9

- i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

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

- 1

9.2 FUNDAMENTAL MODEL EQ UATIONS

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

- 2

9.2.1 Replacement Shipments

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

- 3

9.2.2 Shipments to New Construction

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

- 4

9.2.3 Shipments to New Owners

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

- 5

9.3 DATA INPUTS AND SUPP ORTING CALCULATIONS

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

- 5

9.3.1 Historical Shipments

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

- 5

9.3.2 Shipments to New Construction

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

- 7

9.3.3 Shipments to New Owners

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

- 7

9.4 IMPACT OF STANDARDS ON SHIPMENTS

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

- 8

9.4.1 Purchase Price Elasticity

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

- 8

9.4.2 Impact from Increase in Relative Price

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

- 10

9.5 RESULTS

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

- 11

LIST OF TABLES

Table 9.1.1 Name Abbreviations by Equipment Class

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

- 2
Table 9.3.1 Fractions of Number of Models in the A ir

- Conditioning, Heating, and Refriger ation Institute Model Directory

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

- 6
Table 9.3.2 Saturations of Commercial Packaged Boilers in New Construction in 2019

...... 9

- 7
Table 9.4.1 Change in Relative Price Elasticity Following a Purchase Price Change

......... 9

- 10

LIST OF FIGURES

Figure 9.3.1 Historical Shipments of Commercial Packaged Boilers

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

- 6 Figure 9.5.1 Historic and Projected Base Case Shipments Commercial Packaged Boilers by Equipment Class

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

- 12 Figure 9.5.2 Total Projected Shipments of Mechanical Draft Gas

- Fired Hot Water Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr in

the Base Case and Each Standards Case

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

- 139-1 CHAPTER 9. S HIPMENTS A NALYSIS

9.1 INTRODUCTION

Estimates of future equipment shipments are necessary input s to calculations of national energy savings (NES) and net present value (NPV) , as well as to the manufacturer impact analysis (MIA). This chapter describes the data and methods the U.S. Department of Energy ( DOE ) use s to project annual equipment shipments and presents results for commercial packaged

boiler equipment classes considered in this analysis.

The shipments model divides the shipments of commercial packaged boiler s into specific market segments. The model starts from a historical base year and calculates retirements and shipments by market segment for each year of the analysis period

. Th is approach produces an

estimate of the total equipment stock, broken down by ag e or vintage , in each year of the analysis period. In addition, t he equipment stock efficiency distribution is calculated for the base

case and for each standards case for each equipment class. The stock distribution is used in the

n ational i mpact a nalysis (NIA) to estimate the total 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 ( www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/79 ). Appendix

10

- A discusses how to access and utilize the shipments model spreadsh eet , which is integrated into the spreadsheet for the NIA . This chapter explains how the shipments model is constructed and provides some summary output. Sections 9.2 and 9.3 describe the methodological approach.

Table 9.1.1 summari zes the abbreviations of each product class. The commercial packaged boiler shipments model considers t hree equipment placement channels (hereafter referred to as “channels”) as follows:

1. New construction : a certain fraction of new buildings acquire boiler s in each future year. This fraction is defined as the new construction saturation, which varies by year, and by equipment class

.

2. Existing owners (replacements) : these are defined as existing buildings with boiler s installed. This category receives new sh ipments when existing equipment are replaced.

3. N ew owners : these are defined as existing buildings that acquire boiler s for the first time during the analysis period

. The new owners primarily consist of buildings that during a major remodel add or switch t o hydronic heating using a boiler

. For this analysis, new owners also include buildings switching between different boiler equipment classes.9-2 Table 9.1.1 Name A bbreviations by Product Class

Abbreviation Full product name

SGNDHW Natural Draft Gas

- Fired Hot Water Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr

LGNDHW Natural Draft Gas

- Fired Hot Water Commercial Packaged Boiler >2,500,000 Btu/hr

SONDHW Natural Draft Oil

- Fired Hot Water Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/ hr

LONDHW Natural Draft Oil

- Fired Hot Water Commercial Packaged Boiler >2,500,000 Btu/hr

SGNDST Natural Draft Gas

- Fired Steam Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr

LGNDST Natural Draft Gas

- Fired Steam Commercial Packaged Boiler >2,500,000
Btu/hr

SONDST Natural Draft Oil

- Fired Steam Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr

LONDST Natural Draft Oil

- Fired Steam Commercial Packaged Boiler >2,500,000
Btu/hr

SGMDHW Mechanical Draft Gas

- Fired Hot Water Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr

LGMDHW Mechanical Draft Gas

- Fired Hot Water Commercial Packaged Boiler >2,500,000 Btu/hr

SOMDHW Mechanical Draft Oil

- Fired Hot Water Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr

LOMDHW Mechanical Draft Oil

- Fired Hot Water Commercial Packaged Boiler >2,500,000 Btu/hr

SGMDST Mechanical Draft Gas

- Fired Steam Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr

LGMDST Mechanical Draft Gas

- Fired Steam Commercial Packaged Boiler >2,500,000 Btu/hr

SOMDST Mechanical Draft Oil

- Fired Steam Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr

LOMDST Mechanical Draft Oil

- Fired Steam Commercial Packaged Boiler >2,500,000 Btu/hr

9.2 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 analys is year minus the vintage). The stock function is adjusted in each year of the analysis period by new shipments coming in and broken

or demolished equipment being taken out.

For existing stock:9-3 

## ( ) ( ) ( ) ( ) 1

, 1

, 1

, 1

, − − +

− − − = a j Ship a j Rem a j Stock a j Stock

p p p p Eq. 9

. 1

and for new shipments :



## ( ) ( )

. 1 1

, − = = j Ship a j Stock

p p Eq. 9

. 2
Where:

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

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

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

.

Removals due to equipment failure contain a survival function f p (a) that is used to represent the probability that a unit of a ge a will survive in a given year; equivalently, the probability that this unit will fail is 1

- f p (a)

.

Total removals in the base case are then:



## ( ) ( ) 

# [ ] ( ) a j Stock a f a j Rem

p p p , 1

, ×

− =

. Eq. 9

. 3

In the standards case, there is also a second term that represents the extended repair stock that has been in use for six years following the repair date (see section 9.4.2 for discussion of extended repair).

S hipments are directed to one of the three channels:



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

p p p p + + = Eq. 9

. 4

W here:

Rpl p (j) = number of u nits of equipment p replaced in year j

, which depends on removed units and units in demolished buildings

, N

C

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

N

O

p (j) = number of units shipped to “ new owners ” of product p in year j

.

9.2.1 Replacement Shipments

The shipments model assumes that units that are taken from demolished buildings, Dem(j ), are included in the mix of broken units Rem p (j). As the demolished units do not need to be replaced, they are deducted from Rem p (j) when calculating the required repl acements, as represented by the following expression :9-4 

## ( ) ( ) ( ) j Dem j Rem j Rpl

p p − = Eq. 9

. 5

When a boiler fails , it is removed from the stock or is repaired for extended use . The following retirement function r p (a) is used to represent the probability that a unit will fail at age

a

. 

## ( ) ( ) ( ) a j Stock a r j Rem

p a a p , × = 

## ∑

Eq. 9

. 6

Retirement functions and equipment lifetimes are discussed in more detail in chapter 8.

In each year, equipment is removed from demolished buildings. As represented by the following expression, the shipments model assumes that the saturation of the equipment in the demolished buildings is the same as that of the overall building population.



## ( ) ( ) ( ) 1

, − × = j p sat j D j Dem
Eq. 9

. 7

T he number of demolished buildings is calculated by:



## ( ) ( ) ( ) ( ) j Stock H j Starts H j Stock H j D

_ _ 1

_ − +

− = Eq. 9

. 8

W here:

H_Stock (j) = number of building units in analysis year j

, H_Starts(j) = number of new building units in year j

, D(j) = number of demolished buildings

, Dem (j) = number of equipment demolished in analysis year j

, and sat(p,j) = saturation of equipment of equipment class p for all buildings in year j

.

9.2.2 Shipments to New Construction

DOE multiplie s new construction market saturations by projections of new building s to estimate shipments to the new construction channel. On a n equipment class basis, the

determination o f shipments to new construction is represented by the following expression:



## ( ) ( ) ( ) ( ) ( ) j res Sat NC j res Starts NC j com Sat NC j com Starts NC j NC

p p p

_ _ _ _ _ _ _ _ × + × = Eq. 9

. 9

W here:

NC_Starts_com (j) = number of new commercial building starts in year j

, NC

_

Sat_com p (j) = new commercial housing saturation for equipment class p and year j

,9-5 NC_Starts_res (j) = number of new residential housing starts in year j

, and

NC

_

Sat_res p (j) = new residential housing saturation for equipment class p and year j

, .

9.2.3 Shipments to New Owners

The third market segment consists of new owners of equipment in a given equipment class, and also includes an adjustment for switching to a different equipment class. Because there are no data on the extent of these phenomena, DOE estimate s historical shi pments to this market
segment as a residual, using the following equation:



## ( ) ( ) ( ) ( ) ) ( j NU j RU j Shipment j NO +

− = Eq. 9

. 10

W here:

j = year where historical shipment data is available

NO (j) = new owners (if positive) or adjustment for switching (if negative) for year j

, Shipment (j) = historical shipment in year j

, RU (j) = estimated replacement units in year j

, and

NU (j) = new units for new buildings in year j

.

9.3 DATA INPUTS AND S U PPORTING CALCULATIONS

9.3.1 Historical Shipments

DOE use s (1) historical shipment data about commercial packaged boiler s in the commercial packaged boiler final rule (2009), (2) number of models in the Air

- Conditioning, Heating, and Refrigeration Institute ( AHRI ) Direct ory of Certified Product Performance (2007

- 2014), (3) historical shipment data of residential boilers from Appliance Magazine (AM), (4) the Gas Appliance Manufacturers Association (GAMA), (5) and the Energy Star program to populate its shipments model for commercial packaged boiler s

. a , 1

, 2 , 3, 4, 5 DOE derive s the ratios of gas

- and oil

- fired commercial packaged boiler shipment s to residential boiler shipment s from the shipment data in 2009 and assume s the ratios remain constant from 1960 to 2013. The oil

fired and gas

- fired commercial packaged boiler shipments are further disaggregated to each equipment class using the number of models in the aforementioned AHRI directory. The

fractions of equipment classes in the AHRI directory are summarized in Table 9.3.1

.

a Total shipments reported by AM and GAMA included commercial boilers with input between 300 kBtu/h and 450 kBtu/h. Note that DOE adjust ed for shipments from manufacturers that were not part of GAMA or currently part of

AHRI.9-6 Table 9.3.1 Fractions of Number of Models in the AHRI Model Directory

Year SGNDHW LGNDHW SONDHW LONDHW SGNDST LGNDST SONDST LONDST

2007 4.5% 4.1% 3.1% 5.6% 7.1% 10.0% 2.9% 8.0%

2008 9.1% 2.7% 2.2% 4.0% 8.4% 7.1% 2.0% 5.6%

2009 8.5% 2.8% 2.2% 4.0% 7.9% 7.1% 2.0% 5.7%

2011 5.6% 2.1% 1.6% 3.1% 9.6% 6.1% 1.5% 4.3%

2012 4.7% 2.1% 1.6% 3.0% 9.0% 6.0% 1.5% 4.2%

2013 4.6% 2.0% 1.6% 2.9% 11.9% 6.6% 1.5% 4.2%

2014 7.7% 1.6% 2.5% 0.1% 8.9% 3.7% 1.3% 0.0%

Year SGMDHW LGMDHW SOMDHW LOMDHW SGMDST LGMDST SOMDST LOMDST

2007 11.2% 3.4% 7.7% 4.0% 7.2% 5.6% 7.6% 8.0%

2008 21.6% 3.6% 7.2% 4.2% 5.5% 4.7% 5.8% 6.3%

2009 23.5% 3.0% 7.3% 3.5% 5.6% 4.7% 5.8% 6.4%

2011 34.5% 4.1% 5.7% 2.9% 4.9% 4.1% 4.9% 4.9%

2012 35.8% 5.3% 5.6% 2.9% 4.8% 4.0% 4.8% 4.8%

2013 35.6% 5.1% 5.2% 2.5% 3.9% 3.9% 3.8% 4.7%

2014 42.1% 8.9% 4.9% 3.8% 4.5% 3.6% 3.5% 2.9%

Further details about historical shipments are described in A ppendix 9

- B. Figure 9.3.1 summarizes the historical shipments data that DOE has assembled.

Figure 9.3.1 Historical Shipments of Commercial P a ckaged Boiler s

A fraction of commercial packaged boiler s are shipped to residential buildings ; therefore,

DOE consider s any future shipments of commercial packaged boiler s to residential buildings in 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050

1960 1970 1980 1990 2000 2010Historical shipments (million) LOMDST
SOMDST
LGMDST
SGMDST
LOMDHW
SOMDHW
LGMDHW
SGMDHW
LONDST
SONDST
LGNDST
SGNDST
LONDHW
SONDHW
LGNDHW
SGNDHW9-7 this analysis. DOE use s CBECS 2003 and RECS 2009 to determine the number of commercial packaged boiler s in commercial and residential applications. DOE estimate s that commercial packaged boiler s shipped to residential buildings account for 5.5 percent of the total historical shipments of commercial packaged boiler s

. Details of the methodology to dete rmine the fraction

of commercial packaged boiler s shipped to residential buildings are in A ppendix 9

- B.

9.3.2 Shipments to New Construction

DOE determine s new commercial building and residential housing construction starts by using recorded data through 2012 and projections from EIA’s Annual Energy Outlook 20 1 4 ( AEO 20 1 4 ). 3

DOE develop s new

- building boiler

- type market saturations from CBECS 2003 and RECS

2009

. To estimate future saturations in commercial buildings for gas boilers and oil boilers, DOE

use s the average saturations in buildings built in 1990

- 200 3 (from CBECS 200 3 data) that also use each type of heating equipment

. To estimate future saturations in residential buildings for gas

boilers and oil boilers, DOE use s the average saturations in homes built in 1990

- 2009 ( from

RECS 2009 data ) which use each type o f heating product

. DOE base s the split between natural

draft and mechanical draft on the models in the AHRI database from 2007 to 2014. 2 From 2007 to 2014 the percentage of mechanical draft boilers increases while the percentage of natural draft

boilers decreases

. DOE assume s that after 2014 percentage of mechanical draft boilers continues

to increase, and increases linearly at the same rate as the average growth seen from 2007 to 2014.

Table 9.3.2 shows the projected market saturation s of commercial packaged boiler s by building type in year 2019

.

Table 9.3.2 Saturations of Commercial Packaged Boiler s in New Construction in

2019

Product Class Residential Single Family Residential

Multi

- Family Commercial Buildings

Gas

- fired Boiler 0.14% 2.85% 10.13%

Oil

- fired Boiler 0.04% 1.55% 0.83%

9.3.3 Shipments to New Owners

The new owners primarily consist of ( 1 ) buildings that during a major remodel add hydronic heating

, and (2) buildings that choose to install a boiler for a hydronic air handler to replace a gas furnace. To estimate future shipments to buildings that choose to install a boiler for a hydronic air handler to replace a gas furnace

, DOE use s information from the 2008 American Home Comfort Survey, which indicated that about 9 percent of new central boilers went to new

owners

. 4 Because the potential number of new owners is limited, DOE assume s that in the future

th is fraction would potentially decrease to 3

- percent

.9-8 DOE include s equipment switching from natural draft equipment classes to the corresponding mechanical draft equipment classes. The switching fractions are based on a three year average (2011

- 2013) of the fractions of new owner shipments versus replacement shipments. Additionally, DOE consider s switching from large equipment classes to the corresponding small equipment classes. The switching fraction is assumed to be 50 percent . Each large commercial packaged boiler is assumed to be replaced by 3.75 small b oilers. b

9.4 I MPACT OF S TANDARDS ON S HIPMENTS

For replacements

, commercial consumer purchase decisions are influenced by the purchase price and operating cost of the equipment , and therefore may be different in the base

case and under standards cases at diffe rent efficiency levels ( EL s )

. c These decisions can be modeled by estimating the purchase price elasticity for commercial boiler s specifically; however,

the data needed are not sufficient to perform a robust estimation

. Hence, DOE adopt s the same assumptions used for residential product s and assume s that the purchase price elasticity is similar

between residential products and commercial equipment. The following purchase price elasticity analysis is described in the context of residential products, but DOE assume s it is applicable for commercial equipment as well.

The purchase price elasticity is defined as the change in the percentage of consumer s acquiring a boiler divided by a change in the relative price (defined below) for that equ ipment

. This elasticity , along with information obtained from the life

- cycle cost (LCC) and payback period (PBP) analysis on the change in purchase price and operating costs at different efficiency levels ( E L s )

, are used in the shipments model to estimate the change in ship ments under

standards at different ELs

.

9.4.1 Purchase Price Elasticity

DOE conduct s a literature review and an analysis of appliance price and efficiency data

to estimate the combined effects on equipment shipments from increases in equipment purchase price, decreases in equipment operating costs, and changes to household income. Appendix 9

- A provides a detailed explanation of the methodology DOE use s 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 ”

, d s o

b Number based on the assumed average capacity of the large equipment classes (3,000 kBTU/h) versus the small equipment classes (800 kBTU/h).

c Because the percentage change in the cost of boilers due to amended residential boiler standards is relatively sm all,

DOE assume s that the new construction market is unaffected by changes in either the total installed cost or operating

costs of the product . That is, home builders are not likely to choose to not install a boiler if the installed cost rises by

a small amount.

d A normal good is any good for which demand increases when income increases, and falls when income decreases but price remains constant

.9-9 that rising incomes incre ase the demand for appliances, and that consumer behavior reflects relatively high implicit d iscount rates when comparing appliance prices and appliance operating

costs. e

DOE use s the available data for the period 1980

‒ 2002 on large appliance 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, as well as to potential savings in the operating cost of the appliance, DOE combine s the available economic information into one variable, termed the relative price

. This variable was used in a regression analysis to parameterize historical market

trends

. The relative price is defined with the following expression:

Income PVOC PP

Income TP RP +

= =

Eq. 9

. 11

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.

In Eq. 9.11 , DOE use s real prices, as opposed to nominal, and an implicit discount rate of

37

- percent to estimate the present value of operating costs. The rate of 37

- percent is based on a survey of seve ral studies of different appliances that suggests that the consumer implicit discount rate has a broad range and averages about 37

- percent

. 5

DOE’s regression analysis suggests that the relative price elasticity of demand is

- 0.34

. Th is implies that a relative price increase of 10

- percent results in a 3 .4

- percent decrease in shipments

. Note that the relative price elasticity incorporates the impacts from purchase price, operating cost, and household income, so the impact from any single effect can be mitigated by changes in the other two effects

.

The relative price elasticity of

- 0.34 is consistent with estimate s in the literature

. Nevertheless, DOE stress es that the measure is based on a small data set, using simple statistical analysis. More important ly , the measure is based on an assumption that economic variables, including purchase price, operating costs, an d household income , explain most of the trend in appliances per household in the United States since 1980. Changes in appliance quality and

e An implicit discount rate refers to a rate than can be inferred from observed consumer behavior with regard to
future operating cost savings realized from more

- efficient appliances. An implicit discount rate is not a true discount rate because the observed consumer behavior is affected by lack of information, high transaction costs, and other

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

efficient appliances

.9-10 consumer preferences may have occurred during this period, but DOE d oes not account for them in this analysis. Despi te 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 have on equipment shipments.

Because projection s of shipments and national impacts attributable to standards are calculated for a lengthy time period, DOE consider s how the relative price elasticity is affected

after a new standard takes effect. DOE consider s the relative price elasticity, described above, to

be a short

- term value. It is not able to identify sources specific to household durable goods, such as appliances, to indicate how short

- run and long

- run price elasticities differ. Therefore, to

estimate how the relative pr ice elasticity changes over time, DOE relie s on a study pertaining to

automobiles. 6 This study shows that the automobile price elasticity of demand changes in the years fo llow ing a purchase price change, becom ing smaller (more inelastic) until it reaches a terminal value around the tenth year after the price change. Table 9.4.1 shows the relative change in the price elasticity of demand for automobiles over time. DOE develop s a time series of relative price elasticities based on the relative change in the automobile price elasticity of

demand. For years not shown in Table 9.4.1 , DOE perform s a linear interpolation to obtain the relative price elasticity.

Table 9.4.1 Change in Relative Price Elasticity F ollowing 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

9.4.2 Impact from Increase in Relative Price

Based on the assumption that the price elasticity for residential products is similar to commercial equipment, DOE i s able to apply the relative price elasticity for residential product

to estimate the impact of the increase in relative price from a partic ular standard level for commercial equipment . The impact , as shown in the equation below, is expressed as a percentage drop in market share for each year, dMS p j

.


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

_ RP j std

_ RP dMS RP

p p p j ×    

           

− = 1

Eq. 9

. 12
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 equipment class p

, year j

, RP p (j ) = relative price in the base case for equipment class p

, year j

, and

e

RP (j) = relative price elasticity in year j

.9-11 To model the impact of the increase in relative price from a particular standard level on boiler shipments

, DOE assume s that the affected commercial consumer s would repair their equipment rather than replace it, extending the life of the equipment by six years. When the extended repaired units fail after six more years, they will be replaced with new ones.

The model calculates the relative percentage market drop, dMS p j , due to the equipment price increase from a particular standard level . The extended repair is only applicable to failed equipment that was purchased before 2019

.

The number of failed boiler s that will be repaired instead of being replaced is calculated

as follows:



## ( ) 

## ∑ <

− × =

a p
i i a j for dMS a j m XR 2020 ) (

, Re


## ( ) ( ) 

## ∑

− +

− =

−

a j i j Dem XR XR ) a

, j ( m Re j Rpl 6

Eq. 9

. 13
Where :

d

MS p j = percentage market share drop for class p

, year j

,

a = age of 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

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

.

9.5 R ESULTS

Figure 9.5.1 shows the historic and projected base case shipments of commercial packaged boiler s by equipment c lass

.9-12 Figure 9.5.1 Historic and Projected Base Case Shipments Commercial

P a ckaged Boiler s by Equipment Class

Figure 9.5.2 shows total projected shipments of m echanical d raft g as

- f ired h ot w ater

c ommercial p ackaged b oiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr in t he base case and under

each standards case. Because the elasticity is modeled as a delayed replacement of boiler equipment , the projection for the ELs shows a decline in the early years, but an increase in later years once the delayed replacements are fina lly made. As explained above, the elasticity parameter decreases over time, so the impact of the standards on shipments diminishes. See appendix 9

- B for shipment project ion s in the base case and under each standards case for other

boiler equipment classes. 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.050

1960 1970 1980 1990 2000 2010 2020 2030 2040Shipments (million) LOMDST
SOMDST
LGMDST
SGMDST
LOMDHW
SOMDHW
LGMDHW
SGMDHW
LONDST
SONDST
LGNDST
SGNDST
LONDHW
SONDHW
LGNDHW
SGNDHW9-13 Figure 9.5.2 Total Projected Shipments of Mechanical Draft Gas

- Fired Hot Water Commercial Packaged Boiler ≥300,000 Btu/hr and ≤2,500,000 Btu/hr in the Base Case and Each Standards Case 1.0 1.1 1.1 1.2 1.2 1.3 1.3 1.4 1.4 1.5

2015 2025 2035 2045Shipments (thousand) Base

EL 1

EL 2

EL 3

EL 4

EL 5

EL 6

EL 79-14 REFERENCES

1. U.S. Department of Energy

- Energy Efficiency and Renewable Energy, Technical Support Document: Energy Conservation Program for Certain Industrial Equipment: Energy Conservation Standards and Test Procedures for Commercial Heating, Air

- Conditioning,

and Wate r

- Heating Equipment 2009. Washington, DC. Docket Number EERE

- 2008

- BT

STD

- 0013. < http://www.regulations.gov/#!docketDetail;D=EERE

- 2008

- BT

- STD

- 0013 >

2. Air Conditioning H eating and Refrigeration Institute, Directory of Certified Product Performance for Commercial Boilers, 2007

- 2014. (Last accessed February, 2014.)

< http://www.ahridirectory.org/ahri directory/pages/home.aspx >

3. U.S. Department of Energy

- Energy Information Administration, Annual Energy Outlook 2014 with Projections to 2040 , 2014. Washington, DC.

< http://www.eia.gov/forecasts/aeo/ >

4

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