CHAPTER 9. SHIPMENTS ANALYSIS 

TABLE OF CONTENTS 

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

9.2 SHIPMENTS MODEL EQUATIONS ............................................................................ 9-2
 

9.2.1 Mathematical Formulation of the Shipments Model ........................................ 9-3
 

9.2.1.1 Size of the Stock ............................................................................. 9-3
 

9.2.1.2 Replacement Events ........................................................................ 9-4
 

9.3 DATA INPUTS ............................................................................................................... 9-6
 

9.3.1 Historical Stock................................................................................................. 9-6
 

9.3.2 Commercial Floor Space and Market Saturation .............................................. 9-6
 

9.3.3 Distribution of Walk-ins by Equipment Class.................................................. 9-7
 

9.3.3.1 Equipment Lifetime and Age.......................................................... 9-9
 

9.3.3.2 Age Distributions of Existing Stock ............................................. 9-10
 

9.3.3.3 Price Elasticity .............................................................................. 9-12
 

9.4 RESULTS ...................................................................................................................... 9-12
 

REFERENCES .......................................................................................................................... 9-15
 

LIST OF TABLES 

Table 9.3.1 Forecasted Cumulative Growth By Building Type Since 2007........................... 9-7
 

Table 9.3.2 Percent of Shipped Walk-ins by Product Class ................................................... 9-8
 

Table 9.3.3 Estimates of Walk-in Lifetimes by Equipment Type .......................................... 9-9
 

Table 9.3.4 Cumulative Failure Rates by Equipment Type.................................................. 9-10
 

Table 9.3.5 Assumed Age Distribution of Initial Stock ........................................................ 9-12
 

Table 9.4.1 Forecasted Shipments of New and Replacement WICF Envelopes, 2015-

2045.................................................................................................................... 9-13
 

Table 9.4.2 Forecasted Shipments of New and Replacement WICF Refrigeration 

Systems, 2015-2045........................................................................................... 9-14
 

LIST OF FIGURES 

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model .......................................... 9-2
 

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CHAPTER 9. SHIPMENTS ANALYSIS 

9.1 INTRODUCTION 

The Shipment Model for walk-in coolers and freezers (walk-ins or WICF) provides 

several necessary inputs to the national energy savings (NES), net present value (NPV) 

calculations, and the manufacturer impacts analysis (MIA). Key outputs of the model are the 

number of shipments in every year, the total stock of installed units in every year, and the energy 

efficiency of the installed stock. This chapter describes the U.S. Department of Energy’s 

(DOE’s) methodology for projecting annual shipments and presents results. 

The Shipments Model results are driven primarily by historical stock data for walk-ins 

and assumptions about the rates at which that stock will grow and turn over. The flow chart 

presented in Figure 9.1.1 outlines the structure of the Shipments Model. 

The model assumes that, in each year, any given piece of existing WICF equipment stock 

either ages by 1 year or fails. For the purpose of characterizing replacement rates for the 

Shipments Model, DOE refers to equipment failures. However, DOE recognizes that failure rates 

as used here represent a simplified model of real-world failure conditions. For refrigeration 

systems, it is not uncommon for only part of a unit to fail and be replaced. The failure rates in 

this model represent the average replacement rate for refrigeration system equipment. Similarly, 

failure in WICF envelopes can be less clear-cut than for many types of commercial equipment. 

More information on envelope failures is provided in the Market and Technology Assessment 

(preliminary technical support document [TSD] chapter 3). 

Failed equipment is replaced with equipment of the same type. When any new equipment 

ships (either to replace failed equipment or as part of market growth), it is required to meet 

whatever efficiency standards are in place. In this way, energy efficient equipment is modeled as 

percolating into the stock over time. Provisions are made in the model for either partial or 

complete replacement, depending on the nature of the malfunction in the equipment. In addition, 

new equipment can be shipped into new commercial floor space, and old equipment can be 

removed through demolitions (not shown in Figure 9.1.1). 

The Shipments Model is in a Microsoft Excel spreadsheet format that is accessible on the 

Internet at: http://www1.eere.energy.gov/buildings/appliance_standards/commercial/wicf.html. 

Appendix 10C of this TSD will provide instructions for using the spreadsheet. 

The rest of this chapter explains the Shipments Model in more detail. Section 9.2 presents 

the mathematical formulation of the model, Section 9.3 describes the data input to the model, and 

section 9.4 presents the results for the base-case energy conservation standard level scenario and 

discusses the development of higher standard scenarios. The energy conservation standards for 

walk-ins will set the maximum-rated energy consumption for all equipment within an equipment 

class. DOE refers to each standard level considered as a “candidate standard level” (CSL). Walkins are an atypical product in that they consist of two separate pieces of equipment: the thermal 

envelope and the refrigeration system, which cools the space inside the envelope. DOE will set 

separate standards for envelopes and refrigeration systems. DOE forecasts shipments for each 

equipment class of both envelopes and refrigeration systems. The shipments are related (because 

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each walk-in requires one envelope and one refrigeration system to operate), but are not identical 

(because envelopes and refrigeration systems are replaced at different rates). 

Figure 9.1.1 Flow Chart Showing Inputs to the Shipments Model 

In section 9.2, DOE describes the mathematical formulation of the Shipments Model. In 

section 9.3, DOE lists the different data sources and assumptions used in this analysis. Section 

9.4 provides results. 

9.2 SHIPMENTS MODEL EQUATIONS 

The Shipments Model describes WICF equipment stock flows as a function of time and 

equipment age. The equations presented in this chapter describe each combination of envelope 

type and refrigeration type independently. Results are then aggregated by envelope type and by 

refrigeration system type to determine the shipments of each type of equipment. 

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

function of stock age. The equations distinguish between the age of the envelope, aenv, and the 

age of the refrigeration system, aref. For each year of the analysis period, the stock is tracked for 

all possible combinations of unit ages, aenv and aref. This is because the refrigeration systems 

require more frequent replacement than envelopes on average, so it is not uncommon for a walkin envelope to be of a different age (typically older) than the associated refrigeration system. 

Note that this also means that shipments of refrigeration systems are modeled to always exceed 

shipments of envelopes, even though they are used in a one-to-one ratio in the stock. 

DOE’s approach first combines national statistics on the existing stock of walk-ins with 

expected growth rates to estimate the total stock of walk-ins going forward. DOE estimates these 

growth rates as a function of the extent of relevant commercial floor space. DOE assumes that 

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whenever an envelope or refrigeration system fails, it is replaced with equipment from the same 

equipment class. Therefore, DOE can combine these stock forecasts with estimated lifetimes of 

both envelopes and refrigeration systems to produce shipment forecasts. Sales of new and 

replacement equipment are recorded by the year of the equipment (distinguishing between 

envelope age and refrigeration system age), and each annual vintage is depreciated over the 

estimated life of the equipment to determine the rate of replacement shipments necessary to 

sustain the stock at the forecasted level. 

9.2.1 Mathematical Formulation of the Shipments Model 

The Shipments Model estimates the number of shipments of each type of WICF 

equipment in a given year by tracking stock age and type. At the end of each year, every piece of 

equipment either ages by 1 year or is replaced. Whenever new equipment replaces failed 

equipment, the new equipment is by definition zero years old. Similarly, whenever new stock is 

added through market growth, this new equipment is also zero years old. Therefore, in a given 

year, shipments of WICF equipment are equal in quantity to the number of units at age zero. 

The numerous possible combinations of WICF equipment add considerable complexity to 

the otherwise straightforward calculations used within the Shipments Model. While not all types 

of envelopes and refrigeration systems can be combined, there are 36 (meaningful) combinations 

of envelope and refrigeration system equipment classes. See the energy use characterization 

(preliminary TSD chapter 7, section 2.1) for additional details. For each of these equipment class 

combinations, the model tracks each possible combination of envelope CSL and refrigeration 

system CSL. For each of these possible combinations of equipment class and CSL, the model 

also tracks the number of shipments and size of the stock in each year. And for each of these 

possible combinations, the model tracks the age distribution of units in each year. Thus, the total 

stock of walk-ins in the year t is the sum of all possible combinations of the following: 

STOCKt = ∑ (STOCKEC ,EC ,CSL ,CSL ,a ,a ,t ) Eq. 9.1 env ref env ref env ref 

EC ,EC ,CSL ,CSL ,a ,a env ref env ref env ref 

where 

t = year, 

ECenv = equipment class of the envelope,
 

ECref = equipment class of the refrigeration system,
 

CSLenv = CSL of the envelope, 

CSLref = CSL of the refrigeration system,
 

aenv = age of the envelope, and
 

aref = age of the refrigeration system.
 

9.2.1.1 Size of the Stock 

DOE assumes that the major driver of WICF stock is commercial floor space of the major 

establishments that use walk-ins: food service establishments, food sales establishments 

(comprising both grocery stores and convenience stores), and “other” (encompassing a wide 

variety of minor walk-in applications, e.g., florists and stadiums). In addition, the WICF stock 

accounts for dairy farms for which DOE estimated growth in number of walk-ins using output 

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(estimated by pounds of milk production per year) rather than floor space. For simplicity, the 

following text refers to all these categories as “building types.” 

DOE did not have sufficient information to forecast the change over time in intensity of 

walk-ins per square foot of commercial floor space (or per pound of milk production). Therefore, 

in the model, the stock of walk-ins is assumed to be a constant factor of commercial floorspace 

(and pounds of milk production). As both of these quantities are expected to grow over time, 

DOE forecasts that the installed stock of walk-ins nationwide will also grow over time. However, 

limited data available from the U.S. Economic Census suggest that the walk-in market may have 

been essentially flat from the period 1997 to 2007, rather than steadily growing. DOE intends to 

re-examine the issue of WICF stock growth rates in the next stage of this analysis and seeks 

further comment on growth trends in the walk-in industry. 

For each of the five building types (food service, grocery stores, convenience stores, 

dairy farms, and other), the Shipments Model estimates stock growth in the following manner: 

STOCKB,t = STOCKB,0 × SFt × I B	 Eq. 9.2 

where

STOCKB,t = the stock of walk-ins in building type B in the year t, 

STOCKB,0 = the stock of walk-ins in building type B in year zero (the first year 

before the analysis period, or 2014, 

SFt = square footage (or pounds milk production) of building type B in year t, 

IB = walk-in intensity, i.e., the number of walk-ins per square foot (or pounds 

of milk production) for building type B. 

This equation is repeated across all equipment classes and CSLs. Summing across these 

and across all building types gives the total number of walk-ins in the market. 

9.2.1.2 Replacement Events 

As noted above, the Shipments Model estimates shipments based on the size and age of 

the stock in each year. In any given year, t, the model estimates shipments as the sum of growth 

(i.e., any increase in total stock relative to the previous year) plus any units that are shipped to 

replace failed units. The number of failed units is therefore a key element of the Shipments 

Model. 

The number of replacement units in any given year equals the number of units that break 

or are discarded. In any given year, each individual walk-in experiences one of three possible 

replacement events: 

•	 The envelope fails. In this case, DOE assumes that the refrigeration system is replaced as 

well. 

•	 The refrigeration system fails while the envelope does not. In this case, DOE assumes that 

the refrigeration system is replaced, but the envelope simply ages by 1 year. 

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•	 Neither piece of equipment fails or is removed. In this case, both pieces simply age by one 

year. 

All replacements are assumed to comply with any efficiency standards, whereas existing 

units are not assumed to comply. Because DOE is regulating envelopes and refrigeration systems 

separately, this assumption applies even to the case where only the refrigeration system is 

replaced. In that case, the refrigeration system is assumed to be replaced with a standardscompliant refrigeration system, yet the pre-existing envelope would not necessarily comply with 

the standard. Further detail on the efficiency mix of the stock is provided in the National Impact 

Analysis (preliminary TSD chapter 10). 

Some percentage of each category of walk-ins STOCK (t, ECenv, ECref, CSLenv, CSLref, 

aenv, aref) will experience one of these life events in a given year. The percentage of each category 

that experiences these life events is equal to the probability that a single unit of that type would 

experience a given life event. Those probabilities are as follows. 

•	 Probability of envelope failure: 

Fenv(aenv) = Wenv(aenv)	 Eq. 9.3 

where 

Fenv =	 probability that the envelope fails, whether or not the refrigeration system 

fails, and 

Wenv =	 probability of envelope failure at envelope age aenv based on the Weibull 

lifetime distributions given in Table 9.3.4. 

•	 Probability of refrigeration system failure when the envelope does not fail: 

Frefonly = Wref(aenv, aref) x (1 - Wenv(aenv))	 Eq. 9.4 

where 

Frefonly =	 the probability that the refrigeration system fails, but the envelope does not 

fail, and 

Fenv =	 the probability of refrigeration system failure at refrigeration system age aref 

based on the Weibull lifetime distributions given in Table 9.3.4. 

•	 Probability that neither piece of equipment fails: 

Fnone = 1 – Wenv(aenv) – Wref(aenv, aref) x (1 - Wenv(aenv))	 Eq. 9.5 

where 

Frefonly =	 the probability that neither system fails. 

In the model, all broken equipment is assumed to be replaced in kind, and every store 

renovation that removes a walk-in is assumed to be offset by another store renovation that adds a 

walk-in. The following sections present the equations used to represent each possible event. 

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9.3 DATA INPUTS 

This section outlines the inputs that DOE used in constructing its Shipments Model. 

9.3.1 Historical Stock 

As noted in the Market and Technology Assessment (preliminary TSD chapter 3), some 

high-level data are available on the historical value of walk-ins shipped by year. However, in 

terms of the number of units, DOE was unable to acquire detailed shipment information. In part, 

this is because manufacturers of envelopes typically track shipments in square feet of panel 

rather than number of units. Since more detailed data are available on the historical stock, DOE 

bases its shipment estimates primarily on the historical stock. In the preliminary analysis, DOE 

uses two different approaches to estimate stock trends over time from this information. For the 

Market and Technology Assessment, which focuses on historical shipments, DOE extrapolates 

these numbers linearly. This assessment indicates a very low rate of growth in the WICF market. 

However, in the preliminary analysis Shipments Model, which focuses on future shipments, 

DOE assumes that the long-run growth rate in the WICF market will recover somewhat and uses 

the following approach instead. DOE seeks further comment on what long-run growth rates to 

assume in the WICF market for the final analysis. 

DOE relied on three main sources of historical stock data for walk-ins: the 2003 

Commercial Buildings Energy Consumption Survey (CBECS)1

, the 2007 U.S. Economic 

Census2

, and manufacturer interviews. The CBECS and Census data provided detailed 

information on the number of commercial walk-ins in total nationwide and by category of 

building. 

A small number of walk-ins operate in dairy farms, which are not covered under CBECS 

because they are considered industrial sector applications. For these walk-ins, DOE used the 

2007 U.S. Agricultural Census.3 

In mathematical terms, this means that in equation 9.2, IB was based on data from 

CBECS, whereas SFt was based on data from the U.S. Census. Ideally, DOE would be able to 

base all variables in the same equation from one data source, but this is not done because of the 

different strengths of the two different data sets. While CBECS provides more detailed 

information about the nature of the walk-in stock by building type, the Census is based on a 

larger sample size. 

Within each of the equipment categories, the shipments data must be disaggregated into 

detailed equipment and temperature ranges for the shipment analysis. Since neither of these 

sources distinguished between categories of walk-ins, DOE next used manufacturer input to 

apportion the share of shipments by product class. 

9.3.2 Commercial Floor Space and Market Saturation 

As discussed in section 9.2.1.2, the amount of commercial floor space (and pounds of 

milk production is the main driver for WICF stock and is appropriately one of the basic inputs 

into the Shipments Model. For this analysis, commercial square footage with walk-ins refers to 

both new and existing stock of building types. 

9-6 
 

 

DOE took the projected floor space construction from the National Energy Modeling 

System (NEMS) projection published in April 2009 update to the Annual Energy Outlook 2009 

(AEO 2009-B)4

. AEO 2009-B gives the projections for years 2010, 2015, 2020, 2025, and 2030. 

For years beyond this, DOE derived commercial floor-space data by linear extrapolation of the 

existing years. Beyond 2030, DOE extrapolated floor-space estimates from NEMS-projected 

data between 2020 and 2030. 

As noted above, for the dairy farm building type, DOE used pounds of milk production 

estimates rather than floor space estimates. These estimates were taken from the 2007 U.S. 

Agricultural Census.5 

Table 9.3.1 shows the growth rates applied to each building type. Because NEMS does 

not distinguish between different types of food sales building stock, DOE forecast both grocery 

stores and convenience stores as growing at the rate of the food sales category in NEMS. 

Table 9.3.1 Forecasted Cumulative Growth by Building Type Since 2007 

Food Sales Food Service Dairy Farms Other 

Year Square Feet Square Feet Pounds of Milk Produced Square Feet 

2015 9% 9% 16% 17% 

2020 16% 17% 26% 27% 

2025 25% 26% 38% 38% 

2030 35% 35% 50% 48% 

2035 (extrapolated) 44% 45% 64% 62% 

2040 (extrapolated) 54% 56% 79% 77% 

2045 (extrapolated) 65% 67% 96% 93% 

Following is an example of how to read Table 9.3.1: the stock of walk-ins in food 

services in 2035 is modeled as equal to 145 percent of the initial (2014) stock: 100 percent of the 

initial stock persists as existing shipments, plus 45 percent in the form of growth. 

9.3.3 Distribution of Walk-ins by Equipment Class 

Table 9.3.2 shows DOE’s assumptions as to the share of equipment classes. These 

assumptions are based on manufacturer interviews and DOE estimates. As noted above, many 

possible combinations of envelope and refrigeration system equipment classes would be 

meaningless or clearly inferior to some other combination (e.g., any small cooler and large lowtemperature refrigeration system). Blank entries in the table refer to such equipment class 

combinations. For further information on the size definitions, please see the energy use 

characterization (preliminary TSD chapter 7). 

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Table 9.3.2 Percent of Shipped Walk-ins by Product Class 

9-8 

Class/ Size Code 

Small NonDisplay Cooler 

Medium NonDisplay Cooler 

Large NonDisplay Cooler 

Small Display Cooler 

Medium Display Cooler 

Large Display Cooler 

Small NonDisplay Freezer 

Medium NonDisplay Freezer 

Large NonDisplay Freezer 

SDF

Small Medium-Temperature Indoor Dedicated System 1% Large Medium-Temperature Indoor Dedicated System 2% 0% 0% 0% 0% 

Small Medium-Temperature Outdoor Dedicated System 7% Large Medium-Temperature Outdoor Dedicated System 11% 9% 0% 2% 1% 

Small Medium-Temperature Multiplex System 0% Large Medium-Temperature Multiplex System 20% 6% 0% 1% 4% 

Small Low Temperature Indoor Dedicated System 1% Large Low Temperature Indoor Dedicated System 1% 0% 

Small Low Temperature Outdoor Dedicated System 5% Large Low Temperature Outdoor Dedicated System 5% 5% 

Small Low Temperature Multiplex System 0% Large Low Temperature Multiplex System 13% 2% 

Sources: Manufacturer Interviews 
 

9.3.3.1 Equipment Lifetime and Age 

The lifetime of both types of walk-in equipment (envelopes and refrigeration systems) is 

an important input to the Shipments Model. As the lifetimes of WICF equipment become shorter, 

the shipments of such equipment will need to be more frequent to sustain a given level of stock. 

DOE was unable to obtain detailed data on the distribution of lifetimes by equipment type. 

Public comments from interested parties on the framework document and DOE manufacturer 

interviews did, however, provide several different estimates of walk-in equipment lifetimes. 

These estimates are listed in Table 9.3.3. 

Table 9.3.3 Estimates of Walk-in Lifetimes by Equipment Type 

Estimate Source Estimated Envelope 

Lifetime 

(years) 

Estimated Refrigeration 

System Lifetime 

(years) 

AHRI, No. 33 at p. 7* 12-15 8-12 

Manitowoc Foodservice Group, Public Meeting 

Transcript, No. 15 at p. 204* 25 is “not unreasonable” 10 is “not unreasonable” 

Craig Industries, Public Meeting Transcript, 

No. 15 at p. 205* Less than 10 

Manufacturer Interviews 12-15 is typical, 8-20 not 

unheard of 7-8 

*Public comment on the framework document 

DOE observed significant variation in the available estimates of equipment lifetime and 

concluded that the lifetime of actual walk-in equipment is highly variable. In part, this may be 

because equipment is not always retired due to mechanical failure, but may instead be retired for 

financial reasons (e.g., a restaurant might close down and no longer need its walk-in) or other 

non-mechanical reasons. DOE intends to revisit the issue of equipment lifetimes in the next 

phase of its analysis and seeks further comment on the issue of average equipment lifetimes and 

the distribution of those lifetimes around the average. 

Rather than assuming a single lifetime for all units, DOE created distributions of potential 

lifetimes for both envelopes and refrigeration systems based on the above estimates. The 

distributions were assumed to take the form of a Weibull curve, a common shape for equipment 

failure rates. True Weibull distributions go out to infinity at ever smaller shares, i.e., there might 

be a vanishingly small chance of a unit lasting for many hundreds of years. So for analytical 

tractability, it was necessary to truncate the maximum possible lifetime of walk-ins modeled at 

some cut-off point. DOE therefore assumed that no envelope would last beyond 25 years, and no 

refrigeration system would last beyond 15 years. The resultant lifetime distributions are shown in 

Table 9.3.4: 

9-9 
Table 9.3.4 Cumulative Failure Rates by Equipment Type 

Equipment 

Age 

(years) 

Assumed Envelope 

Cumulative Failure Rate 

(percent) 

Assumed Refrigeration System 

Cumulative Failure Rate 

(percent) 

0 0.0 0.0 

1 0.0 0.0 

2 0.0 0.4 

3 0.0 2.5 

4 0.0 8.3 

5 0.2 19.0 

6 0.8 34.8 

7 1.8 53.8 

8 3.7 72. 

9 6.5 86.3 

10 10.6 94.8 

11 15.9 98.5 

12 22.6 99.7 

13 30.6 >99.9 

14 39.5 >99.9 

15 49.0 >99.9 

16 58.6 100.0 

17 67.8 100.0 

18 76.1 100.0 

19 83.1 100.0 

20 88.8 100.0 

21 93.0 100.0 

22 95.9 100.0 

23 97.8 100.0 

24 98.9 100.0 

25 99.5 100.0 

26 100.0 100.0 

27+ 100.0 100.0 

As shown in Table 9.3.4, refrigeration systems typically exhibit a shorter lifetime than 

envelopes. It is therefore common that a refrigeration system will fail when the associated 

envelope still has useful life remaining. As noted above, DOE assumed that when this happens, 

the refrigeration system is replaced with a standards-compliant system, but the envelope remains. 

However, DOE assumed that when an envelope fails, it is replaced along with a new 

refrigeration system. For these reasons, shipments of refrigeration systems are greater than 

shipments of envelopes, even though the two types of equipment are used in a 1:1 ratio. 

9.3.3.2 Age Distributions of Existing Stock 

The age distribution of the existing walk-in stock is an important input that affects 

forecasted replacement rates for walk-ins in the analysis period. Assumptions were made about 

the ages of existing stock because DOE was unable to find any information on the distribution of 

ages of the existing stock. Because the failure rates in a Weibull distribution are not constant 

every year, it seems unlikely that the Nation’s stock of walk-ins would be evenly distributed 

9-10 
across all possible ages. Instead, over time, the stock would likely gravitate towards whatever 

age distribution is stable at the failure rates and growth rates for these particular products. To 

determine what that age distribution might look like, DOE modeled a hypothetical WICF market 

in which initially all units were of random age and modeled 100 years passing in this 

hypothetical market. This market had a growth rate of zero and failure rates equal to the rates in 

Table 9.3.4. After 100 years, this hypothetical market settled into an equilibrium age distribution, 

shown in 

Table 9.3.5. DOE then used this age distribution as an estimate of the likely age 

distribution of the actual WICF stock in the United States today. DOE also tracked each possible 

combination of envelope age and refrigeration system age (e.g., the percentage of walk-ins with 

a 6-year-old envelope and a 3-year-old refrigeration system), but those results are not presented 

here. As with all other information used in the model, those results are available online at 

http://www.eere.energy.gov/buildings/appliance_standards/ commercial_products/wicf.html. 

9-11 
Table 9.3.5 Assumed Age Distribution of Initial Stock 

Equipment 

Age 

(years) 

Share of Envelopes of this 

Age 

(percent) 

Share of Refrigeration System of 

this Age 

(percent) 

0 6.5% 15.2% 

1 6.4% 14.3% 

2 6.3% 13.5% 

3 6.3% 12.7% 

4 6.2% 11.6% 

5 6.2% 10.2% 

6 6.1% 8.4% 

7 6.1% 6.3% 

8 6.0% 4.1% 

9 5.9% 2.3% 

10 5.7% 1.0% 

11 5.4% 0.3% 

12 5.0% 0.1% 

13 4.5% < 0.1% 

14 4.0% < 0.1% 

15 3.4% 0.0% 

16 2.8% 0.0% 

17 2.2% 0.0% 

18 1.7% 0.0% 

19 1.2% 0.0% 

20 0.8% 0.0% 

21 0.5% 0.0% 

22 0.3% 0.0% 

23 0.2% 0.0% 

24 0.1% 0.0% 

25 0.1% 0.0% 

26 <0.1% 0.0% 

27+ 0.0% 0.0% 

9.3.3.3 Price Elasticity 

Economic theory suggests that changes in the price of WICF equipment resulting from 

this standard might affect the number of shipments, a phenomenon known as “price elasticity.” 

This might take the form of either a decrease in shipments, in cases where purchase costs 

increase; or an increase in shipments, in cases where life-cycle costs decrease. 

However, in practice, DOE has no information with which to calibrate such a relationship 

in the WICF market. Further, manufacturer interviews indicated that manufacturers do not 

believe that the nature of this relationship is particularly significant in either direction in the 

walk-in market. Therefore, for the preliminary analysis, DOE presumes that the shipments do not 

change between the base case and standards case. 

9.4 RESULTS 

Table 9.4.1 shows DOE’s shipments forecast for WICF envelopes by envelope 

equipment class. Table 9.4.2 shows the refrigeration system forecast. As noted above, DOE 

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forecasts that refrigeration system shipments will exceed envelope shipments because the shorter 

equipment lifetime of refrigeration systems means they need more frequent replacements. 

Table 9.4.1 Forecasted Shipments of New and Replacement WICF Envelopes, 2015-2045 

WICF Envelope Systems 

Year and Number of Units Shipped 

2015 2020 2025 2030 2035 2040 2045 

Non-Display Cooler Small 11,933 12,839 13,572 14,486 15,574 16,702 17,835 

Non-Display Cooler Medium 49,895 53,686 56,750 60,570 65,119 69,838 74,576 

Non-Display Cooler Large 22,398 24,100 25,475 27,190 29,232 31,350 33,477 

Display Cooler Small - - - - - - -

Display Cooler Medium 3,880 4,184 4,431 4,747 5,105 5,486 5,871 

Display Cooler Large 7,385 7,965 8,436 9,037 9,718 10,444 11,176 

Non-Display Freezer Small 8,337 8,999 9,527 10,161 10,855 11,645 12,416 

Non-Display Freezer Medium 27,705 29,905 31,661 33,766 36,073 38,697 41,261 

Non-Display Freezer Large 11,217 12,108 12,818 13,671 14,605 15,667 16,705 

Display Freezer Small - - - - - - -

Display Freezer Medium 4,739 5,098 5,405 5,870 6,443 6,963 7,533 

Display Freezer Large 1,185 1,275 1,351 1,468 1,611 1,741 1,883 

Total 148,673 160,160 169,428 180,966 194,336 208,533 222,735 

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Table 9.4.2 Forecasted Shipments of New and Replacement WICF Refrigeration 

Systems, 2015-2045 

WICF Refrigeration Systems 

Year and Number of Units Shipped 

2015 2020 2025 2030 2035 2040 2045 

Dedicated Medium Temperature 

Indoor Small 4,884 5,185 5,539 5,938 6,367 6,809 7,289 

Dedicated Medium Temperature 

Indoor Large 9,437 10,020 10,707 11,483 12,314 13,173 14,106 

Dedicated Medium Temperature 

Outdoor Small 22,251 23,620 25,232 27,051 29,004 31,018 33,205 

Dedicated Medium Temperature 

Outdoor Large 76,903 81,649 87,249 93,569 100,337 107,332 114,927 

Multiplex system Medium 

Temperature Small - - - - - - -

Multiplex system Medium 

Temperature Large 103,619 110,018 117,572 126,098 135,224 144,659 154,905 

Dedicated Low Temperature 

Indoor Small 3,427 3,640 3,884 4,164 4,453 4,756 5,082 

Dedicated Low Temperature 

Indoor Large 3,416 3,629 3,872 4,151 4,440 4,741 5,067 

Dedicated Low Temperature 

Outdoor Small 15,610 16,583 17,695 18,970 20,288 21,665 23,152 

Dedicated Low Temperature 

Outdoor Large 46,668 49,620 53,183 57,199 61,456 65,899 70,745 

Multiplex system Low 

Temperature Small - - - - - - -

Multiplex system Low 

Temperature Large 51,966 55,207 58,907 63,150 67,538 72,124 77,075 

Total 338,182 359,170 383,841 411,774 441,419 472,176 505,552 

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REFERENCES 

1 EIA (U.S. Energy Information Administration). Commercial Buildings Energy Consumption 

Survey 2003. Accessed January 2010 at 

http://www.eia.doe.gov/emeu/cbecs/cbecs2003/public_use_2003/cbecs_pudata2003.html 

2 U.S. Census Bureau. 2007 Economic Census. Accessed March 2010 at 

http://www.census.gov/econ/census07/ 

3 NASS (U.S. National Agricultural Statistics Service.) The 2007 Census of Agriculture. 2007. 

Accessed January 2010 at http://www.nass.usda.gov/QuickStats/PullData_US.jsp. 

4 EIA (U. S. Energy Information Administration). 2009. Update to the Annual Energy Outlook 

2009. DOE/EIA-0383(2007). U.S. Department of Energy, Energy Information Administration, 

Washington D.C. 

5 NASS (U.S. National Agricultural Statistics Service.) The 2007 Census of Agriculture. 2007. 

Accessed January 2010 at http://www.nass.usda.gov/QuickStats/PullData_US.jsp. 

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