9-i

CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 FUNDAMENTAL MODEL EQUATIONS.................................................................... 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 SUPPORTING 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 Air-Conditioning, Heating, and 

Refrigeration 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............................................................ 9-13
9-1

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future equipment shipments are necessary inputs 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) uses 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 boilers 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. This approach produces an 

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

analysis period. In addition, the 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 

national impact analysis (NIA) to estimate the total costs and benefits associated with each 

efficiency level.

The shipments model 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 spreadsheet, 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 summarizes the abbreviations of each product class. The commercial 

packaged boiler shipments model considers three equipment placement channels (hereafter 

referred to as “channels”) as follows:

1. New construction: a certain fraction of new buildings acquire boilers 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 boilers

installed. This category receives new shipments when existing equipment are replaced.

3. New owners: these are defined as existing buildings that acquire boilers for the first time 

during the analysis period. The new owners primarily consist of buildings that during a 

major remodel add or switch to 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 Abbreviations 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 analysis year minus the vintage). The stock 

function is adjusted in each year of the analysis period by new shipments coming in and broken 

or demolished equipment being taken out.

For existing stock:
9-3

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

Eq. 9.1

and for new shipments:

Stock ( j, a = 1) = Ship ( j −1). p p

Eq. 9.2

Where:

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

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

Shipp(j) = number of units of equipment class p shipped in year j.

Removals due to equipment failure contain a survival function fp(a) that is used to 

represent the probability that a unit of age a will survive in a given year; equivalently, the 

probability that this unit will fail is 1- fp(a).

Total removals in the base case are then:

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

Shipments are directed to one of the three channels:

Ship ( j) Rpl ( j) NC ( j) NO ( j) p = p + p + p

Eq. 9.4

Where:

Rplp(j) = number of units of equipment p replaced in year j, which depends on removed units and 

units in demolished buildings,

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

NOp(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 Remp (j). As the demolished units do not need to 

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

represented by the following expression:
9-4

Rpl ( j) Rem ( j) Dem( j) 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 rp(a) is used to represent the probability that a unit will fail at age 

a.

Rem ( j) r (a) Stock ( j a) p a p a = ∑ × ,

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.

Dem( j) = D( j)× sat(p, j −1)

Eq. 9.7

The number of demolished buildings is calculated by:

D( j) = H _ Stock( j −1) + H _ Starts( j) − H _ Stock( j)

Eq. 9.8

Where: 

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 multiplies new construction market saturations by projections of new buildings to 

estimate shipments to the new construction channel. On an equipment class basis, the 

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

NC ( j) NC Starts com( j) NC Sat com ( j) NC Starts res( j) NC Sat res ( j) p = _ _ × _ _ p + _ _ × _ _ p

Eq. 9.9

Where: 

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

NC_Sat_comp (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_resp(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 estimates historical shipments to this market 

segment as a residual, using the following equation:

NO( j) = Shipment( j)− (RU( j)+ NU( j))

Eq. 9.10

Where: 

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 SUPPORTING CALCULATIONS

9.3.1 Historical Shipments

DOE uses (1) historical shipment data about commercial packaged boilers in the 

commercial packaged boiler final rule (2009), (2) number of models in the Air-Conditioning, 

Heating, and Refrigeration Institute (AHRI) Directory 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 boilers.

a, 1, 2, 3, 4, 5 DOE derives the ratios 

of gas- and oil- fired commercial packaged boiler shipments to residential boiler shipments from 

the shipment data in 2009 and assumes the ratios remain constant from 1960 to 2013. The oilfired 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 adjusted 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 Appendix 9-B. Figure 9.3.1

summarizes the historical shipments data that DOE has assembled. 

Figure 9.3.1 Historical Shipments of Commercial Packaged Boilers

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

DOE considers any future shipments of commercial packaged boilers 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 2010

Historical shipments (million)

LOMDST

SOMDST

LGMDST

SGMDST

LOMDHW

SOMDHW

LGMDHW

SGMDHW

LONDST

SONDST

LGNDST

SGNDST

LONDHW

SONDHW

LGNDHW

SGNDHW
9-7

this analysis. DOE uses CBECS 2003 and RECS 2009 to determine the number of commercial 

packaged boilers in commercial and residential applications. DOE estimates that commercial 

packaged boilers shipped to residential buildings account for 5.5 percent of the total historical 

shipments of commercial packaged boilers. Details of the methodology to determine the fraction 

of commercial packaged boilers shipped to residential buildings are in Appendix 9-B.

9.3.2 Shipments to New Construction

DOE determines new commercial building and residential housing construction starts by 

using recorded data through 2012 and projections from EIA’s Annual Energy Outlook 2014

(AEO 2014).3

DOE develops 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 

uses the average saturations in buildings built in 1990-2003 (from CBECS 2003 data) that also

use each type of heating equipment. To estimate future saturations in residential buildings for gas 

boilers and oil boilers, DOE uses the average saturations in homes built in 1990-2009 (from 

RECS 2009 data) which use each type of heating product. DOE bases 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 assumes 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 saturations of commercial packaged boilers by 

building type in year 2019. 

Table 9.3.2 Saturations of Commercial Packaged Boilers 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 uses 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 assumes that in the future 

this fraction would potentially decrease to 3-percent.
9-8

DOE includes 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 considers 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 boilers.b

9.4 IMPACT OF STANDARDS ON SHIPMENTS

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 different efficiency levels (ELs).

c These decisions can be 

modeled by estimating the purchase price elasticity for commercial boilers specifically; however, 

the data needed are not sufficient to perform a robust estimation. Hence, DOE adopts the same 

assumptions used for residential products and assumes 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 assumes it is applicable for 

commercial equipment as well.

The purchase price elasticity is defined as the change in the percentage of consumers 

acquiring a boiler divided by a change in the relative price (defined below) for that equipment. 

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 (ELs), are used in the shipments model to estimate the change in shipments under 

standards at different ELs.

9.4.1 Purchase Price Elasticity

DOE conducts 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 uses to quantify the impacts from these 

variables.

Existing studies of appliance markets suggest that the demand for appliances is priceinelastic. Other information in the literature suggests that appliances are a “normal good”,

d so

 

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 small, 

DOE assumes 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 increase the demand for appliances, and that consumer behavior reflects

relatively high implicit discount rates when comparing appliance prices and appliance operating 

costs. e

DOE uses the available data for the period 1980‒2002 on large appliance purchases to 

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

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

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

purchase decisions are sensitive to income, as well as to potential savings in the operating cost of 

the appliance, DOE combines 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

PP PVOC

Income

TP RP + = =

Eq. 9.11

Where:

RP = relative price,

TP = total price,

Income = household income,

PP = appliance purchase price, and

PVOC = present value of operating cost.

In Eq. 9.11, DOE uses 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 several 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.

This 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 estimates in the literature.

Nevertheless, DOE stresses that the measure is based on a small data set, using simple statistical 

analysis. More importantly, the measure is based on an assumption that economic variables, 

including purchase price, operating costs, and household income, explain most of the trend in 

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

 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 energyefficient appliances.
9-10

consumer preferences may have occurred during this period, but DOE does 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 have on equipment shipments.

Because projections of shipments and national impacts attributable to standards are

calculated for a lengthy time period, DOE considers how the relative price elasticity is affected 

after a new standard takes effect. DOE considers 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 price elasticity changes over time, DOE relies on a study pertaining to 

automobiles.6 This study shows that the automobile price elasticity of demand changes in the 

years following a purchase price change, becoming smaller (more inelastic) until it reaches a 

terminal value around the tenth year after the price change. Table 9.4.1 shows the relative change 

in the price elasticity of demand for automobiles over time. DOE develops 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 performs a linear interpolation to obtain the 

relative price elasticity.

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

Years Following Price Change

1 2 3 5 10 20

Relative Change in 

Elasticity to 1st year

1.00 0.78 0.63 0.46 0.35 0.33

Relative Price Elasticity -0.34 -0.26 -0.21 -0.16 -0.12 -0.11

9.4.2 Impact from Increase in Relative Price 

Based on the assumption that the price elasticity for residential products is similar to 

commercial equipment, DOE is able to apply the relative price elasticity for residential product 

to estimate the impact of the increase in relative price from a particular 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, dMSp

j. 

( )

( ) e (j) RP _base j

RP _std j dMS RP

p

p p

j ×































 = 1 −

Eq. 9.12

Where:

dMSp

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

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

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

eRP(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 assumes that the affected commercial consumers 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, dMSp

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 boilers that will be repaired instead of being replaced is calculated 

as follows: 

= ∑ ( )× − < a

p XRi Rem j,a dMSi for( j a) 2020

( ) = ∑ − + − ( ) −

a

i j Rpl j Re m( j,a ) XR XR Dem j 6

Eq. 9.13

Where:

dMSp

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,

XRj = extended repair units, year j,

Rpl(j) = replacement units in year j, and

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

9.5 RESULTS

Figure 9.5.1 shows the historic and projected base case shipments of commercial 

packaged boilers by equipment class.
9-12

Figure 9.5.1 Historic and Projected Base Case Shipments Commercial 

Packaged Boilers by Equipment Class

Figure 9.5.2 shows 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 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 finally 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 projections 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 2040

Shipments (million)

LOMDST

SOMDST

LGMDST

SGMDST

LOMDHW

SOMDHW

LGMDHW

SGMDHW

LONDST

SONDST

LGNDST

SGNDST

LONDHW

SONDHW

LGNDHW

SGNDHW
9-13

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 2045

Shipments (thousand)

Base

EL 1

EL 2

EL 3

EL 4

EL 5

EL 6

EL 7
9-14

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 Water-Heating Equipment 2009. Washington, DC. Docket Number EERE-2008-BTSTD-0013. <http://www.regulations.gov/#!docketDetail;D=EERE-2008-BT-STD-0013>

2. Air Conditioning Heating and Refrigeration Institute, Directory of Certified Product 

Performance for Commercial Boilers, 2007-2014. (Last accessed February, 2014.) 

<http://www.ahridirectory.org/ahridirectory/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. Decision Analysts, 2008 American Home Comfort Study: Online Database Tool, 2009. 

Arlington, Texas. <http://www.decisionanalyst.com/Syndicated/HomeComfort.dai>

5. Train, K., Discount Rates in Consumers' Energy-Related Decisions. Energy: The 

International Journal, 1985. 10(12): pp. 1243-1253 

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

on Economic Activity, 1971. 1971(1): pp. 234-239