9

- i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

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

- 1 9.2 SHIPMENTS MODEL GENE RAL METHODOLOGY

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

- 1 9.2.1 Stock Accounting Approach

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

- 2 9.2.2 Market Segments

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

- 2 9.3 PRODUCT

– SPECIFIC MODELS

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

- 3 9.3.1 Cooled Cabinets

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

- 4 9.3.1.1 Disaggregation into Market Segments

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

- 4 9.3.1.2 Disaggregation into product categories

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

- 6 9.3.2 N on
- Compressor Refrigerators

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

- 6 9.3.2.2 Disaggregation into product categories

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

- 7 9.3.3 Ice Makers

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

- 8 9.3.3.1 Disaggregation into Market Segments

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

- 8 9.3.3.2 Disaggregation in Product Classes

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

- 9 9.3.4 Hybrid Products

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

- 9 9.3.4.1 Disaggregation into Market Segments

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

- 10 9.3.4.2 Disaggregation into Product Classes

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

- 12 9.4 IMPACT OF ENERGY CON SERVATION STANDARDS ON SHIPMENTS

.......... 9

- 12 9.4.2 Application of Relative Price Elasticity

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

- 14 9.5 AFF ECTED STOCK

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

- 15 9.6 SHIPMENTS FORECASTS IN STANDARDS CASES

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

- 16

`9

- ii LIST OF TABLES

Table 9.3.1 Product Category Market Shares for Cooled Cabinets

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

- 6 Table 9.3.2 Product Category Market Shares for Non

- Compressor Refrigerators

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

- 8 Table 9.3.3 Product Class Market Shares for Ice Makers

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

- 9 Table 9.3.4 Product Class Market Shares for Compa ct Hybrid Refrigerators

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

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

......... 9

- 14

LIST OF FIGURES

Figure 9.3.1 Cooled Cabinets: Survival and Retirement Functions

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

- 5 Figure 9.3.2 Cooled Cabinets: Historical and Base Case Shipments Forecast by Market Segment

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

- 5 Figure 9.3.3 Non

- Compressor Refrigerators: Historical and Base Case Shipments Forecast by Market Segment

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

- 7 Figure 9.3.4 Ice Makers: Base Case Shipments Forecast by Market Segment

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

- 9 Figure 9.3.5 Standard

- sized, Built

- In Hybrid All

- Refrigerator (H

- 3A

- BI) Base Case Shipments Forecast by Market Segment

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

- 11 Figure 9.3.6 Compact Hybrid All

- Refrigerator (H

- 11A and H

- 13A) Base Case Shipments Forecast by Market Segment

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

- 11 Figure 9.3.7 Standard

- sized, Built

- in Hybrid Freezers (H

- 9I

- BI): Base Case Shipments Forecast by Market Segment

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

- 12 Figure 9.6.1 All Cooled Cabinets Shipments Forecasts

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

- 17 Figure 9.6.2 CC

- 1, Vapor Compression Cooled Cabinets Shipment Forecasts

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

- 18 Figure 9.6.3 CC

- 2, Thermoelectric Cooled Cabinets Shipment Forecasts

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

- 199-1 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 manufacturer 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 of the miscellaneous refrigeration products being considered in this analysis.

As described in c hapter 3, DOE defined four types of miscellaneous refr igeration products: cooled cabinets (one product class), non
- compressor refrigerators (one product class), ice makers ( three product classes) and hybrid refrigeration products (four product classes). DOE developed models that estimate shipments of each product type as a proportion of the total stock. The results from these analyses are disaggregated to two market segments : replacements and shipments to new housing. DOE then used various data and assumptions to disaggregate total shipments into t he product classes considered in this rulemaking. To estimate the effects of potential standard levels on product shipments, each shipments model accounts for the combined effects on consumer purchase decisions of changes in product price, annual operatin g cost, and household income. The shipments models were developed as a Microsoft Excel spreadsheet that is accessible on the DOE rulemaking website for miscellaneous refrigerat ion products

. 1 Appendix 10 A discusses how to access and utilize the shipments model spreadsheets, which are integrated into spr eadsheets for the n ational i mpact a nalysis (NIA) . The rest of this chapter explains the shipments models in more detail. Section 9.2 presents the methodology behind the models; section 9.3 describes the data inputs and calibration of each model; section 9. 4 discusses the impacts on shipments from standards (a more detailed explanation of the methodology DOE used to quantify the impacts of these variables on shipments can be found in Appendix 9 A ) ; section 9.5 discusses the affected stock; and section 9.6 pr esents the shipments forecast for different energy conservation standard levels.

9.2 SHIPMENTS MODEL GENE RAL METHODOLOGY As explained in c hapter 3, the data on shipments of the products under consideration

w ere very limited. Therefore , DOE developed a model of the national stock of in

- service appliances for estimating annual shipments for each of the product types considered for this standards rulemaking. This was done using the multi

- decadal time series trends for standard refrigerator s and freezers modelled in the 201 1 Final Rule , 2 scaled to stock and lifetime estimates obtained from survey data. The shipments models used in this rulemaking take an accounting approach, tracking the vintage of units in the existing stock.9-2 9.2.1 Stock Accounting Approach 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 product stocks is a key input to both the NES and NPV calculations , because the operating costs for any year depend on the age distribution of the stock. Older, less efficient units may have higher operating costs, while younger, more

- efficient units have lower operating costs.

DOE dete rmined the total stock of each product from the results of a study that used online surveys

. 3 T he results of this study were significantly different from esti mates based on data available from NPD and AHAM (see chapter 3)

. DOE decided to use the online study in favor of these other sources, as the survey results were believed to be more reliable. This is because the survey covered a wider sample of the populati on, compared to NPD and AHAM data which represented only a portion of the market, and it was difficult to ascertain what fraction of the total the data represented

. As units are added to the stock, some of the older ones retire and exit the stock. To estim ate future shipments, DOE developed a series of equations that define the dynamics and accounting of stocks. For new units, the equation is:

Stock ( j , age = 1 ) = S h ip ( j

− 1 )

Where: Stock(j, age) = number of units of a particular age, j = year for which the stock is being estimated, and Ship (j) = number of units purchased in year j

.
Th is 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 how the model accounts for the existing stock of units:

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

− prob Rtr ( age ) ] In this 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 stock is removed; that fraction is determined by a retirement probability function, prob Rtr (age) , which is described below.

9.2.2 Market Segments The model considers specific market segments in developing a shipments forecast. The two primary market segments are replacements and installations in new homes. For common appliances that have been used by U.S. consumers for a long time, replacements typically constitute the majority of shipments. To estimate shipments of replacement units, the mode ls utilize shipments data from previous years and estimates of the9-3 lifetime of each product. Estimated shipments of replacement units in a given year are equal to the total stock of the appliance minus those units shipped in previous years that remain in t he stock. DOE determines the useful service life of each product class to estimate the number of years products are likely to remain in the stock. The following equation shows how DOE estimates shipments of replacement units

.
) ( ) 1 ( ) ( 

# ∑ ∑

0 1

_ _ _ age prob Ship j Stock j Rpl Rtr ageMax

age j

N j j p p × =

= =

Where:

Stock P (j

- 1) = total stock of 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.
To forecast annual shipments for the new construction market, the model uses forecasts of new housing and the saturation of the product in new housing. The forecast of market shares involves knowledge of histor ical trends, as well as the important drivers of consumer choice and their relative impacts. For many products, DOE models a third market segment, conversion of an existing unit from first to second use. However, the online survey results 3 indicated that in the vast majority of households that own these products, there is only one such product, so second

- use conversion was not considered necessary for the products in this rulemaking.

9.3 PRODUCT

– SPECIFIC MODELS This section describes the models used to forecast shipments of the four types of miscellaneous refrigeration products described in section 9.1. For each model, the section describes the sources of historical shipments data, the market segments consi dered, the approach for disaggregating total shipments into product classes where appropriate, and the forecast of base case shipments.

Forecast s of new housing are used in the shipments models. New housing includes newly constructed single

- and multi

- fa mily units, termed “new housing completions,” and mobile home placements. For new housing completions and mobile home placements, DOE used recorded data through 201 2 and adopted the projections from DOE ’s Energy Information Administration (EIA)’s Annual Energy Outlook 201 4 (AEO201 4 ) for the period 201 3

- 2040. 4 For 2041

- 2050, DOE kept completions at the 2040 level.9-4 9.3.1 Cooled Cabinets
DOE used data from the online survey 3 to gain information on the age distribution of existing stocks of cooled cabinets (designated product class CC). The survey addressed wine chillers with vapor compression and thermoelectric cooling technologies, CC

- 1 and CC

- 2 respe ctively. DOE determined the total stock of cooled cabinets in 2012 was 12.3 million, with a household saturation of 10. 9 percent (1.6 percent for CC

- 1, 9.0 percent for CC

- 2)

. Having no satisfactory data on historical shipments of these products, DOE assumed that the household saturations of these products would remain the same projecting forward to 2050. DOE used the AEO forecasts described earlier to estimate total households

. As explained in chapter 3, DOE determined the number of new shipments in 2012 as the total stock divided by the mean product lifetime. DOE assumed that this ratio would remain the same projecting forward to 2050. DOE used the wine chiller survey result s to determine a survival function based on a product lifetime distribution with an average value of 4.3 years. Chapter 8 provides a detailed discussion of product lifetime.

9.3.1.1 Disaggregation into Market Segments
The market for cooled cabinet s was assumed to be primarily comp o sed of units for new construction and replacement units for products that have been retired. Total shipments are represented by the following equation:

S h i p CC ( j ) = Rp l CC ( j ) + N H CC ( j )

Where: Ship CC (j) = total shipments of wine chil ler cooled cabinets in year j , Rpl CC (j) = replacement shipments in year j , and NH CC (j) = shipments to new homes in year j

.

The following sections discuss these market segments in further detail.
New Construction Shipments . To estimate shipments for new residential construction DOE multiplied the housing starts forecast for each year by the estimated saturation of cooled cabinet s in new housing. Due to lack of data, DOE assumed that the saturation in new construction would be the same as the saturation in the total stock, which was estimated to be 10.6 percent as noted above . This was determined for each year going forward by multiplying 10.6 percent by the total number of projected housing starts taken from AEO201 4

. Replacements . DOE assumed that replace ments would account for the balance of shipments. Over time, some units are retired and removed from the stock, thereby triggering the shipment of replacement units. A certain percentage of units will fail each year and need to be replaced.9-5 Figure 9.3.1 Cooled Cabinets: Survival and Retirement Functions

Shipments forecast by market segment

. Figure 9.3.2 shows the forecasted shipments in the base case (without new energy conservation standards) and the estimated historical sh ipments (through 2011) for cooled cabinets. The figure presents shipments due to retirements and shipments to new housing.

Figure 9.3.2 Cooled Cabinets: Historical and Base Case Shipments Forecast by Market Segment 0% 20% 40% 60% 80% 100%

0 5 10 15 20 Age % surviving % retiring

0.0E+00 5.0E+05 1.0E+06 1.5E+06 2.0E+06 2.5E+06 3.0E+06 3.5E+06 4.0E+06 4.5E+06 5.0E+062000200420082012201620202024202820322036204020442048

YEAR New housing projections

Replacement Units

Modelled Total Shipment9-6 9.3.1.2 Disaggregation into product categories To disaggregate the total shipments of cooled cabinet s into shipments of each of the cooled cabinet product categories (CC

- 1 and CC

- 2 ), DOE used the distribution of products found in the results of the online surveys.

Table 9.3.1 presents the estimated market shares for disaggregating modeled shipments of cooled cabinet s . Because a reliable method for projecting market share changes was unavailable, DOE used these e stimated market shares throughout the forecast period.

Table 9.3.1 Product Category Market Shares for Cooled Cabinets

Vapor compression cooled cabinets (CC

- 1) Thermoelectric cooled cabinets (CC

- 2) 15% 85%

9.3.2 Non

- Compressor Refrigerators
DOE used the results of the online survey 3 to gain information on the age distribution of

non
- compressor refrigeration products, covering both thermoelectric (NCR

- 1) and abso rption (NCR

- 2) technologies. DOE also found around 400,000 absorption units mostly located in hotels. DOE determined the total stock of non

- compressor refrigerators to be 4.9 million units, with a household saturation of 4.2 percent (3.8 percent thermoelec tric and 0.4 percent absorption). The majority of these absorption units (75 percent ) are believed to be in hotels. Having no satisfactory data on historical shipments of these products, DOE assumed that the household saturations of these products would re main the same projecting forward to 2050. DOE used the AEO forecasts described earlier to estimate total households. As explained in chapter 3, DOE determined the number of new shipments in 2012 as the total stock divided by the mean product lifetime. DOE assumed that this ratio would remain the same projecting forward to 2050. DOE found the results of the non

- compressor refrigerator survey insufficient to determine a survival function; therefore DOE assumed that the survival rate would be the same as that determined for cooled cabinets. Chapter 8 provides a detailed discussion of product lifetime. The following sections discuss these market segments in further detail.
New Construction Shipments . To estimate shipments for new residential construction DOE mul tiplied the housing starts forecast for each year by the estimated saturation of non
compressor refrigerators in new housing. Due to lack of data, DOE assumed that the saturation in new construction would be the same as the saturation in the total stock, w hich was estimated to be 4.2 percent as noted above . This was determined for each year going forward by multiplying 4.2 percent by t he total number of projected housing starts taken from AEO201 4

.9-7 Shipments to Hotels. DOE estimated that about 8 percent of non

- compressor refrigerators were currently found in hotels . As no other data were available DOE assumed this value would be the same throughout the projection period

. Replacements . DOE assumed that replacements would account for the balance of shipme nts. Shipments forecast by market segment

. Figure 9.3.3 shows the forecasted shipments in the base case (without new energy conservation standards) and the estimat ed historical shipments (through 2011) for non
- compressor refrigerators . The figure presen ts shipments due to retirements, shipments to new housing , and shipments to hotels

.

Figure 9.3.3 Non

- Compressor Refrigerators: Historical and Base Case Shipments Forecast by Market Segment

9.3.2.2 Disaggregation into product categories To disaggregate the total shipments of non
- compressor refrigerators into shipments of each of the non
- compressor refrigerator product categories ( NCR

- 1 and NCR

- 2 ), DOE used the distribution of products found in the results of the online survey.

Table 9.3.2 presents the estimated market shares for disaggregating modeled shi pments of non
- compressor refrigerators . Because a reliable method for projecting market share changes was unavailable, DOE used these estimated market shares throughout the forecast period. 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000 2,000,0002000200420082012201620202024202820322036204020442048 Hotels

Shipments to Housing Starts Replacement Units

Modelled Total Shipment9-8 Table 9.3.2 Product Category Market Shares for Non

- Compressor Refrigerators Thermoelectric refrigerators (NCR

- 1) Absorption refrigerators (NCR

- 2)

89% 11%

9.3.3 Ice Makers DOE found no available data on historical shipments of ice makers . DOE used the results of the online survey 3 to determine the current stock of ice makers and the age distribution of these products. DOE found that the current total stock of ice makers in th e United States was approximately 5. 5 million, with a household saturation of 4.7 percent. Similar to cooled cabinets , DOE assumed that the household saturation would remain the same going forward and projected total stock to 2050, using the AEO housing fo recasts described previously

. As explained in c hapter 3, DOE determined the proportion of the total stock that corresponded to new shipments by dividing the total stock by the mean product lifetime. Due to insufficient data DOE assumed the lifetime would the same as that of residential compact freezers 2

, 7.5 years

.

9.3.3.1 Disaggregation into Market Segments
The market for ice makers is assumed to be primarily comprised of units that replace products that have been retired from service and units installed in new construction

. New Housing . To estimate the proportion of total shipments for new construction, DOE multiplied the housing starts forecast for each year by the estimated saturation of ice makers in new housing. Due to lack of data, DOE assumed that the saturation in new housing would be the

same proportion as the saturation in the total stock, which was estimated to be 4.7 percent as noted above . This was determined for each year going forward by multiplying this proportion by the projected number of housing starts taken from AEO201 4

. Replacements . DOE assumed shipments to the replacement market would account for the balance of shipments. Shipments Forecast by Market Segment

. Figure 9.3.4 shows the forecasted shipments in the base case (without new energy conservation standards). Historical shipments are estimated by projecting backwards from the current stock of icemakers, as determined by the online survey results. The figure prese nts shipments due to retirements and shipments to new housing.9-9 Figure 9.3.4 Ice M akers: Base Case Shipments Forecast by Market Segment

9.3.3.2 Disaggregation in P roduct C lasses To disaggregate the total shipments of ice makers into shipments of each of the ice maker product classes, DOE use d the distribution of products found in the results of the online survey.

Table 9.3.3 presents the estimated market shares for disaggregating modeled shipments of ice makers . Because a reliable method for projecting market share changes was unavailable, DOE used these estimated market shares throughout the forecast period.

Table 9.3.3 Pr oduct Class Market Shares for Ice Makers Portable ice makers ( IM

P) Non

- portable ice makers with cooled storage ( IM

- C) Non

- portable ice makers with non

- cooled storage (RIM

- U) 69% 8% 23%

9.3.4 Hybrid Products DOE found no available data on historical shipments of hybrid standard all

- refrigerator, standard hybrid freezer, or compact hybrid all

- refrigerator products. DOE used the results of the online survey 3 to determine the current stock of hybrid products and their age. DOE found that

0.0E+00 2.0E+05 4.0E+05 6.0E+05 8.0E+05 1.0E+06 1.2E+06

2000 2007 2014 2021 2028 2035 2042 2049 Year Shipments to Housing Starts Replace-ment Units Modelled Total Shipment9-10 the current total stock of hybrid products in the United States was approximately 2.2 million standard

- sized, b uilt

- in hybrid all

- refrigerators (product class H

- 3A

- BI), 0.3 million compact hybrid all

- refrigerators with manual defrost (product class H

- 11A), 1.1 million compact hybrid all

- refrigerators with automatic defrost (product class H

- 13A) and 0.9 million stan dard

- sized, built

- in hybrid freezers (product class H

- 9I

- BI) , resulting in household saturations of 1.9 percent, 0.2 percent, 1.0 percent, and 0.8 percent respectively. Again , DOE assumed that these household saturations would remain the same through 2050 and used the AEO housing projections to determine the total stocks. DOE assumed that the mean lifetime of these products would be the same as those of their non

- hybrid equivalents, as determined in DOE ’ s final rule for refrigerators

. 2 The mean lifetimes are 17.1 years for standard

- sized refrigerators, 5.6 years for compact refrigerators, and 22.8 years for freezers. DOE estimated new shipments as t he total stock divided by the mean lifetime for all years through 2050.

9.3.4.1 Disaggregation into Market Segments The market for hybrid products is assumed to be primarily comp o sed of units that replace products that have been retired from service and units ins talled in new housing. DOE assumed that the proportion of new shipments to each market segment would be the same as their non

hybrid equivalents, determined in DOE’s final rule for refrigerators

. 2
Shipments Forecast by Market Segment

. Figure 9.3.5 to Figure 9.3.7 show the forecasted shipments in the base case (without new energy conservation standards) for standard hybrid all

- refrigerators, compact hybrid all

- refrigerators, and hybrid freezers respectivel y. Historical and projected shipments were both calculated assuming that the proportion s of shipments to total stock were the same as those previously determined by DOE in the refrigerators and freezers final rule

. 2 The figures present shipments due to retirements and shipments to new housing.9-11 Figure 9.3.5 Standard

- sized, Built

- In Hybrid All

- Refrigerator (H

3A

- BI ) Base Case Shipments Forecast by Market Segment

Figure 9.3.6 Compact Hybrid All

- Refrigerator (H

- 11A and H

- 13A) Base Case Shipments Forecast by Market Segment 0.00 0.05 0.10 0.15 0.20 0.252000200420082012201620202024202820322036204020442048Shipments (millions) Shipments to new housing Replacements

Total new shipments

0 0.05

0.1 0.15

0.2 0.25

0.3 0.35

0.4 0.452000200420082012201620202024202820322036204020442048Shipments (millions)

New housing Replacements Total Shipments9-12 Figure 9.3.7 Standard

- sized, Built

- in Hybrid Freezers (H

- 9I

- BI): Base Case Shipments Forecast by Market Segment

9.3.4.2 Disaggregation into P roduct C lasses As expl ained in chapter 3, compact hybrid all

- refrigerators are divided into two product classes (H

- 11A and H

- 13A). The relative proportion of each was determined from data on available models found on the market; see chapter 7.

Table 9.3.4 Product Class Market Shares for Compact Hybrid Refrigerators Compact refrigerator

cooled cabinets with automatic defrost (H13A) Compact refrigerator

cooled cabinets with manual defrost (H11A) 80% 20%

9.4 IMPACT OF ENERGY CON SERVATION STANDARDS ON SHIPMENTS DOE projects that appliance standards often result in an increase in the price of the product. Economic theory suggests that, all else being equal, an increase in the price of a normal product would lead to a decrease in demand for it. DOE conducted a literature review and an analysi s 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

. Appendix 9 A provides a detailed explanation of the methodology DOE used to quantify the impacts of these variables on shipments. 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.072000200420082012201620202024202820322036204020442048Shipments (Millions)

Housing starts Replacements Total new shipments9-13 In the literature, DOE found only a few studies of appliance markets that are relevant to the issue at hand. DOE identified no studies t hat use time

- series data of product price and shipments data after 1980. The information that can be summarized from the literature suggests appliances are a normal good, such that rising incomes increase the demand for appliances. Finally, the literatu re suggests that market behavio r indicates relatively high “implicit discount rates” when comparing appliance prices and appliance operating costs. a
DOE found insufficient data on product purchase price and operating cost to perform a thorough analysis of dyn amic changes in the appliance market. Instead, it used purchase price and efficiency data specific to residential refrigerators, clothes washers, and dishwashers over the period 1980

- 20 0 2 to evaluate broad market trends and conduct simple regression analys es. These data indicate that there has been a rise in appliance shipments and a decline in appliance purchase price and operating costs over the time period. Household income has also risen during this time. To simplify the analysis, DOE combined the avai lable economic information into one variable, termed the relative price , and used the variable in an analysis of market trends, as well as to conduct a regression analysis. The relative price is defined with the following expression:

RP = TP Income = PP + PVOC Income
Where: RP = relative price, TP = total price, Income = household income PP = appliance purchase price, and PVOC = present value of operating cost.

DOE used an “implicit discount rate” of 37 percent to determine the present value of operating costs. This value is an average from values derived by several studies described in a 1985 journal article. 5
DOE’s regression analysis yields a relative price elasticity of demand, averaged over the three appliances, of

- 0.34. For exampl e, a relative price increase of 10 percent results in a 3.4 percent decrease in shipments. 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 somewhat mitigated by changes in the other two effects.

a A high implicit discount rat e with regard to operating costs means that, based on market behaviour, consumers appear to put relatively low economic value on the operating cost savings realized from more

- efficient appliances. A high value may indicate lack of information, risk aversio n, and other factors, as well as the value consumers place on savings accrued in the future.9-14 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 simpl e statistical analysis. More importantly, the measure is based on an assumption that economic variables, including purchase price, operating costs, and household income, explain most of the trend in appliances per household in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price elasticity of demand provides a reasonable assessment of the impact that purchase price, operating cost, and household income have on product shipments.

DOE considers the relative price elasticity provided by the preceding analysis to be a short

- run value. Because DOE’s forecasts of s hipments 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. 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. To estimate how the relative price elasticity changes over time, DOE relied on a study pertaining to automobiles. 6 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 smaller (more inelastic) until it reaches a termina l 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. DOE develo ped 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, DOE performed a linear interpolation to obtain the relative price elasticity.

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

1 2 3 5 10 20 Change in Elasticity 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

9.4.2 Application of Relative Price Elasticity DOE estimates shipments in each standards case using the relative price elasticities described previously, along with the change in the relative price between a standards case and9-15 the base case. Because household in come is the same in the standards case and the base case, it does not figure into the calculation of the change in the relative price. Note that, in the following equation, the relative price and the relative price elasticity are functions of the year beca use both change with time. b

S h ip STD

_ P ( j ) = ( Rpl BASE

_ P ( j ) + NI BASE

_ P ( j ) + M BASE

_ P ( j ) ) × ( 1

− e RP ( j ) × ∆ RP ( j ) )

Where: Ship STD _P (j) = total shipments under the standards case of product p in year j , Rpl BASE _P (j) = units of product p under the base case retired and replaced in year j , NI BASE _P (j) = number of new construction installations under the base case of product p in year j , M BASE _P (j) = units installed in market M under the base case of product p in year j ( M represents purchases for existing homes),

e

RP (j) = relative price elasticity in year j (equals

- 0.34 for year 1), and ∆

RP(j) = change in relative price due to a standards level in year j

.

9.5 AFFECTED STOCK In addition to the forecast of product s hipments under both the base case and the standards case, the affected stock is a key output of DOE’s shipments models. The affected stock (stock that is affected by a standards level) consists of those in

- service units that are purchased in or after the y ear the standard has taken effect, as described by the following equation :

Aff Stoc k p ( j ) = S h i p P ( j ) + � Stoc k P ( age ) j

− Std

_ yr

age = 1
Where: Aff Stock P (j) =affected stock of units of product p of all vintages that are in service 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 in service in year j , age = age of the units (years), and Std_yr = effective date of the standards.

b The relative price changes slightly over time because the lifetime operating costs are different for each vintage in the forecast period. Operating costs change slightly because forecasted energy prices are changing.9-16 For its analysis, DOE assumed that amended energy conservation standards will become effective in 2021. Thus, all appliances purchased starting in 2021 are affected by the standard level.

9.6 SHIPMENTS FORECASTS IN STANDARDS CASES
This section presents the shipments forecasts for the candidate standard levels (CSLs), also known as efficiency levels (ELs), which DOE considered for each of the miscellaneous refrigeration product types, as well as for the base case. The ELs are described in chapte r 10. DOE generally expects that a standard will increase the per unit price of a given product, which in turn will lead to a decrease in shipments. In the case of hybrid products, however, DOE does not expect the per unit price to increase due to the stan dards (refer to chapter 5, Engineering Analysis, for details). Therefore, it does not expect shipments to be affected. DOE determines the percentage decrease in shipments as:

1

− Pric e basecase + Operatingcost s basecase Pri c e standardscase + Operatingcos t standardscase ∗ elasticity

Pric e basecase = Price per unit in the base case Operatingcost s basecase = Operating cost per unit in the base cas e
Pric e standardscase = Price per unit in the standards case Operatingcos t standardscase =Operating costs per unit in the standards case elasticity = relative price elasticity, as described in 9.4

For ice makers, DOE found the increased price per unit in the standards case was insignificant compared to the decreased operating costs. This would lead to a negative value in the above formula, indicating an increase in shipments. DOE does not ex pect the standard to lead to an increase in shipments and therefore does not expect ice maker shipments to be affected by the standards. Similarly DOE does not expect shipments of non

- compressor refrigerators to be affected by the standards.

D OE does expect the shipments of cooled cabinets to be affected by the standards for the whole 30 year assessment period

. Figure 9.6.1 shows the differences between the base case and standards case for all cooled cabinets

. Figure 9.6.2 and Figure 9.6.3 show the differences between the base case and standards case shipments for CC

- 1 (vapor compression) and CC

- 29-17 (thermoelectric) , c respectively . These forecasts represent the a nnual shipments reductions attributable to the standard levels.

Figure 9.6.1 All Cooled Cabinets Shipments Forecasts

c The CC

- 2 product category is referred to as Thermoelectric Cooled Cabinets in the following figures. The cost

efficiency curve for CC

- 2 uses thermoelectric cooling at baseline and vapo r compressio n technology at efficiency levels 5 through 11. See chapter 5 for more information. 0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5Shipments (millions) Base Case
EL1
EL2
EL3
EL4
EL5
EL6
EL7
EL8
EL9
EL10
EL119-18 Figure 9.6.2 CC

- 1, Vapor Compression Cooled Cabinets Shipment Forecasts 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8Shipments (millions) Base Case
EL1
EL2
EL3
EL4
EL5
EL6
EL7
EL8
EL9
EL10
EL119-19 Figure 9.6.3 CC

- 2, Thermoelectric Cooled Cabinets Shipment Forecasts 0 0.5

1 1.5

2 2.5

3 3.5

4Shipments (millions) Base Case
EL1
EL2
EL3
EL4
EL5
EL6
EL7
EL8
EL9
EL10
EL119-20 REFERENCES

1 http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/71

. Accessed June 26 , 201 4

. This material is available in Docket EERE

- 2011

- BT

- STD

- 0043 at regulations.gov.

2 DOE (2011). Energy Conservation Program: Energy Conservation Standards for Residential Refrigerators, Refrigerator

- Freezers, and Freezers , Final Rule notice (76 FR 57516) U.S. Department of Energy. http://www.gpo.gov/fdsys/pkg/FR

- 2011

- 09

- 15/pdf/2011

- 22329.pdf

.

3 Greenblatt, J. B., S. J. Young, H.

- C. Yang, T. Long , B. Beraki, S. Price, S. Pratt, H. Willem, L.

- B. Desroches, and S. M. Donovan (2013). U.S. Residential Miscellaneous Refrigeration Products: Results from Amazon Mechanical Turk Surveys , Lawrence Berkeley National Laboratory, LBNL

- 6 537 E, November 2013.

4 U.S. Department of Energy

- Energy Information Administration. Annual Energy Outlook 201 4 With Projections to 20 40

. April 201 4 . Washington, DC. DOE/EIA

- 0383(201 4 ). http://www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf

5 Train, K., “Discount Rates in Consumers,” Energy

- Related Decisions,” Energy: The International Journal, Vol.10, No.12, 1985, pp. 1243

- 1253.

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

- 239.