CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

........................................................................................................... 9- 1

9.1.1 Definition of Market Segments for the Shipments Analysis

........................................... 9-1

9.1.2 Fundamental Model Equations

........................................................................................ 9-3

9.2 DATA INPUTS AND SUPPORTING CALCULATIONS

............................................ 9-5

9.2.1 Historical Shipments and the Calcul ation of Replacement Shipments

............................ 9-5

9.2.1.1 Historical Shipments

...................................................................................... 9-5

9.2.1.2 Calculation of Repl acement Shipments

......................................................... 9-7

9.2.2 Shipments to New Construction

...................................................................................... 9-7

9.2.3 Model Calibration and New Owners

............................................................................. 9-10

9.3 DECISION MODEL FOR NEW OWNERS AND REPLACEMENTS

....................... 9-11

9.3.1 Purchase Price Elasticity

............................................................................................... . 9-11

9.3.2 Impact from Increase in Relative Price

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

9.3.2.1 New Owner Shipment Impact s and Equipment Switching

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

9.3.2.2 Replacement Shipment Impacts

................................................................... 9-15

9.4 REGIONAL SHIPMENTS MODEL

............................................................................. 9-15

9.4.1 Allocation of Historical Shipments to Regions

............................................................. 9-16

9.4.2 Regional New Construction and Ne w Construction Saturations

................................... 9-19

9.4.2.1 Regional New Construction

......................................................................... 9-19

9.4.2.2 Regional New Construc tion Saturations

...................................................... 9-20

9.5 RESULTS

................................................................................................................... ... 9-22

9.5.1 Split CAC

............................................................................................................... ........ 9-22

9.5.2 Single Package CAC

...................................................................................................... 9-30

9.5.3 Split HP

................................................................................................................ .......... 9-32

9.5.4 Single Package HP

....................................................................................................... .. 9-35

LIST OF TABLES

Table 9.1.1 CAC and HP Product Classes

.............................................................................. 9-2 Table 9.1.2 Stock Categories, Input Data, De cisions, and Resulting Actions used in the Shipments Model

................................................................................................. 9-3

Table 9.2.1 Central Air Conditioner and Heat Pump Historical Shipments*

......................... 9-6

Table 9.2.2 Historical New Ow ner Market Shares of CAC and HP Equipment

.................. 9-11 Table 9.3.1 Change in Relative Price Elasticit y following a Purchase Price Change

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

Table 9.4.1 CAC Reside ntial Regional Stock Saturations

.................................................... 9-16 Table 9.4.2 HP Residential Regi onal Stock Saturations

....................................................... 9-17 Table 9.4.3 CAC Shipment Regi onal Allocation Factors

..................................................... 9-17 Table 9.4.4 HP Shipment Regional Allocation Factors

........................................................ 9-17

9-iTable 9.4.5 Population Projected for 2020 for Areas defined by the Overlap of Census Divisions with Climate Regions

........................................................................ 9-19 Table 9.5.1 Split CAC: Base Case and St andards Case Cumula tive Shipments from 2016-2046 (millions)

......................................................................................... 9-29 Table 9.5.2 Single Package CAC: Base Case and Standard s Case Cumulative Shipments from 2016-2046 (millions)

............................................................... 9-32 Table 9.5.3 Split HP: Base Case and St andards Case Cumula tive Shipments from 2016-2046 (millions)

......................................................................................... 9-34 Table 9.5.4 Single Package HP: Base Case and Standard s Case Cumulative Shipments from 2016-2046 (millions)

............................................................... 9-36

LIST OF FIGURES

Figure 9.2.1 CAC and HP Saturations in Residential New Construction

................................ 9-8 Figure 9.2.2 CAC and HP Saturation in Commercial New C onstruction

................................ 9-9 Figure 9.4.1 Residential CAC and HP Stock Saturations by Region and the Entire U.S.: American Housing Survey (AHS) vs. Shipments Model (Calculated)

.............. 9-18 Figure 9.4.2 CAC National and Re gional Saturations in Reside ntial New Construction

...... 9-20 Figure 9.4.3 HP National and Re gional Saturations in Reside ntial New Construction

......... 9-21 Figure 9.4.4 CAC and HP National and Re gional Saturations in Commercial New

Construction

....................................................................................................... 9-22 Figure 9.5.1 Forecasted Shipments (Base Case ) for Split-System CACs, Coil-Only

............ 9-23 Figure 9.5.2 Forecasted Shipments (Base Case ) for Split-System CACs, Blower Coil

........ 9-23 Figure 9.5.3 Split Coil-Only CAC: Base Case and CSL 13 (22 SEER) Replacement Shipments Forecasts

........................................................................................... 9-25 Figure 9.5.4 Split Coil-Only CAC: Base Case and CSL 13 (22 SEER) New Owner Shipments Forecasts

........................................................................................... 9-26 Figure 9.5.5 Split Coil-Only CAC: Base Case New Constructi on Shipments Forecasts

....... 9-27 Figure 9.5.6 Split Coil-Only CAC: Base Ca se and CSL 13 (22 SEER) Total Shipments

Forecasts

............................................................................................................ 9-28 Figure 9.5.7 Forecasted Shipments (Base Case) for Single Package CACs

.......................... 9-30 Figure 9.5.8 Single Package CAC: Base Ca se and CSL 7 (16.6 SEER) Total Shipments

Forecasts

............................................................................................................ 9-31 Figure 9.5.9 Forecasted Shipments (Bas e Case) for Split System HPs

................................. 9-32 Figure 9.5.10 Split HP: Base Case a nd CSL 10 (19.5 SEER / 10.3 HSPF) Total Shipments Forecasts

........................................................................................... 9-33 Figure 9.5.11 Forecasted Shipments (Base Case) for Single Package HPs

............................. 9-35 Figure 9.5.12 Single Package HP: Base Ca se and CSL 7 (16.4 SEER / 9.0 HSPF Total Shipments Forecasts

........................................................................................... 9-36

9-iiCHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a n ecessary 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 central air conditioner (CAC) and heat pump (H P) product classes being considered in this analysis. The shipments model divides the popula tion of appliance users into sp ecific market segments and estimates the shipment of new equipment to each of these segments under each candidate standard level (CSL). The model starts from a hist orical base year and calculates, for each year of the analysis period, both shipments and retir ements by market segment. This approach produces an estimate of the total equipment stoc k, broken down by age or vintage, in each year of the analysis period. The equipment stock dist ribution is calculated for the base case and for each CSL as well as for each product class. The st ock distribution is used in the national impact analysis (NIA) to estimate the total dollar and energy costs and benefits associated with each

CSL.

The shipments model was developed as a Microsoft Excel spreadsheet that is accessible on DOE’s Appliance and Commercia l Equipment Standards website ( http://www1.eere.energy.gov/buildings/applianc e_standards/residen tial/central_ac_hp.html ). Appendix 10-A discusses how to access and utilize the shipments model spreadsheet, which is integrated into the spreadsheet for the NIA. The rest of this chapter explains how the shipments

model is constructed a nd provides some summary output. The rest of section 9.1 describes the methodological approach. The analysis incorpor ates a large number of components or market segments, describing different product classes, market sectors and geographic regions; for simplicity, the methodology is explained for a sing le product class for re sidential consumers for the country as a whole. Section 9.2 defines the input data, pre-processi ng and calibration steps

that are used in the model. Section 9.3 defines th e decision models used to differentiate between the base case and the CSLs. Section 9.4 presents the modifications to the input data that were used to develop shipments models specific to th e three regions (hot-humid, hot-dry and rest-of

country) used in the analysis. Section 9.5 pres ents summary output from the shipments model,including the shipments forecast for the base cases and different energy conservation standardlevel scenarios. The methodology used to develop price elasticities is de scribed in Appendix 9

- A.

9.1.1 Definition of Market Segments for the Shipments Analysis

The shipments model disaggregates the total stock according to the following

characteristics:

9-11. Product class: there are four product classe s as summarized in Table 9.1.1. DOE further disaggregated the split system CAC product cl ass into two unit types: coil-only and blower coil units. Although not considered as separate product classes, DOE structured the shipments model to analyze both split CAC coil-only and split CAC blower coil systems separately.

2. Market sector: residential and commercial bu ilding sectors; approx imately 93 percent of shipments are to the residential sector with the balance going to the commercial sector. 1
This ratio is held constant over the analysis period.

3. Repair status: this is define d as default (i.e., normal repair s and maintenance as accounted for in the lifetime of the equipment) or extended repair (ER). The shipments model allows for the price increase associated with a CSL to influence the consumer decision to repair, rather than replace , broken equipment. The extended repair stock category represents equipment that has been repaired after failure. This component of the stock is accounted for separately because it is furthe r assumed that equipm ent that has undergone one extended repair will last another six years and then be replaced. This category is referred to as extended repair because it mode ls major equipment failures; minor repairs

are accounted for in the annual maintenanc e costs and determination of equipment

lifetimes.

Table 9.1.1 CAC and HP Product Classes
Product Type Product Class Unit Type
Coil-Only
Central Air Conditioner (CAC) Split System Blower Coil
Single Package NA

Heat Pump (HP) Split System NA
Single Package NA

Each market sector (i.e., residential and co mmercial) is further disaggregated into three product placement channels (hereafter refe rred to as “channels”) as follows:

1. New construction: a certain fraction of new buildings are assumed to acquire CAC and HP equipment in the year of construction. This fraction is defined as the new construction saturation, which varies by year, by region, and by product type.

2. Existing owners: these are defined as exis ting buildings with CAC and HP equipment installed. This category receives new shipments when existing equipment is replaced.

3. New owners: these are defined as existing bu ildings that acquire CAC and HP equipment for the first time during the analysis period. The fraction of new owners is estimated from trends in overall market saturation. As described later in this chapter, the new owner channel is necessary for calibrating the shipments model to historical shipments.

9-2These three channels are treated differently in the way that the consumer decision is modeled. For new construction the placement of equipment is automatic, in line with observed trends in the data. For new owners, the consumer decision to purchase CAC and HP equipment is affected by the change in purchase price and operating costs; for this category, the number of new purchases will generally decrease for the higher CSLs. In those cases where new owners of CAC and HP equipment have be en modeled to forego a new purchase, DOE assumed that potential new owners would purchase alterna tive space-conditioning systems; room air

conditioners in the case of CAC consumers and combination warm-air electric resistance

furnaces and CACs in the case of HP consumers. For existing owners, the repair versus replace decision is also affected by the change in ownership cost associated with higher efficiency. In those cases where existing owners have been mo deled as being affected by higher ownership costs, DOE assumed they would perform an ex tended repair which prol ongs the life of the equipment by six years, after which the equipm ent would be replaced. The three channels and related decisions modeled in the sp readsheet are summarized in Table 9.1.2.

Table 9.1.2 Stock Categories, Input Data, Decisions, and Resulting Actions used in the Shipments Model

Category Input data Decision Action
Equipment Stock =

+ new construction nc_starts (year) buy => add to equip_stock, age=0 nc_saturation(year)

- demolitions housing demolitions(year)

remove => subtract from equip_stock Equip_stock(age, year)

+ existing owners Equip_stock(age, year) does not break => stays in equip_stock; increment age
survival_function(age) breaks: replace => add to equip_stock, age=0
Repair_probability(year) breaks: repair => convert to ER_equip_stock, a2=0

Extended Repair ER_equip_stock(year,a2) a2 < 6 years => stays in ER_equip_stock; increment a2
a2=6 years, replace => add to equip_stock, age=0

New owners (no CAC or HP) housing_stock(year) buy => add to equip_stock, age=0 Equip_stock(age, year)
max_saturation(year) do not buy => stays in no cooling stock purchase_probability(year)

9.1.2 Fundamental Model Equations

The fundamental dependent va riable in the shipments model is the equipment stock which is represented as a function of analysis year (indexed by j) , and equipment vintage or age (the equipment age is noted as a, and is equal to the analysis year minus the vintage). The stock function is adjusted in each year of the anal ysis period by new shipments coming in and broken or demolished equipm ent being taken out.

For existing stock:

9-3Stock 

## ( j,a 

## )

= Stock 

## ( j

− 1 ,a

− 1 

## )

− Re m 

## ( j,a 

## )

p p p

and for new units:

Stock

p 

## ( j,a = 1 

## )

= Ship

p 

## ( j

− 1 

## )

.

Where:

Stock p (j, a) = number of units of product class p and age a in analysis year j,

Rem p (j, a) = number of units of product class p and age a removed in analysis year j,

and Ship p (j) = number of units of product class p shipped in year j

.

Shipments are directed to one of the three channels:

Ship 

## ( j 

## )

= Rpl 

## ( j 

## ) + NC 

## ( j 

## ) + NO 

## ( j 

## )

p p p p

Where:

Rpl p (j) = number of units of product p replaced in year j ; it depends on removals,

NC p (j) = number of units installed in new construction of product p in year j , and

NO p (j) = number of units shipped to “new owners” of product p in year j

.

Removals due to equipment breakage contains two terms. In the first, a survival function f

p (a) is used to represent the probability that a unit of age a will survive in a given year; equivalently the probability that this unit will fail is 1- f p (a) . The second term is the extended repair stock that has been in use for six years following the repair date. Total removals in the

base case are then:

Re m 

## ( j,a 

## )

= 

# [ 1

− f 

## ( a 

## ) 

# ]
× Stock 

## ( j,a 

## ) + ER

_ Stock 

## ( j,a 2 = 6 

## )

p p p p

Where:

a2 = number of years since the equipment was repaired, and
ER_Stock p (j, a2) = number of extended-repa ir units of product p replaced in year j.

The new construction and new owner markets depe nd on changes to the building stock, which is included as a component of the shipments model. This is discussed in the next section.

9-49.2 DATA INPUTS AND SUPPORTING CALCULATIONS

Data inputs and supporting calc ulations that are necessary for the shipments model are explained in detail in the following sections.

9.2.1 Historical Shipments and the Calculation of Replacement Shipments

9.2.1.1 Historical Shipments

DOE used historical shipments data (i.e., domestic shipments and imports) to populate its shipments model for CAC and HP equipment. As part of its data submittal to the DOE standards rulemaking, the Air-Conditioning, Heating, and Refrigeration Institute (AHRI) provided historical shipments data ove r the 1990-2007 time-period disaggregated into the four primary product classes. 2 Historical shipments from 1972- 1989 were also provided by AHRI from previous data submittals to the Lawrence Berkeley National Laboratory (LBNL). 3 The data for 1972-1989 included some shipments of 3-phase as well as single -phase CAC and HP, whereas

the data for 1990-2007 included onl y single-phase CAC. DOE di saggregated the shipments for single-phase and 3-phase CAC a nd HP equipment in the earlier data and included only single phase units in the analysis.

DOE further broke down split system CAC historical shipments into coil-only and blower coil unit shipments. Because DOE believes that a majority of split CAC shipments are coil-only units, it estimated that coil-only units account for 90 percent of annual split system shipments.

As discussed earlier in section 9.1.1, DOE divi ded all historical sh ipments for all product classes into two market segments, i.e., the residential and commercial building sectors. DOE allocated 93 percent of historical shipments to the residential building sector and the remaining seven percent to the commercial building sector.

Table 9.2.1 summarizes the historical shipme nts data. DOE developed a total stock estimate for each product class and market segment by integrating historical shipments. Over time, units are retired and removed from the stock based on a survival function as discussed in section 9.1.1, triggering the purchase of a new unit. Because of the relationship between retirements and total stock, there is a strong correlation between past and future shipments, independent of cons ervation standards.

9-5Table 9.2.1 Central Air Conditioner and Heat Pump Historical Shipments*

Year Split CAC**

thousands Single Package CAC

thousands Split HP

thousands Single Package HP

thousands

1972 1,489 257 28 52

1973 1,742 318 35 52

1974 1,503 289 49 48

1975 888 159 83 45

1976 1,219 231 177 74

1977 1,364 218 329 89

1978 1,546 253 389 97

1979 1,428 248 376 107

1980 1,233 134 293 61

1981 1,293 122 348 66

1982 1,037 111 267 49

1983 1,558 192 455 82

1984 1,857 189 535 98

1985 1,784 203 619 107

1986 1,876 203 680 106

1987 2,204 237 714 103

1988 2,334 247 649 91

1989 2,516 267 650 91

1990 2,241 334 643 104

1991 2,388 306 615 96

1992 2,254 330 647 101

1993 2,474 346 718 107

1994 3,087 387 825 118

1995 3,221 384 834 120

1996 3,635 418 936 139

1997 3,364 395 917 142

1998 4,010 406 1,019 148

1999 4,317 429 1,044 147

2000 4,280 437 1,073 159

2001 3,931 361 1,180 165

2002 4,381 380 1,229 165

2003 4,281 390 1,350 174

2004 4,561 414 1,584 190

2005 5,461 455 1,810 213

2006 4,038 330 1,807 190

2007 3,582 338 1,570 199

Source: AHRI * For each product class, 93 percent of annual shipments are allocated to the residential building sector and the

remaining seven percent are allocated to the commercial building sector. **Annual split CAC shipments are disaggregated into coil-only and blower coil unit shipments. Coil-only unit

shipments estimated to comprise 90 percent of annual split CAC shipments.

9-69.2.1.2 Calculation of Replacement Shipments

When an equipment unit is broken, it is removed from the stock. The following

retirement function r p (a) is used to represent the probability that a unit will be broken at age a

.

Re m p 

## ( j 

## )

= 

# ∑ r

a 

## (

a 

## ) × Stock

p 

## ( j,a 

## )

a

Retirement functions and produc t lifetimes are discussed in mo re detail in Chapter 8. The average lifetimes are 18.3 years for split and si ngle package CAC equipment and 15.5 years for split and single package heat pumps.

In each year, equipment are removed with demolished buildings. As represented by the following expression, the shipment s model assumes that the saturation of the equipment in the demolished buildings is the same of that of the overall population.

D 

## ( ) j = H

_ Stock 

## ( j

− 1 

## ) + H

_ Starts 

## ( j 

## )

− H

_ Stock 

## ( j 

## )

Dem 

## ( j 

## )

= D 

## ( j 

## ) × sat 

## ( p , j

− 1 

## )

Where:

H_Stock (j) = number of housing units in analysis year j, H_Starts(j) = number of new housing units in year j

, D(j) = number of demolished buildings, and Dem (j) = number of equipment units demolished in analysis year j. sat(p,j) = saturation of equipment of product class p for all buildings in year j

The shipments model assumes that units that are going away with demolished buildings, Dem(j ), are included in the mix of broken units Rem p (j). Since the demolished units do not need to be replaced, they are deducted from Rem p (j) when calculating the required replacements, as represented by the following expression.

Rpl

p 

## ( j 

## )

= Re m

p 

## ( j 

## )

− Dem 

## ( j 

## )

9.2.2 Shipments to New Construction

DOE multiplied new construction market saturations by forecasts of new construction to estimate CAC and HP shipments to the new constr uction channel. On a product class basis, the determination of shipments to new construction is represented by the following expression.

NC

p 

## ( j 

## )

= NC

_ Starts 

## ( j 

## ) × NC _ Sat

p 

## ( j 

## )

Where:

9-7NC_Starts (j) = number of new construction starts in year j, and NC_Sat(j) = new construction saturati on for product class p and year j

.

For the residential market segment, DOE de veloped new constructi on market saturations

from Characteristics of New Housing data from the U.S. Census Bureau. 4 CAC and HP historical and projected saturations are depicted in Figure 9.2.1. DOE used historical new construction saturations for all CAC and HP equipment to project saturations into the future. As evidenced in Figure 9.2.1., DOE used a logistic fit to the historical data to estimate future saturations. DOE used product class market shar es based on historical shipments (as shown in Table 9.2.1) to calculate speci fic market saturations fore e ach CAC and HP product class.

Saturation = 0.10Ln(Year-1977) + 0.52

R2 = 0.94

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 10 20 30 40 50 60 70

Year - 1977Residential New Consturciton Saturaitons

CAC + HP

HP

CAC

Figure 9.2.1 CAC and HP Saturations in Residential New Construction

DOE determined new construction starts to the residential market segment, otherwisereferred to as housing starts, by using recorded data through 2007 and projections from the DOEEnergy Information Administration (EIA)’s Annual Energy Outlook 2009 ( AEO2009 ) for the period 2008–2030. 5 New housing includes newly construc ted single- and multi-family units, termed “new housing completions,” and mobile home placements. AEO2009 provides three sets of economic growth scenarios: a Reference case, a High Economic Growth case, and a Low Economic Growth case. The original AEO2009 analysis published by the EIA in March 2009 was modified in April 2009 to incorporate th e effects of the American Recovery and

Reinvestment Act. 1 DOE used the high and low growth scenarios from the March 2009 release

9-8of the AEO2009 6 , and the Reference case from the m odified April release. For 2031

− 2046, DOE froze completions at the 2030 level for all three economic scenarios.

For the commercial market segment, DOE developed new construction market saturations from data in EIA’s Commercial Building Energy Consumption Survey (CBECS). 7
Past CBECS surveys for the years 1992, 1995, 1999, and 2003 provided the saturation of residential-type central air conditioners in all ne w commercial buildings by Census Division. To determine nationally representative saturation values, DOE calculated a weighted-average saturation based on historical construction commercial starts by Census Division (in millions of square feet) from AEO2009 . DOE considered residential-type central air conditioners to comprise all split system and single package CAC and HP equipment. DOE used product class market shares based on historical shipments to calculate specific market saturations for each CAC and HP product class. Figure 9.2.2 depi cts the new construction saturations for the commercial market segment. Actual data from CBECS are shown as “black diamonds” in Figure 9.2.2. Saturations prior to 1992 were fixed by DOE to the 1992 value. DOE used linear interpolation to develop the saturations for non-CBECS years between 1992 and 2003. DOE averaged the saturations for the four CBECS years to forecast saturation values after the year

2003.

0% 1% 2% 3% 4% 5% 6%

1990 1995 2000 2005 2010 2015 2020 2025 2030

YearSaturation (percent sq.ft. cooledby CAC and HP)

Sources: DOE-EIA, CBECS; AEO2009

Figure 9.2.2 CAC and HP Saturation in Commercial New Construction

As with the residential market segment, DOE determined new construction starts to the commercial building market segment by using re corded data through 2007 and projections from

EIA’s AEO2009 for the period 2008–2030. For 2031

− 2046, DOE froze completions at the 2030

9-9level for all three economic scenarios. DOE used the same data sources that were used for the

residential market to estimate new construction starts fo r a Reference case, a High Economic Growth case, and a Low Economic Growth case.

9.2.3 Model Calibration and New Owners

The shipments model begins with an estimat e of the building stock and equipment stock in the base year, and steps forward adding sh ipments each year and removing retirements. In principle, only building and market saturation data are needed to allow the shipments model to estimate shipments to new construction and re placements. The third product channel, new owners, is more difficult to desc ribe based on existing data. Howe ver, the historical shipments data (as described above in sec tion 9.2.1.1) can be used to ca librate the shipments model and improve the representation of the new owner market.

To calibrate estimated shipments with the historical data, as represented by the following expression, DOE treated the new owner market NO p (j) as a residual after shipments for new construction and replaceme nts were subtracted from total shipments.

NO

p 

## ( j 

## )

= Ship

p 

## ( j 

## )

− Rpl

p 

## ( j 

## )

− NC

p 

## ( j 

## )

The three terms on the left are known quantities (calculation of Rpl p (j) and NC p (j) are described above).

The results of the historical calibration ar e shown in Table 9.2.2. The values are

historical market shares of total CAC and HP equipment in the new owner channel. The

residential market share is th e total CAC and HP shipments to the new owner housing market divided by the number of households without e ither CACs or HPs. The commercial market share is the number of total CAC and HP shipments to the new owner commercial building market divided by the commercial building stoc k not conditioned by CAC and HP equipment. DOE determined the commercial building stock not conditioned by CAC and HP equipment by dividing the total stock in square feet by 500 square feet (i.e., the amount of square feet conditioned by one piece of CA C and HP equipment). DOE used product class market shares based on historical shipments (as shown in Table 9.2.1) to calcu late specific new owner market

saturations fore each CA C and HP product class.

9-10Table 9.2.2 Historical New Owner Market Shares of CAC and HP Equipment

Year Residential Commercial

1990 2.05% 0.10%

1991 1.78% 0.09%

1992 1.35% 0.08%

1993 1.59% 0.07%

1994 2.25% 0.07%

1995 2.43% 0.06%

1996 3.22% 0.11%

1997 2.53% 0.10%

1998 3.34% 0.15%

1999 3.82% 0.19%

2000 4.04% 0.16%

2001 3.31% 0.12%

2002 4.10% 0.12%

2003 3.93% 0.09%

2004 4.81% 0.14%

2005 7.75% 0.15%

2006 3.51% 0.07%

2007 1.94% 0.02%

To forecast future shipments to the new owner market, DOE calculated the average market share over the 1990 to 2007 time-period for the residential and commercial markets, 3.21% and 0.10%, respectively, and held the market share fixed from 2008-2046.

9.3 DECISION MODEL FOR NEW OWNERS AND REPLACEMENTS

In the shipments model, two sets of cons umer purchase decisions are modeled. These decisions are influenced by the purchase price and operating cost of equipment, and therefore will be different in the base case vs. different CSLs. These decisions are modeled by estimating the purchase price elasticity for cooling equipment. The purchase price elasticity is defined as the change in the percentage of consumers acquiring CAC and HP equipment divided by a change in

the relative price (defined below) for that equipment. This elasticity and information obtained from the life-cycle cost (LCC) and payback period (PBP) analysis on the change in purchase price and operating costs under different CSLs ar e used in the shipments model to estimate the resulting change in equipment shipped.

9.3.1 Purchase Price Elasticity

DOE conducted a literature review and an analysis of appliance price and efficiency data to estimate the combined effects on product ship ments from increases in product purchase price, decreases in product operating costs, and chan ges to household income . Appendix 9-A provides

9-11a detailed explanation of the methodology DOE used to quantify the impacts from these

variables.

Existing studies of appliance markets sugge st that the demand for appliances is priceinelastic. Other information in the literature suggests that appliances are a normal good, so that rising incomes increase the demand for applian ces, and that consumers use relatively high implicit discount rates a when comparing appliance prices and appliance operating costs.

DOE used the available data for the pe riod 1980-2002 on large appliance purchases to

evaluate broad market trends and conduct simple regression anal yses. 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 inco me has also risen during this time. Because purchase decisions are sensitive to income, and also to potential savings in the operating cost of the appliance, DOE combined the available economic information 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:

TP PP + PVOC RP = = I

ncome I ncome

Where:

RP = relative price,

TP = total price,

Income = household income,

PP = appliance purchase price, and

PVOC = present value of operating cost, ba sed on an implicit discount rate of 37

percent.

DOE’s regression analysis of the relevant data suggests that the relative price elasticity of demand is approximately -0.34; this implies that a relative price increase of 10 percent results in a 3 to 4 percent decrease in shipments. Note that the relative price elasticity incorporates the impacts from purchase price, operating cost, and household income, so the impact from any single effect can be mitigated by changes from the other two effects. The relative price elasticity of -0.34 is consistent with estimates in the literature, but is based on a small data set. More importantly, the measure is based on an assumpti on that economic variables, including purchase price, operating costs, and household income, ex plain most of the trend in appliances per

household in the United States since 1980. Ch anges in appliance quality and consumer preferences may have occurred during this peri od, but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price elasticity

a 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.

9-12of demand provides a reasonable a ssessment of the impact that purchase price, operating cost,

and household income have on product shipments.

The forecasts of shipments and national imp acts due to standards are for a 30-year time period, and it is possible that over this time pe riod the numerical value of the relative price elasticity could change; in particular, it can be affected by a new standard that changes consumer expectations about equipment performance. DOE considered the relative price elasticity provided by the preceding analysis to be a s hort-run value. No data exists to allow the determination of long run price elasticities for appliances, so DOE relied on a study pertaining to

automobiles. 8 This study shows that the automobile price elasticity of demand changes in the years following a purchase price change, beco ming smaller (more inelastic) until it reaches a terminal value around the tenth year after the pr ice change. Table 9.3.1 shows the relative change in the price elasticity of demand for automobiles over time. DOE developed a time series of relative price elasticities for CAC and CAC-HP based on the relative change in the automobile price elasticity of demand. For years not s hown in Table 9.3.1, DOE performed a linear interpolation to obtain the relative price elasticity.

Table 9.3.1 Change in Relative Pric e 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

9.3.2 Impact from Increase in Relative Price

Using the relative price elasticity, DOE was able to estimate the impact of the increase in relative price from a particular CSL. The impact, as shown in the equation be low, is expressed as a percentage drop in market share for each year, dMS p j , which is applied in the new owner market channel as well as the decision for replacement versus extended repair. As noted in section 9.1.1, for the new construction market channel, the placement of equipment is automatic,

in line with observed trends in the data. In ot her words, DOE assumed that the new construction market channel was unaffected by changes in either the total installed cost or operating costs of the equipment as well as changes in household income.

p ⎡ ⎛ ⎜ RP _ std

p 

## ( j 

## ) ⎞ ⎟ ⎤

dMS j = ⎢ 1

− ⎜ ⎟ ⎥ × e RP 

## ( ) j ⎢ RP _base

p 

## ( ) j ⎥ ⎣ ⎝ ⎠ ⎦

Where:

dMS p j = percentage market share drop for class p, year j

, RP_std p (j)= relative price in the standards case for product class p, year j

, RP p (j)= relative price in the base case for product class p, year j , and

9-13e RP (j) = relative price elasticity in year j

.

9.3.2.1 New Owner Shipment Impacts and Equipment Switching

DOE estimated standards case shipments to the new owner product channel by incorporating the impact of the relative price into the standards case shipments forecast. The relative price and the relative price elasticity are both a function of time.

NO _ std 

## ( j 

## )

= NO 

## ( j 

## ) × 

# ( 1

− dMS p 

# )

p p j

Where:

NO_std p (j)= shipments to new owners in th e standards case for product class p, year j

, and

NO p (j)= shipments to new owners in th e base case for product class p, year j

.

The new owner market is equal to the total building stock minus the building stock with equipment; if each building is assumed to have just one CAC or HP unit then the number of building units with CAC and HP equipment is equal to the CAC and HP equipment stock.

DOE assumed that the new owners impacted by the relative price increase and do not purchase the equipment would switch to purchas e an alternative type of space-conditioning equipment. The number of new ow ners impacted and switching to an alternative equipment type is represented by the following expression.

p NO _ sw p 

## ( j 

## )

= NO

p 

## ( j 

## ) × dMS j

Where:

NO_sw p (j)= new owners for product class p switching to buy other products

, year j.

For new owner purchases of split system and single package CACs, DOE assumed that consumers affected by a relative price increase would switch equipment and purchase a room air conditioner rather than a CAC. Because room air conditioners have lower cooling capacities and lower overall annual energy consumption than CA Cs, DOE did not explicitly account for the

increased sales of room air conditioners in its an alysis. Doing so would have credited a standard with energy savings from displaced CAC shipments. To avoid including savings from displaced shipments to new owners, DOE used the shipment s projection estimates for the standards cases to calculate the annual energy consumption in the base case.

For new owner purchases of split system and single package HPs, DOE assumed that consumers affected by a relative price increase would switch equipment and purchase a combination electric resistance furnace and CAC rather than a HP. In its analysis of HP shipments in the NIA (discussed in chapter 10), DOE tracked purchases of electric furnaces and

9-14CACs due to decreases in shipme nts of HPs in order to quantify the energy, operating cost, and total installed cost impacts for consumers who were estimated to forgo a purchase of a new HP. Because DOE explicitly accounted for the energy c onsumption, operating cost , and total installed cost of the displaced shipments of HPs, it maintained use of the base case shipments to determine the annual energy consumption in the base case.

9.3.2.2 Replacement Shipment Impacts

For replacement purchases of split system and single package CACs and HPs, DOE assumed consumers affected by an increase in to tal installed cost would repair their equipment rather than replace it, extending the life of the product by six years. When the extended repaired units fail after six more years, they will be replaced with new ones.

The model calculates for each year after the standard, the relative percentage market drop, dMS p j , due to the equipment price increase as desc ribed in 9.3.1.1. The extended repair is only applicable to broken equipment that are pu rchased before 2016. For broken equipment that are purchased after 2016, extended repair will not be considered since there will be no longer a price impact from standards between new units and the broken units.

The number of broken units that will be furt her repaired instead of being replaced is

calculated as follows:

p XR i = 

# ∑ Re m 

## ( j,a 

## ) × dMS
i for ( j

− a ) < 2016

a Rpl 

## ( ) j = 

# ∑ Re m( j,a )

− XR i + XR j

− 6 − Dem 

## ( j 

## )

a Where:

dMS p j = percentage market share drop for class p, year j

,

a = age of an equipment j = year, Rem(j,a) = retiring units in year j of age a , XR j = extended repair units, year j

, Rpl(j) = replacement units in year j

, Dem (j) = number of units gone with demolished buildings in analysis year j.

9.4 REGIONAL SHIPMENTS MODEL

As described in Chapter 6, Energy Use De termination, some of the CSLs involve the division of the U.S. into three climate regions : hot-humid, hot-dry, and rest-of-country. For the shipments model, DOE constructed two variants of the national analysis. In the first, the nation as a whole is treated as a single region. In the second, the U.S. is disaggregated into the three climate regions, a separate shipments model is r un for each region, and the results are summed to

9-15provide national-level out put under a regional standard scenar io. In the regional standards case, the mechanics of the shipments model are identical to the one-region case already described. The only changes needed to analyze a specific region ar e definitions of the various data inputs for the region in question. This section describes how these data inputs were disaggregated.

Not every data input can be or needs to be defined at a regional level. DOE assumed the product lifetimes, purchase prices, and relative price elasticities did not vary by region. Product operating costs do vary by region and this in formation is available from the LCC and PBP analysis (see Chapter 8). For the shipments m odel, historical shipments, housing starts and housing stock, commercial buildings starts and stoc k (in square feet), and market saturations for

new construction must be developed for each region. The following sections describe the development of regional histori cal shipments, housing and commerc ial building starts and stock,

and market saturations.

9.4.1 Allocation of Historical Shipments to Regions

DOE developed regional allocation factors to di vide historical shipments into each of the three climatic regions. The regional allocation factors were determined by trial and error by calibrating to historical residential stock saturations in the American Housing Survey (AHS). 9
Tables 9.4.1 and 9.4.2 provide th e historical regional and nationa l stock saturations for CAC and HP equipment for every other year between 1991

− 2007 as specified in the AHS. The stock saturations are specific to each region or the nati on, i.e., the individual regional stock saturations do not add up to the national stock saturation.

Table 9.4.1 CAC Residential Regional Stock Saturations

Year Hot-Dry Hot-Humid Rest of U.S. Entire U.S.

1991 32.5% 48.6% 23.8% 30.8%

1993 33.5% 50.0% 25.0% 32.0%

1995 36.6% 50.7% 27.3% 33.9%

1997 42.4% 47.3% 29.2% 36.3%

1999 48.0% 54.0% 33.3% 41.4%

2001 51.5% 58.1% 36.6% 45.0%

2003 53.8% 58.1% 38.5% 46.5%

2005 55.9% 61.5% 43.0% 50.2%

2007 63.5% 62.8% 42.8% 51.8%

Source: AHS

9-16Table 9.4.2 HP Residential Regional Stock Saturations

Year Hot-Dry Hot-Humid Rest of U.S. Entire U.S.

1991 6.3% 18.1% 3.7% 7.8%

1993 7.5% 19.7% 3.9% 8.5%

1995 7.1% 21.6% 4.1% 9.1%

1997 9.9% 28.0% 4.4% 12.0%

1999 8.3% 26.8% 4.2% 11.4%

2001 8.0% 26.2% 4.4% 11.3%

2003 7.8% 27.4% 4.4% 11.7%

2005 8.8% 29.0% 5.4% 12.7%

2007 8.1% 28.8% 5.3% 12.8%

Source: AHS

Tables 9.4.3 and 9.4.4 provide the ship ment allocation factors for CAC and HP, respectively. For each of the years presented in the tables below, DOE used the allocation factors to assign the fraction of shipments for each region until the market saturations calculated by the shipments model were nearly equal to th e regional stock saturations reported by AHS.

Table 9.4.3 CAC Shipment Regional Allocation Factors

Year Hot-Dry Hot-Humid Rest U.S.

1983 15.0% 50.0% 35.0%

1987 15.0% 45.0% 40.0%

1990 17.0% 38.0% 45.0%

1993 17.0% 35.0% 48.0%

1997 17.0% 30.0% 53.0%

2001 17.0% 30.0% 53.0%

2003 17.0% 30.0% 53.0%

2005 17.0% 32.0% 51.0%

2007 17.0% 32.0% 51.0%

Table 9.4.4 HP Shipment Regional Allocation Factors

Year Hot-Dry Hot-Humid Rest U.S.

1983 14.0% 75.0% 11.0%

1987 14.0% 72.0% 14.0%

1990 14.0% 70.0% 16.0%

1993 12.0% 70.0% 18.0%

1997 9.0% 70.0% 21.0%

2001 9.0% 60.0% 31.0%

2003 9.0% 50.0% 41.0%

2005 9.0% 50.0% 41.0%

2007 9.0% 50.0% 41.0%

9-17Figure 9.4.1 provides the resulting comparison between the CAC and HP stock saturations by region and the entire U.S. from the AHS and those calculated from the shipments model. As shown in the figure below, the calculated values are relatively close to the values from the AHS. After establishing the appropriate CAC and HP shipment allocation factors, DOE used product class market shares based on histor ical shipments (as shown in Table 9.2.1) to calculate specific allocati ons factors for each CAC and HP product class.

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% CAC & HP Saturation Hot-Dry CAC & HP Saturation Hot-Humid

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008Saturation CAC calculated HP calculated

CAC AHS HP AHS

CAC & HP Saturation Entire U.S.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008Saturation CAC calculated HP calculated

CAC AHS HP AHS CAC calculated

CAC AHS HP calculated

HP AHSSaturation

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

CAC & HP Saturation Rest of U.S.
100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 CAC calculated HP calculated

CAC AHS HP AHSSaturation

Figure 9.4.1 Residential CAC and HP Stock Saturations by Region and the Entire U.S.: American Housing Survey (AHS) vs. Shipments Model (Calculated)

9-18It is worth noting that, once historical shipments are allocated to each of the three climatic regions, DOE calculated replacement shipments for each region based on when the equipment is forecasted to break. Refer back to section 9.2.1.2 for a description of the method for determining replacement shipments.

9.4.2 Regional New Construction and New Construction Saturations

9.4.2.1 Regional New Construction

AEO2009 provides housing forecasts by Census Di vision. These were converted to the three climate regions by using population data to allocate the housing stock. Each state can be

allocated to both a Census Division and a climat e region; hence, a set of overlap regions can defined at the state level. For example, Florida is in the hot-humid climate region and the South Atlantic census division; therefore, the intersec tion of hot-humid with S outh Atlantic contains the state of Florida. (The list of states included in each climate region is defined in Chapter 7, section 7.11). The U.S. Census provides historical population, and popul ation projections, by state at five-year intervals for the years 2005-2030. 10 These data were used to define annual populations for each of the intersection regions over the same period. First, the Census data were interpolated onto one-year intervals by assuming a constant rate of growth within the given fiveyear period. Next, the one-year data were aggreg ated to provide regional populations. Projections out to 2046 were constructed by a ssuming that the rate of growth in each region would remain constant for the years after 2030. The constant in each region was defined as the average of the

U.S. Census growth rates for 2025 and 2030. Samp le output from this calculation is represented in Table 9.4.5, which shows the population in each area defined by the overlap of a climate region with a census division. For example, bot h the hot-dry and the rest-of-country regions overlap with the Mountain and P acific census divisions, while the West North Central census division is entirely in the hot-humid region.

Table 9.4.5 Population Projected for 2020 for Areas defined by the Overlap of Census Divisions with Climate Regions

Region

Census Division Hot-Dry Hot-Humid Rest-of-Country Total

Northeast

- - 15,309,528 15,309,528

Mid-Atlantic

- - 41,825,909 41,825,909

East North Central

- - 48,208,733 48,208,733

West North Central

- - 21,246,442 21,246,442

South Atlantic

- 49,782,144 18,659,882 68,442,026

East South Central

- 14,554,397 4,424,431 18,978,828

West South Central

- 40,149,965

- 40,149,965

Mountain 13,993,072

- 11,563,977 25,557,049

Pacific 42,206,743

- 13,879,323 56,086,066
Total by Region 56,199,815 104,486,506 175,118,225 335,804,546

9-19Using these numbers for each census division, the fraction of its population that belongs to a given climate region can be determined. This is used to allocate housing stock to climate regions.

In a similar manner, the year-on-year change in population for each Census Division can be disaggregated onto the climate regions that overlap that Cens us Division. The disaggregation of population growth is used to allocate housing starts to climate regions.

The same coefficients are used to allocate commercial building floor space and commercial building starts to the three climate regions.

9.4.2.2 Regional New Constr uction Saturations

DOE developed residential regiona l new construction saturations from Characteristics of New Housing data from the U.S. Census Bureau. 3 The data breaks down CAC and HP saturations for single-family and multi-family ho useholds into the four Census Regions (i.e., northeast, midwest, south, and west). DOE us ed population allocation factors (as discussedpreviously) to properly allocate saturations into the three climate regions (i.e., hot-humid, hotdry, and rest-of-country). Figures 9.4.2 and 9.4. 2 depict the national and regional saturations for CACs and HPs, respectively. The regional saturations depicted in the figures below were determined using the entire U.S. housing stock. In other words, the regional saturations when summed equal the national saturation.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

YearCAC Residential New Construction Saturations Entire U.S. Rest of Country

Hot-Humid Hot-Dry

Figure 9.4.2 CAC National and Regional Saturations in Residential New Construction

9-200% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

YearHP Residential New Construction Saturations Entire U.S. Rest of Country

Hot-Humid Hot-Dry

Figure 9.4.3 HP National and Regional Saturation s in Residential New Construction

For the commercial market segment, DOE developed new construction market saturations from data in EIA’s Commercial Building Energy Consumption Survey (CBECS). 6
As noted above in section 9.2.2, DOE developed new construction saturations through the use of past CBECS surveys and commercial starts from AEO2009. The assembled data breaks down

CAC and HP saturations into nine Census Divisions. DOE used population allocation factors (as discussed previously) to properly allocate saturations into the three climate regions (i.e., hothumid, hot-dry, and rest-of-country). Figures 9.4. 4 depicts the national an d regional saturations for CAC and HP equipment. The regional satura tions depicted in the Figure 9.4.4 below were determined using the entire commercial building stock. In other words, the regional saturations when summed equal the national saturation.

9-210% 1% 2% 3% 4% 5% 6%

1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035

YearCAC and HP Commercial New Construction Saturations Entire U.S.

Hot-Humid

Rest of Country

Hot-Dry

Figure 9.4.4 CAC and HP National and Regional Saturations in Commercial New

Construction

9.5 RESULTS

This section presents the shipments forecasts for the base case and for various candidate standard levels (CSL) that DOE considered fo r each of the four CAC and HP product classes.

9.5.1 Split CAC

As noted in section 9.2.1.1, split CAC shipments are divided into coil-only and blower coil units. Figures 9.5.1 and 9.5.2 show the ba se case shipments forecasts for split system CAC coil-only and blower coil units, respectively. Shipments are broken down into each of the three regions and designated by market segment ( i.e. , new construction, replacements, and new owners). Shipments to the commercial building sector are estimated to represent only about seven percent of all prod uct shipments. Therefore, the figu res do not differentiate among units going to the residential and commercial sectors.

9-220 1 2 3 4 5 6

1 9 9 0

1 9 9 4 1998
2 0 0 2
2 0 0 6
2 0 1 0 2014
2 0 1 8
2 0 2 2 2026
2 0 3 0
2 0 3 4 2038 2042
2 0 4 6

YearShipments (million) Hot-Dry: New Construction Hot-Dry: New Ow ner Hot-Dry: Replace

Hot-Humid: New Construction Hot-Humid: New Ow ner Hot-Humid: Replace

Rest of Country: New Construction Rest of Country: New Ow ner Rest of Country: Replace

Rest-of-Country

Hot-Humid

Hot-Dry

Figure 9.5.1 Forecasted Shipments (Base Case) for Split-System CACs, Coil-Only

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

1 9 9 0

1 9 9 4 1998
2 0 0 2
2 0 0 6
2 0 1 0 2014
2 0 1 8
2 0 2 2 2026
2 0 3 0
2 0 3 4 2038 2042
2 0 4 6

YearShipments (million) Hot-Dry: New Construction Hot-Dry: New Ow ner Hot-Dry: Replace

Hot-Humid: New Construction Hot-Humid: New Ow ner Hot-Humid: Replace

Rest of Country: New Construction Rest of Country: New Ow ner Rest of Country: Replace

Rest-of-Country

Hot-Humid

Hot-Dry

Figure 9.5.2 Forecasted Shipments (Base Case) for Split-System CACs, Blower Coil

9-23To demonstrate the impact of higher effi ciency standards on base case shipments, the following figures focus on coil-only units, as they represent approximately 90 percent of total split CAC shipments. Figures 9.5.3 through 9.5.6 show the split CAC coil-only shipments

forecasts for the base case and the standards case corresponding to the highest efficiency CSL (i.e., CSL 13 – 22 SEER) for the replacement, new owner, and new construction marketchannels, respectively. In each figure, shipments are disaggregated by climate region (HH = hothumid; HD = hot-dry; ROC = rest-of-country). Th e solid blue line represents the entire U.S. shipments forecast (i.e., the sum of the three climate regions). b

In Figure 9.5.3, representing replacement shipments, a drop in shipments occurs in the year that standards are effective (i.e., 2016) due to the consumers affected by a relative price increase (i.e., the change in total installed cost and operating costs rela tive to household income caused by the CSL). As discussed in section 9.3. 2.2, affected consumers repair their equipment rather than replace it, extending the life of the product by six years. When the extended repaired units fail after six more years, they are repla ced with new ones. The replacement of extended repairs are shown in Figure 9.5. 1 as additional shipments over the base case, i.e., six years after

the effective date of the standard to account fo r CAC purchases delayed by extended repairs. For example, note the increase in shipments in the year 2022 over the base case which is six years after the effective date of the standard. Because the relative price elasticity decreases over time (as summarized in Table 9.3.1), decreases and incr eases in shipments due to extended repairs are not significant after the year 2022.

b Because DOE determined shipments for the entire U.S. independently from the shipments that were determined for each climate region, the regional shipments do not add up precisely to the national value. Because inputs to the NIA, such as product total installed cost, product annual energy consumption, and product repair and maintenance costs, have regional dependencies in addition to being a function of product efficiency, the regional level impacts when summed will differ from the impacts that are determined at the national level. For split CACs, the sum of the regional shipments for any year over the forecast period was less than 0.4 percent of the shipments determined for the nation as a whole. For the three other primary product classes, the percentage differences between the sum of the regional shipments and the shipments determined for the nation as a whole were the following: 0.4 percent for single package CAC, 1.3 percent for split HP, and 1.3 percent for single package HP.

9-240.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044millions HH

HD

ROC

HH (CSL13)

HD (CSL13)

ROC (CSL13)

U.S.

Figure 9.5.3 Split Coil-Only CAC: Base Case and CSL 13 (22 SEER) Replacement Shipments Forecasts

9-25InFigure 9.5.4, representing new owner sh ipments, a drop in shipments occurs in the year that standards are effective (i.e., 2016) due to the change in total installed cost and operating costs caused by the CSL. As discussed in secti on 9.3.2.1, consumers affe cted by a relative price

increase switch equipment and purchase a room air conditioner rather than a CAC. As shown in Figure 9.5.1, the drop in shipments to this market channel is slight.

0.00 0.50 1.00 1.50 2.00 2.50

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044millions HH

HD

ROC

HH (CSL13)

HD (CSL13)

ROC (CSL13)

U.S.

Figure 9.5.4 Split Coil-Only CAC: Base Case and CSL 13 (22 SEER) New Owner Shipments Forecasts

9-26Figure 9.5.5 shows the shipment forecasts fo r the base case. As noted previously in section 9.1.1, the standards case shipments are identical to the base case as shipments to new constructions are assumed to be unaffected by increase in the relative price.

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044millions HH

HD

ROC

U.S.

Figure 9.5.5 Split Coil-Only CAC: Base Case New Construction Shipments Forecasts

9-27Figure 9.5.6 provides the total base case a nd CSL shipments, which are a combination of the shipments depicted from the previous three figures. Most of the decrease in shipments is due to extended repairs.

0.00 1.00 2.00 3.00 4.00 5.00 6.00

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044millions

HH

HD

ROC HH (CSL13) HD (CSL13) ROC (CSL13) U.S. (CSL13)

U.S.

Figure 9.5.6 Split Coil-Only CAC: Base Case and CSL 13 (22 SEER) Total Shipments Forecasts

Table 9.5.1 summarizes the cumulative ship ments over the 30 year period starting from the assumed effective date of the standard (i.e., 2016) for the base case and each of the thirteen CSLs that DOE considered for split CACs. Cumulative shipments are shown for coil-only units, blower coil units, and total CAC shipments. Sh ipments are also disaggregated by region and sector. Also of note, the efficiency levels for CSLs 8 through 13 for coil-only units are equivalent to CSL 7 (16.5 SEER).

9-28Table 9.5.1Split CAC: Base Case and Standards Case Cumulative Shipments from 2016-2046 (millions)Base CaseCSL 1 (13.5 SEER)CSL 2 (14 SEER)CSL 3 (14.5 SEER)CSL 4 (15 SEER)CSL 5 (15.5 SEER)CSL 6 (16 SEER)RegionSectorCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalHotDryRes25.312.8128.1225.302.8128.1225.302.8128.1125.282.8128.1025.272.8128.0825.252.8128.0625.232.8128.04Com2.680.302.982.680.302.982.680.302.982.670.302.972.670.302.972.670.302.962.660.302.96HotHumidRes41.724.6546.3741.714.6546.3741.704.6546.3541.694.6546.3441.674.6546.3241.644.6546.2941.614.6546.26Com4.580.515.094.580.515.094.570.515.094.570.515.084.560.515.084.560.515.074.550.515.06Rest ofU.S.Res71.917.9979.9071.907.9979.8971.857.9979.8471.797.9979.7871.727.9879.7071.637.9879.6171.527.9879.50Com7.330.818.157.330.818.157.330.818.147.320.818.137.310.818.137.300.818.127.290.818.10EntireU.S.Res139.7115.52155.24139.7015.52155.22139.6315.52155.15139.5515.52155.07139.4515.51154.96139.3215.51154.83139.1715.51154.68Com14.321.5915.9114.311.5915.9114.301.5915.8914.291.5915.8814.271.5915.8614.251.5915.8414.231.5915.82CSL 7 (16.5 SEER)CSL 8 (17 SEER)*CSL 9 (18 SEER)*CSL 10 (19SEER)*CSL 11 (20 SEER)*CSL 12(21 SEER)*CSL 13 (22 SEER)*RegionSectorCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalCoil-OnlyBlowerCoilTotalHotDryRes25.212.8128.0225.212.8128.0225.212.8128.0225.212.8128.0225.212.8028.0225.212.8028.0125.212.8028.01Com2.660.302.952.660.302.952.660.302.952.660.302.952.660.302.952.660.292.952.660.292.95HotHumidRes41.594.6546.2441.594.6546.2441.594.6546.2341.594.6446.2341.594.6446.2341.594.6446.2341.594.6346.22Com4.540.515.054.540.515.054.540.515.054.540.515.054.540.515.054.540.515.054.540.515.05Rest ofU.S.Res71.457.9879.4271.457.9779.4271.457.9679.4171.457.9679.4071.457.9579.3971.457.9479.3871.457.9379.38Com7.280.818.107.280.818.107.280.818.097.280.818.097.280.818.097.280.818.097.280.818.09EntireU.S.Res139.0615.50154.56139.0615.50154.55139.0615.49154.54139.0615.48154.53139.0615.46154.52139.0615.45154.50139.0615.43154.49Com14.211.5915.8014.211.5915.8014.211.5815.7914.211.5815.7914.211.5815.7914.211.5815.7914.211.5715.78*CSL 7 is the maximum efficiency level for Blower Coil units. CSLs 8 -13 for Blower Coil units is equivalent to CSL 7.9-299.5.2 Single Package CAC

Figure 9.5.7 shows the base case shipments forecasts for single package CACs. Shipments are broken down into each of the three regions and designated by market segment ( i.e. , new construction, replacements, and new owners). Shipments to the commercial building sector are estimated to represent only about seve n percent of all product sh ipments. Therefore, the figures do not differentiate among units going to the residential and commercial sectors.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

1990 1994 199 8 2002 2006 2010 2014 201 8 2022 2026 2030 2034
2 038 2042 2046

YearShipments (million) Hot-Dry: New Construction Hot-Dry: New Ow ner Hot-Dry: Replace

Hot-Humid: New Construction Hot-Humid: New Ow ner Hot-Humid: Replace
Rest of Country: New Construction Rest of Country: New Ow ner Rest of Country: Replace

Rest-of-Country

Hot-Humid

Hot-Dry

Figure 9.5.7 Forecasted Shipments (Base Case) for Single Package CACs

9-30Figure 9.5.8 shows total single package CA C shipments forecasts for the base case and the standards case corresponding to the highes t efficiency CSL (i.e., CSL 7 – 16.4 SEER). Because the impact of standards on single packag e CAC shipments is similar to that exhibited for split CAC, figures disaggregating total shipments into the replacement, new owner, and new

construction market channels are not provided. In the figure below, shipments are disaggregated by climate region (HH = hot-humid; HD = hot-dry; ROC = rest-of-country). The solid blue line represents the entire U.S. shipments forecast (i.e., the sum of the three climate regions).

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044millions

HH

HD

ROC
HH (CSL7)
HD (CSL7)
ROC (CSL7)

U.S. (CSL7)

U.S.

Figure 9.5.8 Single Package CAC: Base Case and CSL 7 (16.6 SEER) Total Shipments Forecasts

Table 9.5.2 summarizes the cumulative ship ments over the 30 year period starting from the assumed effective date of the standard (i.e ., 2016) for the base case and each of the seven CSLs that DOE considered for single package CACs. Shipments are disaggregated by region

and sector.

9-31Table 9.5.2 Single Package CAC: Base Case and Standards Case Cumulative Shipments from 2016-2046 (millions)

Region Sector Base

Case CSL 1 (13.5 SEER) CSL 2 (14 SEER) CSL 3 (14.5 SEER) CSL 4 (15 SEER) CSL 5 (15.5 SEER) CSL 6 (16 SEER) CSL 7 (16.6 SEER)

Hot-Dry Res 2.77 2.77 2.76 2.76 2.76 2.76 2.76 2.75

Com 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.28

Hot-Humid Res 4.67 4.66 4.66 4.66 4.66 4.65 4.65 4.65

Com 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50

Rest of U.S. Res 7.85 7.84 7.83 7.82 7.81 7.80 7.79 7.78

Com 0.79 0.79 0.79 0.79 0.78 0.78 0.78 0.78

Entire U.S. Res 15.35 15.34 15.33 15.31 15.30 15.29 15.27 15.26

Com 1.55 1.54 1.54 1.54 1.54 1.54 1.53 1.53

9.5.3 Split HP

Figure 9.5.9 shows the base case shipments forecasts for split system HPs. Shipments are broken down into each of the three regions and designated by market segment ( i.e. , new construction, replacements, and new owners). Shipments to the commercial building sector are estimated to represent only about seven percent of all product shipments. Therefore, the figures do not differentiate among units going to the residential and commercial sectors.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

1990 1994 199 8 2002 2006 2010 2014 201 8 2022 2026 2030 2034
2 038 2042 2046

YearShipments (million) Hot-Dry: New Construction Hot-Dry: New Ow ner Hot-Dry: Replace

Hot-Humid: New Construction Hot-Humid: New Ow ner Hot-Humid: Replace
Rest of Country: New Construction Rest of Country: New Ow ner Rest of Country: Replace

Rest-of-Country

Hot-Humid

Hot-Dry

Figure 9.5.9 Forecasted Shipments (Base Case) for Split System HPs

9-32Figure 9.5.10 shows total split HP shipments forecasts for the base case and the standards case corresponding to the highes t efficiency CSL (i.e., CSL 10 – 19.5 SEER / 10.3 HSPF). Because the impact of standards on split HP shipments is similar to that exhibited for split CAC, figures disaggregating total shipments into the replacement, new owner, and new construction market channels are not provided. In the figure below, shipments are disaggregated by climate region (HH = hot-humid; HD = hot-dry; ROC = rest-o f-country). The solid blue line represents the entire U.S. shipments forecast (i.e., the sum of the three climate regions).

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044millions

HH

HD

ROC
HH (CSL10)
HD (CSL10)
ROC (CSL10) U.S. (CSL10)

U.S.

Figure 9.5.10 Split HP: Base Case and CSL 10 (19.5 SEER / 10.3 HSPF) Total Shipments Forecasts

Table 9.5.3 summarizes the cumulative ship ments over the 30 year period starting from the assumed effective date of the standard (i.e., 2016) for the base case and each of the ten CSLs that DOE considered for split HPs. Shipments are disaggregated by region and sector.

9-33Table 9.5.3Split HP: Base Case and Standards CaseCumulative Shipments from 2016-2046 (millions)RegionSectorBase CaseCSL 1(13.5 SEER7.85 HSPF)CSL 2(14 SEER8.0 HSPF)CSL 3(14.5 SEER8.15 HSPF)CSL 4(15 SEER8.3 HSPF)CSL 5(15.5 SEER8.45 HSPF)CSL 6(16 SEER8.6 HSPF)CSL 7(17 SEER9.1 HSPF)CSL 1(18 SEER9.6 HSPF)CSL 2(19 SEER10.1 HSPF)CSL 3(19.5 SEER10.3 HSPF)Hot-DryRes8.438.438.438.438.438.438.438.438.428.428.41Com1.161.161.161.161.161.161.151.151.151.151.14Hot-HumidRes42.4042.4042.4042.4042.3942.3942.3842.3842.3742.3542.34Com3.973.973.973.973.973.973.973.973.963.953.95Rest of U.S.Res25.2625.2625.2525.2525.2425.2325.2125.2125.1825.1525.12Com3.033.033.033.033.023.023.023.023.013.013.00Entire U.S.Res77.1277.1277.1177.1077.0877.0677.0477.0477.0076.9476.89Com8.208.208.198.198.198.188.178.178.168.148.139-349.5.4 Single Package HP

Figure 9.5.11 shows the base case shipme nts forecasts for single package HPs. Shipments are broken down into each of the three regions and designated by market segment ( i.e. , new construction, replacements, and new owners). Shipments to the commercial building sector are estimated to represent only about seve n percent of all product sh ipments. Therefore, the figures do not differentiate among units going to the residential and commercial sectors.

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

1990

1 9 9 4 1998
2 002 200 6 2010 2014 2018 2022
2 026 2030 2034 203 8 2042 2046

YearShipments (million) Hot-Dry: New Construction Hot-Dry: New Ow ner Hot-Dry: Replace

Hot-Humid: New Construction Hot-Humid: New Ow ner Hot-Humid: Replace
Rest of Country: New Construction Rest of Country: New Ow ner Rest of Country: Replace

Rest-of-Country

Hot-Humid

Hot-Dry

Figure 9.5.11 Forecasted Shipments (Base Case) for Single Package HPs

Figure 9.5.12 shows total single package HP shipments forecasts for the base case and the standards case corresponding to the highest ef ficiency CSL (i.e., CSL 7 – 16.4 SEER / 9.0 HSPF). Because the impact of standards on single package HP shipments is similar to that exhibited for split CAC, figures disaggregating total shipments into the replacement, new owner, and new construction market channels are not provided. In the figure below, shipments are disaggregated by climate region (HH = hot-humid ; HD = hot-dry; ROC = rest-of-country). The solid blue line represents the entire U.S. shipments forecast (i.e., the sum of the three climate regions).

9-350.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044millions

HH

HD

ROC
HH (CSL7)
HD (CSL7)
ROC (CSL7) U.S. (CSL7)

U.S.

Figure 9.5.12 Single Package HP: Base Case a nd CSL 7 (16.4 SEER / 9.0 HSPF Total Shipments Forecasts

Table 9.5.4 summarizes the cumulative ship ments over the 30 year period starting from the assumed effective date of the standard (i.e., 2016) for the base case and each of the ten CSLs that DOE considered for single package HPs. Shipments are disaggregated by region and sector.

Table 9.5.4 Single Package HP: Base Case and Standards Case Cumulative Shipments from 2016-2046 (millions)

Region Sector Base

Case CSL 1 (13.5 SEER 7.85 HSPF) CSL 2 (14 SEER 8.0 HSPF) CSL 3 (14.5 SEER 8.0 HSPF) CSL 4 (15 SEER 8.0 HSPF) CSL 5 (15.5 SEER 8.5 HSPF) CSL 6 (16 SEER 9.0 HSPF) CSL 7 (16.4 SEER 9.0 HSPF)

Hot-Dry Res 1.10 1.10 1.10 1.10 1.10 1.10 1.09 1.09

Com 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15

Hot-Humid Res 5.55 5.55 5.55 5.54 5.54 5.54 5.54 5.54

Com 0.52 0.52 0.51 0.51 0.51 0.51 0.51 0.51

Rest of U.S. Res 3.22 3.22 3.21 3.21 3.21 3.20 3.20 3.20

Com 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38

Entire U.S. Res 10.00 10.00 9.99 9.99 9.98 9.97 9.97 9.96

Com 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.04

9-36REFERENCES

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2 Air-Conditioning, Heating and Refrigeration Institute. Historical shipments and shipmentweighted efficiency data submittal. December 2008.

3 Air-Conditioning, Heating and Refrigeration In stitute. Historical shipments data, 1972-1989

–

provided to Lawrence Berkeley National Laboratory.

4 U.S. Census Bureau. Characteristics of New Housing, History Tables, 2008. Washington,

DC. < http://www.census.gov/const/C25Ann/c25ann2008.pdf >

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0383(2009). < http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2009).pdf >

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< http://www.eia.doe.gov/emeu/cbecs />

8 S. Hymans. Consumer Durable Sp ending: Explanation and Prediction, Brookings Papers on Economic Activity , 1971. Vol. 1971, No. 1, pp. 234-239.

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< http://www.census.gov/hhes/www/housing/ahs/ahs.html >

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9-37