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 Calculation of Replacement Shipments ............................ 9-5 

9.2.1.1 Historical Shipments ...................................................................................... 9-5 

9.2.1.2 Calculation of Replacement 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 Impacts 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 New Construction Saturations ................................... 9-19 

9.4.2.1 Regional New Construction ......................................................................... 9-19 

9.4.2.2 Regional New Construction 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, Decisions, 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 Owner Market Shares of CAC and HP Equipment .................. 9-11 

Table 9.3.1 Change in Relative Price Elasticity following a Purchase Price Change .......... 9-13 

Table 9.4.1 CAC Residential Regional Stock Saturations .................................................... 9-16 

Table 9.4.2 HP Residential Regional Stock Saturations ....................................................... 9-17 

Table 9.4.3 CAC Shipment Regional Allocation Factors..................................................... 9-17 

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

9-i 
Table 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 Standards Case Cumulative Shipments from 

2016-2046 (millions) ......................................................................................... 9-29 

Table 9.5.2 Single Package CAC: Base Case and Standards Case Cumulative 

Shipments from 2016-2046 (millions)............................................................... 9-32 

Table 9.5.3 Split HP: Base Case and Standards Case Cumulative Shipments from 

2016-2046 (millions) ......................................................................................... 9-34 

Table 9.5.4 Single Package HP: Base Case and Standards 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 Construction ................................ 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 Regional Saturations in Residential New Construction ...... 9-20 

Figure 9.4.3 HP National and Regional Saturations in Residential New Construction ......... 9-21 

Figure 9.4.4 CAC and HP National and Regional 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 Construction Shipments Forecasts....... 9-27 

Figure 9.5.6 Split Coil-Only CAC: Base Case 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 Case and CSL 7 (16.6 SEER) Total Shipments 

Forecasts ............................................................................................................ 9-31 

Figure 9.5.9 Forecasted Shipments (Base Case) for Split System HPs ................................. 9-32 

Figure 9.5.10 Split HP: Base Case and 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 Case and CSL 7 (16.4 SEER / 9.0 HSPF Total 

Shipments Forecasts........................................................................................... 9-36 

9-ii 
 

 

CHAPTER 9. SHIPMENTS ANALYSIS 

9.1 INTRODUCTION
 

Estimates of future product shipments are a necessary input to calculations of the national 

energy savings (NES) and net present value (NPV), as well as to the manufacturer impact 

analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy 

(DOE) used to forecast annual product shipments, and presents results for each of the central air 

conditioner (CAC) and heat pump (HP) product classes being considered in this analysis. The 

shipments model divides the population of appliance users into specific 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 historical base year and calculates, for each year 

of the analysis period, both shipments and retirements by market segment. This approach 

produces an estimate of the total equipment stock, broken down by age or vintage, in each year 

of the analysis period. The equipment stock distribution is calculated for the base case and for 

each CSL as well as for each product class. The stock 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 Commercial Equipment Standards website 

(http://www1.eere.energy.gov/buildings/appliance_standards/residential/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 and provides some summary output. The rest of section 9.1 describes the 

methodological approach. The analysis incorporates 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 single product class for residential consumers for 

the country as a whole. Section 9.2 defines the input data, pre-processing and calibration steps 

that are used in the model. Section 9.3 defines the 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 the three regions (hot-humid, hot-dry and rest-ofcountry) used in the analysis. Section 9.5 presents 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 described 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: 

9-1 
 

1.	 Product class: there are four product classes as summarized in Table 9.1.1. DOE further 

disaggregated the split system CAC product class 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 building sectors; approximately 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 defined as default (i.e., normal repairs 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 further assumed that equipment 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 models major equipment failures; minor repairs 

are accounted for in the annual maintenance 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 commercial) is further disaggregated into three 

product placement channels (hereafter referred 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 existing 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 buildings 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. 

9-2 
 

 

 

 

 

These 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 been modeled to forego a new purchase, DOE assumed that 

potential new owners would purchase alternative 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 modeled as being affected by higher ownership 

costs, DOE assumed they would perform an extended repair which prolongs the life of the 

equipment by six years, after which the equipment would be replaced. The three channels and 

related decisions modeled in the spreadsheet 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 variable 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 analysis period by new shipments coming in and broken 

or demolished equipment being taken out. 

For existing stock: 

9-3 
 

 

 

 

 

Stock (j,a) = Stock (j − 1,a − 1)− Rem (j,a) p p p

and for new units: 

Stockp (j,a = 1) = Shipp (j − 1).

Where: 

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

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

and 

Shipp(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: 

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

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

NOp(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 

fp(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- fp(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: 

Rem (j,a) = [1 − f (a)]× Stock (j,a)+ ER _Stock (j,a2 = 6) p p p p

Where: 

a2 = number of years since the equipment was repaired, and 

ER_Stockp(j, a2) = number of extended-repair units of product p replaced in year j. 

The new construction and new owner markets depend on changes to the building stock, which is 

included as a component of the shipments model. This is discussed in the next section. 

9-4 
 

 

9.2 DATA INPUTS AND SUPPORTING CALCULATIONS
 

Data inputs and supporting calculations 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 over 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 only single-phase CAC. DOE disaggregated the shipments for 

single-phase and 3-phase CAC and 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 divided all historical shipments 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 shipments 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 conservation standards. 

9-5 
 

Table 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. 

9-6 
 

 

 

 

 

 

 

 

9.2.1.2 Calculation of Replacement Shipments 

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

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

Re mp (j) = ∑ ra (a)× Stockp (j,a) a

Retirement functions and product lifetimes are discussed in more detail in Chapter 8. The 

average lifetimes are 18.3 years for split and single 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 shipments 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 Remp (j). Since the demolished units do not need 

to be replaced, they are deducted from Remp(j) when calculating the required replacements, as 

represented by the following expression. 

Rpl p (j) = Re mp (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 construction 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: 

9-7 
NC_Starts (j) = number of new construction starts in year j, and 

NC_Sat(j) = new construction saturation for product class p and year j. 

For the residential market segment, DOE developed new construction 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 shares based on historical shipments (as shown in 

Table 9.2.1) to calculate specific market saturations fore each 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 - 1977 

Residential 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, otherwise 

referred 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 constructed 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 the effects of the American Recovery and 

Reinvestment Act.1

 DOE used the high and low growth scenarios from the March 2009 release 

9-8 
 

 

of the AEO20096

, and the Reference case from the modified 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 new 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 depicts 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 

Year 

Saturation (percent sq.ft. cooled

by 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 recorded data through 2007 and projections from 

EIA’s AEO2009 for the period 2008–2030. For 2031−2046, DOE froze completions at the 2030 

9-9 
 

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

residential market to estimate new construction starts for 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 estimate of the building stock and equipment stock 

in the base year, and steps forward adding shipments 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 replacements. The third product channel, new 

owners, is more difficult to describe based on existing data. However, the historical shipments 

data (as described above in section 9.2.1.1) can be used to calibrate 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 NOp(j) as a residual after shipments for new 

construction and replacements were subtracted from total shipments. 

NOp (j) = Ship p (j) − Rpl p (j) − NC p (j)

The three terms on the left are known quantities (calculation of Rplp(j) and NCp(j) are described 

above). 

The results of the historical calibration are 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 the total CAC and HP shipments to the new owner housing market 

divided by the number of households without either 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 stock 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 CAC and HP equipment). DOE used product class market shares 

based on historical shipments (as shown in Table 9.2.1) to calculate specific new owner market 

saturations fore each CAC and HP product class. 

9-10 
 

 

 

Table 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 consumer 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 are 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 shipments from increases in product purchase price, 

decreases in product operating costs, and changes to household income. Appendix 9-A provides 

9-11 
 

 

 

 

 

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 priceinelastic. Other information in the literature suggests that appliances are a normal good, so that 

rising incomes increase the demand for appliances, and that consumers use relatively high 

implicit discount ratesa

 when comparing appliance prices and appliance operating costs. 

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

evaluate broad market trends and conduct simple regression analyses. These data indicate that 

there has been a rise in appliance shipments and a decline in appliance purchase price and 

operating costs over the time period. Household income has also risen during this time. 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 = = Income Income

Where: 

RP = relative price, 

TP = total price, 

Income = household income, 

PP = appliance purchase price, and 

PVOC = present value of operating cost, based 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 assumption that economic variables, including purchase 

price, operating costs, and household income, explain most of the trend in appliances per 

household in the United States since 1980. Changes in appliance quality and consumer 

preferences may have occurred during this period, but DOE did not account for them in this 

analysis. Despite these uncertainties, DOE believes that its estimate of the relative price elasticity 

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. 

9-12 
 

 

 

of demand provides a reasonable assessment of the impact that purchase price, operating cost, 

and household income have on product shipments. 

The forecasts of shipments and national impacts due to standards are for a 30-year time 

period, and it is possible that over this time period 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 short-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, becoming smaller (more inelastic) until it reaches a 

terminal value around the tenth year after the price 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 shown in Table 9.3.1, DOE performed a linear 

interpolation to obtain the relative price elasticity. 

Table 9.3.1 Change in Relative Price Elasticity following a Purchase Price Change 

Years Following Price Change 

1 2 3 5 10 20 

Relative Change in 

Elasticity to 1st 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 below, is expressed as 

a percentage drop in market share for each year, dMSp

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 other 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) ⎞

⎟

⎤

dMSj = ⎢1 − ⎜ ⎟

⎥ × eRP ( )j

⎢ RP _base p ( )j ⎥ ⎣ ⎝ ⎠⎦

Where: 

dMSp

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

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

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

9-13 
 

 

 

 

eRP(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_stdp(j)= shipments to new owners in the standards case for product class p, year j, 

and 

NOp(j)= shipments to new owners in the 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 purchase an alternative type of space-conditioning 

equipment. The number of new owners impacted and switching to an alternative equipment type 

is represented by the following expression. 

p NO _ swp (j) = NOp (j)× dMSj

Where: 

NO_swp(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 CACs, DOE did not explicitly account for the 

increased sales of room air conditioners in its analysis. 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 shipments 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 

9-14 
 

 

 

 

 

CACs due to decreases in shipments 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 consumption, 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 total 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, dMSp

j, due to the equipment price increase as described in 9.3.1.1. The extended repair is 

only applicable to broken equipment that are purchased 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 further repaired instead of being replaced is 

calculated as follows: 

p XRi = ∑Re m( j,a)× dMSi for ( j − a )< 2016 a 

Rpl( )j = ∑Re m( j,a ) − XRi + XR j −6 − Dem( j) a 

Where: 

dMSp

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, 

XRj = 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 Determination, 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 run for each region, and the results are summed to 

9-15 
 

 

 

provide national-level output under a regional standard scenario. 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 are 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 information is available from the LCC and PBP 

analysis (see Chapter 8). For the shipments model, historical shipments, housing starts and 

housing stock, commercial buildings starts and stock (in square feet), and market saturations for 

new construction must be developed for each region. The following sections describe the 

development of regional historical shipments, housing and commercial building starts and stock, 

and market saturations. 

9.4.1 Allocation of Historical Shipments to Regions 

DOE developed regional allocation factors to divide 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 the historical regional and national 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 nation, 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 

9-16 
 

 

 

 

 

 

 

 

Table 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 shipment 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 the 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% 

9-17 
 

Figure 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 historical shipments (as shown in Table 9.2.1) to 

calculate specific allocations 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 2008 

Saturation 

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 2008 

Saturation 

CAC calculated HP calculated 

CAC AHS HP AHS 

CAC calculated 

CAC AHS 

HP calculated 

HP AHS 

Saturation 

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 AHS 

Saturation 

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 
 

 

It 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 Division. 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 climate 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 intersection of hot-humid with South 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 population 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 aggregated to provide regional populations. Projections 

out to 2046 were constructed by assuming 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. Sample 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, both the hot-dry and the rest-of-country regions 

overlap with the Mountain and Pacific 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 

9-19 
 

Using 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 Census 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 Construction Saturations 

DOE developed residential regional 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 households into the four Census Regions (i.e., 

northeast, midwest, south, and west). DOE used population allocation factors (as discussed 

previously) 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 

Year 

CAC 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 

9-20 
 

0% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

100% 

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

Year 

HP Residential New Construction Saturations 

Entire U.S. 

Rest of Country 

Hot-Humid 

Hot-Dry 

Figure 9.4.3 HP National and Regional Saturations 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 and regional saturations 

for CAC and HP equipment. The regional saturations 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. 

9-21 
 

 

0% 

1% 

2% 

3% 

4% 

5% 

6% 

1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 

Year 

CAC 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 for 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 base 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 product shipments. Therefore, the figures do not differentiate among units 

going to the residential and commercial sectors. 

9-22 
0 

1 

2 

3 

4 

5 

6 

1990 

1994 

1998 

2002 

2006 

2010 

2014 

2018 

2022 

2026 

2030 

2034 

2038 

2042 

2046 

Year 

Shipments (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 

1990 

1994 

1998 

2002 

2006 

2010 

2014 

2018 

2022 

2026 

2030 

2034 

2038 

2042 

2046 

Year 

Shipments (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 

9-23 
 

 

 

 

 

To demonstrate the impact of higher efficiency 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 market 

channels, respectively. In each figure, shipments are disaggregated by climate region (HH = hothumid; 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).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 relative 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 replaced 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 for 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 increases 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. 

9-24 
0.00 

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 2044 

millions 

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 

9-25 
 InFigure 9.5.4, representing new owner shipments, 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 section 9.3.2.1, consumers affected 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 2044 

millions 

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 

9-26 
 

Figure 9.5.5 shows the shipment forecasts for 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 2044 

millions 

HH 

HD 

ROC 

U.S. 

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

Shipments Forecasts 

9-27 
Figure 9.5.6 provides the total base case and 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 2044 

millions 

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 shipments 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. Shipments 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). 

9-28 
Table 9.5.1 Split CAC: Base Case and Standards Case Cumulative Shipments from 2016-2046 (millions) 

Base Case CSL 1 (13.5 SEER) CSL 2 (14 SEER) CSL 3 (14.5 SEER) CSL 4 (15 SEER) CSL 5 (1

Region Sector Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Bl

C

HotDry Res 25.31 2.81 28.12 25.30 2.81 28.12 25.30 2.81 28.11 25.28 2.81 28.10 25.27 2.81 28.08 25.25 2

Com 2.68 0.30 2.98 2.68 0.30 2.98 2.68 0.30 2.98 2.67 0.30 2.97 2.67 0.30 2.97 2.67 0

HotHumid Res 41.72 4.65 46.37 41.71 4.65 46.37 41.70 4.65 46.35 41.69 4.65 46.34 41.67 4.65 46.32 41.64 4

Com 4.58 0.51 5.09 4.58 0.51 5.09 4.57 0.51 5.09 4.57 0.51 5.08 4.56 0.51 5.08 4.56 0

Rest of U.S. 

Res 71.91 7.99 79.90 71.90 7.99 79.89 71.85 7.99 79.84 71.79 7.99 79.78 71.72 7.98 79.70 71.63 7

Com 7.33 0.81 8.15 7.33 0.81 8.15 7.33 0.81 8.14 7.32 0.81 8.13 7.31 0.81 8.13 7.30 0

Entire U.S. 

Res 139.71 15.52 155.24 139.70 15.52 155.22 139.63 15.52 155.15 139.55 15.52 155.07 139.45 15.51 154.96 139.32 1

Com 14.32 1.59 15.91 14.31 1.59 15.91 14.30 1.59 15.89 14.29 1.59 15.88 14.27 1.59 15.86 14.25 1

CSL 7 (16.5 SEER) CSL 8 (17 SEER)* CSL 9 (18 SEER)* CSL 10 (19 SEER)* CSL 11 (20 SEER)* CSL 12 (

Region Sector Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Blower Coil Total Coil- Only Bl

C

HotDry Res 25.21 2.81 28.02 25.21 2.81 28.02 25.21 2.81 28.02 25.21 2.81 28.02 25.21 2.80 28.02 25.21 2

Com 2.66 0.30 2.95 2.66 0.30 2.95 2.66 0.30 2.95 2.66 0.30 2.95 2.66 0.30 2.95 2.66 0

HotHumid Res 41.59 4.65 46.24 41.59 4.65 46.24 41.59 4.65 46.23 41.59 4.64 46.23 41.59 4.64 46.23 41.59 4

Com 4.54 0.51 5.05 4.54 0.51 5.05 4.54 0.51 5.05 4.54 0.51 5.05 4.54 0.51 5.05 4.54 0

Rest of U.S. 

Res 71.45 7.98 79.42 71.45 7.97 79.42 71.45 7.96 79.41 71.45 7.96 79.40 71.45 7.95 79.39 71.45 7

Com 7.28 0.81 8.10 7.28 0.81 8.10 7.28 0.81 8.09 7.28 0.81 8.09 7.28 0.81 8.09 7.28 0

Entire U.S. 

Res 139.06 15.50 154.56 139.06 15.50 154.55 139.06 15.49 154.54 139.06 15.48 154.53 139.06 15.46 154.52 139.06 1

Com 14.21 1.59 15.80 14.21 1.59 15.80 14.21 1.58 15.79 14.21 1.58 15.79 14.21 1.58 15.79 14.21 1

* CSL 7 is the maximum efficiency level for Blower Coil units. CSLs 8 -13 for Blower Coil units is equivalent to CSL 7. 

9-29 
9.5.2 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 seven percent of all product shipments. 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 

1998 

2002 

2006 

2010 

2014 

2018 

2022 

2026 

2030 

2034 

2038 

2042 

2046 

Year 

Shipments (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 

9-30 
Figure 9.5.8 shows total single package CAC shipments forecasts for the base case and 

the standards case corresponding to the highest efficiency CSL (i.e., CSL 7 – 16.4 SEER). 

Because the impact of standards on single package 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 2044 

millions 

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 shipments 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. 

9-31 
 

 

 

 

 

 

 

 

 

 

Table 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 

1998 

2002 

2006 

2010 

2014 

2018 

2022 

2026 

2030 

2034 

2038 

2042 

2046 

Year 

Shipments (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 

9-32 
 

Figure 9.5.10 shows total split HP shipments forecasts for the base case and the standards 

case corresponding to the highest 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-of-country). The solid blue line represents 

the entire U.S. shipments forecast (i.e., the sum of the three climate regions). 

0.00 

0.50 

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 2044 

millions 

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 shipments 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. 

9-33 
Table 9.5.3 Split 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 

8.15 HSPF) 

CSL 4 

(15 SEER 

8.3 HSPF) 

CSL 5 

(15.5 SEER 

8.45 HSPF) 

CSL 6 

(16 SEER 

8.6 HSPF) 

CSL 7 

(17 SEER 

9.1 HSPF) 

CSL 1 

(18 SEER 

9.6 HSPF

Hot-Dry 

Res 8.43 8.43 8.43 8.43 8.43 8.43 8.43 8.43 8.42 

Com 1.16 1.16 1.16 1.16 1.16 1.16 1.15 1.15 1.15 

Hot-Humid 

Res 42.40 42.40 42.40 42.40 42.39 42.39 42.38 42.38 42.37 

Com 3.97 3.97 3.97 3.97 3.97 3.97 3.97 3.97 3.96 

Rest of U.S. 

Res 25.26 25.26 25.25 25.25 25.24 25.23 25.21 25.21 25.18 

Com 3.03 3.03 3.03 3.03 3.02 3.02 3.02 3.02 3.01 

Entire U.S. 

Res 77.12 77.12 77.11 77.10 77.08 77.06 77.04 77.04 77.00 

Com 8.20 8.20 8.19 8.19 8.19 8.18 8.17 8.17 8.16 

9-34 
9.5.4 Single Package HP 

Figure 9.5.11 shows the base case shipments 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 seven percent of all product shipments. 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 

1994 

1998 

2002 

2006 

2010 

2014 

2018 

2022 

2026 

2030 

2034 

2038 

2042 

2046 

Year 

Shipments (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 efficiency 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). 

9-35 
 

 

 

 

 

 

 

 

 

 

0.00 

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 2044 

millions 

HH 

HD 

ROC 

HH (CSL7) 

HD (CSL7) 

ROC (CSL7) 

U.S. (CSL7) 

U.S. 

Figure 9.5.12 Single Package HP: Base Case and CSL 7 (16.4 SEER / 9.0 HSPF 

Total Shipments Forecasts 

Table 9.5.4 summarizes the cumulative shipments 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 

9-36 
 

 

 

 

 

 

 

 

 

 

 

 

REFERENCES
 

1	 Letter to Mrs. Rachel Schmeltz, Energy Star Program Manager, Environmental Protection 

Agency from Karim Amrane, Vice President, Regulatory & Research, Air-Conditioning, 

Heating, and Refrigeration Institute (AHRI), “EPA Proposed Draft Energy Star Specification 

for Light Commercial HVAC Equipment,” Jan. 30, 2009. 

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

3	 Air-Conditioning, Heating and Refrigeration Institute. 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> 

5	 U.S. Department of Energy-Energy Information Administration. An Updated Annual Energy 

Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and 

Reinvestment Act and Recent Changes in the Economic Outlook, April, 2009. Washington, 

DC. SR/OIAF/2009-03. 

<http://www.eia.doe.gov/oiaf/servicerpt/stimulus/pdf/sroiaf(2009)03.pdf> 

6	 U.S. Department of Energy–Energy Information Administration. Annual Energy Outlook 

2009 with Projections to 2030. March 2009. U.S. DOE–EIA: Washington, D.C. DOE/EIA0383(2009). <http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2009).pdf> 

7	 U.S. Department of Energy–Energy Information Administration. Commercial Building 

Energy Consumption Survey. Washington, D.C. Surveys for 1992, 1995, 1999 and 2003. 

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

8	 S. Hymans. Consumer Durable Spending: Explanation and Prediction, Brookings Papers on 

Economic Activity, 1971. Vol. 1971, No. 1, pp. 234-239. 

9	 U.S. Census Bureau, Housing and Household Economic Statistics Division. American 

Housing Survey. Washington, DC. 

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

10	 U.S. Census Bureau, National Population Projections Released 2008. 

http://www.census.gov/population/www/projections/summarytables.html 

9-37