9-i

CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

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

9.3 DATA INPUTS AND MODEL CALIBRATION .......................................................... 9-3

9.3.1 Historical Shipments........................................................................................................ 9-4

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

9.3.2.1 New Housing ................................................................................................. 9-5

9.3.2.2 Replacements................................................................................................. 9-8

9.4 IMPACTS OF STANDARDS ON SHIPMENTS ......................................................... 9-10

9.4.1 Impact from Increase in Relative Price.......................................................................... 9-12

9.5 AFFECTED STOCK ..................................................................................................... 9-12

9.6 SHIPMENTS IMPACTS CAUSED BY STANDARDS .............................................. 9-13

LIST OF TABLES

Table 9.3.1 Standard and Compact Dishwashers: Historical Shipments, Domestic plus 

Imports................................................................................................................. 9-4

Table 9.3.2 Historical and Forecasted Housing Starts (millions) ........................................... 9-7

Table 9.3.3 Dishwashers: Historical Market Saturations........................................................ 9-8

Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price Change ......... 9-12

Table 9.6.1 Standard-sized Dishwashers: Cumulative Shipments Impacts Caused by 

Standards............................................................................................................ 9-14

Table 9.6.2 Compact-sized Dishwashers: Cumulative Shipments Impacts Caused by 

Standards............................................................................................................ 9-14

LIST OF FIGURES

Figure 9.3.1 Dishwashers: Survival and Retirement Functions.............................................. 9-9

Figure 9.3.2 Standard-Sized and Compact Dishwashers: Historical and Base Case 

Shipments Forecast ............................................................................................ 9-10
9-1

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

The U.S. Department of Energy (DOE) analyzes shipments of affected products as part of 

every rulemaking regarding new or amended energy efficiency standards for appliances. Product 

shipments estimates are a necessary input to the national energy savings (NES) and net present 

value (NPV) calculations, which are required to justify potential new energy efficiency 

standards. Shipments are also a necessary input to the manufacturer impact analysis (MIA). This 

chapter describes DOE’s method and results for projecting annual shipments for standard-sized 

dishwashers and compact-sized dishwashers under base- and standards-case efficiency levels.

DOE estimated shipments for each product class with a computer model that was

calibrated against historical shipments. To estimate the impacts of prospective standard levels on 

product shipments, the model accounts for the combined effects of changes in purchase price, 

annual operating costs, and household income on the consumer purchase decision.

The shipments model considers specific market segments to estimate shipments. These

results are then aggregated to estimate total product shipments. DOE accounted for two market 

segments: (1) shipments to new construction, and (2) replacement shipments going into existing 

buildings. 

The shipments models are prepared as Microsoft Excel spreadsheets that are accessible 

on the Internet (http://www.eere.energy.gov/buildings/appliance_standards/). The rest of this 

chapter explains the shipments model in more detail. Section 9.2 describes the method that 

underlies the development of the model; section 9.3 describes the data inputs and the model 

calibration; section 9.4 discusses impacts on shipments from changes in equipment purchase 

price, operating cost, and household income; and section 9.5 discusses the affected stock. Section 

9.6 presents the model results for different energy-efficiency standard levels, identified as trial 

standard levels (TSLs), for standard-sized and compact-sized dishwashers.

9.2 SHIPMENTS MODEL METHODOLOGY

DOE developed a model of the national stock of in-service appliances for estimating 

annual shipments for this standards rulemaking. The model considers market segments as 

distinct inputs to the shipments forecast. As represented by the following equation, the two 

primary market segments are new installations and replacements. 

Ship ( j ) Rpl ( j ) NI ( j ) p = p

+

p

Where:
9-2

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

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

NIp(j) = Number of new installations of product p in year j.

DOE’s shipments model takes an accounting approach, tracking market shares of each 

product class, vintage of units in the existing stock, and expected construction trends. In 

principle, each market segment and product class responds differently to both the demographic 

and economic trends in the base case (i.e., the case without new standards) than in any of the 

standards cases. Furthermore, retirements, early replacements, and efficiency trendsa are 

dynamic and can vary among product classes. Rather than simply extrapolating a current

shipments trend, the base case shipments analysis uses driver input variables, such as 

construction forecasts and product lifetime distributions, to forecast sales in each market 

segment. For example, the model assumes that construction (i.e., new housing units) drives new 

installations. Therefore, the product shipments for 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. 

The model estimates shipments of replacement units using shipments data from previous 

years and assumptions about the lifetime of the equipment. Therefore, estimated sales of

replacement units in a given year are equal to the total stock of the appliance minus those units 

shipped 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 shows how DOE estimated replacement shipments.

( ) ( 1) ∑ ∑ ( )

0

1

_ _

_

Rpl j Stock j Ship prob age Rtr

ageMax

age

j

j N

p = p j ×

= =

Where:

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

probRtr (age) = Probability that an appliance of a particular age will be retired, and

N = year in which the model begins its stock accounting.

Stock accounting provides an estimate of the age distribution of product stocks for all 

years using product shipments, a retirement function, and initial product stock as inputs. The age 

distribution of in-service product stocks is a key input to both the NES and NPV calculations

because the operating costs for any year depend on the age distribution of the stock. Operating 

cost depends on the product age distribution under a standards-case scenario that produces 

increasing efficiency over time; this means that older, less efficient units may have higher 

operating costs than younger, more-efficient units, which will have lower operating costs. In the 

 

a Efficiency trends affect shipments only in the standards case. A change in the efficiency distribution of the stock 

results in a change in the purchase price and operating cost, which impacts shipments. This is discussed further in 

section 9.4.
9-3

case of early replacements, some units are removed from the in-service stock before expected the 

end of their lifetime and replaced with more efficient units. 

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) =Number 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. Slightly more complicated equations, such as

the following equation, describe the accounting of the existing in-service stock of units: 

( 1, 1) ( , ) [1 ( )] _ Stock j age Stock j age prob age + + = × Rtr

In the above equation, as the year is incremented from j to j+1, the age is also 

incremented from age to age+1. Over time, a fraction of the in-service stock is removed, and 

that fraction is determined by a retirement probability function, probRtr(age), which is described 

in section 9.3. Because the products considered in this rulemaking are common appliances that 

have been used by U.S. consumers for a long time, replacements typically constitute the majority 

of shipments. Most replacements occur when equipment wears out and fails. 

9.3 DATA INPUTS AND MODEL CALIBRATION 

As noted above, shipments are driven primarily by two market segments: new 

construction and replacements. 

DOE used two inputs – new housing forecasts and market saturation data – to estimate 

shipments driven by new construction. New housing includes newly constructed single- and 

multi-family units, termed “new housing completions,” and mobile home placements. For new 

housing completions and mobile home placements, DOE used recorded data through 2007 and 

adopted the projections from the DOE Energy Information Administration (EIA)’s Annual 

Energy Outlook 2011 (AEO2011) for the period of 2008–2035.

1 To determine new construction 

shipments for dishwashers, DOE used forecasts of market saturations combined with forecasts of 

housing starts.
9-4

DOE estimated replacements using product retirement functions that it developed from 

product lifetimes. The retirement function is described in detail in chapter 8.

DOE designed its shipments model for residential dishwashers by developing a single 

model for all dishwashers and then disaggregating the shipments into its two product classes—

standard-sized and compact-sized dishwashers.

9.3.1 Historical Shipments

DOE used historical shipments data (both domestic and imports) to calibrate its 

shipments model. It used four sources to establish historical shipments: (1) data provided by the 

Association of Home Appliance Manufacturers (AHAM) for the period 2000–2010,2 (2) data 

provided by the Association of Home Appliance Manufacturers (AHAM) for the period 1995–

2000,

3 (3) data from the 2000 AHAM Factbook for the period 1989–1994,4 and (4) data from 

Appliance Magazine5,6,7 for the period 1972–1988. b Table 9.3.1 summarizes the historical 

dishwasher shipments data. DOE built up a total stock of dishwashers by integrating historical 

shipments starting in the year 1972. Over time, some of the units will be retired and removed 

from the stock, thus triggering the shipment of a new unit. Because of the relationship between 

retirements and total stock, there is a strong correlation between past and future shipments, 

independent of efficiency standards.

Table 9.3.1 Standard and Compact Dishwashers: Historical Shipments, Domestic plus 

Imports

Year Shipments (thousands) Year Shipments (thousands)

1972 3.040 1992 3.597

1973 3.518 1993 3.914

1974 3.155 1994 4.353

1975 2.568 1995 4.346

1976 2.984 1996 4.606

1977 3.189 1997 4.826

1978 3.380 1998 5.144

1979 3.314 1999 5.712

1980 2.602 2000 5.828

1981 2.360 2001 5.627

1982 2.062 2002 6.207

 

b 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.
9-5

Year Shipments (thousands) Year Shipments (thousands)

1983 2.966 2003 6.428

1984 3.317 2004 7.106

1985 3.397 2005 7.424

1986 3.723 2006 7.252

1987 3.831 2007 6.977

1988 3.713 2008 5.995

1989 3.588 2009 5.404

1990 3.485 2010 5.710

1991 3.375

The shipment data from AHAM and Appliance Magazine did not differentiate between 

standard-sized and compact-sized dishwashers. To determine the portion of the shipments that 

are compact dishwashers, DOE used data from The NPD Group, Inc, which revealed that 0.1% 

of dishwasher shipments were compact-sized dishwashers.8

9.3.2 Markets and Model Calibration

The shipments market for dishwashers primarily comprises units for new construction 

and replacement units for equipment that has been retired. DOE’s shipments model also assumes 

that some existing households without dishwashers will purchase the equipment (i.e., become 

first time owners (FTOs)). Total dishwasher shipments are represented by the following 

equation:

Ship ( j) Rpl ( j) NI ( j) FTO ( j) DW = DW + DW + DW

Where:

ShipDW (j) = Total shipments of dishwashers in year j, 

RplDW (j) = Replacement shipments in year j, 

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

FTODW (j)= Shipments to existing households without dishwashers in year j. 

The following sections discuss the new construction, replacement markets, and first-time 

owners in further detail. 

9.3.2.1 New Housing 

DOE forecasted shipments to the new construction market based on forecasts of housing 

starts coupled with market saturation data for dishwashers. To forecast the shipments to new 

construction for any given year, DOE multiplied the housing starts forecasts by the forecasted 
9-6

saturation of dishwashers for new housing. DOE used historical and forecasted new housing 

starts to calibrate its model. 

Table 9.3.2 presents historical and forecasted new housing starts based on EIA’s

AEO2011 for the period 2008–2035. AEO2011 provides three scenarios for housing starts: a

reference case, a high economic growth case, and a low economic growth case. Table 9.3.2

shows quantities based on three of these forecasts. DOE used only the forecasts from the 

reference case to estimate its shipments to new construction. For 2036−2047, DOE froze 

completions at the level in 2035.

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 AEO2011 data are based on actual completions through the year 2005.
9-7

Table 9.3.2 Historical and Forecasted Housing Starts (millions)

Year Reference High Low Year Reference High Low

2008 0.98 0.98 0.98 2022 1.93 2.31 1.54

2009 0.60 0.60 0.60 2023 1.92 2.31 1.53

2010 0.65 0.65 0.65 2024 1.91 2.33 1.51

2011 0.85 0.85 0.85 2025 1.93 2.37 1.50

2012 1.72 1.96 1.55 2026 1.93 2.39 1.48

2013 1.85 2.13 1.61 2027 1.90 2.38 1.44

2014 1.78 2.07 1.51 2028 1.86 2.36 1.39

2015 1.85 2.16 1.54 2029 1.83 2.35 1.35

2016 1.90 2.23 1.58 2030 1.83 2.37 1.32

2017 1.91 2.26 1.58 2031 1.81 2.37 1.29

2018 1.91 2.29 1.57 2032 1.77 2.33 1.23

2019 1.91 2.29 1.54 2033 1.75 2.30 1.20

2020 1.90 2.27 1.53 2034 1.75 2.30 1.20

2021 1.89 2.27 1.53 2035 1.74 2.29 1.20

Source: EIA, AEO2011.

Table 9.3.3 presents historical market saturations of dishwashers based on various 

sources, including the AHAM 2005 Fact Book,

9 Appliance Magazine,10 NFO World Group,11

and EIA’s Residential Energy Consumption Survey (RECS) for the years 1993,12 1997,13 and 

2001.14 The table presents market saturations for the 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 1997, 2001, and 

2005 was 78.1 percent, 81.5 percent, and 96.7 percent, respectively. DOE decided to use the 

most recent RECS data point because of the much higher rate of saturation to forecast saturations 

over the forecast period. 
9-8

Table 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% 78.1%

1998 56.3%

1999 56.5%

2000 59.0%

2001 59.3% 59.3% 53.6% 53.0% 81.5%

2002 59.5%

2003 59.5%

2004 60.0%

2005 73.7% 60.5% 58.3% 96.7%

2006 61.0%

2007 61.0%

2008 61.0%

2009 59.3%

2010

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

Research Report,” January 2010, September issues; *** Source: NFO World Group, 2001; † Source: DOE-EIA, 

RECS 1997, 2001, and 2005.

9.3.2.2 Replacements 

To determine shipments to the replacement market, DOE used an accounting method that 

tracks the total stock of units by vintage. DOE estimated a stock of dishwashers by vintage by 
9-9

integrating historical shipments starting from the year 1972. Over time, some units are retired 

and removed from stock, triggering the shipment of a replacement unit. Depending on the 

vintage, a certain percentage will fail and need to be replaced. To estimate how long a unit will 

function before failing, DOE used a survival function based on a product lifetime distribution 

with an average value of 15.4 years. For a more complete discussion of dishwasher lifetimes, 

refer back to section 8.2.3.3. Figure 9.3.1 shows the survival and retirement functions that DOE 

used to estimate replacement shipments. 

Figure 9.3.1 Dishwashers: Survival and Retirement Functions

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. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 5 10 15 20 25 30 35 40 45 50

Years

% Surviving

% Retiring
9-10

Figure 9.3.2 Standard-Sized and Compact Dishwashers: Historical 

and Base Case Shipments Forecast

9.4 IMPACTS OF STANDARDS ON SHIPMENTS

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 9A

explains the method DOE used to quantify the impacts from the above variables.

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 derived from the literature suggests that the demand for 

appliances is price-inelastic (i.e., price does not affect demand). Other information in the 

literature suggests that appliances are a normal good, such that rising incomes increase the 

demand for them. Finally, the literature suggests that consumers exhibit relatively high implicit 

discount rates when comparing the price of an appliance to its operating costs.

c

DOE found too few data on equipment purchase price and operating costs to perform a 

thorough analysis of dynamic changes in the appliance market. Rather, it used purchase price and 

efficiency data specific to residential refrigerators, clothes washers, and dishwashers during

1980–2002 to evaluate broad market trends and conduct simple regression analyses. These data 

indicate that, during that period, shipments of appliances increased while purchase prices and 

operating costs decreased. In addition, household income increased during that time period. To 

 

c A high implicit discount rate with regard to operating costs suggests that consumers do not put high economic 

value on the operating cost savings expected from more-efficient appliances. In other words, consumers are much 

more concerned with higher purchase prices than with lower operating costs.
9-11

simplify the analysis, DOE combined the available economic information into one variable, 

termed the relative price, and used this variable to analyze market trends and conduct a

regression analysis. DOE used the following expression to define relative price:

Income

PP PVOC

Income

TP

RP

+ = =

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 real prices, as opposed to nominal, and an implicit 

discount rate of 37 percent to estimate the present value of operating costs. The rate of 37 percent 

is based on a survey of several studies of different appliances suggests that the consumer implicit 

discount rate has a broad range and averages about 37 percent.

15

DOE’s analysis of market trends suggests that the demand for the three appliances is 

relatively price inelastic (i.e., under 1.0) (See appendix 9-A for further description). 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 costs, and household income), the impact from 

any single effect is mitigated by changes from the other two effects.

The relative price elasticity of -0.34 is consistent with estimates in the literature.

Nevertheless, DOE stresses that the measure is based on a small data set, using simple statistical 

analysis. More important, the measure is based on an assumption that economic variables, 

including purchase price, operating costs, and household income, explain most of the trend in 

appliances per household in the United States since 1980. Changes in appliance quality and 

consumer preferences may have occurred during this period, but DOE did not account for them 

in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price 

elasticity of demand provides a reasonable assessment of the impact that purchase price, 

operating costs, and household income have on product shipments.

Because DOE’s forecasts of shipments and national impacts attributable to standards is 

over a lengthy time period, it needed to consider how the relative price elasticity is affected after

a new standard takes effect. DOE considered the relative price elasticity, described above, to be 

a short-term 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 
9-12

automobiles.

16 This study shows that the automobile price elasticity of demand changes in the 

years following a purchase price change. As the number of years increases 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. Several economic textbooks support the concept that price 

elasticity of demand for durable goods, such as appliances and automobiles, declines in the long 

run.

17 18 19 Table 9.4.1 shows the relative change in the price elasticity of demand for 

automobiles over time. 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

Change Relative to 1st year 1.00 0.78 0.63 0.46 0.35 0.33

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

9.4.1 Impact from Increase in Relative Price 

DOE used the following equation to estimate shipments under each standards case for the 

two product classes of dishwashers. The equation incorporates the impact of the relative price

into the forecast of the base case shipments. In the equation, the relative price and the relative 

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

( ) ( ( ) ( ) ( )) (1 ( ) ( )) _

_ _ _ _ Ship j Rpl j NI j M j e j RP j STD p = BASE p + BASE p + BASE p

× RP × ∆

Where:

ShipSTD_p(j) = total shipments of product p in year j under a given standards case;

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

NIBASE_p(j) = number of new construction installations of product p in year j under the 

base case;

MBASE_p(j) = units of product p installed in market M in year j (M represents early 

replacements) under the base case;

eRP(j)= relative price elasticity in year j (equals -0.34 for year 1); and 

ΔRP(j)= change in relative price due to the given standard in year j.

9.5 AFFECTED STOCK

The affected stock is the in-service stock of the product that is affected by a potential 

energy efficiency standard. In addition to the forecast of product shipments under both the base 

case and each standards case (TSL), a key output of DOE’s shipments model is the affected 
9-13

stock, which represents the difference in the stock under the base case and each TSL. DOE 

calculates the affected stock to quantify the effect attributable to a TSL that new product 

shipments have on the appliance stock. Therefore, the affected stock consists of those in-service 

units that are purchased in or after the year a standard takes effect, as described by the following 

equation:

∑

_

1

_

( ) ( ) ( )

j Std yr

age

Aff Stock p j Shipp j Stock p age

=

= +

Where:

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

year j,

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

Stockp(j) = Stock of product p units of all vintages that are operational in year j,

age = Age of the units (years), and

Std_yr = Effective date of the standard.

As the above equation shows, DOE must define the effective date of the standard to

calculate the affected stock. For the NES and NPV results presented in Chapter 10, DOE 

assumed that new energy efficiency standards would become effective in 2018 (2013 for TSL 2). 

Thus, the standard level would affect all appliances purchased beginning the first day of 2018 

(2013 for TSL 2). Further discussion of the TSLs is provided in Chapter 10, Section 10.1.1.

9.6 SHIPMENTS IMPACTS CAUSED BY STANDARDS 

This section presents the shipments impacts resulting from the four trial standard levels 

(TSLs) that DOE considered for dishwashers. TSL 2 consists of the efficiency levels 

recommended in the Joint Petition. TSL 3 consists of the efficiency level that is one level below 

the max-tech efficiency level. TSL 4 consists of the max-tech efficiency level. 

Table 9.6.1 and Table 9.6.2 show the cumulative impacts on shipments during 2018–

2047 (2013–2047 for TSL 2) from each TSL for standard-sized and compact dishwashers. For

standard-sized dishwashers, shipments are forecasted to slightly decrease under each TSL; that 

is, the effect of increased purchase price offsets the effect of decreased operating costs, resulting 

in a net decrease in shipments. For compact dishwashers, the impacts are negligible.
9-14

Table 9.6.1 Standard-sized Dishwashers: Cumulative Shipments Impacts Caused by 

Standards

TSL

Annual Energy Use 

(kWh/year)

Cumulative Impact

(million)

Cumulative Impact

(%)

1 324 -0.021 -0.01%

2 307 -0.176 -0.06%

3 234 -0.883 -0.28%

4 180 -0.310 -0.10%

Table 9.6.2 Compact-sized Dishwashers: Cumulative Shipments Impacts Caused by 

Standards

TSL

Annual Energy Use 

(kWh/year)

Cumulative Impact

(million)

Cumulative Impact

(%)

1 222 0.000 0.06%

2 222 0.000 0.06%

3 154 0.001 0.16%

4 154 0.001 0.16%
9-15

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 1

 Energy Information Administration, Annual Energy Outlook 2011 with Projections to 2035,

2011. Washington, DC. Report Number: DOE/EIA-0383(2011). 

http://www.eia.gov/forecasts/aeo/index.cfm

2 Association of Home Appliance Manufacturers (AHAM). AHAM 2010. Washington, D.C.

3 Association of Home Appliance Manufacturers. AHAM Data on Dishwashers for Efficiency 

Standards Rulemaking, September 7, 2006. Letter to Building Technologies Program, U.S. 

Department of Energy.

4 Association of Home Appliance Manufacturers. AHAM 2000 Fact Book, 2000. Washington, 

DC.

5 Statistical Review. Appliance Magazine, April, 1993.

6 Statistical Review. Appliance Magazine, April, 1990.

7 Statistical Review. Appliance Magazine, April, 1982.

8 The NPD Group, Inc., The NPD Group/NPD Houseworld – POS, Dishwashers 2004-2010. 

Port Washington, NY. 

9 Association of Home Appliance Manufacturers. AHAM 2005 Fact Book, 2005. Washington,

DC. 

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

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Ownership Study, May, 2001. Prepared for Association of Home Appliance Manufacturers 

by NFO World Group, Boston, MA.

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<http://www.eia.doe.gov/emeu/recs/93publicuse.html>

13 U.S. Department of Energy-Energy Information Administration. Residential Energy 

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<http://www.eia.doe.gov/emeu/recs/public.html>

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Consumption Survey, 2001 Public Use Data Files, 2001. Washington, DC. 

<http://www.eia.doe.gov/emeu/recs/recs2001/publicuse2001.html>
9-16

 

15 K. Train. Discount rates in consumer' energy related decisions: a review of the literature, 

Energy, 1985.

16 S. Hymans. Consumer Durable Spending: Explanation and Prediction, Brookings Papers on 

Economic Activity, 1971. Vol. 1971, No. 1, pp. 234-239.

17 D. R. Kamerschen, L. M. Valentine. Intermediate Microeconomic Theory. 1977. SouthWestern Publishing Company, 2nd edition. p.52.

18 H. Köhler. Intermediate Microeconomics. 1990. Scott, Foresman, University of California. 

3rd edition. pp. 82-83.

19 R. Pindyck and D. Rubinfeld. Microeconomics. 2012. Pearson/Prentice Hall, 8th edition. 

Chapter 2.5.