CHAPTER 10. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

10.1 INTRODUCTION

......................................................................................................... 10-1 10.2 SHIPMENTS MODEL METHODOLOGY

.................................................................. 10-2 10.3 DATA INPUTS AND MODEL CALIBRATION

........................................................ 10-4 10.3.1 Historical Shipments

.................................................................................................... .. 10-5 10.3.2 Markets and Model Calibration

..................................................................................... 10-7 10.3.2.1 New Construction

........................................................................................ 10-7 10.3.2.2 Replacements

............................................................................................. 10-11 10.3.2.3 Model Calibration—Non-Replacements

................................................... 10-12 10.3.3 Base Case Shipments

................................................................................................... 10 -14 10.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME IMPACTS

.................................................................................................................... 10-15 10.5 AFFECTED STOCK

................................................................................................... 10-18 10.6 RESULTS

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

LIST OF TABLES Table 10.3.1 Commercial Clothes Washers: Historical Shipments, Domestic plus Imports

............................................................................................................... 10-6 Table 10.3.2 Historical and Forecasted Multi-Family Housing Starts (millions)

................... 10-9 Table 10.3.3 Commercial Clothes Washers: Historical New Multi-Family Building Saturations

........................................................................................................ 10-10 Table 10.3.4 Commercial Clothes Washers: Historical New Multi-Family Building Saturations

........................................................................................................ 10-13 Table 10.3.5 Multi-Family In-Unit Washer Saturations

....................................................... 10-13 Table 10.4.1 Change in Relative Price Elasticity following a Purchase Price Change

........ 10-17 Table 10.6.1 Top-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2013–2043

.............................. 10-19 Table 10.6.2 Front-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2013–2043

.............................. 10-19

LIST OF FIGURE Figure 10.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index

............. 10-7 Figure 10.3.2 Commercial Laundry Saturation in New Multi-Family Housing

.................... 10-11 Figure 10.3.3 Commercial Clothes Washers: Survival and Retirement Functions

............... 10-12

10-iFigure 10.3.4 Commercial Clothes Washers: Historical and Base Case Shipments Forecast

............................................................................................................ 10-14 Figure 10.3.5 Commercial Clothes Washers: Disaggregated Base Case Shipments Figure 10.6.1 Top-Loading Commercial Clothes Washers: Base Case and TSL Figure 10.6.2 Front-Loading Commercial Clothes Washers: Base Case and TSL Forecast

............................................................................................................ 10-15 Shipments Forecasts

......................................................................................... 10-19 Shipments Forecasts

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

10-iiCHAPTER 10. SHIPMENTS ANALYSIS

10.1 INTRODUCTION Product shipments estimates are a necessary input to the national energy savings (NES) and net present value (NPV) calculations. Shipments are also a necessary input to the manufacturer impact analysis (MIA), which DOE conducts for its notices of proposed rulemaking (NOPRs). This chapter describes DOE’s methodology for projecting annual shipments and presents results commercial clothes washers. DOE estimated shipments for commercial clothes washers with a Shipments Model. DOE calibrated the Shipments Model against historical shipments. For purposes of estimating the impacts of prospective trial standard levels (TSL) on product shipments, the Shipments Model accounts for the combined effects of changes in purchase price, annual operating cost, and household income on the consumer purchase decision. The Shipments Model considers specific market segments to estimate shipments, the results for which are then aggregated to estimate total product shipments. In the case of commercial clothes washers, DOE accounted for three market segments: (1) new construction; (2) existing buildings (i.e., replacing failed equipment); and (3) retired units not replaced. The Shipments Model is a Microsoft Excel spreadsheet that is accessible on the Internet ( http://www.eere.energy.gov/buildings/appliance_standards/ ). Appendix 11A discusses how to access the Shipments Model spreadsheet and other related spreadsheets, and provides basic instructions for using them. The rest of this chapter explains the Shipments Model in more detail. Section 10.2 presents the Shipments Model methodology; section 10.3 describes the data inputs and the model calibration; section 10.4 discusses impacts on shipments from changes in equipment purchase price, operating cost, and household income; section 10.5 discusses the affected stock; and section 10.6 presents the results for different TSL scenarios. The technical support document (TSD) for DOE’s notice of proposed rulemaking (NOPR) covered conventional cooking products (i.e., cooktops and ovens), microwave oven energy factor (EF), microwave oven standby power consumption, and commercial clothes washers (CCW). 1 DOE has already completed its standards rulemaking for conventional cooking products and microwave oven EF and has published both a final rule 2 and corresponding TSD. 3 This chapter presents information and results pertaining solely to CCWs. The impact of more-efficient equipment on microwave oven standby power will be addressed in a subsequent TSD.

10-110.2 SHIPMENTS MODEL METHODOLOGY DOE developed a national stock model for estimating annual shipments for commercial clothes washers. The model considers market segmentation as a distinct input to the shipments forecast. As represented by the following equation, the two primary market segments are new installations and replacements.
Ship p ( j ) = Rpl p ( j ) + NI p ( j )

where: Ship p (j) = Total shipments of product p in year j , Rpl p (j) = Units of product p retired and replaced in year j , and NI p (j) = Number of new installations of product p in year j

. As the product-specific sections below discuss, DOE also considered a third market segment for most of the products to calibrate its Shipments Models to historical shipments data. In principle, each market segment and each product class responds differently to both the base case demographic and economic trends and to the implementation of standards. Furthermore, retirements, early replacements, and efficiency trends a are dynamic and can vary among product classes. Rather than simply extrapolating a current shipments trend, the base case shipments analysis (i.e., the case without new standards) uses driver input variables, such as construction forecasts and product lifetime distributions, to forecast sales in each market segment. Thus, DOE’s Shipments Model assumes that construction, i.e., new housing units, drives new installations. In each year, the product shipments from the new construction market segment are equal to the number of new housing units built times the purchase rate, which is determined by the product class market share and the market saturation of the product under consideration. DOE’s Shipments Model takes an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected construction trends. The models estimate shipments due to replacements using sales in previous years and assumptions about the lifetime of the equipment. Therefore, estimated sales due to replacements in a given year are equal to the total stock of the appliance minus the sum of the appliances sold in previous years that still remain in the stock. DOE determined the useful service life of each appliance to estimate how long the appliance is likely to remain in stock. The following equation represents how DOE estimated replacement shipments.

a Efficiency trends affect shipments only in the standards case. A change in the efficiency distribution of the stock results in a change in the purchase price and operating co st and, therefore, produces a purchase price and operating cost impact on the shipments. This is discu ssed later in the ch apter in Section 10.4.

10-2_ _ _ 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-service appliances in year j-1 , prob Rtr (age) = Probability that an appliance of a particular age will be retired, and N

= Start year for when the model begins its stock accounting (start year is specific to each product based on available historical shipments data). Stock accounting takes product shipments, a retirement function, and initial in-service product stock as inputs and provides an estimate of the age distribution of in-service product stocks for all years. The age distribution of in-service product stocks is a key input to both the NES and NPV calculations—the operating costs for any year depend on the age distribution of the stock. The dependence of operating cost on the equipment age distribution occurs under a TSL that produces increasing efficiency over time, where older, less efficient units may have higher operating costs, while younger, more-efficient units will have lower operating costs. Furthermore, in the case of an early replacement scenario, retirements due to early replacement will depend on the age of the units that are subject to early replacement. DOE calculated total in-service stock of equipment by integrating historical shipments data starting from a specific year. The start year depended on the historical data available for the product. As units are added to the in-service stock, some of the older ones retire and exit the stock. To estimate future shipments, DOE developed a series of equations that define the dynamics and accounting of in-service stocks. For new units, the equation is:

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

where: Stock(j, age) = The population of in-service units of a particular age, j = Year for which the in-service stock is being estimated, and Ship (j) = Number of units purchased in year j

. The above equation states that the number of one-year-old units is simply equal to the number of new units purchased the previous year. The slightly more complicated equations (e.g., the following equation) are those that describe the accounting of the existing in-service stock of units:
Stock( j +1 ,age +1 ) = Stock( j,age ) × 

# [ 1 _ prob Rtr ( age ) 

# ]

In the above equation, as the year is incremented from j to j+1 , the age is also incremented from age to age+1 . With time, a fraction of the in-service stock is removed, and that fraction is determined by a retirement probability function, prob Rtr (age) , which is described

10-3in section 10.3. Because commercial clothes washers are considered to be common appliances that have been used by U.S. consumers for a long time, replacements typically constitute the majority of shipments. Most replacements are made when equipment wears out and fails.

10.3 DAT A INPUTS AND MODEL CALIBRATION As discussed above, shipments are driven primarily by two market segments: new construction and replacements. DOE used historical shipments as the basis for calibrating its Shipments Model. Because new construction shipments and replacements were not enough to account for all product shipments, DOE developed another market segment to calibrate its Shipments Model. This additional market segment, retired units not replaced, represented a small share of total shipments. Shipments to each of these market segments are discussed below. DOE designed its Shipments Model for commercial clothes washers by developing a single Shipments Model for all washers destined for the two most predominant product applications—multi-family buildings and laundromats—and then disaggregating the shipments into its two product classes—top-loading and front-loading washers. As described in Chapter 8, section 8.2.6, DOE estimated that 70 percent of the market is comprised of top-loading washers and 30 percent is comprised of front-loading washers. DOE estimated that the market share of top-loading and front-loading washers would remain fixed at 70 percent and 30 percent, respectively, through the forecast period. DOE’s approach for estimating shipments relies on using aggregate historical shipments data for all commercial clothes washers. Tracking of product stock and vintage relies on a retirement function, which describes the likelihood that equipment installed in a certain year will still be in service in any subsequent year. Once a product reaches the end of its useful life it is removed from service. In most cases, retirement is followed by replacement, which is a key driver of further shipments. The difference between replacements and total shipments for the following year provides the shipments going into new construction. The output includes units going into both laundromats and multi-family apartment buildings. DOE based its Shipments Model on the following three assumptions: (1) All product shipments for new construction are driven by the new multi-family housing market, (2) the relative market shares of the two product applications, laundromats and common-area laundry facilities in multi-family housing, are constant over time, and (3) the U.S. Census Bureau’s quantity index data can be used to validate the shipments trend observed in the historical data. DOE’s first assumption is based on the premise that multi-family occupants who do not have their own personal washers and dryers must either wash their laundry in the common area laundry facility within their building or in a laundromat. Because the vast majority of people that use laundromats live in multi-family buildings, the construction of any new laundromats would seem to be driven by new multi-family customers. Thus, DOE believes it is reasonable to

10-4expect that new multi-family construction starts capture all new product shipments, regardless of whether the units are destined for multi-family buildings or laundromats. With regard to the second assumption, i.e., constant market shares, DOE did not have sufficient data to forecast whether the market shares of laundromats and multi-family housing will vary over time. Therefore, DOE assumed that market shares will remain constant at current levels. DOE relied on a report from the Consortium for Energy Efficiency (CEE) to estimate current market shares. Based on the CEE report, multi-family buildings and laundromats comprise 85 percent and 15 percent of the commercial clothes washer market, respectively. 4
The third assumption pertains to the use of the U.S Bureau of Economic Analysis’ quantity index data to validate historical shipments data. 5 The Census Bureau’s quantity index for commercial laundry equipment includes shipments of commercial laundry products beyond those covered by this rulemaking. ��������������������������������������������������� ����������������������������������������������������������������������������������� �������������������������������������� commercial laundry ������������������������������ �������������������������������������������������������������������������������������� ���������������������������������������������������������������������������� to validate the historical shipments.

10.3.1 Historical Shipments DOE used historical shipments data (i.e., domestic shipments and imports) to calibrate its Shipments Model. It used two sources to establish historical shipments: (1) data AHAM provided for the period 2002–2005 6 and (2) data from Appliance Magazine 7 for the period 1988– 1998. For the period 1999–2001, DOE interpolated between the shipments for 1998 and 2002. For the period 1979–1987, DOE used the shipments in1989 as the reference point and the corresponding Census Bureau’s quantity index data to estimate shipments. Lastly, DOE used a five-year moving average to backcast shipments for the period 1972�1978. Table 10.3.1 summarizes the historical commercial clothes washer shipments data.

10-5Table 10.3.1 Commercial Clothes Washers: Historical Shipments, Domestic plus Imports Year Shipments (thousands) Year Shipments (thousands) 1972 167 1989 215 1973 167 1990 225 1974 166 1991 193 1975 167 1992 188 1976 167 1993 190 1977 167 1994 205 1978 166 1995 209 1979 164 1996 232 1980 170 1997 241 1981 171 1998 265 1982 163 1999 239 1983 163 2000 215 1984 167 2001 194 1985 176 2002 175 1986 186 2003 191 1987 194 2004 178 1988 213 2005 177 Sources: 2002–2005: AHAM Data Submi ttal, 2006; 1988–1998: Appliance Magazine, “Statistical Rev iew,” 1998 and 1999. 1972–1987 : Based on Census Bureau’s Quantity Index Data. 1999–2001: DOE interpolated values. As indicated in Table 10.3.1, there was a significant drop in commercial clothes washer shipments between 1998 and 2002. Because DOE used different data sources to estimate the shipments in 1998 and 2002, it validated the shipments data through a review of the U.S. Bureau of Economic Analysis’ quantity index data. 5 DOE compared the historical shipments data against the quantity index for commercial laundry equipment (represented by North American Industry Classification System (NAICS) code 333312). The largest product group in this industry is washers and extractors, which accounts for almost 40 percent of sales and about 25 percent of the shipment value. 4 As shown in Figure 10.3.1 below, the commercial laundry quantity index tracks the shipments trend closely. Thus, DOE concluded that the downward trend observed in the historical shipments between 1998 and 2002 was real.

10-60 20 40 60 80 100 120 140 160

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

0.00 0.05 0.10 0.15 0.20 0.25 0.30Shipments in millions

QI Shipments QI-based Shipments Pre-1988 shipments based on QI Extrapolated after 2005

Figure 10.3.1 Commercial Clothes Washer Shipments and Trend in Quantity Index

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

. The sections below discuss both of these markets in further detail. The model calibration is also discussed.

10.3.2.1 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 commonarea laundry facility (within the multi-family building) or a laundromat. To estimate shipments to new construction, DOE used forecasts of multi-family housing starts coupled with commercial clothes washer saturation data. In other words, to forecast the

10-7shipments for new construction for any given year, DOE multiplied the multi-housing forecasts by the forecasted saturation of commercial clothes washers for new multi-family housing. Because DOE used only multi-family housing starts as a driver for new construction shipments, the saturation represents washers in both the multi-family housing and laundromat markets. Table 10.3.2 presents historical and forecasted new multi-family housing starts based on the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 2009 updated to include provisions of the American Recovery and Reinvestment Act ( AEO2009 ARRA ) for the period 1990–2030. 8 The AEO typically provides three scenarios of multi-family housing starts: the Reference case, the High Economic Growth case, and the Low Economic Growth case. AEO2009 ARRA only provided housing starts for the Reference Case. As result, to estimate housing starts for the high and low economic growth cases, DOE used the AEO2009 March release to estimate the high and low growth forecasts. 9 All three housing starts forecasts are presented in Table 10.3.2. DOE used only the forecasts from the Reference case to estimate its shipments to new construction. For the period 2031�2043, DOE froze completions at the level in the year 2030 for all three economic forecasts.

10-8Table 10.3.2 Historical and Forecasted Multi-Family Housing Starts (millions) Year Reference High Low Year Reference High Low 1990 0.30 0.30 0.30 2017 0.39 0.47 0.32 1991 0.17 0.17 0.17 2018 0.41 0.47 0.34 1992 0.17 0.17 0.17 2019 0.42 0.45 0.33 1993 0.16 0.16 0.16 2020 0.43 0.40 0.29 1994 0.26 0.26 0.26 2021 0.42 0.38 0.28 1995 0.28 0.28 0.28 2022 0.41 0.39 0.28 1996 0.32 0.32 0.32 2023 0.41 0.40 0.29 1997 0.34 0.34 0.34 2024 0.41 0.41 0.29 1998 0.35 0.35 0.35 2025 0.41 0.42 0.29 1999 0.34 0.34 0.34 2026 0.41 0.44 0.29 2000 0.34 0.34 0.34 2027 0.41 0.46 0.30 2001 0.33 0.33 0.33 2028 0.40 0.47 0.30 2002 0.35 0.35 0.35 2029 0.40 0.47 0.30 2003 0.35 0.35 0.35 2030 0.40 0.47 0.30 2004 0.34 0.34 0.34 2031 0.40 0.47 0.30 2005 0.34 0.34 0.34 2032 0.40 0.47 0.30 2006 0.35 0.34 0.34 2033 0.40 0.47 0.30 2007 0.38 0.37 0.30 2034 0.40 0.47 0.30 2008 0.41 0.39 0.20 2035 0.40 0.47 0.30 2009 0.28 0.32 0.22 2036 0.40 0.47 0.30 2010 0.18 0.22 0.24 2037 0.40 0.47 0.30 2011 0.19 0.27 0.26 2038 0.40 0.47 0.30 2012 0.23 0.33 0.26 2039 0.40 0.47 0.30 2013 0.29 0.41 0.26 2040 0.40 0.47 0.30 2014 0.31 0.43 0.26 2041 0.40 0.47 0.30 2015 0.33 0.45 0.27 2042 0.40 0.47 0.30 2016 0.36 0.46 0.28 2043 0.40 0.47 0.30 Source: EIA, AEO2009 ARRA for Reference; EIA, AEO 2009 March release for High and Low. DOE deduced historical commercial laundry saturations for the new construction market based on the remaining portion of historical shipments that did not serve as replacement units. (The determination of replacement shipments is discussed below.) Table 10.3.3 shows the historical saturation of commercial clothes washers in new multi-family buildings.

10-9Table 10.3.3 Commercial Clothes Washers: Historical New Multi-Family Building Saturations Year Saturation 1990 19.4% 1991 14.7% 1992 12.4% 1993 14.3% 1994 13.8% 1995 12.6% 1996 15.8% 1997 14.6% 1998 18.7% Average 15.1%
DOE assumed that the average saturation over the period 1990�1998 of 15.1 percent would remain constant over the period 1999�2043. CEE’s report on the commercial laundry industry estimates a 14 percent saturation (i.e., approximately 14 percent of total domestic laundry is done in non-residential washers). CEE’s 14 percent figure is close to the 15.1 percent saturation value that DOE determined. Thus, DOE has confidence that its calculated saturation is representative of the commercial clothes washer market. Figure 10.3.2 shows the historical variation in the new construction saturation as well as the forecasted saturation for the years after 1998.

10-100% 5% 10% 15% 20% 25%

1985 1995 2005 2015 2025 2035 2045 YearCommercial Laundry Saturation in new MultiFamily Housing

Figure 10.3.2	 Commercial Laundry Saturation in New Multi-Family Housing

10.3.2.2 Replacements DOE determined shipments to the replacement market using an accounting method that tracks the total stock of units by vintage. DOE estimated a stock of commercial clothes washers by vintage by integrating historical shipments starting from the year 1972. Over time, some units will be retired and removed from stock, thereby triggering the shipment of a replacement unit. Depending on the vintage, a certain perc entage of each type of unit will fail and need to be replaced. To determine when a unit fails, DOE used a survival function based on the product lifetime distributions of the two product applications considered in the analysis—laundromats and multi-family buildings. DOE based the retirement function on a Weibull probability distribution for the product lifetime. The Shipments Models assume that no units are retired below a minimum product lifetime and all units are retired before exceeding a maximum product lifetime. The models determine the probability of retirement at a certain age for all products using the following equations:
prob Rtr ( age ) = 0 for age < AgeMin 1 prob Rtr ( age ) =

_ f o r AgeMin � age � AgeMax AgeMax AgeMin
prob Rtr ( age ) = 1 for age > AgeMax

10-11where: prob Rtr (age) = Probability of retirement at the age of the product, AgeMin = Minimum retirement age, and AgeMax = Maximum retirement age. For the commercial clothes washer Shipments Model, DOE combined the average values for the two product applications based on the market share of each application (85 percent for multi-family housing and 15 percent for laundromats). The survival function that DOE used had an average lifetime of 10.6 years with minimum and maximum values of 6.7 years and 14.6 years, respectively. For a more complete discussion of commercial clothes washer lifetimes, refer back to section 8.2.3 of Chapter 8. Figure 10.3.3 shows the survival function that DOE used to estimate replacement shipments.

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

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Age (years)Percent Surviving

Figure 10.3.3	 Commercial Clothes Washers: Survival and Retirement Functions

10.3.2.3 Model Calibration—Non-Replacements To account for the significant drop in shipments after 1998, and to reconcile the historical shipments with the accounting model, DOE assumed every retired unit is not replaced immediately. The market share of retired units not replaced was relatively large due to the significant drop in shipments after 1998 (as discussed above in Section 10.3.1). Staring in 1999 and extending to 2005, DOE estimated the share of retired units that were not replaced. The

10-12share of retired units not replaced fluctuated between nine and 31 percent. Table 10.3.4 shows the share of retired units not replaced between 1999

– 2005.
Table 10.3.4	 Commercial Clothes Washers: Historical New Multi-Family Building Saturations Year Saturation 1999 9.2% 2000 12.9% 2001 19.7% 2002 25.2% 2003 12.1% 2004 30.8% 2005 18.2% Average 18.3%
To determine the share of retired units not replaced after the year 2005, DOE reviewed data from the Census Bureau’s American Housing Survey (AHS) on the saturation of in-unit washers (i.e., residential washers used in multi-family units) in the multi-family stock and new multi-family construction. 10 DOE utilized the in-unit washer trends in multi-family buildings to estimate the associated trends in the use of co mmercial clothes washers in multi-family commonarea laundry facilities. Table 10.3.5 shows the saturation of in-unit washers in both the multifamily stock and new construction from 1997

– 2005.
Table 10.3.5	 Multi-Family In-Unit Washer Saturations In-Unit Washer Saturation Multi-Family Housing 1997 1999 2001 2003 2005 New Construction 70.4% 64.4% 68.7% 68.1% 73.4% Stock 32.3% 32.7% 35.6% 36.1% 37.3% Source: AHS, 1997

– 2005
As evident from Table 10.3.5, the saturation of in-unit washers in new construction has stayed relatively constant while the saturation in the multi-family building stock has increased by approximately 16 percent. Because the saturation of in-unit washers in new construction has stayed relatively constant, there must be another driver for the increase of in-unit washers in the building stock. DOE estimates that the growth in in-unit washer saturations in the multi-family stock over the last ten years was caused by conversions of rental property to condominiums, resulting in the gradual phase out or non-replacement of failed commercial clothes washers in common-area laundry facilities. Based on this apparent trend, DOE used the average percent of non-replacements over the 1999

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

– 2043.

10-1310.3.3 Base Case Shipments Figure 10.3.4 shows the forecasted commercial clothes washer shipments in the base case (i.e., the case without new energy efficiency standards) and the historical shipments DOE used to calibrate the forecast. In the figure below, note that the replacement shipments for the period 1998�2043 are lower than the actual retired units. The area between the two lines showing retired units and replacement shipments represents the retired units that were not replaced. To emphasize, all commercial clothes washers shipments, i.e., top-loading and front-loading units, are represented in the shipments shown in Figure 10.3.4. As previously stated, DOE estimated that 70 percent of shipments in every year of the forecast are comprised of top-loading washers and 30 percent of shipments in every year are comprised of front-loading washers.

0.00 0.05 0.10 0.15 0.20 0.25 0.30

1985 1995 2005 2015 2025 2035 2045 YearShipments (million) Historical Shipments Total Shipments Retired Modeled Units Replacement Modeled Shipments New Construction Modeled Shipments

Figure 10.3.4	 Commercial Clothes Washers: Historical and Base Case Shipments Forecast Figure 10.3.5 presents forecasted base case commercial clothes washer shipments disaggregated into shipments due to retirements and shipments for new construction. DOE determined that replacements accruing from retirements range from 80 percent to 85 percent of total commercial clothes washer shipments over the period 2006–2043.

10-140.00 0.05 0.10 0.15 0.20 0.25

2005 2010 2015 2020 2025 2030 2035 2040 YearShipments (million) New Construction Modeled Shipments Replacement Modeled Shipments

Figure 10.3.5	 Commercial Clothes Washers: Disaggregated Base Case Shipments Forecast

10.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME IMPACTS DOE conducted a literature review and an analysis of appliance price and efficiency data to estimate the combined effects on product shipments from increases in equipment purchase price, decreases in equipment operating costs, and changes to household income. Appendix 10A provides a detailed explanation of the methodology DOE used to quantify the impacts from the above variables. In the literature, DOE found only a few studies of appliance markets that are relevant to this rulemaking analysis, and identified no studies that use time-series data of equipment price and shipments data after 1980. The information that can be summarized from the literature suggests that the demand for appliances is price inelastic. Other information in the literature suggests that appliances are a normal good, such that rising incomes increase the demand for

10-15appliances. Finally, the literature suggests that consumers use relatively high implicit discount rates b when comparing appliance prices and appliance operating costs. DOE did not find enough equipment purchase price and operating cost data to perform a complex analysis of dynamic changes in the appliance market. Rather, it used purchase price and efficiency data specific to residentia l refrigerators, clothes washers, and dishwashers over the period 1980–2002 to evaluate broad market trends and conduct simple regression analyses. These data indicate that there has been a rise in appliance shipments and a decline in appliance purchase price and operating costs over the time period. Household income has also risen during this time. To simplify the analysis, DOE combined the available economic information into one variable, termed the relative price , and used this variable in an analysis of market trends, as well as to conduct a regression analysis. The relative price is defined with the following expression:

TP PP + PVOC RP = = Income Income

where:

RP = Relative price, TP = Total price, Income = Household income, PP = Appliance purchase price, and PVOC = Present value of operating cost. In the above equation, DOE used an implicit discount rate of 37 percent to determine the present value of operating costs. DOE’s analysis of market trends suggests that the relative price elasticity of demand for the three appliances is relatively inelastic (i.e., under 1.0). DOE’s regression analysis suggests that the relative price elasticity of demand, averaged over the three appliances, is -0.34. For example, a relative price increase of 10 percent results in a shipments decrease of 3.4 percent. Note that, because the relative price elasticity incorporates the impacts from three effects (i.e., purchase price, operating cost, and household income), the impact from any single effect is mitigated by changes from the other two effects. The relative price elasticity of -0.34 is consistent with estimates in the literature. Nevertheless, DOE stresses that the measure is based on a small data set, using simple statistical analysis. More important, the measure is based on an assumption that economic variables, including purchase price, operating costs, and household income, explain most of the trend in appliances per household in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes

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

10-16that its estimate of the relative price elasticity of demand provides a reasonable assessment of the impact that purchase price, operating cost, and household income have on product shipments. Because DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time period, it needed to consider how the relative price elasticity is affected once a new standard takes effect. DOE considered the relative price elasticitiy provided above to be a shortrun value. It was unable to identify sources specific to household durable goods, such as appliances, to indicate how short-run and long-run price elasticities differ. Therefore, to estimate how the relative price elasticity changes over time, DOE relied on a study pertaining to automobiles. 11 This study shows that the automobile price elasticity of demand changes in the years following a purchase price change. With increasing years after the purchase price change, the price elasticity becomes more inelastic until it reaches a terminal value around the tenth year after the price change. Table 10.4.1 shows the relative change in the price elasticity of demand for automobiles over time. As shown in the table, DOE developed a time series of relative price elasticities for home appliances based on the relative change in the automobile price elasticity of demand. For years not shown in the table below, DOE performed a linear interpolation to obtain the relative price elasticity.
Table 10.4.1 Change in Relative Price Elasticity following a Purchase Price Change Years Following Price Change 1 2 3 5 10 20 Relative Change in Elasticity to 1 st year 1.00 0.78 0.63 0.46 0.35 0.33
Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11 Based on the following equation, DOE estimated standards case shipments by incorporating the impact of the relative price into the base case shipments forecast. Note that in the equation below, the relative price and the relative price elasticity are functions of the year because both change with time.

_ Ship ( j ) = 

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

# ) × 

# ( 1 e ( j ) × � RP( j ) 

# ) STD _ p BASE _ p BASE _ p BASE _ p RP
where: Ship STD_p (j) = Total shipments under the standards case of product p in year j , Rpl BASE_p (j) = Units of product p under the base case retired and replaced in year j , NI BASE_p (j) = Number of new construction installations under the base case of product p in year j , M

BASE_p (j) = Units installed in Market M under the base case of product p in year j ( M represents early replacements for cooking products and microwave ovens),

e RP (j)= Relative price elasticity in year j (equals -0.34 for year 1), and � RP(j)= Change in relative price due to a standard level in year j

.

10-1710.5 AFFECTED STOCK The affected stock is the in-service stock of the appliance or product that is affected by a TSL. In addition to the forecast of product shipments under both the base case and the standards case, the affected stock (which represents the difference in the appliance stock for the base case and the standards case) is a key output of DOE’s Shipments Models. The affected stock quantifies the impact that new product shipments have on the appliance stock due to a TSL. Therefore, the affected stock consists of those in-service units that are purchased in or after the year the standard has taken effect, as described by the following equation:

_ j Std _ yr 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 calculate the affected stock, DOE must define the effective date of the standard. For the NES and NPV results presented in Chapter 11, DOE assumed that new energy efficiency standards will become effective in the year 2013. Thus, all appliances purchased starting on the first day of the year 2013 are affected by the standard level.

10.6 RESULTS The following sections show the shipments forecasts for the various TSLs that DOE considered for each of the products. The TSLs are identified and described in Chapter 9. Tables 10.6.1 and 10.6.2 show the cumulative shipments impacts over the period 2013– 2043 caused by each of the TSLs considered for top-loading and front-loading commercial clothes washers, respectively. For all TSLs, shipments are forecasted to decrease—the effects from the increase in product purchase prices offset the effects from decreased operating costs, resulting in a net decrease in shipments. Figure 10.6.1 compares the shipments forecasts for the base case and each of the two TSLs for top-loading washers. In Figure 10.6.1, the difference between the base case and TSL shipments forecasts depicts the annual shipments reductions caused by the TSLs. Figure 10.6.2 compares the shipments forecasts for the base case and each of the four TSLs for front-loading washers.

10-18Table 10.6.1 Top-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2013–2043

TSL Modified Energy Factor/ Water Factor Cumulative Impacts (thousands) 1 1.42/9.5 -143 2 1.60/8.5 -302

Table 10.6.2 Front-Loading Commercial Clothes Washers: Cumulative Shipments Impacts Caused by Standards over the Period 2013–2043

TSL Modified Energy Factor/ Water Factor Cumulative Impacts (thousands) 1 1.80/7.5 0 2 2.00/5.5 0 3 2.20/5.1 -14 4 2.34/4.4 -34

0. 1 1 6 0. 1 2 0 0. 1 2 4 0. 1 2 8 0. 1 3 2 0. 1 3 6 0. 1 4 0 0. 1 4 4 0. 1 4 8 0. 1 5 2

2009 20 11 2013 20 15 2017 2019 2 021 202 3 2025 20 27 2029 20 31 2033 2035 20 37 2039 2 041 204 3

YearShipments (million) B a se C a se St d L e v e l 1 St d L e v e l 2

Figure 10.6.1 Top-Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts

10-190. 0 5 4 0. 0 5 6 0. 0 5 8 0. 0 6 0 0. 0 6 2 0. 0 6 4 0. 0 6 6 0. 0 6 8 0. 0 7 0

200 9 20 1 1 201 3 20 1 5 201 7 20 1 9 202 1 20 2 3 202 5 202 7 20 2 9 203 1 20 3 3 203 5 2 03

7 203 9 2 04

1 2043

YearShipments (million) B a se C a se St d L e v e l 1 St d L e v e l 2 St d L e v e l 3 St d L e v e l 4

Figure 10.6.2 Front-Loading Commercial Clothes Washers: Base Case and TSL Shipments Forecasts

10-20REFERENCES

1 U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy. Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Residential Dishwashers, Dehumidifiers, Cooking Products, and Commercial Clothes Washers , September, 2008. Washington, DC.
< http://www1.eere.energy.gov/buildings/appliance_standards/residential/home_appl_tsd.html >

2 U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy, Energy Conservation Standards for Certain Consumer Products (Dishwashers, Dehumidifiers, Microwave Ovens, and Electric and Gas Kitchen Ranges and Ovens) and for Certain Commercial and Industrial Equipment (Commercial Clothes Washers); Final Rule. Federal Register , 2009. 74(66): pp. 16040-16096.

3 U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy. Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Residential Dishwashers, Dehumidifiers, Cooking Products, and Commercial Clothes Washers , March, 2009. Washington, DC.
< http://www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products_final_rul e_tsd.html >

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

5 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 >

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

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

8 U.S. Department of Energy-Energy Information Administration. An Updated Annual Energy Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and Reinvestment Act and Recent Changes in the Economic Outlook , April, 2009. Washington, DC. SR/OIAF/2009-03.
< http://www.eia.doe.gov/oiaf/servicerpt/stimulus/pdf/sroiaf(2009)03.pdf >

9 U.S. Department of Energy-Energy Information Administration. Annual Energy Outlook 2009 with Projections to 2030 , March, 2009. Washington, DC. DOE/EIA-0383(2009).
< http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2009).pdf >

10-2110 U.S. Census Bureau, Housing and Household Economic Statistics Division. The American Housing Survey, 1997—2005. Washington, DC.
< http://www.census.gov/hhes/www/housing/ahs/ahs.html >

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

10-22