9-i CHAPTER 9. SHIPMENTS ANALYSIS
TABLE OF CONTENTS
9.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 9.2 SHIPMENTS MODEL EQUATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-4 9.2.1 Mathematical Formulation of the Shipments Model . . . . . . . . . . . . . . . . . . . . . 9-4 9.2.2 Stock Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5 9.2.2.1 Broken equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5 9.2.2.2 New equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6 9.2.2.3 Demolitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7 9.2.3 Stock Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-8 9.2.4 Probability Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-9 9.3 DATA INPUTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-10 9.3.1 Historical Shipments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-10 9.3.2 Historical Shipped Tonnage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-12 9.3.3 Commercial Floor Space and Market Saturation . . . . . . . . . . . . . . . . . . . . . . 9-13 9.3.3.1 Floor Space - New Construction (NFS) . . . . . . . . . . . . . . . . . . . . . . . . 9-14 9.3.3.2 Floor Space - Existing Stock (EFS) . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-14 9.3.3.3 Demolished Floor Space (DFS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-15 9.3.3.4 Market Saturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-15 9.3.3.5 Eligible Market and Maximum Market Constraint . . . . . . . . . . . . . . . 9-16 9.3.4 Equipment Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-16 9.3.4.1 Equipment Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-16 9.3.4.2 Operating Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-18 9.3.4.3 Discounted Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-19 9.3.4.4 Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-19 9.3.5 Model Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-19 9.3.5.1 Parameter Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-20 9.3.5.2 Model Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-20 9.3.5.3 Market Constraints and Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-20 9.4 RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-21 9.4.1 

### $ 65,000 Btu/h to <135,000 Btu/h Equipment Class . . . . . . . . . . . . . . . . . . . . 9-21 9.4.2 

### $ 65,000 Btu/h to <135,000 Btu/h Equipment Class . . . . . . . . . . . . . . . . . . . . 9-24
LIST OF TABLES
Table 9.3.1 Market Saturations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-16 Table 9.3.2 Model and Market Constraints and Calibration Values . . . . . . . . . . . . . . . . . 9-21 Table 9.4.1 

### $ 65,000 Btu/h to <135,000 Btu/h: Forecasted Shipments for the Base Case and Standards Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-23 Table 9.4.2 

### $ 135,000 Btu/h to <240,000 Btu/h: Forecasted Shipments for the Base Case and Standards Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-269-ii LIST OF FIGURES Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model . . . . . . . . . . . . . . . . . . . . 9-3 Figure 9.3.1 Historical Shipments for Commercial Unitary Air Conditioners . . . . . . . . . . 9-11 Figure 9.3.2 Historical Percent of Shipped Tonnage for Commercial Unitary Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-13 Figure 9.3.3 Base Case Equipment Price Trend for Commercial Unitary Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-17 Figure 9.4.1 

### $ 65,000 Btu/h to <135,000 Btu/h: Base Case Shipments Forecast with Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-21 Figure 9.4.2 

### $ 65,000 Btu/h to <135,000 Btu/h: Forecasted Shipments for the Base Case and Standards Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-24 Figure 9.4.3 

### $ 135,000 Btu/h to <240,000 Btu/h: Base Case Shipments Forecast with Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-25 Figure 9.4.4 

### $ 135,000 Btu/h to <240,000 Btu/h: Forecasted Shipments for the Base Case and Standards Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-279-1 CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION Commercial unitary air-conditioning equipment shipments estimates are a necessary input to the national energy savings (NES) and net present value (NPV) calculations and the manufacturer impacts analysis (MIA). This chapter describes the Department’s methodology for projecting annual shipments and presents results. The Shipments Model results are driven primarily by historical shipments data for the two size categories of commercial unitary air-conditioning equipment under consideration ( 

### $ 65,000 Btu/h to <135,000 Btu/h, and 

### $ 135,000 Btu/h to <240,000 Btu/h). The Shipments Model explicitly accounts for the combined effects of changes in purchase price, annual operating cost, and the value of commercial floor space on the purchase decision. The model also accounts for decisions to extend the life of broken equipment through repairs rather than replacing it. These decisions depend on purchase price, annual operating cost, and commercial floor-space value. The Shipments Model considers three market segments: (1) new commercial buildings acquiring unitary equipment, (2) existing buildings replacing broken equipment, and (3) existing buildings acquiring new equipment for the first time. It considers two stock categories: (1) equipment that has received only normal maintenance repairs, and (2) equipment that has had its useful life extended through additional repairs. Both sets of stock are represented by a distribution giving the number of units as a function of the units’ age. The flow chart presented in Figure 9.1.1 outlines the structure of the Shipments Model. The model assumes that, in each year, the existing commercial unitary air-conditioning equipment stock either ages by one year or breaks. Broken equipment is either replaced or repaired. In addition, new equipment can be shipped into new or existing commercial floor space, and old equipment can be removed through demolitions (not shown in the diagram). The Shipments Model is in a Microsoft Excel spreadsheet format that is accessible on the Internet at: http://www.eere.energy.gov/buildings/appliance_standards/commercial/ac_hp.html. Further, Chapter 10, section 10.5 of this Technical Support Document (TSD) discusses how to access the Shipments Model spreadsheet and other related spreadsheets, and provides basic instructions for using them. The rest of this chapter explains the Shipments Model in more detail. Section 9.2 presents the mathematical formulation of the model, section 9.3 describes the data inputs to the model, and section 9.4 presents the results for different energy-efficiency standard-level scenarios. Shipments forecasts were determined for all of the candidate standard levels for which NES and NPV are required. Candidate standard levels of 10.5, 11.0, 11.5, and 12.0 EER are being considered for the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class while candidate9-2 standard levels of 10.0, 10.5, 11.0, 11.5, and 12.0 EER are being considered for the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class. In addition, the Department determined the NES and NPV due to quick adoption of the ASHRAE/IES NA 90.1-1999 candidate standard levels. Quick adoption in this case is defined as an effective date of 2004 as opposed to an assumed effective date of 2008 for the more stringent standard levels currently under consideration by the Department. In this way, the Department can assess the national benefits of adopting more stringent standards at a later effective date relative to adopting the ASHRAE/IESNA 90.1-1999 standard levels almost immediately.9-3 Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model. (The boxes on the left correspond to the building or customer categories. The diamonds represent stock events and decisions – for example, equipment breaks and is repaired.)9-4 

## ( ) ( ) ( ) U ya U ya U ya , ,, = + 0 1


# ( ) ( ) ( ) StockAge y U ya a U ya a = ⋅ 

# ∑

∑ ,

, 9.2 SHIPMENTS MODEL EQUATIONS The Shipments Model is a description of commercial unitary air-conditioning equipment stock flows as a function of year and age. The Department calibrated the model to historical data for shipments of unitary air-conditioning equipment classes covered by the energy-efficiency standards. While there are two equipment classes, there is no coupling between them, so the equations describe each type of equipment independently. This section begins with a definition of the different commercial unitary air-conditioning stock categories. The Department formulates the equations as updates of the distribution of stock in year yas a function of age ato year y+1 . Then the Department presents the probabilities of different purchase decisions and, in the following section, provides a description of the data sources. In the equations below, DOE has converted the equipment units to squarefeet of air- conditioned space cooled by those units by taking the tonnage of unitary airconditioning equipment and dividing by the national average value of equipment capacity per square foot of commercial building floor space. 9.2.1 Mathematical Formulation of the Shipments Model The Department uses two commercial unitary air-conditioning equipment stock categories. The category U 0 (y,a) is the stock of existing units that have never had life-extending repairs. Whereas these units will undergo normal repairs that do not affect the lifetime of the equipment, such repairs are not explicitly modeled here. The category U 1 (y,a) is the stock of existing units that have had their useful life extended by additional repairs. In the model, repair extends the life of the unit by six years (consiste nt with values used in the residential airconditioning equipment Shipments Model 1 ). The total stock of age ain a given year yis represented by

and the average age of the stock in year yis defined as

where: U(y,a ) = total stock of age ain a given year y , U

0 (y,a ) = stock of existing units without life-extending repairs, U 1 (y,a ) = stock of existing units with extended life, StockAge (y ) = average age of stock in year y , a = age of stock, and y= year.9-5 

## ( ) ( ) 

## ( ) UB y a PB a U y a 0 00 , , = ⋅ 

## ( ) ( ) 

## ( ) UB y a PB a U y a 1 11 , , = ⋅ The shipments of new stock in a given year are Uship(y) . By definition, the age of the equipment is zero in the year that it is shipped, so that Uship(y) = U(y,0)

. 9.2.2 Stock Events In the transition from year y to year y+1 , six things could happen to the stock of commercial unitary air-conditioning equipment:



### C existing equipment could break and be replaced,



### C existing equipment could break and be repaired,



### C new equipment could be purchased to go into a new building,



### C new equipment could be purchased to go into a building that did not previously have it,



### C a building containing the equipment could be demolished, or



### C the stock could simply age by one year. In the model, early replacements (i.e., existing equipment that is replaced before it has broken) are not considered, and all broken equipment is replaced. The following sections present the equations used to represent each possible event. 9.2.2.1 Broken equipment The Department determines the probability that commercial unitary air-conditioning equipment of age afrom stock U 0 will break using a known function PB 0 (a) . Similarly, the probability that equipment of age afrom stock U 1 will break is given by a known function PB 1 (a) . These probabilities do not depend on the model year y . The Department defines the quantities of broken equipment as

where: UB 0 (y,a ) = stock of existing units without life-extending repairs that have broken, UB 1 (y,a ) = stock of existing units with extended life that have broken, PB 0 ( a ) = probability that stock of existing units without life-extending repairs will break, PB 1 ( a ) = probability that stock of existing units with extended life will break, U

0 (y,a ) = stock of existing units that have never had life-extending repairs, U 1 (y,a ) = stock of existing units with extended life,9-6 

## ( ) ( ) ( ) UR y a PR y U y a 0 00 , , = ⋅



## ( ) ( ) ( ) ( ) EFS y EFS y NFS y DFS y += + −

1



## ( ) ( ) ( ) UN y A PP y NFS y =⋅ ⋅ 0 a = age of stock, and y= year.
The stock UB 1 represents commercial unitary air-conditioning equipment that has broken for a second time, and will be entirely replaced. The stock UB 0is divided into two parts: stock that is repaired (and so becomes part of U 1 ), and stock that is replaced. The Department defines the probability that the broken stock represented by UB 0will be repaired as PR 0 (y) . This probability depends on the year, because the decision to repair depends implicitly on assorted cost parameters that vary with time. The probability does not depend on the age of the equipment, which has already been accounted for in the probability of breakdown. The Department then represents the number of repaired units, UR o (y,a), by

where: UR 0 (y,a ) = number of repaired units, PR 0 ( a ) = probability that broken stock of existing units without life-extending repairs will be repaired, U

0 (y,a ) = stock of existing units that have never had life-extending repairs,
a = age of stock, and y= year.
The total number of replaced units is the sum UB 0 (y,a) - UR 0 (y,a) + UB 1 (y,a)

. 9.2.2.2 New equipment New commercial unitary air-conditioning equipment will be purchased to replace the broken units described above. The Department assumes that the purchase of new equipment to go into new buildings, or into existing buildings acquiring this type of equipment for the first time, is driven by the rate of construction of commercial floor space. By definition:

where: EFS(y) = the square footage of existing commercial floor space in year y , NFS(y) = the square footage of new commercial floor space added in year y (described in section 9.3.3), and DFS(y) = the square footage demolished in year y (described in section 9.3.3). PP(y) is the probability of purchasing new commercial unitary air-conditioning equipment to go into a new building in year y . This probability depends on the utility of the equipment and is defined in section 9.2.4. The number of units going into new buildings is defined by:9-7 

# ( ) () () UEx y E y y PP y EFS y = ⋅ −

− ⋅ ⋅ 0 0 2000 2000



# () () ( ) ( ) UDem y a UB y a DFS y EFS y 0 0 

### , , = ⋅ where: UN(y) = the number of units going into new buildings, A 0= an overall scale factor which accounts for the number of units covered by the standard which are not used in all commercial building types, and PP(y) = probability of purchasing new units that to go into a new building in year y
. As stated above, the overall scale factor, A 0 , accounts for unitary air-conditioning equipment covered by the standard which is not used in all commercial building types, so that the eligible market share or maximum possible saturation is less than one. The Department will determine the value of A 0 by a fit to a model of the market saturation for the given equipment category. To describe the purchase of new units to go into existing buildings that did not previously use this type of unitary air-conditioning equipment, DOE must consider various factors. Generally, the probability of purchase is proportional to PP(y) , defined above, and to the quantity of existing floor space EFS(y) . This probability should be at its maximum in the years when the equipment is first introduced to the market, and should decline eventually to zero as purchase of the equipment for new buildings becomes more common. The simplest form this decline can take is a linear function that is at the maximum in year zero of the model (y 0 ), and at zero in some later year. The number of new units that are bought for the first time for existing buildings is defined by,

where: UEx(y) = the number of new units that are bought for the first time for existing buildings, E

0 = is a second scale factor that DOE determined from the model fit, and y 0 = the year 1960. For years y> 2000, UEx is zero.
9.2.2.3 Demolitions A number of commercial air-conditioning units— UDem(y,a) —will disappear with the demolished floor space DFS(y) . Since it is more likely that older units will disappear with older buildings, the age distribution of UDemshould look like the age distribution for broken units. The rate at which buildings are demolished in a given year is the ratio of DFS to EFS , so for the two stock categories, the number of demolished units is9-8 

# () () ( ) ( ) UDem y a UB y a DFS y EFS y 1 1 

### , , = ⋅



## ( ) () ( ) ( ) ( ) () 

# ( ) U y a UN y UEx y UB y a UR y a UB y a
a 0 0 0 1 

### 1 0 + == + + − + 

# ∑
, , , , 

## ( ) ( ) U y a Uship y

0 1 0 + == , 

## ( ) () 

# ( ) ( ) ( ) U y a PB a U y a UDem y a

0 0 0 0 101 1 1 + > =− ⋅ −− − , ,,



## ( ) Uy a

1 1 00 + == , 

## ( ) () 

## ( ) ( ) ( ) ( ) U y a PB a U y a UDem y a UR y a

1 1 1 10 101 1 1 + > =− ⋅ −− −+ , , ,, where: UDem 0 (y,a) = stock of existing units without life-extending repairs that have been removed due to demolished floor space, UDem 1 (y,a) = stock of existing units with extended life that have been removed due to demolished floor space, UB 0 (y,a ) = stock of existing units without life-extending repairs that have broken, and UB 1 (y,a ) = stock of existing units with extended life that have broken.

9.2.3 Stock Equations The Department separated the equations for the unitary air conditioner stock transition from yto y+1 into two components: U(y+1,a = 0) , representing new equipment (in year y ), and U(y+1,a > 0) , representing non-new equipment (i.e., equipment from earlier years with age greater than zero). The equations for the never-repaired stock U 0are:

For the stock U 1 ,

These equations must be initialized with U 0 (y 0 ,a) and U 1 (y 0 ,a) . Because of the lifeextending repairs, the age of units in stock U 1 may exceed the formal definition of the maximum physical lifetime of the unitary air-conditioning equipment. As a calibration, replacements should constitute about 70 percent of the annual shipments in the year 2000.9-9 

# ( ) ( ) ( ) ( ) Utility y ice y OpCost y Income y = + Pr



# ( )

( ) ( ) ln PP y PP y Utility y 1 1 1

−         = +⋅ α β



# ( ) ( ) ( ) ln 1 0

0 2 2 −         = +⋅ PR y PR y Utility y α β 9.2.4 Probability Equations To complete the description of the Shipments Model, this section provides equations for the probabilities PP(y) , PB 0 (a) , PB 1 (a) , and PR 0 (y) . In this analysis, the Department used a logit equation to model customer purchase decisions. In this model, the probability of purchasedepends on the utility of the unitary air-conditioning equipment, which in turn depends on timevarying economic and equipment parameters. The purchase probability is constrained to be between 0 and 1, and relative changes in the probability of purchase are proportional to changes in the utility of the appliance. The Department defines the utility of the equipment in year yas

The components of the utility are defined as follows:



### C Price(y) is the price of a unitary air conditioner of average efficiency in year y
.



### C OpCost(y) is the operating cost of the unitary air conditioner, including maintenance. It depends on the unit efficiency and on the price of electricity.



### C Income(y) is a normalization factor which is necessary because customer decisions to purchase unitary air-conditioning equipment depend on the purchase cost relative to the value of commercial floor space. The Department used the rental income per square foot for commercial buildings as a measure of this value.

The logit model takes the following function form:

An increase in purchase price with no change in income or operating cost would lead to a lower probability of purchase, which constrains the coefficients 

### " 1 and 

### $ 1 to be negative. The probability that a broken unit will be repaired versus replaced is also represented using a logit model, with the same utility but different coefficients. The Department defined the replacement probability to be9-10 

## ( ) () PB a LT s ds a
0 0 

### = 

# ∫

( ) ( ) PB a PB a 1 0 6 = − The equation takes this form because PR 0 is the probability that a unit will be repaired, so (1 - PR 0 ) is the probability that a new unit will be purchased. Because the probability that the unitary air conditioner will be repaired should increase as the purchase price increases (i.e., customers will be more likely to repair an existing unit if replacement unitary air conditioners are more expensive than expected), the coefficients 

### " 2 and 

### $ 2must be positive. The Department determined all four coefficients— 

### " 1 , 

### $ 1 , 

### " 2, and 

### $ 2—by fitting the Shipments Model to historical shipments data. To determine the probability that the stock U 0 of as-yet unrepaired equipment will break, DOE used a Weibull distribution function for the physical age of the stock. The Weibull distribution requires two parameters that are determined in the model by setting the average lifetime of the equipment to be approximately 15 years, and the maximum lifetime to be 30 years. Defining the lifetime function LT(a) to be the fraction of units that would survive to age
awith only normal repairs, the probability that the unit will break is the probability that it will not survive past age a . This gives:

For the stock of unitary air conditioners, U 1 , which have received life-extending repairs, DOE defined the lifetime function by shifting LT(a) by the increase in lifetime (6 years), which gives:

For consistency, DOE imposed the condition that PB 1 ( a < 6 ) = 0.

9.3 DATA INPUTS 9.3.1 Historical Shipments Historical shipments are critical to the development of the Shipments Model, since the Department used the historical data to calibrate the model. The Department’s primary source of historical data for shipments of unitary air-conditioning equipment is the U.S. Census Bureau’s Current Industrial Reports on Refrigeration, Air Conditioning, and Warm Air Heating Equipment

. 2The Air-Conditioning and Refrigeration Institute (ARI) also provided the Department with confidential shipments data. The Department used the ARI data to validate the accuracy of the Census Bureau data and, where necessary, to disaggregate the Census Bureau shipments into finer equipment categories and cooling capacity ranges. For the two unitary air-conditioning equipment classes of interest to this analysis ( 

### $ 65,000 Btu/h to <135,000 Btu/h, and 

### $ 135,000 Btu/h to <240,000 Btu/h), the Census Bureau provides shipments data in the following equipment categories (as defined by the Census9-11 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000 220000

1965 1970 1975 1980 1985 1990 1995 2000 2005 YearUnits 65 to 135 kBtu/h 135 to 250 kBtu/h

Figure 9.3.1 Historical Shipments for Co mmercial Unitary Air Conditioners Bureau): (1) single package air conditioners, with or without evaporator fans, including refrigeration chassis and remote-condenser types, (2) year-round air conditioners, single package and remote-condenser type (except heat pumps), and (3) split-system air-conditioning condensing units. Within each of the above equipment categories, the shipments data are disaggregated into cooling capacity ranges. The capacity ranges and equipment categories are defined in a way that allowed the Department to aggregate the Census Bureau shipments data into the two product classes of interest, with two notable exceptions: (1) the Census Bureau specified a capacity range of 

### $ 135,000 Btu/h to <250,000 Btu/h, rather than the desired range of 

### $ 135,000 Btu/h to <240,000 Btu/h, and (2) the Census Bureau combined water-cooled and aircooled equipment. With respect to the first issue, the Department used the Census Bureau shipments data in the 

### $ 135,000 Btu/h to <250,000 Btu/h capacity range to represent shipments for the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class. With respect to the second issue, the Department used the confidential ARI shipments data to extract the appropriate percentage of water-cooled equipment from the aggregated water-cooled and air-cooled shipments.Figure 9.3.1 shows the historical shipments data for the two commercial unitary airconditioning equipment classes. Although DOE collected data for the years dating back to 1969, it did not obtain data for the following years: 1971–1973, 1976–1979, and 1981–1982. The most recent year for which shipments data are reported is 2000.a For capacity ranges defined as less than a given capacityvalue (e.g., less than 22,000 Btu/h), DOE used 90 percent of the capacity as the representative capacity (e.g., 22,000 Btu/h X .9 = 19,800 Btu/h). For capacity ranges defined as greater than a given capacity value (e.g., 640,000 Btu/h and over), DOE used 110 percent of the capacity as the representative capacity (e.g., 640,000 Btu/h X 1.1 = 704,000 Btu/h).
9-12 9.3.2 Historical Shipped Tonnage Historical shipped tonnage depicts the annual amount of cooling capacity (in tons) shipped and is an alternative way to express shipments data. The Department determined the actual shipped tonnage for any given year by multiplying each unit shipped by its associated cooling capacity or tonnage, and then summing all the tonnage values. The Department converted shipped tonnage to percent of shipped tonnage for use in the Shipments Model, to establish the commercial unitary air-conditioner market share attributed to each equipment class. The market share calculation and its use in forecasting shipments is explained in section 9.3.3.4. The Department calculated the percent of shipped tonnage by dividing the shipped tonnage for each equipment class by the total shipped tonnage for all commercial unitary air-conditioning equipment. The Department used the Census Bureau shipments data to derive shipped tonnages for each of the two equipment classes, as well as for the entire set of commercial unitary air conditioners. The Census Bureau classifies the range of cooling capacities for the entire set of commercial unitary air conditioners from less than 22,000 Btu/h to greater than 640,000 Btu/h. Across this span of cooling capacities, the Census Bureau disaggregated shipments into as many as 16 smaller capacity ranges. For each capacity range, DOE used the mid-point of the range as the representative capacity for the range. a Then the Department determined the shipped tonnage for each capacity range by multiplying the representative capacity by the number of shipments in the range. Because the Census Bureau disaggregated shipments into several capacity ranges, the Department was able to establish the annual shipped tonnage for each of the two equipment classes and the entire set of commercial unitary air conditioners.The confidential ARI shipments data were instrumental in separating water-cooled airconditioning equipment from air-cooled equipment. Further, the ARI shipments data identified which air conditioners with cooling capacities less than 65,000 Btu/h were commercial equipment and which equipment were residential products. Figure 9.3.2 shows the historical percent of shipped tonnage for the 

### $ 65,000 Btu/h to 

### # 135,000 Btu/h and 

### $ 135,000 Btu/h to<250,000 Btu/h classes of commercial unitary airconditioning equipment. Although DOE derived data for the years back to 1969, it did not obtain the necessary shipments data to derive shipped tonnage for the following years: 1971–1973, 1976–1979, and 1981–1982. The most recent year for which shipments data are reported is 2000. In Figure 9.3.2, the percent of shipped tonnage for each equipment class does not vary greatly from year to year. As a result, DOE used average fixed values of 23 percent and9-13 0% 5% 10% 15% 20% 25% 30%

1965 1970 1975 1980 1985 1990 1995 2000 2005 YearPercent of Shipped Tonnage

65 to 135 kBtu/h 135 to 250 kBtu/h

Figure 9.3.2 Historical Percent of Shippe d Tonnage for Commercial Unitary Air Conditioners 16 percent for inputs to the Shipments Model for the 

### $ 65,000 Btu/h to <135,000 Btu/h and 

### $ 135,000 Btu/h to <240,000 Btu/h equipment classes, respectively.

9.3.3 Commercial Floor Space and Market Saturation The amount of commercial floor space is the main driver for commercial unitary equipment shipments and is appropriately one of the basic inputs into the Shipments Model. As discussed in section 9.2.2, the model divides commercial floor space into three components: floor space from new construction (NFS), existing floor space (EFS), and demolished floor space (DFS). For this analysis, commercial floor space refers to non-residential buildings that are used primarily for purposes other than industrial or manufacturing.bThe New Construction includes the following non-residential categories from table No. 933: Commercial, Educational, Health, Public Buildings, Religious , Social and Recreational, and Miscellaneous.
9-14 9.3.3.1 Floor Space - New Construction (NFS)
U.S. Census Bureau databare available for NFS for 1980, 1985, and 1990–2000. For 1980–1985 and 1985–1990, the Department derived NFS data points through linear interpolation of the available Census Bureau data. The Department derived NFS data for years 1960–1980 by linearly extrapolating the Census Bureau data backward in time. 3 The Department took the projected floor space after the year 2000 from the National Energy Modeling System (NEMS) projection published in the Annual Energy Outlook 2003 ( AEO2003 ). 4 AEO2003 lists the projections for years 1999, 2000, 2005, 2010, 2015, 2020, and 2025. For unlisted years in this range, DOE derived NFS data by linear interpolation of the existing years. Beyond 2025, DOE extrapolated NFS from the 10-year trend of NEMS projected data. There is approximately a 100 percent difference between the NEMS projection in 2000 and historical Census Bureau data in 2000. To match the Census Bureau data with the NEMS projections, DOE calculated the rate of change in NFS for each NEMS year and applied it to the Census Bureau data, starting in 2000 and stepping forward each year to 2025. 9.3.3.2 Floor Space - Existing Stock (EFS) The Department derived historical values for existing floor space from data in the Commercial Building Energy Consumption Survey (CBECS). 5 There are CBECS surveys for the years 1979, 1983, 1986, 1989, 1992, 1995, and 1999. For unspecified years with this range, DOE linearly interpolated EFS from the survey data. For the years 1960 to 1980, DOE derived EFS by linearly extrapolating the CBECS data backward in time. The Department took the projected floor space after the year 1999 from the NEMS projection published in AEO2002 . The projections are listed for years 1999, 2000, 2005, 2010, 2015, and 2020. For unlisted years in this range, DOE derived EFS by linear interpolation of the existing years. Beyond 2020, DOE extrapolated EFS from the 10-year trend of NEMS projected data. There is a small difference, less than one percent, between the NEMS projection in 2000 and CBECS data in 2000. In order to match the CBECS data with the NEMS projections, DOE calculated the rate of change in EFS for each NEMS year. The Department then applied this rate to the CBECS data, starting in 2000 and stepping forward each year to 2025.9-15 

## ( ) ( ) ( ) DFS y EFS y SFS y = −+ 1



### MKT SAT y CUACFS y EFS y MKTSHARE _ () ( ) ( ) = ⋅ 9.3.3.3 Demolished Floor Space (DFS) The Department took demolished floor space from NEMS projections. In addition to predicting NFS and EFS , NEMS also predicts surviving square feet (SFS) (i.e., square feet in the building stock that are not demolished). The following formula gives the DFS for any given NEMS year.

After DOE determined the DFS for NEMS years, it calculated the average DFS rate per year, DFS/EFS . The Department then applied this rate to the EFS for each year to get the DFS for all years, as discussed in section 9.2.3.
9.3.3.4 Market Saturation The Department defines market saturation, MKT_SAT(y) , for commercial unitary airconditioning equipment as the ratio of existing cooled floor space to all existing floor space, EFS(y) . It is one of the constraints used to calibrate the Shipments Model. To calculate market saturation for a particular class of unitary air-conditioningequipment, the Department first calculated the market saturation for all commercial unitary airconditioning (CUAC) equipment from historical CBECS data for commercial floor space cooled by unitary air conditioners, CUACFS(y) , and all existing floor space, EFS(y) . Then, to estimate the MKT_SAT for a particular class of commercial unitary air conditioners, DOE used the market share, or percent, of unitary air conditioners in that equipment class. The Department used the above shipments data to estimate the market share, MKTSHARE(y) , of each equipment class. Because the percent of shipments in each class is relatively constant over time and the lifetime of each class is the same, the ratio of shipments to a class is indicative of the market share for that class. The Department estimated the MKTSHARE for a particular class to be a constant value equal to the average of the yearly percent shipments to that class. From these data, DOE calculated the MKT_SAT(y) for each class using the following formula.

Table 9.3.1 summarizes the market saturations for 1992, 1995, and 1999, which are the years for which CUACFS data are available in CBECS.9-16 Table 9.3.1 Market Saturations

$ 65,000 Btu/h to <135,000 Btu/h $ 135,000 Btu/h to 240,000 Btu/h

Year CUAC Market Saturation (CBECS) Market Share MKT_SAT Market Share MKT_SAT 1992 21% 23% 5% 16% 3% 1995 24% 23% 6% 16% 4% 1999 31% 23% 7% 16% 5%

9.3.3.5 Eligible Market and Maximum Market Constraint As discussed in Section 9.2.2.2, the Department scaled NFS by the total eligible market parameter, A 0 . This parameter represents the percent of floor space that could potentially accept commercial unitary air-conditioning equipment from each class and, as such, is the upper bound on the market saturation for each class. However, the true market saturation will be somewhat lower than the total eligible market because the unitary air-conditioning equipment installed on that floor space can be only from one class, and there will be some floor space that is eligible for more than one equipment class. As an additional constraint to the Shipments Model, DOE estimated the market saturation (MMS) for each class of unitary air-conditioning equipment in a mature market. The maximum market saturation is the actual saturation that the market approaches as it reaches maturity. 9.3.4 Equipment Utility The equipment utility is a measure of the economic value of a commercial unitary air conditioner to the customers. The primary economic factors that influence the equipment utility and, thereby, the decision to purchase or not purchase that equipment, are equipment price, operating costs, and the customer’s income. Historical data and forecast values for these economic factors are used for input to the Shipments Model and are discussed below. To makedirect comparisons to floor space data, DOE expresses these economic factors on a per-squarefoot basis. 9.3.4.1 Equipment Price Equipment price is the price paid by the customer for a commercial unitary air conditioner. It includes both the purchase price of the equipment and installation costs. The Department converts the equipment price to a price per square-foot-cooled based on the cooling capacity of the equipment class. For the years prior to 2001, DOE projected unitary air-conditioning equipment prices backward in time using the unitary air conditioner producers price index (UAC PPI) as the scaling factor. 6Since the UAC PPI is scaled in nominal values, the Department rescaled the9-17 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000 $16,000 $18,000

1965 1975 1985 1995 2005 2015 2025 2035 YearEquipment Price (2001$) 135 to 240 kBtu/h 65 to 135 kBtu/h

Figure 9.3.3 Base Case Equipment Pri ce Trend for Commercial Unitary Air Conditioners index in real terms using the gross domestic product (GDP) deflator referenced to 2001 dollars. Also, because PPI data are only available back to 1978, DOE derived unitary air-conditioning equipment prices for the years prior to 1978 by linearly extrapolating the rescaled UAC PPI data backward. As discussed in the engineering analysis (Chapter 5) and the life-cycle cost and payback period analysis (Chapter 8), equipment prices in this analysis are a function of efficiency. The Department based equipment price projections on future equipment efficiency trends. The Department developed a single efficiency tre nd for the base case (i.e., the case without new efficiency standards) and each standard level. The efficiency trends are discussed in detail in Chapter 10, section 10.2.2.1. The efficiency trends’ impact on future equipment prices is discussed in detail in Chapter 10, section 10.3.2.1. As Figure 9.3.3 shows, equipment prices decreased in real terms before 2001. After 2001, the price in the base case is forecast to remain constant. As unitary air-conditioning equipment prices decrease, the probability of purchase is expected to increase. This implies that the parameter 

### $ 1 of the purchase model is negative (see section 9.2.4).9-18 9.3.4.2 Operating Costs Operating costs consist of maintenance costs, repair costs, and energy costs. Maintenance and Repair Costs. Each unit of commercial unitary air-conditioning equipment incurs yearly routine maintenance and standard repair costs. The Department converted these costs to a cost per square foot of cooling capacity for each equipment class. For earlier years, DOE projected maintenance and repair costs backward in time, as with equipment prices above, using the UAC PPI as the scaling factor. Since the UAC PPI is scaled in nominal values, DOE rescaled the index in real terms using the GDP deflator referenced to 2001 dollars. Also, because PPI data are only available back to 1978, DOE derived maintenance and repair costs for the years prior to 1978 by linearly extrapolating the rescaled UAC PPI data backward. Whereas maintenance cost is independent of unitary air-conditioning equipment class and energy efficiency, repair costs are a function of both. Thus, projections for maintenance costs remain constant both in the base and energy efficiency standards cases. However, repair costs are projected to increase with increasing energy-efficiency standards levels. Both maintenance and repair costs are discussed in detail in Chapter 10, section 10.3.2.2. Energy Costs. The annual energy costs for commercial unitary air conditioners depends upon the efficiency of the unit (energy efficiency ratio or EER), the annual energy use per square foot given the efficiency of the unit (energy use intensity or EUI), and the price of electricity. The determination of the EUI is discussed in the building energy use and end-use load characterization analysis (Chapter 6), and the de velopment of electricity prices is discussed in the life-cycle cost and payback period analysis (Chapter 8, section 8.2.3.1). Coupling the EUI with electricity price data enables DOE to derive the annual energy cost by EER. Chapter 8 discusses how the EUI data are combined with the electricity price data to derive annual electricity expenses. The Department assumes that the annual energy costs for any given year change as the efficiency of the shipped units improves. In any given year, there is a distribution of efficiencies for shipped equipment and the Shipments Model uses the shipment-weighted average efficiency for that given year. The Department based annual energy cost projections on future equipment energy-efficiency trends and future electricityprices. The Department developed a single efficiency trend for the base case (i.e., the cas e without new efficiency standards) and each standard level. As noted earlier, the efficiency trends are discussed in detail in Chapter 10, section 10.2.2.1. The efficiency trends’ impact on future energy costs is discussed in detail in Chapter 10, section 10.3.2.2. The equipment efficiency trends allow for the determination of projected annual energy costs as a function of efficiency in 2001 dollars. To factor in the impact of future electricity9-19 

# ( ) M DIFF HSHIP MSHIP i i i _ = −

= 

# ∑ 2

1969 2000 prices, the Department used electricity price projections from the AEO2003 to appropriately scale the above projected energy costs (for scaling purposes, the year 2001 equals 1.0).
9.3.4.3 Discounted Costs When a decision is made to purchase commercial unitary air-conditioning equipment, a sophisticated purchasing agent will consider the to tal lifetime cost of the equipment. Typically, these lifetime costs are discounted to represent the present value of these costs. The Department discounted the total operating costs (i.e., maintenance, repair, and energy costs) over the full lifetime of the equipment. Commercial air conditioner lifetimes range from one to approximately 30 years, with an average value of 15.4 years (see Chapter 8, section 8.2.3.9, Lifetime, for further details). Operating costs are typically heavily discounted in purchase decisions. The Department understands that such operating costs are heavily discounted, and thus it used 75 percent as the discount rate for commercial unitary air conditioners, which was the same rate it used in the residential central air conditioner Shipments Model. 1 As a result, the 75 percent discount rate translates to a discountfactor of 1.33. The Department adjusted all costs associated with commercial unitary air conditioners in this analysis by 1.33.
9.3.4.4 Income Income mitigates customer sensitivity to equipment price and operating cost changes. The Building Owners and Managers Association (BOMA) Commercial Building Surveyreports income per square foot for commercial buildings. BOMA data are available for the years 1979–2001. 5 Because the market for commercial real estate can be highly volatile and exhibit a strong response to macro-economic factors, prices tend to fluctuate greatly. To moderate such volatility, DOE applied a four-year moving average to these data. As a result, the trend in building income, in real terms, appears to be reasonably stable. Thus, for years before 1979 and after 2001, the Department used $21 as the average income per square foot.
9.3.5 Model Calibration The purpose of the Shipments Model is to accurately estimate the number of future shipments of commercial unitary air conditioners. For accuracy, the Shipments Model must first be calibrated by assigning values to the free model (i.e., non-constrained) parameters: A 0 , E 0 , 

### " 1 , 

### "

2 , 

### $ 1 , and 

### $ 2 . The methodology for calibrating the model is to minimize the difference between the shipments predicted by the model (MSHIP ) and the actual historical shipments (HSHIP ) for the years 1969 through 2000. More specifically, the Department defines the difference in shipments (M_DIFF ) as the sum of squares of the differences, in each year, of HSHIP and MSHIP . That is:9-20 M_DIFF can be minimized using a standard Linear Programming (LP) tool, such as Solver in Microsoft Excel.
9.3.5.1 Parameter Constraints To reduce the parameter search space of the LP calculation, DOE constrained several of the model parameters so that the performance of the model would be consistent with rational economic behavior, and historical and predicted shipment characteristics.
9.3.5.2 Model Constraints As mentioned in the equipment price section (section 9.3.4.1), 

### $ 1 should be a negative value. This indicates that, as unitary air-conditioning equipment prices increase, purchases decrease. Conversely, the replacement parameter, 

### $ 2 , should be positive. The assumption is that, as the price of a new unitary air conditioner increases, the user is more likely to repair the old one rather than replace it.
9.3.5.3 Market Constraints and Values Market parameters, A 0and E 0are constrained between zero and one, since market size must be positive and the total market cannot be larger than the available floor space. Another market constraint is the replacement rate. This is the ratio of shipments of unitary air conditioners that replace existing units versus shipments of unitary air conditioners to new construction. The Department constrained the model replacement rate to be between 65 percent and 75 percent in the year 2015. Finally, an additional model constraint is the maximum market saturation. Maximum market saturation is the actual saturation that the market approaches as it reaches maturity. The Department estimated the maximum market saturation as a constraint for each equipment class. Table 9.3.2 presents a summary of the model and market constraints and the calibrated values from the fit to historical shipments for each equipment class.9-21 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 YearShipments Historical Shipments Model Shipments - Base Case

Figure 9.4.1 

### $ 65,000 Btu/h to <135,000 Btu/h: Base Case Shipments Forecast with Historical Data Table 9.3.2 Model and Market Constraints and Calibration Values

$ 65,000 Btu/h to <135,000 Btu/h $ 135,000 Btu/h to 240,000 Btu/h Parameter Constraint Value Constraint Value A 0 >0 and <1 0.19 >0 and <1 0.16 E 0 >0 and <1 0.003 >0 and <1 0.0001

" 1 - 14.7

- 12.4

" 2 - -20.7

- -16.1 $ 1 < 0 -70 < 0 -64 $ 2 > 0 125 > 0 104 MMS 10.6% NA 9.5% NA Replacement Rate $ 60% and # 70% 62% $ 60% and # 70% 61%

9.4 RESULTS
9.4.1 

### $ 65,000 Btu/h to <135,000 Btu/h Equipment Class Figure 9.4.1 shows the base case (i.e., without new standards) shipments forecast for the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class. The figure also provides historical data points to show how closely the Shipments Model base case forecast agrees with historical data.9-22 Table 9.4.1 and Figure 9.4.2 shows the shipments forecast for the 

### $ 65,000 Btu/h to <135,000 Btu/h commercial unitary air-conditioning equipment class for energy-efficiency standards levels from 10.5 EER through 12 EER in 0.5 EER increments. Also, the figure shows the base case forecast for comparison. As equipment purchase price increases with efficiency, a drop in shipments is forecasted.9-23 Table 9.4.1 

### $ 65,000 Btu/h to <135,000 Btu/h: Forecasted Shipments for the Base Case and Standards Cases Standards Case Year Base Case ASHRAE 10.5 EER 11.0 EER 11.5 EER 12.0 EER 2004 205,587 205,164 205,587 205,587 205,587 205,587 2005 197,045 196,637 197,045 197,045 197,045 197,045 2006 202,518 202,128 202,518 202,518 202,518 202,518 2007 208,909 208,525 208,909 208,909 208,909 208,909 2008 216,033 215,648 215,257 213,966 210,811 202,910 2009 223,234 222,839 222,418 221,057 217,731 210,317 2010 230,580 230,188 229,756 228,386 225,040 218,477 2011 237,195 236,804 236,373 235,004 231,663 226,028 2012 243,076 242,693 242,285 240,972 237,772 233,296 2013 247,720 247,338 246,967 245,719 242,679 239,362 2014 251,286 250,900 250,581 249,413 246,574 244,405 2015 254,083 253,691 253,434 252,360 249,758 248,664 2016 256,842 256,431 256,228 255,210 252,748 252,483 2017 259,856 259,432 259,280 258,331 256,049 256,420 2018 262,862 262,412 262,277 261,315 259,010 259,647 2019 266,683 266,217 266,075 265,078 262,705 263,360 2020 270,739 270,240 270,049 268,913 266,216 266,640 2021 275,319 274,812 274,566 273,333 270,416 270,566 2022 279,709 279,171 278,853 277,449 274,123 274,053 2023 284,704 284,155 283,791 282,300 278,989 278,657 2024 289,011 288,434 288,031 286,424 283,344 282,704 2025 293,701 293,124 292,720 291,118 288,503 287,769 2026 298,056 297,467 297,070 295,456 293,241 292,579 2027 302,126 301,529 301,145 299,539 297,723 297,207 2028 305,983 305,378 305,011 303,418 301,970 301,621 2029 309,723 309,110 308,757 307,173 306,037 305,843 2030 313,445 312,821 312,476 310,888 309,994 309,924 2031 317,230 316,592 316,247 314,638 313,914 313,923 2032 321,129 320,474 320,119 318,469 317,851 317,887 2033 325,153 324,478 324,106 322,396 321,830 321,843 2034 329,292 328,596 328,201 326,420 325,892 325,848 2035 333,515 332,797 332,376 330,520 330,046 329,9299-24 190,000 210,000 230,000 250,000 270,000 290,000 310,000 330,000 350,000

2000 2005 2010 2015 2020 2025 2030 2035 YearShipments Base Case ASHRAE 10.5 EER 11.0 EER 11.5 EER 12.0 EER

Figure 9.4.2 

### $ 65,000 Btu/h to <135,000 Btu/h: Foreca sted Shipments for the Base Case and Standards Cases

9.4.2 

### $ 65,000 Btu/h to <135,000 Btu/h Equipment Class Figure 9.4.3 shows the base case (i.e., without new standards) shipments forecast for the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class. Al so, the figure shows historical data points to show how closely the Shipments Model base case forecast agrees with historical data.9-25 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 YearShipments Historical Shipments Model Shipments - Base Case

Figure 9.4.3 

### $ 135,000 Btu/h to <240,000 Btu/h: Base Case Shipments Forecast with Historical Data
Table 9.4.2 and Figure 9.4.4 shows the shipments forecast for the 

### $ 135,000 Btu/h to <240,000 Btu/h commercial unitary air-conditioning equipment class for energy-efficiency standards levels from 10.0 EER through 12 EER in 0.5 EER increments. Also, the figure shows the base case forecast for comparison. As equipment purchase price increases with efficiency, a drop in shipments is forecasted.9-26 Table 9.4.2 

### $ 135,000 Btu/h to <240,000 Btu/h: Forecasted Shipments for the Base Case and Standards Cases Standards Case Year Base Case ASHRAE 10.0 EER 10.5 EER 11.0 EER 11.5 EER 12.0 EER 2004 87,491 87,427 87,491 87,491 87,491 87,491 87,491 2005 83,665 83,601 83,665 83,665 83,665 83,665 83,665 2006 86,310 86,244 86,310 86,310 86,310 86,310 86,310 2007 89,387 89,319 89,387 89,387 89,387 89,387 89,387 2008 92,816 92,746 92,644 92,408 91,832 90,517 87,606 2009 96,302 96,230 96,122 95,873 95,264 93,871 91,104 2010 99,864 99,791 99,679 99,424 98,804 97,386 94,887 2011 103,100 103,026 102,914 102,657 102,032 100,605 98,415 2012 105,997 105,924 105,815 105,564 104,957 103,572 101,781 2013 108,322 108,248 108,147 107,906 107,324 105,996 104,620 2014 110,138 110,064 109,973 109,746 109,197 107,948 106,996 2015 111,583 111,509 111,430 111,220 110,711 109,557 109,010 2016 113,003 112,927 112,859 112,661 112,179 111,087 110,864 2017 114,525 114,447 114,388 114,200 113,747 112,726 112,758 2018 116,040 115,958 115,902 115,713 115,256 114,229 114,384 2019 117,921 117,835 117,776 117,578 117,104 116,044 116,227 2020 119,908 119,817 119,747 119,526 118,992 117,803 117,921 2021 122,121 122,026 121,942 121,699 121,117 119,823 119,856 2022 124,252 124,151 124,052 123,778 123,119 121,655 121,619 2023 126,657 126,551 126,440 126,146 125,440 123,966 123,832 2024 128,755 128,646 128,525 128,210 127,452 126,069 125,806 2025 131,027 130,915 130,791 130,472 129,708 128,502 128,188 2026 133,155 133,040 132,916 132,594 131,820 130,776 130,474 2027 135,152 135,036 134,914 134,592 133,819 132,937 132,679 2028 137,050 136,933 136,813 136,493 135,725 134,996 134,794 2029 138,890 138,771 138,655 138,336 137,570 136,975 136,830 2030 140,716 140,596 140,480 140,160 139,392 138,905 138,810 2031 142,565 142,442 142,325 142,001 141,224 140,816 140,757 2032 144,459 144,332 144,213 143,881 143,085 142,730 142,690 2033 146,405 146,275 146,151 145,807 144,984 144,662 144,622 2034 148,400 148,266 148,135 147,778 146,923 146,625 146,572 2035 150,430 150,292 150,155 149,783 148,893 148,625 148,5499-27 80,000 90,000 100,000 110,000 120,000 130,000 140,000 150,000 160,000

2000 2005 2010 2015 2020 2025 2030 2035 YearShipments Base Case ASHRAE 10.0 EER 10.5 EER 11.0 EER 11.5 EER 12.0 EER

Figure 9.4.4 

### $ 135,000 Btu/h to <240,000 Btu/h: Forecasted Shipments for the Base Case and Standards Cases9-28 1. U.S. Department of Energy, Energy Efficiency and Renewable Energy. Technical Support Document: Energy Efficiency Standards for Consumer Products: Residential Central Air Conditioners and Heat Pumps , May, 2002. Washington, DC. <http://www.eere.energy.gov/buildings/appliance_standards/residential/ac_central.html> 2. U.S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau. Current Industrial Reports – Refrigeration, Air Conditioning, and Warm Air Heating Equipment , 1970, 1975, 1980, and 1984 through 2000 Reports. Washington, DC. Report No. MA333M(XX)-1 for years 1998 through 2000, Report No. MA35M(XX)-1 for years 1970, 1975, 1980, and 1984 through 1997 where XX designates the year. 3. U.S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau. Statistical abstract of the United States : 2001 . Washington, DC. Table No 933 - Construction Contracts-Value of Construc tion and Floor Space of Buildings by Class of Construction: 1980 to 2000. 4. U.S. Department of Energy, Energy Inform ation Administration, Office of Integrated Analysis and Forecasting. Annual Energy Outlook 2002 with Projections to 2020. Washington, DC. Table A5 - Commercial Sector Key Indicators and Consumption. 5. Building Owners and Managers Association International, BOMA Experience Exchange Report, 1986,1989,1992,1995,1997,2001. Washington, DC. Table 4 - Comparison of Average Income and Expenses U.S. Private Sector. 6. U.S. Department of Labor, Bureau of Labor Statistics. Producers Price Index Website http://www.bls.gov/ppi/home.htm : Industry: Refrigeration and Heating Equipment - Product: Unitary Air Conditioners - Series ID: PCU3585#2 (N) REFERENCES