# TECHNICAL SUPPORT DOCUMENT:
ENERGY EFFICIENCY PROGRAM
FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

Commercial Prerinse Spray Valves

June 2 015

U.S. Department of Energy Assistant Secretary
Office of Energy Efficiency and Renewable Energy Building Technologies Program Appliances and Commercial Equipment Standards Washington, DC 20585This D ocument w as pr epared

f or t he Department of

E ne rgy by s taff m embers o f

N avigant C onsulting

, Inc.

and
Pacific Northwest National L aboratory1-i CHAPTER 1. INTRODUCTION

TA BLE OF CONTENTS

1.1 PURPOSE OF THE DOCUMENT .................................................................................... 1-1
1.2 OVERVIEW OF COMMERCIAL EQUIPMENT STANDARDS .................................... 1-1
1.3 OVERVIEW OF STANDARD S FOR COMMERCIAL PRE RINSE 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 ............................................................................................................................ 1-61-1 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. t he total projected a mount 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. t he 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. t he need for national energy and water conservation; and

7. o ther factors the Secretary considers relevant.1-2 (42 U.S.C. 6 295(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, capaci ties, 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 COMM ERCIAL PRERINSE SPRAY

VALVES

1.3.1 Test Procedure and Definitions

Section 323(b)(14) of EPCA (42 U.S.C. 6293(b)(14 )) state s that the test procedure for commercial prerinse spray valves shall be based on the American Society for Testing and Materials (ASTM) Stand ard 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
mark eted 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
manufactu red 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.1-3 1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

After the publication of a f ramework 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 weighti ng 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 Sta ge of the Rulemaking P rocess Preliminary Analyses * NOPR Final Rule

Market and technology assessment Revised preliminary analysis Revised analys is

Screening analysis Consumer sub

- group analysis Revised analys is

Engineering analysis Manufacturer impact analysis Revised analys is

Energy use analysis Emissions analysis Revised analys is

Markups analysis Monetization of emissions reduction
benefits Revised analys is

Life

- cycle cost and payback period analysis Utility impact analysis Revised analys is

Shipments analysis Employment impact analysis Revised analys is

National impact analysis Regulatory impact analysis Revised analys is

Preliminary manufacturer impact analysis Revised analysis Revised analys is

* 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 F ramework document public meeting and
discussed the issues detailed in the CPSV F ramework document. DOE also described the1-4 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 Septemb er 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, payba ck 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 tec hnology 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 efficie ncy 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 compares1-5 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 effect s 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 m anufacturers.

• 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 Analys is : 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.

• Ap pendix 8A, User I nstructions for the L ife -C ycle C ost Analysis S preadsheet : contains a description of the spreadsheet and instructions that allow the user to examine and reproduce the detailed results of the LCC and PBP analys i s.

• 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 10 B , Full Fuel Cycle Multipliers: contains a summary of the methods
used to calculate full

- fuel

- cycle energy savings.1-6 REFERENCES

1. American Energy Manufacturing Technical Corrections Act (AEMTCA), Pub. L. 112-210 (Dec. 18, 2012).2-i CHAPTER 2. ANALYTICAL FRAMEWORK

TA BLE 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 Technolog y Assessment

.......................................................................................... 2-3
2.4 SCREENING ANALYSIS ................................................................................................. 2-3
2.5 ENGINEERING ANALYSIS ............................................................................................. 2-4
2.6 MARKUPS ANALYSIS .................................................................................................... 2-4
2.7 CO NSUMERCONSUMERCONSUMERCONSUMERENERGY 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 ANAL YSIS ................................................................................... 2-6
2.11 CONSUMER SUBGROUP AN ALYSIS ........................................................................... 2-8
2.12 MANUFACTURER IMPACT ANALYSIS

....................................................................... 2-9
2.13 EMISSIONS ANALYSIS ................................................................................................... 2-9
2.14 MONETIZATION OF EMISSIONS REDUCTION BENE FITS .................................... 2-11
2.15 UTILITY IMPACT ANALY SIS ...................................................................................... 2-12
2.16 EMPLOYMENT IMPACT AN ALYSIS .......................................................................... 2-12
2.17 REGULATORY IMPACT AN ALYSIS .......................................................................... 2-13
REFERENCES .......................................................................................................................... 2-142-1 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 feasi ble 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 (M SP ) 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) analys i s 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 commercia l 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.2-2 • 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.

• A n emissions analysis to provide estimates of the effects of amended energy
conservation standards on emissions of CO 2 , SO 2 , NO

x , and mercury (Hg);

• Monetization of emissions reductions benefits estimated to occur from red uced
emissions of CO 2 and NO

x 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, s tate 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) methodolog ies; (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 framew ork 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 Fra mework 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 the2-3 F ramework 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 TECHNOLOG Y ASSESSMENT

The market and technology assessment characterizes the relevant product markets and existing technology options, including prototype designs.

2. 3 .1 Market Asse ss m ent

W hen 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 th is TSD.

2. 3 .2 T echnology Assessment

A s 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 develop ed a list of technologies for consideration in its screening and engineering analyses. Initially, these technologies encomp ass 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
consid eration 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 list2-4 to determine if these options are practicable to ma nufacture, 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 th is TSD contains details on the screening analysis.

2.5 ENGINEERING ANALYS IS

The engineering analysis (chapter 5 of th is 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 manufacturin g 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 e fficiency 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 structur es 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 co sts 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 assessmen t 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 c hapter 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
in to 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.2-5 In this rulemaking, DOE identified four different major channels through which the

consumer s 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 th is 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 o perating 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 . T he analysis capture s and
estimate s 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 Survey 2 (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 b y 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 t he CBECS database to
determine the proportions of buildings that used natural gas or electricity.

2.8 LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYS IS

New or amended energy conservation standards usually decrease product operating

e xpenses and increase the purchase price. DOE analyzes the net effect of new or amendedstandards 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 a nd 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 a2-6 po int 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 consumer s.

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 consumer s to estimate future energy prices in the LCC and PBP analys i s. DOE used EIA’s Annual Energy Outlook 2014 ( AEO 2014 ) 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 w astewater prices in the LCC and PBP analysis.

DOE assumed average product lifetimes for use in the LCC a nd 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
exis ting 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 valve s will slightly increase
over the next several decades. DOE based its estim ates of CPSV stock over time on available
d ata from industry reports, commercial floorspace growth projections, and the Environmental Protection Agency ’s ( EPA ) WaterSense ® Field Study 3

.

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 determ ined 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 typ ical values (as opposed to probability distributions) as inputs.2-7 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 ene rgy and water consumption without standards. The projections include projections of annual shipments, the annual energy and water consumption of new products, and the pu rchase price of new products.

A key component of DOE’s NIA is the efficiencies projecte d over time for the no- new

standards 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 effici encies in the no- new

standards case for the assumed compliance year of new standards for each CPS V 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 t rial standard level s ( 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 o f th is 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 bas ed 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 in2-8 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 an nual savings in operating costs, and (3) a discount factor to calculate the pre sent value of costs and savings. DOE determined the net savings for each year as the difference between the no- new

standards case and each standards case in terms of total savings in operating costs versus total
in creases 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- new

standards case. Total savings in operating costs are the products of savings per unit and the number of units of each vintag e 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 consumer s of CPSV products who may be disproportionately disadvantaged by proposed energy conservation standards. The LCC and PBP analysis described in chapter 8 of th is 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 fina l 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. Therefor e, 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 opera ting
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 o f 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 lea d 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 shar e, 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 e valuated 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 th is TSD

.

2.13 EMISSIONS ANALYSIS

In the emissions analysis, DOE estimated the reduction in power sector emissions of
CO 2 , NO

x , SO 2 , 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 F R 51282 (Aug. 18, 2011)), as amended at 77 FR 49701 (Aug. 17, 2012), the FFC analysis includes impacts on emissions of methane (CH 4 ) and nitrous oxide (N 2 O), both of which are recognized as greenhouse gases (GHGs).

DOE conducted the emissions analysis usin g 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 th is 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 SO 2 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 SO 2 for affected EGUs in the 48 contiguous States and the District of Columbia (D.C.). SO 2 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 SO 2 emissions allowances resulting from the lower electricity demand caused by the adoption of an efficiency standard could be used to permit offsetting increases in SO 2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions covered by the existing cap

- and

- trade system, but it concluded that no reductions in power sector emissions would occur for SO 2 as a result of standards.

Beginning in 2016, however, SO 2 em issions 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
SO 2 (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, SO 2 emissions will be

re duced 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
SO 2 emissions. Under the MATS, NEMS shows a reduction in SO 2 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 SO 2 emissions allowances resulting from the lower electricity and natural gas demand would be needed or used to permit offsetting increase s in SO 2 emissions by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce SO 2 emissions in 2015 and beyond.

CAIR established a cap on NO

x emissions in the eastern States and the District of Columbia. Energy conservation standar ds are expected to have little or no physical effect on
these emissions in those s tates covered by CAIR because excess NO

x emissions allowances resulting from the lower electricity demand could be used to permit offsetting increases in NO

x emissions. However, standards would be expected to reduce NO

x emissions in the states not2- 11 affected by caps, so DOE estimated NO

x emissions reductions from the standards considered in
the NOPR for these states.

The MATS limit mercury emissions from power p lants, 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 CO 2 and NO

x that are expected to accompany each of the standard levels
considered.

To estimate the monetary value of be nefits resulting from reduced emissions of CO 2 , 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 Interagen cy 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 CO 2 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 gi ve preference to consideration of the global benefits of reducing CO 2 emissions.

DOE multiplies the CO 2 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 mone tary 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 CO 2 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 NO

x emissions resulting from the standard levels it considers. Estimates of monetary value for reducing NO

x from

st ationary sources range from $476 to $4,893 per ton in 2013$. 5 DOE calculates monetary benefits using a medium value for NO

x emissions of $2,723 per short ton (in 2014$), and real discount rates of 3 percent and 7 percent, in accordance with OMB guidance. 62- 12 DOE is evaluating an appropriate valuation of avoided SO 2 and Hg emissions. Whether monetization of reduced Hg emissions will occur in this rulemaking is yet to be determined.

2.15 UTILITY IMPACT ANALY SIS

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 thro ugh 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 standard s 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 em ployment 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 indirec t 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 mi nor 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 prepare d 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 suc h 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

Ben efits 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 E nergy Technologies,’’ PNNL–15273. Pacific Northwest National Laboratory (2005).3-i CHAPTER 3. MARKET AND TECHNOLOG Y ASSESSMENT

TA BLE 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 Distrib ution 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 Basi c S tr

u ctur e a n d Op eration o f Commercial Prerins e S pray 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 Ho l e A rea

.............................................................................. 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 Ventur i M eter to Orifice Plate No zzle Geo metries

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

LI ST 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) ............................................................... 3-93- 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........................ 3-113-1 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 metric s, 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 p r erinse 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 t echnology 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 valve s 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 comm ercial dishwas hing 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) Wat erSense ® program. 1 T he 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, where as 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

mea sured 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 covere d 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 par t 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. Sect ion 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 (Sep tember 11, 2014).3-3 3.5 PRODUCT CLASSES

When evaluat ing 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
estab lishing separate product classes based on capacity or other performance related features

. Research showed that CPS V applications range from heavy rinsing to removing loose food particles. Therefore, t he se applications required that commercial prerinse spray valves on the
market have a range of flow rates. Hence, if all commercial prerinse spray valves were with in one product class and subject to one standard, then reducing the maximum flow rate for all CSPVs could have negative impacts on customer util ity f or 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 commerci al 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 spra y 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 t o 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 3.6.4.13-4 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

customer s. Plumbing Manufacturers Internat ional (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 u ltra 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 customer s with two distinct levels of equipment. 5 In response to comments from interested parties , WaterSense changed the spray force requ irement 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 Ta ble 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 spr ay 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 rega rding 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 cl osely with EPA. 73-5 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 equip ment 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 publi cly available sources and manufacturer comments to the 2014 Framework document. The following four distribution channels are bein g 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 ma jor 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 database c

• Manufacturer websites

• Food Service Technology Center 12

DOE is aware that t he market sha re 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 shar e 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 iden tical to the f ederal 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 the se standards on December 15, 2004. 15

EPCA preempts any requirements of s tate 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 exce ption 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 com mercial prer inse 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 s tandard, 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 R egulatory Plan 2014-2016, which will introduce new MEPs for commercial prerinse spray valves. 193-8 3.6.4 Non -Regulatory Initiatives

3.6.4.1 Environmental P rotection A gency 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 org anizations help maintain the WaterSense integrity and credibility by ver ifying and testing products for conformance to WaterSense specifications, efficiency, performance, and label use, and also conduct periodic market surveillance.

The EPA’s water efficie ncy 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 applicati ons. 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 imp act 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

e quipment, 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 f ederal 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 inclu de 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 prerins e 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. I n evaluating overall U.S. shipments of commercial prerinse spray valves , DO E
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 used3-9 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 11,4373- 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 whic h 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 C hapter 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

un its 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 af fect energy consumption, energy efficiency, water consumption,
or water efficienc y. (10 CFR 430.2) Therefore, DOE filtered out the non-unique basic models. DOE also performed internet searches, and used information from the Food S ervice Technology3- 11 C enter w ebsite 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.

F igure 3.6.2 D is tribution of C ommercial P rerinse Spray Valves by F low R ate

3.7 TECHNOLOGY ASSESSMEN T

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 B asi c S t ructu re a nd Operation o f Co mmercial Prerins e S pray Valv es

T h e 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 c hapter 5 of the TSD

.

3.7. 2 Product Features wit h No Impact o n E ff iciency

I n the 2014 Framework document, DOE identified potential technology options that could improve CPSV efficiency. However, furth er research by DOE indicates that several
features from that list do not affect CPSV efficiency. The fol lowing subsections provide some background on the product features that DOE determined had no impact on CPSV efficiency.

3.7. 2 .1 B ackflow Preventers

B ackflow 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 valve s or working prototypes that incorporate backflow preventers into the design. Furthermore , DOE did not identify any means by which incorporating a backf low preventers into a commercial prerinse spray valve could improve its efficiency by limiting the water flow rate

.

3.7. 2 .2 Spray Pa t t erns

I n the 2014 Framework document, DOE identified five different spray patterns that are incorporated in commercial prerinse spray valve s:

(1) Fan spray pattern (single nozzle with a hollow cone stream)

(2) S olid stream pattern (single nozzle with single solid jet stream)

(3) T riple

- action spray pattern (three nozzles with solid jet streams)

(4) K nife

- 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 T echn ology Options for Co m m ercial Prerins e S pray Valv es

T he 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 the3- 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 Add ition o f a Flow Control In sert

A f low 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

. ” S everal faucets and showerheads on the market us e flow control inse rts 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 Small er S pray Ho l e A rea

T he 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 fl ow 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

A n 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 r educing the water pressure. Aerators can also serve other purposes such as preventing splashing and shaping the water

stream

.

3.7. 3

. 4 Additional Valves

P lumbing 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

A s 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 hole s (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 Met er t o Orifi ce P late N o zzl e Geo metries

DOE has o bserved 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 pre rinse 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 hig her 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. E PA 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. E PA 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

W aterSense® 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- n afem/Default.aspx

.

9. About NRA. http://www.restaurant.org/About- U s/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- Fl ow 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/Pre

Rinse%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- AAE R

- 01, available at http://www.energy.ca.gov/appliances/archive/2004rulemaking/notices/2004-12- 22_ORDER_ADOPT.PDF3- 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.n rcan.gc.ca/energy/regulations

- codes -standards/bulletins/7127

.

18. National Resources Canada. Regulatory Update – November 2011. http://www.nr can.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_fina l.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%2 0Spray%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

TA BLE 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 TECHNOLOGI ES ................................................................................. 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

LI ST OF TABLES Table 4.4.1 Technology Options .................................................................................................. 4-44

- 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 Fe deral 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 st andard. Under the guidelines, DOE eliminates from consideration technologies that present unacceptable problems with respect to the following four
factors:

(1) Technological feasibility. DOE screen s 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 uti lity 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. B ased on DOE’s research to date, however, there are no commercially ava i lable 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 criter i a 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. D ifferent size aerators are
available

, which are typically used in faucets and showerheads.

DOE is aware of only one commercial prerinse spray valve that incorporate s 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 th at the performance of this aerated unit differe d 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 g lobe 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 of4

- 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. T hese 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
consider ed 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 CPS V 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 furth er detail on how spray hole shape affects flow rate

.

DOE has preliminary concluded that none of the four screening cri teria apply to this technology option and has consider ed 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 tea ring down units, whi ch 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 cri teria apply to this technology option and has considered orifice plate as a design option in the engineering analysis.

4.4 SCREENING AN AL YSIS SUMMARY

Table 4.4.1 lists the screened -out and retained technology options as described above.
DOE has considered each of the re tained technology options as de sign options in the subsequent engineering analysis described in chapter 5 of this TSD

.

T able 4.4.1 Technology Options

Screened O ut Retained

• Addition of flow control insert

• Aerators

• Additional v alves • Smaller spray hole area

• Changing s pray hole shape

• Venturi meter to o rifice p late nozzle geometry5

- 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

- 245

- ii LIST OF FIGURES
Figure 5.3.1 Relationship between CPSV Flow Rate and Spray Force

................................ ....... 5

- 4 Figu re 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 Cleanabilit y 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 (A 1 )

................................ ....... 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 Optio ns 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 Efficienc y Relationship for the Standard

- Duty Equipment Class (Spray

force > 5 ozf and ≤ 8 ozf)

................................ ................................ ................... 5

- 23 Table 5.5.6 Cost Efficiency Relationsh ip for the Heavy

- Duty Equipment Class (Spray

force > 8.0 ozf)

................................ ................................ ................................ ... 5

- 235

- 1 CHAPTER 5. ENGINEERING ANALYSIS

5.1 I NTRODUCTION

After conducting the screening analysis for commercial prerinse spray valves (CPSV s )

, 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 var y wi th increased levels of efficiency.

The primary inputs to the engineering analysis are information and data from the market and technology assessment ( c hapter 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 reverse

engineering 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 in creas e product efficiency above the baseline levels. In the subsequent markups analysis ( TSD chapter 6), DOE
determine d customer prices by applying manufacturer markups, distribution chain or dealer markups

, and sales tax to the MPCs developed in the engin eering analysis. After applying these markups, the data serve as inputs to the energy use characterization ( TSD chapter 7) and the life

cycle 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 revers e

- 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) other5

- 2

performance

- related features t hat would justify the establishment of a separate ener gy 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 base d 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 class es . The subsections following the table describe how D OE select ed
the baseline, higher

- efficiency, and maximum technologically feasible (“max tech”) efficiency

le vels 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”) 0.945

- 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

‒ 1 3 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 va lves 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 spra y 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 pro posed 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 Pr otection 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 select ed 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 wi th basic characteristics of equipment t hat 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 Hi gher Effic i ency 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 pe rcent 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 t he Energy Policy and Conservation Act , as amended, (EPCA) (4 2 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 ma x

- 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 M ET HODOLOGY

This section describes the general anal y tical 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 teardo wns , 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 forc e

 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 t he results of the theoretical analysis supported and confirmed t he 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

- by

piece, compile a BOM, and estimate the material and labor cost of each component

. DOE refers to this practice as a physical teardown. A suppleme ntary 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 analys e s 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 pair s

. 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 consider s 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 pair5

- 7

In responding to the preceding quest ions, DOE adopt s the following crit eria for selecting models for testing and the teardown analysis:

 T he selected products should span the full range of efficiency levels for each equipment
class under consideration

.

 W ithin each equipment class, if possibl e, the selected products should be produced by
the same manufacturer and belong to the same product series

.

 T he selected products should represent manufacturer s with large market share in that equipment class, although the highest efficiency products are c hosen irrespective of
manufacturer

.

 T he 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 i s 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, spot

welders) 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 int o 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 pr ice 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 us es 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 ident ically structured and provides a description in equal detail to the BOM of a physical teardown

. However, it wa s generated using a slightly different methodology

— modifying the BOM of a similar model that has been physically torn
down

. These modifications re flect 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 (di rect materials, direct5

- 8

labor

, and some overhead costs.) Figure 5.4

. 2 shows the t h ree major steps in generating the manufacturing cost.

Figure 5.4

. 2 Teardown Spreadsheet Model Methodology

The first step in the manufacturing cost assessment wa s the creation of a complete and structured BOM from the disassembly of the models selected for teardown. The models we re dismantled, and each part wa s characterized according to weight, manufacturing processes used, dimensions, material, and quantity. The BOM incorporate d 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 indu stry experience, and information in trade publications

.

Following the development of a detailed BOM, the major manufacturing processes we re 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 f

abrication process cycle times and enter ed 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 wa s finalized, DOE prepared a detailed

summary of relevant components, subassemblies

, and processes. The BOM thus detail ed 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 h ybrid assemblies consisting of both purchased
parts an d parts made on site

. Purchased Parts

Bill of
Materials Fabrication
Processes Assembly
Processes Teardown
Observatio ns Production Cost Raw
Materials Finished
Materials5

- 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, valve s ), raw materials, ( i.e. , cold rolled steel,
copper t ube), 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) depend ing on the industry.

 Indirect labor: Labor costs that scale with fab rication 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.

 Mainten ance: 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 manufac turer 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 expert ise and knowledge of the methods used by the industry.5

- 10

5.4.4 Manufacturer Interviews

V ariability 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 Flo w Rate and Spray Force Test Results5

- 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

p reviously.5

- 12

Figure 5.5

. 3 Spray Forc e 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 wi th 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

p rocedure. A s 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 The oretical Analysis Results

In addition to performing the experimental test s 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 equ ation 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

. 83 C d A 2 √ 2 ( P 1 − P 2 )

ρ ( 1

− ( A 2 A 1 ) 2 )

Eq. 5

. 2

Where:

Q = flow rate (gallons per minute; gpm)

C d = discharge coefficient (unitless)

A 1 = nozzle input area (square feet)

A 2 = spray hole exit area (square feet)

P 1 = water pressure entering the nozzle, assumed to be 60 pounds per square inch (psi), per the DOE test procedure

P 2 = 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
standar d 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 th e 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
theo retical fluid speed, as described by Torricelli’s law, multiplied by the spray hole area. 10 The actual discharge is determined by the nozzle geometry. 115

- 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 √

P 1 Eq. 5

. 3

Where:

F = calculated spray force (ozf) Q = flow rate (gpm)

P 1 = water pressure entering the nozzle, a ssumed 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 T SD, 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 cons tants or negligible. Specifically, water pressures P 1 and P 2 are constant under DOE
test conditions. P 1 is the water pressure during testing (60 psi) and DOE assumes that P 2 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 (A 1 ) to spray hole area (A 2 ) 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 C

d x A 2 Eq. 5

. 4
Where

K = constant term including everything under the square root of equation x

C d = Discharge coefficient (unitless)

A 2 = spray hole area (square feet)

The spray hole area (A 2 ) is the hole through which the water exits the commercial prerinse spray valv e. The area of this hole affects the flow rate, as indicated in Eq. 5

. 2

. DOE used ImageJ software 14 to compute the spray hole area for all the CPSV models that DOE te sted
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 th e 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 Ar ea

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 loo ked further into how changes in the nozzle area (A 1 ) affect the flow rate. DOE analyzed two scenarios: the first uses a very small spray hole area (A 2 ) and the second uses a large spray hole area. The discharge coefficient (C d ) is kept constant at 0.60 in both scenarios, because the discharge coefficients affect flow rate in dependently 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 (A 1 ) 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. Th erefore, 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 N ozzle A rea (A 1 )

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 C PSV test unit using calipers with an accuracy of 1/1000 th 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 dem onstrating 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 G eometry

DOE is aware of two types of nozzle designs: ventur i meters and orifice plates. A v enturi 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 v enturi 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 Orifi ce 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 v enturi meter. 17 DOE calculated the discharge coefficient of each CPSV test unit and notes that the majority of discharge co efficients 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 CS PV valves that currently use a v enturi 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 o f 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

– materia l 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 fiv e 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 efficie nt 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, h andle, 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 Manufacturin g 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 spray5

- 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 use d. 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 manufacturer s . One of the technology pairs has flow rates
that corres pond to t he light duty and heavy duty product classes. The other two technology pairs have flow rates that correspond to t he light duty and standard duty product classes. The
difference in MPC s among the three technology pairs was due to the materials and production processes used. However , within each technology pair, the MPC of both models w as identical. Based on these results, DOE conclude s that there is no incremental cost associated with producing a more efficient ( i.e. , lower flow rate) c ommercial pre rinse 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 remain s 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
cal culated 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

test ing, 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 simpli fication 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 S pray 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 Heavy

Duty 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 S pray hole area = 0.0064 square inch; Discharge coefficient = 0.665

- 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 $0.005

- 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 da ta 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.ht ml

7

. California Energy Commission, 2014 Appliance Efficiency Regulations , Report No. CEC

400

- 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

- orifice

di scharge

- coefficient

- for

- orifice

- flow

- meter

- wit h

- 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

- dyna mics

and

- its

- applications

- 11/bernoulli

- s

- equation

- 99/torricelli

- s

- law

- 358

- 1967/

11

. http://ac.els

- cdn.com/073510979090260V/1

- s2.0

- 073510979090260V

main.pdf?_tid=7c22f84e

- e396

- 11e4

- 9c2f

00000aab0f6b&acdnat=1429119799_d03c13a7631730a16022a552daa03030

12

. http://phy sics.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

- 073510979090260V

main.pdf?_tid=4946ad68

- d968

- 11e4

- 83a7

- 00000aacb35d&acdnat=1428000445_0f25dcfbefdd667c795676515cb7608b

16

. ht tp://www.engineeringtoolbox.com/orifice

- nozzle

- venturi

- d_590.html

17

. http://www.brighthubengineering.com/hydraulics

- civil

- engineering/52906

- orifice

- flow

nozzle

- and

- venturi

- meter

- for

- pipe

- flow

- measurement/6-i CHAPTER 6. MARKUPS ANALYSIS

TA BLE 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

LI ST OF TABLES

T able 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

LI ST OF FIGURES Figure 6.1.1 Components of Manufacturer Selling Price ............................................................ 6-16-1 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 ( NOP R ) analysis for commercial prerinse spray valves (CPSVs)

.

In this rulemakin g, 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 pric es. 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

.

F igure 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, Record

Keeping, 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 Profit6-2 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 th is 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
informat ion 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 ave rage 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 consu mer s . 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 NOP R 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 energy6-3 and water cost savings at each efficiency level in the LCC and PBP analysi s ( chapter 8 of this
TSD ) and NIA analysis (chapter 10 of this TSD ).

As an example, t he following equation shows how DOE determined the product prices
for commercial prerinse spray valves for distribution channel C (Retailer) :



### ) ( TAX RETAIL MFR END 

### MU MUPP ××= Eq. 6.1

Where:

END P = product price to the end

- user ($),

MFR 

### P = MSP of baseline or standard- level product ($),

RETAIL 

### MU = retail er markup, and

TAX MU = 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 m arkup 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) indicat ed 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, t he following equations show the calculation of baseline markups:



### ) (

_ _ _ BASE RETAIL BASE MFR BASE RETAIL 

### MU P P × = Eq. 6.2



### ) (

_ _ TAX BASE RETAIL BASE END 

### MU P P × = Eq. 6.3

Where:

BASE MFR 

### P

_ = MSP of baseline product ($),

BASE RETAIL P

_ = retailer selling price of baseline product ($),

BASE END 

### P

_ = end

- user purchase price for baseline product ($),

BASE RETAIL MU

_ = retail er markup for baseline product, and

TAX MU = sales tax markup.6-4 6.2 ESTIMATION OF COMPANY AND SALES TAX MARKUP S

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).

F iled 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 indirec t 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 Prof it Margin Gross Prof it

Net Sales =

E q. 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 c hanges resulting from enactment of potential standards). Each company ’s baseline markup was calculated as:



### Sales ofCost

SalesNet
)rgin Profit Ma Gross 1( 1 Markup Baseline =

− =

E q. 6.6

Table 6.2.1 through Table 6.2.3 contain s the calculated gross profit margins for a sample
of companies

.6-5 Table 6.2.1 Gross Profit Margin s for Distributors *

Company Financial Figure $ Year

2013 2012 2011 2010 200 9

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
201 3 SEC 10

- K reports

.

Table 6.2.2 Gross Profit Margin s for Retailers *

Company Financial Figure $ Year

2013 2012 2011 2010 200 9

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
201 3 SEC 10

- K reports

.

Table 6.2.3 Gross Profit Margin s for Service Companies *

Company Financial Figure $ Year

2013 2012 2011 2010 200 9

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
201 3 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 V alves
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, r esulting 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 manufactu rer 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 s tate 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 s tate 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 devel oped for the LCC and PBP analysi s. 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
6.0 0
8.0 0
7.00
6.8 0
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
7.0 5
7.45
0.00
6.0 5
7. 95
0.00
6.95
6. 70
8.4 5
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
5.4 5
9.45
7.95
6.65
6.10

5. 60
8.90
6.05
5.45
5.45

National Averag e 7.1 66-8 REFERENCES

1. T he 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

TA BLE OF CONTENTS

7.1 INTRODUCTION .............................................................................................................. 7-2
7.2 ESTIMATING WATER USE OF COMMERCIAL PRERIN SE SPRAY VALVES ....... 7-2
7.2.1 Modeling Annual Water Consumption Using Annual Operating Time ................ 7-2
7.3 ANNUAL WATER CONSUMP TION

............................................................................... 7-5
7.4 ESTIMATING ENERGY US E OF COMMERCIAL PRER INSE SPRAY VALVES ...... 7-6
7.4.1 Modeling Annual Energy Consumption Using Annual Water Consumption ........ 7-6

7.5 ANNUAL ENERGY CONSUM PTION

............................................................................. 7-9
REFERENCES .......................................................................................................................... 7-10

LI ST OF TABLES

T able 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 Prerin se Spray Valves ............. 7-9 Table 7.5.1 Annual Energy Use ................................................................................................... 7-97-2 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 te chnical support document (TSD) and the national impacts analys i s (NIA) found in chapter 10 of this TSD

. This chapter describes the means and methods DOE adopt ed 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 valve s ( CPSV ) to inform the LCC and PBP analysis.

T he goal of the en ergy 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 U nited States . The engineering analysis described in c hapter 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 E STIMATING WATER USE OF COMMERCIAL PRERINSE SPRAY

VALVE S

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 p rerinse 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

Th e annual o perating time of commercial prerinse spray valves was used to annualize water consumption.

T he general equation used to determine annual operating time is as follows:

A nnual Valve Operating Time (minutes per year) =
CPSV Operating Time per Meal (minutes) x Meals Served per Year Eq. 7.1

T o determine annual operating time , DOE was required to determine the following
information:

• bui lding 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

• o perating time per meal by building type

.

DOE utilized the Commercial Building Energy Consumption Survey 1 (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 hous e 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 da taset 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 t o 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 pr erinse 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 2004 3 and 2007, 4 Veritec Consulting, Inc. in the City of Calgary 5 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. Th e 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 Sub

Group Meals Serve d 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 89.77-5 Next, the annual CPSV operating time for each building type was derived from the daily operating time. This was accomplished using the following equation:

C

BECS 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

F inally, 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 opera ting times for individual building types and the weighted average operating time across all buildings are provided in Table 7.2.3

.

T able 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

.

D OE 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 operating7-6 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 2004 3 and 2007 4 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 psi 8 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

T he 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

.

A nnual Water Consumption (gallons per year per unit) = Weighted Average Annual Operating Time (minutes per year) x Flow Rate (gpm) Eq. 7.3

T ab le 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

VALVE S

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 th is equation.

E

nergy Consumption per Gallon of Water = (E req * ΔT * R mix * C)/ Eff wh Eq. 7.4
Where:

E

req = heating constant, 8.30 Btu/gal-F,

ΔT = change in temperature from cold water supply to hot water supply (oF),

R mix = 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 Eff wh = 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 12-2000 11 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 corr elates 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. Th is 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

N ew E ngland 54.06 55.74

Middle A tlantic 57.39 59.24

E ast N orth C entral 53.82 55.67

W est N orth C entral 54.25 56.43

S outh Atlantic 68.30 69.74

E ast South Central 64.63 66.54

W est 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 f or 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 = (T

EU – T S ) ÷ (T H

- T EU

) Eq. 7.5
Where:

T

EU = e nd use water temperature

, T

S = s upply water temperature , and

T

H = h ot 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
commercia l kitchens utilize natural gas or electricity. T he 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 s tandard 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 heate rs 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

.

T able 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 2,71 7 4,361 5,7 20

1 2,57 4 3,932 5,14 8

2 2,43 1 3,46 8 4,57 6

3 2,32 4 3,360 4,43 37-9 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 10,75 9 16,437 21,51 8

2 10,161 14,494 19,12 7

3 9,71 3 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 multiplie d 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

.

T able 7.5.1 Annual Energy Use

Efficiency Level Annual Energy Use (kBtu per y ea r)

Spray Force ≤ 5 ozf Spray Force > 5 ozf and ≤ 8 ozf Spray Force > 8 ozf

Baseline 8,59 2 13,792 18,088

1 8,1 40 12,43 6 16,279

2 7,687 10,96 6 14,470

3 7,348 10,62 7 14,0187-10 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.ep a.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/Pre

Rinse- 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. S BW Co nsulting, Inc. and Koeller and Company. Pre-Rinse Spray Valve Programs: How Are They Really Doing? December 2005. Seattle, WA. Available at: http://www.alli anceforwaterefficiency.org/Commercial_Food_Service_Introduction.aspx.

8. F riedman , 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. AS H RAE. 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

TA BLE 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 INPUT S ......................................................................................... 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 P ERIOD ............................................................................. 8-23
8.5.1 Results ................................................................................................................... 8-24
REFERENCES .......................................................................................................................... 8-25

L IST 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 ................ 8-218- 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 Com mercial Prerinse Spray Valves ..................... 8-24

LI ST 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) .......................... 8-138-1 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 o f commercial prerinse spray valves ( CPSV s)

.

• Li fe

- 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 red uced operating costs.

T he 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).

I nputs 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 rul emaking website for c ommercial prerinse spray valves 1 . 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 Analys is

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 prov ides a detailed explanation of Monte Carlo simulation and the use of probability distributions.

D OE 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.

T he 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 re duction in operating cost . For the purpose of this rulemaking, DOE has concluded that there is no change in8-2 purchase cost for m ore 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 Customer s

DOE used 2 014 population estimates from the United States Census to assign probabilities that a commercial prerinse spray valve is located in a particular s tate 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 analysis 3 . 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 Analys is 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 purch ase 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:

• M anufacturer 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. The8-3 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 e quipment energy consumption: The associated equipment energy consumption is the site energy use associated with operating commercial prerinse spra y 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

.

• E nergy price trends: The energy prices projected over the lifetime of commercial prerinse spray valve s 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 yea r escalated by the water price trends.

• Water price trends: The prices projected over the lifetime of commercial prerinse spray
valve s installed in the compliance year.

• Maintenance costs: The labor and material costs associated with maintaining the operatio n 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: T he 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 prob ability 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 F igure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP

8.2 LIFE

- CYCLE COST ANALYSIS INPUTS

L CC 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 . L CC is defined by the following equation:

E q. 8.1
Where:

L CC = 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.

D OE expresses all the costs in this chapter in 2014$. Total installed cost, operating cost, lifetime, and discount rate are discussed in the following sections. T he year of equipment 

# ∑

= 

### + += N

t t

t 

### r OC ICLCC

1 

### )1 /(8-5 purchase is assumed to be 2019 since the effective date of the energy conservation standards for commercial prerinse spray valve s is expected to be late 2018

.

8.2.1 Total Installed Cost Inputs

DOE calculated the total installed cost customer s pay for commercial prerinse spray valves based on the following equation:



### INST TAX RSPIC +×= ) (

E q. 8.2

W here:

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.

D OE developed the MSP for each equipment class of commercial prerinse spray valves as a part of the engineering analysis ; see Table 8.2.1

.

T able 8.2.1 Mean CPSV MSP s in 2014

Equipment Class 2014 $

Light Duty ( Spray F orce ≤ 5 ozf ) 36.63

Standard Duty ( Spray F orce > 5 ozf and ≤ 8 ozf ) 36.63

Heavy Duty ( Spray F orce > 8 ozf ) 36.63

T he 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 A verage 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 ne cessary to install the CPS V 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 installati on 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 install ation 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 differ ent 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. These8-7 values were from the year 2010, so a Consumer Price Index (CPI) inflation calculator 6 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 wa ge. 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 b y 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. T he 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 relat ively 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 mark ups, 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:

EL EL WATER EL ENERGY EL EL 

### MC RC PRICE AWC PRICE AEC OC ++ ×+ ×= ) () (

Eq. 8.3

Where:

OC EL = operating cost ($),

A EC = annual energy co nsumption associated with operating the equipment,
PRICEENERGY = energy prices ($),

A WC = 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 develo ped equations to estimate annual associated energy and water consumption for commercial prerinse spray valve s 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 will8-8 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 equipmen t.

Because of the wide variation in e nergy 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 s tate level from an Energy Information Administration ( EIA ) database o f commercial electricity and natural gas prices 7

. When this analysis was conducted, t he latest available pric es from this sourc e were for the calendar year 20 13. 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 s tate electricity and natural gas prices

, respectively

.8-9 Table 8.2.3 Average Commercial Electricity Prices

State Average Price,
Commercial $/kWh, 201 4 State Average Price,
Commercial $/kWh, 201 4

Alabama 0. 107 Montana 0.09 7

Alaska 0. 158 Nebraska 0.0 87

Arizona 0.100 Nevada 0.09 2

Arkansas 0.081 New Hampshire 0.1 37

California 0.14 8 New Jersey 0.13 0

Colorado 0.100 New Mexico 0.09 9

Connecticut 0.149 New York 0.15 5

Delaware 0.1 04 North Carolina 0.0 89

District of Columbia 0.12 1 North Dakota 0.084

Florida 0.096 Ohio 0.095

Georgia 0.10 0 Oklahoma 0.0 78

Hawaii 0.3 46 Oregon 0.08 5

Idaho 0.07 5 Pennsylvania 0.094

Illinois 0.0 80 Rhode Island 0.1 33

Indiana 0.09 6 South Carolina 0.100

Iowa 0.08 6 South Dakota 0.08 6

Kansas 0.097 Tennessee 0.10 2

Kentucky 0.0 86 Texas 0.0 81

Louisiana 0.0 91 Utah 0.08 5

Maine 0.11 9 Vermont 0.14 9

Maryland 0.10 9 Virginia 0.08 2

Massachusetts 0.14 7 Washington 0.0 79

Michigan 0.11 2 West Virginia 0.08 3

Minnesota 0.09 7 Wisconsin 0.1 10

Mississippi 0.10 4 Wyoming 0.08 7

Missouri 0.08 8 U.S. Average 0.1048- 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 to8- 11 2014 dollars using the ratio of 2014 and 2012 values for the Bureau of Labor Statistics C PI for

water. The 2014 combined water prices are shown in Table 8.2.5

.

Table 8.2.5 Average Combined Water Prices

State Average Pri ce, 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 8.258- 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 0.0902014201720202023202620292032203520382041204420472050205320562059 Low

Reference

High8- 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 an d 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, t here are no maintenance costs included in the operating cost calculations for the LCC analysis for commercial prerinse spray val ves.

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 3.5201420162018202020222024202620282030203220342036203820402042204420462048205020522054205620582060 Low

Reference

High8- 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 probabilit ies 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

W here:

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 Rate s

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 capita l 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 (k
e ) 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 ( R f ), 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 stoc k 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:


## ( ) ERP Rk

fe



### ×+= β

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. Fo r 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 ca lculating discount rate averages, utilizes the gross domestic equipment price deflator for estimating inflation, and considers the best method for determining8- 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 t he risk

- free rate using Federal Reserve data for the period 1974 through 2013, 12 with a resulting rate of 6. 85 perce nt. 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 Online 13 site and the
risk

- free rate

.

The cost of debt financing ( k d ) is the interest rate paid on money borrowed by a company. The cost of debt is estimated by adding a risk adjustment factor ( R
a ) 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:

aifdi 

### RRk +=
Eq. 8.7
Where:

k

d = cost of debt financing for firm, i,

R

f = expected return on risk- free assets, and

R ai = risk adjustment factor to risk

- free rate for firm, i.

DOE estimates the WACC using the following equation:

d dee 

### wkwkWACC ×+×= 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 i n 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 owner

operators, 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 Ra tes by Building Type

Publicly Traded Small - Disc Rate
Adder S & L Govt / Non

Profit U.S. Government

Building

Type WACC % of

Market Discount
Premium 5 % 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 Fed eral 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 , m ixed quality bonds , based on a nnual ave rage data. Non

- seasonally adjusted.
d ata 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. A rticles d id not indicate t he 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 municipal8- 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. govern ment -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 no t be required until the later part of 2018, DOE calculated the LCC and PBP for all c ustomer s 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 ord er 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 par ticular 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 Center 16 and DOE’s Compliance Certification Database 17 for commercial prerinse spray valves. These data sources, while limited in scope and coverage, gave information regarding the relative distribution of efficiencies f a or 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 prerin se spray valves are shown in Table 8.2.8

. The estimates
come from c hapter 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).

T he equation for PBP is:
PBP =∆IC/∆OC Eq. 8.9

W h ere:

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

.

PB Ps are expressed in years. P BP s 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.

T he data inputs to the PBP are the total installed cost of the equipment to the c ustomer 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 w ater cost s. The PBP uses the same inputs as the LCC analysis, except that e nergy and water price trends are not required. Since the PBP is a “simple” payback, the required energy and water cost is only for t he 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.

D OE 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 a nnual energy and water cost s as calculated from the DOE test procedure. The calculation excludes repair costs and maintenance costs.

S ince 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

.

L CC 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.

D OE 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 level s . 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 valve s.8- 21 Table 8.4.1 Li ght Duty Commercial Prerinse Spray Va lve s: LCC and PBP Results

Light Duty (Spray F orce ≤ 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 1,9 33 0.0

2 2 79 334 1,751 1,8 30 0.0

3 , 4 3 79 319 1,674 1, 753 0.0

Light Duty (Spray F orce ≤ 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 calcula ted relative to the no standards case efficiency distribution

. The
calculation includes customer s with zero LCC savings (no impact)

. † The median payback period is calculated only for affected customer s. Customer s with zero LCC energy savings have an undefined payback period, and are therefore not in cluded in calculating the median PBP

.8- 22 Table 8.4.2 Standard Duty Commercial P rerinse Spray Valve s: LCC and PBP Results

S tandard Duty (Spray F orce > 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 F orce > 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 calcula t ed relative to the no standards case efficiency distribution

. The
calculation includes customer s with zero LCC savings (no impact)

. † The median payback period is calculated only for affected customer s. Customer s with zero LCC energy savings
have an undefin ed payback period, and are therefore not in cluded in calculating the median PBP

.8- 23 Table 8.4.3 Heavy Spray Force Commercial Prerinse S pray Valve s: LCC and PBP Results

Heavy (Spray F orce > 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 F orce > 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 customer s with zero LCC savings (no impact)

. † The median payback period is calculated only for affected customer s. Customer s with zero LCC energy savings have an undefined payback period, and are therefore not in cluded 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))

T he 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 rebu ttable 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 probability8- 24 distribution of energy prices in the distributional PBP analysis, it uses only the national average energy price to determine the rebuttable PBP.

O ther 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.

D OE 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 present s the rebuttable PBPs for each equipment class of commercial prerinse spray valve s.

T able 8.5.1 Rebuttable Payback Periods for Commercial Prerinse Spray V alves

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 0.08- 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

P uerto 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- E nergy 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. C PI Inflation Calculator

. http://www.bls.gov/data/inflation_calculator.htm

.

7. F orm 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. C hicago, 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 Rate s. Last accessed May 21, 2014. www.federalreserve.gov/releases/h15/data.htm

.
13. Damodoran Online. The Data Page. 2014. Last accessed June 24, 2014.
ht tp://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 Certifi cation Database

. http://www.regulations.doe.gov/certificationdata/

.8A -i APPENDIX 8A. USER INSTRUCTIONS FOR L IFE

- CYCLE COST ANALYSIS
SPREADSHEET

TA BLE OF CONTENTS

8A .1 INTRODUCTION ........................................................................................................ 8A-1
8A.2 BASIC INSTRUCTIONS ............................................................................................. 8A-2
REFERENCES ......................................................................................................................... 8A-48A -1 APPENDIX 8A. USER INSTRUCTIONS FOR L IFE

- CYCLE COST ANALYSIS
SPREADSHEET

8A .1 INTRODUCTION

The detailed results of the life

- cycle cost ( LCC ) and payback period ( PBP ) analys is 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 Crysta l Ball ® 2 software. Both applications are comme rcially available. The spreadsheet has b een tested with the 2010 and
2013 versions of Microsoft Excel

, and the latest spreadsheet is avai lable from the DOE website

at : http://www1.eere.energy.go v/buildings/appliance_standards/rulemaking.aspx?ruleid=100 Descrip
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 econom ic 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 consumer s. All
sources used to derive di scount 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

- new

standards 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 ta bles.

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 usi ng 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 unde r "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 Cryst al Ball simulation, click th e “ 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 botto m 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. EER E. Rulemaking for Commercial Prerinse Spray Valves Standard

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

. Accessed May 9 , 2015.

2. O racle. 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

TA BLE 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

LI ST OF FIGURES

F igure 8B.5 -1 Normal, Triangular, Uniform, Weibull, and Custom Probability Distributions 8B-38B -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 equa tion 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 i n a calculation, arguments can arise about what the appropriate value is for each quantity. Explicit analysis of uncertainty and variability is intended to prov ide 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 rare ly 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 el se. 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 inas much 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 fore cast ( e.g. , persons per household).

8B.4 A PPROACHES 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 a ppliance 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 cross over points can be identified. An example of a crossover point is the

ene rgy 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 disadv antage 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 informatio n 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 ou tcome 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 CRYST AL BALL ®

To quantify the uncertainty and variability that exis t in inputs to the engineering and to
the LCC and payback period (PBP) an alyses, 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 Carlo8B -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. T he same
holds true with the va riables 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.

Fi gure 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 CUSTOM9 -i CHAPTER 9. SHIPMENTS ANALYSIS

TA BLE 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 TO TAL SHIPMENTS INTO P RODUCT CLASSES
AND EFFICIENCY LEVEL S ..................................................................................................... 9-2
9.4 IMPACT OF POTENTIAL STANDARDS ON SHIPMEN TS ......................................... 9-3
9.5 RESULTS ........................................................................................................................... 9-5
REFERENCES .......................................................................................................................... 9-11

LI ST OF TABLES

T able 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 ......................................................... 9-109-1 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 shipment s of new products in every product class, and for each efficiency
level (EL) . The model starts from a historical base year a nd calculates, for each year of the analysis period, both shipments and retirements. Th is 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 n ational i mpact a nalysis (NIA) to estimate the total dollar as well as energy and water costs and benefits associated with a proposed standard at each EL

.

T he 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 N IA

.

T his chapter explains how the shipments model is constructed and provides some summary output. Section 9.2 discusses historical shipments . S ection 9.3 outlines the approach used for disaggregating total CPSV shipments into shipments by product classes and EL s. Section 9.4 describes the impacts on shipments due to potential new or amended standards.
S ection 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 EL s, and then partitioning shipments into product
classes and EL s 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 f ramework 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 t he majority of t he CPSV market is comprised of family -owned companies that prefer to not divulge their shipments for competitive reasons.9-2 T he Environmental Protection Agency’s (EPA) WaterSense ® Field Study 1 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.

D OE 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 li fetime of the product is 5 years ; all units
are assumed to fail by the end of their 10 th year of operation. DOE incorporated this variability in product lifetime in its shipments analysis.

D OE 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

.

A nnual 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 EL s according to the no- new -standards case distribution of

EL s. The development of the no- new -standards case distribution of EL s is discussed in chapter 8
of this TSD. DOE used data from the Food Service Technology Center 3 and DOE’s CCMS
Database 4 , and general market research, to develop the no- new

- standards c ase 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.

T able 9.3.2 No Standards Case Relative Shipments Breakdown by EL within Product
Class es

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 EL s. 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.

F or its shipments analysis, DOE therefore assumed that any potential amended energy conservation standards for commercial prerinse spray valve s 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 cas e and standards cases.

D OE 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) pr oduct 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 a nd energy
than their current commercial prerinse spray valve . In particular, if current choices of products correspond to consumer s’ optimal products under the current regulatory environment, it is probable that some consumer s 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 consumer s 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 consumer s’ current choice s of products corresponds to their optima l 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 g allons 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 preferen ce 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 l evel

. ” (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– new

standards 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 4,6259-7 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 St andard D uty 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 K ey Indicators and Consumption, R eference
C ase.” 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

TA BLE 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 RESULT S ...................................................................................... 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 ........................................................................................................................ 10-1910-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: Cumulati ve Net Present Value of Consumer Benefits for Products Shipped in 2019–2048 for TSL 3 .......... 10-16 Table 10.4.13 Commercial Prerinse Spra y 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 .......... 10-1810-1 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 consumer s 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)

.

D OE determin ed 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 spreadsh eet 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 ).

C hapter 9 of this TSD provides a detailed description of the shipments model that DOE used to forecast future purchases of the considered products.

I n its analysis, DOE studied three different product classes in detail , as described in

c hapter 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 ( AEO201 4 ). 1 DOE also calculated NIA results using inputs from the High Economic Growth case and the Low
Economic Growth case in AEO201 4. 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).

STD NOSTD y 

### AEC AEC NES

_ 

### =
E q. 10.1

Where:

NES y = national energy savings in the year y ( quadrillion British thermal units (quads))

, AEC NOSTD = annual national energy consumption in the no- new -standards- case scenario (quads),
and
AEC STD = annual national energy consumption in a standards

- case scenario (quads ).

C umulative 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 evaluatio n period for cumulative energy savings spans 2019-
2058.

D OE 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.

AA AAAA yy = � � ( SSSSSS SS VV × UU VV ff )

VV ∗ CCCC ff
Eq. 10.2
Where:

AEC y = national annual energy consumption each year in quadrillion British thermal units (quads), summed over vintages of the product stock, STOCK V, and fuel, f

, STOCK V = stock of products (units) of vintage V that survive in the year for which DOE
calculated AEC

, UEC V = average energy consumption per unit of product in kilowatt -hours (kWh) or thousand British thermal units (kBtu)

, CONV f = 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.

E lectricity 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 g as consumption is converted from s ite 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 close ly 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 ca se. 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).

STD NOSTD y 

### AWC AWC NWS

_ 

### = Eq. 10.3

Where:

NWS y = national water savings in the year y (billions of gallons)

, AWC NOSTD = annual national water consumption in the no- new -standards- case scenario ( billions of gallons), and
AWC STD = 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 wa ter 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.

AAAAAA yy = � ( SSSSSS SS VV × UU VV )

VV ∗ CCCC

Eq . 10.4
Where:

AWC y = national annual water consumption each year in billions of gallons, summed over vintages of the product stock, STOCK V , STOCK V = stock of products (units) of vintage V that survive in the year for which DOE
calculated AWC

, UWC V = average water consumption per unit of product in thousands of gallons (k Gal ),
CONV = conversion from k Gal 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 Inputs10-4 The inputs to the calculation of NES and NWS are:

• shipments,

• product stock (STOCKV ),

• average energy consumption per unit (UEC) and av erage 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 c hapter 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 th at year. The NIA model track s the number of units shipped each

year . DOE assumes that commercial prerinse spray valves have an incre asing 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 E nergy and Water Consumption: Light Duty ( Spray F orce ≤ 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 18.0810-5 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 F orce > 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 A EC

, DOE first calculated AEC at the site , and then applied conversion factors , described in section s 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 en ergy 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 elect rici ty

. Th e 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 annual10-6 conversion factors based on the version of the National Energy Modeling System (NEMS) a that corresponds to AEO 2014 . 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 AEO201 4 NEMS end
in 2040. DOE assumed that conversion factors remain at the 2040 values throughout the rest of
the forecas t.

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 distrib uting 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

gener ated by the NEMS used for AEO 201 4 . The AEO 201 4 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 co st associated with the difference between the no- new -standards case and each potential standards
case for the considered c ommercial p rerinse s pray v alve 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:



### PVCPVS NPV

_ 

### =
E q. 10.5
Where:

PVS = present value of savings in operating cost, and
PVC = present value of increased total product cost to consumer s.

D OE 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 un der 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 

## ∑

yy DFOCS PVS ×= Eq. 10.6 

## ∑

yy DF TIC PVC ×= Eq. 10.7
Where:

OCS = total annual savings in operating cost each year summed over vintages of the product stock, STOCK V , TIC = total annual increases in product cost each year summed over years of the product shipments, S HIP y , 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 co st by multiplying the number of shipments of the given product class (by v intage) by its per

- unit increase in

cons umer 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.



## ∑

V V y UOCS STOCK OCS × =

Eq. 10.8 

## ∑

y y y UTIC SHIP TIC × =

Eq. 10.9
Where:

STOCK V = stock of product of vintage V that survive in the year for which DOE calculated AEC

, UOCS V = annual per-unit savings in operating cost,

V

= year in which the product was purchased as a new unit

, SHIP y = shipments of products in year y , and
UTIC y = annual per -unit increase in installed product cost in year y

.

DOE determined the total inc reased 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 ha ve 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:

• a verage annual per -unit product installed cost

,

• average annual per -unit sav ings 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 c osts with increased efficiency level s. 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 AEO201 4 . 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 

## ∑

y y y UTIC SHIP TIC × = 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 sta ndards 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.



## ∑

V V UOCS STOCK OCS × = Eq. 10.11

10.3.2.5 Discount Factor

DOE multiplied mo netary values in future years by a discount factor to determine the present value. The discount factor (DF) is described by the equation:

)( )1( 1

yp

y r DF

− + =

Eq. 10.12
Where:

r

= discount rate,

y = year in which the monetary value exists, and

y

P = 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 discoun t 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 se ction 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 the10-10 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 st andards 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 t he higher energy efficiency of products pu rchased 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 WATE R 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 usi ng 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 i nput from the LCC analysis

. Constant for all years

, efficiency
levels , and product classes

. (See chapter 8.)

Annual e nergy and combined

water cost s 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 AEO20 1 4 forecasts (to 20 40 ) 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 s ite

- to

- source conversion factor s 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 th at 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 proj ections generated by DOE

EIA’s NEMS program

. (See chapter 10

- B).

Discount rates 3% and 7% real.

Present year Future expenses are discounted to 20 1 5

.

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 a nticipated standards-induced product- class switching to heavy -duty products by certain consumer groups in10-12 the standard -duty product class . The resulting shifts in standards- case shipments yield negative energy savings for the heavy -duty product class at ev ery 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 * Nation al Water Savings billion gal Primary FFC

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 * Nation al Water Savings billion gal Primary FFC

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 * Nation al Water Savings billion gal Primary FFC

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 * Nation al Water Savings billion gal Primary FFC

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

costs c

. Circular A -4 also directs agencies to consider the variability of key elements underlying
the es timates 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 10.4.9 Table report cumulative

c U.S. Office of Management and Budget, “ Circular A -4 : Regulatory Analysis,” s ection 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 * Nation al Water Savings billion gal Primary FFC

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 * Nation al Water Savings billion gal Primary FFC

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 * Nation al Water Savings billion gal Primary FFC

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 * Nation al Water Savings billion gal Primary FFC

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 NPV s associated with each of
the considered TSLs for each c ommercial 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. Thi s 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 consumer s 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 nee d 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) $1.6 04 $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) $1.81 3 $3.7 24

Heavy Duty

(> 8 ozf) ($1.23 0) ($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) $1.81 3 $3.7 24

Heavy Duty

(> 8 ozf) ($1.157 ) ($2.374 )

TOTAL TSL 3 $0.708 $1.45910-17 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) $3.4 18 $7.0 18

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 $0.39710-18 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 $0.17710-19 REFERENCES

1. Energy I nformation Administration. Annual Energy Outlook 2014 with Projections to
2040 . April 2014. U.S. Department of Energy, Washington, DC.

2. Off ice 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

TA BLE OF CONTENTS

10A .1 INTRODUCTION ...................................................................................................... 10A-1
10A.2 WORKSHEET SUMMARY ...................................................................................... 10A-1
10A.3 BASIC INSTRUCTIONS ........................................................................................... 10A-210A -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 desi gned and tested using Excel 2010.

10A.2 WORKSHEET S UMMARY

The N IA spreadsheet consists of the following worksheets:

I nstructions Contains a table of contents with a short description of each included worksheet, as well as brief instructions for operating the spreadsheet.

G enerate 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” work sheet. The second button populates tables on th e “Generate and View Results ” work sheet
with the NIA results. The results of each of these macros depend

on user- selected sett ings on the “ Calculations ” work sheet.

f

or NIAplus Contains data for use in the NIAplus model.

f

or ImSET Contains calculations used to derive inputs for t he Impact of Sector Energy Technologies ( ImSET ) model.

C alculations Contains user input s as well as most of the calculations necessary
to derive the results of the NIA. This work sheet also contains calculations utilized to populate the “for NIAplus” worksheet and
the “for ImSET worksheet.

C onvFactors 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.

Shi pmen ts Contains estimated historical and projected future shipments and
stock of commercial prerinse spray valve s, by product class and

year. This work sheet includes a distribution of the expected
lifetimes of commercial prerinse spray valves, and also contains10A -2 e ditable definitions of the trial standard levels (TSLs) in terms of efficiency levels for each product class (in the “ TSL/EL Lookup
Table ” ).

I nputs from LCC Contains required data from the life

- cycle cost ( LCC ) spreadsheet

, includin g:

• t otal installed cost ;

• annual operating costs by product class, efficiency level, and economic growth scenario;

• annual water use by product class and efficiency level ;

• a nnual 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;

• a nnual electricity operating costs by product class, efficiency level, and economic growth scenario ; and

• annual natural gas operatin g 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) Us e Excel’s view/z oom commands at the top menu bar to change the size of the display to make it fit your monitor.

3) On the “Calculations” worksheet, t he user can optionally change the following settings :

a) Analysis Start Year: This is used to select the year sta ndards will come into effect.
The default is 2019.

b) Reference/Discount Year: This is used to s elect 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 s elects 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 butt on 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 Vi ew 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

TA BLE OF CONTENTS

10B .1 INTRODUCTION .......................................................................................................10B-1
10B.2 METHODOLOGY ......................................................................................................10B-1
10B.3 FU LL

- FUEL

- CYCLE ENERGY MULTIPL IERS .....................................................10B-3
REFERENCES ........................................................................................................................10B-4

LI ST OF TABLES

T able 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-310B -1 APPENDIX 10 B . 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 incl udes 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 meas ures 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, wit h 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 pri mary 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 fue ls 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 lo sses 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 in10B -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:

• a x is the quantity of fuel x burned per unit of electricity output, on average, for grid electricity. The calculation of a

x includes a factor to account for transmission and distribution system losses.

• b y is the amount of grid electricity used in production of fuel y, in megawatt hour ( MWh ) per physical unit of fuel y.

• c xy is the amount of fuel x consumed in producing one unit of fuel y.

• q x is the heat content of fuel x ( million BTU /physical unit)

.

• z x (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 , q x . To convert electricity in k Wh 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 u sed 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. Pro jections of Full Fuel Cycle Energy and

Emissions Metrics 3 describes the additional data sources used to complete the analysis. However, the time dependence in the FFC multipliers arises exclusively from variables taken from the AEO. The FFC analysis used data from AEO 2014

. 410B -3 Table 10B.2.1 Dependence of FFC Parameters on AEO Inputs

Parameter Fuel AEO Table Variables

q x A ll Conversion Factors MMBTU per P hysical U nit

a x A ll Electricity Supply, Disposition, Prices, and
Emissions Generation by F uel T ype

Energy Consumption by Sector and Source Electric P ower S ector E nergy C onsumption

b

c , c nc , c pc C oal Coal Production by Region and Type Production by C oal T ype and S ulfur
C ontent

b p , c np , c pp P etroleum Refining Industry Energy Consumption Refining only E nergy U se Liquid Fuels Supply and Disposition Crude S upply by S ource

International Liquids Supply and Disposition Crude O il I mports

Oil and Gas Supply Crude O il D omestic P roduction

c nn N atural G as Oil and Gas Supply U.S. D ry G as P roduction

Natural Gas Supply, Disposition

, and Prices Pipeline, L ease , and P lant F uel

z x A ll Electricity Supply, Disposition, Prices

, and
Emissions Power S ector E missions

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 AEO 2014 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 Energ y Multipliers (Based on AEO 2014 )

2020 2025 2030 2035 2040 2045 2050

Electricity (P ower P lant Primary Energy U se) 1.044 1.045 1.046 1.047 1.047 1.047 1.047

Natural Gas ( S ite) 1.10 9 1.111 1.113 1.114 1 .114 1.1 14 1.1 1410B -4 REFERENCES

1. U nited 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- F uel -Cycle Energy and Emissions Metrics. 2013. Lawrence Berkeley National L aboratory. 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

TA BLE OF CONTENTS

11.1 METHODOLOGY ........................................................................................................ 11-1
11.2 IDENTIFICATION OF CO NSUMER SUBGROUPS .................................................. 11-1
11.3 CONSUMER SUBGROUP AN ALYSIS RESULTS .................................................... 11-2

LI ST OF TABLES

T able 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 ...... 11-4Table 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 .... 11-511 -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 ) conduct ed this analysis as one of the analyses for the notice of proposed rulemaking (NOPR). DOE accomplish ed this by analyzing the life

- cycle costs ( LCC s) and payback periods (PBPs) for consumer s that fall into any identifiable groups. DOE
evaluate d 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
obtain ed estimates of each input parameter’s variability and consider ed this variability in its
calculation of consumer impacts.

DOE determ ined the impact on consumer subgroups using the LCC s preadsheet model, which allows for different data inputs. The standard LCC analysis (described in c hapter 8 of the technical support document (TSD) ) analyzes all consumer s that use commercial prerinse sp ray valves

. DOE use d the LCC s preadsheet 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 LCC s and PBPs. In the case of commercial prerinse spray valves, the subgroups
DOE consider ed 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 genera l population of commercial prerinse spray valve consumer s. DOE also identified limited service establishments that purchase commercial prer inse 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 saving s 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 a nnual 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

m inutes

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

T he 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 consu mer 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 RE SULTS

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 consumer s 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-D uty

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

, 9 18 0.0

1 1 79 353 1

, 742 1

, 8 21 0.0

2 2 79 334 1

, 646 1

, 7 25 0.0 3, 4 3 79 319 1

, 573 1

, 6 52 0.0

TSL Efficiency

Level Life

- Cycle Cost Savings
% of Consumer s that Experience
Net Cost Average Savings 2014$

0 0 0

1 1 0 97

2 2 0 126 3, 4 3 0 19811 -3 Table 11.3.2 Single Entity Consumer Subgroup Results for Standard -D uty

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

, 0 31 0.0

1 1 79 540 2

, 662 2

, 7 41 0.0 2, 3 2 79 476 2

, 347 2

, 4 26 0.0

4 3 79 461 2

, 275 2

, 3 54 0.0

TSL Efficiency

Level Life

- Cycle Cost Savings
% of Consumer s 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-D uty

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

, 9 51 0.0

1 1 79 707 3

, 485 3

, 5 64 0.0

2 2 79 628 3

, 098 3

, 1 77 0.0 3, 4 3 79 609 3

, 001 3

, 0 80 0.0

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Consumer s that Experience
Net Cost Average Savings 2014$

0 0 0

1 1 0 387

2 2 0 559 3, 4 3 0 62711 -4 Table 11.3.4 Limited Service Establishment Consumer Subgroup Results for Light-D uty

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

, 6 45 0.0

1 1 79 283 1

, 483 1

, 5 62 0.0

2 2 79 267 1

, 401 1

, 4 80 0.0

3, 4 3 79 255 1

, 339 1

, 4 18 0.0

TSL Efficiency

Level Life

- Cycle Cost Savings
% of Consumer s 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 Standard

D uty

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

, 3 45 0.0 2, 3 2 79 381 1

, 998 2

, 0 77 0.0

4 3 79 369 1

, 936 2

, 0 15 0.0

TSL Efficiency

Level Life

- Cycle Cost Savings
% of Consumer s that Experience
Net Cost Average Savings 2014$

0 0 0

1 1 0 247 2, 3 2 0 378

4 3 0 43911 -5 Table 11.3.6 Limited Service Establishment Consumer Subgroup Results for Heav y-D uty

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

, 3 75 0.0

1 1 79 565 2

, 966 3

, 0 45 0.0

2 2 79 503 2

, 637 2

, 7 16 0.0 3, 4 3 79 487 2

, 554 2

, 6 33 0.0

TSL Efficiency

Level Life

- Cycle Cost Savings
% of Consumer s that Experience
Net Cost Average Savings 2014$

0 0 0

1 1 0 330

2 2 0 476 3, 4 3 0 53312

- 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 Sm all

- 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 ASSU MPTIONS

................................ ................................ ..... 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 Annu al 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 I MPACTS

................................ ................................ ...... 12

- 14
12.5.1 Introduction

................................ ................................ ................................ ............... 12

- 14
12.5.2 Comm ercial Prerinse Spray Valve Industry Financial Impacts

................................ 12

- 16
12.6 IMPACTS ON SMALL COM MERCIAL PRERINSE SPR AY 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

- 2712

- 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

- Du ty Commercial Prerinse

Spray Valves

................................ ................................ ................................ .... 12

- 12 Table 12.4.11 Product and Capital Conversion Costs for Standard Duty Commercial

Prerinse Sp ray 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 C osts

.............................. 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 Compon ents 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

- 1712

- 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

- 2312

- 1 CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether an amended energy conservation standard for commercial prerinse spray valve s is economically justified, DOE is required to consider “the economic impact of the standard on the manufacturers and on the consumers of the products subjec t 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 manufac turing capacity.

The MIA involves both quantitative analys e s and qualitative evaluation. The quantitative
element s 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 flow s 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 charac teristics, 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 mode l 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 sub groups, specifically focusing on the potential for disproportionate impacts on small business manufacturers of commercial prerinse spray valve s. DOE also evaluated potential impacts on direct employment and manufacturing capacity within the commercial prerinse spray valve manufacturing indus try , 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 and12

- 2

characteristics of the industry, product attributes, product shipments, manufacturer markups, and
the cost struc ture for various manufacturers.

The profile also included an analysis of manufacturers in the industry using the U.S. Securities and Exchange Commission 10

- K fili ngs, St andard & 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 depreciati on, 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 investmen t, (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 an alysis ( 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, sm all 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 prerins e spray valves that would warrant a separate analysis, DOE specifically investigated impacts on small business manufacturers. See section s 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

- new

standards case and at each TSL from 2015 to 2048. S ection 12.2.3.3 of this chapter provides more information on direct employment impacts

.

12.2.3.1 Small

- Business Manufacturer

T he 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 th e total number of employees for a business

, including the12

- 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 Certifica tion Manageme nt System (CCMS), t he U.S. Environmenta l Protection Agency’s ( EPA’ s ) WaterSense ® program database, individual company websites, and various marketing resear ch 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 rese arch, 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 manuf acturers 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). S ection 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 energ y 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 no

new

- standards case and at each TSL from 2015 through 2048. The employmen t 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 ene rgy 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 pre rinse spray valve manufacturers,
such as F ederal 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 in dustry cash flow both with and without amended energy conservation standards.

12.4.1 Overview of the GRIM

The basic struct ure of the GRIM, illustrated in Figure 12.4

. 1 , is a n 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 a ssociated 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 12 A provides more technical details and use r 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 ann ual reports, company profiles, c ensus data, credit ratings, the shipments mode l, 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 pub lic. 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 in puts 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 ind ustry’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 methodol o gy 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 equipme nt 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 engineer ing 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 sellin g commercial prerinse spray valve equipment. The values listed are

averages over a 7

- year period (200 7 through 201 3 ).

Table 12.4

. 1 GRIM Financial Parameters B ased on 2007

– 2013 Weighted Company
Financial Data

Parameter Industry

Weighted 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 M anufacturer 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 c alculate 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 equ ipment 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 es timate 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 retur n 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, and12

- 7

Beta (β) = the correlation between the movement in the pric e 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 Industry

Weighted 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 ri sk

- 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 Industry

Weighted 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 21.912

- 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 valve s. 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 D uty: Spray f

orce ≤ 5 ozf

* EL ** 0 (0.76) EL 1

(0.72) EL 2

(0.68) EL3

(0.65) EL 3

(0.65)

Standard D uty: Spray f

orce > 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 D uty: Spray f

orce > 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
, 2 019

Equipment Class Total Industry Shipments

Light Duty 46,206

Standard Duty 115,516

Heavy Duty 69,31012

- 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 produc ts 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 NI A for the commercial prerinse spray valve equipment
classes.

Table 12.4

. 6 No

- New

- Standards Case Distribution of Efficiencies for Commercial Prerinse
S pray V alves
, 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 foreca st 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 commercia l 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
thos e 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 clos est 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 s tandard 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 GRI M 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 cos ts 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.

, decrea sing 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

gp m 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 $36.6312

- 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 th ese 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 conservatio n 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 S pray V alve Product and Capital Conversion Costs

DOE relied on the engineering analysis t o 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 c apital 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). T here 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 r equire 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 ).

O ne 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 assemb le the commercial prerinse spray valves ( i.e., s ourced c omponents s cenario). 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., f abricated c omponents s cenario).

For product conversion costs, DOE assumed a cost of $100,000 per manufacturer to update product brochures and other marketing materials associated with product u pdates 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

- compli ant 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 ra te 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 prerins e 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 0.0012

- 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 Compo nents 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 sc enarios, 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 un certainty 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 simila r 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 profi tability 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 manufactu ring 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 standard s

- case shipments for certain equipment clas ses as a result of equipment class switching ( i.e., a decrease
in s tandard

- d uty commercial prerinse spray valve shipments

, and an equivalent increase in
h eavy

- d uty commercial prerinse spray valve shipments at all TSLs) are controlled for by using the per u nit 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 res ults 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 diffe rent 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 i ndustry’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, an d a long

- term assessment over the 30

- year analysis period immediately thereafter.12

- 15

In the MIA, DOE compare d 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

- cas e 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 s ourced c omponents c apital c onversion c osts scenario and the
f abricated c omponents c apital c onversion c osts 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 t he 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 short

term 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 public ation 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 c ash 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 conservatio n 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 energ y 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 act s 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 standard s , 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

- new

standards

- 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

- New

Standards
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)

* V alues in parenthesis indicate negative numbers

.

Table 12.5

. 2 Manufacturer Impact Analysis for All Commercial Prerinse Spray Valves

– Fabricated Components Capital Conversion Costs

No

- New

Standards
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)

* V alues 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

- New

Standards
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)

* V alues 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

- New

Standards
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)

* V alues 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

- New

Standards
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)

* V alues 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

- New

Standards
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)

* V alues 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

- New

Standards
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

* V alues 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

- New

Standards
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

* V alues 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 Scenario12

- 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 P RERINSE S PRAY V ALVE
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 amende d 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 th e 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 c ommercial 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 ba sic models currently on the market. The small domestic manufacturers are BK Resources, Bricor Analytical, Component Hardware12

- 20

Group, Fisher Manufacturing, Krowne Metal Corp, Niagara Conservation, Strahman Valves, and T&S Brass. The remaining 17 percent of c ommercial 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 m anufacturers. 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 profi t 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 manufactu rers. 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$ million s 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 Sce nario

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 1 20

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 $ 20 0 ,000 for
the s ourced c omponents c apital c onversion c osts scenario

, to $ 30 0 ,000 for the f abricated

c omponents c apital c onversion c osts scenario. This suggests that an average small manufac turer would need to reinvest rough ly 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 $11 0 ,000 for the s ourced c omponents c apital c onversion c osts scenario

, to $18 0 ,000 for the
f abricated c omponents c apital c onversion c osts 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 expen ditures 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 associat ed 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 Manufacture r s ( ASM ) as well as market research, DOE estimate d 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 prod uct, the sales volume, and an assumption that wages in
real terms remain constant.

Using the GRIM, DOE forecast ed 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 estimate d there will be approximately 44 domestic production workers involved in assembling and, to a lesser extent, fabricating comp onents for commercial prerinse spray valves in 2019, the year in which any amended standards would go into effect. In addition, DOE estimate d 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 involve d 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 inc luded 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 scen ario in which manufacturers continue to produce the same scope of covered products in domestic facilities and domestic production is not shifted to lower labor cost12

- 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

- New

Standards
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. T herefore, 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 ind ustry 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 tha t 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 standard s 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

c ombined 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 w ith 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 r egulations 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 a nd expected industry convers ion 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 Pre r inse 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 wit h third

- party and international12

- 24

industry stand ards 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 s tate water conservation standards differ from F ederal standards. Califo rnia 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 summarize s 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 mi llion, compared to the no

new

- 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 shipment s 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 baselin e 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 C omponents 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 significa nt 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 becau se 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 manufacture rs 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 Com ponents 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 to12

- 25

the no

- new

- standards case value of $0.5 million in the year leading up to th e 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 fract ion 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, bec ause 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 du e 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 c ase 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 standa rds 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 Component s 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 v alves 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 ca se 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 stand ards 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 Cap ital 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 the12

- 26

Standard Duty to Heavy Duty equipment class, at this TSL does not have a significa nt 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

- small

business

- 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. CEC

400

- 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

TA BLE OF CONTENTS

13.1 INTRODUCTION ............................................................................................................ 13-1
13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS .................................. 13-2
13.3 POWER SECTOR EMISSIO NS FACTORS ................................................................... 13-3
13.4 UPSTREAM FACTORS .................................................................................................. 13-4
13.5 EMISSIONS IMPACT RES ULTS ................................................................................... 13-5
REFERENCES .......................................................................................................................... 13-9

LI ST OF TABLES

T able 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

LI ST OF FIGURES

F igure 13.5.1 Commercial Prerinse Spray Valves: CO 2 Total Emissions Reduction ............... 13-6 Figure 13.5.2 Commercial Prerinse Spray Valves: SO 2 Total Em issions Reduction ................ 13-6 Figure 13.5.3 Commercial Prerinse Spray Valves: NO

x 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: N 2 O Total Emissions Reduction ............... 13-8 Figure 13.5.6 Commercial Prerinse Spray Valves: CH 4 Total Emissions Reduction ............... 13-813 -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 (CO 2 ), nitrogen oxides ( NO

x ), sulfur dioxide (SO 2 ), and mercury (Hg). The sec ond component estimates the impacts of a potential standard on emissions of tw o additional greenhouse gases (GHG)

, methane (CH 4 ) and nitrous oxide (N 2 O), as well as the reductions to emissions of all species due to “upstream” activities in the fuel product ion chain. These upstream activities comprise extraction , processing, and transporting fuels to the site of combustion. T he associated emissions are referred to as upstream emissions. Together, these emissions account for the fullfuel

- cycle (FFC) , i n acco rdance 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. AEO201 4 generally represents current Federal and s tate legislation and final implementation regulations in place as of the end of Octo ber 2013. Where applicable, s ite emissions of CO 2 and NO

x are estimated using emissions intensity factors from a publication of the Environmental Protection Agency (EPA)

. 2

Combustion emissions of CH 4 and N 2 O (from power plants and from upstream combustion) are estimated using emissions intensity factors publishe d by the EPA , GHG
Emissions Factors Hub. a

The FFC upstream emissions a re 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 CH 4 and CO 2

.

The emissions intensity factors are expressed in terms of physical units per megawatt

hours ( MWh ) or million British thermal units ( MMBtu ) of site energy savings. Total e missions reductions are estimated using the energy savings calculated in the national impact analysis (chapter 10 of this TSD ).

For CH 4 and N 2 O, DOE also presents results in terms of units of carbon dioxide equivalent (CO 2e ). Gases are converted to CO 2 e by multiplying the physical units by the gas’

a http://www.epa.gov/climateleadership/inventory/ghg

- emissions.html13 -2 global warming potential (GWP) over a 100-year time horizon. Based on the Fifth Assessment Report of the Intergover nmental Panel on Climate Change, 4 DOE used GWP values of 28 for
CH 4 and 265 for N 2 O. b

13.2 AIR QUALITY REGULATIONS AN D EMISSIONS IMPACTS

SO 2 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 SO 2 for affected EGUs in the 48 contiguous states and the District of Columbia (D.C.). SO 2 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 a long
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 replace ment 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 analys is , 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 exi sting EPA regulations, any excess SO 2 emissions allowances resulting from the lower electricity demand caused by the imposition of an efficiency standard could be used to permit offsetting increases in
SO 2 emissions by any regulated EGU. In past rulemaking s, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions covered by the existing

cap

- and -trade system, but it concluded that no reductions in power sector emissions would occur
for SO 2 as a result of standards

.
Beginning in 2016, however, SO 2 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 SO 2 (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

- CO 2 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 m ethodology 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, s lip

o p. at 32 ( U.S. April 29, 2014). B ecause DOE is using emissions factors based on AEO2014

, it assumes that CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR i s not relevant for the purpose of DOE's analysis of SO 2 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

- fire d electric utility steam generating units. www.reginfo.gov/public/do/eAgendaViewRule?pubId=201110&RIN=2060

- AP5213 -3 equivalent surrog ate standard for acid gas HAP. The same controls are used to reduce HAP and non-HAP acid gas; thus, SO 2 emissions will be r educed as a result of the control technologies installed on coal-fired power plants to comply with the MATS requirements for acid gas.
AEO201 4 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorben t injection systems installed by 201 6. Both technologies, which are used to reduce acid gas emissions, also reduce SO 2 emissions. Under the MATS, NEMS shows a
reduction in SO 2 emissions when electricity demand decreases ( e.g., as a result of energy
efficie ncy standards). Emissions will be far below the cap established by CAIR , so it is unlikely
that excess SO 2 emissions allowances resulting from the lower electricity and natural gas demand would be needed or used to permit offsetting increases in SO 2 emissi ons by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce SO 2 emissions in 2016 and beyond.

CAIR established a cap on NO

x emissions in 28 eastern states and the District of Columbia. Energy conservation standards are expected to have little effect on NO

x emissions in
those states covered by CSAPR because excess NO

x emissions allowances resulting from the lower electricity and natural gas demand could be used to permit offsetting increases in NO

x emissions. However, standards would be expected to reduce NO

x emissions in the states not affected by CAIR

, so DOE estimate d NOx emissions reductions from potential standards for
th ose 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 AEO201 4

, which incorporates the MATS.

13.3 POWER SECTOR EMISSIO NS FACTORS

The analysis of p ower 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
dema nd 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 defin ed 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 use d may be found in Coughlin (2013 ). 3

Table 13.3.1 presents the average power plant emissions factors for selected years. The se power plant emissions factors are derived from the emissions factors of the plant types used to

su pply 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

CO 2 kg/MWh 765 723 642 579 529 484

SO 2 g/MWh 806 712 556 468 392 351

NO

x g/MWh 626 572 478 417 368 333 Hg g/MWh 0.00249 0.00220 0.00172 0.00144 0.00121 0.00108

N 2 O g/MWh 7.2 7.2 7.1 6.9 6.6 6.4

CH 4 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 (s ee 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 CO 2 occur during oil and gas production, but are small relative to combustion emissions. They comprise about 2.5 percent of total CO 2 emissions for natural gas
and 1.7 percent for pet roleum fuels. Fugitive emissions of methane occur during oil, gas, and coal production. Combustion emissions of CH 4 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.

U pstream emissions factors account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primar y fuels. Fugitive emissions factors for
methane fr om 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 ) reportin g 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 NO

x emissions do not apply to upstream combustion sources.13 -5 Table 13.4.1 Electricity Upstream Emissions Factors

2019 2020 2025 2030 2035 2040

CO 2 kg/MWh 28.84 29.07 29.43 29.72 29.88 29.81

SO 2 g/MWh 5.12 5.0 5 5.08 4.92 4.74 4.64

NO X g/MWh 364.01 368.10 375.14 382.14 386.82 386.81
Hg g/MWh 0.00001 2 0.00001 2 0.00001 1 0.00001 1 0.00001 0 0.00001 0

N 2 O g/MWh 0.25 0.25 0.25 0.24 0.23 0.23

CH 4 g/MWh 2123.48 2149.27 2194.56 2215.68 2247.89 2254.56

T able 13.4.2 Natural Gas Upstream Emissions Factors

2019 2020 2025 2030 2035 2040

CO 2 kg/ mcf 7.069 7.115 7.217 7.330 7.363 7.353

SO 2 g/ mcf 0.030 0.030 0.031 0.031 0.032 0.032

NO X g/mcf 100.239 101.067 102.860 104.857 105.440 105.271

N 2 O g/mcf 0.011 0.011 0.011 0.012 0.012 0.012

CH 4 g/mcf 657.099 659.304 665.033 666.489 669.522 670.441

13. 5 EMISSIONS IMPACT RES ULTS

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
CO 2 ( million metric tons ) 0.78 4.58 5.27 2.05

NO X ( thousand tons ) 0.85 4.99 5.73 2.23 Hg ( tons ) 0.00109 0.00640 0.00736 0.00286

N 2 O ( thousand tons ) 0.00634 0.03712 0.04269 0.01661

CH 4 ( thousand tons) 0.05 0.27 0.31 0.12

SO 2 ( thousand tons ) 0.36 2.09 2.40 0.93 Upstream Emissions
CO 2 ( million metric tons ) 0.07 0.43 0.49 0.19

NO X ( thousand tons ) 1.11 6.51 7.49 2.91 Hg ( tons ) 0.00001 0.00005 0.00006 0.00002

N 2 O ( thousand tons ) 0.00028 0.00166 0.00191 0.00074

CH 4 ( thousand tons ) 6.92 40.55 46.63 18.15

SO 2 ( thousand tons ) 0.004 0.025 0.028 0.011

Total Emissions
CO 2 ( million metric tons ) 0.85 5.01 8.76 2.24

NO X ( thousand tons ) 1.96 11.50 13.22 5.15 Hg ( tons ) 0.00110 0.00645 0.00742 0.00289

N 2 O ( thousand tons ) 0.00662 0.03878 0.04459 0.01735

CH 4 ( thousand tons ) 6.97 40.83 46.94 18.27

SO 2 ( thousand tons ) 0.36 2.11 2.43 0.9413 -6 Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emissions for each type of emission from e ach TSL

. The reductions reflect the lifetime impacts of products
sold from 2019

- 2048.

Figure 13.5.1 Commercial Prerinse Spray V alves: CO 2 Total Emissions Reduction

Figure 13.5.2 Commercial Prerinse Spray V alves: SO 2 Total Emissions Reduction13 -7 Figure 13.5.3 Commercial Prerinse Spray V alves: NO
x Total Emissions Reduction

Figure 13.5.4 Commercial Prerinse Spray V alves: Hg Total Emissions Reduction13 -8 Figure 13.5.5 Commercial Prerinse Spray V alves: N 2 O Total Emissions Reduction

Figure 13.5.6 Commercial Prerinse Spray V alves: CH 4 Total Emissions Reduction13 -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 o f Full

- F uel -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 th e 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

TA BLE OF CONTENTS

14.1 INTRODUCTION ............................................................................................................ 14-1
14.2 MONETIZING CARBON DI OXIDE 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

LI ST OF TABLES

T able 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007$ per metric ton CO 2 ) ............................................................................................ 14-4 Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007$

per metric ton CO 2 ) ............................................................................................ 14-4
Table 14.4.1 Estimates of Global Present Value of CO 2 Emissions Reduction under Commercial Prerinse Spray Valves Trial Standard Levels ................................ 14-6
Table 14.4.2 Estimates of Domestic Present Value of CO 2 Emissions Reduction under Commercial Prerinse Spray Valves Trial Standard Levels ................................ 14-6
Table 14.4.3 Estimates of Present Value of NO

x Emissions Reduction under Commercial Prerinse Spray Valves Trial Standard Levels .................................................... 14-714 -1 CHAPTER 1 4. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

14.1 INTRODUCTION

As part of its assessment of energy conservation standards for commercial prerinse s pray valves, the U.S. Department of Energy ( DOE ) estimated the monetary benefits likely to result
from the reduc ed emissions of carbon dioxide (CO 2 ) and nitrogen oxides ( NO

x ) 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 CO 2 . A domestic SCC v alue is meant to reflect the value of damages in the United States resulting from a unit change in CO 2 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 t he 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 CO 2 emissions into cost- benefit analyses of regulatory actions that have small, or
“mar ginal,” 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 CO 2 emissions, the analyst faces a number of seri ous challenges. A report from the National Research Council 1 points out that any assessment will suffer from uncertainty, speculation, and lack of information14 -2 about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on

t he 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 cli mate 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 c arbon 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 multiplyin g 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 cu mulative 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 CO 2 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 CO 2 emissions. The interagency group did not undertake any original analysi s. 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 CO 2

. 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) comm only 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 damag es 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

- econ omic and emissions trajectories, and (3) discount rates. A prob ability 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 determine d 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
CO 2 emissi ons.

The SCC values used for th is 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 update 4 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 interagenc y 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 includi ng 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 CO 2 )

Year Discount Rate
%

5 3 2.5 3 Average Average Average 95 th 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 CO 2 )

Year Discount Rate
%

5 3 2.5 3 Average Average Average 95 th Percentile

2010 11 3 2 5 1 8 9

2015 1 1 3 7 5 7 109

2020 12 43 6 4 12 8

2025 14 4 7 69 14 3

2030 16 52 7 5 159

2035 19 5 6 8 0 17 5

2040 21 6 1 8 6 19 1

2045 24 66 92 206

2050 2 6 71 9 7 22 0

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 rec ognizes 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 pr ocess 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 CO 2 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 produc t ( 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 int eragency update.

DOE multiplied the CO 2 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 NO

x emissions from the TSLs it considered. As noted in chapter 13 o f this TSD , new or amended energy conservation
standards would reduce NO

x emissions in those 22 s tates that are not affected by caps . DOE
estimated the monetized value of NO

x 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 NO X and real discount rates of 3 percent and 7
percent

. 7

DOE is still evaluating appropriate values to use to monetize avoided SO 2 and Hg
emissions . I t did not monetize these emissions for this NOPR.

14.4 R ESULTS

Table 14.4.1 p resent s the global values of CO 2 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 CO 2 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% disc ount rate,
95 th percentile

million 201 4 $ 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 CO 2 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*

m illion 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 , $6 3. 4 , and $1 2 1 per metric ton (201 4$).

Table 14.4.3 shows the present value of cumulative NO

x 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 NO
x 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 3.914 -8 REFERENCES

1. N ational 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

R egulatory Impact Analysis under Executive Order 12866 . February 2010. United States
Government. www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

Cost-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- social

cost -of-carbon- for -regulator- impact -analysis.pdf

.

5. T echnical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group on Social Cost of Carbon, United
States Government . May 2013; revised November 2013. http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical -update- social

cost -of-carbon- for -regulator- impact -analysis.pdf

.
6. U.S. Office of Management and Budget (OMB). 2006 R eport 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). C ircular 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

TA BLE 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- CO 2 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

14 A.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.10 ANNEX

..................................................................................................................... 14A-42
14A.10.1 Other (Non

- CO 2 ) Gases ............................................................................ 14A-43
14A.10.2 Extrapolating Emissions Projections to 2300 ........................................... 14A-45

L I ST OF TABLES

Table 14A.1.1 Social Cost of CO 2 , 2010–2050 (2007$) ........................................................ 14A-2 Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions .. 14A-14 Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF- 22 Reference
Scenarios ....................................................................................................... 14A-17 Table 14A.5.1 Disaggregated Social Cost of CO 2 Values by Model, So cio -Economic Trajectory, and Discount Rate for 2010 (2007$) .......................................... 14A-27
Table 14A.5.2 Social Cost of CO 2 , 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$/ton CO 2 ) ............................................................................................ 14A-50
Table 14 A.10.3 2010 Global SCC Estimates at 3-Percent Discount Rate (2007$/ton CO 2 ) 14A-5014A

- ii Table 14A.10.4 2010 Global SCC Estimates at 5-Percent Discount Rate (2007$/ton CO 2 ) 14A-51 Table 14A.10.5 Additional Summary Statistics of 2010 Global SCC Estimates ................. 14A-52

LI ST 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 ze ro in 2300) .......................................... 14A-47 Figure 14A.10.4 Global Fossil and Industrial CO 2 Emissions, 2000-2300 (post-2100 extrapolations assume growth rate of CO 2 intensity (CO 2 /GDP) over 2090– 2100 is maintained through 2300) ................................................................ 14A-48 Figure 14A.10.5 Global Net Land Use CO 2 Emission s, 2000–2300 (post-2100 extrapolations assume emissions decline linearly, reaching zero in 2200) .......................... 14A-48 Figure 14A.10.6 Global Non

- CO 2 Radiative Forcing, 2000–2300 (post-2100 extrapolations

assume constant non- CO 2 radiative forcing after 2100) ............................... 14A-49 Figure 14A.10.7 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000–2300 (post-2100 extrapolations assume decline in CO 2 /GDP growth rate over 2 090– 2100 is maintained through 2300) ................................................................ 14A-49 Figure 14A.10.8 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by Discount
Rate* ............................................................................................................. 14A-5114A -1 APPENDIX 14 A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT
ANALYSIS UNDER EXECUTIVE ORDER 12866

P repared by Interagency Working Group on Social Cost of Carbon, United States Government

W ith participation by

C ouncil 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 (CO 2 ) 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 g iven 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 th e 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 95 th percentile SCC
estimate across all three models at a 3 -percent discount rate, is included t o represent higher

- than

expected impacts from temperature change further out in the tails of the SCC distribution.

Table 14A.1.1 Social Cost of CO 2 , 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 CO 2 throughout this document. a

When attempting to assess the incremental economic impacts of CO 2 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 gr eenhouse 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 estimat es can be useful in estimating the social benefits of reducing CO 2 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 CO 2 emissions. Alternatively, one could report the SCC as the cost per metric ton of carbon emissions. The multiplier for translating between mass of

CO 2 and the mass of carb on is 3.67 (the molecular weight of CO 2 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 CO 2 emissions into costbenefit analyses of regulatory actions that have small or “marginal” impacts on cumula tive global emissions. Most F ederal 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 ch ange 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
CO 2 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 parti cipated 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 a nd 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 rule making 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 a nd 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 95 th 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 im portance 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 CO 2 in 2015 and $26 per ton of

CO 2 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 re visiting 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 issues14A -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 CO 2 emi ssions. In the final model year 2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of $2 per ton of CO 2 and a “global” SCC value of $33 per ton of CO 2 for 2007 emission reductions (2007$), increasing both values at 2.4 percent per year. It also included a sensitivity analysis at $80 per ton of CO 2 . A domestic SCC value is meant to reflect the value of damages in the United States resulting from a unit change in CO 2 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 CO 2 (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 re gulation finalized by DOE in October of 2008 used a domestic SCC range of $0 to $20 per ton CO 2 for 2007 emission reductions (2007$). In addition,
the 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified what it described as “very pre liminary” SCC estimates subject to revision. EPA’s global mean values were $68 and $40 per ton CO 2 for discount rates of approximately 2 percent and 3 percent, respectively ( 2006$ for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing CO 2 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 CO 2 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 CO 2. 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 in14A -5 connection with proposed rules, including the join t EPA -DOT fuel economy and CO 2 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 consi dered 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 uncertaint ies 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 incomplet e. 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
li mitations 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 impro ve 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 peer

reviewed 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 mode ls 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 theunderlying 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 decision

makers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Clim ate 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 IAM s 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 mode l’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 cons umption 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 climat e 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
discoun t 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 distri butions. 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 vari able (atmospheric CO 2 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, CO 2 emissions are a function of global GDP and the carbon intensity of economic output, with the latter declining over time due to technological progress. The DICE damage function links global average temperature to the overall impact on the world economy. It varies quadratically with temperature change to capture the more rapid increase in damages expected to occur under more extreme climate change, and is calibrated to include the effects of warming on the production of market and nonmarket goods and services. It incorporates impacts on agriculture, coastal areas (due to sea level rise), “other vulnerable market sectors” (based primarily on changes in energy use), human health (based on climate

related diseases, such as malaria and dengue fever, and pollution), non- market amenities (based on outdoor recreation), and human settlements and ecosystems. The DICE damage function also includes the expected value of damages associated with low probability, high impact “catastrophic” climate change. This last component is calibrated based on a survey of experts(Nordhaus 1994). The expected value of these impacts is then added to the other market and nonmarket impacts mention ed 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 uncertai n, 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
“di scontinuity” ( 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 non

economic impacts through adaptation (Hope 2006).

14A.4.1.3 The FUND Model

L ike 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

. Specificall y, 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 assu med 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 a nd agricultural sectors, economic damages also depend on CO 2 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) th ose due to the level of temperature change (damages can be positive or negative depending on region and temperature); and (3) those from CO 2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorporated into FUND by all owing damages to be smaller if climate change happens more slowly. The combined effect of CO 2 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 globa l- 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 Models e

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, socio

economic, and emissions pa rameters will produce a different realization of damages for each IAM. The damage functions represented in Figure 14A.4 .1 and Figure 14A.4 .2 are the outcome of default assumptions. For instance, under alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater
than 3 °C.14A

- 11 The lack of agreement among the models at lower tem perature increases is underscored by the fact that the damages from FUND are well below the 5 th percentile estimated by PAGE, while the damages estimated by DICE are roughly equal to the 95 th percentile estimated by PAGE. This is significant because at hig her 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 o ccur 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, t he 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 cent er 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 clim ate 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 emiss ions 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 F ederal 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 la w and hence does not intrude on such interests.14A

- 12 co nsiderations 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

mor e 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

- speci fic 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, t he 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 gl obal 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 bec ome available, the Federal government will examine these to determine whether to update its approach.

14A.4.3 Valuing Non- CO 2 Emissions

While CO 2 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 —i s 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) f rom 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 CO 2 . However, because these gases differ in both radiative forcing and atmospheric lifetimes, their relative damages are n ot 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 gase s in ways that are not captured by GWP. For instance, CO 2 emissions, unlike methane and other greenhouse gases, contribute to ocean acidification. Likewise, damages from methane emissions are not offset by the positive effect of
CO 2 fertilization. Thus, transforming gases into CO 2 -equivalents using GWP, and then multiplying the carbon -equivalents by the SCC, would not result in accurate estimates of the
social costs of non- CO 2 gases.

In light of these limitations, and the significant contributions of non- CO 2 emissions to climate change, further research is required to link non- CO 2 emissions to economic impacts. Such work would feed into efforts to develop a monetized value of reductions in non- CO 2 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 CO 2 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 CO 2 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 f eedbacks simulated in [global climate models], we conclude that the global mean equilibrium warming for doubling CO 2 , 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 cl imate 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 tho se 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 c alibrated 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

5 th P ercentile 1.72 1.49 1.37 1.13

10 th P ercentile 1.91 1.74 1.65 1.48
Mode 2.34 2.52 2.65 2.90 Median (50 th percentile) 3.00 3.00 3.00 3.00

Mean 3.50 3.28 3.19 3.07

90 th P ercentile 5.86 5.14 4.93 4.69

95 th P ercentile 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
mathema tical 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 equa l 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 p oint. 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
95 th percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode equal to 3 °C produced 95 th 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 95 th percentile of the calibrated Roe & Baker distribution (7.1 °C) is much closer to the mean and the median (7.2 °C) of the 95 th 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 95 th 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 probabi lity,” 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 cli mate 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 i nstrumental 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, CO 2 emissions, and non- CO 2 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, co ordinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, and emission trajectories are internally consistent for each model and scenario evaluated. The EMF-22 modeling effort also is preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the EMF-22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are recent, peer

reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a small effect on global cumulative emissions, we use socio- economic and emission trajectories tha t 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 CO 2 (only) concentrations ranging from 612 to 889 ppm in 2100. One represents an emissions pathway that achieves stabilization at 550 ppm CO 2 e ( i.e.

, CO 2 - only concentrations of 42 5–484 ppm or a radiative forcing of 3.7 W/m 2 ) 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 pat hways, we used the GDP, population, and emission trajectories from each of these four models. For the 550 ppm CO 2 e 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 emissio ns, 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 CO 2 Emissions
GtCO 2 /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 co untries. 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 quant ity 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 t he many geophysical uncertainties.14A

- 18 most likely outcome span a wide range, from the more optimistic ( e.g. , abundant low- cos t, 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 CO 2 e 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 Ann ual Energy
Outlook projected that global CO 2 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 CO 2 emissions, each EMF scenario provides projections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO 2 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 CO 2 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 CO 2 emitted in a particular year in terms of reduced consumption (or consumption equivalents) d ue 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 CO 2 emissions to 83 percent below 2005 levels by 2050 (per the G

- 8 agreement) b ut all other countries continue along a BAU path CO 2 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, “ I f 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 specifi c 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

appro ach 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 generall y 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 f oundation 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 rat e 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 s uch 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 incorpo rate 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 p references of future generations with regard to consumption versus environmental amenities may not be the same as those today, making thecurrent 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 dam age (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 underlies14A

- 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 d iscount 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 fut ure 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 ch oice 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 the14A

- 21 discount rate typically assumes a repres entative 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 certainty

equivalent 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 i s 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 perio d 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 adjustin g 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 tha t 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). t These are then combined with g (growth rate of per-capita consumption) to equal the interest rate at which future monetized damages are discounted: ρ + η∙g. u In the simplest version of the Ramsey model, with an optimizing representative a gent 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. , A rrow 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 ra te 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 estima ting 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 p ercent. 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 , w e 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 inter est 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 Es timating 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 us e 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 clima te 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

r ate 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 unce rtainty 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,

u tility, 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 0and 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 distributio n 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. I nput 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

tem perature change in that period.

c. In DICE, temperatu re 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 pa ths, 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 st eps 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 CO 2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO 2 in PAGE).

The steps above were repeat ed 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 no te 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 separat e 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 co mbined 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 conte xt, 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 so cio

- 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 95 th 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 of14A

- 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 cli mate 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 g enerally produces the highest estimates.

Table 14A.5.1 Disaggregated Social Cost of CO 2 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 these14A

- 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 pa ths 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 chan ge (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 GD P 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 b y 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 distribut ion 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 forcing14A

- 29 Table 14A.5.2 Social Cost of CO 2 , 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 i n 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 to day 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 CO 2 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 applyi ng 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 complexi ty 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 impa cts 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 CO 2 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 distri bution 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 regulat ory 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 w ithstand 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 treatme nt 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 95 th 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 shou ld 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 r espect 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 contex t 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 econ omic 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, reorganizatio n 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 95 th 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 CO 2 is almost double the value estimated by DICE, $58 per ton in
2010. We cannot evaluate how well the three mo dels account for catastrophic or non

catastrophic 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 t o Incorporate Inter

- Sectoral a nd 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 one14A

- 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 warmin g. 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;
Cam pbell et al . 2007).

14A.7.3 Imperfect S ubstitutability of Environmental A menities

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
Ther mal 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 plant s (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 provid e 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 incorporat e 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 95 th 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. Thes e SCC estimates also grow over time. For instance, the central value increases to $24 per ton of CO 2 in 2015 and $26 per ton of

CO 2 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 of14A

- 35 damages to high temperatures, and assumptions regarding risk aversion. The limited amount of

re search 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

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- 42 14A.10 A NNEX

This A nnex provides additional technical information about the non- CO 2 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 65.0 136.214A

- 43 14A.10.1 Other (N on- CO 2 ) G ases

In addition to fossil and industrial CO 2 emissions, each EMF scenario provides projections of methane (CH 4 ), nitrous oxide (N 2 O), fluorinated gases, and net land use CO 2 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- CO 2 EMF emissions on ly, we calculated the RF associated with the EMF atmospheric CO 2 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- CO 2 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 CH 4 , N 2 O, SF 6 , and the CO 2 emissions from land were replaced with the EMF values.

PAGE : PAGE models CO 2 , CH 4 , sulfur hexafluoride (SF 6 ), and aerosols and contains an

“ excess forcing ” vector that includes the RF for everything else. To include the EMF values, we
removed the default CH 4 and SF 6 factors

, ee decomposed the excess forcing vector, and
constructed a new excess f orcing vector that includes the EMF RF for CH 4 , N 2 O, and fluorinated gases, as well as the model default values for aerosols and other factors. Net land use CO 2 emissions were added to the fossil and industrial CO 2 emissions pathway.

DICE : DICE presents the greatest challenge because all forcing due to factors other than
industrial CO 2 emissions is embedded in an exogenous non- CO 2 RF vector. To decompose this exogenous forcing path into EMF non- CO 2 gases and other gases, we relied on the references in
DICE 2007 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 f orcing
from all non- CO 2 sources is -0.06 W/m 2 in 2005, as reported in AR4, and increases linearly to
0.3 W/m 2 in 2105, based on GISS projections, and then stays constant after that time.

According to AR4, the RF in 2005 from CH 4 , N 2 0, and halocarbons (approximately
similar to the F

- gases in the EMF

- 22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m 2 and RF
from total aerosols was -1.2 W/m 2 . Thus, the -.06 W/m 2 non- CO 2 forcing in DICE can be decomposed into: 0.98 W/m 2 due to the EMF non- CO 2 gases, -1.2 W/m 2 due to aerosols, and the remainder, 0.16 W/m 2 , due to other residual forcing.

dd Note EMF did not provide CO 2 concentrations for the IMAGE reference scenario. Thus, for this scenario, we fed the fossil, industrial and land CO 2 emissions into MAGICC (considered a “ neutral arbiter ” model, which is tuned to emulate the major global climate models) and the resulti ng CO 2 concentrations were used. Note also that MERGE assumes a neutral biosphere so net land CO 2 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 CH 4 emissions and the initial atmospheric CH 4 is set to zero to avoid double counting the effect of past CH 4 emissions.14A

- 44For subsequent years, we calculated the DICE default RF from aerosols and other nonCO 2 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- CO 2 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
emission s, 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 2 000 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/m 2 ; forcing due to other non- CO 2 gases not included in the EMF scenarios declines from
0.160 to 0.153 W/m 2

.

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, 5 th

and 95 th 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 SO 2 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 CO 2 emissions are added to the fossil and
industrial CO 2 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 CO 2 emissions decline linearly, reaching zero in 2200.14A

- 46 5. Non

- CO 2 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, si nce 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 popu lation 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. , CO 2 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 CO 2 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- CO 2 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 CO 2 emissions, net land CO 2 emissions, non- CO 2 radiative forcing, and CO 2 intensity (fossil and industrial CO 2 emissions/GDP) resulting from these assumptions.

ii U nited 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
CO 2 e, full

- participation, not

- to

- exceed scenarios considere d by each of the four models.

Figure 14A.10.3 World GDP, 2000- 2300 (post -2100 extrapolations assume GDP per capita growth declines linearly, reachin g zero in 2300)

Note: In the fifth scenario, 2000

– 2100 GDP is equal to the average of the GDP under the 550 ppm CO 2 e, full

participation, not

- to

- exceed scenarios considered by each of the four models

. 5.5 6.5 7.5 8.5 9.5 10.5 11.5

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsGlobal Population [billion]

Year

0 500 1000 1500 2000

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

YearWorld GDP [$trillion]14A

- 48 Figure 14A.10.4 Global Fossil and Industrial CO 2 Emissions, 2000-2300 (post-2100 extrapolations assume growth rate of CO 2 intensity (CO 2 /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 CO 2 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 CO 2 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

ExtrapolationsFossil&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

ExtrapolationsNet land CO2 emissions [GtCO2/yr]

Year z14A

- 49 Figure 14A.10.6 Global Non

- CO 2 Radiative Forcing, 2000–2300 (post-2100 extrapolations

assume constant non- CO 2 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 CO 2 Intensity (fos sil & industrial CO 2 emissions/GDP), 2000–2300 (post-2100 extrapolations assume decline in CO 2 /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 5 50 ppm CO 2 e, 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

ExtrapolationsnonCO2 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

ExtrapolationsFossil & Industrial CO2 intensity [GtCO2/trillion $]

Year14A

- 50 Table 14A.10.2 2010 Global SCC Estimates at 2.5-Percent Discount Rate (2007$/ton CO 2 )

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 CO 2 )

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 53.614A

- 51 Table 14A.10.4 2010 Global SCC Estimates at 5-Percent Discount Rate (2007$/ton CO 2 )

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 CO 2 ), 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 1 st and 99 th percentiles to better show the data. 0 2000 4000 6000 8000 10000 12000 14000 16000-25-12114274053667992105118131144157170183196209222235248261274

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 23,558.315 -i CHAPTER 15. UTILITY IMPACT ANALYSIS

TA BLE 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

LI ST OF F IGURES

F igure 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 Figu re 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 C ommercial Prerinse Spray Valves: Gas Combined Cycle Generation
Reduction ........................................................................................................... 15-7 Figure 15.3.11 Commercial Prerinse Spray Valves: O il Generation Reduction ....................... 15-8 Figure 15.3.12 Commercial Prerinse Spray Valves: Renewables Generation Reduction ......... 15-8

LI ST OF TABLES

T able 15.3.1 Commercial Prerinse Spray Valves: Summary of Utility Impact Results ............ 15-915 -1 CHAPTER 15. UTILITY IMPACT ANALYSIS

15.1 INTRODUCTION

In the utility impact analysis

, the U.S. Department of Energy ( DOE ) analyze s the changes in electric installed capacity and generation that result for each trial sta ndard 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
Ou tlook (

AEO ). DOE use s 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 t he analysis of standards

on c ommercial p rerinse s pray v alves , DOE used the version of NEMS- BT based on AEO201 4

. 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 re duction 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 a nd 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 R eference C ase, three inter

- related
effects can occur : the annual generation (terawatt -hour s (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 NEMS

BT

. (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed .)15 -2 generating capacity cha nges; 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 ene rgy 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 e ach TSL. The relationship between a
reduction c 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 fou nd 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 i mpact 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 RESUL TS

This section presents results of the analysis for all of t he 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. electric ity installed capacity that result for each TSL by major plant type for selected years.

c These reductions are defined relative to the AEO Reference C ase.15 -3 Figure 15.3.1 Commercial Prerinse Spray Valves: Total Capacity Reduction

Figure 15.3.2 Commercial Prerinse Spray Valves: Coal Capacity Reduction15 -4 Figure 15.3.3 Commercial Prerinse Spray Valves : Nuclear Capacity Reduction

Figure 15.3.4 Commercial Prerinse Spray Valves: Gas Combined Cycle Capacity
Reduction15 -5 Figure 15.3.5 Commercial Prerinse Spray Valves: Peaking Capacity Reduction

Figure 15.3.6 Commercial Prerinse Spray Valves: Renewables Capacity Reduction15 -6 15.3.2 Electricity Generation

Figure 15.3.7 through Figure 15.3.12 show the annual changes in electric ity generation
that result for each TSL by p lant 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 Reduction15 -7 Figure 15.3.9 Commercial Prerinse Spray Valves: Nuclear Generation Reduction

Figure 15.3.10 Commercial Prerinse Spray Valves: Gas Combined Cycle Generation
Reduction15 -8 Fi g ure 15.3.11 Commercial Prerinse Spray Valves: Oil Generation Reduction

Figure 15.3.12 Commercial Prerinse Spray Valves : Renewables Generation Reduction15 -9 15.3.3 Results Summary

Table 15.3.1 presents a summ ary 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 63.1 6

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 73.9015

- 10 REFERENCES

1. U.S. Department of Energy, Energy Information Admin istration. Annual Energy Outlook
2014 . 2014. Washington, DC. (Last accessed May 1, 201 4 .)

< http://www.eia.gov/forecasts/aeo/ >.

2. Coughlin, K. 2013. Projections of Full- Fu el -Cycle Energy and Emissions Metrics. Lawrence Berkeley National Laboratory, Report Number LBNL -6025E.16 -i CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

TA BLE OF CONTENTS

16.1 INTRODUCTION ............................................................................................................ 16-1
16.2 ASSUMPTIONS ............................................................................................................... 16-1
16.3 METHO DOLOGY ........................................................................................................... 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) ...................... 16-316 -1 CHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

The U.S. Department of Energy ’s ( DOE ’s ) emplo yment 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 a mended 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 (s ee 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 rule making

. 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 modelin g 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 Northwes t National Laboratory (PNNL) developed the model using
ImSET 3.1.1 2 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. Im SET esti mates the employment and income effects of building energy technologies.16 -2 Compared with simple economic multiplier approaches, Im SET allows for more complete and automated analysis of the economic impacts of energy

- efficiency investments in buildings.

In an input/output model, the level of employment in an economy is determined by the relationship s of different sectors of the economy and spending flows among them . D ifferent sectors have different levels of labor intensity, and so changes in the level of spen ding ( 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 se ctor 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
mai nta in 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 towa rd firms that supply production inputs. Third, utility sector investment funds are released for use in other sector s 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 a re different

.

16.4 SHORT

- TERM RESULTS

The results in this section refer to impacts of CPSV standards relative to a no

- new

standards scenario . DOE dis aggregated the impact of standards on employment into three component effects : increased capital investment c ost s, 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 e lectricity 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 20 25.

Table 16.4.1 Net Short -T erm 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

, natura l gas

, and water to decline over time and demand for other goods to increase. Because the electricity , natural gas, and water generation sector s are relatively capital intensive compared to the consumer goods sector, the net effect will be an increase in l abor 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 short

term effec ts 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 N orthwest 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

TA BLE 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 ALTERNATI VES ............ 17-5

LI ST OF TABLES

T able 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 .......................................................................... 17-517 -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
evaluate d 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 analyze d 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 reg ulatory action (no

- new

- standards case)

C onsumer rebates

C onsumer tax credits Voluntary efficiency program s Early replacement

17.2 METHODOLOGY

DOE uses the n ational i mpact a nalysis (NIA) s preadsheet m odels 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 c hapter 10 of
this TSD . To compare each alternative quantitatively to the energy conservation standards, DOE
quant ifie s 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 cas e scenario

. Key inputs that DOE typically revise s in these models include the following:

• e nergy and water prices

, and escalation factors

,

• implicit market discount rates for trading off purchase price against operating expense when choosing product efficiency

,

• b usiness purchase price s and operating cost s,

• p urchase price -versus- efficiency relationships

, and17 -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 c umulative 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 a lternatives . By definition, no new regulatory action yields zero
NES , zero NWS , and an NPV of zero dollars.

17.3.2 C onsumer R ebates

Consumer rebates typically cover a portion of the difference in incremental product price
between models meeting baseline effic iency levels and those meeting higher efficiency levels, resulting in a higher percentage of consum ers purc hasing more effic ient models and decreased aggregated energy and water use compared to the no- new

- standards case scenario

.
DOE surveyed the various reba te 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 efficien t model. The ma jority of identified rebate progr ams 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, consume r rebates typically defray the incremental price for higher

efficiency 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 c onsumer rebates would instead simply reduce the installed cost for higher-17 -3 efficiency CPSV models

. Specifically , DOE chose to model a scenario where consume rs are offered, as rebates, 50 percent of the cost of t he 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 . O n 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 wa s 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 assig ned 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- new

standards 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 c onsumer , 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 produc ts purchased in the 2019–2048 ana lysis 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 C onsumer 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 201 4 $

7%
Discount

Rate 3%
Discount

Rate No New Regulatory Action 0 0 0 0

C onsumer 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 C onsumer Tax C redits

T ax 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

perspec tive, 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 Servi ce.

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 201 4 $

7% Discount

Rate 3% Discount

Rate

No new r egulatory a ction 0 0 0 0

C onsumer t ax c redits 0.058 74.563 0.439 0.905 Proposed s tandards 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 ne cessary to quantitatively project the degree to which such voluntary programs for higher- efficiency CPSV products would modify the market.

17.3.5 Early R eplacement

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 of17 -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 incent ives, 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 defini tion. 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 ru le. 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

C onsumer rebates 0.072 93.204
Consumer tax credits 0.058 74.563 Voluntary efficiency programs 0 0 Early replacement 0 0 Proposed s tandards (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

C onsumer 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