# TECHNICAL SUPPORT DOCUMENT:

ENERGY EFFICIENCY PROGRAM

FOR CONSUMER PRODUCTS AND COMMERCIAL AND INDUSTRIAL EQUIPMENT:

COMMERCIAL CLOTHES WASHERS

February 21, 2014

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

and Lawrence Berkeley National Laboratory1

- i CHAPTER 1. INTRODUCTION

TABLE OF CONTENTS

1.1 PURPOSE OF THE DOCUMENT ................................................................

................. 1 -1

1.2 OVERVIEW OF STANDARDS FOR COMMERCIAL CLOTHES WASHERS ......... 1 -1

1.3 PROCESS FOR SETTING ENERGY CONSERVATION STANDARDS ................... 1 -1

1.4 STRUCTURE OF THE DOCUMENT ................................................................

............ 1 -5

LIST OF TABLES

Table 1.3.1 Analyses Under the Process Rule ................................................................

........ 1 -41-1 CHAPTER 1. INTRODUCTION

1.1 PURPOSE OF THE DOCUMENT

This technical support document (TSD) is a stand-alone report that provides the technical analyses and results supporting the information presented in the Notice of Proposed Rulemaking (NOPR) for commercial clothes washers. This TSD reports on the activities and analyses conducted in the period following the publication of the framework document for this rulemaking.

1.2 OVERVIEW OF STANDARDS FOR COMMERCIAL CLOTHES WASHERS

Title III of the Energy Policy and Conservation Act of 1975 (42 U.S.C.6291, et seq; “EPCA”), Pub. L. 94-163, sets forth a variety of provisions designed to improve energy efficiency. (All references to EPCA refer to the statute as amended through the American Energy Manufacturing Technical Corrections Act, Pub. L. 112-210 (Dec. 18, 2012)). 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, as codified), establishes the "Energy Conservation Program for Certain Industrial Equipment." These include commercial clothes washers, the subject of this rulemaking. (42 U.S.C. 6311(1)(H)).

The Energy Policy Act of 2005 (EPACT) established the first energy conservation standards for commercial clothes washers. (42 U.S. C. 6313(e)(1)) EPACT directed DOE to conduct two rulemakings to determine whether the established standards should be amended. DOE published its first final rule amending commercial clothes washer standards on January 8, 2010 (“January 2010 final rule”), which apply to commercial clothes washers manufactured on or after January 8, 2013. EPACT required the second final rule to be published by January 1, 2015, which will apply to commercial clothes washers manufactured three years after the date on which the final amended standard is published. (42 U. S.C. 6313(e)(2)(B)) This current rulemaking will satisfy the requirement to publish the second final rule by January 1, 2015. Compliance with any amended standards would be required three years after the date of publication of the final standards.

1.3 PROCESS FOR SETTING ENERGY C ON SERVATION STANDARDS

Under EPCA, when DOE is studying new or amended standards, it must consider, to the greatest extent practicable, the following seven factors (42 U.S.C. 6295 (o)(2)(B)(i) and 6316(a) ):1-2

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;

3) the total projected amount of energy savings likely to result directly from the imposition of the standard;

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

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

6) the need for national energy conservation; and

7) other factors the Secretary considers relevant.

Other statutory requirements are set forth in 42 U.S.C. 6295 (o)(1)– (2)(A), (2)(B)(ii)– (iii), and (3)– (4) and 42 U.S.C. 6316(e).

DOE considers stakeholder participation to be an important part of the process for setting energy conservation standards. Through formal public notifications ( i.e.

, Federal Register notices), DOE actively encourages the participation and interaction of all stakeholders during the comment period in each stage of the rulemaking. Beginning with the framework document and during subsequent comment periods, interactions among stakeholders provide a balanced discussion of the information that is required for the standards rulemaking.

Before DOE determines whether or not to adopt a proposed energy conservation standard, it must first solicit comments on the proposed standard. (42 U.S.C. 6295(m)(2)(B); 6295(p)(2); and 6316(a)) Any new or amended standard must be designed to achieve significant additional conservation of energy and be technologically feasible and economically justified. (42 U.S.C. 6295(o)(2)(A) and 6316(a)) To determine whether economic justification exists, DOE must review comments on the proposal and determine that the benefits of the proposed standard exceed its burdens to the greatest extent practicable, weighing the seven factors listed above. (42 U.S.C. 6295 (o)(2)(B)(i) and 6316(a))

After the publication of the framework document, the energy conservation standards rulemaking process involves three additional, formal public notices, which DOE publishes in the Federal Register . The first of the rulemaking notices is a notice of public meeting (NOPM ), which is designed to publicly vet the models and tools used in the preliminary rulemaking and to facilitate public participation before the next stage. The second notice is the notice of proposed rulemaking (NOPR), which presents a discussion of comments received in response to the1-3

NOPM and the preliminary analyses and analytical tools; analyses of the impacts of potential amended energy conservation standards on consumers, manufacturers, and the Nation; DOE’s weighting of these impacts of amended energy conservation standards; and the proposed energy conservation standards for the product. The third notice is the final rule, which presents a discussion of the comments received in response to the NOPR; the revised analyses; DOE’s weighting of these impacts; the amended energy conservation standards DOE is adopting for the product; and the compliance dates of the amended energy conservation standards.

DOE initiated the current rulemaking on August 13, 2012 by publishing a notice announcing the availability of the framework document, the Energy Conservation Standards Rulemaking Framework Document for Commercial Clothes Washers . In this notice, DOE also announced a public meeting and requested comment on the matters raised in the framework

document. 77 FR 48108 (August 13, 2012). The framework document described the procedural and analytical approaches that DOE anticipated using to evaluate energy conservation standards

for commercial clothes washers and identified various issues to resolve during the rulemaking.

The NOPM is available at http://www.regulations.gov/#!documentDetail;D=EERE-2012

- BT

STD -0020-0002 and the framework document is available at http://www.regulations.gov/#!documentDetail;D=EERE-2012- BT

- STD -0020 -0001

.

On September 24, 2012, DOE held the framework document public meeting and

disc ussed the issues detailed in the framework document. DOE also described the analyses that it planned to conduct during the rulemaking, listed in Table 1.3 .1 . Through the public meeting, DOE sought feedback from interested parties on these subjects and provided information regarding the rulemaking process that DOE would follow. Interested parties discussed the following major issues at the public meeting: rule making schedule; test procedure revisions; product classes; technology options; efficiency levels; and approaches for each of the analyses performed by DOE as part of the rulemaking process. The comments received since publication of the August 2012 notice, including those received at the September 2012 framework public meeting, have contributed to DOE's proposed resolution of the issues in this rulemaking as they pertain to commercial clothes washers.1-4

Table 1.3. 1 Analyses Under the Process Rule

Preliminary Analyses 1 NOPR Final Rule

Market and technology assessment Revised preliminary analyses Revised NOPR analyses

Screening analysis Life

- cycle cost sub

- group analysis

Engineering analysis Manufacturer impact analysis

Energy use determination Utility impact analysis

Markups for equipment price

determination Emissions analysis

Life

- cycle cost and payback period analysis Employment impact analysis

Shipments analysis Regulatory impact analysis

National impact analysis

Preliminary manufacturer impact analysis

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

Following the framework meeting, DOE gathered additional information, held discussions with manufacturers, performed product testing and teardowns, and performed the various analyses described in the framework document, including the engineering, life-cycle cost, payback period, manufacturer impact, and national impact analyses. The results of these analyses are presented in the NOPR and accompanying TSD chapters.

As part of the information gathering and sharing process, DOE organized and held

interviews with manufacturers of the commercial clothes washers considered in this rulemaking as part of the engineering analysis. DOE selected companies that represented production of all types of products on the market, ranging from small to large manufacturers, and included the Association of Home Appliance Manufacturers (AHAM) member companies. DOE had four objectives for these interviews: (1) solicit manufacturer feedback on the draft inputs to the engineering analysis; (2) solicit feedback on topics related to the manufacturer impact analysis; (3) provide an opportunity, early in the rulemaking process, to express manufacturers’ concerns to DOE; and (4) foster cooperation between manufacturers and DOE. DOE incorporated the information gathered during the engineering interviews with manufacturers into its engineering

an alysis (chapter 5) and the manufacturer impact analysis (chapter 12).

DOE developed spreadsheets for the engineering, Life-Cycle Cost (LCC) Payback Period (PBP)

, and national impact analyses for each product. The LCC spreadsheet calculates the LCC and PBP at various energy efficiency levels. The national impact analysis spreadsheet calculates

th e national energy savings and national net present values at various energy efficiency levels. This spreadsheet includes a model that forecasts the impacts of amended energy conservation standards at various levels on product shipments. All of these spreadsheets are available on the1-5

DOE website for commercial cl othes washers at : http://www1.eere.energy.gov/buildings/appliance_standards/product.aspx/productid/46

.

DOE can also provide quantitative outputs from its analyses in machine-readable format upon request. For example, outputs from trial runs of the LCC Monte Carlo simulations can be provided in such a format.

1.4 STRUCTURE OF THE DOCUMENT

This TSD outlines the analytical approaches used in this rulemaking. The TSD consists of the following chapters and appendices:

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

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

document.

Chapter 2 Analytical Framework: describes the rulemaking process.

Chapter 3 Market and Technology Assessment: characterizes the market for the

considered products and the technologies available for increasing product efficiency.

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

efficiency of the considered products, and determines which technology options are viable for consideration in the engineering analysis.

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

relationship between increased manufacturer price and increased efficiency.

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

for converting manufacturer prices to customer product costs.

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

generating energy-use estimates for the considered products as a

function of standard levels.

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

standards on individual customers and users of the products and compares the LCC and PBP of products with and without higher efficiency standards.1-6

Chapter 9 Shipments Analysis: estimates shipments of the products over the 30-

year analysis period that is used in performing the national impact analysis (NIA).

Chapter 10 National Impact Analysis: assesses the national energy savings, and the

national net present value of total consumer costs and savings, expected to result from specific, potential energy conservation standards for

residential clothes washers.

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

different subgroups of consumers.

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

finances and profitability of product manufacturers.

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

pollutants—sulfur dioxide (SO 2 ), nitrogen oxides (NO

x ), and mercury—

as well as carbon dioxide emissions.

Chapter 14 Monetization of Emission Reductions Benefits

Chapter 15 Utility Impact Analysis: discusses certain effects of the considered

standards on electric and gas utilities.

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

national employment.

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

alternatives to efficiency standards.

Appendix 5 -A AHAM Data Request

Appendix 6 -A Detailed Data for Markups

Appendix 7-A Load Usage Factor in Commercial Laundry Applications

Appendix 8- A User Instructions for Life-Cycle Cost and Payback Period Spreadsheets

Appendix 8- B Uncertainty and Variability

Appendix 8- C Lifetime Distributions

Appendix 9- A Relative Price Elasticity of Demand for Clothes Washers1-7

Appendix 10-A Full-Fuel-Cycle Multipliers

Appendix 10-B User Instructions for Shipments and National Impact Analysis

Spreadsheets

Appendix 10 -C National Energy Savings and Net Present Value Using Alternative

Growth Scenarios

Appendix 10 -D National Net Present Value of Consumer Benefits Using Alternative

Product Price Forecasts

Appendix 12-A Manufacturer Interview Guide

Appendix 12-B Government Regulatory Impact Model

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

Order 12866

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

Analysis Under Executive Order 12866

Appendix 17-A Regulatory Impact Analysis: Supporting Materials2

- i CHAPTER 2. ANALYTICAL FRAMEWORK

TABLE OF CONTENTS

2.1 INTRODUCTION ................................

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

2.2 MARKET AND TECHNOLOGY ASSESSMENT ................................

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

2.2.1 Market Assessment ................................

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

2.2.2 Technology Assessment ................................

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

2.3 SCREENING ANALYSIS ................................

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

2.4 ENGINEERING ANALYSIS ................................................................

.......................... 2 -6

2.5 MARKUPS ANALYSIS ................................

................................................................ . 2 -6

2.6 ENERGY AND WATER USE ANALYSIS ................................................................

... 2 -7

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALSYIS ................................

..... 2 -7

2.8 SHIPMENTS ANALYSIS................................

............................................................... 2 -8

2.9 NATION AL IMPACT ANALYSIS ................................................................

................ 2 -8

2.9.1 National Energy and Water Savings ................................................................

................ 2 -8

2.9.2 Net Present Value of Consumer Benefit ................................................................

.......... 2 -9

2.10 CONSUMER SUBGROUP ANALYSIS ................................................................

........ 2 -9

2.11 MANUFACTURER IMPACT ANALYSIS................................................................

.. 2 -10

2.12 EMISSIONS ANALYSIS ................................

.............................................................. 2 -10

2.13 MONETIZATION OF EMISSIONS REDUCTION BENEFITS ................................

. 2 -12

2.14 UTILITY IMPACT ANALYSIS

................................................................ ................... 2 -13

2.15 EMPLOYMENT IMPACT ANALYSIS

................................................................ ....... 2 -14

2.16 REGULATORY IMPACT ANALYSIS

................................................................ ........ 2 -14

LIST OF FIGURES

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

......... 2 -22-1 CHAPTER 2. ANALYTICAL FRAMEWORK

2.1 INTRODUCTION

Section 6295(o)(2)(A) and 6316(a) of the Energy Policy and Conservation Act (EPCA), Pub. L. 94-163, 42 U.S.C. 6291 et seq . re quires the U.S. Department of Energy (DOE) to set forth energy conservation standards that achieve the maximum improvement in energy efficiency that is technologically feasible and economically justified. This chapter describes the general analytical framework that DOE uses in developing such standards. The analytical framework is a description of the methodology, 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 this figure is the center column, identified as “Analyses.” The columns labeled “Key Inputs” and “Key Outputs” show how the analyses fit into the rulemaking process, and how the analyses relate to each other. Key inputs are the types of data and information that the analyses require. Some key inputs exist in public databases; DOE collects other inputs from interested parties or other knowledgeable experts within the field. Key outputs are analytical results that feed directly into the standards-setting process. Dotted lines connecting analyses show types of information that feed from one analysis to another.

In this rulemaking, DOE proceeded directly to the Notice of Proposed Rulemaking (NOPR) stage after publishing the framework document; therefore, the analyses listed within the Preliminary Analysis period were conducted as part of the NOPR analysis.2-2 Figure 2.1

. 1 Flow Diagram of Analyses for the Rulemaking Process Ut ility Impact Analysis

Notice of Proposed Rulemaking (NOPR)

Final Rule Approaches Key Inputs Analyses Key Outputs

Market and Technology

Assessment

Screening Analysis

Engineering Analysis

Product Price

Determination Life

- Cycle Cost and Payback Period Analyses

National Impact Analysis Energy Use

Revise Preliminary Analyses

Life

- Cycle Cost

Sub

- Group Analysis

Manufacturer Impact Analysis Shipments Analysis

Employment Impact Analysis

Revise Analyses • Product Classes

• Technology Options

• Industry Cash Flow

• Sub

- Group Cash

- Flow

• Direct Employment Impacts

• Competitive Impacts

• Cumulative Regulatory Burden • Product Designs

• Life

- Cycle Costs

• Payback Periods

• National Energy Savings

• Net Present Values

• National Employment Impacts

• Revised Results • Characterize Industry

• Market Segmentation

• Non

- Regulatory Programs Framework Document

• Efficiency

- Level Approach

• Design Option Approach

• Energy Prices

• Installation Costs

• Maintenance & Repair Costs

• Accounting Approach

• Backcast and Forecast

• Market Saturation

• Stakeholder Comments

• Demographics

• Manufacturer Interviews

• GRIM Analysis

• Emission Rates

• National Energy Savings

• Stakeholder Comments • Identify Firms/Products

• Historical Shipments

• Retail Price Collection and Analysis

• Manufacturer Prices

• Average Costs

• Manufacturer Financial Data Design Options Product Classes

Retail

Prices Design Energy/ Use

Advance Notice of Proposed Rulemaking (ANO) • Efficiency/Performance • Commercial Products

Candidate Standard Levels • Analysis of Energy Use

Data

Environmental

Assessment

Regulatory Impact Analysis Technology Options

• Emission Estimates • Utility Load Factors

• National Energy Savings

• Non

- Regulatory
Alternatives • NEMS

- BT

• ImSET

• NEMS

- BT • Design Options

• Energy Price Forecasts

• Site

- to

- Source Factors • Energy

- Efficiency Levels

Direct Employment Impacts

• National Energy Savings

• National Product Costs

• National Operating Costs • Utility Impacts • Analysis of Market Data

• Analysis of Product Data

• Life

- Cycle Costs

• Payback Periods • Installation Costs

• Maint Costs

• Repair Costs • UEC

• Energy Prices Annual Energy Use (UEC)

Preliminary Manufacturer Impact Analysis • Manufacturer Prices

• Average Costs • Conversion Capital Expenditures

• Direct Employment Impacts

• National Energy Savings

• Net Present Values • Trial Standard Levels ( TSLs )

TSLs • Product Prototypes

Energy

- Efficiency

Levels

• Department of Justice Review Product Designs

Utility Impact Analysis

Notice of Proposed Rulemaking (NOPR)

Final Rule Approaches Key Inputs Analyses Key Outputs

Market and Technology

Assessment

Screening Analysis

Engineering Analysis

Product Price

Determination Life

- Cycle Cost and Payback Period Analyses

National Impact Analysis Energy Use

Analyses

Life

- Cycle Cost

Sub

- Group Analysis

Manufacturer Impact Analysis Shipments Analysis

Employment Impact Analysis

Revise Analyses • Product Classes

• Technology Options

• Industry Cash Flow

• Sub

- Group Cash

- Flow

• Direct Employment Impacts

• Competitive Impacts

• Cumulative Regulatory Burden • Product Designs

• Life

- Cycle Costs

• Payback Periods

• National Energy Savings

• Net Present Values

• National Employment Impacts

• Revised Results • Characterize Industry

• Market Segmentation

• Non

- Regulatory Programs Framework Document

• Efficiency

- Level Approach

• Design Option Approach

• Energy Prices

• Installation Costs

• Maintenance & Repair Costs

• Accounting Approach

• Backcast and Forecast

• Market Saturation

• Stakeholder Comments

• Demographics

• Manufacturer Interviews

• GRIM Analysis

• Emission Rates

• National Energy Savings

• Stakeholder Comments • Identify Firms/Products

• Historical Shipments

• Retail Price Collection and Analysis

• Manufacturer Prices

• Average Costs

• Manufacturer Financial Data Design Options Product Classes

Retail

Prices Design Energy/ Use

Publication of Preliminary Analyses • Efficiency/Performance • Commercial Products

Candidate Standard Levels • Analysis of Energy Use

Data

Environmental

Assessment

Regulatory Impact Analysis Technology Options

• Emission Estimates • Utility Load Factors

• National Energy Savings

• Non

- Regulatory
Alternatives • NEMS

- BT

• ImSET

• NEMS

- BT • Design Options

• Energy Price Forecasts

• Site

- to

- Source Factors • Energy

- Efficiency Levels

Direct Employment Impacts

• National Energy Savings

• National Product Costs

• National Operating Costs • Utility Impacts • Analysis of Market Data

• Analysis of Product Data

• Life

- Cycle Costs

• Payback Periods • Installation Costs

• Maint Costs

• Repair Costs • UEC

• Energy Prices Annual Energy Use (UEC)

Preliminary Manufacturer Impact Analysis • Manufacturer Prices

• Average Costs • Conversion Capital Expenditures

• Direct Employment Impacts

• National Energy Savings

• Net Present Values • Trial Standard Levels ( TSLs )

TSLs • Product Prototypes

Energy

- Efficiency

Levels

• Department of Justice Review Product Designs2-3

The analyses performed for this NOPR and reported in this technical support document (TSD) are listed below.

 A market and technology assessment to characterize the relevant product markets and existing 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 distribution channel markups that relate the manufacturer production cost (MPC) to the cost to the consumer.

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

 Life-cycle cost (LCC) and payback period (PBP) analyses to calculate, at the consumer level, the discounted savings in operating costs (minus maintenance and repair costs) throughout the estimated average lifetime of the covered products, compared to any increase in purchase and installation cost likely to result directly from imposition of a given standard.

 A shipments analysis to forecast shipments of residential clothes washers, which are then used to calculate the national impacts of standards on energy, net present value (NPV),

and future manufacturer cash flows.

 A national impact analysis (NIA) to assess aggregate impacts at the national level of potential energy conservation standards for the considered products, as measured by the NPV of total consumer economic impacts and the national energy savings (NES).

 A manufacturer impact analysis to assess the potential impacts of energy conservation standards on manufacturers’ capital conversion expenditures, marketing costs, shipments, and research and development costs.

 An LCC subgroup analysis to evaluate variations in consumer characteristics that might cause a standard to affect particular consumer subpopulations, such as low-income households, differently than the overall population.2-4

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

 A utility impact analysis to estimate the effects of proposed standards on electric, gas, or

oil utilities.

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

 An environmental impact analysis to estimate the effects of amended standards on emissions of carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), nitrogen oxides (NO X ), and mercury (Hg).

 A regulatory impact analysis to examine major alternatives to proposed amended energy conservation standards that potentially could achieve substantially the same regulatory goal at a lower cost.

DOE developed this analytical framework and documented its initial findings in the Energy Conservation Standards Rulemaking Framework Document for Commercial Clothes

Was hers (the framework document). DOE announced the availability of the framework document in a Notice of Public Meeting and Availability of a Framework Document published in

the Federal Register on August 13, 2012 . 7 7 FR 48108 . DOE presented the analytical approach to interested parties during a public meeting on September 24 , 20 12 . The framework document is

available at http://www.regulations.gov/#!documentDetail;D=EERE-2012- BT

- STD -0020 -0001

.

In response to the publication of the framework document and the associated public meeting, DOE received comments from interested parties regarding its analytical approach. In the NOPR, DOE: (1) summarizes the key comments received from interested parties and describes DOE’s responses to those comments; and (2) summarizes any significant changes in the analytical approach made since publishing the framework document. This NOPR TSD contains details of the final analyses conducted for residential clothes washers.2-5

2.2 MARKET AND TECHNOLOGY ASSESSMENT

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

2.2.1 Market Assessment

When DOE begins an energy conservation standards rulemaking, it develops information that provides an overall picture of the market for the products considered, including the nature of the products, the industry structure, and market characteristics for the products. This activity consists of both quantitative and qualitative efforts based primarily on publicly available information. The subjects addressed in the market assessment include manufacturers, trade associations, and the quantities and types of products sold and offered for sale. DOE examined both large and small and foreign and domestic manufacturers. DOE also examined publicly available data from the key trade association for this product category. DOE reviewed shipment data to evaluate annual shipment trends. Finally, DOE reviewed other energy efficiency programs from utilities, individual States, and other organizations. Chapter 3 of the TSD provides additional details on the market and technology assessment.

2.2.2 Technology Assessment

DOE typically uses information relating to existing and past technology options and prototype designs as inputs to determine what technologies manufacturers use to attain higher performance levels. In consultation with interested parties, DOE develops a list of technologies for consideration. Initially, these technologies encompass all those it believes are technologically

feasible.

DOE developed its list of technologically feasible design options through consultation with manufacturers of components and systems, and from trade publications and technical papers. Since many options for improving product efficiency are available in existing units, product literature and direct examination provided additional information.

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

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, in consultation with interested parties, review ed the list to determine if these options are practicable to manufacture, install, and service, would adversely affect product utility or availability, or would

have adverse impacts on health and safety. In addition, DOE removed from the list technology2-6

options that lack energy consumption data as well as technology options whose energy consumption could not be adequately measured by existing DOE test procedures. In the engineering analysis, DOE further consider ed efficiency enhancement options that it did not screen out in the screening analysis. Chapter 4 of the TSD contains details on the screening analysis.

2.4 ENGINEERING ANALYSIS

The engineering analysis establishes the relationship between the manufacturing production cost (MPC) and the efficiency for each class of products. The purpose of the analysis is to estimate the incremental MPCs for a product that would result from increasing efficiency levels above the level of the baseline model in each product class. This relationship serves as the basis for cost/benefit calculations in terms of individual consumers, manufacturers, and the nation. Chapter 5 discusses the product classes DOE analyzed, the representative baseline units, the incremental efficiency levels, the methodology DOE used to develop the manufacturing production costs, the cost-efficiency curves, and the impact of efficiency improvements on the considered products.

The engineering analysis consider ed technologies not eliminated in the screening analysis, although certain technologies were not analyzed due to negligible incremental efficiency improvements or the inability of the existing DOE test procedures to measure any reduction in energy use. DOE consider ed the remaining technologies, designated as design

o ptions, in developing the cost-efficiency curves

.

DOE typically structures its engineering analysis around one of three methodologies: (1) the design-option approach, which calculates the incremental costs of adding specific design options to a baseline model; (2) the efficiency-level approach, which calculates the relative costs of achieving increases in energy efficiency levels without regard to the particular design options used to achieve such increases; and/or (3) the reverse-engineering or cost- asse ssment approach, which involves a “bottom-up” manufacturing cost assessment based on a detailed bill of materials derived from tear-downs of the product being analyzed. DOE used a combination of these approaches for this rulemaking, as described in further detail in Chapter 5.

2.5 MARKUPS ANALYSIS

DOE performed a markup analysis to convert the manufacturer costs estimated in the engineering analysis to consumer prices, which then were used in the LCC and PBP and manufacturer impact analyses. DOE calculated markups for baseline products (baseline markups) and for more efficient products (incremental markups). The incremental markup relates the change in the manufacturer sales price of higher efficiency models (the incremental cost increase) to the change in the retailer or distributor sales price.2-7

To develop markups, DOE identified how the products are distributed from the manufacturer to the consumer. After establishing appropriate distribution channels, DOE relied on economic data from the U.S. Census Bureau and other sources to determine how prices are marked up as the products pass from the manufacturer to the consumer. Chapter 6 of this TSD provides details on DOE’s development of markups.

2.6 ENERGY AND WATER USE ANALYSIS

DOE performed an energy and water use analysis to assess the energy and water savings potential from higher efficiency levels, providing the basis for the energy and water savings values used in the LCC and subsequent analyses. The goal of the energy and water use characterization is to generate a range of energy and water use values that reflects actual product

use. Chapter 7 of this TSD provides more detail about DOE’s approach for characterizing energy

and water use.

2.7 LIFE-CYCLE COST AND PAYBACK PERIOD ANALSYIS

New energy conservation standards usually decrease a product’s operating expenses, and usually increase its price. DOE analyzes the net effect of amended standards on consumers by evaluating the net life cycle cost (LCC). To evaluate the net LCC, DOE uses the cost-efficiency relationship derived in the engineering analysis and the energy and water costs derived from the energy and water use analysis. Inputs to the LCC calculation include the installed cost of a product to the consumer (consumer purchase price plus installation cost), operating expenses (energy expenses and maintenance costs), the lifetime of the unit, and a discount rate.

Because the installed cost of a product typically increases while operating costs typically decrease under new standards, there is a time in the life of products having higher-than-baseline efficiency when the net operating-cost benefit (in dollars) since the time of purchase is equal to the incremental first cost of purchasing the product. The time required for products to reach this cost-equivalence point is known as the payback period (PBP).

Recognizing that several inputs to determining consumer LCC and PBP are either variable or uncertain, DOE uses Monte Carlo simulation and probability distributions to model both the uncertainty and variability in inputs. DOE uses Microsoft Excel combined w ith Crystal Ball (a commercially available add

- in program) to develop LCC and PBP spreadsheet models that incorporate both Monte Carlo simulation and probability distributions.

Chapter 8 of this TSD describes the results from the LCC and PBP analyses.2-8

2.8 SH IPMENTS ANALYSIS

Forecasts of product shipments are needed to calculate the national impacts of standards on energy use, NPV, and future manufacturer cash flows. DOE developed shipment forecasts based on an analysis of key market drivers. In DOE’s shipments model, shipments of products are driven by new construction, stock replacements, and other types of purchases.

The shipments models take an accounting approach, tracking market shares of each product class and the vintage of units in the existing stock. Stock accounting uses product shipments as inputs to estimate the age distribution of in-service product stocks for all years. The age distribution of in-service product stocks is a key input to calculations of both the NES and NPV, because operating costs for any year depend on the age distribution of the stock.

DOE also considers the impacts on shipments from changes in product purchase price and operating cost associated with higher energy efficiency levels. Chapter 9 of this TSD provides additional details on the shipments analysis.

2.9 NATION AL IMPACT ANALYSIS

The national impact analysis (NIA) assesses the net present value (NPV), to the nation, of total consumer life-cycle cost (LCC) and net energy savings (NES). DOE determined both the NPV and NES for the efficiency levels considered for the product classes analyzed. To make the analysis more accessible and transparent to all interested parties, DOE prepared a Microsoft Excel spreadsheet model to forecast NES and the national consumer economic costs and savings resulting from 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 may conduct sensitivity analyses by running scenarios on specific input variables. Chapter 10 of this TSD provides additional details regarding the national impact analysis.

Several of the inputs for determining NES and NPV depend on the forecast trends in product energy efficiency. For the base case, DOE uses the efficiency distributions developed for the LCC analysis, and assumes some rate of change over the forecast period. In this analysis, DOE has used a roll-up scenario in developing its forecasts of efficiency trends after standards take effect. Under a roll-up scenario, all products that perform at levels below a prospective standard are moved, or rolled-up, to the minimum performance level allowed under the standard.

Product efficiencies above the standard level under consideration would remain the same as

before the revised standard takes effect.

2.9.1 National Energy and Water Savings

The inputs for determining the national energy and water savings (NES) for each product class are: (1) annual energy and water consumption per unit, (2) shipments, (3) product stock, (4) national energy and water consumption, and (5) site- to -source conversion factors for energy.2-9

DOE calculated national energy and water consumption by multiplying the number of units, or stock, of each product class (by vintage, or age) by the unit energy and water consumption (also by vintage). DOE calculated annual NES based on the difference in national energy and water consumption for the base case (without new efficiency standards) and for each efficiency standard being considered. DOE estimated energy consumption and savings based on site energy consumption, which it then converted to source energy. DOE estimated water consumption and savings based on site water use. DOE did not use a conversion factor for water because no such factor has been developed. Cumulative energy and water savings are the sum of the NES for each year.

2.9.2 Net Present Value of Consumer Benefit

The inputs for determining NPV of the total costs and benefits experienced by consumers are: (1) total annual installed cost, (2) total annual savings in operating costs, (3) a discount factor, (4) present value of costs, and (5) present value of savings. DOE calculated net savings each year as the difference in total savings in operating costs and total increases in installed costs between the base case and each standards case. DOE calculated savings over the life of each product class, accounting for differences in yearly energy rates. DOE calculated NPV as the difference between the present value of operating cost savings and the present value of total installed costs. DOE used a discount factor based on real discount rates of 3 percent and 7 percent to discount future costs and savings to present values.

DOE calculated the difference in total installed cost between the base case and each standards case ( i.e ., after standards take effect). Because the more efficient products bought in the standards case usually cost more than products bought in the base case, cost increases appear as negative values in the NPV.

DOE expressed savings in operating costs as decreases associated with the lower energy consumption of products bought in the standards case compared to the base efficiency case. Total savings in operating costs are the product of savings per unit and the number of units of each vintage that survive in a given year.

2.10 CONSUMER SUBGROUP ANALYSIS

The consumer subgroup analysis evaluates economic impacts on selected groups of consumers who might be adversely affected by a change in the national energy conservation standards for the considered products. DOE evaluates impacts on particular subgroups of consumers primarily by analyzing the LCC impacts and PBP for those particular consumers using the LCC spreadsheet model. Chapter 11 of this TSD describes the consumer subgroup analysis.2-10

2.11 MANUFACTURER IMPACT ANALYSIS

The MIA assesses the impacts of new energy conservation standards on manufacturers of the considered products. Potential impacts include financial effects, both quantitative and qualitative, that might lead to changes in the manufacturing practices for these products. DOE identified these potential impacts through interviews with manufacturers and other interested parties.

DOE conducted the MIA in three phases, and further tailored the analytical framework based on interested parties’ comments. In Phase I, an industry profile was created to characterize the industry, and a preliminary MIA was conducted to identify important issues that required consideration. In Phase II, an industry cash flow model and an interview questionnaire were prepared to guide subsequent discussions. In Phase III, manufacturers were interviewed, and the impacts of standards were assessed both quantitatively and qualitatively. Industry and subgroup cash flow and NPV were assessed through use of the Government Regulatory Impact Model (GRIM). Then impacts on competition, manufacturing capacity, employment, and cumulative regulatory burden were assessed based on manufacturer interview feedback and discussions. DOE discusses its findings from the MIA in chapter 12 of the TSD.

2.12 EMISSIONS ANALYSIS

In the emissions analysis, DOE estimates the reduction in power sector emissions of carbon dioxide (CO2), nitrogen oxides (NOX), sulfur dioxide (SO2) and mercury (Hg) from potential energy conservation standards for the considered products. In addition, DOE will estimate emissions impacts in production activities (extracting, processing, and transporting fuels) that provide the energy inputs to power plants. These are referred to as “upstream” emissions. Together, these emissions account for the full-fuel -cycle (FFC). In accordance with DOE’s FFC Statement of Policy (76 FR 51282 (Aug. 18, 2011)), the FFC analysis includes impacts on emissions of methane and nitrous oxide, both of which are recognized as greenhouse

gases.

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

EIA’s Annual Energy Outlook 2012 , supplemented by data from other sources. EIA prepares the Annual Energy Outlook using NEMS. Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions.

SO2 emissions from affected electric generating units (EGUs) are subject to nationwide and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual emissions cap on SO2 for affected EGUs in the 48 contiguous states and the District of Columbia (D.C.). SO2 emissions from 28 eastern states and D.C. were also limited under the Clean Air Interstate Rule (CAIR), which created an allowance-based trading program that operates along with the Title IV program in those States and D.C. 70 FR 25162 (May 12, 2005). CAIR was remanded to EPA by the U.S. Court of Appeals for the District of Columbia Circuit (D.C.2-11

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

North Carolina v. EPA , 531 F.3d 896 (D.C. Cir. 2008). On July 6, 2011 EPA issued a replacement for CAIR, the Cross-State Air Pollution Rule (CSAPR; also known as the Transport Rule). 76 FR 48208 (August 8, 2011). The AEO 2012 NEMS assumes the implementation of the

CS APR. 1

The attainment of emissions caps is typically flexible among EGUs and is enforced through the use of emissions allowances and tradable permits. Under existing EPA regulations, any excess SO2 emissions allowances resulting from the lower electricity demand caused by the adoption of an efficiency standard could be used to permit offsetting increases in SO2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO2 emissions covered by the existing cap-and-trade system, but it concluded that no reductions in power sector emissions would occur for SO2 as a

result of standards.

Beginning in 2015, however, SO2 emissions will fall as a result of the Mercury and Air Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 2011. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for HCl as a surrogate for acid gas hazardous air pollutants (HAP), and also established a standard for SO2 (a non-HAP acid gas) as an alternative equivalent surrogate standard for acid gas HAP. The same controls are used to reduce HAP and non-HAP acid gas; thus, SO2 emissions will be reduced as a result of the control technologies installed on coal-fired power plants to comply with the MATS requirements for acid gas. AEO 2012 assumes that, in order to continue operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce SO2 emissions. Under the MATS, NEMS shows a reduction in SO2 emissions when electricity demand decreases (e.g., as a result of energy efficiency standards). Emissions will be far below the cap that would be established by CSAPR, so it is unlikely that excess SO2 emissions allowances resulting from the lower electricity demand would be needed or used to permit

o ffsetting increases in SO2 emissions by any regulated EGU. Therefore, DOE believes that efficiency standards will reduce SO2 emissions in 2015 and beyond.

CSAPR established a cap on NOX emissions in eastern States and the District of Columbia. Energy conservation standards are expected to have little or no physical effect on these emissions in those States covered by CSAPR because excess NOx emissions allowances resulting from the lower electricity demand could be used to permit offsetting increases in NOx emissions. However, standards would be expected to reduce NOx emissions in the States not

1 On December 30, 2011, the D.C. Circuit stayed the new rules while a panel of judges reviews them, and told EPA to continue administering CAIR (see EME Homer City Generation v. EPA , No. 11-1302, Slip Op. at *2 (D.C. Cir. Dec. 30, 2011)). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City
Generation, LP v. EPA , No. 11-1302, 2012 WL 3570721 at *24 (D.C. Cir. Aug. 21, 2012). The court again ordered EPA to continue administering CAIR. AEO 2012 had been finalized prior to this decision, however, DOE understands that CAIR and CSAPR are similar with respect to their effect on emissions impacts of energy efficiency

standards.2-12

affected by CSAPR. Therefore, DOE estimates NOX emissions reductions from potential standards in the States where emissions are not capped.

The MATS limit mercury emissions from power plants, but they do not include emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. For this rulemaking, DOE estimated mercury emissions reductions using emissions factors based on

AEO 2012 , which incorporates the MATS.

With regard to the impact of standards on particulate matter (PM), the great majority of ambient PM associated with power plants is in the form of secondary sulfates, which are produced at a significant distance from power plants by complex atmospheric chemical reactions that often involve the gaseous emissions of power plants, mainly SO2 and NOx. The monetary benefits that DOE estimates for reductions in SO2 and NOx emissions resulting from standards are in fact primarily related to the health benefits of reduced indirect PM. Power plants may also emit particulates from the smoke stack. These direct, or primary, PM emissions can be difficult to quantify, and DOE is not able to quantify them at this time.

2.13 MONETIZATION OF EMISSIONS REDUCTION BENEFITS

In Chapter 14 of the TSD, DOE explains how it plans to monetize the benefits associated with emissions reductions. DOE estimates the monetary benefits likely to result from the reduced emissions of CO2 and NOX that are expected to result from each of the standard levels

considered.

In order to estimate the monetary value of benefits resulting from reduced emissions of CO2 emissions, DOE uses the most current Social Cost of Carbon (SCC) values developed by an interagency process. The SCC is intended to be a monetary measure of the incremental damage resulting from greenhouse gas (GHG) emissions, including, but not limited to, net agricultural productivity loss, human health effects, property damage from sea level rise, and changes in ecosystem services. Any effort to quantify and to monetize the harms associated with climate change will raise serious questions of science, economics, and ethics. But with full regard for the limits of both quantification and monetization, the SCC can be used to provide estimates of the

social benefits of reductions in GHG emissions.

At the time of this analysis, the most recent interagency estimates of the potential global benefits resulting from reduced CO2 emissions in 2015, expressed in 2012$, were $12.9, $40.8, $62.2, and $117.0 per metric ton avoided. 2 For emission reductions that occur in later years, these values grow over time. Additionally, the interagency group determined that a range of

2 Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Technical Model Update for the Social Cost of Carbon (SCC) . Interagency Working Group on Social Cost of Carbon, United States Government, Internal EPA Draft, February 13, 2013.2-13

values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although DOE gives preference to consideration of the global benefits of reducing CO2 emissions. To calculate a present value of the stream of monetary values, DOE

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

obtain the SCC values in each case.

DOE recognizes that scientific and economic knowledge continues to evolve rapidly as to the contribution of CO2 and other GHG to changes in the future global climate and the potential resulting damages to the world economy. Thus, these values are subject to change.

DOE also estimates the potential monetary benefit of reduced NO X emissions resulting from

the standard levels it considers. For NO x emissions, available estimates suggest a very wide range of monetary values, ranging from $468 to $4,809 per ton in 2012$. 3 In accordance with U.S. Office of Management and Budget (OMB) guidance, DOE conducts two calculations of the monetary benefits derived using each of the economic values used for NO x , one using a real discount rate of 3 percent and another using a real discount rate of 7 percent. 4

DOE did not monetize estimates of SO 2 and Hg reduction in this rulemaking.

2.14 UTILITY IMPACT ANALYSIS

The utility impact analysis estimates the effects of amended energy conservation standards on installed electricity generation capacity and electricity generation. For this analysis, DOE adapted NEMS, which is a large multi-sectoral, partial-equilibrium model of the U.S. energy sector that the EIA has developed throughout the past decade, primarily for preparing

EIA’s AEO . In previous rulemakings, a variant of NEMS (currently termed NEMS-BT, BT referring to DOE’s Building Technologies Program), was developed to better address the specific impacts of an energy conservation standard. NEMS, which is available in the public domain, produces a widely recognized baseline energy forecast for the United States. The typical NEMS outputs include forecasts of electricity and natural gas sales, prices, and electric generating capacity.

DOE conducts the utility impact analysis as a scenario that departs from the latest Annual Energy Outlook reference case. In other words, the energy savings impacts from amended energy conservation standards are modeled using NEMS-BT to generate forecasts that deviate from the

AEO reference case. Chapter 15 describes the results of the utility impact analysis.

3 For additional information, refer to U.S. Office of Management and Budget, Office of Information and Regulatory Affairs, 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on
State, Local, and Tribal Entities , Washington, DC.

4 OMB, Circular A-4: Regulatory Analysis (Sept. 17, 2003).2-14

2.15 EMPLOYMENT IMPACT ANALYSIS

New or amended energy conservation standards can impact employment both directly and indirectly. Direct employment impacts are changes in the number of employees at the plants that produce the covered products, and at the affiliated distribution and service companies, resulting from the adoption of new standards. DOE evaluated direct employment impacts in the MIA. Indirect employment impacts may result from expenditures shifting between goods (the substitution effect) and changes in income and overall expenditure levels (the income effect) that occur due to the adoption of standards. Chapter 16 describes the results of the employment impact analysis.

DOE investigated the combined direct and indirect employment impacts of standards using the Pacific Northwest National Laboratory (PNNL)’s “Impact of Sector Energy Technologies” (ImSET) model. The ImSET model, which was developed for DOE’s Office of Planning, Budget, and Analysis, estimates the employment and income effects energy-saving technologies produced in buildings, industry, and transportation. In comparison with simple economic multiplier approaches, ImSET allows for more complete and automated analysis of the economic impacts of energy conservation investments.

2.16 REGULATORY IMPACT ANALYSIS

DOE prepared a regulatory impact analysis (RIA) under Executive Order 12866, “Regulatory Planning and Review,” 58 FR 51735, October 4, 1993, which was subject to review under the Executive Order by the Office of Information and Regulatory Affairs (OIRA) at the Office of Management and Budget. The RIA evaluated non-regulatory alternatives to standards, in terms of their ability to achieve significant energy savings in the considered products at a reasonable cost, and compared the effectiveness of each one to the effectiveness of the adopted

standards. Chapter 17 of this TSD describes the results of the regulatory impact analysis.

DOE recognizes that voluntary or other non-regulatory efforts by manufacturers, utilities, and other interested parties can result in substantial improvements to energy efficiency or reductions in energy consumption. DOE considered the likely effects of non-regulatory initiatives on product energy use, consumer utility, and LCC. DOE based its assessment on the actual impacts of any such initiatives to date, but also considered information presented regarding the impacts that any existing initiative might have in the future.3

- i CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

TABLE OF CONTENTS

3.1 INTRODUCTION ................................

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

3.2 PRODUCT DEFINITIONS ................................

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

3.3 PRODUCT CLASSES ................................

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

3.4 PRODUCT TEST PROCEDURES ................................................................

................. 3 -2

3.5 MANUFACTURER TRADE GROUPS ................................................................

......... 3 -3

3.5.1 Association of Home Appliance Manufacturers .............................................................. 3 -3

3.5.2 Coin Laundry Association ................................

............................................................... 3 -3

3.5.3 Multi-housing Laundry Association ................................................................

................ 3 -4

3.6 MANUFACTURER INFORMATION ................................................................

........... 3 -4

3.6.1 Manufacturers and Market Shares ................................................................

................... 3 -4

3.6.2 Mergers and Acquisitions ................................

................................................................ 3 -4

3.6.3 Small Business Impacts ................................

................................................................ ... 3 -5

3.6.4 Distribution Channels ................................

................................................................ ...... 3 -5

3.7 REGULATORY PROGRAMS ................................................................

....................... 3 -6

3.7.1 Energy Policy Act of 2005 ................................

............................................................... 3 -7

3.7.2 Energy Independence and Security Act of 2007 .............................................................. 3 -7

3.7.3 State Energy Conservation Standards ................................................................

.............. 3 -8

3.7.4 Canadian Energy Conservation Standards ................................................................

....... 3 -8

3.7.5 International Standby Power Regulatory Programs ................................

....................... 3 -10

3.8 VOLUNTARY PROGRAMS ................................................................

........................ 3 -10

3.8.1 ENERGY STAR ................................

................................................................ ............ 3 -11

3.8.2 Co nsortium for Energy Efficiency ................................................................

................. 3 -11

3.8.3 Federal Energy Management Program ................................................................

.......... 3 -11

3.8.4 Manufacturer Tax Credits for Energy-Efficient Appliances ................................

......... 3 -12

3.8.5 Rebates for Highly Energy-Efficient Produc ts

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

3.9 HISTORICAL SHIPMENTS................................................................

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

3.9.1 New Multi-Family Unit Starts

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

3.9.2 Unit Shipments................................

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

3.9.3 AHAM Data ................................

................................................................ ................... 3 -15

3.9.4 Imports and Exports ................................................................................................

....... 3 -16

3.10 HISTORICAL EFFICIENCIES................................................................

..................... 3 -17

3.11 PRODUCT RETAIL PRICES ................................................................

....................... 3 -1 8

3.12 INDUSTRY COST STRUCTURE ................................................................

................ 3 -20

3.13 TECHNOLOGY ASSESSMENT ................................................................

.................. 3 -20

3.13.1 Product Operations and Components ................................................................

............. 3 -20

3.13.2 Technology Options ................................

................................................................ ....... 3 -22

3.13.2.1 Adaptive water fill ................................................................

....................... 3 -23

3.13.2.2 Advanced agitation concepts for top -loading machines .............................. 3 -23

3.13.2.3 Capacity increase ................................................................

......................... 3 -24

3.13.2.4 Direct-drive motor ................................................................

....................... 3 -243

- ii

3.13.2.5 Higher spin speeds to reduce remaining moisture content .......................... 3 -25

3.13.2.6 Increased motor efficiency ................................................................

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

3.13.2.7 Ozonated laundering ................................................................

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

3.13.2.8 Polymer bead cleaning

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

3.13.2.9 Spray rinse ................................

................................................................ ... 3 -27

3.13.2.10 Temperature-differentiated pricing controls ................................

................ 3 -27

3.13.2.11 Thermostatically controlled mixing valves ................................

.................. 3 -27

3.13.2.12 Water circulation loop

................................................................ .................. 3 -28

3.13.3 Energy Efficiency ................................................................................................

.......... 3 -28

LIST OF TABLES

Table 3.6.1 Major and Other Commercial Clothes Washer Manufacturers ................................ 3 -4 Table 3.7.1 Energy Conservation Standards for Commercial Clothes Washers Established

by EPACT 2005 ................................

................................................................ ... 3 -7 Table 3.7.2 Energy Conservation Standards for Commercial Clothes Washers Established

by January 2010 Final Rule

................................................................ ................. 3 -7 Table 3.7.3 Current Canadian Energy Conservation Standards for Household-Style

Commercial Clothes Washers ................................................................

.............. 3 -9 Table 3.7.4 Current Canadian Standard Levels for High-Efficiency Household-Style

Commercial Clothes Washers ................................................................

.............. 3 -9 Table 3.7.5 EU Ecodesign Requirements for Standby and Off Mode ................................

....... 3 -10

Table 3.8.1 ENERGY STAR Criteria for Commercial Clothes Washers ................................

. 3 -11

Tab le 3.8.2 Manufacturer Tax Credits for Energy-Efficient Clothes Washers, 2011-2013

...... 3 -13 Table 3.9.1 New Multi-Family Housing Unit Starts in the United States from 2000 -2012

...... 3 -14 Table 3.9.2 Industry Shipments of Commercial Clothes Washers 2002– 2011

......................... 3 -15 Table 3.9.3 AHAM Commercial Clothes Washer Shipment-Weighted Average Efficiency

Data Submittal ................................

................................................................ ... 3 -15 Table 3.9.4 AHAM Commercial Clothes Washer Market Share Efficiency Data Submittal .... 3 -16 Table 3.9.5 Import and Export Data for Commercial Clothes Washers, 2003-2013

................. 3 -17 Table 3.10.1 Energy Efficiency Trends for Commercial Clothes Washers, 2002-2011

........... 3 -18

Table 3.11.1 Commercial Clothes Washer Retail Prices ........................................................... 3 -19 Table 3.13.1 Design Options Removed from Consideration ................................

..................... 3 -22 Table 3.13.2 Technology Options for Residential Clothes Washers ................................

......... 3 -23

LIST OF FIGURES

Figure 3.6.1 Distribution Channel for Commercial Clothes Washers ................................

......... 3 -6 Figure 3.9.1 Multi-Family Housing Unit Starts, 2000– 2012 ................................

..................... 3 -14 Figure 3.9.2 Annual CCW Shipments 2002-2011 ................................................................

..... 3 -15 Figure 3.9.3 Import and Export Trends for Commercial Clothes Washers, 2003-2013

............ 3 -17 Figure 3.10.1 Energy Efficiency Trends for Commercial Clothes Washers, 2002-2011

.......... 3 -18 Figure 3.13.1 Distribution of Commercial Clothes Washers by MEF, November 2013 ........... 3 -293

- iii

Figure 3.13.2 Distribution of Commercial Clothes Washers by WF, November 2013 ............. 3 -293

- 1 CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

3.1 INTRODUCTION

This chapter provides a profile of the commercial clothes washer (CCW) industry in the United States. The U.S. Department of Energy (DOE) developed the market and technology assessment presented in this chapter primarily from publicly available information. This assessment is helpful in identifying the major manufacturers and their product characteristics, which form the basis for the engineering and the life-cycle cost (LCC) analyses. Present and past industry structure and industry financial information help DOE in the process of conducting the manufacturer impact analysis.

3.2 PRODUCT DEFINITIONS

The Energy Policy and Conservation Act of 1975, as amended, defines commercial

clothes washers as follows:

The term “commercial clothes washer” means a soft-mount, front-loading or soft-mount, top-loading clothes washer that: A) has a clothes compartment that: i) for horizontal-axis clothes washers, is not more than 3.5 cubic feet; and ii) for vertical -axis clothes washers, is not more than 4.0 cubic feet; and B) is defined for use in: i) applications in which the occupants of more than 1 household will be using the clothes washer, such as multi-family housing common areas and coin laundries;

o r ii) other commercial applications.

(42 U.S.C. 6311(21))

3.3 PRODUCT CLASSES

DOE separated commercial clothes washers into product class es and has formulated separate energy conservation standards for each class. The criteria for separation into different product classes are: (1) type of energy used; (2) capacity; or (3) other performance-related features such as those that provide utility to the consumer, or others deemed appropriate by the Secretary that would justify the establishment of a separate energy conservation standard. (42 U.S.C. 6295 (q) and 6316(a))

For the existing energy conservation standards, DOE defines two product classes for

commercial clothes washers based on location of access:3

- 2

 Top-loading

 Front-loading

In the August 13, 2012 framework document, DOE proposed to maintain this product class distinction for the current rulemaking. Interested parties submitted comments either supporting DOE’s proposal to maintain this product class structure or encouraging DOE to reconsider the product class structure by establishing a single product class for all CCWs. Asdescribed in full detail in the NOPR, DOE proposes to maintain separate product classes for toploading and front-loading CCWs for this rulemaking.

3.4 PRODUCT TEST PROCEDU RES

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

, as amended, mandates that the test procedures for commercial clothes washers shall be the same as the test procedures established by DOE for residential clothes washers (RCW). (42 U.S.C. 6314(a)(8)). The DOE test procedures for commercial clothes washers currently being manufactured is codified at title 10 of the Code of Federal Regulations (CFR) part 430, subpart B, appendix J1 (hereafter, “appendix J1”). On March 7, 2012, DOE published a final rule amending its test procedures for residential clothes washers ( “March 2012 final rule”). (77 FR

13888 ) The March 2012 final rule included minor amendments to appendix J1 and also established a new test procedure at appendix J2 (hereafter, “appendix J2”).

Beginning March 7, 2015, manufacturers of commercial clothes washers may use either appendix J1 or appendix J2 to demonstrate compliance with the current standards established by the January 2010 final rule. Manufacturers using appendix J2 would be required to use the conversion equations proposed in a separate test procedure NOPR to translate the measured efficiency metrics into equivalent appendix J1 values. 1 The use of appendix J2 would be required to demonstrate compliance with any amended energy conservation standards to be published in a final rule by January 1, 2015, and the conversion equations would no longer be used at that time.

In the March 2012 final rule, DOE incorporated standby and off mode energy use with active mode energy use into an integrated metric, integrated modified energy factor (IMEF),

contained in the new appendix J2 test procedure. Appendix J2 also retains provisions for calculating modified energy factor (MEF). As described in greater detail in the NOPR, in this rulemaking, DOE proposes amended standards based on MEF as measured using appendix J2, which DOE designates as MEF J2

.

1 DOE published a test procedure NOPR for commercial clothes washers on February 11, 2014 (79 FR 8112). Additional details are available at DOE’s rulemaking webpage: http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=86 . All rulemaking documents

are also available at www.regulations.gov , under Docket # EERE-2013- BT

- TP

- 00 02.3

- 3

As described further in chapter 5 of this TSD, DOE also considered the Association of Home Appliance Manufacturers (AHAM) test method HLW-1 -2010, “Performance Evaluation Procedures for Household Clothes Washers” (referred to here as “AHAM HLW-1 -2010”), in order to evaluate potential impacts on cleaning and rinsing performance as a result of more stringent standard levels. AHAM HLW-1 -2010 is typically used to measure the performance of residential clothes washers; however, given the similarity of CCWs covered in this rulemaking toRCWs, and through consultation with CCW manufacturers, DOE determined that AHAM HLW1 -2010 would be appropriate to use for comparison testing of CCWs. Specifically, DOE performed the soil/stain removal, rinsing effectiveness, and sand removal tests provided in

HLW-1 -2010.

3.5 MANUFACTURER TRADE GROUPS

DOE recognizes the importance of trade groups in disseminating information and promoting the interests of the industry that they support. To gain insight into the commercial clothes washer industry , DOE researched various associations available to manufacturers, suppliers, and users of such equipment.

DOE identified several trade groups that support, or have an interest in, the residential cooking product and/or CCW industries, including the Association of Home Appliance Manufacturers (AHAM), the Coin Laundry Association (CLA), and the Multi-housing Laundry Association (MLA).

3.5.1 Association of Home Appliance Manufacturers

AHAM, 1 formed in 1967, aims to enhance the value of the home appliance industry through leadership, public education and advocacy. AHAM provides services to its members including government relations; certification programs; an active communications program; and technical services and research. In addition, AHAM conducts other market and consumer research studies and publishes periodic industry reports. AHAM also develops and maintains technical standards for various appliances to provide uniform, repeatable procedures for measuring specific product characteristics and performance features.

3.5.2 Coin Laundry Association

The CLA 2 is a trade association representing the 30,000 coin laundry owners in the

United States and around the world. Since 1960, the CLA has provided education and service to the entrepreneurs of the coin laundry industry, promoted their business interests, and promoted the industry to the general public.3

- 4

3.5.3 Multi-housing Laundry Association

The MLA, 3 founded in 1959, is a trade association of operator and supplier companies providing professional laundry services for the multi-housing industry. The MLA works for a favorable legislative and regulatory climate for the industry, facilitates the exchange of technical and business information among members, and demonstrates the value of the industry to target

markets.

3.6 MANUFACTURER INFORMATION

3.6.1 Manufacturers and Market Shares

DOE estimates that there are five domestic and two foreign manufacturers of the types of commercial clothes washers covered by this rulemaking. DOE estimates that two major manufacturers hold over 90 percent of the market share of commercial clothes washers covered by DOE standards: Alliance Laundry Systems LLC 2 and Whirlpool Corporation 3

. Other manufacturers include AB Electrolux, 4 Continental Girbau, Inc., General Electric, and LG

. Table

3.6

. 1 lists these manufacturers.

Table 3.6. 1 Major and Other Commercial Clothes Washer Manufacturers

Major Manufacturers Other Manufacturers

Alliance Laundry Systems AB Electrolux

Whirlpool Continental Girbau

General Electric

LG

3.6.2 Mergers and Acquisitions

DOE described the merger between Whirlpool and Maytag, which was completed on March 31, 2006, in chapter 3 of the TSD accompanying the January 2010 final rule. 4 DOE is not aware of any significant mergers or acquisitions in the commercial clothes washer market since

then.

2 Alliance Laundry Systems markets the following brands: Speed Queen, UniMac, Huebsch, and IPSO.

3 Whirlpool also markets the Maytag brand. Whirlpool merged with Maytag on March 31, 2006.

4 AB Electrolux also markets the Wascomat brand.3

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3.6.3 Small Business Impacts

DOE considers the possibility of small businesses being impacted by the promulgation o f energy conservation standards. Based on the size standards published by the Small Business Association (SBA) (65 FR 30840, May 15, 2000, as amended at 67 FR 52602, Aug. 13, 2002; 74 FR 46313, Sept. 9, 2009), to be categorized as a small business manufacturer of commercial clothes washers under North American Industry Classification System (NAICS) 333318, “Other commercial and service industry machinery manufacturing,” a commercial laundry equipment manufacturer and its affiliates may employ a maximum of 1000 employees. The 1000-employee threshold includes all employees in a business’s parent company and any other subsidiaries. Using this classification in conjunction with a search of industry databases and the SBA member directory, DOE did not identify any manufacturers of commercial clothes washers that qualify as

small businesses.

DOE identified one low-volume manufacturer (LVM) of commercial clothes washers. Unlike small business manufacturers, there is no employment limit associated with LVMs. Instead, LVMs are characterized by their low overall production volumes relative to their competitors, often associated with specialization within a singular industry. In 2012, the LVM derived 98 percent of its revenues from the sale of laundry equipment and service parts, while, for its main competitor, this percentage was 30 percent. Within the clothes washer segment,

DOE estimates that the LVM derived 88 percent of its clothes washer equipment revenues from the sale of commercial clothes washers covered by this rulemaking. Because the commercial clothes washer industry itself is characterized by low total shipments, with less than 200,000 units sold annually in the U.S., the concentration of this manufacturer’s business in this industry qualifies them as an LVM. Where the LVM operates at a much smaller scale and does not manufacture products across a broad range of industries, this rulemaking could have disproportionate impacts on the LVM compared to its large, diversified competitors. Accordingly, DOE performed an in-depth analysis of the issues relating to the commercial clothes washer LVM. The manufacturer subgroup analysis is discussed in greater detail in the NOPR and in chapter 12 of the NOPR TSD.

3.6.4 Distribution Channels

Understanding the distribution channels of products covered by this rulemaking is an important facet of the market assessment. DOE gathered information regarding the distribution

channels for commercial clothes washers from publicly available sources and through

discussions with manufacturers.

For commercial clothes washers, DOE believes the distribution channels identified during the prior rulemaking remain relevant for this current rulemaking. Figure 3.6

. 1 illustrates the CCW distribution channels identified during the prior rulemaking, which were originally described in a 1998 Consortium for Energy Efficiency (CEE) report describing their Commercial, Family-Sized Clothes Washer Initiative. 53

- 6 Figure 3.6

. 1 Distribution Channel for Commercial Clothes Washers

The 1998 CEE report states that the CCW market can be divided into three areas, including: (1) laundromats; (2) private multi-family housing; and (3) large institutions (e.g., military barracks, universities, and housing authorities.) Most large institutions and a majority of private multifamily housing (between 50 and 90 percent) do not purchase clothes washers directly. Rather, these organizations lease their laundry space to a third party known as a route operator. Route operators supply laundry equipment and maintain facilities in exchange for a percentage of the laundry revenue. Laundromats and some private building managers, conversely, will purchase clothes washers directly from distributors. The main difference between route operators and distributors is the length of service provided to their clients. Route operators provide on-going support, while distributor support ends at the point of sale.

3.7 REGULATORY PROGRAMS

The following section details current regulatory programs mandating energy conservation

standards for CCWs.3

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3.7.1 Energy Policy Act of 2005

The Energy Policy Act of 2005 (EPACT 2005), P.L. 109-58, amended EPCA to expand DOE’s energy conservation program to include commercial clothes. This legislation established the first energy conservation standards for CCWs, shown in Table 3.7

. 1 , without consideration of separate product classes. These standards became effective January 1, 2007.

Table 3.7. 1 Energy Conservation Standards for Commercial Clothes Washers Established by EPACT 2005

Metric Standard Effective January 1, 2007

Modified Energy Factor (MEF), ft 3 /kwh/cycle 1.26 (minimum)

Water Factor (WF), gal/cycle/ft 3 9.5 (maximum)

EPACT 2005 further directed DOE to conduct two rulemaking cycles to determine whether to amend these standards: the first final rule was required by January 1, 2010, which was fulfilled by the January 2010 final rule. Table 3.7 .2 shows the amended standard levels from the January 2010 final rule, which became effective January 8, 2013.

Table 3.7. 2 Energy Conservation Standards for Commercial Clothes Washers Established by January 2010 Final Rule

Standard s Effective January 8, 2013

Equipment Class Modified Energy Factor (cu.ft./kWh/cycle) Water Factor (gal/cycle/cu.ft)

Top

- Loading ≥ 1.60 ≤ 8.5

Front

- Loading ≥ 2.00 ≤ 5.5

The second final rule is required by January 1, 2015, which will be fulfilled by the current rulemaking. (42 U.S.C. 6313(e))

3.7.2 Energy Independence and Security Act of 2007

The Energy Independence and Security Act of 2007 (EISA 2007), Pub. L. No. 110-142

, amended EPCA to require any final rule for new or amended energy conservation standards for consumer products promulgated after July 1, 2010 to address standby mode and off mode energy use. (42 U.S.C. 6295(gg)(3)) This requirement applies to residential clothes washers but does not apply to commercial clothes washers, which are covered under DOE’s energy conservation program for certain industrial equipment (42 U.S.C. 6311-6317). Specifically, when DOE adopts a standard for a covered consumer product afte r July 1, 2010, it must, if justified by the criteria for adoption of standards under EPCA (42 U.S.C. 6295(o)), incorporate standby mode and off mode energy use into the standard, or, if that is not feasible, adopt a separate standard for such energy use for that product. (42 U.S.C. 6295(gg)(3)(A)-(B)) In the March 2012 final rule, DOE incorporated standby and off mode energy use with active mode energy use into an3

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integrated metric, integrated modified energy factor (IMEF), contained in the new appendix J2 test procedure. Appendix J2 also retains provisions for calculating modified energy factor (MEF J2 ). As described above, DOE proposes amended standards based on MEF J2 , which does not incorporate standby and off mode power.

3.7.3 State Energy Conservation Standar ds

Prior to the passage of EPACT 2005, several States proposed and adopted State-level efficiency regulations for commercial clothes washers that are identical, or very similar, to EPACT 2005 regulations. Complete details of these State-level regulations are available in chapter 3 of the TSD accompanying the January 2010 final rule. 6 The EPACT 2005 energy and water use standards for CCWs preempt any State efficiency standards since they became effective January 1, 2007. DOE is not aware of any State-level energy conservation programs for

commercial clothes washers since the standards from EPACT 2005 became effective.

3.7.4 Canadian Energy Conservation Standards

The Canadian government regulates the efficiency of household-style commercial clothes

washers. The standard levels are contained in Table 11 of the Canadian Standard Association (CSA) C360-03 test method (“CSA C360-03”), Energy performance, water consumption and capacity of household clothes washers

. 7 CSA C360-03 defines household-style commercial

clothes washers as follows:

Household style commercial clothes washer means a soft-mounted front-loading or softmounted top-loading clothes washer that is designed for use in— (i) Applications in which the occupants of more than one household will be using the clothes washer, such as multi-family housing common areas and coin laundries; or (ii) other commercial applications.

Table 3.7. 3 shows the currently applicable regulations for household-style commercial clothes

washers from Table 11 of CSA C360-03. These standards are harmonized with the U.S. standards that became effective January 8, 2013.3

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Table 3.7. 3 Current Canadian Energy Conservation Standards for Household-Style

Commercial Clothes Washers

Standard s Effective January 8, 2013

Equipment Class Modified Energy Factor, L/kWh/cycle (cu.ft./kWh/cycle) Water Factor, L/cycle/L (gal/cycle/cu.ft)

Vertical

- axis ≥ 45.31 ( ≥ 1.6) ≤ 1.13

(≤ 8.5)

Horizontal

- axis ≥ 56.63 (≥ 2.0) ≤ 0.73

(≤ 5.5)

Canada also defines voluntary standard levels for high-efficiency household-style

commercial clothes washers in Table 13 of CSA C360-03. These standards, shown in Table

3.7

. 4 , are consistent with the U.S. ENERGY STAR version 6.0 standards as described later in this chapter. These high-efficiency standard levels are not defined by product class.

Table 3.7. 4 Current Canadian Standard Levels for High-Efficiency Household-Style

Commercial Clothes Washers

Standard s Effective February 1 , 2013

Equipment Class Modified Energy Factor, L/kWh/cycle (cu.ft./kWh/cycle) Water Factor, L/cycle/L (gal/cycle/cu.ft)

Household

- style commercial

clothes washers ≥ 62.3 ( ≥ 2.2 ) ≤ 0.6

(≤ 4 .5)

Canada has recently revised its C360 test method, designated CSA C360-13, to

harmonize with DOE’s appendix J2 test procedure. CSA C360-1 3 contains a slightly modified definition of household-style commercial clothes washers, defined as follows:

Household style commercial clothes washer means a soft-mounted front-loading or softmounted top-loading clothes washer that— (1) Has a clothes container compartment that

-- (i) For horizontal-axis clothes washers, is not more than 3.5 cubic feet; and (ii) For vertical-axis clothes washers, is not more than 4.0 cubic feet; and (2) is designed for use in applications in which the occupants of more than one household will be using the clothes washer, such as multi-family housing common areas and coin

laundries.

CSA C360-13 does not yet contain updated standards for household-style commercial

cl othes washers based on appendix J2. DOE believes that such standards may be harmonized with the final standards promulgated as a result of this rulemaking, and would be incorporated into CSA C360-13 shortly thereafter.3

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3.7.5 International Standby Power Regulatory Programs

The International Energy Agency (IEA) has raised awareness of standby power through publications, international conferences, and policy advice to governments. In 1999, the IEA developed the “1-Watt Plan,” which proposed reducing standby power internationally in electronic devices and which advocates that all countries harmonize energy policies and adopt the same definition and test procedure. The IEA has advocated a 1 watt (W) requirement for all consumer electrical products (unless specifically excluded) in standby mode. The IEA also stated that IEC 62301 provides an internationally sanctioned definition and test procedure for standby power which is now widely specified and used. 8

The European Union passed the Ecodesign Directive in 2005 which calls for an implementing measure to reduce standby power for a specified list of energy using products,

which include household clothes washers. DOE does not believe that these regulations would apply to commercial clothes washers. On December 18, 2008, the Commission of the European Communities published Commission Regulation (EC) No 1275/2008 containing Ecodesign requirements for standby and off mode electric power consumption of electrical and electronic household and office equipment. Table 3.7

. 5 presents the requirements for energy consumption in standby and off mod e power. 9

Table 3.7. 5 EU Ecodesign Requirements for Standby and Off Mode

Mode Maximum Power ( Watts )

Tier 1 ( effective January 07, 2010 )

Off (no function) 1.0 W

Standby (only reactivation function) 1.0 W

Standby (information or status display plus reactivation function ) 2.0 W

Tier 2 ( effective January 07, 2013 )

Off (no function) 0.5 W

Standby (only reactivation function) 0.5 W

Standby (information or status display plus reactivation function ) 1.0 W

3.8 VOLUNTARY PROGRAMS

DOE reviewed several voluntary programs promoting commercial clothes washers in the United States. Many programs, including ENERGY STAR, CEE, and the Federal Energy Management Program (FEMP ), establish voluntary energy conservation standards for these products.3

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3.8.1 ENERGY STAR

ENERGY STAR, a voluntary labeling program backed by the U.S. Environmental Protection Agency (EPA) and DOE, identifies energy efficient products through a qualification process. To qualify, a product must exceed Federal minimum standards by a specified amount, or if no Federal standard exists, exhibit selected energy saving features. The ENERGY STAR program works to recognize the top quartile of products on the market, meaning that approximately 25 percent of products on the market meet or exceed the ENERGY STAR levels. ENERGY STAR specifications exist for several products, including commercial clothes washers.

ENERGY STAR criteria exist for commercial clothes washers with capacities greater

than 1.6 cubic feet. 10 New criteria for CCWs became effective on February 1, 2013, shown

below in Table 3.8

. 1

. The ENERGY STAR program does not distinguish between product

classes for CCWs.

Table 3.8. 1 ENERGY STAR Criteria for Commercial Clothes Washers

Standard s Effective February 1, 2013

Equipment Type Modified Energy Factor (cu.ft./kWh/cycle) Water Factor (gal/cycle/cu.ft)

Commercial Clothes Washers ≥ 2.2 ≤ 4 .5

3.8.2 Consortium for Energy Efficiency

The CEE 11 develops initiatives for its North American members to promote the manufacture and purchase of energy efficient products and services. The goal of the organization is to induce lasting structural and behavioral changes in the marketplace, resulting in the increased adoption of energy efficient technologies. CEE has historically maintained a Commercial Clothes Washer Initiative that encourages the purchase and use of energy and water

efficient commercial clothes washers. Given recent changes to federal minimum efficiency standards and ENERGY STAR criteria for commercial clothes washers, the CEE Commercial Clothes Washer Specification is not currently active as of the publication date of this NOPR. As a result, publication of the associated qualifying product list is also temporarily suspended. According to information on the CEE website, the specification will be reinstated once the revised levels are finalized through the CEE specification revision process.

3.8.3 Federal Energy Management Program

DOE’s FEMP 12 works to reduce the cost and environmental impact of the Federal government by advancing energy efficiency and water conservation, promoting the use of distributed and renewable energy, and improving utility management decisions at Federal sites. FEMP helps Federal buyers identify and purchase energy efficient equipment, including

commercial clothes washers.3

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The National Energy Conservation Act (P.L. 95-619), Executive Order 13423, and Federal Acquisition Regulations Subpart 23.2 and 53.223 require that Federal agencies purchase ENERGY STAR-qualified products. Currently, the FEMP recommendations for commercial clothes washers are equivalent to the ENERGY STAR standard levels described above.

3.8.4 Manufacturer Tax Credits for Energy-Efficient Appliances

EPACT 2005 amended the Internal Revenue Code of 1986 (26 U.S.C. 1 et seq. ) to establish a tax credit to manufacturers for the production in the United States of energy efficient CCWs. (26 U.S.C. 45M(b)(1)(B)) A credit of $100 was established for CCWs manufactured in the calendar year 2006 or 2007 which met the ENERGY STAR qualifying criteria in effect in 2007. The total credit for CCWs was equal to an applicable credit amount multiplied by the eligible production, subject to a $75 million limit, reduced by the amount of credit taken in previous years. The eligible production for CCWs equaled the number of appliances produced in a year divided by the average number of appliances produced during the preceding 3-calendar

year period.

The Energy Improvement and Extension Act of 2008 (Pub. L. 110 -343; Oct. 3, 2008) further amended the Internal Revenue Code of 1986 to modify and extend the manufacturer tax credits for CCWs to apply to equipment produced in calendar years 2008, 2009, or 2010. (26 U.S.C. 45M(b)(2)) Eligible production was calculated over a 2-calendar-year period and the total credit limit calculation was reduced by the amount of credit taken in prior years beginning after December 31, 2007. Further, the aggregate credit amount did not apply to CCWs meeting or exceeding 2.2 MEF/4.5 WF. (26 U.S.C. 45M(c)(2) and (e))

The Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act of 2010 (Pub. L. 111-312) and the American Taxpayer Relief Act of 2012 (Pub. L. 112 -240) further extended the manufacturing tax credits for all clothes washers, including CCWs, through 2013. (26 U.S.C. 45M(b)(2 )(B Table 3.8

. 2 shows the applicable tax credit amounts based on MEF and

WF. Although CCWs are eligible to receive these tax credits, DOE’s market survey conducted for this rulemaking indicates that no top-loading or front-loading CCWs on the market achieve the MEF and WF levels required to receive the tax credits.3

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Table 3.8. 2 Manufacturer Tax Credits for Energy-Efficient Clothes Washers, 2011-2013

Equipment Type Modified Energy Factor (cu.ft./kWh/cycle) Water Factor (gal/cycle/cu.ft) Credit

Amount

Top

- Loading Clothes Washers 1 ≥ 2.2 ≤ 4 .5 $175

Top

- Loading Clothes Washers ≥ 2.4 ≤ 4

. 2 $225

Front

- Loading Clothes Washer ≥ 2.8 ≤ 3.5 $225

1. Effective only in 2011

3.8.5 Rebates for Highly Energy-Efficient Products

States, electric utilities, municipalities, and other organizations promote the purchase of highly energy-efficient commercial clothes washers through consumer rebates. Typically, these programs offer rebates for products meeting existing ENERGY STAR efficiency levels. DOE jointly manages the Database of State Incentives for Renewables & Efficiency (DSIRE), which lists the financial incentives available in each state for purchasing energy-efficient products, including CCWs. 13

3.9 HISTORICAL SHIPMENTS

Awareness of annual product shipment trends is an important aspect of the market assessment and in the development of the standards rulemaking. DOE reviewed data collected by the U.S. Census Bureau and AHAM to evaluate commercial clothes washer product shipment trends and the value of these shipments, which were used during the shipments analysis (chapter 9 of this TSD).

3.9.1 New Multi-Family Unit Starts

Trends in multi-family unit starts can affect shipments of commercial clothes washers.

Table 3.9. 1 an d Figure 3.9

. 1 show the number of new multi-family housing units started in the

United States from 2000-2012

. 14 Over the 5-year period from 2000– 2005, multi-family unit starts remained stable at around 300,000 units annually. Between 2005 and 200 9 , multi-family

unit starts decreased significantly to around 100,000 units annually. Since 2009, multi-family unit starts have increased steadily, reaching almost 250,000 in 2012.3

- 14

Table 3.9. 1 New Multi-Family Housing Unit Starts in the United States from 2000 -201 2

Year Multi

Unit Housing

Starts (000’s)

Figure 3.9

. 1 Multi

- Family Housing Unit Starts, 2000

– 2012 2000 299

2001 293

2002 308

2003 315

2004 303

2005 311

2006 293

2007 277

2008 266

2009 98

2010 104

2011 167

2012 234

3.9.2 Unit Shipments

AHAM provided shipment estimates during the last rulemaking for the period 2002- 2005, and during the current rulemaking for the period 2010 -201 1 , as presented in Table 3.9

. 2 and Figure 3.9

. 2 below. According to the data, shipments of CCWs peaked in 2003 at 191,000 units, and declined to 161,000 in 2011. AHAM provided the breakdown between top-loading and front-loading CCWs for the period 2010-2011, as shown below. 0 50 100 150 200 250 300 350

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012Housing Units Started (thousands)

Year3

- 15

Table 3.9. 2 Industry Shipments of Commercial Clothes Washers 2002– 201 1

Shipments (000’s)

Figure 3.9

. 2 Annual CCW Shipments 2002

- 2011 Year Total Top

Load Front

Load

2002 175 n/a n/a

2003 191 n/a n/a

2004 178 n/a n/a

2005 177 n/a n/a

2006 n/a n/a n/a

2007 n/a n/a n/a

2008 n/a n/a n/a

2009 n/a n/a n/a

2010 164 104 60

2011 161 107 54

3.9.3 AHAM Data

AHAM provide shipment-weighted average efficiency levels disaggregated by product class for 2010 and 2011, as shown in Table 3.9

. 3

.

Table 3.9. 3 AHAM Commercial Clothes Washer Shipment-Weighted Average Efficiency

Data Submittal

Top

- Loading Front

- Loading

Year Weighted MEF Weighted WF Weighted MEF Weighted WF

2010 1.36 9.10 2.19 4.68

2011 1.32 9.33 2.31 4.43

AHAM also provided market share efficiency data for top-loading and front-loading commercial clothes washers for 2010 and 2011, as shown in Table 3.9

. 4 . The submitted values represent the market shares for clothes washers up to the listed efficiency levels. 0 50 100 150 200 250

2000 2002 2004 2006 2008 2010 2012Annual Shipments (thousands)

Year

Total

Top-Loading

Front-Loading3

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Table 3.9. 4 AHAM Commercial Clothes Washer Market Share Efficiency Data Submittal

Top

- Loading Front

- Loading

Efficiency Bins Market Share (%) Efficiency Bins Market Share (%)

MEF WF 2010 2011 MEF WF 2010 2011

1.26 9.5 82% 80% 1.72 8.0 1% 0%

1.42 9.5 18% 20% 1.80 7.5 1% 0%

1.60 8.5 0% 0% 2.00 5.5 28% 28%

2.20 5.1 68% 34%

2.40 4.0 0% 38%

2.60 3.7 0% 0%

Notes:

1. Models with MEF/WF data not meeting the criteria for both MEF and WF for any of the Efficiency Bins in the table above were included in the Efficiency Bin for which the value of the lesser efficient, MEF or WF, is satisfied. For example, a model with a MEF = 2.01 and WF = 5.6 would have been included in the 1.80/7.5 Efficiency Bin.

2. The percentages for the Front-Loading may not add up to 100% due to the absence of the current Federal Minimum Standard levels from the MEF/WF Efficiency Bins listed in the table.

3.9.4 Imports and Exports

DOE obtained import and export data from the U.S. International Trade Commission’s

DataWeb database. 15 Table 3.9

. 5 and Figure 3.9

. 3 show the number of coin-operated commercial clothes washer imports and exports for the period 2003 -2013. The quantity of exports is substantially greater than imports. Exports have generally decreased from a peak of around 120,000 units in 2005 to around 56,000 units in 2013. Imports have generally increased from around 3,800 units in 2003 to around 6,500 units in 2013.3

- 17

Table 3.9. 5 Import and Export Data for Commercial Clothes Washers, 2003-2013

Unit Shipments

Figure 3.9

. 3 Import and Export Trends for Commercial Clothes Washers, 2003

- 2013 Year Imports Exports

2003 3,780 112,110

2004 3,793 109,307

2005 3,499 120,856

2006 2,113 94,841

2007 4,581 88,382

2008 3,543 77,195

2009 2,858 54,513

2010 6,264 46,006

2011 5,701 52,391

2012 12,037 56,409

2013 6,543 56,227

3.10 HISTORICAL EFFICIENCIES

The average efficiency of new commercial clothes washers has steadily increased since

2002. Table 3.10

. 1 and Figure 3.10

. 1 show the shipment-weighted trend in MEF from 2002-

2011. This data was provided by AHAM during the last rulemaking for the period 2002-2005, and during the current rulemaking for the period 2010 -2011. 0 20 40 60 80 100 120 140Shipments (thousands)

Year Exports

Imports3

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Table 3.10. 1 Energy Efficiency Trends for Commercial Clothes Washers, 2002-2011

Shipment

- Weighted

MEF

Figure 3.10

. 1 Energy Efficiency Trends for Commercial Clothes Washers, 2002

- 2011 Year Total Top

Load Front

Load

2002 1.30 n/a n/a

2003 1.33 n/a n/a

2004 1.33 n/a n/a

2005 1.41 n/a n/a

2006 n/a n/a n/a

2007 n/a n/a n/a

2008 n/a n/a n/a

2009 n/a n/a n/a

2010 1.66 n/a n/a

2011 1.65 1.36 2.19

2012 1.30 1.32 2.31

3.11 PRODUCT RETAIL PRICES

DOE conducted a limited search of retail prices for CCWs by contacting several CCW distributors by phone and by gathering pricing data for CCW models available for sale on the

Internet. Table 3.11

. 1 summarizes the data collected by DOE. Where DOE obtained multiple prices for the same model, the average price is shown in the table. 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6

2000 2002 2004 2006 2008 2010 2012Weighted MEF

Year

Total

Top-Loading

Front-Loading3

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Table 3.11

. 1 Commercial Clothes Washer Retail Prices

Manufacturer Model Product Class Control Panel Type Payment System

Type Vendor Price

GE WNRD2050G Top

- Loading No Vend Display None AppliancesDirect.com $682

Alliance Laundry Systems SWNSX2PP112TW01 Top

- Loading No Vend Display Coin Box Appliances Direct $909

Alliance Laundry Systems SWNBC2PP112TW01 Top

- Loading Vend Price Display Coin Box Appliances Direct $1,052

Alliance Laundry Systems SWNLC2PP112TW01 Top

- Loading Vend Price Display Coin Box Appliances Direct $1,086

Whirlpool CAE2743BQ Top

- Loading No Vend Display Coin Slide Home Depot $764

Whirlpool MHN30PDBWW Front

- Loading Vend Price Display Coin Box Appliances Connection $1,579

Whirlpool CHW8990AW Front

- Loading Vend Price Display Coin Box Whirlsource.com $1,225

Alliance Laundry Systems SFNLCFSP* Front

- Loading Vend Price Display Coin Box Yankee Equipment $1,900

Alliance Laundry Systems SFNNCFSP* Front

- Loading Vend Price Display Coin Box Yankee Equipment $1,900

LG GCW1069* Front

- Loading No Vend Display None Daniels Equipment $1,695

GE WNRD2050G Top

- Loading No Vend Display None AppliancesDirect.com $682

Alliance Laundry Systems SWNSX2PP112TW01 Top

- Loading No Vend Display Coin Box Appliances Direct $9093

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Based on its market survey, DOE determined that retail price is largely a function of the type of loading (i.e., top-loading or front-loading) as well as whether the unit offers a control panel that displays vend price. Vend price display units would be primarily sold to Laundromats and multi-family housing laundries; whereas units with no vend price display would be used mostly in other commercial applications where fare is not charged.

As discussed further in chapter 8 of the T SD, DOE used manufacturing costs developed for specific standard levels in combination with manufacturer and distributor markups, sales taxes, and installation costs to establish the total installed cost differences between baseline and

more efficient commercial clothes washers. DOE used the above range in current retail price differences between top-loading and front-loading clothes washers as an indication of the reasonableness of the total installed cost estimates that it developed for residential clothe s washers.

3.12 INDUSTRY COST STRUCTURE

Chapter 12 of this TSD provides a detailed financial analysis of the industry cost structure for commercial clothes washers. This analysis identifies key financial inputs including cost of capital, working capital, depreciation, and capital expenditures.

3.13 TECHNOLOGY ASSESSMENT

The purpose of the technology assessment is to understand how the product uses energy and potential changes that could reduce energy consumption. DOE typically uses information about “technology options”— existing technologies and prototype designs and concepts— to identify technologies product manufacturers could use to attain higher energy efficiency levels.

3.13.1 Product Operations and Components

In preparation for the screening and engineering analyses, DOE prepared a brief description of the characteristics and operation of commercial clothes washers. These descriptions provide a basis for understanding the technologies used to improve product efficiency.

Commercial clothes washers are appliances designed to clean clothes by using a solution of soap and/or detergent and water with mechanical agitation. Clothes washers use electricity to operate one or more pumps and to power an electric motor that agitates and spins the clothes. Clothes are washed, rinsed, and spun within a tub that is inside a cabinet. Because clothes washers require heated water for hot and warm water cycles, a separate water heater is typically needed to supply hot water. Most of the energy used by clothes washers is for water heating. Water is fed into the clothes washer through electrically operated water valves, one connected to a hot water supply line and a second connected to a cold water supply line.3

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Traditional top-loading clothes washers have an opening on the top of the cabinet, covered by a door, which gives access to the inner basket where the load to be washed is placed. The inner basket is typically perforated and is surrounded by a larger outer tub which holds the water when the machine is running. The inner basket typically contains an agitator along a vertical axis, which undergoes a reversing circular motion. The motion of the agitator, which is powered by an electric motor, circulates the clothes vertically from the bottom to the top of the basket. The spinning action of the inner basket and the drain pump are also powered by the motor. Traditional top-loading clothes washers use more water than other types of washers, and therefore, consume more energy during heated wash cycles. They also typically extract less

wa ter from laundry during the spin cycle, which results in longer drying time and greater overall energy consumption.

Non-agitator top-loading clothes washers may have a “wash plate” rather than an agitator to move the clothes within the basket. These top-loading clothes washers function by partially filling the tub with water for wash and rinse cycles and using very high spin speeds to extract rinse water from the clothes during the spin cycle. These top-loading clothes washers are more efficient in terms of water and energy than traditional top-loaders, but their high spin speeds that reduce drying time (and thus dryer energy consumption) may wrinkle clothing more than traditional top-loader spin speeds. These machines work best with low-foaming, high efficiency detergent.

Front-loading clothes washers utilize a cylindrical tub or drum rotating on a horizontal or nearly-horizontal axis to wash clothes. Clothes are usually loaded along the axis of the cylindrical drum, which has led to this type of clothes washer to be called a front-loader. The clothes are cleaned by tumbling them in the water (i.e., clothes are lifted to the top of the drum by the rotation of the cylinder and then dropped into the water below.) The cylindrical drum is only partially filled with water for wash and rinse cycles. High spin speeds are used to extract the water from the clothes during the spin cycles. These clothes washers are typically more efficient in terms of water and energy than traditional top-loaders, but as with non -agitator top-loading clothes washers, their high spin speeds may wrinkle clothing.

Manufacturers typically base CCWs on existing RCW platforms. This simplifies fabrication and assembly (e.g. commercial and residential clothes washers can be assembled on the same assembly line), and helps reduce the fixed costs associated with tooling, overhead etc. for the (typically) much lower CCW manufacturing volumes. However, some CCW components are selectively upgraded to make them more rugged, reliable, and vandal-resistant. Furthermore, the user interface is usually simplified (presenting the commercial user with fewer wash choices than a residential user) and the control system is designed to interface with various payment systems (ranging from coin slides to magnetic card readers). Lastly, the wash cycles may be redesigned to allow satisfactory operation in an approximately 30-minute wash cycle.3

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3.13.2 Technology Options

In order to gain a deeper understanding of the technological improvements used to increase the efficiency of commercial clothes washers, DOE identified several possible technologies and examined the most common improvements used in today’s market.

For the technology assessment for this rulemaking, DOE considered a subset of the design options considered during the previous CCW standards rulemaking. Based on information published in recent trade publications, technical reports, manufacturer literature, and through product teardowns, DOE has observed that the results of the technology screening analysis performed during the previous rulemaking remain largely relevant for this rulemaking. After further analysis of the both the CCW and RCW markets and discussions with manufacturers of both CCWs and RCWs, DOE concluded that some of the technologies previously included in the analysis are not considered “technology options” for improving efficiency because they have negligible or no impact on the energy efficiency of a commercial clothes washer. Table 3.13

. 1 lists the specific technologies DOE eliminated from consideration and provides a brief justification for each.

Table 3.13. 1 Design Options Removed from Consideration

Design Option Removed

from Consideration Justification for Removal

Adaptive control systems (not including adaptive water fill controls) DOE is not aware of any adaptive control systems for commercial clothes washers other than adaptive water fill controls, which are considered a separate design option. In addition, DOE has not found any indications of adaptive control systems being used to achieve energy savings in clothes washers.

Horizontal

- axis design This would be considered a design option only if DOE were to eliminate the product class distinction based on axis of loading. However, DOE has proposed retaining two separate product cla sses, top

- loading and front

- loading.
Improved horizontal

- axis washer drum design DOE does not have any data to suggest that changes to a horizontal

- axis drum design could provide improvements in efficiency (e.g. drum shape or hole patterns).
Low standby power controls DOE is proposing amended energy standards based on the MEF J2 metric, which would exclude measurement of standby and off mode power.

Reduced thermal mass DOE has not found any data indicating potential energy savings due to reduced

thermal m ass of the wash basket or outer wash tub. DOE believes any such energy savings would be negligible.
Silver ion injection DOE has determined that silver ion injection technology is no longer available on

the market for residential clothes washers and has n ot been considered as a technology option for commercial clothes washers.
Tighter tub tolerance DOE’s engineering analyses for prior rulemakings have indicated that manufacturers have already achieved the maximum feasible reduction in spacing

between the wash basket and outer was tub, leaving little, if any, room for improvement.

Based on comments received from interested parties in response to the framework document, DOE added temperature-differentiated pricing controls as a technology option for

consideration. Table 3.13

. 2 shows the complete list of technology options considered by DOE in3

- 23

the technology assessment. Detailed descriptions of each technology option are provided after

the table.

Table 3.13. 2 Technology Options for Residential Clothes Washers

1. Adaptive water fill

2. Advanced agitation concepts for top

- loading machines

3. Capacity increase

4. Direct

- drive motor

5. Higher spin speeds to reduce remaining moisture content

6. M otor efficiency improvements

7. Ozonated laundering

8. Polymer bead cleaning

9. Spray rinse or similar water

- reducing rinse technology

10. Temperature

- differentiated pricing controls

11. Thermostatically controlled mixing valves

12. W ater re circulation loop

3.13.2.1 Adaptive water fill

Adaptive water fill controls incorporate advanced control technologies to sense the size of the clothing load and adjust the water level accordingly. This technology option can overcome the tendency of consumers to manually select a water level greater than required for a given load. The most common implementation is a water level pressure sensor, which measures the pressure of the pooled water at the bottom of the tub. During the water fill portion of the cycle, dry clothing absorbs most of the incoming water. As the clothes become saturated, water begins pooling in the bottom of the wash tub. Once the pooled water reaches a predetermined height, the pressure will trigger the sensor to shut off the water flow.

This technology is used primarily to save water; however, energy is also saved by reducing the amount of hot water used in the wash cycle. DOE testing indicates that for toploading clothes washers, adaptive water fill controls can provide to up 50% energy and water savings compared to clothes washers with manual water fill controls. Nearly all front-load clothes washers already implement adaptive water fill controls, but higher efficiency can be achieved by implementing more sensitive water level pressure sensors. DOE estimates that such sensors can provide around 10% additional energy and water savings for those machines that already have adaptive water fill control.

3.13.2.2 Advanced agitation concepts for top-loading machines

Advanced agitation designs replace the standard agitator found in traditional top-loading clothes washers. The most common implementation of this technology is a rotating “impeller” wash plate at the bottom of the drum. A traditional agitator requires clothing to be fully suspended in water; as the agitator rotates, the agitator vanes catch the clothing and move the garments through the watear. A rotating wash plate, however, requires a much lower water level3

- 24

inside the wash tub to clean the clothing properly. The wet clothing load sits on top of the wash plate, and as the wash plate rotates, raised fins catch the clothing along the bottom of the wash tub to rotate the garments. If the wash tub contains too much water and the clothing becomes suspended in water, the wash plate is unable to rotate the clothing effectively. DOE believes that the lack of a standard agitator may encourage machine overloading in a coin laundry environment, and that this technology is more susceptible to producing poorer wash performance when overloaded compared to a traditional agitator design.

As with adaptive water fill controls, impeller wash plates are used primarily to save water; however, energy is also saved by reducing the amount of hot water used in the wash cycle. In addition, the absence of the agitator increases the available capacity for the same size wash tub. Advanced agitation systems add considerable complexity to the control strategies for

top -loading clothes washer s.

DOE has identified only one top-loading commercial clothes washer, manufactured by Whirlpool, that uses an advanced agitation concept. This clothes washer uses a rotating wash plate in combination with a vane-less agitator column, which is intended primarily to prevent over-loading of the machine by the end user.

DOE testing indicates that the use of impeller wash plates can directly contribute around 10% energy and water savings in top-loading clothes washers.

3.13.2.3 Capacity increase

Implementing a larger tub capacity can contribute to improved efficiency because a larger amount of clothing can be washed using an incremental increase in the quantity of water that is less than the incremental increase in capacity, therefore reducing the amount of water and energy per pound of clothing. DOE estimates that increased capacity can contribute around 10-15% energy and water savings.

End-users typically value larger capacity clothes washers, so increases in tub capacity provide increased end-user utility. However, a larger clothes container capacity may be considered a detriment to coin-operated laundry owners and route operators because a larger capacity tub may result in fewer wash cycles performed by the end-user customer. In competitive markets, coin-operated laundries may not be able to sustain higher vend fares to compensate for the lower number of “turns” per day. In addition, based on discussions with manufacturers, larger tub capacities may encourage the over-loading of machines by end-user customers. Because of these perceived negative impacts on utility, unlike the residential clothes washer market, manufacturers may not be willing to aggressively increase tub volumes in order to achieve higher efficiency levels.

3.13.2.4 Direct-drive motor

A baseline clothes washer typically uses a relatively simple and inexpensive single-speed or dual-speed capacitor-start induction motor, in combination with a mechanical transmission,3

- 25

and sometimes a pulley belt. A direct-drive motor can replace this conventional motor/transmission system. Rather than using a belt and/or transmission, a direct-drive motor is mounted directly to the wash tub along the axis of rotation, thus directly driving the rotation of the wash tub and eliminating transmission and/or gearbox losses. A direct drive motor for a clothes washer is typically a variable-speed, electronically-controlled permanent magnet motor.

DOE has observed increasingly widespread use of direct-drive motors in the residentialclothes washer market, particularly for front-loading clothes washers and high-efficiency toploading clothes washers. DOE has identified only one front-loading commercial clothes washer, manufactured by LG, that uses a direct drive motor.

DOE testing indicates that direct-drive permanent magnet motors can reduce a clothes washer’s motor energy consumption by up to 50 percent; however, because motor energy represents such a small portion of overall clothes washer energy, total energy is reduced by roughly five percent.

3.13.2.5 Higher spin speeds to reduce remaining moisture content

The clothes washer test procedure requires measuring the remaining moisture content of the clothing load, which is calculated as:

where W

c is the weight of the test cloth immediately after completion of the washer spin cycle,

and W i is the “bone dry” weight of the test cloth. The RMC measurement is used to determine the estimated drying energy that would be needed to dry the clothes in an electric clothes dryer after the wash cycle is complete. The drying energy is included in the MEF equation to encourage manufacturers to reduce the remaining moisture content of the clothing coming out of the clothes washer, since that will subsequently result in clothes dryer energy savings.

The most common ways to reduce the RMC include increasing the spin speed of the wash basket for the final spin cycle, or using a longer spin cycle. Since increasing the duration of the spin cycle would result in a longer total cycle time, which would negatively impact consumer utility, manufacturers focus mainly on increasing the spin speed of the final spin cycle. Implementing faster spin speeds requires more advanced motor technologies, including electronic feedback sensing; stronger tub materials such as stainless steel; more robust bearings; and consumer safety protections such as locking lids.

Since mechanical water removal is more energy efficient than using heat to evaporate it, overall energy consumption for the combined wash and dry process is reduced. DOE testing indicates that faster spin speeds can reduce the calculated drying energy by 30% or more. Because drying energy constitutes a majority of the total energy captured in the MEF metric, this

translates to 15 -20% total energy savings.3

- 26

3.13.2.6 Increased motor efficiency

Around 10 percent of the total energy factored into the MEF metric is used by the electric motor. The typical clothes washer has a 1⁄2 – 2⁄3 horsepower single-speed or dual-speed capacitorstart induction motor. Drive system efficiency can be increased by using a buck-boost power factor correction circuit (PFC). This reduces inverter complexity and cost by controlling inverter output voltage through changing DC bus voltage without affecting performance. Using a PFC circuit can improve efficiency by more than 1.5 times from that of a single-phase induction

motor. 16 Three-phase induction motors have also been shown to consume 50 percent less power than universal commutator motors through better control of motor slip. 17

3.13.2.7 Ozonated laundering

Ozonated laundering systems are available for large-scale commercial or institutional

multi-load clothes washers. An ozone laundry system consists of a separate wall-mounted unit that pumps ambient air through an ozone generator, which is then directly injected into the wash water. The ozone may be injected into the cold water line or directly into the clothes washer through a port located in the clothes washer sump. Once in the water, the ozone reacts with

in soluble soils, making them soluble, after which the mechanical action of the washing separates the soils from the fabric. Ozone also acts as a natural disinfectant, providing sanitization in colder water temperatures.

Ozone laundry technology saves energy because the water is not heated for lightly to moderately soiled laundry. For heavily soiled laundry, temperatures of 100– 110 °F are used,

which are lower than the temperatures required in a conventional washer. The reduced water temperature results in energy savings of up to 89 percent. Also, since fewer chemicals are used, the rinse time is minimized, thereby reducing the total cycle time and water consumption. 18

3.13.2.8 Polymer bead cleaning

Polymer bead cleaning uses the absorbent properties of nylon polymer beads to clean clothes. The nylon beads are added to the wash drum with a small amount of water and detergent to loosen the dirt or stains on the clothing. The clothes are tumbled with the polymer beads for around 45 minutes. The polarity of the nylon polymer attracts stains from the clothing. Under humid conditions, the polymer becomes absorbent. Dirt is attracted to the surface and then absorbed into the center of the bead. At the end of the cycle, the polymer beads are separated from the clothing through an inner drum/outer drum rotation process.

This technology was developed by Xeros Ltd. in the United Kingdom, and the company has recently introduced this technology to the U.S. market. Research conducted by Xeros in the United Kingdom has shown that this technology could reduce water consumption by up to 90

percent compared to a traditional clothes washer. Including lower temperature cleaning and reduced detergent consumption, total laundry energy consumption could be reduced by up to 40

percent. 193

- 27

3.13.2.9 Spray rinse

Traditional vertical-axis clothes washers immerse the clothing completely in water during the wash and rinse phases of the cleaning cycle. Spray-rinse systems eliminate the immersion requirement by spraying rinse water into the drum while the wash basket is rotating. The centrifugal force of the rotating basket pins the clothes against the outer walls and subsequently forces the rinse liquid through the clothes. In the process, the rinsing liquid picks up remaining suds and transports them into the sump, from which they are drained. Since immersion or partial immersion is no longer necessary, less rinse water is required.

On clothes washers that provide only a cold rinse option, spray rinse technology provides water savings but no energy savings. On clothes washers that provide a warm rinse option, spray rinse will provide energy savings by reducing the quantity of hot water consumed during the rinse portion of the cycle. DOE testing indicates that spray rinse technology can provide around 25% water savings.

3.13.2.10 Temperature-differentiated pricing controls

Temperature-differentiated pricing controls offer the potential to incentivize energy savings by providing favorable vend pricing for lower-temperature settings. End-users may thus opt for lower-temperature settings than they might otherwise have select ed under undifferentiated pricing. DOE’s market analysis confirmed the availability of this feature on multiple commercial clothes washer models from multiple manufacturers. DOE is not aware of any information regarding the extent to which this feature changes the temperature selection frequencies of end users.

3.13.2.11 Thermostatically controlled mixing valves

Thermostatically controlled water valves (TCWVs) are clothes washer inlet valves that have the ability to sense and adjust the hot and cold supply water. This technology option achieves energy savings by more accurately controlling inlet water temperature for hot and warm fills. In a typical non-thermostatically-controlled water inlet system, two solenoid valves are used on a timed basis; one valve opens for hot water fills while the other opens for cold water fills. Both solenoid valves are opened if a warm water setting is selected.

Energy can be saved with a TCWV by either reducing the hot water temperature and/or reducing the warm water temperature. For example, the TCWV could be used to lower the hot wash temperature from 135 °F to 130 °F by mixing hot water (at 135 °F test inlet conditions) with cold water. Warm water temperature could be similarly reduced. The energy savings can vary widely depending on the test standard’s specified inlet hot water temperature, the selected temperature of the warm water, and the amount of hot water consumed.

The TCWV technology option has the potential to overstate the real-world energy savings, depending on the proximity of the water heater to the installed clothes washer. When tested under laboratory conditions, the clothes washer is typically close to the hot water source,3

- 2 8

whereas this is generally not the case in a home laundry setting. A large distance between the hot

water source and the clothes washer can create a significant temperature drop due to line losses. Because of these line losses, a clothes washer with a TCWV may use more hot water than predicted on the basis of laboratory testing. Similarly, in a real-world application, savings will also vary with the temperature of incoming cold water. The unheated water temperature will vary with both geographic location and time of year. More energy would likely be saved in cases of warmer “cold” supply water, as a TCWV would then reduce the amount of time a hot water valve is open. The amount of savings is also dependent on the set point of the TCWV, the water heater set point, the temperature selection of the wash (hot or warm cycle), and the location of the sensing thermostat controlling the inlet water valves.

3.13.2.12 Water circulation loop

Water recirculation pumps are used in some high-efficiency clothes washers to recirculate water out of the bottom sump and spray it back into the wash drum during certain parts of the cycle. This reduces the amount of unusable standing water in the sump and reduces the amount of new water that must be periodically introduced into the clothes washer.

DOE testing indicates that water recirculation pumps can provide water and energy savings of around 5-10%.

3.13.3 Energy Efficiency

In preparation for the screening and engineering analyses, DOE gathered data on the energy efficiency of commercial clothes washers available in the marketplace in November

2013

. While this section is not intended to provide a complete characterization of the energy efficiency of all commercial clothes washers currently available and in use, it does provide an

overview of the vast majority of units available currently on the market.

DOE used its Compliance Certification Database to identify CCW models on the

market. 20 As of November 2013, DOE’s database contains 253 CCW models. DOE excluded any models in the database that do not achieve the new standard levels effective January 8, 2013. A large number of these models belong to the same basic model. DOE defines a “basic model” as all units of a given type of covered product (or class thereof) manufactured by one manufacturer, having the same primary energy source, and which have essentially identical electrical, physical, and functional (or hydraulic) characteristics that affect energy consumption, energy efficiency, water consumption, or water efficiency. (10 CFR 430.2) After filtering out non-unique basicmodels and models that do not achieve the January 8, 2013 standards, DOE identified 8 toploading basic models and 11 front-loading basic models available on the market.

Figure 3.13 .1 displays the distribution of commercial clothes washers as a function of MEF, as measured using appendix J1. As shown in the figure, no overlap exists between the MEF ratings of top-loading and front-loading CCWs. Top-loading MEF values range from 1.60 to 1.85, and front-loading MEF values range from 2.20 to 2.60.3

- 29

Figure 3.13 .1 Distribution of Commercial Clothes Washers by MEF, November 2013

Figure 3.13 .2 displays the distribution of commercial clothes washers as a function of

WF , as measured using appendix J1. As shown in the figure, no overlap exists between the WF ratings of top-loading and front-loading CCWs. Top-loading WF values range from 6.2 to 8.5,

and fron t-loading WF values range from 3.7 to 4.5.

Figure 3.13 .2 Distribution of Commercial Clothes Washers by WF, November 2013 0 1 2 3 4 5 6

1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7Frequency

MEF (based on Appendix J1) Top-Loading

Front-Loading

0 1 2 3 4 53.03.43.84.24.65.05.45.86.26.67.07.47.88.28.6Frequency

WF (based on Appendix J1) Top-Loading

Front-Loading3

- 30

REFERENCES

1 Association of Home Appliance Manufacturers website: www.aham.org

.

2 Coin Laundry Association website: For more information, visit www.coinlaundry.org

3 Multi-Housing Laundry Association website: http://www.mla-online.com/

4 Technical Support Document accompanying the January 8, 2010 final rule is available at http://www.regulations.gov/#!documentDetail;D=EERE-2006- STD -0127 -0118

.

5 CEE, Commercial Family-Sized Washers: An Initiative Description, 1998. No longer

accessible online.

6 Technical Support Document accompanying the January 8, 2010 final rule is available at http://www.regulations.gov/#!documentDetail;D=EERE-2006- STD -0127 -0118

.

7 CSA C360-13 available at http://shop.csa.ca/en/canada/energy-efficiency/c360-

13/invt/27019792013

. The CSA C360-03 test method has been superseded by C360-13 and is no longer available online.

8 Internation Energy Agency website: http://www.iea.org/

.

9 Commission of the European Communities. 2008. “Commission Regulation (EC) No. 1275/2008 of 17 December 2008 implementing Directive 2005/32/EC of the European Parliament and of the Council with regard to ecodesign requirements for standby and off mode electric power consumption of electrical and electronic household and office equipment.” December 18, 2008. Available online at http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:339:0045:0052:EN:PDF (Accessed December 2009)

10 ENERGY STAR criteria for commercial clothes washers: http://www.energystar.gov/index.cfm?c=clotheswash.pr_crit_clothes_washers

11 Consortium for Energy Efficiency website: http://www.cee1.org

.

12 Federal Energy Management Program website: http://www.eere.energy.gov/femp

.

13 Database of State Incentives for Renewables & Efficiency website: www.dsireusa.org

14 U.S. Census Bureau. “New Residential Construction.” Accessed online at http://www.census.gov/construction/nrc/

.3

- 31

15 U.S. International Trade Commission. “USITC Interactive Tariff and Trade DataWeb.”

Accessed online at http://dataweb.usitc.gov , Feb. 2014.

16 Hoghani, J.S. and Heidari, M. “High Efficient Low Cost Induction Motor Drive for Residential Applications.” 2006 International Symposium of Power Electronics, Electrical
Drives, Automation and Motion . IEEE. pp. 1399-1402. May 26, 2006.

17 Bourgeois, J.-M. “Energy Saving Motor Controls for Washing Machines.” Electronik . Vol. 50, No. 12, pp. 58-61. July 12, 2001.

18 “Cold Facts: Textile/Linen Wear Study of Traditional versus EcoTex Ozone Laundry Wash Practices.” Clearwater Tech, LLC. Available at http://www.cwtozone.com

.

19 Xeros Bead Cleaning website: http://www.xeroscleaning.com/

20 DOE Compliance Certification Database: http://www.regulations.doe.gov/certificationdata/CCMS-79222370561.html4

- i CHAPTER 4

. SCREENING ANALYSIS

TABLE OF CONTENTS

4.1 INTRODUCTION ................................

................................................................ ........... 4 -1

4.2 DISCUSSION OF TECHNOLOGY OPTIONS .............................................................. 4 -1

4.2.1 Screened-Out Technology Options .................................................................................. 4 -1

4.2.1.1 Ozonated laundering ................................................................

...................... 4 -3

4.2.1.2 Plastic particle cleaning ................................................................

................. 4 -3

4.2.2 Remaining Design Options ................................

.............................................................. 4 -4

LIST OF TABLES

Table 4.2.1 Retained Design Options for Commercial Clothes Washers ............................... 4 -44-1 CHAPTER 4. SCREENING ANALYSIS

4.1 INTRODUCTION

This chapter discusses the screening analysis conducted by the U.S. Department of Energy (DOE) of the technology options identified in the market and technology assessment for

commercial clothes washers (CCW) (chapter 3 of this technical support document ( TSD )). In the market and technology assessment, DOE presented an initial list of technology options that can be used to reduce energy consumption of CCWs. The goal of the screening analysis is to identify any technologies that will be eliminated from further consideration in the rulemaking analyses.

The candidate technology options are assessed based on DOE analysis as well as inputs from stakeholders including manufacturers, trade organizations, and energy efficiency advocates

. Technology options that are judged to be viable approaches for improving energy efficiency are retained as inputs to the subsequent engineering analysis, and are designated as design options. Technology options that are not incorporated in commerci al clothes washer equipment or in working prototypes or that fail to meet certain criteria regarding practicability to manufacture, install and service, impacts on equipment utility or availability, or health or safety will be

eliminated from consideration in accordance with Energy Conservation Program for Consumer Products: Procedures for Consideration of New or Revised Energy Conservation Standards for

Consumer Products

. 61 FR 36974 (July 15, 1996). The rationale for either screening out or retaining each technology option is detailed in the following sections.

4.2 DISCUSSION OF TECHNOLOGY OPTIONS

For commercial clothes washers, the screening criteria specified in section 4.1 were applied to the technology options to either retain or eliminate each technology from the engineering analysis. DOE derived the list of technology options as a subset of options considered in the previous CCW energy conservations standards rulemaking. DOE has included temperature-differentiated pricing controls as an additional technology option based on comments received in response to the framework document for this rulemaking.

4.2.1 Screened-Out Technology Options

The following section describes the technology options that were screened out prior to the engineering analysis, along with the rationale for elimination.

The technologies identified in the market and technology assessment were evaluated pursuant to the criteria set out in the Energy Policy and Conservation Act (EPCA), Pub. L. 94- 163, 42 U.S.C. 6291 et seq

. (42 U.S.C. 63 11 -6317 ) EPCA provides criteria for prescribing new4-2

or amended standards, which will achieve the maximum improvement in energy efficiency that the Secretary of Energy determines is technologically feasible. (42 U.S.C. 6295 (o )( 2)(A) and 6316(a) ) It also est ablishes guidelines for determining whether a standard is economically justified. (42 U.S.C. 6295(o )( 2)(B) and 6316(a) ) Pursuant to the EPCA requirements for determining whether a standard is technologically feasible and economically justified, appendix A to subpart C of Title 10 Code of Federal Regulations part 430 (10 CFR p art 430), Procedures, Interpretations and Policies for Consideration of New or Revised Energy Conservation Standards for Consumer Products (the Process Rule), sets forth procedures to guide DOE in the consideration and promulgation of new or revised product energy conservation standards under

EPCA. These procedures elaborate on the statutory criteria provided in 42 U.S.C. 6295 and in part eliminate problematic technologies early in the process of revising an energy conservation standard. Under the guidelines, at the outset of a rulemaking, DOE eliminates from consideration technologies that present unacceptable problems with respect to the following four factors:

(1) Technolo gical feasibility. If it is determined that a technology has not been incorporated in commercially available products or in working prototypes, then that technology

will not be considered further.

(2) Practicability to manufacture, install, and service. If it is determined that mass production of a technology in commercially available products and reliable installation and servicing of the technology could not 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 not be

considered further.

(3) Impacts on product utility or product availability. If a technology is determined to have significant adverse impact on the utility of the equipment to significant subgroups of consumers, or results in the unavailability of any covered equipment type with performance characteristics (including reliability), features, size, 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) 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.

Based on these factors, DOE screened out ozonated laundering and polymer bead cleaning for the reasons that follow.4-3

4.2.1.1 Ozonated laundering

DOE has tentatively concluded that ozonated laundering presents unacceptable problems with the following EPCA screening criteria:

(1) Technological feasib ility

. Although ozone laundering systems are commercially

available as add-on systems for large-scale commercial or institutional multi-load clothes washers, DOE is unaware of any instances of this technology being incorporated into any commercially available products or prototypes for the type of commercial clothes washers covered by DOE standards, which include primarily single-load soft-mounted clothes washers intended for use in coin-operated laundries and multi-family housing settings.

(2) Practicability to manufacture, install, and service. DOE believes that it would not be practicable to manufacture, install and service this technology on the scale necessary to serve

the U.S. commercial clothes washer market at the time of the effective date of an amended

standard.

(4) Impacts on Health or Safety. Because this technology has not been implemented in coin-operated laundries our multi-family housing settings, DOE is unable to assess whether ozone laundry technology would have any adverse impacts on end-user health or safety.

4.2.1.2 Plastic particle cleaning

DOE has tentatively concluded that plastic particle cleaning presents unacceptable problems with the following EPCA screening criteria:

(1) Technological feasibility . Although plastic particle cleaning systems are commercially available for large-scale commercial or institutional multi-load clothes washers, DOE is unaware of any instances of this technology being incorporated into any commercially available products or working p rototypes for the type of commercial clothes washers covered by DOE standards, which include primarily single-load soft-mounted clothes washers intended for use in coin-operated laundries and multi-family housing settings.

(2) Practicability to manufactu re, install, and service. Plastic particle cleaning is still in the research stage for single-load residential-style clothes washers, so DOE believes that it would not be practicable to manufacture, install and service this technology on the scale necessary to serve the relevant market at the time of the effective date of an amended standard. In addition, the requirements for the potential purchase, handling, reusing, recycling, and/or disposal of the plastic particles are unknown.

(3) Impacts on product utility or product availability. DOE is unable to assess the potential impact of plastic particle cleaning on product utility. DOE notes that as currently implemented, plastic particle cleaning systems are operated by trained staff in large-scale4-4

commercial or institutional laundry settings. Because of the significantly different operating protocol compared to commercial clothes washers currently on the market, DOE does not believethat such technology could be used by untrained users, which would include most users of coinoperated laundries and multi-family housing laundry equipment. DOE is also unable to evaluate impacts on cleaning performance compared to current commercial clothes washer products on

the market.

4.2.2 Remaining Design Options

Table 4.2.1 lists the technology options for commercial clothes washers that were retained by DOE and subsequently designated as design options.

Table 4.2.1 Retained Design Options for Commercial Clothes Washers

1. Adaptive water fill

2. Advanced agitation concepts for top

- loading machines

3. Capacity increase

4. Direct

- drive motor

5. Higher spin speeds to reduce remaining moisture content

6. M otor efficiency improvements

7. Spray rinse or similar water

- reducing rinse technology

8. Temperature

- dif

ferentiated pricing controls

9. Thermostatically controlled mixing valves

10. W ater re circulation loop4-5

REFERENCES

(None)5-i CHAPTER 5

. ENGINEERING ANALYSIS

TABLE OF CONTENTS

5.1 INTRODUCTION ................................

................................................................ ........... 5 -1

5.2 TECHNOLOGIES UNABLE TO BE INCLUDED IN THE ANALYSIS ..................... 5 -2

5.3 PRODUCT CLASSES ANALYZED ................................................................

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

5.4 EFFICIENCY LEVELS................................

................................................................ ... 5 -3

5.4.1 Efficiency Metrics ................................

................................................................ ............ 5 -3

5.4.2 Appendix J1/Appendix J2 Efficiency Metric Translations ................................

.............. 5 -5

5.4.3 Baseline Units ................................

................................................................ .................. 5 -6

5.4.4 Higher Efficiency Levels ................................

................................................................ . 5 -6

5.4.5 Maximum Technologically Feasible Efficiency Levels ................................

.................. 5 -7

5.5 METHODOLOGY OVERVIEW ................................................................

.................... 5 -7

5. 5.1 AHAM Data Request ................................

................................................................ ....... 5 -7

5.5.2 Product Testing Methodology................................................................

.......................... 5 -8

5.5.3 Product Teardown Methodology ................................................................

..................... 5 -8

5.5.3.1 Selection of Units ................................................................

........................... 5 -9

5.5.3.2 Generation of Bill of Materials ................................................................

...... 5 -9

5.5.3.3 Cost Structure of the Spreadsheet Models ................................

..................... 5 -9

5.5.3.4 Cost Model and Definitions ................................................................

......... 5 -11

5.5.3.5 Cost Model Assumptions ................................................................

............. 5 -12

5.5.4 Manufacturer Interviews ................................

................................................................ 5 -12

5.6 ANALYSIS AND RESULTS ................................................................

........................ 5 -12

5. 6.1 AHAM Data ................................

................................................................ ................... 5 -12

5.6.2 Product Testing Results ................................

................................................................ . 5 -12

5.6.2.1 Active Mode Testing................................................................

.................... 5 -14

5.6.2.2 Standby Mode and Off Mode Power ........................................................... 5 -18

5.6.2.3 Cleaning Performance Testing ................................................................

..... 5 -19

5.6.3 Product Teardowns ................................

................................................................ ........ 5 -34

5.6.3.1 Top-Loading Commercial Clothes Washers ................................

................ 5 -34

5.6.3.2 Front-Loading Commercial Clothes Washers ................................

............. 5 -37

5.6.4 Cost-Efficiency Curves ................................

................................................................ .. 5 -41

5.6.4.1 Top-Loading Cost-Efficiency Curves ................................

.......................... 5 -41

5.6.4.2 Front-Loading Cost-Efficiency Curves ................................

........................ 5 -42

LIST OF TABLES

Table 5.4.1 Revisions to the Clothes Washer Test Procedure that Significantly Affect

Efficiency Metrics

................................ ................................................................ 5 -4 Table 5.4.2 Appendix J1 and Appendix J2 Efficiency Level Translations for Top-Loading

CCWs ................................

................................................................ ................... 5 -55-ii

Table 5.4.3 Appendix J1 and Appendix J2 Efficiency Level Translations for FrontLoading CCWs................................

................................................................ ..... 5 -6 Table 5.5.1 Major Manufacturing Processes ................................................................

............. 5 -10 Table 5.6.4 Top-Loading Commercial Clothes Washer Test Unit Features .............................. 5 -13 Table 5.6.5 Front-Loading Commercial Clothes Washer Test Unit Features ........................... 5 -14 Table 5.6.6 Commercial Clothes Washer Standby Power Measurements................................

. 5 -19 Table 5.6.7 Production Cost Distribution for Baseline Top-Loading CCW .............................. 5 -35 Table 5.6.8 Materials Cost Distribution for Baseline Top-Loading CCW ................................ 5 -35

Table 5.6.9 Baseline Front-Load Clothes Washer Production Cost Distribution ...................... 5 -39

Table 5.6.10 Baseline Front-Load Clothes Washer Materials Cost Distribution ...................... 5 -39 Table 5.6.11 Incremental Manufacturing Costs for Top-Loading CCWs Based on MEF J2

...... 5 -41 Table 5.6.12 Incremental Manufacturing Costs for Front-Loading CCWs Based on MEF J2

.... 5 -43

LIST OF FIGURES Figure 5.5.1 Manufacturing Cost Assessment Stages ................................................................ 5 -10 Figure 5.6.1 Relationship Between Equivalent MEF J2 (Appendix J2) and Rated MEF

(Appendix J1) for Top-Loading CCWs ............................................................. 5 -15 Figure 5.6.2 Relationship Between Equivalent IWF (Appendix J2) and Rated WF

(Appendix J1) for Top-Loading CCWs ............................................................. 5 -16 Figure 5.6.3 Relationship Between Equivalent MEF J2 (Appendix J2) and Rated MEF

(Appendix J1) for Front-Loading CCWs ........................................................... 5 -17 Figure 5.6.4 Relationship Between Equivalent IWF (Appendix J2) and Rated WF

(Appendix J1) for Front-Loading CCWs ........................................................... 5 -18 Figure 5.6.5 AHAM Soil/Stain Test Strip ................................................................

................. 5 -20 Figure 5.6.6 Total Cleaning Score vs. Appendix J2 MEF J2 Efficiency Level for TopLoading CCWs................................

................................................................ ... 5 -22Figure 5.6.7 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for TopLoading CCWs................................

................................................................ ... 5 -22 Figure 5.6.8 Total Cleaning Score vs. Cycle Time for Top-Loading CCWs ............................ 5 -23 Figure 5.6.9 Total Cleaning Score vs. Appendix J2 MEF J2 Efficiency Level for FrontLoading CCWs................................

................................................................ ... 5 -24Figure 5.6.10 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs................................

................................................................ ... 5 -24 Figure 5.6.11 Total Cleaning Score vs. Cycle Time for Front-Loading CCWs ........................ 5 -25 Figure 5.6.12 Rinse Score vs. Appendix J2 MEF J2 Efficiency Level for Top-Loading

CCWs ................................

................................................................ ................. 5 -26 Figure 5.6.13 Rinse Score vs. Appendix J2 IWF Efficiency Level for Top-Loading

CCWs ................................

................................................................ ................. 5 -27 Figure 5.6.14 Rinse Score vs. Cycle Time for Top-Loading CCWs ................................

......... 5 -28 Figure 5.6.15 Rinse Score vs. Appendix J2 MEF J2 Efficiency Level for Front-Loading

CCWs ................................

................................................................ ................. 5 -29 Figure 5.6.16 Rinse Score vs. Appendix J2 IWF Efficiency Level for Front-Loading

CCWs ................................

................................................................ ................. 5 -295-iii

Figure 5.6.17 Rinse Score vs. Cycle Time for Front-Loading CCWs ................................

....... 5 -30 Figure 5.6.18 Sand Removal Score vs. Appendix J2 MEF J2 Efficiency Level for TopLoading CCWs................................

................................................................ ... 5 -31Figure 5.6.19 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for TopLoading CCWs................................

................................................................ ... 5 -32 Figure 5.6.20 Sand Removal Score vs. Appendix J2 MEF J2 Efficiency Level for FrontLoading CCWs................................

................................................................ ... 5 -33Figure 5.6.21 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs................................

................................................................ ... 5 -33 Figure 5.6.22 Production Cost Distribution for Baseline Top-Loading CCW .......................... 5 -35 Figure 5.6.23 Baseline Top-Load Standard Clothes Washer Materials Cost Distribution

........ 5 -35 Figure 5.6.24 Production Cost Distribution for Efficiency Level 1 Front-Loading

Commercial Clothes Washer ................................................................

............. 5 -39 Figure 5.6.25 Materials Cost Distribution for Efficiency Level 1 Front-Loading

Commercial Clothes Washer ................................................................

............. 5 -39 Figure 5.6.26 Cost-Efficiency Curve Based on MEF J2 for Top-Loading Commercial

Clothes Washer ................................

................................................................ .. 5 -41 Figure 5.6.27 Cost-Efficiency Curve Based on MEF J2 for Front-Loading Commercial

Clothes Washer ................................

................................................................ .. 5 -435-1 CHAPTER 5. ENGINEERING ANALYSIS

5.1 INTRODUCTION

After conducting the screening analysis, the U.S. Department of Energy (DOE) performed an engineering analysis based on the remaining design options

. The engineering analysis consists of estimating the energy and water consumption and costs of commercial

clothes washers at various levels of increased efficiency.

The primary inputs to the engineering analysis are baseline information from the market and technology assessment (chapter 3 of this technical support document ( TSD )) and technology options from the screening analysis (chapter 4). Additional inputs include cost and energy efficiency data, which DOE received from the Association of Home Appliance Manufacturers (AHAM) and qualified and supplemented through teardown analysis and manufacturer interviews. The primary output of the engineering analysis is a set of cost-efficiency curves.

DOE typically structures its engineering analysis around one of three methodologies,

described as follows:

(1) De sign-option approach, which provides the incremental costs of adding to a baseline model design options that will improve its efficiency;

(2) Efficiency-level approach, which provides the relative costs of achieving increases in energy efficiency levels, without regard to the particular design options used to achieve such increases; and

(3) Cost-assessment (or reverse-engineering) approach, which provides “bottom-up” manufacturing cost assessments for achieving various levels of increased efficiency, based on detailed data regarding costs for parts and material, labor, shipping/packaging, and investment for models that operate at particular efficiency levels.

DOE conducted the engineering analyses for the top-loading and front-loading product classes using a combination of the efficiency-level approach and cost-assessment approach. The cost-assessment approach provides an accurate means for estimating a single manufacturer’s incremental manufacturing costs for achieving various levels of increased efficiency. Thisapproach involves physically disassembling commercially available products to develop costefficiency relationships for each manufacturer’s product lines. Because each manufacturer may choose a different path to achieve higher levels of efficiency, an efficiency-level approach produces an industry-wide cost-efficiency relationship for each product class. DOE developed cost-efficiency relationships for the top-loading and front-loading product classes by calculating the market-weighted average of the individual cost-efficiency relationships it developed for each

manufacturer.5-2

5.2 TECHNOLOGIES UNABLE TO BE INCLUDED IN THE ANALYSIS

In performing the engineering analysis, DOE did not consider for analysis certain technologies that met the screening criteria, described in chapter 4 of this TSD, but were unable to be evaluated for one or more of the following reasons: (1) data are not available to evaluate the energy efficiency characteristics of the technology; (2) available data suggested that the efficiency benefits of the technology would be negligible; or (3) certain technologies cannot be measured according to the conditions and methods specified in the DOE clothes washer test procedure (10 CFR 430, subpart B, appendix J2 ; hereafter, “appendix J2” ).

As described in the Notice of Proposed Rulemaking (NOPR), interested parties suggested adding temperature-differentiated pricing controls to the list of design options for consideration. Such controls would offer the potential to incentive energy savings by providing favorable vend pricing for lower-temperature wash/rinse settings. DOE’s market analysis confirmed the availability of this feature on multiple clothes washer models from multiple manufacturers. However, DOE’s current test procedure at appendix J2 uses a fixed set Temperature Use Factors (TUFs), which represent the percentage of time an end-user would select each wash/rinse temperature selection available on the clothes washer. Because the TUFs in the test procedure are fixed, a clothes washer with temperature-differentiated pricing controls would be tested the same way as an identical clothes washer without temperature-differentiated pricing controls. Therefore, the energy savings of this technology cannot be measured according to the conditions and methods specified in the DOE clothes washer test procedure. Accordingly, DOE did not analyze this technology option in its NOPR analysis.

5.3 PRODUCT CLASSES ANALYZED

DOE separates commercial clothes washers into product classes and has formulated separate energy conservation standards for each class. The criteria for separation into different product classes are: (1) type of energy used; (2) capacity; or (3) other performance-related features such as those that provide utility to the consumer, or others deemed appropriate by the Secretary that would justify the establishment of a separate energy conservation standard. (42 U.S.C. 6295 (q) and 6316(a))

As discussed in chapter 3 of this TSD, DOE has separated commercial clothes washers into the following two product classes for this rulemaking:

 Top-loading

 Front-loading5-3

5.4 EFFICIENCY LEVELS

5.4.1 Efficiency Metrics

The energy conservation standard levels for commercial clothes washers are currently defined by two factors—modified energy factor (MEF) and water factor (WF). MEF is calculated as the clothes container capacity in cubic feet divided by the sum, expressed in kilowatt-hours (kWh), of: (1) the total weighted per-cycle hot water energy consumption; (2) the total weighted per-cycle machine electrical energy consumption; and (3) the per-cycle energy consumption for removing moisture from a test load. WF is calculated as the weighted per-cycle water consumption of the cold wash/cold rinse cycle divided by the clothes container capacity in

cubic feet.

The equations for calculating MEF and WF are as follows:

where:

C= Clothes container capacity (cubic feet)

HE T = Total weighted per-cycle hot water energy consumption (kWh)

ME T = Total weighted per-cycle machine electrical energy consumption (kWh)

D E = Per-cycle energy consumption for removal of moisture from test load (kWh )

where:

Q T = Total weighted per-cycle water consumption of cold wash/cold rinse cycles (gallons) C = Clothes container capacity (cu.ft. ).

On March 7, 2012, DOE published a final rule amending its test procedures for

residential clothes washers ( “March 2012 final rule”). (77 FR 13888 ) The March 2012 final rule included minor amendments to the test procedure at 10 CFR 430, subpart B, appendix J1 and also established a new test procedure at appendix J2. Beginning March 7, 2015, manufacturers of

commercial clothes washers may use either appendix J1 or appendix J2 to demonstrate compliance with the current standards established by the January 2010 final rule. Manufacturers using appendix J2 would be required to use the conversion equations proposed in a separate test procedure NOPR to translate the measured efficiency metrics into equivalent appendix J15-4

values. 1 The use of appendix J2 would be required to demonstrate compliance with any amended energy conservation standards to be published in a final rule by January 1, 2015, and the conversion equations would no longer be used at that time.

In the March 2012 final rule, DOE incorporated standby and off mode energy use with active mode energy use into an integrated metric, integrated modified energy factor (IMEF),

contained in the new appendix J2 test procedure. Appendix J2 also retains provisions for calculating modified energy factor, which DOE designates as MEF J2

. As described in greater detail in the NOPR, DOE proposes amended energy standards based on MEF J2 as measured using appendix J2.

Table 5.4

. 1 shows the major revisions included in the amended test procedure that significantly affect the calculated efficiency metrics for commercial clothes washers.

Table 5.4. 1 Revisions to the Clothes Washer Test Procedure that Significantly Affect Efficiency Metrics
Factors Affecting MEF Appendix J1 Appendix J2

Capacity measurement For top

- loading clothes

washers: Innermost diameter of the tub cover. (Defined as

“Fill Level 3” in DOE guidance document 2 ) For top

- loading clothes washers: Uppermost edge of the rotating portion, including any balance ring. (Defined as “Fill Level 2” in DOE guidance document)

Dryer energy calculation Based on Load Adjustment Factor (LAF) of 0.52 multiplied by maximum test load weight Based o n weighted average load size according to existing Load Usage Factors (LUF).

Dryer Usage Factor (DUF) DUF = 0.84 DUF = 0.91

Factors Affecting WF / IWF Appendix J1 (WF) Appendix J2 (IWF)

Capacity measurement See above. See above.

Water Consumption Based on water usage of

Cold Wash / Cold Rinse cycles only. Based on weighted average of water usage of all Wash / Rinse temperature combinations

1 DOE published a test procedure NOPR for commercial clothes washers on February 11, 2014 (79 FR 8112). Additional details are available at DOE’s rulemaking webpage: http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx?ruleid=86 . All rulemaking documents

are also available at www.regulations.gov , under Docket # EERE-2013- BT

- TP

- 00 02.

2 DOE issued guidance in May 2010 on what is considered the clothes container for purposes of measuring clothes container capacity. The guidance is available at the residential clothes washer rulemaking website, http://www1.eere.energy.gov/guidance/detail_search.aspx?IDQuestion=568&pid=2&spid=15-5

As described in the NOPR, DOE is proposing amended water efficiency standards for commercial clothes washers based on the IWF metric, which incorporates water consumption from all the temperature cycles included as part of the energy test cycle in appendix J2. DOE believes that the IWF metric provides a more representative measure of water consumption than the WF metric. The equation for IWF is as follows:

where:

Q T = Total weighted per-cycle water consumption of all wash cycles (gallons) C = Clothes container capacity (cu.ft. ).

Current CCW product ratings are based on MEF and WF as measured using appendix J1

. To define energy efficiency levels based on MEF J2 and IWF as measured using appendix J2, DOE first conducted testing on a representative sample of CCWs, spanning the entire range of available efficiency levels, to determine each model’s corresponding appendix J2 rating. DOE then established baseline and higher efficiency levels based on the appendix J2 MEF J2 and IWF ratings.

For convenience, throughout this TSD, DOE displays both the appendix J1 and appendix J2 ratings corresponding to each efficiency leve l.

5.4.2 Appendix J1/Appendix J2 Efficiency Metric Translations

Based on the test results described in section 5.6.2 of this TSD chapter, DOE translated the appendix J1 MEF/WF efficiency levels into appendix J2 MEF J2 /IWF efficiency levels for consideration in the NOPR analysis. Table 5.4

. 2 shows the resulting efficiency level translations for top-loading CCWs. Table 5.4

. 3 shows the resulting efficiency level translations for frontloading CCWs.

Table 5.4. 2 Appendix J1 and Appendix J2 Efficiency Level Translations for Top-Loading

CCWs

Efficiency Level Efficiency Level

Source Appendix J1 Metrics Appendix J2 Metrics

MEF WF MEF J2 IWF

Baseline DOE Standard 1.60 8.5 1.15 8.9

Level 1 Gap Fill 1.70 8.4 1.35 8.8

Level 2 Maximum Available 1.85 6.9 1.55 6.95-6

Table 5.4. 3 Appendix J1 and Appendix J2 Efficiency Level Translations for Front-Loading

CCWs

Efficiency Level Efficiency Level

Source Appendix J1 Metrics Appendix J2 Metrics

MEF WF MEF J2 IWF

Baseline DOE Standard 2.00 5.5 1.65 5.2

Level 1 CEE Tier 2 2.20 4.5 1.80 4.5

Level 2 CEE Tier 3 2.40 4.0 2.00 4.1

Level 3 Maximum Available 2.60 3.7 2.20 3.9

The following sections describe the baseline and higher efficiency levels in greater detai l.

5.4.3 Baseline Units

DOE selects baseline units as reference points for each product class. The baseline unit in each product class represents the basic characteristics of equipment in that class. Typically, a baseline unit is a unit that just meets current energy conservation standards and provides basic consumer utility. DOE uses the baseline units in the engineering analysis and the LCC and PBP analysis. To determine energy savings and changes in price, DOE compares each higher energy efficiency design option with the baseline unit.

DOE established the baseline level for the top-loading product class based on the 1. 60

MEF and the 8.5 WF requirements specified by current Federal energy conservation standards, which became effective for commercial clothes washers manufactured on or after January 8,

2013

. For the front-loading product class, DOE established the baseline level based on the 2.00 MEF and 5.5 WF requirements specified by current Federal energy conservation standards.

5.4.4 Higher Efficiency Levels

For the top-loading and front-loading product classes, DOE considered efficiency levels higher than baseline levels based on the Consortium for Energy Efficiency (CEE) former Tier

levels for commercial clothes washers. 3 The highest efficiency levels were defined by the maximum available technology that DOE could identify on the market. For the top-loading product class, DOE added an intermediate “gap fill” levels between the baseline and maximum available efficiency levels.

3 CEE has historically maintained a Commercial Clothes Washer Initiative that encourages the purchase and use of energy and water efficient commercial clothes washers. Given recent changes to federal minimum efficiency standards and ENERGY STAR criteria for commercial clothes washers, the CEE Commercial Clothes Washer Specification is not currently active as of the publication date of this NOPR. As a result, publication of the associated qualifying product list is also temporarily suspended.5-7

5.4.5 Maximum Technologically Feasible Efficiency Levels

Under EPCA, DOE is required to consider the maximum technologically feasible level (“max-tech”). DOE determines max-tech levels based on technologies that are either commercially available or have been demonstrated as working prototypes. If the max-tech design meets DOE’s screening criteria, DOE considers the design in further analysis. DOE also considers consumer utility and availability of features, which may be met by a niche product, as required by EPCA.

DOE identified the max-tech levels for top-loading and front-loading commercial clothes washers based on the maximum performance of products currently on the market in the United

States. DOE has observed that the max-tech units on the market use a combination of reduced water volumes, reduced water temperatures, extended cycle times, high er spin speeds. DOE is not aware of any additional design options that could be used to increase the efficiency beyond the max-tech levels without causing potential negative effects on consumer utility. Nor is DOE aware of any working prototype clothes washers that exceed the efficiency levels of the max-tech units on the market in the United States. Therefore, DOE has tentatively determined that the “max available” efficiency levels identified for top-loading and front-loading commercial clothes washers correspond to the maximum technologically feasible efficiency levels.

5.5 METHODOLOGY OVERVIEW

DOE conducted energy and water testing, cleaning and rinsing performance testing, and standby power testing on a representative sample of top-loading and front-loading CCWs spanning a range of efficiencies and manufacturers. The objective of this testing was to gain insights into the relative performance differences among the CCW models available on the market. DOE conducted its testing at an independent laboratory with significant experience performing clothes washer testing. Following testing, DOE performed detailed product teardowns and cost modeling to generate cost-efficiency curves for each product class. Finally,

DOE conducted interviews with commercial clothes washer manufacturers to better understand and explain the differences among manufacturers in baseline unit construction, design strategies for achieving higher efficiency levels, and overall cost structures.

5.5.1 AHAM Data Request

In support of this rulemaking effort, DOE requested incremental cost data from AHAM

for commercial clothes washers. The AHAM data request is presented in Appendix 5-A to this

TSD. The data represent the average incremental production cost to improve a baseline unit to a specified efficiency level. In addition, DOE requested shipments, shipment-weighted average efficiency, and market share efficiency data.5-8

5.5.2 Product Testing Methodology

DOE conducted performance testing on a range of CCW models for the following

purposes:

 Determine equivalent MEF J2 and IWF ratings using the new appendix J2 test procedure;

 Verify performance trends that are apparent in the publicly available data;

 Develop a better understanding of the operational characteristics of CCWs; and

 Evaluate potential impacts on cleaning and rinsing performance as a result of proposed higher efficiency levels.

DOE conducted energy and water testing, cleaning and rinsing performance testing, and standby power testing on a representative sample of top-loading and front-loading CCWs spanning a range of efficiencies and manufacturers. DOE conducted its testing at an independent laboratory with significant experience performing clothes washer testing. DOE used the following test procedures for its testing:

 10 CFR 430, subpart B, Appendix J1,

 10 CFR 430, subpart B, Appendix J2

 AHAM HLW-1 -2010, Performance Evaluation Procedures for Household Clothes

Washers 4

Within AHAM HLW-1-2010, DOE performed the following tests:

 Soil/Stain Removal (Section 6)

 Sand Removal (Section 8)

 Rinsing Effectiveness Test (Annex E)

Section 5.6.2 provides detailed results of this testing.

5.5.3 Product Teardown Methodology

Other than obtaining detailed manufacturing costs directly from a manufacturer, the most accurate method for determining the production cost of a product is to disassemble representative units piece-by -piece and estimate the material, labor, and overhead costs associated with each component using a process commonly called a physical teardown. A supplementary method, called a catalog teardown, uses published manufacturer catalogs and supplementary component data to estimate the major physical differences between a product that has been physically disassembled and another similar product. DOE performed physical teardown analysis on both top-loading and front-loading commercial clothes washers. The teardown methodology is explained in the following sections.

4 Available from AHAM at http://www.aham.org/ht/d/ProductDetails/sku/4045- 110 -140/from/714/pid/5-9 5.5.3.1 Selection of Units

DOE generally adopts the following criteria for selecting units for teardown analysis:

 The selected products should span the full range of efficiency levels for each product class under consideration;

 Within each product class, the selected products should, if possible, come from the same manufacturer and belong to the same product platform;

 The selected products should, if possible, come from manufacturers with large market shares in that product class, although the highest efficiency products are chosen irrespective of manufacturer; and

 The selected products should have non-efficiency-related features that are the same as, or similar to, features of other products in the same class and at the same efficiency level.

5.5.3.2 Generation of Bill of Materials

The end result of each teardown is a structured bill of materials (BOM), which describes each product part and its relationship to the other parts, in the estimated order of assembly. The BOMs describe each fabrication and assembly operation in detail, including the type of value— added equipment needed ( e.g. , stamping presses, injection molding machines, spot-welders, etc.) and the estimated cycle times associated with each conversion step. The result is a thorough and explicit model of the production process.

Materials in the BOM are divided between raw materials that require conversion steps to be made ready for assembly, while purchased parts are typically delivered ready for installation. The classification into raw materials or purchased parts is based on DOE’s previous industry experience, recent information in trade publications, and discussions with original equipment manufacturers (OEMs). For purchased parts, the purchase price is based on volume-variable price quotations and detailed discussions with suppliers.

For parts fabricated in-house, the prices of the underlying “raw” metals ( e.g. , tube, sheet metal) are estimated on the basis of 5-year averages to smooth out spikes in demand. Other “raw” materials such as plastic resins, insulation materials, etc. are estimated on a current-market basis. The costs of raw materials are based on manufacturer interviews, quotes from suppliers, secondary research, and by subscriptions to publications including the American Metals Market (AMM). 5 Past price quotes are indexed using applicable Bureau of Labor Statistics producer price index tables as well as AMM monthly data.

5.5.3.3 Cost Structure of the Spreadsheet Models

The manufacturing cost assessment methodology uses a detailed, component-focused technique for rigorously calculating the manufacturing cost of a product (direct materials, direct

5 For information on American Metals Market, please visit: www.amm.com

.5-10

labor and some overhead costs). Figure 5.5

. 1 shows the three major steps in generating the manufacturing cost.

Figure 5.5

. 1 Manufacturing Cost Assessment Stages

The first step in the manufacturing cost assessment is the creation of a complete and structured BOM from the disassembly of the units selected for teardown. The units are dismantled, and each part is characterized according to weight, manufacturing processes used, dimensions, material, and quantity. The BOM incorporates all materials, components, and fasteners with estimates of raw material costs and purchased part costs. Assumptions on the sourcing of parts and in-house fabrication are based on industry experience, information in trade publications, and discussions with manufacturers. Interviews and plant visits are conducted with manufacturers to ensure accuracy on methodology and pricing.

Following the development of a detailed BOM, the major manufacturing processes are identified and developed for the spreadsheet model. Some typical processes are listed in Table

5.5

. 1

.

Table 5.5. 1 Major Manufacturing Processes

Fabrication Finishing Assembly/Joining Quality

Control

Fixturing Stamping/Pressing Brake Forming Cutting and Shearing Insulating

Turret Punch Tube Forming Enameling Washing

Powder Coating

De

- burring Polishing Refrigerant Charging Adhesive Bonding

Spot Welding Seam Welding Packaging Inspecting &

Testing

Fabrication process cycle times for each part made in-house are estimated and entered into the BOM. Based on estimated assembly and fabrication time requirements, the labor content of each appliance can be estimated.

Cycle requirements for fabrication steps are similarly aggregated by fabrication machine type while accounting for dedicated vs. non-dedicated machinery and/or change-over times (die swaps in a press, for example). Once the cost estimate for each teardown unit ar e finalized, a detailed summary is prepared for relevant components, subassemblies and processes. The BOM thus details all aspects of unit costs: material, labor, and overhead.5-11

Design options used in units subject to teardown are noted in the summary sheet of each cost model and are cost-estimated individually. Thus, various implementations of design options can be accommodated, ranging from assemblies that are entirely purchased to units that are made entirely from raw materials. Hybrid assemblies, consisting of purchased parts and parts made on

site are thus also accommodated.

5.5.3.4 Cost Model and Definitions

The cost model is based on production activities and divides factory costs into the following categories:

 Materials: Purchased parts ( i.e. , motors, valves, etc .), raw materials, ( i.e. , cold rolled steel, copper tube, etc .), and indirect materials that are used for processing and

fabrication.

 Labor: Fabrication, assembly, indirect, and supervisor labor. Fabrication and assembly

labor cost are burdened with benefits and supervisory costs.

 Overhead: Equipment, tooling, and building depreciation, as well as utilities, equipment and tooling maintenance, insurance, and property taxes.

Cost Definitions Because there are many different accounting systems and methods to monitor costs, DOE

defined the above terms as follows:

 Direct material: Purchased parts (out-sourced) plus manufactured parts (made in-house from raw materials).

 Indirect material: Material used during manufacturing ( e.g. , welding rods, adhesives).

 Fabrication labor: Labor associated with in-house piece manufacturing.

 Assembly labor: Labor associated with final assembly.

 Supervisory labor: Labor associated with fabrication and assembly basis. Assigned on a span basis (x number of employees per supervisor) that depends on the industry.

 Indirect labor: Labor costs that scale with fabrication and assembly labor. These included the cost of technicians, manufacturing engineering support, stocking, etc . that are proportional to all other labor.

 Equipment depreciation: Money allocated to pay for initial equipment installation and replacement as the production equipment is amortized. All depreciation is assigned in a linear fashion and affected equipment life depends on the type of equipment.

 Tooling depreciation: Cost for initial tooling (including non-recurring engineering and debugging of the tools) and tooling replacement as it wears out or is rendered obsolete.

 Building depreciation: Money allocated to pay for the building space and the conveyors that feed and/or make up the assembly line.

 Utilities: Electricity, gas, telephones, etc.

 Maintenance: Annual money spent on maintaining tooling and equipment.

 Insurance: Appropriated as a function of unit cost.

 Property Tax: Appropriated as a function of unit cost.5-12 5.5.3.5 Cost Model Assumptions

As discussed in the previous section, assumptions about manufacturer practices and cost structure played an important role in estimating the final product cost. In converting physical information about the product into cost information, DOE reconstructed manufacturing processes for each component using internal expertise and knowledge of the methods used by the industry. Site visits allowed DOE to confirm its cost model assumptions through direct observation of the manufacturing plant, as well as through manufacturer interviews, reviews of current Bureau of Labor Statistics data, etc.

5.5.4 Manufacturer Interviews

DOE understands that there is variability among manufacturers in baseline units, design strategies, and cost structures. To better understand and explain these variances, DOE conducted

manufacturer interviews. These confidential interviews provided a deeper understanding of the various combinations of technologies used to increase residential clothes washer efficiency, and their associated manufacturing costs

. Sample questions asked during the interviews are contained in appendix 12A.

During the interviews, DOE also gathered information about the capital expenditures required to increase the efficiency of the baseline units to various efficiency levels ( i.e.

, conversion capital expenditures by efficiency or energy-use level). The interviews provided information about the size and the nature of the capital investments. DOE also requested information about the depreciation method used to expense the conversion capital. The manufacturer impact analysis in chapter 12 includes a discussion of this information obtained during manufacturer interviews.

5.6 ANALYSIS AND RESULTS

5.6.1 AHAM Data

In its data submittal to DOE, AHAM stated that it has not collected incremental cost data from CCW manufacturers and therefore cannot provide that data to DOE.

5.6.2 Product Testing Results

DOE tested a total of 10 commercial clothes washer models in support of this rulemaking: five top-loading and five front-loading units. DOE also conducted teardowns on these 10 clothes washer models. DOE selected clothes washer models for testing and teardowns based on the proposed efficiency levels and the range of product efficiencies available on the

market. Table 5.6

. 1 lists the major features of the top-loading CCWs observed at each efficiency

level. Table 5.6

. 2 lists the major features of front-loading CCWs observed at each efficiency

level. The efficiency levels listed in these tables correspond to the levels shown above in Table5-13

5.4

. 2 and Table 5.4

. 3

. Note that for top-loading Units #2 and #3, the Efficiency Level changes depending on whether Appendix J1 or Appendix J2 is used. The next section below provides further details regarding the test result differences between Appendix J1 and Appendix J2.

Table 5.6. 1 Top-Loading Commercial Clothes Washer Test Unit Features

Feature Unit #1 Unit # 2 Unit #3 Unit # 4 Unit #5

Appendix J2 Efficiency Level ( MEF J2 /IWF) Baseline (1.15/8.9) Baseline

(1.15/8.9) Level 1

(1.35/8.8) Level 1

(1.35/8.8) Level 2

(1.55/6.9)

Appendix J1Efficiency Level ( MEF /WF) Baseline ( 1.60 /8.5) Level 1

( 1. 7 0 /8.4) Baseline

(1.15/8.9) Level 1

( 1. 7 0 /8.4) Level 2

( 1.85 /6.9)

Efficiency Level WF (Appendix J2 IWF) [gal/ ft 3 ] 8.5 (8.9) 8.4 (8.8) 8.5 (8.9) 8.4 (8.8) 6.9 (6.9)

Rated Drum Capacity (ft 3 ) 3.5 3.3 3.3 3.3 2.9

RMC (%) 50.8% 48.1% 49.5% 48.0% 47.2%

Control Panel Type No vend display Vend

price display +

advanced

features 1 No vend

display Vend

price display No vend

display

Payment System Type None (“push to start”) Coin box Coin box Coin box Coin slide

Advanced Auditing or

Remote

Communication Features No Yes No No No

Water Fill Control Manual Manual Automatic

Fixed Automatic

Fixed Automatic

Adaptive

Agitator Type Standard Standard Standard Standard Low

Profile

Internal Heater No No No No No

1. Advanced features may include dynamic or cycle-based pricing controls, built-in logging capabilities, and remote auditing features.5-14

Table 5.6. 2 Front-Loading Commercial Clothes Washer Test Unit Features

Feature Unit #1 Unit # 2 Unit #3 Unit # 4 Unit #5

Appendix J2 Efficiency Level ( MEF J2 /IWF) Level 1 (1.80/4.5) Level 1

(1.80/4.5) Level 2

(2.00/4.1) Level 2

(2.00/4.1) Level 3

(2.20/3.9)

Appendix J1 Efficiency Level ( MEF /WF) Level 1 (2.20/4.5) Level 1

(2.20/4.5) Level 2

(2.40/4.0) Level 2

(2.40/4.0) Level 3

(2.60/3.7)

Rated Drum Capacity (ft 3 ) 2.8 3.1 2.8 3.1 3.6

RMC (%) 39.6% 37.9% 39.6% 35.5% 33.4%

Control Panel Type Vend price display +

advanced

features 1 Vend

price display Vend

price display +

advanced

features 1 Vend

price display No vend

display

Payment System Type Coin box Coin box Coin box Coin box None (“push to start”)

Advanced Auditing or

Remote

Communication Features Yes No Yes No No

Water Fill Control Automatic Adaptive Automatic

Adaptive Automatic

Adaptive Automatic

Adaptive Automatic

Adaptive

Internal Heater No No No No No

1. Advanced features may include dynamic or cycle-based pricing controls, built-in logging capabilities, and remote auditing features.

5.6.2.1 Active Mode Testing

DOE conducted testing on a representative sample of five top-loading and five frontloading CCWs using both appendix J1 and appendix J2. DOE used the results from these tests to determine each model’s appendix J2 MEF J2 /IWF ratings in relation to its appendix J1 MEF/WF ratings. These relationships were then used to develop the equivalent Appendix J2 values for each Appendix J1 efficiency level shown in Table 5.4

. 2 and Table 5.4

. 3

.

The DOE test procedure consists of running different load sizes of preconditioned test cloth in the clothes washer at various temperature settings. For all clothes washers, the minimum test load is 3 pounds of test cloth. Average and maximum load sizes are specified based on the clothes washer capacity. During the energy test cycles, the total kWh of electric energy consumed by the clothes washer is measured, in addition to the cold and hot water consumption.5-15

The measurements also include the “bone dry” 6 we ight of the test load and the weight immediately after the completion of certain energy test cycles for the RMC calculation.

Figure 5.6

. 1 shows a plot of the equivalent MEF J2 values (using appendix J2) associated with the rated MEF values (using appendix J1) for each of the top-loading CCWs in the test sample. For reference, the plot also indicates the associated efficiency levels used for the rulemaking analysis, based on appendix J2 MEF J2 ratings.

Figure 5.6. 1 Relationship Between Equivalent MEF J2 (Appendix J2) and Rated MEF (Appendix J1) for Top-Loading CCWs

Figure 5.6

. 1 shows that top -loading clothes washers with manual water fill controls have lower relative appendix J2 MEF J2 ratings compared to similarly-rated units (based on appendix J1) with automatic water fill controls. 7 As a result, some manual water fill units that would have been considered higher-efficiency models under appendix J1 ratings are considered baseline models under appendix J2 ratings.

The primary driver of the lower relative efficiency ratings under appendix J2 for units with manual water fill controls is the amended calculation of drying energy in section 4.3 of

6 “Bone dry” means a condition of a load of test clothes which has been dried in a dryer at maximum temperature for a minimum of 10 minutes, removed and weighed before cool down, and then dried again for 10-minute periods until the final weight change of the load is 1 percent or less.

7 The term “automatic” water fill used here refers to water fill control systems that determine the water fill level without requiring user intervention or actions. This includes “adaptive” water fill control systems and “fixed” water fill control systems, available on some commercial clothes washers, that provide a fixed water level for all load sizes and no water fill selector or water fill control settings available to the user. Clothes washers with fixed water fill control systems are tested in the same manner as clothes washers with adaptive water fill control systems.

1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7

1.5 1.6 1.7 1.8 1.9 2.0Equivalent MEFJ2(Appendix J2)

Rated MEF (Appendix J1)

(Higher MEF is more efficient)

Manual Water Fill

Automatic Water Fill

Baseline: 1.15 MEFJ2

Level 1: 1.35 MEFJ2

Level 2: 1.55 MEFJ25-16

appendix J2. In appendix J2, the drying energy is calculated using the weighted average load size, with the weightings corresponding to the Load Usage Factors (LUF) for manual water fill machines. Whereas, in section 4.3 of appendix J1, the drying energy is calculated using an adjusted load size equal to 0.52 x Maximum Load Size. The calculated drying energy using appendix J2 is a greater quantity than under appendix J1; thus resulting in a lower MEF J2 value. For units with adaptive fill controls, the difference between the weighted average load size in appendix J2 and the adjusted load size in appendix J1 is much less; therefore, the difference in drying energy is not as pronounced as with manual water fill units.

Figure 5.6

. 2 shows a plot of the equivalent IWF values (using appendix J2) associated with the rated WF values (using appendix J1) for each of the top-loading CCWs in the test sample. For reference, the plot also indicates the associated efficiency levels used for the rulemaking analysis, based on appendix J2 IWF ratings.

Figure 5.6. 2 Relationship Between Equivalent IWF (Appendix J2) and Rated WF (Appendix J1) for Top-Loading CCWs

Figure 5.6

. 3 shows a plot of the equivalent MEF J2 values (using appendix J2) associated with the rated MEF values (using appendix J1) for each of the front-loading CCWs in the test sample. For reference, the plot also indicates the associated efficiency levels used for the rulemaking analysis, based on appendix J2 MEF J2 ratings.

6.0 6.5 7.0 7.5 8.0 8.5 9.0

6.0 6.5 7.0 7.5 8.0 8.5 9.0Equivalent IWF (Appendix J2)

Rated WF (Appendix J1)

(Lower IWF is more efficient)

Manual Water Fill

Automatic Water Fill

Baseline: 8.9 IWF

Level 1: 8.8 IWF

Level 2: 6.9 IWF5-17

Figure 5.6. 3 Relationship Between Equivalent MEF J2 (Appendix J2) and Rated MEF (Appendix J1) for Front-Loading CCWs

As noted in the NOPR discussion, DOE is not aware of any front-loading CCWs on the market with a rating of 2.00 MEF, which corresponds to the current minimum standard level. As a result, DOE used data from the prior CCW rulemaking to represent the 2.00 MEF level, indicated in Figure 5.6. 3 as a “virtual model.” To calculate the equivalent appendix J2 MEF J2 for the virtual model, DOE assumed that a model with a 2.00 MEF rating under appendix J1 would have the same measured capacity, machine electrical, and hot water heating energy under appendix J2. The only change would be the drying energy calculation, as described above.

Figure 5.6

. 4 shows a plot of the equivalent IWF values (using appendix J2) associated with the rated WF values (using appendix J1) for each of the front-loading CCWs in the test sample. For reference, the plot also indicates the associated efficiency levels used for the rulemaking analysis, based on appendix J2 IWF ratings. 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3

1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8Equivalent MEFJ2(Appendix J2)

Rated MEF (Appendix J1)

(Higher MEF is more efficient)

Tested Models

Virtual Model

Baseline: 1.65 MEFJ2

Level 1: 1.80 MEFJ2

Level 2: 2.00 MEFJ2

Level 3: 2.20 MEFJ25-18

Figure 5.6. 4 Relationship Between Equivalent IWF (Appendix J2) and Rated WF (Appendix J1) for Front-Loading CCWs

5.6.2.2 Standby Mode and Off Mode Power

As described in greater detail in the NOPR, DOE proposes amended standards for

commercial clothes washers based on MEF J2 , which does not incorporate standby and off mode power. As part of its investigation of standby power for CCWs, DOE measured standby and off mode power for all 10 clothes washers selected in the test sample, using the methodology provided in Appendix J2, which references International Electrotechnical Commission (IEC)

Standard 62301 Second Edition.

CCW manufacturers offer a variety of display and payment functionalities that can be selected independent of the basic model selected. The standby power associated with these different display and payment functionalities varies widely. The lowest standby power levels are associated with models having no vend price display and no coin or card payment options (often referred to as “push- to -start” models). These models are typically used in multi-family housing facilities offering free laundry, or in other commercial applications not requiring fare payment. Such models are not suitable for coin-operated laundry or most multi-family housing facilities. The highest standby power levels are associated with models having a digital vend price display, coin or debit card payment system, and advanced features such as dynamic or cycle-based pricing controls, built-in logging capabilities, and remote auditing features. These models are typically used in coin-operated laundries located in competitive markets.

The results of the standby and off mode measurements are shown in Table 5.6

. 3 below.

3.0 3.5 4.0 4.5 5.0 5.5 6.0

3.0 3.5 4.0 4.5 5.0 5.5 6.0Equivalent IWF (Appendix J2)

Rated WF (Appendix J1)

(Lower IWF is more efficient)

Tested Models

Virtual Model

Baseline: 5.2 IWF

Level 1: 4.5 IWF

Level 2: 4.1 IWF

Level 3: 3.9 IWF5-19

Table 5.6. 3 Commercial Clothes Washer Standby Power Measurements

Product

Class Rated

MEF (ft 3 /kWh) Control Panel Type Payment System Type Power

Consumpti

on (W)

Top

Loading 1.60 No vend display Push

- to

- start 2.84

1.65 No vend display Coin slot 5.44

1.70 Vend price only Coin slot 10.21

1.75 Vend price display + advanced

features 1 Coin slot 11.77

1.85 No vend display Coin slide 0.88

Front

Loading 2.24 Vend price display + advanced

features 1 Coin slot 11.60

2.24 Vend price only Coin slot 7.10

2.46 Vend price display + advanced

features 1 Coin slot 11.30

2.49 Vend price only Coin slot 7.70

2.60 Vend price only Push

- to

- start 5.80

1. Advanced features may include dynamic or cycle-based pricing controls, built-in logging capabilities, and remote auditing features.

5.6.2.3 Cleaning Performance Testing

To investigate potential impacts on cleaning performance, rinsing performance, and solid particle removal, DOE performed additional testing on each model using AHAM’s HLW-1 -2010 test method: Performance Evaluation Procedures for Household Clothes Washers (hereafter,

“AHAM HLW-1 -2 010”). Specifically, DOE performed the soil/stain removal, rinsing effectiveness, and sand removal tests provided in HLW-1 -2010. For each clothes washer, DOE tested the maximum load size specified in appendix J2, rounded to the nearest pound, using the warm wash/cold rinse cycle. DOE believes that the maximum load size is particularly relevant to commercial clothes washer owners and operators because end-users often overload the machines in order to limit their total laundry cost.

DOE consulted with a number of manufacturers who indicated that AHAM HLW-1 -2010 would be the most appropriate test method to determine relative cleaning performance across different commercial clothes washer models. DOE recognizes that AHAM HLW-1 -2010 is typically used to measure the performance of residential clothes washers, but given the similarities in physical construction, DOE believes the test procedure is also appropriate for

commercial clothes washers covered by DOE standards. DOE also acknowledges that the commercial clothes washer industry has not agreed upon acceptable ranges of performance5-20

characteristics; therefore, DOE’s test results should be used for relative comparison purposes only.

Cleaning Performance

DO E performed the Soil/Stain Removal test within section 6 of AHAM HLW-1 -2010 to measure relative cleaning performance among the CCW test sample units. AHAM HLW-1 -2010 states that the purpose of the Soil/Stain Removal test is to evaluate the performance of household clothes washers in removing representative soils and stains from fabric. For each clothes washer, DOE tested the maximum load size specified in appendix J2, rounded to the nearest pound, and used the warm wash/cold rinse cycle.

The AHAM HLW-1 -2010 Soil/Stain Removal test consists of running a wash cycle with

user-defined test load size and machine settings, using AHAM base load materials, a defined number of soil/stain strips, and a defined quantity of powdered AHAM detergent. The AHAM base load materials consist of 100% cotton sheets, towels, and pillowcases. The soil/stain strips consist of square test fabric swatches measuring 4.7 in. x 4.7 in., carrying five different types of

standard stains in addition to a sixth unstained swatch. The swatches are attached to each other, forming a test strip with the different stains in the following order:

1. Unsoiled 2. Pigment/sebum

3. Carbon black/mineral oil.

4. Blood.

5. Chocolate and milk.

6. Red wine

Figure 5.6. 5 shows the soil/stain strips, including typical packaging material. The photo includes a printed copy of the AHAM HLW-1 -2010 test procedure for scale.

Figure 5.6

. 5 AHAM Soil/Stain Test Strip5-21

Multiple stain strips are attached to individual towels comprising the test load. AHAM

HLW-1 -2010 defines the number of test strips based on the total load size. Three replications of the Soil/Stain Removal test are performed on each clothes washer, using fresh soil/stain strips for each replication.

After a single wash cycle replication is performed, the soil/stain test strips are removed

from the base load and measured using a tristimulus colorimeter. The unsoiled swatches are removed and used for the Rinsing Effectiveness test, as described in the next section of this chapter. The reflectance values of each stain swatch are measured; higher reflectance represents

better stain removal. For each stain type, the reflectance values measured from all soil/stain test strips are averaged to produce an average reflectance score, y w lab for e ach stain. For each stain

type, an Individual Cleaning Score, CS i , is obtained, expressed as:

where for each soil/stain:

y max lot = post-wash reflectance as determined by the strip manufacturer using the calibration wash treatment defined in Annex A.A.7.1 of HLW-1-2010;

y w lab = post-wash reflectance as measured by laboratory.

CS i is determined for each stain type after each wash cycle replication. After the three replications have been performed, the three CS i scores for each stain type are averaged, to produce a mean CS i score for each stain type. A Total Cleaning Score, CS t , is then determined by averaging the mean CS i scores fo r all five stain types and multiplying by 100:

The Total Cleaning Score represents cleaning performance as a percentage of that achieved by a reference “maximum” wash cycle determined by the calibration wash treatment performed by the stain strip manufacturer. The Total Cleaning Score may be less than or greater than 100%. A higher Total Cleaning Score represents better cleaning performance.

Figure 5.6

. 6 shows the Total Cleaning Score for each top-loading CCW included in the test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6

. 7 shows the Total Cleaning Score for each top-loading CCW as a function of each washer’s appendix J2 IWF efficiency level. As shown in both figures, some of the higher-efficiency clothes washers in the test sample achieve better Total Cleaning Scores than the baseline models. DOE believes that one of the factors that may contribute to higher-efficiency models achieving

better T otal Cleaning Scores is that using a smaller quantity of wash water in high-efficiency models increases the concentration of detergent during the wash portion of the cycle; thus enhancing stain removal. The proposed standard levels in the NOPR correspond to Efficiency

Level 1.5-22

Figure 5.6

. 6 Total Cleaning Score vs. Appendix J2 MEF J2 Efficiency Level for TopLoading CCWs

Figure 5.6

. 7 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for Top-Loading

CCWs

Figure 5.6

. 8 shows the Total Cleaning Score for each top-loading CCW as a function of each washer’s cycle time, as measured during the AHAM HLW-1 -2010 test. DOE notes that these cycle times may not correspond to the same cycle times when conducting the DOE 80 82 84 86 88 90 92 94 96

1.1 1.2 1.3 1.4 1.5 1.6AHAM Total Cleaning Score

Appendix J2 MEF J2 Efficiency Level (cu.ft./kWh/cycle) Total Cleaning Score vs. Appendix J2 MEF J2 Efficiency Level

(Higher cleaning score is better)

Baseline

Level 1

Level 2

80 82 84 86 88 90 92 94 96

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5AHAM Total Cleaning Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Total Cleaning Score vs. Appendix J2 IWF Efficiency Level

(Higher cleaning score is better)

Baseline

Level 1

Level 25-23

appendix J2 test procedure, due to differences in the base load material that may affect any adaptive control strategies used throughout the wash cycle. However, DOE believes that the cycle times shown in the figure would be representative of consumer usage in a coin-operated laundry or multi-family housing laundry setting. As shown in the figure, one of the CCW models at Efficiency Level 1 achieves a better cleaning performance score than baseline models, at a cycle time of just under 30 minutes.

Figure 5.6

. 8 Total Cleaning Score vs. Cycle Time for Top-Loading CCWs

Figure 5.6

. 9 shows the Total Cleaning Score for each front-loading CCW included in the test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6

. 10 shows the Total Cleaning Score for each front-loading CCW as a function of each washer’s appendix J2 IWF efficiency level. Both figures indicate a general trend of higher efficiency levels resulting in reduced cleaning performance scores; however, DOE notes that one Level 2 clothes washer achieves better a better cleaning score than both Level 1 clothes washers. DOE believes that this clothes washer may utilize advanced control strategies to maintain cleaning performance at the higher efficiency level. The proposed standard levels in the NOPR correspond to Efficiency Level 2.

80 82 84 86 88 90 92 94 96

25 30 35 40 45 50AHAM Total Cleaning Score

Cycle Time (min)

Total Cleaning Score vs. Cycle Time

(Higher cleaning score is better)

Baseline

Level 1

Level 25-24

Figure 5.6

. 9 Total Cleaning Score vs. Appendix J2 MEF J2 Efficiency Level for FrontLoading CCWs

Figure 5.6

. 10 Total Cleaning Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs

Figure 5.6

. 11 shows the Total Cleaning Score for each front-loading CCW as a function of each washer’s cycle time, as measured during the AHAM HLW-1 -2 010 test. DOE notes that these cycle times may not correspond to the same cycle times when conducting the DOE appendix J2 test procedure, due to differences in the base load material that may affect any 80 82 84 86 88 90 92 94 96

1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3AHAM Total Cleaning Score

Appendix J2 MEF J2 Efficiency Level (cu.ft./kWh/cycle) Total Cleaning Score vs. Appendix J2 MEF J2 Efficiency Level

(Higher cleaning score is better)

Level 1

Level 2

Level 3

80 82 84 86 88 90 92 94 96

3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7AHAM Total Cleaning Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Total Cleaning Score vs. Appendix J2 IWF Efficiency Level

(Higher cleaning score is better)

Level 1

Level 2

Level 35-25

adaptive control strategies used throughout the wash cycle. However, DOE believes that the cycle times shown in the figure would be representative of consumer usage in a coin-operated laundry or multi-family housing laundry setting. DOE notes a lack of any discernible trend of cleaning performance as a function of cycle time. As shown in the figure, one of the models at Efficiency Level 2 achieves the best cleaning performance score with a cycle time just over 36 minutes. The cycle times for the Level 1 models are approximately 31 and 34 minutes. DOE believes that the highest cleaning performance score of the Level 2 model may be achieved in part through a slightly longer wash cycle time. Based on conversations with CCW manufacturers, DOE believes that a 36 -minute cycle remains within the range of acceptable cycle times for coin-operated laundry owners and multi-family housing laundry operators.

Figure 5.6

. 11 Total Cleaning Score vs. Cycle Time for Front-Loading CCWs

Rinsing Performance

The AHAM Rinsing Effectiveness test consists of measuring the concentration of detergent remaining in the unstained swatches after performing the Soil/Stain Removal tests described in the previous section. This is accomplished by measuring the UV absorbance at the wavelength particular to LAS (surfactant), a key ingredient of the detergent with a known linear relationship to the quantity of detergent mixture. Using a concentration vs. absorbance curve developed as part of this procedure, the absorbance values are then converted into detergent concentrations, which together with test solution weight data, yields detergent quantities.

After a single wash cycle replication is performed, the unsoiled test strips are removed and each strip is placed into an individual bottle with 100ml distilled water. After all the test strips have been bottled, they are placed on a laboratory shaker for 20 minutes. Following that, 80 82 84 86 88 90 92 94 96

28 30 32 34 36 38 40AHAM Total Cleaning Score

Cycle Time (min)

Total Cleaning Score vs. Cycle Time

(Higher cleaning score is better)

Level 1

Level 2

Level 35-26

samples of the fluid from each bottle are then collected and measured in a UV spectrophotometer. The measured values are translated into detergent concentrations using the detergent concentration curves developed at the beginning of the procedure. The detergent concentration is then used to determine the amount of detergent recovered per gram of swatch material. The final Rinse Score is then calculated as the amount of detergent, in grams, remaining in a pound of test load. A lower Rinse Score represents better rinsing performance.

Figure 5.6

. 12 shows the Rinse Score for each top-loading CCW included in the test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6

. 13 shows the Rinse Score for each top-loading CCW as a function of each washer’s appendix J2 IWF efficiency level. As demonstrated in both figures, DOE notes a lack of any clear trend in rinsing performance as a function of efficiency level. For example, DOE notes that the most efficient Level 2 model achieves comparable rinsing scores with the current baseline models. DOE believes that a major factor affecting the rinse score is the quantity of water used during the rinse portion of the cycle. To maintain rinsing performance in higher-efficiency models, manufacturers may use relatively more water during the rinse portion of the cycle while reducing total water usage overall. The proposed standard levels in the NOPR correspond to Efficiency

Level 1.

Figure 5.6. 12 Rinse Score vs. Appendix J2 MEF J2 Efficiency Level for Top-Loading CCWs 0.5 0.6 0.7 0.8 0.9 1.0

1.1 1.2 1.3 1.4 1.5 1.6AHAM Rinse Score

Appendix J2 MEF J2 Efficiency Level (cu.ft./kWh/cycle) Rinse Score vs. Appendix J2 MEF J2 Efficiency Level

(Lower rinse score is better)

Baseline

Level 1

Level 25-27

Figure 5.6

. 13 Rinse Score vs. Appendix J2 IWF Efficiency Level for Top-Loading CCWs Figure 5.6

. 14 shows the Rinse Score for each top-loading CCW as a function of each washer’s cycle time, as measured during the AHAM HLW-1-2010 test. DOE notes a general trend of better Rinse Scores as a function of increased cycle time; however, DOE notes that the increases in cycle time are not directly correlated with efficiency level. As shown in the figure, one of the baseline models achieves a Rinse Score just over 0.7 with a cycle time of approximately 30 minutes. The Level 1 models achieve slightly higher rinse scores (just under 0.8) with similar cycle times. The most efficient Level 2 model achieves a better rinse score of less than 0.7, but at an increased cycle time of around 39 minutes. The proposed standard levels in the NOPR correspond to Efficiency Level 1. Based on trends in the figure, DOE believes that manufacturers should be able to achieve Rinse Scores comparable to current baseline models with cycle times between 30-35 minutes. Based on conversations with CCW manufacturers,

DOE believes that a cycle time range of 30-35 minutes is within the range of acceptable cycle times for coin-operated laundry owners and multi-family housing laundry operators.

0.5 0.6 0.7 0.8 0.9 1.0

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5AHAM Rinse Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Rinse Score vs. Appendix J2 IWF Efficiency Level

(Lower rinse score is better)

Baseline

Level 1

Level 25-28 Figure 5.6

. 14 Rinse Score vs. Cycle Time for Top-Loading CCWs

Figure 5.6

. 15 shows the Rinse Score for each front-loading CCW included in the test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6

. 16 shows the Rinse Score for each front-loading CCW as a function of each washer’s appendix J2 IWF efficiency level. As demonstrated in both figures, DOE notes a lack of any clear trend in rinsing performance as a function of efficiency level. The proposed standard levels in the NOPR correspond to Efficiency Level 2

. DOE notes that one of the Level 2 models achieves the lowest (best) Rinse Score of the entire test sample, at around 0.8. 0.5 0.6 0.7 0.8 0.9 1.0

25 30 35 40 45 50AHAM Rinse Score

Cycle Time (min)

Rinse Score vs. Cycle Time

(Lower rinse score is better)

Baseline

Level 1

Level 25-29

Figure 5.6

. 15 Rinse Score vs. Appendix J2 MEF J2 Efficiency Level for Front-Loading

CCWs

Figure 5.6. 16 Rinse Score vs. Appendix J2 IWF Efficiency Level for Front-Loading CCWs

Figure 5.6

. 17 shows the Rinse Score for each front-loading CCW as a function of each washer’s cycle time, as measured during the AHAM HLW-1-2010 test. Similar to the Total Cleaning Scores for front-loading CCWs, DOE notes a lack of a discernible trend of cleaning performance as a function of cycle time. As shown in the figure, one of the models at Efficiency 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4

1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3AHAM Rinse Score

Appendix J2 MEF

J2 Efficiency Level (cu.ft./kWh/cycle)

Rinse Score vs. Appendix J2 MEF J2 Efficiency Level

(Lower rinse score is better)

Level 1

Level 2

Level 3

0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4

3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7AHAM Rinse Score

Appendix J2 IWF Efficiency Level (gal/cycle/cu.ft.)

Rinse Score vs. Appendix J2 IWF Efficiency Level

(Lower rinse score is better)

Level 1

Level 2

Level 35-30

Level 2 achieves the best rinsing performance score with a cycle time just over 36 minutes. The cycle times for the Level 1 models are approximately 31 and 34 minutes. Based on conversations with CCW manufacturers, DOE believes that a 36-minute cycle remains within the range of acceptable cycle times for coin-operated laundry owners and multi-family housing laundry

operators.

Figure 5.6

. 17 Rinse Score vs. Cycle Time for Front-Loading CCWs

Sand Removal Performance

DO E performed the Sand Removal test within section 8 of AHAM HLW-1 -2010, the purpose of which is to evaluate the ability of a clothes washers to remove insoluble and heavierthan-water soils from clothes and the system. For each clothes washer, DOE tested the maximum load size specified in appendix J2, rounded to the nearest pound, and used the warm wash/cold rinse cycle. The Sand Removal test is performed separately from the Soil/Stain Removal and Rinsing Effectiveness tests. Three replications of the test are performed.

The AHAM HLW-1 -2010 Sand Removal test consists of running a wash cycle with userdefined test load size and machine settings, using AHAM base load materials, a defined quantity of powdered AHAM detergent, and 50 grams of sand added to the load. During the wash cycle, sand is collected through a filter placed at the end of the clothes washer drain hose. Upon completion of the wash cycle, the test load is removed and any sand particles remaining in the clothing load are captured and measured. The Sand Remaining score, SR, is calculated as the quantity of sand remaining in the load (in grams) divided by the amount of sand added to the load (50 grams). A lower SR score represents better sand removal performance.

During testing, some sand may remain trapped in the clothes washer after each replication. Therefore, after each replication, “cleanup cycles” with no load are performed to 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4

28 30 32 34 36 38 40AHAM Rinse Score

Cycle Time (min) Rinse Score vs. Cycle Time

(Lower rinse score is better)

Level 1

Level 2

Level 35-31

flush out any sand remaining inside the washer. However, in some cases, despite running multiple cleanup cycles, not all the sand was accounted for after each replication of the test. Therefore, the second and third replications of each test may contain sand carried over from prior tests. DOE notes that because of these uncertainties, it is important to look at the SR scores from the three replications individually, rather than averaging them together.

Figure 5.6

. 18 shows the Sand Removal Score for each top-loading CCW included in the test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6

. 19 shows the Sand Removal Score for each top-loading CCW as a function of each washer’s appendix J2 IWF efficiency level. As shown in both figures, current baseline clothes washers achieve a minimum sand removal score of 2%; Level 1 washers achieve a minimum score of around 4%, and Level 2 washers achieve a minimum score of 6%. The upper bound of the range is around 16% at all efficiency levels. The proposed standard levels in the NOPR correspond to Efficiency Level 1.

Figure 5.6

. 18 Sand Removal Score vs. Appendix J2 MEF J2 Efficiency Level for TopLoading CCWs 0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

1.1 1.2 1.3 1.4 1.5 1.6AHAM Sand Removal Score

Appendix J2 MEF

J2 Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 MEF J2 Efficiency Level

(Lower sand removal score is better)

Baseline

Level 1

Level 25-32

Figure 5.6. 19 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for Top-Loading

CCWs

Figure 5.6

. 20 shows the Sand Removal Score for each front-loading CCW included in the test sample as a function of each washer’s appendix J2 MEF J2 efficiency level. Figure 5.6

. 21 shows the Sand Removal Score for each front-loading CCW as a function of each washer’s appendix J2 IWF efficiency level. As shown in both figures, current Level 1 clothes washers achieve sand removal scores ranging from 7-17%; Level 2 washers achieve scores ranging from

5 -10%; and Level 3 washers achieve scores ranging from 12-14%. The proposed standard levels in the NOPR correspond to Efficiency Level 2

. 0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5AHAM Sand Removal Score

Appendix J2 IWF Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 IWF Efficiency Level

(Lower sand removal score is better)

Baseline

Level 1

Level 25-33

Figure 5.6

. 20 Sand Removal Score vs. Appendix J2 MEF J2 Efficiency Level for FrontLoading CCWs

Figure 5.6

. 21 Sand Removal Score vs. Appendix J2 IWF Efficiency Level for FrontLoading CCWs 0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3AHAM Sand Removal Score

Appendix J2 MEF

J2 Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 MEF J2 Efficiency Level

(Lower sand removal score is better)

Level 1

Level 2

Level 3

0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7AHAM Sand Removal Score

Appendix J2 IWF Efficiency Level (cu.ft./kWh/cycle)

Sand Removal Score vs. Appendix J2 IWF Efficiency Level

(Lower sand removal score is better)

Level 1

Level 2

Level 35-34

5.6.3 Product Teardowns

As part of its reverse-engineering process, DOE conducted teardowns of commercial clothes washers to identify design features and corresponding manufacturing costs that are associated with successively higher efficiency levels. The clothes washer teardown analysis performed for this analysis included five top-loading and five front-loading models. Details of

the teardown results are as follows.

5.6.3.1 Top-Loading Commercial Clothes Washers

Top-Loading Baseline Construction (1.15 MEF J2 / 8.9 IWF)
The baseline top-loading CCW is equipped with electronic controls that allow the user to select specific cycle settings, including wash /rinse temperature and load size. Temperature is controlled using a thermal cutout on the water inlet assembly, which consists of two solenoid valves. The load size selector switch determines the fill level for a wash cycle, which is controlled by a pressure switch. Between 2 -4 load size selections may be available on baseline

units.

The motor is typically a 1⁄2 -horsepower permanent split capacitor (PSC) motor which

drives the drum and the agitator. On some baseline models, this motor also drives the drain pump by reversing direction, while other manufacturers opt to include a dedicated drain pump instead.

The baseline wash basket consists of either an enameled steel or stainless steel cylinder with a

ci rcular plate attached to the bottom to form a drum. From the center of the bottom plate a smaller cylinder rises to guide the drive shaft into the agitator and to center the wash basket

inside the tub. Snapped onto the top rim of the drum is a plastic balance ring, which is partially filled with a saline solution. Covering the balance ring is a plastic cover, often referred to as the

tub cover.

The outer wrapper of the baseline unit consists of stamped metal panels which may be made of pre-painted steel or coated post-fabrication with powder-coat paint. Th e top panel houses the lid, which is a smaller stamped piece of sheet metal on hinges. One of these hinges triggers a limit switch that cuts power to the motor when the lid is opened. The top panel and lid are typically enameled to provide greater scratch resistance.

Based upon product teardowns, DOE developed the following baseline production and

materials cost distributions for a typical top-loading CCW. Table 5.6

. 4 and Figure 5.6 .22 show the baseline production cost distributions; and Table 5.6. 5 and Figure 5.6 .23 show the baseline materials cost distribution. Depending on the manufacturer and the production volume, the depreciation costs may vary from those shown in the figures, which assume a “green-field” site.5-35

Table 5.6. 4 Production Cost Distribution for Baseline Top-Loading CCW

Cost Category Total Cost ($201 2 )

Figure 5.6

. 22 Production Cost Distribution for Baseline Top

- Load ing CCW Raw Materials $73.97

Purchased Parts $161.21

Assy Labor Cost $18.15

Fab Labor Cost $27.63

Indirect Labor Cost $2.38

Supervision Cost $15.89

Tooling Dep. Cost $59.41

Indirect Process Costs $25.85

Equipment Dep. Cost $50.09

Building Dep. Cost $21.20

Maintenance $52.48

Utility Cost $5.38

Property Tax $4.62

Insurance $4.15

Total $522.42

Table 5.6. 5 Materials Cost Distribution for Baseline Top-Loading CCW

Sub

- Assembly Total

Materials

Cost ($201 2

)

Figure 5.6

. 23 Baseline Top

- Load Standard Clothes

Washer Materials Cost Distribution Motor/Pump/Capacitor $61.06

Control Assy $37.93

Gear Box/Transm ission $29.97

Tub/Drum $28.85

Outer Wrapper $24.95

Suspension $15.63

Door Assy $10.45

Main Wiring Harness $9.90

Packaging $9.68

Water Temp. Mgmt. $3.91

Final Assy $2.87

Total $235.195-36

Construction at Higher Efficiency Levels

Based on the design options retained from the screening analysis (see chapter 4 of this TSD), the reverse-engineering analyses, and discussions with manufacturers, DOE developed manufacturing costs associated with various design features necessary to achieve higher efficiencies. The following are the design changes DOE believes would be necessary to meet each higher efficiency level, which were subsequently modeled to obtain incremental manufacturing cost estimates.

Top-Loading Efficiency Level 1 (1.35 MEF J2 / 3.8 IWF)

Based on characteristics of units selected for teardown and based on discussions withmanufacturers, DOE research suggests that Efficiency Level (EL) 1 is typically achieved in toploading commercial clothes washers through the following changes:

1. Automatic Water Fill Control Through its observations and discussions with manufacturers, DOE believes that in moving from the baseline level to EL 1, manufacturers would likely replace manual water fill controls, in which the end-user selects the fill level, with automatic water fill controls, in which the machine automatically selects the water fill level. The test procedure assumes a different set of Load Usage Factors (LUF) for manual and automatic water fill machines. For manual water fill machines, the LUF for the maximum load size is 72% (with the minimum load size TUF as 28%), whereas for automatic water fill machines, the maximum load size LUF is 12% (with the average load size TUF as 74%, and minimum load size TUF as 14%).

2. Reduced Hot Water Temperatures DOE believes that in addition to using automatic water fill controls, manufacturers may slightly reduce the temperature of the hot and warm water settings compared to the baseline unit.

Top-Loading Efficiency Level 2 (1.55 MEF J2 / 6.9 IWF)

DOE research suggests that manufacturers would likely significantly change the design platform of a top-loading commercial clothes washer to reach EL 2. A CCW at EL 2 is likely to employ the same features as one at EL 1

, and would incorporate the following additional

features:

1. Improved Fill Level Control Manufacturers would likely improve the automatic fill control at EL2, which would include more accurate water level sensors and a more sophisticated control scheme for determining optimal wash water levels. The improved pressure sensor and control logic would allow the clothes washer to more accurately determine the load size based on the measured volume of water introduced to the clothes container and the transient water pressure measured at the bottom of the tub, which is affected by the load size. These controls would reduce excess water consumption by filling the wash basket with the minimum volume of water needed to soak

the load.5-37

2. Improved Water Flow Control At EL 2, manufacturers would likely use a combination of electronically controlled thermistors and solenoid valves to achieve more precise water flow control. The thermistors sense the internal water temperatures, and the control system actively adjusts the solenoid valves to maintain the desired internal wash temperature.

3. Spray Rinse Manufacturers would likely employ a spray rinse at EL 2. Spray rinse significantly decreases the volume of water used during the rinse cycle. The clothes washers at the baseline level and EL 1 typically use a deep rinse, during which the tub is filled with water to a level in which the clothes are submerged, agitated, and then drained to remove soap suds. A spray rinse uses high-pressure jets of water to rinse the load while the basket is spinning, without requiring

the clothes to be immersed.

4. Increased Capacity At EL2, manufacturers would likely increase capacity slightly by around 0.2 cubic feet above the capacity of the baseline and EL1 units.

5. Low-Profile Agitator Design DOE believes manufacturers would likely move to a low -profile agitator design at EL 2.

The low -profile agitator design consists of a rotating flat plate at the bottom of the wash basket, compared to the standard agitator, which is a finned cylinder that rises axially into the wash basket. The low-profile agitator is designed to manually agitate the clothing using less water than a traditional agitator in a washer with deep water fills.

6. Improved motor efficiency At EL2, manufacturers are likely to use a more efficient motor and transmission system that reduces the machine electrical energy consumption by almost 50%.

5.6.3.2 Front-Loading Commercial Clothes Washers

Front -Loading Baseline Construction (1.65 MEF J2 / 5.5 IWF)
DOE is unaware of any front-loading commercial clothes washers currently available on

the market that are at or above the baseline efficiency level but do not meet EL 1. For this reason, DOE established the baseline construction for a front-loading clothes washer at EL 1.

Front -Loading Efficiency Level 1 Construction (1. 80 MEF J2 / 4.5 IWF)

Th e EL1 front-loading commercial clothes washer is equipped with basic electronic controls that allow the user to select specific cycle settings, including wash cycle and wash/rinse

temperature. Typically up to four wash temperature selections are offered, and the temperature is controlled using two water inlet solenoid valves combined with a third solenoid to control the

flow of the mixed water. From the inlet solenoid assembly, the water flows through a detergent5-38

dispenser tray before entering the wash basket. The water fill level for a wash cycle is automatically controlled using a pressure switch.

The internal tub/drum assembly of the baseline front-loading clothes washer is supported by three or four suspension legs attached to a sturdy metal base pla te

. Two springs attached to the upper sides of the outer wrapper may also support the tub/drum assembly from above. The tub/drum assembly consists of an internal rotating basket and a stainless steel or plastic outer

wash tub. The basket is made of a stainless steel cylinder with an attached front ring and back plate. Attached to the back plate is a cast aluminum connector piece that secures the drive shaft. Molded into the back half of the tub is a cast iron hub that houses two bearings, which guide the drive shaft. A variable speed motor powers the drive shaft using a drive belt and drive wheel.

The motor is attached to the back base plate.

The outer wrapper of the baseline unit typically consists of individual side, back, and top panels made of stamped cold-rolled steel (CRS) that are clinched together. The front panel, which is a smaller stamped piece of CRS, houses the door assembly. A large piece of tempered glass with a plastic frame and handle make up the door assembly, which is attached to the fron t panel by a hinge. A door latch inside the front panel locks the door shut during the wash cycle. Attached to the front of the internal wash tub is a door seal gasket. This is a flexible ring that

extends forward from the wash tub to create a water-tight seal when the door is shut.

Based on product teardowns, DOE developed the following baseline production and materials cost distributions for a typical front-loading CCW. Table 5.6

. 6 and

Figure 5.6. 24 show the total cost distribution, and Table 5.6

. 7 and Figure 5.6

. 25 show the

materials cost distribution. Depending on the manufacturer and the production volume, the depreciation costs may vary from those shown in the figures, which assume a green-field site.5-39

Table 5.6. 6 Baseline Front-Load Clothes Washer Production Cost Distribution

Cost Category Total

Cost ($201 2 )

Figure 5.6

. 24 Production Cost Distribution for Efficiency

Level 1 Front

- Load ing Commercial Clothes Washer Purchased Parts $151.10

Raw Materials $282.48

Assy Labor Cost $28.46

Indirect Labor Cost $40.08

Fab Labor Cost $3.56

Supervision Cost $23.80

Tooling Dep. Cost $72.99

Indirect Process Costs $51.55

Equipment Dep. Cost $62.92

Building Dep. Cost $10.73

Utility Costs $77.49

Property Tax $9.33

Maintenance Costs $7.33

Insurance Costs $6.57

Total $828.40

Table 5.6. 7 Baseline Front-Load Clothes Washer Materials Cost Distribution

Sub

- Assembly Total

Materials

Cost ($201 2

)

Figure 5.6

. 25 Materials Cost Distribution for Efficiency

Level 1 Front

- Loading Commercial Clothes Washer Tub/Drum $139.79

Control Assy $104.61

Motor/Pump/Capacitor $58.50

Outer Wrapper $52.51

Door Assy $19.39

Main Wiring Harness $16.14

Packaging $15.04

Water Temp Mgmt. $13.19

Suspension $10.66

Final Assy $3.74

Total $ 433

. 585-40

Construction at Higher Efficiency Levels

Based on the design options retained from the screening analysis (see chapter 4 of this TSD), the reverse-engineering analyses, and discussions with manufacturers, DOE developed manufacturing costs associated with various design features necessary to achieve higher efficiencies. The following are the design changes DOE believes would be necessary to meet each higher efficiency level, which were subsequently modeled to obtain incremental manufacturing cost estimates.

Front-Loading Efficiency Level 2 (2.00 MEF J2 / 4.1 IWF)

Based on characteristics of units selected for teardown and based on discussions with manufacturers, DOE research suggests that EL2 is typically achieved in front-loading commercial clothes washers through the following changes:

1. Reduced Hot Water Temperatures DOE believes that manufacturers may reduce the temperature of the hot and warm water settings compared to the EL1 unit.

2. Improved Fill Level Control DOE believes manufacturers would use reduced water fill levels compared to the EL2 unit. The reduction in water fill levels would be achieved through changes to the overall control strategy; no hardware changes would be implemented.

Front-Loading Efficiency Level 3 (2.20 MEF J2 / 3.9 IWF)
DOE research suggests that manufacturers would likely significantly change the design platform of a front-loading commercial clothes washer to reach EL 3 . A CCW at EL 3 is likely to employ the same features as one at EL 1

, and would incorporate the following additional

features:

1. Increased Capacity At EL2, manufacturers would likely increase capacity by around 0.6 cubic feet above the capacity of the EL1and EL2 units.

2. Direct Drive Motor Manufacturers would likely use a direct drive motor on their front-loading clothes washers at EL 3. This would replace the standard variable-speed motor, drive belt, and drive wheel used on the clothes washers at lower efficiency levels.

3. Increased Maximum Spin Speed Manufacturers would likely increase the maximum spin speed for a front-loading clothes washer at EL 3. DOE believes the spin speed would be roughly 100 RPM higher than the spin speed at EL 1 and EL 2.5-41

4. Modified Control Scheme DOE believes that some manufacturers would likely adjust control parameters to improve efficiency at EL 3. Through its active mode testing, DOE observed that longer spin cycles and lower wash temperature set-points correspond to lower RMC values and less hot water consumption, respectively. Neither of these changes is associated with any physical design change to the clothes washer, but they may impact consumer utility.

5.6.4 Cost-Efficiency Curves

Based upon product teardowns and cost modeling, DOE developed the following costefficiency relationships for top-loading and front-loading commercial clothes washers.

5.6.4.1 Top-Loading Cost-Efficiency Curves

For top-loading commercial clothes washers, DOE developed incremental manufacturing costs by tearing down units, and creating a cost model at each efficiency level. DOE started with the baseline unit cost model, and at each higher efficiency level, added in the observed changesassociated with improving efficiency. By doing this, DOE excluded the costs of any nonefficiency related components from the more efficient units. The more efficient units are generally sold at a higher price point, and sometimes include more complex control panels, payment options, or other user interface features that are not necessarily efficiency-related. Table

5.6

. 8 and Figure 5.6

. 26 show the incremental manufacturing costs from DOE’s reverseengineering analysis.

Table 5.6. 8 Incremental Manufacturing Costs for Top-Loading CCWs Based on MEF J2

Appendix J2 Efficiency Level Incremental

Costs ($201 2

)

Figure 5.6

. 26 Cost

- Efficiency Curve Based on MEF J2 for

Top

- Loading Commercial Clothes Washer Baseline
(1.15 MEF J2 / 8.9 IWF) -

EL 1
(1.35 MEF J2 / 8.8 IWF) $0.00

EL 2
(1.55 MEF J2 / 6.9 IWF) $38.895-42

Table 5.6

. 8 and Figure 5.6

. 26 show that the incremental cost to achieve EL1 is $0. This is consistent with information gained through product testing and through manufacturer interviews. As described above, the design options used to achieve EL1 are control-based strategies that do not require any additional hardware costs.

The incremental cost to achieve EL2 is much more significant at $38.89. As described above, a more significant design platform change is required to achieve EL2, resulting in additional hardware costs over the EL1 design.

DOE is confident in its estimates of the incremental costs for the units it torn down. However, DOE is aware that these values are only representative of the small number of units torn down, and do not necessarily characterize the entire top-loading commercial clothes washer

market nor the expected typical costs at the time of the effective date of new standards.

5.6.4.2 Front-Loading Cost-Efficiency Curves

For front-loading commercial clothes washers, DOE tore down units at all efficiency

levels

, except for the baseline level because DOE is unaware of any front-loading CCWs

available at that level.

DOE observed that manufacturers each have unique EL1 units depending on their segment of the market. To account for this, DOE developed incremental cost curves for each

ma nufacturer according to their respective product offerings. For a manufacturer only producing front-loading CCWs at higher efficiency levels, the incremental costs associated with the efficiency levels below that manufacturer’s baseline is assumed to be zero. A single costefficiency curve was then calculated from a market share-weighted average of the individual

curves. The DOE incremental costs shown in Table 5.6

. 9 and Figure 5.6

. 27 show the market share-weighted average of the incremental cost curves.5-43

Table 5.6. 9 Incremental Manufacturing Costs for Front-Loading CCWs Based on MEF J2

Appendix J2 Efficiency

Level Incremental

Costs ($201 2

)

Figure 5.6

. 27 Cost

- Efficiency Curve Based on MEF J2 for

Front

- Loading Commercial Clothes Washer Baseline
(1.6 5 MEF J2 /

5.2 IWF) -

EL 1
(1. 80 MEF J2 /

4.5 IWF) -

EL 2
( 2.00 MEF J2 /

4.1 IWF) $0.31

EL 3
( 2.20 MEF J2 /

3.9 IWF) $19.55

Table 5.6

. 9 and Figure 5.6

. 27 show that the incremental cost to achieve EL1 is $0, and the cost to achieve EL2 is relatively small at $0.31. This is consistent with information gained through product testing and through manufacturer interviews. As described above, the design options used to achieve EL1 and EL2 are largely control-based strategies that do not require any

additional hardware costs.

The incremental cost to achieve EL3 is more significant at $19.55. As described above, a more significant design platform change is required to achieve EL3, resulting in additional hardware costs over the EL2 design.

DOE is confident in its estimates of the incremental costs for the units it torn down. However, DOE is aware that these values are only representative of the small number of units torn down, and do not necessarily characterize the entire front-loading commercial clothes washer market nor the expected typical costs at the time of the effective date of new standards.CHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

TABLE OF CONTENTS

6.1 INTRODUCTION ...............................................................................................................2

6.2 DISTRIBUTION CHANNEL S ...........................................................................................2

6.3 MARKUP CALCULATION PROCEDURE ......................................................................3

6.3.1 Manufacturer Markups.............................................................................................. 3

6.3.2 Distributor Markups .................................................................................................. 4

6.3.2.1 Baseline Distributor Markup ............................................................................. 5

6.3.2.2 Incremental Distributor Markup ........................................................................ 5

6.4 SALES TAXES ...................................................................................................................9

6.5 SUMMARY OF MARKUPS ............................................................................................10

LIST OF TABLES

Table 6.3.1 Major Appliance Manufacturer Gross Margins and Markups .................................4

Table 6.3.2 Data for Baseline Markup Calculation: Machinery, Equipment and Supplies Merchant Wholesalers (2007) ...................................................................5

Table 6.3.3 Data for Incremental Markup Calculation: Machinery, Equipment and Supplies Merchant Wholesalers ...............................................................................7

Table 6.3.4 Concentration by Four Firms for Commercial Clothes Washer Distributors and Route Operators ............................................................................8

Table 6.4.1 Average Sales Tax Rates by Censes Division and Large State ...............................9

Table 6.5.1 Summary of Markups ............................................................................................10

LIST OF FIGURES

Figure 6.2.1 Channel for Commercial Clothes Washers ..............................................................2

6-iCHAPTER 6. MARKUPS FOR EQUIPMENT PRICE DETERMINATION

6.1 INTRODUCTION

To carry out the engineering and life- cycle cost (LCC) analyses, the U.S. Department of Energy ( DOE ) needed to determine the cost to the customer of a baseline product and the cost of

more

- efficient units. By applying a multiplier called a “markup” to the manufacturers’ equipment costs estimated in the engineering analysis, DOE estimated the consumer pr ices for baseline models and more-efficient equipment.

6.2 DISTRIBUTION CHANNEL S

The appropriate markups for determining the consumer equipment price depend on the type of distribution channels through which products move 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. For commercial clothes washers, DOE based the distribution channels (see Figure 6.2.1 ) on data developed by the Consortium for Energy Efficiency (CEE).

Source: CEE, 1998 Figure 6.2.1 Channel for Commercial Clothes Washers

2The CEE states that the relevant portions of the commercial, family

- sized clothes washer

market can be divided into three areas: laundromats

, private multi -family housing, and large institutions. Included as part of large institutions are military barracks, universities, housing authorities, lodging establishments, and health care facilities. For these three market areas, the CEE data indicate that an overwhelming majority of commercial clothes washers are sold through either distributors or route operators. Laundromats generally purchase their equipment through distributors, while multi

- family housing and large institutions generally lease their equipment from route operators. For purposes of developing the markups for commercial clothes washers, DOE based its calculations primarily on data for the distribution ch annel that involves only distributors. DOE assumed that the markups and the resulting consumer equipment prices determined for the distribution channel involving distributors would be representative of the prices paid by consumers acquiring their equipment from route operators.

6.3 MARKUP CALCULATION P ROCEDURE

At each point in the distribution channel, companies mark up the price of the equipment to cover their business costs and profit margin. In financial statements, gross margin is the difference

betwee n the company revenue and the company cost of sales or cost of goods sold ( CGS ). Inputs for calculating the gross margin are all corporate costs—including overhead costs (sales, general, and administration) , research and development (R&D) and interest expe nses
, depreciation, and taxes

— and profits. In order for sales of a product to contribute positively to company cash flow, the product’s markup must be greater than the corporate gross margin. Individual products may command a lower or higher markup, depending on their perceived added value and the competition they face from similar products in the market.

In developing markups for manufacturers and distributors, DOE obtained data about the

revenue, CGS , and expenses of firms that produce and sell the products of interest. DOE’s approach categorizes the expenses into two categories: fixed costs, which are fixed labor and occupancy expenses that increase in proportion to the amount of labor required to produce or sell the product, and variable costs , which are variable operating costs that do not scale with labor and vary in proportion to CGS . Together, fixed and variable costs represent the gross margin.

6.3.1 Manufacturer Markups

DOE uses manufacturer markups to transform a manufacturer’s production costs into a manufacturer sales price. Using the CGS and gross margin , DOE calculated the manufacturer markup ( MU MFG ) with the following equation:

MU MFG = CGS MFG + GM MFG
CGS MFG

3W here:

MU MFG = manufacturer markup,

CGS MFG = manufacturer’s cost of goods sold or m anufacturer production cost ( MPC ),

and

GM MFG = manufacturer’s gross margin.

The manufacturer’s CGS (or MPC ) plus its gross margin equals the manufactu rer selling price ( MSP ).

DOE developed an average manufacturer markup by examining the annual Securities and Exchange Commission (SEC) 10- K reports filed by three publicly traded manufacturers primarily engaged in appliance manufacturing and whose combined product range includes

commercial clothes washers ( Table 6.3.1 ).

Table 6.3.1 Major Appliance Manufacturer Gros s Margins and Markups

Mfr A Mfr B Mfr C

Average Net Revenues (Million) $3 59 $ 5,443 $ 10,000

Corporate Gross Margin 26.5 % 14.5 % 22.3 %

Markup 1. 36 1. 17 1. 2 9

6.3.2 Distributor Markups

DOE based the distributor markups for commercial clothes washers on financial data for

the sector Machinery, Equipment and Supplies Merchant Wholesalers from the 2007 U.S. Census Business Expenses Survey (BES), which is the most recent available survey. 1 This sector includes the subsector Laundry Machinery, Equipment, and Supplies, Commercial, Merchant

Whole salers, which specifically sells commercial clothes washers. However, as few data are available for this detailed subsector, DOE relie d on the 2007 BES data for Machinery, Equipment and Supplies Merchant Wholesalers to estimate distributor markups

.

DOE organized the financial data into statements that break down cost components incurred by firms in this category. DOE assumes that the income statements faithfully represent the various average costs incurred by distributors . Although firm s in this sector h andle multiple commodity lines, the data provide the most accurate available indication of expenses for selling

commercial clothes washers

.

The 2007 BES data provided for Machinery, Equipment and Supplies Merchant

Wholesalers only contain total sales and detailed operating expenses. In order to construct a complete data set to estimate markups, DOE needed to estimate CGS and gross margin. The 1997 Business Expenses Survey provides total sales, gross margin, and detailed operating expenses of Machinery, Eq uipment and Supplies Merchant Wholesalers. 2 DOE found that gross

4margin as a percent of sales has been roughly constant in this category from 1992 to 2010, averaging 27.1%. 3 Therefore, DOE assumed that the fractions of CGS and gross margin a s a percent of sales in 200 7 are the same as in 1997. Following this assumption, DOE calculated the

CGS

, gross margin , and net profit for Machinery, Equipment and Supplies Merchant

Wholesalers from the 2007 BES

.

6.3.2.1 Baseline Distributor Markup

The baseline m arkup relates the manufacturer sales price of baseline products to the distributor sales price. DOE considers baseline models to be equipment sold under existing market conditions ( i.e., without new energy efficiency standards). DOE calculated the baseline markup ( MU BASE ) for distributo rs as an average markup using the following equation:

MU BASE = CGS Dist + GM Dist

CGS Dist

W here:

MU BASE = b aseline distributor markup,

CGS Dist = d istributor’s cost of goods sold, and

GM Dist = d istributor ’s gross margin

.

Table 6.3.2 shows the calculation of the baseline distri butor markup.

Table 6.3.2 Data for Baseline Markup Calculation: Machinery, Equipment and Supplies

Merchant Wholesalers (2007)

Kind of business item Amount ($ million )

Sales $328,820 Cost of Goods Sold (CGS) $239,710 Gross Margin (GM) $89,110 Baseline Markup = (CGS+GM)/CGS 1. 37

Source: U.S. Census, 200 7 Business Expenses Survey (for Sales) and 1997 Business Expenses Survey (for CGS and GM shares)

6.3.2.2 Incremental Distributor Markup

Incremental markups are coefficients that relate the change in the manufacturer sales price of higher efficiency models to the change in the distributor sales price. DOE considers

5higher efficiency models to be equipment sold under market conditions with new efficiency standards. The incremental markup reflects a situation in which th e distributo r faces an increase in CGS for a particular product due to new or amended standards.

Unfortunately, empirical evidence regarding appliance distributor markup practices when a product increases in cost (due to increased efficiency or other fac tors) is lacking. DOE understands that real-world markup practices will vary depending on the market conditions faced by distributo rs, on the magnitude of the change in CGS associated with an efficiency increase and on any associated changes in wholesale costs. Pricing may also involve rules of thumb that are difficult to know and to incorporate into DOE’s analysis.

Given the uncertainty about actual markup practices used by appliance distributors , DOE uses an approach that reflects the following two key concepts:

1. Changes in the efficiency of the goods sold are not expected to increase economic profits. Thus, DOE calculates markups/gross margins to allow cost recovery for wholesale companies in the distribution chain (including changes in the cost of c apital) without changes in company profits.

2. Efficiency improvements impact some distribution costs but not others. DOE sets markups and prices to cover the distribution costs expected to change with efficiency but

not the distribution costs that are not expected to change with efficiency.

The incremental markup approach is described in more detail in Dale et al. (2004). 4

To estimate incremental distributor markups, DOE divides distributor s’ operating expenses into two categories: (1) those that do not change when CGS increases due to amended

efficienc y standards (“fixed”), and (2) those that i ncrease proportionately with CGS (“variable”). DOE defines fixed costs to include labor and occupancy expenses, because these costs are not likely to increase as a result of a rise in CGS due to amended efficiency standards. All other expenses, as well as the net profit, are assumed to vary in proportion to CGS. Although it is possible that some of the other expenses may not scale with CGS, DOE is inclined to take a more conservative position and in clude these as variable costs. Note that u nder DOE’s appro ach, a high fixed-cost component yields a low incremental markup.

DOE calculated the incremental markup ( MU INCR ) for distributors using the following equation:

MU INCR = CGS Dist + VC Dist

CGS Dist

W here:

MU INCR = i ncremental distributor markup,

CGS Dist = d istributor ’s cost of goods sold, and

6VC Dist = d istributor ’s variable costs.

Table 6.3.3 shows the breakdown of operating expenses using the 2007 BES data. The incremental markup is calculated as 1.18.

Table 6.3.3 Data for Incremental Markup Calculation: Machinery, Equipment and Supplies Merchant Wholesalers

Amount ($ million )

Sales 328,820
Cost of Goods Sold (CGS) 239,710

Gross Margin (GM) 89,110

Labor & Occupancy Expenses (“Fixed”)
Annual payroll 32,871
Employer costs for fringe benefits 6,638
Contract labor costs, including temporary help 572

Purchased communication services 808
Purchased repairs and maintenance to machinery and equipment 763
Purchased repairs and maintenance to buildings, structures, and offices 462

Lease and rental payments for machinery and equipment 492
Lease and rental payments for buildings, offices, stores 2,655
Purchased electricity 583
Purchased fuels (except motor fuels) 220

Water, sewer, refuse removal, and other utility payments 111

Fixed Subtotal: 46,175

Other Operating Expenses & Profit (“Variable”)
Expensed equipment 329
Purchases of packaging materials and containers 390

Purchases of other materials, parts, and supplies (not for resale) 1,321
Expensed purchases of software 178
Data processing and other purchased computer services 275
Commission expense 1,213
Purchased transportation, shipping and warehousing services 2,446
Purchased advertising and promotional services 1,211
Purchased professional and technical services 1,404
Depreciation and amortization charges 2,461

Taxes and license fees 917
Other operating expenses 2 6,448
Net profit before taxes 24,342

Variable Subtotal: 42,935

Incremental Markup = (CGS+Total Other Operating Expenses and Profit)/CGS 1.18

Source: U.S. Census, 2007 Business Expenses Survey ; U.S. Census, 1997 Business Expenses Survey

By dividing expenses into fixed and variable components, the incremental markup approach envisions that distributors cover costs without ch anging profits. Although distributo rs may be able to reap higher profits for a time, DOE’s approach assumes that competition in the appliance distributor market, combined with relatively inelastic demand ( i.e., the demand is not

7expected to decrease significantly with a relatively small increase in price ), will tend to pressure margins back down.

To measure the degree of competition, DOE estimated the four firm concentration ratio (FFCR) of commercial clothes washer sales in the two sectors included in the distribution channels: distributors and route operators. The FFCR represents the market share of the four largest firms in the relevant industry. G enerally, an FFCR of less than 40% indicates that the industry is not concentrated, and an FFCR of more than 70% indicates that an industry is highl y concentrated. 5 ,6

The 2007 Economic Census reports the FFCR of merchant wholesalers (including laundry equipment distributors) and service providers (including route operators) in aggregate and for many detailed subsectors. 7

, 8 Th e FFCR of commercial clothes washer sales is equal to the sector FFCR times the percent of total sales within each sector accounted for by major appliances , which are estimated from the financial reports of distributors and route

operators

. 9

, 10

, 11

, 12 DOE approximates the weighted aggregate FFCR for commercial clothes washer sales by weighting these sector FFCRs by the market share of each sector ( Table 6.3.4 ).

Table 6.3.4 Concentration by Four Firms for Commercial Clothes Washer Distributors and Route Operators

Sector Four Firm

Concentration Ratio

(% of Sector Sales) % of Revenue

Accounted for by CCW Four Firm

Concentration Ratio (% of CCW Sales ) Market

Share (%

of CCW

Sales )

Distributors 60.3 85 51.3 33.3

Route operators 26.5 5.1 1.4 66.6

Weighted Aggregate FFCR 18.0

Source: 2007 Economic Census (Note that distributors are represented by the sector NAICS 4238503 “Laundry and dry

- cleaning equipment and supplies merchant wholesalers” and route operators are represented by the sector

NAICS 81231 “coin

- operated laundries and drycleaners”)

DOE’s estimation of t he weighted average FFCR for commercial clothes washer sales is further supported by a 2011 IBISWorld Industry Report, which states that concentration in the

sector is low. 13 Note that the sector is referred to in the IBISWorld report as “Janitorial Equipment Supply Wholesaling,” rather than “Service establishment equipment and supplies

merchant wholesalers ” as in the U.S. Economic Census.

86.4 SALES TAXES

The sales tax represents state and local sales taxes that are applied to the consumer equipment price. The sales tax is a multiplicative factor that i ncreases the consumer equipment price.

DOE derived state and local taxes from data provided by the Sales Tax Clearinghouse and population data from the U.S. Census. 14 DOE derived population- weighted average tax values for each Census “Reporta ble Domain” (large states and groups of states) , as shown in

Table 6.4.1

.

Table 6.4.1 Average Sales Tax Rates by Censes Division and Large State

Reportable Domain State(s) Tax Rate (February 2013)

1 CT, ME, NH, RI, VT 5.11%

2 MA 6.25%

3 NY 8.40%

4 NJ 6.95%

5 PA 6.40%

6 IL 8.05%

7 IN, OH 6.87%

8 MI 6.00%

9 WI 5.45%

10 IA, MN, ND, SD 6.86%

11 KS, NB 7.22%

12 MO 6.60%

13 VA 5.00%

14 DE, DC, MD, WV 6.87%

15 GA 7.10%

16 NC, SC 6.93%

17 FL 6.65%

18 AL, KY, MS 5.09%

19 TN 9.45%

20 AK, LA, OK 8.51%

21 TX 7.95%

22 CO 6.15%

923 IA, MT, UT, WY 5.29%

24 AZ 8.15%

25 NV, NM 7.31%

26 CA 8.40%

27 AK, HI, OR, WA 5.28%

DOE then derived a U.S. average tax value of 7.04% for commercial clothes washers based on the product’s saturation within each Census division. 15

6.5 S UMMARY OF MARKUPS

Table 6.5.1 summarizes the markups at each stage in the distribution channel and the average sales tax.

Table 6.5.1 Summary of Markups

Markup Baseline Incremental

Manufacturer 1.285

Distributor 1.37 1.18

Sales Tax 1.07 4

Overall 1.89 1.62

10REFERENCES

1. U.S. Census Bureau, Economic Census, Business Expenses Survey, Wholesale Trade, Machinery, Equipment and Supplies Merchant Wholesalers, 2007. (Last accessed February, 2013.)

< http://www.census.gov/wholesale/www/historic_releases/annual_historic_releases.html

>

2. U.S. Census Bureau, Economic Census, Business Expenses Survey, Wholesale Trade, Machinery, Equipment and Supplies Merchant Wholesalers , 1997. Washington, D.C.

3. U.S. Census Bureau, 2010 Annual Wholesale Trade Survey . 2010: Washington, D.C. http://www2.census.gov/wholesale/pdf/awts/2010_awts_salesinv_nomsbo_table2.pdf

4. Dale, L., D. Millstein, K. Coughlin, R. V. Buskirk, G. Rosenquist, A. Lekov, and S. Bhuyan, An Analysis of Price Determination and Markups in the Air-Conditioning and Heating Equipment Industry , 2004. Lawrence Berkeley National Laboratory.

5. University of Maryland University College, Note on Industry Structure, 2002. (Last accessed February, 2013.)

< http://info.umuc.edu/mba/public/AMBA607/IndustryStructure.html >

6. QuickMBA, Industry Concentration, 2010. (Last accessed February, 2013.)

< http://www.quickmba.com/econ/micro/indcon.shtml >

7. U.S. Census Bureau, 2007 Economic Census, Establishment and Firm Size: (Including Legal Form of Organization), Wholesale Trade Subject Series , 2007.

8. U.S. Census Bureau, 2007 Economic Census, Establishment and Firm Size: (Including Legal Form of Organization), Other Services (Except Public Administration) Subject

Series , 2007.

9. Mac -Gray Corporation, Form 10-K Annual Report Pursuant to Section 13 of 15(d) of the

Securities Excha nge Act of 1934, 2011. (Last accessed November, 2012.)

< http://www.sec.gov/Archives/edgar/data/1038280/000104746912002365/a2207800z10- k.htm#ds11601_item_8._financial_statements_and_supplementary_data >

10. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2005, 2005

< http://www.sec.gov/Archives/edgar/data/91693/000095012305007218/y09552e10vk.ht

m#106 >

11. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2007, 2007.

< http://www.sec.gov/Archives/edgar/data/1282858/000095012307008319/y35770e10vk.

htm >

1112. Alliance Laundry Systems, Alliance Laundry Holdings LLC Reports 2004 Sales and Earnings , 2005. Ripon, W.I.

13. Culbert, K., Janitorial Equipment Supply Wholesaling in the US , 2011. <ibisworld.com>

14. Sales Tax Clearinghouse, I., State sales tax rates along with combined average city and

county rates, 2013. 2013.) < http://thestc.com/STrates.stm >

15. U.S. Energy Information Administration, Residential Energy Consumption Survey , U.S. Department of Energy, Editor. 2009.

127

- i CHAPTER 7. ENERGY AND WATER USE DETERMINATION

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 PER

- CYCLE ENERGY AND WATER CONSUMPTION BY EF FICIENCY

LEVEL ............................................................................................................................. 7-1

7.3 AVERAGE ANNUAL ENERGY AND WATER CONSUMPT ION BY

EFFICIENCY LEVEL ..................................................................................................... 7-5

7.4 VARIABILITY OF ANNUAL ENERGY AND WATER CONSUMPTION .............. 7-10

LIST OF TABLES

Table 7.2.1 Load Usage Factors from the Test Procedure ...................................................... 7-2

Table 7.2.2 Top

- Loading Commercial Clothes W ashers: Per

- Cycle Energy and Water Use by Efficiency Level

................................ ....................................................... 7-5

Table 7.2.3 Front- Loading Commercial Clothes Washers: Per

- Cycle Ener gy and Water Use by Efficiency Level ............................................................................ 7-5

Table 7.3.1 Commercial Clothes Washer Usage Data ............................................................ 7-6

Table 7.3.2 Top

- Loading Commercial Clothes Washers, Multi

- Family Application: Annual Energy and Water Use by Efficiency Level ............................................ 7-8

Table 7.3.3 Top

- Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level ............................................ 7-9

Table 7.3.4 Front- Loading Commercial Clothes Washers, Multi

- Family Application: Annual Energy and Water Use by Efficiency Level ............................................ 7-9

Table 7.3.5 Front-Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level ............................................ 7-9

LIST OF FIGURES

Figure 7.4.1 Probability Distribution for Commercial Clothes Washer Usage in Multi

Family Applications ........................................................................................... 7-10 Figure 7.4.2 Probability Distribution for Commercial Clothes Washer Usage in Laundromat Applications................................................................................... 7-117-1 CHAPTER 7. ENERGY AND WATER USE DETERMINATION

7.1 INTRODUCTION

To carry out the life-cycle cost (LCC) and payback period (PBP) calculations described in Chapter 8, the U.S. Department of Energy ( DOE ) needed to determine the operating cost savings to consumers from more- efficient equipment. The LCC and PBP analysis req uires data on annual energy and water use because, along with energy and water prices, DOE uses these data to establish the most significant component of consumer operating costs. (Maintenance and repair costs are the other contributors to operating cost.) This chapter describes how DOE determined the annual energy and water consumption of commercial clothes washers.

7.2 PER

- CYCLE ENERGY AND WAT ER CONSUMPTION BY EF FICIENCY

LEVEL

DOE established the annual energy and water consumption of commercial clothes

was hers by multiplying the per- cycle energy and water use by the number of cycles per year.

Commercial clothes washer per -cycle energy consumption is composed of three

components: water- heating energy, machine energy, and drying energy. The machine energy is composed of the motor energy needed for turning an agitator or rotating the wash basket. Based on language in the Energy Policy Act of 2005 (EPACT 2005), DOE is required to rate

commercial clothes washers with the same test procedure established for residential clothes

washers. 1 The DOE test procedures for commercial clothes washers currently being

manufactured is codified at Title 10 of the Code of Federal Regulations (CFR) part 430, subpart B, appendix J1. In March 2012 , DOE published a final rule amending its test procedures for

residential clothes washers

. (77 FR 13888 ) The March 2012 final rule included minor amendments to appendix J1 and also established a new test procedure at appendix J2 (hereafter, “appendix J2”). 2

The DOE test procedure provides the following equations to calculate the total per- cycle

clothes washer energy consumption:

ET T

CYCLE D ME c
e HE CW ++×=

W here:

CW CYCLE = t otal commercial clothes washer per

- cycle energy consumption,

HE T = p er

- cycle water heating consumption (kWh/cycle),

e = e fficiency of electric water heater (100%) or gas water heater (75%),

c = conversion factor if water heater is gas (3, 412 Btu/kWh),7-2 ME T = p er -cycle machine electrical energy consumption (kWh/cycle), and

D E = p er -cycle energy consumption for removal of moisture from test load, i.e., dryer energy consumption (kWh/cycle).

DOE determined the per

- cycle machine energy use from the tests results of the considered models, performed according to the current DOE test pro cedure (77 FR 13888, Mar. 7, 2012). It determined the per- cycle clothes drying energy use by using remaining moisture content (RMC) values contained in the cost s and efficiencies data set developed in the engineering analysis. The energy required to remove moisture from clothes, i.e., the dryer energy, is a significant component of total clothes washer energy consumption. The equation used to determine this energy component is as described in the current DOE test procedure.

The following equation from t he current DOE test procedure calculates the per

- cycle energy consumption for removing moisture:



## ( ) 

# ( ) ( ) 

# ( ) 

# ( ) DUF DEF RMC W LUF W LUF W LUF D

avg avg E ́ ́

 ́ ́+ ́+ ́= %4

_ min min max max

Where:

D E = per -cycle energy consumption for removal of moisture from test load,

LUF = load usage factor

, W = weight of test load [pounds (lb)/cycle],

RMC = moisture content that remains after washing (lb),

DEF = nominal energy required for a clothes dryer to remove moisture from clothes (0.5kWh/lb), and

DUF = dryer usage factor, a percentage of washer loads dried in a clothes dryer (0.91).

For clothes washers with manual fill control systems, only the LUF

max and LUF min terms are used. For clothes washers with adaptive fill control systems, all three terms are included. DOE implements the Load Usage Factor, as described in the current test procedure. According to

th e test procedure description, LUF

max , LUF min , and LUF avg are described in the Table 7.2.1

.

Table 7.2.1 Load Usage Factors from the Test Procedure

LUF

max LUF avg LUF min Manual Fill 0.72

- 0.28 Adaptive Fill 0.12 0.74 0.14

Since the test procedure describes the load usage for a typical residential application

, DOE received comments on possible differences in loading in a commercial setting. DOE examined this issue by analyzing data from a study that monitored energy and water usage in multiple sites. a DOE found a pattern based on water usage in 6 sites to establish an alternative

a PNNL 2008. Cal

- UCONS Commercial Laundry Program Measurement and Evaluation

- PNWD

- 3910.7-3 LUF distribution for the range of load usage factors. However, since this pattern does not have

sufficient geographic coverage to conclusively establish a new usage factor, DOE has implemented an optional LUF as a sensitivity in the energy and water use calculations . The optional LUF for adaptive fill category of 0.26, 0.54, and 0.20 for L

max , L avg , and L min captures possible divergence in the load usage factor in the field. Details of this estimation are provided in Appendix 7- A.

When considering gas drying, DOE multiplies the dryer energy consumption by a gas correction factor to account for the additional energy a gas dryer requires relative to an electric dryer. The following equation determines per-cycle energy consumption for gas drying.

E G D GCF D ×=

W here:

D G = p er -cycle energy consumption for removal of moisture from test load due to a gas dryer, i.e., dryer energy consumption (kWh/cycle), and

GCF = g as correction factor = 1.12 3

, D E = per -cycle energy consumption for removal of moisture from test load, i.e., dryer energy consumption (kWh/cycle).

DOE previously used a modified energy factor (MEF) and a water factor (WF) to establish the energy and water efficiencies of clothes washers. The following equations

determine the MEF and WF:

CYCLE CW C MEF =

C Q WF T

=

Where:

C = container capacity [cubic feet (cu ft)],

CW CYCLE = total commercial clothes washer per

- cycle energy consumption, and Q T = per

- cyc le water consumption [gallons (gal)/cycle].

The existing test procedure was revised to include self -cleaning and non- active mode

energy use. 4 Th e revised test procedure us es an integrated modified energy factor (IMEF) that

includes non- active and self -cleaning mode energy use, and this analysis takes those revisions into account by also using the IMEF, which is defined as:7-4 IMEF = C / (CW cycle +cyclic self

- cleaning energy +cyclic off

- mode energy)

The cyclic self

- cleaning and cyclic off

- mode ene rgy are expressed in units of kWh/cycle

and are obtained by dividing the annual energy use for each of those two modes by the number of wash cycles per year.

The amended energy efficiency standards proposed in this rulemaking are based on the

MEF J2 metr ic. In the framework document, DOE stated it would consider establishing amended energy efficiency standards for commercial clothes washers on the IMEF metric, which would incorporate standby and off mode power. As described in Chapter 3 , DOE performed an in -depth evaluation of the standby and off mode power characteristics of a representative sample of

commercial clothes washer s and has tentatively determined that establishing amended standards for commercial clothes washers based on an integrated energy metric would not be technically

feasible. Instead, DOE proposes amended standards based on MEF J2 , which does not incorporate standby and off mode power. The amended water efficiency standards proposed in this rulemaking are based on the IWF metric contained in appendix J2, as the IWF metric provides a more representative measure of water consumption than the WF metric . For further discussion on metrics, please refer to Chapter 3.

As discussed in Chapter 5, DOE analyzed specific efficiency levels for top-loading and

front -loadin g commercial clothes washers. Table 7.2.2 and Table 7.2.3 show the efficiency levels

for top-loading and front-loading commercial clothes washers, respectively, and their corresponding per- cycle energy and water use. Per

- cycle energy use is disaggregated into water heating, machine, and clothes drying. DOE determined the per- cycle clothes drying energy use by first establishing the RMC based on the engineering data

. DOE then calculated the per

- cycle

clothes -drying energy use using the equation that d etermines the per -cycle energy consumption

for the removal of moisture. DOE determined the per -cycle machine energy use from the engineering analysis based on test data . With the per- cycle clothes drying and machine energy known, DOE determined the per- cyc le water

- heating energy use by first determining the total

per -cycle energy use (the clothes container volume divided by the MEF J2 ) and then subtracting from it the per

- cycle clothes -drying and machine energy. The water

- heating energy use assumes

the use of an electric water heater. DOE determined the per -cycle hot water use by dividing the

per

- cycle water

- heating energy use by an assumed temperature rise of 75 oF (21.1 oC) and a specific heat of 0.0024 kWh/gal-oF (4.186 joule/gram-oC). DOE determined the total water use by multiplying the WF by the clothes container volume.7-5 Table 7.2.2 Top-Loading Commercial Clothes Washers: Per -Cycle Energy and Water Use by Efficiency Level

Level MEF J2 I WF

RMC Energy Use Water Use

Machin e Drye r Water Heat † Hot Total

cu.ft./kWh/cyc gal/cu.ft. kWh/cyc kWh/cyc kWh/cyc gal/cyc gal/cyc

Baseline 1.15 8.90 49.0% 0.220 2.08 0.391 2.2 27.6

1 1.35 8.80 48.0% 0.210 1. 66 0. 427 2. 4 27.3

2 1.55 6.90 47.0% 0.100 1. 71 0. 324 1.8 22.8

† Based on the use of an electric water heater at 100% efficiency.

Table 7.2.3 Front -Loading Commercial Clothes Washers: Per

- Cycle Energy and Water Use by Efficiency Level

Level MEF J2 I WF

RMC Energy Use Water Use

Machine Dryer Water Heat † Hot Total

cu.ft./kWh/cyc gal/cu.ft. kWh/cyc kWh/cyc kWh/cyc gal/cyc gal/cyc

Baseline 1.65 5.20 42.0% 0.110 1. 10 0. 264 1. 5 14.6

1 1.80 4.50 39.0% 0.080 1. 080 0. 269 1. 5 13.1

2 2.00 4.10 38.0% 0.100 1. 100 0. 134 0.7 11.9

3 2.20 3.90 36.0% 0.070 1. 0 70 0. 421 2.3 14.0

† Based on the use of an electric water heater at 100% efficiency.

7.3 AVERAGE ANNUAL ENERG Y AND WATER CONSUMPT ION BY

EFFICIENCY LEVEL

DOE determined the average annual energy and water consumption by multiplying the

per

- cycle energy and water consumption by the number of cycles per year. Because the predominant applications of commercial clothes washers are in multi- family buildings and laundromats, DOE focused only on these two building applications to determine the appropriate number of commercial clothes washer cycles per year. Other applications include lodging establishments ( e.g., hotels and motels), inpatient health care facilities, and nursing homes.

Relative to multi -family buildings and laundromats, these other applications are a small segment

of the market. Therefore, DOE believes it is not critical to the analysis to accurately characterize commercial clothes washer usage for these applications.

Table 7.3.1 shows the usage of commercial clothes washers from several studies. The last column of the table shows the variation in usage from each of the studies. Note that the variation shown for the City of Toronto and Multi-Housing Laundry Association (MLA) studies is based on the range of average val ues for either specific buildings or cities. Thus, if individual machine7-6 data were made available, the variation in usage would likely be much greater than indicated by

the average data. For multi -family applications, the usage ranges from 1 to 6 cycles pe r day. Assuming each study has equal representation, the average value from all studies performed on

multi

- family applications is 3 cycles per day. The sparse data on commercial clothes washer use in laundromats show a usage variation of 2.8 to 6 cycles pe r day with an average value of 4.1 cycles per day. Based on 365 days per year of use, the minimum, average, and maximum usage

data for multi

- family applications of 1. 5, 3.0, and 4.3 cycles per day yield 548, 1,095, and 1,570 cycles per year, respectively. For laundromats, the minimum, average, and maximum usage data

of 2.8, 4.1, and 6.0 cycles per day yield 1,022, 1,497, and 2,190 cycles per year, respectively. As a comparison, DOE assumed that residential clothes washers have an annual average usage of

295 cycles per year.

Table 7.3.1 Commercial Clothes Washer Usage Data

Study Application Population Usage per Machine (cycles/day) Variation (cycles/day)

Multi

- Family Buildings

City of Toronto (1999) 5 Multifamily coin

operated (6 buildings, 945 apartments, 39 machines) 3 family; 1 mixed;

1 seniors & singles; and 1

seniors 4.3: metered 3.3 to 6.0 (variation based on average

values from 6 buildings)

SCE/PNNL (2000) 6 Multifamily coin

operated (12 machines) Seniors 2.2 : metered 1 to 4

MLA (2002) 7 Multifamily coin

operated (4 buildings in 4 cities,

50 common area machines) General 2.9: metered *

2.7: coin * 4.5: survey * 2.6 to 3.1 (variation based on average values from 4 cities)

Wisconsin/ PNNL (2004) 8 Multifamily coin

operated (9 machines) Apartments &

Condos 1.5: metere d ** 1 to 3

Lightwash/ Equipoise (2004) 9 Multifamily &

Businesses Not specified. 3: businesses & multif amily <10 units, industry value

***

4: multifamily ≥10 units, industry valu e *** Not available.

PG&E Cal

- UCONS (2008) 10 Multifamily Not specified. 2.6: metered 1.1 to 3.9 (variation based on average

values from 5 metered property sites)

SCGC, SDG&E Cal

UCONS (2008) 11 Multifamily Not specified. 4.2: metered 3.3 to 5.4 (variation based on average

values from 5 metered property sites)

Alliance for Water Efficiency Multifamily Not specified. 2: estimated7-7 Study Application Population Usage per Machine (cycles/day) Variation (cycles/day)

Laundromats

Lightwash/ Equipoise (2004) 10 Laundromat &

Institutional Not specified. 6: industry valu e *** Not available.

Alliance for Water Efficiency Laundromat Not specified. 4.8 : estimated Not available.

San Diego County Water Authority (2006) 12 Laundromat Not specified. 2.8: metered. 2.3 to 4.7 (variation based on average

values from 3 metered sites)

PG&E Cal

- UCONS (2008) 11 Laundromat Not specified. 4.8: metered 4.2 to 5.3 (variation based on average values from top

- load

and front

- load washing machines in

one metered site.

SCGC and SDG & E

Cal

- UCONS (2008) 12 Laundromat Not specified. 3.5: metered 2.4 to 4.6 (variation based on average values from top

- load

and front

- load

washi ng machines in

one metered site.

Sutter, M. (2006) 13 Laundromat Not specified. 3.0 : metered 2.97±0.70

Coin

- op (2013) 14 Laundromat Not specified. 3.6: survey data Not available.

* U sage per machine values derived based on water usage of 32 gallons per cycle.

** R eport states that multifamily industry average is 2.5 cycles per day and attribut es the lower reported usage per

machine value to a lower ratio of tenants to machines.

*** U sage per machine values are “industry values.” Study says that survey data in report may be unreliable.

DOE calculated commercial clothes washer annual energy consumption with the following equation:



### N CW CW CYCLE ANNUAL 

### × =

W here:

CW ANNUAL = total annual commercial clothes washer energy consumption,

CW CYCLE = t otal per -cycle commercial clothes washer energy consumption, and

N = r epresentative average commercial clothes washer use.

DOE calculated commercial clothes washer annual water consumption with the following equation:7-8 N CW CW WATER CYCLE WATER ANNUAL × =

W here:

CW ANNUAL WATER = total annual commercial clothes washer water consumption,

CW CYCLE WATER = t otal per -cycle commercial clothes washer water consumption, and

N = r epresentative average commercial clothes washer use.

Table 7.3.2 and Table 7.3.3 show the annual energy and water consumption for multi

family buildings and laundromats, respectively, for top- loading washers. Table 7.3.4 and Table

7.3.5 show the annual energy and water consumption for multi-family buildings and laundromats, respectively, for front

- loading washers

. The electric and gas water heating and drying consumption in Table 7.3.2 through Table 7.3.5 are weighted by the share of buildings that use electric and gas water heaters and dryers. Based on data from the Residential Energy Consumption Survey (RECS) 2009 15 and Consortium for Energy Efficiency (CEE), 16 the shares of electric and gas water heating and drying in multi -family applications are: 25 percent and 75 percent electric and gas water heating, respectively, and 40 percent and 60 percent electric and gas drying, respectively. Also based on CEE data, 100 percent of laundromats use gas water heating and drying.The energy and water consumption values provided are based on average use cycles of 3.0 cycles per day for multi -family buildings and 4.1 cycles per day for laundromats.

The annual water

- heating a nd clothes-drying energy consumption reflects the use of either an electric or a gas water heater.

Table 7.3.2 Top-Loading Commercial Clothes Washers, Multi

- Family Application: Annual Energy and Water Use by Efficiency Level

Level MEF J2 I WF Annual Energy Use*

Annual

Water Use Water Heatin g** Dryin g

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 107.1 1.46 913.0 5.23 240.9 30.21

1 1.35 8.80 138.6 1.89 692.0 3.97 230.0 29.87

2 1.55 6.90 108.7 1.48 690.9 3.96 91.9 24.26

* Annua l usage based on 3. 0 cycles per day (1 ,095 cycles per year).

** El ectric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.7-9 Table 7.3.3 Top-Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level

Level MEF J2 I WF Annual Energy Use*

Annual

Water Use Water Heatin g** Dryin g

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 0.0 2.66 0.0 11.92 329.3 41.30

1 1.35 8.80 0.0 3.45 0.0 9.04 314.4 40.84

2 1.55 6.90 0.0 2.70 0.0 9.02 133.0 33.17

* Annua l usage based on 4.1 cycles per day ( 1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.

Table 7.3.4 Front -Loading Commercial Clothes Washers, Multi- Family Application: Annual Energy and Water Use by Efficiency Level

Level MEF J2 I WF Annual Energy Use*

Annual

Water Use Water Heatin g** Dryin g

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 88.9 1.21 552.9 3.17 120.5 15.94

1 1.80 4.50 87.9 1.20 516.1 2.96 87.6 14.10

2 2.00 4.10 51.4 0.70 501.3 2.87 95.7 12.85

3 2.20 3.90 119.3 1.63 439.0 2.52 61.2 14.12

* Annua l usage based on 3. 0 cycles per day (1 ,095 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.

Table 7.3.5 Front -Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level

Level MEF J2 I WF Annual Energy Use*

Annual

Water Use Water Heatin g** Dryin g

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 0.0 2.21 0.0 7.22 164.7 21.80

1 1.80 4.50 0.0 2.19 0.0 6.74 119.8 19.28

2 2.00 4.10 0.0 1.28 0.0 6.55 136.7 17.57

3 2.20 3.90 0.0 2.97 0.0 5.73 90.1 19.30

* Annual usage based on 4.1 8 cycles per day ( 1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas.7-10 7.4 VARIABILITY OF ANNUA L ENERGY AND WATER C ONSUMPTION

DOE determined the variability in annual energy and water consumption using the average usage data from Table 7.3.1

. From the table, there are nine average usage values for

multi -family buildings ranging from a low of 1.5 cycles per day to a high of 4.3 cycles per day.

For laundromats, the low and high values are 2.8 and 6.0 cycles per day, respectively.

DOE weighted the usage from each study to v ary the annual energy and water consumption of commercial clothes washers when it conducted the LCC and PBP analysis. As

shown in Figure 7.4.1 and Figure 7.4.2 , DOE weighted the usage studies equally for multi- family and laundromats applications. As will be described in chapter 8 on the LCC and PBP analysis, DOE used the usage variability to vary the annual energy and water consumption for multi

family and laundromat applications when it conducted the LCC and PBP analysis.

Figure 7.4.1 Probability Distribution for Commercial Clothes Washer Usage in

Multi

- Family Applications 0.00 0.02 0.04 0.06 0.08 0.10

1 1.5 2 2.5 3 3.5 4 4.5Probability

Usage (cycles/day)Wisc/PNNLAllianceSCE/PNNLPG&EMLAEquip<10Equip>=10SCGC, etal.Toronto7-11 Figure 7.4.2 Probability Distribution for Commercial Clothes Washer Usage in Laundromat Applications 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5Probability

Usage (cycles/day)San DiegoSutter, M.SCGC & SDG&E; Coin-opPG&E; AllianceEquip7-12 REFERENCES

1 Energy Policy Act of 2005, Title 1— Energy Effici ency, Subtitle C— Energy Efficient Products, Sec. 136., Energy conservation standards for commercial equipment.

2 U.S. Federal Register. Energy Conservation Standards for Certain Consumer Products and Commercial and Industrial Equipment, Final Rule; technical amendment, October 18, 2005.

70 FR 60407.

3 Biermayer, P.J. Preliminary Engineering Analysis for Clothes Washers , October 1996. Berkeley, CA. LBNL -47889: Appendix D.

4 U.S. Office of the Federal Register. Title 10, Code of Federal Regulations, Part 430– Energy Conservation Program for Certain Commercial and Industrial Product, Subpart B, Appendix

JI

—Residential Clothes Washers . 2010. Washington, D.C.

5 City of Toronto Works and Emergency Services and Toronto Community Housing Corporation. Multi -Unit Residential Clothes Washer Replacement Pilot Project 1999 , May, 2003. Toronto, Ontario, Canada.

6 G. P. Sullivan, J. W. Currie, T.C. Hillman, and G.B. Parker, Southern California Edison High -Performance Clothes Washer Demonstration at Leisure World Laguna Woods, Final Report , December 2000. Prepared for Southern California Edison Company by Battelle Pacific Northwest National Laboratory, Richland, WA.

7 National Research Center, Inc., A National Study of Water & Energy Consumption in Multifam ily Housing , November 2002. Prepared for the Multi

- Housing Laundry Association by National Research Center, Inc, Boulder, CO.

8 Sullivan, G.P., G. B. Parker, and J. W. Currie. Wisconsin Focus on Energy Resource Efficient Laundry (REL) Room Demonstration, Final Report , June 2004. Prepared for Wisconsin Focus on Energy by Battelle Pacific Northwest National Laboratory, Richland, WA. Report

No. PNWD -3445.

9 Equipoise Consulting, Inc. Final Report for Local Energy Efficiency Program No. 148-02 Energy Solutions’ 2002/03 LightWash Program , June 30, 2004. Prepared by Equipoise Consulting Incorporated.

10 PNNL 2008a. Cal-UCONS Commercial Laundry Program Measurement and Evaluation: Pacific Gas and Electric Final Report

, PNWD -3910 . Prepared by Battelle.

11 PNNL 2008b. Cal

- UCONS Commercial Laundry Program Measurement and Evaluation: Pacific Gas and Electric Final Report

, PNWD- 3910. Prepared by Battelle.

12 San Diego County Water Authority. Report on the Monitoring and Assessment of Water7-13 Savings from the Coin-operated Multi-Load Clothes Washers Voucher Initiative Program

, October 2006. Prepared by Water Management, Inc.

13 Sutter, M., 2006, Estimating Commercial Clothes Washer Use in California Coin Laundry

Stores , 2006 ACEEE Summer Study on Energy Ef ficiency in Buildings.

< http://www.eceee.org/conference_proceedings/ACEEE_buildings/2006/Panel_9/p9_29/paper >

14 America Coin -Op. State of the Industry, 2012 – 2013 . April 2013.

< http://www.americancoinop.com/ >

15 U.S. Energy Information Administration, Residential Energy Consumption Survey 2009. Washington, DC.

< http://www.eia.gov/consumption/residential/data/2009/ >

16 Consor tium for Energy Efficiency. Commercial, Family

- Sized Washers: An Initiative Description of the Consortium for Energy Efficie ncy , 1998. Boston, MA.

< http://www.cee1.org/com/cwsh/cwsh -main.php3 >CHAPTER 8. LIFE

- CYCLE COST AND PAYBACK PERIOD ANALYSIS

TABLE OF CONTENTS

8.1 INTRODUCTION ........................................................................................................... 8-1

8.1.1 General Approach for LCC and PBP Analysis ................................................................ 8-1

8.1.2 Overview of LCC and PBP Inputs ................................................................................... 8-2

8.2 LIFE

- CYCLE COST INPUTS ........................................................................................ 8-6

8.2.1 Total Installed Cost Inputs ............................................................................................... 8-6

8.2.1.1 Baseline Manufacturer Cost ........................................................................... 8-8

8.2.1.2 Markups ......................................................................................................... 8-9

8.2.1.3 Overall Markup .............................................................................................. 8-9

8.2.1.4 Standard

- Level Manufacturer Cost Increases .............................................. 8-10

8.2.1.5 Installation Cost ........................................................................................... 8-10

8.2.1.6 Total Installed Cost ...................................................................................... 8-11

8.2.1.7 Future Product Prices ................................................................................... 8-12

8.2.2 Operating Cost Inputs .................................................................................................... 8-14

8.2.2.1 Annual Energy and Water Consumption ..................................................... 8-16

8.2.2.2 Energy and Water Prices .............................................................................. 8-19

8.2.2.3 Energy and Water Price Trends ................................................................... 8-25

8.2.2.4 Repair and Maintenance Costs ..................................................................... 8-27

8.2.3 Product Lifetime ............................................................................................................ 8-28

8.2.4 Discount Rates ............................................................................................................... 8-30

8.2.5 Effective Date of Standard ............................................................................................. 8-33

8.2.6 Equipment Assignment for the Base Case ..................................................................... 8-33

8.3 PAYBACK PERIOD INPUT S ...................................................................................... 8-35

8.4 LIFE

- CYCLE COST AND PAYBACK PERIOD RESULTS ...................................... 8-35

8.4.1 Base Case LCC Distributions ........................................................................................ 8-36

8.4.2 Standard

- Level Distributions of LCC Impacts .............................................................. 8-39

8.4.3 Standard

- Level PBP Distributions ................................................................................. 8-39

8.4.4 LCC and PBP Results .................................................................................................... 8-40

8.5 REBUTTABLE PAYBACK P ERIOD .......................................................................... 8-47

8.5.1 Metric ............................................................................................................................. 8-47

8.5.2 Inputs.............................................................................................................................. 8-47

8.5.3 Results ............................................................................................................................ 8-48

LIST OF TABLES

Table 8.1.1 LCC and PBP Input Summary ............................................................................. 8-5

Table 8.2.1 Inputs for Total Installed Cost ............................................................................. 8-7

Table 8.2.2 Commercial Clothes Washers: Baseline Manufacturer Costs ............................. 8-8

Table 8.2.3 Summary of Average Markups ............................................................................ 8-9

8-iTable 8.2.4 Overall Markups ................................................................................................ 8-10

Table 8.2.5 Top

- Loading Commercial Clothes Washers: Standard

- Level Manufacturer

Cost Increases .................................................................................................... 8-10

Table 8.2.6 Front-Loading Commercial Clothes Washers: Standard

- Level

Manufacturer Cost Increases

................................ .............................................. 8-10

Table 8.2.7 Commercial Clothes Washers: Baseline Installation Costs ............................... 8-11

Table 8.2.8 Top -Loading Commercial Clothes Washers: Consumer Equipment Prices, Installation Costs, and Tot al Installed Costs ...................................................... 8-11

Table 8.2.9 Front-Loading Commercial Clothes Washers: Consumer Equipment Prices, Installation Costs, and T otal Installed Costs .......................................... 8-12

Table 8.2.10 Inputs for Operating Cost................................................................................... 8-15

Table 8.2.11 Top

- Loading Commercial Clothes Washers, Multi

- Family Application: Annual Energy and Water Use by Efficiency Level .......................................... 8-17

Table 8.2.12 Top

- Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level .......................................... 8-18

Table 8.2.13 Front- Loading Commercial Clothes Washers, Multi

- Family Application: Annual Energy and Water Use by Efficiency Level .......................................... 8-18

Table 8.2.14 Front-Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level .......................................... 8-19

Table 8.2.15 Average Commercial Electricity Prices in 2011 ................................................ 8-20

Table 8.2.16 Average Commercial Electricity Prices for Commercial Clothes Washers

in 2012 ............................................................................................................... 8-21

Table 8.2.17 Average Commercial Natural Gas Prices in 2012 ............................................. 8-23

Table 8.2.18 Average Per

- Unit

- Volume Water and Waste Water Prices in 2012 .................. 8-25

Table 8.2.19 Top -Loading Commercial Clothes Washer Annualized Repair Cos ts .............. 8-28

Table 8.2.20 Front-Loading Commercial Clothes Washer Annualized Repair Costs ............ 8-28

Table 8.2.21 Commercial Clothes Washers: Product Lifetime Estimates and Sources ........ 8-29

Table 8.2.22 Commercial Clothes Washers: Average, Minimum, and Maximum

Product Lifetimes ............................................................................................... 8-29

Table 8.2.23 Data for the Calculation of Weighted

- Average Cost of Capital for Sectors

that Purchase Commercial Clothes Washers ..................................................... 8-32

Table 8.2.24 Weighted Average Cost of Capital for Sectors that Purchase Commercial

Clothes Washers

................................ ................................ ................................. 8-32

Table 8.2.25 Top

- Loading Commercial Clothes Washers: Base Case Market Shares ........... 8-34

Table 8.2.26 Front- Loading Commercial Clothes Washers: Base Case Market Shares ......... 8-34

Table 8.4.1 Top

- Loading Commercial Clothes Washers, Multi

- Family Application:

LCC and PBP Results ........................................................................................ 8-41

Table 8.4.2 Top

- Loading Commercial Clothes Washers, Laundromat Application:

LCC and PBP Results ........................................................................................ 8-41

Table 8.4.3 Front- Loading Commercial Clothes Washers, Multi

- Family Application:

LCC and PBP Results ........................................................................................ 8-41

Table 8.4.4 Front- Loadin g Commercial Clothes Washers, Laundromat Application:

LCC and PBP Results ........................................................................................ 8-42

Table 8.5.1 Top

- Loading Commercial Clothes Washers , Multi- Family Application: Rebuttable Payback Periods ............................................................................... 8-48

8- iiTable 8.5.2 Top -Loading Commercial Clothes Washers, Laundromat Applica tion: Rebuttable Payback Periods ............................................................................... 8-49

Table 8.5.3 Front- Loading Commercial Clothes Washers, Multi

- Family Application: Rebuttable Payback Periods ............................................................................... 8-49

Table 8.5.4 Front-Loading Commercial Clothes Washers, Laundromat Application: Rebuttable Payback Periods ............................................................................... 8-49

LIST OF FIGURES

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP ....................... 8-4 Figure 8.2.1 Historic Nominal and Deflated Producer Price Indexes for Commercial Laundry and Drycleaning Machinery ................................................................ 8-13 Figure 8.2.2 Historic Nominal and Deflated Producer Price Indexes for Household Laundry Equipment ........................................................................................... 8-14 Figure 8.2.3 Electricity Price Trends

................................ ...................................................... 8-26 Figure 8.2.4 Natural Gas Price Trends ................................................................................... 8-26 Figure 8.2.5 Water Price Trend .............................................................................................. 8-27 Figure 8.4.1 Top

- Loading Commercial Clothes Washers, Multi

- Family Application:

Base Case LCC Distribution .............................................................................. 8-37 Figure 8.4.2 Top

- Loading Commercial Clothes Washers, Laundromat Application:

Base Case LCC Distribution .............................................................................. 8-37 Figure 8.4.3 Front- Loading Commercial Clothes Washers, Multi

- Family Application:

Base Case LCC Distribution .............................................................................. 8-38 Figure 8.4.4 Front-Loading Commercial Clothes Washers, Laundromat Application:

Base Case LCC Distribution .............................................................................. 8-38 Figure 8.4.5 Front- Loading Commercial Clothes Washers, Multi

- Family Application: Distribution of LCC Impacts for EL3 ................................................................ 8-39 Figure 8.4.6 Front- loading Commercial Clothes Washers, Multi

- Family Application:

Distribution of PBPs for EL 3 ............................................................................ 8-40 Figure 8.4.7 Range of LCC Savings for Top- Loading Commercial Clothes Washers,

Multi

- Family ...................................................................................................... 8-43 Figure 8.4.8 Range of LCC Savings for Top- Loading Commercial Clothes Washers,

Laundromat ........................................................................................................ 8-43 Figure 8.4.9 Range of LCC Savings for Front- Loading Commercial Clothes Washers,

Multi

- Family ...................................................................................................... 8-44 Figure 8.4.10 Range of LCC Savings for Front- Loading Commercial Clothes Washers,

Laundromat ........................................................................................................ 8-44 Figure 8.4.11 Range of Payback Periods for Top- Loading Commercial Clothes Washers,

Multi

- Family ...................................................................................................... 8-45 Figure 8.4.12 Range of Payback Periods for Top- Loading Commercial Clothes Washers,

Laundromat ........................................................................................................ 8-45 Figure 8.4.13 Range of Payback Periods for Front- Loading Commercial Clothes Washers, Multi- Family ...................................................................................... 8-46 Figure 8.4.14 Range of Pay back Periods for Front- Loading Commercial Clothes Washers, Laundromat ........................................................................................ 8-46

8- iiiCHAPTER 8. LIFE

- CYCLE COST AND PAYBA CK PERIOD ANALYSIS

8.1 INTRODUCTION

This chapter describes the Department of Energy (DOE)’s methodology for analyzing the economic impacts of possible energy efficiency standards on individual consumers. The effect

of standards on individual consumers includes a change in operating expense (usually decreased) and a change in purchase price (usually increased). This chapter describes three metrics DOE used in the consumer analysis to determine the effect of standards on individual consumers:

• Life

- cycle cost (L CC) is the total consumer expense over the life of an appliance, including purchase expense and operating costs (including energy expenditures). DOE discounts future operating costs to the time of purchase, and sums them over the lifetime of the equipment.

• Payback period (PBP) measures the amount of time it takes customers to recover the assumed higher purchase price of more energy-efficient equipment through lower operating costs.

• Rebuttable payback period is a special case of the PBP. Where LCC and PBP are estimated over a range of inputs reflecting actual conditions, rebuttable payback period is based on laboratory conditions, specifically DOE test procedure inputs.

Inputs to the LCC and PBP are discussed in sections 8.2 and 8.3, respectively, of th is chapter. Results for the LCC and PBP are presented in section 8.4. The rebuttable PBP is discussed in section 8.5. Key variables and calculations are presented for each metric. DOE performed the calculations discussed here us i ng a series of Microsof t Excel ® spreadsheets which are accessible on the Internet ( http://www.eere.energy.gov/buildings/ appliance_standards/). Details and instructions for using the spreadsheets are discussed in Appendix 8A.

8.1.1 General Approach for LCC and PBP Analysis

Recognizing that several inputs to the determination of consumer LCC and PBP are either variable or uncertain, DOE conducted the LCC and PBP analysis by modeling both the uncertainty and variability in the inp uts using Monte Carlo simulation and probability

distributions. A detailed explanation of Monte Carlo simulation and the use of probability distributions is contained in Appendix 8A . DOE developed LCC and PBP spreadsheet models incorporating both Monte Carlo simulation and probability distributions by using Microsoft

Excel ® spreadsheets combined with Crystal Ball ® (a commercially available add -in program).

As described in Chapter 7 , DOE established the variability and uncertai nty in energy and

water use by defining the uncertainty and variability in the usage (in cycles per day) of the

8-1equipment. As will be described later in this chapter, the variability and uncertainty in energy and water pricing are characterized by regional differences in energy and water prices.

DOE displays the LCC and PBP results as distributions of impacts compared to the baseline conditions. Results are presented at the end of this chapter and are based on 10,000 samples per Monte Carlo simulation r un. To illustrate the implications of the analysis, DOE generated a frequency chart depicting the variation in LCC and PBP for each standard level

considered.

8.1.2 Overview of LCC and PBP Inputs

The LCC is the total consumer expense over the life of the equipment, including purchase expense and operating expense (including energy expenditures). DOE discounts future operating expenses to the time of purchase and sums them over the lifetime of the equipment. The PBP is the change in purchase expense due to an increased efficiency standard divided by the change in annual operating expense that results from the standard. It represents the number of years it will take the customer to recover the increased purchase expense through decreased operating

expenses.

DOE categorizes inputs to the LCC and PBP analysis as follows: (1) inputs for establishing the purchase expense, otherwise known as the total installed cost, and (2) inputs for calculating th e operating cost.

The primary inputs for establishing the total installed cost are:

• Baseline manufacturer cost : The costs incurred by the manufacturer to produce equipment meeting existing minimum efficiency standards.

• Standard- level manufacturer cost increases : The change in manufacturer cost associated with producing equipment to meet a particular standard level.

• Markups and sales tax : The markups and sales tax associated with converting the manufacturer cost to a consumer equipment price. The markups and sale tax are described in detail in Chapter 7, Markups for Equipment Price Determination.

• Installation cost : The cost to the consumer of installing the equipment. The installation cost represents all costs required to install the equipment other than the marked-up consumer equipment price. The installation cost includes labor, overhead, and any miscellaneous materials and parts . Thus, the total installed cost equals the consumer equipment price plus the installation cost.

The primary inputs for calculating the operating cost are:

• Equipment energy and water consumption : The equipment energy consumption is the site energy use associated with operating the equipment. The water consumption is the

8-2site water use associated with operati ng the equipment. Chapter 7 , Energy and Water Use Determination, details how DOE determined the equipment energy and water consumption based on various data sources.

• Equipment efficiency : The equipment efficiency dictates the equipment energy and water consumption associated with standard- level equipment (i.e., equipment with efficiencies greater than baseline equipment). Chapter 7, Energy and Water Use Determination, details how energy and water consumption change with increasing equipment efficiency.

• Energy and water prices : Energy and water prices are the prices paid by consumers for energy (i.e., electricity, gas, or oil) and water. DOE determined current energy prices

based on data from the DOE- EIA. DOE determined water prices based on data f rom the American Water Works Association (AWWA).

• Energy and water price trends : DOE used the EIA Annual Energy Outlook 2013 ( AEO20 13 ) to forecast energy prices into the future. For the results presented in this chapter, DOE used the AEO20 13 reference case to forecast future energy prices. DOE used consumer price index data specific to water and sewerage maintenance from the Bureau of Labor Statistics as the basis for its water price trend.

• Repair and maintenance costs : Repair costs are as sociated with repairing or replacing components that have failed. Maintenance costs are associated with maintaining the operation of the equipment.

• Lifetime : The age at which the equipment is retired from service.

• Discount rate : The rate at which DOE discounted future expenditures to establish their present value.

Figure 8.1.1 graphically depicts the relationships between the installed cost and operating cost inputs for the calculation of the LCC and PBP. In the figure below, the yellow boxes indicate the inputs, the green boxes indicate intermediate outputs, and the blue boxes indicate the final outputs (the LCC and PBP).

8-3Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP

Table 8.1.1 summarize s the input values that DOE used to calculate the LCC and PBP.

Each table summarizes the total installed co st inputs and the operating cost inputs including the lifetime, discount rate, and energy and water price trends. DOE characterized all of the total cost inputs with single -point values, but characterized several of the operating cost inputs with probability distributions that capture the input’s uncertainty and/or variability. For those inputs characterized with probability distributions, the values provided in the following tables are the average or typical values. Also listed in the following tables is the section of the technical support document (TSD) where more detailed information on the inputs can be found. Energy and

Water Use

Energy and

Water Prices From Energy and Water Use

Analysis (Use is a

function of Efficiency) Installation

Cost Equipment

Price From Engineering

Analysis (Price is a

function of Efficiency)

From Markups Analysis Baseline

Manufacturer

Price

Std

- Level

Manufacturer

Price

Consumer

Retail Price

Markup

Sales Tax

Repair Cost

Maintenance

Cost Annual Energy

and Water Expense

Lifetime

Discount Rate

Energy and

Water Price

Annual Operating Expense Total Installed

Cost

Lifetime Operating Expense Payback Life

- Cycle

Cost

8-4Table 8.1.1 LCC and PBP Input Summary

Input Product

Class Average or Typical Value Characterization TSD Section

Reference
Total Installed Cost Inputs

Baseline Manufacturer

Cost Top

Loading 1.15 MEF/ 8.90 I WF = $ 522. 42 Single

- Point Value

8.2.1.2 Front

Loading 1.65 MEF/ 5.2 0 I WF = $ 828.40 Single

- Point Value

Standard

- Level

Manufacturer Cost

Increase Top

Loading 1. 35 MEF/ 8.80 I WF = $ 0.00

1. 55 MEF/ 6.90 I WF = $ 39.39 Single

- Point Value

8.2.1.3 Front

Loading 1.80 MEF/ 4 .50 I WF = $0.00

2.00 MEF/4.10 I WF = $ 0.31

2.20 MEF/3.90 I WF = $ 19.55 Single

- Point Value

Manufacturer Markup Both 1. 2 9 Single

- Point Value 6

. 4

Distributor Markup Both Baseline = 1. 3

Incremental = 1.18 Single

- Point Value 6.5

Sales Tax Both 1.0711 Single

- Point Value 6

. 6

Installation Cost Both $ 223 .00 Single

- Point Value 8.2.1. 5
Operating Cost Inputs

Usage Both Multi

- Family = 3 .0 cycles/day

Laundromat = 4.1 cycles/day Uniform distribution:

Multi

- Family = 1. 0 to 6. 0 cyc/day

Laundromat = 2.8 to 6 cyc/day 7 .3, 7 .4

Annual Electricity Use Top

Loading Baseline use*:

Multi

- Family = 1,2 37 kWh

Laundromat = 32 6 kWh Variability based on usage

7 .3

Front

Loading Baseline use*:

Multi

- Family = 7 48 kWh

Laundromat = 163 kWh Variability based on usage

Annual Gas Use Top

Loading Baseline use* : Multi

- Family = 6. 6 MMBtu

Laundromat = 14. 5 MMBtu Variability based on usage

7 .3

Front

Loading Baseline use*:

Multi

- Family = 4. 3 MMBtu

Laundromat = 9. 3 MMBtu Variability based on usage

Annual Water Use Top

Loading Baseline use*:

Multi

- Family = 29. 6 10 3 gallon

Laundromat = 40

. 9 10 3 gallon Variability based on usage

7 .3

Front

Loading Baseline use*:

Multi

- Family = 15 .6 10 3 gallon

Laundromat = 21. 6 10 3 gallon Variability based on usage

Energy Prices Both Elec = 10. 4 ¢/kWh (average)

Gas = 8.74 $/MMBtu (average) Variable based on location 8.2.2.2

Water and Wastewater

Prices Both Water = 3.54 $/10 3 gallon (average)

Wastewater = 4.48 $/10 3 gallon (average) Variable based on region 8.2.2.2

Repair and

Maintenance Costs Both Annualized repair cost =
1⁄2 Equipment price / Lifetime Single

- Point Value 8.2.2.4

Lifetime Both Multi

- Family = 11.25 years Laundromat = 7.125 years Weibull distribution 8.2.3

8-5Discount Rate Both 6.0 % Custom distribution 8.2.4

Energy Price Trend Both AEO 2013 Reference Case Two sensitivities: High & Low Growth Cases 8.2.2.3

Water and Wastewater

Price Trend Both Bureau of Labor Statistics: Water and sewerage CPI Single forecast 8.2.2.3

* Annual use based on electric water heating and electric clothes drying ** Annual use provided for baseline product only. Annual use decreases with increased product efficiency.

8.2 LIFE

- CYCLE COST INPUTS

Life -cycle cost is the total customer expense over the life of an appliance, including purchase expense and operating costs (including energy expenditures). DOE discounts future operating costs to the time of purchase, and sums them over the lifetime of t he equipment. DOE defines LCC by the following equation:



## ( ) 

# ∑

= + += N

t t t

r OC ICLCC

1 1

where:

LCC = Life

- cycle cost in dollars,

IC = Total installed cost in dollars, ∑ = Sum over the lifetime, from year 1 to year N,

N = Lifetime of appliance in years,

OC = Operating cost in dollars,

r

= Discount rate, and

t = Year for which operating cost is being determined.

DOE expresses dollar values in 2012$.

The following sections discuss total installed cost, operating cost, lifetime, and discount

rate.

8.2.1 Total Installed Cost Inputs

DOE defines the total installed cost using the following equation:

INST EQPIC +=

where:

EQP = Equipment price (i.e., customer price for the equipment only), expressed in dollars, and

INST = Installation cost or the customer price to install equipment (i.e., the cost for labor and materials), also in dollars.

8-6The equipment price is based on how the consumer purchases the equipment. As discussed in Chapter 7, Markups for Equipment Price Determination, DOE defined markups and sales taxes for converting manufacturing costs into consumer equipment prices.

Table 8.2.1 summarizes the inputs for the determination of total inst alled cost.

Table 8.2.1 Inputs for Total Installed Cost

Baseline Manufacturer Cost

Standard

- Level Manufacturer Cost

Manufacturer Markup

Retailer or Distributor Markup

Sales Tax

Installation Cost

The baseline manufacturer cost is the cost incurred by the manufacturer to produce equipment meeting existing minimum efficiency standards. Standard-level manufacturer cost

increases are the change in manufacturer cost associated with producing equipment at a standard

level. Markups and sales tax convert the manufacturer cost to a consumer equipment price. The

installation cost is the cost to the consumer of installing the equipment and represents all costs required to install the equipment other than the marked-up consumer equipment price. The installation cost includes labor, overhead, and any miscellaneous materials and parts. Thus, the total installed cost equals the consumer equipment price plus the installation cost. DOE calculated the total installed cost for baseline produc ts based on the following equation:

BASE BASE OVERALL MFG BASE BASE BASE
INST MU COST INST EQP IC

+ × = +=

_

where:

IC BASE = Baseline total installed cost

, EQP BASE = Consumer equipment price for baseline models,

INST BASE = Baseline installation cost,

COST MFG = Manufacturer cost for baseline models, and

MU OVERALL_BASE = Baseline overall markup (product of manufacturer markup, baseline retailer or distributor markup, and sales tax).

DOE calculated the total installed cost for standard -level products based on the following equation:

8-7

## ( ) ( ) ( ) ( ) 

# ( )

STD INCR OVERALL MFG BASE STD STD BASE BASE STD BASE STD BASE STD STD STD

INST MU COST IC INST EQP INST EQP INST INST EQP EQP INST EQP IC

∆+ × ∆+= ∆+∆+ + = ∆+ +∆+ = +=

_

where:

IC STD = Standard

- level total installed cost

, EQP STD = Consumer equipment price for standard

- level models,

INST STD = Standard

- level installation cost, EQP BASE = Consumer equipment price for baseline models,

Δ EQP STD = Change in equipment price for standard- level models,

INST BASE = Baseline installation cost,

Δ INST STD = Change in installation cost for standard- level models,

IC BASE = Baseline total installed cost

, Δ COST MFG = Change in manufacturer cost f or standard-level models, and

MU OVERALL_INCR = Incremental overall markup (product of manufacturer markup, incremental retailer or distributor markup, and sales tax).

The remainder of this section provides information about each of the above input

varia bles that DOE used to calculate the total installed cost for commercial clothes washers.

8.2.1.1 Baseline Manufacturer Cost

DOE developed the manufacturer costs for commercial clothes washer units as described in chapter 5, Engineering Analysis. Based on a manufacturer markup of 1.29, as determined in

section 6.2 of Chapter 6, Markups for Equipment Price Determination, DOE arrived at a baseline

manufacturer cost. Table 8.2.2 presents the baseline manufacturer cost as well as the associated baseline modified energy factor and water factor.

Table 8.2.2 Commercial Clothes Washers: Baseline Manufacturer Costs

Product Class Baseline Modified Energy Factor (cu.ft./kWh/cyc) Baseline Integrated

Water Factor

(gal/cu.ft.) Baseline Manufacturer

Cost ( 2012 $)

Top

- Loading 1. 15 8.90 $522.42

Front

- Loading 1. 65 5.20 $828.40

8-88.2.1.2 Markup s

For a given distribution channel, the overall markup is the value determined by multiplying all the associated markups and the applicable sales tax together to arrive at a single overall distribution chain markup value. The overall markup is multiplied times the baseline or

standard

- compliant manufacturer cost to arrive at the price paid by the customer. Because there are baseline and incremental markups associated with the wholesaler and mechanical and general

contractor s, the overall markup is also divided into a baseline markup ( i.e. , a markup used to convert the baseline manufacturer price into a customer price) and an incremental markup ( i.e. , a markup used to convert a standard -compliant manufacturer cost increase due to an efficiency increase into an incremental customer price). Markups can differ depending on whether the equipment is being purchased for a new construction installation or is being purchased to replace existing equipment. DOE developed the overall baseline markups and incremental markups for both new construction and replacement applications as a part of the markups analysis (chap ter 6 of the TSD).

Table 8.2.3 displays baseline and incremental markups used to calculate customer price from manufacturer cost; Table 8.2.3 presents the values used for the distribution channels involving wholesalers and contractors, as well as the national accounts distribution channels. Because the relative importance o f new construction and replacements in total shipments varies among the equipment classes, the total markup varies as well ( Table 8.2.3 ).

Table 8.2.3 Summary of Average Marku ps Baseline Markup Incremental Markup

Manufacturer 1. 1 to 1. 36 (see Table 6. 3.1 in chapter 6)

Wholesaler 1.3 6 1.10

Mechanical Contractor (new construction/replacement) 1.42/1.50 1.14/1.20

General Contractor (new construction only) 1.31 1.19

Sales Tax (replacement only) 1.0 7 1.0 7

8.2.1.3 Overall Markup

The overall markup is the value determined by multiplying the manufacturer and retailer markups and the sales tax together to arrive at a single markup value. Table 8.2.4 shows the overall baseline and incremental markups for commercial clothes washers. Refer to Chapter 7, Markups for Equipment Price Determination, for details.

8-9Table 8.2.4 Overall Markups

Markup Base line Incremental

Manufacturer 1.2 8 5

Distributor 1. 37 1.18

Sales Tax 1.0 711

Overall 1. 89 1. 62

8.2.1.4 Standard

- Level Manufacturer Cost Increases

DOE used a combination of cost data submitted by AHAM and a reverse engineering analysis to develop commercial clothes washer manufacturer cost increases associated with increases in product standard levels. Refer to Chapter 5, Engineering Analysis, for details.

Table 8.2.5 and Table 8.2.6 present the standard- level manufacturer cost increases as well as the associated modified energy factors and water factors for top -loading and front-loading washers, respectively

.

Table 8.2.5 Top-Loading Commercial Clothes Washers: Standard- Level Manufacturer

Cost Increases

Standard Level Modified Energy Fac tor (cu.ft./kWh/cyc) Integrated Water

Factor (gal/cu.ft.) Standard

- Level

Manufacturer Cost Increase ( 2012 $)

Baseline 1.15 8.90

-
1 1.35 8.80 $ 0.00

2 1.55 6.90 $ 38.39

Table 8.2.6 Front -Loading Commercial Clothes Washers: Standard- Level Manufacturer

Cost Increases

Standard Level Modified Energy Factor (cu.ft./kWh/cyc) Integrated Water

Factor (gal/cu.ft.) Standard

- Level

Manufacturer Cost Increase ( 20 12 $)

Baseline 1.65 5.20

-
1 1.80 4.50 $0.00

2 2.00 4.10 $ 0.31

3 2.20 3.90 $ 19.55

8.2.1.5 Installation Cost

DOE derived baseline installation costs for commercial clothes washers from data in the
RS Means Mechanical Cost Data, 2013

. 1 This book provides estimates on the labor required to

8-10install commercial clothes washers. Table 8.2.7 summarizes the nationally representative

average costs associated with the installation of a four

- cycle, coin operating, commercial clothes washer as presented in RS Means Mechanical Cost Data

. Table 8.2.7 provides both bare costs (i.e., costs before O&P) and installation costs including O&P. DOE determined that installation costs would not be impacted with increased standard levels.

Table 8.2.7 Commercial Clothes Washers: Baseline Installation Costs

Bare Costs (20 13 $) Including Overhead & Profit (20 13 $)

Installation Type Material Labor Total Total Material* Labor**

Average $1, 250 $1 49 $1, 399 $1, 600 $1, 374 $ 225

Average (2012 $) $ 223

* Material costs including O&P equal bare costs plus 10% profit.

** DOE derived labor cost including O&P by subtracting material with O&P from total with O&P.

Source: RS Means, Mechanical Cost Data, 20 13

.

8.2.1.6 Total Installed Cost

The total installed cost is the sum of the consumer equipment price and the installation

cost. Refer back to section 8.2.1 to see the equations that DOE used to calculate the total installed cost for baseline and standard-level products.

Table 8.2.8 and Table 8.2.9 present the consumer equipment price, installation costs, and total installed costs for top -loading and front- loading washers, respectively. Prices and costs are presented at the baseline level and each standard level.

Table 8.2.8 Top- Loading Commercial Clothes Washers: Consumer Equipment Prices, Installation Costs, and Total Installed Costs

Standard

Level Modified Energy Factor (cu.ft./kWh/cyc) Integrated

Water

Factor (gal/cu.ft.) Equipment

Price (2012 $) Installation

Cost (2012 $) Total Installed

Cost (2012 $)

Baseline 1.15 8.90 $ 1, 028 $ 223 $1, 251

1 1.35 8.80 $ 1, 028 $ 223 $1, 2 5 1

. 2 1.55 6.90 $ 1, 090 $ 223 $1, 313

8-11Table 8.2.9 Front -Loading Commercial Clothes Washers: Consumer Equipment Prices, Installation Costs, and Total Installed Costs

Standard

Level Modified Energy Factor (cu.ft./kWh/cyc) Integrated

Water

Factor (gal/cu.ft.) Equipment

Price (2012 $) Installation

Cost (2012 $) Total Installed

Cost (2012 $)

Baseline 1.65 5.20 $1, 630 $223 $1, 853

1 1.80 4.50 $1, 630 $223 $1, 853

2 2.00 4.10 $1, 631 $223 $1, 854

3 2.20 3.90 $1, 662 $223 $1, 885

8.2.1.7 Future Product Prices

DOE obtained historical PPI data for commercial laundry and drycleaning machinery spanning the time period 1982-2012 from the Bureau of Labor Statistics’ (BLS). a The PPI data reflect nominal prices, adjusted for product quality changes. An inflation- adjusted (deflated) price index for commercial laundry and drycleaning machinery was calculated by dividing the

PPI series by the Gross Domestic Product Chained Price Inde x (see ).

a Series ID PCU3333183333182 ; http://www.bls.gov/ppi/

8-12Figure 8.2.1 Historic Nominal and Deflated Producer Price Indexes for Commercial Laundry and Drycleaning Machinery

From the early -1980s to early -1990s, the deflated price index for commercial laundry and drycleaning machinery was decreasing slowy . Since 1993, the deflated price index for this industry has risen consistently and only till the last third year (2010), the prices started to stay flat. The rising prices we observed in commercial laundry and drycleaning machinery industry from early -2000s to 2010 could partly attribute to the concurrent price increase in copper and steel products which are heavily used in commercial clothes washers; however, it is uncertain what was cauing the price to rise between 1993 to early-2000s. Although the prices in this industry remain roughly constant in the last three years, g iven the uncertain cuase of the long

lasting increasing price trend and the slowdown in global economic activity in recent years , DOE

believes that the extent to how the trends will continue is very uncertain. Additionally, in te rms of the types of commerial clothes washers considered in this rulemaking, they are largely similar to the residential clothes washers. Hence, DOE also referred to the household laundry equipment

PPI for comparison and found that the deflated household laundry equipment PPI have actually been decreasing consistenly from 1947 to 2012, as shown in . b

b Series ID PCU335224335224P ; http://www.bls.gov/ppi/ 0.75 0.80 0.85 0.90 0.95 1.00 1.05

0.0 50.0 100.0 150.0 200.0 250.0

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Deflated PPI ($2012; 2012=1)Nominal PPI (1982=100)

Year

Nominal Commercial Laundry & Drycleaning Machinery PPI (2012=1)

Defalted Commercial Laundry & Drycleaning Machinery PPI (2012=1)

8-13Figure 8.2.2 Historic Nominal and Deflated Producer Price Indexes for Household Laundry Equipment

Given the above considerations, DOE decided to use constant prices as the default price assumption to project future commercial clothes washer prices. Thus, projected prices for the LCC and PBP analysis are equal to the 2012 values for each efficiency level in each product

class.

8.2.2 Operating Cost Inputs

DOE defines the operating cost by the following equation:



### MC RC WC EC OC +++=
where:

EC = Energy expenditure associated with operating the equipment,

WC = For dishwashers and commercial clothes washers, the water expenditure associated with operating the equipment,

RC = Repair cost associated with component failure, and

MC = Service cost for maintaining equipment operation. 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.01947195019531956195919621965196819711974197719801983198619891992199519982001200420072010Deflated PPI (2012=1)Nominal PPI (1979=100)

Nominal Household Laundry PPI Deflated Household Laundry PPI (2012=1)

8-14Table 8.2.10 shows the inputs for determining the operating costs. The inputs listed in

Table 8.2.10 are also necessary for determining lifetime operating expenses, which include the energy price trends (and wate r price trends), product lifetime, discount rate, and effective date of

the standard.

Table 8.2.10 Inputs for Operating Cost

Annual Energy (and Water) Consumption

Energy and Water Prices

Repair and Maintenance Costs

Energy and Water Price Trends

Product Lifetime

Discount Rate

Effective Date of Standard

The annual energy consumption is the site energy use associated with operating the equipment. The annual water consumption , which is applicable to commercial clothes washers, is the site water use associated with operating the equipment. The annual energy (and water) consumption vary with the product efficiency. That is, the energy and water consumption

associated with stan dard

- level equipment (i.e., equipment with efficiencies greater than baseline equipment) are less than the consumptions associated with baseline equipment. Energy and
water prices are the prices paid by consumers for energy (i.e., electricity, gas, or oil ) and water. Multiplying the annual energy and water consumption by the energy and water prices yields the annual energy cost and water cost, respectively. Repair costs are associated with repairing or replacing components that have failed. Maintenance costs are associated with maintaining the operation of the equipment. DOE used energy and water price trends to forecast energy and water prices into the future and, along with the product lifetime and discount rate, to establish the lifetime energy and water costs. The product l ifetime is the age at which the equipment is retired

from service. The discount rate is the rate at which DOE discounted future expenditures to establish their present value. DOE calculated the operating cost for baseline products based on the following equation:

BASE BASE WATER BASE ENERGY BASE BASE BASE BASE BASE BASE


### MC RC PRICE AWC PRICE AEC MC RC WC EC OC

++ × + ×= +++=

where:

OC BASE = Baseline operating cost

, EC BASE = Energy expenditure associated with operating the baseline equipment,

WC BASE = For commercial clothes washers, the water expenditure associated with operating the baseline equipment,

8-15RC BASE = Repair cost associated with component failure for the baseline equipment,

MC BASE = Service cost for maintaining baseline equipment operation,

AE C BASE = Annual energy consumption for baseline equipment,

PRICE ENERGY = Energy price,

A WC BASE = Annual water consumption for baseline equipment, and

PRICE WATER = Water price.

DOE calculated the operating cost for standard -level products based on the following equation:



# ( ) ( )

( ) ( )

STD BASE STD BASE WATER STD BASE ENERGY STD BASE STD STD WATER STD ENERGY STD STD STD STD STD STD

MC MC RC RC PRICE AWC AWC PRICE AEC AEC MC RC PRICE AWC PRICE AEC MC RC WC EC OC

∆++∆++ ×∆ + ×∆ = ++ ×+ ×= +++=

__ __

where :

OC STD = Standard

- level operating cost

, EC STD = Energy expenditure associated with operating standard-level equipment,

WC STD = For commercial clothes washers, the water expenditure associated with operating standard

- level equipment,

RC STD = Repair c ost associated with component failure for standard- level equipment,

MC STD = Service cost for maintaining standard -level equipment operation,

AE C STD = Annual energy consumption for standard

- level equipment,

PRICE ENERGY = Energy price,

A WC STD = Annual water consumption for standard- level equipment,

PRICE WATER = Water price,

Δ

AE C STD = Change in annual energy consumption caused by standard- level equipment,

Δ

A WC STD = Change in annual water consumption caused by standard- level equipment,

ΔRC STD = Chang e in repair cost caused by standard -level equipment, and

Δ

MC STD = Change in maintenance cost caused by standard -level equipment.

The remainder of this section provides information about each of the above input variables that DOE used to calculate the op erating costs for cooking products, dishwashers, dehumidifiers, and commercial clothes washers.

8.2.2.1 Annual Energy and Water Consumption

Chapter 6, Energy and Water Use Determination, details how DOE determined the annual energy and water consumption for baseline and standard-level products.

8-16DOE characterized the variability of commercial clothes washer usage based on several

studies. For both multi

- family as well as laundromat applications, DOE equally weighted each study to establish the variability. Refe r back to Chapter 7, section 7.4 to review how DOE characterized commercial clothes washer usage variability.

The tables presented below are based on the energy and water use determination analysis described in Chapter 7. Keep in mind that the annual energy and water consumption values in the tables below are averages. DOE captured the variability in energy (and water) consumption when it conducted its LCC and PBP analysis.

Table 8.2.11 and Table 8.2.12 provide the average annual energy and water consumption by efficiency level for top -loading commercial clothes washers. Table 8.2.13 and Table 8.2.14 provide the average annual energy and water consumption by efficiency level for front-loading

commercial clothes washers. DOE presents annual consumption based on two app lications:

multi -family housing and laundromats.

Table 8.2.11 Top-Loading Commercial Clothes Washers, Multi

- Family Application: Annual Energy and Water Use by Efficiency Level

Standard

Level MEF I WF Annual Energy Use*

Annual

Water Use Water Heating** Drying***

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 107.1 1.46 913.0 5.23 240.9 30.21

1 1.35 8.80 138.6 1.89 692.0 3.97 230.0 29.87

2 1.55 6.90 108.7 1.48 690.9 3.96 91.9 24.26

* Annual usage based on 3. 0 cycles per day ( 1 ,095 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water heater consumption is weighted by the share of buildings with electric ( 2 5 %) and gas ( 75 %) water heaters.

*** Dryer consumption is weighted by the share of buildings with electric (40%) and gas (60%) dryers.

8-17Table 8.2.12 Top-Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level

Standard

Level MEF I WF Annual Energy Use*

Annual

Water Use Water Heating** Drying***

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.15 8.90 0.0 2.66 0.0 11.92 329.3 41.30

1 1.35 8.80 0.0 3.45 0.0 9.04 314.4 40.84

2 1.55 6.90 0.0 2.70 0.0 9.02 133.0 33.17

* Annual usage based on 4.1 cycles per day ( 1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water heater consumption is weighted by the share of buildings with electric (0%) and gas (100%) water heaters.

*** Dryer consumption is weighted by the share of buildings with electric (0%) and gas (100%) dryers.

Table 8.2.13 Front -Loading Commercial Clothes Washers, Multi- Family Application: Annual Energy and Water Use by Efficiency Level

Standard

Level MEF I WF Annual Energy Use*

Annual

Water Use Water Heating** Drying***

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 88.9 1.21 552.9 3.17 120.5 15.94

1 1.80 4.50 87.9 1.20 516.1 2.96 87.6 14.10

2 2.00 4.10 51.4 0.70 501.3 2.87 95.7 12.85

3 2.20 3.90 119.3 1.63 439.0 2.52 61.2 14.12

* Annual usage based on 3. 0 cycles per day ( 1,095 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water heater consumption is weighted by the share of buildings with electric (2 5 %) and gas ( 75 %) water heaters.

*** Dryer consumption is weighted by the share of buildings with electric (40%) and gas (60%) dryers.

8-18Table 8.2.14 Front -Loading Commercial Clothes Washers, Laundromat Application: Annual Energy and Water Use by Efficiency Level

Standard

Level MEF I WF Annual Energy Use*

Annual

Water Use Water Heating** Drying***

Machine Electric Gas Electric Gas

cu.ft./kWh/cyc gal/cu.ft. kWh/yr MMBtu/yr kWh/yr MMBtu/yr kWh/yr 1000 gal/year

Baseline 1.65 5.20 0.0 2.21 0.0 7.22 164.7 21.80

1 1.80 4.50 0.0 2.19 0.0 6.74 119.8 19.28

2 2.00 4.10 0.0 1.28 0.0 6.55 136.7 17.57

3 2.20 3.90 0.0 2.97 0.0 5.73 90.1 19.30

* Annual usage based on 4.1 cycles per day ( 1,497 cycles per year).

** Electric and gas water heating based on water heater efficiencies of 100% for electric and 75% for gas. Water heater consumption is weighted by the share of buildings with electric (0%) and gas (100%) water heaters.

*** Dryer consumption is weighted by the share of buildings with electric (0%) and gas (100%) dryers.

8.2.2.2 Energy and Water Prices

DOE derived energy prices for 27 geographic areas in the U.S. and derived water prices for the four Census regions. Using these data, DOE analyzed the variability of energy and water prices at the regional level for commercial clothes washers

.

DOE characterized energy and water price regional variability with probability distributions. It based the probability associated with each regional energy and water price on the population weight of each region.

The methodology that DOE used for deriving the energy and water prices is presen ted below. Included are tables that summarize the regional energy and water prices for each product.

Energy Prices
DOE derived average commercial electricity prices from data that are published annually

based on EIA Form 86 2 . Those data include annual electricity sales in kilowatt -hours, revenues from electricity sales , and number of customers in the residential, commercial, and industrial

sectors , for every utility serving final consumers . DOE calculated commercial electricity prices

for each of the 27 U.S. geographic areas to accordance with RECS 2009 geographic areas. The calculation of an average commercial electricity price proceeds in two steps:

1. For each utility, estimate an average commercial price by dividing the commercial revenues by commercial sales.

2. Calculate a regional average price, weighting each utility with customers in a region by

the number of commercial consumers served in that region.

8-19Table 8.2.15 shows the results for each geographic region.

Table 8.2.15 Average Commercial Electricity Prices in 2011

Geographic Area 201 1 $/ kWh

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $0.108

2 Massachusetts $0.120

3 New York $0.133

4 New Jersey $0.120

5 Pennsylvania $0.088

6 Illinois $0.079

7 Indiana, Ohio $0.081

8 Michigan $0.106

9 Wisconsin $0.105

10 Iowa, Minnesota, North Dakota, South

Dakota $0.085

11 Kansas, Nebraska $0.092

12 Missouri $0.083

13 Virginia $0.083

14 Delaware, District of Columbia, Maryland, West Virginia $0.094

15 Georgia $0.101

16 North Carolina, South Carolina $0.088

17 Florida $0.100

18 Alabama, Kentucky, Mississippi $0.099

19 Tennessee $0.105

20 Arkansas, Louisiana, Oklahoma $0.083

21 Texas $0.102

22 Colorado $0.096

23 Idaho, Montana, Utah, Wyoming $0.077

24 Arizona $0.099

25 Nevada, New Mexico $0.094

26 California $0.134

27 Alaska

, Hawaii

, Oregon, Washington $0.105

Source: EIA Form 861.

Table 8.2.16 shows the national average commercial electricity prices for commercial clothes washers based on the relative residential consumer weight of each geographic area. DOE used 2012 population es timates from the U.S. Census as a proxy to estimate how the national

saturation of commercial clothes washers was distributed over the 27 geographic areas. 3

Because DOE conducted the LCC and PBP analysis in 2012$, all electricity prices are in 20 12$.

8-20To perform the necessary monetary conversion, DOE used the CPI to convert the electricity prices from 20 11$ to 20 12 $.

Table 8.2.16 Average Commercial Electricity Prices for Commercial Clothes Washers in

2012

Geographic Area 2012 $/kWh Saturation

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $0.110 2.5%

2 Massachusetts $0.123 2.1%

3 New York $0.136 6.2%

4 New Jersey $0.122 2.8%

5 Pennsylvania $0.089 4.1%

6 Illinois $0.081 4.1%

7 Indiana, Ohio $0.082 5.8%

8 Michigan $0.108 3.1%

9 Wisconsin $0.107 1.8%

10 Iowa, Minnesota, North Dakota, South

Dakota $0.086 3.2%

11 Kansas, Nebraska $0.094 1.5%

12 Missouri $0.085 1.9%

13 Virginia $0.085 2.6%

14 Delaware, District of Columbia, Maryland, West Virginia $0.096 2.4%

15 Georgia $0.103 3.2%

16 North Carolina, South Carolina $0.089 4.6%

17 Florida $0.102 6.2%

18 Alabama, Kentucky, Mississippi $0.101 3.9%

19 Tennessee $0.107 2.1%

20 Arkansas, Louisiana, Oklahoma $0.085 3.6%

21 Texas $0.104 8.3%

22 Colorado $0.098 1.7%

23 Idaho, Montana, Utah, Wyoming $0.079 1.9%

24 Arizona $0.101 2.1%

25 Nevada, New Mexico $0.096 1.5%

26 California $0.136 12.1%

27 Alaska , Hawaii

, Oregon, Washington $0.107 4.7%

Source: EIA Form 861

.

Commercial Natural Gas Prices
DOE obtained the data for the natural gas price calculation from the EIA publication

Natural Gas Prices

. 4 This publication includes a compilation of monthly natural gas delivery volumes and average consumer prices by State, for residential, commercial, and industrial

8-21customers. The Department used the complete annual data for 2012 to calculate an average

annu l price for each area. The calculation of average prices proceeds in two steps:

1. Calculate the annul price for each State using a simple average over the appropriate

months.

2. Calculate a regional price, weighting each State in a region by its population.

This method differs from the method used to calculate electricity prices because EIA does not provide consumer- or utility -level data on gas consumption and prices. Table 8.2.17 shows the national average commercial natural gas prices for commercial clothes washers based on the relative commercial consumer weight of each geographic area. DOE used 20 12 population estimates from the U.S. Census as a proxy to estimate how the national saturation of commercial

clothes washers was distributed over the 27 geographic areas.

8-22Table 8.2.17 Average Commercial Natural Gas Prices in 2012

Geographic Area 2012$/MMBtu Saturation

1 Connecticut, Maine, New Hampshire, Rhode Island, Vermont $10.06 2.5%

2 Massachusetts $10.05 2.1%

3 New York $7.28 6.2%

4 New Jersey $8.17 2.8%

5 Pennsylvania $10.39 4.1%

6 Illinois $8.64 4.1%

7 Indiana, Ohio $7.36 5.8%

8 Michigan $8.52 3.1%

9 Wisconsin $6.82 1.8%

10 Iowa, Minnesota, North Dakota, South

Dakota $6.40 3.2%

11 Kansas, Nebraska $8.13 1.5%

12 Missouri $10.29 1.9%

13 Virginia $8.57 2.6%

14 Delaware, District of Columbia, Maryland, West Virginia $10.81 2.4%

15 Georgia $9.70 3.2%

16 North Carolina, South Carolina $8.35 4.6%

17 Florida $10.04 6.2%

18 Alabama, Kentucky, Mississippi $9.70 3.9%

19 Tennessee $8.15 2.1%

20 Arkansas, Louisiana, Oklahoma $8.76 3.6%

21 Texas $6.66 8.3%

22 Colorado $7.56 1.7%

23 Idaho, Montana, Utah, Wyoming $7.12 1.9%

24 Arizona $9.35 2.1%

25 Nevada, New Mexico $6.88 1.5%

26 California $6.87 12.1%

27 Alaska , Hawaii

, Oregon, Washington $12.89 4.7%

Source: EIA, Natural Gas Prices

.

8-23Water Prices
DOE obtained water price data from the year 2012 from the Water and Wastewater Rate Survey conducted by Raftelis Financial Consultants and the American Water Works

Association. 5 The survey covers approximately 290 water ut ilities and 214 wastewater utilities, with each industry analyzed separately. The water survey includes, for each utility, the cost to consumers of purchasing a given volume of water. In this case, the data include a division of the total consumer cost into fixed and volumetric charges. The calculations use only the volumetric charge to calculate water prices, since only this charge would be affected by a change in water consumption. Including the fixed charge in the average would lead to a slightly hig her water price.

The Water and Wastewater Rate Survey provides prices separately for residential and

non- residential customers. For wastewater utilities, the format is similar, but the cost refers to the cost of treating a given volume of wastewater. I n both surveys, the non- residential sector is divided into four sub-sectors based on the average monthly volume of water delivered or, equivalently, the size of the delivery pipe. The sub- sectors are named non -manufacturing, commercial, industrial 1, and industrial 2. DOE’s analysis uses the commercial category to estimate prices for commercial consumers.

A sample of 290 utilities is not large enough to calculate regional prices for all U.S. Census divisions and large States (for comparison, the EIA Form 861 data include more than 3000 utilities). For this reason, DOE calculated regional values at the Census region level (Northeast, South, Midwest, and West). The calculation of average per-unit-volume prices proceeds in three steps:

1. For each utility, calculate the per -unit- volume price by dividing the total volumetric cost by the volume delivered.

2. Calculate a State

- level average price by weighting each utility in a given State by the

number of commercial consumers it serves.

3. Calculate a regional average by combining the State

- level averages, weighting each by the population of that State. This third step helps reduce any bias in the sample that may

occur due to relative under- sampling of large States.

The results of the calculat ion for the commercial sec tor are presented in Table 8.2.18

. The price units in the table are dollars per thousand gallons ($/tg).

8-24Table 8.2.18 Average Per

- Unit -Volume Water and Waste Water Prices in 2012

Commercial

Region 2012 $/tg Water 2012 $/tg Wastewater

Northeast $3.85 $6.48

Midwest $3.28 $3.91

South $3.28 $4.67

West $3.98 $4.69

Source: Raftelis, Raftelis Survey, 201 2

.

8.2.2.3 Energy and Water Price Trends

DOE used price forecasts by the EIA to estimate the trends in natural gas, oil, and electricity prices

. To arrive at prices in future years, it multiplied the average prices described in the preceding section (section 8.2.2.2) by the forecast of annual average price changes in EIA’s

AEO 2013

. 6 To estimate the trend after 2040 , DOE followed past guidelines provided to the Federal Energy Management Program (FEMP) by EIA and used the average rate of change during 2025–2040.

The Department set up its analysis to calculate LCC and PBP using three separate projections: Reference, Low Economic Growth, and High Economic Growth. These three cases reflect the uncertainty of economic growth in the forecast period. The high and low growth cases show the projected effects of alternative growth assumptions on energy markets. Figure

8.2.1 and Fi gure 8.2.2 show the commercial electricity and natural gas price trends, respectively, based on the three projections. For the LCC results presented in section 8.4, DOE used only the energy price forecasts from the AEO 2013 Reference Case.

0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35Electricity Price Trend (2012=100) Reference Case

High Case

Low Case

8-25Figure 8.2.3 Electricity Price Trends

Figure 8.2.4 Natural Gas Price Trends

To estimate the future trend for water and wastewater prices, DOE used data on the historic trend in the national water price index (U.S. city average) from 1970 through 2012. 7

DOE extrapolated the future trend based on the linear growth from 1970 to 2012. But rather than use the extrapolated trend to forecast the prices for the eight years after 2012 , DOE used the annual growth rate between 2012 and 2021 to project water price trend

. Otherwise, forecasted prices for this eight -year time period would have been much lower than the price in 2012. Beyond the eight -year time period, DOE used the extrapolated trend to forecast prices out to the year 2047. Figure 8.2.3 shows the historical and projected water price trend data. DOE used the forecasted data to estimate water and wastewater prices for commercial clothes was hers. 0.90 1.10 1.30 1.50 1.70 1.90 2.10 2.30Natural Gas Price Trend (2012=100) Reference Case

High Case

Low Case

8-26Figure 8.2.5 Water Price Trend

8.2.2.4 Repair and Maintenance Costs

Typically, small incremental changes in product efficiency incur no

, or only very small

, changes in repair and maintenance costs over baseline products. However, equipment with

efficiencies that are significantly higher than the baseline are more likely to incur higher repair

and maintenance costs because its higher complexity and higher part count typically increases the cumulative probability of failure

.

The Whirlpool Corporation estimates that the unit shipments of horizo ntal

- axis

commercial clothes washers are less than half that of vertical

- axis machines while the ir in

warranty repair co sts are double that of vertical- axis machines. 8 This suggests that the repair of

horizontal- axis machines is four time s a s costly as that of vertical

- axis machines. Because in

warranty repair costs, based on Whirlpool’s experience, are greater for horizontal- axis machines

that vertical

- axis machines, DOE estimated that commercial clothes washer repair costs would

increase a s a function of equipment efficiency. DOE utilized an algorithm developed for central air conditioners and heat pumps to estimate repair cost increases

. 9 This algorithm calculates annualized repair costs by dividing the half the equipment retail price by the equipment lifetime as show in the expression below:



### LIFE EQP RC ANNUAL 

### ×

= 5.0

where:

RC ANNUAL = Annualized repair cost associated with equipment, EQP = Consumer equipment price, and y = 8.502x

- 16753
R2 = 0.9558

0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 Water Price CPI Trend (1982

- 1984=100)

Historical Water Price CPI

Extrapolated Water Price CPI
Trend

Linear (Historical Water Price CPI)

8-27LIFE = Product lifetime.

Table 8.2.19 and Table 8.2.20 show for each product class and each product application (multi -family buildings and laundromats) the annualized repair cost s for the commercial clothes washer baseline and efficiency levels. Annualized repair costs for each product application differ because commercial clothes washer lifetime is different for each application. (Product lifetimes are presented below is section 8.2.3.) The tables also present the consumer equipment price s for the baseline level and each efficiency level.

Table 8.2.19 Top-Loading Commercial Clothes Washer Annualized Repair Costs

Level Modified Energy Factor (cu.ft./kWh/cyc) Integrated

Water

Factor (gal/cu.ft.) Equipment

Price (20 12 $) Annualized Repair Cost ( 20 12 $)

Multi

- Family* Laundromat*

Baseline 1.15 8.90 $1,008.84 $47 $73

1 1.35 8.80 $1,008.84 $47 $73

2 1.55 6.90 $1,070.01 $50 $78

* Average product lifetime: Multi

- family = 11.3 years; Laundromat = 7.1 years.

Table 8.2.20 Front -Loading Commercial Clothes Washer Annualized Repair Costs

Level Modified Energy Factor (cu.ft./kWh/cyc) Integrated

Water

Factor (gal/cu.ft.) Equipment

Price (20 12 $) Annualized Repair Cost (20 12 $)

Multi

- Family* Laundromat*

Baseline 1.65 5.20 $1,599.71 $74 $116

1 1.80 4.50 $1,599.71 $74 $116

2 2.00 4.10 $1,600.20 $74 $116

3 2.20 3.90 $1,630.86 $76 $118

* Average product lifetime: Multi

- family = 11.3 years; Laundromat = 7.1 years.

8.2.3 Product Lifetime

DOE used only primary sources of data to estimate product lifetimes. DOE considered

the sources listed in Table 8.2.21 to estimate product lifetime.

8-28Table 8.2.21 Commercial Clothes Washers: Product Lifetime Estimates and Sources

Lifetime (years) Source

7 to 10 CEE (1998) 5

Top

- Loading (12 to 14 lbs.): 5 to 8*

Front

- Loading (18 to 50 lbs.): 10 to 15** CLA 10

8 to 9*** ACEEE (2001) 11

5 (high

- use)* t o 13 (low

- use)** Southern California Edison (2000) 12

15** † CALMAC (2000) 13

* Used to establish laundromat lifetime only.

** Used to establish multi

- family lifetime only.

*** Depending on the usage rate, the life can be shorter.

† Based on engineering judgment

.

Because DOE conducted the LCC analysis based on analyzing two product applications— multi -family and laundromats— it established separate lifetimes for each product application. Because clothes washers in multi -family applications are used less frequently than in laundromats, DOE decided to use only those sources that indicated lifetimes of seven years or

greater for multi

- family applications

. For laundromats applications, where clothes washers receive heavier use, DOE decided to use only those sources that indicated lifetimes of 10 years

or less. DOE then took an average of the lifetime estimates that met the above criteria. For

multi -family applications the average lifetime turned out to be 11.3 years, while the average

lifetime for laundromats was 7.1 years. DOE used the low estimate for the above applicable sources to establish the minimum product lifetime for each product application. For multi- family applications the minimum estimate is 7 years and for laundromats it is 5 years. To establish the maximum product lifetime, DOE took the difference between the minimum and average values (4.3 years for multi -family and 2.1 years for laundromats) and added it to the average product lifetime. The minimum, average, and maximum lifetime estimates for each product appli cation

are shown in Table 8.2.22

.

Table 8.2.22 Commercial Clothes Washers: Average, Minimum, and Maximum Product

Lifetimes

Product Minimum

years Average

years Maximum

years

Multi

- Family

Laundromats 7.0

5.0 11.3

7.1 15.5

9.3

DOE characterized the clothes washer product lifetimes for each product application with Weibull distributions. Appendix 8C presents the Weibull distributions for commercial clothes

washers that DOE used in the LCC and PBP analysis.

8-298.2.4 Discount Rates

The discount rate is the rate at which future expenditures and savings are discounted to establish their present value. The cost of capital is commonly used to estimate the present value of cash flows to be derived from a typical company project or investment. Following financial theory, the finance cost of raising funds for expenditure can be interpreted as: (1) the financial cost of any debt incurred, or (2) the opportunity cost of any equity. Similarly, the value of cost savings can be interpreted as: (1) the cost avoided by decreasing debt, or (2) the return earned from increasing equity holdings. Most companies use both debt and equity capital to fund investments, so their cost of capital is the weighted average of the cost to the firm of equity and debt financing. 10 DOE defines the discount rate as the weighted average cost of capital (WACC), calculated as the weight ed average of the cost of equity financing and the cost of debt financing, as estimated from financial data for publicly traded firms in the sectors that purchase commercial

clothes washers.

For commercial clothes washers, DOE derived the discount rate f rom the cost of capital of publicly traded firms in the sectors that purchase commercial clothes washers. 14 DOE estimated the cost of capital of these firms as the weighted average of the cost of equity financing and the cost of debt financing. The costs of debt and equity financing are usually obtainable from publicly available data about the following types of companies in the sectors that purchase commercial clothes washers:

• personal services (including laundromats),

• educational services (which buy washers for dormitories),

• hotels, and

• real estate investment trusts ( REITs ) (i.e., building owners, including owners of apartment complexes).

Damodaran Online is a widely used source of information about company debt and equity financing for most types of firms, and was the source of data for this analysis on
educational services, hotels, and REITs. 15 Since Damodaran Online does not include data for firms in the personal services sector (SIC 7200), DOE used data from Ibbotson’s Associates for

this sector. 16 Ibbotson Associates is a leading authority on asset allocation with expertise in capital market expectations and portfolio implementation.

DOE estimated the cost of equity using the capital asset pricing model (CAPM). 17 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 isdetermined by several variables: the risk coefficient of the firm (β), the expected return on riskfree assets ( R

f ), and the equity risk premium ( ERP ). The risk coefficient of the firm indicates the

risk associated with that firm relative to the price variability in the stock market. The expected return on risk-free assets is defined by the yield on long-term government bonds. The ERP represents the difference between the expected stock market return and the risk

- free rate

. The

8-30cost of equity financing is estimated using the following equation, where the variables are

defined as above:



## ( ) ERP Rk

fe

×+= 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.

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 fa ctor depends on the variability of stock returns represented by standard deviations in stock prices. So for firm i , the cost of debt financing is:

aifdi RRk +=

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 weighted -average cost of capital (WACC) using the following equation:

ddee 

### wkwkWACC ×+×=

where:

WACC = Weighted average cost of capital,

w e = Proportion of equity financing, and

w d = Proportion of debt financing.

Several parameters of the cost of capital equations can vary substantially over time, and therefore the estimates can vary with the time period over which data is selected and the technical details of the data averaging method. For guidance on the time period for selecting and averaging data for key parameters and the averaging method, DOE used Federal Reserve methodologies for calculating these parameters. In its use of the CAPM, th e 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

8-31risk free rate as one where “the time horizon of the investor is matched with the term of the risk

free security.” 18

Damodaran Online is a widely used source of information about company debt and equity financing for most types of firms. 19 By taking a forty- year geometric average of Damodaran Online data, DOE found for this analysis the following risk free rates for 2010-2012 ( Table 8.2.24 ). DOE also estimated the ERP by calculating the difference between risk free rate and stock market return for the same time period. Since Damodaran Online does not include data for firms in the personal services sector (SIC 7200), DOE used 2008 data from Ibbotson’s Associates

for this sector. 16 Ibbotson Asso ciates is a leading authority on asset allocation with expertise in capital market expectations and portfolio implementation.

The values of the parameters used in the calculations are shown in Table 8.2.23 below.

Table 8.2.23 Data for the Calculation of Weighted-Average Cost of Capital for Sectors

that Purchase Commercial Clothes Washers

Sector β R f ERP R

a w e w d
Personal Services 0.53 5.20% 5.10% 0.80% 67.40% 32.60%

Educational Services 0.85 5.20% 5.10% 2.33% 86.79% 13.21%

Hotels 1.71 5.20% 5.10% 2.17% 67.37% 32.63%

REIT 1.40 5.20% 5.10% 1.17% 74.55% 25.45%

By adjusting for the influence of inflation, DOE estimates the real weighted average cost of capital, or discount rate, for each sector. DOE then aggregates the sector- level real weighted average costs of capital to estimate the discount rate for each of the four ownership types in the commercial clothes washer analysis. An overall average is estimated by weighting each sector’s discount rate by its estimated share of the commercial clothes washer market

. These values are presented in Table 8.2.24 . While WACC valu es for any sector may trend higher or lower over substantial periods of time, these values represent a private sector cost of capital that is averaged over major business cycles.

Table 8.2.24 Weighted Average Cost of Capital for Sectors that Purchase Commercial

Clothes Washers

Sector Discount Rate Market Share

Personal Services 4.7% 14.0%

Educational Services 5.4% 9.7%

Hotels 6.9% 9.7%

R.E.I.T. 6.2% 66.6% Average 6.0 % 100%

8-328.2.5 Effective Date of Standard

The effective date is the future date when a new standard becomes operative. Based on DOE’s implementation report for energy conservation standards activities submitted pursuant to Section 136 of the Energy Policy Act of 2005, a final rule for the commercial clothes washers being considered for this standards rulemaking is scheduled for completion no later than January 1, 2015. Therefore, the effective date of any new energy efficiency standards for these products will be three years after the final rule is published, which is January 1, 2018. The Depa rtment calculated the LCC for all consumers as if they each would purchase a new piece of equipment in the year the standard takes effect.

8.2.6 Equipment Assignment for the Base Case

For purposes of conducting the LCC analysis, DOE analyzed standard levels relative to a baseline efficiency level. However, some consumers already purchase products with efficiencies greater than the baseline levels. Thus, to accurately estimate the percentage of consumers that would be affected by a particula r standard level, DOE took into account the distribution of product efficiencies currently in the marketplace. In other words, rather than analyzing the impacts of a particular standard level assuming that all consumers are currently purchasing products at the baseline level, DOE conducted the analysis by taking into account the full breadth of product efficiencies that consumers purchase under the base case (i.e., the case without new energy efficiency standards).

DOE took into account the mix of commerc ial clothes washer efficiencies by characterizing the current mix of product efficiencies as a probability distribution. In other words, as DOE performed the Monte Carlo simulation, each standard level analyzed was evaluated against the distribution of pr oduct efficiencies in the base case.

Rather than providing market share data, AHAM provided shipment-weighted

efficiencies of all commercial clothes washers. 20 That is, shipment- weighted efficiencies were not provided separately for the top-loading and front-loading product classes. For the year 2005, the shipment- weighted efficiencies of commercial clothes washers a re 1.41 modified energy factor (MEF) and 10.91 water factor (WF). Based on the shipment- weighted efficiency data, DOE was able to derive the market share of each product class. DOE assumed a bi -modal market share distribution consisting of only top- loadi ng equipment meeting current minimum efficiency standards of 1.26 MEF/9.5 WF and top-loading equipment with an efficiency of 2.00 MEF/5.5 WF. Thus, by using a shipment-weighted efficiency of 1.41 MEF, DOE determined that the market share of 1.26 MEF/9.5 WF washers is 79.7 percent and the market share of 2.00 MEF/5.5 WF washers is 20.3 percent. c DOE interprets this result as indicating that in the year 2005, 79.7 percent of the market consists of top-loading washers while 20.3 percent is comprised

of fron t- loading washers.

c DOE did not consider the shipment

- weighted average WF of 10.91 in performin g the market share calculation.

8-33Subsequent to 2005, energy efficiency tax incentives in the Energy Policy Act of 2005 and extensions as well as new provisions in the Economic Stabilization Act of 2008 have resulted in more front-loading washers being produced by ma nufacturers. DOE reviewed the U.S. Securities and Exchange Commission (SEC) 10K report of the low volume manufacturer (LVM) of commercial clothes washers and determined that manufacturer tax credits in this recent federal legislation have resulted in significantly increased sales of the front -loadingwashers for the LVM. When accounting for the LVM’s market share, the increase in frontloading sales results in a current market share of 30 percent for front-loading washers. Although

tax credits are set to e xpire after 2010, DOE estimates that the tax credits would permanently transform the market so that front-loading washers would continue to comprise 30 percent of the market over the entire forecast period.

Knowing the market share of each product class does not provide the distribution of equipment efficiencies within each class. Therefore, DOE relied on anther data source— the California Energy Commission (CEC). 21 As discussed in Section 3.14.3 of Chapter 3, Market and Technology Assessment, the CEC publish es the MEFs and WFs of commercial clothes washers sold in California. Although the range in efficiencies is relatively wide, the distribu tion is based on the number of available models being sold rather than the percent of shipments being

sold. Without any other data available, DOE assumed that the distribution of available models in the CEC data were representative of actual shipments sold in the U.S. Table 8.2.25 and Table

8.2.26 present the market shares of the efficiency levels in the base case for top -loading and

front

- loading washers, respectively. The tables represent the equipment that establishments would have purchased in the year 2018 in the absence of new standards.

Table 8.2.25 Top-Loading Commercial Clothes Washers: Base Case Market Shares

Standard Level MEF I WF Market Share

Baseline 1. 15 8.90 99.5 %*

1 1. 35 8.80 0.3 %**

2 1. 55 6.90 0.3 %***

* Represents units with the following MEF and WF ratings: 1. 15 ≤ MEF < 1. 35 ; 8.9 ≥ I WF > 8. 8

. ** Represents units with the following MEF and WF ratings: 1. 35 ≤ MEF < 1. 55 ; 8.8 ≥ I WF > 6.9

.

** * Represents units with the following MEF and WF ratings: MEF ≥ 1. 55 ; I WF ≤ 6.9

.

Table 8.2.26 Front -Loading Commercial Clothes Washers: Base Case Market Shares

Standard Level MEF WF Market Share

Baseline 1.65 5.20 28.0 %*

1 1.80 4.50 34 .0%**

2 2.00 4.10 38.0 %***

3 2.20 3.90 0.0 % †

* Represents units with the following MEF and WF ratings: 1. 65 ≤ MEF < 1.80; 5.2 ≥ I WF > 4.5

. ** Represents units with the following MEF and WF ratings: 1.80 ≤ MEF < 2.00; 4.5 ≥ I WF > 4.1

.

** * Represents units with the following MEF and WF ratings: 2.00 ≤ MEF < 2.20; 4.9 ≥ I WF > 3.9

. † Represents units with the following MEF and WF ratings: MEF ≥ 2.35; I WF ≤ 3.9

.

8-348.3 PAYBACK PERIOD INPUT S

The payback period is the amount of time it takes the consumer to recover the assumed higher purchase expense of more energy -efficient equipment as a result of lower operating costs. Numerically, the PBP is the ratio of the increase in purchase expense (i.e., from a less efficient design to a more efficient design) to the decrease in annual operating expenditures. This type of calculation is known as a “simple” payback period, because it does not take into account changes in operating expense over time or the time value of money; i.e., the calculation is done at an effective discount rate of zero percent.

The equation for PBP is:



### OC IC PBP ∆ ∆

=

where:

Δ IC = Difference in the total installed cost between the more efficient standard level and the baseline design, and

Δ OC = difference in annual operating expenses. Payback periods are expressed in years. Payback periods greater than the life of the product mean that the increased total installed cost is not recovered in reduced operating

expenses.

The data inputs to PBP are the total installed cost of the equipment to the consumer for each efficiency level and the annual (first year) operating expenditures for each standard level

. The inputs to the total installed cost are the equipment price and the installation cost. The inputs to the operating costs are the annual energy (and water) cost, the annual repair cost, and the annual maintenance cost. The PBP uses the same inputs as the LCC analysis as described in section 8.2, except that energy (and water) price trends and discount rates are not required. Since the PBP is a “simple” payback, the required energy price is only for the year in which a new

standard is to take ef fect

— in this case, the year 2018. The energy price DOE used in the PBP calculation was the price projected for that year. Discount rates are also not required for the simple PBP calculation.

8.4 LIFE

- CYCLE COST AND PAYBA CK PERIOD RESULTS

This section presents the LCC and PBP results for commercial clothes washers. DOE’s approach for conducting the LCC analysis relied on characterizing the uncertainty and variability of many of the inputs to the analysis with probability distributions. DOE used a Monte Carlo simulation technique to perform the LCC calculations. DOE calculated the average LCC and

8-35LCC savings and the median and average PBP for each of the candidate standard levels. These

standard levels are also referred to as candidate standard levels (CSL)

.

DOE calculated LCC savings and PBPs relative to the base case equipment that it assigned to commercial consumers . As discussed in section 8.2.6 , DOE assigned base case equipment that is more efficient than some of the CSLs . For that reason, the average LCC impacts are not equal to the difference between the LCC of a specific and the LCC of the baseline equipment.

In the subsections below, DOE presents figures showing the distribution of LCCs in the base case for each product class. Also presented below for a specific are figures showing the distribution of LCC impacts and the distribution of PBPs. The figures are presented as frequency charts that show the distribution of LCCs, LCC impacts, and PBPs with their corresponding probability of occurrence. DOE generated the figures for the distributions from a Monte Carlo simulation run based on 10,000 samples. The LCC and PBP calculations were performed 10,000 times by sampling from the probability distributions that DOE develop ed to characterize many of the inputs.

Based on the Monte Carlo simulations that DOE performed, for each standard- level, DOE calculated the share of commercial consumers with a net LCC benefit, with a net LCC cost, and with no impact. DOE considered a commercial user to receive no impact at a given if DOE assigned it base case equipment that is the same as or has higher efficiency than the CSL

. To illustrate the range of LCC and PBP impacts among the commercial clothes washer users, the sections below present figures that provide such information for each product class.

8.4.1 Base Case LCC Distributions

Figure 8.4.1 and Figure 8.4.2 show the frequency charts for the base case LCC for both product applications of top-loading commercial clothes washers. Figure 8.4.3 and Figure 8.4.4 show the frequency charts for the base case LCC for both product applications offront-loading

commercial clothes washers. The figures below show the mean LCC of the base case distribution as well as the full range of LCCs.

8-36Figure 8.4.1 Top-Loading Commercial Clothes Washers, Multi

- Family Application: Base Case LCC Distribution

Figure 8.4.2 Top-Loading Commercial Clothes Washers, Laundromat Application: Base Case LCC Distribution

8-37Figure 8.4.3 Front -Loading Commercial Clothes Washers, Multi- Family Application: Base Case LCC Distribution

Figure 8.4.4 Front -Loading Commercial Clothes Washers, Laundromat Application: Base Cas e LCC Distribution

8-388.4.2 Standard

- Level Distributions of LCC Impacts

Figure 8.4.5 is an example of a frequency chart showing the distribution of LCC

differences for the case of EL3 for front-loading washers, multi -family product application. In the figure, a text box next to a verti cal line at that value on the x

- axis shows the mean change in LCC (a savings of $ 12.51 in the example here). The note, “Certainty is 54 .39% from $0 to +Infinity,” means that 54.39 percent of owners of CCW units will have LCC savings or not be impacted due to the EL compared to the base case. Th e large ‘spike’ in Figure 8.4.5 represents the percentage of consumers that are not impacted by an increase in the EL , i.e., consumers that already use commercial clothes washers with efficiencies greater than or equal to the EL . Refer back to section 8.2.6 on the distribution of product efficiencies under the base case. DOE can

ge nerate a frequency chart lik e the one shown in Figure 8.4.5 for every EL and each product application.

Figure 8.4.5 Front -Loading Commercial Clothes Washers, Multi

- Family Application: Distribution of LCC Impacts for EL3

8.4.3 Standard

- Level PBP Distributions

Figure 8.4.6 is an example of a frequency chart showing the distribution of pa yback periods of EL 3 for front -loading washer, multi -family product application. The entire

distribution of PBP value above 0 indicates that all consumers are impacted by an increase in the

EL , i.e., consumers are currently using front -loading commercial clothes washers with

efficiencies lower than then EL . Refer back to section 8.2.6 on the distribution of product

8-39efficiencies under the base case. DOE can generate a frequency chart lik e the one shown in Figure 8.4.6 for every EL and each product application.

Figure 8.4.6 Front -loading Commercial Clothes Washers, Multi

- Family Application: Distribution of PBPs for EL 3

8.4.4 LCC and PBP Results

Table 8.4.1 and Table 8.4.2 show the LCC and PBP results for both produ ct applications

of top-loading commercial clothes washers

. Table 8.4.3 and Table 8.4.4 show the LCC and PBP results for both product applications of front- loading commercial clothes washers. F or example,

in the case of the front -loading washer, multi

- family application, EL 2 (2.00 MEF/4.10 I WF) shows an average LCC savings of $285. Note that for EL 2, approximately 39 percent of

consumers in 2018 are assumed to already be using a commercial clothes washer in the base case

at EL 2 and thus have zero savings due to the standard. With regard to the PBPs shown below, DOE determined the median and average values by excluding the percentage of households not impacted by the standard. For example, in the case of EL 2 for front -loading washers, about 39 percent of the consumers did not factor into the calculation of the median and average PBP.

8-40Table 8.4.1 Top-Loading Commercial Clothes Washers, Multi

- Family Application: LCC

and PBP Results

CSL MEF/WF Life

- Cycle Cost Life

- Cycle Cost Savings Payback Period (years)

Average

Installed

Price Average Operating

Cost Average

LCC Average Savings Households with

Median Average Net

Cost No Impact Net

Benefit

Baseline 1.15 /

8.90 $1,251 $4,617 $5,868

- - - - - -

1 1.35 /

8.80 $1,251 $4,350 $5,601 $268 0% 1% 99% 0.0 0.0

2 1.55 /

6.90 $1,313 $3,708 $5,022 $ 845 0% 0% 100% 0.6 0.7

Table 8.4.2 Top-Loading Commercial Clothes Washers, Laundromat Application: LCC

and PBP Results

CSL MEF/WF Life

- Cycle Cost Life

- Cycle Cost Savings Payback Period (years)

Average

Installed

Price Average Operating

Cost Average

LCC Average Savings Households with

Median Average Net

Cost No Impact Net

Benefit

Baseline 1.15 /

8.90 $1,251 $3,924 $5,176

- - - - - -

1 1.35 /

8.80 $1,251 $3,767 $5,018 $158 0% 1% 99% 0.0 0.0

2 1.55 /

6.90 $1,313 $3,181 $4,494 $681 0% 0% 100% 0.6 0.5

Table 8.4.3 Front -Loading Commercial Clothes Washers, Multi

- Family Application:

LCC and PBP Results

CSL MEF/WF Life

- Cycle Cost Life

- Cycle Cost Savings Payback Period (years)

Average

Installed

Price Average Operating

Cost Average

LCC Average Savings Households with

Median Average Net

Cost No Impact Net

Benefit

Baseline 1.65 /

5.20 $1,853 $2,987 $4,840

- - - - - -

1 1.80 /

4.50 $1,853 $2,749 $4,602 $238 0% 73% 27% 0.0 0.0

2 2.00 /

4.10 $1,854 $2,555 $4,409 $298 0% 39% 61% 0.0 0.0

3 2.20 /

3.90 $1,885 $2,700 $4,585 $8 46% 0% 53% 3. 7 3.9

8-41Table 8.4.4 Front -Loading Commercial Clothes Washers, Laundromat Application:

LCC and PBP Results

CSL MEF/WF Life

- Cycle Cost Life

- Cycle Cost Savings Payback Period (years)

Average

Installed

Price Average Operating

Cost Average

LCC Average Savings Households with

Median Average Net

Cost No Impact Net

Benefit

Baseline 1.65 /

5.20 $1,853 $2,604 $4,458

- - - - - -

1 1.80 /

4.50 $1,853 $2,398 $4,251 $ 204 0% 73% 27% 0.0 0.0

2 2.00 /

4.10 $1,854 $2,242 $4,096 $245 0% 39% 61% 0.0 0.0

3 2.20 /

3.90 $1,885 $2,381 $4,266

- $19 72% 0% 28% 7.3 8 .0

Figure 8.4.7 and Figure 8.4.8 show the range of LCC savings for the CSL s for both product applications of top-loading commercial clothes washers. Figure 8.4.9 and Figure 8.4.10

show the range of LCC savings for the ELs for both product applications of front-loading

commercial clothes washers. For each EL , the top and the bottom of the box indicate the 75 th

and 25 th percentiles, respectively. The bar at the middle of the box indicates the median; 50 percent of the households have LCC savings above this value The ‘whiskers’ at the bottom and the top of the box indicate the 5 th and 95 th percentiles. The small box sh ows the average LCC savings for each EL

.

Figure 8.4.11 and Figure 8.4.12 show the range of PBPs for top-loading commercial

clothes washers. Figure 8.4.13 and Figure 8.4.14 show the range of PBPs for front-loading

commercial clothes washers. In these figures, households which are not impacted by an increase in the standard are not included (this situation applies to all ELs except EL 3 for fr ont-loading

washers ).

8-42Figure 8.4.7 Range of LCC Savings for Top-Loading Commercial Clothes Washers, Multi

- Family

Figure 8.4.8 Range of LCC Savings for Top-Loading Commercial Clothes Washers, Laundromat 0 200 400 600 800 1,000 1,200 1,400 1,600

Level 1 Level 2Life Cycle Cost Savings ($)

Efficiency Level median average

0 200 400 600 800 1,000 1,200

Level 1 Level 2Life Cycle Cost Savings ($)

Efficiency Level median average

8-43Figure 8.4.9 Range of LCC Savings for Front-Loading Commercial Clothes Washers, Multi

- Family

Figure 8.4.10 Range of LCC Savings for Front-Loading Commercial Clothes Washers, Laundromat - 400 - 200 0 200 400 600 800

Level 1 Level 2 Level 3Life Cycle Cost Savings ($)

Efficiency Level median average

- 300 - 200 - 100 0 100 200 300 400 500 600

Level 1 Level 2 Level 3Life Cycle Cost Savings ($)

Efficiency Level median average

8-44Figure 8.4.11 Range of Payback Periods for Top-Loading Commercial Clothes Washers, Multi

- Family

Figure 8.4.12 Range of Paybac k Periods for Top-Loading

Commercial Clothes Washers, Laundromat 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Level 1 Level 2Payback Period (years)

Efficiency Level median average

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Level 1 Level 2Payback Period (years)

Efficiency Level median average

8-45Figure 8.4.13 Range of Payback Periods for Front-Loading Commercial Clothes Washers, Multi

- Family

Figure 8.4.14 Range of Payback Periods for Front-Loading Commercial Clothes Washers, Laundromat 0 2 4 6 8 10 12

Level 1 Level 2 Level 3Payback Period (years)

Efficiency Level median average

0 5 10 15 20 25

Level 1 Level 2 Level 3Payback Period (years)

Efficiency Level median average

8-468.5 REBUTTABLE PAYBACK P ERIOD

DOE presents rebuttable PBPs to provide the legally established rebuttable presumption that an energy efficiency standard is economically justified if the additional product costs

attributed to the standard are less than three times the value of the first -year energy cost savings. (42 U.S.C. §6295 (o)(2)(B)(iii))

8.5.1 Metric

The basic equation for rebuttable PBP is the same as that shown in section 8.3, Payback Period Inputs. Unlike the analyses described in sections 8.2 and 8.3, however, the rebuttable PBP is not based on the use of household samples and probability distributions. Rather than distributions, the rebuttable PBP is based on discrete single-point values. For example, while DOE uses a probability distribution of regional energy prices in the distributional payback period

anal ysis, it uses only the national average energy price from the probability distribution to
determine the rebuttable PBP.

Other than the use of single-point values, the most notable difference between the

distribution PBP and the rebuttable PBP is the latt er’s reliance on the DOE test procedure to determine a product’s annual energy and water consumption. The following identifies the

differenc es between the annual energy and water consumptions determined for the distribution

PBP and the rebuttable PBP.

DOE based the annual energy and water consumption values that it used to determine the rebuttable PBP on the number of cycles per year specified in the DOE test procedure. 22 The commercial clothes washer test procedure cites the residential clothes washer test procedure to establish efficiency ratings as well as annual energy and water consumption. As a result, the annual number of use cycles, 295 cycles per year, for determining the annual energy and water consumption of commercial clothes washers is representative of residential use, not commercial use. Because residential use is significantly lower than the average usage for commercial applications—1460 cycles per year in multi -family buildings and 2190 cycles per year in laundromats—the average annual energy and water consumption used to determine rebuttable PBP will be significantly less than the consumption associated with actual usage. As a result, the

rebuttable PBP will be significantly longer than the distribution PBPs for b oth multi

- family and laundromat applications.

8.5.2 Inputs

Inputs for the rebuttable PBP differ from the distribution PBP in that the calculation uses discrete values, rather than distributions, for inputs. Note that for the calculation of distribution PBP, because inputs for the determination of total installed cost were based on single-point values, only the variability and/or uncertainty in the inputs for determining operating cost

contributed to v ariability in the distribution PBPs. The following summarizes the single -point

values that DOE used in the determination of the rebuttable PBP.

8-47• Manufacturing costs, markups, sales taxes, and installation costs were all based on the single -point values used in the distributional LCC and PBP analysis.

• As described in section 8.5.1, annual energy and water consumption is based on the DOE test procedure.

• Energy and water prices are based on national average values for the year that new

standards are assumed to take effect.

• An average discount rate or lifetime is not required in the rebuttable PBP calculation.

• The effective date of th e standard is assumed to be 2013.

8.5.3 Results

DOE calculated rebuttable PBPs for each CSL relative to the distribution of product efficiencies assumed for the base case (refer back to section 8.2.6 for more details on the base case efficiency distributions for each product). In other words, DOE did not determine the

rebuttable PBP relative to the baseline efficiency level, but relative to the current distribution of product efficiencies DOE determined for the base case (i.e., the case without new standards). The following sections present the results for the sets of products bei ng analyzed for this standards rulemaking.

Table 8.5.1 and Table 8.5.2 present the rebuttable PBPs for top-loading commercial

clothes washer s under both product applications; multi- family build ings and laundromats. Table

8.5.3 and Table 8.5.4 present the rebuttable PBPs for front- loading commercial clothes washers under both product applications; multi-family buildings and laundromats.

Table 8.5.1 Top-Loading Commercial Clothes Washers, Multi

- Family Application : Rebuttable Payback Periods

CSL M EF I WF PBP

years

Baseline 1.15 8.90

-

1 1.35 8.80 0

2 1.55 6.90 2.3

8-48Table 8.5.2 Top-Loading Commercial Clothes Washers, Laundromat Application: Rebuttable Payback Periods

CSL MEF I WF PBP

years

Baseline 1.15 8.90

-

1 1.35 8.80 0

2 1.55 6.90 2.7 2

Table 8.5.3 Front -Loading Commercial Clothes Washers, Multi- Family Application: Rebuttable Payback Periods

CSL MEF I WF PBP

years

Baseline 1.65 5.20

-

1 1.80 4.50 0

2 2.00 4.10 0.04

3 2.20 3.90 8.7 3

Table 8.5.4 Front -Loading Commercial Clothes Washers, Laundromat Application: Rebuttable Payback Periods

CSL MEF I WF PBP

years

Baseline 1.65 5.20

-

1 1.80 4.50 0

2 2.00 4.10 0.05

3 2.20 3.90 11. 24

8-49REFERENCES

1. RS Means, Mechanical Cost Data, 36st Annual Edition , 2013. Kingston, MA.

2. U.S. Department of Energy–Energy Information Administration, Form EIA -861 for 2011,

2012. (Last accessed November, 2013.) < http://www.eia.gov/electricity/data/eia861 >

3. U.S. Census Bureau, Population Estimates, National Totals: Vintage 2012, (Last accessed November, 2013.)

< http://www.ce nsus.gov/popest/data/national/totals/2012/index.html >

4. U.S. Department of Energy- Energy Information Administration, Natural Gas Prices

, 2013. Washington, DC. < http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm >

5. Raftelis Financial Consultants Inc., 2012 RFC/AWWA Water and Wastewater Rate Survey , 2013. Charlotte, NC, Kansas City, MO, and Pasadena, CA.

< http://www.raftelis.com/ratessurvey.html >

6. U.S. Department of Energy- Energy Information Administration, Annual Energy Outlook 2013 with Projections to 2040 , 2013. Washington, DC.

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

7. U.S. Department of Labor- Bureau of Labor Statistics, Consumer Price Indexes, Item: Water and sewerage maintenance, Series Id: CUUR0000SEHG01, U.S. city average (not seasonally adjusted) , 2013. Washington, DC. < http://www.bls.gov/cpi/home.htm#data >

8. Whirlpool Corporation, Comment of Whirlpool Corporation Regarding the ANOPR for Cooking Products and Commercial Clothes Washers

.

9. U.S. Department of Energy- Ene rgy Efficiency and Renewable Energy, Technical Support Document: Energy Efficiency Standards for Consumer Products: Residential Central AirConditioners and Heat Pumps Including: Regulatory Impact Analysis. Chapter 5, LifeCycle Cost and Payback Analysis, May, 2002. Washington, D.C.

< http://www1.eere.energy.gov/buildings/appliance_standards/residential/ac_central_1000 _r.html >

10. Coin Laundry Associ ation, Industry Overview . Downers Grove, IL.

< http://www.coinlaundry.org/resources/industryoverview.cfm >

11. Southern California Edison, 2004-2005 Database for Energy Efficiency Res ources (DEER) Update Study, Final Report, December, 2005. Prepared for Southern California Edison by Itron, Inc. Vavcouver, WA. < http://www.calmac.org/publications/2 004- 05_DEER_Update_Final_Report-Wo.pdf >

8-5012. Sullivan, G. P., J. W. Currie, T. C. Hillman, and G. B. Parker, High -Performance Clothes Washer Demonstration at Leisure World Laguna Woods Final Report, December, 2000. Prepared for Southern California Edison C ompany by Battelle Pacific Northwest Laboratory. Richland, WA. < http://eere.pnl.gov/building-technologies/doc/scefinal%202- 22-01.pdf >

13. California Measurement Advisory Committee, CALMAC Protocols, Appendix F, Effective Useful Live Values for Major Energy Efficiency Measures, September, 2000.

< http://www.calmac.org/events/APX_F.pdf >

14. Ibbotsons Associates, Cost of Capital 2001 Yearbook, Data Through March 2001 , 2001. Chicago, IL.

15. Damodaran Online, The Data Page: Cost of Capital by Industry Sector

. 2012< http://pages.stern.nyu.edu/~adamodar >

16. Ibbotsons Ass ociates, Statistics for SIC code 72 , 2008. < http://www.ibbotson.com >

17. Ibbotsons Associates, SBBI Valuation Edition 2001 Yearbook , 2001. Chicago, IL.

18. The Federal Reserve Board, Federal Reserve Bank Services Private Sector Adjustment

Factor , 2005. Washington, DC.

19. Damodaran Online, The Data Page: Cost of Capital by Industry Sector , 2008, 2010, 2011, 2012. < http://pages.stern.nyu.edu/~adamodar/ >

20. As sociation of Home Appliance Manufacturers, AHAM Data on Commercial Clothes Washers for DOE Efficiency Standards Rulemaking . 2012, Letter to Building Technologies Program,U.S. Department of Energy.

21. California Energy Commission, Appliances Databases – Clothes Washers, 2009. (Last accessed November, 2013.)

< http://www.energy.ca.gov/appliances/database/excel_based_files/Clothes_Washers/ >

22. U.S. Office of t he Federal Register, Code of Federal Regulations, Title 10, Part 431 - Energy Conservation Program for Certain Commercial and Industrial Equipment, Subpart I—Commercial Clothes Washers, January 1, 2007. Washington, DC.

8-519

- i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CO NTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 SHIPMENTS MODEL METH ODOLOGY .................................................................... 9-1

9.3 DATA INPUTS AND MODE L CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments ........................................................................................................ 9-5

9.3.2 Markets and Model Calibration ....................................................................................... 9-7

9.3.2.1 New Construction .......................................................................................... 9-7

9.3.2.2 Replacements ............................................................................................... 9-10

9.3.2.3 Model Calibration

— Non

- Replacements ..................................................... 9-11

9.3.2.4 Base Case Shipments ................................................................................... 9-12

9.3.3 Market Share Forecast ................................................................................................... 9-14

9.4 PURCHASE PRICE AND OPERATING COST IMPACT S ....................................... 9-16

9.5 AFFECTED STOCK ..................................................................................................... 9-17

9.6 RESULTS ...................................................................................................................... 9-18

LIST OF TABLES

Table 9.3.1 Commercial Clothes Washers: Historical Shipments (1972 – 2011) .................... 9-6

Table 9.3.3 Commercial Clothes Washers: Historical New Multi

- Family Building ................ 9-9

Table 9.3.4 Multi- Family In -Unit Washer Saturations .............................................................. 9-9

Table 9.3.5 Commercial Clothes Washers: Historical New Multi

- Family Building .............. 9-12

Table 9.4.1 Pric e Elasticity Decrease after CCW Price Change .............................................. 9-17 Table 9.6.1 Top- Loading Commercial Clothes Washers: Cumulative Shipments Impact s .................................................................................................................. 9-18

Table 9.6.2 Front- Loading Commercial Clothes Washers: Cumulative Shipments ............... 9-19

LIST OF FIGURE

Figure 9.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index ............... 9-7 Figure 9.3.2 Historical and Forecasted Multi

- Family Housing Starts (millions) ..................... 9-8 Figure 9.3.3 Commercial Laundry Saturation in New Multi-Fa mily Housing....................... 9-10 Figure 9.3.4 Commercial Clothes Washers: Survival and Retirement Functions ................. 9-11 Figure 9.3.5 Co mmercial Clothes Washers: Historical and Base Case Shipments ............... 9-13 Figure 9.3.6 Commercial Clothes Washers: Disaggregated Base Case ................................. 9-13 Figure 9.3.7 Historical Market Share of Top -loading Units ................................................... 9-14 Figure 9.3.8 Forecast of Market Share in Base Case and Standards Case TSL 3 for ............. 9-15 Figure 9.6.1 Top- Loading Commercial Clothes Washers: Base Case and TS Shipments

Forecasts ............................................................................................................ 9-19 Figure 9.6.2 Front- Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts

................................ ........................................................... 9-209-1 CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Product shipments estimates are a necessary input to the national energy savings (NES) and net present value (NPV) calculations. Shipments are also a necessary input to the manufacturer impact analysis (MIA), which the U.S. Department of Energy ( DOE ) conducts for

its notic e of proposed rulemaking (NOPR). This chapter describes DOE’s methodology for projecting annual shipments and presents results for commercial clothes washers.

DOE estimated shipments for commercial clothes washers with a shipments model. DOE

calibrated the shipments model against historical shipments. For purposes of estimating the impacts of prospective trial standard levels (TSL s ) on product shipments, the shipments model accounts for the combined effects of changes in purchase price and annual operating cost on the product purchase decision.

The shipments model considers specific market segments to estimate shipments, the

results of which are then aggregated to estimate total product shipments. In the case of

commercial clothes washers , DOE accounted for two market segments: (1) shipments due to

new construction, and (2) replacements of retired units from existing buildings

.

The shipments model is a Microsoft Excel spreadsheet that is accessible on the Internet ( http://www.eere.energy.gov/buildings/appliance_standards/ ). Appendix 10-B discusses how to

access the shipments model contained in the NIA spreadsheet, and provides basic instructions for using them. Th e rest of this chapter explains the shipments model in more detail. Section 9.2 presents the shipments model methodology ; section 9.3 describes the data inputs and the model calibration; section 9.4 discusses impacts on shipments from changes in equipment purchase price and operating cost; section 9.5 discusses the affected stock; and section 9.6 presents the

results for different TSL scenarios.

9.2 SHIPMENTS MODEL METH ODOLOGY

DOE developed a national stock model for estimating annual shipments for commercial

clothes washers

. The model considers market segmentation as a distinct input to the shipments projection . As represented by the following equation, the two primary market segments are new installations and replacements.

)()()( j NI j Rpl j Ship

p p p +=

W here:9-2 Ship p (j) = t otal shipments of product p in year j

, Rpl p (j) = units of product p retired and replaced in year j , and

NI p (j) = number of new installations of product p in year j

.

As section 9.3.2.3 discusses , DOE also considered non- replacements as a factor for most of the products to calibrate its shipments model to historical shipments data.

In principle, each market segment and eac h product class responds differently to both the base case demographic and economic trends and to the implementation of standards. Furthermore, retirement s , early replacement s, and efficiency trends a are dynamic and can vary among product classes. Rathe r than simply extrapolating a current shipments trend, the base case shipments analysis ( i.e., the case without new standards) uses driver input variables, such as construction forecasts and product lifetime distributions, to project sales in each market s egment

. Thus, DOE’s shipments model assume s that construction

-- i.e., new multi

- family housing units

-- drives new installations. In each year, the product shipments from the new construction market segment are equal to the number of new multi

- family housin g units built times the purchase rate, which is determined by the product class market share and the market saturation of the product

under consideration b

.

DOE’s shipments model take s an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected construction trends. The

model estimate s shipments due to replacements using sales in previous years and assumptions

about the lifetime of the equipment. Therefore, estimated sales due to replacements in a given year are equal to the total stock of the appliance minus the sum of the appliances sold in previous years that still remain in the stock. DOE determined the useful servi ce life of each appliance to estimate how long the appliance is likely to remain in stock. The following equation represents how DOE estimated replacement shipments.

Rpl p ( j )

= Stock p ( j − 1 ) − � � Sh ip j j−1

j=N ageMax

age =0 × prob Rt r ( age )

W here:

Stock p (j

- 1) = total stock of in- service appliances in year j-1

, prob Rtr (age) = probability that an appliance of a particular age will be retired, and

a Efficiency trends affect shipments only in the standards case. A change in the efficiency distribution of the stock results in a change in the purchase price and operating cost and, therefore, produces a purchase price and operating cost impact on the shipments. This is discussed later in the chapter in section 9.4.

b In this case, market saturation of the product is related to saturation of in-home clothes washer units in multifamily households.9-3 N = s tart year for when the model begins its stock accounting (start year is specific to each product based on available historical shipments data).

Stock accounting takes product shipments, a retirement function, and initial in- service product stock as inputs and provides an estimate of the age distribution of in- service product stocks for all years. The age distribution of in-service product stocks is a key input to both the

NES and NPV calculations —the operating costs for any year depend on the age distribution of the stock. The dependence of operating cost on the equipment age distribution occurs under a

TSL that produces increasing efficiency over time, where older, less efficient units may have higher operating costs, while younger, more- efficient units will have lower operating costs.

DOE calculated total in

- service stock of equipment by integrating historical shipments data starting from a specific year. The start year depended on the historical data available for the product. As units are added to the in-service stock, some of the older ones retire and exit the stock. To estimate future shipments, DOE developed a series of equations that define the dynamics and accounting of in-service stocks. For new units, the equation is:

)1()1,(

_ j Ship age j Stock ==

W here:

Stock(j, age) = t he p opulation of in- service units of a particular age,
j = y ear for which the in

- service stock is being estimated, and Ship (j) = number of units purchased in year j

.

The previous equation states that the number of one- year -old units is simply equ al to the number of new units purchased the previous year. The slightly more complicated equations ( e.g., the following equation) are those that describe the accounting of the existing in- service

stock of units:

Stock ( j + 1, age + 1 )

= Stock ( j

, a ge ) × [1

− prob Rtr ( age ) ]

In th is equation, as the year is incremented from j to j+1 , the age is also incremented

from age to age+1 . With time, a fraction of the in -service stock is removed, and that fraction is determined by a retirement probability function, prob Rtr (age) , which is described in section 10.3.

Because commercial clothes washers are considered to be common appliances that have been used by U.S. consumers for a long time, replacements typically constitute the majority of shipments. Most replacements are made when equipment wears out and fails.

9.3 DATA INPUTS AND MODE L CALIBRATION

As discussed previously, shipments are driven primarily by two market segments: new construction and replacements. DOE used historical shipments as the basis for calibrating its9-4 shipments model. Because new construction shipments and replacements were not enough to account for all product shipments, DOE developed another factor t o calibrate its shipments

model. This factor , retired units not replaced, represent s a small share of total shipments. Shipments to the different market segments are discussed below.

DOE designed its shipments model for commercial clothes washers by developing a single shipments model for all washers destined for the two most predominant product applications— multi -family buildings and laundromats—and then disaggregating the shipments into its two product classes—top-loading and front-loading washers. DOE developed a cross

price elasticity to estimate the market shares of the considered product classes through the forecast period. The Cross- Price elasticity is covered in section 9.3.3

.

DOE ’s approach for estimating shipments relies on using aggr egate historical shipments

data for all commercial clothes washers. Tracking of product stock and vintage relies on a

retiremen t function, which describes the likelihood that equipment installed in a certain year will still be in service in any subsequent year. Once a product reaches the end of its useful life it is removed from service. In most cases, retirement is followed by replacement, which is a key driver of further shipments. The difference between replacements and total shipments for the following year provides the shipments going into new construction. The output includes units going into both laundromats and multi- fa mily apartment buildings.

DOE based its shipments model on the following three assumptions: (1) all product shipments for new construction are driven by the new multi-family housing market, (2) the relative market shares of the two product applications, laundromats and common- area laundry

facilities in multi -family housing, are constant over time, and (3) the U.S. Census Bureau’s quantity index data can be used to validate the shipments trend observed in the historical data.

DOE’s first assumption is based on the premise that multi -family occupants who do not have their own personal washers and dryers must either wash their laundry in the common area laundry facility within their building or in a laundromat. Because the vast majority of people

that use laundromats live in multi -family buildings, the construction of any new laundromats would seem to be driven by new multi-family customers. Thus, DOE believes it is reasonable to expect that new multi -family construction starts capture all new product shipments, regardless of whether the units are destined for multi-family buildings or laundromats.

With regard to the second assumption , i.e., constant market shares, DOE did not have

sufficient data to project whether the market shares of laundromats and multi- family housing will vary over time. Therefore, DOE assumed that market shares will remain constant at current levels. DOE relied on a report from the Consortium for Energy Efficiency (CEE) to estimate current market shares. Based on the CEE report, multi-family buildings and laundromats comprise 85 percent and 15 percent of the commercial cloth es washer market, respectively. 1
Although the CEE report was published more than 10 years ago, it continues to the most reliable data source for developing a market split for commercial clothes washer shipments that was

available.9-5 The third assumption pertains to the use of the U.S Bureau of Economic Analysis’ quantity index data to validate historical shipments data. 2 The Census Bureau’s quantity index for commercial laundry equipment includes shipments of commercial laundry products beyond those covered by this rulemaking. Because disaggregated quantity index data were not available,

DOE assumed that the market share of commercial clothes washers covered under this rulemaking relative to all commercial laundry equipment was constant over the period 1977– 2011. Based on this assumption, DOE derived quantity index data specific to the commercial clothes washers of interest to this rulemaking and used the se data to validate the historical shipments.

9.3.1 Historical Shipments

DOE used historical shipments data ( i.e., domestic shipments) to calibrate its shipments model. It used two sources to establish historical shipments: (1) data the Association of Home Appliance Manufacturers ( AHAM ) provided for the period 2010–2011 3 and (2) data for the period 1972–2005 4 f rom the last rulemaking . DOE used a five-year moving average to estimate shipments for the period 2006–2009. Table 9.3.1 summarizes the historical commercial clothes washer shipments data.9-6 Table 9.3.1 Commercial Clothes Washers: Historical Shipments (1972 – 2011)

Year Shipments (thousands) Year Shipments (thousands)

1972 167 1992 188

1973 167 1993 190

1974 166 1994 205

1975 167 1995 209

1976 167 1996 232

1977 167 1997 241

1978 166 1998 265

1979 164 1999 239

1980 170 2000 215

1981 171 2001 194

1982 163 2002 175

1983 163 2003 191

1984 167 2004 178

1985 176 2005 177

1986 186 2006 175

1987 194 2007 172

1988 213 2008 169

1989 215 2009 167

1990 225 2010 164

1991 193 2011 161

Sources: 2010

– 2011 : AHAM Data Submittal, 2012 ; 2006

– 2009, DOE interpolated values; 1972

– 2005 : DOE Standards for Commercial Clothes Washers, Technical Support Document, 2010.

As indicated in Table 9.3.1 , there was a significant drop in commercial clothes washer shipments between 1998 and 2011. Therefore, DOE validated the shipments data through a review of the U.S. Bureau of Economic Analysis’ quantity index data. 2 DOE compared the

historical shipments data against the quantity index for commercial laundry equipment (represented by North American Industry Classification System (NAICS) code 333312). As

shown in Figure 9.3.1 below, the commercial laundry quantity index tracks the shipments trend closely . Thus, DOE concluded that the downward trend observed in the historical shipments

between 1998 and 2011 w as real.9-7 Figure 9.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index

9.3.2 Markets and Model Calibration

The market for commercial clothes washers primarily consists of replacement units for equipment that has been retired and units for new housing. Total commercial clothes washers shipments are represented by the following equation:

)()()( j NI j Rpl j Ship CCW CCW CCW + =

W here :

Ship CCW (j) = t otal shipments of commercial clothes washers in yea r j

, Rpl CCW (j) = r eplacement shipments in year j , and

NI CC W (j) = shipments due to new construction in year j

.

The following sections discuss both of these markets in further detail. The model

calibration is also discussed.

9.3.2.1 New Con struction

To project shipments to the new construction market, DOE assumed shipments are driven solely by multi

- family construction starts. Implicit in this assumption is the fact that a certain percentage of multi

- family residents will need to wash their laundry in either a common

- area laundry facility (within the multi -family building) or a laundromat. 0.00 0.05 0.10 0.15 0.20 0.25 0.30

0 20 40 60 80 100 120 140 160Shipments (millions)Quantity Index (2000 =100)

Quantity Index Historical Shipments QI

- based Shipments Pre

- 1988
shipments
based on QI Decling

trend since

19999-8 To estimate shipments to new construction, DOE used projections of multi

- family housing starts coupled with commercial clothes washer saturation data. In other words, to project the shipments for new construction for any given year, DOE multiplied the multi-housing projections by the projected saturation of commercial clothes washers for new multi

- family housing. Because DOE used only multi- family housing starts as a driver for new construction shipments, the saturation represents the fulfilled washer demand in both the multi-family housing

and laundromat markets.

Figure 9.3.2 presents historical and projected new multi -family housing starts based on the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 2013 for the period

2013–2040. 5 The AEO typically provides three scenarios of multi

- family housing starts: the Reference case, the High Economic Growth case, and the Low Economic Growth case. All three housing starts projections are presented in Figure 9.3.2 . DOE used only the projections from the Reference case to estimate its shipments to new construction. For the period 2041−2047 , DOE froze completions at the level in the year 2040 for all three economic projections.

Figure 9.3.2 Historical and Forecasted Multi

- Family Housing Starts (millions)

DOE deduced historical commercial laundry saturations for the new construction market based on the remaining portion of historical shipments that did not serve as replacement units. (The determination of replacement shipments is discussed in section 9.3.2.2 .) Table 9.3.3 shows

the historical saturation of commercial clothes washers in new multi

- family buildings. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90Housing Starts (millions)

Reference

High

Low9-9 Table 9.3.2 Commercial Clothes Washers: Historical New Multi

- Family Building

Saturations

Year Saturation

1990 18.6%

1991 10.8%

1992 9.8%

1993 11.6%

1994 15.6%

1995 11.9%

1996 17.0%

1997 19.0%

1998 23.7%

Average 15.3 %

DOE assumed that the average saturation over the period 1990−1998 of 15.3 percent
would like ly change during the period 1999 – 2011, due to the increasing saturation of in-unit

washers in multi

- family units as shown by historical data from the Census Burea u’s American Housing Survey (AHS). 6 DOE utilized the in -unit washer trends in multi- family buildings to

estimate the associated trends in the use of commercial clothes washers in multi

- family common area laundry facilities.

Table 9.3.4 shows the saturation of in-unit washers in both the multi-family stock and

new construction from 1997 to 2011. Between 1997 and 2011, t he annual growth rate of

saturation of in-unit washers in new multi

- family construction ( i.e., residential washers used in

new ly constructed multi- family units) is 0.68 percent

. Therefore, DOE assume d that the annual growth rate of saturation of commercial clothes washers is -0.68 percent during the period 2000

– 2011 . DOE also assumed that the average saturation over the period 1999 – 2011 of 14.7 percent would remain constant over the period 2012–2047.

Table 9.3.3 M ulti

- Family In -Unit Washer Saturations

In

- Unit Washer Saturation

Multi -Family Housing 1997 1999 2001 2003 2005 2007 2009 2011

New Construction 70.4% 64.4% 68.7% 68.1% 73.4% 73.5% 78.1% 77.41%

Stock 32.3% 32.7% 35.6% 36.1% 37.3% 37.5% 39.1% 39.56%

Source: AHS, 1997

– 2011

.

Figure 9.3.3 sh ows the historical variation in the new construction saturation as well a the project ed saturation for the years after 1998.9-10 0.0% 5.0% 10.0% 15.0% 20.0% 25.0%Commercial Laundry Saturation in NewMulti-Family Housing (%)

Figure 9.3.3 Commercial Laundry Saturation in New Multi

- Family Housing

9.3.2.2 Replacements

DOE determined shipments to the replacement market using an accounting method that tracks the total stock of units by vintage. DOE estimated a st ock of commercial clothes washers by vintage by integrating historical shipments starting from the year 1972. Over time, some units will be retired and removed from stock, thereby triggering the shipment of a replacement unit. Depending on the vintage, a certain percentage of each type of unit will fail and need to be replaced. To determine when a unit fails, DOE used a survival function based on the product lifetime distributions of the two product applications considered in the analysis—laundromats

and multi -family buildings. DOE based the retirement function on a Weibull probability distribution for the product lifetime.

F or the commercial clothes washer shipments model, DOE combined the maximum and minimum age values for the two product applications based on the market share of each application (85 percent for multi-family housing and 15 percent for laundromats). The survival function that DOE used had an average lifetime of 10.6 years with minimum and maximum values of 6.7 years and 14.6 years, respectively. For a more complete discussion of commercial clothes washer lifetimes, ref er back to section 8.2.3 of Chapter 8.

Figure 9.3.4 shows the survival function that DOE used to estimate replacement shipments.9-11 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 % Surviving by Age

Figure 9.3.4 C ommercial Clothes Washers: Survival and Retirement Functions

9.3.2.3 Model Calibration— Non

- Replacements

To account for the significant drop in shipments after 1998, and to reconcile the historical shipments with the accounting model, DOE assumed every retired unit is not replaced immediately. The market share of retired units not replaced was relatively lar ge due to the significant drop in shipments after 1998 (as discussed in section 9 .3.1). Starting in 1999 and extending to 2011 , DOE estimated the share of retired units that were not replaced. The share of retired units not replaced fluctuated between 16 and 42 percent. Table 9.3.5 shows the share of retired units not replaced between 1999

– 2011.9-12 Table 9.3.4 Commercial Clothes Washers: Historical New Multi

- Family Building

Saturations

Year % of Retired units not replaced

1999 0.0%

2000 16.2%

2001 26.7%

2002 37.8%

2003 29.0%

2004 37.1%

2005 37.2%

2006 40.9%

2007 39.5%

2008 41.6%

2009 40.5%

2010 32.8%

2011 31.8%
Average 3 1.6 %

To determine the share of retired units not replaced after the year 2011 , DOE reviewed

data from the American Housing Survey.

As evident from Table 9.3.4 , the saturation of in-unit washers in new construction has increased by approximately 10 percent, while the saturation in the multi

- family building stock has increased by approximately 23 per cent. Because the saturation of in-unit washers in new

construction has increased relatively slow ly , there must be another driver for the increase of inunit washers in the building stock. DOE postu lates that the growth in in -unit washer saturations

in t he multi

- family stock over the last ten years was caused by conversions of rental property to condominiums, resulting in the gradual phase out or non- replacement of failed commercial

clothes washers in common- area laundry facilities. Based on this apparen t trend, DOE used the average percent of non- replacements over the 1999

– 2011 timeframe —31.6 percent —and

maintained it over the entire projection period of 20 12

– 2047.

9.3.2.4 Base Case Shipments

Figure 9.3.5 shows the projected commercial clothes washer shipments in th e base case (i.e., the case without new energy efficiency standards) and the historical shipments DOE used to

calibrate the projection

. To be clear , all commercial clothes washer shipments, including both top-loading and front-loading units, are represented in the shipments shown in Figure 9.3.5

.9-13 0.00 0.05 0.10 0.15 0.20 0.25Shipments (millions)

Year Replacement Modeled Shipments

New Construction Modeled Shipments Figure 9.3.5 Commercial Clothes Washers: Historical and Base Case Shipments

Forecast

Figure 9.3.6 presents projected base case commercial clothes washer shipments disaggregated into shipments due to retirements and shipments for new construction. The unusual jump in shipments going from 2012 to 2013 is driven primarily by the new construction starts in multifamily housing.

Figure 9.3.6 Commercial Clothes Washers: Disaggregated Base Case Shipments Projection 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28Shipments (million)

Year Historical Shipments

Projected Total Shipments9-14 9.3.3 Market Share Forecast

DOE projected the change in market share between the front -loading and top-loading

units in the base case on the basis of available market share data

. For the base case projection

, DOE f it a curve to estimate the top

- loading market share using data submitted by AHAM for the period between 2005 and 2011, as shown in Figure 9.3.7.

Figure 9.3.7 Historical Market Share of Top -loading Units

For the standards case, both available data and cross

- price elasticities were used to forecast market share changes. Due to insufficient data, DOE was not able to estimate cross-price impacts on the market share of top-loading and front-loading commercial clothes washers and instead relies on its recent analysis performed for residential clothes washers. 7

In order to estimate the impact of price changes on market share, DOE applied a logistic regression method to regress market share on price variables and other attributes. The basic form of a logistic regression is:

p i = � 1

1 + e − � β 0 +β 1 x 1

, i +⋯+β k x k

, i � �

With some simple operations, the model can be transformed to an equivalent log-linear form:

ln � p i
1 − p i � β

0 + β 1 x 1

, i + ⋯ + β k x k

, i y = 5E+239x

- 72.62

R2 = 0.8972

0% 20% 40% 60% 80% 100%

2000 2002 2004 2006 2008 2010 2012 2014Market Share y = 0.8104 x

- 0.116

R

2 = 0.929-15 In this case, is the market share of either top

- or front -loading clothes washers, and is the ratio of clothes washer market shares, i.e., the relative probability of the market share of top

- and

front

- loading clothes washers.

These regression results were used to derive the cross

- price impact of a change in the

front -loading washer price on the top- loading market share (and vice versa). For example, these results suggest that a 10% increase in the price of front- lo ading washers would lead to a 10.7% decrease in top -loading washers’ market share, holding other variables constant and measured as changes from the reference case using average values for each variable. In this case, the frontloading cross- price impact ( percent change in top -loading market share over percent change in

front -loading price) is 1.07. c Similarly, these results indicate that a 20% price increase for top

loading washers would yield a 21.49 % increase in front- loading market share, measured as changes from the variable means. Thus, in this example, the top- loading washer cross

- price impact is also 1.07. See the residential clothes washers 2012 Direct Final Rule Technical Support Document for additional details. 7

Figure 9.3.8 shows forecasted base and standards case market share of commercial clothes washers disaggregated by the two product types. The figures show the standards case for top-loading and front-loading clothes washers, TSL 3 . Given the direct correlation between price and market share, as long as the price changes the market share will also continue to change.

Figure 9.3.8 Forecast of Market Share in Base Case and Standards Case TSL 3 for

Front -Loading (E L 3) and for Top-Loading (E L 2)

c Price elasticity varies according to the reference price and market share

– typically measured as changes from mean values for these variables in the data set. In DOE’s data set, the reference top

- loading washer price is $403.78, the reference front-loading washer price is $823.83, the corresponding reference top -loading washer market share is 71%, and the corresponding reference front washer market share is 29%. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Market Share (%) Market Share of Front

- and Top

- loading Washers

Front Loader

- Base Case Top Loader

- Base Case

Front Loader

- Standard Top Loader

- Standard9-16 9.4 PURCHASE PRICE AND OPERATING COST IMPAC TS

DOE conducted a literature review to establish the likely range of values for the price elasticity of demand for commercial clothes washers. The price elasticity of demand describes the responsiveness of the quanti ty purchased of a product to a change in its price and can be generically defined as the ceteris paribus percent change in shipments given a 1% change in price, or:

e d = P 0 Q 0 ∗ ∂Q

∂P

where P and Q represent price and shipments quantity, res pectively

. 8

DOE believes commercial clothes washers to be highly price inelastic and applies a

default price elasticity of demand of zero for this product , meaning that any cost and price increases resulting from efficiency standards are unli kely to substantially affect the quantity of

washers demanded . As these are somewhat niche products , estimates of commercial clothes

washer price elasticity were not found in the literature, and DOE does not have the necessary data to perform an accurate independent calculation of price elasticity. Instead, the value of zero is based on DOE’s understanding of the commercial clothes washer market and distribution channels, particularly in relation to the demand for laundry services.

To validat e the use of a price elasticity of zero, DOE estimate d an implied commercial clothes washer price elasticity based on known variables describing the market for laundry services. The implied price elasticity is approximated as follows:

Implied e d

, CCW = e d

, LaundrySvc ∗ � Price CCW
Price LaundrySvc �

W here :

= the price elasticity of demand for commercial clothes washers,

= the average price elasticity of demand for laundry service,

= the commercial clothes washers wholesale price (or cost), and = the price (or cost) of providing laundry service.

DOE estimate d an implied price elasticity of demand for commercial clothes washers of approximately -0.03 to -0.12 based on laundry equipment distribution channels and the market for laundry services. These values are provided as alternatives to the default price elasticity of

zero.

Additionally, price elasticity for a given product is not constant over time; numerou s studies have demonstrated decreases in price elasticity for durable goods as the period of time since a price change increases. 9

, 10

, 11 Similarly, decreases in price elasticity have been demonstrated through the stages of a product’s life cycle

. 12 DOE proj ects that any non- zero9-17 commercial clothes washer price elasticity will decrease over time, following the proportional decreases demonstrated for automobiles ( Table 9.4.1 ).

Table 9.4.1 Price Elasticity Decrease after CCW Price Change
Years Following Price Change

1 2 3 5 10 20 Price Elasticity of Demand

- 1.20

- 0.93

- 0.75

- 0.55

- 0.42

- 0.40 Relative Change in Elasticity to 1st

year 1.00 0.78 0.63 0.46 0.35 0.33

Implied Price Elasticity (low)

- 0.031

- 0.024

- 0.015

- 0.007

- 0.002

- 0.001 Implied Price Elasticity (high)

- 0.123

- 0.096

- 0.060

- 0.028

- 0.010

- 0.003
Source: Hymans 1979 and DOE calculations

.

Based on the following equation, DOE estimated standards case shipments by incorporating the impact of the price into the base case shipments projection. Note that in the equation below, the price and the price elasticity are functions of the year

, because both change

with time.


# ( ) 

# ( ) )()(1)()( )( )(

_

_ _ _ _ 

### j RP jejMj NI j Rpl j Ship
RP p BASE p BASE p BASE pSTD

### D ́ + + =

W here:

Ship STD_p (j) = t otal shipments under the standards case of product p in year j

, Rpl BASE_p (j) = units of product p under the base case retired and replaced in year j

, NI BASE_p (j) = number of new construction installations under the base case of product p in year j

, M BASE_p (j) = u nits installed in Market M under the base case of product p in year j ( M represents early replacements for commercial clothes washers ),

e d (j)= p rice elasticity in year j (equals -0.031 or -0.123 for year 1), and

Δ

P(j)= c hange in price due to a standard level in year j

.

9.5 AFFECTED STOCK

The affected stock is the in

- service stock of the appliance or product that is affected by a

TSL . In addition to the projection of product shipments under both the base case and the standards case, the affected stock (which represents the difference in the appliance stock for the

base case and the standards case) is a key output of DOE’s shipments model s. The affected stock quantifies the impact that new product shipments have on the appliance stock due to a

TSL . Therefore, the affected stock consists of those in-service units that are purcha sed in or after the year the standard has taken effect, as described by the following equation:9-18 

## ∑ _

1 _ )( )()( yr Std j

age p p p ageStock jShipjStock Aff

= + =

W here:

Aff Stock p (j) = affected stock of units of product p of all vintages that are operational in

year j

, Ship p (j) = shipments of product p in year j

, Stock p (j) = stock of units of product p of all vintages that are operational in year j

, age = age of the units (years), and Std_yr = e ffective date of the standard.

As noted in this equation, to calculate the affected stock, DOE must define the effective date of the standard. For the NES and NPV results presented in Chapter 11, DOE assumed that new energy efficiency standards will become effective in the year 2018. Thus, all appliances purchased starting o n the first d ay of the year 2018 are affected by the standard level.

9.6 RESULTS

The following sections show the shipments projections for the various TSL s that DOE

considered for each of the products. The TSLs are identified and described in chapter 10.

Table 9.6.1 and Table 9.6.2 show the cumulative shipments impacts over t he period

2018–2047 caused by each of the TSLs considered for top-loading and front-loading commercial clothes washers, respectively

. Figure 9.6.1 compares the shipments projections for the b ase case and each of the three TSL s for top

- loading washers . In Figure 9.6.1 , the difference between the

base case and TSL shipments projections depicts the annual shipments reductions caused by

cross

- price elasticities

. Figure 9.6.2 co mpares the shipments projections for the base case and

each of the three TSLs for front -loading washers.

Table 9.6.1 Top-Loading Commercial Clothes Washers: Cumulative Shipments Impacts

Caused b y Standards over the Period 2018–2047

TSL MEF J2 /IWF (cu.ft/kWh/cycle gal/cu.ft) Cumulative Impacts (thousands)

1 1. 3 5 / 8. 8 0 0.00

2 1. 35 / 8.80 0. 62

3 1. 55 / 6.90

- 86.469-19 Table 9.6.2 Front -Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused b y Standards over the Period 2018–2047

TSL MEF J2 /IWF (cu.ft/kWh/cycle gal/cu.ft) Cumulative Impacts (thousands)

1 1. 80 / 4.50 0.00

2 2.00 / 4.10

- 0.62

3 2.20/ 3.9 0 86.46

Figure 9.6.1 Top -Loading Commercial Clothes Washers: Base Case and TS Shipments Forecasts 0.10 0.10 0.10 0.11 0.11 0.11 0.12 0.12 0.12 0.13Shipments (million)

Year TSL 3 Base Case

TSL 2 TSL 19-20 Figure 9.6.2 Front -Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts 0.05 0.06 0.06 0.07 0.07 0.08 0.08 0.09 0.09 0.10 0.10 0.11 0.11 0.12Shipments (million)

Year TSL 3 Base Case

TSL 2 TSL 19-21 REFERENCES

1 Consortium for Energy Efficiency. Commercial Family -Sized Washers: An Initiative Description of the Consortium for Energy Efficiency , 1998. Boston, MA.

2 U.S. Department of Commerce-Bureau of Economic Analysis. Industry Economic Accounts, Gross- Domestic -Product- (GDP)

- by -Industry-Data, 1998-2011 NAICS Data: GDPbyInd_SHIP_NAICS and SIC Data: GDPbyInd_SHIP_SIC , Commercial Laundry Quantity Index Data, NAICS code 333312. Washington, DC.

< http://www.bea.gov/industry/gdpbyind_data.htm >

3 Association of Home Appliance Manufacturers. AHAM Data on Commercial Clothes Washers for DOE Efficiency Standards Rulemaking, November 12, 2012. Letter to Building Technologies Program, U.S. Department of Energy.

4 U.S. Department of Energy, Chapter 10. Shipment Analysis, Commercial Clothe s Washers Final Rule Technical Support Document, EERE -2006- STD -0127 COMMENT 69.1, 2010-

01-19.

< http://www.regulations.gov/#!documentDetail;D=EERE-2006- STD -0127-0118 >

5 U.S. Department of Energy- Energy Information Administration. Annual Energy Outlook 2013 with Projections to 2040 , April 2013. Washington, DC. DOE/EIA -0383(2013).

< http:/ /www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf >

6 U.S. Census Bureau, Housing and Household Economic Statistics Division. The American Housing Survey, 1997—2011. Washington, DC.

< http://www.census.gov/housing/ahs/data/national.html >

7 U.S. Department of Energy, Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Residential Clothes Washers (Direct Final Rule), Building Technologies Program, Editor. 2012: Washinton, D.C.

8 Varian, H. R., Intermediate Microeconomics: A Modern Approach. 1999. Vol. 5th Edition. W.W. Norton & Company: New York, London.

9 Hymans, S., Consumer Durable Spending: Explanation and Prediction. Brookings Papers on Economic Activity, 1971. 1971(1): pp. 234-239.

10 Fibrich, G., A. Gavious, and O. Lowngart, The Dynamics of Price Elasticity of Demand in the Presence of Reference Price Effects. Journal of the Academy of Marketing Science, 2005. 33(1): pp. 66-78.

11 Gowrisankaran, G. and M. Rysman, Dynamics of Consumer Demand for New Durable Goods. 2009, National Bureau of Economic Research Working Paper: Cambridge, MA.9-22 12 Lilien, G. L. and E. Yoon, An Exploratory Analysis of the Dynamic Behavior of Price Elasticity over the Product Life Cycle: An Empirical Analysis of Industrial Chemical

Products

. Issues in Pricing , 1988: pp. 261-408.10

- i CHAPTER 10 : NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION ......................................................................................................... 10-1

10.2 TRIAL STANDARD LEVEL S ..................................................................................... 10-1

10.3 BASE CASE AND STANDA RDS CASE PROJECTED E FFICIENCIES .................. 10-2

10.4 NATIONAL ENERGY AND WATER SAVINGS ...................................................... 10-4

10.4.1 Definition ....................................................................................................................... 10-4

10.4.2 National Energy Savings Inputs..................................................................................... 10-6

10.4.2.1 Annual Energy and Water Consumption per Unit ....................................... 10-6

10.4.2.2 Shipments ..................................................................................................... 10-8

10.4.2.3 Equipment Stock .......................................................................................... 10-8

10.4.2.4 National Annual Energy and Water Consumption ...................................... 10-8

10.4.2.5 Site

- to

- Power Plant Energy Use Factors ...................................................... 10-8

10.4.2.6 Full

- Fuel

- Cycle Energy Factors ................................................................... 10-9

10.5 NET PRESENT VALUE ............................................................................................. 10-10

10.5.1 Net Present Value Definition ....................................................................................... 10-10

10.5.2 Net Present Value Inputs ............................................................................................. 10-11

10.5.2.1 Total Installed Cost per Unit ...................................................................... 10-12

10.5.2.2 Annual Operating Cost Savings per Unit................................................... 10-13

10.5.2.3 Total Annual Installed Cost Increases ....................................................... 10-14

10.5.2.4 Total Annual Operating Cost Savings ....................................................... 10-14

10.5.2.5 Discount Factors ........................................................................................ 10-15

10.5.2.6 Present Value of Costs ............................................................................... 10-15

10.5.2.7 Present Value of Savings ........................................................................... 10-16

10.6 NATIONAL ENERGY AND WATER SAVINGS AND NE T PRESENT

VALUE RESULTS ...................................................................................................... 10-16

10.6.1 National Energy Savings and Net Present Value Input Summary ............................... 10-16

10.6.2 National Energy and Water Savings Results ............................................................... 10-17

10.6.3 Annual Costs and Savings ........................................................................................... 10-18

10.6.4 Net Present Value Results ............................................................................................ 10-18

LIST OF TABLES

Table 10.2.1 Trial Standard Levels for Commercial Clothes Washers .................................. 10-2

Table 10.3.1 Top

- Loading Commercial Clothes Washers: Base Case and Standards Case Efficiency Distributions in 2018 ............................................................... 10-4

Table 10.3.2 Front- Loading Commercial Clothes Washers: Base Case and Standards Case Efficiency Distributions in 2018 ............................................................... 10-4

Table 10.4.1 National Energy Saving Inputs .......................................................................... 10-610

- ii Table 10.4.2 Top

- Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per -Unit Annual Energy and Water Consumption in 2018 ......................................................................................... 10-7

Table 10.4.3 Front- Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per -Unit Annual Energy and Water Consumption in 2018 ......................................................................................... 10-7

Table 10.4.4 Full

- Fuel -Cycle Energy Multipliers (Based on AEO 2013) ............................ 10-10

Table 10.5.1 Net Present Value Inputs ................................................................................. 10-12

Table 10.5.2 Top

- Loading Commercial Clothes Washer s: Base Case and Standards Case Shipment

- Weighted Average Per

- Unit Total Installed Costs ................. 10-12

Table 10.5.3 Front-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per

- Unit Total Installed Costs ................. 10-13

Table 10.5.4 Top

- Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per -Unit Annualized Repair Costs in

2018................................

................................ .................................................. 10-14

Table 10.5.5 Front- Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per -Unit Annualized Repair Costs in

2018................................

................................ .................................................. 10-14

Table 10.6.1 NES and NPV Inputs ....................................................................................... 10-17

Table 10.6.2 Commercial Clothes Washers: Cumulative NES and NWS Results .............. 10-17

Table 10.6.3 Commercial Clothes Washers: Cumulative NPV Results based on Seven

Percent and Three

- Percent Discount Rates ...................................................... 10-19

LIST OF FIGURES

Figure 10.4.1 Site

- to

- Power Plant Energy Use Factor for Commercial Clothes Washers ....... 10-9 Figure 10.6.1 Non -Discounted Annual Installed Cost Increases and Annual Operating Cost Savings for Top-Loading Clothes Washers , TSL 2 ................................. 10-1810-1 CHAPTER 10. NATIONAL IMPACT ANALYSIS

10.1 INTRODUCTION

This chapter describes the method for estimating national impacts for commercial clothes washers, i.e., the quantity and net value to consumers of future national energy savings (NES) and water savings from possible trial standard levels (TSL s). Results described here include: (1) national energy and water consumption and savings, (2) monetary value of energy savings to

the Nation due to TSL s, (3) increased total installed costs to the Nation due to TSL s, and (4) the net present value (NPV) of energy savings (difference between value of energy savings and increased total installed costs). When NES is discussed throughout this chapter, it includes national water savings in addition to national energy savings.

The U.S. Department of Energy ( DOE ) determi ned both the NES and NPV for all of the

TSLs considered for commercial clothes washers. It performed all calculations for each of the considered products using a Microsoft Excel spreadsheet model, which is accessible on the Internet ( http://www.eere.energy.gov/buildings/appliance_standards/ ). The spreadsheet model, referred to as the National Impact Analysis (NIA) model, combined the calculations for determining the NES and NPV as well as input from the relevant shipments model. Details and instructions for using each of the NIA m odels are provided in Appendix 10B.

The important facets of national energy consumption include: (1) shipments of the equipment, (2) customer respons e to changes in total installed cost ( i.e., purchase price plus installation costs), operating expense, and (3) voluntary programs promoting higher energy efficiency of equipment.

Chapter 9 provides a detailed description of the shipments models that DOE used to project future purchases of commercial clothes washers . Included in the discussion are detailed descriptions of customers’ sensitivities to total installed cost, operating expense, and price elasticities, and how DOE captured them within the mode l.

Concerning voluntary programs, although they may increase the share of energy efficient equipment prior to the implementation date of any new TSL , DOE was not able to obtain information that quantified how such programs affect equipment efficiencies on a national basis. Consequently, DOE did not explicitly incorporate the impact of market- based initiatives into the shipments projections detailed i n c hapter 9.

10.2 TRIAL STANDARD LEVEL S

DOE analyzed the benefits and burdens of three TSLs for commercial clothes washers

. These TSLs were developed using combinations of efficiency levels for the front -loading and top-loading product classes analyzed by DOE. Table 10.2.1 presents the TSLs and the10-2 corresponding efficiency levels for CCW . TSL 3 represents the maximum technologically feasible (“max

- tech”) improvements in energy efficiency for commercial clothes washers

. TSL 2 was chosen as an intermediate level between TSL3 and TSL1 to represent the additional efficiency level that has been analy zed for Front -loading equipment class. TSL 1 was set at efficiency level 1 for both Front-loading and Top-loading equipment class.

Table 10.2.1 Trial Standard Levels for Commercial Clothes Washers

Equipment Type Efficiency Levels ( MEF J2 /IWF)

Baseline

FL: EL0

TL: EL0 TSL1 TSL2 TSL3

FL: EL1

TL:EL1 FL: EL2

TL:EL1 FL: EL3

TL:EL2

Front Loading CCW Units 1.65/5.5 1.80/4.5 2.00/4.0 2.20/3.7

Top Loading CCW Units 1.15/8.9 1.35/8.8 1.35/8.8 1.55/6.9

10.3 B ASE C ASE AND S TANDARDS C ASE P ROJECTED E FFICIENCIES

A key component of DOE’s estimates of NES and NPV is the energy efficiencies projected over time for the base case (without new standards) and each of the standards cases (with new standards). The projected efficiencies represent the annual shipment

- weigh ted energy efficiency of the products under consideration over the projection period ( i.e., from the assumed effective date of a new standard to 30 years after the standard becomes effective). Because key inputs to the calculation of the NES and NPV are d ependent on the estimated efficiencies, these efficiencies are of great importance to the analysis. In the case of the N ES, the per-unit annual energy and water consumption is a direct function of product efficiency. Chapter 7, Energy and

Water Use Deter mination, des cribes how the per

- unit energy and water consumption vary as a function of equipment efficiency. With regard to the NPV, two inputs, the per- unit total installed cost and the per-unit annual operating cost, are dependent on efficiency. The p er

- unit total installed cost is a direct function of efficiency

, while the per -unit annual operating cost, because

it is a f unction of the per-unit annual and water consumption, is indirectly dependent on product efficiency. Section 8.2.1 of chapter 8, Li fe -Cycle Cost (LCC) and Payback Period (PBP) Analysis, describes how per-unit total installed costs vary as a function of efficiency for each of the considered products. The above NES and NPV inputs, as well as all other inputs to the calculation of the NES and NPV, are als o discussed in sections 10.3.1 and 10.4.1.

DOE based the development of the product efficiencies in the base case on the assignment of equipment efficiencies in the base case for the year 2011. In other words, DOE

determined the distr ibution of product efficiencies currently in the marketplace to develop a shipment- weigh ted efficiency for the year 2011. For a complete discussion of the assignment of efficiencies in the base case for each of the considered products, refer to section 8. 2.6 of chapter

8.10-3 Using the shipment-weight ed efficiency for the year 2011 as a starting point, DOE developed base case projected efficiencies based on assumptions regarding future efficiency growth. For the period spanning 2012–2018 (2018 being the assumed effective date of a new standard), DOE assumed that there would be no growth in the shipment- weighted efficiency ( i.e., no change in the distribution of product efficiencies). B ecause there are no historical data to indicate how product efficienci es have changed over time, DOE estimated that projected

efficiencies would remain frozen at the 2018 efficiency level until the end of the projection period (30 years after the assu med effective date

— the year 2047 ). Although DOE recognizes the possibility that product efficiencies may change over time, DOE does not want to speculate as to how these product efficiencies may change without historical information.

For its determination of standards- case projected efficiencies, DOE assumed a “roll -up”

scen ario to establish the shipment- weighted efficiency for the year that standards are assumed to become effective ( i.e., 2018 ). DOE assumed that product efficiencies in the base case that did

not meet the standard under consideration would “roll up” to meet the new standard level. Also, DOE assumed that all product efficiencies in the base case that were above the standard under consideration would not be affected. Using the shipment- weighted efficiency in the year 2018 as a starting point, DOE developed standards- case projected efficiencies based on assumptions regarding future efficiency growth. DOE made the same assumptions regarding standards- case efficiency growth rates as for the base case

— namely that projec ted efficiencies would remain

frozen at the 2018 efficiency level until the end of the projection period. By maintaining the same growth rate for projected efficiencies in the standards case as in the base case ( i.e., zero growth), DOE retained a constant efficiency difference or gap between the two cases over the length of the projection period. Although frozen trends may not reflect what happens to base case and standards case product efficiencies in the future, DOE believes that maintaining a frozen efficiency difference between the base case and standards case provides a reasonable estimate of the impact that standards have on product efficiency.

Table 10.3.1 and Table 10.3.2 show the base case and TSL product efficienc y distributions in the year 2018, corresponding to the integrated modified energy factor ( MEF J2 )

and integrated water facto r ( I WF) of top-loading and front- loading clothes washing equipment, respectively, that DOE used in its national impact analysis for commercial clothes washers. The

TSLs are comp o sed of efficiency levels analyzed in the LCC and PBP a nalysis. The TSLs are

also shown in these tables . Note that DOE considered all the candidate standard levels in the TSLs that it developed for commercial clothes washers. Also included in the table below is the shipment- weighted energy factor ( SWEF ) associated with the base case and each TSL

.10-4 Table 10.3.1 Top-Loading Commercial Clothes Washers: Base Case and Standards Case Efficiency Distributions in 201 8

CSL TSL Efficiencies Market Shares

Base Case Trial Standard Level

MEF J2 IWF 1 2 3

Baseline

- 1.15 8.90 99.5% 0.0% 0.0% 0.0%

1 1, 2 1.35 8.80 0.3% 99.8% 99.8% 0.0%

2 3 1.55 6.90 0.3% 0.3% 0.3% 100.0%

SWEF (MEF) 1.15 1.35 1.35 1.55

SWEF (IWF) 8.89 8.80 8.80 6.90

Table 10.3.2 Front -Loading Commercial Clothes Washers: Base Case and Standards Case Efficiency Distributions in 201 8

CSL TSL Efficiencies Market Shares

Base Case Trial Standard Level

MEF J2 IWF 1 2 3

Baseline

- 1.65 5.20 28.0% 0.0% 0.0% 0.0%

1 1 1.80 4.48 34.0% 62.0% 0.0% 0.0%

2 2 2.00 4.13 38.0% 38.0% 100.0% 0.0%

3 3 2.20 3.92 0.0% 0.0% 0.0% 100.0%

SWEF ( MEF J2 ) 1.83 1.88 2.00 2.20

SWEF (IWF) 4.55 4.35 4.13 3.92

Based on the assumption of a zero-percent growth rate in the projected efficiencies for the base case and all standards cases, DOE maintained the SWEFs shown above in Table 10.3.1

and Table 10.3.2 throughout the entire projection period, i.e., from 2018 to 2047.

10.4 NATIONAL ENERGY AND WATER SAVINGS

DOE calculated the national energy and water savings associated with each potential standard for commercial clothes washers. DOE calculated cumulative energy savings throughout

the forecast period, which extends from 2018 to 2047. The equations shown calculate energy savings; DOE used similar equations to calculate water savings.

10.4.1 Definition

As shown in the following equation, DOE calculates annual national energy and water savings as the difference between two projections: a base case (without new standards) and a10-5 standards case (with new standards). Positive values of NES correspond to energy savings ( i.e., national energy consumption with standards is less than national energy consumption in the base case).

STD BASE y 

### AEC AEC NES

_ 

### =

Cumulative energy and water savings are the sum over the projection period, extending

from the assumed effective date

— i.e., 2018— to 30 years afte r the effective date

— i.e., 2047— of the analysis period . Th e calculation is represented by the following equation s:



### = y cumulative 

### NES NES

= y cumulative 

### NWS NWS

DOE calculated the national annual energy and water consumption by multiplying the number or stock of the given product (by vintage) by its unit energy and water consumption ( UEC or UWC; also by vintage). The calculation of the national annual energy and water consumption is represented by the following equation:

= V V y UEC STOCK AEC

= V V y UWC STOCK AWC

For the above expressions, DOE defined the various quantities as follows:

AEC = annual national energy consumption each year in quadrillion British thermal units (Btus)–or quads—summed over vintages of the product stock,

STOCK V , AWC= annual national water consumption each year in trillion gallons—summed over vintages of the product stock, STOCK V ,

NES = annual national energy savings (quads),

NWS = annual national water savings (gallons),

STOCK V = stock of product (millions of units) of vintage V surviving in the year for which DOE calculated annual energy consumption (vintages range from five to approximately 15.5 years, depending on the retirement function of the washer based on its product applica tion ),

UEC V = annual energy consumption per product in either kilowatt-hours (kWh) or million Btus (MMBtu) (electricity and gas consumption are converted from10-6 site energy to source energy (quads) by applying a time -dependent conversion factor),

UWC V = annual water consumption per product in gallons,

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

y = year in the projection ( i.e., 2018 to 2047 ).

The stock of equipment is dependent on annual shipments and the lifetime of the equipmen t. As described in c hapter 9, DOE conducted shipments projections under the base case and standards cases. DOE determined that the shipments projections under the standards cases were slightly lower than those in the base case projection, due to the high er purchase prices of the more-efficient equipment. In other words, DOE believes that the higher purchase prices would cause some consumers to forego new equipment purchases.

DOE assumed that any drop in shipments caused by standards would result in t he purchase of used machines. Therefore, DOE explicitly accounted for the energy and water consumption of the used washers in the standards cases.

10.4.2 National E nergy Savings Inputs

Table 10.4.1 lists the inputs for the determination of NES.

Table 10.4.1 National Energy Saving Inputs
Annual Energy and Water Consumption per Unit ( UEC and UWC )

Shipments

Equipment Stock ( STOCK V )

National Annual Energy and Water Consumption ( AEC and AWC )

Site

- to

- Source Conversion Factor ( src_conv )

10.4.2.1 Annual Energy and Water Consumption per Unit

DOE presen ted the per-unit annual energy and water consumption as a function of product efficiency in chapter 7, Energy and Water Use Determination, and section 8.2.2.1 of chapter 8. Beca use the per

- unit annual energy and water consumption is directly dependent on efficiency, DOE used the base case and standards case SWEFs presented in section 10.3, in combination with the annual energy and water use data presented in c hapters 7 and 8, to estimate the shipment- weighted average annual per-unit energy and water consumption under the base

case and standards cases.

Using the relationship between clo thes washer MEF J2 and IWF and annual energy and water consumption described in chapter 8, Table 10.4.2 and Table 10.4.3 present the per -unit annual electrical and gas energy use, and water consumption, based on the SWEFs corresponding to the base case and each TSL for top -loading and front-loading washers, respectively. Because establishments that use commercial clothes washers use either electric or gas water heaters and dryers, the per -unit annual energy use values in the se table s are provided10-7 by fuel type. The energy and water use values shown in these tables are weighted

- average values reflecting the market share of washers in multi -family buildings (85 percent) and laundromats (15 percent), as provided by the Consortium for Energy Efficiency (CEE). 1 The

water -heating and drying energy consumption data reflect the percentage of gas and electric water heaters and dryers in the combined multi-family and laundromat market. Based on data from the CEE, in the combined market, 22 and 78 percent of water heaters are electric and gas, respectively, while 34 and 66 percent of dryers are electric and gas, respectively. 3

As described in c hapter 9, section 9.3, DOE assumed that market share of washers in

multi -family buildings and laundromats will remain constant at current levels.

Table 10.4.2 Top-Loading Commercial Clo thes Washers: Base Case and Standards Case Shipment

- Weighted Average Per

- Unit Annual Energy and Water Consumption in 2018

Base

Case Trial Standard Level

1 2 3

SWEF ( MEF J2 ) 1.15 1.35 1.35 1.55
SWEF (IWF) 8.89 8.80 8.80 6.90

Annual Energy

Use Electric (kWh/yr) 1,099 930 930 763

Gas (MMBtu/yr) 7.74 6.74 6.74 6.28

Annual Water Use (1000 gal/ yr) 31.31 30.96 30.96 25.16

Table 10.4.3 Front -Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per

- Unit Annual Energy and Water Consumption in 201 8

Base

Case Trial Standard Level

1 2 3

SWEF ( MEF J2 ) 1.83 1.88 2.00 2.2

SWEF (IWF) 4.55 4.35 4.13 3.92

Annual Energy

Use Electric (kWh/yr) 600 582 561 530

Gas (MMBtu/yr) 4.62 4.55 4.14 4.75

Annual Water Use (1000 gal/ yr) 14.66 14.13 13.32 14.64

As noted in section 10.2, DOE assumed that projected efficiencies in the base case and standards

cases would remain frozen at the efficiency levels in 2018 . Therefore, as the per -unit annual energy and water consumption are a function of efficiency, DOE held the va lues shown in Table

10.4.2 and Table 10.4.3 constant over the projection period (2018–2047 ).10-8 10.4.2.2 Shipments

DOE projected shipments for the base case and all standards cases. Several factors, including total installed costs (purchase price plus installation cost s), operating cost, and equipment lifetime, all impact projected shipments. Chapter 9, Shipments Analysis, describes, in detail, the method DOE used to calculate and generate the shipments projections for each product class.

10.4.2.3 Equipment Stock

The equipment stock in a given year is the number of products shipped from earlier years that survive in the given year. The NIA m odels kee p track of the number of units shipped each

year . DOE assumes that the products have an increasing probability of retiring as they age. The probability of survival as a function of years-since-purchase is the survival function. See c hapter 9, Shipments Analysis, for further details on the survival functions that DOE used in its ana lysis

.

10.4.2.4 National Annual Energy and Water Consumption

The national annual energy and water consumption (AEC or AWC) is the product of the annual energy and water consumption per unit and the number of units of each vintage. This approach accounts for differences in unit energy consumption from year to year. The equation for determining the annual energy consumption was shown in section 1 0.3.1 and is repeated

here . As stated previously, the equation for water consumption are the same as the equation for

ene rgy consumption.



### = V V 

### UEC STOCK AEC

In determining national annual energy consumption, DOE initially calculated the annual energy consumption at the site and then applied a conversion factor to calculate primary energy consumption (see section 1 0.3.2.5).

10.4.2.5 Site

- to

- Power Plant Energy Use Factors

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 or

est ablishment). It then used site energy consumption to calculate primary (source) energy consumption by applying a conversion factor to account for losses associated with the generation, transmission, and distribution of electrici ty

. Th e site

- to -power plant conversion factor is a multiplicative factor used to convert site energy consumption into primary or source energy consumption, expressed in quads (quadrillion Btus). DOE used annual site- to -power plant10-9 conversion factors based on the version of the Nation al Energy Modeling System (NEMS) a that corresponds to EIA’s Annual Energy Outlook 20 13 ( AEO 2013 ). 2 The factors are marginal values, which represent the response of the system to an incremental decrease in consumption. For electricity, the conversion factors change over time in response to projected changes in generation sources ( i.e., the types of power plants projected to pro vide electricity to the Nation ). Figure 10.4.1 shows the site- to -power plant factor s from 2018 to the end of the forecast period. The value reported in AEO f or year 2040 (the last year available in AEO ) was extrapolated through the end of the projection period.

Figure 10.4.1 Site

- to

- Power Plant Energy Use Factor for Commercial Clothes Washers

10.4.2.6 Full

- Fuel

- Cycle Energy Factors

The full- fuel

- cycle (FFC) measure includes point-of- use (site) energy, the energy losses associated with generation, transmission, and distribution of electricity, and the energy consumedin extracting, processing, and transporting or distributing primary fuels. To complete the fullfuel

- cycle by e ncompassing the energy consumed in extracting, processing, and transporting or distributing primary fuels, which we refer to as “upstream” activities, DOE developed FFC multipliers b using the data and projections generated by the National Energy Modeling S ystem

a For more information on NEMS, please refer to the U.S. Department of Energy, Energy Information Administration documentation. A useful summary is National Energy Mo deling System: An Overview 2000

, DOE/EIA

- 0581(2000), March 2000. EIA approves use of the name NEMS to describe only an official version of the model without any modification to code or data. Because this analysis entails some minor code modifications

an d the model is run under various policy scenarios that are variations on EIA assumptions, DOE refers to the model by the name NEMS

- BT (BT is DOE’s Building Technologies Program, under whose aegis this work has been performed). NEMS

- BT was previously calle d NEMS

- BRS.

b FFC multipliers discussed in this chapter relate to the upstream part of the FFC process. 7.400 7.600 7.800 8.000 8.200 8.400 8.600 8.800mn Btu/MWh10-10 (NEMS) used for AEO 201 3 . The AEO provides extensive information about the energy system, including projections of future oil, natural gas and coal supply, energy use for oil and gas field and refinery operations, and fuel consumption and emissions r elated to electric power production. This information can be used to define a set of parameters representing the energy intensity of energy production.

Table 10.4.4 shows the FFC energy multipliers used for commercial clothes washers for selected years. The method used to calculate FFC energy multipliers is described in appendix 10-

A.

Table 10.4.4 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO 2013)

20 18 2020 2025 2030 2035 2040 Electricity (power plant energy use) 1.041 1.041 1.040 1.040 1.041 1.040

Natural Gas (site) 1.102 1.101 1.100 1.098 1.099 1.100

10.5 NET PRESENT VALUE

10.5.1 N et P resent Value Definition

The NPV is the value in the present of a time series of costs and savings. The NPV is described by the equation:



### PVCPVS NPV

_ 

### =

W here:

PVS = present value of operating cost savings (including energy, water, repair, and maintenance costs), and

PVC = present value of increased total installed costs (including equipment and installation).

DOE determined the PVS and PVC according to the following expressions:



## ∑

yy DFOCS PVS  ́=



## ∑

yy DF TIC PVC  ́=

DOE calculated the total annual operating cost savings by multiplying the number or stock of the given product (by vintage) by its per-unit operating cost savings (also by vintage). DOE calculated the total annual installed cost increases by multiplying t he number or stock of10-11 the given product (by vintage) by its per- unit total installed cost increase (also by vintage). The calculation of the annual operating costs savings and total annual installed cost increases is represented with the following equations:



## ∑

V V y UOCS STOCK OCS =



## ∑

y y y UTIC SHIP TIC =

For the above expressions, DOE defined the various quantities as follows:

OCS y = total annual operating cost savings in a given year y summed over vintages of the product stock, STOCK V , TIC y = t otal annual installed cost in year y summed over shipments the product in

year y , S HIP y , DF y = discount factor in year y , STOCK V = stock of product (millions of units) of vintage V surviving in the year for which DOE calculated annual energy consumption (vintages range from five to approximately 15.5 years, depending on the retirement function of the washer based on its product application ),

UOCS V = annual operating cost savings per unit of vintage V

, UTIC y = a nnual total installed cost increase per unit in year y , V = year in which the product was purchased as a new unit, and

y = year in the projection ( i.e., 2018 to 2047 ).

DOE determined the PVC for each year, from the effective date of the standard to the

year 2047 . It determined the PVS f

or each year, from the effective date of the standard to the year when units purchased in 2047 retire. DOE calculated costs and savings as the difference between a standards case and a base case ( i.e., without new standards).

DOE calculated a discount factor from the discount rate and the number of years between the “present” ( i.e., year to which the sum is being discounted) and the year in which the costs and savings occur. The NPV is the sum over time of the discounted net savings.

10.5.2 N et P resent Value Inputs

Table 10.5.1 summarizes the inputs to the NPV calculation.10-12 Table 10.5.1 Net Present Value Inputs

Total Installed Cost per Unit

Annual Operating Cost Savings per Unit

Total Annual Installed Cost Increases

Total Annual Operating Costs

Discount Factor

Present Value of Costs

Present Value of Savings

The increase in the total annual installed cost is equal to the annual change in the per-unit total installed cost (difference between base case and standards case) multiplied by the shipments projected in the standards case. As with the calculation of the NES, DOE assumed that any drop in shipments caused by standards would result in the purchase of used machines.

The total annual operating cost savings are equal to the change in the annual operating costs (difference between base case and standards case) per unit multiplied by the shipments projected in the standards case. The annual operating cost includes energy, water, repair, and

maintenance costs.

10.5.2.1 Total Installed Cost per Unit

DOE first presented the per

- unit total installed cost as a function of product efficiency in

section 8.2.1.5 of c hapter 8. Because the per

- unit total annual installed cost is directly dependent on efficiency, DOE used the base case and standards case SWEFs presented in section 10.2, in combination with the total installed costs presented in chapter 8, to estimate the shipmentweighted average annual per

- unit total installed cost under the base case and standards cases.

The total installed cost includes both the equipment price and the installation cost. DOE based average equipment prices on average manufacturer costs multiplied by average overall markup values. Using the relationship between commercial clothes washer MEF J2 and WF and total installed cost pres ented in chapter 8, Table 10.5.2 and Table 10.5.3 show the per

- unit total

install ed cost based on the SWEFs corresponding to the base case and each TSL for top -loading

and front- loading washers, respectively

.

Table 10.5.2 Top-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per

- Unit Total Installed Costs

Base Case Trial Standard Level

1 2 3
SWEF ( MEF J2 ) 1.15 1.35 1.35 1.55

SWEF (IWF) 8.89 8.80 8.80 6.90

Total Installed Cost (2012$) $1,251 $1,251 $1,251 $1,31310-13 Table 10.5.3 Front -Loading Commercial Clothes Washers: Base Case and Standards

Case Shipment

- Weighted Average Per

- Unit Total Installed Costs

Base Case Trial Standard Level

1 2 3
SWEF ( MEF J2 ) 1.83 1.88 2.00 2.20

SWEF (IWF) 4.55 4.35 4.13 3.92 Total Installed Cost (2012$) $1,853 $1,853 $1,854 $1,885

As noted in section 10.2, DOE assumed that projected efficiencies in the base case and

TSLs remain frozen at the efficiency levels in 2018 . Therefore, because the per

- unit total installed costs are a function of efficiency, DOE held the values constant over the projection peri od (2018–2047 ).

10.5.2.2 Annual Operating Cost Savings per Unit

The per

- unit annual operating cost includes the energy, water, repair, and maintenance

costs.

Annual Energy and Water Costs
DOE determined the per -unit annual energy and water cost savings by taking the per -unit annual energy (and water) consumption savings developed for each product and multiplying it by the appropriate energy (and water) price.

For all of the considered products, estimates of the per- unit annual energy an d water consumption for the base case and each standards case were presented earlier in section 10.3.2.1. As described previously, DOE projected the per -unit annual energy (and water) consumption for the base case and each standards case for all products by freezing the consumption at levels estimated for the year 2018.

Energy and water prices are described in section 8.2.2.2 of chapter 8. DOE projected energy prices based on EIA’s AEO2013 . DOE projected water prices based on trends in the

national wat er price index as provided by the Bureau of Labor Statistics. 3 The energy and water price trends are described in section 8.2.2.3 of chapter 8.

Annual Repair and Maintenance Costs
As described in section 8.2.2.4 of chapter 8, DOE estimated increased repair and

maintenance costs for commercial clothes washers

. DOE estimated that repair costs would increase as a function of equipment efficiency. Using the relationship between clothes washer

IMEF and IWF and repair desc ribed in c hap ter 8, Table 10.5.4 and Table 10.5.5 present the perunit annualized r epair cost based on the SWEFs corresponding to the base case and each TSL for top-loading and front- loading washers, respectively. The repair costs are weighted

- average values reflecting the market share of washers in multi -family buildings (85 percent) and laundromats (15 percent), as provided by the Consortium for Energy Efficiency (CEE).10-14 Table 10.5.4 Top-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per

- Unit Annualized Repair Costs in

2018

Base

Case Trial Standard Level

1 2 3
SWEF ( MEF J2 ) 1.15 1.35 1.35 1.55

SWEF (WF) 8.89 8.80 8.80 6.90 Annualized Repair Cost (2012$) $51 $51 $51 $54

Table 10.5.5 Front -Loading Commercial Clothes Washers: Base Case and Standards Case Shipment

- Weighted Average Per

- Unit Annualized Repair Costs in

2018

Base Case Trial Standard Level

1 2 3
SWEF ( MEF J2 ) 1.83 1.88 2.00 2.20

SWEF (WF) 4.55 4.35 4.13 3.92 Annualized Repair Cost (20 12 $) $81 $81 $81 $82

As noted in section 11.2, DOE assumed that projected efficiencies in the base case and

TSLs remain frozen at the effic iency levels in 2013 . Therefore, because the per

- unit total installed costs are a function of efficiency, DOE held the values constant over the projection period (2018–2047 ).

10.5.2.3 Total Annual Installed Cost Increases

The total annual installed cost increase for any given standards case is the product of the total installed cost increase per unit due to the standard and the number of units of each vintage. This approach accounts for difference s in total installed cost from year to year. The equation for determining the total annual installed cost increase for a given standards case was shown in

section 1 0.4.1 and is repeated here

. 

## ∑

V V UTIC STOCK TIC =

As noted earlier for commercial clot hes washers, DOE accounted for the total installed cost of those establishments that were forced into purchasing used clothes washers due to standards. Therefore, the total annual installed cost increase takes into account the total installed

cost of the used washers that were purchased in lieu of the purchase of new washers.

10.5.2.4 Total Annual Operating Cost Savings

The total annual operating cost savings for any given standards case is the product of the annual operating cost savings per unit due to the standard and the number of units of each vintage. This approach accounts for differences in annual operating cost savings from year to year. The equation for determining the total annual operating cost savings for a given standards

case was shown previously a nd is repeated here

.10-15 

## ∑

V V UOCS STOCK OCS =

As noted earlier , DOE accounted for the energy and water use of those establishments that were forced into purchasing used washers due to standards. Therefore, the total annual operating cost savings take into account the energy and water costs of the used washers that were purchased in lieu of the purchase of new washers.

10.5.2.5 Discount Factors

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

)( )1( 1

yp

y r DF

+ =

W here:

r

= discount rate,

y = year of the monetary value, and

y

P

= year in which the present value is being determined.

DOE estimated national impacts with both a three

- percent and a seven

- percent real discount rate as the average real rate of return on private investment in the U.S. economy. It used these discount rates in accordance with the Office of Management and Budget (OMB)’s guidance to Federal agencies on the development of regulatory analysis (OMB Circular A -4, September 17, 2003), and section E., “Identifying and Measuring Benefits and Costs,” therein.

DOE defines the present year as 2013.

10.5.2.6 Present Value of Costs

The present value of increased installed costs is the annual installed cost increase in each year ( i.e., the difference between the standards case and base case), discounted to the present, and summed for the time period over which DOE is considering the installation of equipment ( i.e., from the effective date of standards, 2018, to the year 2047 ).

The increase in total installed cost refers to both equipment cost and installation cost associated with the higher energy efficiency of equipment purchased in the standards case compared to the base case. DOE calculated annual increases in installed costs as the difference in total installed cost for new equipment purchased each year, multiplied by the shipments in the

standards case.10-16 10.5.2.7 Present Value of Savings

The present value of operating cost savings is the annual operating cost savings ( i.e., the difference between the base case and standards case) discounted to the present and summed over the period from the effective date, 2018 , to the time when the last unit installe d in 2047 is retired

from service.

Savings are decreases in operating costs (including electricity, repair, and maintenance costs) associated with the higher energy efficiency of equipment purchased in the standards case compared to the base case. Total annual operating cost savings are the savings per unit multiplied by the number of units of each vintage surviving in a particular year. Equipment consumes energy over its entire lifetime and , for units purchased in 2047, the consumption includes energy consumed until the unit is retired from service.

10.6 N ATIONAL E NERGY AND WATER S AVINGS AND N ET P RESENT

V ALUE RESULTS

The NIA Model provides estimates of the NES and NPV due to various candidate standards levels. The inputs to the NIA mo del have been discussed earlier in sections 10.3.2 (NES Inputs) and 10.4.2 (NPV Inputs). DOE generated the NES and NPV results using a

Microsoft Excel ® spreadsheet, which is accessible on the Internet ( http://www.eere.energy.gov/buildings/ appliance_standards/). Details and instructions for using the spreadsheet are provided in Appendix 10B.

10.6.1 National Energy Savings and Net Present Value Input Summary

Table 1 0.6.1 summarizes the inpu ts to the NIA model. For each input a brief description of the data source is given.10-17 Table 10.6.1 NES and NPV Inputs

Input Data Description
Shipments Annual shipments from Shipments Model. (S ee c hapter 9

. )

Effective Date of Standard 2018

. Base Case Forecasted Efficiencies SWEF determined in the year 2011 . SWEF held const ant over forecast period of 2011

– 2047 . (See s ection 10 .2.)

Standards Case Efficiencies Roll

- up scenario assumed for d etermining SWEF in the year 2018 for

each standards case and for each of th e considered products. SWEF

held constant ov er forecast period of 2018

– 2047 . (See s ection 10 .2.)
Annual Energy Consumption per Unit Annual weighted

- average values are a function of SWEF. (See

s ection 10 .3.2.1.)
Total Installed Cost per Unit Annual weighted

- av erage values are a function of SWEF. (See

s ection 10 .4.2.1.)
Energy and Water Cost per Unit Annual weighted

- average values are a function of the annual energy consumption per unit and energy (and water) prices. (See s ection

8.2.2.3 of c hapter 8 for energy and water prices.)
Repair Cost and Maintenance Cost per Unit A nnual weighted

- average values are a function of SWEF. (See

s ection 8.2.2.4 of c hapter 8 for repair and maintenance costs.)

Escalation of Energy and Water Prices Energy Prices: E IA AEO20 13 forecasts (to 2040 ) and extrapolation to

2047 . (See s ection 8.2.2.3 of c hapter 8.) Water Prices: Linear extrapolation off historical trend in national water price index. (See s ection 8.2.2.3 of c hapter 8.)

Energy Site

- to

- Source Conversion Con version varies yearly and is generated by DOE/EIA’s NEMS* program (a time series conversion factor; includes electric generation, transmission, and distribution losses).

Discount Rate 3 percent and 7 percent real.

Present Year Future expenses are discounted to yea r 2013

.

* Chapter 1 5 on the Utility Impact Analysis and the Environmental Assessment Report provide more detail on NEMS.

10.6.2 National Energy and Water Savings Results

The following sections provide NES results and national water savings (NWS) results for

the TSLs analyzed for commercial clothes washers . NE S results are cumulative to 2047 and are shown as primary energy savings. NWS results are expressed in tr illions of gallons. DOE based the inputs to the NIA model on weighted- average values, yielding results that are discrete point values, rather than a distribution of values as in the LCC and PBP Analysis.

Table 10.6.2 shows the NES and NWS results for the TSL s analyzed for commercial

clothes washers. The results are based on the estimate that multi -family buildings and laundromats comprise 85 percent and 15 percent of the commercial clothes washer market, respectively.

Table 10.6.2 Commercial Clothes Washers: Cumulative NES and NWS Results

Trial Standard Levels Top

- load Front

- load

TSL 1 TSL 2 TSL 3 TSL 1 TSL 2 TSL3
Source Energy Saving 2018 to 2047 (quads) 0.09 0.09 0.16 0.01 0.02 0.01
Water Saving (2018 to 2047) (trillion gallon) 0.01 0.01 0.22 0.01 0.04

- 0.0110-18 10.6.3 Annual Costs and Savings

To illustrate the basic inputs to the NPV calculations, Figure 10.6.1 presents the nondiscounted annual installed cost increases and annual operating cost savings at the national level

for TSL 3 for top-loading washers. The figure also shows the net savings, which is the difference between the savings and costs for each year. The annual equipment cost is the increase in the total installed cost for products purchased each year over the period 2018–2047. The annual operating cost savings is the savings in operating costs for products operating in each year. The NPV is the difference between the cumulative annual discounted savings and the cumulative annual discounted costs. DOE could create figures like the one shown below for

each of the stand ard cases.

Figure 10.6.1 Non

- Discounted Annual Installed Cost Increases and Annual Operating Cost Savings for Top -Loading Clothes Washers, TSL 2

10.6.4 Net Present Value Results

The following sections provide NPV results for the candidate standards levels considered for the product classes of commercial clothes washers. Results are cumulative and are shown as the discounted value of these savings in dollar terms. DOE based the inputs to the NIA m odel on weighted -average values, yielding results that are discrete point values, rather than a distribution of values as in the LCC and PBP Analysis. - 0.0200 0.0300 0.0800Billion 2012$ Operating Cost Savings

Increased Equipment Cost

Net Savings10-19 The present value of increased total installed costs is the total annual installed co st increase ( i.e., the difference between the standards case and base case), discounted to the present, and summed over the time period in which DOE evaluates the impact of standards ( i.e., from the

e ffective date of standards, 2018, to the year 2047 ).

S avings are decreases in operating costs (including energy and water) associated with the higher energy efficiency of equipment purchased in the standards case compared to the base case. Total operating cost savings are the savings per unit multiplied by t he number of units of each vintage ( i.e., the year of manufacture) surviving in a particular year. Energy and water consumption are taken into account over the equipment’s entire lifetime. . For units purchased through 2047, the operating cost includes energy and water consumed until the last unit is retired

from service.

Table 10 .6.3 presents the NPV results for the trial standard levels considered commercial clothes washers. As with the NES results, NPV results are presented by TSL.

Table 10.6.3 Commercial Clothes Washers: Cumulative NPV Results based on Seven

Percent and Three

- Percent Discount Rates

Trial Standard Levels Top

- load Front

- load

TSL 1 TSL 2 TSL 3 TSL 1 TSL 2 TSL 3 NPV @ 3% ( billion 2012$ ) 0.60 0.59 2.13 0.12 0.34

- 0.13 NPV @ 7% ( billion 2012$) 0.26 0.26 0.91 0.05 0.14

- 0.0610-20 REFERENCES

1. Consortium for Energy Efficiency, Commercial Family -Sized Washers: An Initiative Description of the Consortium for Energy Efficiency , 1998. Boston, MA.

2. Energy Information Administration, Annual Energy Outlook 2013 with Projections to

2040 , 2013. Washington, DC. Report No. DOE/EIA-0383.

< http://www.eia.gov/forecasts/ aeo/pdf/0383(2013).pdf >

3. U.S. Department of Labor- Bureau of Labor Statistics, Consumer Price Indexes, Item: Water and sewerage maintenance, Series Id: CUUR0000SEHG01, U.S. city average (not seasonally adjusted) , 2013. Washington, DC. < http://www.bls.gov/cpi/home.htm#data >11

- i CHAPTER 11. LIFE

- CYCLE COST SUBGROUP ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION ......................................................................................................... 11-1

11.2 SUBGROUP DEFINITION

................................ ........................................................... 11-1

11.3 INPUTS TO THE SUBGROUP ANALYSIS FOR SMAL L BUSINESSES ............... 11-1

11.3.1 Small Business Statistics for Commercial Laundry ....................................................... 11-1

11.3.2 Small Company Discount Rates .................................................................................... 11-3

11.4 RESULTS FOR THE CONSIDERED COMMERCIAL CLOTHES WASHER

SUBGROUP .................................................................................................................. 11-5

LIST OF TABLES

Table 11.3.1 Coin-operated laundries & drycleaners: Number of Establishments and Receipts by Nonemployer and Employer Status ............................................... 11-2

Table 11.3.2 Establishment and Firm Size: Legal Form of Organization .............................. 11-2

Table 11.3.3 Establishment and Firm Size: Receipts/Revenue Size of Establishments ......... 11-3

Table 11.3.4 Small Company Discount Rates and Small Cap Premium ................................ 11-4

Table 11.4.1 Top

- Loading Commercia l Clothes Washers : LCC and PBP Results for Small Companies ............................................................................................... 11-5

Table 11.4.2 Front - Loading Commercial Clothes Washers : LCC and P BP Results for Small Companies ............................................................................................... 11-611-1 CHAPTER 11. LIFE

- CYCLE COST SUBGROUP ANALYSIS

11.1 INTRODUCTION

Chapter 8 describes the Life -Cycle Cost (LCC) and Payback Period (PBP) A nalysis that examines impacts of energy efficiency standards on the U.S. population. In analyzing the potential impact of new or amended standards on residentia l and commercial consum ers, DOE

further evaluates the impact on identifiable groups of consumers (i.e., subgroups) that may be disproportionately affected by a national standard level. The LCC subgroup analysis evaluates impacts by analyzing the LCC and PBPs for subgroup s commercial consumers

. For commercial clothes washers, DOE identified one consumer subgroup: small business es that own commercial

laundries.

DOE determine d the impact on consumer subgroups for commercial clothes washers using the LCC Spreadsheet Model, which allows for the examination of particular consumer subgroups. For the small business subgroup, consumer discount rates different than the general population were used . The inputs for the considered subgroup are descr ibed in section 11.3.

11.2 SUBGROUP DEFINITION

DOE analyzed the potential effects of standards on one subgroup: small commercial

clothes washer businesses.

For small commercial clothes washer businesses, DOE analyzed the potential impacts of standards by conducting the analysis with different discount rates, as small businesses do not have the same access to capital as larger businesses. As described below, DOE estimated that for businesses purchasing commercial clothes washers, small companies have an aver age discount rate which is 3.5 percent higher than the industry average.

11.3 INPUTS TO THE SUBGRO UP ANALYSIS FOR SMAL L BUSINESSES

11.3.1 Small Business Statistics for Commercial Laundry

The small business administration (SBA) defines a small business by its annual receipts or its number of employees. Coin- operated laundries and drycleaners are considered small if its

annual revenue is less than $7 million. 1

This industry is comprised of: (1) establishments primarily engaged in operating facilities

with coin

- operated or similar self

- service laundry an d dry- cleaning equipment for customer use11-2on the premises and (2) establishments primarily engaged in supplying and servicing coinoperated or similar self -service laundry and dry cleaning equipment for customer use in places of business operated by others, such as apartments and dormitories. Table 11.3.1 provides the

statistics on coin -operated laundromats and drycleaners. 2

Table 11.3.1 Coin

- operated laundries & drycleaners : Number of Establishments and Receipts by Non

- employer and Employer Status

All Firms Non

- employers* Employers

Establishments Receipts ($1,000) Establishments Receipts ($1,000) Establishments Receipts ($1,000)

25,087 4,249,811 14,126 896,158 10,961 3,353,653

* Non

- employers are establishments or firms with no paid employees.

Source: U.S. Census Bureau, 2007 Economic Census. Other Services (Except Public Administration): Subject

Series

– Estab and Firm Size: Receipts/Revenue Size of Establishments for the United States: 2007

.

The following two tables provide further detail on the establ ishment firm size and receipts and revenues. Table 11.3.2 provides the establishment firm size by the type of legal organization. 3 Table 11.3.3 provides the firm size by the amount of sales per year. 4

Table 11.3.2 Establishment and Firm Size: Legal Form of Organization

Legal form of organization Number of

Establishments Receipts/ Revenue ($1,000) Number of employees

All establishments (employers) 10,961 3,353,653 40,309
Corporations 6,871 2,494,268 26,589
Individual proprietorships 2,631

-- -- Partnerships 1,456 456,629 6,001
Other legal forms of organization 3

-- -- Source: U.S. Census Bureau, 2007 Economic Census. Other Services (Except Public Administration): Subject

Series

– Estab and Firm Size: Legal Form of Organization for the United States: 2007

.11-3 Table 11.3.3 Establishment and Firm Size: Receipts/Revenue Size of Establishments

Meaning of sales/receipts/revenue size categories or establishments operated the entire year Number of

establishments Receipts/

R evenue ($1,000) Number of employees

All establishments (employers) 10,961 3,353,653 40,309
Establishments operated for the entire year 9,396 3,149,554 37,610

Less than $10 0

, 00 0 2,244 140,825 3,776

$10 0 ,000 to $24 9 ,999 3,750 596,516 11,289

$25 0 ,000 to $4 9 9,999 1,739 547,216 8,335

$50 0 ,000 to $9 9 9,999 730 360,393 5,537

$1 ,0 00,000 to $2

, 49 9 ,999 350 201,981 2,926

$2

, 5 0 0,000 to $4 ,9 99,999 * 114 88,630 1,023

$5 ,0 00,000 to $ 9, 999,999 204 100,194 705

$1 0,000,000 or more 265

-- -- * Note that the bins reported by the Census Bureau do not allow DOE to specifically identify establishments with revenues of less than $7 million; $5 million is used as a cut off instead.

Source : U.S. Census Bureau, 2007 Economic Census. Other Services (Except Public Administration): Subject

Series

– Estab and Firm Size: Receipts/Revenue Size of Firms for the United States: 2007

.

B ased on the data in Table 11.3.3 , approximately 95 percent of establishments (operated for the entire year) have receipts/revenues of le ss than $5 million. But only 61 percent of industry receipts/revenues are earned by establishments that make less than $5 million. Therefore, even though almost all establishments are small, a significant percent of the revenues are captured by large establishments (i.e., those that earn at least $5 million). Because small establishments capture less than 70 percent of total industry revenues, small establishments do not represent the entire industry and should be analyzed as a subgroup.

11.3.2 Small Company Discount Rates

To calculate discount rates for small companies that purchase commercial clothes

washers , DOE used the same methodology as the general population of commercial clothes washer consumers as presented in Chapter 8, section 8.2.4.3. Although the methodology is appropriate, t he capital asse t pricing model (CAPM) a used in section 8.2.4 for the general population underestimates the cost of capital for small companies. In CAPM, the risk premium β is used to account for the higher returns associated with greater risk. However, for small companies, particularly very small companies, historic returns have been significantly higher than the CAPM equation predicts. This additional return can be accounted for by adding a size premium to the cost of equity for small firms:



## ( ) S ERP Rk

fe

+ ́+=

k

e = Cost of equity,

a See 8.2.4.3 for more extensive description of CAPM and its parameters.11-4 R

f = Expected return on risk- free assets, β = R isk coefficient of the firm,

ERP = Equity risk premium, and

S = Size Premium.

DOE obtained size premium data from Ibbotson Associates’ Stocks, Bonds, Bills, and Inflation 2009 Yearbook

. 5 For the period of 1926-2008 , the average size premium for the smallest companies in all industries is 5.81%, implying that on average, historic performance of small companies has been 5.81% higher than the CAPM estimate of the small company cost of equity. b

DOE calculated the real weighted average cost of capital (as described in chapter 8) using the cost of equity including a size premium for small companies instead of the CAPM cost of equity. 2 DOE estimates that small companies have average discount rates 1.26% higher than the sector average in the educational services sector, 1.47% higher than the sector average in the hotels/gaming sector, and 2.50% higher than the sector average in the REIT sector (see Table

11.3.4 ). Because there was insufficient data to estimate it separately, DOE assumes that the small company discount rate premium for Person al Services is the same as the average for all small companies in educational services, hotels/gaming, and REIT (1.74%).

Table 11.3.4 Small Company Discount Rates and Small Cap Premium

Sector Discount Rate

Average Std Dev Small Company

Discount Rate

Premium

Educational

Services Entire Sector 5.37% 0.35%

1.26% Small Companies 6.63% 3.61%

Hotel/Gaming Entire Sector 6.94% 0.41%

1.47% Small Companies 8.41% 2.68%

REIT Entire Sector 6.19% 0.06%

2.50% Small Companies 8.69% 2.03%

Personal

Services Entire Sector 4.67%

-- 1.74% Small Companies 6.41%

--

In chapter 8, DOE estimated the average discount rates to be 5.37% for educational services customers, 6.94% for hotel/gaming customers, 6.19% for REIT customers, and 4.67% for personal services customers. Applying the small capitalization discount rate premiums presented

in Table 11.3.4 , the average small business discount rate is 6.63% in the educational services

b In this calculation

, “ small companies ” are defined as the decile of companies with the lowest market capitalization (considered either Decile 10 or Decile 1, according to different sources); the Ibbotson Associates’ definition of Decile 10 includes companies with market capitalization of less than or equal to $218.53 million. 111-5 sector, 8.41% in the hotel/gaming sector, 8.69% in the REIT sector, and 6.41% in the personal

services sector.

11.4 RESULTS FOR THE CONSIDERED COMMERCIAL CLOTHES W ASHER

SUBGROUP

Table 11.4.1 and Table 11.4.2 below summarize the top-loading commercial clothes

w asher LCC results for multi

- family establishments and commercial laundries that are defined as

a small business

. For the two efficiency levels for top-loading washers and the three efficiency

levels for front

- loading washers , the LCC results provide the average or mean LCC, the relative LCC savings or costs, and the percentage of consum ers that are burdened with net costs, realize net savings, or incur no impact.

Table 11.4.1 Top-Loading Commercial Clothes Washers : LCC and PBP Results for Small Companies

Candidate

Standard

Level TSL Life

- Cycle Cost Life

- Cycle Cost Savings Payback Period (Years)**

Average

Installed

Price Average Operati ng Cost Average

LCC Average Savings Customers with

Median Average Net Cost No Impact* Net

Benefit

Multi

- Family Application

Baseline

- $1,251 $4,069 $5,320

- - - - - -
1 1, 2 $1,251 $3,833 $5,084 $236 0% 1% 99% 0.0 0.0

2 3 $1,313 $3,268 $4,582 $737 0% 0% 100% 0.61 0.66

Commercial Laundry Application

Baseline

- $1,251 $3,596 $4,847

- - - - - -
1 1, 2 $1,251 $3,451 $4,702 $144 0% 1% 99% 0.0 0.0

2 3 $1,313 $2,914 $4,228 $618 0% 0% 100% 0.56 0.53

* “No impact” means that the base case forecast commercial clothes washer assigned to the building has greater efficiency than the level indicated, so the household is not affected.

** Based on the payback calculation, a very small change in operating cost can result in a few extremely large paybacks, which will skew the average payback. In these cases, median is probably a better indicator.11-6 Table 11.4.2 Front -Loading Commercial Clothes Washers : LCC and PBP Results for Small Companies

Can didate

Standard

Level TSL Life

- Cycle Cost Life

- Cycle Cost Savings Payback Period (Years)**

Average

Installed

Price Average Operati ng Cost Average

LCC Average Savings Customers with

Median Average Net Cost No Impact* Net

Benefit

Multi

- Family Application

Baseline

- $1, 853 $2,634 $4,487

- - - - - - 1 1 $1, 853 $2,424 $4,278 $209 0% 73% 27% 0.00 0.00

2 2 $1, 854 $2,254 $4,108 $262 0% 39% 61% 0.02 0.02

3 3 $1, 885 $2,381 $4,266 $3 49% 0% 51% 3.66 3.92

Commercial Laundry Application

Baseline

- $1, 853 $2,387 $4,240

- - - - -
1 1 $1, 853 $2,198 $4,051 $187 0% 73% 27% 0.00 0.00

2 2 $1, 854 $2,055 $3,909 $225 0% 39% 61% 0.01 0.02

3 3 $1, 885 $2,182 $4,067

- $20 72% 0% 28% 7.33 7.95

* “No impact” means that the base case forecast commercial clothes washer assigned to the building has greater efficiency than the level indicated, so the household is not affected.

** Based on the payback calculation, a very small change in operating cost can result in a few extremely large paybacks, which will skew the average payback. In these cases, median is pro bably a better indicator.

The small company subgroups shows a similar trend in average LCC differences and consumer impacts (i.e., percentage of consumers significantly or insignificantly impacted) as the overall sample. In general, the benefits of movi ng to a higher efficiency are less in the case of the

small business subgroups.11-7 REFERENCES

1 U.S. Government Printing Office. Title 13: Business Credit and Assistance, Part 121—Small Business Size Regulations, Section 121.201—Small Business Size Standards by NAICS Industry, Subsector 812—Personal and Laundry Services, 812310—Coin-Operated Laundries and Drycleaners, 2013. Washington, DC.

< http://www.ecfr.gov/cgibin/retrieveECFR?gp=&SID=49c0a05ac640b0e9d6f8946ceb23bf45&n=13y1.0.1.1.17.1&r= SUBPART&ty=HTML#13:1.0.1.1.17.1.268.10 >

2 U.S. Census Bureau, 2007 Economic Census. Industry Statistics Sampler, NAICS 81231, Coin-operated laundries and drycleaners , 2007. Washington, DC.

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_20 07_US_00A1&prodType=table >

3 U.S. Census Bureau, 2007 Economic Census. Sector 81: Other Services (Except Public Administration): Subject Series - Estab & Firm Size: Legal Form of Organization for the

United States: 2007 . Washington, DC.

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_20 07_US_81SSSZ7&prodType=table >

4 U.S. Census Bureau, 2007 Economic Census. Sector 81: Other Services (Except Public Administration): Subject Series - Estab & Firm Size: Receipts/Revenue Size of

Establis hments for the United States: 2007 . Wash ington, DC.

< http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_20 07_US_81SSSZ4&prodType= table >

5 Ibbotsons Associates. Stocks, Bonds, Bills, and Inflation 2007 Yearbook, 2007. Chicago, IL.CHAPTER 12. MANUFACTURER IMPACT ANALYSIS

12.1 INTRODUCTION

............................................................................................. 12-1

12.2 METHODOLOGY

............................................................................................ 12-1

12.2.1 Phase I: Industry Profile..................................................................................... 12-2

12.2.2 Phase II: Industry Cash -Flow Model and Interview Guide ............................... 12-2 12.2.2.1 Industry Cash- Flow Analysis

............................................................... 12-2

12.2.2.2 Interview Guide ................................................................................... 12-3

12.2.3 Phase III: Subgroup Analysis............................................................................. 12-3

12.2.3.1 Manufacturing Interviews

.................................................................... 12-3

12.2.3.2 Revised Industry Cash

- Flow Analysis

................................................. 12-3 12.2.3.3 Manufacturer Subgroup Analysis ........................................................ 12-4

12.2.3.4 Manufacturing Capacity Impact

.......................................................... 12-5

12.2.3.5 Employment Impact

............................................................................. 12-5 12.2.3.6 Cumulative Regulatory Burden ........................................................... 12-6

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

.............................. 12-6

12.3.1 Market Model Challenges

.................................................................................. 12-6

12.3.2 Consumer Behavior ........................................................................................... 12-7

12.3.3 Customer Satisfaction Impacts

.......................................................................... 12-7

12.3.4 Technology Limits

............................................................................................. 12-7

12.3.5 Competitive Impacts

.......................................................................................... 12-8

12.4 GRIM INPUTS AND ASSUMPTIONS

............................................................ 12-8

12.4.1 Overview of the GRIM

...................................................................................... 12-8

12.4.2 Sources for GRIM Inputs ................................................................................... 12-9 12.4.2.1 Corporate Annual Reports ................................................................... 12-9 12.4.2.2 Standard and Poor’s Credit Ratings ..................................................... 12-9 12.4.2.3 Shipment Model ................................................................................... 12-9

12.4.2.4 Engineering Analysis

......................................................................... 12-10

12.4.2.5 Manufacturer Interviews

.................................................................... 12-10

12.4.3 Financial Parameters

........................................................................................ 12-10

12.4.4 Corporate Discount Rate .................................................................................. 12-11

12.4.5 Trial Standard Levels

....................................................................................... 12-13

12.4.6 NIA Shipment Forecast.................................................................................... 12-13

12.4.6.1 Base

- Case Shipments Forecast

.......................................................... 12-14

12.4.6.2 Standards- Case Shipments Forecast

.................................................. 12-14

12.4.7 Production Costs .............................................................................................. 12-15

12.4.8 Conversion Costs and Stranded Assets ............................................................ 12-16

12.4.8.1 Commercial Clothes Washer Conversion Costs ................................ 12-16

12.4.9 Markup Scenarios ............................................................................................ 12-17 12.4.9.1 Preservation of Gross Margin Percentage Markup Scenario ............. 12-18 12.4.9.2 Preservation of EBIT Markup Scenario ............................................. 12-18

12.5 INDUSTRY FINANCIAL IMPACTS

............................................................ 12-19

12.5.1 Introduction ...................................................................................................... 12-19

12.5.2 Commercial Clothes Washer Industry Financial Impacts

............................... 12-20

12 -i12.6 IMPACTS ON THE LOW

- VOLUME COMMERCIAL CLOTHES WASHER

MANUFACTURER

........................................................................................ 12-22

12.7 OTHER IMPACTS

.......................................................................................... 12-24

12.7.1 Employment ..................................................................................................... 12-24

12.7.2 Production Capacity ......................................................................................... 12-27

12.7.3 Cumulative Regulatory Burden ....................................................................... 12-27 12.7.3.1 DOE Regulations for Other Products Produced by Commercial Clothes

Washer Manufacturers

......................................................................... 12-28

12.7.3.2 Other Regulations That Could Impact Commercial Clothes Washer

Manufacturers

...................................................................................... 12-28

12.8 CONCLUSION

................................................................................................ 12-29

12.8.1 Commercial Clothes Washers

.......................................................................... 12-29

LIST OF TABLES

Table 12.2.2 SBA and NAICS Classification of Small Businesses Potentially Affected by This Rulemaking ................................................................................................ 12-5 Table 12.4.1 Financial Parameters based on 2006–2012 Weighted Company Financial

Data .................................................................................................................. 12-10 Table 12.4.2 Cost of Equity Calculation ...................................................................... 12-12

Table 12.4.3 Cost of Debt Calculation......................................................................... 12-12

Table 12.4.4 Trial Standard Levels for Commercial Clothes Washers ....................... 12-13

Table 12.4.6 Total Base- Ca se 2018 NIA Shipments ................................................... 12-13

Table 12.4.7 Base-Case Distribution of Efficiencies for Front- Loading Commercial

Clothes Washers in 2018 ................................................................................. 12-14 Table 12.4.8 Base-Case Distribution of Efficiencies for Top- Loading Commercial

Clothes Washers in 2018 ................................................................................. 12-14 Table 12.4.9 MPC Breakdown for Equipment Class 1 – Front- Loading Commercial

Clothes Washers

................................ ............................................................... 12-15 Table 12.4.10 MPC Breakdown for Equipment Class 2 - Top

- Loading Commercial

Clothes Washers

................................ ............................................................... 12-16 Table 12.4.34 Product Conversion Costs for Equipment Class 1 – Front- Loaders by EL

................................ ................................ .......................................................... 12-17 Table 12.4.35 Product Conversion Costs for Equipment Class 2 – Top

- Loaders by EL 12-

17 Table 12.4.36 Capital Conversion Costs for Equipment Class 1 – Front-Loaders by EL12-

17 Table 12.4.37 Capital Conversion Costs for Equipment Class 2 – Top

- Loaders by EL . 12-

17 Table 12.4.40 Preservation of EBIT Markups for Equipment Class 1: Front-Loading

Commercial Clothes Washers .......................................................................... 12-18 Table 12.4.41 Preservation of EBIT Markups for Equipment Class 2: Top-Loading

Comm ercial Clothes Washers .......................................................................... 12-18 Table 12.5.1 Manufacturer Impact Analysis for Commercial Clothes Washers –

Preservation of Gross Mar gin Percentage Markup Scenario ........................... 12-20

12

- iiTable 12.5.2 Manufacturer Impact Analysis for Commercial Clothes Washers – Preservation of EBIT Markup Scenario ........................................................... 12-21 Table 12.7.1 Potential Changes in the Total Number of Domestic Commercial Clothes

Washer Production Workers in 2018 ............................................................... 12-26 Table 12.7.2 Other DOE and Federal Actions Affecting the Commercial Clothes Washer Industry ............................................................................................................ 12-28

LIST OF FIGURES

Figure 12.4.1 Using the GRIM to Calculate Cash Flow ................................................ 12-8 Figure 12.5.1 Annual Industry Net Cash Flows for Commercial Clothes Washers (Preservation of Gross Margin Markup Scenario) ........................................... 12-21 Figure 12.5.2 Annual Industry Net Cash Flows for Commercial clothes washers (Preservation of EBIT Markup Scenario) ........................................................ 12-22 Figure 12.7.1 Commercial Clothes Washer Domestic Production Employment by Year

................................ ................................ .......................................................... 12-26

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. 6295(o)(2)(B)(i) )

The statute also calls for an assessment of the impact of any lessening of competition as determined in writing by the Attorney General. Id . DOE conducted a manufacturer impact analysis (MIA) to estimate the financial impact of more strin gent energy conservation standards

on manufacturers of commercial clothes washer s, and assess the impact of such standards on direct employment and manufacturing capacity.

The MIA has both quantitative and qualitative aspects. The quantitative part of the MIA primarily relies on the Government Regulatory Impact Model (GRIM), an industry cash- flow model adapted to the equipment classes covered by this rulemaking. The GRIM inputs include information on industry cost structure, shipments, and pricing strategies. The GRIM’s key output is the industry net present value (INPV), which is the sum of discounted industry annual cash

flow s over the analysis period . The model estimates the financial impact of more stringent energy conservation standards by comparing ch anges in INPV between a base case and the various trial standard levels (TSLs) in the standards case. The qualitative part of the MIA addresses product characteristics, manufacturer characteristics, market and product trends, as well as the impact of stand ards on subgroups of manufacturers.

12.2 METHODOLOGY

DOE conducted the MIA in three phases. Phase I, “Industry Profile,” consisted of preliminary research directed at characterizing the commercial clothes washer manufacturing industry. This research involved c ollecting data on market share, sales volumes and trends, pricing, employment, and the industry financial structure.

In Phase II, “Industry Cash Flow Model and Interview Guide ,” DOE created a framework

GRIM to analyze the economic impact of amended energy conservation standards on the

commercial clothes washer manufacturing industry as a whole. The DOE also developed a manufacturer interview guide to gather additional information on the potential impacts on

manufacturer s in Phase III

.

In Phase III, “Subgroup Impact Analysis,” DOE interviewed manufacturers representing

an estimated 98 percent of the commercial clothes washer market. Interviewees includedmanufacturers with various market shares and product focus, providing a representative crosssection of the industry. During interviews, DOE discussed financial topics specific to each manufacturer and obtained each manufacturer’s view of the industry. The interviews provided DOE with valuable information for evaluating the impacts of amended energy conservation standards on manufacturer cash flows, investments, and employment.

12 -112.2.1 Phase I: Industry Profile

In Phase I of the MIA, DOE prepared a profile of the commercial clothes washer industry that built upon the market and technology assessment prepared for this rulemaking. (See chapter

3 of this NOPR Technical Support Document (TSD)). Before initiating the detailed impact studies, DOE collected information on the present and past market structure and characteristics of the industry, tracking trends in market share, product attributes, product shipments, manufacturer markups, and the cost structure for various manufacturers.

The profile also included a top-down analysis of manufacturers in the industry using Security and Exchange Commission (SEC) 10– K filings

, a Standard & Poor ’s (S&P) stock

reports, b and corporate annual reports released by both public and privately held companies. DOE used this and other publicly available information to derive preliminary financial inputs for the GRIM ( e.g. revenues; cost of goods sold; depreciation; selling, general and administrative expenses (SG&A); and research and development (R&D) expenses).

12.2.2 Phase II: Industry Cash

- Flow Model and Interview Guide

Phase II focused on the financial impacts of amended energy conservation standards on

the commercial clothes washer manufacturing industry as a whole. Amended energy conservation standards can affect manufacturer cash flows in three distinct ways: (1) by creating

a need for increased inv estment, (2) by raising production costs per unit, and (3) by altering revenue due to higher per -unit prices and/or possible changes in sales volumes. DOE created a

framework GRIM to analyze the economic impact of amended energy conservation standards on

the commercial clothes washer manufacturing industry as a whole. In p reparing the GRIM , DOE used the financial values derived during Phase I and the shipment assumptions from the N IA. Additionally, DOE prepared a written guide for manufacturer interviews to collect additional

data critical to develop ing other inputs for the GRIM

.

12.2.2.1 Industry Cash -Flow Analysis

The GRIM uses several factors to determine a series of annual cash flows ove r a period from the announcement year of the amended energy conservation standards until thirty years after the standards’ compliance date. INPV is the sum of these annual cash flows discounted by the industry weighted average cost of capital. Inputs to the GRIM include the manufacturing costs, markups, and shipment forecasts developed in other analyses as well as the industry weighted average financial parameters developed in Phase I . DOE derived the manufacturing costs from the engineering analysis as pre sented in chapter 5 of this NOPR TSD , information provided by the industry, publicly available financial reports, and interviews with manufacturers. To examine the range of possible impacts, DOE developed alternative markup scenarios based on discussions with manufacturers. DOE’s shipments analysis, presented in chapter 9 of the

NOPR TSD, provided the basis for the shipment projections. DOE derived the financial parameters using publicly available reports and revised them using information received during

confidential manufacturer interviews. DOE used the GRIM to compare INPV in the base case

a A vailable online at www.sec.gov

. b A vailable online at www2.standardandpoors.com

.

12 -2with INPV at various TSLs (the standards cases). The difference in INPV between the base and standards cases represents the financial impact of the amended standard on manufacturers.

12.2.2.2 Interview Guide

During Phase III of the MIA, DOE interviewed manufacturers of commercial clothes

washer s to gather information on the effects of amended energy conservation on revenues and finances, direct employment, capital assets, and i ndustry competitiveness. Before the interviews,

DOE distributed an interview guide to representatives of each participating manufacturer. The interview guide provided a starting point to help identify relevant issues and understand the impacts of amended e nergy conservation standards on individual manufacturers or subgroups of

manufacturers. The information DOE received from these meetings is protected by nondisclosure agreements and resides with DOE’s contractors. The topics covered as part of these

inter views include (1) key issues related to this rulemaking; (2) engineering and life cycle cost; (3) manufacturer markups and profitability; (4) financial parameters; and (5) conversion costs. The interview guide is presented in appendix 12- A.

12.2.3 Phase III: Sub group Analysis

While conducting the MIA, DOE interviewed a representative cross-section of

commercial clothes washer manufacturers. The MIA interviews broadened the discussion to include business-related topics. DOE sought to isolate key issues and concerns, to obtain feedback from industry on the approaches used in the GRIM, and to identify key manufacturer subgroups for analysis.

12.2.3.1 Manufacturing Interviews

The information gathered in Phase I and the cash- flow analysis performed in Phase II are supplemented with information gathered from manufacturer interviews in Phase III. The interview process provides an opportunity for interested parties to express their views on important issues privately, allowing confidential or sensitive information to be considered in the rulemaking process.

DOE used these interviews to tailor the GRIM to reflect the unique financial

characteristics of the commercial clothes washer manufacturing industry. Companies with various market shares and product focus were interviewed to pro vide a representation of the industry. Interviews were scheduled well in advance to provide every opportunity for key individuals to be available for comment. Although a written response to the questionnaire was acceptable, DOE sought interactive interview s, as they help to clarify responses and identify additional issues. The resulting information provides valuable inputs to the GRIM.

12.2.3.2 Revised Industry Cash -Flow Analysis

In Phase II of the MIA, DOE provided manufacturers with preliminary 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 -312.2.3.3 Manufacturer Subgroup Analysis

Using average cost assumptions to develop an industry cash flow estimate is not adequate for assessing differential impacts among subgroups of manufacturers. Small, low-volume manufacturers, or manufacturers exhibiting a cost structure that differs significantly from the industry average could be more negatively affected. Ideally, DOE would consider the imp act on every manufacturer individually; however, it typically uses the results of the industry characterization to group manufacturers exhibiting similar characteristics. During the interview process, DOE discussed the potential subgroups and subgroup memb ers that have been identified for the analysis. DOE looked to the manufacturers and other stakeholders to suggest what subgroups or characteristics are the most appropriate for the analysis.

Small

- Business Manufacturer s

DOE used the Small Business Administration (SBA) small business size standards as amended by the Office of Management and Budget on January 1, 2012, and effective January 7, 2013, and the North American Industry Classification System (NAICS) code, presented in Table

12.2.2 , to determine whether any small entities would be affected by the rulemaking. c For the equipment classes under review, the SBA bases its small business definition on the total number of employees for a business including the total employee count of a parent company and its subsidiaries. An aggregated business entity with fewer employees than the listed limit is

considered a small business.

As there is currently an energy conservation standard for commercial clothes washers,

DOE used its own publically available Compliance Certification Database 1 as a starting point to identify manufacturers of commercial clothes washers. Additionally, DOE used public

certification databases provided by the Cal ifornia Energy Commission (CEC) 2 and ENERGY

STAR 3 to identify commercial clothes washer manufacturers. DOE then checked this list of

commercial clothes washer manufacturers against the employee limit for small businesses using reports from vendors such as Dun & Bradstreet. DOE also consulted publicly available data from the Small Business Association (SBA) to determine the presence of any additional small businesses in the industry. Further, DOE asked interested parties and industry representatives if they were aware of other small business manufacturers and checked any companies identified against the small business criteria

.

For the Framework document , DOE considered businesses with primary NAICS code 333312, “ Commercial Laundry, Dry C leaning , and Pressing Machine Manufacturing .” A search of companies operating under this 2007 NAICS code indicated that there was potentially one small business that manufactures commercial clothes washers covered by this rulemaking. DOE has since confirmed that this small business is not a manufacturer, and thus will not be considered for the purposes of this small business analysis. In 2012, commercial laundry manufacturing was rolled into NAICS code 333318, “ Other Commercial and Service Industry

c The size standards are available on the SBA’s website at www.sba.gov/content/table

- small

- business

- size

standards

.

12 -4Machinery Manufacturi ng,” d DOE has updated its search for small business manufacturers, using this new NAICS classification and corresponding size limit provided by the Small

Business Administration. During this updated research, DOE did not identify any manufacturer

of comme rcial clothes washer s that qualif ies as a small business as specified by the SBA employee limits

.

Table 12.2.1 SBA and NAICS Classification of Small Businesses Potentially Affected by This Rulemaking

Industry Description Revenue Limit Employee Limit NAICS Commercial Laundry, Dry

- cleaning , and Pressing Machine Manufacturing N/A 500 333312

Other Commercial and Service Industry Machinery Manufacturing N/A 1,000 333318

Low

- Volume Manufacturers

While no small business manufacturers of commercial clothes washers were identified during interviews, o ne manufacturer was identified as having significant commercial clothes

washer market share, but much low er revenues than its diversified competitors. This

manufacturer ’s business is highly concentrated in the production of both commercial and residential laundry products. Still , it is not a small business per the SBA definition for

manufacturers of commercial clothes washers . DOE subsequently assigned this manufacturer its

own sub- category, the “Low -Volume Manufacturer” ( LVM ), to evaluate the disproportionate impacts of a mended standards for commercial clothes washers on this manufacturer. The analysis of impacts on this subgroup is found in section 12.6

.

12.2.3.4 Manufacturing Capacity Impact

One possible outcome of amended energy conservation standards is the obsolescence of existing manufacturing assets, including too ling and production equipment. The manufacturer interview guide contains a series of questions to help identify impacts of amended standards on manufacturing capacity, specifically capacity utilization and plant location decisions in the United States and North America, with and without amended standards; the ability of

manufac turers to upgrade or remodel existing facilities to accommodate the new requirements; the nature and value of any stranded assets; and estimates for any one

- time changes to existing plant, property, and equipment (PPE). DOE’s estimates of the one- time capital changes and

stranded assets affect the annual cash flow projections in the GRIM. These estimates can be

found in section 12.4.8 ; DOE’s discussion of the capacity impact can be found in section 12.7.2

.

12.2.3.5 Employment Impact

The impact of amended energy conservation standards on employment is an important consideration in the rulemaking process. To assess how domestic direct employment patterns might be affected, the inter views explored current employment trends in the commercial clothes

washer industry. The interviews also solicited manufacturer views on changes in employment

d NAICS code 333318 includes all manufacturing of commercial and service industry equipment, except optical instruments and lenses, and photographic and photocopying equipment , and has a size limit o f 1,000 employees, as defined by the Small Business Administration.

12 -5patterns that may result from more stringent standards. The employme nt impacts section of the interview guide focused on current emp loyment levels associated with manufacturers at each production facility, expected future employment levels with and without amended energy conservation standards, and differences in workforce skills and issues related to the retraining of employees. The e mployment impacts are reported in section 12.7.1

.

12.2.3.6 Cumulative Regulatory Burden

DOE seeks to mitigate the overlapping effects of regulation on manufacturers of commercial clothes washers. These effects may be the result of other regulatory actions affecting commercial clothes washers, or of amended energy conservation standards for other products and equipment made by the same manufacturers. DOE identified regulations relevant to

commercial clothes washer manufacturers using its own research and discussions with

manufacturers . A d iscussion of the cumulative regulatory burden of energy conservation standards and the impact on manufacturers of multiple, product- specific regulatory actions can

be found in section 12.7.3

.

12.3 MANUFACTURER IMPACT ANALYSIS KEY ISSUES

Each MIA interview starts by asking: “What are the key issues for your company regarding this energy conservation standards rulemaking?” This question prompts manufacturers to identify the issues they feel DOE should explore and discuss further during the interview. The following sect ion describes key issues manufacturers cited for all equipment classes under

review.

12.3.1 Market Model Challenges

The majority of the manufacturers interviewed emphasized that the profit structure of the

commercial clothes washer market fundamentally opposes i ncreased levels of product efficiency. Manufacturers sell their products either to route-operators or to distributors. Route-operators then

lease the machines to multi -family housing unit owners under 5- year to 15- year contract s, and typically provide a 1- 2 day service guarantee on their machines. In contrast, distributors sell

commercial clothe s washers to owners of laundromats.

The profits of both route-operators and laundromat owners are driven by throughput, which is maximized by small capacity machines with short cycle times (less than 35 minutes). In addition to maximizing throughput, one manufacturer noted that consistency of cycle times is necessary for ensuring the correct number of washers and dryers in a given premise or laundromat. Thus, commercial clothes washer manufacturers are constrained by capacity and cycle time limits in any effort to increase the efficiency of their machines

.

Also, due to the length of route- operators lease contracts with their customers, if ener gy efficiency improvements necessitate an increase in manufacturing selling price, any required replacement of units before lease contracts are expired will likely squeeze route -operators’ profits, as they will not be able to pass-through increased unit co sts to lessees. One manufacturer

asserted that in instances when route- operators and laundromat owners pass -through the costs of energy efficiency improvements, this will have a disproportionate negative impact on lower

12 -6income populations , as the majority of commercial laundry users tend to be from low er income

groups.

Finally, most manufacturers affirm that higher efficiency machines are more complex and will require more time and money to repair . Additionally, efficiency changes, such as reduced

water le vel, are likely to be ill

- received by end users and will lead to increases in service calls

and failures. Both outcomes will again potentially cut into route- operator and laundromat owner profits.

12.3.2 Consumer Behavior

All m anufacturers noted that energy efficiency efforts are inherently less effective in the

commercial clothes washer market than in markets for residential appliances, including

residential clothes washers . This is due to the usage patterns of commercial clothes washer end

users, reflecting the fact that end users: 1) do not own the machines, and 2) pay by the load to use

machines . Such usage patterns include putting too much soap into machines (leading to “suds lock”), overfilling machines with oversized loads, choosing to use hot water when unnecessary, and washing clothes twice to deal with suds lock.

Platform changes and reduced water levels of higher efficiency products exacerbate these

issues. One manufacturer notes that there is a steep learning curve for end users relating to adaptation to low-water machines. For instance, end users should be using high efficiency detergents in recommended quantit ies , but are unlikely to do so . As mentioned in the previous section, concerns that machines are not functioning properly will lead to increased service calls.

Another manufacturer noted that end user dissatisfaction with high efficiency products may drive the need for selectable cycle modifiers, which would allow end users to choose less efficient settings in order to reach an acceptable level of cleaning performance to resolve the performance issues caused by their incorrect usage of the machines.

12.3.3 Customer Satisfaction Impacts

All of the manufacturers interviewed expressed concerns that future energy conservation standards would have an adverse impact on cleaning performance and reliability, and would thus compromise consumer utility. One manufacturer noted that reaching the max tech level would require higher spin speeds, which would decrease the reliability of the product. Two manufacturers expressed concerns that the max tech level for top loaders pushes the boundary of acceptable water level in terms of both cleaning performance and market acceptance. The lower water level of max tech products would necessitate lighter loads in order to maintain cleaning performance. A lighter load size requirement would contradict consumer tendencies to overload machines, as mentioned in section 12.3.2

.

12.3.4 Technology Limits

One manufacturer stated that technology has not changed since the last commercial clothes

washers standard came into effect in January 2013 , which places limits on the types of efficiency

12 -7improvements that can be made realistically. Another manufacturer affirms that products currently at the max tech are not truly commercial products.

12.3.5 Competitive Impacts

Manufacturers expressed concerns relating to competitive impacts caused by future energy conservation standards. One manufacturer noted that a conservative approach to meeting amended energy conservation standards would result in a competitive disadvantage for them

relative to other manufacturers . Also, the LVM predicts that it will be impacted disproportionately by any commercial clothes washer standard since its revenue is so closely tied

to commercial clothes washers. More detail is provided on the impact on the LVM in s ection

12.6 of this TSD.

12.4 GRIM INPUTS AND ASSU MPTIONS

The GRIM serves as the main tool for assessing the impacts on industry due to amended energy conservation standards. DOE relies on several sources to obtain inputs for the GRIM. Data and assumptions from these sources are then fed into an accounting model that calculates the industry cash flow both with and without amended energy conservation standards.

12.4.1 Overview of the GRIM

The basic structure of the GRIM, illustrated in Figure 12.4.1 , is an annual cash flow analysis that uses manufacturer prices, manufacturing costs, shipments, and industry financial information as inputs, and accepts a set of regulatory conditions such as changes in costs, the need for additional investments, and changes in associated margins. The GR IM spreadsheet uses a number of inputs to arrive at a series of annual cash flows, beginning with the base year of the analysis, 2013, and continuing to 2047, 30 years after the compliance year of the rulemaking. The model calculates the INPV by summing th e stream of annual discounted cash flows during this period. 4

Figure 12.4.1 Using the GRIM to Calculate Cash Flow

The GRIM projects cash flows using standard accounting principles and compares INPV

between the base

- case and the standard

- case scenario s. The difference in INPV between the base case and the standard case(s) represents the estimated financial impact of the amended energy

12 -8conservation standards on manufacturers. Appendix 12-B provides more techni cal details and

user information for the GRIM.

12.4.2 Sources for GRIM Inputs

The GRIM uses several different sources for data inputs in determining industry cash flow. These sources include corporate annual reports, company profiles, U.S. Census data, credit

rat ings, the shipments model, the engineering analysis, and the manufacturer interviews.

12.4.2.1 Corporate Annual Reports

Corporate annual reports for publicly held companies are freely available to the general public through the SEC as filings of F orm 10 -K. Addition ally, some privately held companies publish annual financial reports on their corporate websites. DOE developed initial financial inputs to the GRIM by examining the publicly available annual reports of companies primarily engaged in the manufacture of home appliances whose combined product range includes

commercial clothes washer s. As these companies do not provide detailed information about their individual product lines, DOE used the aggregate financial information at the corporate level in developing it s initial estimates of the financial parameters to be used in the GRIM. In doing so, DOE assumes that the industry

- average figures calculated for these companies were representative of manufacturing for commercial clothes washers. These figures were later revised using feedback from interviews to be representative of the commercial clothes washer manufacturing industry. DOE used corporate annual reports to derive the following initial inputs

to the GRIM:

• Tax rate ;

• Working capital;

• SG&A ;

• R&D ;

• Depreciation ;

• Capital expenditures; and

• Net PPE

.

12.4.2.2 Standard and Poor ’s 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 weighted average 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). The model relied on historical shipments data for commercial

clothes washer s. Chapter 10 of the NOPR TSD de scribes the methodology and analytical model DOE used to forecast shipments.

12 -912.4.2.4 Engineering Analysis

DOE conducted the engineering analys i s for this rulemaking using the efficiency

- level approach, combined with the cost-assessment approach, to develop a cost for each efficiency

level for commercial clothes washer s. During this analysis, DOE used a manufacturing cost model to develop manufacturer production cost (MPC) estimates for commercial clothes

washer s. The analysis yielded the labor, materials, overhead, and total production costs for products at each efficiency level. The engineering analysis also estimated a manufacturer markup to determine the manufacturer selling price (MSP) for each product at every efficiency level. Chapter 5 of the NOPR TSD describes the engineering analysis in detail.

12.4.2.5 Manufacturer Interviews

As part of the MIA, DOE conducted interviews with a representative cross-section of manufacturers. Through these discussions, DOE obtained information to determine and verify GRIM input assumptions. Key topics discussed during the interviews and reflected in the GRIM

include:

• Capital conversion costs (one- time investments in PPE); • Product conversion costs (one-time investments in research, product development, testing, and marketing); • Product cost structure, or the portion of the MPCs related to materials, labor, overhead, and depreciation costs;

• Projected total shipment and shipment distribution mix; and

• MPCs estimated in the engineering analysis.

12.4.3 Financial Parameters

In the manuf acturer interviews, DOE used the financial parameters from 2006 to 2012 for

three manufacturers of commercial clothes washer s which command a combined market share of over 95 percent as a starting point for determining the commercial clothes washer industr y financial parameters. The industry financial parameters were determined by weighting each manufacturer’s individual financial parameters by their respective market share, and correcting for the fraction of the market that was not represented. Table 12.4.1 below shows the data used to determine the initial financial parameter estimates.

Table 12.4.1 Financial Parameters based on 2006–2012 Weighted Company Financial Data

Parameter Industr y Weighted Average Manufacturer A Manufacturer B Manufacturer C

Tax Rate (% of Taxable Income) 29.2 34.8 23.5 17.6

Working Capital (% of Revenue) 9.5 15.7 3.2 2.4

SG&A (% of Revenue) 11.7 14.1 9.2 17.2

12

- 10R&D (% of Revenue) 2.5 2.3 2.7 1.7

Depreciation (% of Revenue) 3.7 4.3 3.1 4.9

Capital Expenditures (% of Revenue) 2. 1 2.1 2.1 5.6

Net Property, Plant, and Equipment (% of Revenues) 16. 0 15.0 16.9 22.6

During interviews, manufacturers were asked to provide their own figures for the parameters listed in Table 12.4.1

. Where applicable, DOE adjusted the financial parameters according to the manufacturers’ feedback.

12.4.4 Corporate Discount Rate

A company’s assets are financed by a combination of debt and equity, and the wei ghted average cost of capital (WACC) represents the minimum rate of return necessary to cover the debt and equity obligations manufacturers use to finance operations. The WACC is the total cost of debt and equity weighted by their respective proportions in the capital structure of the

company.

DOE estimated the WACC for the commercial clothes washer manufacturing industry

based on three representative companies, using the following formula:

WACC = After -Tax Cost of Debt x (Debt Ratio) + Cost of Equity x (Equity Ratio)

The cost of equity is the rate of return that equity investors (including, potentially, the company) expect to earn on a company’s stock. These expectations are reflected in the market price of the company’s stock. The capital asset pricing m odel (CAPM) provides one widely used means to estimate the cost of equity. According to the CAPM, the cost of equity (expected return) is:

Cost of Equity = Risk- free Rate of Return + β x Risk Premium

where:

Risk

- free rate of return is the rate of return on a “safe” benchmark investment, typically

considered the short- term Treasury Bill (T

- Bill) yield. In practice, investors use a variety of different maturity T- Bills to estimate the risk

- free rate. DOE used the 10

- year T- Bill return because it captures lon g- term inflation expectations and is less volatile than short

- term rates. The

risk

- free rate is estimated to be approximately 5. 2 percent, which is the average 10- year T- Bill

return between 1928 and 2012.

Risk premium is the difference between the expected return on stocks and the risk- free

rate of return . DOE used the average annual return on the S&P 500 between 1928 and 2012 as the expected return on stocks to arrive at an estimated market risk premium of 6.1 percent

.

12

- 11Beta (β) is the correlation between the movement in the price of the stock and that of the broader market. In this case, Beta equals one if the stock is perfectly correlated with the S& P 500

market index. A Beta lower than one means the stock is less volatile than the market index. Values for Beta are only available for publicly traded companies.

DOE used the capital asset pricing model to calculate the cost of equity for th ree commercial clothes washer manufacturers whose combined market share is over 95 percent

. DOE determined that the industry

- average cost of equity for the commercial clothes washer industry is 15.7 percent (see Table 12.4.2 ).

Table 12.4.2 Cost of Equity Calculation

Parameter Industry Weighted Average Manufacturer

A Manufacturer

B Manufacturer

C

(a) Average Beta 1.7 2 1.75 1.75 1.03

(b) Yield on 10 Year T

- Bill (1928

– 2012) (%) 5.16

(c) Market Risk Premium (1928

– 2012) (%) 6.10

Cost of Equity (b) + [(a)*(c)] (%) 15.67

Equity/Total Capital (%) 59.67 45.87 69.20 75.54

Bond ratings are a tool to measure default risk and arrive at a cost of debt. Each bond rating is associated with a particular spread. One way of estimating a company’s cost of debt is to treat it as a spread (usually expressed in basis points) over the ri sk

- free rate. DOE used this

method to calculate the cost of debt for three manufacturers by using S&P and other estimates of corporate credit ratings and adding the relevant spread to the risk- free rate.

Since proceeds from debt issuance are tax deductibl e, DOE adjusted the gross cost of debt by the industry-average tax rate to determine the net cost of debt for the industry. DOE

determined that the after

- tax industry- average cost of debt for the commercial clothes washer industry is 4.9 percent

. Table 12.4.3 presents the derivation of the cost of debt and the capital structure of the industry ( i.e. the debt ratio (debt/total capital)).

Table 12.4.3 Cost of Debt Calculation

Parameter Industry Weighted Average Manufacturer

A Manufacturer

B Manufacturer

C

S&P Bond Rating

-- BBB BBB BBB

(a) Yield on 10 year T

- Bill (1927

– 2011) (%) 5.16

12

- 12(b) Gross Cost of Debt (%) 6.76 6.76 6.76 6.76

(c) Tax Rate (%) 28.14 34.82 23.53 20.54

Net Cost of Debt (b) x[1

- (c)] (%) 4.8 6

Debt/Total Capital (%) 40.33 54.13 30.80 24.46

Correcting for an inflation rate of 3.1 percent over the analysis period, DOE’s calculated

value for the commercial clothes washer industry’s inflation

- adjusted WACC and the initial

estimate of the discount rate is 8.2 percent. During interviews, manufacturers were asked to provide their estimate of the discount rate. Subsequently, DOE adjusted the discount rate to 8.6

percent

.

12.4.5 Trial Standard Levels

DOE developed TSLs to analyze the impact on manufacturers of amended energy efficiency standards for two equipment classes of commercial clothes washer s. Table 12.4.4

present s the TSLs and the corresponding equipment class efficiency levels based on Modified Energy Factor (10 CFR part 430, subpart B, appendix J2).

TSL 3 is comprised of the max

- tech efficiency level s for both equipment classes . TSL 2 is comprised of efficiency level 2 for front

- loading commercial clothes washers and efficiency level 1 for top

- loading commercial clothes washers

. TSL 1 is comprised of efficiency level 1 for each equipment class.

Table 12.4.4 Trial Standard Levels for Commercial Clothes W asher s

Equipment Class Base Case TSL 1 TSL 2 TSL 3

Front

- Loading

Commercial Clothes Washers Efficiency Level Baseline EL 1 EL 2 EL 3

MEF (using Appendix J2) 1.65 1.8 2.0 2.2

Top

- Loading

Commercial Clothes Washers Efficiency Level Baseline EL 1 EL 1 EL 2

MEF (using Appendix J2) 1.15 1.35 1.35 1.55

12.4.6 NIA Shipment Forecast

The GRIM estimates manufacturer revenues based on total -unit-shipment forecasts and the distribution of these values by efficiency level. Changes in the efficiency mix at each standard level are a key driver of manufacturer finances. For this analysis, the GRIM used commercial clothes washer shipment data from the NIA

. Chapter 9 of the NOPR TSD explains DOE’s calculations of total shipments in detail. Table 12.4.6 shows total shipments forecasts for both equipment classes of commercial clothes washer s in 2018, the year new standards for

commercial clothes washers would take effect

.

Table 12.4.5 Total Base- Case 2018 NIA Shipments

12

- 13Equipment Class Total Industry Shipments

Front

- Loading

Commercial Clothes Washers 78,112

Top

- Loading

Commercial Clothes Washers 115,566

12.4.6.1 Base- Case Shipments Forecast

As part of the shipment analysis, DOE estimated the distribution of shipments by efficiency level for both equipment classes of commercial clothes washer s. DOE held the basecase energy efficiency distribution constant throughout the forecast period. Table 12.4.7 and

Table 12.4.8 show the base-case distributions of shipments by effi ciency level estimated in the

NIA for the commercial clothes washer equipment classes.

Table 12.4.6 Base- Case Distribution of Efficiencies for Front -Loading Commercial Clothes

W asher s in 2018

Efficiency Level Baseline EL 1 EL 2 EL 3

MEF 1.65 1.8 2 2.2

% of Shipments 28 .0 % 34 .0 % 38 .0 % 0 .0 %

Table 12.4.7 Base- Case Distribution of Efficiencies for Top-Loading Commercial Clothes

W asher s in 2018

Efficiency Level Baseline EL 1 EL 2

MEF 1.15 1.35 1.55

% of Shipments 99.5% 0.25 % 0.25 %

12.4.6.2 Standards

- Case Shipments Forecast

To examine the impact of amended energy conservation standards on shipments, which in turn affects the INPV, DOE used the base-case shipments described in the previous section as a point of comparison for shipments forecast in the standards case. For each TSL described in the standards case, DOE used the shipments forecasts developed in the NIA for commercial clothes

washer s. DOE assumed the presence of cross

- price elasticity in determin ing efficiency distributions for the standards case, meaning that a portion of shipments moves from one equipment class to the other as the former becomes comparatively more expensive to produce. The remaining portion of shipments for products that fall below the amended energy conservation standards are assumed to “roll-up” to the new standards efficiency level for a given

12

- 14equipment class on the compliance date e and thereafter.

As in the shipmen ts analysis, DOE assumed no relative price elasticity in the commercial

clothes washer market, meaning that amended energy conservation standards that increase the

first cost of commercial clothes washer s would lead to no change the number of total shipmen ts.

12.4.7 Production Costs

Changes in the MPCs of commercial clothes washers can affect revenues, gross margins, and cash flow of the industry, making product cost data key GRIM inputs for DOE’s analysis. In the engineering analysis, DOE created separate cost cu rves for the two commercial clothes

washer equipment classes using data from tear -downs to develop both the baseline MPCs and the incremental costs that correspond to the proposed design options. Generally, manufacturing higher efficiency products is more costly than manufacturing baseline products due to the use of more complex components.

The cost model disaggregated the MPCs at each efficiency level into material, labor, overhead, and depreciation. For materials, DOE used the incremental component and raw material costs that correspond to the proposed design options at each EL. For labor, DOE estimated the labor contribution at each EL by examining how the proposed design options may influence manufacturing and assembly practices. For depreciation, DOE used a depreciation

value that is consistent with historical information in SEC 10

- Ks. Th e remainder of total overhead was allocated to factory overhead.

Later, manufacturers validated these estimates and assumptions during interviews. DOE used the resulting MPCs and cost breakdowns as described in section 12.4.2.4 above, and further detailed in chapter 5 of the NOPR TSD, for each efficiency level analyzed in the GRIM.

The MSP is comprised of production costs (the direct manufacturing costs or MPCs), nonproduction costs (indirect costs including SG&A), and profit. DOE calculated the MSPs for

commercial clothes washer s by multiplying the MPCs by the appropriate manufacturer markup for that product. Table 12.4.9 and Table 12.4.10 show the production cost estimates used in the GRIM for the representative equipment classes for commercial clothes washer s.

Table 12.4.8 MPC Breakdown for Equipment Class 1 – Front -Loading Commercial

Clothes Washers

EL MEF WF Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL1 1.80 4.5 $433. 6 $95.93 $ 31.9 $26 7.0 $828.4 1.2 85 $1,064. 5

EL2 2.00 4.0 $433. 9 $95.88 $31.9 $2 67.0 $828.7 1.2 85 $1,064. 9

EL3 2.20 3.7 $449.4 $97.56 $32. 7 $268. 3 $84 8.0 1.2 85 $1,089.6

e The estimated compliance date for the commercial clothes washer energy conservation standard is 2018.

12

- 15Table 12.4.9 MPC Breakdown for Equipment Class 2 - Top-Loading Commercial Clothes

Washers

EL MEF WF Materials Labor Depreciation Overhead MPC Mfr. Markup MSP

EL1 1.35 8.8 $235. 2 $64. 1 $20.1 $203.0 $522.4 1.2 85 $671.3

EL2 1.55 6.9 $268.7 $7 4.0 $21.6 $197.0 $561.3 1.2 85 $721. 3

12.4.8 Conversion Costs and Stranded Assets

Amended energy conservation standards typically cause manufacturers to incur one

- time conversion costs to bring their production facilities and equipmen t designs into compliance with new regulations. For the MIA, DOE classified these one- time conversion costs into two major groups: product conversion costs and capital conversion costs. Product conversion costs are investments in research, development, testing, marketing, and other non- capitalized costs focused on making equipment designs that comply with the amended energy conservation standard. Capital conversion costs are investments in property, plant, and equipment needed to adapt or change existing production facilities so that new equipment designs can be fabricated and assembled. In the instance where changes to energy conservation standards result in the obsolescence of manufacturing capital, the un- depreciated value of any obsolete equipment is

considered a stranded asset. In addition to product and capital conver sion costs, stranded assets also factor into the GRIM’s calculation of annual cash flows. The following sections describe the inputs DOE used in the GRIM in greater detail.

12.4.8.1 Commercial C lothes W asher Conversion Costs

DOE based its estimates of the conversion costs required to meet each TSL on confidential information received during manufacturer interviews. DOE asked manufacturers to
estimate their investments in product development and new manufacturing capital and their anticipated stranded capital assets at various efficiency levels. DOE then reviewed public

information in the DOE Compliance and Certification, California Energy Commission ( CEC ),

and ENERGY STAR product databases

, as well as manufacturer websit es to understand which products manufacturers would need to upgrade at each efficiency level. DOE mapped

manufacturers’ estimates of investments and stranded assets to a composite database of products to disaggregate their reporting and allocate the estim ated costs across the various equipment classes and efficiency levels that each representative company manufactures . From this, DOE

also developed an average cost estimate to bring non-compliant products in each equipment class into compliance at each efficiency level. DOE applied these average cost estimate s to the remaining products in the database to arrive the compliance costs required by the remainder of industry. These figures were then added to the disaggregated estimates of individual

manufacturer conversion costs to arrive at a total industry estimate of product and capital

conversion costs and stranded assets for each efficiency level of each equipment class

.

Table 12.4.34 through 12.4.37 show DOE’s estimates of the product and capital

12

- 16conversion costs necessary for each commercial clothes washer equipment class at each efficiency level identified

. None of the manufacturers in terviewed provided comment regarding potential stranded assets.

Table 12.4.10 Product Conversion Costs for Equipment Class 1 – Front

- Loaders by EL

EL ( J2 MEF) Product Conversion Costs (Millions 201 2 $)

EL1 (1.80) $0.0

EL2 (2.00) $0.3

EL3 (2.20) $ 11

. 9

Table 12.4.11 Product Conversion Costs for Equipment Class 2 – Top- Loaders by EL

EL ( J2 MEF) Product Conversion Costs (Millions 201 2 $)

EL1 (1.35) $9.9

EL2 (1.55) $50. 6

Table 12.4.12 Capital Conversion Costs for Equipment Class 1 – Front

- Loaders by EL

EL (J2 MEF ) Capital Conversion Costs (Millions 201 2 $)

EL1 (1.80) $0.0

EL2 (2.00) $0.0

EL3 (2.20) $24. 5

Table 12.4.13 Capital Conversion Costs for Equipment Class 2 – Top- Loaders by EL

EL ( J2 MEF) Capital Conversion Costs (Millions 201 2 $)

EL1 (1.35) $0.0

EL2 (1.55) $38. 7

12.4.9 Markup Scenarios

MSP is equal to MPC times a manufacturer markup. The MSP includes direct manufacturing production costs ( i.e.

, material, labor, depreciation, and overhead estimated in DOE’s MPCs) and all non-production costs ( i.e.

, SG&A, R&D, and interest), along with pr ofit.

In the base case, DOE used the same baseline markups calculated in the engineering analysis for both equipment classes. In the standards case, DOE modeled two markup scenarios to represent the uncertainty about the potential impacts on prices and pro fitability following the implementation of amended energy conservation standards: (1) a preservation of gross margin percentage markup scenario , and (2) a preservation of earnings before interest and taxes (EBIT) markup scenario . Modifying these markups fr om the base case to the standards cases yields different sets of impacts on manufacturers by changing industry revenue and cash flow.

To calculate the baseline manufacturer markup, DOE evaluated publicly available financial information for manufacturers whose product offerings include commercial clothes

washer s. During manufacturer interviews, DOE received feedback supporting the calculated

12

- 171.285 baseline manufacturer markup. For both GRIM markup scenarios, DOE assumed a predominantly flat markup structure, placing no premium on higher efficiency products.

12.4.9.1 Preservation of Gross Margin Percentage Markup Scenario

The preservation of gross margin percentage markup scenario assumes that the baseline markup of 1.285 is maintained for all products in the standards case. As production costs increase with efficiency, this scenario implies that the absolute dollar markup will increase as

well. This scenario represents the upper bound of industry profitability as manufactu rers are able to fully mark up and pass through higher production costs to their customers.

12.4.9.2 Preservation of EBIT Markup Scenario

DOE also modeled the preservation of EBIT markup scenario to estimate a lower bound of profitability for the industry. This is similar to the preservation of gross margin percentage markup scenario with the exception that in the standards case, minimally compliant products lose a fraction of the baseline markup. The lower markup for minimally compliant products is derived by cali brating the markup for minimally compliant products such that industry- wide EBIT in the

year after standards go into effect matches EBIT of the same year in the base case. This scenario represents a more substantial impact to the commercial clothes washer industry in the form of reduced gross margin percentage as manufacturers vie to maintain the lowest possible prices for marginally compliant products while securing the same level of EBIT they saw prior to new and

amended standards.

While all compliant pro ducts receive the 1.285 markup in the preservation of gross margin percentage markup scenario, Table 12.4.40 through Table 12.4.41 list the calibrated markups used in the preservation of EBIT markup scenario.

Table 12.4.14 Preservation of EBIT Markups for Equipment Cl ass 1 : Front -Loading

Commercial Clothes Washers

EL J2 MEF Minimally Compliant EL

EL 1 EL 2 EL 3

EL 1 1.8 0 1.285

EL 2 2 .0 0 1.285 1.285

EL 3 2.2 0 1.285 1.285 1.279

Table 12.4.15 Preservation of EBIT Markups for Equipment Class 2 : Top-Loading

Commercial Clothes Washers

EL J2 MEF Minimally Compliant EL

EL 1 EL 2

EL 1 1.35 1.285

12

- 18EL 2 1.55 1.285 1.279

12.5 INDUSTRY FINANCIAL IMPACTS

Using the inputs and scenarios described in the previous sections, DOE used the GRIM to estimate the financial impacts on the c ommercial clothes washer industry. The MIA uses two key financial metrics: INPV and annual cash flows. The main results of the MIA are reported in this

section.

12.5.1 Introduction

The INPV measures the industry value and is used in the MIA to compare the economic impacts of different TSLs in the standards case. The INPV is different from DOE’s NPV, which is applied to the U.S. economy at large. The INPV is specific to the commercial clothes washer manufacturing industry, and is the sum of all annual net cash flows discounted at the industry’s

WACC . The GRIM for the commercial clothes washer industry models cash flows from 2014 to

2047. This timeframe models both the short

- term impacts on the industry from the announcement of the standard until the compliance date in 2018, and a long- term assessment
over the 30- year analysis period immediately thereafter.

In the MIA, DOE compares the INPV at the base case (no amended energy conservation standards) to that at each TSL in the standards case. The difference between the base case and a standards case INPV is an estimate of the economic impacts that implementing that particular TSL would have on the industry. For the commercial clothes washer industry, DOE examined the two markup scenarios described above: the preservation of gross margin percentage markup

scenario and the preservation of EBIT markup scenario. DO E ’s estimates of INPV for the full analysis period (2014 – 2047) for the base case and at each TSL in the standards case are presented in Table 12.5.1 and Table 12.5.2 below. While INPV is useful for evaluating the long

term effects of amended energy conservation standards, short- term changes in cash flow are also important indicat ors of the industry’s financial situation. For example, a large investment over one or two years could strain the industry’s capital reserves and cash flow . Consequently, the sharp drop in financial performance could cause investors to flee, even if recovery is possible. Thus, a short- term disturbance can have long

- term effects that the INPV cannot capture. To get an idea of the behavior of annual net cash flows, Figure 12.5.1 and Figure 12.5.2 below present the annual net or free cash flows from 2014 through 2047 for the base case and each TSL in the

standards case.

Annual cash flows are discounted to the base year, 2014 . Between 2014 and the 2018 compliance date, cash flows are driven by the level of conversion costs and the portion of these

investments made e ach year. After the standard announcement date ( i.e. , the publication date of the final rule), industry cash flows begin to decline as companies use their financial resources to prepare for the amended energy conservation standard. The more stringent the amended energy conservation standard, the greater the impact on industry cash flows in the years leading up to the compliance date, as product conversion costs lower cash flows from operations and capital

12

- 19conversion costs increase outlays of cash for capital expenditures.

Free cash flow in the year the amended energy conservation standards take effect is driven by two competing factors. In addition to capital and product conversion costs, amended energy conservation standards could create stranded assets, i.e. , the residu al un- depreciated value

of 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, the value of which is affected by the amended energy

conservation standards . This one time write down acts as a tax shield that mitigates decreases in cash flow from operations in the year of the write-down. In this year, there is also an increase in working c apital that reduces cash flow from operations. A large increase in working capital can be attributed to more costly production components and materials, higher inventory carrying to sell more expensive products, and higher accounts receivable for more expe nsive products. Depending on these two competing factors, cash flow can either be positively or negatively affected in the year the standard takes effect.

In the years following the compliance date of the standard, the impact on cash flow depends on the operating revenue. U nder the preservation of gross margin percentage markup

scenario , m ore stringent TSLs typically have a positive impact on cash flows relative to the base

case because in marking up more costly equipment, manufacturers are able to earner higher operating profit, which increases cash flow from operations. There is very little impact on cash flow from operations under the preservation of EBIT scenario because this scenario is calibrated to have the same earnings before interest and taxes in the standards case at each TSL as the base case as in the year after the standard takes effect. In this scenario production costs increase, but

EBIT remains approximately equal to the base case, effectively decreasing profit margins as a percentage of revenue.

12.5.2 Commercial Clothes W asher Industry Financial Impacts

Table 12.5.1 and Table 12.5.2 provide the INPV estimates for the commercial clothes

washer manufacturing industry. Figure 12.5.1 and Figure 12.5.2 present the annual net cash

flows for the commercial clothes washer manufacturing industry for each of the markup

scenarios.

Table 12.5.1 Manufacturer Impact Analysis for Commercial Clothes W asher s – Preservation of Gross Margin Percentage M arkup S cenario

Base Case Trial Standard Level

1 2 3

INPV (2012$ millions) $124.2 $118.3 $118.2 $ 33 .0

Change in

INPV (2012$ millions)

- ($5.9) ($6.0) ($9 1.2 )

(%)

- - 4.7%

- 4.9% -7 3.4 %

12

- 20*For tables in section 12.5 , values in parenthesis indicate negative numbers

Table 12.5.2 Manufacturer Impact Analysis for Commercial Clothes W asher s – Preservation of EBIT Markup Scenario

Ba se Case Trial Standard Level

1 2 3

INPV (2012$ millions) $124.2 $118.3 $118.2 $28.8

Change in

INPV (2012$ millions)

- ($5.9) ($6.0) ($95.4)

(%)

- - 4.7%

- 4.9%

- 76.8%

*For tables in section 12.5 , values in parenthesis indicate negative numbers

Figure 12.5.1 Annual Industry Net Cash Flows for Commercial C lothes W asher s ( Preservation of Gross Margin Markup Scenario)

12

- 21Figure 12.5.2 Annual Industry Net Cash Flows for Commercial clothes washer s (Preservation of EBIT Markup Scenario)

12.6 IMPACTS ON THE LOW

- VOLUME COMMERCIAL CL OTHES WASHER

MANUFACTURER

One LVM of commercial clothes washers believes it will be disproportionately affected by any energy efficiency regulation in the commercial clothes washer industry. This business is

concentrated in one specific market segment (commercial laundry) and is at least one order of magnitude smaller than its di versified competitors. Due to this combination of market concentration and size, the viability of this LVM could be at risk depending on the TSL chosen.

S mall and low-volume manufacturers typically pay higher prices for components because

of smaller pu rchasing volumes while their large competitors receive volume discounts. Despite

the fact that the commercial clothes washer industry as a whole is a low-volume industry, larger manufacturers, with a significantly larger proportion of washer revenues derived from the sale of

residential clothes washer s, are able to purchase components in high quantities due to the similarities in commercial and residential clothes washer component parts.

DOE used the annual reports from both the LVM and its larger diversi fied competitors to analyze the extent to which impacts on the LVM may be disproportionate. In its 2009 10- K, the LVM’s major competitor, whose products offerings are “primarily for home use,” reported laundry segment revenues of approximately one- third it s total annual revenues. Component parts that are typically used across both residential and commercial clothes washers include outer sheet metal, doors and lid hardware, wash basket clothes container, outer plastic wash tub, suspension hardware, drain pump, motor and transmission assembly. DOE estimated LVM segment revenues and shipments from publically available sources (see Table 12.6.1). In contrast to the larger commercial clothes washer manufacturers

, the LVM’s washer revenues are mainly

12

- 22generated fro m commercial washers; residential clothes washers only account for an estimated 12 percent of the LVM ’s washer revenues and an estimated 2 percent of total annual...

Because design options associated with TSL1 and TSL2 for both equipment classes relate to control changes and different cycle options rather than material changes to the equipment itself

, DOE assumes that lack of bulk purchasing power would only disadvantage the LVM relative to its major competitor if the max

- tech level is chosen.

Table 12.6.1 DOE Estimates of LVM Segment Revenues and Shipments

2012

Revenues

($MM) % of

Washer

Revenues % of

Total

Revenues Shipment Volume by

Sales Channel % of

Washer Shipments

Commercial Top

- Loader $59 62% 12% 60,762 68%

Front

- Loader $25 26% 5% 13,662 15%

Residential Top

- Loader $10 11% 2% 14,267 16%

Front

- Loader $1 1% 0% 633 1%

All Washers $95 100% 19% 89,324 100%

In addition to component costs, conversion costs associated with product development

and new manufacturing capital are likely to be disproportionately burdensome for the

commercial clothes washer LVM

. DOE assumes for all manufacturers that the same line would be used to build both residential and commercial clothes washers for a given washer type. Hence, the upfront capital and product conversion costs associated with standards compliance, as well as depreciation costs are spread over the entire production volume, not just production volumes of commercial equipment. Although commercial clothes washer shipment volumes are comparable between the LVM and its larger competitor

, the LVM ’s overall clothes washer production volume is at least two orders of magnitude smaller than its major competitor

. Accordingly, the opportunity for the LVM to recoup upfront investments in product development

is substantially smaller than its competitor . Similarly, depreciated manufacturing capital can only be spread across a disproportionately lower volume of shipments, contributing to higher per-unit production costs.

During an interview, the LVM indicated that it could not manufacture top-loading or

front -loading washers at the proposed max- tech level with its existing manufacturing capital and platform constraints. If DOE were to set the standard at the max

- tech level, the LVM believes that a “green field” design for front-loaders would likely be required. Similarly, f or top- loaders, the LVM asserts that it does not have the technology to reach the max-tech level, and it would be

forced to adapt its business model, possibly ceasing production of commercial clothes washer s altogether, sourcing from abroad, shifting production abroad, or some combination thereof . All

of these changes could have nontrivial impacts on manufacturing capacity and employment in the commercial clothes washer industry. Due to its relative concentration in the top -loading commercial washer segment, i f the LVM no longer offers top-loading washers, it would likely cease commercial clothes washer production altogether. DOE estimates that, currently, the LVM’s top

- loading washers account for more than half of the company’s commercial clothes

washer revenues and more than two-thirds of its commercial clothes washer shipments. To shift

12

- 23all top

- loading commercial clothes wa shers to front -loading washers at current production volumes would require substantial investments that the company may not be able to justify. In addition, the LVM derives an estimated 88 percent of its clothes washer revenue from

commercial clothes wash ers, so its sales in the residential clothes washer market would be too low to justify continuing any top-loading clothes washer manufacturing.

In terms of access to the capital required to cover the conversion costs associated with reaching the max

- tec h levels, the LVM would likely be forced to take on additional debt, whereas its larger competitor s would be better equipped to fund purchases with existing cash flow from operations. Additionally, since the recession of 2007 and 2008, small business lendi ng has dropped substantially due to a combination of tightened lending standards, increasing collateral requirements and reduced focus on small business credit markets. Thus, the LVM generally has access to disproportionately lesser capital than its larger competitors.

In summary, DOE recognizes that depending on the TSL selected, amended energy

conservation standards ma y have disproportionate impact s on the LVM relative to its larger diversified competitors . In this industry, l arger manufacturers may have a competitive advantage compared to the LVM due to their production volumes, focus on the residential market, and ability to access capital and to purchase component parts at a discount.

12.7 OTHER IMPACTS

12.7.1 Employment

For commercial clothes washer s, DOE used the GRIM to estimate the domestic labor expenditures and number of domestic production workers in the base case and at each TSL from

2014 to 2047. DOE used statistical data from the most recent U.S Census Bureau’s 2010 Annual Survey of Manufact ures ( 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 for the manufacture of a product are a function of the labor intensity of the product, the sales volume, and an assumption that wages in

real terms remain constant.

In the GRIM, DOE used the labor content of each product and the manufacturing production costs from the engineering analysis to estimate the annual labor expenditures in the

commercial clothes washer industry. DOE used census data and interviews with manufacturers to estimate the portion of the total labor expenditures that is attributable to domestic labor.

The production worker estimates in this section only cover workers up to the linesupervisor level who are directly involved in fabricating and assembling a product within an Original Equipment Manufacturer (OEM) facility. Workers performing services that are closely

associate d with production operations, such as material handling with a forklift, are also included as production labor. DOE’s estimates account only for production workers who manufacture the specific products covered by this rulemaking.

The employment impacts sh own in Table 12.7.1 represent the potential production employment that could result following amended energy conservation standards. The upper end

12

- 24of t he results in this table estimates the total potential increase in the number of production workers after amended energy conservation standards. To calculate the total potential increase, DOE assumed that manufacturers continue to produce the same scope of covered products in domestic production facilities and domestic production is not shifted to lower-labor- cost countries. Because there is a risk of manufacturers evaluating sourcing decisions in response to amended energy conservation standards, the lower end of the range of employment results in

Table 12.7.1 includes the estimated total number of U.S. production workers in the industry who could lose their jobs if all existing production were moved outside of the United States.

While the results present a range of employment impacts following the compliance date of amended energy conservation standards, the discussion below also includes a qualitative

evaluation of the likelihood of negative domestic production employment impacts at the various

TSLs

.

Using the GRIM , DOE forecasts the domestic labor expendit ure for commercial clothes

washer production labor in 2018 will fall approximately within the range of $12.4 to $13.5

million , depending on the TSL chosen. Using the $18.74 hourly wage rate including fringe

benefits and 1,973 production hours per year per employee found in the 2010 ASM

, DOE

estimates there will be 334 to 364 domestic production workers involved in the manufacturing of

commercial clothes washer s in 2018, the year in which amended standards would go into effect for TSL 1, TSL 2, and TSL 3 . In addition, DOE estimates that 271 to 295 non-production employees in the United States will support commercial clothes washer production. f
Approximately 83 percent of comme rcial clothes washer s sold in the United States are manufactured domestically. The employment tab of the commercial clothes washer GRIM

contains more detailed information on the annual domestic employment impacts.

Table 12.7.1 illustrates the range of potential impact s of amended energy conservation standards on domestic production employment levels at each TSL for the commercial clothes

washer mark et.

f As defined in the 2010 ASM , production workers number include “workers (up throug h the line

- supervisor level) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, warehousing, shipping (but not delivering), maintenance, repair, janitorial and guard services, product development, auxiliary p roduction for plant's own use ( e.g. , power plant), recordkeeping, and other services closely associated with these production operations at the establishment covered by the report. Employees above the working

- supervisor level are excluded from this item.” Non

- production workers are defined as “employees of the manufacturing establishment including those engaged in factory supervision above the line

supervisor level. It includes sales (including driver

- salespersons), sales delivery (highway truck drivers and their helpers), advertising, credit, collection, installation and servicing of own products, clerical and routine office functions, executive, purchasing, financing, legal, personnel (including cafeteria, medical, etc.), professional, and technical employ ees. Also included are employees on the payroll of the manufacturing establishment engaged in the construction of major additions or alterations utilized as a separate work force.”

12

- 25Table 12.7.1 Potential Changes i n the Total Number of Domestic Commercial C lothes

W asher Production Workers in 2018

Baseline TSL 1 TSL 2 TSL 3

Total Number of Domestic Production

Workers in 2018 (without changes in production locations) 334 334 334 364

Potential Changes in Domestic

Production Workers in 2018* - 0

- ( 334 ) 0

- ( 334 ) 30

- ( 334 )

* DOE presents a range of potential employment impacts. Numbers in parentheses indicate negative numbers.

Figure 12.7.1 below shows total annual domestic employment levels for each TSL calculated by the GRIM.

Figure 12.7.1 Commercial C lothes W asher Domestic Production Employment by Year

Due to the fact that production employment expenditures are assumed to be a fixed percentage of COGS and the MPCs typically increase with more efficient produ cts, labor tracks the increased prices in the GRIM. As efficiency of washers increases, so does the complexity of the machines, generally requiring more labor to produce. As previously discussed, for TSL 1, there is no change in MPCs from the base case, and, for TSL 2, there is a small increase in MPCs

for front -loaders offset by a shift in shipments from front-loaders to top- loaders. As a result, DOE expects that there would be no employment impacts among domestic commercial clothes

washer manufacturers for TSL 1 and TSL 2. For TSL 3, the GRIM predicts an increase in domestic employment following amended standards based on the increase in complexity and relative price of the equipment.

Approximately 83 percent of commercial clothes washers are current ly produced domestically. In the commercial clothes washer industry, 100 percent of top- loaders are

12

- 26manufactured domestically, while a much larger share of front -loaders are produced abroad. The actual impacts on domestic employment after standards would be different than estimated if any U.S. manufacturer decided to shift remaining U.S. production to lower cost countries. The proposed standard could result in losing all 334 production workers if all U.S. manufacturers source standards-compliant washers or shift U.S. production abroad. However, feedback from manufacturers during NOPR interviews supports the notion that top- loading commercial clothes washers will continue to be produced domestically following amended energy conservation standards, unless th e max

- tech level is chosen.

12.7.2 Production Capacity

According to the majority of commercial clothes washer manufacturers, new energy conservation standards could potentially impact manufacturers’ production capacity depending on the efficiency level required. For TSL 1 and TSL 2, the most significant conve rsion costs are the research and development, testing, and certification of products with more- efficient components, which does not affect production line capacity. Thus, DOE believes manufacturers will be able to maintain manufacturing capacity levels and continue to meet market demand under new energy conservation standards as long as manufacturers continue to offer top-loading and front-loading washers.

However, a very high efficiency standard for top-loading clothes washers could cause

certain manu facturers to abandon further domestic production of top-loading clothes washers after the effective date, and choose instead to cease production of commercial clothes washers altogether , which could possibly lead to a permanently lower production capacity within the commercial clothes washer industry.

12.7.3 Cumulative Regulatory Burden

While any one regulation may not impose a significant burden on manufacturers, the combined effects of several impending regulations may have serious consequences for some

manufac turers, groups of manufacturers, or an entire industry. Regulatory burdens can prompt companies to exit the market or reduce their equipment 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. Assessing the impact of a single regulation may overlook this cumulative regulatory burden. A proposed standard is not economically justified if it contributes to an unacceptable level of cumulative regulatory burden.

For the cumulative regulatory burden analysis, DOE looks at other significant productspecific regulations that will take effect 3 years before or after the 2018 compliance date of the amended energy conservation standards for commercial clothes washer s. In addition to amended energy conservation regulations, several other Federal regulations apply to commercial clothes

washer s. While this analysis focuses on the impacts on manufac turers born of other Federal requirements, DOE also has described a number of other non- Federal regulations in section

12.7.3.2 because it recogniz es that these regulations also impact the equipment covered by this rulemaking.

12

- 2712.7.3.1 DOE Regulations for Other Products Produced by Commercial C lothes

W asher Manufacturers

Companies that produce a wide range of regulated products and equipment may face

more ca pital and product development expenditures than competitors with a narrower scope of products and equipment. The majority of commercial clothes washer manufacturers also produce other appliances and commercial refrigeration equipment . In addition to the amended energy

conservation standards for commercial clothes washer s, these manufacturers contend with several other Federal regulations and pending regulations that apply to other products and equipment. DOE recognizes that each regulation can significantly affect a manufacturer’s financial operations. Multiple regulations affecting the same manufacturer can quickly strain manufacturers’ profits and possibly cause an exit from the market. Table 12.7.2 lists the other DOE energy conservation standards that could also affect manufacturers of commercial clothes

washer s in the 3 years leading up to and after the compliance date of amended energy conservation standards for this equipment.

Table 12.7.2 Other DOE and Federal Actions Affecting the Commercial C lothes W asher Industry

Regulation Approximate Compliance

Date* Number of Impacted Companies Estimated Total Industry

Conversion Costs

Residential Clothes Washers 2015 & 2018 4 $418.5 M

Residential Clothes Dryers 201 5 4 $ 95.4 M

Microwave Oven s 2016 4 $43.1 M

Misc ellaneous Refrigeration 2018 3 N/A †

Dehumidifiers 2019 4 N/A †

*The dates listed are an approximation. The exact dates are pending final DOE action. † For energy conservation standards for rulemakings awaiting DOE final action, DOE does not have a finalized estimated total industry conversion cost.

12.7.3.2 Other Regulations That Could Impact Commercial Clothes W asher

Manufacturers

While the cumulative regulatory burden focuses on the impacts on manufacturers of other Federal requirements, in this section DOE has described a number of other regulations below that could also impact the commercial clothes washer s covered by this rulemaking.

Other Federal Standards

One manufacturer expressed concerns about the c onflict minerals directive included in the Dodd-Frank Act of 2010 (H.R. 4173, section 1502) g . When promulgated, this rule will require that U.S. companies report and make public available their use of certain minerals from

g ht tp://www.sec.gov/rules/proposed/2010/34

- 63547fr.pdf

.

12

- 28the Democratic Republic of the Congo and adjoining countries. This may require manufacturers

to make investments in new tracking sys tems to ensure visibility throughout the ir supply chain.

State Energy Conservation and Environmental Standards

Manufacturers indicated that state

- level regulations, especially in California , contribute to overall cumulative regulatory burden. These regula tions include state energy efficiency standards, for example, proposed standards for commercial clothes dryers in California. Other

state

- level regulations that may impact commercial clothes washer manufacturers include product and packaging, and extended producer responsibility regulations. Additionally, manufacturers expressed concerns over fut u re compliance costs associated with green chemistry

laws and chemical bans , such as those initiated and proposed as part of the California Green Chemistry Initiat ive. h

International Energy Conservation Standards

During the interview process, one manufacturer cited international electromagnetic compatibility ( EMC ) standards as a contributor to cumulative regulatory burden. Such standards serve to standardize the EMC performance in terms of emissions and radio interference of various electrical products, including household appliances. i

12.8 CONCLUSION

The following section summarize s the scenarios DOE believes are most likely to capture the range of impacts on commercial clothes washer manufacturers at each TSL in the standards case. While these scenarios bound the range of the most plausible impacts on manufacturers, some circumstances could cause manufacturers to experience impacts outside this range.

12.8.1 Commercial Clothes Washer s

At TSL 1, DOE estimates the impact on INPV for manufacturers of commercial clothes

washers to be $5.9 million, or a change in INPV of -4.7 percent under either markup scenario. At this TSL, industry fre e cash flow is estimated to decrease by approximately 30. 2 percent to $6.3 million, compared to the base-case value of $9.1 million in the year before the compliance date (2017).

TSL 1 represents an improvement in MEF (as determined using a ppendix J2) f rom the

baseline level of 1.65 to 1.80 (ft 3 /kWh/cycle) for front -loading equipment and an improvement in

MEF from the baseline level of 1.15 to 1.35 (ft 3 /kWh/cycle) for top -loading equipment. The lack of difference in results between the two markup scenari os at TSL 1 can be traced to the fact that for both equipment classes, the baseline MPCs and the MPCs at TSL 1 are the very same. For

front -loading equipment, this is because the 1.8 MEF (as determined using appendix J2) products (on which the EL 1 standard is based) are in fact the lowest efficiency front -loading equipment

h http://www.mofo.com/green

- chemistry/

i http://en.wikipedia.org/wiki/List_of_EMC_directives

12

- 29available on the market today. As such, TSL 1 would have no impact on the front-loading market. Similarly, the design options associated with EL 1 for top- loading equipment relate to

co ntrol changes and different cycle options, rather than material changes to the equipment itself. In turn, there are product conversion costs associated with the research and development needed to make these changes, but no changes in the per unit production costs. Given these conditions, the entirety of impacts on INPV at TSL 1 can be attributed to the $9.9 million in product conversion costs for top-loading equipment.

At TSL 2, DOE estimates the impact on INPV for manufacturers of commercial clothes

washers to be $6.0 million, or a change in INPV of -4.9 percent under either markup scenario. At this TSL, industry free cash flow is estimated to decrea se by approximately 31.2 percent to $6.2 million, compared to the base-case value of $9.1 million in the year before the compliance date (2017).

TSL 2 represents an improvement in MEF from the baseline level of 1.65 to 2.00 (ft 3 /kWh/cycle) for front -loading equipment and a n improvement in MEF from the baseline level

of 1.15 to 1.35 (ft 3 /kWh/cycle) for top -loading equipment. Much like TSL 1, the lack of difference in results between the two markup scenarios at TSL 2 can be traced to the fact that the baseline MPCs and the MPCs at TSL 2 are very close for front -loading equipment, and the very

same for top-loading equipment. For front-loading equipment, this is because the 2.0 MEF EL (as determined using appendix J2) only requires minor changes to baseline equipment needed to

enable slightly faster spin speeds. The standard level for top- loading equip ment at TSL 2 is the same at TSL 1, and again, simply relates to control changes and different cycle options, rather than material changes to the equipment itself. Because there are no substantive changes to MPCs for either equipment class, much as in TSL 1, nearly all of the impacts on INPV at TSL 2 can be attributed to the $10.2 million in product conversion costs.

At TSL 3, DOE estimates impacts on INPV for manufacturers of automatic commercial

clothes washer s to range from -$91. 2 million to -$95.4 million, or a change in INPV of -73.4

percent to -76.8 percent. At this TSL, industry free cash flow is estimated to decrease by over 500 percent to -$36.8 million, compared to the base-case value of $9.1 million in the year bef ore the compliance date (2017).

TSL 3 represents an improvement in MEF from the baseline level of 1.65 to 2.20 (ft 3 /kWh/cycle)

for equipment class 1 and an improvement in MEF from the baseline level of 1.15 to 1.55 (ft 3 /kWh/cycle) for equipment class 2. Unlike TSL 1 and TSL 2, the effi ciency levels specified at TSL 3 would require substantial redesigns of products in both equipment classes. The design options proposed at these efficiency levels include switching to direct-drive motors, hung suspension, non- traditional agitation, and inc reasing the tub capacity – all of which require major platform overhauls and significant changes to manufacturing capital. These design options do not contribute to substantially different MPCs, but the conversion costs associated with product development and testing, as well as the investments in manufacturing capital including retooling of tubs and agitators dramatically impact the INPV in the order of $125.6 million industry- wide

.

12

- 30REFERENCES

1 U.S. Department of Energy. Compliance Certification Database . Last accessed September 3,

2013. Available online at: http://www.regulations.doe.gov/certification- data/

.

2 California Energy Commission. Appliance Efficiency Database . Last accessed September 3,

2013. Available online at: http://www.appliances.energy.ca.gov/

. 3 ENERGY STAR. Commercial Clothes Washers . Last accessed September 11, 2013.

Available online at : http://www.energystar.gov/index.cfm?fuseaction=find_a_product.showProductGroup&pgw_

code=CCW

.

4 McKinsey & Company, Inc. Valuation: Measuring and Managing the Value of Companies , 3rd Edition, Copeland, Koller, Murrin. New York: John Wiley & Sons, 2000.

12

- 31CHAPTER 1 3 . EMISSIONS IMPACT ANALYSIS

TABLE OF CONTENTS

13.1 INTRODUCTION ......................................................................................................... 13-1

13.2 AIR QUALITY REGULATIONS AND EMISSIONS IMPACTS ............................... 13-2

13.3 POWER SECTOR EMISSIO NS FACTORS ................................................................ 13-3

13.4 UPSTREAM FACTORS ............................................................................................... 13-4

13.5 EMISSIONS IMPACT RES ULTS ................................................................................ 13-5

LIST OF TABLES

Table 13.3.1 Power Plant Emissions Factors for Commercial Refrigeration End Use* ........ 13-4

Table 13.4.1 Electricity Upstream Emissions Factors ............................................................ 13-4

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. EMISSIONS IMPACT ANALYSIS

13.1 INTRODUCTION

The emissions analysis consists of two components. The first component estimates the effect of potential energy conservation standards on power sector emissions of carbon dioxide (CO 2 ), nitrogen oxides (NO X ), sulfur dioxide (SO 2 ) and merc 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 “upstrea m” activities in the fuel production chain. These upstream activities comprise extraction, processing, and transporting fuels to the site of combustion. The associated emissions are referred to as upstream emissions. Together, these emissions account for t he full

- fuel

- cycle (FFC) , i n accordance with DOE’s FFC Statement of Policy . 76 FR 51282 (Aug. 18, 2011)

.

The analysis of most power sector emissions uses marginal emissions intensity factors

derived from runs of DOE’s NEMS -BT model, described in Chapter 15. DOE used the version

of NEMS based on the Annual Energy Outlook 2013 (AEO 2013). 1 Each annual version of NEMS incorporates the projected impacts of existing air quality regulations on emissions. AEO

2013 generally represents current Federal and State legisl ation and final implementation regulations in place as of the end of December 2012. Where applicable, s ite emissions of CO 2 and NO X are estimated using emissions intensity factors from a publication of the Environmental Protection Agency (EPA). 2

Combustion emissions of CH 4 and N 2 O (from power plants and from upstream combustion) are estimated using emissions intensity factors published by the EPA , GHG

Emissions Factors Hub. a

The FFC upstream emissions a re estimated based on the methodology developed by Coughlin (2013). 3 The upstream emissions include emissions from fuel combustion during extraction, processing and transportation of fuel, and “fugitive” emissions (direct leakage to the atmosphere) of CH 4 and CO 2

.

The emissions intensity factors are expressed in terms of physical units per 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 by the gas' global warming potential (GWP) over a 100 year time horizon. Based on the Fourth Assessment Report of the

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

- emissions.html

13-1Inte rgovernmental Panel on Climate Change, b DOE used GWP values of 25 for CH 4 and 298 for

N 2 O.

13.2 AIR QUALITY REGULATI ONS AND EMISSIONS IMPACT S

SO 2 emissions from affected electric generating units (EGUs) are subject to nationwide and regional emissions cap and trading programs. Title IV of the Clean Air Act sets an annual emissions cap on SO 2 for affected EGUs in the 48 contiguous states and the Dis trict 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 replacement for CAIR,

the Cross- State Air Pollution Ru le (CSAPR). 76 FR 48208 (August 8, 2011). On August 21, 2012, the D.C. Circuit issued a decision to vacate CSAPR. See EME Homer City Generation,

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

CAIR. The AEO 2013 emissions factors used for this analysis assume that CAIR remains a binding regulation through 2040.

The attainment of emissions caps is typically flexible among affected Electric Generating Units (EGUs) and is enforced through the use of emissions allow ances 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 used to permit offsetting increases in SO 2 emissions by any regulated EGU. In past rulemakings, DOE recognized that there was uncertainty about the effects of efficiency standards on SO 2 emissions covered by the existing cap

- and -trade system, but it concluded that no reductions in power sector

emissions would o ccur for SO 2 as a result of standards.

Beginning in 2015, however, SO 2 emissions will fall as a result of the Mercury and Air Toxics Standards (MATS) for power plants, which were announced by EPA on December 21, 2011. 77 FR 9304 (Feb. 16, 2012). In the final MATS rule, EPA established a standard for 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. The 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 201 3 assumes that, in

order t o continue operating, coal plants must have either flue gas desulfurization or dry sorbent injection systems installed by 2015. Both technologies, which are used to reduce acid gas emissions, also reduce SO 2 emissions. Under the MATS, NEMS shows a reduction in SO 2

b 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. 2007: Changes in Atmospheric Constituents and in Radiative Forcing. In Climate Cha nge 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, Editors. 200 7. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. p. 212.

13 -2emissions when electricity demand decreases ( e.g. , as a result of energy efficiency standards). Emissions will be far below the cap established by CAIR , so it is unlikely that excess 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 that efficiency standards will reduce SO 2 emissions in 2015 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 the States not affected by

CAIR

, so DOE estimate d NOx emissions reductions from potential stan dards for th ose States

.

The MATS limit mercury emissions from power plants, but they do not include emissions caps and, as such, DOE’s energy conservation standards would likely reduce Hg emissions. DOE estimated mercury emissions reductions using the NEM S-BT based on AEO

2013 , which incorporates the MATS.

13.3 POWER SECTOR EMISSIO NS FACTORS

The analysis of power sector emissions uses marginal emissions intensity factors derived

from runs of DOE’s NEMS -BT model, using the version updated to the Annua l Energy Outlook 2013 (AEO 2013). To model the impact of a standard, DOE inputs a reduction to annual energy demand for the corresponding end use in the appropriate start year. The NEMS-BT model is run with the decremented energy demand to determine the modified bu ild -out of capacity, fuel use and power sector emissions. A marginal emissions intensity factor is defined by dividing the reduction in the total emissions of a given pollutant by the reduction in total generation (in billion kWh). DOE uses the site energy savings multiplied by a T&D loss factor to estimate the reduction in generation for each TSL. Details on the approach used may be found in Coughlin (2013 ). 3

Table 13.3.1 presents the power plant emissions factors for selected years. The se power plant emissions factors are derived from the emissions factors of the plan t types used to supply electricity for commercial refrigeration, which DOE assumes will be the same to provide electricity for commercial clothes washers

. The average factors for each year take into account the projected shares of each of the sources in total electricity generation

. The marginal

calculation based on the NEMS

- BT model accounts for the fact that NOx emissions are capped

in some States.

13 -3Table 13.3.1 Power Plant Emissions Factors for Commercial Refrigeration End Use *

Unit* * 201 6 2020 2025 2030 2035 2040

CO 2 kg/MWh 598.1 598.1 558.6 524.4 497.0 309.4

SO 2 g/MWh 570.4 570.4 690.6 732.5 453.6 523.6

NOx g/MWh

- 902.0

- 902.0

- 417.8

- 217.9

- 69.7 43.2 Hg g/MWh 0.00146 0.00146 0.00039 0.00090 0.00067 0.00061

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

CH 4 g/MWh 48.3 50.2 50.1 49.6 49.2 47.8

* DOE assumes these emissions factors to be the same as the emissions factors resulting from providing electricity

for commercial clothes washers.

** Refers to MWh of site electricity savings.

13.4 UPSTREAM FACTORS

The upstream emissions accounting uses the same approach as the upstream energy accounting described in appendix 10-A. See also Coughlin (2013 ). 3 When demand for a particular fuel is reduced, there is a corresponding reduction in the emissions from combustion of that fuel at either the building site or the power plant. The associated reduction in energy use for upstream activities leads to further reductions in emissions. These ups tream emissions are defined to include the combustion emissions from the fuel used upstream, the fugitive emissions associated with the fuel used upstream, and the fugitive emissions associated with the fuel used

on site.

Fugitive emissions of CO 2 occur during oil and gas production, but are small relative to combustion emissions. They comprise about 2.5% of total CO 2 emissions for natural gas and 1.7% for petroleum fuels. Fugitive emissions of methane occur during oil, gas and coal production. Combustion emissions of CH 4 are very small, while fugitive emissions (particularly for gas production) may be relatively large. Hence, fugitive emissions make up over 99% of total methane emissions for natural gas, about 95% for coal, and 93% for petroleum fuels.

U pstream emissions factors account for both fugitive emissions and combustion emissions in extraction, processing, and transport of primary fuels. For ease of application in its analysis, DOE developed all of the emissions factors using site (point of us e) energy savings in

the denominator. Table 13.4.1 presents the electricity upstream emissions factors for selected

years . The caps that apply to power sector NO

x emissions do not apply to upstream combustion

sources.

Table 13.4.1 Electricity Upstream Emissions Factors

Unit* 2015 2020 2025 2030 2035 2040

CO 2 kg/MWh 28.5 27.3 26.9 26.8 26.9 26.3

SO 2 g/MWh 4.9 5.3 5.3 5.2 5.2 5.1

NOx g/MWh 361 340 334 333 336 329 Hg g/MWh 0.00001 0.00001 0.00001 0.00001 0.00001 0.00001

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

CH 4 g/MWh 2142 2025 2008 2025 2057 1999

* Refers to MWh of site electricity savings.

13 -413.5 EMISSIONS IMPACT RES ULTS

Table 13.5.1 presents the estimated cumulative emissions reductions for the lifetime of top-loading and front

- loading commercial clothe s washers sold in 2018-2047 for each TSL.

Table 13.5.1 Cumulative Emissions Reduction for Top- Loading and Front -Loading

Commercial Clothe s Washers Trial Standard Levels

Trial Standard

Level

1 2 3

Power Sector Emissions

CO 2 (million metric tons) 4.53 5.37 8.16

SO 2 (thousand tons) 3.96 4.32 8.61

NO X (thousand tons) 1.22 1.71 1.17 Hg (tons) 0.00 0.01 0.01

N 2 O (thousand tons) 0.07 0.07 0.14

CH 4 (thousand tons) 0.40 0.44 0.83 Upstream Emissions

CO 2 (million metric tons) 0.41 0.50 0.69

SO 2 (thousand tons) 0.04 0.04 0.08

NO X (thousand tons) 6.04 7.39 9.96 Hg (tons) 0.00 0.00 0.00

N 2 O (thousand tons) 0.00 0.00 0.00

CH 4 (thousand tons) 40.37 49.69 65.33

Total Emissions

CO 2 (million metric tons) 4.94 5.87 8.84

SO 2 (thousand tons) 3.99 4.36 8.69

NO X (thousand tons) 7.26 9.10 11.14 Hg (tons) 0.00 0.01 0.01

N 2 O (thousand tons) 0.07 0.08 0.15

N 2 O (thousand tons CO 2 eq) 20.37 22.57 43.25

CH 4 (thousand tons) 40.77 50.14 66.16

CH 4 (thousand tons CO 2 eq) 1.02 1.25 1.65

13 -5Figure 13.5.1 through Figure 13.5.6 show the annual reductions for total emission s for each type of emission from each TSL

. The reductions reflect the lifetime impacts of equipment

sold in 2018-2047.

Figure 13.5.1 CO 2 Total Emissions Reduction

Figure 13.5.2 SO 2 Total Emissions Reduction 0 50 100 150 200 250 300 350 400201820232028203320382043204820532058206320682073kt CO 2 Emissions Reduction

TSL 1 TSL 2 TSL 3

0 50 100 150 200 250 300 350 400201820232028203320382043204820532058206320682073ton SO 2 Emissions Reduction

TSL 1 TSL 2 TSL 3

13 -6Figure 13.5.3 NO

x Total Emissions Reduction

Figure 13.5.4 Hg Total Emissions Reduction -50

0 50 100 150 200 250 300 350 400 450 500201820232028203320382043204820532058206320682073ton NO

x Emissions Reduction

TSL 1 TSL 2 TSL 3

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50201820232028203320382043204820532058206320682073ton Hg Emissions Reduction

TSL 1 TSL 2 TSL 3

13 -7Figure 13.5.5 N 2 O Total Emissions Reduction

Figure 13.5.6 CH 4 Total Emissions Reduction 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0201820232028203320382043204820532058206320682073ton N 2 O Emissions Reduction

TSL 1 TSL 2 TSL 3

0 500 1000 1500 2000 2500201820232028203320382043204820532058206320682073ton CH 4 Emissions Reduction

TSL 1 TSL 2 TSL 3

13 -8REFERENCES

1. Energy Information Administration, Annual Energy Outlook 2013 with Projections to

2040 , 2013. Washington, DC. < http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf >

2. U.S. Environmental Protection Agency, Compilation of Air Pollutant Emission Factors,

AP -42, Fifth Edition, Volume I: Stationary Point and Area Sources . 1998. http://www.epa.gov/ttn/chief/ap42/index.html

3. Coughlin, K., Projections of Full

- Fuel -Cycle Energy and Emissions Metrics , 2013. Lawrence Berkeley National Laboratory. R eport No. LBNL -6025E.

13 -9CHAPTER 1 4. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

TABLE OF CONTENTS

14.1 INTRODUCTION ......................................................................................................... 14-1

14.2 MONETIZING CARBON DIOXIDE EMISSIONS ..................................................... 14-1

14.2.1 Social Cost of Carbon .................................................................................................... 14-1

14.2.2 Monetizing Carbon Dioxide Emissions ......................................................................... 14-2

14.2.3 Current Approach and Key Assumptions ...................................................................... 14-2

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS ........................................... 14-5

14.4 RESULTS ...................................................................................................................... 14-5

LIST OF TABLES

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

Table 14.4.1 Estimates of Present Value of CO 2 Emissions Reduction for Commercial

Clothes Washers Trial Standard Levels ............................................................. 14-6

Table 14.4.2 Estimates of Domestic Present Value of CO 2 Emissions Reduction for

Commercial Clothes Washers Trial Standard Levels ........................................ 14-7

Table 14.4.3 Estimates of Present Value of NO X Emissions Reduction for Commercial

Clothes Washers Trial Standard Levels ............................................................. 14-7

14 -iCHAPTER 14. MONETIZATION OF EMISSION REDUCTIONS BENEFITS

14.1 INTRODUCTION

As part of its assessment of energy conservation standards, DOE estimated the monetary benefits likely to result from the reduced emissions of carbon dioxide (CO 2 ) and nitrogen oxides (NO X ) that are expected to result from each of the TSLs considered. This chapter summarizes the basis for the monetary values used for each of these emissions and presents the benefits estimates

considered.

14.2 MONETIZING CARBON DIOXIDE EMISSIONS

14.2.1 Social Cost of Carbon

The social cost of carbon (SCC) is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services. Estimates of the SCC are provided in dollars per metric ton of carbon dioxide. A domestic SCC value is meant to reflect the value of damages in the United States resulting from a unit change in carbon dioxide emissions, while a global SCC value is meant to reflect the value of damages worldwide.

Under section 1(b) (6) of Executive Order 12866,”Regulatory Planning and Review,” 58 FR 51735 (Oct. 4, 1993), agencies must, to the extent permitted by law, “assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” The purpose of the SCC estimates presented here is to allow agencies to incorporate the monetized social benefits of reducing CO 2 emissions into cost-benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a clear understanding that they should be updated over time to reflect increasing knowledge of the science and economics of climate impacts.

As part of the interagency process that developed these SCC estimates, technical experts from numerous agencies met on a regular basis to explore the technical literature in relevant fields, discuss key model inputs and assumptions, and consider public comments. The main objective of this process was to develop a range of SCC values using a defensible set of input assumptions grounded in the existing scientific and economic literatures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

14 -114.2.2 Monetizing Carbon Dioxide Emissions

When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A report from the National Research

Council 1 points out that any assessment will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in cli mate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science , economics, and ethics and should be viewed as provisional.

Despite the serious limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. Most Federal regulatory actions can be expected to have marginal impacts on global emissions. For such policies, the agency can estimate the benefits from reduced (or costs from increased) emissions in any future year by multiplying the change in emissions in that year by the SCC value appropriate for that year. The net present value of the benefits can then be calculated by multiplying each of these future benefits by an appropriate discount factor and summing across all affected years. This approach assumes that the marginal dam ages from increased emissions are constant for small departures from the baseline emissions path, an approximation that is reasonable for policies that have effects on emissions that are small relative to cumulative global

carbon dioxide emissions.

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing 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. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the existing literature to use as interim values until a more comprehensive analysis could be conducted. The outcome of the preliminary assessment by the interagency group was a set of five interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per ton of CO 2

. 2 These interim values represented the first sustained interagency effort within the U.S. government to develop an SCC for use in regulatory analysis. The results of this preliminary effort were presented in several proposed and final rules.

14.2.3 Current Approach and Key Assumptions

After the release of the interim values, the interagency group reconvened on a regular basis to generate improved SCC estimates, which were considered for this proposed rule. Specifically, the group considered public comments and further explored the technical literature in relevant fields. The interagency group relied on three integrated assessment models ( IAMs) commonly used to estimate the SCC: the FUND, DICE, and PAGE models. a These models are frequently cited in the peer

- reviewed literature and were used in the last assessment of the

a The models are described in appendix 14

- A of the TSD.

14 -2Intergovernmental Panel on Climate Change. Each model was given equal weight in the SCC values that were developed.

Each model takes a slightly different approach to model how changes in emissions result in changes in economic damages. A key objective of the interagency process was to enable a consistent exploration of the three models while respecting the different approaches to quantifying damages taken by the key modelers in the field. An extensive review of the literature was conducted to select three sets of input parameters for these models: (1) climate sensitivity; (2) socio -economic and emissions trajectories; and (3) discount rates. A probability distribution for climate sensitivity was specified as an input into all three models. In addition, the interagency group used a range of scenarios for the socio- economic parameters and a range of values for the discount rate. All other model features were left unchanged, relying on the model developers’ best estimates and judgments.

The interagency group selected four SCC values for use in regulatory analyses. Three

v alues are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95th percentile SCC estimate across all three models at a 3 percent discount rate, is included to represent higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution. The values grow in real terms over time . Additionally, the interagency group determined that a range of values from 7 percent to 23 percent should be used to adjust the global SCC to calculate domestic effects, although preference is given to consideration of the global benefits of reducing

CO 2 emissions. Table 14.2.1 presents the values in the 2010 interagency group report, b which is reproduced in appendix 14- A of the TSD.

The SCC values used for this analysis were generated using the most recent versions of the three integrated assessment models that have been published in the peer- reviewed literature

, as described in the 2013 update 3 from th e interagency working group (revised November 2013). c

Table 14.2.2 shows the updated sets of SCC estimates in five year increments from 2010 to 2050. The full set of annual SCC estimates between 2010 and 2050 is reported in appendix 14- B

of the TSD. The central value that emerges is the average SCC across models at the 3 percent discount rate. However, for purposes of capturing the uncertainties involved in regulatory impact analysis, the interagency group emphasizes the importance of including all four sets of SCC

values. For the years after 2050, DOE applied the average annual growth rate of the SCC

estimates in 2040−2050 associated with each of the four sets of values.

b Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 . Interagency Working Group on Social Cost of Carbon, United States Government, February 2010. http://www.whitehouse.gov/sites/default/files/omb/inforeg/for

- agencies/Social

- Cost

- of

- Carbon

- for

- RIA.pdf

.

c Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working Group on Social Cost of Carbon, United States Government . May 2013 ; revised November

2013

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

- update

- social

- cost

- of

- carbon

- for

regulator

- impact

- analysis.pdf

.

14 -3Table 14.2.1 Annual SCC Values from 2010 Interagency Report, 2010–2050 (in 2007 dollars per metric ton of CO 2 )

Year Discount Rate %

5 3 2.5 3

Average Average Average 95 th Percentile

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

Table 14.2.2 Annual SCC Values from 2013 Interagency Update, 2010–2050 (in 2007 dollars per metric ton of CO 2 )

Year Discount Rate %

5 3 2.5 3

Average Average Average 95 th Percentile

2010 11 3 2 5 1 8 9

2015 11 37 57 109

2020 12 43 6 4 12 8

2025 14 47 69 143

2030 16 52 75 159

2035 19 5 6 8 0 17 5

2040 21 61 86 191

2045 24 66 92 206

2050 26 71 97 220

It is important to recognize that a number of key uncertainties remain, and that current

SCC estimates should be treated as provisional and revisable since they will evolve with improved scientific and economic understanding. The interagency group also recognizes that the existing models are imperfect and incomplete. The National Research Council report mentioned above points out that there is tension between the goal of producing quantified estimates of the economic damages from an incremental ton of carbon and the limits of existing efforts to model

these effects. There are a number of concerns and problems that should be addressed by the research community, including research programs housed in many of the agencies participating in the interagency process to estimate the SCC. The interagency group intends to periodically

review and reconsider es timates 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.

14 -4In summary, in considering the potential global benefits resulting from reduced CO 2

em issions, DOE used the values from the 2013 interagency report, escalated to 2012$ using the GDP price deflator. For each of the four cases specified, the values used for emissions in 2015

are $11.8, $39.7, $61.2, and $117 per metric ton avoided.

DOE mult iplied the CO 2 emissions reduction estimated for each year by the SCC value for that year in each of the four cases. To calculate a present value of the stream of monetary values, the interagency report notes that damages from future emissions should be discounted at

the same rate as that used to calculate the SCC estimates themselves to ensure internal consistency. Thus, DOE discounted the values in each of the four cases using the specific

discount rate that had been used to obtain the SCC values in each case.

14.3 VALUATION OF OTHER EMISSIONS REDUCTIONS

DOE considered the potential monetary benefit of reduced NO X emissions from the TSLs it considered. As noted in chapter 13, new or amended energy conservation standards would

reduce NO X emissions in those Stat es that are not affected by caps. DOE estimated the

monetized value of NO X emissions reductions resulting from each of the TSLs considered based on environmental damage estimates found in the relevant scientific literature. E stimates of monetary value for reducing NOx from stationary sources range from $468 to $4,809 per ton in

2012$. 4 DOE calculated monetary benefits using a medium value for NO X emissions of $2,639 per short ton (in 2012$), and real discount rates of 3 percent and 7 percent.

DOE is still evaluating appropriate values to use to monetize avoided SO 2 and Hg

emissions. It did not monetize these emissions for this analysis

.

14.4 RESULTS

Table 14.4.1 p resent s the global values of CO 2 emissions reductions for each considered

TSL. DOE calculated domestic values as a range from 7 percent to 23 percent of the global values, and these results are presented in Table 14.4.2

.

Table 14.4.3 presents the present value of cumulative NO X emissions reductions for each TSL, calculated using the average dollar- per

- ton values and seven

- percent and three- percent
discount rates.

14 -5Table 14.4.1 Estimates of Present Value of CO 2 Emissions Reduction for Commercial

Clothes Washers Trial Standard Levels

TSL SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate, 95 th
percentile*

Million 2012$

Power Sector Energy Emissions

1 30.1 139 222 431

2 35.5 165 262 509

3 54.4 251 400 777

Upstream Emissions

1 2.65 12.4 19.9 38.5

2 3.22 15.1 24.2 46.8

3 4.43 20.8 33.2 64.4

Total Emissions

1 32.7 152 242 469

2 38.7 180 287 556

3 58.8 272 434 841

* For each of the four cases, the corresponding global SCC value for emissions in 2015 is $11.8

, $ 39 .7 , $ 61 .2 , and $ 117 per metric ton (2012$).

14 -6Table 14.4.2 Estimates of Domestic Present Value of CO 2 Emissions Reduction for

Commercial Clothes Washers Trial Standard Levels

TSL SCC Case*

5% discount rate, average* 3% discount rate, average* 2.5% discount rate, average* 3% discount rate, 95 th
percentile*

Million 2012$

Power Sector Energy Emissions

1 2.1 to 6.9 9.8 to 32.1 15.5 to 51.1 30.1 to 99.0

2 2.5 to 8.2 11.5 to 37.9 18.4 to 60.4 35.6 to 117.1

3 3.8 to 12.5 17.6 to 57.8 28.0 to 92.1 54.4 to 178.7

Upstream Emissions

1 0.2 to 0.6 0.9 to 2.9 1.4 to 4.6 2.7 to 8.9

2 0.2 to 0.7 1.1 to 3.5 1.7 to 5.6 3.3 to 10.8

3 0.3 to 1.0 1.5 to 4.8 2.3 to 7.6 4.5 to 14.8

Total Emissions

1 2.3 to 7.5 10.6 to 34.9 16.9 to 55.6 32.8 to 107.9

2 2.7 to 8.9 12.6 to 41.4 20.1 to 65.9 38.9 to 127.8

3 4.1 to 13.5 19.1 to 62.6 30.3 to 99.7 58.9 to 193.5

* For each of the four cases, the corresponding SCC value for emissions in 2015 is $1 1.8 , $ 39 .7

, $6 1 .2, and $117 per metric ton (2012$).

Table 14.4.3 Estimates of Present Value of NO X Emissions Reduction for Commercial

Clothes Washers Trial Standard Levels

TSL 3% discount rate 7% discount rate

Million 2012$

Power Sector Emissions

1 1.18 0.257

2 1.77 0.496

3 0.63 -0.304

Upstream Emissions

1 7.93 3.60

2 9.66 4.36

3 13.1 5.93

Total Emissions

1 9.10 3.85

2 11.4 4.86

3 13.7 5.63

14 -7REFERENCES

1. National Research Council, Hidden Costs of Energy: Unpriced Consequences of Energy

Production and Use , 2009. National Academies Press. Washington, DC.

< http://www.nap.edu/catalog.php?record_id=12794 >

2. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States

Government. < http://www.whitehouse.gov/sites/default/files/omb/inforeg/for

agencies/Social -Cost-of-Carbon- for -RIA.pdf >

3. I nteragency Working Group on Social Cost of Carbon, Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, May 2013.

United States Gover nment. http://www.whitehouse.gov/sites/default/files/omb/inforeg/social_cost_of_carbon_for_ria _2013_update.pdf

4. U.S. Office of Management and Budget (OMB), 2006 Report to Congress on the Costs and Benefits of Federal Regulations and Unfunded Mandates on State, Local, and Tribal

Entities , 2006. Washington, DC.

< http://www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/2006_cb/2006_c b_final_report.pdf >

14 -8CHAPTER 1 5. UTILITY IMPACT ANALYSIS

TABLE OF CONTENTS

15.1 INTRODUCTION ......................................................................................................... 15-1

15.2 METHODOLOGY ........................................................................................................ 15-1

15.3 UTILITY IMPACT RESULTS ..................................................................................... 15-3

15.3.1 Installed Capacity

................................ ................................ ........................................... 15-3

15.3.2 Electricity Generation .................................................................................................... 15-6

15.3.3 Results Summary ......................................................................................................... 15-10

LIST OF TABLES

Table 15.3.1 Summary of Utility Impact Results ................................................................. 15-10

LIST OF FIGURES

Figure 15.3.1 Total Electric Capacity Reduction ..................................................................... 15-3 Figure 15.3.2 Coal Capacity Reduction ................................................................................... 15-4 Figure 15.3.3 Nuclear Capacity Reduction .............................................................................. 15-4 Figure 15.3.4 Gas Combined Cycle Capacity Reduction ......................................................... 15-5 Figure 15.3.5 Peaking Capacity Reduction .............................................................................. 15-5 Figure 15.3.6 Renewables Capacity Reduction

................................ ........................................ 15-6 Figure 15.3.7 Total Generation Reduction ............................................................................... 15-7 Figure 15.3.8 Coal Generation Reduction ................................................................................ 15-7 Figure 15.3.9 Nuclear Generation Reduction ........................................................................... 15-8 Figure 15.3.10 Gas Combined Cycle Generation Reduction ..................................................... 15-8 Figure 15.3.11 Peaking Generation Reduction .......................................................................... 15-9 Figure 15.3.12 Renewables Generation Reduction .................................................................... 15-9

15 -iCHAPTER 15. UTILITY IMPACT ANALY SIS

15.1 INTRODUCTION

In the utility impact analysis , the U.S. Department of Energy ( DOE ) analyze s the changes in electric installed capacity and power generation that result for each trial standard level (TSL).

The utility impact analysis uses a variant of the DOE Energy Information Administration (EIA)’s National En ergy Modeling System (NEMS). a NEMS is a public domain, multi- sectored, partial equilibrium model of the U.S. energy sector. Each year, DOE/EIA uses NEMS to produce an energy forecast for the United States, the Annual Energy Outlook (AEO) . DOE uses a varian t of this model, referred to as NEMS- BT, b to account for selected utility impacts from energy conservation standards. DOE’s analysis consists of a comparison between model results for the

most recent AEO Reference Case and for cases in which energy use is decremented to reflect the impact s of standards. For the analysis of standards for top-loading and front- loading commercial

clothes washers DOE used the version of NEMS based on AEO 201 3

. 2

NEMS -BT has a number of advantages that have led to its use in the analysis of energy

conservation standards:

• NEMS -BT uses a set of assumptions that are well known and fairly transparent, due to the exposure and scrutiny each AEO r eceives.

• NEMS -BT is updated each year, with each edition of the AEO, to reflect changes in energy prices, supply trends, regulations, etc.

• The comprehensiveness of NEMS- BT permits the modeling of interactions among the various energy supply and demand sectors.

15.2 METHODOLOGY

DOE uses NEMS

- BT to estimate the marginal impacts of re duction in energy demand on

the energy supply sector. In principle, marginal values should provide a better estimate of the actual impact of energy conservation standards . In practice, the numerical differences between marginal and average values may turn out to be smaller than the intrinsic uncertainties in the

AEO.

a For more information on NEMS, refer to the U.S. Department of Energy, Energy Information Administration documentation. A useful summary is National Energy Modeling System: An Overview

. 1

b DOE/EIA approves use of the name NEMS to describe only an official version of the model without any

modification to code or data. Becaus e this analysis entails some minor code modifications and the model is run under various policy scenarios that are variations on DOE/EIA assumptions, DOE refers to it by the name NEMS

BT (BT is DOE’s Building Technologies Program, under whose aegis this wo rk has been performed).

15 -1NEMS uses predicted growth in demand for each end use to build up a projection of the total electric system load growth . The system load shapes are converted internally to load duration curves, which are then used to estimate the most cost

- effective additions to capacity. When electricity demand deviates from the AEO reference case, in general there are three inter

related effects: the annual generation ( terawatt hours, TWh) from the stock of electric generating capacity changes, the total generation capacity itself ( gigawatts

, GW) may change, and the mix of capacity by fuel type may change. Each of these effects can vary for different types of end use. The change in total generating capacity is sensitive to the degree to which the end

- use is peak coincident, while the capacity mix is sensitive to the hourly load shape associated with the

end use.

To model the impact of a standard, DOE inputs a reduction to annual energy demand for the corresponding end use in the appropriate start year. The NEMS -BT model is run with the decremented energy demand to determine the modified build -out of capacity and total generation. Regional effects of a standard can be accounted for by defining the energy demand

decrement as a function of census division.

The output of the NEMS- BT analysis includes the effective marginal heat rate (ratio of the change in energy consumption in quads to the change in generation in TWh), and the capacity reduction by plant type for a given reduction in total generation. DOE uses the site energy savings multiplied by a transmission

- and

- distribution ( T&D ) loss factor to estimate the reduction in generation f or each TSL. The relationship between a reduction c in electricity generation (TWh) and the reduction in capacity (GW) is estimated based on the output of

NEMS -BT model runs using the end- use specific energy demand decrement. Details on the approach used may be found in Coughlin (2013 ). 3

NEMS -BT provides output for the following capacity types: coal, nuclear,

combined cycle (natural gas), renewable sources, oil and natural gas steam, combustion turbine/diesel, pumped storage, fuel cells, and distributed generation (natural gas). DOE groupe d oil and natural gas steam and combustion turbine/diesel into a “peaking” category, and grouped pumped storage, fuel cells, and distributed generation (natural gas) into an “other”

category.

In general, energy conservation standards i mpact primarily fossi l combustion (coal, natural gas and diesel) and renewables. Pumped storage and nuclear power are very insensitive to small changes in demand, while fuel cells and distributed generation make up a very small fraction (less than 1 percent) of the generation capacity base.

For the analysis of top-loading and front- loading commercial clothes washers , DOE derived factors based on the plant types used to supply electricity for commercial refrigeration equipment. DOE assumes that commercial clothes washers operate during the same period of the day as do commercial refrigeration equipment.

c These reductions are defined relative to the AEO Reference case.

15 -215.3 UTILITY IMPACT RESUL TS

This section presents results of the analysis for all of the capacity types except “Other”, for which the impacts are very small.

15.3.1 Installed Capacity

The f igures in this section show the changes in U.S. electricity installed capacity that result for each TSL by major plant type for selected years. The changes have been calculated based on factors ( megawatts ( MW ) of capacity reduction per gigawatt hours ( GWh ) of generation reduction) estimated from a NEMS

- BT model run that simulated a decrement in energy demand for the load shape corresponding to commercial refrigeration

. DOE assumes commercial clothes washers operate during the same period of the day as do co mmercial refrigeration equipment. Note that a positive change means a reduction in capacity under a TSL.

Figure 15.3.1 Total Electric Capacity Reduction 0 20 40 60 80 100 120201820232028203320382043204820532058206320682073MW Total

TSL 1 TSL 2 TSL 3

15 -3Figure 15.3.2 Coal Capacity Reduction

Figure 15.3.3 Nuclear Capacity Reduction 0 2 4 6 8 10 12 14201820232028203320382043204820532058206320682073MW Coal

TSL 1 TSL 2 TSL 3

0.0 0.5 1.0 1.5 2.0 2.5201820232028203320382043204820532058206320682073MW Nuclear

TSL 1 TSL 2 TSL 3

15 -4Figure 15.3.4 Gas Combined Cycle Capacity Reduction

Figure 15.3.5 Peaking Capacity Reduction 0 5 10 15 20 25201820232028203320382043204820532058206320682073MW NGCC

TSL 1 TSL 2 TSL 3

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5201820232028203320382043204820532058206320682073MW Peaking

TSL 1 TSL 2 TSL 3

15 -5Figure 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 plant type. The change by capacity type has been calculated based on shares (percentage of generation reduction for each capacity type over total generation reduction)

estimated from a NEMS -BT model run that simulated a decrement in energy demand for the load shape corresponding to commercial refrigeration. DOE assumes commercial clothes washers operate during the same period of the day as do commercial refrigeration equipment. Note that a positive change means a reduction in generation under a TSL. 0 10 20 30 40 50 60 70 80 90201820232028203320382043204820532058206320682073MW Renewable

TSL 1 TSL 2 TSL 3

15 -6Figure 15.3.7 Total Generation Reduction

Figure 15.3.8 Coal Generation Reduction 0 100 200 300 400 500 600201820232028203320382043204820532058206320682073GWh Total

TSL 1 TSL 2 TSL 3

0 10 20 30 40 50 60 70 80 90201820232028203320382043204820532058206320682073GWh Coal

TSL 1 TSL 2 TSL 3

15 -7Figure 15.3.9 Nuclear Generation Reduction

Figure 15.3.10 Gas Combined Cycle Generation Reduction 0 2 4 6 8 10 12 14 16 18 20201820232028203320382043204820532058206320682073GWh Nuclear

TSL 1 TSL 2 TSL 3

0 50 100 150 200 250 300 350201820232028203320382043204820532058206320682073GWh NGCC

TSL 1 TSL 2 TSL 3

15 -8Figure 15.3.11 Peaking Generation Reduction

Figure 15.3.12 Renewables Generation Reduction -10

-5 0 5 10 15 20201820232028203320382043204820532058206320682073GWh Peaking

TSL 1 TSL 2 TSL 3

0 50 100 150 200 250201820232028203320382043204820532058206320682073GWh Renewable

TSL 1 TSL 2 TSL 3

15 -915.3.3 Results Summary

Table 15.3.1 presents a summary of the utility impact results estimated for top-loading

and front- loading commercial clothes washers

.

Table 15.3.1 Summary of Utility Impact Results

TSL

1 2 3 Installed Capacity Reduction (MW)

2020 2.25 2.43 4.84

2025 8.79 9.51 18.9

2030 19.6 21.3 42.4

2035 30.4 33.1 66.0

2040 50.7 55.5 111 Electricity Generation Reduction (GWh)

2020 61.7 66.6 133

2025 148 161 320

2030 198 215 429

2035 213 232 463

2040 217 237 473

15

- 10REFERENCES

1. U.S. Department of Energy- Energy Information Administration, National Energy Modeling System: An Overview , 2009. Report No. DOE/EIA-0581(2009).

< http://www.eia.gov/oiaf/aeo/overview/ >

2. Energy Information Administration, Annual Energy Outlook 2013 with Projections to

2040 , 2013. Washington, DC. < http://www.eia.gov/forecasts/aeo/p df/0383(2013).pdf >

3. Coughlin, K., H. Shen, P. Chan, B. McDevitt, and A. Sturges, Marginal Utility Sector Impacts of Reduced Electricity Demand , 2012.

15

- 11CHAPTER 1 6 . EMPLOYMENT IMPACT ANALYSIS

TABLE OF CO NTENTS

16.1 INTRODUCTION ......................................................................................................... 16-1

16.2 ASSUMPTIONS ............................................................................................................ 16-1

16.3 METHODOLOGY ........................................................................................................ 16-1

16.4 SHORT

- TERM RESULTS ............................................................................................ 16-2

16.5 LONG

- TERM RESULTS .............................................................................................. 16-3

LIST OF TABLES

Table 16.4.1 Commercial clothes washer Net National Short-term Change in Employment (Number of Jobs) .......................................................................... 16-3

16-iCHAPTER 16. EMPLOYMENT IMPACT ANALYSIS

16.1 INTRODUCTION

DOE’s employment impact analysis is designed to estimate indirect national job creation or elimination resulting from possible standards, due to reallocation of the associated expenditures for purchasing and operating commercial clothes washers . Job incre ases or decreases reported in this chapter are separate from the direct commercial clothes washer production sector employment impacts reported in the manufacturer impact analysis ( Chapter 12), and reflect the employment impact of efficiency standards on a ll other sectors of the

economy.

16.2 ASSUMPTIONS

DOE expects energy conservation standards to decrease energy consumption, and therefore to reduce energy expenditures. The savings in energy expenditures may be spent on new investment or not at all (i.e., t hey may remain “saved”). The standards may increase the purchase price of products , including the retail price plus sales tax, and increase installation

costs.

The ImSET input/output econometric model of the U.S. economy, this analysis estimated

the short- term effect of these expenditure impacts on net economic output and employment. DOE intends this analysis to quantify the indirect employment impacts of these expenditure changes. It evaluated direct employment impacts at manufacturers’ facilities in the manufacturer impact analysis (see Chapter 12).

DOE notes that ImSET is not a general equilibrium forecasting model, and understands the uncertainties involved in projecting employment impacts, especially changes in the later years of the analysis. 1 Because ImSET does not incorporate price changes, the employment effects predicted by ImSET would over- estimate the magnitude of actual job impacts over the long run for this rule. Since input/output models do not allow prices to bring markets into equilibrium, they are best used for short-run analysis. DOE therefore includes a qualitative discussion of how labor markets are likely to respond in the longer term. In future rulemakings, DOE may consider the use of other modeling approaches for examining long run employment impacts

.

16.3 METHODOLOGY

The Department based its analysis on an input/output model of the U.S. economy that estimates the effects of standards on major sectors of the economy related to buildings and the net impact of standards on jobs. The Pacific Northwest National Laboratory developed the model, 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 estimates the employment and income effects of bui lding energy technologies. In comparison with simple

16-1economic multiplier approaches, Im SET allows for more complete and automated analysis of the economic impacts of energy

- efficiency investments in buildings.

In an input/output model, the level of employment in an economy is determined by the relationship 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 o f an efficiency standard) in one sector of the economy will affect flows in other sectors, which affects the overall level of employment.

ImSET uses a 187-sector model of the national economy to predict the economic effects

of residential and commercial buildings technologies. ImSET collects estimates of initial investments, energy savings, and economic activity associated with spending the savings resulting from standards (e.g., changes in final demand in personal consumption, business investment and spending, and government spending). It provides overall estimates of the change in national output for each input-output sector. The model applies estimates of employment and wage income per dollar of economic output for each sector and calculates impacts on national employment and wage income.

Energy -efficiency technology primarily affects the U.S. economy along three spending pathways. First, general investment funds are diverted to sectors that manufacture, install, and maintain energy

- efficient product s. The increased cost of products leads to higher employment in

the product manufacturing sectors and lower employment in other economic sectors. Second, commercial firm and residential spending are redirected from utilities toward firms that supply production inputs. Third, electric utility sector investment funds are released for use in other sectors of the economy. When consumers use less energy, electric utilities experience relative reductions in demand which leads to reductions in utility sector inves tment and employment.

DOE also notes that the employment impacts estimated with ImSET for the entire economy differ from the employment impacts in the commercial clothes washer manufacturing sector estimated in Chapter 12 using the Government Regulatory Impact Model (GRIM). The methodologies used and the sectors analyzed in the ImSET and GRIM models are different.

16.4 SHORT

- TERM RESULTS

The results in this section refer to impacts of commercial clothes washer standards

relative to the base case. DOE disaggregated the impact of standards on employment into three component effects: increased capital investment costs, decreased energy costs, and changes in operations and maintenance costs. DOE presents the summary impact.

16-2Conceptually, one can consid er the impact of the rule in its first year on three aggregate sectors, the commercial clothes washer production sector, the energy generation sector, and the general consumer good sector (as mentioned above ImSET’s calculations are made at a much more disaggregate level) . By r aising energy efficiency, t he rule generally increases the purchase price of commercial clothes washers ; this increase in expenditures causes an increase in employment in this sector. At the sa me time, the improvements in energy efficiency reduce consumer expenditures on electricity , freeing up this money to be spent in other sectors . The reduction in electricity demand causes a reduction in employment in that sector. Finally, based on the net impact of increased expenditures on commercial clothes washers 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 ( e.g. as more workers are hired they consume more goods, which generates more employment ; the converse is true for workers laid off ).

Table 16.4.1 present the modeled net employment impact from the rule in 2015, rounded to the nearest hundred jobs. As most commercial clothes washers are produced domestically, DOE does not model the impact of import spending for this product.

Table 16.4.1 Commercial C lothes W asher Net National Short

- term Change in Employment ( Number of J obs)

Trial

Standard

Level 2015 2020

TSL 1 1 0 8 0

TSL 2 2 0 8 0

TSL 3

- 2 0 11 0

For context, the Office of Management of Budget currently assumes that the unemployment rate may decline to 6.9% in 2014 and drop further to 5.3% in 2017. 5 The unemployment rate in 2017 is projected to be close to “full employment.” When an economy is at full employment any eff ects on net employment are likely to be tr ansitory as workers change jobs, rather t han enter or exit longer- term employment.

16.5 LONG

- TERM RESULTS

Over the long term DOE expect s the energy savings to consumers to increasingly

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. Since the electricity generation sector is relatively capital intensive compared to the consumer good s sector, the net effect will be an increase in labor demand. In equilibrium, this

16-3should lead to upward pressure on wages and a shift in employment away from electricity generation towards consumer goods. Note that in long-run equilibrium there is no net effect on total employment since wages adjust to bring the labor market into equilibrium. Nonetheless, even to the extent that markets are slow to adjust, DOE anticipate s that net labor market impacts

will in general be negligible over time due to the small magnitude of the short- term effects presented in Table 16.4.1 . The ImSET model projections, assuming no price or wage effects until 2020, 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

- AC06 -76RLO 1830.

4. Minnesota IMPLAN Group Inc., IMPLAN Professional: User's Guide, Analysis Guide,

Dat a Guide , 1997. Stillwater, MN.

5. Office of Management and Budget, Fiscal Year 2012 Mid-Session Review: Budget of the
U.S. Government, Washington, DC. <http://www.whitehouse.gov/sites/default/files/omb/budget/fy2012/assets/12msr.pdf>

16-517 -i CHAPTER 1 7 . REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION

................................................................................................... 17-1

17.2 NON

- REGULATORY POLICIES

.......................................................................... 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

................................................................................................... 17-4

17.3.2.1 Methodology .......................................................................................... 17-4

17.3.2.2 Analysis

................................ ................................ .................................. 17-6

17.3.3 Consumer Tax Credits ............................................................................................. 17-8

17.3.4 Manufacturer Tax Credits

...................................................................................... 17-12

17.3.5 Voluntary Energy Efficiency Targets .................................................................... 17-13

17.3.6 Early Replacement

................................................................................................. 17-14

17.3.7 Bulk Government Purchases .................................................................................. 17-18

17.4 IMPACTS OF NON

- REGULATORY ALTERNATIVES

................................... 17-20

LIST OF TABLES

Table 17.1.1 Non

- Regulatory Alternatives to National Standards ...................................... 17-1

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes Washers Equipment Classes ........................................................................... 17-3

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Commercial Clothes

Washer ............................................................................................................ 17-7

Table 17.3.2 Market Penetrations in 2018 Attributable to Consumer Rebates for

Commercial Clothes Washers ......................................................................... 17-8

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000

to 2011 ............................................................................................................ 17-9

Table 17.3.4 Percentages of Increase in Total Shipments of Commercial Clothes Washers Attributed to Tax Credits, Rebates and other Market Effects ........ 17-11

Table 17.3.5 Market Penetrations in 2018 Attributable to Consumer Tax Credits for

Commercial Clothes Washers ....................................................................... 17-11

Table 17.3.6 Market Penetrations in 2018 Attributable to Manufacturer Tax Credits

for Commercial Clothes Washers ................................................................. 17-13

Table 17.3.7 Market Penetrations in 2018 Attributable to Voluntary Energy Efficiency Targets for Commercial Clothes Washers .................................. 17-14

Table 17.3.8 Market Penetrations in 2018 Attributable to Early Replacements for

Commercial Clothes Washers ....................................................................... 17-1817

- ii Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government Purchasing of Commercial Clothes Washers ................................................ 17-20

Table 17.4.1 Impacts of Non-Regulatory Alternatives for Front- Loading Commercial Clothes Washers (TSL 2) .............................................................................. 17-22

Table 17.4.2 Impacts of Non- Regulatory Alternatives for Top

- Loading Commercial Clothes Washers (TSL 2) .............................................................................. 17-23

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Commercial Clothes Washers ....................... 17-8 Figure 17.3.2 Base Case and Policy Case Shipments Projects for the Early Replacement Policy Case (thousand units) ................................................... 17-17 Figure 17.4.1 Market Penetration of Front- Loading Commercial Clothes Washers Meeting the Target Level in Policy Cases (TSL 2) ...................................... 17-21 Figure 17.4.2 Market Penetration of Top

- Loading Commercial Clothes Washers Meeting the Target Level in Policy Cases (TSL 2) ...................................... 17-2117 -1 CHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that energy conservation

standards for consumers of commercial clothes washers constitute an “economically significant regulatory action” under Executive Order (E.O.) 12866, Regulatory Planning and Review. 58 FR 51735 (October 4, 1993). This RIA, which DOE has prepared pursuant to E.O. 12866 , evaluates potential non-regulatory alternatives, comparing the costs and benefits of each to those of the proposed standard s. 58 FR 51735, 51741.

For this analysis , DOE used an integrated National Impact Analysis ( NIA )- RIA model

built on the NIA model discussed in C hapter 11 for its analysis.

DOE identified s even non- regulatory policy alternatives that possibly could provide

incentives for the same energy efficiency levels as the ones in the trial standard level s (TSL) for

the equipment that are the subject of this rulemaking. The non- regulatory policy alternatives are

listed in Table 17.1.1 . 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 standard.

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
Early Replacement

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of six selected policies listed above (excluding the alternative of no new regulatory action). Section 17.4 present s the results of the policy alternatives

.

17.2 NON

- REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost

effectiveness of the non- regulatory policy alternatives for commercial clothes washers. This

section also describes the assumptions underlying the analysis.17 -2 17.2.1 Methodology

DOE used its integrated NIA

- RIA spreadsheet model to calculate the national energy savings (NES) and net present value (NPV) associated with each non-regulatory policy

alternative . Chapter 11 of the technical support document (TSD) describes the NIA spreadsheet model. Appendix 17-A, discusse s the NIA

- RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet the efficiency levels corresponding to the levels set for each TSL . After establishing the quantitative assumptions underlying each alternativ e, DOE appropriately revised inputs to the NIA- RIA spreadsheet model . The primary model inputs revised were market shares of equipment meeting the target efficiency levels set for each TSL . The shipments of equipment for any given year

reflect a distributi on of efficiency levels. DOE assumed , for each TSL, that new energy efficiency standards would affect 100 percent of the shipments of equipment that did not meet

the TSL target level 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 clothes washers attributable to each policy alternative.

Increasing the efficiency of equipment often increases its average installed cost.

However , operating costs generally decrease because energy consumption declines. DOE

therefore calcu lated an NPV for each non- regulatory alternative in the same way it did for the proposed standards. In some policy scenarios, increases in total installed cost are mitigated by government rebates or tax credits. Because DOE assumed that consumers would re- pay credits and rebates in some way (such as additional taxes) , DOE did not include rebates or tax credits as a consumer benefit when calculating national NPV. DOE’s analysis also excluded any administrative costs for the non-regulatory policies; including such costs would decrease the NPVs slightly.

The following are key measures for evaluating the impact of each alternative.

National Energy Savings (NES) , given in quadrillion Btus (quads), describes the cumulative national primary energy saved over the lifetime of equipment purchased during the 30- year analysis period starting in the effective date of the policy (2018-

2047 ).

Net Present Value (NPV)

, represents the value of net monetary savings in 2013, expressed in 2012$, from equipment purchased during the 30- year analysis period starting in the effective date of the policy (2018-2047). DOE calculated the NPV as the difference between the present value of installed equipment cost and operating

a The base case for the NIA is a market

- weighted average energy efficiency calculated from units at several efficiency levels.17 -3 expenditures in the base case and the presen t value of th o se costs in each policy case. DOE calculated operating expenses (including energy costs ) for the life of equipment.

17.2.2 Assumptions Regarding Non- Regulatory Policies

The effects of non

- regulatory policies are by nature uncertain because they de pend on program implementation

, marketing efforts , and on consumers’ responses to a program. Because the projected effects depend on assumptions regarding the rate of consumer participation, they are subject to more uncertainty than are the impacts of mandatory standards, which DOE assumes will meet with full compliance. To increase the robustness of the analysis, DOE conducted a literature review regarding each non -regulatory policy and consulted with recognized experts to gather information on similar incentive programs that have been implemented in the United States. By studying experiences with the various types of programs, DOE sought to make credible assumptions regarding potential market impacts . Section 17.3 presents the sources DOE relied on in developing assumptions about each alternative policy and reports DOE’s conclusions as they affected the assumptions that underlie the modeling of e ach

alternative policy.

Each non-regulatory policy that DOE considered would improve the average efficiency

of new commercial clothes washers relative to their base case efficiency scenario (which involves no new regulatory action). The analysis considered that each alternative policy would induce consumers to purchase units h aving the same efficiency level as required by the proposed standards (the target level ) 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 17.2.1 shows the efficiency level stipulated in the proposed standards for

commercial clothes washers

.

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes Washers Equipment Classes

Equipment Type Efficiency Levels ( MEF J2 /IWF)

Baseline

FL: EL0

TL: EL0 TSL1 TSL2 TSL3

FL: EL1

TL:EL1 FL: EL2

TL:EL1 FL: EL3

TL:EL2

Front Loading CCW Units 1.65/5.5 1.80/4.5 2.00/4.0 2.20/3.7

Top Loading CCW Units 1.15/8.9 1.35/8.8 1.35/8.8 1.55/6.9

DOE assumed that the effects of non- regulatory policies would last from the effective date of standards—2018—through the end of the analysis period, which is 2047.17 -4 17.2.3 Policy Interactions

DOE calculated the effects of each non -regulatory policy separately from those of the other policies. In practice, some policies are most effective when implemented in combination, such as early replacement implemented with consumer rebates, or early replacement implemented with bulk government purchases. However, DOE attempted to make conservative assumptions to avoid double-counting policy impacts. The resulting policy impacts are not additive; the combined effect of several or all policies cannot be inferred from summing their

results.

Section 17.4 presents graphs that show the market penetration estimated under each non

regulatory policy for the commercial clothes washer equipment classes

.

17.3 NON

- REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the six nonregulatory policy alternatives to proposed standard s for commercial clothes washers

. (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 high

- efficiency equipment

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 clothes washers constitutes the base case, as described in C hapter 11, National Impact Analysis. The base case provides the basis of comparison for all other policies. By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 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 appliances

. This policy p rovides a consumer rebate for purchasing commercial clothes washers that operate at (or above) the same efficiency as stipulated in each TSL ( target level ).

17.3.2.1 Methodology

DOE based its evaluation methodology for consumer rebates on a comprehensive study

of C alifornia’s potential for achieving energy efficiency

. Th e study, performed by XENERGY,17 -5 Inc., b 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 referenced XENERGY report was published, 2

, 3

, 4

, 5

, 6

, 7 used different approaches: other economic parameters (e.g., payback period), expert surveys, or model calibrat ion based on specific utility program data rather than

multi

- utility data . Some models in use by energy efficiency program evaluation experts were so

client

- specific that generic relationships between economic parameters and consumer res ponse
could not be established. 5 DOE decided that the most appropriate available method for this RIA analysis was the XENERGY approach of penetration curves based on B/C ratio, which incorporates lifetime operating cost savings.

XENERGY’s model estimate s market impacts induced by financial incentives based on the premise that two types of information diffusion drive the adoption of new technologies.

Internal sources of information encourage consumers to purchase new equipment primarily through word-of- mo uth 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 mod el 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 po licy measure. XENERGY calibrated the curves based on participation data from utility rebate programs. The curves illustrate the increased penetration (i.e., increased market share) of efficient equipment driven by consumer response to changes in B/C ratio induced by rebate programs. The penetration curves depict various diffusion patterns based on perceived

market barriers (from no- barriers to extremely -high- barriers ) to consumer purchase of highefficiency equipment. DOE adjusted the XENERGY former penetra tion curves based on expert advice founded on more recent utility program experience. 5

, 8

DOE modeled the effects of a consumer rebate policy for commercial clothes washers 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 clothes washers . It gathered data on utility or a gency rebates throughout the nation for this equipment, and used this data to calibrate the customized penetration curves it developed for front-loading and top- loading commercial clothes washers so

b XENERGY is now owned by KEMA, Inc. ( www.kema.com )17 -6 they can best reflect the market barrier level faced by each equipment class . Section 17.3.2.2 shows the interpolated curves used in the analysis.

17.3.2.2 Analysis

For the two commercial clothes washer equipment classes it analyzed , DOE estimated t he effect of increasing its B/ C ratio via a rebate that would pay all o f the increased installed cost of

a unit that met the target efficiency level compared to one meeting the baseline efficiency level

. c

To inform its estimate of an appropriate rebate amount, DOE performed a thorough nationwide search for existing rebate pr ograms for commercial clothes washers

. It gathered data from a sample of utility and agency rebate programs that includes 38 rebates for commercial clothes

washers initiated by 26 utilities or agencies in various States d . (Appendix 17-A, identifies the rebate programs .) To represent the rebate level, DOE used the simple average of the rebate

amounts in these programs. DOE assumed that rebates would remain in effect at the same levels throughout the forecast period (2018–2047 ).

For each of the two commercial clothes washer equipment classes

, DOE first calculated

the B/C ratio without a rebate using the difference in total installed costs (C) and lifetime operating cost savings (B) between the unit meeting the target level and the baseline unit. It then

calculated the B/C ratio given a rebate for the unit meeting the target efficiency level

. Because the rebate reduced the incremental cost, the unit receiving the rebate had a larger B/C ratio. Table

17.3.1 show s the effect of consumer rebates on the B/C ratio for each TSL

.

c The baseline technology for each product class is defined in the engineering analysis, Chapter 5, as the technology that represents the basic characteristics of products in that class . A baseline unit typically is one that just meets current Federal energy conservation standards and provides basic consumer utility

.

d Some rebates for commercial clothes washers are offered by electric and /or gas utilities, some by water utilities

and

, in some service te rritories

, by both energy and water utilities

. DOE calculated t he avera ge rebate amount from the total (combined) rebate amount available to the consumer.17 -7 Table 17.3.1 Benefit/Cost Ratios Without and W ith Rebates for Commercial Clothes

Washer

Trial Standard Levels

Front

- Loading 1 2 3

B/C Ratio Without Rebate infinit e 715.8 7.7 Rebate Amount (2012$) 158.16 158.16 158.16

B/C Ratio With Rebate infinit e infinit e infinit e
Calculated Market Barrier s Curve E x tra

- High E x tra

- High E x tra

- High
Top

- Loading 1 2 3

B/C Ratio Without Rebate inf init e inf init e 12.5 Rebate Amount (2012$) 158.16 158.16 158.16

B/C Ratio With Rebate infinit e infinit e infinit e Calculated Market Barrier s Curve E x tra

- High E x tra

- High E x tra

- High

DOE used the se B/C ratios along with the penetration curves shown in Figure 17.3.1 to estimate the percentage of consumers who would purchase commercial clothes washers that meet the target level both with and without a rebate incentive. The penetration curves calculated by DOE to represent the market behavior for front-loading and top- loading commercial clothes

washers at each TSL are indicated in Table 17.3.1

.

Front

- Loading Top

- LoadingTSL 1TSL 217 -8TSL 3

Figure 17.3.1 Market Penetration Curves for Commercial Clothes Washers

For each equipment class, DOE next estimated the percent increase s 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 that meet the target level in the rebate policy case. Table 17.3.2

summarizes the market shares for commercial clothes washers that meet the target level in 2018.

DOE used the resulting annual increase s in market shares as inputs t o represent the rebate policy

case scenario in its NIA -RIA model. Appendix 17-A , shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetration trends for the policy

case of consumer rebates for commercial clothes washers

.

Table 17.3.2 Market Penetrations in 2018 Attributable to Consumer R ebates for

Com mercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3

Base

- Case Market Share 34.0% 38.0% 0.0% Policy Case Market Share 50.0% 50.0% 50.0%

Increased Market Share 16.0% 12.0% 50.0%

Top

- Loading 1 2 3

Base

- Case Market Share 0.3% 0.3% 0.3% Policy Case Market Share 50.0% 50.0% 50.0%

Increased Market Share 49.8% 49.8% 49.8%

17.3.3 Consumer Tax Credits

In preparing its assumptions to estimate the effects of tax credits on consumer purchases of commercial clothes washers, DOE reviewed tax credit programs that have been offered at the Federal level for energy

- efficient appliances. The Energy Policy Act of 2005 (EPACT 2005)

included Federal tax credits for consumers who purchase energy

- efficien t equipment. 10 Those tax credits were in effect in 2006 and 2007, expired in 2008, were reinstated for 2009–2010 by

the American Recovery and Reinvestment Act of 2009 ( ARRA ), exten ded by Congress for 2011

with some modifications , and expired at the end of 2011. 11

, 12 The American Taxpayer Relief17 -9 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. 13 DOE reviewed Internal Revenue Service data on the numbers of taxpayers w ho claimed the tax credits during tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation tax credit program in place in the 1980s. However, DOE did not find data specific enough to commercial clothes washers to warrant its analysis for the consumer t ax credits policy case. Appendix 17-A contains more information on Federal consumer tax credits.

DOE estimated the effects of tax credits on consumer purchases using data from Oregon State’s tax credit program s for energy

- efficient appliances . (Additional discussion of State tax credits for Oregon and other states is in Appendix 17-A .) Oregon has had consumer tax credits for efficient appliances for several years. e DOE reviewed Oregon’s business energy tax credit (BETC) for commercial clothes washers. Oregon’s BETC would typically cover 35–50 percent

of the incremental cost of the efficient equipment. f The majority of these credits w as claimed for

laundries and renta l dwellings, and included commercial clothes washers as well as other appliances. However, ODOE did not track the types of appliances involved nor the number of machines per project; the annual counts are the total number of projects, with each project involving one or more appliances. DOE was therefore unable to use the data to estimate market percentages for specific appliances, i.e. commercial clothes washers. However, DOE did note the trend of increasing numbers of credits claimed from 1998 through 2006 for residential clothes

washers , which contributed to its assumptions regarding the trend line for this policy scenario.

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000 to

2011

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tax Credits* 8

. 2 12 .2 16

. 2 19

. 7 18

. 5 23 .4 25

. 5 23

. 6 23

. 8 23 .3 29

. 7 24

. 6
Shipments** 86

. 4 88

. 3 91

. 0 97

. 7 102

. 0 101 .8 106 .5 118 .2 137 .6 145 .9 155

. 1 142

. 1
Percentage*** 9.5 13.8 17.8 20.2 18.2 23.0 24.0 20.0 17.3 16.0 19.1 17.3

* Number of t ax c redit s claimed (thousand units)

. ** Shipments of clothes washers to Oregon (thousand units)

. *** Percentage of shipments to Oregon with tax credits.

Because tax credits have not been available for commercial clothes washers, DOE analyzed the Oregon resid ential tax credit data for residential clothes washers to inform the analysis of the tax credit policy for commercial clothes washers. DOE obtained data from the Oregon Department of Energy (ODOE) on the number of efficient appliances for which tax

credits were claimed by Oregon residential taxpayers for the years 1998 through 2012, including

e The District of Columbia (Washington D.C.) also passe d a piece of legislation entitled the “ Reside ntial Energy

Conservation Tax Credit Act Of 2005 ” for consumer tax credits, but did not appropriat e the necessary funding

and so the credits did not go into effect.

f The business tax credit is taken over five years: 10 percent in the first and second years and 5 percent each year

thereafter; the taxpayer may carry the unused credit forward up to eight years. Those with eligible project costs of $20,000 or less may take the tax credit in one year.17

- 10 those f or residential clothes washers

. 14 S ince the tax credit for clothes washers expired at the end

of 2011 , DOE assumed that the 2012 credits in ODOE’s database were for systems sold prior to 2012. DOE also obtained shipments data for clothes washers by state (including Oregon) for the

years 2000 through 2012 from the Association of Home Appliance Manufacturers (AH AM). 15 It

then calculated the percentages of clothes washers sold in Oreg on for which taxpayers claimed credits for the years 2000 through 2011. Table 17.3.3 shows the number of Oregon residential tax credits claimed for clothes washers as percentages of total annual shipments these claims represent for the years 2000–2011. DOE used those percentages to estimate the percentage of shipments of commercial clothes washers that would meet the efficiency level at each TSL due to a tax credit program for 2018 through 2029.

Not all of these high- efficiency clothes washers , however, would likely be purchased due to the tax credit program alone. Some taxpayers would be motivated by utility rebate incentives, and others would be "free riders" who would purchase efficient appliances without a tax credit or

rebate incentive but still claime d the tax credit. To better understand this interaction between tax credits, rebates, and free riders/market impacts, in its previous analysis DOE reviewed a report prepared by KEMA on efficient clothes washers in the Northwest. KEMA compared the penetration of “ultra high efficiency” (UHE) g clothes washers in Oregon and Washington, two neighboring States that have similar utility rebate programs for residential clothes washers

. 16 The study attributed the difference in market share of these UHE units (50 percent for Oregon versus 15 percent for Washington) to the Oregon tax credits.

DOE relied on this conclusion to estimate the increase in market share of high

- efficiency clothes washers due to a consumer tax credit program only. For each equipment class, DOE first removed the potential contributions of rebates and free riders to that increase

. For rebates, DOE used the same market penetration it estimated above for the rebate policy case ; f or free riders DOE used the base case market share. Since both the rebate policy case and the base case market shares are constant, the resulting percentages for front-loading and top- loading commercial clothes washers for the years 2018 through 2029 have different values yet same shape. They both present an increasing trend for the first seven years and a d e creasing trend for the remaining five

years. DOE did some interpolations to smooth the shapes of the two market penetration curves for the twelve initial years, and extrapolated the decreasing trend of market s hare for years beyond 2029 until that share reaches zero. Table 17.3.4 shows the percentages of increase in total shipments attributed to the tax credits themselves, to utility rebates, and to other market influences/free riders for 2018 through 2029 for each TSL.

g Units meeting specifications of minimum modified energy factor ( MEF J2 ) of 2.20 and maximum water factor

(WF) of 6.5.17

- 11 Table 17.3.4 P ercentages of I ncrease in Total S hipments of Commercial Clothes Washers

A ttributed to Tax C redits, Rebates and other Market Effects

20 18 201 9 202 0 20 21 20 22 20 23 20 24 20 25 20 26 20 27 20 28 20 29

TSL 1

Front

- L oading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 3.2% 4.7% 6.1% 6.9% 6.2% 7.8% 8.2% 6.8% 5.9% 5.4% 6.5% 5.9%

Tax Credits 1.5% 2.2% 2.9% 3.2% 2.9% 3.7% 3.8% 3.2% 2.8% 2.6% 3.1% 2.8%

Top

- Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.0% 11.4% 11.9% 10.0% 8.6% 7.9% 9.5% 8.6%

TSL 2

Front

- Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 3.6% 5.2% 6.8% 7.7% 6.9% 8.7% 9.1% 7.6% 6.6% 6.1% 7.3% 6.6%

Tax Credits 1.1% 1.7% 2.1% 2.4% 2.2% 2.8% 2.9% 2.4% 2.1% 1.9% 2.3% 2.1%

Top

- Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.0% 11.4% 11.9% 10.0% 8.6% 7.9% 9.5% 8.6%

TSL 3

Front

- Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Top

- Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.1% 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.0% 11.4% 11.9% 10.0% 8.6% 7.9% 9.5% 8.6%

Table 17.3.5 Market Penetrations in 2018 Attributable to Consumer Tax Credits for

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3

Base

- Case Market Share 34.0% 38.0% 0.0% Policy Case Market Share 35.7% 39.3% 5.2%

Increased Market Share 1.7% 1.3% 5.2%

Top

- Loading 1 2 3

Base

- Case Market Share 0.3% 0.3% 0.3% Policy Case Market Share 5.5% 5.5% 5.5%

Increased Market Share 5.2% 5.2% 5.2%17

- 12 Table 17.3.5 summarizes DOE’s assumptions for commercial clothes washers regarding

the market penetration of units in 2018 that meet the efficiency level at each TSL given a

consumer tax credit. The incre ased market shares attributable to consumer tax credit s shown in

Table 17.3.3 were used as inputs in the NIA

- RIA model. Appendix 17- A, Table 17-A.2.1, shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetratio n trends for the policy case of consumer tax credits for commercial clothes washers

that meet the efficiency level for each TSL

.

17.3.4 Manufacturer Tax Credi ts

To analyze the potential effects of a policy that offers tax credits to manufacturers that produce commercial clothes washers that meet the target efficiency level at each TSL

, DOE attempted to investigate manufacturer response to the Energy Efficient Appliance Credits for manufacturers mandated by EPACT 2005. 17 Those manufacturer tax credits have been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009. (Appendix 17- A presents details on Federal manufacturer tax credits.) DOE was unable to locate data from the Internal Revenue Service or other sources on manufacturer response to the Federal credits.

DOE then estimated the effects of tax credits to manufa cturers based on its previous analysis of consumer participation in tax credits. DOE incorporated previous research that disaggregated the effect of rebates and tax credits into a direct price effect , which derives from the savings in purchase price, and an announcement effect , which is independent of the amount

of the incentive. 18

, 19 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 rebate and consumer tax credit policies would encompass both direct price effects and announcement effects, and that half of the increase in market penetration associated with either policy would be due to the direct price

effect and half to the announcement effect. DOE th en assumed that a manufacturer tax credit

would lead manufacturers to pass on some of their reduced costs to consumers, causing a direct price effect. Ho wever, no announcement effect would occur because the program would not be

visible to consumers. h Because the direct price effect is approximately equiva lent to the

announcement effect

, 18 DOE es timated that a manufacturer tax credit would induce half the number of consumers assumed to take advantage of a consumer tax credit to purcha se more efficient equipment. DOE thus applied 50 percent to the increase in market penetrations

estimated above for consumer tax credits (See Table 17.3.5 ) and used the resulting market penetrations to represent the impact s from manufacturer tax credits. Table 17.3.6 summarize s DOE’s assumptions for commercial clothes washers regarding the market penetration of units in

2018 meeting the target efficiency level at each TSL given a manufacturer tax credit.

h Note that this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency programs might promote the models for which manufacturers increase production due to the tax credits, which in turn might induce some announcement effect. However, DOE found no data on such programs on which to base an estimate of the magnitude of this possible announcement effect on consumer behavior.17

- 13 Table 17.3.6 Market Penetrations in 2018 Attributable to Manufacturer Tax Credits for

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3

Base

- Case Market Share 34.0% 38.0% 0.0% Policy Case Market Share 34.8% 38.6% 2.6%

Increased Market Share 0.8% 0.6% 2.6%

Top

- Loading 1 2 3

Base

- Case Market Share 0.3% 0.3% 0.3% Policy Case Market Share 2.9% 2.9% 2.9%

Increased Market Share 2.6% 2.6% 2.6%

The increased market shares attributable to a manufacturer tax credit 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. Section 17.4 presents the resulting market penetratio n trends

for the policy case of manufacturer tax credits for commercial clo thes washers

.

17.3.5 Voluntary Energy Efficiency Targets

For each equipment class , DOE assumed that voluntary energy efficiency targets would be achieved as manufacturers gradually stopped producing units that operated below the efficiency level set for each TSL . DOE assumed that the impetus for phasing out production of

low- efficiency units would be a program similar to the ENERGY STAR labeling program conducted by the Environmental Protection Agency (EPA) and DOE. The ENERGY STAR

program specifie s the minimum e nergy 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 compli ant appliances and estimates the percentage of sales of compliant appliances that are attributable to the ENERGY STAR

program.

Researchers have analyzed the ENERGY STAR program’s effects on sales of several consumer products. Program efforts generally involve a combination of information dissemination and utility or agency rebates. The analyses have been based on State

- specific data

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The analyses generally have conclu ded that the market penetration of ENERGY STAR- qualifying appliances is higher in regions or States where ancillary promotional programs have been

active

. 20

, 21

, 22

DOE based its estimates of market penetration on its previous estimates of t he impact of a voluntary energy efficiency targets program on shipments of commercial clothes washers

. In the absence of data on the impacts of the existing ENERGY STAR program for commercial

clothes washers , in its prior analysis 23 DOE estimated the percentage of market shares17

- 14 attributable to the existing ENERGY STAR program for residential clothes washers for 1996 –

2025. DOE assumed that commercial cloth es washers would experience the same trend of annual increases in market penetration beginning in 2018 as the one presented by residential clothes

washers starting in 1997

. DOE added those percent increases to the market shares of commercial

clothes washer s that met the target level in the RIA base case for each TSL , starting in 2018, to obtain the annual market shares of units meeting the efficiency level set for each TSL in the voluntary energy efficiency targets policy case. Table 17.3.7 summarizes DOE’s assumptions for commercial clothes washers regarding the market penetration of units in 2018 that, given voluntary energy efficiency targets, would meet the target efficiency level set for each TSL

.

Table 17.3.7 Market Penetrations in 2018 Attributable to Voluntary Energy Efficiency

T argets for Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3

Base

- Case Market Share 34.0% 38.0% 0.0% Policy Case Market Share 34.5% 38.5% 0.5%

Increased Market Share 0.5% 0.5% 0.5%

Top

- Loading 1 2 3

Base

- Case Market Share 0.3% 0.3% 0.3% Policy Case Market Share 0.7% 0.7% 0.7%

Increased Market Share 0.5% 0.5% 0.5%

DOE estimated that the program developed in support of the voluntary energy efficiency

targets policy would increase market shares of ef ficient units. Appendix 17-A shows the annual market share increases due to this policy used as inputs to the NIA-RIA model. Section 17.4

presents the resulting market penetratio n trends for the policy case of voluntary energy efficiency

targets for commercial clothes washers.

17.3.6 Early Replacement

The non- regulatory policy of early replacement refers to a program to replace commercial

clothes washers before the end of their useful lives. The purpose of such a policy is to replace old, inefficient units by units at the target efficiency level defined for each TSL. DOE examined

several resources to inform its analysis of th e policy. One report detailed the Connecticut Appliance Retirement Program (ARP) , which was conducted in 2004. 24 Another was a 2006 study of the potentials for electric energy efficiency performed for the State of Vermont. 25

The Connecticut ARP was conducted from June through December 2004 by Nexus Market Research, Inc., and RLW Analytics, Inc., for Northeast Utilities– Connecticut Light and Power and the United Illuminating Company’s State programs. The purpose of t he ARP was to help Connecticut utility customers overcome barriers to recycling room air conditioners (RACs), secondary refrigerators and freezers. The program picked up used appliances at customers’ homes or at turn-in events, paid participants to retire their units, and educated customers about17

- 15 the costs of operating older appliances. In addition, the program provided consumers with financial incentives to replace inefficient RACs with ENERGY STAR -qualified units. Nexus/RLW used program data and surveys t o estimate the number of RACs retired by ARP participants, the percentage of retired units that were replaced with an ENERGY STAR model, and the number of RACs replaced by non-participants during the program. According to the Nexus/RLW analysis, about 7 pe rcent of all RACs retired during the program were retired through the ARP, and 63 percent of those were replaced with ENERGY STAR models. Thus the program resulted directly in about 4 percent of total eligible RAC consumers deciding on early replacement of inefficient units.

In 2006, GDS Associates, Inc. conducted a study for the State of Vermont of the potential for reducing electricity use and peak demand through energy efficiency and fuel conversion measures. The study took an aggressive, multi-progr am approach, one aspect of which was early replacement of appliances. GDS considered that under the program residential appliances, including RACs, would be replaced during 4 years (2006– 2009). GDS estimated achievable market penetrations assuming that consumers would receive a financial incentive equal to 50 percent of the incremental cost of each program measure. GDS assumed a maximum 80- percent penetration. For early replacement of RACs, GDS estimated a maximum achievable participation of 5 percent of el igible single

- family or multi -family homes in the year before the program began (2005).

DOE also reviewed a study it had conducted in the 1990s, under EPACT 1992, which analyzed the effectiveness of Federal programs that support ed early retirement of appliances

. 26

Th e study identified policy options for early replacement that included a direct national program; replacement of Federally

- owned appliances; and promotion through product manufacturers, consumer incentives, incentives to utilities, and building regulations. i

Historically, DOE modeled th e effects of the early replacement policy by increasing by 4 percent per year the retirement rate of units that were in the stock in the first year of the analysis peri od. DOE chose the 4- percent rate from the Connecticut study because it was based on actual program experience. DOE assumed that the early replacement program would continue until it had facilitated the replacement of all eligible units in the stock in the year the program began

. Shipments of new units in the year the program bega n and beyond would not be affected by th e program, but would remain at base

- case efficiency levels. After the stock of inefficient units was completely replaced, the policy would produce no additional impacts.

i The analysis concluded that, although cost

- effective opportunities for early replacement exist, a widespread Federal

program was not justified economically. Because early retirement means that a unit may be replaced by an appliance less efficient than the eventual replacement would h ave been, energy savings would be less than anticipated. Early replacement programs also could increase long

- term sales volatility by encouraging a temporary increase in production, followed by a lull in demand. Early replacement could be economical in loc alities subject to high energy costs or environmental constraints; when replacement appliances are much more efficient than existing stock; or when a major technology breakthrough has occurred, creating the need for a ready market.17

- 16 F or the commercial clothes washer RIA analysis, DOE analyzed a program designed to target installed units with efficiency levels lower than the target level set for each TSL and designed to encourage their early replacement with equipment that perform s at that target level. Rather than relying on a fixed rate of increase in retirement s , DOE based its estimates on t he economic feasibility of early replacement s. The feasibility depends on the energy efficiency of the unit being replaced, the purchase cost of a new efficient unit , and the energy cost savings. DOE performed the feasibility analysis of early replacements for front -loading and top-loading

commercial clothes washers.

DOE started the analysis estimating the stock of existing commercial clothes washers by vintage for each equipment class. To estimate th e stock by vintage DOE relied on the historical shipments from 1989 to 2011 used in the NIA (Chapter 10) . DOE then used the same lifetime distributions used in the NIA (Chapter 10) to estimate the existing stock by vintage for each of

the 30 year s that comprise the analysis period. As a second step to this analysis , DOE estimate d historical market efficiency distributions. DOE applied the historical market efficiency distributions to the stock by vintage that DOE projected. It then estimated : (a) the market average unit lifetime energy cost of a commercial clothes washer from the existing stock, and (b) the unit lifetime energy cost of a replacement, efficient commercial clothes washer. DOE calculated both values as the discounted sum of the corresponding stream of energy costs, taking into account the age of the unit in the replacement year and a stream of annual probabilities that the unit will be in operation until the expected end of its life. The difference between the two lifetime energy costs DOE calculated reflects the estimated energy cost savings that a consumer is likely to enjoy from the early replacement of units in the existing stock by more efficient units . DOE calculated these savings for each of the 30 year s in the analysis per iod for commercial clothes washers from all vintages in the existing stock.

Savings from early replacements, however, come at a cost. Anticipating the time of replacement of a unit in stock either (a) prevents consumers from realizing financial benefits from investing the money they would pay for the new, replacement unit; or (b) imposes financial costs to those consumers who do not hold funds – and therefore would have to loan – to purchase the new, replacement unit. In either case, there is an economic value in delaying the time of replacement. DOE calculated such value for commercial clothes washers from all vintages in the existing stock and for each year in the 30- year analysis period.

While the energy cost savings are an incentive for early replacing a unit in the existing stock ( benefits from replacing ), the financial costs underlying the early replacement ( benefits from waiting ) may hinder the initiative. DOE considered the net- result from the benefits from replacing and the benefits from waiting as an indicator of the decision that consumers would make towards anticipating the replacement of a front-loading and a top-loading commercial clothes washer in the existing stock by a more efficient unit rated at the target efficiency level set

for each TSL. The comparison of the two benefits shows that the early replacement of a u nit

would be cost- effective for front-loading units after their seventh year of operation, and for all top-loading units.17

- 17 Based on the stock by vintage that DOE projected over the 30-year analysis period, and

the net

- results from the benefits from replacing and the benefits from waiting for each vintage, DOE projected the number of potential front-loading and top- loading commercial clothes

washers in the existing stock to be (early) replaced in each of the 30 years of the analysis period. For this projection DOE further considered that, because of lack of information on the benefits from early replacements or any transaction costs involved in the process of early replacing a unit in operation, not all consumers would be informed to or willing to deci de for the replacement. To

estimate the share of the market that would take such initiative DOE used the customized penetration curves it developed to support the analysis of the rebate policy case described above. DOE assumed for this policy case the same monetary incentive and market barrier level it estimated for each equipment class when calculat ing the impacts from rebates . As for the

benefit

- cost ratio , DOE used the ratio of the benefits from replacing divided by the benefits from
waiting

.

The units in the existing stock projected to be replaced early represent additional shipments of front -loading and top- loading units rated at the target efficient level of each TSL. Figure 17.3.2 shows DOE’s projections of shipments of front-loading and top-loading commercial clothes washers in response to an early replacement policy. An early replacement policy would create a fairly immediate jump in shipments of equipment t hat meet the target efficiency level relative to the base case. High -efficiency units would be brought quickly into the stock, leading to an immediate gain in the market share of efficient units compared to the base

case. However, a s opposed to the policy cases discussed previously, an early replacement policy results in market shares of efficient units returning to base- case percentages as the eligible market is depleted. Additionally, because units removed early from the stock would have been replaced later (at the ends of their use ful lives) without the program, the number of shipments in later years drops slightly below the base

- case shipments forecast.

Front

- Loading Top

- Loading

Figure 17.3.2 Base Case and Policy Case Shipments Project ion s for the Early Replacement Policy Case (thousand units)

DOE used the additional shipments for early replacements to calculate the increase in market penetration of more efficient units that would result from an early replacement policy.

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Base Case Policy Case 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047

Base Case Policy Case17

- 18 Table 1 7.3.8 summarizes DOE’s assumptions for commercial clothes washers regarding the market penetration of units in 2018 meeting the target efficiency level at each TSL given early replacement incentives.

Table 17.3.8 Market Penetrations in 2018 Attributable to Early Replacement s for

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3

Base

- Case Market Share 34.0% 38.0% 0.0% Market Share After Policy Implementation 35.4% 39.4% 2.1% Increased Market Share After Policy Implementation 1.4% 1.4% 2.1%

Top

- Loading 1 2 3

Base

- Case Market Share 0.3% 0.3% 0.3% Market Share After Policy Implementation 13.7% 13.7% 15.8% Increased Market Share After Policy Implementation 13.4% 13.4% 15.6%

The resulting increased market shares attributable to early replacements shown in Table

17.3.8 were used as inputs in the NIA-RIA model. Appendix 17-A shows the a nnual market share increases due to this policy. Section 17.4 presents the resulting market penetration trends and cumulative savings for the polic y case of early replacement for commercial clothes washers

.

17.3.7 Bulk Government Purchases

Bulk government purchases can lead to Federal, State, and local governments purchasing large quantities of equipment that meet the target efficiency level . Combining the market

demands of mu ltiple public sectors also can provide a market signal to manufacturers and vendors that some of their largest customers seek equipment that meet an efficiency target at favorable prices. Such a program also can induce “ market pull,” whereby manufacturers and vendors would achieve economies of scale for high efficiency equipment.

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 stud ies the Federal Energy Management Program (FEMP) performed regarding the savings potential of its procurement specifications for appliances and other equipment. FEMP, however, does not track purchasing data, because of the complex range of purchasing systems, large number of vendors, and so on. States, counties, and municipalities have demonstrated increasing interest and activity in “green purchasing." Although many of the programs target office equipment, the growing infrastructure for developing and appl ying efficient purchasing specifications indicates that bulk government purchase programs are

feasible

. 27

, 2817

- 19 DOE assumed that government agencies would administer bulk purchasing programs for

commercial clothes washers . At the federal level, this type of program could modify the current FEMP procurement guidelines for commercial clothes washers, which refer to the ENERGY STAR require ments for commercial clothes washers 29

. DOE reviewed its own previous research on the potential for market transformation through bulk government purcha ses. Its major study analyzed several scenarios based on the assumption that 20 percent of Federal equipment purchas es in 2000 already incorporated energy efficiency requirements based on FEMP guidelines. One scenario in the DOE report showed energy efficient purchasing ramping up during 10 years from 20 percent to 80 percent of all Federal purchases. 30 Based on this study,

DOE estimated that a bulk government purchase program instituted wi thin a 10 -year period

would result in at least 80 percent of government -purchased commercial clothes washers meeting target efficiency levels.

To estimate the impact of a bulk government purchase program for this equipment , DOE

firs t determined the shipments of commercial clothes washers that go to government -owned housing. A report published by Lawrence Berkeley National Laboratory (LBNL) 31 in 2012

estimated that around 3000-3200 commercial clothes washers were purchased annually for Federal buildings from 2005 through 2011. This corresponds to an average of 1.9% of all shipments of commercial clothes washers in the same period. DOE applied the estimated 1.9% of shipments to the forecast of market penetration of more efficient front -loading and top-loading

commercial clothes washers

. DOE esti mated that , for each TSL, starting in 201 8 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 target efficiency level s . DOE

estimated that wit hin 10 years (by the end of 2027), bulk government purchasing programs would result in 80 percent of the front -loading and top- loading commercial clothes washers market for government- owned commercial buildings meeting the target level of each TSL . DOE modeled the bulk government purchase program assuming that the market share for each equipment class achieved in 2027 would be maintained throughout the rest of the analysis period.

Table 17.3.9 summarizes DOE’s assumptions for commercial clothes washers regarding

the market penetration of units in 2018 meeting , for each TSL, the target efficiency level given bulk government purchasing.17

- 20 Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government Purchasing o f

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3

Base

- Case Market Share 34.0% 38.0% 0.0% Market Share After Policy Implementation 34.7% 38.8% 0.1%

Increased Market Share After Policy Implementation 0.7% 0.8% 0.1%

Top

- Loading 1 2 3

Base

- Case Market Share 0.3% 0.3% 0.3% Market Share After Policy Implementation 0.4% 0.4% 0.4% Increased Market Share After Policy Implementation 0.2% 0.2% 0.2%

The resulting increased market shares attributable to bulk government purchasing shown

in Table 17.3.9 were used as inputs in the NIA-RIA model. Appendix 17- A shows the annual market share increases due to this policy that DOE used as inputs to the NIA-RIA model. Section

17.4 below presents the resulting market penetration trends for the policy case of bulk government purchase of commerc ial clothes washers.

17.4 IMPACT S OF NON

- REGULATORY ALTERNATI VE S

Figure 17.4.1 and Figure 17.4.2 show the effects of each non-regulatory policy on market

penetrat ion for front -loading and top-loading commercial clothes washers . R elative to the base case, the policy cas es increase the market shares that meet the target level. Recall the proposed standards (not shown in the figures) would result in a 100- percent market penetration of equipment that meet s the target efficiency level.17

- 21 Figure 17.4.1 Market Penetration of Front -Loading Commercial Clothes Washers Meeting the Target Level in Policy Cases (TSL 2)

Figure 17.4.2 Market Penetration of Top-Loading Commercial Clothes

Was hers Meeting the Target Level in Policy Cases (TSL 2)

Table 17.4.1 and Table 17.4.2 show the national energy savings and net present value for

six non- regulatory policies analyzed in detail for commercial clothes washers . T he target level 0% 10% 20% 30% 40% 50% 60% 70%201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case

0% 10% 20% 30% 40% 50% 60% 70%201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case17

- 22 for each policy equals the efficiency level s proposed for standard s in TSL 2

. The case in which no regulatory action is taken with regard to commercial clothes washers constitute s the base case (or "No New Regulatory Action" scenario ), in which NES and NPV are zero by definition. For comparison, the tables in clude the impacts of the proposed standards. Energy savings are given in quadrillion British thermal units (quads). The NPV s shown in Table 17.4.1 and Table 17.4.2

are based on two discount rates, 7 percent and 3 percent

.

The policies with the highest projected cumulative energy savings are consumer rebates and voluntary energy efficiency targets

. Ea rly replacements result in negative NPV for both equipment classes and at both discount rates. The increase in shipments induced by the policy outweighs the benefits from the higher energy efficiency achieved in the policy case

, in relation

to the base cas e, during the first years of the analysis period. For the remaining years, the decrease in shipments potentia tes the benefits from the higher energy efficiency. However, even at 3 percent discount rate, the positive effects from the last years of the analy sis period do not counterbalance the negative effects observed over the initial years. NPV would become zero for

front -loading commercial clothes washers at 1.8 percent discount rate, and for top-loading at 1.3 percent discount rate.

Table 17.4.1 Impacts of Non- Regulatory Alternatives for Front -Loading Commercial

Clothes Washers (TSL 2)

Policy Alternative Primary Energy Savings quads Net Present Value*

billion 2012 $

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.004 0.028 0.067

Consumer Tax Credits 0.000 0.004 0.008

Manufacturer Tax Credits 0.000 0.002 0.004
Voluntary Energy Efficiency Targets 0.008 0.042 0.113

Early Replacement 0.002

- 0.060

- 0.033

Bulk Government Purchases 0.001 0.003 0.008
Proposed Standards 0.023 0.145 0.344

*For equipment shipped 201 8

– 204 717

- 23 Table 17.4.2 Impacts of Non-Regulatory Alternatives for Top-Loading Commercial

Clothes Washers (TSL 2)

Policy Alternative Primary Energy Savings quads Net Present Value*

billion 2012 $

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.043 0.130 0.298

Consumer Tax Credits 0.005 0.021 0.039

Manufacturer Tax Credits 0.002 0.010 0.020

Voluntary Energy Efficiency Targets 0.018 0.045 0.116
Early Replacement 0.003

- 0.138

- 0.099

Bulk Government Purchases 0.001 0.003 0.007
Proposed Standards 0.085 0.260 0.594

*For equipment shipped 2018

– 204717

- 24 REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy Efficiency, 2002. XENERGY Inc. (Last accessed November, 2013.)

< http://www.issuelab.org/resource/californias_secret_energy_surplus_the_potential_for_ energy_efficiency >

2. ICF International, Arizona Publi c Service Energy Efficient Market Potential Study , 2007. Fairfax, VA.

3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Optimal Energy

. January, 2008.

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Quantec, LLC

. January, 2008.

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Itron, Inc

. January 2008 and March 2009.

6. Itron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last accessed November, 2013.) < http://calmac.org/publications/PGE0264_Final_Report.pdf >

7. Global Energy Partners LLC, AmerenUE D emand Side Management (DSM) Market Potential Study, 2010. (Last accessed November, 2013.)

< http://www.ameren.com/sites/aue/Environment/ Renewables/Documents/AmerenUEVol ume2MarketResearchReport2.pdf >

8. Coito, F., E-mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc

. June, 2010.

9. Blum, H., B. Atkinson, and A. Lekov, A Framework for Comparative Assessments of Energy Efficiency Policy Measures, 2011. (Last accessed November, 2013.)

< http://efficiency.lbl.g ov/drupal.files/ees/A%20Framework%20for%20Comparative%20Assessments%20of%20Energy%20Efficiency%20Policy%20Measures_LBNL4749E.pdf >

10. Energy Policy Act of 2005 , in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC
25C note, . 2005. http://www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf

11. American Recovery and Reinvestment Act of 2009, 2009. Government Printing Office. (Last accessed November, 2013.) < http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf >17

- 25 12. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling Equipment., 2011. (Last accessed November, 2013.)

< www.energytaxincentives.org/consumers/heating-cooling.php >

13. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013. (Last accessed November, 2013.) < http://energytaxincentives.org/ >

14. Barker, D., personal communication. December 2007 –April 2008.

15. AHAM, Estimat ed Distribution Sales by State for Room Air Conditioners - 2006, 2006. Association of Home Appliance Manufacturers (AHAM). (Last accessed November, 2013.) < http://www.aham.org/ht/d/ProductDetails/sku/BDSTATE/from/5256/pid/ >

16. KEMA Inc., Consumer Product Market Progress Evaluation Report 3 , 2007. Prepared for Northwest Energy Efficiency Alliance. Portland, OR. Report No. 07-174.

17. Tax Incentives Assistance Project, Manu facturers Incentives, (Last accessed November, 2013.) < http://energytaxincentives.org/builders/appliances.php >

18. Lawrence Berkeley National Laboratory, Energy End -Use Forecasting: Analysis of Tax Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and Assessment Group (Last accessed November, 2013.)

< http://enduse.lbl.gov/Projects/TaxCredits.html >

19. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment

Purchase Decisions , 1994. Prepared for Southern California Edison by Cambridge Systematics, Pacific Consulting Services, The Technology Applications Group, and

California Su rvey Research Services.

20. Feldman, S., L. Hoegfen, L.Wilson-Wright, and A. Li, Modeling the Effects of U.S. ENERGY STAR® Appliance Programs. In Energy Savings: What Works and Who
Delivers?, May 30 -June 4, 2005. ECEEE Summer Study Proceedings. Mandelieu La Napoule, France.

< http://www.eceee.org/conference_proceedings/eceee/2005c/Panel_4/4228feldman/paper

>

21. Rosenberg, M., The Impact of Regional Programs on the Market Share of Energy Star® Appliances, August 20-22, 2003. Seattle, WA. Published in the 2003 International Energy Program Evaluation Conference Proceedings.

22. 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.17

- 26 23. Technical Support Document: Energy Efficiency Standards for Com mercial Clothes
Washers, 2010. U.S. Department of Energy - Energy Efficiency & Renewable Energy. (Last accessed November, 2013.)

< http://www.regulations.gov/#!docume ntDetail;D=EERE -2006- STD -0127-0118> >

24. Nexus and RLW Analytics, Impact, Process, and Market Study of the Connecticut Appliance Retirement Program: Overall Report, Final, 2005. (Last accessed November, 2013.) < http://www.ctsavesenergy.org/files/Appliance%20Retirement%2012 -05.pdf >

25. GDS Associates Inc., Vermont Electric Energy Efficiency Potential Study: Final Report,

2007. (Last accessed November, 2013.)

< http://publicservice.vermont.gov/energy/vteefinalreportjan07v3andappendices.pdf >

26. McMahon, J. E. and J.P. Harris, Early Replacement of Appliances , 1994. Lawrence

Berke ley National Laboratory. Berkeley, CA. Unpublished report for U.S. Department of Energy.

27. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal

communication. National Association of State Procurement Officials

. April, 2008.

28. Responsible Purchasing Network, (Last accessed November, 2013.)

< www.responsiblepurchasing.org/ >

29. Federal Energy Management Program (FEMP), Covered Products Category: Clothes Washers (Commercial), (Last accessed November, 2013.) <https://www1.eere.energy.gov/femp/technologies/eep_purchasingspecs.html>

30. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase

. ACEEE: Summer Study on Energy Effi ciency in Buildings , 2000: pp. 147-166

31. Fujita, K. S. and M. Taylor, Achieved and Potential Energy Savings through Energy Efficient Procurement , 2012. Lawrence Berkeley National Laboratory. Report No.

LBNL -5737E.

< http://ees.ead.lbl.gov/sites/all/files/achieved_and_potential_energy_savings_through_en ergy_efficient_procurement_l bnl-5737e.pdf >5

- A

- i APPENDIX 5-A. AHAM DATA REQUEST

TABLE OF CONTENTS

5 -A.1 DRAFT ENGINEERING ANALYSIS DATA REQUEST SHEETS ................... 5 -A-1

5 -A.2 MANUFACTURER IMPACT ANALYSIS QUESTIONS: ................................

. 5 -A-5

LIST OF TABLES

Table 5-A.1Commercial Clothes Washer Shipments, Domestic plus Imports, in

Thousands of Units ................................................................

.................... 5 -A-2 Table 5-A.2 Shipment- We ighted Average Efficiency Data .................................................... 5 -A-3 Table 5-A.3 Market Share Efficiency Data* ................................................................

........... 5 -A-3 Table 5-A.4 Aggregate Manufacturer Cost Data: Front-Loading Commercial Clothes

Washers ................................

................................................................ ...... 5 -A-45

- A

- 1 APPENDIX 5 -A. AHAM DATA REQUEST

5-A.1 DRAFT ENGINEERING ANALYSIS DATA REQUEST SHEETS

DOE seeks average incremental production cost to take basic models in the categories shown from the current DOE minimum efficiency level (or proposed baseline level) to the specified efficiency level. For those product classes where more than one basic model may exist, please indicate the minimum and maximum incremental costs that would be incurred across the array of

basic models.

The data sheets are divided by product and contain tables requesting shipment and manufacturer cost data. The shipment-weighted energy use data of Table A-2 should be based on the DOE clothes washer test procedure (10 CFR 430, subpart B, appendix J1, J2).

Shipments

For commercial clothes washers, the AHAM 2005 Fa ct Book offers historical shipments data, but the information is not disaggregated by product class. As shown in the “shipment request” tables below, DOE hopes to collect both shipments data and shipment-weighted average efficiency data dating back to 1995. In addition, DOE hopes to collect market share efficiency data ( i.e. , data on the distribution of equipment shipments by efficiency) for each product class.

Manufacturer Costs

Incremental cost data (in U.S. dollars) include the materials, labor, and overhead needed to take basic models from the current minimum DOE baseline efficiency standard to each higher efficiency level. The depreciation of the conversion capital expenditures is an important component of the overhead for DOE to understand. Therefore, DOE is requesting information about conversion capital expenditures by efficiency level.

DOE requests notification from those interested parties planning to submit data by the close of the framework comment period.

Please provide aggregated industry data for the following tables.5

- A

- 2

Table 5 -A. 1 Commercial Clothes Washer Shipments, Domestic plus Imports, in Thousands

of Units

Year Total Top

- Loading % Imported Front

- Loading % Imported

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

20115

- A

- 3

Table 5 -A. 2 Shipment-Weighted Average Efficiency Data

Top

- Loading Front

- Loading

Year EF / MEF / IMEF* WF / IWF † EF / MEF / IMEF* WF / IWF †

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011 * Please specify whether energy factor (EF), modified energy factor (MEF), or integrated modified energy factor (IMEF). † Please specify whether water factor (WF) or integrated water factor (IWF).

Table 5 -A. 3 Market Share Efficiency Data*

Top

- Loading Front

- Loading

Efficiency Bins Market Share †

(Percent) Efficiency Bins Market Share †

(Percent)

MEF WF 2009 2010 2011 MEF WF 2009 2010 2011

1.26 9.5 1.72 8.0

1.42 9.5 1.80 7.5

1.60‡ 8.5‡ 2.00‡ 5.5‡

2.20 5.1

2.40 4.0

2.60 3.7 * Efficiency bins correspond to those used during the prior CCW rulemaking in addition to the proposed TSLs for this rulemaking. † Total market shares should equal 100%. ‡ January 8, 2013 DOE standard.5

- A

- 4

Table 5 -A. 4 Aggregate Manufacturer Cost Data: Front-Loading Commercial Clothes

Washers Efficiency Level Baseline Level 1 Level 2 Level 3

MEF 2.00 2.20 2.40 2.60

WF 5.5 4.5 4.0 3.7 Average Incremental Costs ($ Per Unit)*

Direct Material 0

Direct Labor 0

Overhead # 0 Minimum Incremental Costs ($ Per Unit)*

Direct Material 0

Direct Labor 0

Overhead # 0

Maximum Incremental Costs ($ Per Unit)*

Direct Material 0

Direct Labor 0

Overhead # 0 Conversion Capital Expenditures ($, Millions) Building CAPX 0 Tooling/ Equipment CAPX 0

R&D 0 Marketing 0 * Incremental costs per unit should be reported relative to the baseline unit’s cost. # Depreciation on the conversion capital expenditure should NOT be included in incremental

overhead.

Direct material – Costs of raw materials including scrap that can be traced to final or end products. Direct material costs do not include indirect material costs, which are attributed to supplies that may be used in the production process but are not assigned to final products (e.g., lubricating oil for production machinery).

Direct labor – The earnings of workers who assemble parts into a finished good or operate machines in the production process. Direct labor includes the fringe benefits of direct laborers, such as group health care, as well as overtime pay. Direct labor does not include indirect labor, which is defined as the earnings of employees who do not work directly in assembling equipment, such as supervisors, janitors, stockroom personnel, and forklift operators.

Overhead –

Factory overhead excluding depreciation. Factory overhead includes indirect labor, downtime, set-up costs, indirect material, expendable tools, maintenance, property taxes, insurance on assets, and utility costs. Factory overhead does not include selling, general, and administrative costs (SG&A); R&D; interest; or profit (accounted for by DOE separately).5

- A

- 5

5-A.2 MANUFACTURER IMPACT ANALYSIS QUESTIONS:

 What fraction of the Commercial Clothes Washer manufacturing industry is represented by AHAM? Please indicate if this is by quantity or value of shipments.

 Are you aware of, or do you represent any small business or low volume manufacturers? Please identify them.

 In the absence of new standards, do you expect any industry consolidation?

 Could new standards disproportionately advance or harm the competitive positions of some

fi rms?

 Could new standards result in disproportionate economic or performance penalties for particular consumer/user subgroups?

 Beyond price and energy efficiency, could new standards result in products that will be more or less desirable to consumers due to changes in product functionality, utility, or other features?

 What distribution channels are used from the manufacturer to the retail outlet? What is the share of product going through each distribution channel?6

- A

- i APPENDIX 6

- A. DETAILED DATA FOR MA RKUPS

TABLE OF CO NTENTS

6- A.1 INTRODUCTION

................................................................................................. 6-A-1

LIST OF TABLES

Table 6 -A.1.1 Sales Tax Rate by State

................................ .............................................. 6-A-16

- A

- 1 APPENDIX 6

- A. DETAILED DATA FOR MA RKUPS

6- A.1 INTRODUCTION

In this appendix, DOE provides state-level sales tax data used in equipment price determination. Population by state was obtained from the U.S. Census Bureau. 1 State and local tax rates were obtained from the Sales Tax Clearinghouse website. 2

Table 6 -A.1.1 Sales Tax R ate by State

Domain State Census Region P opulation, July 2009 (1000s) State and Local

Tax Rate

1 Connecticut 1 3518.288 6.4%

1 Maine 1 1318.301 5.0%

1 New Hampshire 1 1324.575 0.0%

1 Rhode Island 1 1053.209 7.0%

1 Vermont 1 621.76 6.1%

2 Massachusetts 1 6593.587 6.3%

3 New York 1 19541.45 8.4%

4 New Jersey 1 8707.739 7.0%

5 Pennsylvania 1 12604.77 6.4%

6 Illinois 2 12910.41 8.1%

7 Indiana 2 6423.113 7.0%

7 Ohio 2 11542.65 6.8%

8 Michigan 2 9969.727 6.0%

9 Wisconsin 2 5654.774 5.5%

10 Iowa 2 3007.856 6.9%

10 Minnesota 2 5266.214 7.2%

10 North Dakota 2 646.844 5.9%

10 South Dakota 2 812.383 5.4%

11 Kansas 2 2818.747 8.0%

11 Nebraska 2 1796.619 6.0%

12 Missouri 2 5987.58 6.6%

13 Virginia 3 7882.59 5.0%

14 Delaware 3 885.122 0.0%

14 Maryland 3 5699.478 6.0%

14 West Virginia 3 1819.777 6.1%

15 Georgia 3 9829.211 7.1%

16 North Carolina 3 9380.884 6.9%

16 South Carolina 3 4561.242 7.1%6

- A

- 2 17 Florida 3 18537.97 6.7%

Domain State Census Region P opulation, July 2009 (1000s) State and Local

Tax Rate

18 Alabama 3 4708.708 8.6%

18 Kentucky 3 4314.113 0.0%

18 Mississippi 3 2951.996 7.0%

19 Tennessee 3 6296.254 9.5%

20 Arkansas 3 2889.45 8.4%

20 Louisiana 3 4492.076 8.8%

20 Oklahoma 3 3687.05 8.4%

21 Texas 3 24782.3 8.0%

22 Colorado 4 5024.748 6.2%

23 Idaho 4 1545.801 6.1%

23 Montana 4 974.989 0.0%

23 Utah 4 2784.572 6.7%

23 Wyoming 4 544.27 5.4%

24 Arizona 4 6595.778 8.2%

25 Nevada 4 2643.085 7.9%

25 New Mexico 4 2009.671 6.6%

26 California 4 36961.66 8.4%

27 Alaska 4 698.473 1.4%

27 Hawaii 4 1295.178 4.4%

27 Oregon 4 3825.657 0.0%

27 Washington 4 6664.195 8.9%6

- A

- 3 REFERENCES

1. U.S. Census Bureau, The 2012 Statistical Abstract, 2012. (Last accessed May, 2013.)

< http://www.census.gov/compendia/statab/cats/population.html >

2. The Sales Tax Clearinghouse, 2013. (Last accessed May, 2013.) <https://thestc.com/STRates.stm>1 APPENDIX 7

- A. L OAD USAGE FACTOR IN COMMERCIAL LAUNDRY

APPLICATIONS

TABLE OF CO NTENTS

7- A.1 INTRODUCTION

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

7- A.2 METHODOLOGY AND DATA

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

7-A.2.1 Computation of gallons per cycle .................................................................................. 3

7- A.3 FINDINGS

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

LIST OF TABLES

Table 7 -A.1.1 Load Usage Factors from the Test Procedure ....................................................2

Table 7 -A.2.1 Cal UCONS Commercial Laundry P rogram: Number of monitored days for each washer ..........................................................................................3

Table 7 -A.3.1 Load Usage Factors from the Cal -UCONS Commercial Laundry Program ..............................................................................................................4

LIST OF FIGURES

Figure 7 -A.3.1 Distribution of Load Usage Factors .......................................................................42 APPENDIX 7

- A. LOAD USAGE FACTOR IN COMMERCIAL LAUNDRY

APPLICATIONS

7- A.1 INTRODUCTION

The purpose of this appendix is to clarify how the Department of Energy (DOE) made its estimates regarding the load usage factor (LUF) to address potential differences in loading practices between residential clothes washer usage and commercial clothes was her usage.

DOE’s current approach for estimating dryer energy use is based on the existing test procedure for residential clothes washers. The approach utilizes the following equation to calculate the per -cycle energy consumption for removing moisture –



## ( ) 

# ( ) ( ) 

# ( ) 

# ( ) DUF DEF RMC W LUF W LUF W LUF D

avg avg E ××

××+×+×= %4

_ min min max max

Where:

LUF = load usage factor

, W = weight of test load [pounds (lb)/cycle],

RMC = moisture content that remains after washing (lb),

DEF = nominal energy required for a clothes dryer to remove moisture from clothes (0.5kWh/lb), and

DUF = dryer usage factor, a percentage of washer loads dried in a clothes dryer (0.91).

DOE implements the Load Usage Factor, as described in the current test procedure. As per this description, LUF

max , LUF min , and LUF avg are established as described below:

Table 7 -A.1.1 Load Usage Factors from the Test Procedure

LUF

max LUF avg LUF min Manual Fill 0.72

- 0.28 Adaptive Fill 0.12 0.74 0.14

Since the test procedure describes the load usage for a typical residential application, DOE analyzed data from available studies to establish loading practices in a commercial setting.

7- A.2 METHODOLOGY AND DATA

For the purposes of understanding loading patterns, DOE looked at total water consumption as a proxy for loading. DOE collected data from the Cal- UCONS Commercial Laundry Program, which monitored clothes washers from different coin-operated laundromats or multifamily laundry rooms within the Southern California Gas Company (SCG) and the San3 Diego Gas and Electric Company (SDG&E) service territories. 1 The study conducted in situmonitoring of 50 clothes washers at selected properties, either common area laundry in multifamily buildings or coin

- operated laundromats. The CCW units included 21 clothes washers

located at two commercial coin -operated laundromats and 32 clothes washers at four multifamily properties, for a total of 53 clothes washers. Of these, only 6 coin- operated laundromats monitored water usage. The monitored data included hot and cold water gallons dispersed at 5 minute intervals with indicators of a completion of a cycle. In order to assess loading, DOE only examined those washers that had gallons recorded for both cold and hot water. For all sites, only six clothes washers had data for gallons of hot and cold water. Two of these clothes washers were from the same site (CCW#3 and CCW#4). Table 7 -A.2.1 provides the number of days of

data for each clothes washer.

Table 7 -A.2.1 Cal UCONS Commercial Laundry Program: Number of monitored days

for each washer

CCW #1 CCW #2 CCW#3 CCW#4 CCW#5 CCW#6

9 13 12 12 35 35

7-A.2.1 Computation of gallons per cycle

A cycle was flagged as a beginning of the cycle if the number of gallons for hot or cold water during that interval were greater than zero for the current interval and equal to zero for both cold and hot water for at least the previous two consecutive 5- mi nute intervals. The amount of hot and cold water gallons were then summed until at least two intervals following the current

interval recorded zero gallons for both hot and cold water. This interval was then flagged as the completion of the cycle.

DOE fu rther categorized each cycle into different buckets based on their total gallons used during that cycle. The Load Usage Factor is described in the current test procedure.

7- A.3 FINDINGS

Table 7 -A.3.1 shows the different Load Usage Factors for the six different clothes washers we analyzed from the Cal

- UCONS Commercial Laundry Program. These are compared to the Load Usage Factors from DOE’s test procedure. The distribution of LUF min varies from 7% to 36% across all six clothes washers. A wide range of values is also apparent for LUF avg and

LUF

max. Figure 7- A.3 .1 shows the distribution for all clothes washers as well. CCW#1, CCW#5, and CCW#6 are all located in multifamily homes. All other clothes washers are located

in coin- operated Laundromats. Variability may also be due to different clothes washing behaviors of residents living multifamily buildings or customers washing at coin

- operated Laundromats. However, we do not have enough data here to conclude this. Thus the variability of the data is problematic.

In addition, CCW#5 and CCW#6 are monitored for at least 20 more days than the rest of the clothes washers. This may explain why they have a couple of washing cycles with a high4 water usage, but the majority of their washing loads are around the average amount of water per cycle. Furthermore, all of these clothes washers are located in the Southern California area, which may not be representative of the U.S. population. However, since analysis of the Cal

UCONS Commercial Laundry Program does represent a section of the U.S population, DOE

cons iders it for a sensitivity analysis. For default LUF values, DOE decided to use the load usage factors from the current test procedure.

Table 7 -A.3.1 Load Usage Factors from the Cal- UCONS Commercial Laundry Program

DOE TP CCW

#1 CCW

#2 CCW

#3 CCW

#4 CCW

#5 CCW

#6 Average

across CCWs

LUFmin 0.14 0.10 0.26 0.14 0.07 0.36 0.29 0.20 LUFavg 0.74 0.69 0.53 0.43 0.37 0.60 0.61 0.54

LUFmax 0.12 0.22 0.21 0.43 0.56 0.04 0.10 0.26

Figure 7- A.3 .1 Distribution of Load Usage Factors 0% 10% 20% 30% 40% 50% 60% 70% 80%

LUFmin LUFavg LUFmaxFrequency

DOE TP CCW #1 CCW #2 CCW #3 CCW #4 CCW #5 CCW #65 References

1. Pacific Northwest National Laboratory (PNNL), Cal- UCONS Commercial Laundry Program Measurement and Evaluation , 2008. Report No. PNWD-3910

.8 A

- i APPENDIX 8

- A. USER INSTRUCTION S FOR LIFE

- CYCLE COST AND

PAYBACK PERIOD SPREADSHEET

TABLE OF CO NTENTS

8- A.1 DEFINITIONS

....................................................................................................... 8-A-1

8- A.2 BASIC INSTRUCTIONS

...................................................................................... 8-A-28

- A

- 1 APPENDIX 8

- A. USER INSTRUCTION S FOR LIFE

- CYCLE COST AND PAYBA CK

PERIOD SPREADSHEET

8- A.1 DEFINITIONS

The interested reader can examine and reproduce detailed results of the U.S. Department

of Energy’s ( DOE’s ) life

- cycle cost ( LCC ) and payback period ( PBP ) analysi s for commercial

clothes washers by using Microsoft Excel spre adsheet s available on DOE ’s website at http://www1.eere.energy.gov/buildings/appliance_standards/residential/clothes_washers.html

. To fully execute the spreadsheet s requires both Microsoft Excel and Crysta l Ball software. Both applications are comme rcially available. Crystal Ball is available at www.decisioneering.com

.

The latest version of the workbook, which is posted on the DOE website, was tested using Microsoft Excel 2007. The LCC and PBP workbook for commercial clothes washers comprises the following workshe ets.

Summary Presents the results of an analysis in terms of a verage LCC

, LCC savings, and median

, average , and undefined PBP for both frontand top- loading washers. A table includes, for each efficiency level

considered , i nstalled p rice ; lifetime o perating cost; LCC ; average savings; and the percentage of customers that w ould incur a net

cost, no impact, or net savings from each standard level. T he user

can stipulate three parameters for a simulation run: whether the AEO energy price trend reflects an economic case that is reference, low-growth, or high- growth (re ference is default) ; and the nu mber of simulation runs to be performed within a range of 1000–10,000 (10,000 is default).

LCC & Payback L ists the input values used to calculat e LC C and PBP. Parameters include fuel type for water heating and clothes drying ; annual

numbers of cycles or loads; product lifetime ; the discount rateapplied to costs and savings; energy and water prices; the basecase efficiency distribution; product price and total installed cost;

and energy and water use

. The result is annual and cumulative cash

flow for bo th front- and top-loading clothes washers operating at each efficiency level.

Energy & Water Use Contains per

- cycle energy and water use data at each efficiency

level broken down into washer machine energy use, dryer energy use (gas or electric) ; water heating energy use (electric or gas) ; and

both total and hot water use. Identifies the parameters exported to the LCC and PBP analysis.8

- A

- 2 Base- Case Efficiency Gives the market shares for efficiency levels in the base case.

Distribution

Equipment Prices Develops total installed cost for clothes washers in 2012$. This sheet provid es baseline and incremental manufacturer costs, retail price, sales ta x, and installation cost for both product classes and each efficiency level. Includes the assumptions used about markups and sales tax.

Energy & Water Price s Contains the regional prices in 2012$ for electricity, gas, and water used in the LCC and PBP analysis.

Energy Price Tren ds Contains the electricity and natural gas price trends for the reference, high, and low economic gro wth scenarios based on AEO

2013.

Water Price Trend Contains the historical water price trend that is used to forecast future water price

.

Discount Rate Contains data from which an average discount rate and a

distribution of discount rates are determined

.

Forecast Cells Gives details regarding base

- case efficiency distributions for both

classes of clothes washers. Median, minimum, maximum, and average values are given, along with 5 th , 25 th , 50 th , 75 th , and 95 th
percentile values. Included are product prices and details of the

LCC and PBP ( LCC sav ings in terms of money, energy, and water and the percentages of customers that would experience a net cost, no impact, or net savings from each efficiency level ).

Lifetime Presents the average lifetime

, in years

, for front - and top-loading commercial clothes washers, the Weibull parameters used for the survival function, and a graph of the Weibull retirement function

for residential clothes washers

.

8- A.2 BASIC INSTRUCTIONS

Basic instructions fo r operating the LCC spreadsheet are provided below.

1. After download ing the LCC file from DOE’s w eb site

, use Microsoft Excel to open it . At the bottom of the workbook, click on the tab for the sheet labeled Summary

.

2. Use Excel’s “View/Zoom” command in the top menu bar to change the size of the display so that it fit s your monitor.8

- A

- 3 3. Use the graphical interface in the spreadsheet to choose parameters or enter data.

You can change the default choices f or the three inputs listed under "User Input " ( energy price trend , start year, and number of simulation runs). To change a default input, select the desired value from the drop- down choice s by the input box.

4. After selecting the desired parameters, click the “Run” button. The spreadsheet will

minimize until t he simulation is complete, and will then re-open with the updated

results.8 B

- i APPENDIX 8

- B. UNCERTAINTY AND VARI ABILIT Y

TABLE OF CO NTENTS

8- B.1 INTRODUCTION

................................................................................................. 8-B-1

8- B.2 UNCERTAINTY

................................................................................................... 8-B-1

8- B.3 VARIABILITY

...................................................................................................... 8-B-1

8- B.4 APPROACHES TO UNCERTAINTY AND VARIABILIT Y ............................. 8-B-2

8- B.5 PROBABILITY ANALYSIS AND THE USE OF C RYSTAL BALL

................. 8-B-2

LIST OF FIGURE S

Figure 8 -B.5.1 Normal Probability Distribution ................................................................ 8-B-3 Figure 8 -B.5.2 Triangular Probability Distribution ............................................................ 8-B-3 Figure 8

- B. 5.3 Uniform Probability Distribution ............................................................... 8-B-38

- B

- 1 APPENDIX 8

- B. UNCERTAINTY AND VARI ABILITY

8- B.1 INTRODUCTION

Analysis of a potential energy efficiency standard involves calculating effects, for example, the effe ct of a standard on consumer life- cycle cost (LCC). T o perform the calculation, the analyst must first: (1) specify the equation or model that will be used; (2) define the quantities in the equation or model; and (3) provide numer ical values for each quantity. In the simplest case, the equation is unambiguous ( it contains all relevant quantities and no others), each quantity has a single numerical value, and the calculat ion produces a single value. Unambiguousness and precision are rarely the case , however. In most cases, the model and/or the numerical values for each quantity in the model are not completely known ( i.e. , there is

uncert ainty) or the model and/or the numerical values for each quantity in the model depend upon other conditions ( i.e.

, there is variability).

Thorough analysis involves accounting for uncertainty and variability. Although the simplest analysis involves a sin gle numerical value for each quantity in the calculation, arguments can arise about the appropria te value for each quantity. Explicit analysis of uncertainty and variability provides more complete information to the decision

- making process.

8- B.2 UNCERTAINTY

When making observations of past events or speculating about the future, imperfect knowledge is the rule rather than the exception. For example, the energy consumed by a particular type of appliance (such as the average residential cl othes washer ) is not recorde d directly , but rather estimated based on available information. Even direct laboratory

measurements have a margin of error. When estimating numerical values expected for quantities at some future date, the exact outcome rarely is known.

8- B.3 VARIABILITY

Variability in the calculation of a quantity means that different applications or situations produce different numerical values. Specifying an exact value for a quantity may be difficult because the value depend s on something else. For example, the annual number of cycles for a

commercial clothes washer application depends on the specific circumstances and behavior of

the user. Variability makes specifying an appropriate population value more difficult, because n o one value is likely to be representa tive of the entire population. Surveys can be helpful here, and analysis of surveys can relate the variable of interest ( e.g. , cycles per day ) to other variables that are better known or easier to forecast ( e.g. , avera ge number of users of a laundry facility on a given day ).8

- B

- 2 8- B.4 APPROACHES TO UNCERT AINTY AND VARIABILIT Y

This section describes two approaches to uncertainty and variability:

• scenario analysis and

• probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then changes one (or more) of the numerical values and repeats the calculation. Numerous

calculations are performed , which provide some indication of the exten t to which the result depends on the assumptions. For example, the LCC of an appliance could be calculated based on electricity costs of 6, 11, and 14 cents per kilowatt -hour.

The advantages of scenario analysis are that each calculation is simple; a r ange of estimates is used; and cross over points can be identified. (An example of a crossover point is the energy rate above which the LCC is reduced, holding all other inputs constant ; t hat is, the energy rate at which the consumer achieves savings in ope rating costs that more than compensate for the increased purchase price .) The disadvantage of scenario analysis is that there is no information

about the likelihood of each scenario.

Probability analysis considers the probabili ties within a range of valu es. For quantities characterized by variability ( e.g. , electricity rates in different regions), surveys can be used to generate a frequency distribution of numerical values ( e.g. , the number of customers subject to electricity rates at particular levels) t o estimate the probability of each value. For quantities characterized by uncertainty, statistical or subjective measures can be used to provide probabilities ( e.g. , manufacturing cost to improve energy efficiency to a given level may be
estimated to be $1 0 ± $3).

The major disadvantage of the probability approach is that it requires more information, namely information about the shapes and magnitudes of the variability and uncertainty of each quantity. The advantage of the probability approach is that it provides more information about the outcome of the calculations; that is, it provides the probability that the outcome will be in a particular range.

Scenario and probability analysis provide some indication of the robustness of a policy given the identified uncertainties and variability. A policy is robust when the impacts are acceptable over a wide range of possible conditions.

8- B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL

To quantify the uncertainty and variability that exist in inputs to the engineering, LCC, and payback period analyses, DOE used Microsoft Excel spreadsheets combined with Crystal Ball, a commercially available add

- in software , to conduct probability analyses. The probability analyses used Monte Carlo simulation and probability distributions.8

- B

- 3 Simulation refers to any analytical method meant to duplicate a real

- life system, especially when other analyses are too mathematically complex or difficult to reproduce. Without the aid of simulation, a spreadsheet model will reveal only a single outcome, generally the most likely or average outcome. Spreadsheet risk analysis uses both a spreadsheet model and simulation to automatically analyze the effect of varying i nputs on outputs of the modeled system. One type of spreadsheet simulation is Monte Carlo simulation, which randomly generates values for uncertain variables numerous time s. Monte Carlo simulation was named for Monte Carlo, Monaco, where the primary attrac tions are casinos containing games of chance. Games of chance such as roulette wheels, dice, and slot machines, exhibit random behavior. The random behavior in games of chance is similar to how Monte Carlo simulation selects variable values at random to simulate a model. When you roll a die, you know that a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which number for any particular roll. So too with variables that have a known range of values but an uncertain value for any particular time or event ( e.g. , product lifetime, discount rate, and installation cost).

For each uncertain variable (a variable that has a range of possible values), a probability distribution is used to define possible values. The type of distribution selected is based on th e conditions surrounding that variable. Types of p robabi lity distribution s include the following.

Figure 8

- B.5.1 Normal Probability Figure 8

- B.5.2 Triangular Figure 8 -B .5.3 Uniform Probability

Distribution Probability Distribution

Distribution

During a simulation, multiple scenarios are calculated by sampling values repeatedly from the probability distributions for the uncertain variables. Crystal Ball simulations can consist of as many trials (or scenarios) as desired —hundreds or even thousands. During a single trial, Crystal Ball randomly selects a value from the defined possibilities (the range and shape of the probability distribution) for each uncertain variable and then recalculates the spreadsheet. NORMAL TRIANGULAR UNIFORM8

- C

- i APPENDIX 8

- C. LIFETIME DISTRIBUTIO NS

TABLE OF CO NTENTS

8-C.1 INTRODUCTION

................................................................................................. 8-C-1

8-C.2 DERIVATION OF WEIBULL DISTRIBUTION PARAM ETERS

..................... 8-C-1

LIST OF TABLES

Table 8 -C.1.1 Commercial Clothes Washers .................................................................... 8-C-2

LIST OF FIGURE S

Figure 8 -C.2.1 Fraction of the Commercial Clothes Washers Failing, Multi

- Family ....... 8-C-3 Figure 8 -C.2.2 Cumulative Lifetime Length of Commercial Clothes Washers

Multi

- Family .............................................................................................. 8-C-3 Figure 8 -C.2.3 Fraction of the Commercial Clothes Washers Failing, Laundromat .......... 8-C-4 Figure 8

- C.2.4 Cumulative Lifetime Length of Commercial Clothes Washers

Laundromat ................................................................................................ 8-C-48

- C

- 1 APPENDIX 8

- C. LIF ETIME DISTRIBU TIONS

8- C.1 IN TRODUCTION

The U.S. Department of Energy ( DOE ) characterized the lifetime of both product classes

of clothes washers being considered for new energy efficiency standards (front-loading and toploading). DOE characterized commercial clothes washer l ifetimes using a Weibull probability

distribution that ranged from the minimum to maximum lifetime estimates , as described in chapter 8, section 8.2.3. The W eibull distribution is recommended for evaluating lifetime data

, because it can be shaped to match low, most likely ( or average ), and high values. The probability of exceeding the high value is contained in the long tail of the Weibull distribution

. 1

, 2

8- C.2 DERIVATION OF WEIBULL DISTRIBUTION PARAMETERS

Weibull distributions utilize available data to assign low, average, and high values to a

random variable that has unknown distribution parameters. DOE appli ed Weibull distribution s to product lifetime data to de riv e low, average, and high lifetime values, along with a percentile containing a high value. A similar approach is described in a technical note to the softw are Crystal Ball , which uses a most likely value in place of an average value. 3 The Weibull

distribution can be defined as:

W here :

L = location

,

α = scal e, and β = shape.

T he cumulative distribution therefore is :

Based on available data, Weibull distribution parameters are specified as follow s.

1. The output deviates must be greater than the expert opinion of low value.

2. The average , X avg , must be equal to the average value from the available data

. 3. The high value, xb, must correspond to some particular percentile point ( e.g. , 95

percent or 90 percent ).8

- C

- 2 The values for the parameters in the equations we re determined using the approach

outli ned in Crystal Ball’s technical note

. 3

Crystal Ball can be used to check a solution by specifying a Weibull distribution that has the calculated parameters (location, scale, and shape) in an assumption cell , then generat ing a forecast that equals that assumption. The f orecast histogram and statistics will confirm whether the Weibull distribution matches the desired shape.

This solution can be checked using Crystal Ball by specifying a Weibull distribution with the calculated parameters (location, scale, and shape) in an assumption cell and generate a forecast that equals the assumption. Forecast histogram and statistics verify that the Weibull distribution matches the desired shape.

Table 8C.2.1 shows the average, minimum, maximum lifetime, and maximu m percentile values used to determine the Weibull distribution parameters alpha and beta. For commercial clothes washers, DOE developed two lifetime estimates based on product application—one for

multi -family applications and another for Laundromats. DOE estimated that product lifetimes did not vary based on whether the washer was top-loading or front- loading. DOE estimated that the maximum lifetime percentile for both product applications was 99 percent.

Table 8 -C.1.1 Commercial Clothes Washers

Product Application Expert Opinion Values Weibull

Parameters

Minimum

years Average

years Maximum

years Maximum percentile

% Alpha (scale) Beta (shape)

Multi

- Family 7 11.3 15.5 99 4.782 2.655

Laundromat 5 7.1 9.3 99 2.391 2.655

Figures 8C.2.1 to 8C.2.4 show the Weibull distribution as well as the cumulative Weibull distribution for each commercial clothes washer product application.8

- C

- 3 Figure 8- C.2 .1 Fraction of the Commercial Clothes Washers Failing, Multi
- Family

Figure 8- C.2 .2 Cumulative Lifetime Length of Commercial Clothes Washers,

Multi

- Family 0% 5% 10% 15% 20% 25%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Age of Equipment (Years)Probability of Failure(%)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Age of Equipment (Years)Probability (%)8

- C

- 4 Figure 8- C.2 .3 Fraction of the Commercial Clothes Washers Failing, Laundromat

Figure 8- C.2 .4 Cumulative Lifetime Length of Commercial Clothes Washers,

Laundromat 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

0 1 2 3 4 5 6 7 8 9 10 11

Age of Equipment (Years)Probability of Failure (%)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 1 2 3 4 5 6 7 8 9 10 11

Age of Equipment (Years)Probability (%)8

- C

- 5 REFERENCES

1. Barnes, P. R., J. W. Van Dyke, B. W. McConnell, S. M. Cohn, and S. L. Purucker, The Feasibility of Replacing or Upgrading Utility Distribution Transformers During Routine

Maintenance , 1995. Oak Ridge National Laboratory. Oak Ridge, TN. Report No. Report

No. ORNL -6804/R1. < http://www.ornl.gov/~webworks/cpr/v823/rpt/78562.pdf >

2. Karr, T., Making the Most of Life Test Data

. Appliance Magazine , 2003.

< http://www.appliancemagazine.com/print.php?article=197&zone=1&first=1 >

3. Crystal Ball, Technote: Derivation of Weibull Distribution Parameters When the Minimum, Most Likely Value, and a Percentile are Known, 2007. (Last accessed November, 2013.) < http://www.crystalball.com/support/simulation/cbl_gen_021A.html >< <

- i APPENDIX 9

- A RELATIVE PRICE ELASTICITY OF DEMAND FOR CLOTH E S

WASHERS

TABLE OF CONTENTS

9- A.1 INTRODUCTION

................................................................................................. 9-A-1

9- A.2 IMPLIED PRICE ELASTI CITY

........................................................................... 9-A-1

9-A.2.1 Implied Price Elasticity Estimation Method

.......................................................... 9-A-2

9-A.2.2 Price Elasticity Over Time

..................................................................................... 9-A-4

9-A.2.3 Discussion of Relevant Economic Theory ............................................................. 9-A-4

9- A.3 PRICE IMPACTS ON THE MARKET SHARE OF FRONT AND TOP

LOADER CLOTHES WASHE RS

........................................................................ 9-A-6

LIST OF TABLES

Table 9 -A.2.1 Price elasticity of demand for laundry equipment and other appliances

and durables ............................................................................................... 9-A-2

Table 9 -A.2.2 Route Operator Revenue by Business Activity: C oinmach ($ million) ..... 9-A-3

Table 9 -A.2.3 Price Elasticity Decrease after CCW Price Change ................................... 9-A-49

- A

- 1 Appendix 9- A. RELATIVE PRICE ELAST ICITY OF DEMAND FOR CLOTH E S

WASHERS

9- A.1 INTRODUCTION

This appendix describes the methods used by DOE to estimate price elasticity of demand

and cross- price impacts for commercial clothes washers.

9- A.2 IMPLIED PRICE ELASTICITY

The price elasticity of demand describes the responsiveness of the quantity purchased of a product to a change in its price, and can be generically defined as the ceteris paribus percent change in shipments given a 1% change in price, or:

e d = P 0 Q 0 ∗ ∂Q

∂P

where P and Q represent price and shipments quantity, respectively. 1

Goods for which a 1% change in price leads to a greater than 1% change in quantity demanded are considered “elastic,” while goods for which a 1% change in price leads to a less

than 1% change in quantity demanded are “inelastic.” The more elastic a given product, the more a change in cost that translates into a change in price will alter the demand for the product.

DOE believes that CCW are highly price inelastic and applies a defa ult price elasticity of demand of zero for this product , meaning that any cost and price increases resulting from efficiency standards are unlikely to substantially affect the quantity of CCW demanded . As

CCW are a somewhat niche product, estimates of CCW price elasticity were not found in the literature, and DOE does not have the necessary data to perform an accurate independent

calculation of CCW price elasticity. Instead, the value of zero is based on DOE’s understanding

of the CCW market and distribution channels, particularly in relation to the demand for laundry

services

.

In the case of CCW, consumer price elasticity of demand for laundry services , rather than laundry equipment is most relevant. However, in the absence of any specific estimates of laundry service price elasticity, laundry equipment price elasticity can provide useful insights.

Table 1 compares the price elasticity of demand for residential lau ndry equipment and other durable goods, demonstrating that demand for clothes dryers and clothes washers tends to besomewhat less elastic than for other appliances and durables. Inelastic demand for laundryrelated products suggests that demand for laund ry services is likely inelastic as well. As9

- A

- 2 demonstrated in Table 1 , laundry equipment appears to be somewhat less price elastic than other types of appliances and d urables.

Table 9 -A.2.1 Price elasticity of demand for laundry equipment and other appliances

and durables

Product Price elasticity of demand Source
Residential Clothes Dryer

- 0.14 Golder and Tellis 2

Residential Clothes Washer

- 0.27 Dale and Fujita 3

Dishwasher

- 0.43 Dale and Fujita 3

Dishwasher

- 0.42 Golder and Tellis 2

Room Air Conditioner

- 0.37 Golder and Tellis 2

Refrigerator

- 0.37 Revelt and Train 4

Refrigerator

- 0.35 Dale and Fujita 3

Automobiles

- 1.07 Hymans 5

Automobiles

- 0.36 Hymans 5

9-A.2.1 Implied Price Elasticity Estimation Method

To validate the use of a price elasticity of zero, DOE estimates an implied CCW price elasticity bas ed on known variables describing the market for laundry services. CCW price elasticity is approximated as follows:

Implied e d

, CCW = e d

, LaundrySvc ∗ � Price CCW
Price LaundrySvc �

where,

e d

, CCW = price elasticity of demand for commercial clothes washers,

e d

, LaundrySvc = average price elasticity of demand for laundry service,

Price CCW = CCW wholesale price (or cost), and

Price LaundrySvc = price (or cost) of providing laun dry service.

Based on review of the literature, an exact value of the price elasticity of demand was not identified, but a likely range was established:

− 1.0 < e d

, LaundrySvc ≤ − 0.5 . Schettkat and Yocarini (2006) note a range of -0.53 to -0.47 (averaging

- 0.5) for the average price elasticity of

service demand in the U.S. 6 Laundry service is expected to be more of a necessity than luxury, and thus the price elasticity of demand is modeled as inelastic, with a bounding value of -1.0. For example, Kemp (1998) provides a discussion of the relationship between luxury- necessit y status and price elasticity for a variety of goods and services. 7

The provision of laundry services involves other aspects, including equipment and services, beyond the simple rental or sale of a clothes washer. As described by Coinmach Corporation, a major route operator, in a recent 10-K annual financial report to the SEC, route operator core business activities center around “leasing laundry rooms from building owners and9

- A

- 3 property management companies, installing and servicing laundry equipment, collecting revenues generated from laundry machines and operating retail laundromats;” additionally, route operators “rent laundry machines and other household appliances to property owners, managers of multi family housing properties, individuals and corporate entities,” and may engage in laundry equipment distribution. 8

Coinmach reports revenue by individual business activities in their SEC 10-K report: route,
rental, and distribution. 8

, 9 “Route” refers primarily to laundry room leasing and management activities; “rental” refers to revenue from leasing clothes washers and dryers; “distribution” refers to equipment wholesaling activities.

Route activities related to laundry room or laundromat design, installation, and maintenance are not likely to be substantially altered by a change in the price of CCW, but rental and distribution revenue are more directly influenced by CCW features and costs. A route operator generally both rents CCW to a building owne r and manages the laundry space. 10

Table 9 -A.2.2 Route Operator Revenue by Business Activity: Coinmach ($ million) Business Activity FY 2007 FY 2006 FY 2005 FY 2004

Route 492.1 481.7 472.5 469.6

Rental 38.4 36.1 34.4 32.6

Distribution 24.8 25.7 31.7 28.9

Rental % of total 6.9% 6.6% 6.4% 6.1%

Rental +Distribution %

of total 11.4% 11.4% 12.3% 11.6%

Source: Coinmach Corporation (2005 ; 2007)

From Coinmach’s ratios of the route, rental, and distribution revenue categories in 2004 -

2007, DOE estimates that Price CCW

Price LaundrySvc = 0.061 to 0.123.

Combining the above data inputs,

Implied e d

, CCW low = e d

, LaundrySvc ∗ � Price CCW
Price LaundrySvc �

=
− 0.5 ∗ 0. 061 =
− 0. 031

Implied e d

, CCW h ig h = e d

, LaundrySvc ∗ � Price CCW
Price LaundrySvc �

=
− 1.0 ∗ 0. 123 =
− 0. 123

DOE estimates an implied price elasticity of demand for CCW of approximately -0.03 to -0.12 based on the laundry equipment distribution channels and market for laundry services. DOE notes that values in this range are quite low, and feels that this exercise supports the use of a price elasticity of zero.

A further factor to consider is that CCW price is in some cases not readily apparent to the building owner. CCW price is also unknown by the end user of the product, whose demand for laundry services drives building owner demand for laundry equipment and laundry room or laundromat management services. CCW are generally leased at a monthly rate, so the building9

- A

- 4 owner is exposed to only a portion of the total CCW co st increase, based on the ratio of their lease term to the expected CCW useful life. If the CCW price is not visible, the consumer will not have a price signal to react to. As the implied price elasticity estimate assume s consumer

awareness of the CCW pr ice, it is likely an overestimation.

9-A.2.2 Price Elasticity Over Time

Price elasticity for a given product is not constant over time; numerous studies have demonstrated decreases in price elasticity for durable goods as the period of time since a price change increases. 5

, 11

, 12 Similarly, decreases in price elasticity have been demonstrated through the stages of a product’s li fe cycle

. 13 DOE projects that any non- zero CCW price elasticity will decrease over time, following the proportional decreases demon strated for automobiles ( Table 9

- A.2.3 ). 5

Table 9 -A.2.3 Price Elasticity Decrease after CCW Price Change

Year s Following Price Change

1 2 3 5 10 20 Price Elasticity of Demand

- 1.20

- 0.93

- 0.75

- 0.55

- 0.42

- 0.40 Relative Change in Elasticity to 1st year 1.00 0.78 0.63 0.46 0.35 0.33

Implied Price Elasticity (low)

- 0.031

- 0.024

- 0.015

- 0.007

- 0.002

- 0.001 Implied Price Elasticity (high)

- 0.123

- 0.096

- 0.060

- 0.028

- 0 .010

- 0.003

Source: Hymans (1971) and DOE calculations

While estimated to be small initially, the CCW implied price elasticity of demand further approaches zero in the years following any price change due to standards. DOE believes that using a price elasticity of zero, rather than a small non

- zero price elas ticity, will introduce at most a negligible increase in the probability of error in downstream analyses. An alternative scenario using the value of

- 0.12 is also available as a sensitivity analysis.

9-A.2.3 Discussion of Relevant Economic Theory

Demand for comme rcial clothes washers can be viewed as a form of d erived demand ; what end users want are laundry services, what apartment and laundromat owners want is an avenue to provide laundry services, of which the commercial clothes washer as defined in this

rulemak ing is only one input. Other inputs include clothes dryers and other laundry equipment, water and energy, building space, change machines, and labor for maintenance and management.

While many discussions of derived demand in the literature come from the realm of labor economics and focus on the relationship between demand for labor and demand for a final product whose inputs include labor and capital and possibly other inputs, many if the major insights can still be applied to the case of demand for an input ( CCW ) and demand for a service whose inputs include CCW and the other inputs described above.9

- A

- 5 The four key Marshallian rules of derived demand, as restated by Pigou (1932), are as

follows: 14

1) "The demand for anything is likely to be more elastic, the more readily substitutes for
that thing can be obtained." (p. 682)
2) "The demand for anything is likely to be less elastic, the less important is the part played
by the cost of that thing in the total cost of some other thing, in the production of
which it is employed." (p. 683)
3) "The demand for anything is likely to be more elastic, the more elastic is the supply of

co -operant agents of production." (p. 684)
4) "The demand for anything is likely to be more elastic, the more elastic is the demand for
any further thing which it contributes to produce." (p. 685)

In the pre ceding treatment of CCW price elasticity, DOE draws largely on rules 2 and 4, for which some relevant data is available. Below, the rules are discussed in the context of CCW.

Considering rule 1, from the end-user viewpoint some substitutes for laundry s ervices provided by covered CCW in common area laundry facilities or laundromats may exist. It is possible that some end-users could instead purchase and use a residential clothes washer, assuming that their housing unit has appropriate laundry hook-ups. An additional possibility is

that end -users may begin to favor types of CCW not covered by this rule if there is a sizeable increase in the cost of using a residential -style CCW but not of a larger hard-mount CCW. These are fairly similar though imperfec t substitutes; they can potentially accomplish the purpose of providing laundry services, but each involves additional factors that differ from the covered CCW. From the service provider’s perspective, residential- style soft -mount CCW and other CCW types may or may not be interchangeable, depending on the space they have available.

The effect of end -user’s preferences for certain washer types will be limited by physical building constraints. While there are some substitutes available for covered CCW, DOE does not believe that they will be so readily interchanged as to strongly influence the overall price elasticity of

demand for CCW.

Hamermesh (1993) rephrases rule 2: “the demand for an input that accounts for a small share of costs will be less elastic, other things equal, because the scale effect will be very

small.” 15 As th e cost of CCW equipment is only a small portion of the total cost of providing laundry services (as demonstrated by the relative shares of route operator revenue from equipment rental/sale and other services), rule 2 suggests that this fact supports an ine lastic

demand for CCW. This is incorporated into the estimation equation in the factor Price CCW

Price LaundrySvc .

Restating rule 3 in terms of the present example, demand elasticity for CCW is expected to be more inelastic if the p rocess of providing laundry services makes use of other inputs that are highly inelastic in supply. Other inputs, in this case, include other laundry equipment, labor, capital, and building space. DOE does not have sufficient information on the elasticity of supply for the other inputs of production to directly incorporate this factor into the estimation equation;9

- A

- 6 DOE believes that the influence of these other factor is unlikely to overturn the conclusion that CCW price elasticity of demand is quite inelastic.

Regarding rule 4, the demand for CCW is likely to be less elastic, the less elastic the demand for laundry services. DOE believes laundry services to be more of a necessity than luxury, leading to the factor

− 1.0 < e d

, LaundrySvc ≤ − 0.5 incorporated into the estimation equation.

9- A.3 PRICE IMPACTS ON THE MARKET SHARE OF FRON T AND TOP

LOADER CLOTHES WASHE RS

While DOE expects commercial clothes washers in aggregate to be highly price

- inelastic, substitution between types of washers (i.e. front- an d top-loading) may occur if the change in price due to standards is not uniform across types. The cross- price impact is estimated to model likely switching across product classes.

Due to insufficient data, DOE was not able to estimate cross- price impact s on the market share of top-loading and front-loading commercial clothes washers, and instead relies on its recent analysis performed for residential clothes washers. 16 Analysis methods and results are summarized

below.

In order to estimate the impact of price changes on market share, DOE applied a logistic regression method to regress market share on price variables and other attributes. The basic form of a logistic regression is:

p i = � 1

1 + e − � β 0 +β 1 x 1

, i +⋯+β k x k

, i � �

With some simple operations, the model can be transformed to an equivalent log-linear form:

ln � p i

1 − p i � β

0 + β 1 x 1

, i +

⋯ + β k x k

, i

In this case, p i is the market share of either top - or front- load ing clothes washers, and

p i 1−p i is the ratio of clothes washer market shares, i.e., the relative probability of the market share

of top and front

- load ing clothes washers. T he explanatory variables include price

- related variables, washer size and other washer attributes. The logistic regr ess ion model equation estimated in DOE’s residential clothes washer analysis is shown below.

ln � MarketS h are Front

1

− MarketS h are Front �

=
− 2. 985386 + 0. 0035452 ∗ Price Top + 4. 24347 ∗ � Size Front
Size Top �

Where:9

- A

- 7 Mar ketS h are Front

1− MarketS h are Front = relative probability of the market share of front- and top-loading clothes

washers,

Price Top = weighted monthly average price of top-loaders,

Price Front = weighted monthly average price of front -loaders, and

Size Front

Size Top = ratio between the monthly weighted average of front-loaders tub volume

and the monthly weighted average of top-loaders tub volume.

The equation indicates that front loader market share is positively correlated with top loader price and size and negatively correlated with front loader price ( all variables in this regression were statistically significant; the R 2 in the regression was 0.88).

The price coefficients suggest that top - and front- loading washers are substitutes for each other. The positive coefficient associated with the size variable indicates that capacity is a

desirable clothes washer feature for most consumers.

These regression results were used to derive the cross price impact of a change in the front loading washer price on the top loading market share (and vice versa). For example, these results suggest that a 10% increase in the price of front loader washers would lead to a 10.7 % decrease in top

- loader market sh are, holding other variables constant and measured as changes from the reference case using average values for each variable. In this case, the front-loading

cross

- price impact (percent change in top

- loading market share over percent change in front

loading price) is 1.07. Note that p rice elasticity varies according to the reference price and market share, typically measured as changes from mean values for these variables in the data set. In DOE’s dataset, the reference top -loading washer price is $403.78, the reference front -loading washer price is $823.83, the corresponding reference top- loading washer market share is 71% and the corresponding reference front washer market share is 29%.

S imilarly, these results indicate that a 20% price increase for top -loading washers would yield a 21.49 % increase in front-loading market share, measured as changes from the variable means. Thus, in this example the top-loader cross-price impact is also 1.07. See the residential clothes

washers 2012 Direct Final Rule Te chnical Support Document for additional details. 169

- A

- 8 REFERENCES

1. Varian, H. R., Intermediate Microeconomics: A Modern Approach . 1999. Vol. 5th Edition. W.W. Norton & Company: New York, London.

2. Golder, P. and G. Tellis, Beyond Diffusion: An Affordability Model of the Growth of

New Consumer Durables

. Journal of Forecasting , 1998. 17: pp. 259-280

3. Dale, L. and K. S. Fujita, An Analysis of the Price Elasticity of Demand for Household Appliances , 2008. Lawrence Berkeley National Laboratory.

4. Revelt, D. and K. Train, Mixed Logit with Repeated Choices: Household's Choice of Appli ance Efficiency Level

. Review of Economics and Statistics , 1998. 80(4): pp. 647-

657 < http://www.mitpressjournals.org/doi/pdfplus/10.1162/003465398557735 >

5. Hymans, S., Consumer Durable Spending: Explanation and Prediction

. Brookings Papers on Economic Activity , 1971. 1971(1): pp. 234-239

6. Schettkat, R. and L. Yocarini, The shift to services employment: A review of the

literature

. Structural Change and Economic Dynamics , 2006. 17: pp. 127-147

7. Kemp, S., Perceiving luxury and necessity

. Journal of Economic Psychology , 1998. 19: pp. 591-606

8. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2005, 2005. SEC EDGAR. (Last accessed November 19, 2012.)

< http://www.sec.gov/Archives/edgar/data/91693/000095012305007218/y09552e10vk.ht

m#106 >

9. Coinmach Corporation, 10-K Annual Report for Fiscal Year 2007, 2007. SEC EDGAR. (Last accessed November 20, 2012.)

< http://www.sec.gov/Archives/edgar/data/1282858/000095012307008319/y35770e10vk.

htm >

10. Mac -Gray Corporation, 10-K Annual Report for Fiscal Year 2011, 2011. SEC EDGAR. (Last accessed November 20, 2012.)

< http://www.sec.gov/Archives/edgar/data/1038280/000104746912002365/a2207800z10- k.htm#ds11601_item_8._financial_statements_and_supplementary_data >

11. Fibrich, G., A. Gavious, and O. Lowngart, The Dynamics of Price Elasticity of Demand

in the Presence of Reference Price Effects

. Journal of the Academy of Marketing Science

, 2005. 33(1): pp. 66-78

12. Gowrisankaran, G. and M. Rysman, Dynamics of Consumer Demand for New Durable

Goods . 2009, National Bureau of Economic Research Working Paper: Cambridge, MA.9

- A

- 9 13. Lilien, G. L. and E. Yoon, An Exploratory Analysis of the Dynamic Behavior of Price Elasticity over the Product Life Cycle: An Empirical Analysis of Industrial Chemical

Products

. Issues in Pricing , 1988: pp. 261-408

14. Pigou, A. C., The Economics of Welfare . 4 ed. 1932. MacMillian: London. 682-687.

15. Hamermesh, D., Labor Demand . 1993. Princeton University Press: Princeton, N.J.

16. U.S. Department of Energy, Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Residential Clothes Washers (Direct Final Rule) , Building Technologies Program, Editor. 2012: Washinton,

D.C.1 APPENDIX 10

- A. FULL

- FUEL

- CYCLE MULTIPLIERS

TABLE OF CONTENTS

10- A.1 INTRODUCTION

............................................................................................... 10-A-1

10- A.2 METHODOLOGY

.............................................................................................. 10-A-1

10- A.3 FULL

- FUEL

- CYCLE ENERGY MULTIPL IERS

............................................. 10-A-3

LIST OF TABLES

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

Table 10 -A.3.1 Full Fuel Cycle Energy Multipliers (Based on AEO 2013) ..................... 10-A-310

- A

- 1 APPENDIX 10

- A. FULL

- FUEL

- CYCLE MULTIPLIERS

10

- A.1 INTRODUCTION

This appendix summarizes the methods used to calculate full- fuel

- cycle (FFC) energy savings expected to result from potential standards. The FFC measure includes point-of- use (site) energy, the energy losses associated with generation, transmission, and distribution of electricity, and the energy consumed in extracting, processing, and transporting or distributing primary fuels. DOE’s traditional approach encompassed only site energy and the energy losses associated with generation, transmission, and distribution of electricity. 1 Per DOE’s 2011 Statement of Policy for Adopting Full Fuel Cycle Analyses , DOE no w uses FFC measures of energy use and emissions in its energy conservation standards analyses. This appendix summarizes the methods used to incorporate the full- fuel

- cycle impacts into the analysis.

This analysis uses several different terms to reference energy use. The physical sources of energy are the primary fuels such as coal, natural gas, liquid fuels, etc . Primary energy is equal to the heat content (Bt u) of the primary fuels used to provide an end- use service. Site energy use is

defined as the ene rgy consumed at the point-of-use in a building or industrial process. Where natural gas and petroleum fuels are consumed at the site (for example in a furnace), site energy is identical to primary energy, with both equal to the heat content of the primary fuel consumed. For electricity, site energy is measured in kWh. In this case the primary energy is equal to the quads of primary energy required to generate and deliver the site electricity. This primary energy is calculated by multiplying the site kWh tim es the site

- to -power plant energy use factor

, given in chapter 10. For the FFC analysis, the upstream energy use is defined as the energy consumed in extracting, processing, and transporting or distributing primary fuels. FFC energy use is the sum of prim ary plus upstream energy use.

Both primary fuels and electricity are used in upstream activities. The treatment of electricity in fuel cycle analysis must distinguish between electricity generated by fossil fuels and uranium, and electricity generated from renewable fluxes (wind, solar and hydro). For the former, the upstream fuel cycle impacts are derived from the amount of fuel consumed at the power plant. For the latter, no fuel per se is used, so there is no upstream component.

10

- A.2 METHODOLOGY

The mathematical approach is discussed in the paper A Mathematical Analysis of Full Fuel Cycle Energy Use

, 2 and details on the fuel production chain analysis are presented in the paper Projections of Full Fuel Cycle Energy and Emissions Metrics

. 3 The text below provides a brief summary of the methods used to calculate FFC energy.

When all energy quantities are normalized to the same units, the FFC energy use can be represented as the product of the primary energy use and an FFC multiplier

. The FFC multipli er is defined mathematically as a function of a set of parameters representing the energy intensity10

- A

- 2 and material losses at each production stage. The se parameters depend only on physical data, so the calculations do not require any assumptions about prices or other economic data. While in general these parameter values may vary by geographic region , for this analysis national averages are used.

In the notation below, the indices x and y are used to indicate fuel type, with x=c for coal, x=g for natural gas, x=p for petroleum fuels, x=u for uranium and x=r for renewable fluxes. The fuel cycle parameters are:

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

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

• b y is the amount of grid electricity used in production of 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)

The parameters are calculated as a function of time with an annual time step; hence, a time

series of annual val ues is used to estimate the FFC energy and emissions savings in each year of the analysis period. Fossil fuel quantities are converted to energy units using the heat content factors q

x . To convert electricity in kWh to primary energy units, on- site electri city consumption is multiplied by the site

- to -power plant energy use factor. The site

- to -power plant energy use factor is defined as the ratio of the total primary energy consumption by the electric power sector (in quadrillion Btu’ s) divided by the total electricity generation in each year.

The FFC multiplier is denoted μ (mu). A separate multiplier is calculated for each fuel used on site. A multiplier is also calculated for electricity reflecting the fuel mix used in its generation. The multipliers are dimensionless numbers that are applied to primary energy savings to obtain the FFC energy savings. The upstream component of the energy savings is proportional to (μ-1). The fuel type is denoted by a subscript on the multiplier μ.

For DOE’s appliance stan dards energy savings estimates, the fuel cycle analysis methodology is designed to make use of data and projections published in the Annual Energy Outlook (AEO). Table 10 -A.2.1 provides a summary of the AEO data used as inputs to the different parameter calculations. The AEO does not provide all the information needed to estimate total energy use in the fu el production chain. Reference three describes the additional data sources used to complete the analysis. However, the time dependence in the FFC multipliers arises exclusively from variables taken from the AEO. The FFC analysis for CUAC and CU HP equipment used data from AEO 2013

. 410

- A

- 3 Table 10-A.2.1 Dependence of FFC Parameters on AE O Inputs

Parameter Fuel 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 power sector energy consumption

b

c , c nc , c pc coal Coal Production by Region and Type Production by coal 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 Crude oil domestic production

c nn natural

gas Oil and Gas Supply US 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

10

- A.3 FULL

- FUEL

- CYCLE ENERGY MULTIPL IERS

FFC energy multipliers are presented in Table 10 -A.3.1 for selected years. For the analysis period beyond 2040 , the last year in the AEO 2013 projection, the 2040 value was held constant. The multiplier for electricity reflects the shares of vari ous primary fuels in total electricity generation over the forecast period.

Table 10-A.3.1 Full

- Fuel

- Cycle Energy Multipliers (Based on AEO 2013)

2019 2020 2025 2030 2035 2040 Electricity 1.041 1.041 1.040 1.040 1.041 1.040

Natural Gas 1.101 1.101 1.100 1.098 1.099 1.100

Petroleum Fuels 1.139 1.140 1.148 1.158 1.166 1.16810

- A

- 4 REFERENCES

1. United States

- Office of the Federal Register, Federal Register, Volume 76, Number 160, August 18, 2011 . UNT Digital Library. Washington D.C. .

2. Coughlin, K., A Mathematical Analysis of Full Fuel Cycle Energy Use

. Energy , 2012. 37(1): pp. 698-708 <http://www.sciencedirect.com/science/article/pii/S0360544211006803>

3. Coughlin, K., Projections of Full

- Fuel -Cycle Energy and Emissions Metrics , 2013.

Lawrence Berkele y National Laboratory. Report No. LBNL -6025E.

4. Energy Information Administration, Annual Energy Outlook 2013 with Projections to

2040 , 2013. Washington, DC. <http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf>10 A

- i APPENDIX 10 -B. USER INSTRUCTIONS FOR SHIPMENTS AND NATIONAL

IMPACT ANALYSIS SPREADSHEETS

TABLE OF CO NTENTS

10- B.1 INTRODUCTION

............................................................................................... 10-B-1

10- B.2 BASIC INSTRUCTIONS

.................................................................................... 10-B-210

- B

- 1 APPENDIX 10

- B. USER INSTRUCTIONS FOR SHIPMENTS AND NATIONAL

IMPACT ANALYSIS SPREADSHEET S

10

- B.1 INTRODUCTION

The interested reader can examine and reproduce detailed results of the U.S. Department of Energy’s ( DOE’s ) shipments and national impact analysis (NIA) for commercial clothes washers using Microsoft Excel spreadsheets that are available on DOE’s website.

< http://www.eere.energy.gov/buildings/appliance_standards/ >

The latest version of the Microsoft Excel shipments and NIA workbook, which is posted on the DOE website, was tested using Microsoft Excel 2007. To execute the spreadsheet requires

Microsoft Excel 2003 or a later version. The N IA spreadsheet performs calculations to forecast the change in national energy use and net present value due to an energy conservation standard. The energy use and associated costs and savings attributable to a given standard are determined first by calculating the shipments and then the energy use and costs for all products shipped

under that standard. The d ifferences between results under the standar d case and the base case

then can be compared and the nationwide energy savings and net present values (NPVs)

determined.

The shipments and NIA workbook for both front- and top-loading residential clothes washers comprises the following worksheets.

Input and Summary This sheet c ontains user input selections under “User Inputs” an d a

s ummary table, Cumulative Energy Savings and NPV for the selected standard level efficiency distribution . The sheet contains the efficiency levels being considered for c ommercial clothes washers and the associated incremental prices. This sheet also contains efficiency weighted average energy and water use and equipment price for the base and standards cases for all the product

classes.

LCC Input This sheet c ontains the inputs from the Life- cycle cost analysis

.

Efficiency Distribution This sheet contains base and standards case efficiency trends.

Historical Shipment This sheet contains data for historical sales of the equipment.

Base Case This sheet mainly performs the calculations leading up to the estimation of total commercial clothes washer shipments for the base case. The sheet starts with the stock accounting of the equipment and uses the survival function to calculate the surviving stock. It then performs calculations of replacements, and shipments going into new housing, thus yielding total shipments

.10

- B

- 2 Standards Case Contains stock accounting of the equipment that helps calculate annual commercial clothes washer shipment s estimates for the

standards case.

Base Case Calc. This sheet calculates the estimation of shipments for selected product class by taking cross- price elasticity into account for the base case. It also calculates the energy and water consumption and operating cost for selected product class.

Standards Case Calc. This sheet estimates shipments for selected product class by taking

cross

- price elasticity into account for the standards case. It also calculates the energy and wa ter savings and cost savings. The energy and cost savings in a single year are the difference between the base case energy and water use and costs for that year and the standard case energy and water use and costs in the same year

.

Housing Projections This sheet provides projected new housing construction starts by housing type.

Fuel Prices This worksheet contains projected average electricity, gas and water prices for the three economic scenarios.

Heat Rates The sheet contains the marginal site to source conversion factors that are used in the source energy savings calculations.

Lifetime This sheet contains the probability of survival of a commercial clothes washer unit at a given age of the unit. The sheet also provides the average lifetime o f a unit.

10

- B.2 BASIC INSTRUCTIONS

Basic instructions for operating the NIA spreadsheet s are as follows:

1. Once the N IA spreadsheet s have been downloaded from the Web, open the file using

Excel. At the bottom, click on the tab for the worksheet Input and Summ ary

.

2. Use Excel’s View/Zoom commands at the top menu bar to change the size of the display to make it fit your monitor.

3. The user can change the model parameters listed in the box labelled “User Inputs ”. The

parameters are:10

- B

- 3 a. Economic Growth: To the change value, use the drop -down arrow and select the desired Growth level (Reference, High , or Low ).

b. Discount Rate: To the change value, type in the desired Discount Rate.

c. Relative Price Elasticity: To change the applicable elasticity , use the drop-down

menu and select the desired value.

d. Base Case Efficiency Trend: To change value, use the drop-down menu and select the desired efficiency growth value for desired product class (Frozen, 1% growth, or 2% growth).

e. Standards Case Efficiency Trend: To change value, use the drop-down menu and select the desired efficiency growth value for desired product class (Frozen, 1% growth, or 2% growth).

4. Once the parameters have been set, the results are automatically updated and are reported in the “National Impact Summary” table for each product class to the right of the “User Inputs” box.- 10

- C

- i APPENDIX 10

- C. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE USING

ALTERNATIVE GROWTH SCENARIOS

TABLE OF CO NTENTS

10-C.1 INTRODUCTION

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

10-C.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCEN ARIO

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

10-C.3 NIA RESULTS FOR LOW ECONOMIC GROWTH SCEN ARIO

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

LIST OF TABLES

Table 10 -C.2.1 Cumulative National Energy Savings in Quads, National Water Savings in Trillion Gallons, High Economic Growth Scenario .........................2

Table 10 -C.2.2 Cumulative Net Present Value of Consumer Benefits, High Economic

Growth Scenario ................................................................................................3

Table 10 -C.3.1 Cumulative National Energy Savings in Quads, Low Economic

Growth Scenario ................................................................................................3

Table 10 -C.3.2 Cumulative Net Present Value of Consumer Benefits, Low Economic

Growth Scenario ................................................................................................3

LIST OF FIGURE

Figure 10 -C.1.1 Housing Starts Forecast Under Alternative AEO2013 Economic

Growth Scenarios ...............................................................................................1 Figure 10 -C.1.2 Average Commercial Electricity Price Forecasts under Alternative

AEO2013 Economic Growth Scenarios ............................................................210

- C

- 1 APPENDIX 10

- C. NATIONAL ENERGY SAVINGS AND NET PRESENT VALUE USING

ALTERNATIVE GROWTH SCENARIOS

10

- C.1 INTRODUCTION

This appendix presents national energy savings (NES) and net present value (NPV) results using inputs from alternat ive economic growth scenarios . The scenarios use the energy price and housing starts forecasts in the High Economic Growth case and the Low Economic Growth case

fr om EIA’s Annual Energy Outlook 20131 ( AEO 2013 ). 1

Figure 10C.1.1 and Figure 10C.1.2 show the forecasts for housing starts and commercial electricity prices under t he different economic growth scenarios. AEO201 3 provides a forecast to

2040. To estimate the trend after 20 40 , DOE followed guidelines that the EIA had provided to the Federal Energy Management Program, which called for using the average rate of change for electricity during 2025– 20 40.

Figure 10 -C.1.1 Housing Starts Forecast Under Alternative

AEO2013 Economic Growth Scenarios 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90Housing Starts (millions)

Reference

High

Low10

- C

- 2 Figure 10 -C.1.2 Average Commercial Electricity Price For ecasts

under Alternative AEO2013 Economic Growth

Scenarios

10

- C.2 NIA RESULTS FOR HIGH ECONOMIC GROWTH SCEN AR IO

Table 10-C.2.1 Cumulative National Energy Savings in Quads, National Water Savings in Trillion Gallons, High Economic Growth Scenario

Trial Standard

Level Efficiency Level Combination NES NWS

Front

- loading Top

- loading quads trillion gal

1 1 1 0.12 0.0 3

2 2 1 0.14 0. 06

3 3 2 0.21 0. 2610

- C

- 3 Table 10-C.2.2 Cumulative Net Present Value of Consumer Benefits

, High Economic

Growth Scenario

Trial Standard

Level Efficiency Level Combination 3% 7%

Front -loading Top-loading billion

201 2 $ billion

201 2 $

1 1 1 0. 93 0.39

2 2 1 1.21 0. 51

3 3 2 2.55 1.06

10

- C.3 NIA RESULTS FOR LOW ECONOMIC GROWTH SCEN ARIO

Table 10-C.3.1 Cumulative National Energy Savings in Quads, Low Economic Growth

Scenario

Trial Standard

Level Efficiency Level Combination NES NWS

Front

- loading Top

- loading quads trillion gal

1 1 1 0.0 8 0.0 2

2 2 1 0. 10 0. 04

3 3 2 0. 15 0. 18

Table 10-C.3.2 Cumulative Net Present Value of Consumer Benefits

, Low Economic

Growth Scenario

Trial Standard

Level Efficiency Level Combination 3% 7%

Front -loading Top-loading billion

201 2 $ billion

201 2 $

1 1 1 0. 62 0. 27

2 2 1 0. 82 0. 36

3 3 2 1.75 0.7510

- C

- 4 REFERENCES

1. U.S. Department of Energy- Energy Information Administration, Annual Energy Outlook 2013 with Projections to 2040 , 2013. Washington, DC.

< http://www.eia.gov/forecasts/aeo/ >10-D-1 APPENDIX 10

- D. NATIONAL NET PRESENT VALUE OF CONSUMER

BENEFITS USING ALTERNATIVE PRODUCT PRI CE FORECAST S

TABLE OF CO NTENTS

10- D.1 INTRODUCTION

............................................................................................... 10-D-1

10- D.2 ALTERNATIVE COMMERCI AL CLOTHES WASHER PR ICE TREND

SCENARIOS

....................................................................................................... 10-D-1

10-D.2.1 Exponential Fit Approach (Increasing Price Scenario) ....................................... 10-D-2

10-D.2.2 Annual Energy Outloo k 2013 Price Forecast ( Decreasing Price Scenario)

....... 10-D-3

10-D.2.3 Summary

.............................................................................................................. 10-D-3

10- D.3 NET PRESENT VALUE RE SULTS USING ALTERNATIVE PRODUCT

PRICE TRENDS

.................................................................................................. 10-D-4

LIST OF TABLES

Table 10 -D.2.1 Price Trend Sensitivities .......................................................................... 10-D-3

Table 10 -D.3.1 NPV Using Price Trend, Discounted at 3 Percent, for Commercial Clothes Washers (billion 2012$) ............................................................. 10-D-4

Table 10 -D.3.2 NPV Using Price Trend, Discounted at 7 Percent, for Commercial Clothes Washers (billion 2012$) ............................................................. 10-D-5

LIST OF FIGURES Figure 10 -D.2.1 Deflated Commercial Laundry and Drycleaning Machinery PPI versus Year, with Exponential Fit from 1982 to 2012 ........................................ 10-D-2 Figure 10 -D.2.3 Commercial Clothes Washer Price Forecast Indexes ................................. 10-D-410-D-1 APPENDIX 10

- D. NATIONAL NET PRESENT VALUE OF CONSUMER

BENEFITS USING ALTERNATIVE PRODUCT PRI CE FORECAST S

10- D.1 INTRODUCTION

DOE used a constant price assumption for commercial clothes washers’ default price projection in the NIA described in Chapter 10. In order to investigate the impact of different product price forecasts on the consumer net present value (NPV) for the considered TSLs for

commercial clothes washers , DOE also considered two alternative price trends for a sensitivity analysis. This appendix describes the alternative price trends and compares NPV results for these

scenarios with the default price pr ojection

.

In recent rulemakings for several residential products, DOE has us ed the experience curve method to derive learning rates to forecast future prices . In the experience curve method, the real cost of production is related to the cumulative production, or experience, with a manufactured product. That experience usually is measured in terms of cumulative production. As experience (production) accumulates, the cost of 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

assessmen t. 1

For some commercial and industrial products, there are insufficient data to apply a price

based learning curve approach , particularly with respect to cumulative production . In such cases,

DOE used a constant price assumption for the default price pro jection in the NIA, but made use of price indexes that are relevant for the product in question to derive alternative price trends for sensitivity analysis. a Because DOE is using motor price trends as a proxy for furnace fan price trends, this approach was used for furnace fans.

10- D.2 ALTERNATI V E COMMERCIAL CLOTHES W ASHER PRICE

TREND SCENARIOS

DOE considered two alternative price trends for a sensitivity analysis. One of these used

an exponential fit on the deflated Producer Price Index (PPI) for commercial laundry and drycleaning machinery, and the other is based on the “deflator— industrial equipment” that was

forecasted for EIA’s Annual Energy Outlook 2013 ( AEO201 3)

.

a See appendix 10C of the final rule TSD for distribution transformers. http://www.regulations.gov/#!documentDetail;D=EERE

- 2010

- BT

- STD

- 0048

- 076010-D-2 10-D.2.1 Exponential Fit Approach (Increasing Price Scenario)

For the increasing price scenario, DOE used an inflation-adjusted commercial laundry and drycleaning machinery Producer Price Index (PPI) spanning the time period of 1982-2012 from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the explanatory vari able. b The PPI data reflect nominal prices, adjusted for product quality changes.

An inflation -adjusted (deflated) price index for commercial laundry and drycleaning machinery 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:

Y = a ∙ e bX

where Y is the commercial clothes washer price index, X is the time variable, a is the constant

and b is the slope parameter of the time variable.

To estimate these exponential parameters, a least -square fit was performed on the

inflation -adjusted commercial laundry and drycleaning machinery price index versus year from 1982 to 2012. The fitted regresion is shown in Figure 10-D-1

.

Figure 10- D.2 .1 Deflated Commercial Laundry and Drycleaning Machinery PPI versus Year, with Exponential Fit from 1982 to 2012

The regression performed as an exponential trend line fit results in an R-square of 0.74,

which indicates a decent fit to the data. The final estimated exponential function is:

b Series ID PCU3333183333182 ; http://www.bls.gov/ppi/ y = 4E

- 05e 0.005x

R2 = 0.7439

0.820 0.840 0.860 0.880 0.900 0.920 0.940 0.960 0.980 1.000 1.020

1980 1990 2000 2010 2020Price Index (2012=1)

Year Y=Commercial Laundry &
Drycleaning Machinery

Deflated PPI

Expon. (Y=Commercial
Laundry & Drycleaning
Machinery Deflated PPI)10-D-3 Y = 3. 92 × 10 −5 ∙ e 0

. 005 X

DOE then derived a price factor index for this scenario, renormalized with 2012 equal to 1, to project prices in each future year in the analysis period considered in the NIA. The index value in a given year is a function of the exponential parameter and year

.

10-D.2.2 Annual Energy Outlook 2013 Price Forecast ( Decreas ing Price Scenario )

DOE also examined a forecast based on the “deflator

— industrial equipment” that was

forecasted for AEO201 3 out to 2040. AEO 201 3 is the most current price trend projection that DOE acquired. This index is the most disaggregated category that includes commercial laundry and drycleaning machinery. To develop an inflation-adjusted index, DOE normalized the above index with the “chained price index—gross domestic product” forecasted for AEO201 3 , with 2012 equal to 1. To ex tend the price index beyond 2040 , DOE used the average annual price growth rate in 2031 to 2040.

10-D.2.3 Summary

Table 10 -D.2.1 shows the summary of the average annual rates of change for the product price index in each scenario. Figure 10-D-2 shows the resulting price trends under all scenarios

.

Table 10-D.2.1 Price Trend Sensitivities

Sensitivity Price Trend Average Annual R ate

of C hange % Medium (Default) Constant Price Projection 0.0 Decreasing Price

Scenario AEO2011

-- “deflator

— industrial equipment” -0.35

Increasing Price

Scenario Exponential Fit using PPI data from 1982 to

2012 0.5010-D-4 Figure 10- D.2 .2 Commercial Clothes Washer Price Forecast Indexes

10- D.3 N ET PRESENT VALUE RESULTS USING ALTERN ATIVE

PRODUCT PRICE TREND S

This section presents the NPV results using the alternative product price forecast for each key product class. The results are only slightly sensitive to the alternative product price forecast s for TSL 3

.

Table 10-D.3.1 NPV Using Price Trend , Discounted at 3 Percent, for Commercial

Clothes Washers (billion 2012$)

Price Trend Product Class Trial Standard Levels

TSL1 TSL 2 TSL 3

Default Top

- loading 0.596 0.594 2.131

Front

- loadi n g 0.120 0.344

- 0.132

Decreasing Price Top

- loading 0.596 0.594 2.130

Front

- loadi n g 0.120 0.344

- 0.132

Increasing Price Top

- loading 0.596 0.594 2.127

Front

- loadi n g 0.120 0.344

- 0.134 0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400201220142016201820202022202420262028203020322034203620382040204220442046Price Factor Index (2012=1)

Year Constant Price Index
(Default)

PPI Exponential Fit (1982 to
2012) (Increasing Price)

AEO 2013

-- Industrial
Equipment (Decreasing
Price)10-D-5 Table 10-D.3.2 NPV Using Price Trend , Discounted at 7 Percent, for Commercial

Clothes Washers (billion 2012$)

Price Trend Product Class Trial Standard Levels

TSL1 TSL 2 TSL 3

Default Top

- loading 0.261 0.260 0.910

Front

- loadi n g 0.051 0.145

- 0.060

Decreasing Price Top

- loading 0.261 0.260 0.909

Front

- loadi n g 0.051 0.145

- 0.060

Increasing Price Top

- loading 0.261 0.260 0.908

Front

- loadi n g 0.051 0.145

- 0.06112A-i

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

TABLE OF CONTENTS

12A.1 COMMERCIAL CLOTHES WASHERS ................................................................

..... 112A-1

APPENDIX 12A. MANUFACTURER IMPACT ANALYSIS INTERVIEW GUIDE

12A.1 COMMERCIAL CLOTHES WASHERS



## Commercial Clothes Washer Rulemaking Manufacturer Impact Analysis Interview Guide for

Commercial Clothes Washers

May 1, 201312A-2

The U.S. Department of Energy (DOE) conducts the manufacturer impact analysis (MIA) as part of the rulemaking process for amended energy conservation standards for commercial clothes washers (CCWs). In this analysis, DOE uses publicly available information and information provided by manufacturers during interviews to assess possible impacts on manufacturers due to amended energy conservation standards.

The DOE is currently reviewing a set of efficiency levels for front-loading CCWs. The efficiency levels are presented below as they apply to the front-loading CCWs. For top-loading CCWs, the DOE is seeking input relative to the range of efficiency levels that have been certified in the DOE’s Compliance Certification Database since the previous standard went into effect on January 8 th , 2013 . In responding to this questionnaire, please refer to the modified energy factor (M EF) and Water Factor (WF) values described below.

DOE is requesting information on the product classes in the table below.

Table 1.1 Efficiency Levels Under Consideration for Front-Loading Commercial Clothes Washer Analysis

Efficiency Level Efficiency Level

Source MEF

ft 3 /kwh/cycle WF

gal/cycle/ft 3

Baseline DOE Standard 2 5.5

1 CEE Tier 2 2.2 4.5

2 CEE Tier 3 2.4 4

3 Maximum Available 2.6 3.7

Table 1.2 Efficiency Levels for Top-Loading Commercial Clothes Washers Listed in DOE Compliance Certification Database

Efficiency Levels Listed in DOE Certification

Database MEF ft 3 /kwh/cycle WF

gal/cycle/ft 3

Baseline 1.6 8.5

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

1 KEY ISSUES

1.1 In general, what are the key issues for your company regarding amended energy

conservation standards for commercial clothes washers?12A-3

1.2 Are any of the issues more or less significant for the front-loading versus top-loading product classes?

1.3 Do any of the issues become more significant at higher efficiency levels?

1.4 Do you have any suggestions for incorporating any of these issues into the DOE’s engineering analysis or manufacturing impact model?

2 COMPANY OVERVIEW AND ORGANIZATIONAL CHARACTERISTICS

DOE is interested in understanding manufacturer impacts at the plant or profit center level directly pertinent to commercial clothes washer production. However, the context within which

this profit center operates and the details of plant production are not always readily available from public sources. Understanding the organizational setting around the commercial clothes washer industry profit center will help DOE understand the probable future of the manufacturing activity with and without amended energy conservation standards.

2.1 What percentage of your commercial clothes washer manufacturing corresponds to each product class, in terms of both revenue and shipments? Please indicate if you do not manufacture products in any given product class. Please also indicate whether you purchase your commercial

clothes washers from other manufacturers, and whether the factory that supplies the products is

located in the United States.

Table 12. 1 Commercial Clothes Washer Revenue and Shipment Volumes by Product Class

Product Class

Number Product

Type 2012

Revenue 2012

Shipments %

Made %

Bought % Made in

U.S.

1 Front

Loading

2 Top

Loading

2.2 What is your company’s approximate market share in the commercial clothes washer market, for each product class?

3 ENGINEERING AND LIFE-CYCLE COST ANALYSIS

3.1 Please describe the key design features associated with products at the baseline efficiency level as well as design features and associated costs that would be necessary to reach the incremental efficiency levels listed below.12A-4

Front-Loading Commercial Clothes Washer Incremental Costs

MEF (ft 3 /kWh/cycle) WF

( gal/cycle/ ft 3 ) Estimated

Incremental

Cost Description of Design Options

2.0 5.5

-- 2.2 4.5

2.4 4

2.6 3.7

Top-Loading Commercial Clothes Washer Incremental Costs (Exploratory Efficiency Levels*)

MEF (ft 3 /kWh/cycle) WF

( gal/cycle/ ft 3 ) Estimated

Incremental

Cost Description of Design Options

1.60 8.5

--

1.65 8.4

1.7 8.4

1.75 8.4

1.85 6.85 *These top-loading efficiency levels are exploratory only. Since the effective date of new standards on January 8, 2013, top-loading models at these efficiency levels have been certified to DOE and listed in DOE’s Compliance Certification Database. Information obtained for each of these efficiency levels will help DOE define the specific top-loading efficiency levels to be considered during this rulemaking.

3.1.a Are the proposed efficiency levels appropriate?

3.1.b Are the “max tech” efficiency levels appropriate? If not, can you suggest more appropriate max-tech levels?

DOE published a revised clothes washer test procedure, “Appendix J2”, which will be required for use beginning March 7, 2015. Among other changes, Appendix J2 does the following:

 Incorporates standby power energy into a new metric, Integrated Modified Energy Factor (IMEF)

 Incorporates the water consumption of all wash cycle temperatures into a new metric, Integrated Water Factor (IWF)

 Amends the capacity measurement for top-loading clothes washers

 Amends the definition of the energy test cycle

DOE has proposed to translate current MEF/WF standards into their equivalent IMEF/IWF values based on comparison testing of clothes washer models currently on the market. Any new standards as a result of this rulemaking will be defined in terms of IMEF and IWF.12A-5

3.2 Please comment on any issues related to the new DOE clothes washer test procedure as applied to commercial clothes washers:

3.2.a Standby Power Is the standby power measurement procedure in Appendix J2 appropriate for measuring the standby power associated with all types of control systems available on commercial

clothes washers?

Is there a relationship between standby power energy consumption and the overall efficiency level? (i.e. Does standby power energy consumption tend to increase or decrease as overall machine efficiency increases?)

Will the inclusion of standby power affect the design options currently used to reach higher efficiencies?

3.2.b Capacity Measurement Method

Will the revised capacity measurement method for top-loading clothes washers affect the design options you use to reach higher efficiencies?

3.2.C Energy Test Cycle Definition How will the changes to the energy test cycle definition change how your clothes washers are tested? Will this revised definition limit the design options available for reaching higher efficiencies?

3.2.d Co nsumer Use Factors Are the consumer usage factors within the test procedure consistent with actual consumer

use of commercial clothes washers? These include the following:

 Temperature use factors

 Load usage factors

 Dryer usage factor

Can you provide any non -proprietary data related to consumer use of different cycles?

4 MARKUPS AND PROFITABILITY

One of the primary objectives of the MIA is to assess the impact of amended energy

conservation standards on industry profitability. In this section, DOE would like to understand the current markup structure of the industry and how amended energy conservation standards would impact your company’s markup structure and profitability.

DOE will estimate the manufacturer production costs for the two product classes of commercial

clothes washers. DOE defines manufacturer production cost as all direct costs associated with manufacturing a product : direct labor, direct materials, overhead, and depreciation. The12A-6

manufacturer markup is a multiplier applied to manufacturer production cost to cover nonproduction costs, such as SG&A and R&D, as well as profit. It does not reflect a “profit margin.”

The manufacturer production cost times the manufacturer markup equals the manufacturer selling price. Manufacturer selling price is the price manufacturers charge their first customers,

but does not include additional costs along the distribution channels.

DOE estimated a baseline markup of 1.27 for commercial clothes washers.

4.1 Is the 1.27 baseline markup representative of an average industry markup?

4.2 Please comment on the baseline markups DOE calculated as compared to your company’s baseline markups for the commercial clothes washer product classes.

Table 4

. 2 Commercial Clothes Washer Baseline Manufacturer Markups by Product Class

Product

Class Product Type Estimated

Ba seline Markup Manufacturer Comments or Revised Estimates

1 Top

- Loading 1. 2 7

2 Front

- Loading 1.2 7

4.3 How are markups and margins determined in this industry? How would revised standards potentially impact this pricing/margin structure? Please indicate if profit levels vary between product classes or product line. If yes, please explain why.

4.4 One of the possible scenarios DOE uses to model impacts on industry profitability is the impact of commoditization of premium products. Because the market disruption caused by standards can alter the pricing of premium products, DOE is interested in understanding if efficiency is a feature that earns a premium. Within each product class, do markups vary by efficiency level? If yes, please provide estimates for your markups by product class and efficiency level in Table 4.2 and Table 4.3.

Table 4 .2 Estimated Markups for Front-Loading Commercial Clothes W ashers

Efficiency

Level MEF

ft 3 /kwh/cycle WF

gal/cycle/ft 3 Estimated Markup Manufacturer Comments

1 2.2 0 4.5 0

2 2.4 0 4 .00

3 2.6 0 3.7 012A-7

Table 4 .3 Estimated Markups for Top-Loading Commercial Clothes W ashers (Exploratory Efficiency Levels)

Efficiency

Level MEF

ft 3 /kwh/cycle WF

gal/cycle/ft 3 Estimated Markup Manufacturer Comments

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

4.5 What factors or product attributes besides efficiency affect the profitability of

commercial clothes washers within a product class?

4.6 Would you expect changes in your estimated profitability following an amended energy

conservation standard? If so, please explain why.

4.7 What is the structure of your distribution channels and how does that influence your markup? Does that change between product classes or across the industry?

5 FINANCIAL PARAMETERS

DOE’s contractor has developed a draft model of the commercial clothes washer industry financial performance called the Government Regulatory Impact Model (GRIM), using publicly available data. However, this public information might not be reflective of manufacturing at the

commercial clothes washer profit center. This section attempts to understand the financial parameters for commercial clothes washer manufacturing and how your company’s financial situation could differ from the industry aggregate picture.

5.1 In order to accurately collect information about commercial clothes washer manufacturing, please compare your financial parameters to the GRIM parameters tabulated

below.12A-8

Table 5

. 3 Financial Parameters for Commercial Clothes Washer Manufacturing

GRIM Input Definition Industry

Estimated

Value (%) Your Actual (If

Different from

DOE’s Estimate)

Income Tax

Rate Corporate effective income tax paid (percentage of

earnings before taxes, EBT) 29. 2 %

Discount Rate Weighted average cost of capital (inflation

adjusted weighted average of corporate cost of

debt and return on equity) 8.0%

Working Capital Current assets less current liabilities (percentage of revenues) 9.5%

Net PPE Net plant property and equipment (percentage of revenues) 16.0%

SG &A Selling, general, and administrative expenses (percentage of revenues) 11.7%

R&D Research and development expenses (percentage of revenues) 2.5%

Depreciation Amortization of fixed assets (percentage of revenues) 3.7%

Capital Expenditures Outlay of cash to acquire or improve capital assets

(percentage of revenues, not including acquisition

or sale of business units) 2.1%

Cost of Goods

Sold Includes material, labor, overhead, and

depreciation (percentage of revenues) 79.5%

5.2 Do any of the financial parameters in Table 5

. 3 change based on product class? Please describe any differences.

5.3 Do any of the financial parameters in Table 5

. 3 change for a particular subgroup of manufacturers? Please describe any differences.

5.4 DOE accounts for one time product and capital conversion costs including research and development, as well as capital expenditures for facility changes and the depreciation of thes e fixed assets. Beyond these short term changes in cost structure, how would you expect an amended energy conservation standard to impact any of the financial parameters for the industry

over time?12A-9

6 CONVERSION COSTS

Amended energy conservation standards may cause your company to incur capital and product
conversion costs to redesign existing products and make changes to existing production lines to be compliant with the amended energy conservation standard. Depending on their magnitude, the conversion costs can have a substantial impact on the outputs used by DOE to evaluate the industry impacts. Understanding the nature and magnitude of the conversion costs is critical portion of the MIA. The MIA considers two types of conversion costs:

 Capital conversion costs are one-time investments in plant, property, and equipment (PPE) necessitated by an amended energy conservation standard. These may be incremental changes to existing PPE or the replacement of existing PPE. Included are expenditures on buildings, equipment, and tooling.

 Product conversion costs are costs related research, product development, testing, marketing and other costs for redesigning products necessitated by an amended energy

conservation standard.

DOE asks a number of questions to understand the nature and magnitude of your expected capital and product conversion costs.

Table 6.1 through Table 6.4 show the MEF and WF for the different efficiency levels analyzed in the Engineering Analysis for the product categories covered by this rulemaking.

6.1 Please provide estimates for your capital conversion costs by product class and efficiency

level in Table 6.1 and Table 6.2. In the description column, DOE is interested in understanding the kinds of changes that would need to be implemented to production lines and production facilities at each efficiency level. Where applicable, please quantify the number and cost of new production equipment, etc. that would be required to implement the specified design changes.12A-10

Tabl e 6. 4 Expected Capital Conversion Costs for Front-Loading Commercial Clothes

Washers

Efficiency

Level MEF

ft 3 /kwh/cycle WF

gal/cycle/ft 3 Total Capital

Conversion Costs Description

1 2.2 0 4.5 0

2 2.4 0 4 .00

3 2.6 0 3.7 0

Table 6. 5 Expected Capital Conversion Costs for Top-Loading Commercial Clothes Washers (Exploratory Efficiency Levels)

Efficiency

Level MEF

ft 3 /kwh/cycle WF

gal/cycle/ft 3 Total Capital

Conversion Costs Description

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

6.2 Would the changes in question 6.1 be similar across all of your production lines and factories for each product class?

6.3 Are there certain efficiency levels that would require relatively minor changes to existing products? Are there certain efficiency levels where the capital or product conversion costs significantly increase over the previous efficiency levels? Would your answer change for different product classes? Please describe these changes qualitatively.

6.4 For each of the product categories shown in Table 6.1 and Table 6.2, which efficiency level changes could be made within existing platform designs and which would result in major product redesigns?

6.5 For the efficiency levels put forth, which design options would require only minor changes to production lines, major changes to production lines, substantial modifications to existing facilities, or the development of entirely new manufacturing facilities?

6.6 What level of product conversion costs would you expect to incur at each of these efficiency levels for each product class? Please provide your estimates in Table 6

. 3 and Table 6

. 4 considering such expenses as product development expenses, prototyping, testing, certification, and marketing. In the description column, please describe the assumptions behind the estimates provided.12A-11

Table 6.3 Expected Product Conversion Costs for Front-Loading Commercial Clothes

Washers

Efficiency

Level MEF

ft 3 /kwh/cycle WF

gal/cycle/ft 3 Total Product

Conversion Costs Description

1 2.2 0 4.5 0

2 2.4 0 4 .00

3 2.6 0 3.7 0

Table 6.4 Expected Product Conversion Costs for Top-Loading Commercial Clothes Washers (Exploratory Efficiency Levels)

Efficiency

Level MEF

ft 3 /kwh/cycle WF

gal/cycle/ft 3 Total Product

Conversion Costs Description

1 1.65 8.4

2 1.7 8.4

3 1.75 8.4

4 1.85 6.85

6.7 Please provide additional qualitative information to help DOE understand the types and nature of your investments, including the plant and tooling changes and the product development effort required at different efficiency levels.

7 CUMULATIVE REGULATORY BURDEN

In assessing the impact to industry, DOE seeks to understand the cumulative regulatory burden facing manufacturers. Cumulative regulatory burden refers to the financial burden that stems from overlapping effects of new or revised DOE standards and/or other regulatory actions affecting the same product or industry. In this regard, DOE will consider other regulations (beyond energy efficiency) such as materials regulations and building codes that will adversely impact the cost to either the manufacturer or consumer.

7.1 In assessing cumulative regulatory burden, DOE considers the impacts of other regulations for which the effective starting date falls within a six year timeframe extending from three years before to three years after the proposed commercial clothes washer standards become effective. Below is a list of other relevant regulations that could affect manufacturers of commercial clothes washers. Please provide any comments on the listed regulations and provide an estimate for your expected compliance cost.12A-12

Table 7

. 6 Other Regulations Identified by DOE

Regulation Estimated or Actual Effective Date(s) Comments Expected Expense for Compliance (Millions $)

Residential central air conditioners and heat pumps energy

conservation standards 2013 & 2015 211.6

Residential clothes washer energy conservation standards 2015 & 2018 418.5

Residential clothes dryer energy

conservation standards 2015 95.4

Wine chiller and misc. refrigeration energy conservation

standards 2018

Residential furnace fans energy

conservation standards 2018

Dehumidifier energy conservation

standards 2019

7.2 Are there any other recent or impending regulations that commercial clothes washer manufacturers face (from DOE or otherwise)? If so, please identify the regulation, the corresponding effective dates, and your expected compliance cost.

7.3 Under what circumstances would you be able to coordinate any expenditure related to these other regulations with an amended energy conservation standard?

8 DIRECT EMPLOYMENT IMPACT ASSESSMENT

The impact of amended energy conservation standards on employment is an important consideration in the rulemaking process. This section of the interview guide seeks to explore current trends in commercial clothes washer manufacturer employment and solicit manufacturer views on how domestic employment patterns might be affected by amended energy conservation

standards.

8.1 Where are your commercial clothes washer manufacturing facili ties that produce products for the United States located? What types of products are manufactured at each

location? Please provide annual shipment figures for your company’s commercial clothes washer manufacturing at each location by product class. Please also provide employment levels at each

of these facilities.12A-13

Table 8. 7 Commercial Clothes Washer Revenue and Shipment Volumes by Product Class

Facility Location Product Types Manufactured Employees Annual Shipments

Example Sheboygan, WI Commercial Clothes Washers 200 1 00,000

1

2

3

4

5

8.2 Would your domestic employment levels be expected to ch ange significantly under amended energy conservation standards? If so, please explain how they would change if higher efficiency levels are required.

8.3 Would the workforce skills necessary under amended energy conservation standards require extensive retraining or replacement of employees at your manufacturing facilities ?

8.4 Would amended energy conservation standards require extensive retraining of your service/field technicians? If so, could you expand on how your service infrastructure would be impacted in general as a result of amended energy conservation standards?

9 MANUFACTURING CAPACITY AND NON-US SALES

9.1 How would amended energy conservation standards impact your company’s manufacturing capacity?

9.2 For any design changes that would require new production equipment, please describe how much downtime would be required. What impact would downtime have on your business? Are there any design changes that could not be implemented before the compliance date of the

final rule for certain product classes?

9.3 What percentage of your company’s commercial clothes washer sales is made within the

United States?

9.4 What percentage of your U.S. production of commercial clothes washers is exported?

9.5 Are there any foreign companies with North American production facilities?

9.6 Would amended energy conservation standards impact your domestic vs. foreign manufacturing or sourcing decisions? Is there an efficiency level that would cause you to move domestic production facilities outside the U.S.?12A-14

10 IMPACT ON COMPETITION

Amended energy conservation standards can alter the competitive dynamics of the market. This can include prompting companies to enter or exit the market, or to merge. DOE and the Department of Justice are both interested in any potential reduction in competition that would result from an amended energy conservation standard.

10.1 How would amended energy conservation standards affect your ability to compete in the marketplace? Would the effects on your company be different than others in the industry?

10.2 Would you expect your market share to change if amended energy conservation standards

become effective?

10.3 Do any firms hold intellectual property that gives them a competitive advantage following amended energy conservation standards?

10.4 How would industry competition change as a result of amended energy conservation

standards?

11 IMPACTS ON SMALL BUSINESS

11.1 The Small Business Administration (SBA) denotes a small business in the commercial

clothes washer manufacturing industry as having less than 1,000 total employees, including the parent company and all subsidiaries. a By this definition, is your company considered a small

business?

11.2 Are there any reasons that a small business manufacturer might be at a disadvantage relative to a larger business under amended energy conservation standards? Please consider such factors as technical expertise, access to capital, bulk purchasing power for materials/components, engineering resources, and any other relevant issues.

11.3 To your knowledge, are there any small businesses for which the adoption of amended energy conservation standards would have a particularly severe impact? If so, why?

11.4 To your knowledge, are there any niche manufacturers or component manufacturers for which the adoption of amended energy conservation standards would have a particularly severe impact? If so, why?

a DOE uses the small business size standards published on January 1, 20 12 , as amended, by the SBA to determine whether a company is a small business. To be categorized as a small business, a manufacturer of “other commercial and service industry machinery manufacturing” (NAICS #333318, which includes commercial clothes washer manufacturers) and its affiliates may employ a maximum of 1,000 employees. The 1,000 employee threshold includes all employees in a business’s parent company and any other subsidiaries.12 B-i

APPENDIX 12B. GOVERNMENT REGULATORY IMPACT MODEL OVERVIEW

TABLE OF CONTENTS

12B.1 INTRODUCTION AND PURPOSE

................................................................ .... 12B-1

12B.2 MODEL DESCRIPTION ................................................................

..................... 12B-1

12 B.3 DETAILED CASH FLOW EXAMPLE ............................................................... 12B-412 B-1 APPENDIX 12 B. GOVERNMENT REGULATORY IMPACT MODEL

12B.1 INTRODUCTION AND PURPOSE

The purpose of the Government Regulatory Impact Model (GRIM) is to help quantify the impacts of energy conservation standards on manufacturers. The basic mode of analysis is to estimate the change in value of the industry following a regulation or a series of regulations. The model structure also allows an analysis of multiple products with regulations taking effect over a period of time, and of multiple regulations on the same products.

Industry net present value is defined, for the purpose of this analysis, as the discounted sum of industry free cash flows plus a discounted terminal value. The model calculates the annual cash flows and then determines the present value of those cash flows both without an amended energy conservation standard ( i.e ., the base case) and under different trial standard levels (TSLs).

Output from the model consists of summary financial metrics, graphs of major variables, and, when appropriate, access to the complete cash flow calculation.

12B.2 MODEL DESCRIPTION

The basic structure of the GRIM is a standard annual cash flow analysis that uses manufacturer selling prices, manufacturing costs, a shipments forecast, and financial parameters as inputs and accepts a set of regulatory conditions such as changes in costs and investments. The analysis is separated into two major sections: 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 (see section 12. B.3).

Unit Sales: Total annual shipments for the industry were obtained from the National Impact Analysis Spreadsheet;

Revenues: Annual revenues - computed by multiplying unit prices at each efficiency level by the appropriate manufacturer markup;

Labor: The portion of cost of goods sold (COGS) that includes direct labor, commissions, dismissal pay, bonuses, vacation, sick leave, social security contributions, fringe, and assembly labor up-time;

Material: The portion of COGS that includes materials;12 B-2

Overhead: The portion of COGS that includes indirect labor, indirect material, energy use, maintenance, depreciation, property taxes, and insurance related to assets. While included in overhead, the depreciation is shown as a separate line item;

Depreciation: Annual depreciation computed as a percentage of Revenues

. While included in overhead, the depreciation is shown as a separate line item;

Stranded Assets: In the compliance year of the standard, a one-time write-off of net property, plant, and equipment ( PPE ) assets to account for the book value of these assets that would have enjoyed a longer life if not for the standard;

Standard SG&A: Selling, general, and administrative costs are computed as a percentage of

Revenues ;

R&D: the GRIM separately accounts for ordinary research and development (R&D) as a percentage of Revenues ;

Product Conversion Costs: Product conversion costs are one -time investments in research, development, testing, marketing, and other costs focused on making equipment design s comply with the new energy conservation standard. The GRIM allocates these costs over the period between the standard’s announcement and the effective date;

Earnings Before Interest and Taxes (EBIT): Includes profit before deductions for interest paid and taxes;

EBIT as a Percentage of Sales (EBIT/Revenues): the GRIM calculates EBIT as a percentage of sales to compare with the industry’s average reported in financial statements;

Taxes: Taxes on EBIT are calculated by multiplying the tax rate contained in Major Assumptions by EBIT

;

Net Operating Profits After Taxes (NOPAT): Computed by subtracting Cost of Goods Sold

, SG&A

, R&D

, Product Conversion Costs , and Taxes from Revenues ;

NOPAT repeated : NOPAT is repeated in the Statement of Cash Flows;

Depreciation repeated : Depreciation and Stranded Assets are added back in the Statement of Cash Flows because they are non -cash expenses;

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;

Cash Flow From Operations : Calculated by taking NOPAT , adding back non-cash items

such as a Depreciation , and subtracting out Change in Working Capital ;12 B-3

Ordinary Capital Expenditures: Ordinary investments in property, plant, and equipment to maintain and replace existing production assets, computed as a percentage of Revenues ;

Capital Conversion Costs: Capital conversion costs are one-time investments in property, plant, and equipment to adapt or change existing production facilities so that new product designs can be fabricated and assembled under the new regulation;

Capital Investment: Total investments in property, plant, and equipment are computed by adding Ordinary Capital Expenditures and Capital Conversion Costs ;

Free Cash Flow: Annual cash flow from operations and investments; computed by subtracting Capital Investment from Cash Flow from Operations ;

Terminal Value: Estimate of the continuing value of the industry after 2047

. Computed by growing the Free Cash Flow in year 204 7 at a constant rate in perpetuity;

Present Value Factor: Factor used to calculate an estimate of the present value of an amount to be received in the future;

Discounted Cash Flow: Free Cash Flows multiplied by the Present Value Factor

. For 204 7

the discounted cash flow includes the discounted Terminal Value ; and

Industry Value thru 204 7: The sum of Discounted Cash Flows

.12 B-4

12B.3 DET AILED CASH FLOW EXAMPLEAPPENDIX 14

- A. SOCIAL COST OF CARBON FOR REGULATORY IMP ACT

ANALYSIS UNDER EXECU TIVE ORDER 12866

TABLE OF CO NTENTS

14- A.1 EXECUTIVE SUMMARY

................................................................................. 14-A-1

14- A.2 MONETIZING CARBON DIOXIDE EMISS IONS

........................................... 14-A-2

14- A.3 SOCIAL COST OF CARBO N VALUES USED IN PAST REGULATORY

ANALYSES

......................................................................................................... 14-A-4

14- A.4 APPROACH AND KEY ASSUMPTIONS

........................................................ 14-A-5

14-A.4.1 Integrated Assessment Models ............................................................................ 14-A-5

14-A.4.2 Global versus Domestic Measures of SCC

........................................................ 14-A-11

14-A.4.3 Valuing Non- CO 2 Emissions ............................................................................. 14-A-13

14-A.4.4 Equilibrium Climate Sensitivity

........................................................................ 14-A-13

14-A.4.5 Socioeconomic and Emissions Trajectories....................................................... 14-A-16

14-A.4.6 Discount Rate ..................................................................................................... 14-A-19

14- A.5 REVISED SCC ESTIMATE S ........................................................................... 14-A-26

14- A.6 LIMITATIONS OF THE ANALYSIS

.............................................................. 14-A-32

14- A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPACTS AND

DAMAGE FUNCTIONS

.................................................................................. 14-A-34

14- A.8 CONCLUSION

.................................................................................................. 14-A-37

14- A.9 ANNEX

................................ ................................ .............................................. 14-A-43

14-A.9.1 Other (non- CO2) gases

...................................................................................... 14-A-44

14-A.9.2 Extrapolating Emissions Projections to 2300 .................................................... 14-A-46

LIST OF TABLES

Table 14 -A.1.1 Social Cost of CO 2 , 2010 – 2050 (in 2007 dollars) ................................. 14-A-2

Table 14 -A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity

Distributions ........................................................................................... 14-A-14

Table 14 -A.4.2 Socioeconomic and E missions Projections from Select EMF -22

Reference Scenarios ............................................................................... 14-A-18

Table 14 -A.5.1 Disaggregated Social Cost of CO 2 Values by Mode l, Socioeconomic Trajectory, and Discount Rate for 2010 (in 2007 dollars) ..................... 14-A-29

Table 14 -A.5.2 Social Cost of CO 2 , 2010 – 2050 (in 2007 dollars) ............................... 14-A-31

Table 14 -A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates

between 2010 and 2050 ......................................................................... 14-A-31

Table 14 -A.7.1 Probabilities of Various Tipping Points from Expert Elicitation ........... 14-A-35

Table 14 -A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars) ............................... 14-A-43

Table 14 -A.9.2 2010 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton

CO 2 ) ....................................................................................................... 14-A-53

Table 14 -A.9.3 2010 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton

CO 2 ) ....................................................................................................... 14-A-54

14 -A-iTable 14 -A.9.4 2010 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton

CO 2 ) ....................................................................................................... 14-A-55

Table 14 -A.9.5 Additional Summary Statistics of 2010 Global SCC Estimates ............ 14-A-56

LIST OF FIGURE S

Figure 14 -A.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

................................ ......................................................... 14-A-10 Figure 14 -A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE ................................................................................. 14-A-11 Figure 14 -A.4.3 Estimates of the Probability Density Function for Equilibrium Climate Sensitivity (°C) ...................................................................................... 14-A-16 Figure 14 -A.5.1 Level of Global GDP across EMF Scenarios ........................................ 14-A-30 Figure 14 -A.9.2 Sulfur Dioxide Emission Scenarios ....................................................... 14-A-46 Figure 14 -A.9.3 Global Population, 2000-2300 (Post-2100 extrapolations assume the population growth rate changes linearly to reach a zero growth rate by 2200.) ..................................................................................................... 14-A-48 Figure 14 -A.9.4 World GDP, 2000-2300 (Post-2100 extrapolations assume GDP per capita growth declines linearly, reaching zero in the year 2300) .......... 14-A-49 Figure 14 -A.9.5 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) ................................................ 14-A-50 Figure 14 -A.9.6 Global Net Land Use CO 2 Emissions, 2000-2300 (Post-2100 extrapolations assume emissions decline linearly, reaching zero in the year 2200) .............................................................................................. 14-A-50 Figure 14-A.9.7 Global Non- CO 2 Radiative Forcing, 2000-2300 (Post-2100 extrapolations assume constant non- CO 2 radiative forcing after 2100) 14-A-51 Figure 14 -A.9.8 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) ................................. 14-A-52 Figure 14 -A.9.9 Histogram of Global SCC Estimates in 2010 (2007$/ton CO 2 ), by

discount rate ........................................................................................... 14-A-55

14 -A- iiAPPENDIX 14

- A. SOCIAL COST OF CARBON FOR REGULATORY IMPACT

ANALYSIS UNDER EXECUTIVE ORDER 12866 a

14

- A.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 dioxi de (CO 2 ) emissions into cost

- benefit analyses of regulatory actions that have small, or “marginal,” impacts on cumulative global emissions. The estimates are presented with an acknowledgement of the many uncertainties involved and with a

clear understandi ng that they should be updated over time to reflect increasing knowledge of the science and economics of climate impacts.

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services due to climate change.

This document presents a summary of the interagency process that developed these SCC estimates. Technical experts from numerous agencies met on a regular basis to consider public

c omments, 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 scient ific and economic literatures. In this way, key uncertainties and model differences transparently and consistently inform the range of SCC estimates used in the rulemaking process.

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

14 -A-1The interagency group selected four SCC values for use in regulatory an alyses. Three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95 th percentile SCC estimate across all three models at a 3 percent discount rate , is included to represent higher

- than

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

Table 14-A.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

14

- A.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 document. b

When attempting to as sess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC 2009) points out that any assessment will suffer from uncertainty, speculation , and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.

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 the mass of carbon is 3.67 (the molecular weight of CO 2 divided by the molecular weight of carbon = 44/12 = 3.67).

14 -A-2Despite the seri ous limits of both quantification and monetization, SCC estimates can be useful in estimating the social benefits of reducing carbon dioxide emissions. Under Executive Order 12866, agencies are required, to the extent permitted by law, “to assess both the costs and the benefits of the intended regulation and, recognizing that some costs and benefits are difficult to quantify, propose or adopt a regulation only upon a reasoned determination that the benefits of the intended regulation justify its costs.” T he 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 cumulat ive global emissions. Most federal regulatory actions can be expected to have marginal impacts on global emissions.

For such policies, the benefits from reduced (or costs from increased) emissions in any future year can be estimated by multiplying the change in emissions in that year by the SCC value appropriate for that year. The net present value of the benefits can then be calculated by multiplying each of these future benefits by an appropriate discount factor and summing across all affected years. This approach assumes that the marginal damages from increased emissions are constant for small departures from the baseline emissions path, an approximation that is reasonable for policies that have effects on emissions that are small relative to cumul ative global carbon dioxide emissions. For policies that have a large (non

- marginal) impact on global cumulative emissions, there is a separate question of whether the SCC is an appropriate tool for calculating the benefits of reduced emissions; we do not attempt to answer that question here.

An interagency group convened on a regular basis to consider public comments, explore the technical literature in relevant fields, and discuss key inputs and assumptions in order to generate SCC estimates. Agencies that actively participated in the interagency process include the Environmental Protection Agency, and the Departments of Agriculture, Commerce, Energy, Transportation, and Treasury. This process was convened by the Council of Economic Advisers and the Office of Management and Budget, with active participation and regular input from the Council on Environmental Quality, National Economic Council, Office of Energy and Climate Change, and Office of Science and Technology Policy. The main objective of this process was to develop a range of SCC values using a defensible set of input assumptions that are ground ed in the existing literature. In this way, key uncertainties and model differences can more transparently and consistently inform the range of SCC esti mates used in the rulemaking process.

The interagency group selected four SCC estimates for use in regulatory analyses. For 2010, these estimates are $5, $21, $35, and $65 (in 2007 dollars). The first three estimates are based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, respectively. The fourth value is included to represent the higher

than

- expected impacts from temperature change further out in the tails of the SCC dist ribution. 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 i mpact 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 t he full range of annual SCC estimates from 2010 to 2050.

14 -A-3It is important to emphasize that the interagency process is committed to updating these estimates as the science and economic understanding of climate change and its impacts on society improves ove r 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 ex plore the issues raised in this document and consider public comments as part of the ongoing interagency

process.

14

- A.3 SOCIAL COST OF CARBON VALUES USED IN PAST REGULATORY

ANALYSES

To date, economic analyses for Federal regulations have used a wide range of values to estimate the benefits associated with reducing carbon dioxide emissions. In the final model year 2011 CAFE rule, the Department of Transportation (DOT) used both a “domestic” SCC value of $2 per ton of CO 2 and a “global” SCC value of $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 result ing 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 emission reductions (with a range of $0

- $14 for sensitivity analysis), also increasing at 2.4 percent per year. A regulation finalized by DOE in October of 2008 used a domestic SCC range of $0 to $20 per ton CO 2 for 2007 emission reductions (in 2007 dollars). In

additi on, 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 approximately 2 percent and 3 percent, respectively (in 2006 dollars for 2007 emissions).

In 2009, an interagency process was initiated to offer a preliminary assessment of how best to quantify the benefits from reducing carbon dioxide emissions. To ensure consistency in how benefits are evaluated across agencies, the Administration sought to develop a transparent and defensible method, specifically designed for the rulemaking process, to quantify avoided climate change damages from reduced CO 2 emissions. The interagency group did not undertake any original analysis. Instead, it combined SCC estimates from the existing literature to use as interim values until a more comprehensive analysis could be conducted.

The outcome of the preliminary assessment by the interagency group was a set of five interim values: global SCC estimates for 2007 (in 2006 dollars) of $55, $33, $19, $10, and $5 per

ton of CO 2. The $33 and $5 values represented model

- weighted means of the published estimates produced from the most recently available versions of three integrated assessment

models— DICE, PAGE, and FUND

— at approximately 3 and 5 percent discount rates. The $55 and $10 values were derived by adjusting the published estimates for uncertainty in the discount rate (using factors developed by Newell and Pizer (2003)) at 3 and 5 percent discount rates, respectively. The $19 value was chosen as a central value between the $5 and $33 per ton

14 -A-4estimates. All of these values were assumed to increase at 3 percent annually to represent growth

in inc remental damages over time as the magnitude of climate change increases.

These interim values represent the first sustained interagency effort within the U.S. government to develop an SCC for use in regulatory analysis. The results of this preliminary effort were presented in several proposed and final rules and were offered for public comment in connection with proposed rules, including the joint EPA-DOT fuel economy and CO 2 tailpipe emission proposed rules.

14

- A.4 APPROACH AND KEY ASS UMPTIONS

Since the relea se 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 de tails the several choices and assumptions that underlie the resulting estimates of the SCC.

It is important to recognize that a number of key uncertainties remain, and that current SCC estimates should be treated as provisional and revisable

, since they will evolve with improved scientific and economic understanding. The interagency group also recognizes that the existing models are imperfect and incomplete. The National Academy of Science (2009) points out that there is tension between the goal of producing quantified estimates of the economic damages from an incremental ton of carbon and the limits of existing efforts to model these effects. Throughout this document, we highlight a number of concerns and problems that should be addressed by the resear ch community, including research programs housed in many of the agencies participating in the interagency process to estimate the SCC

.

The U.S. Government will periodically review and reconsider estimates of the SCC used

for cost

- benefit analyses to reflect increasing knowledge of the science and economics of climate impacts, as well as improvements in modeling. In this context, statements recognizing the limitations of the analysis and calling for further research take on exceptional significance. T he 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.

14-A.4.1 Integrated Assessment Models

We rely on three integrated assessment models (IAMs) c ommonly used to estimate the SCC: the FUND, DICE, and PAGE models. c These models are frequently cited in the peer

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). T he 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 200 2a, Tol 2002b, Anthoff et al. 2009, Tol 2009).

14 -A-5reviewed literature and used in the IPCC assessment. Each model is given equal weight in the SCC values developed through this process, bea ring in mind their different limitations (discussed below).

These models are useful because they combine climate processes, economic growth, and feedbacks between the climate and the global economy into a single modeling framework. At the same time, t hey 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

form approaches (see NRC 2009 for a more detailed discussion; see Nordhaus 2008 on th e 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 impact s to economic damages, which makes this exercise even more difficult. Underlying the three IAMs selected for this exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ best attempts to synthesize the available scientific and economic research characterizing these relationships.

The three IAMs translate emissions into changes in atmospheric greenhouse concentrations, atmospheric concentrations into changes in temperature, and changes in temperature into economic damages. The emissions projections used in the models are based on specified socio economic (GDP and population) pathways. These emissions are translated into concentrations using the carbon cycle built into each model, and concentrations are translated into warming based on each model’s simplified representation of the climate and a key parameter, climate sensitivity. Each model uses a different approach to translate warming into damages. Finally, transforming the stream of economic damages over time into a single value requires judgments about how to discount them.

Each model takes a slightly different approach to model how changes in emissions result in changes in economic damages. In PAGE, for example, the consumption

- equivalent damages in each period are calculated as a fraction of GDP, depending on the temperature in that period relative to the pre

- industrial average temperature in each region. In FUND, damages in each period also depend on the rate of temperature change from the prior period. In DICE, temperature affects both consumption and investment. We describe each model in greater detail here. In a later section, we discuss key gaps in how the models account for various sc ientific 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 interag ency process was to enable a consistent exploration of the three models while respecting the different approaches to quantifying damages taken by the key modelers in

the field. An extensive review of the literature was conducted to select three sets of in put parameters for these models: climate sensitivity, socioeconomic and emissions trajectories, and discount rates. A probability distribution for climate sensitivity was specified as an input into all three models. In addition, the interagency group use d a range of scenarios for the socioeconomic parameters and a range of values for the discount rate. All other model features were left unchanged, relying on the model developers’ best estimates and judgments. In DICE, these

14 -A-6parameters are handled determ inistically and represented by fixed constants; in PAGE, most parameters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically.

The sensitivity of the results to other aspects of the models (e.g. the carbon cycle or damage function) is also important to explore in the context of future revisions to the SCC but has not been incorporated into these estimates. Areas for future research are highlighted at the

end of this document.

The D ICE 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 inve sting in natural capital today through reductions in emissions

— implying reduced consumption

— harmful effects of climate change can be avoided and future consumption thereby increased.

For purposes of estimating the SCC, carbon dioxide emissions are a function of global GDP and the carbon intensity of economic output, with the latter declining over time due to technological progress. The DICE damage function links global average temperature to the overall impact on the world economy. It varies quadratic ally with temperature change to capture the more rapid increase in damages expected to occur under more extreme climate change, and is calibrated to include the effects of warming on the production of market and nonmarket goods and services. It incorporat es impacts on agriculture, coastal areas (due to sea level rise), “other vulnerable market sectors” (based primarily on changes in energy use), human health (based on

climate

- related diseases, such as malaria and dengue fever, and pollution), non

- market am enities (based on outdoor recreation), and human settlements and ecosystems. The DICE damage function also includes the expected value of damages associated with low probability, high impact “catastrophic” climate change. This last component is calibrat ed 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

inclu ded implicitly through the choice of studies used to calibrate the aggregate damage function. For example, its agricultural impact estimates assume that farmers can adjust land use decisions in response to changing climate conditions, and its health impa ct estimates assume improvements in healthcare over time. In addition, the small impacts on forestry, water systems, construction, fisheries, and outdoor recreation imply optimistic and costless adaptation in these sectors (Nordhaus and Boyer, 2000; Warren et al., 2006). Costs of resettlement due to sea level rise are incorporated into damage estimates, but their magnitude is not clearly reported. Mastrandrea’s (2009) review concludes that “in general, DICE assumes very effective adaptation, and largely ignores adaptation costs."

Note that the damage function in DICE has a somewhat different meaning from the damage functions in FUND and PAGE. Because GDP is endogenous in DICE and because damages in a given year reduce investment in that year, damages propagate forward in time and

14 -A-7reduce GDP in future years. In contrast, GDP is exogenous in FUND and PAGE, so damages in any given year do not propagate forward. d

The PAGE Model

PAGE2002 (version 1.4epm) treats GDP growth as exogenous. It divides impacts into economic, non

- economic, and catastrophic categories and calculates these impacts separately for eight geographic regions. Damages in each region are expressed as a fraction of output, where

the fr action lost depends on the temperature change in each region. Damages are expressed as power functions of temperature change. The exponents of the damage function are the same in all regions but are treated as uncertain, with values ranging from 1 to 3 ( instead of being fixed at 2 as in DICE).

PAGE2002 includes the consequences of catastrophic events in a separate damage sub

function. Unlike DICE, PAGE2002 models these events probabilistically. The probability of a “discontinuity” (i.e., a catastrophic event) is assumed to increase with temperature above a specified threshold. The threshold temperature, the rate at which the probability of experiencing a discontinuity increases above the threshold, and the magnitude of the resulting catastrophe are all modeled probabilistically.

Adaptation is explicitly included in PAGE. Impacts are assumed to occur for temperature increases above some tolerable level (2°C for developed countries and 0°C for developing countries for economic impacts, and 0°C for all regions for non

- economic impacts), but adaptation is assumed to reduce these impacts. Default values in PAGE2002 assume that the developed countries can ultimately eliminate up to 90 percent of all economic impacts beyond

the tolerable 2°C increase and that developing countries can eventually eliminate 50 percent of their economic impacts. All regions are assumed to be able to mitigate 25 percent of the 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 f or sixteen geographic regions. In some impact sectors, the fraction of output lost or gained due to climate change depends not only on

d Using the default assumptions in DICE 2007, this effect generates an approximately 25 percent increase in the SCC relative to damages calculated by fixing GDP. In DICE2007 , the time path of GDP is endogenous. Specifically, the path of GDP depends on the rate of saving and level of abatement in each period chosen by the optimizing representative agent in the model. We made two modifications to DICE to make it consistent wi th EMF GDP trajectories (see next section): we assumed a fixed rate of savings of 20%, and we re

- calibrated the exogenous path of total factor productivity so that DICE would produce GDP projections in the absence of warming that exactly matched the EMF sc enarios.

14 -A-8the absolute temperature change but also on the rate of temperature change and level of regional

income. e In the forestry and agricultural sectors, economic damages also depend on CO 2 concentrations.

Tol (2009) discusses impacts not included in FUND, noting that many are likely to have a relatively small effect on damage estimates (both positive and negative). However, he characterizes several omitted impacts as “big unknowns”: for instance, extreme climate scenarios, biodiversity loss, and effects on economic development and political violence. With regard to potentially catastrophic events, he notes, “Exactly what would cause these sorts of changes or what effects they would have are not well -understood, although the chance of any one of them happening seems low. But they do have the potential to happen relatively quickly, and if they did, the costs could be substantia l. 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 s uch 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 pos itive); (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 incorporated 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 temperature across these sectors can result in negative economic damages from climate change.

Damage Functions

To generate revised SCC values, we rely on the IAM modelers’ current best judgments of how to represent the effects of climate change (represented by the increase in global

- average surface temperature) on the consumption

- equivalent value of both market and non

- market goods (represented as a fraction of global GDP). We recognize that these representations are incomplete and highly uncertain. But given the paucity of data linking the physical impacts to economic damages, we were not able to identify a better way to translate changes in climate into net economic damages, short of launching our own research program.

The damage functions f or the three IAMs are presented in Figures 1 6A.4.1 and 16A.4.2, using the modeler’s default scenarios and mean input assumptions. There are significant differences between the three models both at lower (figure 16A.4.2) and higher (figure 16A.4.1 )

increas es in global- average temperature.

e In the deterministic version of FUND, the majority of damages are attributable to increased air conditioning demand, while reduced cold stress in Europe, North America, and Central and East Asia results in health benefits in those regions at lo w to moderate levels of warming (Warren et al., 2006).

14 -A-9Figure 14 -A.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 rate s we expect that a greater proportion of the SCC value is due to damages in years with lower temperature increases. For example, when the discount rate is 2.5 percent, about 45 percent of the 2010 SCC value in DICE is due to damages that occur in years wh en the temperature is less than or equal to 3 °C. This increases to approximately 55 percent and 80 percent at discount rates of 3 and 5 percent, respectively.

These differences underscore the need for a thorough review of damage functions

— in particular, how the models incorporate adaptation, technological change, and catastrophic damages. Gaps in the literature make modifying these aspects of the models challen ging, which highlights the need for additional research. As knowledge improves, the Federal government is

f The x

- axis represents increases in annual, rather than equilibrium, temperature, while the y

- axis represents the annual stream of benefits as a share of global GDP. Each specific combination of clim ate sensitivity,

socioeconomic , and emissions parameters will produce a different realization of damages for each IAM. The damage functions represented in Figures 1A and 1B are the outcome of default assumptions. For instance, under alternate assumptions , the damages from FUND may cross from negative to positive at less than or greater than 3

°C.

14 -A- 10committed to exploring how these (and other) models can be modified to incorporate more accurate estimates of damages.

Figure 14 -A.4.2 Annual Consumption Loss for Lower Temperature Changes in DICE, FUND, and PAGE

14-A.4.2 Global versus Domestic Measures of SCC

Because of the distinctive nature of the climate change problem, we center our current attention on a global measure of SCC. This approach is the same as that taken for the interim values, but it otherwise represents a departure from past practices, which tended to put greater emphasis on a domestic measure of SCC (limited to impacts of climate change experienced within U.S. borders). As a matter of law, consideration of both global and domestic values is generally permissible; the relevant statutory provisions are usually ambiguous and allow

selection of either measure. g

Global SCC

Under current OMB guidance contained in Circular A

- 4, analysis of economically significant proposed and final regulations from the domestic perspective is required, while analysis from the international perspective is optional. However, the climate change problem is highly unusual in at least two respects. First, it involves a glo bal externality: emissions of most greenhouse gases contribute to damages around the world even when they are emitted in the

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 o f 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.

14 -A- 11United States. Consequently, to address the global nature of the problem, the SCC must incorporate the full (global) damages cause d by GHG emissions. Second, climate change presents a problem that the United States alone cannot solve. Even if the United States were to reduce its greenhouse gas emissions to zero, that step would be far from enough to avoid substantial climate chang e. Other countries would also need to take action to reduce emissions if significant changes in the global climate are to be avoided. Emphasizing the need for a global solution to a global problem, the United States has been actively involved in seeking international agreements to reduce emissions and in encouraging other nations, including emerging major economies, to take significant steps to reduce emissions. When these considerations are taken as a whole, the interagency group concluded that a global measure of the benefits from reducing U.S. emissions is preferable.

When quantifying the damages associated with a change in emissions, a number of analysts (e.g., Anthoff, et al. 2009a) employ “equity weighting” to aggregate changes in consumption across regions. This weighting takes into account the relative reductions in wealth in different regions of the world. A per

- capita loss of $500 in GDP, for instance, is weighted more heavily in a country with a per

- capita GDP of $2,000 than in one with a per

- capita GDP of $40,000. The main argument for this approach is that a loss of $500 in a poor country causes a greater reduction in utility or welfare than does the same loss in a wealthy nation. Notwithstanding the theoretical claims on behalf of equity weighting, the interagency group concluded that this approach would not be appropriate for estimating a SCC value used in domestic regulatory analysis. h For this reason, the group concluded that using the global (rather than domestic) value, without equity weighting, is the appropriate approach.

Domestic SCC

As an empirical matter, the development of a domestic SCC is greatly complicated by the relatively few region

- or country

- specific estimates of the SCC in the literature. One potential

source of es timates comes from the FUND model. The resulting estimates suggest that the ratio of domestic to global benefits of emission reductions varies with key parameter assumptions. For example, with a 2.5 or 3 percent discount rate, the U.S. benefit is about 7

- 10 percent of the global benefit, on average, across the scenarios analyzed. Alternatively, if the fraction of GDP lost due to climate change is assumed to be similar across countries, the domestic benefit would be proportional to the U.S. share of global GDP, which is currently about 23 percent. i

On the basis of this evidence, the interagency workgroup determined that a range of values from 7 to 23 percent should be used to adjust the global SCC to calculate domestic effects. Reported domestic values should use this range. It is recognized that these values are

h It is plausible that a loss of $X inflicts more serious harm on a poor nation than on a wealt hy one, but development of the appropriate "equity weight" is challenging. Emissions reductions also impose costs, and hence a full account would have to consider that a given cost of emissions reductions imposes a greater utility or welfare loss on a poo r nation than on a wealthy one. Even if equity weighting

— for both the costs and benefits of emissions reductions

— is appropriate when considering the utility or welfare effects of international action, the interagency group concluded that it should not be used in developing an SCC for use in regulatory policy at this time.

i Based on 2008 GDP (in current US dollars) from the World Bank Development Indicators Report

.

14 -A- 12approximate, provisional, and highly speculative. There is no a priori reason why domestic benefits should be a constant fraction of net global damages over time. Further, FUND does not

accoun t 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 de termine whether to update its approach.

14-A.4.3 Valuing Non- CO 2 Emissions

While CO 2 is the most prevalent greenhouse gas emitted into the atmosphere, the U.S. included five other greenhouse gases in its recent endangerment finding: methane, nitrous oxide, hydrofl uorocarbons, 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, C O 2 emissions, unlike methane and other greenhouse gases, contribute to ocean acidification. Likewise, damages from methane emissions are not offset by the positive effect of

CO 2 fertilization. Thus, transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accurate estimates of the

social costs of non- CO 2 gases.

In light of these limitations, and the significant contributions of non

- CO 2 emissions to climate change, further res earch 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 interagen cy 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.

14-A.4.4 Equilibrium Climate Sensitivity

Equilibrium climate sensitivity (EC S) is a key input parameter for the DICE, PAGE, and

FUND models. j It is defined as the long

- term increase in the annual global

- average surface temperature from a doubling of atmospheric CO 2 concentration relative to pre

- industrial levels (or stabilization at a concentration of approximately 550 parts per million (ppm)). Uncertainties in this important parameter have received substantial attention in the peer- reviewed literature.

j The equilibrium climate sensitivity includes the response of the climate system to incre ased greenhouse gas concentrations over the short to medium term (up to 100

- 200 years), but it does not include long

- term feedback effects due to possible large

- scale changes in ice sheets or the biosphere, which occur on a time scale of many

hundreds to t housands of years (e.g. Hansen et al. 2007).

14 -A- 13The most authoritative statement about equilibrium climate sensitivity appears in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC):

Basing our assessment on a combination of several independent lines of evidence...including observed climate change and the strength of known feedbacks simulated in [global climate models], we conclude that the global mean equilibrium warming for doubling 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

b e consistent with the above statement: Roe and Baker (2007), log

- normal, gamma, and Weibull.

Table 14 -A.4.1 included below gives summary statistics for the four calibrated distributions.

Table 14-A.4.1 Summary Statistics for Four Calibrated Climate Sensitivity Distribu tions

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:

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.

14 -A- 14(1) a median equal to 3°C, to reflect the judgment of “a most likely value of about 3 °C”; l
(2) two

- thirds probability that the equilibrium climate sensitivity lies between 2 and 4.5 °C;

and (3) zero probability that it is less than 0°C or greater than 10°C (see Hegerl et al. 2006, p. 721).

We selected the calibrated Roe and Baker distribution from the four candidates for two reasons. First, the Roe and Baker distribution is the only one of the four that is based on a theoretical understanding of the response of the climate system to increased greenhouse gas

concentra tions (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 abo ut climate response: (1) absent feedback effects, the equilibrium climate sensitivity is equal to 1.2 °C; (2) feedback factors are proportional to the change in surface temperature; and (3) uncertainties in feedback factors are normally distributed. There is widespread agreement on the first point and the second and third points are common assumptions.

Second, the calibrated Roe and Baker distribution better reflects the IPCC judgment that “values substantially higher than 4.5°C still cannot be excluded.” Although the IPCC made no quantitative judgment, the 95 th percentile of the calibrated Roe & Baker distribution (7.1 °C) is much closer to the mean and the median (7.2 °C) of the 95 th percentiles of 21 previous studies summarized by Newbold and Daigneault (2009). It is also closer to the mean (7.5 °C) and median (7.9 °C) of the nine truncated distributions examined by the IPCC (Hegerl, et al., 2006)

than are the 95 th percentiles of the three other calibrated distributions (5.2-6.0 °C).

Finally, we note the IPCC judgment that the equilibrium climate sensitivity “is very likely larger than 1.5°C.” Although the calibrated Roe & Baker distribution, for which the probability of equilibrium climate sensitivity being greater than 1.5°C is almost 99 percent, is not inconsistent with the IPCC definition of “very likely” as “greater than 90 percent probability,” it reflects a greater degree of certainty about very low values of ECS than was expressed by the

IPCC.

l Strictly speaking, “most likely” refers to the mode of a distribution rather than the median, but common usage would allow the mode, median, or mean to serve as candidates for the central or “most likely” value and the IPCC report is not specific on this point. For the distributions we considered, the median was between the mode and the mean. For the Roe and Baker distribution, setting the median equal to 3°C, rather than the mode or mean, gave a 95 th

percentile that is more consistent with IPCC judgments and the literature. For example, setting the mean and mode equal to 3°C produced 95 th percentiles of 5.6 and 8.6 °C, respectively, which are in the lower and upper end of the range in the literature. Finally, the median is closer to 3°C than is the mode for the truncated distributions selected by the IPCC (Hegerl, et al., 2006); the average median is 3.1 °C and the average mode is 2.3 °C, which is most consistent with a Roe and Baker distribution with the median set equal to 3 °C.

14 -A- 15Figure 14 -A.4.3 Estimates of the Probability Density Function f or 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 16 A.4.3 (above ) overlays it on Figure 9.20 from the IPCC Fourth Assessment Report. These functions are scaled to integrate to unity between 0 °C and 10 °C. The horizontal bars show the respective 5 percent to 95 percent ranges; dots indicate the median estimate. m

14-A.4.5 Socioeconomic and Emissions Trajectories

Another key issue considered by the interagency group is how to select the set of

socioeconomic and emissions parameters for use in PAGE, DICE, and FUND. Socioeconomic pathways are closely tied to climate damages because, all else equal, more and wealthier people tend to emit more greenhouse gases and also have a higher (absolute) willingness to pay to avoid climate disruptions. For this reason, we consider how to mode l several input parameters in tandem: GDP, population, CO 2 emissions, and non

- CO 2 radiative forcing. A wide variety of scenarios have been developed and used for climate change policy simulations (e.g., SRES 2000, CCSP 2007, EMF 2009). In determining which scenarios are appropriate for inclusion, we aimed to select scenarios that span most of the plausible ranges of outcomes for these variables.

m The estimates based on instrumental data are from Andronova and Schlesinger (2001), Forest et al. (2002; dashed line, anthropogenic forcings only), Forest et al. (2006; solid line, anthropogenic and natural forcings), Gregory et al. (2002a), Knutti et al. (2002), Frame et al. (2005), and Forster and Gregory (2006). Hegerl et al. (2006) are based on multiple palaeoclimatic reconstructions of north hemisphere mean temperatures over the last 700 years. Also shown

are the 5

- 95 percent approximate ranges for two estimates from the last glacial maximum (dashed, Annan et al. 2005; solid, Schneider von Deimling et al. 2006), which are based on models with different structural properties.

14 -A-16To accomplish this task in a transparent way, we decided to rely on the recent Stanford Energy Modeling Foru m exercise, EMF

- 22. EMF

- 22 uses ten well

- recognized models to evaluate substantial, coordinated global action to meet specific stabilization targets. A key advantage of relying on these data is that GDP, population, and emission trajectories are

internal ly consistent for each model and scenario evaluated. The EMF

- 22 modeling effort also is preferable to the IPCC SRES due to their age (SRES were developed in 1997) and the fact that 3 of 4 of the SRES scenarios are now extreme outliers in one or more variables. Although the

EMF

- 22 scenarios have not undergone the same level of scrutiny as the SRES scenarios, they are

recent, peer -reviewed, published, and publicly available.

To estimate the SCC for use in evaluating domestic policies that will have a smal l 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 14 -A.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.e., CO 2 - only concentrations of 42 5 – 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 exercise, we selected the trajectories used by MiniCAM, MESSAGE, IMAGE, and the optimistic scenario

from MERGE. For the BAU pathways, we used the GDP, population, and emission trajectories from each of these four models. For the 550 ppm CO 2 e scenario, we averaged the GDP, population, and emission trajectories implied by these same four models.

n Such an emissions path would be consistent with widespread action by countries to mitigate GHG emissions, though it could also result from technological advances. It was chosen because it represents the most stringent case analyzed by the EMF

- 22 where all the models converge: a 550 ppm, not to exceed, full participation scenario.

14 -A- 17Table 14-A.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 proved challenging to do in an analytically rigorous way given the dearth of information on the likelihood of a full range of

future socioeconomic pathways.

o While the EMF

- 22 models used market exchange rates (MER) to calculate global GDP, it is also possible to use purchasing power parity (PPP). PPP takes into account the differen t price levels across countries, so it more accurately describes relative standards of living across countries. MERs tend to make low

- income countries appear poorer than they actually are. Because many models assume convergence in per capita income over time, use of

MER

- adjusted GDP gives rise to projections of higher economic growth in low income countries. There is an ongoing debate about how much this will affect estimated climate impacts. Critics of the use of MER argue that it

leads to overstated ec onomic 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., u sing cross

- sectional PPP measures for relative incomes and outputs and national accounts price and quantity indexes for time

- series extrapolations). However, he notes that it important to keep this debate in perspective; it is by no means clear that exchange

- rate

- conversion issues are as important as uncertainties about population, technological change, or the many geophysical uncertainties.

14 -A- 18T here are a number of caveats. First, EMF BAU scenarios represent the modelers’ judgment of the most likely pathway absent mitigation policies to reduce greenhouse gas emissions, rather than the wider range of possible outcomes. Nevertheless, these views of the most likely outcome span a wide range, from the more optimistic (e.g. abundant low

- cost, low

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 optimal from a

benefit

- cost standpoint. Rather, it is indicative of one possible future outcome. The emission trajectories underlying some BAU scenarios (e.g. MESSAGE’s 612 ppm) also are consistent with some modest policy action to address climate change. q We chose not to include

socioeconomic trajectories that achieve even lower GHG concentrations at this time, given the difficulty many models had in converging to meet these ta rgets.

For comparison purposes, the Energy Information Agency in its 2009 Annual Energy Outlook projected that global carbon dioxide emissions will grow to 30.8, 35.6, and 40.4 gigatons in 2010, 2020, and 2030, respectively, while world GDP is projected to be $51.8, $71.0 and $93.9 trillion (in 2005 dollars using market exchange rates) in 2010, 2020, and 2030, respectively. These projections are consistent with one or more EMF

- 22 scenarios. Likewise, the United Nations’ 2008 Population Prospect projects population will grow from 6.1 billion people in 2000 to 9.1 billion people in 2050, which is close to the population trajectories for the IMAGE, MiniCAM, and MERGE models.

In addition to fossil and industrial CO 2 emissions, each EMF scenario provides

pr ojections of methane, nitrous oxide, fluorinated greenhouse gases, and net land use CO 2 emissions out to 2100. These assumptions also are used in the three models while retaining the default radiative forcings due to other factors (e.g. aerosols and other gases). See the Annex for greater detail.

14-A.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

p For instance, in the MESSAGE model’s reference case total primary energy production from nuclear, biomass, and

non

- biomass renewables is projected to increase from about 15 percent of total primary energy in 2000 to 54 percent in 2100. In comparison, the MiniCAM reference case shows 10 percent in 2000 and 21 percent in 2100.

q For example, MiniCAM projects if all non

- US OECD countries reduce 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 ppmv.

14 -A- 19temperatures, as represented in each of the three IAMs. Then we discount the stream of future damages to its present value in the year when the additional unit of emissions was released using the selected discount rate, which is intended to reflect society's marginal rate of 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 revise that approach here.

Arrow et al. (1996) outlined two main approaches to determine the discount rate for climate change analysis, which they labeled “descriptive” and “prescriptive.” The descriptive approach reflects a positive (non

- normative) 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

14 -A- 20market distortions and uncertainties or inefficiencies in intergenerational transfers of wealth,

which in the Kaldor

- Hicks logic are presumed to compensate future generations for damage (a potentially controversial assumption, as noted above) (Arrow et al. 1996, Weitzman 1999).

Further, a legitimate concern about both descriptive and prescriptive approaches is that they tend to obscure important heterogeneity in the population. The utility function that

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 consistency with the standard contemporary theoretical foundations of benefit-cost analysis and with the approach required by OMB’s existing guidance. The logic of this framework also suggests that market rates should be used for discounting future consumption- 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 imperfections drive a wedge

between the risk

- free rate of return on capital and the consumption rate of interest. Thus, the literature recognizes two conceptual discount concepts

— the consumption rate of interest and the opportunity cost of capital.

According to OMB’s Circular A

- 4, it is appropriate to use the rate of return on capital when a regulation is expected to displace or alter the use of capital in the private sector. In this case, OMB recommends Agencies use a discount rate of 7 percent. When regulation is expected to primarily affect private consumption

— for instance, via higher prices for goods and services

— a lower discount rate of 3 percent is appropriate to reflect how private individuals trade

- off current and future consumption.

14 -A- 21The interagency group examined the economics literature and concluded that the consumption rate of interest is the correct concept to use in evaluating the benefits and costs of a marginal change in carbon emissions (see Lind 1990, Arrow et al 1996, and Arrow 2000). The consumption rate of interest also is appropriate when the impacts of a regulation are measured in 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 policy and baseline consumption.

If the IAM projections of future impacts represent expected values (not certainty

equivalent values), then the appropriate discount rate generally does not equal the risk

- free rate. If the benefits of the policy tend to be high in those states of the world in which consumption 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 Treasury not es 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.

14 -A- 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 revie w of the literature provides some guidance on reasonable parameter values for the Ramsey discounting equation, based on both prescriptive and descriptive approaches.

• η. Most papers in the climate change literature adopt values for η in the range of 0.5 to 3 (Weitzman cites plausible values as those ranging from 1 to 4), although not all authors articulate whether their choice is based on prescriptive or descriptive reasoning. w Dasgupta (2008) argues that η should be greater than 1 and may be as high as 3, since η equal to 1 suggests savings rates that do not conform to observed behavior.

t Cambell et al (2001) estimates that the annual real return from stocks for 1900

- 1995 was about 7 percent. The

annual real rate of return for the S &P 500 from 1950

– 2008 was about 6.8 percent. In the absence of a better way to population

- weight the tax rates, we use the middle of the 20

– 40 percent range to derive a post

- tax interest rate (Kotlikoff and Rapson 2006).

u The parameter ρ  measures th e pure rate of time preference : people’s behavior reveals a preference for an increase in utility today versus the future. Consequently, it is standard to place a lower weight on utility in the future. The parameter η captures diminishing marginal utility : consumption in the future is likely to be higher than consumption today, so diminishing marginal utility of consumption implies that the same monetary damage will cause a smaller reduction of utility for wealthier individuals, either in the future or in current generations. If η = 0, then a one dollar increase in income is equally valuable regardless of level of income; if η = 1, then a one percent increase in income is equally valuable no matter the level of income; and if η > 1, then a one percent increase in

income is less valuable to wealthier individuals.

v In this case, g could be taken from the selected EMF socioeconomic scenarios or alternative assumptions about the rate of consumption growth.

w Empirical estimates of η span a wide range of value s. A benchmark value of 2 is near the middle of the range of values estimated or used by Szpiro (1986), Hall and Jones (2007), Arrow (2007), Dasgupta (2006, 2008), Weitzman (2007, 2009), and Nordhaus (2008). However, Chetty (2006) developed a method of e stimating 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

conclude s that the standard expected utility model cannot generate high levels of risk aversion without contradicting established facts about labor supply. Recent work has jointly estimated the components of the Ramsey equation. Evans and Sezer (2005) estimate η = 1.49 for 22 OECD countries. They also estimate ρ = 1.08 percent per year using data on mortality rates. Anthoff, et al. (2009b) estimate η = 1.18, and ρ = 1.4 percent. When they multiply the

bivariate probability distributions from their work and Evans and Sezer (2005) together, they find η = 1.47, and ρ = 1.07.

14 -A- 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., Ar row et al. 1996, Stern et al. 2006). However, even in an inter

generational setting, it may make sense to use a small positive pure rate of time preference because of the small probability of unforeseen cataclysmic events (Stern et al. 2006).

• g. A commonly accepted approximation is around 2 percent per year. For the

socioeconomic scenarios used for this exercise, the EMF models assume that g is about 1.5-2 percent to 2100.

Some economists and non

- economists have argued for constant discount rates below 2 percent based on the prescriptive approach. When grounded in the Ramsey framework, proponents of this approach have argued that a ρ of zero avoids giving preferential treatment to one generation over another.  The choice of η has also been posed a n 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 a nnual discount rate of 1.4 percent. In the context of permanent income savings behavior, however, Stern’s assumptions suggest that individuals would save 93 percent of their income. x

Recently, Stern (2008) revisited the values used in Stern et al. (2006 ), stating that there is a case to be made for raising η due to the amount of weight lower values place on damages far in the future (over 90 percent of expected damages occur after 2200 with η = 1). Using Stern’s assumption that ρ = 0.1 percent, combined with a η of 1.5 to 2 and his original growth rate, yields a discount rate of greater than 2 percent.

We conclude that arguments made under the prescriptive approach can be used to justify discount rates between roughly 1.4 and 3.1 percent. In light of concerns about the most appropriate value for η , we find it difficult to justify rates at the lower end of this range under the Ramsey framework.

Accounting for Uncertainty in the Discount Rate

While the consumption rate of interest is an important driver of the benefits estimate, it is

uncertain over time. Ideally, we would formally model this uncertainty, just as we do for climate sensitivity. Weitzman (1998, 2001) showed theoretically and Newell and Pizer (2003) and Groom et al. (2006) confirm empirically that discount rate uncertainty can have a large effect on net present values. A main result from these studies is that if there is a persistent element to the

x Stern (2008) argues that building in a positive rate of exogenous technical change over time reduces the implied savings rate and that η at or above 2 are inconsistent with observed behavior with regard to equity . (At the same time, adding exogenous technical change

— all else equal

— would increase g as well.)

14 -A- 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 Weitzma n 2009).

The proper way to model discount rate uncertainty remains an active area of research. Newell and Pizer (2003) employ a model of how long

- term interest rates change over time to forecast future discount rates. Their model incorporates some o f the basic features of how interest rates move over time, and its parameters are estimated based on historical observations of long

- term rates. Subsequent work on this topic, most notably Groom et al. (2006), uses more general models of interest rate dyn amics to allow for better forecasts. Specifically, the volatility of interest rates depends on whether rates are currently low or high and the variation in the level of persistence over time.

While Newell and Pizer (2003) and Groom et al (2006) attempt formally to model uncertainty in the discount rate, others argue for a declining scale of discount rates applied over time (e.g., Weitzman 2001, and the UK’s “Green Book” for regulatory analysis). This approach uses a higher discount rate initially, but applies a graduated scale of lower discount rates further

out in time. y A key question that has emerged with regard to both of these approaches is the

trade

- off between potential time inconsistency and giving greater weight to far future outcomes (see the EPA Science Advisory Board’s recent comments on this topic as part of its review of

their Guidelines for Economic Analysis ). z

The Discount Rates Selected for Estimating SCC

In light of disagreement in the literature on the appropriate market interest r ate to use in this context and uncertainty about how interest rates may change over time, we use three discount rates to span a plausible range of certainty

- equivalent constant discount rates: 2.5, 3, and 5 percent per year. Based on the review in the previous sections, the interagency workgroup determined that these three rates reflect reasonable judgments under both descriptive and prescriptive approaches.

The central value, 3 percent, is consistent with estimates provided in the economics

literature a nd OMB’s Circular A

- 4 guidance for the consumption rate of interest. As previously

y For instance, the UK applies a discount rate of 3.5 percent to the first 30 years; 3 percent for years 31

- 75; 2.5 percent for years 76

- 125; 2 percent for years 126

- 200; 1.5 percent for years 201

- 300; and 1 percent after 300 years. As a sensitivity, it recommends a discount rate of 3 percent for the first 30 years, also decreasing over time.

z Uncertainty in future damages is distinct from uncertainty in the discount rate. Weitzman (2008) argues that Stern’s choice of a low discount rate was “right for the wrong reasons.” He demonstrates how the damages from a low probability, cat astrophic event far in the future dominate the effect of the discount rate in a present value calculation and result in an infinite willingness

- to

- pay for mitigation today. Newbold and Daigneault, (2009) and Nordhaus (2009) find that Weitzman’s result is s ensitive to the functional forms chosen for climate sensitivity, utility, and consumption. Summers and Zeckhauser (2008) argue that uncertainty in future damages can also work in the other direction by increasing the benefits of waiting to learn the approp riate level of mitigation required.

14 -A- 25mentioned, the consumption rate of interest is the correct discounting concept to use when future damages from elevated temperatures are estimated in consumption -equivalent units. Further, 3 percent roughly corresponds to the after

- tax riskless interest rate. 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 use to smooth consumption across periods.

The low value, 2.5 percent, is included to incorporate the concern that interest rates are highly uncertain over time. It represents the aver age 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 th e 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.

14

- A.5 REVISED SCC ESTIMATE S

Our general approach to estimating SCC values is to run the three integrated assessment models (FUND, DICE, and PAGE) using the following inputs agreed upon by the interagency

group:

• A Roe and Baker distribution for the climate sensitivity parameter bounded between 0 and 10 with a median of 3 °C and a cumulative probability between 2 and 4.5 °C of 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 temperatu re effects and (consumption- equivalent) damages in each year resulting from the baseline path of emissions.

aa Calculations done by Pizer et al. using the original simulation program from Newell and Pizer (2003).

14 -A- 26a. In PAGE, the consumption

- equivalent damages in each period are calculated as a fraction of the EMF GDP forecast, depending on the temperature in that period relative to the pre

- industrial average temperature in each region.

b. In FUND, damages in each period depend on both the level and the rate of temperature change in that period.

c. In DICE, temperature affects both consumption and investment, so w e first adjust the EMF GDP paths as follows: Using the Cobb

- Douglas production function with the DICE2007 parameters, we extract the path of exogenous technical change implied by the EMF GDP and population paths, then we recalculate the baseline GDP path t aking into account climate damages resulting from the baseline emissions path.

3. Add an additional unit of carbon emissions in year t. (The exact unit varies by model.)

4. Recalculate the temperature effects and damages expected in all years beyond t resulting from this adjusted path of emissions, as in step 2.

5. Subtract the damages computed in step 2 from those in step 4 in each year. (DICE is run in 10- year time steps, FUND in annual time steps, while the time steps in PAGE vary.)

6. Discount the resulting path of marginal damages back to the year of emissions using the agreed upon fixed discount rates.

7. Calculate the SCC as the net present value of the discounted path of damages computed in step 6, divided by the unit of carbon emissions used to sho ck 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 thre e models has a different default end year. The default time horizon is 2200 for PAGE, 2595 for DICE, and 3000 for the latest version of FUND.

This is an issue for the multi

- model approach because differences in SCC estimates may arise simply due to the model time horizon. Many consider 2200 too short a time horizon because it could miss a significant fraction of damages under certain assumptions about the growth of marginal damages and discounting, so each model is run here through 2300. This step required a small adjustment in the PAGE model only. This step also required assumptions about GDP,

14 -A- 27population, and greenhouse gas emission trajectories after 2100, the last year for which these

data are available from the EMF

- 22 models. (A more detailed discussion of these assumptions is included in the Annex.)

This exercise produces 45 separate distributions of the SCC for a given year, the product of 3 models, 3 discount rates, and 5 socioeconomic scenarios. This is clearly too many separate

distribu tions for consideration in a regulatory impact analysis.

To produce a range of plausible estimates that still reflects the uncertainty in the estimation exercise, the distributions from each of the models and scenarios are equally weighed

and combined to produce three separate probability distributions for SCC in a given year, one for each assumed discount rate. These distributions are then used to define a range of point estimates for the global SCC. In this way, no IAM or socioeconomic scenario is given greater weight than another. Because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context, we present SCCs based on th e 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 sc enarios 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 purpose, we use the SCC value for the 95 th percentile at a 3 percent discount rate. ( The full set of distributions by model and scenario combination is included in the Annex .) As noted above, the 3 percent discount rate is the central value, and so the central value that emerges is the average SCC across models at the 3 percent discount rate. For purposes of capturing the uncertainties involved in regulatory impact analysis, we emphasize the importance and value of considering the full range.

As previously discussed, low probability, high impact events are incorporated into the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensit ivity. 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 probabili stic treatment of the equilibrium climate sensitivity parameter will directly affect the non

- catastrophic damages that are a function of the rate of temperature change.

In Table 14 -A.5.1 , we begin by presenting SCC estimates for 2010 by model, scenario, and discount rate to illustrate the variability in the SCC across each of these input parameters. As expected, higher discount rates consistently result in lower SCC values, while lower discount rates result in higher SCC values for each socioeconomic trajectory. It is also evident that there are differences in the SCC estimated across the three main models. For these estimates, FUND produces the lowest estimates, while PAGE generally produces the highest estimates

.

14 -A- 28Table 14-A.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) close to $8 per ton for a 4 percent discount rate. Note that these comparisons are only approximate since the literature generally relies on Ramsey discounting,

while we have assumed constant discount rates. bb

bb Nordhaus (2008) runs DICE2007 with ρ = 1.5 and η = 2. The default approach in PAGE2002 (version 1.4epm)

treats ρ and η as random parameters, specified using a triangular distribution such that the min, mode, and max = 0.1, 1, and 2 for ρ, and 0.5, 1, and 2 for η, respectively. The FUND default value for η is 1, and Tol generates SCC estimates for values of ρ = 0, 1 , and 3 in many recent papers (e.g. Anthoff et al. 2009). The path of per

- capita consumption growth, g, varies over time but is treated deterministically in two of the three models. In DICE, g is

14 -A- 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 t he models. Specifically in DICE and PAGE, the fraction of economic output lost due to climate damages increases with the level of temperature alone, whereas in FUND the fractional loss also increases with the rate of temperature change. Furthermore, in F UND increases in income over time decrease vulnerability to climate change (a form of adaptation), whereas this does not occur in DICE and PAGE. These structural differences among the models make FUND more sensitive to the path of emissions and less sensitive to GDP compared to DICE and PAGE.

Figure 16A.5.1 shows that IMAGE has the highest GDP in 2100 while MERGE Optimistic has the lowest. The ordering of global GDP levels in 2100 directly corresponds to the rank ordering of SCC for PAGE and DICE. For FUND, the correspondence is less clear, a result that is to be expected given its less direct relationship between its damage function and GDP.

Figure 14 -A.5.1 Level of Global GDP across EMF Scenarios

Table 14 -A.5.2 shows the four selected SCC values in five- year in crements from 2010 to 2050. Values for 2010, 2020, 2030, 2040, and 2050 are calculated by first combining all outputs (10,000 estimates per model run) from all scenarios and models for a given discount rate. Values for the years in between are calculated using a simple linear interpolation.

endogenous. Under Ramsey discounting, as economic growth s lows in the future, the large damages from climate change that occur far out in the future are discounted at a lower rate than impacts that occur in the nearer term. 0 50 100 150 200 250 300 350 400 450

2000 2010 2020 2030 2050 2100 Global

GDP (trillions

of U.S.
dollars) IMAGE

MERGE Optimistic

MESSAGE

MiniCAM

3.7 W/m2 radiative forcing

14 -A- 30Table 14-A.5.2 Social Cost of CO 2 , 2010 – 2050 (in 2007 dollars)

Discount 5% 3% 2.5% 3%

Year Avg Avg Avg 95th

2010 4.7 21.4 35.1 64.9

2015 5.7 23.8 38.4 72.8

2020 6.8 26.3 41.7 80.7

2025 8.2 29.6 45.9 90.4

2030 9.7 32.8 50.0 100.0

2035 11.2 36.0 54.2 109.7

2040 12.7 39.2 58.4 119.3

2045 14.2 42.1 61.7 127.8

2050 15.7 44.9 65.0 136.2

The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change. Note that this approach allows us to estimate the growth rate of the SCC directly using DICE, PAGE, and FUND rather than assuming a constant annual growth rate as was done for the interim estimates (using 3 percent). This helps to ensure that the estimates are internally consistent with other modeling assumption s. Tabl e 14 -A.5.3 illustrates how the growth

rate for these four SCC estimates varies over time. The full set of annual SCC estimates between 2010 and 2050 is reported in the Annex.

Table 14-A.5.3 Changes in the Average Annual Growth Rates of SCC Estimates between

2010 and 2050

Average Annual Growth Rate (%) 5% 3% 2.5% 3.0%
Avg Avg Avg 95th

2010

- 2020 3.6% 2.1% 1.7% 2.2%

2020

- 2030 3.7% 2.2% 1.8% 2.2%

2030

- 2040 2.7% 1.8% 1.6% 1.8%

2040

- 2050 2.1% 1.4% 1.1% 1.3%

While the SCC estimate grows over time, the future monetized value of emissions reductions in each year (the SCC in year t multiplied by the change in emissions in year t ) must be discounted to the present to determine its total net present value for use in regulatory analysis. Damages from future emissions should be discounted at the same rate as that used to calculate

the SCC estimates themselves to ensure internal cons istency

— i.e., future damages from climate change, whether they result from emissions today or emissions in a later year, should be discounted using the same rate. For example, climate damages in the year 2020 that are

14 -A- 31calculated using a SCC based on a 5 percent discount rate also should be discounted back to the analysis year using a 5 percent discount rate. cc

14

- A.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 when interpreting and applying the SCC estimates.

Incomplete treatment of non

- catastrophic damage s. The impacts of climate change are expected to be widespread, diverse, and heterogeneous. In addition, the exact magnitude of these impacts is uncertain because of the inherent complexity of climate processes, the economic behavior of current and future populations, and our inability to accurately forecast technological change and adaptation. Current IAMs do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature (some of which are discussed above) because of lack of precise information on the nature of damages and because the science incorporated into these models understandably lags behind the most recent research. Our ability to quantify and monetize impacts wi ll 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 Ice Sheet, or large releases of methane from melting permafrost and warming oceans. Weitzman (200 9) suggests that catastrophic damages are extremely large

— so large, in fact, that the damages from a low probability, catastrophic event far in the future dominate the effect of the discount rate in a present value calculation and result in an infinite wil lingness- to

- pay for mitigation today. However, Nordhaus (2009) concluded that the conditions under which Weitzman's results hold “are limited and do not apply to a wide range of potential uncertain

scenarios."

Using a simplified IAM, Newbold and Daigneault (2009) confirmed the potential for large catastrophe risk premiums but also showed that the aggregate benefit estimates can be highly sensitive to the shapes of both the climate sensitivity distribution and the damage function at high temperature chan ges. Pindyck (2009) also used a simplified IAM to examine high

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.

14 -A- 32impact , low- probability risks, using a right

- skewed gamma distribution for climate sensitivity as well as an uncertain damage coefficient, but in most cases found only a modest risk premium. Given this difference in opinion, further research in this area is needed before its practical significance can be fully understood and a reasonable approach developed to account for such risks in regulatory analysis. (The next section discusses the scien tific 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 treatm ent of adaptation and technological change: Each of the three integrated assessment models used here assumes a certain degree of low

- or no -cost adaptation. For instance, Tol assumes a great deal of adaptation in FUND, including widespread reliance on

ai r conditioning; so much so, that the largest single benefit category in FUND is the reduced electricity costs from not having to run air conditioning as intensively (NRC 2009).

Climate change also will increase returns on investment to develop technolo gies that allow individuals to cope with adverse climate conditions, and IAMs to do not adequately account for this directed technological change. dd For example, scientists may develop crops that are better able to withstand higher and more variable temper atures. 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 low er the cost of this adaptation over time. On the other hand, the calibrations do not account for increases in climate variability, pests, or diseases, which could make adaptation more difficult than assumed by the IAMs for a given temperature change.

Hen ce, models do not adequately account for potential adaptation or technical change that might alter the emissions pathway and resulting damages. In this respect, it is difficult to determine whether the incomplete treatment of adaptation and technological change in these IAMs

under 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 -aversion.

dd However these research dollars will be diverted from whatever their next best use would have been in the absence of climate change (so productivity/GDP would have been still higher).

14 -A- 33Assuming a risk

- neutral representative agent is consistent with OMB’s Circular A -4,

which a dvises 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 regulator y alternatives. While this may not always be the case, [analysts] should in general assume ‘risk neutrality’ in [their] analysis.”

Nordhaus (2008) points to the need to explore the relationship between risk and income in the context of climate change a cross 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.

14

- A.7 A FURTHER DISCUSSION OF CATASTROPHIC IMPA CTS AND

DAMAGE FUNCTIONS

As noted above, the damage functions underlying the three IAMs used to estimate the SCC may not capture the economic effects of all possible adverse consequences of climate change and may therefore lead to underestimates of the SCC (Mastrandrea 2009). In particular, the models’ functional forms may not adequately capture: (1) potentially discontinuous “tipping point” behavior in Earth systems, (2) inter

- sectoral and inter

- regional interactions, including global security impacts of high

- end warming, and (3) limited near

- term substitutability between damage to natural systems and increased consumption.

It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling. In the meantime, we

discuss some of the a vailable 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 t he upper end of the distribution. Recent science suggests that there are a number of potential climatic “tipping points” at which the Earth system may exhibit discontinuous behavior with potentially severe social and economic consequences (e.g., Lenton et al, 2008, Kriegler et al., 2009). These tipping points include the disruption of the Indian Summer Monsoon, dieback of the Amazon Rainforest and boreal forests, collapse of the Greenland Ice Sheet and the West Antarctic Ice Sheet, reorganization of the A tlantic Meridional Overturning Circulation, strengthening of El Niño

- Southern Oscillation, and the release of methane from melting

14 -A- 34permafrost. Many of these tipping points are estimated to have thresholds between about 3 °C and 5 °C (Lenton et al., 2008). Probabilities of several of these tipping points were assessed through expert elicitation in 2005

– 2006 by Kriegler et al. (2009); results from this study are highlighted in Table 14 -A.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., ig noring potential risk aversion). PAGE models catastrophic impacts in a probabilistic framework (see Figure 16A.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 e valuate how well the three models 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 14-A.7.1 Probabilities of Vario us Tipping Points from Expert Elicitation

Possible Tipping Points Duration before effect is fully realized (in years) Additional Warming by 2100

0.5-1.5 C 1.5-3.0 C 3-5 C

Reorganization of Atlantic Meridional Overturning Circulation about 100 0-18% 6-39% 18-67%

Greenland Ice Sheet collapse at least 300 8-39% 33-73% 67-96%

West Antarctic Ice Sheet collapse at least 300 5-41% 10-63% 33-88%

Dieback of Amazon rainforest about 50 2-46% 14-84% 41-94%

Strengthening of El Niño-Southern Oscillation about 100 1-13% 6-32% 19-49%

Dieback of boreal forests about 50 13-43% 20-81% 34-91%

Shift in Indian Summer Monsoon about 1 Not formally assessed

Release of methane from melting permafrost Less than 100 Not formally assessed.

PAGE treats the possibility of a catastrophic event probabilistically, while DICE treats it deterministically (that is, by adding the expected value of the damage from a catastrophe to the aggregate damage function). In part, this results in different probabilities being assigned to a catastrophic event across the two models. For instance, PAGE places a probability near zero on a catastrophe at 2.5 °C warming, while DICE assumes a 4 percent probability of a catastrophe at 2.5 °C. By comparison, Kriegler e t al. (2009) estimate a probability of at least 16

- 36 percent of

14 -A- 35crossing at least one of their primary climatic tipping points in a scenario with temperatures about 2-4 °C warmer than pre-Industrial levels in 2100.

It is important to note that crossing a climatic tipping point will not necessarily lead to an economic catastrophe in the sense used in the IAMs. A tipping point is a critical threshold across which some aspect of the Earth system starts to shifts into a qualitatively different state (for

i nstance, one with dramatically reduced ice sheet volumes and higher sea levels). In the IAMs, a catastrophe is a low -probability environmental change with high economic impact.

Failure to incorporate inter-sectoral and inter-regional interactions

The damage functions do not fully incorporate either inter

- sectoral or inter -regional interactions. For instance, while damages to the agricultural sector are incorporated, the effects of changes in food supply on human health are not fully captured and depend on the modeler’s choice of studies used to calibrate the IAM. Likewise, the effects of climate damages in one region of the world on another region are not included in some of the models (FUND includes the effects of migration from sea level rise). These inter

- regional interactions, though difficult to quantify, are the basis for climate

- induced national and economic security concerns (e.g., Campbell et al., 2007; U.S. Department of Defense 2010) and are particularly worrisome at higher levels of warming. High

- end warming scenarios, for instance, project water scarcity affecting 4.3

- 6.9 billion people by 2050, food scarcity affecting about 120 million additional people by 2080, and the creation of millions of climate refugees (Easterling et al., 2007; Camp bell et al., 2007).

Imperfect substitutability of environmental amenities

Data from the geological record of past climate changes suggests that 6 °C of warming may have severe consequences for natural systems. For instance, during the Paleocene

- Eocene Thermal Maximum about 55.5 million years ago, when the Earth experienced a geologically rapid release of carbon associated with an approximately 5 °C increase in global mean temperatures, the effects included shifts of about 400

- 900 miles in the range of plants (Wing et al., 2005), and dwarfing of both land mammals (Gingerich, 2006) and soil fauna (Smith et al., 2009).

The three IAMs used here assume that it is possible to compensate for the economic consequences of damages to natural systems through increased consumption of non

- climate goods, a common assumption in many economic models. In the context of climate change, however, it is possible that the damages to natural systems could become so great that no increase in consumption of non

- climate goods would provide complete compensation (Levy et al., 2005). For instance, as water supplies become scarcer or ecosystems become more fra gile

and less bio -diverse, the services they provide may become increasingly more costly to replace. Uncalibrated attempts to incorporate the imperfect substitutability of such amenities into IAMs (Sterner and Persson, 2008) indicate that the optimal degr ee of emissions abatement can be considerably greater than is commonly recognized.

14 -A- 3614

- A.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 fir st three estimates are based on the average SCC across models and socioeconomic and emissions scenarios at the 5, 3, and 2.5 percent discount rates, respectively. The fourth value is included to represent the 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

captu ring 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 pe r ton of CO 2 in 2020.

We noted a number of limitations to this analysis, including the incomplete way in which the integrated assessment models capture catastrophic and non

- catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impacts to economic damages makes this modeling exercise even more difficult. It is the hope of the interagency group that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling.

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Weitzman, M. 2007. “A review of The Stern Review of the Economics of Climate Change .” Journal of Economic Literature 45:703-724.

Weitzman, M. 1999. In Portney P.R. and Weyant J.P. (eds.), Discounting and Intergenerational Equity , Resources for the Future, Washington, D.C.

Weitzman, M. 1998. “Why the Far-Distant Future Should Be Discounted at Its Lowest Possible

Rate.” Journal of Environmental Economics and Management 36 (3): 201-208.

Wing, S. et al. 2005. “Transient floral change and rapid global warming at the Paleocene

- Eocene boundary.” Science 310: 993 -996.

14 -A- 4214

- A.9 A NNEX

Table 14-A.9.1 Annual SCC Values: 2010–2050 (in 2007 dollars)

D iscount 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.

14 -A- 4314-A.9.1 Other (non -CO2) 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 differently (e.g., DICE lumps all other gases into one composite exogenous input), this approach was applied slightly differently in each of the models.

FUND : Rather than relying on RF for these gases, the actual emissions from each

scenario were used in FUND. The model default trajectories for CH 4 , N 2 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 includ es the RF for 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 model default values for aerosols and other factors. Net land use CO 2 emissions were added to the fossil and industrial CO 2 emissions pathway.

DICE : DICE presents the greatest challenge because all forcing due to factors other than

industrial CO 2 emissions is embedded in an exogenous non- CO 2 RF vector. To decompose this exogenous forcing path into EMF non- CO 2 gases and other gases, we relied on the references in 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 increases linearly to 0.3 W/m 2 in 2105, based on GISS projections, and then stays constant after that time.

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

similar to the F

- gases in the EMF -22 scenarios) was 0.48 + 0.16 + 0.34 = 0.98 W/m 2 and RF

from total aerosols was

- 1.2 W/m 2 . Thus, the

- .06 W/m 2 non- CO 2 forcing in DICE can be

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 net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use emissions from the other three models and divide by 4).

ff Both the model default CH 4 emissions and the initial atmospheric CH 4 i s set to zero to avoid double counting the effect of past CH 4 emissions.

14 -A- 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 th ereafter ; and (2) With respect to RF from non

- CO 2 gases not included in the EMF

- 22 scenarios, the share

of non

- aerosol RF matches the share implicit in the AR4 summary statistics cited above and

remains constant over time.

Assumption (1) means that the RF from aerosols in 2100 equals 66 percent of that in 2000, which is the fraction of the TAR projection of total RF from aerosols (including sulfates, black carbon, and organic carbon) in 2100 vs. 2000 under the A1B SRES emissions scenario. Since

the SR ES marker scenarios were not updated for the AR4, the TAR provides the most recent IPCC projection of aerosol forcing. We rely on the A1B projection from the TAR because it provides one of the lower aerosol forecasts among the SRES marker scenarios and is more consistent with the AR4 discussion of the post- SRES literature on aerosols:

Aerosols have a net cooling effect and the representation of aerosol and aerosol precursor emissions, including sulfur dioxide, black carbon and organic carbon, has improved in the

post- SRES scenarios. Generally, these emissions are projected to be lower than reported in SRES. {WGIII 3.2, TS.3, SPM}. gg

Assuming a simple linear decline in aerosols from 2000 to 2100 also is more consistent

with the recent literature on these emissions. For example, the 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 slightly compared to the SRES scenario (IPCC 2007).

gg AR4 Synthesis Report, p. 44, http://www.ipcc.ch/pdf/assessment

- report/ar4/syr/ar4_syr.pdf

hh See Smith, S.J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions, 1850

- 2000: methods and results. Joint Global Research Institute, College Park, 14 pp.

ii See Carmichael, G., D. Streets, G. Calori, M. Amann, M. Jacobson, J. Hansen, and H. Ueda, 2002: Changing trends in sulphur emissions in Asia: implications for acid deposition, air pollution, and climate. Environmental Science and Technology, 36(22):4707

- 4713; Streets, D., K. Jiang, X. Hu, J. Sinton, X.

- Q. Zhang, D. Xu, M. Jacobson, and J. Hansen, 2001: Recent reductions in China’s greenhouse gas emissions. Science, 294(5548): 1835

- 1837.

14 -A- 45With 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

.

. Figure 14 -A.9.2 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 distribution for the full

ensemble of 40 SRES scenarios. The blue area (and the thin dashed lines in blue) illustrates individual scenarios and the range of Smith et al. (2004). Dotted lines indicate

the minimum and maximum of SO 2 emissions scenarios developed pre- SRES. Source: IPCC (2007), AR4 WGIII 3.2, http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch3- ens3 -2-2-4.html

.

Although other approaches to decomposing the DICE exogenous forcing vector are possible , initial sensitivity analysis suggests that the differences among reasonable alternative approaches are likely to be minor. For example, adjusting the TAR aerosol projection above to assume that aerosols will be maintained at 2000 levels through 2100 red uces average SCC values (for 2010) by approximately 3 percent (or less than $2); assuming all aerosols are phased out by 2100 increases average 2010 SCC values by 6

- 7 percent (or $0.50 -$3)– depending on the discount rate. These differences increase slightl y for SCC values in later years but are still well within 10 percent of each other as far out as 2050.

Finally, as in PAGE, the EMF net land use CO 2 emissions are added to the fossil and

industrial CO 2 emissions pathway.

14-A.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

14 -A- 46these projections are available from the EMF

- 22 models. These inputs were extrapolated from

2100 to 2300 as follows:

1. Population growth rate declines linearly, reaching zero in the year 2200. 2. GDP/per capita growth rate declines linearly, reaching zero in the year 2300. 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 scenario. This is based on the idea that increasing scarcity of natural resources and

th e degradation of environmental sinks available for assimilating pollution from economic production activities may eventually overtake the rate of technological progress. Thus, the overall rate of economic growth may slow over the very long run. The interagency group also considered allowing an exponential decline in the growth rate of GDP per capita. However, since this would require an additional assumption about how close to zero the growth rate would get by 2300, the group opted for the simpler and more transparent linear extrapolation to zero by

2300.

The population growth rate is also assumed to decline linearly, reaching zero by 2200. This assumption is reasonably consistent with the United Nations long run population forecast,

which estimates global population to be fairly stable after 2150 in the medium scenario (UN 2004). jj The resulting range of EMF population trajectories (f igure below ) also encompass the UN medium scenario forecasts through 2300

— global population of 8.5 billion by 2200, and 9 billion by 2300.

Maintaining the decline in the 2090

- 2100 carbon intensity growth rate (i.e., CO 2 per dollar of GDP) through 2300 assumes that technological improvements and innovations in the

areas of en ergy efficiency and other carbon reducing technologies (possibly including currently unavailable methods) will continue to proceed at roughly the same pace that is projected to occur towards the end of the forecast period for each EMF scenario. This assum ption implies that total cumulative emissions in 2300 will be between 5,000 and 12,000 GtC, which is within the range of the total potential global carbon stock estimated in the literature.

Net land use CO 2 emissions are expected to stabilize in the long run, so in the absence of any post 2100 projections, the group assumed a linear decline to zero by 2200. Given no a priori reasons for assuming a long run increase or decline in non

- CO 2 radiative forcing, i t is assumed to remain at the 2100 levels for each EMF scenario through 2300.

jj United Nations. 2004. World Population to 2300

. http://www.un.org/esa/population/publications/longrange2/worldpop2300final.pdf

14 -A- 47Figures below show the paths of global population, GDP, fossil and industrial CO 2 emissions, net land CO 2 emissions, non

- CO 2 radiative forcing, and CO 2 intensity (fossil and

industrial CO 2 emissions/GDP) resulting from these assumptions.

Figure 14 -A.9.3 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

14 -A- 48Figure 14 -A.9.4 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. 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

14 -A- 49Figure 14 -A.9.5 Global Fossil and Industrial CO 2 Emissions, 2000- 2300 (Post-2100 extrapolations assume growth rate

of CO 2 intensity (CO 2 /GDP) over 2090-2100 is maintained through 2300)

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm CO 2 e, full

- participation, not

- to

- exceed scenarios considered by each of the four

models.

Figure 14 -A.9.6 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 ne utral biosphere so net land CO 2 emissions are set to zero for all years for the MERGE Optimistic reference scenario, and for the MERGE component of the average 550 scenario (i.e., we add up the land use emissions from the other three models and divide by 4 ). -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

14 -A- 50Figure 14 -A.9.7 Global Non- CO 2 Radiative Forcing, 2000-2300 (Post -2100 extrapolations assume constant non- CO 2 radiative forcing after 2100)

Note: In the fifth scenario, 2000

- 2100 emissions are equal to the average of the emissions under the 550 ppm 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

14 -A- 51Figure 14 -A.9.8 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)

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

14 -A- 52Table 14-A.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

14 -A- 53Table 14-A.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

14 -A- 54Table 14-A.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 14 -A.9.9 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%

14 -A- 55Table 14-A.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

14 -A- 5614

- B

- i APPENDIX 14 -B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

TABLE OF CONTENTS

14- B.1 PREFACE

............................................................................................................ 14-B-2

14- B.2 PURPOSE

............................................................................................................ 14-B-2

14- B.3 SUMMARY OF MODEL UPDATES

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

14-B.3.1 DICE

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

14-B.3.1.1 Carbon Cycle Parameters

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

14-B.3.1.2 Sea Level Dynamics

...................................................................... 14-B-4

14-B.3.1.3 Re

- calibrated Damage Function

.................................................... 14-B-5

14-B.3.2 FUND

................................................................................................................... 14-B-5

14-B.3.2.1 Space Heating

................................................................................ 14-B-6

14-B.3.2.2 Sea Level Rise and Land Loss

...................................................... 14-B-6

14-B.3.2.3 Agriculture ..................................................................................... 14-B-7

14-B.3.2.4 Temperature Response Model ....................................................... 14-B-7

14-B.3.2.5 Methane

......................................................................................... 14-B-7

14-B.3.3 PAGE

................................................................................................................... 14-B-8

14-B.3.3.1 Sea Level Rise

............................................................................... 14-B-8

14-B.3.3.2 Revised Damage Function to Account for Saturation ................... 14-B-8

14-B.3.3.3 Regional Scaling Factors

............................................................... 14-B-8

14-B.3.3.4 Probability of a Discontinuity ....................................................... 14-B-9

14-B.3.3.5 Adaptation ..................................................................................... 14-B-9

14-B.3.3.6 Other Noteworthy Changes

......................................................... 14-B-10

14- B.4 REVISED SCC ESTIMATE S ........................................................................... 14-B-10

14- B.5 OTHER MODEL LIMITATIONS OR RESEARCH GAPS

............................ 14-B-13

LIST OF TABLES

Table 14 -B.4.1 Revised Social Cost of CO 2 , 2010 – 2050 (in 2007 dollars per ton of

CO 2 ) ....................................................................................................... 14-B-12

Table 14 -B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and

2050................................

................................ ........................................ 14-B-13 Table A1. Annual SCC Values: 2010-2050 (2007$/ton CO 2 ) ............................................. 14-B-16 Table A2. 2020 Global SCC Estimates at 2.5 Percent Discount Rate (2007$/ton CO 2 ) ..... 14-B-17 Table A3. 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2) ....... 14-B-17 Table A4. 2020 Global SCC Estimates at 5 Percent Discount Rate (2007$/ton CO 2 ) ........ 14-B-18

LIST OF FIGURES

Figure 14 -B.4.2 Distribution of SCC Estimates for 2020 (in 2007$ per ton CO 2 ) .......... 14-B-1214

- B

- 2 APPENDIX 14 -B. TECHNICAL UPDATE OF SOCIAL COST OF CARBON FOR

REGULATORY IMPACT ANALYSIS UNDER EXECUTIVE ORDER 12866

14

- B.1 PREFACE

The following text is reproduced almost verbatim from the May 2013 report (revised November 2013) of the Interagency Working Group on the Social Cost of Carbon of the United

States Government

. a Minor changes were made to the working group's report to make it more consistent with the rest of this technical support document. Annex B describes the revisions that were made to the May 2013 report.

14

- B.2 PURPOSE

The purpose of this document is to update the schedule of social cost of carbon (SCC) b

estimates f rom the 2010 interagency technical support document (TSD) (Interagency Working Group on Social Cost of Carbon 2010). 1 E.O. 13563 commits the Administration to regulatory decision making “based on the best available science.” c Additionally, the interagency group recommended in 2010 that the SCC estimates be revisited on a regular basis or as model updates that reflect the growing body of scientific and economic knowledge become available. d New versions of the three integrated assessment models used by the U.S. go vernment to estimate the SCC (DICE, FUND, and PAGE), are now available and have been published in the peer reviewed literature. While acknowledging the continued limitations of the approach taken by the interagency group in 2010 (documented in the original 2010 TSD), this document provides an update of the SCC estimates based solely on the latest peer

- reviewed version of the models, replacing model versions that were developed up to ten years ago in a rapidly evolving field. It does not revisit other assumptions with regard to the discount rate, reference case socioeconomic and emission scenarios, or equilibrium climate sensitivity. Improvements in the way damages are modeled are confined to those that have been incorporated into the latest versions of the m odels 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.

a Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. Interagency Working G roup on Social Cost of Carbon, United States Government. May 2013; revised November

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

- update

- social

- cost

- of

- carbon

- for

regulator

- impact

- analysis.pdf

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 the mass of carbon is 3.67

.

c http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf

d See p. 1, 3, 4, 29, and 33 (Interagency Working Group on Social Cost of Carbon 2010). 114

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- 3 Section 3 summarizes the major updates relevant to SCC estimation that are contained in the new versions of the integrated assessment models released since the 2010 intera gency report.
Section 4 presents the updated schedule of SCC estimates for 2010 – 2050 based on these versions of the models. Section 5 provides a discussion of recent workshops to support improvements in SCC estimation.

14

- B.3 SUMMARY OF MODEL UPD ATES

This sect ion briefly summarizes changes integrated into the most recent versions of the three integrated assessment models (IAMs) used by the interagency group in 2010. We focus on describing those model updates that are relevant to estimating the social cost of ca rbon. For example, both the DICE and PAGE models now include an explicit representation of sea level rise damages. Other revisions to PAGE include: updated adaptation assumptions, revisions to ensure damages are constrained GDP, updated regional scaling of damages, and a revised treatment of potentially abrupt shifts in climate damages. In the most recent version of DICE, the model’s simple carbon cycle has been updated to be more consistent with a relatively more complex climate model. The FUND model incl udes updated damage functions for sea level rise impacts, the agricultural sector, and reduced space heating requirements, as well as changes to the response of temperature to the buildup of GHG concentrations and the inclusion of indirect

effects of metha ne 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.

14-B.3.1 DICE

Changes in the DICE model rel evant for the SCC estimates developed by the interagency working group include: 1) updated parameter values for the carbon cycle model, 2) an explicit representation of sea level dynamics, and 3) a re

- calibrated damage function that includes an explicit re presentation 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 utili ty of consumption

— but these components of DICE are superseded by the interagency working group’s assumptions and so will not be discussed here. More details on DICE2007 can be found in Nordhaus (2008) 2

and on DICE2010 in Nordhaus (2010) 3 and the associated on

- line appendix containing supplemental information.

14-B.3.1.1 Carbon Cycle Parameters

DICE uses a three

- box model of carbon stocks and flow s to represent the accumulation and transfer of carbon among the atmosphere, the shallow ocean and terrestrial biosphere, and the deep ocean. These parameters are “calibrated to match the carbon cycle in the Model for the14

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- 4 Assessment of Greenhouse Gas Induced Climate Change (MAGICC)” (Nordhaus 2008 p 44). 2 e 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 decade, 9.7 percent of the carbon in the shallow ocean is transferred to the atmosphere, 85.3 percent remains in the shallow ocean, and 5 percent is transferred to the deep ocean.

The implication of these changes for DICE2010 is in general a weakening of the ocean as a carbon sink and therefore a higher concentration of carbon in the atmosphere than in DICE2007, for a given path of emissions. All else equal, these changes will generally increase the level of warming and therefore the SCC estimates in DICE2010 relative to those from

DICE2007.

14-B.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 l evel rise (SLR) module; a more detailed description can be found on the model developer’s website. f The average global sea level anomaly is modeled as the sum of four terms that represent contributions from: 1) thermal expansion of the oceans, 2) melting of glaciers and small ice caps, 3) melting of the Greenland ice sheet, and 4) melting of the Antarctic ice sheet.

The parameters of the four components of the SLR module are calibrated to match consensus results from the IPCC’s Fourth Assessment Report. 4 g The rise in sea level from thermal expansion in each time period (decade) is 2 percent of the difference between the sea level in the previous period and the long run equilibrium sea level, which is 0.5 meters per degree Celsius (°C) above the average glo bal temperature in 1900. The rise in sea level from the melting of glaciers and small ice caps occurs at a rate of 0.008 meters per decade per °C above the average global temperature in 1900.

The contribution to sea level rise from melting of the Green land ice sheet is more complex. The equilibrium contribution to SLR is 0 meters for temperature anomalies less than 1

o C and increases linearly from 0 meters to a maximum of 7.3 meters. The contribution to SLR in each period is proportional to the differen ce between the previous period’s sea level anomaly and

e MAGICC is a simple climate model initially developed within the U.S. National Center for Atmospheric Research that has been used heavily by the Intergovernmental Panel on Climate Change (IPCC) t o emulate projections from much more sophisticated state of the art earth system simulation models (Randall et al. 2007). 4

f Documentation on the new sea level rise module of DICE is available on William Nordhaus’ website at: http://nordhaus.econ.yale.edu/documents/SLR_021910.pdf.

g For a review of post

- IPCC AR4 r esearch on sea level rise, see Nicholls et al. (2011) 5 and NAS (201 1). 614

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- 5 the equilibrium sea level anomaly, where the constant of proportionality increases with the temperature anomaly in the current period.

The contribution to SLR from the melting of the Antarctic ice sheet is

- 0.001 meters per decade when the temperature anomaly is below 3 °C and increases linearly to a maximum rate of 0.025 meters per decade at a temperature anomaly of 6 °C.

14-B.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 output in each period (net of climate change damages) is consumed and the remainder is invested in the physical capital stock to support future production, so each period’s climate damages will reduce consumption in that period and in all future periods due to the lost investment. The fraction of output in each period that is lost due to climate change impacts is represented as one

minu s a fraction, which is one divided by a quadratic function of the temperature anomaly, producing a sigmoid (“S”

- shaped) function. The loss function in DICE2010 has been expanded by adding a quadratic function of SLR to the quadratic function of temperature. In DICE2010 the temperature anomaly coefficients have been recalibrated to avoid double

- counting damages from sea level rise that were implicitly included in these parameters in DICE2007.

The aggregate damages in DICE2010 are illustrated by Nordhaus (2010 p 3), 3 who notes that “...damages in the uncontrolled (baseline) [i.e., reference] case ... in 2095 are $12 trillion, or 2.8 percent of global output, for a global temperature increase of 3.4 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 ar e lower in most of the early periods but higher in later periods of the time horizon than would be calculated using the DICE2007 damage function. Specifically, the percent difference between damages in the base run of DICE2010 and those that would be calculated using the DICE2007 damage function starts at +7 percent in 2005, decreases to a low of

- 14 percent in 2065, then continuously increases to +20 percent by 2300 (the end of the interagency analysis time horizon), and to +160 percent by the end of the model time horizon in 2595. The large increases in the far future years of the time horizon are due to the permanence associated with damages from sea level rise, along with the assumption that the sea level is projected to continue to rise long after the global average temperature begins to decrease. The changes to the loss function generally decrease the interagency working group SCC estimates slightly, all else equal.

14-B.3.2 F UND

FUND version 3.8 includes a number of changes over the previous version 3.5 used in the interagency report. Documentation supporting FUND and the model’s source code for all

versions of the model is available from the model authors. h Notable changes, due to their impact

h http://www.fund

- model.org/ . This report uses version 3.8 of the FUND model, which represents a modest update

to the most recent version of the model to appear in the literature (version 3.7) (Anthoff and Tol, 2013). 7 For the purpose of computing the SCC, the relevant changes are associated with improving consistency with IPCC AR4 by14

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- 6 on the estimates of expected SCC, are adjustments to the space heating, agriculture, and sea level rise damage functions in addition to changes to the temperature response function and the

inclusion of indirect effects from methane emissions. i We discuss each of these in turn.

14-B.3.2.1 Space Heating

In FUND, the damages associated with the change in energy needs for space heating are based on the estimated impact due to one degree of warming. These baseline damages are scaled based on the forecasted temperature anomaly’s deviation from the one degree benchmark and adjusted for ch anges in vulnerability due to economic and energy efficiency growth. In FUND 3.5, the function that scales the base year damages adjusted for vulnerability allows for the possibility that in some simulations the benefits associated with reduced heating nee ds 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 an y level of warming will not exceed two times the reductions experienced at one degree of warming. Since the reduced need for space heating represents a benefit of climate change in the model, or a negative damage, this change will

increase the estimated SC C. This update accounts for a significant portion of the difference in the expected SCC estimates reported by the two versions of the model when run probabilistically.

14-B.3.2.2 Sea Level Rise and Land Loss

The FUND model explicitly includes damages associated with the inundation of dry land due to sea level rise. The amount of land lost within a region is dependent upon the proportion of the coastline being protected by adequate sea walls and the amount of sea level rise. In FUND 3.5 the function defining the poten tial land lost in a given year due to sea level rise is linear in the rate of sea level rise for that year. This assumption implicitly assumes that all regions are well represented by a homogeneous coastline in length and a constant uniform slope moving

in land. 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 expected SCC estimate. The model has also been updated to assume that the value of dry land at risk of inundation is not uniform across a region but will be a decreasing function of protection measure, thereby implicitly assuming that the most valuable land will be protected

first.

adjusting the a tmospheric lifetimes of CH4 and N2O and incorporating the indirect forcing effects of CH4, along with making minor stability improvements in the sea wall construction algorithm.

i The other damage sectors (water resources, space cooling, land loss, migrati on, ecosystems, human health, and extreme weather) were not the subject of significant updates.14

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- 7 14-B.3.2.3 Agriculture

In FUND, the damages associated with the agricultural sector are me asured as proportional to the sector’s value. The fraction is made up of three additively separable components that represent the effects from carbon fertilization, the rate of temperature change, and the level of the temperature anomaly. In both FUND 3.5 and FUND 3.8, the fraction of the sector’s value lost due to the level of the temperature anomaly is modeled as a quadratic function with an intercept of zero. In FUND 3.5, the linear and quadratic coefficients are modeled as the

ratio of two normal distri butions. 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

coef ficients are drawn directly from truncated normal distributions so that they remain in the

range [0, ) ∞ and ( ,0] −∞ , respectively, where the means for the new distributions are set equal to

the ratio of the means from the normal distributions used in the previous version. In general the impact of this change has been to increase the likelihood that increases in the temperature level will have either larger positive or negative effects on the agricultural sector relative to the previous version (through eliminating simulations in which the “lost” value approached (+/

- ) infinity). The net effect of this change on the SCC estimates is difficult to predict.

14-B.3.2.4 Temperature Response Model

The temperature response model translates changes in global levels of radiative forcing into the current expected temperature anomaly. In FUND, a given year’s increase in the cumulative temperature anomaly is based on a mean reverting function where the mean equals the equilibrium temperature anomaly that would eventually be reached if that year’s level of radiative forcing were sustained. The rate of mean reversion defines the rate at which the transient temperature approaches the equilibrium. In FUND 3.5, the rate of temperature response

is d efined as a decreasing linear function of equilibrium climate sensitivity to capture the fact that the progressive heat uptake of the deep ocean causes the rate to slow at higher values of the equilibrium climate sensitivity. In FUND 3.8, the rate of tempe rature response has been updated to a quadratic function of the equilibrium climate sensitivity. This change reduces the sensitivity of the rate of temperature response to the level of the equilibrium climate sensitivity. Therefore in FUND 3.8, the temperature response will typically be faster than in the previous version. The overall effect of this change is likely to increase estimates of the SCC as higher temperatures are reached during the timeframe analyzed and as the same damages experienced in the pr evious version of the model are now experienced earlier and therefore discounted less.

14-B.3.2.5 Methane

The IPCC notes a series of indirect effects of methane emissions, and has developed methods for proxying such effects when computing the global warming potentia l of methane (Forster et al. 2007). 8 FUND 3.8 now includes the same methods for incorporating the indirect effects of methane emissions. Specifically, the average atmospheric lifetime of methane has been set to 12 years to account for the feedback of CH4 emissions on its own lifetime. The radiative forcing associated with atmospheric methane has also been increase by 40% to account for its net14

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- 8 impact on ozone production and increase in stratospheric water vapor. The general effect of this increased radiative forcing will be to increase the estimated SCC values, where the degree to which this occurs will be dependent upon the relative curvature of the damage functions with respect to the temperature anomaly.

14-B.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, r evisions to the damage function to ensure damages are constrained by GDP, a change in the regional scaling of damages, a revised treatment for the probability of a discontinuity within the damage function, and revised assumptions on adaptation. The model also includes revisions to the carbon cycle feedback and the calculation of regional temperatures. More details on PAGE2009 can be found in three working papers (Hope 2011a, 2011b, 2011c). 10

, 11

, 12 A description of PAGE2002 can be found in Hope (2006). 13

14-B.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 other damage categories. PAGE09 models damages from sea level rise as increasing less than linearly with sea level based on the assumption that low

- lying shoreline areas will be associated with higher damages than current inland areas. Damages from the economic and non

- economic sector were adjusted to account for the introduction of this new category.

14-B.3.3.2 Revised Damage Function to Account for Saturation

In PAGE09, small initial economic and non

- economic benefits (negative damages) are modeled for small temperature increases, but all regions eventually experience positive economic damages from climate change, where damages are the sum of additively separable polynomial functions of temperature and sea level rise. Damages transition from this polynomial function to a logistic path once they exceed a cer tain proportion of remaining Gross Domestic Product (GDP) to ensure that damages do not exceed 100 percent of GDP. This differs from PAGE2002, which allowed Eastern Europe to potentially experience large benefits from temperature increases, and which also did not bound the possible damages that could be experienced.

14-B.3.3.3 Regional Scaling Factors

As in the previous version of PAGE, the PAGE09 model calculates the damages for the European Union (EU) and then, assumes that damages for other regions are proportiona l 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 temperature14

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- 9 increase, but all regions have a positive scaling factor. PAGE2002 based its scaling factors on four studies reported in the IPCC’s third assessment report, and allowed for benefits from temperature increase in Eastern Europe, smaller impacts in developing countries, and higher damages in developing countries.

14-B.3.3.4 Probability of a Discontinuity

In PAGE2002, the damages associated with a “discontinuity” were modeled as an expected value. That is, additional damages from an extreme event, such as extreme melting of the Greenland ice sheet, were multiplied by the probability of the event occurring and added to the damage estimate. In PAGE09, the probability of “discontinuity” is treated as a discrete event for each year in the model. The damages for each model run are estimated either with or without a discontinuity occurring, rather than as an expec ted value. A large

‐ scale discontinuity becomes possible when the temperature rises beyond some threshold value between 2 and 4°C. The probability that a discontinuity will occur beyond this threshold then increases by between 10 and 30 percent for every 1°C rise in temperature beyond the threshold. If a discontinuity occurs, the EU loses an additional 5 to 25 percent of its GDP (drawn from a triangular distribution with a mean of 15 percent) in addition to other damages, and other regions lose an amount det ermined by the regional scaling factor. The threshold value for a possible discontinuity is lower than in PAGE2002, while the rate at which the probability of a discontinuity increases with the temperature anomaly and the damages that result from a discont inuity 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.

14-B.3.3.5 Adaptation

As in PAGE2002, adaptation is available to inc rease the tolerable level of temperature change and can help mitigate any climate change impacts that still occur. In PAGE this adaptation is the same regardless of the temperature change or sea level rise and is therefore akin to what is more commonly considered a reduction in vulnerability. It is modeled by modifying the temperature change and sea level rise used in the damage function or by reducing the damages by some percentage. PAGE09 assumes a smaller decrease in vulnerability than the previous version of the model and assumes that it will take longer for this change in vulnerability to be realized. In the aggregated economic sector, at the time of full implementation, this adaptation will mitigate all damages up to a temperature increase of 1°C, and for temperature anomalies between 1°C and 3°C, it will reduce damages by 15

- 30 percent (depending on the region). However, it takes 20 years to fully implement this adaptation. In PAGE2002, adaptation was assumed to reduce economic sector damages up to 3° C by 50

- 90 percent after 20 years. Beyond 3°C, no adaptation is assumed to be available to mitigate the impacts of climate change.

For the non

- economic sector, in PAGE09 adaptation is available to reduce 15 percent of the damages due to a temperature incre ase 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 meter s of sea level rise but is assumed to be ineffective thereafter. Hope (2011c) 12 estimates14

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- 10 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.

14-B.3.3.6 Other Noteworthy Changes

Two other changes in the model are worth noting. A revised carbon cycle f eedback 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 capped 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 annual temperature anomaly is downscaled to determine annual average regional temperature anomalies to be used in the regional damage functions. In the previous version of PAGE, the scaling was determined solely based on regional difference in emissions of sulfate aerosols. In PAGE09, this regional

temp erature 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.

14

- B.4 REVISED SCC ESTIMATE S

The updated versions of the three integrated assessment models were run using the same methodology detailed in the 2010 TSD. 1 The approach along with the inputs for the socioeconomic emissions scenarios, equilibrium climate sensitivity distribution, and discount

rate remains the same. This includes the five reference scenarios based on the EMF

- 22 modeling exercise, the Roe and Baker equilibrium climate sensitivity distribution calibrated to the Fourth Assessment Report of the IPCC, and three constant discount rates of 2.5, 3, and 5 percent.

As was previously the case, the use of three models, three discount rates, and five scenarios produces 45 separate distributions for the SCC. The approach laid out in the TSD applied equal weight to each model and socioeconomic scenario in order to reduce the dimensionality down to three separate distributions representative of the three discount rates. The interagency group selected four values from these distributions for use in regulatory analysis. Three values are based on the average SCC across models and so cio -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 pu rpose, the 95th percentile of the SCC estimates at a 3 percent discount rate was chosen. (A detailed set of percentiles by model and scenario combination is available in the Annex.) As noted in the original TSD, “the 3 percent discount rate is the central value, and so the central value that emerges is the average SCC across models at the 3 percent discount rate” (TSD, p. 25). However, for purposes of capturing the uncertainties involved in regulatory impact analysis, the interagency group emphasizes the importance and value of including all four SCC values.

Table 14 -B.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 all14

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- 11 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 ful l set of annual SCC estimates between 2010 and 2050 is reported in the Annex.14

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- 12 Table 14-B.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 3 2 5 1 89

2015 1 1 3 7 5 7 109

2020 12 43 6 4 12 8

2025 14 4 7 69 14 3

2030 16 52 7 5 159

2035 19 5 6 8 0 17 5

2040 21 6 1 8 6 19 1

2045 24 66 92 206

2050 2 6 71 9 7 22 0

The SCC estimates using the updated versions of the models are higher than those reported in the TSD due to the changes to the models outlined in the previous section. Figure 14-

B.4.2 illustrates where the four SCC values for 2020 fall within the full distribution for each discount rate based on the combined set of runs for each model and scenario (150,000 estimates in total for each discount rate). In general, the distributions are skewed to the right and have long tails. The Figure also shows that the lower the discount rate, the longer the right tail of the

distribution.

Figure 14 -B.4.2 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 become more stressed in response to greater climatic change. The approach taken by the interagency group is to allow the growth rate to be determined endogenously by the models14

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- 13 through running them for a set of perturbation years out to 2050. Table 14 -B.4.2 illustrates how the growth rate for these four SCC estimates varies over time.

Table 14-B.4.2 Average Annual Growth Rates of SCC Estimates between 2010 and 2050 Average Annual 5.0% 3.0% 2.5% 3.0% Rate (%) Avg Avg Avg 95th

2010

- 2020 1.2% 3. 3 % 2.4% 4. 4 %

2020

- 2030 3.4% 2.1% 1.7% 2.4%

2030

- 2040 3.0% 1. 9 % 1.5% 2. 1 %

2040

- 2050 2.6% 1.6% 1.3% 1.5%

The future monetized value of emission reductions in each year (the SCC in year t multiplied by the change in emissions in year t) must be discounted to the present to determine its total net present value for use in regulatory analysis. As previously discussed in the original TSD, damages from future emissions should be discounted at the same rate as that used to

calculate the SCC estimates themselves to ensure internal consistency – i.e., future damages from climate change, whether they result from emissions today or emissions in a later year, should be discounted using the same rate.

14

- B.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 needed. In particular, the document highlights the need to improve the quantification of both non- catastrophic and catastrophic damages, the treatment of adap tation and technological change, and the way in which inter-regional and intersectoral linkages are modeled. It also discusses the need to more carefully assess the implications of risk aversion for SCC estimation as well as the inability to perfectly substitute between

climate and non -climate goods at higher temperature increases, both of which have implications for the discount rate used. EPA, DOE, and other agencies continue to engage in long- term research work on modeling and valuation of climate impac ts that we expect will inform improvements in SCC estimation in the future.14

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- 14 REFERENCES

1. Interagency Working Group on Social Cost of Carbon, Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866, February, 2010. United States

Government. < http://www.whitehouse.gov/sites/default/files/omb/inforeg/for

agencies/Social -Cost-of-Carbon- for -RIA.pdf >

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. Proceedings of the National Academy of Sciences , 2010. 107(26): pp. 11721-11726

4. Randall, D. A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, Climate Models

and Their Evaluation . In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller, Editor. 2007. Cambridge University

Press: Cambri dge, United Kingdom and New York, NY, USA

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7. Anthoff, D. and R. S. J. Tol, The uncertainty about the social cost of carbon: a decomposition analysis using FUND

. Climatic Change , 2013 (Forthcoming)

8. Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland, Changes in Atmospheric Constituents and in Radiative Forcing . In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller Editor. 2007. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA

9. Hope, C., Critical issues for the calculation of the social cost of CO2: why the estimates from PAGE09 are higher than those from PAGE2002

. Climatic Change

, 2012(Forthcoming)14

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- 15 10. Hope, C., The PAGE09 Integrated Assessment Model: A Technical Description . 2011, Cambridge Judge Business School http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1104.pdf

11. Hope, C., The Social Cost of CO2 from the PAGE09 Model . 2011, Cambridge Judge

Business School http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1105.pdf

12. Hope, C., New Insights from the PAGE09 Model: The Social Cost of CO2 . 2011, Cambridge Judge Business School http://www.jbs.cam.ac.uk/research/working_papers/2011/wp1108.pdf

13. Hope, C., The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern

. The Integrated Assessment

Journal , 2006. 6(1): pp. 19-56

14. Anthoff D. and Tol, R. S. J., Erratum to: The uncertainty about the social cost of carbon: A decomposition analysis using FUND

. Climatic Change , 2013. Advance online publication. doi: 10.1007/s10584-013-0959-114

- B

- 16 ANNEX A

Table A1. 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 32 51 89
2011 11 33 52 93

2012 11 34 54 97

2013 11 35 55 101

2014 11 36 56 105

2015 11 37 57 109

2016 12 38 59 112

2017 12 39 60 116

2018 12 40 61 120

2019 12 42 62 124

2020 12 43 64 128

2021 12 43 65 131

2022 13 44 66 134

2023 13 45 67 137

2024 14 46 68 140

2025 14 47 69 143

2026 15 48 70 146

2027 15 49 71 149

2028 15 50 72 152

2029 16 51 73 155

2030 16 52 75 159

2031 17 52 76 162

2032 17 53 77 165

2033 18 54 78 168

2034 18 55 79 172

2035 19 56 80 175

2036 19 57 81 178

2037 20 58 83 181

2038 20 59 84 185

2039 21 60 85 188

2040 21 61 86 191

2041 22 62 87 194

2042 22 63 88 197

2043 23 64 89 200

2044 23 65 90 203

2045 24 66 92 206

2046 24 67 93 209

2047 25 68 94 211

2048 25 69 95 214

2049 26 70 96 217

2050 26 71 97 22014

- B

- 17 Table A2. 2020 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

- 14

- 2 4 15 31 39 55 86 107 157

MERGE

- 6 1 6 14 27 35 46 70 87 141

MESSAGE

- 16

- 5 1 11 24 31 43 67 83 126
MiniCAM Base

- 7 2 7 16 32 39 55 83 103 158
5th Scenario

- 29

- 13

- 6 4 16 21 32 53 69 103

Table A3. 2020 Global SCC Estimates at 3 Percent Discount Rate (2007$/ton CO2)

Percentile 1st 5th 10th 25th 50th Avg 75th 90th 95th 99th
Scenario PAGE
IMAGE 4 7 10 18 38 91 95 238 385 727

MERGE 2 4 6 11 23 56 58 142 232 481

MESSAGE 3 5 7 13 29 75 74 197 330 641
MiniCAM Base 3 5 8 14 30 73 75 184 300 623
5th Scenario 1 3 4 7 17 58 48 136 264 660

Scenario DICE
IMAGE 16 21 24 32 43 48 60 79 90 102

MERGE 10 13 15 19 25 28 35 44 50 58

MESSAGE 14 18 20 26 35 40 49 64 73 83
MiniCAM Base 13 17 20 26 35 39 49 65 73 85
5th Scenario 12 15 17 22 30 34 43 58 67 79

Scenario FUND
IMAGE

- 13

- 4 0 8 18 23 33 51 65 99

MERGE

- 7

- 1 2 8 17 21 29 45 57 95

MESSAGE

- 14

- 6

- 2 5 14 18 26 41 52 82
MiniCAM Base

- 7

- 1 3 9 19 23 33 50 63 101
5th Scenario

- 22

- 11

- 6 1 8 11 18 31 40 6214

- B

- 18 Table A4. 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

- 4

- 1 2 3 6 10 14 24

MERGE

- 6

- 4

- 2 0 3 4 6 11 15 26

MESSAGE

- 10

- 6

- 4

- 1 1 2 5 9 12 21
MiniCAM Base

- 7

- 4

- 2 0 3 4 6 11 14 25
5th Scenario

- 11

- 7

- 5

- 3 0 0 3 5 7 1314

- B

- 19 ANNEX B

The November 2013 revision of this technical support document is based on two
corrections to the runs based on the FUND model. First, the potential dry land loss in the
algorithm that estimates regional coastal protections was misspecified in the model’s c omputer code. This correction is covered in an erratum to Anthoff and Tol (2013) published in the same

journal ( Climatic Change ) in October 2013

. 14 Second, th e equilibrium climate sensitivity
distribution was inadvertently specified as a truncated Gamma distribution (the default in FUND)
as opposed to the truncated Roe and Baker distribution as was intended. The truncated Gamma
distribution used in the FUND runs had approximately the same mean and upper truncation
point, but lower variance and faster decay of the upper tail, as compared to the intended
specification based on the Roe and Baker distribution. The difference s between the original
estimates reported in the May 2013 version of this technical support document and this revision

are all one dollar or less.17

- A

- i APPENDIX 17

- A. REGULATORY IMPACT ANALYSIS: SUPPORTING

MATERIALS

TABLE OF CO NTENTS

17- A.1 INTRODUCTION

............................................................................................... 17-A-2

17- A.2 MARKET SHARE ANNUAL INCREASES BY POLICY

................................ 17-A-3

17- A.3 NIA

- RIA INTEGRATED MODEL

..................................................................... 17-A-5

17- A.4 CONSUMER REBATE POLI CY MARKET PENETRATIO N CURVES

........ 17-A-5

17-A.4.1 Introduction .......................................................................................................... 17-A-5

17-A.4.2 Adjustment of XENERGY Penetration Curves ................................................... 17-A-7

17-A.4.3 Interpolation of Penetration Curves ..................................................................... 17-A-8

17- A.5 CONSUMER REBATE PROGRAMS

................................................................ 17-A-9

17- A.6 FEDERAL AND STATE TAX CREDITS

........................................................ 17-A-13

17-A.6.1 Federal Tax Credits for Consumers of Residential Appliances ......................... 17-A-13

17-A.6.2 Federal Tax Credits for Manufacturers

.............................................................. 17-A-14

17-A.6.3 State Tax Credits

................................................................................................ 17-A-14

LIST OF TABLES

Table 17 -A.2.1 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Front

- Loading Commercial Clothes Washers ................... 17-A-3

Table 17 -A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy Measures for Top

- Loading Commercial Clothes Washers ...................... 17-A-4

Table 17 -A.5.1 Rebates for Commercial Clothes Washers ............................................ 17-A-10

LIST OF FIGURE S

Figure 17 -A.4.1 S-Curves Showing Effects of External and Internal Sources on Adoption of New Technologies ............................................................... 17-A-717-A-2 APPENDIX 17

- A. REGULATORY IMPACT ANALYSIS: SUPPORTING MATERIALS

17

- A.1 INTRODUCTION

This appendix contains sections discussing the following topics:

Projections of annual market share increases for the alternative policies;

NIA

- RIA Integrated Model; XENERGY penetration curves used to analyze consumer rebates, including:

o Background material,

o DOE’s adjustment of these curves for this analysis, and

o The method DOE used for interpolating the curves; Detailed tables of rebates offered for the considered products; and Background material on Federal and state tax credits for appliances

.17-A-3 17

- A.2 M ARKET SHARE ANNUAL I NCREASES BY POLICY

Table 17 -A.2.1 and Table 17 -A.2.2 show the annual increases in market sh ares for

commercial clothes washers meeting the target efficiency levels of the proposed TSL (TSL 2). DOE used these market share increases as inputs to the NIA

- RIA spreadsheet model.

Table 17-A.2.1 Annual Increases in Market Shares Attributable to Alternative Polic y Measures for Front

- Loading Commercial Clothes Washers

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary Energy Efficiency

Targets Early Replacement Bulk

Government

Purchases

2018 50.0% 5.2% 2.6% 0.5% 2.1% 0.1%

2019 50.0% 8.0% 4.0% 5.6% 2.4% 0.3%

2020 50.0% 10.9% 5.4% 8.9% 2.7% 0.4%

2021 50.0% 12.7% 6.3% 11.2% 3.0% 0.6%

2022 50.0% 13.8% 6.9% 13.0% 3.2% 0.7%

2023 50.0% 14.9% 7.5% 14.5% 3.5% 0.9%

2024 50.0% 15.8% 7.9% 15.8% 3.7% 1.0%

2025 50.0% 14.8% 7.4% 17.0% 3.9% 1.2%

2026 50.0% 13.8% 6.9% 18.0% 3.9% 1.3%

2027 50.0% 12.8% 6.4% 18.8% 4.0% 1.5%

2028 50.0% 11.8% 5.9% 19.7% 4.0% 1.5%

2029 50.0% 10.5% 5.2% 20.4% 3.9% 1.5%

2030 50.0% 9.1% 4.6% 21.1% 3.7% 1.5%

2031 50.0% 7.8% 3.9% 21.7% 3.2% 1.5%

2032 50.0% 6.4% 3.2% 22.3% 2.5% 1.5%

2033 50.0% 5.1% 2.5% 22.9% 1.3% 1.5%

2034 50.0% 3.7% 1.9% 23.4% 0.4% 1.5%

2035 50.0% 2.4% 1.2% 23.9% 0. 0 % 1.5%

2036 50.0% 1.1% 0.5% 24.3% 0. 0 % 1.5%

2037 50.0% 0.0% 0.0% 24.8% 0. 0 % 1.5%

2038 50.0% 0.0% 0.0% 25.2% 0. 0 % 1.5%

2039 50.0% 0.0% 0.0% 25.6% 0. 0 % 1.5%

2040 50.0% 0.0% 0.0% 26.0% 0. 0 % 1.5%

2041 50.0% 0.0% 0.0% 26.3% 0. 0 % 1.5%

2042 50.0% 0.0% 0.0% 26.7% 0. 0 % 1.5%

2043 50.0% 0.0% 0.0% 27.0% 0. 0 % 1.5%

2044 50.0% 0.0% 0.0% 27.3% 0. 0 % 1.5%

2045 50.0% 0.0% 0.0% 27.7% 0. 0 % 1.5%

2046 50.0% 0.0% 0.0% 28.0% 0. 0 % 1.5%

2047 50.0% 0.0% 0.0% 28.2% 0. 0 % 1.5%17-A-4 Table 17-A.2.2 Annual Increases in Market Shares Attributable to Alternative Policy

Measures for Top-Loading Commercial Clothes Washers

Year Consumer

Rebates Consumer

Tax Credits Manufacturer

Tax Credits Voluntary Energy Efficiency

Targets Early Replacement Bulk

Government

Purchases

2018 49.8% 5.2% 2.6% 0.5% 15.6% 0.2%

2019 49.8% 7.9% 4.0% 5.6% 14.9% 0.3%

2020 49.8% 10.8% 5.4% 8.9% 14.3% 0.5%

2021 49.8% 12.6% 6.3% 11.2% 13.7% 0.6%

2022 49.8% 13.7% 6.9% 13.0% 13.0% 0.7%

2023 49.8% 14.8% 7.4% 14.5% 12.4% 0.9%

2024 49.8% 15.7% 7.9% 15.8% 11.7% 1.0%

2025 49.8% 14.7% 7.4% 17.0% 11.0% 1.2%

2026 49.8% 13.7% 6.9% 18.0% 10.2% 1.3%

2027 49.8% 12.8% 6.4% 18.8% 9.3% 1.5%

2028 49.8% 11.8% 5.9% 19.7% 8.2% 1.5%

2029 49.8% 10.4% 5.2% 20.4% 6.8% 1.5%

2030 49.8% 9.1% 4.5% 21.1% 5.1% 1.5%

2031 49.8% 7.7% 3.9% 21.7% 2.7% 1.5%

2032 49.8% 6.4% 3.2% 22.3% 0.2% 1.5%

2033 49.8% 5.1% 2.5% 22.9% 0. 0 % 1.5%

2034 49.8% 3.7% 1.9% 23.4% 0. 0 % 1.5%

2035 49.8% 2.4% 1.2% 23.9% 0. 0 % 1.5%

2036 49.8% 1.1% 0.5% 24.3% 0. 0 % 1.5%

2037 49.8% 0.0% 0.0% 24.8% 0. 0 % 1.5%

2038 49.8% 0.0% 0.0% 25.2% 0. 0 % 1.5%

2039 49.8% 0.0% 0.0% 25.6% 0. 0 % 1.5%

2040 49.8% 0.0% 0.0% 26.0% 0. 0 % 1.5%

2041 49.8% 0.0% 0.0% 26.3% 0. 0 % 1.5%

2042 49.8% 0.0% 0.0% 26.7% 0. 0 % 1.5%

2043 49.8% 0.0% 0.0% 27.0% 0. 0 % 1.5%

2044 49.8% 0.0% 0.0% 27.3% 0. 0 % 1.5%

2045 49.8% 0.0% 0.0% 27.7% 0. 0 % 1.5%

2046 49.8% 0.0% 0.0% 28.0% 0. 0 % 1.5%

2047 49.8% 0.0% 0.0% 28.2% 0. 0 % 1.5%17-A-5 17

- A.3 NIA

- RIA INTEGRATED MODEL

For this analysis, DOE used its integrated NIA

- RIA a model appro ach that built on the

NIA model discussed in Chapter 1 1 and documented in Appendix 11- A. The resulting integrated

NIA -RIA model featured both the NIA analysis inputs and results and the RIA inputs and had

the capability to generate results for each of the RIA policies. A separate module produced results summaries for the tables and figures in the RIA document. For the RIA methodology documentation in Chapter 17 the module created summaries of parameters calculated by the model for the consumer rebates policy, generated the corresponding customized penetration curves (discussed in S ection 17-A.4.3 below), and reported market s hare impacts for the rebate

and tax cred it policies by product class. For the RIA results reported in C hapter 17 the module produced graphs of the market sh are increases resulting from each of the policies analyzed and

created summary tables for the national energy savings (NES) and net present value (NPV)

results

. This module also generated tables of market share i ncreases for each policy reported in

S ection 17- A.2 of this Appendix

.

17

- A.4 CONSUMER REBATE POLI CY MARKET PENETRATION CURVES

This section first discusses the theoretical basis for the market penetration curves that DOE used to analyze the Consumer Rebates policy. Next it discusses the adjustments it made to the maximum penetration rates. It then refers to the method it used to develop interpolated penetration curves for each specific equipment class and efficiency level in the analysis. The resulting customized curves for front-loading and top-loading commercial clothes washer are in chapter 17

.

17-A.4.1 Introduction

XENERGY, Inc. b , dev eloped a re

- parameterized, mixed -source information diffusion model 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 developing analytical models of diffusion patterns applicable to individual consumers or to technologies from competing firms. 2

, 3

, 4 One study records researchers’ attempts to investigate the factors that drive diffusion processes

. 5 Because a new product generally has its own distinct characteristics, few stud ies have been able to conclusively develop a universally applicable model. Some key findings, however , generally are accepted in academia and industry.

a NIA = national impact analysis; RIA = regulatory impact analysis

b XENERGY is now owned by KEMA, Inc. ( www.kema.com )17-A-6 One accepted finding is that, regardless of their economic benefits and technological merits, new technologies are unlikely to be adopted by all potential users. For many products, a ceiling must be pla ced on the adoption rate. A second conclusion is that not all adopters purchase new products at the same time: some act quickly after a new product is introduced; others wait for the product to mature. Third, diffusion processes can be characterized approx imately by asymmetric S

- curves that depict three stages of diffusion: starting, accelerating, and decreasing (as the adoption ceiling is approached).

A so -called epidemic model of diffusion is used widely in marketing and social studies. The epidemic model assumes that (1) all consumers place identical value on the benefits of a new product, and (2) the cost of a new product is constant or declines monotonically over time. What induces a consumer to purchase a new product is information about the availabi lity and benefits of the product. In other words, information diffusion drives consumers’ adoption of a

new product. 3 The model incorporates information diffusion from both internal sources ( spread by word of mouth from early adopters to prospective adopters) and external sources (the “announcement effect” produced by government agencies, institutions, or commercial advertising). The model incorporates both internal and external sources by com bining a logistic function with an exponential function. 4

, 5

Th e relative degree of influence from the internal and external sources determines the general shape of the diffusion curve for a specific product. 4

, 5 If adoption of a product is influenced primarily by external sources of information (the announcement effect), for instance, a high rate of diffusion occurs at the beginning of the process. In this scenario, external sources provide immediate information exposure to a significant number of prospective adopters. In contrast, internal sources (such as a network of prospective adopters) are relatively small in size and reach, producing a more gradual exposure to prospective adopters. Graphically speaking, information diffusion dominated by external sources is represented by a concave curve (the exponential curve in Figure 17-A.4.1 ). If adoption of a new product is influenced most strongly by internal sources of information, the number of adopters increases gradually, forming a convex curve (the logistic cur ve in Figure 17 -A.4.1 ).17-A-7 Figure 17 -A.4.1 S- Curves Showing Effects of External and Internal Sources on Adoption of New Technologies

17-A.4.2 Adjustment of XENERGY Penetration Curves

In co nsultation 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 stu dy 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 penetratio n parameters for some of the curves from 80 percent to the following levels:

Moderate Barriers: 70% High Barriers: 60% Extremely High Barriers: 50%

The low barriers and no barriers curves (the latter used only when a product has a very high base- case -market share) remained, respectively, with 80 percent and 100 percent as their maximum penetration rates. For the interpolated penetration curves (discussed below), DOE set

the no barriers and extremely high barriers curves as the upper and lower bounds, respectively,17-A-8 for any benefit/cost ratio points higher or lower than the curves. It set another constraint such that the policy case market share cannot be great than 100 percent, as might occur for products with high base case market shares of the target

- level technology.

17-A.4.3 Interpolation of Penetration Curves

As discussed above, the XENERGY penetration (implementation) curves followed a functional form to estimate the market implementation rate caused by energy efficiency

measures such as consumer rebates. c The XENERGY report presents five reference market implementation curves that vary according to the level of market barriers to technology penetration. 1 Such curves have been used by DOE in the Regulatory Impact Analyses for rulemakings for appliance energy efficiency standards to estimate market share increases in response to rebate prog rams. d They provide a framework for evaluating technology penetration, yet require matching the studied market to the curve that best represents it. This approximate matching can introduce some inaccuracy to the analysis.

Blum et al (2011, Appendix A) 7 presents an alternative approach to such evaluation: a method to estimate market implementation rates more accurately by performing interpolations of

the reference curves. The referred report describes the market implementation rate function and the reference curves, the method to calibrate the function to a given market, and the l imitations

of the method.

c The RIA chapter refers to these curves as penetration curves . This section, in references to the original source, uses

the term implementation curve

. d DOE has also used this method to estimate market share increase s resulting from consumer tax credit and manufacturer tax credit programs, since the effects of tax credits on market s can be considered proportional to the rebate impacts

.17-A-9 17

- A.5 CONSUMER REBATE PROGRAMS

DOE performed a search for rebate programs that offered incentive s for commercial

clothes washers . Some organizations nationwide, comprising electric utilities and regional agencies, offer rebate prog rams for commercial clothes washers

. Table 17 -A.5.1 provides the organizations’ names, states, rebate amounts and program websites. If there is more than one entry for an organization, it offers different rebates in different states . When an organization offers rebates through several utilities, it is represented only once in each table.

DOE calculated the rebate amount it used in its analysis of the R ebate s policy case from a sample of 38 rebates from 26 organizations. The rebate amount DOE calculated for commercial

clothes washers is $158.16 (in 2012$ ). Th is amount refers to the simple average of the individual

amounts offered by the programs reported in Table 17 -A.5.1

.17-A-10 Table 17-A.5.1 Rebates for Commercial Clothes Washers
Utility or Agency State Rebate $ Website

City of Palo Alto CA $400 http://www.cityofpaloalto.org/gov/depts/utl/business/rebates/water.asp?Bl

obID=14758

Pacific Power

- FinAnswer Express CA $150 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa ve_Energy_Money/CA_FinAnswer_Express_Appliance_Incentives.pdf

Pacific Power

- FinAnswer Express CA $200 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa ve_Energy_Money/CA_FinAnswer_Express_Appliance_Incentives.pdf

PG&E CA $50 http://www.pge.com/includes/docs/pdfs/mybusiness/energysavingsrebates /incentivesbyindustry/appliances_generalimprovements_catalog_final.pdf

Riverside Public Utilities CA $75 http://www.greenriverside.com/Green

- Rebate

- Programs

7/Commercial/Energy_Efficiency -112/Rebate325/Energy_Star_Rated_Product_Incentives Silicon Valley Power CA $400 https://siliconvalleypower.com/index.aspx?page=1932

Questar Gas ID $75 http://www.thermwise.com/business/BusinessRebates.html

Avista Utilities (Electric) ID $200 https://www.avistautilities.com/business/rebates/washington/washer/Page s/default.aspx Avista Utilities (Electric) WA $200 https://www.avistautilities.com/business/rebates/washington/washer/Page s/default.aspx

Cedar Falls Utilities IA $50 http://www.cfu.net/webres/File/2013%20Commerical%20Rebates%20

- %204%20page_web.pdf

Hutchinson Utilities Commission MN $50 http://www.hutchinsonutilities.com/pdf/prescriptiverebate.pdf

New Mexico Gas Company NM $100 http://eeprograms.net/newmexicogas/comsolprescriptive/upgrades.php

NYSERDA: Existing Facilities Program NY $200 http://www.nyserda.ny.gov/BusinessAreas/Energy

- Efficiency

- and

Renewable

- Programs/Commercial -and-Industrial/CI- Programs/Existing

Facilities

- Program/Pre

- Qualified

- Incentives.aspx Dayton Power and Light OH $50 http://www.dpandl.com/save

- money/business

- government/rapid

rebates/motors

- drives

- and

- compressed

- air

- rebates/ State Rebate Program: Business Energy Efficiency Rebate for Existing Buildings OR $200 http://energytrust.org/library/forms/BE_PI0194F.pdf17-A-11 State Rebate Program: Business Energy Efficiency Rebate for Existing Buildings OR $300 http://energytrust.org/library/forms/BE_PI0194F.pdf

Idaho Power OR $200 http://www.idahopower.com/pdfs/EnergyEfficiency/EasyUpgrades/works heet_Plug2011.pdf

SCE&G (Electric) SC $50 http://www.sceg.com/NR/rdonlyres/A464F2E3

- 43EF

- 43B9

- A72B

93013D6E678D/0/Food_Services_High_Efficiency_v9.pdf

Texas Gas Service TX $100 http://www.texasgasservice.com/en/SaveEnergyAndMoney/Conservation Programs/AustinConservation/CommercialPrograms/CommAppliancePro grams/WashWise.aspx
Questar Gas UT $75 http://www.thermwise.com/business/BusinessRebates.html

Cascade Natural Gas WA $180 http://www.cngc.com/docs/conservation

- materials/ci

- incentives

- list

- q2

- 2012.pdf NW Natural (Gas) WA $200 http://energytrust.org/library/forms/BE_PI0194F.pdf

NW Natural (Gas) WA $300 http://energytrust.org/library/forms/BE_PI0194F.pdf

NW Natural (Gas) OR $200 http://energytrust.org/library/forms/BE_PI0194F.pdf

NW Natural (Gas) OR $300 http://energytrust.org/library/forms/BE_PI0194F.pdf

Pacific Power

- FinAnswer Express WA $150 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa ve_Energy_Money/WA_FinAnswer_Express_Appliances_Incentives.pdf

Pacific Power

- FinAnswer Express WA $200 http://www.pacificpower.net/content/dam/pacific_power/doc/Business/Sa ve_Energy_Money/WA_FinAnswer_Express_Appliances_Incentives.pdf Port Angeles Public Works & Utilities WA $180 https://www.cityofpa.us/PDFs/Conservation/Forms/COMMERCIALAPP LIANCEREBATES.pdf Port Angeles Public Works & Utilities WA $75 https://www.cityofpa.us/PDFs/Conservation/Forms/COMMERCIALAPP LIANCEREBATES.pdf Puget Sound Energy WA $200 http://pse.com/savingsandenergycenter/ForBusinesses/Pages/High

efficiency

- Commercial

- Clothes

- Washers.aspx
Questar Gas WY $150 http://www.thermwise.com/wy/business/BusinessRebateApplicationsWY. php Rocky Mountain Power WY $150 http://www.rockymountainpower.net/content/dam/rocky_mountain_powe r/doc/Business/Save_Energy_Money/WY_FinAnswer_Express_Applianc es_Incentives.pdf Rocky Mountain Power WY $200 http://www.rockymountainpower.net/content/dam/rocky_mountain_powe17-A-12 r/doc/Business/Save_Energy_Money/WY_FinAnswer_Express_Applianc es_Incentives.pdf The United Illuminating Company CT $200 http://www.uinet.com/wps/portal/uinet/business/!ut/p/c5/vY_LkpswEEW_xR8QSIltGTMY2TzCBJjgTYuwIkHM2BctgHr68fJalKpJJup9F12nzp9gQSP9O XYHMprc

rLN5ADae3ChAbRClOHblMTUoNqacJWCAYYCJBDY8eP94GqVrGjS

iF3X2au6BR57T1- seaL97qJXU_jmdC5W2hxFkdRO6xhTdDWTz0n2ckjLBYPV_HRxS3j 4f IcnNEYBkj75yfy14vf- R97 -IdxICiAxB_4hLgPPktpamMEsQ6yTz6V1cM_6NL1TXGsim6pZT3S3hkiBMLJNYtmlaOtINIGQBsXuKJs_j4

- WVVU_dl31rMDif54bvjANW50Cb4vWtZ2PfFkoeN9kW6SP3GkLUc1 Tm6RsVcJCtX5aoS_3zVRBTsDzqN6LWlWPeyidEw45tb5f1XhTCvLM h-M7Gs6oDqA1TuCIM1j2u7tVXsbLVdd7XflCHwUka_m3OE7- ycw8pp0YxDrTDYgHi51P3DQzdGFrMRj

jHxbvE29dpA!!/dl3/d3/L2dBISEvZ0FBIS9nQSEh/ Bright Energy Solutions (offered through 5 utilities in ND) ND $50 http://www.brightenergysolutions.org/municipalities/?category=business &state=sd&municipality=37 Bright Energy Solutions (offered through 10 utilities in SD) SD $50 http://www.brightenergysolutions.org/municipalities/?category=business &state=sd&municipality=37 Bright Energy Solutions (offered through 21 utilities in MN) MN $50 http://www.brightenergysolutions.org/municipalities/?category=business &state=mn&municipality=14 Bright Energy Solutions (offered through 16 utilities in IA) IA $50 http://www.brightenergysolutions.org/municipalities/?category=business

&state=ia& municipality=6717

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- 13 17

- A.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.

17-A.6.1 Federal Tax Credits for Consumers of Residential Appliances

EPACT 2005 included Federal tax credits for consumers who installed efficient air conditioners or heat pumps; gas, oil and propane f urnaces 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 b y the American Recovery and Reinvestment Act (ARRA). 10 There was a $1,500 cap on the credit per home, including the amount received for insulation, windows, and air and duct sealing. Congress extended this provision for 2011, with some modifications to eligibility requirements, and reductions in the cap to $500 per home. The American Taxpayer Relief Act of 2012 extended, with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and

water heaters placed in service between January 1, 2012 and December 31, 2013. 8

, 11

The tax credit for furnace fans was $50 in 2011, after which it expired.

The importance of the Federal tax credits has been emphasized in research in the residential heating industry on the impacts of the relatively large credits that were available for HVAC (heating, ventilating, and air conditioning) equipment. In a survey of HVAC distributors conducted by Vermont Energy 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

progr am 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 . Th e decrease in incentive size from a $1,500 cap in 2009-2010 to a $500 cap in 2011, during a period when the economy continued to be sluggish, resulted in a decline in total sales of residential HVAC products. Distributors stated that an incentive needed to cover 25 to 75 percent of the incremental cost of the efficient equipment to influence consumer choice. The industry publication “2011 HVAC Review and Outlook” noted a decline in sales of air conditioning units with >14 SEER in 2011 and a return in sales of units with >16 SEER to 2009 levels (after an increase in 2010). The large majority of distributor observed no impacts from the utility programs with their lower rebate amounts available in 2011. Distributo rs 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 Servic e (IRS) data on the numbers of taxpayers who claimed the tax credits during tax years 2006 and 2007. It estimated the percentage of taxpayers who filed
Form 5695, Residential Energy Credits. 14 It also estimated the percentage of taxpayers with entries under Form 5695’s section 3, Residential energy property costs , line 3b, qualified natural gas, propane, or oil furnace or hot water boiler . DOE reasoned that the percentage of taxpayers17

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- 14 with an entry on Line 3b could serve as a rough indication of the potential of taxpayer participation in a Federal tax credit program for furnaces during the initial program years. DOE found that of all residential taxpayers filing tax returns, 0.8 percent in 2006 and 0.6 percent in 2007, claimed a credit for a furnace or boiler. DOE further found that the percentages of those filing Form 5695 for any qualifying energy property expenditure (which also included installation of efficient windows, doors and roofs) were 3.1 and 3.2 percent in 2006 and 2007 respectively.

DOE also reviewed data from an earlier Federal energy conservation tax credit program in place in the 1980s. While this tax credit was available from 1979 through 1985, DOE located data for only the first three years of the program

. 15

, 16

, 17 For those three years - 1979, 1980, and

1981 - the percentages of taxpayers filing Form 5695 were 6.4 percent, 5.2 percent, and 4.9 percent. Given that the data from this earlier tax credit program were not disaggregated by type of energy property, this data series served only to indicate a possible trend of greater participation in the initi al program year, followed by slightly smaller participation in subsequent years. However, DOE did not find detailed analysis of this program to indicate the possible reasons for such a trend. Also, this trend varies from the more stable trend shown in the EPAct 2005 energy tax credit program data for its first two program years.

17-A.6.2 Federal Tax Credits for Manufacturers

EPACT 2005 provided Federal Energy Efficient Appliance Credits to manufacturers that produced high- efficiency refrigerators, clothes washers, and dishwashers in 2006 and 2007. 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 tax credits were extended again, by the American Taxpayer Relief Act of 2012, for clothes washers, refrigerators, and dishwashers manufactured between January
1, 2012 and December 31, 2013. 20

Manufacturers who produce these appliances receive the credits for increa sing their production of qualifying appliances. These credits had sev eral efficiency tiers in 2011. For 2012- 2013, credits for the higher tiers remain but were eliminated for the lowest (least efficient) tiers

for cloth es washers and dishwashers

. 11 The credit amounts 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 effici ent refrigerator (30%) and clothes washer (2.2 MEF /4.5 WCF ) models was not subject to the cap. The credit to

manufacturers wa s capped at $25 million for 2011, with t he most efficient refrigerators (35%) and clothes washers (2.8 MEF /3.5 WCF) exempted from this cap. 21

17-A.6.3 State Tax Credits

The States of Oregon and Montana have offered consumer tax credits for efficient appliances for several years, and the States of Kentucky, Michigan and Indiana began offering17

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- 15 such credits in 2009. The Oregon Department of Energy (ODOE) has disaggregated data on taxpayer participation in credits for eligible products. (See the discussion in chapter 17, section 17.3.3, on tax credit data for clothes washers.) Montana’s Department of Revenue does not disaggregate participation data by appliance, although DOE reviewed Montana's overall participation trends and found them congruent with its analysis of Oregon's clothes washer tax

credits.

Oregon’s Residential Energy Tax Credit (RE TC) was created in 1977. T he Orego n legislature expanded the RETC program in 1997 to include residential refrigerators, clothes washers, and dishwashers, which significantly increased participation in the program . The program subsequently added credits for high- efficiency heat pump systems , air conditioners, and water heaters (2001); furnaces and boilers (2002); and duct/air sealing, fuel cells, heat recovery, and renewable energy equipment. Beginning in 2012, a Tax Credit Extension Bill (HB3672) eliminated refrigerators, clothes washers, d ishwashers, air conditioners, and boilers from the RETC program, leaving credits for water heaters, furnaces, heat pumps, tankless water heaters

, and heat pump water heaters. 22

, 23 Those technologies recognized by the Oregon Department of Energy as “premium efficiency” are eligible for tax c redit of $0.60 per kWh saved in the first year (up to $1,500). 22

, 24

Oregon’s business energy tax credit (BETC) includes commercial clothes washers. Oregon’s BETC is 35– 50 percent of the incremental cost of the efficient equipment. e The majority of these credit s were claimed for laundries and rental dwellings, and included commercial clothes washers as well as other appliances. However, ODOE did not track the types of appliances involved nor the number of machines per project; the annual counts are the

total nu mber of projects, with each project involving one or more appliances. DOE was therefore unable to use the data to estimate market percentages for specific appliances, i.e. commercial clothes washers. However, DOE did note the trend of increasing numbers of credits claimed from 1998 through 2006, which contributed to its assumptions regarding the trend line for this policy scenario.

Montana has had an Energy Conservation Tax Credit for residential measures since

1998. 25 The tax credit covers various residential energy and water efficient products, including split system central air conditioning; package system central air conditioning; split system air source heat pumps; package system heat pumps; natural gas, propane, or oil furnaces; hot water boilers; advanced main air circulating fans; heat recovery ventilators; gas, oil, or propane water heaters; electric heat pump water heaters; low-flow showerheads and faucets; light fixture s; and controls. In 2002 the amount of the credit was increased from 5 percent of product costs (up to $150) to 25 percent (up to $500) per taxpayer. The credit can be used for products installed in new construction or remodeling projects. The tax credit covers only that part of the cost and

materials that exceed established standards of construction.

e The business tax credit is taken over five years: 10 percent in the first and second years and 5 percent each year

thereafter; the taxpayer may carry the unused credit forward up to eight years. Those with eligible project costs of $20,000 or less may take the tax credit in one year.17

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- 16 REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy Efficiency, 2002. XENERGY Inc. (Last a ccessed November, 2013.)

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2. Hall, B. H. and B. Khan, Adoption of New Technology

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9. Energy Policy Act of 2005 , in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC
25C note, . 2005. http://www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf

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>17

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