9-i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION

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

- 1 9.2 HISTORICAL AND PROJE CTED SHIPMENTS

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

- 2 9.3 FORMULATION OF THE SHIPMENTS MOD EL

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

- 5 9.3.1 Mathematical Formulation of the Shipments Model

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

- 5 9.4 DISAGGREGATION OF TO TAL SHIPMENTS INTO S UBCATEGORIES

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

- 6 9.4.1 Definition of Market Segments for Equipment Type

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

- 6 9.5 IMPACT OF POTENTIAL STANDARDS ON SHIPMEN TS

................................ ...... 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 CO 2 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 CO 2 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 CO 2 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 CO 2 Shipments Forecasts for the Base Case and Considered ELs

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

- 159-1 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 shipmen ts 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

. Th is approach produces an est imate 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 n ational i mpact a nalysis (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 m odel used for commercial equipment . For refrigerated beverage vending machine s not all of the steps are included ; these are shown in gray.

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

on DOE’s Appliance and Commercial Equipment Standards website ( http://www1.eere.energy.gov/buildings/appliance_standards/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 d iscusses historical shipments

. S ection 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 des cribes 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). S ection 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 R ule (74 FR 44968), DOE used this inform ation along with comments from industry to determine the shipments shown in Table 9.2

. 1

. As shown, DOE estimated that s ales of refrigerated beverage vending machines decreased at a rate of approximately 3.9% per year from 2000

- 2007. Estimated projected s hipments 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 t he total stock of ref rigerated 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 BVM stock Initial replacement 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 shipments9-3 Table 9.2

. 1 Refrigerated Beverage Vending Machine Historical (1998

- 2006) and P rojected (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 Data 2

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.

F actors 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 th e 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 wil l 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 pr oportion 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 c rossover 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 . S hipment s 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 subcategori es in proportion to their relative historical sales (see section 9.4 for more information)

. The category U 0 (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 ) = U o ( t
, a )

− � P a a ( a ) × U 0 ( 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 , U

0 (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 as sumed 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 divid ed equipment class es A and B into two subcategories based on their refrigerant type , hydrofluorocarbon (HFC) or carbon dioxide (CO 2 ). Combination machines exist in both fully cooled and non

- fully cooled configurations , so DOE has divided this equipment cla ss into four subcategories: C ombo A HFC, C ombo A CO 2 , C ombo B HFC, and C ombo B CO 2
. 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 s hipments

. DOE assumes that the remaining s hipments 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 cha pter 10 in 2009 BVM Final Rule)

. Because data were unavailable to indicate otherwise, DOE assumed that the proportion of shipments among equipment classes remain s constant throughout the analysis period (2019

- 2048).

The market for refrigerated beverage ven ding 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 CO 2 refrigerant in all new vending machines beginning in 2015. The other has shown a s tronger 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, thei r transition to HFC

- free technology is not considered in this analysis.

At the time of the 2009 BVM Final Rule, CO 2 refrigerant machines were not approved by the EPA; therefore, all machines were assumed to use HFC refrigerant. In 2012 the number of CO 2 ma chines was not zero; although the EPA had not yet approved the use of CO 2 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 Clas s B. In a 2012 in terview the bottler stated that they expected to approve at least two Class A CO 2 models by the end of the
year. 5 Although there are currently more Class B than Class A CO 2 machines , DOE has assumed that by 20 2 0 the market share will be even. This same bottler had previously committed to all newly purchased machines containing CO 2 refrigerant by 2015; however, in their 2012 sustainability report they were significantly behind schedule to meet this target. 6 Theref ore , 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 CO 2 and HFC in 2020. The projected market share b etween HFC and CO 2 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

CO 2 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 replac ement 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 equi pment 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 qua ntify 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 summar ized from the literature suggests appliances are a normal good, such that rising incomes increase the demand for appliances. Finally, the literatu re suggests that market behavio r 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 t he 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 res idential refrigerators, clothes washers, and dishwashers over the period 1980

- 20 0 2 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
purc hase 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 anal ysis of market trends, as well as to conduct a regression analysis. The relative price is defined with the following expression:

RP = TP
Income = PP + PVOC
Income 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 describe d 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 th at, 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 p rice 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 vari ables, including purchase price, operating costs, and household income, explain most of the trend in appliances per household in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but D OE 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 ha ve 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 consi der 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. To9-9 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 purc hase 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 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

9.5.1 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 an d 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

S h ip STD

_ EQ ( j ) = ( Rpl BASE

_ EQ ( j ) + NI BASE

_ EQ ( j ) + M BASE

_ EQ ( j ) ) × ( 1

− e RP ( j ) × ∆ RP ( j ) )

Where: Ship STD _ EQ (j) = total shipments under the standards case of equipment EQ in year j
, Rpl BASE _ EQ (j) = units of equipment EQ under the base case retired and replaced in year j
, NI BASE _ EQ (j) = number of new construction installations under the base case of equipment EQ in year j
, M BASE _ EQ (j) = units installed in market M under the base case of equipment EQ in year j ( M represe nts purchases for existing locations ),

e

RP (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 slightl y 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

− Pric e basecase + Operatingcost s basecase Pric e standardscase + Operatingcos t standardscase ∗ elasticity

Pric e basecase = Price per unit in the base case Operating cost s basecase = Operating cost per unit in the base case Pric e standardscase = Price per unit in the standards case Operatingcos t standardscase = Operating costs per unit in the standards case ela s ticity = 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 lea d 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 CO 2 shipme nts increases, until about 202 0 , 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 EL 4 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 CO 2 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 t he share of CO 2 shipments increases, until about 202 0
, when the market share is projected to be evenly split between the two. Shipments are only 21 22 23 24 25 26 27Shipments (thousands) Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

19 20 21 22 23 24 25Shipments (thousands) Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL99-12 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 27Shipments (thousands) Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

EL10

EL119-13 Figure 9.6

. 4 Equipment Class B CO 2 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 C ombination refrigerated b everage 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 level s , EL 1 0 and EL11. For the two Combo B sub categories standards are only projected to cause a decrease in shipments at the highest efficiency level, EL 8

. 18 19 20 21 22 23 24 25Shipments (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 0.54Shipments (thousands) Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL8

EL9

EL10

EL119-14 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 CO 2 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 0.51Shipments (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 0.54Shipments (thousands) Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL89-15 Figure 9.6

. 8 Equipment Class Combo B CO 2 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 0.51Shipments (thousands) Base Case

EL1

EL2

EL3

EL4

EL5

EL6

EL7

EL89-16 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 ENER GY 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

– aga inst soda taxes. Available online http://www.takepart.com/article/2014/04/01/soda

- industry

- campaigns

- against

- soda

taxes?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 G lobal R eporting I nitiative 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

- 1 253.

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

- 239.