# TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

Commercial and Industrial Pumps

March 2015

U.S. Department of Energy Assistant Secretary

Office of Energy Efficiency and Renewable Energy Building Technologies Program Appliances and Commercial Equipment Standards Washington, DC 20585This Document was prepared for the Department of Energy by staff members of Navigant Consulting, Inc.

and Lawrence Berkeley National LaboratoryCHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 DOCUMENT PURPOSE

................................ ................................ ................................ 1

- 1

1.2 SUMMARY OF NATIONAL BENEFITS

................................ ................................ ..... 1

- 1

1.3 OVERVIEW OF STANDARD S

................................ ................................ ..................... 1

- 3

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

................... 1

- 4

1.5 STRUCTURE OF THE DOC UMENT

................................ ................................ ............ 1

- 6

LIST OF TABLES

Table 1.2.1 Summary of National Economic Benefits and Costs of Proposed Energy Conservation Standards for Commercial and Industrial Pumps*

................................ ................................ ................................ ........... 1

- 2

Table 1.4. 1 Analyses by Rulemaking Stage

................................ ................................ ..... 1

- 6

1

- iCHAPTER 1

. INTRODUCTION

1.1 DOCUMENT PURPOSE

This technical support document (TSD) is a standalone document that presents the technical analyses that the U.S. Department of Energy (DOE) conducted for evaluating new energy conservation standards for commercial and industrial pumps.

1.2 SUMMARY OF NATIONA L BENEFITS

DOE’s analyses indicate that the proposed standards would save a significant amount of energy. The lifetime savings for commercial and industrial pumps purchased in the 30

- year period that begins in the first full year of compliance a with new st andards (2020

– 2049) amount to 0.27 quadrillion Btu (quads). b This is a savings of 1 percent relative to the energy use of this equipment in the base case without new standards.

The cumulative net present value (NPV) of total customer costs and savings of the proposed standards for commercial and industrial pumps ranges from $0.39 billion (at a 7

- percent discount rate) to $1.05 billion (at a 3

- percent discount rate). This NPV expresses the estimated total value of future operating

- cost savings minus the est imated increased equipment costs for equipment purchased in 2020

– 2049.

In addition, the proposed standards would have significant environmental benefits. The energy savings would result in cumulative emission reductions of 16 million metric tons (Mt) c o f carbon dioxide (CO 2 ), 73 thousand tons of methane (CH 4 ) , 12 thousand tons of sulfur dioxide (SO 2 ), 23 thousand tons of nitrogen oxides (NO X ), 0.22 thousand tons of nitrous oxide (N 2 O), and 0.04 tons of mercury (Hg). d The cumulative reduction in CO 2 emis sions through 2030 amounts to 2.6 Mt, which is equivalent to the emissions associated with the annual electricity use

of 0.36 million homes.

The value of the CO 2 reductions is calculated using a range of values per metric ton of

CO 2 (otherwise known as th e Social Cost of Carbon, or SCC) developed by a recent Federal interagency process. e The derivation of the SCC values is discussed in chapter 14 of this NOPR

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

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

b A quad is equal to 10 15 British thermal units (Btu).

c A metric ton is equivalent to 1.1 short tons. Results for emissions other than CO 2 are presented in short tons.

d DOE calculated emissions reductions relative to the Annual Energy Outlook 2014 ( AEO 2014 ) Reference case, which generally represents current legislation and environmental regulations for which implementing regulations

were availa ble as of October 31, 2013.

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

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

2013. www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical

- update

- social

- cost

- of

- carbon

- for

regulator

- impact

- analysis.pdf

.

1

- 1monetary value of the CO 2 emissions reduction is between $0.10 billion and $1.5 billion. DOE also estimates the present monetary value of th e NO X emissions reduction, is $13 million at a 7

- percent discount rate and $29 million at a 3

- percent discount rate. f

Table 1.2

. 1 summarizes the nationa l economic costs and benefits expected to result from the proposed standards for commercial and industrial pumps.

Table 1.2

. 1 Summary of National Economic Benefits and Costs of Proposed Energy

Conservation Sta ndards for Commercial and Industrial Pumps*

Category Present

Value Billion 2013$ Discount

Rate

Benefits

Operating Cost Savings 0.6 7%

1.4 3%

CO 2 Reduction Monetized Value ($12.0/t case)** 0.1 5%

CO 2 Reduction Monetized Value ($40.5/t case)** 0.5 3%

CO 2 Reduction Monetized Value ($62.4/t case)** 0.8 2.5%

CO 2 Reduction Monetized Value ($119/t case)** 1.5 3%

NO X Reduction Monetized Value (at $2,684/ton)** 0.01 7%

0.03 3%

Total Benefits† 1.0 7%

1.9 3%

Costs

Incremental Installed Costs 0.2 7%

0.3 3%

Total Net Benefits

Including Emissions Reduction Monetized Value† 0.9 7%

1.6 3%

* This table presents the costs and benefits associated with commercial and industrial pumps shipped in 2020−2049. These results include benefits to customers which accrue after 2049 from the products purchased in 2020−2049. The results account for the inc remental variable and fixed costs incurred by manufacturers due to the standard, some of which may be incurred in preparation for the rule.

** The CO 2 values represent global monetized values of the SCC, in 2013$, in 2015 under several

scenarios of the u pdated SCC values. The first three cases use the averages of SCC distributions calculated using 5%, 3%, and 2.5% discount rates, respectively. The fourth case represents the 95 th percentile of the SCC distribution calculated using a 3% discount rate. The S CC time series incorporate an escalation

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

with 3

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

f DOE is currently investigating valuation of avoided Hg and SO 2 emissions.

1

- 21.3 OVERVIEW OF STANDARD S

Title III of the Energy Policy and Conservation Act of 1975 (42 U.S.C.6291, et seq; EPCA), sets forth various provisions designed to improve energy efficiency of consumer appliances. Part C of Title III, which for editorial reasons was re

- designated as Part A

- 1 upon incorporation into the U.S. Code (42 U.S.C. 6311

– 6317), establishes the Energy Conservation Program for Certain Industrial Equipment. The covered equipment includes commercial and industrial pumps, the subject of today’s notice. (42 U.S.C. 6311(1)(A)) g There currently are no

ene rgy conservation standards for commercial and industrial pumps.

In considering whether to establish standards for pumps, DOE issued a request for information (RFI) on June 13, 2011. 76 FR 34192. DOE received several comments in response. In December 2011, DOE received a letter from the Appliance Standards Awareness Project (ASAP) and the Hydraulic Institute indicating that efficiency advocates (including ASAP, American Council for an Energy

- Efficient Economy, Natural Resources Defense Council, and

Northwest Energy Efficiency Alliance) and pump manufacturers (as represented by the Hydraulic Institute) had initiated discussions regarding potential energy conservation standards for commercial and industrial pumps. (EERE

– 2011

– BT

– STD

– 0031

- 0011) In subsequent lett ers in March and April 2012, and in a meeting with DOE in May 2012, the interested parties described a tentative path forward regarding three issues: energy conservation standards for water pumps, certification and labeling, and an “extended product” appro ach. (EERE

- 2011

- BT

STD

- 0031

- 0010 and

- 0012)

On February 1, 2013, DOE published a notice in the Federal Register that announced the availability of the commercial and industrial pumps energy conservation standard framework document, solicited comment on the document, and invited all interested parties to a public meeting to discuss the document. 78 FR 7304. The framework document described the procedural and analytical approaches that DOE anticipated using to evaluate energy conservation standards for pum ps, addressed stakeholder comments related to the RFI, and identified and solicited comment on various issues to be resolved in the rulemaking. (EERE

– 2011

– BT

– STD

– 0031

- 0013)

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

DO E established a working group to negotiate a notice of proposed rulemaking (NOPR) for energy conservation standards for commercial and industrial pumps. Specifically, on July 23, 2013, DOE issued a notice of intent to establish the working group to negotia te a NOPR for commercial and industrial pumps. 78 FR 44036. The working group was established under the ASRAC in accordance with the Federal Advisory Committee Act and the Negotiated Rulemaking Act. (5 U.S.C. App. 2; 5 U.S.C. 561

- 570, Pub. L. 104

- 320) The group was directed to discuss and, if possible, reach consensus on a proposed rule for the energy efficiency of commercial and industrial pumps. The working group, which was to consist of representatives of

g All references to EPCA in th is document refer to the statute as amended through the American Energy Manufacturing Technical Corrections Act of 2012, Pub. L. 112

- 210 (Dec. 18, 2012).

1

- 3parties having a defined stake in the outcome of the proposed standards, would consult as appropriate with a range of technical experts.

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

– 19, 2013, and January 30

– 31, March 4

– 5, March 26

– 27, April 29

– 30, May 28

– 29, and June 17

– 19, 2014. On June 19, 2014, the working group reached consensus on proposed energy conservation standards for an agreed

- upon scope of pumps. The working group assembled their

recommendations into a term sheet

. (EERE

- 2013

- BT

- NOC

- 0039

- 0092 ) DOE considered t he intent of the approved term sheet, along with other comments received during the rulemaking process, in developing proposed energy conservation standards.

1.4 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS

Under EPCA, when DOE evaluates new or amended st andards, it must consider, to the greatest extent practicable, the following seven factors. (42 U.S.C. 6295(o)(2)(B)(i) and 6316(a))

1. the economic impact of the standard on the manufacturers and consumers of the affected products;

2. the savings in operating costs throughout the estimated average life of the product compared to any increases in the initial cost or maintenance expense for the products that are likely to result from the imposition of the standard;

3. the total projected amount of energy savings l ikely to result directly from the imposition of the standard;

4. any lessening of the utility or the performance of the covered products likely to result from the imposition of the standard;

5. the impact of any lessening of competition, as determined in writi ng by the Attorney General, that is likely to result from the imposition of the standard;

6. the need for national energy conservation; and

7. other factors the Secretary considers relevant.

Other statutory requirements are set forth in 42 U.S.C. 6295(o)(1)

– (2)(A), (2)(B)(ii)

– (iii), and (3)

– (4).

Before DOE determines whether to adopt a proposed energy conservation standard, it first must solicit comments on the proposed standard. DOE considers the participation of interested parties a vital part of the proces s for setting energy conservation standards. Through formal public notifications (i.e., Federal Register notices), DOE encourages the participation of all interested parties during the comment period provided at each stage of a rulemaking. Beginning with t he preliminary analysis for this rulemaking and during subsequent comment

1

- 4periods, interactions among interested parties provided a balanced discussion of the information required for the standards rulemaking.

Any new or amended standard must be designed t o achieve significant additional conservation of energy and be technologically feasible and economically justified. (42 U.S.C. 6295(o)(2)(A), 6313(a)(6)(A)) To determine whether economic justification exists, DOE must review comments on the proposed standa rd and determine that the benefits of the proposed standard exceed its burdens to the greatest extent practicable, weighing the seven factors listed above. (42 U.S.C. 6295(o)(2)(B)(i), 6316(a))

After the framework document is published, the energy conserv ation standards rulemaking process typically involves three additional, formal public notices, which DOE publishes in the Federal Register. The first rulemaking notice generally is a notice of public meeting (NOPM), which is designed to publicly vet the mo dels and tools used in the preliminary rulemaking and to facilitate public participation before the NOPR stage. Because DOE established a working group under ASRAC that held several meetings open to the public and provided participants with models publical ly available in the docket, DOE did not issue a NOPM for this rulemaking. What is generally the second notice is the NOPR, which presents a discussion of comments received in response to previous activities such as publication of the framework document; an alyses of the impacts of potential energy conservation standards on consumers, manufacturers, and the Nation; DOE’s weighting of those impacts; and the proposed energy conservation standards for each class of equipment. The third notice is the final rule, which presents a discussion of the comments received in response to the NOPR, the revised analyses, DOE’s weighting of impacts, the energy conservation standards DOE is adopting for

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

standards.

T his TSD describes the various analyses DOE performed in developing the NOPR, such as the engineering analysis and the consumer economic analyses [e.g., the life

- cycle cost (LCC) and payback period (PBP) analyses]; the methods u sed for conducting the analyses, and the relationships among the various analyses. Table 1.4.1 outlines the analyses DOE conducts for the NOPR and final rule for a rulemaking.

1

- 5Table 1.4

. 1 Analyses by Rulemaking Stage

NOPR Final Rule Market and technology assessment   Screening analysis   Engineering analysis  
Energy use characterization   Product price determination  

Life

- cycle cost and payback period analyses  

Life

- cycle cost subgroup analysis   Shipments analysis   National impact analysis   Manufacturer impact analysis   Utility impact analysis   Employment impact analysis   Emissions Analysis   Regulatory impact analysis  

DOE developed spreadsheets for the engineering, LCC, PBP, and national impact analyses (NIA) for each equipment class for commercial and industrial pumps . The LCC workbook calculates the LCC and PBP at various energy efficiency levels. The NIA workbook does the same for national energy savings and national net present values (NPVs). All of the spreadsheets are available on the DOE website for commercial and industrial pumps

at www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/14

.

As part of the information gathering and sharing process, DOE intervi ewed commercial and industrial pump manufacturers. DOE selected companies that represent production of all types of equipment, ranging from small to large manufacturers. DOE had five objectives for these interviews: (1) solicit manufacturer feedback on the draft inputs to the engineering analysis; (2 ) solicit manufacturer data for use in the analysis and downstream analyses ; ( 3 ) solicit feedback on topics related to the manufacturer impact analysis; ( 4 ) provide an opportunity, early in the rulemaking proces s, for manufacturers to express their concerns to DOE; and ( 5 ) foster cooperation between manufacturers and DOE. DOE incorporated the information gathered during these interviews into its engineering analysis (chapter 5) and its manufacturer impact analysi s (chapter 12).

1.5 STRUCTURE OF THE DOC UMENT

This TSD describes the analytical approaches and data sources that DOE proposes to use in the rulemaking for commercial and industrial pumps. The TSD consists of the following chapters and appendices.

Chapter 1 In troduction: provides an overview of DOE's standards program for equipment and how it applies to the rulemaking for commercial and industrial pumps;

outlines the structure of the document.

1

- 6Chapter 2 Analytical Framework: describes the methods, analytical t ools, and relationships among the various analyses.

Chapter 3 Market and Technology Assessment: provides DOE’s definition of a pump, lists the proposed equipment classes, and names the major industry players. This chapter also provides an overview of pump technology, including techniques employed to improve pump efficiency.

Appendix 3A Pump Design Speed

Appendix 3B Efficiency Surfaces

Chapter 4 Screening Analysis: after identifying and evaluating design options for improving pump efficiency, determines which of those DOE screened out of further analysis.

Chapter 5 Engineering Analysis: discusses the methods used for developing the relationship between increased manufacturer price and in creased efficiency. Presents detailed cost and efficiency information for equipment classes analyzed.

Appendix 5A Additional Figures: Failing C

- Values Plotted Against Specific Speed and Flow

at BEP

App endix 5B Scaling Parameter

Chapter 6 Markups Analysis: discusses the methods used to establish price markups for converting manufacturer prices to customer equipment prices.

Chapter 7 Energy Use Analysis: discusses the process used for estimating energy use of the considered equipment as a function of efficiency level.

Chapter 8 Life

- Cycle Cost and Payback Period Analysis: discusses the effects of standards on individual customers and users of the equipment and compares

the LCC and PBP of equipment with and without higher energy conservation

standards.

Chapter 9 Shipments Analysis: discusses the methods used for projecting the total number of commercial and industrial pumps that would be affected by

standards.

Chapter 10 National I mpact Analysis: discusses the methods used for projecting national energy consumption and customer economic impacts in the absence and presence of standards.

1

- 7Appendix 10A Full Fuel Cycle

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

Price Forecasts and Economic Growth Scenarios

Chapter 11 Life

- Cycle Cost Subgroup Analysis: discusses the effects of standards on any identifiable subgroups of consumers who may be disproportionately affected by any proposed standard level.

Chapter 12 Manufacturer Impact Analysis: discusses the effects of standards on the finances and profitability of manufacturers of commercial and industrial

pumps.

Appendix 12A GRIM Overview

Chapter 13 Emissions Impact Analysis: discusses the effects of standards on pollutants, including sulfur dioxide, nitrogen oxides (NO X ), and mercury, as well as

carbon emissions.

Chapter 14 Monetization of Emission Reductions Benefits: Assigns monetary values to the benefits likely to result from the reduced emissions of carbo n dioxide (CO 2 ) and nitrogen oxides (NO X ) resulting from standards.

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

12866

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

Under Executive Order 12866

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

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

Chapter 17 Regulatory I mpact Analysis: discusses the effects of non

- regulatory alternatives to efficiency standards.

Appendix 17A Regulatory Impact Analysis: Supporting Materials

1

- 8C HAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION

................................ ................................ ................................ ........... 2

- 1

2.2 MARKET AND TECHNOLOG Y ASSESSMENT

................................ ........................ 2

- 4

2.2.1 Market Assessment

................................ ................................ ................................ ... 2

- 4

2.2.2 Technology Assessment

................................ ................................ ........................... 2

- 4

2.3 SCREENING ANALYSIS

................................ ................................ .............................. 2

- 5

2.4 ENGINEERING ANALYSIS

................................ ................................ .......................... 2

- 5

2.5 MARKUPS ANALYSIS

................................ ................................ ................................ . 2

- 6

2.6 ENERGY USE ANALYSIS

................................ ................................ ............................ 2

- 6

2.7 LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

................................ ..... 2

- 6

2.8 SHIPMENTS ANALYSIS

................................ ................................ .............................. 2

- 7

2.9 NATION AL IMPACT ANALYSIS

................................ ................................ ................ 2

- 7

2.9.1 National Energy Savings

................................ ................................ .......................... 2

- 8

2.9.2 Net Present Value of Customer Benefits

................................ ................................ .. 2

- 8

2.10 CUSTOMER SUBGROUP AN ALYSIS

................................ ................................ ......... 2

- 9

2.11 MANUFACTURER IMPACT ANALYSIS

................................ ................................ .... 2

- 9

2.12 EMISSIONS ANALYSIS

................................ ................................ ................................ 2

- 9

2.13 MONETIZING BENEFITS OF REDUCED EMISSIONS

................................ ........... 2

- 10

2.14 UTILITY IMPACT ANALY SIS

................................ ................................ ................... 2

- 11

2.15 EMPLOYMENT IMPACT AN ALYSIS

................................ ................................ ....... 2

- 12

2.16 REGULATORY IMPACT AN ALYSIS

................................ ................................ ....... 2

- 12

FIGURE

Figure 2.1.1 Flow Diagram of Analyses for the Rulemaking Process

................................ ..... 2

- 2

2

- iCHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

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

6291 et seq . requires the U.S. Department of Energy (DOE) to set forth energy conservation standards that achieve the maximum improvement in energy efficiency that is technologically feasible and economically justified. This chapter describes the general analytical f ramework that DOE uses in developing such standards, and in particular, potential new energy conservation

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

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

Figure 2

. 1 .1 summarizes the analytical components of the standards

- setting process. The

focus of th e figure is the third col umn, labeled “Analyses.” The columns labeled “Key Inputs” and “Key Outputs” show how the analyses fit into the rulemaking process and how the y re late to

each other. Key inputs are the types of data and informat ion that the analyses require. Some key inputs are found in public databases; DOE collects other inputs from interested parties or other knowledgeable experts within the field

. Key outputs are analytical results that feed directly into

the standards

- settin g process. Arrows connecting analyses show the types of information that feed from one analysis to another.

2

- 1Figure 2

. 1 .1 Flow Diagram of Analyses for the Rulemaking Process a

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

2-2L isted below are t he analyses reported in this technical support document (TSD) , which

was develop ed in support of the notice of proposed rulemaking (NOPR) for commercial and industrial pumps

.

• A market and technology assessment to characterize the relevant equipment markets an d technology options, including prototype designs.

• A screening analysis to review each technology option to decide whether it is technologically feasible; is practical to manufacture, install, and service; would adversely affect product utility or product availability; or would have adverse effects on health and safety.

• An engineering analysis to develop cost

- efficiency relationships, which indicate the manufacturer’s cost of achieving increased efficiency.

• A markups analysis to develop markups throughout t he distribution channel (s), relat ing the manufacturer production cost (MPC) to the cost to the c usto mer.

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

with and without standards for a representative group of users

.

• A life

- cycle cost ( LCC ) and payback period (PBP) analysi s to calculate the savings in operating costs at the custo mer level throughout the life of the considered equipment compared with any increase in the installed cost for the equipment likely to result d irectly from imposition of a standard.

• A shipments analysis to project equipment shipments, which then are used to calculate the national impacts of standards on energy, net present value (NPV), and future

manufacturer cash flows.

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

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

total c usto mer economic impacts and the national energy savings (NES).

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

cause a standard to disproportionately affect particular customer subpopulations.

• A manufacturer impact analysis to estimate the financial impact of standards on manufacturers and to calculate impacts on c osts, shipments, competition, employment, and manufacturing capacity.

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

on the environment.

2-3• An emissions monetization to assign values to the benefits associated with emiss ions

reductions.

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

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

• A regulatory impact analysis to examine major alternatives to new energy conservation standards that potentially could achieve substantially the same regulatory goal at a lower

cost.

2.2 MARKET AND TECHNOLOGY ASSESSMENT

The market and technology assessment ch aracterizes the relevant markets for the

considered equipment and technology options for improving efficiency , including prototype designs.

2.2.1 Market Assessment

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

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

nature of the equipment , the industry structure, and market characteristics. This activity assesses the industry and equipment both quantitatively and qualitatively based on publicly available

information . The market assessment encompasses (1) manufacturer market share s and characteristics, (2) current regulatory and non

- regulatory initiatives for improving energy efficiency of the equipment , and (3) trends in equipment cha racteristics and retail markets. This

information serves as a resource throughout the rulemaking.

DOE reviewed literature and interviewed manufacturers to develop an overall picture of the industry that serv es the United States market. Industry publications and trade journals, government agencies, trade organizations, and equipment literature provided the bulk of the information, including (1) names of manufacturers and their approximate market shares, (2) equi pment characteristics, and (3) industry trends. Relevant sections of the NOPR describe the analyses and resulting information that underlie the trial standard levels, while the TSD provides supporting documentation.

2.2.2 Technology Assessment

DOE typically uses information relating to technology options and prototype designs as inputs to determine what technologies manufacturers may use to attain higher performance levels for a given type of equipment

. DOE developed its list of technology options for commercial and industrial pumps from trade publications, technical papers, and consultation with interested parties

. Because many options for improving equipmen t efficiency currently are found in pumps, product literature and direct research provided additional infor mation. Initially, the technologies encompass all those DOE believes are technologically feasible. Chapter 3 of this NOPR TSD

2-4includes the detailed list of all technology options identified for commercial and industrial

pumps

.

2.3 SCREENING ANALYSIS

The screening analysis examines various technologies as to whether they: (1) are technologically feasible; (2) are practicable to manufacture, install, and service; (3) have an adverse impact on product utility or availability; and (4) have adverse impacts on health and safety. DOE developed an initial list of efficiency

- enhancement options from the technologies identified as technologically feasible in the technology assessment. Then DOE review ed the list to determine if these options are practicable to manufa cture, install, and service, would adversely affect product utility or availability, or would have adverse impacts on health and safety. In the engineering analysis, DOE further consider ed efficiency enhancement options that it did not

screen out in the sc reening analysis. Chapter 4 of the NOPR TSD contains details on the screening analysis for commercial and industrial pumps

.

2.4 ENGINEERING ANALYSIS

The engineering analysis establishes the relationship s between manufacturer production cost (MPC) and efficien cy, and manufacturer selling price (MSP) and efficiency for each equipment class of commercial and industrial pumps . Th e s e relationship s serve as the basis for calculating cost/benefit for individual custo mers, manufact urers, and the nation. The engineerin g analysis d escribe s the equipment classes DOE analyzed, the representative baseline configuration , the incremental efficiency levels, the metho d DOE used to develo p the MPC

- and

MSP

- efficiency relationship s , and the impact of efficiency improvements on the considered equipment

. The technologies that were not eliminated in the screening analysis are designated as design options in the engineering analysis.

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

- option approach, which calculates the incremental costs of adding specific design options to a baseline model; (2) the efficiency

- level approach, which calculates the relative costs of achieving increases i n energy efficiency levels without regard to the particular design options used to achieve such increases; or (3) the reverse

- engineering or cost

- assessment approach,

which involves a “bottom

- up” manufacturing cost assessment based on a detailed bill of

ma terials derived from tear

- downs of the equipment being analyzed.

D OE incorporated both the design option and efficiency

- level approaches to develop c ost
estimates at each efficiency level for commercial and industrial pumps

. Based on manufacturer supplied cost and price data , DOE estimated the baseline MPC and MSP for the range of pumps available on the market. DOE then considered the incremental steps manufacturers may take to

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

the incremental impact the design option s have on MPC and MSP at each higher efficiency level

.

2-5Chapter 5 of this NOPR TSD describes the methods and results of the activities undertaken to
derive the MPC

- and MSP

- efficiency relationships.

2.5 MARKUPS ANALYSIS

DOE analyzed equipment markup s to convert the manufacturer costs estimated in the engineering analysis to custo mer prices, which then were used in the LCC and PBP and the manufacturer impact analyses. DOE calculate d markup s for baseline equipment (baseline markups) and for more efficient equipment (incremental markup s ). An incremental markup relates the change in the MPC of higher

- efficiency models (the incremental cost increase) to the change in the retailer or distributor sales price.

To develop markups, DOE identifie d how the equipment is distributed from the

manufacturer to the c usto mer. After establishing appropriate distribution channels, DOE used

data from sources that included the financial filings of manufacturers and distributors to determine how prices are marked up as the equipment passes from the manufacturer to the

c usto mer. Chapter 6 of the TSD provides details on DOE’s development of markups for

commercial and industrial pumps

.

2.6 ENERGY USE ANALYSIS

DOE performed an energy use analysis to assess the energy

- savings potential of higher

efficiency levels, providing the basis for the energy savings used in the LCC and subsequent analyses. The goal of the energy use characteriz ation is to generate a range of energy use values

that reflects actual equipment use in the field

. The analysis uses information on the use of equipment in the field to estimate the energy that would be used by equipment operating at various efficiency levels. Chapter 7 of this NOPR TSD provides more detail about DOE’s approach to characterizing energy use of commercial and industrial pumps

.

2.7 LIFE

- CYCLE COST AND PAYBACK PERIOD ANAL Y SIS

In determining whether an energy efficiency stan dard is economically justified, DOE considers the economic impact s of potential standards on custo mers

. The effect s of new or

amended standards on individual c usto mers usually include a reduction in operating cost and an increase in purchase cost. DOE use d the following two metrics to measure c usto mer impacts

.

• L ife

- cycle cost ( LCC ), the total c usto mer cost of a piece of equipment , generally throughout the life of the equipment

. The LCC calculation includes total installed cost (manufacturer selling price, d istribution markups, sales tax, and installation costs ) ; operating costs (energy, repair, and maintenance costs ) ; equipment lifetime ; and a

2-6discount rate. Future operating costs are discounted to the time of purchase and summed

over the lifetime of the equipment

.

• P ayback period ( PBP) , a measure of the amount of time it takes custo mers to recover the assumed higher purchase price of more energy efficient equipment through reduced operating costs. Inputs to the calculation of payback period incl ude the ins talled cost to

the custo mer and first

- year operating costs.

In determining the LCC and PBP

, DOE used data regarding engineering performance,

energy use, and markups. DOE generated LCC and PBP results using a simulation approach in which certain key input s to the analysis consist of probability distributions rather than single

point values. T hat analytical technique produces outcomes that also can be expressed as probability distributions. As a result, the analysis produces a range of LCC and PBP results

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

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

2.8 SHIPMENTS ANALYSIS

DOE projected future shipment s of commer cial and industrial pumps based on an analysis of key market drivers. Projections of shipments are needed to calculate the potential effects of standards on national energy use, NPV, and future manufacturer cash flows. DOE generated shipments projections f or each equipment class. The projections estimate the total number of pumps shipped each year during the 30

- year analysis period ( 2020

– 2049) . To create the projections, DOE combined current

- year shipments with results of a shipment s model that incorporates key market drivers for commercial and industrial pumps

. Chapter 9 of th is TSD provides additional details on the shipments analysis.

2.9 NA TION AL IMPACT ANALYSIS

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

considered for the equipment classes analyzed for commercial and industrial pumps . To make the analysis more accessible and transparent to all interested parties, DOE prepared a Microsoft Excel spreadsheet model to project NES and the national c usto mer economic costs and savings resulting from potential new standards. The spreadsheet model uses as inputs typical values (as opposed to probability distributions ). To assess the effect of input uncertainty on NES and NPV results, DOE i s able to conduct sensitivity analyses by operating the model using various input

variables.

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

efficiencies . For the base case, DOE uses efficiency distributions developed us ing shipments data supplied by manufacturers. Because the evidence suggests that there is no trend toward greater interest in higher pump efficiency, DOE assumed that the base

- case distribution would remain

2-7constant throughout the analysis period

. For this analysis, DOE used a roll

- up scenario to
establish the equipment market shares by efficiency level for the year in which compliance with

new standards would be required . Under a roll

- up scenario, all equipment that perform s at efficiency levels l ower than a prospective standard are moved, or rolled

- up, to the minimum performance level allowed under the standard. Equipment efficiencies that exceed the new standard level do not change

. Because DOE has no reason to believe that implementation of

standards wou ld increase the demand for equipment that is more efficient than the minimum required

, it did not incorporate an efficiency trend in the standards

- case scenarios either.

2.9.1 National Energy Savings

The inputs for determining the NES for each efficiency leve l for each equipment class are: (1) annual energy consumption per unit, (2) shipments, (3) equipment stock, (4) national energy consumption, and (5 ) site

- to

- primary energy and full

- fuel cycle conversion factors. DOE calculated national energy consumption b y multiplying the number of units, or stock, of each equipment class (by vintage, or age) by the unit energy consumption (also by vintage). DOE calculated annual NES based on the difference in national energy consumption for the base case (without new effi ciency standards) and for each efficiency level being considered.

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

the recommendations of a committee on “Point

- of

- Use and Full

- Fuel

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

DOE announced its intention to use full

- fuel

- cycle (FFC) measures of energy use and greenhouse gas and other emissions in the national impact analyses and emissions analyses included in future energy conservation standards rulemakings. 76 FR 51281 (August 18, 2011). After evaluating the approaches discussed in the August 1 8, 2011 notice, DOE published a statement of amended policy in the Federal Register in which DOE explained its determination

that NEMS is the most appropriate tool for its FFC analysis and its intention to use NEMS for that purpose . 77 FR 49701 (August 17, 2012). The approach used for the NOPR is described in appendix 10 A of the TSD.

2.9.2 Net Present Value of C ustomer Benefit s

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

c usto mers are (1) total annual installed cost, (2) t otal annual savings in operating costs, and (3) a

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

each TSL . Because the more efficient equipment bought in a standards case usually cost s more than equipment b ought in the base case, cost increases appear as negative values in the NPV.

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

and total increases in installed costs between the base case and each TSL

. DOE expressed savings in operating costs as decreases associated with the lower energy consumption of equipment bought in the standards case compared to the base case. DOE calculated savings throughout the life of each equipment class, accounting for diffe rences in yearly energy rates. DOE calculated NPV as the difference between the present value of operating cost savings and

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

to discount future costs and sav ings to present values.

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

2.10 C USTO MER SUBGROUP ANALYSIS

The custo mer subgroup analysis evaluates economic impacts on particular groups of

c usto mers who might be a ffected adversely by the energy conservation standards proposed for

the considered equipment . DOE evaluates impacts on subgroups of customers primarily by applyi ng the LCC spreadsheet model to analyz e the LCC impacts and PBP for those customers

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

of this TSD describes the c usto mer subgroup analysis.

2.11 MANUFACTURER IMPACT ANALYSIS

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

conservation standards on manufacturers of commercial and industrial pumps

. Potential impacts include financial effects, both quantitative and qualitative, that might l ead to changes in the manufacturing practices for the products . DOE conduct s the MIA in three phases, tailor ing the analytical framework based on comments from interested parties. In Phase I, DOE created a profile of the manufacturing industry for commercial and industrial pumps and analyzed publicly

available financ ial information to derive preliminary inputs for the Government Regulatory Impact Model (GRIM) . In Phase II, DOE prepared an industry cash flow model

. In Phase III,

DOE applied the GRIM to assess i ndustry and subgroup cash flow and NPV. DOE then assessed impacts on competition, manufacturing capacity, employment, and cumulative regulatory

burden

. DOE describes its findings from the MIA in chapter 1 2 of th is NOPR TSD.

2.12 E MISSIONS ANALYSIS

In the emissions analysis, DOE estimated the reduction in power secto r emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ), and mercury (Hg) attributable to potential energy conservation standards. In addition to estimating reductions in power plant emissions, DOE estimated the reductions in emiss ions from the activities (extracting, processing, and transporting) that provide the fuels needed by power plants. Emissions from those activities are referred to as upstream emissions. Together, the power plant and upstream emissions account for the full fuel cycle (FFC). In accordance with DOE’s FFC statement of policy (76 FR 51282 (August 18, 2011)), the FFC analysis includes impacts on emissions of methane and nitrous oxide, both of which are recognized as greenhouse gases (GHG).

2-9DOE conducted the emis sions analysis for CO 2 and most of the other gases primarily based on emissions factors derived from the Energy Information Administration’s (EIA) Annual Energy Outlook 2014 (AEO 2014). EIA prepares the Annual Energy Outlook using the National Energy Model ing System (NEMS) for the given AEO year. Each annual version of NEMS incorporates the projected effects of current air quality regulations on emissions. AEO 2014 generally represents current legislation and environmental regulations, including recent

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

DOE estimated combustion emissions of CH 4 and N 2 O using emissions intensity factors published by the Environmental Protection Agency's (EPA's) GHG Emissions Factors H ub.

Power plants may emit particulates from the smoke stack, which are known as emissions of direct particulate matter (PM). NEMS does not account for direct PM emissions from power plants. Almost all ambient PM from power plants is in the form of second ary sulfates and nitrates, primarily SO 2 and NO X , which are produced at a significant distance from the power plant by complex atmospheric chemical reactions. The monetary benefits that DOE estimated for

reductions in SO 2 and NO X emissions resulting from s tandards (see section below) are related primarily to the health benefits of reduced ambient PM.

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

2.13 MONETIZING BENEFITS OF REDUCED EMISSIONS

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

of CO 2 and NO X expected from each TS L considered for commercial and industrial pumps

. To assign a monetary value of benefits resulting from reduced CO 2 emissions , DOE use d the most

current Social Cost of Carbon (SCC ) values developed and/or agreed on by an interagency process. The SCC is intended to be a monetary measure of the incremental damage resulting from GHG emissions, including, but not limited to, net loss in agricultural productivi ty, human health effects, property damage from sea level rise, and changes in ecosystem services. Any effort to quantify or monetize the harms associated with climate change raise serious questions of science, economics, and ethics. But with full regard fo r the limits of both quantification and monetization, the SCC can be used to estimate the social benefits of reductions in GHG

emissions.

I n 2013 t he Interagency Working Group on Social Cost of Carbon released an update of its previous report. b The most recent estimates of the SCC in 2015, expressed in 201 3 $, are $ 12.0, $40.5, $62.4, and $119 per metric ton of CO 2 avoided. For emissions reductions that occur in later years, th o se values increase in real terms. Additionally, the interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC when

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

. Int eragency Working Group on Social Cost of Carbon, United States Government . R evised November 2013. www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical

- update

- social

- cost

- of

- carbon

- for

- regulator

impact

- analysis.pdf . (Last accessed September 16, 2014.)

2-10calculat ing domestic effects, although DOE give s preference to consideration of the global

benefi ts of reducing CO 2 emissions.

DOE multiplied the CO 2 emissions reduction estimated for each year by each of the four

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

discount ed the value s in each of the four ca ses using the discount rates that had been used to

obtain the SCC values in each case. DOE recognizes that scientific and economic knowledge continues to evolve rapidly regarding the contribution of CO 2 and other GHG to changes in the future global climate and the potential resulting damages to the world economy. Thus, the SCC values are subject to change.

DOE also estimated the potential monetary benefit s of reduced NO X emissions resulting

from the TSLs consider ed

. Estimates of monetary value of reducing NO X from stationary sources

range from $476 to $4,893 per ton in 201 3 $

. c DOE calculated monetary benefits using a n

average value for NO X emissions of $2,6 84 per short ton (201 3 $), and real discount rates of 3 percent and 7 percent.

DOE is investigating appropriate valuation s for Hg and SO 2 emissions. For this rulemaking DOE has not monetized estimates of SO 2 and Hg reduction s

.

Further detail on the monetization of emission reduction s is provided in chapter 14 of th is

TSD.

2.14 UTILITY IMPACT ANALYSIS

In th e utility impact analysis, DOE analyzes the changes in electric installed capacity and generation attributable to each TSL for potential energy conservation standards

. The utility impact analysis is based on output of the Energy Information Administration (EIA)’s National Energy Modeling System (NEMS). NEMS is a public

- domain, multi

- sector ial

, partial

equilibrium model of the U.S. energy sector. Each year, the EIA uses NEMS to produce an energy forecast for the United States ( the Annual Energy Outlook ) . The EIA publishes a reference case, which incorporates all energy

- related policies at the time of publication, and a variety of side cases that analyze the impact of different policies, energy price s, and market trends. As of 2014, DOE is using a new met hod based on results published for the AEO 2014

Reference case and a set of alternative c ases that implement a variety of efficiency

- related policies

. Further detail is provided in chapter 15 of th is NOPR TSD.

c U.S. Office of Management and Budget

– Office of Information and Regulatory Affairs

. 200 6 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal Entities

. Washington, D

. C

.

2-112.15 EMPLOYMENT IMPACT ANALYSI S

The adoption of ene rgy conservation standards can affect employment both directly and indirectly. Direct employment effe cts are changes in the number s of employees at the plants that produce commercial and industrial pumps . DOE evaluates direct employment impacts in the

MIA. Indirect employment impacts may result when standards (1) cause expenditures to shift between goods (the substitution effect) or (2) impact income and overall expenditure levels (the income effect). DOE defines indirect employment impacts from standards as net jobs eliminated or created in the general economy as a result of increased spending driven by increased

e q uipment cost s and reduced sp ending on energy.

To investigate i ndirect employment impacts in the employment impact analysis , DOE

uses the Pacific Northwest National Laboratory’s " Impact of Sector Energy Technologies” (ImSET) model. d The ImSET model was developed for DOE’s Office of Planning, Budget, and Analysis to estimate the employment and income effects of energy

- saving technologies in buildings, industry, and transportation

. Compared with approaches that simply apply an economic multiplier, ImSET allows for a more complete a nd automated ana lysis of the economic impacts of energy conservation investments. Further detail is provided in chapter 16 of the

NOPR TSD.

2.16 REGULATORY IMPACT ANALYSIS

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

Executive Order 12866, Regulatory Planning and Revie w

. 58 FR 51735 ( October 4, 1993 ) . The RIA addresses the potential for non

- regulatory alternatives to energy conservation standards in improv ing the energy efficiency or reduc ing the energy consumption of the equipmen t covered under this rulemaking. DOE recognizes that voluntary or other non

- regulatory efforts by manufacturers, utilities, and other interested parties can substantially affect energy efficiency or reduce energy consumption. DOE bases its assessme nt on the recorded impacts of any such initiatives to date, but also considers information presented by interested parties regarding the impacts current initiatives m ay have in the future. Further detail on the RIA is provided in chapter 17 of th is TSD.

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

. PNNL

- 18412, Pacific Northwest National Laboratory

. May 2009. www.pnl.gov/main/publications/external/technical_reports/PNNL

- 18412.pdf

. (Last access ed September 16, 2014.)

2-12CHAPTER 3. MARKET AND TECHNOLOG Y ASSESSMENT

TABLE OF CONTENTS

3.1 INTRODUCTION

................................ ................................ ................................ ........... 3

- 1

3.2 DEFINITIONS AND SCOP E OF COVERAGE

................................ ............................. 3

- 1

3.2.1 Covered Equipment

................................ ................................ ............................. 3

- 1

3.2.2 Scope of this Rulemaking

................................ ................................ .................... 3

- 1

3.3 EQUIPMENT CLASSES

................................ ................................ ................................ 3

- 3

3.4 TEST PROCEDURES AND ENERGY USE METRIC

................................ ................. 3

- 5

3.4.1 Rating of a Minimally Compliant Pump

................................ .............................. 3

- 6

3.5 MARKET ASSESSMENT

................................ ................................ .............................. 3

- 8

3.5.1 Trade Associations

................................ ................................ ............................... 3

- 9

3.5.2 Information about Pump Manufacturers

................................ ............................ 3

- 10

3.5.2.1 Market Shares

................................ ................................ ........................... 3

- 10

3.5.2.2 Potential Impacts on Small Businesses

................................ .................... 3

- 12

3.5.3 Shipments

................................ ................................ ................................ ........... 3

- 14

3.5.4 Mark et Performance Data

................................ ................................ .................. 3

- 14

3.5.5 Regulatory Programs

................................ ................................ ......................... 3

- 19

3.5.5.1 European Commission

................................ ................................ ............. 3

- 19

3.5.5.2 Mexico

................................ ................................ ................................ ...... 3

- 20

3.5.5.3 China

................................ ................................ ................................ ........ 3

- 20

3.5.6 Voluntary Programs

................................ ................................ ........................... 3

- 20

3.5.6.1 ASHRAE Standard 90.1

................................ ................................ ........... 3

- 21

3.5.6.2 DOE Advanced Manufacturing Office (Technical Assistance

– Pump Systems)

................................ ................................ ................................ ... 3

- 21

3.5.6.3 Industry Certifications and Standards

................................ ...................... 3

- 22

3.6 TECHNOLOGY ASSESSMEN T

................................ ................................ .................. 3

- 22

3.6.1 Technology Options

................................ ................................ ........................... 3

- 22

3.6.1.1 Improved Surface Finish on Wetted Components

................................ ... 3

- 22

3.6.1.2 Reduced Running Clearances

................................ ................................ ... 3

- 25

3.6.1.3 Reduced Mechanical Friction in Seals

................................ ..................... 3

- 26

3.6.1.4 Reduction of Other Volumetric Losses

................................ .................... 3

- 28

3.6.1.5 Improved Hydraulic Design (Hydraulic Redesign)

................................ . 3

- 28

3.6.1.6 Addition of a Variable Speed Drive

................................ ......................... 3

- 29

3.6.1.7 Improvement of VSD Efficiency

................................ ............................. 3

- 35

3.6.1.8 Reduced VSD Standby and Off Mode Power Usage

............................... 3

- 36

REFERENCES

................................ ................................ ................................ .......................... 3

- 37

LIST OF TABLES

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

............. 3

- 10

Table 3.5.2 Major Suppliers of Proposed Covered Pump Categories

............................ 3

- 12

3

- iTable 3.5.3 Pump and Pumping Equipment Manufacturing Firms by Company

Size 4

................................ ................................ ................................ ............. 3

- 13

Table 3.5. 4 Estimates of Affected Pump Shipments, 2012

................................ ............ 3

- 14

Table 3.5.5 Worldwide Pump Regulatory Programs

................................ ...................... 3

- 19

Table 3.6.1 Range of Potential Efficiency Gains Resulting from Improved Hydraulic Redesign

................................ ................................ ...................... 3

- 29

LIST OF FIGURES

Figure 3.5.1 Size Distribution (by Number of Employees) of Pump and Pump Equipment Manufacturing Firms in the United States 4

............................... 3

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

...................... 3

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

...................... 3

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

...................... 3

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

...................... 3

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

............................. 3

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

............................. 3

- 18 Figure 3.5.8 Distribution of Efficiency for VT

- S.3600 Equipment Class

....................... 3

- 19 Figure 3.6.1 Estimated Points of Efficiency Increase Due to Improving Surface Finish of Both Impeller and Volute from 500 R

a to 200 R

a ......................... 3

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

Clearance

................................ ................................ ................................ ...... 3

- 26 Figure 3.6.3 Estimated Available Efficiency Improvements from Reduced Seal Friction, as a Function of Shaft Horsepower, for the ESCC.1800 Equipment Class

................................ ................................ .......................... 3

- 27 Figure 3.6.4 Illustration of Pump Affinity Laws for an Example 150 hp Pump with a 100% Dynamic Head System Curve

................................ ......................... 3

- 32 Figure 3.6.5 Illustration of Power Reductions Available for an Example Oversized Pump

................................ ................................ ................................ ............ 3

- 33 Figure 3.6.6 Illustration of Power Reductions Available for an Example Pump Used in a High Static Head Application

................................ ............................... 3

- 35

3

- iiCHAPTER 3

. MARKET AND TECHNOLOG Y ASSESSMENT

3.1 INTRODUCTION

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

United States. The information the U.S. Department of Energy (DOE) gathers for a market and technology assessment serves as resource material throughout the rulemaking. D OE considers both quantitative and qualitative information from publicly available sources and interested parties. DOE consulted primarily publicly available information to develop the assessment

describ ed in this chapter.

This chapter consists of three parts. Sections 3.2 through 3.4 deal with various issues pertinent to the rulemaking for commercial and industrial pump s. The market assessment provides an overall picture of the market for the equipment con sidered , including the scope of

the covere d equipment, equipment classes, industry structure, and manufacturer market shares; regulatory and non

- regulatory programs for improving efficiency of the equipment ; and market trends and quantities of equipment sold. Th e market assessment is helpful in id entifying the major manufacturers and the characteristics of the equipment they produce , which will be

examined further in the engineering and life

- cycle cost analyses. The technology assessment identifies a preliminary list of technology options for increasing pump efficiency

.

3.2 DEFINITIONS AND SCOP E OF COVERAGE

3.2.1 Covered Equipment

Although pumps are listed as covered equipment under 42 U.S.C. 6311(1)(A), the term “pump” is not defined in t he Energy Policy and Conservation Act (42 U.S.C. 6311

– 6316;

EPCA )

. In the pumps test procedure NOPR , DOE proposed a definition for pump to clarif y what would constitute the covered equipment. Under the Appliance Standards and Rulemaking Federal Advisory Committee (ASR AC), DOE also established a working group to develop a notice of proposed rulemaking (NOPR) for energy conservation standards for commercial and industrial pumps. Negotiations among members of t he working group produced the following

consensus definition: Pump means a device that moves liquids (which may include entrained gases, free solids, and totally dissolved solids) by physical or mechanical action and includes a bare pump and, if included by the manufacturer, mechanical equipment, driver and controls. In

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

, mechanical equipment

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

.

3.2.2 Scope of this Rulemaki ng

Although the pumps test procedure NOPR proposed a broad definition for pumps as covered equipment, DOE also proposed limiting the scope of the standards under consideration in this rulemaking to a narrower range of equipment. Specifically , DOE proposed a scope consisting of the following pump categories , which is consistent with the CIP working group’s

recommendations:

3

- 1• End suction close coupled,

• End suction frame mounted/own bearings,

• In

- line,

• Radially split

, multi

- stage

, vertical in

- line casing, diffuser,

• Vertical turbine submersible.

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

.

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

scope to pumps designed for use in pumping clean water. In the pumps test procedure NOPR, DOE proposed the following de finitions

.

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

non

- absorbent free solid content of 0.25 kilograms per cubic meter, and with a maximum dissolved solid content of 50 kilograms per cubic meter, provided that the total gas content of the water does not exceed the saturation volume, and disregarding any additives necessary to prevent the water from freezing at a minimum of

- 10 o C

.
In the test procedure NOPR, DOE als o proposed to define several kinds of pumps that are clean water pumps, as defined, but would not be subject to the proposed test procedure, in accordance with CIP Working Group recommendations. DOE proposes that these pumps would also not be subject to th e proposed energy conservation standards:

a) fire pump s;

b) self

- priming pump s;

c) prime

- assist pump s;

d) sealless pumps;

e) pump s designed to be used in a nuclear facility subject to 10 CFR part 50

-- Domestic Licensing of Production and Utilization Facilities; and

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

- P

- 17639F, “Pumps, Centrifugal, Miscellaneous Service, Naval Shipboard Use” (as amended)

.

The pumps test procedure NOPR included additional definitions for fire pump

, self

priming pump

, prime

- assist pump

, and sealless pump

.

3

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

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

characteristic s

.

• 1 to 200 HP shaft power at best efficiency point ( BEP ) at full impeller diameter for the number of stages required for testing to t he proposed standard

.

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

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

• A design temperature that range s from

- 10 to 120 ° C

.

• D esigned to operate with either: ( 1 ) a 2

- or 4

- pole in duction motor, or ( 2 ) a non

- induction

motor with a speed of rotation operating range that includes speeds of rotation between 2,880 and 4,320 revolutions per minute and/or 1,440 and 2,160 revolutions per minute

.

• 6

- inch or smaller bowl diameter (VT

- S/HI VS0)

.

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

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

3.3 EQUIPMENT CLASSES

When evaluating and establishing energy conservation standards, DOE often divides covered equipment into classes by the type of energy used or by capacity or other performance

related feature that justifies a different standard from that which applies to o ther classes of the same equipment

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

classes based on :

• basic pump equipment type,

• s ale configuration

, and

• nominal design speed.

DOE notes that some bare pumps are sold for use with engines or turbines rather than electric motors, and thus would use a different fuel type ( i.e

. , fossil fuels rather than electricity).

B ecause the market share of clean water pumps that use th o se other fuel types is small

, however,

in the pumps test procedure NOPR DOE proposed that any pump sold for use with a driver other than an electric motor would be rated as a bare pump. a Therefore, DOE did not establish equipme nt classes by fuel type, because all are presumed to be electric.

T he five pump equipment categories DOE proposes for this rulemaking, each of which would form the basis for an individual equipment class, are :

a Such a rating would include the hydraulic efficiency of the bare pump as well as the efficiency of a minimally compliant electric motor, as described in section 3.4.1

.

3

- 3• end

- suction

, close

- coupled (ESCC) ;

• end

- suction

, frame

- mounted/own bearings (ESFM) ;

• in

- line (IL) ;

• radially split

, multi

- stage

, vertical

, in

- line casing

, diffuser (RS

- V) ; and

• vertical turbine submersible (VT

- S)

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

inclusive of motors and continuous or non

- continuous controls (as defined in the pumps test procedure NOPR ), capable of operati ng at multiple driver shaft speeds

, are defined as variable load (VL ) . P umps sold as bare pumps or with motors that lack such controls, capable of operati ng at only a fixed shaft speed, are defined as constant load (CL). b

The working group for commercial and industrial pumps recommended establishing separate energy efficiency standards for the nominal speeds of 2

- and 4

- pole motors. (EERE

2013

- BT

- NOC

- 0039

- 0092, p. 4, Recommendation #9) In its analysis, DOE found that throughout the market, pumps at each nominal speed demonstrate distinctly different performance s

. c Therefore, DOE agrees with the working group's reco mmend ation and in t hi s NOPR propos es that both the CL and VL pump classes be further differentiated on the basis of nominal design speed. Within the scope of this proposed rule making , pumps are defined as having either 3,600 or 1,800 rpm nominal driver speeds. Pumps defined as having a 3,600 nominal driver speed are designed to operate with a 2

- pole induction motor or with a non

induction motor with a speed of rotation operating range that includes speeds of rota tion between 2,8880 and 4,320 revolutions per minute. Pumps defined as having an 1,800 nominal driver speed are designed to operate with a 4

- pole induction motor or with a non

- i nduction motor with a speed of rotation operating range that includes speeds of rotation between 1,440 and 2,160 revolutions per minute. Throughout this document

, a 3,600 rpm no minal speed is abbreviated as 3600, and a 1,800 rpm nominal speed is abbreviated as 1800.

Taking into account basic pump equipment categories , nominal design speed s , and sale configuration s , DOE proposes t o establish 20 equipment classes for the pumps covered by this rulemaking . The classes would be designated as follows

.

• ESCC.1800.CL

• ESCC.3600.CL

• ESCC.1800.VL

b In the framework document, DOE explored identifying equipment types for use only in a variable load application. In response, the Hydraulics Institute (HI) and Grundfos reported that pump type or equipment class does not control whether a pump can be used in a variable load application, but that it is the application that defines this. (Grundfos, No. 24 at p. 21; HI, No. 25 at p. 37 ) The proposal is based upon the assumption that a pump sold with speed controls is intended for a variable load applic ation.

c See appendix 3A for an extended discussion of pump design speed and its relationship to equipment class.

3

- 4• ESCC.3600.VL

• ESFM.1800.CL

• ESFM.3600.CL

• ESFM.1800.VL

• ESFM.3600.VL

• IL.1800.CL

• IL.3600.CL

• IL.1800.VL

• IL.3600.VL

• RS

- V .1800.CL

• RS

- V.3600.CL

• RS

- V.1800.VL

• RS

- V.3600.VL

• VT

- S.1800.CL

• VT

- S.3600.CL

• VT

- S.1800.VL

• VT

- S.3600.VL

3.4 TEST PROCEDURES AND ENERGY USE METRIC

DOE currently is conducting a rulemaking to establish a uniform test procedure for determining the energy efficiency of commercial and industrial pumps. In the pumps test procedure NOPR, DOE proposed to prescribe test methods for measuring or calculating the efficiency of pumps, inclusive of motors and/or controls, by measuring the produced hydraulic

power, and the shaft power and/or electric input power to the motor or controls. Consistent with the recommendations of the working group, DOE proposed that the methods be b ased on Hydraulic Institute (HI) Standard 40.6

- 2014, “Hydraulic Institute Standard for Method for Rotodynamic Pump Efficiency Testing,” hereinafter referred to as “HI 40.6

- 2014.” (See EERE

2013

- BT

- NOC

- 0039

- 0092, Recommendation #10) DOE proposed additions t o HI 40.6

- 2014 to account for the energy performance of motors and/or controls, which is not addressed in HI 40.6

- 2014.

The test procedure NOPR propose s that the energy conservation standards for commercial and industrial pumps be expressed in terms of a constant

- load Pump Energy Index

3

- 5(PEI CL ) for pumps sold without controls or a variable

- load PEI (PEI VL ) for pumps sold with

controls. The PEI CL or PEI VL , as applicable, describes the weighted

- average power input to the pump, inclusive of motor and/or contro ls, at specific load points, normalized with respect to the power input to a “minimally compliant pump” without controls. The metrics are defined as

follows

.

PPPPPP CCCC = � PPPPPP CCCC
PPPPPP SSSSSS �

Eq. 3

. 1

PPPPPP VV = � PPPPPP VV
PPPPPP SSSSSS �

Eq. 3

. 2

Where:

• PER CL is the average electric input power to the pump measured (or calculated) at the driver input for three flow points along a fixed

- speed pressure

- flow curve , as test ed in accordance with the DOE test procedure. This metric applies only to pumps having a fixed speed

.

• PER VL is the average electric input power to the pump measured (or calculated) at the controller input over a specified load profile as tested in accordan ce with the DOE test procedure. This metric applies only to pumps having a variable speed.

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

allowable weighted

- average electric input power to a pump, as calculated by the DOE test procedure. This metric applies to all equipment classes.

A measured value of PEI greater than 1.0 indicate s that the pump fails to meet DOE’s proposed energy conservation standard, whereas a measured value less than 1.0 indicate s that the

pump exceeds requirements of the efficiency standard.

3.4.1 Rating of a Minimally Compliant Pump

As noted in the pumps test procedure NOPR, DOE is proposing the use of a standardized minimally compliant bare pump, inclusive of a minimal ly compliant motor, as a reference pump for each combination of flow at BEP and specific speed for each equipment class . The minimally compliant pump would be defined as a function of certain physical properties of the bare pump, such as flow at BEP and sp ecific speed (Ns), as used in the European Commission’ s (E C 's) minimum efficiency index ( MEI ) . In the MEI approach, a single polynomial equation defines a

three

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

flow and Ns. The EU uses the same equation for all equipment classes, changing only one value (known a s the C

- value) to raise or lower the surface along a vertical axis to eliminate a certain percentage of pumps, but without adjusting any variables that would chan ge the shape of the efficiency surface. In the framework document, 1 DOE suggested developing equations unique to

each equipment class and to change the surface shape from bottom

- to top

- of

- market to more accurately represent the market and efficiency level s. HI and Grundfos , on the other hand, preferred the E C MEI approach, which eliminates the least efficient pumps by type category. (HI, No. 25 at p. 2; Grundfos, No. 24 at p. 14) HI added that although a better data fit could be

3

- 6obtained by changing the su rface shape, such complexity is not warranted and w ould not yield a significant improvement in the ability of the equation to represent a cut

- off level for pump efficiency. (HI, No.25 at p p .4

- 5 , 32, 40 )

Grundfos recommended us ing the EU equation as well as the ir C

- values, which would result in exact harmonization. (Grundfos, No. 24 at p. 14) HI , however, recommended DOE use the EU equation but with a modified C

- value to reflect efficiency cut

- off values in the United

States. (HI, No.25 at p p .4

- 5 , 32, 40 )

After reviewing both comments from interested parties and discussions of the working group, DOE propos e s to base its equation on the EU equation

, changing the C

- values to reflect the U.S. market. Specifically, DOE proposes to use the equation EU used to develop its standard ( i.e. , to determine the shape of the efficiency surface), but translated to 60 Hz electrical input power and English units d as shown in Eq. 3. 3 , to determine the efficiency of a minimally compliant pump

.

ηη pppppppp

, SSSSSS = − 0

. 85 ∗ ln ( QQ ) 2

− 0

. 38 ∗ ln ( NNNN ) ∗ ln ( QQ )

− 11

. 48 ∗ ln ( NNNN ) 2 + 13

. 46 ∗

ln ( QQ ) + 179

. 80 ∗ ln ( NNNN )

− ( CC

− 555

. 6 )

Eq. 3

. 3

Where:

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

• Ns = specific speed at 60 Hz, and

• C = an intercept that is established for the surface based on the speed of rotation and equipment category of the pump model.

The C

- value is the translational component of the three

- dimensional polynomial equation. By adjusting the C

- value, DOE can increase or decrease the efficiency of a minimally compliant pump. For more information on efficiency surfaces and their use in developing pump

metric s , see appendix 3B.

The calculated efficiency of a minimally compliant pump reflect s pump efficiency at

BEP. Using the scaling values implemented i n the E C regulations for clean water pumps, the value is adjusted to determine the efficiency of the minimally compliant pump at 75 percent and 110 percent of BEP flow. T he efficiency at 75 percent of BEP flow is assumed to be 94.7 percent
of that at 100 percent

, and the efficiency at 110 percent of BEP flow is assumed to be 98.5 percent of that at 100 percent of BEP flow, as shown in Eq. 3.4

.

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

coefficients to reflect the fact that t he flow will be reported in m 3 /hr at 50 Hz and the specific speed will be reported in metric units. Specific speed is a dimensionless quantity, but has a different magnitude when calculated using metric versus English units.

3

- 7PPPPPP SSSSSS = ωω 75% � PP HHHHHHHHHH

, 75%

0

. 947 ∗ ηη pppppppp

, SSSSSS + LL 75% �

+ ωω 100% � PP HHHHHHHHHH

, 100%

ηη pppppppp

, SSSSSS + LL 100% �

+ ωω 110% � PP HHHHHHHHHH

, 110%

0

. 985 ∗ ηη pppppppp

, SSSSSS + LL 110% �

Eq. 3

. 4

Where:

• ω i = weighting at each rat ing point (equal weighting

– 0.3333 );

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

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

accordance with Eq. 3.4 ;

• L i = the motor losses at each load point i, as determined in accordance with the procedure specified in the DOE test procedure; and

• i = 75%, 100%, and 110% of BEP flow, as determined in accorda nce with the DOE test procedure.

Eq. 3

. 4 also demonstrates how a ratio of the minimally compliant pump efficiency and the hydraulic output power for the rated pump is used to determine the input power to a minimally compliant pump at each load point. Note that the pump hydraulic output power for the minimally compliant pump is the same as that for the particular pump being evaluated. The calculated shaft input power for the minimally compliant pump at each load point then would be combined with a minimally compliant motor for that default motor type and size and the default

part

- load loss curve, described in the proposed DOE test procedur e, to determine the input power to the motor at each load point. Under DOE's propos ed test procedure, the applicable minimum motor efficiency is determined as a function of pump type (open or enclosed), pole configuration, and horsepower rating as specifie d by DOE’s current energy conservation

standards for electric motors at 10 CFR 431.25. PER STD then is determined as the weighted

average input power to the motor at each load point, as shown in Eq. 3

. 4

.

DOE selected several C

- values to establish the efficiency levels to be analyzed in this rulemaking. Each C

- value and efficiency level represent s a PER STD that would remove a certain percentage of the lowest

- performing pu mp models from the market . The PER STD account s for pump efficiency at all load points as well as motor losses, and does so equivalently across the full scope of flow and specific speed encompassed by this rule making

. C hapter 5 provides a complete examination of the efficiency levels analyzed in this rulemaking.

3.5 MARKET A SSESSMENT

This section discusses trade associations, manufacturers and market share, estimated shipments, and regulatory and non

- regulatory programs related to pumps covered by this rulemaking.

3

- 83.5.1 Trade Associations

DOE recognizes the importance of trade groups in disseminating information and providing growth to the industry they support. To gain insight into the commercial and industrial

pumps industry, DOE researched various associat ions open to manufacturers, suppliers, and

users of industrial and commercial pumps . DOE also used the member lists of th o se groups to
construct a database of domestic manufacturers. DOE identified several trade groups that support

or have an interest in t he commercial and industrial pumps industry covered by this rulemaking

, including the Hydraulic Institute, the Electrical Apparatus Service Association, the American National Standards Institute, the Air

- Conditioning, Heating, and Refrigeration Institute, and the

National Electrical Manufacturers Association.

Hydraulic Institute www.pumps.org/ The Hydraulic Institute (HI) is t he largest association of pump manufacturers in North America

. HI provide s product standards for the pump industry and a forum for exchang ing industry information. The institute has established itself as the leading representative for the North American pump industry. HI provides members with many services an d programs

, including standards development, access to statistical data and economic reports, educational materials, industry news, and participation in industry initiatives.

Electrical Apparatus Service Association

www.easa.com/ The Electrical Apparatus Service Association (EASA) is an international trade organization of more than 1,900 electromechanical sales and service firms in 59 countries. EASA provides many engineering and educational programs and enabl es members to keep up to date with materials, equipment, and technolog ies

.

American National Standards Institute www.ansi.org/

The American National Standards Institute (ANSI) oversees the creation, circulation, and use of thousands of models and guidelines that directly affe ct businesses in nearly every sector of operation and manufacturing. ANSI is engaged in accrediting programs that assess standards and

co n formance.

Air

- Co nditioning, Heating, and Refrigeration Institute www.ahrinet.org /

The Air

- Conditioning, Heating, and Refrigeration Institute ( AHRI ) is one of the largest trade associations, re presenting more than 300 heating, water heating, ventilation, air conditioning

, and commercial refrigeration manufacturers within the HVACR industry. M ember companies account for more than 90 percent of the residential and commercial air conditioning, spac e heating, water heating, and commercial refrigeration equipment manufactured and sold in North

America.

National Electrical Manufacturers Association (NEMA)

www.nema.org

3

- 9NEMA members manufacture products used in the generation, transmission and distribution, control, and end use of electricity. NEMA has made numerous contributions to the electrical industry by contributing to public policy and operating as a central

, confidential agenc y for gathering, compiling, and analyzing market statistics and economics data.

3.5.2 Information about Pump Manufacturers

The following section s detail information DOE collected regarding manufacturers of

commercial and industrial pumps , including estimated ma rket shares (section 3.5.2.1 ) and potential small business impacts (section 3.5.2.2 ).

3.5.2.1 Market Shares

According to HI’s comments on DOE's request for information , approximately 450 pump manufacturers serve the U.S. market, with nine having sales of at least $1 billion, and 72 having

sales of more than $ 100 million. (HI, No. 6 a t p. 4) HI represents 97 pump manufacturers and suppliers. (HI, No. 6 at p. 1)

T en companies represent 60 percent to 70 percent of the total U.S. pumps market: Grundfos, Sulzer, Weir Group, KSB, Xylem, Flowserve, Ebara, Pentair, Roper Industries, and

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

scope of DOE's proposed rulemaking

. T he past 25 years have witnessed significant consolidation in the global pump market, and now th o se 10 companies comprise approxim ately 70 brands or divisions, shown in Table 3.5

. 1 , nearly all of which were formerly independent companies.

Table 3.5

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

Parent Company Major Brands/Operating Units

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

Sulzer ABS , Johnston, Sulzer

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

Weir

KSB GIW, KSB

Xylem A.C., Bell & Gossett, Flygt , Goulds Water Technology, Godwin, Jabsco, Laing, Lowara , Marlow, Red Jacket

Flowserve ACEC , Aldrich, Byron Jackson, Cameron, Durco, Flowserve, IDP, Lawrence, Pacific, Pleuger

, Scienco, Sier Bath, TKL , United, Western Land Roller, Wilson Snyder, Worthington

Ebara Ebara

Pentair Aermotor, Aurora, Berkeley, Edwards, Fairbanks Morse, Flotec, Hydromatic, Hypro, Jung Pumpen , Layne/Vertiline, Myers, Nocchi

, Shurflo, Simer, Sta

- Rite

Roper Industries Abel , Cornell, Neptune, Roper

ITT Goulds AC, Goulds

Note: Brands/ operating units in italics do not have manufacturing locations in the U nited States

.

3

- 10In some cases the pump brands themselves ha ve been consolidated, but in most cases the legacy brand names continue to be used. Table 3.5

. 2 shows major suppliers of various pump equipment categories, some but not all of which are brands of the top 10 companies.

3

- 11Table 3.5

. 2 Major Suppliers of Proposed Covered Pump Categories

Parent company Brand/Operating Unit ESCC ESF

M AS/R

S VT

- STop 10 Parent Companies Ebara Ebara X

Flowserve Byron Jackson X

Flowserve Flowserve X X

Flowserve IDP X X

Grundfos Grundfos X X X X

Grundfos Paco X X

Grundfos Peerless X X X

ITT Goulds AC

ITT Goulds Goulds X X X X

KSB KSB X X

Pentair Aurora X X

Pentair Fairbanks Morse

Pentair Layne/Vertiline

Sulzer Johnston X

Sulzer Sulzer X Weir Group Floway X Xylem Bell & Gossett X X X

Xylem Goulds Water Technology XNon-Top 10 Parent Companies American Turbine Pump American Turbine Pump

Crane Pumps & Systems Burks X

Crane Pumps & Systems Weinman

Cascade Pump Company Cascade Pump Company

Gusher Pump Gusher Pump X National Pump Company National Pump Company X

GE Water Technology Osmonics X Price Pump Company Price Pump Company X

Ardox Scot Pump X Simflo Pumps Simflo Pumps

Taco Taco X

3.5.2.2 Potential Impacts on Small Business es

Figure 3.5

. 1 and Table 3.5

. 3 display the distribution of U.S. pump and pump equipment manufacturing firms by numbe r of employees. The most common size for a pump manufacturing

firm is between 20 and 99 employees ( representing 114 firms

, or 26 percent of all pump manufacturing firms). The Small Business Administration defines small business manufacturing enterprises fo r commercial and industrial pumps as those having 500 or fewer employees

. 3 Firms

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

. 4 That 10 percent, however, controls more than half the total value of the pump market.

3

- 12Figure 3.5

. 1 Size Distribution (by Numbe r of Employees ) of Pump and Pump Equipment Manufacturing Firms in the Unite d States 4

Table 3.5

. 3 Pump and Pumping Equipment Manufacturing Firms by Company Size 4

Enterprise Employment Number of

Firms Number of

Establishments Employment

0

– 4 103 103 202

5

– 9 69 69 469

10

– 19 79 79 1,035

20

– 99 114 118 4,563

100

– 499 34 48 4,840

500+ 46 154 20,163

Total 445 571 31,272

DOE studied the potential impacts of energy conservation standards on small businesses in detail during the manufacturer impact analysis, which was among the analyses conducted for

the NOPR . For more details, consult chapter 12 of th is NOPR TSD. 20

- 99

26%

0

- 4

23%

10

- 19

18% 5

- 9

15% 500+

10% 100

- 499

8%

3

- 133.5.3 Shipments

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

under industry designation MA333P

. 5 Because s ome of th o se pumps may not be for clean water applications , it is difficult to determine what percentage of shipments fall under the scope of this rulemaking. In addition , pump manufacturers expressed doubt that the data were reliable.

Because of those issues , HI conducted a survey of its members and provided DOE with shipments estimates for 2012. 6 Th o se estimate s , shown in Table 3.5

. 4

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

Table 3.5

. 4 Estimates of Affected Pump Shipment s, 2012

Pump Category Industry

Estimate Units

ESCC 171,456

ESFM 44,042

IL 50,424

RS

- V 49,975

VT

- S 104,406

T otal 420,303

Source: Hydraulic Institute.

The Census Bureau also provides information on export ed and import ed shipments, but the data are not disaggregated to the same extent as are the manufacturer shipment data. DOE

notes that the historical C ensus data show the qu antity of imported centrifugal non

- submersible

pumps to rang e from 9 million to 16 million per year f or 2000

– 2010. A market consultant estimated that a large fraction of the imports would be ESCC and IL. Although many of the imports would be less than the 1

- HP limit, it seems reasonable that many would not be

. During meetings of the working group, DOE attempted to ascertain whether HI's shipments estimates properly account for imports. It was determined that imports likely we re fully accounted for in

the sc aling factor HI applied to estimate the market based on the shipments reported to them. As a result, DOE did not add any C ensus data on import ed shipments to HI's shipments data.

3.5.4 Market Performance Data

As a part of this rulemaking, DOE conducted a survey of existing commercial and industrial pumps from major manufacturers and compiled a performance database. The database contained the following fields for each model:

• DOE pump type

• ANSI/HI Pump type

• Number of stages

• Bowl diameter (if applicable)

3

- 14• Rated speed

• Net sales volume in USD

• Shipments volume in number of units sold

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

o Flow in GPM

o Head in feet

o Pump efficiency

o Shaft Input Power in HP

Pump efficiency is dependent on flow and specific speed, making it difficult to evaluate efficiency on an equivalent basis across the entire range of pumps in scope. Therefore, using Eq.

3.3 and Eq. 3.4

, along with each pump’s calculated PER rating based o n the proposed test procedure , DOE calculated “failing C

- values” for each pump. “Failing C

- value” is a calculated efficiency parameter that allows each pump to be evaluated on an equivalent scale. Lower failing

C

- values represent higher efficiency pumps. I f a pump has a failing C

- value less than the C

- value specified for an efficiency level, it would pass that efficiency level. If a pump has a failing C

value greater than or equal to the C

- value specified for an efficiency level, it would fail that

efficien cy level. Within an equipment class, pumps with the same failing C

- value will be of the same relative efficiency. For all pumps, one point of C

- value is always equal to one point of pump efficiency change. Chapter 5 provides additional information on the c oncept of failing C

values. Figure 3.5

. 2 through Figure 3.5

. 8 show the histogram distribution of failing C

- value for the same equipment classes.

3

- 15Figure 3.5

. 2 Distribution of Efficiency for ESCC.1800 E quipment Class

Figure 3.5

. 3 Distribution of Efficiency for ESCC.3600 Equipment Class

3

- 16Figure 3.5

. 4 Distribution of Efficiency for ESFM.1800 Equipment Cla ss

Figure 3.5

. 5 Distribution of Efficiency for ESFM.3600 Equipment Class

3

- 17Figure 3.5

. 6 Distribution of Efficiency for IL.1800 Equipment Class

Figure 3.5

. 7 Distribution of Efficiency for IL .36 00 Equipment Class

3

- 18Figure 3.5

. 8 Distribution of Efficiency for VT

- S.36 00 Equipment Class

3.5.5 Regulatory Programs

Currently

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

industrial pumps . Regulations on commercial and industrial pumps relevant to the scope of this rulemakings are in place outside the United States . Those regulations are sho wn in Table 3.5

. 5

.

Table 3.5

. 5 Worldwide Pump Regulatory Programs Country/Region Product Test Standard

European Union Clean water pumps BS EN ISO

5198:1999

Mexico Submersible deep well clean water pumps ISO 9906

Mexico Deep well water pumping systems ISO 9906

China Clean water pumps ISO 9906

3.5.5.1 European Commission

Under Directive 2009/125/EC, the European Commission (EC) sets E codesign requirements for energy

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

hav e a significant environmental impact, and present significant potential for improvement in environmental impact without entailing excessive costs. 7 On January 1, 2013

, a new regulation

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

3

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

energy

- consuming products. This regulation primarily addresses manufacturers that sell water pumps on the European market.

The E C regulation considers pump efficiency as the metric for water pumps used in commercial buildings, drinking water pumping, the food industry, and agricu lture. The EC stipulates requirements at three points along the pump pressure/flow curve when the pump is operating at its rated speed: full load ( i.e.

, at BEP), part load (75 percent of flow at BEP), and overload (at 110 percent of flow at BEP). The EC st andard set s a required efficiency for BEP ; the minimum pump efficienc y at part load and at overload represent the required BEP efficiency multiplied by factor s of 0.947 and 0.985, respectively. A failure at one or more points mean s the

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

, has the potential to increase pump efficiency throughout a wide range of operating conditions.

The E C regulation has a two

- tier requirement for achieving min imum energy efficiency

levels. The first

- tier requirements were to be met by January 1, 2013, while second

- tier requirements are to be met by January 1, 201 7 . The European C ommission will review the regulation no later than four years after its entry into force and present the result of the review to the Ecodesign Consultation Forum.

3.5.5.2 Mexico

Mexico's federal s tandard NOM

- 010

- ENER

- 2004 contains minimum energy performance standards for submersible deep well type clean

- water motor pumps operated by a submersibl e three

- phase electric motor. 9

, 10 The minimum energy efficiency requirements are the product of the minimum pump efficiency and the minimum motor efficiency. The test procedure for the

standard is equivalent partly to the International Standard Organization ’s (ISO 's ) Standard 9906, “Rotodynamic pump, h ydraulic performance acceptance tests

– Grades 1 and 2,” which is

harmonized with HI 14.6.

Standard NOM

- 006

- ENER

- 1995 contains minimum energy performance standards for deep

- well water pumping systems that consist of vertical centrifugal pumps having external or

submersible electric motors from 7.5 to 350 HP. 11

, 12 Th e standard applies to in

- place systems,

which must be retrofitted if they do not meet the standard.

3.5.5.3 China

China's national standard BG 19762

- 2007, “Minimum allowable values of energy efficiency and evaluating values of energy conservation of centrifugal pump for fresh water,” contains minimum energy performance standards for single

- stage

, single

- suction clear

- water centrifugal pumps ; single

- stage

, d ouble

- suction clear

- water centrifugal pumps ; and multiple

stage clear

- water centrifugal pumps. 13 The test procedure is based on ISO 9906.

3.5.6 Voluntary Programs

Described below are several voluntary programs that establish and recommend performance standards for commercial and industrial pumps.

3

- 203.5.6.1 ASHRAE Standard 90.1

The American Society of Heating, Refrigerating, and Air

- Conditioning Engineers (ASHRAE) releases ASHRAE Standard 90.1 every three years. Currently the latest version is

ASHRAE Standard 90.1

– 201 3

. Although the st andard does not contain requirements for pump efficiency or power , it does pr escribe requirements for “Hydronic Variable Flow Systems” ( section 6.5.4.2) and “Hydronic System Balancing” ( section 6.7.2.3.3). The former includes requirements fo r variable speed control in certain situations. The latter includes requirements for impeller trimming or adjust ing speed to meet design flow conditions. ASHRAE Standard 90.1

- 2013 also includes requirements for “Service Water Pressure Booster Systems

, ” whi ch require pressure sensors to vary pump speed and/or start and stop pumps.

3.5.6.2 DOE Advanced Manufacturing Office ( Technical Assistance

– Pump
Systems )

DOE’s Advanced Manufacturing Office (AMO) provides several resources to help

manufacturers achieve energy and cost savings in pump systems. 14 Th o se resources include software tools, training, and publications.

Software Tools

. AMO's pumping system assessment tool (PSAT) is a free online software tool to help industrial users assess the efficiency of pumping systems. PSAT uses achievable pump performance data from HI standards and motor performance data from the MotorMaster+ database to calculate potential energy and cost savings. The tool also enables users to save and retrieve log files, default values, and system curves for sharing analyses with other users. DOE

al so offers a variable speed drive (VSD) calculator to help determine potential energy savings and simple payback from installing a VSD on a pumping system. A pump s corecard helps identify energy

- saving opportunities for pumping systems.

Training . DOE’s AMO offer s general and component

- specific training. The training tools

include the PSAT

, outlining the basic operation and benefits of using the pump system tool to
target energy

- saving opportunities. The PSAT t raining covers practical issues involved in field

measurements of fluid and electrical data and pr ovide s the PSAT software used to assess the performance of pump systems. The training details how the s oftware functions, what data are required, how to use the software when measured data are not available, and how to interpret the

assessment results. DOE is working with the pumping industry and HI to train and qualify experts in us ing PSAT. W orkshops prep are professionals who have extensive experience in pumping systems to use PSAT in their system assessments.

Technical Information

. " Improving Pumping System Performance: A Sourcebook for Industry " ( May 2006 ) was developed by the DOE Industrial Technologies Program (the predecessor to AMO) and the HI. The sourcebook

, part of a series of publications on motor

driven equipment , was designed to provide pump system users with a reference that outlines opportuniti es to improve system performance. Th e source b ook is intended to make users aware of potential performance improvements, provide practical guidelines, and indicate where the user

can find additional help. AMO also offers case studies, tip sheets, and additi onal publications on

life

- cycle

- cost analysis and variable speed pumping.

3

- 213.5.6.3 Industry Certifications and Standards

T he American National Standards Institute (ANSI) and the Hydraulic Institute have

numerous standards that include definitions and design, applic ation, installation, operation, and maintenance guidelines. 15 There are also standards for how to predict pump efficiency and for hydraulic performance acceptance tests.

3.6 TECHNOLOGY ASSESSMEN T

The purpose of the technology assessment is to develop a prelimi nary list of technologies that could improve the efficiency of commercial and industrial pumps. The following assessment provides descriptions of technologies and designs that unless otherwise noted apply to all commercial and industrial pump equipment cla sses.

3.6.1 Technology Options

For the NOPR analysis, DOE further investigated the technologies presented in the Framework Document, identifying specific sub

- categories for some technology options. The following list summarizes the technology options identified for improving the efficiency of commercial and industrial pumps. Each technology option is discussed in further detail in the subsequent sections.

• Improved surface finish on wetted components

o Improved casting techniques

o Surface polishing and/or honing

o Sur face coating or plating

• Reduced running clearances

• Reduced mechanical friction in seals

• Reduction of other volumetric losses

• Improved hydraulic design

• Addition of a variable speed drive (VSD)

• Improvement of VSD efficiency

• Reduced VSD standby and off mode p ower usage.

3.6.1.1 I mproved Surface Finish o n Wetted Components

Surface finish (sometimes known as surface roughness) is the technical term used to describe the smoothness or roughness of a manufactured surface. Surface finish is generally characterized by measuring the vertical deviations of the roughness profile , wit h respect to the

surface’s mean line . In the pump industry, surface finish typically is represented by the

parameter R

a , which is measured in units of micro inches . Within the scope of this rulemaking,

3

- 22pumps of metallic construction generally will contain impellers and volutes/casings that range

from 700 R

a (rough) to 150 R

a (smoother).

A smoother surface finish (lower R

a ) improves efficiency. The magnitude of the improvement depends greatly on the specific speed of a given pump. Specific speed (N

s ) is a

di mensionless parameter that describ es the geometry of a pump impeller. Specific speed provides an indication of a pump’s pressure/flow ratio at BEP. Pumps with lower specific speeds (narrower internal passages) will benefit more from improved surface finish than those pumps with higher specific speeds (wider internal passages). Figure 3.6

. 1 illustrates this relationship. Pumps with specific speeds of 1,000 achieve improv ements of approximately two efficiency points, while pumps with specific speeds greater than approximately 3,000 (a common design within the NOPR scope) achieve improvements of less than 0.4 efficiency points. Improvements continue to decrease at higher sp ecific speeds.

Figure 3.6

. 1 Estimated Points of Efficiency Increase D ue to Improving S urface F inish of

B oth I mpeller and V olute from 500 R
a to 200 R
a 16

The following subsections discuss the three approaches that are available for achieving improved surface finish on wetted components.

Improved Casting T echniques

. Nearly all pumps within the scope of this rulemaking are assembled from impellers and volutes constructed of one of following: (1) fabricated and w elded sheet metal, and (2) cast

- metal components. The former make up less of the market, and their

sheet

- metal construction provides an inherently smooth surface finish (<150 R

a ). The surface finishes of pumps assembled from cast

- metal components, which co nstitute a larger share of the market, may range from >700 R

a to <150 R

a , depending on the casting technique employed. Each construction type has its own strengths and weaknesses. Equipment class, performance

3

- 23needs, and durability requirements may influenc e a manufacturer’s decision to choose one construction type over the other.

Within the scope of this rulemaking, cast

- metal components generally are produced using one of three methods: (1) sand casting, (2) investment casting, or (3) vacuum die casting. S and casting has the lowest tooling and start

- up costs e of the three casting techniques and produces the roughest surface finish. In the pump industry, sand cast surface finishes of 500 R

a are common. Sand casting can be used to cast all common pump compone nt materials, including cast

- iron, stainless steel, and bronze.

Investment casting and die casting are advanced impeller casting techniques that yield a smoother surface finish, as well as improved material quality and reduced defects. Volutes

cannot be ca st using these techniques, as they will typically exceed the maximum weight limit for both investment and die casting processes. Manufacturer responses to confidential surveys

and interviews f indicate that upfront tooling costs for investment and die casting are approximately 2.5 times more than sand casting. Manufacturers also indicated that on average, externally purchased g , semi

- finished h investment and die cast impellers cost the same as s and cast impellers when scrap rates and machine time for additional cleanup are factored in. Investment and die casting typically yield surface finishes of 200 R

a or less. Investment casting can be used for stainless steel and bronze, whereas die casting c an be only used for bronze. Iron cannot be cast using either of the advanced techniques.

The technology option referred to as i mproved casting technique represents the conversion of specific components from sand casting to an advanced casting technique (i nvestment casting or die casting), in order to achieve an improved surface finish.

Surface P olishing and/or H oning

. Polishing, honing, or similar metal removal/treatment techniques may be used to improve the surface finish of sand cast components. The tec hniques generally are manual operations, performed by a technician. Automated polishing and honing technologies exist, but these techniques are rarely seen in the pump manufacturing industry. Polishing and honing cannot effectively improve the surface fini sh of advanced

- cast parts beyond their baseline level; as such, it is not an applicable design option for advanced

- cast
components.

Surface Coating or P lating

. Sand cast parts may be coated or plated with an additional

material that forms a surface that is smoother than the initial sand cast substrate. Hard epoxies

e In the pump industry manufac turers will typically own the casting patterns (tooling) that is required to begin production of a new cast component. Outside casting vendors will use these patterns to create the cast parts for the

manufacturer.

f As a part of this rulemaking, DOE’s con tractor(s) collect ed information from manufacturers through confidential surveys, interviews, and other instruments.

g All manufacturers interviewed indicated that cast impellers and volutes for ESCC, ESFM, and IL pumps were purchased from external or sub sidiary casting vendors.

h A semi

- finished impeller is one that has been cleaned of casting defects/residue and has most final machined surfaces completed. A semi

- finished impeller typically needs only to be trimmed to a final diameter and balanced

before it can be installed on the pump.

3

- 24are the most common coating currently used in the pump industry, although various alternatives exist. Currently most coatings are only used for applications that require sanitary or wear

resistan t properties. Efficiency improvements arise as a secondary benefit.

3.6.1.2 Reduced Running Clearances

A running clearance is the space between adjacent static and rotational components. For a pump to run properly

, a clearance must be maintained during operation

. In some cases, sacrificial components such as wear rings and sleeves can be used to allow for limited contact under adverse conditions. Without a sacrificial component in place, contact between static and rotational components can cause serious and possibl y catastrophic damage to a pump.

When setting assembly clearances, pump designers must take into account manufacturing tolerances, assembly variations, and operational characteristics in order to ensure that contact is avoided at all times. Within the pump industry, components are designed with clearances that are as tight as possible, while ensuring that no contact will occur under worst

- case assembly stack

up and operational loads.

In general, larger clearance will allow water to leak out of the intended flow path and

flow back to the inlet within the pump casing. The “ backflow ” water must then be re

- pumped out to the intended path. This recirculation action reduces the overall efficiency of the pump. Reducing running clearances reduces leakage and backf low in the pump, thus increasing efficiency.

Manufacturer survey responses and industry research indicate pump manufacturers have already taken measures to tighten running clearances as much as possible, given the limitations of current wear ring materials , machining tolerances, and pump assembly practices. As such, all pumps are believed to be at same the baseline level for running clearances. To further tighten these clearances might require alternate materials for wear rings and other running surfaces,

t ighter machining tolerances, and/or modifications to assembly procedures.

The magnitude of efficiency gains resulting from reduced clearances is strongly

d ependent on the specific speed of a given pump. Pu mps with lower specific speeds will benefit

more from reduced running clearances than tho se pumps with higher specific speeds

. Currently there is no data available to quantify the potential efficiency improvements from tightening clearances beyond the current baseline level. Alternativ ely, HI 20.3

- 2 0 10 provides data on estimated efficiency decrease due to increased wear ring clearance. Figure 3.6

. 2 illustrates this relationship for pumps that have had wear ring clearances increased by 1.5 times the standard

clearance i

.

Although Figure 3.6

. 2 does not provide direct guidance on available improvements from tight ening clearances, it does provide insight into the relationship between specific speed and efficiency gains and the order of magnitude of potential improvements.

i “Standard clearance” is not defined in HI 20.3

- 2010. Standard clearance is inferred to represent the current industry baseline. Figure is used for illustrative purposes only.

3

- 25Figure 3.6

. 2 Estimated E fficiency Decrease Due to a 150% I ncrease in Wear Ring

C learance

3.6.1.3 Reduced Mechanical Friction in Seals

Many pumps are powered by external motors. The shaft that mechanically connects the external motor to the impeller penetrates the pump casing. A shaft seal is used to form a barrier between the liquid inside the pump and the exterior environment. Although many variations exist, in its most basic form

, a shaft seal consists of a rotating face mounted to the shaft and pressed by a spring against a parallel stationary face mount ed to the casing. Both faces are perpendicular to the shaft axis. While the pump is operati ng , pressure in the casing increases to a

level that forms a hydrodynamic film between the two sealing faces. When designed properly this hydrodynamic film lubricate s the seal faces and prevents leakage to the outside environment. This basic design relies on mechanical frictional for a seal to work properly. Frictional losses will be seen during start up as the seal faces rub directly, and during steady state operatio n as the hydrodynamic film is sheared within the seal gap. Because these frictional losses directly affect the power required to drive the pump, a reduction in friction would directly improve pump efficiency.

The practical method to reduce friction is to replace the current seal with a seal composed of lower friction materials, and if possible reduce the spring force on the face of the seal. Currently, carbon

- ceramic seal faces are the most common type used in the industry.

Manufacturers indicate that silicon carbide

- carbon seals represent one of the lowest friction options currently available. All manufacturers interviewed were in consensus that such a change would provide very limited efficiency improvements. Some agreed that gains of up to one efficiency point may be possible for the smallest pumps within the scope, even though most felt

3

- 26that one point of gain was highly optimistic. All agreed that available efficiency improvements quickly decline as pump size increases.

For a constant speed pump, p ower losses in seals have a linear relationship with operating

pressure . To illustrate this relationship, estimates of available efficiency improvements for all

ESCC.1800 pumps in the performance database are present ed in Figure 3.6

. 3 . This plot is based on the premise that a typical one horsepower pump j can gain one efficiency point (reduce losses by 0.01 hp). The improvements f or other pumps are calculated by linearly scaling horsepower losses with BEP operating pressure. The illustration indicates that while a one horsepower pump may be able to achieve one point of efficiency, the available improvement quickly drops off as

pump size increase. On average, pumps larger than 10 horsepower will achieve improvements of less than 0.2 efficiency point, with this decreasing to 0.1 efficiency point for pumps larger than 50 horsepower. Note that Figure 3.6

. 3 is for illustrative purposes only, and shows the general scale and trend of improvements that can be expected from typical pumps.

Figure 3.6

. 3 Estimated Available Efficiency Improvements from Reduced Seal Friction, as a Function of Shaft Horsepower, for the ESCC.1800 Equipment Class

j Typical one horsepower pump for this analys is is assumed to be 60 GPM, 40 feet of head, and 60% efficiency at

BEP.

3

- 273.6.1.4 Reduction of Other Volumetric Losses

Volumetric los s es are a catch

- all category to describe water that, after rea ching the impeller, may backflow within the pump casing or leak out of the pump casing altogether. B ack

flow ed water then must be re

- pumped out to the intended path. T his recirculation action reduces the overall efficiency of the pump. Similarly, when wate r is pumped but leaks out of the casing, the useful flow output and net hydraulic work done by the pump are reduced, which in turn reduces the efficiency of the pump.

Besides running clearances (discussed previously as a specific design option), one of th e most common causes of volumetric losses is thrust balance holes. Thrust balance holes are located in the face of an impeller and act to balance the axial loads on the impeller shaft. Pressurized fluid flows through the holes, from the back to the front s ide of the impeller, to achieve a more balanced axial load distribution. The balanced load helps reduce wear on rub surfaces and bearings. The flow through the balance holes is considered backflow, and the

recirculation of this flow reduces the overall eff iciency of a pump

.

To reduce volumetric losses from thrust balance holes, an impeller can be redesigned with fewer and/or smaller balance holes. Such changes would typically require the bearing to be redesigned to handle increased axial thrust loads from t he new impeller configuration. No data is available to quantify potential efficiency improvements from removal or reduction of balance holes. When surveyed, responses of participating manufacturers indicated that such changes will result in improvements we ll below one efficiency point. The majority of manufacturers indicated that they would consider the resulting improvements to be insignificant with respect to their current efficiency ratings.

3.6.1.5 Improved Hydraulic Design (Hydraulic Redesign)

The performance characteristics of a pump, such as flow, head, and efficiency, are a direct result of the hydraulic design. For the purposes of the DOE analysis, hydraulic design is a broad term used to describe the system design of the wetted components of a bare pump. Although hydraulic design focuses on the specific hydraulic characteristics of the impeller and the volute/casing, it also includes design choices related to clearances, seals, and other volumetric losses. Note that the technology options presented previou sly, such as clearances, seals, and volumetric losses, were evaluated as stand

- alone, incremental options, but in this case they are considered part of the improved hydraulic design technology option.

Impeller and volute /casing geometries, clearances, and associated components can be redesigned to a higher efficiency (at the same flow and head) using a combination of historical best practices and modern computer

- aided design (CAD) and analysis methods. The wide availability of modern CAD packages and techni ques now enables pump designers to more quickly reach designs with improved vane shapes, flow paths, and cutwater designs; all of which work to improve the efficiency of the pump as a whole.

DOE considers hydraulic design to be a mature technology and con cluded that the highest possible efficiency hydraulic designs are already present in the marketplace. Although these high efficiency designs already exist, not all pumps in the market are at this level. As such,

3

- 28DOE assessed the range of efficiencies seen in the current pump marketplace k to determine the efficiency improvements available for each equipment class l

. Table 3.6

. 1 presents potential efficiency gains resulti ng from improved hydraulic redesign, for each equipment class. These potential improvements represent the efficiency spread between the lowest efficiency and highest efficiency pumps already offered in the marketplace m . The listed efficiency spreads are

ap plicable to all pumps in each equipment class, regardless of flow and specific speed. A manufacturer can realize these efficiency improvements in any increment, depending on the level of effort and capital they choose to invest in their redesign program. C hapter 5 of the TSD discusses the relationship between efficiency gains and conversion cost in more detail

.

Table 3.6

. 1 Range of P otential Efficiency Gains Resulting from Improved H ydraulic

R edesign

Equipment Class Potential Efficiency Gains from Hydraulic Redesign *
(Points of Efficiency)

ESCC.1800 10.72

ESCC.3600 10.65

ESFM.1800 11.28

ESFM.3600 10.52

IL.1800 11.47

IL.3600 13.66

RS

- V.1800 N/A **

RS

- V.3600 N/A **

VT

- S.1800 10.33

VT

- S.3600 10.33

* Represents spread between the lowest efficiency and highest efficiency pumps already offered in the marketplace. Chapter 5 provides further details on the spread between lowest and highest efficiency pumps.

**Performance data were not available for the RS

- V equipment class

.

3.6.1.6 Addition of a Variable Speed Drive

A variable speed drive (VSD) is a device that allows a compatible n AC induction motor to operate at varying rotational speeds. The synchronous (no

- load) rotational speed of an AC induction motor typi cally is fixed and controlled by two factors: (1) the number of magnetic

k Assessment performed using DOE’s pump performance database. Complete details on the performance database are found in Chapter 5 of the TSD.

l This assessment assumes that hydraulic d esign is the only available technology option to differentiate pumps currently on the market. This assumption is confirmed in the screening analysis, found in Chapter 4 of the TSD.

m Chapter 5 of the TSD provides additional detail on how bottom and top of the market efficiencies were

established.

n Not all AC induction motors are safe for use with VSDs. A compatible motor is one that is approved by the motor

manufacturer for use with VSDs.

3

- 29poles in the motor housing, and (2) the frequency of the AC power driving the motor. The equation that governs the synchronous speed of an AC induction motor is:

nn ss = 111111 × ff

pp Eq. 3

. 5

Where :

• n s is the synchronous speed in RPM,

• f is the motor ’s supply frequency in hertz

, and

• p is the number of magnetic poles

. In the United States, the standard electricity supply frequency is 60 Hz. Thus the nominal synchronous speed of a four

- pole motor is 1,800 RPM, and that of a two

- pole motor is 3,600

RPM.

The most common form of VSD is a variable frequency drive (VFD). A VFD is an electronic device that can convert the frequency of the incoming AC pow er supply to a range of frequencies, as specified by the user or control logic. Given the relationship in Eq. 3

. 5 , the synchronous speed of the motor will vary linearl y with these changes in supply frequency, allowing a VFD to directly control the speed of a motor. VFDs used for pump applications generally are only designed to reduce the input frequency to the motor, because often neither pump nor motor is designed to r un beyond the nominal 60 Hz speed.

Given the proper application, varying the speed of a pump by use of a VSD has the potential to reduce pump energy consumption significantly. The energy

- saving potential from varying speed results from the relationship bet ween pump speed and pump power consumption. For pumps, power varies with the cube of the speed. This relationship is one of the three pump affinity laws, as they relate to changes in rotational speed:

PP

2
PP

1 = � RR 2 RR 1 � 3

Eq. 3

. 6

HH 2
HH 1 = � RR 2 RR 1 � 2

Eq. 3

. 7
QQ 2 QQ 1 = � RR 2 RR 1 � 1

Eq. 3

. 8

Where :

• RPM 1 , P 1 , H 1 , and Q 1 are the rotational speed, power input, head, and flow at one operating speed, and

3

- 30• RPM 2 , P 2 , H 2 , and Q 2 are the rotational speed, power input, head, and flow at the adjusted operating speed.

Figure 3.6

. 4 illustrates the pump affinity laws for a typical 150 hp o pump, operating on a 100% dynamic head system curve p . For this example, when the pump’s speed is halved, the power is reduced from 133.7 hp to 16.7 hp; a factor of 1/8. At half speed, the pump outputs 50% of its original flow. For the example pump to achieve 50% of the original flow without speed control, a throttling valve q would typically be used. Using this configuration, the example pump would require 96.1 hp . The speed controlled method reduces the required power by 83%, as compared to the uncontrolled method. Given this example and those presented in Figure 3.6

. 4 , it is clear that a VSD has the potential to significantly reduce energy consumption when used in applications that allow the VSD to significantly reduce speed to meet flow and head requireme nts. Actual power and energy consumption reduction results will depend on specific application load profiles and pump characteristics.

o 150 hp represents the nameplate motor horsepower for the pump. It is common to describe pumps by their motor horsepower rating.

p Dynamic head is the component of total dynamic head that results from friction losses. A 100% dynamic head system does not ph ysically lift the fluid. Because all loses are a result of friction, the system requires zero head at zero flow. In a 100% dynamic head system, the head increases with the square of flow.

q A throttling valve adds friction to the system and can be adjust ed to allow a pump to move along its flow

- head

curve until the desired flow is reached.

3

- 31Figure 3.6

. 4 Illustration of Pump Affinity Laws for an Example 150 hp P ump with a 100% Dynamic Head System Curve

VSD are most likely to provide energy saving benefits in one of two general types of pump applications : (1) systems having varying loads (flows and heads), and (2) systems that are significantly oversized to allow for future growth, system degradation, and/or engineering safety margins. For the first example , the VSD, in conjunction with a feedback control loop, will vary the driver speed to meet the demands of the system on a continuous basis. In this scenario, po wer is reduced by running the pump at lower speeds to meet reduced flow requirements

.

For the second application, specifications may call for an oversized pump and motor, but

a VSD can be used to dial back the motor speed to meet current system needs. Wit hout speed control, the operator would typically run the pump at full speed and throttle the system until the required flow condition is met. When a throttling valve is used, the unneeded pump pressure is

lost to friction r . If the system degrades or requir es increased head or flow in the future, the

variable speed system can increase the motor speed to meet the new performance requirements

.

r Alternatively, the operator may also use a bypass valve to route a part of the discharge flow back to the suction side of the pump . This method is less energy efficient than throttling.

3

- 32Figure 3.6

. 5 uses the same 150 hp example pump to illustrate the potential power reduction available for oversized pumps.

Figure 3.6

. 5 Illustration of Po wer Reductions Available for an Example Oversized Pump

Although VSDs can provide significant energy savings in certain applications, they are not universally applicable. For common applications, such as systems that have unvarying flow and head requiremen ts (constant load), on/off operation, or high percentages of static head s VFDs may not save energy and may even increase energy consumption when the efficiency of VFD unit is factored in. If a pump is properly sized for constant load and on/off operation

a pplications, a VFD would save little to no energy, as the system would not spend any time operating at lower speed.

Systems with high static head requirements may still have their flow controlled using logic and speed control, but available power reductio ns are less, when compared to systems with

s Static head is the component of total dynamic head that results from the fluid being lifted a certain height above the pump. Unlike dynamic head, static head requirements stay constant across the system curve, even at zero flow

.

3

- 33high dynamic heads. Figure 3.6

. 6 illustrates the power reductions available for the example 150 hp pump when a variable spee d drive is used on a system with 90% static head t . At 50% flow, this variable speed system only reduces the required power by 23%. At 75% flow the power is reduced by 15%. Savings continue to decrease to zero as the pump approaches 100% flow. For compariso n, in the high dynamic head system discussed earlier, power is reduced by 83% and 52%, at 50% and 75% flow, respectively. With lower improvements available for high dynamic head systems, many systems will not be able to benefit from variable speed after VF D losses are factored in. Actual results will depend on the specific application load profile.

When considering the addition of a VFD, it is important to note that electrical losses

within the VFD must be factored into any potential energy saving calculati ons. As such, the use

of VFDs in less suitable applications may actually increase the energy consumption of the

system.

t 90% of the design point total dynamic head is static head. In this case, 135 feet of static head is required. The remaining dynamic head varies with the square of the flow.

3

- 34Figure 3.6

. 6 Illustration of Power Reductions Available for an Example Pump Used in a High Static Head Application

3.6.1.7 Improvement of VSD E fficiency

When evaluating the potential energy savings from use of a VFD, the efficiency of the VFD itself also must be considered. Efficiency varies from unit to unit and by VFD size

. However, standardized testing of VFDs is still new to the industry and as a result, reliable VFD efficiency values are not yet available . In late 2011 AHRI released “ AHRI 1210: Standard for Performance Rating of Variable Frequency Drives 17 ,” which included standardized procedure s for testing and measuring VFD efficiency at various load fractions. In late 2013 AHRI launched a voluntary certification program and began collecting data related to the standard. The certification program is still incomplete and related performance data are not yet publically available. As such, sufficient data is not yet available to estimate full and part load losses, or characterize the range of VFD efficiency offered in the market.

3

- 353.6.1.8 Reduced VSD Standby and Off Mode Power Usage

Standby and off

- mode p ower usage of VSD also differs from unit to unit, with some units consuming more energy than others. At this time no data are available regarding standby and off

mode power usage. In addition, data availability is unlikely to increase soon, because AHRI di d not include standby and off

- mode testing as a part of AHRI 1210.

3

- 36REFERENCES

1 U.S. Department of Energy, "Energy Conservation Standards Rulemaking Framework Document for Commercial and Industrial Pumps," 2013.

2 Volk, M. 2012. Personal communication.

3 U. S. Small Business Administration, "Table of Small Business Size Standards Matched to North American Industry Classification System Codes," Boston, MA, 2012.

4 U.S. Census Bureau, "U.S., 6- digit NAICS, detailed employment sizes," 2010. (Last accessed September 16, 2014.) < www.census.gov//econ/susb/data/susb2010.html >.

5 U.S. Census Bureau. 2011. Pumps (except hydraulic) and Co mpressors –2010. MA333P(08)-1.

6 Hydraulic Institute (March 21, 2014), DOE/HI Manufacturers Survey for Fiscal Year 2012

, Parsippany, NJ. Docket EERE-2013- BT

- NOC -0039, No. 0068. (Last accessed September 16, 2014.) < www.regulations.gov/#!documentDetail;D=EERE-2013- BT

- NOC -0039-0068 >.

7 Council of the European Union. Directive 2009/125/EC of the European Parliament and of the Council of 21 October 2009 establishing a framework for the setting of ecodesign requirements for energy

- related products (recast). Official Journal of the European Union . L 285, 31 October 2009.

8 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 2012 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to ecodesign requirements for water pumps. Official Journal of the European

Union . L 165, 26 June 2012, pp. 28–36.

9 Norma Oficial Mexicana NOM -010- ENER -2004, eficiencia energetic del conjunto motor bomba submergible tipo pozo profundo, limites y metodo de prueba. Diario Oficial. (Last accessed September 16, 2014.)

< www.conuee.gob.mx/work/sites/CONAE/resources/LocalContent/6933/19/NOM010ENER 2004.pdf >.

10 Additional information identified through CLASP ( National Commission for the Efficient Use of Energy). (Last accessed September 16, 2014.)

< www.clasponline.org/en/Tools/Tools/SL_Search/SL_SearchResults/SL%20Detail%20Page. aspx?m=a037b94d-3219-478c-bb6d-b643d19a4849 >.

11 Norma Oficial Mexicana NOM -006- ENER

- 1995, Eficiencia energetica electromecanica en sistemas de bombeo para pozo profundo en operacion, limtes y metodo de prueba. Diario Oficial. 9 November 1995. (Last accessed September 16, 2014.)

< www.conuee.gob.mx/work/sites/CONAE/resources/LocalContent/6933/19/NOM006ENER 1995.pdf >.

3- 3712 Additional information identified through CLASP ( National Commission for the Efficient Use of Energy ). (Last accessed September 16, 2014.)

< www.clasponline.org/en/Tools/Tools/SL_Search/SL_SearchResults/SL%20Detail%20Page. aspx?m=d0930d37

- 239b

- 4a2a

- 8900

- 88fb995 26c68 >.

13 GB 19762

- 2007. The minimum allowable values of energy efficiency and evaluating values of energy conserv ation of centrifugal pump for fresh water.

Information regarding the standard identified through CLASP ( National Commission for the Efficient Use of Energy ). (Last accessed September 16, 2014.)

< www.clasponline.org/en/Tools/Tools/SL_Search/SL_Sea rchResults/SL%20Detail%20Page. aspx?m=db8c2244

- f128

- 41ad

- 9326

- 70680bd3ae40 >.

14 U.S. Department of Energy. Advanced Manufacturing Office. “Pump Systems.” (Last accessed September 16, 2014.) < http://www.energy.gov/eere/amo/pump

- systems >.

15 Hydraulic Institute e

- Stor e

. (Last accessed September 16, 2014.) < http://estore.pumps.org/ >

.

16 HI 20.3, 2010, “ Hydraulic Institute Guideline for Rotodynamic (Centrifugal and Vertical) Pump Efficiency Prediction ,” Hydraulic Institute, Parsippany, NJ, 2010.

17 AHRI Standard 1210, 2011, “Standard for Performance Rating of Variable Frequency Drives, ” AHRI, Arlington, VA, 2011.

3

- 38APPENDIX 3A. PUMP DESIGN SPEED

TABLE OF CONTENTS

3A.1 INTRODUCTION

................................ ................................ ................................ ........ 3A

- 1

3A.2 OVERVIEW

................................ ................................ ................................ ................. 3A

- 1

3A.2.1 Size Effect

................................ ................................ ................................ ......... 3A

- 2

3A.2.2 Effect of Setting Standards by Speed on Single Pumps Running at Multiple Speeds

................................ ................................ ................................ 3A

- 4

3A.3 OPTIONS FOR ADDRESSI NG DESIGN SPEED

................................ ...................... 3A

- 6

3A.4 CONCLUSION

................................ ................................ ................................ ............. 3A

- 7

REFERENCES

................................ ................................ ................................ ......................... 3A

- 8

LIST OF TABLES

Table 3A.2.1 Minimum Efficiencies for Identical Pumps Running at Two Different Speeds

................................ ................................ ................................ ......... 3A

- 6

LIST OF FIGURES

Figure 3A.2.1 Examples of the Size Effect (Pump A versus B) and the Speed Effect (Same Pump, Configuration A versu s A2)

................................ ................. 3A

- 2 Figure 3A.2.2 Pump Efficiency as a Function of Specific Speed

................................ ...... 3A

- 3 Figure 3A.2.3 Predicted Efficiency Differences Between ESCC Pumps Running with

2

- Pole versus 4

- Pole Motors at Selected Specific Speeds (Using Eq. Set 1)

................................ ................................ ................................ ........... 3A

- 5 Figure 3A.2.4 Predicted Efficiency Differences Between ESCC Pumps Running with

2

- Pole versus 4

- Pole Motors at Selected Speci fic Speeds (Using Eq. Set 2)

................................ ................................ ................................ ........... 3A

- 5

3A

- iAPPENDIX 3A. PUMP DE SIGN SPEED

3A.1 INTRODUCTION

Most of the content of this appendix originally was published in the framework document for the U.S. Department of Energy's (DOE's) rulemaking for commercial and industrial pumps. (78 FR 7304, February 1, 2013). Since that time DOE has revised its database of pump performance to be more specific to the scope of pumps identified for this rulemaking. DOE has not updated the analysis described in chapter 3, however, because DOE believes that the analysis of the original database addresses the relevant issues r egarding design speed for commercial and industrial pumps.

3A.2 OVERVIEW

Because operating speed for a pump affects noise, maintenance costs, net positive suction head required (NPSHr), and controllability requirements, as well as attainable efficiency, DOE is evaluating whether to use operating speed as a feature that differentiates equipment classes. The European Commission (EC) regulation for commercial and industrial pumps contains separate efficiency standards for pumps that operate at 1,450 rpm and pumps that operate at 2,900 rpm (i.e., 4

- pole and 2

- pole motors, equating to 1,750 rpm and 3,500 rpm in the United States). i DOE’s review of the European approach supports discussion of (1) the size effect captured by the EC standards that distinguish design spe ed, (2) the implications for single pumps running at multiple speeds, and (3) DOE’s options for addressing the issue of design speed. To help clarify this discussion, Figure 3A.2.1 illustrates the difference between factors (1) and (2). In the figure, pumps A and B demonstrate the size effect (factor 1), and pump A and A2 demonstrate a single pump running at multiple speeds (factor 2). a

a Note that none of the pumps shown represents an option an end user would select. When end users choose among pumps having different speeds, they choose among pumps that produce the same flow and head but at different specific speeds. This appendix, which focuses on pump manufacturing rather than pump selection, does not concern

customer choices.

3A

- 1Figure 3A.2

. 1 Examples of the Size Effect (Pump A versus B) and the Speed Effect (Same Pump, Configuration A versus A2)

3A.2.1 Size Effect

Using the example of end

- suction close

- coupled (ESCC) pumps, the EC minimum efficiency surface (described in chapter 3, section 3.4.1) for pumps having 4

- pole motors is two to three points higher than the efficiency surface for pumps having 2

- pole motors. b In other words, for a given flow and specific speed, pumps that operat e with 4

- pole mo tors have better pump efficienc ies (and higher E C efficiency standards) than do pumps that operat e with 2

- pole

motors. T his difference is not related specifically to speed, but rather to pump size. Comparing

pumps that have the same specific speed and flow , but different speeds, means comparing two geometrically similar pumps

— a smaller pump running at a higher speed and a larger pump running at a lower speed (such as p umps A and B in Figure 3A.2

. 1 ). The E C ’s standard indicates

that the latter is more efficient.

DOE notes that other resources show efficiency as a function of flow and specific speed without regard to speed or physical size. ii

, iii

, iv Figure 3A.2

. 2 shows an example. Th o se resources may be relying on Anderson (1994)

, iv who analyze d true Reynolds number and direct flow

b Compare the values for C for 1,450 rpm and 2,900 rpm pumps in appendix 3B, Table 3B.2.1, for example. Note that the higher the C

- value, the lower the minimum eff iciency (because C is subtracted in the equation).

3A

- 2quantity , concluding that the latter is a sufficiently accurate arbiter for the efficiency of a group of pumps. Anderson went on to state that “the efficiency of a small pump at high speed can be the same as a large p ump at low speed, providing the shape number [specific speed] and flow are kept the same. That is the reason why [figures] can, in general, show efficiency against quantity [flow] and shape number [specific speed] without reference to physical size or oper ating speed .” iv A Europump Guide , however, notes that, when using flow instead of Reynolds number along with specific speed to predict efficiency, the efficiency at speeds other than 2,900 rpm may be different than predicted. v Because predicted efficiency differs with design speed, flow is not

in itself a sufficient determin ant of pump efficiency.

Figure 3A.2

. 2 Pump Efficiency as a Function of Specific Speed

The EC’s method of assigning minimum efficiency relies on changing the constant in the equation to raise or lower the efficiency surface based on pump speed. (See Eq. Set 1.) DOE’s preliminary analysis based on the EC method produced a result similar to the EC result, with 4

- pole ESCC pumps being on average 2.2 points more efficient than 2

- pole ESCC pumps for a given flow and specific speed. c DOE notes , however, that when fitti ng the surfaces by allowing other parameters besides the constant to change ( i.e. , changing the shape instead of just the vertical position as in Eq

. Set 2), in some areas of flow and specific speed, a 2

- pole design is

more efficient than a 4

- pole

. I n othe r words, the data show that the relationship is not necessarily

fixed.

c See a ppendix D of the framework document for a discussion of how this difference changes depending on the pumps considered within the scope of clean

- water pumps.

3A

- 3Eq

. Set 1 : Vertical Change in Surface

ηη BBBBBB 2

− pppppppp = aa ( llllll ) 2 + bb ( llllll

· llllllll ) + cc ( llllllll ) 2 + dd llllll + ee llllllll + ff 2

ηη BBBBBB 4

− pppppppp = aa ( llllll ) 2 + bb ( llllll

· llllllll ) + cc ( llllllll ) 2 + dd llllll + ee llllllll + ff 4

Eq

. Set 2 : Change in Surface Shape

ηη BBBBBB 2

− pppppppp = aa 2 ( llllll ) 2 + bb 2 ( llllll

· llllllll ) + cc 2 ( llllllll ) 2 + dd 2 llllll + ee 2 llllllll + ff

ηη BBBBBB 4

− pppppppp = aa 4 ( llllll ) 2 + bb 4 ( llllll

· llllllll ) + cc 4 ( llllllll ) 2 + dd 4 llllll + ee 4 llllllll + ff

3A.2.2 Effect of Setting Standards by Speed on Single Pumps Running at Multiple Speeds

T he effe ct of pump size on effici ency affects any efficiency standard for single pumps offered at two or more speeds (such as p ump s A and A2 in Figure 3A.2

. 1 ). DOE understands that many manufacturers offer a given pump model at two or more design speeds, but that the wet end of the pump is identical in both offerings. Pump affinity laws indicate that a pump should have the same efficiency at any speed, and indeed, many pump manufacturers publish the s ame efficiency for multiple speeds. HI 14.6 notes , however, that efficiency changes are not negligible for speed changes of more than 20 percent

. vi Th os e resources that show pump efficiency as a function of flow and specific speed, as discuss ed earlier, pre dict a higher efficiency for higher

flow pumps at a given specific speed. Using the ESCC pump as an example, the E C ’s standards

indicate that higher flow s ( from higher speed, or 2

- pole pumps) provide more efficien cy than lower flows only for pumps of less than a bout 800 gpm (2

- pole)/400 gpm (4

- pole). (See Figure

3A.2

. 3

. ) For operation at higher flows, the E C standards require a higher efficiency for 4

- pole pumps ( i.e.

, the same pump running at a lower speed).

For ESCC pumps in the United States, following the EC method of fitting surfaces (Eq. Set 1) results in higher efficiency for 2

- pole than for 4

- pole pumps, except at low specific speeds and high flows. ( See Figure 3A.2

. 3 , which was produced by calculating ηη BBBBBB 2

− pppppppp and

ηη BBBBBB 4

− pppppppp using the same specific speed in each equation , but half the flow for the 4

- pole pump compared to the 2

- pole pump.) Following the fitting method illustrated in Eq

. Set 2, the same trends apply, but the predicted differences are higher. (See Figure 3A.2

. 4 ) Finally , if DOE produced one surface without regard to design speed ( i.e. , ignoring the size effect), the efficiency

of a pump running at 3,550 rpm (2

- pole) would always be higher than the efficiency of that same

pum p running at 1,750 rpm (4

- pole), and the differences would be greater than in the other

scenarios.

.

3A

- 4Figure 3A.2

. 3 Predicted Efficiency Differences Between ESCC Pumps Running with 2

- Pole versus 4

- Pole Motors at Selected Specific Speeds (Using Eq. Set 1)

Figure 3A.2

. 4 Predicted Efficiency Differences Between ESCC Pumps Running with 2

- Pole versus 4

- Pole Motors at Selected Specific Speeds (Using Eq. Set 2)

Table 3A.2

. 1 summarizes the differences in efficiency between pumps running with 2

- pole and 4

- pole motors for each of the scenarios discussed. -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

50 150 250 350 450 550 650 750 850 950Efficiency Difference (Percentage Points)2-Pole-4-Pole

Flow (gpm)

- 2 pole diff (Ns = 1000)

diff EU (Ns = 1000)

diff (Ns = 2000)

diff EU (Ns = 2000)

diff (Ns = 3000)

diff EU (Ns = 3000)

-10.00

-8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.0010020030040050060070080090010001100120013001400150016001700180019002000Efficiency Difference (Percentage Points)2-Pole-4-Pole

Flow (gpm)

- 2 Pole diff (Ns = 1000)

diff (Ns = 2000)

diff (Ns = 3000)

3A

- 5Table 3A.2

. 1 Minimum Efficiencies for Identical Pumps Running at Two Different Speeds

Pump Ns * (U

. S

. ) Flow

, gpm at

3,550

rpm Flow

, gpm at

1,750

rpm Efficiency Difference (2

- pole versus 4

- pole) %

E C ( E q

.

set 1) DOE ( E q

. set 1) DOE ( E q

. set 2) DOE (single equation**)

A 1,000 200 100 2.0 2.9 2.9 5.0

B 1,000 1,200 600

- 0.1

- 0.7

- 5.3 1.8

C 2,000 400 200 1.0 3.4 4.0 5.6

D 2,000 1,200 600

- 0.4 1.3 2.1 3.6

E 3,000 400 200 0.9 4.6 4.8 6.7

F 3,000 2,000 1,000

- 1.0 1.4 4.6 3.8

* Ns = specific speed.

* *Using the single equation approach, DOE would not account for design speed in developing efficiency surfaces, so a single equation would be used no matter the speed.

Analysis of DOE’s pump performance data indicates that out of approximately 500 pairs of pumps in the ESCC database, d 57 percent identified 2

- pole as more efficient, 29 percent

indicated 2

- pole and 4

- pole were equal, e and 14 percent showed 4

- pole being more efficient than

2

- pole. There appear to be no strong trends regarding which speed is more efficient for a given flow or specific speed. The data indicate much larger differences in efficiency between identical pumps running at different speeds than do the EC standard s. DOE does not know whether the data in its database are based on testing or on assumptions of theoretical relationships such as the affinity laws. In addition, DOE notes that a similar analysis performed on non

- submersible vertical turbine pumps shows a different trend, with 77 percent of pump pairs showing 2

- pole

and 4

- pole to be equal, 10 percent showing 2

- pole to be more efficient, and 13 percent showing

4

- pole to be more efficient (out of about 2,000 pairs). Clearly, any identified trends may not hold true for all equipment classes and may simply be a function of the speeds for which pumps in each equipment class generally are optimized and how much of the data is created using affinity law assumptions.

3A.3 OPTIONS FOR ADDRESSI NG DESIGN SPEED

If DOE does not use design speed to differentiat e equipment classes, it is probable that no pump speeds would be eliminated from the market, because the only models affected would be

those having efficiencies close to the standard level. In addition, pumps can run at multiple speeds, so a given model that might be eliminated at one speed may be able to meet the standard

d Pumps having the same manufacturer, cat alog, type, model, and impeller diameter, but different speeds, were considered pairs. The pumps did not have identical specific speeds (which identical pumps theoretically should have), presumably because the affinity laws are not completely accurate, whi ch would produce different results

based on head and flow.

e DOE does not know whether, for these pumps, manufacturers simply assumed the affinity laws, or whether the data represent test results.

3A

- 6at a different speed. DOE is concerned about the implications for identical pumps offered at multiple speeds, however . Specifically, DOE is committed t o select ing a method of surface fitting that produces reasonable results at all speeds.

Regardless of whether it establishes equipment classes based on design speed, DOE must determine at what pump speed testing and compliance should occur. Not all pumps are designed to operate at all speeds, particularly high speeds, and a given equipment class will offer a range of pump speeds. I t therefore may be difficult for DOE to select a single speed for testing each equipment class. In addition, some manufacturer test loops might be set up for a certain speed, so requiring testing at a different speed could increase manufacturer burden by requiring a change in test loop. Alternatively, DOE could require pumps that operate at multiple speeds to meet the most string ent standard (in other words, test the pump model at specified speeds, calculate the minimum efficiency for the pump at each speed, and require compliance with the highest efficiency, if there is a difference). DOE notes that because not all pumps are desi gned to operate at all speeds, DOE may use manufacturer input to determine the range of speeds at which a particular pump model can be tested safely.

DOE understands that some pumps are optimized for a certain design speed (which may not be the speed at w hich efficiency is the highest or the speed at which DOE requires testing), and that therefore either approach may penalize some pump models.

3A.4 CONCLUSION

In the framework document, DOE requested comment s on the use of pump design speed

t o distinguishes equipment classes

, as well as the burden associated with testing at multiple speeds. HI reported that a manufacturer often will need to modif y a pump that will be run at higher speed to allow for greater bearing loads. Modifications may include changes to the bearing frame size or to the axial thrust balancing device, which will affect the efficiency of the

pump. P otential modifications will differ by equipment class. (HI, No. 25 at p. 37

– 38) Grundfos added that speed is considered during the design of a p ump, specifically as it relates to the design of the shaft and bearings. (Grundfos, No. 24 at p. 23) HI noted that pumps usu ally are tested throughout the range of speeds for which the pumps will be offered for sale. A pump manufacturer offering the same p ump at different speeds must account for any speed

- related effects on efficiency and determine whether the pump is compliant with the required minimum efficiency index at all offered speeds. (HI, No. 25 at p. 38) Both HI and Grundfos recommended

harmonizin g equipment classes with the E C , which regulates 2

- and 4

- pole nominal speeds separately, although of course the DOE classes would be at 60 Hz frequency. (Grundfos, No. 24 at p. 22; HI, No. 25 at p. 38)

The working group for commercial and industrial pump s also recommended separate energy efficiency standards for equipment operating at the nominal speeds for 2

- and 4

- pole motors. (See EERE

- 2013

- BT

- NOC

- 0039

- 0092, p. 4, Recommendation #9) In its analysis, DOE

found that throughout the market, pumps operating at each nominal speed demonstrate d distinctly different performance. Therefore, DOE agrees with the submitted comments from interested parties and in the NOPR proposes that for both constant

- and variable

- load pumps, the equipment classes should also be d ifferentiated on the basis of nominal design speed.

3A

- 7REFERENCES

i Council of the European Union. 2012. Commission Regula tion (EU) No 547/2012 of 25 June 2012 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to ecodesign requirements for water pumps. Official Journal of the European

Union . L 165, 26 June 2012, pp. 28

– 36.

ii HI 20.3

- 2010. Rotodynamic (Centrifugal and Vertical) Pump Efficiency Prediction . Hydraulic

Institute.

iii Volk, M. 2005. Pump Characteristics and Applications

, Second Edition . Taylor & Francis Group, LLC. Boca Raton, FL.

iv Anderson, H. 1994. Centrifugal Pumps and A llied Machinery

, 4th Edition . Elsevier Science Publishers Ltd. Oxford, UK.

v Europump Association of Pump Manufacturers. 1999. Attainable Efficiencies of Volute Casing Pumps: A Reference Guide

.

vi ANSI/HI 14.6

- 2011. Rotodynamic Pumps for Hydraulic Performance Acceptance Tests

. Hydraulic Institute.

3A

- 8APPENDIX 3B. EFFICIENCY SURFACES

TABLE OF CONTENTS

3B.1 INTRODUCTION

................................ ................................ ................................ ......... 3B

- 1

3B.2 METRIC USED IN EUROP EAN REGULATIONS

................................ .................... 3B

- 1

3B.3 SURFACES CONSIDERED FOR STANDARDS

................................ ....................... 3B

- 2

3B.4 CONCLUSION

................................ ................................ ................................ .............. 3B

- 5

REFERENCES

................................ ................................ ................................ .......................... 3B

- 6

LIST OF TABLES

Table 3B.2.1 Sample C

- Values for EC Minimum Efficiency Equation

............................ 3B

- 2

LIST OF FIGURES

Figure 3B.3.1 DOE Average Surface for ESCC Compared with EC's 50

- Percent Cut

Off Surface for a 4

- Pole Motor

................................ ................................ .... 3B

- 3 Figure 3B.3.2 Example Average, Top

- of

- Market, and Bottom

- of

- Market Efficiency

Levels for ESCC Pumps

................................ ................................ .............. 3B

- 4 Figure 3B.3.3 Example 2D Slice (Ns=1,500

– 2,500) Showing Average, Top

- of

Market, and Bottom

- of

- Market Efficiency Levels for ESCC Pumps

.......... 3B

- 5

3B

- iAPPENDIX 3B. EFFICIE NCY SURFACES

3B.1 INTRODUCTION

The efficiency of a pump is determined primarily by (1) size, (2) speed, and (3) ratio of

the diameter of the casing throat to that of the impeller. 1 Research has shown that flow can be used as a proxy for the first two parameters, size and speed. 1 The thi rd parameter can be represented by specific speed. The U.S. Department of Energy ( DOE ) considered whether new energy conservation standard s for commercial and industrial pumps sh ould be a function of specific speed and flow

. Using such standards, modeled after the European Commission ( E C ) regulation s for pumps, would eliminate the need to develop hundreds of equipment classes based

on various combinations of specific speed and flow

.

Most of t he contents of this appendix originally appeared in the framewor k document for DOE's rulemaking regarding energy conservation standards for commercial and industrial

pumps

. Since the framework document was published (January 2013), DOE has revised its database of pump performance to apply more directly to the more limi ted scope of pumps

identified for th e rulemaking

. DOE has not updated the analysis described in chapter 3, however,

because DOE believes that the results of analy zing the original database fairly represent the relevant issues regarding efficiency surfaces and metrics for commercial and industrial pumps.

3B.2 METRIC USED IN EUROP EAN REGULATIONS

For stipulating minimum pump efficiency, the E uropean Commission (EC) applies an equation based on pump type, rotating speed, flow, and specific speed (all parameters that affect efficiency) , as follows

.

( ƞ BBBBBB ) min rrrrrrrr = 88

. 59 xx + 13

. 46 yy

− 11

. 48 xx 2

− 0

. 85 yy 2

− 0

. 38 xx

− CC PPPP TTTT

, rr

Where :

• x = ln(ns ),

o ln= natural lo garithm,

o ns= specific speed,

• y = ln(Q ),

o Q = flow in [m3/h ], and

• C = translational value

. 2

The E C equation, which describes a three

- dimensional surface, was defined using data from a 1998 investigation. 2 The equation is intended to represent pumps that have the same stringency of achievable efficiency across all parts of the surface. The EC sets efficiency

standards , which they refer to as mi nimum efficiency indexes (MEIs)

, based on what percentage
of the market they desire to eliminate (also called the cut

- off percentage ) . By changing the

3B

- 1constant, C, the surface can be raised or lowered until the desired percentage of the market is

eliminate d for a given pump class ( i.e.

, until the percentage of pumps desired to be impacted by

the standard lies below the surface). Table 3B.2

. 1 shows sample values of C. After evaluat ing potential standards from a 5

- percent to an 8 0

- percent cut

- off

, the E C stipulated a final standard that will undergo phased implementation from a 10

- percent to a 40

- percent cut

- off

. 3

Table 3B.2

. 1 Sample C

- Values for EC Minimum Efficiency Equation

Equipment Class C

- value

MEI = 10% MEI = 40%

End

- suction

, own bearings , 1

, 450 rpm 132.58 128.07

End

- suction

, own bearings , 2

, 900 rpm 135.60 130.27

End

- suction

, close

- coupled , 1

, 450 rpm 132.74 128.46

End

- suction

, close

- coupled , 2

, 900 rpm 135.93 130.77

3B.3 SURFACES CONSIDERED FOR STANDARDS

DOE could use the E C equation as the basis for defining pump efficiency levels in its rulemaking

. DOE’s investigations , as well as HI's , however, indicate that the distribution of

efficienc ies in the U.S. pump market differs s ignificantly from that in the European market , so that the E C ’s minimum pump efficiency surface can not adequately represent U.S. efficiency

levels

.

DOE could address the fact that the U.S. market does not match the E C market by simply changing the C

- value to represent a given efficiency level. (As mentioned, the EU uses the same surface shape for all equipment classes and MEIs.) Alternatively, DOE could (1) develop separate equations (surfaces) unique to each equipment class by performing regressions on the data in its database, a and (2) change the surface shape from bottom to top of market to more accurately represent the U.S. market and potential efficiency levels. P umps that provide high

flows , for example, typically can reach higher efficiencies at their best efficiency point ( BEP )

than can pumps having lower flow s . DOE c ould account for the fact that the difference in efficiency of higher

- flow pumps be t ween bottom and top of market is much smaller than the

difference for smaller

- flow pumps : the sur face become s flatter from bottom to top of market. DOE could develop surfaces for all pump classes using the form of the EC equation, but with different coefficients for each parameter in the equation (not just the C

- value). b Details regarding the coefficients and equations can be found in appendix C of the framework document

.

a Researchers in Europe note that they did not change the surface by pump type

, because the primary hydraulic components (impeller and volute) are similar for the considered pumps.

b Note that the coefficients and surfaces provided in this appendix relate to ESCC pumps of both 1800 and 3600 rpm design speeds.

3B

- 2Figure 3B.3

. 1 shows a surface that DOE developed to represent t he average ESCC market (based on model availability), compared to the EC's MEI level of 50 percent

.

Figure 3B.3

. 1 DOE Average Surface for ESCC Compared with EC 's 50

- Percent Cut

- Off

Surface for a 4

- Pole Motor

Figure 3B.3

. 2 shows the U.S. average ESCC surface again, along with a bottom

- of

market and top

- of

- market surface. EU (Blue) DOE (Green) E C (Blue) DOE (Green)

3B

- 3Figure 3B.3

. 2 Example Average, Top

- of

- Market, and Bottom

- of

- Market Efficiency Levels for ESCC Pumps

Figure 3B.3

. 3 shows a two

- dimensional s lice through the 3D surfaces for ESCC pumps

for a speed range of 1,500 to 2, 500 rpm

. The slice demonstrates the flattening of the surface from bottom to top of market. Average (Green)

Bottom

- of

- Market (Red) Top

- of

- Market (Blue)

3B

- 4Figure 3B.3

. 3 Example 2D Slice (Ns=1

, 500

– 2

, 500) Showing Average, Top

- of

- Market, and

Bottom

- of

- Market Efficiency Levels for ESCC Pumps

The figures presented above are intended only to exemplify a m ethod for developing

surface s . As mentioned previously, DOE recognizes that the database used for this analysis does

not adequately represent the market for pumps in the scope of this rulemaking

.

3B.4 CONCLUSION

As discussed in chapter 3, interested parties supported using the EC surface

, changing only the C

- value , to establish efficiency levels for commercial and industrial pumps . In particular, HI noted that although a better data fit could be obtained by changing the surface shape, such complexity is not w arranted and w ould not significant ly improve the ability of the equation to represent a cut

- off level for pump efficiency. (HI, No. 25 at p p .4

– 5 , 32, 40 ) The working group for developing efficiency standards for commercial and industrial pumps (described i n chapter 3) developed recommendations for efficiency level s bas ed on using an efficiency surface identical to that used b y the EC

, except for the C

- value. After reviewing

comments from interested parties and discussions of the working group, DOE propos es to base

its efficiency equation on the EC equation and to change the C

- values to reflect the U.S. market. Average (Green) Thick

- 1500; Thin

- 2500

Bottom

- of

- Market (Red)

Top

- of

- Market (Blue)

3B

- 5REFERENCES

1 Anderson, H. 1994. C entrifugal Pumps and Allied Machinery, 4th Edition . Elsevier Science Publishers Ltd. Oxford, UK.

2 AEA Energy & Environment. 2008. Appendix 6: Lot 11

– " Water Pumps (in commercial buildings, drinking water pumping, food industry, agriculture ." Report to European

Commission.

3 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 2012 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to ecodesign requirements for water p umps. Official Journal of the European

Union . L 165, June 26

, 2012, pp. 28

– 36.

3B

- 6CHAPTER 4. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION

................................ ................................ ................................ ........... 4

- 1

4.2 SCREENED

- OUT TECHNOLOGIES

................................ ................................ ............ 4

- 2

4.2.1 Improved Surface Finish of Wetted Components

................................ ................ 4

- 2

4.2.1.1 Improved Casting Techniques

................................ ................................ .... 4

- 3

4.2.1.2 Surface Polishing and/or Honing

................................ ............................... 4

- 3

4.2.1.3 Surface coating or plating

................................ ................................ .......... 4

- 3

4.2.2 Reduced Running Clearances

................................ ................................ .............. 4

- 4

4.2.3 Reducing Mechanical Friction in Seals

................................ ............................... 4

- 4

4.2.4 Reduction of Other Volumetric Losses

................................ ................................ 4

- 4

4.2.5 Addition of a Variable Speed Drive

................................ ................................ ..... 4

- 5

4.2.6 Improvement of VSD Efficiency

................................ ................................ ......... 4

- 5

4.2.7 Reduced VSD Standby and Off Mode Power Usage

................................ ........... 4

- 5

4.2.8 Remaining Technology

................................ ................................ ........................ 4

- 5

REFERENCES

................................ ................................ ................................ ............................ 4

- 7

4

- iCHAPTER 4

. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter details the screening analysis that the U.S. Department of Energy (DOE) conducted in support of the ongoing energy conservation standards rulemakings for pumps.

In chapter 3, the market and technology assessment (MTA), DOE presented an initial list of technologies that can improve the energy efficiency of pumps. The purpose o f the screening analysis is to evaluate the technologies that improve equipment efficiency to determine which technologies to consider further and which to screen out. DOE consulted with a range of parties, including industry, technical experts, and others to develop a list of technologies for consideration. DOE evaluated the technologies pursuant to the criteria set out in the Energy Policy and Conservation Act (EPCA), as amended. (42 U.S.C. 6311

- 6317)

Section 325(o) EPCA establishes criteria for prescrib ing new or amended standards designed to achieve the maximum improvement in energy efficiency. Further, EPCA directs the Secretary of Energy to determine whether a standard is technologically feasible and economically justified. (42 U.S.C. 6295(o)(2)(A), a s directed by 42 U.S.C. 6316(a)(1)

- (3)). EPCA also establishes guidelines for determining whether a standard is economically justified. (42 U.S.C. 6295(o)(2)(B)) Appendix A to subpart C of Title 10, Code of Federal Regulations, Part 430 (10 CFR Part 430), “Procedures, Interpretations and Policies for Consideration of New or Revised Energy Conservation Standards for Consumer Products” (the Process Rule), sets forth procedures to guide DOE in its consideration and promulgation of new or revised equipment

ener gy conservation standards. These procedures elaborate on the statutory criteria provided in 42 U.S.C. 6295(o) and, in part, eliminate problematic technologies early in the process of prescribing or amending an energy efficiency standard. In particular

, sec tions 4(b)(4) and 5(b) of the Process Rule guide DOE in determining whether to eliminate from consideration any technology that presents unacceptable problems with respect to the following criteria:

Technological feasibility . Technologies incorporated in commercial equipment or in working prototypes will be considered technologically feasible.

Practicability to manufacture, install, and service . If mass production of a technology in commercial equipment and reliable installation and servicing of the techno logy could be achieved on the scale necessary to serve the relevant market at the time of the effective date of the standard, then that technology will be considered practicable to manufacture, install, and

service.

Impacts on equipment utility or equipmen t availability . If a technology is determined to have significant adverse impact on the utility of the equipment to significant subgroups of consumers
, or result in the unavailability of any covered equipment type with performance characteristics (includin g reliability), features, sizes, capacities, and volumes that are substantially the same as equipment generally available in the United States at the time, it will not be considered further.

4

- 1Adverse impacts on health or safety . If it is determined that a technology will have significant adverse impacts on health or safety, it will not be considered further.

In sum, if DOE determines that a technology, or a combination of technologies, has unacceptable impacts on the policies stated in section 5(b) of the Process Rule, it will be eliminated from consideration. If a particular technology fails to meet one or more of the four criteria, it will be screened out. Section 4.2 documents the reasons for eliminating any technology.

4.2 SCREENED

- OUT TECHNOLOGIES

This sec tion describes the technologies that DOE eliminated for failure to meet one of the following four factors: (1) technological feasibility; (2) practicability to manufacture, install, and service; (3) impacts on equipment utility or equipment availability; a nd (4) adverse impacts on health or safety.

DOE also eliminated some technologies that were determined to provide little or no potential for efficiency improvement for one of the following additional reasons: (a) the technology does not significantly impr ove efficiency; (b) the technology is not applicable to the equipment being considered for coverage or does not significantly improve efficiency across the entire scope of each equipment class; and (c) efficiency improvements from the technology degrade qu ickly.

DOE found that most of the technology options identified in the Framework Document have limited potential to improve the efficiency of pumps. In addition, DOE found that several of the options also do not pass the screening criteria listed above.

4.2.1 Improved Surface Finish of Wetted Components

DOE determined that improving surface finish will not significantly improve efficiency across the entire scope of each equipment class. As discussed in c hapter 3

, p umps with higher specific speeds benefit signif icantly less than pu mps with lower specific speeds

. For example,

p umps with specific speeds of 1,000 achieve improvements of approximately two efficiency points, while pumps with specific speeds greater than approximately 3,000 (a common design

within the NOPR scope) achieve improvements of less than 0. 4 efficiency points. Improvements continue to decrease at higher specific speeds.

With other technology options such as hydraulic redesign providing approximately 11 points of efficiency improvement a across each equipment class

, improvements of less than 0.4 efficiency point s cannot be considered significant. A gain of 0. 4 efficiency point s represents approximately only 3. 6 % of the improvement available from hydraulic redesign

. As such, improved surface finish was screened out as it will not significantly improve efficiency across the equipment class.

a Varies by equipment class. See discussion of hydraulic redesign in c hapter 3

.

4

- 2For the NOPR analysis DOE identified three specific sub

- categories of technology options for surface finish improvement. The following sub

- sections provide further detail on each of the specific technologies proposed for improving surface finish.

4.2.1.1 Improved Casting Techniques

In addition to the lack of applicability over the full equipment class, initial efficiency improvements from improved c asting techniques are expected to degrade quickly. With regular

use
, the initially smooth die

- and investment

- cast surfaces will naturally roughen due to factors such pitting, oxidation, and other wear and tear. The Hydraulic Institute, Grundfos, and ACEEE

all agree that gains in efficiency due to improved surface finish ( from either improved casting techniques or surface polishing ) degrade quickly in most applications . (HI, No. 00 25 at p.9, 39) (Grundfos, No. 00 24 at p.25) (ACEEE, Framework Public Meeting Tr anscript at p.299) b

4.2.1.2 Surface Polishing and/or Honing

Similar to improved casting techniques, i mprovements gained from surface polishing and/or honing are also expected degrade quickly . Additionally, surface polishing cannot be considered practical to manufacture in a production environment. Traditional s urface polishing and honing are manual operations that cannot be practically automated. Currently, polishing or honing is only used fo r special cases, such as meeting custom performance requirement s requested by the consumer . DOE did explore additional production processes such as extrude

honing or slurry polishing, but manufacturers indicated that such technologies were known to

cause l arger than acceptable variations in metal removal which results in unpredictable part dimension and thicknesses, thus compromising final product quality and reliability.

4.2.1.3 Surface coating or plating

In addition to lack of applicability over the full equipm ent class, coatings and platings were also screened out due to reliability and durability concerns. Multiple manufacturers have experienced issues will epoxy coatings flaking off during operation. Similarly, manufacturers also expressed concern with locali zed galvanic corrosion known to occur in small plating defects, when these defects are present. Both of these issues can lead to rapid localized degradation in the uncoated or unplated region s . Localized degradation acts as a significant stress

concentrati on and can ultimately lead to unforeseen failures. Surface coatings and platings are traditionally used in applications with heighten ed sanitary or wear

- resistant requirements. As such, pumps in these applications are typically inspected more frequently th an a standard clean water pump. Inspections allow the localized wear issues to be corrected before unforeseen

failures can occur

.

b A notation in this form provides a reference for information that is in the docket of DOE’s rulemaking to develop energy conservati on standards for pump s ( Docket No. EERE

- 2013

- BT

- NOC

- 0039 ), which is maintained at www.regulations.gov. This notation indicate s that the statement preceding the reference is document number [DDDD document number as assigned in regulations.gov] in the docket for the [product] [energy conservation standards or test procedure] rulemaking, and appears at pages [xx] of that document.

4

- 34.2.2 Reduced Running Clearances

Manufacturer survey responses and research performed by DOE indicate pump

manufacturers have alrea dy taken measures to tighten running clearances as much as possible, given the limitations of current wear ring materials, machining tolerances, and pump assembly practices

. To further tighten clearances require s alternate materials for wear rings and othe r running surfaces, tighter machining tolerances, and/or modifications to assembly procedures

. With these tighter clearances comes increased risk for contact between static and rotating

component s during pump operation. This contact , known as an operationa l rub, may remove or deform material

. Ultimately, operational rubs lead to looser clearances

, causing efficiency improvements to quickly degrade

. The Hydraulic Institute and The American Council for an Energy

- Efficient Economy ( ACEEE ) both expressed agreement that efficiency improvements

from tightened running clearances degrade quickly . (HI, Framework Public Meeting Transcript at p.329) (ACEEE, Framework Public Meeting Transcript at p.299) Additionally, l arge or frequent operational rubs can also lea d to accelerated component wear, and in the worst case result in catastrophic seizing or failure of the pump.

To prevent these tighter clearances from causing operational rubs, larger ( stiffer ) shafts,

and larger (stiffer) bearings are typically required. The use of larger components to improve the

stiffness to appropriate levels results in increased mechanical losses. The se losses negate the potential improvements gained from reduced clearances

. Additionally, the use of a larger shaft

may require an increa s ed inlet diameter on the impeller , which may necessitate a more complex hydraulic redesign of the impeller.

Given the limitations associated with reduction of running clearances, and the potential

reduc tion in reliability

, this technology option was screened out

.

DOE notes that clearances may still be tightened as a part of a larger hydraulic redesign, but tighter clearances cannot function as a standalone technology option.

4.2.3 Reducing Mechanical Friction in Seals

Although some advanced seals, suc h as silicon carbide

- carbon, can incrementally reduce

friction losses for some pumps , they cannot significantly improve efficiency across the entire scope of the equipment class. As discussed in chapter 3, the smallest pumps in each equipment class may see improvements of up to one point of efficiency, but the available improvement quickly drops off as pump size increase s . At 10 horsepower, improvements are on th e order of 0.2 efficiency point , with this decreasing to 0.1 efficiency point for pumps larger t han 50 horsepower . Improvement s on the order of 0.1

- 0

. 2 efficiency point are not significant when compared to improvements available from other technology options such as hydraulic redesign. As such, this technology option was screened out, as it does not significantly improve efficiency across the entire scope of each equipment class.

4.2.4 Reduction of Other Volumetric Losses

As discussed in c hapter 3, one of the most common causes of volumetric losses (other than previously discussed technology options) is t hrust balance holes. No quantitative data is available regarding potential efficiency improvements from removal or reduction of balance

4

- 4holes . DOE did survey participating manufacturers , and responses indicated that modifications to

balance holes will resu lt in improvements well below one efficiency point. The majority of manufacturers indicated that they would consider the resulting improvements to be insignificant with respect to their current efficiency ratings. As such, DOE concludes that this technolog y option will not significantly improve efficiency.

Manufacturer responses also indicate that removal of thrust balance holes from existing impellers w ould reduce pump reliability . Thrust balance holes are used to reduce wear on rub surfaces and bearings . Absent a more complex system redesign, thrust balance holes cannot be

r emov ed without compromising reliability

. DOE notes that m anufacturers may be able to reduce

volumetric los s es as a part of a larger hydraulic redesign

.

4.2.5 Addition of a Variable Speed Dr ive

As discussed in c hapter 3, DOE determined that for pumps considered in this NOPR there are many application types and load profiles that would not benefit from a VSD and many applications for which energy use would increase

. As such DOE has eliminated the use of VSDs from the list of technology options, as the technology does not significantly improve efficiency across each equipment class.

4.2.6 Improvement of VSD Efficiency

Based on market reports and manufacturer interviews, VSD market penetration rates ar e be between ten and five percent

. c 1 Given the low market penetration of VSDs and the

elimination of VSDs as a technology option, DOE concludes that improvement of VSD efficiency will not significantly reduce pump energy consumption. As such it was screened out

.

4.2.7 Reduced VSD Standby and Off Mode Power Usage

Based on market reports and manufacturer interviews, VSD market penetration rates are

estimated to be between ten and five percent. Given the low market penetration of VSDs and the

elimination of VSDs as a technology option, DOE concludes that reduced VSD standby and off mode power usage will not significantly reduce pump energy consumption. As such it was screened out

.

4.2.8 Remaining Technology

After eliminating those technologies that provide little o r no potential for efficiency improvement, and screening out those technologies that do not meet the requirements of sections 4(a)(4) and 5(b) of the Process Rule, DOE is considering improved hydraulic design as the only remaining technology option. DOE no tes that some of the screened out technology options discussed in this chapter may be part of a full hydraulic redesign, even though they cannot be considered viable as individual, stand

- alone design options.

c Based on a combination of manufacture survey responses and a penetration rate of 3.2% in 1998 , per the cited DOE study . A 5% annual growth rate was applied to the DOE study.

4

- 5DOE considers hydraulic design to be a mature t echnology and concludes that hydraulic designs of the highest possible efficiency are already present in the marketplace. As such, improvements within the bounds of pump efficiencies currently available in the marketplace are technologically feasible. DOE also finds that improved hydraulic design meets the other screening criteria (i.e., practicable to manufacture, install, and service and do not result in adverse impacts on consumer utility, product availability, health, or safety). As such, hydraulic

rede sign was the only design option passed on to the engineering analysis.

4

- 6REFERENCES

1 United States Industrial Electric Motor Systems Market Opportunities Assessm ent. Tech. Washington DC: U.S. Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE), 1998. Print

4

- 7CHAPTER 5. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION

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

- 1

5.2 METHODOLOGY OVERVIEW

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

- 1

5.3 DATA AVAILABILITY AN D EQUIPMENT CLASSES ANALYZED

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

- 2

5.3.1 Radially Split, Multi

- Stage, Vertical, In

- Line, Diffuser Casing

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

- 3

5.3.2 Vertical Turbine Submersible 1,800 RPM

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

- 6

5.4 SUMMARY OF SIGNIFICA NT DATA SOURCES

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

- 7

5.4.1 Performance, Shipments, and Sales Database

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

- 7

5.4.2 Industry Aggregate Model, Shipment and Sales Revenue Data

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

- 8

5.4.3 Cost of Redesign Survey

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

- 9

5.5 REPRESENTATIVE EQUIP MENT FOR ANALYSIS

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

- 10

5.5.1 ESCC, ESFM, and IL Equipment Classes

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

- 11

5.5.1.1 Impellers

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

- 11

5.5.1.2 Volutes

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

- 12

5.5.1.3 Seals

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

- 13

5.5.2 RSV and VTS Equipment Classes

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

- 13

5.5.3 Summary

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

- 14

5.6 DESIGN OPTIONS

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

- 14

5.7 AVAILABLE ENERGY EFF ICIENCY IMPROVEMENTS

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

- 14

5.8 EFFICIENCY LEVELS

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

- 14

5.8.1 Efficiency Level Structure

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

- 14

5.8.2 Efficiency Levels Analyzed

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

- 16

5.8.3 Establishing the Baseline Efficiency Levels

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

- 17

5.8.3.1 Baseline Configuration

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

- 17

5.8.3.2 Establishing Baseline C

- Values

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

- 17

5.8.4 Establishing the Maximum Technologically Efficiency Levels

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

- 20

5.8.4.1 Max

- Tech Configuration

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

- 20

5.8.4.2 Establishing Max

- Tech C

- Values

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

- 21

5.8.5 Harmonization with EU Analytical Structure and Methods

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

- 22

5.8.6 Establishing Baseline and Max

- Tech for RSV Equipment Classes

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

- 22

5.9 MANUFACTURERS PRODUC TION COST ASSESSMENT

METHODOLOGY

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

- 24

5.9.1 Changes in MPC Associated with Hydraulic Redesign

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

- 24

5.9.2 Manufacturer Production Cost Model

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

- 27

5.10 CONVERSION COSTS

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

- 30

5.10 .1 Hydraulically Redesign Cost Data

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

- 31

5.10.2 Average Per

- Model Cost To Hydraulically Redesign a Pump at Each Efficiency Level

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

- 32

5.10.3 Accounting for Cost Sharing Between Models with Shared Platforms

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

- 33

5.10.4 Adjusted Per

- Model Cost to Hydraulically Redesign a Pump at Each Efficiency Level

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

- 36

5

- i5.10.5 Determining Number of Models Redesigned in Each Equipment Class

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

- 37

5.10.5.3 Number of Models Redesigned in Each Equipment Class at Each Efficiency Level

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

- 38

5.10.6 Aggregate Industry Conversion Cost at Eac h Efficiency Level

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

- 39

5.11 MANUFACTURER MARKUP ANALYSIS

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

- 41

5.11.1 Industry

- Average Markups

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

- 41

5.11.2 Individual Manufacturer Markup Structures

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

- 42

5.11.3 Industry

- Wide Markup Structure

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

- 42

5.11.4 MSP

- Efficiency Relationship

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

- 44

REFERENCES

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

- 46

LIST OF TABLES

Table 5.3.1 Summary of Data Availability, Equipment Groups, and Analysis Type

....... 5

- 3

Table 5.4.1 Summary of Performance, Shipments, and Sales Database Content

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

- 8

Table 5.4.2 Model, Shipment and Sales Revenue Data for 2012

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

- 9

Table 5.4.3 Cost to Redesign One Pump Model from below the 10 th to the 40 th
Percentile of Efficiency

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

- 10

Table 5.8.1 Efficiency Levels Analyzed with Corresponding C

- values

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

- 17

Table 5.8.2 European Union MEI C

- Values for RSV at the 40 th and 70 th Percentile

..... 5

- 23

Table 5.8.3 DOE PEI C

- Values for RSV at Baseline and Max

- Tech

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

- 23

Table 5.10.1 Revised Cost of Redesign Table

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

- 31

Table 5.10.2 Estimated Aggregate Industry Development Costs of New Pumps to Meet Different Minimum Cutoff Values in the European Union

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

- 32

Table 5.10.3 European Conversion Costs Interpolated to DOE Efficiency

Percentiles

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

- 32

Table 5.10.4 Average to Cost Redesign, by Efficiency Level, for Each Equipment Class Grouping

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

- 33

Table 5.10.5 Statistic on Shared Platforms

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

- 34

Table 5.10.6 Refined Statistics on Shared Platforms

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

- 34

Table 5.10.7 Percentage of Redesign Costs that Apply to a Duplicate Model

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

- 35

Table 5.10.8 Percent of Redesign Costs Applicable to each Pump Population Category

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

- 35

Table 5.10.9 Redesign Cost Sharing Adjustment Factor for End Suction and Inline Pumps

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

- 36

Table 5.10.10 End Suction

- Adjusted Per

- Model Cost to Hydraulically Redesign a Pump

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

- 36

Table 5.10.11 Inline

- Adjusted Per

- Model Cost to Hydraulically Redesign a Pump

........ 5

- 37

Table 5.10.12 VTS

- Adjusted Per

- Model Cost to Hydraulically Redesign a Pump

.......... 5

- 37

Table 5.10.13 Ca lculation of Total Industry Model Counts for Each Analysis Group

...... 5

- 37

Table 5.10.14 End Suction

- Number of Models Redesigned at E ach Efficiency Level

.... 5

- 39

Table 5.10.15 Inline

- Number of Models Redesigned at Each Efficiency Level

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

- 39

Table 5.10.16 VTS

- Number of Models Redesigned at Each Efficiency Level

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

- 39

Table 5.10.17 End Suction

- Aggregate Industry Conversion Cost at Each Efficiency

Level

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

- 40

5

- iiTable 5.10.18 Inline

- Aggregate Industry Conversion Cost at Each Efficiency Level

..... 5

- 40

Table 5.10.19 VTS

- Aggregate Industry Conversion Cost at Each Efficiency Level

....... 5

- 40

Table 5.10.20 All Equipment Classes

– Aggregate Industry Conversion Cost at Each Efficiency Level

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

- 41

Table 5.11.1 Industry

- Average Markups for Ea ch Equipment Class Group

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

- 42

LIST OF FIGURES

Figure 5.3.1 Histogram to Compare to the Efficiency of RSV 3600 RPM Models

Sold in the United States to the EU 547/2012 MEI 40 Standard Level

......... 5

- 4 Figure 5.3.2 Histogram to Compare to t he Efficiency of RSV 1800 RPM Models

Sold in the United States to the EU 547/2012 MEI 40 Standard Level

......... 5

- 5 Figure 5.5.1 Two

- Dimen sional Fabricated Stainless Steel Impeller. With and

Without Second Disc Attached

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

- 12 Figure 5.8.1 Failing C

- Values Plotted Agai nst Flow at BEP for the ESCC.1800 Equipment Class

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

- 19 Figure 5.8.2 Failing C

- Values Plotted Against Specific Speed for the ESCC.1800 Equipment Class

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

- 20 Figure 5.9.1 Impeller Production Cost Plotted against the Scaling Parameter

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

- 26 Figure 5.9.2 Impeller Production Cost Premium Plotted Against Efficiency

Percentile

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

- 26 Figure 5.9.3 Trends between the Lower and Upper 50 th Percentile of Efficiency, for Impeller Production Cost Premium

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

- 27 Figure 5.9.4 Average Bare Pump MPC and MSP per Model for IL.1800 and

IL.3600

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

- 29 Figure 5.9.5 Average Bare Pump MPC and MSP per Model for ESCC.1800, ESCC.3600, ESFM.1800, and ESFM.3600

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

- 30 Figure 5.9.6 Average Bare Pump MPC and MSP per Model for VTS.3600

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

- 30 Figure 5.10.1 Process for Refining Statistics on End Suction Shared Platforms

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

- 34 Figure 5.10.2 Percent of Failing Models Not Redesigned at Each Efficiency Level

......... 5

- 38 Figure 5.11.1 MSP Premium as a Function of Efficiency Percentile. Actu al Results

and Trended Fits.

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

- 43

5

- iiiCHAPTER 5

. ENGINEERING ANALYSIS

5.1 I NTRODUCTION

The engineering analysis establishes the relationship between manufacturer selling price (MSP) and energy consumption for the pumps examined in this rulemaking. The “ price

efficiency” rela tionship serves as the basis for downstream cost

- benefit calculations with respect

t o individual consumer s, manufacturers, and the nation.

5.2 METHODOL O GY OVERVIEW

This section describes the analytical methods the U.S. Department of Energy (DOE) used for the engineering analysis. In this rulemaking, DOE adopted a modified design option

appro ach to produce analytically derived curves representing the cost

- efficiency and price

efficiency relationship for each equipment class analyzed. In a general design option approach, the goal of the analysis is to calculate the incremental cost and energy e fficiency impacts of implementing specific energy saving technologies, known as “design options

. ” DOE modified

t he analysis conducted in this rulemaking from the standard approach for the following two

reasons . First, only one design option, hydraulic rede sign, remained after the screening analysis

. Second, DOE ’s analysis concluded that manufacturer production cost (MPC) does not vary with efficiency

.

DOE’s MPC analysis is conducted on a greenfields basis, i.e. the manufacturing costs and

differences in th ese costs for different designs are calculated on the basis of setting up production facilities from scratch for the different designs that are under consideration. The development and conversion costs that a manufacturer might incur do not impact the man ufacturing cost of the product and will not show up when examining the differences in MPC. D evelopment and

conversion costs are considered separately in this analysis. M anufacturers may be able to recoup their investment in new designs and tooling, but these differences would show up in the sales prices rather than the manufacturing costs

.

Within the modified design option approach, DOE analyzed and developed the MPC

- and

MSP

- efficiency relation ship. In its final form, the efficiency side of the MPC

- and MSP

efficiency relationships is characterized by the relative efficiency of a pump with respect to its equipment class; this can also be referred to as an efficiency percentile. This method was

c onsistent with the consumer and industrial pumps ( CIP ) working group’s desire to establish efficiency levels based on efficiency percentile. DOE analyzed t he MPC

- and MSP

- efficiency relationships from the baseline to the maximum technologically feasible (“ max

- tech”) equipment configuration for each class. Sections 5.9

, 5.10 , and 5.11 provide further details on DOE’s analysis of MPC, conversion costs, and MSP, respectively.

5

- 15.3 DATA AVAILABILITY AN D EQUIPMENT CLASSES ANALYZED

DOE attempted to collect data on and analyze all eq uipment classes at the individual equipment class l evel . However, the performance and cost data that DOE obtain ed were not granular to the configuration level

. a Consequently, DOE performed their analyses at the level of basic pump equipment type and nomina l design speed

. The basic pump equipment type and nominal design speed groups used to represent each equipment class can be referred to as

“ equipment groups .” E quipment groups are explained in c hapter 3, and summarized in Table

5.3

. 1

.

DOE was un able to obtain sufficient data to “ directly analyze ” all equipment groups

. A “direct analysis” means that a specific equipment group was analyzed using data from products

in tha t equipment group. If DOE could not directly analyze an equipment group, a “similarity ” or

“indirect” analysis was performed . A “similarity analysis” means that a specific equipment class

was analyzed using data from products in a different , but similar eq uipment class. An “indirect analysis” means that data from markets other than the United States were used to characterize the equipment class. Table 5.3

. 1 summarizes data availability, equipment groups

, and analysis type s used for each equipment class.

a Equipment classes are defined by b asic pump equipment type, nominal design speed, and configuration type (e.g.,

end suction close

- coupled, 1800 RPM, constant load). See chapter 3 for complete definitions.

5

- 2Table 5.3

. 1 Summary of Data Availability, Equ ipment Groups, and Analysis Type

Equipment Class * Equipment Group Direct Data **

Available Analysis Type

ESCC.1800.CL ESCC.1800 Yes Direct ESCC.1800.VL

ESCC.3600.CL ESCC.3600 Yes Direct ESCC.3600.VL

ESFM.1800.CL ESFM.1800 Yes Direct ESFM.1800.VL

ESFM.3600.CL ESFM.3600 Yes Direct ESFM.3600.VL

IL.1800.CL IL.1800 Yes Direct IL.1800.VL

IL.3600.CL IL.3600 Yes Direct IL.3600.VL

RSV .1800.CL RSV .1800 No Indirect RSV .1800.VL

RSV .3600.CL RSV .3600 No Indirect RSV .3600.VL

VTS .1800.CL VTS

. 1800 No Similarity to

VTS .3600 VTS .1800.VL

VTS .3600.CL VTS .3600 Yes Direct VTS .3600.VL

* Equipment class designations consist of a combination (in sequential order separated by periods) of: (1) an equipment family code (ESCC = end suction close

- coupled, ESFM = end suction frame mounted/own bearings, IL =

in

- line, RSV = r adially split, multi

- stage, vertical, in

- line, diffuser casing

, VTS = vertical turbine submersible); (2) a nominal operating speed (1800 = 1800 rpm, 3600 = 3600 rpm); and (3) an operating mode code (CL = constant load, VL = variable load). Complete definitions are availa ble in chapter 3.

* * “Direct data” is performance and cost data pertaining to the given equipment group.

DOE conducted direct analyses for the ESCC.1800, ESCC.3600, ESFM.1800, ESFM.3600, IL.1800, IL.3600 and VTS.3600 equipment classes. The following two s ubsections describe in more detail how DOE addressed the equipment groups that could not be directly analyzed.

5.3.1 Radially Split, Multi

- Stage, Vertical, In

- Line, Diffuser Casing

DOE used available information to identify baseline and maximum technologically

f

easible (“max

- tech”) efficiency levels for this class. Specifically DOE used market research and

confidential manufacturer information to establish a database of RSV models. This database represented models offered for sale in the United States by three m ajor manuf acturers of RSV

pumps. DOE reviewed the efficiency data for these RSV pumps and found no models to be less efficient than the European Union’s MEI 40 standard level, which took effect on January 1,

5

- 32015

. 1 Figure 5.3. 1 and Figure 5.3. 2 illustrate these findings for 3,600 and 1,800 RPM models, respectively.

Figure 5.3. 1 Histogram to Compare to the Efficiency of RSV 3600 RPM Models Sold in

the United States to the EU 547/2012 MEI 40 Standard Level

5

- 4Figure 5.3. 2 Histogram to Compare to the Efficiency of RSV 1800 RPM Models Sold in

the United States to the EU 547/2012 MEI 40 Standard Level

This analysis, in conjunction with confidential discussions with manufacturers led DOE

to conclude that RSV models sold in the United States market are global platforms with hydraulic designs equivalent to those in the European market. As such, DOE present ed this conclusion to the CIP working group for consideration, where it was supported and reaffirmed on numerous occasions (See, e.g. EERE

- 2013

- BT

- NOC

- 0039

- 0109 at pp. 91

- 97, EERE

- 2013

- BT

- NOC

- 0039

- 0105 at pp. 293

- 300, EERE

- 2013

- BT

- NOC

- 0039

- 0106 at pp. 38

- 4 0, 62

- 67, 88

- 95; EERE

- 2013

- BT

- NOC

- 0039

- 0108 at pp. 119.).

As a result of the conclusion that RSV models sold in the United States market are global platforms with hydraulic designs equivalent to those in the European market, DOE proposes to

set the base line and max

- tech levels equal to those established in Europe. Specifically, the baseline would be the Europe an minimum efficiency standard

, b and the max

- tech level would be the European level referred to as “the indicative benchmark fo r the best available technology .” 2

Although DOE was able to establish a baseline and max

- tech level through similarity to the European market, DOE was unable to develop a cost

- efficiency relationship or additional efficiency levels for RSV , due to lack of available cost d ata for this equipment. As a result, DOE has proposed a standard level for RSV that is equivalent to the baseline, consistent with the recommendation of the CIP Working Group. (See EERE

- 2013

- BT

- NOC

- 0039

- 0092, p. 4,

b Note that this NOPR and the European Union regulation use different metrics to represent efficiency. DOE used

available da ta to establish harmonized baseline and max

- tech efficiency levels using the DOE metric.

5

- 5Recommendation #9) Based on the data av ailable and recommendation of the CIP working group, DOE concludes that this standard level is representative of the typical minimum efficiency configuration sold in this equipment class, and no significant impact is expected for

either the consumers or ma nufacturers.

5.3.2 Vertical Turbine Submersible 1

, 800 RPM

Market research, confidential manufacturer data, and direct input from the CIP working

group indicate that the VTS .1800 is a very uncommon configuration of pumps in the marketplace. DOE estimates t otal shipments for this equipment class to be less than 1 percent of

the VTS .3600 equipment class. Consequently, DOE was unable to obtain enough VTS .1800 performance and cost data to directly analyze the equipment class.

Due to lack of data, DOE analyzed the VTS .1800 equipment class by similarity to

VTS .3600. Available information indicates that all VTS .1800 models sold into the market are hydraulically identical to analogous VTS .3600 model s already offered in the market

. This means

that each VTS .1800 mode l in the market use s the same bare pump that is already produced for

use with a VTS .3600 model . The only difference between these products is the nominal speed of the motor. As a result, when a manufacturer performs a hydraulic redesign on a VTS .3600

model , the VTS .1800 model will benefit from improved performance at no additional conversion

cost

. c Given this relationship, DOE assessed the conversion costs for these equipment classes collectively, as a single hydraulic redesign to a VTS .3600 pump will impact the VTS .1800

variant of the same bare pump.

It is important to note that operating speed affect s efficiency. L ower speed VTS .1800 pumps will achieve efficiencies greater

- than

- or

- equal

- to higher speed VTS .3600 pumps of the

same flow a t best efficiency point ( BEP ) and specific speed

. d As such, the available performance and efficiency data for VTS

. 3600 cannot be directly translate d to performance and efficiency

data for VTS

. 1800.

T he CIP working group recommended that VTS .1800 efficienc y level s be set to the same

value as VTS .3600 efficiency levels

. ( EERE

- 2013

- BT

- NOC

- 0039

- 0105 at pp. 300

- 308; EERE

2013

- BT

- NOC

- 0039

- 0106 at pp. 38

- 40, 62

- 67, 88

- 95 ) Given a VTS .1800 pump and a VTS .3600 pump built from the same bare pump, the VTS .1800 will be more efficient than the VTS .3600

pump . Consequently, if VTS .3600 pump is designed to meet a certain efficiency level, the

VTS .1800 will also meet that efficiency level. Ultimately

, given the limitation in direct data

, this similarity method allows highest possible efficiencies levels to be established for VTS .1800.

Given the relationship between VTS .1800 and VTS .3600 and the recommen dation of the CIP working group, DOE proposes efficiency levels for VTS .1800 equal to that of the VTS .3600

c Note that VTS .1800 models will not be independently hydraulically redesigned. Manufacturers indicate that hydraulic designs are optimized for 3,600 RPM operation, an d then sold for use at 1,800 RPM if requested by the

consumer.

d Note that although VTS .1800 pump are more efficient, they have different performance characteristics than

VTS .3600 and cannot be considered direct replacements.

5

- 6equipment class. Section 5.8 provides further details on methods used to establish efficiency

levels.

5.4 SUMMARY OF SIGNIFICANT DATA SOURCES

For the engineering analysis, many data sources were utilized, but the following three

sources provided most of the information ; (1) a performance, shipments, and sales database, (2) aggregate model, shipment and sales revenue data, and (3) a cost of redesign survey. T hese data

source s are described in the following three subsections. Additional data sources are describe d as appropriate throughout this chapter

.

5.4.1 Performance, Shipments, and Sales Database

Through out this c hapter, the performance, shipments, and sales database is called the “performance database.” DOE based m uch of the eng ineering and downstream analysis on this

database. The database includes information for individual pump model s , and was merg ed from two separate, but similar datasets; (1) a database built from confidential manufacturer interview

responses obtained under non

- disclosure agreements with DOE’s contractors , and (2) a database purchased from the Hydraulic Institute (HI) . Both database sources were of the same structure

and contained the same fields. T he only difference being that HI was able to reach some manufacturers that DOE was not, and vice

- versa. As a result DOE concluded that the merged database is the best representation of the industry that is currently available. The final database

contain s the following fields for each model entered into the data base.

• DOE pump type

• ANSI/HI Pump type

• Number of stages

• Bowl diameter (if applicable)

• Rated speed

• Net sales volume in dollars

• Shipments volume in number of units sold

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

o Flow in gallon per minute

o Head in feet

o Pump efficiency

o Shaft i nput p ower in horsepower

Table 5.4

. 1 provides a quantitative breakdown of the number of models in the database and shipmen ts they represent, organized by pump type and speed.

5

- 7Table 5.4

. 1 Summary of Performance, Shipments, and Sales Database Content

Pump Type & Speed Number of

Models Number of Shipments Percent of Total Shipment *

ESCC.1800 307 16,017 40% ESCC.3600 249 52,208

ESFM.1800 344 26,194 67% ESFM.3600 217 3,095

IL.1800 215 17,196 41% IL.3600 106 3,581

VTS .1800 Data Not Available 15% VTS .3600 58 15,817

*Total industry shipments were available at the pump type level only, as estimated by the Hydraulic Institute in

Table 5.4.2

.

5.4.2 Industry A ggregate M odel, S hipment and S ales R evenue D ata

As a part of the CIP working group proceedings , the Hydraulic Institute provided DOE

with a special statistical report bulletin 3 containing aggregate revenues (in USD) and aggregate

unit s sold for the pump industry, for the year 2012, summarized by pump type. HI later supplemented this statistical bulletin with total model counts

. 4

In this bulletin

, HI provided both direct survey r esults as well as scaled estimates of total industry results. The direct results represent 17 United States manufacturers th at participated in

the HI survey. Individual surveys were aggregated by a third party, National Electrical

Manufacturers Association Business Information Services ( NEMA / BIS ) , to avoid improper sharing of unique competitive data. HI developed the scaled total industry estimates by starting with the direct survey results and scaling up based o n industry knowledge

. M odel, shipment, and sales revenue data for the equipment classes relevant to this NOPR are summarized in Table

5.4

. 2

. Note that model and shipme nt counts in Table 5.4

. 1 summ arize the pumps for which DOE has performance data, while the model and shipments counts in Table 5.4

. 2 summarize all pumps reported to NEMA/BIS , including those for which performance data w ere not submitted

. Accordingly, DOE does not have performance data for all pumps represented in Table 5.4

. 2

.

5

- 8Table 5.4

. 2 Model, Shipment and Sales Revenue Data for 2012
Direct Survey Results * Total Industry Esti mate

Pump Type Revenues (USD) Units Sold Number of Unique

Models Revenues (USD) Units

Sold Number of Unique

Models **

ESCC $ 67,450,020 94,278 685 $ 122,666,193 171,456 N/A

ESFM $ 52,141,922 31,587 632 $ 72,701,801 44,042 N/A

IL $ 34,498,011 24,406 381 $ 71,273,906 50,424 N/A

VTS $ 34,845,998 78,624 211 $ 46,272,522 104,406 N/A

RSV $ 72,331,298 44,527 86 $ 81,181,030 49,975 N/A

*Participating Companies: Armstrong Fluid Technology, National Pump Company, Dover Pump Solutions Group, Patterson Pump Company, Flowserve Corporation Pentair, Franklin Electric Price Pump Company, Gorman

- Rupp , Sulzer Pumps (US) Inc., Grundfos USA TACO, Inc., ITT

- Industrial Process Wilo USA LLC, Iwaki America Incorporated, Xylem Inc.

- Applied Water Systems, and Lewis P umps. **Hydraulic Institute did not provide scaled estimates for the number of unique models.

5.4.3 Cost of Redesign Survey

Following the close of the Framework Document comment period, the Hydraulic Institute provided DOE with a special statistical report bulle tin 5 on the average cost to redesign a 1 , 50,

and 200 horsepower pump from below the 10 th percentile of efficiency to the 40 th percentile. HI believes that the presented redesign costs are applicable to all equipment classes covered in the

NOPR. The data i n this bulletin represent the average d results of 15 major United States manufacturers that participated in the survey. The survey was administered and aggregated by

NEMA/BIS for the Hydraulic Institute.

The cost of redesign survey data was ultimately use d as the basis for DOE’s industry

wide conversion cost analysis. Before doing so, DOE ’s contractor validated the HI data through independent collection of similar data (covered under non

- disclosure agreement) from individual manufacturers. DOE compared the individual manufacturer responses to the averaged HI data and ultimately concluded that the HI survey results were reasonable and in line with the independent data. DOE choose to use the HI survey results over their own data collections, as the HI survey reached more manufacturers than the DOE survey. Table 5.4

. 3 summarizes the results of the statistical bulletin on average cost to redesign a 1 , 50, and 200 horsepower pump.

5

- 9Table 5.4

. 3 Cost to Redesign One Pump Model from below the 10 th to the 40 th P ercentile

of E fficiency

Cost Category Average Cost to Redesign One Pump Model

1 HP 50 HP 200 HP

Redesign, Prototype & Initial Test * $52,239 $64,397 $65,918

Pattern & Tooling ** $44,329 $51,280 $72,862

Testing † $22,858 $35,394 $47,293

Working Capital ‡ $37,101 $47,339 $54,975

Marketing †† $32,225 $29,842 $27,155

TOTAL $188,752 $228,252 $268,203

*Pattern & Tooling includes: Supplier Development, Patterns, Tooling (Machine Fixtures, Assembly Fixtures, and

Test Fixtures), Machine Programming, Quality Inspection (Parts, Tooling), New Process Development (Casting and/or Machine Processes), New Machine Tools, Retraining. †Testing includes: Performance (All Speeds), Hydrostatic, Certification Testing, Production Documentation, Training, etc. ‡Working Capital includes: Cost of money to finance upgrades, Inventory ( Cost, Rework Transition, and Inventory

fo r legacy support.) ††Marketing includes: Literature, Data Sheets, Curves, Pump Selection Tools, Sales Training, Compliance
Documentation, etc.

5.5 REPRESENTATIVE EQUIP MENT FOR ANALYSIS

For the engineering analysis, DOE directly analyzed the MPC

- and MSP

- efficiency relationship for the equipment classes specified in s ection 5.3 over the full range of pump sizes. Within the engineering analysis, “size” is defined by a pump’s flow at BEP and specific speed. Analyzing over the full size range allowed DOE to use representative configurations for each equipment class, rather than an approach that analyzes a representative unit from each class. A representative unit has a defined size and defined features, while a representative configuration

defines only the features of the pump, allowing the MPC

- and MSP

- efficiency analysis to consider a large range of data points that occur over the full range of sizes.

In selecting re presentative configurations, DOE researched the offerings of major manufacturers to select configurations generally representative of the typical offerings produced within each equipment class. Configurations and features were based on high

- shipment

- volume designs prevalent in the market. The key features that define each representative configuration include impeller material, impeller production method, volute/casing material, volute/casing production method, and seal type. DOE’ s findings and selections of representative configurations for the directly analyzed pump classes are described in the following sections.

5

- 105.5.1 ESCC, ESFM, and IL Equipment Classes

5.5.1.1 Impellers

Pumps in the ESCC, ESFM, and IL equipment classes are often constructed of similar

components m ade from similar materials. I mpellers used in these pumps may be made from cast iron, cast bronze, cast stainless steel, fabricated stainless steel, or composite materials.

Responses gathered from confidential manufacturer interviews indicate that the vast

m ajority of pumps in these equipment classes are constructed from cast bronze. Bronze is popular due to its ease of manufacture and its non

- corrosive nature. It is relatively easy to

machine and can be cast using various processes, such as sand casting, inv e stment casting, and die casting.

Due to its non

- corrosive nature, stainless steel is generally considered a direct competitor to bronze. Stainless steel is generally a stronger and more wear

- resistant material, however, it is

also more difficult to cast a nd machine . Historically the higher price of stainless steel has kept it

from significantly penetrating the market. Recent fluctuations in steel and copper commodity prices have brought the cost s of finished stainless steel and bronze impellers to similar levels.

This has resulted in increased use of stainless steel impellers . Despite this recent trend

, bronze

and stainless steel prices are still highly volatile, and stainless steel is still not a s widely used as

copper.

Due to its susceptibility to corrosion

, cast iron is not a direct substitute or competitor to bronze and stainless steel impellers. As a result , cast iron impellers make up only small portion of the market for product s within the s cope of this rulemaking . Cast iron is significantly less expensive than bronze or stainless steel , consequently cast iron impellers are often viewed as a low cost alternative. Although some models are offered with only cast iron impellers , it is

common fo r manufacturers to offer the i r pump models in a standard bronze or stainless impeller configuration , with an optional cast iron impeller configuration. T he bronze /stainless fitted

version is generally considered as the primary and high volume configuration

. Consumer s may choose to use cast iron impellers in applications with limited corrosion potential, such as closed

loop applications with limited oxygen content in the water system. Cast iron pumps also excel in

non

- clean water applications where the toug hness of the cast iron, especially when epoxy coated, can provide enhanced wear and impact resistance. Alternatively, consumer s may choose cast iron in scenarios where reduced performance due to corrosion or wear is an acceptable trade off, given the lower first cost.

The term fabricated stainless steel impeller describes an impeller comprised of stainless steel components (vanes and discs) that have been mechanically formed from stainless steel sheet metal and welded together to create a finished impeller . For reference, Figure 5.5

. 1 features a view of a typical two

- dimension al impeller with and without the second of its two discs attached. Although exceptions exist, fabricated impellers are typically limited to higher specific speeds. The stainless steel sheet metal used for these impellers is typically very smooth an d

naturall y corrosion resistant. D ue to its relatively light gauge, the fabricated impeller is not impact resistant and may ben d or den t if struck by suspended particles . Manufacturer interview responses indicate that fabricated stainless steel impellers are not pro duced on pumps with sizes

5

- 11greater than approximately 20 hp . Although larger sizes are technically feasible, manufacturers have indicated that they are not economical to produce, as these larger impellers do not reach the high sales volumes needed to cover the large initial tooling investment requirements. Overall, ESCC, ESFM, and IL pumps with fabricated stainless steel impeller are a niche product and represent a small portion of the market.

Figure 5.5

. 1 Two

- Dimensional Fabricated Stainless Steel Impeller. With and Without

Second Disc Attached 6

The term composite impeller describes an impeller made from plastics, resins, or other

similar non

- metallic material. Composite impellers are typically injection molded parts with low strength and a smooth surface finish. Within the scope of this NOPR, p umps with composite impellers are not sold in high volumes and are generally reserved for niche applications where a metal impeller is undesirable, or where durabi lity and strength is not a primary concern. DOE

does not e that composite impellers are common ly used in less

- than

- one horsepower circulator pumps, which are not covered by this NOPR.

Given the available information, cast bronze impellers have been chosen a s the representative impeller type for ESCC, ESFM, and IL pump s . Additionally, given the current

similar ( but fluctuating ) market prices of stainless steel and bronze, DOE concluded that an analysis based on bronze is also financially representative of stainless steel impeller pumps.

5.5.1.2 Volutes

V olutes (also known as cases) used in ESCC, ESFM, and IL pumps are typically made from cast iron, cast bronze, cast stainless steel, or fabricated stainless steel. Responses gathered

from manufacturer interviews indi cate that the vast majority of pumps in these equipment classes

are constructed from cast iron.

Cast iron is popular for volutes due to its low cost and suitable strength. For general

purpose pumps, normal levels of oxidation and resulting surface rougheni ng of the volute are acceptable, as the oxidation will not compromise the mechanical integrity of the static volute, as it would with the rotating impeller. For application s where oxidation is not acceptable, such as pumps for potable water use, cast iron volutes can be coated with corrosion resistant materials.

5

- 12Rather than use a coated cast iron impeller, consumer s requiring corrosion resistance may choose to purchase a cast bronze or cast stainless volute. These options are generally more costly

than a c oated cast iron volute, but may meet additional requirements imposed by the consumer ’s application.

Finally, fabricated stainless steel volutes are used exclusively with fabricated stainless steel impeller s . These volutes provide the same smooth surface fi nish and corrosion resistance as the fabricated impeller and are also limited in size due to economies of scale.

Given the available information, cast iron volutes have been chosen as the representative volute type for ESCC, ESFM, and IL pumps.

5.5.1.3 Seals

Altho ugh numerous types of seals exist for ESCC, ESFM, and IL pumps, manufacturer interview responses and research of the industry suggests that the most commonly used seal for pumps covered in this NOPR is Type 21, carbon

- ceramic seal. As such, the Type 21, ca rbon

ceramic seal has been chosen for the representative configuration.

5.5.2 RSV and VTS Equipment Classes

Within the scope of this NOPR, pumps in the RSV and VTS equipment classes often

share similar construction and material selection. In these pumps, i mpe llers and bowls e are stacked together to form stages . These impellers and bowls can be manufactured from cast iron, cast bronze, cast stainless steel, or fabricated stainless steel. Sales data and r esponses gathered

from manufacturer interviews indicate that the vast majority of impellers in these equipment classes are constructed from fabricated stainless steel impellers.

Both RSV and VTS are multistage pumps that generally specialize in providing high heads (pressures) at low to moderate flows

. RSV s are typically used for pressure boosting applications, and VTS s are typically used to lift groundwater up from a deep well. Fabricated stainless steel impellers are generally limited to low specific speed, which can provide the low flow and high head per formance that RSV and VTS pumps require. The thin, lightweight fabricated impellers can also be easily stacked into multiple stages to provide the consumer with a wide variety of available head s in a relatively small pump. RSV and VTS pumps are often used for potable water applications, which make the non

- corrosive nature of stainless steel a logical

fit

.

S ome RSV and VTS pumps with in the NOPR scope do use cast impellers and bowls , but this is fairly uncommon

. C ast materials may be used in larger RSV and VT S pumps that are not in the scope of this rulemaking. Cast materials are u ncommon in smaller sizes of these pumps due to the bulkiness and weight of the cast designs , especially when stacked into multiple stages

. This is e specially true for VTS pumps, where well diameters are limit ed, and thick cast bowls reduce the available impeller diameter, which restricts overall performance.

e The “bowl” is the case that function s as the volute on each stage of a vertical turbine

.

5

- 135.5.3 Summary

For the ESCC, ESFM, and IL equipment classes the representative configuration is defined as a pump fitted with a ca st bronze impeller; cast

- iron volute; and a Type 21 mechanical

seal. For the RSV and VTS equipment classes the representative configuration was defined as a pump fitted with sheet metal

- based fabricated stainless

- steel impellers, and sheet metal

- based

f

abr icated stainless

- steel bowl/casing

.

5.6 DESIGN OPTIONS

After conducting the screening analysis and removing technologies that did not warrant inclusion on technical grounds, hydraulic redesign remained as the only design options to be

considered in the NOPR e ngineering analysis.

5.7 AVAILABLE ENERGY EFF ICIENCY IMPROVEMENTS

For each equipment class, DOE assessed the available energy efficiency improvements resulting from a hydraulic redesign. This assessment was informed by manufacturer performance and cost data, c onfidential manufacturer interview responses, general industry research, and stakeholder input gathered at the CIP working group public meetings. Chapter 3 presents potential efficiency gains resulting from improved hydraulic redesign, for each equipment c lass. These potential improvements represent the efficiency spread between the lowest efficiency and highest efficiency pumps already offered in the marketplace. It is technically feasible for any pump of the baseline configuration (discussed in section 5.8 ), regardless of flow and specific speed, to improve efficiency by the values present in chapter 3. A vailable efficiency improvements will be less f or pumps that exceed the baseline. A manufacturer can realize efficiency improvements in any increment, depending on the level of effort and capital they choose to invest in their redesign program. Section 5.10 discusses the relationship between efficiency gains and conversion cost in more detail.

5.8 EFFICIENCY LEVELS

5.8.1 Efficiency Level Structure

DOE established e fficiency levels that are a function of flow and specific speed

. f The

form of this function tracks the inherent efficiency trends for pumps of different flow and specific speeds. Each efficiency level represent s an efficiency percentile. This means that for the

2 5 th percentile efficiency level, 2 5 % of pump models have lower efficiency than the defined efficiency function. T he following example g illustrates this idea :

• If Pump A has a flow of 80 GPM and a specific speed of 720, its attainable efficiency might range from 47% to 5 9 %.

f Low flow, low specific speed pumps are limited to lower efficiencies than high flow, high specific speed pumps

g Example is fictional and used for illustrative purposes only.

5

- 14• If Pump B has a flow of 1250 GPM and a specific speed 1,500, its attainable efficiency might range from 75% to 8 7 %.

• The efficiency level function need s to compensate for differences in the efficiency ranges between these two pumps. At t he efficiency level representing t he 50 th percentile (an efficiency that is higher than 50% percent of pump models) , the efficiency would be 53 % for Pump A and 81 % for Pump B (assuming an even distribution of pump models by efficiency

— or a symmetric distribution)

. 53% and 81% represent mid point (50 th
percentile) of the attainable efficiency range for Pump A and B , respectively

.

Moving past th is simplified example, in this NOPR the metric used to establish a proposed standard level is the p ump energy i ndex ( PEI ) . A pump model would be compl iant if its PEI rating is less than or equal to the PEI of the proposed standard. PEI is defined as the pump efficiency rating (PER) for a given pump model, divided by a calculated minimally compliant PER for the given pump model. PER is defined as a weigh ted average electric input power to the pump over selected operating conditions , represented in units of horsepower.

The minimally compliant PER is unique to each pump model and is a function of each pump model’s flow at BEP and specific speed . This function is a polynomial equation that

represents a three

- dimensional surface that describes the attainable hydraulic efficiency of

pumps. All of the terms of the polynomial function are the same for all efficiency levels and

classes of pumps for th e DOE analysis, except for the constant parameter

. This parameter , the

“C

- value

, ” is unique for each combination of equipment class and efficiency level. Changing the

C

- value moves the otherwise fixed

- shape surface to higher or lower efficiency. Because th e function was developed to represent the inherent pump efficiency trends as flow and specific speed change

, h 7 the C

- value can be used to define an efficiency level, which is equally stringent for all pumps across the scope of flow and specific speed. By adjusting the C

- value, DOE can establish different efficiency levels that represent different efficiency percentiles.

For this NOPR the three

- dimensional polynomial efficiency equation discussed in the previous paragraph is based on the EU minimum efficien cy index (MEI) equation 8 but is adapted

to account for the use of English units and the 60 Hz electrical input power found in the United

States

. i The converted equation is shown in E quation 5.1

.

ηη pppppppp

, SSSSSS = − 0

. 85 ∗ ln ( QQ ) 2

− 0

. 38 ∗ ln ( NNNN ) ∗ ln ( QQ )

− 11

. 48 ∗ ln ( NNNN ) 2 + 13

. 46 ∗ ln ( QQ )

+ 179

. 80 ∗ ln ( NNNN )

− ( CC

− 555

. 6 )

h The function was developed for use in the European Union’s pump efficiency regulation.

i In the EU flow is reported in m 3 /hr at 50 Hz and specific speed is reported in metric units. In the US flow is reported in gpm, and the 60 Hz electricity frequency will cause the same pump to output 120% more flow than in the EU. Increased frequency causes increased motor shaft speed, which causes increased flow. The coefficients of equation 5.1 have been modified to account for these differences.

5

- 15Eq. 5

. 1

Where:

Q = flow at BEP in gallons per minute at 60 Hz input power frequency

,

Ns = specific speed at 60 Hz, and

C = an intercept that is set for the surface, which is set based on the speed of rotation and

equipment type of the pump model.

The method of structuring efficiency levels to represent efficiency percentiles was pursued on the r ecommendation of the CIP w orking group. This method functions well for mature products such as pumps, w h ere the highest levels of attainable efficiency are already present in the marketplace. Additionally, this method follows the precedent set by the EU MEI regulation on pumps, and supports the goals of E xecutive O rder 13609, “ Promoting International Regulatory Cooperation .” 9

5.8.2 Efficiency Levels Analyzed

DOE ana lyzed several efficiency levels for each equipment class , each defined by a single C

- value . DOE chose to analyze efficiency levels at the 10 th , 25 th , 40 th , 55 th , and 70 th
efficiency percentile s . The selection of the 10 th and 40 th percentiles as efficiency levels was recommend by the CIP working group , as an effort to harmonize the efficiency le vels with the efficiency levels that were analyzed and adopted in the EU

. j On the recommendation of the working group

, DOE chose to analyze one efficiency level midway between the 10 th and 40 th
percentile, the 25 th percentile. Given the structure of the first three equipment classes, DOE and

the working group chose to establish the remaining efficiency levels in increments of 15 percentile points, up to max tech (which was found to be the 70 th percentile , and is dis cussed in

section 5.8.4 ). Table 5.8

. 1 presents the C

- valu es corresponding with the efficiency level s analyzed for each equipment class.

j C ommission R egulation (EU) No 547/2 012 enforces a regulation at the 10 th percentile, effective January 1, 2013. The regulation goes to the 40 th percentile, effective January 1, 2015.

5

- 16Table 5.8

. 1 Efficiency Levels Analyzed with Corresponding C

- values

Equipment

Class EL0 EL1 EL 2 EL 3 EL 4 EL 5

Baseline 10 th Eff.

Percentile 25 th Eff.

Percentile 40 th Eff.

Percentile 55 th Eff.

Percentile 70 th Eff.

Percentile /

Max Tech

ESCC.1800 134.43 131.63 128.47 126.67 125.07 123.71

ESCC.3600 135.94 134.60 130.42 128.92 127.35 125.29

ESFM.1800 134.99 132.95 128.85 127.04 125.12 123.71

ESFM.3600 136.59 134.98 130.99 129.26 127.77 126.07

IL.1800 135.92 133.95 129.30 127.30 126.00 124.45

IL.3600 141.01 138.86 133.84 131.04 129.38 127.35

RSV .1800* 129.63 N/A N/A N/A N/A 124.73

RSV .3600* 133.20 N/A N/A N/A N/A 129.10

VTS .1800 137.62 135.93 134.13 130.83 128.92 127.29

VTS .3600 137.62 135.93 134.13 130.83 128.92 127.29

*For RSV equipment, DOE established only baseline and max

- tech efficiency levels due to limited data availability.

5.8.3 Establishing the Baseline Efficiency Levels

5.8.3.1 Baseline Configuration

The baseline configuration represent s the lowest efficiency level commonly available in

the market. Because energy conservation standards do not currently exist for pumps, DOE must

establish a baseline configuration using available information

. The baseline configuration defines the energy consumption and associated cost for the lowest efficiency equipment analyzed in each

class.

As discussed earlier, hydraulic redesign is the only design opt ion available in the NOPR analysis. As such, the baseline configuration represents the lowest efficiency hydraulic design s commonly available in each equipment class

. Because hydraulic re design is a complex design option resulting from the interaction of n umerous interrelated design features, it is impossible to select a physical hydraulic design that represents the physical configuration of all baseline pumps across an entire equipment class. In response to this complication, DOE took the inverse approach. Similar to the process used to define efficiency levels, DOE performed a n analysis on the pump population to determine the C

- value for each equipment class that represent the lowest efficiency hydraulic designs currently in the market . Although pumps at t his baseline level may have different physical hydraulic designs, they all represent the baseline efficiency

.

5.8.3.2 Establishing Baseline C

- Values

Ideally the efficiency percentile used to represent the baseline would be zero

. But setting the baseline to represent the single lowest efficiency pump in the market may result in a baseline with unusually low efficiency that is not representative of baseline performance over the full range of flow and specific speed covered by the eq uipment class. The lowest efficiency pumps in

5

- 17the database may be outliers, misrepresented data, or non

- representative models that do not commonly exist across the entire equipment class. T he baseline must be set at a level that represents the lowest effic iency pumps (normalized for size effects) that commonly exist across the entire scope of sizes

, and it must hedge against the possibility of outliers and bad data

. As such, DOE analyze d the efficiency data for all pumps in each equipment class, and used ju dgement to select a baseline level for each.

To identify the efficiency percentile and associated C

- v alues that best represent the

baseline for each equipment class , DOE analyzed s catter p lot s of failing C

- values versus flow

and failing C

- values versus sp ecific speed.

“Failing C

- v alue” is a calculated efficiency parameter that allows each pump to be evaluated on an equivalent scale. Failing represents the C

- value at which each pump would not

meet a standard. If a pump has a failing C

- v alue less than the C

- v alue specified for an efficiency level, it would pass that efficiency level. If a pump has a failing C

- value greater than or equal to

the C

- value specified for an efficiency level, it would fail that efficiency level. For example, if an ESCC.1800 pump has a calculated failing C

- value of 128.00, it would pass efficienc y level 2, which has a passing C

- value of 128.47, but would fail at efficiency level 3, which has a passing

C

- value of 126.67. Within an equipment clas s, pumps with the same failing C

- value wi ll be of the same relative efficiency. Lower failing C

- values represent higher efficiency and vice

- versa.

F or all pumps, one point of C

- v alue is always equal to one point of pump efficiency change. For example, if two pumps of identical flow and specific s peed exist ; Pump A with a failing C

- v alue of 130 and Pump B with a failing C

- value of 125 ; Pump B might have a pump efficiency of 55% and Pump A might have might have a pump efficiency of 50%

.

Figure 5.8

. 1 presents a scatter plot of failing C

- v alues versus flow at BEP for the ESCC.1800 equipment class . Overlaid on this figure are bands representing the C

- v alues for

each efficiency levels presen ted in Table 5.8

. 1 , as well as the C

- v alues for the 5 th and 85 th
percentile s

. Figure 5.8

. 2 is similar to Figure 5.8

. 1 , except it illustrates failing C

- values versus specific speed for the ESCC.1800 equipment class. Note that higher C

- v alues represent lower efficiency pumps.

To identify the efficiency percentile and associated C

- v alues that best represent the baseline, DOE evaluated the trends and tendencies of pumps that fell below the 10 th percentile. These pumps are represented by data poin ts that fall above the red line in both figures. As the halfway point between zero and the 10 th percentile (EL1), the 5 th percentile was established as a

reasonable initial hypothesi s for the location of the baseline. As the initial hypothesis, the 5 th

per centile was plotted on the figures, and t rend s in the data above and below the line were

evaluated to support or reject the hypothesis.

Review of both figures revealed that data below the 5 th percentile is clearly spread over a larger range of C

- values tha n the other percentile grouping. Specifically, the lowest efficiency pump on the figure is approximately 10 points less efficient than the 5 th percentile line. Comparatively, if 10 efficiency points were added to a pump at the 5 th percentile line, that pum p would reach the 55 th percentile. This illustrates the risk of attempting to set the baseline by the single lowest efficiency pump . I n this case such a pump is not representative of pumps over the

5

- 18range of the equipment class . Additionally, data below the 5 th percentile appears to be less uniformly populated across both flow and specific speed, with the lowest efficiency pumps clustered at flows less than 500 GPM and specific speeds between 1,000 and 2,000. Conversely,

the data below the 5 th and above the 10 th percentile line is more uniformly distributed with respect to flow and specific speed, and is contained within a range of C

- v alues more comparable to the other efficiency bands on the figure. Given the trends of data below the 5 th percentile , and

the more uniform nature of the pumps falling above the 5 th percentile, DOE found it reasonable

to accept the initial hypothesis and estimate the baseline at the 5 th percentile for the ESCC .1800 equipment class.

Th e same hypothesis and figures were established and review ed for all equipment

class es . Figures for f ailing C

- value versus f low at BEP and specific speed for all equipment

classes can be found in A ppendix 5a. The trends for the remaining equipment classes were all

similar to those seen in ESCC.1800

. As such

, DOE found it reasonable to accept the initial hypotheses and set a uniform baseline for all equipment classes at C

- values that represent the 5 th
percentile

.

Figure 5.8

. 1 Failing C

- Values Plotted Against Flow at BEP for the ESCC.1800 Equipment

Class

5

- 19Figure 5.8

. 2 Failing C

- Values Plotted Against Specific Speed for the ESCC.1800 Equipment Class

5.8.4 Establishing the Maximum Technologically Efficiency Levels

5.8.4.1 Max

- Tech Configuration

Max

- tech is the efficiency level that provides the maximum improvement in energy efficiency that is technologically feasible. The max

- tech level must be attainable across the entire scope of the equipment class . Generally, the max

- tech level results from the combination of design options predicted to result in the highest efficiency level possible for an equipment

class. For this rulemaking hydraulic redesign was determined to be the only available design option.

P umps are cons idered mature products with the highest levels of attainable efficiency already present in the marketplace. As such, the max

- tech configuration represents the highest efficiency hydraulic design(s) commonly available in the market. Because hydraulic design is a complex design option resulting from the interaction of numerous interrelated design features, it is impossible to select a physical hydraulic design that represents the physical configuration of

all max

- tech pumps across an entire equipment class. I n response to this complication, DOE took the inverse approach. Similar to the process used to define the baseline , DOE performed a n analysis on the pump population to determine the C

- value for each equipment class that represent

the highest efficiency hydraulic designs currently in the market

.

5

- 205.8.4.2 Establishing Max

- Tech C

- Values

Ideally the 100 th efficiency percentile would be used to represent the max

- tech level. But setting the max

- tech level to represent the single highest efficiency pump in the market may

result in an overestimate d max

- tech that is not representative of the entire equipment class. The lowest efficiency pumps in the database may be outliers, misrepresented data, or non

representative models that do not commonly exist across t he entire equipment class . The max

tech must be set at a level that represents the maximum efficiency pumps (normalized for size effects) that commonly exist across the entire scope of sizes, and it must hedge against the possibility of outliers and bad da ta. As such, DOE analyze d the efficiency data for all pumps in each equipment class, and used its judgement to d evelop the max

- tech for each.

To establish the efficiency percentile and associated C

- v alues that best represent the max

tech for each equipmen t class, DOE evaluated the same failing C

- v alue versus flow and specific speed plots that there used to determine the baseline levels.

For this analysis DOE evaluated the trends and tendencies of pumps that were more efficient than the 55 th percentile. To do so, the 70 th and 85 th efficiency percentile was evaluated

in addition to the 55 th . DOE found it reasonable to evaluate in 1 5 point increments as it was consistent with the existing efficiency level spacing selected by the working group

. DOE did not feel that a more granular analysis was warranted, given the analytical structure.

DOE sought to determine whether the max

- tech level occurred at the 5 5 th , 70 th , or 85 th
percentile. To do so, the trends in the data below each percentile line w e r e evaluated

. Revi ew of both figures revealed that the efficiency range ( C

- v alue range) of the 55 th to 70 th percentile band

is similar to those seen in the lower efficiency bands. The range widened in the 70th to 85th percentile band and widened even furth er beyond the 85th percentile. Pumps beyond the 85th percentile are clearly spread over a larger range of C

- values than the other percentile grouping. Specifically, the highest efficiency pump on the figure is approximately 10 points more efficient

than t he 85 th percentile line. Comparatively, if 10 efficiency points were subtracted from a pump at the 8 5 th percentile line, that pump would drop to the 10 th percentile. This illustrates th e risk of attempting to set max

- tech by the single maximum efficiency pump, as in this case such a pump

would not be representative of the equipment class.

Additionally, pumps at or below the 8 5 th percentile line are less uniformly populated across both flow and specific speed, with the maximum efficiency pumps clustered at flows less

than 500 GPM and specific speeds below 1,500

. Few pumps of higher flows and specific speeds

exist above the 85 th percentile line. As such, the 85 th percentile is considered to not be technologically feasible for these larger pumps. A standard set at such a level would result in pumps of larger flows and specific speeds no longer being available in the market. A similar analysis of the 70 th percentile line shows pumps to be well distributed with respect to flow and specific speed. Thi s suggests that the 70 th percentile is likely a technologically feasible level across the entire equipment class.

Given the trends of the upper 70 th and 85 th percentile data, and the more uniform n ature of the pumps falling between the 55 th and 70 th perce ntile, DOE concluded that the 85 th percentile

5

- 21is beyond max

- tech, and found it reasonable to establish the next level down, the 70 th percentile

, as the max

- tech for the ESCC.1800 equipment class.

The same analysis was performed for all equipment class es

. Figures for f ailing C

- value

versus f low at BEP and specific speed for all equ ipment classes can be found in A ppendix 5a. The trends for the remaining equipment classes were all similar to those seen in ESCC.1800. As such, DOE found it reasonable to set a u niform max

- tech for all equipment classes at C

- values that represent the 70 th percentile. This decision was supported by the CIP working group and

harmonizes with the EU, where the 70 th percentile was determined to be “the indicative

benchmark for the best available technology on the market for water pumps .” 10

5.8.5 Harmonization with EU Analytical Structure and Methods

Review of the failing C

- value versus flow and efficiency charts brings to light the observation that low flow and low specific speed pumps (“sm aller pumps”) clearly have a wider range of available efficienc ies than their high flow and high specific speed counterparts. In general, this observation is a result frictional or volumetric losses making up a larger percent of

output power for smaller pumps that for larger pumps. S mall improvements (or reductions) in the

frictional or volumetric losses will have a larger effect on the overall efficiency of a smaller

pump. T he technology option data presented in chapter 3 clearly demonstrate s this relationship for technology options such as improved surface finish and reduced running clearances.

The issue of attainable efficiency ranges varying across each equipment class was a known limitation of the metric and analytic structure used by DOE for this NOPR. As such, DOE explored options to better account for the varying efficiency ranges. These options included:

• Changing the shape of the efficie ncy surface to better represent the U

. S

. market

• V ary ing the efficiency surface shape at each EL , rather than adjusting just the C

- v alue

• S et ting C

- v alue s for the ELs that var y with flow and specific speed

• B reak ing up equipment classes into multiple sub classes based on flow and specific speed

o Smaller pumps could have a different baseline and max tech than the larger

pumps.

Ultimately, DOE and the CIP working group decided to not pursue the alternate

structures, as they felt the value of an analytical structure that is harmonized to the EU outweighed the potential benefits of the more complex analyses. This decision supports the goals

of E xecutive O rder 13609

, 11 which calls for harmonization among international stand ards

.

5.8.6 Establishing Baseline and Max

- Tech for RSV Equipment Classes

As discussed in section 5.3.1

, DOE propose s to set the RSV .1800 and RSV .3600 baseline

and max

- tec h equivalent to the EU minimum efficiency standard and max

- tech , respectively

. To

5

- 22do so, DOE translate d the EU MEI C

- values to equivalent PEI C

- values. Both MEI and PEI define efficiency level with C

- values, but the MEI and PEI C

- values are not interchangeable. For example a n MEI C

- value of 1 30.38 might represents the 40 th percen tile using the MEI metric

, but a PEI C

- value of 1 30.38 might represent s the 35 th percentile when used on the same dataset

. The EU MEI C

- values for RSV 4 0 th and 70 th percentile s k are presented in Table 5.8

. 2 . Note that the EU refers to these equipment classes as MS 1450 and MS 2900, which correspond to the

RSV .1800 and RSV .3600 equipment classes, respectively.

Table 5.8

. 2 European Union MEI C

- Values for RSV at the 40 th and 70 th Percentile 12

40 th Percentile

MEI C

- value 70 th Percentile

MEI C

- value

MS 1450 ( RSV .1800) 130.38 125.48

MS 2900 ( RSV .3600 ) 133.95 130.37

There is no mathematical function to directly translate between MEI C

- value s and PEI C

value s . Instead, DOE used a two

- step method to estimated PEI C

- values that are approximately equivalent to the MEI C

- values. First, for all equipment classes (except RSV l )

, DOE evaluated

how MEI C

- values at the efficiency percentiles defined by each EL compared to PEI C

- values at

the same efficiency percentiles

. The EU MEI equation and regulation method 13 was applied to each pump in the performance database to find t he MEI C

- values at each efficiency percentile. On average

, PEI C

- values were 0.75 point lower than MEI C

- values. As such , DOE subtracted 0.75 point f r o m the EU MEI C

- values, to create a first estimate of PEI C

- values for RSV

.

Second, DOE evaluated limited RSV model performance data again st the first estimates

of PEI C

- values . DOE found that some RSV .3600 models that passed the EU 70 th percentile

level were failing the estimated DOE 70 th percentile level. As a result, DOE adjusted the PEI C

value s for this equipment class until all models that passed the EU 70 th percentile also passed the

DOE 70 th percentile. The resulting PEI C

- values for RSV baseline a nd max

- tech are presented in

Table 5.8

. 3

.

Table 5.8

. 3 DOE PEI C

- Values for RSV at Baseline and Max

- Tech

Baseline C

- value Max

- Tech C

- value

RSV .1800 129.63 124.73

RSV .3600 133.2 0 129.1 0

k 40 th percentile is the minimum efficiency standard in the EU, effective January 1, 2015.

l Available RSV d ata did not represent enough manufacturers or models to be considered representative for the industry.

5

- 235.9 MANUFACTURERS PRODUC TION COST ASSESSMENT METHODOLOGY

5.9.1 Changes in MPC A ssociated with Hydraulic Redesign

DOE performed an analysis to determine the change in MPC, if any, associated with a hydraulic redesign. For this analysis DOE consider two key inputs ; (1) confidential manufacturer responses to relevant interview questions, and (2) an analysis of confidential impeller cost and efficiency data provided by manufacturers.

During manufacturer interview s, DOE specifically inquired about potential changes in production costs associated with a hydraulic redesign

. Eight manufacturers of pumps covered by the scope of this rulemaking were interview ed

. Although most manufacturers indicated that selling price might be increased to cover the costs of rede sign, no responses indicated that production costs would increase due to hydraulic redesign

. m

I mpeller cost and efficiency data

, gathered under confidentiality agreements from four

manufacturers

, were also analyzed

. Impeller design (and its interaction wit h the volute) is primarily responsible for establishing the performance and efficiency of a pump

. As such, any possible cost increases that might result from a hydraulic redesign were expected to come from the impeller and volute. DOE notes that sufficient data w ere not available to evaluate volute cost and efficiency.

For the impeller cost

- efficiency analysis, t he production costs from four manufacturers

for 24 end suction and inline impellers were first plotted against the normalized scaling

parameter Log((N

s /Q) x (n/1800)) + 1 and regressed using a second order polynomial, as shown

in Figure 5.9

. 1

. n End suction and inline impellers were grouped together as these impellers are typically of similar construction and cost.

Next the “ cost premium ” (or discount) for each impeller was found by calculating each pump’s deviation from the regression line. Cost premium for each impeller was then plotted versus the efficiency percentile of the pump that the impeller belonged to. The results are plotted

in Figure 5.9

. 2

.

Finally, the data points were grouped by manufacture r , and trends betw een pumps in the lower and upper 50 th percentile were evaluated, as shown in Figure 5.9

. 3 . The results indicate that only one manufacturer had a positive cost versus efficiency percentile relationship, with the

other three manufacturers displaying negative or flat relationships.

Given the manufacturer interview responses and the results of th e impeller cost

efficiency analysis, DOE concluded that more

- efficient pump geometries are no more complex

m Note the two manufacturers of products not covered by scope of this rulemaking indicated that they expected to see increases in production cost due to hydraulic redesign.

n Log( ( N

s /Q ) x (n/1800) ) + 1 is a normalized scaling parameter that allows for cost and price modeling in only two dimensions. This factor uses physical speed ( n ), flow at BEP, and specific speed to essentially represent the “size” of a given pump. This factor is used consistently throughout the engineering analysis. See Appendix 5 b for a more detailed explanation of this factor.

5

- 24to manufacturer than less

- efficient geometries. Consequently, a hydraulic redesign is not expected to increase the MPC of the representative pump configuration s used for analysis.

As a part of the engineering analysis, DOE presented this conclusion to the CIP working group at the April 29, 2014 meeting. At this meeting the flat cost

- effic iency relationship was explicitly discussed, and no objections from the working group were received

. (EERE

- 2013

- BT

NOC

- 0039

- 0072, pp.114

- 130 and pp.270

- 273) As a result

, DOE propagated this relationship into subsequent engineering and downstream analyses. DOE does note that in a later meeting, one

member of the wor king group expressed concern with this conclusion (EERE

- 2013

- BT

- NOC

0039

- 109 , p. 264 ). The working group, including this member, ultimately voted to approve the term sheet, which was based on the a nalytical structure of a flat cost

- efficiency relationship (EERE

- 2013

- BT

- NOC

- 0039

- 0092). As such

, the analyses presented in the NOPR and this TSD

are based on a flat cost

- efficiency relationship.

DOE acknowledges that actual changes in MPC experienced by individual manufacturers will vary, and that in some cases redesigns may actually increase or decrease the cost of the impeller and/or volute. However, available information indicates that the flat MPC

- versus

efficiency relationship best represents the pumps included in the scope of this rulemaking.

5

- 25Figure 5.9

. 1 Impeller Production Cost Plotted against the Scaling P arameter

Figure 5.9

. 2 Impeller Production Cost Premium Plotted Against Efficiency Percentile

5

- 26Figure 5.9

. 3 Trends between the Lower and Upper 50 th Percentile of Efficiency, for Impeller Production Cost Premium

5.9.2 Manufacturer Production Cost Model

Using data from the performance database, DOE developed a scalable cost model to estimate MPC across all pump sizes. Given a pump’s specific speed and BEP flow, the cost model outputs an estimated MPC. Because efficiency improvements from hydraulic redes ign

have no associated increase in MPC, the model is efficiency

- independent and predicts the same MPC for all pumps of the identical BEP flow, spec ific speed, and equipment class

.

5.9.2.1 Equipment Class Grouping

D OE aggregated d ata from all end suction pumps and used the data to develop a model that predi ct s MPC for any pump in the ESCC.1800, ESCC.3600, ESFM.1800, and ESFM.3600 equipment classes. Data from all inline pumps were also aggregate d and used to develop a model that could predict MPC for any pump in the IL.1800 and IL.3600 equipment classes. Fina lly,

data from VTS .3600 pumps were used to develop a model that could predict MPC for any pump

in the VTS .1800 and VTS .3600 equipment classes.

5.9.2.2 Model Description

Each MPC model outputs the model

- weight ed

- average production cost of a pump. This

means that the MPC of each model in the equipment class was weighted equally when developing the cost model. The model

- weighted

- average production cost represents only the cost

5

- 27of the bare pump. The following subsection describes the details of the MPC model development.

5.9.2.3 Model Development

The MPC models were developed using data from the performance database. Specifically, the model uses pump revenues, manufacturer markup, shipments volumes, and

motor costs as key inputs

. The remainder of this s ection outlines in detail the steps taken to build the MPC models for each equipment class grouping

.

T he revenue data supplied in the performance database included motor revenues; as such the first step of the analysis was to subtract the value of the mot or s from the revenues and ultimately the MSP. The steps taken to do so are as follows:

1. Calculate the per

- u nit MSP (including motor) for each model

.

a. For each model d ivide total r evenues (including motor revenues ) by t otal shipments volume

. 2. Estimate the baseline motor price for each model in the database

.

a. Estimate motor HP as 120% of BEP required shaft HP

. b. Use manufacturer

- specific motor cost curves to estimate baseline motor price

. o

3. Find a djusted m otor c ost

, which account s for markup and the percent of p umps in the population not sold with motors

.

a. Multiply baseline motor price by a markup to create the marked

- up motor cost.
b. Multiply marked

- up motor cost by manufacturer

- specific percent of shipments

sold with motors to find the adjusted motor cost

, which account s the portion of shipments not sold with motors.

4. Find bare pump MSP by subtracting a djusted m otor c ost from p er

- u nit MSP

With bare pump MS P known for each pump in the database, bare pump MSP could then

be regressed against the scaling parameter . To do so effectively and give equal weight to all pump sizes with in the scope, the bare pump MSP values were grouped and averaged over discrete ranges of the scaling parameter, and a regression was performed. Figures 5.9.4

, 5.9

. 5

, and 5.9

. 6 illustrate the grouping and regressions for each of the three M S P models. The resulting regressions estimate the m odel

- w eighted

- a verage MSP for all pumps in the industry. Th e s e regression s d o not yet take into account any possible relationships between markups and efficiency. They provide only the average MSP for a pump of a given size (as defined by the scaling parameter).

From these regressions, the model

- w eighted

- a verage MPC is calculated. The first step is to

divide the m odel

- w eighted

- a verage MSP regressions by the average manufacturer markup p for each equipment class. The resulting model

- weighted

- average MPC regression represent s the average production cost for a pump of a given size. Beca use efficiency improvements from hydraulic redesign have no associated increase in MPC, the model is efficiency

- independent and

o M anufacturers supplied confiden tial motor cost data that was analyzed and trended for use in this analysis.

p Discussed in Section 5.11

5

- 28predicts the same MPC for all pumps of the identical BEP flow, specific speed, and equipment

class

. Figures 5.9.4

, 5.9.5 , and 5.9.6 also illustrate the grouping and regressions for each of the

three MP C models

.

Figure 5.9

. 4 Average Bare Pump MPC and MSP per Model for IL.1800 and IL.3600

5

- 29Figure 5.9

. 5 Average Bare Pump MPC and MSP per Model for ESCC.1800, ESCC.3600, ESFM.1800, and ESFM.3600

Figure 5.9

. 6 Average Bare Pump MPC and MSP per Model for VTS .3600

5.10 CONVERSION COSTS

DOE expects that hydraulic redesigns will result in significant conversion costs for

m anufacturers as they attempt to bring their pumps into compliance with the proposed standard. DOE classified these conversion costs into two major groups: (1) product conversion costs and (2) capital conversion costs. Product conversion costs are investmen ts in research, development, testing, marketing, and other non

- capitalized costs necessary to make product designs comply with a new or amended energy conservation standard. Capital conversion costs are investments in property, plant, and equipment necessa ry to adapt or change existing production facilities such that new product designs can be fabricated and assembled.

To evaluate the magnitude of the product and capital conversion costs the pump industry would incur to comply with new energy conservation s tandards, DOE used a bottom

- up approach. For this bottom

- up approach, DOE first determined the industry

- average cost, per model, to redesign pumps of varying sizes to meet each of the proposed efficiency levels. DOE then modeled the distribution of unique pump models that would require redesign at each efficiency level. For each efficiency level, DOE multiplied the number of unique failing model s by the associated cost to redesign and summed the total to reach an estimate of the total product and capital co nversion cost for the industry. The following sections describe, in detail, the data and methodologies used to estimate these conversion cost for the industry.

5

- 305.10.1 H ydraulic ally Redesign Cost Data

Data supplied to DOE by the Hydraulic Institute was used as bas is for the industry

average cost, per model, to redesign a failing pump model. The Hydraulic Institute, through an independent third party, surveyed 15 manufacturers regarding the product and conversion costs associated with redesigning 1 , 50, and 200 hors epower pumps from below the 10 th to the 40 th
percentile of market efficiency. The results of HI' s survey were previously discussed in S ection

5.5 and presented in Table 5.4

. 3

. For this analysis, the “cost of capital” cost category was adjusted to b ack out “financing costs” that were included in the category. In this context, financing costs represented the total discounted cost of financing redesign activities in 2012 dollars. These costs were backed out, as they are not considered capital or produc t conversion costs. Financing costs are accounted for in the Manufacturer Impact Analysis ( chapter 12) with

the weighted average cost of capital (WACC) parameter. HI, along with the CIP working group, agreed that it was reasonable to back out 75% of that c ost of capital cost category, leaving the remaining 25% to cover costs associated with the need to carry increased inventory. The revised redesign costs are presented in Table 5.10

. 1

.

Table 5.10

. 1 Revised C ost of R edesign T able

Cost Category Average Cost to Redesign One Pump

Model

1 HP 50 HP 200 HP

Redesign, Prototype & Initial Test * $52,239 $64,397 $65,918

Pattern & Tooling ** $44,329 $51,280 $72,862

Testing † $22,858 $35,394 $47,293

Working Capital ‡ $9,275 $11,835 $13,744

Marketing †† $32,225 $29,842 $27,155

TOTAL $160,926 $192,748 $226,972

*Redesign, Prototype & Initial Test includes: Hydraulic and Mechanical Design, Simulation, Prototype Development, Prototype Tooling, Initial Testing **Pattern & Tooling includes: Supplier Development, Patterns, Tooling (Machine Fixtures, Assembly Fixtures, and Test Fixtures), Machine Programming, Quality Inspectio n (Parts, Tooling), New Process Development (Casting and/or Machine Processes), New Machine Tools, Retraining. †Testing includes: Performance (All Speeds), Hydrostatic, Certification Testing, Production Documentation, Training, etc. ‡Working Capital includ es: Increased inventory holding costs ††Marketing includes: Literature, Data Sheets, Curves, Pump Selection Tools, Sales Training, Compliance
Documentation, etc.

Data from the European Union pumps regulation preparatory study

, 14 along with input from manufacturers, were used to scale HI redesign cost estimates to various efficiency levels

above and below the 40 th percentile. The EU study provided “estimated [aggregate industry]

5

- 31development costs of new pumps to meet different min imum cut off values,” where cut off values are akin to DOE efficiency level and are defined by efficiency percentile. Table 5.10

. 2 presents the EU development cost da ta, as published in their preparatory study.

Table 5.10

. 2 Estimated Aggregate Industry Development Costs of New Pumps to Meet Different Minimum Cutoff Values in the European Union

Cut off

(%) Development costs of

new pumps (M illion s )

10 € 43.2

20 € 120.9

50 € 550.8

80 € 1,382.4

DOE interpolated t he European Union data using a second order polynomial to evaluate development costs at the 10 th , 40 th , and 70 th efficiency percentiles, which represent DOE’s EL1, EL3, and EL5 (max

- tech). The results of the interpolation, as well as subsequent data manipulations are presented in Table 5.10

. 3

. The interpolated aggre gate development costs were

then divided by their respective efficiency percentiles ; doing so illustrates how the development

cost per

- unit to redesign a pump compares at each level. Finally, to clarify this comparison, the development cost per percentile point column was normalized to the 40 th percentile values. This

results in a development cost scaling factor that indicates how much more or less it costs to redesign a model to the 10 th and 70 th percentile, as compared to the 40 th percentile. The development cost scaling factors were slightly adjusted based on U.S. manufacturer input and agreement among the CIP working group. Linear interpolation of these values can be used to directly scale the redesign costs provided by HI to all levels between the 10 th and 70 th percentile of efficiency

.

Table 5.10

. 3 European Conversion Costs Interpolated to DOE Efficiency Percentiles

Efficiency

Percentile Conversion *

costs of new

pumps (M illion s ) Conversion

cost, per percentile point (M illions ) Conversion cost scaling factor Adjusted

conversion cost

scaling factor

10 € 43.2 € 4.32 0.5 0.5

40 € 362.9 € 9.07 1.0 1.0

70 € 1,060.7 € 15.15 1.7 1.8

* Note development costs are now referred to at conversion costs, for consistently with DOE terminology

5.10.2 Average Per

- Model Cost To Hydraulically Redesign a Pump at Each Efficiency

Level

The population of pumps in the DOE pump performance database that will not pass a potential standard at each efficiency level can be segregated. For each pump in that population

, the cost of hydraulic redesign can be calculated by interpolating the data presented in Table

5.10

. 1 by horsepower, and applying the adjusted conversion cost scaling factors in Table 5.10

. 3

.

5

- 32The average hydraulic redesign cost can then be calculated for this population. Table 5.10

. 4 presents the average cost of redesign, by effic iency level, for each equipment class grouping

.

Table 5.10

. 4 Average to Cost Redesign, by E fficiency L evel, for E ach E quipment C lass

G rouping

EL 1 EL 2 EL 3 EL 4 EL 5

10 th

Percentile 25 th

Percentile 40 th

Percentile 55 th

Percentile 70 th

Percentile

ESCC/ESFM $88,970 $134,047 $179,233 $251,567 $32 2

, 753

IL $86,314 $132,134 $177,486 $249,497 $319,936

VTS $85,503 $126,482 $169,389 $240,336 $311,779

5.10.3 Accounting for Cost Sharing B etween Models with Shared Platforms

E SCC/ESFM and 1,800/3,600 rpm pump models are known to share significant amounts

of hardware

. A s such

, every model in the database is not expected to incur the cost of a full hydraulic redesign. To account for the costs shared among multiple models in the database, DOE

collect ed statistics on shared platforms and quantified the percentage of costs shared b etween
them

. A per

- model redesign cost adjustment factor was developed for each group of equipment classes with shared components. The groupings were the same as those used for the MPC and

MSP models:

End Suction

. ESCC.1800, ESCC.3600, ESFM.1800, and ESFM.3600

Inline

. IL.1800 and IL.3600.

VTS

. Statistics were not collect ed . Analysis is based on VTS .3600 only.

5.10.3.1 Statistics on Shared Platforms

Table 5.10

. 5 presents th e statistic on shared platforms, as collected from the DOE pump performance database. For end suction pumps , 46% of models in the database were considered duplicates with respect to pump type (ESCC versus ESFM). This means that 46% percent of

ESFM models i n the database had analogous ESCC models in the database. F or end suction, 23% of models in the database were considered duplicates with respect to pump speed (1,800 RPM versus 3,600 RPM). This means that 23% percent of 3,600 RPM models in the database

had analogous 1,800 RPM models in the database. Finally, for i nline, 20% of models in the database were considered duplicates with respect to pump speed.

5

- 33Table 5.10

. 5 Statistic on Shared Platforms

End Suction Inline

Unique

Model s * Duplicate

Model s ** Unique

Model s Duplicate

Model s

Pump Type:

ESCC vs ESFM 54% 46% N/A N/A

Pump Speed:

2

- Pole vs. 4

- Pole 77% 23% 80% 20%

* Unique model is defined as either a model with no related shared platform, or the first of two shared platform

models found the database

* * Duplicate model is the second of two shared platform models found the database

For end suction pumps, each model in the database will be either unique or duplicate in both type and speed. Figure 5.10

. 1 illustrates the process used to refine the result of Table 5.10

. 5 into combined type and speed grouping. Table 5.10

. 6 summarizes the results of the process, as well as results for inline pumps.

Figure 5.10

. 1 Process for Refining Statistics on End Suction Shared Platforms

Table 5.10

. 6 Refined Statistics on Shared Platforms

ESCC/ESFM Inline

Unique Type, Unique Speed 42% 80%

Unique Type, Duplicate Speed 12% 2 0%

Duplicate Type, Unique Speed 35% N/A

Duplicate Type, Duplicate Speed 11% N/A

5

- 345.10.3.2 Allocation of Shared Redesign Costs

“Unique” models incur the full per

- model hydraulic redesign costs previously defined. “Duplicate” models incur only a percentage of the total redesign costs that a unique model would

see . To find this percentage, each cost category was evaluated to determine the percentage that would apply to a duplicate model. DOE performed this evaluation with direct input from HI, and results were agreed upon by the CIP working group. Results are presented in Table 5.10

. 7

.

Table 5.10

. 7 Percentage of Redesign Costs that Apply to a Duplicate Model

Pump Type Pump Speed

Applicable to: End Suction End Suction and

Inline

Redesign, Prototype & Initial

Test 50% 50%

Pattern, Tooling 34% 66%

Testing 100% 100%

Marketing 100% 100%

Working Capital * *

Total ** 63% 73%

* Working capital remains a constant percentage of the preceding cost categories. If working capital is 7% of the redesign, pattern, testing, and market cost for a unique pump, it will be 7% of the reduced costs applied to duplicate

models. ** “Total” is the weighted average percent of total design cost a unique model would see

. To find it, e ach c ost category percentage is multip lied by its associated cost in Table 5.10.1 , and the result is presented as a percentage of total redesign cost for a unique model

The statistics on shared costs were then further refined to fit into the pump population

ca tegories pres ented in Table 5.10

. 8

.

Table 5.10

. 8 Percent of Redesign Costs Applicable to each Pump Population Category

Percent of Unique

Pump Redesign Costs Applicable

Unique Type, Unique Speed 100%

Unique Type, Duplicate Speed 73%

Duplicate Type, Unique Speed 63%

Duplicate Type, Duplicate Speed 46%

5.10.3.3 Composite per

- model redesign cost sharing adjustment

Finally, the sum of the products of Table 5.10

. 6 and * Table 5.10

. 8 can be ta ken to find the composite per

- model redesign cost sharing adjustment factor . This factor is used to adjust the per model redesign cost to represent the average cost that pump in the population would see

.

5

- 35This factor accounts for all cost s that might be sha r ed across the population of the equipment class. Individual models will experience lower or higher costs, but the population averaged adjustment factor is most useful for analytical purposes. Table 5.10

. 9 presents these results.

Table 5.10

. 9 Redesign Cost Sharing Adjustment Factor for End Suction and Inline Pumps

ESCC/ESFM IL

P er

- model redesign cost sharing adjustment factor 78% 95%

5.10.4 Adjusted P er

- M odel C ost to H ydraulically R edesign a Pump at E ach Efficiency

L evel

The per

- model redesign cost sharing adjustment factor presented in Table 5.10

. 9 can be applied to the average per

- model hydraul ic redesign costs presented in Table 5.10

. 4 , to find th e adjusted per

- model cost to hydraulically redesign a pump at each efficiency level, for ea ch equipment class. Tables 5.10.10

, 5.10.11 , and 5.10.12 present the results for each equipment

class. The adjusted per

- mode l hydraulic redesign cost represents the average cost of a redesign that each failing model in the equipment class will incur.

Table 5.10

. 10 End Suction

- Adjusted Per

- Model Cost to Hydraulically Redesign a P ump

ESCC/ESFM Equipment Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Average per

- model hydraulic redesign costs (40th percentile) $88,970 $134,047 $179,233 $251,567 $32 2

, 753

Cost sharing adjustment

factor 0.78 0.78 0.78 0.78 0.78

Adjusted per

- model hydraulic redesign cost $69,243 $104,325 $139,491 $195,787 $251, 189

5

- 36Table 5.10

. 11 Inline

- Adjusted Per

- Model Cost to Hydraulically Redesign a Pump

Inline Equipment

Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Average per

- model hydraulic redesign costs (40th percentile) $86,314 $132,134 $177,486 $249,497 $319,936

Cost sharing adjustment

factor 0.95 0.95 0.95 0.95 0.95

Adjusted per

- model hydraulic redesign cost $81,653 $124,999 $167,901 $236,024 $302,660

Table 5.10

. 12 VTS

- Adjusted Per

- Model Cost to Hydraulically Redesign a Pump

VTS Equipment

Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Average per

- model hydraulic redesign costs (40th percentile) $85,503 $126,482 $169,389 $240,336 $311,779

Cost sharing adjustment

factor 1.00 1.00 1.00 1.00 1.00

Adjusted per

- model hydraulic redesign cost $85,503 $126,482 $169,389 $240,336 $311,779

5.10.5 Determining Number of Models Redesigned in E ach E quipment C lass

5.10.5.1 Total Models in Each Equipment Class

Because HI did not provide estimates for industry model counts i n the special statistical report bulleti n ( Table 5.4

. 2 ), DOE scaled the provided “survey results” model counts by the same factors used to scale shipments. This method was presented to, and accepted by, the CIP working g roup. Results are presente d in Table 5.10

. 13

.

Table 5.10

. 13 Calculation of Total Industry Model Counts for Each Analysis Group

ESCC/ESFM

2/4 Po le IL

2/4 Pole VTS 2 Pole Only
Shipments: Survey Results 125,865 24,406 78,624

Shipments: Scaled Industry Estimate 215,498 50,424 104,406

Ratio 1.71 2.07 1.33

Models: Survey Results 1,317 381 211

Models: Scaled Industry Estimate 2,255 787 280

5

- 375.10.5.2 Percent of Models Not R edesigned (D ropped from P roduction)

To accurately determine the number of models incurring redesign costs, DOE account ed for the percent of failing models that w ould not be redesign ed at each efficiency level . For many failing models in the performance database , the manufacturer may choose not to redesign the pump, dropping that model from the market. Common reasons for this decision include , but are

not limited to : low sales, which lead to a poor business case for a redesign; a manuf acturer may already have a hydraulically overlapping, passing model (often from a different brand ) that it can leverage , or; a manufacturer may have two hydraulically overlapping models that fail and choose to redesign only one. Based on confidential discussions with manufacturers, and open input from the CIP working group, DOE estimates that 25% of failing models will be dropped at EL 1, tapering down to 15% at higher efficiency levels. Figure 5.10

. 7 illustrates this trend. Note that this trend only applies to ESCC, ESFM, and IL, and does not apply to VTS

, as all VTS models are expected to be redesigned.

Figure 5.10

. 2 Percent of Failing Models Not Redesigned at Each Efficiency Lev el

5.10.5.3 Number of Models Redesigned in E ach Equipment Class at E ach

Efficiency Level

The number of models redesigned in each equipment class, at each efficiency level, can be found by multiplying the tota l models in the equipment class by the percent of models failing at each efficiency level, and adjusting the result to account for the percent of models dropped

5

- 38from production and not redesign ed . The results for each equipment class are presented in Tables

5.10.14

, 5.10.15 , and 5.10.16

.

Table 5.10

. 14 End Suction

- Number of Models Redesigned at Each Efficiency Level

ESCC/ESFM Equipment Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Total Models 2,255 2,255 2,255 2,255 2,255

Percent of Models not Redesigned 25% 20% 15% 15% 15%

Number of Models Redesigned 179 473 793 1,075 1, 3 72

Table 5.10

. 15 Inline

- Number of Models Redesigned at Each Efficiency Level

IL Equipment Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Total Models 787 787 787 787 787

Percent of Models not Redesigned 25% 20% 15% 15% 15%

Number of Models Redesigned 62 160 270 374 476

Table 5.10

. 16 VTS

- Number of Models Redesigned at Each Efficiency Level

VTS Equipment

Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Total Models 280 280 280 280 280

Percent of Models not Redesigned 0% 0% 0% 0% 0%

Number of Models Redesigned 29 74 113 157 197

5.10.6 Aggregate Industry Conversion Cost at Each Efficiency Level

Finally the adjusted per

- model hydraulic redesign cost can be multiplied by the number of models redesigned, to yield the aggregate industry conversion for each equipment class and efficiency level

. Tables 5.10.17

, 5.10.18 , and 5.10

. 19 summarize the results.

5

- 39Table 5.10

. 17 End Suction

- Aggregate Industry Conversion Cost at Each Efficiency Level

ESCC/ESFM Equipment Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Number of Models Redesigned 179 473 793 1,075 1, 3 72

Adjusted per

- model hydraulic redesign cost $69,243 $104,325 $139,491 $195,787 $251, 189

Aggregate Industry

Conversion Cost (Millions) $12.4 M $49.4 M $110.6 M $210.4 M $344.7 M

Table 5.10

. 18 Inline

- Aggregate Industry Conversion Cost at Each Efficiency Level

IL Equipment Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Number of Models Redesigned 62 160 270 374 476

Adjusted per

- model hydraulic redesign cost $81,653 $124,999 $167,901 $236,024 $302,660

Aggregate Industry

Conversion Cost (Millions) $5.1 M $20.0 M $ 45.3 M $88.2 M $144.0 M

Table 5.10

. 19 VTS

- Aggregate Industry Conversion Cost at Each Efficiency Level

VTS Equipment

Classes EL 1 EL 2 EL 3 EL 4 EL 5

10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

Number of Models Redesigned 29 74 113 157 197

Adjusted per

- model hydraulic redesign cost $85,503 $126,482 $169,389 $240,336 $311,779

Aggregate Industry

Conversion Cost (Millions) $2.5 M $9.3 M $19.2 M $37.8 M $61.3 M

The results of Tables 5.10.17

, 5.10.18 , and 5.10.19 ar e summarized for comparison in

Table 5.10

. 20

.

5

- 40Table 5.10

. 20 All Equipment Classes

– Aggregate Industry Conversion Cost at Each Efficiency Level

All Values in Millions

of Dollars EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

Baseline 10th

Percentile 25th

Percentile 40th

Percentile 55th

Percentile 70th

Percentile

ESCC/ESFM * $0 $12.4 M $49.4 M $110.6 M $210.4 M $344.7 M

IL $0 $5.1 M $20.0 M $ 45.3 M $88.2 M $144.0 M

VTS $0 $2.5 M $9.3 M $19.2 M $37.8 M $61.3 M

Total Industry $0 $20.0 M $78.7 M $175.1 M $336.4 M $550.0 M

5.11 MANUFACTURER MARKUP ANALYSIS

To account for manufacturers' non

- production costs and profit margin, DOE applies a

non

- production cost multiplier (the manufacturer markup) to the full MPC. The resulting MSP is the price at which the manufacturer can recover all production and non

- production costs and earn a profit. To meet the new energy conservation standards proposed in this rule, DOE expects that manufacturers will hydraulically redesign their product lines, which may result in new and increased capital and equipment conversion costs. Depending on the competitive environment for this equipment, some or all of the increased conversion costs may be passed from

manufacturers to reta ilers and eventually to consumers in the form of higher purchase prices. The MSP should be high enough to recover the full cost of the equipment (i.e., full production and

non

- production costs) and overhead (including amortized product and capital conversi on costs), and still yield a profit. The manufacturer markup has an important bearing on profitability. A high markup under a standards scenario suggests manufacturers can readily pass along more of the increased capital and equipment conversion costs to c onsumers. A low markup suggests that manufacturers will not be able to recover as much of the necessary investment in plant and equipment.

DOE developed initial estimates of the base case manufacturer markups based on corporate annual reports, Securities a nd Exchange Commission (SEC) 10

- K filings, confidential manufacturer data, and comments made publicly during the CIP working group negotiations.

To support the downstream analyses, DOE investigated industry markups in detail, characterizing industry

- avera ge markups, individual manufacturer markup structures, and the industry

- wide markup structure.

5.11.1 Industry

- A verage Markups

Industry

- average manufacturer markups were developed by weighting individual manufacturer markup estimates on a market share basis, as manufacturers with larger market shares more significantly affect the market average. The industry

- average markups were b roken out by equipment class grouping and are presented in Table 5.11

. 1

5

- 41Table 5.11

. 1 Industry

- Average Markups for Each E quipment Class Group

ESCC/ESFM IL VTS RSV

Industry

- Average Markup 1.34 1.44 1.35 N/A

5.11.2 Individual M anufacturer M arkup S tructures

Using data and information gathered during manufacturer interviews, DOE concluded that within an equipment class, each manufacturer maintains a flat markup. This means that each manufacturer targets a single markup value for models offered in an equipment class, regardless of size, efficiency, or other design features. Tiered product offerings and markups do not exist at

the individual manufacturer level.

5.11.3 Industry

- W ide M arkup S tructure

DOE also used the pump performance data base and markup data gathered during the manufacturer interviews to assess the industry

- wide markup structure. Although tiered product offerings and markups do not exist at the individual manufacturer level, DOE concluded that when analyzed as whole, the industry exhibit s a relationsh ip between manufacturer markup and efficiency.

5.11.3.1 MSP Premium

- Efficiency Relationship

To quantify the MSP premium

- e fficiency relationship, DOE performed an analysis based

on the inline equipment class bare pump MSP cost model presented in S ection 5.10

. The inline equipment class was best suited for this analysis, as 95% of these pumps were sold with motors. As such, inline pumps have the highest level of certainty regarding the individual calculated bare

pump MSPs for each model

. q

For this analysis, DOE determined the “price premium” for each model in the database

. The price premium is the difference between the actual and the predicted price of the p ump (as

defined in Section 5.10 )

, divided by the predicted price. To investigate if a price premium

efficiency relationship existed, DOE evaluated t he price premium data against each pump’s efficiency percentile. To do so, price premium data points were grouped by discrete ranges of ten efficiency percentile point s , and results were plotted ( in blue ), as shown in Figure 5.11

. 1

. The results indicated that on an industry

- wide scale, the lowest efficiency models tend to garner lower markups than higher efficiency models. To make these result suitable for analysis, D OE

fitted a continuous piece

- wise trend to best represent the relationship. The trend is composed of a second order polynomial for lower efficiency transitioning to a flat line at higher efficiency ; the trend is represented in black in Figure 5.11

. 1

.

q The end suction and VTS equipment classes had lower perce ntages of models sold with motors, which made it difficult to define an MSP premium

- efficiency relationship with certainty.

5

- 42Figure 5.11

. 1 MSP Premium as a Function of Efficiency Percentile. Actual Results and

Trended Fits

.

The final trend indicates that the lowest efficiency models tend to garner lower markups than high er efficiency models, up to the 25 th percentile of efficiency. Beyond the 25 th percentile the relationship flattens out, and no correlation is seen between markup and efficie ncy. The data suggests that this relationship is a result of certain manufacturers positioning themselves with more or less efficient product portfolios and charging markups commensurate with their position in the marketplace. The data also suggests that the market does not value efficiency beyond the

25 th percentile

.

The relationship presented in Figure 5.11

. 1 were presented to, and accepted by the CIP working group. When presented to the working group, m anufacturers expressed consensus that efficiency is not currently the primary selling point or cost driver for the majority of pumps within the scope of the proposed rule. Rather, other factors such as reliability ma y influence price significantly and are known to be more influential in the purchaser’s decision making

process.

As mentioned previously, development of the MSP

- efficiency relationship was based on data from the Inline equipment class. DOE, with support of the CIP working group, concludes that the markup structure of the Inline equipment class is representative of the ESCC, ESFM, and

5

- 43VTS equipment classes r . DOE applied the Inline markup

- efficiency relationship to these equipment classes , for use in the analyses presented in this NOPR

5.11.3.2 Markup

- Efficiency Relationship

Finally, the markup

- efficiency relationship can be found for each equipment class simply by multiplying (one plus) the price premium relationship by the appropriate average mar kup for each equipment class (as defined in Table 5.11

. 1 ) . For example, if a 50 th percentile Inline pump is priced 3.5% above the market average MSP, then it is priced 3.5% above the market average markup of 1.44 . Because MPC does not vary with eff iciency, that 3.5% premium must be added to the markup. So the markup for that pump would be 1.035 multiplied by 1.44 , which results in a markup of 1.4 9

.

With the markup

- efficiency relationship defined, the MSP of any pump can be calculated given its flow at BEP, specific speed, and efficiency percentile. The flow and specific speed are

used to calculate the MPC, and the efficiency percentile is used to determine the markup. When multiplied together, the MPC and markup will yield the MSP for the pump.

5.11.4 M SP

- Efficiency Relationship

Ultimately, the goal of the engineering analysis is to develop an MSP

- Efficiency relationship that can be used in downstream rulemaking analyses such as the Life Cycle Cost (LCC) analysis, the Payback Period (PBP) analysis, and t he Manufacturer Impact Analysis (MIA).

For the downstream analyses, DOE evaluated a base case MSP

- Efficiency relationship as well as two separate MSP

- Efficiency relationship scenarios to represent the uncertainty regarding the potential impacts on prices and profitability for manufacturers following the implementation of new energy conservation standards. The two scenarios are: (1) flat pricing, and (2) cost recovery pricing. These scenarios result in varying revenue and cash flow impacts

and were chosen t o represent the lower and upper bounds of potential revenues for

manufacturers.

The base case pricing scenario represents a snapshot of the pump market, as it stands prior to this rulemaking. The base case pricing scenario was developed by applying the mar kup

efficiency relationship presented in section 5.11.3 to the MPC model presented in section 5.9.1 ; both of which are based on confidential data supplied by individual manufacturers. This results in a scalable model that can determine MSP as a function of efficiency, specific speed, and fl ow at BEP.

Under the flat pricing standards case scenario, DOE maintains the same pricing structure
as in the base case. If a pump is redesigned to meet an efficiency level, its price will increase

r Refer to the following transcript in which the conclusion that the markup structure of the Inline equipment class is representativ e of the ESCC, ESFM, and VTS equipment classes is presented to the working group and no negative

feedback is received: EERE

- 2013

- BT

- NOC

- 0039

- 0072, pp.292

- 295

.

5

- 44commensurate with the base case MSP

- Efficiency relationsh ip. T his pricing scenario assumes

that manufacturers will not modify their pricing structure to recover conversion costs result ing

from standards . As a result, this scenario is considered a lower bound for revenues.

In the cost recovery pricing scenario, m anufacturer pricing is set so that manufacturers recover their conversion costs over the analysis period. This cost recovery is enabled by an

increase in mark

- up, which results is higher sales prices for pumps even as manufacturer production costs stay th e same. The cost recovery calculation assumes manufacturers raise prices on models where a redesign is necessitated by the standard. The additional revenue due to the increase in markup results in manufacturers recovering 100 percent of their conversion co sts
over the 30

- year analysis period, taking into account the time

- value of money. Chapter 8 provides specific details on the development of the cost recovery pricing scenario. In the LCC and NIA analysis, DOE evaluated only the cost recovery pricing scena rio, as it would be the

most conservative case for consumer s, resulting in the fewest benefits

. s

s The cost recovery pricing scenario is the most conservative case (i.e., resulting in the fewest benefits) for consumers and the most positive case for manufacturers (i.e., resulting in the fewest negative impacts). In the MIA, DOE analyses this scenario and the flat pricing scenario, which results in the most positive case for consumer and

the most conservative case for manufacturers.

5

- 45REFERENCES

1 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 2012 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to ecodesign requirements fo r water pumps. Official Journal of the European Union. L 165, 26 June 2012, pp. 28

- 36.

2 I bi d.

3 Hydraulic Institute (March 21, 2014), DOE/HI Manufacturers Survey for Fiscal Year 2012, Parsippany, NJ. Docket EERE

- 2013

- BT

- NOC

- 0039, No. 0068. (Last accessed September 16, 2014.) < www.regulations.gov/#!documentDetail;D=EERE

- 2013

- BT

- NOC

- 0039

- 0068 >

4 Silver, M. Personal communication. Hydraulic Institute. E

- mail to Alison Williams, LBNL. May 16, 2014. Docket EERE

- 2013

- BT

- NOC

- 0039, No. 0112 . (Last accessed February 19, 2015.) < www.regulations.gov/#!documentDetail;D=EERE

- 2013

- BT

- NOC

- 0039

- 0112 >

5 Hydraulic Institute (November 14, 2012), Pump Redesign Cost Survey, Parsippany, NJ.

Docket EERE

- 2013

- BT

- NOC

- 0039, No. 0111. (February 19, 2015.)

< www.regulations.gov/#!documentDetail;D=EERE

- 2013

- BT

- NOC

- 0039

- 0111 >

6 Photo Credit. (Last accesse d October 1 , 2014.) < www.jvl.dk/489/grundfos

- using

- jvl

turntable >.

7 AEA Energy & Environment. 2008. Appendix 6: Lot 11

– ‘Circulators in buildings’. Report to European Commission, p p

. 174

- 175

.

8 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 2012 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to ecodesign requirements for water pumps. Official Journal of the European Union. L 165, 26 June 2012, pp. 28

- 36.

9 The President of the United States of America

. Executive Order 13609 of May 1, 2012 ; Promoting International Regulatory Cooperation. Federal Register. May 4

, 2012 . vol. 77 , no. 87 : pp. 26413

– 26415

.

10 Co uncil of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 2012 implementing Directive 2009/125/EC of the European Parliament and of the Council with regard to ecodesign requirements for water pumps. Official Journal of the Europe an Uni on. L 165, 26 June 2012, p. 36

.

11 The President of the United States of America. Executive Order 13609 of May 1, 2012; Promoting International Regulatory Cooperation. Federal Register. May 4, 2012. vol. 77, no. 87: pp. 26413

– 26415.

12 AEA Energy & Env ironment. 2008. Appendix 6: Lot 11

– ‘Circulators in buildings’ . Report to European Commission, p. 175.

5

- 4613 Council of the European Union. 2012. Commission Regulation (EU) No 547/2012 of 25 June 2012 implementing Directive 2009/125/EC of the European Parliam ent and of the Council with regard to ecodesign requirements for water pumps. Official Journal of the European Union. L 165, 26 June 2012, pp. 28

- 36.

14 AEA Energy & Environment. 2008. Appendix 6: Lot 11

– ‘Circulators in buildings’. Report to European Commission , p. 186.

5

- 47APPENDIX 5A

. ADDITIONAL FIGURES: FAILING C

- VA LUES PLOTTED AGAINST

SPECIFIC SPEED AND F LOW AT BEP

TABLE OF CONTENTS

5A.1 ADDITIONAL FIGURES

................................ ................................ ............................ 5 A

- 1

LIST OF FIGURE S

Figure 5A.1.1 Failing C

- Values Plotted Against Flow at BEP for ESCC.1800

....................... 5 A

- 1 Figure 5A.1.2 Failing C

- Values Plotted Against Specific Speed for ESCC.180 0

................... 5 A

- 1 Figure 5A.1.3 Failing C

- Values Plotted Against Flow at BEP for ESCC.3600

....................... 5 A

- 2 Figure 5A.1.4 Failing C

- Values Plotted Against Specific Speed for ESCC.3600

................... 5 A

- 2 Figure 5A.1.5 Failing C

- Values Plotted Against Flow at BEP for ESFM.1800

...................... 5 A

- 3

Fi gure 5A.1.6 Failing C

- Values Plotted Against Specific Speed for ESFM.1800

................... 5 A

- 3 Figure 5A.1.7 Failing C

- Values Plotted Against Flow at BEP for ESFM.3600

...................... 5 A

- 4 Figure 5A.1.8 Failing C

- Values Plotted Against Specific Speed for ESFM.3600

................... 5 A

- 4 Figure 5A.1.9 Failing C

- Values Plotted Against Flow at BEP for IL.1800

............................. 5 A

- 5 Figure 5A.1.10 Failing C

- Values Plotted Against Specific Speed for IL.1800

........................ 5 A

- 5 Figure 5A.1.11 Failing C

- Values Plotted Against Flow at BEP for IL.3600

........................... 5 A

- 6 Figure 5A.1.12 Failing C

- Values Plotted Against Specific Speed for IL.3600

........................ 5 A

- 6 Figure 5A.1.13 Failing C

- Values Plotted Against Flow at BEP for VTS.3600

....................... 5 A

- 7 Figu re 5A.1.14 Failing C

- Values Plotted Against Specific Speed for VTS.3600

.................... 5 A

- 7

5A

- iAPPENDIX 5 A

. ADDITIONAL FIGURES: FAILING C

- VA LUES PLOTTED AGAINST

SPECIFIC SPEED AND F LOW AT BEP

5A.1 ADDITIONAL FIGURES

This appendix presents figures representing failing C

- values plotted against specific speed and flow at BEP, for ESCC, ESFM, IL, and VT

- S equipment classes.

Figure 5A.1. 1 Failing C

- Values Plotted Agains t Flow at BEP for ESCC.1800

Figure 5A.1. 2 Failing C

- Values Plotted Agains t Specific Speed for ESCC.1800

5A

- 1Figure 5A.1. 3 Failing C

- Values Plotted Agains t Flow at BEP for ESCC.3600

Figure 5A.1. 4 Failing C

- Values Plotted Agains t Specific Speed for ESCC.3600

5A

- 2Figure 5A.1. 5 Failing C

- Values Plotted Against Flow at BEP for ESFM

. 18 00

Figure 5A.1. 6 Failing C

- Values Plotted Against Specific Speed for ESFM

. 1800

5A

- 3Figure 5A.1. 7 Failing C

- Values Plotted Agains t Flow at BEP for ESFM.3600

Figure 5A.1. 8 Failing C

- Values Plotted Agains t Specific Speed for ESFM.3600

5A

- 4Figure 5A.1. 9 Failing C

- V alues Plotted Against Flow at BEP for IL

. 18 00

Figure 5A.1. 10 Failing C

- Values Plotted Agains t Specific Speed for IL.1800

5A

- 5Figure 5A.1. 11 Failing C

- Values Plotted Agains t Flow at BEP for IL.3600

Figure 5A.1. 12 Failing C

- Values Plotted Against Specific Speed for IL .3600

5A

- 6Figure 5A.1. 13 Failing C

- Values Plotted Agains t Flow at BEP for VTS.3600

Figure 5A.1. 14 Failing C

- Va lues Plotted Agains t Specific Speed for VTS.3600

5A

- 7APPENDIX 5B

. SCALING PARAMETER

TABLE OF CONTENTS

5B.1 OVERVIEW

................................ ................................ ................................ .................. 5 B

- 1

5B.2 STRUCTURE AND DERIVA TION

................................ ................................ ............. 5 B

- 1

LIST OF FIGURE S

Figure 5B.2.1 Hydraulic Horsepower Plotted Against N

s /Q, for IL.1800 Data

........................ 5 B

- 2 Figure 5B.2.2 Hydraulic Horsepower Plotted Against Log[(N

s /Q)×(n/1800)]+1 , for

IL.1800 Data

................................ ................................ ................................ 5 B

- 3

5B

- iAPPENDIX 5B

. SCALING PARAMETER

5B.1 OVERVIEW

The scaling parameter DOE used to regress and mode l MPC

, MSP , and individual part

costs over the range of pump sizes in this NOPR is:

LLLLLL � � NN ss QQ � × � nn

1800 � � + 1

Eq. 5B. 1

Where:

• N
s is specific speed,

• Q is flow at BEP, and

• n is nominal rotational speed of the motor in RPM

.

T his scaling parameter is designed to represent s the physical size of a pump

. DOE expected cost to scale with physical size, as manufacturer interviews indicate that material costs account for upwards of 75 % of pump MPC. L arger pump s use more material which leads to high er mater ial cost s

.

DOE notes that h ydraulic horsepower is a parameter known in the industry to represent the physical size of a pump. DOE explored the use of hydraulic horsepower in the scaling

parameter , b ut selected Q and Ns because t his NOPR’s metric and effi ciency levels are a

function of Q and N

s . DOE concluded that using Q and N

s consistently in both the efficiency metric and the scaling parameter would reduce the overall complexity of the NOPR analyses.

5B.2 STRUCTURE AND DERIVA TION

The foundation of the scalin g parameter is N

s /Q

. When simplified, N

s /Q is structured

similar to the inverse of hydraulic horsepower:

NN

ss QQ = nn × QQ 0

. 5

HH 0

. 75

QQ = nn QQ 0

. 5 × HH 0

. 75

Eq. 5B. 2
1
HHHHHHHHHHHHHHHHHH HHHH WWWWWWWWWW = 3960

QQ × HH

Eq. 5B. 3

5B

- 1Where:

• H is total dynamic head in feet, and

• Hydraulic H P water is the hydraulic horsepower for a clean water system

.

N

s /Q trends well with hydraulic horsepower . This is expected, given the relationship of

the two parameters. The strong t rend provides confidence that the parameter is a suitable descriptor for size. As an example of this trend, figure 5A.2. 1 presents hydraulic horsepower plotted again st N

s /Q for IL.1800 data

.

Figure 5 B .2. 1 Hydraulic Horsepower Plotted Against N
s /Q, for IL.1800 Data

N

s /Q is modified by the factor (n/1800)

. This normalizes N

s /Q for motor speed. When a bare pump is run at 3,600 RPM, its specific speed r emain s the same, but flow double s , as compared to 1,800 RPM operation. Multiplying Ns/Q by (n/1800) counteracts the doubling of flow for 3,600 RPM pumps, and returns a parameter that always represents the Ns/Q of the pump if it were run at 1,800 RPM. This allows the parameter to represent the physical size of a pump, regardless of its speed. Equations 5B. 4 and 5B. 5 illustrate this normalization:

BB BB PPPP
, MM XXXXXX

, aa 1800 RR : NN ss QQ × 1800

1800 = NN ss QQ Eq. 5B. 4

BB BB PPPP
, MM XXXXXX

, aa 3600 RR : NN ss 2 QQ × 3600

1800 = NN ss 2 QQ × 2 = NN ss QQ Eq. 5B. 5

5B

- 2The parameter (N

s /Q)×(n/1800) was modified once more to allow for more accurate regressions. To more evenly distribute pump sizes along the x

- axis, t he Log 10 of the parameter

was taken. Finally, the whole parameter was shifted by one positive unit on the x

- axis for further ease of regression and to ensure that the scali ng parameter remained positive for all pumps in the

database. The result is the final scaling parameter :

LLLLLL � � NN ss QQ � × � nn

1800 � � + 1

Eq. 5B. 6 Figure 5 B .2. 2 presents hydraulic horsepower plotted again the scaling parameter, for

IL.1800 data. The figure indicates a strong and well distributed relationship between hydraulic horsepower and the scaling parameter.

Figure 5B.2. 2 Hydraulic Horsepower Plotted Against Log[ (N
s /Q)×(n/1800 )]+1 , for IL.1800

Data

5B

- 3CHAPTER 6. MARKUPS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION

................................ ................................ ................................ ........... 6

- 1

6.2 DISTRIBUTION CHANNEL S

................................ ................................ ....................... 6

- 1

6. 3 MARKUP CALCULATION M ETHODOLOGY

................................ ........................... 6

- 1

6.3.1 Assumptions

................................ ................................ ................................ ......... 6

- 2

6.4 APPROACH FOR ORIGINA L EQUIPMENT MANUFACT URER MARKUPS

........ 6

- 3

6.5 APPROACH FOR PUMP DI STRIBUTOR MARKUPS

................................ ................ 6

- 4

6.6 CONTRACTOR OR INSTAL LER MARKUP

................................ ............................... 6

- 6

6.7 SALES TAXES

................................ ................................ ................................ ............... 6

- 7

6.8 OVERALL MARKUP

................................ ................................ ................................ ..... 6

- 7

REFERENCES

................................ ................................ ................................ ............................ 6

- 9

LIST OF TABLES

Table 6.5.1 U.S. Census Business and Industry Annual Wholesale Trade Survey Data Used to Calculate Distributor Markups

................................ ................. 6

- 6

Table 6.7.1 Average Sales Tax Rates by Census Division and Large State

..................... 6

- 7

Table 6.8.1 Summary of Markups for Three Primary Distribution Channels for Pumps

................................ ................................ ................................ ............. 6

- 8

6

- iCHAPTER 6. MARKUPS ANALYSIS

6.1 INTRODUCTION

The objective of the markups analysis is to estimate the price paid by the customer or purchaser for an installed c ommercial and i ndustrial pump (CI P )

. Purchase price and installation cost are necessary inputs to the life

- cycle cost (LCC) and payback period (PBP) analyses.

The engineering analysis ( c hapter 5) provides the manufacturer selling prices (MSPs) for the representative units included in the LC C analysis. DOE derived a set of prices for each representative unit by applying markups to the manufacturer selling price in the form of markup equations.

6.2 DISTRIBUTION CHANNELS

The appropriate markups for determining the end

- user equ ipment price depend on the type of distribution channels through which equipment moves from manufacturers to purchasers. At each point in the distribution channel, companies mark up the price of the equipment to cover

their business costs and profit margin.

Based on data from manufacturer interviews and discussions with the CIP working group (see Docket EERE

- 2013

- BT

- NOC

- 0039 ) , DOE identified five main distribution channels for

pumps and estimated their respective shares of shipments

. The five channels are from the

manufacturer to:

(1) d istributor to contractor to end

- users (70 percent of sales); (2) d istributor to end

- u ser s (17 percent of sales) ; (3) OEM to end

- u ser s (8 percent of sales ) ; (4) end

- u ser s (2 percent of sales ) ; (5) contractor to end

- u ser s (1 percent of sales ) ;

Other distribution channels exist but are estimated to ac count for a minor share of pump sales (2 percent).

6.3 MARKUP CALCULATION METHODOLOGY

A t each point in the distributi on channel, companies mark up the price of the equipment to cover their business costs and profit margin s

. In financial statements, gross margin is the difference between the company revenue and the company cost of sales or cost of goods sold ( CGS ). Inpu ts for calculating the gross margin are all corporate costs, including : overhead costs (sales, general, and administration), research and development (R&D), interest expenses, depreciation, taxes, and profits. For sales of equipment to contribute positive ly to company cash flow, the markup of the equipment must be greater than the corporate gross margin. Individual pieces of equipment may command a lower or higher markup, depending on their perceived added value and the competition they face from similar e quipment in the market.

6

- 1In developing markups for OEMs and distributors, DOE obtained data about the revenue,

CGS , and expenses of firms that produce and sell the equipment of interest. DOE determined that markups are neither fixed

- dollar nor proportional to all direct costs, which means that the selling price of a piece of equipment may not be strictly proportional to the purchase price of the equipment. Using the available data, DOE has found measurable differences between

increm ental markups on direct equipment costs and the average aggregate markup on direct

business costs. Additionally, DOE discovered significant differences between average and incremental markups for pump OEMs and distributors . Section 6.4 and Section 6.5 further

discusses the differences between average and incremental markups.

The main reason that the selling price of a piece of equipment may not be strictly proportional to the purchase price of the equipment is that businesses incur a wide variety of

costs. When the purchase price of equipment and materials increases, only a fraction of the business expenses increase, while the remainder of the business expenses stays relatively

constant. For example, if the unit price of a pump increases by 30 percent, it is unlikely that the cost of secretarial support in an administrative office will increase by 30 percent as well

. Certain business expenses are not correlated to the cost of equipment or cost of goods.

DOE’s approach categorizes the expenses into two categories: invariant costs ( IVC ), which are those costs that are not exp ected to vary in proportion to the change in manufacturer selling price (MSP) , and variant costs ( VC ), which are the costs that scale with the change in manufacturer selling price. Together, IVC and VC represent the gross margin.

For each step in equipment distribution, DOE estimated both a baseline mar kup and an incremental markup. For pumps , DOE understands that no increase in distribution labor is necessary for the distribution of more

- efficient equipment, while the non

- labor

- scaling cost does increase with increasing equipment costs. This allowed DOE to estimate the incremental markup given a breakdown of distribution and manufacturing business expenses for a particular industry.

6.3.1 Assumptions

DOE derived the OEM and pump distributor markups fro m three key assumptions about

the costs associated with pump

- related industrial series. DOE used the financial data from t he 2007 U.S. Economic Census’s Manufacturing Industr y S eries and 2012 Business and Industry

Wholesale Trade S urvey to determine OEM a nd pump distributor markups, respectively. These income statements break down the components of all costs incurred by firms that assemble and

distribute pumps . The key assumptions used to estimate markups using these financial data are:

1. The firm income statements faithfully represent the various average costs incurred by

firms designing, assembling, and distributing pumps

.

2. These costs can be divided into two categories: (1) costs that vary in proportion to the

MSP of pumps (variant costs ); and (2) costs that do not vary with the MSP of pumps (invariant costs).

6

- 23. Overall, OEM and distributor sales prices vary in proportion to OEM and distributor

costs that are included in the balance sheets.

In support of the first assumption, the income s tatements itemize firm costs into a number of expense categories, including CGS , operating labor and occupancy costs, and other operating costs and profit. Although OEMs and pump distributors tend to handle multiple commodity lines, the se data provide the most accurate indication that is available of the expenses associated

with pumps

.

In the following discussion, DOE assumes a division of costs between those that do not scale with the manufacturer price (labor and occupancy expenses ), and those that do (operating expenses and profit). This division of costs led to the estimate of incremental markups addressed

in the next section

.

In support of the third assumption, the wholesaler industries are relatively competitive,

and end

- user demand for pumps is relatively inelastic

— i.e., the demand is not expected to decrease significantly with a relatively small increase in price. Following standard economic theory, competitive firms facing inelastic demand either set prices in line with costs or quickly go out of business. 1

6.4 APPROACH FOR ORIGINAL EQUIPMENT MANUFACTURER MARKUPS

Using the previous assumptions, DOE developed baseline and incremental markups for OEMs using the firm income statement from several manufacturing industries that design, assemble, and brand equipment that contain pumps

. The 2007 Economic Census Manufacturing Industry Serie s 2 reports the payroll (production and total), cost of materials, capital expenditures and total value of shipments, and miscellaneous operating costs for manufacturers of various types of machinery. DOE collected th e s e data for 14 types of OEMs , including :

• farm machinery and equipment manufacturing;

• construction machinery manufacturing;

• mining machinery and equipment manufacturing;

• oil and gas field machinery and equipment manufacturing;

• sawmill and woodworking machinery manufacturing;

• plastics and rubber industry machinery manufacturing;

• paper industry machinery manufacturing;

• textile machinery manufacturing;

• printing machinery and equipment manufacturing;

• food product machinery manufacturing;

• semiconductor machinery manufacturing;

• other industrial machin ery manufacturing;

• heating equipment (except warm

- air furnaces) manufacturing; and

• air conditioning and warm

- air heating and commercial and industrial refrigeration equipment manufacturing

.

6

- 3DOE used the baseline markups, which cover all of the OEM’s costs (both variant and invariant costs), to determine the sales price of baseline models. Variant costs were defined as costs that vary in proportion to the change in MSP induced by increased efficiency standards; in contrast, invariant costs were defined as costs that do not vary in proportion to the change in MSP due to increased efficiency standards. The baseline markup relates the MSP to the OEM selling price. For each of the 14 OEMs identified above, DOE calculated the OEM baseline markup as follows:

SSSSSSSSSS

PP PP + MM MM + CC = MM UU BBBBBBBB

Where:

SALES = value of shipments,

PAY = payroll expenses,

MAT = material input expenses,

CAP = capital expenses, and

MU BASE = baseline markup.

The baseline markups range between 1.32 ( construction machinery manufacturing) and 1.63 ( s emiconductor machinery manufacturing), with the sales

- weighted average of 1.4 3

.

Incremental markups are coefficients that relate the change in the MSP of more

- efficient models, or that equipment that meets the requirements of new energy conservation standards, to the change in the OEM selling price. Incremental markups cover only those costs that scale with a change in the manufacturer’s sales price ( variant costs ). DOE calculated the incremental markup (M U INCR ) for each of the 14 OEMs using the following equation:

OEM OEM OEM

INCR CGS VC CGS MU +

=

Where:

MU INCR = incremental OEM markup,

CGS OEM = OEM’s cost of goods sold, and

VC OEM = OEM’s variant costs.

The incremental markups range between 1. 2 9 ( plastics and rubber industry machinery manufacturing) and 1. 5 3 ( farm machinery and equipment manufacturing ), with the sales

weighted average of 1. 3 8

.

6.5 APPROACH FOR PUMP DISTRIBUTOR MARKUPS

The type of financial data used to estimate markups for OEMs is also available for

distributors. DOE based its distributor markups on financial data from the 2012 U.S. Census

Business and Industry Annual Wholesale Trade Survey

. 3 DOE organized the financial data into

6

- 4income statements that break down cost components incurred by firms that sell equipment and machinery with pumps
, “ Hardware, and Plumbing and Heating Equipment and Supplies

Merchant Wholesalers ” (NAICS 4237 ). a

Using the previously described assumptions, DOE developed baseline and incremental markups and applied them in calculating end

- user equipment prices from manufacturer sales prices. The Annual Wholesale Trade Survey provides gross margin (GM) as percent of sales for

the machinery, equipment, and supplies merchant wholesalers industry; therefore, baseline markups can be derived with the following equation:

(%) (%) (%) MU BASE GM Sales Sales

− =

DOE used financial data from the Annual Wholesale Trade Survey for the categories

“ Hardware, and Plumbing and Heating Equipment and Supplies Merchant Wholesalers

, ” to calculate incremental markups used by wholesalers of pumps

. Incremental markups are coefficients that relate the change in the MSP of higher efficien cy models to the change in the wholesaler selling price. Hence, incremental markups cover only those costs that scale with a change in the manufacturer’s sales price ( i.e., variant costs ). DOE considers higher efficiency models to be equipment sold under market conditions with new efficiency standards. It calculated the incremental markup ( MU INCR ) for distributors using the following equation:

R DISTRIBUTO R DISTRIBUTO R DISTRIBUTO

INCR 

### CGS VC CGS MU +

=

Where:

MU INCR = incremental wholesaler markup,

CGS DISTRIBUTOR = distributor’s cost of goods sold, and

VC DISTRIBUTOR = distributor’s variant costs.

Table 6.5

. 1 shows the data from the Annual Wholesale Trade Survey and the markups DOE estimated using the procedures described previously

.

a The distributors to whom these financial data refer handle multiple commodity lines.

6

- 5Table 6.5

. 1 U.S. Census Business and Industry Annual Wholesale Trade Survey Data Used to Calculate Distributor Markups

Items Amount ($1,000,000)

Sales 109,292

Cost of goods sold (CGS) 78,690

Gross Margin 30,602

Total Operating Expenses 25,043

Labor & Occupancy Expenses

Annual payroll 12,704

Employer costs for fringe benefit 2,728

Contract labor costs including temporary help 169

Purchased utilities, total 358

Cost of purchased repair and maintenance services (equipment, buildings, offices) 337

Purchased Communication Services 303

Purchased professional and technical services 379

Lease and rental payments (buildings, structures, offices) 1,740

Taxes and license fees (mostly income taxes) 356

Other Operating Expenses & Profit

Expensed computer related supplies 150

Cost of purchased packaging and containers 644

Cost of purchased transportation, shipping and warehousing services 1,095

Cost of purchased advertising and promotional services 496

Cost of data processing and other purchased computer services, except

communications 89

Depreciation and amortization charges 648

Commissions paid 434

Net profit before taxes 7,971.76

Baseline Markup=(CGS+GM)/CGS 1.39

Incremental Markup=(CGS + Total Other Operating Expenses and Profit)/CGS 1.15

Source: 2012 Annual Wholesale Trade Survey, Hardware, and Plumbing and Heating Equipment and Supplies

Merchant Wholesalers (NAICS 4237 )

6.6 CONTRACTOR OR INSTALLER MARKUP

DOE used information from RSMeans Electrical Cost Data 4 to estimate markups used by contractors in the installation of equipment with pumps . RSMeans Electrical Cost Data estimates material expense markups for electrical contractors as 10 percent, leading to a markup

factor of 1.10. DOE recognizes that contractors are not used in all installations, as some firms

6

- 6have in

- house technicians who would install equipment or rep lace a pump

. However, DOE has no information on the extent to which this occurs, so it applied a markup of 1.10 in all cases.

6.7 SALES TAXES

The sales tax represents state and local sales taxes t hat are applied to the end

- user equipment price. The sales tax is a multiplicative factor that i ncreases the end

- user equipment price.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse. 5
These data represent weighted averages that include county and city rates. DOE then derived

po pulation

- weighted average tax values for each Census division and large state, as shown in

Table 6.7

. 1 below. This provides a national average tax rate of 7. 1 1 perce nt, which DOE used

for each distribution channel.

Table 6.7

. 1 Average Sales Tax Rates by Census Division and Large State

Census Division/State Population (2013) Tax Rate (2014) New England 14,618,806 5.69%

Middle Atlantic 21,673,140 6.63%

East North Central 46,662,180 6.91%

West North Central 20,885,710 7.09%

South Atlantic 42,230,787 6.07%

East South Central 18,716,202 8.02%

West South Central 11,435,411 8.65%

Mountain 22,881,245 6.44%

Pacific 13,040,657 5.30%

New York 19,651,127 8.40%

California 38,332,521 8.45%

Texas 26,448,193 7.90%

Florida 19,552,860 6.65% Population Weighted Average 7. 1 1%

6.8 OVERALL MARKUP

The overall markup for each distribution channel is the product of the relevant markups,

as well as the sales tax. DOE used the overall baseline markup to estimate the end

- user equipment price of baseline models, given the MSP of the baseline models. As stated previously , DOE considers baseline models to be equipment sold under existing market conditions ( i.e., without new energy efficiency standards).

DOE used the overall incremental markup to estimate changes i n the end

- user equipment price, given ch anges in the manufacturer cost above the baseline model cost resulting from a standard to raise equipment efficiency. The total end

- user equipment price for higher efficiency models is composed of two components: the end

- user equipment price of the baseli ne model and the change in end

- user equipment price associated with the increase in manufacturer cost to meet

6

- 7the new efficiency standard. The following equation shows how DOE used the overall incremental markup to determine the end

- user equipment price for higher efficiency models ( i.e., models meeting new efficiency standards).



## ( )

INCR OVERALL MFG BASE SALES INCR MFG BASE OVERALL MFG STD



### MU MSP EQP Tax MU MSP MU MSP EQP

_ _



### × D + = × × D + × =

Where:

EQP STD = end

- user equipment price for models meeting new efficiency standards, EQP BASE = end

- user equipment price for baseline models,

MSP MFG = manufacturer selling price for baseline models,

Δ MSP MFG = change in manufacturer selling price for higher efficiency models,

MU INCR = incremental OEM or distributor markup,

Tax SALES = sales tax,

MU OVERALL_BASE = baseline overall markup (product of manufacturer markup, baseline OEM or distributor markup, and sales tax), and

MU OVERALL_INCR = incremental overall markup (product of manufactu rer markup,

incremental OEM or dis tributor markup, and sales tax

Table 6.8

. 1 sum marizes the markups and the overall baseline and incremental markups

for each of the five main identified channels. Weighting the values by the respective shares of

each channel and equipment class group yields an average overall baseline markup of 1. 59 and an overall incremental markup of 1. 34

.

Table 6.8

. 1 Summary of Markups for Three Primary Distribution Channels for Pumps

Markup Distributor

to
Contractor

to

End

- User

(70%) Distributor

to End

- User (17%) OEM to

End

- User (8%) End

- User (2%) Contractor

to End

- User (1%) Other (2%)

BL Incr BL Incr BL Incr BL Incr BL Incr BL Incr

OEM

- - - - 1.43 1.38

- - - - - - Distributor 1.39 1.15 1.39 1.15

- - - - - - - Contractor 1.1 1.1

- - - - - - 1.1 1.1

- - Sales Tax 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07

- -

Overall 1.64 1.35 1.49 1.23 1.53 1.48 1.07 1.07 1.18 1.18 1.59 1.34

6

- 8REFERENCES

1 Pindyck, R.S. and D.L. Rubinfeld. (2000), Microeconomics, 5 th ed. , New Jersey: Prentice

Hall.

2 U.S. Census Bureau (2007). Economic Census Manufacturing Industry Series (NAICS 33 Series) <www.census.gov/manufacturing/asm / >.

3 U.S. Census Bureau (2012). Annual Wholesale Trade Survey, Hardware, and Plumbing and Heating Equipment and Supplies Merchant Wholesalers (NAICS 4237 )

. < www.census.gov/wholesale/index.html >.

4 RSMeans Construction Publishers & Consultants. (2013), Electrical Cost Data, 36 th Annual

Edition. 2013. ed. J.H. Chiang, Kingston, MA.

5 Sales Tax Clearinghouse, Inc. (last accessed on January 10, 2014), State sales tax rates along

with combined average city and county rates

. < http://thestc.com/STrates.stm >.

6

- 9CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION

................................ ................................ ................................ ........... 7

- 1

7.2 ENERGY USE METHODOLO GY

................................ ................................ ................. 7

- 1

7.2.1 Duty Point

................................ ................................ ................................ ............ 7

- 2

7.2.1.1 Pump binning process

................................ ................................ ................ 7

- 2

7.2.1.2 Normalization of efficiencies

................................ ................................ ..... 7

- 6

7.2.1.3 Shipment weighting to get average pump curve and efficiency

................ 7

- 6

7.2.2 Pump Sizing

................................ ................................ ................................ ......... 7

- 6

7.2.3 Operating Hours

................................ ................................ ................................ ... 7

- 7

7.2.4 Load Profiles

................................ ................................ ................................ ........ 7

- 7

7.2.5 Eq uipment Losses

................................ ................................ ................................ 7

- 8

7.2.5.1 Pump Losses

................................ ................................ ............................... 7

- 9

7.2.5.2 Motor Losses

................................ ................................ .............................. 7

- 9

7.2.5. 3 Control Losses

................................ ................................ ............................ 7

- 9

7.3 ANNUAL ENERGY USE RE SULTS

................................ ................................ ............ 7

- 9

LIST OF TABLES

Table 7.2.1 Power Bins on a Log Scale, all Pumps

................................ .......................... 7

- 3

Table 7.2.2 Flow Bins (gpm) on a Log Scale by Equipment Class

................................ .. 7

- 4

Table 7.2.3 Distribution of Annual Hours of Operation by Application

.......................... 7

- 7

Table 7.2.4 Typical Lo ading Profile Weights

................................ ................................ ... 7

- 8

Table 7.3.1 Average Annual Energy Use by Equipment Class and Efficiency Level in the Base Case (kWh)

................................ ................................ ................ 7

- 10

Table 7.3.2 Average Annual Energy Use by Equipment Class for each Standards Case/Efficiency Level (kWh)

................................ ................................ ...... 7

- 10

LIST OF FIGURES

Figure 7.2.1 Representative Units and Individual Pumps (ESCC 1800)

........................... 7

- 5 Figure 7.2.2 Typical Loading Profiles

................................ ................................ ................ 7

- 8

7

- iCHAPTER 7. ENERGY US E ANALYSIS

7.1 INTRODUCTION

A key component of the life

- cycle cost (LCC) and payback period (PBP) calculations described in chapter 8 is the savings in operating costs that customers woul d realize from more

energy

- efficient equipment. Energy costs are the most significant component of customer operating costs. The U.S. Department of Energy (DOE) uses annual energy use, along with energy prices, to establish energy costs at various energy e fficiency levels. This chapter describes how DOE determined the annual energy use of commercial and industrial pumps at the considered energy efficiency levels.

Pump efficiency is a function of the pump operating conditions. The operating conditions

are de fined by the required flow and the pressure the pump must overcome to deliver that flow. These conditions are referred to as the pump flow (Q) and head (H). The head is a feature of the system in which the pump is installed. For any pump, the flow that can be provided at a given head is a function of the pump design; this is captured in the "pump curve" which specifies head as a function of flow. Pump efficiency is also a function of flow. The flow at which the pump is

most efficient is referred to as the “ Best Efficiency Point” or BEP. The point at which the pump is most likely to be operated is referred to as the “duty point

. ” While pump users will generally try to match the BEP to the duty point, it can be difficult to achieve a precise match. The

instan taneous pump hydraulic power requirement is determined by the flow times the head divided by the efficiency. This represents the energy that must be transferred to the fluid to produce the required flow. In matching a motor to a pump, users must ensure tha t the motor is capable of delivering the maximum power that may be required. Power delivered by the motor is referred to as the shaft power. The total power input to the motor is equal to shaft power plus motor losses. The energy use calculation includes m otor losses, assuming that the motor used is compliant with the existing DOE standard.

Pump power depends on the duty point, so the energy use calculation requires an accurate estimation of the range of duty points in the field. Pump efficiency depends on the relationship between the pump BEP and the duty point, which is referred to as the pump sizing. For pumps serving variable loads, the power, efficiency and energy use also depend on the load profile. Finally, the annual energy use which is the required input to the LCC depends on the annual hours of operation. DOE's analysis accounts for variability in each of these characteristics.

For each equipment class, DOE developed several representative units each representing a range of flow and power. For each representative unit, DOE estimated the energy use using a range of distributions for the key variables (operating hours, pump sizing, load profiles).

7.2 ENERGY USE METHODOLO GY

For a single pump, the energy use calculation depends on the duty point, load profile, and annual operating hours. The annual energy use equation is :

7

- 1

# ∑ × ×

=

e

LoadProfil

, i

i motor i i i 

### OpHour H Q AEU
η η

where:

• Q i is the flow load point

• H i is the head at Qi, calculated from the pump curve

• η i is the pump efficiency calculated from the efficiency curve

• i motor

, η is the motor efficiency

• i 

### OpHour refers to annual hours of operation at load point i

DOE defines the set of load points in two steps: first, the duty point is related to the pump BEP through the BEP offset variable (which may be positive or negative:

)

_ 1 ( int offset BEP Q Q BEP

DutyPo + × =

Second, the load profile is defined relative to the duty point. DOE uses four types of load profile which represent the diversity of load types in the field. Most generally, load points range

betw een 50% and 120% of the duty point. Based on input from stakeholders, most load profiles range between 75% and 110% of the duty point.

The inputs are discussed in the section below.

7.2.1 Duty Point

DOE r eview ed the available information on duty points by sector ( commercial, industrial, municipal and agricultural ) and application and found very little data. DOE used detailed shipments data provided by the manufacturer to estimate the distribution of duty points. The manufacturer data were organized by pump model and included the shipments, flow and head at BEP, pump and efficiency curves, and the C

- value associated with the pump. The C

- value is used to determine whether the pump is compliant or not with a given EL.

The structure of the LCC requires that, at each E L, the same pump is available with the same practical utility (defined by the pump curve) so that the pump energy use can be compared across ELs under equivalent conditions. The pump data supplied by the manufacturers does not quite meet this criterion, as each pump differs in some design details as well as differing in efficiency. Hence, for the LCC DOE used the manufacturer data to create a set of representative units covering the full range of duty points in the data. The method used to create representa tive

units is described next.

7.2.1.1 Pump binning process

DOE implemented a binning process to determine representative units with varying power capacities from the shipments data

. P ump models are grouped into 9 power bins on a log

7

- 2scale. The upper and lower boun ds of the power bins are shown in Table 7.2

. 1 . Then, for each equipment class, the pump models in each power bin are grouped into 9 flow bins , also on a log

scale. The upper and lower bound of the flow bins depend on the power as shown i n Table 7.2

. 2

.

Table 7.2

. 1 Power Bins on a Log Scale , all Pumps

Power Bin Index Power Lower Bound (HP) Power Upper Bound (HP)

1 1.00 1.79

2 1.80 3.24

3 3.25 5.84

4 5.85 10.53

5 10.54 18.97

6 18.98 34.19

7 34.20 61.61

8 61.62 111.00

9 111.01 200.00

7

- 3Table 7.2

. 2 Flow Bins (gpm) on a Log Scale by Equipment Class

Flow Bin Index ESCC 1800 ESCC 3600

Lower Bound Upper Bound Lower Bound Upper Bound

1 42.00 71.39 31.75 49.04

2 71.40 121.36 49.05 75.76

3 121.37 206.32 75.77 117.06

4 206.33 350.75 117.07 180.85

5 350.76 596.26 180.86 279.40

6 596.27 1013.63 279.41 431.65

7 1013.64 1723.15 431.66 666.87

8 1723.16 2929.29 666.88 1030.27

9 2929.30 4979.80 1030.28 1591.80

Flow Bin Index ESFM 1800 ESFM 3600

Lower Bound Upper Bound Lower Bound Upper Bound

1 42.00 69.07 31.75 49.04

2 69.08 113.61 49.05 75.76

3 113.62 186.87 75.77 117.06

4 186.88 307.37 117.07 180.85

5 307.38 505.56 180.86 279.40

6 505.57 831.53 279.41 431.65

7 831.54 1367.69 431.66 666.87

8 1367.70 2249.54 666.88 1030.27

9 2249.55 3700.10 1030.28 1591.80

Flow Bin Index IL 1800 IL 3600

Lower Bound Upper Bound Lower Bound Upper Bound

1 40.41 65.49 45.00 66.38

2 65.50 106.14 66.39 97.94

3 106.15 172.03 97.95 144.49

4 172.04 278.81 144.50 213.17

5 278.82 451.88 213.18 314.51

6 451.89 732.37 314.52 464.00

7 732.38 1186.96 464.01 684.55

8 1186.97 1923.72 684.56 1009.94

9 1923.73 3117.90 1009.95 1490.10

Flow Bin Index VT

- S 3600

Lower Bound Upper Bound

1 25.00 40.54

2 40.55 65.77

3 65.78 106.69

7

- 44 106.70 173.07

5 173.08 280.74

6 280.75 455.50

DOE defines one representative unit for each of the power and flow bins. The characteristics of the representative unit, consisting of flow and head at BEP, the pump curve and the efficiency curve are defined as the shipment

- weighted averages across all the units in a bin, as shown in the equations below.



# ∑ ∑ ∑

∑ ∑

∑ ∑ ∑

⋅

= ⋅

= ⋅

= ⋅

=

i i i i i

RepUnit i i i BEP i i

BEP RepUnit i i i BEP i i

BEP RepUnit i i i BEP i i

BEP RepUnit

Shipment Ns Shipment Ns Shipment P Shipment P Shipment H Shipment H Shipment Q Shipment Q

,

, ,

, ,

,

Figure 7.2

. 1 illustrates the result of the binning process for ESCC 1800: the red solid circles represent representative units for each of combinations of the power and flow bins, while the individual pumps are shown in blue.

Figure 7.2

. 1 Representative Units and Individual Pumps (ESCC 1800)

7

- 57.2.1.2 Normalization of efficiencies

In each bin, t he C

- values associated with the original set of pumps are left unchanged. DOE uses

the C

- value equation to adjust the efficiency at BEP of each pump to the efficiency appropriate to the representative unit flow and head at BEP . The equation is:

BEP i BEP i i BEP i BEP i BEP RepUnit RepUnit adjusted

BEP i

Q Ns f C C Q Ns f

, , , , , ,

)) ln( ), (ln( where )) ln( ), (ln(

h h

− = − =

The details of the C

- value equation are provided in Chapter 5, Engineering Analysis. The results of this process are a set of pumps in each bin which differ by efficiency at BEP, but are

otherwise identical.

7.2.1.3 Shipment weighting to get average pump curve an d efficiency

For each representative unit DOE calculated the shipment weighted average pump curve

, which describes the pump's available operating points away from the BEP

. First, the pump curve equation is rewritten using normalized variables:



### 3 2 1 2



### par Q

Q par Q

Q par H

H

BEP i

BEP

i

BEP

i 

### +        

⋅ +        

⋅ =

Then, the representative unit values of par1, par2 and par3 are calculated using a regression model that minimizes the difference between the representative unit curve and each individual pump curve.

The e fficiency curve is handled in a simila r manner, with normalized pump efficiency

defined as a polynomial function of normalized flow :



### parC Q

Q parB Q

Q parA

BEP i

BEP

i

BEP

i 

### +        

⋅ +        

⋅ = 2



### η

η

DOE compar ed the normalized pump and efficiency curves for all individual pumps within a power and flow bin and confirmed that the shapes of these curves do not v a ry significantly

. Therefore the shipment

- weighted average curves should provide a sufficiently accurate estimate of the required of pump power across the range of operating points. As the shipment

- weighted average duty points are used to define the representative unit, the total power and energy use assigned to each bin is the same as in the original manufacturer data.

7.2.2 Pump Sizing

DOE introduced a variable called BEP offset t o capture variation in pump sizing practice

in the field. The BEP offset is the relative distance between customer’s duty point and the

7

- 6pump’s BEP point. Pumps are typically sized to operate within 75% to 110% of their BEP flow. Therefore, for this analysi s the BEP offset is assumed to be uniformly distributed between [

- 0.25 (i.e., 25% less than BEP flow), and 0.1 (10% more than BEP flow)].

)

_ 1 ( int offset BEP Q Q BEP

DutyPo + × =

7.2.3 Operating Hours

DOE estimated average annual operating hours by application based on inputs from industry experts and feedback from the CIP working group. Table 7.2

. 3 displays the distribution of annual hours of operation by application. For each pump in the LCC sample, the sector and application are assigned using the procedure described in the LCC chapter. A value for the operating hours is then selected from the distributions defined below.

Table 7.2

. 3 Distribution of Annual Hours of Operation by Application
Application Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

Cooling

Water 2500 3500 5000 6000 7500

Boiler Feed 2000 3000 4000 5000 6000

Circulation 2000 3000 4000 5000 6000

Pressure

Boost 300 600 1000 2500 4500

Irrigation 300 600 1000 1500 2000

Other 1000 1500 2000 3000 4000

7.2.4 Load Profiles

Information on typical load profiles for pumps is not available in the public domain. DOE reviewed resources suggested by stakeholders as well as commercial building pump load s calculated using building simulations

. Building simulation profiles are based on input assumptions and cannot substitute for data, although they do provide an indication of variability in pump loads due to building schedules and weather impacts on space

- conditioning end uses

. Given the lack of data, DOE developed a few canonical load profiles for this analysis. These are defined by weights that specify the percentage of time the pump operates at one of four load points: 50 percent, 75 percent, 100 percent, and 110 percent of the flow at the duty point . The canonical load types are:

• Flat load (LP0): 100% of operating hours at the duty point;

• Flat/over

- sized (LP1): 50% of operating hours at the duty point

, and 50% at flow equal to

75% of the duty point flow;

7

- 7• Variable/over

- sized (LP2): 50% of operating hours at the duty point

, and 35 % at flow equal to 75% of the duty point , and 35% at 50% of the d uty point flow;

• Variable/under

- sized (LP3): 3 0% of operating hours at the duty point

, 30% at 75% of the duty point and 20% at 110% of the duty point.

The four load profiles are illustrated in Figure 7.2

. 2 . The load profile is assigned

rando mly, subject to the constraint that the pump never operates outside of the range of 50% to 110% of BEP. This forces a consistency condition between the definition of the BEP offset and the assignment of the load profile. The percentages assigned to each lo ad profile are provided in

Table 7.2

. 4

.

Figure 7.2

. 2 Typical Loading Profiles

Table 7.2

. 4 Typical Loading Profile Weights

Load Profile 50% duty point 75% duty point 100% duty point 110% duty point Probability

LP0 0 0 1 0 30%

LP1 0 0.5 0.5 0 30%

LP2 0.05 0.3 0.65 0 30%

LP3 0.05 0.25 0.5 0.2 10%

7.2.5 Equipment Losses

The total energy use calculated for the LCC depends on pump losses, motor losses, and

control losses. All these losses represent energy that the pump user must pay for as part of the operating costs. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

50% duty point 75% duty point 100% duty point 110% duty point LP0

LP1

LP2

LP3

7

- 87.2.5.1 Pump Losses

Pump losses account for the differen ces between pump shaft horsepower and hydraulic horsepower due to friction and other factors. DOE takes this into account using the pump efficiency curve calculated for each representative unit, modeled as second

- order polynomial functions, as discussed in section 7.2.1.2

.

7.2.5.2 Motor Losses

For each representative unit DOE selected a motor u sing the sizing procedures for default

motors described in the pumps test procedure notice of proposed rulemaking (NOPR)

. Specifically, the required motor horsepower is based on the pump shaft input power at 120 percent of BEP flow. DOE selects the smallest motor that provides the required power. DOE assign s a pole configuration based on the design speed for each equipment class ( e.g ., 1800 RPM corresponds to 4

- pole motor ). For each motor horsepower and pole configuration, DOE used the minimum motor efficiency values under the existing standard 10 CFR 431.25 for the type of motors with which pumps are typically paired (i.e., NEMA Design B) . For the VT

- S equipment class, pumps are paired with submersible motors, for which there are no federal standards. For this equipment, DOE assigned a motor efficiency value based on review of market data

described in the pumps test procedure NOPR.

T o determine the losses of the motors at part load, DOE used the part

- load loss factor curve developed and presented in the pumps test procedure NOPR. This means that motor losses are standardized across all pumps, and while motor losses do contribute to the total energy use, they do not affect the selection of which pumps pass a given EL.

7.2.5.3 Control Losses

DOE assumed that all users with variable loads were throttling their pumps and therefore

did not include variable speed drive efficiency as part of th e annual energy use calculation. VSD

users are handled in a sub

- group analysis, described in chapter 11

.

7.3 ANNUAL ENERGY USE RE SULTS

Table 7.3

. 1 summarizes the result s of the energy use analysis for each equipment class at each considered energy efficiency level in the base case

. The table shows the average energy use, defined as the total energy use for all pumps in the LCC chapter divided by the total number of

pumps . Given the wide range of pump sizes in the LCC sample, the average results are not representative of any specific pump.

For a given size or capacity of pump, the energy use will decrease as efficiency increases. However, across the market in the base cas e, the distribution of horsepower within each efficiency bin differs, as does the distribution of other variables such as application and operating hours. As a result, average energy use as shown in Table 7.3

. 1 does not decrease monotonically as efficiency level increases. However, as individual pumps are redesigned to meet higher efficiency levels in each standards case (as discussed in section 8. 3 .1 .2 ) , the average energy use of the pump market decreases in each case, as shown in Table 7.3

. 2

.

7

- 9Table 7.3

. 1 Average Annual Energy Use by Equipment Class and Efficiency Level in the

Base Case (kWh)
Equipment

Class EL0 EL1 EL2 EL3 EL4 EL5

ESCC,1800 12,545 31,164 24,001 36,900 31,621 19,721

ESCC,3600 29,817 40,824 14,218 44,787 62,802 9,952

ESFM,1800 47,229 43,025 38,112 35,511 52,331 26,849

ESFM,3600 90,194 59,093 58,300 66,196 72,804 38,108

IL,1800 8,419 21,976 51,256 32,555 16,525 13,625

IL,3600 16,845 15,763 25,627 48,821 54,421 13,091

VT

- S,3600 13,250 9,804 29,161 18,009 39,582 8,928

Table 7.3

. 2 Average Annual Energy Use by Equipment Class for each Standards

Case/ Efficiency Level (kWh)
Equipment

Class EL0 Case EL1 Case EL2 Case EL3 Case EL4 Case EL5 Case

ESCC,1800 24,438 24,333 24,114 23,917 23,663 23,382

ESCC,3600 17,136 17,095 16,944 16,841 16,692 16,442

ESFM,1800 36,870 36,860 36,670 36,442 35,985 35,545

ESFM,3600 56,965 56,891 56,312 55,808 55,217 54,348

IL,1800 20,379 20,296 20,123 19,961 19,775 19,517

IL,3600 22,095 22,012 21,806 21,568 21,386 21,018

VT

- S,3600 13,061 13,056 13,001 12,765 12,605 12,446

The LCC uses the entire sample of energy use values calculated for each specific pump rather than the summary values shown in Table 7.3

. 1

. The individual energy use for each of the pumps in the base case and in each standards case is available in the LCC spreadsheet. The operating cost savings calculated in the LCC under each s tandards case will be directly related to the average energy use values shown in Table 7.3

. 2

.

7

- 10CHAPTER 8. LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION

................................ ................................ ................................ ........... 8

- 1

8.1.1 General Approach for Life

- Cycle Cost and Payback Period Analysis

................ 8

- 1

8.2 DEFINITION OF THE LC C SAMPLE

................................ ................................ .......... 8

- 2

8.2.1 LCC Sampling Method

................................ ................................ ........................ 8

- 2

8.2.2 Definition of the Weights

................................ ................................ ..................... 8

- 3

8.3 LIFE

- CYCLE COST INPUTS

................................ ................................ ........................ 8

- 4

8.3.1 Total Installed Cost Inputs

................................ ................................ ................... 8

- 4

8.3.1.1 Baseline Equipment Price

................................ ................................ .......... 8

- 4

8.3.1.2 Calculation of the Equipment Price for Each Efficiency Level

................. 8

- 5

8.3.1.3 Projection of Future Equipment Prices

................................ ...................... 8

- 5

8.3.1.4 Installation Cost

................................ ................................ .......................... 8

- 6

8.3.2 Operating Cost Inputs

................................ ................................ .......................... 8

- 6

8.3.2.1 Annual Energy Use

................................ ................................ .................... 8

- 6

8.3.2.2 Electricity Prices

................................ ................................ ........................ 8

- 6

8.3.2.3 Maintenance Costs

................................ ................................ ..................... 8

- 7

8.3.2.4 Repair Costs

................................ ................................ ............................... 8

- 7

8.3.2.5 Equipment Lifetime

................................ ................................ .................... 8

- 7

8.3.2.6 Discount Rates

................................ ................................ ............................ 8

- 9

8.3. 3 Base Case Efficiency Distribution

................................ ................................ ..... 8

- 12

8.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS

................................ ...... 8

- 12

REFERENCES

................................ ................................ ................................ .......................... 8

- 24

LIST OF TABLES

Table 8.2.1 Pump applications and associated sectors

................................ ...................... 8

- 2

Table 8.2.2 Summary of Inputs to the LCC

................................ ................................ ...... 8

- 3

Table 8.2.3 Distribution of pumps across sectors for each product class

......................... 8

- 3

Table 8.3.1 Average Mechanical Lifetime and Service Lifetime by Equipment

Class

................................ ................................ ................................ ............... 8

- 8

Table 8.3.2 Risk Free Rate and Equity Risk Premium, 2004

- 2013

................................ 8

- 11

Table 8.3.3 Weighted Average Cost of Capital for Sectors that Purchase Pumps

......... 8

- 12

Table 8.3.4 Base Case Efficiency Distributions by Equipment Class

............................ 8

- 12

Table 8.4.1 Average LCC and PBP Results by Efficiency Level for ESCC.1800

......... 8

- 13

Table 8.4.2 LCC Savings Relative to the Base Case Efficiency Distribution for

ESCC.1800

................................ ................................ ................................ ... 8

- 14

Table 8.4.3 Average LCC and PBP Results by Efficiency Level for ESCC.3600

......... 8

- 14

Table 8.4.4 LCC Savings Relative to the Base Case Efficiency Distribution for

ESCC.3600

................................ ................................ ................................ ... 8

- 15

Table 8.4.5 Average LCC and PBP Results by Efficiency Level for ESFM.1800

......... 8

- 15

8

- iTable 8.4.6 LCC Savings Relative to the Base Case Efficiency Distribution for

ESFM.1800

................................ ................................ ................................ .. 8

- 16

Table 8.4.7 Average LCC and PBP Results by Efficiency Level for ESFM 3600

......... 8

- 16

Table 8.4.8 LCC Savings Relative to the Base Case Efficiency Distribution for

ESFM.3600

................................ ................................ ................................ .. 8

- 17

Table 8.4.9 Average LCC and PBP Results by Efficiency Level for IL.1800

............... 8

- 17

Table 8.4.10 LCC Savings Relative to the Base Case Efficiency Distribution f or IL.1800

................................ ................................ ................................ ......... 8

- 18

Table 8.4.11 Average LCC and PBP Results by Efficiency Level for IL.3600

............... 8

- 18

Table 8.4.12 LCC Savings Relative to the Base Case Efficiency Distribution for

IL.3600

................................ ................................ ................................ ......... 8

- 19

Table 8.4.13 Average LCC and PBP Results by Efficiency Level for VT

- S.3600

.......... 8

- 19

Table 8.4.14 LCC Savings Relative to the Base Case Efficiency Distribution for

VT

- S.3600

................................ ................................ ................................ .... 8

- 20

LIST OF FIGURES

Figure 8.3.1 Commercial and Industrial Electricity Price Projections for Reference

Case

................................ ................................ ................................ ................ 8

- 7 Figure 8.3.2 Lifetime Distribution by Pump Equipment Class

................................ .......... 8

- 9 Figure 8.4.1 Distribution of Life

- Cycle Savings for ESCC.1800

................................ .... 8

- 20 Figure 8.4.2 Distribution of L ife

- Cycle Cost Savings for ESCC.3600

............................ 8

- 21 Figure 8.4.3 Distribution of Life

- Cycle Cost Savings for ESFM.1800

........................... 8

- 21 Figure 8.4.4 Distribution of Life

- Cycle Cost Savings for ESFM.3600

........................... 8

- 22 Figure 8.4.5 Distribution of Life

- Cycle Cost Savings for IL.1800

................................ .. 8

- 22 Figure 8.4.6 Distribution of Life

- Cycle Cost Savings for IL.3600

................................ .. 8

- 23 Figure 8.4.7 Distribut ion of Life

- Cycle Cost Savings for VT

- S.3600

............................. 8

- 23

8

- iiCHAPTER 8. L IFE -C YCLE CO ST AND PAYBACK PERIOD AN A LYSIS

8.1 I NTRODUCTION

The life

- cycle cost (LCC) and payback period (PBP) analysis evaluates the impact of proposed energy conservation standards on the pump users, i.e. the consumers who purchase and operate pumps. The LCC provides a measure of the total cost of ownership, consisting of the

initial purchase pric e and installation costs , and energy, maintenance and repair costs over the lifetime of the pump. For this analysis DOE assumed that neither installation nor repair and maintenance costs are affected by the s tandard, so the LCC focuses only on purchase price and the energy costs over the pump lifetime. DOE accounts for variability in energy use and prices, discount rates, and energy costs by doing individual LCC calculations for a large sample of pumps that are assigned different installation conditions. Installation conditions include customer attributes such as sector and application, and usage attributes such as duty point and annual hours of operation. This sample is used to generate national average LCC sa vings by efficiency level.

8.1.1 General Approach for Life -Cycle Cost and Payback Period Analysis

DOE conducted the LCC and PBP analysis by developing a large sample of pump installations, which represent the general population of pumps that would be affected by proposed energy conservation standards. Separate analyses are conducted for each equipment class. Conceptually, the analy sis distinguishes between the pump installation and the pump itself. The pump installation is characterized by a combination of customer attributes (sector, application, electricity price, discount rate) and usage attributes (duty point, BEP offset, load profile, annual hours of operation, mechanical lifetime) that do not change with TSL. The pump itself is the regulated equipment, so its efficiency and selling price do change with TSL. For each equipment class the LCC sample consists of 10,000 distinct pum p installations.

I n the base case, DOE assigns a specific pump to each pump installation. These pumps are chosen from the set of representative units described in chapter 7, Energy Use A nalysis. DOE also assigns a pump efficiency in the base case. The relative weighting of different representative

units and efficiencies in the LCC sample is determined based on shipments data supplied by the manufacturers. At each TSL, the pump assigned in the base case either passes or fails the efficiency criterion. If th e pump passes, the installation is left unchanged. Equivalently, for that installation, the LCC at the given TSL is the same as the LCC in the base case and the standard does not impact that user. If the pump fails, the base case pump is replaced with a co mpliant unit having a higher selling price and higher efficiency, and the LCC is recalculated. The selling price of the compliant unit is calculated based on information provided by the Engineering analysis. The LCC savings at each TSL are defined as the difference between the LCC in the base case and the LCC for the more efficient pump. The LCC is calculated for each pump installation at each TSL. These calculations are presented in the LCC spreadsheet.

8 -18.2 DEFINITION OF THE LC C SAMPLE

The economic inputs to the LCC (discount rate and electricity price) depend on the sector, while usage criteria such as hours of operation depend on the application. Hence, each pump installation in the LCC sample must be assigned a sector and application. DOE considered

four s ectors: industrial, commercial buildings, agricultural and municipal water utili ties. DOE

c onsidered several applications , with correlation between sector and application as shown in

Table 8.2

. 1

. B ased on a review of available data, DOE determined that there is some correlation between application and operating hours. DOE did not find any information relating either the BEP offset (a pump sizing factor defined in the E nergy Use chapter ) or load profile to either sector or application, so these were assigned randomly.

Table 8.2

. 1 Pump applications and associated sectors Application Commercial Industrial Agriculture Municipal

boiler feed x x circulation x x cooling water x x x irrigation x pressure boost x x x x other x x x

As described in the Energy Use chapter, the shipments data provided by the manufacturers are used to define the relative weightings of the representative units. Each representative unit can be thought of as a pump chosen to operate at a representative duty po int. DOE did not assign the customer attributes (sector, application etc.) to duty points randomly. DOE reviewed several data sources to incorporate correlations between sector, application, product class and the distribution of duty points into the analy sis. Specifically, DOE used a database it collected from case studies and other public information (pump applications only), a database on pump tests provided by Pacific Gas & Electric , and the U.S. Department of Agriculture Farm and Ranch Irrigation Surve y (FRIS) 1 , to estimate the correlations between pumps sector, application, and power bin for each product class. DOE used these distributions to determine the relative weighting of different sectors and applic ations in the LCC sample

.

8.2.1 LCC Sampling Method

The LCC sampling requires a weighting function, discussed in the next section. The power and flow bins are indexed by i and j respectively, and total shipment weight in the bin is

defined as w ij . The weight w ij is defined as the number of pumps in bin (i,j ) divided by the total number of pumps in the shipments data for the given product class. Within each bin, pumps are

distributed across sector k and application a

.

The LCC process is (for each product class) :

1. Create a list of installation types, index ed by ( k

, a , i

, j ) and with a weighting w ij that defines the

percentage of all pumps this installation type is expected to represent.

2. Choose a number N of rows in the LCC sample (N=10,000).

8

- 23. Create an expanded list of N installations with each installation type sampled Nw ij times

4. For each row fill in the additional required information as defined in Table 8.2

. 2

5. For each row generate a list of pumps chosen for each efficiency level (EL) (a) Select the base case ( E L =0) pump from bin ( i

, j ) (b) Select the base case pump efficiency PER 0 (c) For each EL , if the chosen pump passes the new efficiency test, do nothing (d) For each EL , if the chosen pump fails the new efficiency test, assign that installation the redesigned pump for that bin

6. Calculate the annual operating cost, total installed cost and life

- cycle cost for each ( m , L )

Table 8.2

. 2 Summary of I nputs to the LCC

Variable Dependencies Definitions

Annual operating hours ( hpy ) application hours

- per

- year of operation

Load profile type ( l ) application four types (see Energy Use Chapter)

Mechanical lifetime in hours N H none total hours of equipment life; lifetime in years is N H

/ hpy

BEP offset ( x ) none operating point flow is set equal to ( 1+ x ) Q B discount rate ( r ) sector used to discount future operating cost savings

electricity price ( ep ) sector average annual price in $/kWh

8.2.2 Definition of the W eights

A consultant report provided DOE with estimates of the distribution of pumps across sectors for each product class. The sector weights by product class used in the LCC are presented

in Table 8.2

. 3 . For each product class, the fraction assigned to sector k is defined as f k . The sector weights are assumed to apply independent of power or flow bin.

Table 8.2

. 3 Distribution of pumps across sectors for each product class

ESCC

1800 ESCC

3600 ESFM

1800 ESFM

3600 IL 1800 IL 3600 VT

- S

3600

Municipal 6% 6% 6% 6% 6% 6% 26%

Agriculture 10% 10% 10% 10% 10% 10% 31%

Commercial 51% 51% 51% 51% 51% 51% 13%

Industrial 33% 33% 33% 33% 33% 33% 30%

DOE used t he WSU motors database to distribute pumps within a given power bin and product class across applications. The WSU data include motor horsepower, speed and operating

hours for a selected list of commercial and industrial pump applications. These data were allocated to the same set of power bins as were used to construct the pump representative units. For each product class, t he proportion of pumps within each power bin corresponding to a given application was then determined as

d i (a, k ) = m i (a

, k) /( ∑
a m i (a , k ) )

W here :

8

- 3i = the bin index for power

, k = the sector (commercial or industrial)

,

a = the application (as listed in Table 8.2

. 1 )

,

m i (a; k, ω ) = the number of motors in bin i, for fixed (a

, k) and product class , and

d i (a; k) = the distribution of motors by application within bin i for sector k

.

The U.S. Department of Agriculture Farm and Ranch Irrigation Survey (FRIS) data 1 were used to define the distribution of pumps across power and fl ow bins for agricultural end

use s . The FRIS provides average flow and head for well pumps and irrigation pumps. These were used to estimate the duty point and assign a power bin to each pump in the FRIS . The pump counts in each power range were then used t o distribute pump shipments by horsepower to the agricultural sector.

The Pacific Gas & Electric pump test database includes pump duty points for the municipal sector. These were used to estimate the required power and assign a power bin to each pump in t he pump test database. The pump counts in each power bin were used to distribute pump shipments by horsepower to the municipal sector.

8.3 LIFE

- CYCLE COST INPUTS

The LCC is equal to the pump purchase price plus the operating cost over the lifetime of the pump . The operating cost is equal to the annual energy use times the energy price. Annual operating costs are discounted relative to the year in which the standard is passed and summed over the lifetime of the pump. The key inputs to the LCC are thus the purch ase price, the annual energy use, the energy price, the pump lifetime and the discount rate.

8.3.1 Total Inst alled Cost Inputs

8.3.1.1 Baseline Equipment Price

The manufacturer selling price (MSP) is the price charged by the manufacturer for the equipment. The price paid by pump users is equal to the MSP, plus any relevant distributor markup, plus the sales tax

. At each efficiency level, the MSP increases to reflect the additional costs incurred by the pump manufacturers to meet the standard. In the base case, represe nting the market with no standard in place, DOE calculated the equipment price for baseline equipment based on the following equation:

EEEEEE BBBBBBBB = MMMM MMMMMM × MMMM OOOO OO OOOO

_ BBBBBBBB

W here:

EQP BASE = c usto mer equipment price for baseline models,

MSP MFG = manufacturer selling price for baseline models, and

MU OVERALL_BASE = product of retailer or distributor markup and sales tax.

8

- 4DOE used an equation to model the base case equipment price as a function of flow, specific speed and efficiency. This equation is presented in chapter 5, Engineering Analysis.

The overall markups used in the LCC analyses a re discussed in Chapter 6

.

8.3.1.2 Calculation of the E quipment Price for E ach Efficiency Level

As discussed in the e ngineering analysis, for commercial and industrial pumps the principal means of achieving higher efficiencies is through hydraulic redesign. Costs associated with redesign are part of the overheads associated with manufacturing. Based on stakeholder input, DOE assumed that (1) pump manufacturers would maintain the number of functionally

distinct models on the market

, and (2) the cost of redesign would be distributed across all pump shipments affected by the standard . The e ngineering a nalysis provides the to tal cost of redesigning pumps (called conversion costs) at each EL.

The LCC includes a pre

- processing step that calculates the selling price of a redesigned pump at each EL. The analysis assumes that the proportion of total revenue allocated to each bin or representative unit does not change with EL. The revenues for each bin are defined as the number of shipments times the base case equipment price, taking into account the b ase case efficiency distribution. Total revenues are calculated by the sum over bins, and each bin is assigned a fraction of total revenues defined as p ij

.

The next steps in this process are repeated for each EL:

1. For each power and flow bin, count the nu mber of shipments associated with failing

pumps, units fail (i,j

, EL)

2. Given the base case equipment price for each of the failing pumps, calculate the total revenues associated with failing pumps

, rev fail (i,j,EL)

. 3. Calculate the fraction of conversion costs a ssigned to bin (i,j) as p ij CC(EL) , where CC(EL) is the total conversion cost at the given EL, and p ij is the percentage of revenues allocated to bin (i,j).

4. Calculate the new selling price for a redesigned pump as

price(i,j,EL) = [ rev fail (i,j,EL) + p ij CC (EL) ] / units fail (i, j, EL)

Th is approach ensures that manufacturer conversion costs are fully recovered. The redesigned pump is assumed to have the minimum efficiency specified by the EL, and to have the same pump curve and efficiency curve as the representative unit for that bin.

8.3.1.3 P rojection of Future Equipment Prices

To project an equipment price trend for the NOPR, DOE derived an inflation

- adjusted

index of the P roducer P rice I ndex for pumps and pumping equipment over the period 1984

-

8

- 52013. a These data show a general price index increase from 1987 through 2009. Since 2009, there has been no clear trend in the price index. Given the relatively slow global economic activity in 2009 through 2013, DOE believes that the extent to which the future trend can be predicted based on the last two decades is very uncertain and that the observed data do not provide a firm basis for projecting future costs trends for commercial and industrial pump equipment. Therefore, DOE used a constant price assumption as the default trend to project future commercial and industrial pump prices in 2020. Thus, prices projected for the LCC and PBP analysis are equal to the 2012 values for each efficiency level in each equipment class.

8.3.1.4 Installation Cost

In the Framework Document, DOE requested information on whether installation costs would be expected to change with efficiency. Grundfos responded that this was not expected to be true for new installations, but noted that for existing installations, there may be additiona l costs to replace existing equipment with higher efficiency equipment for piping, electrical modifications, base and foundations, and code requirements for equipment rooms. (Grundfos, No. 24 at p. 34) In the CIP working group, Grundfos and ITT Corporation also noted that the assumption of targeting identical flange or feet dimensions during redesign is reasonable, but that as one drives to higher efficiency one may have to stretch the pump (i.e., change the dimensions from the base design) and change confi gurations. ( See EERE

- 2013

- BT

- NOC

- 0039

- 0109, pp.240

- 242 ) Grundfos stated that at some point within the range of efficiency levels under consideration, whether at PER 40 or 70 or some other point, the i nstallation cost might change. Because none of the stakeholders provided data to indicate at what efficiency level DOE may

need to consider an increase in installation costs , DOE has not estimated an installation cost for this analysis.

8.3.2 Operating Cost Inp uts

8.3.2.1 Annual Energy Use

DOE estimated the annual electricity consumed by each class of commercial and industrial pump equipment, by efficiency level, based on the energy use analysis described in chapter 7 of the NOPR TSD.

8.3.2.2 Electricity Prices

Electricity pric es are used to convert changes in the electric consumption from higher

efficiency equipment into energy cost savings. DOE used average national commercial and industrial electricity prices in 2014 from the AEO 2014 reference case. 2 These prices are $ 0.102 6 /kWh and $ 0.0688 /kWh, respectively. DOE applied the commercial price to pump installations in the commercial sector and the industrial price to installations in the industrial, agricultural, and municipal sectors. To arrive at prices in future years, DOE multiplied the average price s described for 2014 by the forecast of average annual electricity price changes from year 2020 to 2040 in EIA’s AEO 2014 for both the commercial and industrial sectors. See Figure 8. 3 .1.

a Series ID PCU333911333911 ; www.bls.gov/ppi/

8

- 6Figure 8.3

. 1 Commercial and Industrial Electricity Price Projections for Reference Case

8.3.2.3 Maintenance Costs

During the CIP working group meetings, DOE expressed that its analysis was using the assumption that maintenance costs are not ex pected to change with efficiency level. DOE did not receive any negative comments on this assumption, so it DOE has not estimated a maintenance cost for this analysis.

8.3.2.4 Repair Costs

DOE received information in response to the Framework Document (Grundfos, N o. 24 at p. 35) and from the CIP working group that repair costs are not expected to change with efficiency level. Therefore, DOE has not estimated a repair cost for this analysis.

8.3.2.5 Equipment Lifetime

DOE estimated average lifetime by equipment class based on inputs from industry experts and used these estimates to develop statistical distributions. DOE defines two types of lifetime: the mechanical lifetime is the total lifetime hours of operation (including routine maintenance and repairs). The service lif etime is the number of years the consumer own and uses the unit, and is equal to the mechanical lifetime divided by the annual hours of operation. The service lifetime is the direct input to the LCC. DOE used a Weibull distribution function to

define the d istribution of mechanical lifetimes. The parameters for the Weibull function (shape and scale parameters) were estimated based on average service life and average annual operating

hours. DOE accounted for life

- extending pump repairs for some equipment clas ses by extending the mechanical lifetime, which is done by multiplying the Weibull scale parameter by the number of repair cycles b . For example if the number of repair cycles is two, the customer would

b DOE estimated the number of repair cycles for each equipment class based on input from industry experts.

8

- 7repair the pump twice before replacing it, which results in a lifetime twice as long as a lifetime without repair . This approach leads to a negative correlation between annual operating hours and

service lifetime measured in years

– pumps with more annual operating hours tend to have

shorter service life times. Based on discussions in the CIP working group meetings (see for example, Docket No. EERE

- 2013

- BT

- NOC

- 0039

- 0073, p. 153), DOE also introduced

mechanical lifetime variation by pump speed , as pumps running faster tend to have a shorter

lifetime. DOE as sumed that pumps with nominal speeds of 3600 RPM would have an average lifetime of 0.75 times equivalent pumps with nominal speeds of 1800 RPM. Table 8.3

. 1

summarizes the average mechanical lifetime in hours and service lifetime in years by equipment

class

.

Table 8.3

. 1 Average Mechanical Lifetime and Service Lifetime by Equipment Class

Equipment Class Average Mechanical

Lifetime (hours) Average Service Lifetime (years)

ESCC,1800 29,228 13.3

ESCC,3600 21,946 10.6

ESFM,1800 53,335 23.4

ESFM,1800 40,754 19.7

IL,1800 35,004 15.8

IL,3600 26,056 12.8

VT

- S,3600 15,392 10.5

The Weibull distribution is a probability distribution commonly used to measure failure

rates. c Its form is similar to an exponential distribution, which models a fixed failure rate, except that a Weibull distribution allows for a failure rate that changes over time in a particular fashion.

The cumulative Weibull distribution takes the form:



# e x



### x P β

α

θ 

    

−

−



### = ) ( for x > θ , and

P(x) = 1 for x ≤ θ

W here:

P(x) = probability that the equipment is still in use at age x ,

x = equipment age,

α = scale parameter, which would be the decay length in an exponential distribution, β = shape parameter, which determines the way in which the failure rate changes through time, and

c For reference on the Weibull distribution, see sections 1.3.6.6.8 and 8.4.1.3 of the NIST/SEMATECH e

- Handbook

of Statistical Methods

, www.itl.nist.gov/div898/handbook/

.

8

- 8θ = delay parameter, or location, which allows for a delay before any failures occur.

When β = 1, the failure rate is constant over time, giving the distribution the form of a cumulative exponential distribution. In the case of mechanical equipment, β commonly is greater than 1, reflecting an increasing failure rate as equipment ages. The parameters for the Weibull distribution are calibrated using average and medi an lifetime es timates from industry experts. For all equipment classes DOE used a shape parameter of 2.65. DOE did not use a delay parameter in the Weibull distribution, but did assume a minimum service lifetime of three years. This reflects the fact that many units are purchased with a warranty that effectively guarantees that the unit will remain in operation during the warranty period. Figure 8.3

. 2 show s the resulting service lifetime histogram by equipment classes. This histogram does not resemble a Weibull, because it incorporates the effect of a broad distribution of operating hours.

Figure 8.3

. 2 Lifetime Distribution by Pump Equipment Class

8.3.2.6 Discount Rates

The commercial discount rate is the rate at which future operating costs are discounted to establish their present value in the L CC analysis. The discount rate value is applied in the LCC to future year energy costs and non

- energy operations and maintenance costs to calculate the

estimated net life

- cycle cost of products of various efficiency levels and life

- cycle cost savings as

c ompared to the baseline for a representative sample of commercial end users.

DOE’s method views the purchase of a higher efficiency appliance as an investment that yields a stream of energy cost savings. DOE derived the discount rates for the LCC analysis by estimating the cost of capital for companies that purchase commercial and industrial pumps. The weighted average cost of capital (WACC) is commonly used to estimate the present value of cash flows to be derived from a typical company project or investme nt. Most companies use both debt and equity capital to fund investments, so their cost of capital is the weighted average of the

8

- 9cost to the firm of equity and debt financing, as estimated from financial data for publicly traded firms in the sectors that p urchase pumps. 3

Damodaran Online is a widely used source of information about company debt and equity financing for most types of firms, and was the primary source of data for this analysis. 4

Detailed sectors included in the Damondaran Online database we re assigned to the aggregate categories of: buildings (commercial and institutional), industrial, and municipal (water utilities). As there is no data available for agricultural companies, the industrial rate is applied to the agricultural sector.

DOE estimated the cost of equity using the capital asset pricing model (CAPM). 5 The CAPM assumes that the cost of equity ( 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 risk

free assets ( R f ), and the equity risk premium ( ERP ). Th e risk coefficient of the firm indicates the risk associated with that firm relative to the price variability in the stock market. The expected

return on risk

- free assets is defined by the yield on long

- term government bonds. The ERP represents the differe nce 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 R k

f

e 

## × + = β

Where:

k

e = cost of equity,

R f = expected return on risk

- free assets, β = risk coefficient of the firm, and

ERP = equity risk premium.

Several parameters of the cost of capital equations can vary substantially over time, and therefore the estimates can vary with the time period over which data is selected and the technical details of the data averaging method. For guidance on the time period for selecting and averaging data for key parameters and the averaging method, DOE used Federal Reserve methodologies for calculating these parameters. In i ts use of the CAPM, the Federal Reserve uses a forty

- year period for calculating discount rate averages, utilizes the gross domestic product price deflator for estimating inflation, and considers the best method for determining the

risk free rate as one wh ere “the time horizon of the investor is matched with the term of the risk

free security.” 6

By taking a forty

- year geometric average of Federal Reserve data on annual nominal

returns for 10

- year Treasury bills, DOE estimated the following risk free rat es for 20 04

- 201 3 ( Table 8.3

. 2 ). 7 DOE also estimated the ERP by calculating the difference between risk free rate and stock market return for the same time period, as estimated using Damodaran Online data

on the historical return to stocks. 8

8

- 10Table 8.3

. 2 Risk Free Rate and Equity Risk Premium, 20 04

- 20 13

Year Risk free rate (%) ERP (%)

2004 7.10% 3.25%

2005 7.11% 3.68%

2006 7.10% 3.49%

2007 7.08% 3.36%

2008 7.01% 2.40%

2009 6.88% 3.07%

2010 6.74% 3.23%

2011 6.61% 2.94%

2012 6.41% 3.99%

2013 6.24% 5.30%

The cost of debt financing ( 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:

ai f di R R k + =

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 d e e 

### w k w k WACC × + × =

Where:

WACC = weighted average cost of capital,

w e = proportion of equity financing, and

w d = proportion of debt financing.

By adjusting for the influence of inflation, DOE estimates the real weighted average cost of capital, or discount rate, for each company. DOE then aggregates the company real weighted average costs of capital to estimate the discount rate for each of the ownership types in the commercial and industrial pumps analysis.

8

- 11Table 8.3

. 3 shows the average WACC values for the major sectors that purchase

commercia l and industrial pumps. While WACC values for any sector may trend higher or lower over substantial periods of time, these values represent a cost of capital that is averaged over major business cycles.

Table 8.3

. 3 Weighted Average Cost of Capital for Sectors that Purchase Pumps

Sector Real Weighted Average Cost of Capital (%) Standard Deviation (%)

Buildings 5.1% 1.3%

Industrial / Agricultural 5.2% 1.2%

Municipal 4.5% 0.5%

8.3.3 Base Case Efficiency Distribution

DOE developed a base case efficiency distribution based on the shipments data provided by the manufacturers. Table 8.3

. 4 shows the base ca se efficiency distribution for each equipment class; however, the LCC uses distributions specific to each power and flow bin. In constructing the binned pump data, within each power and flow bin the original list of pump C

- values, and the number of shipmen ts of each model, was preserved. For each pump in the bin, the C

- value equation was used to normalize the efficiency to the value appropriate to the BEP flow and head assigned to the representative unit. This results in, for each bin, an initial or base c ase distribution of pump efficiencies for each bin. This distribution was used to assign an initial base

case C

- value to each row of the LCC sample.

Table 8.3

. 4 Base Case Efficiency Distributions by Equipment Class
Efficiency

Level ESCC

1800 ESCC

3600 ESFM

1800 ESFM

3600 IL

1800 IL

3600 VT

- S

3600

Baseline 18.5% 3.8% 0.9% 4.7% 14.7% 10.7% 1.6%

PER 10 15.8% 8.1% 20.3% 23.5% 16.0% 20.8% 36.7%

PER 25 18.2% 14.5% 16.1% 15.3% 9.9% 8.8% 7.5%

PER 40 10.7% 3.6% 24.2% 16.0% 11.3% 4.5% 4.7%

PER 55 13.7% 3.9% 10.4% 11.3% 10.7% 12.9% 5.9%

PER 70 23.2% 66.1% 28.1% 29.3% 37.3% 42.3% 43.7%

8.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS

This section presents the results of the LCC and PBP analysis. As discussed previously, DOE used probability distributions to characterize the uncertainty in many of the inputs to the

8

- 12analysis. LCC and PBP calculations were performed 10,000 times, sampling from the probability distributions that DOE developed to characterize many of the inputs to the analysis.

The average costs at each TSL are calculated considering the full sample of customers that have levels of efficiency in the base case equal to or ab ove the given TSL (who are not affected by a standard at that TSL), as well as customers who had non

- compliant pumps in the base case and purchase more expensive and efficient redesigned pumps in the standards case. The simple payback and LCC savings are m easured relative to the base

- case efficiency distribution in the compliance year. Based on simulations that DOE performed, for each standard level DOE also calculated the share of customers receiving a net LCC cost.

Table 8.4

. 1 through Table 8.4

. 14 show the LCC and PBP results for each equipment class. In general, the average LCC savings are positive for nearly all equipment classes and efficiency levels, with the exception of VT

- S.3600 , which has negative LCC saving for the first efficiency level

. Figure 8.4

. 1 through Figure 8.4

. 7 show the distribution of LCC savings for each equipment class.

Table 8.4

. 1 Average LCC and PBP Results by Efficiency Level for ESCC.1800

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,639 $2,271 $17,546 $19,185

-- 13

1 1 $1,672 $2,261 $17,470 $19,142 3.3 13

2 2 $1,704 $2,240 $17,317 $19,021 2.2 13

3 3 $1,768 $2,222 $17,177 $18,945 2.6 13

4 4 $1,863 $2,198 $16,997 $18,861 3.1 13

5 5 $2,026 $2,172 $16,796 $18,822 3.9 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

8

- 13Table 8.4

. 2 LCC Savings Relative to the Base Case Efficiency Distribution for

ESCC.1800

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 12% $43

2 2 11% $164

3 3 23% $240

4 4 30% $324

5 5 42% $362

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4

. 3 Average LCC and PBP Results by Efficiency Level for ESCC.3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,092 $1,592 $9,823 $10,915

-- 11

1 1 $1,098 $1,588 $9,800 $10,898 1.4 11

2 2 $1,111 $1,574 $9,713 $10,823 1.0 11

3 3 $1,141 $1,565 $9,653 $10,794 1.8 11

4 4 $1,170 $1,551 $9,566 $10,736 1.9 11

5 5 $1,215 $1,528 $9,422 $10,638 1.9 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

8

- 14Table 8.4

. 4 LCC Savings Relative to the Base Case Efficiency Distribution for

ESCC.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 0.7% $17

2 2 1.8% $92

3 3 14% $122

4 4 14% $180

5 5 12% $278

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4

. 5 Average LCC and PBP Results by Efficiency Level for ESFM.1800

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,891 $3,424 $40,983 $42,874

-- 23

1 1 $1,893 $3,423 $40,973 $42,866 2.4 23

2 2 $1,943 $3,406 $40,759 $42,701 2.8 23

3 3 $2,004 $3,384 $40,498 $42,502 2.8 23

4 4 $2,151 $3,342 $39,988 $42,139 3.1 23

5 5 $2,314 $3,301 $39,498 $41,812 3.4 23

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

8

- 15Table 8.4

. 6 LCC Savings Relative to the Base Case Efficiency Distribution for

ESFM.1800

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 0.26% $8.0

2 2 6.5% $173

3 3 15% $372

4 4 24% $735

5 5 26% $1,062

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4

. 7 Average LCC and PBP Results by Efficiency Level for ESFM 3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,349 $5,278 $51,268 $52,616

-- 20

1 1 $1,357 $5,271 $51,201 $52,558 1.2 20

2 2 $1,396 $5,218 $50,674 $52,070 0.8 20

3 3 $1,441 $5,171 $50,214 $51,655 0.9 20

4 4 $1,529 $5,117 $49,676 $51,205 1.1 20

5 5 $1,648 $5,036 $48,890 $50,538 1.2 20

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

8

- 16Table 8.4

. 8 LCC Savings Relative to the Base Case Efficiency Distribution for

ESFM.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 0.29% $58

2 2 1.9% $547

3 3 4.7% $961

4 4 7.0% $1,411

5 5 8.4% $2,078

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4

. 9 Average LCC and PBP Results by Efficiency Level for IL.1800

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $2,128 $1,891 $16,760 $18,888

-- 16

1 1 $2,145 $1,884 $16,692 $18,837 2.3 16

2 2 $2,194 $1,868 $16,545 $18,739 2.8 16

3 3 $2,281 $1,852 $16,407 $18,688 3.9 16

4 4 $2,432 $1,835 $16,254 $18,686 5.4 16

5 5 $2,614 $1,811 $16,040 $18,654 6.1 16

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

8

- 17Table 8.4

. 10 LCC Savings Relative to the Base Case Efficiency Distribution for IL.1800

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 1.8% $51

2 2 6.9% $149

3 3 15% $200

4 4 25% $202

5 5 36% $234

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4

. 11 Average LCC and PBP Results by Efficiency Level for IL.3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,473 $2,046 $14,211 $15,684

-- 13

1 1 $1,484 $2,038 $14,155 $15,639 1.4 13

2 2 $1,525 $2,019 $14,020 $15,545 1.9 13

3 3 $1,578 $1,997 $13,865 $15,443 2.1 13

4 4 $1,650 $1,980 $13,747 $15,397 2.7 13

5 5 $1,797 $1,946 $13,510 $15,307 3.2 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

8

- 18Table 8.4

. 12 LCC Savings Relative to the Base Case Efficiency Distribution for IL.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 2.0% $46

2 2 13% $139

3 3 11% $241

4 4 14% $288

5 5 20% $377

* The calculation includes customers with zero LCC savings (no impact).

Table 8.4

. 13 Average LCC and PBP Results by Efficiency Level for VT

- S.3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $692 $1,025 $5,857 $6,549

-- 11

1 1 $697 $1,025 $5,855 $6,551 11 11

2 2 $711 $1,021 $5,830 $6,542 4.2 11

3 3 $732 $1,002 $5,726 $6,458 1.7 11

4 4 $772 $989 $5,654 $6,426 2.2 11

5 5 $821 $977 $5,584 $6,405 2.7 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

8

- 19Table 8.4

. 14 LCC Savings Relative to the Base Case Efficiency Distribution for VT

- S.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 1.4% $(2.4)

2 2 21% $7.2

3 3 4.4% $91

4 4 8.5% $123

5 5 13% $144

* The calculation includes customers with zero LCC savings (no impact).

Figure 8.4

. 1 Distribution of Life

- Cycle Savings for ESCC.1800

8

- 20Figure 8.4

. 2 Distribution of Life

- Cycle Cost Savings for ESCC.3600

Figure 8.4

. 3 Distribution of Life

- Cycle Cost Savings for ESFM.1800

8

- 21Figure 8.4

. 4 Distribution of Life

- Cycle Cost Savings for ESFM.3600

Figure 8.4

. 5 Distribution of Life

- Cycle Cost Savings for IL

. 18 00

8

- 22Figure 8.4

. 6 Distribution of Life

- Cycle Cost Savings for IL .3600

Figure 8.4

. 7 Distribution of Life

- Cycle Cost Savings for VT

- S.3600

8

- 23REFERENCES

1 U.S. Department of Agriculture, 2007 Census of Agriculture United States Summary and State

Data Volume 1 , December, 2009. Washington, DC.

2 U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook 2014

, April, 2014. Washington, DC. Report No. DOE/EIA

- 0383(2014).

< www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf >

.

3 Modigliani, F. and M. Miller, The Cost of Capital, Corporations Finance and the Theory of

Investment. American Economic Review , 1958. 48(3): pp. 261

- 297

< www.jstor.org/stable /1809766 >

.

4 Damodaran Online, Data Page: Costs of Capital by Industry Sector , 2012. Damodaran. (Last accessed July, 2013.) < http://pages.stern.nyu.edu/~adamodar/ >

.

5 Ibbotson Associates, SBBI Valuat ion Edition 2009 Yearbook . 2009. Chicago, IL.

6 Federal Reserve Board, Federal Reserve Bank Services Private Sector Adjustment Factor,

2005 , 2005. Washington, D.C.

< www.federalreserve.gov/boarddocs/Press/other/2005/20051012/attachment.pdf >

.

7 Board of Gove rnors of the Federal Reserve System, H.15 Historical Data Selected Interest

Rates , 2013. (Last accessed February, 2013.)

< www.federalreserve.g ov/releases/h15/data.htm >

.

8 Damodaran Online, Data Page: Historical Returns on Stocks, Bonds and Bills

- United States

, 2012. Damodaran. (Last accessed July, 2013.) < http://pages.stern.nyu.edu/~adamodar/ >

.

8

- 24CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

................................ ................................ ................................ ........... 9

- 1

9.2 CURRENT SHIPMENTS

................................ ................................ ................................ 9

- 1

9.3 SHIPMENTS PROJECTION

................................ ................................ .......................... 9

- 2

9.3.1 Methodology

................................ ................................ ................................ ........ 9

- 2

9.3.2 Shipments in Standards Cases

................................ ................................ ............. 9

- 5

9.3.3 Shipments Results

................................ ................................ ................................ 9

- 5

REFERENCES

................................ ................................ ................................ ............................ 9

- 7

LIST OF TABL ES

Table 9.2.1 In

- Scope Pump Shipment Estimates: FY 2012

................................ .............. 9

- 2

Table 9.2.2 Shipment Distribution by Design Speed

................................ ........................ 9

- 2

Table 9.3.1 Distribution of Shipments by Sector

................................ .............................. 9

- 4

Table 9.3.2 Annual and Cumulative CIP Shipments Projections

................................ ..... 9

- 6

LIST OF FIGURES

Figure 9.3.1 Medium Electric Motor Shipments Index vs. Private Fixed Investment Index in Selected Equipment and Structure

................................ ................... 9

- 3 Figure 9.3.2 Pump Shipments Index vs. Private Fixed Investment Index in All Equipment

................................ ................................ ................................ ...... 9

- 3 Figure 9.3.3 Shipments Projection by Scenario Case Based on Growth in Private

Fixed Investment

................................ ................................ ............................ 9

- 4 Figure 9.3.4 Macroeconomic Indicator

- Based Shipment Projections by Sector and

in Total based on AEO 2014

................................ ................................ .......... 9

- 5 Figure 9.3.5 CIP Shipments Projection by Scenario Case

................................ ................. 9

- 6

9

- iCHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future equipment shipments are a necessary input to calculations of the national energy savings (NES) and net present value (NPV), as well as to the manufacturer impact analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy (DOE) used to project annual equipment shipments and presents results for commercial and industrial pumps c onsidered in this analysis.

DOE developed a shipments model to predict shipments of commercial and industrial pumps covered in this analysis

. T he shipments analysis projects initial shipments forward using

macroeconomic indicators for each sector found in the Energy Information Administration’s Annual Energy Outlook 2014 ( AEO201 4 ) 1

. DOE’s pump shipments projections are based on forecasts of economic growth and do not incorporate a distinction between replacements and purchases for new applications.

DOE b egan with shipments data by equipment type provided by the Hydraulic Institute (HI)

. 2 Based on a database of pumps compiled from manufacturer

- and HI

- provided data, DOE then developed a distribution of shipments across equipment classes that also take into account design speed

.

The shipments model is prepared as a Microsoft Excel spreadsheet that is accessible on the Internet ( http://energy.gov/eere/buildings/appliance

- and

- equipment

- standards

- program ). The rest of this chapter explains the shipments model in more detail. Section 9.2 provides a summary

of the data DOE used to devel op estimates of the shipments commercial and industrial pumps by equipment class and for each sector. Section 9.3 describes the methodology that underlies development of the model and presents the shipments projection

.

9.2 CURRENT SHIPMENTS

As discussed in Cha pter 3, DOE reviewed U.S. Census Bureau data for historical shipments of centrifugal pumps. 3 However, it was difficult to determine what percentage of those shipments reflected the scope of this rulemaking. In addition, in both the written comments

in res ponse to the Framework Document and during meetings of the CIP working group, the pump manufacturers did not believe the data to be reliable. Because of these difficulties, HI conducted a survey of its members and provided DOE with shipments estimates for 2012. 2 These shipments , shown in Table 9.2

. 1

, were reviewed in the CIP working group and found to represent an appropriate amount for the scope of this rulemaking. In order to further disaggregate the shipments into equipment classes in cluding design speed, as shown in Table 9.2

. 2

, DOE used

its database of pump shipment data collected directly from manufacturers and from HI.

9

- 1Table 9.2

. 1 In

- Scope Pump Shipment Estimates: FY 2012
Equipment Category Total Industry Estimate (Units)

ESCC 171,456

ESFM 44,042

IL 50,424

RS

- V 49,975

VT

- S 104,406

TOTAL 420,303
Source: Hydraulic Institute.

Table 9.2

. 2 Shipment Distribution by Design Speed
Equipment Category % 1800 % 3600

ESCC 23% 77%

ESFM 89% 11%

IL 83% 17%

RS

- V 9% 91%

VT

- S 0% 100%

9.3 SHIPMENTS PROJECTION

9.3.1 Methodology

In the Framework Document, DOE stated that shipments of pumps are driven by machinery production growth for equipment incorporating pumps and by the economic growth of commercial and industrial sectors that use this equipment. DOE suggested that h istorical data

would be used to establi sh the relationship between shipments of pumps and the appropriate growth index for sector growth, and that DOE intend ed to use private fixed investment data for equipment incorporating pumps from the U.S. Department of Commerce’s Bureau of Economic Analys is to characterize the production of this equipment.

DOE had successfully used this methodology in the medium electric motors rulemaking. 4
In that rulemaking, DOE identified a close relationship between shipments and private fixed

investments in selected e quipment and structure over an extended time period , as shown in Figure 9.3

. 1 . The R 2 for the relationship was greater than 0.91.

9

- 2Figure 9.3

. 1 Medium Electric Motor Shipments Index vs. Private Fixed Investment Index in Selected Equipment and Structure

When DOE attempted this approach for pumps using the historical C ensus shipments

data and private fixed investmen t data from the Bureau of Economic Analysis (BEA) 5

, 6 , DOE was unable to obtain a historical fit as good as that for motors, especially when selecting equipment that seemed most appropriate (i.e., equipment not related to information processing and transpor tation, such as industrial and agricultural machinery )

.

Figure 9.3

. 2 shows the historical relationship of pump shipments to private fixed investment in all equipment for the last 10 years of the data

, which had an R 2 of 0.82. S hipments projections based on forecasts of real Gross Domestic Product from AEO 2014 1 show a tripling of shipments over the analysis period ( Figure 9.3

. 3 ). This projection is much steeper than the

historical trends

.

Figure 9.3

. 2 Pump Shipments Index vs. Private Fixed Investment Index in All Equipment 0 20 40 60 80 100 120 140 160 180Index 2001=100

Shipments Index

Private Fixed Investment Index in Selected Equipment and Structure

-20 30 80 130 180Index2000=100

Shipments Index

9

- 3Figure 9.3

. 3 Shipments Projection by Scenario Case Based on Growth in Private Fixed

Investment

As a result of the uncertainty regarding whether the historical shipments data are representative of the commercial and industrial pumps market, and the relatively steep resulting growth projections (compared to historical trends), DOE considered an alternative shipments methodology. Specifically, DOE projected shipments using forecasts of various indicators in

AEO 2014 : (1) commercial floor space, (2) value of manufacturing shipments, (3) value of agriculture, mining, and construction shipments, and (4) population (for municipal sector). For the initial year, DOE distributed total shipments into the four sectors using estimates from the

LCC analysis ( Table 9.3

. 1 )

. The distribution of sectors changes over time as a result of each sector’s differing forecast in AEO 2014 ( Figure 9.3

. 4 )

. The projection is still fairly steep

comp ared to the historical trend, although much less so than in the initial model. However, as stated previously, it is uncertain how much the historical trend represents shipments of pumps in the scope of this rulemaking, and the pump manufacturers involved i n the CIP working group felt that the resulting annual growth was likely reasonable.

Table 9.3

. 1 Distribution of Shipments by Sector

Sector % FY 2012 Shipments

Commercial 47% 197,542

Industrial 33% 138,700

Agricultural 12% 50,436

Municipal 8% 33,624

TOTAL 100% 420,303 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800Shipments (1,000)

Historical Reference Case High Economic Growth Case Low Economic Growth Case

9

- 4Figure 9.3

. 4 Macroeconomic Indicator

- Based Shipment Projections by Sector and in

Total based on AEO 2014

The total shipments in each year are then disaggregated into equipment classes using the distributions of 2012 shipments shown previously. This market share by equ ipment class remains

constant over time.

9.3.2 Shipments in Standards Cases

DOE believes that standards would have negligible impact on pump shipments . Because there are no increases in manufacturer production cost associated with improved pump efficiency, any increase in customer price is directly related to the conversion costs incurred by the manufacturer and how all manufacturers choose to price the ir pumps following implementation of a standard. Under most pricing scenarios, it is likely that following a standard, a customer would be able to buy a more efficient pump for the same price as the less efficient pump they would have purchased before or w ithout a standard. Therefore, rather than foregoing a pump purchase under a standards case, a customer might simply switch brands or pumps to purchase a cheaper one that did not have to be redesigned. This customer choice was not

modeled in the LCC because DOE chose to rely on a more conservative scenario; however, such customer choice would likely override any propensity to repair a pump model rather than replace

it. DOE used the same shipments projection in the standards case as in the base case.

9.3.3 Shipments Results

Figure 9.3

. 5 shows annual shipments for the AEO Reference Case as well as AEO high and low economic growth scenarios over the 30

- years analysis peri od starting at the compliance

9

- 5year. The analysis uses 20 20 to represent the first full year of compliance with potential

standards

.

Figure 9.3

. 5 CIP Shipments Projection by Scenario Case

Table 9.3

. 2 shows the annual and cumulative shipments for each equipment class for the

Reference Case

.

Table 9.3

. 2 Annua l and Cumulative CIP Shipments Projections

thousand units

Equipment

Class 2020 2030 2040 2049 Cumulative

over 30

years

ESCC 1800 47 54 61 68 1

, 72 7

ESCC 3600 154 177 200 22 2 5

, 628

ESFM 1800 46 53 60 6 7 1

, 6 90

ESFM 3600 5 6 7 8 200

IL 1800 49 56 6 4 70.5 1

, 790

IL 3600 10 1 2 13 1 5 37 3

RS

- V 1800 5 6 7 7.5 191

RS

- V 3600 54 61 69 7 7 1

, 952

VT

- S 1800 0.4 0.5 0.6 0.6 16

VT

- S 3600 122 140 158 17 6 4

, 462

Total* 494 567 6 40 710 18

, 02 9

*Total may not sum up because of rounding.

9

- 6REFERENCES

1 U.S. Department of Energy- Energy Information Administration ( May 2014 ), Annual Energy

Outlook 2014 , Washington, DC. Retrieved May 2014. < www.eia.gov/forecasts/aeo/ >.

2 Hydraulic Institute (March 21, 2014), DOE/HI Manufacturers Survey for Fiscal Year 2012

, Parsippany, NJ. Docket EERE -2013- BT

- NOC -0039, No. 0068. <www.regulations.gov/#!documentDetail;D=EERE-2013- BT

- NOC -0039-0068 >.

3 U.S. Census Bureau (August 2011), Pumps (except hydraulic) and Compressors - 2010. MA333P(08)-1

. Retrieved December, 2012

, <w ww.census.gov/manufacturing/cir/historical_data/ma333p/index.html >

4 U.S. Department of Energy. Technical Support Document: Energy Conservation Program: Energy Conservation Standards for Commercial and Industrial Electric Motors

. <www.regulations.gov/#!documentDetail;D=EERE-2010- BT

- STD -0027-0078 >.

5 B u r eau of Economic Analysis (May 1, 2014)

, Priva te Fixed Investment in Equipment by Type

, Table 5.5.5U. Retrieved April, 2014. <w ww.bea.gov/iTable/iTab le.cfm?ReqID=12&step=1 >.

6 B u r eau of

E conomic Analysis ( May 1, 2014)

, Priva te Fixed Investment i n S tructures b

y Type

, Table 5.4.5U. Retrieved April , 2014. <w ww.bea.gov/iTable/iTable.cf m?ReqID=12&step=1 >.

9-7CHAPTER 10. NATIONAL IMPACT ANAL YSIS

TABLE OF CONTENTS

10.1 INTRODUCTION

................................ ................................ ................................ ......... 10

- 1

10.2 BASE AND STANDARDS C ASE EFFICIENCIES

................................ .................... 10

- 2

10.3 NATIONAL ENERGY SAVI NGS

................................ ................................ ............... 10

- 3

10.3.1 National Energy Savings Overview

................................ ................................ ... 10

- 3

10.3.2 National Energy Savings Inputs

................................ ................................ ......... 10

- 6

10.3.2.1 Unit Annual Site Energy Consumption

................................ .................... 10

- 6

10.3.2.2 Lifetime Distribution

................................ ................................ .............. 10

- 11

10.3.2.3 Site

- to

- Power Plant Energy Use Factor

................................ ................. 10

- 11

10.3.2.4 Full

- Fuel

- Cycle Energy Factors

................................ ............................. 10

- 12

10.4 NET PRESENT VALUE

................................ ................................ ............................. 10

- 12

10.4.1 Net Present Value Overview

................................ ................................ ............ 10

- 13

10.4.2 Net Present Value Inputs

................................ ................................ ................. 10

- 15

10.4.2.1 Total Per

- Unit Installed Cost

................................ ................................ .. 10

- 15

10.4.2.2 Unit Lifetime Operating Cost

................................ ................................ . 10

- 19

10.4.2.3 Discount Rate

................................ ................................ ......................... 10

- 20

10.5 TRIAL STANDARD LEVEL S

................................ ................................ ................... 10

- 20

10.6 RESULTS

................................ ................................ ................................ .................... 10

- 2 1

REFERENCES

................................ ................................ ................................ ........................ 10

- 23

LIST OF TABLES

Table 10.2.1 Base Case Efficiency Distributions by Equipment Class

............................ 10

- 2

Table 10.2.2 Example Standards Case Distributions Under Roll

- Up Scenario (ESCC 1800)

................................ ................................ ................................ 10

- 3

Table 10.3.1 UEC Inputs to the NIA (kWh): ESCC 1800

................................ ................ 10

- 7

Table 10.3.2 UEC Inputs to the NIA (kWh): ESCC 3600

................................ ................ 10

- 7

Table 10.3.3 UEC Inputs to the NIA (kWh): ESFM 1800

................................ ............... 10

- 8

Table 10.3.4 UEC Inputs to the NIA (kWh): ESFM 3600

................................ ............... 10

- 8

Table 10.3.5 UEC Inputs to the NIA (kWh): IL 1800

................................ ...................... 10

- 9

Table 10.3.6 U EC Inputs to the NIA (kWh): IL 3600

................................ ...................... 10

- 9

Table 10.3.7 UEC Inputs to the NIA (kWh): VT

- S 3600

................................ ............... 10

- 10

Table 10.3.8 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO 2014 )

...................... 10

- 12

Table 10.4.1 Average Per

- Unit Total Installed Cost (2013$): ESCC 1800

.................... 10

- 16

Table 10.4.2 Average Per

- Unit Total Installed Cost (2013$): ESCC 3600

.................... 10

- 16

Table 10.4.3 Average Per

- Unit Total Installed Cost (2013$): ESFM 1 800

................... 10

- 17

Table 10.4.4 Average Per

- Unit Total Installed Cost (2013$): ESFM 3600

................... 10

- 17

Table 10.4.5 Average Per

- Unit Total Installed Cost (2013$): IL 1800

.......................... 10

- 18

Table 10.4.6 Average Per

- Unit Total Installed Cost (2013$): IL 3600

.......................... 10

- 18

Table 10.4.7 Average Per

- Unit Total Installed Cost (2013$): VT

- S 3600

..................... 10

- 19

10

- iTable 10.5.1 Mapping Between TSLs and Efficiency Levels

................................ ........ 10

- 21

Table 10.6.1 Cumulative National Primary Energy Savings for Commercial and Industrial Pump Trial Standard Levels for Units Sold in 2020

- 2049

........ 10

- 21

Table 10.6.2 Cumulative National Full

- Fuel

- Cycle Energy Savings for Commercial and Industrial Pump Trial Standard Levels for Units Sold in 2020

- 2049

................................ ................................ ................................ ............ 10

- 22

Table 10.6.3 Cumulative Net Present Value of Customer Benefit for Commercial and Industrial Pump Trial Standard Levels for Units Sold in 2020

- 2049

................................ ................................ ................................ ............ 10

- 22

LIST OF FIGURES

Figure 10.3.1 Energy Use Adjustment Factors

................................ ................................ 10

- 11 Figure 10.4.1 Weighted

- Average Electricity Price Time Series

................................ ...... 10

- 20

10

- iiCHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter examines national impacts attributable to each trial standard level (TSL)

considered for commercial and industrial pumps. For each equipment class

, DOE evaluated the following impacts: national energy savings (NES) attributable to each potential standard level, monetary value of the lifetime energy savings to customers of the considered equipm ent, increased total lifetime cost of the equipment because of standards, and net present value (NPV) resulting from increased energy efficiency (the difference between the energy cost savings and the increased total lifetime cost of the equipment).

To c onduct its national impacts analysis (NIA), DOE determined the NES and NPV for each TSL being considered as the new standard for commercial and industrial pumps. DOE performed all calculations for each considered equipment class using Microsoft Excel

spre adsheet models, which are accessible on the Internet. a The spreadsheets combine the calculations for determining the NES and NPV for each considered equipment with input from the appropriate shipments model that DOE used to project future purchases of the considered equipment. Chapter 9 provides a detailed description of the shipments model.

The NIA calculation started with the shipments model , which produces a projection of annual shipments of pumps . DOE used the annual projection of shipments to pr oduce an accounting of annual national energy savings, annual national energy cost savings, and annual

national incremental non

- energy costs resulting from purchasing, installing and operating the units projected to be shipped in each year of the analysis period during their estimated lifetime.

To calculate the annual national energy savings, DOE first estimated the lifetime primary

and full

- fuel

- cycle b (FFC) energy consumption at the unit level and for each year in the analysis period

, for pumps of each equipment class

. The unit’s lifetime primary and FFC energy consumptions were then scaled up to the national level based on the annual shipments projection

. This produced, for each equipment class, two streams of annual national energy consumption,

from which DOE derived two streams of annual NES from pumps shipped in each year of the analysis period : one that accounts for primary energy savings, and one that accounts for the FFC energy savings

.

DOE followed a similar procedure to calculate the an nual national energy cost savings

and the annual national incremental non

- energy costs

. DOE first estimated the lifetime energy

cost and the lifetime non

- energy costs at unit level and for each year in the analysis period

, for pumps of each equipment clas s

. The unit lifetime energy and non

- energy costs, estimated for units shipped in each year in the analysis period, were then scaled up to the national level based on the annual shipments projection

. This produced, for each equipment class and sector : (a) two

a See http://energy.gov/eere/buildings/appliance

- and

- equipment

- standards

- program

b The full

- fuel

- cycle energy consumption adds to the primary energy consumptio n the energy consumed by the energy supply chain upstream to power plants

.

10

- 1streams of annual national energy costs, from which DOE derived a stream of annual national energy cost savings associated with each year in the analysis period, and its corresponding

present

- value ; and (b) two streams of annual national non

- energy co sts, from which DOE derived a stream of annual national incremental equipment non

- energy costs associated with each year in the analysis period, and its corresponding present

- value

. DOE then calculated the difference between the national energy cost savin gs and national incremental non

- energy costs to obtain the NPV of each equipment class , and summed these values across equipment classes to produce the total NPV.

Two models included in the NIA are described below

— the NES model in Section 10.2,

and the NPV model in Section 10.3

. Each technical description begins with a summary of the

model

. It then provides a descriptive overview of how DOE performed each model’s calculations and follows with a summary of the inputs

. The final subsections of each tech nical description describe each of the major inputs and computation steps in detail and with equations, when appropriate

. After the technical model descriptions, this chapter presents the results of the

NIA calculations.

10.2 BASE AND STANDARDS C ASE EFFICIENCI ES

For the base case in 2012, DOE developed a distribution of efficiencies from a database

which DOE built using data collected from major manufacturers and the Hydraulic Institute (see

Table 10.2

. 1 ). Because the available evidence suggests that there is no trend toward greater interest in higher pump efficiency, DOE assumed that the base case distribution would remain

constant over time

.

Table 10.2

. 1 Base Case Efficiency Distributions by Equipment Class
Efficiency

Level ESCC

1800 ESCC

3600 ESFM

1800 ESFM

3600 IL

1800 IL

3600 VT

- S

3600

Baseline 18.5% 3.8% 0.9% 4.7% 14.7% 10.7% 1.6%

PER 10 15.8% 8.1% 20.3% 23.5% 16.0% 20.8% 36.7%

PER 25 18.2% 14.5% 16.1% 15.3% 9.9% 8.8% 7.5%

PER 40 10.7% 3.6% 24.2% 16.0% 11.3% 4.5% 4.7%

PER 55 13.7% 3.9% 10.4% 11.3% 10.7% 12.9% 5.9%

PER 70 23.2% 66.1% 28.1% 29.3% 37.3% 42.3% 43.7%

For each efficiency level analyzed, DOE used a “roll

- up” scenario to establish the market shares by efficiency level for the year that compliance would be required with new standards (i.e., 2020). DOE believes that equipment efficiencies in the base case that were above the

standard level under consideration would not be affected. There is anecdotal information from certain manufacturers that for pumps not meeting a potential standard at some of the lower efficiency levels, redesign would likely target an efficiency level higher than the minimum given the level of investment required for a redesign, and the relatively more modest change in

10

- 2investment to design to a given pump to a higher level once redesign is already taking place. However, DOE has no data to clearly indicate what percentage of failing pumps would likely be redesigned to a level higher than the minimum, or how high that level would be. Furthermore, DOE does not want to overestimate savings from this rule by assuming that manufacturers would design to a level higher than required. Table 10.2

. 2 shows an example roll

- up scenario for one of the equipment classes.

Table 10.2

. 2 Example Standards Case Distributions Under Roll

- Up Scenario (ESCC 1800)
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 18.5% 0% 0% 0% 0% 0%

PER 10 15.8% 34.3% 0% 0% 0% 0%

PER 25 18.2% 18.2% 52.4% 0% 0% 0%

PER 40 10.7% 10.7% 10.7% 63.1% 0% 0%

PER 55 13.7% 13.7% 13.7% 13.7% 76.8% 0%

PER 70 23.2% 23.2% 23.2% 23.2% 23.2% 100%

10.3 NATIONAL ENERGY SAVI NGS

DOE developed the NES model to estimate the total national primary and FFC energy savings using information from the life

- cycle cost (LCC) relative to energy consumption

, combined with the results from the shipments model

. The savings shown in the NES reflect increased pump efficiency resulting from the installation of more efficient pumps nationwide (as

a consequence o f new standards) , in comparison to a base case with no national standards

.

10.3.1 National Energy Savings Overview

DOE calculated the cumulative primary and FFC energy savings from a pump efficiency standard, relative to a base case scenario of no standard, ove r the analysis period

. It calculated

NES for each TSL in units of quadrillion British thermal units (Btus) (quads), for standards with an expected compliance date in December 2019. c The NES calculation started with estimates of shipments, which are outpu ts of the shipments model (Chapter 9)

. d DOE then obtained values of

pump parameters from the LCC analysis (Chapter 8), projections of site

- to

- primary conversion

factors e from the Annual Energy Outloo k 2014 1 ( AEO 2014 ) and projections of site

- to

- upstream

c The analysis uses January 1st, 20 20 to represent the expected compliance date in December 2019

. Therefore, the

30

- year analysis period 201 9

- 204 8 is referred to as 20 20

- 204 9 in this chapter.

d Shipments provided by the shipment model do not account for the price

- elasticity of demand. Therefore, NES results reported in this chapter were estimated under the assumption of zero price

- elasticity.

e The site

- to

- primary factors account for electricity generation, transmission and distribution losses.

10

- 3conversion factors f from a NEMS

- based methodology , and calculated the market average of the total primary and FFC energy used over the lifetime of units shipped in each year of the analysis period for both a base case and a standards case

. T he market ave rage energy consumed per unit

decreases in the standards case relative to the base case

. For each year analyzed, the lifetime primary and FFC energy savings from all pumps of a given equipment class , shipped in that year, are the differences in their prim ary and FFC energy use between the corresponding base case and

the standards case scenarios.

This calculation is expressed by the following formulas for a given equipment class :

Lifetime Primary Energy Savings

nnnnnnnn ( yy ) = nnnnnnnnnnnn ( yy )

− nnnnnnnnnnnnnn ( yy ) Eq. 10

. 1

nnnnnnnnnnnn ( yy ) = h pp ( yy )

∙ � � uu cc ( yy )

∙ MM cc ( yy ) �

cc Eq. 10

. 2

nnnnnnnnnnnnnn ( yy ) = h pp ( yy )

∙ � � uu cc ( yy )

∙ MM cc ( yy ) �

cc Eq. 10

. 3

uu cc ( yy ) = ∑ aa cc ( yy
, ii ) ii = 1

. . LLLL Eq. 10

. 4

where:

nnnnnnnn ( yy ) = the lifetime primary energy savings of all pumps shipped in year y,

nnnnnnnnnnnn ( yy ) = the base case, lifetime primary energy consumption of pumps shipped in year y,

nnnnnnnnnnnnnn ( yy ) = the standards case, lifetime primary energy consumption of pumps shipped in year y,

h pp ( yy ) = the number of pumps shipped in year y,

uu cc ( yy ) = the lifetime primary energy consumption of a unit with efficiency level at EL c shipped in year y,

aa cc ( yy
, ii ) = the annual primary energy consumption in the ii

- th year of operation of a unit with efficiency level at EL c, shipped in year y,

MM cc ( yy ) = the base case market share of units with efficiency level at EL c shipped in year y, and

MM cc ( yy ) = the standards case market share of units with efficiency level at EL c shipped in year y.

f The site

- to

- upstream factors translate site energy consumption into the energy consumed in the supply chain of the fuels used for electricity generatio n.

10

- 4Lifetime Full

- Fuel

- Cycle Energy Savings

nnnnnnnn ( yy ) = nn ( yy )

− nnnnnnnnnnnnnn ( yy ) Eq. 10

. 5

nnnnnnnnnnnn ( yy ) = h pp ( yy )

∙ � � uu cc ( yy )

∙ MM cc ( yy ) �

cc Eq. 10

. 6

nnnnnnnnnnnnnn ( yy ) = h pp ( yy )

∙ � � uu cc ( yy )

∙ MM cc ( yy ) �

cc Eq. 10

. 7

uu cc ( yy ) = ∑ � aa cc ( yy
, ii )

∙ ffff ( yy + ii

− 1 ) � ii = 1

. . LLLL Eq. 10

. 8

where:

nnnnnnnn ( yy ) = the lifetime FFC energy savings of all pumps shipped in year y,

nnnnnnnnnn ( yy ) = the base case, lifetime FFC energy consumption of pumps shipped in year y,

nnnnnnnnnnnnnn ( yy ) = the standards case, lifetime FFC energy consumption of pumps shipped in year y,

h pp ( yy ) = the number of pumps shipped in year y,

uu cc ( yy ) = the lifetime FFC energy consumption of a unit with efficiency level at EL c shipped in year y,

aa cc ( yy
, ii ) = the annual primary energy consumption in the ii

- th year of operation of a unit with efficiency level at EL c, shipped in year y, ffff ( yy ) = the primary

- to

- FFC conversion factor in year y,

MM cc ( yy ) = the base case market share of units with efficiency level at EL c shipped in year y, and

MM cc ( yy ) = the standards case market share of units with efficiency level at EL c shipped in year y.

DOE used the lifetime primary and FFC energy savings estimated for all pumps shipped

from 20 20 through 204 9 to calculate the total primary NES ( NN ssss ) and the total FFC NES ( NN FFFFFF ) for the analysis period

. The calculation used the followin g formulas:

NN ssss = � nnnnnnnn ( yy ) 2049

yy = 2020 Eq. 10

. 9

NN FFFFFF = � nnnnnnnn ( yy ) 2049

yy = 2020

Eq. 10

. 10

10

- 5where:

nnnnnnnn ( yy ) = the lifetime primary energy savings of all pumps shipped in year y,

and

nnnnnnnn ( yy ) = the lifetime FFC energy savings of all pumps shipped in year y.

Once the shipments model provides the estimate of shipments

, and the site

- to

- primary

and site

- to

- upstream factors convert site energy consumption respectively into primary and

upstream energy consumption, the key to the NES calculation is in calculating the unit annual

site energy consumption and market share distributions using inputs from the LCC an alysis

. The next section summarizes the inputs necessary for the NES calculation and then presents them individually; the following sections detail, respectively, how the unit lifetime site energy consumption and the standards case efficiency distribution were calculated.

10.3.2 National Energy Savings Inputs

The NES model inputs include: (a) the unit site energy consumption , (b) the site

- to

primary conversion factor s , whic h enable the calculation of primary energy consumption from site energy use , (c) the site

- t o

- upstream conversion factor s whic h

– in addition to the site

- to

primary factors

– enable the calculation of FFC energy consumption from site energy use , and (d) sh ipment efficiency distributions, which were discussed in the previous section

. The list of NES model inputs is as follows:

• u nit annual site energy consumption ;

• lifetime (probability) distribution;

• e lectricity site

- to

- primary conversion factors; and

• electricity site

- to

- upstream conversion factors

.

10.3.2.1 Unit Annual Site Energy Consumption

The unit annual site energy consumption expresses an estimate of the amount of site energy that a pumps of a given equipment class, meeting the efficiency level of a given EL , and shipped in a given year will consume in each year of its lifetime

.

The ave rage per

- unit annual energy consumption for each equipment class and efficiency

level was calculated in the LCC. In the base case, the distribution of horsepower within each efficiency bin differs. As a result, average energy use does not decrease monotoni cally as efficiency level increases. In addition, in each standards case, the per

- unit annual energy consumption at the minimum efficiency level differs from that in the base case. For example, if pumps at the baseline efficiency level tended to be smaller than pumps meeting the efficiency associated with EL1, under the standards case, the unit energy consumption at EL 1 would

decrease. Table 10.3

. 1 through Table 10.3

. 7 show these matrixed inputs.

10

- 6DOE uses a market

- or shipment

- weighted average annual UEC in its calculation of the national AEC. This takes into account that the markets include shipments of equipment with a broad range of efficiencies, as shown in section 10.2

. The market

- weighted average UEC

declines as TSL increases, as would be expected, as shown in Table 10.3

. 1 through Table 10.3

. 7

.

Table 10.3

. 1 UEC Inputs to the NIA (kWh) : ESCC 1800
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 12545 N/A N/A N/A N/A N/A

PER 10 31164 20802 N/A N/A N/A N/A

PER 25 24001 24001 21494 N/A N/A N/A

PER 40 36900 36900 36900 23793 N/A N/A

PER 55 31621 31621 31621 31621 24855 N/A

PER 70 19721 19721 19721 19721 19721 23382

Market Weighted Average 24469 24359 24119 23921 23664 23382

Table 10.3

. 2 UEC Inputs to the NIA (kWh): ESCC 3600
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 29817 N/A N/A N/A N/A N/A

PER 10 40824 36945 N/A N/A N/A N/A

PER 25 14218 14218 23904 N/A N/A N/A

PER 40 44787 44787 44787 26032 N/A N/A

PER 55 62802 62802 62802 62802 29821 N/A

PER 70 9952 9952 9952 9952 9952 16442

Market Weighted Average 17141 17098 16951 16837 16687 16442

10

- 7Table 10.3

. 3 UEC Inputs to the NIA (kWh) : ESFM 1800
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 47229 N/A N/A N/A N/A N/A

PER 10 43025 43159 N/A N/A N/A N/A

PER 25 38112 38112 40471 N/A N/A N/A

PER 40 35511 35511 35511 38147 N/A N/A

PER 55 52331 52331 52331 52331 39553 N/A

PER 70 26849 26849 26849 26849 26849 35545

Market Weighted Average 36876 36867 36676 36447 35983 35545

Table 10.3

. 4 UEC Inputs to the NIA (kWh) : ESFM 3600
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 90194 N/A N/A N/A N/A N/A

PER 10 59093 64014 N/A N/A N/A N/A

PER 25 58300 58300 60674 N/A N/A N/A

PER 40 66196 66196 66196 61309 N/A N/A

PER 55 72804 72804 72804 72804 62309 N/A

PER 70 38108 38108 38108 38108 38108 54348

Market Weighted Average 57030 56956 56377 55871 55281 54403

10

- 8Table 10.3

. 5 UEC Inputs to the NIA (kWh) : IL 1800
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 8419 N/A N/A N/A N/A N/A

PER 10 21976 15208 N/A N/A N/A N/A

PER 25 51256 51256 23584 N/A N/A N/A

PER 40 32555 32555 32555 25223 N/A N/A

PER 55 16525 16525 16525 16525 23437 N/A

PER 70 13625 13625 13625 13625 13625 19517

Market Weighted Average 20357 20272 20104 19941 19754 19497

Table 10.3

. 6 UEC Inputs to the NIA (kWh) : IL 3600
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 16845 N/A N/A N/A N/A N/A

PER 10 15763 15870 N/A N/A N/A N/A

PER 25 25627 25627 17488 N/A N/A N/A

PER 40 48821 48821 48821 20101 N/A N/A

PER 55 54421 54421 54421 54421 27470 N/A

PER 70 13091 13091 13091 13091 13091 21018

Market Weighted Average 22091 22009 21803 21563 21388 21018

10

- 9Table 10.3

. 7 UEC Inputs to the NIA (kWh) : VT

- S 3600
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 13250 N/A N/A N/A N/A N/A

PER 10 9804 9928 N/A N/A N/A N/A

PER 25 29161 29161 12962 N/A N/A N/A

PER 40 18009 18009 18009 12961 N/A N/A

PER 55 39582 39582 39582 39582 15463 N/A

PER 70 8928 8928 8928 8928 8928 12446

Market Weighted Average 13080 13073 13020 12782 12623 12458

In order to properly account for national impacts, DOE made some adjustments to the energy use developed from the LCC spreadsheet. Specifically, in the LCC, DOE does not account for pumps sold with trimmed impellers or pumps used with VSDs, both of which m ay reduce the energy savings resulting from pump efficiency improvements.

DOE reviewed studies on VSD penetration and used an initial penetration of 3.2 percent

in 1998 2 with a 5 percent annual increase

. 3 In order to determine the penetration projection, DOE reviewed historical shipments of controls for AC packaged drives from U.S. Economic

Census data. 4 DOE applied a linear regression to this data, which resulted in a growth rate of 8 percent per year assuming a constant linear trend. However, DOE had no way of knowing whether this trend would be applicable to the pumps market specifically, as growth of controls might be faster in other markets. Therefore, DOE also reviewed a study from the European Commission, which projected that VSD penetration in motor systems will grow from 30 to 50 percent across ten years (2000 to 2010), resulting in a 3 percent an d 5 percent annual growth

rate. 5 Based on the U.S

. and European data, DOE chose to use the 5 percent annual growth rate

as a mid

- growth scenario.

Although these VSD studies are not specific to VFDs, DOE assumed all VSD use was VFD use, as VFDs are the mos t common type of VSD in the pumps market

. Based on DOE’s analysis of VFD users in the customer subgroup analysis (see chapter 11 ), DOE assumed VFDs would reduce energy use by 39 percent on average, which also reduces the potential energy savings from highe r efficiency. However, based on the well

- known difficulties with VFD installation and operation, DOE assumed that the full amount of potential savings would not be realized for all customers. DOE is currently assuming an “effectiveness rate” of 75 percent; in other words DOE is assuming that customers will achieve on average only 75 percent of the 39 percent estimated savings (i.e., 29 percent savings) because of improper installation, operation inconsistent with intended use, or other equipment problems.

10

- 10In the CIP working group meetings, stakeholders stated that half to the vast majority of pumps sold by manufacturers are trimmed. (Louis Starr, EERE

- 2013

- BT

- NOC

- 0039

- 0072, p. 345; Al Huber, EERE

- 2013

- BT

- NOC

- 0039

- 0009, p. 168) DOE also consulted a market ex pert who agreed that a majority of pumps are trimmed, and that the average trim is between 10 to 20 percent. In the NIA, DOE assumed that for all equipment classes except VT

- S, 50 percent of pumps not sold with VFDs are sold with impellers trimmed to 85 pe rcent of full impeller. According to the pump affinity laws, such an impeller trim uses 61 percent of the power of full trim. Accordingly, DOE reduced the energy use for those customers by 39 percent. For the VT

- S equipment class, DOE assumed that pumps we re not sold with trimmed impellers. A large percentage of these pumps are pressed stainless and will never be trimmed; the remainder of these pumps will be significantly less likely to be trimmed than other pump types because variability in the number of s tages would be used in place of trimming the impellers.

DOE used the penetration rate and power reduction values for VFDs and trimmed impellers to create an energy use adjustment factor time series in the NES spreadsheet. ( Figure

10.3

. 1 ) The total energy use adjustment factor applies to all equipment classes except VT

- S, which, as noted, only receive the VFD adjustment.

Figure 10.3

. 1 Energy Use Adjustment Factors

10.3.2.2 Lifetime Distribution

For the NIA, DOE uses a distribution of pump lifetime in years as determined in the LCC (chapter 8). Each pump will consume its annual UEC in each year of its lifetime.

10.3.2.3 Sit e

- to

- Power Plant Energy Use Factor

In determining national annual energy consumption, DOE initially calculated the annual energy consumption at the site (for electricity, the energy in kWh consumed at the household )

. DOE then applied a conversion factor to site energy consumption to account for losses associated with the generation, transmission, and distribution of electricity. This multiplicative site

- to

power

- plant conversion factor converts site energy consumptio n into primary energy consumption, expressed in quadrillion Btus (quads). 70% 75% 80% 85% 90% 95% 100% 105%202020212022202320242025202620272028202920302031203220332034203520362037203820392040204120422043204420452046204720482049Percent Energy Use

Year

Total Energy Usage Adjustment VFD Adjustment Only

10

- 11DOE used annual site

- to

- power

- plant conversion factors based on the version of the National Energy Modeling System (NEMS) g that corresponds to DOE’s Energy Information

Administratio n’s (EIA’s) Annual Energy Outlook 2014 ( AEO2014 ). 1 The factors are marginal values, which represent the response of the system to an incremental decrease in consumption. For electricity, the conversion factors change over time in response to projected changes in generation sources (that is, the types of p ower plants projected to provide electricity to the Nation). The value AEO2014 reported for 2040 (the last year available in AEO2014 ) was extrapolated through the end of the projection period (204 9 ).

10.3.2.4 Full

- Fuel

- Cycle Energy Factors

The full

- fuel

- cycle (FFC) includes point

- of

- use (site) energy; the energy losses associated with generation, transmission, and distribution of electricity; and the energy consumed in extracting, processing, and transporting or distributing primary fuels. To estimate the FFC by

inc luding the energy consumed in extracting, processing, and transporting or distributing primary fuels, which we refer to as “upstream” activities, DOE developed FFC multipliers h using the data and projections generated by NEMS and used for AEO2014 . The AEO2 014 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 producti on. This information can be used to define a set of parameters that represent the energy intensity of energy production.

Table 10.3

. 8 shows the FFC energy multipliers used for selected years. The method used to calculate FFC energy multipliers is described in appendix 10A

.

Table 10.3

. 8 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO 2014 )

Energy Source 2020 2025 2030 2035 2040

Electricity (power plant energy use) 1.04 4 1.04 5 1.04 6 1.04 7 1.04 7

10.4 NET PRESENT VALUE

To derive the NPV of customer benefit from potential standards, DOE combined the output of the shipments model with energy and financial data from the LCC analysis to calculate an annual stream of costs and benefits resulting from candidate pump energy efficiency

standards

. It discounted this time series to the year 2015 and summed the result, yielding the

national NPV.

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

documentation. A useful summary is National Energy Modeling System: An Overview 2000 , DOE/EIA

0581(2000), March 2000.

h FFC multipliers discussed in this chapter relate to the upstream part of the FFC process.

10

- 1210.4.1 Net Present Value Overview

The NPV is the present value of the incremental economic impact of a n efficiency level

. Like the NES, the NPV calculation started with the pump shipments estimate d by the shipments

model

. DOE then obtained pump input data and average electricity costs from the LCC analysis

, and estimate d pump non

- energy and energy lifetime costs . F or both a base case and a standards

case
, DOE first calculated the amount spent on pump purchases, i and then calculated the lifetime

energy cost by applying the average electricity prices to the e lectricity used by pumps shipped at each year of the analysis period over their lifetime. I n the standards case

, more expensive yet

more efficient units replace the less efficient ones. Thus, in the standards case, whereas the market average lifetime equipment non

- energy cost s per unit are greater relative to the base case,

the lifetime energy cost s are lower. When the energy cost decrease outweighs the non

- energy costs increase, the standards have a positive impact on consumer s; otherwise, the standards impact is negative.

DOE discounted the non

- energy and energy expenses with pumps using a national average discount factor

. The discount factor converts a future expense to a present value

. The difference in present value of the non

- energy and energy expenses between the base case and the

standards case scenario s leads to the national NPV impact

. DOE calculated the NPV impact in

201 5 from motors that were purchased between the compliance date of the standard s and 204 9

, inclu sive, to calculate the total NPV impact from purchases during the analysis period

. Mathematically, the NPV is the value in the present time of a time series of costs and savings, described by the equation:

NNNNNN = − Eq. 10

. 11

where:

PVS = the present value of electricity cost savings, and

PVC = the present value of incremental non

- energy costs.

PVS and PVC are determined according to the following expressions:

= � nnnnnnnn ( yy )

∙ ( 1 + rr ) 2015

− yy 2049

yy = 2020 Eq. 10

. 12

nnnnnnnn ( yy ) = nn nnnnnn ( yy )

− nn nnnnnnnn ( yy ) Eq. 10

. 13

i DOE did not acc ount for installation costs

, maintenance costs , or repair costs

. Although these costs might have significant impacts on a user ’ s budget, they do not vary with the efficiency level of the pump and therefore would have no impact in the difference of non

- energy costs between the base case and the standards case scenarios.

10

- 13nn nnnnnn ( yy ) = h pp ( yy )

∙ ∑ � uu cc ( yy )

∙ MM cc ( yy ) �

cc Eq. 10

. 14

nnnnnnnnnnnn ( yy ) = h pp ( yy )

∙ ∑ � uu ( yy )

∙ MM ( yy ) �

Eq. 10

. 15

and:

= � nnnnnnnn ( yy )

∙ ( 1 + rr ) 2015

− yy 2049

yy = 2020 Eq. 10

. 16

nnnnnnnn ( yy ) = nnnnnnnnnn ( yy )

− nnnnnnnnnnnn ( yy )

Eq. 10

. 17

nnnnnnnnnn ( yy ) = h pp ( yy )

∙ � � uu cc ( yy )

∙ MM cc ( yy ) �

cc Eq. 10

. 18

nnnnnnnnnnnn ( yy ) = h pp ( yy )

∙ � � uu cc ( yy )

∙ MM cc ( yy ) �

cc Eq. 10

. 19

where:

nnnnnnnn ( yy ) = the lifetime energy cost savings of all pumps shipped in year y,

nn nnnnnn ( yy ) = the base case, lifetime energy cost of all pumps shipped in year y,

nn nnnnnnnn ( yy ) = the standards case, lifetime energy cost of all pumps shipped in

year y,

uu cc ( yy ) = the lifetime energy cost of a unit with efficiency level at EL c shipped in year y,

nnnnnnnn ( yy ) = the lifetime incremental equipment non

- energy costs of all pumps shippe d in year y, nnnnnnnnnn ( yy ) = the base case, lifetime equipment non

- energy costs of all pumps shipped in year y, nnnnnnnnnnnn ( yy ) = the standards case, lifetime equipment non

- energy costs of all

pumps shipped in year y,

uu cc ( yy ) = the lifetime equipment non

- energy costs of a unit with efficiency level at EL c shipped in year y,

h pp ( yy ) = the number of pumps shipped in year y,

MM cc ( yy ) = the base case market share of units with efficiency level at EL c shipped in year y, and

MM cc ( yy ) = the standard s case market share of units with efficiency level at EL c shipped in year y, and

rr = the discount rate.

10

- 14Once the shipments model provides the estimate of shipments, the following sections

describe the inputs necessary for the N PV calculation and detail how unit lifetime energy and

non

- energy costs are calculated

.

10.4.2 Net Present Value Inputs

The N PV model inputs include: (a) the unit energy consumption , (b) the electricity prices

that enable the calculation of energy costs, (c) equipment first

- and n on

- energy operating costs, and (d) shipment efficiency distributions. The list of N PV model inputs not discussed previously

is as follows:

1. t otal per

- unit installed cost;

2. u nit lifetime energy cost

, and

3. discount rate.

10.4.2.1 Total Per

- Unit Installed Cost

Total installed cost typically accounts for manufacturer selling price (MSP), markups, and installation cost. Because installation cost does not vary by efficiency level for pumps, these costs represent only the equipment cost.

The engineering analysis an d LCC calculated MSP data for each representative unit. For the NIA, a weighted average value for each equipment class was calculated. After calculating MSPs for all equipment classes, DOE used average baseline and incremental markups to calculate equipmen t prices. Chapter 6 provides more details on the markups calculation. Table

10.4

. 1 through Table 10.4

. 7 provide average total installed cost values by efficiency level for all equipment classes. As discussed in section 8.3.3 , DOE developed base case and standards case projections of market share by efficiency level. DOE multiplied the market share by efficiency level in each year by the values in Table 10.4

. 1 through Table 10.4

. 7 to calculate annual shipment

- weighted average total installed costs.

10

- 15Table 10.4

. 1 Average Per

- Unit Total Installed Cost (2013$): ESCC 1800

Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1256 N/A N/A N/A N/A N/A

PER 10 1698 1556 N/A N/A N/A N/A

PER 25 1644 1644 1648 N/A N/A N/A

PER 40 1899 1899 1899 1792 N/A N/A

PER 55 1852 1852 1852 1852 1927 N/A

PER 70 1653 1653 1653 1653 1653 2026

Market Weighted Average 1641 1673 1704 1768 1863 2026

Table 10.4

. 2 Average Per

- Unit Total Installed Cost (2013$): ESCC 36 00
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1015 N/A N/A N/A N/A N/A

PER 10 1220 1201 N/A N/A N/A N/A

PER 25 1101 1101 1193 N/A N/A N/A

PER 40 1303 1303 1303 1307 N/A N/A

PER 55 1408 1408 1408 1408 1403 N/A

PER 70 1049 1049 1049 1049 1049 1215

Market Weighted Average 1092 1098 1111 1141 1169 1215

10

- 16Table 10.4

. 3 Average Per

- Unit Total Installed Cost (2013$): ES FM 1800
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1670 N/A N/A N/A N/A N/A

PER 10 1864 1866 N/A N/A N/A N/A

PER 25 1892 1892 2010 N/A N/A N/A

PER 40 1925 1925 1925 2075 N/A N/A

PER 55 2045 2045 2045 2045 2275 N/A

PER 70 1831 1831 1831 1831 1831 2314

Market Weighted Average 1891 1893 1943 2004 2151 2314

Table 10.4

. 4 Average Per

- Unit Total Installed Cost (2013$): ESFM 3600
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1263 N/A N/A N/A N/A N/A

PER 10 1297 1322 N/A N/A N/A N/A

PER 25 1367 1367 1428 N/A N/A N/A

PER 40 1471 1471 1471 1515 N/A N/A

PER 55 1470 1470 1470 1470 1632 N/A

PER 70 1280 1280 1280 1280 1280 1648

Market Weighted Average 1350 1358 1398 1443 1530 1650

10

- 17Table 10.4

. 5 Average Per

- Unit Total Installed Cost (2013$): IL 1800
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1549 N/A N/A N/A N/A N/A

PER 10 2018 1850 N/A N/A N/A N/A

PER 25 2817 2817 2207 N/A N/A N/A

PER 40 2548 2548 2548 2448 N/A N/A

PER 55 2075 2075 2075 2075 2625 N/A

PER 70 2108 2108 2108 2108 2108 2614

Market Weighted Average 2126 2143 2192 2279 2429 2611

Table 10.4

. 6 Average Per

- Unit Total Installed Cost (2013$): IL 3600
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 1255 N/A N/A N/A N/A N/A

PER 10 1397 1381 N/A N/A N/A N/A

PER 25 1634 1634 1538 N/A N/A N/A

PER 40 1790 1790 1790 1683 N/A N/A

PER 55 1899 1899 1899 1899 1856 N/A

PER 70 1370 1370 1370 1370 1370 1797

Market Weighted Average 1473 1484 1525 1578 1650 1797

10

- 18Table 10.4

. 7 Average Per

- Unit Total Installed Cost (2013$): VT

- S 3600
Efficiency

Level Base

Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Baseline 659 N/A N/A N/A N/A N/A

PER 10 627 642 N/A N/A N/A N/A

PER 25 1159 1159 758 N/A N/A N/A

PER 40 854 854 854 808 N/A N/A

PER 55 1446 1446 1446 1446 946 N/A

PER 70 548 548 548 548 548 821

Market Weighted Average 692 698 712 733 773 822

Future Equipment Prices For reasons discussed in Chapter 8 of the TSD ( Section 8.3.1.3 ), DOE used a constant price assumption for the default projection in the NIA. To investigate the impact of different equipment price projections on the NPV for the considered TSLs, DOE a lso considered two alternative price trends

. One of these used an exponential fit on the deflated price index for pumps and pumping equipment

, j and the other is based on AEO201 4 ’s deflator for industrial equipment

. 1 Details on how these alternative price trends were developed are i n Appendix 10B, which also presents results from the sensitivity analysis DOE developed based on these two equipment price scenarios.

10.4.2.2 Unit Lifetime Operating Cost

T he annual operating cost includes only electricity costs as repair and maintenance costs

do not vary with efficiency level. The unit lifetime energy cost expresses an estimate of the market average expense for electricity that owners of all pumps of a given equipment class, shipped in a given year, will have to operate these pumps over their lifetime.

DOE determined annual energy consumption of pumps through the energy use and LCC analysis. DOE then applied na tional weighted

- average electricity prices based on the sector

specific electricity prices described in Chapter 8, Section 8. 3 .2.2.

As with the total installed cost data, DOE developed project ed annual electricity expenses

based on the annual projections o f market share by efficiency level specified in the base case and

standards case s. DOE multiplied the market share by efficiency level in each year by the per

- unit electricity costs by efficiency level to calculate shipment

- weighted average annual electric ity

j Series ID PCU333911333911 ; www.bls.gov/ppi/

10

- 19costs. DOE then applied electricity price trends from AEO 2014 to scale the elec tricity expenses moving forward, as shown in Figure 10.4

. 1

.

Figure 10.4

. 1 Weighted

- Average Electricity Price Time Series

10.4.2.3 Discount Rate

The discount rate expresses the time value of money

. DOE used real discount rates of 3

percent and 7 percent , as established by the U.S

. Office of Management and Budget (OMB) guidelines on regulatory analysis

. 6 The discount rates DOE used in the LCC are distinct from those it used in the NPV calculations, in that the NPV discount rates represent the societal rate of return on capital investment , whe reas LCC discount rates reflect the owner cost of capital and the

financial environment of electric utilities and commercial and industrial entities.

10.5 TRIAL STANDARD LEVEL S

DOE selected six efficiency levels, including baseline, for the NIA. For all equipment classes, efficiency level 0 (EL 0) is the baseline efficiency level. Based on the results of the LCC analysis, NIA, and the input of the CIP working group, DOE selected five TSLs above the baseline level for each equipment class for the NOPR stage of this rulemaking. TSL 5 was

selected a s the max

- tech level for all equipment classes. TSL 1, TSL 2, TSL 3, and TSL 4 were selected to provide intermediate efficiency levels that fill the gap between the baseline efficiency level and TSL 5 and allow for evaluation of manufacturer impact at each level. Table 10.5

. 1 shows the mapping between TSLs and efficiency levels. 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.16020202024202820322036204020442048205220562060206420682072207620802084208820922096$/kWh

Year

10

- 20Table 10.5

. 1 Mapping Between TSLs and Efficien cy Levels
Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC.1800 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESCC.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM.1800 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL.1800 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

VT

- S.3600 EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

10.6 RESULTS

DOE evaluated NES and NPV for each equipment class and TSL using the inputs and methodologies described in Sections 10.3 and 10.4

. Table 10.6

. 1 and Table 10.6

. 2 present NES results, and Table 10.6

. 3 present s NPV results.

Table 10.6

. 1 Cumulative National Primary Energy Savings for Commercial and Industrial Pump Trial Standard Levels for Units Sold in 2020

- 2049

Equipment

Class Trial Standard Level

1 2 3 4 5 quads

ESCC.1800 0.016 0.05 0.08 0.12 0.16

ESCC.3600 0.016 0.07 0.11 0.17 0.26

ESFM.1800 0.003 0.05 0.11 0.23 0.35

ESFM.3600 0.002 0.02 0.03 0.05 0.07

IL.1800 0.015 0.05 0.08 0.11 0.16

IL.3600 0.003 0.01 0.02 0.02 0.03

VT

- S.3600 0.002 0.02 0.11 0.17 0.22

Total

-- All

Classes 0.056 0.27 0.54 0.87 1.26

Note: Components may not sum to total due to rounding.

10

- 21Table 10.6

. 2 Cumulative National Full

- Fuel

- Cycle Energy Savings for Commercial and Industrial Pump Trial Standard Levels for Units Sold in 2020

- 2049

Equipment

Class Trial Standard Level

1 2 3 4 5 quads

ESCC.1800 0.017 0.05 0.08 0.12 0.17

ESCC.3600 0.017 0.08 0.12 0.18 0.28

ESFM.1800 0.003 0.06 0.12 0.25 0.37

ESFM.3600 0.002 0.02 0.03 0.05 0.07

IL.1800 0.016 0.05 0.08 0.12 0.17

IL.3600 0.003 0.01 0.02 0.02 0.03

VT

- S.3600 0.002 0.02 0.11 0.17 0.24

Total

-- All

Classes 0.059 0.28 0.56 0.91 1.32

Note: Components may not sum to total due to rounding.

Table 10.6

. 3 Cumulative Net Present Value of Customer Benefit for Commercial and Industrial Pump Trial Standard Levels for Units Sold in 2020

- 2049

Equipment

Class Discount Rate ( %) Trial Standard Level

1 2 3 4 5 billion 2013$*

ESCC.1800 3 0.052 0.20 0.29 0.40 0.47

7 0.018 0.07 0.11 0.14 0.15

ESCC.3600 3 0.069 0.34 0.46 0.68 1.06

7 0.028 0.14 0.18 0.26 0.41

ESFM.1800 3 0.010 0.20 0.44 0.88 1.28

7 0.003 0.06 0.14 0.27 0.39

ESFM.3600 3 0.009 0.08 0.14 0.20 0.30

7 0.003 0.03 0.05 0.07 0.11

IL.1800 3 0.063 0.18 0.25 0.28 0.34

7 0.022 0.06 0.08 0.07 0.07

IL.3600 3 0.011 0.04 0.06 0.08 0.11

7 0.004 0.01 0.02 0.03 0.04

VT

- S.3600 3 (0.001) 0.07 0.49 0.71 0.90

7 (0.002) 0.02 0.20 0.28 0.35

Total

-- All

Classes 3 0.213 1.11 2.13 3.23 4.47

7 0.077 0.41 0.77 1.13 1.51
* Numbers in parentheses indicate negative NPV. Note: Components may not sum to total due to rounding.

10

- 22REFERENCES

1 U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook 2014

, April, 2014. Washington, DC. Report No. DOE/EIA

- 0383(2014).

< www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf >

.

2 United States Industrial Electric Motor Systems Market Opportunities Assessment . Tech. Washington DC: U.S. Department of Energy’s (D OE) Office of Energy Efficiency and Renewable Energy (EERE), 1998. Print.

3 Almeida, A., Chretien, B., Falkner, H., Reichert, J., West, M., Nielsen, S., and Both, D. VSDs for Electric Motor Systems . Tech. N.p.: European Commission Directorate

- General for

T ransport and Energy, SAVE II Programme 2000, n.d. Print.

4 U.S. Census Bureau. Manufacturing Industry Series Survey and Current Industrial Reports for Controls for A.C. Packaged Drives . Available data from 1963 to 2012. Web. 15 July 2014.

< http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml >

.

5 Almeida, A., Chretien, B., Falkner, H., Reichert, J., West, M., Nielsen, S., and Both, D. VSDs for Electric Motor Systems

. European Commission Directorate

- General for Transport and Energy, SAVE II Programme 2000.

6 U.S. Office of Management and Budget, Circular A

- 4: Regulatory Analysis, September 17,

2003. < www.whitehouse.gov/sites/default/files/omb/assets/regulatory_matters_pdf/a

- 4.pdf >

.

10

- 23APPENDIX 10A. FULL FUEL CYCLE

TABLE OF CONTENTS

10A.1 INTRODUCTION

................................ ................................ ................................ ...... 10A

- 1

10A.2 METHODOLOGY

................................ ................................ ................................ ..... 10A

- 1

10A.3 EN ERGY MULTIPLIERS FOR THE FULL FUEL CYCLE

................................ .... 10A

- 3

REFERENCES

................................ ................................ ................................ ....................... 10A

- 4

LIST OF TABLES

Table 10A.2.1 Dependence of FFC Parameters on AEO Inputs

................................ ....... 10A

- 3

Table 10A.3.1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014 )

............ 10A

- 3

10A

- iAPPENDIX 10A. FULL F UEL CYCLE

10A.1 INTRODUCTION

This appendix summarizes the methods the U.S. Department of Energy (DOE) used to

calculate the full

- fuel

- cycle (FFC) energy savings estimated from potential standards for commercial and industrial pumps

. The FFC measure includes point

- of

- use (site) energy; the energy losses associated with generation, transmission, and distribution of electricity; and the

energy co nsumed in extracting, processing, and transporting or distributing primary fuels. DOE’s method of analysis previously encompassed only site energy and the energy lost through generation, transmission, and distribution of electricity. In 2011 DOE announced its intention, based on recommendations from the National Academy of Sciences, to use FFC measures of energy use and emissions when analyzing proposed energy conservation standards. 1 This appendix summarizes the methods DOE used to incorporate impacts of t he full fuel cycle into the analysis.

This analysis uses several terms to describe aspects of energy use. The physical sources of energy are primary fuels such as coal, natural gas, or liquid fuel. Primary energy is equal to the heat content (British ther mal units [Btu]) of the primary fuel used to produce an end

- use service. Site energy use is defined as the energy consumed at the point of use in a house or establishment. When natural gas or petroleum fuels are consumed at the site (for example in an

on

- s ite furnace), site energy is identical to primary energy, with both equal to the heat content of the primary fuel consumed.

For electricity generated by an off

- site power plant, site energy is measured in kilowatt

hours (kWh). In such a case the primary energy is equal to the quads (quadrillion Btu) of primary energy required to generate and deliver electricity to the site. For the FFC analysis, upstream energy use is defined as the energy consumed in extracting, processing, and transporting or distributing primary fuels. FFC energy use is the sum of primary plus upstream energy use.

Both primary fuels and electricity are use d in upstream activities. The treatment of electricity in full

- fuel

- cycle analysis must distinguish between electricity generated by fossil fuels and uranium and electricity generated from renewable sources (wind, solar, and hydro). For the former, the ups tream fuel cycle relates to the amount of fuel consumed at the power plant. There is no upstream component for the latter, because no fuel per se is used.

10A.2 METHODOLOGY

The mathematical approach to determining FCC is discussed in Coughlin (2012). 2 Details

o n analyzing the fuel production chain are presented in Coughlin (2013). 3 The methods used to calculate FFC energy use are summarized here. When all energy quantities are normalized to the same units, FFC energy use can be represented as the product of the primary energy use and an FFC multiplier. Mathematically the FFC multiplier is a function of a set of parameters that represent the energy intensity and material losses at each stage of energy production. Those parameters depend only on physical data, so t he calculations require no assumptions about prices

10A

- 1or other economic factors. Although the parameter values often differ by geographic region, this analysis utilizes national averages.

The fuel cycle parameters are defined as follows.

• a x is the quantit y of fuel x burned per unit of electricity produced, on average, for grid electricity. The calculation of a

x includes a factor to account for losses incurred through the transmission and distribution systems.

• b y is the amount of grid electricity used in p roducing fuel y , in 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 (MBtu/physical unit).

• z x (s) is the emissions intensity for fuel x (mass of pollutant s per physical unit of x produced).

All the parameters are calculated as functions of an annual time step; hence, when evaluating the effects of potential new standards, a time series of annual values is used to estimate the FFC energy and emissions savings in each yea r of the analysis period and cumulatively. Fossil fuel quantities are converted to energy units using the heat content factor q

x . To convert electricity in kWh to primary energy units, on

- site electricity consumption is multiplied by the site

- to

- power

- plan t energy use factor, described in chapter 10. The site

- to

power

- plant energy use factor is defined as the ratio of the total primary energy consumption by the electric power sector (in quads) divided by the total electricity generated each year.

The FFC m ultiplier is denoted μ (mu). A separate multiplier is calculated for each fuel used on site. Also calculated is a multiplier for electricity that reflects the fuel mix used in its generation. The multipliers are dimensionless numbers applied to primary ene rgy savings to obtain the FFC energy savings. The upstream component of the energy savings is proportional to (μ

- 1). The fuel type is denoted by a subscript on the multiplier μ.

When DOE estimates energy savings attributable to appliance standards, the me thod for performing the full

- fuel

- cycle analysis utilizes data and projections published in the Annual Energy Outlook ( AEO ); in the case of this rulemaking , the AEO2014

. 4 Table 10A.2

. 1

summarizes the AEO2014 data used as inputs to the calculation of various parameters. The

column titled " AEO Table" gives the name of the table that provided the reference data.

10A

- 2Table 10A.2

. 1 Dependence of FFC Parameters on AEO Inputs
Parameter(s) Fuel(s) AEO Table Variables

q x All Conversion factors MMBtu per physical unit

a x All Electricity supply, disposition, prices, and emissions Generation by fuel type

Energy consumption by sector

and source Electric energy consumption by the power sector

b

c , c nc , c pc Coal Coal production by region and

type Coal production by type and

sulfur content

b p , c np , c pp Petroleum Refining industry energy consumption Refining

- only energy use

Liquid fuels supply and disposition Crude supply by source

International liquids supply and disposition Crude oil imports

Oil and gas supply Domestic crude oil production

c nn Natural gas Oil and gas supply U.S. dry gas production Natural gas supply, disposition, and prices Pipeline, lease, and plant fuel

z x All Electricity supply, disposition, prices, and emissions Power sector emissions

The AEO2014 does not provide all the information needed to estimate total energy use in the fuel production chain. Coughlin (2013) describes the additional data sources needed to complete the analysis. The time dependence in the FFC multipliers for pumps , however, ar ises exclusively from variables taken from the AEO2014

.

10A.3 ENERGY MULTIPLIERS F OR THE FULL FUEL CYC LE

FFC energy multipliers for selected years are presented in Table 10A.3

. 1 . The 2040 value

was held constant for the analysis period beyond 2040, which is the last year in the AEO2014 projection. The multiplier for electricity reflects the shares of various primary fuels in total electricity generation throughout the forecast period.

Table 10A.3

. 1 Energy Multipliers for the Full Fuel Cycle (Based on AEO2014 )

2020 2025 2030 2035 2040

Electricity 1.044 1.045 1.046 1.047 1.047

10A

- 3REFERENCES

1 U.S. Department of Energy

– O ffice of Energy Efficiency and Renewable Energy. Energy Conservation Program for Consumer Products and Certain Commercial and Industrial Equipment. Statement of Policy for Adopting Full Fuel Cycle Analyses into Energy Conservation Standards Program. Federal Register

. August 18, 2011. Vol. 76, n o. 160 :

pp. 51281

– 51289

.

2 Coughlin, K. A Mathematical Analysis of Full Fuel Cycle Energy Use

. Energy. 2012. 37(1): pp. 698

– 708. (Last accessed J une 22, 2014.)

< www.sciencedirect.com/science/article/pii/S0360544211006803 >

.

3 Coughlin, K. Projections of Full

- Fuel

- Cycle Energy and Emissions Metrics

. 2013. Lawrence Berkeley National Laboratory. Report No. LBNL

- 6025E.

4 U.S. Energy Information Administration. Annual Energy Outlook 201 4 with Projections to

2040 . April 201 4 . DOE/EIA

- 0383(201 4 ). Washington, D.C. (Last accessed June 22, 2014.) < www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >

.

10A

- 4APPENDIX 10B. NATIONAL NET PRESENT VALUE OF CUSTOMER BE NEFITS

USING ALTERNATIVE EQ UIPMENT PRICE FORECA STS AND ECONOMIC

GROWTH SCENARIOS

TABLE OF CONTENTS

10B.1 INTRODUCTION

................................ ................................ ................................ ....... 10B

- 1

10B.2 DESCRI PTION OF ALTERNATIVE EQUIPMENT PRICE SCEN ARIOS

............. 10B

- 1

10B.2.1 Decreasing Price Scenario

................................ ................................ ............... 10B

- 2

10B.2.2 Increasing Price Scenario

- Exponential Fit Approach

................................ .... 10B

- 2

10B.2.3 Summary

................................ ................................ ................................ .......... 10B

- 3

10B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

......................... 10B

- 4

10B.4 NET PRESENT VALUE RE SULTS USING ALTERNAT IVE EQUIPMENT

PRICE TRENDS AND ECO NOMIC GROWTH SCENARI OS

................................ 10B

- 6

REFERENCES

................................ ................................ ................................ ........................ 10B

- 9

LIST OF TABLES

Table 10B.2.1 Price Trend Sensitivities

................................ ................................ ............ 10B

- 3

Table 10B.4.1 Cumulative NPV Results for Pumps Using Reference Equipment Price

Forecast and AEO Reference Case Scenario

................................ ............. 10B

- 6

Table 10B.4.2 Cumulative NPV Res ults for Pumps Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth

Case Scenario

................................ ................................ ............................. 10B

- 7

Table 10B.4.3 Cumulative NPV Results for Pumps Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth

Case Scenario

................................ ................................ ............................. 10B

- 8

Table 10B.4.4 Comparison of Total NPV across All Equipment Classes for Alternative Equipment Price Forecasts and Economic Growth Cases

...... 10B

- 8

LIST OF FIGURES

Figure 10B.2.1 Deflated Pump and Pumping Equipment PPI versus Year, with Exponential Fit from 1984 to 2013

................................ ............................ 10B

- 3 Figure 10B.2.2 Pump Price Trend Indexes

................................ ................................ ......... 10B

- 4 Figure 10B.3.1 Electricity Price Projections for Reference Case, High, and Low

Economic Growth Scenarios

................................ ................................ ...... 10B

- 5

10B

- iAPPENDIX 10B. NATION AL NET PRESENT VALUE OF CUSTOMER BENEFITS USING ALTERNATIVE EQ UIPMENT PRICE FORECA STS AND ECONOMIC

GROWTH SCENARIOS

10B.1 INTRODUCTION

This appendix presents National Impact Analysis (NIA) results using alternative equipment price forecasts and economic growth scenarios. As will be discussed below, DOE developed increasing and decreasing equipment price forecasts. In addition, alternat ive economic growth scenarios were utilized based on the High Economic Growth case and the Low

Economic Growth case from Energy Information Administration’s (EIA’s) Annual Energy

Outlook 2014 ( AEO2014 ). 1 DOE developed low and high NPV scenarios based on t he combination of equipment price forecasts and economic growth scenarios. DOE based the low NPV scenario on the combination of an increasing equipment price forecast and the Low Economic Growth case, while the high NPV scenario was based on the combinati on of a decreasing equipment price forecast and the High Economic Growth case. Results are presented for the above two combinations at the end of this appendix.

10B.2 DESCRIPTION OF ALTER NATIVE EQUIPMENT PRI CE SCENARIOS

DOE used a constant price assumption for the default forecast in the NIA described in Chapter 10. In order to investigate the impact of different equipment price forecasts on the consumer net present value (NPV) for the considered TSLs for pumps, DOE also considered two alternative price trends f or a sensitivity analysis. This appendix describes the alternative price trends and compares NPV results for these scenarios with the default forecast.

In recent rulemakings for several residential products, DOE has us ed the experience

curve method to de rive learning rates to forecast future prices . In the experience curve method, the real cost of production is related to the cumulative production, or experience, with a manufactured product. That experience usually is measured in terms of cumulative produ ction.

A s experience (production) accumulates, the cost of prod ucing the next unit decreases. The percentage reduction in cost that occurs with each doubling of cumulative production is known as the learning rate . A recent report from Lawrence Berkeley National Laboratory by Taylor and Fujita provides an overview of some of the major findings of the academi c literature on learning curves, and describes the application of a component

- based learning curve approach ( by the Environmental Protection Agency and the National Highway Transportation Safety

Administration ) and a price

- based learning curve approach (by DOE) in regulatory impact

assessment. 2

For some commercial and industrial equipment, there are insufficie nt data to apply a price

- based learning curve approach , particularly with respect to cumulative production . In such cases, DOE used a constant price assumption for the default forecast in the NIA , but made use of

10B

- 1price indexes that are relevant for the equ ipment in question to derive alternative price trends for

a sensitivity analysis. a DOE is using this approach for pumps.

DOE considered two alternative price trends for a sensitivity analysis. One that assumes decreasing prices is based on t he deflator for industrial equipment that was forecasted for

AEO2014 . The other assumes increasing prices and used an exponential fit on the deflated price

index for “ pumps and pumping equipment ” during the period of 19 84 to 20 13

. b

10B.2.1 Decreasing Price Scenario

DOE examined a forecast based on the “chained price index

— industrial equipment” that

was forecasted for AEO2014 out to 2040. This index is the most disaggregated category that includes pumps. To develop an inflation

- adjusted index, DOE normalized the above index with the “chained price index

— gross domestic product” forecasted for AEO2014 . To extend the price

index beyond 2040, DOE used the average annual price growth rate in 2031 to 2040.

10B.2.2 Increasing Price Scenario

- Exponential Fit Approach

DOE used an inflation

- adjusted “pumps and pumping equipment” Producer Price Index (PPI) spanning the time period of 1984

- 2013 f rom the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the explanatory variable. The PPI during this period of time

shows a generally continuing upward trend, so the exponential fit based on this historical PPI represents the in creasing price scenario of future price projection. The PPI data reflect nominal prices, adjusted for equipment quality changes. An inflation

- adjusted (deflated) price index for “pump and pumping equipment” was calculated by dividing the PPI series by the Gross

Domestic Product Chained Price Index

. In this case, the exponential function takes the form of:

YY = aa ∙ ee bbbb

where Y is the pump price index, X is the time variable, a is the constant and b is the slope parameter of the time variable.

To esti mate these exponential parameters, a least

- square fit was performed on the

inflation

- adjusted pump price index versus year from 1984 to 2013. See Figure 10B.2

. 1

.

a See appendix 10C of the final rule TSD for distribution transformers. www.regulations.gov/#!documentDetail;D=EERE

- 2010

- BT

- STD

- 0048

- 0760

b Series ID PCU333911333911 ; www.bls.gov/ppi/

10B

- 2Figure 10B.2

. 1 Deflated Pump and Pumping Equipment PPI versus Year, with Exponential Fit from 1984 to 2013

The regression performed as an exponential trend line fit results in an R

- square of 0. 92

. The final estimated exponential function is:

YY = 2

. 25 × 10 (

− 8 )

∙ ee 0

. 00875

DOE then derived a price factor index for this scenario, renormalized with 201 2 equal to 1, to project prices in each future year in the analysis period considered in the NIA. The inde x value in a given year is a function of the exponential parameter and year.

10B.2.3 Summary

Table 10B.2

. 1 shows the summary of the average annual rates of change for the equipment price index in each scenario. Figure 10B.2

. 2 shows the resulting price trends.

Table 10B.2

. 1 Price Trend Sensitivities
Sensitivity Price Trend Average Annual Rate of Change %

Medium (Default) Constant Price Projection 0.0

Decreasing Price Scenario AEO 2014

- "chained price

index

- industrial equipment" - 0.39

Increasing Price Scenario Exponential Fit using data

from 1984 to 2013 0.88

10B

- 3Figure 10B.2

. 2 Pump Price Trend Indexes

10B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

To generate NIA results reported in chapter 10, DOE uses t he Reference case energy price projection s from AEO2014

. The reference case is a business

- as

- usual estimate, given known market, demographic, and tech nological trends. For AEO2014 , EIA explored the impacts of alternative assumptions in other scenarios with different macroeconomic growth rates, world oil prices, rates of technology progress, and policy changes.

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO2014 uses High and Low Economic Growth scenarios to project the possible impacts of alternative economic growth assumptions on energy markets. 3

Figure 10B. 3

. 1 shows electricity price projections based on the different AEO 2014

scenarios.

10B

- 4Figure 10B.3

. 1 Electricity Price Projections for Refere nce Case, High, and Low Economic

Growth Scenarios 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20

2011 2016 2021 2026 2031 2036Price Index (2013=1)

Year Reference

High

Low

10B

- 510B.4 NET PRESENT VALUE RE SULTS USING ALTERNAT IVE EQUIPMENT

PRICE TRENDS AND ECO NOMIC GROWTH SCENARI OS

This section presents the NPV results for low and high scenarios using the alternative equipment price projections in combination with the alternative economic growth scenarios. DOE based the low NPV scenario on the combination of an increasing equipment price projection and the AEO Low Economic Growth case, while the high NPV s cenario was based on the combination of a decreasing equipment price projection and the AEO High Economic Growth case. Results are presented below for each equipment class.

Table 10B.4

. 1 Cumulative NPV Res ults for Pumps Using Reference Equipment Price

Forecast and AEO Reference Case Scenario

Equipment

Class Discount Rate (%) Trial Standard Level

1 2 3 4 5 billion 2013$*

ESCC.1800 3 0.052 0.20 0.29 0.40 0.47

7 0.018 0.07 0.11 0.14 0.15

ESCC.3600 3 0.069 0.34 0.46 0.68 1.06

7 0.028 0.14 0.18 0.26 0.41

ESFM.1800 3 0.010 0.20 0.44 0.88 1.28

7 0.003 0.06 0.14 0.27 0.39

ESFM.3600 3 0.009 0.08 0.14 0.20 0.30

7 0.003 0.03 0.05 0.07 0.11

IL.1800 3 0.063 0.18 0.25 0.28 0.34

7 0.022 0.06 0.08 0.07 0.07

IL.3600 3 0.011 0.04 0.06 0.08 0.11

7 0.004 0.01 0.02 0.03 0.04

VT

- S.3600 3 (0.001) 0.07 0.49 0.71 0.90

7 (0.002) 0.02 0.20 0.28 0.35

Total

-- All

Classes 3 0.213 1.11 2.13 3.23 4.47

7 0.077 0.41 0.77 1.13 1.51 * Numbers in parentheses indicate negative NPV. Note: Components may not sum to total due to rounding.

10B

- 6Table 10B.4

. 2 Cumulative NPV Results for Pumps Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case

Scenario

Equipment

Class Discount Rate (%) Trial Standard Level

1 2 3 4 5 billion 2013$*

ESCC.1800 3 0.064 0.23 0.35 0.49 0.59

7 0.022 0.09 0.13 0.17 0.19

ESCC.3600 3 0.081 0.40 0.55 0.81 1.26

7 0.032 0.16 0.21 0.31 0.48

ESFM.1800 3 0.012 0.24 0.52 1.05 1.54

7 0.004 0.08 0.16 0.32 0.47

ESFM.3600 3 0.010 0.09 0.16 0.24 0.35

7 0.004 0.03 0.06 0.08 0.12

IL.1800 3 0.074 0.22 0.31 0.36 0.46

7 0.026 0.07 0.10 0.10 0.11

IL.3600 3 0.013 0.04 0.08 0.10 0.13

7 0.005 0.02 0.03 0.03 0.05

VT

- S.3600 3 0.000 0.09 0.57 0.84 1.07

7 (0.001) 0.03 0.23 0.33 0.41

Total

-- All

Classes 3 0.256 1.31 2.53 3.88 5.40

7 0.091 0.47 0.91 1.34 1.82 * Numbers in parentheses indicate negative NPV. Note: Components may not sum to total due to rounding.

10B

- 7Table 10B.4

. 3 Cumulative NPV Results for Pumps Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case

Scenario

Equipment

Class Discount Rate (%) Trial Standard Level

1 2 3 4 5 billion 2013$*

ESCC.1800 3 0.039 0.16 0.23 0.31 0.33

7 0.013 0.06 0.08 0.10 0.10

ESCC.3600 3 0.057 0.29 0.37 0.55 0.85

7 0.023 0.12 0.15 0.21 0.33

ESFM.1800 3 0.009 0.17 0.36 0.71 1.03

7 0.003 0.05 0.11 0.22 0.31

ESFM.3600 3 0.007 0.07 0.12 0.18 0.26

7 0.003 0.02 0.04 0.06 0.09

IL.1800 3 0.051 0.14 0.19 0.17 0.19

7 0.018 0.05 0.05 0.03 0.01

IL.3600 3 0.010 0.03 0.05 0.06 0.08

7 0.004 0.01 0.02 0.02 0.03

VT

- S.3600 3 (0.004) 0.05 0.41 0.58 0.73

7 (0.003) 0.02 0.17 0.23 0.28

Total

-- All

Classes 3 0.169 0.91 1.73 2.56 3.46

7 0.060 0.33 0.62 0.88 1.14 * Numbers in parentheses indicate negative NPV. Note: Components may not sum to total due to rounding.

Table 10B.4

. 4 Comparison of Total NPV across All Equipment Classes for Alternative Equipment Price Forecasts and Economic Growth Cases

Trial Standard Level

1 2 3 4 5

billion 201 3 $

Reference Case NPV 3% 0.213 1.11 2.13 3.23 4.47

NPV 7% 0.077 0.41 0.77 1.13 1.51
Decreasing Price Trend +

AEO High Economic

Growth Case NPV 3% 0.256 1.31 2.53 3.88 5.40

NPV 7% 0.091 0.47 0.91 1.34 1.82

Increasing Price Trend +

AEO Low Economic

Growth Case NPV 3% 0.169 0.91 1.73 2.56 3.46

NPV 7% 0.060 0.33 0.62 0.88 1.14

10B

- 8REFERENCES

1 Energy Information Administration, Annual Energy Outlook 201 4 with Projections to 2040

, 201 4 . Washington, DC. < www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >.

2 Taylor, Margaret, and Sydny K. Fujita. Accounting for Technological Change in Regula tory Impact Analyses: The Learning Curve Technique . Berkeley: Lawrence Berkeley National Laboratory, 2013. LBNL

- 6195E

3 Energy Information Administration, Macroeconomic Activity Module for Annual Energy

Outlook 201 4 , 201 4 . Washington, DC.

< www.eia.gov/forecasts/aeo/assumptions/pdf/macroeconomic.pdf >

.

10B

- 9CHAPTER 11. LIFE

- CYCLE COST SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION

................................ ................................ ................................ ......... 11

- 1

11.2 VFD SUBGROUP DEFINIT ION

................................ ................................ .................. 11

- 1

11.2.1 Load Profiles

................................ ................................ ................................ ...... 11

- 1

11.2.2 Control Losses

................................ ................................ ................................ ... 11

- 2

11.3 RESULTS

................................ ................................ ................................ ...................... 11

- 3

LIST OF TABLES

Table 11.2.1 Load Profiles and Associated Sample Weights for the VFD Subgroup

...... 11

- 2

Table 11.3.1 Average Annual Energy Use by Equipment Class and Efficiency Level in the Base Case for the VFD Subgroup (kWh)

................................ .......... 11

- 3

Table 11.3.2 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESCC.1800

................................ ................................ ................................ ... 11

- 4

Table 11.3.3 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESCC.1800

................................ ................................ ......... 11

- 4

Table 11.3.4 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESCC.3600

................................ ................................ ................................ ... 11

- 5

Table 11.3.5 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESCC.3600

................................ ................................ ......... 11

- 5

Table 11.3.6 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESFM.1800

................................ ................................ ................................ .. 11

- 6

Table 11.3.7 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESFM.1800

................................ ................................ ........ 11

- 6

Table 11.3.8 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESFM 3600

................................ ................................ ................................ .. 11

- 7

Table 11.3.9 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESFM.3600

................................ ................................ ........ 11

- 7

Table 11.3.10 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

IL.1800

................................ ................................ ................................ ......... 11

- 8

Table 11.3.11 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for IL.1800

................................ ................................ ............... 11

- 8

Table 11.3.12 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

IL.3600

................................ ................................ ................................ ......... 11

- 9

Ta ble 11.3.13 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for IL.3600

................................ ................................ ............... 11

- 9

Table 11.3.14 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

VT

- S.3600

................................ ................................ ................................ .. 11

- 10

Table 11.3.15 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for VT

- S.3600

................................ ................................ ........ 11

- 10

11

- iTable 11.3.16 Comparison of Impacts for VFD Users with Non

- VFD Users,

ESCC.1800

................................ ................................ ................................ . 11

- 11

Table 11.3.17 Comparison of Impacts for VFD Users with Non

- VFD Users,

ESCC.3600

................................ ................................ ................................ . 11

- 11

Table 11.3.18 Comparison of Impacts for VFD Users with Non

- VFD Users,

ESFM.1800

................................ ................................ ................................ 11

- 11

Table 11.3.19 Comparison of Impacts for VFD Users with Non

- VFD Users,

ESFM.3600

................................ ................................ ................................ 11

- 12

Table 11.3.20 Comparison of Impacts for VFD Users with Non

- VFD Users, IL.1800

... 11

- 12

Table 11.3.21 Comparison of Impacts for VFD Users with Non

- VFD Users, IL.3600

... 11

- 12

Table 11.3.22 Comparison of Impacts for VFD Users with Non

- VFD Users, VT

S.3600

................................ ................................ ................................ ........ 11

- 13

LIST OF FIGURES

Figure 11.2.1 Load Profiles Used in the LCC and Subgroup Analyses

............................. 11

- 2

11

- iiCHAPTER 11. LIFE

- CYCLE COST SUBGROUP ANALYSIS

11.1 INTRODUCTION

The customer subgroup analysis evaluates impacts on any identifiable groups o f

c usto mers who may be disproportionately affected by a national energy conservation standard s

. The U.S. Department of Energy (DOE) evaluates impacts on particular subgroups of c onsu mers by perform ing the life

- cycle cost (LCC) impacts and payback period (PBP) for the affected subgroup . DOE uses the LCC spr eadsheet models described in Chapter 8 , with inputs modified to reflect the particular characteristics of the subgroup under consideration. For commercial and industrial pumps , DOE calculated the LCC and PBP for those customers who operate their

pumps with variable

- frequency drives (VFD). Because the VFD lowers the required power input to the pump (relative to throttling), pump users with VFDs will typically have lower energy use

than those without VFDs. This chapter describes the subgroup identification in further detail and gives the results of the LCC and PBP analyses for the VFD subgroup.

11.2 VFD SUBGROUP DEFINIT ION

The VFD subgroup represent s pump users with controls that could lower the annual energy use of pumps serving variable loads . DOE analyzed impacts on those customers by using

a system

- curve based energy calculation and introducing VFD specific load profile . The subgroup analysis uses the same distribution of duty points and other customer characteristics, as

described in the LCC documentation in Chapter 8.

11.2.1 Load Profiles

Load profiles are described by defining weight s that specify the fraction of pump operating hours that are spent at each of four load points: 50 percent , 75 percent , 100 percent

, and 110 percent of the flow at the duty poi nt. DOE used four load profiles for the full LCC sample, with variable loads assumed to be controlled through throttling:

• Flat load (LP0): the pump operates 100 percent of operating time at the duty point flow;

• Flat/over

- sized (LP1): the pump operates 5 0 percent of operating time at the duty point

and 50 percent at 75 percent of the duty point flow;

• Variable/over

- sized (LP2): the pump operates 35 percent of time below the duty point flow;

• Variable/under

- sized (LP3): the pump operates 30% of time belo w the duty point and 20

percent above.

For the VFD subgroup DOE developed a fifth load profile that allows the pump to

operate at 25 percent load:

• VFD

- specific (LP

- VFD): the pump operates equally at each of four points load points, 25

percent , 50 percent , 75 percent , and 100 percent duty point.

11

- 1Figure 11.2

. 1 illustrates these four typical loading profiles used in calculating annual

energy u se , and Table 11.2

. 1 summarizes the weights

.

Figure 11.2

. 1 Load Profiles Used in the LCC and Subgroup Analyses

To construct the VFD subgroup, DOE used the original LCC sample and modified the assignment of load profiles. The 30 percent of pump users that had been assigned the flat load (LP0) in the full LCC analysis are changed to the VFD

- specific load profile for th e subgroup analysis. For the pump users originally assigned to one of the variable profiles (LP1, LP2, LP3),

20 percent of each group are shifted to the LP

- VFD load profile. This results in 50 percent of the LCC subgroup sample having the LP

- VFD profile, with the remaining 50 percent distributed over the other three variable load profiles. These sample weights are summarized in the last

column of Table 11.2

. 1 . All lo ad profiles in the LCC subgroup sample are assumed to be

controlled with a VFD.

Table 11.2

. 1 Load Profiles and Associated Sample Weights for the VFD Subgroup

Load

Profile 25% duty point 50% duty point 75% duty point 100% duty point 110% duty point Sample Weight

LP0 0 0 0 1 0 0

LP1 0 0 0.5 0.5 0 0.214

LP2 0 0.05 0.3 0.65 0 0.214

LP3 0 0.05 0.25 0.5 0.2 0.071

LP

- VFD 0.25 0.25 0.25 0.25 0 0.5

11.2.2 Control Losses

DOE used the same method to size the motor and calculate motor losses as described in Chapter 8. To determine the losses of the control, DOE applied the p art

- load loss factor curve 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

25% duty point 50% duty point 75% duty point 100% duty point 110% duty point LP0

LP1

LP2

LP3

LP-VFD

11

- 2developed and presented in th e pumps test procedure NOPR . Th is allows DOE to calculate control losses at each load point in the energy use analysi s. The annual energy use is calculated using the equation:

AAAAAA = � QQ ii × HH ii

ηη ii nn mmmmmmmmmm + cc cc cc

, ii LL LLLL PP PP PP × OOOO OOOOOO ii

where:

• Q i i s the flow load point

• H i is the head at Qi, calculated from the pump curve

• η i is the pump efficiency calculated from the efficiency curve

• nn mmmmmmmmmm + cc cc cc

, ii is the combined motor plus VFD efficiency

• OOOO OOOOOO ii refers to annual hours of operation at load point i

Duty points, operating hours, and hydraulic efficiencies are the same as in the full LCC analysis.

11.3 RESULTS

Table 11.3

. 1 shows the results of the energy use analysis for each equipment class at each efficiency level (EL). The table provides the average annual energy use per pump.

Table 11.3

. 1 Average Annual Energy Use by Equipment Class and Efficiency Level in the Base Case for the VFD Subgroup (kWh)
Equipment

Class EL0 EL1 EL2 EL3 EL4 EL5

ESCC,1800 8,012 19,604 15,196 23,401 20,367 12,686

ESCC,3600 19,397 27,169 9,657 30,012 41,499 6,840

ESFM,1800 30,047 27,785 24,589 22,831 33,412 17,459

ESFM,3600 59,247 38,621 37,660 43,476 46,703 24,909

IL,1800 5,578 13,780 32,130 20,863 10,724 8,764

IL,3600 10,990 10,579 16,961 30,912 35,003 8,804

VT

- S,3600 9,490 7,157 20,572 12,693 27,943 6,488

Table 11.3

. 2 through Table 11.3

. 15 show the LCC and PBP results for each equipment class. In general, the average LCC savings are positive for nearly all equipment classes and efficiency levels, with the exception of VT

- S.3600, which has negative LCC saving s for the first

11

- 3two efficiency level s, and IL.1800, which has negative LCC savings for the last two efficiency

le vels.

As shown in Table 11.3

. 16 through Table 11.3

. 22 , the results of the life

- cycle cost subgroup analysis indicate that for all equipment classes analyzed, the average LCC savings for

the VFD subgroup is lower , and the payback period is longer, than for the full LCC. This is due

to the lower power use of a pump with a VFD, which leads to lower energy savings from pump efficiency improvements.

Table 11.3

. 2 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESCC.1800

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,639 $1,447 $11,186 $12,824

-- 13

1 1 $1,672 $1,441 $11,141 $12,813 5.6 13

2 2 $1,704 $1,428 $11,050 $12,754 3.6 13

3 3 $1,768 $1,417 $10,965 $12,733 4.4 13

4 4 $1,863 $1,403 $10,857 $12,720 5.2 13

5 5 $2,026 $1,387 $10,735 $12,761 6.5 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

Table 11.3

. 3 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESCC.1800

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 15% $12

2 2 17% $71

3 3 32% $91

4 4 41% $104

5 5 55% $63

* The calculation includes customers with zero LCC savings (no impact).

11

- 4Table 11.3

. 4 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESCC.3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,092 $1,072 $6,602 $7,694

-- 11

1 1 $1,098 $1,070 $6,588 $7,686 2.3 11

2 2 $1,111 $1,061 $6,533 $7,644 1.6 11

3 3 $1,141 $1,055 $6,496 $7,637 2.8 11

4 4 $1,170 $1,046 $6,442 $7,611 3.0 11

5 5 $1,215 $1,032 $6,352 $7,567 3.0 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

Table 11.3

. 5 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESCC.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 1.4% $8.7

2 2 2.9% $51

3 3 16% $57

4 4 17% $83

5 5 18% $127

* The calculation includes customers with zero LCC savings (no impact).

11

- 5Table 11.3

. 6 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESFM.1800

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,891 $2,207 $26,429 $28,320

-- 23

1 1 $1,893 $2,207 $26,423 $28,316 3.9 23

2 2 $1,943 $2,196 $26,292 $28,235 4.6 23

3 3 $2,004 $2,183 $26,131 $28,135 4.6 23

4 4 $2,151 $2,157 $25,815 $27,966 5.1 23

5 5 $2,314 $2,132 $25,512 $27,826 5.6 23

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

Table 11.3

. 7 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESFM.1800

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 0.4% $4.3

2 2 9.5% $85

3 3 20% $186

4 4 32% $355

5 5 37% $494

* The calculation includes customers with zero LCC savings (no impact).

11

- 6Table 11.3

. 8 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

ESFM 3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,349 $3,437 $33,340 $34,688

-- 20

1 1 $1,357 $3,433 $33,298 $34,655 2.0 20

2 2 $1,396 $3,400 $32,973 $34,369 1.3 20

3 3 $1,441 $3,371 $32,689 $34,131 1.4 20

4 4 $1,529 $3,337 $32,357 $33,886 1.8 20

5 5 $1,648 $3,287 $31,872 $33,520 2.0 20

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

Table 11.3

. 9 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for ESFM.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 0.7% $33

2 2 3.4% $319

3 3 8.0% $558

4 4 13% $802

5 5 14% $1,168

* The calculation includes customers with zero LCC savings (no impact).

11

- 7Table 11.3

. 10 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

IL.1800

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $2,128 $1,207 $10,684 $12,811

-- 16

1 1 $2,145 $1,203 $10,640 $12,786 3.6 16

2 2 $2,194 $1,193 $10,550 $12,744 4.5 16

3 3 $2,281 $1,183 $10,466 $12,747 6.4 16

4 4 $2,432 $1,173 $10,373 $12,805 8.8 16

5 5 $2,614 $1,158 $10,243 $12,857 9.9 16

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

Table 11.3

. 11 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for IL.1800

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 3.2% $26

2 2 11% $67

3 3 21% $64

4 4 35% $6.3

5 5 47% $(46)

* The calculation includes customers with zero LCC savings (no impact).

11

- 8Table 11.3

. 12 VFD Subgroup Average LCC and PBP Results by Efficiency Level for

IL.3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $1,473 $1,341 $9,347 $10,820

-- 13

1 1 $1,484 $1,336 $9,312 $10,795 2.2 13

2 2 $1,525 $1,325 $9,228 $10,753 3.1 13

3 3 $1,578 $1,311 $9,131 $10,709 3.5 13

4 4 $1,650 $1,300 $9,057 $10,707 4.3 13

5 5 $1,797 $1,279 $8,911 $10,708 5.2 13

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

Table 11.3

. 13 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for IL.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 3.5% $25

2 2 17% $67

3 3 18% $111

4 4 23% $113

5 5 32% $112

* The calculation includes customers with zero LCC savings (no impact).

11

- 9Table 11.3

. 14 VFD Subgroup Average LCC and PBP Results by Efficiency Level for VT

S.3600

TSL Efficiency

Level Average Costs
2013$ Simple
Payback

years Average

Lifetime

years Installed

Cost First Year’s
Operating

Cost Lifetime
Operating

Cost LCC

-- Base Case $692 $737 $4,223 $4,914

-- 11

1 1 $697 $737 $4,221 $4,918 18 11

2 2 $711 $734 $4,206 $4,917 6.6 11

3 3 $732 $722 $4,139 $4,871 2.7 11

4 4 $772 $714 $4,093 $4,865 3.5 11

5 5 $821 $706 $4,047 $4,869 4.2 11

Note: The results for each TSL are calculated considering all customers. The PBP is measured relative to the base

case.

Table 11.3

. 15 VFD Subgroup LCC Savings Relative to the Base Case Efficiency

Distribution for VT

- S.3600

TSL Efficiency

Level Life

- Cycle Cost Savings

% of Customers that
Experience Average Savings*

Net Cost 2013$

1 1 1.5% $(3.5)

2 2 30% $(2.6)

3 3 9.9% $44

4 4 17% $50

5 5 23% $46

* The calculation includes customers with zero LCC savings (no impact).

11

- 10Table 11.3

. 16 Comparison of Impacts for VFD Users with Non

- VFD Users, ESCC.1800

TSL Energy Efficiency

Level LCC Savings 2013$* Simple Payback Period

years

VFD

- Users Non

- VFD

Users VFD

- Users Non

- VFD

Users

1 1 $12 $43 5.6 3.3

2 2 $71 $164 3.6 2.2

3 3 $91 $240 4.4 2.6

4 4 $104 $324 5.2 3.1

5 5 $63 $362 6.5 3.9

*Parentheses indicate negative values.

Table 11.3

. 17 Comparison of Impacts for VFD Users with Non

- VFD Users, ESCC.3600

TSL Energy Efficiency

Level LCC Savings 2013$* Simple Payback Period

years

VFD

- Users Non

- VFD

Users VFD

- Users Non

- VFD

Users

1 1 $8.7 $17 2.3 1.4

2 2 $51 $92 1.6 1.0

3 3 $57 $122 2.8 1.8

4 4 $83 $180 3.0 1.9

5 5 $127 $278 3.0 1.9

*Parentheses indicate negative values.

Table 11.3

. 18 Comparison of Impacts for VFD Users with Non

- VFD Users, ESFM.1800

TSL Energy Efficiency

Level LCC Savings 2013$* Simple Payback Period

years

VFD

- Users Non

- VFD

Users VFD

- Users Non

- VFD

Users

1 1 $4.3 $8.0 3.9 2.4

2 2 $85 $173 4.6 2.8

3 3 $186 $372 4.6 2.8

4 4 $355 $735 5.1 3.1

5 5 $494 $1,062 5.6 3.4

*Parentheses indicate negative values.

11

- 11Table 11.3

. 19 Comparison of Impacts for VFD Users with Non

- VFD Users, ESFM.3600

TSL Energy Efficiency

Level LCC Savings 2013$* Simple Payback Period

years

VFD

- Users Non

- VFD

Users VFD

- Users Non

- VFD

Users

1 1 $33 $58 2.0 1.2

2 2 $319 $547 1.3 0.8

3 3 $558 $961 1.4 0.9

4 4 $802 $1,411 1.8 1.1

5 5 $1,168 $2,078 2.0 1.2

*Parentheses indicate negative values.

Table 11.3

. 20 Comparison of Impacts for VFD Users with Non

- VFD Users, IL.1800

TSL Energy Efficiency

Level LCC Savings 2013$* Simple Payback Period

years

VFD

- Users Non

- VFD

Users VFD

- Users Non

- VFD

Users

1 1 $26 $51 3.6 2.3

2 2 $67 $149 4.5 2.8

3 3 $64 $200 6.4 3.9

4 4 $6.3 $202 8.8 5.4

5 5 ($46) $234 9.9 6.1

*Parentheses indicate negative values.

Table 11.3

. 21 Comparison of Impacts for VFD Users with Non

- VFD Users, IL.3600

TSL Energy Efficiency

Level LCC Savings 2013$* Simple Payback Period

years

VFD

- Users Non

- VFD

Users VFD

- Users Non

- VFD

Users

1 1 $25 $46 2.2 1.4

2 2 $67 $139 3.1 1.9

3 3 $111 $241 3.5 2.1

4 4 $113 $288 4.3 2.7

5 5 $112 $377 5.2 3.2

*Parentheses indicate negative values.

11

- 12Table 11.3

. 22 Comparison of Impacts for VFD Users with Non

- VFD Users, VT

- S.3600

TSL Energy Efficiency

Level LCC Savings 2013$* Simple Payback Period

years

VFD

- Users Non

- VFD

Users VFD

- Users Non

- VFD

Users

1 1 $(3.5) $(2.4) 18 11

2 2 $(2.6) $7.2 6.6 4.2

3 3 $44 $91 2.7 1.7

4 4 $50 $123 3.5 2.2

5 5 $46 $144 4.2 2.7

*Parentheses indicate negative values.

11

- 13CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

TABLE OF CONTENTS

12.1 INTRODUCTION

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

- 1

12.2 METHODOLOGY

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

- 1

12.2.1 Phase I: Industry Profile

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

- 1

12.2.2 Phase II: Industry Cash

- Flow Analysis and Interview Guide

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

- 2 12.2.2.1 Industry Cash

- Flow Analysis

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

- 2

12.2.2.2 Interview Guides

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

- 2

12.2.3 Phase III: Manufacturer Subgroup Analysis

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

- 3 12.2.3.1 Manufacturing Interviews

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

- 3 12.2.3.2 Revised Industry Cash

- Flow Analysis

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

- 3 12.2.3.3 Manufacturer Subgroup Analysis

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

- 3 12.2.3.4 Manufacturing Capacity Impact

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

- 4 12.2.3.5 Employment Impact

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

- 5 12.2.3.6 Cumulative Regulatory Burden

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

- 5

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

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

- 5

12.3.1 Alignment with European Union Efficiency Standards

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

- 5

12.3.2 Pattern Production and Engineering Constraints

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

- 6

12.3.3 Conversion Requirements

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

- 6

12.3.4 Exclusion of Specific Pump Types

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

- 6

12.4 GRIM INPUTS AND ASSU MPTIONS

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

- 6

12.4.1 Overview of the GRIM

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

- 7

12.4.2 Sources for GRIM Inputs

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

- 7 12.4.2.1 Corporate Annual Reports

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

- 7 12.4.2.2 Standard and Poor Credit Ratings

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

- 8 12.4.2.3 Shipment Model

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

- 8 12.4.2.4 Engineering Analysis

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

- 8

12.4.2.5 Manufacturer Interviews

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

- 8

12.4.3 Financial Parameters

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

- 8

12.4.4 Corporate Discount Rate

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

- 9

12.4.5 Trial Standard Levels

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

- 11

12.4.6 NIA Shipments

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

- 11 12.4.6.1 Shipments Forecast

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

- 12

12.4.7 Production Costs

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

- 13

12 .4.8 Product and Capital Conversion Costs

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

- 14

12.4.9 Markup Scenarios

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

- 15 12.4.9.1 Flat Markup Scenario

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

- 16

12.4.9.2 Cost Recove ry Scenario

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

- 17

12.5 INDUSTRY FINANCIAL I MPACTS

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

- 18

12.5.1 Introduction

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

- 19

12.5.2 Pumps Industry Financial Impacts

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

- 20

12.6 IMPACTS ON SMALL BUS INESS MANUFACTURERS

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

- 22

12.7 OTHER IMPACTS

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

- 24

12

- i12.7.1 Employment

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

- 25 12.7.1.1 Methodology

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

- 25 12.7.1.2 Direct Employment Impacts

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

- 25

12.7.2 Production Capacity

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

- 26

12.7.3 Labeling

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

- 26

12.7.4 Cumulative Regulatory Burden

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

- 26

12.8 CONCLUSION

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

- 27

12.8.1 Con clusions for Pumps

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

- 27

LIST OF TABLES

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by This Rulemaking

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

- 4 Table 12.4.1 GRIM Financial Parameters for Pumps

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

- 9 Table 12.4.2 Cost of Equity Calculation

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

- 10

Table 12.4.3 Cost of Debt Calculation

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

- 11 Table 12.4.4 Trial Standard Levels for Energy Efficiency Analysis of Pumps

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

- 11 Table 12.4.5 Total Base Case NIA Shipments for Pumps (in Units Shipped)

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

- 12 Table 12.4.6 Manufacturer Production Cost Breakdown ($2013) for ESCC Pumps

.. 12

- 13 Table 12.4.7 Manufacturer Production Cost Breakdown ($2013) for ESFM Pumps

.. 12

- 13 Table 12.4.8 Manufacturer Production Cost Breakdown ($2013) for IL Pumps

........ 12

- 13

Table 12.4.9 Manufacturer Production Cost Breakd own ($2013) for VTS Pumps

..... 12

- 14 Table 12.4.10 Cumulative Conversion Cost by Efficiency Level ($M)

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

- 15 Table 12.4.11 Base Case Markup at Each Efficiency Level

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

- 16 Table 12.4.12 Total Industry Conversion Costs at Each Efficiency Level

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

- 17

Table 12.4.13 Cost R ecovery Annuity Payment at Each Efficiency Level

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

- 17 Table 12.4.14 Additional MSP Per Model Requiring a Redesign at Each Effici ency Level

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

- 18 Table 12.4.15 Standards Case Cost Recovery Markups at Each Efficiency Level

..... 12

- 18 Table 12.4.16 Simple Payback Period for Industry Conversion Costs

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

- 18 Table 12.5.1 Flat Markup Scenario Changes in INPV for Pumps

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

- 20

Table 12.5.2 Cost Recovery Markup Scenario Changes in INPV for Pumps

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

- 20 Table 12.6.1 Impacts of Conversion Costs on an Average Small versus Larg e Manufacturer at the Proposed Standard Level

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

- 24 Table 12.7.1 Potential Changes in Total Domestic Production Workers for Pumps in 2020

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

- 25 Table 12.8.1 Trial Standard Levels for Pumps

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

- 27 Table 12.8.2 Manufacturer Impact Analysis Results for Pumps

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

- 27

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow

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

- 7 Figure 12.5.1 Annual Industry Net Cash Flows for Pumps (Flat Markup Scenario)

.. 12

- 21

12

- iiFigure 12.5.2 Annual Industry Net Cash Flows for Pumps (Cost Recovery Markup Scenario)

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

- 22

12

- iiiCHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

In determining whether a standard is economically justified, the U.S. Department of Energy (DOE) is required to consider “the economic impact of the standard on the manufacturers

and on the consumers of the products subject to such a standard.” (42 U.S.C. 6313(a)(6)(B)(i)) The law also calls for an assessment of the impact of any lessening of competition as determined in writing by the Attorney General. Id

. DOE conducted a manufacturer im pact analysis (MIA) to estimate the financial impact of new energy conservation standards on manufacturers of pumps
, and assessed the impact of such standards on direct employment and manufacturing capacity.

The MIA has both quantitative and qualitative a spects. The quantitative part of the MIA primarily relies on the Government Regulatory Impact Model (GRIM), an industry cash

- flow model adapted for each product in this rulemaking. The GRIM inputs include information on industry cost structure, shipments, and pricing strategies. The GRIM’s key output is the industry net present value (INPV). The model estimates the financial impact of more stringent energy conservation standards for each product by comparing changes in INPV between a base case and

the vario us trial standard levels (TSLs) in the standards case . The qualitative part of the MIA addresses product characteristics, manufacturer characteristics , market and product trends, as well as the impact of standards on subgroups of manufacturers.

12.2 METHODOLOG Y

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of preparing an industry characterization for the pumps industr y , including data on sales volumes, pricing, employment, and financial structure. In Phase II, “Industry Cash Flo w,” DOE used the GRIM to assess the impacts of new energy conservation standards on manufacturers. DOE also developed interview guide s to gather information on the potential impacts on these

manufacturers. In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers representing a broad cross

- section of the pumps industry. Using information from P hase II, DOE refined its analysis in the GRIM, developed additional analyses for sub

- groups that requ ired special consideration, and incorporated qualitative data from interviews into its analysis.

12.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the pumps industry that built upon the market and technology assessment prep ared for this rulemaking ( refer to c hapter 3 of the TSD )

. Before initiating the detailed impact studies, DOE collected information on the market

characteristics of the pumps industry. This information included product shipments, manufacturer markups, and the cost structure for various manufacturers. The industry profile includes: (1) further detail on the overall market and product characteristics; ( 2 ) financial parameters such as net plant, property, and equipment; selling, general and administrative (SG&A) expens es; cost of goods sold, etc.; and ( 3 ) trends in the number of firms, market, and product characteristics. The industry profile included a top

- down cost analysis of pumps manufacturers that DOE used to derive the preliminary financial inputs for the GRIM ( e.g. , revenues, depreciation, SG&A, and research and development (R&D) expenses).

12

- 1DOE also used public information to further calibrate its initial characterization of the industry , including Securities and Exchange Commission (SEC) 10

– K reports, 1 Standar d & Poor’s (S&P) stock reports, 2 industry trade association membership directories ( e.g. , the Hydraulic Institute ), market research tools ( e.g. , Hoovers reports)

, corporate annual reports, and

the U.S. Census Bure au’s 2011 Annual Survey of Manufacturers (2 011 ASM). 3 DOE also characterized these industries using information from its engineering analysis and the life

- cycle cost analysis.

12.2.2 Phase II: Industry Cash

- Flow Analysis and Interview Guide

Phase II focused on the financial impacts of potential energy con servation standards on

manufacturers of pumps

. More stringent energy conservation standards can affect manufacturer cash flows in three distinct ways: (1) create a need for increased investment, (2) raise production costs per unit, and (3) alter revenue du e to higher per

- unit prices and/or possible changes in sales

volumes. To quantify these impacts, DOE used the GRIM to perform cash

- flow analyse s for

pumps manufacturers . In performing these analyses, DOE used the financial values derived during Phase I and the shipment scenarios used in the NIA.

12.2.2.1 Industry Cash

- Flow Analysis

The GRIM uses several factors to determine a series of annual cash flows from the

announcement yea r of new energy conservation standards until thirty years after the standard ’ s effective date. These factors include annual expected revenues, costs of goods sold , SG&A, taxes, and capital expenditures related to the new standards. Inputs to the GRIM inclu de manufacturing production costs

, markup assumptions

, and shipments forecasts developed in other analyses. DOE derived the manufacturing costs from the engineering analysis and information provided by the industry and estimated typical manufacturer markup s from public financial reports and interviews with manufacturers. DOE developed alternative markup scenarios for each GRIM based on discussions with manufacturers. DOE’s shipments a nalysis, presented in c hapter 9 of th e TSD, provided the basis for the shi pment projections in the GRIM. The financial parameters were developed using publicly available manufacturer data and were revised with information submitted confidentially during manufacturer interviews. The GRIM provides projections for both the base cas e and the standards case . The financial impact of new energy conservation standards is the difference between the discounted annual cash flows in the

base case and standards case INPV for each TSL

.

12.2.2.2 Interview Guides

During Phase I I of the MIA, DOE interviewed manufacturers to gather information on

the effects of a n energy conservation standard on revenues and finances, direct employment, capital assets, and industry competitiveness. Before the interviews, DOE distributed an intervi ew guide to interviewees . The interview guide provided a starting point for identify ing relevant issues and impacts of new energy conservation standards on individual manufacturers or subgroups of manufacturers. Most of the information received from these meetings is protected by non

- disclosure agreements and resides with DOE’s contractors. The MIA interview topics included (1) key issues to this rulemaking; (2) a company overview and organizational characteristics; (3) engineering and life cycle cost analy sis follow

- up; (4 ) manufactur er markups

12

- 2and profitability; (5 ) shipment projections and market shares; (6 ) financial parameters; (7) conversion costs; (8 ) cumulative regulatory burden; ( 9 ) direct employment impact assessment; (1 0 ) exports, foreign competit ion, and outsourcing; (1 1 ) consolidation; and (12) impacts on small

business es

.

12.2.3 Phase III: Manufacturer Subgroup Analysis

In Phase III, “Subgroup Impact Analysis,” DOE interviewed pumps manufacturers

. DOE

interviewed a range of manufacturers

, including s mall and large companies as well as manufacturers of every covered product class

. The interviews provided DOE with valuable information for evaluating the impacts of energy conservation standards on manufacturer cash flows, investment requirements, and emp loyment. Using information from P hase II and from the interviews, DOE refined its analysis for the equipment class es included in the GRIM.

12.2.3.1 Manufacturing Interviews

The information gathered in Phase I and the cash

- flow analysis performed in Phase II are

su pplemented with information gathered from manufacturer interviews in Phase III. The interview process provides an opportunity for interested parties to express their views on important issues privately, allowing confidential or sensitive information to be considered in the rulemaking process. DOE sought to obtain feedback from industry on the approaches used in the GRIMs and to isolate key issues and concerns.

DOE used these interviews to tailor the GRIM to reflect unique financial characteristics

of each product group. Interviews were scheduled well in advance to provide every opportunity for key individuals to be available for comment. Although a written response to the questionnaire was acceptable, DOE sought interactive interviews, which help clarify re sponses and identify additional issues. The resulting information provides valuable inputs to the GRIM developed for

the equipment class es. The interviewed manufacturers account for approximately 40 percent of

the domestic CIP market.

12.2.3.2 Revised Industry Cash

- Flow Analysis

In Phase II I of the MIA, DOE provided manufacturers with preliminary GRIM input financial figures for review and evaluation. During the interviews, DOE requested comments on the values it selected for the parameters. DOE revised its industry cash

- flow model based on this

feedback. Section 12.4.3 provides more information on how DOE calculated the parameters.

12.2.3.3 Manufacturer Subgroup Analy sis

Using average cost assumptions to develop an industry

- cash

- flow estimate may not adequately assess differential impacts of energy conservation standards among manufacturer subgroups. For example, small m anufacturers, niche players, or manufacturers ex hibiting a cost structure that largely differs from the industry average could be more negatively affected. To address this possible impact, DOE used the results of the industry characterization analysis in

Phase I to group manufacturers that exhibit similar characteristics.

Small

- Business Manufacturer Subgroup

12

- 3DOE investigated whether small business manufacturers should be analyzed as a manufacturer subgroup. DOE used the Small Business Administration (SBA) small business size

standards effective on July 14 , 201 4 , as amended, and the North American Industry Classification System (NAICS) code, presented in T able 12.2.1 , to determine whether any small entities would be affected by the rulemaking. 4 For the equipment class es under review, the SBA bases its small business definition on the total number of employees for a business, its subsidiaries, and its parent comp anies. 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 Rulema king
Industry Description Revenue Limit Employee Limit NAICS Pump and Pumping Equipment Manufacturing N/A 500 333911

DOE used publicly available and proprietary information to identify potential small manufacturers. DOE’s research involved industry trade association membership directories (including the Hydraulic Institute ), industry conference exhibitor lists, individual company and buyer guide websites, and market research tools ( e.g.
, Hoovers.com ) to create a list of companies that manufactures or sells products covered by this rulemaking. DOE also asked stakeholders and industry representatives if they were aware of any other small manufacturers during manufacturer interviews and at previous DOE publ ic meetings. DOE screened out companies that did not offer products covered by this rulemaking, did not meet the definition of a “small business,” or are foreign owned and operated. DOE notes that manufacturers interviewed stated that there are potentially a large number of small pumps manufacturers that serve small regional markets. These small manufacturers are not members of HI and typically have a limited marketing presence. The interviewed manufacturers and those that participated in the CIP working gr oup were not able to provide exact names for these smaller players.

Based on this analysis DOE identified 86 manufacturers of covered pump products sold in the U.S., 39 of these manufacturers met the 500

- employee threshold defined by the SBA to qualify as a small business. Finally, DOE determine d that 2 5 of these small manufacturers were domestic companies

. Based on interviews and industry re search, DOE reports the potential impacts of this rulemaking on small manufacturers in section 12.6

.

12.2.3.4 Manufacturing Capacity Impact

One significant outcome of new e nergy conservation standards could be the obsolescence

of existing manufacturing assets. The manufacturer interview guides have a series of questions to help identify impacts of new standards on manufacturing capacity, specifically capacity

utilization and plant location decisions in the United States and North America, with and without standards; the ability of manufacturers to upgrade or remodel existing facilities to accommodate the new requirements; the nature and value of any stranded assets; and estim ates for any one

time changes to existing plant, property, and equipment (PPE). DOE’s estimates of the one

- time capital changes and stranded assets affect the cash flow estimates in the GRIM. These estimates

can be found in section 12.4.8

. DOE’s discussion of the capacity impact can be found in section

12.7.2

.

12

- 412.2.3.5 Employment Impact

The impact of energy conservation standards on employment is an important consideration in the rulemaking process. To assess how domestic direct employment patterns might be affected, the interviews explored current employment trends in the pumps industry . The interviews also solicited manufacturer views on changes in employment patterns that may result from standards. The employment impacts are reported in section 12.7.1

.

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects on manufacturers due to new energy conservation standards and other regulatory actions affecting the same products. DOE analyzed the imp act on manufacturers of multiple, product

- specific regulatory actions. Discussion of the cumulative regulatory burden can be found in section 12.7.4

.

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company regarding the energy conservation standard rulemaking?” This question prompts manufacturers to identify the issues they feel DOE shoul d explore and discuss further during the interview. The following sections describe the most significant issues identified by manufacturers. These summaries are provided in aggregate to protect manufacturer confidentiality.

12.3.1 Alignment with European Union E fficiency Standards

Multiple manufacturers emphasized the importance of harmonizing U.S. energy conservation standards with existing European Union standards for clean water pumps. Manufacturers stated that harmonized standards would promote regulatory consistency and

would e nable them to better coordinate product redesigns and reduce conversion costs. If U.S. and EU standards are not harmonized, some manufacturers noted they would have to carry a greater number of product lines to service separate markets or to comply with ef ficiency standards in both domestic and European markets. Manufacturers also indicated harmonized standards could help to improve U.S. manufacturers’ access to foreign markets and would help to preempt a situation wherein less stringent domestic standards enable EU

- compliant manufacturers to market their pumps to U.S. consumers as more efficient than pumps manufactured domestically. Manufacturers noted that expansion beyond the EU Directive parameters will add complexity and cost to the tasks of the manufac turers and create a significant financial burden for manufacturers to gain compliance, specifically related to double

- suction pumps and vertical turbines beyond 6

- inch bowl assemblies.

In contrast, one manufacturer also stated that aligning US standards w ith EU standards would give European manufacturers an advantage because of the fact that they would have products that could immediately be compliant and US manufacturers would have conversion costs to achieve new efficiency.

12

- 512.3.2 Pattern Production and Enginee ring Constraints

Many manufacturers raised concerns regarding potential tooling bottlenecks. In general, much of the industry relies on the same resources for patterns used to produce the impeller and

bowl. Manufacturers were concerned there would not be e nough pattern production capacity available if the entire industry attempted to redesign products within the same 3

- 5 year timeframe. Furthermore, manufacturers expressed concern surrounding insufficient availability of engineering resources (mainly design engineers) required to redesign a high volume of pump lines during a short time period. Manufacturers stated that limited pump design expertise in the industry could create time delays in complying with new standards.

12.3.3 Conversion Requirements

Manufacturers raised concerns over potentially significant barriers to achieving compliance with new standards, particularly at more stringent efficiency levels. If U.S. standards exceeded levels comparable to a European Union minimum efficiency index (MEI) a of 0.4, several manufacturers indicated they would have to develop “clean sheet” product platforms at significant cost. At an MEI of 0.7, many indicated they would close manufacturing facilities rather than upgrade them to comply with efficiency standards . Additionally, manufacturers suggested that conversion requirements would likely accelerate trends toward industry consolidation, as smaller manufacturers elect to exit the market rather than invest in product redesigns.

12.3.4 Exclusion of Specific Pump Types

Manufacturers expressed concern over which pumps would be included in the rulemaking. Two m anufacturers further raised concerns over including circulator pumps in this rulemaking. Manufacturers stated that compared to the European market, the U.S. market for circulators is very small and therefore, it is not a large opportunity to save energy. Manufacturers also stated that the investment required by U.S. circulator manufacturers will be too high and that they would have a very limited possibility for a so lid return on investment. They also mentioned that in most situations, due to the higher cost of high

- efficiency product and the relatively low cost of energy in the U.S., consumers would not see a return on investment for a long period of

time.

12.4 GRIM INPU TS AND ASSUMPTIONS

The GRIM serves as the main tool for assessing the impact s on industry due to new energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM. Data and assumptions from these sources are then fed into an ac counting model that calculates

the industry cash flow both with and without new energy conservation standards

.

a The EU sets efficiency standards based on desired percentages of the mar ket to cut off, which they refer to as minimum efficiency indexes, or MEIs. A MEI of 0.4, for example, indicates an efficiency standard designed to eliminate the least efficient 40 percent of products from the market.

12

- 612.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4

. 1 , is an annual cash flow analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial information as inputs, and accepts a set of regulatory conditions such as changes in costs, investments, and associated margins. The GRIM spreadsheet uses a number of inputs to arrive at a series of annual cash flows, beginning with the base year of the analysis, 201 5 , and continuing

to 204 9 . The model calculates the INPV by summing the stream of annual discount ed cash flows during this period and adding a discounted terminal value

. 5

Figure 12.4

. 1 Using the GRIM to Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares changes in INPV between the base case and the standard

- case scenario induced by new energy conservation standards. The difference in INPV between the base case and the standard case(s) represents the estimated financial impact of the new energy conservatio n standard on

manufacturers.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash flow. These sources include corporate annual reports, company profiles, Census data, credit ratings, the shipments model, the engineering analysis, and the manufacturer interviews.

12.4.2.1 Corporate Annual Reports

Corporate annual reports to the SEC (SEC 10

- Ks) provided many of the initial financial inputs to the GRIM. These reports exist for publicly held companies and are fr eely available to the general public. DOE developed initial financial inputs to the GRIM by examining the annual

SEC 10

- K reports filed by publicly

- traded manufacturers that manufactur e pumps

. Since these companies do not provide detailed information about their individual product lines, DOE used the financial information for the entire companies as its initial estimates of the financial parameters in the GRIM analys i s . These figures were later revised using feedback from interviews to be representative of manufacturing for each product grouping . D OE used corporate annual reports to derive the following initial inputs to the GRIM:

12

- 7• Tax rate • Working capital

• SG&A

• R&D • Depreciation • Capital expenditures

• Net PPE

12.4.2.2 Standard and Poor Credit Ratings

S&P provides independent credit ratings, research, and financial information. DOE relied on S&P reports to determine the industry’s average cost of debt when calculating the cost of capital.

12.4.2.3 Shipment Model

The GRIM used shipment projections derived from DOE’s shipments model in the national impact analysis (NIA). Chapter 9 of the TSD describes the methodology and analytical model DOE used to forecast shipments.

12.4.2.4 Engineering Analysis

During the engineering analysis, DOE used a manufacturing cost model to develop MPC estimates. The analysis provided the labor, materials, overhead, and total production costs for different design option for pumps . The engineering analysis also estimated a manufacturer markup and a shipping cost to provide the manufacturer selling pr ice (MSP) for design option

.

12.4.2.5 Manufacturer Interviews

During the course of the MIA, DOE conducted interviews with a representative cross

section of manufacturers. DOE also interviewed manufacturers representing a significant portion

of sales in every equip ment class . During these discussions, DOE obtained information to determine and verify GRIM input assumptions in each industry. Key topics discussed during the

interviews and reflected in the GRIM include:

• C onversion costs ( including capital conversion costs

, one

- time investments in

PPE , and product conversion costs

, one

- time investments in research, product development, testing, and marketing );

• P roduct cost structure, or the portion of the MPCs related to materials, labor, overhead, and depreciation costs;

• MPCs estimated in the engineering analysis; and

• P ossible profitability impacts.

12.4.3 Financial Parameters

Table 12.4.1 provides financial parameters for manufacturers of pumps

.

12

- 8Table 12.4

. 1 GRIM Financial Parameters for Pumps

Parameter Estimate

Tax Rate (% of Taxable Income) 32.0 % Working Capital (% of Revenue) 18.6 %

SG&A (% of Revenue) 21.6 %

R&D (% of Revenues) 1.6 % Depreciation (% of Revenues) 2. 6 % Capital Expenditures (% of Revenues) 2. 4 %

While most of these companies also manufacturer products not covered by this rulemaking, DOE used these parameters as initial estimates. During interviews, manufacturers were asked to provide their own figures for the parameters listed in Table 12.4

. 1 . Where applicable, DOE adjusted the parameters in the GRIM using manufacturer feedback and market share information. Additionally, based on manufacturer feedback, DOE analyzed pumps

.

12.4.4 Corpora te Discount Rate

DOE used the weighted

- average cost of capital (WACC) as the discount rate to calculate the INPV. A company’s assets are financed by a combination of debt and equity. The WACC is the total cost of debt and equity weighted by their respectiv e proportions in the capital structure of the industry. DOE estimated the WACC for the pumps industry based on several representative companies, using the following formula:

WACC = After

- Tax Cost of Debt x (Debt Ratio) + Cost of Equity x (Equity Ratio)

Th e 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) prov ides one widely used means to estimate the cost of equity. According to the CAPM, the cost of equity (expected return) is:

Cost of Equity = Riskless Rate of Return + β x Risk Premium

W here:

Riskless rate of return is the rate of return on a “safe” benchmark investment, typically

considered the short

- term Treasury Bill (T

- Bill) yield.

Risk premium is the difference between the expected return on stocks and the riskless

rate.

12

- 9Beta (β) is the correlation between the m ovement in the price of the stock and that of the broader market. In this case, Beta equals one if the stock is perfectly correlated with the S&P 500

market index. A Beta lower than one means the stock is less volatile than the market index.

DOE calculate d that the industry average cost of equity for the pumps industry is 13.78

percent ( Table 12.4.2 ).

Table 12.4

. 2 Cost of Equity Calculation

Parameter Industry Weighted Average
(1) Average Beta 1. 4 0

(2) Yield on 10

- Year (192 7

- 201 1 ) 5.20

(3) Market Risk

Premium (192 7

- 201 1 ) 6.01

Cost of Equity (2)+[(1)*(3)] 13.78
Equity/Total Capital 67.96%

Bond ratings are a tool to measure default risk and arrive at a cost of debt. Each bond rating is associated with a particular spread. One way of estimating a company’s cost of debt is to treat it as a spread (usually expressed in basis points) over the risk

- free rate . DOE used this

method to calculate the cost of debt for five public manufacturers by using S&P ratings and adding the relevant spread to the risk

- free rate.

In practice, investors use a variety of different maturity Treasury bonds to estimate the

risk

- free rate. DOE used the 10

- year Treasury bond return because it captures long

- term inflation expectations and is less volatile than shor t

- term rates. The risk free rate is estimated to be approximately 5.2 percent, which is the average 10

- year T reasury bond return between 19 27 and

2011

.

For the cost of debt S&P’s Credit Services provided the average spread of corporate

bonds for the five manufacturers for which data was available . DOE added the industry

weighted average spread to the average T

- Bill yield over the same period. Since proceeds from debt issuance are tax deductible, DOE adjusted the gross cost of debt by the industry

average t ax rate to determine the net cost of debt for the industry. Table 12.4.3 presents the derivation of the cost of debt and the capital structure of the industry ( i.e. the debt ratio (debt/total capital) )

.

12

- 10Table 12.4

. 3 Cost of Debt Calculation

Parameter Industry Weighted Average
S&P Bond Rating

(2) Yield on 10

- Year (192 7

- 201 1 ) 5.2 0
(2) Gross Cost of Debt 7. 2 5

(3) Tax Rate 2 7

. 39

Net Cost of Debt (2) x [1

- (3)] 5.26
Debt/Total Capital 32.04%

Using public information for these five companies, the initial estimate for the industry’s

inflation

- adjusted WACC and the initial estimate of the discount rate used in the straw

- man GRIM is 11

. 8 percent. DOE also asked for feedback on the discount rate during manufacturer

interviews

.

12.4.5 Trial Standard Levels

DOE developed a number of efficiency levels for each type of equipment class . TSLs

were then d eveloped by selecting likely groupings of efficiency levels for all equipment types. Each TSL includes combinations of efficiency levels for pumps

. Table 12.4

. 4 presents the TSLs used for energy efficiency analysis in the GRIM. The GRIM analysis does not differentiate pumps by speed.

Table 12.4

. 4 Trial Standard Levels for Energy Efficiency Analysis of Pumps Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

VTS EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

RSV * EL 0 EL 0 EL 0 EL 0 EL 0 EL 0

*Equipment classes not analyzed due to lack of available data (in the case of RSV ).

12.4.6 NIA Shipment s

The GRIM estimates manufacturer revenues based on total

- unit

- shipment forecasts and the distribution of these values by efficiency level. Changes in the efficiency mix at each standard level are a key driver of manufacturer finances. For this analysis, the GRIM applied the NIA shipments forecasts.

Chapter 9 of the TSD explains DOE’s calculations of total shipments in detail. Table

12.4

. 5 show s base case shipments for ecasted in the shipment analysis for the pumps rulemaking

.

12

- 11Table 12.4

. 5 Total Base Case NIA Shipments for Pumps ( in Units Shipped )

Year ESCC ESFM IL VTS

2016 189462 48667 55719 115371

2017 193035 49585 56770 117546

2018 195949 50334 57627 119321

2019 198763 51056 58455 121034

2020 201722 51816 59325 122836

2021 204727 52588 60209 124666

2022 207795 53376 61111 126534

2023 210957 54189 62041 128460

2024 213972 54963 62928 130296

2025 217199 55792 63877 132260

2026 220386 56611 64814 134201

2027 223309 57362 65674 135982

2028 225970 58045 66456 137602

2029 228608 58723 67232 139208

2030 231383 59435 68048 140898

2031 234108 60136 68850 142557

2032 236564 60766 69572 144053

2033 239230 61451 70356 145676

2034 242314 62243 71263 147554

2035 245467 63053 72190 149474

2036 248717 63888 73146 151453

2037 251989 64729 74108 153446

2038 255141 65538 75035 155365

2039 258097 66298 75905 157165

2040 261033 67052 76768 158953

2041 265000 68071 77935 161369

2042 268081 68862 78841 163245

2043 271161 69653 79747 165120

2044 274242 70445 80653 166996

2045 277323 71236 81559 168872

2046 280403 72027 82465 170748

2047 283484 72819 83371 172624

2048 286564 73610 84277 174500

2049 289645 74401 85183 176376

12.4.6.1 Shipments Forecast
As part of the shipments analysis, DOE estimated the base

- case shipment distribution by efficiency level for pumps

.

12

- 12In the standards case, the shipments analysis assumes a roll

- up scenario, where all shipments in the ba se case that do not meet the standard would instead ship at the new standard

level. The key assumptions and methodology used to forecast shipments can be found in chapter

9 of the TSD.

12.4.7 Production Costs

The NIA s hipments estimated the number of units at various representative capacity points. The GRIM used the manufacturing production costs from the engineering analysis and

the NIA Shipments to calculate shipment

- w eighted average MPC for each equipment class

. Additionally, t he GRIM used data from manufacturer interviews to determine labor, materials, overhead, and depreciation percentages that constitute the full MPC.

Table 12.4

. 6 through Table 12.4

. 9 show the production cost estimates used in the GRIM

for each analyzed equipment class

.

Table 12.4

. 6 Manufacturer Production Co st Breakdown ($ 2013 ) for ESCC Pumps

Labor Material Overhead Depreciation MPC

Baseline $38.00 $410.03 $58.08 $16.87 $522.98

EL 1 $38.00 $410.03 $58.08 $16.87 $522.98

EL 2 $38.00 $410.03 $58.08 $16.87 $522.98

EL 3 $38.00 $410.03 $58.08 $16.87 $522.98

EL 4 $38.00 $410.03 $58.08 $16.87 $522.98

EL 5 $38.00 $410.03 $58.08 $16.87 $522.98

Table 12.4

. 7 Manufacturer Production Cost Breakdown ($ 2013 ) for ESFM Pumps

Labor Material Overhead Depreciation MPC

Baseline $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 1 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 2 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 3 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 4 $84.46 $991.60 $125.95 $39.15 $1,241.16

EL 5 $84.46 $991.60 $125.95 $39.15 $1,241.16

Table 12.4

. 8 Manufacturer Production Cost Breakdown ($ 2013 ) for IL Pumps

Labor Material Overhead Depreciation MPC

Baseline $67.24 $786.74 $105.69 $32.26 $991.92

EL 1 $67.24 $786.74 $105.69 $32.26 $991.92

EL 2 $67.24 $786.74 $105.69 $32.26 $991.92

EL 3 $67.24 $786.74 $105.69 $32.26 $991.92

EL 4 $67.24 $786.74 $105.69 $32.26 $991.92

EL 5 $67.24 $786.74 $105.69 $32.26 $991.92

12

- 13Table 12.4

. 9 Manufacturer Production Cost Breakdown ($2013) for VTS P umps

Labor Material Overhead Depreciation MPC

Baseline $44.65 $253.18 $21.14 $9.32 $328.29

EL 1 $44.65 $253.18 $21.14 $9.32 $328.29

EL 2 $44.65 $253.18 $21.14 $9.32 $328.29

EL 3 $44.65 $253.18 $21.14 $9.32 $328.29

EL 4 $44.65 $253.18 $21.14 $9.32 $328.29

EL 5 $44.65 $253.18 $21.14 $9.32 $328.29

12.4.8 Product and Capital Conversion Costs

E nergy conservation standards typically cause manufacturers to incur one

- time conversion costs to bring their production facilities and product designs into compliance with new regulations. For the MIA, DOE classified these one

- time conversion costs into two major groups: capital conversion costs and product conversion costs. Capita l conversion costs are one

time investments in plant, property, and equipment to adapt or change existing production

facilities and tooling in order to fabricate and assemble new product design that comply with energy conservation standards . Product conver sion costs are one

- time investments in research, development, testing, marketing and other costs to make product designs comply with energy

conservation standards. DOE based its estimates of the conversion costs for each efficiency level

on information obt ain ed from manufacturer interviews and the design pathways analyzed in the engineering analysis.

DOE expects that hydraulic redesigns will result in significant conversion costs for manufacturers as they attempt to bring their pumps into compliance with the proposed standard. To evaluate the magnitude of the conversion costs the pump industry would incur to comply with new energy conservation standards, DOE used a bottoms

- up approach. For this bottoms

- up approach, DOE first determined the industry

- average cost, per model, to redesign pumps of varying sizes to meet each of the proposed efficiency levels. DOE then modeled the distribution of unique pump models that would require redesign at each efficiency level. For each efficiency level, DOE multiplied eac h unique failing model by its associated cost to redesign and summed the total to reach an estimate of the total product and capital conversion cost for the industry.

Data supplied to DOE by the Hydraulic Institute was used as basis for the industry

avera ge cost, per model, to redesign a failing pump model. The Hydraulic Institute, through an independent third party, surveyed 15 manufacturers regarding the product and conversion costs associated with redesigning one, 50, and 200 hp pumps from the 10 th to t he 40 th percentile of market efficiency. Specifically, HI’s survey contained cost categories for the following: redesign; prototype and initial test; patterns and tooling; testing; working capital; and marketing.

DOE validated the HI survey data with in dependent analysis and comparable independently collected manufacturer interview data. In addition, data from the European Union

12

- 14pumps regulation preparatory study b was used to augment the HI survey data and scale costs to various efficiency levels above a nd below the 40 th percentile. Complete details can be found in chapter 5 of the NOPR TSD.

DOE used a pump model database, containing various performance parameters, to model the distribution of unique pump models that would require redesign at each effici ency level. The database is comprised of a combination of data supplied by the Hydraulic Institute and data collected independently from manufacturers by the DOE. For the ESCC, ESFM, IL, and VTS equipment classes, DOE believes that the database is of suita ble size to be considered representative of the industry as a whole. Table 12.4.10 presents the resulting product and capital conversion costs for eac h equipment class, at each efficiency level. Complete details on the calculation of industry aggregate conversion costs are found in chapter 5 of the NOPR TSD

.

In section VI of the NOPR , DOE proposes labeling requirements recommended by the CIP Working Group. DOE recognizes that such requirements may result in costs to manufacturers. Costs of updating marketing materials for redesigned pumps in each standards

case were included in the c onversion costs for the industry and are accounted for in the industry

cash

- flow analysis results and industry valuation figures presented in this chapter of the TSD

. However, DOE notes that costs of updating marketing materials for pumps that do not have to be redesigned to meet the standard are not considered in the industry valuation figures because these costs would be incurred by manufacturers in order to make representations of energy use (PEI) according to the proposed test procedure, as well as to include labeling requirements, regardless of whether DOE set an energy conservation standard or what TSL DOE selected.

These costs are discussed in section 12.7.3

.

Table 12.4

. 10 Cumulative Conversion Cost by Efficiency Level ($M)

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Baseline PE R 10 PE R 25 PE R 40 PE R 55 PE R 70

ESCC $0 $ 9.9 $ 39.3 $ 88.0 $ 16 7.4 $ 27 4.3

ESFM $0 $ 2.5 $ 10.1 $ 22.6 $ 43.0 $ 70.5

IL $0 $5.0 $19.7 $44.6 $86.8 $141.7

VTS $0 $2.5 $9.3 $19.2 $37.8 $61.3

12.4.9 Markup Scenarios

To meet the new energy conservation standards proposed in this rule, DOE expects that

manufacturers will need to hydraulically redesign a portion of product lines, which may result in significant conversion costs. In interviews and in the CIP working group discussions

, manufacturer noted two possible implications of the conversion costs and a new energy conservation standard on pricing. On one hand, m anufacturers indicated that competitive

markets would make it difficult to pass cost increases on to their customers. On the other hand, manufacturers noted that they would need to have some level of cost recovery to support their

business.

b AEA Energy & Environment. 2008. App endix 6: Lot 11

– ‘Circulators in buildings’. Report to European

Commission.

12

- 15Given these tw o points of view and the uncertainty about the ability to pass the cost of compliance onto customers, DOE modeled two markup scenarios to reflect the uncertainty about the potential impacts of standards on pricing and profitability : (1) a flat markup scenario and (2)

a cost recovery markup scenario . These scenarios lead to different markups values, which, when applied to the inputted MPCs, result in varying revenue and cash flow impacts.

12.4.9.1 Flat Markup Scenario

DOE developed ini tial estima tes of the base case manufacturer markup s based on corporate annual reports and Securities and Exchan ge Commission (SEC) 10

- K filings. Initial estimates were presented to manufacturers in interviews. Using feedback and data and information gathered during the manufacturer interviews, DOE further refined the base case markups. Reviewing industry data, DOE concluded that when analyzed as whole, the industry does exhibit a relationship between manufacturer markup and efficiency. DOE’s analysis showed

that on the industry

- wide scale, the lowest efficiency models tend to garner lower markups than higher efficiency models, up to a certain efficiency level. Beyond the 25 th percentile of efficiency, the relationship flattens out, and there is no correlation between increasing efficiency and markup. The data suggests that this relationship is a result of certain manufacturers positioning themselves with more or less efficient product portfolios, and charging markups that are commensurate with their position in the ma rketplace. The data also suggests that the market

does not val ue efficiency beyond the first 25 th percentile . M anufacturers are in consensus that efficiency is not currently the primary selling point or cost driver for the majority of pumps

within the sco pe of the proposed rule. Rather, other factors such as reliability may influence price significantly and are known to be more influential in the purchaser’s decision making

process.

DOE notes that the development of the markup

- efficiency relationship was b ased only on data from the Inline equipment class. Sufficient data did not exist to directly analyze this relationship for the ESCC, ESFM, and VTS equipment classes. DOE, with support of the working group, believes that the markup structure of the Inline equipment class is representative of the remaining equipment classes. As such, DOE applied the Inline markup

- efficiency relationship to the remaining equipment classes. The base case markups are presented in Table

12.4

. 11

.

Table 12.4

. 11 Base Case Markup at Each Efficiency Level

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base C ase PER 10 PER 25 PER 40 PER 55 PER 70

ESCC 1.368 1.380 1.387 1.387 1.387 1.387

ESFM 1.330 1.368 1.380 1.387 1.387 1.387

IL 1.425 1.462 1.472 1.472 1.472 1.472

VTS 1.369 1.372 1.397 1.397 1.397 1.397

Under the flat markup scenario, DOE maintains the same markups in the standards case

as in the base case. Markups in the base case and in the Flat Markup scenario are identical.

There is no price change for a given model, even if the model gets redesigned under an energy

conservation standard

. Because this pricing scenario assumes that manufacturers would no t increase their pricing as a result of standards, even as they incur conversion costs, this scenario is

considered a lower bound for revenues.

12

- 1612.4.9.2 Cost Recovery Scenario

In the cost recovery pricing scenario, manufacturer pricing is set so that manufacturers recover their conversion costs over the analysis period. This cost recovery is enabled by an

increase in mark

- up, which results i n higher sales prices for pumps even as manufacturer production costs stay the same. The cost recovery calculation assumes man ufacturers raise prices on models where a redesign is necessitated by the standard. The additional revenue due to the increase in markup results in manufacturers recovering 100 percent of their conversion costs

over the 30

- year analysis period, taking into account the time

- value of money.

The first input into the cost recovery markup scenario calculation is the total conversion costs at each efficiency level for each product class. These figures, derived from the engineering analysis, are listed in Table 12.4

. 12 :

Table 12.4

. 12 Total Industry Conversion Costs at Each Efficiency Level

All Values in Millions

of Dollars EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base C ase PER 10 PER 25 PER 40 PER 55 PER 70

ESCC $0 $ 9.9 $ 39.3 $88.0 $167.4 $ 274.3

ESFM $0 $2.5 $ 10.1 $ 22.6 $ 43.0 $ 70.5

IL $0 $5.1 $ 20.0 $ 45.3 $ 88.2 $ 144.0

VTS $0 $2.5 $9.3 $19.2 $ 37.8 $ 61.3

The recovery of these conversion costs was treated as an annuity payment. DOE used an annuity calculation to determine the additional annual revenue need ed by the industry to recover the full industry conversion cost over the 30

- year analysis perio d, taking into account the time

value of money. The annuity formula used in this calculation was the following:

In the above formula , the present value of the annuity payments “PV(A)” was set to the

conversion cost values in Table 12.4

. 12 for each equipment class; the interest rate “i” was set to 11.8% (as explained in section 12.4.4 ); and the number of payment periods “n” was set at 30

years

. By solving for the value of the annuity payment “A” , DOE was able to determine the additional annual revenue required to recover the upfront conversion cost investments. T he resulting annuity payments were:

Table 12.4

. 13 Cost Recovery Annuity Payment at Each Efficiency Level

All Values in Millions

of Dollars EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base C ase PER 10 PER 25 PER 40 PER 55 PER 70

ESCC $ 0 $1.2 $4.8 $10.8 $20.5 $33.5

ESFM $ 0 $0.3 $1.2 $2.8 $5.3 $8.6

IL $ 0 $0.6 $2.4 $5.5 $10.8 $17.6

VTS $ 0 $0.3 $1.1 $2.3 $4.6 $7.5

To generate this additional revenue, DOE modeled the scenario in which manufacturers raise prices on redesigned products. By evaluating the number of shipment that fail and thus requiring redesign , DOE was able to determine a per

- unit price adder that wou ld achieve the

12

- 17necessary cost recovery revenue. Table 12.4

. 14 displays the additional MSP per unit requiring redesign:

Table 12.4

. 14 Additional MSP P er M odel R equiring a R edesign at Each Efficiency Level

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base C ase PER 10 PER 25 PER 40 PER 55 PER 70

ESCC $0.0 $97.9 $155.3 $183.4 $279.1 $387.3

ESFM $0.0 $97.9 $155.3 $183.4 $279.1 $387.3

IL $0.0 $70.7 $144.3 $253.9 $400.9 $546.5

VTS $0.0 $38.4 $24.5 $45.8 $81.7 $121.4

Finally when taking the original product MSP, adding in the additional product MSP and dividing that total by the MPC you get the markups associated with the cost recovery markup

scenario. Because this markup scenario models the maximum level to which manu facturers would increase their pricing as a result of standard, this markup scenario is considered an upper bound to markups. Table 12.4

. 15 represents the standards ca se cost recovery markups:

Table 12.4

. 15 Standards Case Cost Recovery Markups at Each Efficiency Level

EL0 EL1 EL 2 EL 3 EL 4 EL 5

Base C ase PER 10 PER 25 PER 40 PER 55 PER 70

ESCC 1.368 1.567 1.684 1.738 1.921 2.128

ESFM 1.330 1.447 1.505 1.535 1.612 1.699

IL 1.425 1.533 1.617 1.728 1.876 2.023

VTS 1.369 1.491 1.473 1.538 1.649 1.772

Depending on equipment class and the standard level being analyzed, the cost

- recovery markup results in simple payback period of 7 to 8 years for the industry. This means the total additional revenues due to a higher markup equal the industry conversion cost within 7 to 8 years, not taking into account the time value of money. The simple payback period varies at each

TSL due to differences in the number of models requiring redesign, the total conversion costs, and the number of unit over which costs can be recouped. The simple payback timeframes are as

follows:

Table 12.4

. 16 Simple Payback Per iod for Industry Conversion Costs

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Years 0 8 7 7 7 7

The payback period is greatest at TSL 1 due to the relatively high numbers of models that require redesign as compared to the number of units sold at that level (PER 10).

12.5 INDUSTRY FINANCIAL I MPACTS

Using the inputs and scenarios described in the previous sections, the GRIM estimated indicators of financial impacts on the pumps industr y . The following sections detail addit ional inputs and assumptions for the analysis of pumps

. The main results of the MIA are also reported in this section. The MIA consists of two key financial metrics: INPV and annual cash flows.

12

- 1812.5.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the economic

im pacts of different TSLs in the standards case. The INPV is different from DOE’s NPV, which is applied to the U.S. economy. The INPV is the sum of all net cash flows discounted at the industry’s cost of capital or discount rate. The GRIM for this rulemaking estimates cash flows

from 201 5 to 2049

. This timeframe models both the short

- term impacts on the industry from the

announcement of the standard until the compliance date as well as a long

- term assessment over the 30 year analysis period used in the NIA.

I n the MIA, DOE compares the INPV of the base case (no energy conservation standards) to that of each TSL in the standards case. The difference between the base case and a standards case INPV is an estimate of the economic impacts that implementing that par ticular TSL would have on the industry. The markup scenarios are described in greater detail in Table

12.4

. 11 and Table 12.4

. 15

.

While INPV is useful for evaluating the long

- term effects of new energy conservation standards, short

- term changes in cash flow are also important indicators of the industry’s

financial situation. For example, a large investment over 1 or 2 years could strain the industry’s access to capital. Consequently, the sharp drop in financial performance could cause investors to flee, even though recovery may be possible. Thus, a short

- term disturbance can have long

- term effects that the INPV cannot capture. To get an idea of the behavior of annual net cash flows, Figure 12.5.1 through F igure 12.5

. 2 present the annual net cash flows over the analysis period

.

Annual cash flows are d iscounted to the base year, 2015 . After the standards announcement date ( i.e. , the publicat ion date of the final rule), industry cash flows begin to decline as companies use their financial resources to prepare for the new energy conservation standard. Cash flows between the announcement date and the compliance date are driven by the

level of co nversion costs and the proportion of these investments spent every year. The more stringent the energy conservation standard, the greater the impact on industry cash flows in the years leading up to the compliance date, as product conversion costs lower ca sh inflows from operations and capital conversion costs increase cash outflows for capital expenditures.

Free cash flow c in the year the energy conservation standards take effect is driven by two competing factors. In addition to capital and product conve rsion costs, new energy conservation standards could create stranded assets, i.e. tooling and equipment that would have enjoyed longer use if the energy conservation standard had not made them obsolete. In this year, manufacturers write down the remaining book value of existing tooling and equipment whose value is affected by the new e nergy conservation standard. This one time write down acts as a tax shield that alleviates decreases in cash flow from operations in the year of the write

- down. In this year, there is also an increase in working capital that reduces cash flow from operatio ns. A large increase in working capital is needed due to more costly production components and materials, higher inventory carrying to sell more expensive products, and higher accounts receivable for more

c Free cash flow (FCF) is a metric commonly used in financial valuation. DOE calculates this value by adding back depreciation to net operating profit after tax and subtracting increases in working capital and capital expenditures.

12

- 19expensive products. Depending on these two competin g factors, cash flow can either be positively or negatively affected in the year the standard takes effect.

12.5.2 P umps Industry Financial Impacts

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for pumps products for the two

scenarios. Figure 12.5.1 and Figure 12.5

. 2 present the net annual cash flows for the two

scenarios.

Table 12.5

. 1 Flat Markup Scenario Changes in INPV for P umps

Units Base

Case Trial St andard Level

1 2 3 4 5

INPV $2013 M 121.4 111.6 81.9 22.4 ( 85 ) ( 228.4 )

Change in INPV $2013 M

- ( 9.8 ) ( 39.5 ) ( 99 ) ( 206.3 ) ( 349.8 )
(%)

- ( 8.0 ) ( 32.5 ) ( 81.6 ) ( 170.0 ) ( 288.2 )

Table 12.5

. 2 Cost Recovery Markup Scenario Changes in INPV for P umps

Units Base

Case Trial Standard Level

1 2 3 4 5

INPV $2013 M 121.4 121.8 129.7 125.4 114.1 94.1

Change in INPV $2013 M

- 0.4 8.3 4.0 ( 7.2 ) ( 27.3 )
(%)

- 0.3 6.9 3.3 ( 6.0 ) ( 22.5 )

12

- 20Figure 12.5

. 1 Annual Industry Net Cash Flows for Pumps (Flat Markup Scenario)

($250.0) ($200.0) ($150.0) ($100.0)

($50.0)

$0.0 $50.0 $100.0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 

## Net Cash Flow for Pumps

12

- 21Figure 12.5

. 2 Annual Industry Net Cash Flows for P umps ( Cost Recovery Markup Scenario)

12.6 IMPACTS ON SMALL BUSINESS MANUFACTURERS

DOE conducted a n inquiry of the companies that could be small business manufacturers of products covered by this rulemaking. For “Pump and Pumping Equipment Manufacturing,” the Small Business Administration (SBA) has set a size threshold of 500 employees or less for an

en tity to be considered as a small business for this category. DOE used available public information to identify potential small manufacturers. DOE’s research involved industry trade association membership directories (including the Hydraulic Institute), ind ustry conference exhibitor lists, individual company and buyer guide websites, and market research tools ( e.g. , Hoovers reports) to create a list of companies that manufacture or sell products covered by this rulemaking. DOE also asked stakeholders and ind ustry representatives if they were aware of any other small manufacturers during manufacturer interviews and at previous DOE public meetings. DOE reviewed publicly

- available data and contacted select companies on its list, as necessary, to ($250.0) ($200.0) ($150.0) ($100.0)

($50.0)

$0.0 $50.0 $100.0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029

Base Case TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 

## Net Cash Flow for Pumps

12

- 22determine whethe r they met the SBA’s definition of a small business manufacturer of covered

p ump products. DOE screened out companies that did not offer products covered by this rulemaking, did not meet the definition of a “small business,” or are foreign owned and operat ed.

DOE identified 86 manufacturers of covered pump products sold in the U.S., 39 of these

manufacturers met the 500

- employee threshold defined by the SBA to qualify as a small business. Finally, DOE determined that 25 of these small manufacturers were domestic companies. DOE notes that manufacturers interviewed stated that there are potentially a large number of small pumps manufacturers that serve small regional markets. These small manufacturers are not members of HI and typically have a limited marke ting presence. The interviewed manufacturers and those that participated in the ASRAC working group were not able to provide exact names for these smaller players. Based on this information, it is possible that DOE’s list of 25 small domestic players may not be comprehensive of all small US manufacturers in the industry.

DOE was able to interview one small business manufacturer of pumps

. DOE also obtained information about small business impacts while interviewing large manufacturers. In general, DOE foun d very little information in the public domain about the role of small manufacturers in this industry. DOE was forced to make key assumptions about the market share and product offerings of small manufacturers. For its analysis , DOE estimated that small

m anufacturers accounted for approximately 36% of the total industry model offerings.

The proposed standards for pumps could cause small manufacturers to be at a disadvantage relative to large manufacturers. One way in which small manufacturers could be at a disadvantage is that they may be disproportionately affected by product conversion costs. Product redesign, testing, and certification costs tend to be fixed and do not scale with sales

volume. For each product model, small businesses must make investmen ts in research and development to redesign their products, but because they have lower sales volumes, they must spread these costs across fewer units. Moreover, small pump manufacturers may not have the technical expertise to perform hydraulic redesigns in

- house. These small manufacturers would need to hire outside consultants to support their re

- design efforts. This would put small manufacturers at a disadvantage relative to larger companies that have internal resources and personnel for the redesign proce ss.

T o meet the proposed standard, manufacturers may have to seek outside capital to cover expenses related to testing and product design equipment. Smaller firms typically have a higher cost of borrowing due to higher perceived risk on the part of inves tors, largely attributed to lower cash flows and lower per unit profitability. In these cases, small manufacturers may observe higher costs of debt than larger manufacturers.

At TSL 2, the level proposed in today’s notice, DOE estimates total conversion costs of $. 8 million for an average small manufacturer, compared to total conversion costs of $1.4 million for an average large manufacturer. DOE notes that it estimates a lower total conversion cost for small manufacturers because of the previous assumpt ion that small manufacturers offer fewer models than their larger competitors, which means small manufacturers would likely have fewer product models to redesign. DOE applied the same per

- model product conversion costs for both

12

- 23large and small manufacturer s. Table 12.6

. 1 shows the relative impacts of conversion costs on small manufacturers relative to large manufacturers.

Table 12.6

. 1 Impacts of Conversion Costs on an Average Small versus Large Manufacturer at the Proposed Standard Level Capital Conversion

Cost as a Percentage of Annual Capital

Expenditures Product

Conversion Cost as a Percentage of

Annual R&D Expense Tot al

Conversion Cost as a Percentage

of Annual

Revenue Total

Conversion

Cost as a Percentage of

Annual EBIT Average

Large

Manufacturer 303 % 1 579 % 32% 582 %

Average

Small

Manufacturer 3 74 % 10 13 % 25 % 464 %

The total conversion costs are approximately 2 5 % of revenue and 4 64 % of earnings before interest and tax (EBIT) for a small manufacturer. For large manufacturers, the total conversion costs are approximately 32% of revenue and 5 82 % of EBIT. These initial findings

indicate that small manufacturers would not be disproportionately impacted by conversion costs relative to larger competitors.

However, a s noted in section V.B.2.a of the NOPR , the GRIM free cash flow results in

2019 indicated that some manufacturers may need to access the capital markets in order to fund conversion costs directly related to the proposed standard. Given that small manufacturers have greater difficulty securing outside capital d and that the necessary conversion costs are not insignificant to the size of a small business, it is possible the small manufacturers will be forced to retire a greater portion of product models than large competitors

. Also, s maller companies

often have a higher cost of borrowing due to higher risk on the part of in vestors, largely attributed to lower cash flows and lower per unit profitability. In these cases, small manufacturers may observe higher costs of debt than larger manufacturers.

Though conversion costs are similar in magnitude for small and large manufac turers

, small manufacturers may not have the same resources to make the required conversions. For example, some small pump manufacturers may not have the technical expertise to perform hydraulic redesigns in

- house. These small manufacturers would need to hire outside consultants to support their re

- design efforts. This could be a disadvantage relative to companies that have internal resources and personnel for the redesign process.

12.7 OTHER IMPACTS

d Simon, Ruth, and Angus Loten, "Small

- Business Lending Is Slow to Recover," Wall Street Journal , August 14, 2014. Accessed August 2014, available at http://online.wsj.com/articles/small

- business

- lending

- is

- slow

- to

- recover

1408329562

.

12

- 2412.7.1 Employment

12.7.1.1 Methodology
To quantitatively assess the impact s of energy conservation standards on employment, DOE used the GRIM to estimate the domestic labor expenditures and number of employees in

the bas e case and at each TSL from 2015 through 2049 . DOE used statistical data f rom the U.S.

Census Bureau’s 2011 An nual Survey of Manufacturers (ASM) , the results of the engineering analysis, and interviews with manufacturers to determine the inputs necessary to calculate industry

- wide labor expenditures and domestic employment levels. Labor expenditures related to

man ufacturing of the product are a function of the labor intensity of the product, the sales volume, and an assumption that wages remain fixed in real terms over time. The total labor expenditures in each year are calculated by multiplying the MPCs by the lab or percentage of

MPCs.

The total labor expenditures in the GRIM were then converted to domestic production employment levels by dividing production labor expenditures by the annual payment per production worker (production worker hours times the labor ra te found in the U.S. Census

Bureau’s 2011 ASM ). The estimates of production workers in this section cover workers, including line

- supervisors who are directly involved in fabricating and assembling a product

within the original equipment manufacturer ( OEM ) facility. Workers performing services that are closely associated with production operations, such as materials handling tasks using forklifts, are also included as production labor. DOE’s estimates only account for production

workers who manufacture the specific products covered by this rulemaking.

12.7.1.2 Direct Employment Impacts
Using the GRIM, DOE estimates in the absence of n ew energy conservation standard

, there would be 415 domestic production workers for pumps in 20 20

.

Table 12.7.1 show the range of the impacts of potential new energy conservation standards on U.S. production workers in the pumps industry

.

Table 12.7

. 1 Potential Changes in Total Domestic Production Workers for Pumps in 2020

TSL 1 2 3 4 5

Potential Changes in Domestic

Production Workers 20 20 * - 41

to
0 - 104

to
0 - 166

to
0 - 228

to
0 - 290

to
0 *DOE presents a range of potential employment impacts.

The employment impacts shown in Table 12.7.1 represent the potential production employment cha nges that could result following the compliance date of new energy conservation standards. The upper end of the results in the table estimates the maximum increase in the number of production workers after the implementation of new energy conservation stan dards and it assumes that manufacturers would continue to produce the same scope of covered products

within the United States.

12

- 2512.7.2 Production Capacity

Based on the engineering analysis, DOE believes that higher efficiency pumps require similar production faci lities, tooling, and labor as baseline efficiency pumps. Based on the engineering analysis and interview with manufacturers, DOE does not believe a new energy conservation standard would create production capacity constraints. However, industry

representat ives in interviews and in the CIP working group forum expressed concern about the industry’s ability to complete the necessary number of hydraulic redesigns required to comply with a new standard. In the industry, not all companies have the in

- house capaci ty to redesign pumps. Many companies rely on outside consultants for a portion or all of their hydraulic design projects. Manufacturers were concerned that a new standard above the level selected in the consensus by the CIP working group would create more demand for hydraulic design technical resources than are available in the industry.

12.7.3 Labeling

The costs of updating marketing materials for pumps that must be redesigned to meet the standard were included in the conversion costs for the industry and are accounted for in the industry cash

- flow analysis results and industry valuation figures in section 12.5.2

. For pumps that do not need to be redesigned to meet the standard, DOE estimates that the costs of updating marketing materials to include the labeling requirements would be up to $ 3750 per pump model

. e In the absence of a standard, this would result in additional cost to the industry of approximately $13 million. DOE estimates that the investment could result in a loss of INPV compared to a base case with no labeling requirement of up to approxi mately 5%. For TSL 2 , the additional cost to industry for updating marketing materials for pumps that do not have to be redesigned would be approximately $10 million. DOE estimates that the investment could result in an additional loss of INPV compared to a base case with no labeling requirement of up to approximately 4% beyond that estimated for a standard at TSL 2

. f

12.7.4 Cumulative Regulatory Burden

While any one regulation may not impose a significant burden on manufacturers, the

combined effects of recent or impending regulations may have serious consequences for some

e HI estimated the average cost for updating marketing (literature, data sheets, curves, pump selection tools, sales

tr aining, compliance documentation, etc.) for a hydraulic redesign to range from $32,000 for a 1

- hp model to $27,000 for a 200

- hp model. DOE assumed $30,000 on average. The marketing costs provided by HI were for developing new materials for redesigned pump models. For this exercise only literature and data sheets are relevant, which DOE estimated would represent half of the marketing costs. In addition, in this case, DOE is estimating the incremental cost for making a few additions to literature rather than complete design of new materials. DOE assumed these additions would cost only 25% or less of full material development.

f Approximately 3500 models are in the scope of this rulemaking. In the absence of the standard, none of these

models would have to b e redesigned and would thus incur $3750 each in costs for updating marketing materials. At TSL 2, 25% of pump models would have to be redesigned, and creating new marketing materials for these pumps is already accounted for in the MIA. The 75% of pump mode ls that do not have to be redesigned would incur $3750

each.

12

- 26manufacturers, groups of manufacturers, or an entire industry. Assessing the impact of a single regulation may overlook this cumulative regulatory burden. In addition to energy conserva tion standards, other regulations can significantly affect manufacturers’ financial operations. Multiple regulations affecting the same manufacturer can strain profits and lead companies to abandon product lines or markets with lower expected future return s than competing products. For these reasons, DOE conducts an analysis of cumulative regulatory burden as part of its rulemakings pertaining to appliance efficiency.

Companies that produce a wide range of regulated products may be faced with more capital and product development expenditures than competitors with a narrower scope of products. Regulatory burdens can prompt companies to exit the market or reduce their product offerings, potentially reducing competition. Smaller companies in particular can be affected by regulatory costs since these companies have lower sales volumes over which they can amortize the costs of meeting new regulations. A proposed standard is not economically justified if it contributes to an unacceptable level of cumulative regula tory burden.

For the cumulative regulatory burden analysis, DOE looks at product

- specific Federal regulations that could affect pumps manufacturers and that take effect approximately three years

before or after the 2020 compliance date of standard proposed in this notice. The Department was not able to identify any additional regulatory burdens that met these criteria.

12.8 CONCLUSION
The following sections summarize the impacts for the scenarios DOE believes are most likely to capture the range of impac ts on pumps manufacturers as a result of energy conservation standards. DOE also notes that while these scenarios bound the range of most plausible impacts on manufacturers, there potentially could be circumstances which cause manufacturers to experience i mpacts outside of this range. The TSLs are defined as follows:

Table 12.8

. 1 Trial Standard Levels for Pumps

Equipment Class Baseline TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

ESFM EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

IL EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

VTS EL 0 EL 1 EL 2 EL 3 EL 4 EL 5

RSV * EL 0 EL 0 EL 0 EL 0 EL 0 EL 5

*Equipment classes not analyzed due to lack of available data (in the case of RSV ).

12.8.1 Conclusions for P umps

Table 12.8

. 2 Manufacturer Impact Analysis Results for P umps

Units Base

Case Trial Standard Level

1 2 3 4 5

INPV 2013 $M 121.4 111.6 to

121.8 81.9 to

129.7 22.4 to

125.4 ( 85.0 ) to
114.1 ( 228.4 ) to
94.1

12

- 27Change in INPV 2013 $M

- ( 9.8 ) to
0.4 ( 39.5 )

to 8.3 ( 99 .0 )

to 4 .0 ( 206.3 ) to ( 7.2 ) ( 349.8 ) to ( 27.3 )

%

- ( 8 .0 ) to
0.3 ( 32.5 )

to 6.9 ( 81.6 )

to 3.3 ( 170 .0 ) to ( 6 .0 ) ( 288.2 ) to ( 22.5 )

FCF (2014) 201 3 $M 12.2 5.6 ( 16.1 ) ( 58.7 ) ( 130.1 ) ( 224.4 )

Change in FCF (2014) 2013 $M

- ( 6.6 ) ( 28.3 ) ( 70.9 ) ( 142.2 ) ( 236.6 )

%

- ( 54.3 ) ( 232.5 ) ( 582 .0 ) ( 1167.5 ) ( 1942.4 )

Conversion Costs 2013 $M

- 19.9 78.4 174.3 335 .0 547.7

At TSL 1, DOE models the impacts on pumps INPV under both mark

- up scenarios. The change in INPV ranges from

- $ 9. 8 million to $. 4 million, or a change in INPV of

- 8.0 percent to

. 3 percent. At this level, the pumps industry free cash flow is estima ted to decrease by as much as $6.6 million, or

- 5 4

. 3 percent compared to the ba se

- case value of $ 12.2 million in 2019 , the year before the compliance date. At this level, DOE esti mates conversion costs to be $19. 9 million for the industry.

At TSL 2, the change in INPV ranges from

- $39

. 5 million to $8. 3 million, or a change in

INPV of

- 32. 5 percent to 6. 9 percent. At this level, the pumps industry free cash flow is estimated to decrease by as much as $28

. 3 million, or

- 2 32

. 5 percent compared to the base

- case value of $ 12.2 million in 2019. DOE estimates conversion costs to be $7 8

. 4 million for the industry.

At TSL 3, DOE estimates impacts on panel INPV to range from

- $ 9 9 million to $ 4 million, or a change in INPV of

- 8 1

. 6 percent to 3. 3 percent. At this level, the pumps industry

free cash flow is estimat ed to decrease by as much as $70

. 9 million, or

- 5 82 % compared to the

base

- case value of $ 12.2 million in the year before the compliance date. The large percentage drop in cash flow in the GRIM indicates that conversion costs are high relative to the size o f the industry a nd relative to annual operating profits. Conversion costs are expected to total $ 17 4

. 3

million.

At TSL 4, the change in INPV ranges from

- $20 6 .3 million to

- $7. 2 million, or a change

in INPV of

- 1 70 percent to

- 6 percent. At this level the pumps industry free cash flow is estimated to decrease by as much as $14 2

. 2 , or

- 11 67

. 5 % compared to the base

- case value of $12.2 million in the year before the compliance date. Conversion costs are expected to total $33 5

millio n.

DOE estimates TSL 5 impacts pumps INPV to be range from

- 34 9

. 8 million to

- 2 7

. 3 million, or a change in INPV of

- 28 8

. 2 percent to

- 2 2

. 5 percent. At this level, the pumps industry free cash flow is estimated to decrease by as much as $ 2 36

. 6 million, or

- 19 42

. 4 percent compared to the base

- case value of $ 12.2 million in the year before the compliance date. At this TSL, conversion costs total $54 7 .7 million for the industry.

12

- 28REFERENCES

1 Securities and Exchange Commission, Annual 10

- K Reports, Various Years, Washington DC. < www.sec.gov >.

2 Standard and Poors Financial Services LLC, Company Credit Ratings, Various Companies, New York, NY. < www2.standardandpoors.com >.

3 Annual Survey of Manufacturing: 2011. American FactFinder2. 2012. Bureau of the

Census.

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ASM_2011_ 31GS101&prodType=table >.

4 The size standards are available on the SBA’s website at

< www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf >

.

5 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies , 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

12

- 29APPENDIX 12A. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12A.1 INTRODUCTION AND PUR POSE

................................ ................................ .............. A

- 1

12A.2 MODEL DESCRIPTION

................................ ................................ ............................... A

- 1

LIST OF TABLES

Table 12A.1 Detailed Cash Flow Example

................................ ................................ ................. A

- 4

12 A

- iAPPENDIX 12A

. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

12A.1 INTRODUCTION AND PUR POSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the impacts of energy conservatio n standards and other regulations on manufacturers. The basic mode of analysis is to estimate the change in the value of the industry or manufacturers(s) following a regulation or a series of regulations. The model structure also allows an analysis of

mult iple equipment types with regulations taking effect over a period of time, and of multiple regulations on the same equipment

.

Industry net present value is defined, for the purpose of this analysis, as the discounted sum of industry free cash flows plus a discounted terminal value. The model calculates the actual cash flows by year and then determines the present value of those cash flows both without an energy conservation standard ( i.e. , the base case) and under different trial standard levels ( i.e.

, the standards case).

Output s from the model consist of summary financial metrics, graphs of major variables, and, when appropriate, access to the complete cash flow calculation.

12A.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow a nalysis that uses manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters as inputs and accepts a set of regulatory conditions as changes in costs and investments. The cash flow analysis is separated into two major blocks: income and cash flow. The income calculation determines net operating profit after taxes. The cash flow calculation converts net operating profit after taxes into an annual cash flow by including investment and non

- cash items. Below

are definitions of listed items on the printout of the output sheet of the GRIM

.

1) Revenues: Annual revenues

– computed by multiplying equipment unit prices at each efficiency level by the appropriate manufacturer markup

.

2) Total Shipments : Total annual shipments for the ind ustry were obtained from the National Impact Analysis Spreadsheet.

3) Material: The portion of cost of goods sold (COGS) that includes materials.

4) Labor: The portion of COGS that includes direct labor, commissions, dismissal pay, bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor

up

- time

.

5) Depreciation: The portion of overhead that includes an allowance for the total

amount of fixed assets used to produce that one unit. Annual depreciation is computed as a percentage of COGS. D epreciation is broken out from overhead as a separate line item

.

12 A

- 16) Overhead: The portion of COGS that includes indirect labor, indirect material, energy use, maintenanc e, depreciation, property taxes, and insurance related to assets.

D epreciation is broken out from overhead as a separate line item

.

7) Standard SG&A: Selling, general, and administrative costs are computed as a percentage of Revenues (1 )

.

8) R&D: GRIM separately accounts for ordinary research and development (R&D) as a percentage of Revenues (1 )

.

9) Product Conversion Costs : Product conversion costs are one

- time investments in research, development, testing, marketing, and other costs focused on making equipment des igns comply with the new energy conservation standard. The GRIM allocates these costs over the period between the standard’s announcement and compliance dates

.

10) Stranded Assets: In the year the st andard becomes effective, a one

- time write

- off of

stranded assets is accounted for

.

11) Earnings Before Interest and Taxes (EBIT): Includes profits before deductions for interest paid and taxes

.

12) Per Unit EBIT ($/unit): GRIM calculates Per Unit EBIT as EBIT (11) divided by Shipments ( 2 ).

13) EBIT as a Percentage of Sales (EBIT/Revenues) : GRIM calculates EBIT as a percentage of sales to compare with the industry’s average reported in financial

statements

.

14) Taxes: Taxes on EBIT (11) are calculated by multiplying the tax rate contained in Major Assumptions by EBIT (11)

.

15) Net O perating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods Sold ((3) to (6))

, SG&A ( 7 )

, R&D ( 8

)

, Product Conversion Costs (9 ) , and Taxes (14 ) from Revenues ( 1 )

.

16) NOPAT repeated : NOPAT is repeated in the Statement of Cash Flows

.

17) Depreciation repeated : Depreciation is added back in the Statement of Cash Flows

because it is a non

- cash expense

.

18) Loss on Disposal of Stranded Assets repeated : Stranded Assets are added back in the Statement of Cash Flows because they are non

- cash expenses.

19) Change in Working Capital : Change in cash tied up in accounts receivable, inventory, and other cash investments necessary to support operations is calculated by multiplying working capital (as a percentage of revenues) by the change in annual

revenues.

12 A

- 220) Cash Flow fr om Operations : Calculated by taking NOPAT (15) , adding back non

cash items such as a Depreciation (17 ) and Stranded Assets (18) , and subtracting the Change in Working Capital (19 )

.

21) Ordinary Capital Expenditures: Ordinary investments in property, plant, and equipment to maintain and replace existing production assets, computed as a percentage of Revenues (1 )

.

22) Capital Conversion Costs : Capital conversion costs are one

- time investments in property, plant, and equipment to adapt or change existing production fa cilities so that

new equipment designs can be fabricated and ass embled under the new regulation. The GRIM allocates these costs over the period between the standard’s announcement and compliance dates

.

23) Capital Investment: Total investments in property, pla nt, and equipment are computed by adding Ordinary Capital Expenditures ( 21 ) and Capital Conversion

Costs ( 22 )

.

24) Free Cash Flow: Annual cash flow from operations and investments; computed by subtracting Capital Investment (2 3 ) from Cash Flow from Operations ( 20 )

.

25) Terminal Value: Estimate of the continuing value of the industry after the analysis period. Computed by growing the Free Cash Flow at a constant rate in perpetuity

.

26) Present Value Factor: Factor used to calculate an estimate of the present value of an

amount to be received in the future

.

27) Discounted Cash Flow: Free Cash Flows (2 4 ) multiplied by the Present Value Factor (2 6 ) . For the end of 2048 , the discounted cash flow includes the discoun ted Terminal Value (2 5 )

.

28) Industry Value thru the end of 2048 : The sum of Discounted Cash Flows (2 7 )

.

12 A

- 3Table 12 A

. 1 Detailed Cash Flow Example

12 A

- 4CHAPTER 13. EMISSIONS IMPACT ANA LYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION

................................ ................................ ................................ ......... 13

- 1

13.2 AIR QUALITY REGULATI ONS AND EMISSIONS IM PACTS

............................... 13

- 2

13.3 POWER SECTOR AND SIT E EMISSIONS FACTORS

................................ ............. 13

- 3

13.4 UPSTREAM FACTORS

................................ ................................ ............................... 13

- 4

13.5 EMISSIONS IMPACT RES ULTS

................................ ................................ ................ 13

- 5

REFERENCES

................................ ................................ ................................ .......................... 13

- 1

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors

................................ ................................ .... 13

- 4

Table 13.4.1 Electricity Upstream Emissions Factors

................................ ...................... 13

- 5

Table 13.5.1 Cumulative Emissions Reduction from Potential Standards for Commercial and Industrial Pumps

................................ ............................... 13

- 5

LIST OF FIGURES

Figure 13.5.1 CO 2 Total Emissions Reduction

................................ ................................ .. 13

- 6 Figure 13.5.2 SO 2 Total Emissions Reduction

................................ ................................ ... 13

- 6 Figure 13.5.3 NO

x Total Emissions Reduction

................................ ................................ .. 13

- 7 Figure 13.5.4 Hg Total Emissions Reduction

................................ ................................ .... 13

- 7 Figure 13.5.5 N 2 O Total Emissions Reduction

................................ ................................ .. 13

- 8 Figure 13.5.6 CH 4 Total Emissions Reduction

................................ ................................ .. 13

- 8

13

- iCHAPTER 13. EMISSION S IMPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the effect of potential energy conservation standards on power sector and site combustion emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ) and me rc ury (Hg). The second component estimates the impacts of a potential standard on emissions of tw o additional greenhouse gases, 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 production 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 full

fuel

- cycle ( FFC)

, i n accordance with DOE’s FFC Statement of Policy

. 76 FR 51282 (Aug. 18,

2011 )

.

The analysis of power sector emissions uses marginal emissions intensity factors calculated by DOE

. As of 2014, DOE is using a new methodology based on results published for

the Annual Energy Outlook 2014 ( AEO 2014 ) reference case and a set of side cases that implement a variety of efficiency

- related policies

. 1 The new methodology is described in c hapter 15 and in the report “Utility Sector Impacts of Reduced Electricity Demand” ( Coughlin

, 2014). 2

Site emissions of CO 2 and NO X are estimated using emissions intensity factors from a publication of the Environmental Protection Agency (EP A)

. 3

Combustion emissions of CH 4 and N 2 O are estimated using emissions intensity factors published by the EPA , GHG Emissions Factors Hub. a The FFC upstream emiss ions a re
estimated based on the methodolo gy developed by Coughlin (2013). 4 The upstream emissions include both emissions from fuel combustion during extraction, processing and transportation of fuel, and “fugitive” emissions (direct leakage to the atmosphere) of CH 4 and CO 2

.

The emissions intensity facto rs are expressed in terms of physical units per MWh or MMBtu of site energy savings. Total emissions reductions are estimated using the energy savings calculated in the national impact analysis (chapter 10).

For CH 4 and N 2 O, DOE also presents results in terms of units of carbon dioxide equivalent (CO 2 e). Gases are converted to CO 2 e by multiplying the physical units by the gas global warming potential (GWP) over a 100 year time horizon. Based on the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 5 DOE used GWP values of 28 for

CH 4 and 265 for N 2 O. b

a See EPA GHG Emission Factors Hub www.epa.gov/climateleadership/inventory/ghg

- emissions.html

. b The values are without inclusion of climate

- carbon feedbacks in response to emissions of the indicated non

- CO 2
gases.

13

- 113.2 AIR QUALITY REGULATI ONS AND EMISSIONS IMPACTS

Each annual version of the AEO incorporates the projected impacts of existing air quality regulations on emissions. AEO 2014 generally represents current Federal and State legislation and final implementation regulations in place as of t he end of October 2013.

SO 2 emissions from affected electric generating units (EGUs) are subject to nationwide and regional emissions cap and trad e programs. Title IV of the Clean Air Act sets an annual emissions cap on SO 2 for affected EGUs in t he 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 that operates along with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C. Circuit) but parts of it remained in effect. On July 6, 2011 EPA issued a replacemen t for CAIR,

the Cross

- State Air Pollution Rule (CSAPR). 76 FR 48208 (August 8, 2011 ). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation, LP

v. EPA , 696 F.3d 7, 38 (D.C. Cir. 2012). The court ordered EPA t o continue administering

CAIR. The AEO 201 4 emissions factors used for the present analysis assume that CAIR remains a binding regulation through 2040. c

The attainment of emissions caps is typically flexible among affected Electric Generating Units (EGU s) 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 imposition of an efficiency standard could be use d 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 201 6 , 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). 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 equivalent surrogate standard for acid gas HAP. Th e same controls are used to reduce HAP and

non

- HAP acid gas; thus, SO 2 emissions will be reduced as a result of the control technologies

installed on coal

- fired power plants to comply with the MATS requirements for acid gas. AEO

c On April 29, 2014, the U.S. Supreme Court reversed the judgment of the D.C. Circuit and remanded the case for further proceedings consi stent with the Supreme Court's opinion. The Supreme Court held in part that EPA's methodology for quantifying emissions that must be eliminated in certain states due to their impacts in other downwind states was based on a permissible, workable, and equita ble interpretation of the Clean Air Act provision that provides statutory authority for CSAPR. See EPA v. EME Homer City Generation , No 12

- 1182, slip op. at 32 (U.S. April 29, 2014). Because DOE is using emissions factors based on AEO 2014 , the analysis as sumes that CAIR, not CSAPR, is the regulation in force. The difference between CAIR and CSAPR is not relevant for the purpose of DOE's analysis of SO 2 emissions.

13

- 2201 4 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 201 6 . Both technologies, which are used to reduce acid gas emissions, also reduce SO 2 emissions. Under the MATS, e missions will

be f ar below the cap established by CAIR, so it is unlikely that excess SO 2 emissions allowances resulting from the lower electricity demand would be needed or used to permit offsetting

increases in SO 2 emissions by any regulated EGU. Therefore, DOE believes t hat efficiency

standards will reduce SO 2 emissions in 201 6 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 demand could be used to permit offsetting increases in NO

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

x emissions in t he States not affected by CAIR, so DOE estimated NOx emissions reductions from potential standards for those States.

The MATS limit mercury emissions from power plants, but they do not include emissions caps and, as such, DOE’s energy conservation standar ds would likely reduce Hg emissions. DOE estimated mercury emissions reductions using emissions factors based on AEO

2014 , which incorporates the MATS.

13.3 POWER SECTOR AND SIT E EMISSIONS FACTORS

The analysis of power sector emissions uses marginal emissions intensity factors derived

from analysis of the AEO 2014 reference and a number of side cases incorporating enhanced equipment efficiencies. To model the impact of a standard, DOE calculates factors that relate a unit reduction to annual site electricity d emand for a given end use to corresponding reductions

to installed capacity by fuel type , fuel use for generation, and power sector emissions. Details on the approach use d may be found in Coughlin ( 2014 ).

Table 13.3

. 1 present s 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 supply electricity to commercial buildings

. The average factors for each year take into account

the projected shares of each of the source s in total electricity generation

.

13

- 3Table 13.3

. 1 Power Plant Emissions Factors

Unit* 202 0 2025 2030 2035 2040

CO 2 kg/MWh 710 632 571 522 478

SO 2 g/MWh 688 537 452 379 338

NOx g/MWh 557 466 408 360 326

Hg g/MWh 0.00212 0.00166 0.00139 0.00117 0.00104

N 2 O g/MWh 7.2 7.1 6.9 6.6 6.4

CH 4 g/MWh 50 49 48 46 45

* Refers to site electricity savings

.

13.4 UPSTREAM FACTORS

The upstream emissions accounting uses the same approach as the upstream energy accounting described in A ppendix 10 B

. See also Coughlin (201 3 ) and Coughlin (2014). When demand for a particular fuel is reduced, there is a corre sponding 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 t o 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 emi ssions. They comprise about 2.5 percent of total CO 2 em issions for natural gas and 1.7 percent for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal

p roduction. 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 emis sions for natural gas, about 95 percent for co al, and 93 percent for petroleum fuels.

U pstream emissions factors account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primary fuels. Fugitive emissions factors for methane from coal mining and natura l gas production were estimated based on a review of recent studies compiled by Burnham (2011). 6 This review includes estimates of the difference between fugitive emissions factors for conventional production of natural vs. unconventional (shale or tight gas). These estimates rely in turn on data gathered by EPA under new GHG reporting

r equirements for the petroleum and natural gas industries. 7

, 8 As more data are made available, DOE will continue to update these estimated emissions factors.

For ease of application in its analysis, DOE developed all of the emissions factors using site (point of use) en ergy savings in the denominator. Table 13.4

. 1 presents the electricity upstream emissions factors for selected years

. These were used to estimate the emissions associated with the decreased electricity use. The caps that apply to power sector NO X emissions do not apply to upstream combustion sources.

13

- 4Table 13.4

. 1 Electricit y Upstream Emissions Factors

Unit 202 0 2025 2030 2035 2040

CO 2 kg/MWh 29.1 29.4 29.7 29.9 29.8

SO 2 g/MWh 5.0 5.1 4.9 4.7 4.6

NOx g/MWh 368 375 382 387 387

Hg g/MWh 0 0 0 0 0

N 2 O g/MWh 0.252 0.247 0.241 0.234 0.228

CH 4 g/MWh 2149 2195 2216 2248 2255

* Refers to site electricity savings

.

13.5 EMISSIONS IMPACT RES ULTS

Table 13.5

. 1 presents the estimated cumulative emissions reductions for the lifetime of products sold in 20 20

- 20 49 for each TSL. Negative values indicate that emissions increase.

Table 13. 5

. 1 Cumulative Emissions Reduction f rom Potential Standard s for Commercial and Industrial Pumps

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

Power Sector and Site Emissions

CO 2 (million metric tons) 3.2 15 31 50 72

SO 2 (thousand tons) 2.6 13 25 40 58

NO X (thousand tons) 2.5 12 23 38 55 Hg (tons) 0.008 0.039 0.077 0.124 0.180

CH 4 (thousand tons) 0.32 1.54 3.07 4.95 7.20

N 2 O (thousand tons) 0.05 0.22 0.44 0.71 1.03
Upstream Emissions

CO 2 (million metric tons) 0.19 0.91 1.81 2.93 4.26

SO 2 (thousand tons) 0.03 0.16 0.32 0.51 0.74

NO X (thousand tons) 2.7 13 26 42 61 Hg (tons) 0.0001 0.0004 0.0007 0.0011 0.0016

CH 4 (thousand tons) 16 76 151 244 354

N 2 O (thousand tons) 0.002 0.008 0.016 0.025 0.036

Total Emissions

CO 2 (million metric tons) 3.4 16 33 53 77

SO 2 (thousand tons) 2.7 13 25 41 59

NO X (thousand tons) 5.2 25 49 80 116 Hg (tons) 0.01 0.04 0.08 0.13 0.18

CH 4 (thousand tons) 16 77 154 248 362

N 2 O (thousand tons) 0.05 0.23 0.45 0.73 1.07

13

- 5Figure 13.5

. 1 through Figure 13.5

. 6 show the annual reductions for total emissions for each type of emission from each TSL. The reductions reflect the lifetime impacts of products

sold in 20 20

- 20 49

.

Figure 13. 5

. 1 CO 2 Total Emissions Reduction

Figure 13.5

. 2 SO 2 Total Emissions Reduction

13

- 6Figure 13.5

. 3 NO

x Total Emissions Reduction

Figure 13.5

. 4 Hg Total Emissions Reduction

13

- 7Figure 13.5

. 5 N 2 O Total Emissions Reduction

Figure 13.5

. 6 CH 4 Total Emissions Reduction

13

- 8REFERENCES

1. E nergy Information Administration, Annual Energy Outlook 2014 with Projections to

2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf >.

2. C oughlin, K., Utility Sector Imacts of Reduced Electricity Demand , 2014. Lawrence Berkeley National Laboratory. Report No. LBNL-6864E.

3. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors,

AP -42, Fifth Edition, Volume I: Stationary Point and Area Sources . 1998.

< www.epa.gov/ttn/chief/ap42/index.html >.

4. C oughlin, K., Projections of Full

- Fuel

- Cycle Energ y and Emissions Metrics , 2013. Lawrence Berkeley National Laboratory. Report No. LBNL -6025E.

5. I ntergovernmental Panel On Climate Change, Chapter 8 . In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, Editor. 2013. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA

6. B urnham, A., J. Han, C. E. Clark, M. Wang, J. B. Dunn, I. Palou- Rivera, Life

- Cycle Greenhouse Gas Emissions of Shale Gas, Natural Gas, Coal, and Petroleum

. Environmental Science & Technology , 2011. 46(2): pp. 619-627

7. U .S. Environmental Protection Agency, Fugitive Emissions Reporting from the Petroleum and Natural Gas Industry , 2009. U.S. Environmental Protection Agency. Washington, DC.

8. U .S. Environmental Protection Agency, Oil and Natural Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production, Transmission, and Distribution, Background Supplemental Technical Support Document for the Final New Source Performance Standards , 2012. U.S. Environmental Protection Agency. Washington, DC.

13 -1CHAPTER 14. MONETIZATION OF EMIS SION REDUCTIONS BENE FITS

TABLE 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

- 2

14.3 DEVELOPMENT OF SOCIA L COST OF CARBON VAL UES

................................ . 14

- 2

14.3.1 Current Approach and Key Assumptions

................................ .......................... 14

- 2

14.3.2 Limitations of Current Estimates

................................ ................................ ....... 14

- 4

14.4 VALUATION OF OTHER E MISSIONS REDUCTIONS

................................ ........... 14

- 5

14.5 RESULTS

................................ ................................ ................................ ...................... 14

- 5

REFERENCES

................................ ................................ ................................ .......................... 14

- 9

LIST OF TABLES

Table 14.3.1 Annual SCC Values for 2010

- 2050 from 2010 Interagency Report (in 2007$ per Metric Ton)

................................ ................................ ................. 14

- 3

Table 14.3.2 Annual SCC Values for 2010

– 2050 from 2013 Interagency Update (in

20 07$ per Metric Ton of CO 2 )

................................ ................................ ..... 14

- 4

Table 14.5.1 Estimates of Global Present Value of CO 2 Emissions Reduction under

TSLs for Commercia l and Industrial Pumps

................................ ............... 14

- 6

Table 14.5.2 Estimates of Domestic Present Value of CO 2 Emissions Reduction under TSLs for Commercial and Industrial Pumps

................................ ..... 14

- 7

Table 14.5.3 Estimates of Present Value of NO X Emissions Reduction under TSLs for Commercial and Industrial Pumps

................................ ......................... 14

- 8

14

- iCHAPTER 14. MONETIZA TION OF EMISSION RED UCTIONS BENEFITS

14.1 INTRODUCTION

As part of its assessment of the effe cts of potential energy conservation standards for commercial and industrial pumps , the U.S. Department of Energy ( DOE ) estim ated the monetary

benefits of the reduced emissions of carbon dioxide (CO 2 ) and nitrogen oxides (NO X ) that would

be expected to result from each trial standard level ( TSL ) considered for this rulemaking . This chapter summarizes the basis for the monetary values assigned to emissions and presents the

modeled benefits of e stimated reductions

.

14.2 MONETIZING CARBON DI OXIDE EMISSIONS

One challenge for anyone attempting to calculate the monetary benefits of red uced

emissions of CO 2 is what value to assign to each unit eliminated. The value must encompass a broad range of p h ysical, economic, social, and political effects. Analysts developed the concept of the social cost of carbon (SCC) to represent the broad cos t or value associated with producing

— or reducing

— a quantifiable amount of CO 2 emissions

.

14.2.1 Social Cost of Carbon

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. The SCC is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services. SCC estimates are provided in dollars per metric

ton of carbon dioxide

. A value for the domestic SCC is meant to re present the damages in the United States resulting from a unit change in carbon dioxide emissions, whereas a global SCC is meant to reflect the value of damages worldwide.

Under section 1(b) (6) of Executive Orde r 12866

, 1 agencies must, to the extent permitted by law, “assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose

of the SCC estimates required by the Executive Order is to enable agencies to incorporate the

monetized social be nefits of reducing CO 2 emissions into cost

- benefit analyses of regulatory actions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a clear understanding that they will need updating in response to increasing knowledge of the science and economics of climate impacts.

As part of the interagency process that developed the SCC estimates, technical experts from numerous agencies met regular ly to explore the technical literature in relevant fields, discuss key mod el inputs and assumptions, and consider public comments. The primary objective

of the process was to develop a range of SCC values using a defensible set of assumptions regarding model inputs that was grounded in the scientific and economic literature. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC

estimates developed for use in the rulemaking process.

14

- 114.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces several serious challenges. A report from the National Research

Council 2 points out that any a ssessment will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the effects of changes in climate on the physical and

biolo gical environment, and (4) the translation of those environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change raise s serious questions of science, economics, and ethics and should be viewed as provisional.

Despite the limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing CO 2 emissions. An agency can estimate the benefits from reduced (or costs from increased) em issions in any future year by multiplying the change in emissions in that year by the SCC values appropriate for that year. The n the net present value of the benefits can be calculated by multiplying each of the future benefits by an appropriate

discount f actor and summing across all affected years. This approach assumes that the marginal damages from increased emissions are constant for small departures from the baseline emissions path, an approximation that is reasonable for policies that have effects on emissions that are small relative to cumulative global carbon dioxide emissions.

14.3 DEVELOPMENT OF SOCIA L COST OF CARBON VAL UES

In 2009, an interagency process was initiated to develop a preliminary assessment of how best to quantify the benefits from reduci ng carbon dioxide emissions. To provide consistency in

how benefits are evaluated across Federal agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemaking process, to quantify

avoided clima te change damages from reduced CO 2 emissions. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the literature to use as interim values until a more comprehensive analysis could be conducted. The outcome of the preliminary assessment was a set of five interim values: global SCC estimates for 2007 (in

2006 $ ) of $55, $33, $19, $10, and $5 per ton of CO 2

. 3 Tho se 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 th at preliminary effort were presented in several proposed and final rules.

14.3.1 Current Approach an d Key Assumptions

After the release of the interim values, the interagency group reconvened regular ly to improve the SCC estimates. Speci fic ally, the group considered public comments and further explored the technical literature in relevant fields. The interagency group relied on three integrated assessment models commonly used to estimate the SCC . The models are known by their acronyms of FUND, DICE, and PAGE. Those three models frequently are cited in the peer

reviewed literature and were used in the m ost recent assessment of the Intergovernmental Panel on Climate Change. Each model was given equal weight in developing SCC values.

14

- 2Each model takes a slightly different approach to calculating how increases in emissions produce economic damages. A key o bjective of the interagency process was to enable a consistent exploration of the three models while respecting the different approaches taken by the key modelers in the field. An extensive review of the literature identified three sets of input

parameters for the models: climate sensitivity; socioeconomic and emissions trajectories; and

d iscount rates

. A probability distribution for climate sensitivity was specified as an input to all three models. In addition, the interagency group used a range of scenari os for the socioeconomic parameters and a range of values for the discount rate. All other model features were left unchanged, relying on the model developers’ best estimates and judgments.

The interagency group selected four SCC values for use in regula tory analyses

. Three values are based on the average SCC from the three integrated assessment models, at discount

rates of 2.5 percent , 3 percent , and 5 percent. The fourth value, which represents the 95 th
percentile of the SCC estimate across all three mo dels at a 3

- percent discount rate, is included to

represent larger

- than

- expected effects from temperature change s f a rther out in the tails of the

SCC distribution. The values increase in real terms over time

. Additionally, the interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although preference is given to consideration of the global benefits of reducing CO 2 emissions

. 0 presents the values in the 2010 interagency group

report

. 4

Table 14.3

. 1 Annual SCC Values for 2010

- 2050 from 2010 Interagency Report (in 2007 $

per Metric Ton )

Year Discount Rate ( % )

5 3 2.5 3

Average Average Average 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

The SCC values used for the analysis of the effects of po tential standards for commercial and industrial pumps were generated using the most recent versions of the three integrated assessment models that have been published in the peer

- reviewed literature, as described in the 2013 update from the interagency working group (revised November 2013). Table 14.3

. 2 shows the updated sets of SCC estimates in 5

- year increments from 2010 to 2050. The full set of annual

SCC estimates for 2010

– 2050 is presented in appendix 14 B of this TS D. The central value that emerges is the average SCC across models at a 3

- percent discount rate. To capture the

14

- 3uncertainties involved in regulatory impact analysis, however, the interagency group emphasizes the importance of including all four sets of SCC values.

Table 14.3

. 2 Annual SCC Values for 2010

– 2050 from 2013 Interagency Update (in 2007 $

per Metric Ton of CO 2 )

Year Discount Rate ( % )

5 3 2.5 3

Average Average Average 95 th Percentile

2010 11 32 51 89

2015 11 37 57 109

2020 12 43 64 128

2025 14 47 69 143

2030 16 52 75 159

2035 19 56 80 175

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

14.3.2 Limitations of Current Estimates

The interagency group recognizes that current models are imperfect and incomplete.

Because k e y uncertainties remain

, current SCC estimates should be treated as provisional and

revisable . Estimates doubtless will evolve in response to improved scientific and economic understanding. The 2009 National Research Council report points out the tension between producing quantified estimates of the economic damages from an incremental ton of carbon and

the limits of current modeling efforts. Se veral analytic challenges are being addressed by the research community, some by research programs housed in many of the Federal agencies participating in the interagency process. The interagency group intends to review and reconsider

SCC estimates periodi cally to incorporate expanding knowledge of the science and economics of climate impacts, as well as improvements in modeling.

In summary, in considering the potential global benefits resulting from reduced CO 2 emissions, DOE used the values from the 2013 interagency report , applying the GDP price

deflator to adjust the values to 2013 $. For the four SCC values, the values of emissions in 2015 were $1 2

. 0 , $ 40.5 , $6 2

. 4 , and $11 9 per metric ton avoided (values expr essed in 2013 $). DOE derived values after 2050 using the relevant growth rates for 2040

– 2050 in the interagency update.

DOE multiplied the CO 2 emissions reduction estimated for each year by the SCC value for that year under each discount rate . To calcula te a present value of the stream of monetary values, DOE discounted the values in each of the four cases using the same discount rate that had

been used to obtain the SCC values in each case.

14

- 414.4 VALUATION OF OTHER E MISSIONS REDUCTIONS

DOE also considered the potential monetary benefit s of reduced NO X emissions from the proposed standard s

. As noted in chapter 13, new or amended energy conservation standards

would reduce NO X emissions in those States that are not affected by emissions caps. DOE

estimated the mon etized value of NO X emissions reductions resulting from each TSL based on

estimates of environmental damage found in the scientific literature. E stimates suggest a wide range of monetary values, from $4 7 6 to $4,89 3 per ton ( in 2013 $). 5 DOE calculated monetary benefits using a medi an value fo r NO X emissions of $2,684 per short ton (in 2013$), at real discount rates of 3 percent and 7 percent.

DOE continues to evaluate appropriate values for monetizing avoided SO 2 and Hg

emissions. DOE did not monetize those emissions for this analysis

.

14.5 RESULTS

Table 14.5

. 1 p resent s the global values of CO 2 emissions reductions for each considered

TSL.

14

- 5Table 14.5

. 1 Estimates of Global Present Value of CO 2 Emissions Reduction under TSLs

for Commercial and Industrial Pumps

TSL SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate,

95 th percentile*

Million 201 3 $

Power Sector Emissions

1 21 100 160 310

2 100 474 757 1468

3 199 944 1506 2921

4 319 1517 2421 4695

5 463 2205 3521 6826

Upstream Emissions

1 1.2 5.8 9.3 18

2 5.8 28 44 86

3 11 55 88 170

4 18 88 141 274

5 27 129 206 398

Total Emissions

1 22 106 169 329

2 106 502 801 1554

3 210 999 1594 3092

4 337 1605 2563 4969

5 490 2334 3726 7224

* For each of the four cases , the corresponding global SCC value for emissions in 2015 is $ 12.0

,

$ 40.5 , $ 62.4 , and $ 119 per metric ton ( 2013 $).

After calculating global values of CO 2 emissions reductions for each considered TSL, DOE calculated domestic values as a range of 7 percent to 23 percent of the global values.

Results for domestic values are presented in Table 14.5

. 2

.

14

- 6Table 14.5

. 2 Estimates of Domestic Present Value of CO 2 Emissions Reduction under

TSLs for Commercial and Industrial Pumps

TSL SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate,

95 th percentile*

Million 2013 $

Power Sector Emissions

1 1.5 to 4.9 7.0 to 23.1 11.2 to 36.7 21.7 to 71.4

2 7.0 to 23.0 33.2 to 109.1 53.0 to 174.1 102.8 to 337.7

3 13.9 to 45.7 66.1 to 217.0 105.4 to 346.3 204.5 to 671.9

4 22.3 to 73.3 106.2 to 348.8 169.5 to 556.9 328.7 to 1079.9

5 32.4 to 106.5 154.3 to 507.1 246.4 to 809.7 477.8 to 1570.0

Upstream Emissions

1 0.1 to 0.3 0.4 to 1.3 0.7 to 2.1 1.3 to 4.2

2 0.4 to 1.3 1.9 to 6.4 3.1 to 10.2 6.0 to 19.7

3 0.8 to 2.6 3.8 to 12.6 6.1 to 20.2 11.9 to 39.2

4 1.3 to 4.2 6.2 to 20.3 9.9 to 32.5 19.2 to 63.0

5 1.9 to 6.1 9.0 to 29.6 14.4 to 47.3 27.9 to 91.6

Total Emissions

1 1.6 to 5.2 7.4 to 24.4 11.8 to 38.9 23.0 to 75.6

2 7.4 to 24.3 35.1 to 115.4 56.1 to 184.2 108.8 to 357.4

3 14.7 to 48.4 69.9 to 229.7 111.6 to 366.5 216.4 to 711.1

4 23.6 to 77.5 112.4 to 369.2 179.4 to 589.5 347.8 to 1142.9

5 34.3 to 112.7 163.3 to 536.7 260.8 to 857.1 505.7 to 1661.6

* For each of the four cases , the corresponding SCC value for emissions in 2015 is $ 12.0 , $ 40.5

,

$ 62.4 , and $ 119 per metric ton ( 2013 $).

Table 14.5

. 3 presents the present value of cumulative NO X emissions reductions for each

TSL

. Monetary v alues are calculated using the average dollar

- per

- ton values assigned to NO X emissions at 7

- per cent a nd 3

- percent discount rates.

14

- 7Table 14.5

. 3 Estimates of Present Value of NO X Emissions Reduction under TSLs for Commercial and Industrial Pumps

TSL 3% discount rate 7% discount rate

Million 2013$

Power Sector Emissions

1 3.1 1.4

2 15 6.4

3 29 13

4 47 20

5 68 29

Upstream Emissions

1 3.3 1.4

2 16 6.4

3 31 13

4 50 20

5 72 30

Total Emissions

1 6.5 2.8

2 30 13

3 60 25

4 97 41

5 141 59

14

- 8REFERENCES

1. Presidential Documents

-- Executive Order 12866

-- Regulatory Planning and Review

, October 4, 1993. < www.archives.gov/federal

- register/executive

- orders/pdf/12866.pdf >

.

2. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Use , 2010. National Academies Press. Washington, DC.

< www.nap.edu/catalog.php?record_id=12794 >

.

3. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon fo r Regulatory Impact Analysis under Executive Order 12866, February, February 2010. United States Government. Washington, D.C.

< www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

- Cost

- of

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 , May 2013; revised November 2013. United States Government. Washington, D.C.

< www.whitehouse.gov/sites/default/files/omb/assets/ inforeg/technical

- update

- social

cost

- of

- carbon

- for

- regulator

- impact

- analysis.pdf >

.

5. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal

Entities , 2006. Washington, DC.

< www.whitehouse. gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_cb_fina l_report.pdf >

.

14

- 9APPENDIX 14A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866

TABLE OF CONTENTS

14A.1 EXECUTIVE SUMMARY

................................ ................................ ........................ 14A

- 1

14A.2 MONETIZING CARBON DIOXIDE EMISS IONS

................................ .................. 14A

- 2

14A.3 SOCIAL COST OF CARBO N VALUES USED IN PAS T REGULATORY

ANALYSES

................................ ................................ ................................ ................ 14A

- 4

14A.4 APPROACH AND KEY ASS UMPTIONS

................................ ............................... 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

- 13

14A.4.4 Equilibrium Climate Sensitivity

................................ ................................ ... 14A

- 13

14A.4.5 Socioeconomic and Emissions Trajectories

................................ .................. 14A

- 16

14A.4.6 Discount Rate

................................ ................................ ................................ 14A

- 19

14A.5 REVISED SCC ESTIMATE S

................................ ................................ .................. 14A

- 26

14A.6 LIMITATIONS OF THE A NALYSIS

................................ ................................ ..... 14A

- 32

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPA CTS AND DAMAGE

FUNCTIONS

................................ ................................ ................................ ............ 14A

- 34

14A.8 CONCLUSION

................................ ................................ ................................ ......... 14A

- 36

REFERENCES

................................ ................................ ................................ ..................... 14A

- 38

14A.9 ANNEX

................................ ................................ ................................ ..................... 14A

- 43

14A.9.1 Other (non

- CO 2 ) gases

................................ ................................ .................. 14A

- 44

14A.9.2 Extrapolating Emissions Projections to 2300

................................ ............... 14A

- 46

LIST OF TABLES

Table 14A.1.1 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ .. 14A

- 2

Table 14A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity

Distributions

................................ ................................ ............................ 14A

- 14

Table 14A.4.2 Socioeconomic and Emissions Projections from Select EMF

- 22

Reference Scenarios

................................ ................................ ................ 14A

- 18

Table 14A.5.1 Disaggregated Social Cost of CO 2 Values by Model, Socioeconomic Trajectory , and Discount Rate for 2010 (in 2007 dollars)

...................... 14A

- 29

Table 14A.5.2 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars)

................................ 14A

- 31

Table 14A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates

between 2010 and 2050

................................ ................................ .......... 14A

- 31

Table 14A.7.1 Probabilities of Various Tipping Points from Expert Elicitation

............ 14A

- 35

Table 14A.9.1 Annual SCC Values: 2010

– 2050 (in 2007 dollars)

................................ 14A

- 43

Table 14 A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 14A

- 53

Table 14A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 14A

- 54

14A

- iTable 14A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ........ 14A

- 55

Table 14A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates

............. 14A

- 56

LIST OF FIGURE S

Figure 14A.4.1 Annual Consumption Loss as a Fraction of Global GDP in 2100 Due to an Increase in Annual Global Temperature in the DICE, FUND, and

PAGE models

................................ ................................ .......................... 14A

- 10 Figure 14A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE

................................ ................................ .................. 14A

- 11 Figure 14A.4.3 Estimates of the Probability Density Function for Equilibrium Climate Sensitivity (°C)

................................ ................................ ....................... 14A

- 16 Figure 14A.5.1 Level of Global GDP across EMF Scenarios

................................ ......... 14A

- 30 Figure 14A.9.1 Sulfur Dioxide Emission Scenarios

................................ ........................ 14A

- 46 Figure 14A.9.2 Global Population, 2000

- 2300 (Post

- 2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.)

................................ ................................ ................................ ...... 14A

- 48 Figure 14A.9.3 World GDP, 2000

- 2300 (Post

- 2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in the year 2300)

........... 14A

- 49 Figure 14A.9.4 Global Fossil and Industrial 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

- 49 Figure 14A.9.5 Global Net Land Use CO 2 Emissions, 2000

- 2300 (Post

- 2100 extrapolations assume emissions decline linearly, reaching zero in the

ye ar 2200)

................................ ................................ ............................... 14A

- 50 Figure 14A.9.6 Global Non

- CO 2 Radiative Forcing, 2000

- 2300 (Post

- 2100 extrapolations assume constant non

- CO 2 radiative forcing after 2100)

. 14A

- 51 Figure 14A.9.7 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 2000

- 2300 (Post

- 2100 extrapolations assume decline in CO 2 /GDP growth

rate over 2090

- 2100 is maintained through 2300)

................................ .. 14A

- 52 Figure 14A.9.8 Histo gram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by

discount rate

................................ ................................ ............................ 14A

- 55

14A

- iiAPPENDIX 14A. SOCIAL COST OF CARBO N FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866 a

14A.1 EXECUTIVE SUMMARY

Under Executive Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose of the “social cost of carbon” (SCC) estimates presented here is to allow agencies to incorporate the social benefits of reducing carbon dioxide (CO 2 ) emissions into cost

- benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The

estima tes 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 t he monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagency process that developed these SCC estimates. Technical experts from numerous agencies met on a regular basis to consider public comments, explore the technical literature in relevant fields, and discuss key model inputs and assumptions. The main objective of this process was to develop a range of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic li teratures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC values for use in regulatory analyses. Three

value s are based on the average SCC from three integrated assessment models, at discount rates

a Prepared by Interagency Working Group on Social Cost of Carbon, United States Government. With participation by:

Council of Economic Advisers Council on Environmental Quality Department of Agriculture Department of Commerce Department of Energy Department of Transportation Environmental Protection Agency

National Economic Council Office of Energy and Climate Change Office of Management and Budget Office of Science and Technology Policy Department of the Treasury

14A

- 1of 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 to rep resent 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 (in 2007 dollars)

Discount Rate

5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

14A.2 MONETIZING CARBON DIOXIDE EMISS IONS

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. We report estimates of the SCC in dollars per metric ton of carbon dioxide throughout this d ocument. b

When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC 2009) points out that any assessment will suffer from unc ertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive Order 12866, agencies are required, to the extent permitted by law , “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of

b In this document, we present all values of the SCC as the cost per metric ton of 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 t he mass of carbon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12
= 3.67).

14A

- 2the intended regulation j ustify its costs.” The purpose of the SCC estimates presented here is to make it possible for agencies to incorporate the social benefits from reducing carbon dioxide

emissions into cost

- benefit analyses of regulatory actions that have small, or “marginal ,” impacts on cumulative global emissions. Most federal regulatory actions can be expected to have marginal impacts on global emissions.

For such policies, the benefits from reduced (or costs from increased) emissions in any future year can be estimat ed by multiplying the change in emissions in that year by the SCC value appropriate for that year. The net present value of the benefits can then be calculated by multiplying each of these future benefits by an appropriate discount factor and summing acro ss 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 sm all relative to cumulative global carbon dioxide emissions. For policies that have a large (non

- marginal) impact on global cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore the technical literature in relevant fields, and discuss key inputs and assumptions in order to generate SCC estimates. Agencies that actively participated in the interagency process include the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, Transportation, and Treasury. This process was convened by the Council of Econom ic Advisers and the Office of Management and Budget, with active participation and regular input from the Council on Environmental Quality, National Economic Council, Office of Energy and Climate Change, and Office of Science and Technology Policy. The ma in objective of this process was to develop a range of SCC values using a defensible set of input assumptions that are ground ed in the existing literature. In this way, key uncertainties and model differences can more transparently and consistently inform the range of SCC estimates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are

based on th e average SCC across models and socioeconomic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, respectively. The fourth value is included to represent the 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. 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

section 16

- A .5 for the full range of annual SCC estimates from 2010 to 2050.

It is important to emphasize that the interagency process is committed to updating these estimates as the science and economic understanding of climate change and its impacts on

societ y improves over time. Specifically, we have set a preliminary goal of revisiting the SCC values within two years or at such time as substantially updated models become available, and to continue to support research in this area. In the meantime, we will continue to explore the issues

14A

- 3raised in this document and consider public comments as part of the ongoing interagency

process.

14A.3 SOCIAL COST OF CARBO N VALUES USED IN PAS T REGULATORY

ANALYSES

To date, economic analyses for Federal regulations have used a w ide range of values to estimate the benefits associated with reducing carbon dioxide emissions. In the final model year 2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of $2 per ton of CO 2 and a “global” SCC value o f $33 per ton of CO 2 for 2007 emission reductions (in 2007 dollars), increasing both v alues 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 carbon dioxide emissions, while a global SCC value is meant to reflect the value of damages worldwide.

A 2008 regulation proposed by DOT assumed a domestic SCC value of $7 per ton CO 2 (in 2006 dollars) for 2011 e mission reductions (with a range of $0

- $14 for sensitivity analysis), also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a domestic SCC range of $0 to $20 per ton CO 2 for 2007 emission reductions (in 2007 dollar s). In addition, EPA’s 2008 Advance Notice of Proposed Rulemaking for Greenhouse Gases identified what it described as “very preliminary” SCC estimates subject to revision. EPA’s global mean values were $68 and $40 per ton CO 2 for discount rates of approx imately 2 percent and 3 percent, respectively (in 2006 dollars for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in how benefits are evaluated across agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemaking process, to quantify avoided climate change damages from reduced CO 2 emissions. T he 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 fac tors 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 represen t 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 f or public comment in

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- 4connection with proposed rules, including the joint EPA

- DOT fuel economy and CO 2 tailpipe emission proposed rules.

14A.4 APPROACH AND KEY ASS UMPTIONS

Since the release of the interim values, the interagency group has reconvened on a regular basis to generate improved SCC estimates. Specifically, the group has considered public comments and further explored the technical literature in relevant fields. This section details the several choices and assumptions that underlie the resulting estim ates of the SCC.

It is important to recognize that a number of key uncertainties remain, and that current SCC estimates should be treated as provisional and revisable

, since they will evolve with improved scientific and economic understanding. The inter agency group also recognizes that the existing models are imperfect and incomplete. The National Academy of Science (2009) points out that there is tension between the goal of producing quantified estimates of the economic damages from an incremental ton of carbon and the limits of existing efforts to model these effects. Throughout this document, we highlight a number of concerns and problems that should be addressed by the research community, including research programs housed in many of the agencies pa rticipating in the interagency process to estimate the SCC

.

The U.S. Government will periodically review and reconsider estimates of the SCC used

for cost

- benefit analyses to reflect increasing knowledge of the science and economics of climate impacts , as well as improvements in modeling. In this context, statements recognizing the limitations of the analysis and calling for further research take on exceptional significance. The interagency group offers the new SCC values with all due humility about the uncertainties embedded in them and with a sincere promise to continue work to improve them.

14A.4.1 Integrated Assessment Models

We rely on three integrated assessment models (IAMs) commonly used to estimate the SCC: the FUND, DICE, and PAGE models. c These models are frequently cited in the 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 model s are useful because they combine climate processes, economic growth, and feedbacks between the climate and the global economy into a single modeling framework. At the same time, they gain this advantage at the expense of a more detailed representation of the underlying climatic and economic systems. DICE, PAGE, and FUND all take stylized, reduced

c The DICE (Dynamic Integrated Climate and Economy) model by William Nordhaus evolved from a series of energy models and was first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008). The PAGE (Policy Analysis of the Greenhouse Effect) model was developed by Chris Hope in 1991 for use by European decision

makers in assessing the marginal impact of carbon emissions (Hope 2006, Hope 2008). The FUND (Climate Framework for Uncertainty, Negotiation, and Distribution) model, developed by Richard Tol in the early 1990s, originally to study international capital transfers in climate policy. is now widely used to study climate impacts ( e.g. , Tol 2002a, Tol 2002b, Anthoff et al. 2009, Tol 2009).

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- 5form approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on the possible advantages of this approach). Other IAMs may better reflect the complexity of the science in their modeling frameworks but do not link physical impacts to economic damages. There is currently a limited amount of research linking climate impacts to economic damages,

which makes this exercise even more difficult. Unde rlying 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 thr ee IAMs translate emissions into changes in atmospheric greenhouse concentrations, atmospheric concentrations into changes in temperature, and changes in temperature into economic damages. The emissions projections used in the models are based on specifie d socioeconomic (GDP and population) pathways. These emissions are translated into concentrations using the carbon cycle built into each model, and concentrations are translated into warming based on each model’s simplified representation of the climate an d 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 t akes 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 pe riod relative to the pre

- industrial average temperature in each region. In FUND, damages in each period also depend on the rate of temperature change from the prior period. In DICE, temperature affects both consumption and investment. We describe each m odel in greater detail here. In a later section, we discuss key gaps in how the models account for various scientific and economic processes ( e.g. the probability of catastrophe, and the ability to adapt to climate change and the physical changes it causes).

The parameters and assumptions embedded in the three models vary widely. A key objective of the interagency process was to enable a consiste nt 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: clim ate sensitivity, socioeconomic and emissions trajectories, and discount rates. A probability distribution for climate sensitivity was specified as an input into all three models. In addition, the interagency group used a range of scenarios for the socioe conomic parameters and a range of values for the discount rate. All other model features were left unchanged, relying on the model developers’ best estimates and judgments. In DICE, these parameters are handled deterministically and represented by fixed constants; in PAGE, most parameters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically.

The sensitivity of the results to other aspects of the models ( e.g. the carbon cycle or

dam age 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.

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- 6The DICE Model

The DICE model is an optimal growth model based on a global production function with an extra stock variable (atmospheric carbon dioxide concentrations). Emission reductions are treated as analogous to investment in “natural capital.” By investing in nat ural capital today through reductions in emissions

— implying reduced consumption

— harmful effects of climate change can be avoided and future consumption thereby increased.

For purposes of estimating the SCC, carbon dioxide emissions are a function of gl obal 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 te mperature 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 o n 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 (bas ed on outdoor recreation), and human settlements and ecosystems. The DICE damage function also includes the expected value of damages associated with low probability, high impact “catastrophic” climate change. This last component is calibrated based on a survey of experts (Nordhaus 1994). The expected value of these impacts is then added to the other market

and non

- market impacts mentioned above.

No structural components of the DICE model represent adaptation explicitly, though it is included implicit ly 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., 200 6). 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 adapt ation costs."

Note that the damage function in DICE has a somewhat different meaning from the damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because damages in a given year reduce investment in that year, damages propagate forwa rd in time and reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in any given year do not propagate forward. d

d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the SCC relative to damages calcula ted by fixing GDP. In DICE2007, the time path of GDP is endogenous. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it consistent with EMF GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re

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- 7The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into economic, non

- economic, and catastrophic categories and calculates these impacts separately for eight geographic regions. Damages in each region are expressed as a fraction of output, where the fraction lost depends on the temperature change in each region. Damages are expressed as power functions of temperature change. The exponents of the damage function are the same in all regions but are treated as uncertain, with valu es 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 sub

function. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a

“discontin uity” ( 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 re sulting 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 economi c 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 to lerable 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).

The FUND Model

Like PAGE, the FUND model treats GDP growth as exogenous. It includes separately calibrated damage functions for eight market and nonmarket sectors: agriculture, forestry, water, energy (based on heating and cooling demand), sea level rise (based on the value of land lost and the cost of protection), ecosystems, human health (diarrhea, vector

- borne diseases, and cardiovascular and respiratory mortality), and extreme weather. Each impact sector has a different functional form, and is calculated separately for s ixteen geographic regions. In some impact sectors, the fraction of output lost or gained due to climate change depends not only on the absolute temperature change but also on the rate of temperature change and level of regional

income. e In the forestry a nd agricultural sectors, economic damages also depend on CO 2 concentrations.

calibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the absence of warming t hat exactly matched the EMF scenarios.

e In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health b enefits in those regions at low to moderate levels of warming (Warren et al., 2006).

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- 8Tol (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

c haracterizes 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 c ause these sorts of changes or what effects they would have are not well

- understood, although the chance of any one of them happening seems low. But they do have the potential to happen relatively quickly, and if they did, the costs could be substantial. Only a few studies of climate change have examined

these issues.”

Adaptation is included both implicitly and explicitly in FUND. Explicit adaptation is seen in the agriculture and sea level rise sectors. Implicit adaptation is included in sectors such as energy and human health, where wealthier populations are assumed to be less vulnerable to climate impacts. For example, the damages to agriculture are the sum of three effects: (1) those due to the rate of temperature change (damages are always positive); (2) those due to the level of temperature change (damages can be positive or negative depending on region and temperature); and (3) those from CO 2 fertilization (damages are generally negative but diminishing to zero).

Adaptation is incorpor ated into FUND by allowing 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

temp erature across these sectors can result in negative economic damages from climate change.

Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of how to represent the effects of climate change (represented by the increase in global

- average surface temperature) on the consumption

- equivalent value of both market and non

- market goods (represented as a fraction of global GDP). We recognize that these representations are incomplete and highly uncertain. But giv en 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 th ree IAMs are presented in Figure 14A.4

. 1 and Figure

14A.4

. 2 , using the modeler’s default scenarios and mean input assumptions. There are significant differences between the three models both at lower ( Figure 14A.4

. 2 ) and higher ( Figure 14A.4

. 1 ) increases in global

- average temperature.

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- 9Figure 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 f

The lack of agreement among the models at lower temperature increases is underscored by the fact that the damages from FUND are well below the 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 higher discount rates we expect that a greater proportion of the SCC value is due to damages in y ears with lower temperature increases. For example, when the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to damages that occur in years when the temperature is less than or equal to 3 °C. This increases to approxima tely 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 catastrophi c damages. Gaps in the literature make modifying these aspects of the models challenging, which highlights the need for additional research. As knowledge improves, the Federal government is committed to exploring how these (and other) models can be modif ied to incorporate more accurate estimates of damages.

f The x

- axis represents increases in annual, rather than equilibrium, temperature, while the y

- axis represents the annual stream of benefits as a share of global GDP. Eac h specific combination of climate sensitivity,

socioeconomic , and emissions parameters will produce a different realization of damages for each IAM. The damage functions represented in Figures 1A and 1B are the outcome of default assumptions. For instanc e, under alternate assumptions, the damages from FUND may cross from negative to positive at less than or greater than 3

°C.

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- 10Figure 14A.4

. 2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE

14A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current attention on a global measure of SCC. This approach is the same as that taken for the interim values, but it otherwise represents a departure from past practices, which tended to put greater emphasis on a domestic measure of SCC (limited to impacts of climate change experienced within U.S. borders). As a matter of law, consideration of both global and domestic values is generally permissible; the relevant statutory provisions are usua lly ambiguous and allow

selection of either measure. g

Global SCC

Under current OMB guidance contained in Circular A

- 4, analysis of economically significant proposed and final regulations from the domestic perspective is required, while analysis from th e international perspective is optional. However, the climate change problem is highly unusual in at least two respects. First, it involves a global externality: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in the United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages caused by GHG emissions. Second, climate change presents a problem that the United States alone cannot solve. E ven if the United States were to

g It is true that federal statutes are presumed not to have extraterritorial effect, in part to ensure that the laws of the

United States respect the interests of foreign sovereigns. But use of a global measure for the SCC does not give

extraterritorial effect to federal law and hence does not intrude on such interests.

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- 11reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid substantial climate change. Other countries would also need to take action to reduce emissions if significant changes in the global c limate 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 econo mies, to take significant steps to reduce emissions. When these considerations are taken as a whole, the interagency group concluded that a global measure of the benefits from reducing U.S. emissions is preferable.

When quantifying the damages associate d with a change in emissions, a number of analysts ( e.g. , Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in consumption across regions. This weighting takes into account the relative reductions in wealth in different regions of the world. A per

- capita loss of $500 in GDP, for instance, is weighted more heavily in a country with a per

- capita GDP of $2,000 than in one with a per

- capita GDP of $40,000. The main argument for this approach is that a loss of $500 in a poor country causes a greater reduction in utility or welfare than does the same loss in a wealthy nation. Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group concluded that this approach would not be appropriate for estimating a SCC value used in domestic regulatory analysis. h For this reason, the group concluded that using the global (rather than domestic) value, without equity weighting, is the appropriate approach.

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the relatively few region

- or country

- specific estimates of the SCC in the literature. One potential

source of estimates comes from the FUND model. The resulting estimates suggest that the ratio of domestic to globa l 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

f raction of GDP lost due to climate change is assumed to be similar across countries, the domestic benefit would be proportional to the U.S. share of global GDP, which is currently about 23 percent. i

On the basis of this evidence, the interagency workgrou p determined that a range of values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic effects. Reported domestic values should use this range. It is recognized that these values are approximate, provisional, and highly spe culative. There is no a priori reason why domestic benefits should be a constant fraction of net global damages over time. Further, FUND does not

h It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealthy one, but development of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account would have to consider that a given cost of emissions reductions imposes a greater ut ility or welfare loss on a poor nation than on a wealthy one. Even if equity weighting

— for both the costs and benefits of emissions reductions

— is appropriate when considering the utility or welfare effects of international action, the interagency group con cluded that it should not be used in developing an SCC for use in regulatory policy at this time.

i Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report

.

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- 12account for how damages in other regions could affect the United States ( e.g. , global migration,

economic and political destabilization). If more accurate methods for calculating the domestic SCC become available, the Federal government will examine these to determine whether to update its approach.

14A.4.3 Valuing Non

- CO 2 Emissions

While CO 2 is the most prevalent gree nhouse gas emitted into the atmosphere, the U.S. included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, and sulfur hexafluoride. The climate impact of these gases is commonly discussed in terms of their 100

- year global warming potential (GWP). GWP measures the ability of different gases to trap heat in the atmosphere ( i.e. , radiative forcing per unit of mass) over a particular timeframe relative to CO 2 . However, because these gases differ in both radiative forcing and atmospheric lifetimes, their relative damages are not constant over time. For example, because methane has a short lifetime, its impacts occur primarily in the near term and thus are not discounted as heavily as those caused by longer

- lived gases. Impacts other than temperature change also vary across gases in ways that are not captured by GWP. For instance, 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

socia l 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 carbon dioxide

emissions.

14A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (ECS) is a key input parameter for t he DICE, PAGE, and

FUND models. j It is defined as the long

- term increase in the annual global

- average surface temperature from a doubling of atmospheric CO 2 concentration relative to pre

- industrial levels (or stabilization at a concentration of approximat ely 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 Repor t of the Intergovernmental Panel on Climate Change (IPCC):

j The equilibrium climate sensitivity includes the response of the climate system to increased greenhouse gas concentrations over the short to medium term (up to 100

- 200 years), but it does not include long

- term feedback effects due to possible large

- scale changes in ice sheets or the biosphere, which occur on a ti me scale of many hundreds to thousands of years ( e.g. Hansen et al. 2007).

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- 13Basing our assessment on a combination of several independent lines of evidence...including observed climate change and the strength of known feedbacks simulated in [global climate models], we concl ude 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 climate sensitivity is very likely larger than 1.5 °C. k

For fundamental physical reasons as well as data limitations, values substantially higher than 4.5 °C still cannot be excluded, but agreement with observations and proxy data is generally worse for those high values than for values in the 2 °C to 4.5 °C range. (Meehl et al., 2007, p 799)

After consulting with several lead authors of this chapter of the IPCC report, the interagency workgroup selected four candidate probability distributions and calibrated them to

be consistent with the above statement: Roe and Baker (2007), log

- normal, gamma, and Weibull.

Table 14A.1

. 1 included below gives summary statistics for the four calibrated distributions.

Table 14A.4

. 1 Summary Statistics for Four Calibrated Climate Sensitivity Distributions

Roe & Baker Log

- normal Gamma Weibull Pr(ECS < 1.5°C) 0.013 0.050 0.070 0.102 Pr(2°C < ECS < 4.5°C) 0.667 0.667 0.667 0.667

5 th percentile 1.72 1.49 1.37 1.13

10 th percentile 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 percentile 5.86 5.14 4.93 4.69

95 th percentile 7.14 5.97 5.59 5.17

Each distribution was calibrated by applying three constraints from the IPCC:

(1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”; l

(2) two

- thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C;

and

k This is in accord with the judgment that it “is likely to lie in the range 2 °C to 4.5 °C” and the IPCC definition of “likely” as greater than 66 percent probability (Le Treut et al.2007). “Very likely” indicates a greater than 90 percent probability.

l Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage
would allow the mode, median, or mean to serve as candidates for the c entral or “most likely” value and the IPCC report is not specific on this point. For the distributions we considered, the median was between the mode and the mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode o r 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 Bak er distribution with the median set equal to 3 °C.

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- 14(3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 721).

We selected the calibrated Roe and Baker distribution from the four candidates for two reasons. First, the Roe and Baker distribution is the only one of th e four that is based on a theoretical understanding of the response of the climate system to increased greenhouse gas concentrations (Roe and Baker 2007, Roe 2008). In contrast, the other three distributions are mathematical functions that are arbitrarily chosen based on simplicity, convenience, and general shape. The Roe and Baker distribution results from three assumptions about climate response: (1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback

factors ar e proportional to the change in surface temperature; and (3) uncertainties in feedback factors are normally distributed. There is widespread agreement on the first point and the second and third points are common assumptions.

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that “values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no quantitative judgment, the 95 th percentile of the calibrated Roe & Baker distribution (7.1 °C) is

muc h 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 (H egerl, 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 & Bak er distribution, for which the probability of equilibrium climate sensitivity being greater than 1.5 °C is almost 99 percent, is not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it reflects a greater degr ee of certainty about very low values of ECS than was expressed by the IPCC.

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- 15Figure 14A.4

. 3 Estimates of the Probability Density Function for Equilibrium Climate Sensitivity (°C)

To show how the calibrated Roe and Baker distribution compares to different estimates of the probability distribution function of equilibrium climate sensitivity in the empirical literature, Figure 14A.4

. 3 (above ) overlays it on Figure 9.20 from the IPCC Fourth Assessment Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal bars show the respective 5 percent to 95 percent range s; dots indicate the median estimate. m

14A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic pathwa ys 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 sever al 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.

m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natu ral forcings), Gregory et al. (2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown

are the 5

- 95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.

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- 16To accomplish this task in a transparent way, we decided to rely on the recent St anford Energy Modeling Forum exercise, EMF

- 22. EMF

- 22 uses ten well

- recognized models to evaluate substantial, coordinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, and emissio n 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 out liers 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 pol icies that will have a small effect on global cumulative emissions, we use socioeconomic and emission trajectories that span a range of plausible scenarios. Five trajectories were s e lected from EMF

- 22 (see Table 14A.4

. 2 below). Four of these represent potential business

- as

- usual (BAU) growth in population, wealth, and emissions and are associated with 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 i.e. , CO 2 - only concentrations of 425

– 484 ppm or a radiative forcing of 3.7 W/m 2 ) in 2100, a lower

- than

- BAU trajectory. n Out of the 10 models included in the EMF

- 22 exercis e, we selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories from each of these four models. For the 550 ppm CO 2 e scenario, we average d the GDP, population, and emission trajectories implied by these same four models.

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, though it could also result from technological advances. It was chosen because it represents the most stringent case analyzed by the EMF

- 22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

14A

- 17Table 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 (using market exchange rates in trillion 2005$) o

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 38.6 53.0 73.5 97.2 156.3 396.6 MERGE Optimistic 36.3 45.9 59.7 76.8 122.7 268.0

MESSAGE 38.1 52.3 69.4 91.4 153.7 334.9
MiniCAM 36.1 47.4 60.8 78.9 125.7 369.5 550 ppm average 37.1 49.6 65.6 85.5 137.4 337.9

Global Population (billions)

EMF

– 22 Based Scenarios 2000 2010 2020 2030 2050 2100

IMAGE 6.1 6.9 7.6 8.2 9.0 9.1

MERGE Optimistic 6.0 6.8 7.5 8.2 9.0 9.7
MESSAGE 6.1 6.9 7.7 8.4 9.4 10.4
MiniCAM 6.0 6.8 7.5 8.1 8.8 8.7 550 ppm average 6.1 6.8 7.6 8.2 8.7 9.1

We explore how sensitive the SCC is to various assumptions about how the future will evolve without prejudging what is likely to occur. The interagency group considered formally assigning probability weights to different states of the world, but this prov ed challenging to do in an analytically rigorous way given the dearth of information on the likelihood of a full range of

future socioeconomic pathways.

o While the EMF

- 22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use purchasing power parity (PPP). PPP takes into account the different price levels across co untries, so it more accurately describes relative standards of living across countries. MERs tend to make low

- income countries appear poorer than they actually are. Because many models assume convergence in per capita income over time, use of

MER

- adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it leads to overstated economic growth and hence a significant upward bias in projections of greenhouse gas emissions, and unrealistically high future temperatures ( e.g. , Castles and Henderson 2003). Others argue that convergence of the emissions

- intensity gap across countries at least partially offset the overstated income gap so that differences in exchange rates have less of an effect on emissions (Holtsmark and Alfsen, 2005; Tol, 2006). Nordhaus (2007b) argues that the ideal approach is to use superlative PPP accounts (i.e., using cross

- sectional PPP measures for relative incomes and outputs and national accounts price and quantity indexes for time

- series extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that exchange

- rate

- conversion issue s are as important as uncertainties about population, technological change, or the many geophysical uncertainties.

14A

- 18T here are a number of caveats. First, EMF BAU scenarios represent the modelers’ judgment of the mos t likely pathway absent mitigation policies to reduce greenhouse gas emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the most likely outcome span a wide range, from the more optimistic ( e.g. , abundant low

- cost, lo w

carbon energy) to more pessimistic ( e.g. , constraints on the availability of nuclear and renewables). p Second, the socioeconomic trajectories associated with a 550 ppm CO 2 e concentration scenario are not derived from an assessment of what policy is optim al from a

benefit

- cost standpoint. Rather, it is indicative of one possible future outcome. The emission trajectories underlying some BAU scenarios ( e.g. , MESSAGE’s 612 ppm) also are consistent with some modest policy action to address climate change. q We chose not to include

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the difficulty many models had in converging to meet these targets.

For comparison purposes, the Energy Information Agency in its 2009 Annua l Energy Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion (in 2005 dollars using market exchange rate s) 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 20 50, 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 u se 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 for greater detail.

14A.4.6 Discount Rate

The choice of a discount rate, especially over long periods of time, raises highly contested and exceedingly difficult questions of science, economics, philosophy, and law. Although it is well understood that the discount rate has a large influence on the current value of future damages, there is no consensus about what rates to use in this context. Because carbon dioxide emissions are long

- lived, subsequent damages occur over many years. In calculating the SCC, we first estimate the future damages to ag riculture, human health, and other market and

non

- market sectors from an additional unit of carbon dioxide emitted in a particular year in terms of reduced consumption (or consumption equivalents) due to the impacts of elevated temperatures, as represented in each of the three IAMs. Then we discount the stream of future damages to its present value in the year when the additional unit of emissions was released using

p For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and

non

- biomass renewables is pr ojected to increase from about 15 percent of total primary energy in 2000 to 54 percent in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100.

q For example, MiniCAM projects if all non

- US OECD countries reduce CO 2 emissions to 83 percent below 2005 levels by 2050 (per the G

- 8 agreement) but all other countries continue along a BAU path CO 2 concentrations in 2100 would drop from 794 ppmv in its reference case to 762 p pmv.

14A

- 19the selected discount rate, which is intended to reflect society's marginal rate of substit ution between consumption in different time periods.

For rules with both intra

- and intergenerational effects, agencies traditionally employ constant discount rates of both 3 percent and 7 percent in accordance with OMB Circular A

- 4.

As Circular A

- 4 acknowledges, however, the choice of discount rate for intergenerational problems raises distinctive problems and presents considerable challenges. After reviewing those challenges, Circular A

- 4 states, “ If your rule will have important intergenerational benefits or costs you might consider a further sensitivity analysis using a lower but positive discount rate in addition to calculating net benefits using discount rates of 3 and 7 percent.” For the specific purpose of developing the SCC, we adapt and re vise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive approach reflects a positive (non

- normative) persp ective based on observations of people’s

actual choices

— e.g. , savings versus consumption decisions over time, and allocations of savings among more and less risky investments. Advocates of this approach generally call for inferring

the discount rate from market rates of return “because of a lack of justification for choosing a social welfare function that is any different than what decision makers [individuals] actually use” (Arrow et al. 1996).

One theoretical foundation for the cost

- benefit analyses in which the social cost of carbon

will be used

— the Kaldor

- Hicks potential

- compensation test

— also suggests that market rates should be used to discount future benefits and costs, because it is the market interest rate that would govern the returns potentia lly set aside today to compensate future individuals for climate damages that they bear ( e.g. , Just et al. 2004)

. As some have noted, the word “potentially” is an important qualification; there is no assurance that such returns will actually be set aside to provide compensation, and the very idea of compensation is difficult to define in the intergenerational context. On the other hand, societies provide compensation to future generations through investments in human capital and the resulting increase in knowledge, as well as infrastructure and other physical capital.

The prescriptive approach specifies a social welfare function that formalizes the normative judgments that the decision

- maker wants explicitly to incorporate into the policy

evaluation

— e.g. , how inter

- personal comparisons of utility should be made, and how the welfare of future generations should be weighed against that of the present generation. Ramsey (1928), for example, has argued that it is “ethically indefensible” to apply a positive pure rate of time preference to discount values across generations, and many agree with this view.

Other concerns also motivate making adjustments to descriptive discount rates. In particular, it has been noted that the preferences of future generatio ns with regard to consumption versus environmental amenities may not be the same as those today, making the current market rate on consumption an inappropriate metric by which to discount future climate

related damages. Others argue that the discount rate should be below market rates to correct for market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth,

14A

- 20which in the Kaldor

- Hicks logic are presumed to compensate future generations for damage (a potentially controvers ial 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

underl ies the prescriptive approach assumes a representative agent with perfect foresight and no credit constraints. This is an artificial rendering of the real world that misses many of the

frictions that characterize individuals’ lives and indeed the available descriptive evidence supports this. For instance, many individuals smooth consumption by borrowing with credit cards that have relatively high rates. Some are unable to access traditional credit markets and rely on payday lending operations or other high

- cost forms of smoothing consumption. Whether one puts greater weight on the prescriptive or descriptive approach, the high interest rates that

credit

- constrained individuals accept suggest that some account should be given to the discount

rates revealed by their behavior.

We draw on both approaches but rely primarily on the descriptive approach to inform the choice of discount rate. With recognition of its limitations, we find this approach to be the most defensible and transparent given its consisten cy with the standard contemporary theoretical

foundations of benefit

- cost analysis and with the approach required by OMB’s existing guidance. The logic of this framework also suggests that market rates should be used for discounting future consumption

- equ ivalent damages. Regardless of the theoretical approach used to derive the appropriate discount rate(s), we note the inherent conceptual and practical difficulties of adequately capturing consumption trade

- offs over many decades or even centuries. While relying primarily on the descriptive approach in selecting specific discount rates, the interagency group has been keenly aware of the deeply normative dimensions of both the debate over discounting in the intergenerational context and the consequences of selecting one discount rate

over another.

Historically Observed Interest Rates

In a market with no distortions, the return to savings would equal the private return on investment, and the market rate of interest would be the appropriate choice for the social discount rate. In the real world risk, taxes, and other market imperfection s 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 O MB’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 gro up examined the economics literature and concluded that the consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a

14A

- 21marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The

con sumption rate of interest also is appropriate when the impacts of a regulation are measured in consumption (

- equivalent) units, as is done in the three integrated assessment models used for estimating the SCC.

Individuals use a variety of savings instruments that vary with risk level, time horizon, and tax characteristics. The standard analytic framework used to develop intuition about the discount rate typically assumes a representative agent with perfect fore sight and no credit

constraints. The risk

- free rate is appropriate for discounting certain future benefits or costs, but the benefits calculated by IAMs are uncertain. To use the risk

- free rate to discount uncertain benefits, these benefits first must be transformed into "certainty equivalents," that is the maximum certain amount that we would exchange for the uncertain amount. However, the calculation of the certainty

- equivalent requires first estimating the correlation between the benefits of the polic y 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 is low, then the certainty

- equivalent benefits will be higher than the expected benefits (and vice versa). Since many (though not necessarily all) of the important impacts of climate change will

flow through market sectors such as agriculture and energy, and since willingness to pay for environmental protections typically increases with income, we might expect a positive (though not necessarily perfect) correlation between the net benefits from climate policies and market returns. This line of reasoning suggests that the proper discount rate would exceed the riskless rate. Alternatively, a negative correlation between the returns to climate policies and market returns would imply that a discount rate below the riskless rate is appropriate.

This discussion suggests that both the post

- tax riskless and risky rates can be used to capture individuals’ consumption

- equivalent interest rate. As a measure of the post

- tax riskless rate, we calculate the avera ge real return from Treasury notes over the longest time period available (those from Newell and Pizer 2003) and adjust for Federal taxes (the average marginal rate from tax years 2003 through 2006 is around 27 percent). r This calculation produces a real interest rate of about 2.7 percent, which is roughly consistent with Circular A

- 4’s recommendation to use 3 percent to represent the consumption rate of interest. s A measure of the post

- tax risky rate for investments whose returns are positively correlat ed with overall equity

r The literature argues for a risk

- free rate on government bonds as an appropriate measure of the consumption rate of interest. Arrow (2000) suggests that it is roughly 3

- 4 percent. OMB cites evidence of a 3.1 percent pre

- tax rate for

10

- year Treasu ry notes in the A

- 4 guidance. Newell and Pizer (2003) find real interest rates between 3.5 and 4 percent for 30

- year Treasury securities.

s The positive approach reflects how individuals make allocation choices across time, but it is important to keep in mind that we wish to reflect preferences for society as a whole, which generally has a longer planning horizon.

14A

- 22market returns can be obtained by adjusting pre

- tax rates of household returns to risky investments (approximately 7 percent) for taxes , which yields a real rate of roughly 5 percent. t

The Ramsey Equation

Ramsey discounting als o 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 avers ion or elasticity of the marginal utility of consumption) and ρ ( pure rate of time preference). u These are

then combined with g (growth rate of per

- capita consumption) to equal the interest rate at which future monetized damages are discounted: ρ + η∙g. v In the simplest version of the Ramsey model, with an optimizing representative agent with perfect foresight, what we are calling the “Ramsey discount rate,” ρ + η∙g, will be equal to the rate of return to capital, i.e. , the market

interest rate.

A review of the literature provides some guidance on reasonable parameter values for the Ramsey discounting equation , based on both prescriptive and descriptive approaches.

• η. Most papers in the climate change literature adopt values for η in the range o f 0.5 to 3 (Weitzman cites plausible values as those ranging from 1 to 4), although not all authors articulate whether their choice is based on prescriptive or descriptive reasoning. w Dasgupta (2008) argues that η should be greater than 1 and may be as hi gh as 3, because

η equal to 1 suggests savings rates that do not conform to observed behavior.

t Cambell et al (2001) estimates that the annual real return from stocks for 1900

- 1995 was about 7 percent. The

annual real rate of return for the S&P 500 from 1950

– 2008 was about 6.8 percent. In the absence of a better way to population

- weight the tax rates, we use the middle of the 20

– 40 percent range to derive a post

- tax interest rate (Kotlikoff and Rapson 2006).

u The parameter ρ  measu res the pure rate of time preference : people’s behavior reveals a preference for an increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The parameter η captures diminishing marginal u tility : consumption in the future is likely to be higher than consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in

income is less valuable to wealthier individuals.

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the rate of consumption growth.

w Empirical estimates of η span a wide range of values. A benchmark value of 2 is near the middle of the range of values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman (2007, 2009), and Nordhaus (2008). However, Chetty (20 06) developed a method of estimating η using data on labor supply behavior. He shows that existing evidence of the effects of wage changes on labor supply imposes a tight upper bound on the curvature of utility over wealth (CRRA < 2) with the mean implied value of 0.71 and concludes that the standard expected utility model cannot generate high levels of risk aversion without contradicting established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation.

Evans a nd 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 distribution s from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 1.07.

14A

- 23• ρ. With respect to the pure rate of time preference, most papers in the climate change literature adopt values for ρ in the range of 0 to 3 percent per year. The very low rates tend to follow from moral judgments involving intergenerational neutrality. Some have argued that to use any value other than ρ = 0 would unjustly discriminate against future generations ( e.g. , Arrow et al. 1996, Stern et al. 2006). Ho wever, 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 et al. 2006).

• g. A commonly accepted approximation is around 2 pe rcent per year. For the

socioeconomic scenarios used for this exercise, the EMF models assume that g is about

1.5

- 2 percent to 2100.

Some economists and non

- economists have argued for constant discount rates below 2 percent based on the prescriptive a pproach. When grounded in the Ramsey framework, proponents of this approach have argued that a ρ of zero avoids giving preferential treatment t o one generation over another. The choice of η has also been posed as an ethical choice linked to

the value of an additional dollar in poorer countries compared to wealthier ones. Stern et al. (2006) applies this perspective through his choice of ρ = 0.1 percent per year

, η = 1 and g = 1.3 percent per year, which yields an annual discount rate of 1.4 percent. In the context of permanent income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent of their income. x

Recently, Stern (2008) revisited the values used in Stern et al. (2006), stating that there is a case to be made for raising η due to the amount of weight lower values place on damages far in the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, yields a discount rate of greater than 2 percent.

We conclude that arguments made under the prescriptive approach can be used to j ustify discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most appropriate value for η , we find it difficult to justify rates at the lower end of this range under the Ramsey framework.

Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate sensitivity. Weitzman (1998, 2001) showed theor etically and Newell and Pizer (2003) and Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on net present values. A main result from these studies is that if there is a persistent element to the

x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied savings rate and that η at or above 2 are inconsist ent with observed behavior with regard to equity . (At the same time, adding exogenous technical change

— all else equal

— would increase g as well.)

14A

- 24uncertainty in the discount rate ( e.g. , the rate follows a random walk), then it will result in an effective (or certainty

- equivalent) discount rate that declines over time. Consequently, lower discount rates tend to dominate over the very long term (see Weitzman 1998, 1999, 2001; Newell and Pizer 2003; Groom et al. 2006; Gollier 2008; Summers and Zeckhauser 2008; and Gollier and Weitzman 2009).

The proper way to model discount rate uncertainty remains an active area of research. Newell and Pizer (2003) employ a mo del of how long

- term interest rates change over time to forecast future discount rates. Their model incorporates some of the basic features of how interest rates move over time, and its parameters are estimated based on historical observations of long

- ter m rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more general models of interest rate dynamics to allow for better forecasts. Specifically, the volatility of interest rates depends on whether rates are currently low or high and the variation in the level of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model uncertainty in the discount rate, others argue for a declining scale of discount rates applied over time ( e.g. , Weitz man 2001, and the UK’s “Green Book” for regulatory analysis). This approach uses a higher discount rate initially, but applies a graduated scale of lower discount rates further

out in time. y A key question that has emerged with regard to both of these approaches is the

trade

- off between potential time inconsistency and giving greater weight to far future outcomes (see the EPA Science Advisory Board’s recent comments on this topic as part of its review of

their Guidelines for Economic Analysis ). z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest rate to use in this context and uncertainty about how interest rates may change over time, we use three discount rates to span a plausible range of certainty

- equivalent constant discount rates: 2.5, 3, and 5 percent per year. Based on the review in the previous sections, the interagency workgroup

determined that these three r ates 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. A s previously mentioned, the consumption rate of interest is the correct discounting concept to use when future

y For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31

- 7 5; 2.5 percent for years 76

- 125; 2 percent for years 126

- 200; 1.5 percent for years 201

- 300; and 1 percent after 300 years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time.

z Uncertaint y in future damages is distinct from uncertainty in the discount rate . Weitzman (2008) argues that Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a low probability, catastrophic event far in th e 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 functiona l forms chosen for climate sensitivity, utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in the other direction by increasing the benefits of waiting to learn the appropriate level of mitigation required.

14A

- 25damages from elevated temperatures are estimated in consumption

- equivalent units. Further, 3 percent roughly corresponds to the after

- tax riskle ss interest rate. T he upper value of 5 percent is included to represent the possibility that climate damages are positively correlated with market returns. Additionally, this discount rate may be justified by the high interest rates that many

consumers u se 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 mean

reverting and random walk approaches from Newell and Pizer (2003) starting at a discount rate of 3 percent. Using this approach, the certainty equivalent is about 2.2 percent using the random walk model and 2.8 percent using the mean reverting approach. aa Without giving preference to a particular model, the average of the two rates is 2.5 percent. Further, a rate below the riskless rate would be justified if climate investments are negatively correlated with the overall market rate of return. Use of this lower value also responds to certain judgments using the prescriptive or normative approach and to ethical objections that have been raised about rates of 3 percent or higher.

14A.5 REVISED SCC ESTIMATE S

Our general approach to estimating SCC values is to run the three integrated asses sment models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 and 10 with a median of 3 °C and a cumulative probability between 2 an d 4.5 °C of two

thirds.

• Five sets of GDP, population

, and carbon emissions trajectories based on EMF

- 22.

• Constant annual discount rates of 2.5, 3, and 5 percent.

Because the climate sensitivity parameter is modeled probabilistically, and because PAGE and FUND incorporate uncertainty in other model parameters, the final output from each model run is a distribution over the SCC in year t

.

For each of the IAM s , the basic computational steps for calculating the SCC in a particular year t

are:

1. Input the path of emissions, GDP, and population from the selected EMF

- 22 scenarios, and the extrapolations based on these scenarios for post

- 2100 years.

2. Calculate the temperature effects and (consumption

- equivalent) damages in each year resulting from the base line path of emissions.

a. In PAGE, the consumption

- equivalent damages in each period are calculated as a fraction of the EMF GDP forecast, depending on the

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14A

- 26temperature in that period relative to the pre

- industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of temperature change in that period.

c. In DICE, temperature affects both consumption and investment, so we first adjust the EMF GDP paths as follows: Using the Cobb

- Douglas production funct ion with the DICE2007 parameters, we extract the path of exogenous technical change implied by the EMF GDP and population paths, then we recalculate the baseline GDP path taking into account climate damages resulting from the baseline emissions path.

3. Ad d 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 comp uted in step 2 from those i n step 4 in each year. (DICE is run in 10

- year time steps, FUND in annual time steps, while the time steps in PAGE vary.)

6. Discount the resulting path of marginal damages back to the year of emissions using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed in step 6, divided by the unit of carbon emissions used to shock the models in step 3.

8. Multiply by 12/44 to convert from dollars per ton of carbon to dollars per ton of

CO 2 (2007 dollars) in DICE and FUND. (All calculations are done in tons of CO 2 in PAGE).

The steps above were repeated in each model for multiple future years to cover the time horizons anticipated for upcoming rulemaking analysis. To maintain consistency across the three IAMs, climate damages are calculated as lost consumption in each future year.

It is important to note that each of the three models has a different default end year. The default time horizon is 2200 for PAGE, 259 5 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 si gnificant fraction of damages under certain assumptions about the growth of marginal damages and discounting, so each model is run here through 2300. This step required a small adjustment in the PAGE model only. This step also required assumptions about GDP, population, and greenhouse gas emission trajectories after 2100, the last year for which these

data are available from the EMF

- 22 models. (A more detailed discussion of these assumptions is

included in the Annex .)

14A

- 27This exercise produces 45 separate distributions of the SCC for a given year, the product of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate distributions for consideration in a regulatory impact analysis.

To produce a range of plausible estim ates that still reflects the uncertainty in the estimation exercise, the distributions from each of the models and scenarios are equally weighed and combined to produce three separate probability distributions for SCC in a given year, one for

each assumed discount rate. These distributions are then used to define a range of point estimates for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than another. Because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational co ntext, we present SCCs based on the average values across models and

socioeconomic scenarios for each discount rate.

The interagency group selected four SCC values for use in regulatory analyses. Three values are based on the average SCC across models and socioeconomic and emissions scenarios at the 2.5, 3, and 5 percent discount rates. The fourth value is included to represent the higher

than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpos e, 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 of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range.

As previously di scussed, low probability, high impact events are incorporated into the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high

- temperature outcomes, which in turn lead to higher projections of damages. Although FUND does not include catastrophic damages (in contrast to the other two models), its probabilistic treatment o f 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 low er 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 generall y produces the highest estimates.

14A

- 28Table 14A.5

. 1 Disaggregated Social Cost of CO 2 Values by Model, Socioeconomic Trajectory, and Discount Rate for 2010 (in 2007 dollars)

Discount rate: 5% 3% 2.5% 3%

Model Scenario Avg Avg Avg 95thDICE 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 50.8PAGE 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 42.9 104.7FUND 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 lates t 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) cl ose to $8 per ton for a 4 percent discount rate. Note that these comparisons are only approximate since the literature generally relies on Ramsey discounting,

while we have assumed constant discount rates. bb

bb Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm) treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC estimates for values of ρ = 0, 1, and 3 in many recent papers ( e.g. Anthoff et al. 2009). The path of per

- capita consumption growth, g, varies over time but is treated deterministica lly in two of the three models. In DICE, g is

14A

- 29The SCC estimates from FUND are sensitive to differences in emissions paths but relatively insensitive to differences in GDP paths across scenarios, while the reverse is true for DICE and PAGE. This likely occurs because of several structural differences among the models. Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages increases with the level of temperature alone, whereas in FUND the fractional loss also increases with the rate of temperature change. Furthermor e, in FUND increases in income over time decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and PAGE. These structural differences among the models make FUND more sensitive to the path of emissions and les s 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 g lobal GDP levels in 2100 directly corresponds to the rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result that is to be expected given its less direct relationship between its damage function and GDP.

Figure 14A.5

. 1 Level of Global GDP across EMF Scenarios

Table 14A.5

. 2 shows the four selected SCC values in five

- year increments from 2010 to 2050. Values for 2010, 2020, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using a simple linear interpolation.

endogenous. Under Ramsey discounting, as economic growth slows in the future, the large damages from climate change that occur far out in the future are discounted at a lower rate than impacts that occur in th e nearer term. 0 50 100 150 200 250 300 350 400 450

2000 2010 2020 2030 2050 2100 Global

GDP (trillions

of U.S. dollars) IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

14A

- 30Table 14A.5

. 2 Social Cost of CO 2 , 2010

– 2050 (in 2007 dollar s)

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are internally consistent with other modeling assumption s. Table 14A.5

. 3 illustrates how the growth

rate for these four SCC estimates varies over time. The full set of annual SCC es timates between 2010 and 2050 is reported in the Annex

.

Table 14A.5

. 3 Changes in the Average Annual Growth Rates of SCC Estimates between

2010 and 2050

Average Annual Growth Rate (%) 5% 3% 2.5% 3.0%
Avg Avg Avg 95th

2010

- 2020 3.6% 2.1% 1.7% 2.2%

2020

- 2030 3.7% 2.2% 1.8% 2.2%

2030

- 2040 2.7% 1.8% 1.6% 1.8%

2040

- 2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions reductions in each year (the SCC in year t multiplied by the change in emissions in year t ) must be discounted to the present to determine its total net present value for use 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 t oday or emissions in a later year, should be discounted using the same rate. For example, climate damages in the year 2020 that are

14A

- 31calculated using a SCC based on a 5 percent discount rate also should be discounted back to the analysis year using a 5 perc ent discount rate. cc

14A.6 LIMITATIONS OF THE A NALYSIS

As noted, any estimate of the SCC must be taken as provisional and subject to further refinement (and possibly significant change) in accordance with evolving scientific, economic,

and ethical understandings. During the course of our modeling, it became apparent that there are several areas in particular need of additional exploration and research. These caveats, and additional observations in the following section, are necessary to consider wh en interpreting and applying the SCC estimates.

Incomplete treatment of non

- catastrophic damages. The impacts of climate change are expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain bec ause of the inherent complexity of climate processes, the economic behavior of current and future populations, and our inability to accurately forecast technological change and adaptation. Current IAMs do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature (some of which are discussed above) because of lack of precise information on the nature of damages and because the science incorporated into these models under standably lags behind the most recent research. Our ability to quantify and monetize impacts will undoubtedly improve with time. But it is also likely that even in future applications, a number of potentially significant damage categories will remain non

- monetized. (Ocean acidification is one example of a potentially large damage from 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 I ce 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 domi nate 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 sha pes of both the climate sensitivity distribution and the damage function at high temperature changes. Pindyck (2009) also used a simplified IAM to examine high

impact

, low

- probability risks, using a right

- skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficient, but in most cases found only a modest risk premium.

cc 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

- 32Given this difference in opinion, further research in this area is needed before its practical significance can be fully understood and a reasonable approach d eveloped to account for such risks in regulatory analysis. (The next section discusses the scientific evidence on catastrophic impacts in greater detail.)

Uncertainty in extrapolation of damages to high temperatures: The damage functions in

these IAMs are typically calibrated by estimating damages at moderate temperature increases ( e.g. , DICE was calibrated at 2.5 °C) and extrapolated to far higher temperatures by assuming that damages increase as some power of the temperature change. Hence, estimated damages are far more uncertain under more extreme climate change scenarios.

Incomplete treatment of adaptation and technological change: Each of the three integrated assessment models used here assumes a certain degree of low

- or no

- cost adaptation.

F or 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 intens ively (NRC 2009).

Climate change also will increase returns on investment to develop technologies that allow individuals to cope with adverse climate conditions, and IAMs to do not adequately account for this directed technological change. dd For exampl e, scientists may develop crops that are better able to withstand higher and more variable temperatures. Although DICE and FUND have both calibrated their agricultural sectors under the assumption that farmers will change land use practices in response to climate change (Mastrandrea, 2009), they do not take into account technological changes that lower the cost of this adaptation over time. On the other hand, the calibrations do not account for increases in climate variability, pests, or diseases, which c ould 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 respec t, it is difficult to determine whether the incomplete treatment of adaptation and technological change in these IAMs

under state 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 should be taken into account for regulatory policy. Even if individuals are not

risk

- averse for such scenarios, it is possible that regulatory policy should include a degree of

risk

- aversi on.

dd However these research dollars will be diver ted from whatever their next best use would have been in the absence of climate change (so productivity/GDP would have been still higher).

14A

- 33Assuming a risk

- neutral representative agent is consistent with OMB’s Circular A

- 4, which advises that the estimates of benefits and costs used in regulatory analysis are usually based on the average or the expected value and that “emphasis on these expected values is appropriate as long as society is ‘risk neutral’ with respect to the regulatory alternatives. While this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] analysis.”

Nordhaus (2008) points t o the need to explore the relationship between risk and income in the context of climate change across models and to explore the role of uncertainty regarding various parameters in the results. Using FUND, Anthoff et al (2009) explored the sensitivity of the SCC to Ramsey equation parameter assumptions based on observed behavior. They conclude that “the assumed rate of risk aversion is at least as important as the assumed rate of time preference in determining the social cost of carbon.” Since Circular A

- 4 allows for a different assumption on risk preference in regulatory analysis if it is adequately justified, we plan to continue investigating this issue.

14A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPA CTS AND DAMAGE

FUNCTIONS

As noted above, the damage function s underlying the three IAMs used to estimate the SCC may not capture the economic effects of all possible adverse consequences of climate change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular,

the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping point” behavior in Earth systems, (2) inter

- sectoral and inter

- regional interactions, including global security impacts of high

- end warming, and (3) limited near

- term substitutability between damage to natural systems and increased consumption.

It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal

government will continue to evolve with improvements in modeling. In the meantime, we

discuss some of the available evidence.

Extrapolation of climate damages to high levels of warming

The damage functions in the models are calibrated at moderate levels of warming and should therefore be viewed cautiously when extrapolated to the high temperatures found in the upper end of the distribution. Recent science suggests that there are a number of potential climatic “tipping points” at which the Earth system may e xhibit discontinuous behavior with potentially severe social and economic consequences ( e.g. , Lenton et al, 2008, Kriegler et al., 2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of

the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West Antarctic Ice Sheet, reorganization of the Atlantic Meridional Overturning Circulation, strengthening of El Niño

- Southern Oscillation, and the release of methane from melting permafrost. Many of t hese tipping points are estimated to have thresholds between about 3 °C and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed

14A

- 34through expert elicitation in 2005

– 2006 by Kriegler et al. (2009); results from this stu dy are highlighted in Table 14A.7

. 1

. Ranges of probability are averaged across core experts on each topic.

As previously mentioned, FUND does not include potentially catastrophic effects. DICE assumes a small probability of catastrophic damages that increases with increased warming, but the damages from these risks are incorporated as expected values ( i.e. , ignoring potential risk aversion). PAGE models catastrophic impacts in a probabilistic framework (see Figure 14A.4

. 1 ), so the high

- end output from PAGE potentially offers the best insight into the SCC if the world were to experience catastrophic climate change. For instance, at the 95 th percentile and a 3 percent discount rate, the SCC estimated by PAGE across the five socioeconomic and emission trajectories of $113 per ton of CO 2 is almost double the value estimate d by DICE, $58 per ton in

2010. We cannot evaluate how well the three m odels 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.

Table 14A.7

. 1 Probabilities of Various Tipping Points from Expert Elicitation

Possible Tipping Points Duration before effect is fully realized (in years) Additional Warming by 2100

0.5

- 1.5 C 1.5

- 3.0 C 3

- 5 C

Reorganization of Atlantic Meridional Overturning Circulation about 100 0

- 18% 6

- 39% 18

- 67%

Greenland Ice Sheet collapse at least 300 8

- 39% 33

- 73% 67

- 96%

West Antarctic Ice Sheet collapse at least 300 5

- 41% 10

- 63% 33

- 88%

Dieback of Amazon rainforest about 50 2

- 46% 14

- 84% 41

- 94%

Strengthening of El Niño

- Southern Oscillation about 100 1

- 13% 6

- 32% 19

- 49%

Dieback of boreal forests about 50 13

- 43% 20

- 81% 34

- 91%

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it deterministically (that is, by adding the expected value of the damage from a catastrophe to the aggregate damage function). In part, thi s results in different probabilities being assigned to a catastrophic event across the two models. For instance, PAGE places a probability near zero on a catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at

2.5 °C. By comparison, Kriegler et al. (2009) estimate a probability of at least 16

- 36 percent of crossing at least one of their primary climatic tipping points in a scenario with temperatures

about 2

- 4 °C warmer than pre

- Industrial levels in 2100.

14A

- 35It is impo rtant 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 qualitativ ely different state (for instance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a catastrophe is a low

- probability environmental change with high economic impact.

Failure to incorporate inter

- sectoral and inter

- regi onal interactions

The damage functions do not fully incorporate either inter

- sectoral or inter

- regional interactions. For instance, while damages to the agricultural sector are incorporated, the effects of changes in food supply on human health are not fully captured and depend on the modeler’s choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one region of the world on another region are not included in some of the models (FUND includes the effects of migration fr om sea level rise). These inter

- regional interactions, though difficult to quantify, are the basis for climate

- induced national and economic security concerns ( e.g. , Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at higher levels of warming. High

- end warming scenarios, for instance, project water scarcity affecting 4.3

- 6.9 billion people by 2050, food scarcity affecting about 120 million additional people by 2080, and the creation of millions of climate refugees (Eas terling et al., 2007; Campbell et al., 2007).

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming may have severe consequences for natural systems. For instance, du ring the Paleocene

- Eocene Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically rapid release of carbon associated with an approximately 5 °C increase in global mean temperatures, the effects included shifts of about 400

- 9 00 miles in the range of plants (Wing et al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 2009).

The three IAMs used here assume that it is possible to compensate for the economic consequences of damages to natural systems through increased consumption of non

- climate goods, a common assumption in many economic models. In the context of climate change, however, it is possible that the damages to natural systems could become so great that no increase in consump tion of non

- climate goods would provide complete compensation (Levy et al., 2005). For instance, as water supplies become scarcer or ecosystems become more fragile

and less bio

- diverse, the services they provide may become increasingly more costly to repl ace. Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs (Sterner and Persson, 2008) indicate that the optimal degree of emissions abatement can be considerably greater than is commonly recognized.

14A.8 CONCLUSION

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are

14A

- 36based on the average SCC across models and socioeconomic and emissions sc enarios 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 v alue 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.

We noted a number of limitations to this analysis, including the incompl ete way in which the integrated assessment models capture catastrophic and non

- catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regard ing risk aversion. The limited amount of research linking climate impacts to economic damages makes this modeling exercise even more difficult. It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps a nd 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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- 41Stern N. 2008. “The economics of climate change.” American Economic Review 98(2):1

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14A

- 4214A.9 ANNEX

Table 14A.9

. 1 Annual SCC Values: 2010

– 2050 (in 2007 dollars)

Discount Rate 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2011 4.9 21.9 35.7 66.5

2012 5.1 22.4 36.4 68.1

2013 5.3 22.8 37.0 69.6

2014 5.5 23.3 37.7 71.2

2015 5.7 23.8 38.4 72.8

2016 5.9 24.3 39.0 74.4

2017 6.1 24.8 39.7 76.0

2018 6.3 25.3 40.4 77.5

2019 6.5 25.8 41.0 79.1

2020 6.8 26.3 41.7 80.7

2021 7.1 27.0 42.5 82.6

2022 7.4 27.6 43.4 84.6

2023 7.7 28.3 44.2 86.5

2024 7.9 28.9 45.0 88.4

2025 8.2 29.6 45.9 90.4

2026 8.5 30.2 46.7 92.3

2027 8.8 30.9 47.5 94.2

2028 9.1 31.5 48.4 96.2

2029 9.4 32.1 49.2 98.1

2030 9.7 32.8 50.0 100.0

2031 10.0 33.4 50.9 102.0

2032 10.3 34.1 51.7 103.9

2033 10.6 34.7 52.5 105.8

2034 10.9 35.4 53.4 107.8

2035 11.2 36.0 54.2 109.7

2036 11.5 36.7 55.0 111.6

2037 11.8 37.3 55.9 113.6

2038 12.1 37.9 56.7 115.5

2039 12.4 38.6 57.5 117.4

2040 12.7 39.2 58.4 119.3

2041 13.0 39.8 59.0 121.0

2042 13.3 40.4 59.7 122.7

2043 13.6 40.9 60.4 124.4

2044 13.9 41.5 61.0 126.1

2045 14.2 42.1 61.7 127.8

2046 14.5 42.6 62.4 129.4

2047 14.8 43.2 63.0 131.1

2048 15.1 43.8 63.7 132.8

2049 15.4 44.4 64.4 134.5

2050 15.7 44.9 65.0 136.2

This Annex provides additional technical information about the non

- 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.

14A

- 4314A.9.1 Other (non

- CO 2 ) gases

In addition to fossil and industr ial 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 forcing s (RF) due to other factors ( e.g. , aerosols and other gases). Specifically,

to obtain the RF associated with the non

- CO 2 EMF emissions only, we calculated the RF associated with the EMF atmospheric CO 2 concentrations and subtracted them from the EMF

total RF. ee This approach respects the EMF scenarios as much as possible and at the same time takes account of those components not included in the EMF projections. Since each model treats

non

- CO 2 gases diff erently ( 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 FU ND. The model default trajectories for CH 4 , N 2 0, 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 f or everything else. To include the EMF values, we

removed the default CH 4 and SF 6 factors ff , decomposed the excess forcing vector, and constructed a new excess forcing vector that includes the EMF RF for CH 4 , N 2 0, and fluorinated gases, as well as the mode l 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 DICE2007 to the Intergovernmental Panel on Climate Change’s (IPCC) Fourth Assessment Report (AR4) and the discussion of aerosol forecasts in the IPCC’s Third Assessment Report (TAR) and in AR4, as explained below. In DICE2007, Nordhaus assumes that exogenous forcing from all non

- CO 2 sources is

- 0.06 W/m 2 in 2005, as reported in AR4, and i ncreases 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

ee 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 resulting CO 2 concentrations were used. Note also that MERGE assumes a neutral biosphere so ne t 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).

ff Both the model d efault 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

- 44decomposed 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.

For subsequent years, we calculated the DICE default RF from aerosols and other non

CO 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 ther eafter ; 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 R F 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 m ore consistent with the AR4 discussion of the post

- SRES literature on aerosols:

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the

post

- SRES scenarios. Generally, these emissions are projected to be lower than reported in SRES. {WGIII 3.2, TS.3, SPM}. gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent

with the recent literature on these em issions. For example, the f igure below shows that the sulfur dioxide emissions peak over the short term of some SRES scenarios above the upper bound

estimates of the more recent scenarios. hh Recent scenarios project sulfur emissions to peak earlier

and at lower levels compared to the SRES in part because of new information about present and planned sulfur legislation in some developing countries, such as India and China. ii The lower

bound projections of the recent literature have also shifted downward sligh tly 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

.

gg AR4 Synthesis Report, p. 44, www.ipcc.ch/pdf/asses sment

- report/ar4/syr/ar4_syr.pdf

hh See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850

- 2000: methods and results. Joint Global Research Institute, College Park, 14 pp.

ii See Carmichael, G., D. Streets, G . Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental Science and Technology, 36(22):4707

- 4713; Streets, D., K. Jiang, X. Hu , J. Sinton, X.

- Q. Zhang, D. Xu, M. Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835

- 1837.

14A

- 45. Figure 14A.9

. 1 Sulfur Dioxide Emission Scenarios

Notes: Thick colored lines depict the four SRES marker scenarios and black dashed lines show the median, 5 th

, and 95 th percentile of the frequency distribu tion 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 proj ection 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 pe rcent (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 a dded to the fossil and

industrial CO 2 emissions pathway.

14A.9.2 Extrapolating Emissions Projections to 2300

To run each model through 2300 requires assumptions about GDP, population, greenhouse gas emissions, and radiative forcing trajectories after 2100, the last year for which these projections are available from the EMF

- 22 models. These inputs were extrapolated from

2100 to 2300 as follows:

1. Population growth rate declines linearly, reaching zero in the year 2200.

14A

- 462. GDP/per capita growth rate decline s linearly, reaching zero in the year 2300. 3. The decline in the fossil and industrial carbon intensity (CO 2 /GDP) growth rate over

2090

- 2100 is maintained from 2100 through 2300.

4. Net land use CO 2 emissions decline linearly, reaching zero in the year 2200.

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 sc enario. This is based on the idea that increasing scarcity of natural resources and the degradation of environmental sinks available for assimilating pollution from economic production activities may eventually overtake the rate of technological progress. Thus, the overall rate of economic growth may slow over the very long run. The interagency group also considered allowing an exponential decline in the growth rate of GDP per capita. However, since this would require an additional assumption about how close to zero the growth rate would get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by

2300.

The population growth rate is also assumed to decline linearly, reaching zero by 2200. This assumption is reas onably consistent with the United Nations long run population forecast, which estimates global population to be fairly stable after 2150 in the medium scenario (UN 2004). jj The resulting range o f EMF population trajectories (f igure below ) also encompass t he 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 sam e 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 c arbon 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.

Figures below show the paths of global population, GDP, fossil and industrial CO 2 emissions, net land CO 2 emissio ns, non

- CO 2 radiative forcing, and CO 2 intensity (fossil and

industrial CO 2 emissions/GDP) resulting from these assumptions.

jj United Nations. 2004. World Population to 2300

. www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf

14A

- 47Figure 14A.9

. 2 Global Population, 2000

- 2300 (Post

- 2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.)

Note: In the fifth scenario, 2000

- 2100 population is equal to the average of the population under the 550 ppm 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

14A

- 48Figure 14A.9

. 3 World GDP, 2000

- 2300 (Post

- 2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in the year 2300)

Note: In the fifth scenario, 2000

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

- participation, not

- to

- exceed scenarios considered by each of the four models.

Figure 14A.9

. 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 ) 0 500 1000 1500 2000

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

Extrapolations

YearWorld GDP [$trillion]

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

14A

- 49Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, fu ll

- participation, not

- to

- exceed scenarios considered by each of the four

models.

Figure 14A.9

. 5 Global Net Land Use CO 2 Emissions, 2000

- 2300 (Post

- 2100 extrapolations

assume emissions decline linearly, reaching zero in the year 2200 ) kk

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models.

kk MERGE assumes a neutral biosphere so net land CO 2 emissions are set to zero for all year s for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use emissions from the other three models and divide by 4). -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scneario

ExtrapolationsNet land CO2 emissions [GtCO2/yr]

Year z

14A

- 50Figure 14A.9

. 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 o f the emissions under the 550 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

14A

- 51Figure 14A.9

. 7 Global CO 2 Intensity (fossil & industrial CO 2 emissions/GDP), 20 00

- 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 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models. 0 0 0 0 0 1 1 1 1

2000 2050 2100 2150 2200 2250 2300 IMAGE

MERGE

MESSAGE

MiniCAM

5th scenario

ExtrapolationsFossil & Industrial CO2 intensity [GtCO2/trillion $]

Year

14A

- 52Table 14A.9

. 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

14A

- 53Table 14A.9

. 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 53.6

14A

- 5 4Table 14A.9

. 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.9

. 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

- 55Table 14A.9

. 5 Additional Summary Statistics of 2010 Global SCC Estimates

Discount

Rate Scenario

DICE PAGE FUND

5% Mean 9 6.5

- 1.3

Variance 13.1 136 70.1

Skewness 0.8 6.3 28.2

Kurtosis 0.2 72.4 1,479.00

3% Mean 28.3 29.8 6

Variance 209.8 3,383.70 16,382.50

Skewness 1.1 8.6 128

Kurtosis 0.9 151 18,976.50

2.50% Mean 42.2 49.3 13.6

Variance 534.9 9,546.00 #######

Skewness 1.2 8.7 149

Kurtosis 1.1 143.8 23,558.30

14A

- 56APPENDIX 14B. TECHNICAL UPDATE OF SOCIAL COST OF CARBO N FOR

REGULATORY IMPACT AN ALYSIS UNDER EXECUTI VE ORDER 12866

TABLE OF CONTENTS

14B.1 PREFACE

................................ ................................ ................................ .................... 14B

- 1

14B.2 PURPOSE

................................ ................................ ................................ .................... 14B

- 1

14B.3 SUMMARY OF MODEL UPD ATES

................................ ................................ ......... 14B

- 2

14B.3.1 DICE

................................ ................................ ................................ ................ 14B

- 2

14B.3.1.1 Carbon Cycle Parameters

................................ ................................ ....... 14B

- 2

14B.3.1.2 Sea Level Dynamics

................................ ................................ ............... 14B

- 3

14B.3.1.3 Re

- calibrated Damage Function

................................ ............................. 14B

- 3

14B.3.2 FUND

................................ ................................ ................................ ............... 14B

- 4

14B.3.2.1 Space Heating

................................ ................................ ......................... 14B

- 4

14B.3.2.2 Sea Level Rise and Land Loss

................................ ............................... 14B

- 5

14B.3.2.3 Agriculture

................................ ................................ ............................. 14B

- 5

14B.3.2.4 Temperature Response Model

................................ ................................ 14B

- 6

14B.3.2.5 Methane

................................ ................................ ................................ .. 14B

- 6

14B.3.3 PAGE

................................ ................................ ................................ ............... 14B

- 6

14B.3.3.1 Sea Level Rise

................................ ................................ ........................ 14B

- 7

14B.3.3.2 Revised Damage Function to Account for Saturation

............................ 14B

- 7

14B.3.3.3 Regional Scaling Factors

................................ ................................ ........ 14B

- 7

14B.3.3.4 Probability of a Discontinuity

................................ ................................ 14B

- 7

14B.3.3.5 Adaptation

................................ ................................ .............................. 14B

- 8

14B.3.3.6 Other Noteworthy Changes

................................ ................................ .... 14B

- 8

14B.4 REVISED SCC ESTIMATE S

................................ ................................ ..................... 14B

- 9

14B.5 OTHER MODEL LIMITATI ONS OR RESEARCH GAPS

................................ .... 14B

- 11

REFERENCES

................................ ................................ ................................ ...................... 14B

- 12

14B.6 ANNEX

................................ ................................ ................................ ...................... 14B

- 14

LIST OF TABLES

Table 14B.4.1 Revised Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars per ton of

CO 2 )

................................ ................................ ................................ ........... 14B

- 9

Table 14B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and

2050

................................ ................................ ................................ .......... 14B

- 10

Table 14B.6.1 Annual SCC Values: 2010

- 2050 (2007$/ton CO 2 )

................................ .. 14B

- 14

Table 14B.6.2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ......... 14B

- 15

Table 14B.6.3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

................. 14B

- 15

Table 14B.6.4 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 )

................................ ................................ ................................ ......... 14B

- 16

LIST OF FIGURES

14B

- iFigure 14B.4.1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 )

............ 14B

- 10

14B

- iiAPPENDIX 14B. TECHNI CAL UPDATE OF SOCIAL COST OF CARBON FOR

REGULATORY IMPACT AN ALYSIS UNDER EXECUTI VE ORDER 12866

14B.1 PREFACE

The following text is reproduced almost verbatim from the May 2013 report of the Interagency Working Group on the Social Cost of Carbon of the United States Government

. Minor changes were made to the working group's report to make it more consistent with t he rest of this technical support document.

14B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) a estimates from the 2010 interagency technical support document (TSD) (Interagency Working Group on Social Cost of Carbon 2010). 1 E.O. 13563 commits the Administration to regulatory decision making “based on the best available science.” b Additionally, the interagency group recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates

that reflect t he growing body of scientific and economic knowledge become available. c New versions of the three integrated assessment models used by the U.S. government to estimate the SCC (DICE, FUND, and PAGE), are now available and have been published in the peer

re viewed literature. While acknowledging the continued limitations of the approach taken by the interagency group in 2010 (documented in the original 2010 TSD), this document provides an update of the SCC estimates based solely on the latest peer

- reviewed ve rsion of the models, replacing model versions that were developed up to ten years ago in a rapidly evolving field. It does not revisit other assumptions with regard to the discount rate, reference case socioeconomic and emission scenarios, or equilibrium c limate sensitivity. Improvements in the way damages are modeled are confined to those that have been incorporated into the latest versions of the models by the developers themselves in the peer

- reviewed literature. The Environmental Protection Agency (EPA) , in collaboration with other Federal agencies such as the Department of Energy (DOE), continues to investigate potential improvements to the way in which economic damages associated with changes in CO 2 emissions are quantified.

Section 14B.3 summarizes the major updates relevant to SCC estimation that are contained in the new versions of the integrated assessment models released since the 2010

in teragency report. Section 14B.4 presents the updated schedule of SCC estimates for 2010

– 2050 based on these versions of the models.

a In this document, we present all values of the SCC as the cost per metric ton of 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 carbon is 3.67

. b http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo135 63_01182011.pdf

c See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010). 1

14B

- 114B.3 SUMMARY OF MODEL UPD ATES

This section briefly summarizes changes integrated into the mos t recent versions of the three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on describing those model updates that are relevant to estimating the social cost of carbon. For example, both the DICE and PAGE models now i nclude an explicit representation of sea level rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to ensure damages are constrained GDP, updated regional scaling of damages, and a revised treatment of potentially abrup t shifts in climate damages. In the most recent version of DICE, the model’s simple carbon cycle has been updated to be more consistent with a relatively more complex climate model. The FUND model includes updated damage functions for sea level rise impac ts, the agricultural sector, and reduced space heating requirements, as well as changes to the response of temperature to the buildup of GHG concentrations and the inclusion of indirect effects of methane emissions. Changes made to parts of the models that are superseded by the interagency working group’s modeling assumptions

– regarding climate sensitivity, discounting,

and socioeconomic variables

– are not discussed.

14B.3.1 DICE

Changes in the DICE model relevant for the SCC estimates developed by the interagen cy working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit representation of sea level dynamics, and 3) a re

- calibrated damage function that includes an explicit representation of economic damages from sea level rise. Changes were also made to other parts of the DICE model

— including the equilibrium climate sensitivity parameter, the rate of change of total factor productivity, and the elasticity of the marginal utility of consumption

— but these components of DICE are sup erseded by the interagency working group’s assumptions
and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008) 2

and on DICE2010 in Nordhaus (2010) 3 and the associated on

- line appendix containing supplemental information.

14B.3.1.1 Carbon Cycle Parameters

DICE uses a three

- box model of carbon stocks and flows to represent the accumulation

and transfer of ca rbon among the atmosphere, the shallow ocean and terrestrial biosphere, and the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44). 2 d
Carbon cycle transfer coefficient values in DICE2010 are based on re

- calibration of the model to match the newer version of MAGICC (Nordhaus 2010 p 2). 3 For example, in DICE2010 in each decade, 12 percent of the carbon in the atmosphere is transferred to the shallow ocean, 4.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 94.8 percent remains

in the shallow ocean, and 0.5 percent is transferred to the deep ocean. For comparison, in DICE 2007, 18.9 percent of the carbon in the atmosphere is transferred to the shallow ocean each

d MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) to emulate projections from much more sophisticated state of the art earth system simulation models (Randall et al. 2007). 4

14B

- 2decade, 9.7 percent of the carbon in the shallow ocean is transferred to th e atmosphere, 85.3 percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as a carbon sink and therefore a higher concentration of carbon in the atmosphere than in DICE2007, for a given path of emissions. All else equal, these changes will generally increase the level of warming and therefore the SCC estimates in DICE2010 relative to those from

DICE2007.

14B.3.1.2 Sea Level Dynamics

A new feature of DICE2010 is an explicit representation of the dynamics of the global average sea level anomaly to be used in the updated damage function (discussed below). This section contains a brief description of the sea level rise (SLR) module; a more detailed

descri ption can be found on the model developer’s website. e The average global sea level anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland ice sheet, and 4) melting of the Antarc tic ice sheet.

The parameters of the four components of the SLR module are calibrated to match consensus results from the IPCC’s Fourth Assessment Report. 4

, f The rise in sea level from thermal expansion in each time period (decade) is 2 percent of the difference between the sea level in the previous period an d the long run equilibrium sea level, which is 0.5 meters per degree Celsius (°C) above the average global temperature in 1900. The rise in sea level from the melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above

t he average global temperature in 1900.

The contribution to sea level rise from melting of the Greenland ice sheet is more complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1

o C and increases linearly from 0 me ters to a maximum of 7.3 meters. The contribution to SLR in each period is proportional to the difference between the previous period’s sea level anomaly and the equilibrium sea level anomaly, where the constant of proportionality increases with the

temper ature anomaly in the current period.

The contribution to SLR from the melting of the Antarctic ice sheet is

- 0.001 meters per decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 0.025 meters per decade at a tempe rature anomaly of 6 °C.

14B.3.1.3 Re

- calibrated Damage Function

Economic damages from climate change in the DICE model are represented by a fractional loss of gross economic output in each period. A portion of the remaining economic

e Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: http://nordhaus.econ.yale.edu/documents/SLR_021910.pdf

. f For a review of post

- IPCC AR4 research on sea level rise, see Nicholls et al. (2011 ) 5 and NAS (2011). 6

14B

- 3output in each period (net of cl imate change damages) is consumed and the remainder is invested in the physical capital stock to support future production, so each period’s climate damages will reduce consumption in that period and in all future periods due to the lost investment. The

fr action of output in each period that is lost due to climate change impacts is represented as one minus a fraction, which is one divided by a quadratic function of the temperature anomaly, producing a sigmoid (“S”

- shaped) function. The loss function in DICE 2010 has been expanded by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the temperature anomaly coefficients have been recalibrated to avoid double

- counting damages from sea level rise that were implicitly include d in these parameters in DICE2007.

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3), 3 who notes

that “...d amages in the uncontrolled (baseline) ( i.e. , reference) case ... in 2095 are $12 trillion, or 2.8 percent of global output, for a global temperature increase of 3.4 o C above 1900 levels.” This compares to a loss of 3.2 percent of global output at 3.4 o C in DICE2007. However, in DICE2010 (as downloaded from the homepage of William Nordhaus), annual damages are lower in most of the early periods but higher in later periods of the time horizon than would be calculated using the DICE2007 damage function. Specifi cally, the percent difference between damages in the base run of DICE2010 and those that would be calculated using the DICE2007 damage function starts at +7 percent in 2005, decreases to a low of

- 14 percent in 2065, then continuously increases to +20 perc ent by 2300 (the end of the interagency analysis time horizon), and to +160 percent by the end of the model time horizon in 2595. The large increases in the far future years of the time horizon are due to the permanence associated with damages from sea

lev el rise, along with the assumption that the sea level is projected to continue to rise long after the global average temperature begins to decrease. The changes to the loss function generally decrease the interagency working group SCC estimates slightly, all else equal.

14B.3.2 FUND

FUND version 3.8 includes a number of changes over the previous version 3.5 used in the interagency report. Documentation supporting FUND and the model’s source code for all

versions of the model is available from the model authors. g Notable changes, due to their impact on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level rise damage functions in addition to changes to the temperature response function and the

inclusion of indirect effects from methane emissions. h We discuss each of these in turn.

14B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are based on the estimated impact due to one degree of warming. These baseline damages are scaled

ba sed on the forecasted temperature anomaly’s deviation from the one degree benchmark and

g www.fund

- model.org/ . This report uses version 3.8 of the FUND model, which represents a modest update to the

most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013). 7 For the purpose of computing the SCC, the relevant changes are associated with improving consi stency with IPCC AR4 by adjusting the atmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along with making minor stability improvements in the sea wall construction algorithm.

h The other damage sectors (water resou rces, space cooling, land loss, migration, ecosystems, human health, and extreme weather) were not the subject of significant updates.

14B

- 4adjusted for changes in vulnerability due to economic and energy efficiency growth. In FUND 3.5, the function that scales the base year damages adjusted for vulnerabili ty allows for the possibility that in some simulations the benefits associated with reduced heating needs may be an unbounded convex function of the temperature anomaly. In FUND 3.8, the form of the scaling

has been modified to ensure that the function is everywhere concave, meaning that for every simulation there will exist an upper bound on the benefits a region may receive from reduced space heating needs. The new formulation approaches a value of two in the limit as the temperature anomaly increases, or in other words, assuming no decrease in vulnerability, the reduced expenditures on space heating at any level of warming will not exceed two times the reductions experienced at one degree of warming. Since the reduced need for space heating represents a b enefit of climate change in the model, or a negative damage, this change will increase the estimated SCC. This update accounts for a significant portion of the difference in the expected SCC estimates reported by the two versions of the model when run prob abilistically.

14B.3.2.2 Sea Level Rise and Land Loss

The FUND model explicitly includes damages associated with the inundation of dry land due to sea level rise. The amount of land lost within a region is dependent upon the proportion of the coastline being protec ted by adequate sea walls and the amount of sea level rise. In FUND 3.5 the function defining the potential land lost in a given year due to sea level rise is linear in the rate of sea level rise for that year. This assumption implicitly assumes that all r egions are well represented by a homogeneous coastline in length and a constant uniform slope moving inland. In FUND 3.8 the function defining the potential land lost has been changed to be a non

linear function of sea level rise, thereby assuming that the slope of the shore line is not constant moving inland, with a positive first derivative. The effect of this change is to typically reduce the vulnerability of some regions to sea level rise based land loss, therefore having an effect of lowering the expec ted SCC estimate. The model has also been updated to assume that the value of dry land at risk of inundation is not uniform across a region but will be a decreasing function of protection measure, thereby implicitly assuming that the most valuable land wi ll be protected

first.

14B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are measured as proportional to the sector’s value. The fraction is made up of three additively separable components that represent the effects from carbon fer tilization, the rate of temperature change, and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the ratio of two normal distributions. Within this specification, as draws from the distribution in the denominator approached zero the share of the sector’s value “lost” approaches ( +/

- ) infinity independent of the temperature anomaly itself. In FUND 3.8, the linear and quadratic coefficients are drawn directly from truncated normal distributions so that they remain in the

range [0, ) ∞ and ( ,0]

− ∞ , respectively, where the means for the new distributions are set equal to the ratio of the means from the normal distributions used in the previous version. In general the impact of this change has been to increase the likelihood that increases in the te mperature level

14B

- 5will have either larger positive or negative effects on the agricultural sector relative to the previous version (through eliminating simulations in which the “lost” value approached (+/

- ) infinity). The net effect of this change on the SCC estimates is difficult to predict.

14B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing into the current expected temperature anomaly. In FUND, a given year’s increase in the cumulative tempe rature anomaly is based on a mean reverting function where the mean equals the equilibrium temperature anomaly that would eventually be reached if that year’s level of radiative forcing were sustained. The rate of mean reversion defines the rate at which t he transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response is defined as a decreasing linear function of equilibrium climate sensitivity to capture the fact that the progressive heat uptake of the deep ocean causes t he rate to slow at higher values of the equilibrium climate sensitivity. In FUND 3.8, the rate of temperature response has been updated to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity of the rate of tempe rature response to the level of the equilibrium climate sensitivity. Therefore in FUND 3.8, the temperature response will typically be faster than in the previous version. The overall effect of this change is likely to increase estimates of the SCC as high er temperatures are reached during the timeframe analyzed and as the same damages experienced in the previous version of the model are now experienced earlier and therefore discounted less.

14B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane em issions, and has developed methods for proxying such effects when computing the global warming potential of methane
(Forster et al. 2007). 8 FUND 3.8 now includes the same methods for incorporating the indirect effects of methane emissions. Specifically, the average at mospheric lifetime of methane has been set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative forcing associated with atmospheric methane has also been increase by 40% to account for its net impact on ozone producti on and increase in stratospheric water vapor. The general effect of this increased radiative forcing will be to increase the estimated SCC values, where the degree to which this occurs will be dependent upon the relative curvature of the damage functions w ith respect to the temperature anomaly.

14B.3.3 PAGE

PAGE09 (Hope 2012) 9 includes a number of changes from PAGE2002, the version used in the 2009 SCC interagency report. The changes that most directly affect the SCC estimates include: explicitly modeling the impacts from sea level rise, revisions to the damage function to ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised treatment for the probabi lity of a discontinuity within the damage function, and revised assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and the calculation of regional temperatures. More details on PAGE2009 can be found in three working p apers (Hope 2011a, 2011b, 2011c). 10

, 11

, 12 A description of PAGE2002 can be found in Hope (2006). 13

14B

- 614B.3.3.1 Sea Level Rise

While PAGE2002 aggregates all damages into two categories

– economic and non

economic impacts

- PAGE2009 adds a third explicit category: damages from sea level rise. In the previous version of the model, damages from sea level rise were subsumed by the oth er damage categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea level based on the assumption that low

- lying shoreline areas will be associated with higher damages than current inland areas. Damages from the econom ic and non

- economic sector were adjusted to account for the introduction of this new category.

14B.3.3.2 Revised Damage Function to Account for Saturation

In PAGE09, small initial economic and non

- economic benefits (negative damages) are

modeled for small temperature increases, but all regions eventually experience positive economic damages from climate change, where damages are the sum of additively separable polynomial functions of temperature and sea level rise. Damages transition from this polynomial fu nction to a logistic path once they exceed a certain proportion of remaining Gross Domestic Product (GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, which allowed Eastern Europe to potentially experience large bene fits from temperature increases, and which also did not bound the possible damages that could be experienced.

14B.3.3.3 Regional Scaling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the European Union (EU) and then, assume s that damages for other regions are proportional based on a given scaling factor. The scaling factor in PAGE09 is based on the length of a region’s coastline relative to the EU (Hope 2011b). 11 Because of the long coastline in the EU, other regions are, on average, less vulnerable than the EU for the same sea level and temperature increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on four studies reported in the IPCC’s third assessment report, and allowed for benefits from temperature increase in Eastern Europe, smaller impacts in developing countries, and higher damages in develop ing countries.

14B.3.3.4 Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” were modeled as an expected value. That is, additional damages from an extreme event, such as extreme melting of the Greenland ice sheet, were multi plied by the probability of the event occurring and added to the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event for each year in the model. The damages for each model run are estimated with or without a

discon tinuity occurring, rather than as an expected value. A large

‐ scale discontinuity becomes possible when the temperature rises beyond some threshold value between 2 and 4°C. The probability that a discontinuity will occur beyond this threshold then increases by between 10 and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a mean of 15 percent) in addition to other dam ages, and other regions lose an amount determined by the regional scaling factor. The threshold value for a possible discontinuity is lower than in

14B

- 7PAGE2002, while the rate at which the probability of a discontinuity increases with the temperature anomaly and the damages that result from a discontinuity are both higher than in PAGE2002. The model assumes that only one discontinuity can occur and that the impact is phased in over a period of time, but once it occurs, its effect is permanent.

14B.3.3.5 Adaptation

As i n PAGE2002, adaptation is available to increase the tolerable level of temperature change and can help mitigate any climate change impacts that still occur. In PAGE this adaptation is the same regardless of the temperature change or sea level rise and is t herefore akin to what is more commonly considered a reduction in vulnerability. It is modeled by modifying the temperature change and sea level rise used in the damage function or by reducing the damages by some percentage. PAGE09 assumes a smaller decreas e in vulnerability than the previous version of the model and assumes that it will take longer for this change in vulnerability to be realized. In the aggregated economic sector, at the time of full implementation, this adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature anomalies between 1°C and 3°C, it will reduce damages by 15

- 30 percent (depending on the region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation

was assumed to reduce economic sector damages up to 3°C by 50

- 90 percent after 20 years. Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change.

For the non

- economic sector, in PAGE09 adaptation is available to reduce 15 percent of the damages due to a temperature increase between 0°C and 2°C and is assumed to take 40 years to fully implement, instead of 25 percent of the damages over 20 years assumed in PAGE2002. Similarly, adaptation is assumed to alleviate 25

- 50 percent of the damages from the first 0.20 to 0.25 meters of sea level rise but is assumed to be ineffective thereafter. Hope (2011c) 12 estimates that the less optimistic assumptions regarding the ability to offset impacts of temperature and sea level rise via adaptation increase the SCC by approximately 30 percent.

14B.3.3.6 Other Noteworthy Changes

Two other changes in the mode l are worth noting. A revised carbon cycle feedback is

introduced to simulate decreased CO 2 absorption by the terrestrial biosphere and ocean as the temperature rises. This feedback is linear in the average global and annual temperature anomaly but is capp ed at a maximum value. In the previous version of PAGE, an additional amount was

added to the CO 2 emissions each period to account for a decrease in ocean absorption and a loss of soil carbon. Also updated is the method by which the average global and annu al temperature anomaly is downscaled to determine annual average regional temperature anomalies to be used in the regional damage functions. In the previous version of PAGE, the scaling was determined solely based on regional difference in emissions of sul fate aerosols. In PAGE09, this regional temperature anomaly is further adjusted using an additive factor that is based on the average absolute latitude of a region relative to the area weighted average absolute latitude of the Earth’s

landmass.

14B

- 814B.4 REVISED SCC ESTIMATES

The updated versions of the three integrated assessment models were run using the same methodology detailed in the 2010 TSD. 1 The approach along with the inputs for the socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and di scount
rate remains the same. This includes the five reference scenarios based on the EMF

- 22 modeling exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth Assessment Report of the IPCC, and three constant discou nt rates of 2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD applied equal weight to each model and socioe conomic scenario in order to reduce the dimensionality down to three separate distributions representative of the three discount rates. The interagency group selected four values from these distributions for use in regulatory analysis.

Three values are bas ed on the average SCC across models and socio

- economic

- emissions scenarios at the 2.5, 3, and 5 percent discount rates, respectively. The fourth value was chosen to represent the higher

- than

- expected economic impacts from climate change further out in the tails of the SCC distribution. For this purpose, the 95th percentile of the SCC estimates at a 3 percent discount rate was chosen. (A detailed set of percentiles by model and scenario combination is available in the Annex.) As noted in the original TSD, “ the 3 percent discount rate is the central value, and so the central value that emerges is the average SCC across models at the 3 percent discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in regulatory impact analys is, the interagency group emphasizes the importance and value of including all four SCC values.

Table 14B.4

. 1 shows the four selected SCC estimates in five year increments from 2010 to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using basic linear interpolation. The full set of annual SCC estimates between 2010 and 2050 is reported in the Annex.

Table 14B.4

. 1 Revised Social Cost of CO 2 , 2010

– 2050 (in 2007 dollars per ton of CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 33 52 90

2015 12 38 58 109

2020 12 43 65 129

2025 14 48 70 144

2030 16 52 76 159

2035 19 57 81 176

2040 21 62 87 192
2045 24 66 92 206

2050 27 71 98 221

14B

- 9The SCC estimates using the updated versions of the models are higher than those reported in the TSD due to the changes to the models outlined in the previous section. Figure

14B.4

. 1 illustrates where the four SCC values for 2020 fall within the full distribution for each discount rate based on the combined set of runs for each model and scenario (150,000 estimates

in tot al for each discount rate). In general, the distributions are skewed to the right and have long tails. The Figure also shows that the lower the discount rate, the longer the right tail of the

distribution.

Figure 14B.4

. 1 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 )

As was the case in the original TSD, the SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems

be come more stressed in response to greater climatic change. The approach taken by the interagency group is to allow the growth rate to be determined endogenously by the models through running them for a set of perturbation years out to 2050. Table 14B.4

. 2 illustrates how the growth rate for these four SCC estimates varies over time.

Table 14B.4

. 2 Average Annual Growth Rates of SCC Estimates betwe en 2010 and 2050
Average Annual 5.0% 3.0% 2.5% 3.0% Rate (%) Avg Avg Avg 95th

2010

- 2020 1.2% 3.2% 2.4% 4.3%

2020

- 2030 3.4% 2.1% 1.7% 2.4%

2030

- 2040 3.0% 1.8% 1.5% 2.0%

2040

- 2050 2.6% 1.6% 1.3% 1.5%

14B

- 10The future monetized value of emission reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must be discounted to the present to determine its total net present value for use in regulatory analysis. As previously disc ussed in the original TSD, damages from future emissions should be discounted at the same rate as that used to calculate the SCC estimates themselves to ensure internal consistency

– i.e. , future damages from climate change, whether they result from emissi ons today or emissions in a later year, should be discounted using the same rate.

14B.5 OTHER MODEL LIMITATI ONS OR RESEARCH GAPS

The 2010 interagency SCC technical support report discusses a number of important

limitations for which additional research is neede d. In particular, the document highlights the need to improve the quantification of both non

- catastrophic and catastrophic damages, the treatment of adaptation and technological change, and the way in which inter

- regional and inter

sectoral linkages are mo deled. It also discusses the need to more carefully assess the implications of risk aversion for SCC estimation as well as the inability to perfectly substitute between

climate and non

- climate goods at higher temperature increases, both of which have impli cations for the discount rate used. EPA, DOE, and other agencies continue to engage in long

- term research work on modeling and valuation of climate impacts that we expect will inform improvements in SCC estimation in the future.

14B

- 11REFERENCES

1. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States

Government. < www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

Cost

- of

- Carbon

- for

- RIA.pdf >

.

2. Nordhaus, W., A Question of Balance . 2008. Yale University Press: New Haver.

3. Nordhau s, W., Economic aspects of global warming in a post

- Copenhagen environment

. Proceedings of the National Academy of Sciences , 2010. 107(26): pp. 11721

- 11726

4. Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models

and Their Evaluation . In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climat e Change , S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA

5. Nicholls, R. J., N. Marinova, J.A. Lowe, S. Brown, P. Vellin ga, D. de Gusmão, J. Hinkel and R.S.J. Tol, Sea

- level rise and its possible impacts given a ‘beyond 4°C world’ in the

twenty

- first century

. Phil. Trans. R. Soc. A 2011. 369(1934): pp. 161

- 181

6. National Academy of Sciences, Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia . 2011. National Academies Press, Inc: Washington, DC.

7. Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a decomposition analysis using FUND

. Climati c Change , 2013(Forthcoming)

8. Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland, Changes in Atmospheric Constituents and in Radiat ive Forcing . In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA

9. Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates from PAGE09 are higher than those from PAGE2002

. Climatic Change

, 2012(Forthcoming)

14B

- 1210. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description . 2011, Cambridge Judge Business School

< www.jbs.cam.a c.uk/fileadmin/user_upload/research/workingpapers/wp1104.pdf >.

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model . 2011, Cambridge Judge

Business School

< www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1105.pdf >.

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2 . 2011, Cambridge Judge Business School

< www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp1108.pdf >.

13. Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern

. The Integrated Assessment

Journal , 2006. 6(1): pp. 19

- 56

14B

- 1314B.6 ANNEX

Table 14B.6

. 1 Annual SCC Values: 2010

- 2050 (2007$/ton CO 2 )

Discount Rate 5.0% 3.0% 2.5% 3.0%
Year Avg Avg Avg 95th

2010 11 33 52 90
2011 11 34 54 94

2012 11 35 55 98
2013 11 36 56 102

2014 11 37 57 106

2015 12 38 58 109

2016 12 39 60 113

2017 12 40 61 117

2018 12 41 62 121
2019 12 42 63 125

2020 12 43 65 129

2021 13 44 66 132

2022 13 45 67 135

2023 13 46 68 138

2024 14 47 69 141
2025 14 48 70 144

2026 15 49 71 147

2027 15 49 72 150

2028 15 50 73 153
2029 16 51 74 156

2030 16 52 76 159

2031 17 53 77 163

2032 17 54 78 166

2033 18 55 79 169

2034 18 56 80 172

2035 19 57 81 176

2036 19 58 82 179
2037 20 59 84 182

2038 20 60 85 185
2039 21 61 86 188

2040 21 62 87 192

2041 22 63 88 195

2042 22 64 89 198

2043 23 65 90 200

2044 23 65 91 203

2045 24 66 92 206

2046 24 67 94 209

2047 25 68 95 212

2048 25 69 96 215
2049 26 70 97 218

2050 27 71 98 221

14B

- 14Table 14B.6

. 2 202 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 )

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95 th 99th
Scenario PAGE
IMAGE 6 11 15 27 58 129 139 327 515 991

MERGE 4 6 9 16 34 78 82 196 317 649

MESSAGE 4 8 11 20 42 108 107 278 483 918
MiniCAM Base 5 9 12 22 47 107 113 266 431 872
5th Scenario 2 4 6 11 25 85 68 200 387 955

Scenario DICE
IMAGE 25 31 37 47 64 72 92 123 139 161

MERGE 14 18 20 26 36 40 50 65 74 85

MESSAGE 20 24 28 37 51 58 71 95 109 221
MiniCAM Base 20 25 29 38 53 61 76 102 117 135
5th Scenario 17 22 25 33 45 52 65 91 106 126

Scenario FUND
IMAGE

- 17

- 1 5 17 34 44 59 90 113 176

MERGE

- 7 2 7 16 30 35 49 72 91 146

MESSAGE

- 19

- 4 2 12 27 32 46 70 87 135
MiniCAM Base

- 9 1 8 18 35 45 59 87 108 172
5th Scenario

- 30

- 12

- 5 6 19 24 35 57 72 108

Table 14B.6

. 3 SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th
Scenario PAGE
IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481
MESSAGE 3 5 7 13 29 75 74 197 330 641
MiniCAM Base 3 5 8 14 30 73 75 184 300 623
5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE
IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83
MiniCAM Base 13 17 20 26 35 39 49 65 73 85
5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND IMAGE

- 14

- 3 1 9 20 25 35 54 69 111

MERGE

- 8

- 1 3 9 18 22 31 47 60 97
MESSAGE

- 16

- 5

- 1 6 16 18 28 43 55 88
MiniCAM Base

- 9

- 1 3 10 21 27 35 53 67 107
5th Scenario

- 22

- 10

- 5 2 10 13 20 33 42 63

14B

- 15Table 14B.6

. 4 2020 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 1 2 2 5 10 28 27 71 123 244

MERGE 1 1 2 3 7 17 17 45 75 153

MESSAGE 1 1 2 4 9 24 22 60 106 216
MiniCAM Base 1 1 2 3 8 21 21 54 94 190
5th Scenario 0 1 1 2 5 18 14 41 78 208

Scenario DICE
IMAGE 6 8 9 11 14 15 18 22 25 27

MERGE 4 5 6 7 9 10 12 15 16 18

MESSAGE 6 7 8 10 12 13 16 20 22 25
MiniCAM Base 5 6 7 8 11 12 14 18 20 22
5th Scenario 5 6 6 8 10 11 14 17 19 21

Scenario FUND
IMAGE

- 9

- 5

- 3

- 1 2 3 6 11 15 25

MERGE

- 6

- 3

- 2 0 3 4 7 12 16 27

MESSAGE

- 10

- 6

- 4

- 1 2 2 5 9 13 23
MiniCAM Base

- 7

- 3

- 2 0 3 4 7 11 15 26
5th Scenario

- 11

- 7

- 5

- 2 0 0 3 6 8 14

14B

- 16CHAPTER 15. UTILITY IMPACT ANALY SIS

TABLE OF CONTENTS

15.1 INTRODUCTION

................................ ................................ ................................ ......... 15

- 1

15.2 METHODOLOGY

................................ ................................ ................................ ........ 15

- 2

15.3 UTILITY IMPACT RESUL TS

................................ ................................ ..................... 15

- 4

15.3.1 Installed Capacity

................................ ................................ ............................... 15

- 4

15.3.2 Electricity Generation

................................ ................................ ........................ 15

- 7

15.3.3 Results Summary

................................ ................................ ............................. 15

- 11

RE FERENCES

................................ ................................ ................................ ........................ 15

- 12

LIST OF TABLES

Table 15.3.1 Summary of Utility Impact Results

................................ ........................... 15

- 11

LIST OF FIGURES

Figure 15.3.1 Total Electric Capacity Reduction

................................ ............................... 15

- 4 Figure 15.3.2 Coal Capacity Reduction

................................ ................................ ............. 15

- 5 Figure 15.3.3 Nuclear Capacity Reduction

................................ ................................ ........ 15

- 5 Figure 15.3.4 Gas Combined Cycle Capacity Reduction

................................ ................... 15

- 6 Figure 15.3.5 Peaking Capacity Reduction

................................ ................................ ........ 15

- 6 Figure 15.3.6 Renewables Capacity Reduction

................................ ................................ .. 15

- 7 Figure 15.3.7 Total Generation Reduction

................................ ................................ ......... 15

- 8 Figure 15.3.8 Coal Generation Reduction

................................ ................................ .......... 15

- 8 Figure 15.3.9 Nuclear Generation Reduction

................................ ................................ ..... 15

- 9 Figure 15.3.10 Gas Combined Cycle Generation Reduction

................................ ............... 15

- 9 Figure 15.3.11 Oil Generation Reduction

................................ ................................ .......... 15

- 10 Figure 15.3.12 Renewables Generation Reduction

................................ ............................ 15

- 10

15

- iCHAPTER 15. UTILITY IMPACT ANALY SIS

15.1 INTRODUCTION

In the utility impact analysis, the U.S. Department of Energy (DOE) analyzes the changes in electric installed capacity and power generation that result for each trial standard level (TSL).

The utility impact analysis is based on output of the DOE/Energy Information Administration (EIA)’s National Energy Modeling System (NEMS). a NEMS is a public domain,

multi

- sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO)

. The EIA publishes a reference case, which incorporates all existing energy

- related policies at the time of publication, and a variety of side cases which analyze the impact of different policies, energy price and market trends. As of 2014, DOE is using a new methodology based on results published for the Annual Energy Outlook 2014 ( AEO 2014 ) R eference case and a set of side cases that implement a variety of efficiency

- related policies

. 2

The new approach retains key aspects of DOE’s previous methodo logy, and provides some improvements:

• The assumptions used in the AEO reference case and side cases are fully documented and receive detailed public scrutiny

.

• NEMS is updated each year, with each edition of the AEO, to reflect changes in energy prices, su pply trends, regulations, etc.

• The comprehensiveness of NEMS permits the modeling of interactions among the various energy supply and demand sectors.

• Using EIA published side cases to estimate the utility impacts enhances the transparency of DOE’s analys is.

• The variability in impacts estimates from one edition of AEO to the next will be reduced under the new approach.

On the average, however, over the full analysis period, the results from the new approach are comparable to results from the old approach.

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

document ation. A useful summary is National Energy Modeling System: An Overview

. 1

15

- 115.2 METHODOLOGY

DOE estimate s 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

.

NEMS uses predicted growth in demand for each end use to build up a projection of the total electric system load growth . The system load shapes are converted internally to load duration curves, which are then used to estimate the most cost

- effective additions to capacity.

When electri city demand deviates from the AEO reference case, in general there are three inter

related effects: the annual generation (TWh) from the stock of electric generating capacity changes, the total generation capacity itself (GW) may change, and the mix of cap acity by fuel type may change. Each of these effects can vary for different types of end use. The change in total generating capacity is sensitive to the degree to which the end

- use is peak coincident, while the capacity mix is sensitive to the hourly load shape associated with the end use. Changes in generation by fuel type lead in turn to changes in total power sector emissions of SO 2 , NO

x , Hg

and CO 2

.

DO E ’s new approach examines a series of AEO side cases to estimate the relationship

between demand redu ctions and the marginal energy, emissions and capacity changes. The assumptions for each side case are documented in Appendix E of the AEO. The side cases, or scenarios, that incorporate significant changes to equipment efficiencies relative to the R eferen ce

case are:

• 2013 Technology (leaves all technologies at 2013 efficiencies) ; • Best Available Technology (highest efficiency irrespective of cost) ; • High Technology (higher penetration rates for efficiency and demand management) ; • Extended Policies ( includes efficiency standards that are not in the reference)

.

Scenarios that incorporate policies that directly affect the power sector without changes in energy demand (for example, subsidies for renewables, or high fuel price assumptions) are not

approp riate for this analysis. The methodology proceeds in seven steps:

1. Supply

- side data on generation, capacity and emissions, and demand

- side data on electricity use by sector and end

- use, are extracted from each side case . The data are converted to

differences relative to the AEO R eference case.

2. The changes in electricity use on the demand

- side data are a llocated to one of three categories: on

- peak, shoulder, and off

- peak. These categories are used in the utility sector to

correlate end

- use consumption with supply types. For each of the end

- uses that are modeled explicitly in NEMS , load shape information is used to identify the fraction of annual electricity use assigned to each category. On

- peak hours are defined as noon

- 5pm, June through September. Off

- peak hours are nights and Sundays. All other hours are assigned to the shoulder period.

15

- 23. For each year and each side case , the demand

- side reductions to on

- peak, off

- peak and

shoulder

- period electricity use are matched on the supply

- side to reductions in generation by fuel type. The fuel types are petroleum fuels, natural gas, renewables, nuclear and coal. The

allocation is based on the following rules:

3.1. All petroleum

- based generation is allocated to peak periods ;

3.2. Natura l gas generation is allocated to any remaining peak reduction; this is consistent with the fact that oil and gas steam units are used in NEMS to meet peak demand ;

3.3. Base

- load generation (nuclear and coal) is allocated proportionally to all periods ;

3.4. The remai ning generation of all types is allocated to the remaining off

- peak and shoulder reductions proportionally

.

4. The output of step 3 defines fuel

- share weights giving the fraction of energy demand in each load category that is met by each fuel type as a funct ion of time . These are combined with the weights that define the load category shares by end

- use to produce coefficients that allocate a marginal reduction in end

- use electricity demand to each of the five fuel types

.

5. A regression model is used to relate reductions in generation by fuel type to reductions in emissions of power sector pollutants. The model produces coefficients that define the change in total annual emissions of a given pollutant resulting from a unit change in total annual generation for e ach fuel type , as a function of time . These coefficients are combined with the weights calculated in step 4 to produce coefficients that relate emissions changes to changes

in end

- use demand.

6. A regression model is used to relate reductions in generation b y fuel type to reductions in installed capacity. The categories used for installed capacity are the same as for generation except for peak : NEMS uses two peak capacity types (combustion turbin e/diesel and oil and gas steam) which are combined here into a s ingle “peak” category. The model produces coefficients that define the change in total installed capacity of a given type resulting from a unit change in total annual generation for the corresponding fuel type. These coefficients are combined with the weig hts calculated in step 4 to produce coefficients that relate installed capacity changes to changes in end

- use demand , as a function of time

.

7. The coefficient time

- series for fuel share, pollutant emissions and capacity for the appropriate end use are multi plied by the stream of energy savings calculated in the NIA to produce estimates of the utility impacts.

This analysis ignores p umped storage , fuel cells and distributed generation , as t hese generation types are not affected by the policy changes modeled in the EIA side cases. The methodology is described in more detail in K. Coughlin, “Utility S ector I mpacts of E lectricity

D emand R eductions”

. 3

15

- 315.3 UTILITY IMPACT RESUL TS

This section presents results of the analysis for all of the capacity types used to supply electricity to commercial buildings

.

15.3.1 Installed Capacity

The figures in this section show the changes in U.S. electricity installed capacity that result for each TSL by major plant type for selected years. Note that a negative number means an

increase in capacity under a TSL

.

Figure 15.3

. 1 Total Electric Capacity Reduction 0 500 1,000 1,500 2,000 2,5002020202520302035204020452050205520602065207020752080208520902095MW Total

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

15

- 4Figure 15.3

. 2 Coal Capacity Reduction

Figure 15.3

. 3 Nuclear Capacity Reduction

15

- 5Figure 15.3

. 4 Gas Combined Cycle Capacity Reduction

Figure 15.3

. 5 Peaking Capacity Reduction

15

- 6Figure 15.3

. 6 Renewables Capacity Reduction

15.3.2 Electricity Generation

The figures in this section show the annual change in electricity generation that result for each TSL by fuel type. Note that a negative number means an increase in generation under a

TSL.

15

- 7Figur e 15.3

. 7 Total Generation Reduction

Figure 15.3

. 8 Coal Generation Reduction

15

- 8Figure 15.3

. 9 Nuclear Generation Reduction

Figure 15.3

. 10 Gas Combined Cycle Generation Reduction

1 5

- 9Figure 15.3

. 11 Oil Generation Reduction

Figure 15.3

. 12 Renewables Generation Reduction

15

- 1015.3.3 Results Summary

Table 15.3

. 1 presents a summary of the utility impact results for commercial and industrial pumps

.

Table 15.3

. 1 Summary of Utility Impact Results

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5 Installed Capacity Reduction (MW)

2020 4.12 18.7 38.0 59.5 86.3

2025 27.4 124 249 392 568

2030 47.3 215 430 679 985

2035 64.8 297 592 941 1365

2040 79.7 369 734 1171 1700
Electricity Generation Reduction (GWh)

2020 15.5 70.6 143 224 325

2025 88.5 401 805 1265 1834

2030 144 657 1310 2071 3004

2035 185 848 1689 2682 3891

2040 214 992 1972 3146 4568

15

- 11REFERENCES

1. U.S. Department of Energy

- Energy Information Administration, National Energy Modeling System: An Overview , 2009. Report No. DOE/EIA

- 0581(2009).

< www.eia.gov/oiaf/aeo/overview/ >

.

2. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook 2014 with Projections to 2040 , 2014. Washington, DC. < www.eia.gov/forecasts/aeo/ >

.

3. Coughlin, K., Utility Sector Imacts of Reduced Electricity Demand , 2014. Lawrence Berkeley National Laboratory. Report No. LBNL

- 6864E

.

15

- 12CHAPTER 16. EMPLOYMENT IMPACT AN ALYSIS

TABLE OF CONTENTS

16.1 INTRODUCTION

................................ ................................ ................................ ......... 16

- 1

16.2 ASSUMPTIONS

................................ ................................ ................................ ............ 16

- 1

16 .3 METHODOLOGY

................................ ................................ ................................ ........ 16

- 1

16.4 SHORT

- TERM RESULTS

................................ ................................ ............................ 16

- 2

16 .1 LONG

- TERM RESULTS

................................ ................................ .............................. 16

- 3

REFERENCES

................................ ................................ ................................ .......................... 16

- 5

LIST OF TABLES

Table 16.4.1 Net National Short

- term Change in Employment (Number of Jobs)

.......... 16

- 3

16

- iCHAPTER 16. EMPLOYME NT IMPACT ANALYSIS

16.1 INTRODUCTION

DOE’s employment impact analysis is designed to estimate indirect national job creation or elimination resulting from possible standards due to reallocation of the associated expenditures for purchasing and operating commercial and industrial pumps

. Job in creases or decreases reported in this chapter are separate from the direct pump production sector employment impacts reported in the manufacturer impact analysis ( Chapter 12 ) , and reflect the employment impact of efficiency standards on all other sectors o f the economy.

16.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption, and therefore to reduce energy expenditures. The savings in energy expenditures may be spent on new investment or not at all ( i.e. , they may remain “save d”). The standards may increase the purchase price of products , including the retail price plus sales tax, and increase installation

costs.

Using an input/output econometric model of the U.S. economy, this analysis estimated

the short

- term effect of these expenditure impacts on net economic output and employment.

DOE intends for this analysis to quantify the indirect employment impacts of these expenditure changes. It evaluated direct employment impacts at manufacturers’ facilities in the m anufacturer impact analysis (see C hapter 12).

DOE notes that ImSET is not a general equilibrium forecasting model, and understands the uncertainties involved in projecting employment impacts, especially changes in the later years of the analysis. 1 Because ImSET does not incorporate price changes, the employment effects predicted by ImSET would over

- estimate the magnit ude of actual job impacts over the long run for this rule. Because input/output models do not allow prices to bring markets into equilibrium, they are best used for short

- run analysis. DOE therefore includes a qualitative

discussion of how labor markets ar e 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

The Department based its analysis on an input/output model of the U.S. economy that estimates the effects of standards on major sectors of the economy related to buildings and the net impact of standards on jobs. The Pacific Northwest National Laboratory developed the
model, ImSE T 3.1. 1 2 ( Im pact of S ector E nergy T echnologies) as a successor to ImB uild

, 3 a special

- purpose version of the IMPLAN 4 national input/output model. Im SET esti mates the employment and income effects of building energy technologies. In comparison with simple

16

- 1economic multiplier appro aches, 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 of different sectors of the economy and the spending flows among them. Different sectors have different levels of labor intensity and so changes in the level of spending (e.g., due to the effects of an efficiency standard) in one sector of the economy will affect flows in other

s ectors, 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 sav ings, 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 nat ional output for each input

- output sector. The model applies estimates of employment and wage income per dollar of economic output for each sector and calculates impacts on national employment and wage income.

Energy

- efficiency technology primarily affec ts the U.S. economy along three spending pathways. First, general investment funds are diverted to sectors that manufacture, install, and maintain energy

- efficient product s. The increased cost of product s leads to higher employment in

the product manufactu ring sectors and lower employment in other economic sectors. Second, commercial firm and residential spending are redirected from electric utilities toward firms that supply production inputs for energy

- efficient products

. Third

, investment funds from electric

utilities are released for use in other sectors of the economy. When consumers use less energy, utilities experience relative reductions in demand which leads to reductions in utility sector investment and employment.

DOE also notes that the emp loyment impacts estimated with ImSET for the entire economy differ from the employment impacts in the commercial and industrial pump manufacturing sector estimated in Chapter 12 using the Government Regulatory Impact Model (GRIM). The methodologies used an d the sectors analyzed in the ImSET and GRIM models are

different.

16.4 SHORT

- TERM RESULTS

The results in this section refer to impacts of commercial and industrial pump standards

relative to the base case. DOE dis aggregated the impact of standards on employ ment into three component effects : increased capital investment costs, decreased energy costs, and changes in operations and maintenance costs

. DOE presents the summary impact.

Conceptually, one can consider the impact of a standard in its first year on three

aggregate sectors, the commercial and industrial pump production sector, the electricity generation sector, and the general consumer good s sector (as mentioned above ImSET’s

16

- 2calculations are made at a much more disaggregate level) . By r aising energy efficiency, t he

standard generally increas es the purchase price of pumps ; this increase in expenditures causes an increase in employment in this sector. At the same time, the improvements in energy efficiency reduce consumer expenditures on electricity

. T he reduction in electricity demand causes a reduction in employment in that sector. Finally, based on the net impact of increased expenditures on pumps and reduced expenditures on electricity , consumer expenditures on everything else are either positively or negatively affected, increasing or reducing jobs in that sector accordingly. The model also captures any indirect jobs created or lost by changes in consumption due to changes in employment (as more workers are hired they consume more goods, which gener ates more employment , the converse is true for workers laid off ).

Table 16.4

. 1 present s the modeled net employment impact from the standards in 20 2 1

, in

millions of jobs. For context, the U.S. labor force had 156 million people in July 2014. a DOE did not consider imports in this analysis as most pumps are produced domestically

.

Table 16.4

. 1 Net National Short

- term Change in Employment ( Number of J obs )

Trial Standard Level 2020 2025

TSL 1 10 50

TSL 2 50 220

TSL 3 110 440

TSL 4 180 710

TSL 5 270 1040

For context, the Office of Management of Budget currently assumes that the unemployment rate may decline to 6. 0 percent in the fourth quarter of 2014 an d drop further to

5. 4 percent by 2017. 5 The unemployment rate in 202 0 is projected to be 5.4 percent, which is close to “full employment.” When an economy is at full employment any effects on net employment are likely to be transitory as workers change jobs, rather than enter or exit longer

term employment.

16.1 LONG

- TERM RESULTS

Over the long term DOE expect s the electricity savings to consumers to incr easingly

dominate the increase in product costs, resulting in increased aggregate savings to consumers. As a result, DOE expect s demand for electricity to decline over time and demand for other goods to

increase. Because the el ectric utility sector is rela tively capital intensive compared to the consumer good s sector, the net effect will be an increase in labor demand. In equilibrium, this should lead to upward pressure on wages and a shift in employment away from el ectric utilities towards consumer goods. Note that in long

- run equilibrium there is no net effect on total employment because wages adjust to bring the labor market into equilibrium. Nonetheless, even

a Bureau of Labor Statistics : Labor Force Statistics (Available at: http://data.bls.gov/pdq/SurveyOutputServlet?request_action=wh&graph_name=LN_cpsbref1 ).

16

- 3to the extent that markets are slow to adjust, DOE anticipate s that net labor market impacts wil l in general be negligible over time due to the small magnitude of the short

- term effects presented

in Table 16.4

. 1 for most product classes and TSLs

. The ImSET model projections, assuming no price or wage effects until 2025 , are included in the second column of Table 16.4

. 1

.

16

- 4REFERENCES

1. Scott, M., JM Roop, RW Schultz, DM Anderson, KA Cort, The Impact of DOE Building Technology Energy Efficiency Programs on U.S. Employment, Income, and Investment

. Energy Economics , 2008. 30(5): pp. 2283

- 2301

.

2. Roop, J. M., M. J. Scott, and R. W. Schultz, ImSET: Impact of Sector Energy Technologies , 2005. Pacific Northwest National Laboratory. Richland, WA. Report No.

PNNL

- 15273.

3. Scott, M. J., D. J. Hostick, and D. B. Belzer, ImBuild: Impact of Building Energy Efficiency Programs, April, 1998. Pacific Northwest National Laboratory. Richland, WA. Report No. PNNL

- 11884. Prepared for the U.S. Department of Energy under Contract

DE

- AC0 6

- 76RLO 1830.

4. Minnesota IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide,

Data Guide , 1997. Stillwater, MN.

5. Office of Management and Budget, Fiscal Year 2015 Mid

- Session Review: Budget of the
U.S. Government, < www.whitehouse.gov/sites/default/files/omb/budget/fy2015/assets/15msr.pdf >

.

16

- 5CHAPTER 17. REGULATORY IMPACT AN ALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION

................................ ................................ ................................ ......... 17

- 1

17.2.1 Methodology

................................ ................................ ................................ ...... 17

- 2

17.2.2 Assumptions Regarding Non

- Regulatory Policies

................................ ............ 17

- 3

17.2.3 Policy Interactions

................................ ................................ ............................. 17

- 4

17.3 NON

- REGULATORY POLICY AS SUMPTIONS

................................ ...................... 17

- 4

17.3.1 No New Regulatory Action

................................ ................................ ............... 17

- 4

17.3.2 Consumer Rebates

................................ ................................ ............................. 17

- 5

17.3.2.1 Methodology

................................ ................................ ............................ 17

- 5

17.3.2.2 Analysis

................................ ................................ ................................ .... 17

- 6

17.3.3 C onsumer Tax Credits

................................ ................................ ..................... 17

- 13

17.3.4 Manufacturer Tax Credits

................................ ................................ ................ 17

- 16

17.3.5 Voluntary Energy Efficiency Targets

................................ .............................. 17

- 17

17.3.6 Bulk Government Purchases

................................ ................................ ............ 17

- 22

17.4 IMPACTS OF NON

- REGULATORY ALTERNATI VES

................................ ......... 17

- 24

REFERENCES

................................ ................................ ................................ ........................ 17

- 30

LIST OF TABLES

Table 17.1.1 Non

- Regulatory Alternatives to National Standards

................................ ... 17

- 1

Table 17.1.2 Efficiency Levels by TSL (C

- Value)

................................ ........................... 17

- 4

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates

................................ ........... 17

- 7

Table 17.3.2 Market Penetrations in 2020 Attributable to Consumer Rebates

............... 17

- 12

Table 17.3.3 Market Penetrations in 2020 Attributable to Consumer Tax Credits

........ 17

- 15

Table 17.3.4 Market Penetrations in 2020 Attributable to Manufacturer Tax Credits

... 17

- 17

Table 17.3.5 Market Barriers Changes Attributable to Voluntary Energy Efficiency Targets (TSL#2)

................................ ................................ ......................... 17

- 19

Table 17.3.6 Market Penetrations in 2020 Attributable to Voluntary Energy Efficiency Targets

................................ ................................ ...................... 17

- 20

Table 17.3.7 Market Penetrations in Selected Years Attributable to Voluntary Energy Efficiency Targets for TSL #2

................................ ....................... 17

- 21

Table 17.3 .8 Market Penetrations in 2020 Attributable to Bulk Government

Purchases

................................ ................................ ................................ .... 17

- 24

Table 17.4.1 Impacts of Non

- Regulatory Policy Alte rnatives (TSL #2)

........................ 17

- 29

17

- iLIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Commercial and Industrial Pumps

........... 17

- 11 Figure 17.4.1 Market Penetration of Efficient ESCC 1800 Commercial and Industrial Pumps (TSL 2)

................................ ................................ ........................... 17

- 25 Figure 17.4.2 Market Penetration of Efficient ESCC 3600 Commercial and Industrial Pumps (TSL #2)

................................ ................................ ......................... 17

- 25 Figure 17.4.3 Market Penetration of Efficient ESFM 1800 Commercial and Industrial Pumps (TSL #2)

................................ ................................ ......... 17

- 26 Figure 17.4.4 Market Penetration of Efficient ESFM 3600 Commercial and Industrial Pumps (TSL #2)

................................ ................................ ......... 17

- 26 Figure 17.4.5 Market Penetration of Efficient IL 1800 Commercial and Industrial Pumps (TSL #2)

................................ ................................ ......................... 17

- 27 Figure 17.4.6 Market Penetration of Efficient IL 3600 Commercial and Industrial Pumps (TSL #2)

................................ ................................ ......................... 17

- 27 Figure 17.4.7 Market Penetration of Efficient VT

- S 3600 Commercial and Industrial Pumps (TSL #2)

................................ ................................ ......................... 1 7

- 28

17

- iiCHAPTER 17. REGULATO RY IMPACT ANALYSIS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that the regulatory action described in the Federal Register notice associated with this TSD constitute s an “economically significant regulatory action” under Executive Order (E.O.) 12866

, Regulatory Planning and Review. 58 FR 51735 (October 4, 1993). For such actions, E.O. 12866 requires Federal agencies to provide “a n assessment, including the underlying analysis, of costs and benefits of potentially effective and reasonably feasible alternatives to the planned regulation, identified by the agencies or the public (including impro ving the current regulation and reasonably viable non

- regulatory actions), and an explanation why the planned regulatory action is preferable to the identified potential alternatives. ” 58 FR 51735, 51741.

To conduct this analysis , DOE used an integrate d National Impact Analysis ( NIA )

- RIA

model built on the NIA model discussed in C hapter 10

. DOE identified five non

- regulatory policy alternatives that possibly could provide incentives for the same energy efficiency lev el s as the one s in the proposed trial standard lev el s for the commercial and industrial pumps that are the subject of this rulemaking. The non

- regulatory policy alternatives are listed in Table 17.1

. 1

, wh ich also includes the “no new regulatory action” alternative . DOE evaluated each alternative in terms of its ability to achieve significant e nergy savings at a reasonable cost, and compared

the effectiveness of each to the effectiveness of the proposed sta n dard s for the commercial and industrial pumps covered by this rulemaking.

Table 17.1

. 1 Non

- Regulatory Alternatives to National Standards

No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits Voluntary Energy Efficiency Targets

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed in ( excluding

the alternative of “N o N ew R egulatory A ction ” ) . Section 17.4 present s the results of the policy

alternatives

.

17.2 N ON

- REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost

effectiveness of the non

- regulatory policy alternatives for commercial and industrial pumps

. This

section also describes the assumptions underlying the analysis.

17

- 117.2.1 Methodology

DOE used its integrated NIA

- RIA spreadsheet model to calculate the national energy savings (NES) and net present value (NPV) associated with each non

- regulatory policy

alternative . Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet

model

. Appendix 17 A discusse s the NI A

- RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet s the

efficien cy level s corresponding to each TSL . After establishing the quantitative assumptions underlying each alternative, DOE appropriately revised inputs to the NIA

- RIA spreadsheet

model . The primary model inputs revised were market shares of equipment meeting the target efficiency level s set for each TSL . The shipments of equipment for any given year reflect a

distribution of e fficiency levels. DOE assumed , for each TSL, that new energy efficiency

standard s would affect 100 percent of the shipments of products that did not meet the TSL target
level s in the base case, a whereas the non

- regulatory policies would affect a smaller percentage of those shipments. DOE made certain assumptions about the percentage of shipments affected by each alternative policy

. DOE used those percentages to calculate the shipment

- weighted average energy consumption and costs of commercial and industrial pumps attributable to each policy

alternative.

Increasing the efficiency of a product often increases its average installed cost. However

, operating costs generally decrease because energy consumption declines . DOE therefore

calculated an NPV for each non

- regulatory alternative in the same way it did for the proposed

standard s. In some policy scenarios, increases in total installed cost are mitigated by government

rebates or tax credits. Because DOE assumed that consumers woul d re

- pay credits and rebates in some way (such as additional taxes) , DOE did not include rebates or tax credits as a consumer benefit when calculating national NPV. DOE’s analysis also excluded any administrative costs

for the non

- regulatory policies; incl uding such costs would decrease the NPVs slightly.

The following are key measures for evaluating the impact of each alternative.

• National Energy Savings (NES) , given in quadrillion Btus (quads), describes the cumulative national energy sav ed over the lifetime of equipment purchased during the

30

- year analysis period starting in the effective date of the policy ( 2020

- 2049 )

.

• Net Present Value (NPV)

, represents the value of net monetary savings in 2015

, expressed in 2013 $, from equipment purchased during the 30

- year analysis period starting in the effective date of the policy ( 2020

- 2049 )

. DOE calculated the NPV as

the difference between the present value s of installed equipment cost and operating expenditures in the base case and the present value s of th o se costs in each policy case.

a The base case for the NIA is a market

- weighted average energy efficiency calculated from units at several efficiency levels.

17

- 2DOE calculated operating expenses (including energy costs ) for the life of the product

.

17.2.2 Assumptions Regarding Non

- Regulatory Policies

The effects of non

- regulatory policies are by nature uncertain because they depend on program implementation

, marketing efforts

, and on consumers’ response to a program. Because the projected effects depend on assumptions regarding the rate of consumer participation, they are subject to more uncertainty than are the impacts of mandatory standards, which DOE

assumes will be met with full compliance. To increase the robustness of the analysis, DOE conducted a literature review regarding each non

- regulatory policy and consulted with recognized experts to gather information on similar incenti ve programs that have been implemented in the United States. By studying experiences with the various types of programs, DOE sought to make credible assumptions regarding potential market impacts . Section 17.3 presents the sources DOE relied on in developing assumptions about each alternative policy and reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each

alte rnative policy.

Each non

- regulatory policy that DOE considered would improve the average efficiency

of new commercial and industrial pumps relative to their base case efficiency scenario (which

involve s no new regulatory action). The analysis considered that each alternative policy would induce consumers to purchase units h aving the same technology as required by standard s (the target level ) , according to the minimum energy efficiency set for each TSL . As opposed to the standards case, however, the policy cases may not lead to 100 percent market penetration of units

that meet the target level

.

Table 17.1

. 2 show s the C

- Values b corresponding to each TSL

. The table also include s the Pump Energy Ratings (PER)

, which correspond

– for each TSL

– to the percentage of pumps

that will be removed from th e market if that TSL i s selected

.

b For more on C

- Values please refer to Chapter 5.

17

- 3Table 17.1

. 2 Efficiency Levels by TSL ( C

- Value )

TSL 1 (PER 10) TSL 2 (PER 25) TSL 3 (PER 40) TSL 4 (PER 55) TSL 5 (PER 70)

ESCC 1800 131.63 128.47 126.67 125.07 123.71

ESCC 3600 134.60 130.42 128.92 127.35 125.29

ESFM 1800 132.95 128.85 127.04 125.12 123.71

ESFM 3600 134.98 130.99 129.26 127.77 126.07

IL 1800 133.95 129.30 127.30 126.00 124.45

IL 3600 138.86 133.84 131.04 129.38 127.35

VT

- S 3600 135.93 134.13 130.83 128.92 127.29

DOE assumed that the effects of non

- regulatory policies would last from the effective

date of standards

— 2020

— through the end of the analysis period, which is 2049

.

17.2.3 Policy Interactions

DOE calculated the effects of each non

- regulatory policy separately from those of the other policies. In practice, some policies are most effective when implemented in combination,

such as voluntary efficiency targets implemented with consumer rebates or tax credits. However, DOE attempted to make conserv ative assumptions to avoid double

- counting policy impacts. The resulting policy impacts are therefore not additive , and the combined effect of several or all policies cannot be inferred from summing their results.

Section 17.4 presents graphs that show the market penetration estimated under each non

regulatory policy for commercial and industrial pumps

.

17.3 NON

- REGULATORY POLICY AS SUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five non

regulat ory policy alternatives to the standard s proposed for commercial and industrial pumps

. (Because the alternative of “ No New Regulatory Action ” has no energy or economic impacts, essentially representing the NIA base case, DOE did not perform any additional analysis for that alternative.) DOE developed estimate s of the market penetration of more efficien t products both

with and without each of the non

- regulatory policy alternatives

.

17.3.1 No New Regulatory Action

The case in which no new regulatory action is taken with regard to the energy efficiency

of commercial and industrial pumps constitutes the base case, as described in C hapter 10

, National Impact Analysis. The base ca se provides the basis of comparison for all other policies. By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17

- 417.3.2 Consumer Rebates

DOE considered th e scenario in which the Federal government would provide financial

incentives in the form of rebate s to consumers for purchasing energy

- efficient equipment

. This policy provides a consumer rebate for purchasing commercial and industrial pumps that operate

at the same efficiency level s as stipulated in each TSL.

17.3.2.1 Methodology

DOE based its evaluation methodology for consumer rebates on a comprehensive study of California’s potential for achieving energy efficiency

. Th e study, performed by XENERGY,

Inc., c summarize d experiences with various utility rebate programs. 1 XENE RGY’s analytical

method utilized graphs, or penetration curves
, that estimate the market penetration of a technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other methods of estimating the effect of consumer rebate programs on the market penetration of efficient technologies. The other methods, developed after the referen ced XENERGY report was published, 2

, 3

, 4

, 5

, 6

, 7 used different approach es: other economic parameters (e.g., payback period), expert surveys, or model calibration based on specific utility program data rather than

multi

- utility data

. Some models in use by energy efficiency program evaluation experts were so

client

- specific th at generic relationships between economic parameters and consumer res ponse
could not be established. 5 DOE decided that the most appropriate available method for this RIA was the XENERGY approach of penetration curves based on B/C ratio, which incorporates lifetime operating cost savings

.

XENERGY’s model estimates market impacts induced by financial incentives based on the premise that two types of information diffusion drive the adoption of new technologies.

Internal sources of information encourage cons umers to purchase new equipment primarily through word

- of

- mouth from early adopters. External sources affect consumer purchase decisions through marketing efforts and information from outside the consumer group. Appendix

17 A contains additional details on internal and external information diffusion.

XENERGY’s model equation accounts for the influences of both internal and external sources of information by superimposing the two components. Combining the two mechanisms for information diffusion, XENERGY’s model generates a set of penetration (or implementation)

curves for a policy measure. XENERGY calibrated the curves based on participation data from utility rebate programs. The curves illustrate the increased penetration (i.e., increased market share) of efficient equipment driven by consumer response to changes in B/C ratio induced by rebate programs. The penetration curves depict various diffusion patterns based on perceived

market barriers (from no

- barriers to extremely

- high

- barriers ) to consumer purcha se of high

efficiency equipment

. DOE adjusted the XENERGY former penetration curves based on expert advice founded on more recent utility program experience. 5

, 8

c XENERGY is now owned by KEMA, Inc. ( www.kema.com )

17

- 5DOE modeled the effects of a consumer rebate policy for commercial and industrial

pumps by determining , for each TSL, the increase in market penetration of equipment meeting the target level relative to their market penetration in the base case. It used the interpolation method presented in Blum et al (2011) 9 to create customized penetration curves based on relationships between actual base case market penetrations and actual B/C ratios. To inform its estimate of B/C ratios provided by a rebate program DOE performed a thorough nationwide search for existing rebate programs for commercial and industrial pumps . It gathered data on utility or agency rebates throughout the nation for this equ ipment , and used this data to calibrate the customized penetration curve s it developed for each equipment class covered by this RIA so

t hey can best reflect the market barrier lev el s that consumer rebates for commercial and industrial pumps would face

. Sec tion 17.3.2.2 shows the resulting interpolated curv e s used in the analysis

.

17.3.2.2 Analysis

DOE estimated t he effect of increasing the B/ C ratio of commercial and industrial pumps

via a rebate that would pay

– depending on the equipment class

– part or all of the incr eased

installed cost of unit s that me e t the target efficiency leve l s compared to units meeting the

baseline efficiency l evel

. d To inform its estimate of an appropriate rebate amount, DOE performed a thorough nationwide search for existing rebate pr ograms for commercial and industrial pumps

. It gathered data from a sample of utility and agency rebate programs that

includes 52 rebate program s for commercial and industrial pumps initiated by three utilities or agencies in various States. DOE then estimated a market representative rebate value for each equipment class covered by this RIA which it applied in the calculation of the B/C ratio of commercial and industrial pumps under the effect of consumer rebates. (Appendix 17 A,

identifies the rebate programs and details the methodology DOE used to estimate a market representative rebate amount .) DOE assumed th at rebates would remain in effect at the same

level throughout the forecast period (2020

- 2049)

.

DOE first calculated the B/C ratio of a commercial or industrial pump without a rebate using the difference in total installed costs (C) and lifetime operating cost savings e (B) between a unit meeting the target level and a baseline unit. It then calculated the B/C ratio given a rebate

for the unit meeting the target efficiency level

. Because the rebate reduced the incremental cost,

the unit receiving the rebate had a l arger B/C ratio. Table 17.3

. 1 show s the effect of con sumer rebates for each TSL on the B/C ratio of commercial and industrial pumps shipped in the first year of the analysis period

.

d The baseline technology is defined in the engineering analysis, Chapter 5, as the technology that represents the basic characteristics of commercial and industrial pumps. A baseline unit typically is one th at just meets current Federal energy conservation standards and provides basic consumer utility

.

e The cash flow of the operating cost savings is discounted to the purchase year using a 7 percent discount rate.

17

- 6Table 17.3

. 1 Benefit/Cost Ratios Without and W ith Rebates

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

B/C Ratio Without Rebate 2.1 2.8 2.6 2.3 2.0 Rebate Amount (2013$) 164.16 164.16 164.16 164.16 164.16

B/C Ratio With Rebate 25.6 7.1 4.4 3.4 2.6

Estimated Market Barriers Low

- Mod Low

- Mod Mod Low

- Mod Low

- Mod

ESCC 3600

B/C Ratio Without Rebate 3.5 4.3 3.5 3.3 3.1 Rebate Amount (2013$) 268.27 268.27 268.27 268.27 268.27

B/C Ratio With Rebate infinite infinite 13.1 7.8 5.6

Estimated Market Barriers Mod

- High Mod Mod

- High Mod

- High No

ESFM 1800

B/C Ratio Without Rebate 3.6 3.4 3.3 3.0 2.9 Rebate Amount (2013$) 374.79 374.79 374.79 374.79 374.79

B/C Ratio With Rebate infinite 16.5 8.0 5.3 4.3

Estimated Market Barriers Low

- Mod Low

- Mod Low

- Mod Mod Low

- Mod

ESFM 3600

B/C Ratio Without Rebate 6.6 8.5 8.5 7.6 7.1 Rebate Amount (2013$) 541.04 541.04 541.04 541.04 541.04

B/C Ratio With Rebate infinite infinite infinite 150.4 26.6

Estimated Market Barriers Mod Mod

- High Mod

- High Mod

- High Mod

IL 1800

B/C Ratio Without Rebate 3.2 2.7 2.1 1.6 1.4

Rebate Amount (2013$) 131.11 131.11 131.11 131.11 131.11

B/C Ratio With Rebate infinite 5.1 2.8 1.9 1.6

Estimated Market Barriers Low

- Mod Mod Low

- Mod Low

- Mod No

- Low

IL 3600

B/C Ratio Without Rebate 4.2 3.3 3.0 2.5 2.1 Rebate Amount (2013$) 195.58 195.58 195.58 195.58 195.58

B/C Ratio With Rebate infinite 24.2 6.5 4.0 2.8

Estimated Market Barriers Mod Mod Mod

- High Low

- Mod No

- Low

VT

- S 3600

B/C Ratio Without Rebate 0.7 0.9 1.5 1.7 1.7 Rebate Amount (2013$) 182.62 182.62 182.62 182.62 182.62

B/C Ratio With Rebate 1.6 1.7 2.7 2.7 2.5

Estimated Market Barriers No Low

- Mod Low

- Mod Low

- Mod No

- Low

* No

- Low: No

- to

- Low market barriers; Mod: Moderate market barriers; Low

- Mod: Low

- to

- Moderate market

barriers; Mod

- High: Moderate

- to

- High market barriers; xHigh: Extremely

- H igh market barriers; Hg

- xHg: High

to

- E xtr e mely

- High market barriers.

17

- 7DOE used the B/C ratio along with the customized penetration curve s shown in Figure

17.3

. 1 to estimate the percentage of consumers who would purchase commercial and industrial

pumps that meet the target level s both with and without a rebate incentive. For some equipment

classes

– n amely ESFM 1800 and IL 3600

– the market penetration with rebates is constrained by the market share that is below the target level in the base case, and therefore the policy measure does not fulfill its potential for market transformation. As for equipmen t classes ESCC

3600 and ESFM 3600 , because the rebate amount would cover the upfront costs in full, the B/C in the policy case is infinite and therefore the market penetration with rebate s is not represented

in Figure 17.3

. 1

. The estimated leve l s of market barriers corresponding to the penetration curve s DOE calculated to represent the market behavior for commercial and industrial pumps at the proposed TSL are in dicated (highlight ed) in Table 17.3

. 1

. DOE assumed the estimated market barriers would remain the same over the whole analysis period.

TSL #2ESCC 1800

17

- 8ESCC3600ESFM 1800

17

- 9ESFM 3600IL 1800

17

- 10Figure 17.3

. 1 Market Penetration Curve s for Commercial and Industrial PumpsIL 3600VT-S 3600

17

- 11DOE next estimated the percent increase represented by the change in penetration rate

shown on the corresponding penetration curve. It then added this percent increase to t he market

share of units that meet the target level in the base case to obtain the market share of units th at meet the target level in the rebate policy case.

Table 17.3

. 2 summarizes DOE’s assumptions for commercial and industrial pumps regarding the market penetration of products in 2020 that meet the target lev el s at each TSL given a consumer rebate

.

Table 17.3

. 2 Market Penetrations in 2020 Attributable to Consumer R ebates

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base

- Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2% Policy Case Market Share 34.3% 40.7% 22.6% 21.7% 29.0%

Increased Market Share 18.5% 22.5% 11.9% 8.1% 5.8%

ESCC 3600

Base

- Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1% Policy Case Market Share 11.9% 26.5% 26.6% 17.0% 76.4%

Increased Market Share 3.8% 11.9% 23.0% 13.1% 10.3%

ESFM 1800

Base

- Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1% Policy Case Market Share 21.2% 37.3% 45.3% 22.6% 37.9%

Increased Market Share 0.9% 21.2% 21.1% 12.3% 9.8%

ESFM 3600

Base

- Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3% Policy Case Market Share 28.2% 43.5% 58.7% 54.5% 55.0%

Increased Market Share 4.7% 28.2% 42.8% 43.2% 25.7%

IL 1800

Base

- Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3% Policy Case Market Share 30.7% 23.8% 17.6% 14.1% 40.4%

Increased Market Share 14.7% 13.9% 6.3% 3.4% 3.0%

IL 3600

Base

- Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3% Policy Case Market Share 31.5% 40.3% 16.8% 23.7% 48.8%

Increased Market Share 10.7% 31.5% 12.3% 10.8% 6.5%

VT

- S 3600

Base

- Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7% Policy Case Market Share 38.2% 19.6% 13.1% 13.1% 52.2%

Increased Market Share 1.6% 12.1% 8.4% 7.2% 8.5%

DOE used the resulting annual increase s in market shares as inputs t o rep resent the rebate policy case scenario in its NIA

- RIA model. Appendix 17 A shows the annual market share increases due to this policy for the whole forecast period

. Section 17.4 presents the resulting

17

- 12market penetration trends for the po licy case of consumer rebates for commercial and industrial

pumps

. Because energy prices increase over the analysis period and equipment price s are held constant, the B/C ratio s increase over time. With increasing B/C ratio s and constant market barriers, t he increase s in market penetration of more efficient technolog ies eventually increase over the analysis period.

17.3.3 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous analysis of consumer participation in tax credits. DOE supported its approach using data from Oregon State’s tax credit program for energy

- efficient appliances. DOE also incorporated previous research that disaggregated the effe ct of rebates and tax credits into a direct price effect

, which de riv es from the savings in purchase price

, and an announcement effect , which is independent of the amount of the incentive

. 10

, 11 The announcement effect derives from the credibility that a technology receives from being included in an incentive program, as well as changes in product marketing and modifications in markup and pricing. DOE assumed that the rebate and consumer tax credit policies would encompass both dire ct price effect s and

announcement effect s, and that half the increase in market penetration associated with either policy would be due to the direct price ef fect and ha lf to the announcement effect.

In estimat ing the effects of a tax credit on purchases of consumer products that meet new efficiency standards, DOE assumed the amount of the tax credit would be the same as the corresponding rebate amount discussed above

.

DOE estimated that fewer consumers would participate in a tax credit program than would take advantage of a rebate. Research has shown that the delay required for a consumer to receive a tax credit, plus the added time and cost i n preparing the tax return, make a tax credit

incentive less effective than a rebate received at the time of purchase. Based on previous analyses, DOE assumed that only 60 percent of the consumers who would take advantage of a rebate would take advantage of a tax credit. 12

In preparing its assumptions to estimate the effects of tax credits on consumer purchases

of commercial and industrial pumps , DOE also reviewed other tax credit programs that have

been offered at both the Federal and State levels for en ergy

- efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005 ) included Federal tax credits for

consumers who purchase energy

- efficient products

. 13 Those tax credits we re in effect in 2006

and 2007, expired in 2008, were reinstated for 2009

– 2010 by the American Recovery and

Reinvestment Act of 2009 ( ARRA ), extended by Congress for 2011 with some modifications

, and expired at the end of 2011

. 14

, 15 The American Taxpayer Relief Act of 2012 extended, with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water heaters placed in service between January 1, 2012 and December 31, 2013. 16 DOE reviewed Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy c onservation

17

- 13tax credit program in place in the 1980s. However, DOE did not find data specific enough to commercial and industrial pumps to warrant adjusting its analysis method for the Consumer Tax Credits policy case. Appendix 17 A contains more informati on on Federal consumer tax credits.

DOE also reviewed its previous analysis o f Oregon’s tax credits for clothes washers to provide support for its assumptions. 17 In that previous analysis, DOE compared the market

shares of ultra

- high efficiency (UHE) residential clothes washers in Oregon, which offered both State tax credits and utility rebates, with those in Washington State, which offered only utility rebates during the same per iod. Based on this analysis, DOE estimated that in Oregon the impact

of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equivalent efficiency. This finding supports its original assumption that participation in a tax cred it program would be about 60 percent of participation in a rebate program. Additional discussion of State tax credits for Oregon and other states is in Appendix 17 A.

DOE applied the assumed 60 percent participation described above to the increase in

pe netration rates estimated for the rebate policy to estimate penetration rates attributable to consumer tax credits. In doing so, DOE incorporated the assumptions for consumer response to

financial incentives from the customized penetration curves it develo ped for commercial and industrial pumps (See Figure 17.3

. 1 )

.

Table 17.3

. 3 summarizes DOE’s assumptions for commercial and industrial pumps regarding the market penetration of products in 2020 that meet the target levels at each TSL given a consumer tax credit.

17

- 14Table 17.3

. 3 Market Penetrations in 2020 Attributable to Consumer Tax Credits

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base

- Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2% Policy Case Market Share 26.9% 31.7% 17.9% 18.5% 26.7%

Increased Market Share 11.1% 13.5% 7.1% 4.8% 3.5%

ESCC 3600

Base

- Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1% Policy Case Market Share 10.4% 21.7% 17.4% 11.7% 72.2%

Increased Market Share 2.3% 7.2% 13.8% 7.8% 6.2%

ESFM 1800

Base

- Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1% Policy Case Market Share 10.4% 21.7% 17.4% 11.7% 72.2%

Increased Market Share 2.3% 7.2% 13.8% 7.8% 6.2%

ESFM 3600

Base

- Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3% Policy Case Market Share 26.3% 32.2% 41.6% 37.2% 44.7%

Increased Market Share 2.8% 16.9% 25.7% 25.9% 15.4%

IL 1800

Base

- Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3% Policy Case Market Share 24.8% 18.3% 15.1% 12.8% 39.1%

Increased Market Share 8.8% 8.3% 3.8% 2.1% 1.8%

IL 3600

Base

- Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3% Policy Case Market Share 27.2% 27.7% 11.9% 19.4% 46.2%

Increased Market Share 6.4% 18.9% 7.4% 6.5% 3.9%

VT

- S 3600

Base

- Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7% Policy Case Market Share 37.6% 14.8% 9.7% 10.2% 48.8%

Increased Market Share 0.9% 7.3% 5.0% 4.3% 5.1%

The increased market shares attributable to consumer tax credits shown in Table 17.3

. 3 were used as inputs in the NIA

- RIA model. Appendix 17 A shows the annual market share increases due to this policy for the whole forecast period

. Section 17.4 presents the resulting market penetratio n trends for the policy case of consumer tax credits for commercial and industrial pumps that meet the efficiency level for the proposed TSL

. Because t he increase in market penetration for consumer tax credits is proportional to the increase in market penetration DOE calculated for consumer rebates, the former follows a similar increasing trend over the analysis period as the latter.

17

- 1517.3.4 Manufacturer Tax Cre dits

To analyze the potential effects of a policy that offers tax credits to manufacturers that produce commercial and industrial pumps that meet the target efficiency level s at each TSL, DOE assumed that a manufacturer tax credit would lower the consumer’s purchase cost by an amount equivalent to that provided by the consumer rebates or tax credits described above. DOE

further assumed that manufacturers would pass on some of their red uced costs to consumers, causing a direct price effect. DOE assumed that no announcement effect would occur, because the program would not be visible to consumers. f Because the direct price effect is approximately equivalent to the announcement effect, 10 DOE estimated that a manufacturer tax credit would induce half the number of consumers assumed to take advantage of a consumer tax credit to purchase more efficient products . Thus the assumed participation rate is equal to 30 percent of the number of consumers who would participate in a rebate program.

DOE attempted to investigate manufacturer response to the Energy Efficient Appliance Credits for manufacturers mandated by EPACT 2005. 18 Those manufacturer tax credits have
been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009.

DOE was unable to locate data from the Internal Revenue Service or other sources on manufacturer response to the Federal credits. Appendix 17 A presents details on Federal

manufacturer tax credits.

DOE applied th e assumption of 30 percent participation to the increase in penetration rates predicted for the rebate policy to estimate the effects of a manufacturer tax credit policy. In doing so, DOE incorporated the assumptions for consumer response to financial ince ntives from

the customized penetration curves it developed for commercial and industrial pumps

. (See Figure

17.3

. 1 ).

Table 17.3

. 4 summarizes DOE’s assumptions for commercial and industrial pumps regarding the market penetration of products in 2020 that meet the target level s at each TSL given a manufacturer tax credit.

f Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency programs might promote the models for which manufacturers increase production due to the tax credits, which in turn might induce some announc ement effect. However, DOE found no data on such programs on which to base an estimate of the magnitude of this possible announcement effect on consumer behavior.

17

- 16Table 17.3

. 4 Market Penetrations in 2020 Attributable to Manufacturer Tax Credits

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base

- Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2% Policy Case Market Share 21.3% 24.9% 14.3% 16.1% 24.9%

Increased Market Share 5.6% 6.7% 3.6% 2.4% 1.7%

ESCC 3600

Base

- Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1% Policy Case Market Share 9.3% 18.1% 10.5% 7.8% 69.2%

Increased Market Share 1.1% 3.6% 6.9% 3.9% 3.1%

ESFM 1800

Base

- Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1% Policy Case Market Share 20.6% 22.5% 30.6% 14.0% 31.0%

Increased Market Share 0.3% 6.4% 6.3% 3.7% 3.0%

ESFM 3600

Base

- Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3% Policy Case Market Share 24.9% 23.7% 28.8% 24.3% 37.0%

Increased Market Share 1.4% 8.5% 12.8% 13.0% 7.7%

IL 1800

Base

- Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3% Policy Case Market Share 20.4% 14.1% 13.2% 11.8% 38.2%

Increased Market Share 4.4% 4.2% 1.9% 1.0% 0.9%

IL 3600

Base

- Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3% Policy Case Market Share 24.0% 18.2% 8.2% 16.1% 44.3%

Increased Market Share 3.2% 9.4% 3.7% 3.2% 1.9%

VT

- S 3600

Base

- Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7% Policy Case Market Share 37.1% 11.1% 7.2% 8.0% 46.3%

Increased Market Share 0.5% 3.6% 2.5% 2.2% 2.6%

The increased market shares attributab le to a manufacturer tax credit shown in Table

17.3

. 4 were used as inputs in the NIA

- RIA model. Appendix 17 A shows the annual market share increases due to this policy for the whole forecast period

. Section 17.4 presents the resulting market penetratio n trends for the policy case of manufacturer tax credits for commercial and industrial pumps

. Because the incre ase in market penetration for manufacturer tax credits is proportional to the increase in market penetration DOE calculated for consumer rebates, the

former follows a similar increas ing trend over the analysis period as the latter

.

17.3.5 Voluntary Energy Efficie ncy Targets

DOE assumed that voluntary energy efficiency targets would lead manufacturers of commercial and industrial pumps to gradually stop producing units that operate below the

17

- 17efficiency level s set for each TSL . DOE assumed that the impetus for phasing out production of

low

- efficiency units would be a program with impacts similar to those of the ENERGY STAR labeling program conducted by the Environmental Protection Agency (EPA) and DOE in conjunction with industry partners . The ENERGY STAR progra m specifie s the minimum energy

efficienc ies that various products must have to receive the ENERGY STAR label . ENERGY STAR encourages consumers to purchase efficient products via marketing that promotes consumer label recognition, various incentive programs that adopt the ENERGY STAR specifications, and manufacturers’ promotion of their qualifying appliances. ENERGY STAR projects market penetration of compliant appliances and estimates the percentage of sales of compliant appliances that are attributable to the ENERGY STAR program.

Researchers have analyzed the ENERGY STAR program’s effects on sales of several consumer products. Program efforts generally involve a combination of information dissemination and utility or agency rebates. The analyses have be en based on State

- specific data

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The analyses generally have concluded that the market penetration of ENERGY STAR

- qualifying appliances is higher in regions or States wh ere ancillary promotional programs have been

active

. 19

, 20

, 21

DOE believes that informational incentive programs

– like ENERGY STAR, or any other labeling program sponsored by industry or other organizations

– are likely to reduce the market

barriers to more efficient products over time. During the rebate analysis, when assessing the B/C ratio and market penetration in the base case for commercial and industrial pumps , DOE

observed that the level of market barriers for more e fficient commercial and industrial pumps are in the range of low

- to

- moderate barriers to a moderate

- to

- high level of market barriers . DOE

estimates that voluntary energy efficiency targets could reduce these barriers to low er leve l s over 10 years

. Table 17.3

. 5 presents the levels of market barriers DOE estimated for commercial and industrial pumps in the base case and in the policy case of voluntary energy effi ciency targets.

DOE followed the methodology presented by Blum et al ( 2011) 22 to evaluate the effects that

such a reduction in market barriers would have on the market penetration of efficient commercial

and industrial pumps

. g The methodology relies on interpolated market penetration curves to

calculate

– given a B/C ratio

– how the market penetration of more efficient units increases as

the market barrier level to those units decreases.

g For the calculation of B/C ratios DOE discounted the cash flow of the operating cost saving s to the purchase year using a 7 percent discount rate.

17

- 18Table 17.3

. 5 Market Barriers Changes Attributable to Voluntary Energy Efficiency Targets (TSL #2 )

Base Case Voluntary Energy Efficiency Targets

ESCC 1800 Low

- Mod erate Low

ESCC 3600 Mod erate Mod erate *

ESFM 1800 Low

- Mod erate Low

ESFM 3600 Mod erate

- High Mod erate

IL 1800 Mod erate Low

IL 3600 Mod erate Mod erate *

VT

- S 3600 Low

- Moderate Low
* The policy reduces the market barriers from an upper to a lower level within the range of moderate barriers.

Table 17.3

. 6 summarizes DOE’s assumptions for commercial and industrial pumps regarding the market penetration of products in 2020 that meet the target lev els at each TSL given voluntary energy efficiency targets. Table 17.3

. 7 expands on Table 17.3

. 6 to include

, for

the proposed TSL, DOE’s assumptions regarding the market penetration of units in selected

years

.

17

- 19Table 17.3

. 6 Market Penetrations in 2020 Attributable to Voluntary Energy Efficiency Targets

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base

- Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2% Policy Case Market Share 19.0% 23.1% 16.5% 18.4% 24.9%

Increased Market Share 3.3% 4.9% 5.8% 4.7% 1.7%

ESCC 3600

Base

- Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1% Policy Case Market Share 9.0% 15.4% 4.5% 5.0% 66.8%

Increased Market Share 0.9% 0.9% 0.9% 1.1% 0.8%

ESFM 1800

Base

- Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1% Policy Case Market Share 21.2% 23.3% 28.3% 11.8% 30.4%

Increased Market Share 0.9% 7.2% 4.1% 1.5% 2.3%

ESFM 3600

Base

- Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3% Policy Case Market Share 24.7% 17.5% 18.5% 13.5% 30.0%

Increased Market Share 1.2% 2.3% 2.5% 2.2% 0.7%

IL 1800

Base

- Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3% Policy Case Market Share 22.6% 11.0% 15.7% 13.7% 39.5%

Increased Market Share 6.6% 1.1% 4.4% 2.9% 2.1%

IL 3600

Base

- Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3% Policy Case Market Share 25.5% 9.5% 5.4% 18.2% 45.1%

Increased Market Share 4.8% 0.7% 0.9% 5.3% 2.8%

VT

- S 3600

Base

- Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7% Policy Case Market Share 36.7% 8.5% 8.2% 9.9% 45.6%

Increased Market Share 0.0% 1.0% 3.6% 4.0% 1.9%

17

- 20Table 17.3

. 7 Market Penetrations in Selected Years Attributable to V oluntary Energy

E fficiency T argets for TSL #2

2020 2029 2049

ESCC 1800

Base

- Case Market Share 18.2% 18.2% 18.2% Policy Case Market Share 23.1% 45.7% 48.5%

Increased Market Share 4.9% 27.5% 30.3%

ESCC 3600

Base

- Case Market Share 14.5% 14.5% 14.5% Policy Case Market Share 15.4% 23.4% 26.5%

Increased Market Share 0.9% 8.8% 11.9%

ESFM 1800

Base

- Case Market Share 16.1% 16.1% 16.1% Policy Case Market Share 23.3% 37.3% 37.3%

Increased Market Share 7.2% 21.2% 21.2%

ESFM 3600

Base

- Case Market Share 15.3% 15.3% 15.3% Policy Case Market Share 17.5% 40.8% 43.5%

Increased Market Share 2.3% 25.5% 28.2%

IL 1800

Base

- Case Market Share 9.9% 9.9% 9.9% Policy Case Market Share 11.0% 40.6% 40.6%

Increased Market Share 1.1% 30.7% 30.7%

IL 3600

Base

- Case Market Share 8.8% 8.8% 8.8% Policy Case Market Share 9.5% 16.9% 20.0%

Increased Market Share 0.7% 8.1% 11.2%

VT

- S 3600

Base

- Case Market Share 7.5% 7.5% 7.5% Policy Case Market Share 8.5% 18.2% 20.8%

Increased Market Share 1.0% 10.7% 13.3%

The increased market shares attributable to voluntary energy efficiency targets shown in

Table 17.3

. 6 were used as inputs in the NIA

- RIA model. Appendix 17 A shows the annual market share increases due to this policy for the whole forecast period

. Section 17.4 presents the resulting market penetratio n trends for the policy case of voluntary energy efficiency targets for commercial and industrial pumps that meet the efficiency level fo r the proposed TSL

. Because of

the decrease in the market barriers level over the first 10 years of the analysis period , the market penetration of more efficient commercial and industrial pumps significantly increases over that period . For the remaining 20 years of the fore cast period the increase in market penetration keeps growing because, even though the market barriers level remains constant (at 2029 level ), the

17

- 21increase in energy prices lead s to increasing B/C ratio s and eventually to higher market penetration s

.

17.3.6 Bulk Go vernment Purchases

B ulk government purchases can lead to Federal, State, and local governments purchasing large quantities of products that meet a certain, target efficiency level . Combining the market

demands of mu ltiple public sectors can provide a market signal to manufacturers and vendors that some of their largest customers seek products that meet an efficiency target at favorable

p rices. Such a program also can induce “market pull,” whereby manufacturers and vendors

would achieve econo mies of scale for high efficiency products

.

Most of the previous bulk government purchase (procurement) initiatives at the Federal, State, and municipal levels have not tracked data on numbers of purchases or degree of compliance with procurement specifications. In many cases, procurement programs are decentralized, being part of larger State or regional initiatives. DOE based its assumptions regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) performed rega rding the savings potential of its procurement specifications for appliances and other products. FEMP, however, does not track purchasing data, because of the complex range of purchasing systems, large number of vendors, and so on . States, counties, and mu nicipalities have demonstrated increasing interest and activity in “green purchasing." Although many of the programs target office equipment, the growing infrastructure for developing and applying efficient purchasing specifications indicates that bulk gov ernment purchase programs are

feasible

. 23

, 24

DOE assumed that government agencies would administer bulk purchasing programs for

comm ercial and industrial pumps . At the federal level, this type of program could lead to FEMP procurement guidelines for commercial and industrial pumps

, which would refer to the target

level s of the proposed TSL as the minimum efficiency level s of commercial and industrial

pumps to be purchased. DOE reviewed its own previous research on the potential for market transformation through bulk government purchases. Its major study analyzed several scenarios based on the assumption that 20 percent of Federal equipm ent purchas es in 2000 already incorporated energy efficiency requirements based on FEMP guidelines. One scenario in the DOE report showed energy efficient purchasing ram ping up during 10 years from 20 percent to 80 percent of all Federal purchases. 25 Based on this study, DOE estimated that a bulk government purchas e program instituted within a 10

- year perio d would result in at least 80 percent of government

- purchased commercial and industrial pumps meeting the target efficiency

level.

DOE assumed that bulk government purchases would affect a subset of commercial buildings for which government agencies purch ased or influenced the purchase of commercial and industrial pumps . This subset would consist primarily of public commercial buildings. DOE used the 2003 Commercial Buildings Energy Consumption Survey (CBECS 2003) 26 to calculate the share of floor space in commercial buildings that corresponds to federa l government

- owned

17

- 22buildings. The calculation resulted in 2.6 percent. DOE further estimated that 47 percent of all shipments are directed to commercial buildings. Combined, these two percentages lead to 1.2 percent of all shipment s , which DOE assumed as t he share of the market to which this policy would apply.

DOE estimated that starting in 2020 , each year of a bulk government purchase policy would result in an increasing percent of shipments of government

- purchased units beyond the

base case that would meet the target efficiency level. DOE estimated that within 10 years (by

2029 ) bulk government purchasing programs would result in 80 percent h of the market for commercial and industrial pumps used in publicly owned commercial buildings meeting the

target level. DOE modeled the bulk government purchase program assuming that the market

share for commercial and industrial pumps achieved in 2029 would be at least maintained throughout the rest of the forecast period.

Table 17.3

. 8 summarizes DOE’s assumptions for commercial and industrial pumps regarding the market penetration of products in 2020 that meet the target levels at each TSL given bulk government purchases

.

h The 80 percent target to be achieved within 10 years is not reached for any of the equipment classes analyzed in this RIA, as it is constrained by the market share below the target level in the base case scenario.

17

- 23Table 17.3

. 8 Market Penetrations in 2020 Attributable to Bulk Government Purchases

TSL 1 TSL 2 TSL 3 TSL 4 TSL 5

ESCC 1800

Base

- Case Market Share 15.8% 18.2% 10.7% 13.7% 23.2% Policy Case Market Share 15.8% 18.2% 10.8% 13.7% 23.3%

Increased Market Share 0.0% 0.0% 0.1% 0.1% 0.1%

ESCC 3600

Base

- Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1% Policy Case Market Share 8.1% 14.5% 3.6% 3.9% 66.1%

Increased Market Share 0.0% 0.0% 0.0% 0.0% 0.0%

ESFM 1800

Base

- Case Market Share 20.3% 16.1% 24.2% 10.4% 28.1% Policy Case Market Share 20.3% 16.2% 24.3% 10.4% 28.2%

Increased Market Share 0.0% 0.0% 0.0% 0.1% 0.1%

ESFM 3600

Base

- Case Market Share 23.5% 15.3% 16.0% 11.3% 29.3% Policy Case Market Share 23.5% 15.3% 16.0% 11.4% 29.4%

Increased Market Share 0.0% 0.0% 0.1% 0.1% 0.1%

IL 1800

Base

- Case Market Share 16.0% 9.9% 11.3% 10.7% 37.3% Policy Case Market Share 16.0% 10.0% 11.3% 10.8% 37.4%

Increased Market Share 0.0% 0.0% 0.0% 0.1% 0.1%

IL 3600

Base

- Case Market Share 20.8% 8.8% 4.5% 12.9% 42.3% Policy Case Market Share 20.8% 8.8% 4.5% 13.0% 42.4%

Increased Market Share 0.0% 0.0% 0.0% 0.1% 0.0%

VT

- S 3600

Base

- Case Market Share 36.7% 7.5% 4.7% 5.9% 43.7% Policy Case Market Share 36.7% 7.5% 4.7% 6.0% 43.8%

Increased Market Share 0.0% 0.0% 0.1% 0.1% 0.0%

The increased market shares attributab le to bulk government purchases shown in Table

17.3

. 8 were used as inputs in the NIA

- RIA model. Appendix 17 A shows the annual market share increases due to this policy for the whole forecast period. Section 17.4 presents the resulting market penetration trends for the policy case of bulk government purchases for commercial and industrial pumps

. Market penetrations increase over the first 10 years of the forecast period, and steady for the rest of the analysis period

.

17.4 IMPACTS OF NON

- REGULATORY ALTERNATI VES

Figure 17.4

. 1 through Figure 17.4

. 7 show the effects of each non

- regulatory policy

alternative on the market penetr ation of more efficient commercial and industrial pumps

. R elative to the base case, the alternative policy cas es increase the market sha res that meet the

17

- 24target level. Recall the proposed standards (not shown in the figures) would result in a 100

- percent market penetration of products that meet the more efficient technology

.

Figure 17.4

. 1 Market Penetration of Efficient ESCC 1800 Commercial and Industrial Pumps (TSL 2 )

Figure 17.4

. 2 Market Penetration of Efficient ESCC 3600 Commercial and Industrial Pumps (TSL #2 )

17

- 25Figure 17.4

. 3 Market Penetration of Efficient ESFM 1800 Commercial and Industrial Pumps (TSL #2 )

Figure 17.4

. 4 Market Penetration of Efficient ESFM 3600 Commercial and Industrial Pumps (TSL #2 )

17

- 26Figure 17.4

. 5 Market Penetration of Efficient IL 1800 Commercial and Industrial Pumps (TSL #2)

Figure 17.4

. 6 Market Penetration of Efficient IL 3600 Commercial and Industrial Pumps (TSL #2)

17

- 27Figure 17.4

. 7 Market Penetration of Efficient VT

- S 3600 Commercial and Industrial Pumps (TSL #2)

Table 17.4

. 1 show s the national energy savings and net present value for the five non

regulatory polic y alternatives analyzed in detail for commercial and industrial pumps . T he target level for each polic y corresponds to the same efficien t technology proposed for standard s in TSL

#2

. Th e case in which no regulatory action is taken with regard to commercial and industrial

pumps constitute s the base case (or "No New Regulatory Action" scenario ) , in which NES and

NPV are zero by definition. For comparison, the tables include the impacts of the proposed

standard s. Energy savings are given in quadrillion British thermal units (quads). i The NPVs

shown in Table 17.4

. 1 are based on two discount rates, 7 percent and 3 percent

.

The polic y with the highest projected cumulative energy savings are consumer rebates, followed by consumer tax credits

, manufacturer tax credits and voluntary energy efficiency

targets

. Bulk government purchases, due to the (small) size of the estimated market, lead to negligible cumulative energy savings

. Overall, the energy saving benefits from the alternative policies

, range from 0.2 percent to 77.7 percent of the benefits from the proposed standards.

i For the alternative policies whose market penetration depends on B/C ratio, the energy savings in Table 17.4.1 correspond to the case where the ca sh flow of the operating cost savings was discounted to the purchase year using a 7 percent discount rate.

17

- 28Table 17.4

. 1 Impacts of Non

- Regulatory Policy Alternatives (TSL #2 )

Policy Alternative Energy Savings * quads Net Present Value*

m illion 2013 $

7% Disc Rate 3% Disc Rate

Consumer Rebates 0.077 ( 73 % )** 315.4 837.5

Consumer Tax Credits 0.046 ( 44 % ) 189.2 502.5

Manufacturer Tax Credits 0.023 ( 22 % ) 94.6 251.2
Voluntary Energy Efficiency Targets 0.013 ( 12 % ) 74.3 144.1

Bulk Government Purchases 0.000 ( 0.2% ) 0.8 2.0
Proposed Standards *** 0. 105 ( 100.0% ) 405.8 1107.9

* For products shipped 2020

- 2049

. **The percentages show how the energy savings from each policy alternative compare d to the (site)

energy savings from the proposed standards (represented in the table as 100%). *** Refers to site energy savings.

17

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.

20. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® Applian ces, August 20

- 22, 2003. Seattle, WA. Published in the 2003 International Energy Program Evaluation Conference Proceedings.

17

- 31U.S. Department of Energy: Energy Information Administration ( EIA)

, Co mme rcial

Bu ildings Energy Consumption Survey, ( Last accessed November, 2013.)

< www.eia.gov/consumption/commercial/data/2003/index.cfm?view=microdata >. 21. Titus, E. and S. Feldman, Tracking the Effectiveness of Energy

- Efficient Appliance Programs in the U.S., August 20-22, 2003. In 2003 International Energy Program Evaluation Conference Proceedings. Seattle, WA.

22. Blum, H ., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of Energy Efficiency Policy Measures, May, 2011.

23. Smit h, N., Telephone conversation with Barbara Atkinson of LBNL, personal

communication. National Association of State Procurement Officials

. April, 2008.

24. Respons ible Purchasing Network, (Last accessed March, 2014.)

< www.responsiblepurchasing.org/ >.

25. Harris, J. and F. Johnson, Potential E nergy, Cost, and CO 2 Savings f rom E nergy- Efficient

Government Purchase

. ACEEE: Summer Study on Energy Efficiency in Buildings , 2000: pp. 147-166 < http://aceee.org/files/proceedings/2000/data/papers/ SS00_Panel4_Paper13.pdf >.

26.

17

- 32APPENDIX 17A. REGULATORY IMPACT AN ALYSIS: SUPPORTING M ATERIALS

TABLE OF CONTENTS

17A.1 INTRODUCTION

................................ ................................ ................................ ...... 17A

- 1

17A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

................................ ....... 17A

- 2

17A.3 NIA

- RIA INTEGRATED MODEL

................................ ................................ ............ 17A

- 9

17A.4 MARKET PENETRATION C URVES

................................ ................................ ...... 17A

- 9

17A.4.1 Introduction

................................ ................................ ................................ ..... 17A

- 9

17A.4.2 Adjustment of XENERGY Penetration Curves

................................ ............ 17A

- 11

17A.4.3 Interpolation of Penetration Curve s

................................ .............................. 17A

- 12

17A.5 CONSUMER REBATE PROG RAMS

................................ ................................ ..... 17A

- 13

17A.6 FEDERAL AND STATE TA X CREDITS

................................ ............................... 17A

- 17

17A.1.1 Federal Tax Credits for Consumers

................................ .............................. 17A

- 17

17A.6.1 Federal Tax Credits for Manufacturers

................................ ......................... 17A

- 19

17A.6.2 State Tax Credits

................................ ................................ ........................... 17A

- 19

REFERENCES

................................ ................................ ................................ ..................... 17A

- 21

LIST OF TABLES

Table 17A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy Measures for ESCC 1800 Commercial and Industrial Pumps (TSL #2)

.. 17A

- 2

Table 17A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy Measures for ESCC 3600 Commercial and Industrial Pumps (TSL #2)

.. 17A

- 3

Table 17A.2.3 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for ESFM 1800 Commercial and Industrial Pumps (TSL #2)

.. 17A

- 4

Table 17A.2.4 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for ES FM 3600 Commercial and Industrial Pumps (TSL #2)

.. 17A

- 5

Table 17A.2.5 Annual Increases in Market Shares Attributable to Alternativ e Policy Measures for IL 1800 Commercial and Industrial Pumps (TSL #2)

........ 17A

- 6

Table 17A.2.6 Annual Increases in Market Shares Attr ibutable to Alternative Policy Measures for IL 3600 Commercial and Industrial Pumps (TSL #2)

........ 17A

- 7

Table 17A.2.7 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for VT

- S 3600 Commercial and Industrial Pumps (TSL #2)

... 17A

- 8

Table 17A.5.1 Statistical Results*

................................ ................................ .................. 17A

- 13

Table 17A.5.2 Rebates per Horsepower by Equipment Class

................................ ........ 17A

- 14

Table 17A.5.3 Rebate Programs for Commercial and Industrial Pumps

........................ 17A

- 15

LIST OF FIGURE S

Figure 17A.4.1 S

- Curves Showing Effects of External and Internal Sources on Adoption of New Technologies

................................ .............................. 17A

- 11

17A

- iAPPENDIX 17A. REGULA TORY IMPACT ANALYSIS : SUPPORTING MATERIA LS

17A.1 INTRODUCTION

This appendix contains sections discussing the following topics:

• Projections of annual market share increases for the alternative policies;

• NIA

- RIA Integrated Model;

• Market p enetration curves used to analyze consumer rebates and voluntary energy

efficien cy targets, including:

o Background material on XENERGY’s approach

,

o DOE’s adjustment of these curves for this analysis, and

o The m ethod DOE used to derive interpolat ed, customized curves;

• Detailed table of rebates offered for the considered product , as well as DOE’s approach to estimate a market representative rebate value for this RIA ; and

• Background material on Federal and S tate tax credits for appliances

.

17A

- 117A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

Table 17A.2

. 1 through Table 17A.2

. 7 show the annual increases in market shares of commercial and industrial pumps meeting the target efficiency level s for the proposed TSL (TSL

#2 )

. DOE used these market share increases as inputs to the NIA

- RIA spreadsheet model.

Table 17A.2

. 1 Annual Increases in Market Shares Attributable to Alternative P olicy

Measures for ESCC 1800 Commercial and Industrial Pumps (TSL #2 )

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Vol Energy

Eff Targets Bulk Govt

Purchases

2020 23.2% 13.9% 6.9% 7.1% 0.0%

2021 23.2% 13.9% 6.9% 12.1% 0.1%

2022 23.2% 13.9% 6.9% 15.9% 0.1%

2023 23.2% 13.9% 6.9% 19.0% 0.2%

2024 23.2% 13.9% 6.9% 21.7% 0.2%

2025 23.2% 13.9% 6.9% 24.1% 0.3%

2026 23.2% 13.9% 6.9% 26.4% 0.3%

2027 23.2% 13.9% 6.9% 28.5% 0.3%

2028 23.2% 13.9% 6.9% 30.5% 0.4%

2029 23.2% 13.9% 6.9% 32.4% 0.4%

2030 23.2% 13.9% 6.9% 32.5% 0.4%

2031 23.2% 13.9% 6.9% 32.6% 0.4%

2032 23.2% 13.9% 6.9% 32.7% 0.4%

2033 23.2% 13.9% 6.9% 32.9% 0.4%

2034 23.2% 13.9% 6.9% 33.0% 0.4%

2035 23.2% 13.9% 6.9% 33.1% 0.4%

2036 23.2% 13.9% 6.9% 33.3% 0.4%

2037 23.2% 13.9% 6.9% 33.4% 0.4%

2038 23.2% 13.9% 6.9% 33.5% 0.4%

2039 23.2% 13.9% 6.9% 33.7% 0.4%

2040 23.2% 13.9% 6.9% 33.8% 0.4%

2041 23.2% 13.9% 6.9% 33.9% 0.4%

2042 23.2% 13.9% 6.9% 34.0% 0.4%

2043 23.2% 13.9% 6.9% 34.2% 0.4%

2044 23.2% 13.9% 6.9% 34.3% 0.4%

2045 23.2% 13.9% 6.9% 34.3% 0.4%

2046 23.2% 13.9% 6.9% 34.3% 0.4%

2047 23.2% 13.9% 6.9% 34.3% 0.4%

2048 23.2% 13.9% 6.9% 34.3% 0.4%

2049 23.2% 13.9% 6.9% 34.3% 0.4%

17A

- 2Table 17A.2

. 2 Annual Increases in Market Shares Attributable to Alternative Policy Measures for ESCC 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Vol Energy

Eff Targets Bulk Govt

Purchases

2020 11.9% 7.2% 3.6% 1.4% 0.0%

2021 11.9% 7.2% 3.6% 2.8% 0.0%

2022 11.9% 7.2% 3.6% 4.2% 0.0%

2023 11.9% 7.2% 3.6% 5.6% 0.1%

2024 11.9% 7.2% 3.6% 6.9% 0.1%

2025 11.9% 7.2% 3.6% 8.2% 0.1%

2026 11.9% 7.2% 3.6% 9.5% 0.1%

2027 11.9% 7.2% 3.6% 10.7% 0.1%

2028 11.9% 7.2% 3.6% 11.9% 0.1%

2029 11.9% 7.2% 3.6% 11.9% 0.1%

2030 11.9% 7.2% 3.6% 11.9% 0.1%

2031 11.9% 7.2% 3.6% 11.9% 0.1%

2032 11.9% 7.2% 3.6% 11.9% 0.1%

2033 11.9% 7.2% 3.6% 11.9% 0.1%

2034 11.9% 7.2% 3.6% 11.9% 0.1%

2035 11.9% 7.2% 3.6% 11.9% 0.1%

2036 11.9% 7.2% 3.6% 11.9% 0.1%

2037 11.9% 7.2% 3.6% 11.9% 0.1%

2038 11.9% 7.2% 3.6% 11.9% 0.1%

2039 11.9% 7.2% 3.6% 11.9% 0.1%

2040 11.9% 7.2% 3.6% 11.9% 0.1%

2041 11.9% 7.2% 3.6% 11.9% 0.1%

2042 11.9% 7.2% 3.6% 11.9% 0.1%

2043 11.9% 7.2% 3.6% 11.9% 0.1%

2044 11.9% 7.2% 3.6% 11.9% 0.1%

2045 11.9% 7.2% 3.6% 11.9% 0.1%

2046 11.9% 7.2% 3.6% 11.9% 0.1%

2047 11.9% 7.2% 3.6% 11.9% 0.1%

2048 11.9% 7.2% 3.6% 11.9% 0.1%

2049 11.9% 7.2% 3.6% 11.9% 0.1%

17A

- 3Table 17A.2

. 3 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for ESFM 1800 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Vol Energy

Eff Targets Bulk Govt

Purchases

2020 21.2% 12.7% 6.4% 0.9% 0.0%

2021 21.2% 12.7% 6.4% 2.0% 0.1%

2022 21.2% 12.7% 6.4% 3.0% 0.1%

2023 21.2% 12.7% 6.4% 4.0% 0.1%

2024 21.2% 12.7% 6.4% 5.0% 0.1%

2025 21.2% 12.7% 6.4% 5.9% 0.2%

2026 21.2% 12.7% 6.4% 6.9% 0.2%

2027 21.2% 12.7% 6.4% 7.8% 0.2%

2028 21.2% 12.7% 6.4% 8.7% 0.2%

2029 21.2% 12.7% 6.4% 9.6% 0.3%

2030 21.2% 12.7% 6.4% 9.8% 0.3%

2031 21.2% 12.7% 6.4% 10.0% 0.3%

2032 21.2% 12.7% 6.4% 10.1% 0.3%

2033 21.2% 12.7% 6.4% 10.3% 0.3%

2034 21.2% 12.7% 6.4% 10.5% 0.3%

2035 21.2% 12.7% 6.4% 10.7% 0.3%

2036 21.2% 12.7% 6.4% 10.8% 0.3%

2037 21.2% 12.7% 6.4% 11.0% 0.3%

2038 21.2% 12.7% 6.4% 11.2% 0.3%

2039 21.2% 12.7% 6.4% 11.3% 0.3%

2040 21.2% 12.7% 6.4% 11.5% 0.3%

2041 21.2% 12.7% 6.4% 11.7% 0.3%

2042 21.2% 12.7% 6.4% 11.9% 0.3%

2043 21.2% 12.7% 6.4% 12.0% 0.3%

2044 21.2% 12.7% 6.4% 12.2% 0.3%

2045 21.2% 12.7% 6.4% 12.4% 0.3%

2046 21.2% 12.7% 6.4% 12.5% 0.3%

2047 21.2% 12.7% 6.4% 12.7% 0.3%

2048 21.2% 12.7% 6.4% 12.9% 0.3%

2049 21.2% 12.7% 6.4% 13.0% 0.3%

17A

- 4Table 17A.2

. 4 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for ESFM 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Vol Energy

Eff Targets Bulk Govt

Purchases

2020 28.2% 16.9% 8.5% 1.1% 0.0%

2021 28.2% 16.9% 8.5% 2.2% 0.1%

2022 28.2% 16.9% 8.5% 3.4% 0.1%

2023 28.2% 16.9% 8.5% 4.6% 0.1%

2024 28.2% 16.9% 8.5% 5.8% 0.2%

2025 28.2% 16.9% 8.5% 6.9% 0.2%

2026 28.2% 16.9% 8.5% 8.1% 0.2%

2027 28.2% 16.9% 8.5% 9.2% 0.3%

2028 28.2% 16.9% 8.5% 10.4% 0.3%

2029 28.2% 16.9% 8.5% 11.5% 0.3%

2030 28.2% 16.9% 8.5% 11.6% 0.3%

2031 28.2% 16.9% 8.5% 11.8% 0.3%

2032 28.2% 16.9% 8.5% 12.0% 0.3%

2033 28.2% 16.9% 8.5% 12.1% 0.3%

2034 28.2% 16.9% 8.5% 12.3% 0.3%

2035 28.2% 16.9% 8.5% 12.5% 0.3%

2036 28.2% 16.9% 8.5% 12.6% 0.3%

2037 28.2% 16.9% 8.5% 12.8% 0.3%

2038 28.2% 16.9% 8.5% 13.0% 0.3%

2039 28.2% 16.9% 8.5% 13.1% 0.3%

2040 28.2% 16.9% 8.5% 13.3% 0.3%

2041 28.2% 16.9% 8.5% 13.4% 0.3%

2042 28.2% 16.9% 8.5% 13.6% 0.3%

2043 28.2% 16.9% 8.5% 13.8% 0.3%

2044 28.2% 16.9% 8.5% 13.9% 0.3%

2045 28.2% 16.9% 8.5% 14.1% 0.3%

2046 28.2% 16.9% 8.5% 14.2% 0.3%

2047 28.2% 16.9% 8.5% 14.4% 0.3%

2048 28.2% 16.9% 8.5% 14.6% 0.3%

2049 28.2% 16.9% 8.5% 14.7% 0.3%

17A

- 5Table 17A.2

. 5 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for IL 1800 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Vol Energy

Eff Targets Bulk Govt

Purchases

2020 13.8% 8.3% 4.1% 0.7% 0.0%

2021 13.8% 8.3% 4.1% 1.5% 0.1%

2022 13.8% 8.3% 4.1% 2.4% 0.1%

2023 13.8% 8.3% 4.1% 3.2% 0.1%

2024 13.8% 8.3% 4.1% 4.0% 0.2%

2025 13.8% 8.3% 4.1% 4.9% 0.2%

2026 13.8% 8.3% 4.1% 5.7% 0.3%

2027 13.8% 8.3% 4.1% 6.5% 0.3%

2028 13.8% 8.3% 4.1% 7.4% 0.3%

2029 13.8% 8.3% 4.1% 8.2% 0.4%

2030 13.8% 8.3% 4.1% 8.3% 0.4%

2031 13.8% 8.3% 4.1% 8.5% 0.4%

2032 13.8% 8.3% 4.1% 8.6% 0.4%

2033 13.8% 8.3% 4.1% 8.8% 0.4%

2034 13.8% 8.3% 4.1% 8.9% 0.4%

2035 13.8% 8.3% 4.1% 9.1% 0.4%

2036 13.8% 8.3% 4.1% 9.3% 0.4%

2037 13.8% 8.3% 4.1% 9.4% 0.4%

2038 13.8% 8.3% 4.1% 9.6% 0.4%

2039 13.8% 8.3% 4.1% 9.7% 0.4%

2040 13.8% 8.3% 4.1% 9.9% 0.4%

2041 13.8% 8.3% 4.1% 10.1% 0.4%

2042 13.8% 8.3% 4.1% 10.2% 0.4%

2043 13.8% 8.3% 4.1% 10.4% 0.4%

2044 13.8% 8.3% 4.1% 10.5% 0.4%

2045 13.8% 8.3% 4.1% 10.7% 0.4%

2046 13.8% 8.3% 4.1% 10.9% 0.4%

2047 13.8% 8.3% 4.1% 11.0% 0.4%

2048 13.8% 8.3% 4.1% 11.2% 0.4%

2049 13.8% 8.3% 4.1% 11.3% 0.4%

17A

- 6Table 17A.2

. 6 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for IL 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Vol Energy

Eff Targets Bulk Govt

Purchases

2020 31.5% 18.9% 9.4% 1.3% 0.0%

2021 31.5% 18.9% 9.4% 2.7% 0.1%

2022 31.5% 18.9% 9.4% 4.1% 0.1%

2023 31.5% 18.9% 9.4% 5.5% 0.2%

2024 31.5% 18.9% 9.4% 6.9% 0.2%

2025 31.5% 18.9% 9.4% 8.2% 0.2%

2026 31.5% 18.9% 9.4% 9.5% 0.3%

2027 31.5% 18.9% 9.4% 10.8% 0.3%

2028 31.5% 18.9% 9.4% 12.1% 0.3%

2029 31.5% 18.9% 9.4% 13.3% 0.4%

2030 31.5% 18.9% 9.4% 13.4% 0.4%

2031 31.5% 18.9% 9.4% 13.6% 0.4%

2032 31.5% 18.9% 9.4% 13.7% 0.4%

2033 31.5% 18.9% 9.4% 13.9% 0.4%

2034 31.5% 18.9% 9.4% 14.1% 0.4%

2035 31.5% 18.9% 9.4% 14.3% 0.4%

2036 31.5% 18.9% 9.4% 14.4% 0.4%

2037 31.5% 18.9% 9.4% 14.6% 0.4%

2038 31.5% 18.9% 9.4% 14.8% 0.4%

2039 31.5% 18.9% 9.4% 14.9% 0.4%

2040 31.5% 18.9% 9.4% 15.1% 0.4%

2041 31.5% 18.9% 9.4% 15.3% 0.4%

2042 31.5% 18.9% 9.4% 15.4% 0.4%

2043 31.5% 18.9% 9.4% 15.6% 0.4%

2044 31.5% 18.9% 9.4% 15.8% 0.4%

2045 31.5% 18.9% 9.4% 15.9% 0.4%

2046 31.5% 18.9% 9.4% 16.1% 0.4%

2047 31.5% 18.9% 9.4% 16.3% 0.4%

2048 31.5% 18.9% 9.4% 16.4% 0.4%

2049 31.5% 18.9% 9.4% 16.6% 0.4%

17A

- 7Table 17A.2

. 7 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for VT

- S 3600 Commercial and Industrial Pumps (TSL #2)

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Vol Energy

Eff Targets Bulk Govt

Purchases

2020 12.3% 7.4% 3.7% 1.5% 0.0%

2021 12.3% 7.4% 3.7% 3.1% 0.1%

2022 12.3% 7.4% 3.7% 4.6% 0.1%

2023 12.3% 7.4% 3.7% 6.1% 0.2%

2024 12.3% 7.4% 3.7% 7.6% 0.2%

2025 12.3% 7.4% 3.7% 9.1% 0.3%

2026 12.3% 7.4% 3.7% 10.5% 0.3%

2027 12.3% 7.4% 3.7% 11.9% 0.4%

2028 12.3% 7.4% 3.7% 13.3% 0.4%

2029 12.3% 7.4% 3.7% 14.6% 0.5%

2030 12.3% 7.4% 3.7% 14.7% 0.5%

2031 12.3% 7.4% 3.7% 14.9% 0.5%

2032 12.3% 7.4% 3.7% 15.0% 0.5%

2033 12.3% 7.4% 3.7% 15.1% 0.5%

2034 12.3% 7.4% 3.7% 15.3% 0.5%

2035 12.3% 7.4% 3.7% 15.4% 0.5%

2036 12.3% 7.4% 3.7% 15.6% 0.5%

2037 12.3% 7.4% 3.7% 15.7% 0.5%

2038 12.3% 7.4% 3.7% 15.9% 0.5%

2039 12.3% 7.4% 3.7% 16.0% 0.5%

2040 12.3% 7.4% 3.7% 16.2% 0.5%

2041 12.3% 7.4% 3.7% 16.3% 0.5%

2042 12.3% 7.4% 3.7% 16.4% 0.5%

2043 12.3% 7.4% 3.7% 16.6% 0.5%

2044 12.3% 7.4% 3.7% 16.7% 0.5%

2045 12.3% 7.4% 3.7% 16.9% 0.5%

2046 12.3% 7.4% 3.7% 17.0% 0.5%

2047 12.3% 7.4% 3.7% 17.1% 0.5%

2048 12.3% 7.4% 3.7% 17.3% 0.5%

2049 12.3% 7.4% 3.7% 17.4% 0.5%

17A

- 817A.3 NIA

- RIA INTEGRATED MODEL

For this analysis, DOE used its integrated NIA

- RIA a model appro ach that the Department

built on the NIA model discussed in Chapter 10 and documented in Appendix 10A .The resulting integrated NIA

- RIA model feature s both the NIA and RIA inputs

, analys e s and results . It has the capability to generate results , by equipment class and TSL, for the mandatory standar ds and each of the RIA polic ies. Separate modules estimate increases in market penetration of more efficient equipment for consumer rebates, voluntary energy efficiency targets and bulk government purchases. b The consumer rebates module calculates benefit

- cost (B/C) ratios and market barriers, and generates customized market penetration curves for each equipment class ; t he voluntary energy efficiency targets module relies on the market barriers calculated in the

consumer rebates module to project a reductio n in those barriers over the first ten years of the forecast period

, and estimate the market effects of such a reduction; and t he bulk government purchases module scales down the market for commercial and industrial pumps to Federal

government

- owned commercial buildings. A separate module summarizes the market impacts from mandatory standards and all policy alternatives, and an additional module produces all tables and figures presented in Chapter 17 as well as the tables of market share increases for each policy reported in S ection 17A.2 of this Appendix

.

17A.4 MARKET PENETRATION C URVES

This section first discusses the theoretical basis for the mark et penetration curves that DOE used to analyze the Consumer Rebates and Voluntary Energy Efficiency Targets polic ies

. Next it discusses the adjustments it made to the maximum penetration rates. It then refers to the

method it used to develop interpolated penetration curve s for commercial and industrial pumps that meet the target efficiency level s at each TSL . The resulting curve s are presented in C hapter

17

.

17A.4.1 Introduction

XENERGY, Inc. c , developed a re

- parameterized, mixed

- source information diffusion

mode l to estimate market impacts induced by financial incentives for purchasing energy efficient appliance s. 1 The basic premise of the mixed

- source model is that information diffusion drives the adoption of technology.

Extensive economic literature describes the diffusion of new products as technologies evolve. Some research focuses primarily on dev eloping analytical models of diffusion patterns applicable to individual consumers or to technologies from competing firms

. 2

, 3

, 4 One study records researchers’ attempts to investigate the factors that drive diffusion processes

. 5 Because a

a NIA = National Impact Analysis; RIA = Regulatory Impact Analysis

b As mentioned in Chapter 17, the increase in market penetrations for consumer tax credits and manufacturer tax credits are estimated as a fraction of the increase in market penetration of c onsumer rebates.

c XENERGY is now owned by KEMA, Inc. ( www.kema.com )

17A

- 9ne w product generally has its own distinct characteristics, few studies have been able to conclusively develop a universally applicable model. Some key findings, however, generally are accepted in academia and industry.

One accepted finding is that, regar dless of their economic benefits and technological merits, new technologies are unlikely to be adopted by all potential users. For many products, a ceiling must be placed on the adoption rate. A second conclusion is that not all adopters purchase new produ cts at the same time: some act quickly after a new product is introduced; others wait for the product to mature. Third, diffusion processes can be characterized approximately by asymmetric S

- curves that depict three stages of diffusion: starting, accelerat ing, and decreasing (as the adoption ceiling is approached).

A so

- called epidemic model of diffusion is used widely in marketing and social studies. The epidemic model assumes that (1) all consumers place identical value on the benefits of a new product, and (2) the cost of a new product is constant or declines monotonically over time. What induces a consumer to purchase a new product is information about the availability and benefits of the product. In other words, information diffusion drives consumers’ adoption of a

new product

. 3 The model incorporates information diffusion from both internal sources (spread by word of mouth from early adopters to prospective adopters) and external sources (the “announcement effect” produced by government agencies, institutions, o r commercial advertising). The model incorporates both internal and external sources by combining a logistic function with an exponential function. 4

, 5

The relative degree of influence from the internal and external source s determines the general shape of the diffusion curve for a specific product. 4

, 5 If adoption of a product is influenced primarily by external sources of information (the announcement effect), for instance, a high rate of diffusion occurs at the beginning of the process. In th is scenario, external sources provide immediate information exposure to a significant number of prospective adopters. In contrast, internal sources (such as a network of prospective adopters) are relatively small in size and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, information diffusion dominated by external sources is represented by a concave curve (the exponential curve in Figure 17A.4

. 1 ). If adoption of a new product is influenced most strongly by internal sources of information, the number of adopters increases gradually, forming a convex curve (the logistic curve in Figure 17A.4

. 1 ).

17A

- 10Figure 17A.4

. 1 S

- Curves Showing Effects of External and Internal Sources on Adoption of New Technologies

17A.4.2 Adjustment of XENERGY Penetration Curves

In consultation with the primary authors of the 2002 XENERGY study who later conducted similar California studies, DOE made some adjustments to XENERGY’s original implementation (penetration) curves. 6 The experiences with utility programs since the

XENERGY study indicate that incentive programs have difficulty achieving penetration rates as high as 80 percent. Consumer response is limited by barriers created by consumer utility issues

and other non

- economic factors. DOE therefore adjusted the maximum penetr ation parameters for some of the curves from 80 percent to the following levels:

Moderate Barriers: 70% High Barriers: 60% Extremely High Barriers: 50%

The low barriers and no barriers curves (the latter used only when a product has a very high base

- case

- market share) remained, respectively, with 80 percent and 100 percent as their maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set

the no barri ers and extremely high barriers curves as the upper and lower bounds, respectively, for any benefit/cost ratio points higher or lower than the curves. It set another constraint such

17A

- 11that the policy case market share cannot be great er than 100 percent, as m ight occur for products with high base case market shares of the target

- level technology.

17A.4.3 Interpolation of Penetration Curves

As discussed above, the XENERGY penetration (implementation) curves followed a functional form to estimate the market implementat ion rate caused by energy efficiency

measures such as consumer rebates. d The XENERGY report presents five reference market implementation curves that vary according to the level of market barriers to technology penetration. 1 Such curves have been used by DOE in the Regulatory Impact Analyses for rulemakings for applia nce energy efficiency standards to estimate market share increases in response to rebate programs. e They provide a framework for evaluating technology penetration, yet require matching the studied market to the curve that best represents it. This approxima te matching can introduce some inaccuracy to the analysis.

Blum et al (2011, Appendix A) 7 presents an alternative approach to such evaluation: a method to estimate market implementation rates more accurately by performing interpolations of

the reference curves. The referred report describes the mar ket implementation rate function and the reference curves, the method to calibrate the function to a given market, and the limitations

of the method.

DOE used the above referred method to interpolat e market implementation curves
, to
generate customized curves that were used to estimate the effects of consumer rebates and voluntary energy efficiency targets for each equipment class covered by this RIA. For consumer rebates, DOE derived such curves based on an algorithm that finds the market implementation curve that best fits, for the first year of the analysis period, the B/C ratio of the target efficiency level and the market penetration of equipment with that level of energy efficiency in the base

case. For the analysis of voluntary energy efficiency ta rgets, DOE departs from the market barriers level corresponding to the market implementation curve it derived for consumer rebates

, to linearly decrease it over the ten initial years of the analysis period. For each year, as market barriers decline, the corresponding market implementation curve leads

– for the same B/C ratio

– to higher market penetrations.

d The RIA chapter refers to these curves as penetration curves . This section, in references to the original source, uses

the term implementation curve

.

e DOE has also used this method to estimate market share increase s resulting from consumer tax credit and manufacturer tax credit programs, since the effects of tax credits on market s are considered in this RIA proportional

to the impacts from rebate s

.

17A

- 1217A.5 CONSUMER REBATE PROG RAMS

DOE performed a search for rebate programs that offered incentive s for commercial and industrial pumps

. Some organizations nationwide, comprising elect ric utilities and regional agencies, offer rebat e programs for this equipment , and provide s the orga nizations’ names,
states, rebate amounts

, and program websites. If there is more than one e ntry for an organization,

it offers different rebates in different states

. When an organization offers rebates through several

utilities , it is represented only once in that table.

DOE relied on the data it gathered from the 52 rebate programs offered by the three organizations listed in Table 17A.5

. 3 to calculate a market

- representative rebate amount for commercial and industrial pumps

. DOE performed a statistical analysis over the rebate values and other characteristics of the rebate programs it identified

, to estimate a rebate amount per horse power that would vary with the horse power of a pump :

rrrrrr h pp ( h pp ) = ββ

∙ rrrrrr
xx − αα + εε [1]

where:

rrrrrr h pp ( h pp ) Estimated amount of re bate per horse power ( dollar/hp ) for a given value of

horse power ( hp )

, rrrrrr
xx Rebate per h orse power ( dollar/ hp ) offered by rebate program x ,

αα
, ββ regression coefficients, and

εε statistical error.

Table 17A.5

. 1 presents the statistical results of model [1] above, and Table 17A.5

. 2

presents the rebate per horse power amounts that DOE calculated for each equipment class based

on the representative horse power of the equipment class

.

Table 17A.5

. 1 Statistical Results *

Coefficient Value

αα 0.433

ββ 60.638

* R 2 =0. 403

17A

- 13Table 17A.5

. 2 Rebates per Horse p ower by Equipment Class
Representative

Horse p ower (hp) Rebate per Horse p ower (2013$/hp) Rebate Amount (2013$)

ESCC 1800 5.8 28.30 164.16

ESCC 3600 13.8 19.44 268.27

ESFM 1800 24.9 15.05 374.79

ESFM 3600 47.6 11.37 541.04

IL 1800 3.9 33.62 131.11

IL 3600 7.9 24.76 195.58

VT

- S 3600 7.0 26.09 182.62

17A

- 14Table 17A.5

. 3 Rebate Programs for Commercial and Industrial Pumps

Organization State Rebate* HP Website

Duke Energy IN $250.00/unit 7.5 http://www.duke

- energy.com/indiana

- business/energy

management/pump

- incentive.asp

Duke Energy IN $260.00/unit 10.0 http://www.duke

- energy.com/indiana

- business/energy

management/pump

- incentive.asp

Duke Energy IN $260.00/unit 15.0 http://www.duke

- energy.com/indiana

- business/energy

management/pump

- incentive.asp

Duke Energy IN $400.00/unit 20.0 http://www.duke

- energy.com /indiana

- business/energy

management/pump

- incentive.asp

Indianapolis Power & Light Company IN $22.50/unit 1.5 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Indianapolis Power & Light Company IN $30.00/unit 2.0 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Indianapolis Power & Light Company IN $45.00/unit 3.0 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Indianapolis Power & Light Company IN $75.00/unit 5.0 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Indianapolis Power & Light Company IN $112.50/unit 7.5 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Indianapolis Power & Light Company IN $150.00/unit 10.0 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Indianapolis Power & Light Company IN $225.00/unit 15.0 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Indianapolis Power & Light Company IN $300.00/unit 20.0 http://www.iplpower.com/Business/Business_Energy_Sa vings/Business_Energy_Incentives_Program/

Duke Energy KY $122.00/unit 1.5 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy KY $175.00/unit 2.0 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy KY $175.00/unit 3.0 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy KY $170.00/unit 5.0 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy KY $250.00/unit 7.5 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy KY $165.00/unit 10.0 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy KY $290.00/unit 15.0 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy KY $400.00/unit 20.0 http://www.duke

- energy.com/kentucky

- business/smart

saver/customer/pump

- incentives.asp

DTE Energy MI $20.00/hp 1.5 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/how%20to %20participate/!ut/p/b1/

17A

- 15Organization State Rebate* HP Website

DTE Energy MI $20.00/hp 2.0 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/ho w%20to %20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 3.0 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/how%20to %20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 5.0 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/how%20to %20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 7.5 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/ho w%20to %20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 10.0 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/how%20to %20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 15.0 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/how%20to %20participate/!ut/p/b1/

DTE Energy MI $20.00/hp 20.0 https://www2.dteenergy.com/wps/portal/dte/business/sav eEnergy/details/incentives%20and%20offers/h ow%20to %20participate/!ut/p/b1/

Duke Energy NC $122.00/unit 1.5 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy NC $175.00/unit 2.0 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy NC $175.00/unit 3.0 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy NC $170.00/unit 5.0 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy NC $249.00/unit 7.5 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Ener gy NC $165.00/unit 10.0 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy NC $290.00/unit 15.0 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy NC $400.00/unit 20.0 http://www.duke

- energy.com/north

- carolina

- large

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy OH $122.00/unit 1.5 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy OH $175.00/unit 2.0 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy OH $175.00/unit 3.0 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy OH $170.00/unit 5.0 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

17A

- 16Organization State Rebate* HP Website

Duke Energy OH $249.00/unit 7.5 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy OH $165.00/unit 10.0 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy OH $290.00/unit 15.0 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy OH $400.00/unit 20.0 http://www.duke

- energy.com/ohio

- business/smart

saver/customer/pump

- incentives.asp

Duke Energy SC $122.00/unit 1.5 http://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy SC $175.00/unit 2.0 http://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy SC $175.00/unit 3.0 http://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy SC $170.00/unit 5.0 http://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy SC $249.00/unit 7.5 http://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy SC $165.00/unit 10.0 http://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy SC $290.00/unit 15.0 http://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

Duke Energy SC $400.00/unit 20.0 http ://www.duke

- energy.com/south

- carolina

business/smart

- saver/customer/pump

- incentives.asp

* In 2013 $.

17A.6 FEDERAL AND STATE TA X CREDITS

This section summarizes the Federal and State tax credits available to consumers who purchase energy efficient appliances. This section also describes tax credits available to manufacturers who produce certain energy efficient appliances.

17A.1.1 Federal Tax Credits for Consumers

EPACT 2005 included Federal tax credits for consumers who installed efficient air conditioners or heat pum ps; gas, oil and propane furnaces and boilers ; furnace fans; and/or gas, oil, or electric heat pump water heaters in new or existing homes. 8

, 9 These tax credits were in effect in 2006 and 2007, expired in 2008, and were reinstated for 2009

– 2010 by the American Recovery and Reinvestment Act (ARRA). 10 There was a $1,500 cap on the credit per home, including the amount received for insulation, windows, and air and duct sealing. Congress extended this provision for 2011, with some modifications to eligibility requirements, and reductions in the cap to $500 per home

. The Am erican Taxpayer Relief Act of 2012 extended,

17A

- 17with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and

water heaters placed in service between January 1, 2012 and December 31, 2013. 8

, 11

The tax credit for furnace fans was $50 in 20 1 1, after which it expired.

The importance of the Fed eral tax credits has been emphasized in research in the residential heating industry on the impacts of the relatively large credits that were available for HVAC (heating, ventilating, and air conditioning) equipment

. I n a survey of HVAC distributors conducted by Vermont Energy Inve stment Corporation, respondents indicated that the ample

credit had had a notable impact on sal es of higher

- efficiency heating and cooling equipment

. Some distributors combined the Federal tax credits with manufacturer re bates and ut ility

program rebates for a greater consumer incentive. However, when the amount of the Federal tax

credit was reduced , smaller utility rebate incentives had not ind uced the same levels of equipment sales increases

. T h e decrease in incentive size from a $1,500 cap in 2009

- 20 10 to a $500 cap in 2011 , during a period when the economy continued to be sluggish, resulted in a decline in total sales of residential HVAC p roducts

. Distributors stated that an incentive needed to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence

consumer choice. The industry publication “ 2011 HVAC Review and Outlook ” noted a decline

in sales of air conditi oning units with >14 SEER in 2011 and a return in sales of units with >16 SEER to 2009 levels (after an increase in 2010). The l arge majority of distributor s observed no impacts from the utility p rograms with their lower rebate amounts available in 2011. D istributors also commented on the advantages of the Federal tax credit being nationwide in contrast to utility rebate programs that target regional markets. 12

, 13

In an effort to evaluate the potential impact of a Federal appliance tax credit program

, DOE reviewed Internal Revenue Service (IRS) data on the numbers of taxpayers who claimed the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed
Form 5695, Residential Energy Credits

. 14 It also estimated the percentage of taxpayers with entries under Form 5695’s section 3, Residential energy property costs , line 3b, qualified natural gas, propane, or oil furnac e or hot water boiler . DOE reasoned that the percentage of taxpayers with an entry on Line 3b could serve as a rough indication of the potential of taxpayer participation in a Federal tax credit program for furnaces during the initial program years. DOE

fo und that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 2007, claimed a credit for a furnace or boiler. DOE further found that the percentages of those filing Form 5695 for any qualifying energy property expenditure (which also included installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 respectively.

DOE also reviewed data from an earlier Federal energy conservation tax credit program in place in the 1980s. While this ta x credit was available from 1979 through 1985, DOE located data for only the first three years of the program

. 15

, 16

, 17 For those three years

- 1979, 1980, and

1981

- the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 percent. Given that the data from this earlier tax credit program were not disaggregated by type of energy property, this da ta series served only to indicate a possible trend of greater participation in the initial program year, followed by slightly smaller participation in subsequent years. However, DOE did not find detailed analysis of this program to indicate the possible

17A

- 18re asons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 2005 energy tax credit program data for its first two program years.

As discussed in C hapter 17, DOE analyzed the percentage of participation in consumer

tax cr edit programs using its estimates of consumer participation in rebate programs that was based on benefit/cost data specific to each equipment class of commercial and industrial pumps covered by this RIA . Hence it was difficult to compare these detailed est imates to the more general data analysis described above from the existing Federal tax credit program, or to use the IRS data analysis in its consumer tax credit analysis.

17A.6.1 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient A ppliance Credits to manufacturers that produced high

- efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007. 18

The Emergency Economic Stabilization Act of 2008 19 amended the credits and extended them through 2010. The credits were extended again to 2011 with modifications in the eligibility requirements. Manufacturer t ax credits were extended again, by the American Taxpayer Relief

Act of 2012 , for clothes washers, refrigerators, and dishwashers manufactured between January 1, 2012 and December 31, 2013.

Manufacturers who pr oduce these appliances receive the credits for increasing their production of qualifying appliances. These credits had sev eral efficiency tiers in 2011. From

2012

- 2013, credits for the higher tiers remain ed, but were eliminated for the lowest (least

ef

ficient) tiers for cloth es washers and dishwashers

. 11 The credit amounts were applied to each unit manufactured. The credit to manufacturers of qualifying clothes washers, refrigerators and

dishwashers wa s capped at $75 million for the period of 2008

- 2010. However, the most efficient refrigerator (30 percent ) and clothes washer (2.2 MEF/4.5 wcf) models w ere not subject to the

ca p. The credit to manufacturers wa s capped at $25 million for 2011, with t he most efficient refrigerators (35 percent ) and clothe s washers (2.8 MEF/3.5 WCF) exempted from this cap. 20

17A.6.2 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for energy

- efficient appliances for several years, and the State s of Kentucky, Michigan and Indiana began offering such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on taxpayer participation in credits for eligible products. ( See the discussion in C hapter 17

, S ection

1 7 .3.3, on tax cre dit data for clothes washers.) Montana’s Department of Revenue does not disaggregate participation data by appliance, although DOE reviewed Montana's overall participation trends and found them congruent with its analysis of Oregon's clothes washer tax

cre dits.

Oregon’s Residential Energy Tax Credit (RE TC) was created in 1977. T he Oregon legislature expanded the RETC program in 1997 to include residential refrigerators, clothes washers, and dishwashers, which significantly increased participation in the program . The program subsequently added credits for high

- efficiency heat pump systems, air conditioners, and water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery,

17A

- 19and renewable energy equipment. Beginning in 2 012 a Tax Credit Extension Bill (HB3672) eliminated refrigerators, clothes washers, dishwashers, air conditioners, and boilers from the RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters

, and heat pump water heate rs. 21

, 22 Those technologies recognized by the Oregon Department of Energy a s “premium efficiency” are eligible for a tax credit of $0.60 per kWh saved in the first year (up to $1,500). 21

, 23

Montana has had an Energy Conservation Tax Credit for residential measures since

1998. 24 The tax credit covers various residential energy and water efficient products, including split sys tem central air conditioning; package system central air conditioning; split system air source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water boilers; advanced main air circulating fans; heat recovery ventilators; g as, oil, or propane water heaters; electric heat pump water heaters; low

- flow showerheads and faucets ; light fixtures ; and

controls. In 2002

, the amount of the credit was increased from 5 percent of product costs (up to $150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in new construction or remodeling projects. The tax credit covers only th e part of the cost and

materials that exceed established standards of construction.

17A

- 20REFERENCES

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< www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_energy _efficiency >

.

2. Hall, B. H. and B. Khan, Adoption of New Technology

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- 330

, 2003. Department of Economics, University of California, Berkele y, CA.

3. Lekvall. P and C. Wahlbin, "A Study of Some Assumptions Underlying Innovation

Diffusion Functions"

. Swedish Journal of Economics , 1973

4. Geroski, P. A., Models of Technology Diffusion

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communication. Itron , Inc

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LBNL

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< http://escholarship.org/uc/item/4t32x6vr >

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8. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling Equipment., 2011. (Last accessed March, 2014.)

< www.energytaxincentives.org/consumers/heating

- cooling.php >

.

9. Energy Policy Act of 2005 , in 119 STAT. 594 Public Law 109

- 58. Section 1333, 26 USC
25C note, . 2005. < www.gpo.gov/fdsys/pkg/BILLS

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109hr6pcs.pdf >.

10. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. (Last accessed March, 2014.) < http://frwebgate.access.gpo.gov/cgi

bin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf >

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11. Tax Incentives Assistance Project, The Tax Incentives Assistance Pro ject (TIAP), 2013. (Last accessed March, 2014.) < http://energytaxincentives.org/ >

.

12. Foster, R., J. Grevatt, N. Lange, and T. Gee, Efficiency Programs and Distributors: Opportunities to Fully Leverage th e Supply Chain for Greater Impact, August 12

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- Efficient Economy. Asilomar, CA.

< www.aceee.org/files/proceedings/2012/data/papers/0193

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13. Tusa, C. S., D. Pierson, P. Mammola, and J. P. Vora, JPM/HARDI 2011 HVAC Survey,

2011. (Last accessed March, 2014.) < www.hardinet.org/mid

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survey >

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14. Internal Revenue Service, SOI Tax Stats

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15. Internal Revenue Service, 1979 Annual Report, Commissioner of Int ernal Revenue, (Last accessed March, 2014.) < www.irs.gov/pub/irs

- soi/79dbfullar.pdf >

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accessed Marc h, 2014.) < www.irs.gov/pub/irs

- soi/80dbfullar.pdf >

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17. Internal Revenue Service, 1981 Annual Report: Commissioner of Internal Revenue and the Chief Counsel for the Internal Revenue Service, ( Last accessed March, 2014.)

< www.irs.gov/pub/irs

- soi/81dbfullar.pdf >

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18. U.S. Department of the Treasury

– Internal Revenue Service, Form 8909: Energy Efficient Appliance Credit, (Last access ed March, 2014.) < www.irs.gov/pub/irs

- pdf/f8909.pdf >

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19. Emergency Economic Stabilization Act of 2008, (Last accessed March, 2014.)

< www.gpo.gov/fdsys/pkg/PLAW

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20. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed March, 2014.) < http://energytaxincentives.org/builders/appliances.php >

.

21. Database of State Incentives for Renewables and Efficiency (DSIRE), Oregon Incentives/Policies for Renewable Energy, Residential Ener gy Tax Credit, 2013. (Last accessed March, 2014.) < http://programs.dsireusa.org/system/program/detail/638 >

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22. Oregon Department of Energy, HB 3672 (2011)Tax Credit Extension Bill, 2013. (Last accessed March, 2014.) < www.oregon.gov/energy/CONS/docs/HB3672summary.pdf >

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23. Oregon Department of Energy, ODOE: Energy Conservation, Heat Pumps and Air Conditioning Systems, 2013. (Last accessed March, 2014.)

< www.oregon.gov/energy/CONS/Pages/res/tax/HVAC

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24. Montana Department of Revenue, Energy Related Tax Relief: Energy Conservation Installation Credit, (Last accessed March, 2014.)

< http://revenue.mt.gov/home/individuals/taxrelief_energy.aspx >

.

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