TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

ELECTRIC MOTORS

May, 2014

U.S. Department of Energy

Assistant Secretary

Office of Energy Efficiency and Renewable Energy

Building Technologies Program

Appliances and Commercial Equipment Standards

Washington, DC 20585
This Document was prepared for the Department of Energy

by staff members of

Navigant Consulting, Inc.

and

Lawrence Berkeley National Laboratory
CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 DOCUMENT PURPOSE .................................................................................... 1-1

1.2 SUMMARY OF NATIONAL BENEFITS ......................................................... 1-1

1.3 OVERVIEW OF STANDARDS FOR ELECTRIC MOTORS........................... 1-2

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS ....... 1-4

1.5 STRUCTURE OF THE DOCUMENT ................................................................ 1-6

LIST OF TABLES

Table 1-1 Summary of National Economic Benefits and Costs of Electric Motors Energy 

Conservation Standards ................................................................................................... 1-2

Table 1.4.1 Analyses by Rulemaking Stage ................................................................... 1-5

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CHAPTER 1. INTRODUCTION

1.1 DOCUMENT PURPOSE

This technical support document (TSD) is a standalone report that presents the technical 

analyses that the U.S. Department of Energy (DOE or Department) has conducted in support of 

the electric motors final rule. 

1.2 SUMMARY OF NATIONAL BENEFITS

DOE’s analyses indicate that the standards would save a significant amount of energy. 

Estimated lifetime savings for electric motors purchased over the 30-year period that begins in 

the year of compliance with new and amended standards (2016–2045) would amount to 7.0 

quads (full-fuel-cycle energy).1

 The annualized energy savings (0.23 quads) are equivalent to 

one percent of total U.S. industrial primary energy consumption in 2013.2

The estimated cumulative net present value (NPV) of total consumer costs and savings 

attributed to the standards for electric motors ranges from 11.3 billion (at a 7-percent discount 

rate) to $28.8 billion (at a 3-percent discount rate). This NPV expresses the estimated total value 

of future operating-cost savings minus the estimated increased equipment costs for equipment 

purchased in 2016–2045. 

In addition, the standards would have significant environmental benefits. Estimated 

energy savings would result in cumulative emission reductions of 395 million metric tons (Mt)3

of carbon dioxide (CO2), 673 thousand tons of sulfur dioxide (SO2), 498 thousand tons of 

nitrogen oxides (NOX) and 0.8 tons of mercury (Hg).4

The value of the CO2 reductions is calculated using a range of values per metric ton of 

CO2 (otherwise known as the Social Cost of Carbon (SCC)) developed by an interagency process 

(see Chapter 14 for more details.) DOE estimates the present monetary value of the CO2

emissions reduction is between $2.7 and $38.3 billion. DOE also estimates the present monetary 

value of the NOX emissions reduction is $0.3 billion at a 7-percent discount rate and $0.7 billion 

at a 3-percent discount rate.5

1 One quad (quadrillion Btu) is the equivalent of 293.1 billion kilowatt hours (kWh) or 172.3 million barrels of oil. 2 Based on U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook (AEO) 2013 

data.

3 A metric ton is equivalent to 1.1 short tons. Results for NOX and Hg are presented in short tons. 4 DOE calculates emissions reductions relative to the AEO2013 Reference case, which generally represents current 

legislation and environmental regulations for which implementing regulations were available as of December 31, 

2012

5 DOE is currently investigating valuation of avoided Hg and SO2 emissions.

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Table 1-1 summarizes the national economic costs and benefits expected to result from 

the standards for electric motors.

Table 1-1 Summary of National Economic Benefits and Costs of Electric Motors Energy 

Conservation Standards 

Category 

Present 

Value

Billion 2013$ 

Discount 

Rate

Benefits

Operating Cost Savings 18.2 7%

41.4 3%

CO2 Reduction Monetized Value ($12.0/t case)* 2.7 5%

CO2 Reduction Monetized Value ($40.5/t case)* 12.4 3%

CO2 Reduction Monetized Value ($62.4/t case)* 19.7 2.5%

CO2 Reduction Monetized Value ($119/t case)* 38.3 3%

NOX Reduction Monetized Value (at 

$2,684/ton)** 

0.3 7%

0.7 3%

Total Benefits† 30.9 7%

54.4 3%

Costs

Incremental Installed Costs 6.9 7%

12.5 3%

Net Benefits

Including CO2 and NOX Reduction Monetized 

Value 

24.0 7%

41.9 3%

* The interagency group selected four sets of SCC values for use in regulatory analyses. Three sets of values are 

based on the average SCC from the three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. 

The fourth set, which represents the 95th percentile SCC estimate across all three models at a 3-percent discount 

rate, is included to represent higher-than-expected impacts from temperature change further out in the tails of the 

SCC distribution. The values in parentheses represent the SCC in 2015. The SCC time series incorporate an 

escalation factor.

** The value represents the average of the low and high NOX values used in DOE’s analysis.

† Total Benefits for both the 3% and 7% cases are derived using the series corresponding to SCC value of $40.5/t in 

2015. 

1.3 OVERVIEW OF STANDARDS FOR ELECTRIC MOTORS 

The Energy Policy and Conservation Act (EPCA), 42 U.S.C. § 6311, et seq, as amended 

by the Energy Policy Act of 1992 (EPACT) established energy conservation standards and test 

procedures for certain commercial and industrial electric motors manufactured (alone or as a 

component of another piece of equipment) after October 24, 1997. Then, in December 2007, 

Congress passed into law the Energy Independence and Security Act of 2007 (EISA 2007) (Pub. 

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L. No. 110–140) Section 313(b)(1) of EISA 2007 updated the energy conservation standards for 

those electric motors already covered by EPCA and established energy conservation standards 

for a larger scope of motors not previously covered. (42 U.S.C. 6313(b)(2)) 

EPCA also directs that the Secretary of Energy shall publish a final rule no later than 24 

months after the effective date of the previous final rule to determine whether to amend the 

standards in effect for such product. Any such amendment shall apply to electric motors 

manufactured after a date which is five years after – 

(i) the effective date of the previous amendment; or

(ii) if the previous final rule did not amend the standards, the earliest date by which a 

previous amendment could have been effective. (42 U.S.C. 6313(b)(4)) 

As described previously, EISA 2007 constitutes the most recent amendment to EPCA and 

energy conservation standards for electric motors. The compliance date prescribed by statute 

would require manufacturers to begin manufacturing compliant motors by December 19, 2015 

(as calculated under 42 U.S.C. 6313(b)(4)(B)). DOE, however, recognizes that the statute also 

contemplated a three-year lead time for manufacturers to account for the potential logistical and 

production hurdles that manufacturers may face when transitioning to the new standards. To 

account for these challenges while remaining cognizant of , and the statutory timeline provided 

by Congress, DOE has modified its proposed deadline and sets a compliance date of two years 

after the publication of the final rule, which should provide manufacturers with sufficient leadtime to adjust to the new standards required by today’s rule. 

On September 22, 2010, DOE published an Energy Conservation Standards Rulemaking 

Framework Document for Electric Motors. This document describes the procedural and 

analytical approaches that DOE anticipated that it would use to evaluate the establishment of 

energy conservation standards for electric motors.6 On October 18, 2010, DOE held a public 

meeting to discuss the proposed analytical framework. The analytical framework presented at the 

public meeting described different analyses, such as life-cycle cost and payback, the methods 

proposed for conducting them, and the relationships among the various analyses. Representatives 

of motor manufacturers, trade associations, and energy efficiency advocacy groups attended the 

framework document public meeting and submitted both oral and written comments. DOE also 

published a Request for Information (RFI) seeking public comments from interested parties 

regarding establishment of energy conservation standards for several types of definite and special 

purpose motors for which EISA 2007 did not provide energy conservation standards.7

DOE incorporated comments received in response to the framework document and the 

RFI, as well as information obtained from discussions with manufacturers and subject matter 

experts (SMEs) into its engineering analysis. On July 23, 2012, DOE published the preliminary 

technical support document (TSD). A public meeting was held on August 21, 2012 to discuss 

6 This document is available at the DOE website: 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/42 7 This document is available at the DOE website: 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/42

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these preliminary analyses. Following the preliminary analysis public meeting, DOE held 

additional meetings with manufacturers as part of the consultative process for the manufacturer 

impact analysis (Chapter 12). On December 6, 2013 DOE published a notice of proposed rule 

(NOPR) and a public meeting was held on December 11, 2013. Following the NOPR public 

meeting, DOE received numerous comments regarding the NOPR analyses. DOE has 

incorporated the comments received at the public meeting and submitted in written comments 

into today’s rule. 

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

Under EPCA, when DOE studies new or amended standards, it must consider, to the 

greatest extent practicable, the following seven factors (42 U.S.C. 6295(o)(2)(B)(i)): 

(1) the economic impact of the standard on the manufacturers and consumers of the 

affected products; 

(2) the savings in operating costs throughout the estimated average life of the product 

compared to any increases in the initial cost, or maintenance expense for the products 

that are likely to result from the imposition of the standard;

(3) the total projected amount of energy savings likely to result directly from the 

imposition of the standard; 

(4) any lessening of the utility or the performance of the covered products likely to result 

from the imposition of the standard; 

(5) the impact of any lessening of competition, as determined in writing by the Attorney 

General, that is likely to result from the imposition of the standard;

(6) the need for national energy conservation; and 

(7) other factors the Secretary considers relevant. 

Other statutory requirements are set forth in 42 U.S.C. 6295(o)(1)–(2)(A), (2)(B)(ii)–(iii), 

and (3)–(4).

DOE considers the participation of interested parties a very important part of the process 

for setting energy conservation standards. Through formal public notifications (i.e., Federal 

Register notices), DOE encourages the participation of all interested parties during the comment 

period in each stage of the rulemaking. Beginning with the preliminary analysis for this 

rulemaking and during subsequent comment periods, interactions among interested parties 

provide a balanced discussion of the information that is required for the standards rulemaking. 

Before DOE determines whether to adopt an energy conservation standard, it must first 

solicit comments on the proposed standard. Any new or amended standard must be designed to 

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achieve significant additional conservation of energy and be technologically feasible and 

economically justified. (42 U.S.C. 6295(o)(2)(A), 6313(a)(6)(A)) To determine whether 

economic justification exists, DOE must review comments on the proposal and determine that 

the benefits of the proposed standard exceed its burdens to the greatest extent practicable, 

weighing the seven factors listed above. (42 U.S.C. 6295(o)(2)(B)(i), 6316(a)) 

After the publication of the framework document, the energy conservation standards 

rulemaking process involves three additional, formal public notices, which DOE publishes in the 

Federal Register. The first of the rulemaking notices is a notice of public meeting (NOPM), 

which is designed to publicly vet the models and tools used in the preliminary rulemaking and to 

facilitate public participation before the NOPR stage. The second notice is the notice of proposed 

rulemaking (NOPR), which presents a discussion of comments received in response to the 

NOPM and the preliminary analyses and analytical tools; analyses of the impacts of potential 

amended energy conservation standards on consumers, manufacturers, and the Nation; DOE’s 

weighting of these impacts of amended energy conservation standards; and the proposed energy 

conservation standards for each product. The third notice is the final rule, which presents a 

discussion of the comments received in response to the NOPR; the revised analyses; DOE’s 

weighting of these impacts; the amended energy conservation standards DOE is adopting for 

each product; and the effective dates of the amended energy conservation standards.

The analytical framework presented in this TSD presents the different analyses, such as 

the engineering analysis and the consumer economic analyses (e.g., the life-cycle cost (LCC) and 

payback period (PBP) analyses), the methods used for conducting them, and the relationships 

among the various analyses. Table 1.3.1 outlines the analyses DOE conducts for each stage of 

the rulemaking.

. Table 1.4.1 Analyses by Rulemaking Stage 

Preliminary NOPR Final Rule

Market and technology assessment   

Screening analysis   

Engineering analysis   

Energy use characterization   

Product price determination   

Life-cycle cost and payback period analyses   

Life-cycle cost subgroup analysis  

Shipments analysis   

National impact analysis   

Preliminary manufacturer impact analysis 

Manufacturer impact analysis  

Utility impact analysis  

Employment impact analysis  

Emissions Analysis  

Regulatory impact analysis  

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DOE developed spreadsheets for the engineering, LCC, PBP, and national impact 

analyses (NIA) for each equipment class. The LCC workbook calculates the LCC and PBP at 

various energy efficiency levels. The NIA workbook does the same for national energy savings 

(NES) and national net present values (NPVs). All of these spreadsheets are available on the 

DOE website for electric motors: 

http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/50

As part of the information gathering and sharing process, DOE interviewed electric motor

manufacturers. DOE selected companies that represented production of all types of equipment, 

ranging from small to large manufacturers. DOE had four objectives for these interviews: (1) 

solicit manufacturer feedback on the draft inputs to the engineering analysis; (2) solicit feedback 

on topics related to the preliminary manufacturer impact analysis; (3) provide an opportunity, 

early in the rulemaking process, for manufacturers to express their concerns to DOE; and (4) 

foster cooperation between manufacturers and DOE. 

DOE incorporated the information gathered during these interviews into its engineering 

analysis (chapter 5) and its manufacturer impact analysis (chapter 12). Following the publication 

of the preliminary analyses and the associated public meeting, DOE held additional meetings 

with manufacturers as part of the consultative process for the manufacturer impact analysis for

the NOPR phase of the rulemaking. 

1.5 STRUCTURE OF THE DOCUMENT

The TSD describes the analytical approaches and data sources used in this rulemaking. 

The TSD consists of the following chapters and appendices. 

Chapter 1 Introduction: provides an overview of the appliance standards program 

and how it applies to the electric motor rulemaking, and outlines the 

structure of the document. 

Chapter 2 Analytical Framework: describes the methodology, the analytical tools, 

and relationships among the various analyses, summarizes issues and 

comments DOE received from its preliminary interviews with 

manufacturers, and explains DOE’s responses to those comments. 

Chapter 3 Market and Technology Assessment: provides DOE’s definition of an 

electric motor, lists the equipment classes, and names the major industry 

players. This chapter also provides an overview of electric motor

technology, including techniques employed to improve motor efficiency.

Chapter 4 Screening Analysis: identifies all the design options that improve electric 

motor efficiency, and determines which of these DOE evaluated and 

which DOE screened out of its analysis. 

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Chapter 5 Engineering Analysis: discusses the methods used for developing the 

relationship between increased manufacturer price and increased 

efficiency. Presents detailed cost and efficiency information for the units 

of analysis.

Chapter 6 Markups Analysis: discusses the methods used for establishing markups 

for converting manufacturer prices to customer equipment prices. 

Chapter 7 Energy Use Characterization: discusses the process used for generating 

energy-use estimates for the considered products as a function of 

efficiency levels.

Chapter 8 Life-Cycle Cost and Payback Period Analysis: discusses the effects of 

standards on individual customers and users of the products and compares 

the LCC and PBP of equipment with and without higher energy 

conservation standards. 

Chapter 9 Shipments Analysis: discusses the methods used for projecting the total 

number of electric motors that would be affected by standards. 

Chapter 10 National Impact Analysis: discusses the methods used for forecasting 

national energy consumption and national consumer economic impacts in 

the absence and presence of standards. 

Chapter 11 Customer Subgroup Analysis: discusses the effects of standards on any 

identifiable subgroups of consumers who may be disproportionately 

affected by any standard level. This chapter compares the LCC and PBP 

of products with and without higher energy conservation standards for 

these consumers.

Chapter 12 Manufacturer Impact Analysis: discusses the effects of standards on the 

finances and profitability of electric motor manufacturers. 

Chapter 13 Emissions Analysis: discusses the effects of standards on pollutants 

including – sulfur dioxide (SO2), nitrogen oxides (NOX), and mercury – as 

well as carbon emissions. 

Chapter 14 Monetization of Emission Reductions Benefits: discusses the effects of 

standards on the monetary benefits likely to result from the reduced 

emissions of carbon dioxide (CO2) and nitrogen oxides (NOX). 

Chapter 15 Utility Impact Analysis: discusses the effects of standards on the electric 

utility industry.

Chapter 16 Employment Impact Analysis: discusses the effects of standards on 

national employment.

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Chapter 17 Regulatory Impact Analysis: discusses the impact of non-regulatory 

alternatives to efficiency standards.

Appendices: 

App.5-A Engineering Data

App.5-B Sample Teardown Report

App.5-C Efficiency Modeling Validation

App.7-A Energy Use Scenario for Medium Electric Motors

App.8-A User Instructions for Life-Cycle Cost Spreadsheet

App.8-B Life-Cycle Cost and Payback Period Results 

App.8-C Life-Cycle Cost Sensitivity Analysis

App.10-A User Instructions for Shipments and National Impact Analysis Spreadsheet 

Models 

App.10-B National Impact Analysis Sensitivity Analysis for Alternative Product 

Price Trend Scenarios

App.10-C Full-Fuel-Cycle Multipliers

App.10-D National Impact Analysis Sensitivity for Alternative Scenarios of Price 

Elasticity of Demand

App.12-A Manufacturer Impact Analysis Interview Guide 

App.12-B GRIM Overview

App.14-A Social Cost of Carbon for Regulatory Impact Analysis Under Executive 

Order 12866: Technical Model Update

App.14-B Technical Update of Social Cost of Carbon For Regulatory Impact 

Analysis Under Executive Order 12866 

App.17-A Regulatory Impact Analysis: Supporting Materials

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CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION ........................................................................................................... 2-1

2.2 BACKGROUND ............................................................................................................. 2-4

2.3 MARKET AND TECHNOLOGY ASSESSMENT ........................................................ 2-5

2.4 SCREENING ANALYSIS .............................................................................................. 2-6

2.5 ENGINEERING ANALYSIS .......................................................................................... 2-6

2.6 MARKUPS TO DETERMINE EQUIPMENT PRICE ................................................... 2-7

2.7 ENERGY USE ANALYSIS ............................................................................................ 2-8

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES .................................... 2-8

2.9 SHIPMENTS ANALYSIS .............................................................................................. 2-9

2.10 NATIONAL IMPACT ANALYSIS ................................................................................ 2-9

2.10.1 National Energy Savings Analysis..................................................................... 2-10

2.10.2 Net Present Value Analysis ............................................................................... 2-10

2.11 CONSUMER SUBGROUP ANALYSIS ...................................................................... 2-11

2.12 MANUFACTURER IMPACT ANALYSIS.................................................................. 2-11

2.13 EMISSIONS ANALYSIS .............................................................................................. 2-12

2.14 MONETIZING REDUCED CO2 AND OTHER EMISSIONS .................................... 2-14

2.15 UTILITY IMPACT ANALYSIS ................................................................................... 2-15

2.16 EMPLOYMENT IMPACT ANALYSIS ....................................................................... 2-16

2.17 REGULATORY IMPACT ANALYSIS ....................................................................... 2-16

LIST OF FIGURES

Figure 2.1.1 Flow Diagram of Analyses for the Energy Conservation Standards 

Rulemaking Analysis Process ........................................................................ 2-2

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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section 6295(o)(2)(A) of 42 United States Code (U.S.C.) requires the U.S. Department of 

Energy (DOE) to set forth energy conservation standards that are technologically feasible and 

economically justified, and would achieve the maximum improvement in energy efficiency. This 

provision applies to electric motors via 42 U.S.C. 6316(a). This chapter provides a description of 

the general analytical framework that DOE uses in developing such standards. The analytical 

framework is a description of the methodology, analytical tools, and relationships among the 

various analyses that are part of this rulemaking. For example, the methodology that addresses 

the statutory requirement for economic justification includes analyses of life-cycle cost (LCC), 

economic impact on manufacturers and users, national benefits, impacts, if any, on utility 

companies, and impacts, if any, from lessening competition among manufacturers. 

Figure 2.1.1 summarizes the stages and analytical components of the rulemaking process. 

The focus of this figure is the center column, which lists the analyses that DOE conducts. The 

figure shows how the analyses fit into the rulemaking process, and how they relate to each other. 

Key inputs are the types of data and information that the analyses require. Some key inputs exist 

in public databases; DOE collects other inputs from stakeholders or persons with special 

knowledge. Key outputs are analytical results that feed directly into the standards-setting 

process. Arrows connecting analyses show types of information that feed from one analysis to 

another. 

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Figure 2.1.1 Flow Diagram of Analyses for the Energy Conservation Standards 

Rulemaking Analysis Process 

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The analyses performed prior to the final rule stage as part of the final rule and described 

in the final rule technical support document (TSD) are listed below. These analyses were revised 

for the final rule based in part on comments received, and are reported in this TSD. 

• A market and technology assessment to characterize the relevant equipment markets and 

existing technology options, including prototype designs. 

• A screening analysis to review each technology option and determine if it is 

technologically feasible; is practical to manufacture, install, and service; would adversely 

affect equipment utility or equipment availability; or would have adverse impacts on 

health and safety.

• An engineering analysis to develop cost-efficiency relationships that show the 

manufacturer’s cost of achieving increased efficiency. 

• An energy use analysis to determine the annual energy use in the field of the considered 

equipment as a function of efficiency level. 

• A markups analysis to develop distribution channel markups to convert manufacturer 

selling prices to customer installed prices. 

• An LCC and payback period (PBP) analysis to calculate, at the consumer level, the 

relationship between savings in operating costs compared to any increase in the installed 

cost for equipment at higher efficiency levels. 

• A shipments analysis to forecast equipment shipments, which then are used to calculate 

the national impacts of standards and future manufacturer cash flows.

• A national impact analysis (NIA) to assess the impacts at the national level of potential 

energy conservation standards for each of the considered equipment, as measured by the 

net present value (NPV) of total consumer economic impacts and the national energy 

savings (NES). 

• A manufacturer impact analysis to assess the potential impacts of energy conservation 

standards on manufacturers, such as impacts on capital conversion expenditures, 

marketing costs, shipments, and research and development costs. 

• A consumer subgroup analysis to evaluate variations in customer characteristics that 

might cause a standard to affect particular consumer sub-population differently than the 

overall population. 

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• A manufacturer impact analysis to estimate the financial impact of standards on 

manufacturers and to calculate impacts on competition, employment, and manufacturing 

capacity.

• An emissions analysis to assess the effects of the considered standards on emissions of 

carbon dioxide (CO2), nitrogen oxides (NOx), and mercury (Hg).

• A monetization of emission reduction benefits resulting from reduced emissions 

associated with potential new and amended standards. 

• A utility impact analysis to estimate the effects of the considered energy conservation

standards on installed electricity generation capacity and electricity generation. 

• An employment impact analysis to assess the indirect impacts of the considered energy

conservation standards on national employment. 

• A regulatory impact analysis to assess alternatives to energy conservation standards that 

could achieve substantially the same regulatory goal.

2.2 BACKGROUND

 On October 5, 1999, DOE published in the Federal Register, a final rule to implement the 

EPACT 1992 electric motor requirements. 64 FR 54114. In response to EISA 2007, on March 

23, 2009, DOE updated, among other things, the corresponding electric motor regulations at 10 

CFR part 431 with the new definitions and energy conservation standards. 74 FR 12058. On 

December 22, 2008, DOE proposed to update the test procedures under 10 CFR part 431 both for 

electric motors and small electric motors. 73 FR 78220. DOE finalized key provisions related to 

small electric motor testing in a 2009 final rule at 74 FR 32059 (July 7, 2009), and further 

updated the test procedures for electric motors and small electric motors at 77 FR 26608 (May 4, 

2012). The May 2012 final rule primarily focused on updating various definitions and 

incorporations by reference related to the current test procedure. In that rule, DOE promulgated a 

regulatory definition of “electric motor” to account for EISA 2007’s removal of the previous 

statutory definition of “electric motor.” DOE also clarified definitions related to those motors 

that EISA 2007 laid out as part of EPCA’s statutory framework, including motor types that DOE 

had not previously regulated. See generally, id. at 26613-26619. DOE published a new proposed 

test procedure rulemaking on June 26, 2013 that attempts to further refine some existing electric 

motor definitions and proposes to add certain definitions and test procedure preparatory steps to 

address a wider variety of electric motor types than are currently regulated. 78 FR 38456. 

Regarding the compliance date that would apply to the requirements of today’s proposed 

rule, EPCA directs the Secretary of Energy to publish a final rule no later than 24 months after 

the effective date of the previous final rule to determine whether to amend the standards in effect 

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for such equipment. Any such amendment shall apply to electric motors manufactured after a 

date which is five years after – 

(i) the effective date of the previous amendment; or

(ii) if the previous final rule did not amend the standards, the earliest date by which a 

previous amendment could have been effective. (42 U.S.C. § 6313(b)(4)) 

As described previously, EISA 2007 constitutes the most recent amendment to EPCA and 

energy conservation standards for electric motors. The compliance date prescribed by statute 

would require manufacturers to begin manufacturing compliant motors by December 19, 2015 

(as calculated under 42 U.S.C. 6313(b)(4)(B)). DOE, however, recognizes that the statute also 

contemplated a three-year lead time for manufacturers to account for the potential logistical and 

production hurdles that manufacturers may face when transitioning to the new standards. To 

account for these challenges while remaining cognizant of , and the statutory timeline provided 

by Congress, this analysis estimates a compliance date of June 1, 2016 (two years after issuance 

of the final rule) which should provide manufacturers with sufficient lead-time to adjust to the 

new standards required by today’s rule

2.3 MARKET AND TECHNOLOGY ASSESSMENT

When initiating a standards rulemaking, DOE develops information on the present and 

past industry structure and market characteristics for the equipment concerned. This activity 

assesses the industry and equipment both quantitatively and qualitatively based on publicly 

available information and encompasses the following: (1) manufacturer market share and 

characteristics, (2) existing regulatory and non-regulatory equipment efficiency improvement 

initiatives, and (3) trends in equipment characteristics and retail markets. This information serves 

as resource material throughout the rulemaking. 

DOE reviewed existing literature and interviewed manufacturers to get an overall picture 

of the industry serving the United States market. Industry publications and trade journals, 

government agencies, trade organizations, and equipment literature provided the bulk of the 

information, including: (1) manufacturers and their approximate market shares, (2) equipment 

characteristics, and (3) industry trends. The appropriate sections of the final rule describe the 

analysis and resulting information leading up to the proposed trial standard levels, while 

supporting documentation is provided in the TSD. 

DOE categorizes covered equipment into separate equipment classes and formulates a 

separate energy conservation standard for each equipment class. The criteria for separation into 

different classes are type of energy used, capacity, and other performance-related features such 

as those that provide utility to the consumer or others deemed appropriate by the Secretary that 

2-5 
would justify the establishment of a separate energy conservation standard. (42 U.S.C. 6295(q) 

and 6316(a)). 

As part of the market and technology assessment, DOE developed a list of technologies 

for consideration for improving the efficiency of electric motors. DOE typically uses information 

about existing and past technology options and prototype designs to determine which 

technologies manufacturers use to attain higher performance levels. In consultation with 

interested parties, DOE develops a list of technologies for consideration. Initially, these 

technologies encompass all those DOE believes are technologically feasible.

DOE developed its list of technologically feasible design options for electric motors from 

trade publications, technical papers, research conducted in support of previous rulemakings 

concerning these equipment, and through consultation with manufacturers of components and 

systems. Since many options for improving equipment efficiency are available in existing 

equipment, equipment literature and direct examination provided additional information. Chapter 

3 of the TSD includes the detailed list of all technology options identified. 

2.4 SCREENING ANALYSIS

After DOE identified the technologies that could potentially improve the energy 

efficiency of electric motors, DOE conducted the screening analysis. The purpose of the 

screening analysis is to evaluate these technologies to determine which options to consider 

further and which options to screen out. 

The screening analysis examines whether various technologies: (1) are technologically 

feasible; (2) are practicable to manufacture, install, and service; (3) have an adverse impact on 

equipment utility or availability; and (4) have adverse impacts on health and safety. In 

consultation with interested parties, DOE reviews the list to determine if the technologies 

described in chapter 3 of the TSD are practicable to manufacture, install, and service; would 

adversely affect equipment utility or availability; or would have adverse impacts on health and 

safety. In the engineering analysis, DOE further considers the efficiency enhancement options 

(i.e., technologies) that it did not screen out in the screening analysis. Chapter 4 of the TSD 

contains further detail on the criteria that DOE uses.

2.5 ENGINEERING ANALYSIS

The engineering analysis establishes the relationship between the manufacturing 

production cost and the efficiency of electric motors. This relationship serves as the basis for 

cost/benefit calculations in terms of individual consumers, manufacturers, and the Nation. 

Chapter 5 discusses equipment classes DOE analyzed, the representative baseline units, the 

2-6 
efficiency levels analyzed, the methodology DOE used to develop the manufacturing production 

costs, and the cost-efficiency curves.

In the engineering analysis, DOE evaluates a range of equipment efficiency levels and 

their associated manufacturing costs. The purpose of the analysis is to estimate the incremental 

manufacturer selling prices (MSPs) for equipment that would result from increasing efficiency 

levels above the level of the baseline model in each equipment class. The engineering analysis 

considers technologies not eliminated in the screening analysis. The LCC analysis and NIA use 

the cost-efficiency relationships developed in the engineering analysis. 

DOE typically structures its engineering analysis around one of three methodologies: (1) 

the design-option approach, which calculates the incremental costs of adding specific design 

options to a baseline model; (2) the efficiency-level approach, which calculates the relative costs 

of achieving increases in energy efficiency levels without regard to the particular design options 

used to achieve such increases; and/or (3) the reverse-engineering or cost-assessment approach, 

which involves a “bottom-up” manufacturing cost assessment based on a detailed bill of 

materials derived from teardowns of the equipment being analyzed.

DOE’s analysis for the electric motor rulemaking is based on a combination of the 

efficiency-level approach and the reverse engineering approach. Primarily, DOE elected to 

derive its production costs by tearing down electric motors and recording detailed information 

regarding individual components and designs. DOE used the costs derived from the engineering 

teardowns and the corresponding nameplate nominal efficiency of the torn down motors to report 

the relative costs of achieving improvements in energy efficiency. DOE derived material prices 

from current, publicly available data as well as input from subject matter experts and 

manufacturers. For most representative units analyzed, DOE was not able to test and teardown a 

max-tech unit because such units are generally cost-prohibitive and are not readily available. 

Therefore, DOE supplemented the results of its test and teardown analysis with software 

modeling. 

Chapter 5 of the TSD describes the methodology that DOE used to perform the efficiency 

level analysis and derive the cost-efficiency relationship.

2.6 MARKUPS TO DETERMINE EQUIPMENT PRICE

DOE uses markups to convert the manufacturer selling prices estimated in the 

engineering analysis to consumer prices, which then were used in the LCC, PBP, national 

impact, and manufacturer impact analyses. DOE calculates a separate markup for the baseline 

component of equipment’s cost (baseline markup) and for the incremental increase in cost due to 

standards (incremental markup). 

2-7 
To develop markups, DOE identifies how the equipment is distributed from the 

manufacturer to the customer. After establishing appropriate distribution channels, DOE used 

data from the financial filings of manufacturers and distributors and other sources to determine 

how prices are marked up as the equipment passes from the manufacturer to the end consumer. 

See chapter 6 of the TSD for details on the development of markups. 

2.7 ENERGY USE ANALYSIS

The energy use analysis, which assesses the energy savings potential from higher 

efficiency levels, provides the basis for the energy savings values used in the LCC and 

subsequent analyses. The goal of the energy use analysis is to generate a range of energy use 

values which reflects actual equipment use in the field. The analysis uses information on the use 

of actual equipment in the field to estimate the energy that would be used by new equipment at 

various efficiency levels. Chapter 7 of the TSD provides more detail about DOE’s approach for 

characterizing energy use of electric motors. 

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES

New or amended energy conservation standards affect equipment’s operating expenses—

usually decreasing them—and consumer prices for the equipment—usually increasing them. 

DOE analyzed the net effect of standards on consumers by evaluating the net change in LCC. To 

evaluate the net change in LCC, DOE used the cost-efficiency relationship derived in the 

engineering analysis along with the energy costs derived from the energy use analysis. Inputs to 

the LCC calculation include the installed cost of equipment to the consumer (consumer purchase 

price plus installation cost), operating expenses (energy expenses and maintenance costs), the 

lifetime of the unit, and a discount rate. These inputs are described in detail in chapter 8 of the 

TSD.

Because the installed cost of equipment typically increases while operating cost typically 

decreases in response to new standards, there is a time in the life of equipment having higherthan-baseline efficiency when the operating-cost benefit (in dollars) since the time of purchase is 

equal to the incremental first cost of purchasing the higher-efficiency equipment. The length of 

time required for equipment to reach this cost-equivalence point is known as the payback period 

(PBP).

Recognizing that several inputs used to determine consumer LCC and PBP are either 

variable or uncertain, DOE conducted the LCC and PBP analyses by modeling both the 

uncertainty and variability in the inputs using Monte Carlo simulation and probability 

distributions. DOE developed an LCC and PBP spreadsheet model that incorporates both Monte 

Carlo simulation and probability distributions by using Microsoft Excel spreadsheets combined 

with Crystal Ball, a commercially available add-in program. 

2-8 
The LCC and PBP analyses are described in more detail in chapter 8 of the TSD.

2.9 SHIPMENTS ANALYSIS

 Projections of equipment shipments are needed to calculate the potential effects of 

standards on national energy use, NPV, and future manufacturer cash flows. DOE generated both 

shipments projections for each equipment class. The shipments projections calculate the total 

number of electric motors shipped each year over a 30-year period, beginning in 2016. To create 

these projections, DOE combined current year shipments, discussed in the shipments analysis 

(chapter 9), with a shipment analysis model driven by economic growth and machinery 

production growth for equipment, including electric motors and generated unit shipment values 

through the analysis period. Chapter 9 of the TSD provides additional details on the shipments 

analysis.

2.10 NATIONAL IMPACT ANALYSIS

The national impact analysis estimates energy savings and assesses the NPV of consumer 

LCC savings at the national scale. The results can be used to identify the potential energy 

conservation standard that, for a given equipment class, yields the greatest energy savings while 

remaining cost effective from a consumer perspective. DOE estimated both NES and NPV for all 

candidate standard levels for each electric motors equipment class. To make the analysis more 

accessible and transparent to all interested parties, it is documented in a Microsoft Excel 

spreadsheet model that can be downloaded from the DOE Energy Efficiency & Renewable 

Energy (EERE) website (http://www.eere.energy.gov/buildings/appliance_standards/). 

The NIA considers total installed cost (which includes manufacturer selling prices, 

distribution chain markups, sales taxes, and installation costs), operating expenses (energy, 

repair, and maintenance costs), equipment lifetime, and discount rate. However, where the LCC 

considers the savings and costs associated with standards for a set of representative units, the 

NIA considers the savings and costs associated with all units affected by standards during the 

entire analysis period. Chapter 10 provides additional details regarding the NIA. 

A key component of DOE’s NIA analysis is the energy efficiencies forecasted over time 

for the base case (without new standards) and each of the standards cases. The efficiency forecast 

shows the distribution of shipments of electric motors by efficiency level, which determines the 

percentage of shipments affected by a standard. To develop its efficiency forecast, DOE first 

assessed present-day (2012) efficiency and then considered how the efficiency of new units 

might change by the first year of the analysis period and throughout the analysis period in the 

absence of new or amended Federal standards.

2-9 
To estimate the impact that new or amended standards may have in the year compliance 

is required, DOE used both a “roll-up” scenario and a “shift” scenario. Under the “roll-up” 

scenario, DOE assumes: (1) product efficiencies in the base case that do not meet the standard 

level under consideration would “roll-up” to meet the new standard level; and (2) product 

efficiencies above the standard level under consideration would not be affected. Under the 

“shift” scenario, DOE retains the pattern of the base-case efficiency distribution but re-orients 

the distribution at and above the new minimum energy conservation standard. 

2.10.1 National Energy Savings Analysis 

The major inputs for determining the NES for equipment analyzed are annual unit energy 

consumption, shipments, lifetimes, and site-to-source conversion factors. DOE calculated 

national energy consumption for each year by multiplying unit energy consumption by the 

number of units in the installed base in that year. NES for a given year, then, is the difference in 

national energy consumption between the base case (without new efficiency standards) and each 

standards case. DOE estimated energy consumption and savings first in terms of site energy and 

then converted the savings into source energy. Cumulative energy savings are the sum of the 

NES estimates for each year.

DOE has historically presented NES in terms of primary energy savings. DOE has 

recently published a Statement of Policy regarding its intent to incorporate full-fuel-cycle (FFC) 

metrics into its analyses, and outlining a proposed approach. DOE stated that it intends to 

calculate FFC energy and emission impacts by applying conversion factors generated by the 

Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to 

the NEMS-based results currently used by DOE. 76 FR 51281 (Aug. 18, 2011) as amended at 77 

FR 49701 (August 17, 2012). Additionally, DOE will review alternative approaches to

estimating these factors and may decide to use a model other than GREET to estimate the FFC 

energy and emission impacts in any particular future appliance efficiency standards rulemaking.

For this preliminary analysis, DOE calculated FFC energy savings using a NEMS-based 

methodology described in appendix 10-B. Chapter 10 of this TSD presents both the primary NES 

and the FFC energy savings for the considered trial standard levels (TSLs). 

2.10.2 Net Present Value Analysis 

The inputs for determining net present value (NPV) of consumer benefits are: (1) total 

annual installed cost; (2) total annual savings in operating costs; (3) a discount factor; (4) present 

value of costs; and (5) present value of savings. DOE calculated net savings each year as the 

difference between the base case and each standards case in total savings in operating costs and 

total increases in installed costs. DOE calculated savings over the life of equipment, accounting 

for differences in yearly electricity rates. DOE calculated NPV as the difference between the 

present value of operating cost savings and the present value of total installed costs. DOE used a 

2-10 
discount factor based on real discount rates of 3 percent and 7 percent to discount future costs 

and savings to present values. 

DOE calculated increases in total installed costs as the difference in total installed cost 

between the base case and standards case (i.e., once the standards take effect). Because the more 

efficient equipment bought in the standards case usually cost more than equipment bought in the 

base case, cost increases appear as negative values in the NPV.

DOE expressed savings in operating costs as decreases associated with the lower energy 

consumption of equipment bought in the standards case compared to the base case. Total savings 

in operating costs are the product of savings per unit and the number of units of each vintage that 

survive in a given year. 

2.11 CONSUMER SUBGROUP ANALYSIS

The consumer subgroup analysis evaluates economic impacts on selected groups of 

consumers who might be adversely affected by a change in the national energy conservation 

standards for the considered equipment. DOE performed LCC subgroup analyses for consumers 

from low-electricity price regions, small businesses, and consumers from specific sectors 

(industry, agriculture, commercial). DOE evaluated the potential LCC impacts and PBPs for 

these consumers using the LCC spreadsheet model. Chapter 11 of the TSD provides more detail.

2.12 MANUFACTURER IMPACT ANALYSIS 

DOE performed a manufacturer impact analysis (MIA) to estimate the financial impact of 

energy conservation standards on manufacturers of electric motors, and to calculate the impact of 

such standards on employment and manufacturing capacity. The MIA has both quantitative and 

qualitative aspects. The quantitative part of the MIA relies on the government regulatory impact 

model (GRIM), an industry-cash-flow model customized for this rulemaking. The GRIM inputs 

are information regarding the industry cost structure, shipments, and revenues. This includes 

information from many of the analyses described above, such as manufacturing costs and prices 

from the engineering analysis and shipments forecasts. The key GRIM output is the industry net 

present value (INPV). Different sets of assumptions (scenarios) will produce different results. 

The qualitative part of the MIA addresses factors such as equipment characteristics, 

characteristics of particular firms, and market and equipment trends, and includes assessment of 

the impacts of standards on subgroups of manufacturers. The complete MIA is described in 

chapter 12 of the TSD. 

DOE conducted each MIA in this rulemaking in three phases. In Phase I, DOE created an 

industry profile to characterize the industry and identify important issues that require 

consideration. In Phase II, DOE prepared an industry cash-flow model and an interview 

2-11 
questionnaire to guide subsequent discussions. In Phase III, DOE interviewed manufacturers and 

assessed the impacts of standards both quantitatively and qualitatively. DOE assessed industry 

and subgroup cash flow and NPV using the GRIM. DOE then assessed impacts on competition, 

manufacturing capacity, employment, and regulatory burden based on manufacturer interview 

feedback and discussions. 

2.13 EMISSIONS ANALYSIS

 

In the emissions analysis, DOE will estimate the reduction in power sector emissions of 

carbon dioxide (CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and mercury (Hg) from 

potential energy conservation standards for the considered products. In addition, DOE will 

estimate emissions impacts in production activities (extracting, processing, and transporting 

fuels) that provide the energy inputs to power plants. These are referred to as “upstream” 

emissions. Together, these emissions account for the full-fuel-cycle (FFC). In accordance with 

DOE’s FFC Statement of Policy (76 FR 51282 (Aug. 18, 2011), as amended at 77 FR 49701 

(Aug. 17, 2012)), the FFC analysis includes impacts on emissions of methane and nitrous oxide, 

both of which are recognized as greenhouse gases. 

 DOE conducts the emissions analysis using emissions factors derived from data in the 

latest version of EIA’s Annual Energy Outlook (AEO), supplemented by data from other sources. 

EIA prepares the Annual Energy Outlook using NEMS. Each annual version of NEMS 

incorporates the projected impacts of existing air quality regulations on emissions. The text 

below refers to AEO 2013, which generally represents current legislation and environmental 

regulations, including recent government actions, for which implementing regulations were 

available as of December 31, 2012. 

SO2 emissions from affected electric generating units (EGUs) are subject to nationwide 

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual 

emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that operates along 

with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR was 

remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit), but it remained in effect. See North Carolina v. EPA, 550 F.3d 1176 (D.C. Cir. 2008); 

North Carolina v. EPA, 531 F.3d 896 (D.C. Cir. 2008). On July 6, 2011 EPA issued a 

replacement for CAIR, the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 

2011). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME 

Homer City Generation, LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to 

continue administering CAIR. AEO 2013 assumes that CAIR remains a binding regulation 

through 2040. 

2-12 
The attainment of emissions caps is typically flexible among EGUs and is enforced 

through the use of emissions allowances and tradable permits. Under existing EPA regulations, 

any excess SO2 emissions allowances resulting from the lower electricity demand caused by the 

adoption of an efficiency standard could be used to permit offsetting increases in SO2 emissions 

by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about 

the effects of efficiency standards on SO2 emissions covered by the existing cap-and-trade 

system, but it concluded that no reductions in power sector emissions would occur for SO2 as a 

result of standards. 

 Beginning in 2015, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 

2011. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for HCl 

as a surrogate for acid gas hazardous air pollutants (HAP), and also established a standard for 

SO2 (a non-HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. The 

same controls are used to reduce HAP and non-HAP acid gas; thus, SO2 emissions will be 

reduced as a result of the control technologies installed on coal-fired power plants to comply 

with the MATS requirements for acid gas. AEO 2013 assumes that, in order to continue 

operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems 

installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce 

SO2 emissions. Under the MATS, NEMS shows a reduction in SO2 emissions when electricity 

demand decreases (e.g., as a result of energy efficiency standards). Emissions will be far below 

the cap that would be established by CSAPR, so it is unlikely that excess SO2 emissions 

allowances resulting from the lower electricity demand would be needed or used to permit 

offsetting increases in SO2 emissions by any regulated EGU. Therefore, DOE believes that 

efficiency standards will reduce SO2 emissions in 2015 and beyond. 

CAIR established a cap on NOX emissions in eastern States and the District of Columbia. 

Energy conservation standards are expected to have little or no physical effect on these emissions 

in those States covered by CAIR because excess NOx emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOx emissions. 

However, standards would be expected to reduce NOx emissions in the States not affected by the 

caps, so DOE estimates NOX emissions reductions from potential standards in the States where 

emissions are not capped.

 The MATS limit mercury emissions from power plants, but they do not include emissions 

caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. DOE 

will estimate mercury emissions reduction using emissions factors based on AEO 2013, which 

incorporates the MATS. 

Power plants may emit particulates from the smoke stack, which are known as direct 

particulate matter (PM) emissions. NEMS does not account for direct PM emissions from power 

plants. DOE is investigating the possibility of using other methods to estimate reduction in PM 

2-13 
emissions due to standards. The great majority of ambient PM associated with power plants is in 

the form of secondary sulfates and nitrates, which are produced at a significant distance from 

power plants by complex atmospheric chemical reactions that often involve the gaseous 

emissions of power plants, mainly SO2 and NOx. The monetary benefits that DOE estimates for 

reductions in SO2 and NOx emissions resulting from standards are in fact primarily related to the 

health benefits of reduced ambient PM. 

2.14 MONETIZING REDUCED CO2 AND OTHER EMISSIONS 

DOE plans to consider the estimated monetary benefits likely to result from the reduced 

emissions of CO2 and NOX that are expected to result from each of the standard levels 

considered. 

 To estimate the monetary value of benefits resulting from reduced emissions of CO2, 

DOE plans to use the most current Social Cost of Carbon (SCC) values developed and/or agreed 

to by an interagency process. The SCC is intended to be a monetary measure of the incremental 

damage resulting from greenhouse gas (GHG) emissions, including, but not limited to, net 

agricultural productivity loss, human health effects, property damage from sea level rise, and 

changes in ecosystem services. Any effort to quantify and to monetize the harms associated with 

climate change will raise serious questions of science, economics, and ethics. But with full 

regard for the limits of both quantification and monetization, the SCC can be used to provide 

estimates of the social benefits of reductions in GHG emissions. 

 The Interagency Working Group on Social Cost of Carbon released an update of its 

previous report in 2013.a The most recent estimates of the SCC in 2015, expressed in 2013$, are 

$12.0, $40.5, $62.4, and $119 per metric ton of CO2 avoided. For emissions reductions that occur 

in later years, these values grow in real terms over time. Additionally, the interagency group 

determined that a range of values from 7 percent to 23 percent should be used to adjust the global 

SCC to calculate domestic effects, although DOE will give preference to consideration of the 

global benefits of reducing CO2 emissions. 

DOE multiplies the CO2 emissions reduction estimated for each year by the SCC value 

for that year in each of the four cases. To calculate a present value of the stream of monetary 

values, DOE discounts the values in each of the four cases using the discount rates that had been 

used to obtain the SCC values in each case.

a Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 

Interagency Working Group on Social Cost of Carbon, United States Government, May 2013. 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria_2013_update.pdf

2-14 

 
 DOE recognizes that scientific and economic knowledge continues to evolve rapidly as to 

the contribution of CO2 and other GHG to changes in the future global climate and the potential 

resulting damages to the world economy. Thus, these values are subject to change. 

DOE also estimates the potential monetary benefit of reduced NOX emissions resulting 

from the standard levels it considers. For NOx emissions, estimates suggest a very wide range of 

monetary values, ranging from 476 to $4,893 per ton in 2013$).b In accordance with OMB 

guidance, c DOE calculates a range of monetary benefits using each of the economic values for 

NOX and real discount rates of 3 percent and 7 percent. 

 

 DOE is evaluating appropriate valuation of avoided SO2 and Hg emissions. Whether 

monetization of reduced Hg emissions will occur in this rulemaking is yet to be determined. 

2.15 UTILITY IMPACT ANALYSIS

To estimate the impacts of potential energy conservation standards for electric motors on 

the electric utility industry, DOE uses a variant of the EIA’s National Energy Modeling System 

called NEMS-BT.d

 NEMS is a large, multi-sectoral, partial-equilibrium model of the U.S. 

energy sector that EIA has developed over several years, primarily for the purpose of preparing 

the AEO. NEMS produces a widely recognized forecast for the United States through 2035 and is 

available to the public. 

The utility impact analysis is a comparison between the NEMS-BT model results for the 

base case and standard cases. The utility impact analysis reports the changes in installed capacity 

and generation that result from each standard level by plant type. DOE models the anticipated 

energy savings impacts from potential amended energy conservation standards using NEMS-BT 

to generate forecasts that deviate from the AEO Reference Case.

b

 For additional information, refer to U.S. Office of Management and Budget, Office of Information and Regulatory 

Affairs, 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on 

State, Local, and Tribal Entities, Washington, DC. c OMB, Circular A-4: Regulatory Analysis (Sept. 17, 2003).

d For more information on NEMS, please refer to the U.S. Department of Energy, Energy Information 

Administration documentation. A useful summary is National Energy Modeling System: An Overview 2000, 

DOE/EIA-0581 (March 2000), available at: http://tonto.eia.doe.gov/ftproot/forecasting/05812000.pdf. EIA approves 

use of the name NEMS to describe only an official version of the model without any modification to code or data. 

Because this analysis entails some minor code modifications and the model is run under various policy scenarios 

that are variations on EIA assumptions, DOE refers to the model by the name NEMS-BT. (“BT” refers to DOE’s 

Building Technologies Program, under whose aegis this work is performed.)

2-15 

 
2.16 EMPLOYMENT IMPACT ANALYSIS

The imposition of standards can affect employment both directly and indirectly. Direct 

employment impacts are changes, produced by new standards, in the number of employees at 

plants that produce the covered equipment. DOE evaluated direct employment impacts in the 

manufacturer impact analysis. Indirect employment impacts that occur because of the imposition 

of standards may result from consumers shifting expenditures between goods (the substitution 

effect) and from changes in income and overall expenditure levels (the income effect). DOE 

utilizes Pacific Northwest National Laboratory’s ImSET model to investigate the combined 

direct and indirect employment impacts. The ImSET model, which was developed for DOE’s 

Office of Planning, Budget, and Analysis, estimates the employment and income effects energysaving technologies produced in buildings, industry, and transportation. In comparison with 

simple economic multiplier approaches, ImSET allows for more complete and automated 

analysis of the economic impacts of energy conservation investments. Further detail is provided 

in chapter 16 of the TSD. 

2.17 REGULATORY IMPACT ANALYSIS

DOE prepared a regulatory impact analysis (RIA) pursuant to Executive Order 12866, 

Regulatory Planning and Review, 58 FR 51735, October 4, 1993, which is subject to review by 

the Office of Information and Regulatory Affairs at the Office of Management and Budget. The 

RIA addresses the potential for non-regulatory approaches to supplant or augment energy 

conservation standards in order to improve the energy efficiency or reduce the energy 

consumption of the equipment covered under this rulemaking. 

DOE recognizes that voluntary or other non-regulatory efforts by manufacturers, utilities, 

and other interested parties can substantially affect energy efficiency or reduce energy 

consumption. DOE bases its assessment on the actual impacts of any such initiatives to date, but 

also considers information presented by interested parties regarding the impacts existing 

initiatives might have in the future. Further detail is provided in chapter 17 of the TSD.

2-16 
3-i 

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

3.1 INTRODUCTION ........................................................................................................... 3-1

3.2 MARKET ASSESSMENT .............................................................................................. 3-1

3.2.1 Definitions............................................................................................................ 3-1

3.2.1.1 Statutory ................................................................................................ 3-1

3.2.1.2 2012 Test Procedure ............................................................................. 3-2

3.2.1.3 2013 Test Procedure ............................................................................. 3-4

3.2.2 Equipment Class Groups and Equipment Classes ............................................... 3-6

3.2.2.1 Electric Motor Design ........................................................................... 3-8

3.2.2.2 Fire Pump Electric Motors .................................................................. 3-10

3.2.2.3 Horsepower Rating ............................................................................. 3-10

3.2.2.4 Pole Configuration .............................................................................. 3-10

3.2.2.5 Enclosure Type ................................................................................... 3-10

3.2.3 Expanded Scope of Coverage ............................................................................ 3-14

3.2.4 Advanced Electric Motors ................................................................................. 3-17

3.2.5 Electric Motor Shipments .................................................................................. 3-21

3.2.5.1 NEMA Design Type ........................................................................... 3-21

3.2.5.2 Horsepower Ratings ............................................................................ 3-23

3.2.5.3 Pole Configuration .............................................................................. 3-24

3.2.5.4 Enclosure Types .................................................................................. 3-24

3.2.6 Manufacturers and Market Share ....................................................................... 3-25

3.2.6.1 Small Businesses ................................................................................. 3-25

3.2.7 Application and Performance of Existing Equipment ....................................... 3-26

3.2.8 Trade Associations ............................................................................................. 3-26

3.2.8.1 National Electrical Manufacturers Association .................................. 3-26

3.2.9 Regulatory Programs ......................................................................................... 3-27

3.2.10 Non-Regulatory Programs ................................................................................. 3-28

3.2.10.1 Department of Energy Motor Challenge Program .............................. 3-28

3.2.10.2 National Electrical Manufacturers Association Premium® Efficiency 

Motor Program .................................................................................... 3-29

3.2.10.3 Consortium for Energy Efficiency ...................................................... 3-29

3.3 TECHNOLOGY ASSESSMENT .................................................................................. 3-30

3.3.1 Technology Options for I2R Losses ................................................................... 3-31

3.3.2 Technology Options for Core Losses ................................................................ 3-31

3.3.2.1 Amorphous Metal Laminations .......................................................... 3-32

3.3.2.2 Plastic Bonded Iron Powder................................................................ 3-33

3.3.3 Technology Options for Friction and Windage Losses ..................................... 3-33

3.3.4 Technology Options for Stray-Load Losses ...................................................... 3-33

3.3.5 Summary of the Technology Options under Consideration .............................. 3-34
3-ii

LIST OF TABLES

Table 3.1 Electric Motor Equipment Class Groups .............................................. 3-8

Table 3.2 NEMA Design A and B Equipment Classes....................................... 3-11

Table 3.3 NEMA Design C Equipment Classes ................................................. 3-12

Table 3.4 Fire Pump Electric Motor Equipment Classes .................................... 3-13

Table 3.5 Characteristics of Motors Regulated Under Expanded Scope of 

Coverage ............................................................................................. 3-14

Table 3.6 Electric Motor Types DOE is Regulating under Expanded Scope of 

Conservation Standards ...................................................................... 3-15

Table 3.7 Equipment Specifically Excluded from Coverage .............................. 3-15

Table 3.8 Advanced Electric Motors .................................................................. 3-17

Table 3.9 Technology Options to Increase Electric Motor Efficiency ............... 3-34

LIST OF FIGURES

Figure 3.1 Electric Motor Shipments by Design Type for 2012 .......................... 3-22

Figure 3.2 ECG1 (NEMA Design A & B) Electric Motors Shipments by 

Horsepower Rating for 2012 ............................................................... 3-23

Figure 3.3 ECG 2 (NEMA Design C) Electric Motors Shipments by Horsepower 

Rating for 2012 ................................................................................... 3-23

Figure 3.4 Electric Motor Shipments by Pole Configuration for 2012 ................ 3-24

Figure 3.5 Electric Motor Shipments by Enclosure Type for 2012 ..................... 3-25
3-1 

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter provides a profile of the electric motor industry in the United States. The 

U.S. Department of Energy (DOE) developed the market and technology assessment presented in 

this chapter primarily from publicly available information. This assessment is helpful in 

identifying the major manufacturers and their equipment characteristics, which form the basis for 

the engineering and life-cycle cost (LCC) analysis.

This chapter consists of two sections: the market assessment and the technology 

assessment. The market assessment provides an overall picture of the market for the equipment 

concerned, including a scope of the equipment covered, equipment classes, industry structure, 

manufacturer market shares; regulatory and non-regulatory efficiency improvement programs; 

and market trends and quantities of equipment sold. The technology assessment identifies a 

preliminary list of technology options for reducing motor losses to consider in the screening 

analysis.

The information DOE gathers for the market and technology assessment serves as 

resource material for use throughout the rulemaking. DOE considers both quantitative and 

qualitative information from publicly available sources and interested parties.

3.2 MARKET ASSESSMENT

This section addresses the scope of the rulemaking, identifies potential equipment

classes, estimates national shipments of electric motors, and the market shares of electric motor 

manufacturers. This section also discusses the application and performance of existing 

equipment and regulatory and non-regulatory programs that apply to electric motors.

3.2.1 Definitions 

3.2.1.1 Statutory

The Energy Policy and Conservation Act (EPCA), as amended by the Energy Policy Act 

of 1992 (EPACT 1992), had previously established a definition for “electric motor” as “any 

motor which is a general purpose T-frame, single-speed, foot-mounting, polyphase squirrel-cage 

induction motor of the National Electrical Manufacturers Association [NEMA] Design A and B, 

continuous rated, operating on 230/460 volts and constant 60 Hertz line power as defined in 

NEMA Standards Publication MG1–1987.” (42 U.S.C. 6311(13)(A) (1992)) Through subsequent 

amendments to EPCA and, in particular, the Energy Independence and Security Act that was 

signed into law on December 19, 2007 (EISA 2007), Congress struck the EPACT 1992 

definition and replaced it with language that covered a broader scope of general purpose electric 

motors. (See 42 U.S.C. 6311(13)(A)-(B) (2010))
3-2 

3.2.1.2 2012 Test Procedure 

Consequently, the new terminology adopted as a result of EISA 2007 generated 

confusion over the definitions of the terms “electric motor” and “general purpose electric motor.” 

As a result, DOE sought to clarify its interpretations of these definitions in a rulemaking about 

test procedures for electric motors. On May 4, 2012, DOE published in the Federal Register a 

test procedure final rule for electric motors which clarified the two definitions. 77 FR 26608. A 

regulatory definition of “electric motor” was promulgated in light of EISA 2007’s removal of the 

statutory definition of “electric motor.” The definition of “general purpose motor” (now “general 

purpose electric motor”) was taken directly from the industry standard NEMA MG 1-1993, 

“Motors and Generators,” and was intended to specify a broad category of motors that were 

potentially subject to regulation. 

The test procedure was intended to clear up confusion over the definitions of “electric 

motor” and “general purpose electric motor.” The test procedure final rule defined the two terms 

as follows: 

“Electric motor means a machine that converts electrical power into rotational 

mechanical power.”

and

 “General purpose electric motor means any electric motor that is designed in standard 

ratings with either:

(1) Standard operating characteristics and mechanical construction for use under usual 

service conditions, such as those specified in NEMA MG 1–2009, paragraph 14.2, “Usual 

Service Conditions,” (incorporated by reference, see § 431.15) and without restriction to a 

particular application or type of application; or 

(2) Standard operating characteristics or standard mechanical construction for use under 

unusual service conditions, such as those specified in NEMA MG 1–2009, paragraph 14.3, 

“Unusual Service Conditions,” (incorporated by reference, see §431.15) or for a particular type 

of application, and which can be used in most general purpose applications.” 

 EISA 2007 also introduced and established energy conservation standards for several 

new categories of electric motors. As such, the test procedure final rule sought to clarify DOE’s 

interpretation of these terms. Ultimately, DOE created new definitions for the terms “general 

purpose electric motor (subtype I),” “general purpose electric motor (subtype II),” “NEMA 

Design B motor,” and “fire pump electric motor,” which are shown below. 

As a result of the recent electric motors test procedure final rule, section 431.12 of Title 

10 of the Code of Federal Regulations, Part 431 (10 CFR 431) now defines a general purpose 

electric motor (subtype I) as a general purpose electric motor that:

(1) Is a single-speed, induction motor; 

(2) Is rated for continuous duty (MG 1) operation or for duty type S1 (IEC);
3-3 

(3) Contains a squirrel-cage (MG 1) or cage (IEC) rotor;

(4) Has foot-mounting that may include foot-mounting with flanges or detachable feet;

(5) Is built in accordance with NEMA T-frame dimensions or their IEC metric

equivalents, including a frame size that is between two consecutive NEMA frame 

sizes or their IEC metric equivalents; 

(6) Has performance in accordance with NEMA Design A (MG 1) or B (MG 1) 

characteristics or equivalent designs such as IEC Design N (IEC);

(7) Operates on polyphase alternating current 60-hertz sinusoidal power, and: 

(i) Is rated at 230 or 460 volts (or both) including motors rated at multiple voltages that 

include 230 or 460 volts (or both), or 

(ii) Can be operated on 230 or 460 volts (or both); and 

(8) Includes, but is not limited to, explosion-proof construction. 

Further, the recent electric motors test procedure final rule amended 10 CFR 431.12, 

which now defines a general purpose electric motor (subtype II) as any general purpose electric 

motor that incorporates design elements of a general purpose electric motor (subtype I) but, 

unlike a general purpose electric motor (subtype I), is configured in one or more of the following 

ways: 

(1) Is built in accordance with NEMA U-frame dimensions as described in NEMA MG

1–1967 or in accordance with the IEC metric equivalents, including a frame size that 

is between two consecutive NEMA frame sizes or their IEC metric equivalents;

(2) Has performance in accordance with NEMA Design C characteristics as described in 

MG 1 or an equivalent IEC design(s) such as IEC Design H; 

(3) Is a close-coupled pump motor; 

(4) Is a footless motor; 

(5) Is a vertical solid shaft normal thrust motor (as tested in a horizontal configuration) 

built and designed in a manner consistent with MG 1; 

(6) Is an eight-pole motor (900 rpm); or 

(7) Is a polyphase motor with a voltage rating of not more than 600 volts, is not rated at 

230 or 460 volts (or both), and cannot be operated on 230 or 460 volts (or both). 

Also, as a result of the electric motors test procedure final rule, 10 CFR 431.12 defines a 

NEMA Design B motor as a squirrel-cage motor that is: 

(1) Designed to withstand full-voltage starting;

(2) Develops locked-rotor, breakdown, and pull-up torques adequate for general 

application as specified in sections 12.38, 12.39 and 12.40 of NEMA MG 1– 2009 

(incorporated by reference, see § 431.15); 

(3) Draws locked-rotor current not to exceed the values shown in section 12.35.1 for 60 

hertz and 12.35.2 for 50 hertz of NEMA MG 1–2009; and 

(4) Has a slip at rated load of less than 5 percent for motors with fewer than 10 poles. 

 

Finally, the electric motors test procedure final rule, amended 10 CFR 431.12 by defining 

a fire pump electric motor in the following manner: 
3-4 

Fire pump electric motor means an electric motor, including any IEC-equivalent, that 

meets the requirements of section 9.5 of NFPA 20 (incorporated by reference, see 10 CFR 

431.15). 

3.2.1.3 2013 Test Procedure

In order to facilitate the potential application of energy conservation standards to motors 

built in certain configurations, DOE has adopted definitions for these expanded scope motor 

types. The definitions would address motors currently subject to standards, specific motors DOE 

is considering requiring to meet standards, and some motors that DOE is, at this time, declining 

to regulate through energy conservation standards. Some of these clarifying definitions, such as 

the definitions for NEMA Design A and C motors, come from NEMA MG 1-2009. However, 

DOE understands that some motors, such as partial motors and brake electric motors, do not 

have standard, industry-accepted definitions. For such motor types, DOE worked with subject 

matter experts (SMEs), manufacturers, and the Motor Coalition to create working definitions.a

DOE lists these motors in section 3.2.3 of this TSD chapter, but notes that these definitions are 

discussed in detail in the 2013 Test Procedures for Electric Motors. (78 FR 75961, December 13, 

2013) The 2013 Test Procedure gave the following definitions: 

Air-over electric motor means an electric motor rated to operate in and be cooled by the 

airstream of a fan or blower that is not supplied with the motor and whose primary purpose is 

providing airflow to an application other than the motor driving it. 

 

Brake electric motor means a motor that contains a dedicated mechanism for speed 

reduction, such as a brake, either within or external to the motor enclosure 

 

Component set means a combination of motor parts that require the addition of more than 

two endshields (and their associated bearings) to create an operable motor. These parts may 

consist of any combination of a stator frame, wound stator, rotor, shaft, or endshields. For the 

purpose of this definition, the term “operable motor” means an electric motor engineered for 

performing in accordance with nameplate ratings.

 

Electric motor with encapsulated windings means an electric motor capable of passing 

the conformance test for water resistance described in NEMA MG 1–2009, paragraph 12.62 

(incorporated by reference, see §431.15). 

 

Electric motor with moisture resistant windings means an electric motor that is capable of 

passing the conformance test for moisture resistance generally described in NEMA MG 1–2009, 

paragraph 12.63 (incorporated by reference, see §431.15). 

 

a The members of the Motor Coalition include: National Electrical Manufacturers Association, American Council 

for an Energy‐Efficient Economy, Appliance Standards Awareness Project, Alliance to Save Energy, Earthjustice, 

Natural Resources Defense Council, Northwest Energy Efficiency Alliance, Northeast Energy Efficiency

Partnerships, and Northwest Power and Conservation Council.
3-5 

 

Electric motor with sealed windings means an electric motor capable of passing the 

conformance test for water resistance described in NEMA MG 1–2009, paragraph 12.62 

(incorporated by reference, see §431.15). 

 

IEC Design H motor means an electric motor that

(1) Is an induction motor designed for use with three-phase power; 

(2) Contains a cage rotor; 

(3) Is capable of direct-on-line starting 

(4) Has 4, 6, or 8 poles; 

(5) Is rated from 0.4 kW to 1600 kW at a frequency of 60 Hz; and 

(6) Conforms to sections 8.1, 8.2, and 8.3 of the IEC 60034−12 edition 2.1 

(incorporated by reference, see § 431.15) requirements for starting torque, locked 

rotor apparent power, and starting. 

 

IEC Design N motor means an electric motor that: 

(1) Is an induction motor designed for use with three-phase power; 

(2) Contains a cage rotor; 

(3) Is capable of direct-on-line starting;

(4) Has 2, 4, 6, or 8 poles; 

(5) Is rated from 0.4 kW to 1600 kW at a frequency of 60 Hz; and 

(6) Conforms to sections 6.1, 6.2, and 6.3 of the IEC 60034−12 edition 2.1 

(incorporated by reference, see § 431.15) requirements for torque characteristics, 

locked rotor apparent power, and starting. 

 

Immersible electric motor means an electric motor primarily designed to operate 

continuously in free-air, but is also capable of withstanding complete immersion in liquid for a 

continuous period of no less than 30 minutes. 

 

Inverter-capable electric motor means an electric motor designed to be directly 

connected to polyphase, sinusoidal line power, but that is also capable of continuous operation 

on an inverter drive over a limited speed range and associated load.

 

Inverter-only electric motor means an electric motor that is capable of rated operation 

solely with an inverter, and is not intended for operation when directly connected to polyphase, 

sinusoidal line power. 

 

Liquid-cooled electric motor means a motor that is cooled by liquid circulated using a 

designated cooling apparatus such that the liquid or liquid-filled conductors come into direct 

contact with the parts of the motor. 

 

NEMA Design A motor means a squirrel-cage motor that: 

(1) Is Designed to withstand full-voltage starting and developing locked-rotor torque 

as shown in NEMA MG1–2009, paragraph 12.38.1 (incorporated by reference, see 

§ 431.15); 
3-6 

(2) Has pull-up torque not less than the values shown in NEMA MG1–2009, 

paragraph 12.40.1; 

(3) Has breakdown torque not less than the values shown in NEMA MG1–2009, 

paragraph 12.39.1; 

(4) Has a locked-rotor current not to exceed the values shown in NEMA MG1–2009, 

paragraph 12.35.1 for 60 hertz and NEMA MG1–2009, paragraph 12.35.2 for 50 

hertz; and 

(5) Has a slip at rated load of less than 5 percent for motors with fewer than 10 poles. 

NEMA Design C motor means a squirrel-cage motor that: 

(1) Is Designed to withstand full-voltage starting and developing locked-rotor torque 

for high-torque applications up to the values shown in NEMA MG1–2009, paragraph 

12.38.2 (incorporated by reference, see § 431.15); 

(2) Has pull-up torque not less than the values shown in NEMA MG1–2009, 

paragraph 12.40.2; 

(3) Has breakdown torque not less than the values shown in NEMA MG1–2009, 

paragraph 12.39.2; 

(4) Has a locked-rotor current not to exceed the values shown in NEMA MG1–2009, 

paragraphs 12.35.1 for 60 hertz and 12.35.2 for 50 hertz; and 

(5) Has a slip at rated load of less than 5 percent.

Partial electric motor means an assembly of motor components necessitating the addition 

of no more than two endshields, including bearings, to create an an electric motor capable of 

operation in accordance with the applicable nameplate ratings.

Submersible electric motor means an electric motor that (1) is intended to operate 

continuously only while submerged in liquid, (2) is capable of operation while submerged in 

liquid for an indefinite period of time, and (3) has been sealed to prevent ingress of liquid from 

contacting the motor’s internal parts.

Totally enclosed non-ventilated (TENV) electric motor means an electric motor that is 

built in a frame-surface cooled, totally enclosed configuration that is designed and equipped to 

be cooled only by free convection. 

3.2.2 Equipment Class Groups and Equipment Classes

Within each category of electric motors it addressed, EISA 2007 set separate energy 

conservation standards by horsepower rating, enclosure type, and pole configuration. These 

standards correspond to Table 12-12 of NEMA MG 1–2011 (equivalent to premium efficiency 

levelsb

) for general purpose electric motors (subtype I) and Table 12-11 of NEMA MG 1–2011 

(equivalent to EPACT 1992 values) for 1 to 200 horsepower general purpose electric motors 

 

b Premium efficiency levels refer to the efficiency values in NEMA MG 1-2011 Tables 12-12 
3-7 

(subtype II), fire pump electric motors, and NEMA Design B motors greater than 200 

horsepower.c

 (42 U.S.C. 6313(b)(2)) 

In general, when DOE amends energy conservation standards, it divides covered 

equipment into classes. By statute, these classes are based on: (a) the type of energy used; (b) the 

capacity of the equipment; or (c) any other performance-related feature that justifies different 

efficiency levels, such as features affecting consumer utility. (42 U.S.C. 6295(q)) As a result of 

changes in EISA 2007, particularly with the addition of general purpose electric motors (subtype 

II) as a subset of motors covered by the term “electric motor,” there are a large number of motor 

design features that DOE considered in this rulemaking. In the following sections, DOE 

discusses the design features that it is considering as part of its analysis. 

Due to the number of electric motor characteristics (e.g., horsepower rating, pole 

configuration, and enclosure), DOE is using two constructs, at this stage, to help develop 

appropriate energy conservation standards for electric motors: “equipment class groups” and 

“equipment classes.” An equipment class group is a collection of electric motors that share a 

common design type. Equipment class groups include motors over a range of horsepower 

ratings, enclosure types, and pole configurations. Essentially, each equipment class group is a 

collection of a large number of equipment classes with the same design type. An equipment class 

represents a unique combination of motor characteristics for which DOE will determine an 

energy efficiency conservation standard. For example, given a combination of motor design type, 

horsepower rating, pole configuration, and enclosure type, the motor design type dictates the 

equipment class group, while the combination of the remaining characteristics dictates the 

specific equipment class.

For the final rule analysis, DOE has created three equipment class groups based on two 

main motor characteristics: the designated NEMA (or IEC) design letter and whether the motor 

meets the definition of a fire pump electric motor. DOE’s three resulting equipment class groups 

are :NEMA Design A and B and IEC Design N motors (ECG 1), NEMA Design C motors and 

IEC Design H motors (ECG 2) and fire pump electric motors (ECG 3). Within each of these 

three broad groups, DOE uses combinations of other pertinent motor characteristics to enumerate 

its individual equipment classes. To illustrate the differences between the two terms, consider the 

following example. A NEMA Design B, 50 horsepower (hp), 2-pole enclosed electric motor and 

a NEMA Design B, 100 hp, 6-pole open electric motor would both be in the same equipment 

class group (equipment class group 1), but each motor would represent a unique equipment class, 

which will ultimately have its own efficiency standard. There are 482 potential equipment 

classes which consist of all permutations of electric motor design types (i.e., NEMA Design A 

and B, NEMA Design C and fire pump electric motors), standard horsepower ratings (i.e., 

standard ratings from 1 to 500 horsepower), pole configurations (i.e., 2-, 4-, 6-, or 8-pole), and 

enclosure types (i.e., open or enclosed). Table 3.1 illustrates the relationships between equipment 

class groups and the characteristics used to define equipment classes. In the following sections, 

DOE discusses each of these design features. 

 

c EISA 2007 also set energy conservation standards for general purpose NEMA Design B motors from 201–500 

horsepower at the NEMA MG 1Table 12-11 levels.
3-8 

Table 3.1 Electric Motor Equipment Class Groups

Equipment 

Class Group Electric Motor Design Horsepower Poles Enclosure

1 NEMA Design A & B* 1-500 2, 4, 6, 8 

Open

Enclosed

2 NEMA Design C* 1-200 4, 6, 8 

Open

Enclosed

3 Fire Pump* 1-500 2, 4, 6, 8 

Open

Enclosed

*Including IEC equivalents.

DOE notes that with this arrangement of equipment class groups and equipment classes 

in the amended energy conservation standards for electric motors it would no longer 

disaggregate its standards by general purpose electric motor subtype I and II. Additionally, in 

light of DOE’s expanded scope of energy conservation standards in this rulemaking, the 

equipment class groups listed in Table 3.1 would include motor types that previously may not 

have been subject to energy conservation standards, including motors that may not fall under the 

categories of subtype I or II motors. 

3.2.2.1 Electric Motor Design

 Various industry organizations, such as NEMA and IEC, publish performance criteria

that provide specifications that electric motors must meet in order to be assigned different design 

types. As these design types represent a certain set of performance parameters, they provide 

electric motor users with an easy reference to use when designing their equipment and when 

purchasing a motor to drive their equipment. The electric motors covered under this rulemaking 

must meet one of three NEMA design types. For medium polyphase alternating current (AC) 

induction motors, the three NEMA design types considered general purpose and covered by 

EPCA, as amended by EISA 2007, are Design A, Design B, and Design C. The definitions for 

these three motor types, as codified in 10 CFR Part 431.12, are as follows:

NEMA Design A motor means a squirrel-cage motor that (1) is designed to withstand 

full-voltage starting and developing locked-rotor torque as shown in NEMA MG 1-2009, 

paragraph 12.38.1 (incorporated by reference, see §431.15); (2) has pull-up torque not less than 

the values shown in NEMA MG 1-2009, paragraph 12.40.1; (3) has breakdown torque not less 

than the values shown in NEMA MG 1-2009, paragraph 12.39.1; (4) has a locked-rotor current 

greater than the values shown in NEMA MG 1-2009, paragraph 12.35.1 for 60 hertz and NEMA 

MG 1-2009, paragraph 12.35.2 for 50 hertz; and (5) has a slip at rated load of less than 5 percent 

for motors with fewer than 10 poles. 

 
3-9 

NEMA Design B motor means a squirrel-cage motor that (1) is designed to withstand 

full-voltage starting, (2) develops locked-rotor, breakdown, and pull-up torques adequate for 

general application as specified in sections 12.38, 12.39 and 12.40 of NEMA Standards 

Publication MG 1–2009 (incorporated by reference, see § 431.15), (3) draws locked-rotor current 

not to exceed the values shown in section 12.35.1 for 60 hertz and 12.35.2 for 50 hertz of NEMA 

Standards Publication MG 1–2009, and (4) has a slip at rated load of less than 5 percent for 

motors with fewer than 10 poles. 

NEMA Design C motor means a squirrel-cage motor that: (1) is designed to withstand 

full-voltage starting and developing locked-rotor torque for high-torque applications up to the 

values shown in NEMA MG1-2009, paragraph 12.38.2 (incorporated by reference, see §431.15); 

(2) has pull-up torque not less than the values shown in NEMA MG1-2009, paragraph 12.40.2; 

(3) has breakdown torque not less than the values shown in NEMA MG1-2009, paragraph 

12.39.2; (4) has a locked-rotor current not to exceed the values shown in NEMA MG1-2009, 

paragraphs 12.35.1 for 60 hertz and 12.35.2 for 50 hertz; and (5) has a slip at rated load of less 

than 5 percent. 

NEMA Design A and NEMA Design B motors have different locked-rotor current

requirements. NEMA Design A motors have no locked-rotor current limits whereas NEMA 

Design B motors are required to stay below certain maximums specified in NEMA MG 1-2011 

paragraph 12.35.1. This tolerance for higher locked-rotor current will allow NEMA Design A 

motors to reach the same efficiency levels as NEMA Design B with fewer design changes and 

constraints. However, NEMA Design A and NEMA Design B motors have the same 

requirements for locked-rotor, pull-up, and breakdown torque and are consequently used in many 

of the same applications. Additionally, as is shown in section 3.2.5 below, NEMA Design B 

motors constitute a significantly larger population of the electric motors that are shipped relative 

to NEMA Design A motors.

NEMA Design C motors, on the other hand, have different torque requirements than 

NEMA Design A or B motors. NEMA Design C motors typically have higher torque 

requirements. DOE believes that this performance change represents a change in utility which 

can also affect efficiency. Additionally, the difference in torque requirements will restrict which 

applications can use which NEMA Design types. As a result, NEMA Design C motors will not 

always be replaceable with NEMA Design A or B motors, or vice versa. 

DOE notes that Congress held NEMA Design A and NEMA Design B motors to the 

same energy conservation standards prescribed by EPACT 1992 (42 U.S.C. 6311(13)(A)) and 

EISA 2007 (42 U.S.C. 6311 (13)(A)) (see requirements for general purpose electric motors 

(subtype I)). For the final rule analysis, DOE has followed the precedent set by EPACT 1992 and 

EISA 2007 and has considered NEMA Design A and B motors in a group together, while placing 

NEMA Design C motors in their own equipment class group. Finally, DOE notes that all 

equivalent IEC design types are also covered by this energy conservation standards rulemaking 

and should be considered with their corresponding NEMA Design type. 
3-10 

3.2.2.2 Fire Pump Electric Motors

EISA 2007 prescribed energy conservation standards for fire pump electric motors. (42 

U.S.C. § 6313(b)(2)(B)) Fire pump electric motors are motors with special design characteristics 

that make them more suitable for emergency operation. As stated previously, DOE adopted a 

definition of “fire pump electric motor,” which incorporated portions of the National Fire 

Protection Association (NFPA) Standard 20, “Standard for the Installation of Stationary Pumps 

for Fire Protection” (2010). Such electric motors, per the requirements of NFPA 20, are required 

to be marked as complying with NEMA Design B performance standards and be capable of 

operating even if it overheats or may be damaged due to continued operation. The additional 

requirements for a fire pump electric motor constitute a change in utility, apart from other 

general purpose electric motors, which DOE believes could also affect its performance and 

efficiency. Therefore, DOE established a separate equipment class group for fire pump electric 

motors to account for the special utility offered by these motors. 

3.2.2.3 Horsepower Rating

Horsepower is a measurement directly related to the capacity of an electric motor to 

perform useful work and, therefore, it is one of DOE’s primary criteria in designating equipment 

classes. Horsepower rating defines the output power of an electric motor, where 1 horsepower 

equals 745.7 watts. It is generally true that efficiency scales with horsepower. In other words, a 

50-horsepower motor is usually more efficient than a 10-horsepower motor of similar design and 

technology. Also, because of its larger frame size and additional active material (e.g., copper 

wiring and electrical steel), the 50-horsepower motor will be able to achieve a higher, maximum 

level of efficiency. Horsepower is a critical performance attribute of an electric motor, and 

because there is a direct correlation between horsepower and efficiency, DOE is using 

horsepower rating as an equipment class setting criterion.

3.2.2.4 Pole Configuration 

An electric motor’s pole configuration corresponds to the number of magnetic poles 

present in the motor. Consequently, the number of magnetic poles (or “poles”) dictates the 

revolutions per minute (RPM) of the rotor and shaft. For each pole configuration there is a 

corresponding synchronous speed, in RPMs, which is the theoretical maximum speed at which a 

motor might operate without a load. All of the electric motors covered by this rulemaking are 

asynchronous motors, meaning they cannot reach this speed. There is an inverse relationship 

between the number of poles and a motor’s speed. As the number of poles increases from two to 

four to six to eight, the synchronous speed drops from 3,600 to 1,800 to 1,200 to 900 RPMs. 

Because the number of poles has a direct impact on the rotational speed of a motor shaft, it also 

affects a motor’s utility and performance, including efficiency. Therefore, DOE is also using 

pole configuration as a means of differentiating equipment classes for the final rule analysis.

3.2.2.5 Enclosure Type 

In general, there are two variations of enclosure types, either open or enclosed. DOE 

currently defines both of these terms under 10 CFR 431.12. An electric motor meets the current 

definition of an “enclosed motor” if it is “an electric motor so constructed as to prevent the free 
3-11 

exchange of air between the inside and outside of the case but not sufficiently enclosed to be 

termed airtight.” An open motor is defined under 10 CFR 431.12 as “an electric motor having 

ventilating openings which permit passage of external cooling air over and around the windings 

of the machine.” 

As in EPACT 1992, EISA 2007 prescribes separate energy conservation standards for 

open and enclosed electric motors. (42 U.S.C. 6313 (b)(1)) Electric motors manufactured with 

open construction allow a free interchange of air between the electric motor’s interior and 

exterior. Electric motors with enclosed construction have no direct air interchange between the 

motor’s interior and exterior (but are not necessarily air-tight) and may be equipped with an 

internal fan for cooling. Whether an electric motor is open or enclosed affects its utility; open 

motors are generally not used in harsh operating environments, whereas totally enclosed electri c 

motors often are. The enclosure type also affects an electric motor’s ability to dissipate heat, 

which directly affects efficiency. For these reasons, DOE used an electric motor’s enclosure type 

(open or enclosed) as an equipment class setting criterion in the final rule analysis.

Table 3.2, Table 3.3and Table 3.4 illustrate the relationship between equipment class and 

various motor design characteristics. Yellow highlighted cells mark which equipment class are 

representative units in the engineering analysis. 

Table 3.2 NEMA Design A and B Equipment Classes

Horsepower Enclosure Two Poles Four Poles Six Poles Eight Poles

1.0

Open EC#1 EC#2 EC#3 EC#4

Enclosed EC#5 EC#6 EC#7 EC#8

1.5

Open EC#9 EC#10 EC#11 EC#12

Enclosed EC#13 EC#14 EC#15 EC#16

2.0

Open EC#17 EC#18 EC#19 EC#20

Enclosed EC#21 EC#22 EC#23 EC#24

3.0

Open EC#25 EC#26 EC#27 EC#28

Enclosed EC#29 EC#30 EC#31 EC#32

5.0

Open EC#33 EC#34 EC#35 EC#36

Enclosed EC#37 EC#38 EC#39 EC#40

7.5

Open EC#41 EC#42 EC#43 EC#44

Enclosed EC#45 EC#46 EC#47 EC#48

10.0

Open EC#49 EC#50 EC#51 EC#52

Enclosed EC#53 EC#54 EC#55 EC#56

15.0

Open EC#57 EC#58 EC#59 EC#60

Enclosed EC#61 EC#62 EC#63 EC#64

20.0

Open EC#65 EC#66 EC#67 EC#68

Enclosed EC#69 EC#70 EC#71 EC#72

25.0

Open EC#73 EC#74 EC#75 EC#76

Enclosed EC#77 EC#78 EC#79 EC#80

30.0

Open EC#81 EC#82 EC#83 EC#84

Enclosed EC#85 EC#86 EC#87 EC#88

40.0

Open EC#89 EC#90 EC#91 EC#92

Enclosed EC#93 EC#94 EC#95 EC#96

50.0

Open EC#97 EC#98 EC#99 EC#100

Enclosed EC#101 EC#102 EC#103 EC#104

60.0 Open EC#105 EC#106 EC#107 EC#108
3-12 

Enclosed EC#109 EC#110 EC#111 EC#112

75.0

Open EC#113 EC#114 EC#115 EC#116

Enclosed EC#117 EC#118 EC#119 EC#120

100.0

Open EC#121 EC#122 EC#123 EC#124

Enclosed EC#125 EC#126 EC#127 EC#128

125.0

Open EC#129 EC#130 EC#131 EC#132

Enclosed EC#133 EC#134 EC#135 EC#136

150.0

Open EC#137 EC#138 EC#139 EC#140

Enclosed EC#141 EC#142 EC#143 EC#144

200.0

Open EC#145 EC#146 EC#147 EC#148

Enclosed EC#149 EC#150 EC#151 EC#152

250.0

Open EC#153 EC#154 EC#155 EC#156

Enclosed EC#157 EC#158 EC#159 EC#160

300.0

Open EC#161 EC#162 EC#163

Enclosed EC#164 EC#165 EC#166

350.0

Open EC#167 EC#168 EC#169

Enclosed EC#170 EC#171 EC#172

400.0

Open EC#173 EC#174

Enclosed EC#175 EC#176

450.0

Open EC#177 EC#178

Enclosed EC#179 EC#180

500.0

Open EC#181 EC#182

Enclosed EC#183 EC#184

Table 3.3 NEMA Design C Equipment Classes

Horsepower Enclosure Four Poles Six Poles Eight Poles

1.0

Open EC#185 EC#186 EC#187

Enclosed EC#188 EC#189 EC#190

1.5

Open EC#191 EC#192 EC#193

Enclosed EC#194 EC#195 EC#196

2.0

Open EC#197 EC#198 EC#199

Enclosed EC#200 EC#201 EC#202

3.0

Open EC#203 EC#204 EC#205

Enclosed EC#206 EC#207 EC#208

5.0

Open EC#209 EC#210 EC#211

Enclosed EC#212 EC#213 EC#214

7.5

Open EC#215 EC#216 EC#217

Enclosed EC#218 EC#219 EC#220

10.0

Open EC#221 EC#222 EC#223

Enclosed EC#224 EC#225 EC#226

15.0

Open EC#227 EC#228 EC#229

Enclosed EC#230 EC#231 EC#232

20.0

Open EC#233 EC#234 EC#235

Enclosed EC#236 EC#237 EC#238

25.0

Open EC#239 EC#240 EC#241

Enclosed EC#242 EC#243 EC#244

30.0

Open EC#245 EC#246 EC#247

Enclosed EC#248 EC#249 EC#250

40.0

Open EC#251 EC#252 EC#253

Enclosed EC#254 EC#255 EC#256
3-13 

50.0

Open EC#257 EC#258 EC#259

Enclosed EC#260 EC#261 EC#262

60.0

Open EC#263 EC#264 EC#265

Enclosed EC#266 EC#267 EC#268

75.0

Open EC#269 EC#270 EC#271

Enclosed EC#272 EC#273 EC#274

100.0

Open EC#275 EC#276 EC#277

Enclosed EC#278 EC#279 EC#280

125.0

Open EC#281 EC#282 EC#283

Enclosed EC#284 EC#285 EC#286

150.0

Open EC#287 EC#288 EC#289

Enclosed EC#290 EC#291 EC#292

200.0

Open EC#293 EC#294 EC#295

Enclosed EC#296 EC#297 EC#298

Table 3.4 Fire Pump Electric Motor Equipment Classes

Horsepower Enclosure Two Poles Four Poles Six Poles Eight Poles

1.0

Open EC#299 EC#300 EC#301 EC#302

Enclosed EC#303 EC#304 EC#305 EC#306

1.5

Open EC#307 EC#308 EC#309 EC#310

Enclosed EC#311 EC#312 EC#313 EC#314

2.0

Open EC#315 EC#316 EC#317 EC#318

Enclosed EC#319 EC#320 EC#321 EC#322

3.0

Open EC#323 EC#324 EC#325 EC#326

Enclosed EC#327 EC#328 EC#329 EC#330

5.0

Open EC#331 EC#332 EC#333 EC#334

Enclosed EC#335 EC#336 EC#337 EC#338

7.5

Open EC#339 EC#340 EC#341 EC#342

Enclosed EC#343 EC#344 EC#345 EC#346

10.0

Open EC#347 EC#348 EC#349 EC#350

Enclosed EC#351 EC#352 EC#353 EC#354

15.0

Open EC#355 EC#356 EC#357 EC#358

Enclosed EC#359 EC#360 EC#361 EC#362

20.0

Open EC#363 EC#364 EC#365 EC#366

Enclosed EC#367 EC#368 EC#369 EC#370

25.0

Open EC#371 EC#372 EC#373 EC#374

Enclosed EC#375 EC#376 EC#377 EC#378

30.0

Open EC#379 EC#380 EC#381 EC#382

Enclosed EC#383 EC#384 EC#385 EC#386

40.0

Open EC#387 EC#388 EC#389 EC#390

Enclosed EC#391 EC#392 EC#393 EC#394

50.0

Open EC#395 EC#396 EC#397 EC#398

Enclosed EC#399 EC#400 EC#401 EC#402

60.0

Open EC#403 EC#404 EC#405 EC#406

Enclosed EC#407 EC#408 EC#409 EC#410

75.0

Open EC#411 EC#412 EC#413 EC#414

Enclosed EC#415 EC#416 EC#417 EC#418

100.0

Open EC#419 EC#420 EC#421 EC#422

Enclosed EC#423 EC#424 EC#425 EC#426

125.0

Open EC#427 EC#428 EC#429 EC#430

Enclosed EC#431 EC#432 EC#433 EC#434
3-14 

150.0

Open EC#435 EC#436 EC#437 EC#438

Enclosed EC#439 EC#440 EC#441 EC#442

200.0

Open EC#443 EC#444 EC#445 EC#446

Enclosed EC#447 EC#448 EC#449 EC#450

250.0

Open EC#451 EC#452 EC#453 EC#454

Enclosed EC#455 EC#456 EC#457 EC#458

300.0

Open EC#459 EC#460 EC#461

Enclosed EC#462 EC#463 EC#464

350.0

Open EC#465 EC#466 EC#467

Enclosed EC#468 EC#469 EC#470

400.0

Open EC#471 EC#472

Enclosed EC#473 EC#474

450.0

Open EC#475 EC#476

Enclosed EC#477 EC#478

500.0

Open EC#479 EC#480

Enclosed EC#481 EC#482

3.2.3 Expanded Scope of Coverage 

DOE requires electric motor types beyond those currently covered to meet energy 

conservation standards. DOE has the statutory authority to establish such standards without first 

promulgating a coverage determination rulemaking based on the modifications resulting from 

EISA 2007, which struck the statutory definition for “electric motors.” DOE recognizes the 

energy savings potential of scope expansion for motors not previously covered under energy 

conservation standards, as well as motors that may not fall into the subtype I, subtype II, and fire 

pump electric motor categories. In today’s rule, DOE is establishing conservation standards for 

electric motors that exhibit all of the characteristics listed in Table 3.5, with a limited number of 

exempted motor types, listed in Table 3.6. 

Table 3.5 Characteristics of Motors Regulated Under Expanded Scope of Coverage

Motor Characteristic

Is a single-speed, induction motor,

Is rated for continuous duty (MG 1) operation or for duty type S1 (IEC),

Contains a squirrel-cage (MG 1) or cage (IEC) rotor,

Operates on polyphase alternating current 60-hertz sinusoidal power,

Is rated 600 volts or less,

Has a 2-, 4-, 6-, or 8-pole configuration,

Is built in a three-digit or four-digit NEMA frame size (or IEC metric equivalent), including 

those designs between two consecutive NEMA frame sizes (or IEC metric equivalent), or an 

enclosed 56 NEMA frame size (or IEC metric equivalent), or an enclosed 56 NEMA frame size 

(or IEC metric equivalent),

Produce at least one horsepower (0.746 kW) but not greater than 500 horsepower (373 kW),and 

Meets all of the performance requirements of one of the following motor types: a NEMA Design 

A, B, or C motor or an IEC Design N or H motor.
3-15 

Table 3.6 lists formerly unregulated electric motor types that be covered by today’s rule if 

they meet all of the characteristics listed in Table 3.5 (with the exception of specifically named 

motors that would otherwise not be covered). Such motors fall into the equipment class groups 

listed in Table 3.1 based on their respective design type. 

Table 3.6 Electric Motor Types DOE is Regulating under Expanded Scope of 

Conservation Standards 

Electric Motor Type 

NEMA Design A from 201 to 500 horsepower Electric motors with non-standard endshields or flanges

Electric motors with moisture resistant windings Electric motors with non-standard bases

Electric motors with sealed windings Electric motors with special shafts

Electric motors with encapsulated windings Vertical hollow-shaft electric motors

Totally enclosed non-ventilated (TENV) electric motors Electric motors with sleeve bearings

Immersible electric motors Electric motors with thrust bearings

Brake electric motors Partial electric motors

Electric motors with separately powered blowers

The motor types listed in Table 3.7 are motor types to which new energy conservation 

standards do not apply, at this time. While some of these motors conform to many or all of the 

characteristics listed in Table 3.5, DOE understands that covering such motors might not be 

warranted due to special operating conditions or testing difficulties as discussed below. 

Table 3.7 Equipment Specifically Excluded from Coverage

Electric Motor Type

Air-over electric motors

Component Sets of an electric motor

Liquid-cooled electric motors

Submersible electric motors

Inverter-only electric motors

Air-Over Electric Motors

Air-over electric motors require an external means of cooling to allow continuous duty 

operation. These motors may be subject to over-heating and therefore cannot run continuously 

without a specified amount of air flowing over the motor housing. The required air flow amount 

is usually determined by the manufacturer as part of the motor design and performance 

characteristics. 

DOE is not planning on covering air-over motors because of the test setup complexities 

required for these motors. DOE’s primary test procedure, the Institute of Electrical and 
3-16 

Electronics Engineers, Inc. (IEEE) Standard 112–2004 Test Method B (IEEE 112B), requires

certain measurements to be taken at a steady-state temperaturesd

. Reaching a steady-state 

temperature requires a motor to be rated and operate under continuous-duty conditions; 

otherwise the motor could overheat and be damaged before reaching a steady-state temperature. 

IEEE 112B does not provide directions on how to setup an air-over motor for testing, which 

would otherwise require an external cooling apparatus. DOE is not aware of test procedures that 

provide guidance on how to test such motors. 

Component Sets of an Electric Motor 

Component sets are comprised of any combination of motor parts, such as a stator, rotor, 

shaft, stator housing, shaft bearings, endshields, or other electrical parts. DOE delineated 

between component sets and partial motors in chapter 2 of the final rule TSD when it called out 

partial motors as motors only missing one or both endshields. Component sets are typically sold 

to be turned into complete electric motors or installed in equipment by the end-user. 

DOE believes component sets do not constitute a complete motor that could be tested 

under IEEE 112B. Additionally, DOE is not aware of any test procedures that would 

accommodate the testing of component sets of motors. While DOE is including partial motors in 

the expansion of energy conservation standards by testing them with a custom-built endshield

that could be attached as a ‘dummy’ endplate for testing, DOE believes component sets would 

require too many or various hardware additions to make a complete motor. Therefore, DOE is 

not including component sets in the expanded scope of coverage. 

Liquid-Cooled Electric Motors 

Liquid-cooled electric motors rely on a special cooling apparatus that pumps liquid into 

and around the motor housing. The liquid is circulated around the motor to dissipate heat and 

prevent the motor from overheating during continuous-duty operation. The user of a liquidcooled motor could employ different liquids or liquid temperatures which could affect the 

measured efficiency of a motor. IEEE 112B does not provide standardized direction for testing 

liquid-cooled motors, and therefore DOE is not including them in the scope of coverage. 

Submersible Electric Motors 

Submersible electric motors are similar to liquid-cooled motors in that they use liquid to 

dissipate the heat produced during continuous duty operation. However, unlike liquid-cooled 

motors, submersible motors are only meant to operate while completely submerged in water, as 

opposed to having a hose and pump apparatus circulating liquid around the motor enclosure. 

 

d Section 3.3.2 of IEEE 112B requires the conductor losses to be measured when the machine is at a specified 

temperature.
3-17 

DOE is not aware of any test procedures for motors that can only operate continuously in 

special environments, such as underwater. Therefore, DOE is excluding submersible motors 

from the expanded scope of coverage. 

Inverter-only Electric Motors

Inverter-only motors cannot be run continuously when directly connected to a 60-hertz, 

AC polyphase sinusoidal power source. Therefore a separate, special electronic controller, called 

an inverter, is used to alter the power signal to the motor. 

Inverter controllers are not necessarily 100 percent efficient when manipulating the 

power signal being fed into the motor. Consequently, the IEEE 112B-measured efficiency of an 

inverter-only motor would not reflect the true efficiency of that motor, but would also include 

any losses inherent in the inverter controller. DOE believes testing an inverter-only motor with 

the inverter controller connected would not accurately record the efficiency of the motor per se. 

DOE is not including inverter-only electric motors under the expanded scope motors covered by 

energy conservation standards, because it is not aware of any test procedures that recognize and 

differentiate losses caused by the inverter controller. 

3.2.4 Advanced Electric Motors 

 The motors and motor systems listed in Table 3.8 are technologies that DOE views as 

“advanced electric motors”. DOE believes that these technologies are advanced electric motors 

because there are significant differences between these motors or controllers and general purpose 

motors that run directly on polyphase AC power. DOE believes that if it were to include these 

types of motors as part of its standards analysis, extensive test procedure changes would be 

required because they have drastically different electromechanical properties relative to squirrelcage induction motors and they do not run directly off of polyphase, AC sinusoidal power 

sources, which is required for testing with IEEE 112B. At this time, DOE has chosen not to 

regulate advanced electric motors and knows of no established definitions or test procedures that 

could be applied to them. Because DOE agrees that significant energy savings may be possible 

for some advanced motors, DOE plans to keep abreast of changes to these technologies and their 

use within industry, and may consider regulating them in the future. 

Table 3.8 Advanced Electric Motors 

Motor Description

Inverter Drives

Permanent magnet motors

Electrically commutated motor

Switched-reluctance motors

Inverter Drives
3-18 

 The current scope of coverage includes motors with a single, constant rotational speed. A 

motor’s rotational speed is determined by the frequency of the power source, as well as the pole 

configuration of the motor. The equation determining a motor’s speed is: 

Speed of motor = 120 × (Frequency of power source)

Number of Motor Poles

Inverter drives, also called variable-frequency drives (VFDs), variable-speed drives, 

adjustable frequency drives, AC drives, microdrives, or vector drives, work by changing the 

voltage and frequency of the power source fed into an electric motor. The equation above shows 

that controlling the frequency of the power source of a motor allows the user to control the speed 

of that motor. One of the biggest advantages of a VFD is the ability to reduce the speed of a 

motor when the full, nameplate-rated speed is not needed. This practice can save energy over a 

motor’s lifetime. VFDs can also control start-up characteristics of motors, such as locked-rotor 

current or locked-rotor torque, which allows motors to achieve higher efficiencies when running 

at rated speed.

1

 DOE is aware of the energy saving potential of motors that run on VFDs.2

 However, 

DOE does not know of any relevant test procedures for testing motors run on a VFD. IEEE 112B 

requires a motor to be tested at its nameplate-rated speed, but motors only capable of running on 

an inverter will not have a nameplate rated speed. Furthermore, the energy saving potential of 

electric motors operating on inverter drives is primarily due to operation below rated speed. A 

test procedure that only measures the efficiency of adjustable speed systems at full speed will not 

provide an accurate assessment of efficiency across the range of speeds these systems will 

operate at. 

Permanent Magnet Motors 

 In both polyphase AC induction motors and permanent magnet motors, the stator is 

energized by three-phase alternating current, which induces a magnetic field that rotates around 

the stator. This rotating magnetic flux induces a voltage in the squirrel-cage rotor, which in turn 

creates a current in the squirrel-cage rotor. These currents then create an opposing magnetic field 

in the rotor that causes it to rotate at a slower speed than the stator field.e

 In permanent magnet 

motors, the rotor uses an embedded permanent magnet to create a constant magnetic field that 

causes the rotor to rotate as the stator magnetic field rotates. Since the rotor is rotating at the 

same speed as the rotating stator field, the motor can be referred to as a synchronous motor. 

Permanent magnet motors have several advantages over AC induction motors including a higher 

efficiency potential, higher power/torque density, lower operating temperature, smaller size and 

quieter operation.3 In AC induction motors, some of the stator current is used to induce rotor 

current in order to produce magnetic flux in the rotor. These additional currents generate heat in 

the motor, leading to increased losses. Permanent magnet motors, on the other hand, do not 

require a current in the rotor to produce magnetic flux since the flux is already provided by the 

 

e When a motor operates with the rotor rotating at a speed slower than the rotating stator field, it is considered to be 

“asynchronous.”
3-19 

permanent magnets. With no current in the rotor there are no rotor losses, which contribute to the 

high efficiency of permanent magnet motors. 

Permanent magnet motors can be classified into two major groups: those with permanent 

magnets mounted on the surface of the rotor and those with permanent magnets placed in the 

interior of the rotor core. Surface permanent magnet (SPM) motors employ arc-shaped magnets 

glued or secured to the outer surface of the rotor core. This arrangement is not as structurally 

robust as the arrangement used in interior permanent magnet (IPM) motors, which instead have 

their permanent magnets placed inside of slots made in the interior of the laminated rotor core, 

thereby increasing retention of the magnet during high-speed operation compared to SPM 

designs. Different magnet grades are used in permanent magnet motors, with ceramic-ferrites 

and rare-earth metals being the most common choices. Although rare-earth magnets are more 

expensive than ceramic-ferrites, they have a higher magnetic energy density which permits 

increased energy output from a motor. However, the market for rare-earth metals is highly 

concentrated, with the vast majority of supply coming from China.4

 Wide-spread adoption of 

permanent magnet motors could be hindered by the inability of suppliers to respond to increased 

global demand as well supply disruptions caused by Chinese export policy. 

Synchronous motors are typically not capable of starting from a fixed frequency AC 

power source. If the rotor is stationary when the stator field starts rotating at full speed, the rotor 

will not develop enough starting torque to overcome its own inertia. One popular method for 

overcoming this constraint is to use a VFD to start the motor. By increasing the frequency of the 

AC signal from zero to the desired running speed, the rotor is able to operate at synchronous 

speed with the accelerating stator field. This method of starting has the added benefit of the 

energy savings associated with adjustable speed control. Alternatively, some designs of interior 

permanent magnet motors incorporate a squirrel cage in the rotor, allowing the rotor to start 

across-the-line like an AC induction motor. These types of self-starting motors are called line 

start permanent magnet (LSPM) motors. During the motor transient start up, the squirrel cage in 

the rotor contributes to the production of enough torque to start the rotation of the rotor, albeit at 

an asynchronous speed. When the speed of the rotor approaches synchronous speed, the constant 

magnetic field of the permanent magnet locks to the rotating stator field, thereby pulling the rotor 

into synchronous operation. LSPM motors would be suitable in applications where the higher 

efficiency of permanent magnet motors is desired, but for which the added cost of a VFD 

remains prohibitive. 

 DOE is aware of the energy saving potential of permanent magnet motors. DOE does not 

know of any relevant test procedures for testing these motors. IEEE 112B is specific to 

polyphase induction motors and does not specify how to segregate losses for permanent magnet 

motors. 

Electronically Commutated Motors 

 Electronically commutated motors (ECMs), also called brushless DC motors, are 

permanent-magnet synchronous motors combined with an on-board electronic controller that can 

measure and regulate the motor’s performance. The commutator in older, brushless motors 

previously consisted of a rotary mechanical component that manipulated the power being fed to 
3-20 

the stator. In ECMs, an electronic microprocessor controls the rotary mechanical component − 

and, consequently, the power supply. The use of the microprocessor permits greater customized 

control over motor performance. Some ECMs run on a DC power supply, while others run on a 

single phase or polyphase AC power supply which is rectified (i.e., converted) to DC power in 

the motor’s controllers. The microprocessor in the motor control converts this DC power into a 

trapezoidal three-phase AC signal (unlike the sinusoidal AC signal used to power the permanent 

magnet motors discussed in the previous paragraph), inducing a rotating magnetic field in the 

stator windings. The rotor uses an embedded permanent magnet to create a constant magnetic 

field that causes the rotor to rotate as the stator magnetic field rotates. The position of the rotor is 

monitored by a microprocessor, which adjusts the magnetic fields in the stator to achieve the 

desired operating speed and torque. The motor can also communicate its status to the equipment 

it is powering, offering instant feedback of the unit’s performance. 

 

Like other types of permanent magnet synchronous motors, ECMs have several 

advantages over AC induction motors due to their higher efficiency, higher power/torque 

density, lower operating temperature, smaller size and quieter operation. ECMs also offer 

adjustable speed control with their programmable electronics, which can save energy in a manner 

similar to VFDs, which are discussed earlier in this section. However, the inclusion of 

programmable electronic controls also increases the cost of manufacturing an ECM. 

 However, DOE does not know of any relevant test procedures for testing electronically 

commutated motors. IEEE 112B requires that a motor be tested at its nameplate rated speed. 

However, motors capable of only being run on an electronic commutator will not have a 

nameplate rated speed because they are variable speed motors and can be run at a range of 

speeds as specified by the user. Additionally, the electronic commutator has its own electrical 

losses which are not accounted for in IEEE 112B. These electrical losses are the result of 

manipulating the power source into the motor. 

Switched Reluctance Motors 

Switched reluctance (SR) motors are synchronous motors that operate on the principle of 

magnetic reluctance. Magnetic reluctance is a measure of the permeability of a given material 

with respect to magnetic flux. Compared to high reluctance materials, low reluctance materials 

offer lower resistance to the passage of magnetic lines of force. In a magnetic circuit, the 

presence of a magnetic field causes magnetic flux to follow the path of least magnetic reluctance. 

When low reluctance materials (such as iron) are in the presence of a magnetic field, flux will 

tend to concentrate in the low reluctance material, forming strong temporary poles that cause an 

attractive force toward regions of higher flux. Just as in a DC motor, the stator in a SR motor 

consists of wound field coils. Unlike induction and permanent-magnet motors, the rotor does not 

contain any windings or magnets. The rotor in a SR motor consists of a low reluctance material, 

such as laminated silicon steel, with multiple projections that act as magnetic poles through 

magnetic reluctance. An electronic controller is used to energize each phase in sequence. As each 

phase is energized, the poles of the rotor are drawn to the position of least magnetic reluctance, 

which occurs when the poles of the stator and rotor are aligned. A full rotation of the rotor can be 

achieved by sequentially energizing each phase. 
3-21 

SR motors have several advantages over AC induction motors, such as higher efficiency 

and simpler construction. Unlike permanent-magnet motors, they do not rely on rare-earth 

magnets in their construction. However, they also have several disadvantages including high 

torque ripple (the difference between the maximum and minimum torque during one revolution) 

and noise (associated with torque ripple). Additionally, SR motors cannot be run on 

commercially available drives that can both operate induction and permanent-magnet motors, a 

fact that could discourage users who have already invested in VFDs from adopting SR motors. 

DOE does not know of any relevant test procedures for testing switched reluctance motors. 

3.2.5 Electric Motor Shipments

To prepare an estimate of the national impact of energy conservation standards for 

electric motors, DOE needed to estimate annual motor shipments. For this stage of the 

rulemaking, DOE developed shipment projections based on historical data and an analysis of key 

market drivers for each product. 

DOE used this data for three main purposes. First, the shipment data and market trend 

information contributed to the shipments analysis and base-case forecast for electric motors

(chapter 9 of the TSD). Second, DOE used the shipment and catalog data to select the 

representative equipment classes and units for analysis (chapter 5 of the TSD). Third, DOE used 

the data to develop the installed stock of equipment for the national impact analysis (chapter 10 

of the TSD). Although more detailed shipments data are given in chapter 9, the shipments shown 

in this chapter illustrate which electric motor characteristics were the most common in 2011. 

3.2.5.1 NEMA Design Type

As discussed previously, the scope of DOE’s energy conservation standards for electric 

motors covers four design types: NEMA Design A, NEMA Design B, NEMA Design C, and 

fire pump electric motors.f

In 2011, Design B motors were by far the most common electric 

motor type, comprising of 96.13 percent of all shipments. NEMA Design A was the second most 

common design type, consisting of 1.05 percent of shipments. Electric motors with brakes 

consisted of 2.6 percent of shipments. Finally, NEMA Design C and fire pump electric motors 

constituted just 0.2 percent and 0.02 percent of shipments, respectively. 

 

f DOE notes that IEC-equivalent design types are also covered.
3-22 

Figure 3.1 Electric Motor Shipments by Design Type for 2012g

 As will be discussed in more detail in chapter 5 of the TSD, DOE focused its engineering 

analysis on NEMA Design B motors based on the popularity of the design type. Although 

NEMA Design C motors, fire pump motors electric motors, and electric motors with brakes 

consist of a small portion of the motor market, DOE has separately analyzed these motors 

because of the different utility and performance characteristics that these motors have relative to 

Design A and B motors. 

 

g In the final rule, analytical results for brake electric motors are reported in ECG 1 under subgroup 1b. DOE 

believes most brake electric motors fall into NEMA Design B category, although notes that its definition of “Brake 

Electric Motor” may apply to a motor of any design. 78 FR 75993.
3-23 

3.2.5.2 Horsepower Ratings

For 2012 NEMA supplied shipments data broken down by horsepower rating. Figure 3.2

illustrates the total shipments of electric motors broken down by horsepower rating for 

equipment class group 1, and Figure 3.3 illustrates the total shipments for equipment class group 

2. As is evident by the graph, the vast majority of shipments occurred in the lower range of 

horsepower rating, with 5-horsepower being the most common rating.

Figure 3.2 ECG 1 (NEMA Design A & B) Electric Motors Shipments by Horsepower 

Rating for 2012 

Figure 3.3 ECG 2 (NEMA Design C) Electric Motors Shipments by Horsepower Rating 

for 2012 
3-24 

3.2.5.3 Pole Configuration 

NEMA also supplied 2012 shipments data broken down by pole configuration. As 

illustrated in Figure 3.4, 4-pole electric motors were by far the most commonly shipped. The 

next highest group of shipments was 2-pole motors, constituting 18.1 percent of all shipments. 

Then, 6-pole and 8-pole motors accounted for 10.6 percent and 2.6 percent of electric motor 

shipments, respectively. 

Figure 3.4 Electric Motor Shipments by Pole Configuration for 2012 

3.2.5.4 Enclosure Types

 Finally, NEMA provided shipment estimates broken down by enclosure types, that is, 

open or enclosed. In 2012, enclosed motors were shipped roughly three times as frequently as 

open motors. In 2011, enclosed consisted of about 76 percent of electric motor shipments and 

open electric motors consisted of about 24 percent of motor shipments. 
3-25 

Figure 3.5 Electric Motor Shipments by Enclosure Type for 2012

3.2.6 Manufacturers and Market Share

The major manufacturers that dominate the electric motor market for this rulemaking, in 

alphabetical order, are: 

• Baldor Electric Company; 

• General Electric Company;

• Nidec Motor Corporation; 

• Regal-Beloit Corporation; 

• Siemens Industry, Inc.;

• Toshiba; and 

• WEG

The manufacturers identified above are all major manufacturers with diverse portfolios of 

equipment offerings, including electric motors covered under EPCA. Over the past decade, there 

has been a consolidation of motor manufacturing in the United States and this list is a result of 

those mergers and acquisitions. 

DOE does not have empirical data on the market shares of particular manufacturers of 

electric motors. Nevertheless, estimates of available cumulative data indicate that shipments of 

electric motors from these companies constitute over a significant portion of the total U.S. 

market. Further, DOE believes that the cumulative shipment estimates provided by NEMA 

constitute a good estimate of overall national shipments.

3.2.6.1 Small Businesses

 Although the electric motor market is predominantly supplied by large manufacturers, 

DOE is examining those small businesses that manufacture electric motors during this stage of 
3-26 

the rulemaking. The Small Business Administration (SBA) lists small business size standards for 

industries as they are described in the North American Industry Classification System (NAICS).

For electric motors, the size standard is matched to NAICS code 335312, Motor and Generator 

Manufacturing.5

 In general, the SBA defines a small business manufacturing enterprise for 

“motor and generator manufacturing” as one that has 1,000 or fewer employees. The number of 

employees in a small business is rolled up with the total employees of the parent company; it 

does not represent the division manufacturing electric motors. DOE studies the potential impacts 

to small businesses in greater detail during the manufacturer impact analysis (MIA). Please see 

chapter 12 for more detail on this analysis. 

3.2.7 Application and Performance of Existing Equipment

 The general purpose electric motors as well as the definite and special purpose electric 

motors that can be used in general purpose applications covered in today’s analysis are used in a

wide range of applications that include the following: 

• blowers 

• business equipment 

• commercial food processing

• compressors 

• conveyors 

• crushers

• fans

• farm equipment 

• general industrial applications 

• grinders

• heating, ventilation, and air-conditioning equipment

• machine tools 

• milking machines

• pumps 

• winches

• woodworking machines 

3.2.8 Trade Associations 

DOE is aware of one trade association for manufacturers of medium electric motors, the 

National Electrical Manufacturers Association (NEMA). 

3.2.8.1 National Electrical Manufacturers Association

 NEMA was established as a trade association in 1926, and has since been divided into 

five core departments that provide different functions for its members. Those departments are: 

• Technical Services

• Government Relations
3-27 

• Industry Operations 

• Business Information Services 

• Medical

 Through these groups, NEMA establishes voluntary standards for the performance, size, 

and functionality of electrical equipment to facilitate communication among motor 

manufacturers, original equipment manufacturers, engineers, purchasing agents, and users. An

example of NEMA’s role in standardization is the NEMA Standards Publication MG 1, “Motors 

and Generators,” (MG 1) document, which is a reference document for motor and generator 

manufacturers and users. MG 1 provides guidance to motor manufacturers on performance and 

construction specifications for a broad range of electric motors. By standardizing around certain 

parameters, NEMA makes it easier for users to identify and purchase electric motors. MG 1 is a 

complete industry reference document for standardizing the motors offered in the market. The 

groups above also set up work that NEMA, as a whole, does to contribute to U.S. public policy 

and the economic data analysis it performs. 

In addition to MG 1, NEMA established and promoted a high efficiency standard through 

a “NEMA Premium®” label for qualifying motors. NEMA motor manufacturers attach a label to 

motors that are built to high efficiency standards. These standards exceed those set by EPACT 

1992, which requires general-purpose motors from 1 to 200 horsepower to meet certain 

minimum efficiency levels. See section 3.2.2 and 3.2.10 for more discussion on these minimum 

efficiency levels. 

3.2.9 Regulatory Programs

EPCA, 42 U.S.C. 6311, et seq., as amended by EPACT 1992, established energy 

conservation standards and test procedures for certain commercial and industrial electric motors 

manufactured (alone or as a component of another piece of equipment) after October 24, 1997. 

Then, in December 2007, Congress passed into law EISA 2007. (Pub. L. No. 110–140) Section 

313(b)(1) of EISA 2007 updated the energy conservation standards for those electric motors 

already covered by EPCA and established energy conservation standards for a larger scope of 

motors not previously covered. (42 U.S.C. 6313(b)(2)) 

EPCA also directs that the Secretary [of Energy] shall publish a final rule no later than 24 

months after the effective date of the previous final rule to determine whether to amend the 

standards in effect for such product. Any such amendment shall apply to electric motors 

manufactured after a date which is five years after – 

(i) the effective date of the previous amendment; or

(ii) if the previous final rule did not amend the standards, the earliest date by which a 

previous amendment could have been effective. (42 U.S.C. 6313(b)(4))As described 

previously, EISA 2007 constitutes the most recent amendment to EPCA and energy 

conservation standards for electric motors. The compliance date prescribed by statute 

would require manufacturers to begin manufacturing compliant motors by December 

19, 2015 (as calculated under 42 U.S.C. 6313(b)(4)(B)). DOE, however, recognizes 

that the statute also contemplated a three-year lead time for manufacturers to account 

for the potential logistical and production hurdles that manufacturers may face when 
3-28 

transitioning to the new standards. To account for these challenges while remaining 

cognizant of , and the statutory timeline provided by Congress, DOE has modified its 

proposed deadline and sets a compliance date of June 1, 2016, which should provide 

manufacturers with sufficient lead-time to adjust to the new standards required by 

today’s rule. 

Additionally, DOE covers certain other motors not covered in this rulemaking under the 

requirements of 10 CFR 431, Subpart X, which pertains to Small Electric Motors. Although both 

rules cover 56-frame electric motors above one hp, DOE expects no overlap because Subpart X 

addresses only open, general-purpose motors and Subpart B (this rule) address only enclosed 56-

frame motors.

3.2.10 Non-Regulatory Programs

DOE reviewed voluntary programs that promote energy efficient electric motors in the 

United States, including the DOE Motor Challenge and Best Practices programs, NEMA 

Premium energy efficient motors program, and Consortium for Energy Efficiency (CEE) 

Premium Efficiency motors program.

3.2.10.1 Department of Energy Motor Challenge Program 

In general, motor-driven equipment accounts for almost 70 percent of all electricity 

consumption by U.S. industries. In 1993, DOE launched its industry/government partnership, 

Motor Challenge Program with the goals of increasing the energy-efficiency of electric motordriven systems in domestic industry and enhancing environmental quality. The program uses a 

market-driven approach to promote the design, purchase, installation, and management of 

energy-efficient electric motors and motor-driven systems and equipment, such as pumps, fans, 

and compressors. It was designed to help industry capture 5 billion kilowatt-hours per year of 

electricity savings and 1.2 million metric tons of carbon-equivalent by the year 2000, with 

projections of much larger and longer-term national energy savings opportunities of over 100 

billion kilowatt-hours per year by the year 2010. 

The Motor Challenge program encompasses three-phase 60 Hertz motors rated 1 

horsepower and above. Its elements and offerings include: DOE Energy Efficiency and 

Renewable Energy (EERE) Information Center, which provides up-to-date information about the 

practicality and profitability of electric motor system strategies; design decision tools, such as 

MotorMaster+ software; Showcase Demonstration projects; training; workshops; and 

conferences. In general, the response to the program from industry has been overwhelmingly 

favorable. The Motor Challenge program is no longer active; however, the DOE Energy 

Efficiency and Renewable Energy (EERE) Information Center and the MotorMaster+ database 

of industrial motors remain viable.

The EERE Information Center answers questions on energy efficient products and 

services and refers callers to the most appropriate DOE/EERE resources. Industrial callers are 

eligible for an advanced level of service that includes engineering assistance, research, and 

software support for plant staff and industrial service providers working on industrial energy 

savings projects. 
3-29 

MotorMaster+ is an energy-efficient motor selection and management tool, which 

includes a database of over 20,000 AC motors. It features motor inventory management tools, 

maintenance log tracking, efficiency analysis, savings evaluation, energy accounting, and 

environmental reporting capabilities.h

3.2.10.2 National Electrical Manufacturers Association Premium® Efficiency 

Motor Program

On January 11, 1989, NEMA established voluntary energy efficiency levels for 1 through 

200 horsepower, polyphase squirrel-cage induction motors. For an electric motor to be classified 

as “energy efficient,” it was required to meet certain levels of efficiency in NEMA Standards 

Publication MG 1–1987 (Revised March 1991). In 1992, the NEMA efficiency levels were 

incorporated into section 342(b) of EPACT 1992 and subsequently codified in 10 CFR 431.25. 

In 2001, the NEMA Premium Efficiency Motor Program was established to provide special 

recognition to electric motors that exceed the required efficiency levels established by EPACT 

1992. NEMA Premium-labeled motors help purchasers identify more efficient motors and 

optimize motor system efficiency commensurate with a particular application. i

 Going a step beyond EPACT, NEMA Premium applies to single-speed, polyphase; 1 to 

500 horsepower; 2-, 4-, and 6-pole; squirrel-cage; induction motors; NEMA Designs A or B; 600 

volts or less; and rated for continuous duty operation. Such electric motors are typically used in 

industrial applications operating more than 2000 hours per year. 

DOE notes that NEMA Premium® is a registered trademark of NEMA and should not be used 

without explicit permission. 

3.2.10.3 Consortium for Energy Efficiency

 The Consortium for Energy Efficiency (CEE) is a nonprofit corporation that develops 

initiatives for its North American members to promote the manufacture and purchase of energy 

efficient equipment, including electric motors and services. Its members include utilities, 

statewide and regional market transformation administrators, environmental groups, research 

organizations and state energy offices in the U.S. and Canada. Also included in the CEE 

collaborative process are manufacturers, retailers, and government agencies.

In 1996, CEE began its Premium-Efficiency Motors Initiative to promote the production, 

distribution, and adoption of premium efficiency motors over motors meeting the minimum 

efficiency levels established under EPACT 1992. In 1999, CEE took a systems approach to 

energy savings and launched its Motor Systems Initiative that viewed the motor as a component 

 

h For more information about MotorMaster+, visit 

http://www1.eere.energy.gov/manufacturing/tech_assistance/software_motormaster.html. The July 10, 2013, 

material from this website is available in Docket #EERE–2010–BT–STD–0027 at regulations.gov. i

NEMA’s Premium® Motors program can be reviewed at 

http://www.nema.org/Policy/Energy/Efficiency/Pages/NEMA-Premium-Motors.aspx. The July 10, 2013, material 

from this website is available in Docket #EERE–2010–BT–STD–0027 at regulations.gov.
3-30 

of a larger system, where efficient motors, adjustable-speed drives, and system-specific design 

strategies would provide the greatest opportunity for savings. Then, in 2001, CEE launched its 

Motor Decisions Matter to promote greater awareness of the benefits of motor systems 

efficiency. In June 2001, CEE and NEMA aligned to promote NEMA Premium motor efficiency 

levels that are roughly .5 to 3 percentage points above EPACT 1992 requirements. 

 In May 2007, CEE published the Energy-Efficiency Incentive Programs – PremiumEfficiency Motors & Adjustable Speed Drives in the U.S and Canada, which provides 

information about the incentive-based programs in North America. These programs concentrate 

on 1 to 200 horsepower motors, but some include 201 to 500 horsepower motors. It appears that 

the programs cover commercial and industrial motors rated from 1 to 500 horsepower. There are 

a number of different programs broken down by region. For more information on these 

programs, download the report from CEE.j

3.3 TECHNOLOGY ASSESSMENT 

The electric motors covered in the final rule are all single speed polyphase AC induction 

motors. Induction motors have two core components: a stator and a rotor. The components work 

together to convert electrical energy into rotational mechanical power. This is done by creating a 

rotating magnetic field in the stator which induces currents in the squirrel-cage of the rotor. The 

squirrel-cage used in the rotor of induction motors consists of longitudinal conductive bars (rotor 

bars) connected at both ends by rings (end rings) forming a cage-like shape. The currents in the 

rotor squirrel-cage create magnetic fields in the rotor which then react with the stator’s rotating 

magnetic field to create torque. This torque provides the rotational force delivered to the load via 

the shaft. 

 The purpose of the technology assessment is to develop a preliminary list of technology 

options that may improve the efficiency of electric motors. For the electric motors covered in this 

rulemaking, energy efficiency losses are grouped into five main categories: stator I2

R losses, 

rotor I2

R losses, core losses, friction and windage losses, and stray load losses. 

 Designers have to balance the five basic losses to optimize the various motor 

performance criteria. There are numerous trade-offs that have to be considered. Efficiency is 

only one parameter that has to be met. Reducing one loss may increase another. What may be 

desirable on a 4-pole motor may not be on a 2-pole motor. A complete discussion of these tradeoffs is beyond the scope of this report. Different manufacturers utilize different approaches for 

minimizing motor losses. 

 

j

CEE’s Summary of Member Programs for Motors and Motor Systems can be found at 

http://library.cee1.org/sites/default/files/library/9323/MMSProgSummary2012CEEWebsite_8.xlsx. The July 11, 

2013, material from this website is available in Docket #EERE–2010–BT–STD–0027 at regulations.gov.
3-31 

3.3.1 Technology Options for I2

R Losses

 I2R losses are produced from either the current flow through the copper windings in the 

stator (stator I

2

R losses) or the squirrel cage of the rotor (rotor I

2

R losses). Stator I

2

R losses are 

reduced by decreasing resistance to current flow in the electrical components of a motor. These 

losses are manifested as heat, which can shorten the service life of a motor. 

One method of reducing resistance losses in the stator is decreasing the length of the coil 

extensions at the end turns. Reducing the length of copper wire in the stator slots not only 

reduces the resistive losses, but also reduces the material cost of the electric motor because less 

copper is being used. 

Another way to reduce stator I

2

R losses is to increase the cross-sectional area of the stator 

winding conductors (e.g., copper wire diameter). This can be accomplished by either increasing 

the slot fill and/or increasing the size of the stator slots. However, this method replaces some of 

the stator magnetic cross sectional area and increases the flux density in the stator. Increasing the 

flux density may increase core losses. Furthermore, there are practical limits to how much slot 

fill can be increased. Very high slot fills may require hand winding, a manufacturing technique 

that is far more labor intensive than machine winding. The motor designer must carefully weigh 

the trade-offs to optimize the motor design. 

There are also various ways to reduce rotor I

2

R losses. The squirrel-cage is the part of the 

rotor in which current flows. Squirrel-cages are usually made of aluminum in electric motors. 

However, one method of increasing the efficiency of the motor is to substitute copper for 

aluminum when die-casting the rotor squirrel-cage. Copper has a lower electrical resistivity (1.68 

x 10-8 ohm-m) than aluminum (2.65 x 10-8 ohm-m). Copper’s 63 percent lower electrical 

resistance compared to aluminum can result in reduced rotor I2R losses. There are, however, 

design trade-offs when using die-cast copper in a rotor. Copper’s lower resistivity may result in a 

higher locked-rotor current. This can be mitigated by modifying the geometry of the rotor slots to 

keep locked-rotor current within NEMA Design B limits.

Increasing the cross-sectional area of the rotor conductor bars can also improve motor 

efficiency. Resistance is inversely proportional to the cross-sectional area of the material through 

which current is flowing. By increasing the cross-sectional area, rotor bar resistance will 

decrease which may reduce rotor I

2

R losses. Similarly, increasing the cross-sectional area of the

rotor end rings can also reduce rotor I

2

R losses. Current flows through the end rings of the rotor 

and increasing the size of the end ring may decrease resistance and reduce the associated rotor

I

2

R losses. These two techniques can result in reduced rotor I2R losses if the increase in rotor 

current does not exceed the square of the decrease in the rotor resistance. 

3.3.2 Technology Options for Core Losses 

Core losses are losses created in the electrical steel components of a motor. These losses, 

like I2R losses, manifest themselves as heat. Core losses are generated in the steel by two 

electromagnetic phenomena: hysteresis losses and eddy currents. Hysteresis losses are caused by 
3-32 

magnetic domains in the steel resisting reorientation to the alternating magnetic field. Eddy 

currents are currents that are induced in the steel laminations by the magnetic flux

One technique for reducing core losses is using a higher grade of electrical steel in the 

core. Higher grades of steel exhibit lower core losses as well as higher magnetic permeability. In 

general, higher grades of electrical steel exhibit lower core losses. Lower core losses can be 

achieved by adding silicon and other elements to the steel, thereby increasing its electrical 

resistivity. Lower core losses can also be achieved by subjecting the steel to special heat 

treatments during processing. 

In studying the different types of steel available, DOE considered two types of materials: 

conventional silicon steels, and “exotic” steels, which contain a relatively high percentage of 

boron or cobalt. Conventional steels are commonly used in electric motors manufactured today. 

The exotic steels are not generally manufactured for use specifically in the electric motors 

covered in this rulemaking. These steels offer lower core losses than the best conventional 

electrical steels, but are more expensive per pound. In addition, these steels can present 

manufacturing challenges because they come in nonstandard thicknesses that are difficult to 

manufacture.

Conventional steels are commonly used in electric motors manufactured today. There are 

three types of steel that DOE considers “conventional:” cold-rolled magnetic laminations 

(CRML), fully processed non-oriented electrical steel, and semi-processed non-oriented 

electrical steel. Each steel type is sold in a range of grades. In general, as the grade number goes 

down, so does the amount of core loss associated with the steel (i.e., watts of loss per pound of 

steel). The induction saturation level also drops, causing the need for increased stack length. Of 

these three types, CRML steels are the most commonly used, but also the least efficient. The 

fully processed steels are annealed before punching and therefore do not require annealing after 

being punched and assembled, and are available in a range of steel grades from M56 through 

M15. Semi-processed electrical steels are designed for annealing after punching and assembly. 

 Another possible option for reducing core loss is to use thinner laminations. Thinner 

laminations generally have lower eddy current losses and this contributes toward improving 

motor efficiency.

Adding electrical steel laminations to the rotor and stator to lengthen the motor can also 

reduce the core losses in an electric motor. Increasing the stack length reduces the magnetic flux 

density, which reduces core losses. However, increasing the stack length affects other 

performance attributes of the motor, such as starting torque. 

3.3.2.1 Amorphous Metal Laminations 

 Using amorphous metals in the rotor laminations is another technology option to improve 

the efficiency of electric motors. Amorphous metal is extremely thin, has high electrical 

resistivity, and has little or no magnetic domain definition. Because of amorphous steel’s high 

resistance it exhibits a reduction in hysteresis and eddy current losses, which reduce overall 
3-33 

losses in electric motors. However, amorphous steel is a very brittle material which makes it 

difficult to punch into motor laminations.6

3.3.2.2 Plastic Bonded Iron Powder

Recently, DOE became aware of a new technology that Lund University researchers in 

Sweden developed in the production of magnetic components for electric motors from plastic 

bonded iron powder (PBIP). The technique has the potential to cut production costs by 50 

percent while doubling motor output. 

The method uses two main ingredients: metal powder and plastics. Combining the 

ingredients creates a material with low conductivity and high permeability. The metal particles 

are surrounded by an insulating plastic, which prevents electric current from developing in the 

material. This is critical because it essentially eliminates losses in the core due to eddy currents. 

Properties of PBIP can differ depending on the processing. If the metal particles are too closely 

compacted and begin to touch, the material will gain electrical conductivity, counteracting one of 

its most important features.

 Another advantage of PBIP is a reduction in the number of production steps. The number 

of steps in manufacturing a rotor and stator is reduced from roughly 60 to just a few. A second 

way to increase savings is to build an inductor with PBIP. During processing, the plastic and 

metal are molded together using a centrifugal force. During this process, the inductor core 

consisting of PBIP and pre-wound windings are baked into the core. This inductor is then used as 

a filter for grid power application. The filter then reduces the use of cooling equipment in the 

motor design.7

3.3.3 Technology Options for Friction and Windage Losses 

Friction and windage losses are caused by friction in the bearings of the motor and 

aerodynamic losses associated with the ventilation fan and other rotating parts. 

 One way to reduce these losses is to optimize the selection of bearings and a lubricant. 

Using improved bearings and lubricants can minimize mechanical resistance to the rotation of 

the rotor, which also extends motor life. 

 Optimizing a motor’s cooling system is another technology option to improve the 

efficiency of electric motors. An optimized cooling system design provides ample motor cooling 

while reducing air resistance. 

3.3.4 Technology Options for Stray-Load Losses 

Stray-load loss is defined as the difference between the total motor loss and the sum of the 

other four losses referred to above. Stray-load losses arise from a variety of sources. 

One way to reduce stray-load losses is to reduce the skew in the rotor squirrel cage. The 

rotor conductor bars of the rotor cage are often skewed. This means the conductor bars are 
3-34 

slightly offset from one end of the rotor to the other. By skewing the rotor bars, motor designers 

can reduce harmonics that add cusps to the speed-torque characteristics of the motor. The cusps 

in the speed-torque curves mean that the acceleration of the motor will not be completely 

smooth. The degree of skew matters because reducing the skew will help reduce the rotor 

resistance and reactance, which can result in improved efficiency. However, reducing the skew 

may have adverse impacts on the speed-torque characteristics.

Another way to reduce stray-load losses is to improve insulation between the rotor 

squirrel-cage and the rotor laminations.8

 Motors with insulated rotor cages often exhibit lower 

stray-load losses when compared to motors with un-insulated rotor cages. Manufacturers use 

different methods to insulate rotor cages, such as applying an insulating coating on the rotor slot 

prior to die-casting or heating and quenching the rotor (i.e. rapid cooling, generally by 

immersion in a fluid instead of allowing the rotor temperature to equalize to ambient) to separate 

rotor bars from rotor laminations after die-casting.

3.3.5 Summary of the Technology Options under Consideration 

Table 3.9 summarizes the technology options discussed in this TSD technology 

assessment and those that DOE will consider in the screening analysis (see chapter 4). The 

options that pass all four screening criteria are considered “design options” and are used in the 

engineering analysis (see TSD chapter 5) as a means of improving the efficiency of electric 

motors. 

Table 3.9 Technology Options to Increase Electric Motor Efficiency

Type of Loss to 

Reduce Technology Option

Stator I2R Losses

Increase cross-sectional area of copper in stator slots 

Decrease the length of coil extensions

Rotor I2R Losses

Use a die-cast copper rotor cage

Increase cross-sectional area of rotor conductor bars 

Increase cross-sectional area of end rings

Core Losses

Use electrical steel laminations with lower losses (watts/lb)

Use thinner steel laminations 

Increase stack length (i.e., add electrical steel laminations) 

Friction and 

Windage Losses

Optimize bearing and lubrication selection

Improve cooling system design

Stray-Load Losses Reduce skew on rotor cage 

Improve rotor bar insulation 

 Most of the design changes suggested in Table 3.9 produce interacting effects on the 

motor’s breakdown torque, locked-rotor torque, locked-rotor current, and so forth. Therefore, 
3-35 

motor designers making a specific design change must evaluate the effects against all of a

motor’s performance characteristics and not just focus on efficiency.
3-36 

REFERENCES

 

1

 Li, Harry. Impact of VFD, Starting Method and Driven Load on Motor Efficiency. 

2011.Siemens Industry, Inc.

2

 S. Dereyne, K. Stockman, S. Derammelaere, P. Defreyne. Variable Speed Drive Evaluation 

Using Iso Efficiency Maps. 2011. Technical University College of West-Flanders. Department of 

Electrical Energy, Systems and Automation, Ghent University. 

3

 Rajagopalan, S., B. Vairamohan, and M. Samotyj. Electric Motors for the Modern World - A 

Look at New Motor Technologies and Applications. 2011. Electric Power Research Institute: 

Palo Alto, CA. 

4 U.S. Department of Energy. Critical Materials Strategy. December 2011. Washington, DC. 

5 U.S. Small Business Administration. Small Business Size Standards Matched to North 

American Industry Classification System. 2013. (Last accessed July 10, 2013.) 

<http://www.sba.gov/sites/default/files/files/Size_Standards_Table_updated%20070213%282%2

9.pdf> The July 10, 2013, material from this website is available in Docket #EERE–2010–BT–

STD–0027 at regulations.gov. 

6 S.R. Ning, J. Gao, and Y.G. Wang. Review on Applications of Low Loss Amorphous Metals in 

Motors. 2010. ShanDong University. Weihai, China.

7

Horrdin, H., Olsson, E. Technology Shifts in Power Electronics and Electric Motors for Hybrid 

Electric Vehicles: A Study of Silicon Carbide and Iron Powder Materials. 2007. Chalmers 

University of Technology. Göteborg, Sweden. 

8

 Feng, Y.N.; Apsley, J.; Williamson, S.; Smith, A.C.; Ionel, D.M. Reduced Losses in Die-Cast 

Machines with Insulated Rotors. IEEE Transactions on Industry Applications. May/June 2010. 

46 (3): pp. 928–936.
4-i 

CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 DISCUSSION OF DESIGN OPTIONS .......................................................................... 4-1

4.3 TECHNOLOGY OPTIONS NOT SCREENED OUT OF THE ANALYSIS................. 4-3

4.3.1 Increase the Cross-sectional Area of Copper in the Stator Slots ......................... 4-3

4.3.2 Decrease the Length of Coil Extensions .............................................................. 4-4

4.3.3 Copper Die-Cast Rotor Cage ............................................................................... 4-4

4.3.4 Increase Cross-sectional Area of Rotor Conductor Bars ..................................... 4-6

4.3.5 Increase Cross-sectional Area of Rotor End Rings.............................................. 4-6

4.3.6 Use Electrical Steel Laminations with Lower Losses .......................................... 4-7

4.3.7 Thinner Steel Laminations ................................................................................... 4-7

4.3.8 Increase Stack Length .......................................................................................... 4-8

4.3.9 Optimize Bearing and Lubricant Selection .......................................................... 4-8

4.3.10 Improve Cooling System Design ......................................................................... 4-8

4.3.11 Reduce Skew on Conductor Cage........................................................................ 4-9

4.3.12 Improved Rotor Bar Insulation ............................................................................ 4-9

4.3.13 Summary of Technology Options Not Screened Out .......................................... 4-9

4.4 TECHNOLOGY OPTIONS SCREENED OUT OF THE ANALYSIS ........................ 4-10

4.4.1 Amorphous Metal Laminations ......................................................................... 4-10

4.4.2 Plastic Bonded Iron Powder............................................................................... 4-11

4.4.3 Summary of Technology Options Screened Out of the Analysis ...................... 4-11

LIST OF TABLES

Table 4.1 Summary List of Options from Technology Assessment ........................................ 4-3

Table 4.2 Summary List of Technology Options Not Screened Out ..................................... 4-10

Table 4.3 Technology Options Screened Out of the Analysis ............................................... 4-11
4-1 

CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

 The purpose of the screening analysis is to identify design options that improve electric 

motor efficiency and determine which options the Department of Energy (DOE) will either 

evaluate or screen out. DOE consults with industry, technical experts, and other interested parties 

in developing a list of design options for consideration. Then DOE applies the following set of 

screening criteria to determine which design options are unsuitable for further consideration in 

the rulemaking (See Title 10 of the Code of Federal Regulations, Part 430, Subpart C, Appendix 

A at 4(a)(4) and 5(b)): 

(1) Technological feasibility: Technologies incorporated in commercial equipment or in 

working prototypes will be considered technologically feasible. 

(2) Practicability to manufacture, install, and service: If mass production of a technology in 

commercial equipment and reliable installation and servicing of the technology could be 

achieved on the scale necessary to serve the relevant market at the time of the effective 

date of the standard, then DOE will consider that technology practicable to manufacture, 

install, and service.

(3) Adverse impacts on equipment utility or equipment availability: DOE will not further 

consider a technology if DOE determines that a technology will have significant adverse 

impact on the utility of the equipment to significant subgroups of consumers. DOE will 

also not further consider a technology that will result in the unavailability of any covered 

equipment type with performance characteristics (including reliability), features, sizes, 

capacities, and volumes that are substantially the same as equipments generally available 

in the United States at the time. 

(4) Adverse impacts on health or safety: DOE will not further consider a technology if DOE 

determines that the technology will have significant adverse impacts on health or safety. 

This chapter discusses the design options that DOE considered for improving the energy 

efficiency of electric motors and describes how DOE applied the screening criteria.

4.2 DISCUSSION OF DESIGN OPTIONS

 Several well-established engineering practices and techniques exist for improving the 

efficiency of an electric motor. Improving the construction materials (i.e., the core steel, the rotor 

conductor material) and modifying the motor’s geometric configuration (i.e., the core and 

winding assemblies, the rotor, and stator) can make an electric motor more energy efficient. 

As discussed in the market and technology assessment (chapter 3), there are four general 

areas of efficiency loss in electric motors: I2

R, core, friction and windage, and stray-load. In the 

final rule analysis DOE presented a list of technology options used to reduce energy 
4-2 

consumption and thus improve the efficiency of general purpose induction motors. 

Unfortunately, methods of reducing electrical losses in the equipment are not completely 

independent of one another. This means that some technology options that decrease one type of 

loss may cause an increase in a different type of loss in the motor. Thus, it takes a great degree of 

engineering skill to maximize the efficiency gains in a motor design overall, balancing out the 

loss mechanisms. In some instances, motor design engineers must make design tradeoffs to 

maintain utility when finding the appropriate combination of materials and costs. However, there 

are multiple design pathways to achieve a given efficiency level.

 I2R losses are produced from the current flow through the copper windings in the stator 

(stator I

2

R losses) and the squirrel cage of the rotor (rotor I

2

R losses). These losses are 

manifested as heat, which can shorten the service life of a motor. Core losses are the losses 

created in the electrical steel components of a motor. These losses, like I

2

R losses, manifest 

themselves as heat. Core losses are generated in the steel by two electromagnetic phenomena: 

hysteresis losses and eddy currents. Hysteresis losses are caused by magnetic domains in the 

steel resisting reorientation to the alternating magnetic field. Eddy currents are currents that are 

induced in the steel laminations by the magnetic flux. Although I2

R and core losses account for 

the majority of the losses in an induction motor, friction and windage losses and stray-load losses 

also contribute to the total loss. In an induction motor, friction and windage losses are caused by 

friction in the bearings of the motor and aerodynamic losses associated with the ventilation fan 

and other rotating parts. Any losses that are otherwise unaccounted for and not attributed to I2R 

losses, core losses, or friction and windage losses are considered stray-load losses. 

Table 4.1 presents a general summary of the methods that a manufacturer may use to 

reduce losses in electric motors. The approaches presented in this table refer either to specific 

technologies (e.g., aluminum versus copper die-cast rotor cages, different grades of electrical 

steel) or physical changes to the motor geometries (e.g., cross-sectional area of rotor conductor 

bars, additional stack length).
4-3 

Table 4.1 Summary List of Options from Technology Assessment 

Type of Loss to 

Reduce Technology Option

Stator I2R Losses

Increase cross-sectional area of copper in stator slots 

Decrease the length of coil extensions

Rotor I2R Losses

Use a die-cast copper rotor cage

Increase cross-sectional area of rotor conductor bars 

Increase cross-sectional area of end rings

Core Losses

Use electrical steel laminations with lower losses (watts/lb)

Use thinner steel laminations 

Increase stack length (i.e., add electrical steel laminations) 

Friction and 

Windage Losses

Optimize bearing and lubrication selection

Improve cooling system design

Stray-Load Losses Reduce skew on rotor cage 

Improve rotor bar insulation 

4.3 TECHNOLOGY OPTIONS NOT SCREENED OUT OF THE ANALYSIS

 This section discusses the technology options that DOE considers viable means of 

improving the efficiency of electric motors.

4.3.1 Increase the Cross-sectional Area of Copper in the Stator Slots 

Increasing the cross-sectional area of copper in the stator slots, by either increasing the 

slot fill percentage and/or increasing the size of the stator slots, can increase motor efficiency. 

Motor design engineers can achieve higher slot fills by manipulating the wire gauges to allow for 

a greater total cross-sectional area of wire to be incorporated into the stator slots. This could 

mean either an increase or decrease in wire gauge, depending on the dimensions of the stator 

slots and insulation thicknesses. Motor design engineers may also consider increasing the size of 

the stator slots to accommodate additional copper windings. However, this method replaces some 

of the stator magnetic cross-sectional area and increases the flux density in the stator. Increasing 

the flux density may increase core losses. Furthermore, there are practical limits to how much 

slot fill can be increased. The stator slot openings must be able to fit the wires so that automated 

machinery or manual labor can pull (or push) the wire into the stator slots. Very high slot fills 

may require hand winding, a manufacturing technique that is far more labor intensive than 

machine winding. The motor designer must carefully weigh the trade-offs to optimize the motor 

design. 

 
4-4 

 Considering the four screening criteria for this technology option, DOE did not screen out 

with increasing the cross-sectional area of copper in the stator as a means of improving 

efficiency. Motor design engineers adjust this technology option when manufacturing an electric 

motor to achieve desired performance and efficiency targets. Because this design technique is in 

commercial use today, DOE considers this technology option both technologically feasible and 

practicable to manufacture, install, and service. DOE is not aware of any adverse impacts on 

consumer utility, reliability, health, or safety associated with increasing the cross-sectional area 

of copper in the stator to obtain increased efficiency.

4.3.2 Decrease the Length of Coil Extensions

One method of reducing resistance losses in the stator is decreasing the length of the coil 

extensions at the end turns. Reducing the length of copper wire in the stator slots not only 

reduces the resistive losses, but also reduces the material cost of the electric motor because less 

copper is being used. 

Considering the four screening criteria for this technology option, DOE did not screen out 

decreasing the length of the coil extensions as a means of improving efficiency. Motor design 

engineers adjust this particular variable when manufacturing to obtain performance and 

efficiency targets. Because this design technique is in commercial use today, DOE considers this 

technology option both technologically feasible and practicable to manufacture, install, and 

service. DOE is not aware of any adverse impacts on consumer utility, reliability, health, or 

safety associated with decreasing the length of coil extensions to obtain increased efficiency. 

4.3.3 Copper Die-Cast Rotor Cage

Aluminum is the most common material used today to create die-cast rotor bars in electric 

motors. Some manufacturers that focus on producing high-efficiency designs have started to 

offer electric motors with die-cast rotor bars made of copper. Copper offers better performance 

than aluminum because copper has a higher electrical conductivity (i.e., a lower electrical 

resistance) per unit area. However, copper has a higher melting point than aluminum, so the 

casting process becomes more difficult and is likely to increase both production time and cost for 

manufacturing a motor. 

When assessing the technological feasibility of die-cast rotors, DOE notes that electric 

motors incorporating this technology option are already commercially available. DOE is aware 

of two large manufacturers — Siemens and SEW-Eurodrive — that offer die-cast copper rotor 

motors up to 30-horsepower. Additionally, a French rotor die-casting company called FAVI 

supplies die-cast copper rotors up to 100-horsepower (75 kW) to manufacturers of electric 

motors.a At larger horsepower ratings, DOE recognizes that assessing the technological 

feasibility of die-cast rotors is made more complex by the fact that manufacturers do not offer 

them commercially. That could be for a variety of reasons, among them:

 

a For more information about FAVI die cast copper rotors, visit

http://www.favi.com/download.php?fich=rotor/Plaquette+-+ang.pdf. The July 11, 2013, material from this website 

is available in Docket #EERE–2010–BT–STD–0027 at regulations.gov.
4-5 

1. Large copper die-cast rotors are physically impossible to construct;

2. They are possible to construct, but impossible to construct to required specifications; 

3. They are possible to construct to required specifications, but would require high 

manufacturing capital investment to do so and be so costly that few (if any) consumers 

would choose them. 

DOE is hesitant to screen out copper die-cast rotors on the basis of technological 

feasibility. It has not seen anything to suggest the advantages associated with copper rotors 

would vanish beyond a certain size. Relative to the above list of possible reasons for their 

absence from the high-horsepower market, DOE’s analysis does not conclude copper die-cast 

rotors are either: (1) physically impossible to construct or (2) possible to construct, but 

impossible to construct to required specifications. 

DOE also does not believe it has grounds to screen out copper die-cast rotors on the basis 

of practicability to manufacture, install, and service. The available facts indicate that 

manufacturers are already producing electric motors with die-cast copper rotors. DOE did speak 

with a manufacturer of casting presses (THT Presses) that reports the ability to cast large (up to 

600 lb) pieces of copper in a single run.b

Finally, based on DOE’s own shipments analysis (see TSD Chapter 9) and estimates of 

worldwide annual copper production,c DOE estimates that 0.01–0.02% of worldwide copper 

supply would be required to use copper rotors for every single motor within DOE’s scope of 

coverage. At present, DOE does not believe there is sufficient evidence to screen out copper diecast rotors from the analysis on the basis of adverse impacts to equipment utility or availability.

DOE is aware of the higher melting point of copper (1085 degrees Celsius versus 660 

degrees Celsius for aluminum) and the potential impacts this may have on the health or safety of 

plant workers. However, DOE does not believe at this time that this potential impact is 

sufficiently adverse to screen out copper as a die cast material for rotor conductors. The process 

for die casting copper rotors involves risks similar to those of die casting aluminum. DOE 

believes that manufacturers who die-cast metal at 660 Celsius or 1085 Celsius (the respective 

temperatures required for aluminum and copper) would need to observe strict protocols to 

operate safely. DOE understands that many plants already work with molten aluminum die 

casting processes and believes that similar processes could be adopted for copper. DOE has not 

received any supporting data about the increased risks associated with copper die casting, and 

could not locate any studies suggesting that the die-casting of copper inherently represented 

incrementally more risks to worker safety and health. DOE notes that several OSHA standards 

 

b http://thtpresses.com/copper-rotors/ c For more information about copper production, visit 

http://minerals.usgs.gov/minerals/pubs/commodity/copper/mcs-2012-coppe.pdf. The July 11, 2013, material from 

this website is available in Docket #EERE–2010–BT–STD–0027 at regulations.gov.
4-6 

relate to the safety of “Nonferrous Die-Castings, Except Aluminum,” of which die-cast copper is 

a part. d

Considering the four screening criteria for this technology option, DOE did not screen out 

copper as a die-cast rotor cage conductor material. 

4.3.4 Increase Cross-sectional Area of Rotor Conductor Bars 

Increasing the cross-sectional area of the rotor conductor bars can also improve motor 

efficiency. Resistance is inversely proportional to the cross-sectional area of the material through 

which current is flowing. By increasing the cross-sectional area, rotor bar resistance will 

decrease which may reduce rotor I

2

R losses.This technique can result in reduced rotor I2R losses 

if the increase in rotor current does not exceed the square of the decrease in the rotor resistance.

However, changing the shape of the rotor bars may affect the size of the end rings and can also 

change the torque characteristics of the motor. 

 Considering the four screening criteria for this technology option, DOE did not screen out 

increasing the cross-sectional area of rotor conductor bars as a means of improving efficiency.

Motor design engineers adjust this particular variable when manufacturing to obtain performance 

and efficiency targets. Because this design technique is in commercial use today, DOE considers 

this technology option both technologically feasible and practicable to manufacture, install, and 

service. DOE is not aware of any adverse impacts on consumer utility, reliability, health, or 

safety associated with increasing the cross-sectional area of rotor conductor bars to obtain 

increased efficiency.

4.3.5 Increase Cross-sectional Area of Rotor End Rings 

Increasing the cross-sectional area of the rotor end rings can also reduce rotor I2

R losses. 

Current flows through the end rings of the rotor and increasing the size of the end ring may 

decrease resistance and reduce the associated rotor I

2

R losses. This technique can result in 

reduced rotor I

2

R losses if the increase in rotor current does not exceed the square of the decrease 

in the rotor resistance. 

 Considering the four screening criteria for this technology option, DOE did not screen out 

increasing end ring size as a means of improving efficiency. As with some of the previous 

technology options, motor design engineers adjust this variable when manufacturing an electric 

motor to achieve performance and efficiency targets. Automated production and casting 

equipment, which allow some degree of variability, determine the end ring size. Because this 

design technique is in commercial use today, DOE considers this technology option both 

technologically feasible and practicable to manufacture, install, and service. DOE is not aware of 

any adverse impacts on consumer utility, reliability, health, or safety associated with increasing 

the size of the rotor end rings to obtain increased efficiency.

 

d For a list of OSHA standards , visit 

http://www.osha.gov/pls/imis/citedstandard.sic?p_esize=&p_state=FEFederal&p_sic=3364. The July 11, 2013, 

material from this website is available in Docket #EERE–2010–BT–STD–0027 at regulations.gov.
4-7 

4.3.6 Use Electrical Steel Laminations with Lower Losses

 Using a higher grade of electrical steel in the core can reduce core losses. Higher grades 

of steel exhibit lower core losses as well as higher magnetic permeability. Lower core losses can 

be achieved by adding silicon and other elements to the steel, thereby increasing its electrical 

resistivity. Lower core losses can also be achieved by subjecting the steel to special heat 

treatments during processing. 

 In studying the different types of steel available, DOE considered two types of materials: 

conventional silicon steels and “exotic” steels, which contain a relatively high percentage of 

boron or cobalt. Conventional steels are commonly used in electric motors manufactured today. 

The exotic steels are not generally manufactured for use specifically in the electric motors 

covered in this rulemaking. These steels offer lower core losses than the best conventional 

electrical steels, but are more expensive per pound. In addition, these steels can present 

manufacturing challenges because they come in nonstandard thicknesses that are difficult to 

manufacture.

There are three types of steel that DOE considers “conventional”: cold-rolled magnetic 

laminations (CRML), fully processed non-oriented electrical steel, and semi-processed nonoriented electrical steel. Each steel type is sold in a range of grades. In general, as the grade 

number goes down, so does the amount of core loss associated with the steel (i.e. watts of loss 

per pound of steel). The induction saturation level also drops, causing the need for increased 

stack length. Of these three types, CRML steels are the most commonly used, but also the least 

efficient. The fully processed steels are annealed before punching and therefore do not require 

annealing after being punched and assembled, and are available in a range of steel grades from 

M56 through M15. Semi-processed electrical steels are designed for annealing after punching 

and assembly.

 Considering the four screening criteria for this technology option, DOE did not screen out 

lower loss electrical steel in the core as a means of improving efficiency. Design engineers use 

this approach to achieve desired performance and efficiency targets. Because this design 

technique is in commercial use today, DOE considers this technology option both 

technologically feasible and practicable to manufacture, install, and service. DOE is not aware of 

any adverse impacts on consumer utility, reliability, health, or safety associated with using lower 

loss electrical steel.

4.3.7 Thinner Steel Laminations

 DOE can use thinner laminations of core steel to reduce eddy currents. DOE can either 

change grades of electrical steel as described above, or use a thinner gauge of the same grade of 

electrical steel. The magnitude of the eddy currents induced by the magnetic field becomes 

smaller in thinner laminations, which can result in a more energy efficient motor. 

 Considering the four screening criteria for this technology option, DOE did not screen out 

thinner steel laminations as a means of improving efficiency. Design engineers use this approach 

to achieve desired improvements in performance and efficiency. Because this design technique is 

in commercial use today, DOE considers this technology option both technologically feasible
4-8 

and practicable to manufacture, install, and service. DOE is not aware of any adverse impacts on 

consumer utility, reliability, health, or safety associated with using thinner steel laminations.

4.3.8 Increase Stack Length

Adding electrical steel laminations to the rotor and stator to lengthen the motor can also 

reduce the core losses in an electric motor. Increasing the stack length reduces the magnetic flux 

density, which reduces core losses. However, increasing the stack length affects other 

performance attributes of the motor, such as starting torque. Issues can also arise when installing 

a longer motor in applications with dimensional constraints.

 Considering the four screening criteria for this technology option, DOE did not screen out 

additional stack length as a means of improving efficiency. Design engineers use this approach 

to achieve desired improvements in performance and efficiency. Because this design technique is 

in commercial use today, DOE considers this technology option technologically feasible. 

Regarding the second screening criterion—practicable to manufacture, install, and service—

DOE understands that there are practical limits to lengthening a motor due to dimensional 

constraints of users. However, DOE recognizes that many motor applications are not constrained 

by motor length. Thus, DOE believes that this technology option meets the second screening 

criterion. DOE is not aware of any adverse impacts on consumer utility, reliability, health, or 

safety associated with increased stack length. 

4.3.9 Optimize Bearing and Lubricant Selection

 One way to improve efficiency is to optimize the selection of bearings and lubricant. 

Using improved bearings and lubricants can minimize mechanical resistance to the rotation of 

the rotor, which also extends motor life. 

 Considering the four screening criteria for this technology option, DOE did not screen out 

optimizing bearing and lubricant selection as a means of improving efficiency. Design engineers 

use this approach to achieve desired improvements in performance and efficiency. Because this 

design technique is in commercial use today, DOE considers this technology option both 

technologically feasible and practicable to manufacture, install, and service. DOE is not aware of 

any adverse impacts on consumer utility, reliability, health, or safety associated with better ball 

bearings and lubricant. 

4.3.10 Improve Cooling System Design

 Optimizing a motor’s cooling system is another technology option to improve the 

efficiency of electric motors. An optimized cooling system design provides ample motor cooling 

while reducing air resistance.

 Considering the four screening criteria for this technology option, DOE did not screen out 

an improved cooling system as a means of improving efficiency. Design engineers use this 

approach to achieve desired improvements in performance and efficiency. Because this design 

technique is in commercial use today, DOE considers this technology option both 

technologically feasible and practicable to manufacture, install, and service. DOE is not aware of 
4-9 

any adverse impacts on consumer utility, reliability, health, or safety associated with improved 

cooling systems for electric motors.

4.3.11 Reduce Skew on Conductor Cage 

One way to reduce stray-load losses is to reduce the skew in the rotor squirrel cage. The 

rotor conductor bars of the rotor cage are often skewed. This means the conductor bars are 

slightly offset from one end of the rotor to the other. By skewing the rotor bars, motor designers 

can reduce harmonics that add cusps to the speed-torque characteristics of the motor. The cusps 

in the speed-torque curves mean that the acceleration of the motor will not be completely 

smooth. The degree of skew matters because reducing the skew will help reduce the rotor 

resistance and reactance, which can result in improved efficiency. However, reducing the skew 

may have adverse impacts on the speed-torque characteristics.

 Considering the four screening criteria for this technology option, DOE did not screen out 

adjusting rotor skew as a means of improving efficiency. Rotor skew is one of the variables that 

motor design engineers can manipulate to obtain certain performance and efficiency targets. The 

rotor skew is a part of the overall motor design, which is input into automated production 

equipment that punches and stacks the steel to create a rotor with the desired skew. Because this 

design technique is in commercial use today, DOE considers this technology option both 

technologically feasible and practicable to manufacture, install, and service. DOE is not aware of 

any adverse impacts on consumer utility, reliability, health, or safety associated with properly

manipulating the rotor skew to obtain improved performance. 

4.3.12 Improved Rotor Bar Insulation 

Another way to reduce stray-load losses is to improve insulation between the rotor 

squirrel-cage and the rotor laminations. Motors with insulated rotor cages often exhibit lower 

stray-load losses when compared to motors with un-insulated rotor cages. Manufacturers use 

different methods to insulate rotor cages, such as applying an insulating coating on the rotor slot 

prior to die-casting or heating and quenching the rotor (i.e., rapid cooling, generally by 

immersion in a fluid instead of allowing the rotor temperature to equalize to the ambient

temperature) to separate rotor bars from rotor laminations after die-casting.

 Considering the four screening criteria for this technology option, DOE did not screen out 

improved rotor bar insulation as a means of improving efficiency. Design engineers use this 

approach to achieve desired improvements in performance and efficiency. Because this design 

technique is in commercial use today, DOE considers this technology option both 

technologically feasible and practicable to manufacture, install, and service. DOE is not aware of 

any adverse impacts on consumer utility, reliability, health, or safety associated with improved 

rotor bar insulation. 

4.3.13 Summary of Technology Options Not Screened Out

Table 4.2 summarizes the technology options that DOE did not screen out of the analysis. 
4-10 

Table 4.2 Summary List of Technology Options Not Screened Out

Type of Loss to 

Reduce Technology Option

Stator I2R Losses

Increase cross-sectional area of copper in stator slots 

Decrease the length of coil extensions

Rotor I2R Losses

Use a die-cast copper rotor cage

Increase cross-sectional area of rotor conductor bars 

Increase cross-sectional area of end rings

Core Losses

Use electrical steel laminations with lower losses (watts/lb)

Use thinner steel laminations 

Increase stack length (i.e., add electrical steel laminations) 

Friction and 

Windage Losses

Optimize bearing and lubrication selection

Improve cooling system design

Stray-Load Losses Reduce skew on rotor cage 

Improve rotor bar insulation 

4.4 TECHNOLOGY OPTIONS SCREENED OUT OF THE ANALYSIS

DOE screened out the following design options from further consideration because they

do not meet the screening criteria.

4.4.1 Amorphous Metal Laminations 

Using amorphous metals in the rotor laminations is another technology option to improve 

the efficiency of electric motors. Amorphous metal is extremely thin, has high electrical 

resistivity, and has little or no magnetic domain definition. Because of amorphous steel’s high 

resistance it exhibits a reduction in hysteresis and eddy current losses, which reduce overall 

losses in electric motors. However, amorphous steel is a very brittle material which makes it 

difficult to punch into motor laminations.1

 Considering the four screening criteria for this technology option, DOE screened out 

amorphous metal laminations as a means of improving efficiency. Although amorphous metals 

have the potential to improve efficiency, DOE does not consider this technology option 

technologically feasible, because it has not been incorporated into a working prototype of an 

electric motor. Furthermore, DOE is uncertain whether amorphous metals are practicable to 

manufacture, install, and service, because a prototype amorphous metal electric motor has not 

been made and little information is available on the ability to manufacture this technology to 

make a judgment. DOE is not aware of any adverse impacts on consumer utility, reliability, 

health, or safety associated with amorphous metal laminations. 
4-11 

4.4.2 Plastic Bonded Iron Powder

 Plastic bonded iron powder (PBIP) could cut production costs while increasing the output 

of electric motors. Although other researchers may be working on this technology option, DOE 

is aware of a research team at Lund University in Sweden that published a paper about PBIP. 

This technology option is based on an iron powder alloy that is suspended in plastic, and is used 

in certain motor applications such as fans, pumps, and household appliances.2 The compound is 

then shaped into motor components using a centrifugal mold, reducing the number of 

manufacturing steps. Researchers claim that this technology option could cut losses by as much 

as 50 percent. The Lund University team already produces inductors, transformers, and induction 

heating coils using PBIP, but has not yet produced an electric motor. In addition, it appears that 

PBIP technology is aimed at torus, claw-pole, and transversal flux motors, none of which fall 

under DOE’s scope of analysis as defined by the Energy Policy and Conservation Act, as 

amended by the Energy Independence and Security Act.

Considering the four screening criteria for this technology option, DOE screened out 

PBIP as a means of improving efficiency. Although PBIP has the potential to improve efficiency 

while reducing manufacturing costs, DOE does not consider this technology option 

technologically feasible, because it has not been incorporated into a working prototype of an 

electric motor. Also, DOE is uncertain whether the material has the structural integrity to form 

into the necessary shape of an electric motor steel frame. Furthermore, DOE is uncertain whether 

PBIP is practicable to manufacture, install, and service, because a prototype PBIP electric motor 

has not been made and little information is available on the ability to manufacture this 

technology to make a judgment. However, DOE is not aware of any adverse impacts on 

equipment utility, equipment availability, health, or safety that may arise from the use of PBIP in 

electric motors. 

4.4.3 Summary of Technology Options Screened Out of the Analysis

Table 4.3 shows the criteria DOE used to screen amorphous metal laminations and plastic 

bonded iron powder (PBIP) out of the analysis. 

Table 4.3 Technology Options Screened Out of the Analysis

Technology Option Excluded Eliminating Screening Criteria

Amorphous Metals Technological feasibility

PBIP Technological feasibility
4-12 

REFERENCES

1 S.R. Ning, J. Gao, and Y.G. Wang. Review on Applications of Low Loss Amorphous Metals in 

Motors. 2010. ShanDong University. Weihai, China.

2

Horrdin, H., and E. Olsson. Technology Shifts in Power Electronics and Electric Motors for 

Hybrid Electric Vehicles: A Study of Silicon Carbide and Iron Powder Materials. 2007. 

Chalmers University of Technology. Göteborg, Sweden. 
5-i 

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION .............................................................................................................. 1

5.2 EQUIPMENT CLASSES AND REPRESENTATIVE UNITS ANALYZED ................... 2

5.2.1 Electric Motor Design Type .................................................................................... 2

5.2.2 Horsepower Rating ................................................................................................. 4

5.2.3 Pole-Configuration .................................................................................................. 5

5.2.4 Enclosure Type ....................................................................................................... 6

5.2.5 Equipment Class Group 1 (NEMA Design Aand B Motors) ................................. 8

5.2.6 Equipment Class Group 2 (NEMA Design C Motors) ........................................... 8

5.2.7 Equipment Class Group 3 (Fire Pump Electric Motors) ......................................... 9

5.2.8 (Defunct) Equipment Class Group 4 (Brake Motors) ............................................. 9

5.3 BASELINE AND EFFICIENCY LEVELS........................................................................ 9

5.3.1 Efficiency Levels (ELs) ........................................................................................ 10

5.4 ENGINEERING ANALYSIS METHODOLOGY........................................................... 13

5.4.1 Subcontractor Tear-downs .................................................................................... 13

5.4.2 Subcontractor Software Designs ........................................................................... 14

5.5 COST MODEL ................................................................................................................. 16

5.5.1 Constructing a Bill of Materials ............................................................................ 17

5.5.2 Labor Costs and Assumptions .............................................................................. 18

5.5.3 Manufacturer Markups.......................................................................................... 19

5.5.4 Conversion Costs .................................................................................................. 20

5.6 RESULTS OF ENGINEERING ANALYSIS .................................................................. 21

5.6.1 NEMA Design B, 5-Horsepower, 4-Pole, Enclosed Electric Motor .................... 21

5.6.2 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Electric Motor .................. 23

5.6.3 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Electric Motor .................. 25

5.6.4 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Electric Motor .................... 27

5.6.5 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Electric Motor .................. 29

5.7 SCALING METHODOLOGY ......................................................................................... 31

5.7.1 Scaling Approach Using Incremental Improvements of Motor Losses ................ 31

5.7.2 Scaling Approach Using Regression Equations ................................................... 34
5-ii

LIST OF TABLES

Table 5-1 Electric Motor Equipment Class Groups ................................................................ 2

Table 5-2 Design Characteristics of the Five Representative Units Analyzed ....................... 7

Table 5-3 Baseline Efficiency Ratings of Representative Units ........................................... 10

Table 5-4 Efficiency Levels for ECG 1 ................................................................................. 11

Table 5-5 Efficiency Levels for ECG 2 ................................................................................. 12

Table 5-6 Efficiency Levels for each Representative Unit ................................................... 13

Table 5-7 Stack Length Measurements of Torn Down and Modeled Motors....................... 15

Table 5-8 Comparison of Maximum Stack Lengths Considered for Modeled Designs ....... 16

Table 5-9 Labor Markups for Electric Motor Manufacturers ............................................... 19

Table 5-10 Conversion Cost Adder for ELs above Premium efficiency levels. ..................... 21

Table 5-11 Efficiency and Manufacturer Selling Price Data for the NEMA Design B, ......... 22

Table 5-12 NEMA Design B, 5-Horsepower, 4-Pole, Enclosed Motor Characteristics ......... 23

Table 5-13 Efficiency and Manufacturer Selling Price Data for the NEMA Design B, 30-

Horsepower Motor ................................................................................................ 24

Table 5-14 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Motor Characteristics ....... 25

Table 5-15 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics ....................................................................................................... 25

Table 5-16 Efficiency and Manufacturer Selling Price Data for the NEMA Design B, 75-

Horsepower Motor ................................................................................................ 26

Table 5-17 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Motor Characteristics ....... 27

Table 5-18 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics ....................................................................................................... 27

Table 5-19 Efficiency and Manufacturer Selling Price Data for the NEMA Design C, 5-

Horsepower Motor ................................................................................................ 28

Table 5-20 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Motor Characteristics ......... 29

Table 5-21 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics ....................................................................................................... 29

Table 5-22 Efficiency and Manufacturer Selling Price Data for the NEMA Design C, 50-

Horsepower Motor ................................................................................................ 30

Table 5-23 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Motor Characteristics ....... 31

Table 5-24 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics ....................................................................................................... 31

LIST OF FIGURES

Figure 5.1 Electric Motor Shipments by Design Type for 2012 .............................................. 3

Figure 5.2 ECG 1 (NEMA Design A & B) Electric Motors Shipments by Horsepower Rating 

for 2012 ................................................................................................................... 4

Figure 5.3 ECG 2 (NEMA Design C) Electric Motors Shipments by Horsepower Rating for 

2012......................................................................................................................... 5

Figure 5.4 Electric Motor Shipments by Pole Configuration for 2012 .................................... 6

Figure 5.5 Electric Motor Shipments by Enclosure Type for 2012 ......................................... 7

Figure 5.6 Standard Method of Cost Accounting for Standards Rulemaking........................ 16

Figure 5.7 NEMA Design B, 5-Horsepower, 4-Pole, Enclosed Electric Motor Engineering 

Analysis Curve ...................................................................................................... 22

Figure 5.8 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Electric Motor Engineering 

Analysis Curve ...................................................................................................... 24

Figure 5.9 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Motor Engineering Analysis 

Curve ..................................................................................................................... 26

Figure 5.10 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Motor Engineering Analysis 

Curve ..................................................................................................................... 28

Figure 5.11 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Motor Engineering Analysis 

Curve ..................................................................................................................... 30

Figure 5.12 Segmentation of Electric Motor Market for Representative Units ....................... 33

Figure 5.13 Motor Losses versus Horsepower Rating ............................................................. 35

Figure 5.14 Function of Electric Motor Losses with Horsepower for 4-Pole, Enclosed Electric 

Motors ................................................................................................................... 36

Figure 5.15 Function of Electric Motor Losses with Horsepower Derived for EL 2 and EL 3 

for 4-Pole, Enclosed Electric Motors of NEMA Design A & B ........................... 37
5-iii

Figure 5.16 Scaling Across Electric Motor Configurations ..................................................... 38
5-iv

LIST OF ACRONYMS AND ABBREVIATIONS

A ampere

AC alternating current

AWG American wire gauge

BOM bill of materials

EL efficiency level

DOE United States Department of Energy

EISA Energy Independence and Security Act of 2007 

IEC International Electrotechnical Commission

in inch

lbs pounds 

M* M15, M19, M36, M47, M56 - grade of core steel

MSP manufacturer selling price

Nm Newton meter 

NCI Navigant Consulting, Inc. 

NEMA National Electrical Manufacturers Association

NIA national impact analysis

RPM revolutions per minute 

SEC Securities and Exchange Commission

SME subject matter expert

TSD technical support document 

U.S. United States

V volt
5-1 

5.1 INTRODUCTION

The engineering analysis estimates the increase in manufacturer selling price (MSP) 

associated with technological design changes that improve the efficiency of an electric motor. 

This chapter presents the U.S. Department of Energy’s (DOE’s) assumptions, methodology and 

findings for the electric motor engineering analysis. The output from the engineering analysis is 

a “cost-efficiency” relationship for each electric motor analyzed which describes how its cost 

changes as efficiency increases. The output of the engineering analysis is used as an input to the 

life-cycle cost analysis (Technical Support Document (TSD) chapter 8) and the national impact 

analysis (TSD chapter 10). 

The engineering analysis takes input from the market and technology assessment (see 

TSD chapter 3) and the screening analysis (see TSD chapter 4). These inputs include equipment 

classes, baseline electric motor performance, methods for improving efficiency, and design 

options that have passed the screening criteria. The engineering analysis uses these inputs, 

coupled with material price estimates, design parameters, and other manufacturer inputs to 

develop the relationship between the MSP and nominal full-load efficiency of the representative 

electric motors studied.

At its most basic level, the output of the engineering analysis is a curve that estimates the 

MSP for a range of efficiency values. This output is subsequently marked-up to determine the 

end-user prices based on the various distribution channels (see TSD chapter 6). After 

determining customer prices by applying distribution chain markups, sales tax, and contractor 

markups, the data is combined with the energy-use and end-use load characterization (see TSD 

chapter 7) and used as a critical input to the customer’s life-cycle cost and payback period 

analysis (see TSD chapter 8).

In this chapter, DOE discusses the equipment classes analyzed and the representative 

electric motors selected from all motors considered for energy conservation standards. As 

discussed in chapters 2 and 3 of this TSD, the electric motors in the scope of coverage of this 

rulemaking include single-speed, squirrel-cage induction, alternating current (AC), polyphase 

motors from 1 to 500 horsepower and National Electrical Manufacturers Association (NEMA) 

Design A, B, and C motors, including fire pump electric motors. The engineering analysis 

selected three NEMA Design B motors to analyze the NEMA Design A and B equipment class 

group and two NEMA Design C motors to analyze the NEMA Design C equipment class group. 

The fire pump electric motor equipment class group is based on the three NEMA Design B 

motors. DOE also presents the methodology, inputs, and results associated with the development 

of MSP versus efficiency curves for each of the representative electric motors. Finally, DOE 

discusses the approach used to scale the efficiency levels analyzed to all other equipment classes

for the national impact analysis. 
5-2 

5.2 EQUIPMENT CLASSES AND REPRESENTATIVE UNITS ANALYZED

Due to the large number of equipment classes, DOE did not directly analyze all covered 

electric motors. Instead, DOE selected certain equipment classes to directly analyze after 

reviewing electric motors shipments, examining manufacturers’ catalog data, and soliciting 

feedback from interested parties. The equipment classes that DOE directly analyzes and focuses 

its engineering analysis on are referred to as representative units. Table 5-1 shows the equipment 

class groups discussed in TSD chapter 3 and the corresponding electric motor designs they 

encompass. As mentioned above, DOE selected three representative units to analyze in 

equipment class group 1 and two representative units in equipment class group 2. For equipment 

class group 3, DOE plans on developing any potential amended energy conservation standards 

based off of its analysis of equipment class group 1 because fire pump electric motors are 

required to meet National Electrical Manufacturers Association (NEMA) Design B performance 

standards as per National Fire Protection Association Standard (NFPA) 20. 

Table 5-1 Electric Motor Equipment Class Groups

Equipment 

Class Group 

(ECG)

Electric Motor

Design Type 

Horsepower

Rating

Pole

Configuration Enclosure

1 NEMA Design A & B* 1–500 2, 4, 6, 8 Open

Closed

2 NEMA Design C* 1–200 4, 6, 8 Open

Closed

3 Fire Pump* 1–500 2, 4, 6, 8 

Open

Closed

*Includes International Electrotechnical Commission (IEC) equivalent design types. 

DOE considered each of the characteristics listed in Table 5-1 when selecting its 

representative units. The sections that follow describe the decisions that DOE made with respect 

to each of these electric motor characteristics.

5.2.1 Electric Motor Design Type 

For equipment class group 1 that includes NEMA Design A and B motors, DOE only 

selected NEMA Design B motors as representative units to analyze in the engineering analysis. 

DOE chose NEMA Design B motors because NEMA Design B motors have slightly more 

stringent performance requirements, namely their locked-rotor current has a maximum allowable 

level for a given rating. Consequently, NEMA Design B motors are slightly more restricted in 

terms of their maximum efficiency levels. Therefore, by analyzing NEMA Design B motors, 

DOE could ensure technological feasibility for all designs covered in ECG 1. Additionally, 

NEMA Design B units have much higher shipment volumes than NEMA Design A motors 

because most motor driven equipment is designed (and UL listed) to run with NEMA Design B 

motors.. Figure 5.1 shows the relative shipments of each electric motor design type, which 

demonstrates that NEMA Design B motors constitute the vast majority of all shipments with a

market share of 96 percent. Finally, by choosing NEMA Design B motors, DOE could also apply 
5-3 

the results of its equipment class group 1 analysis to its equipment class group 3 analysis because 

fire pump motor designs are held to very similar design constraints as NEMA Design B motors. 

For equipment class group 2, DOE selected two representative units to analyze directly. 

Since NEMA Design C is the only NEMA design type covered by this equipment class group, 

DOE only selected NEMA Design C motors for analysis as its representative units. 

Figure 5.1 Electric Motor Shipments by Design Type for 2012a

 For equipment class group 3, which consists of fire pump electric motors, DOE based its 

engineering analysis on the NEMA Design B units analyzed for ECG 1. As noted, in order to be 

in compliance with section 9.5 of National Fire Protection Association (NFPA) "Standard for the 

Installation of Stationary Pumps for Fire Protection” Standard 20-2010, which is a requirement 

for a motor to meet DOE’s current definition of a “fire pump electric motor,” the motor must 

comply with NEMA Design Brequirements.b Even though fire pump electric motors have 

additional performance requirements, the analysis of the ECG 1 motors would serve as a 

sufficient approximation for the cost-efficiency relationship for fire pump electric motors. The 

design differences between a NEMA Design B motor (or IEC-equivalent) and fire pump electric 

motor are small and unlikely to greatly affect incremental cost behavior. 

 

a In the final rule, analytical results for brake electric motors are reported in ECG 1 under subgroup 1b. DOE 

believes most brake electric motors fall into NEMA Design B category, although notes that its definition of “Brake 

Electric Motor” may apply to a motor of any design. 78 FR 75993.

b With the exception of having a thermal shutoff switch, which could prevent a fire pump motor from performing its 

duty in hot conditions, NFPA 20 also excludes several motor types not considered in this rulemaking from the 

NEMA Design B requirement. They are direct current, high-voltage (over 600 V), large-horsepower (over 500 hp), 

single-phase, universal-type, and wound-rotor motors.
5-4 

5.2.2 Horsepower Rating

Horsepower rating is an important equipment class setting criterion. When DOE selected 

its representative units, DOE chose those horsepower ratings that constitute a high volume of 

shipments in the market and provide a sufficiently wide range upon which DOE could 

reasonably base a scaling methodology. For NEMA Design B motors, for example, DOE chose 

5-, 30-, and 75-horsepower-rated electric motors to analyze as representative units. DOE selected 

the 5-horsepower rating because it is the rating with the highest shipment volume of the electric 

motors considered. Figure 5.2 shows shipments of electric motors in equipment class group 1 

broken down by horsepower rating and demonstrates that the 5-horsepower rating constituted 

nearly 23 percent of shipments in 2012. DOE selected the 30-horsepower rating as an 

intermediary between the small and large frame number series electric motors. For the largest 

frame number series, DOE elected to analyze a 75-horsepower rated electric motor. DOE 

believes that this rating is an appropriate choice to represent the highest horsepower ratings 

because there tends to be minimal change in efficiency between the highest horsepower ratings. 

For consecutive horsepower ratings above 75, the nominal efficiencies that motors must meet in 

order to be deemed premium efficiency level tend to repeat.c

Figure 5.2 ECG 1 (NEMA Design A & B) Electric Motors Shipments by Horsepower 

Rating for 2012 

For NEMA Design C motors, DOE only selected two horsepower ratings because of the 

relatively low shipment volumes and smaller range of horsepower ratings. As with NEMA 

Design B motors, DOE elected to analyze the 5-horsepower rating because of its relatively high 

 

c In June 2001, NEMA began a program to provide special recognition to certain electric motors whose energy 

efficiency was better than that required under the Energy Policy Act of 1992 (EPACT 1992). NEMA created a 

designation called NEMA Premium. This designation applies to single-speed, polyphase, 1 to 500 horsepower, 2-, 4-

, and 6-pole (3600, 1800 and 1200 rpm) squirrel-cage induction motors, NEMA Designs A or B, 600V or less, (5kV 

or less for medium voltage motors), and continuous rated. The energy efficiency values are defined in NEMA MG 1 

Table 12−12. Section 342(b)(2)(A) of EPCA, as amended, 42 U.S.C. 6313(b)(2)(A), essentially incorporates by 

reference NEMA MG 1 Table 12−12.
5-5 

market share. For an upper bound, DOE selected the 50-horsepower rating due to the smaller 

range of horsepower ratings for NEMA Design C motors. Figure 5.3 shows shipments of electric 

motors in equipment class group 2 broken down by horsepower rating. 

Figure 5.3 ECG 2 (NEMA Design C) Electric Motors Shipments by Horsepower Rating 

for 2012 

5.2.3 Pole-Configuration 

Pole-configuration is another important equipment class setting criterion which DOE had 

to consider when selecting its representative units. For the engineering analysis, DOE selected 4-

pole motors for all of its representative units. DOE chose not to vary the pole configuration of 

the various representative units it analyzed because it believed that doing so would provide the 

strongest relationship upon which to base its scaling. By keeping as many design characteristics 

constant as possible, DOE could more accurately identify how design changes affect efficiency 

across horsepower ratings. For example, if DOE compared the premium efficiencies of a 5-

horsepower, 4-pole electric motor and 50-horsepower, 6-pole electric motor it would be difficult 

to determine how much of the difference was due to the change in horsepower rating and how 

much was due to the change of pole configuration. Additionally, DOE believes that the 

horsepower rating-versus-efficiency relationship is the most important (rather than pole 

configuration and enclosure-type versus efficiency) because there are significantly more 

horsepower ratings to consider. Finally, as illustrated in Figure 5.4, 4-pole electric motors 

constitute the largest fraction of the electric motors market. Electric motors built with 4-poles 

accounted for 69 percent of shipments in 2012, which was more than 2-pole, 6-pole, and 8-pole 

motor shipments combined. 
5-6 

Figure 5.4 Electric Motor Shipments by Pole Configuration for 2012 

5.2.4 Enclosure Type

The final equipment class setting criterion that DOE had to consider when selecting its 

representative units was enclosure type. DOE elected to only analyze electric motors with totally 

enclosed, fan-cooled (TEFC) designs rather than open designs for all of its representative units. 

DOE selected TEFC motors because, as with pole configurations, DOE wanted as many design 

characteristics to remain constant as possible. Again, DOE believed that such an approach would 

allow it to more accurately identify the reasons for efficiency improvements. Finally, TEFC 

electric motors represented more than three times the shipment volume of open motors. Figure 

5.5 shows the relative shipments of open and enclosed motors in the year 2012. 
5-7 

Figure 5.5 Electric Motor Shipments by Enclosure Type for 2012 

As addressed above, when identifying which electric motors to evaluate, DOE considered 

equipment classes that represented motors with a significant volume of shipments. DOE also 

considered the necessity for scaling its engineering results. Therefore, DOE selected electric 

motors that would minimize any error that might be introduced through extrapolating between 

horsepower ratings, pole configurations, and enclosure types. As is discussed in section 5.7, 

DOE scaled the engineering analysis results of its analyzed representative units to all of the 

other, not-analyzed, equipment classes. Such scaling is necessary for the national impacts 

analysis (NIA). For more information on the NIA, please see TSD chapter 10. Table 5-2 presents 

the major design characteristics of the five representative units that DOE analyzed and will 

discuss in detail throughout this engineering analysis. 

Table 5-2 Design Characteristics of the Five Representative Units Analyzed

Equipment Class 

Group Represented

Electric Motor

Design Type 

Horsepower 

Rating

Pole 

Configuration Enclosure

1, 3 NEMA Design B 5 4 Totally Enclosed, 

Fan Cooled

1, 3 NEMA Design B 30 4 Totally Enclosed, 

Fan Cooled

1, 3 NEMA Design B 75 4 Totally Enclosed, 

Fan Cooled

2 NEMA Design C 5 4 Totally Enclosed, 

Fan Cooled

2 NEMA Design C 50 4 Totally Enclosed, 

Fan Cooled
5-8 

5.2.5 Equipment Class Group 1 (NEMA Design Aand B Motors) 

DOE decided to focus the analysis of NEMA Design A and NEMA Design B motors on 

three representative units. When selecting these representative units, DOE used the data in 

Figure 5.2 and Figure 5.12 to select three representative units with high shipping volume that 

also evenly cover the entire range of horsepower ratings in the scope of this analysis. The graph 

in Figure 5.12 shows the average efficiencies of 4-pole, enclosed electric motors versus 

horsepower rating. This data was based on DOE’s electric motor database which was compiled 

from the most current electric motor manufacturer catalog data available. DOE analyzed this 

curve and segmented the graph into three primary sections. 

5.2.6 Equipment Class Group 2 (NEMA Design C Motors)

When selecting the representative units for equipment class group 2 (NEMA Design C 

motors), DOE referred to Figure 5.3 which represents the shipment volumes of NEMA Design C 

motors. Based on Figure 5.2, DOE selected a 5-horsepower electric motor again because of its 

high volume of shipments. To cover the higher horsepower ratings, DOE selected a 50-

horsepower electric motor. DOE chose to base the analysis on the NEMA Design C equipment 

class group on two electric motors instead of three due to the smaller range of horsepower ratings 

as well as the lower production volumes of NEMA Design C motors and therefore somewhat 

limited equipment selection. DOE selected the 50-horsepower rating because it falls between the 

30-horsepower and 75-horsepowerratings selected as representative units for equipment class 

group 1. 
5-9 

5.2.7 Equipment Class Group 3 (Fire Pump Electric Motors)

According to National Fire Protection Association (NFPA) 20, Standard for the 

Installation of Stationary Pumps for Fire Protection, electric motors (as covered under this 

rulemaking) used with a fire pump system must comply with NEMA Standards Publication MG

1, Motors and Generators, (MG 1) requirements , comply with NEMA Design B requirements, 

and be listed for fire pump service. So, with a few exceptions, fire pump electric motors are very 

similar to NEMA Design B motors. Namely, fire pump electric motors are not required to shut 

off if they are overheating, and they require more rigorous start/stop capabilities than general 

purpose NEMA Design B motors. Aside from these operating differences, fire pump electric 

motors are electromechanically similar to NEMA Design B motors. Therefore, DOE decided to 

base the analysis of fire pump electric motors on the engineering data produced from the 

representative units chosen for equipment class group 1. 

5.2.8 (Defunct) Equipment Class Group 4 (Brake Motors) 

 Until the final rule, DOE had analyzed brake electric motors in a separate equipment 

class. Following publication of the 2013 Test Procedured, DOE believes that brake motors can be 

tested fairly and meet the same energy efficiency requirements as any other motor type. 

Although DOE believes that most brake motors within the scope of this rule are built to Design 

B specifications, DOE notes that brake motors may be of any design type and, therefore, fall into 

any equipment class group. 

5.3 BASELINE AND EFFICIENCY LEVELS 

For each representative unit selected, DOE identified a specific baseline electric motor as 

a fundamental design against which it would apply design changes to improve the electric 

motor’s efficiency. DOE chose the baseline electric motors to represent the typical 

characteristics of electric motors in the equipment class of the corresponding representative unit. 

The baseline efficiency level is used to determine energy savings and changes in price associated 

with moving to higher efficiency levels. Efficiency levels (ELs) help characterize the costefficiency relationship. Table 5-3 shows the baseline efficiency levels for each of DOE’s selected 

representative units.

 

d 78 FR 75961
5-10 

Table 5-3 Baseline Efficiency Ratings of Representative Units

Basic Characteristics of Electric Motors 

Analyzed

Baseline 

Efficiency %

Equipment Class 

Group

Design B, 5-horsepower, 4-pole, enclosed frame 82.5 1*

Design B, 30-horsepower, 4-pole, enclosed frame 89.5 1*

Design B, 75-horsepower, 4-pole, enclosed frame 93.0 1*

Design C, 5-horsepower, 4-pole, enclosed frame 87.5 2

Design C, 50-horsepower, 4-pole, enclosed frame 93.0 2

*Analysis of equipment class groups 3 is based on these representative units. However, the baseline for equipment 

class group 3 is slightly higher (equivalent to NEMA MG 1 Table 12-11) because fire pump electric motors have 

conservation standards set by the Energy Independence and Security Act of 2007 (EISA 2007) while equipment 

class group 1 includes electric motors with no existing conservation standards. 

As discussed in chapters 2 and 3, DOE is expanding the scope of energy conservation 

standards to include electric motors that were not previously covered by regulation. Those motor 

types not previously covered and that are now within the scope of coverage are listed in chapter 

3 of the TSD. DOE used a motor database of efficiencies and up-to-date manufacturer motor 

catalogs to find motors with the lowest market efficiency. Since the expanded scope of energy 

conservation standards includes motors not previously subject to efficiency standards, DOE 

selected motors whose baseline efficiencies were below the lowest energy conservation levels 

currently enforced for any motors (levels most recently prescribed by EISA 2007). DOE 

observed NEMA Design B vertical, hollow-shaft motors, currently outside the scope of 

regulation, with efficiency levels listed in Table 5-3. For the NEMA Design C equipment class 

group, DOE selected NEMA MG 1 Table 12-11 values as baseline efficiency levels. This 

approach is based on the lowest efficiency values DOE observed in motor catalogs for NEMA 

Design C motors. The NEMA Design C representative motors with the lowest observed 

efficiencies are also listed in Table 5-3. DOE notes that although the efficiencies in Table 5-3

represent the baseline, DOE’s efficiency distribution for equipment class group 1 shows a 

significant portion of motors already above the baseline efficiency level.

5.3.1 Efficiency Levels (ELs)

NEMA MG 1-2011 contains a table of standardized “nominal” full load efficiency 

values, Table 12-10, from which manufacturers may choose a value to label and market their 

electric motors. NEMA uses these standardized values of efficiency to characterize the efficiency 

of a population of electric motors because of the variability in performance due to materials used 

in electric motors, such as electrical steel and copper, and the laboratory to laboratory test 

variation that can occur. Because of these possible sources of performance variation, NEMA and 

its members in industry use these standardized values of efficiencies, with associated guaranteed 

minimum values of efficiencies, to represent a specific electric motor model’s efficiency with a 

“band” of efficiency. The standardized values of NEMA nominal efficiencies found in Table 12-
5-11 

10 of NEMA MG 1-2011 are fairly evenly spaced in terms of motor losses.e

 Each higher, 

incremental level of nominal efficiency represents a reduction in motor losses of roughly 10 

percent. DOE followed a similar pattern when developing its higher ELs (i.e., those above 

NEMA MG 1-2011 Table 12-11 and Table 12-12).

As mentioned earlier, DOE selected a baseline model for each representative unit as a 

reference point against which to measure changes that may result from increasing an electric 

motor’s efficiency. Each increase in efficiency over the baseline level that DOE analyzed was 

assigned an EL number. DOE based its baseline efficiency level, or EL 0, on the lowest 

efficiency levels observed in motor catalog data for the electric motors DOE plans on including 

in the expanded scope of conservation standards. DOE selected four additional incremental ELs 

for equipment class group 1 and two additional incremental ELs for equipment class group 2 

based on other industry specifications, market data, and software modeling. 

Table 5-4 shows the ELs for equipment class group 1 that DOE used for electric motors 

during the analysis. DOE based its first incremental EL (EL 1) on NEMA MG 1-2011, Table 12-

11 which specify the nominal efficiency levels for motors that NEMA classifies as “energy 

efficient”. Table 12-11 is equivalent to the EPACT 1992 levels for 1 to 200 horsepower NEMA 

Design B motors and the EISA 2007 levels for NEMA Design B motors with a horsepower 

rating greater than 200. EISA 2007 also mandated that general purpose electric motors (subtype 

I) from 1 to 200 horsepower meet efficiency levels that correspond to NEMA MG 1-2011, Table 

12-12 (i.e., equivalent to premium levels). However, equipment class group 1 includes motors 

that are considered general purpose electric motors (subtype II). For these electric motors, EISA 

2007 mandated efficiency standards equivalent to Table 12-11, which is why DOE believes 

Table 12-11 is the appropriate EL 1 to represent equipment class group 1. 

Table 5-4 Efficiency Levels for ECG 1

EL Number EL Name

NEMA 

MG 1-2011 

Table

Note

0 Baseline — Lowest observed efficiency under expanded scope

1 Standard 12-11 EPACT 1992 requirement

2 Premium 12-12 EISA 2007 requirement for general purpose electric 

motors (subtype I) 

3 Best-in-Market — One NEMA nominal efficiency level improvement 

relative to the Premium level

4 Maximum 

Technology — One NEMA nominal efficiency level improvement 

relative to the Best-in-Market

 

e Motor losses (watts) are calculated with the formula 













 −1 1

η

P , where P represents the motor’s rated power in 

watts, and η represents the value of efficiency. 
5-12 

DOE based its second incremental EL (EL 2) on the premium efficiency levels, found in 

NEMA MG 1 Tables 12-12. These tables typically represent a two or three NEMA band 

improvement above the previously mandated EPACT 1992 levels displayed in NEMA MG 1 

Table 12-11. The third incremental EL (EL 3) is based on motors with the highest efficiencies 

observed in DOE’s motor database and up-to-date motor catalogs. Therefore EL 3 motors have 

the “best-in-market” efficiencies for equipment class group 1 (ECG 1). This level was generally 

one NEMA band above the premium efficiency level, or EL 2. This level represents the best or 

near best efficiency level at which current manufacturers are producing electric motors. EL 4 

represents an incremental level between the maximum available efficiency and the maximum 

technology (“max-tech”) EL. EL 4 represents the maximum technologically available or “maxtech” efficiency level. EL 4 is based on a motor which incorporates a combination of the best 

materials potentially available for high-production motor manufacturing. This includes low-loss 

electrical steel and copper rotor motor technology. DOE based its value of efficiencies for EL 4 

on a physical electric motor, computer-modeled designs and subject matter expert (SME) 

feedback.

Table 5-5 shows the ELs for equipment class group 2 (NEMA Design C motors), which

were selected differently than for equipment class group 1. For equipment class group 2, DOE 

selected the NEMA MG 1 Table 12-11 values as the baseline efficiency level. This approach is 

based on the lowest efficiency values DOE observed in manufacturer catalogs for NEMA Design 

C motors, which are the EPACT 1992 equivalent efficiency levels (as mandated by EISA 2007 

under ‘general purpose electric motor (subtype II)’). Further ELs for ECG 2 were selected based 

on computer modeling results, and are displayed in Table 5-6. 

Table 5-5 Efficiency Levels for ECG 2

EL Number EL Name

NEMA 

MG 1-2011 

Table

Note

0 Baseline 12-11 Lowest observed efficiency under expanded scope

(EPACT 1992 requirement)

1 Premium 12-12 EISA 2007 requirement for general purpose electric 

motors (subtype I)

2 Maximum 

Technology — One NEMA nominal efficiency level improvement 

relative to the Premium level

Table 5-6 shows the nominal efficiency values for each representative unit and each EL. 

Cells with a ‘†’ indicate a physical electric motor that DOE purchased and tore down. Cells with 

a ‘*’ indicate the efficiency levels are from software modeling data gathered from DOE’s SME 

which were derived using various technology, material, and geometry changes. Cells with a ‘—’ 

indicate that DOE was not able to further increase efficiency levels for these representative units 

and still keep an electric motor design within the proper specifications. 
5-13 

Table 5-6 Efficiency Levels for each Representative Unit

Efficiency

Level (EL)

5-Horsepower 

Design B 

Efficiency (%)

30-Horsepower 

Design B 

Efficiency (%)

75-Horsepower 

Design B 

Efficiency (%)

5-Horsepower 

Design C 

Efficiency (%)

50-Horsepower 

Design C 

Efficiency (%)

0 82.5† 89.5† 93.0† 87.5† 93.0†

1 87.5† 92.4† 94.1† 89.5* 94.5*

2 89.5† 93.6† 95.4† 91.0* 95.0*

3 90.2† 94.1† 95.8† — —

4 91.0† 94.5* 96.2* — —

†Indicates the efficiency of a purchased and physically torn-down electric motor

*Indicates the efficiency of a software-modeled electric motor

5.4 ENGINEERING ANALYSIS METHODOLOGY

As stated, the engineering analysis estimates the cost increment for the efficiency 

improvement potential of individual design options or combinations of design options that pass 

the four criteria in the screening analysis. DOE uses this cost-efficiency relationship, developed 

in the engineering analysis, in the LCC analysis.

DOE can use three methodologies to generate the manufacturing costs needed for the 

engineering analysis. These methods are:

1. the design-option approach — reporting the incremental costs of adding design options to 

a baseline model;

2. the efficiency-level approach — reporting relative costs of achieving improvements in 

energy efficiency; and

3. the reverse engineering or cost assessment approach — involving a "bottom up" 

manufacturing cost assessment based on a detailed bill of materials derived from electric 

motor teardowns.

Because DOE targeted certain nominal efficiency levels when improving baseline 

efficiencies and relied on tear-downs of electric motors, DOE’s analysis for the electric motor 

rulemaking is a combination of the efficiency-level approach and the reverse engineering 

approach. DOE created baseline costs from bills of materials of electric motor tear-downs and 

then determined the costs of increasing efficiency levels based on material or technology 

changes.

5.4.1 Subcontractor Tear-downs

Due to limited manufacturer feedback concerning cost data and production costs, DOE 

derived its production and material costs by having a professional motor laboratoryf disassemble 

 

f The Center for Electromechanics University of Texas at Austin, a 140,000 sq. ft. lab with 40 years of operating 

experience with teardowns overseen by Dr. Angelo Gattozzi, an electric motor expert with previous industry 

experience. In addition, some teardowns were performed at Advanced Energy, an independent test lab with NVLAP 

(National Voluntary Laboratory Accreditation Program) certification located in North Carolina. 
5-14 

and inventory the physical electric motors purchased. DOE performed tear-downs on the electric 

motors representing EL 0 through 3 for equipment class group 1 as well as electric motors 

representing EL 0 for equipment class group 2. These tear-downs provided DOE the necessary 

data to construct a bill of materials, which DOE could normalize using a standard cost model and 

markup to produce a projected manufacturer selling price (MSP). DOE used the MSP derived 

from the engineering tear-down paired with the corresponding nameplate nominal efficiency to 

report the relative costs of achieving improvements in energy efficiency. DOE derived material 

prices from a consensus of current, publicly available data, manufacturer feedback, and 

conversations with its subject matter experts. DOE supplemented the findings from its tests and 

tear-downs through: (1) a review of data collected from manufacturers about prices, efficiencies, 

and other features of various models of electric motors, and (2) interviews with manufacturers 

about the techniques and associated costs used to improve efficiency. 

DOE’s engineering analysis documents the design changes and associated costs when 

improving electric motor efficiency from the baseline level up to a max-tech level. This includes 

considering improved electrical steel for the stator and rotor, using die-cast copper rotors, 

increasing stack length, and any other applicable design options remaining after the screening 

analysis. As each of these design options are added, the manufacturer’s cost generally increases 

and the electric motor’s efficiency improves. 

5.4.2 Subcontractor Software Designs

 DOE worked with technical experts to develop the highest efficiency levels (i.e., the 

max-tech levels) technologically feasible for each representative unit analyzed. DOE used a 

combination of electric motor software design programs and SME input. DOE retained an 

electric motor expertg

 with design experience and software, who prepared a set of designs with 

increasing efficiency. The design software DOE used is a proprietary software program called 

VICA.h The SME also checked his designs against tear-down data and calibrated his software

using the relevant test results. As new designs were created, careful attention was paid to the 

critical performance characteristics defined in NEMA MG 1-2011 Tables 12-2, 12-3, 12-4, and 

paragraph 12.35.1, which define locked-rotor torque, breakdown torque, pull-up torque and 

maximum locked-rotor currents, respectively. This was done to ensure that the utility of the 

baseline unit was conserved as efficiency was improved through the application of various 

design options. Additionally, DOE limited its modeled stack length increases based on tear-down 

data and the maximum “C” dimensions found in manufacturer’s catalogs.i

DOE limited the amount by which it would increase the stack length of its softwaremodeled electric motors to preserve the utility of the baseline model torn down. The maximum 

stack lengths used in the software-modeled ELs were determined by first analyzing the stack 

lengths and “C” dimensions of torn-down electric motors. Then, DOE analyzed the “C” 

dimensions of various electric motors in the marketplace conforming to the same design 

constraints as the representative units (same NEMA design letter, horsepower rating, NEMA 

 

g Dr. Howard Jordan, Ph.D, an electric motor design expert with over 40 years of industry experience.

h VICA stands for “Veinott Interactive Computer Aid”. i

The “C” dimension of an electric motor is the length of the electric motor from the end of the shaft to the end of the 

opposite side’s fan cover guard. Essentially, the “C” dimension is the overall length of an electric motor including 

its shaft extension.
5-15 

frame series, enclosure type, and pole configuration). For each representative unit, DOE found 

the largest “C” dimension currently available on the marketplace and estimated a maximum 

stack length based on the stack length to “C” dimension ratios of motors it tore down. The 

resulting product was the value that DOE chose to use as the maximum stack length in its 

software modeled designs. Table 5-7 shows the stack lengths of torn down ELs and stack lengths 

used in the software modeled ELs. Table 5-8 shows the estimated maximum stack length that 

was used as an upper bound in the software modeled ELs. The efficiency levels of the software 

modeled ELs are displayed in Table 5-6. 

Table 5-7 Stack Length Measurements of Torn Down and Modeled Motors

Representative Unit EL Stack Length

(in)

5 HP, Design B

0 2.80* 

1 3.47

2 5.14

3 4.65

4 5.02

30 HP, Design B

0 7.87* 

1 5.53

2 8.02

3 6.74

4 7.00**

75 HP, Design B

0 8.15*

1 10.23

2 10.58

3 11.33

4 12.00**

5 HP, Design C

0 4.75

1 4.25**

2 5.32**

50 HP, Design C

0 8.67

1 9.55**

2 9.55**

*Represents stack length of a vertical, hollow-shaft motor.

**Represents stack length of a software modeled motor. 
5-16 

Table 5-8 Comparison of Maximum Stack Lengths Considered for Modeled Designs 

Representative Unit Estimated Maximum 

Stack Length (in.)

Maximum Stack Length of 

a Torn Down Motor (in.)

Maximum Stack Length 

Modeled (in.)

30 Horsepower

Design B 8.87 8.02 (EL 2) 7.00

75 Horsepower

Design B 13.06 11.33 (EL 3) 12.00

5 Horsepower

Design C 5.80 4.75 (EL 0) 5.32

50 Horsepower

Design C 9.55 8.67 (EL 0) 9.55

5.5 COST MODEL

 DOE uses a standard method of cost accounting to determine the costs associated with 

manufacturing. This methodology is illustrated in Figure 5.6, where production costs and nonproduction costs are combined to determine the full cost of a product. 

Figure 5.6 Standard Method of Cost Accounting for Standards Rulemaking

DOE developed estimates of some of the cost multipliers shown in Figure 5.6 by 

reviewing Security and Exchange Commission (SEC) SEC 10-K reports from electric motor 

manufacturers, and examining previous, relevant, rulemakings, and through conversations with 

industry experts. Together, the full production cost and the non-production costs equal the full 

cost of the product. Full production cost is a combination of direct labor, direct materials, and 

overhead. The overhead contributing to full production cost includes indirect labor, indirect 

Direct

Labor

Direct

Labor Direct

Material

Direct

Material Overhead Overhead Selling Selling General &

Admin.

General &

Admin. R & D R & D Interest Interest

Indirect Labor

Full Production Cost 1 Non-Production Cost

Indirect Material

Maintenance

Depreciation

Taxes

Insurance related

to assets

Mkt. research

Advertising

POS promotion

Sales person

salary + travel

+ commission

Logistics

- Warehousing

- Delivery

- Record

 keeping

- Order entry/

 processing

Costs of

service &

staff units

General

corporate

costs

- comp. etc.

Costs

associated

with efforts

to find new

or improved

products or

production

processes

Costs

of

borrowing

funds

1 Tax Reform Act of 1986, essentially, requires companies to measure cost of goods sold as the full production cost of the goods sold.

Full Cost of Product Full Cost of Product

Code

compliance

Modernization

Cust. service
5-17 

material, maintenance, depreciation, taxes, and insurance related to company assets. Nonproduction costs include the cost of selling (market research, advertising, sales representatives, 

logistics), general and administrative costs, research and development, interest payments and 

profit factor (not shown in the figure).

After the designs examined by DOE’s motor experts were completed or the electric 

motors were torn down and the parts were inventoried, the next step was applying a consistent 

cost model to all of them. A standard bill of materials (BOM) was constructed that includes 

direct material costs. From this BOM, labor time estimates (along with associated costs) were 

added and various manufacturer markups were applied to create an MSP. DOE presents a 

summary of the production costs and non-production costs for each of the representative units 

analyzed in Appendix 5A. 

5.5.1 Constructing a Bill of Materials 

The BOM calculated for each design contained three types of material costs: variable, 

insulation, and hardware. The variable costs considered are those portions of the BOM that vary 

based on the cost of the material and the amount of that material used in the design. For example, 

stator and rotor lamination costs are variable costs because the material price for the different 

steel grades changes as does the volume of steel needed for each design. The insulation cost was 

aggregated due to the difficulty in pricing out all components of the insulation system. Based on 

SME feedback, DOE assumed increased efficiency does not incur notable increases in insulation 

system costs. Therefore, insulation costs increase as representative unit horsepower increases, 

but remain constant across all ELs for each representative unit. The total price for insulation was 

also derived from SME input. Finally, hardware costs are an aggregate cost for all electric motor 

hardware components. This includes nuts, bolts, gaskets, washers and other miscellaneous 

hardware components. As with the insulation costs, the hardware cost was aggregated due to the 

difficulty of pricing individual components. DOE believes hardware costs account for a small 

percentage of the total material costs of an electric motor and therefore does not believe this 

aggregation method will have a detrimental impact on the accuracy of the MSP. The aggregate 

hardware cost, which is unique for each horsepower rating, was also derived based on SME input 

and information received about the teardowns. 

Each item in the BOM is organized by the type of cost (i.e., variable, insulation, and 

hardware) and the component of the electric motor to which they apply. The variable costs 

portion of the BOM includes the following subheadings, each with an itemized parts list: stator 

assembly, rotor assembly, and other major costs. The insulation cost section of the BOM 

includes subheadings for each individual component identified during teardown, however they 

are not priced out individually. As discussed above, an aggregate price is used to cover this entire 

section. This aggregate price is unique for different horsepower ratings. The hardware cost 

section of the BOM includes subheadings for individual hardware items identified during the 

teardown, but again like the insulation costs, they are not individually priced. There is one 

aggregate price used that covers all of the hardware components. This aggregate price is unique 

for each horsepower rating.
5-18 

The subheadings that have an itemized list of components include the stator assembly, 

rotor assembly, and other major costs. The stator assembly’s itemized lists include prices for 

steel laminations and copper wire. The rotor assembly portion of the BOM includes prices for 

laminations, rotor conductor material, (either aluminum or copper) and shaft extension material. 

The other major costs heading contains items for the frame material and base, terminal housing 

components, bearing-type, and end-shield material.

DOE presents a detailed BOM for one design from each of the electric motor categories 

analyzed in Appendix 5B. The discussion below describes the level of detail contained in the bill 

of materials presented in the appendix. 

5.5.2 Labor Costs and Assumptions 

Due to the varying degree of automation used in manufacturing electric motors, labor 

costs differ for each representative unit. DOE analyzed teardown results to determine which 

electric motors were machine wound and which electric motors were hand wound and based on 

this analysis, DOE applied a higher labor hour amount for the hand-wound electric motors. For 

the max-tech software modeled electric motors, DOE always assumed hand-winding and 

therefore a higher labor hour amount. Labor hours for each of the representative units were based 

on SME input and manufacturer interviews. 

DOE used the same hourly labor rate for all electric motors analyzed. The base hourly rate 

was developed from the 2007 Economic Census of Industry,j

 published by the U.S. Census 

Bureau, as well as manufacturer and SME input. The base hourly rate is an aggregate rate of a 

foreign labor rate and a domestic labor rate. DOE weighed the foreign labor rate more than the 

domestic labor rate due to manufacturer feedback indicating off-shore production accounts for a 

majority of electric motor production by American-based companies. Several markups were 

applied to this hourly rate to obtain a fully burdened rate which is representative of the labor

costs associated with manufacturing electric motors. Table 5-9 shows the markups that were 

applied, their corresponding markup percentage, and the new burdened labor rate. 

 

j

U.S. Census Bureau, 2007 Economic Census of Industry
5-19 

Table 5-9 Labor Markups for Electric Motor Manufacturers

Item description Markup percentage Rate per hour

Labor cost per hour٭ $ 10.87

Indirect Production٭٭ 33 % $ 14.46

18.79 $ % 30 ٭٭٭Overhead

Fringe† 24 % $ 23.40

Assembly Labor Up-time†† 43 % $ 33.46

Cost of Labor Input to Spreadsheet $ 33.46

٭ Cost per hour is an aggregate number drawn from U.S. Census Bureau, 2007 Economic Census of Industry, 

published December 2010 and foreign labor rate estimates based on manufacturer feedback.

٭٭ Indirect Production Labor (Production managers, quality control, etc.) as a percent of direct labor on a cost basis. 

Navigant Consulting, Inc. (NCI) estimate.

٭٭٭ Overhead includes commissions, dismissal pay, bonuses vacation, sick leave, and social security contributions. 

NCI estimate.

† Fringe includes pension contributions, group insurance premiums, workers compensation. Source: U.S. Census 

Bureau, 2007 Economic Census of Industry, published December 2010. Data for NAICS code 335312 “Electric 

Motor and Generator Manufacturer” total fringe benefits as a percent of total compensation for all employees (not 

just production workers).

†† Assembly labor up-time is a factor applied to account for the time that workers are not assembling product 

and/or reworking unsatisfactory units. The markup of 43 percent represents a 70 percent utilization (multiplying by 

100/70). NCI estimate.

5.5.3 Manufacturer Markups

 DOE used the three markups described below to account for non-production costs that 

are part of each electric motor leaving a manufacturer’s facility. Handling and scrap factor, 

overhead, and non-production markups will vary from manufacturer to manufacturer because 

their profit margins, overheads, prices paid for goods, and business structures vary. DOE 

prepared estimates for these three non-production cost manufacturer markups from Securities 

and Exchange Commission (SEC) Form 10K annual reports, and conversations with 

manufacturers and experts.

• Handling and scrap factor: 2.5 percent markup. This markup was applied to the direct 

material production costs of each electric motor. It accounts for the handling of material 

(loading into assembly or winding equipment) and the scrap material that cannot be used 

in the production of a finished electric motor (e.g., lengths of wire too short to wind). 

• Factory overhead: 17.5 percent markup. Factory overhead includes all the indirect costs 

associated with production, indirect materials and energy use, taxes, and insurance. DOE 

applies factory overhead to the sum of direct material production costs (including the 

handling and scrap factor) and the direct labor costs. The overhead increases to 18.0 

percent when copper die-casting is used in the rotor. This accounts for additional energy, 

insurance, and other indirect costs associated with the copper die-casting process.

• Non-production: 37 – 45 percent markup. This markup reflects costs including sales and 

general administrative, research and development, interest payments, and profit factor. 

DOE applies the non-production markup to the sum of the direct material production, the 
5-20 

direct labor, the factory overhead, and the product conversion costs. For the analyzed 

electric motors at or below 30-horsepower this markup was 37 percent and for electric 

motors above 30-horsepower this markup was 45 percent. This increase accounts for the 

extra profit margin manufacturers may receive on larger electric motors that are sold in 

smaller volumes.

5.5.4 Conversion Costs 

DOE understands that even without new conservation standards, manufacturers will be 

expending resources on research and development, capital equipment replacement, and testing 

and certification for new products in the normal course of their day-to-day business operations. 

However, DOE also realizes that some of the conservation standards under consideration may 

require significant levels of investment, in time and dollars, by manufacturers above and beyond 

their typical operational levels. To account for the additional investments that manufacturers will 

have to make to reach certain ELs, DOE included a conversion cost adder in the cost model. This 

reflects the additional cost passed along to the consumer by manufacturers attempting to recover 

the costs incurred from having to redevelop their product lines as a result of higher energy 

conservation standards. The conversion costs incurred by manufacturers include capital 

investment (i.e., new tooling and machinery), product development (i.e., reengineering each 

motor design offered), and testing and certification costs. 

The conversion cost adder was only applied to ELs above premium efficiency level based 

on manufacturer feedback on conversion costs at each EL. For background, most manufacturers 

now offer premium efficient motors for a significant portion of their product line as a result of 

EISA 2007. Many manufacturers also offer certain ratings with efficiency levels higher than 

premium efficiency levels. However, DOE is not aware of any manufacturer with a complete 

product line above premium efficiency levels. Consequently, DOE believes that energy 

conservation standards above premium efficiency levels would result in manufacturers incurring 

significant conversion costs as they bring their product offerings up to the higher standard. 

DOE developed the various conversion costs from data collected during manufacturer 

interviews that were conducted for the Manufacturer Impact Analysis (MIA). For more 

information on the MIA, see chapter 12 of the TSD. DOE used the manufacturer supplied data to 

estimate industry-wide capital conversion costs and product conversion costs for each EL above 

premium efficiency level. DOE then assumed that manufacturers would markup their motors to 

recover the total conversion costs over a seven year period. By dividing industry-wide 

conversion costs by seven years of expected industry-wide revenue, DOE obtained a percentage 

estimate of how much each motor would be marked up by manufacturers: 

• 1 NEMA band above premium efficiency level: %1.4

108,723,404,20$

452,468,841$ = (Conversion 

costs as a percentage of 7 year revenue)

• 2 NEMA bands above premium efficiency level: %5.6

108,723,404,20$

562,413,320,1$ = (Conversion 

costs as a percentage of 7 year revenue)
5-21 

The percentage markup was then applied to the full production cost (direct material + 

direct labor + overhead) at the premium efficiency levels to derive the per unit adder for levels 

above premium efficiency levels (see Table 5-10).

Table 5-10 Conversion Cost Adder for ELs above Premium efficiency levels. 

Representative Unit

Per Unit Adder for 

1 Band Above Premium 

Efficiency

Per Unit Adder for 

2 Bands Above Premium 

Efficiency

5 HP, Design B $11.06 $17.36

30 HP, Design B $32.89 $51.61

75 HP, Design B $66.18 $103.86

5 HP, Design C $10.68 $16.75

50 HP, Design C $60.59 $95.08

5.6 RESULTS OF ENGINEERING ANALYSIS

DOE used the five representative units to develop five manufacturer selling price versus 

nominal full-load efficiency curves, three for equipment class group 1 (also used for equipment 

class group 3), and two for equipment class group 2. Figure 5.7 through Figure 5.11 provide the 

manufacturer selling price versus efficiency curves and Table 5-11 through Table 5-22 present 

the tabulated results.

5.6.1 NEMA Design B, 5-Horsepower, 4-Pole, Enclosed Electric Motor

Figure 5.7 presents the relationship between MSP and nominal full-load efficiency for the 

5-horsepower, Design B, 4-pole, enclosed electric motor that was analyzed. Using the tear-down 

results for ELs 0 to 3, DOE determined that the manufacturer of these electric motors increased 

the stack length and used various combinations of increasing the stator copper, electrical steel, or 

rotor conductor, as well as design changes, to improve the electric motor’s efficiency. 

DOE increased the efficiency level of these representative units and all other 

representative units by employing a combination of changing the slot fill, increasing stator 

copper or electrical steel amounts, changing the type or amount of rotor conductor material, and 

changing specifications of the motor design such as rotor cage geometry or rotor skew. For EL 4, 

which is the max-tech efficiency level, DOE used a die-cast copper rotor electric motor in lieu of 

a software modeled design. 

Material cost increases, such as low loss electrical steel and increased stator copper, 

contribute to the relatively large increase in MSP from EL 3 to EL 4. Additionally, DOE 

observed a hand-wound stator for EL 4 which adds to the relatively large jump in MSP when 

moving to EL 4. All of the motors torn down and used for ELs 0 through 3 were observed to 

have machine-wound stators. 
5-22 

Figure 5.7 NEMA Design B, 5-Horsepower, 4-Pole, Enclosed Electric Motor 

Engineering Analysis Curve 

Table 5-11 presents the same engineering analysis results in tabular form, including the 

nominal full-load efficiency values and the MSPs. From EL 0 through EL 3, MSP increases by 

amounts varying up to 10 percent. When moving from EL 3 to 4, MSP increases by $260, or 

about 65 percent, for a loss reduction of roughly 10 percent. The large price increase when 

moving to EL 4 is largely a result of the use of increased labor hours and die-cast copper 

conductor in the rotor. At the time of publishing, copper was approximately 3.9 times more 

expensive than aluminum per pound and is three times denser. 

Table 5-11 Efficiency and Manufacturer Selling Price Data for the NEMA Design B, 

5-Horsepower Motor

EL Nominal Full-Load Efficiency (%) MSP (2013$)

EL 0 (Baseline) 82.5 333

EL 1 (EPACT 1992) 87.5 344

EL 2 (Premium Efficiency) 89.5 371

EL 3 (Best-in-Market) 90.2 406

EL 4 (Max-Tech) 91.0 677

Table 5-12 presents some of the design and performance specifications associated with 

the five 5-horsepower NEMA Design B motors presented above including stator copper weight, 

rotor conductor weight, and electrical steel weight. 

$300

$400

$500

$600

$700

82.0% 84.0% 86.0% 88.0% 90.0% 92.0%

Manufacturer Selling Price

Nominal Full-Load Efficiency
5-23 

Table 5-12 NEMA Design B, 5-Horsepower, 4-Pole, Enclosed Motor Characteristics

Parameter Units EL 0 EL 1 EL 2 EL 3 EL 4

Efficiency % 82.5 87.5 89.5 90.2 91.0

Line Voltage V 460 460 460 460 460

Full Load Speed RPM 1,745 1,745 1,750 1,755 1,770

Full Load Torque Nm 20.3 20.3 20.4 20.3 20.1

Current A 6.9 6.5 6.6 6.2 6.5

Steel - M56 M47 M47 M47 M15

Rotor Conductor 

Material - Aluminum Aluminum Aluminum Aluminum Copper

Approximate 

Slot Fill % 38.7% 51.7% 70.0% 54.4% 53.3% 

Stator Wire 

Gauge AWG 19 19 19 20 20.5

Stator Copper 

Weight lbs 8.4 10.1 10.1 12.2 13.4

Rotor Conductor 

Weight lbs 2.63 2.87 2.64 3.42 9.8

Stack Length In 2.8 3.47 5.14 4.65 5.02

Housing Weight lbs 8 15.4 22 20.6 21.4

5.6.2 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Electric Motor

Figure 5.8 presents the relationship between the MSP and nominal full-load efficiency for 

the 30-horsepower, Design B, 4-pole, enclosed electric motor analyzed. Using tear-down results 

for ELs 0, 1, 2 and 3, DOE determined that the manufacturer of these motors used a combination 

of material grade, material quantities, and design changes to increase the electric motor’s 

efficiency.

DOE also used software modeling to develop EL4. For this design DOE used a copper 

rotor and low-loss electrical steel to achieve efficiencies higher than the purchased electric 

motors. Using a die-cast copper conductor in the rotor also reduced the stack length of EL 4 

compared to the other 30 horsepower ELs analyzed. Shortening the stack length helps lower the 

cost of this max-tech design. EL 4’s primary cost increases arise from an increased labor hour 

amount based on a hand-wound labor assumption as well as other material quantity increases. 
5-24 

Figure 5.8 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Electric Motor 

Engineering Analysis Curve 

Table 5-13 presents the engineering analysis results in a tabular form, including the fullload efficiency values and the MSPs. From EL 0 to 3, DOE found that the full-load efficiency 

would increase 4.6 nominal percentage points over the baseline, EL 0, which represents about a 

47 percent reduction in electric motor losses. The increase in MSP to move from EL 0 to EL 3 is 

$435, or about a 51 percent increase in MSP over EL 0. Moving from EL 0 to EL 4 provides a 51 

percent reduction in electric motor losses for a MSP increase of $1,169 or about a 138 percent 

MSP increase over EL 0. 

Table 5-13 Efficiency and Manufacturer Selling Price Data for the NEMA Design B, 30-

Horsepower Motor

EL Nominal Full-Load Efficiency (%) MSP (2013$)

0 (Baseline) 89.5 856

1 (EPACT 1992) 92.4 1,096

2 (Premium Efficiency) 93.6 1,168

3 (Best-in-Market) 94.1 1,308

4 (Max-Tech) 94.5 2,077

Table 5-14 presents some of the design and performance specifications associated with 

the five 30-horsepower designs presented above, including stator copper weight, rotor conductor 

weight, and electrical steel weight. Table 5-15 shows the NEMA MG 1 Design B performance 

criteria as well as those design parameters for the software modeled electric motor.

 $800

 $1,000

 $1,200

 $1,400

 $1,600

 $1,800

 $2,000

 $2,200

89.0% 90.0% 91.0% 92.0% 93.0% 94.0% 95.0%

Manufacturer Selling Price

Nominal Full-Load Efficiency
5-25 

Table 5-14 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Motor Characteristics

Parameter Units EL 0 EL 1 EL 2 EL 3 EL 4*

Efficiency % 89.5 92.4 93.6 94.1 94.5

Line Voltage V 460 460 460 575 460

Full Load Speed RPM 1,755 1,765 1,770 1,773 1,784

Full Load Torque Nm 121.6 120.9 120.0 120.7 119.6

Current A 37 37 37.5 29.2 37

Steel - M56 M56/M47 M47 M47 M36

Rotor Conductor Material - Aluminum Aluminum Aluminum Aluminum Copper

Approximate Slot Fill % 47.5 64.8 50.9 70.0 83.2

Stator Wire Gauge AWG 18 17 18 18 18

Stator Copper Weight lbs 20.2 43.5 49.4 47.4 74.5

Rotor Conductor Weight lbs 8.25 9.5 16.84 13.66 42.6

Stack Length In 7.88 5.53 8.02 6.74 7.00

Housing Weight lbs 21 121 28.3 147 153

* Software modeled motor

Table 5-15 NEMA Design B, 30-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics

Parameter Units Design B Limit EL 4

Efficiency % - 94.5

Breakdown Torque % of full-load 200 (min.) 202

Pull-up Torque % of full-load 105 (min.) 139

Locked Rotor Torque % of full-load 150 (min.) 154

Locked Rotor Current A 217.5(max.) 208

5.6.3 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Electric Motor

Figure 5.9 presents the relationship between the MSP and nominal full-load efficiency for 

the 75-horsepower, Design B, 4-pole enclosed electric motor analyzed. Using tear-down results 

for ELs 0 through 3, DOE determined that the manufacturer of these electric motors increased 

the stack length and other material amounts to increase the electric motor’s efficiency levels 

from 93.0 percent to 95.8 percent. The torn-down electric motor representing EL 3 used 

increased rotor aluminum and stator copper as well as an increased stack length to achieve 95.8 

percent efficiency.

DOE used software modeling to develop the max-tech efficiency level, EL 4. For this 

design, DOE used a die-cast copper conductor in the rotor and low-loss electrical steel in the 

rotor and stator to achieve efficiencies higher than commercially available electric motors. The 

assumption of manual-labor hour amounts and the use of die-cast copper conductors in EL 4’s

rotor accounts for the larger-than-typical price increase between EL 3 and EL 4 for the 75-

horsepower Design B representative unit. 
5-26 

Figure 5.9 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Motor Engineering 

Analysis Curve 

Table 5-16 presents the same engineering analysis results in a tabular form, including the 

nominal full-load efficiency values and the MSPs. Moving from EL 0 to EL 3, DOE found that 

the full-load efficiency would increase 2.4 nominal percentage points over the baseline, EL 0, 

which represents about a 42 percent reduction in electric motor losses. The increase in MSP to 

move from EL 0 to EL 3 is about $860 or about a 45 percent increase in MSP over EL 0. Moving 

from EL 0 to the max-tech efficiency level of EL 4 provides a 48 percent reduction in electric 

motor losses for a MSP increase of $1,520, which constitutes an 87 percent MSP increase over 

the EL 0 electric motor. 

Table 5-16 Efficiency and Manufacturer Selling Price Data for the NEMA Design B, 75-

Horsepower Motor

EL Nominal Full-Load Efficiency 

(%) MSP (2013$)

EL 0 (Baseline) 93.0 1,910

EL 1 (EPACT 1992) 94.1 2,068

EL 2 (Premium Efficiency) 95.4 2,351

EL 3 (Best-in-Market) 95.8 2,804

EL 4 (Max-Tech) 96.2 3,656

Table 5-17 presents some of the design and performance specifications associated with 

the five 75-horsepower designs presented above, including stator copper weight, rotor conductor 

$1,800

$2,000

$2,200

$2,400

$2,600

$2,800

$3,000

$3,200

$3,400

$3,600

$3,800

92.5% 93.0% 93.5% 94.0% 94.5% 95.0% 95.5% 96.0% 96.5%

Manufacturer Selling Price

Nominal Full-Load Efficiency
5-27 

weight, and electrical steel weight. Table 5-18 shows the NEMA MG 1 Design B performance 

criteria as well as those design parameters for the software modeled electric motor.

Table 5-17 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Motor Characteristics

Parameter Units EL 0 EL 1 EL 2 EL 3 EL 4*

Efficiency % 93.0 94.1 95.4 95.8 96.2

Line Voltage V 460 460 460 460 460

Full Load Speed RPM 1,775 1,785 1,775 1,785 1,788

Full Load Torque Nm 300.6 299.6 299.6 299.6 299.6

Current A 88 91.5 85 85.5 89.8

Steel - M56 M47 M27 M36 M36

Rotor Conductor 

Material - Aluminum Aluminum Aluminum Aluminum Copper

Approximate Slot 

Fill % 50.9 35.0 70.0 70.0 85.1

Stator Wire Gauge AWG 17 12 15 16 14

Stator Copper 

Weight lbs 77.8 71 82 136 127

Rotor Conductor 

Weight lbs 30.9 20.7 27.3 38.5 78.9

Stack Length In 8.15 10.23 10.58 11.33 12.00

Housing Weight lbs 127 79 168 180 190

* Software modeled motor

Table 5-18 NEMA Design B, 75-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics

Parameter Units Design B Limit EL 4

Efficiency % - 96.2

Breakdown Torque % of full-load 200 (min.) 218.2

Pull-up Torque % of full-load 100 (min.) 135

Locked Rotor Torque % of full-load 140 (min.) 163.8

Locked Rotor Current A 542.5(max.) 530.7

5.6.4 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Electric Motor 

Figure 5.10 presents the relationship between the MSP and nominal full-load efficiency 

for the 5-horsepower, NEMA Design C, 4-pole, enclosed electric motor analyzed. DOE 

purchased only one 5-horsepower NEMA Design C motor for its tear-down analysis. The 

remaining two ELs were based on software modeled electric motors. Therefore, discussion of the 

NEMA Design C revolves around the design changes DOE’s software modeling expert chose to 

implement to increase the efficiency levels of the electric motors. 

DOE achieved the EL 1 efficiency level by using a lower loss grade of electrical steel and 

increasing the slot fill higher than that of the EL 0 electric motor. The EL 1 electric motor also 

boasts a smaller stack length than the EL 0 electric motor. DOE achieved the max-tech efficiency 

level of the EL 2 motor design by switching to a die-cast copper rotor and increasing the stack 

length to the maximum stack length calculated via the methodology described in section 5.4.2. 
5-28 

This increased the amount of electrical steel and stator copper material by 25 and 29 percent, 

respectively. 

Figure 5.10 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Motor Engineering 

Analysis Curve 

Table 5-19 presents the same engineering analysis results in a tabular form, including the 

nominal full-load efficiency values and the MSPs. Moving from EL 0 to EL 2, DOE found that 

the full-load nominal efficiency would increase 3.5 percentage points over the baseline, EL 0, 

which represents a 31 percent reduction in electric motor losses. The increase in MSP to move 

from EL 0 to EL 2 is $278, or about an 84 percent increase in MSP over EL 0. 

Table 5-19 Efficiency and Manufacturer Selling Price Data for the NEMA Design C, 5-

Horsepower Motor

EL Nominal Full-Load Efficiency (%) MSP (2013$)

EL 0 (Baseline/EPACT 1992) 87.5 334

EL 1 (Premium Efficiency) 89.5 358

EL 2 (Max-Tech) 91.0 627

Table 5-20 presents some of the design and performance specifications associated with 

the three NEMA Design C, 5-horsepower electric motors presented above. The table includes 

stator copper weight, rotor conductor weight, and electrical steel weight. Table 5-21 shows the 

NEMA MG 1 Design C performance requirements as well as the resulting design parameters for 

the two software modeled electric motors.

$300

$400

$500

$600

$700

87.0% 87.5% 88.0% 88.5% 89.0% 89.5% 90.0% 90.5% 91.0% 91.5%

Manufacturer Selling Price

Full-Load Efficiency
5-29 

Table 5-20 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Motor Characteristics

Parameter Units EL 0 EL 1 EL 2

Efficiency % 87.5 89.5 91.0

Line Voltage V 460 460 460

Full Load Speed RPM 1,750 1,762 1,776

Full Load Torque Nm 20.3 20.2 20.1

Current A 7.1 8.4 6.5

Steel - M47 M36 M36

Rotor Conductor Material - Aluminum Aluminum Copper

Approximate Slot Fill % 53.3 79.9 82.9

Stator Wire Gauge AWG 18 18 18

Stator Copper Weight lbs 10 9.9 12.8

Rotor Conductor Weight lbs 2.2 2.0 7.8

Stack Length in 4.75 4.25 5.32

Frame Weight lbs 12 11 14

Table 5-21 NEMA Design C, 5-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics

Parameter Units Design C Limit EL 1 EL 2

Efficiency % - 89.5 91.0

Breakdown Torque % of full-load 200 (min.) 293 260.8

Pull-up Torque % of full-load 180 (min.) 283.9 260.8

Locked Rotor Torque % of full-load 255 (min.) 344.1 260.8

Locked Rotor Current A 46 (max.) 38.5 41.7

5.6.5 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Electric Motor

Figure 5.11 presents the relationship between the MSP and nominal full-load efficiency 

for the 50-horsepower, NEMA Design C, 4-pole, enclosed electric motor analyzed. DOE 

purchased only one NEMA Design C motor for its tear-down analysis. The remaining two ELs 

were based on software-modeled electric motors. Therefore, discussion of the NEMA Design C 

revolves around the design changes DOE’s software modeling expert chose to implement to 

increase the efficiency levels of the electric motors. 

DOE achieved the EL 1 efficiency level by using a higher slot fill, higher grade electrical 

steel and the maximum-calculated stack length found by using the method discussed in section 

5.4.2. DOE then increased the efficiency level to EL 2 by switching to a die-cast copper rotor 

and using a higher grade electrical steel. 
5-30 

Figure 5.11 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Motor Engineering 

Analysis Curve 

Table 5-22 presents the same engineering analysis results in a tabular form, including the 

nominal full-load efficiency values and the MSPs. Moving from the EL 0 to EL 2, DOE found 

that the nominal full-load efficiency would increase 2.0 nominal percentage points over the 

baseline, EL 0, which represents about a 30 percent reduction in electric motor losses. The 

increase in MSP to move from EL 0 to EL 2 is $976, or about a 64 percent increase in MSP over 

EL 0. 

Table 5-22 Efficiency and Manufacturer Selling Price Data for the NEMA Design C, 50-

Horsepower Motor

EL Nominal Full-Load Efficiency (%) MSP (2013$)

EL 0 (Baseline/EPACT 1992) 93.0 1,552

EL 1 (Premium Efficiency) 94.5 2,152

EL 2 (Max-Tech) 95.0 2,612

Table 5-23 presents some of the design and performance specifications associated with 

the three NEMA Design C, 50-horsepower electric motor designs presented above including 

stator copper weight, rotor conductor weight, and electrical steel weight. Table 5-24 shows the 

NEMA MG 1 Design C performance requirements as well as the resulting design parameters for 

the software modeled electric motors.

$1,400

$1,600

$1,800

$2,000

$2,200

$2,400

$2,600

$2,800

92.5% 93.0% 93.5% 94.0% 94.5% 95.0% 95.5%

Manufacturer Selling Price

Full-Load Efficiency
5-31 

Table 5-23 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Motor Characteristics

Parameter Units EL 0 EL 1 EL 2

Efficiency % 93.0 94.5 95.0

Line Voltage V 460 460 460

Full Load Speed RPM 1,770 1,775 1,782

Full Load Torque Nm 200.7 200.6 199.8

Current A 59 63.8 61.3

Steel - M47 M36 M19

Rotor Conductor Material - Aluminum Aluminum Copper

Approximate Slot Fill % 62.5 85.3 81.3

Stator Wire Gauge AWG 17 17 17

Stator Copper Weight lbs 66 90 85

Rotor Conductor Weight lbs 16.5 13.5 36.6

Stack Length In 8.67 9.55 9.55

Frame Weight lbs 125 138 138

Table 5-24 NEMA Design C, 50-Horsepower, 4-Pole, Enclosed Motor Modeled 

Characteristics

Parameter Units Design C Limit EL 1 EL 2

Efficiency % - 94.5 95.0

Breakdown Torque % of full-load 190 (min.) 193.5 233.5

Pull-up Torque % of full-load 150 (min.) 165.1 202.9

Locked Rotor Torque % of full-load 200 (min.) 258.6 202.9

Locked Rotor Current A 362.5 (max.) 356.2 359.6

5.7 SCALING METHODOLOGY

Due to the large number of equipment classes, DOE was not able to perform a detailed 

engineering analysis on each one. Instead, DOE focused its analysis on three NEMA Design B 

equipment classes and two NEMA Design C equipment classes. From these results, DOE scaled 

to other equipment classes not directly analyzed in the engineering analysis. DOE considered 

two methods of scaling, one based on the incremental improvement of motors losses and one that 

develops a set of power law equations based on the relationships found in the NEMA “Energy 

Efficient” and NEMA “Premium Efficient”k tables of efficiency. Ultimately, DOE did not find a 

large discrepancy between the two methods and elected to use the, simpler, incremental 

improvement of motor losses approach. 

5.7.1 Scaling Approach Using Incremental Improvements of Motor Losses

Scaling electric motor efficiencies is a complicated proposition that has the potential to 

result in efficiency standards that are not evenly stringent across all equipment classes. Among 

DOE’s three ECGs, there are several hundred combinations of horsepower rating, pole 

configuration, and enclosure. Within these combinations there is a large number of standardized 

 

k NEMA MG 1-2011 specifies that motors classified as “energy efficient” shall meet or exceed the efficiency values 

listed in Table 12-11 . Motors classified as “premium efficiency” shall meet or exceed the efficiency values listed in 

Table 12-12 .
5-32 

frame number series. Given this sizable number of frame number series, DOE cannot feasibly 

analyze all of these variants — hence, the need for scaling. Scaling across horsepower ratings, 

pole configurations, enclosures, and frame number series is a necessity. For DOE’s first 

approach to scaling, it relied on a relatively simple method of analyzing the motor losses of each 

of its representative units from EL to EL and applying those same losses to various segments of 

the market.

As discussed previously, DOE based the first four of its ELs for ECG 1 on torn-down 

motors. As these motors were marketed and sold with NEMA nominal efficiencies, DOE used 

those values to denote each of those ELs. Consequently, the efficiency levels that DOE scaled to 

for the non-representative units were also selected from the NEMA nominal efficiency levels. 

DOE also used the NEMA nominal efficiency values for the ELs that were achieved for the 

representative units using software modeling. 

For EL 1 and EL 2, DOE had to do minimal scaling. EL 1 is based on NEMA MG 1-

2011 Tables 12-11 which was left unchanged for all electric motors. However, Table 12-11 does 

not specify an efficiency level for 1 horsepower, 2 pole, open motors. DOE scaled the missing 

value by using the same efficiency level as that of 1 horsepower, 2 pole, enclosed motors. By 

observing that 1 horsepower, 2 pole, both open and enclosed motors had the same Table 12-12 

efficiency levels, DOE inferred that a 1 horsepower, 2 pole, open configuration could also meet 

the Table 12-11 efficiency level of its enclosed counterpart.

EL 2 is based on NEMA MG 1-2011 Tables 12-12 which specify the nominal efficiencies 

of electric motors that represent “premium efficiency”. The 2011 version of MG 1 Table 12-12 

omits values for 6-pole motors at 300 and 350 horsepower. DOE’s engineering analysis and past 

tables suggest efficiency is increasing more slowly with horsepower at these levels, and DOE 

derived standards for these motors by simply reusing the value for 250 hp. 

 For the higher ELs, namely 3 and 4, DOE’s conservation of motor losses approach relies 

on NEMA MG 1-2011 Table 12-10 of nominal efficiencies and the relative improvement in 

motor losses of the representative units. As has been discussed, each incremental improvement in 

NEMA nominal efficiency (or NEMA band) corresponds to roughly a 10 percent reduction in 

motor losses. After ELs 3 and 4 were developed for each representative unit, DOE applied the 

same reduction in motor losses (or the same number of NEMA band improvements) to various 

segments of the market based on the representative units. DOE assigned a segment of the electric 

motors market, based on horsepower ratings, to each representative unit analyzed. DOE’s 

assignments of these segments of the markets were in part based on the standardized NEMA 

frame number series that NEMA MG 1 assigns to horsepower and pole configuration 

combinations. That segmentation of the market is shown in Figure 5.12. 
5-33 

Figure 5.12 Segmentation of Electric Motor Market for Representative Units

The first section, shaded blue in Figure 5.12, consists of smaller frame electric motors 

whose efficiencies increase at a quicker rate than larger frame electric motors. A 5-horsepower 

electric motor was selected to represent the electric motors on this section of the graph based on 

high shipment volume and the fact that this electric motor’s efficiency is in middle of this steep 

section of the graph. The electric motors whose analysis is based on the 5-horsepower electric 

motor are electric motors between 1-horsepower and 10-horsepower. 

DOE then analyzed the mid-section of the graph, or electric motors whose efficiencies do 

not change as drastically as the blue-shaded region and determined that a 30-horsepower electric 

motor falls in the middle of this region of the graph. Consequently, DOE selected the 30-

horsepower rating to analyze for the red shaded region of the graph, which represents electric 

motors from 15-horsepower to 50-horsepower. 

For the third section, DOE observed the electric motor efficiencies exhibited a fairly 

“flat” characteristic as frame sizes increase beyond 60-horsepower. DOE selected a 75-

horsepower electric motor to represent the electric motors on the final part of the graph because 

it was large enough to represent electric motors in this horsepower range yet small enough to 

facilitate various aspects of the engineering analysis, such as physical teardowns of the electric 

motor. The 75-horsepower electric motor represents electric motors on the large end of the scope 

of coverage, from 60-horsepower to 500-horsepower.

 In the end, for ECG 1, each EL above EL 2 was one NEMA band above the previous EL

for each representative unit — i.e., EL 3 exceeded Table 12-12 by one band, and EL 4 by two. 

The following bulleted line items summarize each EL for ECG 1:

• EL 0: Lowest-in-scope efficiencies for all equipment classes

• EL 1: NEMA MG 1-2011 Tables 12-11 for all equipment classes

• EL 2: NEMA MG 1-2011 Tables 12-12 for all equipment classes

5 HP

30 HP

75 HP

80.0

82.0

84.0

86.0

88.0

90.0

92.0

94.0

96.0

98.0

100.0

AVerage Efficiency Available (%)

1 to 10 HP 15 to 50 HP 60 to 500 HP
5-34 

• EL 3: One NEMA band above EL 1 for all equipment classes

• EL 4: One NEMA band above EL 2 for all equipment classes

The scaling results for ECG 2 were slightly different. As discussed, there is limited 

equipment selection of NEMA Design C motors, and EL 0 was the only EL based on tear-down 

results. Consequently, ELs 1 through 2 were modeled using a computer software program. 

Relative to the baseline EL (NEMA MG 1 Table 12-11) DOE was able to achieve a max-tech 

efficiency level that corresponded to an improvement of four NEMA bands for both 

representative units. Each incremental EL above EL 1 corresponded to a one NEMA band 

improvement, totaling four NEMA bands of improvement relative to the baseline at EL 2. The 

following bullets summarize each EL for ECG 2.

• EL 0: NEMA MG 1-2011 Table 12-11 for all equipment classes

• EL 1: NEMA MG 1-2011 Table 12-12 for all equipment classes

• EL 2: One NEMA band above EL 1 for all equipment classes

5.7.2 Scaling Approach Using Regression Equations 

DOE developed a second approach for scaling to EL 3 and EL 4 which relied on 

regression equations to predict electric motor losses. The first step DOE took in this approach 

was to create a model that describes electric motor losses as a function of the electric motor’s 

rated horsepower. To do this, DOE examined the standards adopted by EISA 2007. For 

polyphase general-purpose electric motors built in a three digit frame size EISA adopted the 

premium efficiency Standards, shown in NEMA MG 1-2011 in Table 12-12, as the minimum 

efficiency levels. This table has standards for electric motors ranging in horsepower from 1 to 

200-horsepower, in two-, four-, and six-pole configurations, and in open and enclosed 

constructions. DOE plotted this data to observe any trends: 

• Electric motor losses (calculated as 

1

efficiency − 1) versus horsepower 

When plotted on logarithmic scales, DOE observed that as horsepower increased, electric 

motor losses decreased following a power law function, as shown in Figure 5.13. That is: 

• b MotorLosses HP a HP− )( ×= , where a and b vary by pole configuration and electric 

motor category combination. 
5-35 

Figure 5.13 Motor Losses versus Horsepower Rating

 As mentioned in section 5.3, for ECG 1 EL 3 represents a best-in-market efficiency level, 

and EL 4 represents the maximum technology efficiency level. For the representative units, the 

efficiency levels at EL 3 and EL 4 were already known, either through purchased electric motors 

or software modeling. Therefore, DOE scaled the ELs from the representative units to the 

equipment classes that were not analyzed. This was done by using the power law function 

observed in Figure 5.13. Since DOE directly analyzed three horsepower ratings (5-horsepower, 

30-horsepower and 75-horsepower), the electric motor losses continuum was split up into three 

ranges: 1- to 10-horsepower, 15- to 50-horsepower, and 60- to 500-horsepower (as shown in 

Figure 5.12). A power law function was derived for EL 1 and EL 2 for each range in the 

representative ECGs as shown in Figure 5.14. 

y = 0.2166x-0.261

R2 = 0.9871

y = 0.1705x-0.273

R2 = 0.9868

y = 0.1801x-0.266

R2 = 0.9817

0.01

0.10

1.00

1.0 10.0 100.0

Motor Losses (1/efficiency -1)

Horespower

12-12 Open Motor 2 Pole

12-12 Open Motor 4 Pole

12-12 Open Motor 6 Pole
5-36 

Figure 5.14 Function of Electric Motor Losses with Horsepower for 4-Pole, Enclosed 

Electric Motorsl

 For each range, the exponents of EL 1 and EL 2 were averaged to derive the following 

three power law equations: 

 

286. )( − MotorLosses HP a ×= HP for 1 horsepower to 10-horsepower 

 

269. )( − MotorLosses HP a ×= HP for 15-horsepower to 50-horsepower 

 

190. )( − MotorLosses HP a ×= HP for 60-horsepower and greater 

where ‘a’ is a constant that that differs for EL 3 and EL 4. As previously mentioned, the 

efficiency values for EL 3 and EL 4 are known at 5-horsepower, 30-horsepower and 75-

horsepower as they are the efficiency levels of the representative equipment classes. The value of 

‘a’ for EL 3 and EL 4 can be solved for using these known efficiency values. With the constants 

and exponents derived for the EL 3 and EL 4 power functions, the equations can be used to 

 

l

For the purposes of the final rule, the term “candidate standard level” (CSL) refers to “efficiency level” (EL) 
5-37 

derive the EL 3 and EL 4 efficiency levels for the horsepower ratings not analyzed. The results 

of this calculation are shown in Figure 5.15. 

Figure 5.15 Function of Electric Motor Losses with Horsepower Derived for EL 2 and 

EL 3 for 4-Pole, Enclosed Electric Motors of NEMA Design A & B

With EL 3 and EL 4 determined for the 4-pole enclosed electric motors, DOE then had to 

scale these ELs to the other electric motor pole configurations and enclosures. To do this, DOE 

compared the efficiencies, at a given horsepower rating, of the 4-pole enclosed motors with the 

efficiencies of other pole configurations and enclosures at the Table 12-12 levels. The ratio of 

those efficiencies was multiplied by the scaled efficiency (at EL 3 or 4) of the 4-pole enclosed 

electric motor efficiency. The resulting product was a scaled efficiency, at a given horsepower 

rating, of the equipment class not analyzed. To do this, DOE had to assume that the ratio of 

efficiencies of different equipment classes at EL 2 stayed constant for EL 3 and EL 4. The 

following equation was used to derive the scaled efficiencies: 
5-38 

)( )(

)( )( 4

4

Efficiency hp Efficiency hp

Efficiency hp Efficiency hp E

NP E

NP =

where 

• Efficiency- is the resulting scaled efficiency of the desired equipment class at the new EL

(3, 4, or 5). 

• EfficiencyNP-is the premium efficiency level of the desired equipment class.

• EfficiencyNP4E-is the premium efficiency level of a 4-pole enclosed electric motor.

• Efficiency4E- is the scaled efficiency of a 4-pole enclosed electric motor at the EL being 

scaled to (3, 4, or 5). 

Figure 5.16 Scaling Across Electric Motor Configurations

Note: For the purposes of the final rule, the term “candidate standard level” (CSL) refers to “efficiency level” (EL). 

For example, in order to calculate the efficiency of a 15-horsepower, 6-pole, enclosed 

electric motor at EL 3, see the equation below along with Figure 5.16. 

3.920.93

4.92

7.91 )15( )15(

)15( )15( 4

4

= E =×=

NP E

NP Efficiency Efficiency

Efficiency Efficiency

 As shown above, this method results in an efficiency level of 92.3 percent for a 6-pole 

NEMA Design A or B motor of enclosed construction. However, 92.3 percent falls just short of 

the NEMA nominal efficiency (see NEMA MG 1-2011 Table 12-10) of 92.4 percent. Therefore, 

it would have to be “rounded” down to the closest NEMA nominal efficiency level which in this 

case is 91.7 percent. By having to convert the calculated scaled efficiency levels to NEMA 

nominal efficiency levels, DOE observed that some of the efficiency levels that were scaled were 

the same efficiency as the lower EL. For instance, in the example above EL 2 and EL 3 would be 

equal to each other at 15-horsepower since the 92.3 percent efficiency would have to be rounded 

down to the closest NEMA nominal efficiency level. As a result, DOE elected not to use this as 

the primary methodology for scaling the efficiency levels of its representative units. 

CSL 0 CSL 1 CSL 2 CSL 3 CSL 4 CSL 0 CSL 1 CSL 2 CSL 3 CSL 4

7.5 84.0 89.5 91.7 92.4 93.0 82.5 89.5 91.0

10 86.5 89.5 91.7 92.4 93.0 84.0 89.5 91.0

15 86.5 91.0 92.4 93.0 94.1 88.5 90.2 91.7 92.3

20 87.5 91.0 93.0 93.6 94.1 87.5 90.2 91.7

25 89.5 92.4 93.6 94.1 94.5 91.7 91.7 93.0

Efficiency derived from power law equation

Unknown efficiency

HP

4 Pole 6 Pole

Enclosed Frame

Result
CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.1.1 Distribution Channels ...................................................................................................... 6-1

6.2 MARKUP CALCULATION METHODOLOGY ........................................................... 6-2

6.2.1 Assumptions ..................................................................................................................... 6-3

6.3 APPROACH FOR ORIGINAL EQUIPMENT MANUFACTURER MARKUPS ........ 6-4

6.4 APPROACH FOR MOTOR DISTRIBUTOR MARKUPS ............................................ 6-5

6.5 CONTRACTOR OR INSTALLER MARKUP ............................................................... 6-7

6.6 SALES TAXES ............................................................................................................... 6-7

6.7 OVERALL MARKUP ..................................................................................................... 6-8

6.8 SHIPPING COSTS .......................................................................................................... 6-9

LIST OF TABLES

Table 6.4-1 Business Expenses Survey Data Used to Calculate Distributor Markups ................ 6-6

Table 6.6-1 Average Sales Tax Rates by Census Division and Large State................................ 6-8

Table 6.7.1 Summary of Markups for Four Primary Distribution Channels for Electric 

Motors .................................................................................................................. 6-9

6-i
CHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

This chapter of the technical support document (TSD) presents the U.S. Department of 

Energy’s (DOE's) method for deriving electric motor prices. The objective of the equipment 

price determination is to estimate the price paid by the customer or purchaser for an installed 

electric motor. Purchase price and installation cost are necessary inputs to the life-cycle cost 

(LCC) and payback period (PBP) analyses. Chapter 8 presents the LCC calculations; section 

8.2.1 describes how the LCC uses purchase price and installation cost as inputs.

Purchase prices for electric motors are not generally known. Electric motors are often 

sold as part of a project, sometimes custom-built with unlisted prices. The engineering analysis 

(chapter 5) provides the manufacturer selling prices (MSPs) for the representative units included 

in the LCC analysis. DOE derived a set of prices, for each electric motor representative unit 

produced by the engineering analysis, by applying markups to the manufacturer selling price in 

the form of markup equations. 

6.1.1 Distribution Channels

 The appropriate markups for determining the end-user equipment price depend on the 

type of distribution channels through which equipment moves from manufacturers to purchasers. 

At each point in the distribution channel, companies mark up the price of the equipment to cover 

their business costs and profit margin. 

 Distribution channels vary depending on the size of the electric motor. Because smaller 

electric motors used as components in larger pieces of equipment constitute the majority of the 

market, much of the market passes through original equipment manufacturers (OEMs) who 

design, assemble, and brand equipment that contains electric motors. OEMs in turn obtain their 

motors either directly from the motor manufacturers or from manufacturers via distributors. For 

motors with larger horsepower (more than 50 horsepower), direct sales to the end-user and sales 

to contractors become more significant. 

Based on market research1

 and input from interested parties, DOE identified six main 

distribution channels for electric motors and estimated their respective shares of shipments per 

electric motor horsepower range. The seven channels are from the manufacturer to: 

(1) OEMs and then to end-users (25 percent of sales); 

(2) OEMs to distributers to end-users (25 percent of sales);

(3) distributors to end-users (24 percent of sales); 

(4) distributors to OEM and then to end-users (23 percent of sales); 

(5) contractors and then to end-users (less than one percent of sales); 

(6) distributors to end-users through contractors (less than one percent of sales); and

(7) end-users (less than two percent of sales).

6-1
 Other distribution channels exist (e.g., from manufacturer to OEMs to end-users through 

distributors) but are estimated to account for a minor share of motor sales (less than one percent). 

 In addition to these distribution chain markups, DOE estimated the shipping costs of the 

motors and added these to the end-user equipment prices. These costs are a significant factor, 

because more-efficient motors are often larger and heavier than less efficient motors, so this is a 

cost that needs to be included in an accurate cost analysis.

6.2 MARKUP CALCULATION METHODOLOGY

As addressed previously, at each point in the distribution channel, companies mark up the 

price of the equipment to cover their business costs and profit margins. In financial statements, 

gross margin is the difference between the company revenue and the company cost of sales or 

cost of goods sold (CGS). Inputs for calculating the gross margin are all corporate costs, 

including: overhead costs (sales, general, and administration), research and development (R&D), 

interest expenses, depreciation, taxes, and profits. For sales of equipment to contribute positively 

to company cash flow, the markup of the equipment must be greater than the corporate gross 

margin. Individual pieces of equipment may command a lower or higher markup, depending on 

their perceived added value and the competition they face from similar equipment in the market. 

In developing markups for OEMs and distributors, DOE obtained data about the revenue, 

CGS, and expenses of firms that produce and sell the equipment of interest. DOE determined 

that markups are neither fixed-dollar nor proportional to all direct costs, which means that the 

selling price of a piece of equipment may not be strictly proportional to the purchase price of the 

equipment. Using the available data, DOE has found measurable differences between 

incremental markups on direct equipment costs and the average aggregate markup on direct 

business costs. Additionally, DOE discovered significant differences between average and 

incremental markups for electric motor OEMs and distributors. Section 6.3 and Section 6.4

further discusses the differences between average and incremental markups.

The main reason that the selling price of a piece of equipment may not be strictly 

proportional to the purchase price of the equipment is that businesses incur a wide variety of 

costs. When the purchase price of equipment and materials increases, only a fraction of the 

business expenses increases, while the remainder of the business expenses stays relatively 

constant. For example, if the unit price of an electric motor increases by 30 percent, it is unlikely 

that the cost of secretarial support in an administrative office will increase by 30 percent also. 

Certain business expenses are uncorrelated with the cost of equipment or cost of goods.

 

 DOE’s approach categorizes the expenses into two categories: invariant costs (IVC), 

which are those costs that are not expected to vary in proportion to the change in manufacturer 

selling price, and variant costs (VC), which are the costs that scale with the change in 

manufacturer selling price. Together, IVC and VC represent the gross margin. 

 

6-2
 For each step in equipment distribution, DOE estimated both a baseline markup and an 

incremental markup. For electric motors, DOE understands that no increase in distribution labor 

is necessary for the distribution of more-efficient equipment, while the non-labor-scaling cost 

does increase with increasing equipment costs. This allowed DOE to estimate the incremental 

markup given a breakdown of distribution and manufacturing business expenses for a particular 

industry. 

6.2.1 Assumptions

DOE derived the OEM and motor distributor markups from three key assumptions about 

the costs associated with motor-related industrial series. DOE used the financial data from the 

2007 U.S. Economic Census’s manufacturing industrial series and 2007 Business Expenses 

Survey to determine OEM and motor distributor markups, respectively. These income statements 

break down the components of all costs incurred by firms that assemble and distribute electric 

motors. The key assumptions used to estimate markups using these financial data are: 

 

1. The firm income statements faithfully represent the various average costs incurred by 

firms designing, assembling, and distributing electric motors. 

2. These costs can be divided into two categories: (1) costs that vary in proportion to the 

MSP of electric motors (variant costs); and (2) costs that do not vary with the MSP of 

electric motors (invariant costs).

3. Overall, OEM and distributor sales prices vary in proportion to OEM and distributor 

costs that are included in the balance sheets.

In support of the first assumption, the income statements itemize firm costs into a number 

of expense categories, including CGS, operating labor and occupancy costs, and other operating 

costs and profit. Although OEMs and motor distributors tend to handle multiple commodity 

lines, these data provide the most accurate indication that is available of the expenses associated 

with electric motors. 

 In the following discussion, DOE assumes a division of costs between those that do not 

scale with the manufacturer price (labor and occupancy expenses), and those that do (operating 

expenses and profit). This division of costs led to the estimate of incremental markups addressed 

in the next section. 

 In support of the third assumption, the wholesaler industries are relatively competitive, 

and end-user demand for motors and equipment with motors is relatively inelastic—i.e., the 

demand is not expected to decrease significantly with a relatively small increase in price. 

Following standard economic theory, competitive firms facing inelastic demand either set prices 

in line with costs or quickly go out of business.2

6-3
6.3 APPROACH FOR ORIGINAL EQUIPMENT MANUFACTURER MARKUPS 

 Using the previous assumptions, DOE developed baseline and incremental markups for 

OEMs using the firm income statement from several manufacturing industries which design, 

assemble, and brand equipment that contain electric motors. The 2007 Economic Census 

Manufacturing Industry Series reports the payroll (production and total), cost of materials, 

capital expenditures and total value of shipments, and miscellaneous operating costs for 

manufacturers of various types of machinery. DOE collected these data for 25 types of OEMs, 

including: 

• farm machinery and equipment manufacturing;

• construction machinery manufacturing;

• mining machinery and equipment manufacturing;

• oil and gas field machinery and equipment manufacturing;

• sawmill and woodworking machinery manufacturing;

• plastics and rubber industry machinery manufacturing; 

• paper industry machinery manufacturing;

• textile machinery manufacturing; 

• printing machinery and equipment manufacturing;

• food product machinery manufacturing;

• semiconductor machinery manufacturing;

• other industrial machinery manufacturing;

• air-purification equipment manufacturing;

• industrial and commercial fan and blower manufacturing; 

• heating equipment (except warm-air furnaces) manufacturing; 

• air conditioning and warm-air heating and commercial and industrial refrigeration

equipment manufacturing;

• machine-tool (metal-cutting types) manufacturing; 

• machine-tool (metal-forming types) manufacturing; 

• rolling mill machinery and equipment manufacturing;

• pump and pumping equipment manufacturing;

• air and gas compressor manufacturing;

• elevator and moving stairway manufacturing; 

• conveyor and conveying equipment manufacturing;

• packaging machinery manufacturing; and 

• fluid-power pump and motor manufacturing.

 DOE used the baseline markups, which cover all of the OEM’s costs (both variant and 

invariant costs), to determine the sales price of baseline models. Variant costs were defined as 

costs that vary in proportion to the change in MSP induced by increased efficiency standards; in 

contrast, invariant costs were defined as costs that do not vary in proportion to the change in 

MSP due to increased efficiency standards. The baseline markup relates the MSP to the OEM 

selling price. For each of the 25 OEMs identified above, DOE calculated the OEM baseline 

markup as follows: 

6-4
MUBASE PAY MAT CAP

SALES = ++

Where:

SALES = value of shipments, 

PAY = payroll expenses, 

MAT = material input expenses, 

CAP = capital expenses, and

MUBASE = baseline markup. 

 The baseline markups range between 1.32 (machine-tool manufacturing) and 1.63 

(semiconductor machinery manufacturing), with the sales-weighted average of 1.44. 

Incremental markups are coefficients that relate the change in the MSP of more-efficient 

models, or that equipment that meets the requirements of new energy conservation standards, to 

the change in the OEM selling price. Incremental markups cover only those costs that scale with 

a change in the manufacturer’s sales price (variant costs). DOE calculated the incremental 

markup (MUINCR) for each of the 25 OEMs using the following equation: 

OEM

OEM OEM

INCR CGS

CGS VC

MU

+ =

Where:

MUINCR = incremental OEM markup,

CGSOEM = OEM’s cost of goods sold, and 

VCOEM = OEM’s variant costs.

The incremental markups range between 1.27 (machine-tool manufacturing) and 1.56 

(pump and pumping equipment manufacturing), with the sales-weighted average of 1.39. 

6.4 APPROACH FOR MOTOR DISTRIBUTOR MARKUPS 

The type of financial data used to estimate markups for OEMs is also available for 

distributors. DOE based its distributor markups on financial data from the 2007 U.S. Census 

Business Expenses Survey (BES). DOE organized the financial data into income statements that 

break down cost components incurred by firms that sell equipment with electric motors or 

replacement motors, “Electrical Goods Merchant Wholesalers” (NAICS 4236).a

a The distributors to whom these financial data refer handle multiple commodity lines.

6-5

 
 Using the previously described assumptions, DOE developed baseline and incremental 

markups and applied them in calculating end-user equipment prices from manufacturer sales 

prices. The BES provides gross margin (GM) as percent of sales for the electrical goods 

merchant wholesalers industry; therefore, baseline markups can be derived with the following 

equation: 

(%) (%)

(%) MUBASE Sales GMSales

− =

DOE used financial data from the BES for the categories “Electrical Goods Merchant 

Wholesalers” to calculate incremental markups used by wholesalers of motors. Incremental 

markups are coefficients that relate the change in the MSP of higher efficiency models to the 

change in the wholesaler selling price. Hence, incremental markups cover only those costs that 

scale with a change in the manufacturer’s sales price (i.e., variant costs). DOE considers higher 

efficiency models to be equipment sold under market conditions with new efficiency standards. 

It calculated the incremental markup (MUINCR) for distributors using the following equation: 

DISTRIBUTOR

DISTRIBUTOR DISTRIBUTOR

INCR CGS

CGS VC MU + =

Where:

MUINCR = incremental wholesaler markup,

CGSDISTRIBUTOR = distributor’s cost of goods sold, and 

VCDISTRIBUTOR = distributor’s variant costs. 

Table 6.4-1 shows the data from the BES and the markups DOE estimated using the 

procedures described previously. 

 

Table 6.4-1 Business Expenses Survey Data Used to Calculate Distributor Markups

Items Amount ($1,000,000)

Sales 348,960 

Cost of goods sold (CGS) 258,579 

Gross Margin 90,381 

Total Operating Expenses 55,785 

Labor & Occupancy Expenses Amount ($1,000,000)

Annual payroll 26,785 

Employer costs for fringe benefit 5,008 

Contract labor costs including temporary help 894 

Purchased utilities, total 628 

Cost of purchased repair and maintenance services 691 

Cost of purchased management consulting administrative services and other 

professional services 1,863 

Purchased communication services 790 

6-6
Lease and rental payments 2,164 

Taxes and license fees (mostly income taxes) 707 

Other Operating Expenses & Profit Amount ($1,000,000)

Expensed computer related supplies 335 

Cost of purchased packaging and containers 335 

Other materials and supplies not for resale 644 

Lease and rental payments for machinery and equipment 347 

Cost of purchased transportation, shipping and warehousing services 2,486 

Cost of purchased advertising and promotional services 1,890 

Expense purchases of software 353 

Cost of data processing and other purchased computer services, except 

communications 268 

Depreciation and amortization charges 2,170 

Commissions paid 1,444 

Other Operating Expenses 6,004 

Net profit before taxes 34,575 

Baseline Markup=(CGS+GM)/CGS 1.350

Incremental Markup=(CGS+Total Other Operating Expenses and Profit)/CGS 1.197

Source: 2007 Business Expenses Survey, Electrical Goods Merchant Wholesalers (NAICS 4236)

6.5 CONTRACTOR OR INSTALLER MARKUP

 DOE used information from RSMeans Electrical Cost Data3 to estimate markups used by 

contractors in the installation of equipment with small motors or replacement motors. RSMeans 

Electrical Cost Data estimates material expense markups for electrical contractors as 10 percent, 

leading to a markup factor of 1.10. DOE recognizes that contractors are not used in all 

installations, as some firms have in-house technicians who would install equipment or replace a 

motor. However, DOE has no information on the extent to which this occurs, so it applied a 

markup of 1.10 in all cases. 

6.6 SALES TAXES

The sales tax represents state and local sales taxes that are applied to the end-user 

equipment price. The sales tax is a multiplicative factor that increases the end-user equipment 

price.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse.4

 

These data represent weighted averages that include county and city rates. DOE then derived 

population-weighted average tax values for each Census division and large state, as shown in 

Table 6.6-1 below. This provides a national average tax rate of 7.11 percent, which DOE used 

for each distribution channel. 

6-7
Table 6.6-1 Average Sales Tax Rates by Census Division and Large State 

Census Division/State Population (2013) Tax Rate (2014)

New England 14,618,806 5.69%

Middle Atlantic 21,673,140 6.63%

East North Central 46,662,180 6.91%

West North Central 20,885,710 7.09%

South Atlantic 42,230,787 6.07%

East South Central 18,716,202 8.02%

West South Central 11,435,411 8.65%

Mountain 22,881,245 6.44%

Pacific 13,040,657 5.30%

New York 19,651,127 8.40%

California 38,332,521 8.45%

Texas 26,448,193 7.90%

Florida 19,552,860 6.65%

Population Weighted Average 7.11%

6.7 OVERALL MARKUP

 The overall markup for each distribution channel is the product of the relevant markups, 

as well as the sales tax. DOE used the overall baseline markup to estimate the end-user 

equipment price of baseline models, given the MSP of the baseline models. As stated previously, 

DOE considers baseline models to be equipment sold under existing market conditions (i.e., 

without new energy efficiency standards). 

 DOE used the overall incremental markup to estimate changes in the end-user equipment 

price, given changes in the manufacturer cost above the baseline model cost resulting from a 

standard to raise equipment efficiency. The total end-user equipment price for higher efficiency 

models is composed of two components: the end-user equipment price of the baseline model and 

the change in end-user equipment price associated with the increase in manufacturer cost to meet 

the new efficiency standard. The following equation shows how DOE used the overall 

incremental markup to determine the end-user equipment price for higher efficiency models (i.e.,

models meeting new efficiency standards). 

( )

BASE MFG OVERALL INCR

STD MFG OVERALL BASE MFG INCR SALES

EQP MSP MU

EQP MSP MU MSP MU Tax

_

_

×∆+=

×= ××∆+

Where:

6-8
EQPSTD = end-user equipment price for models meeting new efficiency 

standards, 

EQPBASE = end-user equipment price for baseline models, 

MSPMFG = manufacturer selling price for baseline models,

ΔMSPMFG = change in manufacturer selling price for higher efficiency models,

MUINCR = incremental OEM or distributor markup, 

TaxSALES = sales tax,

MUOVERALL_BASE = baseline overall markup (product of manufacturer markup, baseline 

OEM or distributor markup, and sales tax), and 

MUOVERALL_INCR = incremental overall markup (product of manufacturer markup, 

incremental OEM or distributor markup, and sales tax). 

 

Table 6.7.1 summarizes the markups and the overall baseline and incremental markups 

for each of the four main identified channels. Weighting the values by the respective shares of 

each channel yields an average overall baseline markup of 1.77 and an overall incremental 

markup of 1.57. 

Table 6.7.1 Summary of Markups for Four Primary Distribution Channels for Electric Motors

Markup

OEM to 

End-User (25%)

OEM to Distributer 

to End-User (25%)

Distributor to 

End-User (24%)

Distributor to OEM 

to End-User (23 %)

Baseline Incremental Baseline Incremental Baseline Incremental Baseline Incremental

Distributor - - 1.35 1.20 1.35 1.20 1.35 1.20

OEM 1.44 1.39 1.44 1.39 - - 1.44 1.39

Contractor/Installer - - - - - - - -

Sales Tax 1.0711 1.0711 1.0711 1.0711 1.0711 1.0711 1.0711 1.0711

Overall 1.54 1.49 2.08 1.79 1.45 1.29 2.08 1.79

6.8 SHIPPING COSTS

 DOE examined freight shipping costs to evaluate the impact of increased motor weight 

on installed cost. DOE collected quoted shipping costs from 16 freight shipment companies for 

single shipments by “less than truckload” (LTL) ground service weighing between 50 and 2,600 

pounds and over shipping distances of between 350 and 3,000 miles. Marginal shipment costs 

per pound varied from 7.1 cents to $1.44, depending on the total weight, distance shipped, and 

guaranteed delivery times. DOE used a median marginal shipment cost of 67 cents per pound. 

6-9
REFERENCES

1 Arthur D. Little, Inc. (1980), Classification and Evaluation of Electric Motors and Pumps. 

Report DOE/TIC-11339. Prepared for the US. Department of Energy, Office of Industrial 

Programs. Sprindfield, Va.: National Technical Information Service. 

2

 Pindyck, R.S. and D.L. Rubinfeld. (2000), Microeconomics, 5th ed., New Jersey: Prentice 

Hall.

3

 RSMeans Construction Publishers & Consultants. (2013), Electrical Cost Data, 36th Annual 

Edition. 2013. ed. J.H. Chiang, Kingston, MA. 

4

 Sales Tax Clearinghouse, Inc. (last accessed on January 10, 2014), State sales tax rates along 

with combined average city and county rates, http://thestc.com/STrates.stm

 

 

6-10

 
CHAPTER 7. ENERGY USE CHARACTERIZATION

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 ENERGY USE ANALYSIS FOR ELECTRIC MOTORS ............................................. 7-1

7.2.1 Introduction .................................................................................................... 7-1

7.2.2 Motor Losses .................................................................................................. 7-2

7.2.3 Reactive Power .............................................................................................. 7-4

7.2.4 Motor Applications ........................................................................................ 7-6

7.2.5 Load ............................................................................................................... 7-8

7.2.6 Motor Annual Hours of Operation ................................................................. 7-8

7.3 ANNUAL ENERGY USE ............................................................................................. 7-10

LIST OF TABLES

Table 7.1.1 Representative Units ............................................................................................ 7-1

Table 7.2.1 Polynomial Equation Coefficients for Losses vs. Load relationship ................... 7-2

Table 7.2.2 Polynomial Equation Coefficients for Power Factor vs. Load relationship ....... 7-5

Table 7.2.3 Distribution of Motors by Application for NEMA Design A and B Motors ......... 7-6

Table 7.2.4 Distribution of Motors by Application for NEMA Design C Motors ................. 7-7

Table 7.2.5 Distribution of Motors by Application for Brake motors .................................... 7-7

Table 7.2.6 Distribution across Sector by Motor Size ........................................................... 7-8

Table 7.2.7 Average Motor Load ............................................................................................ 7-8

Table 7.2.8 Average Motor Operating Hours by Application and Horsepower Range ........ 7-10

Table 7.3.1 Average Annual Energy Consumption by Efficiency Level for 

Representative Units .......................................................................................... 7-11

LIST OF FIGURES

Figure 7.2.1 Cumulative Distribution for 21-50 Horsepower Motors by Applications in 

Industry Sector ..................................................................................................... 7-9

7-i
CHAPTER 7. ENERGY USE CHARACTERIZATION

7.1 INTRODUCTION

A key component of the life-cycle cost (LCC) and payback period (PBP) calculations 

described in chapter 8 is the savings in operating costs that customers would realize from more 

energy-efficient equipment. Energy costs are the most significant component of customer

operating costs. The U.S. Department of Energy (DOE) uses annual energy use, along with 

energy prices, to establish energy costs at various energy efficiency levels. This chapter 

describes how DOE determined the annual energy use of electric motors. 

The analysis focuses on ten representative units identified in the engineering analysis

(chapter 5) and for which engineering analysis outputs were obtained. (Table 7.1.1) 

Table 7.1.1 Representative Units 

Rep. Unit

Equipment 

Class Group

(ECG)

Description Specifications Horsepower

1

1.a NEMA Designs

A & B NEMA Design B, Tframe, enclosed, 4-

pole 

5 

2 30 

3 75 

9 1.b Brake Motors 5 

10 30 

4

2 NEMA Design C

NEMA Design C, Tframe, enclosed, 4-

pole

5 

5 50 

6

3 Fire Pump Electric Motor

Uses engineering 

outputs derived from 

units 1, 2, and 3 

5 

7 30 

8 75 

7.2 ENERGY USE ANALYSIS FOR ELECTRIC MOTORS

7.2.1 Introduction

The energy use by electric motors is derived from three components: energy converted to 

useful mechanical shaft power, motor losses, and reactive power. Motor losses consist of I2

R 

losses (both stator and rotor), core losses, stray load losses, and friction and windage losses.1

Core losses and friction and windage losses are relatively constant with variations in motor 

loading, while I2

R losses increase with the square of the motor loading. Stray load losses are also 

dependent upon loading. DOE models the I2R losses and stray load losses as load-dependent 

losses.

7-1
7.2.2 Motor Losses

For each representative unit, DOE obtained data on part-load motor losses from test data 

developed in the engineering analysis (chapter 5). Based on the test data, DOE modeled the 

motor losses as a function of loading using a third degree polynomial equation2

: 

Loss(L) = A + B × L + C × L2 + D × L3

Where:

Loss(L ) = the losses of the motor at loading L in watts, 

 L = motor load as a fraction of rated power in percent, and

A/B/C/D = polynomial equation coefficients. 

Table 7.2.1 presents the polynomial equation coefficients for modeling losses as a 

function of load for the ten representative units at each efficiency level analyzed by DOE. These 

efficiency levels correspond to the efficiency levels (ELs) analyzed in the engineering analysis

(chapter 5). 

Table 7.2.1 Polynomial Equation Coefficients for Losses vs. Load relationship

Representative 

Unit EL A B C D

1 

0 364.9 103.8 191.9 130.5

1 169.6 51.2 208.8 103.4

2 158.0 77.8 68.3 133.6

3 141.7 24.0 186.1 53.3

4 118.1 14.6 183.8 52.5

2 

0 903.3 -401.9 2462.5 -338.3

1 863.1 -14.8 856.3 136.1

2 422.1 97.8 654.7 355.7

3 489.0 79.3 601.6 233.3

4 444.6 228.4 358.3 271.3

3 

0 1487.9 190.3 2066.7 466.3

1 1599.4 91.3 1581.4 236.0

2 870.8 280.9 1037.9 508.1

3 1123.5 -13.0 1156.9 185.7

4 765.9 247.6 690.2 506.3

4 

0 220.3 62.5 159.7 90.3

1 200.1 39.2 142.5 55.8

2 180.6 31.8 121.0 35.5

5 

0 1177.8 106.3 1240.8 282.6

1 922.8 178.6 886.5 183.0

2 767.2 204.1 573.1 418.7

7-2
6 

0 169.6 51.2 208.8 103.4

1 158.0 77.8 68.3 133.6

2 141.7 24.0 186.1 53.3

3 118.1 14.6 183.8 52.5

7 

0 863.1 -14.8 856.3 136.1

1 422.1 97.8 654.7 355.7

2 489.0 79.3 601.6 233.3

3 444.6 228.4 358.3 271.3

8 

0 1599.4 91.3 1581.4 236.0

1 870.8 280.9 1037.9 508.1

2 1123.5 -13.0 1156.9 185.7

3 765.9 247.6 690.2 506.3

9 

0 364.9 103.8 191.9 130.5

1 169.6 51.2 208.8 103.4

2 158.0 77.8 68.3 133.6

3 141.7 24.0 186.1 53.3

4 118.1 14.6 183.8 52.5

10 

0 903.3 -401.9 2462.5 -338.3

1 863.1 -14.8 856.3 136.1

2 422.1 97.8 654.7 355.7

3 489.0 79.3 601.6 233.3

4 444.6 228.4 358.3 271.3

To determine the annual energy losses Eloss in kilowatt-hours (kWh), DOE converts the 

full-load losses into part-load losses using the estimate of the motor’s load and multiplies by the 

annual operating hours. Annual energy losses are represented by the following equation: 

Eloss = Hop × Loss(L) 

Where:

 Eloss = annual energy consumed by motor losses in watts per hour, and

 Hop = the annual operating hours, in hours. 

 7.2.2.1 Impact of Higher Operating Speeds

DOE is aware that the installation of a more efficient motor could lead to less energy 

savings than anticipated. According to stakeholder comments, a more efficient motor typically

has less slip than a less efficient motor, an attribute that can result in a higher operating speed 

and a potential overloading of the motor. 

 DOE acknowledges that the cubic relation between speed and power requirement in 

many variable torque applications can affect the benefits gained by efficient motors, which have 

a lower slip. DOE did not obtain sufficient data to incorporate this effect into the LCC analysis. 

Instead, DOE incorporated this effect as a sensitivity analysis in the LCC spreadsheet, allowing 

7-3
the user to consider this effect following a scenario described in Appendix 7-A of the technical 

support document (TSD). 

7.2.3 Reactive Power

In an alternating current power system, the reactive power is the root mean square 

(RMS) voltage times the RMS current, multiplied by the sine of the phase difference between the 

voltage and the current. Reactive power occurs when the inductance or capacitance of the load 

shifts the phase of the voltage relative to the phase of the current. While reactive power does not 

consume energy directly, it can increase losses and costs for the electricity distribution system. 

Motors tend to create reactive power, because the windings in the motor coils have high 

inductance.

Alternating-current power flow has three components: real power (P), measured in watts 

(W); apparent power (S), measured in volt-amperes (VA); and reactive power (Q), measured in 

reactive volt-amperes (VAr). The power factor is defined as P/S. In the case of a perfectly 

sinusoidal waveform, P, Q, and S can be expressed as vectors that form a vector triangle such 

that: S2 = P2 + Q2

. This implies that the formula for reactive power as a function of real power 

and power factor is as follows: 

Q = P * (1/PF2-1)

Where:

 Q = reactive power in reactive volt-amperes, 

 P = real power in watts, and

PF = the motor’s power factor. 

DOE used data on motor power factor as a function of motor loading from test data 

developed in the engineering analysis (chapter 5) to develop a relationship between power factor 

and motor load. This relationship is expressed as a third degree polynomial: 

PF(L) = A + B × L + C × L2 + D × L3 

 Table 7.2.2 presents the polynomial equation coefficients developed to estimate power 

factor for all representative units at each efficiency level analyzed by DOE. 

7-4
Table 7.2.2 Polynomial Equation Coefficients for Power Factor vs. Load relationship

Representative Unit EL A B C D

1 

0 0.042 2.035 -1.883 0.636

1 0.034 2.053 -1.858 0.592

2 0.033 1.835 -1.554 0.476

3 0.035 2.311 -2.289 0.783

4 0.006 1.957 -1.722 0.544

2 

0 0.039 2.716 -2.963 1.068

1 0.032 2.126 -1.975 0.636

2 0.005 2.344 -2.346 0.796

3 0.033 2.188 -2.108 0.698

4 0.048 2.333 -2.386 0.795

3

0 0.044 3.182 -3.823 1.467

1 0.146 1.765 -1.557 0.467

2 0.225 1.827 -1.781 0.578

3 0.160 1.814 -1.663 0.519

4 0.052 2.812 -3.202 1.147

4 

0 0.033 1.612 -1.276 0.381

1 0.040 0.860 -0.269 -0.012

2 0.077 1.746 -1.453 0.420

5 

0 0.040 2.616 -2.835 1.029

1 0.043 1.925 -1.703 0.516

2 0.051 2.402 -2.504 0.851

6 

0 0.034 2.053 -1.858 0.592

1 0.033 1.835 -1.554 0.476

2 0.035 2.311 -2.289 0.783

3 0.006 1.957 -1.722 0.544

7 

0 0.032 2.126 -1.975 0.636

1 0.005 2.344 -2.346 0.796

2 0.033 2.188 -2.108 0.698

3 0.048 2.333 -2.386 0.795

8

0 0.146 1.765 -1.557 0.467

1 0.225 1.827 -1.781 0.578

2 0.160 1.814 -1.663 0.519

3 0.052 2.812 -3.202 1.147

9 

0 0.042 2.035 -1.883 0.636

1 0.034 2.053 -1.858 0.592

2 0.033 1.835 -1.554 0.476

3 0.035 2.311 -2.289 0.783

4 0.006 1.957 -1.722 0.544

10 

0 0.039 2.716 -2.963 1.068

1 0.032 2.126 -1.975 0.636

2 0.005 2.344 -2.346 0.796

3 0.033 2.188 -2.108 0.698

4 0.048 2.333 -2.386 0.795

7-5
7.2.4 Motor Applications 

 The annual operating hours and loading of motors depend on the sector (i.e., industry, 

agriculture, and commercial), motor size (in horsepower), and end-use application (e.g., pump). 

DOE estimated the share of motors in each type of application depending on the National 

Electrical Manufacturers Association (NEMA) Design and size of the motor and used a 

distribution of motors across sectors by motor size. DOE drew upon several data sources to 

develop a model of the applications for which motors covered in this analysis are used. 

 Six motor applications (air compressors, fans, pumps, material handling, fire pumps, and 

others) were selected as representative applications based on a previous DOE study (DOE-ITP 

study)3

. In order to derive distributions of motors across applications, DOE used data from more 

than five hundred field assessments aggregated in two databases: (1) a database of motor 

nameplate and field data compiled by the Washington State University (WSU) Extension Energy 

Program, Applied Proactive Technologies (APT), and New York State Energy Research and 

Development Authority (NYSERDA)4 (“WSU/NYSERDA database”)a; (2) a database of motor 

nameplate and field data compiled by the Industrial Assessment Center (IAC) at Oregon 

University (OSU) (“Northwest Industrial Motor Database”)b,5. 

Table 7.2.3 summarizes the sector-specific distributions of NEMA Design A and B 

motors across applications by horsepower range. Table 7.2.4 summarizes the distribution of 

NEMA Design C motors across applications by horsepower range in all sectors. For Design C 

motors, insufficient data were available to develop similar estimates in the commercial or 

agricultural sector and, instead, the estimates in the industrial sector were used as an 

approximation. Table 7.2.5 represents the sector-specific distribution of brake motors across 

applications by horsepower range. To account for the fact that brake motors are typically not 

used in air compressor, pump, and fan applications, these distributions were derived from 

information on NEMA Design A and B motors distributions across material handling and other 

applications. 

Table 7.2.3 Distribution of Motors by Application for NEMA Design A and B Motors

(%) Air 

Compressor Fan Pump Material Handling Other 

Industry

1-5hp 5.1 14.0 10.6 41.7 28.6

6-20hp 6.3 23.4 17.1 23.2 30.0

21-50hp 12.1 20.2 17.5 19.5 30.7

51-100hp 17.1 20.9 16.1 14.3 31.6

101-200hp 19.0 21.4 14.8 6.6 38.2

a The motors database is composed of information gathered by WSU and APT during 123 industrial motor surveys 

or assessments: 11 motor assessments were conducted between 2005 and 2011 and occurred in industrial plants; 112

industrial motor surveys were conducted between 2005 and 2011 and were funded by NYSERDA and conducted in 

New York State. b The Northwest Industrial Motor Database provides information on motors collected by the Industrial Assessment 

Center (IAC) at Oregon State University (OSU). The database includes more than 22,000 records, each with detailed 

motor application and field usage data.

7-6

 
201-500hp 23.5 13.6 15.6 7.2 40.1

Commercial

1-5hp 5.0 36.7 26.7 3.6 27.9

6-20hp 3.1 28.9 35.0 1.7 31.3

21-50hp 2.8 55.9 20.7 3.6 17.1

51-100hp 8.1 58.5 25.2 0.8 7.3

101-200hp 3.3 43.3 43.3 0.0 10.0

201-500hp 7.1 14.3 78.6 0.0 0.0

Agriculture

1-5hp 0.1 50.1 13.2 20.6 15.9

6-20hp 1.3 23.5 18.8 39.8 16.6

21-50hp 6.3 8.7 37.0 27.6 20.5

51-100hp 11.3 12.4 48.5 17.5 10.3

101-200hp 5.3 2.6 59.2 7.9 25.0

201-500hp 12.8 28.2 33.3 5.1 20.5

Table 7.2.4 Distribution of Motors by Application for NEMA Design C Motors 

(%) Air 

Compressor Fan Pump Material Handling Other 

All Sectors

1-5hp - 25.0 - 25.0 50.0

6-20hp - 11.1 - 11.1 77.8

21-50hp - 0.0 - 20.0 80.0

51-100hp - 11.1 - 11.1 77.8

101-200hp - 11.1 - 14.8 74.1

Table 7.2.5 Distribution of Motors by Application for Brake motors 

(%) Air 

Compressor Fan Pump Material Handling Other 

Industrial

1-5hp - - - 24.8 75.2

6-20hp - - - 43.6 56.4

21-30hp - - - 38.9 61.1

Commercial

1-5hp - - - 11.4 88.6

6-20hp - - - 5.3 94.7

21-30hp - - - 17.5 82.5

Agriculture

1-5hp - - - 56.4 43.6

6-20hp - - - 70.6 29.4

21-30hp - - - 57.4 42.6

7-7
 The distribution of motors across sectors by motor size was extracted from an Easton 

Consultants report,

6

 which provides the distribution of AC integral motors by horsepower across 

various sectors (Table 7.2.6). 

Table 7.2.6 Distribution across Sector by Motor Size

Horsepower range hp Industry Agriculture Commercial

1-50 26.11 0.11 73.78

51-100 63.27 6.98 29.75

101-200 76.03 3.35 20.62

201-500 69.09 3.03 27.88

7.2.5 Load 

To calculate the annual energy consumption at each efficiency level for each equipment 

class, DOE used the efficiencies and losses from the engineering analysis, along with estimates 

of motor operating hours and average load. Because the losses of a motor depend on the motor 

load, DOE estimated average motor load in order to look up the motor losses from the lossesversus-load curves from the engineering analysis (Table 7.2.1). The average motor load mainly 

depends on the motor’s end-use application (e.g., fan, pump) and sector (e.g. industrial). The

DOE-ITP study shows that motor load does not vary significantly across horsepower ranges for a 

specific application. DOE assumed that the motor load distribution took the form of a normal 

distribution, centered on the average value, and estimated application-specific average load and 

standard deviation values from approximately 21,500 field measurements provided by the 

WSU/NYSERDA and the Northwest Industrial Motor databases. Table 7.2.7 presents the 

average motor load by application in the industrial sector. Because sufficient data were not 

available, the same average load values and statistical distribution were used for the commercial 

and agricultural sectors.

Table 7.2.7 Average Motor Load 

Application Load

Air compressors 72.1

Fans 69.6

Pumps 67.0

Material Handling 58.9

Other 62.0

Fire Pumps 67.0

7.2.6 Motor Annual Hours of Operation 

 DOE estimated average annual operating hours by sector, application, and horsepower 

ranges and developed statistical distributions to use in its Monte Carlo analysis. (The Monte 

Carlo analysis is described in chapter 8.) 

7-8
 For the industrial sector, DOE combined data from the WSU/NYSERDA database and 

the Northwest Industrial Motor database to determine average annual operating hours by 

application and horsepower ranges and statistical distributions. For example, Figure 7.2.1 shows 

the cumulative form of the discrete distributions for motors of between 21 and 50 horsepower in 

various applications. 

Figure 7.2.1 Cumulative Distribution for 21-50 Horsepower Motors by Applications in 

Industry Sector

For the commercial and agricultural sectors, DOE derived estimates of average operating 

hours by application and horsepower range from various sources: the 2007 Census of Agriculture 

Farm and Ranch Irrigation Survey7

, and an article by Michael Gallaher et al.

8

. For fire pumps, 

DOE assumed a uniform distribution between 0.5 hours and up to 6 hours. 

Table 7.2.8 displays the average hours of motor operation by application and motor sizes 

for the industrial, commercial, and agricultural sectors. 

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0 2000 4000 6000 8000 10000

Cumulative Distrib ution (%)

Operating Hours (Hours)

Air Compressors

Fans

Pumps

Material Handling and Processing

Others

7-9
Table 7.2.8 Average Motor Operating Hours by Application and Horsepower Range

Horsepower range hp

1-5 6-20 21-50 51-100 101-200 201-500

Industry

Air Compressors 5,729 5,568 5,986 6,440 6,398 6,023

Fans 5,932 6,332 6,469 6,538 6,590 6,817

Pumps 5,936 6,347 6,883 6,848 7,076 7,518

Material Handling 4,902 4,577 4,681 5,488 6,431 5,990

Other 5,289 5,416 5,544 5,377 5,442 5,456

Fire Pump 3.25 3.25 3.25 3.25 3.25 3.25

Commercial

Air Compressors 1,000 1,200 1,500 1,500 1,500 1,500 

Fans 3,000 3,300 3,600 3,900 4,200 4,500 

Pumps 1,500 1,650 1,800 1,950 2,100 2,250 

Material Handling 1,959 2,165 2,380 2,567 2,753 2,939 

Other 1,959 2,165 2,380 2,567 2,753 2,939 

Fire Pump 3.25 3.25 3.25 3.25 3.25 3.25

Agriculture

Air Compressors 1,500 1,500 1,500 1,500 1,500 1,500 

Fans 1,500 1,500 1,500 1,500 1,500 1,500 

Pumps 1,009 1, 009 1, 009 1, 065 1, 121 1, 121

Material Handling 1,500 1,500 1,500 1,500 1,500 1,500 

Other 1,500 1,500 1,500 1,500 1,500 1,500 

Fire Pump 3.25 3.25 3.25 3.25 3.25 3.25

 

7.3 ANNUAL ENERGY USE

 Depending on the hours of operation, the loading, and the efficiency of the motor (which 

varies with the standard level), the annual energy use varies both by efficiency level and from 

motor to motor. The annual energy use is calculated using the following expression:

Hop

HP L E ×× = η

Where:

E = energy use, 

HP = horsepower of the motor, or motor capacity, 

η = operating efficiency, and

Hop = motor operating hours. 

7-10
Table 7.3.1 shows the results of the energy use analysis for the eight representative units 

at each considered energy efficiency level. Results are given for baseline units (EL 0) and the 

higher efficiency levels (ELs) being considered for motors. 

Table 7.3.1 Average Annual Energy Consumption by Efficiency Level for Representative 

Units 

 

ECG* Rep.

Unit Description kilowatt-hours per yearEL 0 EL 1 EL 2 EL 3 EL 4

1 1 Design B, T-frame, 5 hp, 

4 poles, enclosed 8,977 8,287 8,138 8,062 7,969 

1 2 Design B, T-frame, 30 

hp, 4 poles, enclosed 61,611 60,164 58,778 58,698 58,511 

1 3 Design B, T-frame, 75 

hp, 4 poles, enclosed 195,566 194,167 190,458 190,392 188,997 

2 4 Design C, T-frame, 5 hp, 

4 poles, enclosed 8,376 8,206 8,078 - - 

2 5 Design C, T-frame, 50 

hp, 4 poles, enclosed 79,551 78,276 77,653 - - 

3 6 Fire pump electric motor

, 5 hp, 4 poles, enclosed 9.24 9.08 9.00 8.89 - 

3 7 

Fire pump electric 

motor, 30 hp, 4 poles, 

enclosed

53.47 52.22 52.17 52.01 - 

3 8 

Fire pump electric 

motor, 75 hp, 4 poles, 

enclosed

130.24 127.77 127.75 126.81 - 

1 9 Brake motor, 5 hp, 4 

poles, enclosed 8,079 7,430 7,290 7,219 7,132 

1 10 Brake motor, 30 hp, 4 

poles, enclosed 48,394 47,178 45,999 45,934 45,777 

* ECG = Equipment Class Group.

7-11
REFERENCES

1

 National Electrical Manufacturers Association (2002), NEMA Standards Publication: 

Information Guide for General Purpose Industrial AC Small and Medium Squirrel-Cage 

Induction Motor Standards, NEMA, 1300 North 17th Street, Suite 1847, Rosslyn, VA 22209. 

2

 Fernando J. T. E. Ferreira, Aníbal T. De Almeida (September 2011), Technical and 

Economical Considerations of Line-Start PM Motors Including the Applicability of the 

IEC600-34-2-1 Standard, Energy Efficiency in Motor Driven Systems, Alexandria, VA. 

3

 U.S Department of Energy (December 2002), United States Industrial Electric Motor System 

Market Opportunities Assessment. Retrieved February 9, 2011, from 

http://www1.eere.energy.gov/industry/bestpractices/pdfs/mtrmkt.pdf 

4

 Database of motor nameplate and field measurement data compiled by the Washington State 

University Extension Energy Program (WSU) and Applied Proactive Technologies (APT) 

under contract with the New York State Energy Research and Development Authority 

(NYSERDA). 2011. 

5 Strategic Energy Group (January, 2008), Northwest Industrial Motor Database Summary. 

from Regional Technical Forum. Retrieved March 5, 2013 from 

http://rtf.nwcouncil.org/subcommittees/osumotor/Default.htm 

6

 Easton Consultants, I. (2000), Variable Frequency Drive. Retrieved February 9, 2011, from 

http://neea.org/research/reports/E00-054.pdf 

7

 U.S. Department of Agriculture (February 2010), 2007 Census of Agriculture Farm and 

Ranch Irrigation Survey, from

http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/Farm_and_Ranch_Irrig

ation_Survey/index.php 

 

8

 Gallaher, M., Delhotal, K., & Petrusa, J. (2009), Estimating the potential CO2 mitigation 

from agricultural energy efficiency in the United States, Energy Efficiency (2), 207-220. 

7-12

 
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION ........................................................................................................... 8-1

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis ................ 8-1

8.1.2 Overview of Life-Cycle Cost and Payback Period Inputs ................................... 8-3

8.2 LIFE-CYCLE COST INPUTS ........................................................................................ 8-5

8.2.1 Total Installed Cost Inputs ................................................................................... 8-6

8.2.1.1 Projection of Future Product Prices ............................................................... 8-8

8.2.1.2 Baseline Manufacturer Selling Price ............................................................. 8-9

8.2.2 Operating Cost Inputs ........................................................................................ 8-15

8.2.2.1 Annual Energy Consumption ....................................................................... 8-17

8.2.2.2 Energy Prices ............................................................................................... 8-17

8.2.2.3 Energy Price Trends ..................................................................................... 8-18

8.2.2.4 Repair and Maintenance Costs ..................................................................... 8-19

8.2.3 Motor Lifetime ................................................................................................... 8-22

8.2.3.1 The Weibull Distribution ............................................................................. 8-24

8.2.3.2 Mechanical Motor Lifetime and Application Lifetime ................................ 8-25

8.2.4 Discount Rates ................................................................................................... 8-26

8.2.5 Effective Date and Compliance Date of Standard ............................................. 8-28

8.2.6 Product Energy Efficiency in the Base Case ..................................................... 8-29

8.3 PAYBACK PERIOD INPUTS ...................................................................................... 8-32

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR 

REPRESENTATIVE UNITS ........................................................................................ 8-33

8.4.1 Representative Unit 1, NEMA Design B, 5 Horsepower, 4 Poles, Enclosed 

Motor.................................................................................................................. 8-34

8.4.2 Representative Unit 2, NEMA Design B, 30 Horsepower, 4 Poles, 

Enclosed Motor .................................................................................................. 8-36

8.4.3 Representative Unit 3, NEMA Design B, 75 Horsepower, 4 Poles, 

Enclosed Motor .................................................................................................. 8-37

8.4.4 Representative Unit 4, NEMA Design C, 5 Horsepower, 4 Poles, Enclosed 

Motor.................................................................................................................. 8-38

8.4.5 Representative Unit 5, NEMA Design C, 50 Horsepower, 4 Poles, 

Enclosed Motor .................................................................................................. 8-39

8.4.6 Representative Unit 6, Fire Pump, 5 Horsepower, 4 Poles, Enclosed 

Electric Motor .................................................................................................... 8-40

8.4.7 Representative Unit 7, Fire Pump, 30 Horsepower, 4 Poles, Enclosed 

Electric Motor .................................................................................................... 8-41

8.4.8 Representative Unit 8, Fire Pump, 75 Horsepower, 4 Poles, Enclosed 

Electric Motor .................................................................................................... 8-42

8.4.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 

Horsepower, Four Poles, Enclosed Motor ......................................................... 8-43

8.4.10 Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30 

Horsepower, Four Poles, Enclosed Motor ......................................................... 8-44

8-i
8.5 LIFE-CYCLE COST SENSITIVITY CALCULATIONS ............................................ 8-45

8.6 REBUTTABLE PAYBACK PERIOD .......................................................................... 8-47

LIST OF TABLES

Table 8.1.1 Representative Units ............................................................................................ 8-2

Table 8.1.2 Summary Information of Inputs for the Life-Cycle Cost and Payback 

Period Analyses ................................................................................................... 8-5

Table 8.2.1 Inputs for Total Installed Cost .............................................................................. 8-6

Table 8.2.2 Engineering Baseline Manufacturer Selling Price .............................................. 8-10

Table 8.2.3 Efficiency and Manufacturer Selling Price Data by Representative Unit: ......... 8-10

Table 8.2.4 Weighted Average Markups for Electric Motors Covered in this Analysis ...... 8-12

Table 8.2.5 Representative Unit 1: NEMA Design B, T-Frame, 5 hp, 4 Poles, 

Enclosed: Consumer Equipment Prices, Shipping Costs, and Total 

Installed Costs .................................................................................................... 8-12

Table 8.2.6 Representative Unit 2: NEMA Design B, T-Frame, 30 hp, 4 Poles, 

Enclosed: Consumer Equipment Prices, Shipping Costs, and Total 

Installed Costs .................................................................................................... 8-12

Table 8.2.7 Representative Unit 3: NEMA Design B, T-Frame, 75 hp, 4 Poles, 

Enclosed: Consumer Equipment Prices, Shipping Costs, and Total 

Installed Costs .................................................................................................... 8-13

Table 8.2.8 Representative Unit 4: NEMA Design C, T-Frame, 5 hp, 4 Poles, 

Enclosed: Consumer Equipment Prices, Shipping Costs, and Total 

Installed Costs .................................................................................................... 8-13

Table 8.2.9 Representative Unit 5: NEMA Design C, T-Frame, 50 hp, 4 Poles, 

Enclosed: Consumer Equipment Prices, Shipping Costs, and Total 

Installed Costs .................................................................................................... 8-13

Table 8.2.10 Representative Unit 6: Fire Pump Electric Motor, 5 hp, 4 Poles, Enclosed: 

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs ......... 8-13

Table 8.2.11 Representative Unit 7: Fire Pump Electric Motor, 30 hp, 4 Poles, 

Enclosed: Consumer Equipment Prices, Shipping Costs, and Total 

Installed Costs .................................................................................................... 8-14

Table 8.2.12 Representative Unit 8: Fire Pump Electric Motor, 75 hp, 4 Poles, 

Enclosed: Consumer Equipment Prices, Shipping Costs, and Total 

Installed Costs .................................................................................................... 8-14

Table 8.2.13 Representative Unit 9: Brake Motor, 5 hp, 4 Poles, Enclosed: Consumer 

Equipment Prices, Shipping Costs, and Total Installed Costs ........................... 8-14

Table 8.2.14 Representative Unit 10: Brake Motor, 30 hp, 4 Poles, Enclosed: Consumer 

Equipment Prices, Shipping Costs, and Total Installed Costs ........................... 8-15

Table 8.2.15 Inputs for Operating Cost ................................................................................... 8-15

Table 8.2.16 Distribution Across Sector by Motor Size .......................................................... 8-17

Table 8.2.17 Sector-Specific Share of Electric Motors by Census Region ............................. 8-18

Table 8.2.18 Average Electricity Prices in 2012 ..................................................................... 8-18

Table 8.2.19 Repair Cost Calculation Parameters ................................................................... 8-20

Table 8.2.20 Repair Cost Estimates at EL 0 (Equipment Class Group 1.a.) ........................... 8-21

8-ii
Table 8.2.21 Summary of Repair Cost for Each Representative Unit by Energy 

Efficiency Level ($2013) ................................................................................... 8-21

Table 8.2.22 Motor Lifetime by Horsepower Range and Sector ............................................. 8-22

Table 8.2.23 Average Application Lifetime ............................................................................ 8-23

Table 8.2.24 Weighted Average Lifetime by Equipment Class Groups and Sector ................ 8-24

Table 8.2.25 Weibull Parameters for Mechanical Motor Lifetimes by Sector ........................ 8-25

Table 8.2.26 Weibull Parameters for Application Lifetime .................................................... 8-25

Table 8.2.27 Summary of Sampled Motor Lifetimes .............................................................. 8-26

Table 8.2.28 Risk free rate and equity risk premium, 2011-2013 ........................................... 8-27

LIST OF FIGURES

Figure 8.1-1 Flow Diagram of Inputs for the Determination of Life-Cycle Cost and 

Payback Period..................................................................................................... 8-4

Figure 8.2-1 Historical Nominal and Deflated Producer Price Indexes for Integral 

Horsepower Motors and Generators Manufacturing ........................................... 8-8

Figure 8.2-2 Historical Deflated Producer Price Indexes for Copper Smelting, Steel 

Mills Manufacturing and Integral Horsepower Motors and Generators .............. 8-9

Figure 8.2-3 Industrial Electricity Price Trends ..................................................................... 8-19

Figure 8.4-1 Representative Unit 1: Distribution of Life-Cycle Cost Savings for EL 2 ........ 8-34

Figure 8.4-2 Representative Unit 1: Distribution of Payback Periods for EL 2 ..................... 8-35

Figure 8.4-3 Representative Unit 2: Distribution of Life-Cycle Cost Savings for EL 2 ........ 8-36

Figure 8.4-4 Representative Unit 3: Distribution of Life-Cycle Cost Savings for EL 2 ........ 8-37

Figure 8.4-5 Representative Unit 4: Distribution of Life-Cycle Cost Savings for EL 1 ........ 8-38

Figure 8.4-6 Representative Unit 5: Distribution of Life-Cycle Cost Savings for EL1 ......... 8-39

Figure 8.4-7 Representative Unit 6: Distribution of Life-Cycle Cost Savings for EL 1 ........ 8-40

Figure 8.4-8 Representative Unit 7: Distribution of Life-Cycle Cost Savings for EL 1 ........ 8-41

Figure 8.4-9 Representative Unit 8: Distribution of Life-Cycle Cost Savings for EL 1 ........ 8-42

Figure 8.4-10 Representative Unit 9: Distribution of Life-Cycle Cost Savings for EL 2 ........ 8-43

Figure 8.4-11 Representative Unit 10: Distribution of Life-Cycle Cost Savings for EL 2 ...... 8-44

8-iii
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

This chapter of the technical support document (TSD) presents the Department of 

Energy (DOE)’s life-cycle cost (LCC) and payback period (PBP) analysis. It describes the 

method DOE used for analyzing the economic impacts of possible standards on consumers. The 

effect of standards on consumers includes a change in operating expense (usually decreased) and 

a change in purchase price (usually increased). The LCC and PBP analysis produces two basic 

outputs to describe the effect of standards on consumers: 

• LCC is the total (discounted) cost that a consumer pays over the lifetime of the 

equipment, including purchase price, installation cost, and operating expenses. 

• PBP measures the amount of time it takes consumers to recover the estimated higher 

purchase expense of more energy-efficient equipment through lower operating costs. 

This chapter presents inputs and results for the LCC and PBP analysis, as well as key 

variables, current assumptions, and computational equations. DOE performed the calculations 

discussed here using Microsoft Excel spreadsheets, which are accessible on DOE's website 

(http://www.eere.energy.gov/buildings/appliance_standards/). Inputs to the LCC and PBP are 

discussed in sections 8.2 and 8.3, respectively, of this chapter. Results for the LCC and PBP are 

presented in section 8.4, with sensitivity results in section 8.5. Details regarding and instructions 

for using the spreadsheets are discussed in Appendix 8-A.

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis 

 Recognizing that several inputs to the determination of consumer LCC and PBP are 

either variable or uncertain, DOE conducted the LCC and PBP analysis by modeling both the 

uncertainty and variability in the inputs using Monte Carlo simulation and probability 

distributions. DOE developed LCC and PBP spreadsheet models incorporating both Monte Carlo 

simulation and probability distributions by using a Microsoft Excel spreadsheet combined with 

Crystal Ball (a commercially available add-on program). 

 In addition to characterizing several of the inputs to the analysis with probability 

distributions, DOE also developed sector-specific samples of end-use applications for each of the 

ten representative units (Table 8.1.1). These end-use applications determine the use profile of the 

motor and the economic characteristics of the motor owner (see chapter 7 for details). 

8-1
Table 8.1.1 Representative Units 

Rep. Unit

Equipment 

Class Group

(ECG)

Description Specifications Horsepower

1

Subgroup 1.a NEMA Designs

A & B – Non-brake motors NEMA Design B, Tframe, enclosed, 4-

pole 

5 

2 30 

3 75 

9 Subgroup 1.b NEMA Designs

A & B – Brake Motors

5 

10 30 

4

2 NEMA Design C

NEMA Design C, Tframe, enclosed, 4-

pole

5 

5 50 

6

3 Fire Pump Electric Motor

Uses engineering 

outputs derived from 

units 1, 2, and 3 

5 

7 30 

8 75 

In each Monte Carlo iteration, for each representative unit, the sector (i.e., industrial, 

agricultural, and commercial) and the Census region are identified by sampling from 

distributions, and they determine the energy price used in the LCC calculation in each 

simulation. DOE used Energy Information Administration (EIA) data on electricity prices in 

2010 for different customer classes and data from the DOE and the U.S. Department of 

Agriculture to establish the variability in energy pricing by Census region. 

 Further, one of the applications is identified by sampling from a sector-specific 

distribution of applications for that representative unit. The selected application within a sector 

determines the number of operating hours per year as well as the motor loading. The operating 

hours and the motor loading for the application are used in the energy use calculation (see 

chapter 7). 

 Also, the sector to which the motor belongs determines the discount rate used in the LCC 

calculation in each simulation. 

DOE also used data from the literature and field assessments1

 on motor loading and 

motor application characteristics to estimate the variability of annual energy use. Due to the large 

range of applications and motor use characteristics considered in the LCC and PBP analysis, the 

range of annual energy use and energy prices can be quite large. Thus, although the annual 

energy use and energy pricing are known for each sampled motor, their variability across all 

motors contributes to the range of LCCs and PBPs calculated for any particular standard level. 

 Results presented at the end of this chapter are based on 10,000 samples per Monte Carlo 

simulation run. DOE displays the LCC and PBP results as distributions of impacts compared to 

the base case without standards. 

8-2
8.1.2 Overview of Life-Cycle Cost and Payback Period Inputs 

 DOE categorizes inputs to the LCC and PBP analysis as follows: (1) inputs for 

establishing the initial expense, otherwise known as the total installed cost, and (2) inputs for 

calculating the operating cost. 

The primary inputs for establishing the total installed cost are:

• Baseline manufacturer selling price: The price at which the manufacturer sells the 

baseline equipment, which includes the costs incurred by the manufacturer to produce 

equipment meeting existing standards. 

• Manufacturer selling price increases: The change in manufacturer selling price 

associated with producing equipment to meet a particular standard level. 

• Markups and sales tax: The markups and sales tax associated with converting the 

manufacturer cost to a consumer equipment price. The markups and sales tax are 

described in detail in chapter 6, Markups Analysis. 

• Installation cost: The cost to the consumer of installing the equipment. The installation 

cost represents all costs required to install the equipment other than the marked-up 

consumer equipment price. The installation cost includes labor, overhead, and any 

miscellaneous materials and parts. Thus, the total installed cost equals the consumer 

equipment price plus the installation cost. 

The primary inputs for calculating the operating cost are:

• Equipment energy consumption and reactive power: The equipment energy consumption 

is the site energy use associated with operating the equipment. Reactive power is power 

that is reflected back to the electrical system by a change in the phase of alternating 

current power. Chapter 7, Energy Use Characterization, details how DOE determined the 

equipment energy consumption based on various data sources.

• Equipment efficiency: The equipment efficiency dictates the energy consumption 

associated with standard-level equipment (i.e., equipment with efficiencies greater than 

baseline equipment). Chapter 7, Energy Use Characterization, details how energy and 

reactive power change with increasing equipment efficiency and how equipment 

efficiency relates to actual equipment energy use.

• Energy prices: Energy prices are the prices paid by end-users for energy (i.e., electricity). 

DOE determined current energy prices based on data from the EIA.

• Energy price trends: DOE used the EIA Annual Energy Outlook 2012 (AEO2012)

2 to

forecast energy prices into the future. For the results presented in this chapter, DOE used 

the reference case of AEO2012 to forecast future energy prices.

8-3
• Repair and maintenance costs: Repair costs are associated with repairing or replacing 

components that have failed. Maintenance costs are associated with maintaining the 

operation of the equipment. 

• Lifetime: The age at which the equipment is retired from service.

• Discount rate: The rate at which DOE discounted future expenditures to establish their 

present value.

Figure 8.1-1 graphically depicts the relationships between the installed cost and operating 

cost inputs for the calculation of the LCC and PBP. In the figure below, the yellow boxes 

indicate the inputs, the green boxes indicate intermediate outputs, and the blue boxes indicate the 

final outputs (the LCC and PBP). 

Figure 8.1-1 Flow Diagram of Inputs for the Determination of Life-Cycle Cost and 

Payback Period

Table 8.1.2 summarizes the input values that DOE uses to calculate the LCC and PBP for 

electric motors and lists how these inputs changed from the notice of proposed rulemaking 

(NOPR) analysis to the final rule (FR) analysis. Each row summarizes the total installed cost 

inputs and operating costs, including the lifetime, discount rate, and electricity price trend. DOE 

8-4
characterizes several of the inputs with probability distributions that capture the input’s 

uncertainty, variability, or both in the Monte Carlo analysis. 

 

Table 8.1.2 Summary Information of Inputs for the Life-Cycle Cost and Payback Period 

Analyses 

Inputs NOPR Analysis Changes for the final rule

Manufacturer 

Selling Price

From the engineering analysis No change.

Markups Considered six distribution channel pathways. Considered seven distribution channel 

pathways.

Sales Tax Derived weighted-average tax values for each 

Census division and large State from data provided 

by the Sales Tax Clearinghouse.

Updated the sales tax using the latest 

information from the Sales Tax 

Clearinghouse.

Installation 

Cost

Assumed to remain constant across efficiency 

levels.

No change.

Maintenance 

Cost

Assumed to remain constant across efficiency 

levels.

No change.

Repair Costs Based on Vaughen’s 2013 data. No change.

Repair 

frequency 

Determined for each motor horsepower range and 

sector based on multiple data sources.

No change.

Unit Energy 

Consumption

Determined for each application based on sampled

sector and applications which in turns determined 

loading points and usage profile. 

No change.

Electricity 

Prices

Price: Based on EIA’s 2011 Form EIA-861 data.

Variability: Regional energy prices determined for 

4 regions.

Price: Updated with 2012 Form EIA-861 

data. Variability: No change.

Electricity 

Price Trends

Forecasted with EIA’s Annual Energy Outlook 

Early Release 2013.

No change.

Discount 

Rate

Derived discount rates using the cost of capital of 

publicly-traded firms based on data from 

Damodaran Online, the Value Line Investment 

survey, and the Office of Management and Budget 

(OMB) Circular No. A-94.

Same methodology. Updated sources.

Base Case 

Market 

Efficiency 

Distribution

All market efficiency distributions were derived for 

each equipment class group and by horsepower 

range. Distributions were derived from model 

counts derived from catalog data and assumed to 

remain constant over time. Used efficiency trends 

for equipment class groups 1 to derive efficiency 

distributions in the compliance year

No change.

8.2 LIFE-CYCLE COST INPUTS

Life-cycle cost is the total customer expense over the life of a piece of equipment, 

including purchase expense and operating costs (including energy expenditures). DOE discounts future 

operating costs to the time of purchase and sums them over the lifetime of the equipment. DOE 

defines LCC by the following equation: 

8-5
( ) ∑= +

+=

N

t

t

t

r

OC ICLCC

1 1

Where:

LCC = life-cycle cost in dollars,

IC = total installed cost in dollars,

∑ = sum over the lifetime, from year 1 to year N,

N = lifetime of appliance in years,

OC = operating cost in dollars, 

r = discount rate, and 

t = year for which operating cost is being determined. 

 DOE gathered most of its data for the LCC and PBP analysis in 2010, 2011, and 2012 

and updated its inputs to 20123$ using appropriate measures of inflation where necessary. 

Throughout this TSD, DOE expresses dollar values in 2013$. 

8.2.1 Total Installed Cost Inputs

DOE defines the total installed cost, IC, using the following equation:

EQPIC += INST

Where:

EQP = equipment price (i.e., customer cost for the equipment only), expressed in 

dollars, and 

INST = installation cost or the customer price to install equipment (i.e., the cost for 

labor and materials), also in dollars.

 The equipment price is based on how the customer (end-user) purchases the equipment. 

As discussed in chapter 6, DOE defined markups and sales taxes for converting manufacturing 

selling prices into customer equipment prices.

Table 8.2.1 summarizes the inputs for the determination of total installed cost.

Table 8.2.1 Inputs for Total Installed Cost

Baseline Manufacturer Selling Price

Manufacturer Selling Price Increase

Markups and Sales Tax

Installation Cost

The baseline manufacturer selling price is the price charged by the manufacturer to 

produce equipment for the current market. Manufacturer selling price increase is the change in 

8-6
manufacturer price associated with producing equipment at a standard level. Markups and sales 

tax convert the manufacturer selling price to a consumer equipment price. The installation cost is 

the cost to the consumer of installing the equipment and represents all costs required to install the 

equipment other than the marked-up consumer equipment price. The installation cost includes 

labor, overhead, and any miscellaneous materials and parts. Thus, the total installed cost equals 

the consumer equipment price plus the installation cost. DOE calculated the total installed cost 

for baseline products based on the following equation: 

MFG OVERALL BASE BASE

BASE BASE BASE

MSP MU INST

IC EQP INST

×= +

+=

_

Where:

ICBASE = baseline total installed cost,

EQPBASE = consumer equipment price for baseline models, 

INSTBASE = baseline installation and shipping cost, 

MSPMFG = manufacturer selling price for baseline models, and

MUOVERALL_BASE = baseline overall markup (product of manufacturer markup, baseline 

retailer or distributor markup, and sales tax). 

DOE calculated the total installed cost for standard-level products based on the following 

equation: 

( ) ( )

( ) ( )

( ) BASE MFG OVERALL INCR STD

BASE BASE STD STD

BASE STD BASE STD

STD STD STD

IC MSP MU INST

EQP INST EQP INST

EQP EQP INST INST

IC EQP INST

×∆+= ∆+

= + ∆+∆+

= +∆+ ∆+

+=

_

Where:

ICSTD = standard-level total installed cost,

EQPSTD = consumer equipment price for standard-level models, 

INSTSTD = standard-level installation cost,

EQPBASE = consumer equipment price for baseline models, 

ΔEQPSTD = change in equipment price for standard-level models,

INSTBASE = baseline installation and shipping cost, 

ΔINSTSTD = change in installation and shipping cost for standard-level models,

ICBASE = baseline total installed cost,

ΔMSPMFG = change in manufacturer selling price for standard-level models, and 

MUOVERALL_INCR = incremental overall markup (product of manufacturer markup, 

incremental retailer or distributor markup, and sales tax). 

8-7
 DOE found no evidence that installation costs would increase with higher motor energy 

efficiency. Thus, DOE did not incorporate changes in installation costs for motors that are more 

efficient than baseline products. In addition, motor installation cost data from RS Means 

Electrical Cost Data 20133 show a variation in installation costs according to the motor 

horsepower (for three-phase electric motors), but not according to efficiency. Therefore, in the 

preliminary analysis, DOE assumed there is no variation in installation costs between a baseline 

efficiency motor and a higher efficiency motor. 

 The remainder of this section provides information about each of the input variables that 

DOE used to calculate the total installed cost for electric motors. 

8.2.1.1 Projection of Future Product Prices 

To derive a price trend for electric motors, DOE obtained historical Producer Price 

Index (PPI) data for integral horsepower motors and generators manufacturing spanning the time 

period 1969-2013 from the Bureau of Labor Statistics’ (BLS).a The PPI data reflect nominal 

prices, adjusted for product quality changes. An inflation-adjusted (deflated) price index for 

integral horsepower motors and generators manufacturing was calculated by dividing the PPI 

series by the Gross Domestic Product Chained Price Index and is presented in 2013 dollar values 

in Figure 8.2-1. 

Figure 8.2-1 Historical Nominal and Deflated Producer Price Indexes for Integral 

Horsepower Motors and Generators Manufacturing 

a

 Series ID PCU3353123353123; http://www.bls.gov/ppi/ 

0.000

0.200

0.400

0.600

0.800

1.000

1.200

0.0

50.0

100.0

150.0

200.0

250.0

300.0

19691971197319751977197919811983198519871989199119931995199719992001200320052007200920112013

Deflated PPI (2013=1)

Nominal PPI (1983=100)

Year

Nominal Integral Horsepower Motors and Generators Manufacturing

Deflated Integral Horsepower Motors and Generators Manufacturing

8-8

 
 From the mid-1970s to 2005, the deflated price index for electric motors was roughly 

flat. Since then, the index has risen sharply, primarily due to rising prices of copper and steel 

products that go into motors (see Figure 8.2-2). The rising prices for copper and steel products 

were primarily a result of strong demand from China and other emerging economies. Given the 

slowdown in global economic activity in 2011, DOE believes that the extent to which the trends 

of the past five years will continue is very uncertain. DOE performed an exponential fit on the 

deflated price index for electric motors, but the R2 was relatively low (0.58). DOE also 

considered the experience curve approach, in which an experience rate parameter is derived 

using two historical data series on price and cumulative production, but the time series for 

historical shipments was not long enough for a robust analysis. 

 

 Given the above considerations, DOE decided to use a constant price assumption as the 

default price factor index to project future motor prices. 

Figure 8.2-2 Historical Deflated Producer Price Indexes for Copper Smelting, Steel Mills 

Manufacturing and Integral Horsepower Motors and Generators

8.2.1.2 Baseline Manufacturer Selling Price

The engineering analysis provides a baseline manufacturer selling price (MSP) that 

includes all manufacturer markups (see TSD chapter 5). Table 8.2.2 presents the baseline MSP 

and the associated energy efficiency for each representative unit analyzed in the engineering 

analysis.

8-9
Table 8.2.2 Engineering Baseline Manufacturer Selling Price

Representative Unit

Baseline 

Efficiency 

%

Baseline 

MSP 

2013$

1 NEMA Design B, T-frame, 5 hp, 4 poles, enclosed 82.5 333

2 NEMA Design B, T-frame, 30 hp, 4 poles, enclosed 89.5 856

3 NEMA Design B, T-frame, 75 hp, 4 poles, enclosed 93.0 1,910

4 NEMA Design C, T-frame, 5 hp, 4 poles, enclosed 87.5 334

5 NEMA Design C, T-frame, 50 hp, 4 poles, enclosed 93.0 1,552

6 Fire pump electric motor, 5 hp, 4 poles, enclosed 87.5 344

7 Fire pump electric motor, 30 hp, 4 poles, enclosed 92.4 1,096

8 Fire pump electric motor, 75 hp, 4 poles, enclosed 94.1 2,068

9 Brake motor, T-frame, 5 hp, 4 poles, enclosed 82.5 333

10 Brake motor, T-frame, 30 hp, 4 poles, enclosed 89.5 856

DOE determined the MSP associated with motors produced at increasing energy 

efficiency levels (ELs) for electric motors in the engineering analysis (see TSD chapter 5). Table 

8.2.3 presents the MSP, along with the associated energy efficiency, for representative units 1 

through 10. 

Table 8.2.3 Efficiency and Manufacturer Selling Price Data by Representative Unit: 

Representative Unit Energy Efficiency 

Level

Efficiency

%

MSP 

2013$

1 NEMA Design B, T-frame, 5 hp, 4 poles, enclosed Baseline 82.5 333

1 87.5 344

2 89.5 371

3 90.2 406

4 91.0 677

2 NEMA Design B, T-frame, 30 hp, 4 poles, enclosed Baseline 89.5 856

1 92.4 1,096

2 93.6 1,168

3 94.1 1,308

4 94.5 2,077

3 NEMA Design B, T-frame, 75 hp, 4 poles, enclosed Baseline 93.0 1,910

1 94.1 2,068

2 95.4 2,351

3 95.8 2,804

4 96.2 3,656

4 NEMA Design C, T-frame, 5 hp, 4 poles, enclosed Baseline 87.5 334

1 89.5 358

2 91.0 627

8-10
5 NEMA Design C, T-frame, 50 hp, 4 poles, enclosed Baseline 93.0 1,552

1 94.5 2,152

2 95.0 2,612

6 Fire pump electric motor, 5 hp , 4 poles, enclosed Baseline 87.5 344

1 89.5 371

2 90.2 406

3 91.0 677

7 Fire pump electric motor, 30 hp, 4 poles, enclosed Baseline 92.4 1,096

1 93.6 1,168

2 94.1 1,308

3 94.5 2,077

8 Fire pump electric motor, 75 hp, 4 poles, enclosed Baseline 94.1 2,068

1 95.4 2,351

2 95.8 2,804

3 96.2 3,656

9 Brake motor, T-frame, 5 hp, 4 poles, enclosed Baseline 82.5 333

1 87.5 344

2 89.5 371

3 90.2 406

4 91.0 677

10 Brake motor, T-frame, 30 hp, 4 poles, enclosed Baseline 89.5 856

1 92.4 1,096

2 93.6 1,168

3 94.1 1,308

4 94.5 2,077

 

Table 8.2.4 shows the baseline and incremental markups estimated for each point in the 

electric motor supply chain. The overall baseline and incremental markups shown are weighted 

averages based on the share of shipments in each distribution channel. Refer to TSD chapter 6 

for details. 

8-11
Table 8.2.4 Weighted Average Markups for Electric Motors Covered in this Analysis

Point in Supply Chain Baseline* Incremental*

Distributor 1.25 1.15

OEM 1.32 1.29

Contractor/Installer 1.00 1.00

Markup before Tax 1.66 1.47

Sales Tax 1.07

Overall 1.77 1.57

* Weighted average of the seven distribution channels.

Total Installed Cost: The total installed cost is the sum of the end-user equipment price and the 

installation cost. Refer back to section 8.2.1 to see the equations that DOE used to calculate the 

total installed cost for various energy efficiency levels. Table 8.2.5 through Table 8.2.9 present 

the end-user equipment price, shipping cost, and total installed cost for representative units 1 

through 10. 

Table 8.2.5 Representative Unit 1: NEMA Design B, T-Frame, 5 hp, 4 Poles, Enclosed:

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

%

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 82.5 591 65 656

1 87.5 608 69 678

2 89.5 651 81 731

3 90.2 706 83 789

4 91.0 1,133 104 1,237

Table 8.2.6 Representative Unit 2: NEMA Design B, T-Frame, 30 hp, 4 Poles, Enclosed:

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

% 

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 89.5 1,518 230 1,748

1 92.4 1,896 285 2,181

2 93.6 2,010 290 2,300

3 94.1 2,231 327 2,559

4 94.5 3,444 447 3,891

8-12
Table 8.2.7 Representative Unit 3: NEMA Design B, T-Frame, 75 hp, 4 Poles, Enclosed:

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

% 

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 93.0 3,387 496 3,882

1 94.1 3,636 546 4,182

2 95.4 4,082 610 4,692

3 95.8 4,797 675 5,473

4 96.2 6,142 790 6,932

Table 8.2.8 Representative Unit 4: NEMA Design C, T-Frame, 5 hp, 4 Poles, Enclosed:

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

%

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 87.5 593 57 649

1 89.5 631 66 697

2 91.0 1,055 84 1,139

Table 8.2.9 Representative Unit 5: NEMA Design C, T-Frame, 50 hp, 4 Poles, Enclosed:

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

%

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 93.0 2,752 438 3,190

1 94.5 3,699 517 4,216

2 95.0 4,425 533 4,958

Table 8.2.10 Representative Unit 6: Fire Pump Electric Motor, 5 hp, 4 Poles, Enclosed: 

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

%

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 87.5 610 69 680

1 89.5 653 81 733

2 90.2 708 83 792

3 91.0 1,135 104 1,239

8-13
Table 8.2.11 Representative Unit 7: Fire Pump Electric Motor, 30 hp, 4 Poles, Enclosed: 

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

% 

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 92.4 1,943 285 2,228

1 93.6 2,057 290 2,347

2 94.1 2,278 327 2,606

3 94.5 3,491 447 3,938

Table 8.2.12 Representative Unit 8: Fire Pump Electric Motor, 75 hp, 4 Poles, Enclosed: 

Consumer Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

% 

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 94.1 3,667 546 4,213

1 95.4 4,113 610 4,723

2 95.8 4,828 675 5,504

3 96.2 6,173 790 6,963

Table 8.2.13 Representative Unit 9: Brake Motor, 5 hp, 4 Poles, Enclosed: Consumer 

Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

%

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 82.5 591 65 656

1 87.5 608 69 678

2 89.5 651 81 731

3 90.2 706 83 789

4 91.0 1,133 104 1,237

8-14
Table 8.2.14 Representative Unit 10: Brake Motor, 30 hp, 4 Poles, Enclosed: Consumer 

Equipment Prices, Shipping Costs, and Total Installed Costs 

Energy 

Efficiency 

Level

Efficiency

% 

Equipment 

Price 

2013$

Shipping Cost

2013$ 

Total Installed 

Cost

2013$

Baseline 89.5 1,518 230 1,748

1 92.4 1,896 285 2,181

2 93.6 2,010 290 2,300

3 94.1 2,231 327 2,559

4 94.5 3,444 447 3,891

8.2.2 Operating Cost Inputs

 DOE defines the operating cost, OC, by the following equation: 

OC RCEC ++= MC

Where:

EC = energy expenditure associated with operating the equipment, 

RC = repair cost associated with component failure, and 

MC = cost for maintaining equipment operation. 

Table 8.2.15 shows the inputs for determining the operating costs. The inputs listed in 

Table 8.2.15 are also necessary for determining the present value of lifetime operating expenses, 

which include the energy price trends, equipment lifetime, discount rate, and effective date of the 

standard.

Table 8.2.15 Inputs for Operating Cost

Annual Energy Consumption 

Energy Prices

Repair and Maintenance Costs

Energy Price Trends

Product Lifetime

Discount Rate

Effective Date of Standard

The annual energy consumption is the site energy use associated with operating the 

equipment. Energy prices are the prices paid by end-users for energy supply, including both 

energy and demand charges. Multiplying the annual energy and demand by the appropriate 

prices yields the annual energy cost. Repair costs are associated with repairing or replacing 

components that have failed, and maintenance costs are associated with maintaining the 

8-15
operation of the equipment. DOE used energy price trends to forecast energy supply prices into 

the future and, along with the equipment lifetime and discount rate, to establish the lifetime 

energy supply costs. The equipment lifetime is the age at which the equipment is retired from 

service. The discount rate is the rate at which DOE discounted future expenditures to establish 

their present value. DOE calculated the operating cost for the baseline equipment based on the 

following equation: 

BASE ENERGY BASE BASE

BASE BASE BASE BASE

AEC PRICE RC MC

OC EC RC MC

= × ++

++=

Where:

OCBASE = baseline operating cost,

ECBASE = energy expenditures associated with operating the baseline equipment, 

which may include reactive power costs, 

RCBASE = repair cost associated with component failure for the baseline 

equipment, 

MCBASE = cost for maintaining baseline equipment operation, 

AECBASE = annual energy consumption for baseline equipment, and 

PRICEENERGY = energy price.

DOE calculated the operating cost for standard-level equipment based on the following 

equation: 

( ) ( ) ( ) BASE STD ENERGY BASE STD BASE STD

STD ENERGY STD STD

STD STD STD STD

AEC AEC PRICE RC RC MC MC

AEC PRICE RC MC

OC RCEC MC

= ×∆ ∆++∆++

×= ++

++=

__

Where:

OCSTD = standard-level operating cost,

ECSTD = energy expenditures associated with operating standard-level equipment, 

RCSTD = repair cost associated with component failure for standard-level 

equipment, 

MCSTD = cost for maintaining standard-level equipment operation, 

AECSTD = annual energy consumption for standard-level equipment, 

PRICEENERGY = energy price,

ΔAECSTD = decrease in annual energy consumption caused by standard-level 

equipment, 

ΔRCSTD = change in repair cost caused by standard-level equipment, and 

ΔMCSTD = change in maintenance cost caused by standard-level equipment. 

 The remainder of this section provides information about each of the above input 

variables that DOE used to calculate the operating costs for electric motors. 

8-16
8.2.2.1 Annual Energy Consumption

Chapter 7, Energy Use Characterization, details how DOE determined the annual energy 

consumption for baseline and standard-level equipment and provides the average annual energy 

consumption by efficiency level for each representative unit. DOE captured the variability in 

energy consumption by estimating energy consumption for a variety of motor-using applications.

DOE used several assumptions to account for a possible decrease in efficiency each time 

the motor is repaired, which would increase the annual energy consumption. First, for the 

industrial and commercial sector, DOE assumed that 1 to 20 hp motors are not repaired; motors 

from 21 to 100 hp are repaired at half their lifetime; and motors from 101 to 500 hp are repaired 

at a third of their lifetime. For the agricultural sector, DOE did not find sufficient data to 

distinguish by horsepower range and assumed that motors are repaired on average at half of their 

lifetime. Based on the mechanical lifetime estimates (see section 8.2.3) and operating hours 

estimates (see chapter 7), this corresponds to a repair frequency of 48,600 hours in the industrial 

sector. DOE also assumed that fire pump electric motors are not repaired because of their low 

annual operating hours. Second, DOE assumed that 90% of repairs are performed following 

industry recommended practice and, therefore, do not affect the efficiency of the motor; that is, 

there is no degradation of efficiency after a repair4

. In addition, DOE assumed that 10% of 

repairs do not follow good practice and that the repair results in a slight decrease in efficiency. 

This estimate of the number of repairs following industry recommended practices was based on 

the share of motor repair shops that are members of the Electrical Apparatus Service Association 

(EASA), assuming members of EASA follow the EASA recommended practices5

. Lastly, for 

the cases in which good practices were not followed during repairs, DOE assumed the efficiency 

drops by 1 percent in the case of motors of less than 40 hp and by 0.5 percent in the case of 

larger motors6

. 

8.2.2.2 Energy Prices

To estimate the energy prices faced by motor end-users throughout the United States, 

DOE uses sector-specific regional electricity prices, as well as a statistical distribution of motors 

across sectors and regions, to assign an appropriate electricity price to each motor end-user. 

 

First, DOE distributed the motors across the three sectors using data from an Easton 

Consultants report7 (see Table 8.2.16).

Table 8.2.16 Distribution Across Sector by Motor Size 

Horsepower 

range (hp) Industrial Agricultural Commercial

1-50 26.11 0.11 73.78

51-100 63.27 6.98 29.75

101-200 76.03 3.35 20.62

201-500 69.09 3.03 27.88

8-17
Then, for each sector, DOE distributed the motors in four Census regions based on the 

following indicators: 

• industry electricity consumption by region from the AEO2013 for the industrial sector8

; 

• value of shipments of agricultural products from the U.S Census of Agriculture for the 

agricultural sector 9

; and

• commercial floor space from the Commercial Building Energy Consumption Survey for 

the commercial sector10. 

 Table 8.2.17 shows the resulting distribution.

Table 8.2.17 Sector-Specific Share of Electric Motors by Census Region

Census Region Agricultural 

%

Industrial 

%

Commercial 

%

Northeast 4.6 9.7 19.5

Midwest 42.8 29.3 25.3

South 29.5 44.6 37.3

West 23.1 16.4 17.9

 For each sector, DOE then estimated weighted regional average prices using EIA Form 

861 data.11 These data are published annually and include annual electricity usage in kilowatthours (kWh), revenues from electricity sales, and number of consumers for the residential, 

commercial, and industrial sectors for every utility serving final consumers. The calculation used 

the most recent EIA data available at the time the analysis was conducted. Table 8.2.18 shows 

the average agricultural, industrial, and commercial electricity prices in 2012 for each Census 

region. 

Table 8.2.18 Average Electricity Prices in 2012

Census Region Average 

Agricultural Price

2013$/kWh

Average Industrial 

Price

2013$/kWh

Average 

Commercial Price 

2013$/kWh

Northeast 0.096 0.096 0.126

Midwest 0.083 0.083 0.104

South 0.073 0.073 0.112

West 0.084 0.084 0.121

Average (weighted) 0.081 0.080 0.114 

8.2.2.3 Energy Price Trends

DOE used price forecasts by the EIA to estimate the trends in electricity prices for all 

sectors. To arrive at prices in future years, DOE multiplied the average prices described in the 

8-18
preceding section by the forecast of annual average price changes in EIA’s AEO2013. To 

estimate the trend after 2040, DOE followed past guidelines provided to the Federal Energy 

Management Program by the EIA and used the average rate of change during 2025–2040 for 

electricity prices. 

As an example, Figure 8.2-3 shows the projected trends in industrial electricity prices 

based on the AEO2013 reference case. For the LCC results presented in this chapter, DOE used 

only the energy price forecast from the AEO2013 reference case. 

 Figure 8.2-3 Industrial Electricity Price Trends 

8.2.2.4 Repair and Maintenance Costs

DOE defined a motor repair as repair as including rewinding and reconditioning. DOE 

accounted for the differences in repair costs of a higher efficiency motor compared to a baseline 

efficiency motor. Based on data from Vaughen’s12, DOE derived a model to estimate repair costs 

by horsepower, enclosure, and pole for each efficiency level:

RepairCost = R(hp, poles, encl, CSL), 

R(hp, poles, encl, CSL) = R′(hp, poles) ∙ R′′(encl) ∙ A(EL), 

where:

R′(hp, poles) = r2(hp, poles) + r1(hp, poles) + r0(poles), 

with:

0.85

0.95

1.05

1.15

1.25

1.35

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040

Industrial Electricity Price Index 

(2012=1)

Reference High Low

8-19
r2(hp, poles) = (−0.000005 ∙ poles) ∙ hp2, 

r1(hp, poles) = (−0.00024 ∙ poles2 + 0.00178 ∙ poles + 0.03859) ∙ hp, 

r0(poles) = (0.01886 ∙ poles2 − 0.07154 ∙ poles + 0.89775), 

and, 

R′′(encl) = �

1.0, Open, 

1.2, Enclosed,

and A(EL) is given by Table 8.2.19: 

Table 8.2.19 Repair Cost Calculation Parameters 

Efficiency 

level

A(EL) by Equipment Class Group

1.a.

(NEMA Design A 

and B motors) 

1.b. 

(Brake motors)

2

(NEMA Design C 

motors)

3

(Fire pump 

electric motors)

Baseline 0% 15% 0% 0%

EL 1 0% 15% 15% 15%

EL 2 15% 32% 27% 27%

EL 3 27% 45% n/a 39%

EL 4 39% 60% n/a n/a

Table 8.2.20 shows the resulting repair cost estimates for all horsepower, enclosure, and 

pole combinations for equipment class group 1.a. motors (NEMA Design A and B) with baseline 

efficiency. 

8-20
Table 8.2.20 Repair Cost Estimates at EL 0 (Equipment Class Group 1.a.) 

EL 0 Open Enclosed

hp 2 poles 4 poles 6 poles 8 poles 2 poles 4 poles 6 poles 8 poles

1 363 340 433 589 435 408 520 706

1.5 372 349 444 601 447 419 533 721

2 381 358 454 614 458 429 545 736

3 400 375 475 639 480 450 570 766

5 437 410 516 688 525 492 620 826

7.5 484 454 568 751 580 545 682 901

10 530 497 620 813 636 597 744 975

15 622 584 723 936 747 701 867 1123

20 714 670 825 1059 857 805 990 1271

25 806 756 926 1181 967 908 1112 1417

30 897 842 1028 1302 1077 1010 1233 1563

40 1079 1012 1228 1543 1295 1215 1474 1851

50 1259 1181 1427 1780 1510 1417 1712 2136

60 1437 1349 1623 2015 1725 1618 1948 2418

75 1702 1597 1914 2361 2042 1916 2296 2833

100 2136 2004 2387 2924 2563 2405 2865 3508

125 2560 2402 2847 3468 3072 2883 3416 4161

150 2975 2791 3294 3993 3570 3350 3952 4792

200 3776 3544 4146 4990 4532 4252 4975 5987

250 4540 4260 4944 5912 5448 5112 5933 7095

300 5267 4942 5689 6761 6320 5930 6827 8114

350 5955 5588 6380 7537 7146 6705 7655 9044

400 6606 6199 7016 8239 7927 7438 8419 9887

450 7219 6774 7599 8868 8663 8129 9119 10641

500 7795 7314 8128 9423 9354 8777 9753 11307

Table 8.2.21 summarizes the repair cost for representative units by efficiency level.

 

Table 8.2.21 Summary of Repair Cost for Each Representative Unit by Energy Efficiency

Level ($2013) 

Representative Unit

1 2 3 4 5 6 7 8 9 10

Baseline 492 1,010 1,916 492 1,417 492 1,010 1,916 566 1,162 

EL 1 492 1,010 1,916 566 1,630 566 1,162 2,204 566 1,162 

EL 2 566 1,162 2,204 623 1,793 623 1,278 2,424 651 1,336 

EL 3 623 1,278 2,424 n/a n/a 685 1,406 2,667 716 1,470 

EL 4 685 1,406 2,667 n/a n/a n/a n/a n/a 788 1,617 

 For the maintenance costs, DOE did not find data indicating a variation in maintenance 

costs between baseline efficiency and higher efficiency motors. According to Vaughen’s, the 

8-21
price of replacing bearings, which is the most common maintenance practice, is the same at all 

efficiency levels.

8.2.3 Motor Lifetime 

Where data were available, DOE established sector-specific motor lifetime estimates to 

account for differences in maintenance practices and field usage conditions. DOE relied on 

several sources to inform its lifetime model: for the industrial sector, DOE consulted an 

industrial expert and obtained estimates of average mechanical lifetimes by horsepower range13; 

for the agricultural sector, DOE referred to an article by Michael Gallaher et al14 for determining 

average motor lifetimes; and for the commercial sector, because DOE could not find sectorspecific estimates, it used average motor lifetimes by horsepower range from the Energy 

Efficient Motor Systems handbook15 instead.

DOE then converted all lifetimes into mechanical lifetimes in hours based on typical 

annual operating hours by horsepower range and sector (see chapter 6, Energy Use 

Characterization for operating hours). Table 8.2.22 presents the resulting lifetimes. 

Table 8.2.22 Motor Lifetime by Horsepower Range and Sector 

Lifetime Horsepower 

Range Industrial Sector* Agricultural Sector** 

Commercial 

Sector*** 

Mechanical 

Hours

1 – 5 43,800 28,578 37,060

6 – 20 43,800 27,966 44,248

21 – 50 87,600 26,555 63,596

51 – 100 87,600 25,870 88,675

101 – 200 131,400 24,659 85,548

201 – 500 131,400 27,597 73,018

Weighted

Average Across 

Applications†

Years

1 – 5 8 20.0 17.1

6 – 20 8 20.0 19.4

21 – 50 15 20.0 21.8

51 – 100 14 20.0 28.5

101 – 200 21 20.0 28.0

201 – 500 21 20.0 29.0

* Weighted average lifetimes in years were calculated based on the mechanical lifetime estimates and dividing by 

the weighted average annual operating hours across applications and equipment class groups. 13

** Mechanical lifetimes were calculated based on an average 20-year lifetime estimate in agriculture and 

multiplying by the weighted average annual operating hours across applications and equipment class groups.

14

*** Mechanical lifetimes were calculated based on average lifetime estimates by horsepower range and multiplying 

by the weighted average annual operating hours across applications and equipment class groups. 15

In the LCC, DOE used a motor lifetime model that combines annual operating hours by 

application and sector with motor mechanical lifetime in hours to estimate the distribution of 

motor lifetimes in years. This model results in a negative correlation between annual hours of 

operation and motor lifetime; motors operated many hours per year are likely to be retired sooner 

than motors that are used for only a few hundred hours per year. 

8-22
Further, motors that are smaller than 75 horsepower are typically embedded in other 

equipment (i.e., “application”) such as pumps or compressors. For each of these motors (less 

than 75 hp), DOE determined the lifetime in years by dividing the mechanical lifetime in hours 

by the annual hours of operation. DOE then compared this lifetime (in years) with the sampled 

application lifetime (also in years) and assumed that the motor would be retired at the younger of 

these two ages. For example, a pump motor with annual operating hours of 2,500 hours per year 

may have a mechanical lifetime of 30,000 hours (12 years) and an application lifetime of 10 

years. DOE assumed the motor would retire in 10 years, when its application reached the end of 

its lifetime, even if the motor itself could run for two more years. If the pump motor were to run 

for 6,000 hours per year, with the same mechanical and application lifetimes, DOE would 

assume it would retire after 5 years due to motor failure upon reaching its mechanical lifetime of 

30,000 hours. 

Table 8.2.23 presents the average application lifetimes used in the LCC 16,17,18,19. 

Table 8.2.23 Average Application Lifetime

Application Average Lifetime

Yr

Air Compressor 15

Fans 15

Pumps 11

Material Handling 20

Other 15

The DOE’s motor lifetime model relies on four distributions: (1) the annual operating 

hours distribution derived for use in the energy use analysis (see chapter 6); (2) the distribution 

of motor shipments into six application areas, each with its own distribution of annual hours of 

operation; (3) a Weibull distribution of mechanical motor lifetimes, expressed in total hours of 

operation before failure; and (4) a Weibull distribution of application lifetimes, expressed in 

years. DOE used its estimate of motor mechanical lifetime in hours and application lifetime in 

years to develop the parameters for the Weibull distributions for all represented units. DOE’s 

Monte Carlo analysis of a motor’s LCC selected an application, an appropriate number of hours 

of operation, a motor mechanical lifetime, and an application lifetime from these distributions in 

order to calculate the sampled motor’s lifetime in years. 

 

The national impact analysis (NIA) calculation uses average lifetimes in years by 

equipment class group, horsepower range, and sector. DOE used the application-specific annual 

operating hours and application distributions in order to convert the motor mechanical lifetimes

into average lifetimes in years. Results are presented in Table 8.2.24 by equipment class group, 

horsepower range, and sector. Further, based on a literature review,

20,21,22 DOE assumed that the 

maximum motor lifetime in years is 30 years. 

8-23
Table 8.2.24 Weighted Average Lifetime by Equipment Class Groups and Sector

 For fire pump electric motors, DOE assumed an average lifetime of 29 years and 

developed a Weibull distribution around this value (both in the LCC and in the NIA). 

DOE further developed Weibull distributions for each of these average lifetimes in years.

8.2.3.1 The Weibull Distribution 

 The Weibull distribution is a probability distribution commonly used to measure failure 

rates.b Its form is similar to an exponential distribution, which models a fixed failure rate, except 

that a Weibull distribution allows for a failure rate that changes over time in a particular fashion. 

The cumulative Weibull distribution takes the form:

e x

xP

β

α

θ 



 



 − −

)( = for x > θ , and 

P(x) = 1 for x ≤ θ

Where:

P(x) = probability that the equipment is still in use at age x, 

x = equipment age, 

α = scale parameter, which would be the decay length in an exponential distribution, 

β = shape parameter, which determines the way in which the failure rate changes through 

time, and

θ = delay parameter, or location, which allows for a delay before any failures occur.

b For reference on the Weibull distribution, see sections 1.3.6.6.8 and 8.4.1.3 of the NIST/SEMATECH e-Handbook 

of Statistical Methods, <www.itl.nist.gov/div898/handbook/>. 

Horsepower 

Range 

hp

Equipment Class Group

Weighted Average Lifetime Across 

Applications

Yr

Industrial Commercial Agricultural

1-5

Group 1.a. NEMA Design A and B 

Motors

8 17 20 

6-20 8 19 20 

21-50 15 22 20 

51-100 14 29 20 

101-200 21 28 19

201-500 21 29 20 

1-5

Group 1.b. Brake Motors

8 19 19 

6-20 9 20 19 

21-30 17 27 18 

1-5

Group 2 NEMA Design C Motors

8 17 19 

6-20 8 19 19 

21-50 16 27 18

51-100 16 33 17 

101-200 23 29 16 

8-24

 
 When β = 1, the failure rate is constant over time, giving the distribution the form of a 

cumulative exponential distribution. In the case of mechanical equipment, β commonly is greater 

than 1, reflecting an increasing failure rate as equipment ages. 

8.2.3.2 Mechanical Motor Lifetime and Application Lifetime

DOE’s derived Weibull parameters for each representative unit’s mechanical lifetimes

are listed in Table 8.2.25. The Weibull parameters account for a three-year manufacturer 

warranty period. During this period DOE assumes that no motors fail. 

Table 8.2.25 Weibull Parameters for Mechanical Motor Lifetimes by Sector

Representative Unit

Industrial Sector Commercial Sector Agricultural Sector

Α β θ Α β θ Α β θ

1 

NEMA Design B, Tframe, 5 hp, 4 poles, 

enclosed

19,712 2.65 26,280 34,339 2.65 6,540 27,331 2.65 4,287 

2 

NEMA Design B, Tframe, 30 hp, 4 poles, 

enclosed

68,993 2.65 26,280 61,796 2.65 8,672 25,396 2.65 3,983 

3 

NEMA Design B, Tframe, 75 hp, 4 poles, 

enclosed

68,993 2.65 26,280 89,450 2.65 9,173 24,741 2.65 3,880 

4 

NEMA Design C, Tframe, 5 hp, 4 poles, 

enclosed

19,712 2.65 26,280 34,339 2.65 6,540 27,331 2.65 4,287 

5 

NEMA Design C, Tframe, 50 hp, 4 poles, 

enclosed

68,993 2.65 26,280 61,796 2.65 8,672 25,396 2.65 3,983 

9 Brake motor, 5 hp 19,712 2.65 26,280 34,339 2.65 6,540 27,331 2.65 4,287

10 Brake motor, 30 hp 68,993 2.65 26,280 61,796 2.65 8,672 25,396 2.65 3,983

DOE’s derived Weibull parameters for motor applications are listed in Table 8.2.26. 

Table 8.2.26 Weibull Parameters for Application Lifetime

Application Parameters

α Β θ

1 Fan 8.44 2.65 7.50

2 Air Compressor 8.44 2.65 7.50

3 Pump 6.19 2.65 5.50

4 Material Handling 11.25 2.65 10.00

5 Others 8.63 2.65 7.67

6 Fire Pump 26.14 110.09 3.00

8-25
 In the scope of this life-cycle analysis, DOE combines these two distributions with the 

appropriately weighted duty factor distribution to select a lifetime for each motor. 

Table 8.2.27 summarizes calculated motor lifetimes of sampled motors. 

Table 8.2.27 Summary of Sampled Motor Lifetimes 

Representative Unit Median

yr

Min

yr

Max

yr

Average

yr

1 NEMA Design B, T-frame, 5 hp, 4 poles, enclosed 10.5 2.3 31.3 10.1

2 NEMA Design B, T-frame, 30 hp, 4 poles, enclosed 12.2 2.9 35.4 12.5

3 NEMA Design B, T-frame, 75 hp, 4 poles, enclosed 10.3 2.7 30.6 10.9

4 NEMA Design C, T-frame, 5 hp, 4 poles, enclosed 10.9 2.3 31.8 10.5

5 NEMA Design C, T-frame, 50 hp, 4 poles, enclosed 12.8 2.8 33.1 13.1

6 Fire pump, 5 hp, 4 poles, enclosed 28.8 14.8 51.4 29.1

7 Fire pump, 30 hp, 4 poles, enclosed 28.8 14.8 51.4 29.1

8 Fire pump, 75 hp, 4 poles, enclosed 28.8 14.8 51.4 29.1

9 Brake motor, 5 hp 28.8 14.8 51.4 29.1

10 Brake motor, 30 hp 28.8 14.8 51.4 29.1

8.2.4 Discount Rates

DOE derived the discount rates for the LCC and PBP analysis from estimates of the 

finance cost of purchasing the considered products. Following financial theory, the finance cost 

of raising funds to purchase equipment can be interpreted as: (1) the financial cost of any debt 

incurred to purchase equipment, or (2) the opportunity cost of any equity used to purchase 

equipment. DOE defines the discount rate as the weighted average cost of capital (WACC), 

calculated as the weighted average of the cost of equity financing and the cost of debt financing, 

as estimated from financial data for publicly traded firms in the sectors that purchase motors. 

 Damodaran Online is a widely used source of information about company debt and 

equity financing for most types of firms, and was the primary source of data for this analysis.23

Detailed sectors included in the Damondaran Online database were assigned to the aggregate 

categories of industrial or commercial. Due to limited data availability, DOE applies the discount 

rate estimated for the industrial sector to the agricultural sector.

DOE estimated the cost of equity using the capital asset pricing model (CAPM).24 The 

CAPM assumes that the cost of equity (ke) for a particular company is proportional to the 

systematic risk faced by that company, where high risk is associated with a high cost of equity 

and low risk is associated with a low cost of equity. The systematic risk facing a firm is 

determined by several variables: the risk coefficient of the firm (β), the expected return on riskfree assets (Rf), and the equity risk premium (ERP). The risk coefficient of the firm indicates the 

risk associated with that firm relative to the price variability in the stock market. The expected 

return on risk-free assets is defined by the yield on long-term government bonds. The ERP

represents the difference between the expected stock market return and the risk-free rate. The 

8-26
cost of equity financing is estimated using the following equation, where the variables are 

defined as above: 

e Rk f ( ) β ×+= ERP

Where:

 

ke = cost of equity, 

Rf = expected return on risk-free assets,

β = risk coefficient of the firm, and 

ERP = equity risk premium. 

Several parameters of the cost of capital equations can vary substantially over time, and 

therefore the estimates can vary with the time period over which data is selected and the 

technical details of the data averaging method. For guidance on the time period for selecting and 

averaging data for key parameters and the averaging method, DOE used Federal Reserve 

methodologies for calculating these parameters. In its use of the CAPM, the Federal Reserve 

uses a forty-year period for calculating discount rate averages, utilizes the gross domestic 

product price deflator for estimating inflation, and considers the best method for determining the 

risk free rate as one where “the time horizon of the investor is matched with the term of the riskfree security.”25 

By taking a forty-year geometric average of Federal Reserve data on annual nominal 

returns for 10-year Treasury bills, DOE estimated the following risk free rates for 2011-2013 

(Table 8.2.28).26 DOE also estimated the ERP by calculating the difference between risk free 

rate and stock market return for the same time period, as estimated using Damodaran Online data 

on the historical return to stocks.527

Table 8.2.28 Risk free rate and equity risk premium, 2011-2013 

Year Risk free rate (%) ERP (%)

2011 6.61 2.94

2012 6.41 3.46

2013 6.24 5.30

 The cost of debt financing (kd) is the interest rate paid on money borrowed by a company. 

The cost of debt is estimated by adding a risk adjustment factor (Ra) to the risk-free rate. This 

risk adjustment factor depends on the variability of stock returns represented by standard 

deviations in stock prices. So for firm i, the cost of debt financing is: 

+= RRk aifdi

Where:

 

kd = cost of debt financing for firm, i, 

8-27
Rf = expected return on risk-free assets, and

Rai = risk adjustment factor to risk-free rate for firm, i. 

DOE estimates the WACC using the following equation: 

dee ×+×= wkwkWACC d

Where:

WACC = weighted average cost of capital,

we = proportion of equity financing, and 

wd = proportion of debt financing. 

By adjusting for the influence of inflation, DOE estimates the real weighted average cost 

of capital, or discount rate, for each sector. DOE then aggregates the sectoral real weighted 

average costs of capital to estimate the discount rate for each of the three non-residential 

ownership types in the medium electric motors analysis, weighting each sector’s discount rate 

by the number of companies in the sector.c

Table 8.2.29 shows the weighted average WACC values by ownership types in the 

medium electric motors analysis. While WACC values for any sector may trend higher or lower 

over substantial periods of time, these values represent a private sector cost of capital that is 

averaged over major business cycles. For the agricultural sector, DOE used the discount rates as 

calculated in the industrial sector.

Table 8.2.29 Weighted Average Real Weighted Average Cost of Capital for Sectors that 

Purchase Medium Electric Motors

WACCr

Industrial / 

Agricultural Commercial 

6.73% 5.95%

8.2.5 Effective Date and Compliance Date of Standard 

 Any amended standard for electric motors shall apply to electric motors manufactured on 

or after a date which is five years after the effective date of the previous amendment. (42 U.S.C. 

6313(b)(4)) In this case, the effective date of the previous amendment (established by EISA in 

2007) is December 19, 2010. DOE is relying on the Congressionally established two-year spread 

between the effective date of the latest amendments to electric motor energy conservation 

standards and the date by which DOE must amend such standards to arrive at the two-year leadc Giving equal weight to each industry, rather than weighting by number of companies leads to similar estimate of 

discount rates; the mean industrial / agricultural discount rate is estimated to be 6.42% and the mean commercial 

discount rate is estimated to be 6.30%.

8-28

 
time for manufacturers to comply with the standard after its date of issuance. See 42 U.S.C. 

6313(b). This was modeled using a date of January 1st 2016 in the LCC analysis.

8.2.6 Product Energy Efficiency in the Base Case

For purposes of conducting the LCC analysis, DOE analyzed efficiency levels relative to 

a base case (i.e., the case without new energy efficiency standards). This requires an estimate of 

the distribution of product efficiencies in the base case (i.e., what consumers would have 

purchased in the compliance year in the absence of new standards). DOE refers to this 

distribution of product energy efficiencies as the base-case efficiency distribution. 

DOE used six major manufacturer and one distributor’s catalog data to develop the basecase efficiency distributions using the number of models (in all representative units) meeting the 

requirements of each efficiency level in year 2012. The distribution is estimated separately for 

each representative unit. 

Table 8.2.30 shows the energy efficiency distribution for in the base case for all 

representative units in 2012. 

Table 8.2.30 Base Case Energy Efficiency Distribution in 2012 

Unit #1: NEMA Design B, T-Frame, 5 hp, 4 poles, Enclosed

Efficiency Level FL* Nominal Efficiency Share

0 82.5% 13.5%

1 87.5% 29.9%

2 89.5% 34.1%

3 90.2% 14.4%

4 91.0% 8.1%

Unit #2: NEMA Design B, T-Frame, 30 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 89.5% 7.4%

1 92.4% 34.5%

2 93.6% 41.5%

3 94.1% 9.1%

4 94.5% 7.5%

Unit #3: NEMA Design B, T-Frame, 75 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 93.0% 10.3%

1 94.1% 25.2%

2 95.4% 39.2%

3 95.8% 17.0%

4 96.2% 8.3%

Unit #4: NEMA Design C, T-Frame, 5 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 87.5% 91.7%

1 89.5% 8.3%

2 91.0% 0.0%

8-29
Unit #5: NEMA Design C, T-Frame, 50 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 93.0% 73.3%

1 94.5% 26.7%

2 95.0% 0.0%

Unit #6: Fire pump electric motor, 5 h, 4 poles, Enclosed 

Efficiency Level FL Nominal Efficiency Share

0 87.5% 82.1%

1 89.5% 12.8%

2 90.2% 5.1%

3 91.0% 0.0%

Unit #7: Fire pump electric motor, 30 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 92.4% 80.7%

1 93.6% 6.4%

2 94.1% 12.8%

3 94.5% 0.0%

Unit #8: Fire Pump, 75 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 94.1% 80.6%

1 95.4% 10.2%

2 95.8% 9.2%

3 96.2% 0.0%

Unit #9: Brake Motor 5 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 82.5% 36.5%

1 87.5% 34.8%

2 89.5% 27.5%

3 90.2% 0.9%

4 91.0% 0.4%

Unit #10: Brake Motor 30 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 89.5% 25.0%

1 92.4% 62.5%

2 93.6% 12.5%

3 94.1% 0.0%

4 94.5% 0.0%

*FL = Full Load

In order to establish the base case efficiency distribution in the compliance year, DOE 

assumed the efficiency distributions for equipment class group 1.a. and 1.b. vary over time based 

on historical data28 for the market penetration of NEMA Premium motors within the market for 

integral AC induction motors. The assumed trend is detailed in chapter 10 of the NOPR TSD. 

For equipment class group 2 and 3, which represent a very minor share of the market (less than 

0.2 percent), DOE believes the overall trend in efficiency improvement for the total integral AC 

induction motors may not be representative, so it kept the base case efficiency distributions in the 

compliance year equal to 2012 levels. 

8-30
Using the base case efficiency distribution in the compliance year, DOE assigned an 

efficiency rating to each motor unit. If a unit is assigned an efficiency rating that is greater than 

or equal to the efficiency of the standard level under consideration, the LCC calculation shows 

that this unit would not be affected by that standard level. 

Table 8.2.31 Base Case Energy Efficiency Distribution in the Compliance Year

Unit #1: NEMA Design B, T-Frame, 5 hp, 4 poles, Enclosed

Efficiency Level FL* Nominal Efficiency Share

0 82.5% 11.0%

1 87.5% 29.9%

2 89.5% 36.6%

3 90.2% 14.4%

4 91.0% 8.1%

Unit #2: NEMA Design B, T-Frame, 30 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 89.5% 4.9%

1 92.4% 34.5%

2 93.6% 44.0%

3 94.1% 9.1%

4 94.5% 7.5%

Unit #3: NEMA Design B, T-Frame, 75 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 93.0% 7.8%

1 94.1% 25.2%

2 95.4% 41.7%

3 95.8% 17.0%

4 96.2% 8.3%

Unit #4: NEMA Design C, T-Frame, 5 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 87.5% 91.7%

1 89.5% 8.3%

2 91.0% 0.0%

Unit #5: NEMA Design C, T-Frame, 50 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 93.0% 73.3%

1 94.5% 26.7%

2 95.0% 0.0%

Unit #6: Fire pump electric motor, 5 h, 4 poles, Enclosed 

Efficiency Level FL Nominal Efficiency Share

0 87.5% 82.1%

1 89.5% 12.8%

2 90.2% 5.1%

3 91.0% 0.0%

8-31
Unit #7: Fire pump electric motor, 30 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 92.4% 80.7%

1 93.6% 6.4%

2 94.1% 12.8%

3 94.5% 0.0%

Unit #8: Fire Pump, 75 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 94.1% 80.6%

1 95.4% 10.2%

2 95.8% 9.2%

3 96.2% 0.0%

Unit #9: Brake Motor 5 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 82.5% 34.0%

1 87.5% 34.8%

2 89.5% 30.0%

3 90.2% 0.9%

4 91.0% 0.4%

Unit #10: Brake Motor 30 hp, 4 poles, Enclosed

Efficiency Level FL Nominal Efficiency Share

0 89.5% 22.5%

1 92.4% 62.5%

2 93.6% 15.0%

3 94.1% 0.0%

4 94.5% 0.0%

*FL = Full Load

8.3 PAYBACK PERIOD INPUTS 

The PBP is the amount of time it takes the consumer to recover the assumed higher 

purchase expense of more energy-efficient equipment as a result of lower operating costs. 

Numerically, the PBP is the ratio of the increase in purchase expense (i.e., from a less efficient

design to a more efficient design) to the decrease in annual operating expenditures. This type of 

calculation is known as a “simple” PBP, because it does not take into account changes in 

operating expense over time or the time value of money; the calculation is done at an effective 

discount rate of zero percent. 

 The equation for PBP is: 

OC

IC PBP

∆

∆ =

Where:

8-32
ΔIC = change, generally an increase in the total installed cost between the more 

efficient standard level and the baseline design, and 

ΔOC = change, generally decrease in annual operating expenses. 

 A PBP is expressed in years. A PBP that is greater than the life of the product indicates 

that the increased total installed cost is not recovered in reduced operating expenses. 

 The data inputs to PBP are the total installed cost of the equipment to the purchaser for 

each efficiency level and the annual (first-year) operating expenditures for each standard level. 

The inputs to the total installed cost are the equipment price and the installation cost. The inputs 

to the operating costs are the annual energy cost, the annual repair cost, and the annual 

maintenance cost. The PBP uses the same inputs as the LCC analysis as described in section 8.2, 

except that lifetime, energy price trends, and discount rates are not required. Because the PBP is 

a “simple” payback, the required energy price is only for the year in which compliance with a 

new standard is required. The energy price DOE used in the PBP calculation was the price 

projected for that year. 

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS FOR 

REPRESENTATIVE UNITS

This section presents the LCC and PBP results for the representative units analyzed. As 

discussed in section 8.1.1, DOE’s approach for conducting the LCC analysis relied on 

developing samples of customers for each representative unit. DOE also characterized the 

uncertainty of many of the inputs to the analysis with probability distributions. DOE used a 

Monte Carlo simulation technique to perform the LCC calculations on the customers in the 

sample. For each set of sample customers using motors in each representative unit, DOE 

calculated the average LCC and LCC savings and the median and average PBP for each of the 

standard levels. To account for customers that are already purchasing equipment at or above a 

considered energy efficiency level, the LCC savings are calculated relative to a base case (i.e.,

the case without new energy efficiency standards). Referring to the LCC and PBP tables 

presented in this section, the LCC savings are the total savings comparing a particular energy 

efficiency level (e.g. EL1, EL2, EL3, and EL4) to the base case. The base case is characterized 

by the base case efficiency distribution (see Table 8.2.31). 

 In the subsections below, DOE presents figures showing the distribution of LCCs in the 

base case for each representative unit. Also presented below for a specific standard level are 

figures showing the distribution of LCC impacts and the distribution of PBPs. The figures are 

presented as frequency charts that show the distribution of LCCs, LCC impacts, and PBPs with 

their corresponding probabilities of occurrence. DOE generated the figures for the distributions 

from a Monte Carlo simulation run based on 10,000 samples. The LCC and PBP calculations 

were performed 10,000 times by sampling from the probability distributions that DOE developed 

to characterize many of the inputs. 

8-33
Based on the Monte Carlo simulations that DOE performed, for each efficiency level, 

DOE calculated the share of motor users with a net LCC benefit and with a net LCC cost. To 

illustrate the range of LCC and PBP impacts among motor end-users, the sections below present 

figures that provide such information for each representative unit. 

8.4.1 Representative Unit 1, NEMA Design B, 5 Horsepower, 4 Poles, Enclosed Motor

Figure 8.4-1 is an example of a frequency chart showing the distribution of LCC savings

for representative unit 1, at EL 2. 

Figure 8.4-1 Representative Unit 1: Distribution of Life-Cycle Cost Savings for EL 2 

Figure 8.4-2 is an example of a frequency chart showing the distribution of PBPs for the 

efficiency level corresponding to EL 2 for the representative unit 1. Because many motors 

operate for very few hours per year and because the operating cost savings is very small 

compared to the increase in first cost, there are a significant number of motors that may have 

extremely long PBPs. The distribution in the figure illustrates that most motors have a payback 

of less than 30 years, but the mean value of the distribution payback is larger (7.8 years) because 

of the small, but significant number of motors with PBPs longer than 30 years. Because of the 

skewed distribution in PBPs, DOE also considers the PBP of the typical customer, or the median 

of the distribution, which is 3.5 years for Figure 8.4-2. 

8-34
Figure 8.4-2 Representative Unit 1: Distribution of Payback Periods for EL 2 

 The distribution of PBPs for other representative units associated with other efficiency 

levels are illustrated in Appendix 8-B. 

 

Table 8.4.1 summarizes the LCC and PBP results for representative unit 1 based on a run 

of 10,000 Monte Carlo samples. The most rigorous EL that provides positive average LCC 

savings is EL 3. DOE estimates that 45.1 percent of end-users would experience a net benefit 

(i.e., LCC decrease) at this EL. At this EL the increase in average total installed cost (relative to 

the base case) is $133, or 20.3 percent, while operating costs decrease by $90, or 10.2 percent.

Table 8.4.1 Life-Cycle Cost and Payback Period Results for Representative Unit 1: 

NEMA Design B, T-Frame, 5 horsepower, Four Poles, Enclosed Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 82.5 656 8,977 883 6,912 N/A 0.0 0.0 N/A N/A

1 87.5 678 8,287 817 6,462 50 0.0 11.2 0.6 0.4

2 89.5 731 8,138 802 6,413 70 9.3 31.7 7.8 3.5

3 90.2 789 8,062 794 6,411 68 32.4 45.1 10.0 6.5

4 91.0 1,237 7,969 786 6,801 -287 84.7 7.6 69.2 28.7

8-35
8.4.2 Representative Unit 2, NEMA Design B, 30 Horsepower, 4 Poles, Enclosed Motor 

Figure 8.4-3 is an example of a frequency chart showing the distribution of LCC savings 

for representative unit 2, at EL 2. The net benefit of LCC is $415 in this Monte Carlo run. 

Figure 8.4-3 Representative Unit 2: Distribution of Life-Cycle Cost Savings for EL 2 

Table 8.4.2 summarizes the LCC and PBP results for representative unit 2 based on a run 

of 10,000 Monte Carlo samples. The most rigorous EL that provides positive average LCC 

savings is EL 3. DOE estimates that 38.3 percent of end-users would experience a net benefit 

(i.e., LCC decrease) at this EL. At this EL the increase in average total installed cost (relative to 

the base case) is $811, or 46.4 percent, while operating costs decrease by $264, or 4.2 percent.

Table 8.4.2 Life-Cycle Cost and Payback Period Results for Representative Unit 2:

NEMA Design B, T-Frame, 30 hp, Four Poles, Enclosed Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 89.5 1,748 61,611 6,232 54,651 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 60,164 6,091 53,881 42 1.1 3.9 13.6 3.5

2 93.6 2,300 58,778 5,966 52,931 415 1.3 37.9 2.2 1.2

3 94.1 2,559 58,698 5,968 53,200 192 44.6 38.3 39 4.5

4 94.5 3,891 58,511 5,960 54,459 -974 81.9 10.7 257 23.1

8-36
8.4.3 Representative Unit 3, NEMA Design B, 75 Horsepower, 4 Poles, Enclosed Motor

Figure 8.4-4 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 2 for representative unit 3. The LCC net benefit is $702 in this Monte Carlo 

run. 

 

Figure 8.4-4 Representative Unit 3: Distribution of Life-Cycle Cost Savings for EL 2 

 

 Table 8.4.3 summarizes the LCC and PBP results for representative unit 3 based on a run 

of 10,000 Monte Carlo samples. The most rigorous EL that provides positive average LCC 

savings is EL 3. DOE estimates that 26.6 percent of end-users would experience a net benefit 

(i.e., LCC decrease) at this EL. At this EL the increase in average total installed cost (relative to 

the base case) is $1,590, or 41.0 percent, while operating costs decrease by $264, or 2.2 percent.

Table 8.4.3 Life-Cycle Cost and Payback Period Results for Representative Unit 3: 

NEMA Design B, T-Frame, 75 hp, Four Poles, Enclosed Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 93.0 3,882 195,566 17,049 143,892 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 194,167 16,950 143,414 54 2.7 5.3 11.2 3.2

2 95.4 4,692 190,458 16,652 141,460 702 2.5 30.3 3.8 1.7 

3 95.8 5,473 190,392 16,667 142,361 26 48.5 26.6 37.1 6.1

4 96.2 6,932 188,997 16,566 142,975 -537 68.4 23.2 168.6 14.9

8-37
8.4.4 Representative Unit 4, NEMA Design C, 5 Horsepower, 4 Poles, Enclosed Motor

 Figure 8.4-5 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 1 for representative unit 4. The LCC net benefit is $62 in this Monte Carlo 

run. 

Figure 8.4-5 Representative Unit 4: Distribution of Life-Cycle Cost Savings for EL 1 

Table 8.4.4 summarizes the LCC and PBP results for representative unit 4 based on a run 

of 10,000 Monte Carlo samples. The most rigorous EL that provides positive average LCC 

savings is EL 1. DOE estimates that 76.0 percent of end-users would experience a net benefit 

(i.e., LCC decrease) at this EL. At this EL the increase in average total installed cost (relative to 

the base case) is $47, or 7.3 percent, while operating costs decrease by $16, or 1.9 percent.

Table 8.4.4 Life-Cycle Cost and Payback Period Results for Representative Unit 4: NEMA 

Design C, T-Frame, 5 hp, Four Poles, Enclosed Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 87.5 649 8,376 823 6,714 N/A 0.0 0.0 N/A N/A

1 89.5 697 8,206 808 6,646 62 16.0 76.0 8.5 3.8

2 91.0 1,139 8,078 794 6,986 -278 95.2 4.8 31.9 21.5 

8-38
8.4.5 Representative Unit 5, NEMA Design C, 50 Horsepower, 4 Poles, Enclosed Motor

Figure 8.4-6 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 1 for representative unit 5. The LCC net benefit is -$40 in this Monte Carlo 

run. 

Figure 8.4-6 Representative Unit 5: Distribution of Life-Cycle Cost Savings for EL1 

Table 8.4.5 summarizes the LCC and PBP results for representative unit 5 based on a run 

of 10,000 Monte Carlo samples. All ELs lead to negative average LCC savings.

Table 8.4.5 Life-Cycle Cost and Payback Period results for Representative Unit 5: NEMA 

Design C, T-Frame, 50 hp, Four Pole, Enclosed Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 93.0 3,190 79,551 7,933 75,575 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 78,276 7,832 75,655 -40 44.1 29.1 46.1 11.0

2 95.0 4,958 77,653 7,780 75,919 -304 70.1 29.9 35.7 12.9

8-39
8.4.6 Representative Unit 6, Fire Pump, 5 Horsepower, 4 Poles, Enclosed Electric Motor

Figure 8.4-7 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 1 for representative unit 6. The LCC net benefit is -$46 in this Monte Carlo 

run. 

Figure 8.4-7 Representative Unit 6: Distribution of Life-Cycle Cost Savings for EL 1 

Table 8.4.6 summarizes the LCC and PBP results for Unit 6 motors based on a run of 

10,000 Monte Carlo samples. All ELs lead to negative average LCC savings.

Table 8.4.6 Life-Cycle Cost and Payback Period Results for Representative Unit 6: Fire

Pump, NEMA Design B, T-Frame, 5 hp, Four Poles, Enclosed Electric Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 87.5 680 9 2 713 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 3 769 -46 82.0 0.0 7,765 3,730 

2 90.2 792 9 2 822 -96 94.9 0.0 1,234 507 

3 91.0 1,239 9 2 1,274 -548 100.0 0.0 34,521 14,079

8-40
8.4.7 Representative Unit 7, Fire Pump, 30 Horsepower, 4 Poles, Enclosed Electric Motor 

Figure 8.4-8 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 1 for representative unit 7. The LCC net benefit is -$93 in this Monte Carlo 

run. 

Figure 8.4-8 Representative Unit 7: Distribution of Life-Cycle Cost Savings for EL 1 

Table 8.4.7 summarizes the LCC and PBP results for representative unit 7 based on a run 

of 10,000 Monte Carlo samples. All ELs lead to negative average LCC savings.

Table 8.4.7 Life-Cycle Cost and Payback Period Results for Representative Unit 7: Fire 

Pump, NEMA Design B, T-Frame, 30 hp, Four Poles, Enclosed Electric 

Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 92.4 2,228 53 13 2,418 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 13 2,533 -93 80.7 0.0 1,027 362 

2 94.1 2,606 52 13 2,791 -318 87.4 0.0 2,894 1,287

3 94.5 3,938 52 13 4,119 -1,647 100.0 0.0 10,367 2,740

8-41
8.4.8 Representative Unit 8, Fire Pump, 75 Horsepower, 4 Poles, Enclosed Electric Motor 

Figure 8.4-9 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 1 for representative unit 8. The LCC net benefit is -$370 in this Monte Carlo 

run. 

Figure 8.4-9 Representative Unit 8: Distribution of Life-Cycle Cost Savings for EL 1 

Table 8.4.8 summarizes the LCC and PBP results for representative unit 8 based on a run of 

10,000 Monte Carlo samples. All ELs lead to negative average LCC savings.

Table 8.4.8 Life-Cycle Cost and Payback Period Results for Representative Unit 8: Fire

Pump, NEMA Design B, T-Frame, 75 hp, Four Poles, Enclosed Electric 

Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 94.1 4,213 130 30 4,636 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 27 5,096 -370 80.3 0.0 481 148

2 95.8 5,504 128 29 5,906 -1,103 90.5 0.0 3,768 919

3 96.2 6,963 127 26 7,327 -2,524 100.0 0.0 3,483 719

8-42
8.4.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 Horsepower, 

Four Poles, Enclosed Motor 

Figure 8.4-10 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 2 for representative unit 9. The LCC net benefit is $194 in this Monte Carlo 

run. 

Figure 8.4-10 Representative Unit 9: Distribution of Life-Cycle Cost Savings for EL 2 

Table 8.4.9 summarizes the LCC and PBP results for representative unit 9 based on a run 

of 10,000 Monte Carlo samples. The most rigorous EL that provides positive average LCC 

savings is EL 3. DOE estimates that 68.4 percent of end-users would experience a net benefit 

(i.e., LCC decrease) at this EL. At this EL the increase in average total installed cost (relative to 

the base case) is $133, or 20.3 percent, while operating costs decrease by $54, or 6.0 percent.

Table 8.4.9 Life-Cycle Cost and Payback Period Results for Representative Unit 9: 

NEMA Design B, T-Frame, 5 hp, Four Poles, Enclosed Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 82.5 656 8,079 900 6,623 N/A 0.0 0.0 N/A N/A

1 87.5 678 7,430 837 6,167 160 0.0 34.8 0.6 0.4 

2 89.5 731 7,290 841 6,119 194 10.9 58.3 18.0 1.8

3 90.2 789 7,219 846 6,119 191 30.3 68.4 33.9 3.3

4 91.0 1,237 7,132 853 6,507 -193 76.7 22.8 103 14.1

8-43
8.4.10 Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30 Horsepower, 

Four Poles, Enclosed Motor 

Figure 8.4-11 is an example of a frequency chart showing the distribution of LCC savings 

for the case of EL 2 for representative unit 10. The LCC net benefit is $861 in this Monte Carlo 

run. 

 

Figure 8.4-11 Representative Unit 10: Distribution of Life-Cycle Cost Savings for EL 2 

 

 Table 8.4.10 summarizes the LCC and PBP results for representative unit 10 based on a 

run of 10,000 Monte Carlo samples. The most rigorous EL that provides positive average LCC 

savings is EL 3. DOE estimates that 71.4 percent of end-users would experience a net benefit 

(i.e., LCC decrease) at this EL. At this EL the increase in average total installed cost (relative to 

the base case) is $811, or 46.4 percent, while operating costs decrease by $217, or 4.5 percent.

Table 8.4.10 Life-Cycle Cost and Payback Period Results for Representative Unit 10: 

Brake Motor, NEMA Design B, T-Frame, 30 hp, Four Poles, Enclosed Motor 

Energy 

Efficiency 

Level

Efficiency

%

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

%

Net 

Benefit

%

Average Median

0 89.5 1,748 48,394 4,869 46,676 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 47,178 4,753 46,018 138 6.0 16.1 17.1 4.8 

2 93.6 2,300 45,999 4,649 45,173 861 3.8 81.5 4.3 1.5

3 94.1 2,559 45,934 4,652 45,457 577 28.6 71.4 14.6 4.1

4 94.5 3,891 45,777 4,647 46,738 -704 81.5 18.6 102 15.9

8-44
8.5 LIFE-CYCLE COST SENSITIVITY CALCULATIONS

DOE developed a number of sensitivity analyses in order to analyze the particular 

impacts of many inputs to its LCC analysis. These sensitivity analyses include lower and higher 

retail price discounts and two alternative energy price trend scenarios. Table 8.5.1 displays the 

user choices and associated values for each sensitivity parameter analyzed.

Table 8.5.1 Life-Cycle Cost Sensitivity Case Parameters and Values 

Parameter Choices Typical Value

Energy Price 

Trend

Default AEO 2013 Reference Case

High Value AEO 2013 High Case

Low Value AEO 2013 Low Case

Retail Price 

Discount 

Default 1

High Discount 0.7

Medium Discount 0.5

Low Discount 0.3

Table 8.5.2 compares the average LCC savings using the default value for energy price 

trends with the LCC savings using high and low sensitivity values for representative units 2, 5, 

and 7. As expected, DOE observed larger savings with higher energy prices and smaller savings 

with lower energy prices. 

Table 8.5.2 Life-Cycle Cost Results for Energy Price Trend Sensitivity Cases

Representative Unit 2

Energy Efficiency Level Efficiency % Average LCC Savings $

Reference High Low 

0 89.5 N/A N/A N/A

1 92.4 $42 $44 $42

2 93.6 $415 $430 $414

3 94.1 $192 $208 $190

4 94.5 -$974 -$954 -$976

Representative Unit 5

Energy Efficiency Level Efficiency % Average LCC Savings $

Reference High Low 

0 93.0 N/A N/A N/A

1 94.5 -$40 -$16 -$44

2 95.0 -$304 -$263 -$311

8-45
Representative Unit 7

Energy Efficiency Level Efficiency % Average LCC Savings $

Reference High Low

0 92.4 N/A N/A N/A

1 93.6 -$93 -$93 -$93

2 94.1 -$318 -$318 -$318

3 94.5 -$1,647 -$1,646 -$1,647

 

Table 8.5.3 shows an example of retail price discount sensitivity analyses for representative units 

2, 5, and 7. The default case does not include any discounts, whereas the other cases incorporate 

different discounts. The sensitivity results reflect that the higher the discount used, the greater the 

savings that are achieved.

 

Table 8.5.3 Life-Cycle Cost Results for Retail Price Discount Sensitivity Cases

Representative Unit 2

Energy Efficiency 

Level

Efficiency 

%

Average LCC Savings $

Default Low Medium High 

0 89.5 N/A N/A N/A N/A

1 92.4 $42 $55 $51 $48

2 93.6 $415 $460 $447 $434

3 94.1 $192 $364 $315 $266

4 94.5 -$974 -$16 -$290 -$564

Representative Unit 5

Energy Efficiency 

Level

Efficiency 

%

Average LCC Savings $

Default Low Medium High 

0 93.0 N/A N/A N/A N/A

1 94.5 -$40 $444 $305 $167

2 95.0 -$304 $688 $405 $121

Representative Unit 7

Energy Efficiency 

Level

Efficiency 

%

Average LCC Savings $

Default Low Medium High 

0 92.4 N/A N/A N/A N/A

1 93.6 -$93 -$29 -$47 -$65

2 94.1 -$318 -$119 -$176 -$233

3 94.5 -$1,647 -$598 -$898 -$1,197

 

 DOE collected the results of each sensitivity analysis, applied individually, in appendix 

8-C. The Department’s LCC analysis and PBP spreadsheet tool is available for download via the 

Internetd and allows the user to examine the results for the sensitivity scenario of their choice.

d See links from this web site: http://www1.eere.energy.gov/buildings/appliance_standards/ 

8-46

 
8.6 REBUTTABLE PAYBACK PERIOD

A more energy-efficient motor will usually cost more to buy than a motor of standard 

energy efficiency. However, the more efficient motor will usually cost less to operate due to 

reductions in operating costs (i.e., lower energy bills). The PBP is the time (usually expressed in 

years) it takes to recover the additional installed cost of the more efficient motor through energy 

cost savings. The Energy Policy and Conservation Act (EPCA) provides a rebuttable 

presumption that, in essence, an energy conservation standard is economically justified if the 

increased purchase cost for a product that meets the standard is less than three times the value of 

the first-year energy savings resulting from the standard. However, DOE routinely conducts a 

full economic analysis that considers the full range of impacts, including those to the customer, 

manufacturer, nation, and environment, as required under 42 U.S.C. 6295(o)(2)(B)(i) and 42 

U.S.C. 6316(e)(1). The results of this analysis serve as the basis for DOE to evaluate definitively 

the economic justification for a potential standard level (thereby supporting or rebutting the 

results of any preliminary determination of economic justification).

The results of DOE’s rebuttable PBP calculations are shown in Table 8.6.1 below. 

Table 8.6.1 Rebuttable Presumption Payback for All Representative Units

Representative Unit

Payback Period

years

EL 1 EL 2 EL 3 EL 4

1 NEMA Design B, T-frame, 5 hp, 4 poles, enclosed 0.2 0.6 0.9 3.8

2 NEMA Design B, T-frame, 30 hp, 4 poles, enclosed 1.7 1.5 2.0 5.0

3 NEMA Design B, T-frame, 75 hp, 4 poles, enclosed 0.8 1.0 1.8 3.0

4 NEMA Design C, T-frame, 5 hp, 4 poles, enclosed 1.2 7.4 - -

5 NEMA Design C, T-frame, 50 hp, 4 poles, enclosed 5.2 6.8 - -

6 Fire pump, 5 hp, 4 poles, enclosed 1,000 1,557 6,055 -

7 Fire pump, 30 hp, 4 poles, enclosed 135 304 1,120 -

8 Fire pump, 75 hp, 4 poles, enclosed 84.4 164 284 -

9 Brake Motor, T-frame, 5 hp, 4 poles, enclosed 0.2 0.6 1.0 3.9

10 Brake Motor, T-frame, 30 hp, 4 poles, enclosed 1.9 1.8 2.3 5.6

8-47
REFERENCES

1

 Database of motor nameplate and field measurement data compiled by the Washington State 

 University Extension Energy Program (WSU) and Applied Proactive Technologies (APT)

under contract with the New York State Energy Research and Development Authority 

(NYSERDA). 2011. 

 2

 U.S. Department of Energy-Energy Information Administration (June 2012), Annual Energy 

Outlook 2012, Washington, DC. http://www.eia.gov/forecasts/archive/aeo12/index.cfm 

3

 RS Means (2013), Electrical Cost Data, 36th Annual Edition, Kingston, MA. 

4 EASA/Association of Electrical and Mechanical Trades (AEMT) (2003), The Effect of 

Repair/Rewinding on Motor Efficiency.

5 Electrical Apparatus Service Association, Inc. (2010), EASA Standard AR100-2010, 

Recommended Practice for the Repair of Rotating Electrical Apparatus, Missouri. 

http://www.easa.com/sites/default/files/AR100-2010_1010-2.pdf

 6

 Sahni, Sahil, Avid Boustani1, Timothy Gutowski, and Steven Graves (January 2010), 

Electric Motor Remanufacturing and Energy Savings, p. 12. 

7

 Easton Consultants, I. (2000), Variable Frequency Drive. Retrieved February 9, 2011. 

 http://neea.org/research/reports/E00-054.pdf 

8

 U.S. Department of Energy Information Administration (June 2012), Annual 

 Energy Outlook 2012, Washington, DC. 

 http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2012&subject=0- 

AEO2012&table=2-

9

 U.S. Department of Agriculture (2007), The Census of Agriculture. 

http://www.agcensus.usda.gov/Publications/2007/Full_Report/Volume_1,_Chapter_2_US_St

ate_Level/index.asp

10 U.S. Department of Energy Information Administration (2003), Commercial Buildings 

Energy Consumption Survey. http://www.eia.gov/emeu/cbecs/contents.html

11 U.S. Department of Energy-Energy Information Administration (2013), Form EIA-861 for 

2012, Washington, DC. http://www.eia.doe.gov/cneaf/electricity/page/eia861.html

12 Vaughen’s (2013), Vaughen’s Motor & Pump Repair Price Guide, 2013 Edition. 

 http://www.vaughens.com/

13 Research performed by Austin Bonnet (2011) 

14 Gallaher, M., Delhotal, K., & Petrusa, J. (2009), Estimating the potential CO2 mitigation 

from agricultural energy efficiency in the United States, Energy Efficiency (2), 207-220. 

8-48

 
15 Nadel, Steven et al. (2002), Energy Efficient Motor Systems: A Handbook on Technology, 

 Program, and Policy Opportunities, American Council for an Energy-Efficient Economy, 

 Washington, D.C. 

16 Fraunhofer (2008), Eup Lot 11: Fans for Ventilation in Non Residential Building. 

 

17 Falkner, Hugh and Geoff Dollard (2008), Lot 11 - Water Pumps (in commercial buildings, 

drinking water pumping, food industry, agriculture). 

18 Innovation Center for US Dairy, Case Study - Compressed Air Systems. 

http://www.usdairy.com/Sustainability/BestPractices/Documents/CaseStudyCompresedAirSystems.pdf

19 Conveyor Dynamics, Inc., A New Era In Overland Conveyor Belt Design. 

http://www.ckit.co.za/secure/conveyor/papers/troughed/overland/overland.htm

20 John C. Andrea (1982), Energy-Efficient Electric Motors: Selection and Application. 

21 Northwest Power & Conservation Council (2009), Regional Technical Forum. 

http://www.nwcouncil.org/energy/rtf/measures/Default.asp 

 

22 ACEEE (2007), Impact of Proposed Increases to Motor Efficiency Performance Standards, 

Proposed Federal Motor Tax Incentives and Suggested New Directions Forward. 

 http://www.aceee.org/sites/default/files/publications/researchreports/ie073.pdf 

23 Damodaran Online, Data Page: Costs of Capital by Industry Sector, 2013. Damodaran. (Last 

accessed January, 2014.) <http://pages.stern.nyu.edu/~adamodar/> 

24 Ibbotson Associates, SBBI Valuation Edition 2009 Yearbook. 2009. Chicago, IL.

25 Federal Reserve Board, Federal Reserve Bank Services Private Sector Adjustment Factor, 

2005, 2005. Washington, D.C. 

<http://www.federalreserve.gov/boarddocs/Press/other/2005/20051012/attachment.pdf> 

26 Board of Governors of the Federal Reserve System, H.15 Historical Data Selected Interest 

Rates, 2013. (Last accessed January, 2014.) 

<http://www.federalreserve.gov/releases/h15/data.htm> 

27 Damodaran Online, Data Page: Historical Returns on Stocks, Bonds and Bills-United States, 

2013. Damodaran. (Last accessed January, 2014.) <http://pages.stern.nyu.edu/~adamodar/> 

28 Robert Boteler, USA Motor Update 2009, Energy Efficient Motor Driven Systems 

Conference (EEMODS) 2009.

8-49

 
CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-2

9.2 TOTAL SHIPMENTS ..................................................................................................... 9-2

9.2.1 Distribution Across Equipment Class Groups ................................................................. 9-3

9.2.2 Distribution Across Horsepower...................................................................................... 9-4

9.2.3 Distribution Across Pole Configurations and Enclosures ................................................ 9-5

9.2.4 Distribution Across Equipment Class Groups, Sectors and Applications ....................... 9-5

9.3 SHIPMENTS PROJECTION .......................................................................................... 9-5

9.3.1 Shipments Model ............................................................................................................. 9-5

9.3.2 Shipments in Standards Cases ......................................................................................... 9-7

9.3.3 Shipments Data ................................................................................................................ 9-7

LIST OF TABLES

Table 9.2.1 Total Unit Shipped in 2011 by Equipment Class Group (thousand units) .......... 9-3

Table 9.2.2 Share of Motors by Horsepower Range ............................................................... 9-4

Table 9.2.3 Share of Motors by Horsepower Rating .............................................................. 9-4

Table 9.2.4 Share of Motors by Pole Configuration and Enclosure (All Equipment 

Class Groups) ....................................................................................................... 9-5

Table 9.3.1 Annual and Cumulative Shipments Projection .................................................... 9-8

LIST OF FIGURES 

Figure 9.3.1 Shipments Index vs. Private Fixed Investment Index in Selected 

Equipment and Structure...................................................................................... 9-6

Figure 9.3.2 Shipments Projection by Scenario Case ................................................................ 9-8

9-i
CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a necessary input to calculations of the national 

energy savings (NES) and net present value (NPV), as well as to the manufacturer impact 

analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy 

(DOE) used to project annual product shipments and presents results for electric motors 

considered in this analysis under base- and standards-case efficiency levels.

 DOE developed a shipments model to predict shipments of electric motors covered in this 

analysis. The core of the shipments analysis is a model that DOE developed to simulate how 

future purchases are incorporated into an in-service stock of aging motors that are gradually 

replaced. DOE’s motors shipments projections are based on forecasts of economic growth and do 

not incorporate a distinction within shipments between replacements and purchases for new 

applications. 

To formulate its total shipments estimates, DOE began with shipments data from a 

market research report1

, input from interested parties, and responses to the Request for 

Information (RFI) published in the Federal Register (76 FR 17577 (March 30, 2011)). Based on 

two databases of motor field data2,3, U.S. Census Bureau’s Current Industrial Reports4,5, and 

stakeholder input, DOE then developed a distribution of shipments across each of the three

equipment class groups (NEMA Design A and B, NEMA Design C, and fire pump motors). 

Within each category, motor shipments were split into subcategories by horsepower ratings, 

rotational speeds (corresponding to 2-pole, 4-pole, 6-pole, and 8-pole motors), and two enclosure 

types (open or enclosed); projections within each of these subcategories were summed to arrive 

at shipments at the equipment class level.

 The shipments model is prepared as a Microsoft Excel spreadsheet that is accessible on 

the Internet (http://www.eere.energy.gov/buildings/appliance_standards/). Appendix 10-A 

discusses how to access the shipments model and other related spreadsheets and provides basic 

instructions for using them. The rest of this chapter explains the shipments model in more detail. 

Section 9.2 provides a summary of the data DOE used to develop estimates of the shipments of 

covered electric motors by equipment class and for each sector and applications. Section 9.3 

describes the methodology that underlies development of the model and presents the shipments 

projection. 

9.2 TOTAL SHIPMENTS

In the preliminary analysis, DOE estimated total shipments of electric motors to 4.56 

million units in 2011 based on a market research report1 and data provided by the Motor 

9-2
Coalitiona

. This amount did not include NEMA 56-frame size electric motors (one million units)

and 150,000 integral brake motors, as these electric motors were not covered in the preliminary 

analysis scope. 

 In this final rule, DOE included enclosed NEMA 56-frame size electric motors as well as 

integral brake motors. Based on data provided by the Motor Coalition and responses to the RFI, 

annual shipments of covered motors were estimated to total 5.43 million units in 2011. This 

corresponds to the addition of 0.73 million enclosed NEMA 56-frame size electric motorsb and 

0.14 million integral brake motors with 3-digit NEMA frame sizes or enclosed 56-framesc. 

After estimating the total shipments for 2011, DOE drew upon three data sources to 

develop a distribution of the total shipments across the 482 equipment classes: input from 

interested parties, data from extensive field measurements collected by the Washington State 

University Extension Energy Program (WSU), Applied Proactive Technologies and the New 

York State Energy Research and Development Authority (NYSERDA) 2 (“WSU/NYSERDA 

database”), and field data compiled by the Industrial Assessment Center (IAC) at Oregon State 

University (OSU) (“Northwest Industrial Database”)3. The different distributions across 

equipment class groups and motor configurations are presented in sections 9.2.1 to 9.2.4. 

9.2.1 Distribution Across Equipment Class Groups

 DOE derived the distribution by equipment class group (ECG) from the 

WSU/NYSERDA database from the National Electrical Manufacturers Association’s (NEMA’s) 

estimate of the share of brake motors provided in response to the RFI. Results are presented in 

Table 9.2.1. 

Table 9.2.1 Total Unit Shipped in 2011 by Equipment Class Group (thousand units)

ECG 1

ECG 2: NEMA 

Design C

ECG 3: Fire Pump

Electric Motors

1.a.: NEMA Design 

A and B – NonBrake Motors

1.b: NEMA Design 

A and B – Brake 

Motors

5,121 299 9.7 1.3

a The Motor Coalition members include the following: American Council for an Energy-Efficient Economy, 

Alliance to Save Energy, Appliance Standard Awareness Project, Earthjustice, Natural Resources Defense Council, 

Northeast Energy Efficiency Partnerships, Northwest Energy Efficiency Alliance, National Electrical Manufacturers 

Association, and Pacific Gas and Electric. 

b DOE derived market shares of enclosed versus open enclosures in the 1 to 5 horsepower range from two databases 

of motor field usage data2

,

3 and used it to estimate the number of enclosed NEMA 56-frame size electric motors 

based on the one million estimated annual units of NEMA 56-frame size electric motors shipped. c DOE derived market shares of enclosed versus open enclosures and NEMA 56-frames versus NEMA 3-digit 

frames in the 1 to 5 horsepower range from two databases of motor field usage data2

,

3 to estimate the number of 

integral brake motors with enclosed 56-frames and NEMA 3-digit frames shipped in the year 2011.

9-3

 
9.2.2 Distribution Across Horsepower

 Shipments were first distributed by horsepower range, based on the U.S. Census Bureau’s 

Current Industrial Reports and input from the Motor Coalition (Table 9.2.2). 4,5

Table 9.2.2 Share of Motors by Horsepower Range 

Range hp 2011 Shipments

(1,000 units)

Percentage of Total

(%)

1 – 5 3,491 64.3

6 – 20 1,408 25.9

21 – 50 356 6.5

51 – 100 117 2.2

101 – 200 39 0.7

201 – 500 20 0.4

Total 5,431 100.0

 DOE then split shipments by individual horsepower rating, based on the distribution 

observed in the WSU/NYSERDA and the Northwest Industrial databases (Table 9.2.3). For 

some ECG, motors are not available in horsepower ratings, and DOE adjusted the shipments 

distribution to account for this. 

Table 9.2.3 Share of Motors by Horsepower Rating 

Horsepower rating

hp

Percentage of Total

(%)

1 5.95

1.5 3.48

2 6.59

3 8.26

5 13.17

7.5 8.14

10 8.33

15 7.59

20 4.97

25 4.67

30 4.27

40 4.12

50 3.71

60 2.46

75 3.14

100 3.09

125 1.70

150 2.14

200 1.87

250 0.87

300 0.62

350 0.28

9-4
400 0.24

450 0.09

500 0.25

9.2.3 Distribution Across Pole Configurations and Enclosures 

 DOE derived the distribution by pole configuration and enclosure from the 

WSU/NYSERDA and the Northwest Industrial databases (Table 9.2.4).

Table 9.2.4 Share of Motors by Pole Configuration and Enclosure (All Equipment Class 

Groups)

Enclosure Open Enclosed

Range hp 2 

poles 4 poles 6 poles 8 poles 2 poles 4 poles 6 poles 8 poles 

1 – 5 1.4% 8.8% 1.3% 0.1% 5.7% 20.9% 2.9% 1.3%

6 – 20 0.9% 5.6% 0.5% 0.1% 6.0% 15.8% 1.4% 0.2%

21 – 50 0.3% 2.1% 0.2% 0.1% 2.5% 7.6% 1.4% 0.1%

51 – 100 0.1% 0.8% 0.3% 0.1% 0.6% 3.8% 1.0% 0.1%

101 – 200 0.0% 0.3% 0.1% 0.1% 0.2% 2.3% 1.0% 0.2%

201 – 500 0.1% 0.2% 0.2% 0.1% 0.2% 0.6% 0.3% 0.1%

 DOE then combined the distribution by horsepower and the share of motors by pole and 

enclosure configuration to estimate the shipment distribution per equipment class. For some 

ECG, motors are not available in all pole configurations, and DOE adjusted the shipments 

distribution to account for this. 

9.2.4 Distribution Across Equipment Class Groups, Sectors and Applications 

DOE used the data presented in Table 9.2.1, Table 9.2.2, Table 9.2.3, and Table 9.2.4 to 

produce market shares for each of the 482 equipment classes. Further, DOE developed a model 

of the applications and sectors for which motors covered in this analysis are used. These 

distributions are presented in chapter 7, Energy Use Characterization. 

9.3 SHIPMENTS PROJECTION

9.3.1 Shipments Model

 DOE projected shipments of covered motors throughout the 30-year analysis period, 

which starts at the compliance date of the standard. DOE projects total shipments using a model 

driven by forecasted economic growth. Based on a previous publication6

, DOE assumed that 

motors sales are driven by economic growth and machinery production growth for equipment 

including motors. 

 

9-5
Based on historical data for the period 1993-2011 on U.S. shipments provided by the U.S. 

Census Bureau4,7and NEMA8,9 and private fixed investment data from the Bureau of Economic 

Analysis’s (BEA)10,11, DOE assumes that annual shipments growth rates correlate to the annual 

growth rate of private fixed investment in selected equipment and structures12,a including motors 

(Figure 9.3.1). 

Figure 9.3.1 Shipments Index vs. Private Fixed Investment Index in Selected Equipment 

and Structure

 

 DOE developed a relationship between shipments and private fixed investment in 

equipment and structures including motors (indexed to 2001). The relation, derived from a linear 

regression (R2

>0.91), is expressed by the following equation: 

Shipmentsindex(y) = 1.15126 ∙ FixInvestindex(y) − 15.17265 [Equation 1, Step 0] 

Where:

Shipmentsindex (y) is the shipments index based in 2001 in year y, and

FixInvestindex (y) is the private fixed investment index based in 2001 for selected equipment 

and structure including motors in year y. 

DOE projects private fixed investment in selected equipment and structure from 2015 

through 2040 based on the real “gross domestic product” (GDP) growth from the Energy 

Information Administration’s Annual Energy Outlook13 for 2013 (AEO2013) for the period 

2015–2040. DOE then extrapolated the GDP growth trend from 2040 to 2044. The steps for the 

calculation are:

a Heating, ventilating, and air conditioning (HVAC) equipment that incorporates motors is typically included in 

“structures” and not in equipment. Based on RSMeans, DOE estimates that 9 percent of investments in structures are 

related to HVAC equipment.

0

20

40

60

80

100

120

140

160

180

Index 2001=100

Shipments Index

Private Fixed Investment Index in Selected Equipment and Structure (PPI, 2001=100)

9-6

 
1) Based on historical data from the BEA, DOE projected private fixed investment 

in equipment and structure including motors as a share of total private fixed 

investment in equipment and structure for 2015 to 2044. 

2) For 2015 to 2040, DOE used total private fixed investment in equipment and 

structures data (private domestic investment data) from AEO2013 to project 

private fixed investment in equipment and structure including motors. 

3) From 2040 to 2044, DOE used AEO2013data to estimate a trend for private 

domestic investment as a share of GDP using a linear regression (R2>0.96). DOE 

then projected the GDP for 2040 to 2044 using a quadratic regression based on 

AEO2013 data (R2

>0.99). Using the GDP projection, DOE projected private 

domestic investment and estimated private fixed investment in equipment and 

structure including motors. 

4) DOE used the data on projected private fixed investment in equipment and 

structure including motors and Equation 1 to estimate shipments growth over the 

analysis period (2015–2044).

 Following the same methodology, DOE estimated shipments projections for the 

Reference Economic Growth Case, the High Economic Growth Case, and Low Economic 

Growth Case available in AEO2013. 

9.3.2 Shipments in Standards Cases

Sales of electric motors may be sensitive to increases in the installed cost that may result 

from efficiency standards. Increased motor prices could affect the repair versus replace decision 

that the user makes and could lead to increases in the longevity of less efficient motors and 

decreased shipments. However, DOE did not find sufficient data to quantitatively estimate the 

impact of increased efficiency levels on shipments and, therefore, used a price elasticity equal to 

zero as a default. 

9.3.3 Shipments Data 

 Figure 9.3.2 shows annual shipments for each scenario case over the 30-years analysis 

period starting at the compliance year. 

 

9-7
Figure 9.3.2 Shipments Projection by Scenario Case

 Table 9.3.1 shows the annual and cumulative shipments for each equipment class 

grouping for Reference Case

Table 9.3.1 Annual and Cumulative Shipments Projection

Annual Shipments 

thousand units

Equipment Class 2016 2025 2035 2045 Cumulative 

over 30-years

Subgroup 1.a. NEMA Designs 

A & B – Non Brake Motors 5,897 8,197 11,206 15,473 302,880

Subgroup 1.b. NEMA Designs 

A & B – Brake Motors 344 478 654 902 17,666

Group 2. NEMA Design C 11 16 21 29 575

Group 3 Fire Pump 2 2 3 4 77

Total* 6,254 8,693 11,883 16,409 321,198

*Total may not sum up because of rounding.

There are two major assumptions inherent in the shipments model: 

1) The relative market shares of the different equipment classes are constant over 

time. 

2) U.S. production, imports, exports, and, therefore, shipments (i.e., apparent 

consumption) have the same growth rate as described by the shipments index 

provided by NEMA (see section 9.3.1). 8,9

0

5,000

10,000

15,000

20,000

Shipments (Unit: 1,000)

Reference Case High Growth Case Low Growth Case

9-8
REFERENCES

1

 IMS Research (February 2012), The World Market for Low Voltage Motors, 2012 Edition, 

 Austin. 

2

 Database of motor nameplate and field measurements compiled by the Washington State 

University Extension Energy Program (WSU) and Applied Proactive Technologies (APT) 

under contract with the New York State Energy Research and Development Authority 

(NYSERDA). 2011. 

3

 Strategic Energy Group (January, 2008), Northwest Industrial Motor Database Summary. 

Retrieved March, 2013 from Regional Technical Forum: 

 http://rtf.nwcouncil.org/subcommittees/osumotor/Default.htm

4

 U.S. Census Bureau (November 2004), Motors and Generators – 2003.MA335H(03)-1. 

 Retrieved March, 2013 from 

http://www.census.gov/manufacturing/cir/historical_data/discontinued/ma335h/index.html 

5

 U.S. Census Bureau (August 2003), Motors and Generators – 2002.MA335H(02)-1. at 

 Retrieved March, 2013 from 

 http://www.census.gov/manufacturing/cir/historical_data/discontinued/ma335h/ma335h02.xls

6 Iyama, S and Helcio Blum, Estimating the US Motor Market Baseline – Preliminary Results

EEMODS’11 Conference – Energy Efficiency in Motor Driven Systems, Alexandria, VA, 

USA. September, 2011. 

7

 U.S. Census Bureau, Current Industrial Report: Manufacturing Profiles, 1994-1998. 

 

8

 NEMA (2007), Motor Shipments Slip in Third Quarter of 2007, NEMA News, on 05 Dec 

2007. 

9

 NEMA (2011), Motors Shipments on the Upswing in First Half of 2011, NEMA News, on 18 

Aug 2011. 

10 Bureau of Economic Analysis (March 01, 2012), Private Fixed Investment in Equipment and 

Software by Type. Retrieved March, 2013 from 

http://www.bea.gov/iTable/iTable.cfm?ReqID=12&step=1 

11 Bureau of Economic Analysis (March 01, 2012), Private Fixed Investment in Structures by 

Type. Retrieved March, 2013 from 

http://www.bea.gov/iTable/iTable.cfm?ReqID=12&step=1

12 RSMeans (2011), RSMeans Facilities Construction Cost Data 201, Norwell. 

13 U.S. Department of Energy-Energy Information Administration (April 2013), Annual Energy 

Outlook 2013, Washington, DC. Retreived April 2013 from 

http://www.eia.gov/forecasts/aeo/

9-9

 
CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 NATIONAL ENERGY SAVINGS ............................................................................... 10-3

10.2.1 National Energy Savings Overview ............................................................................... 10-3

10.2.2 National Energy Savings Inputs..................................................................................... 10-7

10.2.2.1 Motor Capacity ............................................................................................ 10-7

10.2.2.2 Annual Hours of Operation .......................................................................... 10-7

10.2.2.3 Operating Load ............................................................................................ 10-7

10.2.2.4 Energy Efficiency ........................................................................................ 10-8

10.2.2.5 Lifetime Distribution ................................................................................... 10-9

10.2.2.6 Electricity Site-to-Primary Conversion Factors ........................................... 10-9

10.2.2.7 Electricity Site-to-Upstream Conversion Factors ...................................... 10-10

10.2.2.8 Efficiency Distribution............................................................................... 10-10

10.2.3 Unit Annual Primary Energy Consumption ................................................................. 10-14

10.2.4 Standards Case Shipment Efficiency Distribution ....................................................... 10-15

10.3 NET PRESENT VALUE ............................................................................................. 10-20

10.3.1 Net Present Value Overview ........................................................................................ 10-20

10.3.2 Net Present Value Inputs ............................................................................................. 10-22

10.3.2.1 Manufacturer Selling Price and Price Overheads ...................................... 10-22

10.3.2.2 Projection of Future Equipment Prices ...................................................... 10-25

10.3.2.3 Motor Weight and Shipment Costs ............................................................ 10-26

10.3.2.4 Repair Costs ............................................................................................... 10-27

10.3.2.5 Electricity Prices ........................................................................................ 10-28

10.3.2.6 Discount Rate ............................................................................................. 10-28

10.3.3 Unit Lifetime Energy Cost ........................................................................................... 10-28

10.3.4 Unit Lifetime Non-Energy Costs ................................................................................. 10-29

10.4 TRIAL STANDARD LEVELS ................................................................................... 10-30

10.5 RESULTS .................................................................................................................... 10-31

10.5.1 National Energy Savings and Net Present Value for Trial Standard Levels ............... 10-31

10.5.2 Scenario Analysis......................................................................................................... 10-32

10.5.3 Sensitivity Analysis ..................................................................................................... 10-34

LIST OF TABLES

Table 10.2.1 Factors to Adjust Motor Initial Efficiency to its Efficiency after Repair .......... 10-8

Table 10.2.2 Site-to-Upstream Energy Multipliers (Based on AEO 2013) .......................... 10-10

Table 10.2.3 Base Case Energy Efficiency Distributions in 2012 ........................................ 10-11

Table 10.2.4 Base Case Energy Efficiency Distributions in 2016 ........................................ 10-14

Table 10.3.1 Parameters used to Estimate Manufacturer Selling Price of 4-Pole 

Enclosed Motors across Horsepower ............................................................... 10-23

10-i
Table 10.3.2 Indices used to Scale Manufacturer Selling Price across Poles and 

Enclosures (Equipment Class Subgroup 1.a.) .................................................. 10-25

Table 10.3.3 Parameters used to Estimate the Weight of 4-Pole Enclosed Motors across 

Horsepower ...................................................................................................... 10-26

Table 10.3.4 Indices used to Scale Weight across Poles and Enclosures (Equipment 

Class Subgroup 1.a.) ........................................................................................ 10-27

Table 10.4.1 Trial Standard Levels for Electric Motors ....................................................... 10-30

Table 10.5.1 Cumulative National Energy Savings for Electric Motors Trial Standard 

Levels from Units Sold over the 30-year Analysis Period .............................. 10-31

Table 10.5.2 Net Present Value for Electric Motors for Electric Motors Trial Standard 

Levels from Units Sold over the 30-year Analysis Period .............................. 10-32

Table 10.5.3 Cumulative National Energy Savings for the Low Economic Growth 

Scenario............................................................................................................ 10-33

Table 10.5.4 Net Present Value for the Low Economic Growth Scenario ........................... 10-33

Table 10.5.5 Cumulative National Energy Savings for the High Economic Growth 

Scenario............................................................................................................ 10-34

Table 10.5.6 Net Present Value for the High Economic Growth Scenario .......................... 10-34

Table 10.5.7 Cumulative National Energy Savings Variation in Response to ±10 

Percent Changes in Hours of Operation*......................................................... 10-35

Table 10.5.8 Net Present Value Variation in Response to ±10 Percent Changes in Hours 

of Operation* ................................................................................................... 10-36

Table 10.5.9 Net Present Value Variation in Response to ±10 Percent Changes in 

Manufacturer Selling Price* ............................................................................ 10-36

Table 10.5.10 Net Present Value Variation in Response to ±10 Percent Changes in 

Repair Cost*..................................................................................................... 10-37

LIST OF FIGURES

Figure 10.2.1 National Impact Analysis Model Flowchart ...................................................... 10-4

Figure 10.2.2 Site-to-Power Plant Energy Use Factor for Electric Motors ........................... 10-10

Figure 10.2.3 Estimate of NEMA Premium Motors Market Penetration .............................. 10-12

Figure 10.2.4 Base Case Efficiency Distributions for Equipment Class Subgroup 1.a. ........ 10-13

Figure 10.2.5 Standards Case Efficiency Distributions for Equipment Class Subgroup 

1.a. (EL 1, corresponds to TSL 1) .................................................................... 10-17

Figure 10.2.6 Standards Case Efficiency Distributions for Equipment Class Subgroup 

1.a. (EL 2, corresponds to TSL 2) .................................................................... 10-18

Figure 10.2.7 Standards Case Efficiency Distributions for Equipment Class Subgroup 

1.a. (EL3, corresponds to TSL 3) ..................................................................... 10-19

Figure 10.3.1 Estimated Manufacturer Selling Price by EL for 4-Pole Enclosed 

Equipment Class Subgroup 1.a. Motors .......................................................... 10-24

10-ii
CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter examines selected national impacts attributable to each trial standard level

(TSL) considered for electric motors. Electric motors considered in this analysis have been 

categorized into three distinct Equipment Class Groups (ECG) a: (1) National Electrical 

Manufacturers Association (NEMA) Design A and B motors, (2) NEMA Design C motors, and

(3) fire pump electric motors. For each of these equipment class groups, and for each equipment 

class, DOE evaluated the following impacts: national energy savings (NES) attributable to each 

potential standard level, monetary value of the lifetime energy savings to consumers of the 

considered equipment, increased total lifetime cost of the equipment because of standards, and 

net-present value (NPV) resulting from increased energy efficiency (the difference between the 

energy cost savings and the increased total lifetime cost of the equipment).

 To conduct its national impacts analysis (NIA), DOE determined both the NES and NPV 

for each TSL being considered as the new standard for electric motors. DOE performed all 

calculations for each considered equipment class group and equipment class using Microsoft 

Excel spreadsheet models, which are accessible on the Internet.b

 Details and instructions for 

using the NIA model are provided in Appendix 10-A of the Technical Support Document (TSD). 

The spreadsheets combine the calculations for determining the NES and NPV for each 

considered equipment class group and equipment class with input from the appropriate 

shipments model that DOE used to project future purchases of the considered equipment. 

Chapter 9 provides a detailed description of the shipments models. 

To calculate the national impacts of new and amended standards for all equipment class 

groups considered in this rulemaking DOE used (a) scaling factors (described in Chapter 5 and 

Section 10.3.2 below) to estimate equipment related costs, and (b) operational profiles to 

estimate annual energy consumption for all equipment classes. DOE derived the scaling factors 

from the engineering outputs for the ten representative units, data from manufacturer internet 

catalogs and the usage profiles from the LCC analysis (described in Chapter 8 and Section

10.3.2.1 below). 

Figure 10.2.1 presents a graphical flow diagram of the electric motor NIA spreadsheet 

model. In the diagram, the arrows show the direction of information flow for the calculation. 

The information begins with inputs (shown as parallelograms). As information flows from these 

a

 ECG 1 was analyzed as the sum of two equipment class subgroup: (1.a.) NEMA Design A and B motors – Nonbrake motors and (1.b.) NEMA Design A and B motors – Brake Motors b See www.eere.energy.gov/buildings/appliance_standards/

10-1

 
inputs, it is integrated into intermediate results (shown as rectangles) into major outputs (shown 

as boxes with curved bottom edges). 

 The NIA calculation started with the shipments model. This model produces a projection 

of annual shipments of motors. DOE used the annual projection of such shipments to produce an 

accounting of annual national energy savings, annual national energy cost savings, and annual 

national incremental non-energy costs resulting from purchasing, installing and operating the 

units projected to be shipped in each year of the analysis period during their estimated lifetime. 

The annual values, therefore, refer to the lifetime, cumulative energy related savings and nonenergy related additional costs associated to the units marketed in each year of the analysis 

period. 

To calculate the annual national energy savings, DOE first estimated the lifetime primary

and fuel-fuel-cyclec (FFC) energy consumption at the unit level and for each year in the analysis 

period, for motors of each equipment class used in industry, commercial buildings and 

agriculture. The unit’s lifetime primary and FFC energy consumptions were then scaled up to 

the national level based on the annual shipments projection and according to two scenarios: the 

base case scenario, with no changes in the existing energy efficiency standards; and (b) the 

standards case scenario, where energy efficiency standards are set at the energy efficiency level 

corresponding to one of the TSLs. This produced, for each equipment class and sector, two sets 

of two streams of annual national energy consumption, from which DOE derived two streams of 

annual NES from motors shipped in each year of the analysis period: one that accounts for 

primary energy savings, and one that accounts for the FFC energy savings. The annual national 

primary and FFC energy savings of all equipment classes within an equipment class group and 

sectors were, each one, aggregated over the full analysis period into national energy primary and 

FFC savings by equipment class group. DOE then summed the aggregated national primary and 

FFC energy savings to produce, respectively, the primary and FFC NES of all equipment class 

groups. 

DOE followed a similar procedure to calculate the annual national energy cost savings 

and the annual national incremental non-energy costs. DOE first estimated the lifetime energy 

cost and the lifetime non-energy costs at unit level and for each year in the analysis period, for 

motors of each equipment class, within each equipment class group, used in industry, 

commercial buildings and agriculture. The unit lifetime energy and non-energy costs, estimated 

for units shipped in each year in the analysis period, were then scaled up to the national level 

based on the annual shipments projection and for the same—base case and standards case—

scenarios described above. This produced, for each equipment class and sector: (a) two streams 

of annual national energy costs, from which DOE derived a stream of annual national energy 

cost savings associated with each year in the analysis period, and its corresponding presentvalue, and (b) two streams of annual national non-energy costs, from which DOE derived a 

stream of annual national incremental equipment non-energy costs associated with each year in 

the analysis period, and its corresponding present-value. The present-values of the annual 

national energy cost savings and the annual national incremental non-energy costs of all 

equipment classes within an equipment class group and sectors were aggregated, respectively, 

c The full-fuel-cycle energy consumption adds to the primary energy consumption the energy consumed by the 

energy supply chain upstream to power plants. 

10-2

 
into the total national energy cost savings and national incremental non-energy costs by 

equipment class group. DOE then calculated the difference between the aggregated national 

energy cost savings and national incremental non-energy costs to obtain the NPV of each 

equipment class group, and summed these values across equipment class groups to produce the 

total NPV. Two models included in the NIA are provided below—the NES model in Section

10.2, and the NPV model in Section 10.3. Each technical description begins with a summary of 

the model. It then provides a descriptive overview of how DOE performed each model’s 

calculations and follows with a summary of the inputs. The final subsections of each technical 

description describe each of the major inputs and computation steps in detail and with equations, 

when appropriate. After the technical model descriptions, this chapter presents the results of the 

NIA calculations.

10.2 NATIONAL ENERGY SAVINGS

DOE developed the NES model to estimate the total national primary and FFC energy 

savings using information from the life-cycle cost (LCC) relative to energy consumption, 

combined with the results from the shipments model. The savings shown in the NES reflect 

decreased energy losses resulting from the installation of more efficient electric motors

nationwide (as a consequence of new or amended standards), in comparison to a base case with 

no changes in the current national standards. Positive values of NES correspond to net energy 

savings, that is, a decrease in energy consumption after implementation of a standard in 

comparison to the energy consumption in the base case scenario. 

10.2.1 National Energy Savings Overview 

DOE calculated the cumulative primary and FFC energy savings from an electric motor 

efficiency standard, relative to a base case scenario of no standard, over the analysis period. It 

calculated NES for each TSL in units of quadrillion British thermal units (Btus) (quads), for 

standards with a compliance year of 2016. The NES calculation started with estimates of 

shipments, which are outputs of the shipments model (Chapter 9).d

 DOE then obtained values of 

electric motor parameters from the LCC analysis (Chapter 8), projections of site-to-primary

conversion factorse from the Annual Energy Outlook1

 (AEO) and projections of site-to-upstream

conversion factorsf from a NEMS-based methodology, and calculated the market average of the 

total primary and FFC energy used over the lifetime of units shipped in each year of the analysis 

period for both a base case and a standards case. Since in the standards case part of the units 

shipped is more efficient than its corresponding in the base case, the market average energy 

consumed per unit decreases in the standards case relative to the base case. For each year 

analyzed, the lifetime primary and FFC energy savings from all motors of a given capacity and 

configuration (combination of enclosure and number of poles), shipped in that year to each 

d Shipments provided by the shipment model do not account for the price-elasticity of demand. Therefore, NES 

results reported in this chapter were estimated under the assumption of zero price-elasticity. Appendix 10-C presents 

NES results for a scenario where shipments were adjusted based on a non-zero price-elasticity of demand. e The site-to-primary factors account for electricity generation, transmission and distribution losses. f The site-to-upstream factors translate site energy consumption into the energy consumed in the supply chain of the 

fuels used for electricity generation.

10-3

 
sector, are the differences in their primary and FFC energy use between the corresponding base 

case and the standards case scenarios.

 Figure 10.2.1 National Impact Analysis Model Flowchart

National

Costs

National 

Energy Cost

Natl Energy 

Consumption

Hours of 

Operation

Load

Probability

of Survival

Efficiency 

Conservation

Factor

Full Load 

Efficiency

Load 

Adjustment 

Factor

Base Case 

Efficiency 

Distribution

Heat 

Rates

Efficiency

at Load

Unit Lifetime

Energy 

Consumption

National Energy 

Consumption

Unit Lifetime

Energy Cost

Electricity

Prices

Electricity 

Price Scalar

National 

Energy Cost

National 

Energy

Cost Savings

Manufacturer

Selling Price

Weight

Shipping

Rate

Price

Overheads

Repair 

Cost

Repair 

Events

Probability

of Survival

Lifetime

Repair Cost

Shipping Cost

Unit Lifetime

Non -Energy 

Costs

Base Case 

Efficiency 

Distribution

National

Non -Energy

Costs

National

Incremental

Non-Energy Costs

NPV

Standards Case 

Efficiency

Distribution

Standards Case 

Efficiency

Distribution

Shipments Base case - Standards case

Standards case - Base case

Base case - Standards case

NES NES

10-4
This calculation is expressed by the following formulas:

Lifetime Primary Energy Savings 

nSEShp,g(y) = ∑ ∑ �nSECbchp,g(s, a, y) − nSECstdhp,g as (s, a, y)� i.

nSECbchp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uSEChp,g,c(s, a, y) ∙ Mbchp,c c (y)� ii.

nSECstdhp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uSEChp,g,c(s, a, y) ∙ Mstdhp,c c (y)� iii.

uSEChp,g,c(s, a, y) = ∑ aSEChp,g,c(s, a, y, i) i=1..LT iv. 

where: 

nSEShp,g(y) = the lifetime primary energy savings of all motors with capacity hp

and configuration g shipped in year y, 

nSECbchp,g(s, a, y) = the base case, lifetime primary energy consumption of motors with 

capacity hp and configuration g shipped in year y to be used in 

application a in sector s, 

nSECstdhp,g(s, a, y) = the standards case, lifetime primary energy consumption of motors 

with capacity hp and configuration g shipped in year y to be used in 

application a in sector s, 

Shphp,g(s, y) = the number of motors with capacity hp and configuration g shipped in 

year y to sector s, 

A(a) = the probability of a motor to be used in application a (∑ A(a) = 1), 

uSEChp,g,c(s, a, y) = the lifetime primary energy consumption of a unit with capacity hp, 

configuration g and efficiency level at EL c shipped in year y to be 

used in application a in sector s, 

aSEChp,g,c(s, a, y, i) = the annual primary energy consumption in the i-th year of operation 

of a unit with capacity hp, configuration g and efficiency level at EL

c, shipped in year y to be used in application a in sector s, 

Mbchp,c(y) = the base case market share of units with capacity hp, configuration g

and efficiency level at EL c shipped in year y, and

Mstdhp,c(y) = the standards case market share of units with capacity hp, 

configuration g and efficiency level at EL c shipped in year y. 

Lifetime Full-Fuel-Cycle Energy Savings

nFEShp,g(y) = ∑ ∑ �nFECbchp,g(s, a, y) − nFECstdhp,g as (s, a, y)� v. 

nFECbchp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uFEChp,g,c(s, a, y) ∙ Mbchp,c c (y)� vi. 

10-5
nFECstdhp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uFEChp,g,c(s, a, y) ∙ Mstdhp,c c (y)� vii.

uFEChp,g,c(s, a, y) = ∑ �aSEChp,g,c i=1..LT (s, a, y, i) ∙ ffc(y + i − 1)� viii.

where:

nFEShp,g(y) = the lifetime FFC energy savings of all motors with capacity hp and 

configuration g shipped in year y, 

nFECbchp,g(s, a, y) = the base case, lifetime FFC energy consumption of motors with 

capacity hp and configuration g shipped in year y to be used in 

application a in sector s, 

nFECstdhp,g(s, a, y) = the standards case, lifetime FFC energy consumption of motors with 

capacity hp and configuration g shipped in year y to be used in 

application a in sector s, 

Shphp,g(s, y) = the number of motors with capacity hp and configuration g shipped in 

year y to sector s, 

A(a) = the probability of a motor to be used in application a (∑ A(a) = 1), 

uFEChp,g,c(s, a, y) = the lifetime FFC energy consumption of a unit with capacity hp, 

configuration g and efficiency level at EL c shipped in year y to be 

used in application a in sector s, 

aSEChp,g,c(s, a, y, i) = the annual primary energy consumption in the i-th year of operation 

of a unit with capacity hp, configuration g and efficiency level at EL

c, shipped in year y to be used in application a in sector s, 

ffc(y) = the primary-to-FFC conversion factor in year y, 

Mbchp,c(y) = the base case market share of units with capacity hp, configuration g

and efficiency level at EL c shipped in year y, and

Mstdhp,c(y) = the standards case market share of units with capacity hp, 

configuration g and efficiency level at EL c shipped in year y. 

DOE used the lifetime primary and FFC energy savings estimated for all motors shipped 

from 2016 through 2045 to calculate the total primary NES (NESsrc) and the total FFC NES 

(NESFFC) for the analysis period. The calculation used the following formulas: 

NESsrc = � � � nSEShp,g(y)

2045

hp g y=2016

ix.

NESFFC = � � � nFEShp,g(y)

2045

hp g y=2016

x.

where:

nSEShp,g(y) = the lifetime primary energy savings of all motors with capacity hp and 

configuration g shipped in year y, and

10-6
nFEShp,g(y) = the lifetime FFC energy savings of all motors with capacity hp and 

configuration g shipped in year y. 

Once the shipments model provides the estimate of shipments, and the site-to-primary 

and site-to-upstream factors convert site energy consumption respectively into primary and 

upstream energy consumption, the key to the NES calculation is in calculating the unit annual

site energy consumption and market share distributions using inputs from the LCC analysis. The 

next section summarizes the inputs necessary for the NES calculation and then presents them 

individually; the following sections detail, respectively, how the unit lifetime site energy 

consumption and the standards case efficiency distribution were calculated. 

10.2.2 National Energy Savings Inputs 

 The NES model inputs include: (a) the parameters necessary to calculate the unit site 

energy consumption, (b) the site-to-primary conversion factors, which enable the calculation of 

primary energy consumption from site energy use, (c) the site-to-upstream conversion factors 

which – in addition to the site-to-primary factors – enable the calculation of FFC energy 

consumption from site energy use, and (d) shipment efficiency distributions in the base case. 

The list of NES model inputs is as follows:

1. motor capacity; 

2. annual hours of operation; 

3. operating load; 

4. energy efficiency (at the operating load, and including efficiency adjustment due to 

repairs); 

5. lifetime (probability) distribution; 

6. electricity site-to-primary conversion factors; 

7. electricity site-to-upstream conversion factors, and

8. base case shipments efficiency distribution. 

10.2.2.1 Motor Capacity 

The motor capacity refers to the unit horsepower (hp) rating converted to kilowatts (kW) 

using the following conversion factor: 1 hp = 0.746 kW. 

10.2.2.2 Annual Hours of Operation

 For the NIA, DOE considered the average annual hours of operation by sector, 

application and horsepower ranges described in Chapter 7, Section 7.2.6. 

10.2.2.3 Operating Load 

 For the NIA, DOE considered the average operating load by application described in 

Chapter 7, Section 7.2.5. 

10-7
10.2.2.4 Energy Efficiency

 For the NIA, DOE considered the energy efficiencies by EL presented in Chapter 5. 

Those efficiencies, however, refer to motors performance when operating at full load. Since 

motors usually do not operate at full load, DOE adjusted the full load efficiencies to the part-load 

levels corresponding to the motors’ weighted average operating load across applications, based 

on part load efficiency data from the engineering analysis (Chapter 5). Additionally, DOE 

assumed that ten percent of repaired motors have a slight decrease in their energy efficiency after 

undergoing a repair, and that the repair frequency varies by horsepower size and sector (see 

Chapter 8, Section 8.2.2.1 for more details). To account for the effects of repair on the energy 

efficiency of motors, DOE used a time-varying adjusting factor that reduces the initial motor 

efficiency over its lifetime (see Table 10.2.1).g 

Table 10.2.1 Factors to Adjust Motor Initial Efficiency to its Efficiency after Repair

Year of

Operation 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp 

Industry

Motors < 40 hp

1-7 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000

8-14 1.00000 1.00000 0.99900 0.99900 0.99900 0.99900

15-21 1.00000 1.00000 0.99800 0.99800 0.99800 0.99800

22-28 1.00000 1.00000 0.99700 0.99700 0.99700 0.99700

29-30 1.00000 1.00000 0.99601 0.99601 0.99601 0.99601

Motors ≥ 40 hp

1-7 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000

8-14 1.00000 1.00000 0.99950 0.99950 0.99950 0.99950

15-21 1.00000 1.00000 0.99900 0.99900 0.99900 0.99900

22-28 1.00000 1.00000 0.99850 0.99850 0.99850 0.99850

29-30 1.00000 1.00000 0.99800 0.99800 0.99800 0.99800

Commercial Buildings

Motors < 40 hp

1-9 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000

10 1.00000 1.00000 1.00000 1.00000 0.99900 1.00000

11 1.00000 1.00000 1.00000 1.00000 0.99900 0.99900

12-14 1.00000 1.00000 0.99900 1.00000 0.99900 0.99900

15-18 1.00000 1.00000 0.99900 0.99900 0.99900 0.99900

19-20 1.00000 1.00000 0.99900 0.99900 0.99800 0.99900

21-22 1.00000 1.00000 0.99900 0.99900 0.99800 0.99800

23-27 1.00000 1.00000 0.99800 0.99900 0.99800 0.99800

28 1.00000 1.00000 0.99800 0.99900 0.99700 0.99800

g The Electrical Apparatus Service Association (EASA) commented that a comprehensive study has been done by 

EASA and the Association of Electrical and Mechanical Trades to investigate the effect of repair and rewind on 

electric motor efficiency. EASA commented that the study showed that electric motor efficiency could be 

maintained by following the good practices identified in the study. (EASA, No.7 at pp. 1-2) Both EASA Standard 

AR100-2010 and the EASA/AEMT Rewind Study are available at http://www.easa.com/.

10-8

 
Year of

Operation 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp 

29-30 1.00000 1.00000 0.99800 0.99800 0.99700 0.99800

Motors ≥ 40 hp

1-9 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000

10 1.00000 1.00000 1.00000 1.00000 0.99950 1.00000

11 1.00000 1.00000 1.00000 1.00000 0.99950 0.99950

12-14 1.00000 1.00000 0.99950 1.00000 0.99950 0.99950

15-18 1.00000 1.00000 0.99950 0.99950 0.99950 0.99950

19-20 1.00000 1.00000 0.99950 0.99950 0.99900 0.99950

21-22 1.00000 1.00000 0.99950 0.99950 0.99900 0.99900

23-27 1.00000 1.00000 0.99900 0.99950 0.99900 0.99900

28 1.00000 1.00000 0.99900 0.99950 0.99850 0.99900

29-30 1.00000 1.00000 0.99900 0.99900 0.99850 0.99900

Agriculture

Motors < 40 hp

1-10 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000

11-20 0.99900 0.99900 0.99900 0.99900 0.99900 0.99900

21-30 0.99800 0.99800 0.99800 0.99800 0.99800 0.99800

Motors ≥ 40 hp

1-10 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000

11-20 0.99950 0.99950 0.99950 0.99950 0.99950 0.99950

21-30 0.99900 0.99900 0.99900 0.99900 0.99900 0.99900

10.2.2.5 Lifetime Distribution

 For the NIA, DOE uses motor average lifetime in years derived from motor mechanical 

lifetime in hours (see Chapter 8, Section 8.2.3) and from annual operating hours (see Section 

10.2.2.2).

10.2.2.6 Electricity Site-to-Primary Conversion Factors

DOE calculates primary energy savings (power plant energy consumption) from site 

energy savings by applying a factor to account for losses associated with the generation, 

transmission, and distribution of electricity. DOE derived annual marginal site-to-primary 

factors based on the version of the National Energy Modeling System (NEMS) that corresponds 

to Energy Information Administration (EIA’s) Annual Energy Outlook 2013 (AEO 2013).1

 The 

factors change over time in response to projected changes in the types of power plants projected 

to provide electricity to the country. Figure 10.2.2 shows the site-to-primary factors for the 

projection period. The value reported in AEO for year 2040 (the last year available in AEO) was 

extrapolated through the end of the projection period. 

10-9
Figure 10.2.2 Site-to-Power Plant Energy Use Factor for Electric Motors

10.2.2.7 Electricity Site-to-Upstream Conversion Factors

 The full-fuel-cycle (FFC) measure includes point-of-use (site) energy, the energy losses 

associated with generation, transmission, and distribution of electricity, and the energy consumed 

in extracting, processing, and transporting or distributing primary fuels. To complete the fullfuel-cycle by encompassing the energy consumed in extracting, processing, and transporting or 

distributing primary fuels, which DOE refers to as “upstream” activities, DOE developed site-toupstream multipliersh using the data and projections generated by the National Energy Modeling 

System (NEMS) used for AEO 2013.

1

 The AEO provides extensive information about the 

energy system, including projections of future oil, natural gas and coal supply, energy use for oil 

and gas field and refinery operations, and fuel consumption and emissions related to electric 

power production. This information can be used to define a set of parameters representing the 

energy intensity of energy production. 

Table 10.2.2 shows the energy multipliers used to estimate the energy saved upstream to 

power plants resulting from motors site energy savings for selected years. The method used to 

calculate the site-to-upstream energy multipliers is described in appendix 10-D. 

Table 10.2.2 Site-to-Upstream Energy Multipliers (Based on AEO 2013) 

2015 2020 2025 2030 2035 2040

Electricity 0.043 0.041 0.040 0.040 0.041 0.040

10.2.2.8 Efficiency Distribution

 To estimate market averages for unit energy consumption DOE used statistical 

h FFC multipliers discussed in this chapter relate to the upstream part of the FFC process.

8.000

8.100

8.200

8.300

8.400

8.500

8.600

8.700

2015 2025 2035 2045 2055 2065 2075

10-10

 
distributions of shipments across ELs. For the base case in 2012, DOE developed such 

distributions from a database which DOE built upon data collected from internet catalogs from 

six major manufacturers and one large distributor (see Table 10.2.3). 

Table 10.2.3 Base Case Energy Efficiency Distributions in 2012 

Market Share in 2012

EL 0 EL 1 EL 2 EL 3 EL 4

Equipment Class Subgroup 1.a. (NEMA Design A and B – Non-Brake Motors)

1-5 hp 13.5% 29.9% 34.1% 14.4% 8.1%

6-20 hp 12.1% 31.4% 28.5% 18.1% 9.8%

21-50 hp 7.4% 34.5% 41.5% 9.1% 7.5%

51-100 hp 10.3% 25.2% 39.2% 17.0% 8.3%

101-200 hp 8.1% 24.4% 26.1% 27.0% 14.3%

201-500 hp 20.6% 36.6% 21.3% 15.8% 5.7%

Equipment Class Subgroup 1.a. (NEMA Design A and B – Brake Motors)

1-5 hp 36.5% 34.8% 27.5% 0.9% 0.4%

6-20 hp 35.4% 40.0% 13.8% 10.8% 0.0%

21-30 hp 25.0% 62.5% 12.5% 0.0% 0.0%

Equipment Class Group 2 (NEMA Design C)

1-5 hp 91.7% 8.3% 0.0% - -

6-20 hp 100.0% 0.0% 0.0% - -

21-50 hp 73.3% 26.7% 0.0% - -

51-100 hp 75.0% 25.0% 0.0% - -

101-200 hp 52.2% 34.8% 13.0% - -

Equipment Class Group 3 (Fire Pump Electric Motors)

1-5 hp 82.1% 12.8% 5.1% 0.0% -

6-20 hp 79.2% 20.8% 0.0% 0.0% -

21-50 hp 80.7% 6.4% 12.8% 0.0% -

51-100 hp 80.6% 10.2% 9.2% 0.0% -

101-200 hp 97.1% 0.0% 2.9% 0.0% -

201-500 hp 100.0% 0.0% 0.0% 0.0% -

In order to establish the base case efficiency distribution in the compliance year and over 

the analysis period (2016-2045) DOE made different assumptions regarding the efficiency 

distributions. For equipment class subgroups 1.a. and 1.b. DOE assumed the efficiency 

distributions vary over time and are influenced by the existing NEMA Premium labeling 

program and the energy conservation standard established by the Energy Independence and 

Security Act of 2007 (EISA). (Pub. L. No. 110–140, Section 313(b)(1)). As for equipment class 

groups 2 and 3, which represent a very minor share of the market (less than 0.2 percent), DOE 

believes the overall trend in efficiency improvement for the total integral AC induction motors 

may not be relevant and therefore kept the base case efficiency distributions constant and equal 

to 2012 levels. 

10-11
To estimate the market response to the NEMA Premium program and EISA regulation, 

DOE relied on (a) historical data2 for the market penetration of NEMA Premium motors within 

the market for integral AC induction motors, and (b) the market penetration of NEMA Premium 

motors in 2012 that DOE derived from manufacturer catalogs. Based on these data DOE 

developed the following model to project the market penetration of NEMA Premium motors

(M(t)) in the absence of any new regulations:

M(t) = 0.08604 ∙ ln(1.01031 ∙ t − 0.19634) + 0.11774 xi.

where:

t = the year of existence of the NEMA Premium program (t=1,2,3...), and 

M(t) = the market penetration of NEMA Premium motors. 

Figure 10.2.3 presents the estimated market penetration of NEMA Premium motors since 

the NEMA Premium program was launched through the end of the analysis period of this 

rulemaking. DOE adjusted the base case market share of the EL corresponding to NEMA 

Premium efficiency (EL 2) for equipment class subgroups 1.a. and 1.b. using the market 

penetration of NEMA Premium motors estimated from the model above (xi) to calculate the 

increase in market penetration of these motors for each year in the analysis period relative to 

2012. For each year in the analysis period, the increase in market share of EL 2 relative to 2012 

was compensated with a decrease in market share in lower ELs. The resulting base case 

efficiency distribution in the compliance year is presented in Table 10.2.4. The dynamics of the 

base case efficiency distribution for equipment class subgroup 1.a. is showed in Figure 10.2.4 for 

each horsepower range. 

Figure 10.2.3 Estimate of NEMA Premium Motors Market Penetration 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2001200620112016202120262031203620412046

Market data Estimated market penetration

10-12
Figure 10.2.4 Base Case Efficiency Distributions for Equipment Class Subgroup 1.a. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

1-5 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

6-20 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

21-50 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

51-100 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

101-200 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

201-500 hp

EL4

EL3

EL2

EL1

EL0

10-13
Table 10.2.4 Base Case Energy Efficiency Distributions in 2016 

Market Share in 2016

EL 0 EL 1 EL 2 EL 3 EL 4

Equipment Class Subgroup 1.a. (NEMA Design A and B – Non-Brake Motors)

1-5 hp 10.5% 29.9% 37.1% 14.4% 8.1%

6-20 hp 9.0% 31.4% 31.5% 18.1% 9.8%

21-50 hp 4.4% 34.5% 44.5% 9.1% 7.5%

51-100 hp 7.2% 25.2% 42.3% 17.0% 8.3%

101-200 hp 5.1% 24.4% 29.2% 27.0% 14.3%

201-500 hp 17.6% 36.6% 24.3% 15.8% 5.7%

Equipment Class Subgroup 1.a. (NEMA Design A and B – Brake Motors)

1-5 hp 33.4% 34.8% 30.5% 0.9% 0.4%

6-20 hp 32.3% 40.0% 16.9% 10.8% 0.0%

21-30 hp 22.0% 62.5% 15.5% 0.0% 0.0%

Equipment Class Group 2 (NEMA Design C)

1-5 hp 91.7% 8.3% 0.0% - -

6-20 hp 100.0% 0.0% 0.0% - -

21-50 hp 73.3% 26.7% 0.0% - -

51-100 hp 75.0% 25.0% 0.0% - -

101-200 hp 52.2% 34.8% 13.0% - -

Equipment Class Group 3 (Fire Pump Electric Motors)

1-5 hp 82.1% 12.8% 5.1% 0.0% -

6-20 hp 79.2% 20.8% 0.0% 0.0% -

21-50 hp 80.7% 6.4% 12.8% 0.0% -

51-100 hp 80.6% 10.2% 9.2% 0.0% -

101-200 hp 97.1% 0.0% 2.9% 0.0% -

201-500 hp 100.0% 0.0% 0.0% 0.0% -

10.2.3 Unit Annual Primary Energy Consumption

The unit annual primary energy consumption expresses an estimate of the amount of 

primary energy that a motor of a given equipment class, meeting the efficiency level of a given 

EL, and shipped in a given year to a given sector to be used in a given application will consume 

in each year of its lifetime. It refers to the variable aSEChp,g,c in iv and viii, and is evaluated 

from the following formulas: 

aSEChp,g,c(s, a, y, i) = UEChp,g,c(s, a, i) ∙ Php(s, i) ∙ StoS(y + i − 1) xii.

UEChp,g,c(s, a, i) = (hp × 0.757) ∙ Load(a) ∙ Hourshp(s, a)

fEffc ∙ aEffhp,c(a) ∙ Conserv(i) xiii.

where:

10-14
aSEChp,g,c(s, a, y, i) = the annual primary energy consumption in the i-th year of operation of 

a unit with capacity hp, configuration g and efficiency level at EL c

shipped in year y to be used in application a in sector s, 

UEChp,g,c(s, a, i) = the annual site energy consumption in the i-th year of operation of a 

unit with capacity hp, configuration g and efficiency level at EL c used 

for application a in sector s, 

StoS(t) = the site-to-primary conversion factor projected to year t, 

Php(s, i) = the probability that a unit with capacity hp, used in sector s will be in 

operation in the i-th year of its lifetime, 

hp = the unit capacity (in horse-power), 

Load(a) = the typical load of a motor used in application a, 

Hourshp(s, a) = annual hours of operation of a unit with capacity hp, used for 

application a in sector s, 

fEffc = the full-load efficiency of a unit with efficiency level at EL c, 

aEffhp,c(a) = the factor used to adjust the full-load efficiency of a unit with capacity 

hp and efficiency level at EL c used in application a to the efficiency 

corresponding to its typical load, and

Conserv(i) = the energy efficiency conservation factor used to reduce the unit initial 

efficiency to the efficiency it is estimated to present in its i-th year of 

operation due to repairs. 

10.2.4 Standards Case Shipment Efficiency Distribution

Section 10.2.2.8 described the market efficiency distribution across ELs that DOE used 

for the base case scenario. For the standards case DOE relied on those base case distributions 

and calculated the efficiency distributions following the “roll-up” approach where all shipments 

to the ELs lower than the EL corresponding to the chosen standards level are offset from the 

former to the latter. The market shares in the standards case are calculated from:

Mstdhp,c(y) = �

0, c < c∗

∑ Mbchp,j c∗

j=1 (y), c = c∗

Mbchp,c(y), c > c∗

 xiv.

where:

Mstdhp,c(y) = the standards case market share of units with capacity hp and efficiency level at 

EL c shipped in year y, 

Mbchp,c(y) = the base case market share of units with capacity hp and efficiency level at EL

c shipped in year y, and 

c∗ = the selected EL. 

 For equipment class subgroups 1.a. and 1.b., DOE further assumed in the standards case 

scenario for TSLs 2 and 3 that the EL immediately above the EL corresponding to the standards 

level would behave similarly to the NEMA Premium level, i.e. the share of motors at this EL 

10-15
would follow the same (historical and projected) dynamics of the NEMA Premium market 

penetration. As a consequence, for those equipment class groups, market shares in the standards 

case for TSLs 2 and 3 are calculated from: 

Mstd′hp,c(y) =

⎩

⎪

⎨

⎪

⎧ 0, c < c∗

Mstdhp,c(y) − Prm(t), c = c∗

Mstdhp,c(y) + Prm(t), c = c∗ + 1

Mstdhp,c(y), c > c∗ + 1

xv.

where:

Mstd′hp,c(y) = the standards case market share of units with capacity hp and efficiency level at 

EL c shipped in year y, 

Mstdhp,c(y) = the “rolled-up” standards case market share (as calculated from xiv) of units 

with capacity hp and efficiency level at EL c shipped in year y, 

Prm(t) = the increase in market share penetration of NEMA Premium motors in the tth

year of the NEMA Premium program (t = y − 2015), and 

c∗ = the selected EL (c∗=2, 3). 

Figure 10.2.5 to Figure 10.2.7 show the standards case efficiency distributions by 

horsepower range for equipment class group 1.a. for standard case efficiency levels EL 1, EL 2, 

and EL 3 (which correspond to TSL 1, 2, and 3, respectively). For the standard case efficiency 

level EL 4 (which corresponds to TSL 4), 100 percent of the shipments would be at EL 4. 

10-16
Figure 10.2.5 Standards Case Efficiency Distributions for Equipment Class Subgroup 

1.a. (EL 1, corresponds to TSL 1) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

1-5 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

6-20 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

21-50 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

51-100 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

101-200 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

201-500 hp

EL4

EL3

EL2

EL1

EL0

10-17
Figure 10.2.6 Standards Case Efficiency Distributions for Equipment Class Subgroup 

1.a. (EL 2, corresponds to TSL 2) 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

1-5 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

6-20 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

21-50 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

51-100 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

101-200 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

201-500 hp

EL4

EL3

EL2

EL1

EL0

10-18
Figure 10.2.7 Standards Case Efficiency Distributions for Equipment Class Subgroup 

1.a. (EL3, corresponds to TSL 3)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

1-5 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

6-20 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

21-50 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

51-100 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

101-200 hp

EL4

EL3

EL2

EL1

EL0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

201-500 hp

EL4

EL3

EL2

EL1

EL0

10-19
10.3 NET PRESENT VALUE

DOE estimated the national financial impact on consumers from the imposition of new

and amended energy efficiency standards using a national NPV accounting component in the 

national impact spreadsheet. DOE combined the output of the shipments model with energy and 

financial data from the LCC analysis to calculate an annual stream of costs and benefits resulting 

from candidate electric motors energy efficiency standards. It discounted this time series to the 

year 2014 and summed the result, yielding the national NPV. 

10.3.1 Net Present Value Overview 

The NPV is the present value of the incremental economic impact of an efficiency level. 

Like the NES, the NPV calculation started with the motor shipments estimated by the shipments 

model.i DOE then obtained motor input data and average electricity costs from the LCC 

analysis, and estimated motor non-energy and energy lifetime costs. For both a base case and a 

standards case, DOE first calculated the amount spent on motor purchases and lifetime repairs,

j

and then calculated the lifetime energy cost by applying the average electricity prices to the 

electricity used by motors shipped at each year of the analysis period over their lifetime. In the 

standards case, more expensive yet more efficient units replace the less efficient ones. Thus, in 

the standards case, whereas the market average lifetime equipment non-energy costs per unit are 

greater relative to the base case, the lifetime energy costs are lower. When the energy cost 

decrease outweighs the non-energy costs increase, the standards have a positive impact on 

consumers; otherwise, the standards impact is negative. 

 DOE discounted the non-energy and energy expenses with motors using a national 

average discount factor. The discount factor converts a future expense to a present value. The 

difference in present value of the non-energy and energy expenses between the base case and the 

standards case scenarios leads to the national NPV impact. DOE calculated the NPV impact in 

2014 from motors that were purchased between the compliance date of the standards and 2045, 

inclusive, to calculate the total NPV impact from purchases during the analysis period. 

Mathematically, the NPV is the value in the present time of a time series of costs and savings, 

described by the equation: 

NPV = PVS − PVC xvi. 

where:

PVS = the present value of electricity cost savings, and

PVC = the present value of incremental non-energy costs. 

i

Shipments provided by the shipment model do not account for the price-elasticity of demand. Therefore, NPV 

results reported in this chapter were estimated under the assumption of zero price-elasticity. Appendix 10-C presents 

NPV results for a scenario where shipments were adjusted based on a non-zero price-elasticity of demand. j

DOE did not account for installation costs and maintenance costs. Although these costs might have significant 

impacts on a user’s budget, they do not vary with the efficiency level of the motor and therefore would have no 

impact in the difference of non-energy costs between the base case and the standards case scenarios.

10-20

 
PVS and PVC are determined according to the following expressions: 

PVS = � � � nECShp,g(y) ∙ (1 + r)2013−y

2045

hp g y=2016

xvii.

nECShp,g(y) = ∑ ∑ �nNCbchp,g(s, a, y) − nNCstdhp,g as (s, a, y)� xviii.

nNCbchp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uNChp,g,c(s, a, y) ∙ Mbchp,c c (y)� xix.

nNCstdhp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uNChp,g,c(s, a, y) ∙ Mstdhp,c c (y)� xx.

and: 

PVS = � � � nIEChp,g(y) × (1 + r)2013−y

2045

hp g y=2016

xxi.

nIEChp,g(y) = ∑ ∑ �nQCbchp,g(s, a, y) − nQCstdhp,g as (s, a, y)� xxii.

nQCbchp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uQChp,g,c(s, a, y) ∙ Mbchp,c c (y)� xxiii.

nQCstdhp,g(s, a, y) = Shphp,g(s, y) ∙ A(a) ∙ ∑ �uQChp,g,c(s, a, y) ∙ Mstdhp,c c (y)� xxiv.

where: 

nECShp,g(y) = the lifetime energy cost savings of all motors shipped in year y, 

nNCbchp,g(s, a, y) = the base case, lifetime energy cost of all motors shipped in year y, 

nNCstdhp,g(s, a, y) = the standards case, lifetime energy cost of all motors shipped in year y, 

uNChp,g,c(s, a, y) = the lifetime energy cost of a unit with efficiency level at EL c shipped 

in year y, 

nIEChp,g(y) = the lifetime incremental equipment non-energy costs of all motors 

shipped in year y, 

nQCbchp,g(s, a, y) = the base case, lifetime equipment non-energy costs of all motors 

shipped in year y, 

nQCstdhp,g(s, a, y) = the standards case, lifetime equipment non-energy costs of all motors 

shipped in year y, 

uQChp,g,c(s, a, y) = the lifetime equipment non-energy costs of a unit with efficiency level 

at EL c shipped in year y, 

Shphp,g(s, y) = the number of motors with capacity hp and configuration g shipped in 

year y to sector s, 

10-21
Mbchp,c(y) = the base case market share of units with capacity hp, configuration g

and efficiency level at EL c shipped in year y, and

Mstdhp,c(y) = the standards case market share of units with capacity hp, 

configuration g and efficiency level at EL c shipped in year y, and

r = the discount rate. 

 

Once the shipments model provides the estimate of shipments, the following sections 

describe the inputs necessary for the NPV calculation and detail how unit lifetime energy and 

non-energy costs are calculated. 

10.3.2 Net Present Value Inputs

The NPV model inputs include: (a) the parameters that help calculate the unit energy 

consumption, (b) the electricity prices that enable the calculation of energy costs, (c) equipment 

first- and non-energy operating costs, and (d) shipment efficiency distributions for the base case. 

The list of NPV model inputs is as follows: 

1. motor capacity;

2. annual hours of operation; 

3. operating load; 

4. energy efficiency (at the operating load, and including efficiency degradation due to 

repairs);

5. manufacturer selling price (MSP) and price overheads;

6. motor weight and shipment costs; 

7. repair costs;

8. lifetime (probability) distribution;

9. electricity price; 

10. discount rate; 

11. base case shipments efficiency distribution. 

 Inputs 1-4, 8 and 11 have already been introduced in Section 10.2.2 and therefore are not 

described in this section. 

10.3.2.1 Manufacturer Selling Price and Price Overheads

The Engineering Analysis, Chapter 5 provides MSP data for ten representative units. 

DOE developed scaling relationships to estimate MSPs for all covered equipment classes

following a two-step procedure. 

 

First DOE developed a model to estimate the MSPs of 4-pole enclosed motors for all 

motor horsepower ratings. The model follows a general power law regression, and is expressed 

by the following equation: 

MSP4,e(hp) = a ∙ hpb xxv.

where: 

10-22
MSP4,e(hp) = the MSP of a 4-pole enclosed unit with capacity hp, and

a and b = parameters calibrated for each equipment class group/subgroup and 

EL. 

DOE calibrated the model in equation xxv to each equipment class group and EL level 

using the corresponding MSPs of the representative units provided by the engineering analysis. 

Table 10.3.1 presents the values of parameters a and b that DOE estimated for each equipment 

class group and EL level. As mentioned in Chapter 5, the MSPs for equipment class subgroup 

1.b. and ECG 3 were derived from the MSPs of equipment class subgroup 1.a. 

Table 10.3.1 Parameters used to Estimate Manufacturer Selling Price of 4-Pole 

Enclosed Motors across Horsepower

Equipment Class Subgroup 1.a. (NEMA Design A and B – Non-Brake Motors)

EL 0 EL 1 EL 2 EL 3 EL 4

a 1.15E+02 1.17E+02 1.22E+02 1.26E+02 2.46E+02

b 6.23E-01 6.60E-01 6.75E-01 7.04E-01 6.23E-01

Equipment Class Subgroup 1.b. (NEMA Design A and B – Brake Motors)

EL 0 EL 1 EL 2 EL 3 EL 4

a 1.15E+02 1.17E+02 1.22E+02 1.26E+02 2.46E+02

b 6.23E-01 6.60E-01 6.75E-01 7.04E-01 6.23E-01

Equipment Class Group 2 (NEMA Design C)

EL 0 EL 1 EL 2 EL 3 EL 4

a 1.13E+02 1.13E+02 2.29E+02 - -

b 6.67E-01 7.44E-01 6.19E-01 - -

Equipment Class Group 3 (Fire Pump Electric Motors)

EL 0 EL 1 EL 2 EL 3 EL 4

a 1.17E+02 1.22E+02 1.26E+02 2.46E+02 -

b 6.60E-01 6.75E-01 7.04E-01 6.23E-01 -

Figure 10.3.1 shows how the MSPs estimated for 4-pole enclosed motors in equipment 

class group 1.a. vary with horsepower for each EL level. In the figure, the markers in red 

represent the MSPs of the representative units provided by the engineering analysis. 

 In a second step DOE established an index to describe how MSPs vary with pole and 

enclosure across horsepower ratings (at a fixed EL). DOE established these indices using 

statistical estimates derived from a database of motor prices which DOE built upon data 

collected from internet catalogs from six major manufacturers and one large distributor (see 

Table 10.3.2 for an example of these indices estimated for Designs A and B motors). DOE used 

the indices in Table 10.3.2 and the MSPs estimated from model in equation xxv for 4-pole 

enclosed motors to estimate the MSPs of all other equipment classes. The final MSP estimates 

are available in the NIA spreadsheet.

10-23
After estimating MSPs for all equipment classes, DOE used average baseline and 

incremental markups to calculate equipment prices. Chapter 6 provides more details on the 

markups calculation. 

Figure 10.3.1 Estimated Manufacturer Selling Price by EL for 4-Pole Enclosed 

Equipment Class Subgroup 1.a. Motors

-100

100

300

500

700

900

1100

1 2 3 4 5 6 7 8 9 10

MSP ($2012)

Horsepower Rating

CSL 0 CSL 1 CSL 2 CSL 3 CSL 4

0

500

1000

1500

2000

2500

3000

3500

4000

4500

10 20 30 40 50 60 70 80 90 100

MSP ($2012)

Horsepower Rating

CSL 0 CSL 1 CSL 2 CSL 3 CSL 4

0

2000

4000

6000

8000

10000

12000

100 150 200 250 300 350 400 450 500

MSP ($2012)

Horsepower Rating

CSL 0 CSL 1 CSL 2 CSL 3 CSL 4

10-24
Table 10.3.2 Indices used to Scale Manufacturer Selling Price across Poles and 

Enclosures (Equipment Class Subgroup 1.a.) 

Open Enclosed

hp 2 poles 4 poles 6 poles 8 poles 2 poles 4 poles 6 poles 8 poles

1 0.962 0.974 0.993 1.026 1.013 1.000 1.024 1.074

1.5 0.945 0.963 0.990 1.037 1.018 1.000 1.034 1.105

2 0.930 0.952 0.987 1.047 1.023 1.000 1.044 1.135

3 0.904 0.934 0.982 1.065 1.032 1.000 1.061 1.186

5 0.861 0.905 0.974 1.094 1.047 1.000 1.087 1.268

7.5 0.822 0.879 0.967 1.120 1.060 1.000 1.112 1.343

10 0.793 0.859 0.961 1.140 1.070 1.000 1.130 1.400

15 0.752 0.831 0.953 1.167 1.083 1.000 1.156 1.478

20 0.725 0.813 0.948 1.185 1.092 1.000 1.173 1.530

25 0.706 0.800 0.945 1.198 1.099 1.000 1.185 1.567

30 0.691 0.790 0.942 1.208 1.104 1.000 1.194 1.595

40 0.671 0.776 0.938 1.221 1.110 1.000 1.207 1.634

50 0.658 0.767 0.936 1.230 1.115 1.000 1.215 1.660

60 0.648 0.761 0.934 1.237 1.118 1.000 1.221 1.678

75 0.638 0.754 0.932 1.244 1.122 1.000 1.227 1.697

100 0.628 0.746 0.930 1.251 1.125 1.000 1.234 1.718

125 0.621 0.742 0.929 1.255 1.127 1.000 1.238 1.731

150 0.616 0.739 0.928 1.258 1.129 1.000 1.241 1.740

200 0.610 0.735 0.927 1.262 1.131 1.000 1.245 1.751

250 0.607 0.732 0.926 1.265 1.132 1.000 1.247 1.758

300 0.604 0.731 0.926 N/A 1.133 1.000 1.249 N/A

350 0.602 0.729 0.925 N/A 1.134 1.000 1.250 N/A

400 0.601 0.728 N/A N/A 1.134 1.000 N/A N/A

450 0.600 0.728 N/A N/A 1.134 1.000 N/A N/A

500 0.599 0.727 N/A N/A 1.135 1.000 N/A N/A

10.3.2.2 Projection of Future Equipment Prices

 For reasons discussed in Chapter 8 of the TSD (Section 8.2.1.1), DOE used a constant 

price assumption for the default projection in the NIA. To investigate the impact of different 

equipment price projections on consumers’ net present value (NPV) for the considered TSLs, 

DOE also considered two alternative price trends. One of these used an exponential fit on the 

deflated price index for electric motors, and the other is based on AEO2013’s projected price 

index for industrial equipment3

. Details on how these alternative price trends were developed 

are in Appendix 10-B, which also presents results from the sensitivity analysis DOE developed 

based on these two equipment price scenarios. 

10-25
10.3.2.3 Motor Weight and Shipment Costs 

DOE used the same approach described in Section 10.3.2.1 to derive weight data for all 

covered equipment classes based on outputs from the engineering analysis, Chapter 5. First 

DOE developed a model to estimate the weight of 4-pole enclosed motors for all motor 

horsepowers. The model follows a general power law regression, and is expressed by the 

following equation: 

Weight4,e(hp) = c ∙ hpd xxvi.

where: 

Weight4,e(hp) = the weight of a 4-pole enclosed unit with capacity hp, and

c and d = parameters calibrated for each equipment class group and EL. 

DOE calibrated the model in equation xxvi to each equipment class group and EL level 

using the corresponding weight of the representative units provided by the engineering analysis. 

Table 10.3.3 presents the values of parameters c and d that DOE estimated for each equipment 

class group and EL level. As mentioned in Chapter 5, the weight for equipment class subgroup 

1.b. and ECG 3 were derived from the weight of equipment class subgroup 1.a. 

 

 

Table 10.3.3 Parameters used to Estimate the Weight of 4-Pole Enclosed Motors 

across Horsepower

Equipment Class Subgroup 1.a. (NEMA Design A and B – Non-Brake Motors)

EL 0 EL 1 EL 2 EL 3 EL 4

c 2.66E+01 2.83E+01 3.32E+01 3.32E+01 4.32E+01

d 7.43E-01 7.65E-01 7.43E-01 7.71E-01 7.58E-01

Equipment Class Subgroup 1.b. (NEMA Design A and B – Brake Motors)

EL 0 EL 1 EL 2 EL 3 EL 4

c 2.66E+01 2.83E+01 3.32E+01 3.32E+01 4.32E+01

d 7.43E-01 7.65E-01 7.43E-01 7.71E-01 7.58E-01

Equipment Class Group 2 (NEMA Design C)

EL 0 EL 1 EL 2 EL 3 EL 4

c 1.87E+01 2.16E+01 2.98E+01 - -

d 8.89E-01 8.94E-01 8.33E-01 - -

Equipment Class Group 3 (Fire Pump Motors)

EL 0 EL 1 EL 2 EL 3 EL 4

c 2.83E+01 3.32E+01 3.32E+01 4.32E+01 -

d 7.65E-01 7.43E-01 7.71E-01 7.58E-01 -

 In a second step DOE established an index to describe how weights vary with pole and 

enclosure across horsepower ratings (at a fixed EL). DOE established these indices using 

statistical estimates derived from a database of motor prices which DOE built upon data 

collected from internet catalogs from six major manufacturers and one large distributor (see 

Table 10.3.4 for an example of these indices estimated for Designs A and B motors). DOE used 

10-26
the indices in Table 10.3.4 and the weights estimated from model equation xxvi for 4-pole 

enclosed motors to estimate the weight of all other equipment classes. The final weight

estimates are available in the NIA spreadsheet.

Table 10.3.4 Indices used to Scale Weight across Poles and Enclosures (Equipment 

Class Subgroup 1.a.) 

Open Enclosed

hp 2 poles 4 poles 6 poles 8 poles 2 poles 4 poles 6 poles 8 poles

1 0.970 0.978 1.003 1.011 1.000 1.000 1.016 1.034

1.5 0.956 0.968 1.005 1.016 1.000 1.000 1.024 1.050

2 0.943 0.959 1.006 1.021 1.001 1.000 1.031 1.064

3 0.920 0.942 1.008 1.030 1.001 1.000 1.044 1.091

5 0.881 0.913 1.013 1.044 1.001 1.000 1.065 1.135

7.5 0.842 0.885 1.017 1.059 1.002 1.000 1.086 1.180

10 0.811 0.862 1.020 1.071 1.002 1.000 1.103 1.215

15 0.765 0.829 1.025 1.088 1.003 1.000 1.128 1.267

20 0.733 0.805 1.028 1.100 1.003 1.000 1.146 1.303

25 0.708 0.788 1.031 1.109 1.003 1.000 1.159 1.331

30 0.690 0.774 1.033 1.116 1.003 1.000 1.169 1.352

40 0.663 0.755 1.035 1.126 1.004 1.000 1.184 1.383

50 0.644 0.741 1.037 1.133 1.004 1.000 1.194 1.404

60 0.631 0.731 1.039 1.138 1.004 1.000 1.201 1.419

75 0.616 0.721 1.040 1.143 1.004 1.000 1.209 1.436

100 0.600 0.709 1.042 1.149 1.004 1.000 1.218 1.454

125 0.590 0.702 1.043 1.153 1.004 1.000 1.223 1.465

150 0.583 0.697 1.044 1.155 1.005 1.000 1.227 1.473

200 0.574 0.690 1.045 1.159 1.005 1.000 1.232 1.484

250 0.568 0.686 1.045 1.161 1.005 1.000 1.235 1.490

300 0.564 0.683 1.046 N/A 1.005 1.000 1.237 N/A

350 0.561 0.681 1.046 N/A 1.005 1.000 1.239 N/A

400 0.559 0.679 N/A N/A 1.005 1.000 N/A N/A

450 0.558 0.678 N/A N/A 1.005 1.000 N/A N/A

500 0.556 0.677 N/A N/A 1.005 1.000 N/A N/A

 

10.3.2.4 Repair Costs

DOE calculated the repair costs in two steps. First DOE considered the cost of one repair 

event by motor horsepower, configuration and efficiency level described in Chapter 8, Section

8.2.2.4. Then DOE calculated the lifetime repair cost of a motor with a given horsepower, 

configuration and efficiency level, operating in a certain sector, as the present-value of a stream 

of repair events occurring within a fixed frequency – depending on the sector and horsepower 

range – until the end of the life of the equipment. For the calculation of the present-value DOE 

used the two discount rates discussed in Section 10.3.2.6. However, DOE understands that not 

all motors will operate for 30 years. Consequently, in the calculation of present value, DOE 

multiplied the cost of each repair event by the probability that the motor will be in operation by 

10-27
that time, according to its horsepower rating and the sector where the motor is used. (See 

Section 10.2.2.5 above for more about lifetime distributions.)

10.3.2.5 Electricity Prices

For the NIA, DOE considered the electricity prices by sector as national weighted 

averages of the regional weighted average electricity prices described in Chapter 8, Section

8.2.2.2. 

10.3.2.6 Discount Rate

The discount rate expresses the time value of money. DOE used real discount rates of 3 

percent and 7 percent, as established by the U.S. Office of Management and Budget (OMB) 

guidelines on regulatory analysis. 4

 The discount rates DOE used in the LCC are distinct from 

those it used in the NPV calculations, in that the NPV discount rates represent the societal rate of 

return on capital investment, whereas LCC discount rates reflect the owner cost of capital and the 

financial environment of electric utilities and commercial and industrial entities.

10.3.3 Unit Lifetime Energy Cost

The unit lifetime energy cost expresses an estimate of the market average expense with 

electricity that owners of all motors of a given equipment class, shipped in a given year, will 

have to operate these motors over their lifetime. It refers to the variable uNChp,g,c in xix and xx, 

and is evaluated as the sum of the annual energy cost over the motor lifetime:

uNChp,g,c(s, a, y) = ∑ �UEChp,g,c(s, a, i) ∙ nP(y + i − 1) ∙ (1 + r)1−i ∙ Ohp(s, i)� 30

i=1 xxvii.

UEChp,g,c(s, a, i) = (hp × 0.757) ∙ Load(a) ∙ Hourshp(s, a)

fEffc ∙ aEffhp,c(a) ∙ Conserv(i) xxviii.

where:

uNChp,g,c(s, a, y) = the lifetime energy cost of a unit with capacity hp, configuration g and 

efficiency level at EL c, shipped in year y and used for application a in 

sector s, 

UEChp,g,c(s, a, i) = the site energy consumption in the i-th year of operation of a unit with 

capacity hp, configuration g and efficiency level at EL c used for 

application a in sector s, 

nP(t) = the national average electricity price in year t, 

r = the discount rate, 

Ohp(s, i) = the probability that a unit with capacity hp, used in sector s will be in 

operation in the i-th year of its lifetime, 

hp = the unit capacity (in horse-power), 

Load(a) = the typical load of a motor used in application a, 

10-28
Hourshp(s, a) = annual hours of operation of a unit with capacity hp, used for 

application a in sector s, 

fEffc = the full-load efficiency of a unit with efficiency level at EL c, 

aEffhp,c(a) = the factor used to adjust the full-load efficiency of a unit with capacity 

hp and efficiency level at EL c used in application a to the efficiency 

corresponding to its typical load, and 

Conserv(i) = the energy efficiency conservation factor used to reduce the unit initial 

efficiency to the efficiency it is estimated to present in its i-th year of 

operation due to repairs. 

10.3.4 Unit Lifetime Non-Energy Costs

The unit lifetime non-energy costs expresses an estimate of the market average expenses 

that owners of all motors of a given equipment class, shipped in a given year, will have with 

purchasing and repairing these motors over their lifetime. It refers to the variable uQChp,g,c in 

xxiii and xxiv, and is evaluated as the sum of the motor initial costs with the present-value of all 

repair costs over the motor lifetime:

uQChp,g,c(s, y) = uIChp,g,c(y) + ∑ �uRChp,g,c(i) ∙ (1 + r)1−i ∙ Ohp(s, i)� 30

i=1 xxix.

uIChp,g,c(y) = kP(y) ∙ uQChp,g,c + uSChp,g,c xxx.

uQChp,g,c = MSPhp,g,0 ∙ (OVHbase − OVHinc) + MSPhp,g,c ∙ OVHinc xxxi.

uSChp,g,c = uWeighthp,g,c ∙ sP xxxii.

uRChp,g,c(i) = �

uRCepacthp,g ∙ kRc, i = 6, 11, 16, 21, 26

0, i ≠ 6, 11, 16, 21, 26 xxxiii.

where:

uQChp,g,c(s, a, y) = the lifetime non-energy costs of a unit with capacity hp, configuration g

and efficiency level at EL c, shipped in year y to sector s, 

uIChp,g,c(y) = the total installed cost of a unit with capacity hp, configuration g and 

efficiency level at EL c, shipped in year y, 

kP(y) = the price-trend multiplier for a unit shipped in year y, 

uQChp,g,c = the retail price of a unit with capacity hp, configuration g and efficiency 

level at EL c, 

uSChp,g,c = the shipment cost of a unit with capacity hp, configuration g and 

efficiency level at EL c, 

MSPhp,g,c = the manufacturer price of a unit with capacity hp, configuration g and 

efficiency level at EL c, 

OVHbase = the baseline price overhead, 

OVHinc = the incremental price overhead, 

10-29
uWeighthp,g,c = the weight of a unit with capacity hp, configuration g and efficiency 

level at EL c, 

sP = the per pound shipment cost, 

uRChp,g,c(i) = the repair cost of a unit with capacity hp, configuration g and efficiency 

level at EL c in its i-th year of operation, 

uRCepacthp,g = the repair cost of a unit with capacity hp, configuration g and efficiency 

level below the applicable under the Energy Policy Act of 1992 (EPACT 

1992), 

kRc = the repair cost adder of a unit with efficiency level at EL c relative to the 

repair cost of a unit with efficiency level below EPACT 1992, 

Ohp(s, i) = the probability that a unit with capacity hp, used in sector s will be in 

operation in the i-th year of its lifetime, and

r = the discount rate. 

10.4 TRIAL STANDARD LEVELS 

DOE developed TSLs that combine efficiency levels for each equipment class group. 

Table 10.4.1 presents the efficiency levels for each equipment class group in each TSL. TSL 4 

consists of the max-tech efficiency levels. TSL 3 consists of those efficiency levels that are one 

level above the levels at TSL 2. TSL 2 refers to the efficiency levels closest to the ones 

recommended by the Motor Coalitionk in their comments to the preliminary analysis.

5

 TSL 1 

consists of EL 1 efficiency levels for equipment class groups 1l

 and 2, and of EL 0 for equipment 

class group 3. 

Table 10.4.1 Trial Standard Levels for Electric Motors

Equipment Class Group

Trial Standard Level 

(Efficiency Level)

1 2 3 4

1.: NEMA Design A and B EL 1 EL 2 EL 3 EL 4

2: NEMA Design C EL 1 EL 1 EL 2 EL 2

3: NEMA Fire Pump Electric Motors EL 0 EL 0 EL 1 EL 3

k The members of the Motor Coalition include: National Electrical Manufacturers Association (NEMA), American 

Council for an Energy‐Efficient Economy (ACEEE), Appliance Standards Awareness Project (ASAP), Alliance to 

Save Energy (ASE), Earthjustice, Natural Resources Defense Council (NRDC), Northwest Energy Efficiency 

Alliance (NEEA), Northeast Energy Efficiency Partnerships (NEEP), and Northwest Power and Conservation 

Council (NPCC).

l

Equipment class group 1 is the sum of equipment class subgroups 1.a. and 1.b.

10-30

 
10.5 RESULTS

10.5.1 National Energy Savings and Net Present Value for Trial Standard Levels

DOE evaluated NES and NPV for each equipment class groupm and TSL using the 

inputs and methodologies described in Sections 10.2 and 10.3. Table 10.5.1 and Table 10.5.2

present respectively NES and NPV results. 

Table 10.5.1 Cumulative National Energy Savings for Electric Motors Trial Standard 

Levels from Units Sold over the 30-year Analysis Period 

Energy

Trial Standard Level

Equipment Class 1 2 3 4

(Quads)

Subgroup 1.a.: NEMA Design A and B 

Primary

0.83 6.26 9.83 12.60 

Subgroup 1.b.: Brake Motors 0.26 0.58 0.71 0.81 

Group 2: NEMA Design C 0.02 0.02 0.03 0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00 

Total All Classes 1.10 6.85 10.57 13.45 

Subgroup 1.a.: NEMA Design A and B 

FFC

0.84 6.36 9.99 12.81 

Subgroup 1.b.: Brake Motors 0.26 0.59 0.73 0.83 

Group 2: NEMA Design C 0.02 0.02 0.03 0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00 

Total All Classes 1.12 6.97 10.75 13.67 

m Equipment class group 1 is the sum of equipment class subgroups 1.a. and 1.b.

10-31

 
Table 10.5.2 Net Present Value for Electric Motors for Electric Motors Trial Standard 

Levels from Units Sold over the 30-year Analysis Period 

Discount Rate 

Trial Standard Level

Equipment Class 1 2 3 4

(billion 2013$)

Subgroup 1.a.: NEMA Design A and B

3% 

5.34 25.68 6.61 -38.48

Subgroup 1.b.: Brake Motors 1.57 3.07 2.00 -0.79 

Group 2: NEMA Design C 0.06 0.06 -0.02 -0.02 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 -0.03 

Total All Classes 6.97 28.81 8.59 -39.32

Subgroup 1.a.: NEMA Design A and B 

7% 

2.68 10.04 -2.07 -30.16 

Subgroup 1.b.: Brake Motors 0.66 1.22 0.57 -1.13

Group 2: NEMA Design C 0.02 0.02 -0.03 -0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 -0.02 

Total All Classes 3.36 11.29 -1.54 -31.34 

10.5.2 Scenario Analysis 

 DOE also performed a scenario analysis to assess how changes in economic growth 

would affect the former NES and NPV results reported in Tables Table 10.5.1 and Table 10.5.2. 

Table 10.5.3 through Table 10.5.6 present NES and NPV results for both the low- and high 

economic growth scenarios. 

 

10-32
Table 10.5.3 Cumulative National Energy Savings for the Low Economic Growth 

Scenario 

Energy

Trial Standard Level

Equipment Class 1 2 3 4

(Quads)

Subgroup 1.a.: NEMA Design A and B

Primary

0.71 5.41 8.50 10.90

Subgroup 1.b.: Brake Motors 0.22 0.50 0.62 0.70 

Group 2: NEMA Design C 0.02 0.02 0.03 0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00 

Total All Classes 0.95 5.92 9.14 11.63

Subgroup 1.a.: NEMA Design A and B 

FFC

0.73 5.50 8.64 11.08 

Subgroup 1.b.: Brake Motors 0.23 0.51 0.63 0.72

Group 2: NEMA Design C 0.02 0.02 0.03 0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00 

Total All Classes 0.97 6.02 9.29 11.82 

Table 10.5.4 Net Present Value for the Low Economic Growth Scenario

Discount Rate 

Trial Standard Level

Equipment Class 1 2 3 4

(billion 2013$)

Subgroup 1.a.: NEMA Design A and B

3% 

4.43 20.60 3.22 -36.45

Subgroup 1.b.: Brake Motors 1.30 2.51 1.55 -0.89 

Group 2: NEMA Design C 0.05 0.05 -0.02 -0.02

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 -0.03 

Total All Classes 5.78 23.16 4.74 -37.39 

Subgroup 1.a.: NEMA Design A and B 

7% 

2.24 8.11 -2.66 -27.19 

Subgroup 1.b.: Brake Motors 0.55 1.00 0.43 -1.05

Group 2: NEMA Design C 0.02 0.02 -0.03 -0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 -0.02

Total All Classes 2.81 9.13 -2.26 -28.29 

10-33
Table 10.5.5 Cumulative National Energy Savings for the High Economic Growth 

Scenario 

Energy

Trial Standard Level

Equipment Class 1 2 3 4

(Quads)

Subgroup 1.a.: NEMA Design A and B

Primary

0.95 7.27 11.42 14.64

Subgroup 1.b.: Brake Motors 0.30 0.67 0.83 0.95 

Group 2: NEMA Design C 0.02 0.02 0.03 0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00 

Total All Classes 1.27 7.96 12.29 15.62

Subgroup 1.a.: NEMA Design A and B 

FFC

0.97 7.39 11.61 14.88 

Subgroup 1.b.: Brake Motors 0.30 0.68 0.84 0.96

Group 2: NEMA Design C 0.02 0.02 0.04 0.04 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00 

Total All Classes 1.29 8.09 12.49 15.88 

Table 10.5.6 Net Present Value for the High Economic Growth Scenario

Discount Rate 

Trial Standard Level

Equipment Class 1 2 3 4

(billion 2013$)

Subgroup 1.a.: NEMA Design A and B

3% 

6.48 32.44 11.82 -39.09

Subgroup 1.b.: Brake Motors 1.93 3.81 2.63 -0.56 

Group 2: NEMA Design C 0.08 0.08 0.00 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 -0.04 

Total All Classes 8.50 36.33 14.44 -39.69 

Subgroup 1.a.: NEMA Design A and B 

7% 

3.22 12.63 -0.81 -32.64 

Subgroup 1.b.: Brake Motors 0.81 1.51 0.77 -1.17

Group 2: NEMA Design C 0.03 0.03 -0.03 -0.03 

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 -0.02

Total All Classes 4.06 14.16 -0.07 -33.86 

10.5.3 Sensitivity Analysis

 Besides calculating NES and NPV values for the inputs described in Sections 10.2.2 and 

10.3.2 above, DOE performed a sensitivity analysis for some of those inputs, namely the annual 

hours of operation, MSP and repair cost. While changes in the annual hours of operation affect 

10-34
both the NES and NPV, a variation in the MSP and repair cost impacts only the NPV. Table 

10.5.7 through Table 10.5.10 summarize the impacts that a change of ±10 percent in these 

variables has on the former NES and NPV values, as reported in Table 10.5.1 and Table 10.5.2. 

Table 10.5.7 Cumulative National Energy Savings Variation in Response to ±10 

Percent Changes in Hours of Operation* 

Energy

Trial Standard Level

Equipment Class 1 2 3 4

(billion 2013$)

Subgroup 1.a.: NEMA Design A and B

Primary

±0.083 ±0.626 ±0.983 ±1.260

Subgroup 1.b.: Brake Motors ±0.026 ±0.058 ±0.071 ±0.081

Group 2: NEMA Design C ±0.002 ±0.002 ±0.003 ±0.003 

Group 3: Fire Pump Electric Motors 0.000 0.000 ±0.000 ±0.000

Total All Classes ±0.110 ±0.685 ±1.057 ±1.345

Subgroup 1.a.: NEMA Design A and B 

FFC

±0.084 ±0.636 ±0.999 ±1.281 

Subgroup 1.b.: Brake Motors ±0.026 ±0.059 ±0.073 ±0.083 

Group 2: NEMA Design C ±0.002 ±0.002 ±0.003 ±0.003 

Group 3: Fire Pump Electric Motors 0.000 0.000 ±0.000 ±0.000 

Total All Classes ±0.112 ±0.697 ±1.075 ±1.367

* NES and hours of operation are positively correlated, which means that an increase in NES results from an 

increase in hours of operation. 

10-35
Table 10.5.8 Net Present Value Variation in Response to ±10 Percent Changes in 

Hours of Operation* 

Discount Rate 

Trial Standard Level

Equipment Class 1 2 3 4

(billion 2012$)

Subgroup 1.a.: NEMA Design A and B

3% 

±0.595 ±4.016 ±6.271 ±8.052 

Subgroup 1.b.: Brake Motors ±0.173 ±0.386 ±0.476 ±0.543 

Group 2: NEMA Design C ±0.012 ±0.012 ±0.019 ±0.019 

Group 3: Fire Pump Electric Motors 0.000 0.000 ±0.000 ±0.000 

Total All Classes ±0.780 ±4.414 ±6.766 ±8.614 

Subgroup 1.a.: NEMA Design A and B 

7% 

±0.308 ±1.749 ±2.704 ±3.486 

Subgroup 1.b.: Brake Motors ±0.075 ±0.165 ±0.203 ±0.232 

Group 2: NEMA Design C ±0.005 ±0.005 ±0.008 ±0.008 

Group 3: Fire Pump Electric Motors 0.000 0.000 ±0.000 ±0.000 

Total All Classes ±0.389 ±1.919 ±2.915 ±3.726 

* NPV and hours of operation are positively correlated, which means that an increase in NPV results from an 

increase in hours of operation. 

Table 10.5.9 Net Present Value Variation in Response to ±10 Percent Changes in 

Manufacturer Selling Price* 

Discount Rate 

Trial Standard Level

Equipment Class 1 2 3 4

(billion 2012$)

Subgroup 1.a.: NEMA Design A and B 

3% 

±0.054 ±1.044 ±4.677 ±10.381 

Subgroup 1.b.: Brake Motors ±0.014 ±0.064 ±0.247 ±0.573

Group 2: NEMA Design C ±0.005 ±0.005 ±0.018 ±0.018 

Group 3: Fire Pump Electric Motors 0.000 0.000 ±0.000 ±0.003

Total All Classes ±0.072 ±1.113 ±4.943 ±10.976 

Subgroup 1.a.: NEMA Design A and B 

7% 

±0.035 ±0.57 ±2.497 ±5.794 

Subgroup 1.b.: Brake Motors ±0.008 ±0.036 ±0.132 ±0.32 

Group 2: NEMA Design C ±0.003 ±0.003 ±0.01 ±0.01

Group 3: Fire Pump Electric Motors 0.000 0.000 ±0.000 ±0.002 

Total All Classes ±0.046 ±0.608 ±2.639 ±6.125

* NPV and MSP are negatively correlated, which means that an increase in NPV results from a decrease in MSP. 

10-36
Table 10.5.10 Net Present Value Variation in Response to ±10 Percent Changes in 

Repair Cost* 

Discount Rate 

Trial Standard Level

Equipment Class 1 2 3 4

(billion 2012$)

Subgroup 1.a.: NEMA Design A and B

3% 

±0.000 ±0.273 ±0.556 ±0.802

Subgroup 1.b.: Brake Motors 0.000 ±0.005 ±0.009 ±0.011 

Group 2: NEMA Design C ±0.001 ±0.001 ±0.001 0.001 

Group 3: Fire Pump Electric Motors 0.000 0.000 0.000 0.000 

Total All Classes ±0.001 ±0.278 ±0.566 ±0.815

Subgroup 1.a.: NEMA Design A and B 

7% 

±0.000 ±0.102 ±0.21 ±0.308 

Subgroup 1.b.: Brake Motors 0.000 ±0.002 ±0.003 ±0.004

Group 2: NEMA Design C ±0.000 ±0.000 ±0.000 ±0.000 

Group 3: Fire Pump Electric Motors 0.000 0.000 0.000 0.000 

Total All Classes ±0.000 ±0.104 ±0.214 ±0.313 

* NPV and repair cost are negatively correlated, which means that an increase in NPV results from a decrease in 

repair cost. 

10-37
REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2013, 2013. (Posted May) 

<www.eia.gov/oiaf/aeo>

2. Boteler, R., USA Motor Update 2009, in Energy Efficient Motor Driven Systems 

Conference (EEMODS). 2009. 

3. Energy Information Administration, Annual Energy Outlook 2013, 2013. (Posted May) 

<http://www.eia.doe.gov/oiaf/aeo/>

4. Energy Information Administration - Office of Integrated Analysis and Forecasting, The 

National Energy Modeling System: An Overview, 2009. United States Department of 

Energy. <http://www.eia.doe.gov/oiaf/aeo/overview/>

5. Motor Coalition, Comment #35 from Motor Coalition on Preliminary Analysis for Energy 

Conservation Standards for Electric Motors, 2012. (Posted August 15) 

<http://www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0027-0035>

10-38
CHAPTER 11. LIFE-CYCLE COST SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 SUBGROUPS DEFINITION ........................................................................................ 11-1

11.2.1 Low Electricity Price Regions ....................................................................................... 11-1

11.2.2 Small Businesses ............................................................................................................ 11-1

11.2.3 Customers by Sector of the Economy ............................................................................ 11-3

11.3 RESULTS FOR ELECTRIC MOTOR SUBGROUPS ................................................. 11-3

11.3.1 Representative Unit 1, NEMA Design B, 5 Horsepower, 4 Poles, Enclosed Motor ..... 11-3

11.3.1.1 Low Electricity Price Regions ..................................................................... 11-3

11.3.1.2 Small Businesses .......................................................................................... 11-4

11.3.1.3 Agricultural Sector ....................................................................................... 11-4

11.3.1.4 Industrial Sector ........................................................................................... 11-4

11.3.1.5 Commercial Sector....................................................................................... 11-5

11.3.2 Representative Unit 2, NEMA Design B, 30 Horsepower, 4 Poles, Enclosed 

Motor.............................................................................................................................. 11-5

11.3.2.1 Low Electricity Price Regions ..................................................................... 11-5

11.3.2.2 Small Businesses .......................................................................................... 11-6

11.3.2.3 Agricultural Sector ....................................................................................... 11-6

11.3.2.4 Industrial Sector ........................................................................................... 11-6

11.3.2.5 Commercial Sector....................................................................................... 11-7

11.3.3 Representative Unit 3, NEMA Design B, 75 Horsepower, 4 Poles, Enclosed 

Motor.............................................................................................................................. 11-7

11.3.3.1 Low Electricity Price Regions ..................................................................... 11-7

11.3.3.2 Small Businesses .......................................................................................... 11-8

11.3.3.3 Agricultural Sector ....................................................................................... 11-8

11.3.3.4 Industrial Sector ........................................................................................... 11-8

11.3.3.5 Commercial Sector....................................................................................... 11-9

11.3.4 Representative Unit 4, NEMA Design C, 5 Horsepower, 4 Poles, Enclosed Motor ..... 11-9

11.3.4.1 Low Electricity Price Regions ..................................................................... 11-9

11.3.4.2 Small Businesses .......................................................................................... 11-9

11.3.4.3 Agricultural Sector ..................................................................................... 11-10

11.3.4.4 Industrial Sector ......................................................................................... 11-10

11.3.4.5 Commercial Sector..................................................................................... 11-10

11.3.5 Representative Unit 5, NEMA Design C, 50 Horsepower, 4 Poles, Enclosed 

Motor............................................................................................................................ 11-11

11.3.5.1 Low Electricity Price Regions ................................................................... 11-11

11.3.5.2 Small Businesses ........................................................................................ 11-11

11.3.5.3 Agricultural Sector ..................................................................................... 11-11

11.3.5.4 Industrial Sector ......................................................................................... 11-12

11.3.5.5 Commercial Sector..................................................................................... 11-12

11.3.6 Representative Unit 6, Fire Pump, 5 Horsepower, 4 Poles, Enclosed Electric 

Motor............................................................................................................................ 11-12

11-i 
11.3.6.1 Low Electricity Price Regions ................................................................... 11-12

11.3.6.2 Small Businesses ........................................................................................ 11-13

11.3.6.3 Agricultural Sector ..................................................................................... 11-13

11.3.6.4 Industrial Sector ......................................................................................... 11-13

11.3.6.5 Commercial Sector..................................................................................... 11-14

11.3.7 Representative Unit 7, Fire Pump, 30 Horsepower, 4 Poles, Enclosed Electric 

Motor............................................................................................................................ 11-14

11.3.7.1 Low Electricity Price Regions ................................................................... 11-14

11.3.7.2 Small Businesses ........................................................................................ 11-14

11.3.7.3 Agricultural Sector ..................................................................................... 11-15

11.3.7.4 Industrial Sector ......................................................................................... 11-15

11.3.7.5 Commercial Sector..................................................................................... 11-15

11.3.8 Representative Unit 8, Fire Pump, 75 Horsepower, 4 Poles, Enclosed Electric 

Motor............................................................................................................................ 11-16

11.3.8.1 Low Electricity Price Regions ................................................................... 11-16

11.3.8.2 Small Businesses ........................................................................................ 11-16

11.3.8.3 Agricultural Sector ..................................................................................... 11-16

11.3.8.4 Industrial Sector ......................................................................................... 11-17

11.3.8.5 Commercial Sector..................................................................................... 11-17

11.3.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 Horsepower, 

Four Poles, Enclosed Motor ......................................................................................... 11-18

11.3.9.1 Low Electricity Price Regions ................................................................... 11-18

11.3.9.2 Small Businesses ........................................................................................ 11-18

11.3.9.3 Agricultural Sector ..................................................................................... 11-19

11.3.9.4 Industrial Sector ......................................................................................... 11-19

11.3.9.5 Commercial Sector..................................................................................... 11-19

11.3.10 Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30 

Horsepower, Four Poles, Enclosed Motor ................................................................... 11-20

11.3.10.1 Low Electricity Price Regions ................................................................... 11-20

11.3.10.2 Small Businesses ........................................................................................ 11-20

11.3.10.3 Agricultural Sector ..................................................................................... 11-21

11.3.10.4 Industrial Sector ......................................................................................... 11-21

11.3.10.5 Commercial Sector..................................................................................... 11-21

LIST OF TABLES

Table 11.2.1 Discount Rate Difference Between Small Company and Sector Average ........ 11-2

Table 11.3.1 Representative Unit 1 Low Electricity Price Regions LCC Results .................. 11-3

Table 11.3.2 Representative Unit 1 Small Businesses LCC Results ...................................... 11-4

Table 11.3.3 Representative Unit 1 Agricultural Sector LCC Results ................................... 11-4

Table 11.3.4 Representative Unit 1 Industrial Sector LCC Results ........................................ 11-4

Table 11.3.5 Representative Unit 1 Commercial Sector LCC Results ................................... 11-5

Table 11.3.6 Representative Unit 2 Low Electricity Price Regions LCC Results .................. 11-5

Table 11.3.7 Representative Unit 2 Small Businesses LCC Results ...................................... 11-6

Table 11.3.8 Representative Unit 2 Agricultural Sector LCC Results ................................... 11-6

11-ii
Table 11.3.9 Representative Unit 2 Industrial Sector LCC Results ........................................ 11-6

Table 11.3.10 Representative Unit 2 Commercial Sector LCC Results ................................... 11-7

Table 11.3.11 Representative Unit 3 Low Electricity Price Regions LCC Results .................. 11-7

Table 11.3.12 Representative Unit 3 Small Businesses LCC Results ...................................... 11-8

Table 11.3.13 Representative Unit 3 Agricultural Sector LCC Results ................................... 11-8

Table 11.3.14 Representative Unit 3 Industrial Sector LCC Results ........................................ 11-8

Table 11.3.15 Representative Unit 3 Commercial Sector LCC Results ................................... 11-9

Table 11.3.16 Representative Unit 4 Low Electricity Price Regions LCC Results .................. 11-9

Table 11.3.17 Representative Unit 4 Small Businesses LCC Results ...................................... 11-9

Table 11.3.18 Representative Unit 4 Agricultural Sector LCC Results ................................. 11-10

Table 11.3.19 Representative Unit 4 Industrial Sector LCC Results ...................................... 11-10

Table 11.3.20 Representative Unit 4 Commercial Sector LCC Results ................................. 11-10

Table 11.3.21 Representative Unit 5 Low Electricity Price Regions LCC Results ................ 11-11

Table 11.3.22 Representative Unit 5 Small Businesses LCC Results .................................... 11-11

Table 11.3.23 Representative Unit 5 Agricultural Sector LCC Results ................................. 11-11

Table 11.3.24 Representative Unit 5 Industrial Sector LCC Results ...................................... 11-12

Table 11.3.25 Representative Unit 5 Commercial Sector LCC Results ................................. 11-12

Table 11.3.26 Representative Unit 6 Low Electricity Price Regions LCC Results ................ 11-12

Table 11.3.27 Representative Unit 6 Small Businesses LCC Results .................................... 11-13

Table 11.3.28 Representative Unit 6 Agricultural Sector LCC Results ................................. 11-13

Table 11.3.29 Representative Unit 6 Industrial Sector LCC Results ...................................... 11-13

Table 11.3.30 Representative Unit 6 Commercial Sector LCC Results ................................. 11-14

Table 11.3.31 Representative Unit 7 Low Electricity Price Regions LCC Results ................ 11-14

Table 11.3.32 Representative Unit 7 Small Businesses LCC Results .................................... 11-14

Table 11.3.33 Representative Unit 7 Agricultural Sector LCC Results ................................. 11-15

Table 11.3.34 Representative Unit 7 Industrial Sector LCC Results ...................................... 11-15

Table 11.3.35 Representative Unit 7 Commercial Sector LCC Results ................................. 11-15

Table 11.3.36 Representative Unit 8 Low Electricity Price Regions LCC Results ................ 11-16

Table 11.3.37 Representative Unit 8 Small Businesses LCC Results .................................... 11-16

Table 11.3.38 Representative Unit 8 Agricultural Sector LCC Results ................................. 11-16

Table 11.3.39 Representative Unit 8 Industrial Sector LCC Results ...................................... 11-17

Table 11.3.40 Representative Unit 8 Commercial Sector LCC Results ................................. 11-17

Table 11.3.41 Representative Unit 9 Low Electricity Price Regions LCC Results ................ 11-18

Table 11.3.42 Representative Unit 9 Small Businesses LCC Results .................................... 11-18

Table 11.3.43 Representative Unit 9 Agricultural Sector LCC Results ................................. 11-19

Table 11.3.44 Representative Unit 9 Industrial Sector LCC Results ...................................... 11-19

Table 11.3.45 Representative Unit 9 Commercial Sector LCC Results ................................. 11-19

Table 11.3.46 Representative Unit 10 Low Electricity Price Regions LCC Results .............. 11-20

Table 11.3.47 Representative Unit 10 Small Businesses LCC Results .................................. 11-20

Table 11.3.48 Representative Unit 10 Agricultural Sector LCC Results ............................... 11-21

Table 11.3.49 Representative Unit 10 Industrial Sector LCC Results .................................... 11-21

Table 11.3.50 Representative Unit 10 Commercial Sector LCC Results ............................... 11-21

11-iii
CHAPTER 11. CUSTOMER SUBGROUP ANALYSIS

11.1 INTRODUCTION

 The customer subgroup analysis evaluates impacts on any identifiable groups or 

customers who may be disproportionately affected by any national energy conservation standard. 

The U.S. Department of Energy (DOE) evaluates impacts on particular subgroups of customers 

primarily by analyzing the life-cycle cost (LCC) impacts and payback period (PBP) for those 

customers from the considered energy efficiency levels. DOE determines the impact on 

customer subgroups using the LCC spreadsheet models for electric motors. Chapter 8 explains 

in detail the inputs to the models used in determining LCC impacts and PBPs. 

For the final rule, DOE evaluated impacts on customers located in regions with lower electricity 

prices, customers which are small businesses and customers which are part of the industrial, 

agricultural and commercial sector.

 

 This chapter describes the subgroup identification in further detail and gives the results of 

the LCC and PBP analyses for the considered subgroups. 

11.2 SUBGROUPS DEFINITION

11.2.1 Low Electricity Price Regions 

Customers in the low electricity price regions represent the users of electric motors which 

are located in the regions with lower electricity prices for each sector (industrial, agricultural, 

and commercial). DOE analyzed impacts on those customers by using the lowest electricity rate 

among the four Census regions considered for each sector. DOE used electricity rate in the South 

Census region ($0.073/kWh) for the agricultural and industrial customer subgroups, and the 

electricity rate in the Midwest Census region ($0.104/kWh) for the commercial customer 

subgroups analysis. 

11.2.2 Small Businesses 

The Small Business Administration (SBA) defines a small business by its annual receipts 

or its number of employees. Electric motors are used throughout the U.S. economy, so DOE did 

not assign a different distribution of motor applications or sectors of the economy to this 

subgroup. 

To calculate discount rates for small companies that purchase electric motors, DOE used 

the same methodology as for the general population of electric motor customers as presented in 

11-1 
chapter 8.a

 Although the general methodology is appropriate, the capital asset pricing model 

(CAPM)b described in chapter 8 for the general population underestimates the cost of capital for 

small companies. In CAPM, the risk premium β is used to account for the higher returns 

associated with greater risk. However, for small companies, particularly very small companies, 

historic returns have been significantly higher than the CAPM equation predicts. This additional 

return can be accounted for by adding a size premium to the cost of equity for small firms:

Rk fe ( ) β ERP +×+= S

 ke = Cost of equity, 

 Rf = Expected return on risk-free assets,

β = Risk coefficient of the firm, 

ERP = Equity risk premium, and 

 S = Size Premium.

 DOE obtained size premium data from Ibbotson Associates’ Stocks, Bonds, Bills, and 

Inflation 2009 Yearbook.1 For the period of 1926–2008, the average size premium for the 

smallest companies in all industries is 5.81 percent, implying that on average, historic 

performance of small companies has been 5.81 percent higher than the CAPM estimate of the 

small company cost of equity.c

DOE calculated the real weighted average cost of capital (as described in chapter 8) using 

the cost of equity including a size premium for small companies instead of the CAPM cost of 

equity. DOE estimates that small companies have average discount rates 1.88% higher than the 

sector average in the industrial sector and 2.21% higher than the sector average in the 

commercial sector, based on data from Damodaran2 (see Table 11.2.1).

Table 11.2.1 Discount Rate Difference Between Small Company and Sector Average

Sector

Discount Rate

Average Std Dev

Small Company 

Discount Rate 

Premium

Industrial Entire Sector 6.73% 1.53% 1.88% Small Companies 8.61% 2.75%

Commercial Entire Sector 5.95% 1.30% 2.21% Small Companies 8.16% 2.82%

 In chapter 8, DOE estimated the average discount rate to be 6.73% for industrial 

customers and 5.95% for commercial customers. Applying the additional small capitalization 

a DOE assumed that small businesses as a whole are a reasonable approximation for small businesses which use 

small electric motors.

b See 8.2.4.3 for more extensive description of CAPM and its parameters.

c

 In this calculation, small companies are defined as companies with market capitalization of less than or equal to 

$84.5 million, the Ibbotson Associates’ definition of Decile 10 companies.

11-2 

 
discount rate premiums, as presented in Table 11.2.1, the average small business discount rate is 

8.61% for the industrial sector and 8.16% for the commercial sector. Due to limited data 

availability, DOE applies the small business discount rate estimated for the industrial sector to 

the agricultural sector.

11.2.3 Customers by Sector of the Economy 

 Customers may operate their motors differently depending on the sector: industrial, 

agricultural, or commercial. Typically, customers of the industrial sectors show higher operating 

hours than customers in the agricultural sectors. 

DOE conducted analysis by using the sector specific average operating hours for each 

sectors in chapter 7 to evaluate the impact of standards by sector. 

11.3 RESULTS FOR ELECTRIC MOTOR SUBGROUPS

11.3.1 Representative Unit 1, NEMA Design B, 5 Horsepower, 4 Poles, Enclosed Motor 

11.3.1.1 Low Electricity Price Regions

Table 11.3.1 Representative Unit 1 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,977 805 6,357 N/A 0.0 0.0 N/A N/A

1 87.5 678 8,287 744 5,948 46 0.0 11.2 0.6 0.4

2 89.5 731 8,138 731 5,908 62 10.4 30.5 8.6 3.8

3 90.2 789 8,062 723 5,911 56 36.4 41.1 10.8 7.1

4 91.0 1,237 7,969 716 6,307 -304 85.7 6.5 68.9 31.5

11-3 
11.3.1.2 Small Businesses

Table 11.3.2 Representative Unit 1 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,977 883 6,345 N/A 0.0 0.0 N/A N/A

1 87.5 678 8,287 817 5,937 46 0.0 11.2 0.6 0.4

2 89.5 731 8,138 802 5,897 62 10.7 30.3 7.8 3.5

3 90.2 789 8,062 794 5,901 56 37.2 40.3 10.0 6.5

4 91.0 1,237 7,969 786 6,296 -305 85.8 6.5 69.2 28.7

11.3.1.3 Agricultural Sector

Table 11.3.3 Representative Unit 1 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 4,371 395 4,014 N/A 0.0 0.0 N/A N/A

1 87.5 678 4,037 368 3,803 24 0.0 11.2 1.2 0.8

2 89.5 731 3,964 368 3,856 2 28.1 12.9 63.3 7.2

3 90.2 789 3,927 369 3,922 -50 68.7 8.9 717.6 24.6

4 91.0 1,237 3,882 371 4,382 -473 91.9 0.4 959.9 141.7

11.3.1.4 Industrial Sector

Table 11.3.4 Representative Unit 1 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 15,629 1,247 7,792 N/A 0.0 0.0 N/A N/A

1 87.5 678 14,400 1,150 7,253 60 0.0 11.2 0.3 0.2

2 89.5 731 14,144 1,129 7,191 85 6.5 34.4 4.8 2.3

3 90.2 789 14,009 1,118 7,183 89 21.8 55.7 6.3 4.3

11-4 
4 91.0 1,237 13,844 1,105 7,559 -256 82.5 9.8 29.4 18.6

11.3.1.5 Commercial Sector

Table 11.3.5 Representative Unit 1 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 6,724 763 6,659 N/A 0.0 0.0 N/A N/A

1 87.5 678 6,218 707 6,232 48 0.0 11.2 0.6 0.4

2 89.5 731 6,104 694 6,187 67 10.1 30.9 8.7 3.9

3 90.2 789 6,049 687 6,189 63 36.0 41.6 11.2 7.6

4 91.0 1,237 5,980 680 6,581 -296 85.2 7.1 78.2 32.9

11.3.2 Representative Unit 2, NEMA Design B, 30 Horsepower, 4 Poles, Enclosed Motor 

11.3.2.1 Low Electricity Price Regions

Table 11.3.6 Representative Unit 2 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 61,611 5,683 50,002 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 60,164 5,555 49,340 36 1.2 3.8 15.2 3.8

2 93.6 2,300 58,778 5,442 48,494 369 1.5 37.7 2.7 1.3

3 94.1 2,559 58,698 5,445 48,769 140 46.4 36.5 32 4.9

4 94.5 3,891 58,511 5,438 50,043 -1,039 84.6 7.9 243 25.2

11-5 
11.3.2.2 Small Businesses

Table 11.3.7 Representative Unit 2 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 61,611 6,232 49,284 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 60,164 6,091 48,636 36 1.2 3.7 13.6 3.5

2 93.6 2,300 58,778 5,966 47,794 366 1.5 37.7 2.2 1.2

3 94.1 2,559 58,698 5,968 48,061 144 46.4 36.6 39 4.5

4 94.5 3,891 58,511 5,960 49,328 -1,028 84.5 8.1 257 23.1

11.3.2.3 Agricultural Sector

Table 11.3.8 Representative Unit 2 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 21,097 1,795 16,647 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 20,621 1,759 16,777 -6 3.7 1.2 49.7 13.4

2 93.6 2,300 20,117 1,731 16,655 41 11.2 28.0 14.5 4.9

3 94.1 2,559 20,099 1,739 16,979 -228 76.9 6.0 94 19.9

4 94.5 3,891 20,037 1,745 18,352 -1,499 92.5 0.0 563 92.3

11.3.2.4 Industrial Sector

Table 11.3.9 Representative Unit 2 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 95,482 7,710 62,986 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 93,287 7,539 62,052 49 0.9 4.1 9.3 2.8

2 93.6 2,300 91,064 7,374 60,857 515 0.7 38.5 1.6 0.8

3 94.1 2,559 90,965 7,377 61,131 287 41.6 41.3 29 3.1

11-6 
4 94.5 3,891 90,680 7,365 62,366 -856 78.0 14.5 162 17.3

11.3.2.5 Commercial Sector

Table 11.3.10 Representative Unit 2 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 49,614 5,716 51,803 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 48,435 5,587 51,101 38 1.2 3.8 15.7 4.0

2 93.6 2,300 47,339 5,475 50,233 378 1.5 37.7 42.4 1.3

3 94.1 2,559 47,267 5,476 50,501 157 45.5 37.4 42 4.9

4 94.5 3,891 47,115 5,470 51,768 -1,017 83.4 9.1 252 24.5

11.3.3 Representative Unit 3, NEMA Design B, 75 Horsepower, 4 Poles, Enclosed Motor 

11.3.3.1 Low Electricity Price Regions

Table 11.3.11 Representative Unit 3 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 195,566 15,590 131,907 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 194,167 15,499 131,491 47 2.9 5.2 17.9 3.5

2 95.4 4,692 190,458 15,228 129,764 619 2.8 30.1 5.1 1.9

3 95.8 5,473 190,392 15,244 130,670 -60 49.7 25.3 120.8 6.6

4 96.2 6,932 188,997 15,153 131,370 -701 71.5 20.1 70.9 16.6

11-7 
11.3.3.2 Small Businesses

Table 11.3.12 Representative Unit 3 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 195,566 17,049 131,097 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 194,167 16,950 130,689 47 2.9 5.2 11.2 3.2

2 95.4 4,692 190,458 16,652 128,959 620 2.7 30.1 3.8 1.7

3 95.8 5,473 190,392 16,667 129,848 -47 49.7 25.4 37.1 6.1

4 96.2 6,932 188,997 16,566 130,539 -680 71.5 20.1 168.6 14.9

11.3.3.3 Agricultural Sector

Table 11.3.13 Representative Unit 3 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 50,951 4,293 39,062 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 50,602 4,273 39,199 -10 6.4 1.7 51.0 12.9

2 95.4 4,692 49,621 4,216 39,223 -19 20.5 12.4 18.7 9.4

3 95.8 5,473 49,616 4,237 40,168 -728 74.1 0.9 192.0 34.4

4 96.2 6,932 49,243 4,226 41,523 -1,968 91.5 0.1 953.9 81.2

11.3.3.4 Industrial Sector

Table 11.3.14 Representative Unit 3 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 242,271 19,485 157,793 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 240,549 19,382 157,310 58 2.4 5.7 9.8 2.7

2 95.4 4,692 235,934 19,036 155,069 791 0.9 31.9 6.0 1.5

3 95.8 5,473 235,863 19,053 155,975 110 44.7 30.4 32.5 5.1

11-8 
4 96.2 6,932 234,124 18,933 156,479 -350 65.0 26.6 39.5 12.8

11.3.3.5 Commercial Sector

Table 11.3.15 Representative Unit 3 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 128,827 14,767 138,475 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 127,872 14,676 137,982 52 2.7 5.4 11.5 3.7

2 95.4 4,692 125,484 14,425 136,190 639 1.8 31.0 4.2 2.3

3 95.8 5,473 125,410 14,436 137,062 -17 50.4 24.7 37.2 7.6

4 96.2 6,932 124,520 14,353 137,747 -645 71.4 20.2 176.3 18.1

11.3.4 Representative Unit 4, NEMA Design C, 5 Horsepower, 4 Poles, Enclosed Motor 

11.3.4.1 Low Electricity Price Regions

Table 11.3.16 Representative Unit 4 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 8,376 751 6,175 N/A 0.0 0.0 N/A N/A

1 89.5 697 8,206 737 6,118 52 19.0 72.9 11.0 4.2

2 91.0 1,139 8,078 724 6,466 -296 97.0 3.0 34.6 23.6

11.3.4.2 Small Businesses

Table 11.3.17 Representative Unit 4 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 8,376 823 6,134 N/A 0.0 0.0 N/A N/A

11-9 
1 89.5 697 8,206 808 6,077 52 19.8 72.1 8.5 3.8

2 91.0 1,139 8,078 794 6,427 -298 96.6 3.4 31.9 21.5

11.3.4.3 Agricultural Sector

Table 11.3.18 Representative Unit 4 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 4,109 373 3,832 N/A 0.0 0.0 N/A N/A

1 89.5 697 4,026 373 3,877 -42 81.5 10.5 167.3 16.9

2 91.0 1,139 3,963 371 4,302 -466 100.0 0.0 515.5 103.9

11.3.4.4 Industrial Sector

Table 11.3.19 Representative Unit 4 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 14,756 1,179 7,336 N/A 0.0 0.0 N/A N/A

1 89.5 697 14,455 1,156 7,253 76 7.6 84.3 4.7 2.4

2 91.0 1,139 14,231 1,136 7,586 -257 95.3 4.7 21.1 14.1

11.3.4.5 Commercial Sector

Table 11.3.20 Representative Unit 4 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 6,185 703 6,526 N/A 0.0 0.0 N/A N/A

1 89.5 697 6,059 690 6,463 59 18.5 73.4 10.5 4.3

2 91.0 1,139 5,965 678 6,805 -284 95.2 4.8 35.8 24.4

11-10
11.3.5 Representative Unit 5, NEMA Design C, 50 Horsepower, 4 Poles, Enclosed Motor

11.3.5.1 Low Electricity Price Regions

Table 11.3.21 Representative Unit 5 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 79,551 7,235 69,206 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 78,276 7,145 69,385 -115 48.4 24.8 41.3 12.2

2 95.0 4,958 77,653 7,098 69,699 -428 76.8 23.2 36.9 14.2

11.3.5.2 Small Businesses

Table 11.3.22 Representative Unit 5 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 79,551 7,933 67,671 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 78,276 7,832 67,854 -116 48.9 24.2 46.1 11.0

2 95.0 4,958 77,653 7,780 68,169 -431 77.1 22.9 35.7 12.9

11.3.5.3 Agricultural Sector

Table 11.3.23 Representative Unit 5 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 37,613 3,160 29,897 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 37,009 3,133 30,688 -571 72.3 0.8 141.6 37.4

2 95.0 4,958 36,714 3,120 31,311 -1,194 99.9 0.1 198.8 45.1

11-11
11.3.5.4 Industrial Sector

Table 11.3.24 Representative Unit 5 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 134,548 10,852 93,576 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 132,390 10,706 93,360 174 31.1 42.0 29.4 7.5

2 95.0 4,958 131,336 10,633 93,485 50 53.9 46.1 24.5 8.9

11.3.5.5 Commercial Sector

Table 11.3.25 Representative Unit 5 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 59,646 6,867 69,324 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 58,688 6,782 69,526 -113 48.7 24.4 39.4 12.2

2 95.0 4,958 58,221 6,738 69,838 -425 76.2 23.7 45.9 14.5

11.3.6 Representative Unit 6, Fire Pump, 5 Horsepower, 4 Poles, Enclosed Electric Motor

11.3.6.1 Low Electricity Price Regions

Table 11.3.26 Representative Unit 6 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 680 9 2 712 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 2 768 -46 82.0 0.0 6,431 4,123

2 90.2 792 9 2 821 -96 94.9 0.0 1,310 514

3 91.0 1,239 9 2 1,272 -548 100.0 0.0 30,267 14,660

11-12
11.3.6.2 Small Businesses

Table 11.3.27 Representative Unit 6 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 680 9 2 708 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 3 763 -45 82.0 0.0 7,765 3,730

2 90.2 792 9 2 817 -97 94.9 0.0 1,234 507

3 91.0 1,239 9 2 1,268 -548 100.0 0.0 34,521 14,079

11.3.6.3 Agricultural Sector

Table 11.3.28 Representative Unit 6 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 680 9 2 709 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 2 765 -46 82.0 0.0 7,100 4,804

2 90.2 792 9 2 818 -96 94.9 0.0 1,428 520

3 91.0 1,239 9 2 1,270 -548 100.0 0.0 26,354 15,859

11.3.6.4 Industrial Sector

Table 11.3.29 Representative Unit 6 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 680 9 2 709 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 2 765 -46 82.0 0.0 7,351 4,932

2 90.2 792 9 2 818 -96 94.9 0.0 1,467 521

3 91.0 1,239 9 2 1,270 -548 100.0 0.0 25,819 16,277

11-13
11.3.6.5 Commercial Sector

Table 11.3.30 Representative Unit 6 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 680 9 2 714 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 3 770 -46 82.0 0.0 7,376 3,382

2 90.2 792 9 2 823 -96 94.9 0.0 1,140 502

3 91.0 1,239 9 3 1,275 -548 100.0 0.0 34,558 13,323

11.3.7 Representative Unit 7, Fire Pump, 30 Horsepower, 4 Poles, Enclosed Electric Motor

11.3.7.1 Low Electricity Price Regions

Table 11.3.31 Representative Unit 7 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 53 13 2,410 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 13 2,526 -93 80.7 0.0 1,089 375

2 94.1 2,606 52 13 2,784 -319 87.4 0.0 4,530 1,343

3 94.5 3,938 52 12 4,112 -1,647 100.0 0.0 12,115 2,777

11.3.7.2 Small Businesses

Table 11.3.32 Representative Unit 7 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 53 13 2,386 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 13 2,502 -93 80.7 0.0 1,027 362

2 94.1 2,606 52 13 2,760 -319 87.4 0.0 2,894 1,287

3 94.5 3,938 52 13 4,089 -1,648 100.0 0.0 10,367 2,740

11-14
11.3.7.3 Agricultural Sector

Table 11.3.33 Representative Unit 7 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 53 12 2,395 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 12 2,511 -93 80.7 0.0 1,239 392

2 94.1 2,606 52 12 2,769 -319 87.4 0.0 3,741 1,442

3 94.5 3,938 52 12 4,097 -1,647 100.0 0.0 15,341 2,854

11.3.7.4 Industrial Sector

Table 11.3.34 Representative Unit 7 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 54 12 2,395 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 12 2,511 -93 80.7 0.0 1,140 391

2 94.1 2,606 52 12 2,768 -319 87.4 0.0 4,473 1,431

3 94.5 3,938 52 12 4,097 -1,647 100.0 0.0 13,093 2,846

11.3.7.5 Commercial Sector

Table 11.3.35 Representative Unit 7 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 53 14 2,426 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 14 2,541 -93 80.7 0.0 1,060 354

2 94.1 2,606 52 14 2,798 -318 87.4 0.0 2,745 1,242

3 94.5 3,938 52 13 4,127 -1,646 100.0 0.0 9,908 2,706

11-15
11.3.8 Representative Unit 8, Fire Pump, 75 Horsepower, 4 Poles, Enclosed Electric Motor

11.3.8.1 Low Electricity Price Regions

Table 11.3.36 Representative Unit 8 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 130 29 4,621 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 26 5,082 -370 80.3 0.0 511 149

2 95.8 5,504 128 28 5,892 -1,104 90.5 0.0 4,038 931

3 96.2 6,963 127 25 7,313 -2,524 100.0 0.0 3,292 723

11.3.8.2 Small Businesses

Table 11.3.37 Representative Unit 8 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 130 30 4,573 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 27 5,041 -375 80.3 0.0 481 148

2 95.8 5,504 128 29 5,847 -1,105 90.5 0.0 3,768 919

3 96.2 6,963 127 26 7,273 -2,531 100.0 0.0 3,483 719

11.3.8.3 Agricultural Sector

Table 11.3.38 Representative Unit 8 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 131 29 4,616 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 26 5,076 -370 80.3 0.0 485 149

11-16
2 95.8 5,504 128 28 5,887 -1,104 90.5 0.0 5,439 934

3 96.2 6,963 127 25 7,308 -2,525 100.0 0.0 3,107 726

11.3.8.4 Industrial Sector

Table 11.3.39 Representative Unit 8 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 131 29 4,614 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 26 5,075 -370 80.3 0.0 508 149

2 95.8 5,504 128 28 5,885 -1,104 90.5 0.0 5,897 936

3 96.2 6,963 127 25 7,306 -2,525 100.0 0.0 3,396 729

11.3.8.5 Commercial Sector

Table 11.3.40 Representative Unit 8 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 130 33 4,688 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 30 5,147 -368 80.3 0.0 398 146

2 95.8 5,504 127 32 5,958 -1,102 90.5 0.0 3,047 893

3 96.2 6,963 127 29 7,377 -2,522 100.0 0.0 3,637 709

11-17
11.3.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 Horsepower, 

Four Poles, Enclosed Motor 

11.3.9.1 Low Electricity Price Regions

Table 11.3.41 Representative Unit 9 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,079 831 6,092 N/A 0.0 0.0 N/A N/A

1 87.5 678 7,430 774 5,679 145 0.0 34.8 0.6 0.4

2 89.5 731 7,290 778 5,640 173 12.1 57.0 14.6 1.9

3 90.2 789 7,219 784 5,646 165 34.2 64.4 32.3 3.5

4 91.0 1,237 7,132 792 6,039 -225 79.1 20.5 71 15.2

11.3.9.2 Small Businesses

Table 11.3.42 Representative Unit 9 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,079 900 6,042 N/A 0.0 0.0 N/A N/A

1 87.5 678 7,430 837 5,633 144 0.0 34.8 0.6 0.4

2 89.5 731 7,290 841 5,594 171 12.5 56.7 18.0 1.8

3 90.2 789 7,219 846 5,600 163 35.6 63.1 33.9 3.3

4 91.0 1,237 7,132 853 5,994 -227 79.6 20.0 103 14.1

11-18
11.3.9.3 Agricultural Sector

Table 11.3.43 Representative Unit 9 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 4,291 461 4,092 N/A 0.0 0.0 N/A N/A

1 87.5 678 3,940 433 3,862 79 0.1 34.7 1.2 0.8

2 89.5 731 3,864 444 3,916 42 35.0 34.2 18.4 3.6

3 90.2 789 3,826 453 3,984 -25 72.9 25.8 60.0 7.6

4 91.0 1,237 3,779 464 4,446 -485 98.1 1.4 149 37.8

11.3.9.4 Industrial Sector

Table 11.3.44 Representative Unit 9 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 14,971 1,308 7,568 N/A 0.0 0.0 N/A N/A

1 87.5 678 13,770 1,212 7,037 185 0.0 34.8 0.4 0.2

2 89.5 731 13,521 1,210 6,977 226 7.9 61.3 8.6 1.3

3 90.2 789 13,390 1,212 6,970 231 21.1 77.7 32.0 3.7

4 91.0 1,237 13,228 1,214 7,347 -143 71.2 28.4 180 16.8

11.3.9.5 Commercial Sector

Table 11.3.45 Representative Unit 9 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 5,715 763 6,338 N/A 0.0 0.0 N/A N/A

1 87.5 678 5,257 711 5,905 152 0.0 34.8 0.6 0.4

2 89.5 731 5,156 717 5,860 183 11.8 57.4 20.5 2.0

3 90.2 789 5,106 724 5,865 178 33.0 65.7 35.7 3.1

11-19
4 91.0 1,237 5,044 731 6,254 -209 78.5 21.1 72 13.3

11.3.10Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30 Horsepower, 

Four Poles, Enclosed Motor 

11.3.10.1 Low Electricity Price Regions

Table 11.3.46 Representative Unit 10 Low Electricity Price Regions LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 48,394 4,440 42,713 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 47,178 4,334 42,154 117 6.6 15.5 20.8 5.3

2 93.6 2,300 45,999 4,241 41,405 758 4.4 80.9 5.1 1.7

3 94.1 2,559 45,934 4,244 41,695 468 31.0 69.0 15.4 4.5

4 94.5 3,891 45,777 4,241 42,989 -825 86.5 13.5 112 17.7

11.3.10.2 Small Businesses

Table 11.3.47 Representative Unit 10 Small Businesses LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 48,394 4,869 41,736 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 47,178 4,753 41,198 113 6.7 15.4 17.1 4.8

2 93.6 2,300 45,999 4,649 40,458 747 4.4 80.9 4.3 1.5

3 94.1 2,559 45,934 4,652 40,738 467 31.5 68.5 14.6 4.1

4 94.5 3,891 45,777 4,647 42,024 -819 86.3 13.7 102 15.9

11-20
11.3.10.3 Agricultural Sector

Table 11.3.48 Representative Unit 10 Agricultural Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 22,984 1,953 18,562 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 22,439 1,911 18,632 -17 14.9 7.2 44.1 12.8

2 93.6 2,300 21,863 1,878 18,458 131 21.1 64.2 12.0 4.6

3 94.1 2,559 21,843 1,887 18,787 -198 81.4 18.6 66.7 14.7

4 94.5 3,891 21,772 1,892 20,160 -1,571 99.8 0.2 350 62.1

11.3.10.4 Industrial Sector

Table 11.3.49 Representative Unit 10 Industrial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 80,227 6,498 57,028 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 78,282 6,347 56,163 193 4.8 17.3 16.2 3.7

2 93.6 2,300 76,298 6,202 55,042 1,148 1.7 83.6 2.1 1.1

3 94.1 2,559 76,212 6,206 55,327 863 20.4 79.7 16.4 3.0

4 94.5 3,891 75,958 6,197 56,578 -388 70.4 29.6 70 11.3

11.3.10.5 Commercial Sector

Table 11.3.50 Representative Unit 10 Commercial Sector LCC Results

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 37,094 4,293 43,073 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 36,140 4,187 42,481 128 6.0 16.0 17.1 5.0

2 93.6 2,300 35,241 4,097 41,729 770 4.4 80.9 4.6 1.7

3 94.1 2,559 35,186 4,100 42,014 486 31.4 68.6 16.9 4.6

11-21
4 94.5 3,891 35,063 4,097 43,305 -805 85.3 14.7 109 17.6

11-22
REFERENCES

1

 Ibbotson Associates, SBBI Valuation Edition 2009 Yearbook. 2009. Chicago, IL 

2

 Damodaran Online. The Data Page: Cost of Capital by Industry Sector, 2006. 

<http://pages.stern.nyu.edu/~adamodar/> 

11-23

 
12-i 

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION ......................................................................................................... 12-1

12.2 METHODOLOGY ........................................................................................................ 12-1

12.2.1 Phase I: Industry Profile..................................................................................... 12-2

12.2.2 Phase II: Industry Cash-Flow Analysis and Interview Guide ............................ 12-2

12.2.2.1 Industry Cash-Flow Analysis ....................................................................... 12-2

12.2.2.2 Interview Guides .......................................................................................... 12-3

12.2.3 Phase III: Subgroup Analysis............................................................................. 12-3

12.2.3.1 Manufacturing Interviews ............................................................................ 12-3

12.2.3.2 Revised Industry Cash-Flow Analysis ......................................................... 12-4

12.2.3.3 Small-Business Manufacturer Subgroup ..................................................... 12-4

12.2.3.4 Manufacturing Capacity Impact .................................................................. 12-5

12.2.3.5 Employment Impact ..................................................................................... 12-5

12.2.3.6 Cumulative Regulatory Burden ................................................................... 12-5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES .......................................... 12-5

12.3.1 Efficiency Levels above Premium Efficiency Levels ........................................ 12-6

12.3.2 Increase in the Equipment Repair ...................................................................... 12-6

12.3.3 Enforcement ....................................................................................................... 12-7

12.4 GRIM INPUTS AND ASSUMPTIONS ........................................................................ 12-7

12.4.1 Overview of the GRIM ...................................................................................... 12-7

12.4.2 Sources for GRIM Inputs ................................................................................... 12-8

12.4.2.1 Corporate Annual Reports ........................................................................... 12-8

12.4.2.2 Standard and Poor Credit Ratings ................................................................ 12-8

12.4.2.3 Shipment Model ........................................................................................... 12-8

12.4.2.4 Engineering Analysis ................................................................................... 12-8

12.4.2.5 Manufacturer Interviews .............................................................................. 12-9

12.4.3 Financial Parameters .......................................................................................... 12-9

12.4.4 Corporate Discount Rate .................................................................................. 12-10

12.4.5 Trial Standard Levels ....................................................................................... 12-12

12.4.6 NIA Shipment Forecast.................................................................................... 12-13

12.4.7 Production Costs .............................................................................................. 12-13

12.4.8 Product and Capital Conversion Costs ............................................................ 12-15

12.4.9 Markup Scenarios ............................................................................................ 12-18

12.4.9.1 Flat Markup Scenario ................................................................................. 12-18

12.4.9.2 Two-Tiered Markup Scenario .................................................................... 12-19

12.4.9.3 Preservation of Operating Profit Markup Scenario ................................... 12-21

12.5 INDUSTRY FINANCIAL IMPACTS ........................................................................ 12-24

12.5.1 Impacts on Industry Net Present Value ........................................................... 12-24

12.5.2 Impacts on Annual Cash Flow ......................................................................... 12-25

12.6 IMPACTS ON MANUFACTURER SUBGROUPS ................................................... 12-28

12.6.1 Impacts on Small Business Manufacturers ...................................................... 12-28

12.6.1.1 Description and Estimated Number of Small Entities Regulated .............. 12-28

12.6.1.2 Comparison Between Large and Small Entities ........................................ 12-29
12-ii

12.7 OTHER IMPACTS ...................................................................................................... 12-31

12.7.1 Employment ..................................................................................................... 12-31

12.7.2 Production Capacity ......................................................................................... 12-33

12.7.3 Cumulative Regulatory Burden ....................................................................... 12-34

12.7.3.1 DOE Regulations for Other Equipment Produced by Electric Motors 

Manufacturers .............................................................................................................. 12-34

12.7.3.2 Other Regulations That Could Impact Electric Motors Manufacturers ..... 12-39

12.8 CONCLUSION ............................................................................................................ 12-39

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by This 

Rulemaking .................................................................................................................... 12-4

Table 12.4.1 GRIM Financial Parameters Based on 2004–2011 Weighted Company Financial 

Data ................................................................................................................................ 12-9

Table 12.4.2 GRIM Revised Electric Motor Industry Financial Parameters ........................... 12-10

Table 12.4.3 Cost of Equity Calculation .................................................................................. 12-11

Table 12.4.4 Cost of Debt Calculation..................................................................................... 12-11

Table 12.4.5 Trial Standard Levels for Electric Motors .......................................................... 12-12

Table 12.4.6 Manufacturer Production Cost Breakdown (2013$) for NEMA Design B, 5 

Horsepower, 4 Pole, Enclosed Electric Motor ............................................................. 12-13

Table 12.4.7 Manufacturer Production Cost Breakdown (2013$) for NEMA Design B, 30 

Horsepower, 4 Pole, Enclosed Electric Motor ............................................................. 12-13

Table 12.4.8 Manufacturer Production Cost Breakdown (2013$) for NEMA Design B, 75 

Horsepower, 4 Pole, Enclosed Electric Motor ............................................................. 12-14

Table 12.4.9 Manufacturer Production Cost Breakdown (2013$) for Brake, 5 Horsepower, 4 

Pole, Enclosed Electric Motor ..................................................................................... 12-14

Table 12.4.10 Manufacturer Production Cost Breakdown (2013$) for Brake, 30 Horsepower, 4 

Pole, Enclosed Electric Motor ..................................................................................... 12-14

Table 12.4.11 Manufacturer Production Cost Breakdown (2013$) for NEMA Design C, 5 

Horsepower, 4 Pole, Enclosed Electric Motor ............................................................. 12-14

Table 12.4.12 Manufacturer Production Cost Breakdown (2013$) for NEMA Design C, 50 

Horsepower, 4 Pole, Enclosed Electric Motor ............................................................. 12-14

Table 12.4.13 Manufacturer Production Cost Breakdown (2013$) for Fire Pump, 5 Horsepower, 

4 Pole, Enclosed Electric Motor .................................................................................. 12-15

Table 12.4.14 Manufacturer Production Cost Breakdown (2013$) for Fire Pump, 30 Horsepower, 

4 Pole, Enclosed Electric Motor .................................................................................. 12-15

Table 12.4.15 Manufacturer Production Cost Breakdown (2013$) for Fire Pump, 75 Horsepower, 

4 Pole, Enclosed Electric Motor .................................................................................. 12-15

Table 12.4.16 Product and Capital Conversion Costs for NEMA Design B, 5 Horsepower, 4 

Pole, Closed, Electric Motors by TSL ......................................................................... 12-16

Table 12.4.17 Product and Capital Conversion Costs for NEMA Design B, 30 Horsepower, 4 

Pole, Closed, Electric Motors by TSL ......................................................................... 12-16

Table 12.4.18 Product and Capital Conversion Costs for NEMA Design B, 75 Horsepower, 4 

Pole, Closed, Electric Motors by TSL ......................................................................... 12-17
12-iii

Table 12.4.19 Product and Capital Conversion Costs for Brake, 5 Horsepower, 4 Pole, Closed, 

Electric Motors by TSL ............................................................................................... 12-17

Table 12.4.20 Product and Capital Conversion Costs for Brake, 30 Horsepower, 4 Pole, Closed, 

Electric Motors by TSL ............................................................................................... 12-17

Table 12.4.21 Product and Capital Conversion Costs for NEMA Design C, 5 Horsepower, 4 

Pole, Closed, Electric Motors by TSL ......................................................................... 12-17

Table 12.4.22 Product and Capital Conversion Costs for NEMA Design C, 50 Horsepower, 4 

Pole, Closed, Electric Motors by TSL ......................................................................... 12-17

Table 12.4.23 Product and Capital Conversion Costs for Fire Pump, 5 Horsepower, 4 Pole, 

Closed, Electric Motors by TSL .................................................................................. 12-17

Table 12.4.24 Product and Capital Conversion Costs for Fire Pump, 30 Horsepower, 4 Pole, 

Closed, Electric Motors by TSL .................................................................................. 12-18

Table 12.4.25 Product and Capital Conversion Costs for Fire Pump, 75 Horsepower, 4 Pole, 

Closed, Electric Motors by TSL .................................................................................. 12-18

Table 12.4.26 Product and Capital Conversion Costs for all Electric Motors ......................... 12-18

Table 12.4.27 Two-Tiered Markups for NEMA Design B, 5 Horsepower, 4 Pole, Closed Electric 

Motors .......................................................................................................................... 12-19

Table 12.4.28 Two-Tiered Markups for NEMA Design B, 30 Horsepower, 4 Pole, Closed 

Electric Motors............................................................................................................. 12-19

Table 12.4.29 Two-Tiered Markups for NEMA Design B, 75 Horsepower, 4 Pole, Closed 

Electric Motors............................................................................................................. 12-20

Table 12.4.30 Two-Tiered Markups for Brake, 5 Horsepower, 4 Pole, Closed Electric Motors 12-

20

Table 12.4.31 Two-Tiered Markups for Brake, 30 Horsepower, 4 Pole, Closed Electric Motors

...................................................................................................................................... 12-20

Table 12.4.32 Two-Tiered Markups for NEMA Design C, 5 Horsepower, 4 Pole, Closed Electric 

Motors .......................................................................................................................... 12-20

Table 12.4.33 Two-Tiered Markups for NEMA Design C, 75 Horsepower, 4 Pole, Closed 

Electric Motors............................................................................................................. 12-20

Table 12.4.34 Two-Tiered Markups for Fire Pump, 5 Horsepower, 4 Pole, Closed Electric 

Motors .......................................................................................................................... 12-21

Table 12.4.35 Two-Tiered Markups for Fire Pump, 30 Horsepower, 4 Pole, Closed Electric 

Motors .......................................................................................................................... 12-21

Table 12.4.36 Two-Tiered Markups for Fire Pump, 75 Horsepower, 4 Pole, Closed Electric 

Motors .......................................................................................................................... 12-21

Table 12.4.37 Preservation of Operating Profit Markups for NEMA Design B, 5 Horsepower, 4 

Pole, Closed Electric Motors ....................................................................................... 12-22

Table 12.4.38 Preservation of Operating Profit Markups for NEMA Design B, 30 Horsepower, 4 

Pole, Closed Electric Motors ....................................................................................... 12-22

Table 12.4.39 Preservation of Operating Profit Markups for NEMA Design B, 75 Horsepower, 4 

Pole, Closed Electric Motors ....................................................................................... 12-22

Table 12.4.40 Preservation of Operating Profit Markups for Brake, 5 Horsepower, 4 Pole, Closed 

Electric Motors............................................................................................................. 12-23

Table 12.4.41 Preservation of Operating Profit Markups for Brake, 30 Horsepower, 4 Pole, 

Closed Electric Motors ................................................................................................ 12-23
12-iv

Table 12.4.42 Preservation of Operating Profit Markups for NEMA Design C, 5 Horsepower, 4 

Pole, Closed Electric Motors ....................................................................................... 12-23

Table 12.4.43 Preservation of Operating Profit Markups for NEMA Design C, 50 Horsepower, 4 

Pole, Closed Electric Motors ....................................................................................... 12-23

Table 12.4.44 Preservation of Operating Profit Markups for Fire Pump, 5 Horsepower, 4 Pole, 

Closed Electric Motors ................................................................................................ 12-23

Table 12.4.45 Preservation of Operating Profit Markups for Fire Pump, 30 Horsepower, 4 Pole, 

Closed Electric Motors ................................................................................................ 12-24

Table 12.4.46 Preservation of Operating Profit Markups for Fire Pump, 75 Horsepower, 4 Pole, 

Closed Electric Motors ................................................................................................ 12-24

Table 12.5.1 Changes in Industry Net Present Value for Electric Motors – Flat Markup Scenario

...................................................................................................................................... 12-25

Table 12.5.2 Changes in Industry Net Present Value for Electric Motors – Two-Tiered Markup 

Scenario........................................................................................................................ 12-25

Table 12.5.3 Changes in Industry Net Present Value for Electric Motors – Preservation of 

Operating Profit Markup Scenario ............................................................................... 12-25

Table 12.6.1 Estimated Capital and Product Conversion Costs as a Percentage of Annual Capital 

Expenditures and R&D Expense ................................................................................. 12-30

Table 12.7.1 Potential Changes in the Total Number of All Domestic Electric Motor Production 

Workers in 2016 ........................................................................................................... 12-32

Table 12.7.2 Other DOE and Federal Actions Affecting the Electric Motors Industry .......... 12-36

Table 12.7.3 DOE Regulations on Equipment for Which Electric Motor Manufacturers Hold 

Significant Market Share ............................................................................................. 12-38

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow ............................................................ 12-7

Figure 12.5.1 Annual Industry Free Cash Flows for Electric Motors - Flat Markup Scenario 12-27

Figure 12.5.2 Annual Industry Free Cash Flows for Electric Motors – Two-Tiered Markup 

Scenario........................................................................................................................ 12-27

Figure 12.5.3 Annual Industry Free Cash Flows for Electric Motors - Preservation of Operating 

Profit Markup Scenario ................................................................................................ 12-28
12-1 

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of 

Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers 

and on the consumers of the products subject to such a standard.” (42 U.S.C. 6312(a)(6)(B)(i)) 

The law also calls for an assessment of the impact of any lessening of competition as determined 

in writing by the Attorney General. Id. DOE conducted a manufacturer impact analysis (MIA) to 

estimate the financial impact of new and amended energy conservation standards on 

manufacturers of electric motors, and assessed the impact of such standards on direct 

employment, manufacturing capacity, and the cumulative regulatory burden on electric motor 

manufacturers. 

The MIA has both quantitative and qualitative aspects. The quantitative part of the MIA 

primarily relies on the Government Regulatory Impact Model (GRIM), an industry cash-flow 

model adapted for the equipment in this rulemaking. The GRIM inputs include information on 

industry cost structure, shipments, and pricing strategies. The GRIM’s key output is the industry 

net present value (INPV). The model estimates the financial impact of more stringent energy 

conservation standards for the equipment by comparing changes in INPV between a base case 

and the various trial standard levels (TSLs) in the standards case. The qualitative part of the MIA 

addresses equipment characteristics, manufacturer characteristics, market and equipment trends, 

as well as the impact of standards on subgroups of manufacturers. 

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of 

preparing an industry characterization for the electric motor industry, including data on market 

share, sales volumes and trends, pricing, employment, and financial structure. In phase II, 

“Industry Cash Flow,” DOE used the GRIM to assess the impacts of new and amended energy 

conservation standards on electric motors. 

In phase II, DOE created a GRIM for electric motors and an interview guide to gather 

information on the potential impacts on manufacturers. DOE presented the MIA results for 

electric motors based on a set of considered TSLs. These TSLs are described in section 12.4.5

below. 

In phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers representing 

more than 75 percent of electric motor sales. Interviewees included large and small 

manufacturers with various market shares and market focus, providing a representative crosssection of the industries. During interviews, DOE discussed financial topics specific to each 

manufacturer and obtained each manufacturer’s view of the industry. The interviews provided 

DOE with valuable information for evaluating the impacts of new and amended energy 

conservation standards on manufacturer cash flows, investment requirements, and employment. 
12-2 

12.2.1 Phase I: Industry Profile

In phase I of the MIA, DOE prepared a profile of the electric motor industry that built 

upon the market and technology assessment prepared for this rulemaking (see chapter 3 of this 

Technical Support Document (TSD)). Before initiating the detailed impact studies, DOE 

collected information on the present and past structure and market characteristics of each 

industry. This information included market share data, unit shipments, manufacturer markups, 

and the cost structure for various manufacturers. The industry profile includes: (1) further detail 

on the overall market and equipment characteristics; (2) estimated manufacturer market shares; 

(3) financial parameters such as net plant, property, and equipment; selling, general and 

administrative (SG&A) expenses; cost of goods sold, etc.; and (4) trends in the number of firms, 

market, and equipment characteristics. The industry profile included a top-down cost analysis of 

electric motor manufacturers that DOE used to derive preliminary financial inputs for the GRIM 

(e.g., revenues, depreciation, SG&A, and research and development (R&D) expenses). 

DOE also used public information to further calibrate its initial characterization of the 

electric motor industry, including Securities and Exchange Commission (SEC) 10–K reports,1

Standard & Poor’s (S&P) stock reports,2

 and corporate annual reports. DOE supplemented this 

public information with data released by privately held companies. 

12.2.2 Phase II: Industry Cash-Flow Analysis and Interview Guide 

Phase II focused on the financial impacts of new and amended energy conservation 

standards on manufacturers of electric motors. More stringent energy conservation standards can 

affect manufacturer cash flows in three distinct ways: (1) create a need for increased investment, 

(2) raise production costs per unit, and (3) alter revenue due to higher per-unit prices and/or 

possible changes in sales volumes. To quantify these impacts, DOE used the GRIM to perform a 

cash-flow analysis for electric motors. In performing these analyses, DOE used the financial 

values derived during phase I and the shipment scenarios used in the national impact analysis 

(NIA). In phase II, DOE performed these preliminary industry cash-flow analyses and prepared 

written guides for manufacturer interviews.

12.2.2.1 Industry Cash-Flow Analysis 

The GRIM uses several factors to determine a series of annual cash flows from the 

announcement year of new and amended energy conservation standards until several years after 

the standards’ compliance date. These factors include annual expected revenues, costs of sales, 

SG&A, taxes, and capital expenditures related to the new and amended standards. Inputs to the 

GRIM include manufacturing production costs, selling prices, and shipments forecasts developed 

in other analyses. DOE derived the manufacturing costs from the engineering analysis and 

information provided by the industry and estimated typical manufacturer markups from public 

financial reports and interviews with manufacturers. DOE developed alternative markup 

scenarios for the GRIM based on discussions with manufacturers. DOE’s shipments analysis, 

presented in chapter 9 of this TSD, provided the basis for the shipment projections in the GRIM. 

The financial parameters were developed using publicly available manufacturer data and were 

revised with information submitted confidentially during manufacturer interviews. The GRIM 

results are compared to base case projections for the industry. The financial impact of new and 
12-3 

amended energy conservation standards is the difference between the discounted annual cash 

flows in the base case and standards case at each TSL.

12.2.2.2 Interview Guides

During phase III of the MIA, DOE interviewed manufacturers to gather information on 

the effects of new and amended energy conservation on revenues and finances, direct 

employment, capital assets, and industry competitiveness. Before the interviews, DOE 

distributed an interview guide for the electric motor industry. The interview guide provided a 

starting point to identify relevant issues and help identify the impacts of new and amended 

energy conservation standards on individual manufacturers or subgroups of manufacturers. Most 

of the information DOE received from these meetings is protected by non-disclosure agreements 

and resides with DOE’s contractors. Before each telephone interview or site visit, DOE provided 

company representatives with an interview guide that included the topics for which DOE sought 

input. The MIA interview topics included (1) key issues to this rulemaking; (2) engineering; (3) 

company overview and organizational characteristics; (4) manufacturer production costs and 

scaling prices; (5) manufacturer markups and profitability; (6) shipment projections and market 

shares; (7) equipment mix; (8) financial parameters; (9) conversion costs; (10) cumulative 

regulatory burden; (11) direct employment impact assessment; (12) exports, foreign competition, 

and outsourcing; (13) consolidation; and (12) impacts on small business. The interview guides 

are presented in Appendix 12A. 

12.2.3 Phase III: Subgroup Analysis

Using average cost and financial assumptions to develop an industry cash-flow model is 

not adequate for assessing differential impacts among a potential subgroup of manufacturers. 

Small manufacturers, niche players, or manufacturers exhibiting a cost structure that differs 

largely from the industry average could be more negatively impacted. During interviews, DOE 

identified one potential manufacturer subgroup (small manufacturers) that could be 

disproportionately impacted by new and amended energy conservation standards. As a result, 

DOE analyzed small business manufacturers as a subgroup. 

12.2.3.1 Manufacturing Interviews

The information gathered in phase I and the cash-flow analysis performed in phase II are 

supplemented with information gathered from manufacturer interviews in phase III. The

interview process provides an opportunity for interested parties to express their views on 

important issues privately, allowing confidential or sensitive information to be considered in the 

rulemaking process. 

DOE used these interviews to tailor the GRIM to reflect unique financial characteristics 

for electric motor manufacturers. DOE contacted companies from its database of manufacturers 

and interviewed small and large companies, subsidiaries and independent firms, and public and 

private corporations to provide an accurate representation of the industry. Interviews were 

scheduled well in advance to provide every opportunity for key individuals to be available for 

comment. Although a written response to the questionnaire was acceptable, DOE sought 
12-4 

interactive interviews, which helped clarify responses and identify additional issues. The 

resulting information provides valuable inputs to the GRIM developed for the equipment classes.

12.2.3.2 Revised Industry Cash-Flow Analysis 

In phase II of the MIA, DOE provided manufacturers with preliminary GRIM input 

financial figures for review and evaluation. During the interviews, DOE requested comments on 

the values it selected for the parameters. DOE revised its industry cash-flow models based on 

this feedback. Section 12.4.3 provides more information on how DOE calculated the parameters. 

12.2.3.3 Small-Business Manufacturer Subgroup

DOE investigated whether small business manufacturers should be analyzed as a 

manufacturer subgroup. DOE used the Small Business Administration (SBA) small business size 

standards published on January 22, 2014, as amended, and the North American Industry 

Classification System (NAICS) code, presented in Table 12.2.1, to determine whether any small 

entities would be affected by the rulemaking.a For the equipment classes under review, the SBA 

bases its small business definition on the total number of employees for a business, its 

subsidiaries, and its parent companies. An aggregated business entity with fewer employees than 

the listed limit is considered a small business.

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by 

This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS

Motor and Generator Manufacturing N/A 1,000 335312

DOE used the National Electrical Manufacturers Association (NEMA)3 member 

directory to identify manufacturers of electric motors. DOE also used information from previous 

rulemakings, UL (Underwriters Laboratories) qualification directories, individual company 

websites, and market research tools (e.g., Hoover’s reports) to create a list of companies that 

potentially manufacture electric motors covered by this rulemaking. Additionally, DOE also 

asked interested parties and industry representatives if they were aware of other small business 

manufacturers. DOE contacted select companies on its list, as necessary, to determine whether 

they met the SBA’s definition of a small business manufacturer of covered electric motors. DOE 

screened out companies that did not offer equipment covered by this rulemaking, did not meet 

the definition of a “small business,” or are completely foreign owned and operated. 

During its research, DOE identified approximately 13 companies that manufacture 

equipment covered by this rulemaking and qualify as small businesses per the applicable SBA 

definition. DOE contacted the small businesses to solicit feedback on the potential impacts of

energy conservation standards. Two of the small businesses consented to being interviewed 

during the MIA interviews. In addition to posing the standard MIA interview questions, DOE 

solicited data from manufacturers on differential impacts that these small companies might 

experience from standards. Because DOE was not able to certify that the rulemaking would not 

have a significant economic impact on a substantial number of small entities, DOE has analyzed 

 a The size standards are available on the SBA’s website at http://www.sba.gov/content/table-small-business-sizestandards
12-5 

small manufacturers as a subgroup. The results of this subgroup analysis are presented in section 

12.6. 

12.2.3.4 Manufacturing Capacity Impact 

One significant outcome of new and amended energy conservation standards could be the 

obsolescence of existing manufacturing assets, including tooling and investment. The 

manufacturer interview guides have a series of questions to help identify impacts of new and 

amended standards on manufacturing capacity, specifically capacity utilization and plant location 

decisions in the United States, with and without new and amended standards; the ability of 

manufacturers to upgrade or remodel existing facilities to accommodate the new requirements; 

the nature and value of any stranded assets; and estimates for any one-time changes to existing 

plant, property, and equipment (PPE). DOE’s estimates of the one-time capital changes and 

stranded assets affect the cash-flow estimates in the GRIM. These estimates can be found in 

section 12.4.8; DOE’s discussion of the capacity impact can be found in section 12.7.2. 

12.2.3.5 Employment Impact 

The impact of new and amended energy conservation standards on employment is an 

important consideration in the rulemaking process. To assess how domestic direct employment 

patterns might be affected, the interviews explored current employment trends in the electric 

motor industry. The interviews also solicited manufacturer views on changes in employment 

patterns that may result from more stringent standards. The employment impacts section of the 

interview guide focused on current employment levels associated with manufacturers at each 

production facility, expected future employment levels with and without new and amended 

standards, and differences in workforce skills and issues related to the retraining of employees. 

The employment impacts are reported in section 12.7.1. 

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to new and amended 

energy conservation standards and other regulatory actions affecting the same equipment. DOE 

analyzed the impact on manufacturers of multiple, equipment-specific regulatory actions. Based 

on its own research and discussions with manufacturers, DOE identified regulations relevant to 

electric motor manufacturers, such as state regulations and other Federal regulations that impact 

other equipment made by the same manufacturers. Discussion of the cumulative regulatory 

burden can be found in section 12.7.3. 

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company 

regarding the energy conservation standard rulemaking?” This question prompts manufacturers 

to identify the issues they believe DOE should explore and discuss further during the interview. 

The following sections describe the most significant issues identified by manufacturers. These 

summaries are provided in aggregate to protect manufacturer confidentiality. 
12-6 

12.3.1 Efficiency Levels above Premium Efficiency Levels

Several manufacturers are concerned with the difficulties associated with increasing 

electric motors above premium efficiency levels. Manufacturers stated that even increasing the 

efficiency of motors to one band above premium efficiency levels would require each 

manufacturer to make a significant capital investment to retool their entire production line. It 

would also require manufacturers to completely redesign almost every motor configuration 

offered, which could take several years of engineering time.

According to manufacturers, another potential problem with setting an electric motors 

standard above premium efficiency levels is that this would misalign U.S. electric motor 

standards with global motor standards (e.g., International Electrotechnical Commission (IEC) 

motor standards). There has been an effort to harmonize global motor standards recently and 

manufacturers are concerned that new U.S. electric motor standards set above premium 

efficiency levels would cause U.S. electric motor markets to be out of synchronization with the 

rest of the world’s efficiency standards.

Several manufacturers also commented they believe any standard requiring die-casting 

copper rotors is infeasible. The two main manufacturer concerns are the rising cost of copper and 

the potential health and safety risks of die-casting copper. Copper prices have fluctuated greatly 

over the past five to ten years and if standards required manufacturers to use copper rotors 

manufacturers would be at the mercy of the volatile copper market. Manufacturers noted that 

motor standards that requiring copper rotors for all electric motors would likely increase the 

price of copper due to the increase in demand from the motors industry. Manufacturers also 

stated that since copper has a much higher melting temperature than aluminum and the pressure 

required to die-cast copper is much higher than aluminum, there is a much greater chance that a 

significant accident or injury could occur with copper than with aluminum. Lastly, several 

manufacturers stated they would not be able to produce copper die-cast rotors in-house and 

therefore would have to outsource this production. Manufacturers went on to say that if the entire 

motor industry was forced to outsource their rotor production, due to copper requirements, there 

would be significant supply chain problems in the motor manufacturing process. In summary, 

manufacturers emphasized during interviews that the capacity to produce copper rotors on a 

large commercial scale does not exist and would be almost impossible to implement in a two

year time period.

12.3.2 Increase in the Equipment Repair

Manufacturers have stated that as energy conservation standards increase, customers are 

more likely to rewind old, less efficient motors, as opposed to purchasing newer more efficient 

compliant motors. Therefore, if motor standards significantly increase the price of motors, 

manufacturers believe rewinding older motors might become a more attractive option for some 

customers. These customers would in turn be using more energy than if they simply purchased a 

currently compliant motor, since rewound motors may not always operate at their original 

efficiency level after being rewound. Manufacturers believe that DOE must take the potential 

consumer rewinding decision into account when deciding on an electric motors standard. 
12-7 

12.3.3 Enforcement

Manufacturers have stated that one of their biggest concerns with additional energy 

conservation standards is the lack of enforcement of current electric motor standards. The large 

domestic manufacturers have stated they comply with the current electric motor regulations and 

will continue to comply with any future standards. However, these manufacturers believe there 

are several foreign motor manufacturers that do not comply with the current electric motor 

regulations and certainly will not comply with any future standards if the efficiency standards are 

increased. This could cause compliant manufacturers to be placed at a competitive disadvantage, 

since complying with any increased efficiency standards could be very costly. Some domestic 

manufacturers believe the most cost effective way to reduce energy consumption of electric 

motors is to more strictly enforce the existing electric motor standards rather than increase the 

efficiency standards of electric motors.

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to new and 

amended energy conservation standards. DOE relies on several sources to obtain inputs for the 

GRIM. Data and assumptions from these sources are then fed into an accounting model that 

calculates the industry cash flow both with and without new and amended energy conservation 

standards. 

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4.1, is an annual cash-flow 

analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial 

information as inputs, and accepts a set of regulatory conditions such as changes in costs, 

investments, and associated margins. The GRIM spreadsheet uses a number of inputs to arrive at 

a series of annual cash flows, beginning with the base year of the analysis, 2014, and continuing 

to 2045. The model calculates the INPV by summing the stream of annual discounted cash flows 

during this period and adding a discounted terminal value.4

Figure 12.4.1 Using the GRIM to Calculate Cash Flow 

The GRIM projects cash flows using standard accounting principles and compares 

changes in INPV between the base case and the standard case scenarios induced by new and 
12-8 

amended energy conservation standards. The difference in INPV between the base case and the 

standard case(s) represents the estimated financial impact of the new and amended standards on 

manufacturers. Appendix 12B provides more technical details and user information for the 

GRIM.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash 

flow. These sources include corporate annual reports, company profiles, Census data, credit 

ratings, the shipments model, the engineering analysis, and the manufacturer interviews. 

12.4.2.1 Corporate Annual Reports

Corporate annual reports to the SEC (SEC 10-Ks) provided many of the initial financial 

inputs to the GRIM. These reports exist for publicly held companies and are freely available to 

the general public. DOE developed initial financial inputs to the GRIM by examining the annual 

SEC 10-K reports filed by publicly-traded manufacturers that produce electric motors, among 

other equipment. Since these companies do not provide detailed information about their 

individual production lines, DOE used financial information at the parent company level as its 

initial estimates of the financial parameters in the GRIM analysis. These figures were later 

revised using feedback from interviews to be representative of electric motor manufacturing. 

DOE used corporate annual reports to derive the following initial inputs to the GRIM: 

• Tax rate

• Working capital 

• SG&A

• R&D

• Depreciation

• Capital expenditures

• Net PPE

12.4.2.2 Standard and Poor Credit Ratings 

S&P provides independent credit ratings, research, and financial information. DOE relied 

on S&P reports to determine the industry’s average cost of debt when calculating the cost of 

capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the NIA. 

The model relied on historical shipments data for electric motors. Chapter 9 of the TSD describes 

the methodology and analytical model DOE used to forecast shipments. 

12.4.2.4 Engineering Analysis 

The engineering analysis establishes the relationship between manufacturer production 

cost (MPC) and energy efficiency for electric motors covered in this rulemaking. DOE began its 
12-9 

analysis by conducting industry research to determine equipment class groupings, select baseline 

electric motors, and select representative electric motors for further testing and analysis. Next 

DOE selected specific efficiency levels based on the efficiency levels published in the tables 

contained in NEMA Standards Publication MG 1-2011, Motors and Generators. DOE generated

a bill of materials (BOM) either by tearing down representative electric motors or by using a 

computer software model. DOE also estimated labor costs based on tear downs or computer 

software modeling. Finally, DOE calculated the necessary scrap costs and overhead costs 

(including depreciation) based on markups applied to the BOM to arrive at a final MPC for all 

directly analyzed representative electric motors across all analyzed efficiency levels. See chapter 

5 of this TSD for a complete discussion of the engineering analysis.

12.4.2.5 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative crosssection of manufacturers. DOE also interviewed manufacturers representing a significant portion 

of sales in every equipment class. During these discussions, DOE obtained information to 

determine and verify GRIM input assumptions in each industry. Key topics discussed during the 

interviews and reflected in the GRIM include:

• capital conversion costs (one-time investments in PPE);

• product conversion costs (one-time investments in research, product development, 

testing, certification, and marketing);

• product cost structure, or the portion of the MPCs related to materials, labor, overhead, 

and depreciation costs; 

• possible profitability impacts;

• impacts on small businesses; and

• cost-efficiency curves calculated in the engineering analysis.

12.4.3 Financial Parameters

Table 12.4.1 below provides financial parameters for six public companies engaged in 

manufacturing and selling electric motors. The values listed are averages over an 8-year period 

(2004 to 2011).

Table 12.4.1 GRIM Financial Parameters Based on 2004–2011 Weighted Company 

Financial Data 

Parameter Weighted 

Average

Manufacturer

A B C D E F

Tax Rate % of taxable income 33.3% 37.5% 32.7% 28.0% 30.9% 28.1% 26.5%

Working Capital % of revenues 20.7% 24.8% 23.6% 20.2% 20.1% 7.5% 52.8%

SG&A % of revenues 17.0% 16.5% 13.9% 18.2% 21.3% 16.1% 15.8%

R&D % of revenues 2.6% 2.2% 2.2% 2.5% 2.0% 5.2% 2.3%

Depreciation % of revenues 4.2% 4.0% 3.2% 2.8% 3.3% 4.3% 4.1%

Capital Expenditures % of revenues 3.6% 2.6% 2.3% 3.1% 2.8% 4.1% 6.5%

Net PPE % of revenues 22.5% 21.7% 18.8% 20.1% 15.6% 14.7% 46.1%

During interviews, electric motor manufacturers were asked to provide their own figures 

for the parameters listed in Table 12.4.1. Where applicable, DOE adjusted the parameters in the 
12-10 

GRIM using this feedback, more recent data from publicly traded companies, and values used 

during the small electric motors rulemaking at 75 FR 10874 (March 9, 2010) to reflect the 

current electric motor industry. Table 12.4.2 presents the revised parameters used for electric 

motor manufacturers for this final rule.

Table 12.4.2 GRIM Revised Electric Motor Industry Financial Parameters

Parameter Revised Estimates

Tax Rate % of taxable income 33.3%

Working Capital % of revenues 16.0%

SG&A % of revenues 15.0%

R&D % of revenues 4.8%

Depreciation % of revenues 4.2%

Capital Expenditures % of revenues 4.8%

Net PPE % of revenues 18.4%

12.4.4 Corporate Discount Rate 

DOE used the weighted-average cost of capital (WACC) as the discount rate to calculate 

the INPV. A company’s assets are financed by a combination of debt and equity. The WACC is 

the total cost of debt and equity weighted by their respective proportions in the capital structure 

of the industry. DOE estimated the WACC for the electric motor industry based on several 

representative companies, using the following formula: 

WACC = After-Tax Cost of Debt x (Debt Ratio) + Cost of Equity x (Equity Ratio) Eq. 1 

The cost of equity is the rate of return that equity investors (including, potentially, the 

company) expect to earn on a company’s stock. These expectations are reflected in the market 

price of the company’s stock. The capital asset pricing model (CAPM) provides one widely used 

means to estimate the cost of equity. According to the CAPM, the cost of equity (expected 

return) is:

Cost of Equity = Riskless Rate of Return + β x Risk Premium Eq. 2 

where:

Riskless rate of return is the rate of return on a “safe” benchmark investment, typically 

considered the short-term Treasury Bill (T-Bill) yield.

Risk premium is the difference between the expected return on stocks and the riskless 

rate.

Beta (β) is the correlation between the movement in the price of the stock and that of the 

broader market. In this case, Beta equals one if the stock is perfectly correlated with the S&P 500 

market index. A Beta lower than one means the stock is less volatile than the market index.

DOE determined that the industry average cost of equity for the electric motor industry is 

13.4 percent. 
12-11 

Table 12.4.3 Cost of Equity Calculation 

Parameter

IndustryWeighted 

Average

%

Manufacturer

A B C D E F 

(1) Average Beta 1.4 na 1.27 0.83 1.21 1.58 na

(2) Yield on 10-Year T-Bill (1928-2011) 5.2 - - - - -

(3) Market Risk Premium (1928-2011) 6.0 - - - - -

Cost of Equity (2)+[(1)*(3)] 13.4 - - - - -

Equity/Total Capital 1.2 1.28 1.30 0.95 0.90 0.62 0.63

Bond ratings are a tool to measure default risk and arrive at a cost of debt. Each bond 

rating is associated with a particular spread. One way of estimating a company’s cost of debt is 

to treat it as a spread (usually expressed in basis points) over the risk-free rate. DOE used this 

method to calculate the cost of debt for all six manufacturers by using S&P ratings and adding 

the relevant spread to the risk-free rate.

In practice, investors use a variety of different maturity Treasury bonds to estimate the 

risk-free rate. DOE used the 10-year Treasury bond return because it captures long-term inflation 

expectations and is less volatile than short-term rates. The risk free rate is estimated to be 

approximately 5.2 percent, which is the average 10-year Treasury bond return between 1928 and 

2011. 

For the cost of debt, S&P’s Credit Services provided the average spread of corporate 

bonds for the six public manufacturers. DOE added the industry-weighted average spread to the 

average T-Bill rate. Since proceeds from debt issuance are tax deductible, DOE adjusted the 

gross cost of debt by the industry average tax rate to determine the net cost of debt for the 

industry. Table 12.4.4 presents the derivation of the cost of debt and the capital structure of the 

industry (i.e., the debt ratio (debt/total capital)).

Table 12.4.4 Cost of Debt Calculation 

Parameter

IndustryWeighted 

Average

%

Manufacturer

A B C D E F 

S&P Bond Rating - B+ AAA AAA A A+ AAA

(1) Yield on 10-Year T-Bill 

(1928-2011) 5.2% - - - - - - 

(2) Gross Cost of Debt 7.0% 9.0% 5.7% 5.7% 6.2% 6.1% 5.7%

(3) Tax Rate 33.2% 37.5% 32.7% 28.0% 30.9% 28.1% 26.5%

Net Cost of Debt (2) x (1-(3)) 4.7% - - - - - - 

Debt/Total Capital 94.1% 160.6% 65.1% 38.6% 37.0% 30.1% 28.7%

Using public information for these six companies, the initial estimate for the electric 

motor industry’s WACC was approximately 9.9 percent. Subtracting an inflation rate of 3.1 

percent between 1928 and 2011, the inflation-adjusted WACC and the initial estimate of the 

discount rate used in the straw-man GRIM was 6.8 percent. DOE asked for feedback on the 6.8 
12-12 

percent discount during manufacturer interviews and used this feedback and the WACC used in 

the small electric motors rulemaking to revise this WACC to be 9.1 percent for electric motor 

manufacturers.

12.4.5 Trial Standard Levels

DOE developed TSLs for electric motors. Consistent with the engineering analysis, DOE 

analyzed three equipment class groups (ECGs), consisting of 10 representative units. Table 

12.4.5 shows the efficiency levels at each TSL for the ECGs analyzed by DOE. For more 

information regarding the creation of TSL see chapter 5 of this TSD.

Table 12.4.5 Trial Standard Levels for Electric Motors

ECG Equipment Class 

Group Description TSL 1 TSL 2 TSL 3 TSL 4

1a NEMA Design A & B:

1-500 horsepower EL 1 EL 2 EL 3 EL 4

1b Brake:

1-500 horsepower EL 1 EL 2 EL 3 EL 4

2 NEMA Design C:

1-200 horsepower EL 1 EL 1 EL 2 EL 2

3 Fire Pump:

1-500 horsepower Baseline Baseline EL 1 EL 3

TSL 1 represents efficiency levels equivalent to NEMA MG 1 table 12-11 for all covered 

NEMA Design A and B (ECG 1a) and brake (ECG 1b) electric motors; efficiency levels 

equivalent to NEMA MG 1 table 12-12 for all covered NEMA Design C (ECG 2) electric 

motors; and no standards for all covered fire pump (ECG 3) motors. 

TSL 2 represents efficiency levels equivalent to NEMA MG 1 table 12-12 for all covered 

NEMA Design A and B (ECG 1a), brake (ECG 1b), and NEMA Design C (ECG 2) electric 

motors and no standards for all covered fire pump (ECG 3) motors. 

TSL 3 represents efficiency levels equivalent to NEMA MG 1 table 12-12 for all covered 

fire pump (ECG 3) electric motors; efficiency levels equivalent to one band above NEMA MG 1

table 12-12 for all covered NEMA Design A and B (ECG 1a) electric motors, all covered brake 

(ECG 1b) electric motors, and NEMA Design C (ECG 2) electric motors between 25 to 200 

horsepower; efficiency levels equivalent to two bands above NEMA MG 1 table 12-12 for 

NEMA Design C (ECG 2) electric motors between 1 and 20 horsepower. All NEMA Design C 

electric motors will require the use of copper rotors at this TSL; however, no other motors will 

require copper rotors.

TSL 4 represents max-tech efficiency levels for all covered electric motors. This TSL is 

equivalent to one band above NEMA MG 1 table 12-12 for NEMA Design C (ECG 2) electric 

motors between 25 and 200 horsepower and efficiency levels equivalent to two bands above 

NEMA MG 1 table 12-12 for all covered NEMA Design A and B (ECG 1a) electric motors, all 

covered brake (ECG 1b) electric motors, NEMA Design C (ECG 2) electric motors between 1 

and 20 horsepower, and all covered fire pump (ECG 3) electric motors. All electric motors 

require the use of copper rotors at this TSL.
12-13 

12.4.6 NIA Shipment Forecast

The GRIM estimates manufacturer revenues based on total unit-shipment forecasts and 

the distribution of these values by efficiency level. Changes in the efficiency mix at each 

standard level are a key driver of manufacturer finances. For this analysis, the GRIM used the 

NIA’s annual shipment forecasts from 2014 to 2045, the end of the analysis period. The 

shipments analysis assumes that growth in electric motors shipments will be driven by private 

fixed investment in specific equipment and structure. The assumptions and methodology that 

drive this analysis are described in chapter 9 of this final rule TSD.

12.4.7 Production Costs

During the engineering analysis, DOE developed the MPCs for the representative units at 

each EL analyzed either by teardowns or by software modeling. For units DOE tore down, DOE 

purchased, tested and then tore down a motor to create a BOM for the motor. If DOE could not 

find or purchase specific representative units at specific efficiency levels, DOE created a BOM 

based on a computer software model for a specific motor that complies with the associated 

efficiency level. Once DOE created a BOM for a specific motor, either by tear downs or software 

modeling, DOE then estimated the labor hours and the associated scrap and overhead costs 

necessary to produce a motor with that BOM. DOE was then able to create an aggregated MPC 

based on the material costs from the BOM and the associated scrap costs, the labor costs based 

on an average labor rate and the labor hours necessary to manufacture the motor, and the 

overhead costs, including depreciation, based on a markup applied to the material, labor, and 

scrap costs based on the materials used.

Table 12.4.6 through Table 12.4.15 show the average production cost estimates used in 

the GRIM for each representative unit at each efficiency level. 

Table 12.4.6 Manufacturer Production Cost Breakdown (2013$) for NEMA Design B, 5 

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $156.42 $50.71 $14.00 $0.00 $22.24 $243.38 1.37 $333.43

EL 1 $160.65 $53.25 $14.46 $0.00 $22.97 $251.33 1.37 $344.33

EL 2 $174.74 $55.91 $15.59 $0.00 $24.77 $271.01 1.37 $371.29

EL 3 $184.28 $58.70 $17.07 $11.18 $25.45 $296.68 1.37 $406.45

EL 4 $251.89 $152.13 $28.44 $17.54 $44.29 $494.29 1.37 $677.18

Table 12.4.7 Manufacturer Production Cost Breakdown (2013$) for NEMA Design B, 30

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $397.51 $105.14 $35.97 $0.00 $52.00 $590.61 1.45 $856.39

EL 1 $533.10 $110.40 $46.05 $0.00 $66.56 $756.11 1.45 $1,096.35

EL 2 $569.97 $115.92 $49.08 $0.00 $70.95 $805.91 1.45 $1,168.57

EL 3 $618.07 $121.71 $54.96 $33.23 $74.50 $902.48 1.45 $1,308.59

EL 4 $941.91 $228.20 $87.26 $52.15 $123.36 $1,432.87 1.45 $2,077.67
12-14 

Table 12.4.8 Manufacturer Production Cost Breakdown (2013$) for NEMA Design B, 75

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Mar

kup MSP

Baseline $928.14 $193.38 $80.25 $0.00 $116.01 $1,317.79 1.45 $1,910.79

EL 1 $1,011.30 $203.05 $86.90 $0.00 $125.62 $1,426.86 1.45 $2,068.95

EL 2 $1,167.43 $212.72 $98.76 $0.00 $142.77 $1,621.68 1.45 $2,351.43

EL 3 $1,365.17 $224.32 $117.81 $66.88 $160.35 $1,934.52 1.45 $2,805.06

EL 4 $1,665.27 $383.37 $153.61 $104.94 $215.15 $2,522.34 1.45 $3,657.40

Table 12.4.9 Manufacturer Production Cost Breakdown (2013$) for Brake, 5 Horsepower, 

4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $156.42 $50.71 $14.00 $0.00 $22.24 $243.38 1.37 $333.43

EL 1 $160.65 $53.25 $14.46 $0.00 $22.97 $251.33 1.37 $344.33

EL 2 $174.74 $55.91 $15.59 $0.00 $24.77 $271.01 1.37 $371.29

EL 3 $184.28 $58.70 $17.07 $11.18 $25.45 $296.68 1.37 $406.45

EL 4 $251.89 $152.13 $28.44 $17.54 $44.29 $494.29 1.37 $677.18

Table 12.4.10 Manufacturer Production Cost Breakdown (2013$) for Brake, 30 

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $397.51 $105.14 $35.97 $0.00 $52.00 $590.61 1.45 $856.39

EL 1 $533.10 $110.40 $46.05 $0.00 $66.56 $756.11 1.45 $1,096.35

EL 2 $569.97 $115.92 $49.08 $0.00 $70.95 $805.91 1.45 $1,168.57

EL 3 $618.07 $121.71 $54.96 $33.23 $74.50 $902.48 1.45 $1,308.59

EL 4 $941.91 $228.20 $87.26 $52.15 $123.36 $1,432.87 1.45 $2,077.67

Table 12.4.11 Manufacturer Production Cost Breakdown (2013$) for NEMA Design C, 5 

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $154.32 $53.42 $14.04 $0.00 $22.31 $244.09 1.37 $334.40

EL 1 $166.54 $56.09 $15.05 $0.00 $23.91 $261.58 1.37 $358.37

EL 2 $220.25 $153.48 $26.35 $16.93 $40.92 $457.93 1.37 $627.36

Table 12.4.12 Manufacturer Production Cost Breakdown (2013$) for NEMA Design C, 50 

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $776.22 $135.23 $65.22 $0.00 $94.28 $1,070.96 1.45 $1,552.89

EL 1 $959.25 $304.27 $90.41 $0.00 $130.70 $1,484.63 1.45 $2,152.72

EL 2 $1,126.42 $319.48 $109.76 $96.07 $150.51 $1,802.24 1.45 $2,613.24
12-15 

Table 12.4.13 Manufacturer Production Cost Breakdown (2013$) for Fire Pump, 5 

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $160.65 $53.25 $14.46 $0.00 $22.97 $251.33 1.37 $344.33

EL 1 $174.74 $55.91 $15.59 $0.00 $24.77 $271.01 1.37 $371.29

EL 2 $184.28 $58.70 $17.07 $11.18 $25.45 $296.68 1.37 $406.45

EL 3 $251.89 $152.13 $28.44 $17.54 $44.29 $494.29 1.37 $677.18

Table 12.4.14 Manufacturer Production Cost Breakdown (2013$) for Fire Pump, 30 

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $533.10 $110.40 $46.05 $0.00 $66.56 $756.11 1.45 $1,096.35

EL 1 $569.97 $115.92 $49.08 $0.00 $70.95 $805.91 1.45 $1,168.57

EL 2 $618.07 $121.71 $54.96 $33.23 $74.50 $902.48 1.45 $1,308.59

EL 3 $941.91 $228.20 $87.26 $52.15 $123.36 $1,432.87 1.45 $2,077.67

Table 12.4.15 Manufacturer Production Cost Breakdown (2013$) for Fire Pump, 75 

Horsepower, 4 Pole, Enclosed Electric Motor

EL Materials Labor Depr. Dev. Overhead MPC Markup MSP

Baseline $1,011.30 $203.05 $86.90 $0.00 $125.62 $1,426.86 1.45 $2,068.95

EL 1 $1,167.43 $212.72 $98.76 $0.00 $142.77 $1,621.68 1.45 $2,351.43

EL 2 $1,365.17 $224.32 $117.81 $66.88 $160.35 $1,934.52 1.45 $2,805.06

EL 3 $1,665.27 $383.37 $153.61 $104.94 $215.15 $2,522.34 1.45 $3,657.40

12.4.8 Product and Capital Conversion Costs 

DOE expects new and amended energy conservation standards to cause manufacturers to 

incur one-time conversion costs to bring their production facilities and equipment designs into 

compliance with new and amended standards. For the MIA, DOE classified these one-time 

conversion costs into two major groups: (1) product conversion costs and (2) capital conversion 

costs. Product conversion costs are one-time investments in research, development, testing, 

marketing, and other non-capitalized costs necessary to make equipment designs comply with 

new and amended standards. Capital conversion costs are one-time investments in property, 

plant, and equipment necessary to adapt or change existing production facilities such that new 

equipment designs can be fabricated and assembled.

DOE calculated the product and capital conversion costs using both a top-down approach 

and a bottom-up approach based on feedback from manufacturers during manufacturer 

interviews and manufacturer submitted comments. DOE then adjusted these conversion costs if 

there were any discrepancies in the final costs using the two methods to arrive at a final product 

and capital conversion cost estimate for each representative unit at each EL.
12-16 

During manufacturer interviews, DOE asked manufacturers for their estimated total 

product and capital conversion costs needed to produce electric motors at specific ELs. To arrive 

at top-down industry wide product and capital conversion cost estimates for each representative 

unit at each EL, DOE calculated a market share weighted average value for product and capital 

conversion costs based on the data submitted during interviews and the market share of the 

interviewed manufacturers.

DOE also calculated bottom-up conversion costs based on manufacturer input on the 

types of costs and the dollar amounts necessary to convert a single electric motor frame size to 

each EL. Some of the types of capital conversion costs manufacturers identified were the 

purchase of lamination die sets, winding machines, frame casts, and assembly equipment as well 

as other retooling costs. The two main types of product conversion costs manufacturers shared 

with DOE during interviews were number of engineering hours necessary to re-engineer frames 

to meet higher efficiency standards and the testing and certification costs to comply with higher 

efficiency standards. DOE then took average values (i.e., costs or number of hours) based on the 

range of responses given by manufacturers for each product and capital conversion costs 

necessary for a manufacturer to increase the efficiency of one frame size to a specific EL. DOE 

multiplied the conversion costs associated with manufacturing a single frame size at each EL by 

the number of frames each interviewed manufacturer produces. DOE finally scaled this number 

based on the market share of the manufacturers DOE interviewed, to arrive at industry wide 

bottom-up product and capital conversion cost estimates for each representative unit at each EL. 

The bottom-up conversion costs estimates DOE created were consistent with the manufacturer 

top down estimates provided, so DOE used the bottom-up conversion cost estimates as the final 

values for each representative unit in the MIA.

DOE’s estimates of the product and capital conversion costs for each representative unit 

can be found in Table 12.4.16 through Table 12.4.25 below. Table 12.4.26 summarizes product 

and conversion costs for all electric motors. 

Table 12.4.16 Product and Capital Conversion Costs for NEMA Design B, 5 Horsepower, 4 

Pole, Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 4.5 -

TSL 2 45.1 7.9

TSL 3 451.0 66.7

TSL 4 451.0 217.0

Table 12.4.17 Product and Capital Conversion Costs for NEMA Design B, 30 Horsepower, 

4 Pole, Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 1.0 -

TSL 2 10.1 7.9

TSL 3 101.4 66.7

TSL 4 101.4 217.0
12-17 

Table 12.4.18 Product and Capital Conversion Costs for NEMA Design B, 75 Horsepower, 

4 Pole, Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.2 -

TSL 2 1.8 10.0

TSL 3 18.5 83.3

TSL 4 18.5 254.3

Table 12.4.19 Product and Capital Conversion Costs for Brake, 5 Horsepower, 4 Pole, 

Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.0 -

TSL 2 0.4 0.4

TSL 3 3.8 3.5

TSL 4 3.8 11.0

Table 12.4.20 Product and Capital Conversion Costs for Brake, 30 Horsepower, 4 Pole, 

Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.0 -

TSL 2 0.1 0.3

TSL 3 0.7 2.3

TSL 4 0.7 7.3

Table 12.4.21 Product and Capital Conversion Costs for NEMA Design C, 5 Horsepower, 4 

Pole, Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.4 0.0

TSL 2 0.4 0.0

Table 12.4.22 Product and Capital Conversion Costs for NEMA Design C, 50 Horsepower, 

4 Pole, Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.0 0.0

TSL 2 0.0 0.0

Table 12.4.23 Product and Capital Conversion Costs for Fire Pump, 5 Horsepower, 4 Pole, 

Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.1 0.0

TSL 2 0.0 0.0

TSL 3 0.1 0.0
12-18 

Table 12.4.24 Product and Capital Conversion Costs for Fire Pump, 30 Horsepower, 4 

Pole, Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.0 0.0

TSL 2 0.0 0.0

TSL 3 0.0 0.0

Table 12.4.25 Product and Capital Conversion Costs for Fire Pump, 75 Horsepower, 4 

Pole, Closed, Electric Motors by TSL

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

TSL 1 0.0 0.0

TSL 2 0.0 0.0

TSL 3 0.0 0.0

Table 12.4.26 Product and Capital Conversion Costs for all Electric Motors 

TSL Product Conversion Costs

2013$ millions

Capital Conversion Costs

2013$ millions

Total Conversion Costs

2013$ millions

TSL 1 6.2 0.0 6.2

TSL 2 58.0 26.6 84.6

TSL 3 618.1 222.8 840.9

TSL 4 627.4 707.2 1,334.6

12.4.9 Markup Scenarios

DOE used several standards case markup scenarios to represent the uncertainty about the 

impacts of new and amended energy conservation standards on prices and profitability. In the 

base case, DOE used the same baseline markups calculated in the engineering analysis for all 

representative units. In the standards case, DOE modeled three markup scenarios to represent the 

uncertainty about the potential impacts on prices and profitability following the implementation 

of new and amended energy conservation standards: (1) flat markup scenario, (2) two-tiered 

markup scenario, and (3) a preservation of operating profit markup scenario. These scenarios 

lead to different markup values, which, when applied to the inputted MPCs, result in varying 

revenue and cash-flow impacts.

12.4.9.1 Flat Markup Scenario

Under the flat markup scenario, DOE applied a single uniform markup across all 

efficiency levels. As production costs increase with efficiency, this scenario implies that the 

absolute dollar markup will increase as well. Based on publicly available financial information 

for manufacturers of electric motors and comments from manufacturer interviews, DOE assumed 

the non-production cost markup—which includes SG&A expenses; research and development 

expenses; interest; and profit—to be 1.37 for the 5 horsepower electric motor representative unit 

and 1.45 for all other electric motor representative units. Because this markup scenario assumes 

that manufacturers would be able to maintain their gross margin percentage as production costs 

increase in response to energy conservation standards, it represents a high bound to industry 

profitability under new and amended standards. 
12-19 

12.4.9.2 Two-Tiered Markup Scenario

DOE also modeled two possible lower bound profitability scenario, a two-tiered markup 

scenario and a preservation of operating profit markup scenario. DOE implemented the twotiered markup scenario because during interviews, multiple manufacturers stated they offer two 

tiers of equipment lines that are differentiated, in part, by efficiency level. The high efficiency 

tiers typically earn a premium over the baseline efficiency tier. For electric motors the high 

efficiency tier is typically one or two bands above premium efficiency levels. Several 

manufacturers suggested that the premium currently earned by the high efficiency tiers would 

erode under new and amended standards due to the disappearance of the baseline efficiency tier, 

which would harm profitability. Because of this pricing dynamic described by manufacturers, 

DOE modeled a two-tier markup scenario. In this scenario, DOE assumed that the markup on 

electric motors varies according to two efficiency tiers in both the base case and the standards 

case. During the MIA interviews, manufacturers provided information on the range of typical 

efficiency levels in those two tiers and the change in profitability at each level. DOE used this 

information and industry average gross margins to estimate markups for electric motors under a 

two-tier pricing strategy in the base case. In the standards case, DOE modeled the situation in 

which portfolio reduction squeezes the margin of high efficiency equipment as they become the 

new minimally compliant equipment, and presumably higher volume equipment. 

Table 12.4.27 through Table 12.4.36 lists the representative units DOE analyzed with the 

corresponding two-tier markups at each selected EL. 

Table 12.4.27 Two-Tiered Markups for NEMA Design B, 5 Horsepower, 4 Pole, Closed 

Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.308

EL 1 1.308 1.308

EL 2 1.308 1.308 1.308

EL 3 1.514 1.514 1.308 1.308

EL 4 1.514 1.514 1.514 1.308 1.308

Table 12.4.28 Two-Tiered Markups for NEMA Design B, 30 Horsepower, 4 Pole, Closed 

Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.383

EL 1 1.383 1.383

EL 2 1.383 1.383 1.383

EL 3 1.601 1.601 1.383 1.383

EL 4 1.601 1.601 1.601 1.383 1.383
12-20 

Table 12.4.29 Two-Tiered Markups for NEMA Design B, 75 Horsepower, 4 Pole, Closed 

Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.373

EL 1 1.373 1.373

EL 2 1.373 1.373 1.373

EL 3 1.590 1.590 1.373 1.373

EL 4 1.590 1.590 1.590 1.373 1.373

Table 12.4.30 Two-Tiered Markups for Brake, 5 Horsepower, 4 Pole, Closed Electric 

Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.362

EL 1 1.362 1.362

EL 2 1.362 1.362 1.362

EL 3 1.577 1.577 1.362 1.362

EL 4 1.577 1.577 1.577 1.362 1.362

Table 12.4.31 Two-Tiered Markups for Brake, 30 Horsepower, 4 Pole, Closed Electric 

Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.427

EL 1 1.427 1.427

EL 2 1.427 1.427 1.427

EL 3 1.653 1.653 1.427 1.427

EL 4 1.653 1.653 1.653 1.427 1.427

Table 12.4.32 Two-Tiered Markups for NEMA Design C, 5 Horsepower, 4 Pole, Closed 

Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2

Baseline 1.370

EL 1 1.370 1.370

EL 2 1.586 1.586 1.370

Table 12.4.33 Two-Tiered Markups for NEMA Design C, 75 Horsepower, 4 Pole, Closed 

Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2

Baseline 1.447

EL 1 1.447 1.447

EL 2 1.675 1.675 1.447
12-21 

Table 12.4.34 Two-Tiered Markups for Fire Pump, 5 Horsepower, 4 Pole, Closed Electric 

Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3

Baseline 1.360

EL 1 1.360 1.360

EL 2 1.574 1.360 1.360

EL 3 1.574 1.574 1.574 1.360

Table 12.4.35 Two-Tiered Markups for Fire Pump, 30 Horsepower, 4 Pole, Closed Electric 

Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3

Baseline 1.437

EL 1 1.437 1.437

EL 2 1.664 1.437 1.437

EL 3 1.664 1.664 1.664 1.437

Table 12.4.36 Two-Tiered Markups for Fire Pump, 75 Horsepower, 4 Pole, Closed Electric 

Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3

Baseline 1.430

EL 1 1.430 1.430

EL 2 1.656 1.430 1.430

EL 3 1.656 1.656 1.656 1.430

12.4.9.3 Preservation of Operating Profit Markup Scenario

DOE implemented the preservation of operating profit markup scenario because 

manufacturers stated that they do not expect to be able to markup the full cost of production 

given the highly competitive market, in the standards case. The preservation of operating profit 

markup scenario assumes that manufacturers are able to maintain only the base case total 

operating profit in absolute dollars in the standards case, despite higher production costs and 

investment. The base case total operating profit is derived from marking up the cost of goods 

sold for each product by a flat percentage (the flat markup, discussed in the previous section) to 

cover standard SG&A expenses, R&D expenses, and profit. DOE adjusted the manufacturer 

markups in the GRIM at each TSL to yield approximately the same earnings before interest and 

taxes in the standards case in the year after the compliance date of the new and amended 

standards as in the base case. DOE assumed that the industry-wide impacts would occur under 

the new minimum efficiency levels. DOE altered the markups only for the minimally compliant 

equipment in this scenario, with margin impacts not occurring for equipment that already exceed 

the new and amended energy conservation standards. The preservation of operating profit 

markup scenario represents one of the possible lower bound markup scenarios of industry 

profitability following new and amended standards. Under this scenario, while manufacturers are 
12-22 

not able to earn additional operating profit on higher production costs and the investments 

required to comply with the new and amended standards, like they are in the flat markup 

scenario, they are able to maintain the same operating profit in the standards case as was earned 

in the base case.

Table 12.4.37 through Table 12.4.46 lists the representative units DOE analyzed with the 

corresponding preservation of operating profit markups at each selected EL. 

Table 12.4.37 Preservation of Operating Profit Markups for NEMA Design B, 5 

Horsepower, 4 Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.370

EL 1 1.370 1.369

EL 2 1.370 1.370 1.350

EL 3 1.370 1.370 1.370 1.289

EL 4 1.370 1.370 1.370 1.370 1.282

Table 12.4.38 Preservation of Operating Profit Markups for NEMA Design B, 30 

Horsepower, 4 Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.450

EL 1 1.450 1.442

EL 2 1.450 1.450 1.420

EL 3 1.450 1.450 1.450 1.348

EL 4 1.450 1.450 1.450 1.450 1.330

Table 12.4.39 Preservation of Operating Profit Markups for NEMA Design B, 75 

Horsepower, 4 Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.450

EL 1 1.450 1.446

EL 2 1.450 1.450 1.412

EL 3 1.450 1.450 1.450 1.353

EL 4 1.450 1.450 1.450 1.450 1.342
12-23 

Table 12.4.40 Preservation of Operating Profit Markups for Brake, 5 Horsepower, 4 Pole, 

Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.370

EL 1 1.370 1.368

EL 2 1.370 1.370 1.351

EL 3 1.370 1.370 1.370 1.279

EL 4 1.370 1.370 1.370 1.370 1.267

Table 12.4.41 Preservation of Operating Profit Markups for Brake, 30 Horsepower, 4 Pole, 

Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3 EL 4

Baseline 1.450

EL 1 1.450 1.432

EL 2 1.450 1.450 1.408

EL 3 1.450 1.450 1.450 1.332

EL 4 1.450 1.450 1.450 1.450 1.307

Table 12.4.42 Preservation of Operating Profit Markups for NEMA Design C, 5 

Horsepower, 4 Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2

Baseline 1.370

EL 1 1.370 1.362

EL 2 1.370 1.370 1.267

Table 12.4.43 Preservation of Operating Profit Markups for NEMA Design C, 50

Horsepower, 4 Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2

Baseline 1.450

EL 1 1.450 1.409

EL 2 1.450 1.450 1.315

Table 12.4.44 Preservation of Operating Profit Markups for Fire Pump, 5 Horsepower, 4 

Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3

Baseline 1.370

EL 1 1.370 1.362

EL 2 1.370 1.370 1.370

EL 3 1.370 1.370 1.370 1.268
12-24 

Table 12.4.45 Preservation of Operating Profit Markups for Fire Pump, 30 Horsepower, 4 

Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3

Baseline 1.450

EL 1 1.450 1.439

EL 2 1.450 1.450 1.450

EL 3 1.450 1.450 1.450 1.312

Table 12.4.46 Preservation of Operating Profit Markups for Fire Pump, 75 Horsepower, 4 

Pole, Closed Electric Motors

EL Markups by Selected EL

Baseline EL 1 EL 2 EL 3

Baseline 1.450

EL 1 1.450 1.427

EL 2 1.450 1.450 1.450

EL 3 1.450 1.450 1.450 1.316

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated 

the financial impacts on the electric motor industry. The following sections detail additional 

inputs and assumptions for electric motors. The main results of the MIA are also reported in this 

section. The MIA consists of two key financial metrics: INPV and annual cash flows. 

12.5.1 Impacts on Industry Net Present Value

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs in the standards case. The INPV is different from DOE’s net present 

value, which is applied to the U.S. economy. The INPV is the sum of all net cash flows 

discounted at the industry’s cost of capital, or discount rate. The electric motors GRIM estimates 

cash flows from 2014 to 2045. This timeframe models both the short-term impacts on the 

industry from the announcement of the standard until the compliance date (2014 until an 

estimated compliance date of June 1, 2016) and a long-term assessment over the 30-year analysis 

period used in the NIA (2016 – 2045).

In the MIA, DOE compares the INPV of the base case (no new or amended energy 

conservation standards) to that of each TSL in the standards case. The difference between the 

base case and a standards case INPV is an estimate of the economic impacts that implementing 

that particular TSL would have on the industry. For the electric motor industry, DOE examined 

the three markup scenarios described above, the flat markup scenario, the two-tiered markup 

scenario, and the preservation of operating profit markup scenario. Table 12.5.1 through Table 

12.5.3 provide the INPV estimates for the three markup scenarios for the electric motor industry. 
12-25 

Table 12.5.1 Changes in Industry Net Present Value for Electric Motors – Flat Markup

Scenario

Units Base Case

Trial Standard Level

1 2 3 4 

INPV (2013$ millions) $3,478.0 $3,486.4 $3,870.6 $4,541.9 $5,382.1

Change in INPV (2013$ millions) - $8.4 $392.6 $1,063.9 $1,904.1

(%) - 0.2% 11.3% 30.6% 54.7%

Table 12.5.2 Changes in Industry Net Present Value for Electric Motors – Two-Tiered 

Markup Scenario

Units Base Case

Trial Standard Level

1 2 3 4

INPV (2013$ millions) $3,478.0 $3,481.6 $3,130.4 $2,928.3 $3,282.0

Change in INPV (2013$ millions) - $3.6 $(347.7) $(549.7) $(196.0)

(%) - 0.1% -10.0% -15.8% -5.6%

Table 12.5.3 Changes in Industry Net Present Value for Electric Motors – Preservation of 

Operating Profit Markup Scenario

Units Base Case

Trial Standard Level

1 2 3 4 

INPV (2013$ millions) $3,478.0 $3,461.3 $3,643.0 $3,362.0 $2,048.3

Change in INPV (2013$ millions) - $(16.7) $165.0 $(116.0) $(1,429.8)

(%) - -0.5% 4.7% -3.3% -41.1%

12.5.2 Impacts on Annual Cash Flow

While INPV is useful for evaluating the long-term effects of new and amended energy 

conservation standards, short-term changes in cash flow are also important indicators of the 

industry’s financial situation. For example, a large investment over one or two years could strain 

the industry’s access to capital. Consequently, the sharp drop in financial performance could 

cause investors to flee, even though recovery may be possible. Thus, a short-term disturbance 

can have long-term effects that the INPV cannot capture. To get an idea of the behavior of 

annual free cash flows, Figure 12.5.1 through Figure 12.5.3 below present the annual free cash 

flows from 2013 through 2026 for the base case and different TSLs in the standards case.

Annual cash flows are discounted to the base year, 2014. Between 2014 and the 2016 

compliance date of the new and amended energy conservation standards, cash flows are driven 

by the level of conversion costs and the proportion of these investments spent every year. After 

the standard announcement date (i.e., the publication date of this final rule), industry cash flows 

begin to decline as companies use their financial resources to prepare for the new and amended 

standards. The more stringent the new and amended standards, the greater the impact on industry 

cash flows in the years leading up to the compliance date, as product conversion costs lower cash 

inflows from operations and capital conversion costs increase cash outflows for capital 

expenditures. 
12-26 

Free cash flow in the year the new and amended energy conservation standards take 

effect is driven by two competing factors. In addition to capital and product conversion costs, 

new and amended standards could create stranded assets, i.e., tooling and equipment that would 

have enjoyed longer use if the standards had not made them obsolete. In this year, manufacturers 

write down the remaining book value of existing tooling and equipment whose value is affected 

by the new and amended standards. This one-time write-down acts as a tax shield that alleviates 

decreases in cash flow from operations in the year of the write-down. In this year, there is also an 

increase in working capital that reduces cash flow from operations. A large increase in working 

capital is needed due to more costly production components and materials, higher inventory 

carrying to sell more expensive equipment, and higher accounts receivable for more expensive 

equipment. Depending on these two competing factors, cash flow can either be positively or 

negatively affected in the year the standards takes effect.

In the years following the compliance date of the standards, the impact on cash flow 

depends on the operating revenue. In the flat markup scenario, the manufacture markup is held 

constant to yield the same gross margin percentage in the standards case at each TSL as in the 

base case in the year after the standards take effect. The implicit assumption is that 

manufacturers can freely pass on and mark up higher cost units. The result under this scenario is 

that operating cash flow increases (in absolute terms) as revenue increases. At the highest TSLs 

where MPCs dramatically increase, this scenario drives large increases in operating cash flow 

relative to the base case. The larger the production cost increase, then, the more likely it is that 

the increase in operating cash flow after the standards take affect will outweigh the initial 

conversion costs. 

Under the preservation of operating profit scenario, cash flow decreases at each TSL in 

the standards case compared to the base case because, since the absolute dollar amount of the 

gross margin does not change despite an increase in sales and cost of goods sold, the gross 

margin percentage is reduced. Figure 12.5.1 through Figure 12.5.3 present the annual free cash 

flows for the electric motor industry. 
12-27 

Figure 12.5.1 Annual Industry Free Cash Flows for Electric Motors - Flat Markup 

Scenario

 

Figure 12.5.2 Annual Industry Free Cash Flows for Electric Motors – Two-Tiered Markup 

Scenario

($600)

($400)

($200)

$0

$200

$400

$600

$800

Net Cash Flows (2013$ MM) 

Base Case TSL 1 TSL 2 TSL 3 TSL 4

($600)

($500)

($400)

($300)

($200)

($100)

$0

$100

$200

$300

$400

$500

Net Cash Flows (2013$ MM) 

Base Case TSL 1 TSL 2 TSL 3 TSL 4
12-28 

Figure 12.5.3 Annual Industry Free Cash Flows for Electric Motors - Preservation of 

Operating Profit Markup Scenario

12.6 IMPACTS ON MANUFACTURER SUBGROUPS

As described in section 12.2.3, DOE identified one subgroup of electric motor

manufacturers: small business manufacturers. The results of this subgroup analysis are described 

in the following section. 

12.6.1 Impacts on Small Business Manufacturers

12.6.1.1 Description and Estimated Number of Small Entities Regulated

DOE conducted a more focused inquiry of the companies that could be small business 

manufacturers of equipment covered by this rulemaking. During its market survey, DOE used 

public information available to identify potential small business manufacturers. DOE’s research 

involved industry trade association membership directories (including NEMA), the SBA’s 

database, individual company websites, and market research tools (e.g., Hoover’s reports) to 

create a list of every company that manufactures or sells electric motors covered by this 

rulemaking. DOE also asked stakeholders and industry representatives if they were aware of any 

other small manufacturers during manufacturer interviews and at previous DOE public meetings. 

DOE attempted to contact every potential small electric motor manufacturer on its list to 

determine whether they met the SBA’s definition of a small business manufacturer of covered 

electric motors. DOE screened out companies that did not offer equipment covered by this 

rulemaking, did not meet SBA’s definition of a “small business,” or are completely foreignowned and -operated.

($600)

($500)

($400)

($300)

($200)

($100)

$0

$100

$200

$300

$400

Net Cash Flows (2013$ MM) 

Base Case TSL 1 TSL 2 TSL 3 TSL 4
12-29 

DOE initially identified 60 potential manufacturers of electric motors sold in the United 

States. After reviewing publically available information DOE contacted 27 of the companies that 

DOE believed were small business manufacturers to determine whether they met the SBA 

definition of a small business and whether they manufactured covered equipment. Based on these 

efforts, DOE estimates that there are 13 small business manufacturers of electric motors.

Before issuing the NOPR, DOE attempted to contact the small business manufacturers of 

electric motors it had identified to invite them to take part in a small business manufacturer 

impact analysis interview. Of the electric motor manufacturers DOE contacted, 10 responded and 

three did not. Eight of the 10 responding manufacturers declined to be interviewed. Therefore, 

DOE was able to reach and discuss potential standards with two of the 13 small business 

manufacturers. DOE also obtained information about small business manufacturers and potential 

impacts while interviewing large manufacturers.

Eight major manufacturers supply approximately 90 percent of the market for electric 

motors. None of the major manufacturers of electric motors covered in this rulemaking are a 

small business. DOE estimates that approximately 50 percent of the market is served by imports. 

Many of the small businesses that compete in the electric motor market produce specialized 

motors, many of which have been not been covered under previous standards. Most of these lowvolume manufacturers do not compete directly with large manufacturers and try to find niche 

markets for their equipment. There are a few small business manufacturers that do produce 

general purpose motors; however, these motors are currently at premium efficiency levels, the 

efficiency levels being adopted in today’s final rule. 

12.6.1.2 Comparison Between Large and Small Entities

In its market survey, DOE identified three categories of small business electric motor 

manufacturers that may be impacted differently by today’s final rule. The first group, which 

includes approximately five of the 13 small businesses, consists of manufacturers that produce 

specialty motors that were not covered under previous Federal energy conservation standards, 

but would be covered under the expanded scope of today’s final rule. DOE believes that this 

group would likely be the most impacted by expanding the scope of equipment required to meet 

premium efficiency levels. The second group, which includes approximately five small 

businesses, consists of manufacturers that offer a very limited number of covered equipment and

primarily focus on other types of motors not covered in this rulemaking, such as single-phase or 

direct-current motors. Because generally less than 10 percent of these manufacturers’ revenue 

comes from covered equipment, DOE does not believe new standards will substantially impact 

their business. The third group, which includes approximately three small businesses, consists of 

manufacturers that already offer premium efficiency level general purpose motors. DOE believes 

that these manufacturers already have the design and production experience necessary to meet 

the standards in this final rule without incurring burdensome costs. 

At TSL 2, the level selected in today’s final rule, DOE estimates capital conversion costs 

of $1.37 million and product conversion costs of $3.79 million for a typical small manufacturer 

in the first group (manufacturers that produce specialized motors previously not covered by 

Federal energy conservation standards). Meanwhile, DOE estimates a typical large manufacturer 
12-30 

would incur capital and product conversion costs of $3.32 million and $7.33 million, 

respectively, at the same TSL. Small manufacturers that predominately produce specialty motors 

would face higher relative capital conversion costs at TSL 2 than large manufacturers because 

large manufacturers have been independently pursuing higher efficiency motors and 

consequently have built up more design and production experience. Large manufacturers have 

also been innovating as a result of the 2010 small electric motors rulemaking (75 FR 10874, 

March 9, 2010). This rule did not apply to non-general purpose small electric motors that many 

of these small business manufacturers produce. Many large manufacturers of general purpose 

motors offer equipment that was covered by the 2010 small electric motors rule, as well as 

equipment that falls under this rule. Small manufacturers pointed out that this fact would give 

large manufacturers an advantage in that they already have experience with the technology 

necessary to redesign their equipment and are familiar with the steps they will have to take to 

upgrade their manufacturing equipment and processes. Small manufacturers, whose specialized 

motors were not required to meet the standards prescribed by the small electric motors rule and 

EISA 2007 have not undergone these processes and, therefore, would have to put more time and 

resources into redesign efforts. 

The small businesses whose equipment lines consist of a high percentage of equipment 

that are not currently required to meet efficiency standards would need to make significant 

capital investments relative to large manufacturers to upgrade their production lines with 

equipment necessary to produce motors that can satisfy the levels being adopted today. As Table 

12.6.1 illustrates, these manufacturers would have to drastically increase their capital 

expenditures to purchase new lamination die sets, and new winding and stacking equipment. 

Table 12.6.1 Estimated Capital and Product Conversion Costs as a Percentage of Annual 

Capital Expenditures and R&D Expense

Capital conversion cost as 

a percentage of annual 

capital expenditures

Product conversion cost 

as a percentage of annual 

R&D expense

Total conversion cost as a 

percentage of annual 

revenue

Typical Large 

Manufacturer 14% 31% 2% 

Typical Small 

Manufacturer 188% 490% 75% 

Table 12.6.1 also illustrates that small manufacturers whose production lines contain 

many motors which are not currently covered under Federal energy conservation standards face 

high relative product conversion costs compared to large manufacturers, despite the lower dollar 

value. In interviews, these small manufacturers expressed concern that they would face a large 

learning curve relative to large manufacturers, due to the fact that the equipment they produce 

has not previously been covered under Federal energy conservation standards. In its market 

survey, DOE learned that for some manufacturers, the expanded scope of specialized motors that 

would have to meet premium efficiency levels could affect nearly half the equipment they offer. 

They would need to hire additional engineers and would have to spend considerable time and 

resources redesigning their equipment and production processes. DOE does not expect the small 

businesses that already manufacture premium efficiency level motors or those that offer very few 

alternating-current motors to incur these high costs. 
12-31 

Manufacturers also expressed concern about testing and certification costs associated 

with new and amended standards. They pointed out that these costs are particularly burdensome 

on small businesses that produce several types of different specialized equipment. As a result of 

their wide variety of equipment and relatively low output, small manufacturers are forced to 

certify multiple small batches of motors, the costs of which they have to spread out over far 

fewer units than large manufacturers.

Small manufacturers that produce equipment that is not currently covered also pointed 

out that they would face significant challenges supporting current business while making 

changes to their production lines. While large manufacturers could shift production of certain 

equipment to different plants or production lines while they made updates, small businesses 

would have limited options. Most of these small businesses have only one plant and therefore 

would have to find a way to continue to fulfill customer needs while redesigning production lines 

and installing new equipment. In interviews with DOE, small manufacturers said that it would be 

difficult to quantify the impacts of downtime and the possible need for external support could 

have on their business. 

In summary, while the conversion costs required can be considered substantial for all 

electric motor manufacturers, the impacts could be proportionately high for a typical small 

manufacturer because of much lower production volumes and the relatively fixed nature of the 

R&D and capital investments required. 

12.7 OTHER IMPACTS

12.7.1 Employment

DOE quantitatively assessed the impact of new and amended energy conservation 

standards on direct employment. DOE used the GRIM to estimate the domestic labor 

expenditures and number of domestic production workers in the base case and at each TSL from 

the announcement of any new and amended standards in 2014 to the end of the analysis period in 

2045. DOE used statistical data from the U.S. Census Bureau’s 2011 Annual Survey of 

Manufacturers (ASM), the results of the engineering analysis, and interviews with manufacturers 

to determine the inputs necessary to calculate industry-wide labor expenditures and domestic 

employment levels. Labor expenditures involved with the manufacturing of electric motors are a 

function of the labor intensity of the equipment, the sales volume, and an assumption that wages 

remain fixed in real terms over time.

In the GRIM, DOE used the labor content of each product and the manufacturing 

production costs to estimate the annual labor expenditures of the industry. DOE used Census 

data and interviews with manufacturers to estimate the portion of the total labor expenditures 

attributable to domestic labor. 

The production worker estimates in this employment section cover only workers up to the 

line-supervisor level who are directly involved in fabricating and assembling an electric motor 

within a motor facility. Workers performing services that are closely associated with production 

operations, such as material handling with a forklift, are also included as production labor. 

DOE’s estimates account for only production workers who manufacture the specific equipment 
12-32 

covered by this rulemaking. For example, a worker on an electric motor line manufacturing a 

fractional horsepower motor (i.e., a motor with less than one horsepower) would not be included 

with this estimate of the number of electric motor workers, since fractional motors are not 

covered by this rulemaking. 

The employment impacts shown in the tables below represent the potential production 

employment impact resulting from new and amended energy conservation standards. The upper 

bound of the results estimates the maximum change in the number of production workers that 

could occur after compliance with new and amended standards when assuming that 

manufacturers continue to produce the same scope of covered equipment in the same production 

facilities. It also assumes that domestic production does not shift to lower-labor-cost countries. 

Because there is a real risk of manufacturers evaluating sourcing decisions in response to new 

and amended standards, the lower bound of the employment results includes the estimated total 

number of U.S. production workers in the industry who could lose their jobs if all existing 

production were moved outside of the U.S. While the results present a range of employment 

impacts following 2016, the following sections also include qualitative discussions of the 

likelihood of negative employment impacts at the various TSLs. Finally, the employment 

impacts shown are independent of the indirect employment impacts from the broader U.S. 

economy, which are documented in chapter 16 of this final rule TSD.

Based on 2011 ASM data and interviews with manufacturers, DOE estimates 

approximately 60 percent of electric motors sold in the U.S. are manufactured domestically. 

Using this assumption, DOE estimates that in the absence of new and amended energy 

conservation standards, there would be approximately 7,313 domestic production workers 

involved in manufacturing all electric motors covered by this rulemaking in 2016. The table 

below shows the range of potential impacts of new and amended standards for all ECGs on U.S. 

production workers in the electric motor industry. However, because ECG 1a motors comprise 

more than 97 percent of the electric motors covered by this rulemaking, DOE believes that 

potential changes in domestic employment will be driven primarily by the standards that are 

selected for ECG 1a; Design A and B electric motors.

Table 12.7.1 Potential Changes in the Total Number of All Domestic Electric Motor 

Production Workers in 2016 

Base Case Trial Standard Level

1 2 3 4

Total Number of Domestic 

Production Workers in 2016 

(without changes in production 

locations)

7,313 7,346 7,498 8,374 16,049

Potential Changes in Domestic 

Production Workers in 2016* - 33 - 0 185 - (366) 1,061 -(3,656)

8,736 -

(7,313)

* DOE presents a range of potential employment impacts. Numbers in parentheses indicate negative numbers.

Most manufacturers agree that any standards that involve expanding the scope of 

equipment required to meet premium efficiency levels would not significantly change domestic 

employment levels. At this efficiency level (TSL 2), manufacturers would not be required to 

make major modifications to their production lines nor would they have to undertake new 

manufacturing processes. A few small business manufacturers who primarily make electric 
12-33 

motors currently out of the scope of coverage, but whose equipment would be covered by new 

electric motor standards, could be impacted by efficiency standards at TSL 2. These impacts, 

including employment impacts, are discussed in section 12.6.1. Overall, DOE believes there 

would not be a significant decrease in domestic employment levels at TSL 2, the selected TSL in 

today’s final rule. DOE created a lower bound of the potential loss of domestic employment at 

366 employees for TSL 2. DOE based this lower bound estimate on the fact that approximately 5 

percent of the electric motor market is comprised of manufacturers that do not currently produce 

any motors at premium efficiency levels. Therefore, DOE estimated that at most 5 percent of 

domestic electric motor employment in the base case in 2016 could potentially move abroad or 

exit the market entirely. DOE similarly estimated that all electric motor manufacturers produce 

some electric motors at or above TSL 1 efficiency levels. Therefore, DOE does not believe that 

any potential loss of domestic employment would occur at TSL 1. 

Manufacturers, however, cautioned that any energy conservation standard set above 

premium efficiency levels would require major changes to production lines, large investments in 

capital and labor, and would result in extensive stranded assets. This is largely because 

manufacturers would have to design and build motors with larger frame sizes and could 

potentially have to use copper, rather than aluminum rotors. Several manufacturers pointed out 

that this would require extensive retooling, vast engineering resources, and would ultimately 

result in a more labor-intensive production process. Manufacturers generally agreed that a shift 

toward copper rotors would cause companies to incur higher labor costs. These factors could 

cause manufacturers to consider moving production offshore in an attempt to reduce labor costs 

or they may choose to exit the market entirely. Therefore, DOE believes it is more likely that 

efficiency standards set above premium efficiency levels could result in a decrease of labor. 

Accordingly, DOE set the lower bound on the potential loss of domestic employment at 50 

percent of the domestic labor market in the base case in 2016 for TSL 3 and 100 percent for TSL 

4. However, these values represent the worst-case scenario DOE modeled. Manufacturers also 

stated that larger motor manufacturing, the manufacturing of motors above 200 horsepower, 

would be very unlikely to move abroad since the shipping costs associated with those motors are 

very large. Consequently, DOE does not believe standards set at TSL 3 and TSL 4 would likely 

result in the large losses of domestic employment that is suggested by the lower bound of DOE’s 

direct employment analysis.

12.7.2 Production Capacity 

Most manufacturers agreed that any standards expanding the scope of equipment required 

to meet premium efficiency levels would not have a significant impact on manufacturing 

capacity. Manufacturers pointed out, however, that a standard that required them to use copper 

rotors would severely disrupt manufacturing capacity. Most manufacturers emphasized they do 

not currently have the machinery, technology, or engineering resources to produce copper rotors 

in-house. Some manufacturers claim that the few manufacturers that do have the capability of 

producing copper rotors are not able to produce these motors in volumes sufficient to meet the 

demands of their customers. For manufacturers to either completely redesign their motor 

production lines or significantly expand their fairly limited copper rotor production line would 

require a massive retooling and engineering effort, which could take several years to complete. 

Most manufacturers stated they would have to outsource copper rotor production because they 

would not be able to modify their facilities and production processes to produce copper rotors in-
12-34 

house within a three year time period. Most manufacturers agreed that outsourcing rotor die 

casting would constrain capacity by creating a bottleneck in rotor production, as there are very 

few companies that produce copper rotors. 

Manufacturers also pointed out that there is substantial uncertainty surrounding the global 

availability and price of copper, which has the potential to constrain capacity. Several 

manufacturers expressed concern that the combination of all of these factors would make it 

difficult to support existing business while redesigning production lines and retooling. The need 

to support existing business would also cause the redesign effort to take several years. 

In summary, for those TSLs that require copper rotors, DOE believes there is a likelihood 

of capacity constraints in the near term due to fluctuations in the copper market and limited 

copper die casting machinery and expertise. However, for the levels adopted in this final rule, 

DOE does not foresee any capacity constraints.

12.7.3 Cumulative Regulatory Burden 

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of several impending regulations may have serious consequences for some 

manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a single 

regulation may overlook this cumulative regulatory burden. For the cumulative regulatory 

burden analysis, DOE looks at other significant equipment-specific regulations that could affect 

electric motor manufacturers that will take effect 3 years before or after the compliance date of 

new and amended energy conservation standards for this equipment. In addition to the new and

amended energy conservation regulations on electric motors, several other Federal regulations 

apply to this equipment and other equipment produced by the same manufacturers. While the 

cumulative regulatory burden focuses on the impacts on manufacturers of other Federal 

requirements, DOE also has described a number of other regulations in section 12.7.3.2 because 

it recognizes that these regulations also impact the equipment covered by this rulemaking. 

Companies that produce a wide range of regulated equipment may be faced with more 

capital and product development expenditures than competitors with a narrower scope of 

equipment. Regulatory burdens can prompt companies to exit the market or reduce their 

equipment offerings, potentially reducing competition. Smaller companies in particular can be 

disproportionately affected by regulatory costs since these companies have lower sales volumes 

over which they can amortize the costs of meeting new regulations. An adopted standard is not 

economically justified if it contributes to an unacceptable level of cumulative regulatory burden. 

12.7.3.1 DOE Regulations for Other Equipment Produced by Electric Motors

Manufacturers

In addition to the new and amended energy conservation standards on electric motors, 

several other Federal regulations and pending regulations apply to other equipment produced by 

the same manufacturers. DOE recognizes that each regulation can significantly affect a 

manufacturer’s financial operations. Multiple regulations affecting the same manufacturer can 

quickly strain manufacturers’ profits and possibly cause an exit from the market. Table 12.7.2

lists the other DOE energy conservation standards that could also affect manufacturers of electric 
12-35 

motors in the 3 years leading up to and after the compliance date of new and amended energy 

conservation standards for this equipment. 
12-36 

Table 12.7.2 Other DOE and Federal Actions Affecting the Electric Motors Industry

Regulation Approximate 

Compliance Date

Number of Impacted 

Companies from the 

Market and Technology 

Assessment (MTA) (See 

Chapter 3)

Estimated Industry 

Total Conversion 

Expenses

General Service Incandescent 

Lamps

2012; 2013; & 

2014 1 N/A†

Commercial Clothes 

Washers 2013 1 $20.4 million (2008$)b

Dishwashers 2013 1 $85.3 million (2010$)c

Direct Heating Equipment 2013 & 2015 1 $5.39 million (2009$)d

Residential Furnaces & 

Residential Central Air 

Conditioners and Heat 

Pumps

2013 & 2015 1 $45.7 million (2009$)e

Room Air Conditioners 2014 1 $171 million (2009$)f

Residential Refrigerators and 

Freezers 2014 1 $1,245 million (2009$)g

Fluorescent Lamp Ballasts 2014 1 $82 million (2010$)h

Residential Clothes Dryers 2015 1 $95 million (2009$)i

Small Electric Motors 2015 16 $51.2 million (2009$)j

Residential Water Heaters 2015 2 $95.9 million (2009$)k

Residential Clothes Washers 2015 & 2018 1 $418.5 million (2010$)l

Commercial Distribution 

Transformers 2016 2 $61.0 million (2011$)m

 b Estimated industry conversion expenses were published in the TSD for the January 2010 commercial clothes 

washers final rule. 75 FR 1122. c Estimated industry conversion expenses were published in the TSD for the May 2012 dishwashers direct final rule. 

77 FR 31918. d Estimated industry conversion expenses were published in the TSD for the April 2010 heating products final rule. 

75 FR 20112. e Estimated industry conversion expenses were published in the TSD for the June 2011 furnaces, central air 

conditioners and heating pumps direct final rule. 76 FR 37408. f Estimated industry conversion expenses were published in the TSD for the April 2011 AHAM direct final rule. 76

FR 22454. g Estimated industry conversion expenses were published in the TSD for the September 2011 refrigerators final rule. 

76 FR 57516. h Estimated industry conversion expenses were published in the TSD for the April 2011 AHAM direct final rule. 76

FR 70548. i

Estimated industry conversion expenses were published in the TSD for the November 2011 fluorescent lamp 

ballasts final rule. 76 FR 70548. j

Estimated industry conversion expenses were published in the TSD for the March 2010 small electric motors final 

rule. 75 FR 10874. k Estimated industry conversion expenses were published in the TSD for the April 2010 heating products final rule. 

75 FR 20112. l

Estimated industry conversion expenses were published in the TSD for the May 2012 residential clothes washers 

direct final rule. 77 FR 32308. m Estimated industry conversion expenses were published in the TSD for the April 2013distribution transformers 

final rule. 78 FR 23336. 
12-37 

Microwave Ovens 2016 1 $43.1 million (2011$)n

Metal Halide Lamp Fixtures 2017* 1 $25.0 million (2012$)o

General Service Fluorescent 

Lamps and Incandescent 

Reflector Lamps Update

2017* 1 N/A††

HID Lamps 2018* 1 N/A††

Commercial Packaged AirConditioning and Heating 

Equipment

2018* 1 N/A††

Commercial and Industrial 

Fans and Blowers 2018* 2 N/A††

Commercial and Industrial 

Pumps 2018* 1 N/A††

Commercial Clothes 

Washers Update 2018* 1 N/A††

*The dates listed are an approximation. The exact dates are pending final DOE action.

† For minimum performance requirements prescribed by the Energy Independence and Security Act of 2007 (EISA 

2007), DOE did not estimate total industry conversion costs because an MIA was not completed as part of a 

rulemaking. Pub. L. 110-140. EISA 2007 made numerous amendments to the Energy Policy and Conservation Act 

(EPCA) of 1975, Pub. L. 94-163, (42 U.S.C. 6291–6309), which established an energy conservation program for 

major household appliances and industrial and commercial equipment.

†† For energy conservation standards for rulemakings awaiting DOE final action, DOE does not have a finalized 

estimated total industry conversion cost.

Some Federal energy conservation regulations have a more significant impact on 

manufacturers of electric motors than others because manufacturers hold a significant market 

share of those covered equipment. Several manufacturers expressed concern about the proximity 

between the compliance date of this rulemaking and that of the small electric motors rulemaking 

at 75 FR 10874 (March 9, 2010). Most manufacturers of electric motors covered by this 

rulemaking also produce electric motors that are covered by the small electric motors rulemaking. 

Manufacturers stated that adopting these two regulations in a potentially short timeframe could 

strain R&D and capital expenditure budgets for motor manufacturers. Table 12.7.3 below shows 

the DOE energy conservation standards with compliance dates within three years of electric 

motors where manufacturers are expected to be most impacted due to their market positions. For 

these rulemakings, electric motors manufacturers would likely be burdened by a significant 

portion of the estimated industry conversion costs. In some cases, specific market share data was 

not available, but manufacturers were identified as major or minor manufacturers in the given 

market when this information was publicly available. 

 n Estimated industry conversion expenses were published in the TSD for the May 2013 microwave ovens final rule. 

78 FR 36316. o Estimated industry conversion expenses were published in the TSD for the February 2014 metal halide lamp 

fixture final rule. 79 FR 7746
12-38 

Table 12.7.3 DOE Regulations on Equipment for Which Electric Motor Manufacturers 

Hold Significant Market Share

Manufacturer Market Share in DOE Regulated Product

Regulation

Estimated Industry 

Total Conversion 

Expenses

General 

Electric Baldor Regal 

Beloit Siemens Toshiba

General Service 

Incandescent 

Lamps

N/A

Residential Gas 

Kitchen Ranges 

and Ovens

$22.6 million 

(2006$)

47% 

(electric)

37% (gas)

General Service 

Fluorescent Lamps 

and Incandescent 

Reflector Lamps

$363.1 million 

(2008$) N/A (major)

Packaged Terminal 

Air Conditioners 

and Packaged 

Terminal Heat 

Pumps

$17.3 million 

(2007$) N/A (major)

Commercial 

Clothes Washers

$20.4 million 

(2008$) N/A (major)

Direct Heating 

Equipment

$5.39 million 

(2009$)

N/A 

(major)

Residential 

Furnaces & 

Residential Central 

Air Conditioners 

and Heat Pumps

$45.7 million 

(2009$)

N/A 

(major)

Dishwashers $94.0 million 

(2010$) N/A (major)

Room Air 

Conditioners

$171 million 

(2009$) N/A (major)

Residential 

Refrigerators and 

Freezers

$1,245 million 

(2009$)

27% 

(refrigerators)

Fluorescent Lamp 

Ballasts $82 million (2010$) N/A (major)

Residential Clothes 

Dryers $95 million (2009$)

16% 

(electric)

10% (gas)

Small Electric 

Motors

$51.2 million 

(2009$)

N/A 

(major)

N/A 

(major)

Residential Water 

Heaters

$95.9 million 

(2009$)

N/A 

(major)

Commercial 

Distribution 

Transformers

$61.0 million 

(2011$) N/A (major) N/A (major)

ER, BR, and Small 

Diameter IRLs N/A N/A (major)

General Service 

Fluorescent Lamps 

and Incandescent 

N/A N/A (major)
12-39 

Reflector Lamps 

Update

Metal Halide Lamp 

Fixtures

$25.0 million 

(2012$) N/A (major)

HID Lamps N/A N/A (major)

Commercial 

Clothes Washers 

Update

N/A N/A (major)

12.7.3.2 Other Regulations That Could Impact Electric Motors Manufacturers

European Commission Ecodesign Directive for Lot 30

The European Commission (EC) is currently evaluating expanding conservation 

standards to motor types with no existing standards. The expanded scope being considered 

includes some of the special and definite purpose motors (i.e. brake motors) that DOE has 

included in the expanded scope of the final rule. The EC is also evaluating standards for several 

motor types not included in the final rule by DOE, such as permanent magnet motors, switched 

reluctance motors, and motors operating in conjunction with inverter drives. This could be an 

additional burden for manufacturers that sell motors in Europe. 

NFPA 70 and NFPA 20 

The National Fire Protection Association (NFPA) has issued two codes that impact 

manufacturers of fire pump electric motors – NFPA 70: National Electric Code and NFPA 20:

Standard for the Installation of Stationary Pumps for Fire Protection. To comply with these

standards, manufacturers of fire pump electric motors must undergo additional design and 

engineering efforts and incur increased testing and certification costs. These testing and 

certification costs could add to the compliance costs of new and amended Federal energy 

conservation standards for covered fire pump motors. 

12.8 CONCLUSION

The following section summarizes the impacts for the scenarios DOE believes are most 

likely to capture the range of impacts on electric motor manufacturers as a result of this final 

rule. DOE also notes that while these scenarios bound the range of most plausible impacts on 

manufacturers, there potentially could be circumstances which cause manufacturers to 

experience impacts outside of this range. 

TSL 1 represents EL 1 for ECG 1a, ECG 1b, and ECG 2 motors and baseline for ECG 3 

motors. At TSL 1, DOE estimates impacts on INPV to range from $8.4 million to -$16.7 million, 

or a change in INPV of 0.2 percent to -0.5 percent. At this TSL, industry free cash flow is 

estimated to decrease by approximately 1 percent to $164.3 million, compared to the base case 

value of $166.1 million in 2015. 

The INPV impacts at TSL 1 range from slightly positive to slightly negative. 

Consequently, DOE does not anticipate that manufacturers would lose a significant portion of 

their INPV at this TSL. This is because the vast majority of shipments already meets or exceeds 

the efficiency levels prescribed at TSL 1. DOE estimates that in the year of compliance, 90 

percent of all electric motor shipments (91 percent of ECG 1a, 68 percent of ECG 1b, 8 percent 
12-40 

of ECG 2, and 100 percent of ECG 3 shipments) would meet the efficiency levels at TSL 1 or 

higher in the base case in 2016. Since ECG 1a shipments account for over 97 percent of all 

electric motor shipments, the effects on those motors are the primary driver for the impacts at 

this TSL. Only a few ECG 1a shipments not currently covered by the existing electric motor 

standard and a small amount of ECG 1b and ECG 2 shipments would need to be converted to 

comply with efficiency standards prescribed at TSL 1. 

DOE expects conversion costs to be small compared to the industry value because most 

of the electric motor shipments, on a volume basis, already meet the efficiency levels analyzed at 

this TSL. DOE estimates product conversion costs of $6.2 million due to the expanded scope of 

motors covered by this rulemaking which includes motors previously not covered by the existing 

electric motor energy conservation standards. DOE believes that at this TSL, there will be some 

engineering costs as well as testing and certification costs associated with this scope expansion. 

DOE estimates the capital conversion costs to be minimal at TSL 1. This is mainly because 

almost all manufacturers currently produce some motors that are compliant at TSL 1 efficiency 

levels and it would not be much of a capital investment to bring all motor production to this 

efficiency level.

TSL 2 represents EL 2 for ECG 1a and ECG 1b motors, EL 1 for ECG 2 motors, and 

baseline for ECG 3 motors. At TSL 2, DOE estimates impacts on INPV to range from $392.6 

million to -$347.7 million, or a change in INPV of 11.3 percent to -10.0 percent. At this TSL, 

industry free cash flow is estimated to decrease by approximately 17 percent to $137.1 million, 

compared to the base case value of $166.1 million in 2015. 

The INPV impacts at TSL 2 range from moderately positive to slightly negative. DOE 

estimates that in the year of compliance, 60 percent of all electric motor shipments (60 percent of 

ECG 1a, 31 percent of ECG 1b, 8 percent of ECG 2, and 100 percent of ECG 3 shipments) 

would meet the efficiency levels at TSL 2 or higher in the base case in 2016. The majority of 

shipments are currently covered by an electric motors standard that requires general purpose 

Design A and B motors to meet the efficiency levels at this TSL. Therefore, only previously noncovered Design A and B motors and most ECG 1b and ECG 2 motors would need to be 

converted to comply with efficiency standards prescribed at TSL 2. 

At TSL 2, DOE expects conversion costs to increase significantly from TSL 1. However, 

these conversion costs do not represent a large portion of the base case INPV, since the majority 

of electric motor shipments already meet the efficiency levels required at this TSL. DOE 

estimates product conversion costs of $58.0 million due to the expanded scope of this 

rulemaking, which includes motors not previously covered by the existing electric motor energy 

conservation standards and the inclusion of ECG 1b and ECG 2 motors. DOE believes there will 

be moderate engineering costs as well as testing and certification costs at this TSL associated 

with this scope expansion. DOE estimates the capital conversion costs to be approximately $26.6 

million at TSL 2. While most manufacturers already produce at least some motors that are 

compliant at TSL 2, these manufacturers would likely have to invest in machinery to bring all 

motor production to these efficiency levels. 
12-41 

TSL 3 represents EL 3 for ECG 1a and ECG 1b motors, EL 2 for ECG 2 motors, and EL 

1 for ECG 3 motors. At TSL 3, DOE estimates the impacts on INPV to range from $1,063.9 

million to -$549.7 million, or a change in INPV of 30.6 percent to -15.8 percent. At this TSL, 

industry free cash flow is estimated to decrease by approximately 170 percent to -$116.0 million, 

compared to the base case value of $166.1 million in 2015. 

The INPV impacts at TSL 3 range from significantly positive to moderately negative. 

DOE estimates that in the year of compliance, 23 percent of all electric motor shipments (24 

percent of ECG 1a, 4 percent of ECG 1b, less than 1 percent of ECG 2, and 19 percent of ECG 3 

shipments) would meet the efficiency levels at TSL 3 or higher in the base case in 2016. The 

majority of shipments would need to be converted to comply with efficiency standards 

prescribed at TSL 3.

DOE expects conversion costs to increase significantly at TSL 3 and become a 

substantial investment for manufacturers. DOE estimates product conversion costs of $618.1 

million at TSL 3, since most electric motors in the base case do not exceed the current motor 

standards set at premium efficiency levels for Design A and B motors, which represents EL 2 for 

ECG 1a. DOE believes there would be a massive reengineering effort that manufacturers would 

have to undergo to have all motors meet this TSL. Additionally, motor manufacturers would 

have to increase the efficiency levels for ECG 1b, ECG 2, and ECG 3 motors. DOE estimates the 

capital conversion costs to be approximately $222.8 million at TSL 3. Most manufacturers would 

have to make significant investments to their production facilities in order to convert all their 

motors to be compliant at TSL 3. 

TSL 4 represents EL 4 for ECG 1a and ECG 1b motors, EL 2 for ECG 2 motors, and EL 

3 for ECG 3 motors. At TSL 4, DOE estimates impacts on INPV to range from $1,904.1 million 

to -$1,429.8 million, or a change in INPV of 54.7 percent to -41.1 percent. At this TSL, industry 

free cash flow is estimated to decrease by approximately 303 percent to -$336.6 million, 

compared to the base case value of $166.1 million in 2015. 

The INPV impacts at TSL 4 range from significantly positive to significantly negative. 

DOE estimates that in the year of compliance only 8 percent of all electric motor shipments (9 

percent of ECG 1a, less than 1 percent of ECG 1b, less than 1 percent of ECG 2, and no ECG 3 

shipments) would meet the efficiency levels at TSL 2 or higher in the base case in 2016. Almost 

all shipments would need to be converted to comply with efficiency standards prescribed at TSL 

4. 

DOE expects conversion costs again to increase significantly from TSL 3 to TSL 4. 

Conversion costs at TSL 4 now represent a massive investment for electric motor manufacturers. 

DOE estimates product conversion costs of $627.4 million at TSL 4, which are only slightly 

more than at TSL 3. DOE believes that manufacturers would need to completely reengineer 

almost all electric motors sold as well as test and certify those motors. DOE estimates capital 

conversion costs of $707.2 million at TSL 4. This is a significant increase in capital conversion 

costs from TSL 3 since manufacturers would need to adopt copper die-casting at TSL 4. This 

technology requires a significant level of investment because the majority of manufacturers’ 

machinery would need to be replaced or significantly modified. 
12-42 

REFERENCES

 

1 Securities and Exchange Commission, Annual 10-K Reports, Various Years, 

Washington DC. <www.sec.gov>. 

2 Standard and Poors Financial Services LLC, Company Credit Ratings, Various 

Companies, New York, NY. < www2.standardandpoors.com>. 

3 NEMA – NEMA Members. Last Accessed June 5, 2013. 

<http://www.nema.org/about/members/ >.

4 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of 

Companies, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000. 
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS ............................... 13-1

13.3 POWER SECTOR EMISSIONS FACTORS ................................................................ 13-3

13.4 UPSTREAM FACTORS ............................................................................................... 13-3

13.5 EMISSIONS IMPACT RESULTS ................................................................................ 13-4

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors .......................................................................... 13-3

Table 13.4.1 Electricity Upstream Emissions Factors ............................................................ 13-4

LIST OF FIGURES

Figure 13.5.1 Electric Motors: CO2 Total Emissions Reduction ............................................. 13-6

Figure 13.5.2 Electric Motors: SO2 Total Emissions Reduction .............................................. 13-6

Figure 13.5.3 Electric Motors: NOX Total Emissions Reduction ............................................ 13-7

Figure 13.5.4 Electric Motors: Hg Total Emissions Reduction ............................................... 13-7

Figure 13.5.5 Electric Motors: N2O Total Emissions Reduction ............................................. 13-8

Figure 13.5.6 Electric Motors: CH4 Total Emissions Reduction ............................................. 13-8

13-i 
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector emissions of carbon dioxide 

(CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and mercury (Hg). The second component 

estimates the impacts of a potential standard on emissions of two additional greenhouse gases, 

methane (CH4) and nitrous oxide (N2O), as well as the reductions to emissions of all species due 

to “upstream” activities in the fuel production chain. These upstream activities comprise 

extracting, processing, and transporting fuels to the site of combustion. The associated emissions are 

referred to as upstream emissions. Together, these emissions account for the full-fuel-cycle 

(FFC), in accordance with DOE’s FFC Statement of Policy. 76 FR 51282 (August 18, 2011). 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from runs of DOE’s NEMS-BT model, described in Chapter 15. DOE used the version of NEMS 

based on the Annual Energy Outlook 2013 (AEO 2013).1 Each annual version of NEMS 

incorporates the projected impacts of existing air quality regulations on emissions. AEO 2013

generally represents current Federal and State legislation and final implementation regulations in 

place as of the end of December 2012. Site emissions of CO2 and NOX are estimated using 

emissions intensity factors from a publication of the Environmental Protection Agency (EPA).2

Combustion emissions of CH4 and N2O are estimated using emissions intensity factors 

published by the EPA, GHG Emissions Factors Hub.a The FFC upstream emissions are 

estimated based on the methodology developed by Coughlin (2013).3 The upstream emissions 

include both emissions from fuel combustion during extraction, processing and transportation of 

fuel, and “fugitive” emissions (direct leakage to the atmosphere) of CH4 and CO2. 

The emissions intensity factors are expressed in terms of physical units per MWh or 

MMBtu of site energy savings. Total emissions reductions are estimated using the energy 

savings calculated in the national impact analysis (chapter 10). 

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

SO2 emissions from affected electric generating units (EGUs) are subject to nationwide 

and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual 

emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia 

(D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air 

Interstate Rule (CAIR), which created an allowance-based trading program that operates along 

with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR was 

remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit) but parts of it remained in effect. On July 6, 2011 EPA issued a replacement for CAIR, 

a http://www.epa.gov/climateleadership/guidance/ghg-emissions.html

13-1 

 
the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 

2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, 

LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA to continue administering 

CAIR. The AEO 2013 emissions factors used for this analysis assume that CAIR remains a 

binding regulation through 2040. 

The attainment of emissions caps is typically flexible among affected EGUs and is 

enforced through the use of emissions allowances and tradable permits. Under existing EPA 

regulations, any excess SO2 emissions allowances resulting from the lower electricity demand 

caused by the imposition of an efficiency standard could be used to permit offsetting increases in 

SO2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was 

uncertainty about the effects of efficiency standards on SO2 emissions covered by the existing 

cap-and-trade system, but it concluded that no reductions in power sector emissions would occur 

for SO2 as a result of standards.

Beginning in 2015, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 

2011. 77 FR 9304 (February 16, 2012). In the final MATS rule, EPA established a standard for 

hydrogen chloride as a surrogate for acid gas hazardous air pollutants (HAP), and also 

established a standard for SO2 (a non-HAP acid gas) as an alternative equivalent surrogate 

standard for acid gas HAP. The same controls are used to reduce HAP and non-HAP acid gas; 

thus, SO2 emissions will be reduced as a result of the control technologies installed on coal-fired 

power plants to comply with the MATS requirements for acid gas. AEO 2013 assumes that, in 

order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent 

injection systems installed by 2015. Both technologies, which are used to reduce acid gas 

emissions, also reduce SO2 emissions. Under the MATS, NEMS shows a reduction in SO2

emissions when electricity demand decreases (e.g., as a result of energy efficiency standards). 

Emissions will be far below the cap established by CAIR, so it is unlikely that excess SO2

emissions allowances resulting from the lower electricity demand would be needed or used to 

permit offsetting increases in SO2 emissions by any regulated EGU. Therefore, DOE believes 

that efficiency standards will reduce SO2 emissions in 2015 and beyond. 

CAIR established a cap on NOX emissions in 28 eastern States and the District of 

Columbia. Energy conservation standards are expected to have little effect on NOX emissions in 

those States covered by CSAPR because excess NOX emissions allowances resulting from the 

lower electricity demand could be used to permit offsetting increases in NOX emissions.

However, standards would be expected to reduce NOX emissions in the States not affected by 

CAIR, so DOE estimated NOX emissions reductions from potential standards for those States. 

The MATS limit mercury emissions from power plants, but they do not include 

emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg 

emissions. DOE estimated mercury emissions reductions using the NEMS-BT based on AEO 

2013, which incorporates the MATS. 

13-2 
13.3 POWER SECTOR EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from runs of DOE’s NEMS-BT model, using the version updated to the Annual Energy Outlook 

2013 (AEO 2013) for emissions from power plants and the version updated to the Annual Energy 

Outlook 2012 (AEO 2012) for the upstream emissions. To model the impact of a standard, DOE 

inputs a reduction to annual energy demand for the corresponding end use in the appropriate start 

year. The NEMS-BT model is run with the decremented energy demand to determine the 

modified build-out of capacity, fuel use and power sector emissions. A marginal emissions 

intensity factor is defined by dividing the reduction in the total emissions of a given pollutant by 

the reduction in total generation (in billion kWh). DOE uses the site energy savings multiplied by 

a transmission-and-distribution (T&D) loss factor to estimate the reduction in generation for each 

TSL. Details on the approach used may be found in Coughlin (2013).3

 

Table 13.3.1 presents power plant emissions factors for selected years. These power plant 

emissions factors are derived from the emissions factors of the plant types used to supply 

electricity to customers so they can operate motor driven systems. DOE did not have data on the 

load shape of electric motors, so it used the load shape corresponding to commercial lighting as a 

proxy to the load shape of electric motors. The rationale is that when motor driven systems in 

industry and commercial buildings – which represent together most of the projected shipments – 

are operating lights are likely to be on, and vice-versa. Therefore, electric motors and lighting in 

industry and commercial buildings follow similar daily operational timeframes. The factors 

presented in Table 13.3.1 for each year are weighted averages that take into account the 

projected shares of each of the sources used to generate electricity to support commercial 

lighting. 

 The power plant emissions factor for NOX is an average for the entire U.S. The marginal 

calculation based on the NEMS-BT model accounts for the fact that NOX emissions are capped 

in some States. 

Table 13.3.1 Power Plant Emissions Factors 

Unit* 2016 2020 2025 2030 2035 2040

CO2 kg/MWh 602 602 559 525 480 416

SO2 g/MWh 602 602 728 775 574 765

NOx g/MWh 355 355 329 261 196 194

Hg g/MWh 0.0015 0.0015 0.0006 0.0010 0.0008 0.0009

N2O g/MWh 6.9 7.2 7.2 7.1 7.1 6.9

CH4 g/MWh 48 50 50 50 49 48

* Refers to site electricity savings.

13.4 UPSTREAM FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10-B. See also Coughlin (2013).3

 When demand for a 

13-3 
particular fuel is reduced, there is a corresponding reduction in the emissions from combustion of 

that fuel at either the building site or the power plant. The associated reduction in energy use for 

upstream activities leads to further reductions in emissions. These upstream emissions are 

defined to include the combustion emissions from the fuel used upstream, the fugitive emissions 

associated with the fuel used upstream, and the fugitive emissions associated with the fuel used 

on site. 

Fugitive emissions of CO2 occur during oil and gas production, but are small relative to 

combustion emissions. They comprise about 2.5 percent of total CO2 emissions for natural gas 

and 1.7 percent for petroleum fuels. Fugitive emissions of CH4 occur during oil, gas and coal 

production. Combustion emissions of CH4 are very small, while fugitive emissions (particularly 

for gas production) may be relatively large. Hence, fugitive emissions make up over 99 percent 

of total CH4 emissions for natural gas, about 95 percent for coal, and 93 percent for petroleum 

fuels. 

 Upstream emissions factors account for both fugitive emissions and combustion 

emissions in extraction, processing, and transport of primary fuels. For ease of application in its 

analysis, DOE developed all of the emissions factors using site (point of use) energy savings in 

the denominator. Table 13.4.1 presents the electricity upstream emissions factors for selected 

years. The caps that apply to power sector NOX emissions do not apply to upstream combustion 

sources. 

Table 13.4.1 Electricity Upstream Emissions Factors

Unit* 2016 2020 2025 2030 2035 2040

CO2 kg/MWh 28.5 27.3 26.9 26.8 26.9 26.3

SO2 g/MWh 4.9 5.3 5.3 5.2 5.2 5.1

NOx g/MWh 361 340 334 333 336 329

Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

N2O g/MWh 0.25 0.25 0.25 0.25 0.24 0.24

CH4 g/MWh 2142 2025 2008 2025 2057 1999

* Refers to site electricity savings.

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated, for each TSL, cumulative emissions reductions for 

the lifetime of equipment sold over the 30-year analysis period. 

13-4 
Table 13.5.1 Cumulative Emissions Reduction Estimated for Electric Motors Trial 

Standard Levels 

Trial Standard Level

1 2 3 4 

Primary Emissions

CO2 (million metric tons) 62.7 373 574 731

SO2 (thousand tons) 106 668 1,032 1,312

NOX (thousand tons) 33.6 196 301 383

Hg (tons) 0.132 0.819 1.26 1.61

N2O (thousand tons) 1.24 8.30 12.9 16.3

CH4 (thousand tons) 7.38 46.2 71.4 90.7

Upstream Emissions

CO2 (million metric tons) 3.55 22.0 33.9 43.1

SO2 (thousand tons) 0.761 4.71 7.26 9.23

NOX (thousand tons) 48.8 302 466 593

Hg (tons) 0.002 0.012 0.018 0.023

N2O (thousand tons) 0.036 0.221 0.341 0.433

CH4 (thousand tons) 296 1,837 2,834 3,604

Full Fuel Cycle Emissions

CO2 (million metric tons) 66.2 395 608 774

SO2 (thousand tons) 107 673 1,039 1,321

NOX (thousand tons) 82.5 498 767 977

Hg (tons) 0.134 0.831 1.28 1.63

N2O (thousand tons) 1.27 8.52 13.2 16.8

CH4 (thousand tons) 304 1,883 2,905 3,695

Figure 13.5.1 through Figure 13.5.6 show total annual emissions reductions for each 

pollutant and TSL. The reductions reflect the lifetime impacts of equipment sold over the 30-

year analysis period. 

13-5 
Figure 13.5.1 Electric Motors: CO2 Total Emissions Reduction

Figure 13.5.2 Electric Motors: SO2 Total Emissions Reduction

0

5,000

10,000

15,000

20,000

25,000

30,000

20162021

202620312036

2041204620512056206120662071

kt

CO2 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

0

10,000

20,000

30,000

40,000

50,000

60,000

201620212026203120362041204620512056206120662071

ton

SO2 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

13-6 
Figure 13.5.3 Electric Motors: NOX Total Emissions Reduction

Figure 13.5.4 Electric Motors: Hg Total Emissions Reduction

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

20162021

202620312036

2041204620512056206120662071

ton

NOx Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

201620212026203120362041204620512056206120662071

ton

Hg Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

13-7 
Figure 13.5.5 Electric Motors: N2O Total Emissions Reduction

Figure 13.5.6 Electric Motors: CH4 Total Emissions Reduction

0

100

200

300

400

500

600

700

800

900

20162021

2026203120362041

204620512056206120662071

ton

N2OEmissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

201620212026203120362041204620512056206120662071

ton

CH4 Emissions Reduction

TSL 1 TSL 2 TSL 3 TSL 4

13-8 
REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2013 with Projections to 

2040, 2013. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf> 

2. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors, 

AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources. 1998. 

http://www.epa.gov/ttn/chief/ap42/index.html

3. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

13-9 
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

TABLE OF CONTENTS

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon .................................................................................................... 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ......................................................................... 14-2

14.2.3 Development of Social Cost of Carbon Values ............................................................. 14-2

14.2.4 Current Approach and Key Assumptions ...................................................................... 14-2

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.4 RESULTS ...................................................................................................................... 14-5

LIST OF TABLES

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010-2050 (in 2007 

dollars per metric ton) ........................................................................................ 14-4

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010-2050 (in 2007 

dollars per metric ton CO2) ................................................................................ 14-4

Table 14.4.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

Electric Motors Trial Standard Levels ............................................................... 14-6

Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Electric Motors Trial Standard Levels ............................................................... 14-7

Table 14.4.3 Estimates of Present Value of NOX Emissions Reduction under Electric 

Motors Trial Standard Levels ............................................................................ 14-8

14-i 
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of energy conservation standards for electric motors DOE 

estimated the monetary benefits from the potential reduced emissions of carbon dioxide (CO2) 

and nitrogen oxides (NOX) that are expected to result from each of the trial standard levels (TSL) 

considered. This chapter summarizes the basis for the monetary values used for each of these 

emissions and presents the estimated benefits. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon 

The social cost of carbon (SCC) is an estimate of the monetized damages associated with 

an incremental increase in carbon emissions in a given year. It is intended to include (but is not 

limited to) changes in net agricultural productivity, human health, property damages from 

increased flood risk, and the value of ecosystem services. Estimates of the SCC are provided in 

dollars per metric ton of carbon dioxide. A domestic SCC value is meant to reflect the value of 

damages in the United States resulting from a unit change in carbon dioxide emissions, while a 

global SCC value is meant to reflect the value of damages worldwide.

 Under section 1(b) of Executive Order 12866, agencies must, to the extent permitted by 

law, “assess both the costs and the benefits of the intended regulation and, recognizing that some 

costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

SCC estimates presented in the Executive Order is to allow agencies to incorporate the 

monetized social benefits of reducing CO2 emissions into cost-benefit analyses of regulatory 

actions. The estimates are presented with an acknowledgement of the many uncertainties 

involved and with a clear understanding that they should be updated over time to reflect 

increasing knowledge of the science and economics of climate impacts.

As part of the interagency process that developed these SCC estimates, technical experts 

from numerous agencies met on a regular basis to explore the technical literature in relevant 

fields, discuss key model inputs and assumptions, and consider public comments. The main 

objective of this process was to develop a range of SCC values using a defensible set of input 

assumptions grounded in the existing scientific and economic literatures. In this way, key 

uncertainties and model differences transparently and consistently inform the range of SCC 

estimates used in the rulemaking process.

14-1 
14.2.2 Monetizing Carbon Dioxide Emissions 

 When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A report from the National Research 

Council1

 points out that any assessment will suffer from uncertainty, speculation, and lack of 

information about (1) future emissions of greenhouse gases, (2) the effects of past and future 

emissions on the climate system, (3) the impact of changes in climate on the physical and 

biological environment, and (4) the translation of these environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change will raise serious questions of science, economics, and ethics and should be viewed as 

provisional. 

Despite the limits of both quantification and monetization, SCC estimates can be useful 

in estimating the social benefits of reducing CO2 emissions. The agency can estimate the benefits 

from reduced (or costs from increased) emissions in any future year by multiplying the change in 

emissions in that year by the SCC values appropriate for that year. The net present value of the 

benefits can then be calculated by multiplying each of these future benefits by an appropriate 

discount factor and summing across all affected years. This approach assumes that the marginal 

damages from increased emissions are constant for small departures from the baseline emissions 

path, an approximation that is reasonable for policies that have effects on emissions that are 

small relative to cumulative global carbon dioxide emissions.

14.2.3 Development of Social Cost of Carbon Values 

 In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in 

how benefits are evaluated across Federal agencies, the Administration sought to develop a 

transparent and defensible method, specifically designed for the rulemaking process, to quantify 

avoided climate change damages from reduced CO2 emissions. The interagency group did not 

undertake any original analysis. Instead, it combined SCC estimates from the existing literature 

to use as interim values until a more comprehensive analysis could be conducted. The outcome 

of the preliminary assessment by the interagency group was a set of five interim values: global 

SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per ton of CO2.

2 These 

interim values represented the first sustained interagency effort within the U.S. government to 

develop an SCC for use in regulatory analysis. The results of this preliminary effort were 

presented in several proposed and final rules. 

14.2.4 Current Approach and Key Assumptions

 After the release of the interim values, the interagency group reconvened on a regular 

basis to generate improved SCC estimates. Specially, the group considered public comments and 

further explored the technical literature in relevant fields. The interagency group relied on three 

integrated assessment models commonly used to estimate the SCC: the FUND, DICE, and 

PAGE models. These models are frequently cited in the peer-reviewed literature and were used 

in the last assessment of the Intergovernmental Panel on Climate Change (IPCC). Each model 

was given equal weight in the SCC values that were developed.

14-2 
Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. A key objective of the interagency process was to enable a 

consistent exploration of the three models while respecting the different approaches to 

quantifying damages taken by the key modelers in the field. An extensive review of the literature 

was conducted to select three sets of input parameters for these models: climate sensitivity;

socio-economic and emissions trajectories; and discount rates. A probability distribution for 

climate sensitivity was specified as an input into all three models. In addition, the interagency 

group used a range of scenarios for the socio-economic parameters and a range of values for the 

discount rate. All other model features were left unchanged, relying on the model developers’ 

best estimates and judgments.

 The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate 

across all three models at a 3 percent discount rate, is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution. The values 

grow in real terms over time. Additionally, the interagency group determined that a range of 

values from 7 percent to 23 percent should be used to adjust the global SCC to calculate 

domestic effects, although preference is given to consideration of the global benefits of reducing 

CO2 emissions. Table 14.2.1 presents the values in the 2010 interagency group report,a

 which is 

reproduced in appendix 14-A of the TSD.

The SCC values used for this analysis were generated using the most recent versions of 

the three integrated assessment models that have been published in the peer-reviewed literature, 

as described in the 2013 update3

 from the interagency working group (revised November 2013).b

Table 14.2.2 shows the updated sets of SCC estimates in five year increments from 2010 to 

2050. The full set of annual SCC estimates between 2010 and 2050 is reported in appendix 14-B 

of the TSD. The central value that emerges is the average SCC across models at the 3 percent 

discount rate. However, for purposes of capturing the uncertainties involved in regulatory impact 

analysis, the interagency group emphasizes the importance of including all four sets of SCC 

values.

a Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group 

on Social Cost of Carbon, United States Government, February 2010. 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf. b Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 

Interagency Working Group on Social Cost of Carbon, United States Government. May 2013; revised November 

2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf. 

14-3 

 
Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010-2050 (in 2007 dollars 

per metric ton)

Year

Discount Rate %

5 3 2.5 3

Average Average Average 95th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010-2050 (in 2007 

dollars per metric ton CO2) 

Year

Discount Rate %

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

 It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The 2009 National Research Council report 

mentioned above points out that there is tension between the goal of producing quantified 

estimates of the economic damages from an incremental ton of carbon and the limits of existing 

efforts to model these effects. There are a number of analytic challenges that are being addressed 

by the research community, including research programs housed in many of the Federal agencies 

participating in the interagency process to estimate the SCC. The interagency group intends to 

periodically review and reconsider those estimates to reflect increasing knowledge of the science 

and economics of climate impacts, as well as improvements in modeling.

14-4 
 In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report adjusted to 2012$ using the 

GDP price deflator. For each of the four sets of SCC values, the values for emissions in 2015 

were $11.8, $39.7, $61.2, and $117 per metric ton avoided (values expressed in 2012$). DOE 

derived values after 2050 using the relevant growth rates for the 2040-2050 period in the 

interagency update.

DOE multiplied the CO2 emissions reduction estimated for each year by the SCC value 

for that year in each of the four cases. To calculate a present value of the stream of monetary 

values, DOE discounted the values in each of the four cases using the specific discount rate that 

had been used to obtain the SCC values in each case. 

. 

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NOX emissions from the TSLs 

it considered. As noted in chapter 13, new or amended energy conservation standards would 

reduce NOX emissions in those States that are not affected by caps. DOE estimated the 

monetized value of NOX emissions reductions resulting from each of the TSLs considered based 

on environmental damage estimates found in the relevant scientific literature. Available 

estimates suggest a very wide range of monetary values, ranging from $476 to $4,893 per ton (in 

2013$).4 DOE calculated monetary benefits using a medium value for NOX emissions of $2,684 

per short ton (in 2013$), and real discount rates of 3 percent and 7 percent. 

DOE is still evaluating appropriate values to use to monetize avoided SO2 and Hg 

emissions. It did not monetize these emissions for this analysis. 

14.4 RESULTS

Table 14.4.1 presents the global values of CO2 emissions reductions for each considered 

TSL. DOE calculated domestic values as a range from 7 percent to 23 percent of the global 

values, and these results are presented in Table 14.4.2. 

14-5 
Table 14.4.1 Estimates of Global Present Value of CO2 Emissions Reduction under Electric 

Motors Trial Standard Levels

TSL

SCC Case*

5% discount 

rate, average*

3% discount 

rate, average*

2.5% discount 

rate, average*

3% discount 

rate, 95th

percentile*

Million 2013$ 

Primary Energy Emissions 

1 465 2,070 3,269 6,373

2 2,529 11,720 18,651 36,225

3 3,870 17,985 28,633 55,600

4 4,939 22,923 36,488 70,858

Upstream Emissions

1 25.7 116 183 357

2 146 682 1,087 2,110

3 223 1,049 1,673 3,246

4 285 1,335 2,129 4,132

Full-Fuel-Cycle Emissions

1 491 2,185 3,452 6,730

2 2,675 12,402 19,738 38,335

3 4,094 19,033 30,306 58,845

4 5,223 24,258 38,618 74,991

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.0, 

$40.5, $62.4, and $119 per metric ton (2013$).

14-6 
Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Electric Motors Trial Standard Levels

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount

rate, 95th percentile*

Million 2013$ 

Primary Energy Emissions 

1 32.6 to 107.1 144.9 to 476.0 228.8 to 751.9 446.1 to 1465.9

2 177.0 to 581.7 820.4 to 2695.7 1305.6 to 4289.7 2535.8 to 8331.9

3 270.9 to 890.2 1258.9 to 4136.4 2004.3 to 6585.6 3892.0 to 12788.0

4 345.7 to 1135.9 1604.6 to 5272.2 2554.2 to 8392.3 4960.1 to 16297.4

Upstream Emissions

1 1.8 to 5.9 8.1 to 26.6 12.8 to 42.1 25.0 to 82.1

2 10.2 to 33.5 47.7 to 156.8 76.1 to 250.0 147.7 to 485.2

3 15.6 to 51.4 73.4 to 241.2 117.1 to 384.7 227.2 to 746.5

4 19.9 to 65.5 93.5 to 307.1 149.1 to 489.8 289.2 to 950.4

Full-Fuel-Cycle Emissions

1 34.4 to 113.0 153.0 to 502.6 241.7 to 794.0 471.1 to 1548.0

2 187.2 to 615.2 868.1 to 2852.4 1381.7 to 4539.7 2683.4 to 8817.0

3 286.6 to 941.6 1332.3 to 4377.6 2121.4 to 6970.3 4119.2 to 13534.4

4 365.6 to 1201.4 1698.0 to 5579.3 2703.3 to 8882.1 5249.3 to 17247.8

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $12.0, $40.5, 

$62.4, and $119 per metric ton (2013$).

Table 14.4.3 presents the present value of cumulative NOX emissions reductions for each 

TSL, calculated using the average dollar-per-ton values at 7 percent and 3 percent discount rates.

14-7 
Table 14.4.3 Estimates of Present Value of NOX Emissions Reduction under Electric 

Motors Trial Standard Levels

TSL 3% discount rate 7% discount rate

Million 2013$ 

Primary Energy Emissions

1 52.1 28.8

2 269 131

3 410 197

4 524 253

Upstream Emissions

1 71.5 36.9

2 396 179

3 606 272

4 773 348

Full-Fuel-Cycle Emissions

1 124 65.8

2 664 310

3 1,016 469

4 1,297 601

14-8 
REFERENCES

1. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use, 2009. National Academies Press. Washington, DC. 

<http://www.nap.edu/catalog.php?record_id=12794> 

2. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States 

Government. <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf> 

3. Interagency Working Group on Social Cost of Carbon, Technical Update of the Social 

Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, May 2013. 

United States Government. 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria

_2013_update.pdf 

4. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs 

and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal 

Entities, 2006. Washington, DC. 

<http://www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_c

b_final_report.pdf> 

14-9 
CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-2

15.3.1 Installed Capacity........................................................................................................... 15-3

15.3.2 Electricity Generation .................................................................................................... 15-6

15.3.3 Results Summary ......................................................................................................... 15-10

LIST OF TABLES

Table 15.3.1 Electric Motors: Summary of Utility Impact Results ...................................... 15-10

LIST OF FIGURES

Figure 15.3.1 Electric Motors: Total Electric Capacity Reduction .......................................... 15-3

Figure 15.3.2 Electric Motors: Coal Capacity Reduction ........................................................ 15-4

Figure 15.3.3 Electric Motors: Nuclear Capacity Reduction ................................................... 15-4

Figure 15.3.4 Electric Motors: Gas Combined Cycle Capacity Reduction.............................. 15-5

Figure 15.3.5 Electric Motors: Peaking Capacity Reduction ................................................... 15-5

Figure 15.3.6 Electric Motors: Renewables Capacity Reduction ............................................ 15-6

Figure 15.3.7 Electric Motors: Total Generation Reduction .................................................... 15-7

Figure 15.3.8 Electric Motors: Coal Generation Reduction ..................................................... 15-7

Figure 15.3.9 Electric Motors: Nuclear Generation Reduction ................................................ 15-8

Figure 15.3.10Electric Motors: Gas Combined Cycle Generation Reduction .......................... 15-8

Figure 15.3.11Electric Motors: Peaking Generation Reduction ............................................... 15-9

Figure 15.3.12Electric Motors: Renewables Generation Reduction ......................................... 15-9

15-i 
CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes 

in electric installed capacity and power generation that result for each trial standard level (TSL).

The utility impact analysis uses a variant of the DOE Energy Information Administration 

(EIA)’s National Energy Modeling System (NEMS).a NEMS is a public domain, multi-sectored, 

partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce 

an energy forecast for the United States, the Annual Energy Outlook (AEO). DOE uses a variant 

of this model, referred to as NEMS-BT,b to account for selected utility impacts from energy 

conservation standards. DOE’s analysis consists of a comparison between model results for the 

most recent AEO Reference Case and for cases in which energy use is decremented to reflect the 

impact of standards. For the analysis of standards for electric motors, DOE used the version of 

NEMS based on AEO 2013.

2

 

NEMS-BT has a number of advantages that have led to its use in the analysis of energy 

conservation standards: 

• NEMS-BT uses a set of assumptions that are well known and fairly transparent, 

due to the exposure and scrutiny each AEO receives. 

• NEMS-BT is updated each year, with each edition of the AEO, to reflect changes 

in energy prices, supply trends, regulations, etc. 

• The comprehensiveness of NEMS-BT permits the modeling of interactions 

among the various energy supply and demand sectors. 

15.2 METHODOLOGY 

DOE uses NEMS-BT to estimate the marginal impacts of reduction in energy demand on 

the energy supply sector. In principle, marginal values should provide a better estimate of the 

actual impact of energy conservation standards. In practice, the numerical differences between 

marginal and average values may turn out to be smaller than the intrinsic uncertainties in the 

AEO.

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration 

documentation. A useful summary is National Energy Modeling System: An Overview.

1

b DOE/EIA approves use of the name NEMS to describe only an official version of the model without any 

modification to code or data. Because this analysis entails some minor code modifications and the model is run 

under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the name NEMSBT (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed). 

15-1 

 
NEMS uses predicted growth in demand for each end use to build up a projection of the 

total electric system load growth. The system load shapes are converted internally to load 

duration curves, which are then used to estimate the most cost-effective additions to capacity. 

When electricity demand deviates from the AEO reference case, in general there are three interrelated effects: the annual generation (terawatt hours, TWh) from the stock of electric generating 

capacity changes, the total generation capacity itself (gigawatts, GW) may change, and the mix 

of capacity by fuel type may change. Each of these effects can vary for different types of end 

use. The change in total generating capacity is sensitive to the degree to which the end-use is 

peak coincident, while the capacity mix is sensitive to the hourly load shape associated with the 

end use. 

To model the impact of a standard, DOE inputs a reduction to annual energy demand for 

the corresponding end use in the appropriate start year. The NEMS-BT model is run with the 

decremented energy demand to determine the modified build-out of capacity and total 

generation. Regional effects of a standard can be accounted for by defining the energy demand 

decrement as a function of census division. 

The output of the NEMS-BT analysis includes the effective marginal heat rate (ratio of 

the change in energy consumption in quads to the change in generation in TWh), and the 

capacity reduction by plant type for a given reduction in total generation. DOE uses the site 

energy savings multiplied by a transmission-and-distribution (T&D) loss factor to estimate the 

reduction in generation for each TSL. The relationship between a reductionc in electricity 

generation (TWh) and the reduction in capacity (GW) is estimated based on the output of 

NEMS-BT model runs using the end-use specific energy demand decrement. Details on the 

approach used may be found in Coughlin (2013).3

NEMS-BT provides output for the following capacity types: coal, nuclear, 

combined cycle (natural gas), renewable sources, oil and natural gas steam, 

combustion turbine/diesel, pumped storage, fuel cells, and distributed generation (natural gas). 

DOE grouped oil and natural gas steam and combustion turbine/diesel into a “peaking” category, 

and grouped pumped storage, fuel cells, and distributed generation (natural gas) into an “other” 

category.

In general, energy conservation standards impact primarily fossil combustion (coal, 

natural gas and diesel) and renewables. Pumped storage and nuclear power are very insensitive 

to small changes in demand, while fuel cells and distributed generation make up a very small 

fraction (less than 1 percent) of the generation capacity base.

15.3 UTILITY IMPACT RESULTS

 This section presents results of the analysis for all of the capacity types except “Other”, 

for which the impacts are very small.

c These reductions are defined relative to the AEO Reference case.

15-2 

 
15.3.1 Installed Capacity 

The figures in this section show the changes in U.S. electricity installed capacity that 

result for each TSL by major plant type for selected years. The changes have been calculated 

based on factors (megawatts (MW) of capacity reduction per gigawatt hours (GWh) of 

generation reduction) estimated from a NEMS-BT model run that simulated a decrement in 

energy demand for a load shape that approximates electric motors. Note that a positive change 

means a reduction in capacity under a TSL.

 

Figure 15.3.1 Electric Motors: Total Electric Capacity Reduction

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

201620212026203120362041204620512056206120662071

MW

Total

TSL 1 TSL 2 TSL 3 TSL 4

15-3 
Figure 15.3.2 Electric Motors: Coal Capacity Reduction 

Figure 15.3.3 Electric Motors: Nuclear Capacity Reduction

0

500

1,000

1,500

2,000

2,500

2016202120262031

20362041204620512056206120662071

MW

Coal

TSL 1 TSL 2 TSL 3 TSL 4

-50

0

50

100

150

200

201620212026203120362041204620512056206120662071

MW

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4

15-4 
Figure 15.3.4 Electric Motors: Gas Combined Cycle Capacity 

Reduction

Figure 15.3.5 Electric Motors: Peaking Capacity Reduction 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

20162021

20262031203620412046205120562061

20662071

MW

NGCC

TSL 1 TSL 2 TSL 3 TSL 4

0

500

1,000

1,500

2,000

2,500

201620212026203120362041204620512056206120662071

MW

Peaking

TSL 1 TSL 2 TSL 3 TSL 4

15-5 
Figure 15.3.6 Electric Motors: Renewables Capacity Reduction

15.3.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for 

each TSL by plant type. The change by capacity type has been calculated based on shares 

(percentage of generation reduction for each capacity type over total generation reduction) 

estimated from a NEMS-BT model run that simulated a decrement in energy demand for a load 

shape that approximates electric motors. Note that a positive change means a reduction in 

generation under a TSL. Coal-fired power plants account for most of the generation reduction. 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2016202120262031

20362041204620512056206120662071

MW

Renewable

TSL 1 TSL 2 TSL 3 TSL 4

15-6 
Figure 15.3.7 Electric Motors: Total Generation Reduction 

Figure 15.3.8 Electric Motors: Coal Generation Reduction 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

201620212026

20312036204120462051205620612066

2071

GWh

Total

TSL 1 TSL 2 TSL 3 TSL 4

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

201620212026203120362041204620512056206120662071

GWh

Coal

TSL 1 TSL 2 TSL 3 TSL 4

15-7 
Figure 15.3.9 Electric Motors: Nuclear Generation Reduction

Figure 15.3.10 Electric Motors: Gas Combined Cycle Generation 

Reduction

-400

-200

0

200

400

600

800

1,000

1,200

1,400

1,600

201620212026203120362041204620512056206120662071

GWh

Nuclear

TSL 1 TSL 2 TSL 3 TSL 4

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

201620212026203120362041204620512056206120662071

GWh

NGCC

TSL 1 TSL 2 TSL 3 TSL 4

15-8 
Figure 15.3.11 Electric Motors: Peaking Generation Reduction

Figure 15.3.12 Electric Motors: Renewables Generation Reduction

0

500

1,000

1,500

2,000

2,500

20162021

20262031203620412046205120562061

20662071

GWh

Peaking

TSL 1 TSL 2 TSL 3 TSL 4

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

201620212026203120362041204620512056206120662071

GWh

Renewable

TSL 1 TSL 2 TSL 3 TSL 4

15-9 
15.3.3 Results Summary 

Table 15.3.1 presents a summary of the utility impact results estimated for electric 

motors. 

Table 15.3.1 Electric Motors: Summary of Utility Impact Results

TSL

1 2 3 4

Installed Capacity Reduction (MW)

2020 129 421 619 809

2025 321 1,210 1,796 2,316

2030 565 2,542 3,827 4,898

2035 813 4,460 6,814 8,676

2040 821 5,672 8,801 11,160

Electricity Generation Reduction (GWh)

2020 2,207 7,184 10,547 13,786

2025 3,771 14,201 21,072 27,170

2030 4,452 20,013 30,132 38,565

2035 4,455 24,430 37,326 47,528

2040 4,071 28,117 43,627 55,323

15-10
REFERENCES

1. U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<http://www.eia.gov/oiaf/aeo/overview/> 

2. Energy Information Administration, Annual Energy Outlook 2013 with Projections to 

2040, 2013. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf> 

3. Coughlin, K., H. Shen, P. Chan, B. McDevitt, and A. Sturges, Marginal Utility Sector 

Impacts of Reduced Electricity Demand, 2012. 

15-11
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 ASSUMPTIONS ............................................................................................................ 16-1

16.3 METHODOLOGY ........................................................................................................ 16-1

16.4 SHORT-TERM RESULTS ............................................................................................ 16-2

16.5 LONG-TERM IMPACTS .............................................................................................. 16-3

LIST OF TABLES

Table 16.4.1 Net National Short-Term Change in Employment (Number of Jobs) ............... 16-3

16-i 
CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

 The U.S. Department of Energy’s (DOE’s) employment impact analysis is designed to 

estimate indirect national job creation or elimination resulting from possible standards, due to 

reallocation of the associated expenditures for purchasing and operating motors. Job increases or 

decreases reported in this chapter are separate from the direct manufacturing sector employment 

impacts reported in chapter 12, and reflect the employment impact of efficiency standards on all 

other sectors of the economy. 

16.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption, and 

therefore to reduce energy expenditures. The savings in energy expenditures may be spent on 

other goods and services, or not at all (i.e., they may remain “saved”). The standards may 

increase the purchase price of equipment and increase installation costs. 

 Using an input/output econometric model of the U.S. economy, this analysis estimated 

the short-term effect of these expenditure impacts on net economic output and employment. 

DOE intends this analysis to quantify the indirect employment impacts of these expenditure 

changes. It evaluated direct employment impacts at manufacturers’ facilities in the manufacturer 

impact analysis (see chapter 12).

16.3 METHODOLOGY

 The Department based its analysis on an input/output model of the U.S. economy that 

estimates the effects of standards on major sectors of the economy related to buildings and the 

net impact of standards on jobs. The Pacific Northwest National Laboratory developed the 

model, ImSET 3.1.12

 (Impact of Sector Energy Technologies) as a successor to ImBuild3, a 

special-purpose version of the IMPLAN4 national input/output model. ImSET estimates the 

employment and income effects of building energy technologies. In comparison with simple 

economic multiplier approaches, ImSET allows for more a complete and automated analysis of 

the economic impacts of energy-efficiency investments in buildings. 

 In an input/output model, the level of employment in an economy is determined by the 

relationship of different sectors of the economy and the spending flows among them. Different 

sectors have different levels of labor intensity, and changes in the level of spending (e.g., due to 

the effects of an efficiency standard) in one sector of the economy will affect flows in other 

sectors, which will affect the overall level of employment.

 ImSET uses a 187-sector model of the national economy to predict the economic effects 

of residential and commercial buildings technologies. ImSET collects estimates of initial 

investments, energy savings, and economic activity associated with spending the savings 

16-1 
resulting from standards (e.g., changes in final demand in personal consumption, business 

investment and spending, and government spending). It provides overall estimates of the change 

in national output for each input-output sector. The model applies estimates of employment and 

wage income per dollar of economic output for each sector and calculates impacts on national 

employment and wage income.

Energy-efficiency technology primarily affects the U.S. economy along three spending 

pathways. First, general investment funds are diverted to sectors that manufacture, install, and 

maintain energy-efficient appliances. The increased cost of appliances leads to higher 

employment in the appliance manufacturing sectors and lower employment in other economic 

sectors. Second, commercial firm and residential spending are redirected from utilities toward 

firms that supply production inputs. Third, electric utility sector investment funds are released 

for use in other sectors of the economy. When consumers use less energy, electric utilities 

experience relative reductions in demand which leads to reductions in utility sector investment 

and employment. 

 DOE notes that ImSET is not a general equilibrium forecasting model, and understands 

the uncertainties involved in projecting employment impacts, especially changes in the later 

years of the analysis.1 Because ImSET does not incorporate price changes, the employment 

effects predicted by ImSET would over-estimate the magnitude of actual job impacts over the 

long run for this rule. As input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analyses. DOE therefore includes a qualitative 

discussion of how labor markets are likely to respond in the longer term. In future rulemakings, 

DOE may consider the use of other modeling approaches for examining long-run employment 

impacts. 

 DOE also notes that the employment impacts estimated with ImSET for the entire 

economy differ from the employment impacts in the motor manufacturing sector estimated in 

chapter 12 using the Government Regulatory Impact Model (GRIM). The methodologies used 

and the sectors analyzed in the ImSET and GRIM models are different. 

16.4 SHORT-TERM RESULTS

The results in this section refer to impacts of motor standards relative to the base case. 

DOE disaggregated the impact of standards on employment into three component effects: 

increased capital investment costs, decreased energy costs, and changes in operations and 

maintenance costs. DOE presents the summary impact. 

 Conceptually, one can consider the impact of the rule in its first year on three aggregate 

sectors: the motor production sector, the energy generation sector, and the general consumer 

good sector (as mentioned previously, ImSET’s calculations are made at a much more 

disaggregate level). By raising energy efficiency, the rule increases the purchase price of motors;

this increase in expenditures causes an increase in employment in this sector. At the same time, 

the improvements in energy efficiency reduce consumer expenditures on electricity. The 

16-2 
reduction in electricity demand causes a reduction in employment in that sector. Finally, based 

on the net impact of increased expenditures on motors and reduced expenditures on electricity, 

consumer expenditures on everything else are either positively or negatively affected, increasing 

or reducing jobs in that sector accordingly. The model also captures any indirect jobs created or 

lost by changes in consumption due to changes in employment. (As more workers are hired, they 

consume more goods, in turn generating more employment; the converse is true for workers who 

are laid off.) 

 Table 16.4.1 presents the modeled net employment impact from the rule in 2016. It is 

assumed that 65% of motors are produced domestically and 35% are imported. The net 

employment impact estimate is sensitive to assumptions regarding the return to the U.S. 

economy of money spent on imported motors. The two scenarios bounding the ranges presented 

in Table 16.4.1 represent situations in which: 1) none of the money spent on imported motors 

returns to the U.S. economy and, 2) all of the money spent on imported motors returns to the 

U.S. economy (low and high bounds, respectively). The U.S. trade deficit in recent years 

suggests that between 50% and 75% of the money spent on imported motors is likely to return, 

with employment impacts falling within the ranges presented below. 

Table 16.4.1 Net National Short-Term Change in Employment (Number of Jobs) 

Trial Standard Level 2016 2021 

1 40 – 240 1,190 – 1,370

2 -230 – 980 3,150 – 4,990

3 -1,900 – 2,060 1,680 – 8,930

4 -13,930 – 1,390 -9,210 – 8,760

 For context, the Office of Management and Budget (OMB) currently projects that the 

official unemployment rate may decline to 6.9% in 2014 and drop further to 5.3% in 2017.5 The 

unemployment rate in 2016 is projected to be close to “full employment.” When an economy is 

at full employment, any effects on net employment are likely to be transitory as workers change 

jobs, rather than enter or exit longer-term employment. 

16.5 LONG-TERM IMPACTS

 Due to the short payback period of energy efficiency improvements mandated by this 

rule, over the long term DOE expects the energy savings to consumers to increasingly dominate 

the increase in appliance costs, resulting in increased aggregate savings to consumers. As a 

result, DOE expects demand for electricity to decline over time and demand for other goods to 

increase. As the electricity generation sector is relatively capital intensive compared to the 

consumer goods sector, the net effect will be an increase in labor demand. In equilibrium, this 

should lead to upward pressure on wages and a shift in employment away from electricity 

generation towards consumer goods. Note that in long-run equilibrium there is no net effect on 

total employment because wages adjust to bring the labor market into equilibrium. Nonetheless, 

16-3 
even to the extent that markets are slow to adjust, DOE anticipates that net labor market impacts 

will be negligible over time due to the small magnitude of the short-term effects presented in 

Table 16.4.1. The ImSET model projections, assuming no price or wage effects until 2021, are 

included in the second column of Table 16.4.1. 

 

16-4 
REFERENCES

1. Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Building 

Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment.

Energy Economics, 2008. 30(5): pp. 2283-2301 

2. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy 

Technologies, 2005. Pacific Northwest National Laboratory. Richland, WA. Report No. 

PNNL- 15273. 

3. Scott, M. J., D. J. Hostick, and D. B. Belzer, ImBuild: Impact of Building Energy 

Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. Richland, WA. 

Report No. PNNL-11884. 

4. Minnesota IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide, 

Data Guide, 1997. Stillwater, MN. 

5. Office of Management and Budget, Fiscal Year 2013 Mid-Session Review: Budget of the 

U.S. Government, 

<http://www.whitehouse.gov/sites/default/files/omb/budget/fy2013/assets/13msr.pdf>

16-5 
CHAPTER 17. REGULATORY IMPACT ANALYSIS 

TABLE OF CONTENTS

17.1 INTRODUCTION ................................................................................................... 17-1

17.2 NON-REGULATORY POLICIES .......................................................................... 17-2

17.2.1 Methodology ............................................................................................................ 17-2

17.2.2 Assumptions Regarding Non-Regulatory Policies .................................................. 17-3

17.2.3 Policy Interactions ................................................................................................... 17-3

17.3 NON-REGULATORY POLICY ASSUMPTIONS ................................................ 17-4

17.3.1 Consumer Rebates ................................................................................................... 17-4

17.3.2 Consumer Tax Credits ........................................................................................... 17-10

17.3.3 Manufacturer Tax Credits ...................................................................................... 17-12

17.3.4 Early Replacement ................................................................................................. 17-14

17.3.5 Bulk Government Purchases .................................................................................. 17-21

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES ................................... 17-23

LIST OF TABLES

Table 17.3.1 Benefit/Cost Ratios from and Market Barriers to Premium efficiency 

Motors ............................................................................................................. 17-6

Table 17.3.2 Market Penetrations in 2016 Attributable to Consumer Rebates ................. 17-10

Table 17.3.3 Market Penetrations in 2016 Attributable to Consumer Tax Credits ........... 17-12

Table 17.3.4 Market Penetrations in 2016 Attributable to Manufacturer Tax Credits ...... 17-13

Table 17.3.5 Historical Market Efficiency Distributions................................................... 17-16

Table 17.3.6 Lifetime Energy Cost Savings from Replacing a 101-200 hp Unit in the 

Existing Stock in Industry by a Premium Motor (2013$)* .......................... 17-16

Table 17.3.7 Financial Costs from Replacing a 101-200 hp Unit in the Existing Stock 

by a Premium Motor (2013$)* ..................................................................... 17-17

Table 17.3.8 Number of Units in the Existing Stock Projected to be Early Replaced 

by a Premium Motor (thousands of units) .................................................... 17-19

Table 17.3.9 Market Penetrations Attributable to Early Replacements............................. 17-20

Table 17.3.10 Market Penetrations Attributable to Bulk Government Purchases ............... 17-22

Table 17.4.1 Impacts of Non-Regulatory Alternative Policies for NEMA Design A 

and B Premium Motors in Equipment Class Subgroup 1.a.* ....................... 17-27

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Industry ......................................................... 17-7

Figure 17.3.2 Market Penetration Curve for Commercial Sector ......................................... 17-8

Figure 17.3.3 Market Penetration Curve for Agriculture ...................................................... 17-9

Figure 17.3.4 Estimates of Existing Stock of Motors (thousands of units) ........................ 17-15

Figure 17.3.5 Estimates of Market Efficiency Distributions of Existing Stock .................. 17-18

Figure 17.4.1 Market Penetration of Premium Motors in Industry ..................................... 17-24

17-i
Figure 17.4.2 Market Penetration of Premium Motors in Commercial Sector ................... 17-25

Figure 17.4.3 Market Penetration of Premium Motors in Agriculture ................................ 17-26

17-ii
CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

 The U.S. Department of Energy (DOE) has determined that energy conservation 

standards for electric motors constitute an “economically significant regulatory action” under 

Executive Order (E.O.) 12866, Regulatory Planning and Review. 58 FR 51735, Volume 58, No. 

190, page 51735. (October 4, 1993). Under 10 CFR part 430, subpart C, appendix A, section 

III.12, DOE committed to evaluating non-regulatory alternatives to proposed standards by 

performing a regulatory impact analysis (RIA) of non-regulatory energy efficiency policy 

measures. 61 FR 36981, Volume 61, No. 136, page 36978. (November 15, 1996). This RIA, 

which DOE has prepared pursuant to E.O. 12866, evaluates potential non-regulatory alternatives, 

comparing the costs and benefits of each to those of the proposed standards. 58 FR 51735, page 

51741. As noted in E.O. 12866, this RIA is subject to review by the Office of Management and 

Budget’s Office of Information and Regulatory Affairs. 58 FR 51735, page 51740. 

 For this RIA, DOE used an integrated National Impact Analysis (NIA)-RIA model built 

on the NIA model discussed in Chapter 10. DOE studied the impacts of the non-regulatory 

policies on the medium electric motors category with the predominant market share, which is the 

NEMA Design A and B motors in equipment class subgroup 1.a.a

. Similar to the NIA model, the 

RIA model splits the calculations for the Nation into three sectors and six horsepower ranges. 

While the national energy savings and net present value impacts reported in section 17.4 show 

results for all sectors and horsepower ranges together, the inputs used to generate the changes in 

market share for each of the non-regulatory policies analyzed are reported separately for each 

sector and horsepower range in Section 17.3. 

DOE identified six non-regulatory policy alternatives that possibly could provide 

incentives for the same energy efficiency levels as the standards proposed for electric motors. 

The non-regulatory policy alternatives are: Consumer Rebates, Consumer Tax Credits, 

Manufacturer Tax Credits, Voluntary Energy Efficiency Targets, Early Replacement and Bulk 

Government Purchases. Because in the base case efficiency distribution DOE accounted for the 

dynamic increase in market penetration of Premium motors that is likely to result from NEMA’s 

NEMA Premium labeling program, DOE did not analyze impacts from Voluntary Energy 

Efficiency Targets for this RIA. For each of the five other alternatives, DOE evaluated its ability 

to achieve significant energy savings at a reasonable cost and compared the effectiveness of each 

to the effectiveness of standards set at the same efficiency level as NEMA Premium.

Sections 17.2 and 17.3 discuss the analysis of the five selected policies Section 17.4

presents the results of the policy alternatives. 

a Equipment class group 1 was analyzed as two separate subgroups: (1.a.) NEMA Design A and B – non-brake 

motors and (1.b.) NEMA Design A and B – brake motors.

17-1

 
17.2 NON-REGULATORY POLICIES

 This section describes the method DOE used to analyze the energy savings and cost 

effectiveness of the five non-regulatory policy alternatives for equipment class subgroup 1.a. 

This section also describes the assumptions underlying the analysis. 

17.2.1 Methodology 

DOE used its integrated NIA-RIA spreadsheet model to calculate the national energy 

savings (NES) and net present value (NPV) associated with each non-regulatory policy 

alternative. Appendix 10-A describes the NIA spreadsheet model. Appendix 17-A, section 17-

A.3, discusses the NIA-RIA integrated model approach. 

DOE quantified the effect of each alternative on the purchase of motors that meet the 

Premium efficiency level. After establishing the quantitative assumptions underlying each 

alternative, DOE appropriately revised inputs to the NIA-RIA spreadsheet model. The primary 

model inputs revised were market shares of motors meeting the Premium efficiency level. The 

shipments of motors for any given year reflect a distribution of efficiency levels. DOE assumed 

that the proposed standards would affect 100 percent of the shipments of motors that did not 

meet the Premium efficiency level in the base case,b whereas the non-regulatory policies would 

affect a smaller percentage of those shipments. DOE made certain assumptions about the 

percentage of shipments affected by each alternative policy. DOE used those percentages to 

calculate the shipment-weighted average energy consumption and costs of electric motors 

attributable to each policy alternative. 

Increasing the efficiency of an electric motor often increases its purchase and lifetime 

repair costs. However, operating costs generally decrease because energy consumption declines. 

DOE calculated an NPV for each non-regulatory alternative in the same way it did for the 

proposed standards. In some scenarios, increases in purchase cost are mitigated by government

rebates or tax credits. Because DOE assumed that consumers would re-pay credits and rebates in 

some way (such as additional taxes), DOE did not include the value of rebates or tax credits as a 

consumer benefit when calculating national NPV. DOE’s analysis also excluded any 

administrative costs for the non-regulatory policies; including such costs would decrease the 

NPV slightly.

 

The following are key measures for evaluating the impact of each alternative. 

• National Energy Savings, given in quadrillion Btus (quads), describes the cumulative 

national primary energy savings for motors purchased during the 30-year analysis 

period starting in 2016. 

b

 The base case for the NIA is a market-weighted average of units at several efficiency levels.

17-2

 
• Net Present Value, represents the value in 2013$ (discounted to 2014) of net 

monetary savings from motors purchased during the 30-year analysis period starting 

in 2016. DOE calculated the NPV as the difference between the present value of 

purchase and repair costs against energy expenditures in the base case and the present 

value of those costs in each alternative policy case. DOE calculated repair and energy 

expenses for the life of the motor considering the horsepower rate of the motor, as 

well as the sector where, and the application for which it is used. 

17.2.2 Assumptions Regarding Non-Regulatory Policies

The effects of non-regulatory policies are by nature uncertain because they depend on 

program implementation, marketing efforts, and on consumers’ responses to a program. Because 

the projected effects depend on assumptions regarding the rate of consumer participation, they 

are subject to more uncertainty than are the impacts of mandatory standards, which DOE 

assumes will meet with full compliance. To increase the robustness of the analysis, DOE 

conducted a literature review regarding each non-regulatory policy and consulted with 

recognized experts to gather information on similar incentive programs that have been 

implemented in the United States. By studying experiences with the various types of programs,

DOE sought to make credible assumptions regarding potential market impacts. Section 17.3

presents the sources DOE relied on in developing assumptions about each alternative policy and 

reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each 

alternative policy. 

 Each non-regulatory policy that DOE considered would improve the average efficiency 

of motors purchased for new applications and for replacing motors in stock, relative to their base 

case efficiency scenario (which involves no new regulatory action). The analysis considered that 

each alternative policy would induce consumers to purchase units at Premium efficiency level. 

As opposed to the standards case, however, the alternative policy cases may not lead to 100 

percent market penetration of units that would meet that efficiency level. DOE assumed that the 

effects of non-regulatory policies would last for the 30-year analysis period. 

17.2.3 Policy Interactions 

DOE calculated the effects of each non-regulatory policy separately from those of the 

other policies. In practice, some policies are most effective when implemented in combination, 

such as early replacement implemented with consumer rebates, or early replacement 

implemented with bulk government purchases. However, DOE attempted to make conservative 

assumptions to avoid double-counting policy impacts. The resulting policy impacts are not 

additive; the combined effect of several or all policies cannot be inferred from summing their 

results. Section 17.4 presents graphs that show the market penetration estimated under each nonregulatory policy for NEMA Design A and B motors in equipment class subgroup 1.a. 

17-3
17.3 NON-REGULATORY POLICY ASSUMPTIONS

 The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to proposed standards for electric motors. DOE developed 

estimates of the market penetration of Premium efficiency motors both with and without each of 

the non-regulatory policy alternatives. 

17.3.1 Consumer Rebates

DOE considered the scenario in which the Federal government would provide financial 

incentives in the form of rebates to consumers for purchasing more efficient motors. This policy 

provides a consumer rebate for purchasing motors rated at (or above) Premium efficiency levels.

17.3.1.1 Methodology 

 To inform its estimate of the market impacts of consumer rebates, DOE performed a 

thorough nationwide search for existing rebate programs for electric motors. It gathered data on 

utility or agency rebates throughout the nation for electric motors. 

DOE based its evaluation methodology for consumer rebates on a comprehensive study 

of California’s potential for achieving energy efficiency. This study, performed by XENERGY, 

Inc.,c summarized experiences with various utility rebate programs.1 XENERGY’s analytical 

method utilized graphs, or penetration curves, that estimate the market penetration of a 

technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other 

methods of estimating the effect of consumer rebate programs on the market penetration of 

efficient technologies. The other methods, developed after the referenced XENERGY report was 

published,2, 3, 4, 5, 6, 7

 used different approaches: other economic parameters (e.g., payback 

period), expert surveys, or model calibration based on specific utility program data rather than 

multi-utility data. Some models in use by energy efficiency program evaluation experts were so 

client-specific that generic relationships between economic parameters and consumer response 

could not be established.5

DOE decided that the most appropriate available method for this RIA 

analysis was the XENERGY approach of penetration curves based on B/C ratio, which 

incorporates lifetime operating cost savings. 

XENERGY’s model estimates market impacts induced by financial incentives based on 

the premise that two types of information diffusion drive the adoption of new technologies. 

Internal sources of information encourage consumers to purchase new products primarily 

through word-of-mouth from early adopters. External sources affect consumer purchase 

decisions through marketing efforts and information from outside the consumer group. Appendix 

17-A, section 17-A.4.1, contains additional details on internal and external information diffusion. 

c XENERGY is now owned by KEMA, Inc. (www.kema.com) 

17-4

 
XENERGY’s model equation accounts for the influences of both internal and external 

sources of information by superimposing the two components. Combining the two mechanisms 

for information diffusion, XENERGY’s model generates a set of penetration (or implementation) 

curves for a measure. XENERGY then calibrated the curves based on participation data from 

utility rebate programs. The curves illustrate the increased penetration (i.e., increased market 

share) of efficient products driven by consumer response to changes in B/C ratio induced by 

rebate programs. The penetration curves depict various diffusion patterns based on perceived 

barriers (from no barriers to extremely high barriers) to consumer purchase of high-efficiency 

products. 

DOE adjusted the XENERGY penetration curves based on expert advice founded on 

more recent utility program experience.5,8 In addition, DOE used an interpolation method to 

create penetration curves based on relationships between the actual base case market penetrations 

and actual B/C ratios. The interpolation method DOE used is described in Blum et al (2011, 

Appendix A).28

 DOE modeled the effects of a consumer rebate policy for electric motors by determining 

the increase in market penetration of motors meeting the Premium efficiency level relative to 

their market penetration in the base case. It did this using interpolated penetration curves28 built 

to best reflect the market barrier level faced by NEMA Design A and B motors with different 

combinations of enclosure, number of poles and horsepower rate, to be used in industry, 

commercial buildings and agriculture. Section 17.3.2.2 shows the interpolated curves used in the 

analysis. 

17.3.1.2 Analysis 

DOE estimated the effect of increasing the B/C ratio of NEMA Design A and B motors 

via a rebate that would reduce the increased non-energy costs of a unit that meets the Premium 

efficiency level compared to one meeting the baseline efficiency level.

d

 DOE based the rebate 

amounts on a sample of utility and agency rebate programs for electric motors. DOE gathered 

data on 37 rebate programs for electric motors available from 27 utilities or agencies in various 

States. (Appendix 17-A, section 17-A.5, identifies the rebate programs.) These rebates are 

offered for motors rated at or above the Premium efficiency level. Based on the rebate amounts 

offered by the surveyed programs, DOE calculated rebate values for each horsepower range as 

an amount of dollars per horsepower. To represent these rebate values, DOE first calculated, for 

each horsepower range, the shipment weighted average of the rebate amount per horsepower for 

each of the existing programs, and then used the simple average over all existing programs to 

estimate a rebate amount per horsepower for each horsepower range. 

 

d The baseline technology for each Design A and B equipment class is defined in the engineering analysis, Chapter 

5, as the technology that represents the basic characteristics of products in that class. A baseline unit typically is one

that just meets current Federal energy conservation standards and provides basic consumer utility. 

17-5

 
DOE assumed that rebates would remain in effect at the same levels throughout the 30-

year analysis period. 

For each combination of motors enclosure, number of poles and horsepower rate, used in 

each sector, DOE first calculated the B/C ratio without a rebate using the difference in total 

lifetime incremental non-energy costs and lifetime energy cost savings between a Premium 

motor and a motor meeting the efficiency level corresponding to a baseline unit. It then 

calculated the B/C ratio given a rebate for the Premium motor. Because the rebate reduced the 

incremental non-energy costs, the unit receiving the rebate had a larger B/C ratio. Table 17.3.1

shows the shipment weighted average effects of consumer rebates on the B/C ratio of Premium 

motors for the six horsepower ranges operating in the three covered sectors. The table also 

presents the level of market barriers to Premium motors. The levels of market barrier for each 

horsepower range and sector reported in Table 17.3.1 drive the market penetration curves for 

these motor categories presented in Figure 17.3.1 to Figure 17.3.3. Most of the market for 

NEMA Design A and B Premium motors has low market barrier level.

 DOE used the B/C ratios in Table 17.3.1 along with the market penetration curves shown 

in Figure 17.3.1 to Figure 17.3.3 to estimate the percentage of consumers who would purchase 

Premium efficiency motors both with and without a rebate incentive. 

Table 17.3.1 Benefit/Cost Ratios from and Market Barriers to Premium efficiency 

Motors

Horsepower Range (hp)

1-5 6-20 21-50 51-100 101-200 201-500 

Industry

B/C Ratio Without Rebate 9.1 5.5 6.6 5.3 5.0 2.8

Rebate Amount (2013$/hp) 16.54 8.38 6.70 5.44 5.49 6.08

B/C Ratio With Rebate (infinite) 11.2 10.3 8.0 7.7 5.5

Market Barrier Level* High- Low+ Low+ Low+ Low+ Low+

Commercial

B/C Ratio Without Rebate 6.2 5.7 5.3 4.6 4.0 2.0

Rebate Amount (2013$/hp) 16.54 8.38 6.70 5.44 5.49 6.08

B/C Ratio With Rebate (infinite) 11.6 8.9 7.7 6.3 4.4

Market Barrier Level* Low+ Low+ Low+ Low+ Low+ Low+

Agriculture

B/C Ratio Without Rebate 1.7 1.9 1.6 1.1 1.0 0.3

Rebate Amount (2013$/hp) 16.54 8.38 6.70 5.44 5.49 6.08

B/C Ratio With Rebate 4.3 3.1 2.7 1.9 1.8 1.4

Market Barrier Level* Low- Low+ Low- Low- Low- No

* “Low-” refers to no-to-low market barriers; “Low+” to low-to-moderate market barriers; and “High-“ to 

moderate-to-high market barriers. 

17-6
Figure 17.3.1 Market Penetration Curves for Industry

17-7
Figure 17.3.2 Market Penetration Curve for Commercial Sector

17-8
Figure 17.3.3 Market Penetration Curve for Agriculture

For each horsepower range and sector, DOE next estimated the percent increase 

represented by the change in penetration rate shown on the corresponding penetration curve. It 

then added this percent increase to the market share of Premium motors in the base case to obtain 

the market share of those motors in the rebate policy case. Table 17.3.2 summarizes the market 

penetrations of Premium motors in 2016. DOE used the resulting increased market shares in the 

rebate policy case as inputs to represent the rebate policy case scenario in its NIA-RIA model. 

Appendix 17-A shows the annual market share increases due to this policy. Section 17.4 presents 

17-9
the resulting dynamic market penetration and cumulative savings for the policy case of consumer 

rebates for Premium motors. 

Table 17.3.2 Market Penetrations in 2016 Attributable to Consumer Rebates

Horsepower Range (hp)

1-5 6-20 21-50 51-100 101-200 201-500 

Industry

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

Policy case market share 66.9% 47.6% 52.2% 49.9% 39.6% 39.9%

Commercial

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

Policy case market share 70.7% 47.7% 53.5% 51.6% 39.8% 42.4%

Agriculture

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

Policy case market share 56.5% 42.5% 56.1% 54.9% 42.8% 55.2%

17.3.2 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous 

analysis of consumer participation in tax credits. DOE supported its approach using data from 

Oregon State’s tax credit program for energy-efficient appliances. DOE also incorporated 

previous research that disaggregated the effect of rebates and tax credits into a direct price effect, 

which derives from the savings in purchase price, and an announcement effect, which is 

independent of the amount of the incentive.9, 10 The announcement effect derives from the 

credibility that a technology receives from being included in an incentive program, as well as 

changes in product marketing and modifications in markup and pricing. DOE assumed that the 

rebate and consumer tax credit policies would encompass both direct price effects and 

announcement effects, and that half the increase in market penetration associated with either 

policy would be due to the direct price effect and half to the announcement effect. 

In estimating the effects of a tax credit on purchases of Premium motors DOE assumed a 

financial incentive of 15 dollars per horsepower. This amount corresponds to the tax credit

proposed by the National Electrical Manufacturers Association (NEMA) and the American 

Council for an Energy Efficient Economy (ACEEE) in NEMA’s statement before the Senate 

Finance Subcommittee on Energy, Natural Resources in May 24, 2007.27 

DOE estimated that fewer consumers would participate in a tax credit program than 

would take advantage of a direct, immediate (or quasi-immediate) financial incentive like a 

rebate. Research has shown that the delay required for a consumer to receive a tax credit, plus 

the added time and cost in preparing the tax return, make a tax credit incentive less effective than 

17-10
a rebate received at the time of purchase. Based on previous analyses, DOE assumed that only 60 

percent of the consumers who would take advantage of a rebate would take advantage of a tax 

credit.11

In preparing its assumptions, DOE also reviewed other tax credit programs that have been 

offered at both the Federal and State levels for energy-efficient appliances.

 

 The Energy Policy Act of 2005 (EPACT 2005) included Federal tax credits for 

consumers who purchase energy-efficient products.12 Those tax credits were in effect in 2006 

and 2007, expired in 2008, were reinstated for 2009–2010 by the American Recovery and 

Reinvestment Act of 2009 (ARRA), extended by Congress for 2011 with some modifications, 

and expired at the end of 2011.13, 14 The American Taxpayer Relief Act of 2012 extended, with 

some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water 

heaters placed in service between January 1, 2012 and December 31, 2013.15 DOE reviewed 

Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during 

tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation 

tax credit program in place in the 1980s. However, DOE did not find data specific enough to 

electric motors to warrant adjusting its analysis method for the Consumer Tax Credits policy 

case. Appendix 17-A, section 17-A.6.1, contains more information on Federal and State tax 

credits. 

 DOE also reviewed its previous analysis of Oregon’s tax credits for clothes washers to 

provide support for its assumptions.16 In that previous analysis, DOE compared the market 

shares of ultra-high efficiency (UHE) residential clothes washers in Oregon, which offered both 

State tax credits and utility rebates, with those in Washington State, which offered only utility 

rebates during the same period. Based on this analysis, DOE estimated that in Oregon the impact 

of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equivalent 

efficiency. This finding supports its original assumption that participation in a tax credit program 

would be about 60 percent of participation in a rebate program. Additional discussion of State

tax credits for Oregon and other states is in Appendix 17-A, section 17-A.6.3. 

 

DOE applied the assumed 60 percent participation described above to the penetration 

rates estimated for a rebate policy to estimate penetration rates attributable to consumer tax 

credits. DOE followed the approach described in Section 17.3.1 to develop a whole new rebate 

analysis with a rebate amount of 15 dollars per horsepower for all NEMA Design A and B 

motors. DOE then incorporated the assumptions for consumer response to financial incentives 

from the penetration curves developed for this new, 15 dollars per horsepower rebate scenario, 

along with the 60 percent participation assumption, to estimate the increase in market penetration 

resulting from a consumer tax credit of 15 dollars per horsepower. 

Table 17.3.3 summarizes DOE’s assumptions for market penetrations of Premium motors 

in 2016 given a consumer tax credit of 15 dollars per horsepower. 

17-11
Table 17.3.3 Market Penetrations in 2016 Attributable to Consumer Tax Credits

Horsepower Range (hp)

1-5 6-20 21-50 51-100 101-200 201-500 

Industry

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

Policy case market share 54.8% 54.6% 60.8% 60.3% 53.6% 52.7%

Commercial

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

Policy case market share 57.0% 54.5% 61.7% 60.9% 54.1% 54.1%

Agriculture

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

Policy case market share 47.0% 47.1% 70.3% 72.5% 62.5% 69.5%

 DOE assumed that this policy would transform the market permanently, so that the 

increase in market share seen in the first year of the program would be maintained throughout the 

analysis period. The resulting increased market shares attributable to consumer tax credits shown 

in Table 17.3.3 were used as inputs in the NIA-RIA model. Appendix 17-Ashows the annual 

market share increases due to this policy. Section 17.4 presents the resulting dynamic market 

penetration and cumulative savings for the policy case of consumer tax credits for Premium 

motors. 

17.3.3 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce Premium motors DOE assumed that a manufacturer tax credit would lower the 

consumer’s purchase cost by an amount equivalent to that provided by the consumer tax credits

described above. DOE further assumed that manufacturers would pass on some of their reduced 

costs to consumers, causing a direct price effect. DOE assumed that no announcement effect 

would occur, because the program would not be visible to consumers.e Because the direct price 

effect is approximately equivalent to the announcement effect,

9 DOE estimated that a 

manufacturer tax credit would induce half the number of consumers assumed to take advantage 

of a consumer tax credit to purchase more efficient products. Thus the assumed participation rate 

is equal to 30 percent of the number of consumers who would participate in a rebate program. 

DOE attempted to investigate manufacturer response to the Energy Efficient Appliance 

Credits for manufacturers mandated by EPACT 2005.17 Those manufacturer tax credits have 

e Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency 

programs might promote the models for which manufacturers increase production due to the tax credits, which in 

turn might induce some announcement effect. However, DOE found no data on such programs on which to base an 

estimate of the magnitude of this possible announcement effect on consumer behavior.

17-12

 
been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009. 

DOE was unable to locate data from the Internal Revenue Service or other sources on 

manufacturer response to the Federal credits. Appendix 17-A, section 17-A.6.2, presents details 

on Federal manufacturer tax credits.

DOE applied the assumption of 30 percent participation to the penetration rates predicted 

for the 15 dollars per horsepower rebate policy that DOE developed to support the consumer tax 

credit analysis, to estimate the effects of a manufacturer tax credit policy. In doing so, the 

Department incorporated the assumptions for consumer response to financial incentives from the 

same penetration curves DOE used in Section 17.3.2 for the consumer tax credit analysis. 

Table 17.3.4 summarizes DOE’s assumptions for market penetrations of Premium motors 

in 2016 given a manufacturer tax credit of 15 dollars per horsepower. 

Table 17.3.4 Market Penetrations in 2016 Attributable to Manufacturer Tax Credits

Horsepower Range (hp)

1-5 6-20 21-50 51-100 101-200 201-500 

Industry

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

Policy case market share 45.7% 42.8% 52.4% 51.0% 41.1% 38.2%

Commercial

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

Policy case market share 46.8% 42.8% 52.8% 51.3% 41.4% 39.0%

Agriculture

Base case market share 36.6% 31.0% 44.0% 41.7% 28.6% 23.8%

Market share increase 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

Policy case market share 41.8% 39.0% 57.1% 57.1% 45.6% 46.7%

 DOE assumed that this policy would transform the market permanently, so that the 

increases in market share seen in the first year of the program would be maintained throughout 

the analysis period. The resulting increased market shares attributable to a manufacturer tax 

credit shown in Table 17.3.4 were used as inputs in the NIA-RIA model. Appendix 17-Ashows 

the annual market share increases due to this policy. Section 17.4 presents the resulting dynamic 

market penetration and cumulative savings for the policy case of manufacturer tax credits for 

Premium motors. 

17-13
17.3.4 Early Replacement 

The non-regulatory policy of early replacement refers to a program to replace medium 

electric motors before the ends of their useful lives. The purpose of such a policy is to replace 

old, inefficient units with Premium units. The economic feasibility of early replacement depends 

on the energy efficiency of the unit being replaced, the purchase cost of a new, Premium unit, 

and the energy cost savings. Because electric motors are operated under different regimes – 

depending on their size (horsepower rating) and the sector in which they are used – and energy 

cost savings depend of motors usage, the feasibility of early replacements needs to be evaluated 

for each horsepower rating, motor configuration, and sector where the motor is used. 

DOE started the feasibility analysis of early replacements estimating the stock of existing 

medium electric motors by vintage, sector and horsepower range. To estimate the stock of 

existing motors DOE estimated historical shipments from 1987 to 2015, and disaggregated these 

shipments by sector and horsepower range using the same distributions of shipments across 

sectors and horsepower ranges used in the NIA (Chapter 10). DOE then used the same lifetime 

distributions by sector and horsepower range used in the NIA (Chapter 10) to estimate, for each 

sector and horsepower range, the existing stock by vintage for the 30-year analysis period. Figure 

17.3.4 presents DOE’s projection of the existing stock of motors by sector and horsepower 

range. 

The second step that DOE performed in this analysis was the estimate of historical 

market efficiency distributions. DOE relied on the works of Boteler (2009) and Lowe et al 

(2010) for these estimates. Table 17.3.5 shows the historical market efficiency distributions that 

DOE estimated for selected years. DOE applied these historical market efficiency distributions to 

the stock by vintage that DOE projected for each sector and horsepower range. Figure 17.3.5

presents examples of the resulting projections of market efficiency distribution of the existing 

stock across efficiency levels over the 30-year analysis period for the three sectors. In Figure 

17.3.5, EL 0 corresponds to motors with efficiency level below EPACT 1992 levels; EL 1 

corresponds to EPACT 1992 levels; and EL 2 corresponds to Premium motors (see chapter 5 for 

details). 

17-14
Figure 17.3.4 Estimates of Existing Stock of Motors (thousands of units)

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

12000.0

14000.0

20162017

201820192020202120222023202420252026202720282029203020312032203320342035

20362037203820392040204120422043

20442045

Industry

201-500 hp

101-200 hp

51-100 hp

21-50 hp

6-20 hp

1-5 hp

0.0

5000.0

10000.0

15000.0

20000.0

25000.0

30000.0

35000.0

40000.0

45000.0

50000.0

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

Commercial

201-500 hp

101-200 hp

51-100 hp

21-50 hp

6-20 hp

1-5 hp

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

Agriculture

201-500 hp

101-200 hp

51-100 hp

21-50 hp

6-20 hp

1-5 hp

17-15
Table 17.3.5 Historical Market Efficiency Distributions

Efficiency Level 1987 1990 1995 2000 2005 2010 2015

Below EPACT 1992 

level 82% 75% 50% 30% 23% 20.7% 18.1% 

EPACT 1992 levels 18% 25% 50% 63% 53% 47.8% 46.9%

Premium levels - - - 7% 24% 31.5% 35.1%

In the following, DOE calculated average annual unit energy consumptions across 

efficiency levels for motors of each horsepower range, used in each sector. With these unit 

energy consumptions by efficiency level and the efficiency distributions of the existing stock 

(described above) DOE was able to estimate: (a) the market average unit lifetime energy cost of 

a motor from the existing stock, and (b) the unit lifetime energy cost of a replacement, Premium 

motor. DOE calculated both values as the discounted sum of the corresponding stream of energy 

costs, taking into account the age of the motor in the replacement year and a stream of annual

probabilities that the motor will be in operation until the expected end of its life. The difference 

between the two lifetime energy costs DOE calculated reflects the estimated energy cost savings 

that a consumer is likely to enjoy from the early replacement of motors in the existing stock by 

Premium motors. DOE calculated these savings in each year of the 30-year analysis period for 

motors from all vintages in the existing stock, by horsepower range and sector. Table 17.3.6

presents an example of energy cost savings that a consumer can achieve by replacing a unit in 

the range of 101 horsepower to 200 horsepower operating in industry. The table shows that the 

earlier the replacement the greater the savings.

Table 17.3.6 Lifetime Energy Cost Savings from Replacing a 101-200 hp Unit in the 

Existing Stock in Industry by a Premium Motor (2013$)* 

Shipment

Year

Year of Replacement

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

1990 3277 2535 1738 892 - - - - - -

1991 3814 3148 2435 1670 857 - - - - -

1992 4259 3658 3019 2335 1601 822 - - - -

1993 4618 4077 3502 2890 2235 1533 787 - - -

1994 4899 4412 3895 3345 2761 2135 1464 752 - -

1995 5104 4670 4206 3713 3189 2632 2036 1396 716 -

1996 5295 4904 4486 4041 3567 3064 2529 1956 1341 688

1997 5489 5133 4754 4349 3917 3458 2970 2451 1896 1300

1998 5607 5285 4943 4578 4188 3772 3330 2860 2360 1826

1999 5486 5205 4906 4588 4249 3887 3501 3091 2655 2191

2000 5305 5062 4803 4527 4234 3921 3587 3231 2852 2449

2001 5071 4861 4638 4400 4148 3879 3593 3286 2960 2613

2002 5154 4961 4755 4537 4305 4058 3795 3515 3215 2896

2003 5060 4887 4704 4509 4302 4082 3848 3598 3332 3049

2004 4984 4829 4664 4489 4303 4106 3895 3672 3434 3180

17-16
2005 5047 4903 4750 4588 4416 4233 4038 3832 3612 3378

* Values discounted at 7 percent discount rate.

Savings from early replacements, however, come at a cost. Anticipating the time of 

replacement of a motor in stock either (a) prevents consumers from realizing financial benefits

from investing the money they would pay for the new, replacement motor; or (b) imposes 

financial costs to those consumers who do not hold – and therefore would have to loan – funds to 

purchase the new, replacement motor. In either case, there is an economic value in delaying the 

time of replacement. DOE calculated such value for motors in each horsepower range, from all 

vintages in the existing stock and for each year in the 30-year analysis period. Table 17.3.7

shows an example of the financial costs a consumer would incur by replacing a unit in the range 

of 101 horsepower to 200 horsepower. The table shows that (a) the older the unit the lower the 

financial cost, and (b) the earlier the replacement the greater the financial cost. 

Table 17.3.7 Financial Costs from Replacing a 101-200 hp Unit in the Existing Stock 

by a Premium Motor (2013$)* 

Shipment

Year

Year of Replacement

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

1990 1889 1464 1008 521 - - - - - -

1991 2287 1889 1464 1008 521 - - - - -

1992 2659 2287 1889 1464 1008 521 - - - -

1993 3006 2659 2287 1889 1464 1008 521 - - -

1994 3331 3006 2659 2287 1889 1464 1008 521 - -

1995 3634 3331 3006 2659 2287 1889 1464 1008 521 -

1996 3918 3634 3331 3006 2659 2287 1889 1464 1008 521

1997 4183 3918 3634 3331 3006 2659 2287 1889 1464 1008

1998 4430 4183 3918 3634 3331 3006 2659 2287 1889 1464

1999 4662 4430 4183 3918 3634 3331 3006 2659 2287 1889

2000 4878 4662 4430 4183 3918 3634 3331 3006 2659 2287

2001 5080 4878 4662 4430 4183 3918 3634 3331 3006 2659

2002 5269 5080 4878 4662 4430 4183 3918 3634 3331 3006

2003 5446 5269 5080 4878 4662 4430 4183 3918 3634 3331

2004 5611 5446 5269 5080 4878 4662 4430 4183 3918 3634

2005 5765 5611 5446 5269 5080 4878 4662 4430 4183 3918

* Values discounted at 7 percent discount rate.

Whereas the energy cost savings are an incentive for early replacing a unit in the existing 

stock (benefits from replacing), the financial costs underlying the early replacement (benefits 

from waiting) may hinder the initiative. DOE considered the net-result from the benefits from 

replacing and the benefits from waiting as an indicator of the decision that consumers would 

make towards anticipating the replacement of a unit in the existing stock by a Premium motor. 

The comparison of the two benefits for the ranges of shipment and replacement years listed in 

Table 17.3.6 and Table 17.3.7 shows that the early replacement of a unit in the range of 101-200 

horsepower operating in industry would be cost-effective for units shipped before 2001. 

17-17
Figure 17.3.5 Estimates of Market Efficiency Distributions of Existing Stock

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

20162017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044

Industry, 101-200 hp

EL 2

EL 1

EL 0

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

20162017

20182019202020212022202320242025202620272028202920302031

2032203320342035203620372038203920402041

204220432044

Commercial, 21-50 hp

EL 2

EL 1

EL 0

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

20162017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044

Agriculture, 1-5 hp

EL 2

EL 1

EL 0

17-18
Based on the stock by vintage that DOE projected over the 30-year analysis period, and 

the net-results from the benefits from replacing and the benefits from waiting for each motor 

vintage, DOE projected – for each horsepower range and sector – the number of potential units 

in the existing stock to be (early) replaced in each of the 30 years of the analysis period. For this 

projection DOE further considered that, because of lack of information on the benefits from early 

replacements or any transaction costs involved in the process of early replacing a motor in 

operation, not all consumers would be informed to or willing to decide for the replacement. DOE 

then assumed that 10 percent of the potential replacements would be undertaken. Table 17.3.8

shows DOE’s projection of units to be early replaced by sector and horsepower range. No units 

operating in agriculture would be cost-effective to be early replaced by a Premium motor.

Table 17.3.8 Number of Units in the Existing Stock Projected to be Early Replaced by 

a Premium Motor (thousands of units)

Year of Replacement

2016 2017 2018 2019 2020

Industry

1-5 hp 18.711 11.616 6.282 1.538 0.000

6-20 hp 0.016 0.000 0.000 0.000 0.000

21-50 

hp

24.559 17.434 11.597 6.830 1.341

51-100 

hp

13.432 9.037 3.705 1.836 0.000

101-

200 hp

8.471 6.140 3.682 0.734 0.000

201-

500 hp

1.582 0.959 0.289 0.000 0.000

Commercial

1-5 hp 0.000 0.000 0.000 0.000 0.000

6-20 hp 1.277 0.000 0.000 0.000 0.000

21-50 

hp

3.838 1.957 0.838 0.000 0.000

51-100 

hp

0.042 0.022 0.007 0.000 0.000

101-

200 hp

0.000 0.000 0.000 0.000 0.000

201-

500 hp

0.000 0.000 0.000 0.000 0.000

The units in the existing stock projected to be early replaced represent additional 

shipments of Premium motors. DOE used the additional shipments for early replacements to 

calculate the increase in market penetration of Premium motors that would result from an early 

replacement policy. Table 17.3.9 shows the market penetration of Premium motors attributable 

to early replacements. The resulting increased market shares attributable to early replacements 

shown in Table 17.3.9 were used as inputs in the NIA-RIA model. Appendix 17-A shows the 

17-19
annual market share increases due to this policy. Section 17.4 presents the resulting dynamic 

market penetration and cumulative savings for the policy case of early replacement for electric 

motors. 

Table 17.3.9 Market Penetrations Attributable to Early Replacements

Year of Replacement

2016 2017 2018 2019 2020

Industry

1-5 hp

Base case market share 36.6% 37.1% 37.6% 38.1% 38.5%

Market share increase 1.2% 0.7% 0.4% 0.1% 0.0%

Policy case market share 37.8% 37.8% 38.0% 38.2% 38.5%

6-20 hp

Base case market share 31.0% 31.5% 32.0% 32.5% 33.0%

Market share increase 0.0% 0.0% 0.0% 0.0% 0.0%

Policy case market share 31.0% 31.5% 32.0% 32.5% 33.0%

21-50 hp

Base case market share 44.0% 44.5% 45.0% 45.5% 45.9%

Market share increase 11.0% 7.8% 5.3% 3.1% 0.6%

Policy case market share 55.0% 52.4% 50.3% 48.6% 46.6%

51-100 hp

Base case market share 41.7% 42.3% 42.7% 43.2% 43.7%

Market share increase 8.3% 5.6% 2.3% 1.1% 0.0%

Policy case market share 50.0% 47.8% 45.1% 44.4% 43.7%

101-200 hp

Base case market share 28.6% 29.2% 29.6% 30.1% 30.6%

Market share increase 15.0% 11.0% 6.8% 1.4% 0.0%

Policy case market share 43.6% 40.1% 36.5% 31.5% 30.6%

201-500 hp

Base case market share 23.8% 24.3% 24.8% 25.3% 25.7%

Market share increase 7.2% 4.3% 1.3% 0.0% 0.0%

Policy case market share 31.0% 28.6% 26.1% 25.3% 25.7%

Commercial

1-5 hp

Base case market share 36.6% 37.1% 37.6% 38.1% 38.5%

Market share increase 0.0% 0.0% 0.0% 0.0% 0.0%

Policy case market share 36.6% 37.1% 37.6% 38.1% 38.5%

6-20 hp

Base case market share 31.0% 31.5% 32.0% 32.5% 33.0%

Market share increase 0.1% 0.0% 0.0% 0.0% 0.0%

Policy case market share 31.1% 31.5% 32.0% 32.5% 33.0%

21-50 hp

17-20
Year of Replacement

2016 2017 2018 2019 2020

Base case market share 44.0% 44.5% 45.0% 45.5% 45.9%

Market share increase 0.7% 0.4% 0.1% 0.0% 0.0%

Policy case market share 44.7% 44.9% 45.2% 45.5% 45.9%

51-100 hp

Base case market share 41.7% 42.3% 42.7% 43.2% 43.7%

Market share increase 0.1% 0.0% 0.0% 0.0% 0.0%

Policy case market share 41.8% 42.3% 42.8% 43.2% 43.7%

101-200 hp

Base case market share 28.6% 29.2% 29.6% 30.1% 30.6%

Market share increase 0.0% 0.0% 0.0% 0.0% 0.0%

Policy case market share 28.6% 29.2% 29.6% 30.1% 30.6%

201-500 hp

Base case market share 23.8% 24.3% 24.8% 25.3% 25.7%

Market share increase 0.0% 0.0% 0.0% 0.0% 0.0%

Policy case market share 23.8% 24.3% 24.8% 25.3% 25.7%

17.3.5 Bulk Government Purchases 

 Bulk government purchases can lead to Federal, State, and local governments purchasing 

large quantities of Premium motors. Combining the market demands of multiple public sectors 

also can provide a market signal to manufacturers and vendors that some of their largest 

customers seek motors that meet the Premium efficiency at favorable prices. Such a program 

also can induce “market pull,” whereby manufacturers and vendors would achieve economies of 

scale for producing and marketing Premium motors. 

 Most of the previous bulk government purchase (procurement) initiatives at the Federal, 

State, and municipal levels have not tracked data on numbers of purchases or degree of 

compliance with procurement specifications. In many cases, procurement programs are 

decentralized, being part of larger State or regional initiatives. DOE based its assumptions 

regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) 

performed regarding the savings potential of its procurement specifications for appliances and 

other equipment. FEMP, however, does not track purchasing data, because of the complex range 

of purchasing systems, large number of vendors, and so on. States, counties, and municipalities

have demonstrated increasing interest and activity in “green purchasing." Although many of the 

programs target office equipment, the growing infrastructure for developing and applying 

efficient purchasing specifications indicates that bulk government purchase programs are 

feasible.

22, 23 

 DOE assumed that government agencies would administer bulk purchasing programs for 

Premium motors. FEMP had performance requirements for general-purpose, single-speed, 

polyphase induction motors of 1 to 500 horsepower, but suspended the purchasing specification 

17-21
for these motors as a result of the mandatory minimum standards established by the Energy 

Independence and Security Act (EISA) of 2007, effective since December 2010. Nevertheless, 

FEMP recommends Federal costumers to consider replacing a failed standard motor with a 

Premium one. Further, FEMP emphasizes that “In many cases, it may be cost effective to replace 

a standard motor prior to failure with a premium motor.” The scope of this regulation is much 

broader than the scope of the mandatory standards established in EISA. Therefore, for the 

purpose of this analysis, DOE assumed that the policy would be incorporated into the FEMP 

program, as well as into all other government procurement programs. 

DOE reviewed its own previous research on the potential for market transformation 

through bulk government purchases. Its major study analyzed several scenarios based on the 

assumption that 20 percent of Federal equipment purchases in 2000 already incorporated energy 

efficiency requirements based on FEMP guidelines. One scenario in the DOE report showed 

energy efficient purchasing ramping up during 10 years from 20 percent to 80 percent of all 

Federal purchases.24 Based on this study, DOE estimated that a bulk government purchase 

program instituted within a 10-year period would result in at least 80 percent of governmentpurchased Premium motors.

 

 DOE assumed that bulk government purchases would affect the purchase of electric 

motors to be installed in government-owned buildings. The 2003 Commercial Buildings Energy

Consumption Survey (CBECS 2003) reported that 13.1 percent of all commercial buildings are 

publicly owned .26 CBECS 2003 also estimated that government-owned buildings comprise 21.4 

percent of the floor space of all commercial buildings in the United States. The activities in these 

buildings include education, public assembly, public offices, public order and safety, inpatient 

and outpatient health care, warehousing, lodging and other services. DOE assumed that medium 

electric motors are used in all these buildings – for instance, as part of HVAC (heating, 

ventilating and air conditioning) systems – and, consequently, that this constitutes the market 

affected by this policy. 

DOE estimated that, starting in 2016, each year of a bulk government purchase policy 

would result in an increasing percent of shipments of government-purchased Premium motors. 

DOE estimated that within 10 years (by the end of 2025), bulk government purchasing programs 

would result in 80 percent of the market for medium electric motors in publicly-owned 

commercial building to meet the Premium efficiency level. DOE modeled the bulk government 

purchase program assuming that the market share of Premium motors achieved in 2025 would be 

maintained throughout the rest of the 30-year analysis period. Table 17.3.10 shows the market 

penetration of Premium motors attributable to bulk government purchases. The resulting 

increased market shares attributable to bulk government purchases shown in Table 17.3.10 were 

used as inputs in the NIA-RIA model. Appendix 17-Ashows the annual market share increases 

due to this policy. Section 17.4 below presents the resulting dynamic market penetration and 

cumulative savings for the policy case of bulk government purchases of Premium motors. 

Table 17.3.10 Market Penetrations Attributable to Bulk Government Purchases

Commercial 2016 2020 2025

17-22
1-5 hp

Base case market share 36.6% 38.5% 40.5%

Market share increase 0.9% 4.2% 8.5%

Policy case market share 37.5% 42.8% 48.9%

6-20 hp

Base case market share 31.0% 33.0% 34.9%

Market share increase 1.1% 4.8% 9.7%

Policy case market share 32.1% 37.8% 44.6%

21-50 hp

Base case market share 44.0% 45.9% 47.9%

Market share increase 0.8% 3.4% 6.9%

Policy case market share 44.8% 49.4% 54.8%

51-100 hp

Base case market share 41.7% 43.7% 45.6%

Market share increase 0.8% 3.7% 7.4%

Policy case market share 42.5% 47.4% 53.0%

101-200 hp

Base case market share 28.6% 30.6% 32.5%

Market share increase 1.1% 5.1% 10.2%

Policy case market share 29.7% 35.7% 42.7%

201-500 hp

Base case market share 23.8% 25.7% 27.7%

Market share increase 1.2% 5.6% 11.2%

Policy case market share 25.0% 31.3% 38.9%

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES 

Figure 17.4.1 to Figure 17.4.3 show the effects of each non-regulatory policy on market 

penetration of NEMA Design A and B Premium motors in equipment class subgroup 1.a. The 

graphs show the shipments weighted average impacts in each sector. Relative to the base case, 

the alternative policy cases increase the market shares of Premium motors. Recall that mandatory 

minimum standards result in 100-percent compliance of the market, which leads to higher market 

penetration of Premium motors in comparison to the alternative non-regulatory policies 

discussed in this RIA. The graphs in Figure 17.4.1 to Figure 17.4.3 however do not include the 

market penetration of motors with efficiency levels above Premium. Therefore, the lines 

corresponding to the market penetration of standards in those graphs do not reach 100 percent. 

17-23
Figure 17.4.1 Market Penetration of Premium Motors in Industry

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

2016201720182019202020212022202320242025

2026202720282029

2030203120322033203420352036203720382039204020412042

204320442045

Rebate

Cons Tax Credit

Manf Tax Credit

Early Replacement

Standard

Base Case

17-24
Figure 17.4.2 Market Penetration of Premium Motors in Commercial Sector

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

Rebate

Cons Tax Credit

Manf Tax Credit

Early Replacement

Bulk Govt Purchase

Standard

Base Case

17-25
Figure 17.4.3 Market Penetration of Premium Motors in Agriculture

Table 17.4.1 shows the NES and NPV for the five non-regulatory policies analyzed in 

detail in this RIA. For comparison, the table includes the impacts that new or amended efficiency 

standards set at TSL 2 – the Premium efficiency level – have on NEMA Design A and B motors 

in equipment class subgroup 1.a. Because the analyses of all alternative policies – except Bulk 

Government Purchases – assessed in this RIA rely on consumers’ energy cost savings to estimate 

the market penetration of Premium motors under the influence of the policy, the resulting 

primary energy savings and net present value are sensitive to the discount rate used in the 

calculations. Table 17.4.1 includes results for 7 percent and 3 percent discount rates. 

 The non-regulatory policy with the highest national benefits is Consumer Tax Credits, 

followed by Consumer Rebates and Manufacturer Tax Credits. Bulk Government Purchases and 

Early Replacements have smaller effects on market transformation and, consequently, much 

lower results. Bulk Government Purchases, despite presenting market effects close to the effects 

from Manufacturer Tax Credits in commercial sector for two thirds of the analysis period, affects 

only the commercial sector. 

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

Rebate

Cons Tax Credit

Manf Tax Credit

Early Replacement

Standard

Base Case

17-26
Table 17.4.1 Impacts of Non-Regulatory Alternative Policies for NEMA Design A and 

B Premium Motors in Equipment Class Subgroup 1.a.* 

Policy Alternatives

Primary Energy Savings 

(quads) 

Net Present Value

(billion 2013$) 

7% DR** 3% DR 7% DR 3% DR

Consumer Rebates 2.483 2.349 4.176 9.981 

Consumer Tax Credits 3.064 2.939 4.779 11.731 

Manufacturer Tax Credits 1.532 1.469 2.390 5.866 

Early Replacements 0.022 0.353 0.062 1.782 

Bulk Government Purchases 0.531 0.781 2.247 

Standards 6.273 7.681 20.704 

* For motors shipped 2016-2045. 

** DR: Discount rate.

17-27
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Efficiency, 2002. XENERGY Inc. (Last accessed July, 2013.) < 

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3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal 

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communication. Quantec, LLC. January, 2008. 

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<http://enduse.lbl.gov/Projects/TaxCredits.html>

10. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment 

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California Survey Research Services. 

11. U.S. Department of Energy: Office of Codes and Standards, Technical Support 

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Refrigerator-Freezers, and Freezers, Including Environmental Assessment and 

17-28
Regulatory Impact Analysis, November, 1995. Washington, DC. Report No. DOE/EE0064. 

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13. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed July, 2013.) <http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf>

14. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed July, 2013.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>

15. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed July, 2013.) <http://energytaxincentives.org/>

16. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support 

Document: Energy Efficiency Program for Consumer Products and Commercial and 

Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products, 

and Commercial Clothes Washers, 2008. Washington, DC. 

17. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed July, 2013.) 

<http://energytaxincentives.org/builders/appliances.php>

18. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modeling the Effects of U.S. 

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Delivers?, May 30-June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La 

Napoule, France. (Last accessed July, 2013.) 

<http://www.eceee.org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper

> 

19. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® 

Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy 

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20. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy-Efficient Appliance 

Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program 

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21. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support 

Document: Energy Efficiency Standards for Consumer Products: Residential Furnaces, 

Central Air Conditioners, and Heat Pumps, 2011. Washington, DC. 

17-29
22. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal 

communication. National Association of State Procurement Officials. April, 2008. 

23. Responsible Purchasing Network, (Last accessed July, 2013.) 

<www.responsiblepurchasing.org/>

24. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase. ACEEE: Summer Study on Energy Efficiency in 

Buildings, 2000: pp. 147-166 

25. U.S. Census Bureau: Housing and Household Economic Statistics Division, 2011 

American Housing Survey, 2011. (Last accessed July, 2013.)

<http://www.census.gov/housing/ahs/>

26. U.S. Department of Energy: Energy Information Administration, Residential Energy 

Consumption Survey: 2009 RECS Survey Data, 2012. (Last accessed July, 2013.) 

<http://www.eia.gov/consumption/residential/data/2009/>

27. National Electrical Manufacturers Association (NEMA), Energy Efficiency: Can Tax 

Incentives Reduce Consumption? Statement of the National Electrical Manufacturers 

Association before the Senate Finance Subcommittee on Energy, Natural Resources, & 

Infrastructure, May 24, 2007. 

<http://www.finance.senate.gov/hearings/hearing/?id=e1e225aa-bcce-1a48-45e6-

6cb9da97bea0> 

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energy efficiency policy measures. Report LBNL-4749E. Lawrence Berkeley National 

Laboratory. May, 2011. 

17-30
5-A-i 

APPENDIX 5-A ENGINEERING DATA

TABLE OF CONTENTS

5-A.1 INTRODUCTION .............................................................................................................. 1

5-A.2 BASELINE AND MAXIMUM TECHNOLOGY DESIGN SUMMARIES ..................... 1

5-A.3 DESIGN SPECIFICATIONS AND LOAD PERFORMANCE OF BASELINE MOTORS

............................................................................................................................................. 2

5A.3.1 5-Horsepower, NEMA Design B, Baseline Data and IEEE 112B Test Results ..... 2

5A.3.2 30-Horsepower, NEMA Design B, Baseline Data and IEEE 112B Test Results ... 3

5A.3.3 75-Horsepower, NEMA Design B, Baseline Data and IEEE 112B Test Results ... 5

5A.3.4 5 Horsepower, NEMA Design C, Baseline Data and IEEE 112B Test Results ..... 6

5A.3.5 50-Horsepower, NEMA Design C, Baseline Data and IEEE 112B Test Results ... 7

5-A.4 DESIGN SPECIFICATIONS AND LOAD PERFORMANCE OF MAXIMUM 

TECHNOLOGY MOTORS................................................................................................ 9

5A.4.1 5-Horsepower, NEMA Design B, Maximum-Technology Data ............................ 9

5A.4.2 30-Horsepower, NEMA Design B, Maximum Technology Data and Modeling 

Results ................................................................................................................... 10

5A.4.3 75-Horsepower, NEMA Design B, Maximum Technology Data and Modeling 

Results ................................................................................................................... 11

5A.4.4 5-Horsepower, NEMA Design C, Maximum Technology Data and Modeling 

Results ................................................................................................................... 12

5A.4.5 50-Horsepower, NEMA Design C, Maximum Technology Data and Modeling 

Results ................................................................................................................... 13

5-A.5 EFFICIENCY LEVELS.................................................................................................... 14

5-A.6 MATERIAL PRICING ASSUMPTIONS ........................................................................ 26

5A.6.1 Copper Wire Pricing ............................................................................................. 27

5A.6.2 2011 Material Pricing............................................................................................ 27

5-A.7 LABOR TIME AND COST ASSUMPTIONS................................................................. 28

LIST OF TABLES

Table 2.1 Baseline Design Data ...............................................................................................1

Table 2.2 Maximum-Technology Design Data........................................................................1

Table 3.1 5-Horsepower, NEMA Design B, Nameplate Data .................................................2

Table 3.2 5-Horsepower, NEMA Design B, IEEE 112B Test Results (460 Volts).................3

Table 3.3 30-Horsepower, NEMA Design B, Nameplate Data ...............................................3

Table 3.4 30-Horsepower, NEMA Design B, IEEE 112B Test Results (460 Volts) ..............4

Table 3.5 75-Horsepower, NEMA Design B, Nameplate Data ...............................................5

Table 3.6 75-Horsepower, NEMA Design B, IEEE 112B Test Results (460 Volts) ..............5

Table 3.7 5 Horsepower NEMA Design C Nameplate Data ...................................................6

Table 3.8 5-Horsepower, NEMA Design C, IEEE 112B Test Results ....................................6

Table 3.9 50-Horsepower, NEMA Design C, Nameplate Data ...............................................7

Table 3.10 50-Horsepower, NEMA Design C, IEEE 112B Test Results ..................................8

Table 4.1 5-Horsepower, NEMA Design B, Nameplate Data .................................................9

Table 4.2 5-Horsepower, NEMA Design B, IEEE 112B Test Results ....................................9
5-A-ii

Table 4.3 30-Horsepower, NEMA Design B, Computer Modeling Data ..............................10

Table 4.4 75-Horsepower, NEMA Design B, Computer Modeling Data ..............................11

Table 4.5 5-Horsepower, NEMA Design C, Computer Modeling Data ................................12

Table 4.6 50-Horsepower, NEMA Design C, Computer Modeling Data ..............................13

Table 5.1 Equipment Class Group 1 at Efficiency Level 0 ...................................................15

Table 5.2 Equipment Class Group 1 at Efficiency Level 1 ...................................................16

Table 5.3 Equipment Class Group 1 at Efficiency Level 2 ...................................................17

Table 5.4 Equipment Class Group 1 at Efficiency Level 3 ...................................................18

Table 5.5 Equipment Class Group 1 at Efficiency Level 4 ...................................................19

Table 5.6 Equipment Class Group 2 at Efficiency Level 0 ...................................................20

Table 5.7 Equipment Class Group 2 at Efficiency Level 1 ...................................................21

Table 5.8 Equipment Class Group 2 at Efficiency Level 2 ...................................................22

Table 5.9 Equipment Class Group 3 at Efficiency Level 0 ...................................................23

Table 5.10 Equipment Class Group 3 at Efficiency Level 1 ...................................................24

Table 5.11 Equipment Class Group 3 at Efficiency Level 2 ...................................................25

Table 5.12 Equipment Class Group 3 at Efficiency Level 3 ...................................................26

Table 6.1 Copper Material Pricing .........................................................................................27

Table 6.2 Material Pricing in Constant 2011$ .......................................................................27

Table 7.1 Labor Hour Assumptions by Efficiency Level (EL)..............................................28

Table 7.2 Slot Fill Percentages by Efficiency Level (EL) .....................................................28

LIST OF FIGURES

Figure 3.1 5-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load .........3

Figure 3.2 30-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load.......4

Figure 3.3 75-Horsepower NEMA Design B Efficiency and Power Factor versus Load.........6

Figure 3.4 5-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load .........7

Figure 3.5 50-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load.......8

Figure 4.1 5-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load .......10

Figure 4.2 30-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load.....11

Figure 4.3 75-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load.....12

Figure 4.4 5-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load .......13

Figure 4.5 50-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load.....14
5-A-1 

APPENDIX 5-A ENGINEERING DATA

5-A.1 INTRODUCTION

This appendix presents baseline specifications and detailed cost-efficiency results for 

each of the electric motor representative units analyzed in the engineering analysis (chapter 5 of 

the TSD).

5-A.2 BASELINE AND MAXIMUM TECHNOLOGY DESIGN SUMMARIES 

Table 2.1 and Table 2.2 show the baseline and maximum technology designs for each 

equipment class analyzed, respectively. In the engineering analysis, all changes to cost and 

efficiency are measured relative the levels in Table 2.1. The representative motors chosen from 

each equipment class are all 4-pole, totally enclosed, fan-cooled, continuous duty, 60 hertz, and 

operate on less than 600 volts. 

Table 2.1 Baseline Design Data

Parameter 

(Units) Unit 5 hp (Design B)

30 hp 

(Design B)

75 hp 

(Design B)

5 hp 

(Design C)

50 hp 

(Design C)

Efficiency % 82.5 89.5 93.0 87.5 93.0

Power Factor % 82.7 86.2 86.8 75 85

Cycles Hz 60 60 60 60 60

Tested Voltage V 460 460 460 460 460

Speed RPM 1,745 1,755 1,775 1,750 1,770

Full Load 

Torque Nm 20.3 121.6 300.6 20.3 200.7 

Current A 6.9 37 88 7.1 59

Core Steel - M56 M56 M56 M47 M47

Stack Length in 2.8 7.88 8.15 4.75 8.67

Rotor Material - Aluminum Aluminum Aluminum Aluminum Aluminum

Main Wire AWG 19 18 17 18 17

Breakdown 

Torque 

% of 

Full 

Load

300 250 205 355 257 

Locked-Rotor 

Torque 

% of 

Full 

Load

240 200 170 326 211 

Locked-Rotor 

Current A 45.9 212 505.9 44.7 344 

Pull-Up Torque 

% of 

Full 

Load

187 142 165 248 159 

Table 2.2 Maximum-Technology Design Data 
5-A-2 

Parameter 

(Units) Unit 5 hp (Design B)

30 hp * 

(Design B)

75 hp *

(Design B)

5 hp *

(Design C)

50 hp *

(Design C)

Efficiency % 91.0 94.5 96.2 91.0 95.0

Power Factor % 78.5 79.4 81.3 79.3 80.3

Hertz Hz 60 60 60 60 60

Tested Voltage V 460 460 460 460 460

Speed RPM 1,770 1,784 1,788 1,776 1,782

Torque Nm 20.1 119.6 299.6 20.1 199.8

Current A 6.5 37.3 89.8 6.5 61.3

Core Steel - M15 M36 M36 M36 M19

Stack Length in 5.02 7.0 12.0 5.32 9.55

Rotor Material - Copper Copper Copper Copper Copper

Main Wire AWG 20.5 18 14 18 17

Breakdown 

Torque 

% of 

Full 

Load

305 202 218.2 260.8 233.5 

Locked-Rotor 

Torque 

% of 

Full 

Load

214 154 163.8 260.8 202.9 

Locked-Rotor 

Current A 43.9 208 530.7 41.7 359.6 

Pull-Up Torque 

% of 

Full 

Load

214 139 135 260.8 202.9 

* Software modeled designs 

5-A.3 DESIGN SPECIFICATIONS AND LOAD PERFORMANCE OF BASELINE 

MOTORS

Nameplate data and results of the IEEE Standard 112 (Test Method B) (IEEE 112B) 

testing for the baseline representative motors are displayed in sections 5A.3.1 through 5A.3.5. 

5A.3.1 5-Horsepower, NEMA Design B, Baseline Data and IEEE 112B Test Results 

Table 3.1 5-Horsepower, NEMA Design B, Nameplate Data

Parameter Value

Phases 3

Voltage 230/460

Rated Horsepower 5.0

Rated Current 13.7/6.9

Frame 184TP

NEMA Nameplate Nominal Efficiency 82.5%

Hertz 60

RPM 1745

Enclosure TEFC
5-A-3 

Insulation Class F

Service Factor 1.15

Code Letter (for locked-rotor kVA) J

Table 3.2 5-Horsepower, NEMA Design B, IEEE 112B Test Results (460 Volts) 

Load Efficiency Power Factor Current

% % % Amperes

24.7 71.3 44 3.7

49.6 81.7 64 4.5

74.5 83.9 75 5.6

99.6 84.4 80 6.9

114.7 84.1 82 7.8

Figure 3.1 5-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load 

5A.3.2 30-Horsepower, NEMA Design B, Baseline Data and IEEE 112B Test Results 

Table 3.3 30-Horsepower, NEMA Design B, Nameplate Data

Parameter Value

Phases 3

Voltage 230/460
5-A-4 

Rated Horsepower 30.0

Rated Current 74/37

Frame 286TPA

NEMA Nameplate Nominal Efficiency 89.5%

Hertz 60

RPM 1755

Enclosure TEFC

Insulation Class F

Service Factor 1.15

Code Letter (for locked-rotor kVA) G

Table 3.4 30-Horsepower, NEMA Design B, IEEE 112B Test Results (460 Volts) 

Load Efficiency Power Factor Current

% % % Amperes

25.1 84.3 57 14.6

49.6 89.2 77 20.4

75.0 90.0 84 27.8

99.9 89.4 87 36.2

115.1 88.8 87 41.8

Figure 3.2 30-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load 
5-A-5 

5A.3.3 75-Horsepower, NEMA Design B, Baseline Data and IEEE 112B Test Results 

Table 3.5 75-Horsepower, NEMA Design B, Nameplate Data

Parameter Value

Phases 3

Voltage 460

Rated Horsepower 75.0

Rated Current 88.0

Frame 365TP

NEMA Nameplate Nominal Efficiency 93.0%

Hertz 60

RPM 1775

Enclosure TEFC

Insulation Class F

Service Factor 1.15

Code Letter (for locked-rotor kVA) F

Table 3.6 75-Horsepower, NEMA Design B, IEEE 112B Test Results (460 Volts) 

Load Efficiency Power Factor Current

% % % Amperes

25.4 90.2 67 29.5

50.2 93.4 83 45.3

75.0 93.9 88 63.8

100.2 93.6 89 84.5

115.1 93.3 89 97.5
5-A-6 

Figure 3.3 75-Horsepower NEMA Design B Efficiency and Power Factor versus Load

5A.3.4 5 Horsepower, NEMA Design C, Baseline Data and IEEE 112B Test Results 

Table 3.7 5 Horsepower NEMA Design C Nameplate Data

Parameter Value

Phases 3

Voltage 208-230/460

Rated Horsepower 5.0

Rated Current 15.3-14.16/7.08

Frame 184T

NEMA Nameplate Nominal Efficiency 87.5%

Hertz 60

RPM 1750

Enclosure TEFC

Insulation Class F

Service Factor 1.15

Code Letter (for locked-rotor kVA) J

Table 3.8 5-Horsepower, NEMA Design C, IEEE 112B Test Results

Load Efficiency Power Factor Current

% % % Amperes
5-A-7 

25.8 79.4 37 4.1

51.1 86.2 57 4.9

76.1 87.7 68 5.9

100.9 87.5 75 7.2

116.0 87.0 77 8.1

Figure 3.4 5-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load 

5A.3.5 50-Horsepower, NEMA Design C, Baseline Data and IEEE 112B Test Results 

Table 3.9 50-Horsepower, NEMA Design C, Nameplate Data

Parameter Value

Phases 3

Voltage 208-230/460

Rated Horsepower 50.0

Rated Current 130-118/59

Frame 236T

NEMA Nameplate Nominal Efficiency 93.0%

Hertz 60

RPM 1770
5-A-8 

Enclosure TEFC

Insulation Class F

Service Factor 1.15

Code Letter (for locked-rotor kVA) F

Table 3.10 50-Horsepower, NEMA Design C, IEEE 112B Test Results

Load Efficiency Power Factor Current

% % % Amperes

25.1 88.0 55 24.5

50.1 92.3 75 34.0

75.2 93.2 82 45.9

100.2 93.1 85 59.2

115.2 92.8 86 67.8

Figure 3.5 50-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load 
5-A-9 

5-A.4 DESIGN SPECIFICATIONS AND LOAD PERFORMANCE OF MAXIMUM 

TECHNOLOGY MOTORS

Performance data and speed versus torque curves for the maximum-technology, physical 

motor and computer-modeled motors are displayed in sections 5A.4.1 through 5A.4.5. 

5A.4.1 5-Horsepower, NEMA Design B, Maximum-Technology Data 

Table 4.1 5-Horsepower, NEMA Design B, Nameplate Data

Parameter Value

Phases 3

Voltage 460

Rated Horsepower 5.0

Rated Current 6.5

Frame 184T

NEMA Nameplate Nominal Efficiency 91.0%

Hertz 60

RPM 1770

Enclosure TEFC

Insulation Class F

NEMA Design Letter B

Table 4.2 5-Horsepower, NEMA Design B, IEEE 112B Test Results

Load Efficiency Power Factor Current

% % % Amperes

25.9 88.1 41 3.4

501 91.7 62 4.2

76.1 92.0 72 5.3

101.1 91.4 78 6.7

116.1 90.9 80 7.5

125.8 90.4 80 8.1
5-A-10 

Figure 4.1 5-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load 

5A.4.2 30-Horsepower, NEMA Design B, Maximum Technology Data and Modeling 

Results

Table 4.3 30-Horsepower, NEMA Design B, Computer Modeling Data 

Load Efficiency Power Factor Current

% % % Amperes

25 91.7 50.5 15.16

50 94.4 70.9 20.95

75 94.9 77.9 28.46

100 94.7 79.4 37.30

115 94.4 78.7 43.45

125 94.1 77.5 48.10

150 92.5 70.3 64.50
5-A-11 

Figure 4.2 30-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load 

5A.4.3 75-Horsepower, NEMA Design B, Maximum Technology Data and Modeling 

Results

Table 4.4 75-Horsepower, NEMA Design B, Computer Modeling Data 

Load Efficiency Power Factor Current

% % % Amperes

25 94.3 60 31.20

50 96.0 78 47.15

75 96.3 82 66.76

100 96.2 81 89.82

115 95.9 79 106.27

125 95.6 77 119.46
5-A-12 

Figure 4.3 75-Horsepower, NEMA Design B, Efficiency and Power Factor versus Load 

5A.4.4 5-Horsepower, NEMA Design C, Maximum Technology Data and Modeling Results 

Table 4.5 5-Horsepower, NEMA Design C, Computer Modeling Data

Load Efficiency Power Factor Current

% % % Amperes

25 82.6 43 3.29

50 88.9 64 4.10

75 90.7 75 5.20

100 91.0 79 6.49

115 90.9 81 7.35

125 90.7 81 7.96
5-A-13 

Figure 4.4 5-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load 

5A.4.5 50-Horsepower, NEMA Design C, Maximum Technology Data and Modeling 

Results

Table 4.6 50-Horsepower, NEMA Design C, Computer Modeling Data

Load Efficiency Power Factor Current

% % % Amperes

25 91.70 52 24.51

50 94.50 72 34.25

75 95.10 79 46.75

100 95.00 80 61.35

115 94.70 80 71.42

125 94.40 79 78.96
5-A-14 

Figure 4.5 50-Horsepower, NEMA Design C, Efficiency and Power Factor versus Load 

5-A.5 EFFICIENCY LEVELS

 As part of the scaling process, DOE developed efficiency levels (ELs) for each

equipment class group using NEMA efficiency tables and incremental improvements of motor 

losses. Table 5.1–Table 5.17 show the ELs that were developed for the various NEMA design 

letters, pole configurations, and enclosure types. 
5-A-15 

Table 5.1 Equipment Class Group 1 at Efficiency Level 0 

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 75.5 75.5 75.5 77.0 75.5 80.0 66.0 72.0

1.5 74.0 80.0 77.0 80.0 75.5 75.5 72.0 75.5

2 77.0 82.5 80.0 78.5 78.5 80.0 78.5 80.0

3 80.0 84.0 78.5 80.0 81.5 82.5 80.0 78.5

5 80.0 81.5 82.5 82.5 84.0 85.5 84.0 82.5

7.5 81.5 84.0 84.0 84.0 82.5 81.5 85.5 84.0

10 82.5 85.5 86.5 87.5 84.0 87.5 84.0 85.5

15 85.5 86.5 86.5 87.5 88.5 85.5 88.5 86.5

20 88.5 88.5 87.5 88.5 87.5 87.5 89.5 86.5

25 91.0 89.5 89.5 85.5 91.7 87.5 88.5 87.5

30 89.5 88.5 89.5 87.5 89.5 87.5 91.0 89.5

40 91.0 88.5 91.0 89.5 89.5 88.5 91.0 89.5

50 92.4 88.5 91.0 89.5 90.2 90.2 91.0 91.0

60 92.4 89.5 91.7 90.2 92.4 89.5 91.0 92.4

75 93.0 89.5 93.0 91.0 92.4 89.5 91.7 93.6

100 93.6 91.0 92.4 92.4 93.0 93.0 91.7 93.6

125 94.5 93.6 92.4 93.0 93.6 93.6 92.4 93.6

150 93.6 92.4 93.6 92.4 95.0 93.0 93.6 93.6

200 95.0 93.6 94.5 93.0 94.5 94.1 93.6 93.6

250 94.5 93.6 94.5 93.6 94.1 94.5 94.1 94.5

300 95.4 95.0 94.1 94.5 94.5 95.4

350 95.4 95.0 95.0 94.5 94.5 95.0

400 95.4 95.4 95.4 95.4

450 95.4 95.8 95.4 95.4

500 95.4 95.8 95.4 95.8
5-A-16 

Table 5.2 Equipment Class Group 1 at Efficiency Level 1 

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 75.5 75.5 82.5 82.5 80.0 80.0 74.0 74.0

1.5 82.5 82.5 84.0 84.0 85.5 84.0 77.0 75.5

2 84.0 84.0 84.0 84.0 86.5 85.5 82.5 85.5

3 85.5 84.0 87.5 86.5 87.5 86.5 84.0 86.5

5 87.5 85.5 87.5 87.5 87.5 87.5 85.5 87.5

7.5 88.5 87.5 89.5 88.5 89.5 88.5 85.5 88.5

10 89.5 88.5 89.5 89.5 89.5 90.2 88.5 89.5

15 90.2 89.5 91.0 91.0 90.2 90.2 88.5 89.5

20 90.2 90.2 91.0 91.0 90.2 91.0 89.5 90.2

25 91.0 91.0 92.4 91.7 91.7 91.7 89.5 90.2

30 91.0 91.0 92.4 92.4 91.7 92.4 91.0 91.0

40 91.7 91.7 93.0 93.0 93.0 93.0 91.0 91.0

50 92.4 92.4 93.0 93.0 93.0 93.0 91.7 91.7

60 93.0 93.0 93.6 93.6 93.6 93.6 91.7 92.4

75 93.0 93.0 94.1 94.1 93.6 93.6 93.0 93.6

100 93.6 93.0 94.5 94.1 94.1 94.1 93.0 93.6

125 94.5 93.6 94.5 94.5 94.1 94.1 93.6 93.6

150 94.5 93.6 95.0 95.0 95.0 94.5 93.6 93.6

200 95.0 94.5 95.0 95.0 95.0 94.5 94.1 93.6

250 95.4 94.5 95.0 95.4 95.0 95.4 94.5 94.5

300 95.4 95.0 95.4 95.4 95.0 95.4

350 95.4 95.0 95.4 95.4 95.0 95.4

400 95.4 95.4 95.4 95.4

450 95.4 95.8 95.4 95.8

500 95.4 95.8 95.8 95.8
5-A-17 

Table 5.3 Equipment Class Group 1 at Efficiency Level 2 

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 77.0 77.0 85.5 85.5 82.5 82.5 75.5 75.5

1.5 84.0 84.0 86.5 86.5 87.5 86.5 78.5 77.0

2 85.5 85.5 86.5 86.5 88.5 87.5 84.0 86.5

3 86.5 85.5 89.5 89.5 89.5 88.5 85.5 87.5

5 88.5 86.5 89.5 89.5 89.5 89.5 86.5 88.5

7.5 89.5 88.5 91.7 91.0 91.0 90.2 86.5 89.5

10 90.2 89.5 91.7 91.7 91.0 91.7 89.5 90.2

15 91.0 90.2 92.4 93.0 91.7 91.7 89.5 90.2

20 91.0 91.0 93.0 93.0 91.7 92.4 90.2 91.0

25 91.7 91.7 93.6 93.6 93.0 93.0 90.2 91.0

30 91.7 91.7 93.6 94.1 93.0 93.6 91.7 91.7

40 92.4 92.4 94.1 94.1 94.1 94.1 91.7 91.7

50 93.0 93.0 94.5 94.5 94.1 94.1 92.4 92.4

60 93.6 93.6 95.0 95.0 94.5 94.5 92.4 93.0

75 93.6 93.6 95.4 95.0 94.5 94.5 93.6 94.1

100 94.1 93.6 95.4 95.4 95.0 95.0 93.6 94.1

125 95.0 94.1 95.4 95.4 95.0 95.0 94.1 94.1

150 95.0 94.1 95.8 95.8 95.8 95.4 94.1 94.1

200 95.4 95.0 96.2 95.8 95.8 95.4 94.5 94.1

250 95.8 95.0 96.2 95.8 95.8 95.8 95.0 95.0

300 95.8 95.4 96.2 95.8 95.8 95.8

350 95.8 95.4 96.2 95.8 95.8 95.8

400 95.8 95.8 96.2 95.8

450 95.8 96.2 96.2 96.2

500 95.8 96.2 96.2 96.2
5-A-18 

Table 5.4 Equipment Class Group 1 at Efficiency Level 3 

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 78.5 78.5 86.5 86.5 84.0 84.0 77.0 77.0

1.5 85.5 85.5 87.5 87.5 88.5 87.5 80.0 78.5

2 86.5 86.5 87.5 87.5 89.5 88.5 85.5 87.5

3 87.5 86.5 90.2 90.2 90.2 89.5 86.5 88.5

5 89.5 87.5 90.2 90.2 90.2 90.2 87.5 89.5

7.5 90.2 89.5 92.4 91.7 91.7 91.0 87.5 90.2

10 91.0 90.2 92.4 92.4 91.7 92.4 90.2 91.0

15 91.7 91.0 93.0 93.6 92.4 92.4 90.2 91.0

20 91.7 91.7 93.6 93.6 92.4 93.0 91.0 91.7

25 92.4 92.4 94.1 94.1 93.6 93.6 91.0 91.7

30 92.4 92.4 94.1 94.5 93.6 94.1 92.4 92.4

40 93.0 93.0 94.5 94.5 94.5 94.5 92.4 92.4

50 93.6 93.6 95.0 95.0 94.5 94.5 93.0 93.0

60 94.1 94.1 95.4 95.4 95.0 95.0 93.0 93.6

75 94.1 94.1 95.8 95.4 95.0 95.0 94.1 94.5

100 94.5 94.1 95.8 95.8 95.4 95.4 94.1 94.5

125 95.4 94.5 95.8 95.8 95.4 95.4 94.5 94.5

150 95.4 94.5 96.2 96.2 96.2 95.8 94.5 94.5

200 95.8 95.4 96.5 96.2 96.2 95.8 95.0 94.5

250 96.2 95.4 96.5 96.2 96.2 96.2 95.4 95.4

300 96.2 95.8 96.5 96.2 96.2 96.2

350 96.2 95.8 96.5 96.2 96.2 96.2

400 96.2 96.2 96.5 96.2

450 96.2 96.5 96.5 96.5

500 96.2 96.5 96.5 96.5
5-A-19 

Table 5.5 Equipment Class Group 1 at Efficiency Level 4 

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 80.0 80.0 87.5 87.5 85.5 85.5 78.5 78.5

1.5 86.5 86.5 88.5 88.5 89.5 88.5 81.5 80.0

2 87.5 87.5 88.5 88.5 90.2 89.5 86.5 88.5

3 88.5 87.5 91.0 91.0 91.0 90.2 87.5 89.5

5 90.2 88.5 91.0 91.0 91.0 91.0 88.5 90.2

7.5 91.0 90.2 93.0 92.4 92.4 91.7 88.5 91.0

10 91.7 91.0 93.0 93.0 92.4 93.0 91.0 91.7

15 92.4 91.7 94.1 94.1 93.0 93.0 91.0 91.7

20 92.4 92.4 94.1 94.1 93.0 93.6 91.7 92.4

25 93.0 93.0 94.5 94.5 94.1 94.1 91.7 92.4

30 93.0 93.0 94.5 95.0 94.1 94.5 93.0 93.0

40 93.6 93.6 95.0 95.0 95.0 95.0 93.0 93.0

50 94.1 94.1 95.4 95.4 95.0 95.0 93.6 93.6

60 94.5 94.5 95.8 95.8 95.4 95.4 93.6 94.1

75 94.5 94.5 96.2 95.8 95.4 95.4 94.5 95.0

100 95.0 94.5 96.2 96.2 95.8 95.8 94.5 95.0

125 95.8 95.0 96.2 96.2 95.8 95.8 95.0 95.0

150 95.8 95.0 96.5 96.5 96.5 96.2 95.0 95.0

200 96.2 95.8 96.8 96.5 96.5 96.2 95.4 95.0

250 96.5 95.8 96.8 96.5 96.5 96.5 95.8 95.8

300 96.5 96.2 96.8 96.5 96.5 96.5

350 96.5 96.2 96.8 96.5 96.5 96.5

400 96.5 96.5 96.8 96.5

450 96.5 96.8 96.8 96.8

500 96.5 96.8 96.8 96.8
5-A-20 

Table 5.6 Equipment Class Group 2 at Efficiency Level 0

Horsepower

Nominal Full Load Efficiency (%)

4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open

1 82.5 82.5 80.0 80.0 74.0 74.0

1.5 84.0 84.0 85.5 84.0 77.0 75.5

2 84.0 84.0 86.5 85.5 82.5 85.5

3 87.5 86.5 87.5 86.5 84.0 86.5

5 87.5 87.5 87.5 87.5 85.5 87.5

7.5 89.5 88.5 89.5 88.5 85.5 88.5

10 89.5 89.5 89.5 90.2 88.5 89.5

15 91.0 91.0 90.2 90.2 88.5 89.5

20 91.0 91.0 90.2 91.0 89.5 90.2

25 92.4 91.7 91.7 91.7 89.5 90.2

30 92.4 92.4 91.7 92.4 91.0 91.0

40 93.0 93.0 93.0 93.0 91.0 91.0

50 93.0 93.0 93.0 93.0 91.7 91.7

60 93.6 93.6 93.6 93.6 91.7 92.4

75 94.1 94.1 93.6 93.6 93.0 93.6

100 94.5 94.1 94.1 94.1 93.0 93.6

125 94.5 94.5 94.1 94.1 93.6 93.6

150 95.0 95.0 95.0 94.5 93.6 93.6

200 95.0 95.0 95.0 94.5 94.1 93.6
5-A-21 

Table 5.7 Equipment Class Group 2 at Efficiency Level 1 

Horsepower

Nominal Full Load Efficiency (%)

4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open

1 85.5 85.5 82.5 82.5 75.5 75.5

1.5 86.5 86.5 87.5 86.5 78.5 77.0

2 86.5 86.5 88.5 87.5 84.0 86.5

3 89.5 89.5 89.5 88.5 85.5 87.5

5 89.5 89.5 89.5 89.5 86.5 88.5

7.5 91.7 91.0 91.0 90.2 86.5 89.5

10 91.7 91.7 91.0 91.7 89.5 90.2

15 92.4 93.0 91.7 91.7 89.5 90.2

20 93.0 93.0 91.7 92.4 90.2 91.0

25 93.6 93.6 93.0 93.0 90.2 91.0

30 93.6 94.1 93.0 93.6 91.7 91.7

40 94.1 94.1 94.1 94.1 91.7 91.7

50 94.5 94.5 94.1 94.1 92.4 92.4

60 95.0 95.0 94.5 94.5 92.4 93.0

75 95.4 95.0 94.5 94.5 93.6 94.1

100 95.4 95.4 95.0 95.0 93.6 94.1

125 95.4 95.4 95.0 95.0 94.1 94.1

150 95.8 95.8 95.8 95.4 94.1 94.1

200 96.2 95.8 95.8 95.4 94.5 94.1

 
5-A-22 

Table 5.8 Equipment Class Group 2 at Efficiency Level 2 

Horsepower

Nominal Full Load Efficiency (%)

4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open

1 87.5 87.5 85.5 85.5 78.5 78.5

1.5 88.5 88.5 89.5 88.5 81.5 80.0

2 88.5 88.5 90.2 89.5 86.5 88.5

3 91.0 91.0 91.0 90.2 87.5 89.5

5 91.0 91.0 91.0 91.0 88.5 90.2

7.5 93.0 92.4 92.4 91.7 88.5 91.0

10 93.0 93.0 92.4 93.0 91.0 91.7

15 93.6 94.1 93.0 93.0 91.0 91.7

20 94.1 94.1 93.0 93.6 91.7 92.4

25 94.1 94.1 93.6 93.6 91.0 91.7

30 94.1 94.5 93.6 94.1 92.4 92.4

40 94.5 94.5 94.5 94.5 92.4 92.4

50 95.0 95.0 94.5 94.5 93.0 93.0

60 95.4 95.4 95.0 95.0 93.0 93.6

75 95.8 95.4 95.0 95.0 94.1 94.5

100 95.8 95.8 95.4 95.4 94.1 94.5

125 95.8 95.8 95.4 95.4 94.5 94.5

150 96.2 96.2 96.2 95.8 94.5 94.5

200 96.5 96.2 96.2 95.8 95.0 94.5
5-A-23 

Table 5.9 Equipment Class Group 3 at Efficiency Level 0 

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 75.5 82.5 82.5 80.0 80.0 74.0 74.0

1.5 82.5 82.5 84.0 84.0 85.5 84.0 77.0 75.5

2 84.0 84.0 84.0 84.0 86.5 85.5 82.5 85.5

3 85.5 84.0 87.5 86.5 87.5 86.5 84.0 86.5

5 87.5 85.5 87.5 87.5 87.5 87.5 85.5 87.5

7.5 88.5 87.5 89.5 88.5 89.5 88.5 85.5 88.5

10 89.5 88.5 89.5 89.5 89.5 90.2 88.5 89.5

15 90.2 89.5 91.0 91.0 90.2 90.2 88.5 89.5

20 90.2 90.2 91.0 91.0 90.2 91.0 89.5 90.2

25 91.0 91.0 92.4 91.7 91.7 91.7 89.5 90.2

30 91.0 91.0 92.4 92.4 91.7 92.4 91.0 91.0

40 91.7 91.7 93.0 93.0 93.0 93.0 91.0 91.0

50 92.4 92.4 93.0 93.0 93.0 93.0 91.7 91.7

60 93.0 93.0 93.6 93.6 93.6 93.6 91.7 92.4

75 93.0 93.0 94.1 94.1 93.6 93.6 93.0 93.6

100 93.6 93.0 94.5 94.1 94.1 94.1 93.0 93.6

125 94.5 93.6 94.5 94.5 94.1 94.1 93.6 93.6

150 94.5 93.6 95.0 95.0 95.0 94.5 93.6 93.6

200 95.0 94.5 95.0 95.0 95.0 94.5 94.1 93.6

250 95.4 94.5 95.0 95.4 95.0 95.4 94.5 94.5

300 95.4 95.0 95.4 95.4 95.0 95.4

350 95.4 95.0 95.4 95.4 95.0 95.4

400 95.4 95.4 95.4 95.4

450 95.4 95.8 95.4 95.8

500 95.4 95.8 95.8 95.8
5-A-24 

Table 5.10 Equipment Class Group 3 at Efficiency Level 1

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 77.0 85.5 85.5 82.5 82.5 75.5 75.5

1.5 84.0 84.0 86.5 86.5 87.5 86.5 78.5 77.0

2 85.5 85.5 86.5 86.5 88.5 87.5 84.0 86.5

3 86.5 85.5 89.5 89.5 89.5 88.5 85.5 87.5

5 88.5 86.5 89.5 89.5 89.5 89.5 86.5 88.5

7.5 89.5 88.5 91.7 91.0 91.0 90.2 86.5 89.5

10 90.2 89.5 91.7 91.7 91.0 91.7 89.5 90.2

15 91.0 90.2 92.4 93.0 91.7 91.7 89.5 90.2

20 91.0 91.0 93.0 93.0 91.7 92.4 90.2 91.0

25 91.7 91.7 93.6 93.6 93.0 93.0 90.2 91.0

30 91.7 91.7 93.6 94.1 93.0 93.6 91.7 91.7

40 92.4 92.4 94.1 94.1 94.1 94.1 91.7 91.7

50 93.0 93.0 94.5 94.5 94.1 94.1 92.4 92.4

60 93.6 93.6 95.0 95.0 94.5 94.5 92.4 93.0

75 93.6 93.6 95.4 95.0 94.5 94.5 93.6 94.1

100 94.1 93.6 95.4 95.4 95.0 95.0 93.6 94.1

125 95.0 94.1 95.4 95.4 95.0 95.0 94.1 94.1

150 95.0 94.1 95.8 95.8 95.8 95.4 94.1 94.1

200 95.4 95.0 96.2 95.8 95.8 95.4 94.5 94.1

250 95.8 95.0 96.2 95.8 95.8 95.8 95.0 95.0

300 95.8 95.4 96.2 95.8 95.8 95.8

350 95.8 95.4 96.2 95.8 95.8 95.8

400 95.8 95.8 96.2 95.8

450 95.8 96.2 96.2 96.2

500 95.8 96.2 96.2 96.2
5-A-25 

Table 5.11 Equipment Class Group 3 at Efficiency Level 2

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 78.5 86.5 86.5 84.0 84.0 77.0 77.0

1.5 85.5 85.5 87.5 87.5 88.5 87.5 80.0 78.5

2 86.5 86.5 87.5 87.5 89.5 88.5 85.5 87.5

3 87.5 86.5 90.2 90.2 90.2 89.5 86.5 88.5

5 89.5 87.5 90.2 90.2 90.2 90.2 87.5 89.5

7.5 90.2 89.5 92.4 91.7 91.7 91.0 87.5 90.2

10 91.0 90.2 92.4 92.4 91.7 92.4 90.2 91.0

15 91.7 91.0 93.0 93.6 92.4 92.4 90.2 91.0

20 91.7 91.7 93.6 93.6 92.4 93.0 91.0 91.7

25 92.4 92.4 94.1 94.1 93.6 93.6 91.0 91.7

30 92.4 92.4 94.1 94.5 93.6 94.1 92.4 92.4

40 93.0 93.0 94.5 94.5 94.5 94.5 92.4 92.4

50 93.6 93.6 95.0 95.0 94.5 94.5 93.0 93.0

60 94.1 94.1 95.4 95.4 95.0 95.0 93.0 93.6

75 94.1 94.1 95.8 95.4 95.0 95.0 94.1 94.5

100 94.5 94.1 95.8 95.8 95.4 95.4 94.1 94.5

125 95.4 94.5 95.8 95.8 95.4 95.4 94.5 94.5

150 95.4 94.5 96.2 96.2 96.2 95.8 94.5 94.5

200 95.8 95.4 96.5 96.2 96.2 95.8 95.0 94.5

250 96.2 95.4 96.5 96.2 96.2 96.2 95.4 95.4

300 96.2 95.8 96.5 96.2 96.2 96.2

350 96.2 95.8 96.5 96.2 96.2 96.2

400 96.2 96.2 96.5 96.2

450 96.2 96.5 96.5 96.5

500 96.2 96.5 96.5 96.5
5-A-26 

Table 5.12 Equipment Class Group 3 at Efficiency Level 3

Horsepower

Nominal Full Load Efficiency (%)

2 Pole 4 Pole 6 Pole 8 Pole

Enclosed Open Enclosed Open Enclosed Open Enclosed Open

1 80.0 87.5 87.5 85.5 85.5 78.5 78.5

1.5 86.5 86.5 88.5 88.5 89.5 88.5 81.5 80.0

2 87.5 87.5 88.5 88.5 90.2 89.5 86.5 88.5

3 88.5 87.5 91.0 91.0 91.0 90.2 87.5 89.5

5 90.2 88.5 91.0 91.0 91.0 91.0 88.5 90.2

7.5 91.0 90.2 93.0 92.4 92.4 91.7 88.5 91.0

10 91.7 91.0 93.0 93.0 92.4 93.0 91.0 91.7

15 92.4 91.7 94.1 94.1 93.0 93.0 91.0 91.7

20 92.4 92.4 94.1 94.1 93.0 93.6 91.7 92.4

25 93.0 93.0 94.5 94.5 94.1 94.1 91.7 92.4

30 93.0 93.0 94.5 95.0 94.1 94.5 93.0 93.0

40 93.6 93.6 95.0 95.0 95.0 95.0 93.0 93.0

50 94.1 94.1 95.4 95.4 95.0 95.0 93.6 93.6

60 94.5 94.5 95.8 95.8 95.4 95.4 93.6 94.1

75 94.5 94.5 96.2 95.8 95.4 95.4 94.5 95.0

100 95.0 94.5 96.2 96.2 95.8 95.8 94.5 95.0

125 95.8 95.0 96.2 96.2 95.8 95.8 95.0 95.0

150 95.8 95.0 96.5 96.5 96.5 96.2 95.0 95.0

200 96.2 95.8 96.8 96.5 96.5 96.2 95.4 95.0

250 96.5 95.8 96.8 96.5 96.5 96.5 95.8 95.8

300 96.5 96.2 96.8 96.5 96.5 96.5

350 96.5 96.2 96.8 96.5 96.5 96.5

400 96.5 96.5 96.8 96.5

450 96.5 96.8 96.8 96.8

500 96.5 96.8 96.8 96.8

5-A.6 MATERIAL PRICING ASSUMPTIONS

DOE gathered material pricing information from numerous sources, including subject 

matter experts (SMEs), manufacturers, internal material pricing databases developed from 

research on other rulemakings, the U.S. Census Bureau’s Producer Price Index, the London 

Metal Exchange and the Commodity Exchange, Inc. DOE used a 2012 dollar pricing for a 

majority of the materials, but for copper wire and cast copper prices DOE used a three-year 

average dating from 2010–2012. 
5-A-27 

5A.6.1 Copper Wire Pricing

DOE used a three-year average price for copper due to the large price fluctuations in 

copper wire and copper used for casting. The three-year average copper pricings are displayed in 

Table 6.1. DOE used a constant price for all wire gauges due to the small pricing differences 

between the different wire gauges. After averaging the three years of data, DOE marked up the 

copper wire prices by a constant 30% to account for processing of the raw material. 

Table 6.1 Copper Material Pricing

Material Type 3 Year Average Year

Cu Wire ($/lb) 2012–2010 2012 2011 2010

Cu Wire, Gauge 14 & 14.5 $4.66* $3.61 $4.00 $3.15

Cu Wire, Gauge 15 & 15.5 $4.66* $3.61 $4.00 $3.15

Cu Wire, Gauge 16 & 16.5 $4.66* $3.61 $4.00 $3.15

Cu Wire, Gauge 17 & 17.5 $4.66* $3.61 $4.00 $3.15

Cu Wire, Gauge 18 & 18.5 $4.66* $3.61 $4.00 $3.15

Cu Wire, Gauge 19 , 19.5, 20 &20.5 $4.66* $3.61 $4.00 $3.15

Casting Materials ($/lb)

Casting Materials - Copper $3.59 $3.61 $4.00 $3.15

*Includes 30% markup to account for processing of raw material 

5A.6.2 2011 Material Pricing

 DOE used a constant 2012$ pricing for the remaining materials which include electrical 

steels, aluminum for casting, cast iron, and hot rolled steel. These price assumptions are 

displayed in Table 6.2. 

Table 6.2 Material Pricing in Constant 2011$ 

Motor Frame/End Bell Material ($/lb)

Frame Material - Cast Iron 20k-30k psi $0.58

Frame Material - Steel Fabrication $0.45

Frame Material - Aluminum (extruded or cast) $1.15

Casting Materials ($/lb)

Casting Materials - Aluminum $1.15

Core Steels - ASTM #, Thickness, Processing ($/lb)

26M12, .0185", fully/semi-processed $1.04

26M15, .0185", fully/semi-processed $1.00

26M19, .0185", fully/semi-processed $0.97

26M22, .0185", fully/semi-processed $0.90

26M27, .0185", fully/semi-processed $0.84

26M36, .0185", fully/semi-processed $0.76

26M47, .0185", fully/semi-processed $0.74
5-A-28 

26M56, .0185", fully/semi-processed $0.69

Rotor Shaft ($/lb)

Hot Rolled AISI #1040 Series $0.51

Bearings ($/each)

Front Bearing, 5-HP $4.62

Back Bearing, 5-HP $3.37

Front Bearing, 30-HP $15.31

Back Bearing, 30-HP $10.14

Front Bearing, 50-HP $16.55

Back Bearing, 50-HP $11.12

Front Bearing, 75-HP $32.02

Back Bearing, 75-HP $27.38

5-A.7 LABOR TIME AND COST ASSUMPTIONS

 DOE estimated labor hours for each EL of each representative unit. DOE requested 

information from manufacturers concerning labor time associated with certain electric motor 

horsepower ratings. A summary of these labor time estimates is displayed in Table 7.1. Due to 

the limited manufacturer feedback, DOE relied primarily on SME input to derive the time 

requirements to build the representative units. For the purchased representative units (EL 0-3 for 

the NEMA Design B motors and EL 0 for the NEMA Design C motors) DOE relied on visual 

inspection by motor industry experts to determine if a motor was machine or hand wound. All 

software-modeled motors with a slot fill greater than 82%, including all max-tech ELs, were 

considered hand wound. Approximate slot fill percentages are displayed in Table 7.2. 

Furthermore, any software-modeled motors with a slot fill

Table 7.1 Labor Hour Assumptions by Efficiency Level (EL) 

HP Rating EL 0 EL 1 EL 2 EL 3 EL 4

5, Design B 1.50 hrs 1.58 hrs 1.65 hrs 1.74 hrs 4.50 hrs*

30, Design B 3.11 hrs 3.27 hrs 3.43 hrs 3.60 hrs 6.75 hrs*

75, Design B 5.72 hrs 6.01 hrs 6.29 hrs 6.64 hrs 11.34 hrs*

5, Design C 1.58 hrs 1.66 hrs 4.54 hrs* - -

50, Design C 4.00 hrs 9.00 hrs* 9.45 hrs* - -

* Based on slot fill calculations, DOE assumed a hand-wound labor hour amount for these 

motors 

Table 7.2 Slot Fill Percentages by Efficiency Level (EL) 

HP Rating EL 0 EL 1 EL 2 EL 3 EL 4

5, Design B 38.7% 51.7% 70.0% 54.5% 53.3%

30, Design B 47.5% 64.8% 50.9% 70.0% 83.2%

75, Design B 50.9% 35.0% 70.0% 70.0% 85.1%

5, Design C 53.3% 79.9% 82.9% — —

50, Design C 62.5% 85.3% 81.3% — —
5-B-i 

APPENDIX 5-B SAMPLE TEAR-DOWN REPORT

TABLE OF CONTENTS

5-B.1 FIVE-HORSEPOWER NEMA DESIGN B, 4-POLE ELECTRIC MOTOR TEARDOWN REPORT ................................................................................................................ 1

LIST OF TABLES

Table 5-B.1 Sample Bill of Materials of a 5-HP NEMA Design B, Electric Motor...... .................2 
5-B-1 

APPENDIX 5-B SAMPLE TEAR-DOWN REPORT

5-B.1 FIVE-HORSEPOWER NEMA DESIGN B, 4-POLE ELECTRIC MOTOR

TEAR-DOWN REPORT

The U.S. Department of Energy (DOE) derived the electric motor production and 

material costs for the engineering analysis by purchasing a sample of electric motors, and then 

having a professional motor testing laboratory disassemble each motor and inventory the 

component parts. DOE performed tear downs on the electric motors representing efficiency

levels (EL): EL 0, EL 1, EL 2 and EL 3 for the National Electrical Manufacturers Association 

(NEMA) Design B equipment-class group (ECG1). For the 5-horsepower representative unit in 

ECG 1, DOE also performed a tear down at EL4. Electric motors representing EL 0 were torn 

down for all the representative units for the NEMA Design C equipment-class group (equipmentclass group 2). These tear-downs provided DOE the necessary data to construct a bill of 

materials that DOE could normalize, using a standard cost model and markup, to produce a 

projected manufacturer selling price. Table 5-B.1 shows a sample tear-down report for one of the 

five-horsepower (5-HP) NEMA Design B, 4-pole, totally enclosed, fan cooled electric motors 

purchased by DOE. 

Table 5-B.1 Sample Tear-Down Report of a 5-HP, NEMA Design B, Electric Motor

Stator Assembly

Steel Laminations (M47 Grade, 0.0185" Thick) 29.40 lb

Copper Wire (3#20 AWG) 12.20

Rotor Assembly

Steel Laminations (M47 Grade, 0.0185" Thick) 17.00 lb

Aluminum (Cast) 3.42 lb

Shaft (Steel) 4.50 lb

Other Major Costs

Steel Frame and Bolt on Steel Base (9.6 lb) 9.60 lb

Terminal Housing (Steel) 1.00 lb

Bearing 207 1.00 ea

Bearing 205 1.00 ea

End bell ( Aluminum) (PE) 12 lb

Stator Hardware

Slot Liner (Nomex) 6.05 sq-ft

Top Stick (Nomex) 6.05 sq-ft

Coil Extension Insulation (Phase Paper) 1.00 sq-ft

Lead Wire Thermal Insulation Sleeve 12.00 ft

Lead Wire 0.25 lb

Lace Cord 36.00 ft

Varnish 0.025 Gal

Miscellaneous Hardware
5-B-2 

Fan Cover (Plastic) 1.00 ea

Fan (Plastic) 1.00 ea

Wave Spring (Steel) 1.00 ea

Axial Thrust Nut Ring 2 Holes (Steel) 1.00 ea

Terminal Housing Cover (Steel) 1.00 ea

Rubber Groumet Cover (Over Thrust Bolts) 1.00 ea

Terminal Housing Base Gasket (Foam) 1.00 ea

Terminal Housing Cover Gasket (Foam) 1.00 ea

Axial Thrust Bolts (#10 x 2/75) 2.00 ea

Terminal Housing Mounting Bolts (1/4-20 x .5) 4.00 ea

Terminal Housing Cover Bolts (#10 x .375) 2.00 ea

Stator Axial Tie Bolts (6 mm x 10.5) 4.00 ea

Fan Cover Mounting Bolts (1/4 x .5) 2.00 ea

Ground Connection Screw 1.00 ea

Grease Port Screws 4.00 ea
5-C-5-1 

APPENDIX 5-C EFFICIENCY MODELING VALIDATION

TABLE OF CONTENTS

5-C.1 INTRODUCTION ...................................................................................................1

LIST OF TABLES

Table 5-C.1 Comparison of Tested and Softwared Calculated Efficiency ..................... 1
5-C-1 

APPENDIX 5-C EFFICIENCY MODELING COMPARISON

5-C.1 INTRODUCTION

DOE worked with technical experts to develop certain ELs, in particular, the max-tech 

efficiency levels for each representative unit analyzed. The software program that DOE used for 

its analysis is a proprietary software program called VICA.

1 DOE retained an electric motors 

subject matter expert (SME)2 with design and software experience, who prepared a set of designs 

with increasing efficiency. The SME also checked his designs against tear-down data and 

calibrated his software using the relevant test results. 

Multiple stakeholders requested clarification on how DOE compared its software 

modeled results to the electric motors that it had tested and torn down. Table 5-C.1 details the 

comparisons between the torn down motors and correlated software models. 

Table 5-C.1 Comparison of Tested and Softwared Calculated Efficiency

Horsepower Rotor 

Construction

Rated 

Nominal 

Efficiency

IEEE 112B 

Tested 

Efficiency

Software 

Calculated 

Efficiency

Difference

(Tested −

Calculated) 

5 Aluminum 89.5% 89.1% 88.9% −0.2% 

5 Copper 91.0% 91.5% 91.2% −0.3% 

30 Aluminum 94.1% 93.9% 93.5% −0.4% 

75 Aluminum 95.4% 95.45% 95.3% −0.2% 

75 Aluminum 95.8% 95.3% 95.4% +0.2% 

 

1 VICA stands for “Veinott Interactive Computer Aid.” 2 Dr. Howard Jordan, Ph.D, an electric motor design expert with over 40 years of industry experience served as 

DOE’s subject matter expert.
APPENDIX 7-A ENERGY USE SCENARIO FOR ELECTRIC MOTORS

WITH HIGHER OPERATING SPEEDS

TABLE OF CONTENTS

7-A.1 BACKGROUND ................................................................................................... 7-A-1

7-A.2 METHOD FOR DETERMINING ENERGY SAVING IN VARIABLE 

TORQUE APPLICATIONS .................................................................................. 7-A-1

7-A.3 ASSUMPTIONS TO DETERMINE ENERGY SAVINGS IN VARIABLE 

TORQUE APPLICATIONS .................................................................................. 7-A-2

7-A.3.1 Share of motors negatively impacted by higher operating speeds ......................... 7-A-2

7-A.4 SENSITIVITY ANALYSIS .................................................................................. 7-A-3

7-A-i 
APPENDIX 7-A ENERGY USE SCENARIO FOR ELECTRIC MOTORS 

WITH HIGHER OPERATING SPEEDS

7-A.1 BACKGROUND 

The installation of a higher efficiency motor alone may increase the energy consumption 

for a particular application, instead of realizing energy savings. A more efficient squirrel-cage 

induction motor usually has less slip than an older less efficient motor because of a reduction in 

the resistance of the rotor. This results in higher operating speed and potential overloading of the 

motor. The U.S. Department of Energy (DOE) acknowledges that the cubic relationship between 

speed and power requirement in certain fan, pump, and centrifugal compressor applications can 

affect the benefits gained by efficient motors which have a lower slip. This appendix describes 

the methodology DOE used to estimate this effect as a sensitivity analysis in the Life-Cycle-Cost 

spreadsheet at http://www.eere.energy.gov/buildings/appliance_standards . 

7-A.2 METHOD FOR DETERMINING ENERGY SAVING IN VARIABLE 

TORQUE APPLICATIONS

DOE based its methodology on a previous publicationa

 which states the following: 

In the case where there is a cubic relationship between the power and the speed, 

PoEE(L) = PoBE(L).

ωEE(L)3

ωBE(L)3 

Where: 

L is the load in percentage 

PoEE(L) is the output power of the energy efficient motor 

PoBE (L) is the output power of the baseline efficiency motor 

ωEE (L) is the operating speed of the energy efficient motor 

ωBE (L) is the operating speed of the baseline efficient motor

When the operating speeds are the same then:

PoEE(L) = PoBE(L)

If the more efficient motor has a higher speed then it produces more output power then 

required by the application: 

PoEE(L) > PoBE(L)

a P. Pillay. Practical considerations in applying energy efficient motors in the petrochemical industry . Petroleum 

and Chemical Industry Conference, 1995. Record of Conference Papers., Industry Applications Society 42nd Annual.

7-A-1 

 
 If the only useful power is that generated by the baseline motor (PoBE(L)), then the 

“effective” lossesb

 of the energy efficient motor are:

 

Losses(L) = PinEE(L) − PoBE(L) 

Where: 

PinEE(L) is the input power of the energy efficient motor. 

The efficiency of the EE motor is ηEE(L) and PinEE(L) is:

PinEE(L) = PoEE(L)

ηEE(L) 

And: 

PinEE(L) = PoBE(L).

ωEE(L)3

ωBE(L)3 . 1

ηEE(L)

 

Then the “effective” losses of the EE motor are:

Losses(L) = PoBE(L)(

ωEE(L)3

ωBE(L)3 . 1

ηEE(L) − 1) [Equation 1] 

If the end-user does not adjust for the higher speed of the energy efficient motor, then the 

losses experienced will be greater than if the operating speeds remain constant. 

DOE calculated “effective” losses vs. load tables based on Equation 1 and used these 

values to estimate the energy use of higher efficiency motors in variable torque applications 

which would not benefit from higher operating speeds. 

7-A.3 ASSUMPTIONS TO DETERMINE ENERGY SAVINGS IN VARIABLE 

TORQUE APPLICATIONS

 

 No sufficient solid data was found to estimate the share of motors which are negatively 

impacted by higher operating speeds. DOE therefore considered a scenario described by the two 

following main assumptions: (1) the share of motors which are negatively impacted by higher 

operating speeds, and (2) the actual operating speed of the motor in the field. 

7-A.3.1 Share of motors negatively impacted by higher operating speeds 

 DOE assumed that 60 percent of pumps, fans and compressor applications are variable 

torque applications. 

b The “effective” losses experienced are not losses, they include the increased load imposed by increased speeds 

associated with variable torque applications.

7-A-2 

 
Of these 60 percent, DOE assumed that all fans and a majority (70 percent ) of 

compressors and pumps would be negatively impacted by higher operating speeds; and that 30 

percent of compressors and pumps would not be negatively impacted from higher operating 

speeds as their time of use would decrease as the flow increases with the speed (e.g. a pump 

filling a reservoir).c DOE assumed this revolutions per minute (RPM) effect did not impact fire 

pump motors. 

 When choosing to run the life-cycle cost (LCC) spreadsheet based on the “RPM 

scenario” the LCC results are based on the “effective” losses for 60 percent of all fans and 42 

percent of all compressors and pumps applications. This does not account for the share of users 

who adjust for increased motor speed. 

7-A.4 SENSITIVITY ANALYSIS 

 The results provided by applying this methodology do not account for motors which are 

positively impacted for higher operating speeds and rely on two major assumptions: (1) the share 

of motors which are negatively impacted by higher operating speeds, and (2) the actual operating 

speed of the motor in the field. DOE believes the data supporting these assumptions is not 

sufficiently robust to incorporate this effect in the main analysis and therefore incorporated it as 

a sensitivity scenario in the LCC spreadsheet. 

c This corresponds to 0.6 percent of fan applications being negatively impacted and 0.6x0.7 =0.42 percent of 

compressor and pump applications being negatively impacted by an increase in RPM.

7-A-3 

 
APPENDIX 8-A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND 

PAYBACK PERIOD SPREADSHEETS

TABLE OF CONTENTS

8-A.1 STARTUP .............................................................................................................. 8-A-1

8-A.2 ELECTRIC MOTORS WORKSHEET OVERVIEW ........................................... 8-A-1

8-A.3 BASIC INSTRUCTIONS FOR OPERATING THE LIFE-CYCLE COST 

SPREADSHEET .................................................................................................... 8-A-2

8-A-i 
APPENDIX 8-A. USER INSTRUCTIONS FOR LIFE-CYCLE COST AND 

PAYBACK PERIOD SPREADSHEETS

To utilize the life-cycle cost (LCC) spreadsheet, it is necessary for the user to have the 

appropriate hardware and software tools. The U.S. Department of Energy (DOE) assumes the 

user has a reasonably current computer operating under the Windows operating system. The 

development team uses relatively new systems and has not defined the minimum system 

requirements. Users need Microsoft Excel to execute the spreadsheet. For full functionality, 

users need a copy of Oracle Crystal Ball.

8-A.1 STARTUP

The LCC spreadsheet can be found on the DOE website at 

http://www.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/42. Open the 

file. (Each computer system will have a unique setup for loading a file. Users should refer to 

their software manuals if they have problems loading the spreadsheet file.) For users new to 

Excel and/or Crystal Ball, section 8.8.2 contains basic instructions for operating the LCC 

spreadsheets.

8-A.2 ELECTRIC MOTORS WORKSHEET OVERVIEW

The LCC spreadsheet for electric motors contains the following worksheets: 

Summary Results 

 This worksheet contains input selections and summary results tables. 

LCC and Payback 

This worksheet reports calculations for a single sample.

Definitions 

 This worksheet contains values used to populate the spreadsheet’s form elements. 

Rebuttable Payback 

This worksheet calculates and presents the rebuttable payback period for each of the 

representative units. 

Energy Use 

This worksheet calculates annual electricity use. 

Equipment Price 

This worksheet calculates retail equipment price and total installed cost inputs. 

8-A-1 
Sectors and Applications 

 This worksheet calculates input data regarding sector, application, hours of operation, and 

motor loading. 

Energy Price 

This worksheet calculates electricity price input data for the industrial, commercial, and 

agricultural sectors.

Energy Price Trend 

 This worksheet contains AEO 2013 price trend information. 

Discount Rate

 This worksheet contains the discount rate analysis. 

Lifetime 

This worksheet contains lifetime distributions.

Base Case Eff Dist 

 This worksheet contains efficiency distributions.

8-A.3 BASIC INSTRUCTIONS FOR OPERATING THE LIFE-CYCLE COST 

SPREADSHEET

1. Download and open the spreadsheet. 

2. Select choices from the various user-selectable options.

3. Click the “Run” button to run the simulation using DOE’s parameters. 

To produce sensitivity results directly using Crystal Ball, select custom inputs on the 

“Summary” worksheet and click the “Run” button. Once Crystal Ball has completed the 

simulation, it will produce a series of distributions. To view these distributions, users can either 

interact with Crystal Ball or utilize VBA macros stored in the spreadsheet. To generate various 

charts, after the simulation has completed run the macro “Clear” followed by “CopyAll.” Charts 

will be available in the hidden worksheets “LCC/PBP Box Plots” and LCC/PBP Frequency 

Charts.” 

8-A-2 
APPENDIX 8B. LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

TABLE OF CONTENTS

8B.1 DISTRIBUTION OF LIFE-CYCLE COST RESULTS ..........................................8B-1

8B.1.1 Representative Unit 1, NEMA Design B, 5 Horsepower, Four Poles, Enclosed 

Motor........................................................................................................................8B-1

8B.1.2 Representative Unit 2, NEMA Design B, 30 Horsepower, Four Poles, 

Enclosed Motor ........................................................................................................8B-3

8B.1.3 Representative Unit 3, NEMA Design B, 75 Horsepower, Four Poles, 

Enclosed Motor ........................................................................................................8B-5

8B.1.4 Representative Unit 4, NEMA Design C, 5 Horsepower, Four Poles, Enclosed 

Motor........................................................................................................................8B-7

8B.1.5 Representative Unit 5, NEMA Design C, 50 Horsepower, Four Poles, 

Enclosed Motor ........................................................................................................8B-8

8B.1.6 Representative Unit 6, Fire Pump, 5 Horsepower, Four Poles, Enclosed 

Electric Motor ..........................................................................................................8B-9

8B.1.7 Representative Unit 7, Fire Pump, 30 Horsepower, Four Poles, Enclosed 

Electric Motor ........................................................................................................8B-10

8B.1.8 Representative Unit 8, Fire Pump, 75 Horsepower, Four Poles, Enclosed 

Electric Motor ........................................................................................................8B-12

8B.1.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 

Horsepower, Four Poles, Enclosed Motor .............................................................8B-13

8B.1.10 Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30 

Horsepower, Four Poles, Enclosed Motor .............................................................8B-16

8B.2 DISTRIBUTION OF PAYBACK PERIOD RESULTS .......................................8B-18

8B.2.1 Representative Unit 1, NEMA Design B, 5 Horsepower, Four Poles, Enclosed 

Motor......................................................................................................................8B-18

8B.2.2 Representative Unit 2, NEMA Design B, 30 Horsepower, Four Poles, 

Enclosed Motor ......................................................................................................8B-20

8B.2.3 Representative Unit 3, NEMA Design B, 75 Horsepower, Four Poles, 

Enclosed Motor ......................................................................................................8B-22

8B.2.4 Representative Unit 4, NEMA Design C, 5 Horsepower, Four Poles, Enclosed 

Motor......................................................................................................................8B-24

8B.2.5 Representative Unit 5, NEMA Design C, 50 Horsepower, Four Poles, 

Enclosed Motor ......................................................................................................8B-25

8B.2.6 Representative Unit 6, Fire Pump, 5 Horsepower, Four Poles, Enclosed 

Electric Motor ........................................................................................................8B-26

8B.2.7 Representative Unit 7, Fire Pump, 30 Horsepower, Four Poles, Enclosed 

Electric Motor ........................................................................................................8B-28

8B.2.8 Representative Unit 8, Fire Pump, 75 Horsepower, Four Poles, Enclosed 

Electric Motor ........................................................................................................8B-29

8B.2.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 

Horsepower, Four Poles, Enclosed Motor .............................................................8B-31

8B-i 
8B.2.10 Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30 

Horsepower, Four Poles, Enclosed Motor .............................................................8B-33

TABLE OF FIGURES

Figure 8B.1.1 Representative Unit 1: Distribution of Life-Cycle Cost Savings for EL 

1....................................................................................................................8B-1

Figure 8B.1.2 Representative Unit 1: Distribution of Life-Cycle Cost Savings for EL 

2....................................................................................................................8B-2

Figure 8B.1.3 Representative Unit 1: Distribution of Life-Cycle Cost Savings for EL 

3....................................................................................................................8B-2

Figure 8B.1.4 Representative Unit 1: Distribution of Life-Cycle Cost Savings for EL 

4....................................................................................................................8B-3

Figure 8B.1.5 Representative Unit 2: Distribution of Life-Cycle Cost Savings for EL 

1....................................................................................................................8B-3

Figure 8B.1.6 Representative Unit 2: Distribution of Life-Cycle Cost Savings for EL 

2....................................................................................................................8B-4

Figure 8B.1.7 Representative Unit 2: Distribution of Life-Cycle Cost Savings for EL 

3....................................................................................................................8B-4

Figure 8B.1.8 Representative Unit 2: Distribution of Life-Cycle Cost Savings for EL 

4....................................................................................................................8B-5

Figure 8B.1.9 Representative Unit 3: Distribution of Life-Cycle Cost Savings for EL 

1....................................................................................................................8B-5

Figure 8B.1.10 Representative Unit 3: Distribution of Life-Cycle Cost Savings for EL 

2....................................................................................................................8B-6

Figure 8B.1.11 Representative Unit 3: Distribution of Life-Cycle Cost Savings for EL 

3....................................................................................................................8B-6

Figure 8B.1.12 Representative Unit 3: Distribution of Life-Cycle Cost Savings for EL 

4....................................................................................................................8B-7

Figure 8B.1.13 Representative Unit 4: Distribution of Life-Cycle Cost Savings for EL 

1....................................................................................................................8B-7

Figure 8B.1.14 Representative Unit 4: Distribution of Life-Cycle Cost Savings for EL 

2....................................................................................................................8B-8

Figure 8B.1.15 Representative Unit 5: Distribution of Life-Cycle Cost Savings for EL 

1....................................................................................................................8B-8

Figure 8B.1.16 Representative Unit 5: Distribution of Life-Cycle Cost Savings for EL 

2....................................................................................................................8B-9

Figure 8B.1.17 Representative Unit 6: Distribution of Life-Cycle Cost Savings for EL 

1....................................................................................................................8B-9

Figure 8B.1.18 Representative Unit 6: Distribution of Life-Cycle Cost Savings for EL 

2..................................................................................................................8B-10

Figure 8B.1.19 Representative Unit 6: Distribution of Life-Cycle Cost Savings for EL 

3..................................................................................................................8B-10

8B-ii
Figure 8B.1.20 Representative Unit 7: Distribution of Life-Cycle Cost Savings for EL 

1..................................................................................................................8B-11

Figure 8B.1.21 Representative Unit 7: Distribution of Life-Cycle Cost Savings for EL 

2..................................................................................................................8B-11

Figure 8B.1.22 Representative Unit 7: Distribution of Life-Cycle Cost Savings for EL 

3..................................................................................................................8B-12

Figure 8B.1.23 Representative Unit 8: Distribution of Life-Cycle Cost Savings for EL 

1..................................................................................................................8B-12

Figure 8B.1.24 Representative Unit 8: Distribution of Life-Cycle Cost Savings for EL 

2..................................................................................................................8B-13

Figure 8B.1.25 Representative Unit 8: Distribution of Life-Cycle Cost Savings for EL 

3..................................................................................................................8B-13

Figure 8B.1.26 Representative Unit 9: Distribution of Life-Cycle Cost Savings for EL 

1..................................................................................................................8B-14

Figure 8B.1.27 Representative Unit 9: Distribution of Life-Cycle Cost Savings for EL 

2..................................................................................................................8B-14

Figure 8B.1.28 Representative Unit 9: Distribution of Life-Cycle Cost Savings for EL 

3..................................................................................................................8B-15

Figure 8B.1.29 Representative Unit 9: Distribution of Life-Cycle Cost Savings for EL 

4..................................................................................................................8B-15

Figure 8B.1.30 Representative Unit 10: Distribution of Life-Cycle Cost Savings for 

EL 1 ............................................................................................................8B-16

Figure 8B.1.31 Representative Unit 10: Distribution of Life-Cycle Cost Savings for 

EL 2 ............................................................................................................8B-16

Figure 8B.1.32 Representative Unit 10: Distribution of Life-Cycle Cost Savings for 

EL 3 ............................................................................................................8B-17

Figure 8B.1.33 Representative Unit 10: Distribution of Life-Cycle Cost Savings for 

EL 4 ............................................................................................................8B-17

Figure 8B.2.1 Representative Unit 1: Distribution of Payback Periods for EL 1 .............8B-18

Figure 8B.2.2 Representative Unit 1: Distribution of Payback Periods for EL 2 .............8B-19

Figure 8B.2.3 Representative Unit 1: Distribution of Payback Periods for EL 3 .............8B-19

Figure 8B.2.4 Representative Unit 1: Distribution of Payback Periods for EL 4 .............8B-20

Figure 8B.2.5 Representative Unit 2: Distribution of Payback Periods for EL 1 .............8B-20

Figure 8B.2.6 Representative Unit 2: Distribution of Payback Periods for EL 2 .............8B-21

Figure 8B.2.7 Representative Unit 2: Distribution of Payback Periods for EL 3 .............8B-21

Figure 8B.2.8 Representative Unit 2: Distribution of Payback Periods for EL 4 .............8B-22

Figure 8B.2.9 Representative Unit 3: Distribution of Payback Periods for EL 1 .............8B-22

Figure 8B.2.10 Representative Unit 3: Distribution of Payback Periods for EL 2 .............8B-23

Figure 8B.2.11 Representative Unit 3: Distribution of Payback Periods for EL 3 .............8B-23

Figure 8B.2.12 Representative Unit 3: Distribution of Payback Periods for EL 4 .............8B-24

Figure 8B.2.13 Representative Unit 4: Distribution of Payback Periods for EL 1 .............8B-24

Figure 8B.2.14 Representative Unit 4: Distribution of Payback Periods for EL 2 .............8B-25

Figure 8B.2.15 Representative Unit 5: Distribution of Payback Periods for EL 1 .............8B-25

Figure 8B.2.16 Representative Unit 5: Distribution of Payback Periods for EL 2 .............8B-26

Figure 8B.2.17 Representative Unit 6: Distribution of Payback Periods for EL 1 .............8B-26

Figure 8B.2.18 Representative Unit 6: Distribution of Payback Periods for EL 2 .............8B-27

8B-iii
Figure 8B.2.19 Representative Unit 6: Distribution of Payback Periods for EL 3 .............8B-27

Figure 8B.2.20 Representative Unit 7: Distribution of Payback Periods for EL 1 .............8B-28

Figure 8B.2.21 Representative Unit 7: Distribution of Payback Periods for EL 2 .............8B-28

Figure 8B.2.22 Representative Unit 7: Distribution of Payback Periods for EL 3 .............8B-29

Figure 8B.2.23 Representative Unit 8: Distribution of Payback Periods for EL 1 .............8B-29

Figure 8B.2.24 Representative Unit 8: Distribution of Payback Periods for EL 2 .............8B-30

Figure 8B.2.25 Representative Unit 8: Distribution of Payback Periods for EL 3 .............8B-30

Figure 8B.2.26 Representative Unit 9: Distribution of Payback Periods for EL 1 .............8B-31

Figure 8B.2.27 Representative Unit 9: Distribution of Payback Periods for EL 2 .............8B-31

Figure 8B.2.28 Representative Unit 9: Distribution of Payback Periods for EL 3 .............8B-32

Figure 8B.2.29 Representative Unit 9: Distribution of Payback Periods for EL 4 .............8B-32

Figure 8B.2.30 Representative Unit 10: Distribution of Payback Periods for EL 1 ...........8B-33

Figure 8B.2.31 Representative Unit 10: Distribution of Payback Periods for EL 2 ...........8B-33

Figure 8B.2.32 Representative Unit 10: Distribution of Payback Periods for EL 3 ...........8B-34

Figure 8B.2.33 Representative Unit 10: Distribution of Payback Periods for EL 4 ...........8B-34

8B-iv
APPENDIX 8B. LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

8B.1 DISTRIBUTION OF LIFE-CYCLE COST RESULTS

 The distributions presented in this section each correspond to example runs of 10,000 

Monte Carlo samples. 

8B.1.1 Representative Unit 1, NEMA Design B, 5 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.1.1 Representative Unit 1: Distribution of Life-Cycle 

Cost Savings for EL 1 

8B-1 
Figure 8B.1.2 Representative Unit 1: Distribution of Life-Cycle 

Cost Savings for EL 2 

Figure 8B.1.3 Representative Unit 1: Distribution of Life-Cycle 

Cost Savings for EL 3 

8B-2 
Figure 8B.1.4 Representative Unit 1: Distribution of Life-Cycle 

Cost Savings for EL 4 

8B.1.2 Representative Unit 2, NEMA Design B, 30 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.1.5 Representative Unit 2: Distribution of Life-Cycle 

Cost Savings for EL 1 

8B-3 
Figure 8B.1.6 Representative Unit 2: Distribution of Life-Cycle 

Cost Savings for EL 2 

Figure 8B.1.7 Representative Unit 2: Distribution of Life-Cycle 

Cost Savings for EL 3 

8B-4 
Figure 8B.1.8 Representative Unit 2: Distribution of Life-Cycle 

Cost Savings for EL 4 

8B.1.3 Representative Unit 3, NEMA Design B, 75 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.1.9 Representative Unit 3: Distribution of Life-Cycle 

Cost Savings for EL 1 

8B-5 
Figure 8B.1.10 Representative Unit 3: Distribution of Life-Cycle 

Cost Savings for EL 2 

Figure 8B.1.11 Representative Unit 3: Distribution of Life-Cycle 

Cost Savings for EL 3 

8B-6 
Figure 8B.1.12 Representative Unit 3: Distribution of Life-Cycle 

Cost Savings for EL 4 

8B.1.4 Representative Unit 4, NEMA Design C, 5 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.1.13 Representative Unit 4: Distribution of Life-Cycle 

Cost Savings for EL 1 

8B-7 
Figure 8B.1.14 Representative Unit 4: Distribution of Life-Cycle 

Cost Savings for EL 2 

8B.1.5 Representative Unit 5, NEMA Design C, 50 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.1.15 Representative Unit 5: Distribution of Life-Cycle 

Cost Savings for EL 1 

8B-8 
Figure 8B.1.16 Representative Unit 5: Distribution of Life-Cycle 

Cost Savings for EL 2 

8B.1.6 Representative Unit 6, Fire Pump, 5 Horsepower, Four Poles, Enclosed Electric 

Motor 

Figure 8B.1.17 Representative Unit 6: Distribution of Life-Cycle 

Cost Savings for EL 1 

8B-9 
Figure 8B.1.18 Representative Unit 6: Distribution of Life-Cycle 

Cost Savings for EL 2 

Figure 8B.1.19 Representative Unit 6: Distribution of Life-Cycle 

Cost Savings for EL 3 

8B.1.7 Representative Unit 7, Fire Pump, 30 Horsepower, Four Poles, Enclosed Electric 

Motor 

8B-10
Figure 8B.1.20 Representative Unit 7: Distribution of Life-Cycle 

Cost Savings for EL 1 

Figure 8B.1.21 Representative Unit 7: Distribution of Life-Cycle 

Cost Savings for EL 2 

8B-11
Figure 8B.1.22 Representative Unit 7: Distribution of Life-Cycle 

Cost Savings for EL 3 

8B.1.8 Representative Unit 8, Fire Pump, 75 Horsepower, Four Poles, Enclosed Electric 

Motor 

Figure 8B.1.23 Representative Unit 8: Distribution of Life-Cycle 

Cost Savings for EL 1 

8B-12
Figure 8B.1.24 Representative Unit 8: Distribution of Life-Cycle 

Cost Savings for EL 2 

Figure 8B.1.25 Representative Unit 8: Distribution of Life-Cycle 

Cost Savings for EL 3 

8B.1.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 Horsepower, 

Four Poles, Enclosed Motor 

8B-13
Figure 8B.1.26 Representative Unit 9: Distribution of Life-Cycle 

Cost Savings for EL 1 

Figure 8B.1.27 Representative Unit 9: Distribution of Life-Cycle 

Cost Savings for EL 2 

8B-14
Figure 8B.1.28 Representative Unit 9: Distribution of Life-Cycle 

Cost Savings for EL 3 

Figure 8B.1.29 Representative Unit 9: Distribution of Life-Cycle 

Cost Savings for EL 4 

8B-15
8B.1.10 Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30

Horsepower, Four Poles, Enclosed Motor 

Figure 8B.1.30 Representative Unit 10: Distribution of Life-Cycle 

Cost Savings for EL 1 

Figure 8B.1.31 Representative Unit 10: Distribution of Life-Cycle 

Cost Savings for EL 2 

8B-16
Figure 8B.1.32 Representative Unit 10: Distribution of Life-Cycle 

Cost Savings for EL 3 

Figure 8B.1.33 Representative Unit 10: Distribution of Life-Cycle 

Cost Savings for EL 4 

8B-17
8B.2 DISTRIBUTION OF PAYBACK PERIOD RESULTS 

8B.2.1 Representative Unit 1, NEMA Design B, 5 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.2.1 Representative Unit 1: Distribution of Payback 

Periods for EL 1 

8B-18
Figure 8B.2.2 Representative Unit 1: Distribution of Payback 

Periods for EL 2 

Figure 8B.2.3 Representative Unit 1: Distribution of Payback 

Periods for EL 3 

8B-19
Figure 8B.2.4 Representative Unit 1: Distribution of Payback 

Periods for EL 4 

8B.2.2 Representative Unit 2, NEMA Design B, 30 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.2.5 Representative Unit 2: Distribution of Payback 

Periods for EL 1 

8B-20
Figure 8B.2.6 Representative Unit 2: Distribution of Payback 

Periods for EL 2 

Figure 8B.2.7 Representative Unit 2: Distribution of Payback 

Periods for EL 3 

8B-21
Figure 8B.2.8 Representative Unit 2: Distribution of Payback 

Periods for EL 4 

8B.2.3 Representative Unit 3, NEMA Design B, 75 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.2.9 Representative Unit 3: Distribution of Payback 

Periods for EL 1 

8B-22
Figure 8B.2.10 Representative Unit 3: Distribution of Payback 

Periods for EL 2 

Figure 8B.2.11 Representative Unit 3: Distribution of Payback 

Periods for EL 3 

8B-23
Figure 8B.2.12 Representative Unit 3: Distribution of Payback 

Periods for EL 4 

8B.2.4 Representative Unit 4, NEMA Design C, 5 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.2.13 Representative Unit 4: Distribution of Payback 

Periods for EL 1 

8B-24
Figure 8B.2.14 Representative Unit 4: Distribution of Payback 

Periods for EL 2 

8B.2.5 Representative Unit 5, NEMA Design C, 50 Horsepower, Four Poles, Enclosed 

Motor 

Figure 8B.2.15 Representative Unit 5: Distribution of Payback 

Periods for EL 1 

8B-25
Figure 8B.2.16 Representative Unit 5: Distribution of Payback 

Periods for EL 2 

8B.2.6 Representative Unit 6, Fire Pump, 5 Horsepower, Four Poles, Enclosed Electric 

Motor 

Figure 8B.2.17 Representative Unit 6: Distribution of Payback 

Periods for EL 1 

8B-26
Figure 8B.2.18 Representative Unit 6: Distribution of Payback 

Periods for EL 2 

Figure 8B.2.19 Representative Unit 6: Distribution of Payback 

Periods for EL 3 

8B-27
8B.2.7 Representative Unit 7, Fire Pump, 30 Horsepower, Four Poles, Enclosed Electric 

Motor 

Figure 8B.2.20 Representative Unit 7: Distribution of Payback 

Periods for EL 1 

Figure 8B.2.21 Representative Unit 7: Distribution of Payback 

Periods for EL 2 

8B-28
Figure 8B.2.22 Representative Unit 7: Distribution of Payback 

Periods for EL 3 

8B.2.8 Representative Unit 8, Fire Pump, 75 Horsepower, Four Poles, Enclosed Electric 

Motor 

Figure 8B.2.23 Representative Unit 8: Distribution of Payback 

Periods for EL 1 

8B-29
Figure 8B.2.24 Representative Unit 8: Distribution of Payback 

Periods for EL 2 

Figure 8B.2.25 Representative Unit 8: Distribution of Payback 

Periods for EL 3 

8B-30
8B.2.9 Representative Unit 9, Brake Motor, NEMA Design B, T-Frame, 5 Horsepower, 

Four Poles, Enclosed Motor 

Figure 8B.2.26 Representative Unit 9: Distribution of Payback 

Periods for EL 1 

Figure 8B.2.27 Representative Unit 9: Distribution of Payback 

Periods for EL 2 

8B-31
Figure 8B.2.28 Representative Unit 9: Distribution of Payback 

Periods for EL 3 

Figure 8B.2.29 Representative Unit 9: Distribution of Payback 

Periods for EL 4 

8B-32
8B.2.10 Representative Unit 10, Brake Motor, NEMA Design B, T-Frame, 30 

Horsepower, Four Poles, Enclosed Motor 

Figure 8B.2.30 Representative Unit 10: Distribution of Payback 

Periods for EL 1 

Figure 8B.2.31 Representative Unit 10: Distribution of Payback 

Periods for EL 2 

8B-33
Figure 8B.2.32 Representative Unit 10: Distribution of Payback 

Periods for EL 3 

Figure 8B.2.33 Representative Unit 10: Distribution of Payback 

Periods for EL 4 

8B-34
APPENDIX 8C. LIFE-CYCLE COST SENSITIVITY ANALYSIS

TABLE OF CONTENTS

8C.1 REPRESENTATIVE UNIT 1, NEMA DESIGN B, 5 HORSEPOWER, FOUR 

POLES, ENCLOSED MOTOR .................................................................................................8C-1

8C.2 REPRESENTATIVE UNIT 2, NEMA DESIGN B, 30 HORSEPOWER, 

FOUR POLES, ENCLOSED MOTOR .....................................................................................8C-3

8C.3 REPRESENTATIVE UNIT 3, NEMA DESIGN B, 75 HORSEPOWER, 

FOUR POLES, ENCLOSED MOTOR .....................................................................................8C-5

8C.4 REPRESENTATIVE UNIT 4, NEMA DESIGN C, 5 HORSEPOWER, FOUR 

POLES, ENCLOSED MOTOR .................................................................................................8C-7

8C.5 REPRESENTATIVE UNIT 5, NEMA DESIGN C, 50 HORSEPOWER, 

FOUR POLES, ENCLOSED MOTOR .....................................................................................8C-8

8C.6 REPRESENTATIVE UNIT 6, FIRE PUMP, 5 HORSEPOWER, FOUR 

POLES, ENCLOSED ELECTRIC MOTOR ...........................................................................8C-10

8C.7 REPRESENTATIVE UNIT 7, FIRE PUMP, 30 HORSEPOWER, FOUR 

POLES, ENCLOSED ELECTRIC MOTOR ...........................................................................8C-12

8C.8 REPRESENTATIVE UNIT 8, FIRE PUMP, 75 HORSEPOWER, FOUR 

POLES, ENCLOSED ELECTRIC MOTOR ...........................................................................8C-14

8C.9 REPRESENTATIVE UNIT 9, BRAKE MOTOR, NEMA DESIGN B, TFRAME, 5 HORSEPOWER, FOUR POLES, ENCLOSED MOTOR ...................................8C-16

8C.10 REPRESENTATIVE UNIT 10, BRAKE MOTOR, NEMA DESIGN B, TFRAME, 30 HORSEPOWER, FOUR POLES, ENCLOSED MOTOR .................................8C-18

TABLE OF TABLES

Table 8C.1.1 Representative Unit 1: Reference Scenario ................................................8C-1

Table 8C.1.2 Representative Unit 1: High Energy Price Trend Scenario ........................8C-1

Table 8C.1.3 Representative Unit 1: Low Energy Price Trend Scenario .........................8C-1

Table 8C.1.4 Representative Unit 1: Low Retail Price Discount Scenario ......................8C-2

Table 8C.1.5 Representative Unit 1: Medium Retail Price Discount Scenario ................8C-2

Table 8C.1.6 Representative Unit 1: High Retail Price Discount Scenario .....................8C-2

Table 8C.2.1 Representative Unit 2: Reference Scenario ................................................8C-3

Table 8C.2.2 Representative Unit 2: High Energy Price Trend Scenario ........................8C-3

Table 8C.2.3 Representative Unit 2: Low Energy Price Trend Scenario .........................8C-3

Table 8C.2.4 Representative Unit 2: Low Retail Price Discount Scenario ......................8C-4

Table 8C.2.5 Representative Unit 2: Medium Retail Price Discount Scenario ................8C-4

Table 8C.2.6 Representative Unit 2: High Retail Price Discount Scenario .....................8C-4

Table 8C.3.1 Representative Unit 3: Reference Scenario ................................................8C-5

Table 8C.3.2 Representative Unit 3: High Energy Price Trend Scenario ........................8C-5

Table 8C.3.3 Representative Unit 3: Low Energy Price Trend Scenario .........................8C-5

Table 8C.3.4 Representative Unit 3: Low Retail Price Discount Scenario ......................8C-6

Table 8C.3.5 Representative Unit 3: Medium Retail Price Discount Scenario ................8C-6

8C-i 
Table 8C.3.6 Representative Unit 3: High Retail Price Discount Scenario .....................8C-6

Table 8C.4.1 Representative Unit 4: Reference Scenario ................................................8C-7

Table 8C.4.2 Representative Unit 4: High Energy Price Trend Scenario ........................8C-7

Table 8C.4.3 Representative Unit 4: Low Energy Price Trend Scenario .........................8C-7

Table 8C.4.4 Representative Unit 4: Low Retail Price Discount Scenario ......................8C-7

Table 8C.4.5 Representative Unit 4: Medium Retail Price Discount Scenario ................8C-8

Table 8C.4.6 Representative Unit 4: High Retail Price Discount Scenario .....................8C-8

Table 8C.5.1 Representative Unit 5: Reference Scenario ................................................8C-8

Table 8C.5.2 Representative Unit 5: High Energy Price Trend Scenario ........................8C-8

Table 8C.5.3 Representative Unit 5: Low Energy Price Trend Scenario .........................8C-9

Table 8C.5.4 Representative Unit 5: Low Retail Price Discount Scenario ......................8C-9

Table 8C.5.5 Representative Unit 5: Medium Retail Price Discount Scenario ................8C-9

Table 8C.5.6 Representative Unit 5: High Retail Price Discount Scenario ...................8C-10

Table 8C.6.1 Representative Unit 6: Reference Scenario ..............................................8C-10

Table 8C.6.2 Representative Unit 6: High Energy Price Trend Scenario ......................8C-10

Table 8C.6.3 Representative Unit 6: Low Energy Price Trend Scenario .......................8C-10

Table 8C.6.4 Representative Unit 6: Low Retail Price Discount Scenario ....................8C-11

Table 8C.6.5 Representative Unit 6: Medium Retail Price Discount Scenario ..............8C-11

Table 8C.6.6 Representative Unit 6: High Retail Price Discount Scenario ...................8C-11

Table 8C.7.1 Representative Unit 7: Reference Scenario ..............................................8C-12

Table 8C.7.2 Representative Unit 7: High Energy Price Trend Scenario ......................8C-12

Table 8C.7.3 Representative Unit 7: Low Energy Price Trend Scenario .......................8C-12

Table 8C.7.4 Representative Unit 7: Low Retail Price Discount Scenario ....................8C-13

Table 8C.7.5 Representative Unit 7: Medium Retail Price Discount Scenario ..............8C-13

Table 8C.7.6 Representative Unit 7: High Retail Price Discount Scenario ...................8C-13

Table 8C.8.1 Representative Unit 8: Reference Scenario ..............................................8C-14

Table 8C.8.2 Representative Unit 8: High Energy Price Trend Scenario ......................8C-14

Table 8C.8.3 Representative Unit 8: Low Energy Price Trend Scenario .......................8C-14

Table 8C.8.4 Representative Unit 8: Low Retail Price Discount Scenario ....................8C-15

Table 8C.8.5 Representative Unit 8: Medium Retail Price Discount Scenario ..............8C-15

Table 8C.8.6 Representative Unit 8: High Retail Price Discount Scenario ...................8C-15

Table 8C.9.1 Representative Unit 9: Reference Scenario ..............................................8C-16

Table 8C.9.2 Representative Unit 9: High Energy Price Trend Scenario ......................8C-16

Table 8C.9.3 Representative Unit 9: Low Energy Price Trend Scenario .......................8C-16

Table 8C.9.4 Representative Unit 9: Low Retail Price Discount Scenario ....................8C-17

Table 8C.9.5 Representative Unit 9: Medium Retail Price Discount Scenario ..............8C-17

Table 8C.9.6 Representative Unit 9: High Retail Price Discount Scenario ...................8C-17

Table 8C.10.1 Representative Unit 10: Reference Scenario ............................................8C-18

Table 8C.10.2 Representative Unit 10: High Energy Price Trend Scenario ....................8C-18

Table 8C.10.3 Representative Unit 10: Low Energy Price Trend Scenario .....................8C-18

Table 8C.10.4 Representative Unit 10: Low Retail Price Discount Scenario ..................8C-19

Table 8C.10.5 Representative Unit 10: Medium Retail Price Discount Scenario ............8C-19

Table 8C.10.6 Representative Unit 10: High Retail Price Discount Scenario .................8C-19

8C-ii
APPENDIX 8C. LIFE-CYCLE COST SENSITIVITY ANALYSIS

8C.1 REPRESENTATIVE UNIT 1, NEMA DESIGN B, 5 HORSEPOWER, FOUR 

POLES, ENCLOSED MOTOR

Table 8C.1.1 Representative Unit 1: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,977 883 6,912 N/A 0.0 0.0 N/A N/A

1 87.5 678 8,287 817 6,462 50 0.0 11.2 0.6 0.4

2 89.5 731 8,138 802 6,413 70 9.3 31.7 7.8 3.5

3 90.2 789 8,062 794 6,411 68 32.4 45.1 10.0 6.5

4 91.0 1,237 7,969 786 6,801 -287 84.7 7.6 69.2 28.7

Table 8C.1.2 Representative Unit 1: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,977 898 7,077 N/A 0.0 0.0 N/A N/A

1 87.5 678 8,287 831 6,614 52 0.0 11.2 0.6 0.4

2 89.5 731 8,138 816 6,562 73 9.0 32.0 7.9 3.4

3 90.2 789 8,062 807 6,558 72 31.2 46.4 9.8 6.4

4 91.0 1,237 7,969 799 6,947 -282 84.4 7.9 68.1 28.2

Table 8C.1.3 Representative Unit 1: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,977 901 6,919 N/A 0.0 0.0 N/A N/A

1 87.5 678 8,287 834 6,468 50 0.0 11.2 0.6 0.4

2 89.5 731 8,138 819 6,418 71 9.3 31.7 8.0 3.4

3 90.2 789 8,062 810 6,416 68 32.5 45.1 9.8 6.4

8C-1 
4 91.0 1,237 7,969 802 6,807 -287 84.6 7.6 67.7 28.1

Table 8C.1.4 Representative Unit 1: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 242 8,977 883 6,499 N/A 0.0 0.0 N/A N/A

1 87.5 252 8,287 817 6,036 52 0.0 11.2 0.3 0.2

2 89.5 276 8,138 802 5,957 84 2.9 38.1 3.5 1.5

3 90.2 295 8,062 794 5,917 112 5.2 72.4 3.6 2.3

4 91.0 444 7,969 786 6,008 32 59.0 33.3 23.1 9.6

Table 8C.1.5 Representative Unit 1: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 361 8,977 883 6,617 N/A 0.0 0.0 N/A N/A

1 87.5 373 8,287 817 6,158 51 0.0 11.2 0.3 0.2

2 89.5 406 8,138 802 6,087 80 4.7 36.3 4.7 2.1

3 90.2 436 8,062 794 6,058 99 11.2 66.3 5.4 3.5

4 91.0 670 7,969 786 6,235 -59 74.8 17.5 36.3 15.1

Table 8C.1.6 Representative Unit 1: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 479 8,977 883 6,735 N/A 0.0 0.0 N/A N/A

1 87.5 495 8,287 817 6,280 51 0.0 11.2 0.4 0.3

2 89.5 536 8,138 802 6,218 76 6.5 34.5 5.9 2.6

3 90.2 578 8,062 794 6,199 87 18.9 58.7 7.2 4.7

4 91.0 897 7,969 786 6,461 -150 80.7 11.5 49.4 20.5

8C-2 
8C.2 REPRESENTATIVE UNIT 2, NEMA DESIGN B, 30 HORSEPOWER, FOUR 

POLES, ENCLOSED MOTOR

Table 8C.2.1 Representative Unit 2: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 61,611 6,232 54,651 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 60,164 6,091 53,881 42 1.1 3.9 13.6 3.5

2 93.6 2,300 58,778 5,966 52,931 415 1.3 37.9 2.2 1.2

3 94.1 2,559 58,698 5,968 53,200 192 44.6 38.3 39 4.5

4 94.5 3,891 58,511 5,960 54,459 -974 81.9 10.7 257 23.1

Table 8C.2.2 Representative Unit 2: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 61,611 6,335 56,133 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 60,164 6,192 55,328 44 1.1 3.9 13.4 3.4

2 93.6 2,300 58,778 6,064 54,345 430 1.3 37.9 2.1 1.1

3 94.1 2,559 58,698 6,066 54,612 208 44.1 38.8 27 4.4

4 94.5 3,891 58,511 6,057 55,867 -954 81.1 11.4 307 22.7

Table 8C.2.3 Representative Unit 2: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 61,611 6,359 54,507 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 60,164 6,216 53,740 42 1.1 3.9 13.3 3.4

2 93.6 2,300 58,778 6,088 52,793 414 1.3 37.9 2.1 1.1

3 94.1 2,559 58,698 6,089 53,062 190 44.6 38.3 36 4.4

4 94.5 3,891 58,511 6,081 54,323 -976 82.0 10.6 212 22.6

8C-3 
Table 8C.2.4 Representative Unit 2: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 685 61,611 6,232 53,588 N/A 0.0 0.0 N/A N/A

1 92.4 854 60,164 6,091 52,553 55 0.4 4.5 5.3 1.3

2 93.6 893 58,778 5,966 51,524 460 0.6 38.6 0.7 0.4

3 94.1 997 58,698 5,968 51,638 364 36.9 46.0 16 1.7

4 94.5 1,480 58,511 5,960 52,048 -16 60.5 32.1 96 8.5

Table 8C.2.5 Representative Unit 2: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 989 61,611 6,232 53,892 N/A 0.0 0.0 N/A N/A

1 92.4 1,233 60,164 6,091 52,933 51 0.6 4.4 7.7 2.0

2 93.6 1,295 58,778 5,966 51,926 447 0.8 38.4 1.1 0.6

3 94.1 1,443 58,698 5,968 52,084 315 39.2 43.7 22 2.5

4 94.5 2,169 58,511 5,960 52,737 -290 68.2 24.4 142 12.7

Table 8C.2.6 Representative Unit 2: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,292 61,611 6,232 54,196 N/A 0.0 0.0 N/A N/A

1 92.4 1,612 60,164 6,091 53,312 48 0.8 4.2 10.0 2.6

2 93.6 1,697 58,778 5,966 52,328 434 1.0 38.2 1.5 0.8

3 94.1 1,889 58,698 5,968 52,530 266 41.4 41.6 29 3.3

4 94.5 2,858 58,511 5,960 53,426 -564 74.0 18.5 188 16.8

8C-4 
8C.3 REPRESENTATIVE UNIT 3, NEMA DESIGN B, 75 HORSEPOWER, FOUR 

POLES, ENCLOSED MOTOR

Table 8C.3.1 Representative Unit 3: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 195,566 17,049 143,892 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 194,167 16,950 143,414 54 2.7 5.3 11.2 3.2

2 95.4 4,692 190,458 16,652 141,460 702 2.5 30.3 3.8 1.7

3 95.8 5,473 190,392 16,667 142,361 26 48.5 26.6 37.1 6.1

4 96.2 6,932 188,997 16,566 142,975 -537 68.4 23.2 168.6 14.9

Table 8C.3.2 Representative Unit 3: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 195,566 17,374 148,532 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 194,167 17,273 148,028 56 2.7 5.4 11.0 3.1

2 95.4 4,692 190,458 16,968 145,985 734 2.3 30.6 3.9 1.7

3 95.8 5,473 190,392 16,983 146,885 59 47.9 27.2 70.9 6.0

4 96.2 6,932 188,997 16,880 147,466 -474 67.2 24.3 68.3 14.6

Table 8C.3.3 Representative Unit 3: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,882 195,566 17,346 142,883 N/A 0.0 0.0 N/A N/A

1 94.1 4,182 194,167 17,245 142,411 53 2.7 5.3 11.0 3.1

2 95.4 4,692 190,458 16,941 140,476 695 2.5 30.3 4.0 1.7

3 95.8 5,473 190,392 16,956 141,378 19 48.5 26.5 216.7 6.0

4 96.2 6,932 188,997 16,853 142,000 -551 68.6 22.9 79.8 14.6

8C-5 
Table 8C.3.4 Representative Unit 3: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 1,512 195,566 17,049 141,521 N/A 0.0 0.0 N/A N/A

1 94.1 1,636 194,167 16,950 140,868 68 1.8 6.2 4.7 1.4

2 95.4 1,835 190,458 16,652 138,602 818 0.7 32.2 1.5 0.7

3 95.8 2,114 190,392 16,667 139,002 518 39.1 35.9 13.4 2.3

4 96.2 2,632 188,997 16,566 138,676 816 35.2 56.4 59.4 5.3

Table 8C.3.5 Representative Unit 3: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 2,189 195,566 17,049 142,198 N/A 0.0 0.0 N/A N/A

1 94.1 2,364 194,167 16,950 141,595 64 2.2 5.9 6.6 1.9

2 95.4 2,651 190,458 16,652 139,419 785 1.1 31.8 2.1 1.0

3 95.8 3,074 190,392 16,667 139,962 377 42.0 33.0 20.2 3.4

4 96.2 3,861 188,997 16,566 139,904 429 48.2 43.4 90.6 8.1

Table 8C.3.6 Representative Unit 3: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 2,866 195,566 17,049 142,875 N/A 0.0 0.0 N/A N/A

1 94.1 3,091 194,167 16,950 142,323 60 2.4 5.7 8.4 2.4

2 95.4 3,467 190,458 16,652 140,235 751 1.5 31.3 2.8 1.3

3 95.8 4,033 190,392 16,667 140,921 237 44.8 30.2 27.0 4.5

4 96.2 5,089 188,997 16,566 141,133 43 57.9 33.7 121.8 10.8

8C-6 
8C.4 REPRESENTATIVE UNIT 4, NEMA DESIGN C, 5 HORSEPOWER, FOUR 

POLES, ENCLOSED MOTOR

Table 8C.4.1 Representative Unit 4: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 8,376 823 6,714 N/A 0.0 0.0 N/A N/A

1 89.5 697 8,206 808 6,646 62 16.0 76.0 8.5 3.8

2 91.0 1,139 8,078 794 6,986 -278 95.2 4.8 31.9 21.5

Table 8C.4.2 Representative Unit 4: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 8,376 837 6,876 N/A 0.0 0.0 N/A N/A

1 89.5 697 8,206 821 6,805 65 15.3 76.6 8.4 3.7

2 91.0 1,139 8,078 807 7,142 -272 94.6 5.4 31.4 21.2

Table 8C.4.3 Representative Unit 4: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 649 8,376 840 6,714 N/A 0.0 0.0 N/A N/A

1 89.5 697 8,206 824 6,646 62 16.0 76.0 8.3 3.7

2 91.0 1,139 8,078 810 6,986 -278 95.2 4.8 31.2 21.0

Table 8C.4.4 Representative Unit 4: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

8C-7 
0 87.5 234 8,376 823 6,299 N/A 0.0 0.0 N/A N/A

1 89.5 255 8,206 808 6,204 87 4.6 87.4 3.8 1.7

2 91.0 401 8,078 794 6,248 43 44.9 55.1 10.8 7.3

Table 8C.4.5 Representative Unit 4: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 353 8,376 823 6,418 N/A 0.0 0.0 N/A N/A

1 89.5 381 8,206 808 6,331 80 7.5 84.5 5.1 2.3

2 91.0 612 8,078 794 6,459 -48 71.6 28.4 16.8 11.3

Table 8C.4.6 Representative Unit 4: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 471 8,376 823 6,536 N/A 0.0 0.0 N/A N/A

1 89.5 507 8,206 808 6,457 73 10.7 81.2 6.5 2.9

2 91.0 823 8,078 794 6,670 -140 86.2 13.8 22.9 15.4

8C.5 REPRESENTATIVE UNIT 5, NEMA DESIGN C, 50 HORSEPOWER, FOUR 

POLES, ENCLOSED MOTOR

Table 8C.5.1 Representative Unit 5: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 79,551 7,933 75,575 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 78,276 7,832 75,655 -40 44.1 29.1 46.1 11.0

2 95.0 4,958 77,653 7,780 75,919 -304 70.1 29.9 35.7 12.9

Table 8C.5.2 Representative Unit 5: High Energy Price Trend Scenario

Energy Efficiency Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

8C-8 
Efficiency 

Level

% Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with years

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 79,551 8,066 77,702 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 78,276 7,963 77,750 -16 42.8 30.3 26.5 10.8

2 95.0 4,958 77,653 7,910 77,996 -263 68.4 31.6 28.1 12.7

Table 8C.5.3 Representative Unit 5: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 3,190 79,551 8,093 75,228 N/A 0.0 0.0 N/A N/A

1 94.5 4,216 78,276 7,990 75,314 -44 44.2 28.9 34.6 10.8

2 95.0 4,958 77,653 7,937 75,581 -311 70.5 29.5 46.0 12.6

Table 8C.5.4 Representative Unit 5: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 1,263 79,551 7,933 73,648 N/A 0.0 0.0 N/A N/A

1 94.5 1,627 78,276 7,832 73,066 444 13.3 59.9 16.3 3.9

2 95.0 1,860 77,653 7,780 72,822 688 19.4 80.6 11.8 4.3

Table 8C.5.5 Representative Unit 5: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 1,814 79,551 7,933 74,199 N/A 0.0 0.0 N/A N/A

1 94.5 2,367 78,276 7,832 73,806 305 21.6 51.6 24.8 6.0

2 95.0 2,745 77,653 7,780 73,707 405 34.3 65.7 18.6 6.7

8C-9 
Table 8C.5.6 Representative Unit 5: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 93.0 2,364 79,551 7,933 74,749 N/A 0.0 0.0 N/A N/A

1 94.5 3,106 78,276 7,832 74,546 167 30.8 42.3 33.3 8.0

2 95.0 3,630 77,653 7,780 74,592 121 49.9 50.1 25.5 9.2

8C.6 REPRESENTATIVE UNIT 6, FIRE PUMP, 5 HORSEPOWER, FOUR 

POLES, ENCLOSED ELECTRIC MOTOR

Table 8C.6.1 Representative Unit 6: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 680 9 2 713 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 3 769 -46 82.0 0.0 7,765 3,730

2 90.2 792 9 2 822 -96 94.9 0.0 1,234 507

3 91.0 1,239 9 2 1,274 -548 100.0 0.0 34,521 14,079

Table 8C.6.2 Representative Unit 6: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 680 9 2 714 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 3 770 -46 82.0 0.0 7,641 3,676

2 90.2 792 9 2 823 -96 94.9 0.0 1,214 499

3 91.0 1,239 9 2 1,275 -548 100.0 0.0 33,979 13,863

Table 8C.6.3 Representative Unit 6: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average Average Average Average Average Customers with

8C-10
Installed 

Price

$ 

Energy 

Use 

kWh/yr

Annual 

Operating 

Cost

$

LifeCycle 

Cost

$

Savings

$ Net Cost

% 

Net 

Benefit

% 

Average Median

0 87.5 680 9 2 713 N/A 0.0 0.0 N/A N/A

1 89.5 733 9 3 768 -46 82.0 0.0 7,602 3,645

2 90.2 792 9 2 822 -96 94.9 0.0 1,209 497

3 91.0 1,239 9 3 1,273 -548 100.0 0.0 33,784 13,783

Table 8C.6.4 Representative Unit 6: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 252 9 2 286 N/A 0.0 0.0 N/A N/A

1 89.5 276 9 3 312 -21 82.0 0.0 3,432 1,655

2 90.2 296 9 2 326 -35 94.9 0.0 469 197

3 91.0 444 9 2 479 -188 100.0 0.0 11,746 4,748

Table 8C.6.5 Representative Unit 6: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 375 9 2 408 N/A 0.0 0.0 N/A N/A

1 89.5 407 9 3 442 -28 82.0 0.0 4,670 2,246

2 90.2 437 9 2 468 -52 94.9 0.0 688 285

3 91.0 671 9 2 706 -291 100.0 0.0 18,253 7,426

Table 8C.6.6 Representative Unit 6: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 87.5 497 9 2 530 N/A 0.0 0.0 N/A N/A

1 89.5 538 9 3 573 -35 82.0 0.0 5,908 2,842

2 90.2 579 9 2 609 -70 94.9 0.0 906 374

3 91.0 898 9 2 933 -394 100.0 0.0 24,760 10,087

8C-11
8C.7 REPRESENTATIVE UNIT 7, FIRE PUMP, 30 HORSEPOWER, FOUR 

POLES, ENCLOSED ELECTRIC MOTOR

Table 8C.7.1 Representative Unit 7: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 53 13 2,418 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 13 2,533 -93 80.7 0.0 1,027 362

2 94.1 2,606 52 13 2,791 -318 87.4 0.0 2,894 1,287

3 94.5 3,938 52 13 4,119 -1,647 100.0 0.0 10,367 2,740

Table 8C.7.2 Representative Unit 7: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 53 14 2,425 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 13 2,540 -93 80.7 0.0 1,011 357

2 94.1 2,606 52 13 2,798 -318 87.4 0.0 2,847 1,266

3 94.5 3,938 52 13 4,126 -1,646 100.0 0.0 10,201 2,696

Table 8C.7.3 Representative Unit 7: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 2,228 53 14 2,414 N/A 0.0 0.0 N/A N/A

1 93.6 2,347 52 13 2,530 -93 80.7 0.0 1,006 355

2 94.1 2,606 52 13 2,788 -318 87.4 0.0 2,835 1,261

3 94.5 3,938 52 13 4,116 -1,647 100.0 0.0 10,151 2,682

8C-12
Table 8C.7.4 Representative Unit 7: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 868 53 13 1,057 N/A 0.0 0.0 N/A N/A

1 93.6 907 52 13 1,093 -29 80.7 0.0 339 120

2 94.1 1,011 52 13 1,196 -119 87.4 0.0 1,108 489

3 94.5 1,494 52 13 1,675 -598 100.0 0.0 3,796 1,001

Table 8C.7.5 Representative Unit 7: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 1,256 53 13 1,446 N/A 0.0 0.0 N/A N/A

1 93.6 1,318 52 13 1,504 -47 80.7 0.0 536 189

2 94.1 1,466 52 13 1,652 -176 87.4 0.0 1,618 717

3 94.5 2,193 52 13 2,373 -898 100.0 0.0 5,674 1,498

Table 8C.7.6 Representative Unit 7: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 92.4 1,645 53 13 1,835 N/A 0.0 0.0 N/A N/A

1 93.6 1,730 52 13 1,916 -65 80.7 0.0 732 258

2 94.1 1,922 52 13 2,107 -233 87.4 0.0 2,129 944

3 94.5 2,891 52 13 3,072 -1,197 100.0 0.0 7,551 1,996

8C-13
8C.8 REPRESENTATIVE UNIT 8, FIRE PUMP, 75 HORSEPOWER, FOUR 

POLES, ENCLOSED ELECTRIC MOTOR

Table 8C.8.1 Representative Unit 8: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 130 30 4,636 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 27 5,096 -370 80.3 0.0 481 148

2 95.8 5,504 128 29 5,906 -1,103 90.5 0.0 3,768 919

3 96.2 6,963 127 26 7,327 -2,524 100.0 0.0 3,483 719

Table 8C.8.2 Representative Unit 8: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 130 31 4,654 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 27 5,112 -368 80.3 0.0 472 145

2 95.8 5,504 128 29 5,924 -1,103 90.5 0.0 3,700 903

3 96.2 6,963 127 27 7,343 -2,521 100.0 0.0 3,419 706

Table 8C.8.3 Representative Unit 8: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 4,213 130 31 4,626 N/A 0.0 0.0 N/A N/A

1 95.4 4,723 128 27 5,088 -370 80.3 0.0 473 146

2 95.8 5,504 128 29 5,897 -1,104 90.5 0.0 3,698 904

3 96.2 6,963 127 27 7,319 -2,525 100.0 0.0 3,422 707

8C-14
Table 8C.8.4 Representative Unit 8: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 1,646 130 30 2,069 N/A 0.0 0.0 N/A N/A

1 95.4 1,844 128 27 2,217 -119 80.3 0.0 187 58

2 95.8 2,124 128 29 2,526 -399 90.5 0.0 1,373 341

3 96.2 2,642 127 26 3,006 -879 100.0 0.0 1,253 260

Table 8C.8.5 Representative Unit 8: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 2,379 130 30 2,802 N/A 0.0 0.0 N/A N/A

1 95.4 2,666 128 27 3,039 -191 80.3 0.0 271 83

2 95.8 3,089 128 29 3,492 -600 90.5 0.0 2,057 506

3 96.2 3,876 127 26 4,240 -1,349 100.0 0.0 1,890 392

Table 8C.8.6 Representative Unit 8: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 94.1 3,113 130 30 3,535 N/A 0.0 0.0 N/A N/A

1 95.4 3,489 128 27 3,862 -262 80.3 0.0 355 109

2 95.8 4,055 128 29 4,458 -802 90.5 0.0 2,742 672

3 96.2 5,111 127 26 5,475 -1,819 100.0 0.0 2,528 522

8C-15
8C.9 REPRESENTATIVE UNIT 9, BRAKE MOTOR, NEMA DESIGN B, TFRAME, 5 HORSEPOWER, FOUR POLES, ENCLOSED MOTOR

Table 8C.9.1 Representative Unit 9: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,079 900 6,623 N/A 0.0 0.0 N/A N/A

1 87.5 678 7,430 837 6,167 160 0.0 34.8 0.6 0.4

2 89.5 731 7,290 841 6,119 194 10.9 58.3 18.0 1.8

3 90.2 789 7,219 846 6,119 191 30.3 68.4 33.9 3.3

4 91.0 1,237 7,132 853 6,507 -193 76.7 22.8 103 14.1

Table 8C.9.2 Representative Unit 9: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,079 913 6,785 N/A 0.0 0.0 N/A N/A

1 87.5 678 7,430 850 6,316 165 0.0 34.8 0.6 0.4

2 89.5 731 7,290 853 6,265 201 10.5 58.6 11.1 1.8

3 90.2 789 7,219 858 6,264 199 29.1 69.6 24.8 3.3

4 91.0 1,237 7,132 864 6,650 -183 76.2 23.4 100 14.0

Table 8C.9.3 Representative Unit 9: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 656 8,079 916 6,618 N/A 0.0 0.0 N/A N/A

1 87.5 678 7,430 852 6,163 160 0.0 34.8 0.5 0.4

2 89.5 731 7,290 855 6,115 194 10.9 58.2 12.4 1.8

3 90.2 789 7,219 860 6,115 191 30.4 68.3 27.4 3.3

4 91.0 1,237 7,132 867 6,503 -193 76.8 22.8 78 13.9

8C-16
Table 8C.9.4 Representative Unit 9: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 242 8,079 900 6,209 N/A 0.0 0.0 N/A N/A

1 87.5 252 7,430 837 5,742 164 0.0 34.8 0.2 0.2

2 89.5 276 7,290 841 5,663 219 2.9 66.2 8.0 0.8

3 90.2 295 7,219 846 5,625 254 4.0 94.7 13.2 1.3

4 91.0 444 7,132 853 5,714 168 43.3 56.2 35 4.9

Table 8C.9.5 Representative Unit 9: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 361 8,079 900 6,327 N/A 0.0 0.0 N/A N/A

1 87.5 373 7,430 837 5,863 163 0.0 34.8 0.3 0.2

2 89.5 406 7,290 841 5,793 212 5.1 64.0 10.9 1.1

3 90.2 436 7,219 846 5,766 236 10.5 88.2 19.1 1.9

4 91.0 670 7,132 853 5,941 65 60.8 38.8 55 7.5

Table 8C.9.6 Representative Unit 9: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 82.5 479 8,079 900 6,445 N/A 0.0 0.0 N/A N/A

1 87.5 495 7,430 837 5,985 162 0.0 34.8 0.4 0.3

2 89.5 536 7,290 841 5,923 205 7.5 61.7 13.7 1.4

3 90.2 578 7,219 846 5,908 218 17.6 81.0 25.0 2.4

4 91.0 897 7,132 853 6,167 -38 69.2 30.4 74 10.2

8C-17
8C.10 REPRESENTATIVE UNIT 10, BRAKE MOTOR, NEMA DESIGN B, TFRAME, 30 HORSEPOWER, FOUR POLES, ENCLOSED MOTOR

Table 8C.10.1 Representative Unit 10: Reference Scenario 

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$ 

Average 

LifeCycle 

Cost

$ 

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 48,394 4,869 46,676 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 47,178 4,753 46,018 138 6.0 16.1 17.1 4.8

2 93.6 2,300 45,999 4,649 45,173 861 3.8 81.5 4.3 1.5

3 94.1 2,559 45,934 4,652 45,457 577 28.6 71.4 14.6 4.1

4 94.5 3,891 45,777 4,647 46,738 -704 81.5 18.6 102 15.9

Table 8C.10.2 Representative Unit 10: High Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 48,394 4,950 47,996 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 47,178 4,832 47,306 145 5.8 16.2 16.8 4.7

2 93.6 2,300 45,999 4,726 46,428 895 3.5 81.8 3.8 1.5

3 94.1 2,559 45,934 4,729 46,711 613 27.8 72.2 13.3 4.0

4 94.5 3,891 45,777 4,724 47,987 -663 80.1 19.9 756 15.6

Table 8C.10.3 Representative Unit 10: Low Energy Price Trend Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,748 48,394 4,967 46,452 N/A 0.0 0.0 N/A N/A

1 92.4 2,181 47,178 4,849 45,800 137 6.0 16.0 16.7 4.7

2 93.6 2,300 45,999 4,742 44,960 855 3.8 81.5 5.9 1.5

3 94.1 2,559 45,934 4,745 45,245 571 28.7 71.3 20.6 4.0

4 94.5 3,891 45,777 4,740 46,526 -711 81.8 18.2 92 15.5

8C-18
Table 8C.10.4 Representative Unit 10: Low Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 685 48,394 4,869 45,613 N/A 0.0 0.0 N/A N/A

1 92.4 854 47,178 4,753 44,690 197 2.5 19.6 6.6 1.9

2 93.6 893 45,999 4,649 43,766 987 1.6 83.7 1.5 0.5

3 94.1 997 45,934 4,652 43,895 858 16.3 83.7 5.6 1.6

4 94.5 1,480 45,777 4,647 44,326 426 38.2 61.8 38 5.8

Table 8C.10.5 Representative Unit 10: Medium Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 989 48,394 4,869 45,917 N/A 0.0 0.0 N/A N/A

1 92.4 1,233 47,178 4,753 45,070 180 3.4 18.7 9.6 2.7

2 93.6 1,295 45,999 4,649 44,168 951 2.0 83.3 2.3 0.8

3 94.1 1,443 45,934 4,652 44,341 777 19.5 80.5 8.2 2.3

4 94.5 2,169 45,777 4,647 45,015 104 52.3 47.7 56 8.7

Table 8C.10.6 Representative Unit 10: High Retail Price Discount Scenario

Energy 

Efficiency 

Level

Efficiency

% 

Life-Cycle Cost Life-Cycle Cost Savings Payback Period 

years Average 

Installed 

Price

$ 

Average 

Energy 

Use 

kWh/yr

Average 

Annual 

Operating 

Cost

$

Average 

LifeCycle 

Cost

$

Average 

Savings

$ 

Customers with

Net Cost

% 

Net 

Benefit

%

Average Median

0 89.5 1,292 48,394 4,869 46,221 N/A 0.0 0.0 N/A N/A

1 92.4 1,612 47,178 4,753 45,449 163 4.4 17.6 12.6 3.5

2 93.6 1,697 45,999 4,649 44,570 915 2.6 82.7 3.1 1.1

3 94.1 1,889 45,934 4,652 44,788 697 23.0 77.0 10.8 3.0

4 94.5 2,858 45,777 4,647 45,704 -219 65.5 34.5 75 11.6

8C-19
APPENDIX 10-A. USER INSTRUCTIONS FOR SHIPMENTS AND

NATIONAL IMPACT ANALYSIS SPREADSHEET MODELS 

TABLE OF CONTENTS

10-A.1 USER INSTRUCTIONS ..................................................................................... 10-A-1

10-A.1.1 Shipments Model Spreadsheet Description ......................................................... 10-A-1

10-A.1.2 National Impact Analysis Spreadsheets Description ........................................... 10-A-2

10-A.1.2.1 Summary Spreadsheet Organization ............................................ 10-A-3

10-A.1.2.2 Accountability Spreadsheets Organization .................................. 10-A-3

10-A.1.2.3 National Impact Analysis Spreadsheet Operating Instructions ... 10-A-4

10-A-i
APPENDIX 10-A. USER INSTRUCTIONS FOR SHIPMENTS AND

NATIONAL IMPACT ANALYSIS SPREADSHEET MODELS 

10-A.1 USER INSTRUCTIONS

 The results obtained in the shipments analysis and the national impact analysis (NIA) can 

be examined and reproduced using the Microsoft Excel spreadsheet available on the U.S. 

Department of Energy’s (DOE)’s website at: 

http://www.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/42

The shipments model is in the spreadsheet called “mem_fr_shipments_model.xls,” and 

the NIA in the spreadsheets “mem_fr_nia_summary.xlsm,” “mem_fr_nia_designab.xlsx,” 

“mem_fr_nia_designc.xlsx,” “mem_fr_nia_firepump.xlsx,” and “mem_fr_nia_brake.xlsx.” 

These spreadsheets implement the calculations described in Chapters 9 and 10. Further, the NIA 

spreadsheets enable the user to simulate national impacts under different parameters and 

scenarios. To run the spreadsheets the user needs to have Microsoft Excel 2007 or a later 

version. 

10-A.1.1 Shipments Model Spreadsheet Description

 The shipments model spreadsheet performs calculations to forecast the shipments of 

motors covered by the rulemaking. The methodology for developing the shipments model is 

described in Chapter 9 of the Technical Support Document. The shipments model spreadsheet, or 

workbook, consists of the following worksheets: 

(a) Shipments: Calculates and provides a summary of the shipment forecasts for the entire 

analysis period and beyond. 

(b) Invest. vs. Ship.: Presents how DOE developed a relationship between shipments and private 

fixed investment in selected equipment and structure. 

(c) Invest. vs. Tol. Invest.: Calculates projections for private fixed investment in equipment and 

structure for selected sectors.

(d) Tot. Invest. vs. GDP: Calculates projections for total private fixed investment. 

(e) Census: Presents the Census data used to develop the historical shipments index. 

(f) RSMeans: Presents the data used to estimate the percentage of private fixed investments in 

structures related to heating, ventilating and air conditioning (HVAC) equipment. 

(g) Investment in Structure: Presents the data used to calculate private fixed investments in 

structures related to HVAC equipment. 

10-A-1
(h) Investment in Equipment: Presents the data used to calculate private fixed investments in 

motor related equipment. 

(i) Current Dollar and Real GDP: Presents the data used to adjust the value of dollar. 

10-A.1.2 National Impact Analysis Spreadsheets Description

The NIA spreadsheets perform calculations to forecast the changes in national energy 

savings (NES) and net present value (NPV) due to an energy efficiency standard. For a standard

set at a given trial standard level (TSL), the energy consumption and the costs associated with 

each equipment class, as well as the corresponding NES and NPV results rely on the shipments 

estimated in the shipments spreadsheet and on calculation performed by four accountability 

spreadsheets, each dedicated to a specific equipment class group. A fifth, summary spreadsheet

provides the accountability spreadsheets with general parameters and tables, and summarizes 

their results. Figure 10-A.1.1 presents the general organization and interactions between the 

spreadsheets comprising the NIA model. The following subsections describe, respectively, the 

worksheets comprising the summary and the accountability spreadsheets, and provide 

instructions to operate the NIA model. 

Figure 10-A.1.1 Spreadsheets Architecture of the NIA Model 

Design C

Accountability

NIA

Summary

Brake Motors

Accountability

parameters

and tables

energy savings

energy cost savings

incremental eqpt costs

parameters

and tables

energy savings

energy cost savings

incremental eqpt costs

Fire Pumps

Accountability

parameters

and tables

energy savings

energy cost savings

incremental eqpt costs

Design A & B

Accountability

parameters

and tables

energy savings

energy cost savings

incremental eqpt costs

10-A-2
10-A.1.2.1 Summary Spreadsheet Organization 

The summary spreadsheet consists of the following six worksheets which support the 

accountability spreadsheets and summarize their results. 

(a) Lifetime: Presents, for each equipment class group, motor survival probabilities by sector and 

horsepower (HP) range.

(b) Efficiency Tables: Presents, for each equipment class group, the efficiency levels by CSL 

and equipment class.

(c) General Tables & Parameters: Presents all tables and single-value parameters used by the 

accountability spreadsheets. 

(d) Shipments: Presents forecast of total shipments, as well as shipment distributions across 

equipment class groups, and motor HP and configuration. 

(e) Summary: Enables the user to select TSLs, scenarios and sensitivity levels to be simulated by 

the accountability spreadsheets, and summarize their results. 

(f) Scenario Results: Automatically simulates pre-determined combinations of scenarios and 

sensitivity levels, and summarize results in a pivot-table. 

10-A.1.2.2 Accountability Spreadsheets Organization

The accountability spreadsheets consist of the following 11 worksheets which calculate 

the national energy savings, the national energy cost savings, and the national (non-energy) 

incremental equipment costs for all equipment classes of each equipment class group. 

(a) Shipments: Presents the base case shipments forecast by sector for all equipment classes, and 

estimates shipments for the standards case scenario depending on the elasticity scenario 

selected. 

(b) Efficiency Distribution: Presents the base case energy efficiency distribution by motor HP, 

and calculates the corresponding distributions to the standards case according to the 

efficiency level corresponding to the TSL selected in the Summary spreadsheet.

(c) Unit Energy Consumption: Calculates, for all equipment classes and efficiency levels, the 

lifetime energy consumption of a unit shipped in each year of the analysis period, according 

to the sector to which it is shipped. 

(d) Natl Energy Consumption: Calculates, for all equipment classes, the base case and the 

standards case national lifetime energy consumption from units shipped in each year of the 

analysis period. The calculation is disaggregated by sector. Additionally, this worksheet 

calculates the annual energy consumption from the existing stock not replaced due to effects 

from a non-zero elasticity scenario. 

10-A-3
(e) Natl Energy Savings: Calculates, for all equipment classes, the national energy savings by 

sector.

(f) Unit Energy Cost: Calculates, for all equipment classes and efficiency levels, the lifetime 

energy cost of a unit shipped in each year of the analysis period, according to the sector to 

which it is shipped. 

(g) Natl Energy Cost: Calculates, for all equipment classes, the base case and the standards case 

national lifetime energy costs from units shipped in each year of the analysis period. The 

calculation is disaggregated by sector. Additionally, this worksheet calculates the annual 

energy cost from the existing stock not replaced due to effects from a non-zero elasticity 

scenario.

(h) Natl Energy Cost Savings: Calculates, for all equipment classes, the present-value of the 

national energy cost savings by sector. 

(i) Unit Eqpt Costs: Calculates, for all equipment classes and efficiency levels, the lifetime nonenergy equipment costs of a unit shipped in each year of the analysis period, according to the 

sector to which it is shipped. 

(j) Natl Eqpt Costs: Calculates, for all equipment classes, the base case and the standards case 

national lifetime non-energy equipment costs from units shipped in each year of the analysis 

period. The calculation is disaggregated by sector. 

(k) Natl Eqpt Incr Costs: Calculates, for all equipment classes, the present-value of the national 

(non-energy) incremental equipment costs by sector.

10-A.1.2.3 National Impact Analysis Spreadsheet Operating Instructions

Basic instructions for operating the NIA spreadsheet are as follows:

1. After downloading the NIA set of spreadsheet files from DOE's website, open the Summary 

file using Excel. Once loaded, this spreadsheet will ask if the user wants to open the 

additional files. If you intend only to see the existing results, the answer maybe “No.” 

However, if you plan to do your own simulations you must answer with “Yes,” in which 

case Excel will automatically open the four additional accountability spreadsheet files and 

activate back the Summary spreadsheet.

2. If you intend only to see the existing results, click on the tab for the worksheet “Scenario

Results.” To select results for specific combinations of parameters and scenarios use: the 

pivot-table located at the right side of the results listing.a

a To learn more on how to use Excel pivot-tables refer to “PivotTable I: Get started with PivotTable reports in Excel 

2007” in <http://office.microsoft.com/en-us/excel-help/pivottable-i-get-started-with-pivottable-reports-in-excel2007-RZ010205886.aspx>.

10-A-4

 
3. If you intend to run your own simulations, there are two options: (a) running the model for a 

specific combination of parameters and scenarios, and (b) running the model for predetermined combinations of parameters and scenarios. The two options can be operated as 

follows: 

(a) For a specific combination of parameters and scenarios:

Click on the tab for the worksheet “Summary.” This worksheet serves as the user 

interface for running the model for a particular combination of parameters and scenarios. 

To provide flexibility, the spreadsheet permits some user modifications to the model. 

The user may select a particular:

• Discount rate, which enables the user to set a discount rate (in percentage) and 

affects the present-values of energy savings and incremental equipment (non-energy) 

costs; 

• Economic growth which enables the user to select an annual economic outlook (AEO) 

macroeconomic forecast and determines the electricity prices and level of shipments 

to be used by the model; 

• Product price trend, which enables the user to select a scenario of motor price trends 

and affects motor manufacturer selling prices (MSPs) over the analysis period; 

• Energy savings, which enables the user to select whether the energy savings are to be 

reported as site, primary or full-fuel-cycle energy savings;

• Analysis period, which enables the user to select between 30-year and 9-year impacts 

from standards; 

• TSL, which enables the user to select a TSL that determines the standard level for 

each equipment class group, and affects the standards case efficiency distribution; 

• Elasticity, which enables the user to select a scenario that will either account or not 

account for the effects of price elasticity on shipments; and

• Sensitivity factors, which enables the user to change (with a direct multiplier) all 

motors MSP, repair cost and operating hours values, and affects energy consumption 

and costs, as well as equipment non-energy costs. 

Once the desired parameters are set, the user should start the spreadsheet calculation. 

This can be done either by pressing F9 or navigating through the Excel menu as follows: 

Formulas >> Calculate Now. 

(b) For pre-determined combinations of parameters and scenarios: 

Click on the tab for the worksheet “Results for All Scenarios.” This worksheet can 

automatically calculate results for all equipment class groups, TSLs, and discount rates 

considering all Reference scenarios. It can further extend these calculations to selected 

alternative scenarios (including scenarios for sensitivity analysis). To enable the 

automatic calculation one must answer “Yes” to the “Recalculate all?” question, or 

otherwise the worksheet will just show the results from the earlier run (see item 2 above 

10-A-5
on how to examine results from a model run). After answering with a “Yes” to the 

“Recalculate all?” message, the following alternatives will be posted to the user: 

• “Only Reference scenarios?”

Yes: simulate only the Reference economic growth and the Constant product price 

trend scenarios over the 30-year analysis period with no elasticity

No: enables the selection of additional scenarios to be simulated (see the next item). 

• “Select scenarios to simulate:” 

“A=Economic growth,” 

“P=Prod price trend,” 

“E=Elasticity,”

“Y=Analysis period,” 

“*=All”

A: simulates the Low- and High AEO economic growth scenarios, in addition to the 

Reference one

P: simulates the Decreasing and Increasing product price trend scenarios, in addition 

to the Constant one 

E: simulates the Yes elasticity scenario, in addition to the No one

Y: simulates the 9-year analysis period, in addition to the 30-year period

*: simulates all scenarios. 

• “Include sensitivity analysis?” 

Yes: enables the user to setup the sensitivity level to be simulated (see the next item) 

No: only the reference values for hours of operation, MSP and repair cost will be 

simulated. 

• “Enter percentage:”

Enables the user to type the percentage corresponding to the desired sensitivity level 

to be simulated (for example, to simulate hours of operation, MSP and repair cost 

values 10 percent lower and higher than the former values just enter the number 10). 

• “Run:” 

“<...> scenarios,” 

“<yes/no> sensitivity analysis.” 

This message summarizes what it will be simulated. To start the simulation process, 

click Ok; otherwise, click Cancel. 

During the simulation process, messages in the left side of the lower message bar will 

report the process progress and an estimate of the remaining time. Once the simulation is 

over, the user can then examine the results (see item 2 above on how to examine results 

from a model run). 

10-A-6
APPENDIX 10-B. NATIONAL IMPACT ANALYSIS SENSITIVITY FOR

ALTERNATIVE PRODUCT PRICE TREND SCENARIOS 

TABLE OF CONTENTS

10-B.1 INTRODUCTION ............................................................................................... 10-B-1

10-B.2 ALTERNATIVE MOTOR PRICE TREND SCENARIOS ................................ 10-B-1

10-B.2.1 Exponential Fit Approach (Increasing Price Scenario) ....................................... 10-B-1

10-B.2.2 Annual Energy Outlook 2013 Price Forecast (Decreasing Price Scenario) ........ 10-B-2

10-B.2.3 Summary .............................................................................................................. 10-B-2

10-B.3 NET PRESENT VALUE RESULTS BY PRICE TREND SCENARIO ............ 10-B-4

LIST OF TABLES

Table 10-B.2.1 Price Trend Scenarios .............................................................................. 10-B-3

Table 10-B.3.1 Detailed NPV Results for NEMA Designs A and B Motors (billion 

2012$) ...................................................................................................... 10-B-4

Table 10-B.3.2 Detailed NPV Results for NEMA Design C Motors (billion 2012$) ...... 10-B-5

Table 10-B.3.3 Detailed NPV Results for Fire Pump Motors (billion 2012$) ................. 10-B-5

Table 10-B.3.4 Detailed NPV Results for Brake Motors (billion 2012$) ........................ 10-B-6

10-B-i
APPENDIX 10-B. NATIONAL IMPACT ANALYSIS SENSITIVITY ANALYSIS FOR 

ALTERNATIVE PRODUCT PRICE TREND SCENARIOS

10-B.1 INTRODUCTION

 The U.S. Department of Energy (DOE) used a constant price assumption for the default 

forecast in the National Impact Analysis (NIA) described in Chapter 10. In order to investigate 

the impact of different equipment price forecasts (or product price forecasts) on the consumer net 

present value (NPV) for the considered trial standard levels (TSLs) for electric motors, DOE also 

considered two alternative price trends for a sensitivity analysis. This appendix describes the 

alternative price trends and compares NPV results for these scenarios with the default forecast. 

10-B.2 ALTERNATIVE MOTOR PRICE TREND SCENARIOS

 DOE considered two alternative price trends for a sensitivity analysis. One of these used 

an exponential fit on the deflated Producer Price Index (PPI) for intergral horsepower motors, 

and the other is based on the “chained price index—industrial equipment” that was forecasted for 

EIA’s Annual Energy Outlook 2013 (AEO2013). 

10-B.2.1 Exponential Fit Approach (Increasing Price Scenario) 

For this scenario, DOE used an inflation-adjusted integral horsepower motor and 

generator manufacturing Producer Price Index (PPI) from 1969-2013 to fit an exponential model 

with year as the explanatory variable. DOE obtained historical PPI data for integral horsepower 

motors and generators manufacturing spanning the time period 1969-2013 from the Bureau of 

Labor Statistics’ (BLS).a

 The PPI data reflect nominal prices, adjusted for product quality 

changes. An inflation-adjusted (deflated) price index for integral horsepower motors and 

generators manufacturing was calculated by dividing the PPI series by the Gross Domestic 

Product Chained Price Index. The deflated price index is now presented in 2013 dollar values. In 

this case, the exponential function takes the form of: 

Y = a ∙ ebX 

where Y is the motor price index, X is the time variable, a is the constant and b is the slope 

parameter of the time variable. 

 

To estimate these exponential parameters, a least-square fit was performed on the 

inflation-adjusted motor price index versus year from 1969 to 2013. See Figure 10-B.2.1. 

a

 Series ID PCU3353123353123; http://www.bls.gov/ppi/ 

10-B-1

 
Figure 10-B.2.1 Relative Price of Electric Motors versus Year, with Exponential 

Fit

 The regression performed as an exponential trend line fit results in an R-square of 0.61, 

which indicates a moderate fit to the data. The final estimated exponential function is: 

Y = 2.68 × 10(−7) ∙ e0.00748X 

DOE then derived a price factor index for this scenario, with 2011 equal to 1, to project 

prices in each future year in the analysis period considered in the NIA since 2011. The index 

value in a given year is a function of the exponential parameter and year. 

10-B.2.2 Annual Energy Outlook 2013 Price Forecast (Decreasing Price Scenario) 

DOE also examined a forecast based on the “chained price index—industrial equipment” 

that was forecasted for AEO2013 out to 2040. This index is the most disaggregated category that 

includes electric motors. To develop an inflation-adjusted index, DOE normalized the above 

index with the “chained price index—gross domestic product” forecasted for AEO2013. To 

extend the price index beyond 2040, DOE used the average annual price growth rate in 2031 to 

2040. 

10-B.2.3 Summary

Table 10-B.2.1 shows the summary of the average annual rates of changes for the product 

price index in each scenario. Figure 10-B.2.2 shows the resulting price trends. 

10-B-2
Table 10-B.2.1 Price Trend Scenarios

Scenario Price Trend Average Annual Rate 

of Change (%)

Default Constant Price Projection 0.0

Decreasing Price AEO2013 “chained price index—industrial 

equipment” -0.26 

Increasing Price Exponential Fit using data from 1969 to 2013 0.75

Figure 10-B.2.2 Electric Motor Price Forecast Indexes

10-B-3
10-B.3 NET PRESENT VALUE RESULTS BY PRICE TREND SCENARIO

Table 10-B.3.1 through Table 10-B.3.3 present, for each equipment class group and TSL,

equipment incremental non-energy costs and energy cost savings, with their corresponding NPV 

results, across discount rates and the three product price trend scenarios. 

Table 10-B.3.1 Detailed NPV Results for NEMA Designs A and B Motors (billion 2012$) 

7% discount rate 3% discount rate

Decreasing Constant Increasing Decreasing Constant Increasing

TSL 1

Incr Eqpt Costs 0.342 0.356 0.386 0.527 0.557 0.613

Energy Cost Savings 2.515 2.515 2.515 5.030 5.030 5.030

NPV 2.173 2.159 2.129 4.503 4.473 4.417

TSL 2

Incr Eqpt Costs 6.247 6.697 7.392 12.435 13.549 15.088

Energy Cost Savings 14.378 14.378 14.378 34.254 34.254 34.254

NPV 8.131 7.681 6.986 21.819 20.704 19.165

TSL 3

Incr Eqpt Costs 23.847 25.937 29.070 46.782 51.979 59.031

Energy Cost Savings 22.240 22.240 22.240 53.517 53.517 53.517

NPV -1.607 -3.697 -6.830 6.735 1.538 -5.515

TSL 4

Incr Eqpt Costs 53.503 57.759 64.510 99.394 109.911 124.682

Energy Cost Savings 28.674 28.674 28.674 68.728 68.728 68.728

NPV -24.829 -29.086 -35.836 -30.666 -41.183 -55.954

10-B-4
Table 10-B.3.2 Detailed NPV Results for NEMA Design C Motors (billion 2012$) 

7% discount rate 3% discount rate

Decreasing Constant Increasing Decreasing Constant Increasing

TSL 1

Incr Eqpt Costs 0.025 0.027 0.030 0.048 0.052 0.059

Energy Cost Savings 0.041 0.041 0.041 0.101 0.101 0.101

NPV 0.016 0.014 0.011 0.053 0.049 0.042

TSL 2

Incr Eqpt Costs 0.025 0.027 0.030 0.048 0.052 0.059

Energy Cost Savings 0.041 0.041 0.041 0.101 0.101 0.101

NPV 0.016 0.014 0.011 0.053 0.049 0.042

TSL 3

Incr Eqpt Costs 0.093 0.100 0.112 0.172 0.191 0.217

Energy Cost Savings 0.066 0.066 0.066 0.163 0.163 0.163

NPV -0.027 -0.034 -0.046 -0.009 -0.028 -0.054

TSL 4

Incr Eqpt Costs 0.093 0.100 0.112 0.172 0.191 0.217

Energy Cost Savings 0.066 0.066 0.066 0.163 0.163 0.163

NPV -0.027 -0.034 -0.046 -0.009 -0.028 -0.054

Table 10-B.3.3 Detailed NPV Results for Fire Pump Motors (billion 2012$) 

7% discount rate 3% discount rate

Decreasing Constant Increasing Decreasing Constant Increasing

TSL 1

Incr Eqpt Costs 0.000 0.000 0.000 0.000 0.000 0.000

Energy Cost Savings 0.000 0.000 0.000 0.000 0.000 0.000

NPV 0.000 0.000 0.000 0.000 0.000 0.000

TSL 2

Incr Eqpt Costs 0.000 0.000 0.000 0.000 0.000 0.000

Energy Cost Savings 0.000 0.000 0.000 0.000 0.000 0.000

NPV 0.000 0.000 0.000 0.000 0.000 0.000

TSL 3

Incr Eqpt Costs 0.001 0.002 0.002 0.003 0.003 0.003

Energy Cost Savings 0.000 0.000 0.000 0.000 0.000 0.000

NPV -0.001 -0.002 -0.002 -0.003 -0.003 -0.003

TSL 4

Incr Eqpt Costs 0.015 0.016 0.018 0.027 0.031 0.035

Energy Cost Savings 0.000 0.000 0.000 0.000 0.000 0.000

NPV -0.015 -0.016 -0.018 -0.027 -0.031 -0.035

10-B-5
Table 10-B.3.4 Detailed NPV Results for Brake Motors (billion 2012$) 

7% discount rate 3% discount rate

Decreasing Constant Increasing Decreasing Constant Increasing

TSL 1

Incr Eqpt Costs 0.074 0.079 0.088 0.131 0.144 0.162

Energy Cost Savings 0.611 0.611 0.611 1.454 1.454 1.454

NPV 0.537 0.531 0.523 1.323 1.311 1.292

TSL 2

Incr Eqpt Costs 0.354 0.381 0.423 0.664 0.731 0.824

Energy Cost Savings 1.339 1.339 1.339 3.245 3.245 3.245

NPV 0.985 0.957 0.915 2.580 2.514 2.421

TSL 3

Incr Eqpt Costs 1.187 1.297 1.462 2.268 2.541 2.911

Energy Cost Savings 1.647 1.647 1.647 4.003 4.003 4.003

NPV 0.459 0.349 0.185 1.735 1.462 1.092

TSL 4

Incr Eqpt Costs 2.818 3.053 3.426 5.138 5.719 6.534

Energy Cost Savings 1.883 1.883 1.883 4.567 4.567 4.567

NPV -0.935 -1.170 -1.542 -0.571 -1.152 -1.967

10-B-6
APPENDIX 10-C. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10-C.1 INTRODUCTION ............................................................................................... 10-C-1

10-C.2 METHODOLOGY .............................................................................................. 10-C-1

10-C.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS ............................................. 10-C-3

LIST OF TABLES

Table 10-C.2.1 Dependence of FFC Parameters on AEO Inputs ..................................... 10-C-3

Table 10-C.3.1 Upstream Energy Multipliers (Based on AEO 2013) .............................. 10-C-3

10-C-i 
APPENDIX 10-C. FULL-FUEL-CYCLE MULTIPLIERS

10-C.1 INTRODUCTION

 This appendix summarizes the methods used to calculate full-fuel-cycle (FFC) energy 

savings expected to result from potential standards. The FFC measure includes point-of-use (site)

energy, the energy losses associated with generation, transmission, and distribution of electricity, 

and the energy consumed in extracting, processing, and transporting or distributing primary 

fuels. DOE’s traditional approach encompassed only site energy and the energy losses associated 

with generation, transmission, and distribution of electricity.1 Per DOE’s 2011 Statement of 

Policy for Adopting Full Fuel Cycle Analyses, DOE now uses FFC measures of energy use and 

emissions in its energy conservation standards analyses. This appendix summarizes the methods 

used to incorporate the full-fuel-cycle impacts into the analysis.

This analysis uses several different terms to reference energy use. The physical sources of 

energy are the primary fuels such as coal, natural gas, liquid fuels, etc. Primary energy is equal to 

the heat content (Btu) of the primary fuels used to provide an end-use service. Site energy use is 

defined as the energy consumed at the point-of-use in a building or industrial process. Where 

natural gas and petroleum fuels are consumed at the site (for example in a furnace), site energy is 

identical to primary energy, with both equal to the heat content of the primary fuel consumed. 

For electricity, site energy is measured in kWh. In this case the primary energy – usually 

expressed in quadrillion Btus (quads) – is equal to the energy required to generate and deliver the 

site electricity. This primary energy is calculated by multiplying the site kWh times the site-topower plant energy use factor given in chapter 10. For the FFC analysis, the upstream energy use 

is defined as the energy consumed in extracting, processing, and transporting or distributing 

primary fuels. FFC energy use is the sum of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels 

and uranium, and electricity generated from renewable fluxes (wind, solar and hydro). For the 

former, the upstream fuel cycle impacts are derived from the amount of fuel consumed at the 

power plant. For the latter, no fuel per se is used, so there is no upstream component. 

10-C.2 METHODOLOGY 

The mathematical approach is discussed in the paper A Mathematical Analysis of Full Fuel 

Cycle Energy Use,

2

 and details on the fuel production chain analysis are presented in the paper 

Projections of Full Fuel Cycle Energy and Emissions Metrics.

3

 The text below provides a brief 

summary of the methods used to calculate FFC energy.

When all energy quantities are normalized to the same units, the FFC energy use can be 

represented as the product of the primary energy use and an FFC multiplier. The FFC multiplier 

is defined mathematically as a function of a set of parameters representing the energy intensity 

and material losses at each production stage. These parameters depend only on physical data, so

10-C-1 
the calculations do not require any assumptions about prices or other economic data. While in 

general these parameter values may vary by geographic region, for this analysis national 

averages are used. 

In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, x=g for 

natural gas, x=p for petroleum fuels, x=u for uranium and x=r for renewable fluxes. The fuel 

cycle parameters are: 

• ax is the quantity of fuel x burned per unit of electricity output, on average, for grid 

electricity. The calculation of ax includes a factor to account for transmission and 

distribution system losses. 

• by is the amount of grid electricity used in production of fuel y, in MWh per physical unit 

of fuel y. 

• cxy is the amount of fuel x consumed in producing one unit of fuel y. 

• qx is the heat content of fuel x (MBtu/physical unit) 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x) 

The parameters are calculated as a function of time with an annual time step; hence, a time 

series of annual values is used to estimate the FFC energy and emissions savings in each year of 

the analysis period. Fossil fuel quantities are converted to energy units using the heat content 

factors qx. To convert electricity in kWh to primary energy units, on-site electricity consumption 

is multiplied by the site-to-power plant energy use factor. The site-to-power plant energy use 

factor is defined as the ratio of the total quads of primary energy consumption by the electric 

power sector divided by the total electricity generation in each year.

 The FFC multiplier is a dimensionless number denoted μ (mu). The upstream component 

of the energy savings is proportional to (μ-1). A separate multiplier is calculated for each fuel 

used on site. The fuel type is denoted by a subscript on the multiplier μ. A multiplier is also 

calculated for electricity reflecting the fuel mix used in its generation. 

For DOE’s appliance standards energy savings estimates, the fuel cycle analysis 

methodology is designed to make use of data and projections published in the Annual Energy 

Outlook (AEO). Table 10-C.2.1 provides a summary of the AEO data used as inputs to the 

different parameter calculations. The AEO does not provide all the information needed to 

estimate total energy use in the fuel production chain. Reference [3] describes the additional data 

sources used to complete the analysis. However, the time dependence in the FFC multipliers 

arises exclusively from variables taken from the AEO. The FFC analysis for medium electric 

motors used data from AEO-2013.

4

10-C-2 
Table 10-C.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables 

qx all Conversion Factors MMBtu per physical unit

ax all

Electricity Supply, Disposition,

Prices, and Emissions Generation by fuel type 

Energy Consumption by Sector and 

Source

Electric power sector energy 

consumption

bc, cnc, cpc coal Coal Production by Region and 

Type

Production by coal type and 

sulfur content

bp, cnp, cpp petroleum 

Refining Industry Energy 

Consumption Refining only energy use 

Liquid Fuels Supply and Disposition Crude supply by source

International Liquids Supply and 

Disposition Crude oil imports 

Oil and Gas Supply Crude oil domestic production

cnn

natural 

gas

Oil and Gas Supply US dry gas production

Natural Gas Supply, Disposition and 

Prices Pipeline, lease and plant fuel

zx all Electricity Supply, Disposition,

Prices and Emissions Power sector emissions

10-C.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS 

FFC upstream energy multipliers are presented in Table 10-C.3.1 for selected years. To 

extend the analysis period beyond 2040, the value reported for year 2040 was extrapolated 

through the end of the projection period. The multiplier for electricity reflects the shares of 

various primary fuels in total electricity generation over the forecast period. 

Table 10-C.3.1 Upstream Energy Multipliers (Based on AEO 2013) 

2015 2020 2025 2030 2035 2040

Electricity 0.042 0.041 0.040 0.040 0.041 0.041

Natural Gas (site) 0.102 0.103 0.100 0.099 0.098 0.097

Petroleum Fuels (site) 0.142 0.146 0.153 0.163 0.172 0.181

10-C-3 
REFERENCES

1. United States - Office of the Federal Register, Federal Register, Volume 76, Number 160, 

August 18, 2011. UNT Digital Library. Washington D.C. . 

2. Coughlin, K., A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy, 2012. 

37(1): pp. 698-708 

<http://www.sciencedirect.com/science/article/pii/S0360544211006803> 

3. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013. 

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E. 

4. Energy Information Administration, Annual Energy Outlook 2013 with Projections to 

2040, June 2013. Washington, DC. 

10-C-4 
APPENDIX 10-D. NATIONAL IMPACT ANALYSIS SENSITIVITY FOR

ALTERNATIVE SCENARIOS OF PRICE ELASTICITY OF DEMAND

TABLE OF CONTENTS

10-D.1 INTRODUCTION ............................................................................................... 10-D-1

10-D.2 NON-ZERO PRICE ELASTICITY OF DEMAND ............................................ 10-D-1

10-D.3 EFFECTS OF PRICE ELASTICITY OF DEMAND ON SHIPMENTS ............ 10-D-4

10-D.4 EFFECTS OF PRICE ELASTICITY OF DEMAND ON NATIONAL 

ENERGY SAVINGS ........................................................................................... 10-D-4

10-D.5 EFFECTS OF PRICE ELASTICITY OF DEMAND ON NET PRESENT 

VALUE ................................................................................................................ 10-D-6

10-D.6 RESULTS FOR NON-ZERO PRICE ELASTICITY OF DEMAND ................. 10-D-8

LIST OF TABLES

Table 10-D.2.1 Estimated Historical Shipments (thousands of units) .............................. 10-D-2

Table 10-D.2.2 Estimated Historical Value of Shipments (millions of dollars) ............... 10-D-2

Table 10-D.2.3 Estimated Historical Unit Average Prices (2005 dollars) ........................ 10-D-3

Table 10-D.2.4 Estimated Price elasticity of Demand by Horsepower Range ................. 10-D-3

Table 10-D.6.1 Cumulative National Energy Savings (quads) ......................................... 10-D-8

Table 10-D.6.2 Net Present Value (billion 2013$) ........................................................... 10-D-9

Table 10-D.6.3 Cumulative National Energy Savings with Decreasing Equipment 

Price (quads) .......................................................................................... 10-D-10

Table 10-D.6.4 Net Present Value with Decreasing Equipment Price (billion 2013$) .. 10-D-11

Table 10-D.6.5 Cumulative National Energy Savings with Increasing Equipment Price 

(quads).................................................................................................... 10-D-12

Table 10-D.6.6 Net Present Value with Increasing Equipment Price (billion 2013$) .... 10-D-13

Table 10-D.6.7 Cumulative National Energy Savings for Low Economic Growth 

(quads).................................................................................................... 10-D-14

Table 10-D.6.8 Net Present Value for Low Economic Growth (billion 2013$) ............. 10-D-15

Table 10-D.6.9 Cumulative National Energy Savings for High Economic Growth 

(quads).................................................................................................... 10-D-16

Table 10-D.6.10 Net Present Value for High Economic Growth (billion 2013$) ............ 10-D-17

10-D-i
APPENDIX 10-D. NATIONAL IMPACT ANALYSIS SENSITIVITY ANALYSIS FOR 

ALTERNATIVE SCENARIOS OF PRICE ELASTICITY OF DEMAND

10-D.1 INTRODUCTION

 The U.S. Department of Energy (DOE) used a zero price elasticity of demand 

assumption for the default projection in the National Impact Analysis (NIA) described in Chapter 

10. In order to investigate, for the considered trial standard levels (TSLs) for electric motors, the 

impact from increased equipment cost on shipments, and consequently on national energy 

savings (NES) and consumers’ net present value (NPV), DOE considered a non-zero price 

elasticity of demand for a sensitivity analysis. This appendix describes the method and data DOE 

used to estimate the price elasticity of demand by horsepower range, and compares NES and 

NPV results from this scenario with the default forecast presented in Chapter 10. 

10-D.2 NON-ZERO PRICE ELASTICITY OF DEMAND

 DOE considered one alternative scenario of price elasticity of demand for a sensitivity 

analysis. The elasticity values were estimated based on the following regression model: 

ln Q(t) = ln α + ε ∙ ln P(t) + ε Eq. 1.1

where:

Q(t) = the amount of motors shipped in year t, 

P(t) = the (average) price of motors shipped in year t, 

ε = the price elasticity of demand, 

α = a constant, and 

ε = the regression error. 

 The model on Eq. 1.1 was calibrated for each horsepower range based on historical data 

of annual shipments and value of shipments. DOE used historical data provided by the U.S. 

Census Bureau for Motors and Generators. DOE obtained these data on-line (for years 2001 

through 2003a,b) and from a market reportc (for years 1990 through 2000) for three phase AC 

a U.S. Census Bureau (August 2003), Motors and Generators – 2002.MA335H(02)-1. 

http://www.census.gov/manufacturing/cir/historical_data/discontinued/ma335h/ma335h02.xls b U.S. Census Bureau (November 2004), Motors and Generators – 2003.MA335H(03)-1. 

http://www.census.gov/manufacturing/cir/historical_data/discontinued/ma335h/index.html

10-D-1

 
induction motors between 1 and 500 horsepower. Whereas the data DOE obtained for the period 

1998-2003 were reported separately for single- and polyphase motors and disaggregated across 

horsepower ranges, the remaining data available was aggregated at the level of all integral AC 

motors. DOE then used linear trends to extrapolate the disaggregated data available for 1998-

2003 backwards through 1990. Table 10-D.2.1 and Table 10-D.2.2 present respectively the 

resulting historical time-series of shipments and value of shipments for induction motors 

between 1 and 500 horsepower as derived from the US Census data. Table 10-D.2.3 presents the 

historical unit average price by horsepower range calculated from the amount and value of 

shipments presented in Table 10-D.2.1 and Table 10-D.2.2 and adjusted to 2005 dollars based on 

GDP deflator. Note that the shipments presented here only cover US production and differs from 

the data presented in Chapter 3 which also include imports and exports. 

Table 10-D.2.1 Estimated Historical Shipments (thousands of units)

1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

1990 2000.4 888.7 471.4 86.0 60.0 51.6

1991 1762.8 786.6 399.8 76.4 51.8 44.0

1992 1894.3 849.0 413.1 82.7 54.6 45.7

1993 1985.2 893.5 415.7 87.3 56.0 46.2

1994 2206.9 997.4 443.1 97.7 61.0 49.6

1995 2056.6 933.3 395.3 91.7 55.6 44.5

1996 2388.3 1088.3 438.9 107.3 63.2 49.7

1997 2457.1 1124.2 431.0 111.1 63.7 49.2

1998 1215.1 543.3 216.2 58.5 37.0 23.1

1999 1253.8 538.4 185.0 51.7 25.0 22.5

2000 1208.2 602.5 170.0 53.6 26.2 21.0

2001 969.0 497.5 145.7 48.9 24.0 17.5

2002 897.4 410.7 126.0 45.0 21.0 15.9

2003 931.9 410.4 115.5 40.7 22.2 12.8

Table 10-D.2.2 Estimated Historical Value of Shipments (millions of dollars)

1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

1990 111.0 112.2 110.4 80.1 115.7 458.7

1991 124.8 126.2 120.4 87.8 121.8 468.7

1992 121.6 123.0 114.1 83.5 111.4 415.6

1993 143.4 145.0 131.0 96.3 123.6 446.1

1994 169.2 171.1 150.8 111.3 137.7 479.8

1995 194.7 196.9 169.6 125.6 149.8 503.2

1996 225.6 228.1 192.5 143.1 164.5 531.5

1997 228.4 231.0 191.0 142.5 158.0 490.1

c Business Trend Analysts, The Motor and Generator Industry, 2002.

10-D-2

 
1998 205.4 205.2 173.4 133.8 161.6 412.8

1999 206.0 191.4 158.2 115.1 105.4 356.3

2000 205.5 216.4 152.9 113.9 107.3 316.4

2001 154.3 175.8 133.5 101.3 94.3 217.0

2002 138.1 141.1 112.4 84.2 81.6 213.2

2003 153.2 144.0 109.0 83.9 79.2 165.7

Table 10-D.2.3 Estimated Historical Unit Average Prices (2005 dollars) 

1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

1990 40.09 91.22 169.23 672.87 1393.48 6424.50

1991 52.98 120.04 225.34 859.79 1758.90 7975.25

1992 49.19 110.97 211.53 773.51 1564.57 6970.76

1993 56.54 127.02 246.65 863.26 1728.02 7557.75

1994 61.29 137.14 272.16 910.34 1804.94 7740.97

1995 77.24 172.12 350.17 1117.73 2196.86 9228.05

1996 78.55 174.33 364.70 1109.06 2162.61 8885.52

1997 78.65 173.85 375.11 1084.85 2100.31 8428.29

1998 144.67 323.24 686.41 1957.46 3737.94 15293.97

1999 142.68 308.72 742.62 1933.37 3661.26 13751.91

2000 150.91 318.67 797.99 1885.36 3633.58 13367.60

2001 144.48 320.54 831.40 1878.56 3568.74 11247.36

2002 141.91 316.70 822.26 1723.79 3581.37 12391.51

2003 154.76 330.24 888.60 1941.38 3361.64 12202.28

DOE used the shipments time-series by horsepower range in Table 10-D.2.1 (Q(t)) and 

the price time-series by horsepower range in Table 10-D.2.3 (P(t)) to estimate the price 

elasticity of demand (ε) for each horsepower range according to Eq. 1.1. Table 10-D.2.4 shows 

the resulting elasticity values with additional statistical indicators.

Table 10-D.2.4 Estimated Price elasticity of Demand by Horsepower Range 

1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

Elasticity -0.862 -0.863 -1.111 -1.051 -1.568 -1.658

p-Value 0.000 0.001 0.000 0.001 0.000 0.036

R2 0.661 0.642 0.856 0.632 0.679 0.316

10-D-3
10-D.3 EFFECTS OF PRICE ELASTICITY OF DEMAND ON SHIPMENTS

DOE calculated the effects on non-zero price elasticity of demand on motors shipments 

based on the following model: 

Qstd(t) = Qbc(t) ∙ �1 + ε ∙ �

Pstd(t)

Pbc(t) − 1�� Eq. 1.2

where:

Qstd(t) = the amount of motors shipped in year t in the standards case, 

Qbc(t) = the amount of motors shipped in year t in the base case, 

Pstd(t) = the unit average price of motors shipped in year t in the standards case, 

Pbc(t) = the unit average price of motors shipped in year t in the base case, and

ε = the price elasticity of demand. 

 The model on Eq. 1.2 was evaluated for each horsepower range based on shipments 

projected for 2016 through 2045 (Qbc(t)), the market average equipment cost for the base case 

(Pbc(t)) and the standards case (Pstd(t)), and the price elasticity (ε) values estimated from Eq. 

1.1 and presented in Table 10-D.2.4. Figure 10-D.4.1 compares, for equipment class subgroup 

1.a., the shipments projected for the base case and the standards case considering non-zero price 

elasticity of demand.

10-D.4 EFFECTS OF PRICE ELASTICITY OF DEMAND ON NATIONAL 

ENERGY SAVINGS

DOE calculated the effects of non-zero price elasticity of demand on NES based on a 

model similar to the one presented in Chapter 10 and used to calculate NES for zero price 

elasticity of demand. All formulas and parameters remain the same but (a) shipments in the 

standards case are calculated as described in Section 10-D-4, and (b) the energy consumption 

that would correspond to the units not shipped due to the non-zero price elasticity of demand 

assumption is calculated as the energy consumed by the less efficient, non-replaced motors in the 

stock. A simplified description of the model that accounts for non-zero price elasticity of demand 

to calculate NES is as following:

d

NES = � Qbc(t) ∙ Ubc(t) − �Qstd(t) ∙ Ustd(t) + ∆Q(t) ∙ Ustk(t)�

2045

2016

Eq. 1.3

d The analysis used January 1st 2016 to model the December 19th, 2015 compliance date.

10-D-4

 
Figure 10-D.4.1 Shipments Projected for the Base Case and the Standards Case for 

Equipment Class Subgroup 1.a. (TSL 2)

where:

NES = the cumulative national energy savings, 

Qbc(t) = the amount of motors shipped in year t in the base case, 

Ubc(t) = the base case average unit lifetime energy consumption of motors shipped in 

year t, 

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

12000.0

20162017201820192020

20212022202320242025202620272028202920302031203220332034203520362037203820392040

20412042204320442045

1-5 hp

Base

Std

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

3500.0

4000.0

4500.0

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

6-21 hp

Base

Std

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

21-50 hp

Base

Std

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

51-100 hp

Base

Std

0.0

20.0

40.0

60.0

80.0

100.0

120.0

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

101-200 hp

Base

Std

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

201620172018201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045

201-500 hp

Base

Std

10-D-5
Qstd(t) = the amount of motors shipped in year t in the standards case, 

Ustd(t) = the standards case average unit lifetime energy consumption of motors 

shipped in year t, 

∆Q(t) = the amount of non-shipped motors in year t due to the effect of non-zero price 

elasticity of demand (∆Q(t) = Qbc(t) − Qstd(t)), 

Ustk(t) = the average unit lifetime energy consumption of the non-replaced motors in 

stock in year t. 

DOE evaluated NES for non-zero price elasticity of demand values for each horsepower 

range using the model above. Results are presented in Section 10-D.6. Because in Error! 

Reference source not found., Ustk(t) is greater than Ustd(t), the resulting national energy 

savings is lower than its corresponding value presented in Chapter 10. 

10-D.5 EFFECTS OF PRICE ELASTICITY OF DEMAND ON NET PRESENT 

VALUE

DOE calculated the effects of non-zero price elasticity of demand on NPV based on a 

model similar to the one presented in Chapter 10 and used to calculate NPV for zero price 

elasticity of demand. All formulas and parameters remain the same but (a) shipments in the 

standards case are calculated as described in Section 10-D-4, (b) the energy cost that would 

correspond to the units not shipped due to the non-zero price elasticity of demand assumption is 

calculated as the energy cost of the less efficient, non-replaced motors in the stock, and (c) the 

equipment costs that would correspond to the units not shipped due to the non-zero price 

elasticity of demand assumption are assumed zero. A simplified description of the model that 

accounts for non-zero price elasticity of demand to calculate NPV is as following:

NPV = � �nECS(t) − nIEC(t) + nECK(t)� ∙ (1 + dRate)2013−t

2045

2016

Eq. 1.4

nECS(t) = Qbc(t) ∙ uNCbc(t) − Qstd(t) ∙ uNCstd(t) Eq. 1.5

nIEC(t) = Qstd(t) ∙ uQCstd(t) − Qbc(t) ∙ uQCbc(t) Eq. 1.6

nECK(t) = ∆Q(t) ∙ uNCstk(t) Eq. 1.7

where:

NPV = the net present value, 

nECS(t) = the national lifetime energy cost savings from motors shipped in year t, 

10-D-6
nIEC(t) = the national lifetime incremental equipment non-energy costs from motors 

shipped in year t, 

nECK(t) = the national lifetime energy cost of the non-replaced motors in stock in year t, 

Qbc(t) = the amount of motors shipped in year t in the base case, 

Qstd(t) = the amount of motors shipped in year t in the standards case, 

uNCbc(t) = the base case average unit lifetime energy cost of motors shipped in year t, 

uNCstd(t) = the standards case average unit lifetime energy cost of motors shipped in year 

t, 

uQCbc(t) = the base case average unit lifetime equipment non-energy cost of motors 

shipped in year t, 

uQCstd(t) = the standards case average unit lifetime equipment non-energy cost of motors 

shipped in year t, 

∆Q(t) = the amount of non-shipped motors in year t due to the effect of non-zero price 

elasticity of demand (∆Q(t) = Qbc(t) − Qstd(t)), 

uNCstk(t) = the average unit lifetime energy cost of the non-replaced motors in stock in 

year t. 

 DOE evaluated NPV for non-zero price elasticity of demand values for each horsepower 

range using the model above. Results are presented in Section 10-D.6. Because the national 

lifetime energy cost from the existing, non-replaced stock outweighs the reduction in the national 

lifetime incremental non-energy costs due to the non-zero price elasticity od demand, the 

resulting NPV is lower than its corresponding value presented in Chapter 10. 

10-D-7
10-D.6 RESULTS FOR NON-ZERO PRICE ELASTICITY OF DEMAND

 DOE evaluated the effects of non-zero price elasticity of demand on NES and NPV. 

Table 10-D.6.1 and Table 10-D.6.2 compare respectively, for each TSL and equipment class 

group, NES and NPV results for non-zero price elasticity of demand with their corresponding 

results calculated for zero price elasticity of demand as presented in Chapter 10. 

Table 10-D.6.1 Cumulative National Energy Savings (quads) 

TSL

Equipment Class 

Primary Full-Fuel Cycle

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 0.83 0.44 0.84 0.45 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.26 0.21 0.26 0.21 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 1.10 0.66 1.12 0.67

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 6.26 2.91 6.36 2.96 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.58 0.35 0.59 0.36 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 6.85 3.27 6.97 3.33

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 9.83 2.97 9.99 3.02 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.71 0.36 0.73 0.37 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 10.57 3.33 10.75 3.39

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 12.60 1.84 12.81 1.88 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.81 0.13 0.83 0.13 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 13.45 1.98 13.67 2.01

10-D-8
Table 10-D.6.2 Net Present Value (billion 2013$) 

TSL

Equipment Class 

 

7% discount rate 3% discount rate

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 2.68 1.58 5.34 2.97 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.66 0.54 1.57 1.28 

Group 2: NEMA Design C 0.02 0.01 0.06 0.04

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 3.36 2.14 6.97 4.29

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 10.04 5.04 25.68 12.58 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 1.22 0.75 3.07 1.88 

Group 2: NEMA Design C 0.02 0.01 0.06 0.04

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 11.29 5.80 28.81 14.49

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -2.07 -2.89 6.61 -2.33 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.57 0.29 2.00 1.02 

Group 2: NEMA Design C -0.03 0.00 -0.02 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups -1.54 -2.61 8.59 -1.32

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -30.16 -1.55 -38.48 -0.60 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors -1.13 -0.20 -0.79 -0.15 

Group 2: NEMA Design C -0.03 0.00 -0.02 0.00

Group 3: Fire Pump Electric Motors -0.02 0.00 -0.03 0.00

Total for All Groups -31.34 -1.75 -39.32 -0.75

 DOE additionally evaluated the effects of non-zero price elasticity of demand on 

alternative scenarios. Because under non-zero price elasticity of demand equipment prices affect 

shipments, DOE evaluated the effects of the non-zero price elasticity of demand estimated on

Section 10-D.2 for the non-default scenarios of product price trend and economic growth. Table 

10-D.6.3 through Table 10-D.6.10 compare, for each TSL and equipment class group, results for 

10-D-9
NES and NPV considering zero and non-zero price elasticity of demand values. Table 10-D.6.3

through Table 10-D.6.6 present results for scenarios with decreasing and increasing product price 

trends, and Table 10-D.6.7 through Table 10-D.6.10 present results for the low and high 

economic growth rate scenarios.

Table 10-D.6.3 Cumulative National Energy Savings with Decreasing Equipment Price 

(quads)

TSL

Equipment Class 

Primary Full-Fuel Cycle

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 0.83 0.44 0.84 0.45 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.26 0.21 0.26 0.21 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 1.10 0.66 1.12 0.67

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 6.26 2.96 6.36 3.01 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.58 0.36 0.59 0.36 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 6.85 3.33 6.97 3.38

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 9.83 3.02 9.99 3.07 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.71 0.37 0.73 0.37 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 10.57 3.39 10.75 3.44

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 12.60 1.61 12.81 1.63 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.81 0.14 0.83 0.14 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 13.45 1.75 13.67 1.78

10-D-10
Table 10-D.6.4 Net Present Value with Decreasing Equipment Price (billion 2013$) 

TSL

Equipment Class 

7% discount rate 3% discount rate

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 2.68 1.59 5.35 3.00 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.67 0.54 1.58 1.29 

Group 2: NEMA Design C 0.02 0.01 0.06 0.04

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 3.37 2.15 6.99 4.33

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 10.21 5.18 26.06 12.95 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 1.23 0.76 3.09 1.91 

Group 2: NEMA Design C 0.02 0.01 0.06 0.04

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 11.46 5.95 29.22 14.90

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -1.30 -2.58 8.43 -1.54 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.61 0.31 2.10 1.08 

Group 2: NEMA Design C -0.03 0.00 -0.01 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups -0.73 -2.27 10.51 -0.46

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -28.58 -1.47 -34.78 -0.66 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors -1.04 -0.19 -0.58 -0.12 

Group 2: NEMA Design C -0.03 0.00 -0.01 0.00

Group 3: Fire Pump Electric Motors -0.02 0.00 -0.03 0.00

Total for All Groups -29.67 -1.66 -35.40 -0.78

10-D-11
Table 10-D.6.5 Cumulative National Energy Savings with Increasing Equipment Price 

(quads)

TSL

Equipment Class 

Primary Full-Fuel Cycle

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 0.83 0.41 0.84 0.41 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.26 0.20 0.26 0.21 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 1.10 0.62 1.12 0.63

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 6.26 2.67 6.36 2.71 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.58 0.33 0.59 0.34 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 6.85 3.01 6.97 3.06

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 9.83 2.74 9.99 2.79 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.71 0.33 0.73 0.34 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 10.57 3.07 10.75 3.12

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 12.60 3.02 12.81 3.07 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.81 0.09 0.83 0.09 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 13.45 3.12 13.67 3.17

10-D-12
Table 10-D.6.6 Net Present Value with Increasing Equipment Price (billion 2013$) 

TSL

Equipment Class 

7% discount rate 3% discount rate

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 2.64 1.48 5.27 2.74 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.65 0.52 1.55 1.24 

Group 2: NEMA Design C 0.02 0.01 0.06 0.03

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 3.31 2.02 6.88 4.02

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 9.23 4.33 23.94 10.81 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 1.17 0.68 2.96 1.71 

Group 2: NEMA Design C 0.02 0.01 0.06 0.03

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 10.42 5.02 26.96 12.56

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -5.75 -4.21 -1.38 -5.40 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.37 0.18 1.58 0.74 

Group 2: NEMA Design C -0.05 0.00 -0.04 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups -5.43 -4.03 0.16 -4.66

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -38.11 -2.82 -55.22 -2.26 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors -1.57 -0.21 -1.71 -0.22 

Group 2: NEMA Design C -0.05 0.00 -0.04 0.00

Group 3: Fire Pump Electric Motors -0.02 0.00 -0.04 0.00

Total for All Groups -39.74 -3.03 -57.01 -2.48

10-D-13
Table 10-D.6.7 Cumulative National Energy Savings for Low Economic Growth (quads)

TSL

Equipment Class 

Primary Full-Fuel Cycle

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 0.71 0.38 0.73 0.38 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.22 0.18 0.23 0.18 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 0.95 0.57 0.97 0.58

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 5.41 2.52 5.50 2.56 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.50 0.30 0.51 0.31 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 5.92 2.83 6.02 2.88

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 8.50 2.57 8.64 2.61 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.62 0.31 0.63 0.32 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 9.14 2.88 9.29 2.93

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 10.90 1.60 11.08 1.62 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.70 0.11 0.72 0.12 

Group 2: NEMA Design C 0.03 0.00 0.03 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 11.63 1.71 11.82 1.74

10-D-14
Table 10-D.6.8 Net Present Value for Low Economic Growth (billion 2013$) 

TSL

Equipment Class 

7% discount rate 3% discount rate

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 2.24 1.33 4.43 2.48 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.55 0.45 1.30 1.06 

Group 2: NEMA Design C 0.02 0.01 0.05 0.03

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 2.81 1.79 5.78 3.56

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 8.11 4.09 20.60 10.15 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 1.00 0.61 2.51 1.53 

Group 2: NEMA Design C 0.02 0.01 0.05 0.03

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 9.13 4.72 23.16 11.71

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -2.66 -2.76 3.22 -2.75 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.43 0.22 1.55 0.79 

Group 2: NEMA Design C -0.03 0.00 -0.02 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups -2.26 -2.54 4.74 -1.96

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -27.19 -1.51 -36.45 -1.02 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors -1.05 -0.18 -0.89 -0.16 

Group 2: NEMA Design C -0.03 0.00 -0.02 0.00

Group 3: Fire Pump Electric Motors -0.02 0.00 -0.03 0.00

Total for All Groups -28.29 -1.69 -37.39 -1.19

10-D-15
Table 10-D.6.9 Cumulative National Energy Savings for High Economic Growth (quads)

TSL

Equipment Class 

Primary Full-Fuel Cycle

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 0.95 3.38 0.97 0.51 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.30 0.41 0.30 0.25 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 1.27 3.80 1.29 0.77

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 7.27 3.38 7.39 3.44 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.67 0.41 0.68 0.42 

Group 2: NEMA Design C 0.02 0.01 0.02 0.01

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 7.96 3.80 8.09 3.87

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 11.42 3.45 11.61 3.51 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.83 0.42 0.84 0.43 

Group 2: NEMA Design C 0.03 0.00 0.04 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 12.29 3.87 12.49 3.94

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 14.64 2.14 14.88 2.18 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.95 0.15 0.96 0.16 

Group 2: NEMA Design C 0.03 0.00 0.04 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 15.62 2.30 15.88 2.34

10-D-16
Table 10-D.6.10 Net Present Value for High Economic Growth (billion 2013$) 

TSL

Equipment Class 

7% discount rate 3% discount rate

Elasticity Elasticity

Zero NonZero Zero NonZero

TSL 1

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 3.22 1.89 6.48 3.58 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.81 0.66 1.93 1.58 

Group 2: NEMA Design C 0.03 0.02 0.08 0.05

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 4.06 2.57 8.50 5.21

TSL 2

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors 12.63 6.31 32.44 15.81 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 1.51 0.92 3.81 2.33 

Group 2: NEMA Design C 0.03 0.02 0.08 0.05

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups 14.16 7.25 36.33 18.20

TSL 3

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -0.81 -2.87 11.82 -1.48 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors 0.77 0.39 2.63 1.34 

Group 2: NEMA Design C -0.03 0.00 0.00 0.00

Group 3: Fire Pump Electric Motors 0.00 0.00 0.00 0.00

Total for All Groups -0.07 -2.48 14.44 -0.15

TSL 4

Subgroup 1.a.: NEMA Design A and B –

Non-Brake Motors -32.64 -1.51 -39.09 0.13 

Subgroup 1.b.: NEMA Design A and B –

Brake Motors -1.17 -0.20 -0.56 -0.11 

Group 2: NEMA Design C -0.03 0.00 0.00 0.00

Group 3: Fire Pump Electric Motors -0.02 0.00 -0.04 0.00

Total for All Groups -33.86 -1.72 -39.69 0.01

10-D-17
12-A-i 

APPENDIX 12-A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

12-A.1 ELECTRIC MOTORS RULEMAKING MANUFACTURER IMPACT ANALYSIS 

INTERVIEW GUIDE ......................................................................................................... 1

A KEY ISSUES .................................................................................................................2

B ENGINEERING ANALYSIS ........................................................................................3

C COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS .......15

D MANUFACTURER PRODUCTION COSTS AND SCALING PRICES ..................16

E MARKUPS AND PROFITABILITY ..........................................................................21

F SHIPMENT PROJECTIONS AND MARKET SHARES ..........................................23

G EQUIPMENT MIX ......................................................................................................24

H FINANCIAL PARAMETERS ....................................................................................26

I CONVERSION COSTS ..............................................................................................27

J CUMULATIVE REGULATORY BURDEN..............................................................30

K DIRECT EMPLOYMENT IMPACT ASSESSMENT ................................................31

L EXPORTS / FOREIGN COMPETITION / OUTSOURCING ...................................32

M CONSOLIDATION .....................................................................................................32

N IMPACTS ON SMALL BUSINESS ...........................................................................33

12-A.2 ELECTRIC MOTOR SMALL BUSINESS MANUFACTURER IMPACT ANALYSIS 

INTERVIEW GUIDE ....................................................................................................... 34

A INTRODUCTION .......................................................................................................35

B KEY ISSUES ...............................................................................................................36

C COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS .......36

D MARKUPS AND PROFITABILITY ..........................................................................37

E FINANCIAL PARAMETERS ....................................................................................37

F CONVERSION COSTS ..............................................................................................38

G DIRECT EMPLOYMENT IMPACT ASSESSMENT ................................................39
12-A-1 

APPENDIX 12-A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

12-A.1 ELECTRIC MOTORS RULEMAKING MANUFACTURER IMPACT ANALYSIS 

INTERVIEW GUIDE

As part of the rulemaking process for new energy conservation standards for electric 

motors, the Department of Energy (DOE) conducts the manufacturer impact analysis (MIA). In 

this analysis, DOE uses publicly available information and information provided by 

manufacturers during interviews to assess possible impacts on manufacturers due to new energy 

conservation standards. DOE is currently considering five candidate standard levels of efficiency

(referred to as CSLs) for electric motors in the scope of this rulemaking, including a baseline 

CSL. The motor types covered in this rulemaking are separated into four equipment class groups 

(ECGs), as shown below. 

Table 1.1 Electric Motor Equipment Class Groups

Equipment Class 

Group

Electric Motor 

Design Horsepower Poles Enclosure

I NEMA Design A & 

B* 1-500 2, 4, 6, 8 Open

Closed

II NEMA Design C* 1-200 4, 6, 8 Open

Closed

III Fire Pump* 1-500 2, 4, 6, 8 Open

Closed

IV Integral Brake Motors* 1-30 4, 6, 8 

Open

Closed

*Including IEC equivalents.

DOE analyzed three representative units for ECG I (which are also used to represent 

ECG III and ECG IV) and two representative units for ECG II. The results obtained were then 

extrapolated to other ratings within the respective ECGs.

In responding to this questionnaire, please refer to the CSLs in the table below. 
12-A-2 

Table 1.2 Nominal Efficiency Levels Under Consideration for Equipment Class Group I

and III

Equipment 

Class CSL 1 CSL 2 CSL 3 CSL 4* 

5 horsepower, 4-

pole enclosed

NEMA MG1 Table 

12-11 

(87.5%) 

NEMA MG1 Table 

12-12 

(89.5%) 

1 Band Above 

NEMA MG1 Table 

12-12 

(90.2%)

3 Bands Above 

NEMA MG1 Table 

12-12 

(91.7%)

30 horsepower, 

4-pole enclosed

NEMA MG1 Table 

12-11 

(92.4%) 

NEMA MG1 Table 

12-12 

(93.6%) 

1 Band Above 

NEMA MG1 Table 

12-12 

(94.1%)

2 Bands Above 

NEMA MG1 Table 

12-12 

(94.5%)

75 horsepower, 

4-pole enclosed

NEMA MG1 Table

12-11 

(94.1%) 

NEMA MG1 Table 

12-12 

(95.4%) 

1 Band Above 

NEMA MG1 Table 

12-12 

(95.8%)

2 Bands Above 

NEMA MG1 Table 

12-12 

(96.2%)

*All representative units at this CSL use copper rotor technology.

Table 1.3 Nominal Efficiency Levels under Consideration for Equipment Class Group II

Equipment Class CSL 1 CSL 2*

5 horsepower, 4-pole enclosed NEMA MG1 Table 12-12 

(89.5%) 

2 Bands Above NEMA MG1 

Table 12-12 

(91.0%)

50 horsepower, 4-pole enclosed NEMA MG1 Table 12-12 

(94.5%) 

1 Band Above NEMA MG1 

Table 12-12 

(95.0%)

*All representative units at this CSL use copper rotor technology.

A KEY ISSUES

A.1 In general, what are the key issues for your company regarding new energy conservation 

standards for electric motors and this rulemaking? 

A.2 For the issues identified, how significant are they at each listed CSL? 

A.3 How can we most effectively incorporate these issues in the MIA?
12-A-3 

B ENGINEERING ANALYSIS

The purpose of the engineering analysis is to estimate the relationship between the 

manufacturer’s selling price of an electric motor and its corresponding efficiency rating. This 

relationship serves as the basis for the subsequent cost-benefit calculations for individual 

consumers, manufacturers, and the nation. 

In the engineering analysis, DOE groups electric motors subject to minimum efficiency 

standards into four ECGs that are based on NEMA Design type, and whether the motor is a fire 

pump motor or an integral brake motor. Within the ECGs, each electric motor rating (i.e. 

horsepower rating, enclosure type, and pole configuration) is considered an “equipment class,” 

and unique CSLs are assessed for each individual equipment class. 

Within each ECG, DOE selects representative units for study in the engineering analysis. 

DOE then extrapolates the results from these representative units to the other motor ratings 

within the same ECG. DOE is using this approach because there are over 500 unique equipment 

classes across all covered electric motors, and individually assessing each of these classes is not 

feasible. The representative units and CSLs currently selected for this engineering analysis are 

similar to the ones presented in the preliminary analysis with a few changes and additions. The 

table below illustrates the equipment class groups, as well as characteristics which comprise the 

equipment classes.

Table 1.4 Electric Motor Equipment Class Groups

Equipment Class 

Group Electric Motor Design Horsepower Poles Enclosure

I NEMA Design A & B* 1-500 2, 4, 6, 8 Open

Closed

II NEMA Design C* 1-200 4, 6, 8 Open

Closed

III Fire Pump* 1-500 2, 4, 6, 8 Open

Closed

IV Integral Brake Motors* 1-30 4, 6, 8 Open

Closed

*Including IEC equivalents.

B.1 NEMA Design A and Design B Electric Motors (Equipment Class Group I) 

B.1.1 Efficiency Levels and Representative Units: DOE analyzed three representative 

units for ECG I and III and then extrapolated the results to other ratings within the 

respective ECGs. The table below shows the equipment class and the CSLs analyzed.
12-A-4 

Table 1.5 Nominal Efficiency Levels under Consideration for Equipment Class Group I

and III

Equipment Class CSL 1 CSL 2 CSL 3 CSL 4

5 horsepower, 4-

pole enclosed

NEMA MG1 

Table 12-11 

(87.5%) 

NEMA MG1 Table 

12-12 

(89.5%) 

1 Band Above 

NEMA MG1 

Table 12-12 

(90.2%)

3 Bands Above 

NEMA MG1 

Table 12-12 

(91.7%)

30 horsepower, 4-

pole enclosed

NEMA MG1 

Table 12-11 

(92.4%) 

NEMA MG1 Table 

12-12 

(93.6%) 

1 Band Above 

NEMA MG1 

Table 12-12 

(94.1%)

2 Bands Above 

NEMA MG1 

Table 12-12 

(94.5%)

75 horsepower, 4-

pole enclosed

NEMA MG1 

Table 12-11 

(94.1%) 

NEMA MG1 Table 

12-12 

(95.4%) 

1 Band Above 

NEMA MG1 

Table 12-12 

(95.8%)

2 Bands Above 

NEMA MG1 

Table 12-12 

(96.2%)

B.1.2 ECG I Efficiency Levels: Please comment on the appropriateness of the CSLs and 

representative units chosen for ECG I. Does your company produce electric motors at or 

above CSL 3? Are these motors typically NEMA Design A or NEMA Design B motors? 

B.1.3 Dimension Constraints: DOE utilized an increase in the length of the electric 

motor stack to increase the efficiency while keeping the NEMA frame designation the 

same for that representative unit. The table below lists the maximum stack lengths that 

DOE used for the highest-efficiency software models. Do these numbers look practical 

for electric motors of these horsepower ratings? For each of the representative units, are 

there any dimensional constraints, especially with respect to an increased stack length, for 

customer applications that DOE should be aware of? If so, please specify the maximum 

C-dimensions or stack length of the representative units that would still be feasible for 

building those motors. Could you provide feedback on how stack length is restricted as 

pole configurations change but horsepower remains constant? DOE is also looking for 

feedback on how stack length is restricted as the frame enclosure changes from open to 

enclosed.

Table 1.6 Maximum Stack Lengths Used for ECG I Software Modeling

Representative Unit Stack Length (in)

5 Horsepower, NEMA Design B 5.32

30 Horsepower, NEMA Design B 7.00

75 Horsepower, NEMA Design B 12.0

B.1.4 Design Option Combinations: For each representative unit, DOE is considering 

several design option combinations that characterize a range of CSLs. This range spans 

from the CSL requirements set forth in EISA 2007 (NEMA MG1-2011 Table 12-11 and 

Table 12-12) to the maximum technologically feasible level (“max tech”) The following 
12-A-5 

tables present the CSLs and requests feedback on the primary design options available to 

reach that CSL, as well as the associated burdens with reaching those CSLs.

Table 1.7 Design Options for 5 Horsepower, NEMA Design B, 4-pole Enclosed Motors

Efficiency Level Observed Design Options Associated Burden

Baseline

(82.5%) 

Stack Length = 2.69”

Stator Copper Weight = 8.4 lbs 

Electrical Steel = M56 

CSL 1 

(NEMA MG1 Table 12-11) 

Stack Length = 3.47”

Stator Copper Weight = 10.1 lbs 

Electrical Steel = M56

CSL 2 

(NEMA MG1 Table 12-12) 

Stack Length = 5.14”

Stator Copper Weight = 10.1 lbs 

Electrical Steel = M47

CSL 3

(1 Band Above NEMA MG1 

Table 12-12)

Stack Length = 4.65”

Stator Copper Weight = 12.2 lbs 

Electrical Steel = M47 

CSL 4 

(3 Bands Above NEMA MG1 

Table 12-12) 

Stack Length = 5.32”

Stator Copper Weight = 14.4 lbs 

Electrical Steel = M36 

Rotor Conductor = Copper

Table 1.8 Design Options for 30 Horsepower, NEMA Design B, 4-pole Enclosed Motors

Efficiency Level Design Options Associated Burden

Baseline

(89.5%) 

Stack Length = 7.96”

Stator Copper Weight = 20.2 lbs 

Electrical Steel = M56

CSL 1 

(NEMA MG1 Table 12-11) 

Stack Length = 5.53”

Stator Copper Weight = 43.5 lbs 

Electrical Steel = M47/M56

CSL 2 

(NEMA MG1 Table 12-12) 

Stack Length = 6.00”

Stator Copper Weight = 45.2 lbs 

Electrical Steel = M47

CSL 3

(1 Band Above NEMA MG1 

Table 12-12)

Stack Length = 6.74”

Stator Copper Weight = 47.4 lbs 

Electrical Steel = M47

CSL 4 

(2 Bands Above NEMA MG1 

Table 12-12) 

Stack Length = 7.00”

Stator Copper Weight = 74.5 lbs 

Electrical Steel = M36 

Rotor Conductor = Copper
12-A-6 

Table 1.9 Design Options and for 75 Horsepower, NEMA Design B, 4-pole Enclosed Motors

Efficiency Level Design Options Associated Burden

Baseline

(93.0%) 

Stack Length = 8.02”

Stator Copper Weight = 77.8 lbs 

Electrical Steel = M56

CSL 1 

(NEMA MG1 Table 12-11) 

Stack Length = 10.23”

Stator Copper Weight = 71.0 lbs 

Electrical Steel = M47

CSL 2 

(NEMA MG1 Table 12-12) 

Stack Length = 10.58”

Stator Copper Weight = 81.8 lbs 

Electrical Steel = M22/M27

CSL 3 

(1 Band Above NEMA MG1 

Table 12-12) 

Stack Length = 11.33” 

Stator Copper Weight = 136 lbs 

Electrical Steel = M36

CSL 4 

(2 Bands Above NEMA MG1 

Table 12-12) 

Stack Length = 12.0”

Stator Copper Weight = 127 lbs 

Electrical Steel = M36

Rotor Conductor = Copper

B.2 NEMA Design C (Equipment Class Group II) 

B.2.1 Design Lines and Representative Units: The following table represents the CSLs 

and representative units DOE is considering for ECG II. 

Table 1.10 Efficiency Levels under Consideration for Equipment Class Group II

Equipment Class CSL 1 CSL 2

5 horsepower, 4-pole enclosed NEMA MG1 Table 12-12 

(89.5%) 

2 Bands Above NEMA MG1 

Table 12-12 

(91.0%)

50 horsepower, 4-pole enclosed NEMA MG1 Table 12-12 

(94.5%) 

1 Band Above NEMA MG1 

Table 12-12 

(95.0%)

B.2.2 ECG II Efficiency Levels: Please comment on the appropriateness of the CSLs 

and representative units chosen for ECG II. Does your company produce ECG II-type 

electric motors at or above CSL 2? 
12-A-7 

B.2.3 Dimension Constraints: For each of the representative units, are there any 

dimensional constraints for customer applications that DOE should be aware of? If so, 

please specify the maximum dimensions that are feasible. Are there any additional design 

constraints for NEMA Design C motors when compared to Design A or B motors? 

Table 1.11 Maximum Stack Lengths Used for ECG II Software Modeling

Representative Unit Stack Length (in)

5 Horsepower, NEMA Design C 5.32

50 Horsepower, NEMA Design C 9.55

B.2.4 Design Option Combinations: For each representative unit, DOE is considering 

several design option combinations that characterize a range of CSLs electric motors. 

This range spans from the CSL requirements set forth in EISA 2007 (NEMA MG1-2011 

Table 12-11 and Table 12-12) to the maximum technologically available level (“max 

tech”). The following tables present the CSLs and requests feedback on the primary 

design options available to reach each CSL, as well as the associated burdens with 

reaching those CSLs.

Table 1.12 Design Options for 5 Horsepower, NEMA Design C, 4-pole Enclosed Motors

Efficiency Level Observed Design Options Associated Burden

Baseline

(NEMA MG1 Table 12-11) 

Stack Length = 4.75”

Stator Copper Weight = 10.0 lbs 

Electrical Steel = M47

CSL 1 

(NEMA MG1 Table 12-12) 

Stack Length = 4.25”

Stator Copper Weight = 9.90 lbs 

Electrical Steel = M36

CSL 2 

(2 Bands Above NEMA MG1 

Table 12-12) 

Stack Length = 5.32”

Stator Copper Weight = 12.8 lbs 

Electrical Steel = M36

Rotor Conductor = Copper
12-A-8 

Table 1.13 Design Options for 50 Horsepower, NEMA Design C, 4-pole Enclosed Motors

Efficiency Level Observed Design Options Associated Burden

Baseline

(NEMA MG1 Table 12-11) 

Stack Length = 8.67”

Stator Copper Weight = 66.0 lbs 

Electrical Steel = M47

CSL 1 

(NEMA MG1 Table 12-12) 

Stack Length = 9.55”

Stator Copper Weight = 89.5 lbs 

Electrical Steel = M36

CSL 2

(1 Band Above NEMA MG1 

Table 12-12) 

Stack Length = 9.55”

Stator Copper Weight = 85.0 lbs 

Electrical Steel = M36

Rotor Conductor = Copper

B.3 Materials Prices, Markups, and Labor Rates

DOE gathers materials price data for the five years between 2008 and 2012, and plans to 

use a 2012 materials price for the reference case of its analysis. The current materials prices 

DOE uses are based on a 2012 reference price.

B.3.1 Copper: Due to copper’s relatively large price fluctuations, DOE sets its copper 

materials prices based on a 5-year-avergae of the commodity’s index price plus a 

processing cost markup. DOE is considering the current (2012) index value and will scale 

it back through 2008 using the producer price index. DOE will then apply a processing 

cost to the commodity price that varies for each of DOE’s groupings (e.g., wire vs. 

casting material). Please comment on DOE’s proposed methodology for deriving copper 

prices.

B.3.2 Copper Wire Processing Costs: To account for the processing costs of converting 

copper into wire, is it more appropriate to apply a percentage markup to the underlying 

commodity price, or a straight adder? DOE currently assumes that converting all copper 

wire gauges cost the same. If this is not accurate, please indicate the appropriate markup 

for each of the types of copper in the table below. Also, please comment on the base 

commodity price. 
12-A-9 

Table 1.14 Five Year Average Copper Prices and Markups (2012$-2008$) 

Item Cost (lb) Markup 5 yr 

Avg. 

Year

2012 2011 2010 2009 2008

Cu Wire, Gauge 14 & 14.5 $4.14 30% $3.18 $3.61 $4.00 $3.15 $2.22 $2.92

Cu Wire, Gauge 15 & 15.5 $4.14 30% $3.18 $3.61 $4.00 $3.15 $2.22 $2.92

Cu Wire, Gauge 16 & 16.5 $4.14 30% $3.18 $3.61 $4.00 $3.15 $2.22 $2.92

Cu Wire, Gauge 17 & 17.5 $4.14 30% $3.18 $3.61 $4.00 $3.15 $2.22 $2.92

Cu Wire, Gauge 18 & 18.5 $4.14 30% $3.18 $3.61 $4.00 $3.15 $2.22 $2.92

Cu Wire, Gauge 19 - 20.5 $4.14 30% $3.18 $3.61 $4.00 $3.15 $2.22 $2.92

Casting Materials – Copper $3.18 0% $3.18 $3.61 $4.00 $3.15 $2.22 $2.92

B.3.3 Materials Prices: The following table contains DOE’s estimates for material 

prices used in the preliminary analysis. The prices listed do not include any markups for 

scrap, handling, factory overhead, non-production costs, or profit, but rather represent the 

price a manufacturer would pay for the material, including any bulk purchase discounts. 

All prices are listed in 2012 dollars. Does your company pay a similar price for these 

materials? If not, what price does your company pay? 

Table 1.15 Metal Prices (2012$) 

Item Cost per lb Manufacturer Feedback

Frame Material

Cast Aluminum* $1.30

Cast Iron $0.60

Rolled Steel $0.47

Core Steels -

M15, fully/semi-processed $1.10

M19, fully/semi-processed $1.05

M22, fully/semi-processed $1.02

M27, fully/semi-processed $0.95

M36, fully/semi-processed $0.89

M47, fully/semi-processed $0.80

M56, fully/semi-processed $0.78

Shaft Steels -

Hot Rolled Steel $0.52

*Same cost used for housing material and rotor conductor bar material.

B.3.4 Aggregated Costs: DOE seeks feedback on the costs of components of electric 

motors that DOE aggregated into a bulk price. The table below lists DOE’s estimates of 

the costs for those components. Do these costs seem appropriate? If not, can you provide 

another estimate? Would these costs remain constant regardless of CSL? 
12-A-10 

Table 1.16 Electric Motor Purchased Components and Aggregated Costs

Item Unit of

Measure Cost Manufacturer Feedback

Bearings

Front Bearing, 5HP each $10

Back Bearing, 5HP each $5

Front Bearing, 30HP each $31

Back Bearing, 30HP each $14

Front Bearing, 50HP each $49

Back Bearing, 50HP each $25

Front Bearing, 75HP each $67

Back Bearing, 75HP each $36

Aggregate

Hardware*, 5HP each $8

Hardware*, 30HP each $10

Hardware*, 50HP each $15

Hardware*, 75HP each $20

Stator Insulation, 5HP each $40

Stator Insulation, 30HP each $60

Stator Insulation, 50HP each $70

Stator Insulation, 75HP each $80

*Includes nuts, bolts, fan apparatus, and other hardware used to assemble the motor.

B.3.5 Additional Comments on Table 1.16: Do you have any comments on DOE’s 

approach to aggregate hardware and stator insulation for each representative horsepower 

rating? Do hardware or insulation costs change significantly as efficiency changes?

B.4 Markups 

DOE applies markups to all costs to reflect a manufacturer’s internal markups. A 

summary of the markups used in the analysis is provided in Table 1.17 below. 

DOE uses the following three markups when generating it Manufacturer Selling Prices 

for electric motors:

Handling and scrap factor: This markup was applied to the direct material production costs of 

each electric motor. It accounts for the handling of material (loading into assembly or winding 

equipment) and the scrap material that cannot be used in the production of a finished electric 

motor (e.g., lengths of wire too short to wind). 

Factory overhead: Factory overhead includes all the indirect costs associated with production, 

indirect materials and energy use, taxes, and insurance. DOE only applies factory overhead to the 

direct material production costs (including the handling and scrap factor). The overhead 
12-A-11 

increases when copper die casting is used in the rotor. This accounts for additional energy, 

insurance, and other indirect costs associated with the copper die-casting process.

Non-production: This markup reflects costs including sales and general administrative, research 

and development, interest payments, and profit factor. DOE applies the non-production markup 

to the sum of the direct material production, the direct labor, and the factory overhead. For ECG 

I, analyzed electric motors at or below 5-horsepower this markup was 37 percent and for electric 

motors above 5-horsepower this markup was 45 percent. This increase accounts for the extra 

profit margin manufacturers may receive on larger electric motors that are sold in smaller 

volumes. DOE assumed a 45 percent markup for all ECG II motors due to the low production 

volume of NEMA Design C motors. 

Table 1.17 Markups Used in the Engineering Analysis

Markup Percentage Manufacturer Comment

Scrap Costs 2.50%

Overhead Costs* 17.5%

Non-Production Markup (1-5 

Horsepower Motors) 37.0% 

Non-Production Markup (7.5-500 

Horsepower Motors) 45.0% 

*DOE used an 18.0% markup for copper rotor overhead production costs.

**Manufacturer Production Cost is the sum of the bill of material, labor, scrap, and overhead costs. 

B.5 Labor Rates

DOE used the same hourly labor rate for all electric motors analyzed. The base hourly 

rate was developed from the 2007 Economic Census of Industry, published by the U.S. Census 

Bureau, as well as manufacturer and subject matter expert (SME) input. The base hourly rate is 

an aggregate rate of a foreign and domestic labor rates. DOE weighed the foreign labor rate more 

than the domestic labor rate due to manufacturer feedback indicating off-shore production 

accounts for a majority of electric motor production by American-based companies. Several 

markups were applied to this hourly rate to obtain a fully burdened rate which was intended to be 

representative of the labor costs associated with manufacturing electric motors. Table 1.18 shows 

the labor markups that were applied, their corresponding markup percentage, and the new 

burdened labor rate.

Indirect Production: Accounts for the cost of production managers, quality control, and other 

indirect costs associated with production. 

Fringe: Includes pension contributions, group insurance premiums, workers compensation, 

etc.

Assembly Labor Up-Time: Accounts for the time that workers are not assembling products 

and/or reworking unsatisfactory units. 
12-A-12 

B.5.1 Aggregate Hourly Labor Rate: Please comment on the appropriateness of the 

following labor rate, as well as the labor rate markups. Does the fully-burdened cost of 

labor represent your company’s fully-burdened labor rate for production? If not, what is 

your company’s fully-burdened labor rate, and which markups would you adjust? 

Table 1.18 Labor Markups for Electric Motor Manufacturers

Type of Markup Markup Rate 

($/hr) Comments

Aggregate labor cost per hour* 10.87

Indirect Production** 33 % 14.46

Overhead*** 30 % 18.79

Fringe† 24 % 23.40

Assembly Labor Up-time†† 43 % 33.46

Fully-Burdened Cost of Labor 33.46

٭ Cost per hour is an aggregate number drawn from U.S. Census Bureau, 2007 Economic Census of Industry, 

published December 2010 and foreign labor rate estimates based on manufacturer feedback.

٭٭ Indirect Production Labor (Production managers, quality control, etc.) as a percent of direct labor on a cost basis. 

Navigant Consulting, Inc. (NCI) estimate.

٭٭٭ Overhead includes commissions, dismissal pay, bonuses vacation, sick leave, and social security contributions. 

NCI estimate.

† Fringe includes pension contributions, group insurance premiums, workers compensation. Source: U.S. Census 

Bureau, 2007 Economic Census of Industry, published December 2010. Data for NAICS code 335312 “Electric 

Motor and Generator Manufacturer” total fringe benefits as a percent of total compensation for all employees (not 

just production workers).

†† Assembly labor up-time is a factor applied to account for the time that workers are not assembling product and/or 

reworking unsatisfactory units. The markup of 43 percent represents a 70 percent utilization (multiplying by 

100/70). NCI estimate.

B.5.2 Labor Time Estimates: Below are DOE’s labor time estimates for each 

representative unit as the CSLs increase. These estimates are based on SME and 

manufacturer feedback. Do these numbers accurately reflect how long it takes or would 

take to make each horsepower rating, and are labor hour increases accurate for the 

efficiency increases? If not, please comment on what adjustments you would make.
12-A-13 

Table 1.19 Labor Hour Estimates (And Percentage Increase Over Baseline) For Equipment 

Class Group I Candidate Standard Levels 

ECG I Representative Units

Efficiency Level 5HP, Design B 30HP, Design B 75HP, Design B

Baseline

(Below NEMA MG1 

Table 12-11)

1.25 Hours 2.00 Hours 3.50 Hours 

CSL 1

(NEMA MG1 Table 12-

11)

1.31 Hours (+5%) 2.10 Hours (+5%) 3.68 Hours (+5%)

CSL 2

(NEMA MG1 Table 12-

12)

1.38 Hours (+10%) 2.21 Hours (+10%) 3.86 Hours (+10%) 

CSL 3

(1 Band Above NEMA 

MG1 Table 12-12)

1.45 Hours (+16%) 2.32 Hours (+16%) 4.06 Hours (+16%) 

CSL 4*

(2 Bands Above NEMA 

MG1 Table 12-12)

3.68 Hours (+155%)** 6.00 Hours (+184%)** 9.45 Hours (+170%)** 

*CSL 4 for the 5 horsepower representative unit is 3 bands above NEMA MG1 Table 12-12. 

**This motor has a hand wound stator.

Table 1.20 Labor Hour Estimates (And Percentage Increase Over Baseline) For Equipment 

Class Group II Candidate Standard Levels

ECG II Representative Unit ECG II Representative Unit

Efficiency Level 5HP, Design C Efficiency Level 50HP, Design 

C

Baseline

(NEMA MG1 Table 

12-11)

1.25 hours 

Baseline

(NEMA MG1 Table 

12-11)

2.75 hours 

CSL 1

(NEMA MG1 Table 

12-12)

1.31 hours (+5%)

CSL 1

(NEMA MG1 Table 

12-12)

2.89 hours 

(+5%)

CSL 2

(2 Bands Above NEMA 

MG1 Table 12-12)

3.50 hours 

(+184%)* 

CSL 2

(1 Band Above NEMA 

MG1 Table 12-12)

7.50 hours 

(+173%)* 

*This motor has a hand-wound stator.

B.5.3 Part of the manufacturing selling prices calculation relates to labor expenses.

DOE applied a fully burdened labor rate to estimated manufacturing time. DOE estimates 

that additional manufacturing time will be needed to implement the design options shown 

below. Are these assumptions fair? If not, how much of a change in manufacturing time 

would you expect by implementing each of the design options listed? Please comment on 

the table below.
12-A-14 

Table 1.21 Additional Manufacturing Times for Design Options

Design Option Additional Time

Increasing Slot Fill (70% to 85%)

Lengthening Stack (20%-30% increase)

Changing Grades of Steel (no increase)

Die-Casting Copper Rotor Cage (no increase)

B.6 SCALING RESULTS

DOE scales its analysis of the representative units to the other horsepower and pole 

configurations that are not directly analyzed. In the preliminary analysis, DOE relied on an

incremental improvement of motor losses for each CSL. 

B.6.1 When selecting its representative units, DOE also considered horsepower ratings 

which were built in the last iteration of their frame designation. For example, both a 3 and 

5 horsepower motor are designated as 180-frame series motors in NEMA MG1, but the 5 

horsepower is the largest rating to be built in a 180-frame series designation. Which other 

horsepower ratings are currently at or near their maximum achievable efficiencies for 

their frame designations when they are produced at the NEMA MG1 Table 12-12 

efficiency level?

B.6.2 Software Modeling: In an effort to analyze more ratings, DOE is considering 

analyzing additional models using software analysis. This would allow DOE to assess

many additional ratings that would not be feasible to purchase, test, and teardown. Are 

there any particular characteristics or design parameters DOE should consider when 

conducting software modeling for un-analyzed ratings?

B.6.3 DOE was unable to directly analyze all equipment classes and must scale the 

efficiencies from the motors analyzed in the engineering analysis to the remaining 

equipment classes. DOE has examined the product lines of various manufacturers and 

created efficiency relationships based on data found in manufacturer catalogs. Is it 

appropriate to assume that manufacturers will use the same design options for a given 

product line and thus create a line of motors with similar efficiency ratings for their given 

equipment class?
12-A-15 

C COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

DOE is interested in understanding manufacturer impacts at the plant or profit center 

level directly pertinent to electric motors. However, the context within which the plant operates 

and the details of plant production and costs are not readily available from the published 

literature. Therefore, DOE invites you to provide these details confidentially in your own words 

to the extent possible and practical. Understanding the organizational setting around the electric 

motor industry profit center will help DOE understand the probable future of the manufacturing 

activity with and without new energy conservation standards. 

C.1 Do you have a parent company, and/or any subsidiaries relevant to the electric motor

industry? 

C.2 Do you manufacture any equipment other than electric motors? If so, what other 

equipment do you manufacture? 

C.3 What percentage of your total manufacturing corresponds to electric motors covered by 

this rulemaking? 

C.4 Where are your production facilities located, and what type of equipment is manufactured 

at each location? Could you provide figures for your company’s manufacturing at each location 

by equipment type, horsepower, number of poles and efficiency? 

C.5 At your manufacturing facilities, would potential electric motor redesigns be difficult to 

implement? If so, would your company modify the existing facility or develop a new facility? 

C.6 What are your employment levels at each of these facilities?

C.7 What are your product lines, niches, and relative strengths in the electric motors market?

C.8 What is your company’s approximate market share for polyphase electric motors from 1-

500 horsepower covered in this rulemaking? 

C.9 Would you expect your market share to change once new energy conservation standards 

become effective? 
12-A-16 

D MANUFACTURER PRODUCTION COSTS AND SCALING PRICES

For the MIA, DOE defines manufacturer production cost as all direct costs associated 

with manufacturing a piece of equipment. It includes direct labor, direct materials,1 and 

overhead (which includes depreciation costs). The breakdown of manufacturer production cost 

has implications for the quantitative impacts on electric motors manufacturers. The per unit 

production costs are necessary for DOE to estimate labor expenditures and other cash flow 

calculations.

Manufacturer selling price is the average cost manufacturers charge their first consumers, 

but does not include costs along distribution channels. The manufacturer selling price includes a 

per unit research and development cost; selling, general, and administrative expense; shipping 

cost; and profit. The manufacturer markup is a multiplier applied to manufacturer production 

cost to cover the per-unit research and development, selling, general, and administrative expense, 

shipping, and profit. 

In the engineering analysis, DOE developed manufacturer production costs for one 

representative motor in each motor equipment class grouping. By multiplying the manufacturer 

production costs by the manufacturer markup, DOE calculated the manufacturer selling prices 

for these motors at each CSL. In order to determine manufacturer selling prices for all other 

equipment classes (over 500 in total), DOE scaled the manufacturer selling prices to all covered 

equipment classes (i.e., scale the prices developed using the motors analyzed in the engineering 

analysis by horsepower, number of poles, and enclosure type). For scaling, DOE is using 

engineering data as a basis for examining how prices vary by horsepower, and catalog prices to 

assess how prices vary by enclosure and number of poles for each of the efficiency levels 

analyzed. 

To calculate manufacturer production costs for all other covered equipment (i.e., all 

equipment classes other than the three representative units for ECG I, III and IV and the two 

representative units for ECG II), DOE will divide the scaled manufacturer selling prices by the 

manufacturer markup. As shown in Equation 1.1 below, the manufacturer selling price divided 

by the baseline manufacturer markup would convert the scaled manufacturer selling price to a 

manufacturer production cost. 

Equation 1.1 Calculation of Manufacturer Production Costs and Manufacturer Selling 

Price

Manufacturer Selling Price

Manufacturer Markup = Manufacturer Production Cost

 

1 Included in direct materials are the costs associated with the handling of material (loading into assembly or 

winding equipment) and the scrap material that cannot be used in the production of a finished electric motor (e.g., 

lengths of wire too short to wind).
12-A-17 

D.1 Table 1.22 through Table 1.26 provide DOE’s estimates of the manufacturer production 

costs and manufacturer selling prices for electric motors at the representative horsepower, 

number of poles, and enclosure type as well as each CSL being considered. Could you please 

provide any comments on the estimated values? 

Table 1.22 Estimated Manufacturer Production Costs and Manufacturer Selling Prices for 

5 Horsepower, NEMA Design B, 4-pole Enclosed Motor

Efficiency Level

(Full Load 

Efficiency)

DOE’s Manufacturer 

Production Cost 

Estimates

(2012$)

DOE’s Manufacturer 

Selling Price Estimates

(2012$)

Manufacturer 

Comments or Revised 

Estimates

Baseline Level

(82.5%) 230 316 

CSL 1

(NEMA MG1 Table 

12-11)

233 320 

CSL 2

(NEMA MG1 Table 

12-12)

256 350 

CSL 3

(1 Band Above 

NEMA Premium)

263 361 

CSL 4

(3 Bands Above 

NEMA Premium)

414 567 
12-A-18 

Table 1.23 Estimated Manufacturer Production Costs and Manufacturer Selling Prices for 

30 Horsepower, NEMA Design B, 4-pole Enclosed Motor

Efficiency Level

(Full Load 

Efficiency)

DOE’s Manufacturer 

Production Cost 

Estimates

(2012$)

DOE’s Manufacturer 

Selling Price Estimates

(2012$)

Manufacturer 

Comments or Revised 

Estimates

Baseline Level

(89.5%) 553 801 

CSL 1

(NEMA MG1 Table 

12-11)

699 1,014 

CSL 2

(NEMA MG1 Table 

12-12)

797 1,156 

CSL 3

(1 Band Above 

NEMA MG1 Table 

12-12)

804 1,166 

CSL 4

(2 Bands Above 

NEMA MG1 Table 

12-12)

1,293 1,875 

Table 1.24 Estimated Manufacturer Production Costs and Manufacturer Selling Prices for 

75 Horsepower, NEMA Design B, 4-pole Enclosed Motor

Efficiency Level

(Full Load 

Efficiency)

DOE’s Manufacturer 

Production Cost 

Estimates*

(2012$)

DOE’s Manufacturer 

Selling Price Estimates

(2012$)

Manufacturer 

Comments or Revised 

Estimates

Baseline Level

(93.0%) 1,221 1,771 

CSL 1

(NEMA MG1 Table 

12-11)

1,329 1,927 

CSL 2

(NEMA MG1 Table 

12-12)

1,511 2,191 

CSL 3

(1 Band Above 

NEMA MG1 Table 

12-12)

1,715 2,486 

CSL 4

(2 Bands Above 

NEMA MG1 Table 

12-12)

2,235 3,240 
12-A-19 

Table 1.25 Estimated Manufacturer Production Costs and Manufacturer Selling Prices for 

5 Horsepower, NEMA Design C, 4-pole Enclosed Motor

Table 1.26 Estimated Manufacturer Production Costs and Manufacturer Selling Prices for 

50 Horsepower, NEMA Design C, 4-pole Enclosed Motor

Efficiency Level

(Full Load 

Efficiency)

DOE’s Manufacturer 

Production Cost 

Estimates*

(2012$)

DOE’s Manufacturer 

Selling Price Estimates

(2012$)

Manufacturer 

Comments or Revised 

Estimates

Baseline Level

(NEMA MG1 Table 

12-11)

232 336 

CSL 1

(NEMA MG1 Table 

12-12)

248 360 

CSL 2

(2 Bands Above 

NEMA MG1 Table 

12-12)

400 580 

Efficiency Level

(Full Load 

Efficiency)

DOE’s Manufacturer 

Production Cost 

Estimates*

(2012$)

DOE’s Manufacturer 

Selling Price Estimates

(2012$)

Manufacturer 

Comments or Revised 

Estimates

Baseline Level

(NEMA MG1 Table 

12-11)

1,026 1,487 

CSL 1

(NEMA MG1 Table 

12-12)

1,410 2,045 

CSL 2

(1 Band Above 

NEMA MG1 Table 

12-12)

1,534 2,225 
12-A-20 

D.2 Please compare your manufacturer production cost percentages2 to the estimates 

tabulated below. The manufacturer production cost breakdown is used to calculate the total cost 

of goods sold (COGS) for the industry. Having an accurate estimate of the production costs for 

the industry allows DOE to better examine impacts on profitability and employment due to new 

energy conservation standards. Are the different percentages of each cost representative of your 

company or the electric motor industry? Please explain any differences. As mentioned in section 

D, the overhead component of the manufacturer production cost includes depreciation. In the 

tables below could you separate depreciation from overhead and include it as a percentage of 

manufacturer production cost? 

Table 1.27 Breakdown of Manufacturer Production Costs for ECG I, CSL 1, 5 

Horsepower, 4-pole Enclosed Motor

Components of 

Manufacturer Production 

Cost

DOE’s Estimated Percentage of 

Manufacturer Production Cost Manufacturer Feedback

Materials 66%

Labor 18%

Overhead 15%

Depreciation Included in overhead

Table 1.28 Breakdown of Manufacturer Production Costs for ECG I, CSL 1, 30 

Horsepower, 4-pole Enclosed Motor

Components of 

Manufacturer Production 

Cost

DOE’s Estimated Percentage of 

Manufacturer Production Cost Manufacturer Feedback

Materials 75%

Labor 10%

Overhead 15%

Depreciation Included in overhead

Table 1.29 Breakdown of Manufacturer Production Costs for ECG I, CSL 1, 75 

Horsepower, 4-pole Enclosed Motor

Components of 

Manufacturer Production 

Cost

DOE’s Estimated Percentage of 

Manufacturer Production Cost Manufacturer Feedback

Materials 76%

Labor 9%

Overhead 15%

Depreciation Included in overhead

D.3 Do the percentages presented on Table 1.27 through Table 1.29 change at higher 

 

2 The manufacture production cost percentages shown in Table 1.27 through Table 1.29 are the values that make up 

COGS. These are percentages of total COGS. 
12-A-21 

efficiencies? Do the percentages change for NEMA Design C motors? Please explain any 

differences.

D.4 Within a motor ECG, does the production cost breakdown change with horsepower? 

Does it vary with the number of poles? 

E MARKUPS AND PROFITABILITY

One of the primary objectives of the MIA is to assess the impact of new energy 

conservation standards on industry profitability. In this section, DOE would like to understand 

the current markup structure of the industry and how setting a new energy conservation standard 

would impact your company’s markup structure and profitability. As discussed in Section D, the 

manufacturer markup is a multiplier applied to manufacturer production cost to cover per unit 

research and development, selling, general, and administrative expenses, shipping costs, and 

profit. Currently, DOE estimates an industry-wide markup of 37%–45% for electric motor

equipment classes. 

E.1 Do profit levels currently vary by equipment class? Do profit levels vary by CSL? Please 

explain why or why not. 

E.2 Within each motor ECG, do profit levels vary by horsepower, number of poles, and/or 

enclosure type? 

E.3 DOE currently assumes that the manufacturer markup does not vary by motor ECG, CSL, 

or equipment class. DOE would like to understand how the manufacturer markup changes at 

higher CSLs. If so, could you provide your company’s markup for any motors that meet the 

CSLs shown below? 
12-A-22 

Table 1.30 Manufacturer Markups for 5 Horsepower, NEMA Design B, 4-pole Enclosed 

Motors 

Efficiency Level

(Full Load 

Efficiency)

Estimated Manufacturer Markup

Baseline ( 82.5% )

CSL 1

(NEMA MG1 Table 12-11)

CSL 2

(NEMA MG1 Table 12-12)

CSL 3

(1 Band Above NEMA MG1 Table 12-12)

CSL 4

(3 Bands Above NEMA MG1 Table 12-12)

Table 1.31 Manufacturer Markups for 30 Horsepower, NEMA Design B 4-pole Enclosed 

Motor 

Efficiency Level

(Full Load 

Efficiency)

Estimated Manufacturer Markup

Baseline ( 89.5% )

CSL 1

(NEMA MG1 Table 12-11)

CSL 2

(NEMA MG1 Table 12-12)

CSL 3

(1 Band Above NEMA MG1 Table 12-12)

CSL 4

(2 Bands Above NEMA MG1 Table 12-12)

Table 1.32 Manufacturer Markups for 75 Horsepower, NEMA Design B 4-pole Enclosed 

Motor 

Efficiency Level

(Full Load 

Efficiency)

Estimated Manufacturer Markup

Baseline ( 93.0% )

CSL 1

(NEMA MG1 Table 12-11)

CSL 2

(NEMA MG1 Table 12-12)

CSL 3

(1 Band Above NEMA MG1 Table 12-12)

CSL 4

(2 Bands Above NEMA MG1 Table 12-12)
12-A-23 

Table 1.33 Manufacturer Markups for 5 Horsepower, NEMA Design C 4-pole Enclosed 

Motor 

Table 1.34 Manufacturer Markups for 50 Horsepower, NEMA Design C 4-pole Enclosed 

Motor 

E.4 Could you explain how the manufacturer markup varies for each motor ECG as the 

horsepower, number of poles, and enclosure type varies. 

E.5 Would you expect changes in your estimated profitability following a new energy 

conservation standard? If so, please explain why.

F SHIPMENT PROJECTIONS AND MARKET SHARES

A new energy conservation standard can change overall shipments by altering equipment 

attributes, marketing approaches, equipment availability, and price. The industry revenue 

calculations are based on the shipment projections developed by DOE’s shipments model. 

F.1 Please compare DOE’s projections of annual industry-wide shipments for covered 

electric motors with your company’s projections of industry-wide shipments. 

Efficiency Level

(Full Load 

Efficiency)

Estimated Manufacturer Markup

Baseline ( NEMA MG1 Table 12-11)

CSL 1

(NEMA MG1 Table 12-12)

CSL 2

(2 Bands Above NEMA MG1 Table 12-12)

Efficiency Level

(Full Load 

Efficiency)

Estimated Manufacturer Markup

Baseline (NEMA MG1 Table 12-11)

CSL 1

(NEMA MG1 Table 12-12)

CSL 2

(1 Band Above NEMA MG1 Table 12-12)
12-A-24 

Table 1.35 Annual Industry-Wide Shipment Projections for Polyphase Motors Absent 

Amended Energy Conservation Standards 

2008

Total IndustryWide 

Shipments

Projected Total 

Industry-Wide 

Shipments in 

2015*

Projected Total 

Industry-Wide 

Shipments in 

2025

Projected Total

Industry-Wide 

Shipments in 

2035

DOE’s Estimate for 

Total Industry 

Shipments (Millions)

.750 .838 .990 1.22 

Manufacturer 

Feedback

G EQUIPMENT MIX

Equipment mix describes the distribution of current shipments by CSL. Changes in the 

equipment mix due to new energy conservation standards can have a large impact on industry 

revenues. Having an accurate estimate of the current equipment mix allows DOE to better 

estimate how revenues might change due to new energy conservation standards.

G.1 Does your company offer multiple product lines at different CSLs? Could you provide a 

description of your company’s product lines and their respective CSLs?

G.2 Table 1.36 through Table 1.38 shows DOE’s estimate for the mix of shipments by 

efficiency in 2015. Could you provide feedback on DOE’s estimates based on your knowledge of 

the industry? Note: Though the CSLs defined in the introduction of this interview guide apply to 

one representative motor in each motor ECG, the CSLs in the following tables are meant to 

represent levels that would require manufacturers to implement similar design options for all 

horsepower, enclosure and pole configurations for a given motor ECG. 
12-A-25 

Table 1.36 Percentage of Industry-Wide Shipments by Efficiency Level for Equipment 

Class Group I Motors in 2015 

Percentage of 

Total 

Shipments at 

Each 

Efficiency

Horsepower 

range 

CSL 0 

Baseline

CSL 1 

(NEMA 

MG1 Table 

12-11)

CSL 2 

(NEMA 

MG1 Table 

12-12)

CSL 3 CSL 4

DOE's 

estimate 2015

1 - 5 10.3% 36.8% 41.5% 10.1% 1.3%

6 - 20 4.7% 35.3% 44.3% 12.0% 3.6%

21 - 50 5.3% 30.3% 47.8% 8.8% 7.9%

51 - 100 5.4% 28.6% 48.4% 10.1% 7.5%

101 -200 5.4% 23.3% 53.9% 12.0% 5.4%

201 -500 11.2% 49.9% 32.0% 5.9% 0.9%

Manufacturer 

feedback

1 - 5

6 - 20

21 - 50

51 - 100

101 -200

201 -500

Table 1.37 Percentage of Industry-Wide Shipments by Efficiency Level for Equipment 

Class Group II Motors in 2015 

Percentage of 

Total Shipments 

at Each 

Efficiency

Horsepower 

range 

CSL 0 (NEMA 

MG1 Table 12-

11) 

CSL 1 (NEMA 

MG1 Table 12-

12) 

CSL 2 (1 to 2 

Bands Above 

NEMA MG1 

Table 12-12)

DOE's estimate 

2015 

1 - 5 23.1% 69.2% 7.7%

6 - 20 0.0% 100.0% 0.0%

21 - 50 73.3% 26.7% 0.0%

51 - 100 50.0% 50.0% 0.0%

101 -200 47.8% 30.4% 21.7%

Manufacturer 

feedback

1 - 5

6 - 20

21 - 50

51 - 100

101 -200
12-A-26 

Table 1.38 Percentage of Industry-Wide Shipments by Efficiency Level for Equipment 

Class Group III Motors in 2015 

Percentage of 

Total 

Shipments at 

Each 

Efficiency

Horsepower 

range 

CSL 0 

Baseline

CSL 1 

(NEMA 

MG1 Table 

12-11)

CSL 2 

(NEMA 

MG1 Table 

12-12)

CSL 3 CSL 4

DOE's 

estimate 2015 

1 - 5 94.9% 5.1% 0.0% 0.0% 0.0%

6 - 20 Close to 

100% Insignificant

21 - 50 81.7% 5.5% 12.8% 0.0% 0.0%

51 - 100 80.6% 2.0% 17.3% 0.0% 0.0%

101 -200 73.5% 17.6% 8.8% 0.0% 0.0%

201 -500 75.0% 25.0% 0.0% 0.0% 0.0%

Manufacturer 

feedback

1 - 5

6 - 20

21 - 50

51 - 100

101 -200

201 -500

H FINANCIAL PARAMETERS

Navigant Consulting, Inc. (NCI) has developed a “strawman” model of the electric motor

industry financial performance called the Government Regulatory Impact Model (GRIM) using 

publicly available data. This section attempts to understand how your company’s financial 

situation differs from our industry aggregate picture. 

H.1 Please compare your financial parameters to the GRIM parameters tabulated below.
12-A-27 

Table 1.39 Financial Parameters for Electric Motor Manufacturers

GRIM Input Definition

Industry 

Estimated

Value

Your Actual (If 

Significantly 

Different from 

DOE’s Estimate)

Income Tax Rate

Corporate effective income tax 

paid (percentage of earnings 

before taxes, EBT)

28.6% 

Discount Rate

Weighted average cost of capital 

(inflation-adjusted weighted 

average of corporate cost of debt 

and return on equity)

9.1% 

Working Capital 

Current assets less current 

liabilities (percentage of 

revenues)

2.5% 

SG&A

Selling, general, and 

administrative expenses 

(percentage of revenues)

16.6% 

R&D Research and development 

expenses (percentage of revenues) 4.2% 

Depreciation Amortization of fixed assets 

(percentage of revenues) 5.1% 

Capital Expenditures

Outlay of cash to acquire or 

improve capital assets (percentage 

of revenues, not including 

acquisition or sale of business 

units)

5.1% 

Cost of Goods Sold 

Includes material, labor, 

overhead, and depreciation 

(percentage of revenues)

71.9% 

H.2 How would you expect an amended energy conservation standard to impact any of the 

financial parameters for the industry? 

I CONVERSION COSTS

New and amended energy conservation standards may cause your company to incur 

capital and equipment conversion costs to redesign existing equipment and make changes to 

existing production lines to be compliant with the new and amended energy conservation 

standards. Capital conversion costs are one-time investments in plant, property, and equipment 

(PPE) necessitated by new and amended energy conservation standards. These may be 

incremental changes to existing PPE or the replacement of existing PPE. Replacing existing PPE 

could strand existing assets before the end of their useful lives. In addition to capital conversion 

costs, equipment conversion costs are costs related to research, product development, testing, 

marketing and other costs for redesigning equipment necessitated by new and amended energy 
12-A-28 

conservation standards. For the industry cash flow model, DOE must estimate the conversion 

costs for all covered equipment. It is difficult to estimate these costs due to variations in 

efficiency between equipment classes. The questions below attempt to capture the capital and 

equipment conversion costs that would be required to convert all covered equipment at the CSLs 

studied by DOE. 

I.1 Are different motor categories manufactured on the same line? Within a motor ECG, are 

motors of varying horsepower and number of poles manufactured on the same line?

I.2 Does the production equipment and manufacturing processes used to manufacturer 

motors differ by motor ECG, horsepower, or number of poles? 

I.3 Are production lines shared between covered general purpose motors and motors notcurrently covered by standards?

I.4 What capitol conversion costs do you expect if your company had to manufacture diecast copper rotor motors? How would these costs change if only motors from 1-30 horsepower 

were die-cast copper rotor motors? 

In the tables below, DOE asks you to provide your expected capital and equipment 

conversion costs for the representative combination of horsepower and number of poles for each 

electric motor ECG. Following the tables, DOE asks a series of questions to determine how the 

capital conversion and equipment conversion costs for these motors compare to costs to convert 

the remaining combinations of horsepower and number of poles. 

Table 1.40 Conversion Costs for 5 Horsepower, NEMA Design B, 4-pole Enclosed Motors

Efficiency Level

(Full Load Efficiency)

5 Horsepower, NEMA Design B, 4-pole Enclosed Motor

Capital 

Conversion 

Costs (2012$)

Equipment 

Conversion 

Costs (2012$)

Stranded Assets

(2012$)

Baseline (82.5%) 

CSL 1

(NEMA MG1 Table 12-11)

CSL 2

(NEMA MG1 Table 12-12)

CSL 3

(1 Band Above NEMA MG1 Table 12-12)

CSL 4

(3 Bands Above NEMA MG1 Table 12-

12)
12-A-29 

Table 1.41 Conversion Costs for 30 Horsepower, NEMA Design B 4-pole Enclosed Motors

Efficiency Level

(Full Load Efficiency) 30 Horsepower, NEMA Design B, 4-pole Enclosed Motor

Capital 

Conversion 

Costs (2012$)

Equipment 

Conversion 

Costs (2012$)

Stranded Assets 

(2012$) 

Baseline (89.5%) 

CSL 1

(NEMA MG1 Table 12-11)

CSL 2

(NEMA MG1 Table 12-12)

CSL 3

(1 Band Above NEMA MG1 Table 12-12)

CSL 4

(2 Bands Above NEMA MG1 Table 12-12)

Table 1.42 Conversion Costs for 75 Horsepower, NEMA Design B 4-pole Enclosed Motors

Efficiency Level

(Full Load Efficiency)

75 Horsepower, NEMA Design B, 4-pole Enclosed Motor

Capital 

Conversion 

Costs (2012$)

Equipment 

Conversion 

Costs (2012$)

Stranded Assets 

(2012$) 

Baseline Level

(93.0%)

CSL 1

(NEMA MG1 Table 12-11)

CSL 2

(NEMA MG1 Table 12-12)

CSL 3

(1 Band Above NEMA MG1 Table 12-12)

CSL 4

(2 Bands Above NEMA MG1 Table 12-12)

Table 1.43 Conversion Costs for 5 Horsepower, NEMA Design C 4-pole Enclosed Motors

Efficiency Level

(Full Load Efficiency)

5 Horsepower, NEMA Design C, 4-pole Enclosed Motor

Capital 

Conversion 

Costs (2012$)

Equipment 

Conversion 

Costs (2012$)

Stranded Assets 

(2012$) 

Baseline (NEMA MG1 Table 12-11)

CSL 1

(NEMA MG1 Table 12-12)

CSL 2

(2 Bands Above NEMA MG1 Table 12-

12)
12-A-30 

Table 1.44 Conversion Costs for 50 Horsepower, NEMA Design C 4-pole Enclosed 

Motors

Efficiency Level

(Full Load Efficiency)

50 Horsepower, NEMA Design C, 4-pole Enclosed Motor

Capital 

Conversion 

Costs (2012$)

Equipment 

Conversion 

Costs (2012$)

Stranded Assets 

(2012$) 

Baseline (NEMA MG1 Table 12-11)

CSL 1

(NEMA MG1 Table 12-12)

CSL 2

(1 Band Above NEMA MG1 Table 12-12)

Table 1.45 Conversion Costs for all Covered Fire Pump Motors

Efficiency Level

(Percentage increase over baseline)

Covered Fire Pump Motors

Capital 

Conversion 

Costs (2012$)

Equipment 

Conversion 

Costs (2012$)

Stranded Assets 

(2012$) 

CSL 1

(NEMA MG1 Table 12-11)

CSL 2

(NEMA MG1 Table 12-12)

CSL 3

(1 Band Above NEMA MG1 Table 12-12)

CSL 4

(2 Bands Above NEMA MG1 Table 12-12)

I.5 Within each motor ECG, will the conversion costs presented above be shared across 

equipment with different horsepower and number of poles? For example, will the conversion 

costs for 5 hp, 4-pole enclosed motors include the conversion costs for a 10 hp, 6-pole motors 

open frame motors, 3 hp, 2-pole enclosed motors, etc.? 

I.6 For the conversion costs provided above, would any of these conversion costs be shared

across different motor types? 

I.7 In order to increase the efficiencies by the percentages shown in the tables above, would 

non-covered motors also require a corresponding improvement in efficiency? 

J CUMULATIVE REGULATORY BURDEN

Cumulative regulatory burden refers to the burden that industry faces from overlapping 

effects of new or revised DOE standards, voluntary standards, and/or other regulatory actions 

affecting the same equipment or industry. 
12-A-31 

J.1 Are there other recent or impending regulations that electric motor manufacturers face 

(from DOE or otherwise)? If so, could you identify the regulation and the corresponding possible 

effective dates for those regulations?

J.2 What level of expense are you expecting to incur as a result of these regulations? 

J.3 Under what circumstances would you be able to coordinate any expenditures related to 

these other regulations with this electric motor energy conservation standard, thereby lessening 

the cumulative burden? 

K DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of new and amended energy conservation standards on employment is an 

important consideration in the rulemaking process. This section of the interview guide seeks to 

explore current trends in electric motor production employment and solicit manufacturer views 

on how domestic employment patterns might be affected by new and amended energy 

conservation standards. 

K.1 Where are your facilities that produce electric motors for the United States located? What 

types of products are manufactured at each location? Please provide annual shipment figures for 

your company’s electric motor manufacturing at each location by equipment class. Please also 

provide employment levels at each of these facilities.

Table 1.46 Electric Motor Manufacturing Facilities

Facility Location Equipment Types 

Manufactured Employees Annual Shipments

Example Jackson, TN Equipment Class Group I, II 650 

300,000 for 

ECG 1, 200,000 

for ECG 2

1

2

3

4

5

K.2 Would your domestic employment levels be expected to change significantly under new

and amended energy conservation standards? If so, please explain how they would change if 

higher efficiency levels are required.
12-A-32 

K.3 Would the workforce skills necessary under new and amended energy conservation 

standards require extensive retraining or replacement of employees at your manufacturing 

facilities?

L EXPORTS / FOREIGN COMPETITION / OUTSOURCING

Disparity between domestic and foreign energy conservation standards could impact 

exports or imports. Labor content and material changes, resulting from new and amended energy 

conservation standards, may impact sourcing decisions. 

L.1 What percentage of your company’s electric motors sales is domestic? Absent new and 

amended energy conservation standards, are production facilities being relocated to foreign 

countries? Would new and amended energy conservation standards impact your domestic vs. 

foreign manufacturing decision? 

L.2 If applicable, to what foreign countries or regions do you export your equipment? What 

percentage of sales can be attributed to each?

L.3 Would new and amended energy conservation standards be expected to affect your export 

sales? What would the resulting impact be, if any, on your manufacturing operations and 

profitability?

L.4 Are your foreign exports affected by new and amended energy conservation standards in 

other countries? 

L.5 What percentage of the U.S. market for electric motor is imported? Would new and 

amended energy conservation standards have an impact on foreign competition? 

L.6 What is your outlook for electric motors exports? 

M CONSOLIDATION

New and amended energy conservation standards can alter the competitive dynamics of 

the market. This can include prompting companies to enter or exit the market, or to merge. DOE 

and the Department of Justice are both interested in any potential reduction in competition that 

would result from new and amended energy conservation standards. 
12-A-33 

M.1 Please comment on industry consolidation and related trends over the last 5 years. 

M.2 In the absence of new and amended energy conservation standards, do you expect any 

further industry consolidation? Please describe your expectations.

M.3 How would new and amended energy conservation standards affect your ability to 

compete?

N IMPACTS ON SMALL BUSINESS

N.1 The Small Business Association (SBA) denotes a small business in the electric motor

industry as having less than 1,000 employees3. By this definition, is your company considered a 

small business?

N.2 Are there any reasons that a small business might be at a disadvantage relative to a larger 

business under new and amended energy conservation standards? Please consider such factors as 

technical expertise, access to capital, bulk purchasing power for materials/components, 

engineering resources, and any other relevant issues. 

N.3 Are there any niche manufacturers, small businesses, and/or component manufacturers 

for which the adoption of new and amended energy conservation standards would have a severe 

impact? If so, would manufacturers of these motors have different incremental impacts from 

implemented new and amended energy conservation standards than the rest of the industry? 

 

3 DOE uses the small business size standards published on August 22, 2008, as amended, by the Small Business 

Administration (SBA) to determine whether a company is a small business. To be categorized as a small business, 

an electric motor manufacturer and its affiliates may employ a maximum of 1,000 employees. The 1,000 employee 

threshold includes all employees in a business’s parent company and any other subsidiaries. 
12-A-34 

12-A.2 ELECTRIC MOTOR SMALL BUSINESS MANUFACTURER IMPACT 

ANALYSIS INTERVIEW GUIDE

As part of the rulemaking process for new energy conservation standards for electric 

motors, the Department of Energy (DOE) conducts the manufacturer impact analysis (MIA). In 

this analysis, DOE uses publicly available information and information provided by 

manufacturers during interviews to assess possible impacts on manufacturers due to new energy 

conservation standards. DOE is currently considering five candidate standard levels of efficiency 

(referred to as CSLs) for electric motors in the scope of this rulemaking, including a baseline 

CSL. The motor types covered in this rulemaking are separated into four equipment class groups 

(ECGs), as shown below.

Table 2.1 Electric Motor Equipment Class Groups

Equipment Class 

Group

Electric Motor 

Design Horsepower Poles Enclosure

I NEMA Design A & 

B* 1-500 2, 4, 6, 8 Open

Closed

II NEMA Design C* 1-200 4, 6, 8 Open

Closed

III Fire Pump* 1-500 2, 4, 6, 8 Open

Closed

IV Integral Brake Motors* 1-30 4, 6, 8 

Open

Closed

*Including IEC equivalents.

DOE analyzed three representative units for ECG I (which are also used to represent ECG III and 

ECG IV) and two representative units for ECG II. The results obtained were then extrapolated to 

other ratings within the respective ECGs.

In responding to this questionnaire, please refer to the CSLs in the table below. 
12-A-35 

Table 2.2 Nominal Efficiency Levels Under Consideration for Equipment Class Group I 

and III

Equipment 

Class CSL 1 CSL 2 CSL 3 CSL 4*

5 horsepower, 4-

pole enclosed

NEMA MG1 Table 

12-11 

(87.5%) 

NEMA MG1 Table 

12-12 

(89.5%) 

1 Band Above 

NEMA MG1 Table 

12-12 

(90.2%)

3 Bands Above 

NEMA MG1 Table 

12-12 

(91.7%)

30 horsepower, 

4-pole enclosed

NEMA MG1 Table 

12-11 

(92.4%) 

NEMA MG1 Table 

12-12 

(93.6%) 

1 Band Above 

NEMA MG1 Table 

12-12 

(94.1%)

2 Bands Above 

NEMA MG1 Table 

12-12 

(94.5%)

75 horsepower, 

4-pole enclosed

NEMA MG1 Table 

12-11 

(94.1%) 

NEMA MG1 Table 

12-12 

(95.4%) 

1 Band Above 

NEMA MG1 Table 

12-12 

(95.8%)

2 Bands Above 

NEMA MG1 Table 

12-12 

(96.2%)

*All representative units at this CSL use copper rotor technology.

Table 2.3 Nominal Efficiency Levels under Consideration for Equipment Class Group II

Equipment Class CSL 1 CSL 2*

5 horsepower, 4-pole enclosed NEMA MG1 Table 12-12 

(89.5%) 

2 Bands Above NEMA MG1 

Table 12-12 

(91.0%)

50 horsepower, 4-pole enclosed NEMA MG1 Table 12-12 

(94.5%) 

1 Band Above NEMA MG1 

Table 12-12 

(95.0%)

*All representative units at this CSL use copper rotor technology.

A INTRODUCTION

Are you aware of DOE’s ongoing rulemaking to establish new national minimum energy 

conservation standards for electric motors above 1 horsepower? If you are not already in it, 

would you like to be added to DOE’s email database for updates relating to this rulemaking? 

We are assessing the impacts of a potential energy conservation standard on small 

businesses. Is your company a small business (defined as less than 1,000 employees by the US 

Small Business Administration (SBA), including all subsidiaries and parent companies, and 

employees in all countries where you operate)?

A.1 Are there any reasons that a small business manufacturer might be at a disadvantage 

relative to a larger business under adopted energy conservation standards? Please consider such 

factors as technical expertise, access to capital, bulk purchasing power for materials/components, 
12-A-36 

engineering resources, and any other relevant issues.

A.2 To your knowledge, are there any small businesses for which the adoption of energy 

conservation standards would have a particularly severe impact? If so, why? 

B KEY ISSUES

B.1 In general, what are the key issues for your company regarding new energy conservation 

standards for electric motors and this rulemaking?

B.2 For the issues identified, how significant are they at each listed CSL?

C COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS 

C.1 Do you have a parent company, and/or any subsidiaries relevant to the electric motor 

industry? 

C.2 What types of electric motors do you manufacture? What is your company’s approximate 

market share of the electric motors market?

C.3 Do you manufacture any equipment other than electric motors? If so, what other 

equipment do you manufacture? What percentage of your total manufacturing corresponds to 

electric motors covered by this rulemaking?

C.4 Please complete Table 2.4 to the best of your ability. If possible, please express revenue 

in both dollar amount and in percentage of total electric motor sales. Additionally, please express 

shipments in both volume and percentage of electric motor shipments. Please indicate if you do 

not manufacture products in any given equipment class group. 
12-A-37 

Table 2.4 Electric Motor Revenue and Shipment Volumes

ECG ECG Description 2012 Revenue

($) (%)

2012 Shipments

(volume) (%)

1 NEMA Design A & B*: 1-500 horsepower 

2 NEMA Design C*: 1-200 horsepower 

3 Fire Pump* 

4 Integral Brake Motors*

*Including IEC equivalents

D MARKUPS AND PROFITABILITY

One of the primary objectives of the MIA is to assess the impact of new energy 

conservation standards on industry profitability. In this section, DOE would like to understand 

the current markup structure of the industry and how setting a new energy conservation standard 

would impact your company’s markup structure and profitability. The manufacturer markup is a 

multiplier applied to manufacturer production cost (which DOE defines as all direct costs 

associated with manufacturing a product: direct labor, direct materials, overhead, and 

depreciation) to cover per unit research and development; selling, general, and administrative 

expenses; shipping costs; and profit. It does not reflect a “profit margin.”

The manufacturer production cost times the manufacturer markup equals the 

manufacturer selling price. Manufacturer selling price is the price manufacturers charge their 

first customers, but does not include additional costs along the distribution channels. 

DOE estimates an industry-wide markup of 37% - 45% for electric motor equipment 

classes.

D.1 Is the 37% - 45% markup representative of an average industry markup? 

D.2 Do profit levels currently vary by equipment class? Do profit levels vary by CSL? Please 

explain why or why not. 

D.3 Would you expect changes in your estimated profitability following a new energy 

conservation standard? If so, please explain why. 

E FINANCIAL PARAMETERS

Navigant Consulting, Inc. (NCI) has developed a “strawman” model of the electric motor 

industry financial performance called the Government Regulatory Impact Model (GRIM) using 
12-A-38 

publicly available data. This section attempts to understand how your company’s financial 

situation differs from our industry aggregate picture. 

E.1 Please compare your financial parameters to the GRIM parameters tabulated below. 

Table 2.5 Financial Parameters for Electric Motor Manufacturers

GRIM Input Definition

Industry 

Estimated

Value

Your Actual (If 

Significantly 

Different from 

DOE’s Estimate)

Income Tax Rate Corporate effective income tax paid 

(percentage of earnings before taxes, EBT) 28.6% 

Discount Rate

Weighted average cost of capital (inflationadjusted weighted average of corporate cost 

of debt and return on equity)

9.1% 

Working Capital Current assets less current liabilities 

(percentage of revenues) 2.5% 

SG&A Selling, general, and administrative expenses 

(percentage of revenues) 16.6% 

R&D Research and development expenses 

(percentage of revenues) 4.2% 

Depreciation Amortization of fixed assets (percentage of 

revenues) 5.1% 

Capital 

Expenditures 

Outlay of cash to acquire or improve capital 

assets (percentage of revenues, not including 

acquisition or sale of business units)

5.1% 

Cost of Goods 

Sold

Includes material, labor, overhead, and 

depreciation (percentage of revenues) 71.9% 

E.2 How would you expect a new energy conservation standard to impact any of the financial 

parameters for the industry? 

F CONVERSION COSTS

New and amended energy conservation standards may cause your company to incur 

capital and equipment conversion costs to redesign existing equipment and make changes to 

existing production lines to be compliant with the new and amended energy conservation 

standards. 

Capital conversion costs are one-time investments in plant, property, and equipment (PPE) 

necessitated by new and amended energy conservation standards. These may be incremental 

changes to existing PPE or the replacement of existing PPE. Replacing existing PPE could 

strand existing assets before the end of their useful lives.
12-A-39 

Equipment conversion costs are costs related to research, product development, testing, 

marketing, and other costs for redesigning equipment necessitated by new and amended 

energy conservation standards. 

For the industry cash flow model, DOE must estimate the conversion costs for all 

covered equipment. It is difficult to estimate these costs due to variations in efficiency between 

equipment classes. The questions below attempt to capture the capital and equipment conversion 

costs that would be required to convert all covered equipment at the CSLs studied by DOE. 

F.1 For the electric motors you manufacture within each product class, what capitol and 

equipment conversion costs do you expect to incur at each designated CSL? 

Table 2.6 Expected Capital and Equipment Conversion Costs for Electric Motors

ECG ECG 

Description CSL

Total Capital 

Conversion Costs 

($)

Total Equipment 

Conversion Costs 

($)

Notes

1 

NEMA Design 

A & B:

1 - 500 hp 

CSL 1

CSL 2

CSL 3

CSL 4

2 

NEMA Design 

C:

1 - 200 hp 

CSL 1

CSL 2

F.2 Please provide additional qualitative information to help DOE understand the types and 

nature of your investments, including the plant and tooling changes and the product development 

effort required at different efficiency levels.

G DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of new and amended energy conservation standards on employment is an 

important consideration in the rulemaking process. This section of the interview guide seeks to 

explore current trends in electric motor production employment and solicit manufacturer views 

on how domestic employment patterns might be affected by new and amended energy 

conservation standards. 
12-A-40 

G.1 Where are your facilities that produce electric motors for the United States located? What 

types of products are manufactured at each location? Please provide annual shipment figures for 

your company’s electric motor manufacturing at each location by equipment class. Please also 

provide employment levels at each of these facilities.

Table 2.7 Electric Motor Manufacturing Facilities

Facility Location Equipment Types 

Manufactured Employees Annual Shipments

Example Jackson, TN Equipment Class Group I, II 650 

300,000 for 

ECG 1, 

200,000 for 

ECG 2

1 

2 

3 

4 

5 

G.2 Would your domestic employment levels be expected to change significantly under new 

and amended energy conservation standards? If so, please explain how they would change if 

higher efficiency levels are required.
12-B-i 

APPENDIX 12-B GOVERNMENT REGULATORY IMPACT MODEL (GRIM)

OVERVIEW

TABLE OF CONTENTS

12-B.1 INTRODUCTION AND PURPOSE .................................................................................. 1

12-B.2 ELECTRIC MOTORS MODEL DESCRIPTION.............................................................. 1

12-B.3 ELECTRIC MOTORS DISCOUNTED CASH FLOW EXAMPLE ................................. 4
12-B-1 

12-B.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the 

impacts of energy conservation standards and other regulations on manufacturers. The basic 

mode of analysis is to estimate the change in the value of the industry or manufacturers 

following a regulation or a series of regulations. The model structure also allows an analysis of 

multiple products with regulations taking effect over a period of time, and of multiple regulations 

on the same products. 

Industry net present value is defined, for the purpose of this analysis, as the discounted 

sum of industry free cash flows plus a discounted terminal value. The model calculates the actual 

cash flows by year and then determines the present value of those cash flows both without an 

energy conservation standard (i.e., the base case) and under different trial standard levels (TSLs) 

(i.e., the standards case).

Output from the model consists of summary financial metrics, graphs of major variables, 

and, when appropriate, access to the complete cash flow calculation.

12-B.2 ELECTRIC MOTORS MODEL DESCRIPTION

DOE analyzed the impacts of standards on the electric motor manufacturers. The basic 

structure of the GRIM is a standard annual cash flow analysis that uses manufacturer selling 

prices, manufacturing costs, a shipments forecast, and financial parameters as inputs and accepts 

a set of regulatory conditions as changes in costs and investments. The cash flow analysis is 

separated into two major blocks: income and cash flow. The income calculation determines net 

operating profit after taxes. The cash flow calculation converts net operating profit after taxes 

into an annual cash flow by including investment and non-cash items. The line items below 

relate to the electric motor manufacturers and are definitions of listed items on the printout of the 

output sheet (see section 12-B.3).

(1) Revenues: Annual revenues - computed by multiplying products’ unit prices at each 

efficiency level by the appropriate manufacturer markup;

(2) Material: The portion of cost of goods sold (COGS) that includes materials;

(3) Labor: The portion of COGS that includes direct labor, commissions, dismissal pay, 

bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor 

up-time;

(4) Depreciation: The portion of COGS that includes an allowance for the total amount 

of fixed assets used to produce that one unit; 

(5) Development Amortization: The portion of COGS that includes an allowance for the 

total product and capital conversion costs needed to produce that one unit. This is only 

applied to electric motors above NEMA Premium efficiency levels;
12-B-2 

(6) Overhead: The portion of COGS that includes indirect labor, indirect material, energy 

use, maintenance, property taxes, and insurance related to assets; 

(7) Standard SG&A: Selling, general, and administrative costs are computed as a 

percentage of Revenues (2); 

(8) R&D: GRIM separately accounts for ordinary research and development (R&D) as a 

percentage of Revenues (2); 

(9) Product Conversion Costs: Product conversion costs are one-time investments in

research, development, testing, marketing, and other costs focused on making products 

designs comply with the new and amended energy conservation standard. The GRIM 

allocates these costs over the period between the standard’s announcement and 

compliance dates; 

(10) Stranded Assets: In the year the standard becomes effective, a one-time write-off of 

stranded assets is accounted for;

(11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions for 

interest paid and taxes;

(12) EBIT/Revenues: GRIM calculates EBIT as a percentage of sales to compare with 

the industry’s average reported in financial statements;

(13) Taxes: Taxes on EBIT (11) are calculated by multiplying the tax rate contained in 

the Financials tab by EBIT (11); 

(14) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Taxes (13)

from EBIT (12); 

(15) NOPAT repeated: NOPAT (14) is repeated in the Statement of Cash Flows;

(16) Depreciation repeated: Depreciation (4) is added back in the Statement of Cash 

Flows because it is a non-cash expense;

(17) Development Amortization repeated: Development Amortization (5) is added back 

in the Statement of Cash Flows because it is already accounted for in product and capital 

conversion costs; 

(18) Loss on Disposal of Stranded Assets repeated: Stranded Assets (10) is added back 

in the Statement of Cash Flows because it is a non-cash expense;

(19) Change in Working Capital: Change in cash tied up in accounts receivable, 

inventory, and other cash investments necessary to support operations is calculated by 

multiplying working capital (as a percentage of revenues) by the change in annual 

revenues;
12-B-3 

(20) Cash Flow From Operations: Calculated by taking NOPAT (15), adding back noncash items such as Depreciation (16), Development Amortization (17), and Stranded 

Assets (18), and subtracting the Change in Working Capital (19); 

(21) Ordinary Capital Expenditures: Ordinary investments in property, plant, and 

equipment to maintain and replace existing production assets, computed as a percentage 

of Revenues (2); 

(22) Capital Conversion Costs: Capital conversion costs are one-time investments in 

property, plant, and equipment to adapt or change existing production facilities so that 

new product designs can be fabricated and assembled under the new regulation; the 

GRIM allocates these costs over the period between the standard’s announcement and 

compliance dates;

(23) Production Equipment Maintenance: The additional costs associated with 

maintaining capital equipment purchased due to a standard.

(24) Free Cash Flow: Annual cash flow from operations and investments; computed by 

subtracting (21) Ordinary Capital Expenditures and (22) Capital Conversion Costs from 

Cash Flows from Operations (20); 

(25) Free Cash Flow repeated: Free Cash Flow (24) is repeated in the Discounted Cash 

Flow section;

(26) Terminal Value: Estimate of the continuing value of the industry after the analysis 

period. Computed by growing the Free Cash Flow at the beginning of 2045 at a constant 

rate in perpetuity;

(27) Present Value Factor: Factor used to calculate an estimate of the present value of an 

amount to be received in the future; 

(28) Discounted Cash Flow: Free Cash Flow (24) multiplied by the Present Value 

Factor (27). For the end of 2045, the discounted cash flow includes the discounted 

Terminal Value (26); and

Industry Value thru the end of 2045: The sum of Discounted Cash Flows (28). 
12-B-4 

12-B.3 ELECTRIC MOTORS DISCOUNTED CASH FLOW EXAMPLE
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

TABLE OF CONTENTS

14A.1 EXECUTIVE SUMMARY ........................................................................................ 14A-1

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS .................................................. 14A-2

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES ................................................................................................................ 14A-3

14A.4 APPROACH AND KEY ASSUMPTIONS ............................................................... 14A-4

14A.4.1 Integrated Assessment Models ....................................................................... 14A-5

14A.4.2 Global versus Domestic Measures of SCC ................................................... 14A-11

14A.4.3 Valuing Non-CO2 Emissions ........................................................................ 14A-12

14A.4.4 Equilibrium Climate Sensitivity ................................................................... 14A-13

14A.4.5 Socioeconomic and Emissions Trajectories.................................................. 14A-16

14A.4.6 Discount Rate ................................................................................................ 14A-18

14A.5 REVISED SCC ESTIMATES .................................................................................. 14A-25

14A.6 LIMITATIONS OF THE ANALYSIS ..................................................................... 14A-31

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS ............................................................................................................ 14A-33

14A.8 CONCLUSION ......................................................................................................... 14A-35

14A.9 ANNEX..................................................................................................................... 14A-42

14A.9.1 Other (non-CO2) gases ................................................................................. 14A-43

14A.9.2 Extrapolating Emissions Projections to 2300 ............................................... 14A-45

a Prepared by Interagency Working Group on Social Cost of Carbon, United States Government.

With participation by:

Council of Economic Advisers 

Council on Environmental Quality 

Department of Agriculture

Department of Commerce

Department of Energy

Department of Transportation

Environmental Protection Agency

National Economic Council

Office of Energy and Climate Change

Office of Management and Budget

Office of Science and Technology Policy 

Department of the Treasury

14A-i 

 
LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 14A-1

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity 

Distributions ............................................................................................ 14A-14

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 

Reference Scenarios ................................................................................ 14A-16

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars) ...................... 14A-28

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 14A-30

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 14A-30

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation ............ 14A-34

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 14A-42

Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-53

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-54

Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) ........................................................................................................ 14A-55

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates ............. 14A-56

LIST OF FIGURES 

Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due 

to an Increase in Annual Global Temperature in the DICE, FUND, and 

PAGE models.......................................................................................... 14A-10

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE .................................................................................. 14A-11

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C) ....................................................................................... 14A-15

Figure 14A.5.1 Level of Global GDP across EMF Scenarios ......................................... 14A-29

Figure 14A.9.1 Sulfur Dioxide Emission Scenarios ........................................................ 14A-45

Figure 14A.9.2 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) ...................................................................................................... 14A-47

Figure 14A.9.3 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per 

capita growth declines linearly, reaching zero in the year 2300) ........... 14A-48

Figure 14A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 

2090-2100 is maintained through 2300) ................................................. 14A-49

Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200) ............................................................................................... 14A-50

14A-ii
Figure 14A.9.6 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 

extrapolations assume constant non-CO2 radiative forcing after 2100) . 14A-51

Figure 14A.9.7 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000-

2300 (Post-2100 extrapolations assume decline in CO2/GDP growth 

rate over 2090-2100 is maintained through 2300) .................................. 14A-52

Figure 14A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by 

discount rate ............................................................................................ 14A-55

14A-iii
14A.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to 

assess both the costs and the benefits of the intended regulation and, recognizing that some costs 

and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

“social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the 

social benefits of reducing carbon dioxide (CO2) emissions into cost-benefit analyses of 

regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The 

estimates are presented with an acknowledgement of the many uncertainties involved and with a 

clear understanding that they should be updated over time to reflect increasing knowledge of the 

science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. It is intended to include (but is not limited to) 

changes in net agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change. 

This document presents a summary of the interagency process that developed these SCC 

estimates. Technical experts from numerous agencies met on a regular basis to consider public 

comments, explore the technical literature in relevant fields, and discuss key model inputs and 

assumptions. The main objective of this process was to develop a range of SCC values using a 

defensible set of input assumptions grounded in the existing scientific and economic literatures. 

In this way, key uncertainties and model differences transparently and consistently inform the 

range of SCC estimates used in the rulemaking process. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from three integrated assessment models, at discount rates 

of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate 

across all three models at a 3 percent discount rate, is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution. 

 

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) 

Discount Rate

5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

14A-1 
2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

14A.2 MONETIZING CARBON DIOXIDE EMISSIONS

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. We report estimates of the SCC in dollars per 

metric ton of carbon dioxide throughout this document.b 

 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

Despite the serious limits of both quantification and monetization, SCC estimates can be 

useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive 

Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and 

the benefits of the intended regulation and, recognizing that some costs and benefits are difficult 

to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of 

the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to 

make it possible for agencies to incorporate the social benefits from reducing carbon dioxide 

emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts 

on cumulative global emissions. Most federal regulatory actions can be expected to have 

marginal impacts on global emissions. 

For such policies, the benefits from reduced (or costs from increased) emissions in any 

future year can be estimated by multiplying the change in emissions in that year by the SCC 

value appropriate for that year. The net present value of the benefits can then be calculated by 

multiplying each of these future benefits by an appropriate discount factor and summing across 

all affected years. This approach assumes that the marginal damages from increased emissions 

are constant for small departures from the baseline emissions path, an approximation that is 

reasonable for policies that have effects on emissions that are small relative to cumulative global 

carbon dioxide emissions. For policies that have a large (non-marginal) impact on global 

cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for 

calculating the benefits of reduced emissions; we do not attempt to answer that question here. 

b In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 

14A-2 

 
An interagency group convened on a regular basis to consider public comments, explore 

the technical literature in relevant fields, and discuss key inputs and assumptions in order to 

generate SCC estimates. Agencies that actively participated in the interagency process include 

the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, 

Transportation, and Treasury. This process was convened by the Council of Economic Advisers 

and the Office of Management and Budget, with active participation and regular input from the 

Council on Environmental Quality, National Economic Council, Office of Energy and Climate 

Change, and Office of Science and Technology Policy. The main objective of this process was 

to develop a range of SCC values using a defensible set of input assumptions that are grounded 

in the existing literature. In this way, key uncertainties and model differences can more 

transparently and consistently inform the range of SCC estimates used in the rulemaking process. 

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. See 

Annex for the full range of annual SCC estimates from 2010 to 2050. 

It is important to emphasize that the interagency process is committed to updating these 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, we have set a preliminary goal of revisiting the SCC 

values within two years or at such time as substantially updated models become available, and to 

continue to support research in this area. In the meantime, we will continue to explore the issues 

raised in this document and consider public comments as part of the ongoing interagency 

process. 

14A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY 

ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to 

estimate the benefits associated with reducing carbon dioxide emissions. In the final model year 

2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of 

$2 per ton of CO2 and a “global” SCC value of $33 per ton of CO2 for 2007 emission reductions 

(in 2007 dollars), increasing both values at 2.4 percent per year. It also included a sensitivity 

analysis at $80 per ton of CO2. A domestic SCC value is meant to reflect the value of damages 

in the United States resulting from a unit change in carbon dioxide emissions, while a global 

SCC value is meant to reflect the value of damages worldwide. 

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO2

(in 2006 dollars) for 2011 emission reductions (with a range of $0-$14 for sensitivity analysis), 

also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a 

14A-3 
domestic SCC range of $0 to $20 per ton CO2 for 2007 emission reductions (in 2007 dollars). In 

addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified 

what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean 

values were $68 and $40 per ton CO2 for discount rates of approximately 2 percent and 3 

percent, respectively (in 2006 dollars for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in 

how benefits are evaluated across agencies, the Administration sought to develop a transparent 

and defensible method, specifically designed for the rulemaking process, to quantify avoided 

climate change damages from reduced CO2 emissions. The interagency group did not undertake 

any original analysis. Instead, it combined SCC estimates from the existing literature to use as 

interim values until a more comprehensive analysis could be conducted. 

The outcome of the preliminary assessment by the interagency group was a set of five 

interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per 

ton of CO2. The $33 and $5 values represented model-weighted means of the published 

estimates produced from the most recently available versions of three integrated assessment 

models—DICE, PAGE, and FUND—at approximately 3 and 5 percent discount rates. The $55 

and $10 values were derived by adjusting the published estimates for uncertainty in the discount 

rate (using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, 

respectively. The $19 value was chosen as a central value between the $5 and $33 per ton 

estimates. All of these values were assumed to increase at 3 percent annually to represent growth 

in incremental damages over time as the magnitude of climate change increases.

 These interim values represent the first sustained interagency effort within the U.S. 

government to develop an SCC for use in regulatory analysis. The results of this preliminary 

effort were presented in several proposed and final rules and were offered for public comment in 

connection with proposed rules, including the joint EPA-DOT fuel economy and CO2 tailpipe 

emission proposed rules. 

14A.4 APPROACH AND KEY ASSUMPTIONS

Since the release of the interim values, the interagency group has reconvened on a regular 

basis to generate improved SCC estimates. Specifically, the group has considered public 

comments and further explored the technical literature in relevant fields. This section details the 

several choices and assumptions that underlie the resulting estimates of the SCC. 

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable, since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Academy of Science (2009) points 

out that there is tension between the goal of producing quantified estimates of the economic 

damages from an incremental ton of carbon and the limits of existing efforts to model these 

effects. Throughout this document, we highlight a number of concerns and problems that should 

be addressed by the research community, including research programs housed in many of the 

agencies participating in the interagency process to estimate the SCC. 

14A-4 
The U.S. Government will periodically review and reconsider estimates of the SCC used 

for cost-benefit analyses to reflect increasing knowledge of the science and economics of climate 

impacts, as well as improvements in modeling. In this context, statements recognizing the 

limitations of the analysis and calling for further research take on exceptional significance. The 

interagency group offers the new SCC values with all due humility about the uncertainties 

embedded in them and with a sincere promise to continue work to improve them. 

14A.4.1 Integrated Assessment Models 

We rely on three integrated assessment models (IAMs) commonly used to estimate the 

SCC: the FUND, DICE, and PAGE models.c

 These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below).

 

These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At 

the same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

science in their modeling frameworks but do not link physical impacts to economic damages. 

There is currently a limited amount of research linking climate impacts to economic damages, 

which makes this exercise even more difficult. Underlying the three IAMs selected for this 

exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ 

best attempts to synthesize the available scientific and economic research characterizing these 

relationships. 

The three IAMs translate emissions into changes in atmospheric greenhouse 

concentrations, atmospheric concentrations into changes in temperature, and changes in 

temperature into economic damages. The emissions projections used in the models are based on 

specified socioeconomic (GDP and population) pathways. These emissions are translated into 

concentrations using the carbon cycle built into each model, and concentrations are translated 

into warming based on each model’s simplified representation of the climate and a key 

parameter, climate sensitivity. Each model uses a different approach to translate warming into 

damages. Finally, transforming the stream of economic damages over time into a single value 

requires judgments about how to discount them.

c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of 

energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy 

Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decisionmakers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate 

Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, 

originally to study international capital transfers in climate policy. is now widely used to study climate impacts (e.g.,

Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).

14A-5 

 
Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. In PAGE, for example, the consumption-equivalent damages 

in each period are calculated as a fraction of GDP, depending on the temperature in that period 

relative to the pre-industrial average temperature in each region. In FUND, damages in each 

period also depend on the rate of temperature change from the prior period. In DICE, 

temperature affects both consumption and investment. We describe each model in greater detail 

here. In a later section, we discuss key gaps in how the models account for various scientific and 

economic processes (e.g. the probability of catastrophe, and the ability to adapt to climate change 

and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key 

objective of the interagency process was to enable a consistent exploration of the three models 

while respecting the different approaches to quantifying damages taken by the key modelers in 

the field. An extensive review of the literature was conducted to select three sets of input 

parameters for these models: climate sensitivity, socioeconomic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socioeconomic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. In DICE, these 

parameters are handled deterministically and represented by fixed constants; in PAGE, most 

parameters are represented by probability distributions. FUND was also run in a mode in which 

parameters were treated probabilistically.

The sensitivity of the results to other aspects of the models (e.g. the carbon cycle or 

damage function) is also important to explore in the context of future revisions to the SCC but 

has not been incorporated into these estimates. Areas for future research are highlighted at the 

end of this document.

The DICE Model 

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric carbon dioxide concentrations). Emission reductions are 

treated as analogous to investment in “natural capital.” By investing in natural capital today 

through reductions in emissions—implying reduced consumption—harmful effects of climate 

change can be avoided and future consumption thereby increased. 

For purposes of estimating the SCC, carbon dioxide emissions are a function of global 

GDP and the carbon intensity of economic output, with the latter declining over time due to 

technological progress. The DICE damage function links global average temperature to the 

overall impact on the world economy. It varies quadratically with temperature change to capture 

the more rapid increase in damages expected to occur under more extreme climate change, and is 

calibrated to include the effects of warming on the production of market and nonmarket goods 

and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other 

vulnerable market sectors” (based primarily on changes in energy use), human health (based on 

climate-related diseases, such as malaria and dengue fever, and pollution), non-market amenities 

(based on outdoor recreation), and human settlements and ecosystems. The DICE damage 

14A-6 
function also includes the expected value of damages associated with low probability, high 

impact “catastrophic” climate change. This last component is calibrated based on a survey of 

experts (Nordhaus 1994). The expected value of these impacts is then added to the other market 

and non-market impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is 

included implicitly through the choice of studies used to calibrate the aggregate damage function. 

For example, its agricultural impact estimates assume that farmers can adjust land use decisions 

in response to changing climate conditions, and its health impact estimates assume 

improvements in healthcare over time. In addition, the small impacts on forestry, water systems, 

construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these 

sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level 

rise are incorporated into damage estimates, but their magnitude is not clearly reported. 

Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective 

adaptation, and largely ignores adaptation costs." 

Note that the damage function in DICE has a somewhat different meaning from the 

damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because 

damages in a given year reduce investment in that year, damages propagate forward in time and 

reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in 

any given year do not propagate forward.d

 

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into 

economic, non-economic, and catastrophic categories and calculates these impacts separately for 

eight geographic regions. Damages in each region are expressed as a fraction of output, where 

the fraction lost depends on the temperature change in each region. Damages are expressed as 

power functions of temperature change. The exponents of the damage function are the same in 

all regions but are treated as uncertain, with values ranging from 1 to 3 (instead of being fixed at 

2 as in DICE). 

PAGE2002 includes the consequences of catastrophic events in a separate damage subfunction. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a 

“discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a 

specified threshold. The threshold temperature, the rate at which the probability of experiencing 

a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are 

all modeled probabilistically.

d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the 

SCC relative to damages calculated by fixing GDP. In DICE2007, the time path of GDP is 

endogenous. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period 

chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it 

consistent with EMF GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we recalibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the 

absence of warming that exactly matched the EMF scenarios.

14A-7 

 
 Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature 

increases above some tolerable level (2°C for developed countries and 0°C for developing 

countries for economic impacts, and 0°C for all regions for non-economic impacts), but 

adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the 

developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond 

the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of 

their economic impacts. All regions are assumed to be able to mitigate 25 percent of the noneconomic impacts through adaptation (Hope 2006). 

The FUND Model 

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately 

calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, 

energy (based on heating and cooling demand), sea level rise (based on the value of land lost and 

the cost of protection), ecosystems, human health (diarrhea, vector-borne diseases, and 

cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a 

different functional form, and is calculated separately for sixteen geographic regions. In some 

impact sectors, the fraction of output lost or gained due to climate change depends not only on 

the absolute temperature change but also on the rate of temperature change and level of regional 

income.e

 In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations. 

 Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns”: for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is 

seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such 

as energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero). 

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

e In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning 

demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in 

those regions at low to moderate levels of warming (Warren et al., 2006).

14A-8 

 
positive impacts to some regions from higher temperatures, and sufficiently slow increases in 

temperature across these sectors can result in negative economic damages from climate change.

Damage Functions 

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of 

how to represent the effects of climate change (represented by the increase in global-average 

surface temperature) on the consumption-equivalent value of both market and non-market goods 

(represented as a fraction of global GDP). We recognize that these representations are 

incomplete and highly uncertain. But given the paucity of data linking the physical impacts to 

economic damages, we were not able to identify a better way to translate changes in climate into 

net economic damages, short of launching our own research program. 

 The damage functions for the three IAMs are presented in Figure 14A.4.1 and Figure 

14A.4.2, using the modeler’s default scenarios and mean input assumptions. There are 

significant differences between the three models both at lower (Figure 14A.4.2) and higher 

(Figure 14A.4.1) increases in global-average temperature. 

14A-9 
Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due to an 

Increase in Annual Global Temperature in the DICE, FUND, and PAGE 

modelsf

The lack of agreement among the models at lower temperature increases is underscored 

by the fact that the damages from FUND are well below the 5th percentile estimated by PAGE, 

while the damages estimated by DICE are roughly equal to the 95th percentile estimated by 

PAGE. This is significant because at higher discount rates we expect that a greater proportion of 

the SCC value is due to damages in years with lower temperature increases. For example, when 

the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to 

damages that occur in years when the temperature is less than or equal to 3 °C. This increases to 

approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively. 

These differences underscore the need for a thorough review of damage functions—in 

particular, how the models incorporate adaptation, technological change, and catastrophic 

damages. Gaps in the literature make modifying these aspects of the models challenging, which 

highlights the need for additional research. As knowledge improves, the Federal government is 

committed to exploring how these (and other) models can be modified to incorporate more 

accurate estimates of damages.

f The x-axis represents increases in annual, rather than equilibrium, temperature, while the y-axis represents the 

annual stream of benefits as a share of global GDP. Each specific combination of climate sensitivity, 

socioeconomic, and emissions parameters will produce a different realization of damages for each IAM. The 

damage functions represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under 

alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater than 3 

°C.

14A-10 

 
Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE

14A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current 

attention on a global measure of SCC. This approach is the same as that taken for the interim 

values, but it otherwise represents a departure from past practices, which tended to put greater 

emphasis on a domestic measure of SCC (limited to impacts of climate change experienced 

within U.S. borders). As a matter of law, consideration of both global and domestic values is 

generally permissible; the relevant statutory provisions are usually ambiguous and allow 

selection of either measure.g

 

Global SCC 

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

considerations are taken as a whole, the interagency group concluded that a global measure of 

the benefits from reducing U.S. emissions is preferable. 

When quantifying the damages associated with a change in emissions, a number of 

analysts (e.g., Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in 

consumption across regions. This weighting takes into account the relative reductions in wealth 

in different regions of the world. A per-capita loss of $500 in GDP, for instance, is weighted 

more heavily in a country with a per-capita GDP of $2,000 than in one with a per-capita GDP of 

$40,000. The main argument for this approach is that a loss of $500 in a poor country causes a 

greater reduction in utility or welfare than does the same loss in a wealthy nation. 

Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group 

concluded that this approach would not be appropriate for estimating a SCC value used in 

g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the 

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give 

extraterritorial effect to federal law and hence does not intrude on such interests.

14A-11 

 
domestic regulatory analysis.h

 For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach. 

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the 

relatively few region- or country-specific estimates of the SCC in the literature. One potential 

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio 

of domestic to global benefits of emission reductions varies with key parameter 

assumptions. For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7-10 

percent of the global benefit, on average, across the scenarios analyzed. Alternatively, if the 

fraction of GDP lost due to climate change is assumed to be similar across countries, the 

domestic benefit would be proportional to the U.S. share of global GDP, which is currently about 

23 percent.i

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach. 

14A.4.3 Valuing Non-CO2 Emissions

While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these 

gases is commonly discussed in terms of their 100-year global warming potential (GWP). GWP 

measures the ability of different gases to trap heat in the atmosphere (i.e., radiative forcing per 

unit of mass) over a particular timeframe relative to CO2. However, because these gases differ in 

both radiative forcing and atmospheric lifetimes, their relative damages are not constant over 

time. For example, because methane has a short lifetime, its impacts occur primarily in the near 

term and thus are not discounted as heavily as those caused by longer-lived gases. Impacts other 

than temperature change also vary across gases in ways that are not captured by GWP. For 

instance, CO2 emissions, unlike methane and other greenhouse gases, contribute to ocean 

h It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development 

of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account 

would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poor 

nation than on a wealthy one. Even if equity weighting—for both the costs and benefits of emissions reductions—is 

appropriate when considering the utility or welfare effects of international action, the interagency group concluded 

that it should not be used in developing an SCC for use in regulatory policy at this time. 

i

Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report. 

14A-12 

 
acidification. Likewise, damages from methane emissions are not offset by the positive effect of 

CO2 fertilization. Thus, transforming gases into CO2-equivalents using GWP, and then 

multiplying the carbon-equivalents by the SCC, would not result in accurate estimates of the 

social costs of non-CO2 gases. 

 

In light of these limitations, and the significant contributions of non-CO2 emissions to 

climate change, further research is required to link non-CO2 emissions to economic impacts. 

Such work would feed into efforts to develop a monetized value of reductions in non-CO2

greenhouse gas emissions. As part of ongoing work to further improve the SCC estimates, the 

interagency group hopes to develop methods to value these other greenhouse gases. The goal is 

to develop these estimates by the time we issue revised SCC estimates for carbon dioxide 

emissions. 

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.j It is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 concentration relative to pre-industrial levels 

(or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties 

in this important parameter have received substantial attention in the peer-reviewed literature.

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C. k

 

For fundamental physical reasons as well as data limitations, values substantially higher 

than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is 

generally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et 

al., 2007, p 799) 

After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull. 

Table 14A.4.1 included below gives summary statistics for the four calibrated distributions.

j The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas 

concentrations over the short to medium term (up to 100-200 years), but it does not include long-term feedback 

effects due to possible large-scale changes in ice sheets or the biosphere, which occur on a time scale of many 

hundreds to thousands of years (e.g. Hansen et al. 2007).

k This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of 

“likely” as greater than 66 percent probability (Le Treut et al.2007). “Very likely” indicates a greater than 90 percent 

probability.

14A-13 

 
Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Roe & Baker Log-normal Gamma Weibull

Pr(ECS < 1.5°C) 0.013 0.050 0.070 0.102

Pr(2°C < ECS < 4.5°C) 0.667 0.667 0.667 0.667

5th percentile 1.72 1.49 1.37 1.13

10th percentile 1.91 1.74 1.65 1.48

Mode 2.34 2.52 2.65 2.90

Median (50th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90th percentile 5.86 5.14 4.93 4.69

95th percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by applying three constraints from the IPCC: 

(1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”;l

(2) two-thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C; 

and

(3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 

721). 

We selected the calibrated Roe and Baker distribution from the four candidates for two 

reasons. First, the Roe and Baker distribution is the only one of the four that is based on a 

theoretical understanding of the response of the climate system to increased greenhouse gas 

concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are 

mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general 

shape. The Roe and Baker distribution results from three assumptions about climate response: 

(1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback 

factors are proportional to the change in surface temperature; and (3) uncertainties in feedback 

factors are normally distributed. There is widespread agreement on the first point and the second 

and third points are common assumptions. 

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that 

“values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no 

quantitative judgment, the 95th percentile of the calibrated Roe & Baker distribution (7.1 °C) is 

much closer to the mean and the median (7.2 °C) of the 95th percentiles of 21 previous studies 

l Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage 

would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC 

report is not specific on this point. For the distributions we considered, the median was between the mode and the 

mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode or mean, gave a 95th

percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode 

equal to 3°C produced 95th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the 

range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected 

by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most 

consistent with a Roe and Baker distribution with the median set equal to 3 °C.

14A-14 

 
summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and 

median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006) 

than are the 95th percentiles of the three other calibrated distributions (5.2-6.0 °C). 

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very 

likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the 

probability of equilibrium climate sensitivity being greater than 1.5°C is almost 99 percent, is not 

inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it 

reflects a greater degree of certainty about very low values of ECS than was expressed by the 

IPCC. 

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity (°C)

 To show how the calibrated Roe and Baker distribution compares to different estimates 

of the probability distribution function of equilibrium climate sensitivity in the empirical 

literature, Figure 14A.4.3 (above) overlays it on Figure 9.20 from the IPCC Fourth Assessment 

Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal 

bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate.m 

m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed 

line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. 

(2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on 

multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown 

are the 5-95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 

2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.

14A-15 

 
14A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of 

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic 

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we 

aimed to select scenarios that span most of the plausible ranges of outcomes for these variables. 

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22. EMF-22 uses ten well-recognized models to 

evaluate substantial, coordinated global action to meet specific stabilization targets. A key 

advantage of relying on these data is that GDP, population, and emission trajectories are 

internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is 

preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 

of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the 

EMF-22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are 

recent, peer-reviewed, published, and publicly available. 

To estimate the SCC for use in evaluating domestic policies that will have a small effect 

on global cumulative emissions, we use socioeconomic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (see Table 14A.4.2

below). Four of these represent potential business-as-usual (BAU) growth in population, 

wealth, and emissions and are associated with CO2 (only) concentrations ranging from 612 to 

889 ppm in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm 

CO2e (i.e., CO2-only concentrations of 425 – 484 ppm or a radiative forcing of 3.7 W/m2

) in 

2100, a lower-than-BAU trajectory.n

 Out of the 10 models included in the EMF-22 exercise, we 

selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario 

from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories 

from each of these four models. For the 550 ppm CO2e scenario, we averaged the GDP, 

population, and emission trajectories implied by these same four models. 

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, 

though it could also result from technological advances. It was chosen because it represents the most stringent case 

analyzed by the EMF-22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

14A-16 

 
Reference Fossil and Industrial CO2 Emissions (GtCO2/yr)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 26.6 31.9 36.9 40.0 45.3 60.1

MERGE Optimistic 24.6 31.5 37.6 45.1 66.5 117.9

MESSAGE 26.8 29.2 37.6 42.1 43.5 42.7

MiniCAM 26.5 31.8 38.0 45.1 57.8 80.5

550 ppm average 26.2 31.1 33.2 32.4 20.0 12.8

Reference GDP (using market exchange rates in trillion 2005$)o

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6

MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9

MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5

550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9

Global Population (billions)

EMF – 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1

MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7

MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4

MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7

550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to various assumptions about how the future will 

evolve without prejudging what is likely to occur. The interagency group considered formally 

assigning probability weights to different states of the world, but this proved challenging to do in 

an analytically rigorous way given the dearth of information on the likelihood of a full range of 

future socioeconomic pathways. 

 There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

most likely outcome span a wide range, from the more optimistic (e.g. abundant low-cost, lowo While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP). PPP takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the 

many geophysical uncertainties.

14A-17 

 
carbon energy) to more pessimistic (e.g. constraints on the availability of nuclear and 

renewables).p Second, the socioeconomic trajectories associated with a 550 ppm CO2e 

concentration scenario are not derived from an assessment of what policy is optimal from a 

benefit-cost standpoint. Rather, it is indicative of one possible future outcome. The emission 

trajectories underlying some BAU scenarios (e.g. MESSAGE’s 612 ppm) also are consistent 

with some modest policy action to address climate change.q We chose not to include 

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the 

difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy 

Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 

gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 

and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, 

respectively. These projections are consistent with one or more EMF-22 scenarios. Likewise, 

the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion 

people in 2000 to 9.1 billion people in 2050, which is close to the population trajectories for the 

IMAGE, MiniCAM, and MERGE models. 

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO2

emissions out to 2100. These assumptions also are used in the three models while retaining the 

default radiative forcings due to other factors (e.g. aerosols and other gases). See the Annex for 

greater detail.

14A.4.6 Discount Rate

The choice of a discount rate, especially over long periods of time, raises highly 

contested and exceedingly difficult questions of science, economics, philosophy, and law. 

Although it is well understood that the discount rate has a large influence on the current value of 

future damages, there is no consensus about what rates to use in this context. Because carbon 

dioxide emissions are long-lived, subsequent damages occur over many years. In calculating the 

SCC, we first estimate the future damages to agriculture, human health, and other market and 

non-market sectors from an additional unit of carbon dioxide emitted in a particular year in terms 

of reduced consumption (or consumption equivalents) due to the impacts of elevated 

temperatures, as represented in each of the three IAMs. Then we discount the stream of future 

damages to its present value in the year when the additional unit of emissions was released using 

the selected discount rate, which is intended to reflect society's marginal rate of substitution 

between consumption in different time periods. 

p For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and 

non-biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent 

in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100. 

q For example, MiniCAM projects if all non-US OECD countries reduce CO2 emissions to 83 percent below 2005 

levels by 2050 (per the G-8 agreement) but all other countries continue along a BAU path CO2 concentrations in 

2100 would drop from 794 ppmv in its reference case to 762 ppmv.

14A-18 

 
For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing 

those challenges, Circular A-4 states, “If your rule will have important intergenerational benefits 

or costs you might consider a further sensitivity analysis using a lower but positive discount rate 

in addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996). 

One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view. 

Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999). 

14A-19 
Further, a legitimate concern about both descriptive and prescriptive approaches is that 

they tend to obscure important heterogeneity in the population. The utility function that 

underlies the prescriptive approach assumes a representative agent with perfect foresight and no 

credit constraints. This is an artificial rendering of the real world that misses many of the 

frictions that characterize individuals’ lives and indeed the available descriptive evidence 

supports this. For instance, many individuals smooth consumption by borrowing with credit 

cards that have relatively high rates. Some are unable to access traditional credit markets and 

rely on payday lending operations or other high-cost forms of smoothing consumption. Whether 

one puts greater weight on the prescriptive or descriptive approach, the high interest rates that 

credit-constrained individuals accept suggest that some account should be given to the discount 

rates revealed by their behavior. 

We draw on both approaches but rely primarily on the descriptive approach to inform the 

choice of discount rate. With recognition of its limitations, we find this approach to be the most 

defensible and transparent given its consistency with the standard contemporary theoretical 

foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. 

The logic of this framework also suggests that market rates should be used for discounting future 

consumption-equivalent damages. Regardless of the theoretical approach used to derive the 

appropriate discount rate(s), we note the inherent conceptual and practical difficulties of 

adequately capturing consumption trade-offs over many decades or even centuries. While 

relying primarily on the descriptive approach in selecting specific discount rates, the interagency 

group has been keenly aware of the deeply normative dimensions of both the debate over 

discounting in the intergenerational context and the consequences of selecting one discount rate 

over another. 

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital. 

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The 

consumption rate of interest also is appropriate when the impacts of a regulation are measured in 

14A-20 
consumption (-equivalent) units, as is done in the three integrated assessment models used for 

estimating the SCC. 

Individuals use a variety of savings instruments that vary with risk level, time horizon, 

and tax characteristics. The standard analytic framework used to develop intuition about the 

discount rate typically assumes a representative agent with perfect foresight and no credit 

constraints. The risk-free rate is appropriate for discounting certain future benefits or costs, but 

the benefits calculated by IAMs are uncertain. To use the risk-free rate to discount uncertain 

benefits, these benefits first must be transformed into "certainty equivalents," that is the 

maximum certain amount that we would exchange for the uncertain amount. However, the 

calculation of the certainty-equivalent requires first estimating the correlation between the 

benefits of the policy and baseline consumption. 

If the IAM projections of future impacts represent expected values (not certaintyequivalent values), then the appropriate discount rate generally does not equal the risk-free rate. 

If the benefits of the policy tend to be high in those states of the world in which consumption is 

low, then the certainty-equivalent benefits will be higher than the expected benefits (and vice 

versa). Since many (though not necessarily all) of the important impacts of climate change will 

flow through market sectors such as agriculture and energy, and since willingness to pay for 

environmental protections typically increases with income, we might expect a positive (though 

not necessarily perfect) correlation between the net benefits from climate policies and market 

returns. This line of reasoning suggests that the proper discount rate would exceed the riskless 

rate. Alternatively, a negative correlation between the returns to climate policies and market 

returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post-tax riskless and risky rates can be used to 

capture individuals’ consumption-equivalent interest rate. As a measure of the post-tax riskless 

rate, we calculate the average real return from Treasury notes over the longest time period 

available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal 

rate from tax years 2003 through 2006 is around 27 percent).r

 This calculation produces a real 

interest rate of about 2.7 percent, which is roughly consistent with Circular A-4’s 

recommendation to use 3 percent to represent the consumption rate of interest.s

 A measure of 

the post-tax risky rate for investments whose returns are positively correlated with overall equity 

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes, which yields a real rate of roughly 5 percent.t

 

The Ramsey Equation

r The literature argues for a risk-free rate on government bonds as an appropriate measure of the consumption rate of 

interest. Arrow (2000) suggests that it is roughly 3-4 percent. OMB cites evidence of a 3.1 percent pre-tax rate for 

10-year Treasury notes in the A-4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 

percent for 30-year Treasury securities. 

s The positive approach reflects how individuals make allocation choices across time, but it is important to keep in 

mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.

t Cambell et al (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950 – 2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20 – 40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).

14A-21 

 
Ramsey discounting also provides a useful framework to inform the choice of a discount 

rate. Under this approach, the analyst applies either positive or normative judgments in selecting 

values for the key parameters of the Ramsey equation: η (coefficient of relative risk aversion or 

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).u These are 

then combined with g (growth rate of per-capita consumption) to equal the interest rate at which 

future monetized damages are discounted: ρ + η∙g.v In the simplest version of the Ramsey 

model, with an optimizing representative agent with perfect foresight, what we are calling the 

“Ramsey discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e., the market 

interest rate.

A review of the literature provides some guidance on reasonable parameter values for the 

Ramsey discounting equation, based on both prescriptive and descriptive approaches. 

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 

(Weitzman cites plausible values as those ranging from 1 to 4), although not all authors 

articulate whether their choice is based on prescriptive or descriptive reasoning.w 

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η

equal to 1 suggests savings rates that do not conform to observed behavior. 

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern et al. 2006). However, even in an intergenerational setting, it may make sense to use a small positive pure rate of time 

u The parameter ρ measures the pure rate of time preference: people’s behavior reveals a preference for an 

increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the 

future. The parameter η captures diminishing marginal utility: consumption in the future is likely to be higher than 

consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will 

cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, 

then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent 

increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in 

income is less valuable to wealthier individuals. 

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

w Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of 

values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman 

(2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of estimating η using data on 

labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a 

tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and 

concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting 

established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. 

Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year 

using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the 

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 

1.07. 

14A-22 

 
preference because of the small probability of unforeseen cataclysmic events (Stern et al. 

2006). 

• g. A commonly accepted approximation is around 2 percent per year. For the 

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 

1.5-2 percent to 2100. 

Some economists and non-economists have argued for constant discount rates below 2 

percent based on the prescriptive approach. When grounded in the Ramsey framework, 

proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to 

one generation over another. The choice of η has also been posed as an ethical choice linked to 

the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. 

(2006) applies this perspective through his choice of ρ = 0.1 percent per year, η = 1 and g = 1.3 

percent per year, which yields an annual discount rate of 1.4 percent. In the context of 

permanent income savings behavior, however, Stern’s assumptions suggest that individuals 

would save 93 percent of their income.x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is 

a case to be made for raising η due to the amount of weight lower values place on damages far in 

the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s 

assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, 

yields a discount rate of greater than 2 percent. 

We conclude that arguments made under the prescriptive approach can be used to justify 

discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most 

appropriate value for η, we find it difficult to justify rates at the lower end of this range under the 

Ramsey framework. 

Accounting for Uncertainty in the Discount Rate 

While the consumption rate of interest is an important driver of the benefits estimate, it is 

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate 

sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and 

Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on 

net present values. A main result from these studies is that if there is a persistent element to the 

uncertainty in the discount rate (e.g., the rate follows a random walk), then it will result in an 

effective (or certainty-equivalent) discount rate that declines over time. Consequently, lower 

discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; 

Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and 

Gollier and Weitzman 2009). 

The proper way to model discount rate uncertainty remains an active area of research. 

Newell and Pizer (2003) employ a model of how long-term interest rates change over time to 

x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied 

savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity. (At the same 

time, adding exogenous technical change—all else equal—would increase g as well.)

14A-23 

 
forecast future discount rates. Their model incorporates some of the basic features of how 

interest rates move over time, and its parameters are estimated based on historical observations 

of long-term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more 

general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility 

of interest rates depends on whether rates are currently low or high and the variation in the level 

of persistence over time. 

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model 

uncertainty in the discount rate, others argue for a declining scale of discount rates applied over 

time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach 

uses a higher discount rate initially, but applies a graduated scale of lower discount rates further 

out in time.y A key question that has emerged with regard to both of these approaches is the 

trade-off between potential time inconsistency and giving greater weight to far future outcomes 

(see the EPA Science Advisory Board’s recent comments on this topic as part of its review of 

their Guidelines for Economic Analysis).z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

The central value, 3 percent, is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 3 

percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent is 

included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods. 

The low value, 2.5 percent, is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the meany For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31 - 75; 2.5 

percent for years 76 - 125; 2 percent for years 126 - 200; 1.5 percent for years 201 - 300; and 1 percent after 300 

years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time. 

z Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that 

Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a 

low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value 

calculation and result in an infinite willingness-to-pay for mitigation today. Newbold and Daigneault, (2009) and 

Nordhaus (2009) find that Weitzman’s result is sensitive to the functional forms chosen for climate sensitivity, 

utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in 

the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required. 

14A-24 

 
reverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate 

of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random 

walk model and 2.8 percent using the mean reverting approach.aa Without giving preference to a 

particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless 

rate would be justified if climate investments are negatively correlated with the overall market 

rate of return. Use of this lower value also responds to certain judgments using the prescriptive 

or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher. 

14A.5 REVISED SCC ESTIMATES 

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group: 

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds. 

• Five sets of GDP, population, and carbon emissions trajectories based on EMF-22. 

• Constant annual discount rates of 2.5, 3, and 5 percent. 

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and 

FUND incorporate uncertainty in other model parameters, the final output from each model run 

is a distribution over the SCC in year t. 

For each of the IAMs, the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 

scenarios, and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each 

year resulting from the baseline path of emissions. 

a. In PAGE, the consumption-equivalent damages in each period are 

calculated as a fraction of the EMF GDP forecast, depending on the 

temperature in that period relative to the pre-industrial average 

temperature in each region. 

b. In FUND, damages in each period depend on both the level and the rate of 

temperature change in that period. 

c. In DICE, temperature affects both consumption and investment, so we 

first adjust the EMF GDP paths as follows: Using the Cobb-Douglas 

production function with the DICE2007 parameters, we extract the path of 

exogenous technical change implied by the EMF GDP and population 

paths, then we recalculate the baseline GDP path taking into account 

climate damages resulting from the baseline emissions path. 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A-25 

 
3. Add an additional unit of carbon emissions in year t. (The exact unit varies by 

model.) 

4. Recalculate the temperature effects and damages expected in all years beyond t

resulting from this adjusted path of emissions, as in step 2. 

5. Subtract the damages computed in step 2 from those in step 4 in each year. 

(DICE is run in 10-year time steps, FUND in annual time steps, while the time 

steps in PAGE vary.) 

6. Discount the resulting path of marginal damages back to the year of emissions 

using the agreed upon fixed discount rates. 

7. Calculate the SCC as the net present value of the discounted path of damages 

computed in step 6, divided by the unit of carbon emissions used to shock the 

models in step 3. 

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of 

CO2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 

in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons 

anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, 

climate damages are calculated as lost consumption in each future year. 

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND. 

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Annex.) 

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than 

14A-26 
another. Because the literature shows that the SCC is quite sensitive to assumptions about the 

discount rate, and because no consensus exists on the appropriate rate to use in an 

intergenerational context, we present SCCs based on the average values across models and 

socioeconomic scenarios for each discount rate. 

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC across models and socioeconomic and emissions scenarios 

at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higherthan-expected economic impacts from climate change further out in the tails of the SCC 

distribution. For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount 

rate. (The full set of distributions by model and scenario combination is included in the Annex.) 

As noted above, the 3 percent discount rate is the central value, and so the central value that 

emerges is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. 

As previously discussed, low probability, high impact events are incorporated into the 

SCC values through explicit consideration of their effects in two of the three models as well as 

the use of a probability density function for equilibrium climate sensitivity. Treating climate 

sensitivity probabilistically results in more high-temperature outcomes, which in turn lead to 

higher projections of damages. Although FUND does not include catastrophic damages (in 

contrast to the other two models), its probabilistic treatment of the equilibrium climate sensitivity 

parameter will directly affect the non-catastrophic damages that are a function of the rate of 

temperature change.

In Table 14A.5.1, we begin by presenting SCC estimates for 2010 by model, scenario, 

and discount rate to illustrate the variability in the SCC across each of these input parameters. 

As expected, higher discount rates consistently result in lower SCC values, while lower discount 

rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there 

are differences in the SCC estimated across the three main models. For these estimates, FUND 

produces the lowest estimates, while PAGE generally produces the highest estimates. 

14A-27 
Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars) 

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95th DICE

IMAGE 10.8 35.8 54.2 70.8 

MERGE 7.5 22.0 31.6 42.1 

Message 9.8 29.8 43.5 58.6 

MiniCAM 8.6 28.8 44.4 57.9 

550 Average 8.2 24.9 37.4 50.8 

PAGE

IMAGE 8.3 39.5 65.5 142.4 

MERGE 5.2 22.3 34.6 82.4 

Message 7.2 30.3 49.2 115.6 

MiniCAM 6.4 31.8 54.7 115.4 

550 Average 5.5 25.4 42.9 104.7 

FUND

IMAGE -1.3 8.2 19.3 39.7 

MERGE -0.3 8.0 14.8 41.3 

Message -1.9 3.6 8.8 32.1 

MiniCAM -0.6 10.2 22.2 42.6 

550 Average -2.7 -0.2 3.0 19.4 

These results are not surprising when compared to the estimates in the literature for the 

latest versions of each model. For example, adjusting the values from the literature that were 

used to develop interim SCC values to 2007 dollars for the year 2010 (assuming, as we did for 

the interim process, that SCC grows at 3 percent per year), FUND yields SCC estimates at or 

near zero for a 5 percent discount rate and around $9 per ton for a 3 percent discount rate. There 

are far fewer estimates using the latest versions of DICE and PAGE in the literature: Using 

similar adjustments to generate 2010 estimates, we calculate a SCC from DICE (based on 

Nordhaus 2008) of around $9 per ton for a 5 percent discount rate, and a SCC from PAGE 

(based on Hope 2006, 2008) close to $8 per ton for a 4 percent discount rate. Note that these 

comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.bb

bb Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g. Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

14A-28 

 
The SCC estimates from FUND are sensitive to differences in emissions paths but 

relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for 

DICE and PAGE. This likely occurs because of several structural differences among the models. 

Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages 

increases with the level of temperature alone, whereas in FUND the fractional loss also increases 

with the rate of temperature change. Furthermore, in FUND increases in income over time 

decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in 

DICE and PAGE. These structural differences among the models make FUND more sensitive to 

the path of emissions and less sensitive to GDP compared to DICE and PAGE. 

Figure 14A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE 

Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the 

rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result 

that is to be expected given its less direct relationship between its damage function and GDP.

Figure 14A.5.1 Level of Global GDP across EMF Scenarios 

Table 14A.5.2 shows the four selected SCC values in five-year increments from 2010 to 

2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all outputs 

(10,000 estimates per model run) from all scenarios and models for a given discount rate. Values 

for the years in between are calculated using a simple linear interpolation.

0

50

100

150

200

250

300

350

400

450

2000 2010 2020 2030 2050 2100

Global 

GDP

(trillions

of U.S. 

dollars)

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

14A-29 

 
Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) 

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as 

was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 14A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Annex. 

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050 

Average Annual 

Growth Rate (%)

5% 3% 2.5% 3.0%

Avg Avg Avg 95th

2010-2020 3.6% 2.1% 1.7% 2.2%

2020-2030 3.7% 2.2% 1.8% 2.2%

2030-2040 2.7% 1.8% 1.6% 1.8%

2040-2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions 

reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must 

be discounted to the present to determine its total net present value for use in regulatory analysis. 

Damages from future emissions should be discounted at the same rate as that used to calculate 

the SCC estimates themselves to ensure internal consistency—i.e., future damages from climate 

change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. For example, climate damages in the year 2020 that are 

calculated using a SCC based on a 5 percent discount rate also should be discounted back to the 

analysis year using a 5 percent discount rate.cc 

cc However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 

14A-30 

 
14A.6 LIMITATIONS OF THE ANALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further 

refinement (and possibly significant change) in accordance with evolving scientific, economic, 

and ethical understandings. During the course of our modeling, it became apparent that there are 

several areas in particular need of additional exploration and research. These caveats, and 

additional observations in the following section, are necessary to consider when interpreting and 

applying the SCC estimates.

Incomplete treatment of non-catastrophic damages. The impacts of climate change are 

expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these 

impacts is uncertain because of the inherent complexity of climate processes, the economic 

behavior of current and future populations, and our inability to accurately forecast technological 

change and adaptation. Current IAMs do not assign value to all of the important physical,

ecological, and economic impacts of climate change recognized in the climate change literature 

(some of which are discussed above) because of lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. Our ability to quantify and monetize impacts will undoubtedly improve 

with time. But it is also likely that even in future applications, a number of potentially significant 

damage categories will remain non-monetized. (Ocean acidification is one example of a 

potentially large damage from CO2 emissions not quantified by any of the three models. Species 

and wildlife loss is another example that is exceedingly difficult to monetize.) 

Incomplete treatment of potential catastrophic damages. There has been considerable 

recent discussion of the risk of catastrophic impacts and how best to account for extreme 

scenarios, such as the collapse of the Atlantic Meridional Overturning Circulation or the West 

Antarctic Ice Sheet, or large releases of methane from melting permafrost and warming oceans. 

Weitzman (2009) suggests that catastrophic damages are extremely large—so large, in fact, that 

the damages from a low probability, catastrophic event far in the future dominate the effect of 

the discount rate in a present value calculation and result in an infinite willingness-to-pay for 

mitigation today. However, Nordhaus (2009) concluded that the conditions under which 

Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain 

scenarios." 

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for 

large catastrophe risk premiums but also showed that the aggregate benefit estimates can be 

highly sensitive to the shapes of both the climate sensitivity distribution and the damage function 

at high temperature changes. Pindyck (2009) also used a simplified IAM to examine highimpact, low-probability risks, using a right-skewed gamma distribution for climate sensitivity as 

well as an uncertain damage coefficient, but in most cases found only a modest risk premium. 

Given this difference in opinion, further research in this area is needed before its practical 

significance can be fully understood and a reasonable approach developed to account for such 

risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic 

impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

14A-31 
that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 

Incomplete treatment of adaptation and technological change: Each of the three 

integrated assessment models used here assumes a certain degree of low- or no-cost adaptation. 

For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on 

air conditioning; so much so, that the largest single benefit category in FUND is the reduced 

electricity costs from not having to run air conditioning as intensively (NRC 2009). 

Climate change also will increase returns on investment to develop technologies that 

allow individuals to cope with adverse climate conditions, and IAMs to do not adequately 

account for this directed technological change.dd For example, scientists may develop crops that 

are better able to withstand higher and more variable temperatures. Although DICE and FUND 

have both calibrated their agricultural sectors under the assumption that farmers will change land 

use practices in response to climate change (Mastrandrea, 2009), they do not take into account 

technological changes that lower the cost of this adaptation over time. On the other hand, the 

calibrations do not account for increases in climate variability, pests, or diseases, which could 

make adaptation more difficult than assumed by the IAMs for a given temperature change. 

Hence, models do not adequately account for potential adaptation or technical change that might 

alter the emissions pathway and resulting damages. In this respect, it is difficult to determine 

whether the incomplete treatment of adaptation and technological change in these IAMs 

understate or overstate the likely damages.

Risk aversion: A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability, but lower-impact, damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion. 

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of 

dd However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).

14A-32 

 
the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude

that “the assumed rate of risk aversion is at least as important as the assumed rate of time 

preference in determining the social cost of carbon.” Since Circular A-4 allows for a different 

assumption on risk preference in regulatory analysis if it is adequately justified, we plan to 

continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the 

SCC may not capture the economic effects of all possible adverse consequences of climate 

change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular,

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping 

point” behavior in Earth systems, (2) inter-sectoral and inter-regional interactions, including 

global security impacts of high-end warming, and (3) limited near-term substitutability between 

damage to natural systems and increased consumption. 

It is the hope of the interagency group that over time researchers and modelers will work 

to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. In the meantime, we 

discuss some of the available evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al, 2008, Kriegler et al., 

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West 

Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, 

strengthening of El Niño-Southern Oscillation, and the release of methane from melting 

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 14A.7.1. Ranges of probability are averaged across core experts on each 

topic. 

As previously mentioned, FUND does not include potentially catastrophic effects. DICE 

assumes a small probability of catastrophic damages that increases with increased warming, but 

the damages from these risks are incorporated as expected values (i.e., ignoring potential risk 

aversion). PAGE models catastrophic impacts in a probabilistic framework (see Figure 14A.4.1), 

so the high-end output from PAGE potentially offers the best insight into the SCC if the world 

were to experience catastrophic climate change. For instance, at the 95th percentile and a 3 

percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission 

trajectories of $113 per ton of CO2 is almost double the value estimated by DICE, $58 per ton in 

14A-33 
2010. We cannot evaluate how well the three models account for catastrophic or noncatastrophic impacts, but this estimate highlights the sensitivity of SCC values in the tails of the 

distribution to the assumptions made about catastrophic impacts. 

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation 

Possible Tipping Points

Duration before

effect is fully

realized (in years)

Additional Warming by 2100 

0.5-1.5 C 1.5-3.0 C 3-5 C 

Reorganization of Atlantic Meridional 

Overturning Circulation about 100 0-18% 6-39% 18-67% 

Greenland Ice Sheet collapse at least 300 8-39% 33-73% 67-96% 

West Antarctic Ice Sheet collapse at least 300 5-41% 10-63% 33-88% 

Dieback of Amazon rainforest about 50 2-46% 14-84% 41-94% 

Strengthening of El Niño-Southern Oscillation about 100 1-13% 6-32% 19-49% 

Dieback of boreal forests about 50 13-43% 20-81% 34-91% 

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (that is, by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different probabilities being assigned to a 

catastrophic event across the two models. For instance, PAGE places a probability near zero on a 

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16-36 percent of 

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2-4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact. 

Failure to incorporate inter-sectoral and inter-regional interactions

The damage functions do not fully incorporate either inter-sectoral or inter-regional 

interactions. For instance, while damages to the agricultural sector are incorporated, the effects 

of changes in food supply on human health are not fully captured and depend on the modeler’s 

choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one 

region of the world on another region are not included in some of the models (FUND includes 

14A-34 
the effects of migration from sea level rise). These inter-regional interactions, though difficult to 

quantify, are the basis for climate-induced national and economic security concerns (e.g., 

Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at 

higher levels of warming. High-end warming scenarios, for instance, project water scarcity 

affecting 4.3-6.9 billion people by 2050, food scarcity affecting about 120 million additional 

people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; 

Campbell et al., 2007). 

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming 

may have severe consequences for natural systems. For instance, during the Paleocene-Eocene 

Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically 

rapid release of carbon associated with an approximately 5 °C increase in global mean 

temperatures, the effects included shifts of about 400-900 miles in the range of plants (Wing et 

al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 

2009). 

The three IAMs used here assume that it is possible to compensate for the economic 

consequences of damages to natural systems through increased consumption of non-climate 

goods, a common assumption in many economic models. In the context of climate change, 

however, it is possible that the damages to natural systems could become so great that no 

increase in consumption of non-climate goods would provide complete compensation (Levy et 

al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile 

and less bio-diverse, the services they provide may become increasingly more costly to replace. 

Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs 

(Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be 

considerably greater than is commonly recognized. 

14A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 

2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are 

based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, 

and 2.5 percent discount rates, respectively. The fourth value is included to represent the higherthan-expected impacts from temperature change further out in the tails of the SCC distribution. 

For this purpose, we use the SCC value for the 95th percentile at a 3 percent discount rate. The 

central value is the average SCC across models at the 3 percent discount rate. For purposes of 

capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance 

and value of considering the full range. These SCC estimates also grow over time. For instance, 

the central value increases to $24 per ton of CO2 in 2015 and $26 per ton of CO2 in 2020. 

We noted a number of limitations to this analysis, including the incomplete way in which 

the integrated assessment models capture catastrophic and non-catastrophic impacts, their 

incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of 

damages to high temperatures, and assumptions regarding risk aversion. The limited amount of 

research linking climate impacts to economic damages makes this modeling exercise even more 

14A-35 
difficult. It is the hope of the interagency group that over time researchers and modelers will 

work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal 

government will continue to evolve with improvements in modeling. 

 

14A-36 
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14A-41 
14A.9 ANNEX

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) 

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non-CO2 emission 

projections used in the modeling and the method for extrapolating emissions forecasts through 

2300 and shows the full distribution of 2010 SCC estimates by model and scenario combination. 

14A-42 
14A.9.1 Other (non-CO2) gases 

In addition to fossil and industrial CO2 emissions, each EMF scenario provides 

projections of methane (CH4), nitrous oxide (N2O), fluorinated gases, and net land use CO2

emissions to 2100. These assumptions are used in all three IAMs while retaining each model’s 

default radiative forcings (RF) due to other factors (e.g., aerosols and other gases). Specifically, 

to obtain the RF associated with the non-CO2 EMF emissions only, we calculated the RF 

associated with the EMF atmospheric CO2 concentrations and subtracted them from the EMF 

total RF.ee This approach respects the EMF scenarios as much as possible and at the same time 

takes account of those components not included in the EMF projections. Since each model treats 

non-CO2 gases differently (e.g., DICE lumps all other gases into one composite exogenous 

input), this approach was applied slightly differently in each of the models. 

FUND: Rather than relying on RF for these gases, the actual emissions from each 

scenario were used in FUND. The model default trajectories for CH4, N20, SF6, and the CO2

emissions from land were replaced with the EMF values. 

PAGE: PAGE models CO2, CH4, sulfur hexafluoride (SF6), and aerosols and contains an 

"excess forcing" vector that includes the RF for everything else. To include the EMF values, we 

removed the default CH4 and SF6 factorsff, decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N20, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2

emissions were added to the fossil and industrial CO2 emissions pathway. 

DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous 

forcing from all non-CO2 sources is -0.06 W/m2 in 2005, as reported in AR4, and increases 

linearly to 0.3 W/m2 in 2105, based on GISS projections, and then stays constant after that time. 

According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2 non-CO2 forcing in DICE can be 

decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2, due to other residual forcing. 

ee Note EMF did not provide CO2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed 

the fossil, industrial, and land CO2 emissions into MAGICC (considered a "neutral arbiter" model, which is tuned to 

emulate the major global climate models) and the resulting CO2 concentrations were used. Note also that MERGE 

assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE Optimistic 

reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use 

emissions from the other three models and divide by 4). ff Both the model default CH4 emissions and the initial atmospheric CH4 is set to zero to avoid double counting the 

effect of past CH4 emissions.

14A-43 

 
For subsequent years, we calculated the DICE default RF from aerosols and other nonCO2 gases based on the following two assumptions: 

(1) RF from aerosols declines linearly from 2005 to 2100 at the rate projected by the TAR 

and then stays constant thereafter; and 

(2) With respect to RF from non-CO2 gases not included in the EMF-22 scenarios, the share 

of non-aerosol RF matches the share implicit in the AR4 summary statistics cited above and 

remains constant over time. 

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, 

which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black 

carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since 

the SRES marker scenarios were not updated for the AR4, the TAR provides the most recent 

IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because it 

provides one of the lower aerosol forecasts among the SRES marker scenarios and is more 

consistent with the AR4 discussion of the post-SRES literature on aerosols: 

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor 

emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the 

post-SRES scenarios. Generally, these emissions are projected to be lower than reported in 

SRES. {WGIII 3.2, TS.3, SPM}.gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent 

with the recent literature on these emissions. For example, the figure below shows that the sulfur 

dioxide emissions peak over the short term of some SRES scenarios above the upper bound 

estimates of the more recent scenarios.hh Recent scenarios project sulfur emissions to peak earlier 

and at lower levels compared to the SRES in part because of new information about present and 

planned sulfur legislation in some developing countries, such as India and China.ii

 The lowerbound projections of the recent literature have also shifted downward slightly compared to the 

SRES scenario (IPCC 2007). 

With these assumptions, the DICE aerosol forcing changes from -1.2 in 2005 to -0.792 in 

2105 W/m2

; forcing due to other non-CO2 gases not included in the EMF scenarios declines from 

0.160 to 0.153 W/m2. 

gg AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf

hh See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850-2000: 

methods and results. Joint Global Research Institute, College Park, 14 pp. ii See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing 

trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental 

Science and Technology, 36(22):4707- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.-Q. Zhang, D. Xu, M. 

Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835-

1837.

14A-44 

 
. 

Figure 14A.9.1 Sulfur Dioxide Emission Scenarios 

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines 

show the median, 5th, and 95th percentile of the frequency distribution for the full 

ensemble of 40 SRES scenarios. The blue area (and the thin dashed lines in blue) 

illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines indicate 

the minimum and maximum of SO2 emissions scenarios developed pre-SRES.

Source: IPCC (2007), AR4 WGIII 3.2, 

http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3-ens3-2-2-4.html. 

Although other approaches to decomposing the DICE exogenous forcing vector are 

possible, initial sensitivity analysis suggests that the differences among reasonable alternative 

approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to 

assume that aerosols will be maintained at 2000 levels through 2100 reduces average SCC values 

(for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 

2100 increases average 2010 SCC values by 6-7 percent (or $0.50-$3)–depending on the 

discount rate. These differences increase slightly for SCC values in later years but are still well 

within 10 percent of each other as far out as 2050. 

Finally, as in PAGE, the EMF net land use CO2 emissions are added to the fossil and 

industrial CO2 emissions pathway. 

14A.9.2 Extrapolating Emissions Projections to 2300 

To run each model through 2300 requires assumptions about GDP, population, 

greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which 

these projections are available from the EMF-22 models. These inputs were extrapolated from 

2100 to 2300 as follows: 

1. Population growth rate declines linearly, reaching zero in the year 2200. 

2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300. 

14A-45 
3. The decline in the fossil and industrial carbon intensity (CO2/GDP) growth rate over 

2090-2100 is maintained from 2100 through 2300. 

4. Net land use CO2 emissions decline linearly, reaching zero in the year 2200. 

5. Non-CO2 radiative forcing remains constant after 2100. 

Long run stabilization of GDP per capita was viewed as a more realistic simplifying 

assumption than a linear or exponential extrapolation of the pre-2100 economic growth rate of 

each EMF scenario. This is based on the idea that increasing scarcity of natural resources and 

the degradation of environmental sinks available for assimilating pollution from economic 

production activities may eventually overtake the rate of technological progress. Thus, the 

overall rate of economic growth may slow over the very long run. The interagency group also 

considered allowing an exponential decline in the growth rate of GDP per capita. However, 

since this would require an additional assumption about how close to zero the growth rate would 

get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by 

2300. 

The population growth rate is also assumed to decline linearly, reaching zero by 2200. 

This assumption is reasonably consistent with the United Nations long run population forecast, 

which estimates global population to be fairly stable after 2150 in the medium scenario (UN 

2004).jj The resulting range of EMF population trajectories (figure below) also encompass the 

UN medium scenario forecasts through 2300—global population of 8.5 billion by 2200, and 9 

billion by 2300. 

Maintaining the decline in the 2090-2100 carbon intensity growth rate (i.e., CO2 per 

dollar of GDP) through 2300 assumes that technological improvements and innovations in the 

areas of energy efficiency and other carbon reducing technologies (possibly including currently 

unavailable methods) will continue to proceed at roughly the same pace that is projected to occur 

towards the end of the forecast period for each EMF scenario. This assumption implies that total 

cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range 

of the total potential global carbon stock estimated in the literature.

 

Net land use CO2 emissions are expected to stabilize in the long run, so in the absence of 

any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori 

reasons for assuming a long run increase or decline in non-CO2 radiative forcing, it is assumed to 

remain at the 2100 levels for each EMF scenario through 2300. 

Figures below show the paths of global population, GDP, fossil and industrial CO2

emissions, net land CO2 emissions, non-CO2 radiative forcing, and CO2 intensity (fossil and 

industrial CO2 emissions/GDP) resulting from these assumptions. 

jj United Nations. 2004. World Population to 2300. 

http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf

14A-46 

 
Figure 14A.9.2 Global Population, 2000-2300 (Post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 

2200.) 

Note: In the fifth scenario, 2000-2100 population is equal to the average of the population under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

5.5

6.5

7.5

8.5

9.5

10.5

11.5

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Global Population [billion]

Year

14A-47 
Figure 14A.9.3 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per capita 

growth declines linearly, reaching zero in the year 2300) 

Note: In the fifth scenario, 2000-2100 GDP is equal to the average of the GDP under the 550 

ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]

14A-48 
Figure 14A.9.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 

2090-2100 is maintained through 2300) 

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

0

50

100

150

200

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil&Industrial CO2 Emissions [GtCO2/decade]

Year

14A-49 
Figure 14A.9.5 Global Net Land Use CO2 Emissions, 2000-2300 (Post-2100 

extrapolations assume emissions decline linearly, reaching zero in the 

year 2200)kk

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

kk MERGE assumes a neutral biosphere so net land CO2 emissions are set to zero for all years for the MERGE 

Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land 

use emissions from the other three models and divide by 4).

-3.00

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

Extrapolations

Net land CO2 emissions [GtCO2/yr]

Year

z

14A-50 

 
Figure 14A.9.6 Global Non-CO2 Radiative Forcing, 2000-2300 (Post-2100 extrapolations 

assume constant non-CO2 radiative forcing after 2100) 

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

0.00

0.75

1.50

2.25

3.00

3.75

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

nonCO2 RF [W/m^2]

Year

14A-51 
Figure 14A.9.7 Global CO2 Intensity (fossil & industrial CO2 emissions/GDP), 2000-2300 

(Post-2100 extrapolations assume decline in CO2/GDP growth rate over 

2090-2100 is maintained through 2300) 

Note: In the fifth scenario, 2000-2100 emissions are equal to the average of the emissions under 

the 550 ppm CO2e, full-participation, not-to-exceed scenarios considered by each of the four 

models. 

0

0

0

0

0

1

1

1

1

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Fossil & Industrial CO2 intensity [GtCO2/trillion $]

Year

14A-52 
Table 14A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 3.3 5.9 8.1 13.9 28.8 65.5 68.2 147.9 239.6 563.8

MERGE optimistic 1.9 3.2 4.3 7.2 14.6 34.6 36.2 79.8 124.8 288.3

Message 2.4 4.3 5.8 9.8 20.3 49.2 50.7 114.9 181.7 428.4

MiniCAM base 2.7 4.6 6.4 11.2 22.8 54.7 55.7 120.5 195.3 482.3

5th scenario 2.0 3.5 4.7 8.1 16.3 42.9 41.5 103.9 176.3 371.9

Scenario DICE

IMAGE 16.4 21.4 25 33.3 46.8 54.2 69.7 96.3 111.1 130.0

MERGE optimistic 9.7 12.6 14.9 19.7 27.9 31.6 40.7 54.5 63.5 73.3

Message 13.5 17.2 20.1 27 38.5 43.5 55.1 75.8 87.9 103.0

MiniCAM base 13.1 16.7 19.8 26.7 38.6 44.4 56.8 79.5 92.8 109.3

5th scenario 10.8 14 16.7 22.2 32 37.4 47.7 67.8 80.2 96.8

Scenario FUND

IMAGE -33.1 -18.9 -13.3 -5.5 4.1 19.3 18.7 43.5 67.1 150.7

MERGE optimistic -33.1 -14.8 -10 -3 5.9 14.8 20.4 43.9 65.4 132.9

Message -32.5 -19.8 -14.6 -7.2 1.5 8.8 13.8 33.7 52.3 119.2

MiniCAM base -31.0 -15.9 -10.7 -3.4 6 22.2 21 46.4 70.4 152.9

5th scenario -32.2 -21.6 -16.7 -9.7 -2.3 3 6.7 20.5 34.2 96.8

14A-53 
Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 2.0 3.5 4.8 8.1 16.5 39.5 41.6 90.3 142.4 327.4

MERGE optimistic 1.2 2.1 2.8 4.6 9.3 22.3 22.8 51.3 82.4 190.0

Message 1.6 2.7 3.6 6.2 12.5 30.3 31 71.4 115.6 263.0

MiniCAM base 1.7 2.8 3.8 6.5 13.2 31.8 32.4 72.6 115.4 287.0

5th scenario 1.3 2.3 3.1 5 9.6 25.4 23.6 62.1 104.7 222.5

Scenario DICE

IMAGE 11.0 14.5 17.2 22.8 31.6 35.8 45.4 61.9 70.8 82.1

MERGE optimistic 7.1 9.2 10.8 14.3 19.9 22 27.9 36.9 42.1 48.8

Message 9.7 12.5 14.7 19 26.6 29.8 37.8 51.1 58.6 67.4

MiniCAM base 8.8 11.5 13.6 18 25.2 28.8 36.9 50.4 57.9 67.8

5th scenario 7.9 10.1 11.8 15.6 21.6 24.9 31.8 43.7 50.8 60.6

Scenario FUND

IMAGE -25.2 -15.3 -11.2 -5.6 0.9 8.2 10.4 25.4 39.7 90.3

MERGE optimistic -24.0 -12.4 -8.7 -3.6 2.6 8 12.2 27 41.3 85.3

Message -25.3 -16.2 -12.2 -6.8 -0.5 3.6 7.7 20.1 32.1 72.5

MiniCAM base -23.1 -12.9 -9.3 -4 2.4 10.2 12.2 27.7 42.6 93.0

5th scenario -24.1 -16.6 -13.2 -8.3 -3 -0.2 2.9 11.2 19.4 53.6

14A-54 
Table 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 0.5 0.8 1.1 1.8 3.5 8.3 8.5 19.5 31.4 67.2

MERGE optimistic 0.3 0.5 0.7 1.2 2.3 5.2 5.4 12.3 19.5 42.4

Message 0.4 0.7 0.9 1.6 3 7.2 7.2 17 28.2 60.8

MiniCAM base 0.3 0.6 0.8 1.4 2.7 6.4 6.6 15.9 24.9 52.6

5th scenario 0.3 0.6 0.8 1.3 2.3 5.5 5 12.9 22 48.7

Scenario DICE

IMAGE 4.2 5.4 6.2 7.6 10 10.8 13.4 16.8 18.7 21.1

MERGE optimistic 2.9 3.7 4.2 5.3 7 7.5 9.3 11.7 12.9 14.4

Message 3.9 4.9 5.5 7 9.2 9.8 12.2 15.4 17.1 18.8

MiniCAM base 3.4 4.2 4.7 6 7.9 8.6 10.7 13.5 15.1 16.9

5th scenario 3.2 4 4.6 5.7 7.6 8.2 10.2 12.8 14.3 16.0

Scenario FUND

IMAGE -11.7 -8.4 -6.9 -4.6 -2.2 -1.3 0.7 4.1 7.4 17.4

MERGE optimistic -10.6 -7.1 -5.6 -3.6 -1.3 -0.3 1.6 5.4 9.1 19.0

Message -12.2 -8.9 -7.3 -4.9 -2.5 -1.9 0.3 3.5 6.5 15.6

MiniCAM base -10.4 -7.2 -5.8 -3.8 -1.5 -0.6 1.3 4.8 8.2 18.0

5th scenario -10.9 -8.3 -7 -5 -2.9 -2.7 -0.8 1.4 3.2 9.2

Figure 14A.9.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by discount 

rate 

* The distribution of SCC values ranges from -$5,192 to $66,116, but the X-axis has been 

truncated at approximately the 1st and 99th percentiles to better show the data.

0

2000

4000

6000

8000

10000

12000

14000

16000

-25-12

1

142740

53667992

105

118131144157

170183196209222235

248261274

All 2010, 5.0% All 2010, 3.0% All 2010, 2.5%

14A-55 
Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate Scenario

DICE PAGE FUND

5% 

Mean 9 6.5 -1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3% 

Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50% 

Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30

14A-56 
APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14B.1 PREFACE ....................................................................................................................14B-1

14B.2 PURPOSE ....................................................................................................................14B-1

14B.3 SUMMARY OF MODEL UPDATES.........................................................................14B-2

14B.3.1 DICE ................................................................................................................14B-2

14B.3.1.1 Carbon Cycle Parameters .......................................................................14B-2

14B.3.1.2 Sea Level Dynamics ...............................................................................14B-3

14B.3.1.3 Re-calibrated Damage Function .............................................................14B-3

14B.3.2 FUND ...............................................................................................................14B-4

14B.3.2.1 Space Heating .........................................................................................14B-5

14B.3.2.2 Sea Level Rise and Land Loss ...............................................................14B-5

14B.3.2.3 Agriculture .............................................................................................14B-5

14B.3.2.4 Temperature Response Model ................................................................14B-6

14B.3.2.5 Methane ..................................................................................................14B-6

14B.3.3 PAGE ...............................................................................................................14B-6

14B.3.3.1 Sea Level Rise ........................................................................................14B-7

14B.3.3.2 Revised Damage Function to Account for Saturation ............................14B-7

14B.3.3.3 Regional Scaling Factors ........................................................................14B-7

14B.3.3.4 Probability of a Discontinuity ................................................................14B-7

14B.3.3.5 Adaptation ..............................................................................................14B-8

14B.3.3.6 Other Noteworthy Changes ....................................................................14B-8

14B.4 REVISED SCC ESTIMATES .....................................................................................14B-9

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS ....................................14B-11

14B.6 ANNEX A ..................................................................................................................14B-14

14B.7 ANNEX B ..................................................................................................................14B-17

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) .........................................................................................................14B-10

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................14B-11

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) ..................................14B-14

Table 14B.6.2 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.6.3 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-16

14B-i 
LIST OF FIGURES

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) ............14B-10

14B-ii
14B.1 PREFACE 

 The following text is reproduced almost verbatim from the May 2013 report (revised 

November 2013) of the Interagency Working Group on the Social Cost of Carbon of the United 

States Government.

a

 Minor changes were made to the working group's report to make it more 

consistent with the rest of this technical support document. Annex B describes the revisions that 

were made to the May 2013 report. 

14B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC)b

estimates from the 2010 interagency technical support document (TSD) (Interagency Working 

Group on Social Cost of Carbon 2010).1 E.O. 13563 commits the Administration to regulatory 

decision making “based on the best available science.”c Additionally, the interagency group 

recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates 

that reflect the growing body of scientific and economic knowledge become available.d

 New 

versions of the three integrated assessment models used by the U.S. government to estimate the 

SCC (DICE, FUND, and PAGE), are now available and have been published in the peer

reviewed literature. While acknowledging the continued limitations of the approach taken by the 

interagency group in 2010 (documented in the original 2010 TSD), this document provides an 

update of the SCC estimates based solely on the latest peer-reviewed version of the models, 

replacing model versions that were developed up to ten years ago in a rapidly evolving field. It 

does not revisit other assumptions with regard to the discount rate, reference case socioeconomic 

and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are 

modeled are confined to those that have been incorporated into the latest versions of the models 

by the developers themselves in the peer-reviewed literature. The Environmental Protection 

Agency (EPA), in collaboration with other Federal agencies such as the Department of Energy 

(DOE), continues to investigate potential improvements to the way in which economic damages 

associated with changes in CO2 emissions are quantified. 

Section 3 summarizes the major updates relevant to SCC estimation that are contained in 

the new versions of the integrated assessment models released since the 2010 interagency report. 

Section 4 presents the updated schedule of SCC estimates for 2010 – 2050 based on these 

versions of the models. Section 5 provides a discussion of recent workshops to support 

improvements in SCC estimation.

a Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. 

Interagency Working Group on Social Cost of Carbon, United States Government. May 2013; revised November 

2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf b

 In this document, we present all values of the SCC as the cost per metric ton of CO2 emissions. Alternatively, one 

could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of 

CO2 and the mass of carbon is 3.67. c http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

d See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010).1

14B-1 

 
14B.3 SUMMARY OF MODEL UPDATES

This section briefly summarizes changes integrated into the most recent versions of the 

three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on 

describing those model updates that are relevant to estimating the social cost of carbon. For 

example, both the DICE and PAGE models now include an explicit representation of sea level 

rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to 

ensure damages are constrained GDP, updated regional scaling of damages, and a revised 

treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, 

the model’s simple carbon cycle has been updated to be more consistent with a relatively more 

complex climate model. The FUND model includes updated damage functions for sea level rise 

impacts, the agricultural sector, and reduced space heating requirements, as well as changes to 

the response of temperature to the buildup of GHG concentrations and the inclusion of indirect 

effects of methane emissions. Changes made to parts of the models that are superseded by the 

interagency working group’s modeling assumptions – regarding climate sensitivity, discounting, 

and socioeconomic variables – are not discussed. 

14B.3.1 DICE

Changes in the DICE model relevant for the SCC estimates developed by the interagency 

working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit 

representation of sea level dynamics, and 3) a re-calibrated damage function that includes an 

explicit representation of economic damages from sea level rise. Changes were also made to 

other parts of the DICE model—including the equilibrium climate sensitivity parameter, the rate 

of change of total factor productivity, and the elasticity of the marginal utility of consumption—

but these components of DICE are superseded by the interagency working group’s assumptions 

and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008)2

and on DICE2010 in Nordhaus (2010)3 and the associated on-line appendix containing 

supplemental information. 

14B.3.1.1 Carbon Cycle Parameters

DICE uses a three-box model of carbon stocks and flows to represent the accumulation 

and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and 

the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the 

Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44).2e

Carbon cycle transfer coefficient values in DICE2010 are based on re-calibration of the model to 

match the newer version of MAGICC (Nordhaus 2010 p 2).3 For example, in DICE2010 in each 

decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 

percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains 

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 

2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each 

e MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research 

that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from 

much more sophisticated state of the art earth system simulation models (Randall et al. 2007).4

14B-2 

 
decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 

percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean. 

The implication of these changes for DICE2010 is in general a weakening of the ocean as 

a carbon sink and therefore a higher concentration of carbon in the atmosphere than in 

DICE2007, for a given path of emissions. All else equal, these changes will generally increase 

the level of warming and therefore the SCC estimates in DICE2010 relative to those from 

DICE2007. 

14B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global 

average sea level anomaly to be used in the updated damage function (discussed below). This 

section contains a brief description of the sea level rise (SLR) module; a more detailed 

description can be found on the model developer’s website.f The average global sea level 

anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal 

expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland 

ice sheet, and 4) melting of the Antarctic ice sheet. 

The parameters of the four components of the SLR module are calibrated to match 

consensus results from the IPCC’s Fourth Assessment Report.4 g The rise in sea level from 

thermal expansion in each time period (decade) is 2 percent of the difference between the sea 

level in the previous period and the long run equilibrium sea level, which is 0.5 meters per 

degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the 

melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above 

the average global temperature in 1900. 

 

The contribution to sea level rise from melting of the Greenland ice sheet is more 

complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1

o

C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in 

each period is proportional to the difference between the previous period’s sea level anomaly and 

the equilibrium sea level anomaly, where the constant of proportionality increases with the 

temperature anomaly in the current period. 

The contribution to SLR from the melting of the Antarctic ice sheet is -0.001 meters per 

decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 

0.025 meters per decade at a temperature anomaly of 6 °C. 

14B.3.1.3 Re-calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a 

fractional loss of gross economic output in each period. A portion of the remaining economic 

f Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: 

http://nordhaus.econ.yale.edu/documents/SLR_021910.pdf.

g For a review of post-IPCC AR4 research on sea level rise, see Nicholls et al. (2011)5 and NAS (2011).6

 

14B-3 

 
output in each period (net of climate change damages) is consumed and the remainder is invested 

in the physical capital stock to support future production, so each period’s climate damages will 

reduce consumption in that period and in all future periods due to the lost investment. The 

fraction of output in each period that is lost due to climate change impacts is represented as one 

minus a fraction, which is one divided by a quadratic function of the temperature anomaly, 

producing a sigmoid (“S”-shaped) function. The loss function in DICE2010 has been expanded 

by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the 

temperature anomaly coefficients have been recalibrated to avoid double-counting damages from 

sea level rise that were implicitly included in these parameters in DICE2007. 

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3),3 who notes 

that “...damages in the uncontrolled (baseline) [i.e., reference] case ... in 2095 are $12 trillion, 

or 2.8 percent of global output, for a global temperature increase of 3.4 oC above 1900 levels.” 

This compares to a loss of 3.2 percent of global output at 3.4 oC in DICE2007. However, in 

DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages are lower 

in most of the early periods but higher in later periods of the time horizon than would be 

calculated using the DICE2007 damage function. Specifically, the percent difference between 

damages in the base run of DICE2010 and those that would be calculated using the DICE2007 

damage function starts at +7 percent in 2005, decreases to a low of -14 percent in 2065, then 

continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), 

and to +160 percent by the end of the model time horizon in 2595. The large increases in the far 

future years of the time horizon are due to the permanence associated with damages from sea 

level rise, along with the assumption that the sea level is projected to continue to rise long after 

the global average temperature begins to decrease. The changes to the loss function generally 

decrease the interagency working group SCC estimates slightly, all else equal.

14B.3.2 FUND

FUND version 3.8 includes a number of changes over the previous version 3.5 used in 

the interagency report. Documentation supporting FUND and the model’s source code for all 

versions of the model is available from the model authors.

h Notable changes, due to their impact 

on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level 

rise damage functions in addition to changes to the temperature response function and the 

inclusion of indirect effects from methane emissions.i

 We discuss each of these in turn. 

h http://www.fund-model.org/. This report uses version 3.8 of the FUND model, which represents a modest update 

to the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013).7 For the 

purpose of computing the SCC, the relevant changes are associated with improving consistency with IPCC AR4 by 

adjusting the atmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along 

with making minor stability improvements in the sea wall construction algorithm.

i

The other damage sectors (water resources, space cooling, land loss, migration, ecosystems, human health, and 

extreme weather) were not the subject of significant updates.

14B-4 

 
14B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are 

based on the estimated impact due to one degree of warming. These baseline damages are scaled 

based on the forecasted temperature anomaly’s deviation from the one degree benchmark and 

adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 

3.5, the function that scales the base year damages adjusted for vulnerability allows for the 

possibility that in some simulations the benefits associated with reduced heating needs may be an 

unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling 

has been modified to ensure that the function is everywhere concave, meaning that for every 

simulation there will exist an upper bound on the benefits a region may receive from reduced 

space heating needs. The new formulation approaches a value of two in the limit as the 

temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the 

reduced expenditures on space heating at any level of warming will not exceed two times the 

reductions experienced at one degree of warming. Since the reduced need for space heating 

represents a benefit of climate change in the model, or a negative damage, this change will 

increase the estimated SCC. This update accounts for a significant portion of the difference in the 

expected SCC estimates reported by the two versions of the model when run probabilistically.

14B.3.2.2 Sea Level Rise and Land Loss 

The FUND model explicitly includes damages associated with the inundation of dry land 

due to sea level rise. The amount of land lost within a region is dependent upon the proportion of 

the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 

3.5 the function defining the potential land lost in a given year due to sea level rise is linear in 

the rate of sea level rise for that year. This assumption implicitly assumes that all regions are 

well represented by a homogeneous coastline in length and a constant uniform slope moving 

inland. In FUND 3.8 the function defining the potential land lost has been changed to be a nonlinear function of sea level rise, thereby assuming that the slope of the shore line is not constant 

moving inland, with a positive first derivative. The effect of this change is to typically reduce the 

vulnerability of some regions to sea level rise based land loss, therefore having an effect of 

lowering the expected SCC estimate. The model has also been updated to assume that the value 

of dry land at risk of inundation is not uniform across a region but will be a decreasing function 

of protection measure, thereby implicitly assuming that the most valuable land will be protected 

first.

14B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as 

proportional to the sector’s value. The fraction is made up of three additively separable 

components that represent the effects from carbon fertilization, the rate of temperature change, 

and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the 

sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function 

with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the 

ratio of two normal distributions. Within this specification, as draws from the distribution in the 

denominator approached zero the share of the sector’s value “lost” approaches (+/-) infinity 

14B-5 
independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic 

coefficients are drawn directly from truncated normal distributions so that they remain in the 

range [0,∞) and (−∞,0], respectively, where the means for the new distributions are set equal to 

the ratio of the means from the normal distributions used in the previous version. In general the 

impact of this change has been to increase the likelihood that increases in the temperature level 

will have either larger positive or negative effects on the agricultural sector relative to the 

previous version (through eliminating simulations in which the “lost” value approached (+/-) 

infinity). The net effect of this change on the SCC estimates is difficult to predict. 

14B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing 

into the current expected temperature anomaly. In FUND, a given year’s increase in the 

cumulative temperature anomaly is based on a mean reverting function where the mean equals 

the equilibrium temperature anomaly that would eventually be reached if that year’s level of 

radiative forcing were sustained. The rate of mean reversion defines the rate at which the 

transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response 

is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact 

that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the 

equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated 

to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity 

of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore 

in FUND 3.8, the temperature response will typically be faster than in the previous version. The 

overall effect of this change is likely to increase estimates of the SCC as higher temperatures are 

reached during the timeframe analyzed and as the same damages experienced in the previous 

version of the model are now experienced earlier and therefore discounted less. 

14B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emissions, and has developed 

methods for proxying such effects when computing the global warming potential of methane 

(Forster et al. 2007).8 FUND 3.8 now includes the same methods for incorporating the indirect 

effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been 

set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative 

forcing associated with atmospheric methane has also been increase by 40% to account for its net 

impact on ozone production and increase in stratospheric water vapor. The general effect of this 

increased radiative forcing will be to increase the estimated SCC values, where the degree to 

which this occurs will be dependent upon the relative curvature of the damage functions with 

respect to the temperature anomaly.

14B.3.3 PAGE

PAGE09 (Hope 2012)9 includes a number of changes from PAGE2002, the version used 

in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates 

include: explicitly modeling the impacts from sea level rise, revisions to the damage function to 

ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised 

14B-6 
treatment for the probability of a discontinuity within the damage function, and revised 

assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and 

the calculation of regional temperatures. More details on PAGE2009 can be found in three 

working papers (Hope 2011a, 2011b, 2011c).10, 11, 12 A description of PAGE2002 can be found 

in Hope (2006).13

14B.3.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories – economic and noneconomic impacts - PAGE2009 adds a third explicit category: damages from sea level rise. In the 

previous version of the model, damages from sea level rise were subsumed by the other damage 

categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea 

level based on the assumption that low-lying shoreline areas will be associated with higher 

damages than current inland areas. Damages from the economic and non-economic sector were 

adjusted to account for the introduction of this new category. 

14B.3.3.2 Revised Damage Function to Account for Saturation 

In PAGE09, small initial economic and non-economic benefits (negative damages) are 

modeled for small temperature increases, but all regions eventually experience positive economic 

damages from climate change, where damages are the sum of additively separable polynomial 

functions of temperature and sea level rise. Damages transition from this polynomial function to 

a logistic path once they exceed a certain proportion of remaining Gross Domestic Product 

(GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, 

which allowed Eastern Europe to potentially experience large benefits from temperature 

increases, and which also did not bound the possible damages that could be experienced. 

14B.3.3.3 Regional Scaling Factors 

As in the previous version of PAGE, the PAGE09 model calculates the damages for the 

European Union (EU) and then, assumes that damages for other regions are proportional based 

on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s 

coastline relative to the EU (Hope 2011b).11 Because of the long coastline in the EU, other 

regions are, on average, less vulnerable than the EU for the same sea level and temperature 

increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on 

four studies reported in the IPCC’s third assessment report, and allowed for benefits from 

temperature increase in Eastern Europe, smaller impacts in developing countries, and higher 

damages in developing countries. 

14B.3.3.4 Probability of a Discontinuity 

In PAGE2002, the damages associated with a “discontinuity” were modeled as an 

expected value. That is, additional damages from an extreme event, such as extreme melting of 

the Greenland ice sheet, were multiplied by the probability of the event occurring and added to 

the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event 

for each year in the model. The damages for each model run are estimated either with or without 

14B-7 
a discontinuity occurring, rather than as an expected value. A large‐scale discontinuity becomes 

possible when the temperature rises beyond some threshold value between 2 and 4°C. The 

probability that a discontinuity will occur beyond this threshold then increases by between 10 

and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, 

the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a 

mean of 15 percent) in addition to other damages, and other regions lose an amount determined 

by the regional scaling factor. The threshold value for a possible discontinuity is lower than in 

PAGE2002, while the rate at which the probability of a discontinuity increases with the 

temperature anomaly and the damages that result from a discontinuity are both higher than in 

PAGE2002. The model assumes that only one discontinuity can occur and that the impact is 

phased in over a period of time, but once it occurs, its effect is permanent. 

14B.3.3.5 Adaptation 

As in PAGE2002, adaptation is available to increase the tolerable level of temperature 

change and can help mitigate any climate change impacts that still occur. In PAGE this 

adaptation is the same regardless of the temperature change or sea level rise and is therefore akin 

to what is more commonly considered a reduction in vulnerability. It is modeled by modifying 

the temperature change and sea level rise used in the damage function or by reducing the 

damages by some percentage. PAGE09 assumes a smaller decrease in vulnerability than the 

previous version of the model and assumes that it will take longer for this change in vulnerability 

to be realized. In the aggregated economic sector, at the time of full implementation, this 

adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature 

anomalies between 1°C and 3°C, it will reduce damages by 15-30 percent (depending on the 

region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation 

was assumed to reduce economic sector damages up to 3°C by 50-90 percent after 20 years. 

Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change. 

For the non-economic sector, in PAGE09 adaptation is available to reduce 15 percent of the 

damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to 

fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. 

Similarly, adaptation is assumed to alleviate 25-50 percent of the damages from the first 0.20 to 

0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c)12 estimates 

that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea 

level rise via adaptation increase the SCC by approximately 30 percent.

14B.3.3.6 Other Noteworthy Changes

 Two other changes in the model are worth noting. A revised carbon cycle feedback is 

introduced to simulate decreased CO2 absorption by the terrestrial biosphere and ocean as the 

temperature rises. This feedback is linear in the average global and annual temperature anomaly 

but is capped at a maximum value. In the previous version of PAGE, an additional amount was 

added to the CO2 emissions each period to account for a decrease in ocean absorption and a loss 

of soil carbon. Also updated is the method by which the average global and annual temperature 

anomaly is downscaled to determine annual average regional temperature anomalies to be used 

in the regional damage functions. In the previous version of PAGE, the scaling was determined 

solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional 

14B-8 
temperature anomaly is further adjusted using an additive factor that is based on the average 

absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s 

landmass.

14B.4 REVISED SCC ESTIMATES 

The updated versions of the three integrated assessment models were run using the same 

methodology detailed in the 2010 TSD.1 The approach along with the inputs for the 

socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount 

rate remains the same. This includes the five reference scenarios based on the EMF-22 modeling 

exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth 

Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent. 

As was previously the case, the use of three models, three discount rates, and five 

scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD 

applied equal weight to each model and socioeconomic scenario in order to reduce the 

dimensionality down to three separate distributions representative of the three discount rates. The 

interagency group selected four values from these distributions for use in regulatory analysis. 

Three values are based on the average SCC across models and socio-economic-emissions 

scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to 

represent the higher-than-expected economic impacts from climate change further out in the tails 

of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent 

discount rate was chosen. (A detailed set of percentiles by model and scenario combination is 

available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central

value, and so the central value that emerges is the average SCC across models at the 3 percent 

discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance and value of 

including all four SCC values. 

Table 14B.4.1 shows the four selected SCC estimates in five year increments from 2010 

to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all 

outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. 

Values for the years in between are calculated using basic linear interpolation. The full set of 

annual SCC estimates between 2010 and 2050 is reported in the Annex. 

14B-9 
Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

 The SCC estimates using the updated versions of the models are higher than those 

reported in the TSD due to the changes to the models outlined in the previous section. Figure 

14B.4.1 illustrates where the four SCC values for 2020 fall within the full distribution for each 

discount rate based on the combined set of runs for each model and scenario (150,000 estimates 

in total for each discount rate). In general, the distributions are skewed to the right and have long 

tails. The Figure also shows that the lower the discount rate, the longer the right tail of the 

distribution. 

Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) 

As was the case in the original TSD, the SCC increases over time because future 

emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The approach taken by the 

interagency group is to allow the growth rate to be determined endogenously by the models 

14B-10 
through running them for a set of perturbation years out to 2050. Table 14B.4.2 illustrates how 

the growth rate for these four SCC estimates varies over time.

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050 

Average Annual 5.0% 3.0% 2.5% 3.0%

Rate (%) Avg Avg Avg 95th

2010-2020 1.2% 3.3% 2.4% 4.4%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.9% 1.5% 2.1%

2040-2050 2.6% 1.6% 1.3% 1.5%

 The future monetized value of emission reductions in each year (the SCC in year t 

multiplied by the change in emissions in year t) must be discounted to the present to determine 

its total net present value for use in regulatory analysis. As previously discussed in the original 

TSD, damages from future emissions should be discounted at the same rate as that used to 

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from 

climate change, whether they result from emissions today or emissions in a later year, should be 

discounted using the same rate. 

14B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS

 The 2010 interagency SCC technical support report discusses a number of important 

limitations for which additional research is needed. In particular, the document highlights the 

need to improve the quantification of both non-catastrophic and catastrophic damages, the 

treatment of adaptation and technological change, and the way in which inter-regional and intersectoral linkages are modeled. It also discusses the need to more carefully assess the implications 

of risk aversion for SCC estimation as well as the inability to perfectly substitute between 

climate and non-climate goods at higher temperature increases, both of which have implications 

for the discount rate used. EPA, DOE, and other agencies continue to engage in long-term

research work on modeling and valuation of climate impacts that we expect will inform 

improvements in SCC estimation in the future.

14B-11 
REFERENCES

1

 Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States 

Government. <http://www.whitehouse.gov/sites/default/files/omb/inforeg/foragencies/Social-Cost-of-Carbon-for-RIA.pdf> 

2

 Nordhaus, W., A Question of Balance. 2008. Yale University Press: New Haver. 

3

 Nordhaus, W., Economic aspects of global warming in a post-Copenhagen environment.

Proceedings of the National Academy of Sciences, 2010. 107(26): pp. 11721-11726 

4

 Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. 

Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models 

and Their Evaluation. In Climate Change 2007: The Physical Science Basis. 

Contribution of Working Group I to the Fourth Assessment Report of the 

Intergovernmental Panel on Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, 

M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University 

Press: Cambridge, United Kingdom and New York, NY, USA 

5

 Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. Vellinga, D. de Gusmão, J. Hinkel 

and R.S.J. Tol, Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the 

twenty-first century. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161-181 

6 National Academy of Sciences, Climate Stabilization Targets: Emissions, 

Concentrations, and Impacts over Decades to Millennia. 2011. National Academies 

Press, Inc: Washington, DC.

7

 Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a 

decomposition analysis using FUND. Climatic Change, 2013 (Forthcoming) 

8

 Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, 

J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van 

Dorland, Changes in Atmospheric Constituents and in Radiative Forcing. In Climate 

Change 2007: The Physical Science Basis. Contribution of Working Group I to the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. 

Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. 

Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New 

York, NY, USA

9

 Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates 

from PAGE09 are higher than those from PAGE2002. Climatic Change, 

2012(Forthcoming) 

10 Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf

14B-12 
11 Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf

12 Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School 

http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf

13 Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment 

Model Incorporating the IPCC’s Five Reasons for Concern. The Integrated Assessment 

Journal, 2006. 6(1): pp. 19-56 

14 Anthoff D. and Tol, R. S. J., Erratum to: The uncertainty about the social cost of carbon: 

A decomposition analysis using FUND. Climatic Change, 2013. Advance online 

publication. doi: 10.1007/s10584-013-0959-1 

14B-13 
14B.6 ANNEX A 

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2) 

Discount Rate 5.0% 3.0% 2.5% 3.0%

Year Avg Avg Avg 95th

2010 11 32 51 89

2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 220

14B-14 
Table 14B.6.2 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918

MiniCAM Base 5 9 12 22 47 107 113 266 431 872

5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE

IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221

MiniCAM Base 20 25 29 38 53 61 76 102 117 135

5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND

IMAGE -14 -2 4 15 31 39 55 86 107 157

MERGE -6 1 6 14 27 35 46 70 87 141

MESSAGE -16 -5 1 11 24 31 43 67 83 126

MiniCAM Base -7 2 7 16 32 39 55 83 103 158

5th Scenario -29 -13 -6 4 16 21 32 53 69 103

Table 14B.6.3 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641

MiniCAM Base 3 5 8 14 30 73 75 184 300 623

5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE

IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83

MiniCAM Base 13 17 20 26 35 39 49 65 73 85

5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND

IMAGE -13 -4 0 8 18 23 33 51 65 99

MERGE -7 -1 2 8 17 21 29 45 57 95

MESSAGE -14 -6 -2 5 14 18 26 41 52 82

MiniCAM Base -7 -1 3 9 19 23 33 50 63 101

5th Scenario -22 -11 -6 1 8 11 18 31 40 62

14B-15 
Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO2) 

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th

Scenario PAGE

IMAGE 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216

MiniCAM Base 1 1 2 3 8 21 21 54 94 190

5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE

IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25

MiniCAM Base 5 6 7 8 11 12 14 18 20 22

5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND

IMAGE -9 -5 -4 -1 2 3 6 10 14 24

MERGE -6 -4 -2 0 3 4 6 11 15 26

MESSAGE -10 -6 -4 -1 1 2 5 9 12 21

MiniCAM Base -7 -4 -2 0 3 4 6 11 14 25

5th Scenario -11 -7 -5 -3 0 0 3 5 7 13

14B-16 
14B.7 ANNEX B 

The November 2013 revision of this technical support document is based on two 

corrections to the runs based on the FUND model. First, the potential dry land loss in the 

algorithm that estimates regional coastal protections was misspecified in the model’s computer 

code. This correction is covered in an erratum to Anthoff and Tol (2013) published in the same 

journal (Climatic Change) in October 2013.

14 Second, the equilibrium climate sensitivity 

distribution was inadvertently specified as a truncated Gamma distribution (the default in FUND) 

as opposed to the truncated Roe and Baker distribution as was intended. The truncated Gamma 

distribution used in the FUND runs had approximately the same mean and upper truncation 

point, but lower variance and faster decay of the upper tail, as compared to the intended 

specification based on the Roe and Baker distribution. The differences between the original 

estimates reported in the May 2013 version of this technical support document and this revision 

are all one dollar or less.

14B-17 
APPENDIX 17-A. REGULATORY IMPACT ANALYSIS: SUPPORTING 

MATERIALS

TABLE OF CONTENTS

17-A.1 INTRODUCTION ............................................................................................... 17-A-1

17-A.2 MARKET SHARE ANNUAL INCREASES BY POLICY ................................ 17-A-1

17-A.3 NIA-RIA INTEGRATED MODEL ................................................................... 17-A-14

17-A.4 CONSUMER REBATE POLICY MARKET PENETRATION CURVES ...... 17-A-14

17-A.4.1 Introduction ........................................................................................................ 17-A-14

17-A.4.2 Adjustment of XENERGY Penetration Curves ................................................. 17-A-16

17-A.4.3 Interpolation of Penetration Curves ................................................................... 17-A-17

17-A.5 CONSUMER REBATE PROGRAMS .............................................................. 17-A-17

17-A.6 FEDERAL AND STATE TAX CREDITS........................................................ 17-A-22

17-A.6.1 Federal Tax Credits for Consumers of Residential Appliances ......................... 17-A-22

17-A.6.2 Federal Tax Credits for Manufacturers .............................................................. 17-A-23

17-A.6.3 State Tax Credits ................................................................................................ 17-A-24

LIST OF TABLES

Table 17-A.2.1 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Consumer Rebates ............................................. 17-A-2

Table 17-A.2.2 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Consumer Tax Credits ....................................... 17-A-3

Table 17-A.2.3 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Manufacturer Tax Credits ................................. 17-A-4

Table 17-A.2.4 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Early Replacement ............................................ 17-A-5

Table 17-A.2.5 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Consumer Rebates ........................... 17-A-6

Table 17-A.2.6 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Consumer Tax Credits ..................... 17-A-7

Table 17-A.2.7 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Manufacturer Tax Credits ................ 17-A-8

Table 17-A.2.8 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Early Replacement........................... 17-A-9

Table 17-A.2.9 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Bulk Government Purchases ......... 17-A-10

Table 17-A.2.10 Annual Increases in Market Share of NEMA Premium Motors in 

Agriculture Attributable to Consumer Rebates...................................... 17-A-11

Table 17-A.2.11 Annual Increases in Market Share of NEMA Premium Motors in 

Agriculture Attributable to Consumer Tax Credits ............................... 17-A-12

Table 17-A.2.12 Annual Increases in Market Share of NEMA Premium Motors in 

Agriculture Attributable to Manufacturer Tax Credits .......................... 17-A-13

17-A-i 
Table 17-A.5.1 Rebate Amounts for Medium Electric Motors (2013$/hp) .................... 17-A-18

Table 17-A.5.2 References for Rebate Programs for Medium Electric Motors ............. 17-A-19

LIST OF FIGURES 

Figure 17-A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies ............................................................. 17-A-16

17-A-ii
APPENDIX 17-A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS 

17-A.1 INTRODUCTION

 This appendix contains sections discussing the following topics: 

• Projections of annual market share increases for the alternative policies;

• NIA-RIA Integrated Model;

• XENERGY penetration curves used to analyze consumer rebates, including: 

o Background material,

o DOE’s adjustment of these curves for this analysis, and 

o Method for interpolating the curves; 

• Detailed tables of rebates offered for the considered products; and 

• Background material on Federal and state tax credits for appliances. 

17-A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Tables 17-A.2.1 to 17-A.2.12 show the annual increases in market shares of NEMA 

Premium motors, by horsepower range and sector, for the alternative policies analyzed in this 

RIA. DOE used these market share increases to calculate the market penetration of NEMA 

Premium motors in each of the analyzed alternative policies. The market penetrations of the 

alternative policies are ultimately the inputs DOE use to the NIA-RIA spreadsheet model.

17-A-1 
Table 17-A.2.1 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Consumer Rebates

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2017 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2018 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2019 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2020 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2021 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2022 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2023 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2024 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2025 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2026 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2027 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2028 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2029 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2030 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2031 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2032 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2033 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2034 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2035 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2036 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2037 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2038 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2039 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2040 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2041 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2042 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2043 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2044 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

2045 30.3% 16.6% 8.2% 8.1% 11.0% 16.1%

 

17-A-2 
Table 17-A.2.2 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Consumer Tax Credits

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2017 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2018 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2019 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2020 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2021 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2022 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2023 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2024 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2025 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2026 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2027 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2028 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2029 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2030 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2031 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2032 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2033 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2034 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2035 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2036 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2037 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2038 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2039 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2040 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2041 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2042 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2043 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2044 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

2045 18.2% 23.6% 16.8% 18.6% 25.0% 28.9%

 

17-A-3 
Table 17-A.2.3 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Manufacturer Tax Credits

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2017 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2018 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2019 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2020 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2021 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2022 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2023 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2024 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2025 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2026 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2027 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2028 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2029 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2030 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2031 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2032 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2033 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2034 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2035 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2036 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2037 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2038 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2039 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2040 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2041 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2042 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2043 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2044 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

2045 9.1% 11.8% 8.4% 9.3% 12.5% 14.4%

 

17-A-4 
Table 17-A.2.4 Annual Increases in Market Share of NEMA Premium Motors in 

Industry Attributable to Early Replacement

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 1.2% 0.0% 11.0% 8.3% 15.0% 7.2%

2017 0.7% 0.0% 7.8% 5.6% 11.0% 4.3%

2018 0.4% 0.0% 5.3% 2.3% 6.8% 1.3%

2019 0.1% 0.0% 3.1% 1.1% 1.4% 0.0%

2020 0.0% 0.0% 0.6% 0.0% 0.0% 0.0%

2021 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2022 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2023 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2024 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2025 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2026 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2027 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2028 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2029 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2030 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2031 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2032 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2033 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2034 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2035 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2036 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2037 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2038 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2039 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2040 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2041 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2042 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2043 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2044 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2045 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

 

17-A-5 
Table 17-A.2.5 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Consumer Rebates

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2017 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2018 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2019 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2020 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2021 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2022 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2023 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2024 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2025 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2026 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2027 34.1% 16.7f % 9.5% 9.9% 11.2% 18.6%

2028 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2029 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2030 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2031 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2032 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2033 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2034 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2035 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2036 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2037 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2038 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2039 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2040 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2041 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2042 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2043 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2044 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

2045 34.1% 16.7% 9.5% 9.9% 11.2% 18.6%

 

17-A-6 
Table 17-A.2.6 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Consumer Tax Credits

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2017 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2018 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2019 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2020 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2021 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2022 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2023 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2024 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2025 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2026 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2027 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2028 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2029 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2030 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2031 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2032 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2033 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2034 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2035 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2036 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2037 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2038 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2039 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2040 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2041 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2042 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2043 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2044 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

2045 20.4% 23.5% 17.7% 19.2% 25.5% 30.3%

 

17-A-7 
Table 17-A.2.7 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Manufacturer Tax Credits

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2017 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2018 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2019 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2020 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2021 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2022 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2023 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2024 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2025 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2026 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2027 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2028 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2029 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2030 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2031 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2032 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2033 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2034 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2035 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2036 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2037 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2038 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2039 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2040 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2041 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2042 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2043 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2044 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

2045 10.2% 11.8% 8.8% 9.6% 12.8% 15.2%

 

17-A-8 
Table 17-A.2.8 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Early Replacement

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 0.0% 0.1% 0.7% 0.1% 0.0% 0.0%

2017 0.0% 0.0% 0.4% 0.0% 0.0% 0.0%

2018 0.0% 0.0% 0.1% 0.0% 0.0% 0.0%

2019 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2020 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2021 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2022 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2023 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2024 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2025 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2026 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2027 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2028 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2029 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2030 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2031 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2032 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2033 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2034 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2035 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2036 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2037 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2038 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2039 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2040 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2041 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2042 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2043 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2044 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2045 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

 

17-A-9 
Table 17-A.2.9 Annual Increases in Market Share of NEMA Premium Motors in 

Commercial Sector Attributable to Bulk Government Purchases

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 0.9% 1.1% 0.8% 0.8% 1.1% 1.2%

2017 1.7% 2.0% 1.4% 1.5% 2.1% 2.3%

2018 2.6% 2.9% 2.1% 2.2% 3.1% 3.4%

2019 3.4% 3.9% 2.8% 3.0% 4.1% 4.5%

2020 4.2% 4.8% 3.4% 3.7% 5.1% 5.6%

2021 5.1% 5.8% 4.1% 4.4% 6.1% 6.7%

2022 5.9% 6.8% 4.8% 5.2% 7.1% 7.8%

2023 6.8% 7.7% 5.5% 5.9% 8.1% 9.0%

2024 7.6% 8.7% 6.2% 6.6% 9.2% 10.1%

2025 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2026 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2027 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2028 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2029 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2030 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2031 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2032 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2033 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2034 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2035 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2036 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2037 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2038 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2039 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2040 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2041 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2042 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2043 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2044 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

2045 8.5% 9.7% 6.9% 7.4% 10.2% 11.2%

 

17-A-10 
Table 17-A.2.10 Annual Increases in Market Share of NEMA Premium Motors in 

Agriculture Attributable to Consumer Rebates

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2017 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2018 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2019 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2020 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2021 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2022 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2023 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2024 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2025 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2026 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2027 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2028 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2029 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2030 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2031 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2032 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2033 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2034 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2035 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2036 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2037 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2038 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2039 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2040 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2041 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2042 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2043 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2044 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

2045 19.9% 11.5% 12.1% 13.2% 14.2% 31.4%

 

17-A-11 
Table 17-A.2.11 Annual Increases in Market Share of NEMA Premium Motors in 

Agriculture Attributable to Consumer Tax Credits

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2017 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2018 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2019 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2020 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2021 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2022 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2023 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2024 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2025 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2026 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2027 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2028 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2029 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2030 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2031 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2032 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2033 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2034 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2035 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2036 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2037 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2038 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2039 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2040 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2041 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2042 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2043 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2044 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

2045 10.4% 16.1% 26.3% 30.8% 33.9% 45.7%

 

17-A-12 
Table 17-A.2.12 Annual Increases in Market Share of NEMA Premium Motors in 

Agriculture Attributable to Manufacturer Tax Credits

Year 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

2016 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2017 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2018 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2019 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2020 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2021 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2022 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2023 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2024 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2025 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2026 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2027 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2028 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2029 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2030 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2031 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2032 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2033 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2034 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2035 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2036 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2037 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2038 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2039 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2040 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2041 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2042 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2043 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2044 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

2045 5.2% 8.0% 13.2% 15.4% 16.9% 22.9%

 

 

17-A-13 
17-A.3 NIA-RIA INTEGRATED MODEL

 For this analysis, DOE used its integrated NIA-RIAa model approach that built on the 

NIA model discussed in Chapter 10 and documented in Appendix 10-A.The resulting integrated 

NIA-RIA model featured both the NIA analysis inputs and results and the RIA inputs and had 

the capability to generate results for each of the RIA policies. For the RIA methodology 

documentation in Chapter 17, Section 17.3, the model created summaries of parameters 

calculated by the model for the consumer rebates and tax credit policies, generated their 

penetration curves (discussed in Section 17-A.4.3 below) and reported market share impacts for 

these policies by sector and horsepower range. For the RIA results reported in Chapter 17, 

Section 17.4, the model produced graphs of the market share increases by sector resulting from 

each of the policies analyzed and created summary tables for the national energy savings and net

present value results. The model also generated tables of market share increases for each policy 

reported in Section 17-A.2 of this Appendix.

17-A.4 CONSUMER REBATE POLICY MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that 

DOE used to analyze the Consumer Rebates, as well as the Consumer Tax Credits and 

Manufacturer Tax Credits policies. Next it discusses the adjustments it made to the maximum 

penetration rates. The adjusted penetration curves set the framework for the development of 

interpolated market penetration curves. For the method DOE used to develop interpolated market 

penetration curves please refer to Blum et al (2011, Appendix A).25 The resulting interpolated 

market penetration curves for NEMA Premium motors by sector and horsepower range are

presented in Chapter 17, Section 17.3.1.2. 

17-A.4.1 Introduction

XENERGY, Inc.b, developed a re-parameterized, mixed-source information diffusion 

model to estimate market impacts induced by financial incentives for purchasing energy efficient 

appliances.

1 The basic premise of the mixed-source model is that information diffusion drives 

the adoption of technology. 

Extensive economic literature describes the diffusion of new products as technologies 

evolve. Some research focuses primarily on developing analytical models of diffusion patterns 

applicable to individual consumers or to technologies from competing firms.2, 3, 4 One study 

records researchers’ attempts to investigate the factors that drive diffusion processes.

5 Because a 

new product generally has its own distinct characteristics, few studies have been able 

conclusively to conclusively develop a universally applicable model. Some key findings, 

however, generally are accepted in academia and industry. 

a NIA = national impact analysis; RIA = regulatory impact analysis

b XENERGY is now owned by KEMA, Inc. (www.kema.com) 

17-A-14 

 
 One accepted finding is that, regardless of their economic benefits and technological 

merits, new technologies are unlikely to be adopted by all potential users. For many products, a 

ceiling must be placed on the adoption rate. A second conclusion is that not all adopters purchase 

new products at the same time: some act quickly after a new product is introduced; others wait 

for the product to mature. Third, diffusion processes can be characterized approximately by 

asymmetric S-curves that depict three stages of diffusion: starting, accelerating, and decreasing 

(as the adoption ceiling is approached). 

A so-called epidemic model of diffusion is used widely in marketing and social studies. 

The epidemic model assumes that (1) all consumers place identical value on the benefits of a 

new product, and (2) the cost of a new product is constant or declines monotonically over time. 

What induces a consumer to purchase a new product is information about the availability and 

benefits of the product. In other words, information diffusion drives consumers’ adoption of a 

new product.

3

 The model incorporates information diffusion from both internal sources (spread 

by word of mouth from early adopters to prospective adopters) and external sources (the 

“announcement effect” produced by government agencies, institutions, or commercial 

advertising). The model incorporates both internal and external sources by combining a logistic 

function with an exponential function.4,5 

The relative degree of influence from the internal and external sources determines the 

general shape of the diffusion curve for a specific product.4,5

 If adoption of a product is 

influenced primarily by external sources of information (the announcement effect), for instance, 

a high rate of diffusion occurs at the beginning of the process. In this scenario, external sources 

provide immediate information exposure to a significant number of prospective adopters. In 

contrast, internal sources (such as a network of prospective adopters) are relatively small in size 

and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, 

information diffusion dominated by external sources is represented by a concave curve (the 

exponential curve in Figure 17-A.4.1). If adoption of a new product is influenced most strongly 

by internal sources of information, the number of adopters increases gradually, forming a convex 

curve (the logistic curve in Figure 17-A.4.1).

17-A-15 
Figure 17-A.4.1 S-Curves Showing Effects of External and Internal 

Sources on Adoption of New Technologies 

17-A.4.2 Adjustment of XENERGY Penetration Curves

 In consultation with the primary authors of the 2002 XENERGY study who later 

conducted similar California studies, DOE made some adjustments to XENERGY’s original 

implementation (penetration) curves.6 The experiences with utility programs since the 

XENERGY study indicate that incentive programs have difficulty achieving penetration rates as 

high as 80 percent. Consumer response is limited by barriers created by consumer utility issues 

and other non-economic factors. DOE therefore adjusted the maximum penetration parameters 

for some of the curves from 80 percent to the following levels: 

Moderate Barriers: 70% 

High Barriers: 60% 

Extremely High Barriers: 50% 

The low barriers and no barriers curves (the latter used only when a product has a very 

high base-case-market share) remained, respectively, with 80 percent and 100 percent as their 

maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set 

the no barriers and extremely high barriers curves as the upper and lower bounds, respectively, 

17-A-16 
for any benefit/cost ratio points higher or lower than the curves. It set another constraint such 

that the policy case market share cannot be great than 100 percent, as might occur for products 

with high base case market shares of the target-level technology.

17-A.4.3 Interpolation of Penetration Curves 

As discussed above, the XENERGY penetration (implementation) curves followed a 

functional form to estimate the market implementation rate caused by energy efficiency 

measures such as consumer rebates.c The XENERGY report presents five reference market 

implementation curves that vary according to the level of market barriers to technology 

penetration.1

 Such curves have been used by DOE in the Regulatory Impact Analyses for 

rulemakings for appliance energy efficiency standards to estimate market share increases in 

response to rebate programs.d They provide a framework for evaluating technology penetration, 

yet require matching the studied market to the curve that best represents it. This approximate 

matching can introduce some inaccuracy to the analysis. 

Blum et al (2011, Appendix A)25 presents an alternative approach to such evaluation: a 

method to estimate market implementation rates more accurately by performing interpolations of 

the reference curves. The referred report describes the market implementation rate function and 

the reference curves, the method to calibrate the function to a given market, and the limitations 

of the method. 

17-A.5 CONSUMER REBATE PROGRAMS 

DOE performed a search for rebate programs that offered incentives for medium electric 

motors. Some organizations nationwide, comprising electric utilities and regional agencies, offer 

rebate programs for medium electric motors. DOE calculated the average rebate amount per 

horsepower for each horsepower range from a sample of 37 rebates from 27 organizations. For 

those programs offering fixed rebate amounts DOE first converted these amounts into their 

corresponding values of dollar per horsepower. DOE then calculated the average rebate value per 

horsepower for each horsepower, enclosure and number of poles across all programs, and used 

shipments weighted average to calculate a rebate value per horsepower for each horsepower 

range, which DOE eventually used in the analysis described in Chapter 17. Tables 17-A.5.1 and 

17-A.5.2 provide the organizations’ name and state, shipments weighted average rebate amounts

that DOE summarized for each program, and program websites. When there is more than one 

entry for an organization, it offers different rebates in different states. The final rebate amounts 

per horsepower for each horsepower range are presented in 2013$ at the end of the table. 

c The RIA chapter refers to these curves as penetration curves. This section, in references to the original source, uses 

the term implementation curve. d DOE has also used this method to estimate market share increases resulting from consumer tax credit and 

manufacturer tax credit programs, since the effects of tax credits on markets can be considered proportional to the 

rebate impacts. 

17-A-17 

 
Table 17-A.5.1 Rebate Amounts for Medium Electric Motors (2013$/hp) 

Utility State 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

AEP Ohio OH 6.26 4.47 2.80 1.96 1.90 0.71

Alliant Energy IA 19.06 11.86 5.97 5.21 3.33 4.24

Alliant Energy MN 3.98 1.95 1.10 0.23 0.32

Arizona Public Service AZ 2.65 1.56 0.47 0.33 0.16

Avista ID 14.45 8.16 6.34 5.01 3.84

Burbank Water & power CA 17.27 6.42 6.71 6.84 7.46

Cedar Falls Utilities IA 29.03 16.64 11.33

Consolidated Edison (ConEd) NY 20.97 7.62 4.37 4.03 3.95

Dayton Power and Light OH 25.00 15.00 10.00 10.00 10.00 3.73

Empire District Electric AR 8.58 5.90 5.00 4.70 3.55

Empire District Electric MO 24.95 9.31 1.45

Groton Utilities CT 23.16 8.60 4.41 3.76 3.94

Gulf Power FL 25.00 8.00 8.00 3.00 3.00 2.57

Hawaii Energy Efficiency HI 10.00 10.00 9.44 10.00 10.00

Imperial Irrigation District CA 17.27 6.42 6.71 6.84 7.46

Kentucky Utilties Company KY 6.00 6.00 6.00 6.00 6.00 2.24

Mass Save MA 23.16 8.60 4.41 3.76 3.94

MidAmerican Energy Company IA 18.36 9.90 9.83 7.30 8.25

MidAmerican Energy Company IL 18.36 9.90 9.83 7.30 8.25

Montana Dakota Utilities MT 4.00 4.00 4.00 4.00 4.00

New York State Energy Research and 

Development Authority (NYSERDA)

NY 0.99 0.01

Otter Tail Power Company SD 17.14 15.09 10.98 8.33 7.67 6.64

PECO Energy PA 6.26 4.47 2.80 1.96 1.90 0.71

PPL Electric Utilities PA 11.12 7.74 4.52 3.56 3.29

Progress Energy FL 2.82 2.28 1.39 1.25 0.89 0.41

Sacramento Municipal Utility District CA 0.42 0.31

Tampa Electric FL 6.00 6.00 6.00 6.00 6.00 6.00

17-A-18 
Utility State 1-5 hp 6-20 hp 21-50 hp 51-100 hp 101-200 hp 201-500 hp

Tennessee Valley Authority AL 5.23 2.99 3.12 1.86 1.47

Tennessee Valley Authority GA 5.23 2.99 3.12 1.86 1.47

Tennessee Valley Authority KY 5.23 2.99 3.12 1.86 1.47

Tennessee Valley Authority MS 5.23 2.99 3.12 1.86 1.47

Tennessee Valley Authority NC 5.23 2.99 3.12 1.86 1.47

Tennessee Valley Authority TN 5.23 2.99 3.12 1.86 1.47

Tucson Electric Power AZ 3.07 1.74 1.19 0.82 0.75

Xcel Energy CO 30.04 13.70 11.53 10.19 9.67 9.00

Xcel Energy MN 15.02 6.85 5.77 5.09 4.84 4.50

Xcel Energy NM 30.04 13.70 11.53 10.19 9.67 9.00

Average 16.54 8.38 6.70 5.44 5.49 6.08

Table 17-A.5.2 References for Rebate Programs for Medium Electric Motors 

Utility State Websites

AEP Ohio OH https://www.aepohio.com/account/

Alliant Energy IA http://www.alliantenergy.com/SaveEnergyAndMoney/Rebates/BusIA/031057

Alliant Energy MN http://www.alliantenergy.com/SaveEnergyAndMoney/Rebates/BusMN/032412

Arizona Public Service AZ http://www.aps.com/en/business/Pages/home.aspx

Avista ID https://www.avistautilities.com/business/rebates/washington/Pages/incentive_12.as

px

Burbank Water & power CA http://www.burbankwaterandpower.com/index.php/incentives-forbusinesses/energy-solutions-business-rebate-programs

Cedar Falls Utilities IA http://www.cfu.net/save-energy/business-rebates.aspx

Consolidated Edison (ConEd) NY https://www.conedci.com/Motors.aspx

Dayton Power and Light OH http://www.dpandl.com/save-money/business-government/rapid-rebates/motorsdrives-and-compressed-air-rebates/

Empire District Electric AR http://empirearkansas.programprocessing.com/content/prescriptiverebates

Empire District Electric MO http://empire.programprocessing.com/content/Home

Groton Utilities CT http://www.grotonutilities.com/conserv.asp?l=2

17-A-19 
Utility State Websites

Gulf Power FL http://www.gulfpower.com/commercial/motors.asp

Hawaii Energy Efficiency HI http://www.hawaiienergyefficiency.com/59/for-your-business

Imperial Irrigation District CA http://www.iid.com/index.aspx?page=293

Kentucky Utilties Company KY http://www.lge-ku.com/rebate/commercial/download_application.asp

Mass Save MA http://www.masssave.com/business/new-construction-and-equipment/findincentives/incentive-details-business-motor-up-nstar?p=77d80d5d-ff4e-4423-b226-

5619a7729641

MidAmerican Energy Company IA http://www.midamericanenergy.com/ee/ia_bus_rebates_motors.aspx

MidAmerican Energy Company IL http://www.midamericanenergy.com/ee/il_bus_rebates_motors.aspx

Montana Dakota Utilities MT http://www.montana-dakota.com/conservation/savings-for-your-business

New York State Energy Research and 

Development Authority (NYSERDA) 

NY http://www.nyserda.ny.gov/Energy-Efficiency-and-RenewablePrograms/Commercial-and-Industrial/Business-Partners/Motors-Systems/MotorPurchasers/Purchasers-Information.aspx

Otter Tail Power Company SD https://www.otpco.com/SaveEnergyMoney/SD%20-

EEP/Pages/commercialMotorsEEP_SD.aspx

PECO Energy PA https://www.peco.com/Savings/ProgramsandRebates/Business/Pages/PECOSmartE

quipmentIncentives.aspx

PPL Electric Utilities PA https://www.pplelectric.com/save-energy-and-money/rebate-and-incentiveprograms/customer-rebates-applications.aspx

Progress Energy FL https://www.progress-energy.com/florida/business/save-energy-money/energyefficiency-for-business.page?

Sacramento Municipal Utility District CA https://www.smud.org/en/business/save-energy/rebates-incentivesfinancing/industrial-process-improvement/express-incentives.htm

Tampa Electric FL http://www.tampaelectric.com/business/saveenergy/energyefficiencientmotors/

Tennessee Valley Authority AL http://www.energyright.com/industrial/lessthan_how.html

Tennessee Valley Authority GA http://www.energyright.com/industrial/lessthan_how.html

Tennessee Valley Authority KY http://www.energyright.com/industrial/lessthan_how.html

Tennessee Valley Authority MS http://www.energyright.com/industrial/lessthan_how.html

Tennessee Valley Authority NC http://www.energyright.com/industrial/lessthan_how.html

Tennessee Valley Authority TN http://www.energyright.com/industrial/lessthan_how.html

17-A-20 
Utility State Websites

Tucson Electric Power AZ http://websafe.kemainc.com/Projects/Default.aspx?tabid=1030

Xcel Energy CO http://www.xcelenergy.com/Save_Money_&_Energy/For_Your_Business/Equipme

nt_Efficiency/Motor_and_Drive_Efficiency_-_CO

Xcel Energy MN http://www.xcelenergy.com/Save_Money_&_Energy/For_Your_Business/Equipme

nt_Efficiency/Motor_and_Drive_Efficiency_-_MN

Xcel Energy NM http://www.xcelenergy.com/Save_Money_&_Energy/For_Your_Business/Equipme

nt_Efficiency/Motor_and_Drive_Efficiency_-_NM

17-A-21 
17-A.6 FEDERAL AND STATE TAX CREDITS

This section summarizes the Federal and State tax credits available to consumers who 

purchase energy efficient appliances. This section also describes tax credits available to 

manufacturers who produce certain energy efficient appliances.

17-A.6.1 Federal Tax Credits for Consumers of Residential Appliances

EPACT 2005 included Federal tax credits for consumers who installed efficient air 

conditioners or heat pumps; gas, oil and propane furnaces and boilers; furnace fans; and/or gas, 

oil, or electric heat pump water heaters in new or existing homes.7, 8 These tax credits were in 

effect in 2006 and 2007, expired in 2008, and were reinstated for 2009–2010 by the American 

Recovery and Reinvestment Act (ARRA).9

 There was a $1,500 cap on the credit per home, 

including the amount received for insulation, windows, and air and duct sealing. Congress 

extended this provision for 2011, with some modifications to eligibility requirements, and 

reductions in the cap to $500 per home. The American Taxpayer Relief Act of 2012 extended, 

with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and 

water heaters placed in service between January 1, 2012 and December 31, 2013.7, 10

The tax credit for furnace fans was $50 in 2011, after which it expired. 

The importance of the Federal tax credits has been emphasized in research in the 

residential heating industry on the impacts of the relatively large credits that were available for 

HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors 

conducted by Vermont Energy Investment Corporation, respondents indicated that the ample 

credit had had a notable impact on sales of higher-efficiency heating and cooling equipment. 

Some distributors combined the Federal tax credits with manufacturer rebates and utility 

program rebates for a greater consumer incentive. However, when the amount of the Federal tax 

credit was reduced, smaller utility rebate incentives had not induced the same levels of 

equipment sales increases. The decrease in incentive size from a $1,500 cap in 2009-2010 to a 

$500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a 

decline in total sales of residential HVAC products. Distributors stated that an incentive needed 

to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence 

consumer choice. The industry publication “2011 HVAC Review and Outlook” noted a decline 

in sales of air conditioning units with >14 SEER in 2011 and a return in sales of units with >16 

SEER to 2009 levels (after an increase in 2010). The large majority of distributor observed no 

impacts from the utility programs with their lower rebate amounts available in 2011. Distributors 

also commented on the advantages of the Federal tax credit being nationwide in contrast to 

utility rebate programs that target regional markets.11, 12

In an effort to evaluate the potential impact of a Federal appliance tax credit program, 

DOE reviewed Internal Revenue Service (IRS) data on the numbers of taxpayers who claimed 

the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed 

Form 5695, Residential Energy Credits.13 It also estimated the percentage of taxpayers with 

entries under Form 5695’s section 3, Residential energy property costs, line 3b, qualified natural 

gas, propane, or oil furnace or hot water boiler. DOE reasoned that the percentage of taxpayers 

17-A-22
with an entry on Line 3b could serve as a rough indication of the potential of taxpayer 

participation in a Federal tax credit program for furnaces during the initial program years. DOE 

found that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 

2007, claimed a credit for a furnace or boiler. DOE further found that the percentages of those 

filing Form 5695 for any qualifying energy property expenditure (which also included 

installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 

respectively. 

 

DOE also reviewed data from an earlier Federal energy conservation tax credit program 

in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located 

data for only the first three years of the program.

14, 15, 16 For those three years - 1979, 1980, and 

1981 - the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 

percent. Given that the data from this earlier tax credit program were not disaggregated by type 

of energy property, this data series served only to indicate a possible trend of greater 

participation in the initial program year, followed by slightly smaller participation in subsequent 

years. However, DOE did not find detailed analysis of this program to indicate the possible 

reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 

2005 energy tax credit program data for its first two program years. 

As discussed in Chapter 17, Section 17.3.3, DOE analyzed the percentage of participation 

in consumer tax credit programs using its estimates of consumer participation in rebate programs 

that was based on benefit/cost data specific to each product class. Hence it was difficult to 

compare these detailed estimates to the more general data analysis described above from the 

existing Federal tax credit program, or to use the IRS data analysis in its consumer tax credit 

analysis.

17-A.6.2 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that 

produced high-efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007.17

The Emergency Economic Stabilization Act of 200818 amended the credits and extended them 

through 2010. The credits were extended again to 2011 with modifications in the eligibility 

requirements. Manufacturer tax credits were extended again, by the American Taxpayer Relief 

Act of 2012, for clothes washers, refrigerators, and dishwashers manufactured between January 

1, 2012 and December 31, 2013.19 

Manufacturers who produce these appliances receive the credits for increasing their 

production of qualifying appliances. These credits had several efficiency tiers in 2011. For 2012-

2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers 

for clothes washers and dishwashers.

10 The credit amounts applied to each unit manufactured. 

The credit to manufacturers of qualifying clothes washers, refrigerators and dishwashers was 

capped at $75 million for the period of 2008-2010. However, the most efficient refrigerator 

(30%) and clothes washer (2.2 MEF/4.5 wcf) models was not subject to the cap. The credit to 

manufacturers was capped at $25 million for 2011, with the most efficient refrigerators (35%) 

and clothes washers (2.8 MEF/3.5 WCF) exempted from this cap.20

17-A-23
17-A.6.3 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for efficient 

appliances for several years, and the States of Kentucky, Michigan and Indiana began offering 

such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on 

taxpayer participation in credits for eligible products. (See the discussion in chapter 17, section 

17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not 

disaggregate participation data by appliance, although DOE reviewed Montana's overall 

participation trends and found them congruent with its analysis of Oregon's clothes washer tax 

credits. 

Oregon’s Residential Energy Tax Credit (RETC) was created in 1977. The Oregon 

legislature expanded the RETC program in 1997 to include residential refrigerators, clothes 

washers, and dishwashers, which significantly increased participation in the program. The 

program subsequently added credits for high-efficiency heat pump systems, air conditioners, and 

water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, 

and renewable energy equipment. Beginning in 2012 a Tax Credit Extension Bill (HB3672) 

eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the 

RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters, 

and heat pump water heaters.21, 22 Those technologies recognized by the Oregon Department of 

Energy as “premium efficiency” are eligible for tax credit of $0.60 per kWh saved in the first 

year (up to $1,500).21, 23 

Montana has had an Energy Conservation Tax Credit for residential measures since 

1998.24 The tax credit covers various residential energy and water efficient products, including 

split system central air conditioning; package system central air conditioning; split system air 

source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water 

boilers; advanced main air circulating fans; heat recovery ventilators; gas, oil, or propane water 

heaters; electric heat pump water heaters; low-flow showerheads and faucets; light fixtures; and 

controls. In 2002 the amount of the credit was increased from 5 percent of product costs (up to 

$150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in 

new construction or remodeling projects. The tax credit covers only that part of the cost and 

materials that exceed established standards of construction.

17-A-24
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