CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

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

.................................................................... 9-1 9.3 DATA INPUTS AND MARKET SEGMENTS

.............................................................. 9-1 9.3.1 Room Air Conditioners

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

...................................................................................... 9-1 9.3.1.2 Markets and Mode l Calibration

..................................................................... 9-1 9.3.1.3 Base Case Shipments

..................................................................................... 9-3 9.3.1.4 Disaggregation into Product Classes

............................................................. 9-3 9.3.2 Clothes Dryers

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

...................................................................................... 9-5 9.3.2.2 Markets and Mode l Calibration

..................................................................... 9-6 9.3.2.3 Base Case Shipments

..................................................................................... 9-8 9.4 IMPACT OF PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME

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

..................................................................................................... 9-11 9.6 CANDIDATE STANDARD LEVELS

.......................................................................... 9-12 9.7 SHIPMENTS FORECASTS FOR CANDIDATE STANDARD LEVELS

.................. 9-13 9.7.1 Room Air Conditioners

.................................................................................................. 9- 13 9.7.2 Clothes Dryers

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

LIST OF TABLES

Table 9.3.1 Room Air Conditioner Product Class Market Shares

.......................................... 9-4 Table 9.3.2 Clothes Dryer Produc t Class Market Shares

........................................................ 9-6 Table 9.4.1 Change in Relative Price Elasticity following a Purchase Price Change

.......... 9-11 Table 9.6.1 Integrated Energy Efficiency Rating (IEER) Levels Corresponding to Candidate Standard Levels fo r Room Air Conditioners

.................................... 9-12 Table 9.6.2 Integrated Efficiency Factor (IEF) Levels Corresponding to Candidate Standard Levels for Clothes Dryers

................................................................... 9-13

LIST OF FIGURES

Figure 9.3.1 Room Air Conditi oners: Retirement Function

..................................................... 9-2 Figure 9.3.2 Room Air Conditioners: Base Case Shipments Forecast by Market Segment

................................................................................................................ 9-3 Figure 9.3.3 Room Air Conditioners: Base Case Shipments Forecast by Product Class

......... 9-5 Figure 9.3.4 Historical Shipments of Electric and Gas Clothes Dryers

................................... 9-6 Figure 9.3.5 Clothes Dryers: Retirement Function

.................................................................. 9-7 Figure 9.3.6 Clothes Dryers: Base Case Shipment s Forecast by Market Segment

.................. 9-8 Figure 9.3.7 Clothes Dryers: Base Case Shipments Forecast by Product Class

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

9-iFigure 9.7.1 Room Air Conditioners, < 6,00 0 Btu/h Without Reverse Cycle and With Louvered Sides: Base Case and Standards Case Sh ipments Forecasts.............. 9-14 Figure 9.7.2 Clothes Dryers, Electric St andard: Base Case and Standards Case Shipments Forecasts........................................................................................... 9-15

9-iiCHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION
Product shipments estimates are a necessary input to the national energy savings (NES) and net present value (NPV) calculations. Ship ments are also a necessary input to the manufacturer impact analysis (MIA), which is described in chapter 12. This chapter describes DOE’s methodology for projecting annual shipments a nd 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. E ach shipments model considers specific market segments to estimate shipments, the results for which are then aggregated to estimate total product shipments. For purposes of estimating the impacts of pr ospective standard levels on product shipments, each shipment s model accounts for the combined effects of changes in purchase price, annual operating cost, and househ old income on the consumer purchase decision. The shipments models are Microsoft Excel spreadsheets that are accessible on the Internet ( http://www.eere.energy.gov/buildings/appliance_standards / ). Appendix 9A discusses how to access the shipments model spreadsheet and ot her related spreadsheets, and provides basic instructions for using them.

The rest of this chapter explains the shipments models in more detail. Section 9.2 presents the shipments model methodology for each product; section 9.3 describes the data inputs and the model calibration; section 9.4 di scusses impacts on shipments from changes in equipment purchase price, operating cost, and household income; section 9.5 discusses the affected stock; and section 9.6 presents the results for different energy-efficiency standard-level scenarios.

9.2 SHIPMENTS MODEL METHODOLOGY
DOE developed national stock models for es timating annual shipments for the products considered for this standards rulemaking. The mo dels consider market se gments appropriate for the considered products. Typically, the primary market segments are new home installations, replacements and first-time owners:


## Ship ( j ) = Rpl ( j ) + NI ( j ) + FTO ( j ) p p p p

Where:

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

9-1_ _ NI p (j) = Number of product p going to first-time owners in year j

.
For room air conditioners, DOE did not consid er the new construction market since this product, unlike some major house hold appliances, is not a sta ndard 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 models take an accounting approach, tracking market shares of each product class, the vintage of units in the existi ng 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 determin ed the useful service life of each appliance to estimate how long the appliance is likely to remain in stock. The following equation represents how DOE estimated replacement shipments.

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

# L L _ Ship j × prob Rtr ( age )

age =0 j = N Where:

Stock p (j-1) = Total stock of in-servi ce appliances in year j-1

, prob Rtr (age) = Probability that an 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 ava ilable historical shipments data).

Stock accounting takes product shipments, a retirement func tion, 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 fo r 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 time, where older, less efficient units may have higher operating costs, while younger, more-efficient units will have lower operating costs. Furthermore, in the case of an early replacement scenario, retirements due to early replacement will depend on the age of the units that are subject to early replacement.

DOE estimated replacements using product retirement functions that it developed from product lifetimes. For a ll 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 a ll 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:



### P ( x ) = − ( x

− θ

α J β for x > e and 

# e

9-2P(x) = 1 for x : e Where:

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

x = appliance age;

a = scale parameter, which would be the decay length in an exponential distribution; f

= shape parameter, which determines the way in which the failure rate changes through time; and e

= 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 st ock, some of the older ones retire and exit the stock. To estimate future shipments, DOE deve loped a series of equations that define the dynamics and accounting of in-service stocks. For new units, the equation is:

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

Where:

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

. The above equation states that the number of one-year-old units is simply equal to the number of new units purchased the previous year. The following equation describes the accounting of the existing in-service stock of units:

Stock ( j +1 ,age +1 ) = Stock ( j,age ) × 

# [ 1

_ prob Rtr ( age ) 

# ]

In the above equation, as th e year is incremented from j to j+1 , the age is also incremented from age to age+1 . With time, a fraction of the in -service stock is removed, and that fraction is determined by a retirement probability function, prob Rtr (age) , which is described 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 rulemaking are common appliances that have been used by U.S. consumers for a long time, replacements constitute the majority of shipments.

9-39.3 DATA INPUTS AND MARKET SEGMENTS
The sections below describe the data input s and market segments considered for each product.

9.3.1 Room Air Conditioners

9.3.1.1 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 th e Association of Home Appliance Manufacturers (AHAM) for the period 1995–2005, 1 (2) data from the 2000 AHAM Factbook for the period 1989–1994, 2 and (3) data from Appliance Magazine 3

, 4

, 5 for the period 1972–1988. a The shipments data aggregates a ll product classes and include un its for the residential and commercial sectors.

9.3.1.2 Markets and Model Calibration
The market for room air conditioners primarily consists of replacement units for equipment that has been retired. DOE’s shipments model also assumes that some existing households without room air conditioners as well as households who already own units will purchase the equipment. Total room air conditi oner shipments are represented by the following equation:

Ship ( j ) = Rpl ( j ) + NP ( j )

rac rac rac

Where:

Ship rac (j) = Total shipments of room air conditioners in year j
, Rpl rac (j) = Replacement shipments in year j
, NP rac (j) = Shipments to existing households addi ng a new room air conditioners in
year j

.

The sections below discuss these markets in further detail.

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 domestic shipments plus imports.

9-1Replacements. 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 unit. 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 9.3.1 shows the retirement function that DOE used to estimate replacement shipments.

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

0 2 4 6 8 10 12 14 1 6 18 20 22 24 26 28 30**Annual Retirement Rate**

Figure 9.3.1 Room Air Conditioners: 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, al so referred to as first-time owners (FTOs). Through 2008, DOE derived new purchases as the difference between the historic total shipments and the calculated replacement shipments.

For 2009 and onward, DOE estimated new purch ases as follows: on average, households with room air conditioners own 1.7 units per household, according to the American Housing Survey (AHS) 2007. The AHS i ndicates that 21 million househol ds did not own a room air conditioner in 2007. Assuming that the house holds 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

9-2decreases as some of the households purchase room air conditioners. DOE estimated that 13 percent of the remaining segment in each ye ar in 2009–2044 purchases a unit. It based this fraction on the estimated purchases by FTOs in 2007–2008 compared to the potential market.

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.

9.3.1.3 Base Case Shipments
Figure 9.3.2 presents forecasted room ai r 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.

0 2 4 6 8 10 12

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Millions

Shipments to Commercial Buildings New Purchases

RAC Replacements

Historical Shipments

Figure 9.3.2 Room Air Conditioners: Base Case Shipments Forecast by Market Segment

9.3.1.4 Disaggregation into Product Classes
DOE’s shipments model for room air conditi oners forecasted aggregate shipments. DOE considered 16 product classes for room air cond itioners. DOE estimated th e market shares for

9-3each of the 16 product classes using ma rket share data AHAM provided for 2005–2007. 2 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 9.3.1 shows the market shares used for the forecast period, and Figure 9.3.3 shows the shipments forecast by product class.

Table 9.3.1 Room Air Conditioner Product Class Market Shares

Product Share of Share Used

Class 2005 2006 2007 Models* 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 0.1% 0.1% 7 0.9% 0.4% 8 7.0% 12.0% 7.0% 19.1% 8.0% 9 0.7% 0.3% 10 0.0% 0.0% 11 12 0.7% 0.1% 1.0% NA 0.6% 0.5% 0.8% 0.1% 13 14 0.4% 0.6% NA 0.7% 0.0% 0.3% 0.0% 15 16 0.4% 0.6% 0.4% 1.1% 2.6% 0.1% 0.3%
* Based on 2007 CEC directory

.

9-40 1 2 3 4 5 6 7 8 9

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Millions Other PC PC8 PC5 PC3 PC1

Figure 9.3.3 Room Air Conditioners: Base Case Shipments Forecast by Product Class

9.3.2 Clothes Dryers

9.3.2.1 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 2003–2005, (2) data from the 2000 3
and 2003 2 AHAM Factbooks for the period 1989–2002, and (3) data from Appliance Magazine 4,5,6 for the period 1972–1988. Figure 9.3.4 shows the historical shipments of electric and gas clothes dryers.

9-5Figure 9.3.4 Historical Shipments of Electric and Gas Clothes Dryers

DOE then disaggregated the shipments of el ectric clothes dryers based on the market shares for 2007 according to data provided by AHAM (see Table 9.3.2).

Table 9.3.2 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

9.3.2.2 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 purcha se the equipment (referred to as first-time owners, or FTOs). Total clothes dryer shipment s are represented by the following equation:

Ship ( j ) = Rpl ( j ) + NH ( j ) + FTO ( j ) cd cd cd cd

9-6Where:

Ship cd (j) = Total 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 vintage, a certain percentage of clothes dryers will fail a nd 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 maximu m values of 5 years and 30 years, respectively. For a more complete discussion of clothes dr yer lifetimes, refer back to chapter 8. Figure 9.3.5 shows the survival and retirement functions that DOE used to estimate replacement shipments.

Figure 9.3.5 Clothes Dryers: Retirement Function

New Home Shipments . For clothes dryers, DOE estimated new home shipments using new housing forecasts and product saturation. Ne w 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 pl acements, DOE used actual data through 2007,

9-7and adopted the projections from the DOE En ergy Information Administration (EIA)’s Annual Energy Outlook 2009 ( AEO2009 ) for 2008–2030. 6 DOE estimated the frac tion of new homes for which the owner or builder purchased an electri c or gas clothes dryer based on AHS data. The saturation over the forecast period is 90 percen t. DOE then disaggregated the projected new home shipments of clothes dryers using the market shares in Table 9.3.2.

First-Time Owners. To estimate the number of households without clothes dryers who purchase the equipment, DOE estim ated that 3.7 percent of exis ting households without a clothes dryer would annually purchase this product in 2008–2044. DOE derived this rate by dividing historical shipments from 1990–2007 that were not accounted for by replacement or new construction installations by the number of existing households without a clothes dryer, which was determined from AHS data.

9.3.2.3 Base Case Shipments
Figure 9.3.6 shows the forecasted total clothes dryer shipments in the base case (i.e., the case without amended standards) , disaggregated into the thr ee market segments, and the historical shipments DOE used to calibrate the forecast. Figure 9.3.7 shows the forecasted clothes dryer shipments in the base case, disagg regated into the considered product classes, and the historical shipments.

Figure 9.3.6 Clothes Dryers: Base Case Shipments Forecast by Market Segment

9-8Figure 9.3.7 Clothes Dryers: Base Case Shipments Forecast by Product Class

9.4 IMPACT OF PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME
Economic theory suggests that, all else bein g 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 increase the demand for appliances. Finally, the literatu re suggests that consumers use relatively high implicit discount
rates b when comparing appliance prices and appliance operating costs.

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 concerned with higher purchase prices.

9-9DOE did not find enough equipment purchase price and operating cost data to perform a complex analysis of dynamic changes in the appliance market. Rather, it used purchase price and efficiency data specific to residential refrigera tors, 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 costs over the time period. Household income has also risen during this time. To simplify the anal ysis, DOE combined the available economic information into one variable, termed the relative price , and used this variable in an analysis of market trends, as well as to conduct a regression analysis. The relative price is defined with the following expression:

TP PP + PVOC RP = = Income Income

Where:

RP = Relative price, TP = Total price, Income = Household income, PP = Appliance purchase price, and PVOC = Present value of operating cost.

In the above equation, DOE used an implicit discount rate of 37 per cent to determine the present value of operating costs.

DOE’s analysis of market trends suggests that the relative price elasticity of demand for the three appliances is relatively inelastic (i.e., under 1.0). DOE’s regression analysis suggests that the relative price elasticity of demand, averaged over the three appliances, is -0.34. For example, a relative price increase of 10 percent results in a shipments decrease of 3.4 percent. Note that, because the relative price elasticity incorporates the impacts from three effects (i.e., purchase price, operating cost, and household income), the impact from any single effect is mitigated by changes from the other two effects. The relative price elasticity of -0.34 is consistent with estimates in the literature. Nevertheless, DOE stresses that the measure is based on a small data set, using simple statistical anal ysis. 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 pref erences 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, a nd household income have on product shipments 10

. Because DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time period, it needed to consider how the relative price elasticity is affected once a new standard takes effect. DOE considered the relative price elasticitiy provided above to be a short9-10run 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. 7

, 8 This study shows that the automobile price elasticity of demand changes in the years following a purchase price change. With increasing years after the purchase price change, the price elasticity becomes more inelastic until it reaches a terminal value around the tenth year after the price change. Table 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 appliances based on the relative change in the automobile price elasticity of demand. For years not shown in the table below, DOE performed a linear interpolation to obtain the relative price elasticity.

Table 9.4.1 Change in Relative Price Elasticity following a Purchase Price Change Years Following Price Change 1 2 3 5 10 20 Relative Change in Elasticity to 1 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 es timated standards case shipments by incorporating the impact of the relative price into the base case shipments forecast. Note that in the equation below, the relative price and the relative price elasticity are functions of the year because both change with time.

_ Ship ( j ) = 

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

# ) × 

# ( 1 e ( j ) × Δ RP ( j ) 

# ) STD _ p BASE _ p BASE _ p BASE _ p RP

Where:

Ship STD_p (j) = Total shipments under the standards case of product p in year j
, Rpl BASE_p (j) = Units of product p under the base case retired and replaced in year j
, NI BASE_p (j) = Number of new home installa tions 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 M RP(j)= Change in relative price due to a standard level in year j

.

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 shipment s under both the base case and the standards case, the affected stock (which represents the difference in the appliance stock for the base case and the standards case) is a key output of DOE’s shipments models. The affected stock quantifies the impact that new product shipments have on the appliance stock due to a standard

9-11level. Therefore, the affected stock consists of those in-service units that are purchased in or after the year the standard has taken effect, as described by the following equation:

j Std

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

# L Stock ( age ) p p p age =1 Where:

Aff Stock p (j) = Affected stock of units of product p of all vintages that are operational in
year j
, Ship p (j) = Shipments of product p in year j
, Stock p (j) = Stock of units of product p of all vintages that are operational in year j
, age = Age of the units (years), and Std_yr = Effective date of the standard.

As noted in the above equation, to calculate the affected stock, DOE must define the effective date of the standard. For the NES and NPV results presented in chapter 10, DOE assumed that new energy efficiency standards will become effective in 2014. Thus, all appliances purchased starting in 2014 are affected by the standard level.

9.6 CANDIDATE STANDARD LEVELS

Tables 9.6.1 and 9.6.2 show the efficien cy levels corresponding to each candidate standard level (CSL) for each product class.

Table 9.6.1 Integrated Energy Efficiency Rating (IEER) Levels Corresponding to Candidate Standard Levels for Room Air Conditioners Product Class Candidate Standard Level
1 2 3 4 5 Less than 6,000 Btu/h, with Louvers 10.1 10.6 11.1 11.6 12.0 8,000–13,999 Btu/h, with Louvers 10.3 10.8 11.3 11.5 20,000 Btu/h or more, with Louvers 9.0 9.4 9.8 10.2 8,000–13,999 Btu/h, without Louvers 9.0 9.4 9.8

9-12Table 9.6.2 Integrated Efficiency Factor (IEF) Levels Corresponding to Candidate Standard Levels for Clothes Dryers Product Class Candidate Standard Level 1 2 3 4 5 6 Vented Electric Standard 3.04 3.10 3.33 3.35 3.40 4.52 Vented Electric Compact 120V 3.08 3.15 3.37 3.41 3.53 4.69 Vented Electric Compact 240V 2.86 2.96 3.06 3.10 3.19 4.34 Vented Gas 2.71 2.80 2.97 2.98 3.02 Vent-less Electric 240V 2.31 2.37 2.39 2.59 3.54 Vent-less Combination Washer/D ryer 2.15 2.34 2.36 2.42 3.31

9.7 SHIPMENTS FORECASTS FOR CANDIDATE STANDARD LEVELS
The following sections show the shipments fo recasts for the various standard levels that DOE considered for room air c onditioners and clothes dryers.

9.7.1 Room Air Conditioners
Figure 9.6.1 compares the shipments forecast s for the base case and CSL 1 through 5 for units less than 6,000 Btu/h without reverse cycle a nd with louvered sides. In Figure 9.6.1, the difference between the base case and standard level shipments forecasts depicts the annual shipments reductions caused by the standard le vels. For all standard levels, shipments are forecasted to decrease compared to the ba se 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.

9-131.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0

2014 2019 2024 2029 2034 2039**Millions** Base Case
CSL 1
CSL 2
CSL 3
CSL 4
CSL 5

Figure 9.7.1 Room Air Conditioners, < 6,000 Btu/h Without Reverse Cycle and With Louvered Sides: Base Case and Standards Case Shipments Forecasts

9.7.2 Clothes Dryers
Figure 9.6.2 compares the shipments forecast s for the base case and CSL 1 through 6 for electric standard clothes dryers. In Figure 9.6. 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 clothes dryer product classes.

9-14Figure 9.7.2 Clothes Dryers, Electric Standard: Base Case and Standards Case Shipments Forecasts

9-15REFERENCES

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

. 2008

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

3. Statistical Review

. Appliance Magazine , April, 1993

4. Statistical Review

. Appliance Magazine , April, 1990

5. Statistical Review

. Appliance Magazine , April, 1992

6. Energy Information Administration, Updated Annual Energy Outlook 2009 Reference Case Service Report , 2009. Washington, DC. Report No. DOE/EIA-0383(2009).

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

7. Hymans, S., Consumer Durable Sp ending: Explanation and Prediction

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

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

. LBNL 326E , 2008

9-16