CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS
9.1 INTRODUCTION

........................................................................................................... 9-1 9.2 SHIPMENTS MODEL METHODOLOGY

.................................................................... 9-2 9.3 DATA INPUTS AND M ODEL CALIBRATION

.......................................................... 9-4 9.3.1 Dishwashers

.............................................................................................................. ....... 9-5 9.3.1.1 Historical Shipments

...................................................................................... 9-5 9.3.1.2 Markets and Model Calibration

..................................................................... 9-6 9.3.1.3 Base Case Shipments

................................................................................... 9-10 9.3.2 Dehumidifiers

............................................................................................................ .... 9-12 9.3.2.1 Historical Shipments

.................................................................................... 9-12 9.3.2.2 Markets and Model Calibration

................................................................... 9-13 9.3.2.3 Base Case Shipments

................................................................................... 9-16 9.3.3 Cooktops and Ovens

...................................................................................................... 9 -17 9.3.3.1 Historical Shipments

.................................................................................... 9-21 9.3.3.2 Markets and Model Calibration

................................................................... 9-22 9.3.3.3 Base Case Shipments

................................................................................... 9-27 9.3.4 Microwave Ovens

.......................................................................................................... 9-30 9.3.4.1 Historical Shipments

.................................................................................... 9-30 9.3.4.2 Markets and Model Calibration

................................................................... 9-30 9.3.4.3 Base Case Shipments

................................................................................... 9-34 9.3.5 Commercial Clothes Washers

........................................................................................ 9-35 9.3.5.1 Historical Shipments

.................................................................................... 9-36 9.3.5.2 Markets and Model Calibration

................................................................... 9-38 9.3.5.3 Base Case Shipments

................................................................................... 9-43 9.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME IMPACTS

...................................................................................................................... 9-45 9.5 AFFECTED STOCK

..................................................................................................... 9-47 9.6 RESULTS

.................................................................................................................... .. 9-48 9.6.1 Dishwashers

.............................................................................................................. ..... 9-48 9.6.2 Dehumidifiers

............................................................................................................ .... 9-49 9.6.3 Cooktops and Ovens

...................................................................................................... 9 -50 9.6.4 Microwave Ovens

.......................................................................................................... 9-51 9.6.5 Commercial Clothes Washers

........................................................................................ 9-52

LIST OF TABLES
Table 9.3.1 Dishwashers: Historical Shipments, Do mestic plus Imports

.............................. 9-6 Table 9.3.2 Historical and Forecaste d Housing Starts (millions)

........................................... 9-8 Table 9.3.3 Dishwashers: Histor ical Market Saturations

....................................................... 9-9

9-iTable 9.3.4 Product Class Market Share of Dehumidifiers

.................................................. 9-12 Table 9.3.5 Dehumidifiers: Historical Shipments, Do mestic plus Imports

.......................... 9-13 Table 9.3.6 Dehumidifiers: Histor ical Market Saturations

................................................... 9-14 Table 9.3.7 Cooktops and Ovens: Product Categories, Product Types, and Product Classes

................................................................................................................ 9-18 Table 9.3.8 Electric Cooktops and Ovens: Historical Shipment Market Shares by Product Type

...................................................................................................... 9-19 Table 9.3.9 Gas Cooktops and Ovens: Histor ical Shipment Market Shares by Product Type

................................................................................................................... 9-20 Table 9.3.10 Cooktops and Ovens including Ranges and Built-Ins: Historical Shipments

........................................................................................................... 9-21 Table 9.3.11 Electric Cooking Products: Historical Market Saturations

............................... 9-24 Table 9.3.12 Gas Cooking Products: Hist orical Market Saturations

..................................... 9-25 Table 9.3.13 Microwave Ovens: Historical Shipments, Domestic plus Imports

................... 9-30 Table 9.3.14 Microwave Ovens: Hist orical Market Saturations

............................................ 9-32 Table 9.3.15 Commercial Clothes Washers: Historical Shipments, Domestic plus Imports

............................................................................................................... 9-37 Table 9.3.16 Historical and Forecasted Multi -Family Housing Starts (millions)

................... 9-39 Table 9.3.17 Commercial Clothes Washers: Historical New Multi-Family Building Saturations

.......................................................................................................... 9-40 Table 9.4.1 Change in Relative Price Elasticity following a Purchase Price Change

.......... 9-47 Table 9.6.1 Dishwashers: Cumulative Ship ments Impacts Caused by Standards over the Period 2012–2042

........................................................................................ 9-49 Table 9.6.2 Dehumidifiers: Cumulative Sh ipments Impacts to all Product Classes caused by Standards over the period 2012–2042

............................................... 9-50 Table 9.6.3 Microwave Ovens: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042

................................................................................ 9-51 Table 9.6.4 Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042

............................................................... 9-53

LIST OF FIGURES Figure 9.3.1 Dishwashers: Survival and Retirement Functions

............................................ 9-10 Figure 9.3.2 Dishwashers: Historical and Base Case Shipments Forecast

............................ 9-11 Figure 9.3.3 Dishwashers: Disaggregated Base Case Shipments Forecast

........................... 9-11 Figure 9.3.4 Dehumidifiers: Surviv al and Retirement Functions

......................................... 9-15 Figure 9.3.5 Dehumidifiers: Historical and Base Case Shipments Forecast

......................... 9-16 Figure 9.3.6 Dehumidifiers: Disaggregated Base Case Shipments Forecast

........................ 9-17 Figure 9.3.7 Electric and Gas Cooking Pr oducts: Retirement Functions

.............................. 9-26 Figure 9.3.8 Electric Cooking Products: Historic al and Base Case Shipments Forecast

...... 9-27 Figure 9.3.9 Gas Cooking Products: Historical and Base Case Shipments Forecast

............ 9-28 Figure 9.3.10 Electric Cooking Products: Disaggre gated Base Case Shipments Forecast

..... 9-29 Figure 9.3.11 Gas Cooking Products: Disaggregat ed Base Case Shipments Forecast

........... 9-29

9-iiFigure 9.3.12 Microwave Ovens: Surv ival and Retirement Functions

................................... 9-33 Figure 9.3.13 Microwave Ovens: Historical and Base Case Shipments Forecast

.................. 9-34 Figure 9.3.14 Microwave Ovens: Disaggregated Base Case Shipments Forecast

.................. 9-35 Figure 9.3.15 Commercial Clothes Washer Ship ments and Trend in Quantity Index

............. 9-38 Figure 9.3.16 Commercial Laundry Saturati on in New Multi-Family Housing

...................... 9-41 Figure 9.3.17 Commercial Clothes Washers: Survival and Retirement Functions

................. 9-42 Figure 9.3.18 Commercial Clothes Washers: Percent of Retired Units not Replaced over the period 1998

─2013

........................................................................................ 9-43 Figure 9.3.19 Commercial Clothes Washers: Historical and Base Case Shipments Forecast

.............................................................................................................. 9-44 Figure 9.3.20 Commercial Clothes Washers: Disaggregated Base Case Shipments Forecast

.............................................................................................................. 9-45 Figure 9.6.1 Standard-Sized Dishwashers: Base Case and Standards Case Shipments Forecasts

............................................................................................................ 9-49 Figure 9.6.2 Dehumidifiers: Base Case and Standards Case Shipments Forecasts

.............. 9-50 Figure 9.6.3 Microwave Ovens: Base Case and Standards Case Shipments Forecasts

........ 9-52 Figure 9.6.4 Commercial Clothes Washers: Base Case and Standards Level 6 Shipments Forecasts

........................................................................................... 9-53

9-iiiCHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION Product shipments estimates are a necessary input to the national energy savings (NES) and net present value (NPV) calculations. Sh ipments are also a necessary input to the manufacturer impact analysis (MIA), whic h DOE conducts for its notices of proposed rulemaking (NOPRs). This chapter desc ribes DOE’s methodology for projecting annual shipments and presents results for each of the four sets of products bei ng considered for this standards rulemaking (i.e., c ooking products, dishwashers, de humidifiers, 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. Theref ore, DOE developed a total of five separate Shipments Models; two for cooking products and one each for dishwashers, dehumidifiers, and commercial clothes washers. It calibrated each Shipments Model ag ainst historical shipments. For purposes of estimating the impacts of prospe ctive standard levels on product shipments, each Shipments Model accounts for the combined ef fects 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 to tal product shipments. In the case of all of the considered products, with the exception of dehumi difiers, DOE accounted for at least two market segments: (1) new construction and (2) existing bu ildings (i.e., replacing failed equipment). For dehumidifiers, DOE did not consider the new cons truction market since this product, unlike most major household appliances, is not standard equi pment for new households. For dehumidifiers, rather than a new construction market, DOE acc ounted for the market of existing households acquiring new dehumidifiers for the first time, in addition to accounting for replacements. In addition, for the following products, DOE account ed for a third market segment: cooking products—early replacements; dishwashers—exis ting households acquiri ng the equipment for the first time; and 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 10A 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 9.2 presents the Ship ments Model methodology for each product; section 9.3 describes the data inputs and the model calibration; se ction 9.4 discusses impacts on shipments from changes in equipment purchase price, operating cost, and household income; section 9.5 discusses the affected stock; and sec tion 9.6 presents the resu lts for different energyefficiency standard-level scenarios.

9-19.2 SHIPMENTS MODEL METHODOLOGY DOE developed a national stock model fo r estimating annual shipments for the four product categories considered for this standards rulemaking. The model considers market segmentation as a distinct input to the shipments forecast. As represented by the following equation, the two primary market segments (which are common to all products except dehumidifiers) are new installations and replacements. a 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 in stallations of product p in year j

. As the product-specific sections below di scuss, 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 re placements, and efficiency trends b are dynamic and can vary among product classes. Rather than simply ex trapolating a current shipments trend, the base case shipments analysis (i.e., the cas e without new standards) uses dr iver 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 produ ct shipments from the new construction market segment are equal to the number of new housi ng 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 existi ng stock, and expected co nstruction 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 determ ined the useful service life of each appliance to

aFor dehumidifiers, DOE accounts for shipments to existing households that do not own the product, since dehumidifiers are not typically purchased with a new home.
b Efficiency trends affect sh ipments only in the standards case. A change in the efficiency distribution of the stock results in a change in the purchase price and operating cost and, therefore, produces a purchase price and operating cost impact on the shipments. This is discussed later in the chapter in section 9.4.

9-2_ _ estimate how long the appliance is likely to remain in stock. The following equation represents how DOE estimated replacement shipments.

_ ageMax j 1 Rpl p ( j ) = Stock p ( j 1 ) 

## ∑ ∑ Ship j × prob Rtr ( age )
age =0 j = N where:
Stock p (j-1) = Total stock of in-servi ce appliances in year j-1 , prob Rtr (age) = Probability that an ap pliance of a particular age will be retired, and N

= Start year for when the model begi ns 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 provide s 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 ope rating costs for any year depe nd on the age distribution of the stock. The dependence of operating cost on the equipment age distribution occurs under a standards case scenario that pr oduces increasing efficiency over ti me, 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 anearly replacement scenario, retirements due to early replacement will depend on the age of the un its that are subject to early replacement.
DOE calculated total in-service stock of e quipment by integrating historical shipments data starting from a specific year. The start ye ar 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 deve loped a series of equations that define the dynamics and accounting of in-service stoc ks. 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-servi ce 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 ye ar. The slightly more complicated equations (e.g., the following equation) ar e 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 ) 

# ]

9-3In the above equation, as the year is incremented from jto 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 9.3. Because the pr oducts 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.

9.3 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 us ing two inputs: new housing forecasts and market saturation data. New housing includes ne wly 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 2007 (

AEO2007 ) for the period of 2006–2030. 1 To determine new construction shipments for each product (except dehumidifiers c ), DOE used forecasts of mark et saturations, combined with forecasts of housing starts. DOE estimated replacements using product reti rement functions that it developed from product lifetimes. For all produc ts, with the exception of mi crowave ovens, DOE based the retirement function on a uniform probabil ity distribution for the product lifetime. d The Shipments Models assume that no units are re tired 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 ) =

_ f o r 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,

c For dehumidifiers, because they are not typically installed at the time of new construction, DOE did not account for shipments to new construction.
d As described in section 8.2.3.2 in Chapter 8, in the case of microwave ovens, DOE assumed a triangular retirement function.

9-4AgeMin = 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.

9.3.1 Dishwashers DOE divided residential dishwashers into two product classes: standard-sized and compact-sized units. However, because DOE did not conduct an engineer ing analysis or a lifecycle cost and payback period analysis specific to compact dishwashers, it conducted the shipments analysis without disagg regating dishwasher shipments into its two product classes. DOE included only built-in units (notportable units) in its analysis.

9.3.1.1 Historical Shipments DOE used historical shipment s data (i.e., data on both dom estic shipments and imports) to calibrate its Shipments Model for dishwashers. It used three sources to establish historical shipments: (1) data provided by the Associati on of Home Appliance Manufacturers (AHAM) for the period 1995–2005, 2 (2) data from the 2000 AHAM Factbook for the period 1989–1994, 3 and (3) data from Appliance Magazine 4,5,6 for the period 1972–1988. e Table 9.3.1 summarizes the historical dishwasher shipments data. DOE built up a total stock of dishwashers by integrating historical shipments starting in th e year 1972. Over time, some of the units will be retired and removed from the stock, thus tr iggering 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.

e Shipments estimates from Appliance Magazine included exports. Thus, DOE reduced total shipments by 4.1 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 data) to estimate total domestic shipments plus imports.

9-5Table 9.3.1 Dishwashers: Historical Shipm ents, Domestic plus Imports Year Shipments (thousands) Year Shipments (thousands) 1972 2,756 1989 3,390 1973 3,189 1990 3,291 1974 2,860 1991 3,195 1975 2,328 1992 3,424 1976 2,705 1993 3,722 1977 2,891 1994 4,125 1978 3,064 1995 4,254 1979 3,005 1996 4,317 1980 2,258 1997 4,655 1981 2,104 1998 5,073 1982 1,876 1999 5,425 1983 2,743 2000 5,834 1984 3,113 2001 5,752 1985 3,192 2002 5,906 1986 3,514 2003 6,467 1987 3,610 2004 6,868 1988 3,499 2005 7,280 Sources: 1995–2005: AHAM Data Submittal, 2006; 1989–1994: AHAM Factbook 2000; 1972–1988: Appliance Magazine, “Statistical Review,” 1982, 1990, and 1993.

9.3.1.2 Markets and Model Calibration The shipments market for dishwashers prim arily comprises the following: units for new construction, and replacement units for equipment that has been retired. DOE’s Shipments Model also assumes that some existing house holds without dishwash ers will purchase the equipment (i.e., become first time owners (FTOs) ). Total dishwasher shipments are represented by the following equation: Ship DW ( j ) = Rpl DW ( j ) + NI DW ( j ) + FTO DW ( j )
where: Ship DW (j) = Total shipments of dishwashers in year j , Rpl DW (j) = Replacement shipments in year j , NI DW (j) = Shipments to new households in year j , and FTO DW (j) = Shipments to existing households without dishwashers in year j

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

9-6New Construction. To estimate shipments to new cons truction, DOE used forecasts of housing starts coupled with dishwasher saturation data. In other words, to forecast the shipments for new construction for any given year, DOE multiplied the housing starts forecasts by the forecasted saturation of di shwashers for new housing. Table 9.3.2 presents hist orical and forecasted new hous ing starts based on EIA’s AEO2007 for the period 1990–2030. 1 AEO2007 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 9.3.2. DOE used the forecasts from the Reference case to estimate its shipments to new construction. 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 AEO2007 data are based on actual comp letions through the year 2005. For the Reference case, the forecast shows a decl ine in housing completions from 2.22 million in 2005 to 1.8 million by 2030. For the period 2031

− 2042, DOE froze completions at the level in the year 2030 for all three economic forecasts.

9-7Table 9.3.2 Historical and Forecasted Housing Starts (millions) Year Reference High Low Year Reference High Low 1990 1.38 1.38 1.38 2017 1.91 2.30 1.53 1991 1.18 1.18 1.18 2018 1.91 2.30 1.53 1992 1.41 1.41 1.41 2019 1.90 2.31 1.50 1993 1.54 1.54 1.54 2020 1.90 2.33 1.48 1994 1.76 1.76 1.76 2021 1.88 2.32 1.44 1995 1.69 1.69 1.69 2022 1.85 2.31 1.40 1996 1.82 1.82 1.82 2023 1.83 2.30 1.37 1997 1.49 1.49 1.49 2024 1.83 2.32 1.35 1998 1.54 1.54 1.54 2025 1.85 2.37 1.32 1999 1.64 1.64 1.64 2026 1.86 2.41 1.31 2000 1.70 1.70 1.70 2027 1.85 2.41 1.28 2001 1.72 1.72 1.72 2028 1.81 2.38 1.24 2002 1.75 1.75 1.75 2029 1.80 2.36 1.23 2003 1.79 1.79 1.79 2030 1.80 2.41 1.20 2004 1.80 1.80 1.80 2031 1.80 2.41 1.20 2005 2.22 2.22 2.22 2032 1.80 2.41 1.20 2006 2.03 2.03 2.03 2033 1.80 2.41 1.20 2007 1.91 2.11 1.71 2034 1.80 2.41 1.20 2008 1.93 2.20 1.66 2035 1.80 2.41 1.20 2009 1.92 2.21 1.63 2036 1.80 2.41 1.20 2010 1.94 2.26 1.62 2037 1.80 2.41 1.20 2011 1.94 2.27 1.62 2038 1.80 2.41 1.20 2012 1.90 2.23 1.57 2039 1.80 2.41 1.20 2013 1.88 2.21 1.55 2040 1.80 2.41 1.20 2014 1.88 2.25 1.52 2041 1.80 2.41 1.20 2015 1.91 2.28 1.53 2042 1.80 2.41 1.20 2016 1.92 2.30 1.55 Source: EIA, AEO2007. Table 9.3.3 presents historical market saturations of dishwash ers based on various sources, including the AHAM 2005 Fact Book , 7 Appliance Magazine , 8 NFO World Group, 9 and EIA’s Residential Energy Consumpti on Survey (RECS) for the years 1993, 10 1997, 11 and 2001. 12 The table presents market saturations for th e overall housing stock and for new households. Because the forecast of shipments for the new housing market depends on the saturation of dishwashers in new housing, DOE focused its attention on the market saturations for new housing. According to RECS, dishwasher saturation in new housing for 1993, 1997, and 2001 was 74.9 percent, 74.1 percent, and 73.2 percent, respectively. Because the downward trend in new household dishwasher saturation is statistica lly insignificant, DOE decided to freeze the saturation at 73.2 percent to estimate the shipments to new construction for future years.

9-8Table 9.3.3 Dishwashers: Historical Market Saturations

Year Overall Household New Households AHAM* Appl** NFO*** RECS † RECS † 1970 18.9% 1978 41.9% 1982 44.5% 1983 45.0% 1987 47.7% 1990 53.9% 45.4% 1991 47.7% 1992 50.0% 1993 51.0% 45.4% 74.9% 1994 52.2% 1995 54.4% 1996 54.9% 49.9% 1997 55.6% 50.3% 74.1% 1998 56.3% 1999 56.5% 2000 59.0% 2001 59.3% 59.3% 53.6% 53.0% 73.2% 2002 59.5% 2003 59.5% 2004 60.0% 2005 73.7% * 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 to tal stock of units by vintage. DOE estimated a stock of dishwashers by vintage by integrat ing historical shipments starti ng from the year 1972. Over time, some units will be retired and removed fr om stock, thereby trigge ring 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 un it fails, DOE used a survival function based on a product lifetime distribution with an average value of 12.3 years, and minimum and maximum values of 5.3 years and 17.6 years, respectively. For a more complete discussion of dishwasher lifetimes, refer back to section 8.2.3.3 of Ch apter 8. Figure 9.3.1 shows the survival and retirement functions that DOE used to estimate replacement shipments.

9-90% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent Surviving Percent Retiring

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Age (years) Figure 9.3.1 Dishwashers: Survival a nd Retirement Functions
Model Calibration—Existing Households Without Dishwashers. To calibrate the estimated shipments with the historical data , DOE introduced into the model a new market segment corresponding to existing households without dishwashers, also re ferred to as FTOs. DOE estimated that three percen t of existing households without a dishwasher would annually purchase this product over the period 2005

─2042.

9.3.1.3 Base Case Shipments Figure 9.3.2 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.

9-100.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

1985 1995 2005 2015 2025 2035 YearShipments (million units) Historical Shipments modelled Shipments

Figure 9.3.2 Dishwashers: Historical and Base Case Shipments Forecast Figure 9.3.3 presents forecas ted dishwasher shipments in the base case, disaggregated into the following market segments: shipments due to retirements, shipments to new housing, and shipments for FTOs. DOE determined that replacements due to retirements range from 60 percent to 85 percent of total dishwasher shipme nts over the period 2006–2042.

0.0 2.5 5.0 7.5 10.0 12.5 15.0

1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2 020 2023 2026 2029 2032 2035 2038 2 041
YearShipments (million units) First Time Ow ners New Units Repl. Units Total Shipments

Figure 9.3.3 Dishwashers: Disaggregated Base Case Shipments Forecast

9-119.3.2 Dehumidifiers DOE considered six product classes of dehumi difiers for the current analysis. It based the six product classes on the following size categories: ≤ 25.00, 25.01
─ 35.00, 35.01

− 45.00, 45.01

− 54.00, 54.01

− 74.99, and ≥ 75.00 pints/day. DOE’s Ship ments Model for dehumidifiers used the aggregate shipments, i.e., the shipments for all six product classes, as the basis for its forecasts. In other words, DOE did not de velop a separate Shipments Model for each dehumidifier product class. As provided in Tabl e 9.3.4, DOE based the market shares for each of the six product classes on data AHAM provided for the years 2003–2005. 13 DOE used the average market shares over the period 2003–2005 to disaggregate forecasted shipments into each of the six dehumidifi er product classes.
Table 9.3.4 Product Class Market Share of Dehumidifiers
Year Product Class (pints/day) ≤ 25.00 25.01-35.00 35.01-45.00 45.01-54.00 54.01-74.99 ≥ 75.00 2003 13.4% 21.3% 23.2% 21.8% 19.3% 1.0% 2005 10.0% 23.0% 23.4% 20.3% 22.3% 1.0% 2005 6.4% 25.7% 23.0% 20.5% 23.3% 1.0% Average 10.0% 23.4% 23.2% 20.9% 21.6% 1.0% Source: AHAM Data Submittal, 2006.

9.3.2.1 Historical Shipments DOE used historical shipment s data (i.e., data on domes tic shipments and imports) to calibrate its Shipments Model. It used three sour ces to establish historical shipments: (1) data provided by AHAM for the period 2003–2005, 13 (2) data from the 2000 3 and 2003 14 AHAM Factbooks for the period 1989–2002, and (3) data from Appliance Magazine 4,5,6 for the period 1972–1988. f Table 9.3.5 summarizes the historical dehumidifier shipments data. DOE built up a total stock of dehumidifiers by integrating historical shipments st arting in the year 1972. Over time, some of the units will be retired and remove d from the stock, triggering the shipment of a new unit. Because of the relationship between retirements and total st ock, there is a strong correlation between past and future shipment s, independent of ef ficiency standards.

f Shipments estimates from Appliance Magazine included exports. However, DOE saw no difference between shipments in the AHAM Fact Book 2000, which exclude exports, and those reported in Appliance Magazine for almost all years. Thus, DOE made no adjustments to the Appliance Magazine shipments.

9-12Table 9.3.5 Dehumidifiers: Historical Shipm ents, Domestic plus Imports Year Shipments (thousands) Year Shipments (thousands) 1972 461 1989 605 1973 646 1990 743 1974 586 1991 745 1975 392 1992 803 1976 440 1993 983 1977 314 1994 1,059 1978 442 1995 1,003 1979 685 1996 977 1980 673 1997 820 1981 536 1998 1,031 1982 440 1999 950 1983 437 2000 975 1984 591 2001 806 1985 588 2002 799 1986 555 2003 1,311 1987 704 2004 1,672 1988 673 2005 1,957 Sources: 2003–2005: AHAM Data Submittal, 2006; 1989–2002: AHAM Factbooks 2000, 2003; 1972–1988: Appliance Magazine, “Statistical Review,” 1982, 1990, and 1993.

9.3.2.2 Markets and Model Calibration Table 9.3.6 presents historical market saturations of dehumidifi ers based on various sources, including the AHAM 2005 Fact Book , 6 Appliance Magazine , 7 NFO World Group, 8 and EIA’s 2001 RECS. 11

9-13Table 9.3.6 Dehumidifiers: HistoricalMarket Saturations

Year Overall Housing AHAM* Appl** NFO*** RECS † 1970 1978 8.5% 1982 11.0% 1983 11.0% 1987 11.4% 1990 16.7% 16.3% 1991 12.0% 1992 15.5% 1993 16.5% 1994 17.0% 1995 18.0% 1996 18.0% 14.8% 1997 18.0% 1998 18.4% 1999 10.5% 2000 14.0% 2001 14.1% 14.5% 14.2% 11.3% 2002 16.0% 2003 19.0% 2004 20.0% 2005 18.9% * Source: AHAM, Fact Book, 2005; ** Source: Appliance Magazine, “The Saturation Picture,” September issues; *** Source: NFO World Group, 2001; † Source: DOE-EIA, RECS, 2001. The shipments market for dehumidifiers primarily comprises replacement units for equipment that has been retired. DOE’s Ship ments Model also assumes that some existing households without dehumidifiers will purchase the equipment (i.e., become FTOs). Total dehumidifiers shipments are represented by the following equation: Ship DEH ( j ) = Rpl DEH ( j ) + FTO DEH ( j )
where: Ship DEH (j) = Total shipments of dehumidifiers in year j , Rpl DEH (j) = Replacement shipments in year j , and FTO DEH (j) = Shipments to existing households without dehumidifiers in year j

.

9-14DOE discusses the two markets for dehum idifiers in further detail below. Replacements. DOE determined shipments to the replacement market using an accounting method that tracks the to tal stock of units by vintage. DOE estimated a stock of dehumidifiers by vintage by integrat ing historical shipments starti ng from the year 1972. Over time, some units will be retired and removed from the stock, thereby trig gering 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 un it fails, DOE used a survival function based on a product lifetime distribution with an average value of 11 years, and minimum and maximum values of 5 years and 17 years, respectively. For a more complete discussion of dehumidifier lifetimes, refer back to section 8.2.3.4 of Ch apter 8. Figure 9.3.4 shows the survival and retirement functions that DOE used to estimate replacement shipments.

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

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Age (years) Figure 9.3.4 Dehumidifiers: Survival and Retirement Functions
Model Calibration—Existing Households Without Appliance. To calibrate the estimated shipments with the historical data, DOE introduced into the model a market segment identified as existing households without de humidifiers, also referred to as FTOs. DOE estimated that 0.6 percent of existing house holds without a dehumidifier would annually purchase this product over the period 2005

─2042.

9-159.3.2.3 Base Case Shipments Figure 9.3.5 shows the forecasted shipments in the base case (i.e., the case without new energy efficiency standards) and th e historical shipments DOE used to calibrate the forecast. As stated above, DOE used the average market shar es of the six dehumidif ier product classes over the period 2003–2005 to disaggregate forecasted ba se case shipments into each of the six dehumidifier product classes.

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

1980 1990 2000 2010 2020 2030 2040 YearShipments (million units) Shipments Modeled Shipments

Figure 9.3.5 Dehumidifiers: Historical and Base Case Shipments Forecast Figure 9.3.6 presents forecaste d dehumidifier shipments in the base case, disaggregated into the following market segments: shipment s due to retirements and shipments for FTOs. DOE determined that replacements due to reti rements range from 68 percent to 80 percent of total dehumidifier shipme nts over the period 2006–2042.

9-161991 1995 1 999 2003 2007 2011 2015 2019 2023 2027 2031 2035 2039Shipments (million units) 4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0 M odel ed F i r s t T i m e O w n e r s R epl . U n i t s A ggr egate S h i p m ent s

Year Figure 9.3.6 Dehumidifiers: Disaggregated Base Case Shipments Forecast

9.3.3 Cooktops and Ovens DOE designed its Shipments Model for cookt ops and ovens by di viding these products into two general fuel categories: electric and ga s. 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 estim ated the electric cooking product shipments while the other Model estimated gas cooking product shipments. After DOE estimated shipments for each fuel category, it then disaggr egated 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 9.3.7 shows the product types and produc t classes under each gene ral 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 pr oducts there are five pr oduct types and three product classes. Because ranges are comprise d of cooktop and oven product classes, DOE needed to disaggregate range shipments into the appropriate cooktop an d oven product classes and then combine those shipment s with the appropriate built-in cooktop and oven shipments to obtain the total shipments for each product class.

9-17Table 9.3.7 Cooktops and Ovens: Product Cate gories, 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 Classes: 4 electric, 3 gas Ovens Cooktops Ovens 3.Cooktops 1. Standard 2. Self-Clean 3. Coil 4. Smooth 1. Standard 2. Self-Clean Std = standard; SC = self cleaning; Smth = smooth
Table 9.3.8 shows the historical market shar es of the eight product types that comprise total electric cooking product shipments. For an y 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 15 and (2) data from DOE’s supplemental analysis to its 1996 technical support document (TSD) on cooki ng products covering the period 1989–1996. 16 For the time periods for which no data were available, DOE either applied the market share percentages from th e above two data sources or inte rpolated from the two available data sources. The notes in Table 9.3.8 provide further details. To estimate forecasted market shares of each product type, DOE decided to freeze the percentage s at the levels for the year 2005, i.e., DOE used the market shares in th e year 2005 to disaggregate forecasted shipments into each of the eight electric cooking product ty pes for the period 2006–2042.

9-18Table 9.3.8 Electric Cooktops and Ovens: Hist orical Shipment Market Shares by Product Type

Year Percent of Total Shipments Ranges Built-In Cooktops Built-In OvensCoilStandard 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 9.3.9 shows the historical market shar es of the five produc t types that comprise total gas cooking product shipments. For any gi ven 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 produc ts covering the period 1989–1996, 16 and (3) data from the Appliance Recycling Information Center (ARIC) for the year 2004. 17 For the time periods for which no data were available, DOE either applie d 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 9.3.9 provide further details. Note that in Table 9.3.9, 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, Life9-19Cycle 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 estimat e the national impacts of elimin ating pilot lights for gas cooking pilots. DOE’s method for estimating the national im pacts is discussed in detail in Chapter 10, National Impact Analysis. To estimate the futu re percentages of each product type (with the exception of gas standard ranges with standing p ilots), 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 th e five gas cooking product types for the period 2006–2042. Section 10.2.3 of Chapter 10, Natio nal Impact Analysis, discusses how DOE forecasted the market share of gas standard ranges with standing pilots.
Table 9.3.9 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.

9-209.3.3.1 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 1989–2002, 3 and (3) data from Appliance Magazine 4,5,6 for the period 1972–1988. g Table 9.3.10 summarizes the historical cooking product shipme nts data. DOE built up a total stock of cooking products by integrating hist orical shipments starting in the year 1972. Over time, some units are retired and, therefore, removed from stoc k, 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, i ndependent of effici ency standards. Table 9.3.10 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

gShipments 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.

9-219.3.3.2 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 th at has been retired from service and units for new housing. In a ddition to normal replacements, DOE’s Shipments Model for each general category also assumed th at a certain fraction of the stock would be replaced early. Total electric cooktop and oven shipments are represented by the following equation: Ship ELEC ( j ) = Rpl ELEC ( j ) + NI ELEC ( j ) + ER ELEC ( j )
where: Ship ELEC (j) = Total shipments of electr ic cooking products in yearj , RplELEC (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 GAS ( j ) + NI GAS ( j ) + ER GAS ( j )
where: Ship GAS (j) = Total shipments of ga s 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 cons truction, 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 cooki ng products for new housing.
Historical and forecasted new housing starts were presented earlier for cooking products. Refer back to Table 9.3.2 for the housing starts data. Table 9.3.11 presents historical market saturations of electric c ooking products based on various sources, including AHAM’s 2005 Fact Book , 7 Appliance Magazine , 8 NFO World Group, 9 and EIA’s RECS for the years 1997 11 and 2001. 12 The table presents market saturations for the overall housing stock and for new housing. Note that the overall electric cooking product

9-22saturations provided by AHAM and Appliance Magazine are roughly the same. NFO breaks out the saturations of range s, built-in ovens, and built-in cook tops 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 source s. 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 satu rations 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 disaggregat ing the total shipments using the historical market share data in Table 9.3.8, 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 cookt ops, which is 77.5 percent.

9-23Table 9.3.11 Electric Cooking Products: Hi storical Market Saturations

Year 1970 1978 1982 1983 Overall Housing New Housing AHAM* Appl** NFO*** RECS † All Elec Products

40.6%

58.0%

62.6%

66.3%

65.3% All Elec Products Ranges

60.0%

61.0%

62.0% Built-In Oven

9.4%

9.2%

9.6% Built-In Cooktop

9.2%

10.1%

9.4% Range SC

26.6%

30.1% Range Standard

39.7%

32.5% Built-In SC Oven

8.1%

3.5% Built-In Std Oven

4.0%

1.7% Built-In Cooktop

6.5%

9.6% 51.0%

58.0% 59.4%

56.7% 56.4% 57.5% 58.5% 61.0% 60.2% 60.0% 60.5% 60.0% 60.0% 61.0% 62.0% 61.0% 61.0% 1987 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Source: AHAM, Fact Book 2005 ; ** Source: Appliance Magazine, “The Saturation Picture,” September issues; *** Source: NFO World Group; † Source: DOE-EIA, RECS, 1997, 2001. Table 9.3.12 presents historical market saturations of gas cooking products based on various sources, including AHAM’s 2005 Fact Book , 6 Appliance Magazine , 7 NFO World Group, 8 and EIA’s RECS for the years 1997 10 and 2001. 11 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 range s, built-in ovens, and built-in cook tops 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 source s. 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 satu rations for the year 2001 to estimate the shipments to new construction for future years. Because DOE conducted its shipments analysis

9-24by first forecasting overall gas cooking product sh ipments and then disaggregating the total shipments using the historical market share data in Table 9.3.9, it used the overall saturation of gas cooking products to estimate sh ipments to new construction. So as not to overstate the overall saturation, DOE based the saturation on th e sum of the values for ranges, built-in ovens, and built-in cooktops, whic h is 30.1 percent.
Table 9.3.12 Gas Cooking Products: Historical Market Saturations

Year Overall Housing New Housing AHAM* Appl** NFO*** RECS † All Elec Products All Elec Products Ranges Built-In Oven Built-In Cooktop Range SC Range Standard Built-In SC Oven Built-In Std Oven Built-In 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 to tal stock of units by vintage. DOE estimated stocks of electric and gas cooking products by vintage by integrati ng 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

9-25stock, there is a strong correlati on between past and future shipme nts, 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 co oking product fails, DOE used a product survival function based on a lifetime distribution with anaverage value of 19 years, and minimum and maximum values of 10 years and 28 years, resp ectively. 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 9.3.7 shows the survival and retirement functions that DOE used to estimate replacement shipments.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Gas - Percent Surviving Elec - Percent Surviving Gas - Percent Retiring Elec - Percent Retiring

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Age (years) Figure 9.3.7 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 ear ly replacement rate is distributed among equipment of ages ranging from 10 to 28 years fo r electric cooking products and 12 to 26 years for gas cooking products; DOE weighted these e quipment by their respec tive population in the surviving stock. The following equation illustrates the calculations:

9-26_ AgeMax 1 Stock( j

_ 1 ,age )

_ ER MWO ( j ) = age 

# ∑

=4 

# ∑ Stock( j 1 ,age ) × ERRate × Stock( j 1 )

_ 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 , ERRate = Early replacement rate, and AgeMax = Maximum age of the unit before it retires.

9.3.3.3 Base Case Shipments Figures 9.3.8 and 9.3.9 show the forecas ted shipments of electric and gas cooking products, respectively, in the base case (i.e., the case without ne w 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 (millons) Historical Shipments Modeled Shipments

Figure 9.3.8 Electric Cooking Products: Histor ical and Base Case Shipments Forecast

9-270.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 YearShipments (millons) Historical Shipments Modeled Shipments

Figure 9.3.9 Gas Cooking Products: Histori cal and Base Case Shipments Forecast

Figures 9.3.10 and 9.3.11 present forecasted base case electric and gas cooking product shipments, respectively, disaggregated into th e following market segments: shipments due to retirements, shipments to new housing, and ship ments due to early replacements. Note that replacement shipments comprised 85–92 percent of total gas and elec tric 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.

9-280.0 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 9.3.10 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 9.3.11 Gas Cooking Products: Disaggregated Base Case Shipments Forecast

9-299.3.4 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.

9.3.4.1 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: (1) AHAM Factbooks for the years 2000, 3 2003 14 , and 2005 7 covering the period 1989

− 2005 and (2) Appliance Magazine 4,5,6 for the period 1981

− 1988. hTable 9.3.13 summarizes the historical microwave oven shipments data.
Table 9.3.13 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.

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

hShipments 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.

9-30would be replaced early. Total microwave ove n shipments are represented by the following equation: Ship MWO ( j ) = Rpl MWO ( j ) + NI MWO ( j ) + ER MWO ( j )
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 c onstruction, 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 microwav e ovens for new housing.
Historical and forecasted new housing starts were presented earlier for cooking products. Refer back to Table 9.3.2 for the housing starts data. Table 9.3.14 presents historical market saturations of microwave ovens based on various sources, including AHAM’s 2005 Fact Book , 7 Appliance Magazine , 8 NFO World Group, 9 and EIA’s RECS for the years 1993, 10 1997, 11 and 2001. 12 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 mark et saturations for new housing as well as the saturations of housing wi th multiple microwave ovens. According to RECS, microwave oven saturation in new hous ing for 1993, 1997, and 2001 was 92 percent, 87.5 percent, and 93.3 percent, respectively. Ac cording to NFO, households with more than one microwave oven represented 3.7 percent, 4.9 per cent, and 4.1 percent of the housing market for the years 1990, 1996, and 2001, resp ectively. Therefore, DOE incr emented 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 y ear 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.

9-31Table 9.3.14 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 to tal stock of units by vintage. DOE estimated a stock of microwave ovens by vintage by integrating histor ical shipments starting from the year 1981. Over time, some units will be retired and re moved from the stock, thereby triggering the shipment of a replacement unit. Depending on the vi ntage, a certain percenta ge of each type of unit will fail and need to be replaced. To de termine when a microwave oven fails, DOE used a product survival function based on a lifetime distri bution with an average value of nine years, and minimum and maximum values of seven year s and 10 years, respec tively. For a more complete discussion of microwave lifetimes, refer back to section 8.2.3.2 of Chapter 8. Figure 9.3.12 shows the survival and retirement functio ns that DOE used to estimate replacement shipments.

9-32100% 90% 80% 70% 60% 50% 40% 30% 20% 10%

0% 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Percent Surviving Percent Retiring

Age (years) Figure 9.3.12 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. Th e early replacement rate is distributed among equipment of ages ranging from four thr ough 10 years; DOE weighted these equipment by their respective populati on in the surviving stock. The following equation illustrates the calculations:

AgeMax

_ 1 Stock( j

_ 1 ,age )

_ ER MWO ( j ) = age 

# ∑

=4 

# ∑ Stock( j 1 ,age ) × ERRate × Stock( j 1 )

_ 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.

9-339.3.4.3 Base Case Shipments Figure 9.3.13 shows the forecasted shipments in the base case (i.e., the case without new energy efficiency standards) and the historical sh ipments DOE used to calibrate the forecast.

0 5 10 15 20 25

1985 1995 2005 2015 2025 2035 YearShipments (million units) Historical Shipments modelled Shipments

Figure 9.3.13 Microwave Ovens: Historical and Base Case Shipments Forecast Figure 9.3.14 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. No te that replacement shipments comprised 85–92 percent of total microwave ove n shipments over the period 2006–2035. The percentage of total shipments represented by replacements is high because the microwave ove n market is fairly mature.

9-340.0 5.0 10.0 15.0 20.0 25.0199019931996199920022005200820112014201720202023202620292032203520382041

YearShipments (million units) New Units Early Repl Normal Repl Aggregate Shipments

Figure 9.3.14 Microwave Ovens: Disaggregated Base Case Shipments Forecast

9.3.5 Commercial Clothes Washers DOE’s Shipments Model for commercial clothes washers forecasted shipments to the two most predominant product applications: multi-family buildings and laundromats. DOE adopted the following approach for estimating shipments. The analysis began with aggregate histori cal shipments data for commercial clothes washers. Tracking of product stoc k and vintage relies on a retire ment function, which describes the likelihood that equipment installed in a certain year will still be in se rvice in any subsequent year. Once a product reaches the end of its useful lif e it is removed from se rvice. In most cases, retirement is followed by replacement, which is a key driver of further shipments. The difference between replacements and total sh ipments for the following year provides the shipments going into new construction. The out put includes units going into both laundromats and multi-family apartment buildings. DOE based its Shipments Model on the follo wing three assumpti ons: (1) All product shipments for new construction are driven by the new multi-family housing market, (2) the relative market shares of th e 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 sh ipments trend observed in the historical data. DOE’s first assumption is based on the premise that multi-family occupants who do not have their own personal washers and dryers musteither wash their laundry in the common area

9-35laundry facility within their build ing 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 cust omers. 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 mu lti-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 re port, multi-family buildings and laundromats comprise 85 percent and 15 percent of the comm ercial clothes washer market, respectively. 18 The third assumption pertains to the use of the U.S Bureau of Economic Analysis’ quantity index data to validat e historical shipments data. 19 The Census Bureau’s quantity index for commercial laundry equipm ent includes shipments of co mmercial laundry products beyond those covered by this rulemaking. Because disaggre gated quantity index data were not available, DOE assumed that the market share of commercial clothe s 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.

9.3.5.1 Historical Shipments DOE used historical shipments data (i.e., domestic shipments and imports) to calibrate its Shipments Model. It used two sources to es tablish historical shipments: (1) data AHAM provided for the period 2002–2005 20 and (2) data from Appliance Magazine 21 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 9.3.15 summarizes the historical commercial clothes washer shipments data.

9-36Table 9.3.15 Commercial Clothes Washers: HistoricalShipments, 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 9.3.15, there was a si gnificant drop in commer cial clothes washer shipments between 1998 and 2002. Because DOE used different data sources to estimate the shipments in 1998 and 2002, it validat ed the shipments data through a review of the U.S. Bureau of Economic Analysis’ quantity inde x data. DOE compared the historical shipments data against the quantity index for commercial laundry equipm ent (represented by North American Industry Classification System (NAICS) code 333312). Th e 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 9.3.15 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.

9-370 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 9.3.15 Commercial Clothes Washer Shipmen ts and Trend in Quantity Index

9.3.5.2 Markets and Model Calibration The market for commercial clothes washer s 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 CCW ( j ) = Rpl CCW ( j ) + NI CCW ( j )
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 yearj

. 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 sa turation data. In other words, to forecast the shipments for new construction for any given ye ar, DOE multiplied the multi-housing forecasts by the forecasted saturation of commercial cl othes washers for new multi-family housing.

9-38Because 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 9.3.16 presents historical and for ecasted new multi-family housing starts based on EIA’s AEO2007 for the period 1990–2030. 1 AEO2007 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 9.3.16. DOE used onlythe forecasts from the Reference case to estimate its shipments to new construction. The multifamily housing construction forecast shows anannual 0.5 percent increase in housing completions. Therefore, DOE assumed that comp letions grow at 0.5 percent per year (the projected average annual growth rate in the 2004

− 2030 period) for the 2031

− 2042 period.
Table 9.3.16 Historical and Forecasted Multi-Family Housing Starts (millions) Year Reference High Low Year Reference High Low 1990 0.30 0.30 0.30 2017 0.33 0.28 0.39 1991 0.17 0.17 0.17 2018 0.34 0.29 0.39 1992 0.17 0.17 0.17 2019 0.33 0.28 0.39 1993 0.16 0.16 0.16 2020 0.32 0.27 0.38 1994 0.26 0.26 0.26 2021 0.31 0.26 0.36 1995 0.28 0.28 0.28 2022 0.31 0.25 0.37 1996 0.32 0.32 0.32 2023 0.31 0.25 0.37 1997 0.34 0.34 0.34 2024 0.31 0.25 0.38 1998 0.35 0.35 0.35 2025 0.32 0.25 0.38 1999 0.34 0.34 0.34 2026 0.32 0.25 0.40 2000 0.34 0.34 0.34 2027 0.34 0.26 0.41 2001 0.33 0.33 0.33 2028 0.34 0.27 0.42 2002 0.35 0.35 0.35 2029 0.35 0.27 0.43 2003 0.35 0.35 0.35 2030 0.36 0.28 0.43 2004 0.34 0.34 0.34 2031 0.36 0.28 0.43 2005 0.34 0.34 0.34 2032 0.36 0.28 0.43 2006 0.32 0.25 0.39 2033 0.36 0.28 0.43 2007 0.34 0.27 0.42 2034 0.36 0.28 0.43 2008 0.34 0.27 0.42 2035 0.36 0.28 0.43 2009 0.33 0.25 0.41 2036 0.36 0.28 0.43 2010 0.32 0.25 0.40 2037 0.36 0.28 0.43 2011 0.31 0.24 0.39 2038 0.36 0.28 0.43 2012 0.31 0.24 0.38 2039 0.36 0.28 0.43 2013 0.31 0.24 0.37 2040 0.36 0.28 0.43 2014 0.31 0.25 0.37 2041 0.36 0.28 0.43 2015 0.31 0.25 0.37 2042 0.36 0.28 0.43 2016 0.32 0.26 0.37 Source: EIA, AEO2007.

9-39DOE deduced historical commercial laundry saturations for the new construction market based on the remaining portion of historical ship ments that did not serve as replacement units. (The determination of replacement shipments is discussed below.) Table 9.3.17 shows the historical saturation of commercial clothe s washers in new multi-family buildings.
Table 9.3.17 Commercial Clothes Washers: Historical New Multi-Family Building Saturations Year Saturation 1990 19.7% 1991 14.9% 1992 11.4% 1993 11.9% 1994 11.7% 1995 10.5% 1996 14.7% 1997 14.8% 1998 20.7% Average 14.5% DOE assumed that the averag e saturation over the period 1990

− 1998 of 14.5 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 figur e is close to the 14.5 percent saturation value that DOE determined. Thus, DOE has confidence that its calculated saturation is representative of the commercial clothes wash er market. Figure 9.3.16 shows the historical variation in the new construction saturation as well as the forecasted saturation for the years after 1998.

9-400. 0% 5. 0% 10 . 0 % 15 . 0 % 20 . 0 % 25 . 0 %

1984 1988 1992 1996 2000 2004 2008 2012 2 016 2020 2024 2028 2032 2036 2040

YearCommercial Laundry Saturation in new MFHousing

Figure 9.3.16 Commercial Laundry Saturation in New Multi-Family Housing Replacements . DOE determined shipments to the replacement market using an accounting method that tracks the to tal stock of units by vintage. DOE estimated a stock of commercial clothes washers by vintage by integrati ng 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 vi ntage, a certain percenta ge of each type of unit will fail and need to be replaced. To de termine 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 build ings. For the commercial clothes washer Shipments Model, DOE combined the average valu es for the two product applications based on the market share of each application (85 perc ent for multi-family housing and 15 percent for laundromats). The survival function that DOE us ed had an average life time 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 8.2.3.5 of Chapter 8. Figure 9.3.17 shows the survival and retirement functions that DOE used to estimate replacement shipments.

9-410% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent Surviving Percent Retiring

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Age (years) Figure 9.3.17 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 histor ical shipments with th e accounting model, DOE assumed every retired unit is not replaced immediat ely. 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 9.3.5.1). Staring in 1999 and extending to 2005, DOE estimated an evergrowing share of retired units th at were not replaced. DOE gra dually reduced the percentage of retired units not replaced to zero over the period 2006

─2013. Figure 9.3.18 shows the percentage of retired units not replaced over the period 1998

─2013.

9-420% 5% 10% 15% 20% 25% 30% 35% 40%

1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 YearPercent

Figure 9.3.18 Commercial Clothes Washers: Percent of Retired Units not Replaced over the period 1998
─2013

9.3.5.3 Base Case Shipments Figure 9.3.19 shows the forecas ted commercial clothes washer shipments in the base case (i.e., the case without new energy ef ficiency standards) and the hist orical shipments DOE used to calibrate the forecast. In the figure below, not e that the replacement shipments for the period 1998
─2013 are lower than the actualretired units. The area be tween the two lines showing retired units and replacement shipments represen ts the retired units that were not replaced.

9-430.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

1985 1995 2005 2015 2025 2035 YearShipments (million units) Historical Shipments Total Shipments Retired Units Replacement Shipments

Figure 9.3.19 Commercial Clothes Washers: Historical and Base Case Shipments Forecast Figure 9.3.20 presents forecasted base case commercial clothes washer shipments disaggregated into shipments due to retireme nts and shipments for new construction. DOE determined that replacements accruing from retirem ents range from 80 percent to 85 percent of total commercial clothes washer shipments over the period 2006–2042.

9-441986 1990 1994 1998 2 002 2006 2010 2014 2018 2022 2026 2030 2034 2038 2042 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40Shipments (million units) Shipments due to New cons Replacements Aggregate Shipments

Year Figure 9.3.20 Commercial Clothes Washers: Disaggregated Base Case Shipments Forecast

9.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME IMPACTS DOE conducted a literature review and an anal ysis of appliance pri ce 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 9A provides a detailed explanation of the methodology DOE used to quantify the impacts from the above variables. In the literature, DOE found onl y 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, su ch that rising incomes increase the demand for appliances. Finally, the literature suggests th at consumers use relatively high implicit discount rates i when comparing appliance prices and appliance operating costs.

i 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.

9-45DOE did not find enough equipment purchase pr ice and operating cost data to perform a complex analysis of dynamic changes in the appl iance market. Rather, it used purchase price and efficiency data specific to residential refrig erators, clothes washers, and dishwashers over the period 1980–2002 to evaluate broad market trends and conduct simple regression analyses. These data indicate that there ha s been a rise in appliance ship ments and a decline in appliance purchase price and operating costs over the time pe riod. Household income has also risen during this time. To simplify the analysis, DOE combin ed the available economic information into one variable, termed the relative price , and used this variable in an an alysis 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 relative ly inelastic (i.e., under 1.0). DOE’s regression analysis suggests that the relative price elasticity of demand, averaged over th e 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. Neve rtheless, 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, operati ng 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 reason able 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 need ed 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

9-46appliances, to indicate how short-run and long-run pr ice elasticities differ. Therefore, to estimate how the relative price elasticity changes over time, DOE relied on a study pertaining to automobiles. 22 This study shows that the automobile price elasticity of demand changes in the years following a purchase price change. With in creasing 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 9.4.1 shows the relati ve 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 9.4.1 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 styear 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 STD _ p ( j ) = 

# ( Rpl BASE _ p ( j ) + NI BASE _ p ( j ) + M BASE _ p ( j ) 

# ) × 

# ( 1 e RP ( j ) × ∆ RP( j ) 

# )

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 retire d and replaced in year j , NI BASE_p (j) = Number of new construction instal lations under the base case of product p in year j(not applicable to dehumidifiers), 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, and first time owners for dishwashers and dehumidifiers),
e RP (j)= Relative price elasticity in year j(equals -0.34 for year 1), and ∆RP(j)= Change in relative price due to a standard level in year j

.

9.5 AFFECTED STOCK The affected stock is the in-s ervice stock of the appliance or product that is affected by a standard level. 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

9-47stock quantifies the impact that new product sh ipments have on the appliance stock due to a standard level. Therefore, the affected stock consists of those in-service units that are purchased in or after the year the standard has taken effect, as desc ribed by the following equation:

_ j Std _ yr Aff Stock ( j ) = Ship ( j ) + 

## ∑ Stock ( age ) p p p =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 calculat e the affected stock, DOE must define the effective date of the standard. For the NES and NPV results presented in Chapter 10, DOE assumed that new energy efficien cy standards will become effectiv e in the year 2012. Thus, all appliances purchased starting on the first day of the year 2012 are affected by the standard level.

9.6 RESULTS The following sections show the shipments for ecasts for the various standard levels that DOE considered for each of th e four sets of products.

9.6.1 Dishwashers Table 9.6.1 shows the cumulative ship ments impacts over the period 2012–2042 caused by each of the seven standards cases considered fo r standard-sized dishwashers. For standard level 1, shipments are forecasted to increase slightly—the decrease in operating costs due to the efficiency standard offsets the increase in purchase prices, resulting in a net increase in shipments. For standard levels 2 through 7, th e effect due to the incr ease in product purchase prices exceeds the effects from decreased ope rating costs, resulting in a net decrease in shipments. Figure 9.6.1 compares the shipments fo recasts for the base case and standard levels 4 through 7 (annual shipments forecasts for standard levels 1 through 3 are not appreciably different from the base case). In Figure 9.6.1, the difference between the base case and standard level shipments forecasts depicts the annual shipments reductions caused by the standard levels. As can be seen in this figure, the decrease in forecasted shipments is significant for standard levels 5 through 7.

9-482008 2 01

0 2012 2 01

4 2016 2 01

8 2020 2 02

2 2024 2 02

6 2028 2 03

0 2032 2 03

4 2036 2 03

8 2040 2 04

2 Table 9.6.1 Dishwashers: Cumulative Shipments Im pacts Caused by Standards over the Period 2012–2042 Standard Level Energy Factor Cumulative Impacts (million) 1 0.58 +0.056 2 0.62 -0.002 3 0.65 -0.676 4 0.68 -2.925 5 0.72 -9.010 6 0.80 -17.413 7 1.11 -23.936

13

12

11

10

9

8

7 Ba s e C a s e S t d Le v e l 4 S t d Le v e l 5 S t d Le v e l 6 S t d Le v e l 7Shipments (million)

Year Figure 9.6.1 Standard-Sized Dishwashers: Base Case and Standards Case Shipments Forecasts

9.6.2 Dehumidifiers Table 9.6.2 shows the cumulative ship ments impacts over the period 2012–2042 caused by each of the five standards cases considered for dehumidifiers. For all standard levels, 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 9.6.2 compares the shipments forecasts for the ba se case and standard levels 3 through 5 (annual shipments forecasts for standard levels 1 and 2 ar e not appreciably different from the base case). In Figure 9.6.2, the difference between the base case and standard level shipments forecasts

9-492 008 2 010 201 2 2014 2 016 2 018 2 020 202 2 2024 2 026 2 028 203 0 203 2 2034 2036 2 038 204 0 204 2 depicts the annual shipments reductions caused by th e standard levels. As can be seen in the figure, the decrease in forecasted shipments is significant for standard levels 4 and 5.
Table 9.6.2 Dehumidifiers: Cumulative Shipments Impacts to all Product Classes caused by Standards over the period 2012–2042

Standard Level Cumulative Impacts (million) 1 -0.251 2 -0.810 3 -1.635 4 -2.957 5 -7.179

1. 5 2. 0 2. 5 3. 0 3. 5 4. 0Shipments (million) B a se C a se S t d Lev e l 3 S t d Lev e l 4 S t d Lev e l 5

Year Figure 9.6.2 Dehumidifiers: Base Case and Standards Case Shipments Forecasts

9.6.3 Cooktops and Ovens Because the combined market of electric and gas cooki ng products is completely saturated, DOE assumed that all electric and ga s cooking product standard levels would neither impact base case shipments nor cause shifts inelectric 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, forecas ted shipments for all cooking product standard levels are equivalent to the base case shipments forecasts. Refer back to Figures 9.3.8

9-50and 9.3.9 to review the base case shipments fo recasts for electric and gas cooking products, respectively.

9.6.4 Microwave Ovens Table 9.6.3 shows the cumulative ship ments impacts over the period 2012–2042 caused by each of the four standards cases considered for microwave ovens. For all standard levels, 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 9.6.3 compares the shipments forecasts for the ba se case and standard levels 1 through 4. In Figure 9.6.3, the difference between the base case and standard level shipments forecasts depicts the annual shipments reductions caused by the standard levels. As can be seen in the figure, the decrease in forecasted shipments is significant for all standard levels.
Table 9.6.3 Microwave Ovens: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042 Standard Level Energy Factor Cumulative Impacts (million) 1 0.586 -10.978 2 0.588 -22.550 3 0.597 -40.406 4 0.602 -62.567

9-512 0 0 8 20 1 0 2012 2 0

14 2 0

1 6 2018 2020 2022 20 2 4 2026 2 0 28 2 0 3 0 2 0 3 2 2034 2036 2038 2040 2042Shipments (million) 22

21

20
19
18

17

16
15

14 B a se C a se S t d L e v el 1 S t d L e v el 2 S t d L e v el 3 S t d L e v el 4

Year Figure 9.6.3 Microwave Ovens: Base Case and Standards Case Shipments Forecasts

9.6.5 Commercial Clothes Washers Table 9.6.4 shows the cumulative ship ments impacts over the period 2012–2042 caused by each of the six standards cases considered for commercial clothes washers. For all standard levels, shipments are forecasted to decrease—the effects from the increase in product purchase prices offset the effects from decreased operating co sts, resulting in a net decrease in shipments. Figure 9.6.4 compares the shipments forecasts for the base case and standard level 6. Standard levels 2 through 6 have very similar impacts becau se the purchase price in crease associated with each of the standard levels is the same. In Fi gure 9.6.4, the difference between the base case and standard level shipments forecasts depicts the annual shipments reductions caused by the standard levels.

9-522008 2 010 2 012 201 4 201 6 201 8 2020 2022 2024 2 026 2 0 2 8 2 0 3 0 2 0 3 2 2 0 3 4 2 0 3 6 2038 2040 2042 Table 9.6.4 Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2012–2042

Standard Level Modified Energy Factor/ Water Factor Cumulative Impacts (thousands) 1 1.42/9.5 -0.141 2 1.60/8.5 -0.596 3 1.72/8.0 -0.595 4 1.80/7.5 -0.595 5 2.00/5.5 -0.593 6 2.20/5.1 -0.592

0. 1 5 0. 2 0 0. 2 5 0. 3 0 0. 3 5 0. 4 0Shipments (million) Ba s e C a s e St d L e v e l 6

Year Figure 9.6.4 Commercial Clothes Washers: Base Case and Standards Level 6 Shipments Forecasts

9-53REFERENCES

1 U.S. Department of Energy- Energy Information Administration. Annual Energy Outlook 2007 with Projections to 2030 , February, 2007. Washingt on, DC. DOE/EIA-0383(2007).

2 Association of Home App liance Manufacturers. AHAM Data on Dishwashers for Efficiency Standards Rulemaking , September 7, 2006. Letter to Bu ilding Technologies Program, U.S. Department of Energy.

3 Association of Home Ap pliance Manufacturers. AHAM 2000 Fact Book , 2000. Washington, DC.

4 Statistical Review. Appliance Magazine , April, 1993.

5 Statistical Review. Appliance Magazine , April, 1990.

6 Statistical Review. Appliance Magazine , April, 1982.

7 Association of Home Ap pliance Manufacturers. AHAM 2005 Fact Book , 2005. Washington, DC.

8 The Saturation Picture. Appliance Magazine , September, 1993, 1995, 2004, 2005.

9 NFO World Group. Final Report: Home Appliance Saturation and Length of First Ownership Study , May, 2001. Prepared for Associati on of Home Appliance Manufacturers by NFO World Group, Boston, MA.

10 U.S. Department of Energy-En ergy Information Administration. Residential Energy Consumption Survey, 1993 Public Use Data Files , 1993. Washington, DC. < http://www.eia.doe.gov/emeu/recs/93publicuse.html >

11 U.S. Department of Energy-En ergy Information Administration. Residential Energy Consumption Survey, 1997 Public Use Data Files , 1997. Washington, DC. < http://www.eia.doe.gov/emeu/recs/public.html >

12 U.S. Department of Energy-En ergy Information Administration. Residential Energy Consumption Survey, 2001 Public Use Data Files , 2001. Washington, DC. < http://www.eia.doe.gov/emeu/recs/recs2001/publicuse2001.html >

13 Association of Home Ap pliance Manufacturers. AHAM Data on Dehumidifiers for Efficiency Standards Rulemaking , August 23, 2006. Letter to Building Technologies Program, U.S. Department of Energy.

14 Association of Home Ap pliance Manufacturers. AHAM 2003 Fact Book , 2003. Washington, DC.

9-5415 Association of Home App liance Manufacturers. AHAM Data on Cooking Products for Efficiency Standards Rulemaking , August 23, 2006. Letter to Building Technologies Program, U.S. Department of Energy.

16 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/applianc e_standards/residential/pdfs/chap3_sup.pdf >

17 Appliance Recycling Information Center. INFOBulletin #8, Applications in Appliances , March 2005. Washington, DC. < http://www.aham.org/industry/ht/a ction/GetDocumentAction/id/5370 >

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

19 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>

20 Association of Home App liance 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.

21 Statistical Review. Appliance Magazine , April, 1998, 1999.

22 S. Hymans. Consumer Durable Spen ding: Explanation and Prediction, Brookings Papers on Economic Activity , 1971. Vol. 1971, No. 1, pp. 234-239.

9-55