9

- i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

................................ ................................ ................................ ........... 9

- 1

9.2 SHIPMENTS MODEL METH ODOLOGY

................................ ................................ .... 9

- 1

9.3 DATA INPUTS AND MODE L 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 CA USED 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 Pr ice Elasticity Following a Purchase Price Change

......... 9

- 12

Table 9.6.1 Standard

- sized Dishwashers: Cumulative Shipments Impacts Caused b y

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

- 109-1 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 me thod 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 wi th a computer model that was calibrated again st historical shipments. To estimat e the impacts of prospective standard levels on

product shipments, the model accounts for the combined effects of changes in purchase price,
annual operating cost s , and household income on the consumer purchase decision.

The s hipments m odel 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 g oing into existing
buildings.

The s hipments m odels are prepared as Microsoft Excel spreadsheets that are accessible

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

chapter explains the s hipments 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 o n shipments from changes in equipment purchase
price, operating cost, and household income; and section 9.5 discusses the affected stock . S ection

9.6 presents the model results for different energy

- efficiency standard level s, identified as trial

standard l evels (TSLs), for standard

- sized and compact

- sized dishwashers

.

9.2 SHIPMENTS MODEL METH ODOLOGY

DOE developed a model of the national stock of in

- service appliances for estimating
annual shipments for this standards rulemaking

. The model considers market segment s as distinct input s to the shipments forecast. As represented by the following equation, the two
primary market segments are new installations and replacements.

) j ( NI ) j ( Rpl ) j ( Ship

p p p + =

W here:9-2 Ship
p (j)

= T otal 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

.

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, retirement s , early replacement s, 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 uses driver input variables, such as

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

segment

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

installations . Therefore, the product shipments for the new constructio n 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 estimate s shipments of replacemen t units using shipments data from previous
years and assumptions about the lifetime of the equipment. Therefore, estimated sales of

replacement unit s 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 show s how DOE estimated replacement shipments.

) ( ) 1 ( ) ( 

## ∑ ∑

0 1

_ _ _

age prob Ship j Stock j Rpl

Rtr ageMax

age j

N j j p p **× =** 

**= =**  W here:

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 = 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 oper ating 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 efficien t 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, som e 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:

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

_

W here:

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

numbe r of new units purchased the previous year. S lightly more complicated equations , such as
the following equation

, describe the accounting of the existing in

- service stock of units:



# [ **]** ) ( 1 )

, ( ) 1

, 1 (

_ age prob age j Stock age j Stock

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

, p rob Rtr (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 eq uipment wears out and fails.

9.3 DATA INPUTS AND MODE L CALIBRATION

As not ed 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 plac ements, DOE used recorded data through 200 7 and
adopted the projections from the DOE Energy Information Administration (EIA)’s Annual
Energy Outlook 20 11 ( AEO20 11 ) for the period of 200 8

– 203 5

. 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 dish washers by developing a single

model for all dish washers and then disaggregating the shipments into its two product classes

— standard

- sized and compact

- sized dish washers

.

9.3.1 Historical Shipments

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

s hipments m odel. It used four sources to es tablish historical shipments: (1) data provided by the

Association of Home Appliance Manufacturers (AHAM) for the period 2000

– 2010, 2 (2 ) data

provi ded by the Association of Home Appliance Manufacturers (AHAM) for the period 1995

– 200 0

, 3 (3 ) data from the 2000 AHAM Factbook for the period 1989

– 1994, 4 and (4 ) data from

Appliance Magazine 5

, 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 un its 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 standard s.

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 Shipment s 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 Magaz ine 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 constru ction

and replacement units for equipment that has been retired. DOE’s s hipments m odel also assumes
that some existing households without dishwashers will purchase the equipment (i.e., become

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

equation:



### ) ( ) ( ) ( ) ( j FTO j NI j Rpl j Ship

DW DW DW DW 

### + **+ =** 

W here :

Ship DW (j)

= T otal shipments of dishwashers in year j

, Rpl DW (j)

= Replacement shipments in year j

, NI D W (j) = Shipments to new households in year j , and

FTO D W (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 b ased on forecasts of housing

starts coupled with market saturation data for dishwasher s

. To forecast the shipments to new construction for any given year, DOE multiplied the housing starts forecasts by the forecasted9-6 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

AEO20 11 for the period 2008

– 2035

. AEO201 1 provides three scenario s for housing starts: a

r eference case, a h igh e conomic g rowth case, and a l ow e conomic g rowth case. Table 9.3.2

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

r eference case to estimate its shipments to new construction. For 2036 −204 7 , 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 AEO20 11 data are based on actual comp l etions 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 Con sumption Survey (RECS) for the years 1993, 12 1997, 13 and

2001. 14 The table presents m arket 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 satu ration 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 us ed an accounting method that
tracks the total stock of units by vintage. DOE estimated a stock of dishwashers by vintage by9-9 integrating historical shipments starting from the year 1972. Over time, s ome 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 esti mate 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 u sed 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

% Retiring9-10 Figure 9.3.2 Standard

- Sized and Compact Dishwashers: Historical

and Base Case Shipments Forecast

9.4 IMPACTS OF STANDARDS ON SHIPMEN TS

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

expla i n s 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 deriv ed from the literature sugge sts 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 incre ase the

demand for them

. Finally, the literature sugge sts 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 cost s to perform a thorough analysis of dynamic changes in the appliance market. Rather, it use d 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 analyse s. These data

indicate that

, during that period, shipments of appliances increased while purchase prices and
operating costs decreased

. In addition, h ousehold income increased during th at 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 purcha se prices than with lower operating costs.9-11 simplify the analysis, DOE combined the avail able 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 PVOC PP

Income TP RP +

= =

W here:

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 o f market trends suggests that the demand for the three appliances is
relatively price inelastic (i.e., under 1.0) (See a ppendix 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., purc hase price, operating cost s , 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 stress es t hat 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

ap pliances 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 estima te of the relative price
elasticity of demand provides a reasonable assessment of the impact that purchase price,
operating cost s , and household income have on product shipments.

Because DOE’s forecasts of shipments and national impacts attributable to s tandards 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 identif y 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 to9-12 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 ti me. 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 relativ e 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 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

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 . I n the equation, the relative price and the relative

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



# ( **)** 

# ( **)** ) ( ) ( 1 ) ( ) ( ) ( ) (

_

_ _ _ _ j RP j e j M j NI j Rpl j Ship

RP p BASE p BASE p BASE p STD **∆ ×**  **× +**  **+ =** 

Where:

Ship STD_p (j)

= t otal shipments of product p in year j under a given standards case;

Rpl BASE_p (j)

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

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

base case;

M BASE_p (j)

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

replacements) under the base case;

e

RP (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 s hipments m odel is the affected9-13 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 take s effect, as described by the following

equation:



## ∑ _

1 _ ) ( ) ( ) ( yr Std j

age p p p age Stock j Ship j Stock Aff

**= +**  **=**

W here:

Aff Stock
p (j)

= Affected stock of product p units of all vintages th at are operational in

year j

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

, Stock
p (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 w ould become effective in 201 8 (2013 for TSL 2)

. Thus, the standard level would affect all appliances purchased beginning the first day of 201 8
(2013 for TSL 2)

. Further discussion of the TSLs is prov ided in Chapter 10, Section 10.1.1

.

9.6 SHIPMENTS IMPACTS CAUSED BY STANDARDS

This section presents the shipmen ts impacts resulting from the four t rial 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 effi ciency level. TSL 4 consists of the max

- tech efficiency level.

Table 9.6.1 a n d Table 9.6.2 show the cumulative impacts on shipments during 201 8

– 204 7 (2013

– 2047 for TSL 2) from each TSL for standard

- sized and compact dish washers. For

standard

- sized dishwashers , shipments are fore casted to slightly decrease under each TSL ; that

is, the effect of increased pu rchase 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 Dish washers: 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 REFERENCES

1 Energy Information Administration, Annual Energy Outlook 2011 with Projections to 2035,
2011. Washington, DC. Report Number: DOE/EIA

- 03 83(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 200 0 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

.

11 NFO World Group

. Final Report: Home Appliance Saturation and Length of First
Ownership Study , May, 2001

. Prepared for Association of Home Appliance Manufacturers
by NFO World Group, Boston, MA.

12 U.S. Department of Energy

- Energy Information Administration. Residential Energy

Consumption Survey , 1993 Public Use Data Files , 1993. Washington, DC.

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

13 U.S. Department of Energy

- Energy Information Administration. Residential E nergy Consumption Survey , 1997 Public Use Data Files , 1997. Washington, DC.

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

14 U.S. Department of Energy

- Energy Information Administration. Residential Energy

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 rate s in consumer' energy related decisions: a review of the literature,

Energy , 1985.

16 S. Hyma ns

. Consumer Durable Spendi ng: 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. South

Western Publishing Company , 2 nd edition . p .52.

18 H. Köhler

. Intermediate Microeconomics . 1990. Scott, Foresman, University of California

. 3 rd edition. pp. 82

- 83.

19 R. Pindyck and D. Rubinfeld

. Microeconomics

. 2012. Pearson/Prentice Hall , 8 th ed ition

. Chapter 2.5

.