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
 

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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
 

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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 

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_ _ 

 

 

 

 

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: 

Shipp(j) = Total shipments of product p in year j,
 

Rplp(j) = Units of product p retired and replaced in year j, and
 

NIp(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 trendsa 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. 

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Stockp (j-1) = Total stock of in-service appliances in year j-1, 

probRtr (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, probRtr(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. 

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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: 

probRtr ( age ) = 0 for age < AgeMin 

1 

prob Rtr ( age ) = for AgeMin ≤ age ≤ AgeMax _ AgeMax AgeMin 

probRtr ( age ) = 1 for age > AgeMax 

where: 

probRtr(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. 

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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 2003–20052 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., 

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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. 

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Note that in Table 10.3.3, gas standard range market shares are broken down into 

equipment 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. 

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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. 

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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: 

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

RplELEC (j) = Replacement shipments in year j,
 

NIELEC (j) = Shipments to new households in year j, and
 

ERELEC (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: 

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

RplGAS (j) = Replacement shipments in year j,
 

NIGAS (j) = Shipments to new households in year j, and
 

ERGAS (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. 

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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 

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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. 

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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. 

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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 

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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 _ Stock( j 1,age )

MWO ( j )ER = ∑ × _ ERRate × 1)Stock( j

age =4 ∑ _ Stock( j 1,age )

age 

where: 

ERMWO (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,
 

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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 

Year 

Shipments (million) 

Historical Shipments 

Modeled Shipments 

Figure 10.3.2 Electric Cooking Products: Historical and Base Case Shipments 

Forecast 

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0.0 

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 

Year 

Shipments (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. 

10-16 

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0.0 

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 

Year 

Shipments (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 

Year 

Shipments (million) 

New Units 

Early Repl Units 

Normal Repl 

Figure 10.3.5	 Gas Cooking Products: Disaggregated Base Case 

Shipments Forecast 

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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. 

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

equation: 

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

where: 

ShipMWO (j) = Total shipments of microwave ovens in year j,
 

RplMWO (j) = Replacement shipments in year j,
 

NIMWO (j) = Shipments to new households in year j, and
 

ERMWO (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. 

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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. 

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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 

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: 

ERMWO (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.
 

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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 

Year 

Shipments (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. 

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0.0 

5.0 

10.0 

15.0 

20.0 

25.0 

2000 2005 2010 2015 2020 2025 2030 2035 2040 

Year 

Shipments (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. 

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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 2002–20057 and (2) data from Appliance Magazine8 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. 

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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. 

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0 

20 

40 

60 

80 

100 

120 

140 

160 

1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 

Quantity Index (2000 base) 

0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

0.30 

Shipments 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: 

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

RplCCW (j) = Replacement shipments in year j, and 

NICCW (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. 

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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. 

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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. 

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0% 

5% 

10% 

15% 

20% 

25% 

1985 1995 2005 2015 2025 2035 2045 

Year 

Commercial 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. 

10-29 

EE-2006-STD-0127 COMMENT 41.2
 

 

 

 

 

 

 

 

 

 

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 (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 common 

area 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 not 

replaced. 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. 

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0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

0.30 

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

Year 

Shipments (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. 

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0.00 

0.05 

0.10 

0.15 

0.20 

0.25 

2000 2005 2010 2015 2020 2025 2030 2035 2040 

Year 

Shipments (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. 

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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 

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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 1st 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 )× ΔRP( j )) STD _ p BASE _ p BASE _ p BASE _ p RP 

where: 

ShipSTD_p(j) = Total shipments under the standards case of product p in year j, 

RplBASE_p(j) = Units of product p under the base case retired and replaced in year j, 

NIBASE_p(j) = Number of new construction installations under the base case of product p 

in year j, 

MBASE_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), 

eRP(j)= Relative price elasticity in year j (equals -0.34 for year 1), and 

ΔRP(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. 

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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 Aff Stock ( j ) + Ship ( j ) p ∑ p Stock ( age )

age =1

where: 

Aff Stockp(j) = Affected stock of units of product p of all vintages that are operational in 

year j, 

Shipp(j) = Shipments of product p in year j, 

Stockp(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 

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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 

2008 

2010 

2012 

2014 

2016 

2018 

2020 

2022 

2024 

2026 

2028 

2030 

2032 

2034 

2036 

2038 

2040 

2042 

Year 

Shipments (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 

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13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

2008 

2010 

2012 

2014 

2016 

2018 

2020 

2022 

2024 

2026 

2028 

2030 

2032 

2034 

2036 

2038 

2040 

2042 

Year 

Shipments (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 

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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 

2008 

2010 

2012 

2014 

2016 

2018 

2020 

2022 

2024 

2026 

2028 

2030 

2032 

2034 

2036 

2038 

2040 

2042 

Year 

Shipments (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 

10-39 

EE-2006-STD-0127 COMMENT 41.2
 

 

 

 

 

 

0.036 

0.038 

0.040 

0.042 

0.044 

0.046 

0.048 

2008 

2010 

2012 

2014 

2016 

2018 

2020 

2022 

2024 

2026 

2028 

2030 

2032 

2034 

2036 

2038 

2040 

2042 

Year 

Shipments (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 

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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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