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 MARK ET SEGMENTS .............................................................. 9-1

9.3.1 Clothes Dryers ................................................................................................................. 9-1

9.3.1.1 Historical Shipments ...................................................................................... 9-1

9.3.1.2 Markets and Model Calibration ..................................................................... 9-2

9.3.1.3 Base Case Shipments ..................................................................................... 9-4

9.3.2 Room Air Conditioners .................................................................................................... 9-5

9.3.2.1 Historical Shipments ...................................................................................... 9-5

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

9.3.2.3 Base Case Shipments ..................................................................................... 9-8

9.3.2.4 Disaggregation into Product Classes ............................................................. 9-8

9.4 IMPACT OF PURCHASE PRICE INCREASE ON SHI PMENTS ............................. 9-10

9.5 AFFECTED STOCK ..................................................................................................... 9-13

9.6 CANDIDATE STANDARD L EVELS .......................................................................... 9-13

9.7 SHIPMENTS FORECASTS .......................................................................................... 9-14

9.7.1 Clothes Dryers ............................................................................................................... 9-14

9.7.2 Room Air Conditioners .................................................................................................. 9-16

LIST OF TABLES

Table 9.3.1 Clothes Dryer Product Class Market Shares ........................................................ 9-2

Table 9.3.2 Room Air Conditioner Product Class Market Shares .......................................... 9-9

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

Table 9.6.1 Combined Energy Factor (CEF) Levels Corresponding to Candidate Standard Levels for Clothes Dryers ................................................................... 9-14

Table 9.6.2 Combined Energy Efficiency Rating (CEER) Levels Corresponding to

Candidate Standard Levels for Room Air Conditioners .................................... 9-14

LIST OF FIGURES

Figure 9.3.1 Historical Shipments of Electric and Gas Clothes Dryers ................................... 9-1 Figure 9.3.2 Clothes Dryers: Retirement Function .................................................................. 9-3 Figure 9.3.3 Clothes Dryers: Base Case Shipments Forecast by Market Segment .................. 9-4 Figure 9.3.4 Clothes D ryers: Base Case Shipments Forecast by Product Class ...................... 9-5 Figure 9.3.5 Room Air Conditioners: Retirement Function ..................................................... 9-7 Figure 9.3.6 Room Air Conditioners: Base Case Shipments Forecast by Market Segment ................................................................................................................ 9-8 Figure 9.3.7 Room Air Conditioners: Base Case Shipments Forecast by Product Class ....... 9-10 Figure 9.7.1 Clothes Dryers, Electric Standard: Base Case and Standards Case Shipments Forecasts ........................................................................................... 9-159

- ii Figure 9.7.2 Room Air Conditioners, < 6,000 Btu/h Without Reverse Cycle and With Louvered Sides: Base Case and Trial Standards Level Shipments

Forecasts ............................................................................................................ 9-169-1 CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a necessary input to calculations of the national energy savings (NES) and net present value (NPV), as well as to the manufactur er impact analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy (DOE) used to forecast annual product shipments and presents results for each set of products being considered for this standards rulemaking.

DOE estimated shipments for each product with a separate shipments model, which is

calibrated with historical shipments data

. Each shipments m odel considers specific market segments to estimate shipments , the results for which are then aggregated to estimate total product shipments. To estimat e the impacts of potential standard levels on product shipments, each shipments model accounts for the combined effects of changes in purchase price, annual operating cost, and household income on the consumer purchase decision.

The shipments models we re developed as Microsoft Excel spreadsheets that are accessible on the Internet ( http ://www.eere.energy.gov/buildings/appliance_standards/ ). Appendix 10- A 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 m odels in more detail. Section 9.2 presents the shipments model methodology for each product; 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; section 9.5 discusses the affected stock; section 9.6 shows the candidate standard levels considered for each product class, and section 9.7 presents the results for different e nergy

- efficiency standard

- level scenarios.

9.2 SHIPMENTS MODEL METH ODOLOGY

DOE developed national stock models for estimating annual shipments for the products considered for this standards rulemaking. The model s consider market segment s appropriate for

the c onsidered products. Typically , the primary market segments are new home installations

, replacements

, and first

- time owners :



## )()()()( j FTO jNIjRpljShip

p p p p 

## ++=

W here:

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

,9-2 NI p (j) = Number of new home installations of product p in year j , and

NI p (j) = Number of product p going to first- time owners in year j

.

For room air conditioners, DOE did not consider the new construction market since this product, unlike some major household appliances, is not a standard product for new homes (as opposed to central air conditioners). However, DOE did include a market segment for new purchases of room air conditioners.

DOE’s shipments m odels take an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected construction trends. The models estimate shipments due to replacements using sales in previous years and assumptions

about the lifetime of the equipment. Therefore, estimated sales due to replacements in a given year are equal to the total stock of the appliance minus the sum of the appliances sold in previous years that still remain in the stock. DOE determined the useful service life of ea ch appliance to estimate how long the appliance is likely to remain in stock. The following equation represents how DOE estimated replacement shipments :

)( )1()( 

## ∑ ∑

0 1

__ _ ageprob Ship jStockjRpl
Rtr ageMax

age j

Nj 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 = 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 standards case scenario that produces increasing efficiency over t ime, 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 estimated replacements using product retirement functions that it developed from product lifetimes. For all products, DOE based the retirement function on a Weibull distribution for the product lifetime. The shipments model assumes 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 a Weibull equation:9-3 

# e x 

### xP **β**

**α**

**θ ** 

** **  ** **  ****

**−**

**−**



### = )( for x > θ and

P(x) = 1 for x ≤ θ

Where:

P(x) = probability that the appliance is still in use at age x ;

x = appliance age;

α = scale parameter, which would be the decay length in an exponential distribution; β = shape parameter, which determines the way in which the failure rate changes through time; and

θ = delay parameter, which allows for a delay before any failures occur.

The retirement probability is the difference in the survival function from one year to

another.

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:

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

_

W here:

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

# [ **]** )(1),()1,1(

_ ageprob agejStock agejStock
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

. With time, a fraction of the in -service stock is removed, and that fraction is determined by a retirement probability function, prob Rtr (age) , wh ich is described

in section 9.3. Most replacements are made when equipment wears out and fails. Over time, some of the units will be retired and removed from the stock, thus triggering the shipment of a new unit. Because the products considered in this rul emaking are common appliances that have been used by U.S. consumers for a long time, replacements constitute the majority of shipments.9-1 9.3 DATA INPUTS AND MARKET SEGMENTS

The sections below describe the data inputs and market segments considered for each product.

9.3.1 Clothes Dryers

9.3.1.1 Historical Shipments

DOE used historical shipments data (domestic shipments and imports) to calibrate its shipments model. It used three sources to establish historical shipments of electric and gas clothes dryers : (1) data provided by AHAM for the period 1999–2009 1

, (2) data from the 2000 2

and 2003 3 AHAM Factbooks for the period 1989–1998, and (3) data from Appliance Magazine 4,

5, 6 for the period 1972–1988. Figure 9.3.1 shows the historical shipments of electric and gas clothes dryers.

Figure 9.3.1 Historical Shipments of Electric and Gas Clothes Dryers

DOE then disaggregated the shipments of electric clothes dryers based on the market

shares for 2007 according to data provided by AHAM (see Table 9.3.1 ). 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0

1980 1985 1990 1995 2000 2005 2010Shipments (millions)

Electric

Gas9-2 Table 9.3.1 Clothes Dryer Product Class Market Shares

Product Class Market Share (%)

Vented Electric Standard 77.7 Vented Electric Compact (120V) 0.2 Vented Electric Compact (240V) 0.8

Vented Gas 20.9

Vent

- less Electric Compact (240V) 0.5

Vent

- less Combination Washer/Dryer 0.5

9.3.1.2 Markets and Model Calibration

The shipments market for clothes dryers consists of three main segments: (1) replacement units for equipment that has been retired, (2) units purchased by households in new homes, and (3) households without clothes dryers who purchase the equipment (referr ed to as first

- time owners, or FTOs). Total clothes dryer shipments are represented by the following equation:

)()()()( j FTO jNHjRpljShip cd cd cd cd **+ +=** 

W here :

Ship cd (j) = T otal shipments of clothes dryers in year j

, Rpl cd (j) = Replacement shipments in year j

, NH cd (j) = New home shipments in year j , and

FTO cd (j) = Shipments to households without clothes dryers in year j

.

Replacements. DOE determined shipments for the replacement clothes dryer market

segment using an accounting method that tracks the stock of units. Depending on vinta ge, a certain percentage of clothes dryers will fail and be replaced. To determine when a unit fails, DOE used a survival function based on a product lifetime distribution with an average value of 16 years, and minimum and maximum values of 5 years and 30 years, respectively. For a more complete discussion of clothes dryer lifetimes, refer back to chapter 8. Figure 9.3.2 shows the

survival an d retirement functions that DOE used to estimate replacement shipments.9-3 Figure 9.3.2 Clothes Dryers: Retirement Function

New Home Shipments . For clothes dryers, DOE estimated new home shipments usingnew housing forecasts and product saturation. New housing includes newly constructed singleand multi-family units, referred to as “new housing completions,” and mobile home placements. For new housing completions and mobile home placements, DOE used actual data through 2008, and adopted the projections from the DOE Energy Information Administration (EIA)’s Annual Energy Outlook 2010 ( AEO20 10 ) for 200 9–2035. 7 DOE estimated the fraction of new homes for which the owner or builder purchased an electric or gas clothes dryer based on RECS 2005 data. The saturation over the forecast period is 90 percent. DOE then disaggregated the projected new home shipments of clothes dryers using the market shares in Table 9.3.1

.

First

- Time Owners. To estimate the number of households without clothes dryers who purchase the equipment, DOE considered the historical percentage of households which purchased a clothes dryer as a first -time owner. DOE de rived this rate by dividing historical shipments from 1989-2009 that were not accounted for by replacement or new construction installations by the number of existing households without a clothes dryer. DOE determined that the average first

- time owner rate over the past 20 years was 3.3 percent. Taking into account the current economic climate, DOE estimated that this average would be reached by 2013, and would then decrease at a rate of 5 percent annually, as the market becomes saturated with clothes dryer s. 0% 1% 2% 3% 4% 5% 6% 7%Retirement Probability

Years9-4 9.3.1.3 Base Case Shipments

Figure 9.3.3 shows the forecasted total clothes dryer shipments in the base case (i.e., the case without amended standards), disaggregated into the three market segments, and the historical shipments DOE used to calibrate the forecast. Figure 9.3.4 shows the forecasted clothes dryer shipments in the base case, disaggregated into the considered product classes, and the historical shipments.

Figure 9.3.3 Clothes Dryers: Base Case Shipments Forecast by Market Segment 0 2 4 6 8 10 12

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Shipments (Millions) First Time Owners

New Homes

CD Replacements

Historical Shipments9-5 Figure 9.3.4 Clothes Dryers: Base Case Shipments Forecast by

Product Class

9.3.2 Room A ir Conditioners

9.3.2.1 Historical Shipments

DOE used historical shipments data (domestic shipments and imports) to populate and

calibrate its shipments model for room air conditioners. It used three sources to establish historical shipments: (1) data provided by the Association of Home Appliance Manufacturers (AHAM) for the period 1995–2009, 1 (2) data from the 2000 AHAM Factbook for the period
1989–1994, 3 and (3) data from Appliance Magazine 4

, 5

, 6 for the period 1972–1988. a

9.3.2.2 Markets and Model Calibration The shipments data aggregates all product classes and include units for t he residential and

commercial sectors.

The market for room air conditioners primarily consists of replacement units for equipment that has been retired. DOE’s shipments m odel also assumes that some existing

a 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 domesti c shipments plus imports. 0 2 4 6 8 10 12

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Shipments (Millions) Elec Combination Washer/Dryer

Electric Compact 240v

- Ventless

Electric Compact (240v)

Elec Compact (120v)

Gas Shipments

Electric Standard Shipments

Historical Shipments9-6 households without room air conditioners as well as households who already own units will purchase the equipment. Total room air conditioner shipments are represented by the following equation:

)()()( jNPjRpljShip

rac rac rac **+ =** 

W here :

Ship

rac (j) = T otal shipments of room air conditioners in year j

, Rpl

rac (j) = Replacement shipments in year j

, and

NP

rac (j) = Shipments to existing households adding a new room air conditioners in

year j

.

The sections below discuss these markets in further detail.

Replacements. DOE determined shipments to the replacement market using an accounting method that tracks the total stock of units by vintage. Over time, some units will be retired and removed from stock, thereby triggering the shipment of a replacement uni t. Depending on the vintage, a certain percentage of each type of unit will fail and need to be replaced. To determine when a unit fails, DOE used a survival function based on a product lifetime distribution with an average value of 10.5 years, and minimum and maximum values of

3 years and 20 years, respectively . For a more complete discussion of room air conditioner lifetimes, refer back to chapter 8. Figure 9.3.5 shows the retirement function that DOE used to estimate replacement shipments.9-7 Figure 9.3.5 Room A ir C onditioners : Retirement Function

New Purchases

. To calibrate the modeled shipments with the historical data, DOE introduced a market segment for new purchases of room air conditioners. These include purchase of additional units by households that already own a room air conditioner, as well as purchases by households without room air conditioners , also referred to as first

- time owners ( FTOs ).

Throu gh 2009, DOE derived new purchases as the diff erence between the historic total shipments and the calculated replacement shipments.

For 2010 and onward, DOE estimated new purchase s in the following way. O n average
, households with room air conditioners own 1.7 units per household, according to the American Housing Survey (AHS) 2007. T he AHS indicates that 21 million households did not own a room air conditioner in 2007. Assuming that the households without a room air conditioner would potentially purchase 1.7 units on average, DOE estimated that the total potential for room air

conditioner sales in the FTO segment in 2007 was around 36 million. Over time, this pool decreases as some of the households purchase room air conditioners. In 2009, after subtrac ting the replacement market sales, DOE estimated that the remaining sales imply that less than 1 percent of the FTO potential sales were realized, as opposed to 10 percent over the period 2007- 2009. DOE assumed that this would be the case in the next two years until the economy recovers

from the economic downturn. In the following years, DOE estimated that 10 percent of the remaining segment in each year in 2012–2044 purchases a unit. DOE based these fraction s on the estimated purchases by FTOs in 2009 and in the period 2007–2009 compared to the potential

market. 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Annual Retirement Rate9-8 Commercial Sector Shipments . As described in chapter 8

, DOE estimated that 12 percent of total room air conditioner shipments go to the commercial sector. DOE used this fraction to estimate shipments to the commercial sector for all years in the forecast period.

9.3.2.3 Base Case Shipments

Figure 9.3.6 presents forecasted room air conditioner shipments in the base case
, disaggregated into the considered market segments . The leveling off in shipments forecast by the shipments model reflects the saturation of the potential market for this product.

Figure 9.3.6 Room A ir C onditioners : Base Case Shipments Forecast by Market Segment

9.3.2.4 Disaggregation i nto Product Classes

DOE’s shipments model for room air conditioners forecasted aggregate shipments . DOE considered 16 product classes for room air conditioners. DOE estimate d the market shares for each of the 16 product classes using market share data AHAM provided for 2005–2007. 3 Where

AHAM did not disaggregate shipments into all of the product classes

, DOE assumed that the

distribution of models for the product classes is representative of the distribution of shipments.

Table 9.3.2 shows the market shares used fo r the forecast period , and Figure 9.3.7 shows the shipments forecast by product class.9-9 Table 9.3.2 Room Air Conditioner Product Class Market Shares

Product

Class 2005 2006 2007 Share of

Models * Share Used

After 2007

1 37.0% 23.0% 32.0% 11.6% 30.7%

2 19.0% 19.0% 16.0% 9.9% 18.0%

3 30.0% 34.0% 36.0% 31.1% 33.4%

4 3.0% 5.5% 6.0% 12.1% 4.8%

5 2.0% 3.9% 2.6% 9.0% 2.8%

6

7.0% 12.0% 7.0% 0.1% 0.1%

7 0.9% 0 .4%

8 19.1% 8.0%

9 0.7% 0.3%

10 0. 0% 0.0%

11 0.7% 1.0% NA 0.6% 0.8%

12 0.1% 0.5% 0.1%

13 0.4% 0.6% NA 0.7% 0.3%

14 0.0% 0.0%

15 0.4% 0.6% 0.4% 1.1% 0.1%

16 2.6% 0.3%

* B ased on 2007 CEC directory and AHAM email communication for disaggregation between the product classes

5a and 5b, and 8a and 8b

.9-10 Figure 9.3.7 Room Air Conditioners: Base Case Shipments Forecast by Product Class

9.4 IMPACT OF PURCHASE PRICE INCREASE ON SHIPMENT S

Economic theory suggests that, all else being equal, an increase in the price of a good would lead to a decrease in demand for it. Because DOE projects that appliance standards often result in an increase in the price of the product, DOE conducted a literature review and an analysis of appliance price and efficiency data to estimate the effects on product shipments from

increases in product price . DOE also considered the decreases in operating costs from higher energy efficiency and changes over time in household income.

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 incre ase the demand for9-11 appliances. Finally, the lit erature suggests that consumers use relatively high implicit discount

rates b when comparing appliance prices and appliance operating costs.

DOE found insufficient data on product purchase price and operating cost 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 room air conditioners 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 cost s 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:

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 an implicit discount rate of 37 percent to deter mine 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 th ree 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 stress es 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 , exp lain most of the trend in

b A high implicit discount rate with regard to operating costs means that consumers do not put much economic value on the operating cost savings realized from more

- efficient appliances. In other words, consumers are much more

con cerned with higher purchase prices.9-12 appliances per household in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price elasticity of demand provides a reasonable assessment of the impact that purchase price, operating cost, and household income have on product shipments.

Because DOE’s forecasts of shipments and national impacts due to standards are 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 elasticit y 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. 8, 9 This study shows that the automobile price elasticity of demand changes in th e 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 9.4.1 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 applian ces based on the relative change in the automobile price elasticity of demand. For years not shown in the table below, DOE performed a linear interpolation to obtain

the relative price elasticity.

Table 9.4.1 Change in Relative Price Elasticity following a Purchase Price Change
Years Following Price Change

1 2 3 5 10 20
Relative Change in Elasticity to 1 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 funct ions of the year because both change with time. 

# ( **)** 

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

_

_ _ _ _ 

### j RP jejMj NI j Rpl j Ship
RP p BASE p BASE p BASE pSTD 

### ∆× **× +**  **+ =** 

W here:

Ship STD_p (j) = T otal 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 home installations under the base case of product p in year j

, M BASE_p (j) = First time owners market M of product p in year j under the base case

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

Δ RP( j)= Change in relative price due to a standard level in year j

.9-13 9.5 AFFECTED STOCK

The affected stock is the in

- service stock of the appliance or product that is affected by a

standard level. In addition to the forecast of product shipments under both the bas e 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 standard

level. Therefore, the affected stock consists of those in-service units that are purchased in or after the year the standard has taken effect, as described by the following equation:



## ∑ _

1 _ )( )()( yr Std j

age p p p ageStock jShipjStock Aff

**= +**  **=**

W here:

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 o f 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 10, DOE assumed that new energy efficiency standards will become effective in 201 4. Thus, all appliances purchased starting in 2014 are affected by the standard level.

9.6 CANDIDATE STANDARD L EVELS

Table 9.6.1 and Table 9.6.2 show the efficiency levels corresponding to each candidate standard level ( CSL ) for each product class.9-14 Table 9.6.1 Combined Energy Factor ( CEF ) Levels Corresponding to Candidate

Standard Level s for Clothes Dryers

Product Class Candidate Standard Level

1 2 3 4 5 6

Vented Elec tric Standard 3.56 3.61 3.73 3.81 4.08 5.42

Vented Elec tric Compact 120 V 3.48 3.61 3.72 3.80 4.08 5.41

Vented Elec tric Compact 240 V 3.16 3.27 3.36 3.48 3.60 4.89

Vented Gas 3.16 3.20 3.30 3.41 3.61

Vent

- less Elec tric 240 V 2.59 2.69 2.71 2.80 4.03

Vent

- less Combination Washer/Dryer 2.35 2.38 2.46 2.56 3.69

Table 9.6.2 Combined Energy Efficiency Rating (CEER) Levels Corresponding to

Candidate Standard Level s for Room Air Conditioners

Product Class Base

Case Candidate Standard Level

1 2 3 4 5
Less than 6,000 Btu/h, with Louvers 9.5 10.1 10.6 11.1 11.4 11.7 8,000

- 13,999 Btu/h, with Louvers 9.7 10.2 10.7 10.9 11.5 12.0 20,000

- 24,999 Btu/h, with Louvers 8.5 9.0 9.4 9.8 10.2 25,000 Btu/h or more, with Louvers
8.5 9.0 9.4 9.8 8,000

- 10,999 Btu/h, without Louvers 8.4 9.3 9.6 10.0 10.4
11,000

- 13,999 Btu/h, without Louvers 8.4 9.3 9.5 9.8 10.0

9.7 SHIPMENTS FORECASTS

The following sections show the shipments forecasts for the various standard levels that DOE considered for clothes dryers and room air c onditioners, as well as for the base case.

9.7.1 Clothes Dryers

Figure 9.7.1 compares the shipments forecasts for the base case and TSLs 1 through 6 for electric standard clothes dryers. In Figure 9.7.1 , the difference between the base case and standard level shipments forecasts depicts the annual shipments reductions caused by the standard levels. For all standard levels, shipments are forecasted to decrease compared to the

base case

— the effects from the increase in product purchase prices offset the effects from decreased operating costs, resulting in a net decrease in shipments. Similar effects are seen for the other clothes dryer product classes.9-15 Figure 9.7.1 Clothes Dryers, Electric Standard: Base Case and Standards Case Shipments Forecasts 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00

2014 2019 2024 2029 2034 2039 2044Shipments (Millions) Base Case

TSL 1

TSL 2

TSL 3, TSL 4

TSL 5

TSL 69-16 9.7.2 Room A ir C onditioners

Figure 9.7.2 compares the shipments forecasts for the base case and TSL 1 through 6 for units less than 6,000 Btu/h without reverse cycle and with louvered sides. In Figure 9.7.2 , the

difference between the base case and standard level shipments forecasts depicts the annual shipments reductions caused by the standard levels. For all standard levels, shipments are

forecasted to decrease compared to the base case ; the effects from the increase in product purchase prices offset the effects from decreased operating costs, resulting in a net decrease in shipments. Similar effects are seen for the other room air conditioner product classes.

Figure 9.7.2 Room A ir C onditioners , < 6,000 Btu/h W ithout

R everse C ycle and W ith L ouvered S ides : Base Case

and Tria l Standards Level Shipments Forecasts9-17 REFERENCES

1. Association of Home Appliance Manufacturers, AHAM Data on Room Air Conditioners and Clothes Dryers

. 2009

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

3. Association of Home Appliance Manufacturers, AHAM 2003 Fact Book . 2003. Washington, DC.

4. Statistical Review

. Appliance Magazine , April, 1990

5. Statistical Review

. Appliance Magazine , April, 1992

6. Statistical Review

. Appliance Magazine , April, 1993

7. Energy Information Administration, Annual Energy Outlook 2010 with Projections to

2035 , 2010. Washington, DC. Report No. DOE/EIA-0383(2010).

< http://www.eia.doe.gov/oiaf/aeo/ >

8. Hyman s, S., Consumer Durable Spending: Explanation and Prediction

. Brookings Papers on Economic Activity , 1971. 1971(1): pp. 234-239

9. Dale, L. and K. Fujita, An Analysis of the Price Elasticity of Demand of Household Appliances

. LBNL 326E , 2008< http://www.osti.gov/bridge/servlets/purl/929429- Vm1S9X/929429.pdf >