TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

Commercial and Industrial Pumps

March 2015

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 ..................................................................................... 1-3

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

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

LIST OF TABLES

Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed 

Energy Conservation Standards for Commercial and Industrial 

Pumps*........................................................................................................... 1-2

Table 1.4.1 Analyses by Rulemaking Stage ..................................................................... 1-6

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CHAPTER 1. INTRODUCTION

1.1 DOCUMENT PURPOSE

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

technical analyses that the U.S. Department of Energy (DOE) conducted for evaluating new 

energy conservation standards for commercial and industrial pumps. 

1.2 SUMMARY OF NATIONAL BENEFITS

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

energy. The lifetime savings for commercial and industrial pumps purchased in the 30-year 

period that begins in the first full year of compliancea with new standards (2020–2049) amount 

to 0.27 quadrillion Btu (quads).b This is a savings of 1 percent relative to the energy use of this 

equipment in the base case without new standards.

The cumulative net present value (NPV) of total customer costs and savings of the 

proposed standards for commercial and industrial pumps ranges from $0.39 billion (at a 7-

percent discount rate) to $1.05 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 2020–2049. 

In addition, the proposed standards would have significant environmental benefits. The 

energy savings would result in cumulative emission reductions of 16 million metric tons (Mt)c of 

carbon dioxide (CO2), 73 thousand tons of methane (CH4), 12 thousand tons of sulfur dioxide 

(SO2), 23 thousand tons of nitrogen oxides (NOX), 0.22 thousand tons of nitrous oxide (N2O), 

and 0.04 tons of mercury (Hg).d The cumulative reduction in CO2 emissions through 2030 

amounts to 2.6 Mt, which is equivalent to the emissions associated with the annual electricity use 

of 0.36 million homes.

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, or SCC) developed by a recent Federal 

interagency process.e The derivation of the SCC values is discussed in chapter 14 of this NOPR 

TSD. Using discount rates appropriate for each set of SCC values, DOE estimates the present 

a In this case, the compliance date is expected to be very late 2019, so the analysis period begins in 2020.

b A quad is equal to 1015 British thermal units (Btu). c A metric ton is equivalent to 1.1 short tons. Results for emissions other than CO2 are presented in short tons. d DOE calculated emissions reductions relative to the Annual Energy Outlook 2014 (AEO 2014) Reference case, 

which generally represents current legislation and environmental regulations for which implementing regulations 

were available as of October 31, 2013. e 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. www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-forregulator-impact-analysis.pdf.

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monetary value of the CO2 emissions reduction is between $0.10 billion and $1.5 billion. DOE 

also estimates the present monetary value of the NOX emissions reduction, is $13 million at a 7-

percent discount rate and $29 million at a 3-percent discount rate.f

Table 1.2.1summarizes the national economic costs and benefits expected to result from 

the proposed standards for commercial and industrial pumps.

Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed Energy 

Conservation Standards for Commercial and Industrial Pumps*

Category

Present 

Value

Billion 2013$

Discount 

Rate

Benefits

Operating Cost Savings 0.6 7%

1.4 3%

CO2 Reduction Monetized Value ($12.0/t case)** 0.1 5%

CO2 Reduction Monetized Value ($40.5/t case)** 0.5 3%

CO2 Reduction Monetized Value ($62.4/t case)** 0.8 2.5%

CO2 Reduction Monetized Value ($119/t case)** 1.5 3%

NOX Reduction Monetized Value (at $2,684/ton)** 0.01 7%

0.03 3%

Total Benefits†

1.0 7%

1.9 3%

Costs

Incremental Installed Costs 0.2 7%

0.3 3%

Total Net Benefits

Including Emissions Reduction Monetized Value† 0.9 7%

1.6 3%

* This table presents the costs and benefits associated with commercial and industrial pumps shipped in 

2020−2049. These results include benefits to customers which accrue after 2049 from the products 

purchased in 2020−2049. The results account for the incremental variable and fixed costs incurred by 

manufacturers due to the standard, some of which may be incurred in preparation for the rule. 

** The CO2 values represent global monetized values of the SCC, in 2013$, in 2015 under several 

scenarios of the updated SCC values. The first three cases use the averages of SCC distributions calculated 

using 5%, 3%, and 2.5% discount rates, respectively. The fourth case represents the 95th percentile of the 

SCC distribution calculated using a 3% discount rate. The SCC time series incorporate an escalation 

factor. 

† Total Benefits for both the 3% and 7% cases are derived using the series corresponding to average SCC 

with 3-percent discount rate ($40.5/t case).

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

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1.3 OVERVIEW OF STANDARDS

Title III of the Energy Policy and Conservation Act of 1975 (42 U.S.C.6291, et seq; 

EPCA), sets forth various provisions designed to improve energy efficiency of consumer 

appliances. Part C of Title III, which for editorial reasons was re-designated as Part A-1 upon 

incorporation into the U.S. Code (42 U.S.C. 6311–6317), establishes the Energy Conservation 

Program for Certain Industrial Equipment. The covered equipment includes commercial and 

industrial pumps, the subject of today’s notice. (42 U.S.C. 6311(1)(A))g There currently are no 

energy conservation standards for commercial and industrial pumps.

In considering whether to establish standards for pumps, DOE issued a request for 

information (RFI) on June 13, 2011. 76 FR 34192. DOE received several comments in response. 

In December 2011, DOE received a letter from the Appliance Standards Awareness Project 

(ASAP) and the Hydraulic Institute indicating that efficiency advocates (including ASAP, 

American Council for an Energy-Efficient Economy, Natural Resources Defense Council, and 

Northwest Energy Efficiency Alliance) and pump manufacturers (as represented by the 

Hydraulic Institute) had initiated discussions regarding potential energy conservation standards 

for commercial and industrial pumps. (EERE–2011–BT–STD–0031-0011) In subsequent letters 

in March and April 2012, and in a meeting with DOE in May 2012, the interested parties 

described a tentative path forward regarding three issues: energy conservation standards for 

water pumps, certification and labeling, and an “extended product” approach. (EERE-2011-BTSTD-0031-0010 and -0012) 

On February 1, 2013, DOE published a notice in the Federal Register that announced the 

availability of the commercial and industrial pumps energy conservation standard framework 

document, solicited comment on the document, and invited all interested parties to a public 

meeting to discuss the document. 78 FR 7304. The framework document described the 

procedural and analytical approaches that DOE anticipated using to evaluate energy conservation 

standards for pumps, addressed stakeholder comments related to the RFI, and identified and 

solicited comment on various issues to be resolved in the rulemaking. (EERE–2011–BT–STD–

0031-0013)

Under the Appliance Standards and Rulemaking Federal Advisory Committee (ASRAC), 

DOE established a working group to negotiate a notice of proposed rulemaking (NOPR) for 

energy conservation standards for commercial and industrial pumps. Specifically, on July 23, 

2013, DOE issued a notice of intent to establish the working group to negotiate a NOPR for 

commercial and industrial pumps. 78 FR 44036. The working group was established under the 

ASRAC in accordance with the Federal Advisory Committee Act and the Negotiated 

Rulemaking Act. (5 U.S.C. App. 2; 5 U.S.C. 561-570, Pub. L. 104-320) The group was directed 

to discuss and, if possible, reach consensus on a proposed rule for the energy efficiency of 

commercial and industrial pumps. The working group, which was to consist of representatives of 

g All references to EPCA in this document refer to the statute as amended through the American Energy 

Manufacturing Technical Corrections Act of 2012, Pub. L. 112-210 (Dec. 18, 2012).

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parties having a defined stake in the outcome of the proposed standards, would consult as 

appropriate with a range of technical experts. 

The working group met in person during seven sets of meetings held December 18–19, 

2013, and January 30–31, March 4–5, March 26–27, April 29–30, May 28–29, and June 17–19, 

2014. On June 19, 2014, the working group reached consensus on proposed energy conservation 

standards for an agreed-upon scope of pumps. The working group assembled their 

recommendations into a term sheet. (EERE-2013-BT-NOC-0039-0092) DOE considered the 

intent of the approved term sheet, along with other comments received during the rulemaking 

process, in developing proposed energy conservation standards.

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

Under EPCA, when DOE evaluates 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) and 6316(a))

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).

Before DOE determines whether to adopt a proposed energy conservation standard, it 

first must solicit comments on the proposed standard. DOE considers the participation of 

interested parties a vital 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 provided at each stage of a rulemaking. 

Beginning with the preliminary analysis for this rulemaking and during subsequent comment 

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periods, interactions among interested parties provided a balanced discussion of the information 

required for the standards rulemaking.

Any new or amended standard must be designed to 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 proposed standard 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 framework document is published, the energy conservation standards 

rulemaking process typically involves three additional, formal public notices, which DOE 

publishes in the Federal Register. The first rulemaking notice generally 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. Because DOE 

established a working group under ASRAC that held several meetings open to the public and 

provided participants with models publically available in the docket, DOE did not issue a NOPM 

for this rulemaking. What is generally the second notice is the NOPR, which presents a 

discussion of comments received in response to previous activities such as publication of the 

framework document; analyses of the impacts of potential energy conservation standards on 

consumers, manufacturers, and the Nation; DOE’s weighting of those impacts; and the proposed 

energy conservation standards for each class of equipment. 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 impacts, the energy conservation standards DOE is adopting for 

each class of equipment, and the compliance date of the new or amended energy conservation 

standards.

This TSD describes the various analyses DOE performed in developing the NOPR, 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 the analyses, and the 

relationships among the various analyses. Table 1.4.1 outlines the analyses DOE conducts for the 

NOPR and final rule for a rulemaking.

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Table 1.4.1 Analyses by Rulemaking Stage

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  

Manufacturer impact analysis  

Utility impact analysis  

Employment impact analysis  

Emissions Analysis  

Regulatory impact analysis  

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

analyses (NIA) for each equipment class for commercial and industrial pumps. The LCC 

workbook calculates the LCC and PBP at various energy efficiency levels. The NIA workbook 

does the same for national energy savings and national net present values (NPVs). All of the 

spreadsheets are available on the DOE website for commercial and industrial pumps 

atwww1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/14.

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

and industrial pump manufacturers. DOE selected companies that represent production of all 

types of equipment, ranging from small to large manufacturers. DOE had five objectives for 

these interviews: (1) solicit manufacturer feedback on the draft inputs to the engineering 

analysis; (2) solicit manufacturer data for use in the analysis and downstream analyses; (3) solicit 

feedback on topics related to the manufacturer impact analysis; (4) provide an opportunity, early 

in the rulemaking process, for manufacturers to express their concerns to DOE; and (5) 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).

1.5 STRUCTURE OF THE DOCUMENT

This TSD describes the analytical approaches and data sources that DOE proposes to use 

in the rulemaking for commercial and industrial pumps. The TSD consists of the following 

chapters and appendices.

Chapter 1 Introduction: provides an overview of DOE's standards program for equipment 

and how it applies to the rulemaking for commercial and industrial pumps; 

outlines the structure of the document.

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Chapter 2 Analytical Framework: describes the methods, analytical tools, and 

relationships among the various analyses.

Chapter 3 Market and Technology Assessment: provides DOE’s definition of a pump, 

lists the proposed equipment classes, and names the major industry players. 

This chapter also provides an overview of pump technology, including 

techniques employed to improve pump efficiency.

Appendix 3A Pump Design Speed

Appendix 3B Efficiency Surfaces

Chapter 4 Screening Analysis: after identifying and evaluating design options for 

improving pump efficiency, determines which of those DOE screened out of 

further analysis.

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 equipment classes 

analyzed.

Appendix 5A Additional Figures: Failing C-Values Plotted Against Specific Speed and Flow 

at BEP

Appendix 5B Scaling Parameter

Chapter 6 Markups Analysis: discusses the methods used to establish price markups for 

converting manufacturer prices to customer equipment prices.

Chapter 7 Energy Use Analysis: discusses the process used for estimating energy use of 

the considered equipment as a function of efficiency level.

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

standards on individual customers and users of the equipment 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 commercial and industrial pumps that would be affected by 

standards.

Chapter 10 National Impact Analysis: discusses the methods used for projecting national 

energy consumption and customer economic impacts in the absence and 

presence of standards.

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Appendix 10A Full Fuel Cycle

Appendix 10B National Net Present Value of Customer Benefits Using Alternative Equipment 

Price Forecasts and Economic Growth Scenarios

Chapter 11 Life-Cycle Cost Subgroup Analysis: discusses the effects of standards on any 

identifiable subgroups of consumers who may be disproportionately affected 

by any proposed standard level. 

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

finances and profitability of manufacturers of commercial and industrial 

pumps. 

Appendix 12A GRIM Overview

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

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

carbon emissions.

Chapter 14 Monetization of Emission Reductions Benefits: Assigns monetary values to the 

benefits likely to result from the reduced emissions of carbon dioxide (CO2) 

and nitrogen oxides (NOX) resulting from standards.

Appendix 14A Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 

12866

Appendix 14B Technical Update of Social Cost of Carbon for Regulatory Impact Analysis 

Under Executive Order 12866

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

utility industry.

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

employment.

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

alternatives to efficiency standards.

Appendix 17A Regulatory Impact Analysis: Supporting Materials

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CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

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

2.2 MARKET AND TECHNOLOGY ASSESSMENT........................................................ 2-4

2.2.1 Market Assessment................................................................................................... 2-4

2.2.2 Technology Assessment........................................................................................... 2-4

2.3 SCREENING ANALYSIS .............................................................................................. 2-5

2.4 ENGINEERING ANALYSIS.......................................................................................... 2-5

2.5 MARKUPS ANALYSIS ................................................................................................. 2-6

2.6 ENERGY USE ANALYSIS............................................................................................ 2-6

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS ..................................... 2-6

2.8 SHIPMENTS ANALYSIS .............................................................................................. 2-7

2.9 NATIONAL IMPACT ANALYSIS................................................................................ 2-7

2.9.1 National Energy Savings.......................................................................................... 2-8

2.9.2 Net Present Value of Customer Benefits.................................................................. 2-8

2.10 CUSTOMER SUBGROUP ANALYSIS......................................................................... 2-9

2.11 MANUFACTURER IMPACT ANALYSIS.................................................................... 2-9

2.12 EMISSIONS ANALYSIS................................................................................................ 2-9

2.13 MONETIZING BENEFITS OF REDUCED EMISSIONS........................................... 2-10

2.14 UTILITY IMPACT ANALYSIS................................................................................... 2-11

2.15 EMPLOYMENT IMPACT ANALYSIS....................................................................... 2-12

2.16 REGULATORY IMPACT ANALYSIS ....................................................................... 2-12

FIGURE

Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Process..................................... 2-2

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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section 6295(o)(2)(A) of the Energy Policy and Conservation Act (EPCA), 42 U.S.C. 

6291 et seq. requires the U.S. Department of Energy (DOE) to set forth energy conservation 

standards that achieve the maximum improvement in energy efficiency that is technologically 

feasible and economically justified. This chapter describes the general analytical framework that 

DOE uses in developing such standards, and in particular, potential new energy conservation 

standards for commercial and industrial pumps. The analytical framework encompasses the 

methods, the analytical tools, and the relationships among the various analyses that are part of 

this rulemaking.

Figure 2.1.1 summarizes the analytical components of the standards-setting process. The 

focus of the figure is the third column, labeled “Analyses.” The columns labeled “Key Inputs” 

and “Key Outputs” show 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 are found in public databases; DOE collects other inputs from interested parties or other 

knowledgeable experts within the field. Key outputs are analytical results that feed directly into

the standards-setting process. Arrows connecting analyses show the types of information that 

feed from one analysis to another.

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Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Processa

a As explained in chapter 1, this rulemaking bypassed the second key output (the preliminary analysis) and went 

directly to the NOPR stage.

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Listed below are the analyses reported in this technical support document (TSD), which 

was developed in support of the notice of proposed rulemaking (NOPR) for commercial and 

industrial pumps.

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

technology options, including prototype designs.

• A screening analysis to review each technology option to decide whether it is 

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

affect product utility or product availability; or would have adverse effects on health and 

safety.

• An engineering analysis to develop cost-efficiency relationships, which indicate the 

manufacturer’s cost of achieving increased efficiency.

• A markups analysis to develop markups throughout the distribution channel(s), relating

the manufacturer production cost (MPC) to the cost to the customer.

• An energy use analysis to determine the annual energy use of the considered equipment

with and without standards for a representative group of users.

• A life-cycle cost (LCC) and payback period (PBP) analysis to calculate the savings in 

operating costs at the customer level throughout the life of the considered equipment

compared with any increase in the installed cost for the equipment likely to result directly 

from imposition of a standard.

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

the national impacts of standards on energy, net present value (NPV), and future 

manufacturer cash flows.

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

proposed energy conservation standards for the equipment, as measured by the NPV of 

total customer economic impacts and the national energy savings (NES).

• An LCC subgroup analysis to evaluate variations in customer characteristics that might 

cause a standard to disproportionately affect particular customer subpopulations.

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

manufacturers and to calculate impacts on costs, shipments, competition, employment, 

and manufacturing capacity.

• An emissions analysis to assess the impacts of proposed energy conservation standards 

on the environment.

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• An emissions monetization to assign values to the benefits associated with emissions 

reductions.

• A utility impact analysis to estimate the effects of potential standards on electric utilities.

• An employment impact analysis to assess the aggregate impacts on national employment.

• A regulatory impact analysis to examine major alternatives to new energy conservation 

standards that potentially could achieve substantially the same regulatory goal at a lower 

cost.

2.2 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the relevant markets for the 

considered equipment and technology options for improving efficiency, including prototype 

designs.

2.2.1 Market Assessment

When DOE begins a rulemaking for energy conservation standards, it obtains information 

needed to develop an overall picture of the market for the considered equipment, including the 

nature of the equipment, the industry structure, and market characteristics. This activity assesses 

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

information. The market assessment encompasses (1) manufacturer market shares and 

characteristics, (2) current regulatory and non-regulatory initiatives for improving energy 

efficiency of the equipment, and (3) trends in equipment characteristics and retail markets. This 

information serves as a resource throughout the rulemaking. 

DOE reviewed literature and interviewed manufacturers to develop an overall picture of 

the industry that serves the United States market. Industry publications and trade journals, 

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

information, including (1) names of manufacturers and their approximate market shares, (2) 

equipment characteristics, and (3) industry trends. Relevant sections of the NOPR describe the 

analyses and resulting information that underlie the trial standard levels, while the TSD provides 

supporting documentation.

2.2.2 Technology Assessment

DOE typically uses information relating to technology options and prototype designs as 

inputs to determine what technologies manufacturers may use to attain higher performance levels

for a given type of equipment. DOE developed its list of technology options for commercial and 

industrial pumps from trade publications, technical papers, and consultation with interested 

parties. Because many options for improving equipment efficiency currently are found in pumps, 

product literature and direct research provided additional information. Initially, the technologies 

encompass all those DOE believes are technologically feasible. Chapter 3 of this NOPR TSD 

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includes the detailed list of all technology options identified for commercial and industrial 

pumps.

2.3 SCREENING ANALYSIS

The screening analysis examines various technologies as to whether they: (1) are 

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

adverse impact on product utility or availability; and (4) have adverse impacts on health and 

safety. DOE developed an initial list of efficiency-enhancement options from the technologies 

identified as technologically feasible in the technology assessment. Then DOE reviewed the list 

to determine if these options are practicable to manufacture, install, and service, would adversely 

affect product utility or availability, or would have adverse impacts on health and safety. In the 

engineering analysis, DOE further considered efficiency enhancement options that it did not 

screen out in the screening analysis. Chapter 4 of the NOPR TSD contains details on the 

screening analysis for commercial and industrial pumps.

2.4 ENGINEERING ANALYSIS

The engineering analysis establishes the relationships between manufacturer production 

cost (MPC) and efficiency, and manufacturer selling price (MSP) and efficiency for each 

equipment class of commercial and industrial pumps. These relationships serve as the basis for 

calculating cost/benefit for individual customers, manufacturers, and the nation. The engineering 

analysis describes the equipment classes DOE analyzed, the representative baseline 

configuration, the incremental efficiency levels, the method DOE used to develop the MPC- and 

MSP-efficiency relationships, and the impact of efficiency improvements on the considered 

equipment. The technologies that were not eliminated in the screening analysis are designated as 

design options 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; 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 tear-downs of the equipment being analyzed.

DOE incorporated both the design option and efficiency-level approaches to develop cost

estimates at each efficiency level for commercial and industrial pumps. Based on manufacturer 

supplied cost and price data, DOE estimated the baseline MPC and MSP for the range of pumps 

available on the market. DOE then considered the incremental steps manufacturers may take to 

reach various efficiency levels. In its modeling, DOE started with the baseline MPC and assessed 

the incremental impact the design options have on MPC and MSP at each higher efficiency level.

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Chapter 5 of this NOPR TSD describes the methods and results of the activities undertaken to 

derive the MPC- and MSP-efficiency relationships.

2.5 MARKUPS ANALYSIS

DOE analyzed equipment markups to convert the manufacturer costs estimated in the 

engineering analysis to customer prices, which then were used in the LCC and PBP and the 

manufacturer impact analyses. DOE calculated markups for baseline equipment (baseline 

markups) and for more efficient equipment (incremental markups). An incremental markup 

relates the change in the MPC of higher-efficiency models (the incremental cost increase) to the 

change in the retailer or distributor sales price.

To develop markups, DOE identified how the equipment is distributed from the 

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

data from sources that included the financial filings of manufacturers and distributors to 

determine how prices are marked up as the equipment passes from the manufacturer to the 

customer. Chapter 6 of the TSD provides details on DOE’s development of markups for 

commercial and industrial pumps.

2.6 ENERGY USE ANALYSIS

DOE performed an energy use analysis to assess the energy-savings potential of higherefficiency levels, providing the basis for the energy savings used in the LCC and subsequent 

analyses. The goal of the energy use characterization is to generate a range of energy use values 

that reflects actual equipment use in the field. The analysis uses information on the use of 

equipment in the field to estimate the energy that would be used by equipment operating at 

various efficiency levels. Chapter 7 of this NOPR TSD provides more detail about DOE’s 

approach to characterizing energy use of commercial and industrial pumps.

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

In determining whether an energy efficiency standard is economically justified, DOE 

considers the economic impacts of potential standards on customers. The effects of new or 

amended standards on individual customers usually include a reduction in operating cost and an 

increase in purchase cost. DOE used the following two metrics to measure customer impacts.

• Life-cycle cost (LCC), the total customer cost of a piece of equipment, generally 

throughout the life of the equipment. The LCC calculation includes total installed cost 

(manufacturer selling price, distribution markups, sales tax, and installation costs);

operating costs (energy, repair, and maintenance costs); equipment lifetime; and a 

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discount rate. Future operating costs are discounted to the time of purchase and summed 

over the lifetime of the equipment.

• Payback period (PBP), a measure of the amount of time it takes customers to recover the 

assumed higher purchase price of more energy efficient equipment through reduced 

operating costs. Inputs to the calculation of payback period include the installed cost to 

the customer and first-year operating costs.

In determining the LCC and PBP, DOE used data regarding engineering performance, 

energy use, and markups. DOE generated LCC and PBP results using a simulation approach in 

which certain key inputs to the analysis consist of probability distributions rather than singlepoint values. That analytical technique produces outcomes that also can be expressed as 

probability distributions. As a result, the analysis produces a range of LCC and PBP results,

which enables DOE to identify the fraction of customers achieving LCC savings or incurring net 

cost at each considered efficiency level. Chapter 8 of this TSD describes the LCC and PBP 

analysis.

2.8 SHIPMENTS ANALYSIS

DOE projected future shipments of commercial and industrial pumps based on an 

analysis of key market drivers. Projections of shipments are needed to calculate the potential 

effects of standards on national energy use, NPV, and future manufacturer cash flows. DOE 

generated shipments projections for each equipment class. The projections estimate the total 

number of pumps shipped each year during the 30-year analysis period (2020–2049). To create 

the projections, DOE combined current-year shipments with results of a shipments model that 

incorporates key market drivers for commercial and industrial pumps. Chapter 9 of this TSD 

provides additional details on the shipments analysis.

2.9 NATIONAL IMPACT ANALYSIS

The NIA the NES and the NPV of total customer benefit for the efficiency levels 

considered for the equipment classes analyzed for commercial and industrial pumps. To make 

the analysis more accessible and transparent to all interested parties, DOE prepared a Microsoft 

Excel spreadsheet model to project NES and the national customer economic costs and savings 

resulting from potential new standards. The spreadsheet model uses as inputs typical values (as 

opposed to probability distributions). To assess the effect of input uncertainty on NES and NPV 

results, DOE is able to conduct sensitivity analyses by operating the model using various input 

variables. 

Several inputs for determining NES and NPV depend on the distribution of equipment 

efficiencies. For the base case, DOE uses efficiency distributions developed using shipments data 

supplied by manufacturers. Because the evidence suggests that there is no trend toward greater 

interest in higher pump efficiency, DOE assumed that the base-case distribution would remain 

2-7
constant throughout the analysis period. For this analysis, DOE used a roll-up scenario to 

establish the equipment market shares by efficiency level for the year in which compliance with 

new standards would be required. Under a roll-up scenario, all equipment that performs at 

efficiency levels lower than a prospective standard are moved, or rolled-up, to the minimum 

performance level allowed under the standard. Equipment efficiencies that exceed the new 

standard level do not change. Because DOE has no reason to believe that implementation of 

standards would increase the demand for equipment that is more efficient than the minimum 

required, it did not incorporate an efficiency trend in the standards-case scenarios either.

2.9.1 National Energy Savings 

The inputs for determining the NES for each efficiency level for each equipment class 

are: (1) annual energy consumption per unit, (2) shipments, (3) equipment stock, (4) national 

energy consumption, and (5) site-to-primary energy and full-fuel cycle conversion factors. DOE 

calculated national energy consumption by multiplying the number of units, or stock, of each 

equipment class (by vintage, or age) by the unit energy consumption (also by vintage). DOE 

calculated annual NES based on the difference in national energy consumption for the base case 

(without new efficiency standards) and for each efficiency level being considered. 

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

the recommendations of a committee on “Point-of-Use and Full-Fuel-Cycle Measurement 

Approaches to Energy Efficiency Standards” appointed by the National Academy of Science, 

DOE announced its intention to use full-fuel-cycle (FFC) measures of energy use and 

greenhouse gas and other emissions in the national impact analyses and emissions analyses 

included in future energy conservation standards rulemakings. 76 FR 51281 (August 18, 2011). 

After evaluating the approaches discussed in the August 18, 2011 notice, DOE published a 

statement of amended policy in the Federal Register in which DOE explained its determination 

that NEMS is the most appropriate tool for its FFC analysis and its intention to use NEMS for 

that purpose. 77 FR 49701 (August 17, 2012). The approach used for the NOPR is described in 

appendix 10A of the TSD.

2.9.2 Net Present Value of Customer Benefits

The inputs for determining the NPV of the total costs and benefits experienced by 

customers are (1) total annual installed cost, (2) total annual savings in operating costs, and (3) a 

discount factor. DOE calculated the difference in total installed cost between the base case and 

each TSL. Because the more efficient equipment bought in a standards case usually costs more 

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

DOE calculated net savings each year as the difference in total savings in operating costs 

and total increases in installed costs between the base case and each TSL. 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. DOE calculated savings 

throughout the life of each equipment class, accounting for differences in yearly energy rates. 

DOE calculated NPV as the difference between the present value of operating cost savings and 

2-8
the present value of total installed costs. DOE used real discount rates of 3 percent and 7 percent 

to discount future costs and savings to present values.

Chapter 10 of the NOPR TSD provides additional details regarding the national impact 

analysis.

2.10 CUSTOMER SUBGROUP ANALYSIS

The customer subgroup analysis evaluates economic impacts on particular groups of 

customers who might be affected adversely by the energy conservation standards proposed for 

the considered equipment. DOE evaluates impacts on subgroups of customers primarily by 

applying the LCC spreadsheet model to analyze the LCC impacts and PBP for those customers.

For this rulemaking, DOE analyzed users of variable frequency drives as a subgroup. Chapter 11 

of this TSD describes the customer subgroup analysis.

2.11 MANUFACTURER IMPACT ANALYSIS 

The manufacturer impact analysis (MIA) assesses the impacts of new energy 

conservation standards on manufacturers of commercial and industrial pumps. Potential impacts 

include financial effects, both quantitative and qualitative, that might lead to changes in the 

manufacturing practices for the products. DOE conducts the MIA in three phases, tailoring the 

analytical framework based on comments from interested parties. In Phase I, DOE created a 

profile of the manufacturing industry for commercial and industrial pumps and analyzed publicly 

available financial information to derive preliminary inputs for the Government Regulatory 

Impact Model (GRIM). In Phase II, DOE prepared an industry cash flow model. In Phase III, 

DOE applied the GRIM to assess industry and subgroup cash flow and NPV. DOE then assessed

impacts on competition, manufacturing capacity, employment, and cumulative regulatory 

burden. DOE describes its findings from the MIA in chapter 12 of this NOPR TSD.

2.12 EMISSIONS ANALYSIS

In the emissions analysis, DOE estimated the reduction in power sector emissions of 

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

attributable to potential energy conservation standards. In addition to estimating reductions in 

power plant emissions, DOE estimated the reductions in emissions from the activities 

(extracting, processing, and transporting) that provide the fuels needed by power plants. 

Emissions from those activities are referred to as upstream emissions. Together, the power plant 

and upstream emissions account for the full fuel cycle (FFC). In accordance with DOE’s FFC 

statement of policy (76 FR 51282 (August 18, 2011)), the FFC analysis includes impacts on 

emissions of methane and nitrous oxide, both of which are recognized as greenhouse gases 

(GHG).

2-9
DOE conducted the emissions analysis for CO2 and most of the other gases primarily 

based on emissions factors derived from the Energy Information Administration’s (EIA) Annual 

Energy Outlook 2014 (AEO 2014). EIA prepares the Annual Energy Outlook using the National 

Energy Modeling System (NEMS) for the given AEO year. Each annual version of NEMS 

incorporates the projected effects of current air quality regulations on emissions. AEO 2014 

generally represents current legislation and environmental regulations, including recent 

government actions, for which implementing regulations were available as of October 31, 2013. 

DOE estimated combustion emissions of CH4 and N2O using emissions intensity factors 

published by the Environmental Protection Agency's (EPA's) GHG Emissions Factors Hub. 

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

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

plants. Almost all ambient PM from power plants is in the form of secondary sulfates and 

nitrates, primarily SO2 and NOX, which are produced at a significant distance from the power 

plant by complex atmospheric chemical reactions. The monetary benefits that DOE estimated for 

reductions in SO2 and NOX emissions resulting from standards (see section below) are related 

primarily to the health benefits of reduced ambient PM. 

Further detail regarding DOE's emissions analysis is provided in chapter 13 of this TSD.

2.13 MONETIZING BENEFITS OF REDUCED EMISSIONS

DOE assigned monetary values to the benefits likely to result from the reduced emissions 

of CO2 and NOX expected from each TSL considered for commercial and industrial pumps. To 

assign a monetary value of benefits resulting from reduced CO2 emissions, DOE used the most 

current Social Cost of Carbon (SCC) values developed and/or agreed on by an interagency 

process. The SCC is intended to be a monetary measure of the incremental damage resulting 

from GHG emissions, including, but not limited to, net loss in agricultural productivity, human 

health effects, property damage from sea level rise, and changes in ecosystem services. Any 

effort to quantify or monetize the harms associated with climate change 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 estimate the social benefits of reductions in GHG 

emissions. 

In 2013 the Interagency Working Group on Social Cost of Carbon released an update of 

its previous report.b 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, those values increase in real terms. Additionally, the interagency group determined 

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

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. Revised November 2013.

www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-for-regulatorimpact-analysis.pdf. (Last accessed September 16, 2014.)

2-10

 
calculating domestic effects, although DOE gives preference to consideration of the global 

benefits of reducing CO2 emissions. 

DOE multiplied the CO2 emissions reduction estimated for each year by each of the four

SCC values for that year. To calculate a present value of the stream of monetary values, DOE 

discounted the values in each of the four cases using the discount rates that had been used to 

obtain the SCC values in each case. DOE recognizes that scientific and economic knowledge 

continues to evolve rapidly regarding the contribution of CO2 and other GHG to changes in the 

future global climate and the potential resulting damages to the world economy. Thus, the SCC 

values are subject to change. 

DOE also estimated the potential monetary benefits of reduced NOX emissions resulting 

from the TSLs considered. Estimates of monetary value of reducing NOX from stationary sources 

range from $476 to $4,893 per ton in 2013$.

c DOE calculated monetary benefits using an 

average value for NOX emissions of $2,684 per short ton (2013$), and real discount rates of 3 

percent and 7 percent.

DOE is investigating appropriate valuations for Hg and SO2 emissions. For this 

rulemaking DOE has not monetized estimates of SO2 and Hg reductions.

Further detail on the monetization of emission reductions is provided in chapter 14 of this

TSD.

2.14 UTILITY IMPACT ANALYSIS

In the utility impact analysis, DOE analyzes the changes in electric installed capacity and 

generation attributable to each TSL for potential energy conservation standards. The utility 

impact analysis is based on output of the Energy Information Administration (EIA)’s National 

Energy Modeling System (NEMS). NEMS is a public-domain, multi-sectorial, partialequilibrium model of the U.S. energy sector. Each year, the EIA uses NEMS to produce an 

energy forecast for the United States (the Annual Energy Outlook). The EIA publishes a 

reference case, which incorporates all energy-related policies at the time of publication, and a 

variety of side cases that analyze the impact of different policies, energy prices, and market 

trends. As of 2014, DOE is using a new method based on results published for the AEO 2014

Reference case and a set of alternative cases that implement a variety of efficiency-related 

policies. Further detail is provided in chapter 15 of this NOPR TSD.

c 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, D.C.

2-11

 
2.15 EMPLOYMENT IMPACT ANALYSIS

The adoption of energy conservation standards can affect employment both directly and 

indirectly. Direct employment effects are changes in the numbers of employees at the plants that 

produce commercial and industrial pumps. DOE evaluates direct employment impacts in the 

MIA. Indirect employment impacts may result when standards (1) cause expenditures to shift 

between goods (the substitution effect) or (2) impact income and overall expenditure levels (the 

income effect). DOE defines indirect employment impacts from standards as net jobs eliminated 

or created in the general economy as a result of increased spending driven by increased 

equipment costs and reduced spending on energy.

To investigate indirect employment impacts in the employment impact analysis, DOE 

uses the Pacific Northwest National Laboratory’s "Impact of Sector Energy Technologies” 

(ImSET) model.d The ImSET model was developed for DOE’s Office of Planning, Budget, and 

Analysis to estimate the employment and income effects of energy-saving technologies in 

buildings, industry, and transportation. Compared with approaches that simply apply an

economic multiplier, ImSET allows for a more complete and automated analysis of the economic 

impacts of energy conservation investments. Further detail is provided in chapter 16 of the 

NOPR TSD.

2.16 REGULATORY IMPACT ANALYSIS

In the NOPR stage, DOE prepares a regulatory impact analysis (RIA) pursuant to 

Executive Order 12866, Regulatory Planning and Review. 58 FR 51735 (October 4, 1993). The 

RIA addresses the potential for non-regulatory alternatives to energy conservation standards in

improving the energy efficiency or reducing 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 recorded impacts of any such 

initiatives to date, but also considers information presented by interested parties regarding the 

impacts current initiatives may have in the future. Further detail on the RIA is provided in 

chapter 17 of this TSD.

d M.J. Scott, O.V. Livingston, P.J. Balducci, J.M. Roop, and R.W. Schultz. ImSET 3.1: Impact of Sector Energy

Technologies. PNNL-18412, Pacific Northwest National Laboratory. May 2009. 

www.pnl.gov/main/publications/external/technical_reports/PNNL-18412.pdf. (Last accessed September 16, 2014.)

2-12

 
CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

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

3.2 DEFINITIONS AND SCOPE OF COVERAGE............................................................. 3-1

3.2.1 Covered Equipment ............................................................................................. 3-1

3.2.2 Scope of this Rulemaking .................................................................................... 3-1

3.3 EQUIPMENT CLASSES ................................................................................................ 3-3

3.4 TEST PROCEDURES AND ENERGY USE METRIC ................................................. 3-5

3.4.1 Rating of a Minimally Compliant Pump.............................................................. 3-6

3.5 MARKET ASSESSMENT.............................................................................................. 3-8

3.5.1 Trade Associations............................................................................................... 3-9

3.5.2 Information about Pump Manufacturers............................................................ 3-10

3.5.2.1 Market Shares........................................................................................... 3-10

3.5.2.2 Potential Impacts on Small Businesses.................................................... 3-12

3.5.3 Shipments........................................................................................................... 3-14

3.5.4 Market Performance Data .................................................................................. 3-14

3.5.5 Regulatory Programs ......................................................................................... 3-19

3.5.5.1 European Commission ............................................................................. 3-19

3.5.5.2 Mexico...................................................................................................... 3-20

3.5.5.3 China ........................................................................................................ 3-20

3.5.6 Voluntary Programs........................................................................................... 3-20

3.5.6.1 ASHRAE Standard 90.1........................................................................... 3-21

3.5.6.2 DOE Advanced Manufacturing Office (Technical Assistance–Pump 

Systems) ................................................................................................... 3-21

3.5.6.3 Industry Certifications and Standards...................................................... 3-22

3.6 TECHNOLOGY ASSESSMENT.................................................................................. 3-22

3.6.1 Technology Options........................................................................................... 3-22

3.6.1.1 Improved Surface Finish on Wetted Components ................................... 3-22

3.6.1.2 Reduced Running Clearances................................................................... 3-25

3.6.1.3 Reduced Mechanical Friction in Seals..................................................... 3-26

3.6.1.4 Reduction of Other Volumetric Losses.................................................... 3-28

3.6.1.5 Improved Hydraulic Design (Hydraulic Redesign) ................................. 3-28

3.6.1.6 Addition of a Variable Speed Drive ......................................................... 3-29

3.6.1.7 Improvement of VSD Efficiency ............................................................. 3-35

3.6.1.8 Reduced VSD Standby and Off Mode Power Usage ............................... 3-36

REFERENCES .......................................................................................................................... 3-37

LIST OF TABLES

Table 3.5.1 Top 10 U.S. Pump Suppliers with Major Brands/Operating Units ............. 3-10

Table 3.5.2 Major Suppliers of Proposed Covered Pump Categories ............................ 3-12

3-i
Table 3.5.3 Pump and Pumping Equipment Manufacturing Firms by Company 

Size4 ............................................................................................................. 3-13

Table 3.5.4 Estimates of Affected Pump Shipments, 2012 ............................................ 3-14

Table 3.5.5 Worldwide Pump Regulatory Programs...................................................... 3-19

Table 3.6.1 Range of Potential Efficiency Gains Resulting from Improved 

Hydraulic Redesign...................................................................................... 3-29

LIST OF FIGURES

Figure 3.5.1 Size Distribution (by Number of Employees) of Pump and Pump 

Equipment Manufacturing Firms in the United States4 ............................... 3-13

Figure 3.5.2 Distribution of Efficiency for ESCC.1800 Equipment Class...................... 3-16

Figure 3.5.3 Distribution of Efficiency for ESCC.3600 Equipment Class...................... 3-16

Figure 3.5.4 Distribution of Efficiency for ESFM.1800 Equipment Class...................... 3-17

Figure 3.5.5 Distribution of Efficiency for ESFM.3600 Equipment Class...................... 3-17

Figure 3.5.6 Distribution of Efficiency for IL.1800 Equipment Class............................. 3-18

Figure 3.5.7 Distribution of Efficiency for IL.3600 Equipment Class............................. 3-18

Figure 3.5.8 Distribution of Efficiency for VT-S.3600 Equipment Class ....................... 3-19

Figure 3.6.1 Estimated Points of Efficiency Increase Due to Improving Surface 

Finish of Both Impeller and Volute from 500 Ra to 200 Ra......................... 3-23

Figure 3.6.2 Estimated Efficiency Decrease Due to a 150% Increase in Wear Ring 

Clearance...................................................................................................... 3-26

Figure 3.6.3 Estimated Available Efficiency Improvements from Reduced Seal 

Friction, as a Function of Shaft Horsepower, for the ESCC.1800 

Equipment Class.......................................................................................... 3-27

Figure 3.6.4 Illustration of Pump Affinity Laws for an Example 150 hp Pump with 

a 100% Dynamic Head System Curve......................................................... 3-32

Figure 3.6.5 Illustration of Power Reductions Available for an Example Oversized 

Pump ............................................................................................................ 3-33

Figure 3.6.6 Illustration of Power Reductions Available for an Example Pump Used 

in a High Static Head Application ............................................................... 3-35

3-ii
CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter provides a profile of the commercial and industrial pump industry in the 

United States. The information the U.S. Department of Energy (DOE) gathers for a market and 

technology assessment serves as resource material throughout the rulemaking. DOE considers 

both quantitative and qualitative information from publicly available sources and interested 

parties. DOE consulted primarily publicly available information to develop the assessment 

described in this chapter. 

This chapter consists of three parts. Sections 3.2 through 3.4 deal with various issues 

pertinent to the rulemaking for commercial and industrial pumps. The market assessment 

provides an overall picture of the market for the equipment considered, including the scope of 

the covered equipment, equipment classes, industry structure, and manufacturer market shares; 

regulatory and non-regulatory programs for improving efficiency of the equipment; and market 

trends and quantities of equipment sold. The market assessment is helpful in identifying the 

major manufacturers and the characteristics of the equipment they produce, which will be 

examined further in the engineering and life-cycle cost analyses. The technology assessment 

identifies a preliminary list of technology options for increasing pump efficiency.

3.2 DEFINITIONS AND SCOPE OF COVERAGE

3.2.1 Covered Equipment

Although pumps are listed as covered equipment under 42 U.S.C. 6311(1)(A), the term 

“pump” is not defined in the Energy Policy and Conservation Act (42 U.S.C. 6311–6316; 

EPCA). In the pumps test procedure NOPR, DOE proposed a definition for pump to clarify what 

would constitute the covered equipment. Under the Appliance Standards and Rulemaking 

Federal Advisory Committee (ASRAC), DOE also established a working group to develop a 

notice of proposed rulemaking (NOPR) for energy conservation standards for commercial and 

industrial pumps. Negotiations among members of the working group produced the following 

consensus definition: Pump means a device that moves liquids (which may include entrained 

gases, free solids, and totally dissolved solids) by physical or mechanical action and includes a 

bare pump and, if included by the manufacturer, mechanical equipment, driver and controls. In 

the pumps test procedure NOPR, DOE also proposed definitions for bare pump, mechanical 

equipment, driver, and controls, as recommended by the working group. 

3.2.2 Scope of this Rulemaking

Although the pumps test procedure NOPR proposed a broad definition for pumps as 

covered equipment, DOE also proposed limiting the scope of the standards under consideration 

in this rulemaking to a narrower range of equipment. Specifically, DOE proposed a scope 

consisting of the following pump categories, which is consistent with the CIP working group’s

recommendations:

3-1
• End suction close coupled,

• End suction frame mounted/own bearings,

• In-line,

• Radially split, multi-stage, vertical in-line casing, diffuser,

• Vertical turbine submersible.

The pumps test procedure NOPR also proposes definitions for these pump categories.

For the equipment classes included in this rulemaking, DOE proposed to further limit the 

scope to pumps designed for use in pumping clean water. In the pumps test procedure NOPR, 

DOE proposed the following definitions.

Clean water pump means a pump that is designed for use in pumping water with a maximum 

non-absorbent free solid content of 0.25 kilograms per cubic meter, and with a maximum 

dissolved solid content of 50 kilograms per cubic meter, provided that the total gas 

content of the water does not exceed the saturation volume, and disregarding any 

additives necessary to prevent the water from freezing at a minimum of -10 oC. 

In the test procedure NOPR, DOE also proposed to define several kinds of pumps that are 

clean water pumps, as defined, but would not be subject to the proposed test procedure, in 

accordance with CIP Working Group recommendations. DOE proposes that these pumps would 

also not be subject to the proposed energy conservation standards:

a) fire pumps;

b) self-priming pumps;

c) prime-assist pumps;

d) sealless pumps;

e) pumps designed to be used in a nuclear facility subject to 10 CFR part 50 --

Domestic Licensing of Production and Utilization Facilities; and

f) a pump meeting the design and construction requirements set forth in Military 

Specification MIL-P-17639F, “Pumps, Centrifugal, Miscellaneous Service, Naval 

Shipboard Use” (as amended).

The pumps test procedure NOPR included additional definitions for fire pump, selfpriming pump, prime-assist pump, and sealless pump.

3-2
For pumps that meet the definition of a clean water pump, with certain exceptions as 

noted above, DOE proposed to limit the scope of this rulemaking to pumps having the following 

characteristics.

• 1 to 200 HP shaft power at best efficiency point (BEP) at full impeller diameter for the 

number of stages required for testing to the proposed standard.

• 25 gallons/minute and greater (at BEP at full impeller diameter).

• 459 feet of head maximum (at BEP at full impeller diameter).

• A design temperature that ranges from -10 to 120 °C.

• Designed to operate with either: (1) a 2- or 4-pole induction motor, or (2) a non-induction 

motor with a speed of rotation operating range that includes speeds of rotation between 

2,880 and 4,320 revolutions per minute and/or 1,440 and 2,160 revolutions per minute.

• 6-inch or smaller bowl diameter (VT-S/HI VS0).

DOE also proposed that all pump models be certified in a full impeller configuration 

only. DOE proposed a definition for full impeller in the pumps test procedure NOPR.

3.3 EQUIPMENT CLASSES

When evaluating and establishing energy conservation standards, DOE often divides 

covered equipment into classes by the type of energy used or by capacity or other performancerelated feature that justifies a different standard from that which applies to other classes of the 

same equipment. DOE proposes that commercial and industrial pumps be divided into equipment 

classes based on:

• basic pump equipment type,

• sale configuration, and

• nominal design speed.

DOE notes that some bare pumps are sold for use with engines or turbines rather than 

electric motors, and thus would use a different fuel type (i.e., fossil fuels rather than electricity). 

Because the market share of clean water pumps that use those other fuel types is small, however, 

in the pumps test procedure NOPR DOE proposed that any pump sold for use with a driver other 

than an electric motor would be rated as a bare pump.a Therefore, DOE did not establish

equipment classes by fuel type, because all are presumed to be electric.

The five pump equipment categories DOE proposes for this rulemaking, each of which 

would form the basis for an individual equipment class, are:

a Such a rating would include the hydraulic efficiency of the bare pump as well as the efficiency of a minimally 

compliant electric motor, as described in section 3.4.1.

3-3

 
• end-suction, close-coupled (ESCC);

• end-suction, frame-mounted/own bearings (ESFM);

• in-line (IL);

• radially split, multi-stage, vertical, in-line casing, diffuser (RS-V); and

• vertical turbine submersible (VT-S)

A pump’s sale configuration depends on the equipment with which it is sold. Pumps sold

inclusive of motors and continuous or non-continuous controls (as defined in the pumps test 

procedure NOPR), capable of operating at multiple driver shaft speeds, are defined as variable 

load (VL). Pumps sold as bare pumps or with motors that lack such controls, capable of 

operating at only a fixed shaft speed, are defined as constant load (CL).b

The working group for commercial and industrial pumps recommended establishing 

separate energy efficiency standards for the nominal speeds of 2- and 4-pole motors. (EERE2013-BT-NOC-0039-0092, p. 4, Recommendation #9) In its analysis, DOE found that 

throughout the market, pumps at each nominal speed demonstrate distinctly different 

performances.

c Therefore, DOE agrees with the working group's recommendation and in this 

NOPR proposes that both the CL and VL pump classes be further differentiated on the basis of 

nominal design speed. Within the scope of this proposed rulemaking, pumps are defined as 

having either 3,600 or 1,800 rpm nominal driver speeds. Pumps defined as having a 3,600 

nominal driver speed are designed to operate with a 2-pole induction motor or with a noninduction motor with a speed of rotation operating range that includes speeds of rotation between 

2,8880 and 4,320 revolutions per minute. Pumps defined as having an 1,800 nominal driver 

speed are designed to operate with a 4-pole induction motor or with a non-induction motor with a 

speed of rotation operating range that includes speeds of rotation between 1,440 and 2,160 

revolutions per minute. Throughout this document, a 3,600 rpm nominal speed is abbreviated as 

3600, and a 1,800 rpm nominal speed is abbreviated as 1800. 

Taking into account basic pump equipment categories, nominal design speeds, and sale 

configurations, DOE proposes to establish 20 equipment classes for the pumps covered by this 

rulemaking. The classes would be designated as follows.

• ESCC.1800.CL

• ESCC.3600.CL

• ESCC.1800.VL

b In the framework document, DOE explored identifying equipment types for use only in a variable load application. 

In response, the Hydraulics Institute (HI) and Grundfos reported that pump type or equipment class does not control 

whether a pump can be used in a variable load application, but that it is the application that defines this. (Grundfos, 

No. 24 at p. 21; HI, No. 25 at p. 37) The proposal is based upon the assumption that a pump sold with speed controls 

is intended for a variable load application. c See appendix 3A for an extended discussion of pump design speed and its relationship to equipment class.

3-4

 
• ESCC.3600.VL

• ESFM.1800.CL

• ESFM.3600.CL

• ESFM.1800.VL

• ESFM.3600.VL

• IL.1800.CL

• IL.3600.CL

• IL.1800.VL

• IL.3600.VL

• RS-V.1800.CL

• RS-V.3600.CL

• RS-V.1800.VL

• RS-V.3600.VL

• VT-S.1800.CL

• VT-S.3600.CL

• VT-S.1800.VL

• VT-S.3600.VL

3.4 TEST PROCEDURES AND ENERGY USE METRIC

DOE currently is conducting a rulemaking to establish a uniform test procedure for 

determining the energy efficiency of commercial and industrial pumps. In the pumps test 

procedure NOPR, DOE proposed to prescribe test methods for measuring or calculating the 

efficiency of pumps, inclusive of motors and/or controls, by measuring the produced hydraulic 

power, and the shaft power and/or electric input power to the motor or controls. Consistent with 

the recommendations of the working group, DOE proposed that the methods be based on 

Hydraulic Institute (HI) Standard 40.6-2014, “Hydraulic Institute Standard for Method for 

Rotodynamic Pump Efficiency Testing,” hereinafter referred to as “HI 40.6-2014.” (See EERE2013-BT-NOC-0039-0092, Recommendation #10) DOE proposed additions to HI 40.6-2014 to 

account for the energy performance of motors and/or controls, which is not addressed in HI 40.6-

2014. 

The test procedure NOPR proposes that the energy conservation standards for 

commercial and industrial pumps be expressed in terms of a constant-load Pump Energy Index

3-5
(PEICL) for pumps sold without controls or a variable-load PEI (PEIVL) for pumps sold with 

controls. The PEICL or PEIVL, as applicable, describes the weighted-average power input to the 

pump, inclusive of motor and/or controls, at specific load points, normalized with respect to the 

power input to a “minimally compliant pump” without controls. The metrics are defined as 

follows.

PPPPPPCCCC = �

PPPPPPCCCC

PPPPPPSSSSSS

�

Eq. 3.1

PPPPPPVV = �

PPPPPPVV

PPPPPPSSSSSS

�

Eq. 3.2

Where:

• PERCL is the average electric input power to the pump measured (or calculated) at the 

driver input for three flow points along a fixed-speed pressure-flow curve, as tested in 

accordance with the DOE test procedure. This metric applies only to pumps having a 

fixed speed.

• PERVL is the average electric input power to the pump measured (or calculated) at the 

controller input over a specified load profile as tested in accordance with the DOE test 

procedure. This metric applies only to pumps having a variable speed.

• PERSTD is the PER rating of a minimally compliant pump. PERSTD can be described as the 

allowable weighted-average electric input power to a pump, as calculated by the DOE 

test procedure. This metric applies to all equipment classes.

A measured value of PEI greater than 1.0 indicates that the pump fails to meet DOE’s 

proposed energy conservation standard, whereas a measured value less than 1.0 indicates that the 

pump exceeds requirements of the efficiency standard. 

3.4.1 Rating of a Minimally Compliant Pump

As noted in the pumps test procedure NOPR, DOE is proposing the use of a standardized 

minimally compliant bare pump, inclusive of a minimally compliant motor, as a reference pump 

for each combination of flow at BEP and specific speed for each equipment class. The minimally 

compliant pump would be defined as a function of certain physical properties of the bare pump, 

such as flow at BEP and specific speed (Ns), as used in the European Commission’s (EC's) 

minimum efficiency index (MEI). In the MEI approach, a single polynomial equation defines a 

three-dimensional surface over which minimum efficiency varies throughout a range of both 

flow and Ns. The EU uses the same equation for all equipment classes, changing only one value 

(known as the C-value) to raise or lower the surface along a vertical axis to eliminate a certain 

percentage of pumps, but without adjusting any variables that would change the shape of the 

efficiency surface. In the framework document,1 DOE suggested developing equations unique to 

each equipment class and to change the surface shape from bottom- to top-of-market to more 

accurately represent the market and efficiency levels. HI and Grundfos, on the other hand,

preferred the EC MEI approach, which eliminates the least efficient pumps by type category. 

(HI, No. 25 at p. 2; Grundfos, No. 24 at p. 14) HI added that although a better data fit could be 

3-6
obtained by changing the surface shape, such complexity is not warranted and would not yield a 

significant improvement in the ability of the equation to represent a cut-off level for pump 

efficiency. (HI, No.25 at pp.4-5, 32, 40) 

Grundfos recommended using the EU equation as well as their C-values, which would 

result in exact harmonization. (Grundfos, No. 24 at p. 14) HI, however, recommended DOE use 

the EU equation but with a modified C-value to reflect efficiency cut-off values in the United 

States. (HI, No.25 at pp.4-5, 32, 40)

After reviewing both comments from interested parties and discussions of the working 

group, DOE proposes to base its equation on the EU equation, changing the C-values to reflect 

the U.S. market. Specifically, DOE proposes to use the equation EU used to develop its standard 

(i.e., to determine the shape of the efficiency surface), but translated to 60 Hz electrical input 

power and English unitsd as shown in Eq. 3.3, to determine the efficiency of a minimally 

compliant pump.

ηηpppppppp,SSSSSS = −0.85 ∗ ln(QQ)2 − 0.38 ∗ ln(NNNN) ∗ ln(QQ) − 11.48 ∗ ln(NNNN)2 + 13.46 ∗

ln(QQ) + 179.80 ∗ ln(NNNN) − (CC − 555.6)

Eq. 3.3

Where: 

• Q = flow at BEP in gallons per minute at 60 Hz,

• Ns = specific speed at 60 Hz, and

• C = an intercept that is established for the surface based on the speed of rotation and 

equipment category of the pump model. 

The C-value is the translational component of the three-dimensional polynomial 

equation. By adjusting the C-value, DOE can increase or decrease the efficiency of a minimally 

compliant pump. For more information on efficiency surfaces and their use in developing pump 

metrics, see appendix 3B.

The calculated efficiency of a minimally compliant pump reflects pump efficiency at 

BEP. Using the scaling values implemented in the EC regulations for clean water pumps, the

value is adjusted to determine the efficiency of the minimally compliant pump at 75 percent and 

110 percent of BEP flow. The efficiency at 75 percent of BEP flow is assumed to be 94.7 percent

of that at 100 percent, and the efficiency at 110 percent of BEP flow is assumed to be 98.5 

percent of that at 100 percent of BEP flow, as shown in Eq. 3.4.

d The EC equation for defining the minimally complaint pump takes the same form, but employs different 

coefficients to reflect the fact that the flow will be reported in m3

/hr at 50 Hz and the specific speed will be reported 

in metric units. Specific speed is a dimensionless quantity, but has a different magnitude when calculated using 

metric versus English units. 

3-7

 
PPPPPPSSSSSS = ωω75% � PPHHHHHHHHHH,75%

0.947 ∗ ηηpppppppp,SSSSSS

+ LL75%� + ωω100% �

PPHHHHHHHHHH,100%

ηηpppppppp,SSSSSS

+ LL100%�

+ωω110% � PPHHHHHHHHHH,110%

0.985∗ηηpppppppp,SSSSSS

+ LL110%�

Eq. 3.4

Where: 

• ωi = weighting at each rating point (equal weighting – 0.3333);

• PHydro,i = pump power output at rating point i of the tested pump;

• ηpump,STD = efficiency of the minimally compliant pump, as determined in 

accordance with Eq. 3.4;

• Li = the motor losses at each load point i, as determined in accordance with 

the procedure specified in the DOE test procedure; and

• i = 75%, 100%, and 110% of BEP flow, as determined in accordance 

with the DOE test procedure. 

Eq. 3.4 also demonstrates how a ratio of the minimally compliant pump efficiency and 

the hydraulic output power for the rated pump is used to determine the input power to a 

minimally compliant pump at each load point. Note that the pump hydraulic output power for the 

minimally compliant pump is the same as that for the particular pump being evaluated. The

calculated shaft input power for the minimally compliant pump at each load point then would be 

combined with a minimally compliant motor for that default motor type and size and the default 

part-load loss curve, described in the proposed DOE test procedure, to determine the input power 

to the motor at each load point. Under DOE's proposed test procedure, the applicable minimum 

motor efficiency is determined as a function of pump type (open or enclosed), pole 

configuration, and horsepower rating as specified by DOE’s current energy conservation 

standards for electric motors at 10 CFR 431.25. PERSTD then is determined as the weightedaverage input power to the motor at each load point, as shown in Eq. 3.4.

DOE selected several C-values to establish the efficiency levels to be analyzed in this 

rulemaking. Each C-value and efficiency level represents a PERSTD that would remove a certain 

percentage of the lowest-performing pump models from the market. The PERSTD accounts for 

pump efficiency at all load points as well as motor losses, and does so equivalently across the 

full scope of flow and specific speed encompassed by this rulemaking. Chapter 5 provides a 

complete examination of the efficiency levels analyzed in this rulemaking.

3.5 MARKET ASSESSMENT

This section discusses trade associations, manufacturers and market share, estimated 

shipments, and regulatory and non-regulatory programs related to pumps covered by this 

rulemaking. 

3-8
3.5.1 Trade Associations

DOE recognizes the importance of trade groups in disseminating information and 

providing growth to the industry they support. To gain insight into the commercial and industrial 

pumps industry, DOE researched various associations open to manufacturers, suppliers, and 

users of industrial and commercial pumps. DOE also used the member lists of those groups to 

construct a database of domestic manufacturers. DOE identified several trade groups that support 

or have an interest in the commercial and industrial pumps industry covered by this rulemaking,

including the Hydraulic Institute, the Electrical Apparatus Service Association, the American 

National Standards Institute, the Air-Conditioning, Heating, and Refrigeration Institute, and the 

National Electrical Manufacturers Association.

Hydraulic Institute

www.pumps.org/

The Hydraulic Institute (HI) is the largest association of pump manufacturers in North America. 

HI provides product standards for the pump industry and a forum for exchanging industry 

information. The institute has established itself as the leading representative for the North 

American pump industry. HI provides members with many services and programs, including 

standards development, access to statistical data and economic reports, educational materials, 

industry news, and participation in industry initiatives. 

Electrical Apparatus Service Association

www.easa.com/

The Electrical Apparatus Service Association (EASA) is an international trade organization of 

more than 1,900 electromechanical sales and service firms in 59 countries. EASA provides many 

engineering and educational programs and enables members to keep up to date with materials, 

equipment, and technologies. 

American National Standards Institute

www.ansi.org/

The American National Standards Institute (ANSI) oversees the creation, circulation, and use of 

thousands of models and guidelines that directly affect businesses in nearly every sector of 

operation and manufacturing. ANSI is engaged in accrediting programs that assess standards and 

conformance.

Air-Conditioning, Heating, and Refrigeration Institute

www.ahrinet.org/ 

The Air-Conditioning, Heating, and Refrigeration Institute (AHRI) is one of the largest trade 

associations, representing more than 300 heating, water heating, ventilation, air conditioning, and 

commercial refrigeration manufacturers within the HVACR industry. Member companies 

account for more than 90 percent of the residential and commercial air conditioning, space 

heating, water heating, and commercial refrigeration equipment manufactured and sold in North 

America.

National Electrical Manufacturers Association (NEMA)

www.nema.org

3-9
NEMA members manufacture products used in the generation, transmission and distribution, 

control, and end use of electricity. NEMA has made numerous contributions to the electrical 

industry by contributing to public policy and operating as a central, confidential agency for 

gathering, compiling, and analyzing market statistics and economics data. 

3.5.2 Information about Pump Manufacturers

The following sections detail information DOE collected regarding manufacturers of 

commercial and industrial pumps, including estimated market shares (section 3.5.2.1) and 

potential small business impacts (section 3.5.2.2).

3.5.2.1 Market Shares

According to HI’s comments on DOE's request for information, approximately 450 pump 

manufacturers serve the U.S. market, with nine having sales of at least $1 billion, and 72 having 

sales of more than $100 million. (HI, No. 6 at p. 4) HI represents 97 pump manufacturers and 

suppliers. (HI, No. 6 at p. 1)

Ten companies represent 60 percent to 70 percent of the total U.S. pumps market: 

Grundfos, Sulzer, Weir Group, KSB, Xylem, Flowserve, Ebara, Pentair, Roper Industries, and 

ITT Goulds.2 Except for Roper Industries, all the manufacturers make pumps that fall into the 

scope of DOE's proposed rulemaking. The past 25 years have witnessed significant consolidation 

in the global pump market, and now those 10 companies comprise approximately 70 brands or 

divisions, shown in Table 3.5.1, nearly all of which were formerly independent companies. 

Table 3.5.1 Top 10 U.S. Pump Suppliers with Major Brands/Operating Units

Parent Company Major Brands/Operating Units

Grundfos Chicago, Grundfos, Morris, Paco, Peerless, Yeomans

Sulzer ABS, Johnston, Sulzer

Weir Group Begeman, Envirotech, Floway, Geho, Hazleton, Lewis, Multiflo, Warman, 

Weir

KSB GIW, KSB

Xylem A.C., Bell & Gossett, Flygt, Goulds Water Technology, Godwin, Jabsco, 

Laing, Lowara, Marlow, Red Jacket

Flowserve

ACEC, Aldrich, Byron Jackson, Cameron, Durco, Flowserve, IDP, 

Lawrence, Pacific, Pleuger, Scienco, Sier Bath, TKL, United, Western 

Land Roller, Wilson Snyder, Worthington

Ebara Ebara

Pentair

Aermotor, Aurora, Berkeley, Edwards, Fairbanks Morse, Flotec, 

Hydromatic, Hypro, Jung Pumpen, Layne/Vertiline, Myers, Nocchi, 

Shurflo, Simer, Sta-Rite

Roper Industries Abel, Cornell, Neptune, Roper

ITT Goulds AC, Goulds

Note: Brands/operating units in italics do not have manufacturing locations in the United States.

3-10
In some cases the pump brands themselves have been consolidated, but in most cases the 

legacy brand names continue to be used. Table 3.5.2 shows major suppliers of various pump 

equipment categories, some but not all of which are brands of the top 10 companies.

3-11
Table 3.5.2 Major Suppliers of Proposed Covered Pump Categories

Parent company Brand/Operating Unit ESCC ESF

M

AS/R

S VT-S

Top 10 Parent Companies

Ebara Ebara X

Flowserve Byron Jackson X

Flowserve Flowserve X X

Flowserve IDP X X

Grundfos Grundfos X X X X

Grundfos Paco X X

Grundfos Peerless X X X

ITT Goulds AC

ITT Goulds Goulds X X X X

KSB KSB X X

Pentair Aurora X X

Pentair Fairbanks Morse

Pentair Layne/Vertiline

Sulzer Johnston X

Sulzer Sulzer X

Weir Group Floway X

Xylem Bell & Gossett X X X

Xylem Goulds Water 

Technology X

Non-Top 10 Parent Companies

American Turbine 

Pump American Turbine Pump

Crane Pumps & 

Systems Burks X

Crane Pumps & 

Systems Weinman

Cascade Pump 

Company Cascade Pump Company

Gusher Pump Gusher Pump X

National Pump 

Company

National Pump 

Company X

GE Water Technology Osmonics X

Price Pump Company Price Pump Company X

Ardox Scot Pump X

Simflo Pumps Simflo Pumps

Taco Taco X

3.5.2.2 Potential Impacts on Small Businesses

Figure 3.5.1 and Table 3.5.3 display the distribution of U.S. pump and pump equipment 

manufacturing firms by number of employees. The most common size for a pump manufacturing 

firm is between 20 and 99 employees (representing 114 firms, or 26 percent of all pump 

manufacturing firms). The Small Business Administration defines small business manufacturing 

enterprises for commercial and industrial pumps as those having 500 or fewer employees.

3 Firms 

that have more than 500 employees represent only about 10 percent of the total.

4 That 10

percent, however, controls more than half the total value of the pump market. 

3-12
Figure 3.5.1 Size Distribution (by Number of Employees) of Pump and Pump Equipment 

Manufacturing Firms in the United States4

Table 3.5.3 Pump and Pumping Equipment Manufacturing Firms by Company Size4

Enterprise 

Employment

Number of 

Firms

Number of 

Establishments Employment

0–4 103 103 202

5–9 69 69 469

10–19 79 79 1,035

20–99 114 118 4,563

100–499 34 48 4,840

500+ 46 154 20,163

Total 445 571 31,272

DOE studied the potential impacts of energy conservation standards on small businesses 

in detail during the manufacturer impact analysis, which was among the analyses conducted for

the NOPR. For more details, consult chapter 12 of this NOPR TSD.

20-99

26%

0-4

23%

10-19

18%

5-9

15%

500+

10%

100-499

8%

3-13
3.5.3 Shipments

The U.S. Census Bureau has collected data on pumps (except hydraulic) and compressors 

under industry designation MA333P.

5 Because some of those pumps may not be for clean water 

applications, it is difficult to determine what percentage of shipments fall under the scope of this 

rulemaking. In addition, pump manufacturers expressed doubt that the data were reliable.

Because of those issues, HI conducted a survey of its members and provided DOE with 

shipments estimates for 2012.6 Those estimates, shown in Table 3.5.4, were reviewed in the 

working group and found to reflect the number and types of pumps covered by this rulemaking.

Table 3.5.4 Estimates of Affected Pump Shipments, 2012

Pump 

Category

Industry 

Estimate Units

ESCC 171,456

ESFM 44,042

IL 50,424

RS-V 49,975

VT-S 104,406

Total 420,303

Source: Hydraulic Institute.

The Census Bureau also provides information on exported and imported shipments, but 

the data are not disaggregated to the same extent as are the manufacturer shipment data. DOE 

notes that the historical Census data show the quantity of imported centrifugal non-submersible 

pumps to range from 9 million to 16 million per year for 2000–2010. A market consultant 

estimated that a large fraction of the imports would be ESCC and IL. Although many of the

imports would be less than the 1-HP limit, it seems reasonable that many would not be. During 

meetings of the working group, DOE attempted to ascertain whether HI's shipments estimates 

properly account for imports. It was determined that imports likely were fully accounted for in 

the scaling factor HI applied to estimate the market based on the shipments reported to them. As 

a result, DOE did not add any Census data on imported shipments to HI's shipments data.

3.5.4 Market Performance Data

As a part of this rulemaking, DOE conducted a survey of existing commercial and 

industrial pumps from major manufacturers and compiled a performance database. The database 

contained the following fields for each model:

• DOE pump type

• ANSI/HI Pump type

• Number of stages

• Bowl diameter (if applicable)

3-14
• Rated speed

• Net sales volume in USD

• Shipments volume in number of units sold

• For BEP flow, 50% of BEP flow, 75% of BEP flow, and 110% of BEP flow

o Flow in GPM

o Head in feet

o Pump efficiency

o Shaft Input Power in HP

Pump efficiency is dependent on flow and specific speed, making it difficult to evaluate 

efficiency on an equivalent basis across the entire range of pumps in scope. Therefore, using Eq. 

3.3 and Eq. 3.4, along with each pump’s calculated PER rating based on the proposed test 

procedure, DOE calculated “failing C-values” for each pump. “Failing C-value” is a calculated 

efficiency parameter that allows each pump to be evaluated on an equivalent scale. Lower failing 

C-values represent higher efficiency pumps. If a pump has a failing C-value less than the C-value 

specified for an efficiency level, it would pass that efficiency level. If a pump has a failing Cvalue greater than or equal to the C-value specified for an efficiency level, it would fail that 

efficiency level. Within an equipment class, pumps with the same failing C-value will be of the 

same relative efficiency. For all pumps, one point of C-value is always equal to one point of 

pump efficiency change. Chapter 5 provides additional information on the concept of failing Cvalues. Figure 3.5.2 through Figure 3.5.8 show the histogram distribution of failing C-value for 

the same equipment classes. 

3-15
Figure 3.5.2 Distribution of Efficiency for ESCC.1800 Equipment Class

Figure 3.5.3 Distribution of Efficiency for ESCC.3600 Equipment Class

3-16
Figure 3.5.4 Distribution of Efficiency for ESFM.1800 Equipment Class

Figure 3.5.5 Distribution of Efficiency for ESFM.3600 Equipment Class

3-17
Figure 3.5.6 Distribution of Efficiency for IL.1800 Equipment Class

Figure 3.5.7 Distribution of Efficiency for IL.3600 Equipment Class

3-18
Figure 3.5.8 Distribution of Efficiency for VT-S.3600 Equipment Class

3.5.5 Regulatory Programs

Currently, in the United States there are no regulatory programs for commercial and 

industrial pumps. Regulations on commercial and industrial pumps relevant to the scope of this 

rulemakings are in place outside the United States. Those regulations are shown in Table 3.5.5.

Table 3.5.5 Worldwide Pump Regulatory Programs

Country/Region Product Test Standard

European Union Clean water pumps BS EN ISO 

5198:1999

Mexico Submersible deep well 

clean water pumps ISO 9906

Mexico Deep well water 

pumping systems ISO 9906

China Clean water pumps ISO 9906

3.5.5.1 European Commission

Under Directive 2009/125/EC, the European Commission (EC) sets Ecodesign 

requirements for energy-using products that represent significant volumes of sales and trade, 

have a significant environmental impact, and present significant potential for improvement in 

environmental impact without entailing excessive costs.7 On January 1, 2013, a new regulation 

was established requiring minimum efficiencies for rotodynamic water pumps.8 The regulation 

3-19
sets minimum efficiency requirements for water pumps as part of the Ecodesign Directive for 

energy-consuming products. This regulation primarily addresses manufacturers that sell water 

pumps on the European market. 

The EC regulation considers pump efficiency as the metric for water pumps used in 

commercial buildings, drinking water pumping, the food industry, and agriculture. The EC 

stipulates requirements at three points along the pump pressure/flow curve when the pump is 

operating at its rated speed: full load (i.e., at BEP), part load (75 percent of flow at BEP), and 

overload (at 110 percent of flow at BEP). The EC standard sets a required efficiency for BEP;

the minimum pump efficiency at part load and at overload represent the required BEP efficiency 

multiplied by factors of 0.947 and 0.985, respectively. A failure at one or more points means the 

pump does not meet the standard. This approach, which accounts for the fact that pumps do not 

always operate at BEP, has the potential to increase pump efficiency throughout a wide range of 

operating conditions.

The EC regulation has a two-tier requirement for achieving minimum energy efficiency 

levels. The first-tier requirements were to be met by January 1, 2013, while second-tier 

requirements are to be met by January 1, 2017. The European Commission will review the 

regulation no later than four years after its entry into force and present the result of the review to 

the Ecodesign Consultation Forum.

3.5.5.2 Mexico

Mexico's federal standard NOM-010-ENER-2004 contains minimum energy performance 

standards for submersible deep well type clean-water motor pumps operated by a submersible 

three-phase electric motor.9,10 The minimum energy efficiency requirements are the product of 

the minimum pump efficiency and the minimum motor efficiency. The test procedure for the 

standard is equivalent partly to the International Standard Organization’s (ISO's) Standard 9906, 

“Rotodynamic pump, hydraulic performance acceptance tests–Grades 1 and 2,” which is 

harmonized with HI 14.6.

Standard NOM-006-ENER-1995 contains minimum energy performance standards for 

deep-well water pumping systems that consist of vertical centrifugal pumps having external or 

submersible electric motors from 7.5 to 350 HP.11,12 The standard applies to in-place systems, 

which must be retrofitted if they do not meet the standard.

3.5.5.3 China

China's national standard BG 19762-2007, “Minimum allowable values of energy 

efficiency and evaluating values of energy conservation of centrifugal pump for fresh water,” 

contains minimum energy performance standards for single-stage, single-suction clear-water 

centrifugal pumps; single-stage, double-suction clear-water centrifugal pumps; and multiplestage clear-water centrifugal pumps.13 The test procedure is based on ISO 9906.

3.5.6 Voluntary Programs

Described below are several voluntary programs that establish and recommend 

performance standards for commercial and industrial pumps.

3-20
3.5.6.1 ASHRAE Standard 90.1

The American Society of Heating, Refrigerating, and Air-Conditioning Engineers 

(ASHRAE) releases ASHRAE Standard 90.1 every three years. Currently the latest version is 

ASHRAE Standard 90.1–2013. Although the standard does not contain requirements for pump 

efficiency or power, it does prescribe requirements for “Hydronic Variable Flow Systems” 

(section 6.5.4.2) and “Hydronic System Balancing” (section 6.7.2.3.3). The former includes 

requirements for variable speed control in certain situations. The latter includes requirements for 

impeller trimming or adjusting speed to meet design flow conditions. ASHRAE Standard 90.1-

2013 also includes requirements for “Service Water Pressure Booster Systems,” which require 

pressure sensors to vary pump speed and/or start and stop pumps.

3.5.6.2 DOE Advanced Manufacturing Office (Technical Assistance–Pump 

Systems)

DOE’s Advanced Manufacturing Office (AMO) provides several resources to help 

manufacturers achieve energy and cost savings in pump systems.14 Those resources include 

software tools, training, and publications.

Software Tools. AMO's pumping system assessment tool (PSAT) is a free online software 

tool to help industrial users assess the efficiency of pumping systems. PSAT uses achievable 

pump performance data from HI standards and motor performance data from the MotorMaster+ 

database to calculate potential energy and cost savings. The tool also enables users to save and 

retrieve log files, default values, and system curves for sharing analyses with other users. DOE 

also offers a variable speed drive (VSD) calculator to help determine potential energy savings 

and simple payback from installing a VSD on a pumping system. A pump scorecard helps 

identify energy-saving opportunities for pumping systems.

Training. DOE’s AMO offers general and component-specific training. The training tools 

include the PSAT, outlining the basic operation and benefits of using the pump system tool to 

target energy-saving opportunities. The PSAT training covers practical issues involved in field

measurements of fluid and electrical data and provides the PSAT software used to assess the 

performance of pump systems. The training details how the software functions, what data are 

required, how to use the software when measured data are not available, and how to interpret the 

assessment results. DOE is working with the pumping industry and HI to train and qualify 

experts in using PSAT. Workshops prepare professionals who have extensive experience in 

pumping systems to use PSAT in their system assessments.

Technical Information. "Improving Pumping System Performance: A Sourcebook for 

Industry" (May 2006) was developed by the DOE Industrial Technologies Program (the 

predecessor to AMO) and the HI. The sourcebook, part of a series of publications on motordriven equipment, was designed to provide pump system users with a reference that outlines

opportunities to improve system performance. The sourcebook is intended to make users aware 

of potential performance improvements, provide practical guidelines, and indicate where the user 

can find additional help. AMO also offers case studies, tip sheets, and additional publications on 

life-cycle-cost analysis and variable speed pumping.

3-21
3.5.6.3 Industry Certifications and Standards

The American National Standards Institute (ANSI) and the Hydraulic Institute have 

numerous standards that include definitions and design, application, installation, operation, and 

maintenance guidelines.15 There are also standards for how to predict pump efficiency and for 

hydraulic performance acceptance tests.

3.6 TECHNOLOGY ASSESSMENT

The purpose of the technology assessment is to develop a preliminary list of technologies 

that could improve the efficiency of commercial and industrial pumps. The following assessment 

provides descriptions of technologies and designs that unless otherwise noted apply to all 

commercial and industrial pump equipment classes.

3.6.1 Technology Options

For the NOPR analysis, DOE further investigated the technologies presented in the 

Framework Document, identifying specific sub-categories for some technology options. The 

following list summarizes the technology options identified for improving the efficiency of 

commercial and industrial pumps. Each technology option is discussed in further detail in the 

subsequent sections. 

• Improved surface finish on wetted components

o Improved casting techniques

o Surface polishing and/or honing

o Surface coating or plating

• Reduced running clearances

• Reduced mechanical friction in seals

• Reduction of other volumetric losses

• Improved hydraulic design

• Addition of a variable speed drive (VSD)

• Improvement of VSD efficiency

• Reduced VSD standby and off mode power usage. 

3.6.1.1 Improved Surface Finish on Wetted Components

Surface finish (sometimes known as surface roughness) is the technical term used to 

describe the smoothness or roughness of a manufactured surface. Surface finish is generally 

characterized by measuring the vertical deviations of the roughness profile, with respect to the 

surface’s mean line. In the pump industry, surface finish typically is represented by the 

parameter Ra, which is measured in units of micro inches. Within the scope of this rulemaking, 

3-22
pumps of metallic construction generally will contain impellers and volutes/casings that range 

from 700 Ra (rough) to 150 Ra (smoother).

A smoother surface finish (lower Ra) improves efficiency. The magnitude of the 

improvement depends greatly on the specific speed of a given pump. Specific speed (Ns) is a 

dimensionless parameter that describes the geometry of a pump impeller. Specific speed

provides an indication of a pump’s pressure/flow ratio at BEP. Pumps with lower specific speeds 

(narrower internal passages) will benefit more from improved surface finish than those pumps 

with higher specific speeds (wider internal passages). Figure 3.6.1 illustrates this relationship. 

Pumps with specific speeds of 1,000 achieve improvements of approximately two efficiency 

points, while pumps with specific speeds greater than approximately 3,000 (a common design 

within the NOPR scope) achieve improvements of less than 0.4 efficiency points. Improvements 

continue to decrease at higher specific speeds. 

Figure 3.6.1 Estimated Points of Efficiency Increase Due to Improving Surface Finish of 

Both Impeller and Volute from 500 Ra to 200 Ra

16

The following subsections discuss the three approaches that are available for achieving 

improved surface finish on wetted components.

Improved Casting Techniques. Nearly all pumps within the scope of this rulemaking are 

assembled from impellers and volutes constructed of one of following: (1) fabricated and welded 

sheet metal, and (2) cast-metal components. The former make up less of the market, and their 

sheet-metal construction provides an inherently smooth surface finish (<150 Ra). The surface 

finishes of pumps assembled from cast-metal components, which constitute a larger share of the 

market, may range from >700 Ra to <150 Ra, depending on the casting technique employed. 

Each construction type has its own strengths and weaknesses. Equipment class, performance 

3-23
needs, and durability requirements may influence a manufacturer’s decision to choose one 

construction type over the other.

Within the scope of this rulemaking, cast-metal components generally are produced using 

one of three methods: (1) sand casting, (2) investment casting, or (3) vacuum die casting. Sand 

casting has the lowest tooling and start-up costse of the three casting techniques and produces the 

roughest surface finish. In the pump industry, sand cast surface finishes of 500 Ra are common. 

Sand casting can be used to cast all common pump component materials, including cast-iron, 

stainless steel, and bronze.

Investment casting and die casting are advanced impeller casting techniques that yield a 

smoother surface finish, as well as improved material quality and reduced defects. Volutes 

cannot be cast using these techniques, as they will typically exceed the maximum weight limit 

for both investment and die casting processes. Manufacturer responses to confidential surveys 

and interviewsf indicate that upfront tooling costs for investment and die casting are 

approximately 2.5 times more than sand casting. Manufacturers also indicated that on average, 

externally purchasedg

, semi-finishedh investment and die cast impellers cost the same as sand 

cast impellers when scrap rates and machine time for additional cleanup are factored in. 

Investment and die casting typically yield surface finishes of 200 Ra or less. Investment casting 

can be used for stainless steel and bronze, whereas die casting can be only used for bronze. Iron 

cannot be cast using either of the advanced techniques.

The technology option referred to as improved casting technique represents the 

conversion of specific components from sand casting to an advanced casting technique 

(investment casting or die casting), in order to achieve an improved surface finish. 

Surface Polishing and/or Honing. Polishing, honing, or similar metal removal/treatment 

techniques may be used to improve the surface finish of sand cast components. The techniques 

generally are manual operations, performed by a technician. Automated polishing and honing 

technologies exist, but these techniques are rarely seen in the pump manufacturing industry. 

Polishing and honing cannot effectively improve the surface finish of advanced-cast parts 

beyond their baseline level; as such, it is not an applicable design option for advanced-cast 

components.

Surface Coating or Plating. Sand cast parts may be coated or plated with an additional 

material that forms a surface that is smoother than the initial sand cast substrate. Hard epoxies 

e In the pump industry manufacturers will typically own the casting patterns (tooling) that is required to begin 

production of a new cast component. Outside casting vendors will use these patterns to create the cast parts for the 

manufacturer. 

f As a part of this rulemaking, DOE’s contractor(s) collected information from manufacturers through confidential 

surveys, interviews, and other instruments.

g All manufacturers interviewed indicated that cast impellers and volutes for ESCC, ESFM, and IL pumps were 

purchased from external or subsidiary casting vendors. h A semi-finished impeller is one that has been cleaned of casting defects/residue and has most final machined 

surfaces completed. A semi-finished impeller typically needs only to be trimmed to a final diameter and balanced 

before it can be installed on the pump. 

3-24

 
are the most common coating currently used in the pump industry, although various alternatives 

exist. Currently most coatings are only used for applications that require sanitary or wearresistant properties. Efficiency improvements arise as a secondary benefit.

3.6.1.2 Reduced Running Clearances

A running clearance is the space between adjacent static and rotational components. For a 

pump to run properly, a clearance must be maintained during operation. In some cases, sacrificial 

components such as wear rings and sleeves can be used to allow for limited contact under 

adverse conditions. Without a sacrificial component in place, contact between static and 

rotational components can cause serious and possibly catastrophic damage to a pump.

When setting assembly clearances, pump designers must take into account manufacturing 

tolerances, assembly variations, and operational characteristics in order to ensure that contact is 

avoided at all times. Within the pump industry, components are designed with clearances that are 

as tight as possible, while ensuring that no contact will occur under worst-case assembly stackup and operational loads.

In general, larger clearance will allow water to leak out of the intended flow path and 

flow back to the inlet within the pump casing. The “backflow” water must then be re-pumped 

out to the intended path. This recirculation action reduces the overall efficiency of the pump. 

Reducing running clearances reduces leakage and backflow in the pump, thus increasing 

efficiency.

Manufacturer survey responses and industry research indicate pump manufacturers have 

already taken measures to tighten running clearances as much as possible, given the limitations 

of current wear ring materials, machining tolerances, and pump assembly practices. As such, all 

pumps are believed to be at same the baseline level for running clearances. To further tighten 

these clearances might require alternate materials for wear rings and other running surfaces, 

tighter machining tolerances, and/or modifications to assembly procedures. 

The magnitude of efficiency gains resulting from reduced clearances is strongly 

dependent on the specific speed of a given pump. Pumps with lower specific speeds will benefit 

more from reduced running clearances than those pumps with higher specific speeds. Currently 

there is no data available to quantify the potential efficiency improvements from tightening 

clearances beyond the current baseline level. Alternatively, HI 20.3-2010 provides data on 

estimated efficiency decrease due to increased wear ring clearance. Figure 3.6.2 illustrates this

relationship for pumps that have had wear ring clearances increased by 1.5 times the standard 

clearancei

. 

Although Figure 3.6.2 does not provide direct guidance on available improvements from 

tightening clearances, it does provide insight into the relationship between specific speed and 

efficiency gains and the order of magnitude of potential improvements. 

i

“Standard clearance” is not defined in HI 20.3-2010. Standard clearance is inferred to represent the current industry 

baseline. Figure is used for illustrative purposes only.

3-25

 
Figure 3.6.2 Estimated Efficiency Decrease Due to a 150% Increase in Wear Ring 

Clearance

3.6.1.3 Reduced Mechanical Friction in Seals

Many pumps are powered by external motors. The shaft that mechanically connects the 

external motor to the impeller penetrates the pump casing. A shaft seal is used to form a barrier 

between the liquid inside the pump and the exterior environment. Although many variations 

exist, in its most basic form, a shaft seal consists of a rotating face mounted to the shaft and 

pressed by a spring against a parallel stationary face mounted to the casing. Both faces are 

perpendicular to the shaft axis. While the pump is operating, pressure in the casing increases to a 

level that forms a hydrodynamic film between the two sealing faces. When designed properly 

this hydrodynamic film lubricates the seal faces and prevents leakage to the outside environment.

This basic design relies on mechanical frictional for a seal to work properly. Frictional losses 

will be seen during start up as the seal faces rub directly, and during steady state operation as the 

hydrodynamic film is sheared within the seal gap. Because these frictional losses directly affect 

the power required to drive the pump, a reduction in friction would directly improve pump 

efficiency.

The practical method to reduce friction is to replace the current seal with a seal composed 

of lower friction materials, and if possible reduce the spring force on the face of the seal. 

Currently, carbon-ceramic seal faces are the most common type used in the industry. 

Manufacturers indicate that silicon carbide-carbon seals represent one of the lowest friction 

options currently available. All manufacturers interviewed were in consensus that such a change 

would provide very limited efficiency improvements. Some agreed that gains of up to one 

efficiency point may be possible for the smallest pumps within the scope, even though most felt 

3-26
that one point of gain was highly optimistic. All agreed that available efficiency improvements 

quickly decline as pump size increases.

For a constant speed pump, power losses in seals have a linear relationship with operating 

pressure. To illustrate this relationship, estimates of available efficiency improvements for all 

ESCC.1800 pumps in the performance database are presented in Figure 3.6.3. This plot is based 

on the premise that a typical one horsepower pumpjcan gain one efficiency point (reduce losses 

by 0.01 hp). The improvements for other pumps are calculated by linearly scaling horsepower 

losses with BEP operating pressure. The illustration indicates that while a one horsepower pump 

may be able to achieve one point of efficiency, the available improvement quickly drops off as 

pump size increase. On average, pumps larger than 10 horsepower will achieve improvements of 

less than 0.2 efficiency point, with this decreasing to 0.1 efficiency point for pumps larger than 

50 horsepower. Note that Figure 3.6.3 is for illustrative purposes only, and shows the general 

scale and trend of improvements that can be expected from typical pumps.

Figure 3.6.3 Estimated Available Efficiency Improvements from Reduced Seal Friction, 

as a Function of Shaft Horsepower, for the ESCC.1800 Equipment Class

j

Typical one horsepower pump for this analysis is assumed to be 60 GPM, 40 feet of head, and 60% efficiency at 

BEP.

3-27

 
3.6.1.4 Reduction of Other Volumetric Losses

Volumetric losses are a catch-all category to describe water that, after reaching the 

impeller, may backflow within the pump casing or leak out of the pump casing altogether. Backflowed water then must be re-pumped out to the intended path. This recirculation action reduces 

the overall efficiency of the pump. Similarly, when water is pumped but leaks out of the casing, 

the useful flow output and net hydraulic work done by the pump are reduced, which in turn 

reduces the efficiency of the pump.

Besides running clearances (discussed previously as a specific design option), one of the 

most common causes of volumetric losses is thrust balance holes. Thrust balance holes are 

located in the face of an impeller and act to balance the axial loads on the impeller shaft. 

Pressurized fluid flows through the holes, from the back to the front side of the impeller, to 

achieve a more balanced axial load distribution. The balanced load helps reduce wear on rub 

surfaces and bearings. The flow through the balance holes is considered backflow, and the 

recirculation of this flow reduces the overall efficiency of a pump.

To reduce volumetric losses from thrust balance holes, an impeller can be redesigned 

with fewer and/or smaller balance holes. Such changes would typically require the bearing to be 

redesigned to handle increased axial thrust loads from the new impeller configuration. No data is 

available to quantify potential efficiency improvements from removal or reduction of balance 

holes. When surveyed, responses of participating manufacturers indicated that such changes will 

result in improvements well below one efficiency point. The majority of manufacturers indicated 

that they would consider the resulting improvements to be insignificant with respect to their 

current efficiency ratings. 

3.6.1.5 Improved Hydraulic Design (Hydraulic Redesign)

The performance characteristics of a pump, such as flow, head, and efficiency, are a 

direct result of the hydraulic design. For the purposes of the DOE analysis, hydraulic design is a 

broad term used to describe the system design of the wetted components of a bare pump. 

Although hydraulic design focuses on the specific hydraulic characteristics of the impeller and 

the volute/casing, it also includes design choices related to clearances, seals, and other 

volumetric losses. Note that the technology options presented previously, such as clearances, 

seals, and volumetric losses, were evaluated as stand-alone, incremental options, but in this case 

they are considered part of the improved hydraulic design technology option.

Impeller and volute/casing geometries, clearances, and associated components can be 

redesigned to a higher efficiency (at the same flow and head) using a combination of historical 

best practices and modern computer-aided design (CAD) and analysis methods. The wide 

availability of modern CAD packages and techniques now enables pump designers to more 

quickly reach designs with improved vane shapes, flow paths, and cutwater designs; all of which 

work to improve the efficiency of the pump as a whole. 

DOE considers hydraulic design to be a mature technology and concluded that the 

highest possible efficiency hydraulic designs are already present in the marketplace. Although 

these high efficiency designs already exist, not all pumps in the market are at this level. As such, 

3-28
DOE assessed the range of efficiencies seen in the current pump marketplacek to determine the 

efficiency improvements available for each equipment classl. Table 3.6.1 presents potential 

efficiency gains resulting from improved hydraulic redesign, for each equipment class. These 

potential improvements represent the efficiency spread between the lowest efficiency and highest 

efficiency pumps already offered in the marketplacem. The listed efficiency spreads are 

applicable to all pumps in each equipment class, regardless of flow and specific speed. A 

manufacturer can realize these efficiency improvements in any increment, depending on the level 

of effort and capital they choose to invest in their redesign program. Chapter 5 of the TSD

discusses the relationship between efficiency gains and conversion cost in more detail.

Table 3.6.1 Range of Potential Efficiency Gains Resulting from Improved Hydraulic 

Redesign

Equipment Class

Potential Efficiency Gains 

from Hydraulic Redesign*

(Points of Efficiency)

ESCC.1800 10.72

ESCC.3600 10.65

ESFM.1800 11.28

ESFM.3600 10.52

IL.1800 11.47

IL.3600 13.66

RS-V.1800 N/A**

RS-V.3600 N/A**

VT-S.1800 10.33

VT-S.3600 10.33

* Represents spread between the lowest efficiency and highest efficiency pumps already offered in the marketplace. 

Chapter 5 provides further details on the spread between lowest and highest efficiency pumps.

**Performance data were not available for the RS-V equipment class.

 

3.6.1.6 Addition of a Variable Speed Drive

A variable speed drive (VSD) is a device that allows a compatiblen AC induction motor 

to operate at varying rotational speeds. The synchronous (no-load) rotational speed of an AC 

induction motor typically is fixed and controlled by two factors: (1) the number of magnetic 

k Assessment performed using DOE’s pump performance database. Complete details on the performance database 

are found in Chapter 5 of the TSD.

l

This assessment assumes that hydraulic design is the only available technology option to differentiate pumps 

currently on the market. This assumption is confirmed in the screening analysis, found in Chapter 4 of the TSD.

m Chapter 5 of the TSD provides additional detail on how bottom and top of the market efficiencies were 

established. 

n Not all AC induction motors are safe for use with VSDs. A compatible motor is one that is approved by the motor 

manufacturer for use with VSDs.

3-29

 
poles in the motor housing, and (2) the frequency of the AC power driving the motor. The 

equation that governs the synchronous speed of an AC induction motor is:

nnss = 111111 × ff

pp

Eq. 3.5

Where:

• ns is the synchronous speed in RPM,

• f is the motor’s supply frequency in hertz, and 

• p is the number of magnetic poles.

In the United States, the standard electricity supply frequency is 60 Hz. Thus the nominal 

synchronous speed of a four-pole motor is 1,800 RPM, and that of a two-pole motor is 3,600 

RPM.

The most common form of VSD is a variable frequency drive (VFD). A VFD is an 

electronic device that can convert the frequency of the incoming AC power supply to a range of 

frequencies, as specified by the user or control logic. Given the relationship in Eq. 3.5, the 

synchronous speed of the motor will vary linearly with these changes in supply frequency, 

allowing a VFD to directly control the speed of a motor. VFDs used for pump applications 

generally are only designed to reduce the input frequency to the motor, because often neither 

pump nor motor is designed to run beyond the nominal 60 Hz speed.

Given the proper application, varying the speed of a pump by use of a VSD has the 

potential to reduce pump energy consumption significantly. The energy-saving potential from 

varying speed results from the relationship between pump speed and pump power consumption. 

For pumps, power varies with the cube of the speed. This relationship is one of the three pump 

affinity laws, as they relate to changes in rotational speed:

PP2

PP1

= � RR2

RR1

�

3

Eq. 3.6

HH2

HH1

= � RR2

RR1

�

2

Eq. 3.7

QQ2

QQ1

= � RR2

RR1

�

1

Eq. 3.8

Where:

• RPM1, P1, H1, and Q1 are the rotational speed, power input, head, and flow at one 

operating speed, and 

3-30
• RPM2, P2, H2, and Q2 are the rotational speed, power input, head, and flow at the adjusted 

operating speed.

Figure 3.6.4 illustrates the pump affinity laws for a typical 150 hpo pump, operating on a 

100% dynamic head system curvep. For this example, when the pump’s speed is halved, the 

power is reduced from 133.7 hp to 16.7 hp; a factor of 1/8. At half speed, the pump outputs 50% 

of its original flow. For the example pump to achieve 50% of the original flow without speed 

control, a throttling valveq would typically be used. Using this configuration, the example pump 

would require 96.1 hp. The speed controlled method reduces the required power by 83%, as 

compared to the uncontrolled method. Given this example and those presented in Figure 3.6.4, it 

is clear that a VSD has the potential to significantly reduce energy consumption when used in 

applications that allow the VSD to significantly reduce speed to meet flow and head 

requirements. Actual power and energy consumption reduction results will depend on specific 

application load profiles and pump characteristics.

o 150 hp represents the nameplate motor horsepower for the pump. It is common to describe pumps by their motor 

horsepower rating.

p Dynamic head is the component of total dynamic head that results from friction losses. A 100% dynamic head 

system does not physically lift the fluid. Because all loses are a result of friction, the system requires zero head at 

zero flow. In a 100% dynamic head system, the head increases with the square of flow. 

q A throttling valve adds friction to the system and can be adjusted to allow a pump to move along its flow-head 

curve until the desired flow is reached.

3-31

 
Figure 3.6.4 Illustration of Pump Affinity Laws for an Example 150 hp Pump with a 

100% Dynamic Head System Curve

VSD are most likely to provide energy saving benefits in one of two general types of 

pump applications: (1) systems having varying loads (flows and heads), and (2) systems that are 

significantly oversized to allow for future growth, system degradation, and/or engineering safety 

margins. For the first example, the VSD, in conjunction with a feedback control loop, will vary 

the driver speed to meet the demands of the system on a continuous basis. In this scenario, power 

is reduced by running the pump at lower speeds to meet reduced flow requirements. 

For the second application, specifications may call for an oversized pump and motor, but

a VSD can be used to dial back the motor speed to meet current system needs. Without speed 

control, the operator would typically run the pump at full speed and throttle the system until the 

required flow condition is met. When a throttling valve is used, the unneeded pump pressure is 

lost to frictionr

. If the system degrades or requires increased head or flow in the future, the

variable speed system can increase the motor speed to meet the new performance requirements.

r

Alternatively, the operator may also use a bypass valve to route a part of the discharge flow back to the suction side 

of the pump. This method is less energy efficient than throttling.

3-32

 
Figure 3.6.5 uses the same 150 hp example pump to illustrate the potential power reduction 

available for oversized pumps. 

Figure 3.6.5 Illustration of Power Reductions Available for an Example Oversized Pump

Although VSDs can provide significant energy savings in certain applications, they are 

not universally applicable. For common applications, such as systems that have unvarying flow 

and head requirements (constant load), on/off operation, or high percentages of static heads

VFDs may not save energy and may even increase energy consumption when the efficiency of 

VFD unit is factored in. If a pump is properly sized for constant load and on/off operation 

applications, a VFD would save little to no energy, as the system would not spend any time 

operating at lower speed. 

Systems with high static head requirements may still have their flow controlled using 

logic and speed control, but available power reductions are less, when compared to systems with 

s

Static head is the component of total dynamic head that results from the fluid being lifted a certain height above the 

pump. Unlike dynamic head, static head requirements stay constant across the system curve, even at zero flow. 

3-33

 
high dynamic heads. Figure 3.6.6 illustrates the power reductions available for the example 150 

hp pump when a variable speed drive is used on a system with 90% static headt. At 50% flow, 

this variable speed system only reduces the required power by 23%. At 75% flow the power is 

reduced by 15%. Savings continue to decrease to zero as the pump approaches 100% flow. For 

comparison, in the high dynamic head system discussed earlier, power is reduced by 83% and 

52%, at 50% and 75% flow, respectively. With lower improvements available for high dynamic 

head systems, many systems will not be able to benefit from variable speed after VFD losses are 

factored in. Actual results will depend on the specific application load profile.

When considering the addition of a VFD, it is important to note that electrical losses 

within the VFD must be factored into any potential energy saving calculations. As such, the use 

of VFDs in less suitable applications may actually increase the energy consumption of the 

system.

t

90% of the design point total dynamic head is static head. In this case, 135 feet of static head is required. The 

remaining dynamic head varies with the square of the flow.

3-34

 
Figure 3.6.6 Illustration of Power Reductions Available for an Example Pump Used in a 

High Static Head Application

3.6.1.7 Improvement of VSD Efficiency

When evaluating the potential energy savings from use of a VFD, the efficiency of the 

VFD itself also must be considered. Efficiency varies from unit to unit and by VFD size. 

However, standardized testing of VFDs is still new to the industry and as a result, reliable VFD 

efficiency values are not yet available. In late 2011 AHRI released “AHRI 1210: Standard for 

Performance Rating of Variable Frequency Drives17,” which included standardized procedures 

for testing and measuring VFD efficiency at various load fractions. In late 2013 AHRI launched 

a voluntary certification program and began collecting data related to the standard. The 

certification program is still incomplete and related performance data are not yet publically 

available. As such, sufficient data is not yet available to estimate full and part load losses, or 

characterize the range of VFD efficiency offered in the market.

3-35
3.6.1.8 Reduced VSD Standby and Off Mode Power Usage

Standby and off-mode power usage of VSD also differs from unit to unit, with some units 

consuming more energy than others. At this time no data are available regarding standby and offmode power usage. In addition, data availability is unlikely to increase soon, because AHRI did 

not include standby and off-mode testing as a part of AHRI 1210. 

3-36
REFERENCES

1 U.S. Department of Energy, "Energy Conservation Standards Rulemaking Framework 

Document for Commercial and Industrial Pumps," 2013. 

2 Volk, M. 2012. Personal communication. 

3 U. S. Small Business Administration, "Table of Small Business Size Standards Matched to 

North American Industry Classification System Codes," Boston, MA, 2012. 

4 U.S. Census Bureau, "U.S., 6-digit NAICS, detailed employment sizes," 2010. (Last accessed 

September 16, 2014.) <www.census.gov//econ/susb/data/susb2010.html>. 

5 U.S. Census Bureau. 2011. Pumps (except hydraulic) and Compressors–2010. MA333P(08)-1. 

6 Hydraulic Institute (March 21, 2014), DOE/HI Manufacturers Survey for Fiscal Year 2012, 

Parsippany, NJ. Docket EERE-2013-BT-NOC-0039, No. 0068. (Last accessed September 16, 

2014.) <www.regulations.gov/#!documentDetail;D=EERE-2013-BT-NOC-0039-0068>.

7 Council of the European Union. Directive 2009/125/EC of the European Parliament and of the 

Council of 21 October 2009 establishing a framework for the setting of ecodesign 

requirements for energy-related products (recast). Official Journal of the European Union. L 

285, 31 October 2009. 

8 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 

2012 implementing Directive 2009/125/EC of the European Parliament and of the Council 

with regard to ecodesign requirements for water pumps. Official Journal of the European 

Union. L 165, 26 June 2012, pp. 28–36. 

9 Norma Oficial Mexicana NOM-010-ENER-2004, eficiencia energetic del conjunto motor 

bomba submergible tipo pozo profundo, limites y metodo de prueba. Diario Oficial. (Last 

accessed September 16, 2014.) 

<www.conuee.gob.mx/work/sites/CONAE/resources/LocalContent/6933/19/NOM010ENER

2004.pdf>.

10 Additional information identified through CLASP (National Commission for the Efficient 

Use of Energy). (Last accessed September 16, 2014.) 

<www.clasponline.org/en/Tools/Tools/SL_Search/SL_SearchResults/SL%20Detail%20Page.

aspx?m=a037b94d-3219-478c-bb6d-b643d19a4849>.

11 Norma Oficial Mexicana NOM-006-ENER-1995, Eficiencia energetica electromecanica en 

sistemas de bombeo para pozo profundo en operacion, limtes y metodo de prueba. Diario 

Oficial. 9 November 1995. (Last accessed September 16, 2014.) 

<www.conuee.gob.mx/work/sites/CONAE/resources/LocalContent/6933/19/NOM006ENER

1995.pdf>. 

3-37
12 Additional information identified through CLASP (National Commission for the Efficient 

Use of Energy). (Last accessed September 16, 2014.) 

<www.clasponline.org/en/Tools/Tools/SL_Search/SL_SearchResults/SL%20Detail%20Page.

aspx?m=d0930d37-239b-4a2a-8900-88fb99526c68>.

13 GB 19762-2007. The minimum allowable values of energy efficiency and evaluating values 

of energy conservation of centrifugal pump for fresh water. 

Information regarding the standard identified through CLASP (National Commission for the 

Efficient Use of Energy). (Last accessed September 16, 2014.) 

<www.clasponline.org/en/Tools/Tools/SL_Search/SL_SearchResults/SL%20Detail%20Page.

aspx?m=db8c2244-f128-41ad-9326-70680bd3ae40>.

14 U.S. Department of Energy. Advanced Manufacturing Office. “Pump Systems.” (Last 

accessed September 16, 2014.) <http://www.energy.gov/eere/amo/pump-systems>.

15 Hydraulic Institute e-Store. (Last accessed September 16, 2014.) <http://estore.pumps.org/>.

16 HI 20.3, 2010, “Hydraulic Institute Guideline for Rotodynamic (Centrifugal and Vertical) 

Pump Efficiency Prediction,” Hydraulic Institute, Parsippany, NJ, 2010. 

17 AHRI Standard 1210, 2011, “Standard for Performance Rating of Variable Frequency 

Drives,” AHRI, Arlington, VA, 2011.

3-38
APPENDIX 3A. PUMP DESIGN SPEED

TABLE OF CONTENTS

3A.1 INTRODUCTION ........................................................................................................ 3A-1

3A.2 OVERVIEW ................................................................................................................. 3A-1

3A.2.1 Size Effect......................................................................................................... 3A-2

3A.2.2 Effect of Setting Standards by Speed on Single Pumps Running at 

Multiple Speeds ................................................................................................ 3A-4

3A.3 OPTIONS FOR ADDRESSING DESIGN SPEED...................................................... 3A-6

3A.4 CONCLUSION............................................................................................................. 3A-7

REFERENCES ......................................................................................................................... 3A-8

LIST OF TABLES

Table 3A.2.1 Minimum Efficiencies for Identical Pumps Running at Two Different 

Speeds......................................................................................................... 3A-6

LIST OF FIGURES

Figure 3A.2.1 Examples of the Size Effect (Pump A versus B) and the Speed Effect 

(Same Pump, Configuration A versus A2) ................................................. 3A-2

Figure 3A.2.2 Pump Efficiency as a Function of Specific Speed ...................................... 3A-3

Figure 3A.2.3 Predicted Efficiency Differences Between ESCC Pumps Running with 

2-Pole versus 4-Pole Motors at Selected Specific Speeds (Using Eq. 

Set 1)........................................................................................................... 3A-5

Figure 3A.2.4 Predicted Efficiency Differences Between ESCC Pumps Running with 

2-Pole versus 4-Pole Motors at Selected Specific Speeds (Using Eq. 

Set 2)........................................................................................................... 3A-5

3A-i
APPENDIX 3A. PUMP DESIGN SPEED

3A.1 INTRODUCTION

Most of the content of this appendix originally was published in the framework document 

for the U.S. Department of Energy's (DOE's) rulemaking for commercial and industrial pumps. 

(78 FR 7304, February 1, 2013). Since that time DOE has revised its database of pump 

performance to be more specific to the scope of pumps identified for this rulemaking. DOE has 

not updated the analysis described in chapter 3, however, because DOE believes that the analysis 

of the original database addresses the relevant issues regarding design speed for commercial and 

industrial pumps.

3A.2 OVERVIEW 

Because operating speed for a pump affects noise, maintenance costs, net positive suction 

head required (NPSHr), and controllability requirements, as well as attainable efficiency, DOE is

evaluating whether to use operating speed as a feature that differentiates equipment classes. The 

European Commission (EC) regulation for commercial and industrial pumps contains separate 

efficiency standards for pumps that operate at 1,450 rpm and pumps that operate at 2,900 rpm 

(i.e., 4-pole and 2-pole motors, equating to 1,750 rpm and 3,500 rpm in the United States).i

DOE’s review of the European approach supports discussion of (1) the size effect captured by 

the EC standards that distinguish design speed, (2) the implications for single pumps running at 

multiple speeds, and (3) DOE’s options for addressing the issue of design speed. To help clarify 

this discussion, Figure 3A.2.1 illustrates the difference between factors (1) and (2). In the figure, 

pumps A and B demonstrate the size effect (factor 1), and pump A and A2 demonstrate a single 

pump running at multiple speeds (factor 2).a

a Note that none of the pumps shown represents an option an end user would select. When end users choose among 

pumps having different speeds, they choose among pumps that produce the same flow and head but at different 

specific speeds. This appendix, which focuses on pump manufacturing rather than pump selection, does not concern 

customer choices.

3A-1

 
Figure 3A.2.1 Examples of the Size Effect (Pump A versus B) and the Speed Effect (Same 

Pump, Configuration A versus A2) 

3A.2.1 Size Effect

Using the example of end-suction close-coupled (ESCC) pumps, the EC minimum 

efficiency surface (described in chapter 3, section 3.4.1) for pumps having 4-pole motors is two 

to three points higher than the efficiency surface for pumps having 2-pole motors.b In other 

words, for a given flow and specific speed, pumps that operate with 4-pole motors have better 

pump efficiencies (and higher EC efficiency standards) than do pumps that operate with 2-pole 

motors. This difference is not related specifically to speed, but rather to pump size. Comparing 

pumps that have the same specific speed and flow, but different speeds, means comparing two 

geometrically similar pumps—a smaller pump running at a higher speed and a larger pump 

running at a lower speed (such as pumps A and B in Figure 3A.2.1). The EC’s standard indicates 

that the latter is more efficient. 

DOE notes that other resources show efficiency as a function of flow and specific speed 

without regard to speed or physical size.ii,iii,iv Figure 3A.2.2 shows an example. Those resources 

may be relying on Anderson (1994),

iv who analyzed true Reynolds number and direct flow 

b Compare the values for C for 1,450 rpm and 2,900 rpm pumps in appendix 3B, Table 3B.2.1, for example. Note 

that the higher the C-value, the lower the minimum efficiency (because C is subtracted in the equation).

3A-2

 
quantity, concluding that the latter is a sufficiently accurate arbiter for the efficiency of a group 

of pumps. Anderson went on to state that “the efficiency of a small pump at high speed can be 

the same as a large pump at low speed, providing the shape number [specific speed] and flow are 

kept the same. That is the reason why [figures] can, in general, show efficiency against quantity 

[flow] and shape number [specific speed] without reference to physical size or operating 

speed.”iv A Europump Guide, however, notes that, when using flow instead of Reynolds number 

along with specific speed to predict efficiency, the efficiency at speeds other than 2,900 rpm may 

be different than predicted.v Because predicted efficiency differs with design speed, flow is not 

in itself a sufficient determinant of pump efficiency.

Figure 3A.2.2 Pump Efficiency as a Function of Specific Speed

The EC’s method of assigning minimum efficiency relies on changing the constant in the 

equation to raise or lower the efficiency surface based on pump speed. (See Eq. Set 1.) DOE’s 

preliminary analysis based on the EC method produced a result similar to the EC result, with 4-

pole ESCC pumps being on average 2.2 points more efficient than 2-pole ESCC pumps for a 

given flow and specific speed.c DOE notes, however, that when fitting the surfaces by allowing 

other parameters besides the constant to change (i.e., changing the shape instead of just the 

vertical position as in Eq. Set 2), in some areas of flow and specific speed, a 2-pole design is 

more efficient than a 4-pole. In other words, the data show that the relationship is not necessarily 

fixed. 

c See appendix D of the framework document for a discussion of how this difference changes depending on the 

pumps considered within the scope of clean-water pumps.

3A-3

 
Eq. Set 1: Vertical Change in Surface

ηηBBBBBB 2−pppppppp = aa (llllll)2 + bb (llllll · llllllll) + cc (llllllll)2 + dd llllll + ee llllllll + ff2

ηηBBBBBB 4−pppppppp = aa (llllll)2 + bb (llllll · llllllll) + cc (llllllll)2 + dd llllll + ee llllllll + ff4

Eq. Set 2: Change in Surface Shape 

ηηBBBBBB 2−pppppppp = aa2 (llllll)2 + bb2 (llllll · llllllll) + cc2 (llllllll)2 + dd2 llllll + ee2 llllllll + ff

ηηBBBBBB 4−pppppppp = aa4 (llllll)2 + bb4 (llllll · llllllll) + cc4 (llllllll)2 + dd4 llllll + ee4 llllllll + ff

3A.2.2 Effect of Setting Standards by Speed on Single Pumps Running at Multiple Speeds

The effect of pump size on efficiency affects any efficiency standard for single pumps 

offered at two or more speeds (such as pumps A and A2 in Figure 3A.2.1). DOE understands that 

many manufacturers offer a given pump model at two or more design speeds, but that the wet 

end of the pump is identical in both offerings. Pump affinity laws indicate that a pump should 

have the same efficiency at any speed, and indeed, many pump manufacturers publish the same 

efficiency for multiple speeds. HI 14.6 notes, however, that efficiency changes are not negligible 

for speed changes of more than 20 percent.

vi Those resources that show pump efficiency as a 

function of flow and specific speed, as discussed earlier, predict a higher efficiency for higherflow pumps at a given specific speed. Using the ESCC pump as an example, the EC’s standards 

indicate that higher flows (from higher speed, or 2-pole pumps) provide more efficiency than 

lower flows only for pumps of less than about 800 gpm (2-pole)/400 gpm (4-pole). (See Figure 

3A.2.3.) For operation at higher flows, the EC standards require a higher efficiency for 4-pole 

pumps (i.e., the same pump running at a lower speed).

For ESCC pumps in the United States, following the EC method of fitting surfaces (Eq. 

Set 1) results in higher efficiency for 2-pole than for 4-pole pumps, except at low specific speeds 

and high flows. (See Figure 3A.2.3, which was produced by calculating ηηBBBBBB 2−ppppppppand 

ηηBBBBBB 4−pppppppp using the same specific speed in each equation, but half the flow for the 4-pole pump 

compared to the 2-pole pump.) Following the fitting method illustrated in Eq. Set 2, the same 

trends apply, but the predicted differences are higher. (See Figure 3A.2.4) Finally, if DOE 

produced one surface without regard to design speed (i.e., ignoring the size effect), the efficiency 

of a pump running at 3,550 rpm (2-pole) would always be higher than the efficiency of that same 

pump running at 1,750 rpm (4-pole), and the differences would be greater than in the other 

scenarios..

3A-4
Figure 3A.2.3 Predicted Efficiency Differences Between ESCC Pumps Running with 2-

Pole versus 4-Pole Motors at Selected Specific Speeds (Using Eq. Set 1)

Figure 3A.2.4 Predicted Efficiency Differences Between ESCC Pumps Running with 2-

Pole versus 4-Pole Motors at Selected Specific Speeds (Using Eq. Set 2)

Table 3A.2.1 summarizes the differences in efficiency between pumps running with 2-

pole and 4-pole motors for each of the scenarios discussed.

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

Efficiency Difference (Percentage Points)

50 150 250 350 450 550 650 750 850 950

2-Pole - 4-Pole

Flow (gpm) - 2 pole

diff (Ns = 1000)

diff EU (Ns = 1000)

diff (Ns = 2000)

diff EU (Ns = 2000)

diff (Ns = 3000)

diff EU (Ns = 3000)

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

100200300400500600700800900

10001100120013001400150016001700180019002000

Efficiency Difference (Percentage Points)

2-Pole - 4-Pole

Flow (gpm) - 2 Pole

diff (Ns = 1000)

diff (Ns = 2000)

diff (Ns = 3000)

3A-5
Table 3A.2.1 Minimum Efficiencies for Identical Pumps Running at Two Different 

Speeds

Pump

Ns*

(U.S.)

Flow,

gpm at 

3,550 

rpm

Flow,

gpm at 

1,750 

rpm

Efficiency Difference (2-pole versus 4-pole) %

EC (Eq.

set 1)

DOE (Eq.

set 1)

DOE (Eq.

set 2)

DOE (single 

equation**)

A 1,000 200 100 2.0 2.9 2.9 5.0

B 1,000 1,200 600 -0.1 -0.7 -5.3 1.8

C 2,000 400 200 1.0 3.4 4.0 5.6

D 2,000 1,200 600 -0.4 1.3 2.1 3.6

E 3,000 400 200 0.9 4.6 4.8 6.7

F 3,000 2,000 1,000 -1.0 1.4 4.6 3.8

* Ns = specific speed.

**Using the single equation approach, DOE would not account for design speed in developing efficiency surfaces, 

so a single equation would be used no matter the speed.

Analysis of DOE’s pump performance data indicates that out of approximately 500 pairs 

of pumps in the ESCC database,d 57 percent identified 2-pole as more efficient, 29 percent 

indicated 2-pole and 4-pole were equal,e and 14 percent showed 4-pole being more efficient than 

2-pole. There appear to be no strong trends regarding which speed is more efficient for a given 

flow or specific speed. The data indicate much larger differences in efficiency between identical 

pumps running at different speeds than do the EC standards. DOE does not know whether the 

data in its database are based on testing or on assumptions of theoretical relationships such as the 

affinity laws. In addition, DOE notes that a similar analysis performed on non-submersible 

vertical turbine pumps shows a different trend, with 77 percent of pump pairs showing 2-pole 

and 4-pole to be equal, 10 percent showing 2-pole to be more efficient, and 13 percent showing 

4-pole to be more efficient (out of about 2,000 pairs). Clearly, any identified trends may not hold

true for all equipment classes and may simply be a function of the speeds for which pumps in 

each equipment class generally are optimized and how much of the data is created using affinity 

law assumptions.

3A.3 OPTIONS FOR ADDRESSING DESIGN SPEED

If DOE does not use design speed to differentiate equipment classes, it is probable that no

pump speeds would be eliminated from the market, because the only models affected would be 

those having efficiencies close to the standard level. In addition, pumps can run at multiple 

speeds, so a given model that might be eliminated at one speed may be able to meet the standard 

d Pumps having the same manufacturer, catalog, type, model, and impeller diameter, but different speeds, were 

considered pairs. The pumps did not have identical specific speeds (which identical pumps theoretically should 

have), presumably because the affinity laws are not completely accurate, which would produce different results 

based on head and flow.

e DOE does not know whether, for these pumps, manufacturers simply assumed the affinity laws, or whether the 

data represent test results.

3A-6

 
at a different speed. DOE is concerned about the implications for identical pumps offered at 

multiple speeds, however. Specifically, DOE is committed to selecting a method of surface 

fitting that produces reasonable results at all speeds. 

Regardless of whether it establishes equipment classes based on design speed, DOE must 

determine at what pump speed testing and compliance should occur. Not all pumps are designed 

to operate at all speeds, particularly high speeds, and a given equipment class will offer a range 

of pump speeds. It therefore may be difficult for DOE to select a single speed for testing each 

equipment class. In addition, some manufacturer test loops might be set up for a certain speed, so 

requiring testing at a different speed could increase manufacturer burden by requiring a change 

in test loop. Alternatively, DOE could require pumps that operate at multiple speeds to meet the 

most stringent standard (in other words, test the pump model at specified speeds, calculate the 

minimum efficiency for the pump at each speed, and require compliance with the highest

efficiency, if there is a difference). DOE notes that because not all pumps are designed to operate 

at all speeds, DOE may use manufacturer input to determine the range of speeds at which a 

particular pump model can be tested safely. 

DOE understands that some pumps are optimized for a certain design speed (which may 

not be the speed at which efficiency is the highest or the speed at which DOE requires testing), 

and that therefore either approach may penalize some pump models.

3A.4 CONCLUSION

In the framework document, DOE requested comments on the use of pump design speed 

to distinguishes equipment classes, as well as the burden associated with testing at multiple 

speeds. HI reported that a manufacturer often will need to modify a pump that will be run at 

higher speed to allow for greater bearing loads. Modifications may include changes to the 

bearing frame size or to the axial thrust balancing device, which will affect the efficiency of the 

pump. Potential modifications will differ by equipment class. (HI, No. 25 at p. 37–38) Grundfos 

added that speed is considered during the design of a pump, specifically as it relates to the design 

of the shaft and bearings. (Grundfos, No. 24 at p. 23) HI noted that pumps usually are tested 

throughout the range of speeds for which the pumps will be offered for sale. A pump 

manufacturer offering the same pump at different speeds must account for any speed-related 

effects on efficiency and determine whether the pump is compliant with the required minimum 

efficiency index at all offered speeds. (HI, No. 25 at p. 38) Both HI and Grundfos recommended 

harmonizing equipment classes with the EC, which regulates 2- and 4-pole nominal speeds 

separately, although of course the DOE classes would be at 60 Hz frequency. (Grundfos, No. 24 

at p. 22; HI, No. 25 at p. 38) 

The working group for commercial and industrial pumps also recommended separate 

energy efficiency standards for equipment operating at the nominal speeds for 2- and 4-pole 

motors. (See EERE-2013-BT-NOC-0039-0092, p. 4, Recommendation #9) In its analysis, DOE 

found that throughout the market, pumps operating at each nominal speed demonstrated

distinctly different performance. Therefore, DOE agrees with the submitted comments from 

interested parties and in the NOPR proposes that for both constant- and variable-load pumps, the 

equipment classes should also be differentiated on the basis of nominal design speed. 

3A-7
REFERENCES

i

Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 

2012 implementing Directive 2009/125/EC of the European Parliament and of the Council 

with regard to ecodesign requirements for water pumps. Official Journal of the European 

Union. L 165, 26 June 2012, pp. 28–36.

ii HI 20.3- 2010. Rotodynamic (Centrifugal and Vertical) Pump Efficiency Prediction. Hydraulic 

Institute.

iii Volk, M. 2005. Pump Characteristics and Applications, Second Edition. Taylor & Francis 

Group, LLC. Boca Raton, FL.

iv Anderson, H. 1994. Centrifugal Pumps and Allied Machinery, 4th Edition. Elsevier Science 

Publishers Ltd. Oxford, UK.

v Europump Association of Pump Manufacturers. 1999. Attainable Efficiencies of Volute Casing 

Pumps: A Reference Guide.

vi ANSI/HI 14.6-2011. Rotodynamic Pumps for Hydraulic Performance Acceptance Tests.

Hydraulic Institute.

3A-8
APPENDIX 3B. EFFICIENCY SURFACES

TABLE OF CONTENTS

3B.1 INTRODUCTION .........................................................................................................3B-1

3B.2 METRIC USED IN EUROPEAN REGULATIONS ....................................................3B-1

3B.3 SURFACES CONSIDERED FOR STANDARDS .......................................................3B-2

3B.4 CONCLUSION..............................................................................................................3B-5

REFERENCES ..........................................................................................................................3B-6

LIST OF TABLES

Table 3B.2.1 Sample C-Values for EC Minimum Efficiency Equation............................3B-2

LIST OF FIGURES

Figure 3B.3.1 DOE Average Surface for ESCC Compared with EC's 50-Percent CutOff Surface for a 4-Pole Motor....................................................................3B-3

Figure 3B.3.2 Example Average, Top-of-Market, and Bottom-of-Market Efficiency 

Levels for ESCC Pumps..............................................................................3B-4

Figure 3B.3.3 Example 2D Slice (Ns=1,500–2,500) Showing Average, Top-ofMarket, and Bottom-of-Market Efficiency Levels for ESCC Pumps..........3B-5

3B-i
APPENDIX 3B. EFFICIENCY SURFACES

3B.1 INTRODUCTION

The efficiency of a pump is determined primarily by (1) size, (2) speed, and (3) ratio of 

the diameter of the casing throat to that of the impeller.1 Research has shown that flow can be 

used as a proxy for the first two parameters, size and speed.1 The third parameter can be 

represented by specific speed. The U.S. Department of Energy (DOE) considered whether new 

energy conservation standards for commercial and industrial pumps should be a function of 

specific speed and flow. Using such standards, modeled after the European Commission (EC)

regulations for pumps, would eliminate the need to develop hundreds of equipment classes based 

on various combinations of specific speed and flow.

Most of the contents of this appendix originally appeared in the framework document for 

DOE's rulemaking regarding energy conservation standards for commercial and industrial 

pumps. Since the framework document was published (January 2013), DOE has revised its 

database of pump performance to apply more directly to the more limited scope of pumps 

identified for the rulemaking. DOE has not updated the analysis described in chapter 3, however, 

because DOE believes that the results of analyzing the original database fairly represent the 

relevant issues regarding efficiency surfaces and metrics for commercial and industrial pumps.

3B.2 METRIC USED IN EUROPEAN REGULATIONS

For stipulating minimum pump efficiency, the European Commission (EC) applies an 

equation based on pump type, rotating speed, flow, and specific speed (all parameters that affect 

efficiency), as follows.

(ƞBBBBBB)min rrrrrrrr = 88.59xx + 13.46yy − 11.48xx2 − 0.85yy2 − 0.38xx − CC PPPP TTTT ,rr

Where:

• x= ln(ns), 

o ln= natural logarithm, 

o ns= specific speed, 

• y= ln(Q), 

o Q = flow in [m3/h], and

• C= translational value.

2

The EC equation, which describes a three-dimensional surface, was defined using data 

from a 1998 investigation.2 The equation is intended to represent pumps that have the same 

stringency of achievable efficiency across all parts of the surface. The EC sets efficiency 

standards, which they refer to as minimum efficiency indexes (MEIs), based on what percentage 

of the market they desire to eliminate (also called the cut-off percentage). By changing the

3B-1
constant, C, the surface can be raised or lowered until the desired percentage of the market is 

eliminated for a given pump class (i.e., until the percentage of pumps desired to be impacted by 

the standard lies below the surface). Table 3B.2.1 shows sample values of C. After evaluating

potential standards from a 5-percent to an 80-percent cut-off, the EC stipulated a final standard 

that will undergo phased implementation from a 10-percent to a 40-percent cut-off.

3

Table 3B.2.1 Sample C-Values for EC Minimum Efficiency Equation

Equipment Class

C-value

MEI = 10% MEI = 40%

End-suction, own bearings, 1,450 rpm 132.58 128.07

End-suction, own bearings, 2,900 rpm 135.60 130.27

End-suction, close-coupled, 1,450 rpm 132.74 128.46

End-suction, close-coupled, 2,900 rpm 135.93 130.77

3B.3 SURFACES CONSIDERED FOR STANDARDS

DOE could use the EC equation as the basis for defining pump efficiency levels in its 

rulemaking. DOE’s investigations, as well as HI's, however, indicate that the distribution of 

efficiencies in the U.S. pump market differs significantly from that in the European market, so

that the EC’s minimum pump efficiency surface cannot adequately represent U.S. efficiency

levels.

DOE could address the fact that the U.S. market does not match the EC market by simply 

changing the C-value to represent a given efficiency level. (As mentioned, the EU uses the same 

surface shape for all equipment classes and MEIs.) Alternatively, DOE could (1) develop 

separate equations (surfaces) unique to each equipment class by performing regressions on the 

data in its database,a and (2) change the surface shape from bottom to top of market to more 

accurately represent the U.S. market and potential efficiency levels. Pumps that provide high 

flows, for example, typically can reach higher efficiencies at their best efficiency point (BEP)

than can pumps having lower flows. DOE could account for the fact that the difference in 

efficiency of higher-flow pumps between bottom and top of market is much smaller than the 

difference for smaller-flow pumps: the surface becomes flatter from bottom to top of market. 

DOE could develop surfaces for all pump classes using the form of the EC equation, but with 

different coefficients for each parameter in the equation (not just the C-value).b Details regarding 

the coefficients and equations can be found in appendix C of the framework document.

a Researchers in Europe note that they did not change the surface by pump type, because the primary hydraulic 

components (impeller and volute) are similar for the considered pumps. b Note that the coefficients and surfaces provided in this appendix relate to ESCC pumps of both 1800 and 3600 rpm

design speeds.

3B-2

 
Figure 3B.3.1 shows a surface that DOE developed to represent the average ESCC 

market (based on model availability), compared to the EC's MEI level of 50 percent. 

Figure 3B.3.1 DOE Average Surface for ESCC Compared with EC's 50-Percent Cut-Off 

Surface for a 4-Pole Motor

Figure 3B.3.2 shows the U.S. average ESCC surface again, along with a bottom-ofmarket and top-of-market surface. 

EU (Blue)

DOE (Green)

EC (Blue)

DOE (Green)

3B-3
Figure 3B.3.2 Example Average, Top-of-Market, and Bottom-of-Market Efficiency Levels 

for ESCC Pumps

Figure 3B.3.3 shows a two-dimensional slice through the 3D surfaces for ESCC pumps

for a speed range of 1,500 to 2,500 rpm. The slice demonstrates the flattening of the surface from 

bottom to top of market. 

Average (Green)

Bottom-of-Market (Red)

Top-of-Market (Blue)

3B-4
Figure 3B.3.3 Example 2D Slice (Ns=1,500–2,500) Showing Average, Top-of-Market, and 

Bottom-of-Market Efficiency Levels for ESCC Pumps

The figures presented above are intended only to exemplify a method for developing

surfaces. As mentioned previously, DOE recognizes that the database used for this analysis does 

not adequately represent the market for pumps in the scope of this rulemaking. 

3B.4 CONCLUSION

As discussed in chapter 3, interested parties supported using the EC surface, changing 

only the C-value, to establish efficiency levels for commercial and industrial pumps. In 

particular, HI noted that although a better data fit could be obtained by changing the surface 

shape, such complexity is not warranted and would not significantly improve the ability of the 

equation to represent a cut-off level for pump efficiency. (HI, No. 25 at pp.4–5, 32, 40) The 

working group for developing efficiency standards for commercial and industrial pumps 

(described in chapter 3) developed recommendations for efficiency levels based on using an 

efficiency surface identical to that used by the EC, except for the C-value. After reviewing 

comments from interested parties and discussions of the working group, DOE proposes to base 

its efficiency equation on the EC equation and to change the C-values to reflect the U.S. market.

Average (Green) Thick-1500; Thin-2500

Bottom-of-Market (Red)

Top-of-Market (Blue)

3B-5
REFERENCES

1 Anderson, H. 1994. Centrifugal Pumps and Allied Machinery, 4th Edition. Elsevier Science 

Publishers Ltd. Oxford, UK.

2 AEA Energy & Environment. 2008. Appendix 6: Lot 11–"Water Pumps (in commercial 

buildings, drinking water pumping, food industry, agriculture." Report to European 

Commission.

3 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 

2012 implementing Directive 2009/125/EC of the European Parliament and of the Council 

with regard to ecodesign requirements for water pumps. Official Journal of the European 

Union. L 165, June 26, 2012, pp. 28–36.

3B-6
CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 SCREENED-OUT TECHNOLOGIES............................................................................ 4-2

4.2.1 Improved Surface Finish of Wetted Components................................................ 4-2

4.2.1.1 Improved Casting Techniques.................................................................... 4-3

4.2.1.2 Surface Polishing and/or Honing ............................................................... 4-3

4.2.1.3 Surface coating or plating .......................................................................... 4-3

4.2.2 Reduced Running Clearances.............................................................................. 4-4

4.2.3 Reducing Mechanical Friction in Seals ............................................................... 4-4

4.2.4 Reduction of Other Volumetric Losses................................................................ 4-4

4.2.5 Addition of a Variable Speed Drive..................................................................... 4-5

4.2.6 Improvement of VSD Efficiency......................................................................... 4-5

4.2.7 Reduced VSD Standby and Off Mode Power Usage........................................... 4-5

4.2.8 Remaining Technology........................................................................................ 4-5

REFERENCES ............................................................................................................................ 4-7

4-i
CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter details the screening analysis that the U.S. Department of Energy (DOE) 

conducted in support of the ongoing energy conservation standards rulemakings for pumps.

In chapter 3, the market and technology assessment (MTA), DOE presented an initial list 

of technologies that can improve the energy efficiency of pumps. The purpose of the screening 

analysis is to evaluate the technologies that improve equipment efficiency to determine which 

technologies to consider further and which to screen out. DOE consulted with a range of parties, 

including industry, technical experts, and others to develop a list of technologies for 

consideration. DOE evaluated the technologies pursuant to the criteria set out in the Energy 

Policy and Conservation Act (EPCA), as amended. (42 U.S.C. 6311-6317) 

Section 325(o) EPCA establishes criteria for prescribing new or amended standards 

designed to achieve the maximum improvement in energy efficiency. Further, EPCA directs the 

Secretary of Energy to determine whether a standard is technologically feasible and 

economically justified. (42 U.S.C. 6295(o)(2)(A), as directed by 42 U.S.C. 6316(a)(1)-(3)). 

EPCA also establishes guidelines for determining whether a standard is economically justified. 

(42 U.S.C. 6295(o)(2)(B)) Appendix A to subpart C of Title 10, Code of Federal Regulations, 

Part 430 (10 CFR Part 430), “Procedures, Interpretations and Policies for Consideration of New 

or Revised Energy Conservation Standards for Consumer Products” (the Process Rule), sets forth 

procedures to guide DOE in its consideration and promulgation of new or revised equipment 

energy conservation standards. These procedures elaborate on the statutory criteria provided in 

42 U.S.C. 6295(o) and, in part, eliminate problematic technologies early in the process of 

prescribing or amending an energy efficiency standard. In particular, sections 4(b)(4) and 5(b) of 

the Process Rule guide DOE in determining whether to eliminate from consideration any 

technology that presents unacceptable problems with respect to the following criteria: 

Technological feasibility. Technologies incorporated in commercial equipment or in working 

prototypes will be considered technologically feasible.

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 that technology will be considered practicable to manufacture, install, and 

service.

Impacts on equipment utility or equipment availability. If a technology is determined to have 

significant adverse impact on the utility of the equipment to significant subgroups of consumers, 

or 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 

equipment generally available in the United States at the time, it will not be considered further. 

4-1
Adverse impacts on health or safety. If it is determined that a technology will have significant 

adverse impacts on health or safety, it will not be considered further.

In sum, if DOE determines that a technology, or a combination of technologies, has 

unacceptable impacts on the policies stated in section 5(b) of the Process Rule, it will be 

eliminated from consideration. If a particular technology fails to meet one or more of the four 

criteria, it will be screened out. Section 4.2 documents the reasons for eliminating any 

technology.

4.2 SCREENED-OUT TECHNOLOGIES

This section describes the technologies that DOE eliminated for failure to meet one of the 

following four factors: (1) technological feasibility; (2) practicability to manufacture, install, and 

service; (3) impacts on equipment utility or equipment availability; and (4) adverse impacts on 

health or safety. 

DOE also eliminated some technologies that were determined to provide little or no 

potential for efficiency improvement for one of the following additional reasons: (a) the 

technology does not significantly improve efficiency; (b) the technology is not applicable to the 

equipment being considered for coverage or does not significantly improve efficiency across the 

entire scope of each equipment class; and (c) efficiency improvements from the technology 

degrade quickly. 

DOE found that most of the technology options identified in the Framework Document 

have limited potential to improve the efficiency of pumps. In addition, DOE found that several of 

the options also do not pass the screening criteria listed above.

4.2.1 Improved Surface Finish of Wetted Components

DOE determined that improving surface finish will not significantly improve efficiency 

across the entire scope of each equipment class. As discussed in chapter 3, pumps with higher

specific speeds benefit significantly less than pumps with lower specific speeds. For example, 

pumps with specific speeds of 1,000 achieve improvements of approximately two efficiency 

points, while pumps with specific speeds greater than approximately 3,000 (a common design 

within the NOPR scope) achieve improvements of less than 0.4 efficiency points. Improvements 

continue to decrease at higher specific speeds. 

With other technology options such as hydraulic redesign providing approximately 11 

points of efficiency improvementa across each equipment class, improvements of less than 0.4 

efficiency points cannot be considered significant. A gain of 0.4 efficiency points represents

approximately only 3.6% of the improvement available from hydraulic redesign. As such, 

improved surface finish was screened out as it will not significantly improve efficiency across 

the equipment class.

a Varies by equipment class. See discussion of hydraulic redesign in chapter 3.

4-2

 
For the NOPR analysis DOE identified three specific sub-categories of technology 

options for surface finish improvement. The following sub-sections provide further detail on 

each of the specific technologies proposed for improving surface finish. 

4.2.1.1 Improved Casting Techniques

In addition to the lack of applicability over the full equipment class, initial efficiency 

improvements from improved casting techniques are expected to degrade quickly. With regular 

use, the initially smooth die- and investment-cast surfaces will naturally roughen due to factors 

such pitting, oxidation, and other wear and tear. The Hydraulic Institute, Grundfos, and ACEEE

all agree that gains in efficiency due to improved surface finish (from either improved casting 

techniques or surface polishing) degrade quickly in most applications. (HI, No.0025 at p.9, 39) 

(Grundfos, No.0024 at p.25) (ACEEE, Framework Public Meeting Transcript at p.299)b

4.2.1.2 Surface Polishing and/or Honing

Similar to improved casting techniques, improvements gained from surface polishing 

and/or honing are also expected degrade quickly. Additionally, surface polishing cannot be 

considered practical to manufacture in a production environment. Traditional surface polishing 

and honing are manual operations that cannot be practically automated. Currently, polishing or 

honing is only used for special cases, such as meeting custom performance requirements

requested by the consumer. DOE did explore additional production processes such as extrudehoning or slurry polishing, but manufacturers indicated that such technologies were known to 

cause larger than acceptable variations in metal removal which results in unpredictable part 

dimension and thicknesses, thus compromising final product quality and reliability. 

4.2.1.3 Surface coating or plating

In addition to lack of applicability over the full equipment class, coatings and platings 

were also screened out due to reliability and durability concerns. Multiple manufacturers have 

experienced issues will epoxy coatings flaking off during operation. Similarly, manufacturers 

also expressed concern with localized galvanic corrosion known to occur in small plating 

defects, when these defects are present. Both of these issues can lead to rapid localized 

degradation in the uncoated or unplated regions. Localized degradation acts as a significant stress 

concentration and can ultimately lead to unforeseen failures. Surface coatings and platings are 

traditionally used in applications with heightened sanitary or wear-resistant requirements. As 

such, pumps in these applications are typically inspected more frequently than a standard clean 

water pump. Inspections allow the localized wear issues to be corrected before unforeseen 

failures can occur. 

b A notation in this form provides a reference for information that is in the docket of DOE’s rulemaking to develop 

energy conservation standards for pumps (Docket No. EERE-2013-BT-NOC-0039), which is maintained at 

www.regulations.gov. This notation indicates that the statement preceding the reference is document number 

[DDDD document number as assigned in regulations.gov] in the docket for the [product] [energy conservation 

standards or test procedure] rulemaking, and appears at pages [xx] of that document.

4-3

 
4.2.2 Reduced Running Clearances

Manufacturer survey responses and research performed by DOE indicate pump 

manufacturers have already taken measures to tighten running clearances as much as possible, 

given the limitations of current wear ring materials, machining tolerances, and pump assembly 

practices. To further tighten clearances requires alternate materials for wear rings and other 

running surfaces, tighter machining tolerances, and/or modifications to assembly procedures.

With these tighter clearances comes increased risk for contact between static and rotating 

components during pump operation. This contact, known as an operational rub, may remove or 

deform material. Ultimately, operational rubs lead to looser clearances, causing efficiency 

improvements to quickly degrade. The Hydraulic Institute and The American Council for an 

Energy-Efficient Economy (ACEEE) both expressed agreement that efficiency improvements 

from tightened running clearances degrade quickly. (HI, Framework Public Meeting Transcript 

at p.329) (ACEEE, Framework Public Meeting Transcript at p.299) Additionally, large or 

frequent operational rubs can also lead to accelerated component wear, and in the worst case 

result in catastrophic seizing or failure of the pump.

To prevent these tighter clearances from causing operational rubs, larger (stiffer) shafts, 

and larger (stiffer) bearings are typically required. The use of larger components to improve the 

stiffness to appropriate levels results in increased mechanical losses. These losses negate the

potential improvements gained from reduced clearances. Additionally, the use of a larger shaft

may require an increased inlet diameter on the impeller, which may necessitate a more complex

hydraulic redesign of the impeller. 

Given the limitations associated with reduction of running clearances, and the potential 

reduction in reliability, this technology option was screened out.

DOE notes that clearances may still be tightened as a part of a larger hydraulic redesign, 

but tighter clearances cannot function as a standalone technology option.

4.2.3 Reducing Mechanical Friction in Seals

Although some advanced seals, such as silicon carbide-carbon, can incrementally reduce 

friction losses for some pumps, they cannot significantly improve efficiency across the entire 

scope of the equipment class. As discussed in chapter 3, the smallest pumps in each equipment 

class may see improvements of up to one point of efficiency, but the available improvement 

quickly drops off as pump size increases. At 10 horsepower, improvements are on the order of 

0.2 efficiency point, with this decreasing to 0.1 efficiency point for pumps larger than 50 

horsepower. Improvements on the order of 0.1-0.2 efficiency point are not significant when 

compared to improvements available from other technology options such as hydraulic redesign. 

As such, this technology option was screened out, as it does not significantly improve efficiency 

across the entire scope of each equipment class. 

4.2.4 Reduction of Other Volumetric Losses

As discussed in chapter 3, one of the most common causes of volumetric losses (other 

than previously discussed technology options) is thrust balance holes. No quantitative data is 

available regarding potential efficiency improvements from removal or reduction of balance 

4-4
holes. DOE did survey participating manufacturers, and responses indicated that modifications to 

balance holes will result in improvements well below one efficiency point. The majority of 

manufacturers indicated that they would consider the resulting improvements to be insignificant 

with respect to their current efficiency ratings. As such, DOE concludes that this technology 

option will not significantly improve efficiency. 

Manufacturer responses also indicate that removal of thrust balance holes from existing 

impellers would reduce pump reliability. Thrust balance holes are used to reduce wear on rub 

surfaces and bearings. Absent a more complex system redesign, thrust balance holes cannot be 

removed without compromising reliability. DOE notes that manufacturers may be able to reduce 

volumetric losses as a part of a larger hydraulic redesign.

4.2.5 Addition of a Variable Speed Drive

As discussed in chapter 3, DOE determined that for pumps considered in this NOPR

there are many application types and load profiles that would not benefit from a VSD and many 

applications for which energy use would increase. As such DOE has eliminated the use of VSDs 

from the list of technology options, as the technology does not significantly improve efficiency 

across each equipment class.

4.2.6 Improvement of VSD Efficiency

Based on market reports and manufacturer interviews, VSD market penetration rates are 

be between ten and five percent.

c 1 Given the low market penetration of VSDs and the 

elimination of VSDs as a technology option, DOE concludes that improvement of VSD 

efficiency will not significantly reduce pump energy consumption. As such it was screened out.

4.2.7 Reduced VSD Standby and Off Mode Power Usage

Based on market reports and manufacturer interviews, VSD market penetration rates are 

estimated to be between ten and five percent. Given the low market penetration of VSDs and the 

elimination of VSDs as a technology option, DOE concludes that reduced VSD standby and off 

mode power usage will not significantly reduce pump energy consumption. As such it was

screened out.

4.2.8 Remaining Technology

After eliminating those technologies that provide little or no potential for efficiency 

improvement, and screening out those technologies that do not meet the requirements of sections 

4(a)(4) and 5(b) of the Process Rule, DOE is considering improved hydraulic design as the only 

remaining technology option. DOE notes that some of the screened out technology options 

discussed in this chapter may be part of a full hydraulic redesign, even though they cannot be 

considered viable as individual, stand-alone design options.

c Based on a combination of manufacture survey responses and a penetration rate of 3.2% in 1998, per the cited 

DOE study. A 5% annual growth rate was applied to the DOE study.

4-5

 
DOE considers hydraulic design to be a mature technology and concludes that hydraulic 

designs of the highest possible efficiency are already present in the marketplace. As such, 

improvements within the bounds of pump efficiencies currently available in the marketplace are 

technologically feasible. DOE also finds that improved hydraulic design meets the other 

screening criteria (i.e., practicable to manufacture, install, and service and do not result in 

adverse impacts on consumer utility, product availability, health, or safety). As such, hydraulic 

redesign was the only design option passed on to the engineering analysis.

4-6
REFERENCES

1 United States Industrial Electric Motor Systems Market Opportunities Assessment. Tech. 

Washington DC: U.S. Department of Energy’s (DOE) Office of Energy Efficiency and 

Renewable Energy (EERE), 1998. Print

4-7
CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ........................................................................................................... 5-1

5.2 METHODOLOGY OVERVIEW.................................................................................... 5-1

5.3 DATA AVAILABILITY AND EQUIPMENT CLASSES ANALYZED ...................... 5-2

5.3.1 Radially Split, Multi-Stage, Vertical, In-Line, Diffuser Casing.......................... 5-3

5.3.2 Vertical Turbine Submersible 1,800 RPM........................................................... 5-6

5.4 SUMMARY OF SIGNIFICANT DATA SOURCES ..................................................... 5-7

5.4.1 Performance, Shipments, and Sales Database ..................................................... 5-7

5.4.2 Industry Aggregate Model, Shipment and Sales Revenue Data .......................... 5-8

5.4.3 Cost of Redesign Survey...................................................................................... 5-9

5.5 REPRESENTATIVE EQUIPMENT FOR ANALYSIS ............................................... 5-10

5.5.1 ESCC, ESFM, and IL Equipment Classes......................................................... 5-11

5.5.1.1 Impellers................................................................................................... 5-11

5.5.1.2 Volutes ..................................................................................................... 5-12

5.5.1.3 Seals ......................................................................................................... 5-13

5.5.2 RSV and VTS Equipment Classes..................................................................... 5-13

5.5.3 Summary............................................................................................................ 5-14

5.6 DESIGN OPTIONS....................................................................................................... 5-14

5.7 AVAILABLE ENERGY EFFICIENCY IMPROVEMENTS....................................... 5-14

5.8 EFFICIENCY LEVELS................................................................................................. 5-14

5.8.1 Efficiency Level Structure ................................................................................. 5-14

5.8.2 Efficiency Levels Analyzed............................................................................... 5-16

5.8.3 Establishing the Baseline Efficiency Levels...................................................... 5-17

5.8.3.1 Baseline Configuration............................................................................. 5-17

5.8.3.2 Establishing Baseline C-Values............................................................... 5-17

5.8.4 Establishing the Maximum Technologically Efficiency Levels........................ 5-20

5.8.4.1 Max-Tech Configuration.......................................................................... 5-20

5.8.4.2 Establishing Max-Tech C-Values ............................................................ 5-21

5.8.5 Harmonization with EU Analytical Structure and Methods.............................. 5-22

5.8.6 Establishing Baseline and Max-Tech for RSV Equipment Classes................... 5-22

5.9 MANUFACTURERS PRODUCTION COST ASSESSMENT

METHODOLOGY ........................................................................................................ 5-24

5.9.1 Changes in MPC Associated with Hydraulic Redesign..................................... 5-24

5.9.2 Manufacturer Production Cost Model ............................................................... 5-27

5.10 CONVERSION COSTS ................................................................................................ 5-30

5.10.1 Hydraulically Redesign Cost Data..................................................................... 5-31

5.10.2 Average Per-Model Cost To Hydraulically Redesign a Pump at Each 

Efficiency Level................................................................................................. 5-32

5.10.3 Accounting for Cost Sharing Between Models with Shared Platforms............. 5-33

5.10.4 Adjusted Per-Model Cost to Hydraulically Redesign a Pump at Each 

Efficiency Level................................................................................................. 5-36

5-i
5.10.5 Determining Number of Models Redesigned in Each Equipment Class........... 5-37

5.10.5.3 Number of Models Redesigned in Each Equipment Class at Each 

Efficiency Level ....................................................................................... 5-38

5.10.6 Aggregate Industry Conversion Cost at Each Efficiency Level ........................ 5-39

5.11 MANUFACTURER MARKUP ANALYSIS................................................................ 5-41

5.11.1 Industry-Average Markups................................................................................ 5-41

5.11.2 Individual Manufacturer Markup Structures ..................................................... 5-42

5.11.3 Industry-Wide Markup Structure....................................................................... 5-42

5.11.4 MSP-Efficiency Relationship ............................................................................ 5-44

REFERENCES .......................................................................................................................... 5-46

LIST OF TABLES

Table 5.3.1 Summary of Data Availability, Equipment Groups, and Analysis Type....... 5-3

Table 5.4.1 Summary of Performance, Shipments, and Sales Database Content............. 5-8

Table 5.4.2 Model, Shipment and Sales Revenue Data for 2012 ..................................... 5-9

Table 5.4.3 Cost to Redesign One Pump Model from below the 10th to the 40th

Percentile of Efficiency................................................................................ 5-10

Table 5.8.1 Efficiency Levels Analyzed with Corresponding C-values......................... 5-17

Table 5.8.2 European Union MEI C-Values for RSV at the 40th and 70th Percentile..... 5-23

Table 5.8.3 DOE PEI C-Values for RSV at Baseline and Max-Tech............................. 5-23

Table 5.10.1 Revised Cost of Redesign Table .................................................................. 5-31

Table 5.10.2 Estimated Aggregate Industry Development Costs of New Pumps to 

Meet Different Minimum Cutoff Values in the European Union................ 5-32

Table 5.10.3 European Conversion Costs Interpolated to DOE Efficiency 

Percentiles.................................................................................................... 5-32

Table 5.10.4 Average to Cost Redesign, by Efficiency Level, for Each Equipment 

Class Grouping............................................................................................. 5-33

Table 5.10.5 Statistic on Shared Platforms....................................................................... 5-34

Table 5.10.6 Refined Statistics on Shared Platforms........................................................ 5-34

Table 5.10.7 Percentage of Redesign Costs that Apply to a Duplicate Model................. 5-35

Table 5.10.8 Percent of Redesign Costs Applicable to each Pump Population 

Category....................................................................................................... 5-35

Table 5.10.9 Redesign Cost Sharing Adjustment Factor for End Suction and Inline 

Pumps........................................................................................................... 5-36

Table 5.10.10 End Suction - Adjusted Per-Model Cost to Hydraulically Redesign a 

Pump ............................................................................................................ 5-36

Table 5.10.11 Inline - Adjusted Per-Model Cost to Hydraulically Redesign a Pump........ 5-37

Table 5.10.12 VTS - Adjusted Per-Model Cost to Hydraulically Redesign a Pump.......... 5-37

Table 5.10.13 Calculation of Total Industry Model Counts for Each Analysis Group ...... 5-37

Table 5.10.14 End Suction - Number of Models Redesigned at Each Efficiency Level.... 5-39

Table 5.10.15 Inline - Number of Models Redesigned at Each Efficiency Level .............. 5-39

Table 5.10.16 VTS - Number of Models Redesigned at Each Efficiency Level................ 5-39

Table 5.10.17 End Suction - Aggregate Industry Conversion Cost at Each Efficiency 

Level ............................................................................................................ 5-40

5-ii
Table 5.10.18 Inline - Aggregate Industry Conversion Cost at Each Efficiency Level ..... 5-40

Table 5.10.19 VTS - Aggregate Industry Conversion Cost at Each Efficiency Level ....... 5-40

Table 5.10.20 All Equipment Classes – Aggregate Industry Conversion Cost at Each 

Efficiency Level........................................................................................... 5-41

Table 5.11.1 Industry-Average Markups for Each Equipment Class Group.................... 5-42

LIST OF FIGURES 

Figure 5.3.1 Histogram to Compare to the Efficiency of RSV 3600 RPM Models 

Sold in the United States to the EU 547/2012 MEI 40 Standard Level......... 5-4

Figure 5.3.2 Histogram to Compare to the Efficiency of RSV 1800 RPM Models 

Sold in the United States to the EU 547/2012 MEI 40 Standard Level......... 5-5

Figure 5.5.1 Two-Dimensional Fabricated Stainless Steel Impeller. With and 

Without Second Disc Attached.................................................................... 5-12

Figure 5.8.1 Failing C-Values Plotted Against Flow at BEP for the ESCC.1800 

Equipment Class.......................................................................................... 5-19

Figure 5.8.2 Failing C-Values Plotted Against Specific Speed for the ESCC.1800 

Equipment Class.......................................................................................... 5-20

Figure 5.9.1 Impeller Production Cost Plotted against the Scaling Parameter ................ 5-26

Figure 5.9.2 Impeller Production Cost Premium Plotted Against Efficiency 

Percentile...................................................................................................... 5-26

Figure 5.9.3 Trends between the Lower and Upper 50th Percentile of Efficiency, for 

Impeller Production Cost Premium ............................................................. 5-27

Figure 5.9.4 Average Bare Pump MPC and MSP per Model for IL.1800 and 

IL.3600......................................................................................................... 5-29

Figure 5.9.5 Average Bare Pump MPC and MSP per Model for ESCC.1800, 

ESCC.3600, ESFM.1800, and ESFM.3600................................................. 5-30

Figure 5.9.6 Average Bare Pump MPC and MSP per Model for VTS.3600 ................... 5-30

Figure 5.10.1 Process for Refining Statistics on End Suction Shared Platforms............... 5-34

Figure 5.10.2 Percent of Failing Models Not Redesigned at Each Efficiency Level......... 5-38

Figure 5.11.1 MSP Premium as a Function of Efficiency Percentile. Actual Results 

and Trended Fits. ......................................................................................... 5-43

5-iii
CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

The engineering analysis establishes the relationship between manufacturer selling price 

(MSP) and energy consumption for the pumps examined in this rulemaking. The “priceefficiency” relationship serves as the basis for downstream cost-benefit calculations with respect 

to individual consumers, manufacturers, and the nation. 

5.2 METHODOLOGY OVERVIEW

This section describes the analytical methods the U.S. Department of Energy (DOE) used 

for the engineering analysis. In this rulemaking, DOE adopted a modified design option 

approach to produce analytically derived curves representing the cost-efficiency and priceefficiency relationship for each equipment class analyzed. In a general design option approach, 

the goal of the analysis is to calculate the incremental cost and energy efficiency impacts of 

implementing specific energy saving technologies, known as “design options.” DOE modified 

the analysis conducted in this rulemaking from the standard approach for the following two 

reasons. First, only one design option, hydraulic redesign, remained after the screening analysis. 

Second, DOE’s analysis concluded that manufacturer production cost (MPC) does not vary with 

efficiency. 

DOE’s MPC analysis is conducted on a greenfields basis, i.e. the manufacturing costs and 

differences in these costs for different designs are calculated on the basis of setting up production 

facilities from scratch for the different designs that are under consideration. The development 

and conversion costs that a manufacturer might incur do not impact the manufacturing cost of the 

product and will not show up when examining the differences in MPC. Development and 

conversion costs are considered separately in this analysis. Manufacturers may be able to recoup 

their investment in new designs and tooling, but these differences would show up in the sales 

prices rather than the manufacturing costs.

Within the modified design option approach, DOE analyzed and developed the MPC- and 

MSP-efficiency relationship. In its final form, the efficiency side of the MPC- and MSPefficiency relationships is characterized by the relative efficiency of a pump with respect to its 

equipment class; this can also be referred to as an efficiency percentile. This method was 

consistent with the consumer and industrial pumps (CIP) working group’s desire to establish 

efficiency levels based on efficiency percentile. DOE analyzed the MPC- and MSP-efficiency 

relationships from the baseline to the maximum technologically feasible (“max-tech”) equipment 

configuration for each class. Sections 5.9, 5.10, and 5.11 provide further details on DOE’s 

analysis of MPC, conversion costs, and MSP, respectively.

5-1
5.3 DATA AVAILABILITY AND EQUIPMENT CLASSES ANALYZED

DOE attempted to collect data on and analyze all equipment classes at the individual 

equipment class level. However, the performance and cost data that DOE obtained were not

granular to the configuration level.

a Consequently, DOE performed their analyses at the level of

basic pump equipment type and nominal design speed. The basic pump equipment type and 

nominal design speed groups used to represent each equipment class can be referred to as 

“equipment groups.” Equipment groups are explained in chapter 3, and summarized in Table 

5.3.1.

DOE was unable to obtain sufficient data to “directly analyze” all equipment groups. A 

“direct analysis” means that a specific equipment group was analyzed using data from products 

in that equipment group. If DOE could not directly analyze an equipment group, a “similarity” or 

“indirect” analysis was performed. A “similarity analysis” means that a specific equipment class 

was analyzed using data from products in a different, but similar equipment class. An “indirect 

analysis” means that data from markets other than the United States were used to characterize the 

equipment class. Table 5.3.1 summarizes data availability, equipment groups, and analysis types

used for each equipment class.

a Equipment classes are defined by basic pump equipment type, nominal design speed, and configuration type (e.g., 

end suction close-coupled, 1800 RPM, constant load). See chapter 3 for complete definitions. 

5-2

 
Table 5.3.1 Summary of Data Availability, Equipment Groups, and Analysis Type

Equipment Class* Equipment Group Direct Data**

Available Analysis Type

ESCC.1800.CL

ESCC.1800 Yes Direct

ESCC.1800.VL

ESCC.3600.CL

ESCC.3600 Yes Direct

ESCC.3600.VL

ESFM.1800.CL

ESFM.1800 Yes Direct

ESFM.1800.VL

ESFM.3600.CL

ESFM.3600 Yes Direct

ESFM.3600.VL

IL.1800.CL

IL.1800 Yes Direct

IL.1800.VL

IL.3600.CL

IL.3600 Yes Direct

IL.3600.VL

RSV.1800.CL

RSV.1800 No Indirect

RSV.1800.VL

RSV.3600.CL

RSV.3600 No Indirect

RSV.3600.VL

VTS.1800.CL

VTS.1800 No Similarity to 

VTS.1800.VL VTS.3600

VTS.3600.CL

VTS.3600 Yes Direct

VTS.3600.VL *

Equipment class designations consist of a combination (in sequential order separated by periods) of: (1) an 

equipment family code (ESCC = end suction close-coupled, ESFM = end suction frame mounted/own bearings, IL = 

in-line, RSV = radially split, multi-stage, vertical, in-line, diffuser casing, VTS = vertical turbine submersible); (2) a 

nominal operating speed (1800 = 1800 rpm, 3600 = 3600 rpm); and (3) an operating mode code (CL = constant load, 

VL = variable load). Complete definitions are available in chapter 3. ** “Direct data” is performance and cost data pertaining to the given equipment group.

DOE conducted direct analyses for the ESCC.1800, ESCC.3600, ESFM.1800, 

ESFM.3600, IL.1800, IL.3600 and VTS.3600 equipment classes. The following two subsections 

describe in more detail how DOE addressed the equipment groups that could not be directly 

analyzed.

5.3.1 Radially Split, Multi-Stage, Vertical, In-Line, Diffuser Casing

DOE used available information to identify baseline and maximum technologically 

feasible (“max-tech”) efficiency levels for this class. Specifically DOE used market research and 

confidential manufacturer information to establish a database of RSV models. This database 

represented models offered for sale in the United States by three major manufacturers of RSV

pumps. DOE reviewed the efficiency data for these RSV pumps and found no models to be less 

efficient than the European Union’s MEI 40 standard level, which took effect on January 1, 

5-3
2015.

1 Figure 5.3.1 and Figure 5.3.2 illustrate these findings for 3,600 and 1,800 RPM models, 

respectively.

Figure 5.3.1 Histogram to Compare to the Efficiency of RSV 3600 RPM Models Sold in 

the United States to the EU 547/2012 MEI 40 Standard Level

5-4
Figure 5.3.2 Histogram to Compare to the Efficiency of RSV 1800 RPM Models Sold in 

the United States to the EU 547/2012 MEI 40 Standard Level

This analysis, in conjunction with confidential discussions with manufacturers led DOE 

to conclude that RSV models sold in the United States market are global platforms with 

hydraulic designs equivalent to those in the European market. As such, DOE presented this 

conclusion to the CIP working group for consideration, where it was supported and reaffirmed 

on numerous occasions (See, e.g. EERE-2013-BT-NOC-0039-0109 at pp. 91-97, EERE-2013-

BT-NOC-0039-0105 at pp. 293-300, EERE-2013-BT-NOC-0039-0106 at pp. 38-40, 62-67, 88-

95; EERE-2013-BT-NOC-0039-0108 at pp. 119.). 

As a result of the conclusion that RSV models sold in the United States market are global 

platforms with hydraulic designs equivalent to those in the European market, DOE proposes to 

set the baseline and max-tech levels equal to those established in Europe. Specifically, the 

baseline would be the European minimum efficiency standard,

b and the max-tech level would be 

the European level referred to as “the indicative benchmark for the best available technology.”2 

Although DOE was able to establish a baseline and max-tech level through similarity to the 

European market, DOE was unable to develop a cost-efficiency relationship or additional 

efficiency levels for RSV, due to lack of available cost data for this equipment. As a result, DOE 

has proposed a standard level for RSV that is equivalent to the baseline, consistent with the 

recommendation of the CIP Working Group. (See EERE-2013-BT-NOC-0039-0092, p. 4, 

b Note that this NOPR and the European Union regulation use different metrics to represent efficiency. DOE used 

available data to establish harmonized baseline and max-tech efficiency levels using the DOE metric.

5-5

 
Recommendation #9) Based on the data available and recommendation of the CIP working 

group, DOE concludes that this standard level is representative of the typical minimum 

efficiency configuration sold in this equipment class, and no significant impact is expected for 

either the consumers or manufacturers.

5.3.2 Vertical Turbine Submersible 1,800 RPM

Market research, confidential manufacturer data, and direct input from the CIP working 

group indicate that the VTS.1800 is a very uncommon configuration of pumps in the 

marketplace. DOE estimates total shipments for this equipment class to be less than 1 percent of 

the VTS.3600 equipment class. Consequently, DOE was unable to obtain enough VTS.1800 

performance and cost data to directly analyze the equipment class. 

Due to lack of data, DOE analyzed the VTS.1800 equipment class by similarity to 

VTS.3600. Available information indicates that all VTS.1800 models sold into the market are 

hydraulically identical to analogous VTS.3600 models already offered in the market. This means 

that each VTS.1800 model in the market uses the same bare pump that is already produced for 

use with a VTS.3600 model. The only difference between these products is the nominal speed of 

the motor. As a result, when a manufacturer performs a hydraulic redesign on a VTS.3600 

model, the VTS.1800 model will benefit from improved performance at no additional conversion 

cost.

c Given this relationship, DOE assessed the conversion costs for these equipment classes 

collectively, as a single hydraulic redesign to a VTS.3600 pump will impact the VTS.1800 

variant of the same bare pump.

It is important to note that operating speed affects efficiency. Lower speed VTS.1800 

pumps will achieve efficiencies greater-than-or-equal-to higher speed VTS.3600 pumps of the 

same flow at best efficiency point (BEP) and specific speed.

d As such, the available performance 

and efficiency data for VTS.3600 cannot be directly translated to performance and efficiency 

data for VTS.1800.

The CIP working group recommended that VTS.1800 efficiency levels be set to the same 

value as VTS.3600 efficiency levels. (EERE-2013-BT-NOC-0039-0105 at pp. 300-308; EERE2013-BT-NOC-0039-0106 at pp. 38-40, 62-67, 88-95) Given a VTS.1800 pump and a VTS.3600 

pump built from the same bare pump, the VTS.1800 will be more efficient than the VTS.3600

pump. Consequently, if VTS.3600 pump is designed to meet a certain efficiency level, the 

VTS.1800 will also meet that efficiency level. Ultimately, given the limitation in direct data, this 

similarity method allows highest possible efficiencies levels to be established for VTS.1800. 

Given the relationship between VTS.1800 and VTS.3600 and the recommendation of the 

CIP working group, DOE proposes efficiency levels for VTS.1800 equal to that of the VTS.3600 

c Note that VTS.1800 models will not be independently hydraulically redesigned. Manufacturers indicate that 

hydraulic designs are optimized for 3,600 RPM operation, and then sold for use at 1,800 RPM if requested by the 

consumer.

d Note that although VTS.1800 pump are more efficient, they have different performance characteristics than 

VTS.3600 and cannot be considered direct replacements.

5-6

 
equipment class. Section 5.8 provides further details on methods used to establish efficiency 

levels.

5.4 SUMMARY OF SIGNIFICANT DATA SOURCES

For the engineering analysis, many data sources were utilized, but the following three 

sources provided most of the information; (1) a performance, shipments, and sales database, (2) 

aggregate model, shipment and sales revenue data, and (3) a cost of redesign survey. These data 

sources are described in the following three subsections. Additional data sources are described as 

appropriate throughout this chapter.

5.4.1 Performance, Shipments, and Sales Database

Throughout this chapter, the performance, shipments, and sales database is called the 

“performance database.” DOE based much of the engineering and downstream analysis on this

database. The database includes information for individual pump models, and was merged from 

two separate, but similar datasets; (1) a database built from confidential manufacturer interview 

responses obtained under non-disclosure agreements with DOE’s contractors, and (2) a database 

purchased from the Hydraulic Institute (HI). Both database sources were of the same structure

and contained the same fields. The only difference being that HI was able to reach some 

manufacturers that DOE was not, and vice-versa. As a result DOE concluded that the merged 

database is the best representation of the industry that is currently available. The final database 

contains the following fields for each model entered into the database.

• DOE pump type

• ANSI/HI Pump type

• Number of stages

• Bowl diameter (if applicable)

• Rated speed

• Net sales volume in dollars

• Shipments volume in number of units sold

• For BEP flow, 50% of BEP flow, 75% of BEP flow, and 110% of BEP flow

o Flow in gallon per minute

o Head in feet

o Pump efficiency

o Shaft input power in horsepower

Table 5.4.1 provides a quantitative breakdown of the number of models in the database 

and shipments they represent, organized by pump type and speed.

5-7
Table 5.4.1 Summary of Performance, Shipments, and Sales Database Content

Pump Type & Speed Number of 

Models Number of Shipments Percent of Total Shipment*

ESCC.1800 307 16,017 40%

ESCC.3600 249 52,208

ESFM.1800 344 26,194 67%

ESFM.3600 217 3,095

IL.1800 215 17,196 41%

IL.3600 106 3,581

VTS.1800 Data Not Available

15%

VTS.3600 58 15,817

*Total industry shipments were available at the pump type level only, as estimated by the Hydraulic Institute in 

Table 5.4.2.

5.4.2 Industry Aggregate Model, Shipment and Sales Revenue Data

As a part of the CIP working group proceedings, the Hydraulic Institute provided DOE 

with a special statistical report bulletin3 containing aggregate revenues (in USD) and aggregate 

units sold for the pump industry, for the year 2012, summarized by pump type. HI later 

supplemented this statistical bulletin with total model counts.

4

In this bulletin, HI provided both direct survey results as well as scaled estimates of total 

industry results. The direct results represent 17 United States manufacturers that participated in

the HI survey. Individual surveys were aggregated by a third party, National Electrical 

Manufacturers Association Business Information Services (NEMA/BIS), to avoid improper 

sharing of unique competitive data. HI developed the scaled total industry estimates by starting 

with the direct survey results and scaling up based on industry knowledge. Model, shipment, and 

sales revenue data for the equipment classes relevant to this NOPR are summarized in Table 

5.4.2. Note that model and shipment counts in Table 5.4.1 summarize the pumps for which DOE 

has performance data, while the model and shipments counts in Table 5.4.2 summarize all pumps 

reported to NEMA/BIS, including those for which performance data were not submitted. 

Accordingly, DOE does not have performance data for all pumps represented in Table 5.4.2. 

5-8
Table 5.4.2 Model, Shipment and Sales Revenue Data for 2012

Direct Survey Results* Total Industry Estimate

Pump 

Type

Revenues 

(USD) Units Sold

Number of 

Unique 

Models

Revenues 

(USD)

Units 

Sold

Number of 

Unique 

Models**

ESCC $67,450,020 94,278 685 $122,666,193 171,456 N/A

ESFM $52,141,922 31,587 632 $72,701,801 44,042 N/A

IL $34,498,011 24,406 381 $71,273,906 50,424 N/A

VTS $34,845,998 78,624 211 $46,272,522 104,406 N/A

RSV $72,331,298 44,527 86 $81,181,030 49,975 N/A

*Participating Companies: Armstrong Fluid Technology, National Pump Company, Dover Pump Solutions Group, 

Patterson Pump Company, Flowserve Corporation Pentair, Franklin Electric Price Pump Company, Gorman-Rupp , 

Sulzer Pumps (US) Inc., Grundfos USA TACO, Inc., ITT - Industrial Process Wilo USA LLC, Iwaki America 

Incorporated, Xylem Inc. - Applied Water Systems, and Lewis Pumps.

**Hydraulic Institute did not provide scaled estimates for the number of unique models.

5.4.3 Cost of Redesign Survey

Following the close of the Framework Document comment period, the Hydraulic Institute 

provided DOE with a special statistical report bulletin5 on the average cost to redesign a 1, 50, 

and 200 horsepower pump from below the 10th percentile of efficiency to the 40th percentile. HI 

believes that the presented redesign costs are applicable to all equipment classes covered in the 

NOPR. The data in this bulletin represent the averaged results of 15 major United States 

manufacturers that participated in the survey. The survey was administered and aggregated by 

NEMA/BIS for the Hydraulic Institute. 

The cost of redesign survey data was ultimately used as the basis for DOE’s industrywide conversion cost analysis. Before doing so, DOE’s contractor validated the HI data through 

independent collection of similar data (covered under non-disclosure agreement) from individual 

manufacturers. DOE compared the individual manufacturer responses to the averaged HI data 

and ultimately concluded that the HI survey results were reasonable and in line with the 

independent data. DOE choose to use the HI survey results over their own data collections, as the 

HI survey reached more manufacturers than the DOE survey. Table 5.4.3 summarizes the results 

of the statistical bulletin on average cost to redesign a 1, 50, and 200 horsepower pump.

5-9
Table 5.4.3 Cost to Redesign One Pump Model from below the 10th to the 40th Percentile 

of Efficiency

Cost Category Average Cost to Redesign One Pump Model

1 HP 50 HP 200 HP

Redesign, Prototype & Initial Test* $52,239 $64,397 $65,918 

Pattern & Tooling** $44,329 $51,280 $72,862 

Testing† $22,858 $35,394 $47,293 

Working Capital‡ $37,101 $47,339 $54,975 

Marketing†† $32,225 $29,842 $27,155 

TOTAL $188,752 $228,252 $268,203 

*Pattern & Tooling includes: Supplier Development, Patterns, Tooling (Machine Fixtures, Assembly Fixtures, and 

Test Fixtures), Machine Programming, Quality Inspection (Parts, Tooling), New Process Development (Casting 

and/or Machine Processes), New Machine Tools, Retraining.

†Testing includes: Performance (All Speeds), Hydrostatic, Certification Testing, Production Documentation, 

Training, etc.

‡Working Capital includes: Cost of money to finance upgrades, Inventory (Cost, Rework Transition, and Inventory

for legacy support.)

††Marketing includes: Literature, Data Sheets, Curves, Pump Selection Tools, Sales Training, Compliance 

Documentation, etc.

5.5 REPRESENTATIVE EQUIPMENT FOR ANALYSIS

For the engineering analysis, DOE directly analyzed the MPC- and MSP-efficiency 

relationship for the equipment classes specified in section 5.3 over the full range of pump sizes. 

Within the engineering analysis, “size” is defined by a pump’s flow at BEP and specific speed. 

Analyzing over the full size range allowed DOE to use representative configurations for each 

equipment class, rather than an approach that analyzes a representative unit from each class. A 

representative unit has a defined size and defined features, while a representative configuration 

defines only the features of the pump, allowing the MPC- and MSP-efficiency analysis to 

consider a large range of data points that occur over the full range of sizes. 

In selecting representative configurations, DOE researched the offerings of major 

manufacturers to select configurations generally representative of the typical offerings produced 

within each equipment class. Configurations and features were based on high-shipment-volume

designs prevalent in the market. The key features that define each representative configuration 

include impeller material, impeller production method, volute/casing material, volute/casing 

production method, and seal type. DOE’s findings and selections of representative configurations 

for the directly analyzed pump classes are described in the following sections.

5-10
5.5.1 ESCC, ESFM, and IL Equipment Classes

5.5.1.1 Impellers

Pumps in the ESCC, ESFM, and IL equipment classes are often constructed of similar

components made from similar materials. Impellers used in these pumps may be made from cast 

iron, cast bronze, cast stainless steel, fabricated stainless steel, or composite materials. 

Responses gathered from confidential manufacturer interviews indicate that the vast 

majority of pumps in these equipment classes are constructed from cast bronze. Bronze is 

popular due to its ease of manufacture and its non-corrosive nature. It is relatively easy to 

machine and can be cast using various processes, such as sand casting, investment casting, and 

die casting.

Due to its non-corrosive nature, stainless steel is generally considered a direct competitor 

to bronze. Stainless steel is generally a stronger and more wear-resistant material, however, it is 

also more difficult to cast and machine. Historically the higher price of stainless steel has kept it 

from significantly penetrating the market. Recent fluctuations in steel and copper commodity 

prices have brought the costs of finished stainless steel and bronze impellers to similar levels. 

This has resulted in increased use of stainless steel impellers. Despite this recent trend, bronze 

and stainless steel prices are still highly volatile, and stainless steel is still not as widely used as 

copper.

Due to its susceptibility to corrosion, cast iron is not a direct substitute or competitor to 

bronze and stainless steel impellers. As a result, cast iron impellers make up only small portion 

of the market for products within the scope of this rulemaking. Cast iron is significantly less 

expensive than bronze or stainless steel, consequently cast iron impellers are often viewed as a 

low cost alternative. Although some models are offered with only cast iron impellers, it is 

common for manufacturers to offer their pump models in a standard bronze or stainless impeller 

configuration, with an optional cast iron impeller configuration. The bronze/stainless fitted 

version is generally considered as the primary and high volume configuration. Consumers may 

choose to use cast iron impellers in applications with limited corrosion potential, such as closedloop applications with limited oxygen content in the water system. Cast iron pumps also excel in 

non-clean water applications where the toughness of the cast iron, especially when epoxy coated, 

can provide enhanced wear and impact resistance. Alternatively, consumers may choose cast iron 

in scenarios where reduced performance due to corrosion or wear is an acceptable trade off, 

given the lower first cost. 

The term fabricated stainless steel impeller describes an impeller comprised of stainless 

steel components (vanes and discs) that have been mechanically formed from stainless steel 

sheet metal and welded together to create a finished impeller. For reference, Figure 5.5.1 features 

a view of a typical two-dimensional impeller with and without the second of its two discs 

attached. Although exceptions exist, fabricated impellers are typically limited to higher specific 

speeds. The stainless steel sheet metal used for these impellers is typically very smooth and 

naturally corrosion resistant. Due to its relatively light gauge, the fabricated impeller is not 

impact resistant and may bend or dent if struck by suspended particles. Manufacturer interview 

responses indicate that fabricated stainless steel impellers are not produced on pumps with sizes 

5-11
greater than approximately 20 hp. Although larger sizes are technically feasible, manufacturers 

have indicated that they are not economical to produce, as these larger impellers do not reach the 

high sales volumes needed to cover the large initial tooling investment requirements. Overall, 

ESCC, ESFM, and IL pumps with fabricated stainless steel impeller are a niche product and 

represent a small portion of the market. 

Figure 5.5.1 Two-Dimensional Fabricated Stainless Steel Impeller. With and Without 

Second Disc Attached6

 

The term composite impeller describes an impeller made from plastics, resins, or other 

similar non-metallic material. Composite impellers are typically injection molded parts with low 

strength and a smooth surface finish. Within the scope of this NOPR, pumps with composite 

impellers are not sold in high volumes and are generally reserved for niche applications where a 

metal impeller is undesirable, or where durability and strength is not a primary concern. DOE 

does note that composite impellers are commonly used in less-than-one horsepower circulator 

pumps, which are not covered by this NOPR.

Given the available information, cast bronze impellers have been chosen as the 

representative impeller type for ESCC, ESFM, and IL pumps. Additionally, given the current 

similar (but fluctuating) market prices of stainless steel and bronze, DOE concluded that an 

analysis based on bronze is also financially representative of stainless steel impeller pumps.

5.5.1.2 Volutes

Volutes (also known as cases) used in ESCC, ESFM, and IL pumps are typically made 

from cast iron, cast bronze, cast stainless steel, or fabricated stainless steel. Responses gathered 

from manufacturer interviews indicate that the vast majority of pumps in these equipment classes 

are constructed from cast iron.

Cast iron is popular for volutes due to its low cost and suitable strength. For general 

purpose pumps, normal levels of oxidation and resulting surface roughening of the volute are

acceptable, as the oxidation will not compromise the mechanical integrity of the static volute, as 

it would with the rotating impeller. For applications where oxidation is not acceptable, such as 

pumps for potable water use, cast iron volutes can be coated with corrosion resistant materials. 

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Rather than use a coated cast iron impeller, consumers requiring corrosion resistance may 

choose to purchase a cast bronze or cast stainless volute. These options are generally more costly 

than a coated cast iron volute, but may meet additional requirements imposed by the consumer’s 

application.

Finally, fabricated stainless steel volutes are used exclusively with fabricated stainless 

steel impellers. These volutes provide the same smooth surface finish and corrosion resistance as 

the fabricated impeller and are also limited in size due to economies of scale.

Given the available information, cast iron volutes have been chosen as the representative 

volute type for ESCC, ESFM, and IL pumps.

5.5.1.3 Seals

Although numerous types of seals exist for ESCC, ESFM, and IL pumps, manufacturer 

interview responses and research of the industry suggests that the most commonly used seal for 

pumps covered in this NOPR is Type 21, carbon-ceramic seal. As such, the Type 21, carbonceramic seal has been chosen for the representative configuration. 

5.5.2 RSV and VTS Equipment Classes

Within the scope of this NOPR, pumps in the RSV and VTS equipment classes often 

share similar construction and material selection. In these pumps, impellers and bowlse are 

stacked together to form stages. These impellers and bowls can be manufactured from cast iron, 

cast bronze, cast stainless steel, or fabricated stainless steel. Sales data and responses gathered 

from manufacturer interviews indicate that the vast majority of impellers in these equipment 

classes are constructed from fabricated stainless steel impellers.

Both RSV and VTS are multistage pumps that generally specialize in providing high 

heads (pressures) at low to moderate flows. RSVs are typically used for pressure boosting 

applications, and VTSs are typically used to lift groundwater up from a deep well. Fabricated 

stainless steel impellers are generally limited to low specific speed, which can provide the low 

flow and high head performance that RSV and VTS pumps require. The thin, lightweight 

fabricated impellers can also be easily stacked into multiple stages to provide the consumer with 

a wide variety of available heads in a relatively small pump. RSV and VTS pumps are often used 

for potable water applications, which make the non-corrosive nature of stainless steel a logical 

fit.

Some RSV and VTS pumps within the NOPR scope do use cast impellers and bowls, but 

this is fairly uncommon. Cast materials may be used in larger RSV and VTS pumps that are not 

in the scope of this rulemaking. Cast materials are uncommon in smaller sizes of these pumps 

due to the bulkiness and weight of the cast designs, especially when stacked into multiple stages.

This is especially true for VTS pumps, where well diameters are limited, and thick cast bowls 

reduce the available impeller diameter, which restricts overall performance.

e The “bowl” is the case that functions as the volute on each stage of a vertical turbine.

5-13

 
5.5.3 Summary

For the ESCC, ESFM, and IL equipment classes the representative configuration is

defined as a pump fitted with a cast bronze impeller; cast-iron volute; and a Type 21 mechanical 

seal. For the RSV and VTS equipment classes the representative configuration was defined as a 

pump fitted with sheet metal-based fabricated stainless-steel impellers, and sheet metal-based 

fabricated stainless-steel bowl/casing. 

5.6 DESIGN OPTIONS

After conducting the screening analysis and removing technologies that did not warrant 

inclusion on technical grounds, hydraulic redesign remained as the only design options to be 

considered in the NOPR engineering analysis.

5.7 AVAILABLE ENERGY EFFICIENCY IMPROVEMENTS

For each equipment class, DOE assessed the available energy efficiency improvements 

resulting from a hydraulic redesign. This assessment was informed by manufacturer performance 

and cost data, confidential manufacturer interview responses, general industry research, and 

stakeholder input gathered at the CIP working group public meetings. Chapter 3 presents 

potential efficiency gains resulting from improved hydraulic redesign, for each equipment class. 

These potential improvements represent the efficiency spread between the lowest efficiency and 

highest efficiency pumps already offered in the marketplace. It is technically feasible for any

pump of the baseline configuration (discussed in section 5.8), regardless of flow and specific 

speed, to improve efficiency by the values present in chapter 3. Available efficiency 

improvements will be less for pumps that exceed the baseline. A manufacturer can realize 

efficiency improvements in any increment, depending on the level of effort and capital they 

choose to invest in their redesign program. Section 5.10 discusses the relationship between 

efficiency gains and conversion cost in more detail. 

5.8 EFFICIENCY LEVELS

5.8.1 Efficiency Level Structure

DOE established efficiency levels that are a function of flow and specific speed.

f The 

form of this function tracks the inherent efficiency trends for pumps of different flow and 

specific speeds. Each efficiency level represents an efficiency percentile. This means that for the 

25th percentile efficiency level, 25% of pump models have lower efficiency than the defined 

efficiency function. The following exampleg illustrates this idea:

• If Pump A has a flow of 80 GPM and a specific speed of 720, its attainable efficiency 

might range from 47% to 59%. 

f Low flow, low specific speed pumps are limited to lower efficiencies than high flow, high specific speed pumps

g Example is fictional and used for illustrative purposes only.

5-14

 
• If Pump B has a flow of 1250 GPM and a specific speed 1,500, its attainable efficiency 

might range from 75% to 87%.

• The efficiency level function needs to compensate for differences in the efficiency ranges 

between these two pumps. At the efficiency level representing the 50th percentile (an 

efficiency that is higher than 50% percent of pump models), the efficiency would be 53% 

for Pump A and 81% for Pump B (assuming an even distribution of pump models by 

efficiency—or a symmetric distribution). 53% and 81% represent midpoint (50th

percentile) of the attainable efficiency range for Pump A and B, respectively.

Moving past this simplified example, in this NOPR the metric used to establish a 

proposed standard level is the pump energy index (PEI). A pump model would be compliant if 

its PEI rating is less than or equal to the PEI of the proposed standard. PEI is defined as the 

pump efficiency rating (PER) for a given pump model, divided by a calculated minimally 

compliant PER for the given pump model. PER is defined as a weighted average electric input 

power to the pump over selected operating conditions, represented in units of horsepower. 

The minimally compliant PER is unique to each pump model and is a function of each 

pump model’s flow at BEP and specific speed. This function is a polynomial equation that 

represents a three-dimensional surface that describes the attainable hydraulic efficiency of 

pumps. All of the terms of the polynomial function are the same for all efficiency levels and 

classes of pumps for the DOE analysis, except for the constant parameter. This parameter, the 

“C-value,” is unique for each combination of equipment class and efficiency level. Changing the 

C-value moves the otherwise fixed-shape surface to higher or lower efficiency. Because the 

function was developed to represent the inherent pump efficiency trends as flow and specific 

speed change,

h 7 the C-value can be used to define an efficiency level, which is equally stringent

for all pumps across the scope of flow and specific speed. By adjusting the C-value, DOE can 

establish different efficiency levels that represent different efficiency percentiles.

For this NOPR the three-dimensional polynomial efficiency equation discussed in the 

previous paragraph is based on the EU minimum efficiency index (MEI) equation8 but is adapted

to account for the use of English units and the 60 Hz electrical input power found in the United 

States.

i

The converted equation is shown in Equation 5.1.

ηηpppppppp,SSSSSS = −0.85 ∗ ln(QQ)2 − 0.38 ∗ ln(NNNN) ∗ ln(QQ) − 11.48 ∗ ln(NNNN)2 + 13.46 ∗ ln(QQ)

+ 179.80 ∗ ln(NNNN) − (CC − 555.6)

h The function was developed for use in the European Union’s pump efficiency regulation. i

In the EU flow is reported in m3/hr at 50 Hz and specific speed is reported in metric units. In the US flow is 

reported in gpm, and the 60 Hz electricity frequency will cause the same pump to output 120% more flow than in 

the EU. Increased frequency causes increased motor shaft speed, which causes increased flow. The coefficients of 

equation 5.1 have been modified to account for these differences. 

5-15

 
Eq. 5.1

Where: 

Q = flow at BEP in gallons per minute at 60 Hz input power frequency,

Ns = specific speed at 60 Hz, and

C = an intercept that is set for the surface, which is set based on the speed of rotation and 

equipment type of the pump model. 

The method of structuring efficiency levels to represent efficiency percentiles was 

pursued on the recommendation of the CIP working group. This method functions well for 

mature products such as pumps, where the highest levels of attainable efficiency are already 

present in the marketplace. Additionally, this method follows the precedent set by the EU MEI 

regulation on pumps, and supports the goals of Executive Order 13609, “Promoting International 

Regulatory Cooperation.”9

5.8.2 Efficiency Levels Analyzed

DOE analyzed several efficiency levels for each equipment class, each defined by a 

single C-value. DOE chose to analyze efficiency levels at the 10th, 25th, 40th, 55th, and 70th

efficiency percentiles. The selection of the 10th and 40th percentiles as efficiency levels was 

recommend by the CIP working group, as an effort to harmonize the efficiency levels with the 

efficiency levels that were analyzed and adopted in the EU.

j

On the recommendation of the 

working group, DOE chose to analyze one efficiency level midway between the 10th and 40th

percentile, the 25th percentile. Given the structure of the first three equipment classes, DOE and 

the working group chose to establish the remaining efficiency levels in increments of 15 

percentile points, up to max tech (which was found to be the 70th percentile, and is discussed in 

section 5.8.4). Table 5.8.1 presents the C-values corresponding with the efficiency levels 

analyzed for each equipment class. 

j

Commission Regulation (EU) No 547/2012 enforces a regulation at the 10th percentile, effective January 1, 2013. 

The regulation goes to the 40th percentile, effective January 1, 2015.

5-16

 
Table 5.8.1 Efficiency Levels Analyzed with Corresponding C-values

Equipment 

Class

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Baseline 10th Eff. 

Percentile

25th Eff. 

Percentile

40th Eff. 

Percentile

55th Eff. 

Percentile

70th Eff. 

Percentile/ 

Max Tech

ESCC.1800 134.43 131.63 128.47 126.67 125.07 123.71

ESCC.3600 135.94 134.60 130.42 128.92 127.35 125.29

ESFM.1800 134.99 132.95 128.85 127.04 125.12 123.71

ESFM.3600 136.59 134.98 130.99 129.26 127.77 126.07

IL.1800 135.92 133.95 129.30 127.30 126.00 124.45

IL.3600 141.01 138.86 133.84 131.04 129.38 127.35

RSV.1800* 129.63 N/A N/A N/A N/A 124.73

RSV.3600* 133.20 N/A N/A N/A N/A 129.10

VTS.1800 137.62 135.93 134.13 130.83 128.92 127.29

VTS.3600 137.62 135.93 134.13 130.83 128.92 127.29

*For RSV equipment, DOE established only baseline and max-tech efficiency levels due to limited data 

availability. 

5.8.3 Establishing the Baseline Efficiency Levels

5.8.3.1 Baseline Configuration

The baseline configuration represents the lowest efficiency level commonly available in 

the market. Because energy conservation standards do not currently exist for pumps, DOE must 

establish a baseline configuration using available information. The baseline configuration defines 

the energy consumption and associated cost for the lowest efficiency equipment analyzed in each 

class.

As discussed earlier, hydraulic redesign is the only design option available in the NOPR 

analysis. As such, the baseline configuration represents the lowest efficiency hydraulic designs

commonly available in each equipment class. Because hydraulic redesign is a complex design 

option resulting from the interaction of numerous interrelated design features, it is impossible to 

select a physical hydraulic design that represents the physical configuration of all baseline pumps 

across an entire equipment class. In response to this complication, DOE took the inverse 

approach. Similar to the process used to define efficiency levels, DOE performed an analysis on

the pump population to determine the C-value for each equipment class that represent the lowest 

efficiency hydraulic designs currently in the market. Although pumps at this baseline level may 

have different physical hydraulic designs, they all represent the baseline efficiency.

5.8.3.2 Establishing Baseline C-Values

Ideally the efficiency percentile used to represent the baseline would be zero. But setting 

the baseline to represent the single lowest efficiency pump in the market may result in a baseline 

with unusually low efficiency that is not representative of baseline performance over the full 

range of flow and specific speed covered by the equipment class. The lowest efficiency pumps in 

5-17
the database may be outliers, misrepresented data, or non-representative models that do not 

commonly exist across the entire equipment class. The baseline must be set at a level that 

represents the lowest efficiency pumps (normalized for size effects) that commonly exist across 

the entire scope of sizes, and it must hedge against the possibility of outliers and bad data. As 

such, DOE analyzed the efficiency data for all pumps in each equipment class, and used 

judgement to select a baseline level for each.

To identify the efficiency percentile and associated C-values that best represent the 

baseline for each equipment class, DOE analyzed scatter plots of failing C-values versus flow

and failing C-values versus specific speed.

“Failing C-value” is a calculated efficiency parameter that allows each pump to be 

evaluated on an equivalent scale. Failing represents the C-value at which each pump would not 

meet a standard. If a pump has a failing C-value less than the C-value specified for an efficiency 

level, it would pass that efficiency level. If a pump has a failing C-value greater than or equal to 

the C-value specified for an efficiency level, it would fail that efficiency level. For example, if an 

ESCC.1800 pump has a calculated failing C-value of 128.00, it would pass efficiency level 2, 

which has a passing C-value of 128.47, but would fail at efficiency level 3, which has a passing 

C-value of 126.67. Within an equipment class, pumps with the same failing C-value will be of 

the same relative efficiency. Lower failing C-values represent higher efficiency and vice-versa.

For all pumps, one point of C-value is always equal to one point of pump efficiency 

change. For example, if two pumps of identical flow and specific speed exist; Pump A with a 

failing C-value of 130 and Pump B with a failing C-value of 125; Pump B might have a pump 

efficiency of 55% and Pump A might have might have a pump efficiency of 50%. 

Figure 5.8.1 presents a scatter plot of failing C-values versus flow at BEP for the 

ESCC.1800 equipment class. Overlaid on this figure are bands representing the C-values for 

each efficiency levels presented in Table 5.8.1, as well as the C-values for the 5th and 85th

percentiles. Figure 5.8.2 is similar to Figure 5.8.1, except it illustrates failing C-values versus 

specific speed for the ESCC.1800 equipment class. Note that higher C-values represent lower 

efficiency pumps.

To identify the efficiency percentile and associated C-values that best represent the 

baseline, DOE evaluated the trends and tendencies of pumps that fell below the 10th percentile. 

These pumps are represented by data points that fall above the red line in both figures. As the 

halfway point between zero and the 10th percentile (EL1), the 5th percentile was established as a 

reasonable initial hypothesis for the location of the baseline. As the initial hypothesis, the 5th

percentile was plotted on the figures, and trends in the data above and below the line were

evaluated to support or reject the hypothesis.

Review of both figures revealed that data below the 5th percentile is clearly spread over a 

larger range of C-values than the other percentile grouping. Specifically, the lowest efficiency 

pump on the figure is approximately 10 points less efficient than the 5th percentile line. 

Comparatively, if 10 efficiency points were added to a pump at the 5th percentile line, that pump 

would reach the 55th percentile. This illustrates the risk of attempting to set the baseline by the 

single lowest efficiency pump. In this case such a pump is not representative of pumps over the 

5-18
range of the equipment class. Additionally, data below the 5th percentile appears to be less 

uniformly populated across both flow and specific speed, with the lowest efficiency pumps 

clustered at flows less than 500 GPM and specific speeds between 1,000 and 2,000. Conversely, 

the data below the 5th and above the 10th percentile line is more uniformly distributed with 

respect to flow and specific speed, and is contained within a range of C-values more comparable 

to the other efficiency bands on the figure. Given the trends of data below the 5th percentile, and 

the more uniform nature of the pumps falling above the 5th percentile, DOE found it reasonable 

to accept the initial hypothesis and estimate the baseline at the 5th percentile for the ESCC.1800 

equipment class. 

The same hypothesis and figures were established and reviewed for all equipment 

classes. Figures for failing C-value versus flow at BEP and specific speed for all equipment 

classes can be found in Appendix 5a. The trends for the remaining equipment classes were all 

similar to those seen in ESCC.1800. As such, DOE found it reasonable to accept the initial 

hypotheses and set a uniform baseline for all equipment classes at C-values that represent the 5th

percentile. 

Figure 5.8.1 Failing C-Values Plotted Against Flow at BEP for the ESCC.1800 Equipment 

Class

5-19
Figure 5.8.2 Failing C-Values Plotted Against Specific Speed for the ESCC.1800 

Equipment Class

5.8.4 Establishing the Maximum Technologically Efficiency Levels

5.8.4.1 Max-Tech Configuration

Max-tech is the efficiency level that provides the maximum improvement in energy 

efficiency that is technologically feasible. The max-tech level must be attainable across the 

entire scope of the equipment class. Generally, the max-tech level results from the combination 

of design options predicted to result in the highest efficiency level possible for an equipment 

class. For this rulemaking hydraulic redesign was determined to be the only available design 

option.

Pumps are considered mature products with the highest levels of attainable efficiency 

already present in the marketplace. As such, the max-tech configuration represents the highest 

efficiency hydraulic design(s) commonly available in the market. Because hydraulic design is a 

complex design option resulting from the interaction of numerous interrelated design features, it 

is impossible to select a physical hydraulic design that represents the physical configuration of 

all max-tech pumps across an entire equipment class. In response to this complication, DOE took 

the inverse approach. Similar to the process used to define the baseline, DOE performed an

analysis on the pump population to determine the C-value for each equipment class that represent 

the highest efficiency hydraulic designs currently in the market. 

5-20
5.8.4.2 Establishing Max-Tech C-Values

Ideally the 100th efficiency percentile would be used to represent the max-tech level. But 

setting the max-tech level to represent the single highest efficiency pump in the market may 

result in an overestimated max-tech that is not representative of the entire equipment class. The 

lowest efficiency pumps in the database may be outliers, misrepresented data, or nonrepresentative models that do not commonly exist across the entire equipment class. The maxtech must be set at a level that represents the maximum efficiency pumps (normalized for size 

effects) that commonly exist across the entire scope of sizes, and it must hedge against the 

possibility of outliers and bad data. As such, DOE analyzed the efficiency data for all pumps in 

each equipment class, and used its judgement to develop the max-tech for each. 

To establish the efficiency percentile and associated C-values that best represent the maxtech for each equipment class, DOE evaluated the same failing C-value versus flow and specific 

speed plots that there used to determine the baseline levels.

For this analysis DOE evaluated the trends and tendencies of pumps that were more 

efficient than the 55th percentile. To do so, the 70th and 85th efficiency percentile was evaluated 

in addition to the 55th. DOE found it reasonable to evaluate in 15 point increments as it was 

consistent with the existing efficiency level spacing selected by the working group. DOE did not 

feel that a more granular analysis was warranted, given the analytical structure.

DOE sought to determine whether the max-tech level occurred at the 55th, 70th, or 85th

percentile. To do so, the trends in the data below each percentile line were evaluated. Review of 

both figures revealed that the efficiency range (C-value range) of the 55th to 70th percentile band 

is similar to those seen in the lower efficiency bands. The range widened in the 70th to 85th 

percentile band and widened even further beyond the 85th percentile. Pumps beyond the 85th 

percentile are clearly spread over a larger range of C-values than the other percentile grouping.

Specifically, the highest efficiency pump on the figure is approximately 10 points more efficient 

than the 85th percentile line. Comparatively, if 10 efficiency points were subtracted from a pump 

at the 85th percentile line, that pump would drop to the 10th percentile. This illustrates the risk of 

attempting to set max-tech by the single maximum efficiency pump, as in this case such a pump 

would not be representative of the equipment class. 

Additionally, pumps at or below the 85th percentile line are less uniformly populated 

across both flow and specific speed, with the maximum efficiency pumps clustered at flows less 

than 500 GPM and specific speeds below 1,500. Few pumps of higher flows and specific speeds 

exist above the 85th percentile line. As such, the 85th percentile is considered to not be 

technologically feasible for these larger pumps. A standard set at such a level would result in 

pumps of larger flows and specific speeds no longer being available in the market. A similar 

analysis of the 70th percentile line shows pumps to be well distributed with respect to flow and 

specific speed. This suggests that the 70th percentile is likely a technologically feasible level 

across the entire equipment class. 

Given the trends of the upper 70th and 85th percentile data, and the more uniform nature 

of the pumps falling between the 55th and 70th percentile, DOE concluded that the 85th percentile 

5-21
is beyond max-tech, and found it reasonable to establish the next level down, the 70th percentile,

as the max-tech for the ESCC.1800 equipment class. 

The same analysis was performed for all equipment classes. Figures for failing C-value 

versus flow at BEP and specific speed for all equipment classes can be found in Appendix 5a. 

The trends for the remaining equipment classes were all similar to those seen in ESCC.1800. As 

such, DOE found it reasonable to set a uniform max-tech for all equipment classes at C-values 

that represent the 70th percentile. This decision was supported by the CIP working group and 

harmonizes with the EU, where the 70th percentile was determined to be “the indicative 

benchmark for the best available technology on the market for water pumps.”10

5.8.5 Harmonization with EU Analytical Structure and Methods

Review of the failing C-value versus flow and efficiency charts brings to light the 

observation that low flow and low specific speed pumps (“smaller pumps”) clearly have a wider 

range of available efficiencies than their high flow and high specific speed counterparts. In 

general, this observation is a result frictional or volumetric losses making up a larger percent of 

output power for smaller pumps that for larger pumps. Small improvements (or reductions) in the 

frictional or volumetric losses will have a larger effect on the overall efficiency of a smaller

pump. The technology option data presented in chapter 3 clearly demonstrates this relationship

for technology options such as improved surface finish and reduced running clearances.

The issue of attainable efficiency ranges varying across each equipment class was a 

known limitation of the metric and analytic structure used by DOE for this NOPR. As such, DOE 

explored options to better account for the varying efficiency ranges. These options included:

• Changing the shape of the efficiency surface to better represent the U.S. market

• Varying the efficiency surface shape at each EL, rather than adjusting just the C-value

• Setting C-values for the ELs that vary with flow and specific speed

• Breaking up equipment classes into multiple sub classes based on flow and specific speed

o Smaller pumps could have a different baseline and max tech than the larger 

pumps.

Ultimately, DOE and the CIP working group decided to not pursue the alternate 

structures, as they felt the value of an analytical structure that is harmonized to the EU 

outweighed the potential benefits of the more complex analyses. This decision supports the goals 

of Executive Order 13609,

11 which calls for harmonization among international standards.

5.8.6 Establishing Baseline and Max-Tech for RSV Equipment Classes

As discussed in section 5.3.1, DOE proposes to set the RSV.1800 and RSV.3600 baseline

and max-tech equivalent to the EU minimum efficiency standard and max-tech, respectively. To 

5-22
do so, DOE translated the EU MEI C-values to equivalent PEI C-values. Both MEI and PEI

define efficiency level with C-values, but the MEI and PEI C-values are not interchangeable. For 

example an MEI C-value of 130.38 might represents the 40th percentile using the MEI metric, 

but a PEI C-value of 130.38 might represents the 35th percentile when used on the same dataset.

The EU MEI C-values for RSV 40th and 70th percentiles

k are presented in Table 5.8.2. Note that 

the EU refers to these equipment classes as MS 1450 and MS 2900, which correspond to the 

RSV.1800 and RSV.3600 equipment classes, respectively.

Table 5.8.2 European Union MEI C-Values for RSV at the 40th and 70th Percentile12

40th Percentile

MEI C-value

70th Percentile

MEI C-value

MS 1450

(RSV.1800) 130.38 125.48

MS 2900

(RSV.3600) 133.95 130.37

 

There is no mathematical function to directly translate between MEI C-values and PEI Cvalues. Instead, DOE used a two-step method to estimated PEI C-values that are approximately 

equivalent to the MEI C-values. First, for all equipment classes (except RSVl), DOE evaluated 

how MEI C-values at the efficiency percentiles defined by each EL compared to PEI C-values at

the same efficiency percentiles. The EU MEI equation and regulation method13 was applied to 

each pump in the performance database to find the MEI C-values at each efficiency percentile. 

On average, PEI C-values were 0.75 point lower than MEI C-values. As such, DOE subtracted 

0.75 point from the EU MEI C-values, to create a first estimate of PEI C-values for RSV.

Second, DOE evaluated limited RSV model performance data against the first estimates 

of PEI C-values. DOE found that some RSV.3600 models that passed the EU 70th percentile 

level were failing the estimated DOE 70th percentile level. As a result, DOE adjusted the PEI Cvalues for this equipment class until all models that passed the EU 70th percentile also passed the 

DOE 70th percentile. The resulting PEI C-values for RSV baseline and max-tech are presented in 

Table 5.8.3. 

Table 5.8.3 DOE PEI C-Values for RSV at Baseline and Max-Tech

Baseline C-value Max-Tech C-value

RSV.1800 129.63 124.73

RSV.3600 133.20 129.10

k 40th percentile is the minimum efficiency standard in the EU, effective January 1, 2015. l

Available RSV data did not represent enough manufacturers or models to be considered representative for the 

industry.

5-23

 
5.9 MANUFACTURERS PRODUCTION COST ASSESSMENT METHODOLOGY

5.9.1 Changes in MPC Associated with Hydraulic Redesign

DOE performed an analysis to determine the change in MPC, if any, associated with a 

hydraulic redesign. For this analysis DOE consider two key inputs; (1) confidential manufacturer 

responses to relevant interview questions, and (2) an analysis of confidential impeller cost and 

efficiency data provided by manufacturers.

During manufacturer interviews, DOE specifically inquired about potential changes in

production costs associated with a hydraulic redesign. Eight manufacturers of pumps covered by 

the scope of this rulemaking were interviewed. Although most manufacturers indicated that 

selling price might be increased to cover the costs of redesign, no responses indicated that 

production costs would increase due to hydraulic redesign.

m

Impeller cost and efficiency data, gathered under confidentiality agreements from four 

manufacturers, were also analyzed. Impeller design (and its interaction with the volute) is 

primarily responsible for establishing the performance and efficiency of a pump. As such, any 

possible cost increases that might result from a hydraulic redesign were expected to come from

the impeller and volute. DOE notes that sufficient data were not available to evaluate volute cost 

and efficiency.

For the impeller cost-efficiency analysis, the production costs from four manufacturers 

for 24 end suction and inline impellers were first plotted against the normalized scaling 

parameter Log((Ns/Q) x (n/1800)) + 1 and regressed using a second order polynomial, as shown 

in Figure 5.9.1. n End suction and inline impellers were grouped together as these impellers are 

typically of similar construction and cost.

Next the “cost premium” (or discount) for each impeller was found by calculating each 

pump’s deviation from the regression line. Cost premium for each impeller was then plotted 

versus the efficiency percentile of the pump that the impeller belonged to. The results are plotted 

in Figure 5.9.2.

Finally, the data points were grouped by manufacturer, and trends between pumps in the 

lower and upper 50th percentile were evaluated, as shown in Figure 5.9.3. The results indicate 

that only one manufacturer had a positive cost versus efficiency percentile relationship, with the 

other three manufacturers displaying negative or flat relationships. 

Given the manufacturer interview responses and the results of the impeller costefficiency analysis, DOE concluded that more-efficient pump geometries are no more complex 

m Note the two manufacturers of products not covered by scope of this rulemaking indicated that they expected to 

see increases in production cost due to hydraulic redesign. n Log((Ns/Q) x (n/1800)) + 1 is a normalized scaling parameter that allows for cost and price modeling in only two 

dimensions. This factor uses physical speed (n), flow at BEP, and specific speed to essentially represent the “size” of 

a given pump. This factor is used consistently throughout the engineering analysis. See Appendix 5b for a more 

detailed explanation of this factor. 

5-24

 
to manufacturer than less-efficient geometries. Consequently, a hydraulic redesign is not 

expected to increase the MPC of the representative pump configurations used for analysis.

As a part of the engineering analysis, DOE presented this conclusion to the CIP working 

group at the April 29, 2014 meeting. At this meeting the flat cost-efficiency relationship was 

explicitly discussed, and no objections from the working group were received. (EERE-2013-BTNOC-0039-0072, pp.114-130 and pp.270-273) As a result, DOE propagated this relationship into 

subsequent engineering and downstream analyses. DOE does note that in a later meeting, one 

member of the working group expressed concern with this conclusion (EERE-2013-BT-NOC0039-109, p.264). The working group, including this member, ultimately voted to approve the 

term sheet, which was based on the analytical structure of a flat cost-efficiency relationship

(EERE-2013-BT-NOC-0039-0092). As such, the analyses presented in the NOPR and this TSD 

are based on a flat cost-efficiency relationship.

DOE acknowledges that actual changes in MPC experienced by individual manufacturers 

will vary, and that in some cases redesigns may actually increase or decrease the cost of the 

impeller and/or volute. However, available information indicates that the flat MPC-versusefficiency relationship best represents the pumps included in the scope of this rulemaking.

5-25
Figure 5.9.1 Impeller Production Cost Plotted against the Scaling Parameter

Figure 5.9.2 Impeller Production Cost Premium Plotted Against Efficiency Percentile

5-26
Figure 5.9.3 Trends between the Lower and Upper 50th Percentile of Efficiency, for 

Impeller Production Cost Premium

5.9.2 Manufacturer Production Cost Model

Using data from the performance database, DOE developed a scalable cost model to 

estimate MPC across all pump sizes. Given a pump’s specific speed and BEP flow, the cost 

model outputs an estimated MPC. Because efficiency improvements from hydraulic redesign

have no associated increase in MPC, the model is efficiency-independent and predicts the same 

MPC for all pumps of the identical BEP flow, specific speed, and equipment class. 

5.9.2.1 Equipment Class Grouping

DOE aggregated data from all end suction pumps and used the data to develop a model 

that predicts MPC for any pump in the ESCC.1800, ESCC.3600, ESFM.1800, and ESFM.3600 

equipment classes. Data from all inline pumps were also aggregated and used to develop a model 

that could predict MPC for any pump in the IL.1800 and IL.3600 equipment classes. Finally, 

data from VTS.3600 pumps were used to develop a model that could predict MPC for any pump 

in the VTS.1800 and VTS.3600 equipment classes.

5.9.2.2 Model Description

Each MPC model outputs the model-weighted-average production cost of a pump. This 

means that the MPC of each model in the equipment class was weighted equally when 

developing the cost model. The model-weighted-average production cost represents only the cost 

5-27
of the bare pump. The following subsection describes the details of the MPC model 

development.

5.9.2.3 Model Development

The MPC models were developed using data from the performance database. 

Specifically, the model uses pump revenues, manufacturer markup, shipments volumes, and 

motor costs as key inputs. The remainder of this section outlines in detail the steps taken to build 

the MPC models for each equipment class grouping. 

The revenue data supplied in the performance database included motor revenues; as such 

the first step of the analysis was to subtract the value of the motors from the revenues and 

ultimately the MSP. The steps taken to do so are as follows:

1. Calculate the per-unit MSP (including motor) for each model.

a. For each model divide total revenues (including motor revenues) by total 

shipments volume.

2. Estimate the baseline motor price for each model in the database.

a. Estimate motor HP as 120% of BEP required shaft HP.

b. Use manufacturer-specific motor cost curves to estimate baseline motor price.

o

3. Find adjusted motor cost, which accounts for markup and the percent of pumps in the 

population not sold with motors.

a. Multiply baseline motor price by a markup to create the marked-up motor cost.

b. Multiply marked-up motor cost by manufacturer-specific percent of shipments 

sold with motors to find the adjusted motor cost, which accounts the portion of 

shipments not sold with motors.

4. Find bare pump MSP by subtracting adjusted motor cost from per-unit MSP

With bare pump MSP known for each pump in the database, bare pump MSP could then

be regressed against the scaling parameter. To do so effectively and give equal weight to all 

pump sizes within the scope, the bare pump MSP values were grouped and averaged over 

discrete ranges of the scaling parameter, and a regression was performed. Figures 5.9.4, 5.9.5, 

and 5.9.6 illustrate the grouping and regressions for each of the three MSP models. The resulting 

regressions estimate the model-weighted-average MSP for all pumps in the industry. These

regressions do not yet take into account any possible relationships between markups and 

efficiency. They provide only the average MSP for a pump of a given size (as defined by the 

scaling parameter).

From these regressions, the model-weighted-average MPC is calculated. The first step is to 

divide the model-weighted-average MSP regressions by the average manufacturer markupp for 

each equipment class. The resulting model-weighted-average MPC regression represents the 

average production cost for a pump of a given size. Because efficiency improvements from 

hydraulic redesign have no associated increase in MPC, the model is efficiency-independent and 

o Manufacturers supplied confidential motor cost data that was analyzed and trended for use in this analysis. p Discussed in Section 5.11

5-28

 
predicts the same MPC for all pumps of the identical BEP flow, specific speed, and equipment 

class. Figures 5.9.4, 5.9.5, and 5.9.6 also illustrate the grouping and regressions for each of the 

three MPC models.

Figure 5.9.4 Average Bare Pump MPC and MSP per Model for IL.1800 and IL.3600

5-29
Figure 5.9.5 Average Bare Pump MPC and MSP per Model for ESCC.1800, ESCC.3600, 

ESFM.1800, and ESFM.3600

Figure 5.9.6 Average Bare Pump MPC and MSP per Model for VTS.3600

5.10 CONVERSION COSTS

DOE expects that hydraulic redesigns will result in significant conversion costs for 

manufacturers as they attempt to bring their pumps into compliance with the proposed standard. 

DOE classified these conversion costs into two major groups: (1) product conversion costs and 

(2) capital conversion costs. Product conversion costs are investments in research, development, 

testing, marketing, and other non-capitalized costs necessary to make product designs comply 

with a new or amended energy conservation standard. Capital conversion costs are investments 

in property, plant, and equipment necessary to adapt or change existing production facilities such 

that new product designs can be fabricated and assembled.

To evaluate the magnitude of the product and capital conversion costs the pump industry 

would incur to comply with new energy conservation standards, DOE used a bottom-up 

approach. For this bottom-up approach, DOE first determined the industry-average cost, per 

model, to redesign pumps of varying sizes to meet each of the proposed efficiency levels. DOE 

then modeled the distribution of unique pump models that would require redesign at each 

efficiency level. For each efficiency level, DOE multiplied the number of unique failing models

by the associated cost to redesign and summed the total to reach an estimate of the total product 

and capital conversion cost for the industry. The following sections describe, in detail, the data 

and methodologies used to estimate these conversion cost for the industry.

5-30
5.10.1 Hydraulically Redesign Cost Data

Data supplied to DOE by the Hydraulic Institute was used as basis for the industryaverage cost, per model, to redesign a failing pump model. The Hydraulic Institute, through an 

independent third party, surveyed 15 manufacturers regarding the product and conversion costs 

associated with redesigning 1, 50, and 200 horsepower pumps from below the 10th to the 40th

percentile of market efficiency. The results of HI's survey were previously discussed in Section 

5.5 and presented in Table 5.4.3. For this analysis, the “cost of capital” cost category was 

adjusted to back out “financing costs” that were included in the category. In this context, 

financing costs represented the total discounted cost of financing redesign activities in 2012 

dollars. These costs were backed out, as they are not considered capital or product conversion 

costs. Financing costs are accounted for in the Manufacturer Impact Analysis (chapter 12) with 

the weighted average cost of capital (WACC) parameter. HI, along with the CIP working group, 

agreed that it was reasonable to back out 75% of that cost of capital cost category, leaving the 

remaining 25% to cover costs associated with the need to carry increased inventory. The revised 

redesign costs are presented in Table 5.10.1.

Table 5.10.1 Revised Cost of Redesign Table

Cost Category

Average Cost to Redesign One Pump 

Model

1 HP 50 HP 200 HP

Redesign, Prototype & Initial Test* $52,239 $64,397 $65,918 

Pattern & Tooling** $44,329 $51,280 $72,862 

Testing† $22,858 $35,394 $47,293 

Working Capital‡ $9,275 $11,835 $13,744 

Marketing†† $32,225 $29,842 $27,155 

TOTAL $160,926 $192,748 $226,972 

*Redesign, Prototype & Initial Test includes: Hydraulic and Mechanical Design, Simulation, Prototype 

Development, Prototype Tooling, Initial Testing

**Pattern & Tooling includes: Supplier Development, Patterns, Tooling (Machine Fixtures, Assembly Fixtures, and 

Test Fixtures), Machine Programming, Quality Inspection (Parts, Tooling), New Process Development (Casting 

and/or Machine Processes), New Machine Tools, Retraining.

†Testing includes: Performance (All Speeds), Hydrostatic, Certification Testing, Production Documentation, 

Training, etc.

‡Working Capital includes: Increased inventory holding costs

††Marketing includes: Literature, Data Sheets, Curves, Pump Selection Tools, Sales Training, Compliance 

Documentation, etc.

Data from the European Union pumps regulation preparatory study,

14 along with input 

from manufacturers, were used to scale HI redesign cost estimates to various efficiency levels 

above and below the 40th percentile. The EU study provided “estimated [aggregate industry] 

5-31
development costs of new pumps to meet different minimum cut off values,” where cut off 

values are akin to DOE efficiency level and are defined by efficiency percentile. Table 5.10.2

presents the EU development cost data, as published in their preparatory study.

Table 5.10.2 Estimated Aggregate Industry Development Costs of New Pumps to Meet 

Different Minimum Cutoff Values in the European Union

Cut off 

(%)

Development costs of 

new pumps

(Millions)

10 € 43.2

20 € 120.9

50 € 550.8

80 € 1,382.4

DOE interpolated the European Union data using a second order polynomial to evaluate 

development costs at the 10th, 40th, and 70th efficiency percentiles, which represent DOE’s EL1, 

EL3, and EL5 (max-tech). The results of the interpolation, as well as subsequent data 

manipulations are presented in Table 5.10.3. The interpolated aggregate development costs were 

then divided by their respective efficiency percentiles; doing so illustrates how the development 

cost per-unit to redesign a pump compares at each level. Finally, to clarify this comparison, the 

development cost per percentile point column was normalized to the 40th percentile values. This 

results in a development cost scaling factor that indicates how much more or less it costs to 

redesign a model to the 10th and 70th percentile, as compared to the 40th percentile. The 

development cost scaling factors were slightly adjusted based on U.S. manufacturer input and 

agreement among the CIP working group. Linear interpolation of these values can be used to 

directly scale the redesign costs provided by HI to all levels between the 10th and 70th percentile 

of efficiency.

Table 5.10.3 European Conversion Costs Interpolated to DOE Efficiency Percentiles

Efficiency 

Percentile

Conversion*

costs of new 

pumps 

(Millions)

Conversion

cost, per 

percentile point

(Millions)

Conversion cost 

scaling factor

Adjusted 

conversion cost 

scaling factor

10 € 43.2 € 4.32 0.5 0.5

40 € 362.9 € 9.07 1.0 1.0

70 € 1,060.7 € 15.15 1.7 1.8 *

Note development costs are now referred to at conversion costs, for consistently with DOE terminology

5.10.2 Average Per-Model Cost To Hydraulically Redesign a Pump at Each Efficiency 

Level

The population of pumps in the DOE pump performance database that will not pass a 

potential standard at each efficiency level can be segregated. For each pump in that population,

the cost of hydraulic redesign can be calculated by interpolating the data presented in Table 

5.10.1 by horsepower, and applying the adjusted conversion cost scaling factors in Table 5.10.3. 

5-32
The average hydraulic redesign cost can then be calculated for this population. Table 5.10.4

presents the average cost of redesign, by efficiency level, for each equipment class grouping.

Table 5.10.4 Average to Cost Redesign, by Efficiency Level, for Each Equipment Class 

Grouping

EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile

25th

Percentile

40th

Percentile

55th

Percentile

70th

Percentile

ESCC/ESFM $88,970 $134,047 $179,233 $251,567 $322,753

IL $86,314 $132,134 $177,486 $249,497 $319,936

VTS $85,503 $126,482 $169,389 $240,336 $311,779

5.10.3 Accounting for Cost Sharing Between Models with Shared Platforms

ESCC/ESFM and 1,800/3,600 rpm pump models are known to share significant amounts 

of hardware. As such, every model in the database is not expected to incur the cost of a full 

hydraulic redesign. To account for the costs shared among multiple models in the database, DOE 

collected statistics on shared platforms and quantified the percentage of costs shared between 

them. A per-model redesign cost adjustment factor was developed for each group of equipment 

classes with shared components. The groupings were the same as those used for the MPC and 

MSP models:

End Suction. ESCC.1800, ESCC.3600, ESFM.1800, and ESFM.3600

Inline. IL.1800 and IL.3600.

VTS. Statistics were not collected. Analysis is based on VTS.3600 only.

5.10.3.1 Statistics on Shared Platforms

Table 5.10.5 presents the statistic on shared platforms, as collected from the DOE pump 

performance database. For end suction pumps, 46% of models in the database were considered 

duplicates with respect to pump type (ESCC versus ESFM). This means that 46% percent of 

ESFM models in the database had analogous ESCC models in the database. For end suction, 

23% of models in the database were considered duplicates with respect to pump speed (1,800 

RPM versus 3,600 RPM). This means that 23% percent of 3,600 RPM models in the database 

had analogous 1,800 RPM models in the database. Finally, for inline, 20% of models in the 

database were considered duplicates with respect to pump speed. 

5-33
Table 5.10.5 Statistic on Shared Platforms

End Suction Inline

Unique 

Models

*

Duplicate 

Models

**

Unique 

Models

Duplicate 

Models

Pump Type: 

ESCC vs ESFM 54% 46% N/A N/A

Pump Speed: 

2-Pole vs. 4-Pole 77% 23% 80% 20%

* Unique model is defined as either a model with no related shared platform, or the first of two shared platform 

models found the database

** Duplicate model is the second of two shared platform models found the database

For end suction pumps, each model in the database will be either unique or duplicate in 

both type and speed. Figure 5.10.1 illustrates the process used to refine the result of Table 5.10.5

into combined type and speed grouping. Table 5.10.6 summarizes the results of the process, as 

well as results for inline pumps. 

Figure 5.10.1 Process for Refining Statistics on End Suction Shared Platforms

Table 5.10.6 Refined Statistics on Shared Platforms

ESCC/ESFM Inline

Unique Type, Unique Speed 42% 80%

Unique Type, Duplicate Speed 12% 20%

Duplicate Type, Unique Speed 35% N/A

Duplicate Type, Duplicate Speed 11% N/A

5-34
5.10.3.2 Allocation of Shared Redesign Costs

“Unique” models incur the full per-model hydraulic redesign costs previously defined. 

“Duplicate” models incur only a percentage of the total redesign costs that a unique model would 

see. To find this percentage, each cost category was evaluated to determine the percentage that 

would apply to a duplicate model. DOE performed this evaluation with direct input from HI, and 

results were agreed upon by the CIP working group. Results are presented in Table 5.10.7. 

Table 5.10.7 Percentage of Redesign Costs that Apply to a Duplicate Model

Pump Type Pump Speed

Applicable to: End Suction End Suction and

Inline

Redesign, Prototype & Initial 

Test 50% 50%

Pattern, Tooling 34% 66%

Testing 100% 100%

Marketing 100% 100%

Working Capital * *

Total** 63% 73%

* Working capital remains a constant percentage of the preceding cost categories. If working capital is 7% of the 

redesign, pattern, testing, and market cost for a unique pump, it will be 7% of the reduced costs applied to duplicate 

models.

** “Total” is the weighted average percent of total design cost a unique model would see. To find it, each cost 

category percentage is multiplied by its associated cost in Table 5.10.1, and the result is presented as a percentage of 

total redesign cost for a unique model

The statistics on shared costs were then further refined to fit into the pump population 

categories presented in Table 5.10.8.

Table 5.10.8 Percent of Redesign Costs Applicable to each Pump Population Category

Percent of Unique 

Pump Redesign 

Costs Applicable

Unique Type, Unique Speed 100%

Unique Type, Duplicate Speed 73%

Duplicate Type, Unique Speed 63%

Duplicate Type, Duplicate Speed 46%

5.10.3.3 Composite per-model redesign cost sharing adjustment

Finally, the sum of the products of Table 5.10.6 and *Table 5.10.8 can be taken to find 

the composite per-model redesign cost sharing adjustment factor. This factor is used to adjust 

the per model redesign cost to represent the average cost that pump in the population would see. 

5-35
This factor accounts for all costs that might be shared across the population of the equipment 

class. Individual models will experience lower or higher costs, but the population averaged 

adjustment factor is most useful for analytical purposes. Table 5.10.9 presents these results.

Table 5.10.9 Redesign Cost Sharing Adjustment Factor for End Suction and Inline Pumps

ESCC/ESFM IL

Per-model redesign cost 

sharing adjustment factor 78% 95%

5.10.4 Adjusted Per-Model Cost to Hydraulically Redesign a Pump at Each Efficiency 

Level

The per-model redesign cost sharing adjustment factor presented in Table 5.10.9 can be 

applied to the average per-model hydraulic redesign costs presented in Table 5.10.4, to find the 

adjusted per-model cost to hydraulically redesign a pump at each efficiency level, for each 

equipment class. Tables 5.10.10, 5.10.11, and 5.10.12 present the results for each equipment 

class. The adjusted per-model hydraulic redesign cost represents the average cost of a redesign 

that each failing model in the equipment class will incur.

Table 5.10.10 End Suction - Adjusted Per-Model Cost to Hydraulically Redesign a Pump 

ESCC/ESFM 

Equipment Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Average per-model 

hydraulic redesign costs 

(40th percentile)

$88,970 $134,047 $179,233 $251,567 $322,753

Cost sharing adjustment 

factor 0.78 0.78 0.78 0.78 0.78

Adjusted per-model 

hydraulic redesign cost $69,243 $104,325 $139,491 $195,787 $251,189

5-36
Table 5.10.11 Inline - Adjusted Per-Model Cost to Hydraulically Redesign a Pump 

Inline Equipment 

Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Average per-model 

hydraulic redesign costs 

(40th percentile)

$86,314 $132,134 $177,486 $249,497 $319,936

Cost sharing adjustment 

factor 0.95 0.95 0.95 0.95 0.95

Adjusted per-model 

hydraulic redesign cost $81,653 $124,999 $167,901 $236,024 $302,660

Table 5.10.12 VTS - Adjusted Per-Model Cost to Hydraulically Redesign a Pump

VTS Equipment 

Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Average per-model 

hydraulic redesign costs 

(40th percentile)

$85,503 $126,482 $169,389 $240,336 $311,779

Cost sharing adjustment 

factor 1.00 1.00 1.00 1.00 1.00

Adjusted per-model 

hydraulic redesign cost $85,503 $126,482 $169,389 $240,336 $311,779

5.10.5 Determining Number of Models Redesigned in Each Equipment Class

5.10.5.1 Total Models in Each Equipment Class

Because HI did not provide estimates for industry model counts in the special statistical 

report bulletin (Table 5.4.2), DOE scaled the provided “survey results” model counts by the 

same factors used to scale shipments. This method was presented to, and accepted by, the CIP 

working group. Results are presented in Table 5.10.13.

Table 5.10.13 Calculation of Total Industry Model Counts for Each Analysis Group

ESCC/ESFM

2/4 Pole

IL

2/4 Pole

VTS

2 Pole Only

Shipments: Survey Results 125,865 24,406 78,624

Shipments: Scaled Industry Estimate 215,498 50,424 104,406

Ratio 1.71 2.07 1.33

Models: Survey Results 1,317 381 211

Models: Scaled Industry Estimate 2,255 787 280

5-37
5.10.5.2 Percent of Models Not Redesigned (Dropped from Production)

To accurately determine the number of models incurring redesign costs, DOE accounted

for the percent of failing models that would not be redesigned at each efficiency level. For many 

failing models in the performance database, the manufacturer may choose not to redesign the 

pump, dropping that model from the market. Common reasons for this decision include, but are 

not limited to: low sales, which lead to a poor business case for a redesign; a manufacturer may 

already have a hydraulically overlapping, passing model (often from a different brand) that it can 

leverage, or; a manufacturer may have two hydraulically overlapping models that fail and choose 

to redesign only one. Based on confidential discussions with manufacturers, and open input from 

the CIP working group, DOE estimates that 25% of failing models will be dropped at EL 1, 

tapering down to 15% at higher efficiency levels. Figure 5.10.7 illustrates this trend. Note that 

this trend only applies to ESCC, ESFM, and IL, and does not apply to VTS, as all VTS models 

are expected to be redesigned.

Figure 5.10.2 Percent of Failing Models Not Redesigned at Each Efficiency Level

5.10.5.3 Number of Models Redesigned in Each Equipment Class at Each 

Efficiency Level

The number of models redesigned in each equipment class, at each efficiency level, can 

be found by multiplying the total models in the equipment class by the percent of models failing 

at each efficiency level, and adjusting the result to account for the percent of models dropped 

5-38
from production and not redesigned. The results for each equipment class are presented in Tables 

5.10.14, 5.10.15, and 5.10.16.

Table 5.10.14 End Suction - Number of Models Redesigned at Each Efficiency Level

ESCC/ESFM 

Equipment Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Total Models 2,255 2,255 2,255 2,255 2,255

Percent of Models not 

Redesigned 25% 20% 15% 15% 15%

Number of Models 

Redesigned 179 473 793 1,075 1,372

Table 5.10.15 Inline - Number of Models Redesigned at Each Efficiency Level

IL Equipment Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Total Models 787 787 787 787 787

Percent of Models not 

Redesigned 25% 20% 15% 15% 15%

Number of Models 

Redesigned 62 160 270 374 476

Table 5.10.16 VTS - Number of Models Redesigned at Each Efficiency Level

VTS Equipment 

Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Total Models 280 280 280 280 280

Percent of Models not 

Redesigned 0% 0% 0% 0% 0%

Number of Models 

Redesigned 29 74 113 157 197

5.10.6 Aggregate Industry Conversion Cost at Each Efficiency Level

Finally the adjusted per-model hydraulic redesign cost can be multiplied by the number 

of models redesigned, to yield the aggregate industry conversion for each equipment class and 

efficiency level. Tables 5.10.17, 5.10.18, and 5.10.19 summarize the results.

5-39
Table 5.10.17 End Suction - Aggregate Industry Conversion Cost at Each Efficiency Level

ESCC/ESFM 

Equipment Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Number of Models 

Redesigned 179 473 793 1,075 1,372

Adjusted per-model 

hydraulic redesign cost $69,243 $104,325 $139,491 $195,787 $251,189

Aggregate Industry 

Conversion Cost

(Millions)

$12.4 M $49.4 M $110.6 M $210.4 M $344.7 M

Table 5.10.18 Inline - Aggregate Industry Conversion Cost at Each Efficiency Level

IL Equipment Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Number of Models 

Redesigned 62 160 270 374 476

Adjusted per-model 

hydraulic redesign cost $81,653 $124,999 $167,901 $236,024 $302,660

Aggregate Industry 

Conversion Cost 

(Millions)

$5.1 M $20.0 M $45.3 M $88.2 M $144.0 M

Table 5.10.19 VTS - Aggregate Industry Conversion Cost at Each Efficiency Level

VTS Equipment 

Classes

EL 1 EL 2 EL 3 EL 4 EL 5

10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

Number of Models 

Redesigned 29 74 113 157 197

Adjusted per-model 

hydraulic redesign cost $85,503 $126,482 $169,389 $240,336 $311,779

Aggregate Industry 

Conversion Cost 

(Millions)

$2.5 M $9.3 M $19.2 M $37.8 M $61.3 M

The results of Tables 5.10.17, 5.10.18, and 5.10.19 are summarized for comparison in 

Table 5.10.20.

5-40
Table 5.10.20 All Equipment Classes – Aggregate Industry Conversion Cost at Each 

Efficiency Level

All Values in Millions 

of Dollars

EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

Baseline 10th 

Percentile

25th 

Percentile

40th 

Percentile

55th 

Percentile

70th 

Percentile

ESCC/ESFM* $0 $12.4 M $49.4 M $110.6 M $210.4 M $344.7 M

IL $0 $5.1 M $20.0 M $45.3 M $88.2 M $144.0 M

VTS $0 $2.5 M $9.3 M $19.2 M $37.8 M $61.3 M

Total Industry $0 $20.0 M $78.7 M $175.1 M $336.4 M $550.0 M 

5.11 MANUFACTURER MARKUP ANALYSIS

To account for manufacturers' non-production costs and profit margin, DOE applies a 

non-production cost multiplier (the manufacturer markup) to the full MPC. The resulting MSP is 

the price at which the manufacturer can recover all production and non-production costs and earn 

a profit. To meet the new energy conservation standards proposed in this rule, DOE expects that 

manufacturers will hydraulically redesign their product lines, which may result in new and 

increased capital and equipment conversion costs. Depending on the competitive environment 

for this equipment, some or all of the increased conversion costs may be passed from 

manufacturers to retailers and eventually to consumers in the form of higher purchase prices. The 

MSP should be high enough to recover the full cost of the equipment (i.e., full production and 

non-production costs) and overhead (including amortized product and capital conversion costs), 

and still yield a profit. The manufacturer markup has an important bearing on profitability. A 

high markup under a standards scenario suggests manufacturers can readily pass along more of 

the increased capital and equipment conversion costs to consumers. A low markup suggests that 

manufacturers will not be able to recover as much of the necessary investment in plant and 

equipment.

DOE developed initial estimates of the base case manufacturer markups based on 

corporate annual reports, Securities and Exchange Commission (SEC) 10-K filings, confidential 

manufacturer data, and comments made publicly during the CIP working group negotiations. 

To support the downstream analyses, DOE investigated industry markups in detail, 

characterizing industry-average markups, individual manufacturer markup structures, and the 

industry-wide markup structure.

5.11.1 Industry-Average Markups

Industry-average manufacturer markups were developed by weighting individual 

manufacturer markup estimates on a market share basis, as manufacturers with larger market 

shares more significantly affect the market average. The industry-average markups were broken 

out by equipment class grouping and are presented in Table 5.11.1

5-41
Table 5.11.1 Industry-Average Markups for Each Equipment Class Group

ESCC/ESFM IL VTS RSV

Industry-Average 

Markup 1.34 1.44 1.35 N/A

5.11.2 Individual Manufacturer Markup Structures

Using data and information gathered during manufacturer interviews, DOE concluded 

that within an equipment class, each manufacturer maintains a flat markup. This means that each 

manufacturer targets a single markup value for models offered in an equipment class, regardless 

of size, efficiency, or other design features. Tiered product offerings and markups do not exist at 

the individual manufacturer level. 

5.11.3 Industry-Wide Markup Structure

DOE also used the pump performance database and markup data gathered during the 

manufacturer interviews to assess the industry-wide markup structure. Although tiered product 

offerings and markups do not exist at the individual manufacturer level, DOE concluded that 

when analyzed as whole, the industry exhibits a relationship between manufacturer markup and 

efficiency. 

5.11.3.1 MSP Premium-Efficiency Relationship

To quantify the MSP premium-efficiency relationship, DOE performed an analysis based 

on the inline equipment class bare pump MSP cost model presented in Section 5.10. The inline 

equipment class was best suited for this analysis, as 95% of these pumps were sold with motors. 

As such, inline pumps have the highest level of certainty regarding the individual calculated bare 

pump MSPs for each model.

q

 

For this analysis, DOE determined the “price premium” for each model in the database. 

The price premium is the difference between the actual and the predicted price of the pump (as 

defined in Section 5.10), divided by the predicted price. To investigate if a price premiumefficiency relationship existed, DOE evaluated the price premium data against each pump’s 

efficiency percentile. To do so, price premium data points were grouped by discrete ranges of ten 

efficiency percentile points, and results were plotted (in blue), as shown in Figure 5.11.1. The 

results indicated that on an industry-wide scale, the lowest efficiency models tend to garner 

lower markups than higher efficiency models. To make these result suitable for analysis, DOE 

fitted a continuous piece-wise trend to best represent the relationship. The trend is composed of a 

second order polynomial for lower efficiency transitioning to a flat line at higher efficiency; the 

trend is represented in black in Figure 5.11.1.

q The end suction and VTS equipment classes had lower percentages of models sold with motors, which made it 

difficult to define an MSP premium-efficiency relationship with certainty.

5-42

 
Figure 5.11.1 MSP Premium as a Function of Efficiency Percentile. Actual Results and 

Trended Fits. 

The final trend indicates that the lowest efficiency models tend to garner lower markups 

than higher efficiency models, up to the 25th percentile of efficiency. Beyond the 25th percentile 

the relationship flattens out, and no correlation is seen between markup and efficiency. The data 

suggests that this relationship is a result of certain manufacturers positioning themselves with 

more or less efficient product portfolios and charging markups commensurate with their position 

in the marketplace. The data also suggests that the market does not value efficiency beyond the 

25th percentile.

The relationship presented in Figure 5.11.1 were presented to, and accepted by the CIP 

working group. When presented to the working group, manufacturers expressed consensus that 

efficiency is not currently the primary selling point or cost driver for the majority of pumps 

within the scope of the proposed rule. Rather, other factors such as reliability may influence 

price significantly and are known to be more influential in the purchaser’s decision making 

process.

As mentioned previously, development of the MSP-efficiency relationship was based on 

data from the Inline equipment class. DOE, with support of the CIP working group, concludes 

that the markup structure of the Inline equipment class is representative of the ESCC, ESFM, and 

5-43
VTS equipment classesr

. DOE applied the Inline markup-efficiency relationship to these 

equipment classes, for use in the analyses presented in this NOPR

5.11.3.2 Markup-Efficiency Relationship

Finally, the markup-efficiency relationship can be found for each equipment class simply 

by multiplying (one plus) the price premium relationship by the appropriate average markup for 

each equipment class (as defined in Table 5.11.1). For example, if a 50th percentile Inline pump 

is priced 3.5% above the market average MSP, then it is priced 3.5% above the market average 

markup of 1.44. Because MPC does not vary with efficiency, that 3.5% premium must be added 

to the markup. So the markup for that pump would be 1.035 multiplied by 1.44, which results in 

a markup of 1.49.

With the markup-efficiency relationship defined, the MSP of any pump can be calculated 

given its flow at BEP, specific speed, and efficiency percentile. The flow and specific speed are 

used to calculate the MPC, and the efficiency percentile is used to determine the markup. When 

multiplied together, the MPC and markup will yield the MSP for the pump. 

5.11.4 MSP-Efficiency Relationship

Ultimately, the goal of the engineering analysis is to develop an MSP-Efficiency 

relationship that can be used in downstream rulemaking analyses such as the Life Cycle Cost 

(LCC) analysis, the Payback Period (PBP) analysis, and the Manufacturer Impact Analysis 

(MIA). 

For the downstream analyses, DOE evaluated a base case MSP-Efficiency relationship as 

well as two separate MSP-Efficiency relationship scenarios to represent the uncertainty 

regarding the potential impacts on prices and profitability for manufacturers following the 

implementation of new energy conservation standards. The two scenarios are: (1) flat pricing, 

and (2) cost recovery pricing. These scenarios result in varying revenue and cash flow impacts 

and were chosen to represent the lower and upper bounds of potential revenues for 

manufacturers.

The base case pricing scenario represents a snapshot of the pump market, as it stands 

prior to this rulemaking. The base case pricing scenario was developed by applying the markupefficiency relationship presented in section 5.11.3 to the MPC model presented in section 5.9.1; 

both of which are based on confidential data supplied by individual manufacturers. This results 

in a scalable model that can determine MSP as a function of efficiency, specific speed, and flow 

at BEP. 

Under the flat pricing standards case scenario, DOE maintains the same pricing structure

as in the base case. If a pump is redesigned to meet an efficiency level, its price will increase 

r Refer to the following transcript in which the conclusion that the markup structure of the Inline equipment class is 

representative of the ESCC, ESFM, and VTS equipment classes is presented to the working group and no negative 

feedback is received: EERE-2013-BT-NOC-0039-0072, pp.292-295.

5-44

 
commensurate with the base case MSP-Efficiency relationship. This pricing scenario assumes 

that manufacturers will not modify their pricing structure to recover conversion costs resulting 

from standards. As a result, this scenario is considered a lower bound for revenues.

In the cost recovery pricing scenario, manufacturer pricing is set so that manufacturers 

recover their conversion costs over the analysis period. This cost recovery is enabled by an 

increase in mark-up, which results is higher sales prices for pumps even as manufacturer 

production costs stay the same. The cost recovery calculation assumes manufacturers raise prices 

on models where a redesign is necessitated by the standard. The additional revenue due to the 

increase in markup results in manufacturers recovering 100 percent of their conversion costs 

over the 30-year analysis period, taking into account the time-value of money. Chapter 8 

provides specific details on the development of the cost recovery pricing scenario. In the LCC 

and NIA analysis, DOE evaluated only the cost recovery pricing scenario, as it would be the 

most conservative case for consumers, resulting in the fewest benefits.

s

s The cost recovery pricing scenario is the most conservative case (i.e., resulting in the fewest benefits) for 

consumers and the most positive case for manufacturers (i.e., resulting in the fewest negative impacts). In the MIA, 

DOE analyses this scenario and the flat pricing scenario, which results in the most positive case for consumer and 

the most conservative case for manufacturers. 

5-45

 
REFERENCES

1 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 

2012 implementing Directive 2009/125/EC of the European Parliament and of the Council 

with regard to ecodesign requirements for water pumps. Official Journal of the European 

Union. L 165, 26 June 2012, pp. 28-36.

2 Ibid.

3 Hydraulic Institute (March 21, 2014), DOE/HI Manufacturers Survey for Fiscal Year 2012, 

Parsippany, NJ. Docket EERE-2013-BT-NOC-0039, No. 0068. (Last accessed September 16, 

2014.) <www.regulations.gov/#!documentDetail;D=EERE-2013-BT-NOC-0039-0068>

4 Silver, M. Personal communication. Hydraulic Institute. E-mail to Alison Williams, LBNL. 

May 16, 2014. Docket EERE-2013-BT-NOC-0039, No. 0112. (Last accessed February 19, 

2015.) <www.regulations.gov/#!documentDetail;D=EERE-2013-BT-NOC-0039-0112> 

5 Hydraulic Institute (November 14, 2012), Pump Redesign Cost Survey, Parsippany, NJ. 

Docket EERE-2013-BT-NOC-0039, No. 0111. (February 19, 2015.) 

<www.regulations.gov/#!documentDetail;D=EERE-2013-BT-NOC-0039-0111>

6 Photo Credit. (Last accessed October 1, 2014.) <www.jvl.dk/489/grundfos-using-jvlturntable>.

7 AEA Energy & Environment. 2008. Appendix 6: Lot 11 – ‘Circulators in buildings’. Report to 

European Commission, pp. 174-175.

8 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 

2012 implementing Directive 2009/125/EC of the European Parliament and of the Council 

with regard to ecodesign requirements for water pumps. Official Journal of the European 

Union. L 165, 26 June 2012, pp. 28-36.

9 The President of the United States of America. Executive Order 13609 of May 1, 2012;

Promoting International Regulatory Cooperation. Federal Register. May 4, 2012. vol. 77, no. 

87: pp. 26413–26415.

10 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 

2012 implementing Directive 2009/125/EC of the European Parliament and of the Council 

with regard to ecodesign requirements for water pumps. Official Journal of the European 

Union. L 165, 26 June 2012, p. 36.

11 The President of the United States of America. Executive Order 13609 of May 1, 2012; 

Promoting International Regulatory Cooperation. Federal Register. May 4, 2012. vol. 77, no. 

87: pp. 26413–26415.

12 AEA Energy & Environment. 2008. Appendix 6: Lot 11 – ‘Circulators in buildings’. Report 

to European Commission, p. 175.

5-46
13 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 

2012 implementing Directive 2009/125/EC of the European Parliament and of the Council 

with regard to ecodesign requirements for water pumps. Official Journal of the European 

Union. L 165, 26 June 2012, pp. 28-36.

14 AEA Energy & Environment. 2008. Appendix 6: Lot 11 – ‘Circulators in buildings’. Report 

to European Commission, p. 186.

5-47
APPENDIX 5A. ADDITIONAL FIGURES: FAILING C-VALUES PLOTTED AGAINST

SPECIFIC SPEED AND FLOW AT BEP

TABLE OF CONTENTS

5A.1 ADDITIONAL FIGURES............................................................................................ 5A-1

LIST OF FIGURES

Figure 5A.1.1 Failing C-Values Plotted Against Flow at BEP for ESCC.1800....................... 5A-1

Figure 5A.1.2 Failing C-Values Plotted Against Specific Speed for ESCC.1800 ................... 5A-1

Figure 5A.1.3 Failing C-Values Plotted Against Flow at BEP for ESCC.3600....................... 5A-2

Figure 5A.1.4 Failing C-Values Plotted Against Specific Speed for ESCC.3600 ................... 5A-2

Figure 5A.1.5 Failing C-Values Plotted Against Flow at BEP for ESFM.1800 ...................... 5A-3

Figure 5A.1.6 Failing C-Values Plotted Against Specific Speed for ESFM.1800................... 5A-3

Figure 5A.1.7 Failing C-Values Plotted Against Flow at BEP for ESFM.3600 ...................... 5A-4

Figure 5A.1.8 Failing C-Values Plotted Against Specific Speed for ESFM.3600................... 5A-4

Figure 5A.1.9 Failing C-Values Plotted Against Flow at BEP for IL.1800 ............................. 5A-5

Figure 5A.1.10 Failing C-Values Plotted Against Specific Speed for IL.1800........................ 5A-5

Figure 5A.1.11 Failing C-Values Plotted Against Flow at BEP for IL.3600 ........................... 5A-6

Figure 5A.1.12 Failing C-Values Plotted Against Specific Speed for IL.3600........................ 5A-6

Figure 5A.1.13 Failing C-Values Plotted Against Flow at BEP for VTS.3600 ....................... 5A-7

Figure 5A.1.14 Failing C-Values Plotted Against Specific Speed for VTS.3600.................... 5A-7

5A-i
APPENDIX 5A. ADDITIONAL FIGURES: FAILING C-VALUES PLOTTED AGAINST

SPECIFIC SPEED AND FLOW AT BEP

5A.1 ADDITIONAL FIGURES

This appendix presents figures representing failing C-values plotted against specific 

speed and flow at BEP, for ESCC, ESFM, IL, and VT-S equipment classes.

Figure 5A.1.1 Failing C-Values Plotted Against Flow at BEP for ESCC.1800

Figure 5A.1.2 Failing C-Values Plotted Against Specific Speed for ESCC.1800

5A-1
Figure 5A.1.3 Failing C-Values Plotted Against Flow at BEP for ESCC.3600

Figure 5A.1.4 Failing C-Values Plotted Against Specific Speed for ESCC.3600

5A-2
Figure 5A.1.5 Failing C-Values Plotted Against Flow at BEP for ESFM.1800

Figure 5A.1.6 Failing C-Values Plotted Against Specific Speed for ESFM.1800

5A-3
Figure 5A.1.7 Failing C-Values Plotted Against Flow at BEP for ESFM.3600

Figure 5A.1.8 Failing C-Values Plotted Against Specific Speed for ESFM.3600

5A-4
Figure 5A.1.9 Failing C-Values Plotted Against Flow at BEP for IL.1800

Figure 5A.1.10 Failing C-Values Plotted Against Specific Speed for IL.1800

5A-5
Figure 5A.1.11 Failing C-Values Plotted Against Flow at BEP for IL.3600

Figure 5A.1.12 Failing C-Values Plotted Against Specific Speed for IL.3600

5A-6
Figure 5A.1.13 Failing C-Values Plotted Against Flow at BEP for VTS.3600

Figure 5A.1.14 Failing C-Values Plotted Against Specific Speed for VTS.3600

5A-7
APPENDIX 5B. SCALING PARAMETER

TABLE OF CONTENTS

5B.1 OVERVIEW ..................................................................................................................5B-1

5B.2 STRUCTURE AND DERIVATION.............................................................................5B-1

LIST OF FIGURES

Figure 5B.2.1 Hydraulic Horsepower Plotted Against Ns/Q, for IL.1800 Data ........................5B-2

Figure 5B.2.2 Hydraulic Horsepower Plotted Against Log[(Ns/Q)×(n/1800)]+1, for 

IL.1800 Data ................................................................................................5B-3

5B-i
APPENDIX 5B. SCALING PARAMETER 

5B.1 OVERVIEW

The scaling parameter DOE used to regress and model MPC, MSP, and individual part 

costs over the range of pump sizes in this NOPR is:

LLLLLL ��

NNss

QQ � × � nn1800�� + 1

Eq. 5B.1

Where:

• Ns is specific speed,

• Q is flow at BEP, and

• n is nominal rotational speed of the motor in RPM.

This scaling parameter is designed to represents the physical size of a pump. DOE 

expected cost to scale with physical size, as manufacturer interviews indicate that material costs 

account for upwards of 75% of pump MPC. Larger pumps use more material which leads to 

higher material costs. 

DOE notes that hydraulic horsepower is a parameter known in the industry to represent 

the physical size of a pump. DOE explored the use of hydraulic horsepower in the scaling 

parameter, but selected Q and Ns because this NOPR’s metric and efficiency levels are a 

function of Q and Ns. DOE concluded that using Q and Ns consistently in both the efficiency 

metric and the scaling parameter would reduce the overall complexity of the NOPR analyses.

5B.2 STRUCTURE AND DERIVATION

The foundation of the scaling parameter is Ns/Q. When simplified, Ns/Q is structured 

similar to the inverse of hydraulic horsepower:

NNss

QQ =

nn × QQ0.5

HH0.75

QQ = nnQQ0.5 × HH0.75

Eq. 5B.2

1

HHHHHHHHHHHHHHHHHH HHHHWWWWWWWWWW

= 3960

QQ × HH

Eq. 5B.3

5B-1
Where:

• H is total dynamic head in feet, and

• Hydraulic HPwater is the hydraulic horsepower for a clean water system.

Ns/Q trends well with hydraulic horsepower. This is expected, given the relationship of 

the two parameters. The strong trend provides confidence that the parameter is a suitable 

descriptor for size. As an example of this trend, figure 5A.2.1 presents hydraulic horsepower 

plotted against Ns/Q for IL.1800 data.

Figure 5B.2.1 Hydraulic Horsepower Plotted Against Ns/Q, for IL.1800 Data

Ns/Q is modified by the factor (n/1800). This normalizes Ns/Q for motor speed. When a 

bare pump is run at 3,600 RPM, its specific speed remains the same, but flow doubles, as 

compared to 1,800 RPM operation. Multiplying Ns/Q by (n/1800) counteracts the doubling of 

flow for 3,600 RPM pumps, and returns a parameter that always represents the Ns/Q of the pump 

if it were run at 1,800 RPM. This allows the parameter to represent the physical size of a pump, 

regardless of its speed. Equations 5B.4 and 5B.5 illustrate this normalization:

BB BB PPPP, MM XXXXXX, aa 1800 RR :

NNss

QQ

×

1800

1800 = NNssQQ

Eq. 5B.4

BB BB PPPP, MM XXXXXX, aa 3600 RR :

NNss

2QQ

×

3600

1800 = NNss2QQ

× 2 = NNss

QQ

Eq. 5B.5

5B-2
The parameter (Ns/Q)×(n/1800) was modified once more to allow for more accurate 

regressions. To more evenly distribute pump sizes along the x-axis, the Log10 of the parameter 

was taken. Finally, the whole parameter was shifted by one positive unit on the x-axis for further 

ease of regression and to ensure that the scaling parameter remained positive for all pumps in the 

database. The result is the final scaling parameter:

LLLLLL ��

NNss

QQ � × � nn

1800�� + 1

Eq. 5B.6

Figure 5B.2.2 presents hydraulic horsepower plotted again the scaling parameter, for 

IL.1800 data. The figure indicates a strong and well distributed relationship between hydraulic 

horsepower and the scaling parameter. 

Figure 5B.2.2 Hydraulic Horsepower Plotted Against Log[(Ns/Q)×(n/1800)]+1, for IL.1800 

Data

5B-3
CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6-1

6.2 DISTRIBUTION CHANNELS ....................................................................................... 6-1

6.3 MARKUP CALCULATION METHODOLOGY........................................................... 6-1

6.3.1 Assumptions......................................................................................................... 6-2

6.4 APPROACH FOR ORIGINAL EQUIPMENT MANUFACTURER MARKUPS ........ 6-3

6.5 APPROACH FOR PUMP DISTRIBUTOR MARKUPS................................................ 6-4

6.6 CONTRACTOR OR INSTALLER MARKUP............................................................... 6-6

6.7 SALES TAXES ............................................................................................................... 6-7

6.8 OVERALL MARKUP..................................................................................................... 6-7

REFERENCES ............................................................................................................................ 6-9

LIST OF TABLES

Table 6.5.1 U.S. Census Business and Industry Annual Wholesale Trade Survey 

Data Used to Calculate Distributor Markups................................................. 6-6

Table 6.7.1 Average Sales Tax Rates by Census Division and Large State ..................... 6-7

Table 6.8.1 Summary of Markups for Three Primary Distribution Channels for 

Pumps............................................................................................................. 6-8

6-i
CHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

The objective of the markups analysis is to estimate the price paid by the customer or 

purchaser for an installed commercial and industrial pump (CIP). Purchase price and installation 

cost are necessary inputs to the life-cycle cost (LCC) and payback period (PBP) analyses. 

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 

representative unit by applying markups to the manufacturer selling price in the form of markup 

equations.

6.2 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. 

Based on data from manufacturer interviews and discussions with the CIP working group 

(see Docket EERE-2013-BT-NOC-0039), DOE identified five main distribution channels for 

pumps and estimated their respective shares of shipments. The five channels are from the 

manufacturer to: 

(1) distributor to contractor to end-users (70 percent of sales);

(2) distributor to end-users (17 percent of sales);

(3) OEM to end-users (8 percent of sales);

(4) end-users (2 percent of sales);

(5) contractor to end-users (1 percent of sales);

Other distribution channels exist but are estimated to account for a minor share of pump sales 

(2 percent). 

6.3 MARKUP CALCULATION METHODOLOGY

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.

6-1
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 pump OEMs and distributors. Section 6.4 and Section 6.5 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 increase, while the remainder of the business expenses stays relatively 

constant. For example, if the unit price of a pump increases by 30 percent, it is unlikely that the 

cost of secretarial support in an administrative office will increase by 30 percent as well. Certain 

business expenses are not correlated to 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 (MSP), 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. 

For each step in equipment distribution, DOE estimated both a baseline markup and an 

incremental markup. For pumps, 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.

6.3.1 Assumptions

DOE derived the OEM and pump distributor markups from three key assumptions about 

the costs associated with pump-related industrial series. DOE used the financial data from the 

2007 U.S. Economic Census’s Manufacturing Industry Series and 2012 Business and Industry 

Wholesale Trade Survey to determine OEM and pump distributor markups, respectively. These 

income statements break down the components of all costs incurred by firms that assemble and 

distribute pumps. 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 pumps.

2. These costs can be divided into two categories: (1) costs that vary in proportion to the 

MSP of pumps (variant costs); and (2) costs that do not vary with the MSP of pumps

(invariant costs).

6-2
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 pump distributors tend to handle multiple commodity 

lines, these data provide the most accurate indication that is available of the expenses associated 

with pumps.

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 pumps 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.1

6.4 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 that design, 

assemble, and brand equipment that contain pumps. The 2007 Economic Census Manufacturing 

Industry Series

2 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 14 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;

• heating equipment (except warm-air furnaces) manufacturing; and

• air conditioning and warm-air heating and commercial and industrial refrigeration 

equipment manufacturing.

6-3
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 14 OEMs identified above, DOE calculated the OEM baseline

markup as follows:

SSSSSSSSSS

PP PP + MM MM + CC = MMUUBBBBBBBB

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 (construction machinery manufacturing) and 

1.63 (semiconductor machinery manufacturing), with the sales-weighted average of 1.43.

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 14 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 1.29 (plastics and rubber industry machinery

manufacturing) and 1.53 (farm machinery and equipment manufacturing), with the salesweighted average of 1.38.

6.5 APPROACH FOR PUMP 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 2012 U.S. Census 

Business and Industry Annual Wholesale Trade Survey.

3 DOE organized the financial data into 

6-4
income statements that break down cost components incurred by firms that sell equipment and 

machinery with pumps, “Hardware, and Plumbing and Heating Equipment and Supplies 

Merchant Wholesalers” (NAICS 4237).a

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 Annual Wholesale Trade Survey provides gross margin (GM) as percent of sales for

the machinery, equipment, and supplies merchant wholesalers industry; therefore, baseline 

markups can be derived with the following equation:

(%) (%)

(%) MUBASE Sales GMSales

− =

DOE used financial data from the Annual Wholesale Trade Survey for the categories

“Hardware, and Plumbing and Heating Equipment and Supplies Merchant Wholesalers,” to 

calculate incremental markups used by wholesalers of pumps. 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 6.5.1 shows the data from the Annual Wholesale Trade Survey and the markups 

DOE estimated using the procedures described previously.

a The distributors to whom these financial data refer handle multiple commodity lines.

6-5

 
Table 6.5.1 U.S. Census Business and Industry Annual Wholesale Trade Survey Data 

Used to Calculate Distributor Markups

Items Amount ($1,000,000)

Sales 109,292

Cost of goods sold (CGS) 78,690

Gross Margin 30,602

Total Operating Expenses 25,043

Labor & Occupancy Expenses

Annual payroll 12,704

Employer costs for fringe benefit 2,728

Contract labor costs including temporary help 169

Purchased utilities, total 358

Cost of purchased repair and maintenance services 

(equipment, buildings, offices)

337

Purchased Communication Services 303

Purchased professional and technical services 379

Lease and rental payments (buildings, structures, offices) 1,740

Taxes and license fees (mostly income taxes) 356

Other Operating Expenses & Profit

Expensed computer related supplies 150

Cost of purchased packaging and containers 644

Cost of purchased transportation, shipping and warehousing services 1,095

Cost of purchased advertising and promotional services 496

Cost of data processing and other purchased computer services, except 

communications 

89

Depreciation and amortization charges 648

Commissions paid 434

Net profit before taxes 7,971.76

Baseline Markup=(CGS+GM)/CGS 1.39

Incremental Markup=(CGS + Total Other Operating Expenses and Profit)/CGS 1.15

Source: 2012 Annual Wholesale Trade Survey, Hardware, and Plumbing and Heating Equipment and Supplies 

Merchant Wholesalers (NAICS 4237)

6.6 CONTRACTOR OR INSTALLER MARKUP

DOE used information from RSMeans Electrical Cost Data4 to estimate markups used by 

contractors in the installation of equipment with pumps. 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 

6-6
have in-house technicians who would install equipment or replace a pump. However, DOE has 

no information on the extent to which this occurs, so it applied a markup of 1.10 in all cases.

6.7 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.5 

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 6.7.1 below. This provides a national average tax rate of 7.11 percent, which DOE used 

for each distribution channel.

Table 6.7.1 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%

6.8 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 

6-7
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

_

_

= + D ×

= × + D × ×

Where:

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 6.8.1 summarizes the markups and the overall baseline and incremental markups 

for each of the five main identified channels. Weighting the values by the respective shares of 

each channel and equipment class group yields an average overall baseline markup of 1.59 and 

an overall incremental markup of 1.34.

Table 6.8.1 Summary of Markups for Three Primary Distribution Channels for Pumps

Markup Distributor 

to 

Contractor 

to 

End-User

(70%)

Distributor 

to End-User

(17%)

OEM to 

End-User

(8%)

End-User

(2%)

Contractor 

to End-User

(1%)

Other

(2%)

BL Incr BL Incr BL Incr BL Incr BL Incr BL Incr

OEM - - - - 1.43 1.38 - - - - - -

Distributor 1.39 1.15 1.39 1.15 - - - - - - -

Contractor 1.1 1.1 - - - - - - 1.1 1.1 - -

Sales Tax 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07 - -

Overall 1.64 1.35 1.49 1.23 1.53 1.48 1.07 1.07 1.18 1.18 1.59 1.34

6-8
REFERENCES

1 Pindyck, R.S. and D.L. Rubinfeld. (2000), Microeconomics, 5th ed., New Jersey: Prentice 

Hall.

2 U.S. Census Bureau (2007). Economic Census Manufacturing Industry Series (NAICS 33 

Series) <www.census.gov/manufacturing/asm/>.

3 U.S. Census Bureau (2012). Annual Wholesale Trade Survey, Hardware, and Plumbing and 

Heating Equipment and Supplies Merchant Wholesalers (NAICS 4237). 

<www.census.gov/wholesale/index.html>. 

4

 RSMeans Construction Publishers & Consultants. (2013), Electrical Cost Data, 36th Annual 

Edition. 2013. ed. J.H. Chiang, Kingston, MA.

5

 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>.

6-9
CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 ENERGY USE METHODOLOGY................................................................................. 7-1

7.2.1 Duty Point ............................................................................................................ 7-2

7.2.1.1 Pump binning process ................................................................................ 7-2

7.2.1.2 Normalization of efficiencies..................................................................... 7-6

7.2.1.3 Shipment weighting to get average pump curve and efficiency ................ 7-6

7.2.2 Pump Sizing......................................................................................................... 7-6

7.2.3 Operating Hours................................................................................................... 7-7

7.2.4 Load Profiles........................................................................................................ 7-7

7.2.5 Equipment Losses................................................................................................ 7-8

7.2.5.1 Pump Losses............................................................................................... 7-9

7.2.5.2 Motor Losses.............................................................................................. 7-9

7.2.5.3 Control Losses............................................................................................ 7-9

7.3 ANNUAL ENERGY USE RESULTS ............................................................................ 7-9

LIST OF TABLES

Table 7.2.1 Power Bins on a Log Scale, all Pumps.......................................................... 7-3

Table 7.2.2 Flow Bins (gpm) on a Log Scale by Equipment Class.................................. 7-4

Table 7.2.3 Distribution of Annual Hours of Operation by Application .......................... 7-7

Table 7.2.4 Typical Loading Profile Weights................................................................... 7-8

Table 7.3.1 Average Annual Energy Use by Equipment Class and Efficiency Level 

in the Base Case (kWh)................................................................................ 7-10

Table 7.3.2 Average Annual Energy Use by Equipment Class for each Standards 

Case/Efficiency Level (kWh) ...................................................................... 7-10

LIST OF FIGURES

Figure 7.2.1 Representative Units and Individual Pumps (ESCC 1800) ........................... 7-5

Figure 7.2.2 Typical Loading Profiles................................................................................ 7-8

7-i
CHAPTER 7. ENERGY USE ANALYSIS

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 commercial and industrial pumps at the 

considered energy efficiency levels.

Pump efficiency is a function of the pump operating conditions. The operating conditions 

are defined by the required flow and the pressure the pump must overcome to deliver that flow. 

These conditions are referred to as the pump flow (Q) and head (H). The head is a feature of the 

system in which the pump is installed. For any pump, the flow that can be provided at a given 

head is a function of the pump design; this is captured in the "pump curve" which specifies head 

as a function of flow. Pump efficiency is also a function of flow. The flow at which the pump is 

most efficient is referred to as the “Best Efficiency Point” or BEP. The point at which the pump 

is most likely to be operated is referred to as the “duty point.” While pump users will generally 

try to match the BEP to the duty point, it can be difficult to achieve a precise match. The 

instantaneous pump hydraulic power requirement is determined by the flow times the head 

divided by the efficiency. This represents the energy that must be transferred to the fluid to 

produce the required flow. In matching a motor to a pump, users must ensure that the motor is 

capable of delivering the maximum power that may be required. Power delivered by the motor is 

referred to as the shaft power. The total power input to the motor is equal to shaft power plus 

motor losses. The energy use calculation includes motor losses, assuming that the motor used is 

compliant with the existing DOE standard. 

Pump power depends on the duty point, so the energy use calculation requires an accurate 

estimation of the range of duty points in the field. Pump efficiency depends on the relationship 

between the pump BEP and the duty point, which is referred to as the pump sizing. For pumps 

serving variable loads, the power, efficiency and energy use also depend on the load profile. 

Finally, the annual energy use which is the required input to the LCC depends on the annual 

hours of operation. DOE's analysis accounts for variability in each of these characteristics.

For each equipment class, DOE developed several representative units each representing 

a range of flow and power. For each representative unit, DOE estimated the energy use using a 

range of distributions for the key variables (operating hours, pump sizing, load profiles). 

7.2 ENERGY USE METHODOLOGY

For a single pump, the energy use calculation depends on the duty point, load profile, and 

annual operating hours. The annual energy use equation is:

7-1
∑ ×

× =

LoadProfile ,

i

i motor i

i i OpHour Q H AEU

η η

where:

• Qi is the flow load point

• Hi is the head at Qi, calculated from the pump curve

• ηi is the pump efficiency calculated from the efficiency curve

• η motor,i is the motor efficiency

• OpHouri refers to annual hours of operation at load point i

DOE defines the set of load points in two steps: first, the duty point is related to the pump 

BEP through the BEP offset variable (which may be positive or negative:

(1 _ ) Q int Q BEP offset DutyPo = BEP × +

Second, the load profile is defined relative to the duty point. DOE uses four types of load 

profile which represent the diversity of load types in the field. Most generally, load points range 

between 50% and 120% of the duty point. Based on input from stakeholders, most load profiles 

range between 75% and 110% of the duty point.

The inputs are discussed in the section below.

7.2.1 Duty Point

DOE reviewed the available information on duty points by sector (commercial, industrial, 

municipal and agricultural) and application and found very little data. DOE used detailed 

shipments data provided by the manufacturer to estimate the distribution of duty points. The 

manufacturer data were organized by pump model and included the shipments, flow and head at 

BEP, pump and efficiency curves, and the C-value associated with the pump. The C-value is 

used to determine whether the pump is compliant or not with a given EL.

The structure of the LCC requires that, at each EL, the same pump is available with the 

same practical utility (defined by the pump curve) so that the pump energy use can be compared 

across ELs under equivalent conditions. The pump data supplied by the manufacturers does not 

quite meet this criterion, as each pump differs in some design details as well as differing in 

efficiency. Hence, for the LCC DOE used the manufacturer data to create a set of representative 

units covering the full range of duty points in the data. The method used to create representative 

units is described next.

7.2.1.1 Pump binning process

DOE implemented a binning process to determine representative units with varying 

power capacities from the shipments data. Pump models are grouped into 9 power bins on a log7-2
scale. The upper and lower bounds of the power bins are shown in Table 7.2.1. Then, for each 

equipment class, the pump models in each power bin are grouped into 9 flow bins, also on a logscale. The upper and lower bound of the flow bins depend on the power as shown in Table 7.2.2.

Table 7.2.1 Power Bins on a Log Scale, all Pumps

Power Bin Index

Power Lower Bound 

(HP)

Power Upper Bound

(HP)

1 1.00 1.79

2 1.80 3.24

3 3.25 5.84

4 5.85 10.53

5 10.54 18.97

6 18.98 34.19

7 34.20 61.61

8 61.62 111.00

9 111.01 200.00

7-3
Table 7.2.2 Flow Bins (gpm) on a Log Scale by Equipment Class

Flow Bin Index

ESCC 1800 ESCC 3600

Lower Bound Upper Bound Lower Bound Upper Bound

1 42.00 71.39 31.75 49.04

2 71.40 121.36 49.05 75.76

3 121.37 206.32 75.77 117.06

4 206.33 350.75 117.07 180.85

5 350.76 596.26 180.86 279.40

6 596.27 1013.63 279.41 431.65

7 1013.64 1723.15 431.66 666.87

8 1723.16 2929.29 666.88 1030.27

9 2929.30 4979.80 1030.28 1591.80

Flow Bin Index

ESFM 1800 ESFM 3600

Lower Bound Upper Bound Lower Bound Upper Bound

1 42.00 69.07 31.75 49.04

2 69.08 113.61 49.05 75.76

3 113.62 186.87 75.77 117.06

4 186.88 307.37 117.07 180.85

5 307.38 505.56 180.86 279.40

6 505.57 831.53 279.41 431.65

7 831.54 1367.69 431.66 666.87

8 1367.70 2249.54 666.88 1030.27

9 2249.55 3700.10 1030.28 1591.80

Flow Bin Index

IL 1800 IL 3600

Lower Bound Upper Bound Lower Bound Upper Bound

1 40.41 65.49 45.00 66.38

2 65.50 106.14 66.39 97.94

3 106.15 172.03 97.95 144.49

4 172.04 278.81 144.50 213.17

5 278.82 451.88 213.18 314.51

6 451.89 732.37 314.52 464.00

7 732.38 1186.96 464.01 684.55

8 1186.97 1923.72 684.56 1009.94

9 1923.73 3117.90 1009.95 1490.10

Flow Bin Index

VT-S 3600

Lower Bound Upper Bound

1 25.00 40.54

2 40.55 65.77

3 65.78 106.69

7-4
4 106.70 173.07

5 173.08 280.74

6 280.75 455.50

DOE defines one representative unit for each of the power and flow bins. The 

characteristics of the representative unit, consisting of flow and head at BEP, the pump curve and 

the efficiency curve are defined as the shipment-weighted averages across all the units in a bin, 

as shown in the equations below. 

∑

∑

∑

∑

∑

∑

∑

∑

⋅ =

⋅ =

⋅ =

⋅ =

i i

i i i

RepUnit

i i

i i i BEP

RepUnit BEP

i i

i i i BEP

RepUnit BEP

i i

i i i BEP

RepUnit BEP

Shipment

Shipment Ns

Ns

Shipment

Shipment P

P

Shipment

Shipment H

H

Shipment

Shipment Q Q

,

,

,

,

,

,

Figure 7.2.1 illustrates the result of the binning process for ESCC 1800: the red solid 

circles represent representative units for each of combinations of the power and flow bins, while 

the individual pumps are shown in blue.

Figure 7.2.1 Representative Units and Individual Pumps (ESCC 1800)

7-5
7.2.1.2 Normalization of efficiencies

In each bin, the C-values associated with the original set of pumps are left unchanged. DOE uses 

the C-value equation to adjust the efficiency at BEP of each pump to the efficiency appropriate 

to the representative unit flow and head at BEP. The equation is:

i BEP i i BEP i BEP

RepUnit RepUnit BEP i BEP

adjusted

i BEP

C f Ns Q

f Ns Q C

, , ,

, , ,

(ln( ),ln( ))

where

(ln( ),ln( ))

h

h

= −

= −

The details of the C-value equation are provided in Chapter 5, Engineering Analysis. The 

results of this process are a set of pumps in each bin which differ by efficiency at BEP, but are 

otherwise identical. 

7.2.1.3 Shipment weighting to get average pump curve and efficiency

For each representative unit DOE calculated the shipment weighted average pump curve, 

which describes the pump's available operating points away from the BEP. First, the pump curve 

equation is rewritten using normalized variables:

1 2 3

2

par Q

Q

par Q

Q

par H

H

BEP

i

BEP

i

BEP

i +















+ ⋅ 













 = ⋅

Then, the representative unit values of par1, par2 and par3 are calculated using a 

regression model that minimizes the difference between the representative unit curve and each 

individual pump curve. 

The efficiency curve is handled in a similar manner, with normalized pump efficiency 

defined as a polynomial function of normalized flow:

parC Q

Q parB QQ parA

BEP

i

BEP

i

BEP

i +















+ ⋅ 













 = ⋅

2

η

η

DOE compared the normalized pump and efficiency curves for all individual pumps 

within a power and flow bin and confirmed that the shapes of these curves do not vary 

significantly. Therefore the shipment-weighted average curves should provide a sufficiently 

accurate estimate of the required of pump power across the range of operating points. As the 

shipment-weighted average duty points are used to define the representative unit, the total power 

and energy use assigned to each bin is the same as in the original manufacturer data.

7.2.2 Pump Sizing

DOE introduced a variable called BEP offset to capture variation in pump sizing practice 

in the field. The BEP offset is the relative distance between customer’s duty point and the 

7-6
pump’s BEP point. Pumps are typically sized to operate within 75% to 110% of their BEP flow. 

Therefore, for this analysis the BEP offset is assumed to be uniformly distributed between [-0.25 

(i.e., 25% less than BEP flow), and 0.1 (10% more than BEP flow)]. 

(1 _ ) Q int Q BEP offset DutyPo = BEP × +

7.2.3 Operating Hours

DOE estimated average annual operating hours by application based on inputs from 

industry experts and feedback from the CIP working group. Table 7.2.3 displays the distribution 

of annual hours of operation by application. For each pump in the LCC sample, the sector and 

application are assigned using the procedure described in the LCC chapter. A value for the 

operating hours is then selected from the distributions defined below.

Table 7.2.3 Distribution of Annual Hours of Operation by Application

Application Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Cooling 

Water 2500 3500 5000 6000 7500

Boiler Feed 2000 3000 4000 5000 6000

Circulation 2000 3000 4000 5000 6000

Pressure 

Boost 300 600 1000 2500 4500

Irrigation 300 600 1000 1500 2000

Other 1000 1500 2000 3000 4000

7.2.4 Load Profiles

Information on typical load profiles for pumps is not available in the public domain. DOE 

reviewed resources suggested by stakeholders as well as commercial building pump loads 

calculated using building simulations. Building simulation profiles are based on input 

assumptions and cannot substitute for data, although they do provide an indication of variability 

in pump loads due to building schedules and weather impacts on space-conditioning end uses. 

Given the lack of data, DOE developed a few canonical load profiles for this analysis. These are 

defined by weights that specify the percentage of time the pump operates at one of four load 

points: 50 percent, 75 percent, 100 percent, and 110 percent of the flow at the duty point. The 

canonical load types are:

• Flat load (LP0): 100% of operating hours at the duty point;

• Flat/over-sized (LP1): 50% of operating hours at the duty point, and 50% at flow equal to 

75% of the duty point flow;

7-7
• Variable/over-sized (LP2): 50% of operating hours at the duty point, and 35% at flow 

equal to 75% of the duty point, and 35% at 50% of the duty point flow;

• Variable/under-sized (LP3): 30% of operating hours at the duty point, 30% at 75% of the 

duty point and 20% at 110% of the duty point.

The four load profiles are illustrated in Figure 7.2.2. The load profile is assigned 

randomly, subject to the constraint that the pump never operates outside of the range of 50% to 

110% of BEP. This forces a consistency condition between the definition of the BEP offset and 

the assignment of the load profile. The percentages assigned to each load profile are provided in 

Table 7.2.4. 

Figure 7.2.2 Typical Loading Profiles

Table 7.2.4 Typical Loading Profile Weights

Load Profile

50% duty 

point

75% duty 

point

100% duty 

point

110% duty 

point Probability

LP0 0 0 1 0 30%

LP1 0 0.5 0.5 0 30%

LP2 0.05 0.3 0.65 0 30%

LP3 0.05 0.25 0.5 0.2 10%

7.2.5 Equipment Losses

The total energy use calculated for the LCC depends on pump losses, motor losses, and 

control losses. All these losses represent energy that the pump user must pay for as part of the 

operating costs.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

50% duty point 75% duty point 100% duty point 110% duty point

LP0

LP1

LP2

LP3

7-8
7.2.5.1 Pump Losses

Pump losses account for the differences between pump shaft horsepower and hydraulic 

horsepower due to friction and other factors. DOE takes this into account using the pump 

efficiency curve calculated for each representative unit, modeled as second-order polynomial 

functions, as discussed in section 7.2.1.2.

7.2.5.2 Motor Losses

For each representative unit DOE selected a motor using the sizing procedures for default 

motors described in the pumps test procedure notice of proposed rulemaking (NOPR). 

Specifically, the required motor horsepower is based on the pump shaft input power at 120 

percent of BEP flow. DOE selects the smallest motor that provides the required power. DOE 

assigns a pole configuration based on the design speed for each equipment class (e.g., 1800 RPM

corresponds to 4-pole motor). For each motor horsepower and pole configuration, DOE used the 

minimum motor efficiency values under the existing standard 10 CFR 431.25 for the type of 

motors with which pumps are typically paired (i.e., NEMA Design B). For the VT-S equipment 

class, pumps are paired with submersible motors, for which there are no federal standards. For 

this equipment, DOE assigned a motor efficiency value based on review of market data 

described in the pumps test procedure NOPR.

To determine the losses of the motors at part load, DOE used the part-load loss factor 

curve developed and presented in the pumps test procedure NOPR. This means that motor losses 

are standardized across all pumps, and while motor losses do contribute to the total energy use, 

they do not affect the selection of which pumps pass a given EL.

7.2.5.3 Control Losses

DOE assumed that all users with variable loads were throttling their pumps and therefore 

did not include variable speed drive efficiency as part of the annual energy use calculation. VSD 

users are handled in a sub-group analysis, described in chapter 11. 

7.3 ANNUAL ENERGY USE RESULTS

Table 7.3.1 summarizes the results of the energy use analysis for each equipment class at 

each considered energy efficiency level in the base case. The table shows the average energy use, 

defined as the total energy use for all pumps in the LCC chapter divided by the total number of 

pumps. Given the wide range of pump sizes in the LCC sample, the average results are not 

representative of any specific pump. 

For a given size or capacity of pump, the energy use will decrease as efficiency increases. 

However, across the market in the base case, the distribution of horsepower within each 

efficiency bin differs, as does the distribution of other variables such as application and operating 

hours. As a result, average energy use as shown in Table 7.3.1 does not decrease monotonically 

as efficiency level increases. However, as individual pumps are redesigned to meet higher 

efficiency levels in each standards case (as discussed in section 8.3.1.2), the average energy use 

of the pump market decreases in each case, as shown in Table 7.3.2.

7-9
Table 7.3.1 Average Annual Energy Use by Equipment Class and Efficiency Level in the 

Base Case (kWh)

Equipment 

Class EL0 EL1 EL2 EL3 EL4 EL5

ESCC,1800 12,545 31,164 24,001 36,900 31,621 19,721 

ESCC,3600 29,817 40,824 14,218 44,787 62,802 9,952 

ESFM,1800 47,229 43,025 38,112 35,511 52,331 26,849 

ESFM,3600 90,194 59,093 58,300 66,196 72,804 38,108 

IL,1800 8,419 21,976 51,256 32,555 16,525 13,625 

IL,3600 16,845 15,763 25,627 48,821 54,421 13,091 

VT-S,3600 13,250 9,804 29,161 18,009 39,582 8,928 

Table 7.3.2 Average Annual Energy Use by Equipment Class for each Standards 

Case/Efficiency Level (kWh)

Equipment 

Class EL0 Case EL1 Case EL2 Case EL3 Case EL4 Case EL5 Case

ESCC,1800 24,438 24,333 24,114 23,917 23,663 23,382 

ESCC,3600 17,136 17,095 16,944 16,841 16,692 16,442 

ESFM,1800 36,870 36,860 36,670 36,442 35,985 35,545 

ESFM,3600 56,965 56,891 56,312 55,808 55,217 54,348 

IL,1800 20,379 20,296 20,123 19,961 19,775 19,517 

IL,3600 22,095 22,012 21,806 21,568 21,386 21,018 

VT-S,3600 13,061 13,056 13,001 12,765 12,605 12,446 

The LCC uses the entire sample of energy use values calculated for each specific pump 

rather than the summary values shown in Table 7.3.1. The individual energy use for each of the 

pumps in the base case and in each standards case is available in the LCC spreadsheet. The 

operating cost savings calculated in the LCC under each standards case will be directly related to 

the average energy use values shown in Table 7.3.2. 

7-10
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

8.2 DEFINITION OF THE LCC SAMPLE .......................................................................... 8-2

8.2.1 LCC Sampling Method........................................................................................ 8-2

8.2.2 Definition of the Weights..................................................................................... 8-3

8.3 LIFE-CYCLE COST INPUTS ........................................................................................ 8-4

8.3.1 Total Installed Cost Inputs................................................................................... 8-4

8.3.1.1 Baseline Equipment Price .......................................................................... 8-4

8.3.1.2 Calculation of the Equipment Price for Each Efficiency Level ................. 8-5

8.3.1.3 Projection of Future Equipment Prices ...................................................... 8-5

8.3.1.4 Installation Cost.......................................................................................... 8-6

8.3.2 Operating Cost Inputs.......................................................................................... 8-6

8.3.2.1 Annual Energy Use .................................................................................... 8-6

8.3.2.2 Electricity Prices ........................................................................................ 8-6

8.3.2.3 Maintenance Costs ..................................................................................... 8-7

8.3.2.4 Repair Costs ............................................................................................... 8-7

8.3.2.5 Equipment Lifetime.................................................................................... 8-7

8.3.2.6 Discount Rates............................................................................................ 8-9

8.3.3 Base Case Efficiency Distribution..................................................................... 8-12

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS...................................... 8-12

REFERENCES .......................................................................................................................... 8-24

LIST OF TABLES

Table 8.2.1 Pump applications and associated sectors...................................................... 8-2

Table 8.2.2 Summary of Inputs to the LCC...................................................................... 8-3

Table 8.2.3 Distribution of pumps across sectors for each product class......................... 8-3

Table 8.3.1 Average Mechanical Lifetime and Service Lifetime by Equipment 

Class............................................................................................................... 8-8

Table 8.3.2 Risk Free Rate and Equity Risk Premium, 2004-2013................................ 8-11

Table 8.3.3 Weighted Average Cost of Capital for Sectors that Purchase Pumps ......... 8-12

Table 8.3.4 Base Case Efficiency Distributions by Equipment Class............................ 8-12

Table 8.4.1 Average LCC and PBP Results by Efficiency Level for ESCC.1800 ......... 8-13

Table 8.4.2 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESCC.1800................................................................................................... 8-14

Table 8.4.3 Average LCC and PBP Results by Efficiency Level for ESCC.3600 ......... 8-14

Table 8.4.4 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESCC.3600................................................................................................... 8-15

Table 8.4.5 Average LCC and PBP Results by Efficiency Level for ESFM.1800......... 8-15

8-i
Table 8.4.6 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESFM.1800 .................................................................................................. 8-16

Table 8.4.7 Average LCC and PBP Results by Efficiency Level for ESFM 3600......... 8-16

Table 8.4.8 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESFM.3600 .................................................................................................. 8-17

Table 8.4.9 Average LCC and PBP Results by Efficiency Level for IL.1800 ............... 8-17

Table 8.4.10 LCC Savings Relative to the Base Case Efficiency Distribution for 

IL.1800......................................................................................................... 8-18

Table 8.4.11 Average LCC and PBP Results by Efficiency Level for IL.3600 ............... 8-18

Table 8.4.12 LCC Savings Relative to the Base Case Efficiency Distribution for 

IL.3600......................................................................................................... 8-19

Table 8.4.13 Average LCC and PBP Results by Efficiency Level for VT-S.3600 .......... 8-19

Table 8.4.14 LCC Savings Relative to the Base Case Efficiency Distribution for 

VT-S.3600.................................................................................................... 8-20

LIST OF FIGURES

Figure 8.3.1 Commercial and Industrial Electricity Price Projections for Reference 

Case................................................................................................................ 8-7

Figure 8.3.2 Lifetime Distribution by Pump Equipment Class.......................................... 8-9

Figure 8.4.1 Distribution of Life-Cycle Savings for ESCC.1800 .................................... 8-20

Figure 8.4.2 Distribution of Life-Cycle Cost Savings for ESCC.3600............................ 8-21

Figure 8.4.3 Distribution of Life-Cycle Cost Savings for ESFM.1800 ........................... 8-21

Figure 8.4.4 Distribution of Life-Cycle Cost Savings for ESFM.3600 ........................... 8-22

Figure 8.4.5 Distribution of Life-Cycle Cost Savings for IL.1800 .................................. 8-22

Figure 8.4.6 Distribution of Life-Cycle Cost Savings for IL.3600 .................................. 8-23

Figure 8.4.7 Distribution of Life-Cycle Cost Savings for VT-S.3600............................. 8-23

8-ii
CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS 

8.1 INTRODUCTION

The life-cycle cost (LCC) and payback period (PBP) analysis evaluates the impact of 

proposed energy conservation standards on the pump users, i.e. the consumers who purchase and 

operate pumps. The LCC provides a measure of the total cost of ownership, consisting of the 

initial purchase price and installation costs, and energy, maintenance and repair costs over the 

lifetime of the pump. For this analysis DOE assumed that neither installation nor repair and 

maintenance costs are affected by the standard, so the LCC focuses only on purchase price and 

the energy costs over the pump lifetime. DOE accounts for variability in energy use and prices, 

discount rates, and energy costs by doing individual LCC calculations for a large sample of 

pumps that are assigned different installation conditions. Installation conditions include customer 

attributes such as sector and application, and usage attributes such as duty point and annual hours 

of operation. This sample is used to generate national average LCC savings by efficiency level. 

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis 

DOE conducted the LCC and PBP analysis by developing a large sample of pump 

installations, which represent the general population of pumps that would be affected by 

proposed energy conservation standards. Separate analyses are conducted for each equipment 

class. Conceptually, the analysis distinguishes between the pump installation and the pump itself. 

The pump installation is characterized by a combination of customer attributes (sector, 

application, electricity price, discount rate) and usage attributes (duty point, BEP offset, load 

profile, annual hours of operation, mechanical lifetime) that do not change with TSL. The pump 

itself is the regulated equipment, so its efficiency and selling price do change with TSL. For each 

equipment class the LCC sample consists of 10,000 distinct pump installations.

In the base case, DOE assigns a specific pump to each pump installation. These pumps 

are chosen from the set of representative units described in chapter 7, Energy Use Analysis. DOE 

also assigns a pump efficiency in the base case. The relative weighting of different representative 

units and efficiencies in the LCC sample is determined based on shipments data supplied by the 

manufacturers. At each TSL, the pump assigned in the base case either passes or fails the 

efficiency criterion. If the pump passes, the installation is left unchanged. Equivalently, for that 

installation, the LCC at the given TSL is the same as the LCC in the base case and the standard 

does not impact that user. If the pump fails, the base case pump is replaced with a compliant unit 

having a higher selling price and higher efficiency, and the LCC is recalculated. The selling price 

of the compliant unit is calculated based on information provided by the Engineering analysis. 

The LCC savings at each TSL are defined as the difference between the LCC in the base case 

and the LCC for the more efficient pump. The LCC is calculated for each pump installation at 

each TSL. These calculations are presented in the LCC spreadsheet.

8-1 
8.2 DEFINITION OF THE LCC SAMPLE

The economic inputs to the LCC (discount rate and electricity price) depend on the 

sector, while usage criteria such as hours of operation depend on the application. Hence, each 

pump installation in the LCC sample must be assigned a sector and application. DOE considered 

four sectors: industrial, commercial buildings, agricultural and municipal water utilities. DOE 

considered several applications, with correlation between sector and application as shown in 

Table 8.2.1. Based on a review of available data, DOE determined that there is some correlation 

between application and operating hours. DOE did not find any information relating either the 

BEP offset (a pump sizing factor defined in the Energy Use chapter) or load profile to either 

sector or application, so these were assigned randomly.

Table 8.2.1 Pump applications and associated sectors 

Application Commercial Industrial Agriculture Municipal

boiler feed x x

circulation x x

cooling water x x x

irrigation x

pressure boost x x x x

other x x x

As described in the Energy Use chapter, the shipments data provided by the 

manufacturers are used to define the relative weightings of the representative units. Each 

representative unit can be thought of as a pump chosen to operate at a representative duty point. 

DOE did not assign the customer attributes (sector, application etc.) to duty points randomly. 

DOE reviewed several data sources to incorporate correlations between sector, application, 

product class and the distribution of duty points into the analysis. Specifically, DOE used a 

database it collected from case studies and other public information (pump applications only), a 

database on pump tests provided by Pacific Gas & Electric, and the U.S. Department of 

Agriculture Farm and Ranch Irrigation Survey (FRIS)1

, to estimate the correlations between 

pumps sector, application, and power bin for each product class. DOE used these distributions to 

determine the relative weighting of different sectors and applications in the LCC sample.

8.2.1 LCC Sampling Method

The LCC sampling requires a weighting function, discussed in the next section. The 

power and flow bins are indexed by i and j respectively, and total shipment weight in the bin is 

defined as wij. The weight wij is defined as the number of pumps in bin (i,j) divided by the total 

number of pumps in the shipments data for the given product class. Within each bin, pumps are 

distributed across sector k and application a. 

The LCC process is (for each product class):

1. Create a list of installation types, indexed by (k,a,i,j) and with a weighting wij that defines the 

percentage of all pumps this installation type is expected to represent. 

2. Choose a number N of rows in the LCC sample (N=10,000). 

8-2
3. Create an expanded list of N installations with each installation type sampled Nwij times 

4. For each row fill in the additional required information as defined in Table 8.2.2

5. For each row generate a list of pumps chosen for each efficiency level (EL)

(a) Select the base case (EL=0) pump from bin (i,j) 

(b) Select the base case pump efficiency PER0

(c) For each EL, if the chosen pump passes the new efficiency test, do nothing 

(d) For each EL, if the chosen pump fails the new efficiency test, assign that 

installation the redesigned pump for that bin

6. Calculate the annual operating cost, total installed cost and life-cycle cost for each (m,L) 

Table 8.2.2 Summary of Inputs to the LCC 

Variable Dependencies Definitions

Annual operating hours (hpy) application hours-per-year of operation

Load profile type (l) application four types (see Energy Use Chapter)

Mechanical lifetime in hours NH none total hours of equipment life; lifetime in years is NH/hpy

BEP offset (x) none operating point flow is set equal to (1+x)QB

discount rate (r) sector used to discount future operating cost savings

electricity price (ep) sector average annual price in $/kWh

8.2.2 Definition of the Weights

A consultant report provided DOE with estimates of the distribution of pumps across 

sectors for each product class. The sector weights by product class used in the LCC are presented 

in Table 8.2.3. For each product class, the fraction assigned to sector k is defined as fk. The 

sector weights are assumed to apply independent of power or flow bin.

Table 8.2.3 Distribution of pumps across sectors for each product class 

ESCC 

1800

ESCC 

3600

ESFM 

1800

ESFM 

3600 IL 1800 IL 3600

VT-S

3600

Municipal 6% 6% 6% 6% 6% 6% 26%

Agriculture 10% 10% 10% 10% 10% 10% 31%

Commercial 51% 51% 51% 51% 51% 51% 13%

Industrial 33% 33% 33% 33% 33% 33% 30%

DOE used the WSU motors database to distribute pumps within a given power bin and 

product class across applications. The WSU data include motor horsepower, speed and operating 

hours for a selected list of commercial and industrial pump applications. These data were 

allocated to the same set of power bins as were used to construct the pump representative units. 

For each product class, the proportion of pumps within each power bin corresponding to a given 

application was then determined as

di(a, k) = mi(a, k)/( ∑a mi(a, k) )

Where:

8-3
i = the bin index for power,

k = the sector (commercial or industrial),

a = the application (as listed in Table 8.2.1),

mi(a; k, ω) = the number of motors in bin i, for fixed (a, k) and product class, and

di(a; k) = the distribution of motors by application within bin i for sector k.

The U.S. Department of Agriculture Farm and Ranch Irrigation Survey (FRIS) data1

were used to define the distribution of pumps across power and flow bins for agricultural end 

uses. The FRIS provides average flow and head for well pumps and irrigation pumps. These 

were used to estimate the duty point and assign a power bin to each pump in the FRIS. The pump 

counts in each power range were then used to distribute pump shipments by horsepower to the 

agricultural sector.

The Pacific Gas & Electric pump test database includes pump duty points for the 

municipal sector. These were used to estimate the required power and assign a power bin to each 

pump in the pump test database. The pump counts in each power bin were used to distribute 

pump shipments by horsepower to the municipal sector.

8.3 LIFE-CYCLE COST INPUTS

The LCC is equal to the pump purchase price plus the operating cost over the lifetime of 

the pump. The operating cost is equal to the annual energy use times the energy price. Annual 

operating costs are discounted relative to the year in which the standard is passed and summed 

over the lifetime of the pump. The key inputs to the LCC are thus the purchase price, the annual 

energy use, the energy price, the pump lifetime and the discount rate. 

8.3.1 Total Installed Cost Inputs

8.3.1.1 Baseline Equipment Price

The manufacturer selling price (MSP) is the price charged by the manufacturer for the 

equipment. The price paid by pump users is equal to the MSP, plus any relevant distributor 

markup, plus the sales tax. At each efficiency level, the MSP increases to reflect the additional 

costs incurred by the pump manufacturers to meet the standard. In the base case, representing the 

market with no standard in place, DOE calculated the equipment price for baseline equipment 

based on the following equation:

EEEEEEBBBBBBBB = MMMM MMMMMM × MMMMOOOO OO OOOO_BBBBBBBB

Where:

EQPBASE = customer equipment price for baseline models, 

MSPMFG = manufacturer selling price for baseline models, and

MUOVERALL_BASE = product of retailer or distributor markup and sales tax.

8-4
DOE used an equation to model the base case equipment price as a function of flow, 

specific speed and efficiency. This equation is presented in chapter 5, Engineering Analysis.

The overall markups used in the LCC analyses are discussed in Chapter 6.

8.3.1.2 Calculation of the Equipment Price for Each Efficiency Level

As discussed in the engineering analysis, for commercial and industrial pumps the 

principal means of achieving higher efficiencies is through hydraulic redesign. Costs associated 

with redesign are part of the overheads associated with manufacturing. Based on stakeholder 

input, DOE assumed that (1) pump manufacturers would maintain the number of functionally 

distinct models on the market, and (2) the cost of redesign would be distributed across all pump 

shipments affected by the standard. The engineering analysis provides the total cost of 

redesigning pumps (called conversion costs) at each EL.

The LCC includes a pre-processing step that calculates the selling price of a redesigned 

pump at each EL. The analysis assumes that the proportion of total revenue allocated to each bin 

or representative unit does not change with EL. The revenues for each bin are defined as the 

number of shipments times the base case equipment price, taking into account the base case 

efficiency distribution. Total revenues are calculated by the sum over bins, and each bin is 

assigned a fraction of total revenues defined as pij.

The next steps in this process are repeated for each EL:

1. For each power and flow bin, count the number of shipments associated with failing 

pumps, unitsfail(i,j,EL)

2. Given the base case equipment price for each of the failing pumps, calculate the total 

revenues associated with failing pumps, revfail(i,j,EL).

3. Calculate the fraction of conversion costs assigned to bin (i,j) as pijCC(EL), where 

CC(EL) is the total conversion cost at the given EL, and pij is the percentage of revenues 

allocated to bin (i,j).

4. Calculate the new selling price for a redesigned pump as

 

price(i,j,EL) =[ revfail(i,j,EL)+ pijCC(EL)] / unitsfail(i, j, EL)

This approach ensures that manufacturer conversion costs are fully recovered. The 

redesigned pump is assumed to have the minimum efficiency specified by the EL, and to have 

the same pump curve and efficiency curve as the representative unit for that bin. 

8.3.1.3 Projection of Future Equipment Prices

To project an equipment price trend for the NOPR, DOE derived an inflation-adjusted 

index of the Producer Price Index for pumps and pumping equipment over the period 1984-

8-5
2013.a These data show a general price index increase from 1987 through 2009. Since 2009, 

there has been no clear trend in the price index. Given the relatively slow global economic 

activity in 2009 through 2013, DOE believes that the extent to which the future trend can be 

predicted based on the last two decades is very uncertain and that the observed data do not 

provide a firm basis for projecting future costs trends for commercial and industrial pump 

equipment. Therefore, DOE used a constant price assumption as the default trend to project 

future commercial and industrial pump prices in 2020. Thus, prices projected for the LCC and 

PBP analysis are equal to the 2012 values for each efficiency level in each equipment class. 

8.3.1.4 Installation Cost

In the Framework Document, DOE requested information on whether installation costs 

would be expected to change with efficiency. Grundfos responded that this was not expected to 

be true for new installations, but noted that for existing installations, there may be additional 

costs to replace existing equipment with higher efficiency equipment for piping, electrical 

modifications, base and foundations, and code requirements for equipment rooms. (Grundfos, 

No. 24 at p. 34) In the CIP working group, Grundfos and ITT Corporation also noted that the 

assumption of targeting identical flange or feet dimensions during redesign is reasonable, but that 

as one drives to higher efficiency one may have to stretch the pump (i.e., change the dimensions 

from the base design) and change configurations. (See EERE-2013-BT-NOC-0039-0109, 

pp.240-242) Grundfos stated that at some point within the range of efficiency levels under 

consideration, whether at PER 40 or 70 or some other point, the installation cost might change. 

Because none of the stakeholders provided data to indicate at what efficiency level DOE may 

need to consider an increase in installation costs, DOE has not estimated an installation cost for 

this analysis.

8.3.2 Operating Cost Inputs

8.3.2.1 Annual Energy Use

DOE estimated the annual electricity consumed by each class of commercial and 

industrial pump equipment, by efficiency level, based on the energy use analysis described in 

chapter 7 of the NOPR TSD.

8.3.2.2 Electricity Prices

Electricity prices are used to convert changes in the electric consumption from higherefficiency equipment into energy cost savings. DOE used average national commercial and 

industrial electricity prices in 2014 from the AEO 2014 reference case.2 These prices are 

$0.1026/kWh and $0.0688/kWh, respectively. DOE applied the commercial price to pump 

installations in the commercial sector and the industrial price to installations in the industrial, 

agricultural, and municipal sectors. To arrive at prices in future years, DOE multiplied the 

average prices described for 2014 by the forecast of average annual electricity price changes

from year 2020 to 2040 in EIA’s AEO 2014 for both the commercial and industrial sectors. See 

Figure 8.3.1.

a Series ID PCU333911333911; www.bls.gov/ppi/

8-6

 
Figure 8.3.1 Commercial and Industrial Electricity Price Projections for Reference Case

8.3.2.3 Maintenance Costs

During the CIP working group meetings, DOE expressed that its analysis was using the 

assumption that maintenance costs are not expected to change with efficiency level. DOE did not 

receive any negative comments on this assumption, so it DOE has not estimated a maintenance

cost for this analysis.

8.3.2.4 Repair Costs

DOE received information in response to the Framework Document (Grundfos, No. 24 at 

p. 35) and from the CIP working group that repair costs are not expected to change with 

efficiency level. Therefore, DOE has not estimated a repair cost for this analysis.

8.3.2.5 Equipment Lifetime

DOE estimated average lifetime by equipment class based on inputs from industry 

experts and used these estimates to develop statistical distributions. DOE defines two types of 

lifetime: the mechanical lifetime is the total lifetime hours of operation (including routine 

maintenance and repairs). The service lifetime is the number of years the consumer own and 

uses the unit, and is equal to the mechanical lifetime divided by the annual hours of operation. 

The service lifetime is the direct input to the LCC. DOE used a Weibull distribution function to 

define the distribution of mechanical lifetimes. The parameters for the Weibull function (shape 

and scale parameters) were estimated based on average service life and average annual operating 

hours. DOE accounted for life-extending pump repairs for some equipment classes by extending 

the mechanical lifetime, which is done by multiplying the Weibull scale parameter by the 

number of repair cyclesb

. For example if the number of repair cycles is two, the customer would 

b DOE estimated the number of repair cycles for each equipment class based on input from industry experts.

8-7

 
repair the pump twice before replacing it, which results in a lifetime twice as long as a lifetime 

without repair. This approach leads to a negative correlation between annual operating hours and 

service lifetime measured in years – pumps with more annual operating hours tend to have 

shorter service lifetimes. Based on discussions in the CIP working group meetings (see for 

example, Docket No. EERE-2013-BT-NOC-0039-0073, p. 153), DOE also introduced

mechanical lifetime variation by pump speed, as pumps running faster tend to have a shorter 

lifetime. DOE assumed that pumps with nominal speeds of 3600 RPM would have an average 

lifetime of 0.75 times equivalent pumps with nominal speeds of 1800 RPM. Table 8.3.1

summarizes the average mechanical lifetime in hours and service lifetime in years by equipment 

class.

Table 8.3.1 Average Mechanical Lifetime and Service Lifetime by Equipment Class

Equipment Class Average Mechanical 

Lifetime (hours)

Average Service Lifetime 

(years)

ESCC,1800 29,228 13.3

ESCC,3600 21,946 10.6

ESFM,1800 53,335 23.4

ESFM,1800 40,754 19.7

IL,1800 35,004 15.8

IL,3600 26,056 12.8

VT-S,3600 15,392 10.5

The Weibull distribution is a probability distribution commonly used to measure failure 

rates.c 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

P x

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

c 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/. 

8-8

 
θ = delay parameter, or location, which allows for a delay before any failures occur.

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. The parameters for the Weibull 

distribution are calibrated using average and median lifetime estimates from industry experts.

For all equipment classes DOE used a shape parameter of 2.65. DOE did not use a delay 

parameter in the Weibull distribution, but did assume a minimum service lifetime of three years. 

This reflects the fact that many units are purchased with a warranty that effectively guarantees 

that the unit will remain in operation during the warranty period. Figure 8.3.2 shows the resulting 

service lifetime histogram by equipment classes. This histogram does not resemble a Weibull, 

because it incorporates the effect of a broad distribution of operating hours.

Figure 8.3.2 Lifetime Distribution by Pump Equipment Class

8.3.2.6 Discount Rates

The commercial discount rate is the rate at which future operating costs are discounted to 

establish their present value in the LCC analysis. The discount rate value is applied in the LCC 

to future year energy costs and non-energy operations and maintenance costs to calculate the 

estimated net life-cycle cost of products of various efficiency levels and life-cycle cost savings as 

compared to the baseline for a representative sample of commercial end users.

DOE’s method views the purchase of a higher efficiency appliance as an investment that 

yields a stream of energy cost savings. DOE derived the discount rates for the LCC analysis by 

estimating the cost of capital for companies that purchase commercial and industrial pumps. The 

weighted average cost of capital (WACC) is commonly used to estimate the present value of 

cash flows to be derived from a typical company project or investment. Most companies use both 

debt and equity capital to fund investments, so their cost of capital is the weighted average of the 

8-9
cost to the firm of equity and debt financing, as estimated from financial data for publicly traded 

firms in the sectors that purchase pumps.3

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.4

Detailed sectors included in the Damondaran Online database were assigned to the aggregate 

categories of: buildings (commercial and institutional), industrial, and municipal (water utilities). 

As there is no data available for agricultural companies, the industrial rate is applied to the 

agricultural sector.

DOE estimated the cost of equity using the capital asset pricing model (CAPM).5 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 

cost of equity financing is estimated using the following equation, where the variables are 

defined as above:

k R ( ERP) e = f + β ×

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.”6

 

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 2004-

2013(Table 8.3.2).7 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.8

8-10
Table 8.3.2 Risk Free Rate and Equity Risk Premium, 2004-2013 

Year Risk free rate (%) ERP (%)

2004 7.10% 3.25%

2005 7.11% 3.68%

2006 7.10% 3.49%

2007 7.08% 3.36%

2008 7.01% 2.40%

2009 6.88% 3.07%

2010 6.74% 3.23%

2011 6.61% 2.94%

2012 6.41% 3.99%

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:

kdi = Rf + Rai

Where:

kd = cost of debt financing for firm, i,

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:

e e d wd WACC = k ×w + k ×

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 company. DOE then aggregates the company real weighted 

average costs of capital to estimate the discount rate for each of the ownership types in the 

commercial and industrial pumps analysis.

8-11
Table 8.3.3 shows the average WACC values for the major sectors that purchase 

commercial and industrial pumps. While WACC values for any sector may trend higher or lower 

over substantial periods of time, these values represent a cost of capital that is averaged over 

major business cycles. 

Table 8.3.3 Weighted Average Cost of Capital for Sectors that Purchase Pumps 

Sector Real Weighted Average 

Cost of Capital (%)

Standard 

Deviation (%)

Buildings 5.1% 1.3%

Industrial / 

Agricultural

5.2% 1.2%

Municipal 4.5% 0.5%

8.3.3 Base Case Efficiency Distribution

DOE developed a base case efficiency distribution based on the shipments data provided 

by the manufacturers. Table 8.3.4 shows the base case efficiency distribution for each equipment 

class; however, the LCC uses distributions specific to each power and flow bin. In constructing 

the binned pump data, within each power and flow bin the original list of pump C-values, and the 

number of shipments of each model, was preserved. For each pump in the bin, the C-value 

equation was used to normalize the efficiency to the value appropriate to the BEP flow and head 

assigned to the representative unit. This results in, for each bin, an initial or base case 

distribution of pump efficiencies for each bin. This distribution was used to assign an initial base 

case C-value to each row of the LCC sample.

Table 8.3.4 Base Case Efficiency Distributions by Equipment Class 

Efficiency 

Level

ESCC 

1800

ESCC 

3600

ESFM 

1800

ESFM 

3600

IL 

1800

IL 

3600

VT-S 

3600

Baseline 18.5% 3.8% 0.9% 4.7% 14.7% 10.7% 1.6%

PER 10 15.8% 8.1% 20.3% 23.5% 16.0% 20.8% 36.7%

PER 25 18.2% 14.5% 16.1% 15.3% 9.9% 8.8% 7.5%

PER 40 10.7% 3.6% 24.2% 16.0% 11.3% 4.5% 4.7%

PER 55 13.7% 3.9% 10.4% 11.3% 10.7% 12.9% 5.9%

PER 70 23.2% 66.1% 28.1% 29.3% 37.3% 42.3% 43.7%

8.4 LIFE-CYCLE COST AND PAYBACK PERIOD RESULTS

This section presents the results of the LCC and PBP analysis. As discussed previously, 

DOE used probability distributions to characterize the uncertainty in many of the inputs to the 

8-12
analysis. LCC and PBP calculations were performed 10,000 times, sampling from the probability 

distributions that DOE developed to characterize many of the inputs to the analysis. 

The average costs at each TSL are calculated considering the full sample of customers 

that have levels of efficiency in the base case equal to or above the given TSL (who are not 

affected by a standard at that TSL), as well as customers who had non-compliant pumps in the 

base case and purchase more expensive and efficient redesigned pumps in the standards case. 

The simple payback and LCC savings are measured relative to the base-case efficiency 

distribution in the compliance year. Based on simulations that DOE performed, for each standard 

level DOE also calculated the share of customers receiving a net LCC cost.

Table 8.4.1 through Table 8.4.14 show the LCC and PBP results for each equipment 

class. In general, the average LCC savings are positive for nearly all equipment classes and 

efficiency levels, with the exception of VT-S.3600, which has negative LCC saving for the first 

efficiency level. Figure 8.4.1 through Figure 8.4.7 show the distribution of LCC savings for each 

equipment class.

Table 8.4.1 Average LCC and PBP Results by Efficiency Level for ESCC.1800 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,639 $2,271 $17,546 $19,185 -- 13

1 1 $1,672 $2,261 $17,470 $19,142 3.3 13

2 2 $1,704 $2,240 $17,317 $19,021 2.2 13

3 3 $1,768 $2,222 $17,177 $18,945 2.6 13

4 4 $1,863 $2,198 $16,997 $18,861 3.1 13

5 5 $2,026 $2,172 $16,796 $18,822 3.9 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

8-13
Table 8.4.2 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESCC.1800 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 12% $43

2 2 11% $164

3 3 23% $240

4 4 30% $324

5 5 42% $362

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4.3 Average LCC and PBP Results by Efficiency Level for ESCC.3600 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,092 $1,592 $9,823 $10,915 -- 11

1 1 $1,098 $1,588 $9,800 $10,898 1.4 11

2 2 $1,111 $1,574 $9,713 $10,823 1.0 11

3 3 $1,141 $1,565 $9,653 $10,794 1.8 11

4 4 $1,170 $1,551 $9,566 $10,736 1.9 11

5 5 $1,215 $1,528 $9,422 $10,638 1.9 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

8-14
Table 8.4.4 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESCC.3600 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 0.7% $17

2 2 1.8% $92

3 3 14% $122

4 4 14% $180

5 5 12% $278

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4.5 Average LCC and PBP Results by Efficiency Level for ESFM.1800 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,891 $3,424 $40,983 $42,874 -- 23

1 1 $1,893 $3,423 $40,973 $42,866 2.4 23

2 2 $1,943 $3,406 $40,759 $42,701 2.8 23

3 3 $2,004 $3,384 $40,498 $42,502 2.8 23

4 4 $2,151 $3,342 $39,988 $42,139 3.1 23

5 5 $2,314 $3,301 $39,498 $41,812 3.4 23

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

8-15
Table 8.4.6 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESFM.1800 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 0.26% $8.0

2 2 6.5% $173

3 3 15% $372

4 4 24% $735

5 5 26% $1,062

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4.7 Average LCC and PBP Results by Efficiency Level for ESFM 3600 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,349 $5,278 $51,268 $52,616 -- 20

1 1 $1,357 $5,271 $51,201 $52,558 1.2 20

2 2 $1,396 $5,218 $50,674 $52,070 0.8 20

3 3 $1,441 $5,171 $50,214 $51,655 0.9 20

4 4 $1,529 $5,117 $49,676 $51,205 1.1 20

5 5 $1,648 $5,036 $48,890 $50,538 1.2 20

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

8-16
Table 8.4.8 LCC Savings Relative to the Base Case Efficiency Distribution for 

ESFM.3600 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 0.29% $58

2 2 1.9% $547

3 3 4.7% $961

4 4 7.0% $1,411

5 5 8.4% $2,078

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4.9 Average LCC and PBP Results by Efficiency Level for IL.1800 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $2,128 $1,891 $16,760 $18,888 -- 16

1 1 $2,145 $1,884 $16,692 $18,837 2.3 16

2 2 $2,194 $1,868 $16,545 $18,739 2.8 16

3 3 $2,281 $1,852 $16,407 $18,688 3.9 16

4 4 $2,432 $1,835 $16,254 $18,686 5.4 16

5 5 $2,614 $1,811 $16,040 $18,654 6.1 16

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

8-17
Table 8.4.10 LCC Savings Relative to the Base Case Efficiency Distribution for IL.1800 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 1.8% $51

2 2 6.9% $149

3 3 15% $200

4 4 25% $202

5 5 36% $234

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4.11 Average LCC and PBP Results by Efficiency Level for IL.3600 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,473 $2,046 $14,211 $15,684 -- 13

1 1 $1,484 $2,038 $14,155 $15,639 1.4 13

2 2 $1,525 $2,019 $14,020 $15,545 1.9 13

3 3 $1,578 $1,997 $13,865 $15,443 2.1 13

4 4 $1,650 $1,980 $13,747 $15,397 2.7 13

5 5 $1,797 $1,946 $13,510 $15,307 3.2 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

8-18
Table 8.4.12 LCC Savings Relative to the Base Case Efficiency Distribution for IL.3600 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 2.0% $46

2 2 13% $139

3 3 11% $241

4 4 14% $288

5 5 20% $377

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4.13 Average LCC and PBP Results by Efficiency Level for VT-S.3600 

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $692 $1,025 $5,857 $6,549 -- 11

1 1 $697 $1,025 $5,855 $6,551 11 11

2 2 $711 $1,021 $5,830 $6,542 4.2 11

3 3 $732 $1,002 $5,726 $6,458 1.7 11

4 4 $772 $989 $5,654 $6,426 2.2 11

5 5 $821 $977 $5,584 $6,405 2.7 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

8-19
Table 8.4.14 LCC Savings Relative to the Base Case Efficiency Distribution for VT-S.3600 

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 1.4% $(2.4)

2 2 21% $7.2

3 3 4.4% $91

4 4 8.5% $123

5 5 13% $144

* The calculation includes customers with zero LCC savings (no impact).

Figure 8.4.1 Distribution of Life-Cycle Savings for ESCC.1800

8-20
Figure 8.4.2 Distribution of Life-Cycle Cost Savings for ESCC.3600

Figure 8.4.3 Distribution of Life-Cycle Cost Savings for ESFM.1800

8-21
Figure 8.4.4 Distribution of Life-Cycle Cost Savings for ESFM.3600

Figure 8.4.5 Distribution of Life-Cycle Cost Savings for IL.1800

8-22
Figure 8.4.6 Distribution of Life-Cycle Cost Savings for IL.3600

Figure 8.4.7 Distribution of Life-Cycle Cost Savings for VT-S.3600

 

8-23
REFERENCES

1 U.S. Department of Agriculture, 2007 Census of Agriculture United States Summary and State 

Data Volume 1, December, 2009. Washington, DC. 

2 U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 2014, 

April, 2014. Washington, DC. Report No. DOE/EIA-0383(2014). 

<www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf>.

3 Modigliani, F. and M. Miller, The Cost of Capital, Corporations Finance and the Theory of 

Investment. American Economic Review, 1958. 48(3): pp. 261-297 

<www.jstor.org/stable/1809766>.

4 Damodaran Online, Data Page: Costs of Capital by Industry Sector, 2012. Damodaran. (Last 

accessed July, 2013.) <http://pages.stern.nyu.edu/~adamodar/>.

5 Ibbotson Associates, SBBI Valuation Edition 2009 Yearbook. 2009. Chicago, IL.

6 Federal Reserve Board, Federal Reserve Bank Services Private Sector Adjustment Factor, 

2005, 2005. Washington, D.C. 

<www.federalreserve.gov/boarddocs/Press/other/2005/20051012/attachment.pdf>.

7 Board of Governors of the Federal Reserve System, H.15 Historical Data Selected Interest 

Rates, 2013. (Last accessed February, 2013.) 

<www.federalreserve.gov/releases/h15/data.htm>.

8 Damodaran Online, Data Page: Historical Returns on Stocks, Bonds and Bills-United States, 

2012. Damodaran. (Last accessed July, 2013.) <http://pages.stern.nyu.edu/~adamodar/>.

8-24
CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 CURRENT SHIPMENTS................................................................................................ 9-1

9.3 SHIPMENTS PROJECTION .......................................................................................... 9-2

9.3.1 Methodology........................................................................................................ 9-2

9.3.2 Shipments in Standards Cases ............................................................................. 9-5

9.3.3 Shipments Results................................................................................................ 9-5

REFERENCES ............................................................................................................................ 9-7

LIST OF TABLES

Table 9.2.1 In-Scope Pump Shipment Estimates: FY 2012.............................................. 9-2

Table 9.2.2 Shipment Distribution by Design Speed........................................................ 9-2

Table 9.3.1 Distribution of Shipments by Sector.............................................................. 9-4

Table 9.3.2 Annual and Cumulative CIP Shipments Projections..................................... 9-6

LIST OF FIGURES

Figure 9.3.1 Medium Electric Motor Shipments Index vs. Private Fixed Investment 

Index in Selected Equipment and Structure................................................... 9-3

Figure 9.3.2 Pump Shipments Index vs. Private Fixed Investment Index in All 

Equipment...................................................................................................... 9-3

Figure 9.3.3 Shipments Projection by Scenario Case Based on Growth in Private 

Fixed Investment............................................................................................ 9-4

Figure 9.3.4 Macroeconomic Indicator-Based Shipment Projections by Sector and 

in Total based on AEO 2014.......................................................................... 9-5

Figure 9.3.5 CIP Shipments Projection by Scenario Case ................................................. 9-6

9-i
CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future equipment 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 equipment shipments and presents results for commercial 

and industrial pumps considered in this analysis.

DOE developed a shipments model to predict shipments of commercial and industrial 

pumps covered in this analysis. The shipments analysis projects initial shipments forward using 

macroeconomic indicators for each sector found in the Energy Information Administration’s 

Annual Energy Outlook 2014 (AEO2014)

1

. DOE’s pump shipments projections are based on 

forecasts of economic growth and do not incorporate a distinction between replacements and 

purchases for new applications.

DOE began with shipments data by equipment type provided by the Hydraulic Institute 

(HI).

2 Based on a database of pumps compiled from manufacturer- and HI-provided data, DOE 

then developed a distribution of shipments across equipment classes that also take into account 

design speed.

The shipments model is prepared as a Microsoft Excel spreadsheet that is accessible on 

the Internet (http://energy.gov/eere/buildings/appliance-and-equipment-standards-program). 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 commercial and industrial pumps by 

equipment class and for each sector. Section 9.3 describes the methodology that underlies 

development of the model and presents the shipments projection.

9.2 CURRENT SHIPMENTS

As discussed in Chapter 3, DOE reviewed U.S. Census Bureau data for historical 

shipments of centrifugal pumps.3 However, it was difficult to determine what percentage of 

those shipments reflected the scope of this rulemaking. In addition, in both the written comments 

in response to the Framework Document and during meetings of the CIP working group, the 

pump manufacturers did not believe the data to be reliable. Because of these difficulties, HI 

conducted a survey of its members and provided DOE with shipments estimates for 2012.2 These 

shipments, shown in Table 9.2.1, were reviewed in the CIP working group and found to 

represent an appropriate amount for the scope of this rulemaking. In order to further disaggregate 

the shipments into equipment classes including design speed, as shown in Table 9.2.2, DOE used 

its database of pump shipment data collected directly from manufacturers and from HI.

9-1
Table 9.2.1 In-Scope Pump Shipment Estimates: FY 2012

Equipment Category Total Industry Estimate (Units)

ESCC 171,456

ESFM 44,042

IL 50,424

RS-V 49,975

VT-S 104,406

TOTAL 420,303

Source: Hydraulic Institute.

Table 9.2.2 Shipment Distribution by Design Speed

Equipment Category % 1800 % 3600

ESCC 23% 77%

ESFM 89% 11%

IL 83% 17%

RS-V 9% 91%

VT-S 0% 100%

9.3 SHIPMENTS PROJECTION

9.3.1 Methodology

In the Framework Document, DOE stated that shipments of pumps are driven by 

machinery production growth for equipment incorporating pumps and by the economic growth 

of commercial and industrial sectors that use this equipment. DOE suggested that historical data 

would be used to establish the relationship between shipments of pumps and the appropriate 

growth index for sector growth, and that DOE intended to use private fixed investment data for 

equipment incorporating pumps from the U.S. Department of Commerce’s Bureau of Economic 

Analysis to characterize the production of this equipment.

DOE had successfully used this methodology in the medium electric motors rulemaking.4

In that rulemaking, DOE identified a close relationship between shipments and private fixed 

investments in selected equipment and structure over an extended time period, as shown in 

Figure 9.3.1. The R2 for the relationship was greater than 0.91.

9-2
Figure 9.3.1 Medium Electric Motor Shipments Index vs. Private Fixed Investment Index 

in Selected Equipment and Structure

When DOE attempted this approach for pumps using the historical Census shipments 

data and private fixed investment data from the Bureau of Economic Analysis (BEA)5,6, DOE 

was unable to obtain a historical fit as good as that for motors, especially when selecting 

equipment that seemed most appropriate (i.e., equipment not related to information processing 

and transportation, such as industrial and agricultural machinery).

Figure 9.3.2 shows the historical relationship of pump shipments to private fixed 

investment in all equipment for the last 10 years of the data, which had an R2 of 0.82. Shipments 

projections based on forecasts of real Gross Domestic Product from AEO 20141 show a tripling 

of shipments over the analysis period (Figure 9.3.3). This projection is much steeper than the 

historical trends.

Figure 9.3.2 Pump Shipments Index vs. Private Fixed Investment Index in All 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

-20

30

80

130

180

Index 2000=100

Shipments Index

9-3
Figure 9.3.3 Shipments Projection by Scenario Case Based on Growth in Private Fixed 

Investment

As a result of the uncertainty regarding whether the historical shipments data are 

representative of the commercial and industrial pumps market, and the relatively steep resulting 

growth projections (compared to historical trends), DOE considered an alternative shipments 

methodology. Specifically, DOE projected shipments using forecasts of various indicators in 

AEO 2014: (1) commercial floor space, (2) value of manufacturing shipments, (3) value of 

agriculture, mining, and construction shipments, and (4) population (for municipal sector). For 

the initial year, DOE distributed total shipments into the four sectors using estimates from the 

LCC analysis (Table 9.3.1). The distribution of sectors changes over time as a result of each 

sector’s differing forecast in AEO 2014 (Figure 9.3.4). The projection is still fairly steep 

compared to the historical trend, although much less so than in the initial model. However, as 

stated previously, it is uncertain how much the historical trend represents shipments of pumps in 

the scope of this rulemaking, and the pump manufacturers involved in the CIP working group 

felt that the resulting annual growth was likely reasonable.

Table 9.3.1 Distribution of Shipments by Sector

Sector % FY 2012 Shipments

Commercial 47% 197,542 

Industrial 33% 138,700 

Agricultural 12% 50,436 

Municipal 8% 33,624 

TOTAL 100% 420,303 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800 Shipments (1,000)

Historical Reference Case

High Economic Growth Case Low Economic Growth Case

9-4
Figure 9.3.4 Macroeconomic Indicator-Based Shipment Projections by Sector and in 

Total based on AEO 2014

The total shipments in each year are then disaggregated into equipment classes using the 

distributions of 2012 shipments shown previously. This market share by equipment class remains 

constant over time.

9.3.2 Shipments in Standards Cases

DOE believes that standards would have negligible impact on pump shipments. Because 

there are no increases in manufacturer production cost associated with improved pump 

efficiency, any increase in customer price is directly related to the conversion costs incurred by 

the manufacturer and how all manufacturers choose to price their pumps following 

implementation of a standard. Under most pricing scenarios, it is likely that following a standard, 

a customer would be able to buy a more efficient pump for the same price as the less efficient 

pump they would have purchased before or without a standard. Therefore, rather than foregoing 

a pump purchase under a standards case, a customer might simply switch brands or pumps to 

purchase a cheaper one that did not have to be redesigned. This customer choice was not 

modeled in the LCC because DOE chose to rely on a more conservative scenario; however, such 

customer choice would likely override any propensity to repair a pump model rather than replace 

it. DOE used the same shipments projection in the standards case as in the base case.

9.3.3 Shipments Results

Figure 9.3.5 shows annual shipments for the AEO Reference Case as well as AEO high 

and low economic growth scenarios over the 30-years analysis period starting at the compliance 

9-5
year. The analysis uses 2020 to represent the first full year of compliance with potential 

standards.

Figure 9.3.5 CIP Shipments Projection by Scenario Case

Table 9.3.2 shows the annual and cumulative shipments for each equipment class for the 

Reference Case.

Table 9.3.2 Annual and Cumulative CIP Shipments Projections

thousand units

Equipment 

Class 2020 2030 2040 2049

Cumulative 

over 30

years

ESCC 1800 47 54 61 68 1,727

ESCC 3600 154 177 200 222 5,628

ESFM 1800 46 53 60 67 1,690

ESFM 3600 5 6 7 8 200

IL 1800 49 56 64 70.5 1,790

IL 3600 10 12 13 15 373

RS-V 1800 5 6 7 7.5 191

RS-V 3600 54 61 69 77 1,952

VT-S 1800 0.4 0.5 0.6 0.6 16

VT-S 3600 122 140 158 176 4,462

Total* 494 567 640 710 18,029

*Total may not sum up because of rounding.

9-6
REFERENCES

1

 U.S. Department of Energy-Energy Information Administration (May 2014), Annual Energy 

Outlook 2014, Washington, DC. Retrieved May 2014. <www.eia.gov/forecasts/aeo/>.

2

 Hydraulic Institute (March 21, 2014), DOE/HI Manufacturers Survey for Fiscal Year 2012, 

Parsippany, NJ. Docket EERE-2013-BT-NOC-0039, No. 0068. 

<www.regulations.gov/#!documentDetail;D=EERE-2013-BT-NOC-0039-0068>.

3

 U.S. Census Bureau (August 2011), Pumps (except hydraulic) and Compressors - 2010. 

MA333P(08)-1. Retrieved December, 2012, 

<www.census.gov/manufacturing/cir/historical_data/ma333p/index.html> 

4

 U.S. Department of Energy. Technical Support Document: Energy Conservation Program: 

Energy Conservation Standards for Commercial and Industrial Electric Motors. 

<www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0027-0078>.

5 Bureau of Economic Analysis (May 1, 2014), Private Fixed Investment in Equipment by Type, 

Table 5.5.5U. Retrieved April, 2014. <www.bea.gov/iTable/iTable.cfm?ReqID=12&step=1>. 

6 Bureau of Economic Analysis (May 1, 2014), Private Fixed Investment in Structures by Type,

Table 5.4.5U. Retrieved April, 2014. <www.bea.gov/iTable/iTable.cfm?ReqID=12&step=1>. 

9-7 
CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 BASE AND STANDARDS CASE EFFICIENCIES .................................................... 10-2

10.3 NATIONAL ENERGY SAVINGS ............................................................................... 10-3

10.3.1 National Energy Savings Overview................................................................... 10-3

10.3.2 National Energy Savings Inputs......................................................................... 10-6

10.3.2.1 Unit Annual Site Energy Consumption.................................................... 10-6

10.3.2.2 Lifetime Distribution.............................................................................. 10-11

10.3.2.3 Site-to-Power Plant Energy Use Factor ................................................. 10-11

10.3.2.4 Full-Fuel-Cycle Energy Factors............................................................. 10-12

10.4 NET PRESENT VALUE............................................................................................. 10-12

10.4.1 Net Present Value Overview............................................................................ 10-13

10.4.2 Net Present Value Inputs ................................................................................. 10-15

10.4.2.1 Total Per-Unit Installed Cost.................................................................. 10-15

10.4.2.2 Unit Lifetime Operating Cost................................................................. 10-19

10.4.2.3 Discount Rate ......................................................................................... 10-20

10.5 TRIAL STANDARD LEVELS ................................................................................... 10-20

10.6 RESULTS .................................................................................................................... 10-21

REFERENCES ........................................................................................................................ 10-23

LIST OF TABLES

Table 10.2.1 Base Case Efficiency Distributions by Equipment Class............................ 10-2

Table 10.2.2 Example Standards Case Distributions Under Roll-Up Scenario 

(ESCC 1800)................................................................................................ 10-3

Table 10.3.1 UEC Inputs to the NIA (kWh): ESCC 1800................................................ 10-7

Table 10.3.2 UEC Inputs to the NIA (kWh): ESCC 3600................................................ 10-7

Table 10.3.3 UEC Inputs to the NIA (kWh): ESFM 1800 ............................................... 10-8

Table 10.3.4 UEC Inputs to the NIA (kWh): ESFM 3600 ............................................... 10-8

Table 10.3.5 UEC Inputs to the NIA (kWh): IL 1800 ...................................................... 10-9

Table 10.3.6 UEC Inputs to the NIA (kWh): IL 3600 ...................................................... 10-9

Table 10.3.7 UEC Inputs to the NIA (kWh): VT-S 3600............................................... 10-10

Table 10.3.8 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014) ...................... 10-12

Table 10.4.1 Average Per-Unit Total Installed Cost (2013$): ESCC 1800.................... 10-16

Table 10.4.2 Average Per-Unit Total Installed Cost (2013$): ESCC 3600.................... 10-16

Table 10.4.3 Average Per-Unit Total Installed Cost (2013$): ESFM 1800 ................... 10-17

Table 10.4.4 Average Per-Unit Total Installed Cost (2013$): ESFM 3600 ................... 10-17

Table 10.4.5 Average Per-Unit Total Installed Cost (2013$): IL 1800 .......................... 10-18

Table 10.4.6 Average Per-Unit Total Installed Cost (2013$): IL 3600 .......................... 10-18

Table 10.4.7 Average Per-Unit Total Installed Cost (2013$): VT-S 3600..................... 10-19

10-i
Table 10.5.1 Mapping Between TSLs and Efficiency Levels ........................................ 10-21

Table 10.6.1 Cumulative National Primary Energy Savings for Commercial and 

Industrial Pump Trial Standard Levels for Units Sold in 2020-2049 ........ 10-21

Table 10.6.2 Cumulative National Full-Fuel-Cycle Energy Savings for Commercial 

and Industrial Pump Trial Standard Levels for Units Sold in 2020-

2049............................................................................................................ 10-22

Table 10.6.3 Cumulative Net Present Value of Customer Benefit for Commercial 

and Industrial Pump Trial Standard Levels for Units Sold in 2020-

2049............................................................................................................ 10-22

LIST OF FIGURES

Figure 10.3.1 Energy Use Adjustment Factors ................................................................ 10-11

Figure 10.4.1 Weighted-Average Electricity Price Time Series...................................... 10-20

10-ii
CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter examines national impacts attributable to each trial standard level (TSL) 

considered for commercial and industrial pumps. 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 customers 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 the NES and NPV for 

each TSL being considered as the new standard for commercial and industrial pumps. DOE 

performed all calculations for each considered equipment class using Microsoft Excel 

spreadsheet models, which are accessible on the Internet.a

 The spreadsheets combine the 

calculations for determining the NES and NPV for each considered equipment 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 model. 

The NIA calculation started with the shipments model, which produces a projection of 

annual shipments of pumps. DOE used the annual projection of 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. 

To calculate the annual national energy savings, DOE first estimated the lifetime primary 

and full-fuel-cycleb (FFC) energy consumption at the unit level and for each year in the analysis 

period, for pumps of each equipment class. The unit’s lifetime primary and FFC energy 

consumptions were then scaled up to the national level based on the annual shipments projection. 

This produced, for each equipment class, two streams of annual national energy consumption,

from which DOE derived two streams of annual NES from pumps shipped in each year of the 

analysis period: one that accounts for primary energy savings, and one that accounts for the FFC 

energy savings.

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 

pumps of each equipment class. 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. This produced, for each equipment class and sector: (a) two 

a See http://energy.gov/eere/buildings/appliance-and-equipment-standards-program b The full-fuel-cycle energy consumption adds to the primary energy consumption the energy consumed by the 

energy supply chain upstream to power plants.

10-1

 
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 

present-value; 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. DOE then calculated the 

difference between the national energy cost savings and national incremental non-energy costs to 

obtain the NPV of each equipment class, and summed these values across equipment classes to 

produce the total NPV. 

Two models included in the NIA are described 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 BASE AND STANDARDS CASE EFFICIENCIES

For the base case in 2012, DOE developed a distribution of efficiencies from a database 

which DOE built using data collected from major manufacturers and the Hydraulic Institute (see 

Table 10.2.1). Because the available evidence suggests that there is no trend toward greater 

interest in higher pump efficiency, DOE assumed that the base case distribution would remain 

constant over time. 

Table 10.2.1 Base Case Efficiency Distributions by Equipment Class

Efficiency 

Level

ESCC 

1800

ESCC 

3600

ESFM 

1800

ESFM 

3600

IL 

1800

IL 

3600

VT-S 

3600

Baseline 18.5% 3.8% 0.9% 4.7% 14.7% 10.7% 1.6%

PER 10 15.8% 8.1% 20.3% 23.5% 16.0% 20.8% 36.7%

PER 25 18.2% 14.5% 16.1% 15.3% 9.9% 8.8% 7.5%

PER 40 10.7% 3.6% 24.2% 16.0% 11.3% 4.5% 4.7%

PER 55 13.7% 3.9% 10.4% 11.3% 10.7% 12.9% 5.9%

PER 70 23.2% 66.1% 28.1% 29.3% 37.3% 42.3% 43.7%

For each efficiency level analyzed, DOE used a “roll-up” scenario to establish the market 

shares by efficiency level for the year that compliance would be required with new standards 

(i.e., 2020). DOE believes that equipment efficiencies in the base case that were above the 

standard level under consideration would not be affected. There is anecdotal information from 

certain manufacturers that for pumps not meeting a potential standard at some of the lower 

efficiency levels, redesign would likely target an efficiency level higher than the minimum given 

the level of investment required for a redesign, and the relatively more modest change in 

10-2
investment to design to a given pump to a higher level once redesign is already taking place. 

However, DOE has no data to clearly indicate what percentage of failing pumps would likely be 

redesigned to a level higher than the minimum, or how high that level would be. Furthermore, 

DOE does not want to overestimate savings from this rule by assuming that manufacturers would

design to a level higher than required. Table 10.2.2 shows an example roll-up scenario for one of 

the equipment classes.

Table 10.2.2 Example Standards Case Distributions Under Roll-Up Scenario (ESCC 

1800)

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 18.5% 0% 0% 0% 0% 0%

PER 10 15.8% 34.3% 0% 0% 0% 0%

PER 25 18.2% 18.2% 52.4% 0% 0% 0%

PER 40 10.7% 10.7% 10.7% 63.1% 0% 0%

PER 55 13.7% 13.7% 13.7% 13.7% 76.8% 0%

PER 70 23.2% 23.2% 23.2% 23.2% 23.2% 100%

10.3 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 

increased pump efficiency resulting from the installation of more efficient pumps nationwide (as 

a consequence of new standards), in comparison to a base case with no national standards. 

10.3.1 National Energy Savings Overview

DOE calculated the cumulative primary and FFC energy savings from a pump 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 

an expected compliance date in December 2019.c

 The NES calculation started with estimates of 

shipments, which are outputs of the shipments model (Chapter 9).

d

 DOE then obtained values of 

pump parameters from the LCC analysis (Chapter 8), projections of site-to-primary conversion 

factorse from the Annual Energy Outlook 20141 (AEO 2014) and projections of site-to-upstream

c The analysis uses January 1st, 2020 to represent the expected compliance date in December 2019. Therefore, the 

30-year analysis period 2019-2048 is referred to as 2020-2049 in this chapter. 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. e The site-to-primary factors account for electricity generation, transmission and distribution losses.

10-3

 
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. 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 pumps of a given equipment class, shipped in that year, 

are the differences in their primary and FFC energy use between the corresponding base case and 

the standards case scenarios.

This calculation is expressed by the following formulas for a given equipment class:

Lifetime Primary Energy Savings

nnnnnnnn(yy) = nnnnnnnnnnnn(yy) − nnnnnnnnnnnnnn(yy)

Eq. 10.1

nnnnnnnnnnnn(yy) = hpp(yy) ∙ � �uu cc(yy) ∙ MM cc(yy)� cc

Eq. 10.2

nnnnnnnnnnnnnn(yy) = hpp(yy) ∙ � �uu cc(yy) ∙ MM cc(yy)� cc

Eq. 10.3

uu cc(yy) = ∑ aa cc(yy, ii) ii=1..LLLL

Eq. 10.4

where:

nnnnnnnn(yy) = the lifetime primary energy savings of all pumps shipped in year y, 

nnnnnnnnnnnn(yy) = the base case, lifetime primary energy consumption of pumps

shipped in year y, 

nnnnnnnnnnnnnn(yy) = the standards case, lifetime primary energy consumption of pumps

shipped in year y, 

hpp(yy) = the number of pumps shipped in year y, 

uu cc(yy) = the lifetime primary energy consumption of a unit with efficiency 

level at EL c shipped in year y, 

aa cc(yy, ii) = the annual primary energy consumption in the ii-th year of 

operation of a unit with efficiency level at EL c, shipped in year y, 

MM cc(yy) = the base case market share of units with efficiency level at EL c 

shipped in year y, and

MM cc(yy) = the standards case market share of units with efficiency level at EL 

c shipped in year y.

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.

10-4

 
Lifetime Full-Fuel-Cycle Energy Savings

nnnnnnnn(yy) = nn (yy) − nnnnnnnnnnnnnn(yy)

Eq. 10.5

nnnnnnnnnnnn(yy) = hpp(yy) ∙ � �uu cc(yy) ∙ MM cc(yy)� cc

Eq. 10.6

nnnnnnnnnnnnnn(yy) = hpp(yy) ∙� �uu cc(yy) ∙ MM cc(yy)� cc

Eq. 10.7

uu cc(yy) = ∑ �aa cc ii=1..LLLL (yy, ii) ∙ ffff (yy + ii − 1)�

Eq. 10.8

where:

nnnnnnnn(yy) = the lifetime FFC energy savings of all pumps shipped in year y, 

nnnnnnnnnn (yy) = the base case, lifetime FFC energy consumption of pumps shipped 

in year y, 

nnnnnnnnnnnnnn(yy) = the standards case, lifetime FFC energy consumption of pumps 

shipped in year y, 

hpp(yy) = the number of pumps shipped in year y, 

uu cc(yy) = the lifetime FFC energy consumption of a unit with efficiency 

level at EL c shipped in year y, 

aa cc(yy, ii) = the annual primary energy consumption in the ii-th year of 

operation of a unit with efficiency level at EL c, shipped in year y, 

ffff (yy) = the primary-to-FFC conversion factor in year y, 

MM cc(yy) = the base case market share of units with efficiency level at EL c 

shipped in year y, and

MM cc(yy) = the standards case market share of units with efficiency level at EL 

c shipped in year y.

DOE used the lifetime primary and FFC energy savings estimated for all pumps shipped 

from 2020 through 2049 to calculate the total primary NES (NN ssss ) and the total FFC NES 

(NN FFFFFF) for the analysis period. The calculation used the following formulas: 

NN ssss = � nnnnnnnn(yy)

2049

yy=2020

Eq. 10.9

NN FFFFFF = � nnnnnnnn(yy)

2049

yy=2020

Eq. 10.10

10-5
where:

nnnnnnnn(yy) = the lifetime primary energy savings of all pumps shipped in year y, 

and

nnnnnnnn(yy) = the lifetime FFC energy savings of all pumps 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.

10.3.2 National Energy Savings Inputs

The NES model inputs include: (a) the unit site energy consumption, (b) the site-toprimary 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-toprimary factors – enable the calculation of FFC energy consumption from site energy use, and 

(d) shipment efficiency distributions, which were discussed in the previous section. The list of 

NES model inputs is as follows:

• unit annual site energy consumption;

• lifetime (probability) distribution;

• electricity site-to-primary conversion factors; and

• electricity site-to-upstream conversion factors.

10.3.2.1 Unit Annual Site Energy Consumption

The unit annual site energy consumption expresses an estimate of the amount of site

energy that a pumps of a given equipment class, meeting the efficiency level of a given EL, and 

shipped in a given year will consume in each year of its lifetime. 

The average per-unit annual energy consumption for each equipment class and efficiency 

level was calculated in the LCC. In the base case, the distribution of horsepower within each 

efficiency bin differs. As a result, average energy use does not decrease monotonically as 

efficiency level increases. In addition, in each standards case, the per-unit annual energy 

consumption at the minimum efficiency level differs from that in the base case. For example, if 

pumps at the baseline efficiency level tended to be smaller than pumps meeting the efficiency 

associated with EL1, under the standards case, the unit energy consumption at EL1 would 

decrease. Table 10.3.1 through Table 10.3.7 show these matrixed inputs.

10-6
DOE uses a market- or shipment-weighted average annual UEC in its calculation of the 

national AEC. This takes into account that the markets include shipments of equipment with a 

broad range of efficiencies, as shown in section 10.2. The market-weighted average UEC 

declines as TSL increases, as would be expected, as shown in Table 10.3.1 through Table 10.3.7.

Table 10.3.1 UEC Inputs to the NIA (kWh): ESCC 1800

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 12545 N/A N/A N/A N/A N/A

PER 10 31164 20802 N/A N/A N/A N/A

PER 25 24001 24001 21494 N/A N/A N/A

PER 40 36900 36900 36900 23793 N/A N/A

PER 55 31621 31621 31621 31621 24855 N/A

PER 70 19721 19721 19721 19721 19721 23382

Market 

Weighted 

Average

24469 24359 24119 23921 23664 23382

Table 10.3.2 UEC Inputs to the NIA (kWh): ESCC 3600

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 29817 N/A N/A N/A N/A N/A

PER 10 40824 36945 N/A N/A N/A N/A

PER 25 14218 14218 23904 N/A N/A N/A

PER 40 44787 44787 44787 26032 N/A N/A

PER 55 62802 62802 62802 62802 29821 N/A

PER 70 9952 9952 9952 9952 9952 16442

Market 

Weighted 

Average

17141 17098 16951 16837 16687 16442

10-7
Table 10.3.3 UEC Inputs to the NIA (kWh): ESFM 1800

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 47229 N/A N/A N/A N/A N/A

PER 10 43025 43159 N/A N/A N/A N/A

PER 25 38112 38112 40471 N/A N/A N/A

PER 40 35511 35511 35511 38147 N/A N/A

PER 55 52331 52331 52331 52331 39553 N/A

PER 70 26849 26849 26849 26849 26849 35545

Market 

Weighted 

Average

36876 36867 36676 36447 35983 35545

Table 10.3.4 UEC Inputs to the NIA (kWh): ESFM 3600

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 90194 N/A N/A N/A N/A N/A

PER 10 59093 64014 N/A N/A N/A N/A

PER 25 58300 58300 60674 N/A N/A N/A

PER 40 66196 66196 66196 61309 N/A N/A

PER 55 72804 72804 72804 72804 62309 N/A

PER 70 38108 38108 38108 38108 38108 54348

Market 

Weighted 

Average

57030 56956 56377 55871 55281 54403

10-8
Table 10.3.5 UEC Inputs to the NIA (kWh): IL 1800

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 8419 N/A N/A N/A N/A N/A

PER 10 21976 15208 N/A N/A N/A N/A

PER 25 51256 51256 23584 N/A N/A N/A

PER 40 32555 32555 32555 25223 N/A N/A

PER 55 16525 16525 16525 16525 23437 N/A

PER 70 13625 13625 13625 13625 13625 19517

Market 

Weighted 

Average

20357 20272 20104 19941 19754 19497

Table 10.3.6 UEC Inputs to the NIA (kWh): IL 3600

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 16845 N/A N/A N/A N/A N/A

PER 10 15763 15870 N/A N/A N/A N/A

PER 25 25627 25627 17488 N/A N/A N/A

PER 40 48821 48821 48821 20101 N/A N/A

PER 55 54421 54421 54421 54421 27470 N/A

PER 70 13091 13091 13091 13091 13091 21018

Market 

Weighted 

Average

22091 22009 21803 21563 21388 21018

10-9
Table 10.3.7 UEC Inputs to the NIA (kWh): VT-S 3600

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 13250 N/A N/A N/A N/A N/A

PER 10 9804 9928 N/A N/A N/A N/A

PER 25 29161 29161 12962 N/A N/A N/A

PER 40 18009 18009 18009 12961 N/A N/A

PER 55 39582 39582 39582 39582 15463 N/A

PER 70 8928 8928 8928 8928 8928 12446

Market 

Weighted 

Average

13080 13073 13020 12782 12623 12458

In order to properly account for national impacts, DOE made some adjustments to the 

energy use developed from the LCC spreadsheet. Specifically, in the LCC, DOE does not 

account for pumps sold with trimmed impellers or pumps used with VSDs, both of which may 

reduce the energy savings resulting from pump efficiency improvements. 

DOE reviewed studies on VSD penetration and used an initial penetration of 3.2 percent 

in 19982 with a 5 percent annual increase.

3 In order to determine the penetration projection,

DOE reviewed historical shipments of controls for AC packaged drives from U.S. Economic 

Census data.4 DOE applied a linear regression to this data, which resulted in a growth rate of 8 

percent per year assuming a constant linear trend. However, DOE had no way of knowing 

whether this trend would be applicable to the pumps market specifically, as growth of controls 

might be faster in other markets. Therefore, DOE also reviewed a study from the European 

Commission, which projected that VSD penetration in motor systems will grow from 30 to 50 

percent across ten years (2000 to 2010), resulting in a 3 percent and 5 percent annual growth 

rate.5 Based on the U.S. and European data, DOE chose to use the 5 percent annual growth rate 

as a mid-growth scenario.

Although these VSD studies are not specific to VFDs, DOE assumed all VSD use was 

VFD use, as VFDs are the most common type of VSD in the pumps market. Based on DOE’s 

analysis of VFD users in the customer subgroup analysis (see chapter 11), DOE assumed VFDs 

would reduce energy use by 39 percent on average, which also reduces the potential energy 

savings from higher efficiency. However, based on the well-known difficulties with VFD 

installation and operation, DOE assumed that the full amount of potential savings would not be 

realized for all customers. DOE is currently assuming an “effectiveness rate” of 75 percent; in 

other words DOE is assuming that customers will achieve on average only 75 percent of the 39 

percent estimated savings (i.e., 29 percent savings) because of improper installation, operation 

inconsistent with intended use, or other equipment problems.

10-10
In the CIP working group meetings, stakeholders stated that half to the vast majority of 

pumps sold by manufacturers are trimmed. (Louis Starr, EERE-2013-BT-NOC-0039-0072, p. 

345; Al Huber, EERE-2013-BT-NOC-0039-0009, p. 168) DOE also consulted a market expert 

who agreed that a majority of pumps are trimmed, and that the average trim is between 10 to 20 

percent. In the NIA, DOE assumed that for all equipment classes except VT-S, 50 percent of 

pumps not sold with VFDs are sold with impellers trimmed to 85 percent of full impeller. 

According to the pump affinity laws, such an impeller trim uses 61 percent of the power of full 

trim. Accordingly, DOE reduced the energy use for those customers by 39 percent. For the VT-S 

equipment class, DOE assumed that pumps were not sold with trimmed impellers. A large 

percentage of these pumps are pressed stainless and will never be trimmed; the remainder of 

these pumps will be significantly less likely to be trimmed than other pump types because 

variability in the number of stages would be used in place of trimming the impellers.

DOE used the penetration rate and power reduction values for VFDs and trimmed 

impellers to create an energy use adjustment factor time series in the NES spreadsheet. (Figure 

10.3.1) The total energy use adjustment factor applies to all equipment classes except VT-S, 

which, as noted, only receive the VFD adjustment.

Figure 10.3.1 Energy Use Adjustment Factors

10.3.2.2 Lifetime Distribution

For the NIA, DOE uses a distribution of pump lifetime in years as determined in the LCC 

(chapter 8). Each pump will consume its annual UEC in each year of its lifetime.

10.3.2.3 Site-to-Power Plant Energy Use Factor

In determining national annual energy consumption, DOE initially calculated the annual 

energy consumption at the site (for electricity, the energy in kWh consumed at the household). 

DOE then applied a conversion factor to site energy consumption to account for losses associated 

with the generation, transmission, and distribution of electricity. This multiplicative site-topower-plant conversion factor converts site energy consumption into primary energy 

consumption, expressed in quadrillion Btus (quads). 

70%

75%

80%

85%

90%

95%

100%

105%

202020212022202320242025202620272028202920302031203220332034203520362037203820392040204120422043204420452046204720482049

Percent Energy Use

Year

Total Energy Usage Adjustment VFD Adjustment Only

10-11
DOE used annual site-to-power-plant conversion factors based on the version of the 

National Energy Modeling System (NEMS)g that corresponds to DOE’s Energy Information 

Administration’s (EIA’s) Annual Energy Outlook 2014 (AEO2014).1 The factors are marginal 

values, which represent the response of the system to an incremental decrease in consumption. 

For electricity, the conversion factors change over time in response to projected changes in 

generation sources (that is, the types of power plants projected to provide electricity to the 

Nation). The value AEO2014 reported for 2040 (the last year available in AEO2014) was 

extrapolated through the end of the projection period (2049).

10.3.2.4 Full-Fuel-Cycle Energy Factors

The full-fuel-cycle (FFC) 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 estimate the FFC by 

including the energy consumed in extracting, processing, and transporting or distributing primary 

fuels, which we refer to as “upstream” activities, DOE developed FFC multipliersh using the data 

and projections generated by NEMS and used for AEO2014. The AEO2014 provides extensive 

information about the energy system, including projections of future oil, natural gas, and coal 

supplies; 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 that represent the energy intensity of energy production.

Table 10.3.8 shows the FFC energy multipliers used for selected years. The method used 

to calculate FFC energy multipliers is described in appendix 10A.

Table 10.3.8 Full-Fuel-Cycle Energy Multipliers (Based on AEO 2014)

Energy Source 2020 2025 2030 2035 2040

Electricity

(power plant energy use) 1.044 1.045 1.046 1.047 1.047

10.4 NET PRESENT VALUE

To derive the NPV of customer benefit from potential standards, 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 pump energy efficiency 

standards. It discounted this time series to the year 2015 and summed the result, yielding the 

national NPV.

g 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 2000, DOE/EIA0581(2000), March 2000. 

h FFC multipliers discussed in this chapter relate to the upstream part of the FFC process.

10-12

 
10.4.1 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 pump shipments estimated by the shipments 

model. DOE then obtained pump input data and average electricity costs from the LCC analysis, 

and estimated pump non-energy and energy lifetime costs. For both a base case and a standards 

case, DOE first calculated the amount spent on pump purchases,iand then calculated the lifetime 

energy cost by applying the average electricity prices to the electricity used by pumps 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 pumps 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 

2015 from motors that were purchased between the compliance date of the standards and 2049,

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:

NNNNNN = −

Eq. 10.11

where:

PVS = the present value of electricity cost savings, and

PVC = the present value of incremental non-energy costs. 

PVS and PVC are determined according to the following expressions:

= � nnnnnnnn(yy) ∙ (1 + rr)2015−yy

2049

yy=2020

Eq. 10.12

nnnnnnnn(yy) = nn nnnnnn(yy) − nn nnnnnnnn(yy)

Eq. 10.13

i

DOE did not account for installation costs, maintenance costs, or repair costs. Although these costs might have 

significant impacts on a user’s budget, they do not vary with the efficiency level of the pump and therefore would 

have no impact in the difference of non-energy costs between the base case and the standards case scenarios.

10-13

 
nn nnnnnn(yy) = hpp(yy) ∙ ∑ �uu cc(yy) ∙ MM cc cc (yy)�

Eq. 10.14

nnnnnnnnnnnn(yy) = hpp(yy) ∙ ∑ �uu (yy) ∙ MM (yy)�

Eq. 10.15

and:

= � nnnnnnnn(yy) ∙ (1 + rr)2015−yy

2049

yy=2020

Eq. 10.16

nnnnnnnn(yy) = nnnnnnnnnn(yy) − nnnnnnnnnnnn(yy)

Eq. 10.17

nnnnnnnnnn(yy) = hpp(yy) ∙ � �uu cc(yy) ∙ MM cc(yy)� cc

Eq. 10.18

nnnnnnnnnnnn(yy) = hpp(yy) ∙ � �uu cc(yy) ∙ MM cc(yy)� cc

Eq. 10.19

where:

nnnnnnnn(yy) = the lifetime energy cost savings of all pumps shipped in year y,

nn nnnnnn(yy) = the base case, lifetime energy cost of all pumps shipped in year y, 

nn nnnnnnnn(yy) = the standards case, lifetime energy cost of all pumps shipped in 

year y, 

uu cc(yy) = the lifetime energy cost of a unit with efficiency level at EL c 

shipped in year y, 

nnnnnnnn(yy) = the lifetime incremental equipment non-energy costs of all pumps

shipped in year y,

nnnnnnnnnn(yy) = the base case, lifetime equipment non-energy costs of all pumps

shipped in year y, 

nnnnnnnnnnnn(yy) = the standards case, lifetime equipment non-energy costs of all 

pumps shipped in year y, 

uu cc(yy) = the lifetime equipment non-energy costs of a unit with efficiency 

level at EL c shipped in year y, 

hpp(yy) = the number of pumps shipped in year y, 

MM cc(yy) = the base case market share of units with efficiency level at EL c 

shipped in year y, and

MM cc(yy) = the standards case market share of units with efficiency level at EL 

c shipped in year y, and

rr = the discount rate.

10-14
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.

10.4.2 Net Present Value Inputs

The NPV model inputs include: (a) 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. The list of NPV model inputs not discussed previously

is as follows:

1. total per-unit installed cost;

2. unit lifetime energy cost, and

3. discount rate.

10.4.2.1 Total Per-Unit Installed Cost

Total installed cost typically accounts for manufacturer selling price (MSP), markups, 

and installation cost. Because installation cost does not vary by efficiency level for pumps, these 

costs represent only the equipment cost. 

The engineering analysis and LCC calculated MSP data for each representative unit. For 

the NIA, a weighted average value for each equipment class was calculated. After calculating

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. Table 

10.4.1 through Table 10.4.7 provide average total installed cost values by efficiency level for all 

equipment classes. As discussed in section 8.3.3, DOE developed base case and standards case 

projections of market share by efficiency level. DOE multiplied the market share by efficiency 

level in each year by the values in Table 10.4.1 through Table 10.4.7 to calculate annual 

shipment-weighted average total installed costs.

10-15
Table 10.4.1 Average Per-Unit Total Installed Cost (2013$): ESCC 1800

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1256 N/A N/A N/A N/A N/A

PER 10 1698 1556 N/A N/A N/A N/A

PER 25 1644 1644 1648 N/A N/A N/A

PER 40 1899 1899 1899 1792 N/A N/A

PER 55 1852 1852 1852 1852 1927 N/A

PER 70 1653 1653 1653 1653 1653 2026

Market 

Weighted 

Average

1641 1673 1704 1768 1863 2026

Table 10.4.2 Average Per-Unit Total Installed Cost (2013$): ESCC 3600

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1015 N/A N/A N/A N/A N/A

PER 10 1220 1201 N/A N/A N/A N/A

PER 25 1101 1101 1193 N/A N/A N/A

PER 40 1303 1303 1303 1307 N/A N/A

PER 55 1408 1408 1408 1408 1403 N/A

PER 70 1049 1049 1049 1049 1049 1215

Market 

Weighted 

Average

1092 1098 1111 1141 1169 1215

10-16
Table 10.4.3 Average Per-Unit Total Installed Cost (2013$): ESFM 1800

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1670 N/A N/A N/A N/A N/A

PER 10 1864 1866 N/A N/A N/A N/A

PER 25 1892 1892 2010 N/A N/A N/A

PER 40 1925 1925 1925 2075 N/A N/A

PER 55 2045 2045 2045 2045 2275 N/A

PER 70 1831 1831 1831 1831 1831 2314

Market 

Weighted 

Average

1891 1893 1943 2004 2151 2314

Table 10.4.4 Average Per-Unit Total Installed Cost (2013$): ESFM 3600

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1263 N/A N/A N/A N/A N/A

PER 10 1297 1322 N/A N/A N/A N/A

PER 25 1367 1367 1428 N/A N/A N/A

PER 40 1471 1471 1471 1515 N/A N/A

PER 55 1470 1470 1470 1470 1632 N/A

PER 70 1280 1280 1280 1280 1280 1648

Market 

Weighted 

Average

1350 1358 1398 1443 1530 1650

10-17
Table 10.4.5 Average Per-Unit Total Installed Cost (2013$): IL 1800

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1549 N/A N/A N/A N/A N/A

PER 10 2018 1850 N/A N/A N/A N/A

PER 25 2817 2817 2207 N/A N/A N/A

PER 40 2548 2548 2548 2448 N/A N/A

PER 55 2075 2075 2075 2075 2625 N/A

PER 70 2108 2108 2108 2108 2108 2614

Market 

Weighted 

Average

2126 2143 2192 2279 2429 2611

Table 10.4.6 Average Per-Unit Total Installed Cost (2013$): IL 3600

Efficiency

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1255 N/A N/A N/A N/A N/A

PER 10 1397 1381 N/A N/A N/A N/A

PER 25 1634 1634 1538 N/A N/A N/A

PER 40 1790 1790 1790 1683 N/A N/A

PER 55 1899 1899 1899 1899 1856 N/A

PER 70 1370 1370 1370 1370 1370 1797

Market 

Weighted 

Average

1473 1484 1525 1578 1650 1797

10-18
Table 10.4.7 Average Per-Unit Total Installed Cost (2013$): VT-S 3600

Efficiency 

Level

Base 

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 659 N/A N/A N/A N/A N/A

PER 10 627 642 N/A N/A N/A N/A

PER 25 1159 1159 758 N/A N/A N/A

PER 40 854 854 854 808 N/A N/A

PER 55 1446 1446 1446 1446 946 N/A

PER 70 548 548 548 548 548 821

Market 

Weighted 

Average

692 698 712 733 773 822

Future Equipment Prices

For reasons discussed in Chapter 8 of the TSD (Section 8.3.1.3), DOE used a constant 

price assumption for the default projection in the NIA. To investigate the impact of different 

equipment price projections on the 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 

pumps and pumping equipment,

j

and the other is based on AEO2014’s deflator for industrial 

equipment.

1 Details on how these alternative price trends were developed are in Appendix 10B, 

which also presents results from the sensitivity analysis DOE developed based on these two 

equipment price scenarios.

10.4.2.2 Unit Lifetime Operating Cost

The annual operating cost includes only electricity costs as repair and maintenance costs 

do not vary with efficiency level. The unit lifetime energy cost expresses an estimate of the 

market average expense for electricity that owners of all pumps of a given equipment class, 

shipped in a given year, will have to operate these pumps over their lifetime. 

DOE determined annual energy consumption of pumps through the energy use and LCC 

analysis. DOE then applied national weighted-average electricity prices based on the sectorspecific electricity prices described in Chapter 8, Section 8.3.2.2.

As with the total installed cost data, DOE developed projected annual electricity expenses 

based on the annual projections of market share by efficiency level specified in the base case and 

standards cases. DOE multiplied the market share by efficiency level in each year by the per-unit 

electricity costs by efficiency level to calculate shipment-weighted average annual electricity 

j

Series ID PCU333911333911; www.bls.gov/ppi/

10-19

 
costs. DOE then applied electricity price trends from AEO 2014 to scale the electricity expenses 

moving forward, as shown in Figure 10.4.1.

Figure 10.4.1 Weighted-Average Electricity Price Time Series

10.4.2.3 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.

6 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.

10.5 TRIAL STANDARD LEVELS

DOE selected six efficiency levels, including baseline, for the NIA. For all equipment 

classes, efficiency level 0 (EL 0) is the baseline efficiency level. Based on the results of the LCC 

analysis, NIA, and the input of the CIP working group, DOE selected five TSLs above the 

baseline level for each equipment class for the NOPR stage of this rulemaking. TSL 5 was 

selected as the max-tech level for all equipment classes. TSL 1, TSL 2, TSL 3, and TSL 4 were 

selected to provide intermediate efficiency levels that fill the gap between the baseline efficiency 

level and TSL 5 and allow for evaluation of manufacturer impact at each level. Table 10.5.1

shows the mapping between TSLs and efficiency levels.

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

20202024202820322036204020442048205220562060206420682072207620802084208820922096

$/kWh

Year

10-20
Table 10.5.1 Mapping Between TSLs and Efficiency Levels

Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC.1800 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESCC.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM.1800 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL.1800 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

VT-S.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

10.6 RESULTS

DOE evaluated NES and NPV for each equipment class and TSL using the inputs and 

methodologies described in Sections 10.3 and 10.4. Table 10.6.1 and Table 10.6.2 present NES 

results, and Table 10.6.3 presents NPV results.

Table 10.6.1 Cumulative National Primary Energy Savings for Commercial and 

Industrial Pump Trial Standard Levels for Units Sold in 2020-2049 

Equipment 

Class

Trial Standard Level

1 2 3 4 5

quads

ESCC.1800 0.016 0.05 0.08 0.12 0.16

ESCC.3600 0.016 0.07 0.11 0.17 0.26

ESFM.1800 0.003 0.05 0.11 0.23 0.35

ESFM.3600 0.002 0.02 0.03 0.05 0.07

IL.1800 0.015 0.05 0.08 0.11 0.16

IL.3600 0.003 0.01 0.02 0.02 0.03

VT-S.3600 0.002 0.02 0.11 0.17 0.22

Total -- All 

Classes 0.056 0.27 0.54 0.87 1.26

Note: Components may not sum to total due to rounding.

10-21
Table 10.6.2 Cumulative National Full-Fuel-Cycle Energy Savings for Commercial and 

Industrial Pump Trial Standard Levels for Units Sold in 2020-2049

Equipment 

Class

Trial Standard Level

1 2 3 4 5

quads

ESCC.1800 0.017 0.05 0.08 0.12 0.17

ESCC.3600 0.017 0.08 0.12 0.18 0.28

ESFM.1800 0.003 0.06 0.12 0.25 0.37

ESFM.3600 0.002 0.02 0.03 0.05 0.07

IL.1800 0.016 0.05 0.08 0.12 0.17

IL.3600 0.003 0.01 0.02 0.02 0.03

VT-S.3600 0.002 0.02 0.11 0.17 0.24

Total -- All 

Classes 0.059 0.28 0.56 0.91 1.32

Note: Components may not sum to total due to rounding.

Table 10.6.3 Cumulative Net Present Value of Customer Benefit for Commercial and 

Industrial Pump Trial Standard Levels for Units Sold in 2020-2049

Equipment 

Class

Discount 

Rate (%)

Trial Standard Level

1 2 3 4 5

billion 2013$*

ESCC.1800 3 0.052 0.20 0.29 0.40 0.47

7 0.018 0.07 0.11 0.14 0.15

ESCC.3600 3 0.069 0.34 0.46 0.68 1.06

7 0.028 0.14 0.18 0.26 0.41

ESFM.1800 3 0.010 0.20 0.44 0.88 1.28

7 0.003 0.06 0.14 0.27 0.39

ESFM.3600 3 0.009 0.08 0.14 0.20 0.30

7 0.003 0.03 0.05 0.07 0.11

IL.1800 3 0.063 0.18 0.25 0.28 0.34

7 0.022 0.06 0.08 0.07 0.07

IL.3600 3 0.011 0.04 0.06 0.08 0.11

7 0.004 0.01 0.02 0.03 0.04

VT-S.3600 3 (0.001) 0.07 0.49 0.71 0.90

7 (0.002) 0.02 0.20 0.28 0.35

Total -- All 

Classes

3 0.213 1.11 2.13 3.23 4.47

7 0.077 0.41 0.77 1.13 1.51

* Numbers in parentheses indicate negative NPV.

Note: Components may not sum to total due to rounding.

10-22
REFERENCES

1 U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 2014, 

April, 2014. Washington, DC. Report No. DOE/EIA-0383(2014). 

<www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf>.

2 United States Industrial Electric Motor Systems Market Opportunities Assessment. Tech. 

Washington DC: U.S. Department of Energy’s (DOE) Office of Energy Efficiency and 

Renewable Energy (EERE), 1998. Print.

3 Almeida, A., Chretien, B., Falkner, H., Reichert, J., West, M., Nielsen, S., and Both, D. VSDs 

for Electric Motor Systems. Tech. N.p.: European Commission Directorate-General for 

Transport and Energy, SAVE II Programme 2000, n.d. Print.

4 U.S. Census Bureau. Manufacturing Industry Series Survey and Current Industrial Reports for 

Controls for A.C. Packaged Drives. Available data from 1963 to 2012. Web. 15 July 2014. 

<http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml>.

5 Almeida, A., Chretien, B., Falkner, H., Reichert, J., West, M., Nielsen, S., and Both, D. VSDs 

for Electric Motor Systems. European Commission Directorate-General for Transport and

Energy, SAVE II Programme 2000.

6 U.S. Office of Management and Budget, Circular A-4: Regulatory Analysis, September 17, 

2003. <www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a-4.pdf>. 

10-23
APPENDIX 10A. FULL FUEL CYCLE

TABLE OF CONTENTS 

10A.1 INTRODUCTION ...................................................................................................... 10A-1

10A.2 METHODOLOGY ..................................................................................................... 10A-1

10A.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE.................................... 10A-3

REFERENCES ....................................................................................................................... 10A-4

LIST OF TABLES 

Table 10A.2.1 Dependence of FFC Parameters on AEO Inputs....................................... 10A-3

Table 10A.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014)............ 10A-3

10A-i
APPENDIX 10A. FULL FUEL CYCLE

10A.1 INTRODUCTION

This appendix summarizes the methods the U.S. Department of Energy (DOE) used to 

calculate the full-fuel-cycle (FFC) energy savings estimated from potential standards for 

commercial and industrial pumps. 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 

method of analysis previously encompassed only site energy and the energy lost through 

generation, transmission, and distribution of electricity. In 2011 DOE announced its intention, 

based on recommendations from the National Academy of Sciences, to use FFC measures of 

energy use and emissions when analyzing proposed energy conservation standards.1 This 

appendix summarizes the methods DOE used to incorporate impacts of the full fuel cycle into 

the analysis.

This analysis uses several terms to describe aspects of energy use. The physical sources 

of energy are primary fuels such as coal, natural gas, or liquid fuel. Primary energy is equal to 

the heat content (British thermal units [Btu]) of the primary fuel used to produce an end-use 

service. Site energy use is defined as the energy consumed at the point of use in a house or 

establishment. When natural gas or petroleum fuels are consumed at the site (for example in an 

on-site furnace), site energy is identical to primary energy, with both equal to the heat content of 

the primary fuel consumed.

For electricity generated by an off-site power plant, site energy is measured in kilowatthours (kWh). In such a case the primary energy is equal to the quads (quadrillion Btu) of primary 

energy required to generate and deliver electricity to the site. For the FFC analysis, 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 full-fuel-cycle analysis must distinguish between electricity generated by fossil 

fuels and uranium and electricity generated from renewable sources (wind, solar, and hydro). For 

the former, the upstream fuel cycle relates to the amount of fuel consumed at the power plant. 

There is no upstream component for the latter, because no fuel per se is used.

10A.2 METHODOLOGY

The mathematical approach to determining FCC is discussed in Coughlin (2012).2 Details 

on analyzing the fuel production chain are presented in Coughlin (2013).3 The methods used to 

calculate FFC energy use are summarized here. When all energy quantities are normalized to the 

same units, FFC energy use can be represented as the product of the primary energy use and an 

FFC multiplier. Mathematically the FFC multiplier is a function of a set of parameters that 

represent the energy intensity and material losses at each stage of energy production. Those 

parameters depend only on physical data, so the calculations require no assumptions about prices 

10A-1
or other economic factors. Although the parameter values often differ by geographic region, this 

analysis utilizes national averages. 

The fuel cycle parameters are defined as follows.

• ax is the quantity of fuel x burned per unit of electricity produced, on average, for grid 

electricity. The calculation of ax includes a factor to account for losses incurred through 

the transmission and distribution systems. 

• by is the amount of grid electricity used in producing 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 

produced).

All the parameters are calculated as functions of an annual time step; hence, when 

evaluating the effects of potential new standards, a time series of annual values is used to 

estimate the FFC energy and emissions savings in each year of the analysis period and 

cumulatively. Fossil fuel quantities are converted to energy units using the heat content factor 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, described in chapter 10. The site-topower-plant energy use factor is defined as the ratio of the total primary energy consumption by 

the electric power sector (in quads) divided by the total electricity generated each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. Also calculated is a multiplier for electricity that reflects the fuel mix used in its 

generation. The multipliers are dimensionless numbers applied to primary energy savings to 

obtain the FFC energy savings. The upstream component of the energy savings is proportional to 

(μ-1). The fuel type is denoted by a subscript on the multiplier μ.

When DOE estimates energy savings attributable to appliance standards, the method for 

performing the full-fuel-cycle analysis utilizes data and projections published in the Annual 

Energy Outlook (AEO); in the case of this rulemaking, the AEO2014.

4 Table 10A.2.1

summarizes the AEO2014 data used as inputs to the calculation of various parameters. The 

column titled "AEO Table" gives the name of the table that provided the reference data.

10A-2
Table 10A.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter(s) Fuel(s) 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 energy consumption 

by the power sector

bc, cnc, cpc Coal

Coal production by region and 

type

Coal production by 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 Domestic crude oil production

cnn Natural gas

Oil and gas supply U.S. 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

The AEO2014 does not provide all the information needed to estimate total energy use in 

the fuel production chain. Coughlin (2013) describes the additional data sources needed to 

complete the analysis. The time dependence in the FFC multipliers for pumps, however, arises 

exclusively from variables taken from the AEO2014.

10A.3 ENERGY MULTIPLIERS FOR THE FULL FUEL CYCLE

FFC energy multipliers for selected years are presented in Table 10A.3.1. The 2040 value 

was held constant for the analysis period beyond 2040, which is the last year in the AEO2014

projection. The multiplier for electricity reflects the shares of various primary fuels in total 

electricity generation throughout the forecast period. 

Table 10A.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014)

2020 2025 2030 2035 2040

Electricity 1.044 1.045 1.046 1.047 1.047

10A-3
REFERENCES 

1 U.S. Department of Energy–Office of Energy Efficiency and Renewable Energy. Energy 

Conservation Program for Consumer Products and Certain Commercial and Industrial 

Equipment. Statement of Policy for Adopting Full Fuel Cycle Analyses into Energy 

Conservation Standards Program. Federal Register. August 18, 2011. Vol. 76, no. 160: 

pp. 51281–51289.

2 Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy. 2012. 37(1): 

pp. 698–708. (Last accessed June 22, 2014.) 

<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 U.S. Energy Information Administration. Annual Energy Outlook 2014 with Projections to 

2040. April 2014. DOE/EIA-0383(2014). Washington, D.C. (Last accessed June 22, 

2014.) <www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >.

10A-4
APPENDIX 10B. NATIONAL NET PRESENT VALUE OF CUSTOMER BENEFITS 

USING ALTERNATIVE EQUIPMENT PRICE FORECASTS AND ECONOMIC 

GROWTH SCENARIOS

TABLE OF CONTENTS

10B.1 INTRODUCTION .......................................................................................................10B-1

10B.2 DESCRIPTION OF ALTERNATIVE EQUIPMENT PRICE SCENARIOS.............10B-1

10B.2.1 Decreasing Price Scenario ...............................................................................10B-2

10B.2.2 Increasing Price Scenario - Exponential Fit Approach....................................10B-2

10B.2.3 Summary..........................................................................................................10B-3

10B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS .........................10B-4

10B.4 NET PRESENT VALUE RESULTS USING ALTERNATIVE EQUIPMENT 

PRICE TRENDS AND ECONOMIC GROWTH SCENARIOS................................10B-6

REFERENCES ........................................................................................................................10B-9

LIST OF TABLES

Table 10B.2.1 Price Trend Sensitivities............................................................................10B-3

Table 10B.4.1 Cumulative NPV Results for Pumps Using Reference Equipment Price 

Forecast and AEO Reference Case Scenario.............................................10B-6

Table 10B.4.2 Cumulative NPV Results for Pumps Using Alternative Equipment 

Price Forecast (Decreasing Trend) and AEO High Economic Growth 

Case Scenario.............................................................................................10B-7

Table 10B.4.3 Cumulative NPV Results for Pumps Using Alternative Equipment 

Price Forecast (Increasing Trend) and AEO Low Economic Growth 

Case Scenario.............................................................................................10B-8

Table 10B.4.4 Comparison of Total NPV across All Equipment Classes for 

Alternative Equipment Price Forecasts and Economic Growth Cases......10B-8

LIST OF FIGURES

Figure 10B.2.1 Deflated Pump and Pumping Equipment PPI versus Year, with 

Exponential Fit from 1984 to 2013............................................................10B-3

Figure 10B.2.2 Pump Price Trend Indexes.........................................................................10B-4

Figure 10B.3.1 Electricity Price Projections for Reference Case, High, and Low 

Economic Growth Scenarios......................................................................10B-5

10B-i
APPENDIX 10B. NATIONAL NET PRESENT VALUE OF CUSTOMER BENEFITS

USING ALTERNATIVE EQUIPMENT PRICE FORECASTS AND ECONOMIC 

GROWTH SCENARIOS

10B.1 INTRODUCTION

This appendix presents National Impact Analysis (NIA) results using alternative 

equipment price forecasts and economic growth scenarios. As will be discussed below, DOE 

developed increasing and decreasing equipment price forecasts. In addition, alternative 

economic growth scenarios were utilized based on the High Economic Growth case and the Low 

Economic Growth case from Energy Information Administration’s (EIA’s) Annual Energy 

Outlook 2014 (AEO2014).1 DOE developed low and high NPV scenarios based on the 

combination of equipment price forecasts and economic growth scenarios. DOE based the low 

NPV scenario on the combination of an increasing equipment price forecast and the Low 

Economic Growth case, while the high NPV scenario was based on the combination of a 

decreasing equipment price forecast and the High Economic Growth case. Results are presented 

for the above two combinations at the end of this appendix.

10B.2 DESCRIPTION OF ALTERNATIVE EQUIPMENT PRICE SCENARIOS

DOE used a constant price assumption for the default forecast in the NIA described in 

Chapter 10. In order to investigate the impact of different equipment price forecasts on the 

consumer net present value (NPV) for the considered TSLs for pumps, 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. 

In recent rulemakings for several residential products, DOE has used the experience 

curve method to derive learning rates to forecast future prices. In the experience curve method, 

the real cost of production is related to the cumulative production, or experience, with a 

manufactured product. That experience usually is measured in terms of cumulative production. 

As experience (production) accumulates, the cost of producing the next unit decreases. The 

percentage reduction in cost that occurs with each doubling of cumulative production is known 

as the learning rate. A recent report from Lawrence Berkeley National Laboratory by Taylor and 

Fujita provides an overview of some of the major findings of the academic literature on learning 

curves, and describes the application of a component-based learning curve approach (by the 

Environmental Protection Agency and the National Highway Transportation Safety 

Administration) and a price-based learning curve approach (by DOE) in regulatory impact 

assessment.2

For some commercial and industrial equipment, there are insufficient data to apply a

price-based learning curve approach, particularly with respect to cumulative production. In such 

cases, DOE used a constant price assumption for the default forecast in the NIA, but made use of 

10B-1
price indexes that are relevant for the equipment in question to derive alternative price trends for 

a sensitivity analysis.a DOE is using this approach for pumps.

 

DOE considered two alternative price trends for a sensitivity analysis. One that assumes 

decreasing prices is based on the deflator for industrial equipment that was forecasted for 

AEO2014. The other assumes increasing prices and used an exponential fit on the deflated price 

index for “pumps and pumping equipment” during the period of 1984 to 2013.

b

10B.2.1 Decreasing Price Scenario

DOE examined a forecast based on the “chained price index—industrial equipment” that 

was forecasted for AEO2014 out to 2040. This index is the most disaggregated category that 

includes pumps. To develop an inflation-adjusted index, DOE normalized the above index with 

the “chained price index—gross domestic product” forecasted for AEO2014. To extend the price 

index beyond 2040, DOE used the average annual price growth rate in 2031 to 2040.

10B.2.2 Increasing Price Scenario - Exponential Fit Approach

DOE used an inflation-adjusted “pumps and pumping equipment” Producer Price Index 

(PPI) spanning the time period of 1984-2013 from the Bureau of Labor Statistics’ (BLS) to fit an 

exponential model with year as the explanatory variable. The PPI during this period of time 

shows a generally continuing upward trend, so the exponential fit based on this historical PPI 

represents the increasing price scenario of future price projection. The PPI data reflect nominal 

prices, adjusted for equipment quality changes. An inflation-adjusted (deflated) price index for 

“pump and pumping equipment” was calculated by dividing the PPI series by the Gross 

Domestic Product Chained Price Index. In this case, the exponential function takes the form of:

YY = aa ∙ eebbbb 

where Y is the pump 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 pump price index versus year from 1984 to 2013. See Figure 10B.2.1.

a See appendix 10C of the final rule TSD for distribution transformers. 

www.regulations.gov/#!documentDetail;D=EERE-2010-BT-STD-0048-0760 b Series ID PCU333911333911; www.bls.gov/ppi/

10B-2

 
Figure 10B.2.1 Deflated Pump and Pumping Equipment PPI versus Year, with 

Exponential Fit from 1984 to 2013

The regression performed as an exponential trend line fit results in an R-square of 0.92. 

The final estimated exponential function is:

YY = 2.25 × 10(−8) ∙ ee0.00875 

DOE then derived a price factor index for this scenario, renormalized with 2012 equal to 

1, to project prices in each future year in the analysis period considered in the NIA. The index 

value in a given year is a function of the exponential parameter and year.

10B.2.3 Summary

Table 10B.2.1 shows the summary of the average annual rates of change for the 

equipment price index in each scenario. Figure 10B.2.2 shows the resulting price trends.

Table 10B.2.1 Price Trend Sensitivities

Sensitivity Price Trend Average Annual Rate of 

Change %

Medium (Default) Constant Price Projection 0.0

Decreasing Price Scenario AEO 2014 - "chained price 

index - industrial equipment" -0.39

Increasing Price Scenario Exponential Fit using data 

from 1984 to 2013 0.88

10B-3
Figure 10B.2.2 Pump Price Trend Indexes

10B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS

To generate NIA results reported in chapter 10, DOE uses the Reference case energy 

price projections from AEO2014. The reference case is a business-as-usual estimate, given 

known market, demographic, and technological trends. For AEO2014, EIA explored the impacts 

of alternative assumptions in other scenarios with different macroeconomic growth rates, world 

oil prices, rates of technology progress, and policy changes. 

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO2014 uses 

High and Low Economic Growth scenarios to project the possible impacts of alternative 

economic growth assumptions on energy markets.3

 

Figure 10B.3.1 shows electricity price projections based on the different AEO 2014

scenarios.

10B-4
Figure 10B.3.1 Electricity Price Projections for Reference Case, High, and Low Economic 

Growth Scenarios

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

2011 2016 2021 2026 2031 2036

Price Index (2013=1)

Year

Reference

High

Low

10B-5
10B.4 NET PRESENT VALUE RESULTS USING ALTERNATIVE EQUIPMENT 

PRICE TRENDS AND ECONOMIC GROWTH SCENARIOS

This section presents the NPV results for low and high scenarios using the alternative 

equipment price projections in combination with the alternative economic growth scenarios. 

DOE based the low NPV scenario on the combination of an increasing equipment price 

projection and the AEO Low Economic Growth case, while the high NPV scenario was based on 

the combination of a decreasing equipment price projection and the AEO High Economic 

Growth case. Results are presented below for each equipment class. 

Table 10B.4.1 Cumulative NPV Results for Pumps Using Reference Equipment Price 

Forecast and AEO Reference Case Scenario

Equipment 

Class

Discount 

Rate (%)

Trial Standard Level

1 2 3 4 5

billion 2013$*

ESCC.1800 3 0.052 0.20 0.29 0.40 0.47

7 0.018 0.07 0.11 0.14 0.15

ESCC.3600 3 0.069 0.34 0.46 0.68 1.06

7 0.028 0.14 0.18 0.26 0.41

ESFM.1800 3 0.010 0.20 0.44 0.88 1.28

7 0.003 0.06 0.14 0.27 0.39

ESFM.3600 3 0.009 0.08 0.14 0.20 0.30

7 0.003 0.03 0.05 0.07 0.11

IL.1800 3 0.063 0.18 0.25 0.28 0.34

7 0.022 0.06 0.08 0.07 0.07

IL.3600 3 0.011 0.04 0.06 0.08 0.11

7 0.004 0.01 0.02 0.03 0.04

VT-S.3600 3 (0.001) 0.07 0.49 0.71 0.90

7 (0.002) 0.02 0.20 0.28 0.35

Total -- All 

Classes

3 0.213 1.11 2.13 3.23 4.47

7 0.077 0.41 0.77 1.13 1.51

* Numbers in parentheses indicate negative NPV.

Note: Components may not sum to total due to rounding.

10B-6
Table 10B.4.2 Cumulative NPV Results for Pumps Using Alternative Equipment Price 

Forecast (Decreasing Trend) and AEO High Economic Growth Case 

Scenario

Equipment 

Class

Discount 

Rate (%)

Trial Standard Level

1 2 3 4 5

billion 2013$*

ESCC.1800 3 0.064 0.23 0.35 0.49 0.59

7 0.022 0.09 0.13 0.17 0.19

ESCC.3600 3 0.081 0.40 0.55 0.81 1.26

7 0.032 0.16 0.21 0.31 0.48

ESFM.1800 3 0.012 0.24 0.52 1.05 1.54

7 0.004 0.08 0.16 0.32 0.47

ESFM.3600 3 0.010 0.09 0.16 0.24 0.35

7 0.004 0.03 0.06 0.08 0.12

IL.1800 3 0.074 0.22 0.31 0.36 0.46

7 0.026 0.07 0.10 0.10 0.11

IL.3600 3 0.013 0.04 0.08 0.10 0.13

7 0.005 0.02 0.03 0.03 0.05

VT-S.3600 3 0.000 0.09 0.57 0.84 1.07

7 (0.001) 0.03 0.23 0.33 0.41

Total -- All 

Classes

3 0.256 1.31 2.53 3.88 5.40

7 0.091 0.47 0.91 1.34 1.82

* Numbers in parentheses indicate negative NPV.

Note: Components may not sum to total due to rounding.

10B-7
Table 10B.4.3 Cumulative NPV Results for Pumps Using Alternative Equipment Price 

Forecast (Increasing Trend) and AEO Low Economic Growth Case 

Scenario

Equipment 

Class

Discount 

Rate (%)

Trial Standard Level

1 2 3 4 5

billion 2013$*

ESCC.1800 3 0.039 0.16 0.23 0.31 0.33

7 0.013 0.06 0.08 0.10 0.10

ESCC.3600 3 0.057 0.29 0.37 0.55 0.85

7 0.023 0.12 0.15 0.21 0.33

ESFM.1800 3 0.009 0.17 0.36 0.71 1.03

7 0.003 0.05 0.11 0.22 0.31

ESFM.3600 3 0.007 0.07 0.12 0.18 0.26

7 0.003 0.02 0.04 0.06 0.09

IL.1800 3 0.051 0.14 0.19 0.17 0.19

7 0.018 0.05 0.05 0.03 0.01

IL.3600 3 0.010 0.03 0.05 0.06 0.08

7 0.004 0.01 0.02 0.02 0.03

VT-S.3600 3 (0.004) 0.05 0.41 0.58 0.73

7 (0.003) 0.02 0.17 0.23 0.28

Total -- All 

Classes

3 0.169 0.91 1.73 2.56 3.46

7 0.060 0.33 0.62 0.88 1.14

* Numbers in parentheses indicate negative NPV.

Note: Components may not sum to total due to rounding.

Table 10B.4.4 Comparison of Total NPV across All Equipment Classes for Alternative 

Equipment Price Forecasts and Economic Growth Cases

Trial Standard Level

1 2 3 4 5

billion 2013$

Reference Case NPV 3% 0.213 1.11 2.13 3.23 4.47

NPV 7% 0.077 0.41 0.77 1.13 1.51

Decreasing Price Trend + 

AEO High Economic 

Growth Case

NPV 3% 0.256 1.31 2.53 3.88 5.40

NPV 7% 0.091 0.47 0.91 1.34 1.82

Increasing Price Trend + 

AEO Low Economic 

Growth Case

NPV 3% 0.169 0.91 1.73 2.56 3.46

NPV 7% 0.060 0.33 0.62 0.88 1.14

10B-8
REFERENCES

1 Energy Information Administration, Annual Energy Outlook 2014 with Projections to 2040, 

2014. Washington, DC. <www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf>.

2 Taylor, Margaret, and Sydny K. Fujita. Accounting for Technological Change in Regulatory 

Impact Analyses: The Learning Curve Technique. Berkeley: Lawrence Berkeley National 

Laboratory, 2013. LBNL-6195E

3 Energy Information Administration, Macroeconomic Activity Module for Annual Energy 

Outlook 2014, 2014. Washington, DC. 

<www.eia.gov/forecasts/aeo/assumptions/pdf/macroeconomic.pdf>.

10B-9
CHAPTER 11. LIFE-CYCLE COST SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 VFD SUBGROUP DEFINITION.................................................................................. 11-1

11.2.1 Load Profiles...................................................................................................... 11-1

11.2.2 Control Losses ................................................................................................... 11-2

11.3 RESULTS ...................................................................................................................... 11-3

LIST OF TABLES

Table 11.2.1 Load Profiles and Associated Sample Weights for the VFD Subgroup ...... 11-2

Table 11.3.1 Average Annual Energy Use by Equipment Class and Efficiency Level 

in the Base Case for the VFD Subgroup (kWh) .......................................... 11-3

Table 11.3.2 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESCC.1800................................................................................................... 11-4

Table 11.3.3 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESCC.1800......................................................................... 11-4

Table 11.3.4 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESCC.3600................................................................................................... 11-5

Table 11.3.5 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESCC.3600......................................................................... 11-5

Table 11.3.6 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESFM.1800 .................................................................................................. 11-6

Table 11.3.7 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESFM.1800 ........................................................................ 11-6

Table 11.3.8 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESFM 3600 .................................................................................................. 11-7

Table 11.3.9 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESFM.3600 ........................................................................ 11-7

Table 11.3.10 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

IL.1800......................................................................................................... 11-8

Table 11.3.11 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for IL.1800............................................................................... 11-8

Table 11.3.12 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

IL.3600......................................................................................................... 11-9

Table 11.3.13 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for IL.3600............................................................................... 11-9

Table 11.3.14 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

VT-S.3600.................................................................................................. 11-10

Table 11.3.15 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for VT-S.3600........................................................................ 11-10

11-i
Table 11.3.16 Comparison of Impacts for VFD Users with Non-VFD Users, 

ESCC.1800................................................................................................. 11-11

Table 11.3.17 Comparison of Impacts for VFD Users with Non-VFD Users, 

ESCC.3600................................................................................................. 11-11

Table 11.3.18 Comparison of Impacts for VFD Users with Non-VFD Users, 

ESFM.1800 ................................................................................................ 11-11

Table 11.3.19 Comparison of Impacts for VFD Users with Non-VFD Users, 

ESFM.3600 ................................................................................................ 11-12

Table 11.3.20 Comparison of Impacts for VFD Users with Non-VFD Users, IL.1800 ... 11-12

Table 11.3.21 Comparison of Impacts for VFD Users with Non-VFD Users, IL.3600 ... 11-12

Table 11.3.22 Comparison of Impacts for VFD Users with Non-VFD Users, VTS.3600 ........................................................................................................ 11-13

LIST OF FIGURES

Figure 11.2.1 Load Profiles Used in the LCC and Subgroup Analyses............................. 11-2

11-ii
CHAPTER 11. LIFE-CYCLE COST SUBGROUP ANALYSIS

11.1 INTRODUCTION

The customer subgroup analysis evaluates impacts on any identifiable groups of

customers who may be disproportionately affected by a national energy conservation standards. 

The U.S. Department of Energy (DOE) evaluates impacts on particular subgroups of consumers 

by performing the life-cycle cost (LCC) impacts and payback period (PBP) for the affected 

subgroup. DOE uses the LCC spreadsheet models described in Chapter 8, with inputs modified 

to reflect the particular characteristics of the subgroup under consideration. For commercial and 

industrial pumps, DOE calculated the LCC and PBP for those customers who operate their 

pumps with variable-frequency drives (VFD). Because the VFD lowers the required power input 

to the pump (relative to throttling), pump users with VFDs will typically have lower energy use 

than those without VFDs. This chapter describes the subgroup identification in further detail and 

gives the results of the LCC and PBP analyses for the VFD subgroup.

11.2 VFD SUBGROUP DEFINITION

The VFD subgroup represents pump users with controls that could lower the annual 

energy use of pumps serving variable loads. DOE analyzed impacts on those customers by using 

a system-curve based energy calculation and introducing VFD specific load profile. The 

subgroup analysis uses the same distribution of duty points and other customer characteristics, as 

described in the LCC documentation in Chapter 8.

11.2.1 Load Profiles

Load profiles are described by defining weights that specify the fraction of pump 

operating hours that are spent at each of four load points: 50 percent, 75 percent, 100 percent, 

and 110 percent of the flow at the duty point. DOE used four load profiles for the full LCC 

sample, with variable loads assumed to be controlled through throttling: 

• Flat load (LP0): the pump operates 100 percent of operating time at the duty point flow;

• Flat/over-sized (LP1): the pump operates 50 percent of operating time at the duty point 

and 50 percent at 75 percent of the duty point flow;

• Variable/over-sized (LP2): the pump operates 35 percent of time below the duty point

flow;

• Variable/under-sized (LP3): the pump operates 30% of time below the duty point and 20

percent above.

For the VFD subgroup DOE developed a fifth load profile that allows the pump to 

operate at 25 percent load: 

• VFD-specific (LP-VFD): the pump operates equally at each of four points load points, 25

percent, 50 percent, 75 percent, and 100 percent duty point.

11-1
Figure 11.2.1 illustrates these four typical loading profiles used in calculating annual 

energy use, and Table 11.2.1 summarizes the weights.

Figure 11.2.1 Load Profiles Used in the LCC and Subgroup Analyses

To construct the VFD subgroup, DOE used the original LCC sample and modified the 

assignment of load profiles. The 30 percent of pump users that had been assigned the flat load 

(LP0) in the full LCC analysis are changed to the VFD-specific load profile for the subgroup 

analysis. For the pump users originally assigned to one of the variable profiles (LP1, LP2, LP3), 

20 percent of each group are shifted to the LP-VFD load profile. This results in 50 percent of the 

LCC subgroup sample having the LP-VFD profile, with the remaining 50 percent distributed 

over the other three variable load profiles. These sample weights are summarized in the last 

column of Table 11.2.1. All load profiles in the LCC subgroup sample are assumed to be 

controlled with a VFD.

Table 11.2.1 Load Profiles and Associated Sample Weights for the VFD Subgroup

Load 

Profile

25% duty 

point

50% duty 

point

75% duty

point

100% duty 

point

110% duty 

point

Sample 

Weight

LP0 0 0 0 1 0 0

LP1 0 0 0.5 0.5 0 0.214

LP2 0 0.05 0.3 0.65 0 0.214

LP3 0 0.05 0.25 0.5 0.2 0.071

LP-VFD 0.25 0.25 0.25 0.25 0 0.5

11.2.2 Control Losses

DOE used the same method to size the motor and calculate motor losses as described in 

Chapter 8. To determine the losses of the control, DOE applied the part-load loss factor curve 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

25% duty point 50% duty point 75% duty point 100% duty point 110% duty point

LP0

LP1

LP2

LP3

LP-VFD

11-2
developed and presented in the pumps test procedure NOPR. This allows DOE to calculate 

control losses at each load point in the energy use analysis. The annual energy use is calculated 

using the equation:

AAAAAA = � QQii × HHii

ηηiinnmmmmmmmmmm+cc cc cc,ii LL LLLL PP PP PP

× OOOO OOOOOOii

where:

• Qi is the flow load point

• Hi is the head at Qi, calculated from the pump curve

• ηi is the pump efficiency calculated from the efficiency curve

• nnmmmmmmmmmm+cc cc cc,ii is the combined motor plus VFD efficiency

• OOOO OOOOOOii refers to annual hours of operation at load point i

Duty points, operating hours, and hydraulic efficiencies are the same as in the full LCC 

analysis.

11.3 RESULTS

Table 11.3.1 shows the results of the energy use analysis for each equipment class at each 

efficiency level (EL). The table provides the average annual energy use per pump. 

Table 11.3.1 Average Annual Energy Use by Equipment Class and Efficiency Level in the 

Base Case for the VFD Subgroup (kWh)

Equipment 

Class EL0 EL1 EL2 EL3 EL4 EL5

ESCC,1800 8,012 19,604 15,196 23,401 20,367 12,686 

ESCC,3600 19,397 27,169 9,657 30,012 41,499 6,840 

ESFM,1800 30,047 27,785 24,589 22,831 33,412 17,459 

ESFM,3600 59,247 38,621 37,660 43,476 46,703 24,909 

IL,1800 5,578 13,780 32,130 20,863 10,724 8,764 

IL,3600 10,990 10,579 16,961 30,912 35,003 8,804 

VT-S,3600 9,490 7,157 20,572 12,693 27,943 6,488 

Table 11.3.2 through Table 11.3.15 show the LCC and PBP results for each equipment 

class. In general, the average LCC savings are positive for nearly all equipment classes and 

efficiency levels, with the exception of VT-S.3600, which has negative LCC savings for the first 

11-3
two efficiency levels, and IL.1800, which has negative LCC savings for the last two efficiency 

levels. 

As shown in Table 11.3.16 through Table 11.3.22, the results of the life-cycle cost 

subgroup analysis indicate that for all equipment classes analyzed, the average LCC savings for 

the VFD subgroup is lower, and the payback period is longer, than for the full LCC. This is due 

to the lower power use of a pump with a VFD, which leads to lower energy savings from pump 

efficiency improvements.

Table 11.3.2 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESCC.1800

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,639 $1,447 $11,186 $12,824 -- 13

1 1 $1,672 $1,441 $11,141 $12,813 5.6 13

2 2 $1,704 $1,428 $11,050 $12,754 3.6 13

3 3 $1,768 $1,417 $10,965 $12,733 4.4 13

4 4 $1,863 $1,403 $10,857 $12,720 5.2 13

5 5 $2,026 $1,387 $10,735 $12,761 6.5 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

Table 11.3.3 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESCC.1800

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 15% $12

2 2 17% $71

3 3 32% $91

4 4 41% $104

5 5 55% $63

* The calculation includes customers with zero LCC savings (no impact).

11-4
Table 11.3.4 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESCC.3600

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,092 $1,072 $6,602 $7,694 -- 11

1 1 $1,098 $1,070 $6,588 $7,686 2.3 11

2 2 $1,111 $1,061 $6,533 $7,644 1.6 11

3 3 $1,141 $1,055 $6,496 $7,637 2.8 11

4 4 $1,170 $1,046 $6,442 $7,611 3.0 11

5 5 $1,215 $1,032 $6,352 $7,567 3.0 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

Table 11.3.5 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESCC.3600

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 1.4% $8.7

2 2 2.9% $51

3 3 16% $57

4 4 17% $83

5 5 18% $127

* The calculation includes customers with zero LCC savings (no impact).

11-5
Table 11.3.6 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESFM.1800

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,891 $2,207 $26,429 $28,320 -- 23

1 1 $1,893 $2,207 $26,423 $28,316 3.9 23

2 2 $1,943 $2,196 $26,292 $28,235 4.6 23

3 3 $2,004 $2,183 $26,131 $28,135 4.6 23

4 4 $2,151 $2,157 $25,815 $27,966 5.1 23

5 5 $2,314 $2,132 $25,512 $27,826 5.6 23

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

Table 11.3.7 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESFM.1800

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 0.4% $4.3

2 2 9.5% $85

3 3 20% $186

4 4 32% $355

5 5 37% $494

* The calculation includes customers with zero LCC savings (no impact).

11-6
Table 11.3.8 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

ESFM 3600

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,349 $3,437 $33,340 $34,688 -- 20

1 1 $1,357 $3,433 $33,298 $34,655 2.0 20

2 2 $1,396 $3,400 $32,973 $34,369 1.3 20

3 3 $1,441 $3,371 $32,689 $34,131 1.4 20

4 4 $1,529 $3,337 $32,357 $33,886 1.8 20

5 5 $1,648 $3,287 $31,872 $33,520 2.0 20

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

Table 11.3.9 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for ESFM.3600

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 0.7% $33

2 2 3.4% $319

3 3 8.0% $558

4 4 13% $802

5 5 14% $1,168

* The calculation includes customers with zero LCC savings (no impact).

11-7
Table 11.3.10 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

IL.1800

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $2,128 $1,207 $10,684 $12,811 -- 16

1 1 $2,145 $1,203 $10,640 $12,786 3.6 16

2 2 $2,194 $1,193 $10,550 $12,744 4.5 16

3 3 $2,281 $1,183 $10,466 $12,747 6.4 16

4 4 $2,432 $1,173 $10,373 $12,805 8.8 16

5 5 $2,614 $1,158 $10,243 $12,857 9.9 16

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

Table 11.3.11 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for IL.1800

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 3.2% $26

2 2 11% $67

3 3 21% $64

4 4 35% $6.3

5 5 47% $(46)

* The calculation includes customers with zero LCC savings (no impact).

11-8
Table 11.3.12 VFD Subgroup Average LCC and PBP Results by Efficiency Level for 

IL.3600

TSL Efficiency 

Level

Average Costs 

2013$ Simple 

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $1,473 $1,341 $9,347 $10,820 -- 13

1 1 $1,484 $1,336 $9,312 $10,795 2.2 13

2 2 $1,525 $1,325 $9,228 $10,753 3.1 13

3 3 $1,578 $1,311 $9,131 $10,709 3.5 13

4 4 $1,650 $1,300 $9,057 $10,707 4.3 13

5 5 $1,797 $1,279 $8,911 $10,708 5.2 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

Table 11.3.13 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for IL.3600

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 3.5% $25

2 2 17% $67

3 3 18% $111

4 4 23% $113

5 5 32% $112

* The calculation includes customers with zero LCC savings (no impact).

11-9
Table 11.3.14 VFD Subgroup Average LCC and PBP Results by Efficiency Level for VTS.3600

TSL Efficiency 

Level

Average Costs 

2013$ Simple

Payback

years

Average 

Lifetime

years Installed 

Cost

First Year’s 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

-- Base Case $692 $737 $4,223 $4,914 -- 11

1 1 $697 $737 $4,221 $4,918 18 11

2 2 $711 $734 $4,206 $4,917 6.6 11

3 3 $732 $722 $4,139 $4,871 2.7 11

4 4 $772 $714 $4,093 $4,865 3.5 11

5 5 $821 $706 $4,047 $4,869 4.2 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base 

case. 

Table 11.3.15 VFD Subgroup LCC Savings Relative to the Base Case Efficiency 

Distribution for VT-S.3600

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers that 

Experience Average Savings*

Net Cost 2013$

1 1 1.5% $(3.5)

2 2 30% $(2.6)

3 3 9.9% $44

4 4 17% $50

5 5 23% $46

* The calculation includes customers with zero LCC savings (no impact).

11-10
Table 11.3.16 Comparison of Impacts for VFD Users with Non-VFD Users, ESCC.1800

TSL Energy 

Efficiency 

Level

LCC Savings

2013$*

Simple Payback Period

years

VFD-Users Non-VFD 

Users VFD-Users Non-VFD Users

1 1 $12 $43 5.6 3.3

2 2 $71 $164 3.6 2.2

3 3 $91 $240 4.4 2.6

4 4 $104 $324 5.2 3.1

5 5 $63 $362 6.5 3.9

*Parentheses indicate negative values.

Table 11.3.17 Comparison of Impacts for VFD Users with Non-VFD Users, ESCC.3600

TSL Energy 

Efficiency 

Level

LCC Savings

2013$*

Simple Payback Period

years

VFD-Users Non-VFD 

Users VFD-Users Non-VFD Users

1 1 $8.7 $17 2.3 1.4

2 2 $51 $92 1.6 1.0

3 3 $57 $122 2.8 1.8

4 4 $83 $180 3.0 1.9

5 5 $127 $278 3.0 1.9

*Parentheses indicate negative values.

Table 11.3.18 Comparison of Impacts for VFD Users with Non-VFD Users, ESFM.1800

TSL Energy 

Efficiency 

Level

LCC Savings

2013$*

Simple Payback Period

years

VFD-Users Non-VFD 

Users VFD-Users Non-VFD Users

1 1 $4.3 $8.0 3.9 2.4

2 2 $85 $173 4.6 2.8

3 3 $186 $372 4.6 2.8

4 4 $355 $735 5.1 3.1

5 5 $494 $1,062 5.6 3.4

*Parentheses indicate negative values.

11-11
Table 11.3.19 Comparison of Impacts for VFD Users with Non-VFD Users, ESFM.3600

TSL Energy 

Efficiency 

Level

LCC Savings

2013$*

Simple Payback Period

years

VFD-Users Non-VFD 

Users VFD-Users Non-VFD Users

1 1 $33 $58 2.0 1.2

2 2 $319 $547 1.3 0.8

3 3 $558 $961 1.4 0.9

4 4 $802 $1,411 1.8 1.1

5 5 $1,168 $2,078 2.0 1.2

*Parentheses indicate negative values.

Table 11.3.20 Comparison of Impacts for VFD Users with Non-VFD Users, IL.1800

TSL Energy 

Efficiency 

Level

LCC Savings

2013$*

Simple Payback Period

years

VFD-Users Non-VFD 

Users VFD-Users Non-VFD Users

1 1 $26 $51 3.6 2.3

2 2 $67 $149 4.5 2.8

3 3 $64 $200 6.4 3.9

4 4 $6.3 $202 8.8 5.4

5 5 ($46) $234 9.9 6.1

*Parentheses indicate negative values.

Table 11.3.21 Comparison of Impacts for VFD Users with Non-VFD Users, IL.3600

TSL Energy 

Efficiency 

Level

LCC Savings

2013$*

Simple Payback Period

years

VFD-Users Non-VFD 

Users VFD-Users Non-VFD Users

1 1 $25 $46 2.2 1.4

2 2 $67 $139 3.1 1.9

3 3 $111 $241 3.5 2.1

4 4 $113 $288 4.3 2.7

5 5 $112 $377 5.2 3.2

*Parentheses indicate negative values.

11-12
Table 11.3.22 Comparison of Impacts for VFD Users with Non-VFD Users, VT-S.3600

TSL Energy 

Efficiency 

Level

LCC Savings

2013$*

Simple Payback Period

years

VFD-Users Non-VFD 

Users VFD-Users Non-VFD Users

1 1 $(3.5) $(2.4) 18 11

2 2 $(2.6) $7.2 6.6 4.2

3 3 $44 $91 2.7 1.7

4 4 $50 $123 3.5 2.2

5 5 $46 $144 4.2 2.7

*Parentheses indicate negative values.

11-13
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 ............................................................................. 12-1

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........................................................................... 12-2

12.2.3 Phase III: Manufacturer Subgroup Analysis............................................... 12-3

12.2.3.1 Manufacturing Interviews............................................................. 12-3

12.2.3.2 Revised Industry Cash-Flow Analysis.......................................... 12-3

12.2.3.3 Manufacturer Subgroup Analysis................................................. 12-3

12.2.3.4 Manufacturing Capacity Impact ................................................... 12-4

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 Alignment with European Union Efficiency Standards ............................. 12-5

12.3.2 Pattern Production and Engineering Constraints........................................ 12-6

12.3.3 Conversion Requirements........................................................................... 12-6

12.3.4 Exclusion of Specific Pump Types............................................................. 12-6

12.4 GRIM INPUTS AND ASSUMPTIONS............................................................ 12-6

12.4.1 Overview of the GRIM............................................................................... 12-7

12.4.2 Sources for GRIM Inputs............................................................................ 12-7

12.4.2.1 Corporate Annual Reports............................................................ 12-7

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............................................................... 12-8

12.4.3 Financial Parameters................................................................................... 12-8

12.4.4 Corporate Discount Rate............................................................................. 12-9

12.4.5 Trial Standard Levels................................................................................ 12-11

12.4.6 NIA Shipments ......................................................................................... 12-11

12.4.6.1 Shipments Forecast..................................................................... 12-12

12.4.7 Production Costs....................................................................................... 12-13

12.4.8 Product and Capital Conversion Costs..................................................... 12-14

12.4.9 Markup Scenarios..................................................................................... 12-15

12.4.9.1 Flat Markup Scenario ................................................................. 12-16

12.4.9.2 Cost Recovery Scenario.............................................................. 12-17

12.5 INDUSTRY FINANCIAL IMPACTS ............................................................ 12-18

12.5.1 Introduction............................................................................................... 12-19

12.5.2 Pumps Industry Financial Impacts............................................................ 12-20

12.6 IMPACTS ON SMALL BUSINESS MANUFACTURERS........................... 12-22

12.7 OTHER IMPACTS.......................................................................................... 12-24

12-i
12.7.1 Employment.............................................................................................. 12-25

12.7.1.1 Methodology............................................................................... 12-25

12.7.1.2 Direct Employment Impacts....................................................... 12-25

12.7.2 Production Capacity.................................................................................. 12-26

12.7.3 Labeling .................................................................................................... 12-26

12.7.4 Cumulative Regulatory Burden ................................................................ 12-26

12.8 CONCLUSION................................................................................................ 12-27

12.8.1 Conclusions for Pumps............................................................................. 12-27

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 for Pumps.................................................... 12-9

Table 12.4.2 Cost of Equity Calculation...................................................................... 12-10

Table 12.4.3 Cost of Debt Calculation......................................................................... 12-11

Table 12.4.4 Trial Standard Levels for Energy Efficiency Analysis of Pumps........... 12-11

Table 12.4.5 Total Base Case NIA Shipments for Pumps (in Units Shipped) ............ 12-12

Table 12.4.6 Manufacturer Production Cost Breakdown ($2013) for ESCC Pumps.. 12-13

Table 12.4.7 Manufacturer Production Cost Breakdown ($2013) for ESFM Pumps.. 12-13

Table 12.4.8 Manufacturer Production Cost Breakdown ($2013) for IL Pumps ........ 12-13

Table 12.4.9 Manufacturer Production Cost Breakdown ($2013) for VTS Pumps..... 12-14

Table 12.4.10 Cumulative Conversion Cost by Efficiency Level ($M)...................... 12-15

Table 12.4.11 Base Case Markup at Each Efficiency Level........................................ 12-16

Table 12.4.12 Total Industry Conversion Costs at Each Efficiency Level.................. 12-17

Table 12.4.13 Cost Recovery Annuity Payment at Each Efficiency Level................. 12-17

Table 12.4.14 Additional MSP Per Model Requiring a Redesign at Each Efficiency Level

...................................................................................................................................... 12-18

Table 12.4.15 Standards Case Cost Recovery Markups at Each Efficiency Level ..... 12-18

Table 12.4.16 Simple Payback Period for Industry Conversion Costs........................ 12-18

Table 12.5.1 Flat Markup Scenario Changes in INPV for Pumps............................... 12-20

Table 12.5.2 Cost Recovery Markup Scenario Changes in INPV for Pumps............. 12-20

Table 12.6.1 Impacts of Conversion Costs on an Average Small versus Large 

Manufacturer at the Proposed Standard Level............................................................. 12-24

Table 12.7.1 Potential Changes in Total Domestic Production Workers for Pumps in 2020

...................................................................................................................................... 12-25

Table 12.8.1 Trial Standard Levels for Pumps............................................................ 12-27

Table 12.8.2 Manufacturer Impact Analysis Results for Pumps................................. 12-27

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow................................................ 12-7

Figure 12.5.1 Annual Industry Net Cash Flows for Pumps (Flat Markup Scenario) .. 12-21

12-ii
Figure 12.5.2 Annual Industry Net Cash Flows for Pumps (Cost Recovery Markup 

Scenario) ...................................................................................................................... 12-22

12-iii
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. 6313(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 energy conservation standards on manufacturers of pumps, 

and assessed the impact of such standards on direct employment and manufacturing capacity. 

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 each product 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 each product 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 product characteristics, manufacturer characteristics, market and product 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 pumps industry, including data on sales volumes, 

pricing, employment, and financial structure. In Phase II, “Industry Cash Flow,” DOE used the 

GRIM to assess the impacts of new energy conservation standards on manufacturers. DOE also 

developed interview guides to gather information on the potential impacts on these 

manufacturers. In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers 

representing a broad cross-section of the pumps industry. Using information from Phase II, DOE 

refined its analysis in the GRIM, developed additional analyses for sub-groups that required 

special consideration, and incorporated qualitative data from interviews into its analysis.

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the pumps industry that built upon the 

market and technology assessment prepared for this rulemaking (refer to chapter 3 of the TSD).

Before initiating the detailed impact studies, DOE collected information on the market 

characteristics of the pumps industry. This information included product shipments, 

manufacturer markups, and the cost structure for various manufacturers. The industry profile 

includes: (1) further detail on the overall market and product characteristics; (2) financial 

parameters such as net plant, property, and equipment; selling, general and administrative 

(SG&A) expenses; cost of goods sold, etc.; and (3) trends in the number of firms, market, and 

product characteristics. The industry profile included a top-down cost analysis of pumps

manufacturers that DOE used to derive the preliminary financial inputs for the GRIM (e.g.,

revenues, depreciation, SG&A, and research and development (R&D) expenses).

12-1
DOE also used public information to further calibrate its initial characterization of the 

industry, including Securities and Exchange Commission (SEC) 10–K reports,1 Standard & 

Poor’s (S&P) stock reports,2 industry trade association membership directories (e.g., the 

Hydraulic Institute), market research tools (e.g., Hoovers reports), corporate annual reports, and 

the U.S. Census Bureau’s 2011 Annual Survey of Manufacturers (2011 ASM).3 DOE also 

characterized these industries using information from its engineering analysis and the life-cycle 

cost analysis.

12.2.2 Phase II: Industry Cash-Flow Analysis and Interview Guide

Phase II focused on the financial impacts of potential energy conservation standards on 

manufacturers of pumps. 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 cash-flow analyses for

pumps manufacturers. In performing these analyses, DOE used the financial values derived 

during Phase I and the shipment scenarios used in the NIA. 

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 energy conservation standards until thirty years after the standard’s 

effective date. These factors include annual expected revenues, costs of goods sold, SG&A, 

taxes, and capital expenditures related to the new standards. Inputs to the GRIM include 

manufacturing production costs, markup assumptions, 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 each GRIM based on discussions with manufacturers. DOE’s shipments analysis, 

presented in chapter 9 of the 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 

provides projections for both the base case and the standards case. The financial impact of new

energy conservation standards is the difference between the discounted annual cash flows in the 

base case and standards case INPV for each TSL.

12.2.2.2 Interview Guides

During Phase II of the MIA, DOE interviewed manufacturers to gather information on 

the effects of an energy conservation standard on revenues and finances, direct employment, 

capital assets, and industry competitiveness. Before the interviews, DOE distributed an interview 

guide to interviewees. The interview guide provided a starting point for identifying relevant 

issues and impacts of new energy conservation standards on individual manufacturers or 

subgroups of manufacturers. Most of the information received from these meetings is protected 

by non-disclosure agreements and resides with DOE’s contractors. The MIA interview topics 

included (1) key issues to this rulemaking; (2) a company overview and organizational 

characteristics; (3) engineering and life cycle cost analysis follow-up; (4) manufacturer markups 

12-2
and profitability; (5) shipment projections and market shares; (6) financial parameters; (7) 

conversion costs; (8) cumulative regulatory burden; (9) direct employment impact assessment; 

(10) exports, foreign competition, and outsourcing; (11) consolidation; and (12) impacts on small 

businesses.

12.2.3 Phase III: Manufacturer Subgroup Analysis

In Phase III, “Subgroup Impact Analysis,” DOE interviewed pumps manufacturers. DOE 

interviewed a range of manufacturers, including small and large companies as well as 

manufacturers of every covered product class. The interviews provided DOE with valuable 

information for evaluating the impacts of energy conservation standards on manufacturer cash 

flows, investment requirements, and employment. Using information from Phase II and from the 

interviews, DOE refined its analysis for the equipment classes included in the GRIM. 

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 sought to obtain feedback from industry on the approaches used in the 

GRIMs and to isolate key issues and concerns. 

DOE used these interviews to tailor the GRIM to reflect unique financial characteristics 

of each product group. 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 interactive interviews, which help clarify responses and identify 

additional issues. The resulting information provides valuable inputs to the GRIM developed for 

the equipment classes. The interviewed manufacturers account for approximately 40 percent of 

the domestic CIP market.

12.2.3.2 Revised Industry Cash-Flow Analysis

In Phase III 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 model based on this 

feedback. Section 12.4.3 provides more information on how DOE calculated the parameters.

12.2.3.3 Manufacturer Subgroup Analysis 

Using average cost assumptions to develop an industry-cash-flow estimate may not 

adequately assess differential impacts of energy conservation standards among manufacturer 

subgroups. For example, small manufacturers, niche players, or manufacturers exhibiting a cost 

structure that largely differs from the industry average could be more negatively affected. To 

address this possible impact, DOE used the results of the industry characterization analysis in 

Phase I to group manufacturers that exhibit similar characteristics. 

Small-Business Manufacturer Subgroup

12-3
DOE investigated whether small business manufacturers should be analyzed as a 

manufacturer subgroup. DOE used the Small Business Administration (SBA) small business size 

standards effective on July 14, 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.4 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

Pump and Pumping Equipment 

Manufacturing N/A 500 333911

DOE used publicly available and proprietary information to identify potential small 

manufacturers. DOE’s research involved industry trade association membership directories 

(including the Hydraulic Institute), industry conference exhibitor lists, individual company and 

buyer guide websites, and market research tools (e.g., Hoovers.com) to create a list of companies

that manufactures or sells products 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 screened out companies 

that did not offer products covered by this rulemaking, did not meet the definition of a “small 

business,” or are foreign owned and operated. DOE notes that manufacturers interviewed stated 

that there are potentially a large number of small pumps manufacturers that serve small regional 

markets. These small manufacturers are not members of HI and typically have a limited 

marketing presence. The interviewed manufacturers and those that participated in the CIP 

working group were not able to provide exact names for these smaller players.

Based on this analysis DOE identified 86 manufacturers of covered pump products sold 

in the U.S., 39 of these manufacturers met the 500-employee threshold defined by the SBA to 

qualify as a small business. Finally, DOE determined that 25 of these small manufacturers were 

domestic companies. Based on interviews and industry research, DOE reports the potential 

impacts of this rulemaking on small manufacturers in section 12.6.

12.2.3.4 Manufacturing Capacity Impact

One significant outcome of new energy conservation standards could be the obsolescence 

of existing manufacturing assets. The manufacturer interview guides have a series of questions to 

help identify impacts of new standards on manufacturing capacity, specifically capacity 

utilization and plant location decisions in the United States and North America, with and without 

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 onetime 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.

12-4
12.2.3.5 Employment Impact 

The impact of 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 pumps industry. The 

interviews also solicited manufacturer views on changes in employment patterns that may result 

from standards. 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 energy 

conservation standards and other regulatory actions affecting the same products. DOE analyzed 

the impact on manufacturers of multiple, product-specific regulatory actions. Discussion of the 

cumulative regulatory burden can be found in section 12.7.4. 

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 feel 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. 

12.3.1 Alignment with European Union Efficiency Standards

Multiple manufacturers emphasized the importance of harmonizing U.S. energy 

conservation standards with existing European Union standards for clean water pumps. 

Manufacturers stated that harmonized standards would promote regulatory consistency and 

would enable them to better coordinate product redesigns and reduce conversion costs. If U.S. 

and EU standards are not harmonized, some manufacturers noted they would have to carry a 

greater number of product lines to service separate markets or to comply with efficiency 

standards in both domestic and European markets. Manufacturers also indicated harmonized 

standards could help to improve U.S. manufacturers’ access to foreign markets and would help to 

preempt a situation wherein less stringent domestic standards enable EU-compliant 

manufacturers to market their pumps to U.S. consumers as more efficient than pumps 

manufactured domestically. Manufacturers noted that expansion beyond the EU Directive 

parameters will add complexity and cost to the tasks of the manufacturers and create a significant 

financial burden for manufacturers to gain compliance, specifically related to double-suction 

pumps and vertical turbines beyond 6-inch bowl assemblies. 

In contrast, one manufacturer also stated that aligning US standards with EU standards 

would give European manufacturers an advantage because of the fact that they would have 

products that could immediately be compliant and US manufacturers would have conversion 

costs to achieve new efficiency.

12-5
12.3.2 Pattern Production and Engineering Constraints

Many manufacturers raised concerns regarding potential tooling bottlenecks. In general, 

much of the industry relies on the same resources for patterns used to produce the impeller and 

bowl. Manufacturers were concerned there would not be enough pattern production capacity 

available if the entire industry attempted to redesign products within the same 3-5 year 

timeframe. Furthermore, manufacturers expressed concern surrounding insufficient availability 

of engineering resources (mainly design engineers) required to redesign a high volume of pump 

lines during a short time period. Manufacturers stated that limited pump design expertise in the 

industry could create time delays in complying with new standards.

12.3.3 Conversion Requirements

Manufacturers raised concerns over potentially significant barriers to achieving 

compliance with new standards, particularly at more stringent efficiency levels. If U.S. standards 

exceeded levels comparable to a European Union minimum efficiency index (MEI)a of 0.4, 

several manufacturers indicated they would have to develop “clean sheet” product platforms at 

significant cost. At an MEI of 0.7, many indicated they would close manufacturing facilities 

rather than upgrade them to comply with efficiency standards. Additionally, manufacturers 

suggested that conversion requirements would likely accelerate trends toward industry 

consolidation, as smaller manufacturers elect to exit the market rather than invest in product 

redesigns.

12.3.4 Exclusion of Specific Pump Types

Manufacturers expressed concern over which pumps would be included in the 

rulemaking. Two manufacturers further raised concerns over including circulator pumps in this 

rulemaking. Manufacturers stated that compared to the European market, the U.S. market for 

circulators is very small and therefore, it is not a large opportunity to save energy. Manufacturers 

also stated that the investment required by U.S. circulator manufacturers will be too high and that 

they would have a very limited possibility for a solid return on investment. They also mentioned 

that in most situations, due to the higher cost of high-efficiency product and the relatively low 

cost of energy in the U.S., consumers would not see a return on investment for a long period of 

time.

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to new

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 energy conservation standards.

a The EU sets efficiency standards based on desired percentages of the market to cut off, which they refer to as 

minimum efficiency indexes, or MEIs. A MEI of 0.4, for example, indicates an efficiency standard designed to 

eliminate the least efficient 40 percent of products from the market. 

12-6
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, 2015, and continuing 

to 2049. The model calculates the INPV by summing the stream of annual discounted cash flows 

during this period and adding a discounted terminal value.

5

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 scenario induced by new energy 

conservation standards. The difference in INPV between the base case and the standard case(s) 

represents the estimated financial impact of the new energy conservation standard on 

manufacturers. 

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 manufacture pumps. Since these 

companies do not provide detailed information about their individual product lines, DOE used 

the financial information for the entire companies 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 manufacturing for each product grouping. DOE used corporate 

annual reports to derive the following initial inputs to the GRIM:

12-7
• 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 

national impact analysis (NIA). Chapter 9 of the TSD describes the methodology and analytical 

model DOE used to forecast shipments.

12.4.2.4 Engineering Analysis 

During the engineering analysis, DOE used a manufacturing cost model to develop MPC 

estimates. The analysis provided the labor, materials, overhead, and total production costs for 

different design option for pumps. The engineering analysis also estimated a manufacturer 

markup and a shipping cost to provide the manufacturer selling price (MSP) for design option. 

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:

• Conversion costs (including capital conversion costs, one-time investments in 

PPE, and product conversion costs, one-time investments in research, product 

development, testing, and marketing);

• Product cost structure, or the portion of the MPCs related to materials, labor, 

overhead, and depreciation costs;

• MPCs estimated in the engineering analysis; and

• Possible profitability impacts.

12.4.3 Financial Parameters

Table 12.4.1 provides financial parameters for manufacturers of pumps. 

12-8
Table 12.4.1 GRIM Financial Parameters for Pumps

Parameter Estimate

Tax Rate 

(% of Taxable Income) 32.0%

Working Capital

(% of Revenue) 18.6%

SG&A 

(% of Revenue) 21.6%

R&D 

(% of Revenues) 1.6%

Depreciation 

(% of Revenues) 2.6%

Capital Expenditures 

(% of Revenues) 2.4%

While most of these companies also manufacturer products not covered by this rulemaking, DOE 

used these parameters as initial estimates. During interviews, 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 GRIM using manufacturer feedback and market share information.

Additionally, based on manufacturer feedback, DOE analyzed pumps.

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 pumps 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) 

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 

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.

12-9
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 calculated that the industry average cost of equity for the pumps industry is 13.78

percent (Table 12.4.2).

Table 12.4.2 Cost of Equity Calculation

Parameter

Industry 

Weighted 

Average

(1) Average Beta 1.40

(2) Yield on 10-Year

(1927-2011) 5.20

(3) Market Risk 

Premium (1927-2011) 6.01

Cost of Equity 

(2)+[(1)*(3)] 13.78

Equity/Total Capital 67.96%

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 five public 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 1927 and 

2011.

For the cost of debt S&P’s Credit Services provided the average spread of corporate 

bonds for the five manufacturers for which data was available. DOE added the industryweighted average spread to the average T-Bill yield over the same period. 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 12.4.3 presents the 

derivation of the cost of debt and the capital structure of the industry (i.e. the debt ratio 

(debt/total capital)).

12-10
Table 12.4.3 Cost of Debt Calculation

Parameter

Industry 

Weighted 

Average

S&P Bond Rating

(2) Yield on 10-Year

(1927-2011) 5.20

(2) Gross Cost of Debt 7.25

(3) Tax Rate 27.39

Net Cost of Debt

(2) x [1-(3)] 5.26

Debt/Total Capital 32.04%

Using public information for these five companies, the initial estimate for the industry’s

inflation-adjusted WACC and the initial estimate of the discount rate used in the straw-man 

GRIM is 11.8 percent. DOE also asked for feedback on the discount rate during manufacturer 

interviews.

12.4.5 Trial Standard Levels 

DOE developed a number of efficiency levels for each type of equipment class. TSLs 

were then developed by selecting likely groupings of efficiency levels for all equipment types. 

Each TSL includes combinations of efficiency levels for pumps. Table 12.4.4 presents the TSLs 

used for energy efficiency analysis in the GRIM. The GRIM analysis does not differentiate 

pumps by speed.

Table 12.4.4 Trial Standard Levels for Energy Efficiency Analysis of Pumps

Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

VTS EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

RSV* EL 0 EL 0 EL 0 EL 0 EL 0 EL 0

*Equipment classes not analyzed due to lack of available data (in the case of RSV).

12.4.6 NIA Shipments

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 applied the 

NIA shipments forecasts. 

Chapter 9 of the TSD explains DOE’s calculations of total shipments in detail. Table 

12.4.5 shows base case shipments forecasted in the shipment analysis for the pumps rulemaking. 

12-11
Table 12.4.5 Total Base Case NIA Shipments for Pumps (in Units Shipped)

Year ESCC ESFM IL VTS

2016 189462 48667 55719 115371

2017 193035 49585 56770 117546

2018 195949 50334 57627 119321

2019 198763 51056 58455 121034

2020 201722 51816 59325 122836

2021 204727 52588 60209 124666

2022 207795 53376 61111 126534

2023 210957 54189 62041 128460

2024 213972 54963 62928 130296

2025 217199 55792 63877 132260

2026 220386 56611 64814 134201

2027 223309 57362 65674 135982

2028 225970 58045 66456 137602

2029 228608 58723 67232 139208

2030 231383 59435 68048 140898

2031 234108 60136 68850 142557

2032 236564 60766 69572 144053

2033 239230 61451 70356 145676

2034 242314 62243 71263 147554

2035 245467 63053 72190 149474

2036 248717 63888 73146 151453

2037 251989 64729 74108 153446

2038 255141 65538 75035 155365

2039 258097 66298 75905 157165

2040 261033 67052 76768 158953

2041 265000 68071 77935 161369

2042 268081 68862 78841 163245

2043 271161 69653 79747 165120

2044 274242 70445 80653 166996

2045 277323 71236 81559 168872

2046 280403 72027 82465 170748

2047 283484 72819 83371 172624

2048 286564 73610 84277 174500

2049 289645 74401 85183 176376

12.4.6.1 Shipments Forecast

As part of the shipments analysis, DOE estimated the base-case shipment distribution by 

efficiency level for pumps. 

12-12
In the standards case, the shipments analysis assumes a roll-up scenario, where all 

shipments in the base case that do not meet the standard would instead ship at the new standard 

level. The key assumptions and methodology used to forecast shipments can be found in chapter 

9 of the TSD.

12.4.7 Production Costs

The NIA shipments estimated the number of units at various representative capacity 

points. The GRIM used the manufacturing production costs from the engineering analysis and

the NIA Shipments to calculate shipment-weighted average MPC for each equipment class. 

Additionally, the GRIM used data from manufacturer interviews to determine labor, materials, 

overhead, and depreciation percentages that constitute the full MPC. 

Table 12.4.6 through Table 12.4.9 show the production cost estimates used in the GRIM 

for each analyzed equipment class.

Table 12.4.6 Manufacturer Production Cost Breakdown ($2013) for ESCC Pumps 

Labor Material Overhead Depreciation MPC

Baseline $38.00 $410.03 $58.08 $16.87 $522.98

EL 1 $38.00 $410.03 $58.08 $16.87 $522.98

EL 2 $38.00 $410.03 $58.08 $16.87 $522.98

EL 3 $38.00 $410.03 $58.08 $16.87 $522.98

EL 4 $38.00 $410.03 $58.08 $16.87 $522.98

EL 5 $38.00 $410.03 $58.08 $16.87 $522.98

Table 12.4.7 Manufacturer Production Cost Breakdown ($2013) for ESFM Pumps

Labor Material Overhead Depreciation MPC

Baseline $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 1 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 2 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 3 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 4 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 5 $84.46 $991.60 $125.95 $39.15 $1,241.16

Table 12.4.8 Manufacturer Production Cost Breakdown ($2013) for IL Pumps

Labor Material Overhead Depreciation MPC

Baseline $67.24 $786.74 $105.69 $32.26 $991.92

EL 1 $67.24 $786.74 $105.69 $32.26 $991.92

EL 2 $67.24 $786.74 $105.69 $32.26 $991.92

EL 3 $67.24 $786.74 $105.69 $32.26 $991.92

EL 4 $67.24 $786.74 $105.69 $32.26 $991.92

EL 5 $67.24 $786.74 $105.69 $32.26 $991.92

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Table 12.4.9 Manufacturer Production Cost Breakdown ($2013) for VTS Pumps

Labor Material Overhead Depreciation MPC

Baseline $44.65 $253.18 $21.14 $9.32 $328.29

EL 1 $44.65 $253.18 $21.14 $9.32 $328.29

EL 2 $44.65 $253.18 $21.14 $9.32 $328.29

EL 3 $44.65 $253.18 $21.14 $9.32 $328.29

EL 4 $44.65 $253.18 $21.14 $9.32 $328.29

EL 5 $44.65 $253.18 $21.14 $9.32 $328.29

12.4.8 Product and Capital Conversion Costs

Energy conservation standards typically cause manufacturers to incur one-time 

conversion costs to bring their production facilities and product designs into compliance with 

new regulations. For the MIA, DOE classified these one-time conversion costs into two major 

groups: capital conversion costs and product conversion costs. Capital conversion costs are onetime investments in plant, property, and equipment to adapt or change existing production 

facilities and tooling in order to fabricate and assemble new product design that comply with 

energy conservation standards. Product conversion costs are one-time investments in research, 

development, testing, marketing and other costs to make product designs comply with energy 

conservation standards. DOE based its estimates of the conversion costs for each efficiency level 

on information obtained from manufacturer interviews and the design pathways analyzed in the 

engineering analysis. 

DOE expects that hydraulic redesigns will result in significant conversion costs for 

manufacturers as they attempt to bring their pumps into compliance with the proposed standard.

To evaluate the magnitude of the conversion costs the pump industry would incur to comply 

with new energy conservation standards, DOE used a bottoms-up approach. For this bottoms-up 

approach, DOE first determined the industry-average cost, per model, to redesign pumps of 

varying sizes to meet each of the proposed efficiency levels. DOE then modeled the distribution 

of unique pump models that would require redesign at each efficiency level. For each efficiency 

level, DOE multiplied each unique failing model by its associated cost to redesign and summed 

the total to reach an estimate of the total product and capital conversion cost for the industry.

Data supplied to DOE by the Hydraulic Institute was used as basis for the industryaverage cost, per model, to redesign a failing pump model. The Hydraulic Institute, through an 

independent third party, surveyed 15 manufacturers regarding the product and conversion costs 

associated with redesigning one, 50, and 200 hp pumps from the 10th to the 40th percentile of 

market efficiency. Specifically, HI’s survey contained cost categories for the following: 

redesign; prototype and initial test; patterns and tooling; testing; working capital; and 

marketing. 

DOE validated the HI survey data with independent analysis and comparable 

independently collected manufacturer interview data. In addition, data from the European Union 

12-14
pumps regulation preparatory studyb was used to augment the HI survey data and scale costs to 

various efficiency levels above and below the 40th percentile. Complete details can be found in 

chapter 5 of the NOPR TSD.

DOE used a pump model database, containing various performance parameters, to model 

the distribution of unique pump models that would require redesign at each efficiency level. The 

database is comprised of a combination of data supplied by the Hydraulic Institute and data 

collected independently from manufacturers by the DOE. For the ESCC, ESFM, IL, and VTS

equipment classes, DOE believes that the database is of suitable size to be considered 

representative of the industry as a whole. Table 12.4.10 presents the resulting product and 

capital conversion costs for each equipment class, at each efficiency level. Complete details on 

the calculation of industry aggregate conversion costs are found in chapter 5 of the NOPR TSD.

In section VI of the NOPR, DOE proposes labeling requirements recommended by the 

CIP Working Group. DOE recognizes that such requirements may result in costs to 

manufacturers. Costs of updating marketing materials for redesigned pumps in each standards 

case were included in the conversion costs for the industry and are accounted for in the industry 

cash-flow analysis results and industry valuation figures presented in this chapter of the TSD. 

However, DOE notes that costs of updating marketing materials for pumps that do not have to 

be redesigned to meet the standard are not considered in the industry valuation figures because 

these costs would be incurred by manufacturers in order to make representations of energy use 

(PEI) according to the proposed test procedure, as well as to include labeling requirements, 

regardless of whether DOE set an energy conservation standard or what TSL DOE selected. 

These costs are discussed in section 12.7.3.

Table 12.4.10 Cumulative Conversion Cost by Efficiency Level ($M)

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Baseline PER 10 PER 25 PER 40 PER 55 PER 70

ESCC $0 $9.9 $39.3 $88.0 $167.4 $274.3

ESFM $0 $2.5 $10.1 $22.6 $43.0 $70.5

IL $0 $5.0 $19.7 $44.6 $86.8 $141.7

VTS $0 $2.5 $9.3 $19.2 $37.8 $61.3

12.4.9 Markup Scenarios

To meet the new energy conservation standards proposed in this rule, DOE expects that 

manufacturers will need to hydraulically redesign a portion of product lines, which may result in 

significant conversion costs. In interviews and in the CIP working group discussions, 

manufacturer noted two possible implications of the conversion costs and a new energy 

conservation standard on pricing. On one hand, manufacturers indicated that competitive 

markets would make it difficult to pass cost increases on to their customers. On the other hand, 

manufacturers noted that they would need to have some level of cost recovery to support their 

business. 

b AEA Energy & Environment. 2008. Appendix 6: Lot 11 – ‘Circulators in buildings’. Report to European 

Commission. 

12-15
Given these two points of view and the uncertainty about the ability to pass the cost of 

compliance onto customers, DOE modeled two markup scenarios to reflect the uncertainty about 

the potential impacts of standards on pricing and profitability: (1) a flat markup scenario and (2) 

a cost recovery markup scenario. These scenarios lead to different markups values, which, when 

applied to the inputted MPCs, result in varying revenue and cash flow impacts.

12.4.9.1 Flat Markup Scenario

DOE developed initial estimates of the base case manufacturer markups based on 

corporate annual reports and Securities and Exchange Commission (SEC) 10-K filings. Initial 

estimates were presented to manufacturers in interviews. Using feedback and data and 

information gathered during the manufacturer interviews, DOE further refined the base case 

markups. Reviewing industry data, DOE concluded that when analyzed as whole, the industry 

does exhibit a relationship between manufacturer markup and efficiency. DOE’s analysis showed 

that on the industry-wide scale, the lowest efficiency models tend to garner lower markups than 

higher efficiency models, up to a certain efficiency level. Beyond the 25th percentile of 

efficiency, the relationship flattens out, and there is no correlation between increasing efficiency 

and markup. The data suggests that this relationship is a result of certain manufacturers 

positioning themselves with more or less efficient product portfolios, and charging markups that 

are commensurate with their position in the marketplace. The data also suggests that the market 

does not value efficiency beyond the first 25th percentile. Manufacturers are in consensus that 

efficiency is not currently the primary selling point or cost driver for the majority of pumps 

within the scope of the proposed rule. Rather, other factors such as reliability may influence 

price significantly and are known to be more influential in the purchaser’s decision making 

process.

DOE notes that the development of the markup-efficiency relationship was based only on 

data from the Inline equipment class. Sufficient data did not exist to directly analyze this 

relationship for the ESCC, ESFM, and VTS equipment classes. DOE, with support of the 

working group, believes that the markup structure of the Inline equipment class is representative 

of the remaining equipment classes. As such, DOE applied the Inline markup-efficiency 

relationship to the remaining equipment classes. The base case markups are presented in Table 

12.4.11. 

Table 12.4.11 Base Case Markup at Each Efficiency Level

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base Case PER 10 PER 25 PER 40 PER 55 PER 70

ESCC 1.368 1.380 1.387 1.387 1.387 1.387

ESFM 1.330 1.368 1.380 1.387 1.387 1.387

IL 1.425 1.462 1.472 1.472 1.472 1.472

VTS 1.369 1.372 1.397 1.397 1.397 1.397

Under the flat markup scenario, DOE maintains the same markups in the standards case 

as in the base case. Markups in the base case and in the Flat Markup scenario are identical. 

There is no price change for a given model, even if the model gets redesigned under an energy 

conservation standard. Because this pricing scenario assumes that manufacturers would not 

increase their pricing as a result of standards, even as they incur conversion costs, this scenario is 

considered a lower bound for revenues.

12-16
12.4.9.2 Cost Recovery Scenario

In the cost recovery pricing scenario, manufacturer pricing is set so that manufacturers 

recover their conversion costs over the analysis period. This cost recovery is enabled by an 

increase in mark-up, which results in higher sales prices for pumps even as manufacturer 

production costs stay the same. The cost recovery calculation assumes manufacturers raise prices 

on models where a redesign is necessitated by the standard. The additional revenue due to the 

increase in markup results in manufacturers recovering 100 percent of their conversion costs 

over the 30-year analysis period, taking into account the time-value of money.

The first input into the cost recovery markup scenario calculation is the total conversion costs at 

each efficiency level for each product class. These figures, derived from the engineering 

analysis, are listed in Table 12.4.12:

Table 12.4.12 Total Industry Conversion Costs at Each Efficiency Level

All Values in Millions 

of Dollars

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base Case PER 10 PER 25 PER 40 PER 55 PER 70

ESCC $0 $9.9 $39.3 $88.0 $167.4 $274.3 

ESFM $0 $2.5 $10.1 $22.6 $43.0 $70.5 

IL $0 $5.1 $20.0 $45.3 $88.2 $144.0 

VTS $0 $2.5 $9.3 $19.2 $37.8 $61.3 

The recovery of these conversion costs was treated as an annuity payment. DOE used an 

annuity calculation to determine the additional annual revenue needed by the industry to recover 

the full industry conversion cost over the 30-year analysis period, taking into account the timevalue of money. The annuity formula used in this calculation was the following:

In the above formula, the present value of the annuity payments “PV(A)” was set to the 

conversion cost values in Table 12.4.12 for each equipment class; the interest rate “i” was set to 

11.8% (as explained in section 12.4.4); and the number of payment periods “n” was set at 30 

years. By solving for the value of the annuity payment “A”, DOE was able to determine the 

additional annual revenue required to recover the upfront conversion cost investments. The 

resulting annuity payments were:

Table 12.4.13 Cost Recovery Annuity Payment at Each Efficiency Level

All Values in Millions 

of Dollars

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base Case PER 10 PER 25 PER 40 PER 55 PER 70

ESCC $0 $1.2 $4.8 $10.8 $20.5 $33.5

ESFM $0 $0.3 $1.2 $2.8 $5.3 $8.6

IL $0 $0.6 $2.4 $5.5 $10.8 $17.6

VTS $0 $0.3 $1.1 $2.3 $4.6 $7.5

To generate this additional revenue, DOE modeled the scenario in which manufacturers 

raise prices on redesigned products. By evaluating the number of shipment that fail and thus 

requiring redesign, DOE was able to determine a per-unit price adder that would achieve the 

12-17
necessary cost recovery revenue. Table 12.4.14 displays the additional MSP per unit requiring 

redesign:

Table 12.4.14 Additional MSP Per Model Requiring a Redesign at Each Efficiency Level

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base Case PER 10 PER 25 PER 40 PER 55 PER 70

ESCC $0.0 $97.9 $155.3 $183.4 $279.1 $387.3

ESFM $0.0 $97.9 $155.3 $183.4 $279.1 $387.3

IL $0.0 $70.7 $144.3 $253.9 $400.9 $546.5

VTS $0.0 $38.4 $24.5 $45.8 $81.7 $121.4

Finally when taking the original product MSP, adding in the additional product MSP and 

dividing that total by the MPC you get the markups associated with the cost recovery markup 

scenario. Because this markup scenario models the maximum level to which manufacturers 

would increase their pricing as a result of standard, this markup scenario is considered an upper 

bound to markups. Table 12.4.15 represents the standards case cost recovery markups:

Table 12.4.15 Standards Case Cost Recovery Markups at Each Efficiency Level

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base Case PER 10 PER 25 PER 40 PER 55 PER 70

ESCC 1.368 1.567 1.684 1.738 1.921 2.128

ESFM 1.330 1.447 1.505 1.535 1.612 1.699

IL 1.425 1.533 1.617 1.728 1.876 2.023

VTS 1.369 1.491 1.473 1.538 1.649 1.772

Depending on equipment class and the standard level being analyzed, the cost-recovery 

markup results in simple payback period of 7 to 8 years for the industry. This means the total 

additional revenues due to a higher markup equal the industry conversion cost within 7 to 8

years, not taking into account the time value of money. The simple payback period varies at each 

TSL due to differences in the number of models requiring redesign, the total conversion costs, 

and the number of unit over which costs can be recouped. The simple payback timeframes are as 

follows:

Table 12.4.16 Simple Payback Period for Industry Conversion Costs

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Years 0 8 7 7 7 7

The payback period is greatest at TSL 1 due to the relatively high numbers of models that 

require redesign as compared to the number of units sold at that level (PER 10).

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated 

indicators of financial impacts on the pumps industry. The following sections detail additional 

inputs and assumptions for the analysis of pumps. 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.

12-18
12.5.1 Introduction

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 NPV, 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 GRIM for this rulemaking estimates cash flows 

from 2015 to 2049. This timeframe models both the short-term impacts on the industry from the 

announcement of the standard until the compliance date as well as a long-term assessment over 

the 30 year analysis period used in the NIA.

In the MIA, DOE compares the INPV of the base case (no 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. The markup scenarios are described in greater detail in Table 

12.4.11 and Table 12.4.15.

While INPV is useful for evaluating the long-term effects of new 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 1 or 2 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 net cash flows,

Figure 12.5.1 through Figure 12.5.2 present the annual net cash flows over the analysis period. 

Annual cash flows are discounted to the base year, 2015. After the standards 

announcement date (i.e., the publication date of the final rule), industry cash flows begin to 

decline as companies use their financial resources to prepare for the new energy conservation 

standard. Cash flows between the announcement date and the compliance date are driven by the 

level of conversion costs and the proportion of these investments spent every year. The more 

stringent the energy conservation standard, 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. 

Free cash flowc in the year the energy conservation standards take effect is driven by two 

competing factors. In addition to capital and product conversion costs, new energy conservation 

standards could create stranded assets, i.e. tooling and equipment that would have enjoyed longer 

use if the energy conservation standard 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 energy conservation standard. 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 products, and higher accounts receivable for more 

c Free cash flow (FCF) is a metric commonly used in financial valuation. DOE calculates this value by adding back 

depreciation to net operating profit after tax and subtracting increases in working capital and capital expenditures.

12-19
expensive products. Depending on these two competing factors, cash flow can either be 

positively or negatively affected in the year the standard takes effect. 

12.5.2 Pumps Industry Financial Impacts

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for pumps products for the two 

scenarios. Figure 12.5.1 and Figure 12.5.2 present the net annual cash flows for the two 

scenarios.

Table 12.5.1 Flat Markup Scenario Changes in INPV for Pumps

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV $2013 M 121.4 111.6 81.9 22.4 (85) (228.4)

Change in INPV $2013 M - (9.8) (39.5) (99) (206.3) (349.8)

(%) - (8.0) (32.5) (81.6) (170.0) (288.2)

Table 12.5.2 Cost Recovery Markup Scenario Changes in INPV for Pumps

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV $2013 M 121.4 121.8 129.7 125.4 114.1 94.1

Change in INPV $2013 M - 0.4 8.3 4.0 (7.2) (27.3)

(%) - 0.3 6.9 3.3 (6.0) (22.5)

12-20
Figure 12.5.1 Annual Industry Net Cash Flows for Pumps (Flat Markup Scenario)

($250.0)

($200.0)

($150.0)

($100.0)

($50.0)

$0.0

$50.0

$100.0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Net Cash Flow for Pumps

12-21
Figure 12.5.2 Annual Industry Net Cash Flows for Pumps (Cost Recovery Markup 

Scenario)

12.6 IMPACTS ON SMALL BUSINESS MANUFACTURERS

DOE conducted an inquiry of the companies that could be small business manufacturers 

of products covered by this rulemaking. For “Pump and Pumping Equipment Manufacturing,” 

the Small Business Administration (SBA) has set a size threshold of 500 employees or less for an 

entity to be considered as a small business for this category. DOE used available public 

information to identify potential small manufacturers. DOE’s research involved industry trade 

association membership directories (including the Hydraulic Institute), industry conference 

exhibitor lists, individual company and buyer guide websites, and market research tools (e.g., 

Hoovers reports) to create a list of companies that manufacture or sell products 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 reviewed publicly-available data and contacted select companies on its list, as necessary, to 

($250.0)

($200.0)

($150.0)

($100.0)

($50.0)

$0.0

$50.0

$100.0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Net Cash Flow for Pumps

12-22
determine whether they met the SBA’s definition of a small business manufacturer of covered 

pump products. DOE screened out companies that did not offer products covered by this 

rulemaking, did not meet the definition of a “small business,” or are foreign owned and operated.

DOE identified 86 manufacturers of covered pump products sold in the U.S., 39 of these 

manufacturers met the 500-employee threshold defined by the SBA to qualify as a small 

business. Finally, DOE determined that 25 of these small manufacturers were domestic 

companies. DOE notes that manufacturers interviewed stated that there are potentially a large 

number of small pumps manufacturers that serve small regional markets. These small 

manufacturers are not members of HI and typically have a limited marketing presence. The 

interviewed manufacturers and those that participated in the ASRAC working group were not 

able to provide exact names for these smaller players. Based on this information, it is possible 

that DOE’s list of 25 small domestic players may not be comprehensive of all small US 

manufacturers in the industry.

DOE was able to interview one small business manufacturer of pumps. DOE also 

obtained information about small business impacts while interviewing large manufacturers. In 

general, DOE found very little information in the public domain about the role of small 

manufacturers in this industry. DOE was forced to make key assumptions about the market 

share and product offerings of small manufacturers. For its analysis, DOE estimated that small 

manufacturers accounted for approximately 36% of the total industry model offerings.

The proposed standards for pumps could cause small manufacturers to be at a 

disadvantage relative to large manufacturers. One way in which small manufacturers could be at 

a disadvantage is that they may be disproportionately affected by product conversion costs.

Product redesign, testing, and certification costs tend to be fixed and do not scale with sales 

volume. For each product model, small businesses must make investments in research and 

development to redesign their products, but because they have lower sales volumes, they must 

spread these costs across fewer units. Moreover, small pump manufacturers may not have the 

technical expertise to perform hydraulic redesigns in-house. These small manufacturers would 

need to hire outside consultants to support their re-design efforts. This would put small 

manufacturers at a disadvantage relative to larger companies that have internal resources and 

personnel for the redesign process. 

To meet the proposed standard, manufacturers may have to seek outside capital to cover 

expenses related to testing and product design equipment. Smaller firms typically have a higher 

cost of borrowing due to higher perceived risk on the part of investors, largely attributed to lower 

cash flows and lower per unit profitability. In these cases, small manufacturers may observe 

higher costs of debt than larger manufacturers. 

At TSL 2, the level proposed in today’s notice, DOE estimates total conversion costs of 

$.8 million for an average small manufacturer, compared to total conversion costs of $1.4 million 

for an average large manufacturer. DOE notes that it estimates a lower total conversion cost for 

small manufacturers because of the previous assumption that small manufacturers offer fewer 

models than their larger competitors, which means small manufacturers would likely have fewer 

product models to redesign. DOE applied the same per-model product conversion costs for both 

12-23
large and small manufacturers. Table 12.6.1 shows the relative impacts of conversion costs on 

small manufacturers relative to large manufacturers.

Table 12.6.1 Impacts of Conversion Costs on an Average Small versus Large Manufacturer

at the Proposed Standard Level

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

Total 

Conversion 

Cost as a 

Percentage of 

Annual EBIT

Average 

Large 

Manufacturer

303% 1579% 32% 582%

Average 

Small 

Manufacturer

374% 1013% 25% 464%

The total conversion costs are approximately 25% of revenue and 464% of earnings 

before interest and tax (EBIT) for a small manufacturer. For large manufacturers, the total 

conversion costs are approximately 32% of revenue and 582% of EBIT. These initial findings 

indicate that small manufacturers would not be disproportionately impacted by conversion costs 

relative to larger competitors. 

However, as noted in section V.B.2.a of the NOPR, the GRIM free cash flow results in 

2019 indicated that some manufacturers may need to access the capital markets in order to fund 

conversion costs directly related to the proposed standard. Given that small manufacturers have 

greater difficulty securing outside capitald and that the necessary conversion costs are not 

insignificant to the size of a small business, it is possible the small manufacturers will be forced 

to retire a greater portion of product models than large competitors. Also, smaller companies

often have a higher cost of borrowing due to higher risk on the part of investors, largely 

attributed to lower cash flows and lower per unit profitability. In these cases, small 

manufacturers may observe higher costs of debt than larger manufacturers. 

Though conversion costs are similar in magnitude for small and large manufacturers, 

small manufacturers may not have the same resources to make the required conversions. For 

example, some small pump manufacturers may not have the technical expertise to perform 

hydraulic redesigns in-house. These small manufacturers would need to hire outside consultants 

to support their re-design efforts. This could be a disadvantage relative to companies that have 

internal resources and personnel for the redesign process. 

12.7 OTHER IMPACTS 

d Simon, Ruth, and Angus Loten, "Small-Business Lending Is Slow to Recover," Wall Street Journal, August 14, 

2014. Accessed August 2014, available at http://online.wsj.com/articles/small-business-lending-is-slow-to-recover1408329562. 

12-24
12.7.1 Employment

12.7.1.1 Methodology

To quantitatively assess the impacts of energy conservation standards on employment, 

DOE used the GRIM to estimate the domestic labor expenditures and number of employees in 

the base case and at each TSL from 2015 through 2049. 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 related to 

manufacturing of the product are a function of the labor intensity of the product, the sales 

volume, and an assumption that wages remain fixed in real terms over time. The total labor 

expenditures in each year are calculated by multiplying the MPCs by the labor percentage of 

MPCs. 

The total labor expenditures in the GRIM were then converted to domestic production 

employment levels by dividing production labor expenditures by the annual payment per 

production worker (production worker hours times the labor rate found in the U.S. Census 

Bureau’s 2011 ASM). The estimates of production workers in this section cover workers, 

including line-supervisors who are directly involved in fabricating and assembling a product 

within the original equipment manufacturer (OEM) facility. Workers performing services that 

are closely associated with production operations, such as materials handling tasks using 

forklifts, are also included as production labor. DOE’s estimates only account for production 

workers who manufacture the specific products covered by this rulemaking.

12.7.1.2 Direct Employment Impacts

Using the GRIM, DOE estimates in the absence of new energy conservation standard, 

there would be 415 domestic production workers for pumps in 2020. 

Table 12.7.1 show the range of the impacts of potential new energy conservation 

standards on U.S. production workers in the pumps industry.

Table 12.7.1 Potential Changes in Total Domestic Production Workers for Pumps in 2020

TSL 1 2 3 4 5

Potential Changes in Domestic 

Production Workers 2020*

-41

to

0

-104

to

0

-166

to

0

-228

to

0

-290

to

0

*DOE presents a range of potential employment impacts. 

The employment impacts shown in Table 12.7.1 represent the potential production 

employment changes that could result following the compliance date of new energy conservation 

standards. The upper end of the results in the table estimates the maximum increase in the 

number of production workers after the implementation of new energy conservation standards 

and it assumes that manufacturers would continue to produce the same scope of covered products 

within the United States. 

12-25
12.7.2 Production Capacity

Based on the engineering analysis, DOE believes that higher efficiency pumps require 

similar production facilities, tooling, and labor as baseline efficiency pumps. Based on the 

engineering analysis and interview with manufacturers, DOE does not believe a new energy 

conservation standard would create production capacity constraints. However, industry 

representatives in interviews and in the CIP working group forum expressed concern about the 

industry’s ability to complete the necessary number of hydraulic redesigns required to comply 

with a new standard. In the industry, not all companies have the in-house capacity to redesign 

pumps. Many companies rely on outside consultants for a portion or all of their hydraulic design 

projects. Manufacturers were concerned that a new standard above the level selected in the 

consensus by the CIP working group would create more demand for hydraulic design technical 

resources than are available in the industry.

12.7.3 Labeling

The costs of updating marketing materials for pumps that must be redesigned to meet the 

standard were included in the conversion costs for the industry and are accounted for in the 

industry cash-flow analysis results and industry valuation figures in section 12.5.2. For pumps 

that do not need to be redesigned to meet the standard, DOE estimates that the costs of updating 

marketing materials to include the labeling requirements would be up to $3750 per pump model.

e

In the absence of a standard, this would result in additional cost to the industry of approximately 

$13 million. DOE estimates that the investment could result in a loss of INPV compared to a 

base case with no labeling requirement of up to approximately 5%. For TSL 2, the additional 

cost to industry for updating marketing materials for pumps that do not have to be redesigned 

would be approximately $10 million. DOE estimates that the investment could result in an 

additional loss of INPV compared to a base case with no labeling requirement of up to 

approximately 4% beyond that estimated for a standard at TSL 2.

f

12.7.4 Cumulative Regulatory Burden 

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of recent or impending regulations may have serious consequences for some 

e HI estimated the average cost for updating marketing (literature, data sheets, curves, pump selection tools, sales 

training, compliance documentation, etc.) for a hydraulic redesign to range from $32,000 for a 1-hp model to 

$27,000 for a 200-hp model. DOE assumed $30,000 on average. The marketing costs provided by HI were for 

developing new materials for redesigned pump models. For this exercise only literature and data sheets are relevant, 

which DOE estimated would represent half of the marketing costs. In addition, in this case, DOE is estimating the 

incremental cost for making a few additions to literature rather than complete design of new materials. DOE 

assumed these additions would cost only 25% or less of full material development. 

f Approximately 3500 models are in the scope of this rulemaking. In the absence of the standard, none of these 

models would have to be redesigned and would thus incur $3750 each in costs for updating marketing materials. At 

TSL 2, 25% of pump models would have to be redesigned, and creating new marketing materials for these pumps is 

already accounted for in the MIA. The 75% of pump models that do not have to be redesigned would incur $3750 

each.

12-26
manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a single 

regulation may overlook this cumulative regulatory burden. In addition to energy conservation 

standards, other regulations can significantly affect manufacturers’ financial operations. Multiple 

regulations affecting the same manufacturer can strain profits and lead companies to abandon 

product lines or markets with lower expected future returns than competing products. For these 

reasons, DOE conducts an analysis of cumulative regulatory burden as part of its rulemakings 

pertaining to appliance efficiency. 

Companies that produce a wide range of regulated products may be faced with more 

capital and product development expenditures than competitors with a narrower scope of 

products. Regulatory burdens can prompt companies to exit the market or reduce their product 

offerings, potentially reducing competition. Smaller companies in particular can be affected by 

regulatory costs since these companies have lower sales volumes over which they can amortize 

the costs of meeting new regulations. A proposed standard is not economically justified if it 

contributes to an unacceptable level of cumulative regulatory burden. 

For the cumulative regulatory burden analysis, DOE looks at product-specific Federal 

regulations that could affect pumps manufacturers and that take effect approximately three years 

before or after the 2020 compliance date of standard proposed in this notice. The Department 

was not able to identify any additional regulatory burdens that met these criteria.

12.8 CONCLUSION

The following sections summarize the impacts for the scenarios DOE believes are most 

likely to capture the range of impacts on pumps manufacturers as a result of energy conservation 

standards. 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. The TSLs are defined as follows:

Table 12.8.1 Trial Standard Levels for Pumps

Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

VTS EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

RSV* EL 0 EL 0 EL 0 EL 0 EL 0 EL 5

*Equipment classes not analyzed due to lack of available data (in the case of RSV).

12.8.1 Conclusions for Pumps

Table 12.8.2 Manufacturer Impact Analysis Results for Pumps

Units Base 

Case

Trial Standard Level

1 2 3 4 5

INPV 2013 $M 121.4 111.6 to 

121.8

81.9 to 

129.7

22.4 to 

125.4

(85.0) to 

114.1

(228.4) to 

94.1

12-27
Change in INPV

2013 $M - (9.8) to 

0.4

(39.5)

to 8.3

(99.0)

to 4.0

(206.3) to 

(7.2)

(349.8) to 

(27.3)

% - (8.0) to 

0.3

(32.5)

to 6.9

(81.6)

to 3.3

(170.0) to 

(6.0)

(288.2) to 

(22.5)

FCF (2014) 2013 $M 12.2 5.6 (16.1) (58.7) (130.1) (224.4)

Change in FCF

(2014)

2013 $M - (6.6) (28.3) (70.9) (142.2) (236.6)

% - (54.3) (232.5) (582.0) (1167.5) (1942.4)

Conversion Costs 2013 $M - 19.9 78.4 174.3 335.0 547.7

At TSL 1, DOE models the impacts on pumps INPV under both mark-up scenarios. The 

change in INPV ranges from -$9.8 million to $.4 million, or a change in INPV of -8.0 percent to 

.3 percent. At this level, the pumps industry free cash flow is estimated to decrease by as much 

as $6.6 million, or -54.3 percent compared to the base-case value of $12.2 million in 2019, the 

year before the compliance date. At this level, DOE estimates conversion costs to be $19.9

million for the industry.

At TSL 2, the change in INPV ranges from -$39.5 million to $8.3 million, or a change in 

INPV of -32.5 percent to 6.9 percent. At this level, the pumps industry free cash flow is 

estimated to decrease by as much as $28.3 million, or -232.5 percent compared to the base-case 

value of $12.2 million in 2019. DOE estimates conversion costs to be $78.4 million for the 

industry.

At TSL 3, DOE estimates impacts on panel INPV to range from -$99 million to $4

million, or a change in INPV of -81.6 percent to 3.3 percent. At this level, the pumps industry 

free cash flow is estimated to decrease by as much as $70.9 million, or -582% compared to the 

base-case value of $12.2 million in the year before the compliance date. The large percentage 

drop in cash flow in the GRIM indicates that conversion costs are high relative to the size of the 

industry and relative to annual operating profits. Conversion costs are expected to total $174.3

million.

At TSL 4, the change in INPV ranges from -$206.3 million to -$7.2 million, or a change 

in INPV of -170 percent to -6 percent. At this level the pumps industry free cash flow is 

estimated to decrease by as much as $142.2, or -1167.5% compared to the base-case value of 

$12.2 million in the year before the compliance date. Conversion costs are expected to total $335

million.

DOE estimates TSL 5 impacts pumps INPV to be range from -349.8 million to -27.3

million, or a change in INPV of -288.2percent to -22.5 percent. At this level, the pumps industry 

free cash flow is estimated to decrease by as much as $236.6 million, or -1942.4 percent 

compared to the base-case value of $12.2 million in the year before the compliance date. At this

TSL, conversion costs total $547.7 million for the industry. 

12-28
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 Annual Survey of Manufacturing: 2011. American FactFinder2. 2012. Bureau of the 

Census. 

<http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_2011_

31GS101&prodType=table>.

4 The size standards are available on the SBA’s website at 

<www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf>.

5 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of

Companies, 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

12-29

 
APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12A.1 INTRODUCTION AND PURPOSE.............................................................................. A-1

12A.2 MODEL DESCRIPTION ............................................................................................... A-1

LIST OF TABLES

Table 12A.1 Detailed Cash Flow Example................................................................................. A-4

12A-i
APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

12A.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(s) 

following a regulation or a series of regulations. The model structure also allows an analysis of 

multiple equipment types with regulations taking effect over a period of time, and of multiple 

regulations on the same equipment.

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 (i.e., 

the standards case).

Outputs from the model consist of summary financial metrics, graphs of major variables, 

and, when appropriate, access to the complete cash flow calculation.

12A.2 MODEL DESCRIPTION

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. Below 

are definitions of listed items on the printout of the output sheet of the GRIM.

1) Revenues: Annual revenues – computed by multiplying equipment unit prices at each 

efficiency level by the appropriate manufacturer markup.

2) Total Shipments: Total annual shipments for the industry were obtained from the 

National Impact Analysis Spreadsheet.

3) Material: The portion of cost of goods sold (COGS) that includes materials.

4) 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.

5) Depreciation: The portion of overhead that includes an allowance for the total 

amount of fixed assets used to produce that one unit. Annual depreciation is 

computed as a percentage of COGS. Depreciation is broken out from overhead as a 

separate line item.

12A-1
6) Overhead: The portion of COGS that includes indirect labor, indirect material, 

energy use, maintenance, depreciation, property taxes, and insurance related to assets. 

Depreciation is broken out from overhead as a separate line item.

7) Standard SG&A: Selling, general, and administrative costs are computed as a 

percentage of Revenues (1).

8) R&D: GRIM separately accounts for ordinary research and development (R&D) as a 

percentage of Revenues (1).

9) Product Conversion Costs: Product conversion costs are one-time investments in 

research, development, testing, marketing, and other costs focused on making 

equipment designs comply with the new 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) Per Unit EBIT ($/unit): GRIM calculates Per Unit EBIT as EBIT (11) divided by 

Shipments (2).

13) EBIT as a Percentage of Sales (EBIT/Revenues): GRIM calculates EBIT as a 

percentage of sales to compare with the industry’s average reported in financial 

statements.

14) Taxes: Taxes on EBIT (11) are calculated by multiplying the tax rate contained in 

Major Assumptions by EBIT (11).

15) Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of 

Goods Sold ((3) to (6)), SG&A (7), R&D (8), Product Conversion Costs (9), and 

Taxes (14) from Revenues (1).

16) NOPAT repeated: NOPAT is repeated in the Statement of Cash Flows.

17) Depreciation repeated: Depreciation is added back in the Statement of Cash Flows 

because it is a non-cash expense.

18) Loss on Disposal of Stranded Assets repeated: Stranded Assets are added back in the 

Statement of Cash Flows because they are non-cash expenses.

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.

12A-2
20) Cash Flow from Operations: Calculated by taking NOPAT (15), adding back noncash items such as a Depreciation (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 (1).

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 equipment 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) Capital Investment: Total investments in property, plant, and equipment are 

computed by adding Ordinary Capital Expenditures (21) and Capital Conversion 

Costs (22).

24) Free Cash Flow: Annual cash flow from operations and investments; computed by 

subtracting Capital Investment (23) from Cash Flow from Operations (20).

25) Terminal Value: Estimate of the continuing value of the industry after the analysis 

period. Computed by growing the Free Cash Flow at a constant rate in perpetuity.

26) Present Value Factor: Factor used to calculate an estimate of the present value of an 

amount to be received in the future.

27) Discounted Cash Flow: Free Cash Flows (24) multiplied by the Present Value 

Factor (26). For the end of 2048, the discounted cash flow includes the discounted 

Terminal Value (25).

28)Industry Value thru the end of 2048: The sum of Discounted Cash Flows (27).

12A-3
Table 12A.1 Detailed Cash Flow Example

12A-4
CHAPTER 13. EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS............................... 13-2

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS............................................. 13-3

13.4 UPSTREAM FACTORS ............................................................................................... 13-4

13.5 EMISSIONS IMPACT RESULTS................................................................................ 13-5

REFERENCES .......................................................................................................................... 13-1

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors.................................................................... 13-4

Table 13.4.1 Electricity Upstream Emissions Factors...................................................... 13-5

Table 13.5.1 Cumulative Emissions Reduction from Potential Standards for 

Commercial and Industrial Pumps............................................................... 13-5

LIST OF FIGURES

Figure 13.5.1 CO2 Total Emissions Reduction .................................................................. 13-6

Figure 13.5.2 SO2 Total Emissions Reduction................................................................... 13-6

Figure 13.5.3 NOx Total Emissions Reduction .................................................................. 13-7

Figure 13.5.4 Hg Total Emissions Reduction .................................................................... 13-7

Figure 13.5.5 N2O Total Emissions Reduction .................................................................. 13-8

Figure 13.5.6 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 and site combustion 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 extraction, processing, and transporting fuels to the site of combustion. The associated 

emissions are referred to as upstream emissions. Together, these emissions account for the fullfuel-cycle (FFC), in accordance with DOE’s FFC Statement of Policy. 76 FR 51282 (Aug. 18, 

2011). 

The analysis of power sector emissions uses marginal emissions intensity factors 

calculated by DOE. As of 2014, DOE is using a new methodology based on results published for 

the Annual Energy Outlook 2014 (AEO 2014) reference case and a set of side cases that 

implement a variety of efficiency-related policies.

1 The new methodology is described in chapter 

15 and in the report “Utility Sector Impacts of Reduced Electricity Demand” (Coughlin, 2014).2

Site emissions of CO2 and NOX are estimated using emissions intensity factors from a 

publication of the Environmental Protection Agency (EPA).

3

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).4 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).

For CH4 and N2O, DOE also presents results in terms of units of carbon dioxide 

equivalent (CO2e). Gases are converted to CO2e by multiplying the physical units by the gas

global warming potential (GWP) over a 100 year time horizon. Based on the Fifth Assessment 

Report of the Intergovernmental Panel on Climate Change,5 DOE used GWP values of 28 for 

CH4 and 265 for N2O.b

a See EPA GHG Emission Factors Hub www.epa.gov/climateleadership/inventory/ghg-emissions.html. b The values are without inclusion of climate-carbon feedbacks in response to emissions of the indicated non-CO2 

gases.

13-1

 
13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

Each annual version of the AEO incorporates the projected impacts of existing air quality 

regulations on emissions. AEO 2014 generally represents current Federal and State legislation 

and final implementation regulations in place as of the end of October 2013.

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

and regional emissions cap and trade 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 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, 

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 2014 emissions factors used for the present analysis assume that CAIR remains 

a binding regulation through 2040. c

The attainment of emissions caps is typically flexible among affected Electric Generating 

Units (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 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

Toxics Standards (MATS) for power plants. 77 FR 9304 (Feb. 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 

c On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit and remanded the case for 

further proceedings consistent with the Supreme Court's opinion. The Supreme Court held in part that EPA's 

methodology for quantifying emissions that must be eliminated in certain states due to their impacts in other 

downwind states was based on a permissible, workable, and equitable interpretation of the Clean Air Act provision 

that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation, No 12-1182, slip op. at 32 

(U.S. April 29, 2014). Because DOE is using emissions factors based on AEO 2014, the analysis assumes that 

CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not relevant for the 

purpose of DOE's analysis of SO2 emissions.

13-2

 
2014 assumes that, in order to continue operating, coal plants must have either flue gas 

desulfurization or dry sorbent injection systems installed by 2016. Both technologies, which are 

used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, 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 2016 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 emissions factors based on AEO 

2014, which incorporates the MATS.

13.3 POWER SECTOR AND SITE EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from analysis of the AEO 2014 reference and a number of side cases incorporating enhanced 

equipment efficiencies. To model the impact of a standard, DOE calculates factors that relate a 

unit reduction to annual site electricity demand for a given end use to corresponding reductions 

to installed capacity by fuel type, fuel use for generation, and power sector emissions. Details on 

the approach used may be found in Coughlin (2014).

Table 13.3.1 presents the average 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 commercial buildings. The average factors for each year take into account 

the projected shares of each of the sources in total electricity generation. 

13-3
Table 13.3.1 Power Plant Emissions Factors

Unit* 2020 2025 2030 2035 2040

CO2 kg/MWh 710 632 571 522 478

SO2 g/MWh 688 537 452 379 338

NOx g/MWh 557 466 408 360 326

Hg g/MWh 0.00212 0.00166 0.00139 0.00117 0.00104

N2O g/MWh 7.2 7.1 6.9 6.6 6.4

CH4 g/MWh 50 49 48 46 45

* 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 10B. See also Coughlin (2013) and Coughlin (2014). When 

demand for a 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 methane 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 methane 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. Fugitive emissions factors for 

methane from coal mining and natural gas production were estimated based on a review of recent 

studies compiled by Burnham (2011).6 This review includes estimates of the difference between 

fugitive emissions factors for conventional production of natural vs. unconventional (shale or 

tight gas). These estimates rely in turn on data gathered by EPA under new GHG reporting 

requirements for the petroleum and natural gas industries.7, 8 As more data are made available, 

DOE will continue to update these estimated emissions factors.

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. These were used to estimate the emissions 

associated with the decreased electricity use. The caps that apply to power sector NOX emissions 

do not apply to upstream combustion sources.

13-4
Table 13.4.1 Electricity Upstream Emissions Factors

Unit 2020 2025 2030 2035 2040

CO2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO2 g/MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 368 375 382 387 387

Hg g/MWh 0 0 0 0 0

N2O g/MWh 0.252 0.247 0.241 0.234 0.228

CH4 g/MWh 2149 2195 2216 2248 2255

* Refers to site electricity savings.

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

products sold in 2020-2049 for each TSL. Negative values indicate that emissions increase.

Table 13.5.1 Cumulative Emissions Reduction from Potential Standards for Commercial 

and Industrial Pumps

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Power Sector and Site Emissions

CO2 (million metric tons) 3.2 15 31 50 72

SO2 (thousand tons) 2.6 13 25 40 58

NOX (thousand tons) 2.5 12 23 38 55

Hg (tons) 0.008 0.039 0.077 0.124 0.180

CH4 (thousand tons) 0.32 1.54 3.07 4.95 7.20

N2O (thousand tons) 0.05 0.22 0.44 0.71 1.03

Upstream Emissions

CO2 (million metric tons) 0.19 0.91 1.81 2.93 4.26

SO2 (thousand tons) 0.03 0.16 0.32 0.51 0.74

NOX (thousand tons) 2.7 13 26 42 61

Hg (tons) 0.0001 0.0004 0.0007 0.0011 0.0016

CH4 (thousand tons) 16 76 151 244 354

N2O (thousand tons) 0.002 0.008 0.016 0.025 0.036

Total Emissions

CO2 (million metric tons) 3.4 16 33 53 77

SO2 (thousand tons) 2.7 13 25 41 59

NOX (thousand tons) 5.2 25 49 80 116

Hg (tons) 0.01 0.04 0.08 0.13 0.18

CH4 (thousand tons) 16 77 154 248 362

N2O (thousand tons) 0.05 0.23 0.45 0.73 1.07

13-5
Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for 

each type of emission from each TSL. The reductions reflect the lifetime impacts of products 

sold in 2020-2049.

Figure 13.5.1 CO2 Total Emissions Reduction

Figure 13.5.2 SO2 Total Emissions Reduction

13-6
Figure 13.5.3 NOx Total Emissions Reduction

Figure 13.5.4 Hg Total Emissions Reduction

13-7
Figure 13.5.5 N2O Total Emissions Reduction

Figure 13.5.6 CH4 Total Emissions Reduction

13-8
REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2014 with Projections to

2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf>.

2. Coughlin, K., Utility Sector Imacts of Reduced Electricity Demand, 2014. Lawrence

Berkeley National Laboratory. Report No. LBNL-6864E.

3. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors,

AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources. 1998. 

<www.epa.gov/ttn/chief/ap42/index.html>.

4. Coughlin, K., Projections of Full-Fuel-Cycle Energy and Emissions Metrics, 2013.

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E.

5. Intergovernmental Panel On Climate Change, Chapter 8. In Climate Change 2013: The

Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report

of the Intergovernmental Panel on Climate Change, T.F. Stocker, D. Qin, G.-K. Plattner,

M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley,

Editor. 2013. Cambridge University Press: Cambridge, United Kingdom and New York,

NY, USA

6. Burnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, I. Palou-Rivera, Life-Cycle

Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum.

Environmental Science & Technology, 2011. 46(2): pp. 619-627

7. U.S. Environmental Protection Agency, Fugitive Emissions Reporting from the

Petroleum and Natural Gas Industry, 2009. U.S. Environmental Protection Agency.

Washington, DC.

8. U.S. Environmental Protection Agency, Oil and Natural Gas Sector: Standards of

Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution,

Background Supplemental Technical Support Document for the Final New Source

Performance Standards, 2012. U.S. Environmental Protection Agency. Washington, DC.

13-1 
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

14.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES................................. 14-2

14.3.1 Current Approach and Key Assumptions.......................................................... 14-2

14.3.2 Limitations of Current Estimates....................................................................... 14-4

14.4 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.5 RESULTS ...................................................................................................................... 14-5

REFERENCES .......................................................................................................................... 14-9

LIST OF TABLES

Table 14.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 

2007$ per Metric Ton)................................................................................. 14-3

Table 14.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 

2007$ per Metric Ton of CO2)..................................................................... 14-4

Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

TSLs for Commercial and Industrial Pumps ............................................... 14-6

Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction 

under TSLs for Commercial and Industrial Pumps ..................................... 14-7

Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs 

for Commercial and Industrial Pumps......................................................... 14-8

14-i
CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of the effects of potential energy conservation standards for 

commercial and industrial pumps, the U.S. Department of Energy (DOE) estimated the monetary 

benefits of the reduced emissions of carbon dioxide (CO2) and nitrogen oxides (NOX) that would 

be expected to result from each trial standard level (TSL) considered for this rulemaking. This 

chapter summarizes the basis for the monetary values assigned to emissions and presents the 

modeled benefits of estimated reductions. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

One challenge for anyone attempting to calculate the monetary benefits of reduced 

emissions of CO2 is what value to assign to each unit eliminated. The value must encompass a 

broad range of physical, economic, social, and political effects. Analysts developed the concept 

of the social cost of carbon (SCC) to represent the broad cost or value associated with 

producing—or reducing—a quantifiable amount of CO2 emissions.

14.2.1 Social Cost of Carbon 

The SCC is an estimate of the monetized damages associated with an incremental 

increase in carbon emissions in a given year. The SCC 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. SCC estimates are provided in dollars per metric 

ton of carbon dioxide. A value for the domestic SCC is meant to represent the damages in the 

United States resulting from a unit change in carbon dioxide emissions, whereas a global SCC is 

meant to reflect the value of damages worldwide.

Under section 1(b)(6) of Executive Order 12866,

1 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 required by the Executive Order is to enable 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 will need updating in response to increasing 

knowledge of the science and economics of climate impacts.

As part of the interagency process that developed the SCC estimates, technical experts 

from numerous agencies met regularly to explore the technical literature in relevant fields, 

discuss key model inputs and assumptions, and consider public comments. The primary objective 

of the process was to develop a range of SCC values using a defensible set of assumptions 

regarding model inputs that was grounded in the scientific and economic literature. In this way, 

key uncertainties and model differences transparently and consistently inform the range of SCC 

estimates developed for use 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 several serious challenges. A report from the National Research 

Council2 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 effects of changes in climate on the physical and 

biological environment, and (4) the translation of those environmental impacts into economic 

damages. As a result, any effort to quantify and monetize the harms associated with climate 

change raises 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. An 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. Then the net present value of 

the benefits can be calculated by multiplying each of the 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.

14.3 DEVELOPMENT OF SOCIAL COST OF CARBON VALUES

In 2009, an interagency process was initiated to develop a preliminary assessment of how 

best to quantify the benefits from reducing carbon dioxide emissions. To provide 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 literature to use as 

interim values until a more comprehensive analysis could be conducted. The outcome of the 

preliminary assessment was a set of five interim values: global SCC estimates for 2007 (in 

2006$) of $55, $33, $19, $10, and $5 per ton of CO2.

3 Those 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 that preliminary effort were presented in several proposed and final rules.

14.3.1 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened regularly to 

improve the SCC estimates. Specifically, 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 models are known by 

their acronyms of FUND, DICE, and PAGE. Those three models frequently are cited in the peerreviewed literature and were used in the most recent assessment of the Intergovernmental Panel 

on Climate Change. Each model was given equal weight in developing SCC values.

14-2
Each model takes a slightly different approach to calculating how increases in emissions 

produce economic damages. A key objective of the interagency process was to enable a 

consistent exploration of the three models while respecting the different approaches taken by the 

key modelers in the field. An extensive review of the literature identified three sets of input 

parameters for the models: climate sensitivity; socioeconomic and emissions trajectories; and 

discount rates. A probability distribution for climate sensitivity was specified as an input to 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.

The interagency group selected four SCC values for use in regulatory analyses. Three 

values are based on the average SCC from the three integrated assessment models, at discount 

rates of 2.5 percent, 3 percent, and 5 percent. The fourth value, which represents the 95th

percentile of the SCC estimate across all three models at a 3-percent discount rate, is included to 

represent larger-than-expected effects from temperature changes farther out in the tails of the 

SCC distribution. The values increase 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. 0 presents the values in the 2010 interagency group 

report.

4

Table 14.3.1 Annual SCC Values for 2010-2050 from 2010 Interagency Report (in 2007$

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

The SCC values used for the analysis of the effects of potential standards for commercial 

and industrial pumps 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 update from the interagency working group (revised November 2013). Table 14.3.2 shows 

the updated sets of SCC estimates in 5-year increments from 2010 to 2050. The full set of annual 

SCC estimates for 2010–2050 is presented in appendix 14B of this TSD. The central value that 

emerges is the average SCC across models at a 3-percent discount rate. To capture the 

14-3
uncertainties involved in regulatory impact analysis, however, the interagency group emphasizes 

the importance of including all four sets of SCC values.

Table 14.3.2 Annual SCC Values for 2010–2050 from 2013 Interagency Update (in 2007$

per Metric Ton of 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

14.3.2 Limitations of Current Estimates

The interagency group recognizes that current models are imperfect and incomplete. 

Because key uncertainties remain, current SCC estimates should be treated as provisional and 

revisable. Estimates doubtless will evolve in response to improved scientific and economic 

understanding. The 2009 National Research Council report points out the tension between 

producing quantified estimates of the economic damages from an incremental ton of carbon and 

the limits of current modeling efforts. Several analytic challenges are being addressed by the 

research community, some by research programs housed in many of the Federal agencies 

participating in the interagency process. The interagency group intends to review and reconsider 

SCC estimates periodically to incorporate expanding knowledge of the science and economics of 

climate impacts, as well as improvements in modeling.

In summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, applying the GDP price 

deflator to adjust the values to 2013$. For the four SCC values, the values of emissions in 2015 

were $12.0, $40.5, $62.4, and $119 per metric ton avoided (values expressed in 2013$). DOE 

derived values after 2050 using the relevant growth rates for 2040–2050 in the interagency 

update.

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

for that year under each discount rate. To calculate a present value of the stream of monetary 

values, DOE discounted the values in each of the four cases using the same discount rate that had 

been used to obtain the SCC values in each case.

14-4
14.4 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE also considered the potential monetary benefits of reduced NOX emissions from the 

proposed standards. As noted in chapter 13, new or amended energy conservation standards 

would reduce NOX emissions in those States that are not affected by emissions caps. DOE 

estimated the monetized value of NOX emissions reductions resulting from each TSL based on 

estimates of environmental damage found in the scientific literature. Estimates suggest a wide 

range of monetary values, from $476 to $4,893 per ton (in 2013$).5 DOE calculated monetary 

benefits using a median value for NOX emissions of $2,684 per short ton (in 2013$), at real 

discount rates of 3 percent and 7 percent.

DOE continues to evaluate appropriate values for monetizing avoided SO2 and Hg 

emissions. DOE did not monetize those emissions for this analysis.

14.5 RESULTS

Table 14.5.1 presents the global values of CO2 emissions reductions for each considered 

TSL. 

14-5
Table 14.5.1 Estimates of Global Present Value of CO2 Emissions Reduction under TSLs 

for Commercial and Industrial Pumps

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2013$

Power Sector Emissions

1 21 100 160 310

2 100 474 757 1468

3 199 944 1506 2921

4 319 1517 2421 4695

5 463 2205 3521 6826

Upstream Emissions

1 1.2 5.8 9.3 18

2 5.8 28 44 86

3 11 55 88 170

4 18 88 141 274

5 27 129 206 398

Total Emissions

1 22 106 169 329

2 106 502 801 1554

3 210 999 1594 3092

4 337 1605 2563 4969

5 490 2334 3726 7224

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $12.0, 

$40.5, $62.4, and $119 per metric ton (2013$).

After calculating global values of CO2 emissions reductions for each considered TSL, 

DOE calculated domestic values as a range of 7 percent to 23 percent of the global values. 

Results for domestic values are presented in Table 14.5.2.

14-6
Table 14.5.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

TSLs for Commercial and Industrial Pumps

TSL

SCC Case*

5% discount

rate, average*

3% discount

rate, average*

2.5% discount

rate, average*

3% discount rate,

95th percentile*

Million 2013$

Power Sector Emissions

1 1.5 to 4.9 7.0 to 23.1 11.2 to 36.7 21.7 to 71.4

2 7.0 to 23.0 33.2 to 109.1 53.0 to 174.1 102.8 to 337.7

3 13.9 to 45.7 66.1 to 217.0 105.4 to 346.3 204.5 to 671.9

4 22.3 to 73.3 106.2 to 348.8 169.5 to 556.9 328.7 to 1079.9

5 32.4 to 106.5 154.3 to 507.1 246.4 to 809.7 477.8 to 1570.0

Upstream Emissions

1 0.1 to 0.3 0.4 to 1.3 0.7 to 2.1 1.3 to 4.2

2 0.4 to 1.3 1.9 to 6.4 3.1 to 10.2 6.0 to 19.7

3 0.8 to 2.6 3.8 to 12.6 6.1 to 20.2 11.9 to 39.2

4 1.3 to 4.2 6.2 to 20.3 9.9 to 32.5 19.2 to 63.0

5 1.9 to 6.1 9.0 to 29.6 14.4 to 47.3 27.9 to 91.6

Total Emissions

1 1.6 to 5.2 7.4 to 24.4 11.8 to 38.9 23.0 to 75.6

2 7.4 to 24.3 35.1 to 115.4 56.1 to 184.2 108.8 to 357.4

3 14.7 to 48.4 69.9 to 229.7 111.6 to 366.5 216.4 to 711.1

4 23.6 to 77.5 112.4 to 369.2 179.4 to 589.5 347.8 to 1142.9

5 34.3 to 112.7 163.3 to 536.7 260.8 to 857.1 505.7 to 1661.6

* 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 14.5.3 presents the present value of cumulative NOX emissions reductions for each 

TSL. Monetary values are calculated using the average dollar-per-ton values assigned to NOX

emissions at 7-percent and 3-percent discount rates.

14-7
Table 14.5.3 Estimates of Present Value of NOX Emissions Reduction under TSLs for 

Commercial and Industrial Pumps

TSL

3% discount rate 7% discount rate

Million 2013$

Power Sector Emissions

1 3.1 1.4

2 15 6.4

3 29 13

4 47 20

5 68 29

Upstream Emissions

1 3.3 1.4

2 16 6.4

3 31 13

4 50 20

5 72 30

Total Emissions

1 6.5 2.8

2 30 13

3 60 25

4 97 41

5 141 59

14-8
REFERENCES

1. Presidential Documents--Executive Order 12866--Regulatory Planning and Review, 

October 4, 1993. <www.archives.gov/federal-register/executive-orders/pdf/12866.pdf>.

2. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy 

Production and Use, 2010. National Academies Press. Washington, DC. 

<www.nap.edu/catalog.php?record_id=12794>.

3. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866, February, February 2010. 

United States Government. Washington, D.C. 

<www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-ofCarbon-for-RIA.pdf>.

4. 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; revised November 2013. United States Government. Washington, D.C. 

<www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf>.

5. 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. 

<www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_fina

l_report.pdf>.

14-9
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

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-4

14A.4 APPROACH AND KEY ASSUMPTIONS ............................................................... 14A-5

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-13

14A.4.4 Equilibrium Climate Sensitivity ................................................................... 14A-13

14A.4.5 Socioeconomic and Emissions Trajectories.................................................. 14A-16

14A.4.6 Discount Rate................................................................................................ 14A-19

14A.5 REVISED SCC ESTIMATES .................................................................................. 14A-26

14A.6 LIMITATIONS OF THE ANALYSIS ..................................................................... 14A-32

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS ............................................................................................................ 14A-34

14A.8 CONCLUSION......................................................................................................... 14A-36

REFERENCES ..................................................................................................................... 14A-38

14A.9 ANNEX..................................................................................................................... 14A-43

14A.9.1 Other (non-CO2) gases.................................................................................. 14A-44

14A.9.2 Extrapolating Emissions Projections to 2300 ............................................... 14A-46

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) .................................. 14A-2

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-18

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socioeconomic 

Trajectory, and Discount Rate for 2010 (in 2007 dollars)...................... 14A-29

Table 14A.5.2 Social Cost of CO2, 2010 – 2050 (in 2007 dollars) ................................ 14A-31

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates 

between 2010 and 2050 .......................................................................... 14A-31

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation............ 14A-35

Table 14A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ................................ 14A-43

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

14A-i
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-16

Figure 14A.5.1 Level of Global GDP across EMF Scenarios ......................................... 14A-30

Figure 14A.9.1 Sulfur Dioxide Emission Scenarios........................................................ 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.) ...................................................................................................... 14A-48

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-49

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

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-ii
APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866a

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 

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-1

 
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

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 

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

 
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.

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 

section 16-A.5 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 

14A-3
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 

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 

14A-4
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. 

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, reducedc 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

 
form 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.

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.

14A-6
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 

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

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 re14A-7

 
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.

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.

calibrated 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.

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

 
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, 

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 

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

 
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 

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 

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

 
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 

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):

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).

14A-13

 
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.1.1 included below gives summary statistics for the four calibrated distributions.

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

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.

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

 
(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 

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. 

14A-15
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

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. 

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-16

 
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 (ii.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. 

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-17

 
Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF-22 Reference 

Scenarios

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. 

o 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.

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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, lowcarbon 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 

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-19

 
the selected discount rate, which is intended to reflect society's marginal rate of substitution 

between consumption in different time periods. 

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, 

14A-20
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).

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 

14A-21
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 

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 

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.

14A-22

 
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

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, because

η equal to 1 suggests savings rates that do not conform to observed behavior. 

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).

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-23

 
• 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 

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 

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-24

 
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 

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 

y 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-25

 
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 meanreverting 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 

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A-26

 
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. 

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.)

14A-27
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 

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-28
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 

14A-29

 
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, 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.

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.

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-30

 
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.5.3illustrates 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 

14A-31
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 

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. 

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-32

 
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 

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.

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-33

 
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 

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 

14A-34
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 

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. 

14A-35
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 

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 

14A-36
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 

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-37
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14A-42
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-43
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 

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-44

 
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. 

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, 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-45

 
.

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.

14A-46
2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300.

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. 

www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf

14A-47

 
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-48
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. 

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)

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]

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
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. 

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

14274053667992

105118131144157170183196209222235248261274

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-4

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

REFERENCES ......................................................................................................................14B-12

14B.6 ANNEX......................................................................................................................14B-14

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO2, 2010 – 2050 (in 2007 dollars per ton of 

CO2) ...........................................................................................................14B-9

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 

2050..........................................................................................................14B-10

Table 14B.6.1 Annual SCC Values: 2010-2050 (2007$/ton CO2)..................................14B-14

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton 

CO2) .........................................................................................................14B-15

Table 14B.6.3 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

LIST OF FIGURES

14B-i
Figure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO2) ............14B-10

14B-ii
APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR 

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

14B.1 PREFACE

The following text is reproduced almost verbatim from the May 2013 report of the 

Interagency Working Group on the Social Cost of Carbon of the United States Government. 

Minor changes were made to the working group's report to make it more consistent with the rest 

of this technical support document.

14B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) a

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.”b 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.c 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 14B.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 14B.4 presents the updated schedule of SCC estimates for 2010 –

2050 based on these versions of the models.

a

 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. b http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf c 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).2d

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 

d 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.e 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,f 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 

e 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. f 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.g 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.h We discuss each of these in turn.

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 

g 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.

h 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

 
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 

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 

14B-5
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 

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-6
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 with or without 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 

14B-7
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 

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-8
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.

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 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192

2045 24 66 92 206

2050 27 71 98 221

14B-9
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 

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% 3.2% 2.4% 4.3%

2020-2030 3.4% 2.1% 1.7% 2.4%

2030-2040 3.0% 1.8% 1.5% 2.0%

2040-2050 2.6% 1.6% 1.3% 1.5%

14B-10
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. <www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/SocialCost-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)

14B-12
10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description. 2011, 

Cambridge Judge Business School 

<www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1104.pdf>.

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model. 2011, Cambridge Judge 

Business School 

<www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1105.pdf>.

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2. 2011, 

Cambridge Judge Business School 

<www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/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 

14B-13
14B.6 ANNEX

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 33 52 90

2011 11 34 54 94

2012 11 35 55 98

2013 11 36 56 102

2014 11 37 57 106

2015 12 38 58 109

2016 12 39 60 113

2017 12 40 61 117

2018 12 41 62 121

2019 12 42 63 125

2020 12 43 65 129

2021 13 44 66 132

2022 13 45 67 135

2023 13 46 68 138

2024 14 47 69 141

2025 14 48 70 144

2026 15 49 71 147

2027 15 49 72 150

2028 15 50 73 153

2029 16 51 74 156

2030 16 52 76 159

2031 17 53 77 163

2032 17 54 78 166

2033 18 55 79 169

2034 18 56 80 172

2035 19 57 81 176

2036 19 58 82 179

2037 20 59 84 182

2038 20 60 85 185

2039 21 61 86 188

2040 21 62 87 192

2041 22 63 88 195

2042 22 64 89 198

2043 23 65 90 200

2044 23 65 91 203

2045 24 66 92 206

2046 24 67 94 209

2047 25 68 95 212

2048 25 69 96 215

2049 26 70 97 218

2050 27 71 98 221

14B-14
Table 14B.6.2 202 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 -17 -1 5 17 34 44 59 90 113 176

MERGE -7 2 7 16 30 35 49 72 91 146

MESSAGE -19 -4 2 12 27 32 46 70 87 135

MiniCAM Base -9 1 8 18 35 45 59 87 108 172

5th Scenario -30 -12 -5 6 19 24 35 57 72 108

Table 14B.6.3 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 -14 -3 1 9 20 25 35 54 69 111

MERGE -8 -1 3 9 18 22 31 47 60 97

MESSAGE -16 -5 -1 6 16 18 28 43 55 88

MiniCAM Base -9 -1 3 10 21 27 35 53 67 107

5th Scenario -22 -10 -5 2 10 13 20 33 42 63

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 -3 -1 2 3 6 11 15 25

MERGE -6 -3 -2 0 3 4 7 12 16 27

MESSAGE -10 -6 -4 -1 2 2 5 9 13 23

MiniCAM Base -7 -3 -2 0 3 4 7 11 15 26

5th Scenario -11 -7 -5 -2 0 0 3 6 8 14

14B-16
CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-2

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-4

15.3.1 Installed Capacity............................................................................................... 15-4

15.3.2 Electricity Generation ........................................................................................ 15-7

15.3.3 Results Summary ............................................................................................. 15-11

REFERENCES ........................................................................................................................ 15-12

LIST OF TABLES

Table 15.3.1 Summary of Utility Impact Results........................................................... 15-11

LIST OF FIGURES

Figure 15.3.1 Total Electric Capacity Reduction............................................................... 15-4

Figure 15.3.2 Coal Capacity Reduction ............................................................................. 15-5

Figure 15.3.3 Nuclear Capacity Reduction ........................................................................ 15-5

Figure 15.3.4 Gas Combined Cycle Capacity Reduction................................................... 15-6

Figure 15.3.5 Peaking Capacity Reduction........................................................................ 15-6

Figure 15.3.6 Renewables Capacity Reduction.................................................................. 15-7

Figure 15.3.7 Total Generation Reduction......................................................................... 15-8

Figure 15.3.8 Coal Generation Reduction.......................................................................... 15-8

Figure 15.3.9 Nuclear Generation Reduction..................................................................... 15-9

Figure 15.3.10 Gas Combined Cycle Generation Reduction ............................................... 15-9

Figure 15.3.11 Oil Generation Reduction .......................................................................... 15-10

Figure 15.3.12 Renewables Generation Reduction............................................................ 15-10

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 is based on output 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). 

The EIA publishes a reference case, which incorporates all existing energy-related policies at the 

time of publication, and a variety of side cases which analyze the impact of different policies, 

energy price and market trends. As of 2014, DOE is using a new methodology based on results 

published for the Annual Energy Outlook 2014 (AEO 2014) Reference case and a set of side 

cases that implement a variety of efficiency-related policies.

2

The new approach retains key aspects of DOE’s previous methodology, and provides 

some improvements:

• The assumptions used in the AEO reference case and side cases are fully 

documented and receive detailed public scrutiny.

• NEMS is updated each year, with each edition of the AEO, to reflect changes in 

energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS permits the modeling of interactions among 

the various energy supply and demand sectors. 

• Using EIA published side cases to estimate the utility impacts enhances the 

transparency of DOE’s analysis.

• The variability in impacts estimates from one edition of AEO to the next will be 

reduced under the new approach.

On the average, however, over the full analysis period, the results from the new approach 

are comparable to results from the old approach.

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

15-1

 
15.2 METHODOLOGY 

DOE estimates 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. 

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 (TWh) from the stock of electric generating capacity 

changes, the total generation capacity itself (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. Changes in 

generation by fuel type lead in turn to changes in total power sector emissions of SO2, NOx, Hg 

and CO2.

DOE’s new approach examines a series of AEO side cases to estimate the relationship 

between demand reductions and the marginal energy, emissions and capacity changes. The 

assumptions for each side case are documented in Appendix E of the AEO. The side cases, or 

scenarios, that incorporate significant changes to equipment efficiencies relative to the Reference 

case are: 

• 2013 Technology (leaves all technologies at 2013 efficiencies);

• Best Available Technology (highest efficiency irrespective of cost);

• High Technology (higher penetration rates for efficiency and demand management);

• Extended Policies (includes efficiency standards that are not in the reference).

Scenarios that incorporate policies that directly affect the power sector without changes 

in energy demand (for example, subsidies for renewables, or high fuel price assumptions) are not 

appropriate for this analysis. The methodology proceeds in seven steps:

1. Supply-side data on generation, capacity and emissions, and demand-side data on electricity 

use by sector and end-use, are extracted from each side case. The data are converted to 

differences relative to the AEO Reference case.

2. The changes in electricity use on the demand-side data are allocated to one of three 

categories: on-peak, shoulder, and off-peak. These categories are used in the utility sector to

correlate end-use consumption with supply types. For each of the end-uses that are modeled 

explicitly in NEMS, load shape information is used to identify the fraction of annual 

electricity use assigned to each category. On-peak hours are defined as noon-5pm, June 

through September. Off-peak hours are nights and Sundays. All other hours are assigned to 

the shoulder period.

15-2
3. For each year and each side case, the demand-side reductions to on-peak, off-peak and 

shoulder-period electricity use are matched on the supply-side to reductions in generation by 

fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. The 

allocation is based on the following rules:

3.1. All petroleum-based generation is allocated to peak periods;

3.2. Natural gas generation is allocated to any remaining peak reduction; this is consistent 

with the fact that oil and gas steam units are used in NEMS to meet peak demand;

3.3. Base-load generation (nuclear and coal) is allocated proportionally to all periods;

3.4. The remaining generation of all types is allocated to the remaining off-peak and shoulder 

reductions proportionally.

4. The output of step 3 defines fuel-share weights giving the fraction of energy demand in each 

load category that is met by each fuel type as a function of time. These are combined with the 

weights that define the load category shares by end-use to produce coefficients that allocate a 

marginal reduction in end-use electricity demand to each of the five fuel types.

5. A regression model is used to relate reductions in generation by fuel type to reductions in 

emissions of power sector pollutants. The model produces coefficients that define the change 

in total annual emissions of a given pollutant resulting from a unit change in total annual 

generation for each fuel type, as a function of time. These coefficients are combined with the 

weights calculated in step 4 to produce coefficients that relate emissions changes to changes 

in end-use demand.

6. A regression model is used to relate reductions in generation by fuel type to reductions in 

installed capacity. The categories used for installed capacity are the same as for generation 

except for peak: NEMS uses two peak capacity types (combustion turbine/diesel and oil and 

gas steam) which are combined here into a single “peak” category. The model produces 

coefficients that define the change in total installed capacity of a given type resulting from a 

unit change in total annual generation for the corresponding fuel type. These coefficients are 

combined with the weights calculated in step 4 to produce coefficients that relate installed 

capacity changes to changes in end-use demand, as a function of time.

7. The coefficient time-series for fuel share, pollutant emissions and capacity for the 

appropriate end use are multiplied by the stream of energy savings calculated in the NIA to 

produce estimates of the utility impacts.

This analysis ignores pumped storage, fuel cells and distributed generation, as these 

generation types are not affected by the policy changes modeled in the EIA side cases. The 

methodology is described in more detail in K. Coughlin, “Utility Sector Impacts of Electricity 

Demand Reductions”.

3

15-3
15.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types used to supply 

electricity to commercial buildings.

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. Note that a negative number means an 

increase in capacity under a TSL.

Figure 15.3.1 Total Electric Capacity Reduction

0

500

1,000

1,500

2,000

2,500

20202025

20302035

204020452050205520602065

20702075

2080208520902095

MW

Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15-4
Figure 15.3.2 Coal Capacity Reduction

Figure 15.3.3 Nuclear Capacity Reduction

15-5
Figure 15.3.4 Gas Combined Cycle Capacity Reduction

Figure 15.3.5 Peaking Capacity Reduction

15-6
Figure 15.3.6 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 fuel type. Note that a negative number means an increase in generation under a 

TSL.

15-7
Figure 15.3.7 Total Generation Reduction

Figure 15.3.8 Coal Generation Reduction

15-8
Figure 15.3.9 Nuclear Generation Reduction

Figure 15.3.10 Gas Combined Cycle Generation Reduction

15-9
Figure 15.3.11 Oil Generation Reduction

Figure 15.3.12 Renewables Generation Reduction

15-10
15.3.3 Results Summary 

Table 15.3.1presents a summary of the utility impact results for commercial and 

industrial pumps.

Table 15.3.1 Summary of Utility Impact Results

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Installed Capacity Reduction (MW)

2020 4.12 18.7 38.0 59.5 86.3

2025 27.4 124 249 392 568

2030 47.3 215 430 679 985

2035 64.8 297 592 941 1365

2040 79.7 369 734 1171 1700

Electricity Generation Reduction (GWh)

2020 15.5 70.6 143 224 325

2025 88.5 401 805 1265 1834

2030 144 657 1310 2071 3004

2035 185 848 1689 2682 3891

2040 214 992 1972 3146 4568

15-11
REFERENCES

1. U.S. Department of Energy-Energy Information Administration, National Energy 

Modeling System: An Overview, 2009. Report No. DOE/EIA-0581(2009). 

<www.eia.gov/oiaf/aeo/overview/>.

2. U.S. Department of Energy-Energy Information Administration, Annual Energy Outlook 

2014 with Projections to 2040, 2014. Washington, DC. <www.eia.gov/forecasts/aeo/>.

3. Coughlin, K., Utility Sector Imacts of Reduced Electricity Demand, 2014. Lawrence 

Berkeley National Laboratory. Report No. LBNL-6864E. 

15-12
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

16.1 LONG-TERM RESULTS.............................................................................................. 16-3

REFERENCES .......................................................................................................................... 16-5

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

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 commercial and industrial pumps. Job increases or 

decreases reported in this chapter are separate from the direct pump production sector 

employment impacts reported in the manufacturer impact analysis (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 

new investment or not at all (i.e., they may remain “saved”). The standards may increase the 

purchase price of products, including the retail price plus sales tax, 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 for 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).

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. Because input/output models do not allow prices to bring markets into 

equilibrium, they are best used for short-run analysis. 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.

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 3.1.1

2 (Impact of Sector Energy Technologies) as a successor to ImBuild,3

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 

16-1
economic multiplier approaches, ImSET allows for more 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 so 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 affects 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 

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 products. The increased cost of products leads to higher employment in 

the product manufacturing sectors and lower employment in other economic sectors. Second, 

commercial firm and residential spending are redirected from electric utilities toward firms that 

supply production inputs for energy-efficient products. Third, investment funds from electric 

utilities are released for use in other sectors of the economy. When consumers use less energy, 

utilities experience relative reductions in demand which leads to reductions in utility sector 

investment and employment.

DOE also notes that the employment impacts estimated with ImSET for the entire 

economy differ from the employment impacts in the commercial and industrial pump

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 commercial and industrial pump 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 a standard in its first year on three 

aggregate sectors, the commercial and industrial pump production sector, the electricity 

generation sector, and the general consumer goods sector (as mentioned above ImSET’s 

16-2
calculations are made at a much more disaggregate level). By raising energy efficiency, the 

standard generally increases the purchase price of pumps; 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 reduction in electricity demand causes a 

reduction in employment in that sector. Finally, based on the net impact of increased 

expenditures on pumps 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, which generates more employment, the converse is true for workers laid off).

Table 16.4.1 presents the modeled net employment impact from the standards in 2021, in 

millions of jobs. For context, the U.S. labor force had 156 million people in July 2014.a DOE did 

not consider imports in this analysis as most pumps are produced domestically.

Table 16.4.1 Net National Short-term Change in Employment (Number of Jobs)

Trial Standard Level 2020 2025

TSL 1 10 50

TSL 2 50 220

TSL 3 110 440

TSL 4 180 710

TSL 5 270 1040

For context, the Office of Management of Budget currently assumes that the 

unemployment rate may decline to 6.0 percent in the fourth quarter of 2014 and drop further to 

5.4 percent by 2017.5 The unemployment rate in 2020 is projected to be 5.4 percent, which is 

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 longerterm employment.

16.1 LONG-TERM RESULTS

Over the long term DOE expects the electricity savings to consumers to increasingly 

dominate the increase in product 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. Because the electric utility 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 electric utilities

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, even 

a Bureau of Labor Statistics: Labor Force Statistics (Available at: 

http://data.bls.gov/pdq/SurveyOutputServlet?request_action=wh&graph_name=LN_cpsbref1).

16-3

 
to the extent that markets are slow to adjust, DOE anticipates that net labor market impacts will 

in general be negligible over time due to the small magnitude of the short-term effects presented 

in Table 16.4.1 for most product classes and TSLs. The ImSET model projections, assuming no 

price or wage effects until 2025, 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. Prepared for the U.S. Department of Energy under Contract 

DE-AC06-76RLO 1830. 

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 2015 Mid-Session Review: Budget of the 

U.S. Government, <

www.whitehouse.gov/sites/default/files/omb/budget/fy2015/assets/15msr.pdf>.

16-5
CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION ......................................................................................................... 17-1

17.2.1 Methodology...................................................................................................... 17-2

17.2.2 Assumptions Regarding Non-Regulatory Policies ............................................ 17-3

17.2.3 Policy Interactions ............................................................................................. 17-4

17.3 NON-REGULATORY POLICY ASSUMPTIONS ...................................................... 17-4

17.3.1 No New Regulatory Action ............................................................................... 17-4

17.3.2 Consumer Rebates ............................................................................................. 17-5

17.3.2.1 Methodology ............................................................................................ 17-5

17.3.2.2 Analysis.................................................................................................... 17-6

17.3.3 Consumer Tax Credits..................................................................................... 17-13

17.3.4 Manufacturer Tax Credits................................................................................ 17-16

17.3.5 Voluntary Energy Efficiency Targets.............................................................. 17-17

17.3.6 Bulk Government Purchases............................................................................ 17-22

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES......................................... 17-24

REFERENCES ........................................................................................................................ 17-30

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to National Standards................................... 17-1

Table 17.1.2 Efficiency Levels by TSL (C-Value)........................................................... 17-4

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates........................................... 17-7

Table 17.3.2 Market Penetrations in 2020 Attributable to Consumer Rebates............... 17-12

Table 17.3.3 Market Penetrations in 2020 Attributable to Consumer Tax Credits ........ 17-15

Table 17.3.4 Market Penetrations in 2020 Attributable to Manufacturer Tax Credits... 17-17

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Energy Efficiency 

Targets (TSL#2)......................................................................................... 17-19

Table 17.3.6 Market Penetrations in 2020 Attributable to Voluntary Energy 

Efficiency Targets...................................................................................... 17-20

Table 17.3.7 Market Penetrations in Selected Years Attributable to Voluntary 

Energy Efficiency Targets for TSL #2....................................................... 17-21

Table 17.3.8 Market Penetrations in 2020 Attributable to Bulk Government 

Purchases.................................................................................................... 17-24

Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives (TSL #2)........................ 17-29

17-i
LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Commercial and Industrial Pumps........... 17-11

Figure 17.4.1 Market Penetration of Efficient ESCC 1800 Commercial and Industrial 

Pumps (TSL 2)........................................................................................... 17-25

Figure 17.4.2 Market Penetration of Efficient ESCC 3600 Commercial and Industrial 

Pumps (TSL #2)......................................................................................... 17-25

Figure 17.4.3 Market Penetration of Efficient ESFM 1800 Commercial and 

Industrial Pumps (TSL #2)......................................................................... 17-26

Figure 17.4.4 Market Penetration of Efficient ESFM 3600 Commercial and 

Industrial Pumps (TSL #2)......................................................................... 17-26

Figure 17.4.5 Market Penetration of Efficient IL 1800 Commercial and Industrial 

Pumps (TSL #2)......................................................................................... 17-27

Figure 17.4.6 Market Penetration of Efficient IL 3600 Commercial and Industrial 

Pumps (TSL #2)......................................................................................... 17-27

Figure 17.4.7 Market Penetration of Efficient VT-S 3600 Commercial and Industrial 

Pumps (TSL #2)......................................................................................... 17-28

17-ii
CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that the regulatory action

described in the Federal Register notice associated with this TSD constitutes an “economically 

significant regulatory action” under Executive Order (E.O.) 12866, Regulatory Planning and 

Review. 58 FR 51735 (October 4, 1993). For such actions, E.O. 12866 requires Federal agencies 

to provide “an assessment, including the underlying analysis, of costs and benefits of potentially 

effective and reasonably feasible alternatives to the planned regulation, identified by the agencies 

or the public (including improving the current regulation and reasonably viable non-regulatory 

actions), and an explanation why the planned regulatory action is preferable to the identified 

potential alternatives.” 58 FR 51735, 51741. 

To conduct this analysis, DOE used an integrated National Impact Analysis (NIA)-RIA

model built on the NIA model discussed in Chapter 10. DOE identified five non-regulatory 

policy alternatives that possibly could provide incentives for the same energy efficiency levels as 

the ones in the proposed trial standard levels for the commercial and industrial pumps that are the 

subject of this rulemaking. The non-regulatory policy alternatives are listed in Table 17.1.1, 

which also includes the “no new regulatory action” alternative. DOE evaluated each alternative 

in terms of its ability to achieve significant energy savings at a reasonable cost, and compared 

the effectiveness of each to the effectiveness of the proposed standards for the commercial and 

industrial pumps covered by this rulemaking. 

Table 17.1.1 Non-Regulatory Alternatives to National Standards 

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits

Voluntary Energy Efficiency Targets

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed in (excluding 

the alternative of “No New Regulatory Action”). Section 17.4 presents the results of the policy 

alternatives.

17.2 NON-REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost 

effectiveness of the non-regulatory policy alternatives for commercial and industrial pumps. This 

section also describes the assumptions underlying the analysis. 

17-1
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. Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet 

model. Appendix 17A discusses the NIA-RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meets the 

efficiency levels corresponding to each TSL. 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 equipment meeting the target 

efficiency levels set for each TSL. The shipments of equipment for any given year reflect a 

distribution of efficiency levels. DOE assumed, for each TSL, that new energy efficiency 

standards would affect 100 percent of the shipments of products that did not meet the TSL target 

levels in the base case,a 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 commercial and industrial pumps attributable to each policy 

alternative. 

Increasing the efficiency of a product often increases its average installed cost. However, 

operating costs generally decrease because energy consumption declines. DOE therefore 

calculated an NPV for each non-regulatory alternative in the same way it did for the proposed

standards. In some policy scenarios, increases in total installed 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 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 NPVs slightly.

The following are key measures for evaluating the impact of each alternative. 

• National Energy Savings (NES), given in quadrillion Btus (quads), describes the 

cumulative national energy saved over the lifetime of equipment purchased during the 

30-year analysis period starting in the effective date of the policy (2020-2049).

• Net Present Value (NPV), represents the value of net monetary savings in 2015, 

expressed in 2013$, from equipment purchased during the 30-year analysis period 

starting in the effective date of the policy (2020-2049). DOE calculated the NPV as 

the difference between the present values of installed equipment cost and operating 

expenditures in the base case and the present values of those costs in each policy case. 

a The base case for the NIA is a market-weighted average energy efficiency calculated from units at several 

efficiency levels.

17-2

 
DOE calculated operating expenses (including energy costs) for the life of the 

product.

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’ response 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 be met 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 new commercial and industrial pumps 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 having the same technology as required by standards (the 

target level), according to the minimum energy efficiency set for each TSL. As opposed to the 

standards case, however, the policy cases may not lead to 100 percent market penetration of units 

that meet the target level.

Table 17.1.2 shows the C-Valuesb corresponding to each TSL. The table also includes the 

Pump Energy Ratings (PER), which correspond – for each TSL – to the percentage of pumps 

that will be removed from the market if that TSL is selected.

b For more on C-Values please refer to Chapter 5.

17-3

 
Table 17.1.2 Efficiency Levels by TSL (C-Value)

TSL 1

(PER 10)

TSL 2

(PER 25)

TSL 3

(PER 40)

TSL 4

(PER 55)

TSL 5

(PER 70)

ESCC 1800 131.63 128.47 126.67 125.07 123.71

ESCC 3600 134.60 130.42 128.92 127.35 125.29

ESFM 1800 132.95 128.85 127.04 125.12 123.71

ESFM 3600 134.98 130.99 129.26 127.77 126.07

IL 1800 133.95 129.30 127.30 126.00 124.45

IL 3600 138.86 133.84 131.04 129.38 127.35

VT-S 3600 135.93 134.13 130.83 128.92 127.29

DOE assumed that the effects of non-regulatory policies would last from the effective 

date of standards—2020—through the end of the analysis period, which is 2049. 

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 voluntary efficiency targets implemented with consumer rebates or tax credits. However, 

DOE attempted to make conservative assumptions to avoid double-counting policy impacts. The 

resulting policy impacts are therefore not additive, and 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 commercial and industrial pumps.

17.3 NON-REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to the standards proposed for commercial and industrial pumps.

(Because the alternative of “No New Regulatory Action” has no energy or economic impacts, 

essentially representing the NIA base case, DOE did not perform any additional analysis for that 

alternative.) DOE developed estimates of the market penetration of more efficient products both 

with and without each of the non-regulatory policy alternatives.

17.3.1 No New Regulatory Action

The case in which no new regulatory action is taken with regard to the energy efficiency 

of commercial and industrial pumps constitutes the base case, as described in Chapter 10,

National Impact Analysis. The base case provides the basis of comparison for all other policies. 

By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17-4
17.3.2 Consumer Rebates

DOE considered the scenario in which the Federal government would provide financial 

incentives in the form of rebates to consumers for purchasing energy-efficient equipment. This 

policy provides a consumer rebate for purchasing commercial and industrial pumps that operate 

at the same efficiency levels as stipulated in each TSL. 

17.3.2.1 Methodology

DOE based its evaluation methodology for consumer rebates on a comprehensive study 

of California’s potential for achieving energy efficiency. The 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.5DOE decided that the most appropriate available method for this RIA 

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 equipment 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 

17A contains additional details on internal and external information diffusion.

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 policy measure. XENERGY calibrated the curves based on participation data from 

utility rebate programs. The curves illustrate the increased penetration (i.e., increased market 

share) of efficient equipment driven by consumer response to changes in B/C ratio induced by 

rebate programs. The penetration curves depict various diffusion patterns based on perceived 

market barriers (from no-barriers to extremely-high-barriers) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetration curves based on expert 

advice founded on more recent utility program experience.5, 8

c XENERGY is now owned by KEMA, Inc. (www.kema.com)

17-5

 
DOE modeled the effects of a consumer rebate policy for commercial and industrial 

pumps by determining, for each TSL, the increase in market penetration of equipment meeting 

the target level relative to their market penetration in the base case. It used the interpolation 

method presented in Blum et al (2011)9 to create customized penetration curves based on 

relationships between actual base case market penetrations and actual B/C ratios. To inform its 

estimate of B/C ratios provided by a rebate program DOE performed a thorough nationwide 

search for existing rebate programs for commercial and industrial pumps. It gathered data on 

utility or agency rebates throughout the nation for this equipment, and used this data to calibrate 

the customized penetration curves it developed for each equipment class covered by this RIA so 

they can best reflect the market barrier levels that consumer rebates for commercial and 

industrial pumps would face. Section 17.3.2.2 shows the resulting interpolated curves used in the 

analysis.

17.3.2.2 Analysis 

DOE estimated the effect of increasing the B/C ratio of commercial and industrial pumps

via a rebate that would pay – depending on the equipment class – part or all of the increased

installed cost of units that meet the target efficiency levels compared to units meeting the 

baseline efficiency level.

d To inform its estimate of an appropriate rebate amount, DOE 

performed a thorough nationwide search for existing rebate programs for commercial and 

industrial pumps. It gathered data from a sample of utility and agency rebate programs that 

includes 52 rebate programs for commercial and industrial pumps initiated by three utilities or 

agencies in various States. DOE then estimated a market representative rebate value for each

equipment class covered by this RIA which it applied in the calculation of the B/C ratio of

commercial and industrial pumps under the effect of consumer rebates. (Appendix 17A,

identifies the rebate programs and details the methodology DOE used to estimate a market 

representative rebate amount.) DOE assumed that rebates would remain in effect at the same 

level throughout the forecast period (2020-2049). 

DOE first calculated the B/C ratio of a commercial or industrial pump without a rebate 

using the difference in total installed costs (C) and lifetime operating cost savingse (B) between a

unit meeting the target level and a baseline unit. It then calculated the B/C ratio given a rebate

for the unit meeting the target efficiency level. Because the rebate reduced the incremental cost, 

the unit receiving the rebate had a larger B/C ratio. Table 17.3.1 shows the effect of consumer 

rebates for each TSL on the B/C ratio of commercial and industrial pumps shipped in the first 

year of the analysis period.

d The baseline technology is defined in the engineering analysis, Chapter 5, as the technology that represents the 

basic characteristics of commercial and industrial pumps. A baseline unit typically is one that just meets current 

Federal energy conservation standards and provides basic consumer utility. 

e The cash flow of the operating cost savings is discounted to the purchase year using a 7 percent discount rate.

17-6

 
Table 17.3.1 Benefit/Cost Ratios Without and With Rebates

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

B/C Ratio Without Rebate 2.1 2.8 2.6 2.3 2.0

Rebate Amount (2013$) 164.16 164.16 164.16 164.16 164.16

B/C Ratio With Rebate 25.6 7.1 4.4 3.4 2.6

Estimated Market Barriers Low-Mod Low-Mod Mod Low-Mod Low-Mod

ESCC 3600

B/C Ratio Without Rebate 3.5 4.3 3.5 3.3 3.1

Rebate Amount (2013$) 268.27 268.27 268.27 268.27 268.27

B/C Ratio With Rebate infinite infinite 13.1 7.8 5.6

Estimated Market Barriers Mod-High Mod Mod-High Mod-High No

ESFM 1800

B/C Ratio Without Rebate 3.6 3.4 3.3 3.0 2.9

Rebate Amount (2013$) 374.79 374.79 374.79 374.79 374.79

B/C Ratio With Rebate infinite 16.5 8.0 5.3 4.3

Estimated Market Barriers Low-Mod Low-Mod Low-Mod Mod Low-Mod

ESFM 3600

B/C Ratio Without Rebate 6.6 8.5 8.5 7.6 7.1

Rebate Amount (2013$) 541.04 541.04 541.04 541.04 541.04

B/C Ratio With Rebate infinite infinite infinite 150.4 26.6

Estimated Market Barriers Mod Mod-High Mod-High Mod-High Mod

IL 1800

B/C Ratio Without Rebate 3.2 2.7 2.1 1.6 1.4

Rebate Amount (2013$) 131.11 131.11 131.11 131.11 131.11

B/C Ratio With Rebate infinite 5.1 2.8 1.9 1.6

Estimated Market Barriers Low-Mod Mod Low-Mod Low-Mod No-Low

IL 3600

B/C Ratio Without Rebate 4.2 3.3 3.0 2.5 2.1

Rebate Amount (2013$) 195.58 195.58 195.58 195.58 195.58

B/C Ratio With Rebate infinite 24.2 6.5 4.0 2.8

Estimated Market Barriers Mod Mod Mod-High Low-Mod No-Low

VT-S 3600

B/C Ratio Without Rebate 0.7 0.9 1.5 1.7 1.7

Rebate Amount (2013$) 182.62 182.62 182.62 182.62 182.62

B/C Ratio With Rebate 1.6 1.7 2.7 2.7 2.5

Estimated Market Barriers No Low-Mod Low-Mod Low-Mod No-Low

* No-Low: No-to-Low market barriers; Mod: Moderate market barriers; Low-Mod: Low-to-Moderate market 

barriers; Mod-High: Moderate-to-High market barriers; xHigh: Extremely-High market barriers; Hg-xHg: Highto-Extremely-High market barriers.

17-7
DOE used the B/C ratio along with the customized penetration curves shown in Figure 

17.3.1 to estimate the percentage of consumers who would purchase commercial and industrial 

pumps that meet the target levels both with and without a rebate incentive. For some equipment 

classes – namely ESFM 1800 and IL 3600 – the market penetration with rebates is constrained 

by the market share that is below the target level in the base case, and therefore the policy 

measure does not fulfill its potential for market transformation. As for equipment classes ESCC 

3600 and ESFM 3600, because the rebate amount would cover the upfront costs in full, the B/C 

in the policy case is infinite and therefore the market penetration with rebates is not represented 

in Figure 17.3.1. The estimated levels of market barriers corresponding to the penetration curves

DOE calculated to represent the market behavior for commercial and industrial pumps at the 

proposed TSL are indicated (highlighted) in Table 17.3.1. DOE assumed the estimated market 

barriers would remain the same over the whole analysis period. 

TSL #2

ESCC 1800

17-8
ESCC 3600 ESFM 1800

17-9
ESFM 3600 IL 1800

17-10
Figure 17.3.1 Market Penetration Curves for Commercial and Industrial Pumps IL 3600 VT-S 3600

17-11
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 units that meet the target level in the base case to obtain the market share of units that 

meet the target level in the rebate policy case. 

Table 17.3.2 summarizes DOE’s assumptions for commercial and industrial pumps

regarding the market penetration of products in 2020 that meet the target levels at each TSL

given a consumer rebate. 

Table 17.3.2 Market Penetrations in 2020 Attributable to Consumer Rebates

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base-Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2%

Policy Case Market Share 34.3% 40.7% 22.6% 21.7% 29.0%

Increased Market Share 18.5% 22.5% 11.9% 8.1% 5.8%

ESCC 3600

Base-Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Policy Case Market Share 11.9% 26.5% 26.6% 17.0% 76.4%

Increased Market Share 3.8% 11.9% 23.0% 13.1% 10.3%

ESFM 1800

Base-Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1%

Policy Case Market Share 21.2% 37.3% 45.3% 22.6% 37.9%

Increased Market Share 0.9% 21.2% 21.1% 12.3% 9.8%

ESFM 3600

Base-Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3%

Policy Case Market Share 28.2% 43.5% 58.7% 54.5% 55.0%

Increased Market Share 4.7% 28.2% 42.8% 43.2% 25.7%

IL 1800

Base-Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3%

Policy Case Market Share 30.7% 23.8% 17.6% 14.1% 40.4%

Increased Market Share 14.7% 13.9% 6.3% 3.4% 3.0%

IL 3600

Base-Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3%

Policy Case Market Share 31.5% 40.3% 16.8% 23.7% 48.8%

Increased Market Share 10.7% 31.5% 12.3% 10.8% 6.5%

VT-S 3600

Base-Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7%

Policy Case Market Share 38.2% 19.6% 13.1% 13.1% 52.2%

Increased Market Share 1.6% 12.1% 8.4% 7.2% 8.5%

DOE used the resulting annual increases in market shares as inputs to represent the rebate 

policy case scenario in its NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

17-12
market penetration trends for the policy case of consumer rebates for commercial and industrial 

pumps. Because energy prices increase over the analysis period and equipment prices are held

constant, the B/C ratios increase over time. With increasing B/C ratios and constant market 

barriers, the increases in market penetration of more efficient technologies eventually increase 

over the analysis period.

17.3.3 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.

10, 11 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 consumer products that meet new 

efficiency standards, DOE assumed the amount of the tax credit would be the same as the 

corresponding rebate amount discussed above. 

DOE estimated that fewer consumers would participate in a tax credit program than 

would take advantage of 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 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.12

In preparing its assumptions to estimate the effects of tax credits on consumer purchases 

of commercial and industrial pumps, 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.

13 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.

14, 15 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, 2013.16 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 

17-13
tax credit program in place in the 1980s. However, DOE did not find data specific enough to 

commercial and industrial pumps to warrant adjusting its analysis method for the Consumer Tax 

Credits policy case. Appendix 17A contains more information on Federal consumer tax credits. 

DOE also reviewed its previous analysis of Oregon’s tax credits for clothes washers to 

provide support for its assumptions.17 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 17A.

DOE applied the assumed 60 percent participation described above to the increase in 

penetration rates estimated for the rebate policy to estimate penetration rates attributable to 

consumer tax credits. In doing so, DOE incorporated the assumptions for consumer response to 

financial incentives from the customized penetration curves it developed for commercial and 

industrial pumps (See Figure 17.3.1).

Table 17.3.3 summarizes DOE’s assumptions for commercial and industrial pumps

regarding the market penetration of products in 2020 that meet the target levels at each TSL

given a consumer tax credit. 

17-14
Table 17.3.3 Market Penetrations in 2020 Attributable to Consumer Tax Credits

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base-Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2%

Policy Case Market Share 26.9% 31.7% 17.9% 18.5% 26.7%

Increased Market Share 11.1% 13.5% 7.1% 4.8% 3.5%

ESCC 3600

Base-Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Policy Case Market Share 10.4% 21.7% 17.4% 11.7% 72.2%

Increased Market Share 2.3% 7.2% 13.8% 7.8% 6.2%

ESFM 1800

Base-Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Policy Case Market Share 10.4% 21.7% 17.4% 11.7% 72.2%

Increased Market Share 2.3% 7.2% 13.8% 7.8% 6.2%

ESFM 3600

Base-Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3%

Policy Case Market Share 26.3% 32.2% 41.6% 37.2% 44.7%

Increased Market Share 2.8% 16.9% 25.7% 25.9% 15.4%

IL 1800

Base-Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3%

Policy Case Market Share 24.8% 18.3% 15.1% 12.8% 39.1%

Increased Market Share 8.8% 8.3% 3.8% 2.1% 1.8%

IL 3600

Base-Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3%

Policy Case Market Share 27.2% 27.7% 11.9% 19.4% 46.2%

Increased Market Share 6.4% 18.9% 7.4% 6.5% 3.9%

VT-S 3600

Base-Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7%

Policy Case Market Share 37.6% 14.8% 9.7% 10.2% 48.8%

Increased Market Share 0.9% 7.3% 5.0% 4.3% 5.1%

The increased market shares attributable to consumer tax credits shown in Table 17.3.3

were used as inputs in the NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of consumer tax credits for commercial and 

industrial pumps that meet the efficiency level for the proposed TSL. Because the increase in 

market penetration for consumer tax credits is proportional to the increase in market penetration 

DOE calculated for consumer rebates, the former follows a similar increasing trend over the 

analysis period as the latter.

17-15
17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that 

produce commercial and industrial pumps that meet the target efficiency levels at each TSL, 

DOE assumed that a manufacturer tax credit would lower the consumer’s purchase cost by an 

amount equivalent to that provided by the consumer rebates or 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.f Because the direct price effect is approximately 

equivalent to the announcement effect,10 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 2005.18 Those manufacturer tax credits have 

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 17A presents details on Federal 

manufacturer tax credits.

DOE applied the assumption of 30 percent participation to the increase in penetration 

rates predicted for the rebate policy to estimate the effects of a manufacturer tax credit policy. In 

doing so, DOE incorporated the assumptions for consumer response to financial incentives from 

the customized penetration curves it developed for commercial and industrial pumps. (See Figure 

17.3.1).

Table 17.3.4 summarizes DOE’s assumptions for commercial and industrial pumps

regarding the market penetration of products in 2020 that meet the target levels at each TSL 

given a manufacturer tax credit.

f 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.

17-16

 
Table 17.3.4 Market Penetrations in 2020 Attributable to Manufacturer Tax Credits

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base-Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2%

Policy Case Market Share 21.3% 24.9% 14.3% 16.1% 24.9%

Increased Market Share 5.6% 6.7% 3.6% 2.4% 1.7%

ESCC 3600

Base-Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Policy Case Market Share 9.3% 18.1% 10.5% 7.8% 69.2%

Increased Market Share 1.1% 3.6% 6.9% 3.9% 3.1%

ESFM 1800

Base-Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1%

Policy Case Market Share 20.6% 22.5% 30.6% 14.0% 31.0%

Increased Market Share 0.3% 6.4% 6.3% 3.7% 3.0%

ESFM 3600

Base-Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3%

Policy Case Market Share 24.9% 23.7% 28.8% 24.3% 37.0%

Increased Market Share 1.4% 8.5% 12.8% 13.0% 7.7%

IL 1800

Base-Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3%

Policy Case Market Share 20.4% 14.1% 13.2% 11.8% 38.2%

Increased Market Share 4.4% 4.2% 1.9% 1.0% 0.9%

IL 3600

Base-Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3%

Policy Case Market Share 24.0% 18.2% 8.2% 16.1% 44.3%

Increased Market Share 3.2% 9.4% 3.7% 3.2% 1.9%

VT-S 3600

Base-Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7%

Policy Case Market Share 37.1% 11.1% 7.2% 8.0% 46.3%

Increased Market Share 0.5% 3.6% 2.5% 2.2% 2.6%

The 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 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of manufacturer tax credits for commercial and 

industrial pumps. Because the increase in market penetration for manufacturer tax credits is 

proportional to the increase in market penetration DOE calculated for consumer rebates, the 

former follows a similar increasing trend over the analysis period as the latter.

17.3.5 Voluntary Energy Efficiency Targets 

DOE assumed that voluntary energy efficiency targets would lead manufacturers of 

commercial and industrial pumps to gradually stop producing units that operate below the

17-17
efficiency levels set for each TSL. DOE assumed that the impetus for phasing out production of 

low-efficiency units would be a program with impacts similar to those of the ENERGY STAR 

labeling program conducted by the Environmental Protection Agency (EPA) and DOE in 

conjunction with industry partners. The ENERGY STAR program specifies the minimum energy 

efficiencies that various products must have to receive the ENERGY STAR label. ENERGY 

STAR encourages consumers to purchase efficient products via marketing that promotes 

consumer label recognition, various incentive programs that adopt the ENERGY STAR 

specifications, and manufacturers’ promotion of their qualifying appliances. ENERGY STAR 

projects market penetration of compliant appliances and estimates the percentage of sales of 

compliant appliances that are attributable to the ENERGY STAR program. 

Researchers have analyzed the ENERGY STAR program’s effects on sales of several 

consumer products. Program efforts generally involve a combination of information 

dissemination and utility or agency rebates. The analyses have been based on State-specific data 

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The 

analyses generally have concluded that the market penetration of ENERGY STAR-qualifying 

appliances is higher in regions or States where ancillary promotional programs have been 

active.

19, 20, 21

DOE believes that informational incentive programs – like ENERGY STAR, or any other 

labeling program sponsored by industry or other organizations – are likely to reduce the market 

barriers to more efficient products over time. During the rebate analysis, when assessing the B/C 

ratio and market penetration in the base case for commercial and industrial pumps, DOE 

observed that the level of market barriers for more efficient commercial and industrial pumps are

in the range of low-to-moderate barriers to a moderate-to-high level of market barriers. DOE 

estimates that voluntary energy efficiency targets could reduce these barriers to lower levels over

10 years. Table 17.3.5 presents the levels of market barriers DOE estimated for commercial and 

industrial pumps in the base case and in the policy case of voluntary energy efficiency targets. 

DOE followed the methodology presented by Blum et al (2011)22 to evaluate the effects that 

such a reduction in market barriers would have on the market penetration of efficient commercial 

and industrial pumps.

g The methodology relies on interpolated market penetration curves to 

calculate – given a B/C ratio – how the market penetration of more efficient units increases as 

the market barrier level to those units decreases.

g For the calculation of B/C ratios DOE discounted the cash flow of the operating cost savings to the purchase year 

using a 7 percent discount rate.

17-18

 
Table 17.3.5 Market Barriers Changes Attributable to Voluntary Energy Efficiency 

Targets (TSL#2)

Base Case Voluntary Energy 

Efficiency Targets

ESCC 1800 Low-Moderate Low

ESCC 3600 Moderate Moderate*

ESFM 1800 Low-Moderate Low

ESFM 3600 Moderate-High Moderate

IL 1800 Moderate Low

IL 3600 Moderate Moderate*

VT-S 3600 Low-Moderate Low

* The policy reduces the market barriers from an upper to a lower level within the range of moderate barriers.

Table 17.3.6 summarizes DOE’s assumptions for commercial and industrial pumps

regarding the market penetration of products in 2020 that meet the target levels at each TSL 

given voluntary energy efficiency targets. Table 17.3.7 expands on Table 17.3.6 to include, for 

the proposed TSL, DOE’s assumptions regarding the market penetration of units in selected 

years.

17-19
Table 17.3.6 Market Penetrations in 2020 Attributable to Voluntary Energy Efficiency 

Targets

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base-Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2%

Policy Case Market Share 19.0% 23.1% 16.5% 18.4% 24.9%

Increased Market Share 3.3% 4.9% 5.8% 4.7% 1.7%

ESCC 3600

Base-Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Policy Case Market Share 9.0% 15.4% 4.5% 5.0% 66.8%

Increased Market Share 0.9% 0.9% 0.9% 1.1% 0.8%

ESFM 1800

Base-Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1%

Policy Case Market Share 21.2% 23.3% 28.3% 11.8% 30.4%

Increased Market Share 0.9% 7.2% 4.1% 1.5% 2.3%

ESFM 3600

Base-Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3%

Policy Case Market Share 24.7% 17.5% 18.5% 13.5% 30.0%

Increased Market Share 1.2% 2.3% 2.5% 2.2% 0.7%

IL 1800

Base-Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3%

Policy Case Market Share 22.6% 11.0% 15.7% 13.7% 39.5%

Increased Market Share 6.6% 1.1% 4.4% 2.9% 2.1%

IL 3600

Base-Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3%

Policy Case Market Share 25.5% 9.5% 5.4% 18.2% 45.1%

Increased Market Share 4.8% 0.7% 0.9% 5.3% 2.8%

VT-S 3600

Base-Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7%

Policy Case Market Share 36.7% 8.5% 8.2% 9.9% 45.6%

Increased Market Share 0.0% 1.0% 3.6% 4.0% 1.9%

17-20
Table 17.3.7 Market Penetrations in Selected Years Attributable to Voluntary Energy 

Efficiency Targets for TSL #2

2020 2029 2049

ESCC 1800

Base-Case Market Share 18.2% 18.2% 18.2%

Policy Case Market Share 23.1% 45.7% 48.5%

Increased Market Share 4.9% 27.5% 30.3%

ESCC 3600

Base-Case Market Share 14.5% 14.5% 14.5%

Policy Case Market Share 15.4% 23.4% 26.5%

Increased Market Share 0.9% 8.8% 11.9%

ESFM 1800

Base-Case Market Share 16.1% 16.1% 16.1%

Policy Case Market Share 23.3% 37.3% 37.3%

Increased Market Share 7.2% 21.2% 21.2%

ESFM 3600

Base-Case Market Share 15.3% 15.3% 15.3%

Policy Case Market Share 17.5% 40.8% 43.5%

Increased Market Share 2.3% 25.5% 28.2%

IL 1800

Base-Case Market Share 9.9% 9.9% 9.9%

Policy Case Market Share 11.0% 40.6% 40.6%

Increased Market Share 1.1% 30.7% 30.7%

IL 3600

Base-Case Market Share 8.8% 8.8% 8.8%

Policy Case Market Share 9.5% 16.9% 20.0%

Increased Market Share 0.7% 8.1% 11.2%

VT-S 3600

Base-Case Market Share 7.5% 7.5% 7.5%

Policy Case Market Share 8.5% 18.2% 20.8%

Increased Market Share 1.0% 10.7% 13.3%

The increased market shares attributable to voluntary energy efficiency targets shown in 

Table 17.3.6 were used as inputs in the NIA-RIA model. Appendix 17A shows the annual 

market share increases due to this policy for the whole forecast period. Section 17.4 presents the 

resulting market penetration trends for the policy case of voluntary energy efficiency targets for

commercial and industrial pumps that meet the efficiency level for the proposed TSL. Because of 

the decrease in the market barriers level over the first 10 years of the analysis period, the market 

penetration of more efficient commercial and industrial pumps significantly increases over that 

period. For the remaining 20 years of the forecast period the increase in market penetration keeps 

growing because, even though the market barriers level remains constant (at 2029 level), the

17-21
increase in energy prices leads to increasing B/C ratios and eventually to higher market 

penetrations.

17.3.6 Bulk Government Purchases 

Bulk government purchases can lead to Federal, State, and local governments purchasing

large quantities of products that meet a certain, target efficiency level. Combining the market 

demands of multiple public sectors can provide a market signal to manufacturers and vendors 

that some of their largest customers seek products that meet an efficiency target at favorable 

prices. Such a program also can induce “market pull,” whereby manufacturers and vendors 

would achieve economies of scale for high efficiency products.

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 products. 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.

23, 24

DOE assumed that government agencies would administer bulk purchasing programs for

commercial and industrial pumps. At the federal level, this type of program could lead to FEMP 

procurement guidelines for commercial and industrial pumps, which would refer to the target 

levels of the proposed TSL as the minimum efficiency levels of commercial and industrial 

pumps to be purchased. 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.25 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 government-purchased commercial and industrial pumps meeting the target efficiency 

level.

DOE assumed that bulk government purchases would affect a subset of commercial 

buildings for which government agencies purchased or influenced the purchase of commercial 

and industrial pumps. This subset would consist primarily of public commercial buildings. DOE 

used the 2003 Commercial Buildings Energy Consumption Survey (CBECS 2003)26 to calculate 

the share of floor space in commercial buildings that corresponds to federal government-owned 

17-22
buildings. The calculation resulted in 2.6 percent. DOE further estimated that 47 percent of all 

shipments are directed to commercial buildings. Combined, these two percentages lead to 1.2 

percent of all shipments, which DOE assumed as the share of the market to which this policy 

would apply.

DOE estimated that starting in 2020, each year of a bulk government purchase policy 

would result in an increasing percent of shipments of government-purchased units beyond the 

base case that would meet the target efficiency level. DOE estimated that within 10 years (by 

2029) bulk government purchasing programs would result in 80 percenth of the market for

commercial and industrial pumps used in publicly owned commercial buildings meeting the 

target level. DOE modeled the bulk government purchase program assuming that the market 

share for commercial and industrial pumps achieved in 2029 would be at least maintained 

throughout the rest of the forecast period. 

Table 17.3.8 summarizes DOE’s assumptions for commercial and industrial pumps

regarding the market penetration of products in 2020 that meet the target levels at each TSL 

given bulk government purchases.

h The 80 percent target to be achieved within 10 years is not reached for any of the equipment classes analyzed in 

this RIA, as it is constrained by the market share below the target level in the base case scenario.

17-23

 
Table 17.3.8 Market Penetrations in 2020 Attributable to Bulk Government Purchases

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base-Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2%

Policy Case Market Share 15.8% 18.2% 10.8% 13.7% 23.3%

Increased Market Share 0.0% 0.0% 0.1% 0.1% 0.1%

ESCC 3600

Base-Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Policy Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Increased Market Share 0.0% 0.0% 0.0% 0.0% 0.0%

ESFM 1800

Base-Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1%

Policy Case Market Share 20.3% 16.2% 24.3% 10.4% 28.2%

Increased Market Share 0.0% 0.0% 0.0% 0.1% 0.1%

ESFM 3600

Base-Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3%

Policy Case Market Share 23.5% 15.3% 16.0% 11.4% 29.4%

Increased Market Share 0.0% 0.0% 0.1% 0.1% 0.1%

IL 1800

Base-Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3%

Policy Case Market Share 16.0% 10.0% 11.3% 10.8% 37.4%

Increased Market Share 0.0% 0.0% 0.0% 0.1% 0.1%

IL 3600

Base-Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3%

Policy Case Market Share 20.8% 8.8% 4.5% 13.0% 42.4%

Increased Market Share 0.0% 0.0% 0.0% 0.1% 0.0%

VT-S 3600

Base-Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7%

Policy Case Market Share 36.7% 7.5% 4.7% 6.0% 43.8%

Increased Market Share 0.0% 0.0% 0.1% 0.1% 0.0%

The increased market shares attributable to bulk government purchases shown in Table 

17.3.8 were used as inputs in the NIA-RIA model. Appendix 17A shows the annual market share 

increases due to this policy for the whole forecast period. Section 17.4 presents the resulting 

market penetration trends for the policy case of bulk government purchases for commercial and 

industrial pumps. Market penetrations increase over the first 10 years of the forecast period, and 

steady for the rest of the analysis period.

17.4 IMPACTS OF NON-REGULATORY ALTERNATIVES

Figure 17.4.1 through Figure 17.4.7 show the effects of each non-regulatory policy 

alternative on the market penetration of more efficient commercial and industrial pumps.

Relative to the base case, the alternative policy cases increase the market shares that meet the 

17-24
target level. Recall the proposed standards (not shown in the figures) would result in a 100-

percent market penetration of products that meet the more efficient technology.

Figure 17.4.1 Market Penetration of Efficient ESCC 1800 Commercial and Industrial 

Pumps (TSL 2)

Figure 17.4.2 Market Penetration of Efficient ESCC 3600 Commercial and Industrial 

Pumps (TSL #2)

17-25
Figure 17.4.3 Market Penetration of Efficient ESFM 1800 Commercial and Industrial 

Pumps (TSL #2)

Figure 17.4.4 Market Penetration of Efficient ESFM 3600 Commercial and Industrial 

Pumps (TSL #2)

17-26
Figure 17.4.5 Market Penetration of Efficient IL 1800 Commercial and Industrial Pumps 

(TSL #2)

Figure 17.4.6 Market Penetration of Efficient IL 3600 Commercial and Industrial Pumps 

(TSL #2)

17-27
Figure 17.4.7 Market Penetration of Efficient VT-S 3600 Commercial and Industrial 

Pumps (TSL #2)

Table 17.4.1 shows the national energy savings and net present value for the five nonregulatory policy alternatives analyzed in detail for commercial and industrial pumps. The target 

level for each policy corresponds to the same efficient technology proposed for standards in TSL 

#2. The case in which no regulatory action is taken with regard to commercial and industrial 

pumps constitutes the base case (or "No New Regulatory Action" scenario), in which NES and 

NPV are zero by definition. For comparison, the tables include the impacts of the proposed

standards. Energy savings are given in quadrillion British thermal units (quads).i

The NPVs

shown in Table 17.4.1 are based on two discount rates, 7 percent and 3 percent.

The policy with the highest projected cumulative energy savings are consumer rebates, 

followed by consumer tax credits, manufacturer tax credits and voluntary energy efficiency 

targets. Bulk government purchases, due to the (small) size of the estimated market, lead to 

negligible cumulative energy savings. Overall, the energy saving benefits from the alternative 

policies, range from 0.2 percent to 77.7 percent of the benefits from the proposed standards.

i

For the alternative policies whose market penetration depends on B/C ratio, the energy savings in Table 17.4.1

correspond to the case where the cash flow of the operating cost savings was discounted to the purchase year using a 

7 percent discount rate. 

17-28

 
Table 17.4.1 Impacts of Non-Regulatory Policy Alternatives (TSL #2)

Policy Alternative

Energy Savings*

quads

Net Present Value*

million 2013$

7% Disc Rate 3% Disc Rate

Consumer Rebates 0.077 (73%)** 315.4 837.5

Consumer Tax Credits 0.046 (44%) 189.2 502.5

Manufacturer Tax Credits 0.023 (22%) 94.6 251.2

Voluntary Energy Efficiency Targets 0.013 (12%) 74.3 144.1

Bulk Government Purchases 0.000 (0.2%) 0.8 2.0

Proposed Standards*** 0.105 (100.0%) 405.8 1107.9

* For products shipped 2020-2049.

**The percentages show how the energy savings from each policy alternative compared to the (site) 

energy savings from the proposed standards (represented in the table as 100%).

*** Refers to site energy savings.

17-29
REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy 

Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.) 

<www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_energy

_efficiency>.

2. ICF International, Arizona Public Service Energy Efficient Market Potential Study, 2007. 

Fairfax, VA. 

3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Optimal Energy. January, 2008.

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Quantec, LLC. January, 2008.

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal 

communication. Itron, Inc. January 2008 and March 2009.

6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last 

accessed March, 2014.) <http://calmac.org/publications/PGE0264_Final_Report.pdf>

7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market 

Potential Study, 2010. (Last accessed March, 2014.) <www.ameren.com/-

/media/missourisite/Files/Environment/Renewables/AmerenUEVolume2MarketResearchReport2.pdf>. 

8. Coito, F., E-mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc. 

June, 2010.

9. Blum, H., B. Atkinson, and A. Lekov, A Framework for Comparative Assessments of 

Energy Efficiency Policy Measures, May, 2011. Report No. LBNL-4749E.

<http://eetd.lbl.gov/sites/all/files/a_framework_for_comparative_assessments_of_energy

_efficiency_policy_measures_lbnl-4749e.pdf>.

10. Lawrence Berkeley National Laboratory, Energy End-Use Forecasting: Analysis of Tax 

Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and 

Assessment Group (Last accessed March, 2014.) 

<http://enduse.lbl.gov/Projects/TaxCredits.html>.

11. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment 

Purchase Decisions, 1994. Prepared for Southern California Edison by Cambridge 

17-30
Systematics, Pacific Consulting Services, The Technology Applications Group, and 

California Survey Research Services. 

12. U.S. Department of Energy: Office of Codes and Standards, Technical Support 

Document: Energy Efficiency Standards for Consumer Products: Refrigerators, 

Refrigerator-Freezers, and Freezers, Including Environmental Assessment and 

Regulatory Impact Analysis, November, 1995. Washington, DC. Report No. DOE/EE0064. 

13. Energy Policy Act of 2005, in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC 

25C note,. 2005. <www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf>.

14. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. 

(Last accessed March, 2014.) <http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf>.

15. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling 

Equipment., 2011. (Last accessed March, 2014.) 

<www.energytaxincentives.org/consumers/heating-cooling.php>.

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013.

(Last accessed March, 2014.) <http://energytaxincentives.org/>.

17. 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. 

18. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed March, 

2014.) <http://energytaxincentives.org/builders/appliances.php>.

19. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modeling the Effects of U.S. 

ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who 

Delivers?, May 30-June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La 

Napoule, France. 

<www.eceee.org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper>.

20. 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 

Program Evaluation Conference Proceedings. 

17-31
U.S. Department of Energy: Energy Information Administration (EIA), Commercial

Buildings Energy Consumption Survey, (Last accessed November, 2013.)

<www.eia.gov/consumption/commercial/data/2003/index.cfm?view=microdata>.

21. 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

Evaluation Conference Proceedings. Seattle, WA.

22. Blum, H., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of

Energy Efficiency Policy Measures, May, 2011.

23. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal

communication. National Association of State Procurement Officials. April, 2008.

24. Responsible Purchasing Network, (Last accessed March, 2014.)

<www.responsiblepurchasing.org/>.

25. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from Energy-Efficient

Government Purchase. ACEEE: Summer Study on Energy Efficiency in Buildings, 2000:

pp. 147-166 <http://aceee.org/files/proceedings/2000/data/papers/

SS00_Panel4_Paper13.pdf>.

26.

17-32
APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

TABLE OF CONTENTS

17A.1 INTRODUCTION ...................................................................................................... 17A-1

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY....................................... 17A-2

17A.3 NIA-RIA INTEGRATED MODEL............................................................................ 17A-9

17A.4 MARKET PENETRATION CURVES ...................................................................... 17A-9

17A.4.1 Introduction..................................................................................................... 17A-9

17A.4.2 Adjustment of XENERGY Penetration Curves............................................ 17A-11

17A.4.3 Interpolation of Penetration Curves.............................................................. 17A-12

17A.5 CONSUMER REBATE PROGRAMS..................................................................... 17A-13

17A.6 FEDERAL AND STATE TAX CREDITS............................................................... 17A-17

17A.1.1 Federal Tax Credits for Consumers.............................................................. 17A-17

17A.6.1 Federal Tax Credits for Manufacturers......................................................... 17A-19

17A.6.2 State Tax Credits........................................................................................... 17A-19

REFERENCES ..................................................................................................................... 17A-21

LIST OF TABLES

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESCC 1800 Commercial and Industrial Pumps (TSL #2).. 17A-2

Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESCC 3600 Commercial and Industrial Pumps (TSL #2).. 17A-3

Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESFM 1800 Commercial and Industrial Pumps (TSL #2).. 17A-4

Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESFM 3600 Commercial and Industrial Pumps (TSL #2).. 17A-5

Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for IL 1800 Commercial and Industrial Pumps (TSL #2) ........ 17A-6

Table 17A.2.6 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for IL 3600 Commercial and Industrial Pumps (TSL #2) ........ 17A-7

Table 17A.2.7 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for VT-S 3600 Commercial and Industrial Pumps (TSL #2)... 17A-8

Table 17A.5.1 Statistical Results*.................................................................................. 17A-13

Table 17A.5.2 Rebates per Horsepower by Equipment Class........................................ 17A-14

Table 17A.5.3 Rebate Programs for Commercial and Industrial Pumps........................ 17A-15

LIST OF FIGURES

Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on 

Adoption of New Technologies.............................................................. 17A-11

17A-i
APPENDIX 17A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS 

17A.1 INTRODUCTION

This appendix contains sections discussing the following topics: 

• Projections of annual market share increases for the alternative policies;

• NIA-RIA Integrated Model;

• Market penetration curves used to analyze consumer rebates and voluntary energy 

efficiency targets, including:

o Background material on XENERGY’s approach,

o DOE’s adjustment of these curves for this analysis, and

o The method DOE used to derive interpolated, customized curves;

• Detailed table of rebates offered for the considered product, as well as DOE’s approach 

to estimate a market representative rebate value for this RIA; and

• Background material on Federal and State tax credits for appliances.

17A-1
17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17A.2.1 through Table 17A.2.7 show the annual increases in market shares of

commercial and industrial pumps meeting the target efficiency levels for the proposed TSL (TSL 

#2). DOE used these market share increases as inputs to the NIA-RIA spreadsheet model.

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESCC 1800 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2020 23.2% 13.9% 6.9% 7.1% 0.0%

2021 23.2% 13.9% 6.9% 12.1% 0.1%

2022 23.2% 13.9% 6.9% 15.9% 0.1%

2023 23.2% 13.9% 6.9% 19.0% 0.2%

2024 23.2% 13.9% 6.9% 21.7% 0.2%

2025 23.2% 13.9% 6.9% 24.1% 0.3%

2026 23.2% 13.9% 6.9% 26.4% 0.3%

2027 23.2% 13.9% 6.9% 28.5% 0.3%

2028 23.2% 13.9% 6.9% 30.5% 0.4%

2029 23.2% 13.9% 6.9% 32.4% 0.4%

2030 23.2% 13.9% 6.9% 32.5% 0.4%

2031 23.2% 13.9% 6.9% 32.6% 0.4%

2032 23.2% 13.9% 6.9% 32.7% 0.4%

2033 23.2% 13.9% 6.9% 32.9% 0.4%

2034 23.2% 13.9% 6.9% 33.0% 0.4%

2035 23.2% 13.9% 6.9% 33.1% 0.4%

2036 23.2% 13.9% 6.9% 33.3% 0.4%

2037 23.2% 13.9% 6.9% 33.4% 0.4%

2038 23.2% 13.9% 6.9% 33.5% 0.4%

2039 23.2% 13.9% 6.9% 33.7% 0.4%

2040 23.2% 13.9% 6.9% 33.8% 0.4%

2041 23.2% 13.9% 6.9% 33.9% 0.4%

2042 23.2% 13.9% 6.9% 34.0% 0.4%

2043 23.2% 13.9% 6.9% 34.2% 0.4%

2044 23.2% 13.9% 6.9% 34.3% 0.4%

2045 23.2% 13.9% 6.9% 34.3% 0.4%

2046 23.2% 13.9% 6.9% 34.3% 0.4%

2047 23.2% 13.9% 6.9% 34.3% 0.4%

2048 23.2% 13.9% 6.9% 34.3% 0.4%

2049 23.2% 13.9% 6.9% 34.3% 0.4%

17A-2
Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESCC 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2020 11.9% 7.2% 3.6% 1.4% 0.0%

2021 11.9% 7.2% 3.6% 2.8% 0.0%

2022 11.9% 7.2% 3.6% 4.2% 0.0%

2023 11.9% 7.2% 3.6% 5.6% 0.1%

2024 11.9% 7.2% 3.6% 6.9% 0.1%

2025 11.9% 7.2% 3.6% 8.2% 0.1%

2026 11.9% 7.2% 3.6% 9.5% 0.1%

2027 11.9% 7.2% 3.6% 10.7% 0.1%

2028 11.9% 7.2% 3.6% 11.9% 0.1%

2029 11.9% 7.2% 3.6% 11.9% 0.1%

2030 11.9% 7.2% 3.6% 11.9% 0.1%

2031 11.9% 7.2% 3.6% 11.9% 0.1%

2032 11.9% 7.2% 3.6% 11.9% 0.1%

2033 11.9% 7.2% 3.6% 11.9% 0.1%

2034 11.9% 7.2% 3.6% 11.9% 0.1%

2035 11.9% 7.2% 3.6% 11.9% 0.1%

2036 11.9% 7.2% 3.6% 11.9% 0.1%

2037 11.9% 7.2% 3.6% 11.9% 0.1%

2038 11.9% 7.2% 3.6% 11.9% 0.1%

2039 11.9% 7.2% 3.6% 11.9% 0.1%

2040 11.9% 7.2% 3.6% 11.9% 0.1%

2041 11.9% 7.2% 3.6% 11.9% 0.1%

2042 11.9% 7.2% 3.6% 11.9% 0.1%

2043 11.9% 7.2% 3.6% 11.9% 0.1%

2044 11.9% 7.2% 3.6% 11.9% 0.1%

2045 11.9% 7.2% 3.6% 11.9% 0.1%

2046 11.9% 7.2% 3.6% 11.9% 0.1%

2047 11.9% 7.2% 3.6% 11.9% 0.1%

2048 11.9% 7.2% 3.6% 11.9% 0.1%

2049 11.9% 7.2% 3.6% 11.9% 0.1%

17A-3
Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESFM 1800 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2020 21.2% 12.7% 6.4% 0.9% 0.0%

2021 21.2% 12.7% 6.4% 2.0% 0.1%

2022 21.2% 12.7% 6.4% 3.0% 0.1%

2023 21.2% 12.7% 6.4% 4.0% 0.1%

2024 21.2% 12.7% 6.4% 5.0% 0.1%

2025 21.2% 12.7% 6.4% 5.9% 0.2%

2026 21.2% 12.7% 6.4% 6.9% 0.2%

2027 21.2% 12.7% 6.4% 7.8% 0.2%

2028 21.2% 12.7% 6.4% 8.7% 0.2%

2029 21.2% 12.7% 6.4% 9.6% 0.3%

2030 21.2% 12.7% 6.4% 9.8% 0.3%

2031 21.2% 12.7% 6.4% 10.0% 0.3%

2032 21.2% 12.7% 6.4% 10.1% 0.3%

2033 21.2% 12.7% 6.4% 10.3% 0.3%

2034 21.2% 12.7% 6.4% 10.5% 0.3%

2035 21.2% 12.7% 6.4% 10.7% 0.3%

2036 21.2% 12.7% 6.4% 10.8% 0.3%

2037 21.2% 12.7% 6.4% 11.0% 0.3%

2038 21.2% 12.7% 6.4% 11.2% 0.3%

2039 21.2% 12.7% 6.4% 11.3% 0.3%

2040 21.2% 12.7% 6.4% 11.5% 0.3%

2041 21.2% 12.7% 6.4% 11.7% 0.3%

2042 21.2% 12.7% 6.4% 11.9% 0.3%

2043 21.2% 12.7% 6.4% 12.0% 0.3%

2044 21.2% 12.7% 6.4% 12.2% 0.3%

2045 21.2% 12.7% 6.4% 12.4% 0.3%

2046 21.2% 12.7% 6.4% 12.5% 0.3%

2047 21.2% 12.7% 6.4% 12.7% 0.3%

2048 21.2% 12.7% 6.4% 12.9% 0.3%

2049 21.2% 12.7% 6.4% 13.0% 0.3%

17A-4
Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for ESFM 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2020 28.2% 16.9% 8.5% 1.1% 0.0%

2021 28.2% 16.9% 8.5% 2.2% 0.1%

2022 28.2% 16.9% 8.5% 3.4% 0.1%

2023 28.2% 16.9% 8.5% 4.6% 0.1%

2024 28.2% 16.9% 8.5% 5.8% 0.2%

2025 28.2% 16.9% 8.5% 6.9% 0.2%

2026 28.2% 16.9% 8.5% 8.1% 0.2%

2027 28.2% 16.9% 8.5% 9.2% 0.3%

2028 28.2% 16.9% 8.5% 10.4% 0.3%

2029 28.2% 16.9% 8.5% 11.5% 0.3%

2030 28.2% 16.9% 8.5% 11.6% 0.3%

2031 28.2% 16.9% 8.5% 11.8% 0.3%

2032 28.2% 16.9% 8.5% 12.0% 0.3%

2033 28.2% 16.9% 8.5% 12.1% 0.3%

2034 28.2% 16.9% 8.5% 12.3% 0.3%

2035 28.2% 16.9% 8.5% 12.5% 0.3%

2036 28.2% 16.9% 8.5% 12.6% 0.3%

2037 28.2% 16.9% 8.5% 12.8% 0.3%

2038 28.2% 16.9% 8.5% 13.0% 0.3%

2039 28.2% 16.9% 8.5% 13.1% 0.3%

2040 28.2% 16.9% 8.5% 13.3% 0.3%

2041 28.2% 16.9% 8.5% 13.4% 0.3%

2042 28.2% 16.9% 8.5% 13.6% 0.3%

2043 28.2% 16.9% 8.5% 13.8% 0.3%

2044 28.2% 16.9% 8.5% 13.9% 0.3%

2045 28.2% 16.9% 8.5% 14.1% 0.3%

2046 28.2% 16.9% 8.5% 14.2% 0.3%

2047 28.2% 16.9% 8.5% 14.4% 0.3%

2048 28.2% 16.9% 8.5% 14.6% 0.3%

2049 28.2% 16.9% 8.5% 14.7% 0.3%

17A-5
Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for IL 1800 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2020 13.8% 8.3% 4.1% 0.7% 0.0%

2021 13.8% 8.3% 4.1% 1.5% 0.1%

2022 13.8% 8.3% 4.1% 2.4% 0.1%

2023 13.8% 8.3% 4.1% 3.2% 0.1%

2024 13.8% 8.3% 4.1% 4.0% 0.2%

2025 13.8% 8.3% 4.1% 4.9% 0.2%

2026 13.8% 8.3% 4.1% 5.7% 0.3%

2027 13.8% 8.3% 4.1% 6.5% 0.3%

2028 13.8% 8.3% 4.1% 7.4% 0.3%

2029 13.8% 8.3% 4.1% 8.2% 0.4%

2030 13.8% 8.3% 4.1% 8.3% 0.4%

2031 13.8% 8.3% 4.1% 8.5% 0.4%

2032 13.8% 8.3% 4.1% 8.6% 0.4%

2033 13.8% 8.3% 4.1% 8.8% 0.4%

2034 13.8% 8.3% 4.1% 8.9% 0.4%

2035 13.8% 8.3% 4.1% 9.1% 0.4%

2036 13.8% 8.3% 4.1% 9.3% 0.4%

2037 13.8% 8.3% 4.1% 9.4% 0.4%

2038 13.8% 8.3% 4.1% 9.6% 0.4%

2039 13.8% 8.3% 4.1% 9.7% 0.4%

2040 13.8% 8.3% 4.1% 9.9% 0.4%

2041 13.8% 8.3% 4.1% 10.1% 0.4%

2042 13.8% 8.3% 4.1% 10.2% 0.4%

2043 13.8% 8.3% 4.1% 10.4% 0.4%

2044 13.8% 8.3% 4.1% 10.5% 0.4%

2045 13.8% 8.3% 4.1% 10.7% 0.4%

2046 13.8% 8.3% 4.1% 10.9% 0.4%

2047 13.8% 8.3% 4.1% 11.0% 0.4%

2048 13.8% 8.3% 4.1% 11.2% 0.4%

2049 13.8% 8.3% 4.1% 11.3% 0.4%

17A-6
Table 17A.2.6 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for IL 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2020 31.5% 18.9% 9.4% 1.3% 0.0%

2021 31.5% 18.9% 9.4% 2.7% 0.1%

2022 31.5% 18.9% 9.4% 4.1% 0.1%

2023 31.5% 18.9% 9.4% 5.5% 0.2%

2024 31.5% 18.9% 9.4% 6.9% 0.2%

2025 31.5% 18.9% 9.4% 8.2% 0.2%

2026 31.5% 18.9% 9.4% 9.5% 0.3%

2027 31.5% 18.9% 9.4% 10.8% 0.3%

2028 31.5% 18.9% 9.4% 12.1% 0.3%

2029 31.5% 18.9% 9.4% 13.3% 0.4%

2030 31.5% 18.9% 9.4% 13.4% 0.4%

2031 31.5% 18.9% 9.4% 13.6% 0.4%

2032 31.5% 18.9% 9.4% 13.7% 0.4%

2033 31.5% 18.9% 9.4% 13.9% 0.4%

2034 31.5% 18.9% 9.4% 14.1% 0.4%

2035 31.5% 18.9% 9.4% 14.3% 0.4%

2036 31.5% 18.9% 9.4% 14.4% 0.4%

2037 31.5% 18.9% 9.4% 14.6% 0.4%

2038 31.5% 18.9% 9.4% 14.8% 0.4%

2039 31.5% 18.9% 9.4% 14.9% 0.4%

2040 31.5% 18.9% 9.4% 15.1% 0.4%

2041 31.5% 18.9% 9.4% 15.3% 0.4%

2042 31.5% 18.9% 9.4% 15.4% 0.4%

2043 31.5% 18.9% 9.4% 15.6% 0.4%

2044 31.5% 18.9% 9.4% 15.8% 0.4%

2045 31.5% 18.9% 9.4% 15.9% 0.4%

2046 31.5% 18.9% 9.4% 16.1% 0.4%

2047 31.5% 18.9% 9.4% 16.3% 0.4%

2048 31.5% 18.9% 9.4% 16.4% 0.4%

2049 31.5% 18.9% 9.4% 16.6% 0.4%

17A-7
Table 17A.2.7 Annual Increases in Market Shares Attributable to Alternative Policy 

Measures for VT-S 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates

Consumer

Tax Credits

Manufacturer

Tax Credits

Vol Energy

Eff Targets

Bulk Govt 

Purchases

2020 12.3% 7.4% 3.7% 1.5% 0.0%

2021 12.3% 7.4% 3.7% 3.1% 0.1%

2022 12.3% 7.4% 3.7% 4.6% 0.1%

2023 12.3% 7.4% 3.7% 6.1% 0.2%

2024 12.3% 7.4% 3.7% 7.6% 0.2%

2025 12.3% 7.4% 3.7% 9.1% 0.3%

2026 12.3% 7.4% 3.7% 10.5% 0.3%

2027 12.3% 7.4% 3.7% 11.9% 0.4%

2028 12.3% 7.4% 3.7% 13.3% 0.4%

2029 12.3% 7.4% 3.7% 14.6% 0.5%

2030 12.3% 7.4% 3.7% 14.7% 0.5%

2031 12.3% 7.4% 3.7% 14.9% 0.5%

2032 12.3% 7.4% 3.7% 15.0% 0.5%

2033 12.3% 7.4% 3.7% 15.1% 0.5%

2034 12.3% 7.4% 3.7% 15.3% 0.5%

2035 12.3% 7.4% 3.7% 15.4% 0.5%

2036 12.3% 7.4% 3.7% 15.6% 0.5%

2037 12.3% 7.4% 3.7% 15.7% 0.5%

2038 12.3% 7.4% 3.7% 15.9% 0.5%

2039 12.3% 7.4% 3.7% 16.0% 0.5%

2040 12.3% 7.4% 3.7% 16.2% 0.5%

2041 12.3% 7.4% 3.7% 16.3% 0.5%

2042 12.3% 7.4% 3.7% 16.4% 0.5%

2043 12.3% 7.4% 3.7% 16.6% 0.5%

2044 12.3% 7.4% 3.7% 16.7% 0.5%

2045 12.3% 7.4% 3.7% 16.9% 0.5%

2046 12.3% 7.4% 3.7% 17.0% 0.5%

2047 12.3% 7.4% 3.7% 17.1% 0.5%

2048 12.3% 7.4% 3.7% 17.3% 0.5%

2049 12.3% 7.4% 3.7% 17.4% 0.5%

17A-8
17A.3 NIA-RIA INTEGRATED MODEL

For this analysis, DOE used its integrated NIA-RIAa model approach that the Department 

built on the NIA model discussed in Chapter 10 and documented in Appendix 10A.The resulting 

integrated NIA-RIA model features both the NIA and RIA inputs, analyses and results. It has the 

capability to generate results, by equipment class and TSL, for the mandatory standards and each 

of the RIA policies. Separate modules estimate increases in market penetration of more efficient 

equipment for consumer rebates, voluntary energy efficiency targets and bulk government 

purchases.b The consumer rebates module calculates benefit-cost (B/C) ratios and market 

barriers, and generates customized market penetration curves for each equipment class; the 

voluntary energy efficiency targets module relies on the market barriers calculated in the 

consumer rebates module to project a reduction in those barriers over the first ten years of the 

forecast period, and estimate the market effects of such a reduction; and the bulk government 

purchases module scales down the market for commercial and industrial pumps to Federal 

government-owned commercial buildings. A separate module summarizes the market impacts 

from mandatory standards and all policy alternatives, and an additional module produces all 

tables and figures presented in Chapter 17 as well as the tables of market share increases for each 

policy reported in Section 17A.2 of this Appendix.

17A.4 MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that 

DOE used to analyze the Consumer Rebates and Voluntary Energy Efficiency Targets policies. 

Next it discusses the adjustments it made to the maximum penetration rates. It then refers to the 

method it used to develop interpolated penetration curves for commercial and industrial pumps

that meet the target efficiency levels at each TSL. The resulting curves are presented in Chapter 

17.

17A.4.1 Introduction

XENERGY, Inc.c

, 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 

a NIA = National Impact Analysis; RIA = Regulatory Impact Analysis

b As mentioned in Chapter 17, the increase in market penetrations for consumer tax credits and manufacturer tax 

credits are estimated as a fraction of the increase in market penetration of consumer rebates. c XENERGY is now owned by KEMA, Inc. (www.kema.com)

17A-9

 
new product generally has its own distinct characteristics, few studies have been able to 

conclusively develop a universally applicable model. Some key findings, however, generally are 

accepted in academia and industry. 

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 17A.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 17A.4.1).

17A-10
Figure 17A.4.1 S-Curves Showing Effects of External and Internal Sources on Adoption of 

New Technologies

17A.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, 

for any benefit/cost ratio points higher or lower than the curves. It set another constraint such 

17A-11
that the policy case market share cannot be greater than 100 percent, as might occur for products 

with high base case market shares of the target-level technology.

17A.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.d 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.e 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)7 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. 

DOE used the above referred method to interpolate market implementation curves, to 

generate customized curves that were used to estimate the effects of consumer rebates and 

voluntary energy efficiency targets for each equipment class covered by this RIA. For consumer 

rebates, DOE derived such curves based on an algorithm that finds the market implementation

curve that best fits, for the first year of the analysis period, the B/C ratio of the target efficiency 

level and the market penetration of equipment with that level of energy efficiency in the base 

case. For the analysis of voluntary energy efficiency targets, DOE departs from the market 

barriers level corresponding to the market implementation curve it derived for consumer rebates,

to linearly decrease it over the ten initial years of the analysis period. For each year, as market 

barriers decline, the corresponding market implementation curve leads – for the same B/C ratio –

to higher market penetrations. 

d The RIA chapter refers to these curves as penetration curves. This section, in references to the original source, uses 

the term implementation curve. e 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 are considered in this RIA proportional 

to the impacts from rebates. 

17A-12

 
17A.5 CONSUMER REBATE PROGRAMS

DOE performed a search for rebate programs that offered incentives for commercial and 

industrial pumps. Some organizations nationwide, comprising electric utilities and regional 

agencies, offer rebate programs for this equipment, and provides the organizations’ names, 

states, rebate amounts, and program websites. If there is more than one entry for an organization, 

it offers different rebates in different states. When an organization offers rebates through several 

utilities, it is represented only once in that table. 

DOE relied on the data it gathered from the 52 rebate programs offered by the three 

organizations listed in Table 17A.5.3 to calculate a market-representative rebate amount for 

commercial and industrial pumps. DOE performed a statistical analysis over the rebate values 

and other characteristics of the rebate programs it identified, to estimate a rebate amount per 

horsepower that would vary with the horsepower of a pump:

rrrrrrhpp(hpp) = ββ ∙ rrrrrrxx

−αα + εε [1]

where:

rrrrrrhpp(hpp) Estimated amount of rebate per horsepower (dollar/hp) for a given value of 

horsepower (hp),

rrrrrrxx Rebate per horsepower (dollar/hp) offered by rebate program x,

αα, ββ regression coefficients, and

εε statistical error.

Table 17A.5.1 presents the statistical results of model [1] above, and Table 17A.5.2

presents the rebate per horsepower amounts that DOE calculated for each equipment class based 

on the representative horsepower of the equipment class.

Table 17A.5.1 Statistical Results*

Coefficient Value

αα 0.433

ββ 60.638

* R2=0.403

17A-13
Table 17A.5.2 Rebates per Horsepower by Equipment Class

Representative

Horsepower (hp)

Rebate per Horsepower

(2013$/hp)

Rebate Amount

(2013$)

ESCC 1800 5.8 28.30 164.16

ESCC 3600 13.8 19.44 268.27

ESFM 1800 24.9 15.05 374.79

ESFM 3600 47.6 11.37 541.04

IL 1800 3.9 33.62 131.11

IL 3600 7.9 24.76 195.58

VT-S 3600 7.0 26.09 182.62

17A-14
Table 17A.5.3 Rebate Programs for Commercial and Industrial Pumps

Organization State Rebate* HP Website

Duke Energy IN $250.00/unit 7.5 http://www.duke-energy.com/indiana-business/energymanagement/pump-incentive.asp

Duke Energy IN $260.00/unit 10.0 http://www.duke-energy.com/indiana-business/energymanagement/pump-incentive.asp

Duke Energy IN $260.00/unit 15.0 http://www.duke-energy.com/indiana-business/energymanagement/pump-incentive.asp

Duke Energy IN $400.00/unit 20.0 http://www.duke-energy.com/indiana-business/energymanagement/pump-incentive.asp

Indianapolis Power 

& Light Company IN $22.50/unit 1.5 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Indianapolis Power 

& Light Company IN $30.00/unit 2.0 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Indianapolis Power 

& Light Company IN $45.00/unit 3.0 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Indianapolis Power 

& Light Company IN $75.00/unit 5.0 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Indianapolis Power 

& Light Company IN $112.50/unit 7.5 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Indianapolis Power 

& Light Company IN $150.00/unit 10.0 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Indianapolis Power 

& Light Company IN $225.00/unit 15.0 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Indianapolis Power 

& Light Company IN $300.00/unit 20.0 http://www.iplpower.com/Business/Business_Energy_Savings/Business_Energy_Incentives_Program/

Duke Energy KY $122.00/unit 1.5 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

Duke Energy KY $175.00/unit 2.0 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

Duke Energy KY $175.00/unit 3.0 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

Duke Energy KY $170.00/unit 5.0 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

Duke Energy KY $250.00/unit 7.5 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

Duke Energy KY $165.00/unit 10.0 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

Duke Energy KY $290.00/unit 15.0 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

Duke Energy KY $400.00/unit 20.0 http://www.duke-energy.com/kentucky-business/smartsaver/customer/pump-incentives.asp

DTE Energy MI $20.00/hp 1.5

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

17A-15
Organization State Rebate* HP Website

DTE Energy MI $20.00/hp 2.0

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 3.0

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 5.0

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 7.5

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 10.0

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 15.0

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 20.0

https://www2.dteenergy.com/wps/portal/dte/business/sav

eEnergy/details/incentives%20and%20offers/how%20to

%20participate/!ut/p/b1/

Duke Energy NC $122.00/unit 1.5 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy NC $175.00/unit 2.0 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy NC $175.00/unit 3.0 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy NC $170.00/unit 5.0 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy NC $249.00/unit 7.5 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy NC $165.00/unit 10.0 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy NC $290.00/unit 15.0 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy NC $400.00/unit 20.0 http://www.duke-energy.com/north-carolina-largebusiness/smart-saver/customer/pump-incentives.asp

Duke Energy OH $122.00/unit 1.5 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

Duke Energy OH $175.00/unit 2.0 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

Duke Energy OH $175.00/unit 3.0 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

Duke Energy OH $170.00/unit 5.0 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

17A-16
Organization State Rebate* HP Website

Duke Energy OH $249.00/unit 7.5 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

Duke Energy OH $165.00/unit 10.0 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

Duke Energy OH $290.00/unit 15.0 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

Duke Energy OH $400.00/unit 20.0 http://www.duke-energy.com/ohio-business/smartsaver/customer/pump-incentives.asp

Duke Energy SC $122.00/unit 1.5 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

Duke Energy SC $175.00/unit 2.0 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

Duke Energy SC $175.00/unit 3.0 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

Duke Energy SC $170.00/unit 5.0 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

Duke Energy SC $249.00/unit 7.5 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

Duke Energy SC $165.00/unit 10.0 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

Duke Energy SC $290.00/unit 15.0 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

Duke Energy SC $400.00/unit 20.0 http://www.duke-energy.com/south-carolinabusiness/smart-saver/customer/pump-incentives.asp

* In 2013$.

17A.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.

17A.1.1 Federal Tax Credits for Consumers

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.8, 9 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).10 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, 

17A-17
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, 2013.8, 11

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 distributors 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.12, 13

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.

14 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 

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.

15, 16, 17 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 

17A-18
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, 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 equipment class of commercial and industrial pumps

covered by this RIA. 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.

17A.6.1 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 2007.18

The Emergency Economic Stabilization Act of 200819 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.

Manufacturers who produce these appliances receive the credits for increasing their 

production of qualifying appliances. These credits had several efficiency tiers in 2011. From

2012-2013, credits for the higher tiers remained, but were eliminated for the lowest (least 

efficient) tiers for clothes washers and dishwashers.

11 The credit amounts were 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 percent) and clothes washer (2.2 MEF/4.5 wcf) models were not subject to the 

cap. The credit to manufacturers was capped at $25 million for 2011, with the most efficient 

refrigerators (35 percent) and clothes washers (2.8 MEF/3.5 WCF) exempted from this cap.20

17A.6.2 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for energy-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, 

17A-19
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 a 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 the part of the cost and 

materials that exceed established standards of construction.

17A-20
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Efficiency, 2002. XENERGY Inc. (Last accessed March, 2014.) 

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17A-21
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the Chief Counsel for the Internal Revenue Service, (Last accessed March, 2014.) 

<www.irs.gov/pub/irs-soi/81dbfullar.pdf>.

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