TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND 

COMMERCIAL AND INDUSTRIAL EQUIPMENT:

Commercial Prerinse Spray Valves

June 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 

Pacific Northwest National Laboratory
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CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUMENT .................................................................................... 1-1

1.2 OVERVIEW OF COMMERCIAL EQUIPMENT STANDARDS .................................... 1-1

1.3 OVERVIEW OF STANDARDS FOR COMMERCIAL PRERINSE SPRAY 

VALVES ............................................................................................................................. 1-2

1.3.1 Test Procedure and Definitions............................................................................... 1-2

1.3.2 Current Standards ................................................................................................... 1-2

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS ...................... 1-3

1.5 STRUCTURE OF THE DOCUMENT .............................................................................. 1-4

REFERENCES ............................................................................................................................ 1-6
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CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

This technical support document (TSD) is a stand-alone report that documents the 

technical analyses and results supporting the notice of proposed rulemaking (NOPR) that 

proposes amended energy conservation standards for commercial prerinse spray valves (CPSV). 

This TSD reports on the activities and analyses conducted in the period following the publication 

of the framework document for this rulemaking.

1.2 OVERVIEW OF COMMERCIAL EQUIPMENT STANDARDS

The Energy Policy and Conservation Act (EPCA) of 1975, Pub. L. 94-163 (42 U.S.C. 6291–

6309), sets forth a variety of provisions designed to improve energy efficiency.1 Part B of title 

III, which for editorial reasons was redesignated as Part A upon incorporation into the U.S. Code 

(42 U.S.C. 6291–6309, as codified), establishes the “Energy Conservation Program for 

Consumer Products Other Than Automobiles.” These include commercial prerinse spray valves

that are the subject of this TSD. 

Pursuant to EPCA, the U.S. Department of Energy (DOE) is required to design each 

standard for these products to (1) achieve the maximum improvement in energy efficiency that is 

technologically feasible and economically justified, and (2) result in significant conservation of 

energy. (42 U.S.C. 6295(o)(2)(A)) To determine whether a proposed standard is economically 

justified, DOE will, after receiving comments on the proposed standard, determine whether the 

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

following seven factors: 

1. the economic impact of the standard on the manufacturers and on the consumers of the 

equipment subject to such standard; 

2. the savings in operating costs throughout the estimated average life of the covered 

equipment in the type (or class) compared to any increase in the price of, or in the initial 

charges for maintenance expenses of, the covered equipment that are likely to result from 

the imposition of the standard; 

3. the total projected amount of energy, or as applicable, water savings likely to result 

directly from the imposition of the standard; 

4. any lessening of the utility or the performance of the covered equpiment 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 and water conservation; and 

7. other factors the Secretary considers relevant.
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(42 U.S.C. 6295(o)(2)(B)(i)(I)-(VII))

EPCA also directs that DOE may not prescribe an amended or new standard if the 

standard is likely to result in the unavailability in the United States of performance 

characteristics (including reliability), features, sizes, capacities, and volumes that are 

substantially the same as those generally available in the United States at the time that the 

standard is prescribed. (42 U.S.C. 6295(o)(4)) 

For commercial prerinse spray valves, DOE is applying those statutory factors in a 

manner consistent with its other energy conservation standards rulemakings to ascertain the 

maximum improvement in energy efficiency that is technologically feasible and economically 

justified for these products.

1.3 OVERVIEW OF STANDARDS FOR COMMERCIAL PRERINSE SPRAY 

VALVES

1.3.1 Test Procedure and Definitions

Section 323(b)(14) of EPCA (42 U.S.C. 6293(b)(14)) states that the test procedure for 

commercial prerinse spray valves shall be based on the American Society for Testing and 

Materials (ASTM) Standard F2324, “Standard Test Method for Pre Rinse Spray Valves.” The 

current test procedure is codified at 10 CFR 431, Subpart O. 

EPCA establishes the following definition:

The term “commercial prerinse spray valve” means a handheld device designed and 

marketed for use with commercial dishwashing and ware washing equipment that sprays 

water on dishes, flatware, and other food service items for the purpose of removing food 

residue before cleaning the items. (42 U.S.C. 6291(33)(A))

1.3.2 Current Standards

In a final rule published on October 18, 2005, DOE prescribed the current energy 

conservation standards at 10 CFR 431.266 for commercial prerinse spray valves. 70 FR 60407, 

60409-10. DOE’s current CPSV standards require all commercial prerinse spray valves 

manufactured on or after January 1, 2006 to have a flow rate of not more than 1.6 gpm. 

EPCA mandates DOE to review its standards for covered products within 6 years after 

issuance of any final rule establishing or amending a standard. (42 U.S.C. 6295(m)(1)) Under 

this statutory provision, DOE is required to publish either: 1) a notice of determination that 

energy conservation standards for the product do not need to be amended, or 2) a NOPR

including new proposed energy conservation standards. 
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1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

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

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

publishes in the Federal Register. The first of the rulemaking notices is a notice of public 

meeting (NOPM), which is designed to publicly vet the models and tools used in the preliminary 

rulemaking and to facilitate public participation before the NOPR stage. The second notice is the 

NOPR, which presents a discussion of comments received in response to the NOPM and the 

preliminary analyses and analytical tools; analyses of the impacts of potential amended energy 

conservation standards on consumers, manufacturers, and the Nation; DOE’s weighting of these 

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

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

comments received in response to the NOPR, the revised analyses, DOE’s weighting of these 

impacts, the amended energy conservation standards DOE is adopting for each product, and the 

effective dates of the amended energy conservation standards. Upon assessing the scope and 

complexity of potential amended CPSV standards, DOE elected to not include a publication of a 

NOPM or preliminary analysis in this rulemaking. Instead, the NOPR analysis included all 

analyses typically included in the preliminary analysis. Table 1.4.1 shows the analyses that 

typically occur during each phase of the rulemaking process. 

DOE considers the participation of interested parties to be an important part of the 

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

Federal Register notices), DOE actively encourages the participation and interaction of all 

interested parties during the comment period in each stage of the rulemaking. Beginning with the 

framework document, and during subsequent comment periods, interactions among interested 

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

rulemaking. 

Table 1.4.1 Analyses Performed at Each Stage of the Rulemaking Process

Preliminary Analyses* NOPR Final Rule

Market and technology assessment Revised preliminary analysis Revised analysis

Screening analysis Consumer sub-group analysis Revised analysis

Engineering analysis Manufacturer impact analysis Revised analysis

Energy use analysis Emissions analysis Revised analysis

Markups analysis Monetization of emissions reduction 

benefits Revised analysis

Life-cycle cost and payback period analysis Utility impact analysis Revised analysis

Shipments analysis Employment impact analysis Revised analysis

National impact analysis Regulatory impact analysis Revised analysis

Preliminary manufacturer impact analysis Revised analysis Revised analysis

* In the current rulemaking, DOE conducted the analyses listed under Preliminary Analyses as part of the NOPR 

analysis.

On September 30, 2014, DOE held the CPSV Framework document public meeting and 

discussed the issues detailed in the CPSV Framework document. DOE also described the 
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analyses that it planned to conduct during the rulemaking, listed in Table 1.4.1. Through the 

public meeting, DOE sought feedback from interested parties on these subjects and provided 

information regarding the rulemaking process that DOE would follow. Interested parties 

discussed the following major issues at the public meeting: rulemaking schedule, test procedure 

revisions, product classes, technology options, efficiency levels, and approaches for each of the 

analyses performed by DOE as part of the rulemaking process. The comments received since 

publication of the September 2014 notice, including those received at the September 2014 

Framework public meeting, have contributed to DOE's proposed resolution of the issues in this 

rulemaking as they pertain to commercial prerinse spray valves. 

Following the Framework meeting, DOE gathered additional information, performed 

product testing and teardowns, and performed the various analyses described in the framework 

document, including the engineering, life-cycle cost, payback period, manufacturer impact, and 

national impact analyses. The results of these analyses are presented in the NOPR and 

accompanying TSD chapters.

All the analyses for this NOPR, as well as the relevant documents from previous 

rulemaking phases and a link to the docket for this rulemaking, are available at the DOE website 

for this rulemaking: 

www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=100

1.5 STRUCTURE OF THE DOCUMENT

This TSD outlines the analytical approaches used in this rulemaking. The TSD consists of 

17 chapters, as well as appendices.

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

and how it applies to the CPSV rulemaking, and outlines the structure of the 

document. 

• Chapter 2, Analytical Framework: describes the rulemaking process. 

• Chapter 3, Market and Technology Assessment: characterizes the CPSV product 

market and the technologies available for increasing product efficiency.

• Chapter 4, Screening Analysis: determines which technology options are viable for 

consideration in the engineering analysis.

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

relationship between increased manufacturer price and increased efficiency.

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

for converting manufacturer prices to consumer product prices.

• Chapter 7, Energy and Water Use Analysis: discusses the process used for 

generating energy use estimates for each product class and efficiency level.

• Chapter 8, Life-Cycle Cost and Payback Period Analysis: discusses the economic 

effects of standards on individual consumers and users of the products and compares 
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the life-cycle cost (LCC) and payback period (PBP) of products with and without 

higher efficiency standards.

• Chapter 9, Shipments Analysis: discusses the methods used for forecasting 

shipments with and without higher efficiency standards. 

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

national energy and water consumption, and national economic impacts based on 

annual product shipments and estimates of future efficiency distributions in the 

absence and presence of higher efficiency standards.

• Chapter 11, Consumer Subgroup Analysis: discusses the effects of standards on 

subgroups of CPSV consumers and compares the LCC and PBP of products with and 

without higher efficiency standards. 

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

the finances and profitability of product manufacturers.

• Chapter 13, Emissions Analysis: discusses the effects of standards on airborne 

emissions of electric utilities.

• Chapter 14, Monetization of Emissions Reductions Benefits: considers the 

estimated monetary benefits likely to result from reduced emissions of carbon dioxide 

and nitrogen oxides expected to result from the standards. 

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

installed generation capacity of electric utilities.

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

national employment.

• Chapter 17, Regulatory Impact Analysis: discusses the present regulatory actions 

as well as the impact of non-regulatory alternatives to setting energy efficiency 

standards. 

• Appendix 8A, User Instructions for the Life-Cycle Cost Analysis Spreadsheet: 

contains a description of the spreadsheet and instructions that allow the user to 

examine and reproduce the detailed results of the LCC and PBP analysis.

• Appendix 8B, Uncertainty and Variability in LCC Analysis: explains how the 

LCC model accounts for the uncertainty and variability of the numerical values. 

• Appendix 10A, User Instructions for Shipments and National Impact Analysis 

(NIA) Spreadsheets: contains a description of the NIA spreadsheet and instructions 

on how to use it to examine and reproduce the NIA results.

• Appendix 10B, Full Fuel Cycle Multipliers: contains a summary of the methods 

used to calculate full-fuel-cycle energy savings.
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REFERENCES

1. American Energy Manufacturing Technical Corrections Act (AEMTCA), Pub. L. 112-210 

(Dec. 18, 2012). 
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CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

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

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

2.3 MARKET AND TECHNOLOGY ASSESSMENT ........................................................... 2-3

2.3.1 Market Assessment ................................................................................................. 2-3

2.3.2 Technology Assessment.......................................................................................... 2-3

2.4 SCREENING ANALYSIS ................................................................................................. 2-3

2.5 ENGINEERING ANALYSIS ............................................................................................. 2-4

2.6 MARKUPS ANALYSIS .................................................................................................... 2-4

2.7 CONSUMERCONSUMERCONSUMERCONSUMERENERGY AND WATER USE 

ANALYSIS .................................................................................................................................. 2-5

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS ........................................ 2-5

2.9 SHIPMENTS ANALYSIS ................................................................................................. 2-6

2.10 NATIONAL IMPACT ANALYSIS ................................................................................... 2-6

2.11 CONSUMER SUBGROUP ANALYSIS ........................................................................... 2-8

2.12 MANUFACTURER IMPACT ANALYSIS....................................................................... 2-9

2.13 EMISSIONS ANALYSIS ................................................................................................... 2-9

2.14 MONETIZATION OF EMISSIONS REDUCTION BENEFITS .................................... 2-11

2.15 UTILITY IMPACT ANALYSIS ...................................................................................... 2-12

2.16 EMPLOYMENT IMPACT ANALYSIS .......................................................................... 2-12

2.17 REGULATORY IMPACT ANALYSIS .......................................................................... 2-13

REFERENCES .......................................................................................................................... 2-14
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CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

The Energy Policy and Conservation Act (EPCA), as amended (42 U.S.C. 6291 et. seq.), 

requires that when prescribing new or amended energy conservation standards for covered 

products, the U.S. Department of Energy (DOE) must promulgate standards that achieve the 

maximum improvements in energy efficiency that are technologically feasible and economically 

justified. (42 U.S.C. 6295(o)(2)(A)) This chapter describes the general analytical framework that 

DOE uses in developing such standards, and in particular, the energy conservation standards that 

it is examining for commercial prerinse spray valves (CPSV). Included in this chapter are 

descriptions of the methodology, the analytical tools, and the relationships among the various 

analyses that are part of this rulemaking.

The analyses performed as part of the notice of proposed rulemaking (NOPR) stage and 

reported in this technical support document (TSD) are listed below. 

• A market and technology assessment to characterize the markets for commercial

prerinse spray valves and the applicable existing technology options.

• 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 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 manufacturer markups that convert manufacturer

production costs (MPC) to selling prices and distribution channel markups that relate

the manufacturer selling price (MSP) to the cost to the consumer.

• An analysis of the annual energy and water use of the considered products.

• Life-cycle cost (LCC) and payback period (PBP) analysis to calculate, at the

commercial prerinse spray valve owner level, the savings in operating costs

throughout the estimated average lifetime of the covered product, compared to any

increase in purchase and installation cost.

• A shipments analysis to project shipments of commercial prerinse spray valves,

which are then used to calculate the national impacts of standards on energy and

water, net present value (NPV), and future manufacturer cash flows.

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

potential energy conservation standards for commercial prerinse spray valves, as

measured by the NPV of total consumer economic impacts, the national energy

savings (NES), and national water savings.
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• A consumer sub-group analysis conducted to evaluate variations in commercial

business characteristics (e.g., energy prices, product use patterns, and installation and

maintenance costs) that might cause a standard to affect particular consumer subpopulations differently than the overall population.

• A manufacturer impact analysis (MIA) to estimate the financial impact of standards

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

manufacturing capacity.

• An emissions analysis to provide estimates of the effects of amended energy

conservation standards on emissions of CO2, SO2, NOx, and mercury (Hg);

• Monetization of emissions reductions benefits estimated to occur from reduced

emissions of CO2 and NOx as a result of the standards.

• A utility impact analysis to estimate selected effects of considered standards on the

electric power sector.

• An employment impact analysis to assess the aggregate impacts of considered

standards on national employment.

• A regulatory impact analysis to examine major alternatives to proposed amended

energy conservation standards that potentially could achieve substantially the same

regulatory goal at a lower cost.

The analyses and results published by DOE in the NOPR reflect comments received 

subsequent to the publication of a Framework document in September 2014. 

2.2 BACKGROUND

As described in chapter 1 of this TSD, DOE announced a formal effort to consider further 

improvements to the process used to develop appliance efficiency standards. DOE called on 

energy efficiency groups, manufacturers, trade associations, state agencies utilities, and other 

interested parties to provide input to this effort. As a result of this combined effort, DOE 

published Procedures, Interpretations and Policies for Consideration of New or Revised Energy 

Conservation Standards for Consumer Products (the “Process Rule”), 10 CFR part 430 subpart 

C, appendix A. The Process Rule outlined the procedural improvements identified by the 

interested parties and included a review of the (1) economic models; (2) analytical tools; (3) 

methodologies; (4) non-regulatory approaches; and (5) prioritization of future rules. The Process 

Rule recommended that DOE take into account uncertainty and variability by carrying out 

scenario or probability analysis. Consistent with the improved rulemaking framework of the 

Process Rule, the following sections provide a general description of the analytical components 

for this rulemaking. 

DOE developed the analytical framework pertaining to commercial prerinse spray valves

in the Commercial Prerinse Spray Valve Framework document. DOE announced the availability 

of the Framework document in a notice of public meeting and availability of a Framework 

document published in the Federal Register. 79 FR 54213 (September 11, 2014). A copy of the 
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Framework document is available on DOE’s website.

1

 On September 30, 2014, DOE presented 

the analytical approach contained in the Framework document to interested parties during a 

public meeting. DOE used comments gathered during the Framework public meeting as the basis 

for the analysis presented in the NOPR. 

2.3 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment characterizes the relevant product markets and 

existing technology options, including prototype designs. 

2.3.1 Market Assessment 

When DOE updates an energy conservation standard rulemaking, it develops information 

that provides an overall picture of the market for the product considered, including the nature of 

the product, the industry structure, and market characteristics for the product. This activity 

consists of both quantitative and qualitative efforts based primarily on publicly available 

information. 

For the NOPR, DOE reviewed relevant literature to develop an overall picture of the 

commercial prerinse spray valve market in the United States. Industry publications, government 

agencies, trade organizations, publically available product databases, and other online resources 

provided the bulk of the information, including (1) manufacturers and industry structure, (2) 

shipments, (3) detailed product information, (4) industry trends, and (5) existing regulatory and 

non-regulatory product efficiency improvement initiatives. The analysis developed as part of the 

market and technology assessment is described in chapter 3 of this TSD.

2.3.2 Technology Assessment 

As part of the technology assessment, DOE developed a list of technologies to consider 

for improving the efficiency of commercial prerinse spray valves. DOE typically uses 

information about existing and past technology options and prototype designs to help determine 

which technologies manufacturers use to attain higher energy performance levels. In consultation 

with interested parties, DOE developed a list of technologies for consideration in its screening 

and engineering analyses. Initially, these technologies encompass all those that DOE believes are 

technologically feasible. DOE identified several technology options for improving product 

efficiency based on existing products, product literature, and a direct examination of commercial 

prerinse spray valve units currently on the market. DOE identified other technology options by 

examining technologies used to improve efficiency of other similar water products. Chapter 3 of 

this TSD includes the detailed list of all technology options DOE identified for potential 

consideration in this rulemaking.

2.4 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 
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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 

addition, DOE removed from the list technology options that lack energy or water consumption 

data. In the engineering analysis, DOE further considered efficiency enhancement options that it 

did not screen out in the screening analysis. Chapter 4 of this TSD contains details on the 

screening analysis.

2.5 ENGINEERING ANALYSIS

The engineering analysis (chapter 5 of this TSD) establishes the relationship between 

manufacturing selling price and efficiency for commercial prerinse spray valves. This 

relationship serves as the basis for cost-benefit calculations in terms of individual consumers, 

manufacturers, and the Nation. Chapter 5 discusses the product classes analyzed, representative 

baseline units, efficiency levels, methodology used to develop manufacturing production costs, 

cost-efficiency curves, and impact of efficiency improvements on the considered products. To 

determine the cost to consumers of commercial prerinse spray valves at various efficiency levels, 

DOE estimated manufacturing costs, markups in the distribution chain, installation costs, and 

maintenance costs. Efficiency levels for products included in this rulemaking were calculated 

according to the proposed DOE test procedure, ASTM Standard F2324-13 (see chapter 5 of this 

TSD for more information). 

In the engineering analysis pertaining to commercial prerinse spray valves, DOE 

evaluated a range of product efficiency levels and MPCs, which it converted to MSPs using 

information regarding typical markups. The purpose of the analysis is to estimate the incremental 

increase to selling prices that would result from increasing efficiency levels above the baseline 

level in each product class. The engineering analysis considers technologies not eliminated in the 

screening analysis. DOE considered the remaining technologies, designated as design options, in 

developing the cost-efficiency curves. The LCC analysis uses the cost-efficiency relationships 

developed in the engineering analysis.

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

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

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

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

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

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

materials derived from tear-downs of the product being analyzed. DOE used a combination of 

these approaches for this rulemaking, as described in further detail in chapter 5 of this TSD. 

2.6 MARKUPS ANALYSIS

DOE first converts the MPC to anMSP using manufacturer markups. DOE then uses 

manufacturer-to-consumer markups to convert the MSP estimates from the engineering analysis 

into retail prices, which are then used in the LCC and PBP analysis and the MIA. Retail prices 

are necessary for the baseline efficiency level and all other efficiency levels under consideration.
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In this rulemaking, DOE identified four different major channels through which the 

consumers purchase commercial prerinse spray valves. DOE then determined the market shares 

of each distribution channel and calculated the overall markup as a weighted average of the four 

channels. DOE relied on economic data from the U.S. Census Bureau and input from the 

industry to determine to what extent product prices are increased as they pass from the 

manufacturer to the consumer (see chapter 6 of this TSD).

2.7 CONSUMERCONSUMERCONSUMERCONSUMERENERGY AND WATER

USE ANALYSIS

The purpose of the energy and water use analysis is to establish the annual energy and 

water consumption of the product and assess the energy and water savings potential of different 

product efficiencies. DOE uses the annual energy and water consumption and energy and water

savings potential in the LCC and PBP analysis to establish the consumer operating savings of 

considered efficiency levels.

The energy and water use analysis assessed the annual energy and water consumption of 

the commercial prerinse spray valves located in commercial kitchens. The analysis captures and 

estimates water savings as a result of reduced flow rate and the related energy savings as a result 

of reduced hot water use. DOE calculated the energy and water use by determining the 

representative daily operating time of the product by major building types found in the 

Commercial Building Energy Consumption Survey2 (CBECS) that contain commercial kitchens. 

The daily commercial prerinse spray valve operating time was annualized based on operating 

schedules for each building type. The annual operating time was multiplied by the flow rate at an 

operating pressure of 60 pounds per square inch for each efficiency level to estimate the annual 

water use. 

Energy use was calculated by multiplying the annual hot water use in gallons by the 

energy required to heat each gallon of water supplied to the commercial prerinse spray valve. 

The energy required to heat each gallon of water was established using cold water supply 

temperatures, the hot water supply temperature from the water heater, and the end use 

temperature of the commercial prerinse spray valve. DOE used the CBECS database to 

determine the proportions of buildings that used natural gas or electricity.

2.8 LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS 

New or amended energy conservation standards usually decrease product operating 

expenses and increase the purchase price. DOE analyzes the net effect of new or amended 

standards on consumers by evaluating the net LCC. To evaluate the net LCC, DOE uses the costefficiency relationship derived in the engineering analysis and the energy and water costs derived 

from the energy and water use analysis. Inputs to the LCC calculation include the installed cost 

of products to the consumer (consumer purchase price plus installation cost), operating expenses 

(energy and water expenses and maintenance and repair costs), the lifetime of the unit, and a 

discount rate. 

Under new standards, the cost of installation typically increases while operating costs 

typically decrease. During the lifetime of higher-than-baseline efficiency products, there is a 
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point at which the net operating cost benefit (in dollars) is equal to the incremental first cost of 

implementing the product. The time required for products to reach this cost-equivalence point is 

known as the PBP. 

Recognizing that several inputs to determining consumer LCC and PBP are either 

variable or uncertain, DOE uses Monte Carlo simulation and probability distributions to model 

both the uncertainty and variability in inputs. DOE uses Microsoft Excel® combined with Crystal 

Ball® (a commercially available program) to develop LCC and PBP spreadsheet models that 

incorporate both Monte Carlo simulation and probability distributions. The consumer subgroup 

analysis includes an assessment of impacts on consumer subgroups that have potential to be 

adversely affected by the proposed standard when compared to other consumers. 

For commercial prerinse spray valves, it was necessary to determine several of the input 

values for the LCC estimation, including retail prices; energy and water prices; energy and water 

price trends; discount rates; maintenance, service, and installation costs; and product lifetimes.

DOE reviewed the Energy Information Administration’s (EIA) energy price data to 

establish energy prices. DOE relied on regional average energy price data for the commercial 

sector. DOE used projections of these energy prices for commercial consumers to estimate future 

energy prices in the LCC and PBP analysis. DOE used EIA’s Annual Energy Outlook 2014

(AEO2014) as the default source of projections for future energy prices. DOE reviewed the 

American Water Works Association (AWWA) survey to establish water and wastewater prices. 

DOE relied on Consumer Price Index (CPI) data to estimate future water and wastewater prices 

in the LCC and PBP analysis.

DOE assumed average product lifetimes for use in the LCC and subsequent analyses. 

2.9 SHIPMENTS ANALYSIS

Shipments projections are required to calculate the national impacts of standards (NES 

and NPV) and to calculate the future cash flows of manufacturers. DOE developed shipments 

projections based on an analysis of key market drivers for the products and estimates around the 

existing and future stock of commercial prerinse spray valves in the United States. Specifically, 

DOE’s shipments model assumes that shipments of products are driven by stock replacements, 

assuming that the overall population of commercial prerinse spray valves will slightly increase 

over the next several decades. DOE based its estimates of CPSV stock over time on available 

data from industry reports, commercial floorspace growth projections, and the Environmental 

Protection Agency’s (EPA) WaterSense® Field Study3

. 

2.10 NATIONAL IMPACT ANALYSIS

The NIA assesses the NES, national water savings, and the NPV from a national 

perspective of total consumer costs and savings expected to result from new or amended 

standards at specific efficiency levels. DOE determined the NPV, NES, and national water 

savings for the standard levels considered for the CPSV product classes analyzed. To make the 

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

Excel spreadsheet that uses typical values (as opposed to probability distributions) as inputs. 
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Analyzing impacts of potential standards for commercial prerinse spray valves requires 

comparing projections of U.S. energy and water consumption with new standards against 

projections of energy and water consumption without standards. The projections include 

projections of annual shipments, the annual energy and water consumption of new products, and 

the purchase price of new products. 

A key component of DOE’s NIA is the efficiencies projected over time for the no-newstandards case and each of the standards cases. The projected efficiencies represent the annual 

shipment-weighted efficiency of the product under consideration during the projection period 

(i.e., from the assumed compliance date of a new standard to 30 years after compliance is 

required).

As discussed previously, DOE developed a distribution of efficiencies in the no-newstandards case for the assumed compliance year of new standards for each CPSV product class. 

Because no information was available to suggest a different trend, DOE assumed that the 

efficiency distribution in the no-new-standards case would remain the same in future years. In 

each standards case, a “roll-up” scenario approach was applied to establish the efficiency 

distribution for the compliance year. Under the “roll-up” scenario, DOE assumed (1) product 

efficiencies in the no-new-standards case that do not meet the standard level under consideration 

would “roll-up” to meet the new standard level, and (2) product efficiencies above the standard 

level under consideration would not be affected. 

The nature of the market for commercial prerinse spray valves makes it possible that 

consumers may, under examined trial standard levels (TSL) and product classes, opt to switch 

product classes to a valve that consumes more water and energy than their current products. DOE 

implemented a mechanism in the shipments model to estimate such consumer choices. The 

economics resulting from product class switching may result in lower optimal efficiency levels 

and reduced estimates of water and energy savings, as compared to the case without class 

switching. A detailed description of DOE’s method to model product class switching is 

contained in chapter 9 of this TSD.

The inputs for determining the national energy and water savings are: (1) annual energy 

and water consumption per unit, (2) shipments, (3) product stock, and, in the case of NES, (4) 

site-to-source conversion factors for electricity. DOE calculated the national energy and water 

consumption by multiplying the number of units (stock) of each type of product (by vintage or 

age) by the unit energy and water consumption (also by vintage). Then, DOE calculated annual 

NES and national water savings based on the difference in national energy and water 

consumption for the no-new-standards case and for each higher efficiency standard. DOE 

estimated energy consumption and savings based on site energy and converted the electricity 

consumption and savings to primary energy using annual conversion factors derived from the 

AEO2014 version of the National Energy Modeling System (NEMS). Cumulative energy and 

water savings are the sum of the annual NES and national water savings for each year over the 

timeframe of the analysis.

DOE has historically presented NES in terms of primary energy savings. On August 18, 

2011, DOE published a final statement of policy in the Federal Register announcing its intention 

to use full-fuel-cycle (FFC) measures of energy use and greenhouse gas and other emissions in 
2-8 

the national impact analyses and emissions analyses included in future energy conservation 

standards rulemakings. 76 FR 51281. Although DOE stated in that notice that it intended to use 

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

to conduct the analysis, it also said it would review alternative methods, including the use of 

NEMS. After evaluating both models and the approaches discussed in the August 18, 2011 

notice, DOE published an amended statement of policy, articulating its determination that NEMS 

is a more appropriate tool for this purpose. 77 FR 49701 (August 17, 2012). 

The inputs for determining NPV are: (1) total annual installed cost, (2) total annual 

savings in operating costs, and (3) a discount factor to calculate the present value of costs and 

savings. DOE determined the net savings for each year as the difference between the no-newstandards case and each standards case in terms of total savings in operating costs versus total 

increases in installed costs. DOE calculated savings over the lifetime of products shipped in the 

projection period. DOE calculated NPV as the difference between the present value of operating 

cost savings and the present value of total installed costs. 

For the NPV analysis, DOE calculates increases in total installed costs as the difference 

in total installed cost between the no-new-standards case and standards case (i.e., once the 

standards take effect). Because the more-efficient products bought in the standards case usually 

cost more than products bought in the no-new-standards case, cost increases appear as negative 

values in the NPV. 

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

and water consumption of products bought in the standards case compared to the no-newstandards case. Total savings in operating costs are the products of savings per unit and the 

number of units of each vintage that survive in a given year.

2.11 CONSUMER SUBGROUP ANALYSIS

The consumer subgroup analysis in chapter 11 of this TSD evaluates impacts on 

identifiable groups of consumers of CPSV products who may be disproportionately 

disadvantaged by proposed energy conservation standards. The LCC and PBP analysis described 

in chapter 8 of this TSD is applied to five major types of businesses (healthcare, lodging, 

education, restaurant, and retail) that use a majority of the CPSV products. Although the inputs 

for different types of businesses are different in the LCC and PBP analysis, the final results may 

not reflect the results experienced by certain consumer subgroups. In other words, some of the 

adverse impacts on businesses that are disproportionately disadvantaged may be masked by the 

averaging effect of the LCC and PBP analysis. Therefore, DOE carried out the consumer 

subgroup analysis by using the LCC and PBP analysis spreadsheet, but applying the inputs that 

are applicable only to the identified subgroups. 

In general, the subgroups that face higher discount rates and lower product operating 

times are more disadvantaged than others. Higher discount rates impose burdens on the 

businesses because they have to borrow additional capital to purchase products that meets new or 

amended standards, compared to the case of where there are no new or amended standards. 

Lower product operating times result in lower savings in energy and water costs and, 

consequently, lower LCC savings and higher PBPs. 
2-9 

DOE carried out one consumer subgroup analyses for limited service establishments and 

single entity businesses, by using the LCC spreadsheet described in chapter 8 of this TSD with 

certain modifications. The input for discount rate or product operating time was fixed to the 

identified subgroup, which ensured that the discount rates and product operating times associated 

with only that subgroup were selected in the Monte Carlo simulations (see chapter 8 of this 

TSD). Apart from these changes, all other inputs for consumer subgroup analysis were the same 

as those in the LCC analysis described in chapter 8 of this TSD. 

2.12 MANUFACTURER IMPACT ANALYSIS

The MIA assesses the impacts of potential new energy conservation standards on 

manufacturers of the considered products. Potential impacts include financial effects, both 

quantitative and qualitative, that might lead to changes in the manufacturing practices for this 

product. 

DOE conducted the MIA in three phases. Phase I consisted of preliminary research 

directed at characterizing the CPSV manufacturing industry. This research involved collecting 

data on market share, sales volumes and trends, pricing, employment, and the industry financial 

structure. In Phase II, DOE created a Government Regulatory Impact Model (GRIM) to model 

the economic impact of amended energy conservation standards on the CPSV manufacturing 

industry as a whole. In Phase III, “Subgroup Impact Analysis,” DOE evaluated impacts on 

manufacturer subgroups, specifically focusing on the potential for disproportionate impacts on 

small business manufacturers of commercial prerinse spray valves. DOE also evaluated potential 

impacts on direct employment and manufacturing capacity within the CPSV manufacturing 

industry, as well as cumulative regulatory burdens experienced by CPSV manufacturers. DOE 

discusses its findings from the MIA in chapter 12 of this TSD. 

2.13 EMISSIONS ANALYSIS

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

CO2, NOx, SO2, and Hg from potential energy conservation standards for CPSV products. In 

addition, DOE estimated emissions impacts in production activities (extracting, processing, and 

transporting fuels) that provide the energy inputs to power plants. These are referred to as 

“upstream” emissions. Together, these emissions account for the FFC. In accordance with 

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

(Aug. 17, 2012), the FFC analysis includes impacts on emissions of methane (CH4) and nitrous 

oxide (N2O), both of which are recognized as greenhouse gases (GHGs). 

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

EIA’s AEO2014, supplemented by data from other sources. DOE developed separate emissions 

factors for power sector emissions and upstream emissions. The method that DOE used to derive 

emissions factors is described in chapter 13 of this TSD.

EIA prepares the AEO using the NEMS. Each annual version of NEMS incorporates the 

projected impacts of existing air quality regulations on emissions. AEO2014 generally represents 

current legislation and environmental regulations, including recent government actions, for 

which implementing regulations were available as of October 31, 2013. 
2-10

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

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

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

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

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

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

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

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

North Carolina v. EPA, 531 F.3d 896 (D.C. Cir. 2008). In 2011 EPA issued a replacement for 

CAIR, the Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (Aug. 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 emissions factors used for this rule, which are based on AEO2014, assume that CAIR 

remains a binding regulation through 2040. 

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

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

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

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

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

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

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

result of standards. 

Beginning in 2016, however, SO2 emissions will fall as a result of the Mercury and Air 

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

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

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

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

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

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

with the MATS requirements for acid gas. AEO2014 assumes that, in order to continue 

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

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

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

and natural gas demand decreases (e.g., as a result of energy efficiency standards). Emissions 

will be far below the cap established by CAIR, so it is unlikely that excess SO2 emissions 

allowances resulting from the lower electricity and natural gas demand would be needed or used 

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

that efficiency standards will reduce SO2 emissions in 2015 and beyond. 

CAIR established a cap on NOx emissions in the eastern States and the District of 

Columbia. Energy conservation standards are expected to have little or no physical effect on 

these emissions in those states covered by CAIR because excess NOx emissions allowances 

resulting from the lower electricity demand could be used to permit offsetting increases in NOx

emissions. However, standards would be expected to reduce NOx emissions in the states not 
2-11

affected by caps, so DOE estimated NOx emissions reductions from the standards considered in 

the NOPR for these 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 

will estimate mercury emissions reduction using emissions factors based on AEO2014, which 

incorporates the MATS. 

2.14 MONETIZATION OF EMISSIONS REDUCTION BENEFITS

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

emissions of CO2 and NOx that are expected to accompany each of the standard levels 

considered.

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

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

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

damage resulting from GHG emissions, including, but not limited to, net agricultural 

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

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

change will raise serious questions of science, economics, and ethics. However, with full regard 

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

of the social benefits of reductions in GHG emissions. 

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

previous report in 2013.4 The most recent estimates of the SCC in 2015, expressed in 2014$, are 

$12.2, $41.1, $63.3, and $121.0 per metric ton of CO2 avoided. For emissions reductions that 

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

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

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

the global benefits of reducing CO2 emissions.

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

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

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

used to obtain the SCC values in each case.

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

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

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

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

from the standard levels it considers. Estimates of monetary value for reducing NOx from 

stationary sources range from $476 to $4,893 per ton in 2013$.5 DOE calculates monetary 

benefits using a medium value for NOx emissions of $2,723 per short ton (in 2014$), and real 

discount rates of 3 percent and 7 percent, in accordance with OMB guidance.6
2-12

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

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

2.15 UTILITY IMPACT ANALYSIS

To estimate the impacts of potential energy conservation standards for commercial 

prerinse spray valves on the electric utility industry, DOE used a variant of the EIA’s NEMS 

called NEMS-BT.a NEMS is a large, multi-sectorial, partial-equilibrium model of the U.S. 

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

the AEO. NEMS produces a widely recognized forecast for the United States through 2040 and 

is available to the public.

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

no-new-standards case and standards cases. Outputs of the utility impact analysis usually parallel 

results that appear in the latest AEO, with some additions. The utility impact analysis reports the 

changes in installed capacity and generation that result from each standard level by plant type. 

DOE modeled the anticipated energy savings impacts from potential amended energy 

conservation standards using NEMS-BT to generate projections that deviate from the AEO 

Reference Case. See chapter 15 of this TSD for more information on DOE’s utility impact 

analysis for this rulemaking. 

2.16 EMPLOYMENT IMPACT ANALYSIS

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

indirectly. Direct employment impacts are changes in the number of employees at the plants that 

produce the covered products, along with affiliated distribution and service companies. DOE will 

evaluate direct employment impacts in the MIA.

Indirect employment impacts may result from expenditures shifting between goods (the 

substitution effect) and changes in income and overall expenditure levels (the income effect) that 

occur due to standards. 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 product prices and reduced spending on energy. 

The indirect employment impacts will be investigated in the employment impact analysis 

using the Pacific Northwest National Laboratory’s “Impact of Sector Energy Technologies” 

(ImSET) model.7

 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 simple economic multiplier approaches, 

ImSET allows for more complete and automated analysis of the economic impacts of energy 

conservation investments. 

 a EIA approves use of the name NEMS to describe only an official version of the model without any modification to 

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

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

DOE’s Building Technologies Program, under whose aegis this work is performed.)
2-13

2.17 REGULATORY IMPACT ANALYSIS

In the NOPR stage, DOE prepared a regulatory impact analysis (RIA) that evaluates 

potential non-regulatory policy alternatives, comparing the costs and benefits of each to those of 

the proposed standards. DOE recognizes that non-regulatory policy alternatives can affect 

product efficiency and reduce energy and water consumption, and estimates the actual impacts of 

any such initiatives in the RIA. 
2-14

REFERENCES

1. U.S. Department of Energy—Office of Energy Efficiency and Renewable Energy Building 

Technologies Program. Rulemaking Framework for Commercial Prerinse Spray Valves. 

2014. Washington, D.C. 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=100

2. U.S. Department of Energy-Energy Information Administration. Commercial Building 

Energy Consumption Survey. 2005. Washington DC. 

3. U.S. Environmental Protection Agency. WaterSense. Pre-Rinse Spray Valve Field Study 

Report. March 2011. Washington DC. 

www.epa.gov/watersense/partners/prsv_background.html#study. 

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

Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under 

Executive Order 12866. May 2013; revised November 2013. 

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. 

6. OMB. Circular No. A-4, Regulatory Analysis. 2003. Washington, DC. 

7. Roop, J. M., M. J. Scott, and R. W. Schultz, ‘‘ImSET: Impact of Sector Energy 

Technologies,’’ PNNL–15273. Pacific Northwest National Laboratory (2005). 
3-i 

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

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

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

3.3 TEST PROCEDURE ..................................................................................................... 3-1

3.4 EFFICIENCY METRIC ................................................................................................ 3-2

3.5 PRODUCT CLASSES .................................................................................................. 3-3

3.6 MARKET ASSESSMENT ............................................................................................ 3-4

3.6.1 Manufacturer Trade Groups .......................................................................................... 3-4

3.6.2 Manufacturers and Market Shares ................................................................................. 3-5

3.6.2.1 Distribution Channels ..................................................................................... 3-5

3.6.2.2 Major Manufacturers ....................................................................................... 3-5

3.6.3 Regulatory Programs ..................................................................................................... 3-6

3.6.3.1 Current Federal Energy Conservation Standard and Test Procedure.............. 3-6

3.6.3.2 California Energy Commission (CEC) Appliance Standard ........................... 3-7

3.6.3.3 Other State Regulations................................................................................... 3-7

3.6.3.4 Canadian Energy Conservation Standard........................................................ 3-7

3.6.4 Non-Regulatory Initiatives ............................................................................................ 3-8

3.6.4.1 Environmental Protection Agency WaterSense .............................................. 3-8

3.6.4.2 Federal Energy Management Program............................................................ 3-8

3.6.5 Historical Shipments ..................................................................................................... 3-8

3.6.6 Product Lifetime .......................................................................................................... 3-10

3.6.7 Market Performance Data ............................................................................................ 3-10

3.7 TECHNOLOGY ASSESSMENT ............................................................................... 3-11

3.7.1 Basic Structure and Operation of Commercial Prerinse Spray Valves....................... 3-11

3.7.2 Product Features with No Impact on Efficiency ......................................................... 3-12

3.7.2.1 Backflow Preventers ..................................................................................... 3-12

3.7.2.2 Spray Patterns ............................................................................................... 3-12

3.7.3 Technology Options for Commercial Prerinse Spray Valves ..................................... 3-12

3.7.3.1 Addition of a Flow Control Insert ................................................................. 3-13

3.7.3.2 Smaller Spray Hole Area .............................................................................. 3-13

3.7.3.3 Aerators......................................................................................................... 3-13

3.7.3.4 Additional Valves.......................................................................................... 3-13

3.7.3.5 Changing Spray Hole Shape ......................................................................... 3-13

3.7.3.6 Venturi Meter to Orifice Plate Nozzle Geometries....................................... 3-14

LIST OF TABLES

Table 3.5.1 Product Classes Based on Spray Force ..................................................................... 3-4

Table 3.6.1 Commercial Prerinse Spray Valve Historical Shipments for Light-Duty Class 

(Spray Force ≤ 5 ozf) ..................................................................................... 3-9

Table 3.6.2 Commercial Prerinse Spray Valve Historical Shipments for Standard-Duty 

Class (Spray Force >5 and ≤ 8 ozf) ............................................................... 3-9
3-ii

Table 3.6.3 Commercial Prerinse Spray Valve Historical Shipments for Heavy-Duty 

Class (Spray Force > 8 ozf) ......................................................................... 3-10

LIST OF FIGURES

Figure 3.6.1 Percentage of Basic Models per Manufacturer in the United States ....................... 3-6

Figure 3.6.2 Distribution of Commercial Prerinse Spray Valves by Flow Rate........................ 3-11
3-1 

CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This technical support document (TSD) chapter consists of six sections: scope of 

coverage, test procedure, efficiency metrics, product classes, market assessment, and technology 

assessment. The scope of coverage describes the product covered under this rulemaking and 

discusses certain amendments to the definitions of the product being considered by the U.S. 

Department of Energy (DOE). The test procedure section describes the updates from the 

concurrent commercial prerinse spray valve (CPSV) test procedure rulemaking. The efficiency 

metric section addresses DOE’s proposed metric to measure efficiency for commercial prerinse 

spray valves. The product classes section discusses the class-setting factors DOE considered for 

this rulemaking. The market assessment provides an overall picture of the market for the product 

concerned, including the nature of the product, industry structure, manufacturer market shares, 

and regulatory and non-regulatory efficiency improvement programs. The technology assessment 

identifies a preliminary list of technologies to consider in the screening analysis. 

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

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

qualitative information from publicly available sources and interested parties. 

3.2 SCOPE OF COVERAGE

The Energy Policy and Conservation Act (EPCA), as amended, defines “commercial 

prerinse spray valve” in 42 U.S.C. 6291(33)(A) as “a handheld device designed and marketed for 

use with commercial dishwashing and ware washing equipment that sprays water on dishes, 

flatware, and other food service items for the purpose of removing food residue before cleaning 

the items.” DOE incorporates this same definition in 10 CFR 431.262. Currently, all covered 

commercial prerinse spray valves are included in a single product class that is subject to a

maximum flow rate of 1.6 gallons per minute (gpm). 10 CFR 431.266 

DOE recently proposed updates to its concurrent CPSV test procedure in a proposed rule. 

The 2015 CPSV test procedure NOPR for commercial prerinse spray valves clarifies what 

products are covered under the current EPCA definition of commercial prerinse spray valves. 

Additionally, 42 U.S.C. 6291(33)(B) allows DOE to modify the CPSV definition to include 

products that are extensively used in conjunction with commercial dishwashing and ware 

washing equipment. To ensure coverage of products not explicitly addressed in the current 

definition, DOE proposed an amended definition for commercial prerinse spray valves in the 

2015 CPSV test procedure NOPR. The scope of products considered throughout the analyses 

conducted for the accompanying NOPR is consistent with this proposed definition from the 2015 

CPSV test procedure NOPR.

3.3 TEST PROCEDURE

EPCA established the current maximum flow rate for commercial prerinse spray valves 

and the test procedure to measure the flow rate. (42 U.S.C. 6295(dd), 42 U.S.C. 6293(b)(14)) 
3-2 

In the December 8, 2006 final rule, DOE incorporated by reference American Society for Testing 

and Materials (ASTM) Standard F2324‒03 under 10 CFR 431.263, and prescribed it as the 

uniform test method to measure flow rate of commercial prerinse spray valves under 10 CFR 

431.264. 71 FR 71340, 71374. On October 23, 2013, DOE incorporated by reference ASTM 

Standard F2324‒03 (2009) for testing commercial prerinse spray valves, which updated the 2003 

version. 78 FR 62970, 62980. 

In 2013, ASTM amended Standard F2324‒03 (2009) to replace the cleanability test with 

a spray force test, based on research conducted by the U.S. Environmental Protection Agency’s 

(EPA) WaterSense® program.1

 The cleanability test evaluated the cleaning time of a standard 

dinner plate. ASTM Standard F2324‒13 includes a procedure for measuring spray force, 

measured in ounce-force (ozf), which is a measure of the amount of force exerted by the water 

on a target surface. In addition, whereas ASTM Standard F2324–03 (2009) required measuring 

the prerinse spray valve flow rate at water pressures of both 60 ± 1 pounds per square inch (psi) 

and 60 ± 2 psi (in sections 4.2 and 10.2.2, respectively), ASTM Standard F2324‒13 requires 

measuring commercial prerinse spray valve flow rate only at 60 ± 2 psi. DOE also has identified 

other differences between ASTM Standard F2324–03 (2009) and F2324–13, which include: (1) 

minimum flow rate of flex tubing, (2) water temperature for testing, and (3) length of water pipe 

required to be insulated. 

In the 2015 CPSV test procedure NOPR, DOE proposed to incorporate by reference the 

amended ASTM Standard F2324‒13. Additionally, DOE proposed requiring spray force to be 

measured based on the procedure in ASTM Standard F2324‒13. For commercial prerinse spray 

valves with multiple spray patterns, DOE proposed that both flow rate and spray force be 

measured for each possible spray pattern. 

3.4 EFFICIENCY METRIC

Currently, all covered commercial prerinse spray valves are included in a single product 

class that is subject to a 1.6 gpm standard for maximum flow rate. 10 CFR 431.266 As part of 

the 2014 Framework document, DOE considered adopting an alternative metric to replace the 

existing flow rate (gpm) metric.a

 

Although the purpose of the rulemaking is to achieve water savings, EPCA, as amended, 

requires DOE to consider any lessening of the utility or the performance of a covered product 

likely to result from the imposition of an amended standard. (42 U.S.C. 6295(o)(2)(B)(i)(IV)) As 

described in the NOPR, DOE proposes to retain flow rate (in gallons per minute) as the 

efficiency metric, since it is well established in the industry. However, DOE also proposes to 

include spray force in the analysis, by incorporating it as a characteristic to establish and

distinguish product classes. This will ensure that utility of the commercial prerinse spray valves 

is maintained. Section 3.5 provides further discussion on incorporating spray force as a 

characteristic to differentiate product classes.

 a In initiating this rulemaking, DOE prepared a Framework document, “Rulemaking Framework for Commercial 

Prerinse Spray Valves” (2014 Framework document), and published a notice that announced both the availability of 

the 2014 Framework document and a public meeting to discuss the proposed analytical framework for the 

rulemaking. That notice also invited written comments from the public. 79 FR 54213 (September 11, 2014).
3-3 

3.5 PRODUCT CLASSES

When evaluating and establishing energy conservation standards, DOE considers 

dividing covered products into classes by (a) the type of energy used; (b) the capacity of the 

product; or (c) other performance-related features that justify different standard levels, such as 

features affecting customer utility. (42 U.S.C. 6295(q)) 

The current standard was established by the October 2005 final rule, where all 

commercial prerinse spray valves are part of one product class. Therefore, all commercial 

prerinse spray valves are subject to one standard, which is a 1.6 gpm flow rate as tested using the

current DOE test procedure.

In the Framework document, DOE stated that as part of this rulemaking, it is considering 

establishing separate product classes based on capacity or other performance related features. 

Research showed that CPSV applications range from heavy rinsing to removing loose food 

particles. Therefore, these applications required that commercial prerinse spray valves on the 

market have a range of flow rates. Hence, if all commercial prerinse spray valves were within

one product class and subject to one standard, then reducing the maximum flow rate for all 

CSPVs could have negative impacts on customer utility for heavy duty applications. 

Additionally, the lower flow rates required by ultra-light duty applications may justify different 

standard levels for products used in that application. Therefore, DOE proposes to establish 

multiple product classes for commercial prerinse spray valves. 

With the proposal to establish multiple product classes, DOE considered spray force as a

characteristic to establish the product classes. As mentioned previously, there is a large range of 

applications for commercial prerinse spray valves. Heavy-duty cleaning applications require 

more cleaning force than light rinsing. Heavy-duty cleaning involves removing baked-on foods 

from silverware, dishes, pots and pans, for example. Baked-on foods may be more common in 

certain kitchens due to the type of foods served in those places. However, in other settings with 

less baked-on food, a lower spray force may be sufficient.2

 In each of these applications, the 

amount of spray force is an important aspect of the customer utility of the CPSV. 

The relationship between spray force, flow rate, and customer utility has been 

investigated by the EPA WaterSense program.b The WaterSense program has its own label for 

CPSVs, which requires both a maximum flow rate of 1.28 gpm, and a minimum spray force of 4 

ozf. EPA published a field study report for CPSVs, which found that low water pressure, or 

spray force, is a source of user dissatisfaction.3 WaterSense evaluated 14 commercial prerinse 

spray valve models and collected 56 customer satisfaction reviews, of which nine were 

unsatisfactory. Seven of the nine unsatisfactory scores were attributed, among other factors, to 

the water pressure, or the user-perceived force of the spray. Id. 

Moreover, stakeholder comments to the draft WaterSense specification support the idea 

that high spray force is needed for some, but not all, prerinse spray valve applications. The draft 

specification proposed that to qualify for the WaterSense program, commercial prerinse spray 

valves must have a minimum spray force of 5.0 ozf.4 In response, WaterSense received 

 b EPA released WaterSense specifications for commercial prerinse spray valves on September 19, 2013. More 

details on the EPA WaterSense program are in section 3.6.4.1
3-4 

comments from interested parties who were concerned that the 5.0 ozf spray force minimum 

would exclude many units from the WaterSense program and would thus negatively affect 

customers. Plumbing Manufacturers International (PMI) specifically commented that the 

WaterSense proposal did not account for applications where lower flow rates are needed for 

specific niche markets. PMI suggested the addition of an ultra low-flow category for WaterSense 

qualified prerinse spray valves with a minimum spray force of 4.0 ozf, and a flow rate less than 

0.8 gpm. This would provide manufacturers and customers with two distinct levels of 

equipment.5 In response to comments from interested parties, WaterSense changed the spray 

force requirement from the proposed minimum of 5.0 ozf to 4.0 ozf. In its justification, 

WaterSense agreed that ultra-low flow spray valves are satisfactory to end-users and did not 

intend to exclude these models from the specification.6

 

The information presented above suggests that spray force is an important indicator of 

customer utility and an essential characteristic in evaluating CPSV performance. As described 

further in the engineering analysis, the amount of spray force is directly proportional to the water 

flow rate. To amend the standard for CPSVs while maintaining customer utility for the entire 

spectrum of end use applications, DOE is proposing in this rulemaking to establish three product 

classes based on spray force, as listed in Table 3.5.1. Chapter 5 of this TSD provides additional 

details on the spray force bounds that DOE is proposing for each product class.

Table 3.5.1 Product Classes Based on Spray Force

Product Class Definition

Light-Duty Spray force ≤ 5.0 ozf

Standard-Duty 5.0 ozf < Spray force ≤ 8.0 ozf

Heavy-Duty Spray force > 8.0 ozf

3.6 MARKET ASSESSMENT 

The following market assessment for commercial prerinse spray valves identifies the 

relevant manufacturer trade group and domestic manufacturers; discusses manufacturer market 

share; regulatory and non-regulatory initiatives; and provides historical shipment data and 

product lifetime estimates.

3.6.1 Manufacturer Trade Groups

DOE recognizes the importance of trade groups in distributing information and 

promoting the interests of the industry that they support. DOE identified PMI as the primary 

trade group that supports and has an interest in commercial prerinse spray valves. PMI represents 

various plumbing product manufacturers working to promote the industry’s growth, while 

promoting water efficiency, environmental sustainability, health, and safety. PMI advocates the 

interests of the plumbing product industry regarding federal and state legislative and regulatory 

issues. In addition to being a charter member of the Alliance for Water Efficiency and a member 

of U.S. Green Building Council, PMI also promotes and supports the WaterSense program as a 

partner working closely with EPA.7
3-5 

The North American Association of Food Equipment Manufacturers (NAFEM) is another 

organization that has an interest in prerinse spray valves. NAFEM is a trade association of more 

than 500 food service equipment and supplies manufacturers, including some that manufacture 

prerinse spray valves.8 

The National Restaurant Association (NRA) is the largest food service trade association 

in the world, supporting nearly 500,000 restaurant businesses. NRA advocates for food service 

industry interests, tracking regulatory and legislative developments at the Federal, State and local 

levels.9 

3.6.2 Manufacturers and Market Shares 

This section describes commercial prerinse spray valve distribution channels, major 

manufacturers and their market shares. 

3.6.2.1 Distribution Channels

Understanding the distribution channels of product covered by this rulemaking is an 

important facet of the market assessment. DOE gathered information regarding the distribution 

channels for commercial prerinse spray valves from publicly available sources and manufacturer 

comments to the 2014 Framework document. The following four distribution channels are being 

considered in this NOPR, and more details on distribution channels can be found in chapter 6 of 

the TSD.

1. Manufacturer  Final Customer (Direct Sales)

2. Manufacturer  Authorized Distributor  Final Customer

3. Manufacturer  Retailer  Final Customer

4. Manufacturer  Service Company  Final Customer

3.6.2.2 Major Manufacturers

In the 2014 Framework document, DOE presented a pie chart of the major manufacturers 

based on the number of basic models observed through the DOE Compliance Certification 

Management System (CCMS) database, WaterSense database, and web searches.10, 11 DOE 

requested comments on the manufacturers presented. DOE did not receive any specific 

comments on market shares or manufacturers, but received comments stating that a small 

number of manufacturers control the majority of the market, because CPSVs are a niche market, 

and most manufacturers are family-owned businesses. 

Figure 3.6.1 provides an updated pie chart with the most current information to describe 

major CPSV manufacturers and their estimated market shares. DOE identified 54 basic models 

from 11 different manufacturers. The percentages shown the chart indicate the number of basic 

models per manufacturer as a share of the total number of basic models in the U.S. market. 
3-6 

Figure 3.6.1 Percentage of Basic Models per Manufacturer in the United States

DOE consulted the following sources to identify CPSV models available on the market:

• DOE CCMS database

• WaterSense databasec

• Manufacturer websites

• Food Service Technology Center12

DOE is aware that the market share in terms of sales could differ significantly from the 

fraction of basic models available for sale. For example, in a study conducted by the California 

Urban Water Conservation Council, Fisher Manufacturing was described as having a 60 percent 

market share in the western United States.13 DOE did not receive any submitted comments from 

interested parties on market shares by sales, and research indicated that these market shares are 

not publicly available for all manufacturers.

3.6.3 Regulatory Programs

3.6.3.1 Current Federal Energy Conservation Standard and Test Procedure

In a final rule published on October 18, 2005, (the 2005 CPSV final rule), DOE 

prescribed the current energy conservation standards for commercial prerinse spray valves. 70 

FR 60407, 60409-10. The 2005 CPSV final rule established that all covered commercial prerinse 

 c Six models contained in the WaterSense database were not found in the DOE CCMS database, manufacturer 

websites, or internet searches. Therefore, DOE concluded that these six units are not available in the United States 

and excluded them from Figure 3.1. 
3-7 

spray valves manufactured on or after January 1, 2006, shall have a flow rate of not more than 

1.6 gpm. 10 CFR 431.266 

In a final rule published on October 23, 2013, DOE prescribed the current test procedure 

for commercial prerinse spray valves. This existing test procedure for commercial prerinse spray 

valves incorporates by reference ASTM Standard F2324‒03 (2009). 10 CFR 431.263 

3.6.3.2 California Energy Commission (CEC) Appliance Standard 

California has a flow rate standard for commercial prerinse spray valves of 1.6 gpm at 60 

psi, which is identical to the federal standard, as well as a cleanability standard that requires that 

“commercial pre-rinse spray valves manufactured on or after January 1, 2006 shall be capable of 

cleaning 60 plates in an average time of not more than 30 seconds per plate.”14 California 

adopted these standards on December 15, 2004.15

EPCA preempts any requirements of state and local governments concerning the energy 

efficiency or energy use of products and equipment covered by EPCA, with certain exceptions. 

See, e.g., 42 U.S.C. 6297(c). An exception from the general rule of preemption exists for 

regulations concerning standards for commercial prerinse spray valves adopted by the California 

Energy Commission (CEC) before January 1, 2005. 42 U.S.C. 6297 (c)(7). This exemption 

allows California to not adopt a federal standard. Any state regulations concerning commercial 

prerinse spray valves adopted by the CEC after January 1, 2005, would not have an exception 

from standard preemption. 

3.6.3.3 Other State Regulations

Besides California, no other state energy conservation standards for commercial prerinse 

spray valves differ from federal standards. 

3.6.3.4 Canadian Energy Conservation Standard 

Canada’s Energy Efficiency Regulations do not mandate energy conservation standards 

for commercial prerinse spray valves.16 In October 2010, the Natural Resources Canada’s Office 

of Energy Efficiency (NRCan OEE) published a first bulletin to propose to amend Canada’s 

Energy Efficiency Regulations to add minimum energy performance standard (MEPS) for 

commercial prerinse spray valves. In this bulletin, NRCan OEE proposed to adopt the U.S. 

Federal energy conservation standard, and to incorporate the cleanability requirement adopted in 

the California regulation.17 

In November 2011, NRCan OEE removed the requirements for cleanability and added 

the requirement that the testing procedure be conducted without the use of a removable flow 

restrictor.18 Currently, there is Amendment 14 to Canada’s Energy Efficiency Regulations, 

whose regulatory initiative is the Forward Regulatory Plan 2014-2016, which will introduce new 

MEPs for commercial prerinse spray valves.19
3-8 

3.6.4 Non-Regulatory Initiatives 

3.6.4.1 Environmental Protection Agency WaterSense

The EPA WaterSense program works to recognize products that meet or exceed the 

WaterSense levels, which exceed federal minimum standard levels. WaterSense specifications exist 

for several products, including commercial prerinse spray valves. Certifying organizations help 

maintain the WaterSense integrity and credibility by verifying and testing products for 

conformance to WaterSense specifications, efficiency, performance, and label use, and also 

conduct periodic market surveillance. 

The EPA’s water efficiency requirements sets the maximum flow rate for WaterSense 

labeled prerinse spray valves at 1.28 gpm, or 20-percent less water than the federal standard. The 

EPA’s performance requirements for commercial prerinse spray valves include a minimum spray 

force of 4 ozf, and requirement to perform for a minimum of 250,000 cycles. All WaterSense 

labeled models are required to include flow rate and spray force on product packaging or in 

literature to help purchasers select products most suitable for their applications. WaterSense 

labeled prerinse spray valves are independently certified to ensure efficient cleaning while using 

less water.20

3.6.4.2 Federal Energy Management Program 

DOE’s Federal Energy Management Program (FEMP) works to reduce the cost and 

environmental impact of the federal government by advancing energy and water conservation, 

promoting the use of distributed and renewable energy, and improving utility management 

decisions at federal sites. FEMP helps federal buyers identify and purchase energy efficient 

equipment, including certain commercial appliances.

Federal agencies are required by Executive Order 13693 to reduce water consumption 

and its associated energy use in their facilities.

21 Executive Order 13693 requires federal 

agencies to have a preference to purchase water-saving products labeled by the EPA WaterSense 

program or those designated by FEMP as being among the highest 25 percent in efficiency for 

equivalent products. For commercial prerinse spray valves, the efficiency requirements for 

FEMP include requiring a flow rate of 1.25 gpm or less, and cleanability of 26 seconds per plate 

or less. These requirements are based on ASTM Standard F2324‒03. 

3.6.5 Historical Shipments 

In the Framework document, DOE provided calculations for historical shipments based 

on the WaterSense field study and a study by California Urban Water Conservation Council.22, 23

Because information on historical shipments data was limited, DOE requested information on 

nationwide historical shipments and prices for commercial prerinse spray valves. DOE did not 

receive any shipment information from interested parties. Therefore, DOE used any available 

shipment data to estimate both historical and future shipments of commercial prerinse spray 

valves. In evaluating overall U.S. shipments of commercial prerinse spray valves, DOE 

considered data from the WaterSense Field Study and other industry sources.24 While the sources 

did not provide specific historical shipments for each year considered in the analysis, DOE used 
3-9 

the data from the sources to estimate historical shipments. The historical shipments calculated for 

each product class from 2009 onward is provided in chapter 9 of the TSD. 

Table 3.6.1 Commercial Prerinse Spray Valve Historical Shipments for Light-Duty Class 

(Spray Force ≤ 5 ozf) 

Year Spray Force < 5 ozf

EL0 EL1 EL2 EL3

2009 5,742 13,398 0 19,140

2010 5,857 13,666 0 19,523

2011 5,974 13,940 0 19,914

2012 6,094 14,218 0 20,312

2013 6,215 14,503 0 20,718

2014 6,340 14,793 0 21,132

2015 6,467 15,089 0 21,555

2016 6,596 15,390 0 21,986

2017 6,728 15,698 0 22,426

2018 6,862 16,012 0 22,874

Table 3.6.2 Commercial Prerinse Spray Valve Historical Shipments for Standard-Duty 

Class (Spray Force >5 and ≤ 8 ozf)

Year Spray Force > 5 and ≤ 8 ozf

EL0 EL1 EL2 EL3

2009 38,281 47,851 0 9,570

2010 39,046 48,808 0 9,762

2011 39,827 49,784 0 9,957

2012 40,624 50,780 0 10,156

2013 41,436 51,795 0 10,359

2014 42,265 52,831 0 10,566

2015 43,110 53,888 0 10,778

2016 43,972 54,965 0 10,993

2017 44,852 56,065 0 11,213

2018 45,749 57,186 0 11,437
3-10

Table 3.6.3 Commercial Prerinse Spray Valve Historical Shipments for Heavy-Duty Class 

(Spray Force > 8 ozf)

Year Spray Force > 8 ozf

EL0 EL1 EL2 EL3

2009 22,968 28,710 2,871 2,871

2010 23,428 29,285 2,928 2,928

2011 23,896 29,870 2,987 2,987

2012 24,374 30,468 3,047 3,047

2013 24,862 31,077 3,108 3,108

2014 25,359 31,699 3,170 3,170

2015 25,866 32,333 3,233 3,233

2016 26,383 32,979 3,298 3,298

2017 26,911 33,639 3,364 3,364

2018 27,449 34,312 3,431 3,431

3.6.6 Product Lifetime

In establishing the WaterSense specification for CPSVs, EPA stated that their average 

useful lifetime was 5 years, as determined through conversations with manufacturers and input 

from the American Society of Mechanical Engineers, Canadian Standards Association, PMI, and 

other research conducted on this product category.25 The assumption is also supported by an

EPA 2010 field study, in which it found several CPSVs in use that were not compliant with the 

Energy Policy Act of 2005, indicating that these spray valves were at least 4 years old.26 Survey 

respondents to the field study said that spray valves last between 2 and 7 years. However, a 

commenter to EPA stated that on average, prerinse spray valves last only 1 to 1.5 years, and 

some last as little as 3 months. Additionally, WaterSense set the life-cycle requirements for a 

commercial prerinse spray valve to 250,000 cycles based on its field study in 2010.27 Chapter 8

of the TSD provides more detail on how product lifetime was used in the analysis.

3.6.7 Market Performance Data

In preparation for the screening and engineering analyses, DOE gathered data on the 

efficiency of commercial prerinse spray valves available in the marketplace. While this section is 

not intended to provide a complete characterization of the efficiency of all commercial prerinse 

spray valves currently available and in use, it does provide an overview of the vast majority of 

units available currently on the market.

DOE used its CCMS Database to identify CPSV models on the market.28 As of May 

2015, DOE’s CCMS database contains 1844 CPSV models. A large number of these models 

belong to the same basic model. DOE defines a “basic model” as all units of a given type of 

covered product (or class thereof) manufactured by one manufacturer, having the same primary 

energy source, and which have essentially identical electrical, physical, and functional (or 

hydraulic) characteristics that affect energy consumption, energy efficiency, water consumption, 

or water efficiency. (10 CFR 430.2) Therefore, DOE filtered out the non-unique basic models. 

DOE also performed internet searches, and used information from the Food Service Technology 
3-11

Center website to identify other basic models. Figure 3.6.2 displays the distribution of 

commercial prerinse spray valves as a function of flow rate, as measured using the current DOE 

test procedure under 10 CFR 431.264. The flow rates identified in the figure also include the 

individual flow rates of each spray pattern for multiple spray pattern units.

Figure 3.6.2 Distribution of Commercial Prerinse Spray Valves by Flow Rate

3.7 TECHNOLOGY ASSESSMENT 

In the technology assessment, DOE identifies technology options that appear to be 

feasible means of decreasing CPSV water consumption. They are based on existing technologies, 

as well as prototype designs and concepts. In the 2014 Framework document, DOE developed a 

list of potential technology options for consideration in this rulemaking. To develop a list of 

technology options for the NOPR, DOE reviewed manufacturer catalogs, retail listings, 

published literature, and patents, and consulted with industry experts. The following sections 

describe the basic structure and operation of CPSVs, product features that DOE determined have 

no impact on efficiency, and design options that provide a feasible means of increasing the 

efficiency of CPSVs.

3.7.1 Basic Structure and Operation of Commercial Prerinse Spray Valves 

The basic structure of a CPSV has four main components: 

1) Main body, which is the housing that keeps the nozzle, handle and valve together;

2) Handle, which is the manual control to activate the flow of water;

3) Valve, which is the component that allows water to flow to the spray nozzle when

handle is pressed; and

4) Spray nozzle, which is the component that the water flows through before exiting the

CPSV.
3-12

The design of the spray nozzle generally determines the flow rate out of the CPSV. DOE 

has investigated both the spray hole area, spray hole shape and spray nozzle geometry as 

important factors that determine flow rate. More details on how these design aspects affect flow 

rate are described in chapter 5 of the TSD. 

3.7.2 Product Features with No Impact on Efficiency 

In the 2014 Framework document, DOE identified potential technology options that 

could improve CPSV efficiency. However, further research by DOE indicates that several 

features from that list do not affect CPSV efficiency. The following subsections provide some 

background on the product features that DOE determined had no impact on CPSV efficiency.

3.7.2.1 Backflow Preventers 

Backflow preventers prevent reverse flow of water. They are mainly used in plumbing 

devices to protect water supplies from contamination or pollution. Based on DOE’s research, 

DOE did not identify any commercially available commercial prerinse spray valves or working 

prototypes that incorporate backflow preventers into the design. Furthermore, DOE did not 

identify any means by which incorporating a backflow preventers into a commercial prerinse 

spray valve could improve its efficiency by limiting the water flow rate. 

3.7.2.2 Spray Patterns 

In the 2014 Framework document, DOE identified five different spray patterns that are 

incorporated in commercial prerinse spray valves: 

(1) Fan spray pattern (single nozzle with a hollow cone stream) 

(2) Solid stream pattern (single nozzle with single solid jet stream)

(3) Triple-action spray pattern (three nozzles with solid jet streams)

(4) Knife-like spray pattern (single nozzle with a flat stream)

(5) Rose spray pattern (multiple nozzles resembling a common showerhead). 

DOE performed several tests on various CPSV units with different spray patterns using 

the ASTM Standard F2324‒13 test procedure. While the units provided different flow rate and 

spray force results, DOE research showed no direct correlation between the type of spray pattern 

and flow rate. Hence, DOE found no indication that a different spray pattern can be used to 

reduce water consumption. DOE concluded that the type of spray pattern is more relevant to a 

specific CPSV application. Therefore, DOE determined that spray patterns do not have any 

correlation with flow rate, and therefore do not impact CPSV efficiency.

3.7.3 Technology Options for Commercial Prerinse Spray Valves 

The following list of technology options are those that DOE identified could have an 

impact on water flow rate, and therefore, CPSV efficiency. Chapter 4 of the TSD describes the 
3-13

results of the screening analysis on these technology options to determine which options were 

retained as design options for the engineering analysis.

3.7.3.1 Addition of a Flow Control Insert 

A flow control insert is a component that can be installed within certain plumbing 

products to limit the amount of water that flows out of the product. They are also called “flow 

restrictors.” Several faucets and showerheads on the market use flow control inserts to reduce 

water consumption; therefore, DOE included flow control inserts as a technology option that 

could reduce the flow rate of a commercial prerinse spray valve and thus improve its efficiency.

3.7.3.2 Smaller Spray Hole Area 

The spray hole(s) are located at the exterior of the commercial prerinse spray valve and 

allow water to flow out of the nozzle. The total spray hole area is the sum of all the areas of the 

individual spray holes. As described in chapter 5, the spray hole area and flow rate are directly 

related; therefore, DOE considered smaller spray hole area or nozzle tip opening as a technology 

option that could improve CPSV efficiency. 

3.7.3.3 Aerators 

An aerator is a device that can be used for aeration, or mixing air with water, to maintain 

steady pressure for even water flow. This component can be installed within certain plumbing 

products such as faucets and showerheads. An aerator can improve the efficiency of a plumbing 

product by reducing the flow of water from the device without reducing the water pressure. 

Aerators can also serve other purposes such as preventing splashing and shaping the water 

stream. 

3.7.3.4 Additional Valves 

Plumbing fixtures often use globe valves and butterfly valves to regulate water flow. 

Globe valves are comprised of a movable disk-like element and a stationary ring seated in a 

generally spherical body. The most common application of a globe valve is in a standard water 

faucet, such that when the handle is turned, a disc is lowered or raised. Butterfly valves regulate 

flow by means of a disc that rotates on an axis across the diameter of a pipe. 

3.7.3.5 Changing Spray Hole Shape 

As described in chapter 5, DOE found evidence that spray hole shape affects flow rate.

DOE found that circular-shaped spray holes (i.e., spray holes where the spray hole width equals 

the spray hole length) have higher discharge coefficients than oval-shaped spray holes (i.e., spray 

holes where the spray hole width doesn’t equal the spray hole length). Therefore, if all other 

design elements are identical, commercial prerinse spray valves with circular holes have higher 

flow rates than commercial prerinse spray valves with oval spray holes. Chapter 5 of the TSD 

provides a more detailed discussion on this topic. Therefore, DOE included spray hole shape as a 

technology option that could improve CPSV efficiency. 
3-14

3.7.3.6 Venturi Meter to Orifice Plate Nozzle Geometries 

DOE has observed that the nozzle geometry affects the flow rate of commercial prerinse 

spray valves. Based on DOE testing, reverse-engineering teardowns, and information available in 

literature, DOE has determined that “venturi meter” and “orifice plate” geometries in the nozzle 

of commercial prerinse spray valves can affect flow rate. Research shows that a venturi meter

geometry allows water to pass through the nozzle more easily than an “orifice plate” geometry. 

Therefore, commercial prerinse spray valves with a venturi meter geometry have a higher flow 

rate than commercial prerinse spray valves with an orifice plate geometry, if all other design 

elements of a commercial prerinse spray valve remain the same. Chapter 5 of the TSD provides a 

more detailed discussion on this topic. 
3-15

REFERENCES

1. EPA WaterSense program, September 19, 2013. WaterSense Specification for Commercial

Pre-Rinse Spray Valves Supporting Statement. Version 1.0.

http://www.epa.gov/watersense/partners/prsv_final.html.

2. Based on phone calls with two manufacturers.

3. EPA WaterSense. Pre-Rinse Spray Valves Field Study Report. March 31, 2011.

http://www.epa.gov/WaterSense/docs/prsv_field_study_report_033111v2_508.pdf.

4. EPA WaterSense. Draft Specification for Commercial Pre-Rinse Spray Valves. Version

1.0, February 7, 2013. http://www.epa.gov/watersense/docs/prsv_draftspec_508.pdf.

5. EPA WaterSense. Response to Public Comments Received on February 2013

WaterSense® Draft Specification for Commercial Pre-Rinse Spray Valves. September 19,

2013. 

http://www.epa.gov/watersense/docs/prsv_finalspec_publiccommentresponse_09.19.13_fin

al_508.pdf, page 7

6. Id, page 5

7. About PMI. www.pmihome.org/files/public/About-PMI(1).pdf.

8. About NAFEM. http://www.nafem.org/about-nafem/Default.aspx.

9. About NRA. http://www.restaurant.org/About-Us/What-We-Do.

10. U.S. Department of Energy. Compliance Certification Database.

www.regulations.doe.gov/certification-data/.

11. U.S. EPA, Water Sense. www.epa.gov/watersense/product_search.html.

12. Food Service Technology Center, Low-Flow Pre-Rinse Spray Valves.

http://www.fishnick.com/equipment/sprayvalves/.

13. SBW Consulting, Inc., Impact and Process Evaluation Final Report (Feb. 2007), available

at http://www.cuwcc.org/Portals/0/PreRinse%20Spray%20Valve%20Program%20Phase%202.pdf.

14. California Energy Commission, 2014 Appliance Efficiency Regulations, Report No. CEC400-2014-009-CMF, available at http://www.energy.ca.gov/2014publications/CEC-400-

2014-009/CEC-400-2014-009-CMF.pdf.

15. State of California, Order No. 04-1215-08, Docket No. 04-AAER-01, available at

http://www.energy.ca.gov/appliances/archive/2004rulemaking/notices/2004-12-

22_ORDER_ADOPT.PDF
3-16

16. National Resources Canada. Warewashing equipment. http://www.nrcan.gc.ca/node/14098. 

17. National Resources Canada. Pre-Rinse Spray Valves – October 2010. 

http://www.nrcan.gc.ca/energy/regulations-codes-standards/bulletins/7127. 

18. National Resources Canada. Regulatory Update – November 2011. 

http://www.nrcan.gc.ca/energy/regulations-codes-standards/bulletins/7145#PreRinse_Spray_Valves. 

19. National Resources Canada. Amendment 14 to Canada’s Energy Efficiency Regulations. 

https://www.nrcan.gc.ca/energy/regulations-codes-standards/6855. 

20. WaterSense®. Final Specifications for Commercial Pre-Rinse Spray Valves. 

http://www.epa.gov/watersense/partners/prsv_final.html. 

21. Gpo.gov. Executive Order 13693-Planning for Federal Sustainability in the Next Decade. 

March 25, 2015. http://www.gpo.gov/fdsys/pkg/FR-2015-03-25/pdf/2015-07016.pdf. 

22. U.S. EPA, WaterSense Specification for Commercial Pre-Rinse Spray Valves Supporting 

Statement (Sept. 2013). 

www.epa.gov/watersense/docs/prsv_finalspec_suppstate_09.19.13_final_508.pdf. 

23. SBW Consulting, Inc., Impact and Process Evaluation Final Report (Feb. 2007). 

www.cuwcc.org/Portals/0/Pre-Rinse%20Spray%20Valve%20Program%20Phase%202.pdf. 

24. U.S. EPA, WaterSense Specification for Commercial Pre-Rinse Spray Valves Supporting 

Statement (Sept. 2013). 

www.epa.gov/watersense/docs/prsv_finalspec_suppstate_09.19.13_final_508.pdf. 

25. Id.

26. EPA WaterSense. Pre-Rinse Spray Valves Field Study Report. March 31, 2011. 

http://www.epa.gov/WaterSense/docs/prsv_field_study_report_033111v2_508.pdf. 

27. U.S. EPA, WaterSense Specification for Commercial Pre-Rinse Spray Valves Supporting 

Statement (Sept. 2013). 

www.epa.gov/watersense/docs/prsv_finalspec_suppstate_09.19.13_final_508.pdf. 

28. U.S. Department of Energy. Compliance Certification Database. 

http://www.regulations.doe.gov/certification-data/CCMS-79222348801.html. 
4-i

CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION ......................................................................................................... 4-1

4.2 SCREENED-OUT TECHNOLOGIES ......................................................................... 4-2

4.2.1 Addition of Flow Control Insert .................................................................................... 4-2

4.2.2 Aerators ......................................................................................................................... 4-2

4.2.3 Additional Valves .......................................................................................................... 4-2

4.3 REMAINING TECHNOLOGIES ................................................................................. 4-3

4.3.1 Smaller Spray Hole Area ............................................................................................... 4-3

4.3.2 Changing Spray Hole Shape .......................................................................................... 4-3

4.3.3 Venturi Meter to Orifice Plate Nozzle Geometries ....................................................... 4-3

4.4 SCREENING ANALYSIS SUMMARY ...................................................................... 4-4

LIST OF TABLES

Table 4.4.1 Technology Options .................................................................................................. 4-4
4-1

CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter discusses the screening analysis conducted by the U.S. Department of 

Energy (DOE) of the technology options identified in the market and technology assessment for 

commercial prerinse spray valves (CPSV), as described in chapter 3 of the technical support 

document (TSD). The purpose of the screening analysis is to determine which technologies DOE 

should consider further in the rulemaking analyses, and to identify any technology options that 

will be eliminated from further consideration. In the market and technology assessment, DOE 

presented an initial list of technology options that can be used to reduce water consumption and 

associated energy consumption of the product covered in this rulemaking.

The following section details the specific technology options that DOE screened out prior 

to the engineering analysis, along with the rationale for elimination. DOE evaluated the 

technologies identified in the market and technology assessment pursuant to the criteria set out in 

the Energy Policy and Conservation Act (EPCA), as amended. (42 U.S.C. 6291‒6309) EPCA 

provides criteria for prescribing new or amended standards that will achieve the maximum 

improvement in energy efficiency that is technologically feasible and economically justified. (42 

U.S.C. 6295(o)(2)(A)(B)) 

In view of the EPCA requirements for determining whether a standard is technologically 

feasible and economically justified, 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 the consideration and promulgation of new 

or revised product efficiency standards under EPCA. These procedures elaborate on the statutory 

criteria provided in 42 U.S.C. 6295 and in part eliminate problematic technologies early in the 

process of revising an energy conservation standard. Under the guidelines, DOE eliminates from 

consideration technologies that present unacceptable problems with respect to the following four 

factors: 

(1) Technological feasibility. DOE screens out technologies that are not incorporated in 

commercially available products or working prototypes. 

(2) Practicability to manufacture, install, and service. If DOE determines that mass 

production of a technology in commercial products and reliable installation and servicing of the 

technology could not be achieved on the scale necessary to serve the relevant market by the time 

of the compliance date of the standard, it will not consider that technology further. 

(3) Adverse impacts on product or product utility or availability. If DOE determines 

a technology has a significant adverse impact on the utility of the product for significant 

consumer subgroups or results in the unavailability of any covered product type with 

performance characteristics (including reliability), features, size, capacities, and volumes that are 

substantially the same as products generally available in the United States at the time, it will not 

consider that technology further. 
4-2

(4) Adverse impacts on health or safety. If DOE determines that a technology will have 

significant adverse impacts on health or safety, it will not consider that technology further. 

4.2 SCREENED-OUT TECHNOLOGIES

This section describes the technologies that DOE eliminated based on the four factors 

described above and provides the reasons why each was eliminated. 

4.2.1 Addition of Flow Control Insert

As described in chapter 3 of this TSD, a flow control insert is a component that can be 

installed within certain plumbing products to limit the amount of water that flows out of the 

product. They are also called “flow restrictors.” Several faucets and showerheads on the market 

use flow control inserts to reduce water consumption. Based on DOE’s research to date, 

however, there are no commercially available products or working prototypes of commercial 

prerinse spray valves that use flow control inserts. Therefore, DOE has preliminarily concluded 

that flow control inserts do not meet the screening criteria for technologically feasibility. 

4.2.2 Aerators

An aerator is a device that can be used for aeration, or mixing air with water, to reduce 

the flow of water from the device without reducing the water pressure. This component can be 

installed within certain plumbing products and serve several different purposes, which include 

preventing splashing, shaping the water stream, and conserving water. Different size aerators are 

available, which are typically used in faucets and showerheads.

DOE is aware of only one commercial prerinse spray valve that incorporates an aerator.

This unit has a faucet-shaped spray head, which differs from more conventional circular or 

cylindrical spray heads. DOE tested this unit to determine how the aerator reduces water 

consumption, as summarized in chapter 5 of this TSD. DOE testing indicated that the 

performance of this aerated unit differed substantially from the more common non-aerated units: 

it exhibited a very low spray force, and did not demonstrate the same linear relationship between 

flow rate and spray force that is typical of most other CPSVs that DOE tested. At the present 

time, DOE does not have enough information to determine (1) whether the addition of an aerator 

represents a technologically feasible design option for improving CPSV efficiency, or (2) 

whether aerators can be applied more generally to other CPSV designs. Therefore, DOE is 

tentatively screening out aerators from the analysis. However, DOE notes that aerators are used 

in showerhead designs, which are similar in nature to the design of some CPSVs. 

4.2.3 Additional Valves 

Plumbing fixtures often use globe valves and butterfly valves to regulate water flow. 

Globe valves are comprised of a movable disk-like element and a stationary ring seated in a 

generally spherical body. The most common application of a globe valve is in a standard water 

faucet, such that when the handle is turned, a disc is lowered or raised. Butterfly valves regulate 

flow by means of a disc that rotates on an axis across the diameter of a pipe. Based on DOE’s 

research to date, however, there are no commercially available products or working prototypes of 
4-3

commercial prerinse spray valves that use these additional valves. Therefore, DOE has 

preliminarily concluded that additional valves do not meet the screening criteria for 

technologically feasibility.

4.3 REMAINING TECHNOLOGIES

The following sections list the technology options that DOE retained for the products 

covered by this rulemaking. These technologies are evaluated further as design options in the 

subsequent engineering analysis (TSD chapter 5). 

4.3.1 Smaller Spray Hole Area 

The spray hole(s) are located at the exterior of the CPSV and allow water to flow out of 

the nozzle. The total spray hole area is the sum of all the areas of the individual spray holes. As 

described in more detail in chapter 5 of the TSD, DOE determined that the flow rate and total 

spray hole area are directly related: smaller spray hole area results in a lower flow rate. 

Reducing the spray hole area is a relatively simple design change that is technologically 

feasible; would be practicable to manufacture, install, and service; and would not have adverse 

impacts on product utility, availability, or health and safety.i

 Accordingly, DOE has preliminarily 

concluded that none of the four screening criteria apply to this technology option and has

considered smaller spray hole area as a design option in the engineering analysis.

4.3.2 Changing Spray Hole Shape

DOE determined through testing and teardowns that CPSV flow rate can be affected by 

characteristics of the spray hole shape (e.g., circular vs. oval shapes). DOE found that circular 

spray holes (i.e., spray holes where the spray hole width equals the spray hole length) have 

higher discharge coefficients than oval spray holes (i.e. spray holes where the spray hole width 

doesn’t equal the spray hole length). Therefore, if all other design elements are identical, a 

commercial prerinse spray valves with circular holes have higher flow rates than commercial 

prerinse spray valves with oval-shaped spray holes. Chapter 5 provides further detail on how 

spray hole shape affects flow rate. 

DOE has preliminary concluded that none of the four screening criteria apply to this 

technology option and has considered changing spray hole shape as a design option in the 

engineering analysis.

4.3.3 Venturi Meter to Orifice Plate Nozzle Geometries

DOE determined through testing, teardowns and literature research that CPSV flow rate 

can be affected by the inside geometry of the spray nozzle. DOE identified two specific type of 

i

Although smaller spray hole area would result in lower flow rates and thus a lower amount of force, DOE’s 

proposed revised product class structure would preserve product utility for heavy-duty applications.
4-4

spray nozzle geometries when tearing down units, which include venturi meter and orifice plate.

Based on DOE testing, reverse-engineering teardowns and information available in the literature, 

DOE has determined that a “venturi meter” geometry allows water to pass through the nozzle 

more easily than an “orifice plate” geometry. Therefore, if all other design elements are identical, 

commercial prerinse spray valves with an orifice plate geometry have a lower flow rate than 

commercial prerinse spray valves with a venturi meter geometry. Chapter 5 provides more 

details on these nozzle geometries.

DOE has preliminary concluded that none of the four screening criteria apply to this 

technology option and has considered orifice plate as a design option in the engineering analysis.

4.4 SCREENING ANALYSIS SUMMARY

Table 4.4.1 lists the screened-out and retained technology options as described above. 

DOE has considered each of the retained technology options as design options in the subsequent 

engineering analysis described in chapter 5 of this TSD. 

Table 4.4.1 Technology Options

Screened Out Retained

• Addition of flow control insert

• Aerators

• Additional valves

• Smaller spray hole area

• Changing spray hole shape

• Venturi meter to orifice plate nozzle geometry
5-i

CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION......................................................................................................... 5-1

5.2 PRODUCT CLASSES ANALYZED............................................................................ 5-1

5.3 EFFICIENCY LEVELS ANALYZED ......................................................................... 5-2

5.3.1 Efficiency Metric........................................................................................................... 5-3

5.3.2 Higher Efficiency Levels............................................................................................... 5-4

5.3.3 Maximum Technologically Feasible Efficiency Levels................................................ 5-5

5.4 METHODOLOGY........................................................................................................ 5-5

5.4.1 Testing Methodology..................................................................................................... 5-5

5.4.2 Supplemental Analysis using Theoretical Model.......................................................... 5-6

5.4.3 Reverse-Engineering Teardown Methodology.............................................................. 5-6

5.4.3.1 Selection of Models ........................................................................................ 5-6

5.4.3.2 Generation of Bill of Materials....................................................................... 5-7

5.4.3.3 Cost Structure of the Spreadsheet Models...................................................... 5-7

5.4.3.4 Cost Model and Definitions............................................................................ 5-9

5.4.3.5 Cost Model Assumptions................................................................................ 5-9

5.4.4 Manufacturer Interviews ............................................................................................. 5-10

5.5 ANALYSIS AND RESULTS ..................................................................................... 5-10

5.5.1 Equipment Testing Results.......................................................................................... 5-10

5.5.2 Theoretical Analysis Results....................................................................................... 5-14

5.5.2.1 Flow Rate ...................................................................................................... 5-14

5.5.2.2 Spray Force ................................................................................................... 5-15

5.5.3 Analysis of Design Options......................................................................................... 5-15

5.5.3.1 Design Option - Smaller Spray Hole Area.................................................... 5-15

5.5.3.2 Alternate Design Option - Smaller Nozzle Area........................................... 5-16

5.5.3.3 Design Option – Increasing Spray Hole Eccentricity ................................... 5-17

5.5.3.4 Design Option – Venturi Meter to Orifice Plate Nozzle Geometry.............. 5-18

5.5.4 Product Teardowns...................................................................................................... 5-19

5.5.5 Example Design Paths to Achieve Higher Efficiency Levels..................................... 5-22

5.5.6 Cost-Efficiency Relationships..................................................................................... 5-23

REFERENCES .......................................................................................................................... 5-24
5-ii

LIST OF FIGURES

Figure 5.3.1 Relationship between CPSV Flow Rate and Spray Force....................................... 5-4

Figure 5.4.1 Teardown Methodology for Commercial Prerinse Spray Valves ........................... 5-6

Figure 5.4.2 Teardown Spreadsheet Model Methodology........................................................... 5-8

Figure 5.5.1 Flow Rate and Spray Force Test Results............................................................... 5-10

Figure 5.5.2 Flow Rate Ranges for Light, Standard and Heavy Duty Product Classes............. 5-11

Figure 5.5.3 Spray Force Ranges for Light, Standard and Heavy Duty Product Classes.......... 5-12

Figure 5.5.4 Flow Rate and Cleanability Time Test Results, All Data Points .......................... 5-13

Figure 5.5.5 Flow Rate and Cleanability Time Test Results, Outlier Excluded........................ 5-13

Figure 5.5.6 Measured Flow Rate vs. Measured Spray Hole Area............................................ 5-16

Figure 5.5.7 Relationship between Flow Rate and Nozzle Area (A1) ....................................... 5-17

Figure 5.5.8 Spray Hole Eccentricity vs. Discharge Coefficient............................................... 5-18

Figure 5.5.9 Venturi Meter (above) and Orifice Plate (below).................................................. 5-18

Figure 5.5.10 Total Manufacturing Production Costs for All Torn Down Units ...................... 5-20

Figure 5.5.11 Manufacturing Production Costs for the Spray Nozzle Component................... 5-20

Figure 5.5.12 Flow Rate vs. MSP for Three Technology Pairs................................................. 5-21

LIST OF TABLES

Table 5.2.1 Proposed Product Classes Based on Spray Force..................................................... 5-2

Table 5.3.1 Efficiency Levels Analyzed for CPSV Light Duty Product Class (Spray Force 

≤ 5 ozf)................................................................................................................. 5-2

Table 5.3.2 Efficiency Levels Analyzed for CPSV Medium Duty Product Class (5.0 ozf < 

Spray force ≤ 8.0 ozf) .......................................................................................... 5-2

Table 5.3.3 Efficiency Levels Analyzed for CPSV Heavy Duty Product Class (Spray 

force > 8.0 ozf)..................................................................................................... 5-3

Table 5.4.1 Major Manufacturing Processes ............................................................................... 5-8

Table 5.5.1 Example Design Options for Achieving Higher Efficiency Levels for the 

Light Duty Product Class (Spray Force ≤ 5 ozf) ............................................... 5-22

Table 5.5.2 Example Design Options for Achieving Higher Efficiency Levels for the 

Standard-Duty Equipment Class (Spray force > 5 ozf and ≤ 8 ozf).................. 5-22

Table 5.5.3 Example Design Options for Achieving Higher Efficiency Levels for the 

Heavy-Duty Equipment Class (Spray force > 8.0 ozf)...................................... 5-22

Table 5.5.4 Cost Efficiency Relationship for the Light Duty Product Class (Spray Force ≤ 

5 ozf).................................................................................................................. 5-23

Table 5.5.5 Cost Efficiency Relationship for the Standard-Duty Equipment Class (Spray 

force > 5 ozf and ≤ 8 ozf)................................................................................... 5-23

Table 5.5.6 Cost Efficiency Relationship for the Heavy-Duty Equipment Class (Spray 

force > 8.0 ozf)................................................................................................... 5-23
5-1

CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

After conducting the screening analysis for commercial prerinse spray valves (CPSVs), 

the U.S. Department of Energy (DOE) performed an engineering analysis based on the 

remaining design options. The purpose of the engineering analysis is to establish the relationship 

between the manufacturing production cost (MPC) and increased levels of efficiency for each 

product class. This relationship serves as the basis for cost/benefit calculations in terms of 

individual consumers, manufacturers, and the nation. As part of the engineering analysis, DOE 

also tests a sample of products to determine how water consumption, energy consumption, and 

other aspects of performance vary with increased levels of efficiency.

The primary inputs to the engineering analysis are information and data from the market 

and technology assessment (chapter 3 in the technical support document (TSD)) and design 

options from the screening analysis (TSD chapter 4). Additional inputs include water 

consumption data from DOE testing, publicly available product literature, and research reports;

product information from manufacturers; information about product component and material 

costs obtained through DOE research; and MPC estimates developed through reverseengineering teardown analysis.

The primary output of the engineering analysis is a set of cost-efficiency relationships

that represent the average incremental cost of implementing design options that increase product 

efficiency above the baseline levels. In the subsequent markups analysis (TSD chapter 6), DOE 

determined customer prices by applying manufacturer markups, distribution chain or dealer 

markups, and sales tax to the MPCs developed in the engineering analysis. After applying these 

markups, the data serve as inputs to the energy use characterization (TSD chapter 7) and the lifecycle cost and payback period analyses (TSD chapter 8).

DOE typically structures its engineering analysis around one of three following 

methodologies:

1) Design-option approach, which calculates the incremental costs of adding specific 

design options to a baseline model;

2) Efficiency-level approach, which calculates the relative costs of achieving increases 

in energy efficiency levels without regard to design options used to achieve such 

increases; and/or

3) Reverse-engineering or cost-assessment approach, which involves a “bottom-up” 

manufacturing cost assessment based on a detailed bill of materials (BOM) derived 

from teardowns of the products being analyzed. 

In this rulemaking, DOE structured its engineering analysis using a combination of the 

design-option approach and the reverse-engineering approach.

5.2 PRODUCT CLASSES ANALYZED

When evaluating and establishing energy conservation standards, DOE considers 

dividing covered product into classes by: (1) type of energy used; (2) capacity; or (3) other 
5-2

performance-related features that would justify the establishment of a separate energy 

conservation standard, such as features affecting consumer utility. (42 U.S.C. 6295 (q) and 

6316(a))

In the market and technology assessment (chapter 3 of this TSD), DOE identified three 

proposed product classes for commercial prerinse spray valves based on spray force (measured 

in ounce force, ozf). Table 5.2.1 summarizes the proposed spray force thresholds for each 

product class. Chapter 3 of the TSD provides more details about the product class selection.

Table 5.2.1 Proposed Product Classes Based on Spray Force

Product Class Definition

Light Duty Spray force ≤ 5.0 ozf

Standard Duty 5.0 ozf < Spray force ≤ 8.0 ozf

Heavy Duty Spray force > 8.0 ozf

5.3 EFFICIENCY LEVELS ANALYZED

Table 5.3.1, Table 5.3.2, and Table 5.3.3 summarize the efficiency levels analyzed for the 

three proposed product classes. The subsections following the table describe how DOE selected 

the baseline, higher-efficiency, and maximum technologically feasible (“max tech”) efficiency

levels for each product class.

Table 5.3.1 Efficiency Levels Analyzed for CPSV Light Duty Product Class (Spray Force ≤

5 ozf)

Efficiency Level Efficiency Level Source Flow Rate

(gpm)1

Baseline Linear interpolation 0.76

Level 1 5% improvement over baseline 0.72

Level 2 10% improvement over baseline 0.68

Level 3 Maximum available (“max tech”) 0.65

1. Flow rate measured in gallons per minute (gpm)

Table 5.3.2 Efficiency Levels Analyzed for CPSV Medium Duty Product Class (5.0 ozf < 

Spray force ≤ 8.0 ozf)

Efficiency Level Efficiency Level Source Flow Rate

(gpm)

Baseline Linear interpolation 1.22

Level 1 10% improvement over baseline 1.10

Level 2 20% improvement over baseline 0.97

Level 3 Maximum available (“max tech”) 0.94
5-3

Table 5.3.3 Efficiency Levels Analyzed for CPSV Heavy Duty Product Class (Spray force > 

8.0 ozf)

Efficiency Level Efficiency Level Source Flow Rate

(gpm)

Baseline Current DOE standard 1.60

Level 1 10% improvement over baseline 1.44

Level 2 20% improvement over baseline 1.28

Level 3 Maximum available (“max tech”) 1.24

5.3.1 Efficiency Metric

The current efficiency metric for commercial prerinse spray valves is a maximum flow 

rate, measured in gallons per minute (gpm). The flow rate measurement is based on American 

Society for Testing and Materials (ASTM) Standard F2324‒03 (2009), Standard Test Method for 

Prerinse Spray Valves. As described in the NOPR, DOE proposes to maintain the use of flow 

rate as the efficiency metric for commercial prerinse spray valves. DOE proposes to incorporate 

by reference the most recent amended ASTM Standard F2324‒13 for performing the flow rate 

measurement.

Additionally, DOE is proposing three CPSV product classes based on spray force, 

measured in ounce-force, as measured using the most recently updated version of ASTM 

Standard F2324–13. This standard defines spray force is the amount of force exerted onto the 

center of a disc, eight inches from the prerinse spray valve at maximum flow. DOE proposes 

requiring spray force to be measured based on the procedure in ASTM Standard F2324‒13. For 

commercial prerinse spray valves with multiple spray patterns, DOE proposes that both flow rate 

and spray force be measured for each possible spray pattern.

As part of its engineering analysis, DOE investigated the flow rate and spray force 

characteristics of commercial prerinse spray valves. Figure 5.3.1 shows the relationship between 

flow rate and spray force for a wide variety of CPSV products on the market. The figure also 

indicates DOE’s proposed thresholds defining the light duty, standard duty, and heavy duty 

product classes. The data points represent a combination of DOE test data and supplementary 

data from DOE’s Compliance Certification Management System (CCMS), the U.S. 

Environmental Protection Agency’s (EPA) WaterSense® program, and Food Service 

Technology Center (FSTC) reports.1,2,3 The data in the figure indicate a strongly linear 

relationship between flow rate and spray force, with few outliers.
5-4

Figure 5.3.1 Relationship between CPSV Flow Rate and Spray Force

Flow Rate (gpm) = 0.1523 x spray force (ozf)

Eq. 5.1

DOE selected baseline models as reference points for each equipment class, against 

which DOE compared the energy and water savings and cost increases resulting from amended 

energy conservation standards. The baseline model in each equipment class typically represents

the most common, least efficient model with basic characteristics of equipment that just meet 

current energy conservation standard and provides basic customer utility.

For the heavy duty product class, DOE used the current maximum flow rate standard of 

1.6 gpm to characterize the baseline efficiency level.

For the standard duty and light duty product classes, DOE used the linear relationship 

shown in Figure 5.3.1 to establish the baseline flow rates. For the standard duty product class, the 

8 ozf spray force threshold corresponds to a flow rate of 1.22 gpm. For the light duty product 

class, the 5 ozf spray force threshold corresponds to a flow rate of 0.76 gpm.

5.3.2 Higher Efficiency Levels

For each product class, DOE defined the higher efficiency levels as a percentage 

reduction from the baseline level. For the heavy duty and standard duty product classes, Level 1 

and Level 2 flow rates represent a 10 percent and 20 percent improvement from the baseline, 
5-5

respectively (i.e., a 10 percent or 20 percent reduction in flow rate). For the light duty product 

class, Level 1 and Level 2 represent a 5 percent and 10 percent improvement from the baseline, 

respectively. DOE assumed smaller percentages for the light duty product class because of the 

smaller range of flow rates observed in CPSV products on the market.

5.3.3 Maximum Technologically Feasible Efficiency Levels

Under the Energy Policy and Conservation Act, as amended, (EPCA) (42 U.S.C. 6311–

6316), DOE is required to consider the maximum technologically feasible (“max-tech”) level. 

DOE determines max-tech levels based on technologies that are either commercially available or 

have been demonstrated as working prototypes. If the max-tech design meets DOE’s screening 

criteria, DOE considers the design in further analysis. DOE also considers customer utility and 

availability of features, which may be met by a niche product, as required by EPCA.

For each of the three product classes, DOE established the max-tech efficiency level 

based on the tested commercial prerinse spray valve with the lowest flow rate in each product 

class: 0.65, 0.94, and 1.24 gpm for the light duty, standard duty, and heavy duty product classes 

respectively. 

5.4 METHODOLOGY

This section describes the general analytical methodology used in the engineering 

analysis.

DOE conducted water flow rate testing, and spray force testing on a representative 

sample of commercial prerinse spray valves spanning a range of efficiencies and manufacturers. 

The objective of this testing was to gain insights into the relative performance differences among 

the various CPSV models available on the market. Following testing, DOE performed detailed 

product teardowns, cost modeling, and performance modeling to generate cost-efficiency curves 

for each equipment class.

5.4.1 Testing Methodology

DOE conducted performance testing on a range of CPSV models for the following 

purposes:

 Measure water flow rate

 Measure spray force

 Verify performance trends that are apparent in the publicly available data

 Evaluate impacts on product performance as a result of higher efficiency levels

DOE conducted its testing at an independent laboratory with demonstrated experience 

performing CPSV testing. DOE used ASTM Standard F2324–13 for all testing, as referenced in 

DOE’s recently proposed updates to its CPSV test procedure, as proposed in the concurrent 

CPSV test procedure rulemaking.
5-6

5.4.2 Supplemental Analysis using Theoretical Model

In addition to analyzing test results and information contained in publicly available 

databases, DOE also performed a supplemental analysis based on the theoretical physics of 

nozzle flow. In some cases the results of the theoretical analysis supported and confirmed the 

conclusions reached through the analysis of product testing results. In other cases, the results 

suggested by the theoretical analysis were inconclusive. Additional details about the theoretical 

model are provided in Section 5.5.2.

5.4.3 Reverse-Engineering Teardown Methodology

Other than obtaining detailed manufacturing costs directly from a manufacturer, the most 

accurate method for determining the production cost of a product is to disassemble it piece-bypiece, compile a BOM, and estimate the material and labor cost of each component. DOE refers 

to this practice as a physical teardown. A supplementary method, called a catalog teardown (or 

virtual teardown), uses published manufacturer product literature and supplementary component 

data to estimate the major physical differences between the catalog teardown model and a similar 

physical teardown model. DOE performed physical and catalog teardown analyses on products 

for this rulemaking.

Figure 5.4.1 provides a summary of the teardown analysis methodology that DOE used 

for analyzing commercial prerinse spray valves. DOE calculated the incremental manufacturing 

costs associated with higher efficiency design options by comparing the cost of relevant spray 

valve components in technology pairs. A technology pair is a set of two commercial prerinse 

spray valves from the same manufacturer that have similar design and construction 

characteristics, but different flow rates.

Figure 5.4.1 Teardown Methodology for Commercial Prerinse Spray Valves

5.4.3.1 Selection of Models

DOE selected a range of models for testing, generally ranging from baseline to max-tech 

models. During the process of selecting models for testing and teardown, DOE considers three

main questions:

 What efficiency levels should be captured in the teardown analysis?

 Are models available on the market that span all potential efficiency levels and design 

options?

 Which of the available models are most representative?

 Identify baseline models 

and more efficient 

models that incorporate 

design options being 

considered by DOE 

 Select CPSV technology 

pairs for every design 

option

 Conduct teardowns to 

generate BOMs

 Create spreadsheet 

model to calculate 

component costs and 

total MPC

 Calculate incremental 

manufacturing costs by 

comparing component 

costs for each CPSV

technology pair
5-7

In responding to the preceding questions, DOE adopts the following criteria for selecting 

models for testing and the teardown analysis:

 The selected products should span the full range of efficiency levels for each equipment 

class under consideration.

 Within each equipment class, if possible, the selected products should be produced by

the same manufacturer and belong to the same product series.

 The selected products should represent manufacturers with large market share in that 

equipment class, although the highest efficiency products are chosen irrespective of 

manufacturer.

 The selected products should have non-efficiency-related features that are the same as, 

or similar to, features of other products in the same equipment class to minimize any 

cost differences due to non-efficiency-related design differences among models.

5.4.3.2 Generation of Bill of Materials 

The result of each teardown is a structured BOM, describing each product component and 

its relationship to the other parts in the estimated order in which manufacturers assembled them. 

The BOMs describe each fabrication and assembly operation in detail, including the type of 

equipment needed for fabrication (e.g., stamping presses, injection molding machines, spotwelders) and the process cycle times. The result is a thorough and explicit model of the 

production process. 

The BOMs incorporate all materials, components, and fasteners classified as either raw 

materials or purchased parts and assemblies. The classification into raw materials or purchased 

parts is based on DOE’s previous industry experience, recent information in trade publications, 

and, typically, discussions with manufacturers. 

For purchased parts, the purchase price is an estimate based on volume-variable price 

quotations and detailed discussions with suppliers. For fabricated parts, the price of intermediate 

materials (e.g., tube, sheet metal) and the cost of transforming them into finished parts is an 

estimate based on current industry pricing. 

The cost of raw materials is determined using prices for copper, steel, and aluminum 

from the American Metals Market.4 DOE uses a 5-year average in material prices to smooth out 

year-to-year fluctuations in raw material prices.

The BOM for a catalog teardown is identically structured and provides a description in 

equal detail to the BOM of a physical teardown. However, it was generated using a slightly 

different methodology—modifying the BOM of a similar model that has been physically torn 

down. These modifications reflect the major physical differences among the models.

5.4.3.3 Cost Structure of the Spreadsheet Models

The manufacturing cost assessment methodology used is a detailed, component-focused 

technique for rigorously calculating the manufacturing cost of a product (direct materials, direct 
5-8

labor, and some overhead costs.) Figure 5.4.2 shows the three major steps in generating the 

manufacturing cost.

Figure 5.4.2 Teardown Spreadsheet Model Methodology

The first step in the manufacturing cost assessment was the creation of a complete and 

structured BOM from the disassembly of the models selected for teardown. The models were 

dismantled, and each part was characterized according to weight, manufacturing processes used, 

dimensions, material, and quantity. The BOM incorporated all materials, components, and 

fasteners with estimates of raw material costs and purchased part costs. Assumptions on the 

sourcing of parts and in-house fabrication are based on industry experience, and information in 

trade publications.

Following the development of a detailed BOM, the major manufacturing processes were 

identified and developed for the spreadsheet model. Some typical processes are listed in Table 

5.4.1.

Table 5.4.1 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality Control

Fixturing

Stamping/Pressing

Brake Forming

Die Casting

Cutting and Shearing

Machining

Insulating

Turret Punching

Tube Forming

Enameling

Washing

Powder Coating

De-burring

Polishing

Refrigerant Charging

Adhesive Bonding

Spot Welding

Seam Welding

Packing

Inspecting & Testing

DOE estimated fabrication process cycle times and entered them into the BOM. In the 

final step of the cost assessment, DOE estimated assembly times and associated direct labor 

costs. Once the cost estimate for each teardown model was finalized, DOE prepared a detailed 

summary of relevant components, subassemblies, and processes. The BOM thus detailed all 

aspects of unit costs.

Design options used in units subject to teardown are noted in the summary sheet of each 

cost model and are cost-estimated individually. Thus, various implementations of design options 

can be accommodated, ranging from assemblies that are entirely purchased, to models that are 

made entirely from raw materials, as well as hybrid assemblies consisting of both purchased 

parts and parts made on site.

Purchased Parts

Bill of

Materials

Fabrication

Processes Assembly

Processes

Teardown 

Observations

Production

Cost

Raw

Materials

Finished

Materials
5-9

5.4.3.4 Cost Model and Definitions

The cost model is based on production activities and divides factory costs into the 

following categories:

 Materials: Purchased parts (i.e., motors, valves), raw materials, (i.e., cold rolled steel, 

copper tube), and indirect materials that are used for processing and fabrication.

 Labor: Fabrication, assembly, indirect, and supervisor labor. Fabrication and 

assembly labor cost are burdened with benefits and supervisory costs.

 Overhead: Equipment, tooling, and building depreciation, as well as utilities, 

equipment and tooling maintenance, insurance, and property taxes.

Because there are many different accounting systems and methods to monitor costs, DOE 

defined the above terms as follows:

 Direct material: Purchased parts (outsourced) plus manufactured parts (made in house 

from raw materials).

 Indirect material: Material used during manufacturing (e.g., welding rods, adhesives).

 Fabrication labor: Labor associated with in-house piece manufacturing.

 Assembly labor: Labor associated with final assembly.

 Supervisory labor: Labor associated with fabrication and assembly basis. Assigned on 

a span basis (x number of employees per supervisor) depending on the industry.

 Indirect labor: Labor costs that scale with fabrication and assembly labor. These 

included the cost of technicians, manufacturing engineering support, stocking that are 

proportional to all other labor. 

 Equipment depreciation: Money allocated to pay for initial equipment installation and 

replacement as the production equipment is amortized. All depreciation is assigned in 

a linear fashion and affected equipment life depends on the type of equipment.

 Tooling depreciation: Cost for initial tooling (including non-recurring engineering 

and debugging of the tools) and tooling replacement as it wears out or is rendered 

obsolete.

 Building depreciation: Money allocated to pay for the building space and the 

conveyors that feed and/or make up the assembly line.

 Utilities: Electricity, gas, telephones, etc.

 Maintenance: Annual money spent on maintaining tooling and equipment.

 Insurance: Appropriated as a function of unit cost.

 Property Tax: Appropriated as a function of unit cost.

5.4.3.5 Cost Model Assumptions

As discussed in the previous section, assumptions about manufacturer practices and cost 

structure played an important role in estimating the final product cost. In converting physical 

information about the product into cost information, DOE reconstructed manufacturing processes 

for each component using internal expertise and knowledge of the methods used by the industry. 
5-10

5.4.4 Manufacturer Interviews

Variability exists among manufacturers regarding baseline models, design strategies, and 

cost structures. To better understand and explain these variances, DOE attempted to conduct

manufacturer interviews. These confidential interviews were intended to provide a deeper 

understanding of the various combinations of technologies used to increase commercial prerinse 

spray valve efficiency, and their associated manufacturing costs. 

Further, these interviews were meant to gather information about the capital expenditures 

required to increase the efficiency of the baseline models to various efficiency levels (i.e., 

conversion capital expenditures by efficiency or energy-use level). DOE contacted all 

commercial prerinse spray valve manufacturers that were identified in the market and technology 

assessment as listed in TSD chapter 3 to participate in a manufacturer interview. However none 

of the manufacturers responded to DOE’s request.

5.5 ANALYSIS AND RESULTS

5.5.1 Equipment Testing Results

DOE tested the flow rate and spray force characteristics of a wide variety of CPSV 

products on the market using industry standard ASTM Standard F2324–13. Figure 5.5.1 shows 

the relationship between flow rate and spray force for the tested units. This data is a subset of the 

data shown previously in Figure 5.3.1. The figure also indicates DOE’s proposed thresholds 

defining the light duty, standard duty, and heavy duty product classes.

Figure 5.5.1 Flow Rate and Spray Force Test Results
5-11

As described previously, the results indicate a strongly linear relationship between flow 

rate and spray force, which is described by Eq. 5.1.

DOE complemented these test results with flow rate data from the FSTC and spray force 

data from the WaterSense Database.5,6 Figure 5.5.2 box and whisker plots show the range of flow 

rates for the three equipment classes. The ranges for the flow rate for light duty, standard duty, 

and heavy duty product classes are 0.65 to 0.97, 0.94 to 1.15, and 1.24 to 1.34 gpm, respectively.

Figure 5.5.2 Flow Rate Ranges for Light, Standard and Heavy Duty Product Classes

Figure 5.5.3 box and whisker plots show the range of spray forces for the three product

classes. The figure shows that spray force increases across the equipment classes, which is 

expected, based on the direct relationship between flow rate and spray force discussed 

previously.
5-12

Figure 5.5.3 Spray Force Ranges for Light, Standard and Heavy Duty Product Classes

DOE also tested the cleanability time of each CPSV unit in the test sample using ASTM 

Standard F2324–03. In the state of California, cleanability time is regulated in addition to flow 

rate. The regulation requires that commercial prerinse spray valves shall be capable of cleaning 

tomato sauce from 60 plates in an average time of not more than 30 seconds per plate.7 Figure 

5.5.4 shows the results of flow rate and cleanability time. The results indicate a weak correlation 

between flow rate and cleanability time. Excluding the data point at approximately 100 seconds 

of cleaning time at a flow rate of 0.7 gpm, the relationship between flow rate and cleanability 

time is even weaker, as shown in Figure 5.5.5. DOE notes that the unit with the lowest 

cleanability time of around 37 seconds corresponds to one of the lowest flow rates of around 

0.97 gpm. These results suggest that relatively low cleanability times can be maintained, and 

even reduced, even as flow rate is reduced.
5-13

Figure 5.5.4 Flow Rate and Cleanability Time Test Results, All Data Points

Figure 5.5.5 Flow Rate and Cleanability Time Test Results, Outlier Excluded

Finally, during its testing, DOE found that the cleaning time is a subjective measure, 

because operator judgment is required to determine, for example, when all the tomato sauce has 

been rinsed from a plate, as previously required by industry standard ASTM F2324-03 test 

procedure. As discussed in the market and technology assessment in chapter 3 of the TSD, the 

latest version of Standard F2324–13 replaces the cleanability test with a spray force test as a 

more objective measure for characterizing end user satisfaction.
5-14

5.5.2 Theoretical Analysis Results

In addition to performing the experimental tests on flow rate, spray force, and 

cleanability time, DOE also performed a supplemental analysis to investigate various theoretical 

aspects of nozzle flow. In particular, DOE investigated the theoretical effects of various physical 

parameters on flow rate and spray force.

5.5.2.1 Flow Rate 

As described previously, DOE uses flow rate in gallons per minute as the efficiency 

metric on which the CPSV standard is based. DOE derived an equation representing the flow rate 

of a commercial prerinse spray valve using Bernoulli’s principle of incompressible flow and the 

concept of conservation of mass in a fluid system.

Q = 448. 83CdA2√

2(P1 − P2)

ρ (1 − (

A2

A1

)

2

)

Eq. 5.2

Where:

Q = flow rate (gallons per minute; gpm)

Cd = discharge coefficient (unitless)

A1 = nozzle input area (square feet)

A2 = spray hole exit area (square feet)

P1 = water pressure entering the nozzle, assumed to be 60 pounds per square inch (psi), per the 

DOE test procedure

P2 = water pressure exiting the CPSV, equal to 0 psi 

ρ = water density, equal to 1.94 slugs per cubic foot

DOE verified this equation with the orifice sizing equation in the current industry 

standard ISO 5167, Measurement of fluid flow by means of pressure differential devices inserted 

in circular cross-section conduits running full, that describes flow rate using the same equation.8

Eq. 5.2 indicates that flow rate is a function of the discharge coefficient, spray hole area, nozzle

geometry, water density, and the pressure difference between the inlet and outlet. DOE used this 

equation to predict how changing the spray hole area and nozzle geometry affects the flow rate.

DOE notes that the discharge coefficient is the ratio between the actual discharge and the 

theoretical discharge and is a dimensionless unit.9 The theoretical discharge is the product of the 

theoretical fluid speed, as described by Torricelli’s law, multiplied by the spray hole area.10 The 

actual discharge is determined by the nozzle geometry.11
5-15

5.5.2.2 Spray Force

DOE developed an equation to represent theoretical spray force using Bernoulli’s 

principle of incompressible flow, which describes the behavior of fluid in motion.12

F = 0.8416 x Q x √P1

Eq. 5.3

Where:

F = calculated spray force (ozf)

Q = flow rate (gpm)

P1 = water pressure entering the nozzle, assumed to be 60 pounds per square inch (psi), per the 

DOE test procedure

DOE verified this equation with a study that one spray nozzle manufacturer posted on its 

website.13 The equation shows that spray force increases linearly with flow rate and increases 

with the square root of pressure. Since the pressure is held constant according to the proposed 

DOE test procedure, the flow rate is the only variable that can be adjusted to change spray force.

5.5.3 Analysis of Design Options

As described in chapter 4 of the TSD, DOE retained the following three design options to 

be considered as part of the engineering analysis:

 Smaller spray hole area

 Increasing spray hole eccentricity

 Venturi meter to orifice plate nozzle geometry

The following subsections describe DOE’s analysis of each of these design options in 

more detail.

5.5.3.1 Design Option - Smaller Spray Hole Area

Based on an analysis of Eq. 5.2 above, DOE expects to find a linear relationship between 

spray hole area and flow rate. DOE finds that all the terms under the square root of Eq. 5.2 are 

either constants or negligible. Specifically, water pressures P1 and P2 are constant under DOE 

test conditions. P1 is the water pressure during testing (60 psi) and DOE assumes that P2 is 0 psi 

at the exit of the valve. Furthermore, DOE assumes that the water density is a constant value that 

cannot be changed. Further, DOE found that the ratio of spray nozzle area (A1) to spray hole area 

(A2) is approximately zero, as described in the next subsection. During teardowns, DOE found 

that the spray nozzle area is 25 to 55 times greater than the spray hole area. Therefore, Eq. 5.2

can be simplified as follows:
5-16

Q = K x Cd x A2

Eq. 5.4

Where 

K = constant term including everything under the square root of equation x

Cd = Discharge coefficient (unitless)

A2 = spray hole area (square feet)

The spray hole area (A2) is the hole through which the water exits the commercial 

prerinse spray valve. The area of this hole affects the flow rate, as indicated in Eq. 5.2. DOE

used ImageJ software14 to compute the spray hole area for all the CPSV models that DOE tested 

and tore down. Using this software package was preferred over using simple geometrical 

equations for calculating the area of a circle, because many spray holes are not circular. Figure 

5.5.6 shows the relationship between flow rate and spray hole area and indicates that the 

relationship is fairly linear. Therefore, DOE concludes that reducing spray hole area is an 

effective means for reducing CPSV flow rate.

Figure 5.5.6 Measured Flow Rate vs. Measured Spray Hole Area

5.5.3.2 Alternate Design Option - Smaller Nozzle Area

Although smaller nozzle area was not identified as a design option in the screening 

analysis, DOE looked further into how changes in the nozzle area (A1) affect the flow rate. DOE 

analyzed two scenarios: the first uses a very small spray hole area (A2) and the second uses a 

large spray hole area. The discharge coefficient (Cd) is kept constant at 0.60 in both scenarios, 

because the discharge coefficients affect flow rate independently of the nozzle area. The results 

for the two scenarios are shown in Figure 5.5.7.

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0.000 0.002 0.004 0.006 0.008 0.010 0.012

Flow Rate (gpm)

Spray Hole Area (sq. inch)
5-17

The results indicate that the nozzle area (A1) doesn’t affect the flow rate once it is greater 

than approximately 0.15 square inch (sq.in.). DOE measured the nozzle areas of the tested CPSV 

models and found that all nozzle areas are greater than 0.15 sq.in. Therefore, DOE concludes that 

manufacturers would not be able to achieve any efficiency gains through further reductions in 

nozzle area.

Figure 5.5.7 Relationship between Flow Rate and Nozzle Area (A1)

5.5.3.3 Design Option – Increasing Spray Hole Eccentricity

DOE found a study that related the coefficient of discharge to the eccentricity, or the 

roundness, of the spray hole. The spray hole orifice eccentricity is calculated as the ratio between 

the long and the short diameter of the hole. The study concludes that a rounder spray hole 

provides a higher coefficient of discharge, and therefore a higher flow rate, compared to a more 

oval-shaped spray hole.15

DOE measured the spray hole width and spray hole length of each CPSV test unit using 

calipers with an accuracy of 1/1000th of an inch. Figure 5.5.8 shows the relationship between 

spray hole eccentricity and discharge coefficient.
5-18

Figure 5.5.8 Spray Hole Eccentricity vs. Discharge Coefficient

These results indicate a weak relationship between spray hole eccentricity and discharge 

coefficient. DOE’s test results are inconclusive in demonstrating whether increasing spray hole 

eccentricity can provide an effective means for reducing flow rate.

5.5.3.4 Design Option – Venturi Meter to Orifice Plate Nozzle Geometry

DOE is aware of two types of nozzle designs: venturi meters and orifice plates. A venturi 

meter is a nozzle where the fluid accelerates through a converging cone of 15–20 degrees.16 An 

orifice plate is a flat plate with a circular hole drilled in it. Figure 5.5.9 provides a visual 

representation of a venturi meter and an orifice plate. DOE observed both types of spray nozzles 

used in CPSV units on the market.

Figure 5.5.9 Venturi Meter (above) and Orifice Plate (below)
5-19

DOE research indicates that a common discharge coefficient for an orifice plate is about 

0.60, and about 0.95 for a venturi meter.17 DOE calculated the discharge coefficient of each 

CPSV test unit and notes that the majority of discharge coefficients were between 0.60 and 0.95. 

Three tested spray patterns had discharge coefficients below 0.60, all within the light duty 

product class.

Since Eq. 5.2 above indicates a direct relationship between discharge coefficient and 

flow rate (i.e., a smaller discharge coefficient leads to a lower flow rate), all other parameters 

being equal, DOE believes that CSPV valves that currently use a venturi meter nozzle geometry 

could reduce flow rate by switching to an orifice plate geometry.

5.5.4 Product Teardowns

As part of its reverse-engineering process, DOE conducted teardowns of a representative 

sample of commercial prerinse spray valves to identify design features and corresponding 

manufacturing costs that are associated with successively higher efficiency levels. Details of the 

teardown results are as follows.

DOE’s teardowns indicated that commercial prerinse spray valves have five main 

components:

1) Packaging – material containing and protecting the commercial prerinse spray valve.

2) Main body – housing that keeps the nozzle, handle and valve together.

3) Handle – activates the flow of water when being pressed by opening the valve.

4) Valve – the valve opens when the handle is pressed and allows water to flow to the 

spray nozzle.

5) Spray nozzle – component that water flows through before exiting the unit.

DOE estimated the manufacturing production costs for each commercial prerinse spray 

valves by pricing out the costs for all five parts. For most products, DOE typically finds that the 

manufacturing production costs increase for more efficient models. However, for commercial 

prerinse spray valves, DOE teardowns indicated that the manufacturing costs do not increase for 

more efficient models.

Figure 5.5.10 shows the total manufacturer production cost (MPC) for each torn down 

unit. This includes the costs for packaging, spray nozzle, main body, handle, and valve. The 

results indicate very little difference in manufacturing costs across a range of flow rates. The 

average MPC among all the units torn down was $28.18. 
5-20

Figure 5.5.10 Total Manufacturing Production Costs for All Torn Down Units

DOE examined the costs of the spray nozzle component in greater detail based on 

comments received in response to the 2014 Framework document that suggested that the spray 

nozzle is the component that determines the flow rate. Figure 5.5.11 shows the MPC for only the 

spray nozzle component of each torn down unit.

Figure 5.5.11 Manufacturing Production Costs for the Spray Nozzle Component

DOE did not observe any relationship between the costs of the spray nozzle and the flow 

rate of a commercial prerinse spray valve. Figure 5.5.12 shows that the most common spray 
5-21

nozzle cost of around $7.00 can correspond to flow rates ranging from 0.65 to nearly 1.60 gpm. 

The cost differences among spray nozzles is primarily due to the amount of material used and the 

type of material used. Less expensive nozzles are generally made from plastics, whereas more 

expensive nozzles are made from brass or stainless steel.

Lastly, DOE analyzed the costs of “technology pairs.” DOE considered a technology pair 

to be a set of two commercial prerinse spray valves from the same manufacturer that have similar 

physical design characteristics, but different flow rates. DOE analyzed the MPCs of three 

technology pairs from three different manufacturers. One of the technology pairs has flow rates 

that correspond to the light duty and heavy duty product classes. The other two technology pairs 

have flow rates that correspond to the light duty and standard duty product classes. The 

difference in MPCs among the three technology pairs was due to the materials and production 

processes used. However, within each technology pair, the MPC of both models was identical.

Based on these results, DOE concludes that there is no incremental cost associated with 

producing a more efficient (i.e., lower flow rate) commercial prerinse spray valve.

Figure 5.5.12 Flow Rate vs. MSP for Three Technology Pairs

In summary, DOE concluded that manufacturing production costs are unaffected by 

efficiency level; i.e., MPC remains constant as efficiency increases. This applies to CPSV units 

within each product class and across all three product classes. Therefore, DOE assumed the same 

MPCs for all three product classes across all efficiency levels within each product class. DOE 

calculated the final MPC as the average MPC of all commercial prerinse spray valves that were 

torn down as $28.18.
5-22

5.5.5 Example Design Paths to Achieve Higher Efficiency Levels

Using the theoretical analysis described above and observations from empirical lab 

testing, DOE determined an example set of design parameters that could be implemented to 

achieve the higher efficiency levels analyzed for each product class. These are shown in Table 

5.5.1, Table 5.5.2, and Table 5.5.3. In these examples, DOE assumed that all efficiency levels in 

each equipment class would have the same discharge coefficient as the max-tech model. DOE 

realizes that this is a simplification and requests comments in the notice on what design decisions 

manufacturers make to adjust the discharge coefficient in spray nozzles. DOE also recognizes 

that individual manufacturers could employ a different set of design options other than those

listed in the tables; the examples in the tables provide just one possible design path.

Table 5.5.1 Example Design Options for Achieving Higher Efficiency Levels for the Light 

Duty Product Class (Spray Force ≤ 5 ozf)

Efficiency Level Flow Rate

gpm

Example Design Options

Baseline 0.76 Spray hole area = 0.00291 square inch;

Same discharge coefficient as max-tech

Level 1 0.72 5% smaller spray hole area than baseline (0.00276 square inch); 

Same discharge coefficient as max-tech

Level 2 0.68 10% smaller spray hole area than baseline (0.00262 square inch); 

Same discharge coefficient as max-tech

Level 3 0.65 Spray hole area = 0.00250 square inch;

Discharge coefficient = 0.884

Table 5.5.2 Example Design Options for Achieving Higher Efficiency Levels for the 

Standard-Duty Equipment Class (Spray force > 5 ozf and ≤ 8 ozf)

Efficiency Level Flow Rate

gpm

Example Design Options

Baseline 1.22 Spray hole area = 0.0044 square inch;

Same discharge coefficient as max-tech

Level 1 1.10 10% smaller spray hole area than baseline (0.0040 square inch);

Same discharge coefficient as max-tech

Level 2 0.97 20% smaller spray hole area than baseline (0.0035 square inch); 

Same discharge coefficient max-tech

Level 3 0.94 Spray hole area = 0.00340 sq inch;

Discharge coefficient = 0.940

Table 5.5.3 Example Design Options for Achieving Higher Efficiency Levels for the HeavyDuty Equipment Class (Spray force > 8.0 ozf)

Efficiency Level Flow Rate

gpm

Example Design Options

Baseline 1.60 Spray hole area = 0.0082 square inch;

Same discharge coefficient as max-tech

Level 1 1.44 10% smaller spray hole area than baseline (0.0074 square inch);

Same discharge coefficient as max-tech

Level 2 1.28 20% smaller spray hole area than baseline (0.0066 square inch);

Same discharge coefficient as max-tech

Level 3 1.24 Spray hole area = 0.0064 square inch;

Discharge coefficient = 0.66
5-23

5.5.6 Cost-Efficiency Relationships

Based on results of the reverse-engineering teardowns describe above, Table 5.5.4, Table 

5.5.5, and Table 5.5.6 summarize the cost efficiency relationships for the light duty, standard 

duty, and heavy duty product classes, respectively.

Table 5.5.4 Cost Efficiency Relationship for the Light Duty Product Class (Spray Force ≤ 5 

ozf)

Efficiency Level Flow Rate

gpm

Manufacturer 

Production 

Cost MPC

Incremental 

Cost over 

Baseline

Baseline 0.76 $28.18 --

Level 1 0.72 $28.18 $0.00

Level 2 0.68 $28.18 $0.00

Level 3 0.65 $28.18 $0.00

Table 5.5.5 Cost Efficiency Relationship for the Standard-Duty Equipment Class (Spray 

force > 5 ozf and ≤ 8 ozf)

Efficiency Level Flow Rate

gpm

Manufacturer 

Production 

Cost MPC

Incremental 

Cost over 

Baseline

Baseline 1.22 $28.18 --

Level 1 1.10 $28.18 $0.00

Level 2 0.97 $28.18 $0.00

Level 3 0.94 $28.18 $0.00

Table 5.5.6 Cost Efficiency Relationship for the Heavy-Duty Equipment Class (Spray force 

> 8.0 ozf)

Efficiency Level Flow Rate

gpm

Manufacturer 

Production 

Cost MPC

Incremental 

Cost over 

Baseline

Baseline 1.60 $28.18 --

Level 1 1.44 $28.18 $0.00

Level 2 1.28 $28.18 $0.00

Level 3 1.24 $28.18 $0.00
5-24

REFERENCES

1. DOE compliance certification data for commercial prerinse spray valves available at 

www.regulations.doe.gov/certification-data/.

2. EPA WaterSense program, September 19, 2013. WaterSense Specification for Commercial 

Pre-Rinse Spray Valves Supporting Statement. Version 1.0. 

http://www.epa.gov/watersense/partners/prsv_final.html

3. Food Service Technology Center test data for prerinse spray valves available at 

www.fishnick.com/equipment/sprayvalves/

4. American Metals Market. Available online at http://www.amm.com/. (Last accessed 

December 2012)

5. http://www.fishnick.com/equipment/sprayvalves/

6. http://www.epa.gov/watersense/product_search.html

7. California Energy Commission, 2014 Appliance Efficiency Regulations, Report No. CEC400-2014-009-CMF, available at http://www.energy.ca.gov/2014publications/CEC-400-

2014-009/CEC-400-2014-009-CMF.pdf. 

8. http://www.brighthubengineering.com/hydraulics-civil-engineering/63291-finding-orificedischarge-coefficient-for-orifice-flow-meter-with-iso-5167/

9. http://www.codecogs.com/library/engineering/fluid_mechanics/orifice/index.php

10. https://www.boundless.com/physics/textbooks/boundless-physics-textbook/fluid-dynamicsand-its-applications-11/bernoulli-s-equation-99/torricelli-s-law-358-1967/

11. http://ac.els-cdn.com/073510979090260V/1-s2.0-073510979090260Vmain.pdf?_tid=7c22f84e-e396-11e4-9c2f00000aab0f6b&acdnat=1429119799_d03c13a7631730a16022a552daa03030

12. http://physics.bu.edu/~duffy/py105/Bernoulli.html

13. http://www.spray.com/cat70/cat70pdf_m/ssco_cat70_a07.pdf

14. ImageJ software available at http://imagej.nih.gov/ij

15. http://ac.els-cdn.com/073510979090260V/1-s2.0-073510979090260Vmain.pdf?_tid=4946ad68-d968-11e4-83a7-

00000aacb35d&acdnat=1428000445_0f25dcfbefdd667c795676515cb7608b

16. http://www.engineeringtoolbox.com/orifice-nozzle-venturi-d_590.html

17. http://www.brighthubengineering.com/hydraulics-civil-engineering/52906-orifice-flownozzle-and-venturi-meter-for-pipe-flow-measurement/
6-i 

CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION .............................................................................................................. 6-1

6.1.1 Manufacturer Selling Price ..................................................................................... 6-1

6.1.1.1 Manufacturer Markup .............................................................................. 6-2

6.1.2 Distribution Channels ............................................................................................. 6-2

6.1.3 Markup Calculation Methodology .......................................................................... 6-2

6.2 ESTIMATION OF COMPANY AND SALES TAX MARKUPS .................................... 6-4

6.2.1 Financial Information Sources ................................................................................ 6-4

6.2.2 Sales Tax ................................................................................................................. 6-6

REFERENCES ............................................................................................................................ 6-8

LIST OF TABLES

Table 6.2.1 Gross Profit Margins for Distributors*...................................................................... 6-5

Table 6.2.2 Gross Profit Margins for Retailers* .......................................................................... 6-5

Table 6.2.3 Gross Profit Margins for Service Companies* .......................................................... 6-5

Table 6.2.4 Calculated Baseline Markups for Commercial Prerinse Spray Valves .................... 6-6

Table 6.2.5 Weighted Baseline Markup for Commercial Prerinse Spray Valves ....................... 6-6

Table 6.2.6 State and Local Sales Tax Rates ............................................................................... 6-7

LIST OF FIGURES

Figure 6.1.1 Components of Manufacturer Selling Price ............................................................ 6-1
6-1 

CHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

This technical support document (TSD) chapter describes the methodology the U.S. 

Department of Energy (DOE) followed in developing markups for the notice of proposed 

rulemaking (NOPR) analysis for commercial prerinse spray valves (CPSVs). 

In this rulemaking, DOE performed teardown analyses and a manufacturer markup 

analysis to develop manufacturer selling prices (MSPs) for representative CPSV designs (chapter 

5 of this TSD). DOE then applied distribution channel markups and sales tax to derive end-user 

prices. By combining the engineering analysis results and the distribution channel markups 

analysis, DOE derived inputs for use in the life-cycle cost (LCC) analysis and the national 

impact analysis (NIA). In particular, DOE developed end-user prices for CPSV designs 

associated with any given trial standard level.

6.1.1 Manufacturer Selling Price

To obtain the MSP, DOE multiplied the manufacturer production cost (MPC) by the 

manufacturer markup. The MSP includes all production and non-production costs as well as 

profit. The markup includes sales, general and administrative, research and development, other 

corporate expenses, and profit. Figure 6.1.1 shows the components of MSP. DOE derived the 

manufacturer markup costs as described in section 6.1.1.1. 

Figure 6.1.1 Components of Manufacturer Selling Price

Manufacturer 

Selling Price

Full Cost of 

Product

Manufacturer 

Production Cost

Direct 

Labor

Direct 

Material Overhead

Indirect Labor

Indirect Material

Maintenance

Depreciation

Taxes

Insurance related 

to assets

Non-Production 

Cost

Selling

Market 

Research

Advertising

POS Promotion

Salesperson 

Comp & Travel

Logistics 

(Warehousing, 

Delivery, RecordKeeping, Order 

Processing)

General & 

Admin.

Costs of 

Service & 

Staff Units

General 

Corporate 

Costs 

(Comp, etc)

R & D

Costs associated 

with finding new 

and improved 

products or 

processes

Code 

Compliance

Modernization

Interest

Cost of 

borrowing 

funds

Profit
6-2 

6.1.1.1 Manufacturer Markup

At each stage of the distribution chain, manufacturers, wholesalers, and contractors apply 

a markup to cover their operating costs and profit margins. In the engineering analysis, DOE 

determined a manufacturer markup, and applied it to the MPC to arrive at the MSP.

The manufacturer markup was calculated as the market share weighted average value for 

the industry. DOE developed this manufacturer markup by examining several major commercial 

prerinse spray valve manufacturers’ gross margin information from annual reports and Securities 

and Exchange Commission 10-K reports. Because the 10-K reports do not provide gross margin 

information at the subsidiary level, the estimated markups represent the average markups that the 

parent company applies over its entire range of product offerings, and does not necessarily 

represent the manufacturer markup of the subsidiary. The result of the markup analysis shows 

that the average manufacturer markup is 1.30. 

6.1.2 Distribution Channels

The appropriate markups for determining consumer product prices depend on the type of 

distribution channels through which product moves from manufacturers to final consumers. At 

each point in the distribution channel, companies mark up the price of the product to cover their 

business costs and profit margin. For the NOPR analysis, DOE assumed four primary 

distribution channels through which CPSV manufacturers sell product to a final consumer: 

A. Manufacturer  Final Consumer (Direct Sales)

B. Manufacturer  Authorized Distributor  Final Consumer 

C. Manufacturer  Retailer  Final Consumer 

D. Manufacturer  Service Company  Final Consumer 

6.1.3 Markup Calculation Methodology 

At each point in the distribution channel, companies apply a “markup” to the MSP to 

cover their business costs and profit margin. DOE models this markup as a multiplier. In 

financial statements, gross profit is the difference between the company revenue and the 

company cost of sales. It includes all corporate overhead costs (sales, general, and 

administration), materials and labor costs, research and development and interest expenses, 

depreciation and taxes, and profits. In order for sales of product to contribute positively to 

company cash flow, the product’s markup must be greater than the sum of cost of sales and 

business costs for that product. DOE calculates the end-user sales price by multiplying the MSP 

by the various markups and applying sales tax. 

The end-user prices and installation costs are key inputs to the LCC and PBP analysis, as 

well as the NIA. Through use of the distribution channel markups and installation costs, DOE 

can calculate the first costs that consumers would face under the various efficiency levels 

evaluated. DOE evaluates the tradeoff between the increase in first cost and the resulting energy
6-3 

and water cost savings at each efficiency level in the LCC and PBP analysis (chapter 8 of this 

TSD) and NIA analysis (chapter 10 of this TSD).

As an example, the following equation shows how DOE determined the product prices 

for commercial prerinse spray valves for distribution channel C (Retailer): 

END MFR MUPP RETAIL ××= MUTAX ) (

Eq. 6.1 

Where:

PEND = product price to the end-user ($),

PMFR = MSP of baseline or standard-level product ($),

MURETAIL = retailer markup, and 

MUTAX = sales tax markup.

For the product subject to potential energy conservation standards, markups are typically 

differentiated between a “baseline markup” and an “incremental markup.” The baseline markup 

would relate the MSP of baseline product to a retailer sales price, and the incremental markup 

would relate changes in the MSP of higher-efficiency product to changes in the retailer sales 

price. DOE applied only baseline markups to the MSPs for commercial prerinse spray valves

because DOE’s engineering analysis (chapter 5 of this TSD) indicated no significant price or cost 

structure change with increasing CPSV efficiency. Therefore, incremental markups were not 

used to develop end-user prices for commercial prerinse spray valves. 

Using distribution channel C (Retailer) as an example, the following equations show the 

calculation of baseline markups: 

PRETAIL _ BASE = PMFR _ BASE × ( MURETAIL _ BASE)

Eq. 6.2 

PEND _ BASE = (PRETAIL _ BASE × MUTAX ) 

Eq. 6.3 

Where:

PMFR _ BASE = MSP of baseline product ($),

PRETAIL _ BASE = retailer selling price of baseline product ($),

PEND _ BASE = end-user purchase price for baseline product ($),

MURETAIL _ BASE = retailer markup for baseline product, and 

MUTAX = sales tax markup.
6-4 

6.2 ESTIMATION OF COMPANY AND SALES TAX MARKUPS 

6.2.1 Financial Information Sources 

Publicly owned companies are required by law to disclose financial information on a 

regular basis by filing different forms with the U.S. Securities and Exchange Commission (SEC). 

Filed annually, the SEC form 10-K provides a comprehensive overview of the company’s 

business and financial conditions. Relevant information in the 10-K report includes the 

company’s revenues and direct and indirect costs. For the company markup, DOE used 10-K 

reports from publicly-owned distributors, retailers, and service companies, where available. The 

financial figures necessary for calculating the company markup are net sales, costs of sales, and 

gross profit. The income statement section of the 10-K reports often lists these figures. DOE 

used averages of the financial figures spanning 2009 to 2013 to calculate the markups.

DOE used the following equations to calculate the gross profit and gross profit margins: 

Gross Profit ($) = Net Sales – Cost of Sales

Eq. 6.4 

Gross Profit Margin

Gross Profit

Net Sales =

Eq. 6.5 

To calculate the time-average gross profit margin for each company, DOE first summed 

the gross profit for all the years analyzed and then divided the result by the sum of the net sales 

for the same years. DOE then used the gross profit margins to calculate baseline markups on 

existing product (i.e., prior to efficiency changes resulting from enactment of potential 

standards). Each company’s baseline markup was calculated as:

 ofCost Sales

SalesNet 

1( Gross Profit Ma )rgin

1 Baseline Markup = − =

Eq. 6.6 

Table 6.2.1 through Table 6.2.3 contains the calculated gross profit margins for a sample 

of companies. 
6-5 

Table 6.2.1 Gross Profit Margins for Distributors*

Company

Financial 

Figure

$

Year

2013 2012 2011 2010 2009 

A 

Net Sales 9,437,758 8,950,045 8,078,185 7,182,158 6,221,991

Cost of Sales 5,301,275 5,033,885 4,567,393 4,176,474 3,623,465

Gross Profit 4,136,483 3,916,160 3,510,792 3,005,684 2,598,526

Gross Profit 

Margin (%) 43.8 43.8 43.5 41.8 41.8

Average Gross Profit Margin: 42.9%

B 

Net Sales 1,598,055 1,322,345 1,249,484 1,086,989 1,059,278

Cost of Sales 1,045,084 840,382 787,017 672,745 665,327

Gross Profit 552,971 481,963 462,467 414,244 393,951

Gross Profit 

Margin (%) 34.6 36.4 37.0 38.1 37.2

Average Gross Profit Margin: 36.7% * Unless noted, all numbers are in thousands of dollars. This table includes 2009, 2010, 2011, 2012, and 

2013 SEC 10-K reports. 

Table 6.2.2 Gross Profit Margins for Retailers*

Company

Financial 

Figure

$

Year

2013 2012 2011 2010 2009 

A 

Net Sales 74,754,000 70,395,000 67,997,000 66,176,000 71,288,000

Cost of Sales 48,912,000 46,133,000 44,693,000 43,764,000 47,298,000

Gross Profit 25,842,000 24,262,000 23,304,000 22,412,000 23,990,000

Gross Profit 

Margin (%) 34.6 34.5 34.3 33.9 33.7

Average Gross Profit Margin: 34.2% * Unless noted, all numbers are in thousands of dollars. This table includes 2009, 2010, 2011, 2012, and 

2013 SEC 10-K reports. 

Table 6.2.3 Gross Profit Margins for Service Companies*

Company

Financial 

Figure

$

Year

2013 2012 2011 2010 2009 

A 

Net Sales 13,253,400 11,838,700 6,798,500 6,089,700 5,900,600

Cost of Sales 7,240,100 6,483,500 3,475,600 3,013,800 2,978,000

Gross Profit 6,013,300 5,355,200 3,322,900 3,075,900 2,922,600

Gross Profit 

Margin (%) 45.4 45.2 48.9 50.5 49.5

Average Gross Profit Margin: 47.9% * Unless noted, all numbers are in thousands of dollars. This table includes 2009, 2010, 2011, 2012, and 

2013 SEC 10-K reports.

The baseline markup covers non-production costs and profit. Table 6.2.4 shows the 

baseline markups using this method for the analyzed companies. 
6-6 

Table 6.2.4 Calculated Baseline Markups for Commercial Prerinse Spray Valves 

Company Baseline Markup

Distributor A 1.75

Distributor B 1.58

Distributor Average 1.67

Retailer A 1.52

Service Company A 1.92

DOE assumed that different percentages of commercial prerinse spray valves pass 

through all four distribution channels, resulting in the weighted average baseline markup shown 

in Table 6.2.5. The 1.73 weighted total is the markup that is applied to the manufacturer selling 

price, which quantity is then combined with sales taxes to get final product purchase prices.

Table 6.2.5 Weighted Baseline Markup for Commercial Prerinse Spray Valves

Distribution Channel Baseline Markup Percentage Through Channel

A (Direct Sales) 1.67* 17

B (Distributor) 1.67 33

C (Retailer) 1.52 17

D (Service Company) 1.92 33

WEIGHTED TOTAL 1.73

* Direct sales baseline markup assumed equal to that for distributors (i.e., manufacturers would not undercut authorized distributors).

6.2.2 Sales Tax 

The sales tax rate represents state and local sales taxes applied to commercial prerinse 

spray valves, and is a multiplicative factor that increases the end-user cost. DOE obtained 

information on state and local sales tax from the Sales Tax Clearinghouse (Table 6.2.6).1 These 

data represent weighted averages that include county and city rates. DOE also calculated a 

national population-weighted average sales tax for use in the NIA, where DOE did not use a 

distribution of inputs.2

Table 6.2.6 shows the distribution of sales tax rates that DOE developed for the LCC and 

PBP analysis. The distribution ranges from a minimum of 0 percent in some states to a maximum 

of 9.45 percent in Tennessee (as shown in Table 6.2.6), with a national, population-weighted 

average of 7.16 percent. In the LCC analysis, the distribution of sales taxes are used (weighted 

by state populations), and the average total installed cost (discussed in chapter 8 of this TSD) is 

used in national-level analyses.
6-7 

Table 6.2.6 State and Local Sales Tax Rates 

State

Combined State

and Local Tax Rate

%

State

Combined State

and Local Tax Rate

%

State

Combined State

and Local Tax Rate

%

Alabama

Alaska

Arizona

Arkansas

California

Colorado

Connecticut

Delaware

D.C.

Florida

Georgia

Hawaii

Idaho

Illinois

Indiana

Iowa

Kansas 

8.60

1.30

7.20

8.95

8.45

6.10

6.35

0.00

5.75

6.65

7.00 

4.35

6.00 

8.00 

7.00

6.80 

7.90

Kentucky

Louisiana

Maine

Maryland

Massachusetts

Michigan

Minnesota

Mississippi

Missouri

Montana

Nebraska 

Nevada

New Hampshire

New Jersey

New Mexico

New York

North Carolina 

6.00

8.80

5.50 

6.00

6.25

6.00

7.20

7.05 

7.45

0.00

6.05 

7.95 

0.00

6.95

6.70 

8.45 

6.90

North Dakota

Ohio

Oklahoma

Oregon

Pennsylvania

Rhode Island

South Carolina

South Dakota

Tennessee

Texas

Utah

Vermont

Virginia

Washington

West Virginia

Wisconsin

Wyoming 

6.00

7.10

8.40

0.00

6.35

7.00

7.10 

5.45 

9.45

7.95

6.65

6.10

5.60 

8.90

6.05

5.45

5.45

National 

Average 7.16 
6-8 

REFERENCES

1. The Sales Tax Clearinghouse. State Aggregate Tax Rates. 1999-2015. STC: Garnet Valley,

PA <http://thestc.com/STrates.stm> (Last accessed February 16, 2015)

2. U.S. Census Bureau. State Totals: Vintage 2014, Annual Estimates of the Resident

Population for the United States, Regions, States, and Puerto Rico. 2014. U.S. Department

of Commerce: Suitland, MD. <www.census.gov/popest/data/state/totals/2014/index.html>

(Last accessed February 16, 2015)
7-i 

CHAPTER 7. ENERGY AND WATER USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION .............................................................................................................. 7-2

7.2 ESTIMATING WATER USE OF COMMERCIAL PRERINSE SPRAY VALVES ....... 7-2

7.2.1 Modeling Annual Water Consumption Using Annual Operating Time ................ 7-2

7.3 ANNUAL WATER CONSUMPTION............................................................................... 7-5

7.4 ESTIMATING ENERGY USE OF COMMERCIAL PRERINSE SPRAY VALVES...... 7-6

7.4.1 Modeling Annual Energy Consumption Using Annual Water Consumption ........ 7-6

7.5 ANNUAL ENERGY CONSUMPTION............................................................................. 7-9

REFERENCES .......................................................................................................................... 7-10

LIST OF TABLES

Table 7.2.1 Weighted Average Daily Operating Time Data from End-Use Studies ................... 7-4

Table 7.2.2 Results of CPSV Annual Operating Time ................................................................ 7-4

Table 7.2.3 Weighted Average Annual CPSV Operating Time .................................................. 7-5

Table 7.3.1 Total Annual Water Consumption ............................................................................ 7-6

Table 7.4.1 Weighted Average Annual Main Water Temperature by Census Division and 

Building Type ........................................................................................................... 7-7

Table 7.4.2 Annual Electricity Consumption for Commercial Prerinse Spray Valves ............... 7-8

Table 7.4.3 Annual Natural Gas Consumption for Commercial Prerinse Spray Valves ............. 7-9

Table 7.5.1 Annual Energy Use ................................................................................................... 7-9
7-2 

CHAPTER 7. ENERGY AND WATER USE ANALYSIS

7.1 INTRODUCTION

The U.S. Department of Energy (DOE) conducts an energy and water use analysis to 

determine the savings in operating cost accrued to consumers from the use of more-efficient 

products. The results are used as inputs to inform the life-cycle cost (LCC) and payback period 

(PBP) calculations described in chapter 8 of this technical support document (TSD) and the 

national impacts analysis (NIA) found in chapter 10 of this TSD. This chapter describes the means 

and methods DOE adopted to determine the annual energy consumption in kilowatt-hours per 

year (kWh/yr) for electricity and thousand British thermal units per year (kBtu/yr) for natural gas

as well as the annual water consumption in thousand gallons of water per year (kgal/yr) of 

commercial prerinse spray valves (CPSV) to inform the LCC and PBP analysis.

The goal of the energy and water use analysis is to generate a range of energy and water 

use values that reflects actual product use of commercial prerinse spray valves at varying 

efficiency levels throughout the United States. The engineering analysis described in chapter 5 of 

this TSD reports the flow rate derived from the conditions and requirements of the DOE test 

procedure. Actual water usage in the field may differ from what is estimated in a test laboratory

because of variation in operating conditions, the behavior of users, and other factors. 

7.2 ESTIMATING WATER USE OF COMMERCIAL PRERINSE SPRAY 

VALVES 

Water use for commercial prerinse spray valves was calculated by multiplying the flow 

rate of the product by an estimated number of annual operating hours of commercial prerinse 

spray valves across varying building types. This section describes the analysis and data used in 

these calculations.

7.2.1 Modeling Annual Water Consumption Using Annual Operating Time

The annual operating time of commercial prerinse spray valves was used to annualize 

water consumption. 

The general equation used to determine annual operating time is as follows: 

Annual Valve Operating Time (minutes per year) = 

CPSV Operating Time per Meal (minutes) x Meals Served per Year

Eq. 7.1 

To determine annual operating time, DOE was required to determine the following 

information: 

• building types in the US that contain commercial prerinse spray valves,

• building operating schedules by building type,
7-3 

• meals served per day by building type, and 

• operating time per meal by building type. 

DOE utilized the Commercial Building Energy Consumption Survey1 (CBECS) to 

identify buildings that contain commercial prerinse spray valves and to estimate the operating 

time for each building type. The CBECS database provides specific information on each 

building, including building type, specific attributes to the building, and operating hours. This 

was used to identify buildings that house commercial prerinse spray valves and to estimate 

operating time.

DOE identified buildings in the CBECS dataset that contain commercial prerinse spray 

valves as buildings that house a commercial food preparation area, large restaurant, or cafeteria. 

The CBECS building types in this dataset are educational facilities, food retail, healthcare, 

lodging, restaurants, and select office buildings that house a dining facility. The CBECS dataset 

includes sub-groups within each building type that further differentiates the category by 

operating schedule. For example, educational buildings have sub-groups of college/university, 

K-12, and preschool. Healthcare includes sub-groups of outpatient and inpatient facilities. The 

CBECS dataset provides information on the daily operating hours for each building sub-group, 

broken out by buildings that operate only during the weekdays and buildings that operate 7 days 

per week. This information was used to estimate the number of meals served per day for each 

building type. 

DOE gathered data regarding CPSV daily operating time from field studies that 

calculated actual operating time by measuring the total daily volume of water consumed by the 

commercial prerinse spray valve and dividing by the flow rate of the valve. These CPSV field 

studies are from the Environmental Protection Agency WaterSense® program,2 California Urban 

Water Conservation Council (CUWCC) programs in 20043

 and 2007,4 Veritec Consulting, Inc. 

in the City of Calgary5

 and Region of Waterloo,6 and a study performed for Puget Sound 

Energy.

7

 The average CPSV daily operating time for individual building types was weighted 

based on the number of sites evaluated in the field studies. Using this data, DOE estimated the 

typical CPSV operating time per meal by dividing the total daily operating time by the number of 

meals served per day for each building type. The results are in Table 7.2.1. 
7-4 

Table 7.2.1 Weighted Average Daily Operating Time Data from End-Use Studies

Building 

Type Field Study

Weighted 

Average Daily 

Operating Time

minutes

Estimated 

Number of 

Meals Served 

per Day

Estimated CPSV 

Operating Time 

per Meal minutes

Number of 

Sites 

Measured

Education WaterSense 46.1 2.0 23.1 20

Food retail Waterloo and 

CUWCC 2007 6.4 NA NA 8 

Lodging Waterloo 89.0 3.5 25.4 1

Restaurant

WaterSense, CUWCC

2004, CUWCC 2007, 

City of Calgary, and 

Waterloo

74.7 2.5 29.9 66

Not 

Identified 

Puget Sound Energy, 

CUWCC 2007 80.6 2.5 32.3 5 

Next, DOE correlated the estimated CPSV operating time per meal found in the end-use 

studies to the CBECS dataset. This was done by determining the number of daily meals served 

by each CBECS building group based on the buildings’ operating schedule provided in CBECS. 

DOE approximated that educational buildings serve between two and three meals per day, 

healthcare facilities serve between three and five meals daily, lodging serves three meals daily, 

and restaurants serve between two and three meals daily. The food retail building category uses 

commercial prerinse spray valves for delicatessen and food preparation activities and not 

scheduled meals. Then, the number of daily meals by building sub-group was multiplied by the 

CPSV daily operating time (Table 7.2.1) for that specific building group, which produced the 

total daily CPSV operating time. The results of the CPSV daily operating time are shown in 

Table 7.2.2. 

Table 7.2.2 Results of CPSV Annual Operating Time

CBECS 

Building 

Type

CBECS Building SubGroupMeals Served 

per Day

CPSV Daily 

Operating 

Time

minutes

Education K-12 2 46.1

College/University 3 69.2

Food retail All NA 6.4

Healthcare Outpatient 3 96.8

Inpatient 5 161.3

Lodging Dormitory 3 76.3

Hotel/Motel 3.5 89.0

Restaurant Open for Two Meals 

per Day 2 59.8

Open for Three Meals 

per Day 3 89.7
7-5 

 Next, the annual CPSV operating time for each building type was derived from the daily 

operating time. This was accomplished using the following equation: 

CBECS Building Sub-Group Annual Valve Operating Time (hours per year) = 

CPSV Operating Time per Meal (minutes) x Meals Served per Day x Building Operating Days 

per Week x Building Operating Weeks per Year ÷ 60 minutes per hour 

Eq. 7.2 

Finally, the weighted annual operating time was calculated for each building type by 

weighting the annual operating time for each building sub-group by the total number of buildings 

in each building type found in the CBECS dataset. The average annual operating times for 

individual building types and the weighted average operating time across all buildings are 

provided in Table 7.2.3. 

Table 7.2.3 Weighted Average Annual CPSV Operating Time

Building Type Schedule

Number of 

Buildings in 

CBECS

Average Annual 

CPSV Operating 

Time

hours

Total Operating 

Time for 

Building Group 

hours

Education

K-12 Weekday only 55,227 135 7,428,943 

K-12 7 days per week 24,890 188 4,687,402 

College/University 7 days per week 819 282 231,214 

Food Retail

All groups 7 days per week 34,366 39 1,323,882 

Healthcare

Outpatient 7 days per week 400 587 234,871 

Inpatient 7 days per week 16,076 978 15,729,615 

Lodging

Dormitory 7 days per week 1,150 463 532,303 

Motel/Hotel 7 days per week 16,907 540 9,128,550 

Restaurant

All groups Weekday only 15,313 259 3,966,988 

All groups 7 days per week 230,264 544 125,266,753 

Weighted Average Operating Time Across Building Groups 426

7.3 ANNUAL WATER CONSUMPTION

DOE estimated the annual water consumption for each product class and efficiency level 

using the annual operating time established previously and the flow rate of each product class 

and efficiency level at an operating pressure of 60 pounds per square inch (psi). This section 

describes the method and data used to calculate the annual water consumption. 

DOE used the flow rates at a water pressure of 60 psi for each efficiency level in the 

energy and water use analysis. This is the water pressure required in the 2013 DOE CPSV test 

procedure final rule. 78 FR 62970. DOE acknowledges that there is a range of operating 
7-6 

pressures supplied to buildings across the country depending on local operating conditions. 

Based on lab testing of commercial prerinse spray valves for the CUWCC in 20043

and 20074 to 

show the relationship between pressure and flow rate, lower water pressures correlate to lower 

flow rates of commercial prerinse spray valves, while higher pressures correlate to higher flow 

rates. The range of water pressures supplied from water utilities is typically between 35 psi8 and 

80 psi.9 DOE also notes that the Environmental Protection Agency (EPA) WaterSense Program 

field study found that the water pressure of test facilities ranged between 38 psi and 83 psi.10

The weighted average CPSV operating time across all building types was 426 hours per 

year, the equivalent of 25,558 minutes per year, which was used to estimate the annual water 

consumption for each product class and efficiency level (see chapter 5 of this TSD). The flow 

rate for each product class was multiplied by the weighted average annual operating time to 

determine the annual water consumption per unit, as shown in Eq. 7.3. The annual water 

consumption results are shown in Table 7.3.1. 

Annual Water Consumption (gallons per year per unit) = 

Weighted Average Annual Operating Time (minutes per year) x Flow Rate (gpm) 

Eq. 7.3 

Table 7.3.1 Total Annual Water Consumption 

Efficiency Level

Total Water Consumption

gallons per year

Light Duty

(Spray Force ≤ 5 ozf)

Standard Duty

(Spray Force > 5 ozf and ≤ 8 ozf)

Heavy Duty

(Spray Force > 8 ozf)

Baseline 19,424 31,181 40,893

1 18,402 28,114 36,804

2 17,379 24,791 32,714

3 16,613 24,025 31,692

7.4 ESTIMATING ENERGY USE OF COMMERCIAL PRERINSE SPRAY 

VALVES 

7.4.1 Modeling Annual Energy Consumption Using Annual Water Consumption

The energy use for commercial prerinse spray valves was calculated by multiplying the 

annual water use by the energy required to heat each gallon of water in the water heater, then 

divided by the water heating efficiency, as shown in Eq. 7.4. This section describes the data used 

in this equation. 

Energy Consumption per Gallon of Water = (Ereq * ΔT * Rmix * C)/ Effwh

Eq. 7.4 

Where:

Ereq = heating constant, 8.30 Btu/gal-F, 

ΔT = change in temperature from cold water supply to hot water supply (oF),

Rmix = ratio of hot water to cold water in the mixing valve at the point of use (%),
7-7 

C = conversion factor from Btu to kBtu (natural gas) or to kWh (electricity), and 

Effwh = efficiency of water heater (%).

The change in temperature was calculated from the difference in temperature between the 

cold water supply and the hot water produced by the water heater. The temperature of the hot 

water is taken from an American Society of Heating, Refrigeration, and Air-Conditioning 

Engineers (ASHRAE) Standard 12-200011 specifying that the water should be stored at a 

minimum temperature of 140 oF to prevent bacterial growth in the water.

Cold water temperatures vary depending on location and time of year. The cold water 

supply temperature for this analysis was derived from an equation relating ambient air 

temperatures to cold water temperatures supplied to the building.12 This equation correlates 

ambient air temperatures to cold water temperatures supplied to the building by applying a lag 

between changes in air temperatures to changes in the cold water temperatures. This relationship 

was applied to ambient temperatures in each of the nine U.S. census subdivisions: New England, 

Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, 

West South Central, Mountain, and Pacific. DOE then evaluated a weighted average main water 

temperature to account for the difference in building operation between educational facilities and 

all other building types. DOE assumed that educational facilities operate in the 9 months 

between September and June, while the other buildings operate year round. This resulted in two 

sets of main water temperature data for each census division, displayed in Table 7.4.1. 

Table 7.4.1 Weighted Average Annual Main Water Temperature by Census Division and 

Building Type

Census Division

Main Water Temperature for 

Education Buildings

°F

Main Water Temperature for All 

Other Buildings

°F

New England 54.06 55.74

Middle Atlantic 57.39 59.24

East North Central 53.82 55.67

West North Central 54.25 56.43

South Atlantic 68.30 69.74

East South Central 64.63 66.54

West South Central 69.72 71.60

Mountain 63.95 65.78

Pacific 65.14 65.30

The ratio of hot water and cold water at the point of use was derived using the average 

mixed temperature found in field studies on commercial prerinse spray valves. DOE investigated 

the temperatures measured at the commercial prerinse spray valve from five field studies used in 

establishing the operating times for the water use analysis.3,4,5,6,7 These field evaluations showed 

an average end use temperature of 108 oF. The percent of hot water was calculated using the 

following mass balance equation: 
7-8 

Hot Water to Cold Water Mass Balance = 

(TEU – TS) ÷ (TH - TEU) 

Eq. 7.5 

Where:

TEU = end use water temperature,

TS = supply water temperature, and 

TH = hot water temperature.

The percent of hot water in this analysis ranges between 69 percent and 76 percent, 

depending on the cold water supply temperature for the census divisions. 

Because over 88 percent of the buildings in the CBECS dataset with commercial kitchens 

used electricity or natural gas, DOE assumed that hot water heating equipment used in 

commercial kitchens utilize natural gas or electricity. The efficiency of these two types of water 

heaters was used in the calculation to solve for the amount of energy used by the commercial 

prerinse spray valves. The current standard for commercial water heating equipment requires that 

natural gas water heaters meet a minimum thermal efficiency of 80 percent. 10 CFR Part 

431.110 There is no current thermal efficiency standard for electric water heaters, so DOE 

assumed the thermal efficiency of electric water heaters is 98 percent since there are minimal 

losses in electric energy when converted to thermal energy to heat the water. 

The annual associated energy use for water heaters of both fuel types was determined by 

multiplying the energy requirement by the annual water use for each efficiency level and product 

class by U.S. census division (as described in section 7.4). These results were averaged by 

census division and then weighted by the number of buildings in the CBECS data by building 

type. The results for electric water heaters are in Table 7.4.2 and results for natural gas water 

heaters are in Table 7.4.3. 

Table 7.4.2 Annual Electricity Consumption for Commercial Prerinse Spray Valves

Efficiency Level

Annual Electricity Consumption

kWh per year

Spray Force ≤ 5 ozf Spray Force > 5 ozf and ≤ 8 ozf Spray Force > 8 ozf

Baseline 2,717 4,361 5,720

1 2,574 3,932 5,148

2 2,431 3,468 4,576

3 2,324 3,360 4,433
7-9 

Table 7.4.3 Annual Natural Gas Consumption for Commercial Prerinse Spray Valves 

Efficiency Level

Annual Natural Gas Consumption

kBtu per year

Spray Force ≤ 5 ozf Spray Force > 5 ozf and ≤ 8 ozf Spray Force > 8 ozf

Baseline 11,356 18,230 23,908

1 10,759 16,437 21,518

2 10,161 14,494 19,127

3 9,713 14,046 18,529

7.5 ANNUAL ENERGY CONSUMPTION

DOE analyzed the proportion of buildings that used natural gas or electricity for water 

heating using the CBECS building database. DOE found that natural gas water heaters comprised 

68 percent of the buildings in this analysis, while electric water heaters made up the other 32 

percent.

13 These proportions were used to calculate the weighted average energy consumption 

(in kBtu/yr) by water heater type of each efficiency level and product class of commercial 

prerinse spray valve. The proportions were multiplied by the energy consumption of each kind of 

water heater to obtain the total energy consumption of each product class and efficiency level of 

commercial prerinse spray valve. The results are shown in Table 7.5.1. 

Table 7.5.1 Annual Energy Use

Efficiency Level

Annual Energy Use (kBtu per year)

Spray Force ≤ 5 ozf Spray Force > 5 ozf and ≤ 8 ozf Spray Force > 8 ozf

Baseline 8,592 13,792 18,088

1 8,140 12,436 16,279

2 7,687 10,966 14,470

3 7,348 10,627 14,018
7-10 

REFERENCES

1. U.S. Department of Energy-Energy Information Administration. Commercial Building

Energy Consumption Survey. 2005. Washington DC.

2. U.S. Environmental Protection Agency WaterSense. Pre-Rinse Spray Valve Field Study

Report. March 2011. Washington DC. Available at:

http://www.epa.gov/watersense/partners/prsv_background.html#study.

3. SBW Consulting, Inc. Evaluation, Measurement, and Verification Report for CUWCC PreRinse Spray Head Distribution Program. May 2004. Sacramento, CA. Available at:

https://www.cuwcc.org/Resources/Product-Information/Commercial-Food-Service/PreRinse-Spray-Valves-PRSVs.

4. SBW Consulting, Inc. Impact and Process Evaluation Final Report for CUWCC 2004-5

Pre-Rinse Spray Head Installation Program (Phase 2). February 2007. Sacramento, CA.

Available at: https://www.cuwcc.org/Resources/Product-Information/Commercial-FoodService/Pre-Rinse-Spray-Valves-PRSVs.

5. Veritec Consulting, Inc. City of Calgary Pre-Rinse Spray Valve Pilot Study Final Report.

December 2005. Mississauga, Ontario. Available at:

http://www.allianceforwaterefficiency.org/Commercial_Food_Service_Introduction.aspx.

6. Veritec Consulting, Inc. Region of Waterloo Pre-Rinse Spray Valve Pilot Study Final

Report. January 2005. Mississauga, Ontario. Available at:

http://www.allianceforwaterefficiency.org/Commercial_Food_Service_Introduction.aspx.

7. SBW Consulting, Inc. and Koeller and Company. Pre-Rinse Spray Valve Programs: How

Are They Really Doing? December 2005. Seattle, WA. Available at:

http://www.allianceforwaterefficiency.org/Commercial_Food_Service_Introduction.aspx.

8. Friedman, M., Kirmeyer, G., Lemieux, J., LeChevallier, M., and Seidl, S., Routt, J. 2010.

Criteria for Optimized Distribution Systems. Water Research Foundation. Denver. CO.

9. International Association of Plumbing and Mechanical Officials. Uniform Plumbing Code.

2012. Ontario. Canada.

10. U.S. Environmental Protection Agency’s (EPA’s) WaterSense Program, March 31, 2011.

Pre-Rinse Spray Valves Field Study Report, p. 14.

11. ASHRAE. ASHRAE Standard 12-2000: Minimizing the Risk of Legionellosis Associated

with Building Water Systems. February 2000. Atlanta, GA.

12. Burch, J., and C. Christensen. 2007. “Towards Development of an Algorithm for Mains

Water Temperature.” Proceedings of the 2007 ASES Annual Conference, Cleveland, OH.
7-11 

13. U.S. Energy Information Administration. 2003 Commercial Buildings Energy 

Consumption Survey (CBECS). December 2008. Available at: 

http://www.eia.gov/consumption/commercial/. 
8-i 

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION .............................................................................................................. 8-1

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis ................... 8-1

8.1.2 Development of Customers .................................................................................... 8-2

8.1.3 Overview of Life-Cycle Cost and Payback Period Analysis Inputs ....................... 8-2

8.2 LIFE-CYCLE COST ANALYSIS INPUTS....................................................................... 8-4

8.2.1 Total Installed Cost Inputs ...................................................................................... 8-5

8.2.1.1 Manufacturer Selling Price ...................................................................... 8-5

8.2.1.2 Overall Markup ........................................................................................ 8-6

8.2.1.3 Installation Cost ....................................................................................... 8-6

8.2.1.4 Total Installed Cost .................................................................................. 8-7

8.2.2 Operating Cost Inputs ............................................................................................. 8-7

8.2.2.1 Annual Energy and Water Consumption ................................................. 8-7

8.2.2.2 Energy and Water Prices .......................................................................... 8-8

8.2.2.3 Energy and Water Price Trends ............................................................. 8-12

8.2.2.4 Repair and Maintenance Costs ............................................................... 8-13

8.2.2.5 Lifetime .................................................................................................. 8-13

8.2.2.6 Discount Rates ....................................................................................... 8-15

8.2.2.7 Compliance Date of Standard ................................................................ 8-18

8.2.2.8 No Standards Case Distribution of Efficiency Levels ........................... 8-18

8.3 PAYBACK PERIOD INPUTS ......................................................................................... 8-19

8.4 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS .............. 8-20

8.4.1 Summary of LCC and PBP Results ...................................................................... 8-20

8.5 REBUTTABLE PAYBACK PERIOD ............................................................................. 8-23

8.5.1 Results ................................................................................................................... 8-24

REFERENCES .......................................................................................................................... 8-25

LIST OF TABLES

Table 8.1.1 Building Types Used for CPSV Life-Cycle Cost and Payback Analysis ................. 8-2

Table 8.2.1 Mean CPSV MSPs in 2014 ....................................................................................... 8-5

Table 8.2.2 Summary of National Average Markups for Commercial Prerinse Spray 

Valves ....................................................................................................................... 8-6

Table 8.2.3 Average Commercial Electricity Prices .................................................................... 8-9

Table 8.2.4 Average Commercial Natural Gas Prices ............................................................... 8-10

Table 8.2.5 Average Combined Water Prices ............................................................................ 8-11

Table 8.2.6 Probability of CPSV Failure by Year ..................................................................... 8-14

Table 8.2.7 Derivation of Typical Discount Rates by Building Type ....................................... 8-17

Table 8.2.8 Commercial Prerinse Spray Valve No-Standards Case Efficiency Distribution 

by Equipment Class in 2019 ................................................................................... 8-19

Table 8.4.1 Light Duty Commercial Prerinse Spray Valves: LCC and PBP Results ................ 8-21
8-ii

Table 8.4.2 Standard Duty Commercial Prerinse Spray Valves: LCC and PBP Results .......... 8-22

Table 8.4.3 Heavy Spray Force Commercial Prerinse Spray Valves: LCC and PBP 

Results..................................................................................................................... 8-23

Table 8.5.1 Rebuttable Payback Periods for Commercial Prerinse Spray Valves ..................... 8-24

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP ........................... 8-4

Figure 8.2.1 National Average Electricity Price Projections (2014$/kWh) .............................. 8-12

Figure 8.2.2 National Average Natural Gas Price Projections (2014$/therm) .......................... 8-13
8-1 

CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

8.1 INTRODUCTION

The effect of standards on individual customers usually includes a reduction in operating 

cost and an increase in purchase cost. This chapter describes two metrics used in the analysis to 

determine the economic impact of standards on individual customers of commercial prerinse 

spray valves (CPSVs). 

• Life-cycle cost (LCC) is the total customer cost over the life of equipment, including

purchase costs and operating costs (which in turn include maintenance, repair, and energy

and water costs). Future operating costs are discounted to the time of purchase and

summed over the lifetime of the equipment.

• Payback period (PBP) measures the amount of time it takes customers to recover the

potentially higher purchase price of more energy-efficient equipment through reduced

operating costs.

The U.S. Department of Energy (DOE) conducted the LCC and PBP analysis using a 

spreadsheet model developed in Microsoft Excel®. When combined with Crystal Ball® (a 

commercially available software program), the LCC and PBP model generates a Monte Carlo 

simulation to perform the analysis by incorporating uncertainty and variability considerations in 

several of the key parameters as discussed further in section 8.1.1 of this technical support 

document (TSD). 

Inputs to the LCC and PBP analysis are discussed in sections 8.2 and 8.3 of this TSD, 

respectively. Results for each metric are presented in section 8.4 of this TSD. Key variables and 

calculations are presented for each metric. The calculations discussed here were performed with 

a Microsoft Excel spreadsheet available on the DOE’s rulemaking website for commercial 

prerinse spray valves1. Details of the spreadsheet and instructions for using it are discussed in 

Appendix 8-A of this TSD. 

8.1.1 General Approach for Life-Cycle Cost and Payback Period Analysis

Recognizing that several inputs to the analysis of customer LCC and PBP are either 

variable or uncertain, DOE used a Monte Carlo simulation and probability distributions to model 

both the uncertainty and variability of inputs. Appendix 8-B of this TSD provides a detailed 

explanation of Monte Carlo simulation and the use of probability distributions. 

DOE displays LCC and PBP results as distributions of impacts compared to baseline 

conditions. Results, presented in section 8.4 of this TSD, were derived from 10,000 samples for 

each Monte Carlo simulation run; the tables show the averages of the distributions. 

The LCC and PBP analysis typically reflects the situation in which a single customer 

pays the increase in purchase cost for more-efficient equipment and also receives the reduction in 

operating cost. For the purpose of this rulemaking, DOE has concluded that there is no change in 
8-2 

purchase cost for more efficient commercial prerinse spray valves. This is explained in further 

detail in section 8.2.1.2 of this TSD. Because of the lack of increase of purchase cost of more 

efficient equipment, the LCC and PBP analysis for this rulemaking reflects the situation in which 

a single customer receives a reduction in operating cost from the more-efficient commercial 

prerinse spray valve. 

8.1.2 Development of Customers 

DOE used 2014 population estimates from the United States Census to assign 

probabilities that a commercial prerinse spray valve is located in a particular state or the District 

of Columbia (DC).2

 DOE used the 2003 Commercial Building Energy Consumption Survey 

(CBECS) data regarding commercial kitchen spaces to determine which building types should be 

included in the analysis3. These building types are intended to represent CPSV customers. The 

probabilities that a commercial prerinse spray valve is located in a particular type of building are 

given in Table 8.1.1. DOE used the same set of probabilities for all equipment classes. Building 

type is also used as an input parameter for estimating discount rates and equipment operating 

times.

Table 8.1.1 Building Types Used for CPSV Life-Cycle Cost and Payback Analysis 

Building Type Percentage of CPSV Stock

Healthcare 19.4

Lodging 14.3

Education 24.2

Restaurant 30.7

Retail/Grocery 11.4

8.1.3 Overview of Life-Cycle Cost and Payback Period Analysis Inputs

The LCC is the total customer cost over the life of the equipment, including purchase 

price (including retail markups and sales taxes) and operating cost (including energy and water 

cost). Future operating costs are discounted to the time of purchase and summed over the lifetime 

of the equipment. The PBP is the increase in purchase cost of higher efficiency equipment 

divided by the change in operating cost during the first year of the equipment’s life. It represents 

the number of years that it will take the customer to recover the increased purchase cost through 

decreased operating costs, based on the costs in the first year of the equipment’s life. In the 

calculation of PBP, future costs are not discounted. 

Inputs to the LCC and PBP analysis are categorized as (1) inputs for establishing the 

purchase cost, otherwise known as the total installed cost and (2) inputs for calculating the 

operating cost.

The primary inputs for establishing the total installed cost are:

• Manufacturer selling price: The manufacturer selling price (MSP) is the price charged by 

the manufacturer to a wholesaler, or end user for commercial prerinse spray valves. The 
8-3 

MSP includes a markup that converts the cost of production (i.e., the manufacturer 

production cost) to a MSP. 

• Markups and sales tax: Markups and sales tax are the retail margins and state and local 

retail sales taxes associated with converting the MSP to a customer’s final purchase price.

• Installation cost: The installation cost is the sum of the labor and material costs associated 

with installing commercial prerinse spray valves after purchase.

The primary inputs for calculating the operating cost are:

• Associated equipment energy consumption: The associated equipment energy 

consumption is the site energy use associated with operating commercial prerinse spray 

valves. 

• Energy prices: The energy prices are the prices customers paid for energy (electricity and 

natural gas) in a recent year escalated by the energy price trends. 

• Energy price trends: The energy prices projected over the lifetime of commercial prerinse 

spray valves installed in the compliance year.

• Equipment water consumption: The equipment water consumption is the site water use 

associated with operating equipment. 

• Water prices: The water prices are the prices consumers paid for water in a recent year 

escalated by the water price trends.

• Water price trends: The prices projected over the lifetime of commercial prerinse spray 

valves installed in the compliance year.

• Maintenance costs: The labor and material costs associated with maintaining the 

operation of commercial prerinse spray valves. 

• Repair costs: The labor and material costs associated with repairing or replacing 

components that have failed. 

• Lifetime: The age at which the commercial prerinse spray valves are retired from service.

• Discount rate: The rate at which future costs and savings are reduced to establish their 

present value.

Figure 8.1.1 graphically depicts the relationships among the installed cost and operating 

cost inputs for the calculation of the LCC and PBP. As noted earlier, most of the inputs are 

characterized by probability distributions that capture variability in the input variables. All of the 

inputs depicted in Figure 8.1.1 are discussed in sections 8.2 and 8.3 of this TSD. 
8-4 

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP 

8.2 LIFE-CYCLE COST ANALYSIS INPUTS

LCC is the total customer cost over the life of equipment, including purchase cost and 

operating costs (which are composed of energy costs, combined water costs, maintenance costs, 

and repair costs). Future operating costs are discounted to the time of purchase and summed over 

the lifetime of the equipment. LCC is defined by the following equation: 

Eq. 8.1 

Where:

LCC = life-cycle cost ($),

IC = total installed cost ($),

∑ = sum over the lifetime, from year 1 to year N, where N = lifetime of equipment (years),

OC = operating cost ($),

r = discount rate, and 

t = year for which operating cost is being determined.

DOE expresses all the costs in this chapter in 2014$. Total installed cost, operating cost, 

lifetime, and discount rate are discussed in the following sections. The year of equipment 

∑=

+= +

N

t

t

t ICLCC OC r

1

/( )1
8-5 

purchase is assumed to be 2019 since the effective date of the energy conservation standards for 

commercial prerinse spray valves is expected to be late 2018. 

8.2.1 Total Installed Cost Inputs

DOE calculated the total installed cost customers pay for commercial prerinse spray 

valves based on the following equation: 

(RSPIC TAX ) +×= INST

Eq. 8.2 

Where:

IC = customer installed cost for equipment, 

RSP = retail selling price for equipment 

TAX = sales tax, and

INST = installation cost.

8.2.1.1 Manufacturer Selling Price

The MSP is the price charged by manufacturers to either a wholesaler/distributor or very 

large customer for equipment. The MSP includes a markup that converts from the cost to 

manufacture the equipment (i.e., the manufacturing cost). DOE developed the MSP for 

commercial prerinse spray valves through a design option analysis. Refer to chapter 5 of this

TSD, Engineering Analysis, for details. 

DOE developed the MSP for each equipment class of commercial prerinse spray valves 

as a part of the engineering analysis; see Table 8.2.1. 

Table 8.2.1 Mean CPSV MSPs in 2014 

Equipment Class 2014$

Light Duty

(Spray Force ≤ 5 ozf) 36.63

Standard Duty

(Spray Force > 5 ozf and ≤ 8 ozf) 36.63

Heavy Duty

(Spray Force >8 ozf) 36.63

The standard-level MSP increase is the change in MSP associated with producing 

equipment at higher efficiency levels. DOE concluded that there were no increases in MSP 

associated with higher efficiency commercial prerinse spray valves, making the standard-level 

MSP increase zero and not necessary to include in this LCC and PBP analysis. Refer to chapter 5

of this TSD, Engineering Analysis, for details. The engineering analysis also identified

efficiency levels that were used in the LCC and PBP analyses. The highest efficiency level in 

each equipment class subcategory is considered the maximum technologically achievable, or 

max-tech, level.
8-6 

8.2.1.2 Overall Markup

For a given distribution channel, the overall markup is the value determined by 

multiplying all the associated retail markups and the applicable sales tax.

The following distribution channels were identified: 

A. Manufacturer  Final Customer (Direct Sales)

B. Manufacturer  Authorized Distributor  Final Customer

C. Manufacturer  Retailer  Final Customer

D. Manufacturer  Service Company  Final Customer

Table 8.2.2 Summary of National Average Markups for Commercial Prerinse Spray Valves

Distribution 

Channel Baseline Markup

Percentage 

Through 

Channel

Sales Tax Combined 

Total

A (Direct Sales) 1.67* 17 1.0716 1.78

B (Distributor) 1.67 33 1.0716 1.78

C (Retailer) 1.52 17 1.0716 1.63

D (Service Company) 1.92 33 1.0716 2.06

WEIGHTED 

TOTAL 1.73 1.0716 1.85

* Direct sales baseline markup assumed equal to that for distributors (i.e., manufacturers would not undercut authorized distributors).

Based on the percentages of the market share attributed to each of the distribution 

channel described above, the weighted-average overall baseline markup equals 1.73. Since there 

is no increase in MSP between standard levels, this markup was used for all equipment and there 

was no need to calculate incremental markups. For more details of the markup analysis, see 

chapter 6 of this TSD. 

8.2.1.3 Installation Cost 

The installation cost is the price to the customer of labor and any materials other than the 

actual equipment potentially necessary to install the CPSV equipment. DOE derived the labor 

installation costs based on data from the United States Department of Labor (DOL)4,

5

. DOE 

determined that commercial prerinse spray valves do not typically require any additional 

materials for installation. The installation costs in this rulemaking only consist of the labor costs 

of the individual installing the commercial prerinse spray valve and are assumed to be the same 

for each equipment class and efficiency level.

To determine the labor costs associated with installation of commercial prerinse spray 

valves, DOE assumed that the customer maintenance personnel would be installing the 

equipment and that it would take a single employee one hour to completely install the equipment. 

Because maintenance employees for different types of businesses and buildings have different 

hourly wages, DOE allowed the installation costs to vary by building type. DOE found hourly 

wage data for grounds maintenance employees, as well as national minimum wage data. These 
8-7 

values were from the year 2010, so a Consumer Price Index (CPI) inflation calculator6 was used 

to convert the wages into 2014 values. For restaurant and retail customers, installation costs for 

all equipment classes and efficiency levels are the value of one hour of minimum wage. For 

healthcare, lodging, and education customers, installation costs for all equipment classes and 

efficiency levels are the value of one hour of grounds maintenance wage.

8.2.1.4 Total Installed Cost

As presented in section 8.2.1 of this TSD, the total installed cost is the sum of the 

equipment price and the installation cost. DOE derived the customer equipment price for any 

given standard level by multiplying the MSP by the baseline markup. There is no variation in 

MSP or installation costs between equipment classes or efficiency levels; the total installed costs 

will be the same for each equipment class at each efficiency level. The resulting total installed 

cost will not be a single-point value, but a distribution of values because Monte Carlo 

simulations were implemented to account for the variability in sales tax and installation costs for 

the LCC and PBP analysis.

The derivation of the total installed cost is relatively straightforward. The baseline MSP 

is the starting point for determining the total installed cost (values are taken directly from Table 

8.2.1). The baseline markups, the sales tax, and the installation costs are used to convert the 

MSPs into total installed costs for commercial prerinse spray valves. The average installed cost 

in 2019 is included in the results tables presented in section 8.4 of this TSD. 

8.2.2 Operating Cost Inputs

DOE defined the operating cost for each efficiency level by the following equation: 

OCEL AECEL ×= PRICEENERGY AWCEL ×+ PRICEWATER RCEL ++ MCEL ( () )

Eq. 8.3 

Where:

OCEL = operating cost ($), 

AEC = annual energy consumption associated with operating the equipment, 

PRICEENERGY = energy prices ($), 

AWC = annual water consumption associated with operating the equipment, 

PRICEWATER = water price ($),

RC = repair cost associated with component failure ($), 

MC = annual maintenance cost for maintaining equipment operation ($), and

EL = efficiency level under consideration, 

8.2.2.1 Annual Energy and Water Consumption 

As described in chapter 7 of this TSD, DOE developed equations to estimate annual

associated energy and water consumption for commercial prerinse spray valves from the tested 

flow rate of the commercial prerinse spray valve and estimated operating time. Because different 

building types have different equipment operating times, the energy and water consumption will 
8-8 

vary by building type. Annual energy and water consumption will also vary by census division in 

this analysis. For more details, see chapter 7 of this TSD. 

8.2.2.2 Energy and Water Prices

This section describes the price analysis used to develop the energy and water portion of 

the annual operating costs for CPSV equipment.

Because of the wide variation in energy consumption patterns and rates across the 

country, it is important to consider regional differences in energy and water prices. For this 

reason, DOE divided the United States into the 50 states and the District of Columbia. DOE used 

reported average commercial electricity and natural gas prices (which are assumed to apply to all

building types in this rulemaking) at the state level from an Energy Information Administration

(EIA) database of commercial electricity and natural gas prices7

. When this analysis was 

conducted, the latest available prices from this source were for the calendar year 2013. The LCC 

analysis reports all prices in terms of 2014 year dollars, so a gross domestic product (GDP) 

deflator from the Bureau of Economic Analysis was applied to the 2013 energy prices from the 

EIA. The resulting prices are representative of the 2014 commercial energy prices. Table 8.2.3

and Table 8.2.4 provide the state electricity and natural gas prices, respectively. 
8-9 

Table 8.2.3 Average Commercial Electricity Prices

State

Average Price, 

Commercial 

$/kWh, 2014

State

Average Price, 

Commercial 

$/kWh, 2014

Alabama 0.107 Montana 0.097

Alaska 0.158 Nebraska 0.087

Arizona 0.100 Nevada 0.092 

Arkansas 0.081 New Hampshire 0.137

California 0.148 New Jersey 0.130

Colorado 0.100 New Mexico 0.099 

Connecticut 0.149 New York 0.155 

Delaware 0.104 North Carolina 0.089

District of Columbia 0.121 North Dakota 0.084

Florida 0.096 Ohio 0.095

Georgia 0.100 Oklahoma 0.078

Hawaii 0.346 Oregon 0.085

Idaho 0.075 Pennsylvania 0.094

Illinois 0.080 Rhode Island 0.133

Indiana 0.096 South Carolina 0.100

Iowa 0.086 South Dakota 0.086 

Kansas 0.097 Tennessee 0.102 

Kentucky 0.086 Texas 0.081

Louisiana 0.091 Utah 0.085

Maine 0.119 Vermont 0.149 

Maryland 0.109 Virginia 0.082

Massachusetts 0.147 Washington 0.079

Michigan 0.112 West Virginia 0.083 

Minnesota 0.097 Wisconsin 0.110

Mississippi 0.104 Wyoming 0.087

Missouri 0.088 U.S. Average 0.104
8-10

Table 8.2.4 Average Commercial Natural Gas Prices

State

Average Price, 

Commercial 

$/therm, 2014

State

Average Price, 

Commercial 

$/therm 2014

Alabama 1.286 Montana 0.881

Alaska 0.848 Nebraska 0.668

Arizona 0.867 Nevada 0.688

Arkansas 0.820 New Hampshire 1.334

California 0.769 New Jersey 0.969

Colorado 0.800 New Mexico 0.721

Connecticut 0.928 New York 0.802

Delaware 1.287 North Carolina 0.926

District of Columbia 1.243 North Dakota 0.681

Florida 1.121 Ohio 0.734

Georgia 0.992 Oklahoma 1.102

Hawaii 4.262 Oregon 0.914

Idaho 0.627 Pennsylvania 1.105

Illinois 0.927 Rhode Island 1.393

Indiana 0.821 South Carolina 0.929

Iowa 0.732 South Dakota 0.703

Kansas 1.059 Tennessee 0.891

Kentucky 0.937 Texas 0.764

Louisiana 0.867 Utah 0.744

Maine 1.185 Vermont 0.732

Maryland 1.081 Virginia 0.936

Massachusetts 1.073 Washington 0.956

Michigan 0.854 West Virginia 0.940

Minnesota 0.728 Wisconsin 0.713

Mississippi 0.769 Wyoming 0.717

Missouri 1.012 U.S. Average 0.850

The water prices used in the LCC and PBP analysis were developed using the American 

Water Works Association (AWWA) 2012 rate survey data. Prices were extracted for each utility 

in the AWWA database for commercial sector customers. Water and wastewater prices were 

extracted and combined to a single combined water price. The 2012 prices were escalated to 
8-11

2014 dollars using the ratio of 2014 and 2012 values for the Bureau of Labor Statistics CPI for 

water. The 2014 combined water prices are shown in Table 8.2.5. 

Table 8.2.5 Average Combined Water Prices

State

Average Price, 

Commercial 

$/kGal, 2014

State

Average Price, 

Commercial 

$/kGal, 2014

Alabama 7.91 Montana 7.37

Alaska 11.32 Nebraska 4.08

Arizona 7.01 Nevada 6.56

Arkansas 5.36 New Hampshire 7.58

California 7.15 New Jersey 8.62

Colorado 6.08 New Mexico 7.47

Connecticut 6.84 New York 10.18

Delaware 12.89 North Carolina 6.05

District of Columbia 10.72 North Dakota 8.68

Florida 9.03 Ohio 7.82

Georgia 9.01 Oklahoma 10.15

Hawaii 8.48 Oregon 7.67

Idaho 3.30 Pennsylvania 9.34

Illinois 7.64 Rhode Island 7.74

Indiana 8.25 South Carolina 9.41

Iowa 5.40 South Dakota 9.65

Kansas 7.24 Tennessee 7.54

Kentucky 8.00 Texas 3.44

Louisiana 8.90 Utah 3.96

Maine 4.55 Vermont 10.23

Maryland 11.12 Virginia 10.23

Massachusetts 12.97 Washington 9.66

Michigan 4.64 West Virginia 7.71

Minnesota 8.46 Wisconsin 5.32

Mississippi 7.24 Wyoming 8.29

Missouri 7.06 U.S. Average 8.25
8-12

8.2.2.3 Energy and Water Price Trends

To arrive at energy prices in future years, DOE multiplied the prices described in the 

preceding section by the appropriate projected annual average price changes in EIA’s Annual 

Energy Outlook 2014 (AEO2014).8 Figure 8.2.1 and Figure 8.2.2 show the national weighted

average commercial electricity and natural gas price projections that were used in the LCC and 

PBP analysis. AEO2014 currently projects prices to the year 2040 for low, reference, and high 

economic growth scenarios for each census division and the national average. For each customer 

sampled, DOE applied energy prices that were derived by escalating the 2014 price in the 

customer’s state times the AEO annual change projections for the census division in which the 

customer was located. To estimate the trend after 2040, DOE used the average rate of change 

during 2030–2040. The default energy price trend scenario used in the LCC is the reference case; 

the LCC model spreadsheet does have the capability to analyze the high and low growth trends 

for additional sensitivity analysis.

Figure 8.2.1 National Average Electricity Price Projections (2014$/kWh) 

0.065

0.070

0.075

0.080

0.085

0.090

2014201720202023202620292032203520382041204420472050205320562059

Low

Reference

High
8-13

Figure 8.2.2 National Average Natural Gas Price Projections (2014$/therm) 

To arrive at water prices in future years, DOE examined the CPI for water and sewerage 

maintenance. DOE used a 40-year historical series; in nominal terms, the annual average growth 

rate in this price index was 6.14 percent. The overall CPI for the same period averaged 4.06 

percent. For the analysis period, a real water price escalation of 1.99 percent (1.0614/1.0406) 

was used for a reference water price escalation rate. The default water price growth rate used in 

the LCC analysis was the reference rate. DOE analyzed a 20-year rolling average of real water 

price escalations and used the minimum and maximum real price escalation from the most recent 

20 years as a low and high escalation rate, respectively. The low and high water price escalation 

rates used in this analysis are 1.41 and 2.19 percent, respectively. Since the escalation rates are 

based on national CPI data, they do not vary by location. These growth rates are applied to the 

state-level combined water prices discussed in the previous section. 

8.2.2.4 Repair and Maintenance Costs

The repair cost is defined as the cost to the customer for replacing or repairing 

components in the CPSV equipment that have failed. The maintenance cost is the cost to the 

customer of maintaining equipment operation. DOE assumed that commercial prerinse spray 

valves are typically replaced entirely upon failure rather than repairing or replacing the failed 

components of the equipment. Because of this, there are no repair costs included in the operating 

cost calculations for the LCC analysis. DOE also assumed, based on the components of the 

equipment, that there is no general maintenance required for commercial prerinse spray valves. 

Consequently, there are no maintenance costs included in the operating cost calculations for the 

LCC analysis for commercial prerinse spray valves. 

8.2.2.5 Lifetime

DOE defined lifetime as the age when a CPSV unit is retired from service. DOE assumes 

that the average CPSV lifetime is 5 years and that the maximum lifetime is 10 years. DOE 

1

1.5

2

2.5

3

3.5

201420162018202020222024202620282030203220342036203820402042204420462048205020522054205620582060

Low

Reference

High
8-14

further assumes that 10% of commercial prerinse spray valves will have a lifetime of one year or 

less; this is attributed to businesses failing and the commercial prerinse spray valve no longer 

being in use. The probabilities of commercial prerinse spray valve failure by year, shown in 

Table 8.2.6, were used in the LCC model for the Monte Carlo simulation.

Table 8.2.6 Probability of CPSV Failure by Year 

Years 

Since 

Installation

Probability of 

Failure

1 10.09%

2 1.50%

3 6.63%

4 16.16%

5 25.08%

6 24.09%

7 12.86%

8 3.26%

9 0.32%

10 0.01%

DOE used a Weibull distribution, which is a probability distribution commonly used to 

measure failure rates.a Its form is similar to an exponential distribution, which models a fixed 

failure rate; however, a Weibull distribution allows for a failure rate that changes over time. The 

cumulative Weibull distribution takes the form:

P(x) = e−(

x−θ

α )β

for x > θ 

Eq. 8.4

and

P(x) = 1 for x ≤ θ

Eq. 8.5 

Where:

P(x) = probability that the appliance is still in use at age x, 

x = appliance age,

α = scale parameter, which would be the decay length in an exponential distribution, 

β = shape parameter, which determines the way in which the failure rate changes through time, 

and

θ = delay parameter, which allows for a delay before any failures occur.

 

a 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/>
8-15

When β = 1, the failure rate is constant over time, giving the distribution the form of a 

cumulative exponential distribution. In the case of appliances, β is commonly greater than 1, 

reflecting an increasing failure rate as appliances age.

8.2.2.6 Discount Rates 

The discount rate is the percentage rate at which future expenditures are discounted 

(reduced) to establish their present value. DOE derived the discount rates for the LCC analysis 

by estimating the cost of capital for companies that purchase commercial prerinse spray valves. 

The cost of capital 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 cost to the firm of 

equity and debt financing.9 DOE defines the discount rate as the weighted average cost of capital 

(WACC), calculated as the weighted average of the cost of equity financing and the cost of debt 

financing, as estimated from financial data for publicly traded firms in the sectors that purchase 

commercial prerinse spray valves. 

DOE estimated the cost of equity using the capital asset pricing model (CAPM).10 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 overall 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: 

Rk fe ( ) β ×+= ERP

Eq. 8.6 

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 

equipment price deflator for estimating inflation, and considers the best method for determining 
8-16

the risk free rate as one where “the time horizon of the investor is matched with the term of the 

risk-free security.”11

DOE defined the risk-free rate as the 40-year geometric average yield on long-term 

government bonds. DOE calculated the risk-free rate using Federal Reserve data for the period 

1974 through 2013,12 with a resulting rate of 6.85 percent. DOE used a 4.04 percent estimate for 

the ERP based on the difference between a 40-year geometric average yield on the Standard & 

Poor’s 500 Index calculated using data downloaded from the Damodaran Online13 site and the 

risk-free rate.

The cost of debt financing (kd) is the interest rate paid on money borrowed by a company. 

The cost of debt is estimated by adding a risk adjustment factor (Ra) to the risk-free rate. This 

risk adjustment factor depends on the variability of stock returns represented by standard 

deviations in stock prices. So for firm i, the cost of debt financing is: 

+= RRk aifdi

Eq. 8.7 

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:

dee ×+×= wkwkWACC d

Eq. 8.8

Where:

WACC =weighted average cost of capital,

we = proportion of equity financing, and

wd = proportion of debt financing. 

The cost of capital is a nominal rate, because it includes anticipated future inflation in the 

expected returns from stocks and bonds. The real discount rate or WACC deducts expected 

inflation (r) from the nominal rate. DOE calculated expected inflation (3.55 percent) over the 

same 1973 through 2013 historical period used for the other financial data calculations.

To estimate the WACC of commercial prerinse spray valve customers, DOE used a 

dataset of companies involved in each of the building types being analyzed, drawn from a 

database of U.S. companies given on the Damodaran Online individual company worksheet cited 

earlier. The Damodaran database includes most of the publicly traded companies in the United 

States.
8-17

DOE divided the companies into categories according to their type of activity (e.g., 

education or restaurant). DOE used financial information for firms in the Damodaran database 

that would be likely to occupy one of five building types that DOE assumed likeliest to use 

commercial prerinse spray valves. DOE used all observations from the Damodaran data set for 

which sufficiently complete data were available. 

Table 8.2.7 outlines the building type and ownership categories used for determining 

discount rates. For each building type, there is a mixture of large companies with stock traded on 

major U.S. stock exchanges, and smaller companies that either are not publicly traded(e.g., 

single-store or small, local chains of restaurants) or are state and local government facilities. The 

cost of capital for small independent grocers, restaurant owner-operators, small hotel owneroperators, and others with more limited access to capital is more difficult to determine than for 

publicly traded companies. Individual credit worthiness varies considerably and some 

franchisees have access to the financial resources of the franchising corporation. The Small 

Business Administration has collected data from which DOE was able to estimate the premium 

small businesses pay on loans relative to AAA corporate bonds. For small business loans under 

$100,000, the premium was approximately 1.9 percent for fixed rate loans.14 This assumption 

was applied in the LCC modeling. 

Table 8.2.7 Derivation of Typical Discount Rates by Building Type

Publicly Traded Small - Disc Rate 

Adder

S & L Govt / NonProfit U.S. Government

Building 

Type WACC % of 

Market

Discount 

Premium5

% of 

Market

Muni 

Bond 

Rate

% of 

Market

Treasury 

Rate

% of 

Market

Healthcare*,** 9.30% 19.57% 0.00% 0.00% 2.47% 76.73% 3.19% 3.70%

Lodging† 10.11% 60.00% 1.90% 40.00% 0.00% 0.00% 0.00% 0.00%

Education‡ 10.50% 25.11% 0.00% 0.00% 2.47% 74.89% 0.00% 0.00%

Restaurant 9.07% 58.74% 1.90% 41.26% 0.00% 0.00% 0.00% 0.00%

Retail 8.37% 50.00% 1.90% 50.00% 0.00% 0.00% 0.00% 0.00%

* Percentage of market from American Hospital Association, Fast Facts on Hospitals, Chicago, IL, January 3, 2012. 

www.aha.org/. Percentages based on number of community hospitals.

** Municipal bond rate from Federal Reserve Bank of St. Louis, and Federal Reserve H15 data (for 2012 and 2013 only). Value 

shown in the table is 40-year geometric average, mixed quality bonds, based on annual average data. Non-seasonally adjusted.

data for 1973–2011 from the Federal Reserve Bank of St. Louis, and 2012–2013 from the H15 data.

† Press reports indicate the percentage of “branded” hotels ranges from 60 to 70 percent of total hotel rooms. Articles did not 

indicate the percentage of branded hotel rooms that are franchisees essentially indistinguishable from local, small businesses. The 

low end of the range was used for the percentage of the market.

‡ Source for the percentage of education that is public versus private: http://nces.ed.gov/programs/digest/d10/tables/dt10_090.asp

5 Source: Small Business Administration data on loans between $10,000 and $99,000 compared to AAA Corporate Rates. 

www.sba.gov/advocacy/7540/6282. Last accessed June 10, 2013.

For buildings with an education application, the Damodaran database covers some 

privately operated schools, but not public schools. For the education sector, DOE obtained a 

40-year series of municipal bond rates from the Federal Reserve.15 As shown in the discount rate 

calculation, DOE assumed 75 percent of schools are public schools facing the average municipal 
8-18

bond rates calculated from the Federal Reserve data. The same municipal bond rate was used in 

the calculation of other facilities owned by state and local governments. The long-term, real 

Treasury bond rates were used for the cost of capital of U.S. government-owned facilities. 

8.2.2.7 Compliance Date of Standard 

Pursuant to 42 U.S.C. 6295(l), the compliance date of any new or amended energy 

efficiency standard for commercial prerinse spray valves would be three years after the final rule 

is published. Consistent with its published regulatory agenda, DOE assumed that the final rule 

would be issued late in 2015 and therefore, that the updated standards for commercial prerinse 

spray valves would require compliance beginning in late 2018. Given that compliance would not 

be required until the later part of 2018, DOE calculated the LCC and PBP for all customers as if 

they each would purchase new equipment in 2019, making 2019 the first year of the analysis 

period. 

8.2.2.8 No Standards Case Distribution of Efficiency Levels

In order to assess the impact of standards on individual customers, DOE must first 

determine the existing mix of efficiencies in the current stock of installed equipment. To 

accurately estimate the share of customers that would be affected by a standard at a particular 

efficiency level, DOE’s LCC and PBP analysis considered the projected distribution of 

equipment efficiencies that customers purchase under the no-new-standards case (i.e., the case 

without new energy efficiency standards). DOE refers to this distribution of equipment 

efficiencies as a no-new-standards case efficiency distribution.

To estimate the no-new-standards case efficiency distribution of commercial prerinse 

spray valves in 2019 (the first year of the analysis period), DOE relied on data from the Food 

Service Technology Center16 and DOE’s Compliance Certification Database17 for commercial 

prerinse spray valves. These data sources, while limited in scope and coverage, gave information 

regarding the relative distribution of efficiencies faor commercial prerinse spray valves being 

certified and installed. Additionally, DOE conducted general internet searches to understand the 

characteristics of the spray valves currently offered on the market. DOE assumed that the nonew-standards case percentages in 2019 would stay the same through the analysis period. 

The estimated shares for the no-new-standards case efficiency distribution within each 

equipment class for commercial prerinse spray valves are shown in Table 8.2.8. The estimates 

come from chapter 9 of this TSD, which describes DOE’s shipments analysis, including the 

breakdown of shipments by equipment class in both the no standards and standards scenarios. 
8-19

Table 8.2.8 Commercial Prerinse Spray Valve No-Standards Case Efficiency Distribution 

by Equipment Class in 2019 

Efficiency 

Level

Light Duty

% of Shipments

Standard Duty

% of Shipments

Heavy Duty

% of Shipments

Baseline 15 40 40

1 35 50 50

2 0 0 5

3 50 10 5

8.3 PAYBACK PERIOD INPUTS

The PBP is the amount of time it takes the customer to recover the assumed higher 

purchase cost of more energy-efficient equipment as a result of lower operating costs. 

Numerically, the PBP is the ratio of the increase in purchase cost (i.e., from a less efficient 

design to a more efficient design) to the decrease in first year annual operating expenditures

(using first-year energy and water costs and the annualized values for repair and maintenance 

discussed in section 8.2.2.4 of this TSD). 

The equation for PBP is:

PBP =∆IC/∆OC

Eq. 8.9 

Where:

PBP = payback period in years, 

∆IC = difference in the total installed cost between the more efficient standard-level equipment 

(efficiency levels 2, 3, etc.) and the baseline efficiency equipment, and 

∆OC = difference in first year annual operating costs. 

PBPs are expressed in years. PBPs can be greater than the life of the equipment if the 

increased total installed cost of the more efficient equipment is not recovered fast enough in 

reduced operating costs.

The data inputs to the PBP are the total installed cost of the equipment to the customer 

for each efficiency level and the annual (first year) operating costs for each efficiency level. The 

inputs to the total installed cost are the equipment price and the installation cost. The inputs to 

the operating costs are the annual energy and water costs. The PBP uses the same inputs as the 

LCC analysis, except that energy and water price trends are not required. Since the PBP is a 

“simple” payback, the required energy and water cost is only for the year in which a new or 

amended efficiency standard is to take effect—in this case, 2019 was used rather than the 

compliance year of 2018. See section 8.2.2.7 of this TSD for more information regarding the use 

of 2019 as the first year of the analysis period rather than the compliance year of 2018. 

DOE also calculates a rebuttable PBP, which is the time it takes the customer to recover 

the assumed higher purchase cost of more efficient equipment as a result of lower energy and 

water costs. Numerically, the rebuttable PBP is the ratio of the increase in purchase cost (i.e.,
8-20

from a less efficient design to a more efficient design) to the decrease in annual energy 

expenditures, which is the decrease in first year annual energy and water costs as calculated from 

the DOE test procedure. The calculation excludes repair costs and maintenance costs.

Since DOE determined that there is no increase in purchase cost from the less efficient to 

the more efficient commercial prerinse spray valves (see chapter 5 of this TSD for more details), 

the results of both the simple and rebuttable PBP will equal zero years for each efficiency level.

8.4 RESULTS OF LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS

This section presents the results of the LCC and PBP analysis for commercial prerinse 

spray valves. DOE used probability distributions to characterize the uncertainty in many of the 

inputs to the analysis. DOE used Monte Carlo simulation to perform the LCC calculations for 

commercial prerinse spray valve customers. 

LCC and PBP calculations were performed 10,000 times on the sample of customers 

established for commercial prerinse spray valves. Each LCC and PBP calculation was performed 

on a single customer selected from the sample. A customer was selected based on its weight (i.e.,

how representative that particular customer was of other customers in the distribution). Each 

LCC and PBP calculation also sampled from the probability distributions that DOE developed to 

characterize many of the inputs to the analysis.

DOE calculated the average LCC and LCC savings and the median PBP for each 

efficiency level. DOE calculated LCC savings and PBPs relative to the no standards case 

equipment efficiency distribution that it assigned to the sample of customers. For some 

customers, DOE assigned no standards case equipment that is more efficient than some of the 

efficiency levels. For that reason, the average LCC impacts are not equal to the difference 

between the LCC of a specific standard level and the LCC of the baseline equipment. For each 

efficiency level, DOE also calculated the share of customers experiencing a net LCC cost. DOE 

considered a customer to receive no effect at a given standard level if DOE assigned no standards 

case equipment having the same or higher efficiency than the standard level. 

8.4.1 Summary of LCC and PBP Results

Table 8.4.1 through Table 8.4.3 show the LCC and PBP results by efficiency level for all 

equipment classes and subcategories of commercial prerinse spray valves.
8-21

Table 8.4.1 Light Duty Commercial Prerinse Spray Valves: LCC and PBP Results

Light Duty (Spray Force ≤ 5 ozf)

TSL Efficiency 

Level

Average Costs

2014$ Simple 

Payback 

Period 

years

Installed 

Cost 

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC* 

0 79 373 1,957 2,036 0.0

1 1 79 353 1,854 1,933 0.0

2 2 79 334 1,751 1,830 0.0

3 , 4 3 79 319 1,674 1,753 0.0

Light Duty (Spray Force ≤ 5 ozf)

TSL Efficiency 

Level

Life-Cycle Cost Savings**

% of Customers 

that Experience 

Net Cost

Average 

Savings 

2014$

0 0 0

1 1 0 103

2 2 0 134

3 , 4 3 0 211

*The average discounted LCC for each EL is calculated assuming that all purchases are for equipment only with that 

EL. This allows the LCCs for each EL to be compared under the same conditions.

** The LCC savings for each EL are calculated relative to the no standards case efficiency distribution. The 

calculation includes customers with zero LCC savings (no impact). 

†The median payback period is calculated only for affected customers. Customers with zero LCC energy savings 

have an undefined payback period, and are therefore not included in calculating the median PBP. 
8-22

Table 8.4.2 Standard Duty Commercial Prerinse Spray Valves: LCC and PBP Results

Standard Duty (Spray Force > 5 ozf and ≤ 8 ozf)

TSL Efficiency 

Level

Average Costs

2014$ Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC*

0 79 599 3,141 3,220 0.0

1 1 79 540 2,832 2,911 0.0

2 , 3 2 79 476 2,498 2,577 0.0

4 3 79 461 2,420 2,499 0.0

Standard Duty (Spray Force > 5 ozf and ≤ 8 ozf)

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers 

that Experience 

Net Cost

Average 

Savings** 

2014$

0 0 0

1 1 0 309

2 , 3 2 0 472

4 3 0 549

*The average discounted LCC for each EL is calculated assuming that all purchases are for equipment only with that 

EL. This allows the LCCs for each EL to be compared under the same conditions.

** The LCC savings for each EL are calculated relative to the no standards case efficiency distribution. The 

calculation includes customers with zero LCC savings (no impact). 

†The median payback period is calculated only for affected customers. Customers with zero LCC energy savings 

have an undefined payback period, and are therefore not included in calculating the median PBP. 
8-23

Table 8.4.3 Heavy Spray Force Commercial Prerinse Spray Valves: LCC and PBP Results

Heavy (Spray Force > 8 ozf)

TSL Efficiency

Level

Average Costs 

2014$ Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC*

0 79 785 4,120 4,199 0.0

1 1 79 707 3,708 3,787 0.0

2 2 79 628 3,296 3,375 0.0

3 , 4 3 79 609 3,193 3,272 0.0

Heavy (Spray Force > 8 ozf)

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Customers 

that Experience 

Net Cost

Average 

Savings** 

2014$

0 0 0 

1 1 0 412

2 2 0 595

3 , 4 3 0 667

*The average discounted LCC for each EL is calculated assuming that all purchases are for equipment only with that

EL. This allows the LCCs for each EL to be compared under the same conditions.

** The LCC savings for each EL are calculated relative to the no standards case efficiency distribution. The 

calculation includes customers with zero LCC savings (no impact). 

†The median payback period is calculated only for affected customers. Customers with zero LCC energy savings 

have an undefined payback period, and are therefore not included in calculating the median PBP. 

8.5 REBUTTABLE PAYBACK PERIOD

DOE presents rebuttable PBPs to provide the legally established rebuttable presumption 

that an energy efficiency standard is economically justified if the additional product costs 

attributed to the standard are less than three times the value of the first-year energy cost savings. 

(42 U.S.C. §6295 (o)(2)(B)(iii)) 

The basic equation for rebuttable PBP is the same as that shown for the PBP in section

8.3 of this TSD. Unlike the analyses described in section 8.3 of this TSD, the rebuttable PBP is 

not based on the use of customer samples and probability distributions; it is based not on 

distributions but on discrete single-point values. For example, although DOE uses a probability 
8-24

distribution of energy prices in the distributional PBP analysis, it uses only the national average

energy price to determine the rebuttable PBP.

Other than the use of single-point values, the most notable difference between the 

distribution PBP and the rebuttable PBP is the latter’s reliance on the DOE test procedure to 

determine a product’s annual energy and water consumption. The rebuttable PBP also excludes 

any maintenance and repair costs.

DOE based the annual energy and water consumption values that it used to determine the 

rebuttable PBP for commercial prerinse spray valves on the average energy and water

consumption presented in chapter 7 of this TSD. 

8.5.1 Results

DOE calculated rebuttable PBPs for each standard level relative to the baseline EL. Table 

8.5.1 presents the rebuttable PBPs for each equipment class of commercial prerinse spray valves.

Table 8.5.1 Rebuttable Payback Periods for Commercial Prerinse Spray Valves 

EL

Rebuttable PBP

years

Light Standard Heavy

0 0.0 0.0 0.0

1 0.0 0.0 0.0

2 0.0 0.0 0.0

3 0.0 0.0 0.0
8-25

REFERENCES

1. DOE. Rulemaking for Commercial Prerinse Spray Valves Standard.

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=100.

2. Annual Estimates of the Resident Population for the United States, Regions, States, and

Puerto Rico: April 1, 2010 to July 1,

2014.http://www.census.gov/popest/data/state/totals/2014/index.html.

3. U.S. Department of Energy-Energy Information Administration. Commercial Building

Energy Consumption Survey. 2005. Washington DC.

4. Minimum Wage. http://www.dol.gov/whd/minimumwage.htm.

5. National Compensation. http://www.bls.gov/ncs/.

6. CPI Inflation Calculator. http://www.bls.gov/data/inflation_calculator.htm.

7. Form EIA-826 Database Monthly Electric Utility Sales and Revenue Data (EIA-826 Sales

and Revenue Spreadsheets). http://www.eia.gov/electricity/data.cfm#sales.

8. Energy Information Administration, Annual Energy Outlook 2014 with Projections to

2040. 2014. Washington, DC.

9. 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.

http://www.jstor.org/stable/1809766.

10. Ibbotson Associates, Cost of Capital 2001 Yearbook. 2001. Chicago, IL.

11. Board of Governors of the Federal Reserve System, Federal Reserve Bank Services Private

Sector Adjustment Factor. 2005: Washington, DC.

12. U.S. Federal Reserve. H.15: Selected Interest Rates. Last accessed May 21, 2014.

www.federalreserve.gov/releases/h15/data.htm.

13. Damodoran Online. The Data Page. 2014. Last accessed June 24, 2014.

http://pages.stern.nyu.edu/~adamodar/New_Home_Page/data.html.

14. U.S. Small Business Administration, Office of Advocacy. The Small Business Economy

2012. 2012. Last accessed June 4, 2013. http://www.sba.gov/advocacy/7540/6282. See

also: U.S. Small Business Administration, Office of Advocacy. The Small Business

Economy 2009. 2009. www.sba.gov/advocacy/7540/6282.

15. Federal Reserve Bank of St. Louis. http://research.stlouisfed.org/fred2/ and Federal

Reserve H15 data www.federalreserve.gov/releases/h15/data.htm.
8-26

16. Food Service Technology Center. Low-Flow Pre-Rinse Spray Valves.

www.fishnick.com/equipment/sprayvalves/. 

17. DOE. Compliance Certification Database. http://www.regulations.doe.gov/certificationdata/. 
8A-i 

APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST ANALYSIS 

SPREADSHEET

TABLE OF CONTENTS

8A.1 INTRODUCTION ........................................................................................................ 8A-1

8A.2 BASIC INSTRUCTIONS ............................................................................................. 8A-2

REFERENCES ......................................................................................................................... 8A-4
8A-1 

APPENDIX 8A. USER INSTRUCTIONS FOR LIFE-CYCLE COST ANALYSIS 

SPREADSHEET

8A.1 INTRODUCTION

The detailed results of the life-cycle cost (LCC) and payback period (PBP) analysis can 

be examined and reproduced using a Microsoft Excel spreadsheet available on the U.S. 

Department of Energy’s (DOE’s) rulemaking website for commercial prerinse spray valves.

1

Executing the spreadsheets fully requires both Microsoft Excel® and Crystal Ball®2 software. 

Both applications are commercially available. The spreadsheet has been tested with the 2010 and 

2013 versions of Microsoft Excel, and the latest spreadsheet is available from the DOE website 

at: 

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=100Descrip

tion of Life-Cycle Cost Workbook. 

For all of the product classes, DOE created a single LCC workbook file containing a 

collection of worksheets. Each worksheet represents a conceptual component within the LCC 

calculation. The LCC workbook contains the following worksheets: 

LCC Results The LCC Results worksheet contains a user interface to control the economic 

growth scenario, analysis mode, consumer subgroup selection, and which 

operating costs to analyze. The capability of selecting which operating costs 

to analyze provides the user with water only, electricity only, or natural gas 

only savings. It also contains a button that will save the LCC results once the 

Monte Carlo simulations have been run. 

Water Prices The Water Prices worksheet contains the water prices and growth rates used 

for each state, and the sources used to derive them. 

Energy Prices The Energy Prices worksheet contains the electricity and natural gas prices 

and growth rates used for each state and the sources used to derive them.

Population 

Taxes 

The Population Taxes worksheet contains the population and sales tax 

estimates for each state and the District of Columbia. Population and sales tax 

data and sources are contained in this worksheet as well. 

Discount Rate The Discount Rate worksheet includes the distribution of discount rates by 

building type, the discount rate calculation, and the financial data available for 

publicly traded potential commercial prerinse spray valve consumers. All 

sources used to derive discount rates can be found in this worksheet. 

Installation 

Costs

The Installation Costs worksheet includes labor and material costs and 

installation time for each building type and product class as well as any 

sources that were used.

Engineering 

Inputs

The Engineering Inputs worksheet contains information from the Engineering 

Analysis that is needed for the LCC and PBP analysis. 
8A-2 

Energy Use 

Inputs

The Energy Use Inputs worksheet contains information from the Energy and 

Water Use Analysis that is needed for the LCC and PBP analysis. This 

worksheet also includes the energy used to heat a gallon of water, commercial 

prerinse spray valve operating times and prevalence by building type, and 

water heating energy source breakdowns by census division. 

Other Inputs The Other Inputs worksheet contains information from the markups analysis, 

the commercial prerinse spray valve lifetime distribution, and the no-newstandards case efficiency distribution. 

Lifetime Costs The Lifetime Costs worksheet contains the projected operating and 

maintenance costs and a user interface to select the beginning of the analysis 

period. 

Monte Carlo 

Forecasts

The Monte Carlo Forcasts worksheet contains the LCC and PBP analysis 

results tables.

Output to NIA The Output to the National Impact Analysis (NIA) worksheet contains all the 

information that needs to be exported to the NIA model from the LCC and 

PBP analysis.

NIA Forecasts The NIA Forecasts worksheet contains the Crystal Ball forecast cells that 

calculate the projected operating and maintenance costs, water use, and 

energy use.

8A.2 BASIC INSTRUCTIONS

Basic instructions for operating the LCC spreadsheet are as follows:

1) Once the LCC spreadsheet has been downloaded, open the file using Excel. Click

“Enable Content” when prompted and then click on the tab for the LCC Results

worksheet.

2) Use Excel's View/Zoom commands at the top menu bar to change the size of the

display to fit your monitor.

3) The user can change the parameters listed under "User Options" on the LCC Results

worksheet. There are four drop-down lists on the LCC Results worksheet and three

additional drop-down lists on other worksheets. The default parameters are:

a) Economic Growth: Defaults to “Ref” To change the input, use the drop-down

menu and select the desired trend (Reference, Low, or High).

b) Analysis Mode: Defaults to Monte Carlo. To change the analysis mode, use the

drop-down menu and select weighted average. The weighted average option will

use weighted average values to calculate the LCC rather than an entire

distribution of values that the Monte Carlo analysis uses.

c) Costs Included: Defaults to “All Savings”. To change the operating costs being

used in the analysis, use the drop-down menu.
8A-3 

d) Subgroup: Defaults to “No Subgroup” To change the value, use the drop-down 

menu and select the desired consumer subgroup. 

e) Analysis Start Period: Default is “2019”. To change this value, use the drop-down 

menue on the Lifetime Costs worksheet. 

4) To run the Crystal Ball simulation, click the “Crystal Ball” tab, set the trial value to 

10,000, then hit the start button (you must reset and re-run after changing 

parameters). The status bar shows the progress of the simulation and can be 

monitored by the count of iterations displayed in the bottom left corner. When the 

simulation is finished, press the “Save LCC Results” button on the LCC Results 

worksheet. The results for all product classes on the LCC Results worksheet reflect 

the results of the new simulation. 
8A-4 

REFERENCES

1. EERE. Rulemaking for Commercial Prerinse Spray Valves Standard.

http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=100.

Accessed May 9, 2015.

2. Oracle. Crystal Ball.

http://www.oracle.com/us/products/applications/crystalball/overview/index.html. Accessed

May 9, 2014. 
8B-i 

APPENDIX 8B. UNCERTAINTY AND VARIABILITY IN LIFE-CYCLE COST

ANALYSIS

TABLE OF CONTENTS

8B.1 Introduction ....................................................................................................................8B-1

8B.2 Uncertainty .....................................................................................................................8B-1

8B.3 Variability ......................................................................................................................8B-1

8B.4 Approaches to Uncertainty and Variability ...................................................................8B-1

8B.5 Probability Analysis and the Use of Crystal Ball® ........................................................8B-2

LIST OF FIGURES

Figure 8B.5-1 Normal, Triangular, Uniform, Weibull, and Custom Probability Distributions 8B-3
8B-1 

8B. UNCERTAINTY AND VARIABILITY IN LIFE-CYCLE COST ANALYSIS

8B.1 INTRODUCTION

Analysis of energy conservation standards involves calculations of impacts; for example, 

the impact of a standard on consumer life-cycle cost (LCC). In order to perform the calculation, 

the analyst must first 1) specify the equation or model that will be used, 2) define the quantities 

in the equation, and 3) provide numerical values for each quantity. In the simplest case, the 

equation is unambiguous (contains all relevant quantities and no others), each quantity has a 

single numerical value, and the calculation results in a single value. However, unambiguity and 

precision are rarely encountered in real-world situations. In almost all cases, the model and/or the 

numerical values for each quantity in the model are not completely known (i.e., there is 

uncertainty) or the model and/or the numerical values for each quantity in the model depend 

upon other conditions (i.e., there is variability).

Thorough analysis involves accounting for uncertainty and variability. While the simplest 

analysis involves a single numerical value for each quantity in a calculation, arguments can arise 

about what the appropriate value is for each quantity. Explicit analysis of uncertainty and 

variability is intended to provide more complete information in the decision-making process.

8B.2 UNCERTAINTY

When making observations of past events or speculating about the future, imperfect 

knowledge is the rule rather than the exception. For example, the energy actually consumed by a 

particular appliance type (such as the average U.S. water heater, direct heating equipment, or 

pool heater) is not directly recorded, but rather estimated based upon available information. Even 

direct laboratory measurements have some margin of error. When estimating numerical values 

expected for quantities at some future date, the exact outcome is rarely known in advance. 

8B.3 VARIABILITY

Variability means that different applications or situations produce different numerical 

values when calculating a quantity. Specifying an exact value for a quantity may be difficult 

because the value depends on something else. For example, water heater energy consumption 

depends upon the specific circumstances and behaviors of the occupants (e.g., number of 

persons, length and temperature of showers, etc.). Variability makes specifying an appropriate 

population value more difficult inasmuch as any one value may not be representative of the 

entire population. Surveys can be helpful here, and analysis of surveys can relate the variable of 

interest (e.g., hours of use) to other variables that are better known or easier to forecast (e.g., 

persons per household). 

8B.4 APPROACHES TO UNCERTAINTY AND VARIABILITY

This section describes two approaches to uncertainty and variability: 

• scenario analysis, and

• probability analysis.
8B-2 

Scenario analysis uses a single numerical value for each quantity in a calculation, then 

changes one (or more) of the numerical values and repeats the calculation. A number of 

calculations are done that provide some indication of the extent to which the result depends upon 

the assumptions. For example, the LCC of an appliance could be calculated for energy rates of 2, 

8, and 14¢ per kWh. 

The advantages of scenario analysis are that each calculation is simple: a range of 

estimates is used and crossover points can be identified. An example of a crossover point is the 

energy rate above which the LCC is reduced, holding all other inputs constant. That is, the 

crossover point is the energy rate at which the consumer achieves savings in operating expense 

that more than compensate for the increased purchase expense. The disadvantage of scenario 

analysis is that there is no information about the likelihood of each scenario. 

Probability analysis considers the probabilities within a range of values. For quantities 

with variability (e.g., electricity rates in different households), surveys can be used to generate a 

frequency distribution of numerical values (e.g., the number of households with electricity rates 

at particular levels) to estimate the probability of each value. For quantities with uncertainty, 

statistical or subjective measures can be used to provide probabilities (e.g., manufacturing cost to 

improve energy efficiency to some level may be estimated to be $10 ± $3). 

The major disadvantage of the probability approach is that it requires more information, 

namely information about the shapes and magnitudes of the variability and uncertainty of each 

quantity. The advantage of the probability approach is that it provides greater information about 

the outcome of the calculations; that is, it provides the probability that the outcome will be in a 

particular range.

Scenario and probability analysis provide some indication of the robustness of the policy 

given the uncertainties and variability. A policy is robust when the impacts are acceptable over a 

wide range of possible conditions. 

8B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL®

To quantify the uncertainty and variability that exist in inputs to the engineering and to 

the LCC and payback period (PBP) analyses, DOE used Microsoft Excel® spreadsheets 

combined with Crystal Ball, a commercially available add-in, to conduct probability analyses. 

The probability analyses used Monte Carlo simulation and probability distributions. 

Simulation refers to any analytical method meant to imitate a real-life system, especially 

when other analyses are too mathematically complex or too difficult to reproduce. Without the 

aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most 

likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and 

simulation to automatically analyze the effect of varying inputs on outputs of the modeled 

system. One type of spreadsheet simulation is Monte Carlo simulation, which randomly 

generates values for uncertain variables repeatedly to simulate a model. Monte Carlo simulation 

was named for Monte Carlo, Monaco, where the primary attractions are casinos containing 

games of chance. Games of chance, such as roulette wheels, dice, and slot machines, exhibit 

random behavior. The random behavior in games of chance is similar to how Monte Carlo 
8B-3 

simulation selects variable values at random to simulate a model. When a die is rolled, possible 

outcomes are 1, 2, 3, 4, 5, or 6, but the actual value is unknown for any particular roll. The same 

holds true with the variables that have a known range of values but an uncertain value for any 

particular time or event (e.g., product lifetime, discount rate, and installation cost).

For each uncertain variable (one that has a range of possible values), possible values are 

defined with a probability distribution. The type of distribution selected is based on the 

conditions surrounding that variable. Probability distribution types include normal, triangular, 

uniform, Weibull, and custom, as shown in Figure 8B.5-1. 

Figure 8B.5-1 Normal, Triangular, Uniform, Weibull, and Custom Probability 

Distributions

During a simulation, multiple scenarios of a model are calculated by repeatedly sampling 

values from the probability distributions for the uncertain variables and using those values for the 

cell. Crystal Ball simulations can consist of as many trials (or scenarios) as desired—hundreds or 

even thousands. During a single trial, Crystal Ball randomly selects a value from the defined 

possibilities (the range and shape of the probability distribution) for each uncertain variable and 

then recalculates the spreadsheet. 

NORMAL TRIANGULAR UNIFORM

WEIBULL CUSTOM
9-i

CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION .............................................................................................................. 9-1

9.2 FORMULATION OF TOTAL SHIPMENTS .................................................................... 9-1

9.2.1 Historical and Current Shipments ........................................................................... 9-1

9.2.2 Projected Shipments ............................................................................................... 9-2

9.3 DISAGGREGATION OF TOTAL SHIPMENTS INTO PRODUCT CLASSES 

AND EFFICIENCY LEVELS ..................................................................................................... 9-2

9.4 IMPACT OF POTENTIAL STANDARDS ON SHIPMENTS ......................................... 9-3

9.5 RESULTS ........................................................................................................................... 9-5

REFERENCES .......................................................................................................................... 9-11

LIST OF TABLES

Table 9.3.1 No-New-Standards Case Relative Shipments Breakdown by Product Class ........... 9-3

Table 9.3.2 No Standards Case Relative Shipments Breakdown by EL within Product 

Classes............................................................................................................ 9-3

Table 9.4.1 Product-Class Switching of Consumers with Existing Standard-Duty 

Products.......................................................................................................... 9-4

Table 9.5.1 Shipments by Product Class and EL – No-New-Standards Scenario ....................... 9-6

Table 9.5.2 Shipments by Product Class and EL – TSL 1 ........................................................... 9-7

Table 9.5.3 Shipments by Product Class and EL – TSL 2 ........................................................... 9-8

Table 9.5.4 Shipments by Product Class and EL – TSL 3 ........................................................... 9-9

Table 9.5.5 Shipments by Product Class and EL – TSL 4 ......................................................... 9-10
9-1

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a necessary input to calculations of the national 

energy savings (NES) and net present value (NPV), as well as to the manufacturer impact 

analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy 

(DOE) used to project annual product shipments and presents results for each of the commercial 

prerinse spray valve (CPSV) product classes being considered in this analysis. The shipments 

model estimates the shipments of new products in every product class, and for each efficiency 

level (EL). The model starts from a historical base year and calculates, for each year of the 

analysis period, both shipments and retirements. This approach produces an estimate of the total 

product stock, broken down by age or vintage, in each year of the analysis period. The product 

stock distribution is calculated for the no-standards case and for each EL for every product class. 

The stock distribution is used in the national impact analysis (NIA) to estimate the total dollar as 

well as energy and water costs and benefits associated with a proposed standard at each EL. 

The shipments model was developed as a Microsoft Excel® spreadsheet that is accessible 

on DOE’s Appliance and Commercial Equipment Standards website 

(www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=100). Appendix 

10A of this technical support document (TSD) discusses how to access and utilize the shipments 

model spreadsheet, which is integrated into the spreadsheet for the NIA. 

This chapter explains how the shipments model is constructed and provides some 

summary output. Section 9.2 discusses historical shipments. Section 9.3 outlines the approach 

used for disaggregating total CPSV shipments into shipments by product classes and ELs.

Section 9.4 describes the impacts on shipments due to potential new or amended standards. 

Section 9.5 presents the results of the shipments forecasts in the no-new-standards case and at 

each of the standards cases. 

9.2 FORMULATION OF TOTAL SHIPMENTS

DOE developed its shipments projections by analyzing total historical shipments figures 

and trends including all product classes and ELs, and then partitioning shipments into product 

classes and ELs as appropriate. This section describes DOE’s approach for developing total 

CPSV shipments. 

9.2.1 Historical and Current Shipments

Historical shipped units reflect the annual amount of newly-installed CPSV product 

capacity introduced into the marketplace in past years. In the framework stage of this 

rulemaking, DOE requested interested party input and data regarding commercial prerinse spray 

valve shipments. DOE received no data during the Framework public meeting or during the 

comment period. DOE learned in the Framework stage of this rulemaking that the majority of the 

CPSV market is comprised of family-owned companies that prefer to not divulge their shipments 

for competitive reasons. 
9-2

The Environmental Protection Agency’s (EPA) WaterSense® Field Study1 estimated that 

approximately 1.35 million commercial prerinse spray units were in operation as of 2011. An 

industry source provided a series of confidential reports indicating that there were approximately 

200,000 shipments of commercial prerinse spray valves in 2010. 

DOE has assumed an product lifetime distribution for commercial prerinse spray valves, 

discussed in chapter 8 of this TSD. Approximately 10 percent of new units installed are assumed 

to fail and are replaced in the first year. The average lifetime of the product is 5 years; all units 

are assumed to fail by the end of their 10th year of operation. DOE incorporated this variability in 

product lifetime in its shipments analysis.

DOE obtained information from an industry contact suggesting that shipments of 

commercial prerinse spray valves increased by approximately 2 percent annually between 2009 

and 2014. DOE thus modeled total annual shipments growing by 2 percent annually between 

2009 and 2018 (the year of compliance, which is also the year prior to the beginning of the 

analysis period).

9.2.2 Projected Shipments

The Energy Information Administration’s (EIA) Annual Energy Outlook (AEO2014)

2

provides both historical and projected commercial floorspace figures in the United States. 

Between 2018 and 2048 (the last year of the analysis period), the net growth in commercial floor 

space square feet averaged approximately 1 percent annually. To model the future growth of 

CPSV shipments, DOE applied a 1 percent growth to total shipments from 2018–2048 based on 

floor space growth. 

Annual product stock resulting from product shipments projections, when combined with 

the product lifetime distribution, ranges from just over 1 million in 2019 to just under 1.5 million 

in 2048. 

9.3 DISAGGREGATION OF TOTAL SHIPMENTS INTO PRODUCT CLASSES 

AND EFFICIENCY LEVELS

In the shipments analysis, total shipments are broken down into shipments by product 

class, which are then apportioned into ELs according to the no-new-standards case distribution of 

ELs. The development of the no-new-standards case distribution of ELs is discussed in chapter 8 

of this TSD. DOE used data from the Food Service Technology Center3 and DOE’s CCMS 

Database4, and general market research, to develop the no-new-standards case distribution across 

product classes as well as the no-new-standards breakdown of total shipments by efficiency level 

within product classes. Table 9.3.1 presents the relative breakdown of total shipments by product 

class.
9-3

Table 9.3.1 No-New-Standards Case Relative Shipments Breakdown by Product Class

Product Class

Light Duty

% of Shipments

Standard Duty

% of Shipments

Heavy Duty

% of Shipments

Percent of Total Shipments 

Allocated to Product Class 20 50 30

Table 9.3.2 gives the relative percentages of shipments to each EL within all product 

classes. For the light duty product class, DOE found that approximately half of the market is 

already at EL 3. For the standard duty and heavy duty product classes, the majority of the current 

market is at EL 0 or EL 1 levels.

Table 9.3.2 No Standards Case Relative Shipments Breakdown by EL within Product

Classes

EL Light Duty

% of Shipments

Standard Duty

% of Shipments

Heavy Duty

% of Shipments

Baseline 15 40 40

1 35 50 50

2 0 0 5

3 50 10 5

9.4 IMPACT OF POTENTIAL STANDARDS ON SHIPMENTS

As outlined nn the Engineering Analysis found in chapter 5 of this TSD, DOE found that 

more efficient commercial prerinse spray valves are no more costly to produce and therefore not 

more expensive to consumers than products at the baseline ELs. Economic theory ordinarily 

suggests that, all else being equal, an increase in the price of products would lead to a decrease in 

demand for it. However, because there is no change in manufacturer selling price (MSP) or 

installed cost for more efficient products within product classes in this analysis, DOE’s 

shipments analysis does not include a change in total shipments as a result of changes in product 

price.

For its shipments analysis, DOE therefore assumed that any potential amended energy 

conservation standards for commercial prerinse spray valves would not impact the total volume 

of shipments over the analysis period. Rather, in response to the proposed standards, product 

shipments may move from one EL to another, but the total number of units shipped remains the 

same between no-new-standards case and standards cases.

DOE determined that a “roll-up” scenario is most appropriate to establish the distribution 

of efficiencies for the year that compliance with amended CPSV standards would be required. 

Under the “roll-up” scenario, DOE assumes: (1) product efficiencies in the no-new-standards 

case that do not meet the standard level under consideration would “roll-up” to meet the new 

standard level and (2) product efficiencies above the standard level under consideration would 

not be affected.
9-4

At the same time, however, the nature of the market for commercial prerinse spray valves 

makes it possible that consumers may, under the examined trial standard levels (TSLs) and 

product classes, opt to switch product classes to a valve that consumes more water and energy 

than their current commercial prerinse spray valve. In particular, if current choices of products 

correspond to consumers’ optimal products under the current regulatory environment, it is 

probable that some consumers would switch from the standard-duty product class to the 

heavy-duty product class in response to proposed standards, given the lack of restrictions on 

doing so. 

One method with which to model product-class switching is to utilize a decision rule 

wherein consumers switch to products with a flow rate closest in magnitude to the flow rate of 

the products they currently use. This switch to the “nearest neighbor” is based on the previouslymentioned assumption that consumers’ current choices of products corresponds to their optimal 

product under the current regulatory environment.a

 DOE implemented such a rule in the 

shipments model to estimate such consumer choices. See Table 9.4.1 for illustrations of CPSV 

switching under such a proximity-based rule. It is important to note that under such a rule, 

switching only occurs from the standard-duty to the heavy-duty class with the current 

engineering data structure; no switching occurs from the light-duty or heavy-duty classes. Any 

switching to the heavy-duty class involves a move to EL 3 (1.24 gallons per minute) of that 

class, as that product already exists on the market (i.e., its existence does not depend on a new 

standard). 

Table 9.4.1 Product-Class Switching of Consumers with Existing Standard-Duty 

Products

EL of 

Standard

Consumer EL in 

No-Standards 

Case

No Standards Case 

Flow Rate

gallons per minute

Flow Rate Required by 

Standard

gallons per minute

Actual Standards 

Case Flow Rate

gallons per minute

1 

0 1.22 1.10 1.24

1 1.10 1.10 1.10

2 0.97 0.97 0.97

3 0.94 0.94 0.94

2 

0 1.22 0.97 1.24

1 1.10 0.97 0.97

2 0.97 0.97 0.97

3 0.94 0.94 0.94

3 

0 1.22 0.94 1.24

1 1.10 0.94 1.24

2 0.97 0.94 0.94

3 0.94 0.94 0.94

 

a The following text reflects the basic concept: “The first proposition is that each buyer has an innate preference location 

somewhere on the dimension. This innate preference location, sometimes referred to as an ideal point, is the buyer’s true 

preference and the location that he will prefer over all other locations on the dimension. The second proposition is that the 

buyer’s preference ordering of various dimension levels reflects the nearness of the dimension levels to his innate or ideal 

preference level.” (from Reevaluation of Preference Distribution Analysis, James R. Taylor, Journal of Marketing Research, Vol. 

5, No. 4 (Nov., 1968), pp. 434-438).
9-5

9.5 RESULTS

This section presents the shipments projections by product class for the no–newstandards shipments case (without revised standards) as well as for all TSLs that DOE 

considered. Table 9.5.1 through Table 9.5.5 give the shipments for the first year of the analysis 

period through the final year of the analysis period (2019 through 2048).
9-6

Table 9.5.1 Shipments by Product Class and EL – No-New-Standards Scenario

Light Duty

Product Class

Standard Duty

Product Class

Heavy Duty

Product Class

Year EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3

2019 6,931 16,172 0 23,103 46,206 57,758 0 11,552 27,724 34,655 3,465 3,465 

2020 7,000 16,334 0 23,334 46,668 58,336 0 11,667 28,001 35,001 3,500 3,500 

2021 7,070 16,497 0 23,568 47,135 58,919 0 11,784 28,281 35,351 3,535 3,535 

2022 7,141 16,662 0 23,803 47,606 59,508 0 11,902 28,564 35,705 3,570 3,570 

2023 7,212 16,829 0 24,041 48,083 60,103 0 12,021 28,850 36,062 3,606 3,606 

2024 7,285 16,997 0 24,282 48,563 60,704 0 12,141 29,138 36,423 3,642 3,642 

2025 7,357 17,167 0 24,524 49,049 61,311 0 12,262 29,429 36,787 3,679 3,679 

2026 7,431 17,339 0 24,770 49,539 61,924 0 12,385 29,724 37,155 3,715 3,715 

2027 7,505 17,512 0 25,017 50,035 62,544 0 12,509 30,021 37,526 3,753 3,753 

2028 7,580 17,687 0 25,268 50,535 63,169 0 12,634 30,321 37,901 3,790 3,790 

2029 7,656 17,864 0 25,520 51,041 63,801 0 12,760 30,624 38,280 3,828 3,828 

2030 7,733 18,043 0 25,775 51,551 64,439 0 12,888 30,931 38,663 3,866 3,866 

2031 7,810 18,223 0 26,033 52,066 65,083 0 13,017 31,240 39,050 3,905 3,905 

2032 7,888 18,406 0 26,294 52,587 65,734 0 13,147 31,552 39,440 3,944 3,944 

2033 7,967 18,590 0 26,557 53,113 66,391 0 13,278 31,868 39,835 3,983 3,983 

2034 8,047 18,775 0 26,822 53,644 67,055 0 13,411 32,186 40,233 4,023 4,023 

2035 8,127 18,963 0 27,090 54,181 67,726 0 13,545 32,508 40,635 4,064 4,064 

2036 8,208 19,153 0 27,361 54,722 68,403 0 13,681 32,833 41,042 4,104 4,104 

2037 8,290 19,344 0 27,635 55,270 69,087 0 13,817 33,162 41,452 4,145 4,145 

2038 8,373 19,538 0 27,911 55,822 69,778 0 13,956 33,493 41,867 4,187 4,187 

2039 8,457 19,733 0 28,190 56,381 70,476 0 14,095 33,828 42,285 4,229 4,229 

2040 8,542 19,931 0 28,472 56,944 71,180 0 14,236 34,167 42,708 4,271 4,271 

2041 8,627 20,130 0 28,757 57,514 71,892 0 14,378 34,508 43,135 4,314 4,314 

2042 8,713 20,331 0 29,044 58,089 72,611 0 14,522 34,853 43,567 4,357 4,357 

2043 8,800 20,534 0 29,335 58,670 73,337 0 14,667 35,202 44,002 4,400 4,400 

2044 8,888 20,740 0 29,628 59,257 74,071 0 14,814 35,554 44,442 4,444 4,444 

2045 8,977 20,947 0 29,925 59,849 74,811 0 14,962 35,909 44,887 4,489 4,489 

2046 9,067 21,157 0 30,224 60,448 75,559 0 15,112 36,269 45,336 4,534 4,534 

2047 9,158 21,368 0 30,526 61,052 76,315 0 15,263 36,631 45,789 4,579 4,579 

2048 9,249 21,582 0 30,831 61,663 77,078 0 15,416 36,998 46,247 4,625 4,625 
9-7

Table 9.5.2 Shipments by Product Class and EL – TSL 1

Light Duty

Product Class

Standard Duty

Product Class

Heavy Duty

Product Class

Year EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3*

2019 0 23,103 0 23,103 0 57,758 0 11,552 0 62,379 3,465 49,672

2020 0 23,334 0 23,334 0 58,336 0 11,667 0 63,002 3,500 50,169

2021 0 23,568 0 23,568 0 58,919 0 11,784 0 63,632 3,535 50,670

2022 0 23,803 0 23,803 0 59,508 0 11,902 0 64,269 3,570 51,177

2023 0 24,041 0 24,041 0 60,103 0 12,021 0 64,911 3,606 51,689

2024 0 24,282 0 24,282 0 60,704 0 12,141 0 65,561 3,642 52,206

2025 0 24,524 0 24,524 0 61,311 0 12,262 0 66,216 3,679 52,728

2026 0 24,770 0 24,770 0 61,924 0 12,385 0 66,878 3,715 53,255

2027 0 25,017 0 25,017 0 62,544 0 12,509 0 67,547 3,753 53,787

2028 0 25,268 0 25,268 0 63,169 0 12,634 0 68,223 3,790 54,325

2029 0 25,520 0 25,520 0 63,801 0 12,760 0 68,905 3,828 54,869

2030 0 25,775 0 25,775 0 64,439 0 12,888 0 69,594 3,866 55,417

2031 0 26,033 0 26,033 0 65,083 0 13,017 0 70,290 3,905 55,971

2032 0 26,294 0 26,294 0 65,734 0 13,147 0 70,993 3,944 56,531

2033 0 26,557 0 26,557 0 66,391 0 13,278 0 71,703 3,983 57,097

2034 0 26,822 0 26,822 0 67,055 0 13,411 0 72,420 4,023 57,667

2035 0 27,090 0 27,090 0 67,726 0 13,545 0 73,144 4,064 58,244

2036 0 27,361 0 27,361 0 68,403 0 13,681 0 73,875 4,104 58,827

2037 0 27,635 0 27,635 0 69,087 0 13,817 0 74,614 4,145 59,415

2038 0 27,911 0 27,911 0 69,778 0 13,956 0 75,360 4,187 60,009

2039 0 28,190 0 28,190 0 70,476 0 14,095 0 76,114 4,229 60,609

2040 0 28,472 0 28,472 0 71,180 0 14,236 0 76,875 4,271 61,215

2041 0 28,757 0 28,757 0 71,892 0 14,378 0 77,644 4,314 61,827

2042 0 29,044 0 29,044 0 72,611 0 14,522 0 78,420 4,357 62,446

2043 0 29,335 0 29,335 0 73,337 0 14,667 0 79,204 4,400 63,070

2044 0 29,628 0 29,628 0 74,071 0 14,814 0 79,996 4,444 63,701

2045 0 29,925 0 29,925 0 74,811 0 14,962 0 80,796 4,489 64,338

2046 0 30,224 0 30,224 0 75,559 0 15,112 0 81,604 4,534 64,981

2047 0 30,526 0 30,526 0 76,315 0 15,263 0 82,420 4,579 65,631

2048 0 30,831 0 30,831 0 77,078 0 15,416 0 83,244 4,625 66,287

* Includes transfers from Standard Duty Equipment EL 0. 
9-8

Table 9.5.3 Shipments by Product Class and EL – TSL 2

Light Duty

Product Class

Standard Duty

Product Class

Heavy Duty

Product Class*

Year EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3

2019 0 0 23,103 23,103 0 0 57,758 11,552 0 0 65,844 49,672 

2020 0 0 23,334 23,334 0 0 58,336 11,667 0 0 66,503 50,169 

2021 0 0 23,568 23,568 0 0 58,919 11,784 0 0 67,168 50,670 

2022 0 0 23,803 23,803 0 0 59,508 11,902 0 0 67,839 51,177 

2023 0 0 24,041 24,041 0 0 60,103 12,021 0 0 68,518 51,689 

2024 0 0 24,282 24,282 0 0 60,704 12,141 0 0 69,203 52,206 

2025 0 0 24,524 24,524 0 0 61,311 12,262 0 0 69,895 52,728 

2026 0 0 24,770 24,770 0 0 61,924 12,385 0 0 70,594 53,255 

2027 0 0 25,017 25,017 0 0 62,544 12,509 0 0 71,300 53,787 

2028 0 0 25,268 25,268 0 0 63,169 12,634 0 0 72,013 54,325 

2029 0 0 25,520 25,520 0 0 63,801 12,760 0 0 72,733 54,869 

2030 0 0 25,775 25,775 0 0 64,439 12,888 0 0 73,460 55,417 

2031 0 0 26,033 26,033 0 0 65,083 13,017 0 0 74,195 55,971 

2032 0 0 26,294 26,294 0 0 65,734 13,147 0 0 74,937 56,531 

2033 0 0 26,557 26,557 0 0 66,391 13,278 0 0 75,686 57,097 

2034 0 0 26,822 26,822 0 0 67,055 13,411 0 0 76,443 57,667 

2035 0 0 27,090 27,090 0 0 67,726 13,545 0 0 77,207 58,244 

2036 0 0 27,361 27,361 0 0 68,403 13,681 0 0 77,979 58,827 

2037 0 0 27,635 27,635 0 0 69,087 13,817 0 0 78,759 59,415 

2038 0 0 27,911 27,911 0 0 69,778 13,956 0 0 79,547 60,009 

2039 0 0 28,190 28,190 0 0 70,476 14,095 0 0 80,342 60,609 

2040 0 0 28,472 28,472 0 0 71,180 14,236 0 0 81,146 61,215 

2041 0 0 28,757 28,757 0 0 71,892 14,378 0 0 81,957 61,827 

2042 0 0 29,044 29,044 0 0 72,611 14,522 0 0 82,777 62,446 

2043 0 0 29,335 29,335 0 0 73,337 14,667 0 0 83,605 63,070 

2044 0 0 29,628 29,628 0 0 74,071 14,814 0 0 84,441 63,701 

2045 0 0 29,925 29,925 0 0 74,811 14,962 0 0 85,285 64,338 

2046 0 0 30,224 30,224 0 0 75,559 15,112 0 0 86,138 64,981 

2047 0 0 30,526 30,526 0 0 76,315 15,263 0 0 86,999 65,631 

2048 0 0 30,831 30,831 0 0 77,078 15,416 0 0 87,869 66,287 

* Includes transfers from Standard Duty Equipment EL 0. 
9-9

Table 9.5.4 Shipments by Product Class and EL – TSL 3

Light Duty

Product Class

Standard Duty

Product Class

Heavy Duty

Product Class

Year EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3*

2019 0 0 0 46,206 0 0 57,758 11,552 0 0 0 115,516 

2020 0 0 0 46,668 0 0 58,336 11,667 0 0 0 116,671 

2021 0 0 0 47,135 0 0 58,919 11,784 0 0 0 117,838 

2022 0 0 0 47,606 0 0 59,508 11,902 0 0 0 119,016 

2023 0 0 0 48,083 0 0 60,103 12,021 0 0 0 120,206 

2024 0 0 0 48,563 0 0 60,704 12,141 0 0 0 121,408 

2025 0 0 0 49,049 0 0 61,311 12,262 0 0 0 122,622 

2026 0 0 0 49,539 0 0 61,924 12,385 0 0 0 123,849 

2027 0 0 0 50,035 0 0 62,544 12,509 0 0 0 125,087 

2028 0 0 0 50,535 0 0 63,169 12,634 0 0 0 126,338 

2029 0 0 0 51,041 0 0 63,801 12,760 0 0 0 127,601 

2030 0 0 0 51,551 0 0 64,439 12,888 0 0 0 128,877 

2031 0 0 0 52,066 0 0 65,083 13,017 0 0 0 130,166 

2032 0 0 0 52,587 0 0 65,734 13,147 0 0 0 131,468 

2033 0 0 0 53,113 0 0 66,391 13,278 0 0 0 132,783 

2034 0 0 0 53,644 0 0 67,055 13,411 0 0 0 134,110 

2035 0 0 0 54,181 0 0 67,726 13,545 0 0 0 135,452 

2036 0 0 0 54,722 0 0 68,403 13,681 0 0 0 136,806 

2037 0 0 0 55,270 0 0 69,087 13,817 0 0 0 138,174 

2038 0 0 0 55,822 0 0 69,778 13,956 0 0 0 139,556 

2039 0 0 0 56,381 0 0 70,476 14,095 0 0 0 140,951 

2040 0 0 0 56,944 0 0 71,180 14,236 0 0 0 142,361 

2041 0 0 0 57,514 0 0 71,892 14,378 0 0 0 143,784 

2042 0 0 0 58,089 0 0 72,611 14,522 0 0 0 145,222 

2043 0 0 0 58,670 0 0 73,337 14,667 0 0 0 146,675 

2044 0 0 0 59,257 0 0 74,071 14,814 0 0 0 148,141 

2045 0 0 0 59,849 0 0 74,811 14,962 0 0 0 149,623 

2046 0 0 0 60,448 0 0 75,559 15,112 0 0 0 151,119 

2047 0 0 0 61,052 0 0 76,315 15,263 0 0 0 152,630 

2048 0 0 0 61,663 0 0 77,078 15,416 0 0 0 154,156 

*Includes transfers from Standard Duty at Levels EL0 only because TSL 3 for Standard Duty is the same as TSL2. 
9-10 

Table 9.5.5 Shipments by Product Class and EL – TSL 4

Light Duty

Product Class

Standard Duty

Product Class

Heavy Duty

Product Class

Year EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3 EL 0 EL 1 EL 2 EL 3*

2019 0 0 0 46,206 0 0 0 11,552 0 0 0 173,274 

2020 0 0 0 46,668 0 0 0 11,667 0 0 0 175,007 

2021 0 0 0 47,135 0 0 0 11,784 0 0 0 176,757 

2022 0 0 0 47,606 0 0 0 11,902 0 0 0 178,524 

2023 0 0 0 48,083 0 0 0 12,021 0 0 0 180,309 

2024 0 0 0 48,563 0 0 0 12,141 0 0 0 182,113 

2025 0 0 0 49,049 0 0 0 12,262 0 0 0 183,934 

2026 0 0 0 49,539 0 0 0 12,385 0 0 0 185,773 

2027 0 0 0 50,035 0 0 0 12,509 0 0 0 187,631 

2028 0 0 0 50,535 0 0 0 12,634 0 0 0 189,507 

2029 0 0 0 51,041 0 0 0 12,760 0 0 0 191,402 

2030 0 0 0 51,551 0 0 0 12,888 0 0 0 193,316 

2031 0 0 0 52,066 0 0 0 13,017 0 0 0 195,249 

2032 0 0 0 52,587 0 0 0 13,147 0 0 0 197,202 

2033 0 0 0 53,113 0 0 0 13,278 0 0 0 199,174 

2034 0 0 0 53,644 0 0 0 13,411 0 0 0 201,166 

2035 0 0 0 54,181 0 0 0 13,545 0 0 0 203,177 

2036 0 0 0 54,722 0 0 0 13,681 0 0 0 205,209 

2037 0 0 0 55,270 0 0 0 13,817 0 0 0 207,261 

2038 0 0 0 55,822 0 0 0 13,956 0 0 0 209,334 

2039 0 0 0 56,381 0 0 0 14,095 0 0 0 211,427 

2040 0 0 0 56,944 0 0 0 14,236 0 0 0 213,541 

2041 0 0 0 57,514 0 0 0 14,378 0 0 0 215,677 

2042 0 0 0 58,089 0 0 0 14,522 0 0 0 217,834 

2043 0 0 0 58,670 0 0 0 14,667 0 0 0 220,012 

2044 0 0 0 59,257 0 0 0 14,814 0 0 0 222,212 

2045 0 0 0 59,849 0 0 0 14,962 0 0 0 224,434 

2046 0 0 0 60,448 0 0 0 15,112 0 0 0 226,678 

2047 0 0 0 61,052 0 0 0 15,263 0 0 0 228,945 

2048 0 0 0 61,663 0 0 0 15,416 0 0 0 231,235 

* Includes transfers from Standard Duty Product EL 0 and EL 1.
9-11

REFERENCES

1 U.S. Environmental Protection Agency. WaterSense. Pre-Rinse Spray Valve Field Study 

Report. March 2011. Washington DC. 

www.epa.gov/watersense/partners/prsv_background.html#study. 

2 EIA. AEO2014. “Table 5, Commercial Sector Key Indicators and Consumption, Reference 

Case.” http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2014&subject=0-

AEO2014&table=5-AEO2014®ion=0-0&cases=ref2014-d102413a.

3 Food Service Technology Center. Low-Flow Pre-Rinse Spray Valves. 

www.fishnick.com/equipment/sprayvalves/. 

4. DOE. Compliance Certification Database. http://www.regulations.doe.gov/certificationdata/.
10-i

CHAPTER 10. NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ............................................................................................................ 10-1

10.1.1 Alternative Scenarios ............................................................................................ 10-1

10.2 NATIONAL ENERGY AND WATER SAVINGS ......................................................... 10-1

10.2.1 Definition of NES ................................................................................................. 10-2

10.2.2 Definition of NWS ................................................................................................ 10-3

10.2.3 Inputs .................................................................................................................... 10-3

10.2.3.1 Shipments ............................................................................................... 10-4

10.2.3.2 Product Stock ......................................................................................... 10-4

10.2.3.3 Energy and Water Consumption per Unit .............................................. 10-4

10.2.3.4 National Annual Energy and Water Consumption ................................ 10-5

10.2.3.5 Site-to-Primary Energy Conversion Factors .......................................... 10-5

10.2.3.6 Full-Fuel-Cycle Energy Factors ............................................................. 10-6

10.3 NET PRESENT VALUE .................................................................................................. 10-6

10.3.1 Definition .............................................................................................................. 10-6

10.3.2 Inputs .................................................................................................................... 10-8

10.3.2.1 Average Annual Installed Cost per Unit ................................................ 10-8

10.3.2.2 Average Annual Operating Cost Savings per Unit ................................ 10-8

10.3.2.3 Total Annual Increases in Product Cost................................................. 10-8

10.3.2.4 Total Annual Savings in Operating Cost ............................................... 10-9

10.3.2.5 Discount Factor ...................................................................................... 10-9

10.3.2.6 Present Value of Costs ........................................................................... 10-9

10.3.2.7 Present Value of Savings ..................................................................... 10-10

10.4 NATIONAL ENERGY SAVINGS, NATIONAL WATER SAVINGS, AND NET 

PRESENT VALUE RESULTS ...................................................................................... 10-10

10.4.1 Summary of Inputs.............................................................................................. 10-10

10.4.2 Product-Class Switching ..................................................................................... 10-11

10.4.3 National Energy and Water Savings Results ...................................................... 10-11

10.4.4 Consumer Net Present Value Results ................................................................. 10-15

REFERENCES ........................................................................................................................ 10-19
10-ii

LIST OF TABLES

Table 10.2.1 Average Per-Unit Energy and Water Consumption: Light Duty (Spray Force 

≤ 5 ozf) ......................................................................................................... 10-4

Table 10.2.2 Average Per-Unit Energy and Water Consumption: Standard Duty (Spray 

Force > 5 ozf and ≤ 8 ozf) ............................................................................ 10-5

Table 10.2.3 Average Per-Unit Energy and Water Consumption: Heavy Duty (Spray 

Force > 8 ozf) ............................................................................................... 10-5

Table 10.4.1 Inputs to Calculation of National Energy Savings and Net Present Value ......... 10-11

Table 10.4.2 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 1 ............................ 10-12

Table 10.4.3 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 2 ............................ 10-12

Table 10.4.4 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 3 ............................ 10-13

Table 10.4.5 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 4 ............................ 10-13

Table 10.4.6 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 1 ............................ 10-14

Table 10.4.7 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 2 ............................ 10-14

Table 10.4.8 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 3 ............................ 10-14

Table 10.4.9 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 4 ............................ 10-15

Table 10.4.10 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2048 for TSL 1 .......... 10-16

Table 10.4.11 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2048 for TSL 2 .......... 10-16

Table 10.4.12 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2048 for TSL 3 .......... 10-16

Table 10.4.13 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Productss Shipped in 2019–2048 for TSL 4......... 10-17

Table 10.4.14 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 1 .......... 10-17

Table 10.4.15 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 2 .......... 10-17

Table 10.4.16 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 3 .......... 10-18

Table 10.4.17 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 4 .......... 10-18
10-1

CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method the U.S. Department of Energy (DOE) used to conduct 

a national impacts analysis (NIA) of each standard level for commercial prerinse spray valves

(CPSV). DOE evaluated the following impacts: (1) national energy savings (NES) and national 

water savings (NWS) attributable to each possible standard, (2) monetary value of those energy 

and water savings to consumers of the considered products, (3) increased total installed cost of 

the product because of standards, and (4) net present value (NPV) of energy and water savings 

(the difference between the value of energy and water savings and increased total installed cost). 

DOE determined the NES, NWS, and NPV for all the efficiency levels considered for 

commercial prerinse spray valves. DOE performed all calculations for the considered products 

using a Microsoft Excel® spreadsheet model, which is accessible on the Internet at 

http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/54. The 

spreadsheet combines the calculations for determining the NES, NWS, and NPV for each 

considered product class with input from the shipments and life-cycle cost (LCC) models. 

Details and instructions for using the NIA model are provided in appendix 10-A of this technical 

support document (TSD). 

Chapter 9 of this TSD provides a detailed description of the shipments model that DOE 

used to forecast future purchases of the considered products. 

In its analysis, DOE studied three different product classes in detail, as described in 

chapter 3 of this TSD, Market and Technology Assessment. To estimate the national impacts of 

potential standards for the products considered in this rulemaking, DOE applied the product cost 

and annual energy and water consumption estimates to each product class considered in this 

analysis.

10.1.1 Alternative Scenarios

The results in this chapter were calculated using selected inputs from the Reference case 

in the Energy Information Administration’s (EIA’s) Annual Energy Outlook 2014 (AEO2014).1

DOE also calculated NIA results using inputs from the High Economic Growth case and the Low 

Economic Growth case in AEO2014. These results can be viewed in the NIA spreadsheet by 

selecting the high or low growth economic scenario. 

10.2 NATIONAL ENERGY AND WATER SAVINGS

DOE calculated the NES associated with the difference between the no-new-standards 

case and the case associated with each potential standard for the CPSV products considered 

herein. DOE calculated cumulative energy savings throughout the forecast period, which extends 

from 2019 to 2048. DOE used full-fuel-cycle (FFC) energy factors to estimate NES; see section 

10.2.3.6. 
10-2

10.2.1 Definition of NES

The following equation shows that DOE calculated annual NES as the difference between 

two projections: a no-new-standards case (without amended standards) and a standards case. 

Positive values of NES represent energy savings (that is, national annual energy consumption 

(AEC) under a standard is less than in the no-new-standards case).

NOSTD STD NESy AEC AEC _ =

Eq. 10.1 

Where:

NESy = national energy savings in the year y (quadrillion British thermal units (quads)), 

AECNOSTD = annual national energy consumption in the no-new-standards-case scenario (quads), 

and

AECSTD = annual national energy consumption in a standards-case scenario (quads). 

Cumulative energy savings are the sum of annual NES throughout the forecast period, 

which extends over the lifetime of products shipped in 2019-2048. Since the analysis includes

lifetimes of up to 10 years, the evaluation period for cumulative energy savings spans 2019-

2058. 

DOE calculated the national annual site energy consumption by multiplying the number 

or stock of each product class (by vintage) by its unit energy consumption (UEC; also by 

vintage). The calculation of national AEC is represented by the following equation. 

AAAAAAyy = � � (SSSSSS SSVV × UU VV

ff

)

VV

∗ CCCCff

Eq. 10.2

Where:

AECy = national annual energy consumption each year in quadrillion British thermal units 

(quads), summed over vintages of the product stock, STOCKV, and fuel, f, 

STOCKV = stock of products (units) of vintage V that survive in the year for which DOE 

calculated AEC, 

UECV = average energy consumption per unit of product in kilowatt-hours (kWh) or thousand 

British thermal units (kBtu), 

CONVf = fuel-specific conversion from kWh (electricity) or kBtu (natural gas) to quads 

V = year in which the product was purchased as a new unit, 

f = fuel type (either electricity or natural gas), and

y = year in the forecast.

Electricity consumption is subsequently converted from site energy to source energy 

(quads), and from source to FFC energy (quads), by applying time-dependent conversion factors. 

Similarly, natural gas consumption is converted from site to FFC energy (quads) by applying 

time-dependent conversion factors. These conversions are discussion in sections 10.2.3.5 and 

10.2.3.6. 
10-3

The stock of products depends on annual shipments and the product lifetime. As 

described in chapter 9 of this TSD, DOE projected shipments under the no-new-standards case 

and the standards cases. 

10.2.2 Definition of NWS

The derivation of NWS closely parallels that for the NES. The following equation shows 

that DOE calculated annual NWS as the difference between two projections: a no-new-standards

case (without amended standards) and a standards case. Positive values of NWS represent water 

savings (that is, national annual water consumption (AWC) under a standard is less than in the 

no-new-standards case).

NOSTD STD NWSy AWC AWC _ =

Eq. 10.3 

Where:

NWSy = national water savings in the year y (billions of gallons), 

AWCNOSTD = annual national water consumption in the no-new-standards-case scenario (billions 

of gallons), and 

AWCSTD = annual national water consumption in a standards-case scenario (billions of gallons). 

Cumulative water savings are the sum of annual NWS throughout the forecast period, 

which extends over the lifetime of products shipped in 2019-2048. Since the analysis includes 

lifetimes of up to 10 years, the evaluation period for cumulative water savings spans 2019-2058. 

DOE calculated the national AWC by multiplying the number or stock of each product 

class (by vintage) by its unit water consumption (UWC; also by vintage). The calculation of 

national AWC is represented by the following equation. 

AAAAAAyy = � (SSSSSS SSVV × UU VV)

VV

∗ CCCC

Eq. 10.4 

Where:

AWCy = national annual water consumption each year in billions of gallons, summed over 

vintages of the product stock, STOCKV, 

STOCKV = stock of products (units) of vintage V that survive in the year for which DOE 

calculated AWC, 

UWCV = average water consumption per unit of product in thousands of gallons (kGal),

CONV = conversion from kGal to billions of gallons, 

V = year in which the product was purchased as a new unit, and

y = year in the forecast.

10.2.3 Inputs
10-4

The inputs to the calculation of NES and NWS are:

• shipments, 

• product stock (STOCKV),

• average energy consumption per unit (UEC) and average water consumption per unit 

(UWC), 

• national AEC and national AWC, and

• conversion factors (units conversion factors, as well as energy conversions between site, 

source, and FFC energy). 

10.2.3.1 Shipments

DOE forecasted shipments of each considered product class under the no-new-standards 

case and each of the standards cases. The method DOE used to calculate and generate the 

shipments forecasts for each considered product class is described in detail in chapter 9 of this 

TSD, Shipments Analysis. 

10.2.3.2 Product Stock

The product stock in a given year is the number of each type of product shipped from 

earlier years that survive in that year. The NIA model tracks the number of units shipped each 

year. DOE assumes that commercial prerinse spray valves have an increasing probability of 

retiring as they age. The probability of survival as a function of years since purchase is the 

survival function. Chapter 8 of this TSD provides additional details on the survival functions 

used by DOE. 

10.2.3.3 Energy and Water Consumption per Unit

DOE used average energy and water consumption per unit data from the LCC analysis. 

The average per-unit energy and water consumption for each product category is shown in Table 

10.2.1 through Table 10.2.3.

Table 10.2.1 Average Per-Unit Energy and Water Consumption: Light Duty (Spray Force 

≤ 5 ozf)

Efficiency Level

0 1 2 3 

Unit electricity use 

kWh 897.86 850.60 803.34 767.90

Unit natural gas use 

kBtu 8,335.80 7,897.07 7,458.35 7,129.30

Unit water use

kGal 21.14 20.02 18.91 18.08
10-5

Table 10.2.2 Average Per-Unit Energy and Water Consumption: Standard Duty (Spray 

Force > 5 ozf and ≤ 8 ozf)

Efficiency Level

0 1 2 3 

Unit electricity use 

kWh 1,441.29 1,299.53 1,145.95 1,110.51

Unit natural gas use 

kBtu 13,381.15 12,064.97 10,639.11 10,310.07

Unit water use

kGal 33.93 30.59 26.98 26.14

Table 10.2.3 Average Per-Unit Energy and Water Consumption: Heavy Duty (Spray Force 

> 8 ozf)

Efficiency Level

0 1 2 3 

Unit electricity use 

kWh 1,890.22 1,701.20 1,512.18 1,464.92

Unit natural gas use 

kBtu 17,549.05 15,794.15 14,039.24 13,600.51

Unit water use

kGal 44.50 40.05 35.60 34.48

10.2.3.4 National Annual Energy and Water Consumption

The national AEC is the product of the average energy consumption per unit and the 

number of units of each vintage. The national AWC is the product of the average water 

consumption per unit and the number of units of each vintage. In determining national AEC, 

DOE first calculated AEC at the site, and then applied conversion factors, described in sections 

10.2.3.5 and 10.2.3.6, to calculate source and FFC energy consumption, respectively. 

10.2.3.5 Site-to-Primary Energy Conversion Factors 

In determining national AEC, DOE initially calculated the AEC at the site (for electricity, 

the energy in kWh consumed at the establishment). DOE then used site energy consumption to 

calculate primary (source) energy consumption by applying a conversion factor to account for 

losses associated with the generation, transmission, and distribution of electricity. The 

conversion factor is a multiplicative factor used to convert site energy consumption into primary 

or source energy consumption, expressed in quads (quadrillion Btu). DOE used annual 
10-6

conversion factors based on the version of the National Energy Modeling System (NEMS)a that 

corresponds to AEO2014. 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 (i.e., the types of power plants 

projected to provide electricity to the Nation). The values derived from the AEO2014 NEMS end 

in 2040. DOE assumed that conversion factors remain at the 2040 values throughout the rest of 

the forecast.

10.2.3.6 Full-Fuel-Cycle Energy Factors

The FFC measures 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 calculate the FFC by incorporating 

the energy consumed in extracting, processing, and transporting or distributing primary fuels, 

referred to as upstream activities, DOE developed FFC multipliers using the data and projections 

generated by the NEMS used for AEO 2014. The AEO 2014 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 help define a set of parameters that 

represents the energy intensity of energy production. 

10.3 NET PRESENT VALUE

DOE calculated the NPV of the increased product cost and reduced operating cost 

associated with the difference between the no-new-standards case and each potential standards 

case for the considered commercial prerinse spray valve product classes. 

10.3.1 Definition

The NPV is the current value of a time-series of costs and savings. The NPV is described 

by the equation: 

NPV PVCPVS

_ =

Eq. 10.5 

Where:

PVS = present value of savings in operating cost, and 

PVC = present value of increased total product cost to consumers. 

DOE determined the PVS and PVC according to the following expressions. 

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

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

DOE/EIA-0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the 

model without any modification to code or data. Because this analysis entails some minor code modifications and 

the model is run under various policy scenarios that are variations on EIA assumptions, DOE refers to the model by 

the name NEMS-BT (BT is DOE’s Building Technologies Program, under whose aegis this work has been 

performed). NEMS-BT was previously called NEMS-BRS.
10-7

PVS ∑ ×= DFOCS yy

Eq. 10.6 

PVC ∑TIC ×= DFyy

Eq. 10.7 

Where:

OCS = total annual savings in operating cost each year summed over vintages of the product 

stock, STOCKV, 

TIC = total annual increases in product cost each year summed over years of the product 

shipments, SHIPy, 

DF =discount factor in each year, and

y = year in the forecast.

DOE calculated the total annual consumer savings in operating cost by multiplying the 

number or stock of a given product class (by vintage) by its per-unit operating cost savings (also 

by vintage). DOE calculated the total annual increases in consumer product cost by multiplying 

the number of shipments of the given product class (by vintage) by its per-unit increase in 

consumer product cost (also by vintage). The calculation of total annual operating cost savings 

and total annual product cost increases is represented by the following equations.

OCSy = ∑STOCKV ×UOCSV

Eq. 10.8 

TICy = ∑SHIPy×UTICy

Eq. 10.9 

Where:

STOCKV = stock of product of vintage V that survive in the year for which DOE calculated AEC, 

UOCSV = annual per-unit savings in operating cost,

V = year in which the product was purchased as a new unit, 

SHIPy = shipments of products in year y, and

UTICy = annual per-unit increase in installed product cost in year y. 

DOE determined the total increased product cost for each year from the effective date of 

a potential standard (2019) to 2048,b as well as the present value of operating cost savings for 

each year from the effective date of the standard to the year when all units purchased through 

2048 have been retired. DOE calculated costs and savings as the difference between a standards 

case and a no-new-standards case without amended standards. 

 

b Because the anticipated compliance date is late in the year 2018, for analytical purposes, DOE conducted its 

analyses utilizing shipments associated with the 2019–2048 period. The analytical effect is equivalent to the use of a 

2019 compliance year.
10-8

DOE developed a discount factor from the national discount rate (see section 10.3.2.5) 

and the number of years between the present (i.e., year to which the sum is being discounted) and 

the year in which the costs and savings occur. The NPV is the sum over time of the discounted 

net savings. 

10.3.2 Inputs

The inputs to calculation of the NPV are: 

• average annual per-unit product installed cost, 

• average annual per-unit savings in operating cost, 

• total annual increases in product cost, 

• total annual savings in operating cost, 

• discount factor, 

• present value of costs, and

• present value of savings.

10.3.2.1 Average Annual Installed Cost per Unit

The average annual installed cost was obtained as an input from the LCC analysis. In the 

case of commercial prerinse spray valves, the cost is constant, regardless of efficiency level, 

product class, and year of the analysis. The average annual installed cost, in 2014 dollars, is 

$79.04. 

10.3.2.2 Average Annual Operating Cost Savings per Unit

The average annual operating costs include the costs for energy, water, repair, and 

maintenance. As with the installed cost, the LCC analysis provided these data as inputs to the 

NIA. As described in chapter 8 of this TSD, for all the considered products DOE assumed there

would be no change in repair and maintenance costs with increased efficiency levels. Therefore, 

for all the considered products, DOE determined the per-unit annual savings in operating cost 

based on the savings in energy and water costs attributable to a standard. DOE determined the 

per-unit annual savings in operating cost by multiplying the per-unit annual savings in energy 

and water consumption developed for each product class by the appropriate energy and water 

price, respectively. As described in chapter 8 of this TSD, DOE forecasted energy prices based 

on projected average annual price changes in EIA’s AEO2014. As also described in chapter 8 of 

this TSD, to derive water prices in future years, DOE developed forecasts utilizing the historical 

Consumer Price Index for water and sewerage maintenance.

10.3.2.3 Total Annual Increases in Product Cost

The total annual increase in product cost for any given standards case is the product of 

the average cost increase per unit due to the standard, and the number of units of each vintage 

shipped. This method accounts for differences in product cost from year to year. The equation 

for determining the total annual increase in product cost for a given standards case, which is 

shown in section 10.3.1, is repeated here.
10-9

TICy = ∑SHIPy×UTICy

Eq. 10.10 

However, as discussed in section 10.3.2.1, the costs of commercial prerinse spray valves 

do not vary with efficiency level, product class, or year. As a result, there are no increases in 

product costs associated with increased efficiency levels in the standards case. 

10.3.2.4 Total Annual Savings in Operating Cost 

The total annual savings in operating cost for any given standards case is the product of 

the annual savings in operating cost per unit attributable to the standard, and the number of units 

of each vintage. This method accounts for differences in annual savings in operating cost from 

year to year. The equation for determining the total annual savings in operating cost for a given 

standards case, which was presented in section 10.3.1, is repeated here.

OCS =∑STOCKV ×UOCSV

Eq. 10.11 

10.3.2.5 Discount Factor 

DOE multiplied monetary values in future years by a discount factor to determine the 

present value. The discount factor (DF) is described by the equation: 

)( )1(

1

y yp r

DF − + =

Eq. 10.12 

Where:

r = discount rate, 

y = year in which the monetary value exists, and

yP = year in which the present value is being determined. 

Although DOE used consumer discount rates to determine the life-cycle cost of 

commercial prerinse spray valves (chapter 8 of this TSD), it used national discount rates to 

calculate national NPV. DOE estimated NPV using both a 3-percent and a 7-percent real 

discount rate, in accordance with the Office of Management and Budget’s guidance to Federal 

agencies on the development of regulatory analysis, particularly section E therein, Identifying 

and Measuring Benefits and Costs.

2 DOE defined the present year as 2015. 

10.3.2.6 Present Value of Costs

The present value of increased product costs is the annual total cost increase in each year 

(the difference between a standards case and the no-new-standards case), discounted to the 

present, and summed throughout the period in which DOE is considering the installation of 

products (2019 through 2048). DOE calculated annual increases in installed cost as the 
10-10

difference in total product cost for new products purchased each year, multiplied by the 

shipments in that year. 

10.3.2.7 Present Value of Savings

The present value of savings in operating cost is the annual savings in operating cost (the 

difference between the no-new-standards case and a standards case), discounted to the present 

and summed from the effective date to the time when the last unit installed in 2048 is retired 

from service (2058). Savings are decreases in operating cost associated with the higher energy 

efficiency of products purchased in the standards case compared to the no-new-standards case. 

Total annual savings in operating cost are the savings per unit multiplied by the number of units 

of each vintage that survive in a particular year. 

10.4 NATIONAL ENERGY SAVINGS, NATIONAL WATER SAVINGS, AND NET 

PRESENT VALUE RESULTS

The NIA model produces estimates of the NES, NWS, and NPV attributable to a given 

efficiency level. The inputs to the NIA model were discussed in sections 10.2.3 (inputs to NES

and NWS) and 10.3.2 (inputs to NPV). DOE generated the NES, NWS and NPV results using a 

Microsoft Excel spreadsheet, which is accessible on the Internet at 

http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/54. Details 

regarding and instructions for using the spreadsheet are provided in appendix 10-A. 

10.4.1 Summary of Inputs

Table 10.4.1 summarizes the inputs to the NIA model. 
10-11

Table 10.4.1 Inputs to Calculation of National Energy Savings and Net Present Value 

Input Description

Shipments and stock Annual shipments and stock obtained or derived from shipments model. (See 

chapter 9.)

First year of analysis period 2019 (See footnote b.)

Annual energy and water 

consumption per unit

Provided, by efficiency level and product class, as inputs from the LCC 

analysis. (See chapter 8.)

Total installed cost per unit Provided as input from the LCC analysis. Constant for all years, efficiency 

levels, and product classes. (See chapter 8.)

Annual energy and combined 

water costs per unit

Provided, by efficiency level and product class, as inputs from the LCC 

analysis. Products of annual energy and water consumption per unit, and their 

respective prices. (See chapter 8.)

Repair and maintenance costs 

per unit

Repair cost and maintenance costs are the same at all efficiency levels. (See 

chapter 8.) 

Forecast of energy prices EIA AEO2014 forecasts (to 2040) and extrapolation thereafter. (See chapter 8.)

Forecast of water and 

wastewater prices

Historic Consumer Price Index for water-related expenditures extrapolated out 

to 2058. (See chapter 8.)

Electricity site-to-source 

conversion factors 

A time-series conversion factor that includes electric generation, transmission, 

and distribution losses. Conversion, which changes yearly, is generated by 

DOE-EIA’s NEMS program.

FFC conversion factors

Time-series conversion factors that convert source electricity and site natural 

gas to full-fuel-cycle energy consumption. These factors incorporate the energy 

consumed in extracting, processing, and transporting or distributing fuels. DOE 

developed FFC multipliers using the data and projections generated by DOEEIA’s NEMS program. (See chapter 10-B).

Discount rates 3% and 7% real.

Present year Future expenses are discounted to 2015.

10.4.2 Product-Class Switching

The nature of the market for commercial prerinse spray valves makes it possible that 

consumers may, under examined technical standard levels (TSLs) and product classes, opt to 

switch product classes to a valve that consumes more water and energy than their current 

products. DOE implemented a mechanism in the shipments model to estimate such consumer 

choices. The economics resulting from product-class switching may result in lower optimal 

efficiency levels and reduced estimates of water and energy savings, as compared to the case 

without class switching. A discussion of DOE’s method to model product class switching is 

contained in chapter 9 of this TSD.

10.4.3 National Energy and Water Savings Results

This section provides the cumulative national energy and water savings that DOE 

calculated for each of the TSLs analyzed for commercial prerinse spray valves. 

Note that although the total NES and NWS are positive for every TSL, no TSL 

technically provides positive savings for all product classes. This is due to the anticipated 

standards-induced product-class switching to heavy-duty products by certain consumer groups in 
10-12

the standard-duty product class. The resulting shifts in standards-case shipments yield negative 

energy savings for the heavy-duty product class at every TSL. Those negative savings are 

entirely attributable to the shifting of consumers from the standard-duty product class. Absent the 

effects of this switching, the NES and NWS for the heavy-duty product class are positive at all 

efficiency levels, with the highest efficiency level maximizing the savings. Due to product-class 

switching, the results of the standard and heavy-duty product classes actually need to be judged 

in combination. 

Table 10.4.2 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 1

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

1 

Light Duty

(≤5 ozf) 0.001 0.001 1.305

Standard Duty

(>5 ozf and ≤8 ozf) 0.206 0.223 265.371

Heavy Duty

(>8 ozf) (0.193) (0.209) (248.840)

TOTAL TSL 1 0.014 0.015 17.836

* quads = quadrillion British thermal units.

Table 10.4.3 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 2

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

2 

Light Duty

(≤5 ozf) 0.004 0.005 5.655 

Standard Duty

(>5 ozf and ≤8 ozf) 0.234 0.252 300.718 

Heavy Duty

(>8 ozf) (0.157) (0.169) (201.856)

TOTAL TSL 2 0.081 0.088 104.517 

* quads = quadrillion British thermal units.
10-13

Table 10.4.4 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 3

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

3 

Light Duty

(≤5 ozf) 0.007 0.007 8.918 

Standard Duty

(>5 ozf and ≤8 ozf) 0.234 0.252 300.718 

Heavy Duty

(>8 ozf) (0.147) (0.159) (189.458)

TOTAL TSL 3 0.093 0.101 120.178 

* quads = quadrillion British thermal units.

Table 10.4.5 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2048 for TSL 4

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

4 

Light Duty

(≤5 ozf) 0.007 0.007 8.918 

Standard Duty

(>5 ozf and ≤8 ozf) 0.439 0.474 564.457 

Heavy Duty

(>8 ozf) (0.409) (0.442) (526.609)

TOTAL TSL 4 0.036 0.039 46.766 

* quads = quadrillion British thermal units.

OMB Circular A-4 requires agencies to present analytical results, including separate 

schedules of the monetized benefits and costs that show the type and timing of benefits and 

costsc

. Circular A-4 also directs agencies to consider the variability of key elements underlying 

the estimates of benefits and costs. For this rulemaking, DOE undertook a sensitivity analysis 

using nine, rather than 30, years of product shipments. The choice of a nine-year period is a 

proxy for the timeline in the Energy Policy and Conservation Act (EPCA) for the review of 

certain energy conservation standards and potential revision of and compliance with such revised 

standards.d The review timeframe established in EPCA is generally not synchronized with the 

product lifetime, product manufacturing cycles, or other factors specific to CPSV products. Thus, 

such results are presented for informational purposes only and are not indicative of any change in 

DOE’s analytical methodology. Table 10.4.6 through Table 10.4.9Table report cumulative 

 

c U.S. Office of Management and Budget, “Circular A-4: Regulatory Analysis,” section E, (Sept. 17, 2003)

(Available at: http://www.whitehouse.gov/omb/circulars_a004_a-4/) 

d EPCA requires DOE to review its standards at least once every 6 years, and requires, for certain products, a 3-year 

period after any new standard is promulgated before compliance is required, except that in no case may any new 

standards be required within 6 years of the compliance date of the previous standards. (42 U.S.C. 6313(a)(6)(C)) 

While adding a 6-year review to the 3-year compliance period adds up to 9 years, DOE notes that it may undertake 

reviews at any time within the 6-year period and that the 3-year compliance date may yield to the 6-year backstop. A 

9-year analysis period may not be appropriate given the variability that occurs in the timing of standards reviews and 

the fact that for some consumer products, the compliance period is 5 years rather than 3 years.
10-14

national energy and water savings associated with this shorter analysis period of 2019-2027. The 

impacts are counted over the lifetime of products purchased during this period. 

Table 10.4.6 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 1

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

1 

Light Duty

(≤5 ozf) 0.000 0.000 0.352 

Standard Duty

(>5 ozf and ≤8 ozf) 0.057 0.062 71.472 

Heavy Duty

(>8 ozf) (0.054) (0.058) (67.019)

TOTAL TSL 1 0.004 0.004 4.804 

* quads = quadrillion British thermal units.

Table 10.4.7 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 2 

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

2 

Light Duty

(≤5 ozf) 0.001 0.001 1.523 

Standard Duty

(>5 ozf and ≤8 ozf) 0.065 0.070 80.992 

Heavy Duty

(>8 ozf) (0.044) (0.047) (54.365)

TOTAL TSL 2 0.023 0.024 28.149 

* quads = quadrillion British thermal units.

Table 10.4.8 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 3 

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

3 

Light Duty

(≤5 ozf) 0.002 0.002 2.402 

Standard Duty

(>5 ozf and ≤8 ozf) 0.065 0.070 80.992 

Heavy Duty

(>8 ozf) (0.041) (0.044) (51.026)

TOTAL TSL 3 0.026 0.028 32.367 

* quads = quadrillion British thermal units.
10-15

Table 10.4.9 Commercial Prerinse Spray Valves: Cumulative National Energy and Water 

Savings for Products Shipped in 2019–2027 for TSL 4

TSL Product Class

National Energy Savings

quads* National Water 

Savings

Primary FFC billion gal

4 

Light Duty

(≤5 ozf) 0.002 0.002 2.402 

Standard Duty

(>5 ozf and ≤8 ozf) 0.122 0.131 152.024 

Heavy Duty

(>8 ozf) (0.114) (0.122) (141.830)

TOTAL TSL 4 0.010 0.011 12.595 

* quads = quadrillion British thermal units.

10.4.4 Consumer Net Present Value Results

Table 10.4.10 through Table 10.4.13 show the cumulative NPVs associated with each of

the considered TSLs for each commercial prerinse spray valves product class.

As with the NES and NWS, product-class switching significantly impacts the NPV 

results. Although the total NPV is positive for every TSL, no TSL technically provides positive 

NPVs for all product classes. This is due to the anticipated standards-induced switching to 

heavy-duty products by certain consumer groups in the standard-duty product class. The 

resulting shifts in standards-case shipments yield negative NPVs for the heavy-duty product class 

at every TSL. Those negative results are entirely attributable to the shifting of consumers from 

the standard-duty product class. Absent the effects of this switching, the NPVs for the heavyduty product class are positive at all efficiency levels, with the highest efficiency level 

maximizing the NPV. Due to product-class switching, the results of the standard and heavy-duty 

product classes actually need to be judged in combination. 
10-16

Table 10.4.10 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2048 for TSL 1

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

1 

Light Duty

(≤5 ozf) $0.008 $0.016

Standard Duty

(>5 ozf and ≤8 ozf) $1.604 $3.295

Heavy Duty

(>8 ozf) ($1.507) ($3.095) 

TOTAL TSL 1 $0.105 $0.216

Table 10.4.11 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2048 for TSL 2

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

2 

Light Duty

(≤5 ozf) $0.033 $0.069

Standard Duty

(>5 ozf and ≤8 ozf) $1.813 $3.724

Heavy Duty

(>8 ozf) ($1.230) ($2.524) 

TOTAL TSL 2 $0.616 $1.269

Table 10.4.12 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2048 for TSL 3

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

3 

Light Duty

(≤5 ozf) $0.053 $0.108 

Standard Duty

(>5 ozf and ≤8 ozf) $1.813 $3.724 

Heavy Duty

(>8 ozf) ($1.157) ($2.374) 

TOTAL TSL 3 $0.708 $1.459 
10-17

Table 10.4.13 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Productss Shipped in 2019–2048 for TSL 4

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

4 

Light Duty

(≤5 ozf) $0.053 $0.108 

Standard Duty

(>5 ozf and ≤8 ozf) $3.418 $7.018 

Heavy Duty

(>8 ozf) ($3.195) ($6.559) 

TOTAL TSL 4 $0.276 $0.568 

As described previously in the discussion of the energy and water savings results, DOE 

also determined financial impacts for a sensitivity case utilizing a nine-year analysis period. 

Table 10.4.14 through Table 10.4.17 report NPV results associated with this shorter analysis 

period. The impacts are counted over the lifetime of products purchased in 2019–2027. As 

mentioned previously, this information is presented for informational purposes only and is not 

indicative of any change in DOE’s analytical methodology or decision criteria.

Table 10.4.14 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 1 

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

1 

Light Duty

(≤5 ozf) $0.003 $0.005 

Standard Duty

(>5 ozf and ≤8 ozf) $0.708 $1.034 

Heavy Duty

(>8 ozf) ($0.665) ($0.971)

TOTAL TSL 1 $0.046 $0.068 

Table 10.4.15 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 2 

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

2 

Light Duty

(≤5 ozf) $0.015 $0.021 

Standard Duty

(>5 ozf and ≤8 ozf) $0.800 $1.168 

Heavy Duty

(>8 ozf) ($0.544) ($0.793)

TOTAL TSL 2 $0.271 $0.397 
10-18

Table 10.4.16 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 3 

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

3 

Light Duty

(≤5 ozf) $0.023 $0.034 

Standard Duty

(>5 ozf and ≤8 ozf) $0.800 $1.168 

Heavy Duty

(>8 ozf) ($0.511) ($0.746)

TOTAL TSL 3 $0.312 $0.456 

Table 10.4.17 Commercial Prerinse Spray Valves: Cumulative Net Present Value of 

Consumer Benefits for Products Shipped in 2019–2027 for TSL 4 

TSL Product Class

Net Present Value

billion $2014

7-Percent

Discount Rate

3-Percent

Discount Rate

4 

Light Duty

(≤5 ozf) $0.023 $0.034 

Standard Duty

(>5 ozf and ≤8 ozf) $1.509 $2.203 

Heavy Duty

(>8 ozf) ($1.411) ($2.059)

TOTAL TSL 4 $0.121 $0.177 
10-19

REFERENCES

1. Energy Information Administration. Annual Energy Outlook 2014 with Projections to 

2040. April 2014. U.S. Department of Energy, Washington, DC. 

2. Office of Management and Budget. OMB Circular A-4, Regulatory Analysis. 2003. OMB: 

Washington, D.C. September 17, 2003. 

<www.whitehouse.gov/omb/assets/omb/circulars/a004/a-4.pdf> 
10A-i 

APPENDIX 10A. USER INSTRUCTIONS FOR NIA AND SHIPMENTS 

SPREADSHEET

TABLE OF CONTENTS

10A.1 INTRODUCTION ...................................................................................................... 10A-1

10A.2 WORKSHEET SUMMARY ...................................................................................... 10A-1

10A.3 BASIC INSTRUCTIONS ........................................................................................... 10A-2
10A-1 

APPENDIX 10A. USER INSTRUCTIONS FOR NIA AND SHIPMENTS SPREADSHEET

10A.1 INTRODUCTION

The results obtained in this analysis can be examined and reproduced using a Microsoft

Excel spreadsheet available on the Internet at 

http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/54. 

The spreadsheet enables the user to perform the Department of Energy’s (DOE) national 

impact analysis (NIA) for the three product classes of commercial prerinse spray valves. Also 

included in the file are the calculations utilized to derive annual shipments and stock data. The 

spreadsheet was designed and tested using Excel 2010.

10A.2 WORKSHEET SUMMARY

The NIA spreadsheet consists of the following worksheets: 

Instructions Contains a table of contents with a short description of each 

included worksheet, as well as brief instructions for operating the 

spreadsheet.

Generate and View Results Contains two buttons that trigger macros used to generate results. 

The first button generates inputs for the NIAplus model. These are 

written to the “for NIAplus” worksheet. The second button 

populates tables on the “Generate and View Results” worksheet 

with the NIA results. The results of each of these macros depend 

on user-selected settings on the “Calculations” worksheet.

for NIAplus Contains data for use in the NIAplus model. 

for ImSET Contains calculations used to derive inputs for the Impact of Sector 

Energy Technologies (ImSET) model.

Calculations Contains user inputs as well as most of the calculations necessary 

to derive the results of the NIA. This worksheet also contains 

calculations utilized to populate the “for NIAplus” worksheet and 

the “for ImSET worksheet. 

ConvFactors Contains the following energy conversion factors used in the NIA: 

electricity site-to-source, electricity source-to-full-fuel-cycle 

(FFC), and natural gas site-to-FFC.

Shipments Contains estimated historical and projected future shipments and 

stock of commercial prerinse spray valves, by product class and 

year. This worksheet includes a distribution of the expected 

lifetimes of commercial prerinse spray valves, and also contains 
10A-2 

editable definitions of the trial standard levels (TSLs) in terms of 

efficiency levels for each product class (in the “TSL/EL Lookup 

Table”).

Inputs from LCC Contains required data from the life-cycle cost (LCC) spreadsheet, 

including:

• total installed cost;

• annual operating costs by product class, efficiency level,

and economic growth scenario;

• annual water use by product class and efficiency level;

• annual site electricity use by product class and efficiency

level;

• annual site natural gas use by product class and efficiency

level;

• annual water operating costs by product class, efficiency

level, and economic growth scenario;

• annual electricity operating costs by product class,

efficiency level, and economic growth scenario; and

• annual natural gas operating costs by product class,

efficiency level, and economic growth scenario.

10A.3 BASIC INSTRUCTIONS

Basic instructions for operating the NIA spreadsheet are as follows:

1) Once the NIA spreadsheet has been downloaded from the Internet, open the file using

Excel.

2) Use Excel’s view/zoom commands at the top menu bar to change the size of the

display to make it fit your monitor.

3) On the “Calculations” worksheet, the user can optionally change the following

settings:

a) Analysis Start Year: This is used to select the year standards will come into effect.

The default is 2019.

b) Reference/Discount Year: This is used to select the year to which all monetary

values are discounted. The default is 2015.

c) $ Year: This drives the labeling of dollar amounts within the spreadsheet. The

default is 2014.

d) TSL: This is used to select the TSL used in calculations.
10A-3 

e) Discount Rate (Real): This is used to set the rate at which future monetary values 

are discounted to the Reference (or Discount) Year. 

f) Economic Growth Scenario: This selects the desired growth level (Reference, 

High, or Low). The scenarios refer to the Energy Information Administration’s 

(EIA’s) AEO2014 scenarios.

g) Shipments Scalar: This provides the option to quickly scale shipments and stock 

values by the factor entered in this cell. This is generally utilized only for model 

testing, and should typically be left at 1.00. It is also used for analysis of the 

impacts of tax credits and rebates, for the Regulatory Impact Analysis (RIA).

h) Product Class Switching: This provides the option to exclude product-class 

switching from the analysis. The default is “On.” “Off” is used for analysis of 

the impacts of tax credits and rebates, for the RIA.

4) Population of the NIA results tables and derivation of the inputs for NIAplus is 

performed on the “Generate and View Results” worksheet. This worksheet contains 

two buttons that trigger macros used to generate results. The first button generates 

outputs for the NIAplus model. These outputs are written to the “for NIAplus” 

worksheet. The second button populates tables on the “Generate and View Results” 

worksheet with the NIA results. The results of each of these macros depend on some 

of the user-selected settings on the “Calculations” worksheet.

5) Note that the settings for TSL and Discount Rate do not affect macro-based 

generation of the NIA results via a button on the “Generate and View Results” 

worksheet. The macro code automatically cycles through all the relevant TSL and 

discount rate combinations to populate the NIA results on the “Generate and View 

Results” worksheet. Similarly, the macro code triggered by another button on the 

“Generate and View Results” worksheet automatically cycles through all of the 

relevant TSL, discount rate, and economic growth scenarios for the NIAplus inputs 

generated on the “for NIAplus” worksheet. 
10B-i 

APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

TABLE OF CONTENTS

10B.1 INTRODUCTION .......................................................................................................10B-1

10B.2 METHODOLOGY ......................................................................................................10B-1

10B.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS .....................................................10B-3

REFERENCES ........................................................................................................................10B-4

LIST OF TABLES

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs..............................................10B-3

Table 10B.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO2014) ..............................10B-3
10B-1 

APPENDIX 10B. FULL-FUEL-CYCLE MULTIPLIERS

10B.1 INTRODUCTION

This appendix summarizes the methods used to calculate full-fuel-cycle (FFC) energy 

savings expected to result from amended standards. The FFC measure includes point-of-use 

(site) energy; the energy losses associated with generation, transmission, and distribution of 

electricity; and the energy consumed in extracting, processing, and transporting or distributing 

primary fuels. DOE’s traditional approach encompassed only site energy and the energy losses 

associated with generation, transmission, and distribution of electricity. Per DOE’s 2011 

Statement of Policy for Adopting Full Fuel Cycle Analyses, DOE now uses FFC measures of 

energy use and emissions in its energy conservation standards analyses.1

 This appendix 

summarizes the methods used to incorporate the FFC impacts into the analysis.

This analysis uses several different terms to reference energy use. The physical sources of 

energy are the primary fuels such as coal, natural gas, and liquid fuels. Primary energy is equal to 

the heat content (BTU) of the primary fuels used to provide an end-use service. Site energy use is 

defined as the energy consumed at the point-of-use in a building or industrial process. Where 

natural gas and petroleum fuels are consumed at the site (e.g., in a furnace), site energy is 

identical to primary energy, with both equal to the heat content of the primary fuel consumed. 

For electricity, site energy is measured in kilowatt hours (kWh). In this case, the primary energy 

is equal to the quads of primary energy required to generate and deliver the site electricity. This 

primary energy is calculated by multiplying the site kWh times the power sector primary energy 

use factor. For the FFC analysis, the upstream energy use is defined as the energy consumed in 

extracting, processing, and transporting or distributing primary fuels. FFC energy use is the sum 

of primary plus upstream energy use. 

Both primary fuels and electricity are used in upstream activities. The treatment of 

electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels 

and uranium, and electricity generated from renewable fluxes (wind, solar, and hydro). For the 

former, the upstream fuel cycle impacts are derived from the amount of fuel consumed at the 

power plant. For the latter, no fuel per se is used, so there is no upstream component. 

10B.2 METHODOLOGY

The mathematical approach is discussed in the paper A Mathematical Analysis of Full 

Fuel Cycle Energy Use,

2

 and details on the fuel production chain analysis are presented in the 

paper Projections of Full Fuel Cycle Energy and Emissions Metrics.

3

 The text below provides a 

brief summary of the methods used to calculate FFC energy.

When all energy quantities are normalized to the same units, the FFC energy use can be 

represented as the product of the primary energy use and an FFC multiplier. The FFC multiplier 

is defined mathematically as a function of a set of parameters representing the energy intensity 

and material losses at each production stage. These parameters depend only on physical data, so 

the calculations do not require any assumptions about prices or other economic data. While in 
10B-2 

general, these parameter values may vary by geographic region, for this analysis, national 

averages are used. 

In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, 

x=g for natural gas, x=p for petroleum fuels, x=u for uranium, and x=r for renewable fluxes. The 

fuel cycle parameters are as follows: 

• ax is the quantity of fuel x burned per unit of electricity output, on average, for grid 

electricity. The calculation of ax includes a factor to account for transmission and 

distribution system losses. 

• by is the amount of grid electricity used in production of fuel y, in megawatt hour 

(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 (million BTU/physical unit). 

• zx(s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x). 

The parameters are calculated as a function of time with an annual time step; hence, a 

time series of annual values is used to estimate the FFC energy and emissions savings in each 

year of the analysis period. Fossil fuel quantities are converted to energy units using the heat 

content factor, qx. To convert electricity in kWh to primary energy units, on-site electricity 

consumption is multiplied by the power sector primary energy use factor. The power sector 

primary energy use factor is defined as the ratio of the total primary energy consumption by the 

electric power sector (in quadrillion BTUs) divided by the total electricity generation in each 

year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel 

used on site. A multiplier is also calculated for electricity reflecting the fuel mix used in its 

generation. The multipliers are dimensionless numbers that are 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.

For DOE’s appliance standards energy savings estimates, the fuel cycle analysis 

methodology is designed to make use of data and projections published in the Annual Energy 

Outlook (AEO). Table 10B.2.1 provides a summary of the AEO data used as inputs to the 

different parameter calculations. The AEO does not provide all the information needed to 

estimate total energy use in the fuel production chain. Projections of Full Fuel Cycle Energy and 

Emissions Metrics3

 describes the additional data sources used to complete the analysis. However, 

the time dependence in the FFC multipliers arises exclusively from variables taken from the 

AEO. The FFC analysis used data from AEO2014.

4

 
10B-3 

Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables

qx All Conversion Factors MMBTU per Physical Unit

ax All

Electricity Supply, Disposition, Prices, and 

Emissions Generation by Fuel Type

Energy Consumption by Sector and Source Electric Power Sector Energy 

Consumption

bc, cnc, cpc Coal Coal Production by Region and Type Production by Coal Type and Sulfur 

Content

bp, cnp, cpp Petroleum

Refining Industry Energy Consumption Refining only Energy Use

Liquid Fuels Supply and Disposition Crude Supply by Source

International Liquids Supply and 

Disposition Crude Oil Imports

Oil and Gas Supply Crude Oil Domestic Production

cnn Natural Gas

Oil and Gas Supply 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

10B.3 FULL-FUEL-CYCLE ENERGY MULTIPLIERS

FFC energy multipliers are presented in Table 10B.3.1 for selected years. To extend the 

analysis period beyond 2040, the last year in the AEO2014 projection, the 2040 value was held 

constant. The multiplier for electricity reflects the shares of various primary fuels in total 

electricity generation over the forecast period. 

Table 10B.3.1 Full-Fuel-Cycle Energy Multipliers (Based on AEO2014) 

2020 2025 2030 2035 2040 2045 2050

Electricity

(Power Plant Primary 

Energy Use)

1.044 1.045 1.046 1.047 1.047 1.047 1.047

Natural Gas (Site) 1.109 1.111 1.113 1.114 1.114 1.114 1.114
10B-4 

REFERENCES

1. United States - Office of the Federal Register, Federal Register, Volume 76, Number 160,

August 18, 2011. UNT Digital Library. Washington D.C.

2. Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use. Energy. 2012.

37(1): pp. 698-708. http://www.sciencedirect.com/science/article/pii/S0360544211006803.

3. Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013.

Lawrence Berkeley National Laboratory. Report No. LBNL-6025E.

4. Energy Information Administration. Annual Energy Outlook 2014 with Projections to

2040. 2014. Washington, DC. http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf.
11-i

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 METHODOLOGY ........................................................................................................ 11-1

11.2 IDENTIFICATION OF CONSUMER SUBGROUPS .................................................. 11-1

11.3 CONSUMER SUBGROUP ANALYSIS RESULTS .................................................... 11-2

LIST OF TABLES

Table 11.2.1 Building Types Used for CPSV Life-Cycle Cost and Payback Analysis ............. 11-2

Table 11.3.1 Single Entity Consumer Subgroup Results for Light-Duty .................................. 11-2

Table 11.3.2 Single Entity Consumer Subgroup Results for Standard-Duty ............................ 11-3

Table 11.3.3 Single Entity Consumer Subgroup Results for Heavy-Duty ................................ 11-3

Table 11.3.4 Limited Service Establishment Consumer Subgroup Results for Light-Duty ...... 11-4

Table 11.3.5 Limited Service Establishment Consumer Subgroup Results for StandardDuty.................................................................................................................... 11-4

Table 11.3.6 Limited Service Establishment Consumer Subgroup Results for Heavy-Duty .... 11-5
11-1 

CHAPTER 11. CONSUMER SUBGROUP ANALYSIS

11.1 METHODOLOGY

The consumer subgroup analysis evaluates impacts on any identifiable groups or 

consumers who may be disproportionately affected by a national energy conservation standard. 

The U.S. Department of Energy (DOE) conducted this analysis as one of the analyses for the 

notice of proposed rulemaking (NOPR). DOE accomplished this by analyzing the life-cycle costs 

(LCCs) and payback periods (PBPs) for consumers that fall into any identifiable groups. DOE 

evaluated variations in energy and water use, and variations in discount rates that might affect 

the cost-effectiveness of a standard to consumer subpopulations. To the extent possible, DOE 

obtained estimates of each input parameter’s variability and considered this variability in its 

calculation of consumer impacts.

DOE determined the impact on consumer subgroups using the LCC spreadsheet model, 

which allows for different data inputs. The standard LCC analysis (described in chapter 8 of the 

technical support document (TSD)) analyzes all consumers that use commercial prerinse spray

valves. DOE used the LCC spreadsheet model to analyze the LCC for any subgroup by sampling 

only that subgroup. Chapter 8 of the NOPR TSD explains in detail the inputs to the model used 

in determining LCCs and PBPs. In the case of commercial prerinse spray valves, the subgroups 

DOE considered were single entities and limited service establishment end users. 

11.2 IDENTIFICATION OF CONSUMER SUBGROUPS

For this rulemaking, DOE identified single entities that purchase commercial prerinse 

spray valves as a relevant subgroup. These businesses typically have higher discount rates than 

the general population of commercial prerinse spray valve consumers. DOE also identified 

limited service establishments that purchase commercial prerinse spray valves as a relevant 

subgroup. These establishments typically have lower annual product operating times than the 

general population of commercial prerinse spray valve consumers. Both of these subgroups will 

experience lower operating cost savings than the general consumer. 

The data in Table 11.2.1, which originates from chapter 8 of the TSD, gives the 

percentage of CPSV stock, discount rates, and operating times for buildings in each of the five 

building types considered in this rulemaking. In the standard LCC Monte Carlo simulations, 

building types (and by extension) discount rates and operating times are selected according to the 

probabilities represented by the percentage of CPSV stock. For example, the restaurant discount 

rate and annual operating time is used in approximately 31 percent of the simulation iterations, 

and healthcare is used approximately 19 percent of the time.
11-2 

Table 11.2.1 Building Types Used for CPSV Life-Cycle Cost and Payback Analysis 

Building Type Percentage of CPSV Stock

%

Discount Rate

%

Annual Operating Time

minutes

Restaurant 31 7.72 31,514

Education 24 3.53 9,154

Healthcare 19 3.10 58,136

Lodging 14 8.68 32,101

Retail 11 7.54 2,311

The single entity consumer subgroup used the average of the discount rate between 

lodging and restaurants. These building types have the highest discount rates and are assumed to 

be the most likely building types that would be single entities. In the consumer subgroup 

analysis, when analyzing the limited service consumer subgroup, the operating times for each 

building type were scaled back by 20 percent. 

11.3 CONSUMER SUBGROUP ANALYSIS RESULTS

The consumer subgroup results for single entities or limited service establishments that 

purchase commercial prerinse spray valves are given in Table 11.3.1 through Table 11.3.6. In 

general, LCC savings are smaller in magnitude than the standard analysis LCC savings, and the 

PBPs are slightly longer for the subgroups relative to the standard analysis. However, LCC 

savings are still positive, all consumers experience a net benefit, and PBPs are still zero for the 

proposed standard level. 

Table 11.3.1 Single Entity Consumer Subgroup Results for Light-Duty

TSL Efficiency 

Level

Average Costs 2014$ Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

0 79 373 1,839 1,918 0.0

1 1 79 353 1,742 1,821 0.0

2 2 79 334 1,646 1,725 0.0

3, 4 3 79 319 1,573 1,652 0.0

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers 

that Experience 

Net Cost

Average 

Savings 

2014$

0 0 0

1 1 0 97

2 2 0 126

3, 4 3 0 198
11-3 

Table 11.3.2 Single Entity Consumer Subgroup Results for Standard-Duty

TSL Efficiency 

Level

Average Costs 2014$ Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

0 79 599 2,952 3,031 0.0

1 1 79 540 2,662 2,741 0.0

2, 3 2 79 476 2,347 2,426 0.0

4 3 79 461 2,275 2,354 0.0

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers 

that Experience 

Net Cost

Average 

Savings 

2014$

0 0 0

1 1 0 290

2, 3 2 0 444

4 3 0 516

Table 11.3.3 Single Entity Consumer Subgroup Results for Heavy-Duty

TSL Efficiency 

Level

Average Costs 2014$

Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

0 79 785 3,872 3,951 0.0

1 1 79 707 3,485 3,564 0.0

2 2 79 628 3,098 3,177 0.0

3, 4 3 79 609 3,001 3,080 0.0

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers 

that Experience 

Net Cost

Average 

Savings 

2014$

0 0 0

1 1 0 387

2 2 0 559

3, 4 3 0 627
11-4 

Table 11.3.4 Limited Service Establishment Consumer Subgroup Results for Light-Duty

TSL Efficiency 

Level

Average Costs 2014$ Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

0 79 298 1,566 1,645 0.0

1 1 79 283 1,483 1,562 0.0

2 2 79 267 1,401 1,480 0.0

3, 4 3 79 255 1,339 1,418 0.0

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers 

that Experience 

Net Cost

Average 

Savings 

2014$

0 0 0

1 1 0 82

2 2 0 107

3, 4 3 0 169

Table 11.3.5 Limited Service Establishment Consumer Subgroup Results for StandardDuty

TSL Efficiency 

Level

Average Costs 2014$ Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

0 79 479 2,513 2,592 0.0

1 1 79 432 2,266 2,345 0.0

2, 3 2 79 381 1,998 2,077 0.0

4 3 79 369 1,936 2,015 0.0

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers 

that Experience 

Net Cost

Average 

Savings 

2014$

0 0 0

1 1 0 247

2, 3 2 0 378

4 3 0 439
11-5 

Table 11.3.6 Limited Service Establishment Consumer Subgroup Results for Heavy-Duty

TSL Efficiency 

Level

Average Costs 2014$ Simple 

Payback 

Period 

years

Installed 

Cost

First Year's 

Operating 

Cost

Lifetime 

Operating 

Cost

LCC

0 79 628 3,296 3,375 0.0

1 1 79 565 2,966 3,045 0.0

2 2 79 503 2,637 2,716 0.0

3, 4 3 79 487 2,554 2,633 0.0

TSL Efficiency 

Level

Life-Cycle Cost Savings

% of Consumers 

that Experience 

Net Cost

Average 

Savings 

2014$

0 0 0

1 1 0 330

2 2 0 476

3, 4 3 0 533
12-i

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION....................................................................................................... 12-1

12.2 METHODOLOGY...................................................................................................... 12-1

12.2.1 Phase I: Industry Profile .............................................................................................. 12-1

12.2.2 Phase II: Industry Cash Flow Analysis ....................................................................... 12-2

12.2.3 Phase III: Subgroup Impact Analysis.......................................................................... 12-2

12.2.3.1 Small-Business Manufacturer....................................................................... 12-2

12.2.3.2 Manufacturing Capacity Impact.................................................................... 12-3

12.2.3.3 Employment Impact...................................................................................... 12-3

12.2.3.4 Cumulative Regulatory Burden .................................................................... 12-3

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES........................................ 12-4

12.4 GRIM INPUTS AND ASSUMPTIONS ..................................................................... 12-4

12.4.1 Overview of the GRIM................................................................................................ 12-4

12.4.2 Sources for GRIM Inputs ............................................................................................ 12-4

12.4.2.1 Corporate Annual Reports............................................................................. 12-5

12.4.2.2 Standard and Poor Credit Ratings................................................................. 12-5

12.4.2.3 Shipment Model............................................................................................ 12-5

12.4.2.4 Engineering Analysis.................................................................................... 12-5

12.4.2.5 Manufacturer Interviews............................................................................... 12-5

12.4.3 Financial Parameters ................................................................................................... 12-6

12.4.4 Corporate Discount Rate ............................................................................................. 12-6

12.4.5 Trial Standard Levels .................................................................................................. 12-8

12.4.6 NIA Shipment Forecast ............................................................................................... 12-8

12.4.6.1 No-New-Standards-Case Shipments Forecast .............................................. 12-9

12.4.6.2 Standards-Case Shipments Forecast ............................................................. 12-9

12.4.7 Production Costs........................................................................................................ 12-10

12.4.8 Conversion Costs....................................................................................................... 12-11

12.4.8.1 Commercial Prerinse Spray Valve Product and Capital Conversion Costs 12-11

12.4.9 Markup Scenarios...................................................................................................... 12-13

12.4.9.1 Preservation of Gross Margin Markup Scenario......................................... 12-14

12.4.9.2 Preservation of Per Unit EBIT Markup Scenario ....................................... 12-14

12.5 INDUSTRY FINANCIAL IMPACTS...................................................................... 12-14

12.5.1 Introduction ............................................................................................................... 12-14

12.5.2 Commercial Prerinse Spray Valve Industry Financial Impacts ................................ 12-16

12.6 IMPACTS ON SMALL COMMERCIAL PRERINSE SPRAY VALVE 

MANUFACTURERS................................................................................................ 12-19

12.7 OTHER IMPACTS ................................................................................................... 12-21

12.7.1 Employment .............................................................................................................. 12-21

12.7.2 Production Capacity .................................................................................................. 12-23

12.7.3 Cumulative Regulatory Burden................................................................................. 12-23

12.8 CONCLUSION ......................................................................................................... 12-24

REFERENCES ........................................................................................................................ 12-27
12-ii

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow............................................................ 12-4

Figure 12.5.1 Industry Annual Free Cash Flows for All Commercial Prerinse Spray 

Valves - Sourced Components Capital Conversion Costs Scenario................ 12-18

Figure 12.5.2 Industry Annual Free Cash Flows for All Commercial Prerinse Spray 

Valves–Fabricated Components Capital Conversion Costs Scenario ............. 12-19

Figure 12.7.1 Total Commercial Prerinse Spray Valve Industry Domestic Employment by 

Year.................................................................................................................. 12-22

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by 

This Rulemaking................................................................................................ 12-3

Table 12.4.1 GRIM Financial Parameters Based on 2007–2013 Weighted Company 

Financial Data .................................................................................................... 12-6

Table 12.4.2 Cost of Equity Calculation.................................................................................... 12-7

Table 12.4.3 Cost of Debt Calculation....................................................................................... 12-7

Table 12.4.4 Efficiency Level to Trial Standard Level Mapping, With Flow Rates................. 12-8

Table 12.4.5 Total No-New-Standards-Case NIA Shipments in the Reference NIA 

Shipment Scenario, 2019 ................................................................................... 12-8

Table 12.4.6 No-New-Standards Case Distribution of Efficiencies for Commercial 

Prerinse Spray Valves, 2019.............................................................................. 12-9

Table 12.4.7 MPC Breakdown for Light-Duty Commercial Prerinse Spray Valves............... 12-10

Table 12.4.8 MPC Breakdown for Standard-Duty Commercial Prerinse Spray Valves......... 12-10

Table 12.4.9 MPC Breakdown for Heavy-Duty Commercial Prerinse Spray Valves............. 12-11

Table 12.4.10 Product and Capital Conversion Costs for Light-Duty Commercial Prerinse 

Spray Valves.................................................................................................... 12-12

Table 12.4.11 Product and Capital Conversion Costs for Standard Duty Commercial 

Prerinse Spray Valves...................................................................................... 12-13

Table 12.4.12 Product and Capital Conversion Costs for Heavy Duty Commercial 

Prerinse Spray Valves...................................................................................... 12-13

Table 12.5.1 Manufacturer Impact Analysis for All Commercial Prerinse Spray Valves –

Sourced Components Capital Conversion Costs ............................................. 12-16

Table 12.5.2 Manufacturer Impact Analysis for All Commercial Prerinse Spray Valves –

Fabricated Components Capital Conversion Costs.......................................... 12-16

Table 12.5.3 Manufacturer Impact Analysis for Light Duty Commercial Prerinse Spray 

Valves – Sourced Components Capital Conversion Costs.............................. 12-16

Table 12.5.4 Manufacturer Impact Analysis for Light Duty Commercial Prerinse Spray 

Valves – Fabricated Components Capital Conversion Costs .......................... 12-17

Table 12.5.5 Manufacturer Impact Analysis for Standard Duty Commercial Prerinse 

Spray Valves – Sourced Components Capital Conversion Costs.................... 12-17

Table 12.5.6 Manufacturer Impact Analysis for Standard Duty Commercial Prerinse 

Spray Valves – Fabricated Components Capital Conversion Costs................ 12-17

Table 12.5.7 Manufacturer Impact Analysis for Heavy Duty Commercial Prerinse Spray 

Valves – Sourced Components Capital Conversion Costs.............................. 12-17
12-iii

Table 12.5.8 Manufacturer Impact Analysis for Heavy Duty Commercial Prerinse Spray 

Valves – Fabricated Components Capital Conversion Costs .......................... 12-18

Table 12.6.1 Comparison of Conversion Costs for an Average Small and an Average 

Large Manufacturer at TSL 3 – Sourced Components Capital Conversion 

Costs Scenario.................................................................................................. 12-20

Table 12.6.2 Comparison of Conversion Costs for an Average Small and an Average 

Large Manufacturer at TSL 3 – Fabricated Components Capital 

Conversion Costs Scenario .............................................................................. 12-20

Table 12.7.1 Total Number of Domestic Commercial Prerinse Spray Valve Production 

Workers, 2019.................................................................................................. 12-22

Table 12.7.2 Compliance Dates and Expected Conversion Expenses of Federal Energy 

Conservation Standards Affecting Commercial Prerinse Spray Valve 

Manufacturers.................................................................................................. 12-23
12-1

CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether an amended energy conservation standard for commercial 

prerinse spray valves is economically justified, DOE is required to consider “the economic 

impact of the standard on the manufacturers and on the consumers of the products subject to such 

standard.” (42 U.S.C. 6295(o)(2)(B)(i)(I)) The statute also calls for an assessment of the impact 

of any lessening of competition that is likely to result from the adoption of a standard as 

determined by the Attorney General. (42 U.S.C. 6295(o)(2)(B)(i)(V)) The U.S. Department of 

Energy (DOE) conducted a manufacturer impact analysis (MIA) to estimate the financial impact 

of amended energy conservation standards on manufacturers, and to assess the impacts of such 

standards on employment and manufacturing capacity.

The MIA involves both quantitative analyses and qualitative evaluation. The quantitative 

elements of the MIA rely on the Government Regulatory Impact Model (GRIM), an industry 

cash-flow model customized for 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), which is the sum of discounted annual industry cash flows over the 

analysis period. The model estimates the financial impact of more stringent energy conservation 

standards by comparing changes in INPV between a no-new-standards case and various trial 

standard levels (TSLs) in the standards case. The qualitative part of the MIA addresses trends in 

product characteristics, manufacturer characteristics, and the impact of standards on subgroups 

of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of 

preliminary research directed at characterizing the commercial prerinse spray valve

manufacturing industry. This research involved collecting data on market share, sales volumes 

and trends, pricing, employment, and the industry financial structure.

In Phase II, “Industry Cash Flow Analysis,” DOE created a GRIM to model the economic 

impact of amended energy conservation standards on the commercial prerinse spray valve

manufacturing industry as a whole. In Phase III, “Subgroup Impact Analysis,” DOE evaluated

impacts on manufacturer subgroups, specifically focusing on the potential for disproportionate 

impacts on small business manufacturers of commercial prerinse spray valves. DOE also 

evaluated potential impacts on direct employment and manufacturing capacity within the 

commercial prerinse spray valve manufacturing industry, as well as cumulative regulatory 

burdens experienced by commercial prerinse spray valve manufacturers.

12.2.1 Phase I: Industry Profile

In Phase 1 of the MIA, DOE prepared a profile of the commercial prerinse spray valve 

manufacturing industry based on the market and technology assessment prepared for this 

rulemaking (see chapter 3 of this technical support document (TSD)). Before initiating the 

detailed impact studies, DOE collected information on the present and past market structure and 
12-2

characteristics of the industry, product attributes, product shipments, manufacturer markups, and 

the cost structure for various manufacturers.

The profile also included an analysis of manufacturers in the industry using the U.S. 

Securities and Exchange Commission 10-K filings, Standard & Poor’s stock reports, and 

corporate annual reports released by publicly held companies. DOE used this and other publicly 

available information to derive preliminary financial inputs for the GRIM, including cost of 

goods sold, and depreciation, as well as selling, general, and administrative (SG&A), and 

research and development (R&D) expenses.

12.2.2 Phase II: Industry Cash Flow Analysis 

Phase II focused on the financial impacts of potential amended energy conservation 

standards on the industry as a whole. Amended energy conservation standards can affect 

manufacturer cash flows in three distinct ways: (1) by creating a need for increased investment, 

(2) by potentially raising per-unit production costs, and (3) by altering manufacturer revenue due 

to possible changes in sales volumes and/or manufacturer’s per-unit gross margins. DOE used 

the GRIM to model these effects in a cash-flow analysis of the commercial prerinse spray valve

manufacturing industry. In performing this analysis, DOE used the financial parameters 

developed in Phase I, the cost-efficiency curves from the engineering analysis, and the shipment 

assumptions from the national impact analysis (NIA).

12.2.3 Phase III: Subgroup Impact Analysis

Using average cost assumptions to develop an industry cash-flow estimate may not 

adequately assess differential impacts of amended energy conservation standards among 

manufacturer subgroups. For example, small businesses, manufacturers of niche products, or 

companies exhibiting a cost structure that differs significantly from the industry average could be 

more negatively affected. While DOE did not identify any subgroup of manufacturers of 

commercial prerinse spray valves that would warrant a separate analysis, DOE specifically 

investigated impacts on small business manufacturers. See sections 12.2.3.1 and 12.6 of this TSD 

chapter for more information.

The MIA also addresses the direct impact on employment tied to the manufacturing of 

commercial prerinse spray valves. Using the GRIM and census data, DOE estimated the 

domestic labor expenditures and number of domestic production workers in the no-newstandards case and at each TSL from 2015 to 2048. Section 12.2.3.3 of this chapter provides 

more information on direct employment impacts.

12.2.3.1 Small-Business Manufacturer

The U.S. Small Business Administration (SBA) has set a size threshold to define entities 

classified as “small businesses” for the purposes of the statute. DOE used the SBA’s small 

business size standards as effective January 22, 2014, 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.1 For the equipment classes under review, the SBA 

bases its small business definition on the total number of employees for a business, including the 
12-3

total employee count of a parent company and its subsidiaries. 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 Code

Other metal valve and pipe fitting manufacturing N/A 500 332919

To estimate the number of small businesses that could be affected by amended energy 

conservation standards, DOE conducted a market survey using public information to identify 

potential small manufacturers. DOE reviewed its Compliance Certification Management System 

(CCMS), the U.S. Environmental Protection Agency’s (EPA’s) WaterSense® program database, 

individual company websites, and various marketing research tools (e.g., Hoover’s reports) to 

create a list of companies that import, assemble, or otherwise manufacture commercial prerinse 

spray valves covered by this rulemaking. DOE screened out companies that do not offer 

equipment covered by this rulemaking, do not meet the definition of a “small business,” or are 

foreign owned and operated. During its research, DOE identified eight manufacturers of 

commercial prerinse spray valves that meet the small business criteria as specified by the SBA.

12.2.3.2 Manufacturing Capacity Impact

One significant outcome of amended energy conservation standards could be the 

obsolescence of existing manufacturing assets, including tooling and production equipment. The 

MIA evaluated impacts of amended standards on manufacturing capacity, specifically capacity 

utilization, with and without amended standards; the ability of manufacturers to upgrade or 

remodel existing facilities to accommodate the new requirements; the nature and value of any 

stranded assets; and estimates for any one-time changes to existing plant, property, and 

equipment (PPE). Section 12.7.2 presents DOE’s discussion of the capacity impact, and section 

12.4.8 presents estimate of product and capital conversion costs.

12.2.3.3 Employment Impact

The impact of amended energy conservation standards on employment is an important 

consideration in the rulemaking process. To quantitatively assess the impacts of energy 

conservation standards on employment in the commercial prerinse spray valve industry, DOE 

used the GRIM to estimate the domestic labor expenditures and number of employees in the nonew-standards case and at each TSL from 2015 through 2048. The employment impacts are 

reported in section 12.7.1.

12.2.3.4 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers of amended energy 

conservation standards and other regulatory actions affecting the same products. DOE analyzed 

the impact on manufacturers of multiple, product-specific regulatory actions. Based on its own 

research, DOE identified regulations relevant to commercial prerinse spray valve manufacturers, 

such as Federal regulations that affect other products made by the same manufacturers. 

Discussion of the cumulative regulatory burden can be found in section 12.7.3.
12-4

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

DOE contacted manufacturers representing an estimated 100 percent of the U.S. 

commercial prerinse spray valve market for the purpose of conducting interviews. However, 

despite multiple requests from DOE to participate in manufacturer interviews, no manufacturer 

agreed to participate in an interview to inform the notice of proposed rulemaking (NOPR).

12.4 GRIM INPUTS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to amended 

energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM. 

Data and assumptions from these sources are then fed into an accounting model that calculates 

the industry cash flow both with and without amended energy conservation standards.

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4.1, is an annual cash-flow 

analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial 

information as inputs to arrive at a series of annual cash flows, beginning with the base year of 

the analysis, 2015, and continuing to 2048. The model calculates the INPV by summing the 

annual discounted cash flows during this period.2

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 no-new-standards case and the standard case scenarios induced by 

amended energy conservation standards including changes in costs, investments, and associated 

margins. The difference in INPV between the no-new-standards case and the standard cases 

represents the estimated financial impact of the amended energy conservation standards on 

manufacturers. Appendix 12A provides more technical details and user information for the 

GRIM.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash 

flow. These sources include corporate annual reports, company profiles, census data, credit 

ratings, the shipments model, and the engineering analysis.
12-5

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 of commercial prerinse spray valve 

equipment. Because some of these companies do not provide detailed information about their 

individual product lines, for these companies, DOE used the financial information for the entire 

companies as its initial estimates of the financial parameters in the GRIM analysis. DOE used 

corporate annual reports to derive the following initial inputs to the GRIM:

 Tax rate

 Working capital

 SG&A

 R&D

 Depreciation

 Capital expenditures and

 Net PPE.

12.4.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. DOE relied 

on S&P reports to determine the industry’s average cost of debt when calculating the cost of 

capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the NIA. 

The model relied on historical shipments data for commercial prerinse spray valves. Chapter 9 of 

the NOPR TSD describes the methodology and analytical model DOE used to forecast 

shipments.

12.4.2.4 Engineering Analysis 

DOE conducted the engineering analysis for this rulemaking using a combination of a 

design-option approach and a reverse-engineering approach. The analysis is performed in terms 

of incremental decreases in water consumption due to the implementation of selected design 

options, while the estimated manufacturer production costs (MPCs) for each successive design 

option are based on equipment teardowns and a bottom-up manufacturing cost assessment. Using 

this hybrid approach, DOE developed the relationship between manufacturer selling price (MSP) 

and commercial prerinse spray valve efficiency. Chapter 5 of the NOPR TSD describes the 

engineering analysis in greater detail.

12.4.2.5 Manufacturer Interviews

As previously discussed, manufacturer interviews were not used to inform the GRIM 

because no manufacturer agreed to participate in an interview, despite repeated attempts by DOE 

to conduct interviews with manufacturers.
12-6

12.4.3 Financial Parameters

Table 12.4.1 provides financial parameters for two large, public companies engaged in 

manufacturing and selling commercial prerinse spray valve equipment. The values listed are 

averages over a 7-year period (2007 through 2013).

Table 12.4.1 GRIM Financial Parameters Based on 2007–2013 Weighted Company 

Financial Data

Parameter

IndustryWeighted 

Average

Manufacturer

A B

Tax Rate % of Taxable Income 38.0 38.0 N/A*

Working Capital % of Revenue 29.4 29.5 29.4

SG&A % of Revenue 11.6 20.3 6.1

R&D % of Revenue 0.6** 0.6

Depreciation % of Revenue 2.3 3.3 1.7

Capital Expenditures % of Revenue 1.8 2.5 1.3

Net Property, Plant, and Equipment % of Revenue 16.6 24.7 11.4

*Tax rate for Manufacturer B was not used to calculate the industry-weighted average tax rate because it was negative from 2007 to 2013.

**Annual R&D expenditures were not reported in Manufacturer B’s publically available financials. DOE rounded R&D to the nearest whole 

percent for the GRIM.

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 CPSV equipment industry based on several 

representative companies, using the following formula:

WACC = After-Tax Cost of Debt x (Debt Ratio) + Cost of Equity x (Equity Ratio)

Eq. 12.1

The cost of equity is the rate of return that equity investors (including, potentially, the 

company) expect to earn on a company’s stock. These expectations are reflected in the market 

price of the company’s stock. The Capital Asset Pricing Model (CAPM) provides one widely 

used means to estimate the cost of equity. According to the CAPM, the cost of equity (expected 

return) is:

Cost of Equity = Riskless Rate of Return + β x Risk Premium

Eq. 12.2

Where:

Riskless rate of return = the rate of return on a “safe” benchmark investment, typically 

considered the short-term Treasury Bill (T-Bill) yield,

Risk premium = the difference between the expected return on stocks and the riskless rate, and
12-7

Beta (β) = 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 the industry average cost of equity for the commercial prerinse spray 

valve industry at 13.2 percent (Table 12.4.2). 

Table 12.4.2 Cost of Equity Calculation

Parameter

IndustryWeighted 

Average 

%

Manufacturer

A B

(1) Average Beta 1.3 1.5 1.1

(2) Yield on 10-Year 

T-Bill (1928-2013) 5.1

(3) Market Risk Premium (1928-2013) 6.4

Cost of Equity (2)+[(1)*(3)] 13.2

Equity/Total Capital 56.5 22.4 78.1

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 two manufacturers by using S&P ratings and adding the 

relevant spread to the risk-free rate.

Because 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. DOE 

determined that the after-tax industry-average cost of debt for the commercial prerinse spray 

valve industry is 5.7 percent. 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)).

Table 12.4.3 Cost of Debt Calculation

Parameter

IndustryWeighted 

Average 

%

Manufacturer

A B

S&P Bond Rating -- BB BB

(1) Yield on 10-Year T-Bill (1928-2010) 5.1

(2) Gross Cost of Debt 9.1 9.1 9.1

(3) Tax Rate 38.0 38.0 N/A

Net Cost of Debt [(2) x ((1)-(3))] 5.7

Debt/Total Capital 43.5 77.6 21.9
12-8

Correcting for an inflation rate of 3.1 percent over the analysis period, DOE’s calculated 

value for the commercial prerinse spray valve industry’s inflation-adjusted WACC and the initial 

estimate of the discount rate is 6.9 percent.

12.4.5 Trial Standard Levels

DOE developed TSLs to analyze the impact on manufacturers of amended energy 

efficiency standards for three equipment classes of commercial prerinse spray valves—light 

duty, standard duty, and heavy duty. Table 12.4.4 presents the TSLs and the corresponding 

equipment class efficiency levels based on estimated flow rate (measured in gallons per minute 

(gpm)) according to the proposed test procedure (ASTM F2324‒13).

TSL 4 represents the maximum technologically feasible (max-tech) improvements in 

energy and water efficiency for all commercial prerinse spray valves. It also represents the 

highest efficiency levels with a positive total NPV. TSL 3 maximizes total NPV, energy savings, 

and water savings. TSL 2 is the second efficiency level considered above the baseline for all 

equipment classes. TSL 1 corresponds to the first efficiency level considered above the baseline 

for all equipment classes.

Table 12.4.4 Efficiency Level to Trial Standard Level Mapping, With Flow Rates

Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4

Light Duty: Spray force ≤ 5 ozf*

EL** 0 

(0.76)

EL 1 

(0.72)

EL 2 

(0.68)

EL3 

(0.65)

EL 3 

(0.65)

Standard Duty: Spray force > 5 ozf and ≤ 8 ozf EL 0 

(1.22)

EL 1 

(1.10)

EL 2 

(0.97)

EL 2 

(0.97)

EL 3 

(0.94)

Heavy Duty: Spray force > 8 ozf EL 0 

(1.60)

EL 1 

(1.44)

EL 2 

(1.28)

EL 3 

(1.24)

EL3 

(1.24)

* ozf = ounce-force

** EL = efficiency level

12.4.6 NIA Shipment Forecast

The GRIM estimates manufacturer revenues based on total-unit-shipment forecasts and 

the distribution of these values by efficiency level. Changes in the efficiency mix at each 

standard level are a key driver of manufacturer finances. For this analysis, the GRIM used 

commercial prerinse spray valve shipment data from the Shipments Analysis. Chapter 9 of the 

NOPR TSD explains DOE’s calculations of total shipments in detail. Table 12.4.5 shows total 

shipments forecasts for commercial prerinse spray valves in 2019.

Table 12.4.5 Total No-New-Standards-Case NIA Shipments in the Reference NIA Shipment 

Scenario, 2019

Equipment Class Total Industry Shipments

Light Duty 46,206

Standard Duty 115,516

Heavy Duty 69,310
12-9

12.4.6.1 No-New-Standards-Case Shipments Forecast

As part of the shipment analysis, DOE estimated the shipment distribution by efficiency 

level for commercial prerinse spray valves. As described in chapter 9 of the NOPR TSD, DOE’s 

shipment forecast does not indicate a trend toward higher efficiency products over the forecast 

period. Table 12.4.6 shows the 2019 no-new-standards case distributions of shipments by 

efficiency level (EL) estimated in the NIA for the commercial prerinse spray valve equipment

classes.

Table 12.4.6 No-New-Standards Case Distribution of Efficiencies for Commercial Prerinse 

Spray Valves, 2019

Equipment Class Baseline EL 1 EL 2 EL 3

Light Duty

Flow Rate 

gpm

0.76 0.72 0.68 0.65

% of the 

Market at EL 15 35 0 50

Standard Duty

Flow Rate 

gpm

1.22 1.1 0.97 0.97

% of the 

Market at EL 40 50 0 10

Heavy Duty

Flow Rate 

gpm

1.6 1.44 1.28 1.24

% of the 

Market at EL 40 50 5 5

12.4.6.2 Standards-Case Shipments Forecast

To examine the impact of amended energy conservation standards on shipments, which 

in turn affect the INPV, DOE used the no-new-standards case shipments described in the 

previous section as a point of comparison for shipments forecast in the standards case. For each 

TSL described in the standards case, DOE used the shipments forecasts developed in the NIA for 

commercial prerinse spray valves. DOE assumed that commercial prerinse spray valve customers 

would choose to buy the commercial prerinse spray valve that has the flow rate that is closest to 

the flow rate of the equipment they currently use and that complies with the new standard (and, 

accordingly, manufacturers would choose to produce equipment with the closest flow rate to 

those they currently produce). Due to the structure of the equipment classes and efficiency levels 

for this rule, in certain instances, equipment class switching is predicted to occur, wherein 

customers choose to buy the product with the flow rate that is closest to their current product’s 

flow rate even if it has a higher spray force (putting those products into a different equipment 

class). Where equipment class switching does not occur, no-new-standards case shipments of 

equipment that did not meet the new standard would “roll up” to meet the standard starting in the 

compliance year.
12-10

12.4.7 Production Costs

Changes in the MPCs of commercial prerinse spray valves can affect revenues, gross 

margins, and cash flow of the industry, making these product cost data key GRIM inputs for 

DOE’s analysis. In the engineering analysis, DOE created separate cost curves for Light, 

Standard, and Heavy Duty equipment classes using data from tear-downs. DOE used this 

information to develop both the baseline MPCs and the incremental costs that correspond to the 

design options that DOE expects manufacturers would incorporate at each efficiency level. For 

commercial prerinse spray valves, it was determined that there is only one design option for 

increasing product efficiency (i.e., decreasing the spray hole area of the nozzle) and this design 

option does not increase production costs for any equipment class, at any efficiency level (i.e.,

engineering cost curves are identical and flat for each equipment class).

The cost model disaggregated the MPCs into material, labor, overhead, and depreciation. 

Material and labor costs were determined during teardowns. For depreciation, DOE used a 

depreciation value that is consistent with historical information in SEC 10-Ks. The remainder of 

product cost was allocated to factory overhead.

The MSP is comprised of MPCs (direct manufacturing costs), non-production costs 

(indirect costs such as SG&A and R&D), and profit. DOE calculated the MSPs for commercial 

prerinse spray valves by multiplying the MPCs by manufacturer markup. Table 12.4.7, Table 

12.4.8, and Table 12.4.9 show the production cost estimates used in the GRIM for the 

representative equipment classes.

Table 12.4.7 MPC Breakdown for Light-Duty Commercial Prerinse Spray Valves

EL Flow Rate 

gpm

Material

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Mfr. 

Markup

MSP

$

Baseline 0.76 5.75 8.54 0.86 13.03 28.18 1.30 36.63

1 0.72 5.75 8.54 0.86 13.03 28.18 1.30 36.63

2 0.68 5.75 8.54 0.86 13.03 28.18 1.30 36.63

3 0.65 5.75 8.54 0.86 13.03 28.18 1.30 36.63

Table 12.4.8 MPC Breakdown for Standard-Duty Commercial Prerinse Spray Valves

EL Flow Rate 

gpm

Material

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Mfr. 

Markup

MSP

$

Baseline 1.22 $5.75 $8.54 $0.86 $13.03 $28.18 1.30 $36.63

1 1.1 $5.75 $8.54 $0.86 $13.03 $28.18 1.30 $36.63

2 0.97 $5.75 $8.54 $0.86 $13.03 $28.18 1.30 $36.63

3 0.97 $5.75 $8.54 $0.86 $13.03 $28.18 1.30 $36.63
12-11

Table 12.4.9 MPC Breakdown for Heavy-Duty Commercial Prerinse Spray Valves

EL Flow Rate 

gpm

Material

$

Labor

$

Depreciation

$

Overhead

$

MPC

$

Mfr. 

Markup

MSP

$

Baseline 1.6 5.75 8.54 0.86 13.03 28.18 1.30 36.63

1 1.44 5.75 8.54 0.86 13.03 28.18 1.30 36.63

2 1.28 5.75 8.54 $0.86 13.03 28.18 1.30 36.63

3 1.24 5.75 8.54 0.86 13.03 28.18 1.30 36.63

12.4.8 Conversion Costs

Amended energy conservation standards typically cause manufacturers to incur one-time 

conversion costs to redesign products to comply with amended standards and upgrade production 

facilities to manufacture compliant products. 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 investments in property, plant, and equipment necessary to adapt or 

change existing production facilities so that newly compliant product designs can be fabricated 

and assembled. Product conversion costs are investments in research, development, testing, 

marketing, and other non-capitalized costs focused on designing products that comply with the 

amended energy conservation standard. These one-time conversion costs are separate and do not 

directly affect the manufacturer production cost as described in chapter 5 of the NOPR TSD. The 

following sections describe these inputs in greater detail.

12.4.8.1 Commercial Prerinse Spray Valve Product and Capital Conversion Costs

DOE relied on the engineering analysis to estimate the level of capital conversion costs 

manufacturers would likely incur to comply with amended energy conservation standards.

DOE developed two model scenarios for the capital conversion costs required to meet 

each TSL. The assumption underlying both scenarios is that capital conversion costs associated 

with increasing the efficiency of commercial prerinse spray valves are exclusively related to the 

fabrication of plastic nozzles, as manufacturers would have to redesign nozzle molds to produce 

a nozzle with fewer or smaller spray holes. The capital investment associated with redesigning 

the molds is estimated to be $50,000 (i.e., $50,000 per spray pattern). There would not be capital 

conversion costs associated with the in-house fabrication of metal nozzles because design 

options for more efficient spray valves include drilling fewer holes or replacing a drill bit in 

order to drill smaller holes. The first option does not require any additional tooling, and the 

second only requires the replacement of a drill bit (which wears out regularly and would be 

replaced anyway in the time period from the publication of a final rule and the compliance date 

for the amended standards).

One capital conversion cost scenario, representing the upper bound of industry 

profitability, assumes that the majority of commercial prerinse spray valve manufacturers source 

components (including the nozzle) from component suppliers and then simply assemble the 

commercial prerinse spray valves (i.e., sourced components scenario). The second scenario, 
12-12

representing the lower bound of industry profitability, assumes that all of the commercial 

prerinse spray valve manufacturers currently selling products with plastic spray nozzles fabricate 

these nozzles in house (i.e., fabricated components scenario).

For product conversion costs, DOE assumed a cost of $100,000 per manufacturer to 

update product brochures and other marketing materials associated with product updates 

regardless of efficiency level. Additionally, DOE assumed an average testing cost of $425 per 

spray pattern based on test quotes from third-party testing facilities.

Only some manufacturers currently produce spray valves with a range of efficiencies. 

However, even for manufacturers that offer both a low-efficiency and high-efficiency model, 

DOE does not assume any consolidation of models as a result of amended standards (i.e.,

manufacturers would likely continue to offer two models–a standards-compliant lower efficiency 

model and a high-efficiency model). This assumption is based on DOE’s shipments analysis, 

which assumes that commercial prerinse spray valve customers would choose to buy the spray 

valve that has a flow rate that is closest to the flow rate of the equipment they currently use and 

that complies with the new standard. Accordingly, manufacturers would choose to produce 

equipment with the closest flow rate to those they currently produce, which does not necessarily 

correspond to the flow rate of their high-efficiency product.

DOE aggregated the above-mentioned costs per manufacturer and per spray pattern to 

reflect the industrywide investment using market information about the number of manufacturers 

and currently available spray patterns affected at each efficiency level. DOE estimated the 

number of spray patterns using publicly available information from manufacturer websites and 

product databases.

Table 12.4.10 through Table 12.4.12 show the DOE estimates of product and capital 

conversion costs (both scenarios) necessary for both commercial prerinse spray valve equipment

classes at each efficiency level.

Table 12.4.10 Product and Capital Conversion Costs for Light-Duty Commercial Prerinse 

Spray Valves

EL Flow Rate

gpm

Product Conversion 

Costs

2014$ millions

Capital Conversion 

Costs -

Sourced Components 

Scenario

2014$ millions

Capital Conversion 

Costs -

Fabricated 

Components Scenario

2014$ millions

Baseline 0.76 - - -

1 0.72 0.20 0.00 0.00

2 0.68 0.40 0.00 0.00

3 0.65 0.40 0.00 0.00
12-13

Table 12.4.11 Product and Capital Conversion Costs for Standard Duty Commercial 

Prerinse Spray Valves

EL Flow Rate

gpm

Product Conversion 

Costs

2014$ millions

Capital Conversion 

Costs -

Sourced Components 

Scenario

2014$ millions

Capital Conversion 

Costs -

Fabricated 

Components Scenario

2014$ millions

Baseline 1.22 - - -

1 1.10 0.40 0.00 0.20

2 0.97 0.71 0.00 0.20

3 0.94 0.71 0.00 0.20

Table 12.4.12 Product and Capital Conversion Costs for Heavy Duty Commercial Prerinse 

Spray Valves

EL Flow Rate

gpm

Product Conversion 

Costs

2014$ millions

Capital Conversion 

Costs -

Sourced Components 

Scenario

2014$ millions

Capital Conversion 

Costs -

Fabricated 

Components Scenario

2014$ millions

Baseline 1.60 - - -

1 1.44 $0.51 $0.05 $0.70

2 1.28 $0.61 $0.15 $0.95

3 1.24 $0.71 $0.15 $0.95

12.4.9 Markup Scenarios

MSP is equal to MPC multiplied by a manufacturer markup. The MSP includes direct 

manufacturing production costs (i.e., labor, material, and overhead estimated in DOE’s MPCs) 

and all non-production costs (i.e., SG&A, R&D, and interest), along with profit.

DOE used the baseline manufacturer markup, 1.30, developed during Phase I and 

subsequently revised, of the MIA for all products when modeling the no-new-standards case in 

the GRIM. This was calculated by evaluating publicly available financial information for 

manufacturers whose product offerings include commercial prerinse spray valves. For both 

GRIM markup scenarios, DOE placed no premium on higher efficiency products. This is based 

on the assumption that efficiency is not the primary factor influencing customers’ purchasing 

decisions.

In the standards case, DOE modeled two markup scenarios to represent the uncertainty 

about the potential impacts on prices and profitability following the implementation of amended 

energy conservation standards: (1) a preservation of gross margin markup scenario, and (2) a 

preservation of per-unit earnings before interest and taxes (EBIT) markup scenario.
12-14

12.4.9.1 Preservation of Gross Margin Markup Scenario

The preservation of gross margin markup scenario assumes that the baseline markup of 

1.30 is maintained for all products in the standards case. Typically, this represents the upper 

bound of industry profitability as manufacturers are able to fully pass through to customers their 

additional costs due to standards in this scenario.

12.4.9.2 Preservation of Per Unit EBIT Markup Scenario

The preservation of per unit EBIT markup scenario is similar to the preservation of gross 

margin as a percentage of revenues markup scenario, with the exception that in the standards 

case, minimally compliant products lose a fraction of the baseline markup. Typically, this 

scenario represents the lower bound profitability and a more substantial impact on the industry as 

manufacturers accept a lower profit margin in an attempt to offer price-competitive entry level 

products while maintaining the same level of EBIT, on a per unit basis, that they saw prior to 

amended standards.

For the commercial prerinse spray valve industry, there is no difference between the 

preservation of gross margin as a percentage of revenues and the preservation of per unit EBIT 

markup scenarios described previously. This is because manufacturing production costs are 

estimated to be constant across all standard efficiency levels (i.e., baseline, EL 1, EL 2, EL 3), 

total shipments are constant across standards efficiency levels, and changes in standards-case 

shipments for certain equipment classes as a result of equipment class switching (i.e., a decrease 

in standard-duty commercial prerinse spray valve shipments, and an equivalent increase in 

heavy-duty commercial prerinse spray valve shipments at all TSLs) are controlled for by using 

the per unit EBIT in modeling the lower bound of industry profitability. Because the preservation 

of gross margin as a percentage of revenues and the preservation of per unit EBIT markup 

scenarios produce the same results in the GRIM, DOE does not break out the results of each in 

the presentation of INPV impacts in the following discussion.

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, DOE used the GRIM to 

estimate the financial impacts on the commercial prerinse spray valve industry. The MIA uses 

two key financial metrics: INPV and annual cash flows. The main results of the MIA are 

reported in this section.

12.5.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the economic 

impacts of different TSLs. The INPV is different from DOE’s NPV, which is applied to the U.S. 

economy at large. The INPV is specific to the commercial prerinse spray valve manufacturing 

industry, and is the sum of all net cash flows discounted to the present year at the industry’s cost 

of capital. The GRIM for the commercial prerinse spray valve industry models cash flows from 

2015 to 2048. This timeframe models both the short-term impacts on the industry, from the 

announcement of the standard until the compliance date, and a long-term assessment over the 30-

year analysis period immediately thereafter.
12-15

In the MIA, DOE compared the INPV of the no-new-standards case to that of each TSL 

in the standards case. The difference between the no-new-standards case and a standards-case 

INPV is an estimate of the economic impact on the industry of implementing that particular TSL. 

For the commercial prerinse spray valve industry, DOE examined the two capital conversion cost 

scenarios described above, the sourced components capital conversion costs scenario and the 

fabricated components capital conversion costs scenario. While INPV is useful for evaluating the 

long-term effects of amended energy conservation standards, short-term changes in cash flow are 

also important indicators of the industry’s financial situation. For example, a large investment 

over one or two years could strain the industry’s access to capital. Consequently, the sharp drop 

in financial performance could cause investors to flee, even if recovery is possible. Thus, a shortterm disturbance can have long-term effects that the INPV does not capture. To get an idea of the 

behavior of annual net cash flows, Figure 12.5.1 and Figure 12.5.2 present the annual free cash 

flows from 2015 through 2048 for the no-new-standards- case and at each TSL in the standards 

case.

Annual cash flows are discounted to the base year, 2015. Between 2015 and the 2019 

analysis compliance date, annual cash flows are driven by the level of conversion costs and the 

portion of these investments made each year. After the standard announcement date (i.e., the 

publication date of the final rule), industry cash flows decline as companies use their financial 

resources to prepare for the amended energy conservation standard. The more stringent the 

amended 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 flow in the year the amended energy conservation standards take effect is 

driven by two competing factors. In addition to capital and product conversion costs, amended 

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 undepreciated book value of existing 

tooling and equipment rendered obsolete by the amended 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 can be attributed to more costly 

production components and materials, higher inventory carrying to sell more products with more 

expensive components, and higher accounts receivable for more expensive products. Depending 

on these two competing factors, cash flow can either be positively or negatively affected in the 

year the standard takes effect.

In the years following the compliance date of the standards, the impact on cash flow 

depends on the operating revenue. Under the preservation of gross margin markup scenario, 

more stringent TSLs typically have a positive impact on cash flows relative to the no-newstandards-case because manufacturers are able to earner higher operating profit at each TSL in 

the standards case, which increases cash flow from operations.
12-16

12.5.2 Commercial Prerinse Spray Valve Industry Financial Impacts

Table 12.5.1 through Table 12.5.8 provide the INPV estimates for the commercial 

prerinse spray valves for each conversion cost scenario. Additionally, these impacts are 

presented for the industry as a whole, as well as each equipment class individually. Figure 12.5.1

and Figure 12.5.2 present the annual net cash flows for all commercial prerinse spray valve 

manufacturing for each conversion cost scenario.

Table 12.5.1 Manufacturer Impact Analysis for All Commercial Prerinse Spray Valves –

Sourced Components Capital Conversion Costs

No-NewStandards

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 9.1 8.5 8.1 8.0 8.0

Change in 

INPV*

2014$ millions - (0.6) (1.0) (1.1) (1.1)

% - (7.0) (11.5) (12.1) (12.1)

*Values in parenthesis indicate negative numbers.

Table 12.5.2 Manufacturer Impact Analysis for All Commercial Prerinse Spray Valves –

Fabricated Components Capital Conversion Costs

No-NewStandards

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 9.1 7.7 7.2 7.1 7.1

Change in 

INPV*

2014$ millions - (1.4) (1.9) (2.0) (2.0)

% - (15.0) (21.0) (21.6) (21.6)

*Values in parenthesis indicate negative numbers.

Table 12.5.3 Manufacturer Impact Analysis for Light Duty Commercial Prerinse Spray 

Valves – Sourced Components Capital Conversion Costs

No-NewStandards

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 1.8 1.7 1.6 1.6 1.6

Change in 

INPV*

2014$ millions - (0.1) (0.2) (0.2) (0.2)

% - (5.9) (11.8) (11.8) (11.8)

*Values in parenthesis indicate negative numbers.
12-17

Table 12.5.4 Manufacturer Impact Analysis for Light Duty Commercial Prerinse Spray 

Valves – Fabricated Components Capital Conversion Costs

No-NewStandards 

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 1.8 1.7 1.6 1.6 1.6

Change in 

INPV*

2014$ millions - (0.1) (0.2) (0.2) (0.2)

% - (5.9) (11.8) (11.8) (11.8)

*Values in parenthesis indicate negative numbers.

Table 12.5.5 Manufacturer Impact Analysis for Standard Duty Commercial Prerinse Spray 

Valves – Sourced Components Capital Conversion Costs

No-NewStandards

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 4.6 3.2 3.1 3.1 1.7

Change in 

INPV*

2014$ millions - (1.3) (1.5) (1.5) (2.8)

% - (28.7) (32.3) (32.3) (62.3)

*Values in parenthesis indicate negative numbers.

Table 12.5.6 Manufacturer Impact Analysis for Standard Duty Commercial Prerinse Spray 

Valves – Fabricated Components Capital Conversion Costs

No-NewStandards

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 4.6 3.1 2.9 2.9 1.5

Change in 

INPV*

2014$ millions - (1.5) (1.6) (1.6) (3.0)

% - (32.5) (36.1) (36.1) (66.1)

*Values in parenthesis indicate negative numbers.

Table 12.5.7 Manufacturer Impact Analysis for Heavy Duty Commercial Prerinse Spray 

Valves – Sourced Components Capital Conversion Costs

No-NewStandards

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 2.7 3.5 3.4 3.3 4.7

Change in 

INPV*

2014$ millions - 0.8 0.6 0.6 1.9

% - 28.5 23.3 20.8 70.8

*Values in parenthesis indicate negative numbers.
12-18

Table 12.5.8 Manufacturer Impact Analysis for Heavy Duty Commercial Prerinse Spray 

Valves – Fabricated Components Capital Conversion Costs

No-NewStandards

Case

Trial Standard Level

1 2 3 4

INPV 2014$ millions 2.7 2.9 2.7 2.6 4.0

Change in 

INPV*

2014$ millions - 0.2 (0.1) (0.1) 1.2

% - 8.0 (2.0) (4.5) 45.6

*Values in parenthesis indicate negative numbers.

Figure 12.5.1 Industry Annual Free Cash Flows for All Commercial Prerinse Spray Valves 

- Sourced Components Capital Conversion Costs Scenario
12-19

Figure 12.5.2 Industry Annual Free Cash Flows for All Commercial Prerinse Spray 

Valves–Fabricated Components Capital Conversion Costs Scenario

12.6 IMPACTS ON SMALL COMMERCIAL PRERINSE SPRAY VALVE 

MANUFACTURERS

DOE conducted a more focused inquiry of the companies that could be small business 

manufacturers of products covered by this rulemaking. Commercial prerinse spray valves 

manufacturing is classified under NAICS 332919, “Other metal valve and pipe fitting 

manufacturing.” The SBA sets a threshold of 500 employees or less for an entity to be 

considered as a small business for this category.

To estimate the number of small businesses that could be affected by amended energy 

conservation standards, DOE conducted a market survey using public information to identify 

potential small manufacturers. DOE reviewed its CCMS, EPA’s WaterSense program database, 

individual company websites, and various marketing research tools (e.g., Hoover’s reports) to 

create a list of companies that import, assemble, or otherwise manufacture commercial prerinse 

spray valves covered by this rulemaking. DOE excluded companies that do not offer equipment 

covered by this rulemaking, do not meet the definition of a “small business,” or are foreign 

owned and operated.

DOE identified 11 commercial spray valve manufacturers selling commercial prerinse 

spray valves in the United States, eight of which are small businesses. DOE contacted all 

identified commercial prerinse spray valve manufacturers for interviews. Ultimately, no 

manufacturers agreed to participate in an interview.

The eight small domestic commercial spray valve manufacturers account for 

approximately 83 percent of commercial spray valve basic models currently on the market. The 

small domestic manufacturers are BK Resources, Bricor Analytical, Component Hardware 
12-20

Group, Fisher Manufacturing, Krowne Metal Corp, Niagara Conservation, Strahman Valves, and 

T&S Brass. The remaining 17 percent of commercial spray valve basic models on the market are 

offered by three large manufacturers—Chicago Faucet, Mueller Industries, and Zurn Industries.

Using basic model counts, DOE estimated industry conversion costs among the 

distribution of small and large manufacturers. Using its count of manufacturers, DOE calculated 

capital conversion costs (under both capital conversion costs scenarios) and product conversion 

costs for an average small manufacturer versus an average large manufacturer. To provide 

context on the size of the conversion costs relative to the size of the businesses, DOE examined

the conversion costs relative to annual revenue and annual operating profit under the proposed 

standard level for the two capital conversion cost scenarios considered in the MIA, as shown in

Table 12.6.1 and Table 12.6.2. The current annual revenue and annual operating profit estimates 

are derived from the GRIM’s industry revenue calculations and the market share breakdowns of 

small versus large manufacturers. Due to the lack of direct market share data for individual 

manufacturers, DOE used basic model counts as a percent of total basic models available on the 

market as a proxy for market share.

Table 12.6.1 Comparison of Conversion Costs for an Average Small and an Average Large 

Manufacturer at TSL 3 – Sourced Components Capital Conversion Costs Scenario

Capital 

Conversion Cost

2014$ millions

Product 

Conversion Cost

2014$ millions

Conversion Costs 

/ Conversion 

Period Revenue*

%

Conversion Costs / 

Conversion Period 

Operating Profit*

%

Small 

Manufacturer 0.01 0.19 9 81

Large 

Manufacturer 0.01 0.10 8 79

* The conversion period, the time between the final rule publication year and the analysis compliance year for this rulemaking, is 

3 years.

Table 12.6.2 Comparison of Conversion Costs for an Average Small and an Average Large 

Manufacturer at TSL 3 – Fabricated Components Capital Conversion Costs Scenario

Capital 

Conversion Cost

2014$ millions

Product 

Conversion Cost

2014$ millions

Conversion Costs 

/ Conversion 

Period Revenue*

%

Conversion Costs / 

Conversion Period 

Operating Profit*

%

Small 

Manufacturer 0.11 0.19 13 120

Large 

Manufacturer 0.08 0.10 14 129

* The conversion period, the time between the final rule publication year and the analysis compliance year for this rulemaking, is 

3 years.

At the proposed level (TSL 3), depending on the capital conversion cost scenario, DOE 

estimates total conversion costs for an average small manufacturer to range from $200,000 for 

the sourced components capital conversion costs scenario, to $300,000 for the fabricated 

components capital conversion costs scenario. This suggests that an average small manufacturer 

would need to reinvest roughly 81 to 120 percent of its operating profit per year over the 3-year 

conversion period to comply with standards.
12-21

For an average large manufacturer, at TSL 3 the total conversion costs range from 

$110,000 for the sourced components capital conversion costs scenario, to $180,000 for the 

fabricated components capital conversion costs scenario, respectively. This suggests that an 

average large manufacturer would need to reinvest roughly 79 percent to 129 percent of its 

commercial prerinse spray valve-related operating profit per year over the 3-year conversion 

period.

12.7 OTHER IMPACTS

12.7.1 Employment

For commercial prerinse spray valves, DOE used the GRIM to estimate the domestic 

labor expenditures and number of domestic production workers in the no-new-standards case and 

at each TSL from 2014 to 2048. DOE used the labor content of each product and the MPCs from 

the engineering analysis to estimate the total annual labor expenditures associated with 

commercial prerinse spray valves sold in the United States. Using statistical data from the most 

recent U.S. Census Bureau’s 2011 Annual Survey of Manufacturers (ASM) as well as market 

research, DOE estimated that 100 percent of commercial prerinse spray valves sold in the United 

States are assembled domestically, and hence that portion of total labor expenditures is 

attributable to domestic labor. Labor expenditures for the manufacturing of equipment is a 

function of the labor intensity of the product, the sales volume, and an assumption that wages in 

real terms remain constant.

Using the GRIM, DOE forecasted the domestic labor expenditure for commercial 

prerinse spray valve production labor in 2019 will be approximately $2.0 million. Using the 

$21.86 hourly wage rate, including fringe benefits and 2,039 production hours per year per 

employee found in the 2011 ASM, DOE estimated there will be approximately 44 domestic 

production workers involved in assembling and, to a lesser extent, fabricating components for 

commercial prerinse spray valves in 2019, the year in which any amended standards would go 

into effect. In addition, DOE estimated that 22 non-production employees in the United States 

will support commercial prerinse spray valve production. The employment spreadsheet of the 

commercial prerinse spray valve GRIM shows the annual domestic employment impacts in 

further detail.

The production worker estimates in this section only cover workers up to the line 

supervisor level who are directly involved in fabricating and assembling commercial prerinse 

spray valves within an original equipment manufacturer facility. Workers performing services 

that are closely associated with production operations, such as material handling with a forklift, 

are also included as production labor. Additionally, the employment impacts shown are 

independent of the employment impacts from the broader U.S. economy, which are documented 

in chapter 16 of the TSD.

Table 12.7.1 depicts the potential levels of production employment that could result 

following amended energy conservation standards as calculated by the GRIM. The employment 

levels shown reflect the scenario in which manufacturers continue to produce the same scope of 

covered products in domestic facilities and domestic production is not shifted to lower labor cost 
12-22

countries. The following discussion includes a qualitative evaluation of the likelihood of 

negative domestic production employment impacts at the various TSLs.

Table 12.7.1 Total Number of Domestic Commercial Prerinse Spray Valve Production 

Workers, 2019

No-NewStandards

Case

Trial Standard Level

1 2 3 4

Total Number of Domestic Production 

Workers in 2019 (without changes in 

production locations)

44 44 44 44 44

Potential Changes in Domestic 

Production Workers in 2019 - - - - -

Figure 12.7.1 shows total annual domestic employment levels for each TSL as calculated 

by the GRIM.*

*Employment is not predicted to change between the No-New-Standards Case and the Standards Case TSLs. Therefore, only one employment 

trend line is visible.

Figure 12.7.1 Total Commercial Prerinse Spray Valve Industry Domestic Employment by 

Year

The design option specified for achieving greater efficiency levels (i.e., changing the total 

spray hole area of the commercial prerinse spray valve nozzle) does not increase the labor 

content (measured in dollars) of commercial prerinse spray valves at any EL, nor does it increase 

total MPC. Additionally, total industry shipments are forecasted to be constant across TSLs. 

Therefore, DOE predicts no change in domestic manufacturing employment levels, provided 

manufacturers do not relocate production facilities outside of the United States.
12-23

12.7.2 Production Capacity

Less than 20 percent of shipments of commercial prerinse spray valves already comply 

with the proposed amended energy conservation standards proposed in this rulemaking. Not 

every manufacturer that ships commercial prerinse spray valves offers products that meet these 

proposed amended energy conservation standards. However, because manufacturers would not 

need to make substantial platform changes by the 2019 compliance date in order to upgrade their 

equipment to meet the amended energy conservation standards proposed in this rulemaking, 

DOE does not foresee any impact on manufacturing capacity during the period leading up to the 

compliance date.

12.7.3 Cumulative Regulatory Burden 

While any one regulation may not impose a significant burden on manufacturers, the 

combined effects of several current or impending regulations may have serious consequences for 

some manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a 

single regulation may overlook this cumulative regulatory burden. In addition to energy 

conservation standards, other regulations can significantly affect manufacturers’ financial 

operations. Multiple regulations affecting the same manufacturer can strain profits and can 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 energy conservation standards rulemakings.

For the cumulative regulatory burden, DOE considers other DOE regulations that could 

affect commercial prerinse spray valve manufacturers that will take effect approximately 3 years 

before or after the analysis compliance date of amended energy conservation standards. The 

compliance years and expected industry conversion costs of energy conservation standards that 

may also affect commercial prerinse spray valve manufacturers are indicated in Table 12.7.2.

Table 12.7.2 Compliance Dates and Expected Conversion Expenses of Federal Energy 

Conservation Standards Affecting Commercial Prerinse Spray Valve Manufacturers

Regulation Approximate Compliance 

Date Estimated Conversion Costs

General Service Fluorescent Lamps

80 FR 4041 (January 26, 2015) 1/26/2018 $38.6 million

Commercial Refrigerators, Freezers and 

Refrigerator-Freezers

79 FR 17725 (March 28, 2014)

3/27/2017 $43.1 million

External Power Supplies

79 FR 7846 (February 10, 2014) 2/10/2016 $43.4 million

In addition to DOE’s energy conservation regulations for commercial prerinse spray 

valves and other products also sold by commercial prerinse spray valve manufacturers, other 

existing and pending regulations apply to commercial prerinse spray valves. In response to the 

Framework document and public meeting for this rulemaking, manufacturers and trade groups 

provided comments relating to regulatory burdens associated with third-party and international 
12-24

industry standards and certification programs, including standards set by American Society for 

Testing and Materials (ASTM F2324).and American Society for Mechanical Engineers (ASME 

A112.18.1/CSA B125.1).

Manufacturers also commented that state water efficiency regulations (e.g., California, 

Texas, and Massachusetts) would contribute to cumulative regulatory burdens. However, besides 

California, no other state water conservation standards differ from Federal standards. California has 

an additional cleanability standard, which requires that “commercial pre-rinse spray valves 

manufactured on or after January 1, 2006, shall be capable of cleaning 60 plates in an average 

time of not more than 30 seconds per plate.”3 However, this standard has been in place for over a 

decade.

12.8 CONCLUSION

The following section summarizes the likely range of impacts on commercial prerinse 

spray valve manufacturers at each TSL in the standards case. While DOE believes these 

scenarios bound the range of the most plausible impacts on manufacturers, some circumstances 

could cause manufacturers to experience impacts outside this range.

At TSL 1, DOE estimates impacts on INPV to range from -$1.4 million to -$0.6 million, 

or a change in INPV of -15.0 percent to -7.0 percent for the Fabricated Components and Sourced 

Components Capital Conversion Costs scenarios, respectively. At this level, industry free cash 

flow is estimated to decrease by as much as 142.8 percent to -$0.2 million, compared to the nonew-standards case value of $0.5 million in the year leading up to the amended energy 

conservation standards. As DOE forecasts that approximately 65 percent of commercial prerinse 

spray valves in the no-new-standards case shipments scenario will meet TSL 1 in the first year 

that standards are in effect (2019), 35 percent of the market is affected at this standard level. The 

impact on INPV at TSL 1 stems exclusively from the conversion costs associated with the 

conversion of baseline units to those meeting the standards set at TSL 1. At TSL 1, because the 

industry already produces a substantial number of products at this efficiency level, product and 

capital conversion costs are limited to approximately $1.2 million for the Sourced Components 

Capital Conversion Costs scenario and $2.0 million for the Fabricated Components Capital 

Conversion Costs scenario.

DOE notes that the shift of 20 percent of shipments from the Standard Duty to Heavy 

Duty equipment class does not have a significant impact on overall INPV because MPCs are the 

same across all equipment classes. For this reason, and because per unit product conversion costs 

are the same for any product that has a change in flow rate and spray force at each efficiency 

level, and because capital conversion costs are a function of the material of the spray nozzle 

rather than the spray force (i.e., equipment class), DOE does not believe equipment class 

switching will have a detrimental impact on commercial prerinse spray valve manufacturers 

beyond the impact felt in the absence of equipment class switching.

At TSL 2, DOE estimates impacts on INPV to range from -$1.9 million to -$1.0 million, 

or a change in INPV of -21.0 percent to -11.5 percent for the Fabricated Components and 

Sourced Components Capital Conversion Costs scenarios, respectively. At this level, industry 

free cash flow is estimated to decrease by as much as 198.8 percent to -$0.5 million, compared to 
12-25

the no-new-standards case value of $0.5 million in the year leading up to the amended energy 

conservation standards. As it is estimated that only approximately 20 percent of commercial 

prerinse spray valves will meet the efficiency levels specified at TSL 2 in the first year that 

standards are in effect (2019), a substantial fraction of the market is affected at this standard 

level. As with TSL 1, the impact on INPV at TSL 2 stems exclusively from the conversion costs 

associated with the conversion of lower efficiency units to those meeting the standards set at 

TSL 2. At TSL 2, because the majority of commercial prerinse spray valves will have to be 

updated to reach the standard level, product and capital conversion costs are estimated to be 

approximately $1.9 million for the Sourced Components Capital Conversion Costs scenario and 

$2.9 million for the Fabricated Components Capital Conversion Costs scenario. Again, DOE 

notes that the shift of 20 percent of shipments from the Standard Duty to Heavy Duty equipment 

class, at this TSL does not have a significant impact on overall INPV due to the fact that MPCs 

are constant across all equipment classes and conversion costs are not a function of equipment 

class.

At TSL 3, DOE estimates impacts on INPV to range from -$2.0 million to -$1.1 million, 

or a change in INPV of -21.6 percent to -12.1 percent for the Fabricated Components and 

Sourced Components Capital Conversion Cost scenarios, respectively. At this level, industry free 

cash flow is estimated to decrease by as much as 204.4 percent to -$0.5 million, compared to the 

no-new-standards case value of $0.5 million in the year leading up to the amended energy 

conservation standards. As it is estimated that less than 20 percent of commercial prerinse spray 

valves will meet the efficiency levels specified at TSL 3 in the first year that standards are in 

effect (2019), a substantial fraction of the market is affected at this standard level. Again, the 

impact on INPV at TSL 3 stems exclusively from the conversion costs associated with the 

conversion of lower efficiency units to those meeting the standards set at TSL 3. At this TSL, 

because the majority of commercial prerinse spray valves will have to be updated to reach the 

standard level, product and capital conversion costs are estimated to be approximately $2.0 

million for the Sourced Components Capital Conversion Costs scenario and $3.0 million for the 

Fabricated Components Capital Conversion Costs model. Again, DOE notes that the shift of 20 

percent of shipments from the Standard Duty to Heavy Duty equipment class, at this TSL does 

not have a significant impact on overall INPV due to the fact that MPCs are constant across all 

equipment classes and conversion costs are not a function of equipment class.

Finally, at TSL 4, DOE estimates impacts on INPV to range from -$2.0 million to -$1.1 

million, or a change in INPV of -21.6 percent to -12.1 percent for the Fabricated Components 

and Sourced Components Capital Conversion Cost scenarios, respectively. Impacts are the same 

as at TSL 3 due to the fact that no Standard Duty commercial prerinse spray valves at efficiency 

level 2 (greater than 0.94 gpm and less than or equal to 0.97 gpm) are currently marketed. At this 

level, industry free cash flow is estimated to decrease by as much as 204.4 percent to -$0.5 

million, compared to the no-new-standards case value of $0.5 million in the year leading up to 

the amended energy conservation standards. Again, the impact on INPV at TSL 4 stems 

exclusively from the conversion costs associated with the conversion of lower efficiency units to 

those meeting the standards set at TSL 4. At this TSL, because the majority of commercial 

prerinse spray valves will have to be updated to reach the standard level, product and capital 

conversion costs are estimated to be approximately $2.0 million for the Sourced Components 

Capital Conversion Costs scenario and $3.0 million for the Fabricated Components Capital 

Conversion Costs scenario. DOE notes that the shift of 45 percent of shipments from the 
12-26

Standard Duty to Heavy Duty equipment class, at this TSL does not have a significant impact on 

overall INPV due to the fact that MPCs are constant across all equipment classes and conversion 

costs are not a function of equipment class.
12-27

REFERENCES

1. The size standards are available on the SBA’s website at www.sba.gov/content/table-smallbusiness-size-standards.

2. McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies, 

3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

3. California Energy Commission, 2014 Appliance Efficiency Regulations, Report No. CEC400-2014-009-CMF, available at http://www.energy.ca.gov/2014publications/CEC-400-

2014-009/CEC-400-2014-009-CMF.pdf. 
13-i 

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 EMISSIONS FACTORS ................................................................... 13-3

13.4 UPSTREAM FACTORS .................................................................................................. 13-4

13.5 EMISSIONS IMPACT RESULTS ................................................................................... 13-5

REFERENCES .......................................................................................................................... 13-9

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors ............................................................................. 13-4

Table 13.4.1 Electricity Upstream Emissions Factors ............................................................... 13-5

Table 13.4.2 Natural Gas Upstream Emissions Factors ............................................................ 13-5

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Commercial 

Prerinse Spray Valves ........................................................................................ 13-5

LIST OF FIGURES

Figure 13.5.1 Commercial Prerinse Spray Valves: CO2 Total Emissions Reduction ............... 13-6

Figure 13.5.2 Commercial Prerinse Spray Valves: SO2 Total Emissions Reduction ................ 13-6

Figure 13.5.3 Commercial Prerinse Spray Valves: NOx Total Emissions Reduction ............... 13-7

Figure 13.5.4 Commercial Prerinse Spray Valves: Hg Total Emissions Reduction ................. 13-7

Figure 13.5.5 Commercial Prerinse Spray Valves: N2O Total Emissions Reduction ............... 13-8

Figure 13.5.6 Commercial Prerinse Spray Valves: CH4 Total Emissions Reduction ............... 13-8
13-1 

CHAPTER 13. EMISSIONS IMPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the 

effect of potential energy conservation standards on power sector emissions of carbon dioxide 

(CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), and mercury (Hg). The second component 

estimates the impacts of a potential standard on emissions of two additional greenhouse gases 

(GHG), 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 the U.S. Department of Energy’s (DOE’s) FFC Statement 

of Policy. 76 FR 51282 (Aug. 18, 2011). 

The analysis of most power sector emissions uses marginal emissions intensity factors 

derived from runs of DOE’s National Energy Modeling System – Building Technologies 

(NEMS-BT) model, described in chapter 15 of this TSD. DOE used the version of NEMS based 

on the Annual Energy Outlook 2014 (AEO2014).

1

 Each annual version of NEMS incorporates 

the projected impacts of existing air quality regulations on emissions. AEO2014 generally 

represents current Federal and state legislation and final implementation regulations in place as 

of the end of October 2013. Where applicable, site emissions of CO2 and NOx are estimated 

using emissions intensity factors from a publication of the Environmental Protection Agency 

(EPA).

2

Combustion emissions of CH4 and N2O (from power plants and from upstream 

combustion) are estimated using emissions intensity factors published by the EPA, GHG 

Emissions Factors Hub.a 

The FFC upstream emissions are estimated based on the methodology developed by 

Coughlin (2013).3

 The upstream emissions include both emissions from fuel combustion during 

extraction, processing, and transportation of fuel, and “fugitive” emissions (direct leakage to the 

atmosphere) of CH4 and CO2. 

The emissions intensity factors are expressed in terms of physical units per megawatthours (MWh) or million British thermal units (MMBtu) of site energy savings. Total emissions 

reductions are estimated using the energy savings calculated in the national impact analysis 

(chapter 10 of this TSD).

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’

a http://www.epa.gov/climateleadership/inventory/ghg-emissions.html
13-2 

global warming potential (GWP) over a 100-year time horizon. Based on the Fifth Assessment 

Report of the Intergovernmental Panel on Climate Change,4 DOE used GWP values of 28 for 

CH4 and 265 for N2O.b

 

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS

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

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

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

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

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

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

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

Circuit), but parts of it remained in effect. In 2011, EPA issued a replacement for CAIR, the 

Cross-State Air Pollution Rule (CSAPR). 76 FR 48208 (Aug. 8, 2011). On August 21, 2012, the 

D.C. Circuit issued a decision to vacate CSAPR.c The court ordered EPA to continue 

administering CAIR. The emissions factors used for this analysis, which are based on AEO2014, 

assume that CAIR remains a binding regulation through 2040.d

 

The attainment of emissions caps is typically flexible among affected EGUs and is 

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

regulations, any excess SO2 emissions allowances resulting from the lower electricity demand 

caused by the imposition of an efficiency standard could be used to permit offsetting increases in 

SO2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was 

uncertainty about the effects of efficiency standards on SO2 emissions covered by the existing 

cap-and-trade system, but it concluded that no reductions in power sector emissions would occur 

for SO2 as a result of standards. 

Beginning in 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).e 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 

b The values are without inclusion of climate-carbon feedbacks in response to emissions of the indicated non-CO2 

gases.

c See EME Homer City Generation, LP v. EPA, 696 F.3d 7, 38 (D.C. Cir. 2012).

d On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit. The case will now return to 

the D.C. Circuit 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 onAEO2014, it 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. e On July 20, 2012, EPA announced a partial stay, for a limited duration, of the effectiveness of national new source 

emission standards for hazardous air pollutants from coal- and oil-fired electric utility steam generating units. 

www.reginfo.gov/public/do/eAgendaViewRule?pubId=201110&RIN=2060-AP52
13-3 

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. 

AEO2014 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, NEMS shows a 

reduction in SO2 emissions when electricity demand decreases (e.g., as a result of energy 

efficiency standards). Emissions will be far below the cap established by CAIR, so it is unlikely 

that excess SO2 emissions allowances resulting from the lower electricity and natural gas 

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 and natural gas 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 Hg emissions reductions using the NEMS-BT based on AEO2014, 

which incorporates the MATS. 

13.3 POWER SECTOR EMISSIONS FACTORS 

The analysis of power sector emissions uses marginal emissions intensity factors derived 

from runs of DOE’s NEMS-BT model, using the version updated to the Annual Energy Outlook 

2014 (AEO2014). To model the impact of a standard, DOE inputs a reduction to annual energy 

demand for the corresponding end use in the appropriate start year. The NEMS-BT model is run 

with the decremented energy demand to determine the modified build-out of capacity, fuel use 

and power sector emissions. A marginal emissions intensity factor is defined by dividing the 

reduction in the total emissions of a given pollutant by the reduction in total generation (in 

billion kWh). DOE uses the site energy savings multiplied by a transmission and distribution 

(T&D) loss factor to estimate the reduction in generation for each trial standard level (TSL). 

Details on the approach used may be found in Coughlin (2013).3 

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 buildings. DOE used the commercial water heating end use load shape. The 

average factors for each year take into account the projected shares of each of the sources in total 

electricity generation. 
13-4 

The power plant emissions factor for NOx is an average for the entire United States. The 

marginal calculation based on the NEMS-BT model accounts for the fact that NOx emissions are 

capped in some states. 

Table 13.3.1 Power Plant Emissions Factors 

2019 2020 2025 2030 2035 2040

CO2 kg/MWh 765 723 642 579 529 484

SO2 g/MWh 806 712 556 468 392 351

NOx g/MWh 626 572 478 417 368 333

Hg g/MWh 0.00249 0.00220 0.00172 0.00144 0.00121 0.00108

N2O g/MWh 7.2 7.2 7.1 6.9 6.6 6.4

CH4 g/MWh 50 50 49 48 46 45

13.4 UPSTREAM FACTORS 

The upstream emissions accounting uses the same approach as the upstream energy 

accounting described in appendix 10-B of this TSD (see also Coughlin 2013).3 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).5 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 greenhouse gas 

(GHG) reporting requirements for the petroleum and natural gas industries.6,7 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 and Table 13.4.2 present the 

electricity and natural gas upstream emissions factors for selected years, respectively. The caps 

that apply to power sector NOx emissions do not apply to upstream combustion sources. 
13-5 

Table 13.4.1 Electricity Upstream Emissions Factors

2019 2020 2025 2030 2035 2040

CO2 kg/MWh 28.84 29.07 29.43 29.72 29.88 29.81

SO2 g/MWh 5.12 5.05 5.08 4.92 4.74 4.64

NOX g/MWh 364.01 368.10 375.14 382.14 386.82 386.81

Hg g/MWh 0.000012 0.000012 0.000011 0.000011 0.000010 0.000010

N2O g/MWh 0.25 0.25 0.25 0.24 0.23 0.23

CH4 g/MWh 2123.48 2149.27 2194.56 2215.68 2247.89 2254.56

Table 13.4.2 Natural Gas Upstream Emissions Factors 

2019 2020 2025 2030 2035 2040

CO2 kg/mcf 7.069 7.115 7.217 7.330 7.363 7.353

SO2 g/mcf 0.030 0.030 0.031 0.031 0.032 0.032

NOX g/mcf 100.239 101.067 102.860 104.857 105.440 105.271

N2O g/mcf 0.011 0.011 0.011 0.012 0.012 0.012

CH4 g/mcf 657.099 659.304 665.033 666.489 669.522 670.441

13.5 EMISSIONS IMPACT RESULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of 

products sold from 2019-2048 for each TSL.

Table 13.5.1 Cumulative Emissions Reduction for Potential Standards for Commercial 

Prerinse Spray Valves 

Emissions TSL

1 2 3 4

Power Sector and Site Emissions

CO2 (million metric tons) 0.78 4.58 5.27 2.05

NOX (thousand tons) 0.85 4.99 5.73 2.23

Hg (tons) 0.00109 0.00640 0.00736 0.00286

N2O (thousand tons) 0.00634 0.03712 0.04269 0.01661

CH4 (thousand tons) 0.05 0.27 0.31 0.12

SO2 (thousand tons) 0.36 2.09 2.40 0.93

Upstream Emissions

CO2 (million metric tons) 0.07 0.43 0.49 0.19

NOX (thousand tons) 1.11 6.51 7.49 2.91

Hg (tons) 0.00001 0.00005 0.00006 0.00002

N2O (thousand tons) 0.00028 0.00166 0.00191 0.00074

CH4 (thousand tons) 6.92 40.55 46.63 18.15

SO2 (thousand tons) 0.004 0.025 0.028 0.011

Total Emissions

CO2 (million metric tons) 0.85 5.01 8.76 2.24

NOX (thousand tons) 1.96 11.50 13.22 5.15

Hg (tons) 0.00110 0.00645 0.00742 0.00289

N2O (thousand tons) 0.00662 0.03878 0.04459 0.01735

CH4 (thousand tons) 6.97 40.83 46.94 18.27

SO2 (thousand tons) 0.36 2.11 2.43 0.94
13-6 

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 from 2019-2048. 

Figure 13.5.1 Commercial Prerinse Spray Valves: CO2 Total Emissions Reduction

Figure 13.5.2 Commercial Prerinse Spray Valves: SO2 Total Emissions Reduction
13-7 

Figure 13.5.3 Commercial Prerinse Spray Valves: NOx Total Emissions Reduction

Figure 13.5.4 Commercial Prerinse Spray Valves: Hg Total Emissions Reduction 
13-8 

Figure 13.5.5 Commercial Prerinse Spray Valves: N2O Total Emissions Reduction

Figure 13.5.6 Commercial Prerinse Spray Valves: CH4 Total Emissions Reduction
13-9 

REFERENCES

1. U.S. Department of Energy—Energy Information Administration. Annual Energy Outlook

2014 with Projections to 2040. 2014. Washington, DC.

http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf.

2. U.S. Environmental Protection Agency. Compilation of Air Pollutant Emission Factors,

AP-42, Fifth Edition, Volume I: Stationary Point and Area Sources. 1998.

http://www.epa.gov/ttn/chief/ap42/index.html.

3. Coughlin, K. Projections of Full-Fuel-Cycle Energy and Emissions Metrics. 2013.

Lawrence Berkeley National Laboratory: Berkeley, CA. Report No. LBNL-6025E.

4. IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group

I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

(Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y.

Xia, V. Bex and P.M. Midgley, editors). 2013. Cambridge University Press, Cambridge,

United Kingdom and New York, NY, USA. Chapter 8.

5. 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.

6. U.S. Environmental Protection Agency. Fugitive Emissions Reporting from the Petroleum

and Natural Gas Industry. 2009. U.S. Environmental Protection Agency. Washington, DC.

7. 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.
14-i 

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 ................................................................ 14-1

14.2.3 Current Approach and Key Assumptions ............................................................. 14-2

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS .............................................. 14-5

14.4 RESULTS ......................................................................................................................... 14-5

REFERENCES .......................................................................................................................... 14-8

LIST OF TABLES

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007$ 

per metric ton CO2) ............................................................................................ 14-4

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007$ 

per metric ton CO2) ............................................................................................ 14-4

Table 14.4.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

Commercial Prerinse Spray Valves Trial Standard Levels................................ 14-6

Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Commercial Prerinse Spray Valves Trial Standard Levels................................ 14-6

Table 14.4.3 Estimates of Present Value of NOx Emissions Reduction under Commercial 

Prerinse Spray Valves Trial Standard Levels .................................................... 14-7
14-1 

CHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS 

14.1 INTRODUCTION

As part of its assessment of energy conservation standards for commercial prerinse spray 

valves, the U.S. Department of Energy (DOE) estimated the monetary benefits likely to result 

from the reduced emissions of carbon dioxide (CO2) and nitrogen oxides (NOx) that are expected 

to result from each of the trial standard levels (TSLs) considered. This chapter summarizes the 

basis for the monetary values used for each of these emissions and presents the benefits estimates 

considered. 

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon 

The social cost of carbon (SCC) is an estimate of the monetized damages associated with 

an incremental increase in carbon emissions in a given year. It is intended to include (but is not 

limited to) changes in net agricultural productivity, human health, property damages from 

increased flood risk, and the value of ecosystem services. Estimates of the SCC are provided in 

dollars per metric ton of CO2. A domestic SCC value is meant to reflect the value of damages in 

the United States resulting from a unit change in CO2 emissions, while a global SCC value is 

meant to reflect the value of damages worldwide.

Under section 1(b) of Executive Order 12866, agencies must, to the extent permitted by 

law, “assess both the costs and the benefits of the intended regulation and, recognizing that some 

costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned 

determination that the benefits of the intended regulation justify its costs.” The purpose of the 

SCC estimates presented here is to allow agencies to incorporate the monetized social benefits of 

reducing 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.

As part of the interagency process that developed these SCC estimates, technical experts 

from numerous agencies met on a regular basis to explore the technical literature in relevant 

fields, discuss key model inputs and assumptions, and consider public comments. The main 

objective of this process was to develop a range of SCC values using a defensible set of input 

assumptions grounded in the existing scientific and economic literatures. In this way, key 

uncertainties and model differences transparently and consistently inform the range of SCC 

estimates used in the rulemaking process.

14.2.2 Monetizing Carbon Dioxide Emissions 

When attempting to assess the incremental economic impacts of CO2 emissions, the 

analyst faces a number of serious challenges. A report from the National Research Council1

points out that any assessment will suffer from uncertainty, speculation, and lack of information 
14-2 

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. Most Federal 

regulatory actions can be expected to have marginal impacts on global emissions. For such 

policies, the agency can estimate the benefits from reduced (or costs from increased) emissions 

in any future year by multiplying the change in emissions in that year by the SCC 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. 

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing CO2 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.

2 These interim values 

represented the first sustained interagency effort within the U.S. government to develop an SCC 

for use in regulatory analysis. The results of this preliminary effort were presented in several 

proposed and final rules. 

14.2.3 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular 

basis to generate improved SCC estimates, which were considered for this notice of proposed 

rulemaking (NOPR). Specifically, the group considered public comments and further explored 

the technical literature in relevant fields. The interagency group relied on three integrated 

assessment models (IAMs) commonly used to estimate the SCC: the FUND, DICE, and PAGE 

models.a These models are frequently cited in the peer-reviewed literature and were used in the 

last assessment of the Intergovernmental Panel on Climate Change. Each model was given equal 

weight in the SCC values that were developed. 

Each model takes a slightly different approach to model how changes in emissions result 

in changes in economic damages. A key objective of the interagency process was to enable a 

 

a The models are described in appendix 14A of this technical support document (TSD).
14-3 

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: (1) climate sensitivity, 

(2) socio-economic and emissions trajectories, and (3) discount rates. A probability distribution 

for climate sensitivity was specified as an input into all three models. In addition, the interagency 

group used a range of scenarios for the socio-economic parameters and a range of values for the 

discount rate. All other model features were left unchanged, relying on the model developers’ 

best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses.3 The 

2010 report is reproduced in appendix 14A of this TSD. Three values are based on the average 

SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The 

fourth value, which represents the 95th percentile SCC estimate across all three models at a 3-

percent discount rate, is included to represent higher-than-expected impacts from temperature 

change further out in the tails of the SCC distribution. The values grow in real terms over time, 

as depicted in Table 14.2.1. 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,b although preference is given to consideration of the global benefits of reducing 

CO2 emissions. 

The SCC values used for this NOPR 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 update4 from the interagency working group (revised November 2013).5

Table 14.2.2 shows the updated sets of SCC estimates in 5-year increments from 2010 to 2050. 

Appendix 14B of this TSD provides the full set of SCC estimates, as well as the 2013 report 

from the interagency group. The central value that emerges is the average SCC across models at 

the 3-percent discount rate. However, for purposes of capturing the uncertainties involved in 

regulatory impact analysis, the interagency group emphasizes the importance of including all 

four sets of SCC values. 

 

b It is recognized that this calculation for domestic values is 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.
14-4 

Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007$ per 

metric ton CO2) 

Year

Discount Rate 

%

5 3 2.5 3

Average Average Average 95th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007$ per 

metric ton CO2) 

Year

Discount Rate 

%

5 3 2.5 3

Average Average Average 95th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

It is important to recognize that a number of key uncertainties remain, and that current 

SCC estimates should be treated as provisional and revisable since they will evolve with 

improved scientific and economic understanding. The interagency group also recognizes that the 

existing models are imperfect and incomplete. The National Research Council report mentioned 

previously points out that there is tension between the goal of producing quantified estimates of 

the economic damages from an incremental ton of carbon and the limits of existing efforts to 

model these effects. There are a number of 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 interagency group intends to 

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 summary, in considering the potential global benefits resulting from reduced CO2

emissions, DOE used the values from the 2013 interagency report, which is reprinted in appendix 

14A of this TSD, escalated to 2014$ using the gross domestic product (GDP) price deflator. For 

each of the four cases specified, the values used for emissions in 2015 were $12.2, $41.2, $63.4, 
14-5 

and $121 per metric ton avoided. DOE derived values after 2050 using the relevant growth rates 

for the 2040–2050 period in the interagency update.

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

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

values, DOE discounted the values in each of the four cases using the specific discount rate that 

had been used to obtain the SCC values in each case. 

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NOx emissions from the TSLs 

it considered. As noted in chapter 13 of this TSD, new or amended energy conservation 

standards would reduce NOx emissions in those 22 states that are not affected by caps. DOE 

estimated the monetized value of NOx emissions reductions resulting from each of the TSLs 

considered based on environmental damage estimates found in the relevant scientific literature. 

Available estimates suggest a very wide range of monetary values, ranging from $483 to $4,965 

per ton in 2014$).6 In accordance with OMB guidance, DOE calculated a range of monetary 

benefits using each of the economic values for NOX and real discount rates of 3 percent and 7 

percent.

7

DOE is still evaluating appropriate values to use to monetize avoided SO2 and Hg 

emissions. It did not monetize these emissions for this NOPR.

14.4 RESULTS

Table 14.4.1 presents the global values of CO2 emissions reductions for each considered 

TSL. DOE calculated domestic values as a range from 7 percent to 23 percent of the global 

values, and these results are presented in Table 14.4.2. 
14-6 

Table 14.4.1 Estimates of Global Present Value of CO2 Emissions Reduction under 

Commercial Prerinse Spray Valves Trial Standard Levels

TSL

SCC Case*

5% discount 

rate, average

3% discount 

rate, average

2.5% discount 

rate, average

3% discount rate, 

95th percentile

million 2014$

Primary Energy Emissions

1 6.01 26.67 42.01 82.41

2 35.24 156.28 246.19 482.87

3 40.53 179.70 283.08 555.23

4 15.77 69.93 110.16 216.06

Upstream Emissions

1 0.55 2.46 3.88 7.61

2 3.23 14.42 22.74 44.59

3 3.72 16.58 26.15 51.27

4 1.45 6.45 10.18 19.95

Total Emissions

1 6.57 29.13 45.89 90.01

2 38.48 170.70 268.93 527.46

3 44.24 196.27 309.23 606.50

4 17.22 76.38 120.33 236.01

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $12.2, $41.2, 

$63.4, and $121 per metric ton (2014$).

Table 14.4.2 Estimates of Domestic Present Value of CO2 Emissions Reduction under 

Commercial Prerinse Spray Valves Trial Standard Levels 

TSL

SCC Case*

5% discount 

rate, average*

3% discount rate, 

average*

2.5% discount 

rate, average*

3% discount rate, 

95th percentile*

million 2014$

Primary Energy Emissions

1 0.4 to 1.4 1.9 to 6.1 2.9 to 9.7 5.8 to 19.0

2 2.5 to 8.1 10.9 to 35.9 17.2 to 56.6 33.8 to 111.1

3 2.8 to 9.3 12.6 to 41.3 19.8 to 65.1 38.9 to 127.7

4 1.1 to 3.6 4.9 to 16.1 7.7 to 25.3 15.1 to 49.7

Upstream Emissions

1 0.0 to 0.1 0.2 to 0.6 0.3 to 0.9 0.5 to 1.8

2 0.2 to 0.7 1.0 to 3.3 1.6 to 5.2 3.1 to 10.3

3 0.3 to 0.9 1.2 to 3.8 1.8 to 6.0 3.6 to 11.8

4 0.1 to 0.3 0.5 to 1.5 0.7 to 2.3 1.4 to 4.6

Total Emissions

1 0.5 to 1.5 2.0 to 6.7 3.2 to 10.6 6.3 to 20.7

2 2.7 to 8.8 11.9 to 39.3 18.8 to 61.9 36.9 to 121.3

3 3.1 to 10.2 13.7 to 45.1 21.6 to 71.1 42.5 to 139.5

4 1.2 to 4.0 5.3 to 17.6 8.4 to 27.7 16.5 to 54.3

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $12.2, $41.2, $63.4, and $121 per metric ton 

(2014$).

Table 14.4.3 shows the present value of cumulative NOx emissions reductions for each 

TSL, calculated using the average dollar-per-ton values and 3-percent and 7-percent discount 

rates.
14-7 

Table 14.4.3 Estimates of Present Value of NOx Emissions Reduction under Commercial 

Prerinse Spray Valves Trial Standard Levels

TSL 3% discount rate 7% discount rate

million 2014$

Power Sector Emissions

1 1.3 0.7

2 7.6 3.9

3 8.7 4.5

4 3.4 1.8

Upstream Emissions

1 1.7 0.8

2 9.7 4.9

3 11.1 5.6

4 4.3 2.2

Total Emissions

1 2.9 1.5

2 17.2 8.8

3 19.8 10.1

4 7.7 3.9
14-8 

REFERENCES

1. National Research Council. Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Use. 2009. National Academies Press. Washington, D.C.

www.nap.edu/catalog.php?record_id=12794.

2. Interagency Working Group on Social Cost of Carbon. Social Cost of Carbon for

Regulatory Impact Analysis under Executive Order 12866. February 2010. United States

Government. www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/SocialCost-of-Carbon-for-RIA.pdf.

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

Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. February

2010. http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Costof-Carbon-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. November 2013.

United States Government.

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf.

5. Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under

Executive Order 12866. Interagency Working Group on Social Cost of Carbon, United

States Government. May 2013; revised November 2013.

http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf.

6. 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, D.C.

www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_final_r

eport.pdf.

7. U.S. Office of Management and Budget (OMB). Circular A-4: Regulatory Analysis. 2003.

(Posted September 17, 2003) (Last accessed April 2013.)

http://www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a-4.pdf.
14A-i 

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-12

14A.4.4 Equilibrium Climate Sensitivity ............................................................... 14A-13

14A.4.5 Socio-Economic and Emissions Trajectories............................................ 14A-16

14A.4.6 Discount Rate ............................................................................................ 14A-18

14A.5 REVISED SCC ESTIMATES .................................................................................. 14A-25

14A.6 LIMITATIONS OF THE ANALYSIS ..................................................................... 14A-29

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND DAMAGE 

FUNCTIONS ............................................................................................................ 14A-32

14A.7.1 Extrapolation of Climate Damages to High Levels of Warming .............. 14A-32

14A.7.2 Failure to Incorporate Inter-Sectoral and Inter-Regional Interactions ...... 14A-33

14A.7.3 Imperfect Substitutability of Environmental Amenities ........................... 14A-34

14A.8 CONCLUSION ......................................................................................................... 14A-34

14A.9 REFERENCES ......................................................................................................... 14A-36

14A.10ANNEX..................................................................................................................... 14A-42

14A.10.1 Other (Non-CO2) Gases ............................................................................ 14A-43

14A.10.2 Extrapolating Emissions Projections to 2300 ........................................... 14A-45

LIST OF TABLES

Table 14A.1.1 Social Cost of CO2, 2010–2050 (2007$) ........................................................ 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-17

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$) .......................................... 14A-27

Table 14A.5.2 Social Cost of CO2, 2010–2050 (2007$) ...................................................... 14A-29

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between 

2010 and 2050 ............................................................................................... 14A-29

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation.................. 14A-33

Table 14A.10.1 Annual SCC Values: 2010–2050 (2007$) .................................................. 14A-42

Table 14A.10.2 2010 Global SCC Estimates at 2.5-Percent Discount Rate 

(2007$/ton CO2) ............................................................................................ 14A-50

Table 14A.10.3 2010 Global SCC Estimates at 3-Percent Discount Rate (2007$/ton CO2) 14A-50
14A-ii

Table 14A.10.4 2010 Global SCC Estimates at 5-Percent Discount Rate (2007$/ton CO2) 14A-51

Table 14A.10.5 Additional Summary Statistics of 2010 Global SCC Estimates ................. 14A-52

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-10

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity ..................................................................................................... 14A-15

Figure 14A.5.1 Level of Global GDP Across EMF Scenarios ............................................. 14A-28

Figure 14A.10.1 Sulphur Dioxide Emission Scenarios ........................................................ 14A-45

Figure 14A.10.2 Global Population, 2000–2300 (post-2100 extrapolations assume the 

population growth rate changes linearly to reach a zero growth rate by 2200)14A-47

Figure 14A.10.3 World GDP, 2000-2300 (post-2100 extrapolations assume GDP per capita 

growth declines linearly, reaching zero in 2300) .......................................... 14A-47

Figure 14A.10.4 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-48

Figure 14A.10.5 Global Net Land Use CO2 Emissions, 2000–2300 (post-2100 extrapolations 

assume emissions decline linearly, reaching zero in 2200) .......................... 14A-48

Figure 14A.10.6 Global Non-CO2 Radiative Forcing, 2000–2300 (post-2100 extrapolations 

assume constant non-CO2 radiative forcing after 2100) ............................... 14A-49

Figure 14A.10.7 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-49

Figure 14A.10.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO2), by Discount 

Rate* ............................................................................................................. 14A-51
14A-1 

APPENDIX 14A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT 

ANALYSIS UNDER EXECUTIVE ORDER 12866

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 EXECUTIVE SUMMARY

Under Executive Order (E.O.) 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.
14A-2 

The interagency group selected four SCC values for use in regulatory analyses (Table 

14A.1.1. Three values are based on the average SCC from three integrated assessment models, at 

discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC 

estimate across all three models at a 3-percent discount rate, is included to represent higher-thanexpected impacts from temperature change further out in the tails of the SCC distribution. 

Table 14A.1.1 Social Cost of CO2, 2010–2050 (2007$) 

Year Discount Rate

%

5 3 2.5 3

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. 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. We report estimates of the social cost of carbon in 

dollars per metric ton of CO2 throughout this document.a

 

When attempting to assess the incremental economic impacts of CO2 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 CO2 emissions. Under E.O. 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 

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 (the molecular weight of CO2 divided by the molecular weight of carbon = 44/12 

= 3.67). 
14A-3 

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 CO2 emissions into costbenefit 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 

CO2 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 $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5-, 

3-, and 2.5-percent discount rates, respectively. The fourth value is included to represent the 

higher-than-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 the Annex (section 14A.10) 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 2 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-4 

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 CO2 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 

(2007$), 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 CO2 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 $20 per ton CO2 for 2007 emission reductions (2007$). In addition, 

the 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 (2006$ for 2007 emissions). 

In 2009, an interagency process was initiated to offer a preliminary assessment of how 

best to quantify the benefits from reducing CO2 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-5 

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, 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.b These models are frequently cited in the peerreviewed literature and used in the IPCC assessment. Each model is given equal weight in the 

SCC values developed through this process, bearing in mind their different limitations (discussed 

below). 

These models are useful because they combine climate processes, economic growth, and 

feedbacks between the climate and the global economy into a single modeling framework. At the 

same time, they gain this advantage at the expense of a more detailed representation of the 

underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reducedform approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the 

possible advantages of this approach). Other IAMs may better reflect the complexity of the 

b 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-6 

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 socio-economic (gross domestic product (GDP) gross domestic product (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, socio-economic and emissions trajectories, and 

discount rates. A probability distribution for climate sensitivity was specified as an input into all 

three models. In addition, the interagency group used a range of scenarios for the socio-economic 

parameters and a range of values for the discount rate. All other model features were left 

unchanged, relying on the model developers’ best estimates and judgments. 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.
14A-7 

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.4.1.1 The DICE Model

The DICE model is an optimal growth model based on a global production function with 

an extra stock variable (atmospheric CO2 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, CO2 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 climaterelated 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 nonmarket 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.” 
14A-8 

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.c 

14A.4.1.2 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). 

14A.4.1.3 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 

 

c 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 re- 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.
14A-9 

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.d In the forestry and agricultural sectors, economic damages also depend on CO2

concentrations. 

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a 

relatively small effect on damage estimates (both positive and negative). However, he 

characterizes several omitted impacts as “big unknowns:” for instance, extreme climate 

scenarios, biodiversity loss, and effects on economic development and political violence. With 

regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of 

changes or what effects they would have are not well-understood, although the chance of any one 

of them happening seems low. But they do have the potential to happen relatively quickly, and if 

they did, the costs could be substantial. Only a few studies of climate change have examined 

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen 

in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as 

energy and human health, where wealthier populations are assumed to be less vulnerable to 

climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those 

due to the rate of temperature change (damages are always positive); (2) those due to the level of 

temperature change (damages can be positive or negative depending on region and temperature); 

and (3) those from CO2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by allowing damages to be smaller if climate 

change happens more slowly. The combined effect of CO2 fertilization in the agricultural sector, 

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.

14A.4.1.4 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. 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.4.1) and higher (Figure 14A.4.2) 

increases in global-average temperature. 

 

d 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-10

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 Modelse

Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, 

FUND, and PAGE

 

e 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 Figure 14A.4.1 and Figure 14A.4.2are 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-11

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.

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.f 

14A.4.2.1 Global SCC 

Under current OMB guidance contained in Circular A-4, analysis of economically 

significant proposed and final regulations from the domestic perspective is required, while 

analysis from the international perspective is optional. However, the climate change problem is 

highly unusual in at least two respects. First, it involves a global externality: emissions of most 

greenhouse gases contribute to damages around the world even when they are emitted in the 

United States. Consequently, to address the global nature of the problem, the SCC must 

incorporate the full (global) damages caused by GHG emissions. Second, climate change 

presents a problem that the United States alone cannot solve. Even if the United States were to 

reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid 

substantial climate change. Other countries would also need to take action to reduce emissions if 

significant changes in the global climate are to be avoided. Emphasizing the need for a global 

solution to a global problem, the United States has been actively involved in seeking 

international agreements to reduce emissions and in encouraging other nations, including 

emerging major economies, to take significant steps to reduce emissions. When these 

 

f 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-12

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.g For this reason, the group concluded that using the global (rather 

than domestic) value, without equity weighting, is the appropriate approach. 

14A.4.2.2 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.h

On the basis of this evidence, the interagency workgroup determined that a range of 

values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic 

effects. Reported domestic values should use this range. It is recognized that these values are 

approximate, provisional, and highly speculative. There is no a priori reason why domestic 

benefits should be a constant fraction of net global damages over time. Further, FUND does not 

account for how damages in other regions could affect the United States (e.g., global migration, 

economic and political destabilization). If more accurate methods for calculating the domestic 

SCC become available, the Federal government will examine these to determine whether to 

update its approach. 

14A.4.3 Valuing Non-CO2 Emissions

While CO2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. 

included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, 

 

g 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. h Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report. 
14A-13

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 CO2 emissions.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for the DICE, PAGE, and 

FUND models.i It is defined as the long-term increase in the annual global-average surface 

temperature from a doubling of atmospheric CO2 concentration relative to pre-industrial levels 

(or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties 

in this important parameter have received substantial attention in the peer-reviewed literature.

The most authoritative statement about equilibrium climate sensitivity appears in the 

Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC): 

Basing our assessment on a combination of several independent lines of evidence...including 

observed climate change and the strength of known feedbacks simulated in [global climate 

models], we conclude that the global mean equilibrium warming for doubling CO2, or 

‘equilibrium climate sensitivity,’ is likely to lie in the range 2 °C to 4.5 °C, with a most likely 

value of about 3 °C. Equilibrium climate sensitivity is very likely larger than 1.5 °C.j

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 

 

i

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).

j

This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of 

“likely” as greater than 66 percent probability (Le Treut et al. 2007). “Very likely” indicates a greater than 

90 percent probability.
14A-14

worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et al. 2007, 

p. 799) 

After consulting with several lead authors of this chapter of the IPCC report, the 

interagency workgroup selected four candidate probability distributions and calibrated them to 

be consistent with the above statement: Roe and Baker (2007), log-normal, gamma, and Weibull. 

Table 14A.4.1 gives summary statistics for the four calibrated distributions. 

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Rank 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;”k

2. two-thirds probability that the equilibrium climate sensitivity lies between 2 and 

4.5 °C; and 

3. zero probability that it is less than 0 °C or greater than 10 °C (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. 

 

k 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-15

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. 

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 overlays it on Figure 14A.10.2 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.l

Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate 

Sensitivity

 

l

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. 

(2002), 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

14A.4.5 Socio-Economic 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. Socio-economic 

pathways are closely tied to climate damages because, all else equal, more and wealthier people 

tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid 

climate disruptions. For this reason, we consider how to model several input parameters in 

tandem: GDP, population, CO2 emissions, and non-CO2 radiative forcing. A wide variety of 

scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, 

CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed 

to select scenarios that span most of the plausible ranges of outcomes for these variables. 

To accomplish this task in a transparent way, we decided to rely on the recent Stanford 

Energy Modeling Forum exercise, EMF-22, which 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, peerreviewed, 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 socio-economic and emission trajectories that span a 

range of plausible scenarios. Five trajectories were selected from EMF-22 (Table 14A.4.2 ). Four 

of these represent potential business-as-usual (BAU) growth in population, wealth, and 

emissions and are associated with CO2 (only) concentrations ranging from 612 to 889 ppm in 

2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO2e (i.e., 

CO2-only concentrations of 425–484 ppm or a radiative forcing of 3.7 W/m2) in 2100, a lowerthan-BAU trajectory.m 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.

 

m 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

market exchange rates in trillion 2005$n

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 socio-economic pathways. 

There are a number of caveats. First, EMF BAU scenarios represent the modelers’ 

judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas 

emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the 

 

n While the EMF-22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use 

purchasing power parity (PPP), which takes into account the different price levels across countries, so it more 

accurately describes relative standards of living across countries. MERs tend to make low-income countries appear 

poorer than they actually are. Because many models assume convergence in per capita income over time, use of 

MER-adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an 

ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it 

leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas 

emissions, and unrealistically high future temperatures (e.g., Castles and Henderson 2003). Others argue that 

convergence of the emissions-intensity gap across countries at least partially offset the overstated income gap so that 

differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). 

Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross-sectional PPP 

measures for relative incomes and outputs and national accounts price and quantity indexes for time-series 

extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that 

exchange-rate-conversion issues are as important as uncertainties about population, technological change, or the 

many geophysical uncertainties.
14A-18

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).o

 Second, the socio-economic 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.

p

 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 CO2 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 

(2005$ 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 

(section 14A.10) 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 CO2

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 nonmarket sectors from an additional unit of CO2 emitted in a particular year in terms of reduced 

consumption (or consumption equivalents) due to the impacts of elevated temperatures, as 

represented in each of the three IAMs. Then we discount the stream of future damages to its 

present value in the year when the additional unit of emissions was released using the selected 

discount rate, which is intended to reflect society’s marginal rate of substitution between 

consumption in different time periods. 

 

o 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. 

p 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

For rules with both intra- and intergenerational effects, agencies traditionally employ 

constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A-4. 

As Circular A-4 acknowledges, however, the choice of discount rate for intergenerational 

problems raises distinctive problems and presents considerable challenges. After reviewing those 

challenges, Circular A-4 states, “If your rule will have important intergenerational benefits or 

costs you might consider a further sensitivity analysis using a lower but positive discount rate in 

addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific 

purpose of developing the SCC, we adapt and revise that approach here. 

Arrow et al. (1996) outlined two main approaches to determine the discount rate for 

climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive 

approach reflects a positive (non-normative) perspective based on observations of people’s 

actual choices—e.g., savings versus consumption decisions over time, and allocations of savings 

among more and less risky investments. Advocates of this approach generally call for inferring 

the discount rate from market rates of return “because of a lack of justification for choosing a 

social welfare function that is any different than what decision makers [individuals] actually use” 

(Arrow et al. 1996). 

One theoretical foundation for the cost-benefit analyses in which the social cost of carbon 

will be used—the Kaldor-Hicks potential-compensation test—also suggests that market rates 

should be used to discount future benefits and costs, because it is the market interest rate that 

would govern the returns potentially set aside today to compensate future individuals for climate 

damages that they bear (e.g., Just et al. 2004). As some have noted, the word “potentially” is an 

important qualification; there is no assurance that such returns will actually be set aside to 

provide compensation, and the very idea of compensation is difficult to define in the 

intergenerational context. On the other hand, societies provide compensation to future 

generations through investments in human capital and the resulting increase in knowledge, as 

well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the 

normative judgments that the decision-maker wants explicitly to incorporate into the policy 

evaluation—e.g., how inter-personal comparisons of utility should be made, and how the welfare 

of future generations should be weighed against that of the present generation. Ramsey (1928), 

for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time 

preference to discount values across generations, and many agree with this view. 

Other concerns also motivate making adjustments to descriptive discount rates. In 

particular, it has been noted that the preferences of future generations with regard to 

consumption versus environmental amenities may not be the same as those today, making the 

current market rate on consumption an inappropriate metric by which to discount future climaterelated damages. Others argue that the discount rate should be below market rates to correct for 

market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth, 

which in the Kaldor-Hicks logic are presumed to compensate future generations for damage (a 

potentially controversial assumption, as noted above; Arrow et al. 1996, Weitzman 1999). 

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 
14A-20

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. 

14A.4.6.1 Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on 

investment, and the market rate of interest would be the appropriate choice for the social 

discount rate. In the real world risk, taxes, and other market imperfections drive a wedge 

between the risk-free rate of return on capital and the consumption rate of interest. Thus, the 

literature recognizes two conceptual discount concepts—the consumption rate of interest and the 

opportunity cost of capital. 

According to OMB’s Circular A-4, it is appropriate to use the rate of return on capital 

when a regulation is expected to displace or alter the use of capital in the private sector. In this 

case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected 

to primarily affect private consumption—for instance, via higher prices for goods and services—

a lower discount rate of 3 percent is appropriate to reflect how private individuals trade-off 

current and future consumption. 

The interagency group examined the economics literature and concluded that the 

consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a 

marginal change in carbon emissions (Lind 1990, Arrow et al. 1996, 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 
14A-21

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,” i.e., 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).q 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.r A measure of the 

post-tax risky rate for investments whose returns are positively correlated with overall equity 

market returns can be obtained by adjusting pre-tax rates of household returns to risky 

investments (approximately 7 percent) for taxes yields a real rate of roughly 5 percent.s

14A.4.6.2 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 

 

q 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. r 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. s Cambell et al. (2001) estimates that the annual real return from stocks for 1900-1995 was about 7 percent. The 

annual real rate of return for the S&P 500 from 1950–2008 was about 6.8 percent. In the absence of a better way to 

population-weight the tax rates, we use the middle of the 20–40 percent range to derive a post-tax interest rate 

(Kotlikoff and Rapson 2006).
14A-22

elasticity of the marginal utility of consumption) and ρ (pure rate of time preference).tThese 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.u

 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.v

Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η 

equal to 1 suggests savings rates that do not conform to observed behavior. 

• ρ. With respect to the pure rate of time preference, most papers in the climate change 

literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates 

tend to follow from moral judgments involving intergenerational neutrality. Some have 

argued that to use any value other than ρ = 0 would unjustly discriminate against future 

generations (e.g., Arrow et al. 1996, Stern 2006). However, even in an inter-generational 

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 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. 

 

t

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. u In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the 

rate of consumption growth.

v 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

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 (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.w

Recently, Stern (2008) revisited the values used in Stern (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 greater 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.

14A.4.6.3 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 

Panipoulou et al. (2004) confirm empirically that discount rate uncertainty can have a large 

effect on net present values. A main result from these studies is that if there is a persistent 

element to the uncertainty in the discount rate (e.g., the rate follows a random walk), then it will 

result in an effective (or certainty-equivalent) discount rate that declines over time. 

Consequently, lower discount rates tend to dominate over the very long term (Weitzman 1998, 

1999, 2001; Newell and Pizer 2003; Panipoulou et al. (2004); 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 Panipoulou et al. (2004), 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 variation in the 

level of persistence over time. 

 

w 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

While Newell and Pizer (2003) and Panipoulou et al. (2004) 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.x 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).y

14A.4.6.4 The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in 

this context and uncertainty about how interest rates may change over time, we use three 

discount rates to span a plausible range of certainty-equivalent constant discount rates: 2.5, 3, 

and 5 percent per year. Based on the review in the previous sections, the interagency workgroup 

determined that these three rates reflect reasonable judgments under both descriptive and 

prescriptive approaches.

The central value (3 percent) is consistent with estimates provided in the economics 

literature and OMB’s Circular A-4 guidance for the consumption rate of interest. As previously 

mentioned, the consumption rate of interest is the correct discounting concept to use when future 

damages from elevated temperatures are estimated in consumption-equivalent units. Further, 

3 percent roughly corresponds to the after-tax riskless interest rate. The upper value of 5 percent 

is included to represent the possibility that climate damages are positively correlated with market 

returns. Additionally, this discount rate may be justified by the high interest rates that many 

consumers use to smooth consumption across periods. 

The low value (2.5 percent) is included to incorporate the concern that interest rates are 

highly uncertain over time. It represents the average certainty-equivalent rate using the 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.z

 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 

 

x 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. 

y 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. 

z Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).
14A-25

or normative approach and to ethical objections that have been raised about rates of 3 percent or 

higher. 

14A.5 REVISED SCC ESTIMATES

Our general approach to estimating SCC values is to run the three integrated assessment 

models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency 

group: 

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0

and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of twothirds.

• Five sets of GDP, population and carbon emissions trajectories based on EMF-22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because

PAGE and FUND incorporate uncertainty in other model parameters, the final output from each 

model run is a distribution over the SCC in year t. 

For each of the IAMS, the basic computational steps for calculating the SCC in a 

particular year t are:

1. Input the path of emissions, GDP, and population from the selected EMF-22 scenarios,

and the extrapolations based on these scenarios for post-2100 years.

2. Calculate the temperature effects and (consumption-equivalent) damages in each year

resulting from the baseline path of emissions.

a. In PAGE, the consumption-equivalent damages in each period are calculated as a

fraction of the EMF GDP forecast, depending on the temperature in that period

relative to the pre-industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of

temperature change in that period.

c. In DICE, temperature affects both consumption and investment, so we first adjust the

EMF GDP paths as follows: Using the Cobb-Douglas production function with the

DICE2007 parameters, we extract the path of exogenous technical change implied by

the EMF GDP and population paths, then we recalculate the baseline GDP path

taking into account climate damages resulting from the baseline emissions path.

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.)
14A-26

6. Discount the resulting path of marginal damages back to the year of emissions using the 

agreed upon fixed discount rates. 

7. Calculate the SCC as the net present value of the discounted path of damages computed 

in step 6, divided by the unit of carbon emissions used to shock the models in step 3. 

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of CO2

(2007 dollars) in DICE and FUND. (All calculations are done in tons of CO2 in PAGE).

The steps above were repeated in each model for multiple future years to cover the time 

horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three 

IAMs, climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The 

default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND. 

This is an issue for the multi-model approach because differences in SCC estimates may arise 

simply due to the model time horizon. Many consider 2200 too short a time horizon because it 

could miss a significant fraction of damages under certain assumptions about the growth of 

marginal damages and discounting, so each model is run here through 2300. This step required a 

small adjustment in the PAGE model only. This step also required assumptions about GDP, 

population, and greenhouse gas emission trajectories after 2100, the last year for which these 

data are available from the EMF-22 models. (A more detailed discussion of these assumptions is 

included in the Annex.) 

This exercise produces 45 separate distributions of the SCC for a given year, the product 

of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate 

distributions for consideration in a regulatory impact analysis. 

To produce a range of plausible estimates that still reflects the uncertainty in the 

estimation exercise, the distributions from each of the models and scenarios are equally weighed 

and combined to produce three separate probability distributions for SCC in a given year, one for 

each assumed discount rate. These distributions are then used to define a range of point estimates 

for the global SCC. In this way, no integrated assessment model 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 socio-economic 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 
14A-27

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. 

Table 14A.5.1 Disaggregated Social Cost of CO2 Values by Model, Socio-Economic 

Trajectory, and Discount Rate for 2010 (2007$) 

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 
14A-28

comparisons are only approximate since the literature generally relies on Ramsey discounting, 

while we have assumed constant discount rates.aa

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. Further, 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 5-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.

 

aa Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) 

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 

0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC 

estimates for values of ρ = 0, 1, and 3 in many recent papers (e.g., Anthoff et al. 2009). The path of per-capita 

consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is 

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate 

change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term.

0

50

100

150

200

250

300

350

400

450

2000 2010 2020 2030 2050 2100

Global 

GDP

(trillions

of U.S. 

dollars)

IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing
14A-29

Table 14A.5.2 Social Cost of CO2, 2010–2050 (2007$) 

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

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC 

directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as 

was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are 

internally consistent with other modeling assumptions. Table 14A.5.3 illustrates how the growth 

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 

2010 and 2050 is reported in the Annex (section 14A.10). 

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%

Year Range 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 2020 that are calculated using a 

SCC based on a 5-percent discount rate also should be discounted back to the analysis year using 

a 5-percent discount rate.bb 

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 

bb However, it is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be 

discounted at rates that differ from those used to develop the SCC estimates. 
14A-30

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. 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 high-impact 

low-probability risks, using a right-skewed gamma distribution for climate sensitivity as well as 

an uncertain damage coefficient, but in most cases found only a modest risk premium. Given this 

difference in opinion, further research in this area is needed before its practical significance can 

be fully understood and a reasonable approach developed to account for such risks in regulatory 

analysis. (The next section discusses the scientific evidence on catastrophic impacts in greater 

detail.)

Uncertainty in extrapolation of damages to high temperatures. The damage functions in 

these IAMs are typically calibrated by estimating damages at moderate temperature increases 

(e.g., DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming 

that damages increase as some power of the temperature change. Hence, estimated damages are 

far more uncertain under more extreme climate change scenarios. 
14A-31

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.cc 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 under or 

overstate the likely damages.

Risk aversion. A key question unanswered during this interagency process is what to 

assume about relative risk aversion with regard to high-impact outcomes. These calculations do 

not take into account the possibility that individuals may have a higher willingness to pay to 

reduce the likelihood of low-probability, high-impact damages than they do to reduce the 

likelihood of higher-probability but lower-impact damages with the same expected cost. (The 

inclusion of the 95th percentile estimate in the final set of SCC values was largely motivated by 

this concern.) If individuals do show such a higher willingness to pay, a further question is 

whether that fact should be taken into account for regulatory policy. Even if individuals are not 

risk-averse for such scenarios, it is possible that regulatory policy should include a degree of 

risk-aversion. 

Assuming a risk-neutral representative agent is consistent with OMB’s Circular A-4, 

which advises that the estimates of benefits and costs used in regulatory analysis are usually 

based on the average or the expected value and that “emphasis on these expected values is 

appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While 

this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] 

analysis.” 

Nordhaus (2008) points to the need to explore the relationship between risk and income 

in the context of climate change across models and to explore the role of uncertainty regarding 

various parameters in the results. Using FUND, Anthoff et al. (2009) explored the sensitivity of 

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 

 

cc However these research dollars will be diverted from whatever their next best use would have been in the absence 

of climate change (so productivity/GDP would have been still higher).
14A-32

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.

14A.7.1 Extrapolation of Climate Damages to High Levels of Warming 

The damage functions in the models are calibrated at moderate levels of warming and 

should therefore be viewed cautiously when extrapolated to the high temperatures found in the 

upper end of the distribution. Recent science suggests that there are a number of potential 

climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with 

potentially severe social and economic consequences (e.g., Lenton et al. 2008, Kriegler et al.

2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of 

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West 

Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, 

strengthening of El Niño-Southern Oscillation, and the release of methane from melting 

permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C 

and 5 °C (Lenton et al. 2008). Probabilities of several of these tipping points were assessed 

through expert elicitation in 2005–2006 by Kriegler et al. (2009); results from this study are 

highlighted in Table 14A.7.1. Ranges of probability are averaged across core experts on each 

topic. 
14A-33

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points Duration before 

effect is fully realized

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

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 (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 socio-economic 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. 

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it 

deterministically (i.e., by adding the expected value of the damage from a catastrophe to the 

aggregate damage function). In part, this results in different probabilities being assigned to a 

catastrophic event across the two models. For instance, PAGE places a probability near zero on a 

catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16–36 percent of 

crossing at least one of their primary climatic tipping points in a scenario with temperatures 

about 2–4 °C warmer than pre-Industrial levels in 2100. 

It is important to note that crossing a climatic tipping point will not necessarily lead to an 

economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across 

which some aspect of the Earth system starts to shifts into a qualitatively different state (for 

instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a 

catastrophe is a low-probability environmental change with high economic impact. 

14A.7.2 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 
14A-34

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). 

14A.7.3 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 econoic 

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 $4.7, $21.4, $35.1, and $64.9 (2007$). The first three estimates are 

based on the average SCC across models and socio-economic and emissions scenarios at the 5-, 

3-, and 2.5-percent discount rates, respectively. The fourth value is included to represent the 

higher-than-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 
14A-35

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-36

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14A-42

14A.10ANNEX

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. 

Annual SCC values for the next 40 years are provided in Table 14A.10.1. 

Table 14A.10.1 Annual SCC Values: 2010–2050 (2007$) 

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
14A-43

14A.10.1 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.dd 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, N2O, 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 factors,

ee decomposed the excess forcing vector, and 

constructed a new excess forcing vector that includes the EMF RF for CH4, N2O, and fluorinated 

gases, as well as the model default values for aerosols and other factors. Net land use CO2

emissions were added to the fossil and industrial CO2 emissions pathway. 

DICE: DICE presents the greatest challenge because all forcing due to factors other than 

industrial CO2 emissions is embedded in an exogenous non-CO2 RF vector. To decompose this 

exogenous forcing path into EMF non-CO2 gases and other gases, we relied on the references in 

DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment 

Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report 

(TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing 

from all non-CO2 sources is -0.06 W/m2

 in 2005, as reported in AR4, and increases linearly to 

0.3 W/m2

 in 2105, based on GISS projections, and then stays constant after that time. 

According to AR4, the RF in 2005 from CH4, N20, and halocarbons (approximately 

similar to the F-gases in the EMF-22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m2

 and RF 

from total aerosols was -1.2 W/m2. Thus, the -.06 W/m2 non-CO2 forcing in DICE can be 

decomposed into: 0.98 W/m2 due to the EMF non-CO2 gases, -1.2 W/m2 due to aerosols, and the 

remainder, 0.16 W/m2

, due to other residual forcing. 

dd 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).

ee 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

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 sulphur 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}.ff

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent 

with the recent literature on these emissions. For example, Figure 14A.10.1 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.gg 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.hh The lower 

bound 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

. 

 

ff AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr.pdf gg 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.

hh 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.10.1 Sulphur 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.10.2 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 2200. 

2. GDP/per capita growth rate declines linearly, reaching zero in 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 2200. 
14A-46

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).ii The resulting range of EMF population trajectories (Table 14A.10.2) 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. 

Figure 14A.10.2 through Figure 14A.10.8 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. 

 

ii United Nations. 2004. World Population to 2300. 

http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf. 
14A-47

Figure 14A.10.2 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. 

Figure 14A.10.3 World GDP, 2000-2300 (post-2100 extrapolations assume GDP per capita 

growth declines linearly, reaching zero in 2300) 

Note: In the fifth scenario, 2000–2100 GDP is equal to the average of the GDP under the 550 ppm CO2e, fullparticipation, 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

0

500

1000

1500

2000

2000 2050 2100 2150 2200 2250 2300

IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

Year

World GDP [$trillion]
14A-48

Figure 14A.10.4 Global Fossil and Industrial CO2 Emissions, 2000-2300 (post-2100 

extrapolations assume growth rate of CO2 intensity (CO2/GDP) over 2090–2100 is 

maintained through 2300) 

Note: In the fifth scenario, 2000–2100 emissions are equal to the average of the emissions under the 550 ppm 

CO2e, full-participation, not-to-exceed scenarios considered by each of the four models. 

Figure 14A.10.5 Global Net Land Use CO2 Emissions, 2000–2300 (post-2100 extrapolations 

assume emissions decline linearly, reaching zero in 2200)

jj

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. 

 

jj 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).

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

-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-49

Figure 14A.10.6 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. 

Figure 14A.10.7 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. 

0.00

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

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-50

Table 14A.10.2 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

Table 14A.10.3 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-51

Table 14A.10.4 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.10.8 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-52

Table 14A.10.5 Additional Summary Statistics of 2010 Global SCC Estimates 

Discount 

Rate

5% 3% 2.5%

Scenario Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis Mean Variance Skewness Kurtosis

DICE 9.0 13.1 0.8 0.2 28.3 209.8 1.1 0.9 42.2 534.9 1.2 1.1

PAGE 6.5 136.0 6.3 72.4 29.8 3,383.7 8.6 151.0 49.3 9,546.0 8.7 143.8

FUND -1.3 70.1 28.2 1,479.0 6.0 16,382.5 128.0 18,976.5 13.6 150,732.6 149.0 23,558.3
15-i 

CHAPTER 15. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-2

15.3.1 Installed Capacity............................................................................................... 15-2

15.3.2 Electricity Generation ........................................................................................ 15-6

15.3.3 Results Summary ............................................................................................... 15-9

REFERENCES ........................................................................................................................ 15-10

LIST OF FIGURES

Figure 15.3.1 Commercial Prerinse Spray Valves: Total Capacity Reduction.......................... 15-3

Figure 15.3.2 Commercial Prerinse Spray Valves: Coal Capacity Reduction .......................... 15-3

Figure 15.3.3 Commercial Prerinse Spray Valves: Nuclear Capacity Reduction ..................... 15-4

Figure 15.3.4 Commercial Prerinse Spray Valves: Gas Combined Cycle Capacity 

Reduction ........................................................................................................... 15-4

Figure 15.3.5 Commercial Prerinse Spray Valves: Peaking Capacity Reduction ..................... 15-5

Figure 15.3.6 Commercial Prerinse Spray Valves: Renewables Capacity Reduction ............... 15-5

Figure 15.3.7 Commercial Prerinse Spray Valves: Total Generation Reduction ...................... 15-6

Figure 15.3.8 Commercial Prerinse Spray Valves: Coal Generation Reduction ....................... 15-6

Figure 15.3.9 Commercial Prerinse Spray Valves: Nuclear Generation Reduction .................. 15-7

Figure 15.3.10 Commercial Prerinse Spray Valves: Gas Combined Cycle Generation 

Reduction ........................................................................................................... 15-7

Figure 15.3.11 Commercial Prerinse Spray Valves: Oil Generation Reduction ....................... 15-8

Figure 15.3.12 Commercial Prerinse Spray Valves: Renewables Generation Reduction ......... 15-8

LIST OF TABLES

Table 15.3.1 Commercial Prerinse Spray Valves: Summary of Utility Impact Results ............ 15-9
15-1 

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 generation that result for each trial standard level (TSL). 

The utility impact analysis uses a variant of the DOE Energy Information 

Administration’s (EIA’s) National Energy Modeling System (NEMS).a NEMS is a public 

domain, multi-sectored, partial equilibrium model of the U.S. energy sector. Each year, 

DOE/EIA uses NEMS to produce an energy forecast for the United States—the Annual Energy 

Outlook (AEO). DOE uses a variant of this model, referred to as NEMS-BT,b to account for 

selected utility impacts of energy conservation standards. DOE’s analysis consists of a 

comparison between model results for the most recent AEO Reference Case and for cases in 

which energy use is decremented to reflect the impact of standards. For the analysis of standards 

on commercial prerinse spray valves, DOE used the version of NEMS-BT based on AEO2014.

1

NEMS-BT has a number of advantages that have led to its use in the analysis of energy 

conservation standards: 

• NEMS-BT uses a set of assumptions that are well known and fairly transparent, due

to the exposure and scrutiny each AEO receives.

• NEMS-BT is updated each year, with each edition of the AEO, to reflect changes in

energy prices, supply trends, regulations, and other variables.

• The comprehensiveness of NEMS-BT permits the modeling of interactions among the

various energy supply and demand sectors.

15.2 METHODOLOGY

DOE uses NEMS-BT to estimate the marginal impacts of reduction in energy demand on 

the energy supply sector. In principle, marginal values should provide a better estimate of the 

actual impact of energy conservation standards. In practice, the numerical differences between 

marginal and average values may turn out to be smaller than the intrinsic uncertainties in the 

AEO. 

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. In 

general, when electricity demand deviates from the AEO Reference Case, three inter-related 

effects can occur: the annual generation (terawatt-hours (TWh)) from the stock of electric 

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 2003, DOE/EIA-0581(2003), 

March 2003.

b DOE/EIA approves use of the name NEMS to describe only an official version of the model without any 

modification to code or data. Because this analysis entails some minor code modifications and the model is run 

under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the name NEMSBT. (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed.) 
15-2 

generating capacity changes; the total generation capacity itself (gigawatts (GW)) may change, 

and the mix of capacity by fuel type may change. Each of these effects can vary for different 

types of end use. The change in total generating capacity is sensitive to the degree to which the 

end-use is peak coincident, while the capacity mix is sensitive to the hourly load shape 

associated with the end use.

To model the impact of a standard, DOE inputs a reduction to annual energy demand for 

the corresponding end use in the appropriate start year. The NEMS-BT model is run with the 

decremented energy demand to determine the modified build-out of capacity and total 

generation. Regional effects of a standard can be accounted for by defining the energy demand 

decrement as a function of census division. 

The output of the NEMS-BT analysis includes the effective marginal heat rate (i.e., ratio 

of the change in fuel consumption in quadrillion British thermal units (quads) to the change in 

generation in TWh) and the capacity reduction by fuel type for a given reduction in total 

generation. DOE uses the site-energy savings multiplied by a transmission and distribution loss 

factor to estimate the reduction in generation for each TSL. The relationship between a 

reductionc in electricity generation (TWh) and the reduction in capacity (GW) is estimated based 

on the output of NEMS-BT model runs using the end-use-specific energy demand decrement. 

Details on this approach may be found in Coughlin (2013).2

NEMS-BT provides output for the following capacity types: coal, nuclear, combined

cycle (natural gas), renewable sources, oil and natural gas steam, combustion turbine/diesel, 

pumped storage, fuel cells, and distributed generation (natural gas). DOE grouped oil and natural

gas steam and combustion turbine/diesel into a peaking category, and grouped pumped storage, 

fuel cells, and distributed generation (natural gas) into an “other” category.

In general, energy conservation standards impact primarily fossil combustion (i.e., coal,

natural gas, and diesel) and renewables. Pumped storage and nuclear power are very insensitive 

to small changes in demand, while fuel cells and distributed generation make up a very small 

fraction (less than 1 percent) of the generation capacity base.

15.3 UTILITY IMPACT RESULTS

This section presents results of the analysis for all of the capacity types except “other,” 

for which the impacts are very small.

15.3.1 Installed Capacity 

Figure 15.3.1 through Figure 15.3.6 show the changes in U.S. electricity installed 

capacity that result for each TSL by major plant type for selected years.

c These reductions are defined relative to the AEO Reference Case.
15-3 

Figure 15.3.1 Commercial Prerinse Spray Valves: Total Capacity Reduction 

Figure 15.3.2 Commercial Prerinse Spray Valves: Coal Capacity Reduction 
15-4 

Figure 15.3.3 Commercial Prerinse Spray Valves: Nuclear Capacity Reduction

Figure 15.3.4 Commercial Prerinse Spray Valves: Gas Combined Cycle Capacity 

Reduction
15-5 

Figure 15.3.5 Commercial Prerinse Spray Valves: Peaking Capacity Reduction 

Figure 15.3.6 Commercial Prerinse Spray Valves: Renewables Capacity Reduction
15-6 

15.3.2 Electricity Generation

Figure 15.3.7 through Figure 15.3.12 show the annual changes in electricity generation 

that result for each TSL by plant type. Gas combined cycle power plants account for most of the 

generation reduction. 

Figure 15.3.7 Commercial Prerinse Spray Valves: Total Generation Reduction 

Figure 15.3.8 Commercial Prerinse Spray Valves: Coal Generation Reduction
15-7 

Figure 15.3.9 Commercial Prerinse Spray Valves: Nuclear Generation Reduction

Figure 15.3.10 Commercial Prerinse Spray Valves: Gas Combined Cycle Generation 

Reduction
15-8 

Figure 15.3.11 Commercial Prerinse Spray Valves: Oil Generation Reduction

Figure 15.3.12 Commercial Prerinse Spray Valves: Renewables Generation Reduction
15-9 

15.3.3 Results Summary

Table 15.3.1 presents a summary of the utility impact results for commercial prerinse 

spray valves. 

Table 15.3.1 Commercial Prerinse Spray Valves: Summary of Utility Impact Results

 TSL

1 2 3 4

Installed Capacity Reduction

MW

2020 2.24 13.15 15.12 5.88

2025 6.64 38.94 44.77 17.42

2030 7.50 43.97 50.56 19.68

2035 8.46 49.59 57.02 22.19

2040 9.47 55.50 63.81 24.83

Electricity Generation Reduction

GWh

2020 9.18 53.76 61.82 24.06

2025 24.09 141.16 162.31 63.16

2030 25.51 149.51 171.91 66.90

2035 26.82 157.14 180.68 70.31

2040 28.18 165.15 189.90 73.90
15-10

REFERENCES

1. U.S. Department of Energy, Energy Information Administration. Annual Energy Outlook

2014. 2014. Washington, DC. (Last accessed May 1, 2014.)

<http://www.eia.gov/forecasts/aeo/>.

2. Coughlin, K. 2013. Projections of Full-Fuel-Cycle Energy and Emissions Metrics.

Lawrence Berkeley National Laboratory, Report Number LBNL-6025E.
16-i

CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION ............................................................................................................ 16-1

16.2 ASSUMPTIONS ............................................................................................................... 16-1

16.3 METHODOLOGY ........................................................................................................... 16-1

16.4 SHORT-TERM RESULTS ............................................................................................... 16-2

16.5 LONG-TERM RESULTS ................................................................................................. 16-3

REFERENCES .......................................................................................................................... 16-4

LIST OF TABLES

Table 16.4.1 Net Short-Term Change in Employment (Number of Employees) ...................... 16-3
16-1

CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy’s (DOE’s) employment impact analysis is designed to 

estimate indirect national job creation or elimination resulting from proposed standards due to 

reallocation of associated expenditures for purchasing and operating commercial prerinse spray

valves. Job increases or decreases reported in this chapter are separate from the direct 

commercial prerinse spray valve employment impacts reported in chapter 12 of this technical 

support document (TSD), and reflect the employment impact of efficiency standards on all other 

sectors of the economy. DOE conducted this analysis as part of the notice of proposed 

rulemaking (NOPR). 

16.2 ASSUMPTIONS

DOE expects amended energy conservation standards to decrease energy and water use 

and, therefore, reduce energy and water expenditures. The savings in energy and water

expenditures may be spent on new investments or not spent at all (i.e., they may remain 

“saved”). DOE found that amended standards for commercial prerinse spray valves will not

increase the installed costs of commercial prerinse spray valves. See chapter 8 of this TSD for 

information about the installed cost of commercial prerinse spray valves. 

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. DOE evaluated direct employment impacts at manufacturers’ facilities in the 

manufacturer impact analysis (see chapter 12 of this TSD). 

DOE notes that the Impact of Sector Energy Technologies (ImSET) model 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 overestimate 

the magnitude of actual job impacts over the long run for this rulemaking. Because input/output 

models do not allow prices to bring markets into equilibrium, they are best used for short-run 

analysis. Therefore, DOE included 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

DOE 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. Pacific Northwest National Laboratory (PNNL) developed the model using 

ImSET 3.1.12 as a successor to Impact of Building Energy Efficiency Programs (ImBuild),3 a 

special purpose version of the Impact Analysis for Planning (IMPLAN)

4

 national input/output 

model. ImSET estimates the employment and income effects of building energy technologies. 
16-2

Compared with simple 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 

relationships of different sectors of the economy and 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 utilities toward firms that supply 

production inputs. Third, utility sector investment funds are released for use in other sectors of 

the economy. When consumers use less energy or water, electric, natural gas, and water utilities 

experience relative reductions in demand, which leads to reductions in utility sector investment 

and employment. DOE notes that ImSET water utilities, which are estimated to be a highly 

capital-intensive industry, are grouped with solid waste management, which is a highly laborintensive industry, which can lead to overestimated water cost savings impacts. To avoid this 

overestimation, water cost savings were entered into ImSET as electricity cost savings for this 

analysis. 

DOE notes that the employment impacts estimated with ImSET for the entire economy 

differ from the employment impacts in the CPSV manufacturing sector estimated in chapter 12 

of this TSD, which uses 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 CPSV standards relative to a no-newstandards scenario. DOE disaggregated the impact of standards on employment into three 

component effects: increased capital investment costs, decreased energy and water costs, and 

changes in operations and maintenance costs. There were no increased capital investment costs 

or changes in operations and maintenance costs for this rulemaking. Energy cost savings were 

further disaggregated into electricity and natural gas cost savings.
16-3

Table 16.4.1 presents the modeled net employment impact from the amended standards 

for commercial prerinse spray valves in 2020 and 2025. 

Table 16.4.1 Net Short-Term Change in Employment (Number of Employees)

Trial Standard Level 2020 2025

1 16 45

2 95 266

3 109 306

4 43 119

16.5 LONG-TERM RESULTS

Because there are no increases in installed costs of commercial prerinse spray valves 

associated with energy and water-efficiency improvements, over the long term, DOE expects 

increased aggregate savings to consumers. As a result, DOE expects demand for electricity, 

natural gas, and water to decline over time and demand for other goods to increase. Because the 

electricity, natural gas, and water generation sectors are relatively capital intensive compared to 

the consumer goods sector, the net effect will be an increase in labor demand. In equilibrium, 

this should lead to upward pressure on wages and a shift in employment away from electricity, 

natural gas, and water generation toward 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 to the extent that markets are slow to adjust, DOE anticipates that 

net labor market impacts will be negligible over time due to the small magnitude of the shortterm effects presented in Table 16.4.1. 
16-4

REFERENCES

1. Scott, M., J.M. Roop, R.W. Schultz, D.M. Anderson, and K.A. 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. Scott M.J., O.V. Livingston, P.J. Balducci, J.M. Roop, and R.W. Schultz. ImSET: Impact 

of Sector Energy Technologies Model Description and User’s Guide. 2009. Pacific 

Northwest National Laboratory: Richland, WA. Report No. PNNL-18412. 

3. Scott, M.J., D.J. Hostick, and D.B. Belzer. ImBuild: Impact of Building Energy Efficiency 

Programs.1998. Pacific Northwest National Laboratory, Richland, WA. Report No. PNNL11884. 

4. Minnesota IMPLAN Group Inc. IMPLAN Professional: User’s Guide, Analysis Guide, 

Data Guide. 1997. Stillwater, MN. 
17-i 

CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION ......................................................................................................... 17-1

17.2 METHODOLOGY ........................................................................................................ 17-1

17.3 NON-REGULATORY POLICIES ................................................................................ 17-2

17.3.1 No New Regulatory Action ............................................................................... 17-2

17.3.2 Consumer Rebates ............................................................................................. 17-2

17.3.3 Consumer Tax Credits ....................................................................................... 17-4

17.3.4 Voluntary Efficiency Programs ......................................................................... 17-4

17.3.5 Early Replacement ............................................................................................. 17-4

17.4 SUMMARY OF RESULTS FOR NON-REGULATORY ALTERNATIVES ............ 17-5

LIST OF TABLES

Table 17.1.1 Non-Regulatory Alternatives to Standards ........................................................... 17-1

Table 17.3.1 Consumer Rebate NES, NWS, and NPV Comparison to TSL 3 .......................... 17-3

Table 17.3.2 Tax Credit NES, NWS and NPV Comparison to TSL 3 ...................................... 17-4

Table 17.4.1 Cumulative NES and NWS of Non-Regulatory Alternatives Compared to the 

Proposed Energy Conservation Standard ........................................................... 17-5

Table 17.4.2 Cumulative NPV of Non-Regulatory Alternatives Compared to the Proposed 

Energy Conservation Standard .......................................................................... 17-5
17-1 

CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

Under the Process Rule (Procedures for Consideration of New or Revised Energy 

Conservation Standards for Consumer Products, 61 FR 36974 (July 15, 1996)), the U.S.

Department of Energy (DOE) is committed to continually explore non-regulatory alternatives to 

standards. DOE will prepare a draft regulatory impact analysis pursuant to Executive Order

12866, Regulatory Planning and Review, which will be subject to review under the Executive 

Order by the Office of Management and Budget’s Office of Information and Regulatory Affairs. 

58 FR 51735 (Sept. 30, 1993). 

DOE has identified five major alternatives to standards that represent potential policy 

options to reduce commercial prerinse spray valve (CPSV) energy and water consumption. DOE 

evaluated each alternative’s ability to achieve significant energy and water savings at a 

reasonable cost, and compared the effectiveness of each one to that of the standards in the 

proposed rule.

The non-regulatory means of achieving energy savings that DOE analyzed are listed in 

Table 17.1.1. In support of DOE’s proposed rule, DOE includes, where possible, a quantitative 

analysis of each alternative, the methodology for which is discussed briefly in this technical 

support document (TSD). 

Table 17.1.1 Non-Regulatory Alternatives to Standards 

No new regulatory action (no-new-standards case)

Consumer rebates

Consumer tax credits

Voluntary efficiency programs

Early replacement

17.2 METHODOLOGY

DOE uses the national impact analysis (NIA) spreadsheet models to calculate the national 

energy savings (NES), national water savings (NWS), and the net present value (NPV) 

corresponding to each non-regulatory alternative. The NIA model is discussed in chapter 10 of 

this TSD. To compare each alternative quantitatively to the energy conservation standards, DOE 

quantifies the effect of each alternative on the purchase and use of CPSV products. After 

quantifying each alternative, DOE makes the appropriate revisions to the inputs in the NIA 

models to estimate energy and water savings compared to the no-new-standards case scenario. 

Key inputs that DOE typically revises in these models include the following: 

• energy and water prices, and escalation factors,

• implicit market discount rates for trading off purchase price against operating expense

when choosing product efficiency,

• business purchase prices and operating costs,

• purchase price-versus-efficiency relationships, and
17-2 

• product shipment and/or stock data.

The key measures of the effects of each alternative include the following: 

• Energy and water use reflects the cumulative energy and water use of the products

shipped from the effective date of the potential amended standard through the year

2048. Because the analysis includes product lifetimes of up to 10 years, the

evaluation period for cumulative energy and water use spans 2019-2058 (10 years

after the last unit shipped).

• NES represents the cumulative national energy use from the no-new-standards case

projection minus the alternative policy case projection, given in quadrillion British

thermal units (quads).

• NWS represents the cumulative national water use from the no-new-standards case

projection minus the alternative policy case projection, in terms of billions of gallons.

• NPV represents the value in 2014$ (discounted to 2015) of net monetary savings

from products bought during the period from the effective date of the policy (2019)

through the end of the analysis period (2048).

17.3 NON-REGULATORY POLICIES

17.3.1 No New Regulatory Action 

The no-new-standards case scenario is the one in which no new regulatory action is taken 

with regard to the energy and water efficiency of CPSV products, as described in the NIA

(chapter 10 of this TSD). The no-new-standards case scenario provides the basis of comparison 

for all other non-regulatory alternatives. By definition, no new regulatory action yields zero 

NES, zero NWS, and an NPV of zero dollars. 

17.3.2 Consumer Rebates

Consumer rebates typically cover a portion of the difference in incremental product price 

between models meeting baseline efficiency levels and those meeting higher efficiency levels, 

resulting in a higher percentage of consumers purchasing more efficient models and decreased 

aggregated energy and water use compared to the no-new-standards case scenario. 

DOE surveyed the various rebate programs available in the United States in 2015. 

Typically, local utility companies and energy efficiency organizations offer rebates to

commercial consumers that replace their existing commercial prerinse spray valve with a more 

efficient model. The majority of identified rebate programs offer a 50 percent rebate for retrofit 

CPSV products and installation. A smaller number of programs offered free higher-efficiency 

CPSV replacements, or a cash rebate (ranging from $10 - $75, with an average rebate of 

approximately $30). 

As noted previously, consumer rebates typically defray the incremental price for higherefficiency CPSV models. DOE’s engineering analysis (chapter 5 of this TSD) indicated no 

significant price or cost structure change with increasing CPSV efficiency. Therefore, DOE 

assumed that consumer rebates would instead simply reduce the installed cost for higher-
17-3 

efficiency CPSV models. Specifically, DOE chose to model a scenario where consumers are 

offered, as rebates, 50 percent of the cost of the CPSV retrofit products and installation. 

To estimate the market shares of efficiency levels (see chapter 8 of this TSD) that would 

result from such a rebate, DOE estimated efficiency choices in product classes covered by both 

the WaterSense® program and rebate programs. On average, 60 percent of total shipments in 

those product classes meet or exceed the WaterSense level. For determining the potential impact 

of rebate programs targeting TSL 3, DOE developed a shift scenario. In the shift scenario, market

share was shifted upward such that the total market share of shipments at or above TSL 3 was 60 

percent. To do this, DOE calculated the existing market shares of shipments above TSL 3 

(16.5%), and a portion of the remainder of the market (52.1%) was assigned to the TSL 3 

efficiency level, such that the total percentage of shipments at the TSL 3 level was 60 percent.. 

Shipments below TSL 3 were distributed across efficiency levels in proportion to no-newstandards case efficiency distributions. In equation form, this can be represented as:

0.165 + 0.521 * (1 – 0.165) = 0.60. 

Eq. 17.1 

Although the rebate program reduces the total installed cost to the consumer, it is 

financed by tax revenues or by utility revenues. Therefore, from a societal perspective, the 

installed cost at any efficiency level does not change with the rebate program; rather, part of the 

cost is transferred from the consumer to taxpayers/ratepayers as a whole. Consequently, DOE 

assumed that product costs in the rebates scenario were identical to the NIA no-new-standards

case scenario. 

DOE assumed that rebates would remain in effect for the duration of the analysis period. 

Table 17.3.1 presents the NES, NWS, and NPV values for the 50 percent rebate scenario and 

compares them against the NES, NWS, and NPV values at TSL 3. NES, NWS, and NPV are 

calculated for products purchased in the 2019–2048 analysis period. Any energy and water 

savings, operation and maintenance costs, and savings extend for the life of products purchased 

in or before 2048. 

Table 17.3.1 Consumer Rebate NES, NWS, and NPV Comparison to TSL 3 

Policy Alternatives

Cumulative Primary 

Energy Savings

quads

Cumulative Water 

Savings

billion gal

Net Present Value*

billion 2014$

7% 

Discount 

Rate

3% 

Discount 

Rate

No New Regulatory Action 0 0 0 0

Consumer Rebates 0.072 93.204 0.549 1.131

TSL 3 0.093 120.178 0.708 1.459

* Net present value is the value in the present of a time series of costs and savings.
17-4 

17.3.3 Consumer Tax Credits

Tax credits are considered a viable non-regulatory market transformation program, as 

shown by the inclusion of Federal consumer tax credits in the Energy Policy Act of 2005 (EPAct

2005; Pub L. 109-58, 119 Stat 1026 (2005)) for various residential appliances. From a consumer 

perspective, the most important difference between rebate and tax credit programs is that a rebate 

can be obtained relatively quickly, whereas receipt of tax credits is delayed until income taxes 

are filed or a tax refund is provided by the Internal Revenue Service.

As with consumer rebates, DOE assumed that consumer tax credits paid 50 percent of the 

CPSV retrofit product and installation cost, but estimated a different response rate. The delay in 

reimbursement makes tax credits less attractive than rebates. Consequently, DOE estimated a 

response rate that is 80 percent of that for rebate programs (52.1 percent of existing sub-TSL 3 

shipments impacted by rebates) and, therefore, a corresponding shift of 41.7 percent of sub-TSL 

3 shipments to or above TSL 3 with no change in total shipments. DOE estimated NPV and NES 

values under these assumptions, and the results are presented in Table 17.3.2.

From a societal perspective, tax credits (like rebates) do not change the installed cost of 

the product, but rather transfer a portion of the cost from the consumer to taxpayers as a whole. 

DOE, therefore, assumed that product costs in the consumer tax credits scenario were identical to 

the NIA base case.

DOE assumed that tax credits would remain in effect for the duration of the analysis 

period. Table 17.3.2 presents the NES, NWS, and NPV values for the tax credit scenario and 

compares them against the NES, NWS, and NPV values at TSL 3. 

Table 17.3.2 Tax Credit NES, NWS and NPV Comparison to TSL 3 

Policy Alternatives

Cumulative Primary 

Energy Savings

quads

Cumulative Water 

Savings

billion gal

Net Present Value*

billion 2014$

7% Discount 

Rate

3% Discount 

Rate

No new regulatory action 0 0 0 0

Consumer tax credits 0.058 74.563 0.439 0.905

Proposed standards at TSL 3 0.093 120.178 0.708 1.459 * Net present value is the value in the present of a time series of costs and savings. 

17.3.4 Voluntary Efficiency Programs 

Although voluntary programs for CPSV products could be effective, DOE lacks a 

quantitative basis to determine their effectiveness. As previously noted, broader economic and 

social considerations are in play other than simple economic return to the product purchaser. 

DOE lacks the data necessary to quantitatively project the degree to which such voluntary 

programs for higher-efficiency CPSV products would modify the market. 

17.3.5 Early Replacement

Early replacement refers to the replacement of products before the end of its useful life. 

The purpose of this policy is to retrofit or replace old, inefficient products with high-efficiency 

units. DOE studied the feasibility of a Federal program to promote early replacement of 
17-5 

appliances and products under the Energy Policy Act of 1992 (EPAct 1992, Pub L. 102-486, 106 

Stat 2776 (1995)). In this study, DOE identified Federal policy options for early replacement that 

include a direct national program, replacement of Federally-owned products, promotion through 

product manufacturers, consumer incentives, incentives to utilities, market behavior research, 

and building regulations. 

Although cost-effective opportunities to install more-efficient units exist, DOE 

determined that a Federal early replacement program is not economically justified because the 

market for commercial prerinse spray valves is relatively small, especially for Federally-owned 

products, and is distributed across a broad set of consumer; thus, the savings would not be likely 

to be significant.

17.4 SUMMARY OF RESULTS FOR NON-REGULATORY ALTERNATIVES

Table 17.4.1 and Table 17.4.2 show the NES, NWS, and NPV for the non-regulatory 

alternatives analyzed. The case in which no new regulatory action is taken constitutes the nonew-standards case scenario. Because this is the no-new-standards case scenario, NES, NWS, 

and NPV are zero by definition. For comparison, the tables include the results of the NES, NWS, 

and NPV at TSL 3 associated with the proposed energy conservation standard. 

As shown in Table 17.4.1 and Table 17.4.2, none of the policy alternatives DOE 

examined would achieve the amount of energy or monetary savings that could be realized under 

the proposed rule. In addition, implementing either tax credits or consumer rebates would incur 

initial and/or administrative costs not considered in this analysis.

Table 17.4.1 Cumulative NES and NWS of Non-Regulatory Alternatives Compared to the 

Proposed Energy Conservation Standard

Policy Alternatives Cumulative Primary NES

quads

Cumulative Water Savings

billion gal

No new regulatory action 0 0

Consumer rebates 0.072 93.204

Consumer tax credits 0.058 74.563

Voluntary efficiency programs 0 0

Early replacement 0 0

Proposed standards (TSL 3) 0.093 120.178

Table 17.4.2 Cumulative NPV of Non-Regulatory Alternatives Compared to the Proposed

Energy Conservation Standard

Policy Alternatives

Cumulative Net Present Value

billion 2013$

7% Discount 3% Discount

No new regulatory action 0 0

Consumer rebates 0.549 1.131

Consumer tax credits 0.439 0.905

Voluntary efficiency programs 0 0

Early replacement 0 0

Proposed standards (TSL 3) 0.708 1.459