9

- i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

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

- 1

9.2 APPROACH TO SHIPMENT S MODEL

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

- 1

9.3 DATA INPUTS AND MARK ET SEGMENTS

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

- 3

9.3.1 Historical Shipments

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

- 3

9.3.2 Markets and Model Calibration

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

- 4

9.3.2.1 Replacements

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

- 5

9.3.2.2 First

- time Owners

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

- 5

9. 3.2.3 Commercial Sector

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

- 6

9.3.3 Base

- Case Shipments

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

- 6

9.4 EFFECT OF INCREASED PURCHASE PRICE ON SH IPMENTS

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

- 6

9.5 AFFECTED STOCK

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

- 9

9.6 EFFE CT OF CANDIDATE STAN DARD LEVEL ON PROJEC TED

SHIPMENTS

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

- 9

LIST OF TABLES

Table 9.3.1 Historical Shipments of Room ACs (1972

– 2012)

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

- 4

Table 9.4.1 Change in Relative Price Elasticity following a Change in Purchase P rice

........ 9

- 8

Table 9.6.1 Shipments Projected for Base Case and Each Standards Case, millions

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

- 10

LIST OF FIGURES

Figure 9.3.1 Portable ACs: Survival and Retirement Functions

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

- 5
Figure 9.3.2 Portable ACs: Projected Shipments for Base Case by Market Segment

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

- 69

- 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) related to potential energy efficiency
standards. Estimated shipments also are required for the manufactur er impact analysis . This
chapter describes the data and methods the U.S. Department of Energy (DOE) used to project

annual shipments of portable air conditioners (ACs) under base

- case and standards

- case efficiency levels.

DOE estimated shipments for portable ACs using a s hipments m odel calibrate d with
historical shipments data

. The s hipments m odel first estimates shipments for both of the

residential and commercial sectors

, and then dis aggregates results to estimate individual sector

. To estimat e the effect of potential standard levels on product shipments, the shipments model
accounts for the effects of changes in both purchase price and annual operating cost on the
consumer purchase decision. The shipments model was developed as a part of the spreadsheet

for the national impacts analysis (NIA) . Appendix 10A d e sc rib es how to access the NIA

workbook and provides basic instructions for its use.

The rest of this chapter explains the s hipments m odel in more detail. Section 9.2 presents

the approach to building the model ; section 0 describes the data inputs and model calibration;

section 9.4 discusses impacts on shipments from changes in product purchase price, operating

cost, and household income; section 9.5 discusses the affected stock of portable ACs ; and section

9.6 presents the model results for various candidate st andard level s for product efficiency

.

9.2 APPROACH TO SHIPMENTS MODEL

DOE has developed national stock model s to estimate annual shipments of various
products under potential new energy efficiency standards

. The model s consider market segments

as distinct input s to projected shipments. Typically , the primary market segments are new home

installations

, replacements

, and first

- time owners . The following is the general equation for
calculating product shipments to the three market segments.

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

, NI p (j) = number of new home installations of product p in year j , and

FTO p (j) = number of product p purchased by first

- time owners in year j

.9

- 2

For portable ACs , DOE did not consider the new construction market because this
product, unlike some household appliances, is not common ly installed in new homes ( unlike

central ACs ). DOE include d a market segment for purchases of portable ACs by first

- time

owners [or consumers who purchase an additional (rather than replacement) portable AC.]

DOE’s s hipments m odels take an accounting approach, tracking market shares of each
product or product class, the vintage of units in the ex isting stock, and expected housing stock

trends. The models estimate shipments attributable to replacements using sales in previous years
and assumptions about the lifetime of the product

. In a given year e stimated sales attributable to replacements are equal to the total stock of the appliance minus the number of appliances sold in
previous years that remain in the stock. The following equation shows how DOE estimates
replacement shipments of consumer appliances.

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 base d on available historical shipments data).

Stock accounting takes product shipments, a retirement function, and initial in

- service
product stock as inputs and develops 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

calculations of both the NES and NPV , because the product operating cost for any year depend s on the age distribution of the stock. The dependence of operating cost on the product age

dist ribution occurs under a standards

- case scenario that produces increasing efficiency over time,

where by older, less efficient units may have higher operating costs, while younger, more

efficient units have lower operating costs.

DOE estimates replacement s using product retirement functions developed from product

lifetimes. DOE bases the retirement function on a Weibull distribution for the product lifetime.
The shipments model assumes that no units are retired before reaching a minimum product

lifetime

, a nd all units are retired before exceeding a maximum product lifetime. For all products

, t he models use a Weibull equation to determine the probability of retirement at a certain age

.

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 = age of appliance ;9

- 3

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 the

next

.

DOE calculates the total in

- service stock of produc ts by integrating historical shipments
data starting from a specified year. The start year depends on the historical data available for

each product. As units are added to the in

- service stock, some 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:

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 equal simply 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

.

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

incremented from age to age+1

. With time, a fraction of the in

- service stock is removed, a

fraction that is determined by a retirement probability function, p rob Rtr (age) , which is described

in section 9.3. Over time, some units will be retired and removed from the stock, triggering the
shipment of a new unit.

9.3 DATA INPUTS AND MARKET SEGMENTS

The following sections describe the data inputs and market segments considered for
portable ACs

. Those data inputs a re used in the general equations presented in section 9.2 for
each market segment identified for portable ACs.

9.3.1 Historical Shipments

DOE used historical data regarding shipments of room AC s (domestic shipments and
imports) to populate and calibrate its s hip ments m odel for portable ACs

. DOE assumed that9

- 4

shipments of portable ACs account for approximately 10 percent of the total shipments of room

AC s

. 1 DOE consulted two sources for historical shipments data for room AC : (1) for 1972

– 2009, DOE’s rulemaking for room ACs

, 2 and (2) for 2010

– 2012, data from UBM

, Market Research

. 3

Table 9.3.1 presents the combined shipments data for 1972

– 2012.

Table 9.3.1 Historical Shipments of Room ACs (1972

– 2012)

Year Shipments

millions Year Shipments

millions Year Shipments

millions

1972 4.15 1986 2.75 2000 6.50

1973 4.81 1987 3.68 2001 5.58

1974 3.78 1988 4.40 2002 6.15

1975 2.16 1989 4.68 2003 8.22

1976 2.16 1990 3.80 2004 8.08

1977 2.46 1991 2.56 2005 8.03

1978 3.16 1992 2.59 2006 10.06

1979 2.92 1993 2.80 2007 9.46

1980 2.44 1994 3.85 2008 9.09

1981 2.94 1995 4.00 2009 5.79

1982 2.27 1996 4.53 2010 6.42

1983 1.77 1997 3.84 2011 7.26

1984 2.98 1998 4.53 2012 7.55

1985 2.94 1999 6.29

Source s : For 1972

– 2009: DOE’s previous rulemaking for room ACs , 2011.

For 2010

– 2012: U BM

, Market Research, 1954

- 2012.

9.3.2 Markets and Model Calibration

The market for portable ACs consists primarily of replacement units for products that

have been retired. DOE’s s hipments m odel also assumes that some existing households without a portable AC will purchase one (first

- time owners)

. Total shipments of portable AC are represented by the following equation

.

W here :

Ship p ac (j) = t otal shipments of portable ACs in year j

, Rpl p ac (j) = replacement shipments in year j

, and

FTO p ac (j) = shipments to existing households that purchase a new portable AC in year j

.9

- 5

The sections below discuss the market segment s in further detail.

9.3.2.1 Replacements

DOE determined shipments to the replacement market using an accounting method that
tracks the total stock of units by vintage

. Over time, as s ome units are retired and removed from

stock, the sh ipment of a replacement unit is triggered

. Depending on the vintage, a certain
percentage of unit s will fail and need to be replaced every year

. DOE determined the useful
service life of portable ACs to estimate how long the appliance is likely to remain i n stock. To
determine when a unit fails, DOE used a survival function based on a product lifetime

distribution having an average value of 1 0.5 years

. For a more complete discussion of DOE's

estimated lifetime of portable AC s , refer to c hapter 8. Figure 9.3.1 shows the survival and

retirement function s that DOE used to estimate replacement shipments.

Figure 9.3.1 Portable ACs : Survival and Retirement Function s

9.3.2.2 First

- time Owners

To calibrate the modeled shipments with the historical data, DOE introduced a market

segment for new purchases of portable ACs. It include s purchase of new units by households

without portable ACs. DOE derived first

- time owners through 2012, as the difference between
the historic total shipments and the calculated replacement shipments. For 2013 through 20 48

, DOE estimated that 0. 17 percent of households w ill purchase th e product annually , as an initial
purchase

.9

- 6

9.3.2.3 Commercial Sector

As described in chapter 8

, DOE estimated that 1 percent of total portable AC shipments

are used in the commercial sector. DOE used this percentage to estimate shipments to the
commercial sector for all years in the analysis period.

9.3.3 Base

- Case Shipments

Figure 9.3.2 presents projected shipments of portable AC s in the base case

, divided into
market segments

. The leveling off in shipments project ed by the model reflect s the saturation of
the potential market for this product.

Figure 9.3.2 Portable ACs : Projected Shipments for Base Case by

Market Segment

9.4 EFFE CT OF INCREASED PURCHASE PRICE ON SHIPMENTS

DOE conducted a literature review and an analysis of appliance price and efficiency data
to estimate the combined effects on product shipments from increases in product purchase price,
decreases in product operating costs, and changes to household income. Appendix 9

- A provides
a detailed explanation of the methodology DOE used to quantify the impacts from these

variables.

Existing studies of appliance markets suggest that the demand for appliances is price

inelastic. Other information in the literature su ggests that appliances are a normal good, s o that

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 Shipments (million) Shipments by Market Segement

Replacements FTOs9

- 7

rising incomes incre ase the demand for appliances, and that consumer behavior reflects relatively
high implicit discount rates a when comparing appliance prices and appliance operating costs.

DOE used the available data for the period 1980

- 2002 on large appliance purchases t o

evaluate broad market trends and conduct simple regression analyse s. 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 . Because
purchase decisions are sensitive to income, as well as to potential savings in the operating cost of
the appliance, DOE combined the available economic info rmation into one variable, termed the
relative price . This variable was used in a regression analysis to parameterize historical market

trends

. The relative price is defined with the following expression:

W here:

RP = relative price,

TP = t otal price,

Income = household income,

PP = appliance purchase price, and

PVOC = present value of operating cost s

.

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

DOE’s regression analysis suggests that the relative price elasticity of demand , averaged
over the three appliances, is

- 0.34. Thus, f or example, a relative price increase of 10 percent

would result in a shipments decrease of 3.4 percent. Note that, because the relative price
elasticity incorporates the effects of three factors ( purchase price, operating cost, and household
income), the impact of any single factor is mitigated by changes in the other two

.

A relative price elasticity of

- 0.34 is consistent with estimates of appliance price effect s

in the literature. Nevertheless, DOE stress es that the measure is based on a small data set, using
simple statistical analysis. More important ly , the measure is based on the assumption that

econo mic 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 perio d, but DOE did

a An implicit discount rate refers to a rate than can be inferred from observed consumer behavior with regard to

future operating cost savings realized from more

- efficient appliances. An implicit discount rate is not a true discount

rate because the observed consumer behavior is affected by lack of information, high transaction costs, and other

market barriers. However, implicit discount rates can predict consumer purchase behavior with re spect to energy

efficient appliances

. A high implicit discount rate with regard to operating costs means that consumer reflects a high

discounting of future operating cost savings realized from more

- efficient appliances. In other words, consumers are

much more concerned with higher purchase prices.9

- 8

not account for them in this analysis. Despite the uncertainties, DOE believes that its estimate of
the relative price elasticity of demand provides a reasonable assessment of the effect that
purchase price, operating cost, and household income have on product shipments

.

Because DOE’s projection s of shipments and national impacts from potential standards

consider a 30

- year period, DOE needed to consider how relative price elasticity is affected after a

new standard takes effect. DOE cons idered the relative price elasticit y developed above to be a

short

- term value , but was unable to identify sources specific to household durable goods, such as
appliances, to indicate how short

- and long

- term price elasticities differ. Therefore, to estimat e

how the relative price elasticity changes through time, DOE relied on two stud ies pertaining to

automobiles. 4

, 5 Th ose studies show that the price elasticity of demand for automobiles changes
in the years following a change in purchase price. With increa sing years after the change in
purchase price, 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 over time in
the price elasticity of demand for automobiles. As shown in the table, DOE developed a time

series of relative price elasticities for home appliances ba sed on the relative change in the price
elasticity of demand for automobiles

. 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 Change in Purchase Price

Years Following Price Change

1 2 3 5 10 20

Change in elasticity

relative to first 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

Using the following equation, DOE estimated standards

- case shipments by incorporating

the effect of the relative price into the base

- case shipments projection

. Note that in the equation

below, the relative price and the relative price elasticity are functions of the year because both
change with time.

W here:

Ship STD_p (j) = t otal shipments of product p in year j under the standards case

, Rpl BASE_p (j) = units of product p retired and replaced in year j under the base case

, M BASE_p (j) = first

- time owners of product p in year j under the base case

,

e RP (j) = r elative 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

- 9

9.5 AFFECTED STOCK

For any energy efficiency rulemaking, t he affected stock is the in

- service stock of the
appliance or product that is affected by a standard level. In addition to the projection of product
shipments under the base case and each standards case, the affected stock (which represents the
difference in the appliance stock f or the base case and the standards case) is a key output of

DOE’s s hipments m odels. The affected stock quantifies the effect that shipments of new product s under a standard level have on the appliance stock. Therefore, the affected stock

consists of those in

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

.

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 of units (years), and
Std_yr = effective date of standard.

As required for the above equation, to calculate affected stock DOE must define the

effective date of a standard. For the NES and NPV results presented in chapter 10, DOE assumed

that any new energy efficiency standards for portable ACs would become effective in 2019

. Thus, all appliances purchased starting in 2019 are affected by the standard level.

9.6 EFFECT OF CANDIDATE STANDARD LEVEL ON PROJECTED

SHIPMENTS

This section compares the shipments projected under the base case with those projected

for all the candidate st andard levels (CSLs) established for portable ACs . For all CSLs,
shipments are projected to increase

— the effects of the increase in product purch ase price offset

the effects of decreased operating costs, resulting in a net increase in shipments.

Table 9.6.1 shows total projected shipments of portable ACs in the base case and under

each CSL.9

- 10

Table 9.6.1 Shipments Projected for Base Case and Each Standards Case

, millions

CSL 2019 2025 2030 2035 2040 2045 2048

Base c ase 0.98 1.070 1.177 1.272 1.375 1.472 1.531

CSL 1 1.00 1.082 1.189 1.286 1.390 1.490 1.550

CSL 2 1.01 1.086 1.193 1.290 1.395 1.495 1.555

CSL 3 1.02 1.088 1.195 1.293 1.398 1.499 1.559

CSL 4 0.99 1.079 1.187 1.285 1.391 1.492 1.5529

- 11

REFERENCES

1 Natural Resources Canada (NRCan). Survey of Household Energy Use 2007 : Detailed
Statistical Report

. Statistics Canada and NRCan, Ottawa ON . Report SHEU

- 2007. (Last
accessed September 6, 2014.)

< http://oee.nrcan.gc.ca/publications/statistics/sheu07/pdf/sheu07.pdf >.

2 U.S. Department of Energy

– Office of Energy Efficiency and Renewable Energy. Energy
Conservation Program for Consumer Products: Technical Support Document: Energy
Efficiency Standards for Consumer Products: Residential Clothes Dryers and Room Air

Conditioners

. April 2011. Washington, D.C. (Last accessed September 6 , 201 4 .)

< http://www.regulations.gov/#!documentDetail;D=EERE

- 2007

- BT

- STD

- 0010

- 0053 >.

3 UBM

, Market Research. Appliance Historical Statistical Review 1954

– 2012 . January
2014. UBM, Hong Kong.

4 Hymans, S. Consumer Durable Spending: Explanation and Prediction

. Brookings Papers

on Economic Activity . 1971(1): pp. 234

– 239.

5 Dale, L., and K.S. Fujita. An Analysis of the Price Elasticity of Demand of Household
Appliances . 2008. Lawrence Berkeley National Labo ratory: Berkeley, CA. Report No.
LBNL 326E. OSTI ID: 929429. (Last accessed September 6, 2014.)

< http://eetd.lbl.gov/sites/all/files/lbnl

- 326e.pdf >.