### CHAPTER 10. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION

......................................................................................................... 10-1

10.2 SHIPMENTS MODEL METHODOLOGY

.................................................................. 10-1

10.3 DATA INPUTS AND MODEL CALIBRATION

........................................................ 10-4

10.3.1 Cooktops and Ovens

...................................................................................................... 10-5

10.3.1.1 Historical Shipments

.................................................................................... 10-8

10.3.1.2 Markets and Model Calibration

................................................................... 10-9

10.3.1.3 Base Case Shipments

................................................................................. 10-15

10.3.2 Microwave Ovens

........................................................................................................ 10-18

10.3.2.1 Historical Shipments

.................................................................................. 10-18

10.3.2.2 Markets and Model Calibration

................................................................. 10-18

10.3.2.3 Base Case Shipments

................................................................................. 10-22

10.3.3 Commercial Clothes Washers

...................................................................................... 10-23

10.3.3.1 Historical Shipments

.................................................................................. 10-24

10.3.3.2 Markets and Model Calibration

................................................................. 10-26

10.3.3.3 Base Case Shipments

................................................................................. 10-31

10.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME

IMPACTS

.................................................................................................................... 10-33

10.5 AFFECTED STOCK

................................................................................................... 10-35

10.6 RESULTS

.................................................................................................................... 10-36

10.6.1 Cooktops and Ovens

.................................................................................................... 10-36

10.6.2 Microwave Ovens

........................................................................................................ 10-36

10.6.3 Commercial Clothes Washers

...................................................................................... 10-38

LIST OF TABLES

Table 10.3.1 Cooktops and Ovens: Product Categories, Product Types, and Product

Classes

................................................................................................................ 10-5 Table 10.3.2 Electric Cooktops and Ovens: Historical Shipment Market Shares by Product Type

...................................................................................................... 10-6 Table 10.3.3 Gas Cooktops and Ovens: Historical Shipment Market Shares by Product Type

................................................................................................................... 10-7 Table 10.3.4 Cooktops and Ovens including Ranges and Built-Ins: Historical Shipments

........................................................................................................... 10-8 Table 10.3.5 Historical and Forecasted Housing Starts (millions)

....................................... 10-10

Table 10.3.6 Electric Cooking Products: Historical Market Saturations

............................. 10-11 Table 10.3.7 Gas Cooking Products: Historical Market Saturations

................................... 10-13 Table 10.3.8 Microwave Ovens: Historical Shipments, Domestic plus Imports

................. 10-18

Table 10.3.9 Microwave Ovens: Historical Market Saturations

.......................................... 10-20

10-i

### Table 10.3.10 Commercial Clothes Washers: Historical Shipments, Domestic plus Imports

............................................................................................................. 10-25 Table 10.3.11 Historical and Forecasted Multi-Family Housing Starts (millions)

................. 10-27 Table 10.3.12 Commercial Clothes Washers: Historical New Multi-Family Building

Saturations

........................................................................................................ 10-28 Table 10.3.13 Commercial Clothes Washers: Historical New Multi-Family Building

Saturations

........................................................................................................ 10-30

Table 10.3.14 Multi-Family In-Unit Washer Saturations

....................................................... 10-31 Table 10.4.1 Change in Relative Price Elasticity following a Purchase Price Change

........ 10-35 Table 10.6.1 Microwave Ovens: Cumulative Shipments Impacts Caused by EF

Standards over the Period 2012–2042

............................................................. 10-37 Table 10.6.2 Microwave Ovens: Cumulative Shipments Impacts Caused by Standby

Power Standards over the Period 2012–2042

.................................................. 10-37 Table 10.6.3 Top-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042

.............................. 10-39 Table 10.6.4 Front-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042

.............................. 10-39

LIST OF FIGURE

Figure 10.3.1 Electric and Gas Cooking Products: Retirement Functions

............................ 10-14 Figure 10.3.2 Electric Cooking Products: Historical and Base Case Shipments Forecast

.... 10-15 Figure 10.3.3 Gas Cooking Products: Historical and Base Case Shipments Forecast

.......... 10-16 Figure 10.3.4 Electric Cooking Products: Disaggregated Base Case Shipments Forecast

... 10-17 Figure 10.3.5 Gas Cooking Products: Disaggregated Base Case Shipments Forecast

......... 10-17 Figure 10.3.6 Microwave Ovens: Survival and Retirement Functions

................................. 10-21 Figure 10.3.7 Microwave Ovens: Historical and Base Case Shipments Forecast

................ 10-22 Figure 10.3.8 Microwave Ovens: Disaggregated Base Case Shipments Forecast

................ 10-23 Figure 10.3.9 Commercial Clothes Washer Shipments and Trend in Quantity Index

........... 10-26 Figure 10.3.10 Commercial Laundry Saturation in New Multi-Family Housing

.................... 10-29 Figure 10.3.11 Commercial Clothes Washers: Survival and Retirement Functions

............... 10-30 Figure 10.3.12 Commercial Clothes Washers: Historical and Base Case Shipments

Forecast

............................................................................................................ 10-32 Figure 10.3.13 Commercial Clothes Washers: Disaggregated Base Case Shipments

Forecast

............................................................................................................ 10-33 Figure 10.6.1 Microwave Ovens: Base Case and TSL Shipments Forecasts for EF

Standard Levels

................................................................................................ 10-37 Figure 10.6.2 Microwave Ovens: Base Case and TSL Shipments Forecasts for Standby

Power Standard Levels

.................................................................................... 10-38 Figure 10.6.3 Top-Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts

......................................................................................... 10-39 Figure 10.6.4 Front-Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts

......................................................................................... 10-40

10-ii

### CHAPTER 10. SHIPMENTS ANALYSIS

10.1 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 DOE conducts for its notices of proposed rulemaking (NOPRs). This chapter describes DOE’s methodology for projecting annual shipments and presents results for each of the two sets of products being considered for this standards rulemaking (i.e., cooking products and commercial clothes washers).

DOE estimated shipments for each product with a separate Shipments Model. In the case of cooking products, DOE developed two separate Shipments Models: one for cooktops and ovens and another for microwave ovens. Therefore, DOE developed a total of three separate Shipments Models; two for cooking products and one for commercial clothes washers. It calibrated each Shipments Model against historical shipments. For purposes of estimating the impacts of prospective trial standard levels (TSL) on product shipments, each Shipments Model accounts for the combined effects of changes in purchase price, annual operating cost, and household income on the consumer purchase decision.

Each Shipments Model considers specific market segments to estimate shipments, the results for which are then aggregated to estimate total product shipments. In the case of all of the considered products, DOE accounted for at least two market segments: (1) new construction and (2) existing buildings (i.e., replacing failed equipment). In addition, DOE accounted for a third market segment: for cooking products—early replacements and for commercial clothes washers—retired units not replaced.

The Shipments Models are Microsoft Excel spreadsheets that are accessible on the Internet ( http://www.eere.energy.gov/buildings/appliance_standards/ ). Appendix 11A discusses how to access the Shipments Model spreadsheet and other related spreadsheets, and provides basic instructions for using them. The rest of this chapter explains the Shipments Models in more detail. Section 10.2 presents the Shipments Model methodology for each product; section

10.3 describes the data inputs and the model calibration; section 10.4 discusses impacts on shipments from changes in equipment purchase price, operating cost, and household income; section 10.5 discusses the affected stock; and section 10.6 presents the results for different TSL

scenarios.

10.2 SHIPMENTS MODEL METHODOLOGY

DOE developed a national stock model for estimating annual shipments for the four product categories considered for this standards rulemaking. The model considers market

10-1_ _ 

### segmentation as a distinct input to the shipments forecast. As represented by the following equation, the two primary market segments are new installations and replacements.

Ship
p 

### ( j ) = Rpl
p 

### ( j ) + NI p 

### ( j )

where:



### Ship p 

### (j) = Total 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 the product-specific sections below discuss, DOE also considered a third market segment for most of the products to calibrate its Shipments Models to historical shipments data.

In principle, each market segment and each product class responds differently to both the base case demographic and economic trends and to the implementation of standards. Furthermore, retirements, early replacements, and efficiency trends a 

### are dynamic and can vary among product classes. Rather 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 forecast sales in each market segment. Thus, DOE’s Shipments Models assume that construction, i.e., new housing units, drives new installations. In each year, the product shipments from the new construction market segment are equal to the number of new housing units built times the purchase rate, which is determined by the product class market share and the market saturation of the product under

consideration.

DOE’s Shipments Models take an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected construction trends. The models estimate 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 service 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.

_ ageMax j 1 

### Rpl ( j ) = Stock ( j 1 ) 

# ∑ ∑ Ship × prob ( age )
p p j Rtr

age =0 j = N 

### where:

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 10.4.

10-2

### 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



### N = Start 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. Furthermore, in the case of an early replacement scenario, retirements due to early replacement will depend on the age of the units that are subject to early replacement.

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:

_ 

### Stock( j,age = 1 ) = Ship( j 1 )

where:



### Stock(j, age) = The population of in-service units of a particular age, 

### j = Year for which the in-service stock is being estimated, and 

### Ship (j) = Number of units purchased in year 

### j

.

The above equation states that the number of one-year-old units is simply equal 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,age ) × 

# [ 1 prob Rtr 

### ( age ) 

# ]

In the above 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 the products considered in this rulemaking are 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.

10-3

### 10.3 DATA INPUTS AND MODEL CALIBRATION

As discussed above, shipments are driven primarily by two market segments: new construction and replacements.

DOE estimated new construction shipments using two inputs: new housing forecasts and market saturation data. New housing includes newly constructed single- and multi-family units, referred to as “new housing completions,” and mobile home placements. For new housing completions and mobile home placements, DOE used actual data through 2005, and adopted the projections from the DOE Energy Information Administration (EIA)’s 

### Annual Energy Outlook

2008 ( 

### AEO2008 ) for the period of 2006–2030. 1 

### To determine new construction shipments for each product, DOE used forecasts of market saturations, combined with forecasts of housing

starts.

DOE estimated replacements using product retirement functions that it developed from product lifetimes. For all products, with the exception of microwave ovens, DOE based the retirement function on a Weibull probability distribution for the product lifetime. The Shipments Models assume that no units are retired below a minimum product lifetime and all units are retired before exceeding a maximum product lifetime. The models determine the probability of retirement at a certain age for all products using the following equations:



### prob Rtr 

### ( age ) = 0 for 

### age < 

### AgeMin

1 

### prob Rtr 

### ( age ) = for 

### AgeMin ≤ age ≤ AgeMax

_ 

### AgeMax AgeMin

prob Rtr 

### ( age ) = 1 for 

### age > 

### AgeMax

where:



### prob Rtr 

### (age) = Probability of retirement at the age of the product, 

### AgeMin = Minimum retirement age, and 

### AgeMax = Maximum retirement age.

DOE used historical shipments as the basis for calibrating its Shipments Models. For all products, because new construction shipments and replacements were not enough to account for all product shipments, DOE developed another market segment to calibrate its Shipments Models. This additional market segment represented a small share of total shipments.

The sections below explain in detail each of the data inputs for each product, including the third market segment that DOE developed to calibrate its Shipments Model for each product.

10-4

### 10.3.1 Cooktops and Ovens

DOE designed its Shipments Model for cooktops and ovens by dividing these products into two general fuel categories: electric and gas. Both the electric and gas categories comprised the following product configurations: ranges, built-in cooktops, and built-in ovens. DOE developed two Shipments Models; one Model estimated the electric cooking product shipments while the other Model estimated gas cooking product shipments. After DOE estimated shipments for each fuel category, it then disaggregated the shipments into product types—eight product types for electric cooking products and five types for gas cooking products. Since each product type (except gas cooktops) consists of two or more product classes, DOE then disaggregated shipments for each product type into their appropriate product classes.

Table 10.3.1 shows the product types and product classes under each general fuel category (i.e., electric and gas cooking products). For electric cooking products there are eight product types and four product classes; for gas cooking products there are five product types and three product classes. Because ranges are comprised of cooktop and oven product classes, DOE needed to disaggregate range shipments into the appropriate cooktop and oven product classes and then combine those shipments with the appropriate built-in cooktop and oven shipments to obtain the total shipments for each product class.



### Table 10.3.1	 Cooktops and Ovens: Product Categories, Product Types, and Product

Classes

Product Categories Electric Gas

Product Types: 8 electric, 5 gas Ranges Built-In Cooktop Built-In Oven Ranges Built-In Cooktop Built-In Oven

1.

CoilStd 2.

CoilSC 3.

SmthStd 4.

SmthSC 5.

Coil 6.

Smooth 7.

Std 8.

SC 1.

GasStd 2.

GasSC 3.

Gas 4.

Std 5.

SC

Product Ovens Cooktops Ovens 3.

Cooktops Classes: 4 electric, 3 gas 1.

Standard 2.

Self-Clean 3.

Coil 4.

Smooth 1.

Standard 2.

Self-Clean

Std = standard; SC = self cleaning; Smth = smooth



### Table 10.3.2 shows the historical market shares of the eight product types that comprise total electric cooking product shipments. For any given year, the sum of the product type market shares equals 100 percent. DOE used two sources to establish historical market shares: (1) data that AHAM provided for the period 2003–2005 2 

### and (2) data from DOE’s supplemental analysis to its 1996 technical support document (TSD) on cooking products covering the period 1989–

1996. 3 

### For the time periods for which no data were available, DOE either applied the market share percentages from the above two data sources or interpolated from the two available data sources. The notes in Table 10.3.2 provide further details. To estimate forecasted market shares of each product type, DOE decided to freeze the percentages at the levels for the year 2005, i.e.,

10-5

### DOE used the market shares in the year 2005 to disaggregate forecasted shipments into each of the eight electric cooking product types for the period 2006–2042.



### Table 10.3.2	 Electric Cooktops and Ovens: Historical Shipment Market Shares by Product Type

Year Percent of Total Shipments

Ranges Built-In Cooktops Built-In Ovens

CoilStandard CoilSelf-Clean SmoothStandard SmoothSelf-Clean Coil Smooth Standard Self-Clean

1970–1989 26% 13% 9% 21% 4% 9% 6% 13%

1990 22% 15% 8% 23% 4% 9% 7% 12%

1991 25% 14% 9% 22% 4% 8% 4% 14%

1992 26% 14% 9% 22% 4% 8% 4% 14%

1993 26% 14% 9% 22% 4% 8% 3% 14%

1994 22% 17% 8% 26% 3% 7% 5% 11%

1995 24% 16% 9% 26% 3% 7% 3% 12%

1996 22% 18% 8% 27% 3% 7% 3% 12%

1997 21% 18% 7% 28% 3% 7% 3% 12%

1998 20% 19% 7% 29% 3% 8% 2% 12%

1999 19% 20% 7% 30% 3% 7% 2% 12%

2000 19% 20% 7% 31% 3% 7% 2% 12%

2001 18% 20% 6% 31% 3% 7% 2% 12%

2002 17% 21% 6% 32% 3% 7% 2% 13%

2003 14% 22% 5% 34% 3% 6% 2% 13%

2004 14% 22% 5% 34% 3% 6% 2% 14%

2005 15% 21% 6% 34% 3% 6% 1% 14%

Source: 2003–2005: AHAM Data Submittal, 2006; 1989–1996, Standard and Self-Clean Range and Built-In Oven percentages: DOE, Supplemental Analysis, 1996; 1997–2002, Standard and Self-Clean Range and Built-In Oven percentages: interpolated between 1996 and 2003 data; 1989–2002, Smooth and Coil Range and Built-In Cooktop percentages: 3-year average from AHAM Data Submittal for the period 2003–2005; 1970-1988: Set equal to percentages for 1989.



### Table 10.3.3 shows the historical market shares of the five product types that comprise total gas cooking product shipments. For any given year, the sum of the product type market shares equals 100 percent. DOE used three sources to establish historical market shares: (1) data provided by AHAM for the period 2003–2005, 15 

### (2) data from DOE’s supplemental analysis to its 1996 TSD on cooking products covering the period 1989–1996, 16 

### and (3) data from the Appliance Recycling Information Center (ARIC) for the year 2004. 4 

### For the time periods for which no data were available, DOE either applied the market share percentages from the above AHAM and DOE data sources or interpolated between the data from these two sources. The notes in Table 10.3.3 provide further details.

10-6

### Note that in Table 10.3.3, gas standard range market shares are broken down intoequipment with and without standing pilots. As discussed in section 8.2.6 of Chapter 8, LifeCycle Cost and Payback Period Analysis, only gas standard ranges are still being produced with standing pilot lights. As a result, DOE used the ARIC data to track the percentage of gas ranges with pilots, to accurately estimate the national impacts of eliminating pilot lights for gas cooking pilots. DOE’s method for estimating the national impacts is discussed in detail in Chapter 10, National Impact Analysis. To estimate the future percentages of each product type (with the exception of gas standard ranges with standing pilots), DOE decided to freeze the percentages at the levels shown for the year 2005, i.e., DOE used the market shares in the year 2005 to disaggregate forecasted shipments into each of the five gas cooking product types for the period 2006–2042. Section 10.2.3 of Chapter 10, National Impact Analysis, discusses how DOE forecasted the market share of gas standard ranges with standing pilots.



### Table 10.3.3	 Gas Cooktops and Ovens: Historical Shipment Market Shares by Product Type

Year Percent of Total Shipments
Ranges Built-In Cooktop Built-In Ovens

Std w/ Pilot Std no Pilot Self-Clean Standard Self-Clean

1970–1988 33% 30% 22% 10% 4% 1%

1989 35% 28% 22% 10% 4% 1%

1990 36% 27% 22% 11% 4% 1%

1991 37% 25% 23% 11% 3% 1%

1992 38% 24% 24% 12% 3% 1%

1993 37% 21% 27% 12% 2% 1%

1994 40% 21% 25% 11% 3% 0%

1995 39% 19% 30% 9% 2% 1%

1996 38% 18% 31% 10% 2% 1%

1997 38% 16% 34% 10% 2% 1%

1998 36% 14% 36% 11% 2% 1%

1999 35% 13% 38% 12% 2% 1%

2000 34% 12% 40% 12% 2% 1%

2001 33% 10% 42% 13% 2% 1%

2002 32% 10% 44% 13% 2% 1%

2003 32% 9% 44% 13% 1% 1%

2004 29% 8% 47% 14% 1% 0%

2005 30% 7% 47% 15% 1% 0%

Source: 2003–2005: AHAM Data Submittal, 2006 (with the exception of Standard Ranges with pilot); 1989–1996, Standard and Self-Clean Range and Built-In Oven percentages: DOE, TSD, 1996; 1997–2002, Standard and Self-Clean Range and Built-In Oven percentages: interpolated between 1996 and 2003 data; 1989–1996, Standard Range with pilot percentages: DOE, Supplemental Analysis, 1998; 2004, Standard Ranges with pilot percentage: ARIC, 2005; 1997–2003, 2005, Standard Range with pilot percentages: based on interpolation between 1996 and 2004 data; 1970-1988: Set equal to percentages for 1989.

10-7

### 10.3.1.1 Historical Shipments

DOE used data on historical shipments (i.e., domestic shipments and imports) to calibrate its Shipments Model. It obtained historical shipments data from three sources: (1) data provided by AHAM for the period 2003–2005, 15 

### (2) data from the 2000 AHAM 

### Factbook for the period

3 4,5,6 b 

### 1989–2002, and (3) data from 

### Appliance Magazine for the period 1972–1988. Table 10.3.4 summarizes the historical cooking product shipments data. DOE built up a total stock of cooking products by integrating historical shipments starting in the year 1972. Over time, some units are retired and, therefore, removed from stock, thus triggering the shipment of a new unit. Because of the relationship between retirements and total stock, there is a strong correlation between past and future shipments, independent of efficiency standards.



### Table 10.3.4 Cooktops and Ovens including Ranges and Built-Ins: Historical Shipments

Year Shipments (thousands)

Year Shipments (thousands)

Electric Gas Electric Gas

1972 2,871 2,366 1989 3,411 2,326

1973 3,532 2,381 1990 3,349 2,354

1974 3,126 1,871 1991 3,206 2,301

1975 2,281 1,553 1992 3,450 2,492

1976 2,355 1,750 1993 3,725 2,631

1977 3,183 1,683 1994 4,047 2,840

1978 3,361 1,725 1995 3,917 2,715

1979 3,123 1,726 1996 4,155 2,710

1980 2,606 1,476 1997 4,240 2,744

1981 2,389 1,435 1998 4,639 2,950

1982 2,077 1,312 1999 4,983 3,137

1983 3,033 1,509 2000 5,026 3,176

1984 3,348 1,662 2001 5,066 3,036

1985 3,440 1,740 2002 5,338 3,268

1986 3,620 2,006 2003 5,620 3,419

1987 3,719 2,249 2004 6,144 3,719

1988 3,589 2,359 2005 6,195 3,763

Sources: 2003–2005: AHAM Data Submittal, 2006; 1989–2002: AHAM Fact Books , 2000, 2003, 2005; 1972– 1988: Appliance Magazine, “Statistical Review,” 1990, 1993

b Shipments estimates from Appliance Magazine included exports. Thus, DOE reduced total electric and gas cooking product shipments by 3.2 and 4.0 percent, respectively (the average percentage of exports for the years 1989–1993 based on the five-year average difference between the AHAM Fact Book 2000 and Appliance Magazine ) to estimate total domestic shipments plus imports.

10-8

### 10.3.1.2 Markets and Model Calibration

For each general fuel category of cooktops and ovens, i.e., electric and gas, the market is primarily comprised of the following: replacement units for equipment that has been retired from service and units for new housing. In addition to normal replacements, DOE’s Shipments Model for each general category also assumed that a certain fraction of the stock would be replaced early. Total electric cooktop and oven shipments are represented by the following equation:



### Ship ( j ) = 

### Rpl ( j ) + 

### NI ( j ) + 

### ER ( j ) ELEC ELEC ELEC ELEC



### where:



### Ship ELEC 

### (j) = Total shipments of electric cooking products in year 

### j

, 

### Rpl ELEC 

### (j) = Replacement shipments in year 

### j

, 

### NI ELEC 

### (j) = Shipments to new households in year 

### j , and



### ER ELEC 

### (j) = Early replacement shipments in year 

### j

.

Total gas cooktop and oven shipments are represented by the same basic equation:



### Ship GAS 

### ( j ) = 

### Rpl ( j ) + 

### NI GAS 

### ( j ) + 

### ER ( j ) GAS GAS



### where:



### Ship GAS 

### (j) = Total shipments of gas cooking products in year 

### j

, 

### Rpl GAS 

### (j) = Replacement shipments in year 

### j

, 

### NI GAS 

### (j) = Shipments to new households in year 

### j , and



### ER GAS 

### (j) = Early replacement shipments in year 

### j

.

The sections below discuss in further detail all three of these markets for each general cooking product category.



### New Construction. To estimate shipments to new construction, DOE used forecasts of housing starts coupled with cooking product saturation data. In other words, to forecast the shipments for new construction for any given year, DOE multiplied the housing forecasts by the forecasted saturation of cooking products for new housing.

Table 10.3.5 presents historical and forecasted new housing starts based on EIA’s



### AEO2008 for the period 1990–2030. 1 

### AEO2008 provides three sets of housing starts: the Reference case, the High Economic Growth case, and the Low Economic Growth case. All three housing starts forecasts are presented in Table 10.3.5. DOE used the forecasts from the Reference case to estimate its shipments to new construction.

10-9

### New housing is comprised of single- and multi-family units (also referred to as “new housing completions”) and mobile home placements. For new housing completions and mobile home placements, the 

### AEO2008 data are based on actual completions through the year 2005. For the Reference case, the forecast shows a decline in housing completions from 2.22 million in 2005 to 1.7 million by 2030. For the period 2031−2042, DOE froze completions at the level in the year 2030 for all three economic forecasts.



### Table 10.3.5 Historical and Forecasted Housing Starts (millions)

Year Reference High Low Year Reference High Low

1990 1.38 1.38 1.38 2017 1.86 2.24 1.49

1991 1.18 1.18 1.18 2018 1.83 2.22 1.46

1992 1.41 1.41 1.41 2019 1.81 2.18 1.43

1993 1.54 1.54 1.54 2020 1.78 2.17 1.39

1994 1.76 1.76 1.76 2021 1.76 2.16 1.36

1995 1.69 1.69 1.69 2022 1.74 2.16 1.33

1996 1.82 1.82 1.82 2023 1.72 2.14 1.29

1997 1.49 1.49 1.49 2024 1.72 2.17 1.27

1998 1.54 1.54 1.54 2025 1.74 2.22 1.27

1999 1.64 1.64 1.64 2026 1.75 2.26 1.25

2000 1.70 1.70 1.70 2027 1.73 2.26 1.21

2001 1.72 1.72 1.72 2028 1.70 2.23 1.17

2002 1.75 1.75 1.75 2029 1.69 2.23 1.15

2003 1.79 1.79 1.79 2030 1.70 2.24 1.15

2004 1.80 1.80 1.80 2031 1.70 2.24 1.15

2005 2.22 2.22 2.22 2032 1.70 2.24 1.15

2006 1.93 1.93 1.93 2033 1.70 2.24 1.15

2007 1.44 1.44 1.44 2034 1.70 2.24 1.15

2008 1.09 1.21 0.96 2035 1.70 2.24 1.15

2009 1.35 1.54 1.16 2036 1.70 2.24 1.15

2010 1.68 1.93 1.42 2037 1.70 2.24 1.15

2011 1.88 2.19 1.57 2038 1.70 2.24 1.15

2012 1.88 2.20 1.56 2039 1.70 2.24 1.15

2013 1.86 2.19 1.54 2040 1.70 2.24 1.15

2014 1.86 2.20 1.52 2041 1.70 2.24 1.15

2015 1.88 2.27 1.51 2042 1.70 2.24 1.15

2016 1.90 2.29 1.51

Source: EIA, AEO2008.



### Table 10.3.6 presents historical market saturations of electric cooking products based on various sources, including AHAM’s 2005 

### Fact Book

, 7 

### Appliance Magazine

, 8 

### NFO World

9 11 12 

### Group, and EIA’s RECS for the years 1997 and 2001. The table presents market saturations for the overall housing stock and for new housing. Note that the overall electric cooking product saturations provided by AHAM and 

### Appliance Magazine are roughly the same. NFO breaks out

10-10

### the saturations of ranges, built-in ovens, and built-in cooktops but does not indicate which households have multiple cooking appliances. Thus, it is not possible to directly compare the saturations from NFO to the previous two sources. Because the forecast of shipments for the new housing market depends on the saturation of electric cooking products in new housing, DOE focused its attention on the market saturations for new housing that RECS provided for the years 1997 and 2001. DOE decided to use only the saturations for the year 2001 to estimate the shipments to new construction for future years. Because DOE conducted its shipments analysis by first forecasting overall electric cooking product shipments and then disaggregating the total shipments using the historical market share data in Table 10.3.6, it used the overall saturation of electric cooking products to estimate shipments to new construction. DOE based the saturation on the sum of the values for ranges, built-in ovens, and built-in cooktops, which is 77.5 percent.



### Table 10.3.6 Electric Cooking Products: Historical Market Saturations

Overall Housing New Housing

AHAM* Appl** NFO*** RECS †

All Elec All Elec Built-In Built-In Range Range Built-In Built-In Built-In
Year Products Products Ranges Oven Cooktop SC Standard SC Oven Std Oven Cooktop

1970 40.6%

1978 51.0%

1982 58.0%

1983 58.0%

1987 59.4%

1990 62.6% 60.0% 9.4% 9.2%

1991 56.7%

1992 56.4%

1993 57.5%

1994 58.5%

1995 61.0%

1996 60.2% 61.0% 9.2% 10.1%

1997 60.0% 26.6% 39.7% 8.1% 4.0% 6.5%

1998 60.5%

1999 60.0%

2000 60.0%

2001 66.3% 61.0% 62.0% 9.6% 9.4% 30.1% 32.5% 3.5% 1.7% 9.6%

2002 62.0%

2003 61.0%

2004 61.0%

2005 65.3%

Source: AHAM, Fact Book 2005 ; ** Source: Appliance Magazine, “The Saturation Picture,” September issues;

*** Source: NFO World Group; † Source: DOE-EIA, RECS, 1997, 2001.

10-11

### Table 10.3.7 presents historical market saturations of gas cooking products based on various sources, including AHAM’s 2005 

### Fact Book

, 6 

### Appliance Magazine

, 7 

### NFO World

8 10 11 

### Group, and EIA’s RECS for the years 1997 and 2001. The table presents market saturations for the overall housing stock and for new households. Note that the overall gas cooking product saturations provided by AHAM and 

### Appliance Magazine are roughly the same. NFO breaks out the saturations of ranges, built-in ovens, and built-in cooktops but does not indicate which households have multiple cooking appliances. Thus, it is not possible to directly compare the saturations from NFO to the previous two sources. Because the forecast of shipments for the new housing market depends on the saturation of gas cooking products in new housing, DOE focused its attention on the market saturations for new housing that RECS provided for the years 1997 and 2001. DOE decided to use only the saturations for the year 2001 to estimate the shipments to new construction for future years. Because DOE conducted its shipments analysis by first forecasting overall gas cooking product shipments and then disaggregating the total shipments using the historical market share data in Table 10.3.7, it used the overall saturation of gas cooking products to estimate shipments to new construction. So as not to overstate the overall saturation, DOE based the saturation on the sum of the values for ranges, built-in ovens, and built-in cooktops, which is 30.1 percent.

10-12

### Table 10.3.7 Gas Cooking Products: Historical Market Saturations

Overall Housing New Housing

AHAM* Appl** NFO*** RECS †

All Elec All Elec Built-In Built-In Range Range Built-In Built-In Built-In
Year Products Products Ranges Oven Cooktop SC Standard SC Oven Std Oven Cooktop

1970 57.7%

1978 48.0%

1982 42.7%

1983 42.7%

1987 42.2%

1990 38.7% 40.0% 3.1%

1991 45.7%

1992 46.0%

1993 44.8%

1994 45.0%

1995 42.0%

1996 39.0% 39.0% 2.9% 4.3%

1997 39.4% 8.2% 11.0% 1.0% 0.8% 7.0%

1998 40.2%

1999 40.1%

2000 40.0%

2001 37.8% 40.0% 38.0% 2.5% 4.8% 8.3% 12.4% 0.8% 2.4% 6.2%

2002 38.0%

2003 39.0%

2004 39.0%

2005 39.0%

Source: AHAM, Fact Book 2005 ; ** Source: Appliance Magazine, “The Saturation Picture,” September issues;

*** Source: NFO World Group; † Source: DOE-EIA, RECS, 1993, 1997, 2001.



### Replacements. DOE determined shipments to the replacement market using an accounting method that tracks the total stock of units by vintage. DOE estimated stocks of electric and gas cooking products by vintage by integrating historical shipments starting from the year 1972. Over time, some of the units will be retired and removed from the stock, thus triggering the shipment of a new unit. Because of the relationship between retirements and total stock, there is a strong correlation between past and future shipments, independent of efficiency

standards.

Depending on the vintage, a certain percentage of each type of unit will fail and need to be replaced. To determine when an electric cooking product fails, DOE used a product survival function based on a lifetime distribution with an average value of 19 years, and minimum and

10-13

### maximum values of 10 years and 28 years, respectively. For gas cooking products, DOE used a product survival function based on a lifetime distribution with an average value of 19 years, and minimum and maximum values of 12 years and 26 years, respectively. For a more complete discussion of cooking product lifetimes, refer back to section 8.2.3.1 of Chapter 8. Figure 10.3.1 shows the survival functions that DOE used to estimate replacement shipments.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Age (years)Percent Surviving Electric

Gas



### Figure 10.3.1 Electric and Gas Cooking Products: Retirement Functions

Model Calibration—Early Replacements. To calibrate estimated shipments with the historical data, DOE introduced into the model an early replacement market function. DOE assumed that replacements in this category occur at the rate of two percent of the surviving stock per annum over the time period 2005–2042. The early replacement rate is distributed among equipment of ages ranging from 10 to 28 years for electric cooking products and 12 to 26 years for gas cooking products; DOE weighted these equipment by their respective population in the surviving stock. The following equation illustrates the calculations:

_ AgeMax 1

_ 

### 1 ,age ) Stock( j

MWO 

### ( j )ER = 

# ∑ × _ 

### 1 × )Stock( j ERRate

=4 age 

# ∑

_ 

### 1 ,age ) Stock( j

age



### where:



### ER MWO 

### (j) = Number of cooking products replaced early in year 

### j

, 

### Stock (j-1, age) = Population of in-service units of a particular 

### age in year 

### j-1

,

10-14

### ERRate = Early replacement rate, and 

### AgeMax = Maximum age of the unit before it retires.



### 10.3.1.3 Base Case Shipments

Figures 10.3.2 and 10.3.3 show the forecasted shipments of electric and gas cooking products, respectively, in the base case (i.e., the case without new energy efficiency standards) and the historical shipments DOE used to calibrate the forecast.

0 1 2 3 4 5 6 7 8 9

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045

YearShipments (million) Historical Shipments

Modeled Shipments



### Figure 10.3.2 Electric Cooking Products: Historical and Base Case Shipments

Forecast

10-150.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045

YearShipments (million) Historical Shipments

Modeled Shipments



### Figure 10.3.3 Gas Cooking Products: Historical and Base Case Shipments

Forecast

Figures 10.3.4 and 10.3.5 present forecasted base case electric and gas cooking product shipments, respectively, disaggregated into the following market segments: shipments due to retirements, shipments to new housing, and shipments due to early replacements. Note that replacement shipments comprised 85–92 percent of total gas and electric cooking product shipments over the period 2006–2042. The percentage of total shipments represented by replacements is high because the gas and electric cooking product market is fairly mature.

10-160.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

2000 2005 2010 2015 2020 2025 2030 2035 2040

YearShipments (million) New Units

Early Repl Units

Normal Repl



### Figure 10.3.4 Electric Cooking Products: Disaggregated Base Case Shipments Forecast

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

2000 2005 2010 2015 2020 2025 2030 2035 2040

YearShipments (million) New Units

Early Repl Units

Normal Repl



### Figure 10.3.5	 Gas Cooking Products: Disaggregated Base Case Shipments Forecast

10-17

### 10.3.2 Microwave Ovens

Microwave ovens are a specific product category of the cooking products that DOE considered for this rulemaking. Although microwave oven shipments are tracked by configuration (i.e., countertop, convection, over-the-range), DOE represented microwave ovens as a single product class.



### 10.3.2.1 Historical Shipments

DOE used data on historical shipments (i.e., domestic shipments and imports) to calibrate its Shipments Model for microwave ovens. DOE’s sources for historical shipments data were:

3 14 7 

### (1) AHAM 

### Factbooks for the years 2000, 2003 , and 2005 covering the period 1989−2005 and

4,5,6 

### (2) 

### Appliance Magazine for the period 1981−1988. c 

### Table 10.3.8 summarizes the historical microwave oven shipments data.



### Table 10.3.8 Microwave Ovens: Historical Shipments, Domestic plus Imports

Year Shipments (thousands) Year Shipments (thousands)

1980 3,144 1993 7,240

1981 3,864 1994 8,574

1982 3,469 1995 8,162

1983 5,012 1996 8,771

1984 7,638 1997 8,883

1985 9,543 1998 10,365

1986 10,912 1999 11,422

1987 10,891 2000 12,644

1988 9,479 2001 13,446

1989 9,778 2002 13,311

1990 7,693 2003 14,274

1991 6,623 2004 15,526

1992 7,190 2005 13,862

Source: 1989–2005: AHAM Fact Books , 2000, 2003, 2005; 1981–1988: Appliance Magazine, “Statistical Review,” 1990, 1993.



### 10.3.2.2 Markets and Model Calibration

The market for microwave ovens is primarily comprised of the following: replacement units for equipment that has been retired from service and units for new housing. In addition to normal replacements, DOE’s Shipments Model also assumed that a certain fraction of the stock

c Shipments estimates from Appliance Magazine included exports. Thus, DOE reduced total shipments by 12.3 percent (the average percentage of exports for the years 1989–1993 based on the five-year average difference

between the AHAM Fact Book 2000 and Appliance Magazine ) to estimate total domestic shipments plus imports.

10-18

### would be replaced early. Total microwave oven shipments are represented by the following equation:

Ship ( j ) = Rpl ( j ) + NI ( j ) + ER ( j ) MWO MWO MWO MWO



### where:



### Ship MWO 

### (j) = Total shipments of microwave ovens in year 

### j

, 

### Rpl MWO 

### (j) = Replacement shipments in year 

### j

, 

### NI MWO 

### (j) = Shipments to new households in year 

### j , and



### ER MWO 

### (j) = Early replacement shipments in year 

### j

.

The sections below discuss all three of these markets in further detail.

New Construction. To estimate shipments to new construction, DOE used forecasts of housing starts coupled with microwave oven saturation data. In other words, to forecast the shipments for new construction for any given year, DOE multiplied the housing forecasts by the forecasted saturation of microwave ovens for new housing.

Historical and forecasted new housing starts were presented earlier for cooking products. Refer back to Table 10.3.5 for the housing starts data.

Table 10.3.9 presents historical market saturations of microwave ovens based on various

7 8 9 

### sources, including AHAM’s 2005 

### Fact Book

, 

### Appliance Magazine , NFO World Group, and

10 11 12 

### EIA’s RECS for the years 1993, 1997, and 2001. The table presents market saturations for the overall housing stock, new households only, and households with two or more microwave ovens. Because the forecast of shipments for the new housing market depends on the saturation of microwave ovens in new housing, DOE focused its attention on the market saturations for new housing as well as the saturations of housing with multiple microwave ovens. According to RECS, microwave oven saturation in new housing for 1993, 1997, and 2001 was 92 percent,

87.5 percent, and 93.3 percent, respectively. According to NFO, households with more than one microwave oven represented 3.7 percent, 4.9 percent, and 4.1 percent of the housing market for the years 1990, 1996, and 2001, respectively. Therefore, DOE incremented the saturation levels for new housing estimated by RECS by the amounts provided by NFO to estimate the new housing saturations. As a result, DOE determined the effective saturation of microwave ovens in new housing to be 95.7 percent, 92.4 percent, and 97.4 percent for 1993, 1997, and 2001, respectively. Because 2001 is the most recent year in which data are available, DOE decided to freeze the saturation at 97.4 percent to estimate the shipments to new construction for future

years.

10-19

### Table 10.3.9 Microwave Ovens: Historical Market Saturations

Year Overall Household New

Households Households

with ≥ 2 ovens

AHAM* Appl** NFO*** RECS † RECS † NFO***

1978 7.2%

1982 25.6%

1983 33.3%

1987 65.9%

1990 82.7% 81.1% 3.9%

1991 85.2%

1992 84.5%

1993 85.5% 84.1% 92.0%

1994 88.8%

1995 88.9%

1996 90.6% 88.7% 4.9%

1997 91.0% 83.0% 87.5%

1998 91.3%

1999 93.1%

2000 95.6%

2001 90.7% 95.7% 91.8% 86.1% 93.3% 4.1%

2002 95.0%

2003 95.0%

2004 95.5%

2005 89.3%

* Source: AHAM, Fact Book, 2005; ** Source: Appliance Magazine, “The Saturation Picture,” September issues; *** Source: NFO World Group, 2001; † Source: DOE-EIA, RECS, 1993, 1997, and 2001.



### Replacements. DOE determined shipments to the replacement market using an accounting method that tracks the total stock of units by vintage. DOE estimated a stock of microwave ovens by vintage by integrating historical shipments starting from the year 1981. Over time, some units will be retired and removed from the 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 microwave oven fails, DOE used a product survival function based on a lifetime distribution with an average value of nine years, and minimum and maximum values of seven years and 10 years, respectively. For a more complete discussion of microwave lifetimes, refer back to section 8.2.3.2 of Chapter 8. Figure

10.3.6 shows the survival function that DOE used to estimate replacement shipments.

10-200% 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

Avge (years)Percent Surviving



### Figure 10.3.6 Microwave Ovens: Survival and Retirement Functions

Model Calibration—Early Replacements. To calibrate estimated shipments with the historical data, DOE introduced into the model an early replacement market function. DOE determined that replacements in this category occur at the rate of two percent of the surviving stock per annum over the time period 2005–2042. The early replacement rate is distributed among equipment of ages ranging from four through 10 years; DOE weighted these equipment by their respective population in the surviving stock. The following equation illustrates the

calculations:

_ AgeMax 1

_ 

### Stock( j 1 ,age )

_ 

### ER MWO 

### ( j ) = 

# ∑ × ERRate × Stock( j 1 )

_ age =4 

# ∑ Stock( j 1 ,age )

age



### where:



### ER MWO 

### (j) = Number of microwave ovens replaced early in year 

### j

, 

### Stock (j-1, age) = Population of in-service units of a particular 

### age in year 

### j-1

, 

### ERRate = Early replacement rate, and 

### AgeMax = Maximum age of the unit before it retires.

10-21

### 10.3.2.3 Base Case Shipments

Figure 10.3.7 shows the forecasted shipments in the base case (i.e., the case without new energy efficiency standards) and the historical shipments DOE used to calibrate the forecast.

0 2 4 6 8 10 12 14 16 18 20 22

1985 1995 2005 2015 2025 2035 2045

YearShipments (million) Historical Shipments

Modeled Shipments



### Figure 10.3.7 Microwave Ovens: Historical and Base Case Shipments

Forecast

Figure 10.3.8 presents forecasted base case microwave oven shipments disaggregated into the following market segments: shipments due to retirements, shipments to new housing, and shipments due to early replacements. Note that replacement shipments comprised 85–92 percent of total microwave oven shipments over the period 2006–2035. The percentage of total shipments represented by replacements is high because the microwave oven market is fairly

mature.

10-220.0 5.0 10.0 15.0 20.0 25.0

2000 2005 2010 2015 2020 2025 2030 2035 2040

YearShipments (million units) New Units

Early Repl

Normal Repl



### Figure 10.3.8 Microwave Ovens: Disaggregated Base Case Shipments Forecast

10.3.3 Commercial Clothes Washers

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. As described in Chapter 8, section 8.2.6.4, DOE estimated that 79.7 percent of the market is comprised of top-loading washers and 20.3 percent is comprised of front-loading washers. DOE estimated that the market share of top-loading and front-loading washers would remain fixed at 79.3 percent and 20.3 percent, respectively, through the forecast period.

DOE adopted the following approach for estimating shipments. The approach relies on aggregate historical shipments data for all commercial clothes washers. Tracking of product stock and vintage relies on a retirement 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-family 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.

10-23

### 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 forecast 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 clothes washer market, respectively. 5



### The third assumption pertains to the use of the U.S Bureau of Economic Analysis’ quantity index data to validate historical shipments data. 6 

### 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 was constant over the period 1977–2004. Based on this assumption, DOE derived quantity index data specific to the commercial clothes washers of interest to this rulemaking and used the data to validate the historical shipments.



### 10.3.3.1 Historical Shipments

DOE used historical shipments data (i.e., domestic shipments and imports) to calibrate its Shipments Model. It used two sources to establish historical shipments: (1) data AHAM provided for the period 2002–2005 7 

### and (2) data from 

### Appliance Magazine 8 

### for the period 1988– 1998. For the period 1999–2001, DOE interpolated between the shipments for 1998 and 2002. For the period 1979–1987, DOE used the shipments in1989 as the reference point and the corresponding Census Bureau’s quantity index data to estimate shipments. Lastly, DOE used a five-year moving average to backcast shipments for the period 1972−1978. Table 10.3.10 summarizes the historical commercial clothes washer shipments data.

10-24

### Table 10.3.10 Commercial Clothes Washers: Historical Shipments, Domestic plus Imports

Year Shipments (thousands) Year Shipments (thousands)

1972 167 1989 215

1973 167 1990 225

1974 166 1991 193

1975 167 1992 188

1976 167 1993 190

1977 167 1994 205

1978 166 1995 209

1979 164 1996 232

1980 170 1997 241

1981 171 1998 265

1982 163 1999 239

1983 163 2000 215

1984 167 2001 194

1985 176 2002 175

1986 186 2003 191

1987 194 2004 178

1988 213 2005 177

Sources: 2002–2005: AHAM Data Submittal, 2006; 1988–1998: Appliance Magazine, “Statistical Review,” 1998 and 1999. 1972–1987: Based on Census Bureau’s Quantity Index Data. 1999–2001: DOE interpolated values.



### As indicated in Table 10.3.10, there was a significant drop in commercial clothes washer shipments between 1998 and 2002. Because DOE used different data sources to estimate the shipments in 1998 and 2002, it validated the shipments data through a review of the U.S. Bureau of Economic Analysis’ quantity index data. DOE compared the historical shipments data against the quantity index for commercial laundry equipment (represented by North American Industry Classification System (NAICS) code 333312). The largest product group in this industry is washers and extractors, which accounts for almost 40 percent of sales and about 25 percent of the shipment value. 18 

### As shown in Figure 10.3.9 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 2002 was real.

10-250 20 40 60 80 100 120 140 160

1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013Quantity Index (2000 base)

0.00 0.05 0.10 0.15 0.20 0.25 0.30Shipments in millions

QI Shipments QI-based Shipments Pre-1988 shipments

based on QI Extrapolated

after 2005



### Figure 10.3.9 Commercial Clothes Washer Shipments and Trend in Quantity

Index

10.3.3.2 Markets and Model Calibration

The market for commercial clothes washers primarily comprises replacement units for equipment that has been retired and units for new housing. Total commercial clothes washers shipments are represented by the following equation:

Ship ( j ) = Rpl ( j ) + NI ( j ) CCW CCW CCW



### where:



### Ship CCW 

### (j) = Total shipments of commercial clothes washers in year 

### j

, 

### Rpl CCW 

### (j) = Replacement shipments in year 

### j , and



### NI CCW 

### (j) = Shipments to new households in year 

### j

.

The sections below discuss both of these markets in further detail.

New Construction. To forecast 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.

To estimate shipments to new construction, DOE used forecasts of multi-family housing starts coupled with commercial clothes washer saturation data. In other words, to forecast the shipments for new construction for any given year, DOE multiplied the multi-housing forecasts by the forecasted saturation of commercial clothes washers for new multi-family housing.

10-26

### Because DOE used only multi-family housing starts as a driver for new construction shipments, the saturation represents washers in both the multi-family housing and laundromat markets.

Table 10.3.11 presents historical and forecasted new multi-family housing starts based on

EIA’s 

### AEO2008 for the period 1990–2030. 1 

### AEO2008 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 forecasts are presented in Table 10.3.11. DOE used only the forecasts from the Reference case to estimate its shipments to new construction. For the period 2031−2042, DOE froze completions at the level in the year 2030 for all three economic

forecasts.



### Table 10.3.11 Historical and Forecasted Multi-Family Housing Starts (millions)

Year Reference High Low Year Reference High Low

1990 0.30 0.30 0.30 2017 0.35 0.42 0.29

1991 0.17 0.17 0.17 2018 0.35 0.42 0.29

1992 0.17 0.17 0.17 2019 0.35 0.41 0.30

1993 0.16 0.16 0.16 2020 0.35 0.41 0.30

1994 0.26 0.26 0.26 2021 0.35 0.41 0.30

1995 0.28 0.28 0.28 2022 0.36 0.41 0.30

1996 0.32 0.32 0.32 2023 0.36 0.41 0.30

1997 0.34 0.34 0.34 2024 0.36 0.42 0.30

1998 0.35 0.35 0.35 2025 0.36 0.43 0.30

1999 0.34 0.34 0.34 2026 0.37 0.44 0.30

2000 0.34 0.34 0.34 2027 0.38 0.45 0.30

2001 0.33 0.33 0.33 2028 0.38 0.46 0.30

2002 0.35 0.35 0.35 2029 0.39 0.47 0.30

2003 0.35 0.35 0.35 2030 0.39 0.48 0.31

2004 0.34 0.34 0.34 2031 0.39 0.48 0.31

2005 0.34 0.34 0.34 2032 0.39 0.48 0.31

2006 0.34 0.34 0.34 2033 0.39 0.48 0.31

2007 0.30 0.30 0.30 2034 0.39 0.48 0.31

2008 0.25 0.31 0.20 2035 0.39 0.48 0.31

2009 0.29 0.35 0.22 2036 0.39 0.48 0.31

2010 0.31 0.38 0.24 2037 0.39 0.48 0.31

2011 0.33 0.41 0.26 2038 0.39 0.48 0.31

2012 0.34 0.42 0.26 2039 0.39 0.48 0.31

2013 0.34 0.42 0.26 2040 0.39 0.48 0.31

2014 0.34 0.41 0.26 2041 0.39 0.48 0.31

2015 0.34 0.42 0.27 2042 0.39 0.48 0.31

2016 0.35 0.42 0.28

Source: EIA, AEO2007.

10-27

### 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 below.) Table 10.3.12 shows the historical saturation of commercial clothes washers in new multi-family buildings.



### Table 10.3.12 Commercial Clothes Washers: Historical New Multi-Family Building

Saturations

Year Saturation

1990 19.4%

1991 14.7%

1992 12.4%

1993 14.3%

1994 13.8%

1995 12.6%

1996 15.8%

1997 14.6%

1998 18.7%

Average 15.1%



### DOE assumed that the average saturation over the period 1990−1998 of 15.1 percent would remain constant over the period 1999−2042. CEE’s report on the commercial laundry industry estimates a 14 percent saturation (i.e., approximately 14 percent of total domestic laundry is done in non-residential washers). CEE’s 14 percent figure is close to the 15.1 percent saturation value that DOE determined. Thus, DOE has confidence that its calculated saturation is representative of the commercial clothes washer market. Figure 10.3.10 shows the historical variation in the new construction saturation as well as the forecasted saturation for the years after

1998.

10-280% 5% 10% 15% 20% 25%

1985 1995 2005 2015 2025 2035 2045

YearCommercial Laundry Saturation in new MultiFamily Housing



### Figure 10.3.10	 Commercial Laundry Saturation in New Multi-Family Housing

Replacements. DOE determined shipments to the replacement market using an accounting method that tracks the total stock of units by vintage. DOE estimated a stock 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. For the commercial clothes washer Shipments Model, DOE combined the average 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, refer back to section 8.2.3.3 of Chapter 8. Figure 10.3.11 shows the survival function that DOE used to estimate replacement shipments.

10-290% 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 (years)Percent Surviving



### Figure 10.3.11	 Commercial Clothes Washers: Survival and Retirement

Functions

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 large due to the significant drop in shipments after 1998 (as discussed above in Section 10.3.3.1). Staring in 1999 and extending to 2005, DOE estimated the share of retired units that were not replaced. The share of retired units not replaced fluctuated between nine and 31 percent. Table 10.3.13 shows the share of retired units not replaced between 1999 

### – 2005.



### Table 10.3.13 Commercial Clothes Washers: Historical New Multi-Family Building

Saturations

Year Saturation

1999 9.2%

2000 12.9%

2001 19.7%

2002 25.2%

2003 12.1%

2004 30.8%

2005 18.2%
Average 18.3%

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### To determine the share of retired units not replaced after the year 2005, DOE reviewed data from the Census Bureau’s American Housing Survey (AHS) on the saturation of in-unit washers (i.e., residential washers used in multi-family units) in the multi-family stock and new multi-family construction. 9 

### 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 commonarea laundry facilities. Table 10.3.14 shows the saturation of in-unit washers in both the multifamily stock and new construction from 1997



### – 2005.



### Table 10.3.14 Multi-Family In-Unit Washer Saturations

In-Unit Washer Saturation

Multi-Family Housing 1997 1999 2001 2003 2005

New Construction 70.4% 64.4% 68.7% 68.1% 73.4%

Stock 32.3% 32.7% 35.6% 36.1% 37.3%

Source: AHS, 1997

– 2005



### As evident from Table 10.3.14, the saturation of in-unit washers in new construction has stayed relatively constant while the saturation in the multi-family building stock has increased by approximately 16 percent. Because the saturation of in-unit washers in new construction has stayed relatively constant, there must be another driver for the increase of in-unit washers in the building stock. DOE estimates that the growth in in-unit washer saturations in the 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 apparent trend, DOE used the average percent of non-replacements over the 1999



### – 2005 timeframe—18 percent—and maintained it over the entire forecast period of 2006



### – 2042.



### 10.3.3.3 Base Case Shipments

Figure 10.3.12 shows the forecasted commercial clothes washer shipments in the base case (i.e., the case without new energy efficiency standards) and the historical shipments DOE used to calibrate the forecast. In the figure below, note that the replacement shipments for the period 1998



### ─ 2042 are lower than the actual retired units. The area between the two lines showing retired units and replacement shipments represents the retired units that were notreplaced. To emphasize, all commercial clothes washers shipments, i.e., top-loading and frontloading units, are represented in the shipments shown in Figure 10.3.12. As previously stated,DOE estimated that 79.7 percent of shipments in every year of the forecast are comprised of toploading washers and 20.3 percent of shipments in every year are comprised of front-loading

washers.

10-310.00 0.05 0.10 0.15 0.20 0.25 0.30

1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

YearShipments (million) Historical Shipments Total Modeled Shipments

Retired Modeled Units Replacement Modeled Shipments New Construction Modeled Shipments



### Figure 10.3.12	 Commercial Clothes Washers: Historical and Base Case Shipments Forecast

Figure 10.3.13 presents forecasted base case commercial clothes washer shipments disaggregated into shipments due to retirements and shipments for new construction. DOE determined that replacements accruing from retirements range from 80 percent to 85 percent of total commercial clothes washer shipments over the period 2006–2042.

10-320.00 0.05 0.10 0.15 0.20 0.25

2000 2005 2010 2015 2020 2025 2030 2035 2040

YearShipments (million) New Units

Replacement Units



### Figure 10.3.13	 Commercial Clothes Washers: Disaggregated Base Case Shipments Forecast

10.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME

IMPACTS

DOE conducted a literature review and an analysis of appliance price and efficiency data to estimate the combined effects on product shipments from increases in equipment purchase price, decreases in equipment operating costs, and changes to household income. Appendix 10A provides a detailed explanation of the methodology DOE used to quantify the impacts from the

above variables.

In the literature, DOE found only a few studies of appliance markets that are relevant to this rulemaking analysis, and identified no studies that use time-series data of equipment price and shipments data after 1980. The information that can be summarized from the literature suggests that the demand for appliances is price inelastic. Other information in the literature suggests that appliances are a normal good, such that rising incomes increase the demand for appliances. Finally, the literature suggests that consumers use relatively high implicit discount

rates d 

### when comparing appliance prices and appliance operating costs.

d A high implicit discount rate with regard to operating costs means that consumers do not put much economic value on the operating cost savings realized from more-efficient appliances. In other words, consumers are much more concerned with higher purchase prices.

10-33

### DOE did not find enough equipment purchase price and operating cost data to perform a complex analysis of dynamic changes in the appliance market. Rather, it used purchase price and efficiency data specific to residential refrigerators, clothes washers, and dishwashers over the period 1980–2002 to evaluate broad market trends and conduct simple regression analyses. These data indicate that there has been a rise in appliance shipments and a decline in appliance purchase price and operating costs over the time period. Household income has also risen during this time. To simplify the analysis, DOE combined the available economic information into one variable, termed the 

### relative price , and used this variable in an analysis of market trends, as well as to conduct a regression analysis. The 

### relative price is defined with the following expression:

TP PP + PVOC RP = = Income Income

where:



### RP = Relative price,



### TP = Total price,



### Income = Household income,



### PP = Appliance purchase price, and



### PVOC = Present value of operating cost.

In the above equation, DOE used an implicit discount rate of 37 percent to determine the present value of operating costs.

DOE’s analysis of market trends suggests that the 

### relative price elasticity of demand for the three appliances is relatively inelastic (i.e., under 1.0). DOE’s regression analysis suggests

that the 

### relative price elasticity of demand, averaged over the three appliances, is -0.34. For example, a 

### relative price increase of 10 percent results in a shipments decrease of 3.4 percent. Note that, because the 

### relative price elasticity incorporates the impacts from three effects (i.e., purchase price, operating cost, and household income), the impact from any single effect is mitigated by changes from the other two effects. The 

### relative price elasticity of -0.34 is consistent with estimates in the literature. Nevertheless, DOE stresses that the measure is based on a small data set, using simple statistical analysis. More important, the measure is based on an assumption that economic variables, including purchase price, operating costs, and household income, explain most of the trend in appliances per household in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price elasticity of demand provides a reasonable assessment of the impact that purchase price, operating cost, and household income have on product shipments.

Because DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time period, it needed to consider how the 

### relative price elasticity is affected once a new

standard takes effect. DOE considered the 

### relative price elasticitiy provided above to be a shortrun value. It was unable to identify sources specific to household durable goods, such as

10-34

### appliances, to indicate how short-run and long-run price elasticities differ. Therefore, to estimate

how the 

### relative price elasticity changes over time, DOE relied on a study pertaining to

automobiles. 10 

### This study shows that the automobile price elasticity of demand changes in the years following a purchase price change. With increasing years after the purchase price change, the price elasticity becomes more inelastic until it reaches a terminal value around the tenth year after the price change. Table 10.4.1 shows the relative change in the price elasticity of demand for automobiles over time. As shown in the table, DOE developed a time series of 

### relative price elasticities for home appliances based on the relative change in the automobile price elasticity of demand. For years not shown in the table below, DOE performed a linear interpolation to obtain

the 

### relative price elasticity.



### Table 10.4.1 Change in Relative Price 

### Elasticity following a Purchase Price Change
Years Following Price Change

1 2 3 5 10 20
Relative Change in Elasticity to 1 st year 1.00 0.78 0.63 0.46 0.35 0.33

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11



### Based on the following equation, DOE estimated standards case shipments by incorporating the impact of the 

### relative price into the base case shipments forecast. Note that in the equation below, the 

### relative price and the 

### relative price elasticity are functions of the year because both change with time.

_ 

### Ship ( j ) = 

# ( Rpl ( j ) + NI ( j ) + M ( j ) 

# )

× 

# ( 1 e ( j ) × 

### Δ RP( j ) 

# )

STD _ p BASE _ p BASE _ p BASE _ p RP



### where:



### Ship STD_p 

### (j) = Total 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) = Units installed in Market 

### M under the base case of product 

### p in year 

### j ( 

### M represents early replacements for cooking products and microwave ovens),



### e RP 

### (j)= Relative price elasticity in year 

### j (equals -0.34 for year 1), and



### Δ RP(j)= Change in 

### relative price due to a standard level in year 

### j

.



### 10.5 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 forecast 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 Models. The affected stock quantifies the impact that new product shipments have on the appliance stock due to a TSL.

10-35

### Therefore, the affected stock consists of those in-service units that are purchased in or after the year the standard has taken effect, as described by the following equation:

_ j Std _ yr



### = p 

### ( j ) Aff Stock + p 

### Ship ( j ) 

# ∑

p 

### ( age ) Stock

=1 age 

### where:



### 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 = Effective date of the standard .

As noted in the above 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 2012. Thus, all appliances purchased starting on the first day of the year 2012 are affected by the standard level.



### 10.6 RESULTS

The following sections show the shipments forecasts for the various TSLs that DOE considered for each of the products. The TSLs are identified and described in Chapter 9.



### 10.6.1 Cooktops and Ovens

Because the combined market of electric and gas cooking products is completely saturated, DOE assumed that all electric and gas cooking product TSLs would neither impact base case shipments nor cause shifts in electric and gas cooking product market shares. Thus, DOE’s Shipments Model for electric and gas cooking products does not incorporate the use of 

### relative price elasticities. As a result, forecasted shipments for all cooking product TSLs are equivalent to the base case shipments forecasts. Refer back to Figures 10.3.2 and 10.3.3 to review the base case shipments forecasts for electric and gas cooking products, respectively.



### 10.6.2 Microwave Ovens

Tables 10.6.1 and 10.6.2 show the cumulative shipments impacts over the period 2012– 2042 caused by each of the TSLs considered for microwave ovens. Table 10.6.1 shows the results for the four energy factor (EF) TSLs while Table 10.6.2 shows the results for the four standby power TSLs. For all TSLs, shipments are forecasted to decrease—the effects from the increase in product purchase prices offset the effects from decreased operating costs, resulting in a net decrease in shipments. Figure 10.6.1 compares the shipments forecasts for the base case

10-36

### and the four EF TSLs. Figure 10.6.2 compares the shipments forecasts for the base case and four standby power TSLs. In Figures 10.6.1 and 10.6.2, the difference between the base case and TSL shipments forecasts depicts the annual shipments reductions caused by the TSLs. As can be seen in Figure 10.6.1, the decrease in forecasted shipments is significant for all four EF TSLs. In Figure 10.6.2, only TSL 4b is shown as the annual shipments forecasts for TSLs 1b through 3b are not dramatically different than the base case.



### Table 10.6.1	 Microwave Ovens: Cumulative Shipments Impacts Caused by EF Standards

over the Period 2012–2042

TSL Energy Factor Cumulative Impacts (million)

1a 0.586 -5.12

2a 0.588 -10.56

3a 0.597 -19.04

4a 0.602 -29.71



### Table 10.6.2 Microwave Ovens: Cumulative Shipments Impacts Caused by Standby

Power Standards over the Period 2012–2042

TSL Standby Power (W) Cumulative Impacts (million)

1b 2.0 -0.17

2b 1.5 -0.57

3b 1.0 -1.65

4b 0.02 -6.61

13 14 15 16 17 18 19 20 21 22

2 00

8 2010 2012
2 01

4 2016
2 018
2 0 20 2022
2 02

4
2 0 26 2028
2 03

0 2032
2 034
2 03

6 2038
2 040
2 0 42

YearShipments (million) Base Case

Std Level 1a

Std Level 2a

Std Level 3a

Std Level 4a



### Figure 10.6.1 Microwave Ovens: Base Case and TSL Shipments Forecasts for EF

Standard Levels

10-3713 14 15 16 17 18 19 20 21 22

2 00

8
2 01

0
2 01

2
2 01

4
2 01

6
2 01

8
2 02

0
2 02

2
2 02

4
2 02

6
2 02

8 20 3 0 20 3 2
2 03

4
2 03

6 20 3 8 20 4 0 20 4 2

YearShipments (million) Base Case

Std Level 4b



### Figure 10.6.2 Microwave Ovens: Base Case and TSL Shipments Forecasts for Standby Power Standard Levels

10.6.3 Commercial Clothes Washers

Tables 10.6.3 and 10.6.4 show the cumulative shipments impacts over the period 2012– 2042 caused by each of the TSLs considered for top-loading and front-loading commercial clothes washers, respectively. For all TSLs, shipments are forecasted to decrease—the effects from the increase in product purchase prices offset the effects from decreased operating costs, resulting in a net decrease in shipments. Figure 10.6.3 compares the shipments forecasts for the base case and each of the three TSLs for top-loading washers. In Figure 10.6.3, the difference between the base case and TSL shipments forecasts depicts the annual shipments reductions caused by the TSLs. Figure 10.6.4 compares the shipments forecasts for the base case and each of the four TSLs for front-loading washers.

10-38

### Table 10.6.3 Top-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042

TSL Modified Energy Factor/

Water Factor Cumulative Impacts (thousands)

1 1.42/9.5 -155

2 1.60/8.5 -328

3 1.76/8.3 -374



### Table 10.6.4 Front-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042

TSL Modified Energy Factor/

Water Factor Cumulative Impacts (thousands)

1 1.80/7.5 0

2 2.00/5.5 -1

3 2.20/5.1 -13

4 2.34/4.4 -26

0.132 0.136 0.140 0.144 0.148 0.152 0.156 0.160 0.164 0.168 0.172

2 00

8
2 0

10 2012 2014
2 016
2 01

8
2 0 20 2022
2 024
2 026
2 02

8
2 0 30 2032
2 034
2 03

6
2 03

8
2 0 40 2042

YearShipments (million) Base Case

Std Level 1

Std Level 2

Std Level 3



### Figure 10.6.3 Top-Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts

10-390.036 0.038 0.040 0.042 0.044 0.046 0.048

20

0 8 20 1 0 20 1 2 20 14 20 16 20 1 8 20 2 0 20 22 20 24 20 2 6 20 2 8 20 30 20 32 20 3 4 20 3 6 20 38 20 40 20 4 2

YearShipments (million) Base Case

Std Level 1

Std Level 2

Std Level 3

Std Level 4



### Figure 10.6.4 Front-Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts

10-40

### REFERENCES

1 

### U.S. Department of Energy-Energy Information Administration. 

### Annual Energy Outlook 2007 with Projections to 2030 , February, 2008. Washington, DC. DOE/EIA-0383(2008).

2 

### Association of Home Appliance Manufacturers. 

### AHAM Data on Cooking Products for Efficiency Standards Rulemaking , August 23, 2006. Letter to Building Technologies Program, U.S. Department of Energy.

3 

### U.S. Department of Energy-Office of Codes and Standards. 

### Supplemental Analysis for Ranges and Ovens, Supplemental Chapter 3 – Projected National Impacts of Standard

Levels , 1998. Washington, DC.

< http://www.eere.energy.gov/buildings/appliance_standards/residential/pdfs/chap3_sup.pdf >

4 

### Appliance Recycling Information Center. 

### INFOBulletin #8, Applications in Appliances

, March 2005. Washington, DC.

< http://www.aham.org/industry/ht/action/GetDocumentAction/id/5370 >

5 

### Consortium for Energy Efficiency. 

### Commercial Family-Sized Washers: An Initiative Description of the Consortium for Energy Efficiency , 1998. Boston, MA.

6 

### U.S. Department of Commerce-Bureau of Economic Analysis. 

### Industry Economic Accounts, Gross-Domestic-Product-(GDP)-by-Industry-Data, 1998-2005 NAICS Data: GDPbyInd_SHIP_NAICS and SIC Data: GDPbyInd_SHIP_SIC, Commercial Laundry Quantity Index Data, NAICS code 333312 . Washington, DC.

< http://preview.bea.gov/industry/gdpbyind_data.htm >

7 

### Association of Home Appliance Manufacturers. 

### AHAM Data on Commercial Clothes Washers for DOE Efficiency Standards Rulemaking , October 12, 2006. Letter to Building Technologies Program, U.S. Department of Energy.

8 

### Statistical Review. 

### Appliance Magazine , April, 1998, 1999.

9 

### U.S. Census Bureau, Housing and Household Economic Statistics Division. 

### The American Housing Survey, 1997—2005. Washington, DC.

< http://www.census.gov/hhes/www/housing/ahs/ahs.html >

10 

### S. Hymans. Consumer Durable Spending: Explanation and Prediction, 

### Brookings Papers on Economic Activity , 1971. Vol. 1971, No. 1, pp. 234-239.

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