9-i

CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

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

9.2 HISTORICAL AND PROJECTED SHIPMENTS.......................................................... 9-2

9.3 FORMULATION OF THE SHIPMENTS MODEL....................................................... 9-5

9.3.1 Mathematical Formulation of the Shipments Model ........................................... 9-5

9.4 DISAGGREGATION OF TOTAL SHIPMENTS INTO SUBCATEGORIES .............. 9-6

9.4.1 Definition of Market Segments for Equipment Type .......................................... 9-6

9.5 IMPACT OF POTENTIAL STANDARDS ON SHIPMENTS ...................................... 9-7

9.5.1 Application of Relative Price Elasticity............................................................... 9-9

9.6 RESULTS ...................................................................................................................... 9-10

LIST OF TABLES

Table 9.2.1 Refrigerated Beverage Vending Machine Historical (1998-2006) and 

Projected (2007-2012) Shipments from 2009 BVM Final Rule.................... 9-3

Table 9.2.2 ENERGY STAR Market Data ....................................................................... 9-4

Table 9.4.1 Market Shares by Refrigerant ........................................................................ 9-7

Table 9.5.1 Change in Relative Price Elasticity Following a Purchase Price 

Change ........................................................................................................... 9-9

LIST OF FIGURES

Figure 9.1.1 Flow chart showing inputs to the shipments analysis model......................... 9-2

Figure 9.6.1 Equipment Class A HFC Shipments Forecasts for the Base Case and 

Considered ELs............................................................................................ 9-11

Figure 9.6.2 Equipment Class A CO2 Shipments Forecasts for the Base Case and 

Considered ELs............................................................................................ 9-11

Figure 9.6.3 Equipment Class B HFC Shipments Forecasts for the Base Case and 

Considered ELs............................................................................................ 9-12

Figure 9.6.4 Equipment Class B CO2 Shipments Forecasts for the Base Case and 

Considered ELs............................................................................................ 9-13

Figure 9.6.5 Equipment Class Combo A HFC Shipments Forecasts for the Base 

Case and Considered ELs ............................................................................ 9-14

Figure 9.6.6 Equipment Class Combo A CO2 Shipments Forecasts for the Base 

Case and Considered ELs ............................................................................ 9-14

Figure 9.6.7 Equipment Class Combo B HFC Shipments Forecasts for the Base 

Case and Considered ELs ............................................................................ 9-14

Figure 9.6.8 Equipment Class Combo B CO2 Shipments Forecasts for the Base 

Case and Considered ELs ............................................................................ 9-15
9-1

CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

Estimates of future equipment shipments are a necessary input to calculations of the 

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

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

Energy (DOE) used to forecast annual equipment shipments and presents results for each of the

refrigerated beverage vending machine (BVM) equipment classes being considered in this 

analysis. The shipments model estimates the shipment of new equipment under each efficiency 

level. The model starts from a historical base year and calculates, for each year of the analysis 

period, both shipments and retirements by market segment. This approach produces an estimate 

of the total equipment stock, broken down by age or vintage, in each year of the analysis period. 

The equipment stock distribution is calculated for the base case and for each efficiency level for 

each equipment class. The stock distribution is used in the national impact analysis (NIA) to 

estimate the total dollar and energy costs and benefits associated with each efficiency level.

Figure 9.1.1 outlines the structure of the shipments model used for commercial equipment. For

refrigerated beverage vending machines not all of the steps are included; these are shown in gray. 

The shipments model was developed as a Microsoft Excel spreadsheet that is accessible 

on DOE’s Appliance and Commercial Equipment Standards website 

(http://www1.eere.energy.gov/buildings/appliance_standards/rulemaking.aspx/ruleid/73). 

Appendix 10A discusses how to access and utilize the shipments model spreadsheet, which is

integrated into the spreadsheet for the National Impact Analysis (NIA).

This chapter explains how the shipments model is constructed and provides some 

summary output. Section 9.2 discusses historical shipments. Section 9.3 defines the 

mathematical approach to developing the shipments model. Section 9.4 discusses the 

disaggregation of total shipments into subcategories. Section 9.5 describes the impacts on 

shipments due to potential new or amended standards (a more detailed explanation of the 

methodology DOE used to quantify the impacts of these variables on shipments can be found in 

Appendix 9A). Section 9.6 presents the results of the shipments forecasts in the base case and at 

each of the standards cases. 
9-2

Figure 9.1.1 Flow chart showing inputs to the shipments analysis model

9.2 HISTORICAL AND PROJECTED SHIPMENTS

Historical shipments are useful to the development of the shipments model. Historical 

shipped units reflect the annual amount of newly-built BVM equipment capacity introduced into 

the marketplace in past years.

Historical shipments data on equipment class A and equipment class B equipment were 

available online from U.S. Bureau of the Census Current Industrial Reports for most years in the 

1990s; however, this series was discontinued after 2000.

1 In the 2009 BVM Final Rule (74 FR 

44968), DOE used this information along with comments from industry to determine the 

shipments shown in Table 9.2.1. As shown, DOE estimated that sales of refrigerated beverage 

vending machines decreased at a rate of approximately 3.9% per year from 2000-2007.

Estimated projected shipments after 2006 begin at 90,000 per year for 2008-2009, increase to 

150,000 in 2010, and then to 190,000 per year in 2011-2012. At this time the total stock of 

refrigerated beverage vending machines was estimated to have fallen from approximately 3.3 

million in 2000 to 2.5 million in 2007. 

Population forecast % shipments by 

equipment class

Initial replacement Initial BVM stock

shipments

Shipments to new 

locations Initial shipments to 

new locations

Total shipments by 

equipment class

Estimated lifetime 

of BVM stock

BVM stock by 

equipment class 

Replacement 

shipments

Industry estimate of 

shipments
9-3

Table 9.2.1 Refrigerated Beverage Vending Machine Historical (1998-2006) and 

Projected (2007-2012) Shipments from 2009 BVM Final Rule 

Year

Estimated 

Shipments

Assumed 

Replacements New Shipments Estimated Stock

thousands thousands thousands thousands

1998 372 250 122 2,789

1999 477 250 227 3,046

2000 338 250 88 3,164

2001 300 250 50 3,242

2002 250 250 - 3,267

2003 200 200 - 3,229

2004 200 200 - 3,170

2005 200 200 - 3,070

2006 125 125 - 2,690

2007 90 90 - 2,500

2008 90 90 - 2,300

2009 90 90 -

2010 150 150 -

2011 190 190 -

2012 190 190 -

In the 2009 BVM Final Rule, DOE projected an increase in shipments after 2009 that 

would stabilize at roughly 190,000 units annually. However, as discussed in chapter 2, DOE 

received comments from Crane stating that the vending industry has been declining over the past 

several years with shipments stagnant and the number of units in the field declining. (Crane, No. 

7 at p.113)

ENERGY STAR’s unit shipment and market penetration reports for new and rebuilt 

refrigerated beverage vending machines over the period 2005-2012 are shown in Table 9.2.2. 

This table indicates that total shipments of refrigerated beverage vending machines have been in 

the range of 240,000-250,000 annually over this time period. However, the size of the market 

that is newly built machines only account for approximately 100,000 annually, with the 

remainder being rebuilt machines. (The lower values in 2008-2010 reflect the economic 

recession in that period.)
9-4

Table 9.2.2 ENERGY STAR Market Data2

 

Year

ENERGY

STAR units 

shipped

Market 

penetration 

estimate (%)

Total shipments 

(including 

rebuilt 

machines)

New shipments

2005 68,581 28 244,932 104,932

2006 77,340 31 249,484 104,484

2007 63,812 26 245,431 95,431

2008 77,742 32 242,944 87,944

2009 45,000 18 250,000 90,000

2010 68,000* 28 242,857 91,667

2011 77,000* 31 248,387 93,333

2012 81,000 33 245,455 95,000

*In these years not all partners provided ENERGY STAR with shipments data. ENERGY STAR does not correct 

for missing data; therefore, these numbers are likely to be slightly underestimated. 

Factors that may increase the newly built proportion of the market include the 

commitment the two major bottlers have made to phasing in hydrofluorocarbon (HFC)-free 

vending machines and the recent agreement between the signatories to the Montreal Protocol, 

including the United States, to extend the commitment to addressing high global warming 

potential (GWP) gases. But at the same time there are indications that stocks of BVMs may 

decline due to concerns about the incidence of obesity, particularly in children. Such measures 

include the banning of refrigerated beverage vending machines in schools, exemplified by the 

statewide ban on vending machines in elementary schools in Arkansas,3 and the proposed 

introduction of a “soda tax” in some areas. Studies into regions where such soda taxes are 

already in place have found that they do lead to a reduction in sales of soda.

4 While soda is not 

the only item sold by refrigerated beverage vending machines, such developments do indicate a 

potential shrinkage of the BVM market. As discussed in chapter 2, NAMA and Royal 

commented that the market trend indicates a shrinking industry-wide sales volume for new 

refrigerated beverage vending machines, and that this trend is generally expected to continue. 

(NAMA, No.8 at p. 21; Royal, No. 11 at p. 16) 

Given the above information, DOE has decided to assume sales of newly built machines 

will maintain a steady rate of 100,000 annually, approximately the level they have maintained 

over the past eight years according to the ENERGY STAR reports. This will lead to a decline in 

the total stock of equipment, while at the same time increasing the proportion of newly built 

machines sold. 
9-5

9.3 FORMULATION OF THE SHIPMENTS MODEL

9.3.1 Mathematical Formulation of the Shipments Model

The shipments analysis model is a description of BVM equipment stock flows as a 

function of year and age. DOE assumes there is no crossover among equipment classes, so the 

equations describe each type of equipment independently. 

DOE formulated the equations as updates of the distribution of stock in year t as a 

function of age a to year t+1. Shipments of new equipment are recorded by the year sold, and 

each annual vintage is depreciated over the estimated life of the equipment (averaging 

approximately 13.4 years, with a 30-year maximum). Shipments in each year are allocated to the 

eight equipment class subcategories in proportion to their relative historical sales (see section 9.4

for more information).

The category U0(t,a) is the stock of existing units. The total stock of age a in a given year 

t is represented by

U(t, a) = Uo(t, a) − � Pa a

(a) × U0 (t, a)

The total stock of equipment is:

� (U(t, a)) a

and the average age of the stock in year t is defined as

StockAge(t) = � U(t, a) × a/ a

� �U(t, a)� a

Where:

U(t,a) = total stock of age a in a given year t,

U0(t,a) = initial stock of existing units of age a,

StockAge(t) = average age of stock in year t, 

P(a) = probability that stock of initial units will break or retire in year a, 

a = age of stock in years, and

t = year. 

As mentioned in section 9.2, DOE estimated total shipments as constant in each year. 

Therefore, in the model, the net purchase of equipment for new locations is assumed to be zero, 

and any equipment purchases are for replacement only. Although DOE acknowledges that 

shipments to new locations will occur, it assumes that these will be offset by non-replacement at 

current locations.
9-6

9.4 DISAGGREGATION OF TOTAL SHIPMENTS INTO SUBCATEGORIES

9.4.1 Definition of Market Segments for Equipment Type 

The shipments model disaggregates the total shipments into three equipment classes: A 

(fully cooled machines), B (non-fully cooled machines), and Combination machines. DOE has 

further divided equipment classes A and B into two subcategories based on their refrigerant type, 

hydrofluorocarbon (HFC) or carbon dioxide (CO2). Combination machines exist in both fully 

cooled and non-fully cooled configurations, so DOE has divided this equipment class into four 

subcategories: Combo A HFC, Combo A CO2, Combo B HFC, and Combo B CO2. In total, DOE 

disaggregates shipments into eight equipment class subcategories. More details on the three 

equipment classes are found in chapter 3.

DOE estimates that Combination machines make up 2 percent of the total shipments. 

DOE assumes that the remaining shipments are split evenly (49 percent each) between 

equipment classes A and B, as it did for the 2009 BVM Final Rule based on comments from 

stakeholders (see chapter 10 in 2009 BVM Final Rule). Because data were unavailable to 

indicate otherwise, DOE assumed that the proportion of shipments among equipment classes 

remains constant throughout the analysis period (2019-2048).

The market for refrigerated beverage vending machines is dominated by two companies. 

Both have indicated a desire to transition to HFC-free technology. One of these companies has 

made a firm commitment to use CO2 refrigerant in all new vending machines beginning in 2015. 

The other has shown a stronger interest in hydrocarbon refrigerant technologies, which are not 

yet permitted in the United States for vending applications under the U.S. Environmental 

Protection Agency (EPA) Significant New Alternatives Policy (SNAP) regulations; therefore, 

their transition to HFC-free technology is not considered in this analysis.

At the time of the 2009 BVM Final Rule, CO2 refrigerant machines were not approved by 

the EPA; therefore, all machines were assumed to use HFC refrigerant. In 2012 the number of 

CO2 machines was not zero; although the EPA had not yet approved the use of CO2 refrigerant in 

refrigerated beverage vending machines, they had permitted one of the major bottlers to install a 

small number of them on a trial basis. These trial machines were Class B. In a 2012 interview the 

bottler stated that they expected to approve at least two Class A CO2 models by the end of the 

year.5 Although there are currently more Class B than Class A CO2 machines, DOE has assumed 

that by 2020 the market share will be even. This same bottler had previously committed to all 

newly purchased machines containing CO2 refrigerant by 2015; however, in their 2012 

sustainability report they were significantly behind schedule to meet this target.6 Therefore, DOE 

assumed that the bottler would reach this goal in 2017.

Overall, DOE expects the market share by refrigerant type to shift from nearly all HFC 

before 2012 to an even share (50 percent each) between CO2 and HFC in 2020. The projected 

market share between HFC and CO2 is shown in Table 9.4.1. These shares are assumed to be the 

same for all equipment classes. DOE assumes that shares by refrigerant will continue to be 

evenly split throughout the rest of the analysis period (until 2048).
9-7

Table 9.4.1 Market Shares by Refrigerant 

Market Share (%)

Refrigerant 2012 2013 2014 2015 2016 2017 2018 2019 2020

CO2 2.5 10.0 17.5 25.0 32.5 40.0 43.3 46.7 50.0

HFC 97.5 90.0 82.5 75.0 67.5 60.0 56.7 53.3 50.0

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

DOE used the above shipments data to estimate the market shipments in year t of each 

BVM equipment class subcategory y (MKTSHIP(t,y)) from the equipment of the percentage of 

shipments in each equipment class subcategory (MKT(y)) and total shipments, being the sum of 

shipments to new locations (UN(t), assumed to be zero) and replacement shipments (UB(t)):

MKTSHIP(t, y) = MKT(y)[UN(t) + UB(t)]

Where:

MKTSHIP(t,y) = total market shipments of equipment class y in year t, 

MKT(y) = percentage of total market shipments of equipment class y, 

UN(t) = total shipment to new locations in year t (assumed to be zero), and

UB(t) = total replacement shipments in year t. 

9.5 IMPACT OF POTENTIAL STANDARDS ON SHIPMENTS

DOE projects that appliance standards often result in an increase in the price of the 

equipment. Economic theory suggests that, all else being equal, an increase in the price of 

equipment would lead to a decrease in demand for it. DOE conducted a literature review and an 

analysis of appliance price and efficiency data to estimate the effects on equipment shipments 

from increases in equipment price. DOE also considered the decreases in operating costs from 

higher energy efficiency and changes over time in household income. Appendix 9-A provides a 

detailed explanation of the methodology DOE used to quantify the impacts of these variables on 

shipments. Although not directly applicable to commercial equipment, it is the only analysis of 

its kind and so DOE relied upon it to estimate the impacts of higher equipment prices for 

refrigerated beverage vending machines. 

In the literature, DOE found only a few studies of appliance markets that are relevant to 

the issue at hand. DOE identified no studies that use time-series data of equipment price and 

shipments data after 1980. The information that can be summarized from the literature suggests 

appliances are a normal good, such that rising incomes increase the demand for appliances. 

Finally, the literature suggests that market behavior indicates relatively high “implicit discount 

rates” when comparing appliance prices and appliance operating costs.a

 a A high implicit discount rate with regard to operating costs means that, based on market behavior, consumers 

appear to put relatively low economic value on the operating cost savings realized from more-efficient appliances. A 

high value may indicate lack of information, risk aversion, and other factors, as well as the value consumers place 

on savings accrued in the future. 
9-8

DOE found insufficient data on equipment purchase price and operating cost to perform a 

thorough analysis of dynamic changes in the appliance market. Instead, it used purchase price 

and efficiency data specific to residential refrigerators, clothes washers, and dishwashers over the 

period 1980-2002 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. 

To simplify the analysis, DOE combined the available economic information into one 

variable, termed the relative price, and used the variable in an analysis of market trends, as well 

as to conduct a regression analysis. The relative price is defined with the following expression: 

RP = TP

Income = PP + PVOCIncome

Where:

RP= relative price, 

TP= total price,

Income= household income

PP= appliance purchase price, and 

PVOC= present value of operating cost. 

DOE used an “implicit discount rate” of 37 percent to determine the present value of 

operating costs. This value is an average from values derived by several studies described in a 

1985 journal article.7

DOE’s regression analysis yields a relative price elasticity of demand, averaged over the 

three appliances, of -0.34. For example, a relative price increase of 10 percent results in a 3.4 

percent decrease in shipments. Note that, because the relative price elasticity incorporates the 

impacts from three effects (i.e., purchase price, operating cost, and household income), the 

impact from any single effect is somewhat 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 

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

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

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

operating cost, and household income have on equipment shipments. 

DOE considers the relative price elasticity provided by the preceding analysis to be a 

short-run value. Because DOE’s forecasts of shipments and national impacts due to standards is 

over a 30-year time period, it needed to consider how the relative price elasticity is affected once 

a new standard takes effect. It was unable to identify sources specific to household durable 

goods, such as appliances, to indicate how short-run and long-run price elasticities differ. To 
9-9

estimate how the relative price elasticity changes over time, DOE relied on a study pertaining to 

automobiles.8 This study shows that the automobile price elasticity of demand changes in the 

years following a purchase price change. With increasing years after the purchase price change, 

the price elasticity becomes smaller (more inelastic) until it reaches a terminal value around the 

tenth year after the price change. Table 9.5.1 shows the relative change in the price elasticity of 

demand for automobiles over time. DOE developed a time series of relative price elasticities for 

home appliances based on the relative change in the automobile price elasticity of demand. For 

years not shown in the table, DOE performed a linear interpolation to obtain the relative price

elasticity. 

Table 9.5.1 Change in Relative Price Elasticity Following a Purchase Price Change 

Years Following Price Change

1 2 3 5 10 20

Change in 

Elasticity 

Relative 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

9.5.1 Application of Relative Price Elasticity

DOE estimates shipments in each standards case using the relative price elasticities 

described previously, along with the change in the relative price between a standards case and 

the base case. Because household income is the same in the standards case and the base case, it 

does not figure into the calculation of the change in the relative price. Note that, in the following 

equation, the relative price and the relative price elasticity are functions of the year because both 

change with time. b

ShipSTD _EQ (j) = (RplBASE _EQ (j) + NIBASE _EQ(j) + MBASE _EQ(j)) × (1 − eRP (j) × ∆RP(j))

Where:

ShipSTD _EQ(j)= total shipments under the standards case of equipment EQ in year j,

RplBASE _EQ(j)= units of equipment EQ under the base case retired and replaced in year j, 

NIBASE _EQ(j)= number of new construction installations under the base case of equipment 

EQ in year j, 

MBASE _EQ(j)= units installed in market M under the base case of equipment EQ in year j

(M represents purchases for existing locations), 

eRP(j)= relative price elasticity in year j (equals -0.34 for year 1), and

∆RP(j)= change in relative price due to a standards level in year j. 

 b The relative price changes slightly over time because the lifetime operating costs are different for each vintage in 

the forecast period. Operating costs change slightly because forecasted energy prices are changing. 
9-10

DOE determines the percentage decrease in shipments as:

1 − Pricebasecase + Operatingcostsbasecase

Pricestandardscase + Operatingcoststandardscase

∗ elasticity

Pricebasecase= Price per unit in the base case

Operatingcostsbasecase= Operating cost per unit in the base case

Pricestandardscase= Price per unit in the standards case

Operatingcoststandardscase= Operating costs per unit in the standards case

elasticity= relative price elasticity, as described in 9.4

For all equipment classes, DOE found the increased price per unit in the standards case 

was insignificant compared to the decreased operating costs for some efficiency levels. This 

would lead to a negative value in the above formula, indicating an increase in shipments. DOE 

does not expect the standard to lead to an increase in shipments and therefore does not expect 

shipments to be affected by the standards at these efficiency levels.

9.6 RESULTS

This section presents the shipments forecasts for the candidate standard levels (CSLs), 

also known as efficiency levels (ELs), which DOE considered for each of the equipment class 

subcategories, as well as for the base case without revised standards (see chapter 10 for base case 

description). The ELs are described in chapter 10. Figure 9.6.1 and Figure 9.6.2 show the 

shipments forecast for the two subcategories of class A equipment. As can be seen in the early 

years the share of HFC shipments is expected to decrease, while the share of CO2 shipments 

increases, until about 2020, when the market share is projected to be evenly split between the 

two. The standards cases are only projected to lead to a decrease in shipments at EL4 and higher. 
9-11

Figure 9.6.1 Equipment Class A HFC Shipments Forecasts for the Base Case and 

Considered ELs

Figure 9.6.2 Equipment Class A CO2 Shipments Forecasts for the Base Case and 

Considered ELs

Figure 9.6.3 and Figure 9.6.4 show the shipments forecast for the two equipment class B 

equipment subcategories. Similar to equipment class A, in the early years the share of HFC 

shipments is expected to decrease, while the share of CO2 shipments increases, until about 2020, 

when the market share is projected to be evenly split between the two. Shipments are only 

21

22

23

24

25

26

27

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

19

20

21

22

23

24

25

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9
9-12

projected to decrease with the introduction of standards, at EL7 and higher for both equipment 

class B subcategories. 

Figure 9.6.3 Equipment Class B HFC Shipments Forecasts for the Base Case and 

Considered ELs

20

21

22

23

24

25

26

27

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

EL10

EL11
9-13

Figure 9.6.4 Equipment Class B CO2 Shipments Forecasts for the Base Case and 

Considered ELs

Figure 9.6.5 to Figure 9.6.8 show the shipments forecasts for the four subcategories of 

Combination refrigerated beverage vending machines. As can be seen for the two Combo A 

subcategories standards are only projected to cause a decrease in shipments at the two highest 

efficiency levels, EL10 and EL11. For the two Combo B sub categories standards are only 

projected to cause a decrease in shipments at the highest efficiency level, EL8 .

18

19

20

21

22

23

24

25

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

EL10

0.45

0.46

0.47

0.48

0.49

0.50

0.51

0.52

0.53

0.54

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

EL10

EL11
9-14

Figure 9.6.5 Equipment Class Combo A HFC Shipments Forecasts for the Base Case and 

Considered ELs

Figure 9.6.6 Equipment Class Combo A CO2 Shipments Forecasts for the Base Case and 

Considered ELs

Figure 9.6.7 Equipment Class Combo B HFC Shipments Forecasts for the Base Case and 

Considered ELs

0.41

0.42

0.43

0.44

0.45

0.46

0.47

0.48

0.49

0.50

0.51

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

EL10

EL11

0.44

0.45

0.46

0.47

0.48

0.49

0.50

0.51

0.52

0.53

0.54

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8
9-15

Figure 9.6.8 Equipment Class Combo B CO2 Shipments Forecasts for the Base Case and 

Considered ELs

0.41

0.42

0.43

0.44

0.45

0.46

0.47

0.48

0.49

0.50

0.51

Shipments (thousands)

Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8
9-16

REFERENCES

1 For example, see “Vending Machines (Coin-Operated).” Current Industrial Reports 

MA333U(99)-1. September 2000. (Last accessed January 22, 2014) 

http://www.census.gov/industry/1/ma35u99.pdf

2 ENERGY STAR (2014) Unit Shipment and sales data archives Available online 

http://www.energystar.gov/index.cfm?c=partners.unit_shipment_data_archives [accessed 

2/12/2014] 

3 Arkansas Department of Education (2012) Rules governing nutrition and physical activity 

standards and body mass index for age assessment protocols in Arkansas public schools. 

ADE 259-2.

4

Jason Best (2014) Soda industry campaigns early – and hard – against soda taxes. Available 

online http://www.takepart.com/article/2014/04/01/soda-industry-campaigns-against-sodataxes?cmpid=foodinc-fb [accessed 4/1/2014]

5 R744, “Coca-Cola to approve 9 models of CO2 vending machine – exclusive interview,” 

Available online http://www.r744.com/news/view/3466 [accessed 6/11/2014]

6 The Coca-Cola Company (2013)2012/2013 Global Reporting Initiative Report. Available 

online http://assets.coca-colacompany.com/44/d4/e4eb8b6f4682804bdf6ba2ca89b8/2012-

2013-gri-report.pdf [accessed 6/12/2014]

7 Train, K., “Discount Rates in Consumers,” Energy-Related Decisions,” Energy: The 

International Journal, Vol.10, No.12, 1985, pp. 1243-1253.

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

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