9

- i 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

- 4

9.2 DATA INPUTS AND SUPP ORTING 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

- 9

9.2.2 Shipments to New Construction

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

- 10

9.2.2.1 Central Air Conditioners and Heat Pumps

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

- 11

9.2.2.2 Furnaces

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

- 13

9.2.3 Model Ca libration and New Owners

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

- 13

9.2.3.1 Central Air Conditioners and Heat Pumps

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

- 14

9.2.3.2 Furnaces

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

- 15

9.3 DECISION MODEL FOR R EPLACEMENTS AND EQUI PMENT

SWITCHING

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

- 15

9.3.1 Decision Model for Replacements

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

- 15

9.3.1.1 Purchase Price Elasticity

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

- 16

9.3.1.2 Impact from Increase in Relative Price

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

- 17

9.3.1.3 Replacement Shipment Impacts

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

- 18

9.3.2 Equipment Switching

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

- 19

9.3.2.1 Split System Heat Pumps: Switch to Combination Split System

Central Air Conditioner and Electric Furnace

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

- 19

9.3.2.2 Gas Furnace Equipment Switching

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

- 20

9.3.2.3 Other Equipment Switching

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

- 20

9.4 REGIONAL SHIPMENTS M ODEL

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

- 20

9.4.1 Central Air Conditioners and Heat Pumps

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

- 21

9.4.1.1 Allocation of Historical Shipments to Regions

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

- 21

9.4.1.2 Regional New Construction and New Construction S aturations

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

- 25

9.4.2 Furnaces

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

- 29

9.4.2.1 Allocation of Historical Shipments to Regions

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

- 29

9.4.2.2 Regional New Construction and New Construction Saturations

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

- 30

9.5 RESULTS

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

- 33

9.5.1 Base Case Shipments

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

- 33

9.5.1.1 Central Air Conditioners

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

- 33

9.5.1.2 Heat Pumps

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

- 35

9.5.1.3 Furnaces

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

- 36

9.5.2 Shipment s Impacts due to Standards

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

- 38

`9

- ii LIST OF TABLES

Table 9.1.1 Central Air Conditioner, Heat Pump and Furnace Product Classes

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

- 2

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

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

- 3

Table 9.2.1 Central Air Conditioner and Heat Pump Historical Shipments*

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

- 7

Table 9.2.2 Furnace Historical Shipments

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

- 9

Table 9.2.3 Historical New Owner Market Shares of Central Air Conditioner and Heat Pump Equipment

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

- 15

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

.......... 9

- 17

Table 9.4.1 CAC Residential Regional Stock Saturations

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

- 21

Table 9.4.2 HP Residential Regional Stock Saturations

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

- 22

Table 9.4.3 CAC Shipment Regional Allocation Factors

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

- 22

Table 9.4.4 HP Shipment Regional Allocation Factors

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

- 22

Table 9.4.5 Central Air Conditioner and Heat Pump Regional Shipment Market

Shares*

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

- 23

Table 9.4.6 CAC Shipment Regional Allocation Factors by Product Class

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

- 23

Table 9.4.7 HP Shipment Regional Allocation Factors by Product Class

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

- 24

Table 9.4.8 Population Projected for 2020 for Areas defined by the Overlap of Census Divisions with Climate Regions

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

- 26

Table 9.4.9 Furnace Regional Shipment Market Shares*

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

- 30

Table 9.5.1 Trial Standard Levels for Central Air Conditioners and Heat Pumps and Furnaces (Energy Efficiency)

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

- 39

Table 9.5.2 Base Case Cumulative Shipments and Shipments Impacts due to TSLs

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

- 40

LIST OF FIGURES

Figure 9.2.1 Central Air Conditioner and Heat Pump Saturations in Residential New

Construction

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

- 11 Figure 9.2.2 Central Air Conditioner and Heat Pump Saturation in Commercial New

Construction

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

- 12 Figure 9.2.3 Furnace Saturations in Residential New Construction

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

- 13 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

- 25 Figure 9.4.2 Central Air Conditioner National and Regional Saturations in Residential

New Construction

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

- 27 Figure 9.4.3 Heat Pump National and Regional Saturations in Residential New

Construction

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

- 28 Figure 9.4.4 Central Air Conditioner and Heat Pump National and Regional Saturations

in Commercial New Construction

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

- 29 Figure 9.4.5 Non

- Weatherized Gas Furnace National and Regional Saturations in

Residential New Construction

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

- 31 Figure 9.4.6 Mobile Home Gas Nationa l and Regional Saturations in Residential New

Construction

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

- 329

- iii Figure 9.4.7 Oil Furnace National and Regional Saturations in Residential New

Construction

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

- 32 Figure 9.5.1 Central Air Conditioners: Base Case Shipments by Product Class and Product Type

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

- 33 Figure 9.5.2 Central Air Conditioners: Base Case Shipments by Product Channel

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

- 34 Figure 9.5.3 Central A ir Conditioners: Base Case Shipments by Region

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

- 34 Figure 9.5.4 Heat Pumps: Base Case Shipments by Product Class

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

- 35 Figure 9.5.5 Heat Pumps: Base Case Shipments by Product Channel

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

- 35 Figure 9.5.6 Heat Pumps: Base Case Shipments by Region

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

- 36 Figure 9.5.7 Furnaces: Base Case Shipments by Product Class

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

- 36 Figure 9.5.8 Furnaces: Base Case Shipments by Product Channel

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

- 37 Figure 9.5.9 Furnaces: Base Case Shipments by Region

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

- 379-1 CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future product shipments are a necessary input to calculations of the national energy savings (NES) and net present value (NPV) , as well a s 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

condition er (CAC)

, heat pump (HP) , and furnace 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

. Th is approach produces an estimate of the total equipment stock, broken down by age or vintage , in each year of the analysis period. The product stock distribution is calculated for the base case and for each efficiency level for each product class. The stock distribution is used in the n ational

i mpact a nalysis (NIA) to estimate the total dollar and ene rgy costs and benefits associated with

each efficiency level

.

The shipments m odel 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 National Impact Analysis ( NIA ) . 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 num ber of

component s or market segments

, describing different product class es , market sector s and geographic region s ; 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. S ection 9.3 defines the decision

models used to differentiate between the base case and efficiency levels . Section 9.4 presents the modifications to the input data tha t were used to develop shipments models specific to the regions used in the analysis : hot

- humid, hot

- dry and rest

- of

- country for central air conditioners and heat pumps and North and South regions for furnaces

. Although r e gional standards were not

conside red for single package central air conditioners and heat pumps, the shipments models which were developed still analyzed this equipment on a regional basis in order to properly track shipments to the different regions of the country. S ection 9. 5 presents summary o utput from the shipments model for the base cases

.

9.1.1 Definition of Market Segments for the Shipments Analysis

The shipments model disaggregates the total stock according to the following

characteristics:9-2 1. P roduct class : there are two central air conditioner product classes, two heat pump product classes and three furnace product classes as summarized in Table 9.1. 1. DOE

further disaggregated the split system central air conditioner product class into two unit types: coil

- only and blower coil unit s. Although not considered as separate product classes, DOE structured the shipments model to analyze both split central air conditioner

coil

- only and split central air conditioner blower coil systems separately.

2. M arket sector : For central air condition ers and heat pumps, r esidential and commercial building sectors ; approximately 93 percent of shipment s are to the residential sector with the balance going to the commercial sector. 1 This ratio is held constant over the analysis period. For furnaces, 100 p ercent of shipments are to the residential sector.

3. R epair 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 increa se assoc iated with a CSL to influence the con sumer 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 separat ely because it is further assumed that equipment that has undergone one extended repair will last another six years and then be replaced. T his 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 Central Air Conditioner

, Heat Pump and Furnace P roduct C lasses

Product Type Product Class Unit Type

Central Air Conditioner (CAC) Split System Coil

- Only

Blower Coil
Single Package NA

Heat Pump (HP) Split System NA
Single Package NA

Furnaces Non

- Weatherized Gas Furnaces NA

Mobile Home Furnaces NA

Oil Furnaces NA

Each market sector (i.e., residential and commercial) is further disaggregated into t hree product placement channels (hereafter referred to as “channels”) as follows:

1. New construction: a certain fraction of new buildings are assumed to acquire central air

conditioner s
, heat pump s , and furnaces 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 central air conditioner

, heat

pump , and furnaces installed. This category receives new shipmen ts when existing equipment is replaced.9-3 3. N ew owners : these are defined as existing buildings that acquire central air conditioner

, heat pump , and furnaces for the first time during the analysis period

. The fracti on of new

owners is estimated f r o m 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.

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, DOE also estimated the placement of equipment is

automatic. For existing owners, the repair versus replace decision is af fected by the change in ownership cost associated with higher efficiency. In those case s where e xisting 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 e quipment 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 D ata, D ecisions

, and Resulting Actions used in the Shipments M ode l

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 E quip_stock(age, year)

+ existing owners E quip_stock(age, year) d oes not break => stays in equip_stock; increment

age survival_function(age) breaks: replace => add to equip_stock, age=0

R epair_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 (n o CAC, HP , or
Furnaces ) housing_stock(year) buy => add to equip_stock, age=0 E quip_stock(age, year)

max_saturation(year) d o not buy => stays in no cooling stock purchase_probability(year)

One decision not reflected in Table 9.1.2 is the potential equipment switching for split system heat pump consumers to combination split system central air conditioning and electric furnace systems. DOE specifically modeled such equipment switching because of the potential9-4 of high efficiency standards fo r heat pumps to drive consumers into using more energy intensive

electric

- resistance heating systems. Equipment switching is discussed in section 9.3.3.

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:



## ( **) (**  **) (**  **)** a , j m Re a , j Stock a , j Stock

p p p 

### − **− −**  **=** 1 1

and for new units:



## ( **) (**  **)**

. j Ship a , j Stock

p p 

### 1 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

.

S hipments are directed to one of the three channels:



## ( **) (**  **) (**  **) (**  **)** j NO j NC j Rpl j Ship

p p p p 

### + **+ =** 

W here:

Rpl p (j) = number of u nits of product p replaced in year j ; i t depends on removals

, N

C

p (j) = n umber of units installed in new construction of product p in year j , and

N

O

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 ;

equ ivalently 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:



## ( **) (**  **)** 

# [ **] (**  **) (**  **)** 6 2 1 **= +**  **×**

**− =**  a , j Stock

_ ER a , j Stock a f a , j m Re

p p p p9-5 Where:

a2 = number of years since the equipment was repaired , and ER_ Stock p (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 componen t of the shipments model. This is discussed in the next section.

9.2 DATA INPUTS AND SUPP ORTING CALCULAT ION S

Data inputs and supporting calculations that are necessary for the shipments model are explained in detail in the following sections. Data inputs are discussed separately for central air

conditioner and heat pump and furnaces

.

9.2.1 Historical Shipments and the Calculation of Replacement Shipments

9.2.1.1 Historical Shipments

Central Air Conditioners and Heat Pumps

DOE used historical shipments data (i.e., domestic shipments and imports) to populate its shipments model for central air conditioner and heat pump equipment

. As part of its data submittal to the DOE standards rulemaking, the Air

- Conditioning, Heating, and Refrige ration Institute (AHRI) provided historical shipments data over 1990

- 200 9 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 central air

conditioner and heat pump , whereas the data for 1990

- 200 9 included only single

- phase central

air conditioner . DOE disaggregated the shipments for single

- phase and 3

- phase central air

conditioner and heat pump equipment in the earlier data and included only single phase units in the analysis.

DOE further broke down split system central air conditioner historical shipments into

coil

- o nly and blower coil unit shipments. Because DOE believes that a majority of sp lit central

air conditioner shipments are coil

- only units, it estimated that coil

- only units account for 90 percent of annual split system s hipments.

As discussed earlier in se ction 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.

0 summa rizes 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 se ction 9.1.1

,9-6 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.9-7 Table 9.2.1 Central Air Conditioner and Heat Pump Historical Shipments *

Year Split CAC ** Single Package CAC Split HP Single Package HP

thousands thousands thousands 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

2008 3,171 274 1,566 189

2009 2,887 234 1,389 162

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 central air conditioner shipments are disaggregated into coil

- only and blower coil unit shipments.

Coil

- only unit shipments estimated to comprise 90 percent of annual split central air conditioner shipments.

Furnaces9-8 DOE used historical shipments data (i.e., domestic shipments and imports) to populate its shipments model for furnace 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 1988

- 2009 time

- period disaggregated into two categories: (1)

the non

- weatherized gas and mobile home gas furnaces and (2) oil furnaces. 2 Historical shipments from 1972

- 1988 were also provided by the Gas Applian ce Manufacturers Association (GAMA) from previous data submittals to the Lawrence Berkeley National Laboratory (LBNL). 4

For non

- weatherized gas and mobile home gas furnaces, DOE disaggregated mobile home gas furnace shipments from the gas furnace total by using a combination of data from U.S. Census 5

and American Housing Survey (AHS). 6

Table 9.1. 2 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 betwee n past and future shipments, independent of conservation standards.9-9 Table 9.2.2 Furnace Historical Shipments

Year Non

- weather ized Gas Mobile Home Gas Oil

- fired

thousands thousands thousands

1972 1,855 211 311

1973 1,504 215 328

1974 1,337 139 209

1975 1,085 101 213

1976 1,439 115 236

1977 1,384 124 228

1978 1,506 130 263

1979 1,730 133 158

1980 1,328 118 64

1981 1,291 126 81

1982 1,028 128 104

1983 1,513 149 127

1984 1,698 151 148

1985 1,673 150 147

1986 1,966 139 201

1987 1,936 137 206

1988 1,958 134 204

1989 2,033 129 179

1990 1,823 127 138

1991 1,934 123 131

1992 1,969 137 141

1993 2,431 153 149

1994 2,526 171 164

1995 2,419 182 135

1996 2,683 188 152

1997 2,593 186 124

1998 2,785 192 128

1999 2,937 189 125

2000 2,940 164 121

2001 2,915 148 123

2002 3,060 142 117

2003 3,134 131 127

2004 3,388 131 130

2005 3,378 134 111

2006 3,071 125 93

2007 2,665 117 84

2008 2,169 111 59

2009 2,073 101 56

Source: AHRI

9.2.1.2 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

.9-10 

## ( **) (**  **) (**  **)** a , j Stock a r j m Re

p a a p **× =**  

## ∑

Retirement functions and product lifetimes are disc ussed in more detail in Chapter 8. The average lifetimes are 19.5 years for split and single package central air conditioner equipment,

16.7 years for split and single package heat pumps , 24.0 years for non

- weatherized gas furnaces, 19.1 years for mobile home gas furnaces, and 27.0 years for oil furnaces

.

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



## ( **) (**  **) (**  **) (**  **)** j Stock

_ H j Starts

_ H j Stock

_ H j D

**− +** 

**− =**  1



## ( **) (**  **) (**  **)** 1

, **− ×**  **=** j p sat j D j Dem

W here:

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

. 

## ( **) (**  **) (**  **)** j Dem j m Re j Rpl

p p 

### − **=**

9.2.2 Shipments to New Construction

DOE multiplied new construction market saturations by forecasts of new construction to estimate central air conditioner and heat pump shipments to the new construction channel. On a product class basis, the determination of shipments to new construction is represented by the following expression:



## ( **) (**  **) (**  **)** j Sat

_ NC j Starts

_ NC j NC

p p 

### × **=**

W here:

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

.9-11 9.2.2.1 C entral Air Cond itioners and Heat Pumps

For the residential market segment, DOE developed new construction market saturations

from Characteristics of New Housing data from the U.S. Census Bureau. 7

Figure 9.2.1 C entral air

conditioner and heat pump historical and projected saturations are depicted in

. DOE

used historical new construction saturations for all central air conditioner and heat pump equipment to project future saturations. As evidenced in Figure 9.2.1

, DOE used a logistic fit to

the historical data to estimate future saturations. DOE used pro duct class market shares based on historical shipments (as shown in 0 ) to calculate specific market saturations for each central air

conditioner and heat pump prod uct class.

Figure 9.2.1 C entral A ir C onditioner and H eat P ump S aturations 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 200 8 and projections from the DOE

Energy Information Administration (EIA)’s Annual Energy Outlook 20 10 ( AEO20 10 ) for the period 2010

– 203 5

. 8 New housing includes newly constructed single

- and multi

- family units, termed “new housing completions,” and mobile hom e placements. AEO20 10 provides three sets

of economic growth scenarios : a Reference case, a High Economic Growth case, and a Low

Economic Growth case. Fo r 2036 −2045 , DOE froze completions at the 2035 level for all three

economic scenarios

. 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

CAC9-12 For the commercial market segment, DOE developed new construction market

saturations from data in EIA’s Commercial Building Energy Consumption Survey (CBECS). 9

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 AEO20 10

. DOE considered residential

- type central air conditioners to comprise all split system and single package central air conditioner and heat pump equipment. DOE used product class market shares based on historical shipm ents to calculate specific market

saturations for each central air conditioner and heat pump 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 F igure 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.

Figure 9.2.2 C entral A ir C onditioner and H eat P ump 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 200 8 and projections from

EIA’s AEO20 10 for the period 2010

– 203 5

. Fo r 203 6 −204 5 , DOE froze completions at the 203 5

level for all three economic scenarios

. DOE used the same data sources that were used for the

residential market to estimate n ew construction starts for a Reference case, a High Economic Growth case, and a Low Economic Growth case. 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; AEO20099-13 9.2.2.2 Furnaces

For the residential market segment, DOE developed new construction market saturations

from Characteristics of New Housing data from the U.S . Census Bureau. Furnace historical and projected saturations are depicted in Figure 9.2.3 . DOE used historical new construction

saturations for each furnace product class to project future saturations for each class

. Unlike

central air conditioners and h eat pumps, furnace saturations have been relatively unstable. As a result, DOE used a ten

- year average from 2000

- 2009 to estimate future saturation s for non

weatherized gas (43.9% ) and mobile home gas furnaces (30.7% ), and a five

- year average from

2005

- 200 9 to estimate future saturations for oil furnaces (0.4% )

.

Figure 9.2.3 Furnace 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 2008 and projections from the DOE

Energy Information Administration (EIA)’s Annual Energy Outlook 20 10 ( AEO20 1 0 ) for the period 2010

– 2035. 8

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

1970 1975 1980 1985 1990 1995 2000 2005 2010

YearResidential New Construction Saturations Non-Weatherized Gas

Mobile Home Gas

Oil9-14 principle, only building and market saturation data are needed to allow the sh ipments model to estimate shipments to new construction and replacements. The third product channel, new owners, is more difficult to describe based on existing data.

9.2.3.1 Central Air Conditioners and Heat Pumps

For central air conditioners and heat pumps, th e historical shipments data (as described

above in section 9.2.1.1 ) can be used to calibrat e the shipments model and improve the representation of the new owner mar ket

.

To calibrate estimated shipments with the historical data, as represented by the following expression, DOE treated the new owner market N O

p (j) as a residual after shipments for new construction and replacements were subtracted from tot al shipments.



## ( **) (**  **) (**  **) (**  **)** j NC j Rpl j Ship j NO

p p p p 

### − **− =** 

The three terms on the left are known quantities (calculation of Rpl p (j) and N C

p (j ) are described above).

The results of the historical cali bration are shown in Table 9.2.3 . The values are

historical market shares of total central air conditioner and heat pump equipment in the new

owner channel. The residential market share is the total central air conditioner and heat pump shipments t o the new owner housing market divided by the number of households without either

central air conditioner s or heat pump s. The commercial market share is the number of total

central air conditioner and heat pump shipments to the new owner commercial building market divided by the c ommercial building stock not conditioned by central air conditioner and heat

pump equipment

. DOE determined the commercial building stock not conditioned by central air

conditioner and heat pump equipment by dividing the total stock in square feet by 500 square

fee t (i.e., the amount of square feet conditioned by one piece of central air conditioner and heat

pump equipment )

. DOE used product class market shares based on historical shipm ents (as

shown in 0 ) to calculate specific new owner market saturations for each central air conditioner

and heat pump product class.9-15 Table 9.2.3 Historical New Owner Market Shares of C entral A ir C onditioner and H eat

P ump 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 fro m 2008

- 2045

.

9.2.3.2 Furnaces

For furnaces, DOE did not deduce new owner shipments by calibrating to historical shipm ents. F or non

- weatherized gas furn aces, DOE used data from Decision Analysts’ American Home Comfort Survey to estimates the 7.7% of total shipments are to new owner. 10

9.3 DECISION MODEL FOR R EPLACEMENT S AND EQUIPMENT

SWITCHING For mobile home gas and oil furnaces, DOE assumed that there were no new owner shipments.

9.3.1 Decision Model for Replacements

In the Shipments Model

, replacements are modeled based on two sets of consumer purchase decisions. These decisions are influenced by the purchase price and operating cost of equipment, and therefore will be different in the base case and different CSLs. These decisions are modeled by estimating the purchase price elasticity for cooling and heating equipment. The purchase price elasticity is defined as the change in the percentage of consumers acquiring9-16 central air conditioner and heat pump 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.

9.3.1.1 Purchase Price Elasticity

DOE conducted a literature review and an analysis of appl iance price and efficiency data to estimate the combined effects on product shipments from increases in product purchase price,

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

- A provides a detailed explanation of the metho dology DOE used to quantify the impacts from the se variables.

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

inelastic. Other information in the literature suggests that appliances are a normal good, s o that rising incomes incre ase the demand for appliances, 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 period 1980

- 2002 on large applian ce purchase s t o evaluate broad market trends and conduct simple regression analyse s. These data indicate that there has been a rise in appliance shipments and a decline in appliance purchase price and operating cost s over the time period. Household income has also risen during this time

. Because purchase decisions are sensitive to income, 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 va riable was used in a regression analysis to parameterize historical market

trends

. The relative price is defined with the following expression:

Income PVOC PP

Income TP RP **+**

**= =** 

W here:

RP = r elative price,

TP = t otal price,

Income = h ousehold income,

PP = a ppliance purchase price, and

PVOC = p resent value of operating cost , based on an impli cit discount rate of 37

percent

.

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.9-17 DOE’s regression analysis of the relevant data suggests that the relative price elasticity of

demand is approximately

- 0.34 ; this impl ies 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 ef fect 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 important ly , the measure is based on an assumption that economic vari ables, including purchase price, operating costs, and household income , explain most of the trend in appliances per

household in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but DOE did not account for them in this analysis. Despite these uncertainties, DOE believes that its estimate of the relative price elasticity of demand provides a reasonable assessment of the impact that purchase price, operating cost,

and household income h ave 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 c an be affected by a new standard that changes consumer expectations about equipment performance

. DOE considered the relative price elasticity provided by the prece ding 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. 11

Table 9.3.1 This study shows that the automobile price elasticity of demand changes in the years fo llowing a purchase price change, becom ing smaller (more inelastic) until it reach es 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 , 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 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

9.3.1.2 Impact from Increase in Relative Price

Using the r elative 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, dMS p j , which is applied in the decision for replacement versus extended repair. As noted in section 9.1.1, for the new construction and new

owner market channel s , the placement of equipment is automatic. In other words, DOE assumed

that the new construction and new owner market channel s were unaffected by changes in either the total installed cost or operating costs of the equipment as well as changes in household

income.9-18 

## ( **) (**  **) (**  **)** j e j base

_ RP j std

_ RP dMS
RP

p p p j **× **  ** **  ****

** **  ** ** 
** **  ** **  ** **  ** ** 

**− =**  1

Where:

d

MS 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

e

RP (j) = relative price elasticity in year j

.

9.3.1.3 Replacement Shipment Impacts

For replacement purchases of central air conditioner s, heat pump s, and furnaces, 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, dMS p 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:



## ( **)** 

## ∑ **<**

**− ×**  **=**

a p

i i ) a j ( for dMS a

, j m Re XR 2016



## ( **) (**  **)** 

## ∑

**− +** 

**− =** 

**−**

a j i j Dem XR XR ) a

, j ( m Re j Rpl
6

Where :

d

MS 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

, R

p l(j) = replacement units in year j

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

.9-19 9.3.2 Equipment Switching

9.3.2.1 Split System Heat Pump s: Switch to Combination Split System Central Air

Conditioner and Electric Furnace

DOE conducted an analysis of the potential for equipment s witching between a split system heat pump and the combination of a split system central air conditioner and electric furnace. To estimate the likelihood of equipment switching between these two systems, DOE utilized proprietar y data from Decision Analysts, 10 which identified for a representative sample of consumers their willingness to purchase more

- efficient space

- conditioning systems. From these data, DOE deduced the payback period that consumers would expect for a more

- expensive

but more

- efficient produc t. For each pairing of split heat pump and split air conditioner efficiency levels, DOE applied the payback period criterion to estimate the fraction of consumers who would be expected to switch to the other type of equipment. For example, when comparing a 15 SEER split system heat pump and a combination of a 14 SEER split air conditioner and an electric furnace, DOE calculated the payback period of the more

- efficient split system heat pump

relative to the less

- expensive combination of split air conditione r and electric furnace. If the resulting payback period for the split system heat pump exceeded the expected payback period deduced from the Decision Analysts’ data, DOE forecasted that the consumer would switch to the combination of split air conditioner and electric furnace.

For every possible pairing of split system heat pump and split system air conditioner efficiencies, DOE calculated the fraction of consumers who would be expected to switch from split system heat pumps to combination split system a ir conditioners and electric furnaces. The fraction of consumers switching was in turn used by DOE to forecast split system heat pump and split system air conditioner shipments in specific standards cases, as well as the increase in electric furnace shipme nts. Including the latter in accounting for the impacts of equipment switching is important for proper determination of national energy savings and national economic impacts.

For those split system heat pump consumers switching to a combination split sy stem air conditioners and electric furnace, DOE determined the space

- cooling annual energy use or unit energy consumption (UEC) of the air conditioner by multiplying the split system heat pump

space

- cooling UEC by the ratio of the heat pump SEER to the air conditioner SEER. DOE determined the space

- heating UEC of the electric furnace by multiplying the split system heat

pump space

- heating by the ratio of the heat pump HSPF to the electric furnace efficiency ( coefficie nt of performance (COP) of 1.0 which is equivalent to a 3.41 HSPF)

. DOE determined the total installed cost of the combination system based on the SEER level of the split system air conditioner plus the cost of an electric furnace.

Appendix 9

- B provides a more detailed explanation of the met hodology and results DOE used to quantify the impacts of equipment switching on split system heat pump consumers

.9-20 9.3.2.2 Gas Furnace Equipment Switching

DOE did not explicitly quantify the potential for fuel switching from gas furnaces to electric heating equip ment. DOE conducted a thorough review of the 2005 Residential Energy Consumption Survey (RECS) to assess the type of space

- heating system utilized by consumers as a function of house heating load. 12 Gas furnaces are primarily utilized in households with hig h heating loads, while electric space heating systems are almost exclusively used in households with low heating loads. Generally, this is because the operating costs of electric space heating systems are relatively high due to the price of electricity, s o using an electric system in a cold climate is significantly more expensive than using a gas furnace. Based on the above finding, DOE inferred that consumers with high heating loads would be unlikely to switch to electric space heating systems as a resul t of amended standards.

In addition, for a household with a gas furnace to switch to electric space heating, a separate circuit up to 30

- amps would need to be installed at a cost of approximately $300 to power the electric resistance heater within an electric furnace or heat pump system. On average, the electrical circuit cost is approximately 60 percent of the added installation cost of a more expensive venting system required for high

- efficiency, condensing furnaces, further diminishing

the likeliho od of a consumer switching from gas to electric heating.

Appendix 9

- B provides a detailed explanation of the methodology and results DOE used

to estimate that fuel switching from gas furnaces to electric heating equipment is very unlikely.

9.3.2.3 Other Equipment Switching

DOE did not consider any other equipment switching other than for split system heat pumps and gas furnaces. In other words, DOE estimated that consumers of split system and single package air conditioners, single package heat pumps, mo bile home gas furnaces, and oil furnaces would not switch to another type of space

- conditioning system regardless of the

standard level.

9.4 REGIONAL SHIPMENTS M ODEL

As described in c hap ter 7 , Energy Use Determination, some of the CSLs involve the

division of the U.S. into climate regions; hot

- humid, hot

- dry, and rest

- of

- country for central air conditioners and heat pumps and the North and South for gas furnaces . 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 climate regions, a separate shipments model is run for each region, and the results are summed to provide national

level output under a regional standard sce nario. 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 definition s of the various data inputs for the region in question. This section describes how these data inputs were disaggregated.9-21 Not every dat a input can be or needs to be defined at a regional level. DOE assumed t he product lifetimes, purchase prices

, and relative price elasticities did not vary by region. Product

o perating costs do vary by region and this information is available from the LCC and PBP analysis (see c hapter 8) . For the shipments model, historical shipments, housing starts and housing stock, commer cial buildings starts and stock (for central air condi tioners and heat pump only)

, and market saturations for new construction must be developed for each region . The following sections describe the development of regional historical shipments, building starts and stock, and market saturations.

9.4.1 Central Air Conditioners and Heat Pumps

9.4.1.1 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). 13

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 spe cified 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: AHS9-22 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.

Table 9.4.3 CAC Shipment Regional Allocation Factors

Year Hot

- Dry Hot

- Humid Rest U.S.

1983 16.0% 50.0% 34.0%

1987 16.0% 45.0% 39.0%

1990 16.0% 40.0% 44.0%

1993 16.0% 40.0% 44.0%

1997 16.0% 40.0% 44.0%

2001 16.0% 40.0% 44.0%

2003 16.0% 40.0% 44.0%

2005 16.0% 40.0% 44.0%

2007 16.0% 40.0% 44.0%

Table 9.4.4 HP Shipment Regional Allocation Factors

Year Hot

- Dry Hot

- Humid Rest U.S.

1983 12.0% 70.0% 18.0%

1987 12.0% 70.0% 18.0%

1990 12.0% 70.0% 18.0%

1993 12.0% 70.0% 18.0%

1997 12.0% 70.0% 18.0%

2001 12.0% 70.0% 18.0%

2003 12.0% 70.0% 18.0%

2005 12.0% 70.0% 18.0%

2007 12.0% 70.0% 18.0%

DOE further divide d the above allocation factors for CAC and HP into factors specific to the split and single package product classes. The regional allocation factors by product class were based on regional shipments data submitted by AHRI for the years 2008 and 2009. Table9-23 9.4.5 provides regional shipment market share data for the three climate regions (hot

- dry, hot

humid, and rest

- of

- country). For each product class, the market share values total to 100%.

Table 9.4.5 Central Air Conditioner and Heat Pump Regional Shipment Market Sha res*

Year Product Class Split System CAC Split System HP Single Package CAC Single Package HP

Hot

Dry Hot

Humid Rest of

U.S. Hot

Dry Hot

Humid Rest of

U.S. Hot

Dry Hot

Humid Rest of

U.S. Hot

Dry Hot

Humid Rest of

U.S.

2008 10.9% 50.2% 38.9% 7.2% 72.3% 20.5% 25.7% 55.4% 18.9% 24.6% 72.0% 3.4%

2009 11.4% 52.7% 35.9% 6.8% 74.9% 18.3% 23.9% 60.2% 15.9% 25.0% 71.8% 3.2%

Source : AHRI

* Market shares for each product class add up to 100%.

To estimate product class allocation factors for years prior to 2008, DOE first estimated single package CAC and HP market shares for 2008 were representative of prior years. With this assumption, DOE was able to deduce the markets shares for the split sy stem product classes. Tables 9.4.6 and 9.4.7 provide the regional allocation factors for the split and single package central air conditioners and heat pumps.

Table 9.4.6 CAC Shipment Regional Allocation Factors by Product Class

Split System CAC Single Package CAC

Year Hot

- Dry Hot

- Humid Rest U.S. Hot

- Dry Hot

- Humid Rest U.S.

1983 14.8% 49.3% 35.9% 25.7% 55.4% 18.9%

1987 15.0% 43.9% 41.2% 25.7% 55.4% 18.9%

1989 15.0% 38.4% 46.7% 25.7% 55.4% 18.9%

1991 14.8% 38.0% 47.2% 25.7% 55.4% 18.9%

1993 14.6% 37.8% 47.5% 25.7% 55.4% 18.9%

1995 14.8% 38.2% 47.0% 25.7% 55.4% 18.9%

1997 14.9% 38.2% 46.9% 25.7% 55.4% 18.9%

1999 15.0% 38.5% 46.5% 25.7% 55.4% 18.9%

2001 15.1% 38.6% 46.3% 25.7% 55.4% 18.9%

2003 15.1% 38.6% 46.3% 25.7% 55.4% 18.9%

2005 15.2% 38.7% 46.1% 25.7% 55.4% 18.9%

2008 10.9% 50.2% 38.9% 25.7% 55.4% 18.9%

2009 11.4% 52.7% 35.9% 23.9% 60.2% 15.9%9-24 Table 9.4.7 HP Shipment Regional Allocation Factors by Product Class

Split System HP Single Package HP

Year Hot

- Dry Hot

- Humid Rest U.S. Hot

- Dry Hot

- Humid Rest U.S.

1983 9.7% 69.6% 20.6% 24.6% 72.0% 3.4%

1987 10.2% 69.7% 20.1% 24.6% 72.0% 3.4%

1989 10.2% 69.7% 20.0% 24.6% 72.0% 3.4%

1991 10.0% 69.7% 20.3% 24.6% 72.0% 3.4%

1993 10.1% 69.7% 20.2% 24.6% 72.0% 3.4%

1995 10.2% 69.7% 20.1% 24.6% 72.0% 3.4%

1997 10.0% 69.7% 20.3% 24.6% 72.0% 3.4%

1999 10.2% 69.7% 20.0% 24.6% 72.0% 3.4%

2001 10.2% 69.7% 20.0% 24.6% 72.0% 3.4%

2003 10.4% 69.7% 19.9% 24.6% 72.0% 3.4%

2005 10.5% 69.8% 19.7% 24.6% 72.0% 3.4%

2008 7.2% 72.3% 20.5% 24.6% 72.0% 3.4%

2009 6.8% 74.9% 18.3% 25.0% 71.8% 3.2%

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 value s are relatively close to the values

from the AHS.9-25 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)

9.4.1.2 R egional New Construction and New Construction Saturations

Regional New Construction

AEO20 10 provides housing forecasts by Census D ivision. 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 inter section of hot

- humid with South Atlantic contains

the state of Flori da. (T he list of states included in each climate region is defined in c hapter 7 ). 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

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

- Dry

CAC calculated HP calculated

CAC RECS/AHS HP RECS/AHS

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

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

- Humid

CAC calculated HP calculated

CAC RECS/AHS HP RECS/AHS

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

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008Saturation CAC & HP Saturation Rest of U.S.

CAC calculated HP calculated

CAC RECS/AHS HP RECS/AHS

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

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008Saturation CAC & HP Saturation Entire U.S.

CAC calculated HP calculated

CAC RECS/AHS HP RECS/AHS9-26 The U.S. Census provides historical population, and population projections, by state at five

- year intervals for the years 2005

- 2030. 14 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 five

- year period. Next,

th e one

- year data were aggregated to provide r egion al populations

. P rojections out to 2045 were constructed by assuming that the r ate of growth in each region would remain c onstant for the years after 2030 . The constant in each region was defined as the aver age of the U.S. Census growth rates for 2025 and 2030. Sample output from this calculation is represented in Table

9.4. 2 , which shows the population in each area defined by the overlap of a cli mate region with a

census division. For example, both the hot

- d ry 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 9.4.8 Population Projected for 2020 for A rea s defined by the Overlap of Census

Division s with Climate R egions

Region

Census Division H ot

- D ry H ot

- H umid R est

- of

- C ountry 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

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 regio ns.

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.

Regional New Construction Saturations DOE developed residential regional ne w construction saturations from Characteristics of New Housing data from the U.S. Census Bureau. 3 The data breaks down central air conditioner

and heat pump saturations for single

- family and multi

- family households into the four Cen sus Regions (i.e., northeast, midwest, south, and west). DOE used population allocation factors (as9-27 discussed previously) to properly allocate saturations into the three climate regions (i.e., hot

humid, hot

- dry, and rest

- of

- country). Figures 9.4.1 and 9 .4.2 depict the national and regional

saturations for central air conditioner s and heat pump s, respectively. The regional saturations depicted in the figures below were determined using the entire U.S. housing stock. I n other words, the regional saturati ons when summed equal the national saturation.

Figure 9.4.2 C entral A ir C onditioner National and Regional Saturations in

Residential New Construction 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-Dry9-28 Figure 9.4.3 H eat P ump National and Regional Saturations in Residential New

Construction

For the commercial market segment, D OE 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 st arts from AEO2009

. The assembled data breaks down

central air conditioner and heat pump saturations into nine Census Divisions. DOE used population allocation factors (as discussed previously) to properly allocate saturations into the three climate regions (i.e., hot

- humid, hot

- dry, and rest

- of

- country). Figures 9.4.3 depicts the

national and regional saturations for central air conditioner and heat pump 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 equ al 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

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

Hot-Humid
Hot-Dry9-29 Figure 9.4.4 C entral A ir C onditioner and H eat P ump National and Regional

Saturations in Commercial New Construction

9.4.2 Furnaces

9.4.2.1 Allocation of Historical Shipments to Regions

DOE developed regional allocation factors to divide historical shipments into each of the climatic regions. To maintain consistency with the treatment of central air conditioning and heat pump equipment, DOE divided furnace shipments into three regions: ho t

- humid and hot

- dry, which comprise the South, and the rest

- of

- the

- country, which comprises the North. The regional

allocation factors from non

- weatherized gas and mobile home gas furnaces were based on regional shipments data submitted by AHRI for the yea rs 1992 through 2009. For oil furnaces,

based the market shares on data from the AHS. Table 9.4. 3 provides the reg ional shipment market share data for the three climate regions . For each product class, the market share values

total to 100%. 0% 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-Dry9-30 Table 9.4.9 Furnace R egional Shipment Market Shares*

Year Product Class

Non

- Weatherized Gas Mobile Home Gas Oil

Hot

- Dry Hot

Humid Rest of

U.S. Hot

- Dry Hot

Humid Rest of

U.S. Hot

- Dry Hot

Humid Rest of

U.S.

1992 13.6% 31.8% 54.5% 11.7% 30.0% 58.4%

- 19.1% 80.9%

1993 13.2% 35.0% 51.7% 11.7% 30.0% 58.4%

- 19.1% 80.9%

1994 11.9% 34.2% 53.9% 11.7% 30.0% 58.4%

- 19.1% 80.9%

1995 11.6% 34.2% 54.2% 11.9% 31.4% 56.7%

- 19.1% 80.9%

1996 11.0% 35.9% 53.1% 12.9% 33.2% 53.9%

- 19.1% 80.9%

1997 12.5% 34.8% 52.6% 13.6% 33.2% 53.2%

- 19.1% 80.9%

1998 13.1% 35.6% 51.3% 13.7% 34.1% 52.1%

- 19.1% 80.9%

1999 13.0% 35.2% 51.9% 14.3% 32.1% 53.6%

- 19.1% 80.9%

2000 13.4% 34.7% 51.8% 15.4% 28.5% 56.1%

- 19.1% 80.9%

2001 14.6% 36.7% 48.7% 16.7% 26.8% 56.5%

- 19.1% 80.9%

2002 14.1% 36.8% 49.2% 17.3% 25.5% 57.2%

- 19.1% 80.9%

2003 15.1% 35.2% 49.8% 20.8% 23.3% 55.9%

- 19.1% 80.9%

2004 13.8% 38.0% 48.2% 24.3% 24.8% 50.9%

- 19.1% 80.9%

2005 13.6% 37.5% 48.8% 26.0% 29.0% 45.0%

- 19.1% 80.9%

2006 13.7% 37.9% 48.4% 26.4% 28.1% 45.5%

- 19.1% 80.9%

2007 13.4% 36.5% 50.1% 21.0% 30.9% 48.1%

- 19.1% 80.9%

2008 12.2% 32.8% 55.0% 17.4% 35.5% 47.1%

- 19.1% 80.9%

2009 12.3% 32.6% 55.1% 15.5% 36.2% 48.3%

- 19.1% 80.9%

Source s : Gas Furnaces: AHRI ; Oil Furnaces: AHS

* Market shares for each product class add up to 100%.

9.4.2.2 Regional New Construction and New Construction Saturations

Regional New Construction For furnaces, residential regional new construction data were based on the same data sources and methodology used for central air conditioners and heat pumps. Refer to the description in section 9.4.1.2.

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 furance 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, hot

- dry, and rest

- of

country). Figures 9.4.4 and 9.4.5 depict the national and regional saturations for central air conditioners and heat pumps, respectively. The regional saturations depicted in the figures9-31 below were determined using the entire U.S. housing stock. In other words, the regional

saturations when summed equal the national saturation.

Figure 9.4.5 Non

- Weatherized Gas Furnace National and Regional Saturations in

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

1980 1990 2000 2010 2020 2030 2040 2050

YearNon-Weatherized Gas Furnace ResidentialNew Consturction Saturations National

North

South - Hot Humid

South - Hot Dry

`9-32 Figure 9.4.6 Mobile Home Gas National and Regional Saturations in Residential New

Construction

Figure 9.4.7 Oil Furnace National and Regional Satu rations in Residential New

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

1980 1990 2000 2010 2020 2030 2040 2050

YearMobile Home Gas Furnace Residential NewConsturction Saturations National

North

South - Hot Humid

South - Hot Dry

`

0.00% 0.20% 0.40% 0.60% 0.80% 1.00% 1.20% 1.40% 1.60% 1.80% 2.00%

1980 1990 2000 2010 2020 2030 2040 2050

YearOil Furnace Residential New ConsturctionSaturations National

North

South - Hot Humid

`9-33 9.5 R ESULTS

This section presents the shipments forecasts for the base case for central air conditioner s
, heat pump s , and furnaces. For each major product category, figures are presented showing base case shipments by product class, product channel (new construction, replacements, and new owners), and region (hot

- dry, hot

- humid, and rest

- of

- country for central air conditioners and heat pumps, and North and South for furnaces).

Also presented are the cumulative impacts to central air conditioner, heat pump, and furnace shipments due to various standard levels.

9.5.1 Base Case Shipments

9.5.1.1 C entral Air Conditioners

Figure 9.5.1 shows the base case shipments for central air conditioners by product class. Because split central air conditioner shipments are divided into coil

- only and blower coil units, shipments for sp lit systems are shown by product type. Figure 9.5.2 shows the base case shipments for central air conditioners by product channel while Figure 9.5.3 shows the base case shipments for central air conditioners by region.

Figure 9.5.1 C entral A ir C onditioner s : Base Case Shipments by Product Class and Product Type 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

Single Package

Split System Blower Coil

Split System

- Coil Only9-34 Figure 9.5.2 Central Air Conditioners: Base Case Shipments by Product Channel

Figure 9.5.3 Central Air Conditioners: Base Case Shipments by Region 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

Replacement and New Owners

New Construction

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions) Rest of Country

Hot Humid

Hot Dry9-35 9.5.1.2 Heat Pump s

Figure 9.5.4 shows the base case shipments for heat pumps by product class. Figure 9.5.5 shows the base case shipments for central air conditioners by product channel while Figure 9.5.6 shows the base case shipments for central air conditioners by region.

Figure 9.5.4 Heat Pumps: Base Case Shipments by Product Class

Figure 9.5.5 Heat Pumps: Base Case Shipments by Product Channel 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

Single Package

Split System

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

Replacements and New

Owners

New Construction9-36 Figure 9.5.6 Heat Pumps: Base Case Shipments by Region

9.5.1.3 Furnaces

Figure 9.5.7 shows the base case shipments for heat pumps by product class. Figure 9.5.8 shows the base case shipments for central air conditioners by product channel while Figure 9.5.9 shows the base case shipments for central air conditioners by region.

Figure 9.5.7 Furnaces: Base Case Shipments by Product Class 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

Rest of Country

Hot Dry

Hot Humid

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

Mobile Home Furnaces

Oil Furnaces

Non

- Weatherized Gas

Furnaces9-37 Figure 9.5.8 Furnaces: Base Case Shipments by Product Channel

Figure 9.5.9 Furnaces: Base Case Shipments by Region 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

Replacements and New

Owners

New Construction

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

2010 2015 2020 2025 2030 2035 2040 2045Shipments (millions)

South

North9-38 9.5.2 Shipments Impacts due to Standards

As detailed i n chapter 10, DOE created trial standard levels ( TSLs) t hat combine specific efficiency levels across product classes. DOE analyzed the benefits and burdens of a number of TSLs for the products considered in this rulemaking.

Table 9.5.1 shows the TSLs DOE analyzed for furnaces and central air conditioners an d heat pumps. In the table SAC means split system central air conditioners, SHP means split system heat pumps, PAC means single package central air conditioners, PHP means single package heat pumps, SDHV means small duct high velocity systems, NWGF means non

weatherized gas furnaces, OF means oil furnaces, and MHF means mobile home furnaces. SDHV systems are not explicitly addressed in the shipments analysis due to their very low shipments.9-39 Table 9.5.1 Trial Standard Levels for Central Air Conditioners and Heat Pumps and Furnaces (Energy Efficiency)

SAC SHP PAC PHP SDHV NWGF OF MHF

TSL Region Applicable SEER SEER/HSPF SEER SEER/HSPF SEER/HSPF AFUE AFUE AFUE

7 Rest of Country* 22** 20 / 9.9 † 16.5 16.5 / 9.0 14.5 / 8.6 98% 97% 96%
North (Furnace) 98% 96%
Hot Humid (CAC

- HP) 22** 20 / 9.9 † 16.5 16.5 / 9.0 14.5 / 8.6
Hot Dry (CAC

- HP) 22** 20 / 9.9 † 16.5 16.5 / 9.0 14.5 / 8.6

6 Rest of Country* 14 15 / 8.5 15 15 / 8.4 14 / 8.5 80% 85% 80%
North (Furnace) 95% 96%
Hot Humid (CAC

- HP) 15 15 / 8.5 15 15 / 8.4 14 / 8.5
Hot Dry (CAC

- HP) 15 15 / 8.5 15 15 / 8.4 14 / 8.5

5 Rest of Country* 13 14 / 8.2 14 14 / 8.0 13 / 7.7 80% 85% 80%
North (Furnace) 95% 96%
Hot Humid (CAC

- HP) 14 14 / 8.2 14 14 / 8.0 13 / 7.7

Hot Dry (CAC

- HP) 14 14 / 8.2 14 14 / 8.0 13 / 7.7

4 †† Rest of Country* 13 14 / 8.2 14 14 / 8.0 13 / 7.7 80% 83% 80%
North (Furnace) 90% 90%
Hot Humid (CAC

- HP) 14 14 / 8.2 14 14 / 8.0 13 / 7.7

Hot Dry (CAC

- HP) 14 /

12.2 &

11.7 EER 14 / 8.2 14 /

11 EER 14 / 8.0 13 / 7.7

3 Rest of Country* 13 14 / 8.2 14 14 / 8.0 13 / 7.7 80% 83 % 80%
North (Furnace) 90% 90%
Hot Humid (CAC

- HP) 14 14 / 8.2 14 14 / 8.0 13 / 7.7
Hot Dry (CAC

- HP) 14 14 / 8.2 14 14 / 8.0 13 / 7.7

2 Rest of Country* 13.5 13.5 / 8.1 13 13 / 7.7 13 / 7.7 80% 82 % 80%
North (Furnace) 92% 80%
Hot Humid (CAC

- HP) 13.5 ‡ 13.5 / 8.1 ‡ 13 ‡ 13 / 7.7 ‡ 13 / 7.7
Hot Dry (CAC

- HP) 13.5 ‡ 13.5 / 8.1 ‡ 13 ‡ 13 / 7.7 ‡ 13 / 7.7

1 Nation 13.5 13.5 / 8.1 13 13 / 7.7 13 / 7.7 80% 82% 80%

* The values presented under “Rest of Country” are the national standards, but they effectively apply to those States not subject to regional standards. Rest of Country refers to the Northern region for air conditioners and heat pumps and to the Southern region for NWGF and MHF. For OF, the value refers to the entire Nation.

** Max

- tech of 22 SEER pertains to 3

- ton blower

- coil units only, which is the most common cooling capacity of products on

the market. Max

- tech efficiencies vary by tonna ge and type. See chapter 5 of the TSD for more information on the max

- tech efficiency levels.

† Max

- tech of 20 SEER / 9.9 HSPF pertains to 3

- ton units only, which is the most common cooling capacity of products on

the market. Max

- tech efficiencies vary by tonnage. See chapter 5 of the TSD for more information on the max

- tech efficiency

levels.

†† Compliance date is 1/1/2015 for central air conditioners and heat pumps and 5/1/2013 for furnaces. For SAC, TSL 4 has separate EER levels based on capacity (see s ection III.B.2).

‡ Largest market share unknown; assumed to be equal to the market share for entire Nation.

Table 9.5.2 shows the base case cumulative shipments and the impacts to cumulative shipments due to the various TSLs. Decreases in shipments are represented as positive values while increases in shipments are represented by negative values. Shipments impac ts for the various product classes are for the most part small. The most significant impact on product shipments is due to equipment switching between split system coil

- only central air conditioners9-40 (SAC

- CO) and split system heat pumps (SHP). For TSLs 3, 4, 5, and 6 where there is a difference in the efficiency standards between SAC

- CO and SHP for the ‘rest of country’ region, the impact from the TSLs is to increase SAC

- CO shipments and to decrease SHP shipments.

Table 9.5.2 Base Case Cumulative Shipments and Shipme nts Impacts due to TSLs

Region SAC

- CO SAC

- BC PAC SHP PHP NWGF MHF OF

Base Case Shipments (millions)

Hot

- Dry 24.203 2.689 3.930 6.723 3.307 16.920 0.892 0.000

Hot

- Humid 47.717 5.302 6.407 44.365 5.632 32.335 0.989 0.291

Rest of U.S. 73.507 8.167 4.410 13.715 2.381 55.228 1.966 1.493

U.S. 145.427 16.159 14.747 64.803 11.319 104.483 3.846 1.784

TSL 1 Shipments Impacts * (millions)

Hot

- Dry 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hot

- Humid 0.001 0.000 0.000 0.001 0.000 0.000 0.000 0.000

Rest of U.S. 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000

U.S. 0.004 0.001 0.000 0.001 0.000 0.000 0.000 0.000

TSL 2 Shipments Impacts * (millions)

Hot

- Dry 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hot

- Humid 0.001 0.000 0.000 0.001 0.000 0.000 0.000 0.000

Rest of U.S. 0.002 0.000 0.000 0.000 0.000 0.040 0.000 0.000

U.S. 0.004 0.001 0.000 0.001 0.000 0.040 0.000 0.000

TSL 3 Shipments Impacts* (millions)

Hot

- Dry 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hot

- Humid 0.005 0.001 0.000 0.003 0.000 0.000 0.000 0.000

Rest of U.S.

- 0.046 0.000 0.000 0.052 0.000 0.035 0.006 0.000

U.S.

- 0.040 0.001 0.000 0.056 0.000 0.035 0.006 0.000

TSL 4 Shipments Impacts* (millions)

Hot

- Dry 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hot

- Humid 0.001 0.000 0.000 0.002 0.000 0.000 0.000 0.000

Rest of U.S.

- 0.048 0.000 0.000 0.054 0.000

- 0.001 0.000 0.000

U.S.

- 0.047 0.000 0.000 0.055 0.000

- 0.001 0.000 0.000

TSL 5 Shipments Impacts* (millions)

Hot

- Dry 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hot

- Humid 0.005 0.001 0.000 0.003 0.000 0.000 0.000 0.000

Rest of U.S.

- 0.046 0.000 0.000 0.052 0.000 0.061 0.012 0.002

U.S.

- 0.040 0.001 0.000 0.056 0.000 0.061 0.012 0.002

TSL 6 Shipments Impacts* (millions)

Hot

- Dry 0.065 0.001 0.000 0.001 0.000 0.000 0.000 0.000

Hot

- Humid 0.166 0.002 0.000 0.012 0.000 0.000 0.000 0.000

Rest of U.S.

- 0.038 0.001 0.000 0.058 0.000 0.061 0.012 0.002

U.S. 0.193 0.004 0.000 0.071 0.000 0.061 0.012 0.002

TSL 7 Shipments Impacts* (millions)

Hot

- Dry 0.172 0.009 0.000 0.012 0.000 0.116 0.008 0.000

Hot

- Humid 0.433 0.022 0.000 0.104 0.000 0.172 0.006 0.002

Rest of U.S. 0.632 0.034 0.000 0.039 0.000 0.083 0.012 0.006

U.S. 1.237 0.065 0.000 0.155 0.000 0.372 0.026 0.007 *Positive values represent decreases in shipments; negative values represent increases in shipments.

DOE also created trial standard levels ( TSLs) for specific efficiency levels that limit off

mode power consumption across product classes. Although the TSLs for off

- mode power9-41 consumption did increase product cost, DOE estimated the cost increase was so small as to not impact product shipments.9-42 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 Energ y Star Specification for Light Commercial HVAC Equipment,” Jan. 30, 2009.

2 Air

- Conditioning, Heating and Refrigeration Institute. Historical shipments and shipment

weighted efficiency data submittal. December 2008.

3 Air

- Conditioning, Heating and Re frigeration Institute. Historical shipments data, 1972

- 1989

– provided to Lawrence Berkeley National Laboratory.

4 Air

- Conditioning, Heating and Refrigeration Institute. Historical shipments data, 1972

- 1989

– provided to Lawrence Berkeley National La boratory.

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

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- Energy Information Administration. Annual Energy Outlook

2010

, May, 2010

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9 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/>

10 Decision Analyst. 2008 American Home Comfort Survey: O nline Database Tool. 2009. Arlington, Texas. (URL: http://www.decisionanalyst.com/Syndicated/HomeComfort.dai )

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

- 239.

12 U.S. Department of Energy

– Energy Information Administration. Residential Energy Consumption Survey, 2005 Public Use Data Files . 2009. U.S. Department of Energy: Washington, D.C. < www.eia.doe.gov/emeu /recs/recspubuse05/pubuse05.html >9-43 13 U.S. Census Bureau, Housing and Household Economic Statistics Division. American Housing Survey . Washington, DC.

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

14 U.S. Census Bureau, National Population Projections Released 2008. http://www.census.gov/population/www/projections/summarytables.ht ml