Table 8.2.22 Standard-Size Refrigerator-Freezers: Base-Case Efficiency Distributions

Efficiency Level

(% less than baseline energy use) Product Class 3: Top-Mount Refrigerator-Freezer Product Class 5: Bottom-Mount Refrigerator-Freezer Product Class 7: Side-by-Side Refrigerator-Freezer with TTD*
Market Share % Market Share % Market Share %
2007 2014 2007 2014 2007 2014
Baseline 81 81 12 12 25 25

1 (10) 6 6 0 0 43 43 2 (15) 13 0 70 0 30 0 3 (20) † 0 13 18 88 2 32 4 (25) 0 0 0 0 0 0 5 (30) 0 0 0 0 0 0
* Through-the-door ice service. † Meets 2008 ENERGY STAR criteria.

ENERGY STAR requirements for standard-s ize freezers and compact products have not changed since 2001. Those ENERGY STAR requirements correspond to efficiency level 1 for standard-size freezers and level 3 for compact produc ts. DOE assumed that the market shares of those ENERGY STAR products would remain the same between 2007 and 2014 (Tables 8.2.24 and 8.2.25).

Table 8.2.23 Standard-Size Freezers: Base-C ase Efficiency Distributions

Efficiency Level

(% less than baseline energy Product Class 9: Upright Freezer Product Class 10: Chest Freezer
Market Share % Market Share % use) 2007 2014 2007 2014
Baseline 82 82 85 85 1 (10)* 17 17 14 14 2 (15) 1 1 1 1 3 (20) 0 0 0 0 4 (25) 0 0 2 2 5 (30) 0 0 0 0
* Meets 2008 ENERGY STAR criteria.

8-36Table 8.2.24 Compact Refrigeration Products: Ba se-Case Efficiency Distributions

Efficiency Level

(% less than baseline energy use) Product Class 11: Compact Refrigerator Product Class 18: Compact Freezer
Market Share % Market Share %
2007 2014 2007 2014
Baseline 97 97 95 95 1 (10) 0 0 5 5 2 (15) 0 0 0 0 3 (20)* 1 1 0 0 4 (25) 2 2 0 0 5 (30) 0 0 0 0
* Meets 2008 ENERGY STAR criteria.

8.3 INPUTS TO PAYBACK PERIOD ANALYSIS
The payback period (PBP) refers to the time it takes a consumer to recover, through lower operating costs, the assumed higher purchas e cost of more energy efficient products. Numerically, the PBP is the ratio of the increase in purchase cost (from a less to a more efficient design) to the decrease in annual operating cost. This type of calculation is known as a simple payback period, because it does not account for cha nges in operating cost over time or the time value of money. That is, the calculation is performe d at an effective discount rate of 0 percent.

The equation for determining PBP is:

Δ IC PBP = Δ OC Where:

Δ IC = the difference in total installed cost betw een the more efficient design based on the new standard level and the baseline design, and Δ OC = the difference in annual operati ng cost between th e two units.

Payback periods are expressed in years. Payback periods grea ter than the life of a product mean that the increased total installed cost is no t recovered through re duced operating cost.

The data inputs to calculating PBP are the total installed cost to the consumer for each product at each efficiency level and the annual (first-year) operating co st for each efficiency level. The inputs to calculating total installed cost are the product and installation costs. The inputs to calculating operating cost are the annual energy, repair , and maintenance costs. The PBP uses the same inputs as the LCC analysis de scribed in section 8.2, except that no energy

8-37price trends, product lifetimes, or discount rates are required. Because the PBP is a simple payback, the only energy price required is for the year in which a new standard takes effect—in this case, the price projected for 2014.

8.4 RESULTS OF ANALYSES
This section presents the results of the lif e-cycle cost (LCC) and payback period (PBP) analyses for the considered efficiency levels fo r refrigeration products. As discussed in section 8.1.1, DOE’s approach to the LCC analysis i nvolved developing a sample of consumers who use each product. DOE also used probability distributi ons to characterize the uncertainty in many of the analytical inputs. DOE used a Monte Ca rlo simulation technique to perform the LCC calculations on data pertaining to the consumer s in each sample. For each set of sample consumers who use the appliance in each product class, DOE calculated the average LCC, the LCC savings, and the median and average PBP for each standard level.

DOE calculated LCC savings and PBPs relative to the base-case produc t that it assigned to the sampled consumers. As discussed in sec tion 8.2.6, DOE assigned some consumers a basecase product that is more efficient than the baseline product. For that reason, average LCC impacts are not equal to the diffe rence between the LCC of a speci fic possible standard level and the LCC of the baseline product.

In the following subsections, DOE presents figures showing the distribution of LCCs in the base case for each product cla ss. Also presented are figures showing the distribution of LCC impacts and the distribution of PBPs for specific possible standard levels. The figures represent frequency charts that include probabilities of occurrence for th e distributions of LCC impacts and PBPs. DOE generated the figures for the dist ributions from a Monte Carlo simulation that utilized 10,000 samples.

LCC and PBP calculations were performed 10,000 times on the sample of consumers developed for each product. Each calculati on was performed on a single consumer who was selected from the sample. The selection was ba sed on weight, that is on how representative a particular consumer was of other consumers in the national distribution. Each LCC and PBP calculation also sampled from the probability di stributions that DOE de veloped to characterize many of the inputs to the analysis.

Based on the Monte Carlo simulations that DOE performed, for each efficiency level, DOE calculated the percentage of consumers wh o would experience a net LCC benefit, a net LCC cost, or no impact. DOE considered a consumer to receive no impact at a given efficiency level if the base-case product DOE assigned to th at consumer had the same or higher efficiency than that of the new standard being evaluated. Note that the average LCC savings at each efficiency level are relative to the base-case effi ciency distribution, not the baseline efficiency level.

8-388.4.1 Standard-Size Refrigerator-Freezers
The following subsections summarize results of LCC and PBP analyses for standard-size refrigerator-freezers and present fi gures showing the distribution of LCCs in the base case for each product class. Also presente d are figures showing the distri bution of LCC impacts and the distribution of PBPs for sp ecific efficiency levels.

8.4.1.1 Summary of Results
Tables 8.4.1 through 8.4.3 show the LCC a nd PBP results for each refrigerator-freezer product class considered for new standards. DOE determined the median and average values for PBPs at each efficiency level by excluding those households that would not be affected by the standard.

Table 8.4.1 Product Class 3, Top-Mount Refrigerator-Freezers: Analytical Results

Efficiency

Level

(% less than Life-Cycle Cost 2008$ Life-Cycle Cost Savings Payback Period (years)

Average Average Annual Average % of Households that Experience

Median Average baseline
energy use ) Product Cost Operating Cost Average LCC Savings 2008$ Net Cost No Impact Net Benefit
Baseline 591 77 1,505

1 (10) 595 71 1,434 71 0.0 18.5 81.5 0.6 0.7 2 (15) 599 67 1,398 106 0.0 13.0 86.9 0.9 1.1 3 (20) 609 64 1,368 137 0.1 13.0 86.8 1.5 1.7 4 (25) 631 60 1,343 162 2.6 0.0 97.4 2.8 3.2 5 (30) 665 56 1,329 176 7.0 0.0 93.0 4.0 4.6 6 (35) 716 52 1,333 172 16.5 0.0 83.5 5.6 6.4 7 (40) 781 48 1,350 155 28.2 0.0 71.8 7.3 8.3 8 (45) 845 44 1,367 138 36.3 0.0 63.7 8.5 9.7

8-39Table 8.4.2 Product Class 5, Bottom-Mount Refrigerator-Freezers: Analytical Results
Efficiency

Level

( % less than

baseline
energy use ) Life-Cycle Cost 2008$ Life-Cycle Cost Savings Payback Period
years

Average Product

Cost Average Annual Operating Cost Average LCC Average Savings 2008$ % of Households that Experience

Median Average Net Cost No Impact Net Benefit
Baseline 1,758 70 2,591

1 (10) 1,759 69 2,581 11 0.0 88.5 11.5 0.3 0.4 2 (15) 1,759 69 2,576 16 0.0 88.4 11.6 0.7 0.8 3 (20) 1,760 68 2,571 20 0.0 88.4 11.6 1.2 1.4 4 (25) 1,782 64 2,542 49 14.8 0.0 85.2 5.1 6.0 5 (30) 1,799 60 2,508 83 11.2 0.0 88.8 4.6 5.4 6 (35) 1,879 55 2,538 54 44.1 0.0 55.9 9.5 11.0 7 (40) 1,969 51 2,577 14 60.5 0.0 39.5 12.6 14.7 8 (45) 2,053 47 2,610 -19 67.0 0.0 33.0 14.3 16.6

Table 8.4.3 Product Class 7, Side-by-Side Refrigerator-Freezers with Through-theDoor Ice Service: Analytical Results
Efficiency

Level

( % less than Life-Cycle Cost 2008$ Life-Cycle Cost Savings Payback Period
years

Average Average Annual Average % of Households that Experience

Median Average baseline
energy use ) Product Cost Operating Cost Average LCC Savings 2008$ Net Cost No Impact Net Benefit
Baseline 1,459 116 2,840

1 (10) 1,463 113 2,804 35 0.0 74.3 25.7 1.1 1.3 2 (15) 1,469 108 2,759 80 0.1 31.6 68.4 1.5 1.6 3 (20) 1,499 104 2,738 102 3.1 31.6 65.4 3.8 4.2 4 (25) 1,563 97 2,724 116 23.8 0.0 76.2 6.7 7.6 5 (30) 1,638 91 2,722 117 32.7 0.0 67.3 8.1 9.0 6 (35) 1,714 84 2,721 119 38.5 0.0 61.5 8.9 9.9 7 (40) 1,815 78 2,744 95 46.2 0.0 53.9 10.1 11.2

8.4.1.2 Distributions of Impacts
Figure 8.4.1 presents a frequency chart that shows the distribution of LCC impacts for the case of efficiency level 5 for top-mount refriger ator-freezers. The note, “Certainty is 93.01% from $0 to +Infinity,” means that 93.01 percen t of households owning t op-mount refrigeratorfreezers either will not be affected or will have LCC savings under the standard compared to the base case. DOE could generate a similar frequency chart for every efficiency level.

8-40Frequency Chart

Certainty is 93.01% from $0 to +Infinity .000 .005 .011 .016 .021

0 53.5 107 160.5 214

($76) $109 $294 $479 $663 10,000 Trials 9,715 Displayed Forecast: LCCSav_Level5

Figure 8.4.1 Product Class 3, Top-Mount Refrigerator-Freezers: Distribution of Life-Cycle Cost Impacts for Efficiency Level 5

Figure 8.4.2 is an example of a frequenc y chart showing the di stribution of payback periods for efficiency level 5 for top-mount refrigerator-freezers. DOE could generate a similar frequency chart for every efficiency level.

Frequency Chart

.000 .007 .013 .020 .027

0 66.5 133 199.5 266

-1.00 2.46 5.93 9.39 12.86 10,000 Trials 9,812 Displayed Forecast: PP_Level5

Figure 8.4.2 Product Class 3, Top-Mount Refrigerator-Freezers: Distribution of Payback Periods for Efficiency Level 5

8-41Figures 8.4.3 through 8.4.5 show the range of LCC savings for all the efficiency levels considered for each refrigerator-freezer product class. For each efficiency level, the top and bottom of the box in the figure indicate the 75 th and 25 th percentiles, respectively. The bar at the middle of the box indicates the median: 50 percent of households have LCC savings that exceed this value. The horizontal lines above and be low each box indicate the 95 th and 5 th percentiles, respectively. The small box indi cates the average LCC savings for each efficiency level.

-200 -100 0 100 200 300 400 500 600 700 800

1 2 3 4 5 6 7 8 Efficiency LevelLife-cycle Cost Savings($)

Mean Median

Figure 8.4.3 Product Class 3, Top-Mount Refrigerator-Freezers: Range of Life-Cycle Cost Savings by Efficiency Level

8-42-300 -200 -100 0 100 200 300 400 500

1 2 3 4 5 6 7 8 Efficiency LevelLife-cycle Cost Savings($)

Mean Median

Figure 8.4.4 Product Class 5, Bottom-Mount Refrigerator-Freezers: Range of LifeCycle Cost Savings by Efficiency LevelLife-cycle Cost Savings($) 800
700
600

500
400
300
200
100
0

-100 Mean Median -200
-300 1 2 3 4 5 6 7 Efficiency Level

Figure 8.4.5 Product Class 7, Side-by-Side Refrigerator-Freezers: Range of Life-Cycle Cost Savings by Efficiency Level

Figures 8.4.6 through 8.4.8 show the range of PBPs for all efficiency levels considered for each analyzed refrigerator-freezer product clas s. For each efficiency level, the top and bottom of the box in the figure indicate the 75 th and 25 th percentiles, respectively. The bar at the middle

8-43of the box indicates the median: 50 percent of the households have a PBP above this value. The horizontal lines above and be low each box indicate the 95 th and 5 th percentiles, respectively. The small box indicates the average PB P for each efficiency level.

0 2 4 6 8 10 12 14 16 18 20

1 2 3 4 5 6 7 8 Efficiency LevelPayback Period(yr) Mean Median

Figure 8.4.6 Product Class 3, Top-Mount Refrigerator-Freezers: Range of Payback Periods by Efficiency Level

0 5 10 15 20 25 30 35

1 2 3 4 5 6 7 8 Efficiency LevelPayback Period(yr) Mean Median

Figure 8.4.7 Product Class 5, Bottom-Mount Refrigerator-Freezers: Range of Payback Periods by Efficiency Level

8-440 5 10 15 20 25

1 2 3 4 5 6 7 Efficiency LevelPayback Period(yr) Mean Median

Figure 8.4.8 Product Class 7, Side-by-Side Refrigerator-Freezers: Range of Payback Periods by Efficiency Level

8.4.2 Standard-Size Freezers
The following subsections summarize results of LCC and PBP analyses for standard-size freezers and present figures showi ng the distribution of LCCs in the base case for both product classes. Also presented are figur es showing the distribution of LCC impacts and the distribution of PBPs for specific efficiency levels.

8.4.2.1 Summary of Results
Tables 8.4.4 and 8.4.5 show the LCC and PBP results for each freezer product class considered for new standards. DOE determined the median and average values for PBPs at each efficiency level by excluding those house holds not affected by a standard.

8-45Table 8.4.4 Product Class 9, Upright Freezers: Analytical Results
Efficiency

Level

( % less than

baseline
energy use ) Life-Cycle Cost 2008$ Life-Cycle Cost Savings Payback Period
years

Average Equipment Cost Average Annual Operating Cost Average LCC Average Savings 2008$ % of Households that Experience

Median Average Net Cost No Impact Net Benefit
Baseline 505 113 2,064

1 (10) 511 104 1,942 122 0.0 19.4 80.6 0.7 0.8 2 (15) 517 98 1,870 194 0.0 1.6 98.4 0.9 1.1 3 (20) 520 92 1,793 271 0.0 0.5 99.5 0.7 0.9 4 (25) 537 87 1,731 333 0.0 0.4 99.6 1.3 1.5 5 (30) 561 81 1,675 389 0.3 0.2 99.6 1.8 2.1 6 (35) 598 75 1,633 432 1.2 0.0 98.8 2.5 3.0 7 (40) 656 69 1,611 453 4.0 0.0 96.0 3.5 4.3 8 (45) 745 63 1,621 443 9.9 0.0 90.1 5.0 6.0

Table 8.4.5 Product Class 10, Chest Freezer: Analytical Results

Efficiency

Level

( % less than Life-Cycle Cost 2008$ Life-Cycle Cost Savings Payback Period
years

Average Average Annual Average % of Households that Experience

Median Average baseline
energy use ) Equipment Cost Operating Cost Average LCC Savings 2008$ Net Cost No Impact Net Benefit
Baseline 325 69 1,275

1 (10) 328 63 1,196 79 0.0 15.3 84.8 0.4 0.5 2 (15) 332 60 1,153 122 0.0 1.1 98.9 0.8 1.0 3 (20) 355 56 1,127 148 1.2 0.2 98.6 2.5 3.0 4 (25) 361 53 1,085 190 0.7 0.2 99.2 2.4 2.8 5 (30) 368 49 1,044 231 0.4 0.2 99.4 2.3 2.7 6 (35) 416 46 1,044 231 5.3 0.0 94.7 4.2 4.9 7 (40) 420 42 999 276 3.7 0.0 96.3 3.8 4.4 8 (45) 493 39 1,024 251 13.8 0.0 86.2 6.0 6.9

8.4.2.2 Distributions of Impacts
Figure 8.4.9 presents a frequency chart showi ng the distribution of LCCs for the case of standard level 5 for upright freezers. The note, “Certainty is 99.67 % from $0 to +Infinity,” means that 99.67 percent of households owning upright freezers either will not be affected or will have LCC savings under the standard level co mpared to the base case. DOE could generate a similar frequency chart for every efficiency level.

8-46Frequency Chart

Certainty is 99.67% from $0 to +Infinity .000 .004 .009 .013 .017

0 43.25 86.5 129.7 173

($28) $261 $550 $840 $1,129 10,000 Trials 9,739 Displayed Forecast: LCCSav_Level5

Figure 8.4.9 Product Class 9, Upright Freezers: Di stribution of Life-Cycle Cost Impacts for Efficiency Level 5

Figure 8.4.10 presents a frequency chart s howing the distribution of payback periods for standard level 5 for upright freezers. DOE could generate a similar frequency chart for every efficiency level within each product class.

Frequency Chart

.000 .012 .024 .036 .048

0 118.7 237.5 356.2 475

-1.09 0.73 2.54 4.36 6.18 10,000 Trials 9,699 Displayed Forecast: PP_Level5

Figure 8.4.10 Product Class 9, Upright Freezers: Distribution of Payback Periods for Efficiency Level 5

8-47Figures 8.4.11 and 8.4.12 show the range of LCC savings for the efficiency levels considered for each freezer product class. For ea ch standard level, the top and bottom of the box indicate the 75 th and 25 th percentiles, respectively. The bar at the middle of the box indicates the median: 50 percent of the households have LCC sa vings that exceed this value. The horizontal lines above and below the box indicate the 95 th and 5 th percentiles, respectively. The small box shows the average LCC savings for each efficiency level.

-200 0 200 400 600 800 1000 1200 1400

1 2 3 4 5 6 7 8 Efficiency LevelLife-cycle Cost Savings($)

Mean Median

Figure 8.4.11 Product Class 9, Upright Freezers: Range of Life-Cycle Cost Savings by Efficiency Level

8-48-100 0 100 200 300 400 500 600 700 800 900

1 2 3 4 5 6 7 8 Efficiency LevelLife-cycle Cost Savings($)

Mean Median

Figure 8.4.12 Product Class 10, Chest Freezers: Range of Life-Cycle Cost Savings by Efficiency Level

Figures 8.4.13 and 8.4.14 show the range of PBPs for the efficiency levels considered for each freezer product class. For each standard leve l, the top and bottom of the box indicate the 75 th and 25 th percentiles, respectiv ely. The bar at the middle of the box indicates the median: 50 percent of the households have PBPs that exceed this value. The horizontal lines above and below the box indicate the 95 th and 5 th percentiles, respectively. The small box shows the average PBP for each efficiency level.

8-490 2 4 6 8 10 12 14

1 2 3 4 5 6 7 8 Efficiency LevelPayback Period(yr) Mean Median

Figure 8.4.13 Product Class 9, Upright Freezers: Range of Payback Periods by Efficiency Level

0 2 4 6 8 10 12 14 16

1 2 3 4 5 6 7 8 Efficiency LevelPayback Period(yr) Mean Median

Figure 8.4.14 Product Class 10, Chest Freezers: Range of Payback Periods by Efficiency Level

8.4.3 Compact Refrigeration Products
The following subsections summarize result s of LCC and PBP analyses for compact refrigeration products and present figures showing the distribution of LCCs in the base case for

8-50both product classes. Also presen ted are figures showing the dist ribution of LCC impacts and the distribution of PBPs for speci fic efficiency levels.

8.4.3.1 Summary of Results

Tables 8.4.6 and 8.4.7 show the result s of LCC and PBP analyses for compact refrigerators and freezers. DOE determined the me dian and average values of PBPs by excluding those households not affected by a sta ndard at each efficiency level.

Table 8.4.6 Product Class 11, Compact Refrigerators: Analytical Results

Efficiency

Level

( % less than

baseline
energy use ) Life-Cycle Cost $2008 Life-Cycle Cost Savings Payback Period
years

Average Product

Cost Average Annual Operating Cost Average LCC Average Savings $2008 % of Consumers who Experience

Median Average Net Cost No Impact Net Benefit
Baseline 166 33 325

1 (10) 170 30 314 12 4.2 2.9 92.8 1.4 1.3 2 (15) 173 28 309 16 8.1 2.6 89.4 1.7 1.7 3 (20) 176 26 305 21 9.3 2.6 88.1 1.8 1.8 4 (25) 191 25 311 14 34.1 1.7 64.2 3.4 3.3 5 (30) 192 23 304 21 26.9 0.0 73.2 2.9 2.8 6 (35) 198 21 303 23 30.5 0.0 69.6 3.1 3.0 7 (40) 214 20 310 15 44.6 0.0 55.4 4.0 3.9 8 (45) 236 18 324 1 62.2 0.0 37.8 5.3 5.1

Table 8.4.7 Product Class 18, Compact Freezers: Analytical Results

Efficiency

Level

( % less than Life-Cycle Cost $2008 Life-Cycle Cost Savings Payback Period
years

Average Average Annual Average % of Consumers who Experience

Median Average baseline
energy use ) Product Cost Operating Cost Average LCC Savings $2008 Net Cost No Impact Net Benefit
Baseline 207 34 399

1 (10) 214 30 388 11 9.6 5.0 85.4 2.4 2.3 2 (15) 218 29 382 16 11.7 0.0 88.3 2.5 2.5 3 (20) 248 27 403 -4 65.8 0.0 34.2 6.7 6.8 4 (25) 259 25 404 -5 65.6 0.0 34.4 6.7 6.7 5 (30) 281 24 416 -17 76.5 0.0 23.5 7.9 7.9 6 (35) 284 22 410 -11 69.3 0.0 30.7 7.1 7.1 7 (40) 299 20 415 -16 72.0 0.0 28.0 7.4 7.4 8 (45) 317 19 423 -24 75.9 0.0 24.1 7.8 7.8

8-518.4.3.2 Distributions of Impacts
Figure 8.4.15 presents a fre quency chart showing the distribu tion of LCCs for the case of efficiency level 5 for compact refrigerators. The note, “Certainty is 71.04% from $0 to +Infinity,” means that 71.04 percent of households owning compact refriger ators either will not be affected or will have LCC savings under the st andard level compared to the base case. DOE could generate a similar frequency ch art for every efficiency level.

Frequency Chart

Certainty is 71.04% from $0 to +Infinity .000 .006 .011 .017 .022

0 56 112 168 224

($22) $15 $53 $91 $129 10,000 Trials 9,759 Displayed Forecast: LCCSav5

Figure 8.4.15 Product Class 11, Compact Refrigerators : Distribution of Life-Cycle Cost Impacts for Efficiency Level 5

Figure 8.4.16 presents a frequency chart s howing the distribution of payback periods for efficiency level 5 for compact refrigerators. DOE could generate a similar frequency chart for every efficiency level wi thin each product class.

8-52Frequency Chart

.000 .010 .020 .030 .040

0 99.5 199 298.5 398

1.05 2.06 3.07 4.08 5.09 10,000 Trials 9,812 Displayed Forecast: PPLev5

Figure 8.4.16 Product Class 11, Compact Refrigerators: Distribution of Payback Period for Efficiency Level 5

Figures 8.4.17 and 8.4.18 show the range of LCC savings for the standard levels considered for compact refrigerators and freezers. For each standard level, the top and bottom of the box indicate the 75 th and 25 th percentiles, respectively. Th e bar at the middle of the box indicates the median: 50 percent of the households have LCC savings that exceed this value. The horizontal lines above and below the box indicate the 95 th and 5 th percentiles, respectively. The small box shows the average LCC savings for each efficiency level. Figures 8.4.19 and 8.4.20 show the range of PBPs for each efficiency level considered.

8-53-60 -40 -20

0 20 40 60 80 100 120

1 2 3 4 5 6 7 8 Efficiency LevelLife-cycle Cost Savings($)

Mean Median

Figure 8.4.17 Product Class 11, Compact Refrigerators: Range of Life-Cycle Cost Savings by Efficiency LevelLife-cycle Cost Savings($) 80

60

40

20

0

-20

-40

-60

-80 Mean Median

-100 1 2 3 4 5 6 7 8 Efficiency Level

Figure 8.4.18 Product Class 18, Compact Freezers: Range of Life-Cycle Cost Savings by Efficiency Level

Figures 8.4.19 and 8.4.20 show the range of PB Ps for the standard levels considered for compact refrigerators and freezers. For each st andard level, the top and bottom of the box indicate the 75 th and 25 th percentiles, respectively. The bar at the middle of the box indicates the median: 50 percent of the households have PBPs that exceed this value. The horizontal lines

8-54above and below the box indicate the 95 th and 5 th percentiles, respectivel y. The small box shows the average PBP for each efficiency level.

0 1 2 3 4 5 6 7 8

1 2 3 4 5 6 7 8 Efficiency LevelPayback Period(yr) Mean Median

Figure 8.4.19 Product Class 11, Compact Refrigerators: Range of Payback Periods by Efficiency Level

0 2 4 6 8 10 12

1 2 3 4 5 6 7 8 Efficiency LevelPayback Period(yr) Mean Median

Figure 8.4.20 Product Class 18, Compact Freezers: Range of Payback Periods by Efficiency Level

8-558.5 REBUTTABLE PAYBACK PERIOD
DOE presents rebuttable PBPs to provide information for considering the legally established rebuttable presumption that an energy efficiency standard is economically justified if the additional product costs attribut ed to the standard are less than three times the value of the first-year savings in energy cost s. (42 U.S.C. §6295 (o)(2)(B)(iii))

8.5.1 Metric
The basic equation for rebuttable PBP is the sa me as that shown in section 8.3, Inputs to Analysis of Payback Period. Unlike the analyses described in sections 8. 2 and 8.3, however, the rebuttable PBP is not based on the use of hous ehold samples and probability distributions. Rather, it is based on discrete, single-point values. For example, although DOE uses a probability distribution of regional energy prices in the analysis of payback pe riod, it uses only the national average energy price from the probability di stribution to determine the rebuttable PBP.

Other than the use of single-point valu es, the most notable difference between the distribution PBP and the rebuttable PBP is the la tter’s reliance on the DOE test procedure to determine a product’s annual energy consumption.

8.5.2 Inputs to Rebuttable Payback Period Analysis
The following were the key inputs that DOE used in de termining rebuttable PBP.

• Incremental manufacturing costs, markup, baseline retail price, sales tax, and installation costs were all base d on single-point valu es derived from, or used as inputs to, the distributional LCC and PBP analysis.

• Annual energy consumption was based on resu lts of the proposed DOE test procedure for a product with th e average adjusted volume from the LCC and PBP analysis.

• Energy prices were based on average values for the year that new standards are assumed to take effect (2014).

An average discount rate or lifetime is not required in ca lculating the rebuttable PBP.

8.5.3 Results of Analysis

DOE calculated rebuttable PBPs for each sta ndard level relative to the purchase and operating costs of an average baseline product.

Table 8.5.1 presents the rebuttable PBPs fo r each standard-size refrigerator product class that DOE analyzed for the LCC and PBP analysis.

8-56Table 8.5.1 Standard-Size Refrigerator-Freezers: Rebuttable Payback Periods Product Class 3:Top-Mount RefrigeratorFreezer Product Class 5:Bottom-Mount RefrigeratorFreezer Product Class 7: Side-by-Side Refrigerator-Freezer with TTD* Efficiency Level ( % less than baseline
energy use ) PBP years Efficiency Level ( % less than baseline
energy use ) PBP years Efficiency Level ( % less than baseline
energy use ) PBP years Baseline

- Baseline

- Baseline

-

1 (10) 0.7 1 (10) 0.3 1 (10) 1.4 2 (15) 1.0 2 (15) 0.6 2 (15) 1.6 3 (20) 1.6 3 (20) 1.0 3 (20) 3.4 4 (25) 2.6 4 (25) 1.8 4 (25) 5.4 5 (30) 3.9 5 (30) 2.2 5 (30) 7.1 6 (35) 5.6 6 (35) 4.6 6 (35) 8.3 7 (40) 7.4 7 (40) 6.6 7 (40) 9.9 8 (45) 8.7 8 (45) 8.1

- - *Through-the-door ice service.
Table 8.5.2 presents the rebuttable PBPs fo r each standard-size freezer product class that DOE analyzed for the LCC and PBP analysis.

Table 8.5.2 Standard-Size Freezers: Re buttable Payback Periods Product Class 9: Upright Freezer Product Class 10: Chest Freezer Efficiency Level ( % less than baseline energy use ) PBP years Efficiency Level ( % less than baseline energy use ) PBP years Baseline

- Baseline

-

1 (10) 1.0 1 (10) 0.6 2 (15) 1.2 2 (15) 1.0 3 (20) 1.1 3 (20) 3.0 4 (25) 1.8 4 (25) 3.0 5 (30) 2.5 5 (30) 2.9 6 (35) 3.5 6 (35) 5.4 7 (40) 5.0 7 (40) 4.9 8 (45) 7.0 8 (45) 7.7

Table 8.5.3 presents the rebuttable PBPs fo r each compact refrigeration product class that DOE analyzed for the LCC and PBP analysis.

8-57Table 8.5.3 Compact Refrigerators and Freezers: Rebuttable Payback Periods Product Class 11: Compact Refrigerator Product Class 18: Compact Freezer Efficiency Level ( % less than baseline energy use ) PBP years Efficiency Level ( % less than baseline energy use ) PBP years Baseline

- Baseline

-

1 (10) 1.3 1 (10) 2.1 2 (15) 1.7 2 (15) 2.3 3 (20) 1.8 3 (20) 6.0 4 (25) 3.3 4 (25) 6.0 5 (30) 2.9 5 (30) 7.0 6 (35) 3.0 6 (35) 6.3 7 (40) 3.9 7 (40) 6.6 8 (45) 5.1 8 (45) 7.0

8-58REFERENCES

1 U.S. Department of Energy–Ene rgy Information Administration. Residential Energy Consumption Survey, 2005 Public Use Data Files. 2009. U.S. Department of Energy: Washington, D.C. < www.eia.doe.gov/emeu/r ecs/recspubuse05/pubuse05.html >

2 U.S. Department of Energy-En ergy Information Administration. An Updated Annual Energy Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and Reinvestment Act and Recent Change s in the Economic Outlook , April, 2009. Washington, DC. SR/OIAF/2009-03.

< http://www.eia.doe.gov/oiaf/servicerpt/stimulus/pdf/sroiaf(2009)03.pdf >

3 The NPD Group, Inc., The NPD Group/NPD Houseworld – POS, Refrigerators, January – December 2007 , 2007. Port Washington, NY.

4 Association of Home Appliance Manufacturers. Aggregated Refrigerator

‐ Freezer Incremental Cost Data for DOE Refrigerator

‐ Freezer Energy Efficiency Standards Rulemaking , March 2, 2009. Memo to Detlef We stphalen, Navigant Consulting, Inc.

5 U.S. Department of Energy–E nergy Information Administration. Form EIA-861

. Washington, D.C. < www.eia.doe.gov/cneaf/ele ctricity/page/eia861.html >

6 U.S. Census Bureau, Housing and H ousehold Economic Stat istics Division. American Housing Survey . Washington, DC.

< http://www.census.gov/hhes/www/housing/ahs/ahs.html >

7 U.S. Department of Energy–Ene rgy Information Administration. Commercial Building Energy Consumption Survey, 2003 Public Use Data Files. 2008. U.S. Department of Energy: Washington, D.C.

< http://www.eia.doe.gov/emeu/cbecs/cbecs 2003/public_use_2003/cbecs_pudata2003.ht ml >

8 The Federal Reserve Board. Survey of Consumer Finances . 1989, 1992, 1995, 1998, 2001, and 2004. < www.federalreserve.gov/pubs /oss/oss2/scfindex.html >

9 The Federal Reserve Board. Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: Annual, Description: Average rate on 6-mont h negotiable certificates of deposit (secondary market), quoted on an investment basis . 2009. Washington, D.C.

< www.federalreserve.gov/releases/H15/data.htm >

10 The Federal Reserve Board. Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: Monthly,

8-59Description: Bond buyer go 20-bond municipal bond index . 2009. Washington, D.C.

< www.federalreserve.gov/releases/H15/data.htm >

11 The Federal Reserve Board. Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: Corporate bonds/Moody’s Seasoned AAA, Frequency: Annual, Description: Moody’s yield on season ed corporate bonds—all industries, AAA . 2009. Washington, D.C. < www.federalreserve.gov/releases/H15/data.htm >

12 Mortgage-X, Mortgage Information Service. Cost of Savings Index (COSI), Index History

. 2009. < http://mortgage-x.com/general/indexes/default.asp > (Last accessed, February 25, 2009.)

13 Damodaran Online. The Data Page: Historical Returns on Stocks, Bonds, and Bills—United States . 2009. < http://pages.stern.nyu.edu/~adamodar/ >

14 Ibbotsons Associates. SBBI Valuation Edition 2009 Yearbook . 2009. Chicago, IL. pp. 45–48.

15 Damodaran Online. The Data Page: Cost of Capital by Industry Sector . 2001–2008.

< http://pages.stern.nyu.edu/~adamodar/ >

16 Damodaran Online. The Data Page: Historical Returns on Stocks, Bonds and Bills—United States . < http://pages.stern.nyu.edu/~adamodar/ >

17 Bureau of Economic Analysis–Natio nal Income and Product Accounts Tables. Table 1.1.9. Implicit Price Deflators for Gross Domestic Product.

< www.bea.gov/national/ni paweb/SelectTabl e.asp?Selected=N >

18 U.S. Department of Energy. Implementation Report: Energy Conservation Standards Activities. Submitted Pursuant to Secti on 141 of the Energy Policy Act of 2005 . August 2006. Washington, D.C.

< www.eere.energy.gov/buildings/appliance_standards/pdfs/implementation_report_0806.pdf

>

19 Association of Home Appliance Manufacturers. Aggregated Refrigerator

‐ Freezer Shipments and Efficiency Data for DOE Refrigerator

‐ Freezer Energy Efficiency Standards Rulemaking , January 16, 2009. Memo to Detlef We stphalen, Navigant Consulting, Inc.

8-60APPENDIX 8-A. USER INSTRUCTIONS FOR LCC AND PBP SPREADSHEETS

TABLE OF CONTENTS

8-A.1 INTRODUCTION .................................................................................................8-A-1 8-A.1.1 Standard Size Refrigerat or-Freezers and Freezers.................................................8-A-1 8-A.1.2 Compact Refrigerators and Compact Freezers ......................................................8-A-2 8-A.2 BASIC INSTRUCTIONS......................................................................................8-A-3

8A-iAPPENDIX 8-A. USER INSTRUCTIONS FOR LCC AND PBP SPREADSHEETS

8-A.1 INTRODUCTION
It is possible to examine and reproduce the de tailed results of the life-cycle cost (LCC) and payback period (PBP) analyses using Microsoft Excel spread sheets available on the U.S. Department of Energy’s website at: http://www.eere.energy.gov/build ings/appliance_standards/. To fully execute the spreadsheets requires both Microsoft Excel and Crystal Ball software. Both applications are commercially availa ble. Crystal Ball is available at: http://www.decisioneering.com

. The seven spreadsheets posted on the DOE webs ite represent the latest versions and have been tested with Microsoft Excel 2003.

8-A.1.1 Standard Size Refrigerator-Freezers and Freezers
The Standard Size Refrigerator-Freezers and Freezers LCC and PBP spreadsheets or workbooks consist of the following worksheets:

LCC Summary Contains the input selections and a summary table of energy use, operating costs, LCC, and Payback. This worksheet also works as an interface between user inputs a nd the rest of the worksheets — do not modify this sheet.

RECS Households For each RECS household being sampled, contains the equipment usage data, along with product char acteristics (i.e., size, volume, product age) and household charact eristics (e.g., Census division, income).

RECS UAF Contains the unit adjustment factor calculation which converts tested energy consumption into field energy consumption

Base Case Eff Dist Contains market efficiency dist ribution in the year the standard takes effect

Equipment Price Contains manufacturer price da ta for the considered design options. Also includes the manufact urer and retail mark-ups, sales
tax.

Energy Use Contains unit energy use data (tested and field)

Energy Price Contains regional electricity prices for the reference year.

8-A-1Energy Price Trend Contains the electricity price tr ends for the reference, high, and low economic growth scenarios based on AEO 2009.

Discount Rate Contains data from which an average discount rate and a distribution of discount rates are determined.

Lifetime Contains the survival functi on and average lifetime in years.

Standards Contains past and existing st andards by product class along with historical trends of energy consum ption prior to first standards.

AV Equations Contains average relation between volume and adjusted volume by product class.

ESAF Contains efficiency standard adjustment factor.

UAF Range Contains parameters allowing for the sample selection, and minimum and maximum UAF assignments.

EStarModel Contains Energy Star model assigning energy star to households based on income.

8-A.1.2 Compact Refrigerators and Compact Freezers
The Compact Refrigerators and Compact Freezers LCC and PBP spreadsheet or workbook consists of the following worksheets:

LCC Summary Contains the input selections and a summary table of energy use, operating costs, LCC, and Payback. This worksheet also works as an interface between user inputs a nd the rest of the worksheets — do not modify this sheet.

Base Case Eff Dist Contains market efficiency dist ribution in the year the standard takes effect.

Division Contains number of customer s for residential and commercial sector by region.

Equipment Price Contains manufacturer price da ta for the considered design options. Also includes the manufact urer and retail mark-ups, sales
tax.

Energy Use Contains unit energy use data (tested and field)

Energy Price Contains regional electricity prices for the reference year.

8-A-2Energy Price Trend Contains the electricity price tr ends for the reference, high, and low economic growth scenarios based on AEO 2009.

Discount Rate Contains data from which an average discount rate and a distribution of discount rates are determined.

Lifetime Contains the survival functi on and average lifetime in years.

AV Equations Contains average relation between volume and adjusted volume by product class.

8-A.2 BASIC INSTRUCTIONS
Basic instructions for operating the LCC spreadsheets are as follows:

1. Once you have downloaded the LCC file from the Web, open the file using Excel. At the bottom, click on the tab for sheet ‘LCC Summary’.

2. Use Excel’s “View/Zoom” commands at the t op menu bar to change the size of the display to make it fit your monitor.

3. The user interacts with the spreadsheet by clicking choices or entering data using the graphical interface that comes with the spr eadsheet. Select choices from the various inputs listed under "User Options" heading.

4. Under the "User Options" heading, select c hoices from the selection buttons and boxes for the following: (1) type of calculation (Sample or Crys tal Ball®), (2) energy price Trend, (3) start year, and (4) efficiency market share scenario. By overwriting the code in the LCC summary sheet, a new discount rate or lifetime can be ente red if a value other than the default value is wanted . The Department does not recommend saving the spreadsheet after the code is changed.

5. To change inputs listed under "User Input", sel ect the input you wish to change by either clicking on the appropriate button or selecti ng the appropriate input from the input box.

6. This spreadsheet gives the user two types of calculation methods:

a. If the "Sample Calc" is selected, then all calculations are performed for single input values, usually an average. The new result s are shown on the same sheet as soon as the new values are entered.

b. Alternately, if the "CB Calculation" is sele cted, the spreadsheet generates results that are distributions. Some of th e inputs are also distributions . The results from the LCC distribution are shown as singl e values and refer only to the results from the last

8-A-3Monte Carlo sample and are therefore not m eaningful. To run the distribution version of the spreadsheet, the Microsoft Excel® ad d-in software called Crystal Ball® must be enabled.

To produce sensitivity results using Crystal Ball, simply select R un from the Run menu (on the menu bar). To make basic changes in the run sequence, including altering the number of trials, select Run Preferences from the Run menu. After each simulation run, the user needs to select Reset (also from the Run menu) before Run can be selected again. Once Crystal Ball has completed its run sequence it will produce a series of distributions. Using the menu bars on the distribution results, it is possi ble to obtain further statistica l information. The time taken to complete a run sequence can be reduced by mi nimizing the Crystal Ball window in Microsoft Excel. A step-by-step summary of the procedure for running a distribution analysis is outlined below:

1. Find the Crystal Ball toolbar (at top of screen)

2. Click on Run from the menu bar

3. Select Run Preferences and choos e from the following choices:

a. Monte Carlo a b. Latin Hypercube (recommended)

c. Initial seed choices and whether you want it to be constant between runs d. Select number of Monte Carl o Trials (DOE suggests 10,000).

4. To run the simulation, follow the followi ng sequence (on the Cr ystal Ball toolbar)

Run Reset Run

5. Now wait until the program informs you that the simulation is completed.

The following instructions are provided to view the output genera ted by Crystal Ball.

1. After the simulation has finished, to see th e distribution charts generated, click on the Windows tab bar that is labeled Crystal Ball.

2. The life-cycle cost savings and payback pe riods are defined as Forecast cells. The frequency charts display the results of the simulations, or trials, performed by Crystal Ball. Click on any chart to bring it into vi ew. The charts show the low and high endpoints of the forecasts. The View selection on the Crystal Ball toolbar can be used to specify whether you want cumulative or frequency plots shown.

a Because of the nature of the program, there is some variation in results due to random sampling when Monte Carlo or Latin Hypercube sampling is used. 8-A-43. To calculate the probability that a particular value of LCC savings will occur, either type 0 in the box by the left arrow, or move the a rrow key with the cursor to 0 on the scale. The value in the Certainty box shows the like lihood that the LCC savings will occur. To calculate the certainty of payback period bei ng below a certain number of years, choose that value as the high endpoint. 4. To generate a printout repor t, select Create Report fr om the Run menu. The toolbar choice of Forecast Windows allows you to select the charts and statistics in which you are interested. For further information on Crystal Ball outputs, please refer to Understanding the Forecast Chart in the Crystal Ball manual.

8-A-5APPENDIX 8-B. UNCERTAINTY AND VARIABILITY

TABLE OF CONTENTS

8-B.1 INTRODUCTION ................................................................................................. 8-B-1 8-B.2 UNCERTAINTY ................................................................................................... 8-B-1 8-B.3 VARIABILITY...................................................................................................... 8-B-1 8-B.4 APPROACHES TO UNCERTAI NTY AND VARIABILITY ............................. 8-B-2 8-B.5 PROBABILITY ANALY SIS AND THE USE OF CRYSTAL BALL................. 8-B-2

LIST OF FIGURES

Figure 8-B.5.1 Normal Probabi lity Distribution ................................................................ 8-B-3 Figure 8-B.5.2 Triangular Pr obability Distribution............................................................ 8-B-3 Figure 8-B.5.3 Uniform Probability Distribution............................................................... 8-B-3

8B-iAPPENDIX 8-B. UNCERTAINTY AND VARIABILITY

8-B.1 INTRODUCTION
Analysis of an energy-efficiency standard involves calculations of impacts, for example, the impact of a standard on consumer life-cycle cost (LCC). In order to perform the calculation, the analyst must first: 1) specify the equation or model that will be used; 2) define the quantities in the equation; and 3) provide numerical values for each quantity. In the simplest case, the equation is unambiguous (contains all relevant quantities and no others), each quantity has a single numerical value, and the ca lculation results in a single valu e. However, unambiguity and precision are rarely the case. In almost all cases , the model and/or the numerical values for each quantity in the model are not completely known (i .e., there is uncertainty) or the model and/or the numerical values for each quantity in the m odel depend upon other conditions (i.e., there is variability).

Thorough analysis involves accounting for un certainty and variability. While the simplest analysis involves a single numerical va lue for each quantity in a calculation, arguments can arise about what the appropriate value is for each quantity. Explicit analysis of uncertainty and variability is intended to provide more comple te information to the decision-making process.

8-B.2 UNCERTAINTY
When making observations of past events or speculating about the future, imperfect knowledge is the rule rather than the exception. For example, the energy actually consumed by a particular appliance type (such as the average U.S. commercial ai r conditioner or heat pump) is not directly recorded, but rather estimated based upon available information. Even direct laboratory measurements have some margin of error. When estimating numerical values expected for quantities at some future date, th e exact outcome is rare ly known in advance.

8-B.3 VARIABILITY
Variability means that differe nt applications or situations produce different numerical values when calculating a quantity. Specifying an exact value for a quantity may be difficult because the value depends on something else. For example, the number of hours an air conditioner is operated by a household depends upon the specific circumstances and behaviors of the occupants (e.g., number of persons, personal habits about how comfor table the person wants to be, etc.). Variability makes specifying an appropriate population valu e more difficult in as much as any one value may not be representati ve of the entire population. Surveys can be helpful here, and analysis of surveys can relate the variable of interest (e.g., hours of use) to other variables that are better known or easie r to forecast (e.g., persons per household).

8-B-18-B.4 APPROACHES TO UNCERT AINTY AND VARIABILITY
This section describes two approach es to uncertainty and variability:

• scenario analysis, and

• probability analysis.

Scenario analysis uses a single numerical value for each quantity in a calculation, then changes one (or more) of the numerical values and repeats the calcula tion. A number of calculations are done, which provide some indicati on of the extent to which the result depends upon the assumptions. For example, the life-cycle co st of an appliance co uld be calculated for energy rates of 2, 8, and 14¢ per kWh.

The advantages of scenario analysis are that each calculation is simple; a range of estimates is used; and crossover points can be iden tified. (An example of a crossover point is the energy rate above which the life-c ycle cost is reduced, holding a ll other inputs constant. That is, the crossover point is the ener gy rate at which the consumer achieves savings in operating expense that more than compensate for the in creased purchase expense. ) The disadvantage of scenario analysis is that th ere is no information about th e likelihood of each scenario.

Probability analysis consider s the probabilities within a ra nge of values. For quantities with variability (e.g., electricity rates in different households), surveys can be used to generate a frequency distribution of numerical values (e.g., the number of households with electricity rates at particular levels) to estimate the probability of each value. For quantities with uncertainty, statistical or subjective measures can be used to provide probab ilities (e.g., manufact uring cost to improve energy efficiency to some level may be estimated to be $10 ± $3).

The major disadvantage of the probability appr oach is that it requires more information, namely information about the shapes and magnit udes of the variability and uncertainty of each quantity. The advantage of the probability appro ach is that it provides greater information about the outcome of the calculations, that is, it provide s the probability that the outcome will be in a particular range.

Scenario and probability analys is provide some indication of the robustness of the policy given the uncertainties and variability. A polic y is robust when the impacts are acceptable over a wide range of possible conditions.

8-B.5 PROBABILITY ANALYSIS AND THE USE OF CRYSTAL BALL
To quantify the uncertainty and variability th at exist in inputs to the engineering, LCC, and payback period (PBP) analyses, the Depart ment used Microsoft Excel spreadsheets combined with Crystal Ball, a commercially av ailable add-in, to conduct probability analyses. The probability analyses used Monte Carlo simulation and probability distributions.

8-B-2Simulation refers to any analytical method m eant to imitate a real-l ife system, especially when other analyses are too mathematically comp lex or too difficult to reproduce. Without the aid of simulation, a spreadsheet model will only reveal a single outcome, generally the most likely or average scenario. Spreadsheet risk analysis uses both a spreadsheet model and simulation to automatically analyze the effect of varying inputs on out puts of the modeled system. One type of spreadsheet simulati on is Monte Carlo simulation, which randomly generates values for uncertain variables again and again to s imulate a model. Monte Carlo simulation was named for Monte Carlo, Monaco, where the primary attractions are casinos containing games of chance. Games of chance such as roulette wheels, dice, and slot machines, exhibit random behavior. The random behavior in games of chance is similar to how Monte Carlo simulation selects variable values at random to simulate a model. When you roll a die, you know that either a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular roll. It's the same with the vari ables that have a known range of values but an uncertain value for any particular time or event (e.g., equipment lif etime, discount rate, and installation cost).

For each uncertain variable (one that has a range of possible values), possible values are defined with a probability distribution. The t ype of distribution selected is based on the conditions surrounding that va riable. Probability dist ribution types include:

NORMAL TRIANGULAR UNIFORM

Figure 8-B.5.1 Normal Probability Figure 8-B.5.2 Triangular Figure 8-B.5.3 Uniform Probability Distribution Probab ility Distribution Distribution
During a simulation, multiple scenarios of a model are calculated by repeatedly sampling values from the probability distri butions for the uncertain variables and using those values for the cell. Crystal Ball simulations can consist of as many trials (or scenarios) as desired—hundreds or even thousands. During a singl e trial, Crystal Ball randomly selects a value from the defined possibilities (the range and shape of the probabilit y distribution) for each uncertain variable and then recalculates the spreadsheet.

For each uncertain variable (one that has a range of possible values), possible values are defined with a probability distribution. The t ype of distribution selected is based on the conditions surrounding that va riable. Probability dist ribution types include:

8-B-3APPENDIX 8-C. CONSUMER RETAIL PRICE DISTRIBUTIONS FOR BASELINE REFRIG ERATOR-FREEZERS AND FREEZERS

TABLE OF CONTENTS

8-C.1 INTRODUCTION ................................................................................................. 8-C-1 8-C.2 DISTRIBUTION HISTOGRAMS......................................................................... 8-C-2

LIST OF FIGURES

Figure 8-C.2.1 Baseline Retail Price Distri bution for Product Cla ss 3 (Standard-sized Top-mount Refrigerator-Freezers wit hout Through-the-door Service)..... 8-C-2 Figure 8-C.2.2 Baseline Retail Price Distri bution for Product Cla ss 5 (Standard-sized Bottom-mount Refrigerator-Free zers without Through-the-door Service) ...................................................................................................... 8-C-3 Figure 8-C.2.3 Baseline Retail Price Distri bution for Product Cla ss 7 (Standard-sized Side-mount Refrigerator-Freezers wi th Through-the-door Service) ......... 8-C-4 Figure 8-C.2.4 Baseline Retail Price Distri bution for Product Cla ss 9 (Standard-sized Upright Freezers) ....................................................................................... 8-C-5 Figure 8-C.2.5 Baseline Retail Price Distri bution for Product Cla ss 10 (Standard-sized Chest Freezers)........................................................................................... 8-C-6 Figure 8-C.2.6 Baseline Retail Price Dist ribution for Product Class 11 (Compact Refrigerators) ............................................................................................. 8-C-7 Figure 8-C.2.7 Baseline Retail Price Dist ribution for Product Class 18 (Compact Freezers)..................................................................................................... 8-C-8

8C-iAPPENDIX 8-C. CONSUMER RETAIL PRICE DISTRIBUTIONS FOR BASELINE REFRIGERATOR-FREEZERS AND FREEZERS

8-C.1 INTRODUCTION
DOE’s engineering analysis did not attemp t to estimate the manufacturing cost for baseline models. Instead, it developed incremen tal increases in manufacturer selling price associated with increases in efficiency levels . This approach required DOE to estimate retail prices for the baseline mode l in each product class.

DOE drew upon proprietary retail pr ice data collected by The NPD Group. 1 These data reflect retail prices and sales at a large number of reta il outlets in the United States (including over 50 percent of retail sales), and include in formation regarding model number, refrigerated volume, configuration of doors and ice-maki ng, and whether the unit is an ENERGY STAR product. The data include enough information to a ssign each model to the correct product class. DOE developed a sales-weighted price distribu tion for non-ENERGY STAR appliances in each product class from this data. DOE grouped models by selling price in bi ns of varying width (generally $25 for compact produ cts and $50 to $100 for standard -sized products) in order to balance the accuracy and usability of the distributions. These distributions are shown in the following section.

DOE assumed that prices for non-ENER GY STAR models are a reasonable approximation of prices for the baseline models. These models may be “baseline” in efficiency, but span a wide range of other features and mate rials, and therefore have a broad distribution of prices. DOE chose not to develop volume-dependen t baseline retail prices because the data did not show a strong relationship between volume and retail price. The price distributions within most volume ranges and product classes are al most as broad as the volume-independent distributions DOE chose to use, and regression analysis indicated very weak dependence of average price on volume.

8-C-18-C.2 DISTRIBUTION HISTOGRAMS

40%

0% 5% 10% 15% 20% 25% 30% 35%Percent of Sales$150$350$550$750$950$1,150$1,350$1,550$1,750$1,950$2,150$2,350$2,550$2,750$2,950$4,500

Selling Price Figure 8-C.2.1 Baseline Retail Price Distribution for Product Class 3 (Standard-sized Top-mount Refrigerator-Freezers without Through-the-door Service)

8-C-2Percent of Sales 18%

16%

14%

12%

10%

8%

6%

4%

2%

0%$150$350$550$750$950$1,150$1,350$1,550$1,750$1,950$2,150$2,350$2,550$2,750$2,950$3,150$3,350$3,550$3,750$3,950$4,150$4,350$4,550$4,750$4,950$5,300$5,700

Selling Price

Figure 8-C.2.2 Baseline Retail Price Distribution for Product Class5 (Standard-sized Bottom-mount RefrigeratorFreezers without Through-the-door Service)

8-C-3Percent of Sales 16%

14%

12%

10%

8%

6%

4%

2%

0%$150$350$550$750$950$1,150$1,350$1,550$1,750$1,950$2,150$2,350$2,550$2,750$2,950$3,150$3,350$3,550$3,750$3,950$4,150$4,350$4,550$4,750$4,950

Selling Price
Figure 8-C.2.3 Baseline Retail Price Distribution for Product Class 7 (Standard-sized Side-mount Refrigerator-Freezers with Through-the-door Service)

8-C-4Percent of Sales 25%

20%

15%

10%

5%

0%$75$175$275$375$475$575$675$775$875$975$1,075$1,175$1,275$1,375$1,475$1,650$1,850$2,050$2,250$2,450$2,650$2,850$3,250$4,250$5,250$6,250

Selling Price
Figure 8-C.2.4 Baseline Retail Price Distribution for Product Class 9 (Standard-sized Upright Freezers)

8-C-5Percent of Sales 35%

30%

25%

20%

15%

10%

5%

0%$75$125$175$225$275$325$375$425$475$525$575$625$675$725$775$825$875$925$975$1,050$1,150$1,250$1,350$1,450$1,550$1,650$1,750$1,850$1,950$2,250$2,750$3,500$4,500$5,500

Selling Price
Figure 8-C.2.5 Baseline Retail Price Distribution for Product Class 10 (Standard-sized Chest Freezers)

8-C-6Percent of Sales 20%

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%$25$50$75$100$125$150$175$200$225$250$275$300$325$350$375$400$425$450$475$500$525$550$575$600$625

Selling Price
Figure 8-C.2.6 Baseline Retail Price Distribution for Product Class 11 (Compact Refrigerators)

8-C-7Percent of Sales 70%

60%

50%

40%

30%

20%

10%

0%$25$50$75$100$125$150$175$200$225$250$275$300$325$350$375$400$425$450$475$500$750$1,500$2,500$3,500

Selling Price
Figure 8-C.2.7 Baseline Retail Price Distribution for Product Class 18 (Compact Freezers)

8-C-81 REFERENCES

The NPD Group, Inc., The NPD Group/NPD Houseworld – POS, Refrigerators, Freezers January – December 2007 , 2008. Port Washington, NY.

8-C-9APPENDIX 8-D. HOUSEHOLD DISCOUNT RATE DISTRIBUTIONS

TABLE OF CONTENTS

8-D.1 INTRODUCTION .................................................................................................8-D-1 8-D.2 DISTRIBUTION OF MORT GAGE INTEREST RATES ....................................8-D-1 8-D.3 DISTRIBUTION OF RATES FOR TY PES OF DEBT AND EQUITY USED TO FINANCE REPLACEMENT FURNACES....................................................8-D-2 8-D.4 DISTRIBUTION OF RATES FO R TYPES OF EQUITY USED TO FINANCE REPLACEMENT FURNACES ..........................................................8-D-5

LIST OF FIGURES

Figure 8-D.2.1 Distribution of New Home Mortgage Interest Rates .................................8-D-1 Figure 8-D.3.1 Distribution of Home Equity Loan Interest Rates .....................................8-D-2 Figure 8-D.3.2 Distribution of Cred it Card Interest Rates .................................................8-D-3 Figure 8-D.3.3 Distribution of Installment Loan Interest Rates.........................................8-D-3 Figure 8-D.3.4 Distribution of Other Residence Loan Interest Rates ................................8-D-4 Figure 8-D.3.5 Distribution of Other Lines of Credit Loan Interest Rates ........................8-D-4 Figure 8-D.4.1 Distribution of Annual Rate of Return on CD’s ........................................8-D-5 Figure 8-D.4.2 Distribution of Annual Rate of Return on Savings Bonds.........................8-D-6 Figure 8-D.4.3 Distribution of Annual Rate of Return on Corporate AAA Bonds............8-D-6 Figure 8-D.4.4 Distribution of Annual Rate of Savings Accounts.....................................8-D-7 Figure 8-D.4.5 Distribution of Annual Rate of Return on S&P 500..................................8-D-7 Figure 8-D.4.6 Distribution of Annual Rate of Return on Mutual Funds ..........................8-D-8

8-D-iAPPENDIX 8-D. HOUSEHOLD DISCOUNT RATE DISTRIBUTIONS

8-D.1 INTRODUCTION

DOE derived discount rates for the LCC analysis using data on interest or return rates for various types of debt and equ ity. To account for variation among households in rates for each of the types, DOE sampled a rate for each household from a distribution of rates for each debt and equity type. This appendix de scribes the distributions used.

8-D.2 DISTRIBUTION OF MORTGAGE INTEREST RATES
Figure 8-D.2.1 shows the distribution of real in terest rates for new home mortgages. The data source DOE used for mortgage inte rest rates is the Federal Reserve Board’s Survey of Consumer Finances (SCF) in 1989, 1992, 1995, 1998, 2001, 2004 and 2007. 1 Using the appropriate SCF data for each year, DOE adjusted the nominal mortgage interest rate for each relevant household in the SCF for mortgage tax deduction and inflation. In cases where the effective interest rate is equal to or below the inflation rate (resulting in a negative real interest rate), DOE set the real effective interest rate to zero.

0% 5% 10% 15% 20% 25% 30% 35%

0 .5 % 1 .6 % 2 .5 % 3.5% 4.4% 5.4% 6.4% 7 .4 % 8 .5 % 9 .5 %

1 0.

5 %

1 1. 6 % 12.4% 13.5% 14.4%

1 6.

7 % 2 2.

9 %

Real Interest RateProbability

Figure 8-D.2.1 Distribution of New Home Mortgage Interest Rates

8-D-18-D.3 DISTRIBUTION OF RATES FOR TYPES OF DEBT AND EQUITY USED TO FINANCE REPLACEMENT FURNACES
Figure 8-D.3.1 through Figure 8-D.3.5 show th e distribution of real interest rates for different types of debt used to finance replacemen t furnaces. The data source for the interest rates for home equity loans, credit cards, installment loans, other residence lo ans, and other lines of credit is the Federal Reserve Board’s SCF in 1989, 1992, 1995, 1998, 2001, 2004, and 2007. 1 DOE adjusted the nominal rates to real rates us ing the annual inflation rate in each year. For home equity loans, DOE calculated effective inte rest rates in a similar manner as for mortgage rates, since interest on such loans is tax deductible.

0% 5% 10% 15% 20% 25% 30% 35%

0.4% 1 .5 % 2 .6 % 3.5% 4 .5 % 5.4% 6.4% 7 .5 % 8.5% 9.5%

1 0.

6 % 11.8% 12.7%

1 3.

7 % 14.7%

1 6.

7 %

1 7.

5 % 42.5%

Real Interest RateProbability

Figure 8-D.3.1 Distribution of Home Equity Loan Interest Rates

8-D-20% 2% 4% 6% 8% 10% 12%

0.0% 1.4% 2.5% 3.5% 4 .5 %

5.6% 6 .7 %

7 .5 %

8 .7 %

9 .6 % 10.6% 11.5% 12.6%

1 3.

6 % 14.7%

1 5.

6 %

1 6.

4 %

1 7.

6 %

1 8.

5 % 19.4% 2 0.

5 % 21.4% 22.5% 23.6% 24.6% 2 5.

5 % 26.3% 2 7. 3 %

Real Interest RateProbability

Figure 8-D.3.2 Distribution of Credit Card Interest Rates

0% 5% 10% 15% 20% 25% 30% 35%

0 .0 % 2 .4 % 4 .5 % 6 .7 % 8 .6 % 10.6%

1 2.

5 %

1 4.

7 %

1 6.

5 %

1 8.

5 % 2 0.

5 % 2 2.

3 % 2 4.

6 % 2 6.

6 % 2 8.

5 % 3 4.

1 % 4 3.

6 % 4 7. 2 %

Real Interest RateProbability

Figure 8-D.3.3 Distribution of Installment Loan Interest Rates

8-D-30% 5% 10% 15% 20%

0 .2 % 1 .5 % 2 .5 % 3.5% 4.5% 5.5% 6.5% 7 .5 % 8 .6 % 9 .4 %

1 0.

6 %

1 1. 6 % 12.6% 14.7% 15.7%

1 6.

6 %

1 7.

9 %

Real Interest RateProbability

Figure 8-D.3.4 Distribution of Other Residence Loan Interest Rates

0% 2% 4% 6% 8% 10% 12% 14% 16%

0.1% 1.4% 2 .5 % 3.5% 4.5% 5.5% 6 .7 % 7 .6 % 8.7% 9.5%

1 0.

5 %

1 1. 6 %

1 2.

5 %

13.5%

1 4.

7 %

1 5.

6 %

16.5% 17.3%

1 8.

5 %

19.1% 20.2% 2 1. 5 %

2 2.

1 %

2 3.

5 %

Real Interest RateProbability

Figure 8-D.3.5 Distribution of Other Lines of Credit Loan Interest Rates

8-D-48-D.4 DISTRIBUTION OF RATES FOR TYPES OF EQUITY USED TO FINANCE REPLACEMENT FURNACES
Figure 8-D.4.1 through Figure 8-D.4.6 show th e distribution of real interest rates for different types of equity used to finance replace ment furnaces. Data for equity classes are not available from the Federal Reserve Board’s SCF , so the Department derived data for these classes from national-level histor ical data. The interest rates as sociated with certificates of deposit (CDs), 2 savings bonds, 3 and bonds (AAA corporate bonds) 4 are from Federal Reserve Board time-series data covering 1977–2007. DOE assumed rates on checking accounts to be zero. Rates on savings and money ma rket accounts are from Cost of Savings Index data covering 1984–2007. 5 The rates for stocks are th e annual returns on the Standa rd and Poor’s (S&P) 500 in the 1977–2007 period. 6 The mutual fund rates are a weighted average of the stock rates (two

thirds weight) and the bond rates (one-third weight) in each year of the 1977–2007 period. DOE adjusted the nominal rates to real rates using the annual in flation rate in each year.

-2 % 0% 2% 4% 6% 8%

1977 1 97

9 1981 1983 1 98

5 1987 1989 1 99

1 1 99

3 1995 1 99

7 1999 2 00

1 2003 2005 2 00

7

YearAnnual Rate of Return

Figure 8-D.4.1 Distribution of Annual Rate of Return on CD’s

8-D-5-2 % 0% 2% 4% 6% 8%

1977 1 97

9 1981 1983 1 98

5 1987 1989 1 99

1 1 99

3 1995 1 99

7 1999 2 00

1 2003 2005 2 00

7

YearAnnual Rate of Return

Figure 8-D.4.2 Distribution of Annual Rate of Return on Savings Bonds

-2 % 0% 2% 4% 6% 8%

1977 1 97

9 1981 1983 1 98

5 1987 1989 1 99

1 1 99

3 1995 1 99

7 1999 2 00

1 2003 2005 2 00

7

YearAnnual Rate of Return

Figure 8-D.4.3 Distribution of Annual Rate of Return on Corporate AAA Bonds

8-D-6-2% 0% 2% 4% 6% 8%1984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

YearAnnual Rate of Return

Figure 8-D.4.4 Distribution of Annual Rate of Savings Accounts

-30% -20% -10%

0% 10% 20% 30% 40%

1977 1979 1 98

1 1 98

3 1985 1987 1 98

9 1991 1 99

3 1 99

5 1997 1 99

9 2001 2003 2 00

5 2007

YearAnnual Rate of Return

Figure 8-D.4.5 Distribution of Annual Rate of Return on S&P 500

8-D-7-15% -10% -5 % 0% 5% 10% 15% 20% 25%

1977 1979 1 98

1 1 98

3 1985 1987 1 98

9 1991 1 99

3 1 99

5 1997 1 99

9 2001 2003 2 00

5 2007

YearAnnual Rate of Return

Figure 8-D.4.6 Distribution of Annual Rate of Return on Mutual Funds

8-D-8REFERENCES

1. The Federal Reserve Board, Survey of Consumer Finances 1989, 1992, 1995, 1998, 2001, 2004, 2007 . < http://www.federalreserve.gov/pubs/oss/oss2/scfindex.html >

2. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: CDs (secondary market), Maturity: 6-month, Frequency: Annual, Description: Average rate on 6- month negotiable certificates of deposit (secondary market), quoted on an investment basis, 2008.

< http://www.federalreserve.gov/releases/H15/data.htm >

3. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: State and local bonds, Maturity: 20-year, Frequency: Monthly, Description: Bond buyer go 20-bond municipal bond index, 2008.

< http://www.federalreserve.gov/releases/H15/data.htm >

4. The Federal Reserve Board, Federal Reserve Statistical Release, Selected Interest Rates, Historical Data, Instrument: Corporate bonds/Moody's Seasoned AAA, Frequency: Annual, Description: Moody's yield on seasoned corporate bonds - all industries, AAA, 2008. < http://www.federalreserve .gov/releases /H15/data.htm >

5. Wachovia, Cost of Savings Index (COSI) Index History, 2008. (Last accessed March 10, 2008.) < http://mortgage-x.com/general/indexes/default.asp >

6. Damodaran Online, Damodaran Online:The Data Page (Historical Returns on Stocks, Bonds, and Bills - United States), 2008. Stern School of Business, New York University. (Last accessed March 10, 2008.) < http://pages.stern.nyu.edu/~adamodar/ >

8-D-9CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1 9.2 SHIPMENTS MODEL METHODOLOGY.................................................................... 9-1 9.3 DATA INPUTS AND M ODEL CALIBRATION .......................................................... 9-3 9.3.1 Standard-size Refrigerator-Freezers ................................................................................ 9-4 9.3.1.1 Historical Shipments...................................................................................... 9-4 9.3.1.2 Markets and Model Calibration ..................................................................... 9-5 9.3.1.3 Disaggregation into Separate Refrig erator-Freezer Product Classes............. 9-9 9.3.1.4 Base Case Shipments ................................................................................... 9-14 9.3.2 Standard-size Freezers ................................................................................................... 9-15 9.3.2.1 Historical Shipments.................................................................................... 9-15 9.3.2.2 Markets and Model Calibration ................................................................... 9-16 9.3.2.3 Disaggregation into Separate Freezer Product Classes................................9-18 9.3.2.4 Base Case Shipments ................................................................................... 9-19 9.3.3 Compact Refrigerators and Refrigerator-Freezers......................................................... 9-21 9.3.3.1 Historical Shipments.................................................................................... 9-21 9.3.3.2 Markets and Model Calibration ................................................................... 9-21 9.3.3.3 Disaggregation into Separate Compact Refrigerator Product Classes......... 9-24 9.3.3.4 Base Case Shipments ................................................................................... 9-24 9.3.4 Compact Freezers......................................................................................................... .. 9-26 9.3.4.1 Historical Shipments.................................................................................... 9-26 9.3.4.2 Markets and Model Calibration ................................................................... 9-26 9.3.4.3 Disaggregation into Separate Compact Freezer Product Classes ................ 9-28 9.3.4.4 Base Case Shipments ................................................................................... 9-28 9.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME IMPACTS ...................................................................................................................... 9-29 9.5 AFFECTED STOCK ..................................................................................................... 9-31 9.6 RESULTS .................................................................................................................... .. 9-32

LIST OF TABLES

Table 9.3.1 Standard-size Refrigerator-F reezers: Historical Shipments................................. 9-5 Table 9.3.2 Historical and Forecaste d Housing Starts (millions) ........................................... 9-8 Table 9.3.3 Market Shares of Side-by-Side Refrigerator-Freezers....................................... 9-10 Table 9.3.4 Modeled Homeowner Side -by-Side Market Share............................................ 9-11 Table 9.3.5 Product Class Market Shares of Standard-size Refrigerator-Freezers............... 9-13 Table 9.3.6 Standard-size Freezers : Historical Shipments ................................................... 9-16 Table 9.3.7 Saturation of Freezers in New Housing............................................................. 9-17 Table 9.3.8 Product Class Market Share of Standard-size Freezers ..................................... 9-19

9-iTable 9.3.9 Compact Refrigerators: Historical Shipments ................................................... 9-21 Table 9.3.10 Product Class Market Shares of Compact Refrigerators ................................... 9-24 Table 9.3.11 Compact Freezers: Hi storical Shipments........................................................... 9-26 Table 9.3.12 Product Class Market Sh ares of Compact Freezers........................................... 9-28 Table 9.4.1 Change in Relative Price Elasti city following a Purchase Price Change .......... 9-31

LIST OF FIGURES

Figure 9.3.1 Standard-size Refrigerator-Freezer s: Survival and Retirement Functions .......... 9-7 Figure 9.3.2 Projected Market Share of Side- And Bottom-Fr eezer Product Classes ........... 9-12 Figure 9.3.3 Standard-Size Refrigerator-Freeze rs: Historical and Base Case Shipments Forecast by Market Segment ............................................................................. 9-14 Figure 9.3.4 Standard-size Refrigerator-Free zers: Base Case Shipments Forecast by Product Class Group .......................................................................................... 9-15 Figure 9.3.5 Standard-size Freezers: Surv ival and Retirement Functions ............................. 9-18 Figure 9.3.6 Standard-size Freezers: Historical and Base Case Shipments Forecast by Market Segment................................................................................................. 9-20 Figure 9.3.7 Standard-size Freezers: Base Case Shipments Forecast by Product Class........ 9-20 Figure 9.3.8 Forecast of Saturation of Comp act Refrigerators in New Construction Market Segments ............................................................................................... 9-23 Figure 9.3.9 Compact Refrig erators: Survival and Retirement Functions............................. 9-24 Figure 9.3.10 Compact Refriger ators: Base Case Shipments Forecast by Market Segment.............................................................................................................. 9-25 Figure 9.3.11 Compact Refrigerat ors: Base Case Shipments Forecast by Product Class........ 9-25 Figure 9.3.12 Compact Freezers: Survival and Retirement Functions..................................... 9-27 Figure 9.3.13 Compact Freezers: Historical and Base Case Shipments Forecast by Market Segment................................................................................................. 9-28 Figure 9.3.14 Compact Freezers: Base Case Shipments Forecast Disaggregated by Product Class ..................................................................................................... 9-29 Figure 9.6.1 Standard-Size Refrigerator-Fr eezers: Base Case and Standards Case Shipments Forecasts........................................................................................... 9-33 Figure 9.6.2 Standard-size Freezers: Base Case and Standards Case Shipments Forecasts ............................................................................................................ 9-34 Figure 9.6.3 Compact Refriger ators: Base Case and St andards Case Shipments Forecasts ............................................................................................................ 9-35 Figure 9.6.4 Compact Freezers: Base Case and Standards Case Shipments Forecasts.......... 9-36

9-iiCHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION
Estimates of future product 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 da ta and methods the U.S. Department of Energy (DOE) used to forecast annual product shipme nts and presents results for each of the refrigeration product classes being considered in this analysis.

DOE defined four refrigeration product types, and developed models to estimate shipments for each type. The four types are: (1) st andard-size refrigerator-freezers, (2) standardsize freezers, (3) compact refrigerators, and (4 ) compact freezers. DOE calibrated each model against historical shipments. To estimate the eff ects of potential standard levels on shipments of each product class, each model was structured to account for the combined effects on consumer purchase decisions of changes in product cost , annual operating cost, and household income.

Each model considers specific market segments to estimate shipments of individual product classes within each segment. The anal ytical results from these segments then are aggregated to estimate total shipments for each product class and type . For the product types included in this analysis, DOE considered thr ee market segments: (1) new construction or new owners; (2) replacements for faile d products; and (3) for standard-s ize refrigerators, the purchase of an additional refrigerator.

The shipments models were developed as Microsoft Excel spreadsheets that are accessible on the Internet ( www.eere.energy.gov/buildings/appliance_standards/ ). Appendix 10­ A discusses how to access and utilize the shipme nts model spreadsheets, which are integrated into spreadsheets for the National Impact Analys is. The rest of this chapter explains the shipments models in more detail. Section 9.2 presents methodology behind the models; section 9.3 describes the data inputs and calibration of each model; section 9.4 discusses impacts on shipments from changes in product cost, opera ting cost, and household income; section 9.5 discusses the affected stock; and section 9.6 pres ents the shipments forecast for different energy conservation standard-level scenarios.

9.2 SHIPMENTS MODEL METHODOLOGY
DOE developed a model of the national stock of in-service appliances for estimating annual shipments for each of the four product ty pes considered for this standards rulemaking. The model considers market segments as distinct inputs to the shipments forecast. As represented by the following equation, the two primary market segments for standard-size products are new installations and replacements.

9-1_ _ Ship ( j ) = Rpl ( j ) + NI ( j ) p p p

Where:

Ship p (j) = total shipments of product p in year j
, Rpl p (j) = units of product p retired and replaced in year j , and NI p (j) = number of new in stallations of product p in year j

. DOE’s shipments models take an accounting approach, tracking market shares of each product class, the vintage of units in the existing stock, and expected construction trends. Rather than simply extrapolating a current shipments tre nd, the analysis of the base case for each type (i.e., the case without new standards) uses input variables as drivers, including construction forecasts and product lifetime distributions, to for ecast sales in each market segment. As a result, DOE’s shipments models assume that new construction drives shipments.

To estimate shipments of replacement units, the models utilize shipments data from previous years and assumptions about the lifet ime of each product. Estimated shipments of replacement units in a given year are equal to th e total stock of the appliance minus those units shipped in previous years that remain in the st ock. DOE determined the useful service life of each product class to estimate how many years it is likely to remain in stock. The following equation shows how DOE estimated shipments of replacement units.

_ ageMax j 1 Rpl p ( j ) = Stock p ( j 1 ) 

# ∑ ∑ Ship j × prob Rtr ( age )

age =0 j = N Where:

Stock p (j-1) = total stock of appliances in year j-1
, prob Rtr (age) = probability that an appliance of a particular age will be retired, and N

= year in which the model begins its stoc k accounting (start year is specific to each product type, and is based on availa ble historical shipments data).

Stock accounting provides an estimate of th e age distribution of product stocks for all years, using product shipments, a retirement function, and initial product stock as inputs. The age distribution of product stocks is a key input to both the NES and NPV calculations because the operating costs for any year depend on the age di stribution of the stock. Operating cost is dependent on the product age distribution unde r a standards case scen ario that produces increasing efficiency over time, where older, less efficient units may have higher operating costs, while younger, more-efficient units will have lower operating costs.

DOE calculated total stock of each product by integrating historical shipments data beginning with a specific year. The start year de pended on the historical data available for the product. As units are added to the stock, some of the older ones retire and exit the stock. To

9-2estimate future shipments, DOE developed a series of equations that define the dynamics and accounting of stocks. For ne w units, the equation is:

_ Stock( j,age = 1 ) = Ship( j 1 )

Where:

Stock(j, age) = number of units of a particular age, j = year for which the stoc k is being estimated, and Ship (j) = number of units purchased in year j

. The above equation states that the number of one-y ear-old units is simply equal to the number of new units purchased the previous year. Slight ly more complicated equations, such as the following equation, describe how the model accounts for the existing stock of units.

Stock( j +1 ,age +1 ) = Stock( j,age ) × 

# [ 1

_ prob Rtr ( age ) 

# ]

In this equation, as the year is incremented from j to j+1 , the age is also incremented from age to age+1 . Over time, a fraction of the stock is removed; that fraction is determined by a retirement probability function, prob Rtr (age) , which is described in section 9.3. Because the products considered in this rulemaking ar e common appliances that have been used by U.S. consumers for a long time, replacements typically cons titute the majority of shipments.

9.3 DATA INPUTS AND MODEL CALIBRATION
For standard-size products designed for resi dential application, DOE used two inputs to estimate shipments driven by new construction: new housing forecasts and saturation of the product in new housing. New housing includes newl y constructed single- and multi-family units, termed “new housing completions,” and mobile home placements. For new housing completions and mobile home placements, DOE used r ecorded data through 2007, and adopted the projections from the DOE Energy In formation Administration (EIA)’s Annual Energy Outlook 2009 ( AEO2009 ) for the period 2008–2030. 1 To determine new construction shipments for standard-size products, DOE used forecasts of ma rket saturations combined with forecasts of housing starts. For compact products, which ar e used in both residential and commercial applications, DOE used market saturations in co mbination with forecasts of housing starts and new commercial construction.

DOE estimated replacements using product reti rement functions that it developed based on product lifetimes. The retirement functions fo r all product types are described in detail in chapter 8. The retirement functions use the fo llowing equation to determine the probability of retirement at a certain age for the considered products:

9-3_ _ _ ageMax j 1 Rpl p ( j ) = Stock p ( j 1 ) 

# ∑ ∑ Ship j × prob Rtr ( age )

age =0 j = N Where:

Stock p (j-1) = total stock of appliances in year j-1
, Shipj = shipments in year j
, prob Rtr (age) = probability that an appliance of a particular age will be retired, and N

= year in which the model begins its stoc k accounting (start year is specific to each product type, and is based on availa ble historical shipments data).

DOE used historical shipments data to calib rate its shipments mode ls. For standard-size refrigerator-freezers and standard-size freezer s, modeled new construction shipments and replacements were insufficient to account fo r all product shipments. DOE therefore developed two additional market segments to calibrate its model: “additional refrigerator purchase” for refrigerators and “existing homes without the appliance” for freezers. The “additional refrigerator purchase” segment represents those purchases that are meant to be used as a new primary refrigerator while the former primary refr igerator is converted to a second refrigerator, as well as a small number of di rect purchases of second refrige rators. Chapter 8 describes in detail how DOE estimated the probability that a primary refrigerator will be converted to a second refrigerator.

The following sections explain in detail each of the data inputs for each product type.

9.3.1 Standard-size Refrigerator-Freezers
DOE considered seven product classes of st andard-size refrigerator-freezers for this rulemaking. DOE’s shipments model for these produc t classes used the aggr egate shipments, that is, the shipments for all seven product classes, as the basis for its forecasts. DOE did not develop a separate shipments model for each refrigerator-freezer product class. Instead, DOE disaggregated total shipments into product cl asses using various data and assumptions.

9.3.1.1 Historical Shipments
DOE used historical shipme nts data (i.e., domestic shipments and imports) to populate and calibrate its shipments model for standard-size refrigerator-f reezers. It used the following sources to establish hi storical shipments: Appliance magazine’s Statistical Review, 2,3,4 AHAM Factbooks, 5,6 and an AHAM data submittal related to this rulemaking. 7 Table 9.3.1 summarizes the historical shipments data. DOE developed a total stock estimate by integrating historical shipments. Over time, some of the units are re tired and removed from the stock, triggering the purchase of a new unit. Because of the relationship between retirements and total stock, there is a strong correlation between past and future shipments, independent of conservation standards.

9-4Table 9.3.1 Standard-size Refrigerator-Fre ezers: Historical Shipments

Year Shipments millions Year Shipments millions
1951 4.075 1980 5.124
1952 3.570 1981 4.944
1953 3.650 1982 4.364
1954 3.600 1983 5.340
1955 4.200 1984 5.882
1956 3.700 1985 6.002
1957 3.350 1986 6.410
1958 3.117 1987 6.748
1959 3.785 1988 6.733
1960 3.475 1989 6.453
1961 3.480 1990 6.456
1962 3.775 1991 6.411
1963 4.125 1992 6.721
1964 4.545 1993 7.047
1965 4.931 1994 7.589
1966 4.974 1995 7.650
1967 4.713 1996 7.975
1968 5.150 1997 7.924
1969 5.296 1998 8.774
1970 5.286 1999 9.099
1971 5.691 2000 9.217
1972 6.315 2001 9.305
1973 6.774 2002 9.744
1974 5.982 2003 10.021
1975 4.577 2004 10.913
1976 4.817 2005 11.134
1977 5.707 2006 11.078
1978 5.890 2007 10.402
1979 5.707 2008 9.314
Sources: 2005–2007: AHAM Data Submittal, 2009; 1995–2004: AHAM Fact Book 2005; 1992–1994: AHAM Factbook 2003; 1951–1992: Appliance Magazine, “Statistical Review,” various issues.

9.3.1.2 Markets and Model Calibration
The market for standard-size refrigerator-fr eezers is primarily comprised of units for new construction, replacement units for products that have been retired, and purchases driven by the

9-5conversion of an existing unit from first to second refrigerator. Total shipments are represented by the following equation:

Ship SRRF ( j ) = Rpl SRRF ( j ) + NH SRRF ( j ) + Conv SRRF ( j )

Where:

Ship SRRF (j) = total shipments of standard-s ize refrigerator-freezers in year j
, Rpl SRRF (j) = replacement shipments in year j
, NH SRRF (j) = shipments to new households in year j , and Conv SRRF (j) = shipments due to additional refrigerato r purchase (conversion of first to second refrigerator) in year j

. The following sections discuss these three markets in further detail.

New Construction. To estimate shipments driven by new residential construction, DOE multiplied the housing starts forecast for each year by the estimated saturation of standard-size refrigerator-freezers in new housing. DOE based saturation for a given year on the calculated saturation in the previous year . DOE froze the saturation at th e level corresponding to year 2008 (1.3 per new home), the last year for which sh ipments data are available. The following equation describes the method used for calcula ting saturation for new construction:

Sat NC ( j

− 1) = Stock ( j ) / HStock ( j )

Sat ( j ) = Sat (2008) ∀ j > 2008 NC NC Where:

Sat NC (j) = market saturation of standard-size refr igerator-freezers in the new housing market segment in year j
, Stock(j) = total stock of standard-size re frigerator-freezers in year j
, HStock(j) = total number of housing units in year j , and Sat NC (2008) = market saturation of standard-size refr igerator-freezers in the new housing market segment in 2008.

Table 9.3.2 presents hist orical and forecasted new hous ing starts based on EIA’s AEO2009 for the period 1990–2030. AEO2009 provides three sets of housing starts forecasts, based on economic growth scenarios: a Refere nce case, a High Economic Growth case, and a Low Economic Growth case. DOE used the Referen ce Case that incorporates the effects from the American Recovery and Reinvestment Act. 1 DOE used the high and low growth scenarios from the March 2009 release of the AEO2009

. 8 DOE used the forecasts from the Reference case to estimate its shipments to new housing, which is comprised of single- and multi-family units (new housing completions) and mobile home placements. For 2031

− 2043, DOE froze completions at the 2030 level for all three economi c scenarios.

9-6% Retiring Replacements. DOE determined refrigerator-freezer shipments to the replacement market using an accounting method that tr acks the total stock of units by vintage. DOE estimated a stock by vintage by integrating historic al shipments. Over time, some units are retired and removed from the stock, thereby triggering the shipme nt of replacement units. A certain percentage (depending on the age) of units w ill fail each year and need to be replaced. To determine when a unit fails, DOE used a survival function based on a product lifetime distri bution with an average value of 17.1 years. Chapter 8 provides a mo re thorough discussion of product lifetimes. Figure 9.3.1 shows the survival and retirement func tions that DOE used to estimate replacement shipments for all product classes of standard-size refrigerator-freezers.

% Surviving 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% 0 5 10 15 20 25 30 35 40 45 50 Age
Figure 9.3.1 Standard-size Refrigerator-Freezers: Survival and Retirement Functions

9-7Table 9.3.2 Historical and Forecasted Housing Starts (millions)

Year Reference Case High Growth Low Growth Year Reference Case High Growth Low Growth 1990 1.38 1.38 1.38 2017 1.99 2.40 1.60 1991 1.18 1.18 1.18 2018 2.00 2.42 1.59 1992 1.41 1.41 1.41 2019 2.01 2.34 1.53 1993 1.54 1.54 1.54 2020 1.99 2.16 1.39 1994 1.76 1.76 1.76 2021 1.93 2.11 1.33 1995 1.69 1.69 1.69 2022 1.89 2.10 1.30 1996 1.82 1.82 1.82 2023 1.87 2.11 1.27 1997 1.49 1.49 1.49 2024 1.87 2.15 1.26 1998 1.54 1.54 1.54 2025 1.89 2.21 1.27 1999 1.64 1.64 1.64 2026 1.88 2.27 1.25 2000 1.70 1.70 1.70 2027 1.84 2.28 1.22 2001 1.72 1.72 1.72 2028 1.79 2.26 1.19 2002 1.75 1.75 1.75 2029 1.76 2.27 1.17 2003 1.79 1.79 1.79 2030 1.77 2.30 1.18 2004 1.80 1.80 1.80 2031 1.77 2.30 1.18 2005 2.22 2.22 2.22 2032 1.77 2.30 1.18 2006 1.92 1.92 1.92 2033 1.77 2.30 1.18 2007 1.75 1.75 1.75 2034 1.77 2.30 1.18 2008 1.48 1.61 1.28 2035 1.77 2.30 1.18 2009 1.30 1.70 1.28 2036 1.77 2.30 1.18 2010 1.08 1.38 1.02 2037 1.77 2.30 1.18 2011 1.51 1.82 1.31 2038 1.77 2.30 1.18 2012 1.73 2.06 1.47 2039 1.77 2.30 1.18 2013 1.86 2.23 1.56 2040 1.77 2.30 1.18 2014 1.89 2.30 1.59 2041 1.77 2.30 1.18 2015 1.93 2.39 1.59 2042 1.77 2.30 1.18 2016 1.98 2.42 1.60 2043 1.77 2.30 1.18
Source: EIA, AEO2009

. Shipments Due to Additional Refrigerator Purchase. To calibrate estimated shipments with historical data, DOE introduced into the model a market se gment corresponding to purchases of additional standard-size refrigerator -freezers that are not intended as replacements. Because such purchases involve converting a first unit to a sec ond refrigerator, DOE estimated shipments to this market segment by applying the probability of conversion (developed in chapter 8) to the stock of surviv ing refrigerators. To determine when a household converts a first refrigerator to a second one, DOE used a convers ion function based on the total installed stock of refrigerators of a certain age. Chapter 8 provides a detailed discu ssion of the conversion function. The following equation shows how DOE calculate d shipments to this market segment.

9-8_ ageMax j 1 SNew SRRF ( j ) = C j × 

# ∑∑ Stock ( age ) j × prob

conv ( age )

age = 0 j = N

Where:

SNew SRRF (j ) = shipments due to additional refrigerato r purchase (conversion of first to second refrigerator) in year j
, C

j = a calibration factor, equal to the ratio of new shipments in year j to the installed stock in that year, and Prob conv (age) = probability that the refrigerator has been converted at a given age

9.3.1.3 Disaggregation into Separate Refrigerator-Freezer Product Classes

DOE examined the historical trends in the ma rket shares of various refrigerator-freezer configurations to disaggre gate the total shipments of refrigerat or-freezers into sh ipments to each of the three considered refr igerator-freezer product categories (top-mount, bottom-mount and side-by-side configurations). Th e market share of side-by-side refrigerator-freezers models has grown significantly during the past two decades. Bottom-freezer models historically have had a small market share, but that sh are has grown in recen t years. To forecast the market share for these three configurations throughout the 30-year analysis period (beginning in 2014), DOE built a simple model of aggregate consum er behavior, fit its model to th e historical grow th in side-byside market share, and then used its model to estimate future market shares for all three configurations.

DOE assumed that bottom-freezer models were an insignificant portion of the market prior to 2005, and that consumer behavior related to these cla sses in the future would mirror behavior regarding side- by-side models. Therefore, DOE fore cast the combined market share for side-by-side and bottom-freezer products. DOE assumed that the ratio between the market share of bottom-mount and the market share of side -mount products would re main fixed at its 2008 value.

DOE based its model on the market share of each product category as reported by households in the Energy Information Admini stration’s Residential Energy Consumption Survey 9 (RECS) and as reported by the Associ ation of Home Appliance Manufacturers (AHAM). 7 RECS reports whether a household owns a side-by-side refriger ator, but does not distinguish between top- and bottom-mount units . For the purpose of this model, DOE assumed that all recently-purch ased top-or-bottom-mount refrigerat ors reported in the RECS surveys conducted in 1990, 1993, 1997, 2001, and 2005 are top-mount units.

DOE used RECS data first to estimate the maximum (“limiting”) market share that sideby-side and bottom-mount product cl asses would attain in the futu re. RECS reports the income of each household in its sample. DOE assumed that households that have annual incomes greater than $100,000 and that own their own homes are able to select the appliance that best meets their needs and preferences. The market share of side-b y-side units in these hou seholds is significantly

9-9higher than the current overall market share of side-by-side products. DOE determined market shares for recent purchases from RECS by considering only house holds which reported purchasing their appliance within two years prior to the survey. The market share of side-by-side refrigerator purchases in the se lected household group from each RECS survey is shown in Table 9.3.3. Over time, DOE assumed that all homeowner s will be free to choose the product of their choice, as DOE assumed high-in come homeowners can today. As a result, DOE assumed that market share of side-by-side units among all ho meowners will increase un til it equals the mean market share among high-income hom e-owning households, 66.4 percent.

Table 9.3.3 Market Shares of Side-by- Side Refrigerator-Freezers

RECS Survey Year Market Share of Side-by-Side Units for HighIncome Homeowners %
1990 57.7
1993 71.7
1997 73.4
2001 61.6
2005 67.5
Mean 66.4
Source : EIA, Residential Energy Consumption Survey, for the years listed.

DOE used RECS to determine what percent of side-by-side units is sold to home-owners in order to convert a limiting market share am ong home-owning households into a market share for all shipments. This pecentage was roughly cons tant in the five most recent RECS surveys, and DOE assumed that it would remain equal to their mean, 90.9 percent, throughout the forecast period. DOE also assumed that the percent of American households that own their home would return to its historical le vel of roughly 65 percent by 2025. (American home ownership was between 63 percent and 66 percen t from 1962 to 1997, peaked at 69 percent in 2004, and fell to 67.8 percent in 2008.)

DOE combined the three factors (the obser ved preference of high-income consumers, percent of side-by-side refrigerator-freezers purchased by homeowners, and homeownership) to predict the limiting market shar e for the side- and bottom-freezer product classes. This limit is

MS × HO HO

− LIM LIM MS = = 47.45% SF

− LIM SF HO

Where:

MS SF-LIM = limiting market share for side-by- side and bottom-mount product classes, MS HO-LIM = limiting market share for side-by-si de and bottom- mount product classes among homeowners (66.4 percent), HO LIM = DOE’s assumed value for the eventual percentage of households that will own their own home (65 percent), and

9-10SF HO = the percentage of si de-by-side and bottom-mount products sold to homeowners (90.9 percent).

DOE modeled the approach to this limiting value as an e xponential curve, and fit the model parameters to data from RECS and AHAM in order to determine the rate at which the market will approach the limit. RECS micro-da ta enabled DOE to determine the approximate market share of side-by-side products among all homeowners in the year s preceding each RECS survey (1989, 1992, 1996, 2000, and 2004). DOE also calculated th e market share among homeowners from the AHAM data by multiplying by 90.9 percent ( SF HO ) and dividing by the homeownership ( HO ) for each year. The resulting estimates for market shares of side-by-side products among homeowners are shown in Table 9.3.4.

Table 9.3.4 Modeled Homeowner Side-by-Side Market Share

Survey Year Homeowner Side-freezer Market Share AHAM 1998 41.0% 1999 41.9% 2000 42.2% 2001 43.0% 2002 43.9% 2003 45.9% 2004 46.2% 2005 49.1% 2006 59.8% 2007 61.4% RECS 1989 32.5% 1992 31.9% 1996 41.1% 2000 48.9% 2004 53.0%

DOE fit an exponential curve to the data in Table 9.3.4, approaching the limiting value MS SF-LIM :

α ( β

− year ) MS = MS

− , HO

− year HO

− LIM 

# e Where:

MS HO-year = market share of side-by-side and bo ttom-mount refrigerator-freezers among all homeowners in a given year , α , β = fit parameters, and MS HO-LIM = limiting market share for side- and bottom-freezer product classes among homeowners (66.4 percent).

9-11The best fit parameters are: α = 0.05555 and β = 1971.62.

The R-squared value for the fit is 0.924, i ndicating that the model is a relatively good fit. DOE multiplied the best-fit estimate of MS HO-year by homeownership in ea ch year, then divided by SF HO to account for side- and bottom-mount re frigerators sold to non-homeowners. The results are estimated market shares of side- by-side or bottom-mount products throughout the analysis period. This estimate, along with the markets shares derived from the RECS and AHAM data, is shown in Figure 9.3.2.

20.00% 25.00% 30.00% 35.00% 40.00% 45.00% 50.00%Side- and Bottom-freezer Market Share

Forecast market share
AHAM Shipments Share

RECS Side-freezer
Limiting fraction

1985 1995 2005 2015 2025 2035 2045
Year Figure 9.3.2 Projected Market Share of Side- And Bottom-Freezer Product Classes

DOE disaggregated the side and bottom-m ount freezer classes in to their respective product classes based on the AHAM data submittal for the years 2005-2008. For future years, DOE maintained the market shares of the produc t classes within each c onsidered category of side-by-side and bottom-mount, and top-mount re frigerator-freezer as they existed in 2008. Table 9.3.5 presents the resulting market share forecast DOE used to disaggregate total modeled shipments.

9-12Table 9.3.5 Product Class Market Shares of Standard-size Refrigerator-Freezers
Year PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC5A 2005 0.2% 0.1% 62.4% 1.0% 1.6% 0.1% 34.1% 0.4% 2006 0.2% 0.1% 54.4% 1.0% 8.4% 0.1% 33.6% 2.2% 2007 0.1% 0.0% 53.8% 0.8% 10.7% 0.1% 31.6% 2.9% 2008 0.1% 0.0% 55.8% 0.8% 10.3% 0.1% 30.2% 2.7% 2009 0.1% 0.0% 57.7% 0.7% 9.8% 0.1% 28.8% 2.6% 2010 0.1% 0.0% 59.6% 0.7% 9.4% 0.1% 27.6% 2.5% 2011 0.1% 0.0% 59.3% 0.7% 9.5% 0.1% 27.8% 2.5% 2012 0.1% 0.0% 59.0% 0.7% 9.5% 0.1% 28.0% 2.5% 2013 0.1% 0.0% 58.7% 0.7% 9.6% 0.1% 28.2% 2.6% 2014 0.1% 0.0% 58.4% 0.7% 9.7% 0.1% 28.4% 2.6% 2015 0.1% 0.0% 58.1% 0.7% 9.7% 0.1% 28.6% 2.6% 2016 0.1% 0.0% 57.8% 0.7% 9.8% 0.1% 28.8% 2.6% 2017 0.1% 0.0% 57.6% 0.7% 9.9% 0.1% 28.9% 2.6% 2018 0.1% 0.0% 57.3% 0.8% 9.9% 0.1% 29.1% 2.6% 2019 0.1% 0.0% 57.1% 0.8% 10.0% 0.1% 29.3% 2.7% 2020 0.1% 0.0% 56.8% 0.8% 10.0% 0.1% 29.5% 2.7% 2021 0.1% 0.0% 56.7% 0.8% 10.1% 0.1% 29.6% 2.7% 2022 0.1% 0.0% 56.5% 0.8% 10.1% 0.1% 29.7% 2.7% 2023 0.1% 0.0% 56.3% 0.8% 10.2% 0.1% 29.8% 2.7% 2024 0.1% 0.0% 56.1% 0.8% 10.2% 0.1% 29.9% 2.7% 2025 0.1% 0.0% 56.0% 0.8% 10.2% 0.1% 30.0% 2.7% 2026 0.1% 0.0% 55.8% 0.8% 10.3% 0.1% 30.2% 2.7% 2027 0.1% 0.0% 55.6% 0.8% 10.3% 0.1% 30.3% 2.7% 2028 0.1% 0.0% 55.4% 0.8% 10.4% 0.1% 30.4% 2.8% 2029 0.1% 0.0% 55.3% 0.8% 10.4% 0.1% 30.5% 2.8% 2030 0.1% 0.0% 55.1% 0.8% 10.4% 0.1% 30.7% 2.8% 2031 0.1% 0.0% 55.0% 0.8% 10.5% 0.1% 30.7% 2.8% 2032 0.1% 0.0% 54.8% 0.8% 10.5% 0.1% 30.8% 2.8% 2033 0.1% 0.0% 54.7% 0.8% 10.5% 0.1% 30.9% 2.8% 2034 0.1% 0.0% 54.5% 0.8% 10.6% 0.1% 31.0% 2.8% 2035 0.1% 0.0% 54.4% 0.8% 10.6% 0.1% 31.1% 2.8% 2036 0.1% 0.0% 54.3% 0.8% 10.6% 0.1% 31.2% 2.8% 2037 0.1% 0.0% 54.2% 0.8% 10.6% 0.1% 31.3% 2.8% 2038 0.1% 0.0% 54.1% 0.8% 10.7% 0.1% 31.3% 2.8% 2039 0.1% 0.0% 54.0% 0.8% 10.7% 0.1% 31.4% 2.8% 2040 0.1% 0.0% 53.9% 0.8% 10.7% 0.1% 31.5% 2.8% 2041 0.1% 0.0% 53.8% 0.8% 10.7% 0.1% 31.6% 2.9% 2042 0.1% 0.0% 53.5% 0.8% 10.8% 0.1% 31.7% 2.9% 2043 0.1% 0.0% 53.4% 0.8% 10.8% 0.1% 31.8% 2.9%

9-139.3.1.4 Base Case Shipments
Figure 9.3.3 shows the forecasted shipment s in the base case (without new energy conservation standards) and the historical shipments DOE used to calibrate the forecast. The figure also presents shipments due to retirements, shipments to new housing, and shipments due to conversion from first to second unit. Figure 9.3.4 presents forecasted refrigerator-freezer shipments disaggregated by product category.

25.0 Conversion New construction

0.0 5.0 10.0 15.0 20.0Millions Replacements Modeled Shipments Historical Shipments19901995200020052010201520202025203020352040

Figure 9.3.3 Standard-Size Refrigerator-Fre ezers: Historical and Base Case Shipments Forecast by Market Segment

9-1425.0 PC: 5, 5A

0.0 5.0 10.0 15.0 20.0Millions PC: 4, 7 PC: 1, 1A, 3, 3A, 6 Historical Shipments Total Shipments19901995200020052010201520202025203020352040

Figure 9.3.4 Standard-size Refrigerator-Fre ezers: Base Case Shipments Forecast by Product Class Group

9.3.2 Standard-size Freezers
DOE considered four product classes of st andard-size freezers in its analysis. DOE’s shipments model for these classes uses the aggregate shipments for all four product classes as the basis for its forecasts. In other words, DOE did not develop a separate shipments model for each freezer product class. Instead, DOE used variou s data and assumptions to disaggregate total shipments into product classes.

9.3.2.1 Historical Shipments
DOE used data on historical shipments (i .e., domestic shipments and imports) from Appliance magazine’s Statistical Review, 10 AHAM Factbook, 5 and an AHAM data submittal 7 to populate and calibrate its shipments model. DOE built up a total stock of freezers by integrating historical shipments. Over time, some of the units are retired and removed from the stock, triggering the shipment of new units to repl ace them. Because of the relationship between retirements and total stock, there is a strong correlation between past and future shipments, independent of conservation standa rds. Table 9.3.6 summarizes the historical shipments data for standard-size freezers.

9-15Table 9.3.6 Standard-size Freezers: Historical Shipments

Year Shipments millions Year Shipments millions
1983 1.320 1996 1.548
1984 1.261 1997 1.490
1985 1.222 1998 1.628
1986 1.209 1999 1.988
1987 1.242 2000 1.963
1988 1.316 2001 2.215
1989 1.172 2002 2.535
1990 1.241 2003 2.523
1991 1.324 2004 2.516
1992 1.525 2005 2.214
1993 1.438 2006 2.148
1994 1.546 2007 1.992
1995 1.558
Sources: 2005–2007: AHAM Data Submittal, 2009; 1997–2004: AHAM Fact Book 2005; 1983–1996: Appliance Magazine, “Statistical Review,” various issues

9.3.2.2 Markets and Model Calibration
The shipments market for standard-size free zers is primarily comprised of units for new construction and replacement units for products th at have been retired. DOE’s shipments model also assumes that some existing households th at do not currently own the appliance (i.e. EHA ) will enter the market as new owners. Total shipments are represented by the following equation:

Ship ( j ) = Rpl ( j ) + NH ( j ) + EHA ( j ) SF SF SF SF

Where:

Ship SF (j) = total shipments of sta ndard-size freezers in year j
, Rpl SF (j) = replacement shipments in year j
, NH SF (j) = shipments to new households in year j , and EHA SF (j) = shipments to existing households without the appliance in year j

. The following sections discuss all three of these markets in further detail.

New Construction . To forecast the shipments of standard-size freezers for new construction for any given year, DOE multiplied the forecasted housing starts by the forecasted saturation of standard-size freezers for new housi ng. DOE used saturation in new housing from the 1993, 1997, 2001, and 2005 RECS surveys. 8 DOE determined the saturations in new homes by using a sample of RECS household records whos e home is less than 5 years old, with freezers

9-16of age less than 5 years. DOE froze the saturati on at the estimated leve l corresponding to year 2007 (0.119 per new home). Table 9.3.7 presents the freezer saturation data in new housing units.

Table 9.3.7 Saturation of Freezers in New Housing

Year New Housing Units that Contain New Freezers % 1993 7.78 1994 1995 1996 1997 11.31 1998 1999 2000 2001 11.22 2002 2003 2004 2005 11.67 Source: Source : EIA, Residential Energy Consumption Survey, for the years listed.

Replacements . DOE determined shipments to the replacement market using an accounting method that tracks the to tal stock of units by vintage. DOE estimated the stock of standard-size freezers by vintage by integrating hist orical shipments. Over time, some units are retired and removed from the st ock, triggering the shipment of a replacement unit. A certain percentage (depending on the age) of units wi ll fail each year and need to be replaced. To determine when a unit fails, DOE used a su rvival function based on a product lifetime distribution that had an averag e value of 22.7 years. Chapter 8 presents a more thorough discussion of the lifetimes of standard-size freezers. Figure 9.3.5 shows the survival and retirement functions that DOE used to estimate shipments of replacement freezers.

9-17100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% 0 5 10 15 20 25 30 35 40 45 50 % Surviving % Retiring

Age Figure 9.3.5 Standard-size Freezers: Survival and Retirement Functions

Model Calibration—New Owners. To calibrate estimated shipments with the historical data, DOE introduced a market segment that cons ists of households that currently do not own a freezer. DOE estimated shipments to this market segment as residual historical shipments after shipments for new housing and replacements were subtracted from total shipments. DOE then used a 3-year moving average to estimate the percent of households who enter the market as new owners for 2008-2043. The following e quation illustrates the calculations.

EHA SF ( j ) = (1

− StockSat j ) × HStock j × EHAfrac j

Where:

EHA SF (j) = number of freezers shipped to existing households without the appliance in year j
, StockSat j = stock saturation of freezers in year j
, HStock j = housing stock in year j , and EHAfrac j = fraction of housing units without th e appliance and obtain a freezer in year j

.

9.3.2.3 Disaggregation into Separate Freezer Product Classes
To disaggregate the total shipments of standa rd-size freezers into shipments to each of the freezer product classes, DOE used the market share information submitted by AHAM. 7 The data submitted by AHAM provided an aggregated market share of 50.6 percent for product

9-18classes 8 and 10. DOE then used the shipments data from the 2005 AHAM Fact Book 5 and Appliance magazine 2 to disaggregate market shares fo r product classes 8 and 10. The AHAM Fact Book indicates a 59.5 per cent market share for the combination of product classes 10 and 10A. Since the market share for 10A is near zero, DOE attributed the comb ined market share of product classes 8 and 10 from the AHAM data submittal (50.6 percent) entirely to product class 10 in order to be as close as possible to the AHAM Fact Book data. Table 9.3.8 presents the market share forecast used for disaggregating modeled shipments. DOE used these estimated market shares throughout the forecast period.

Table 9.3.8 Product Class Market Share of Standard-size Freezers
Year PC8 PC9 PC10 PC10A
2008 0.0% 49.4% 50.6% 0.0%

9.3.2.4 Base Case Shipments
Figure 9.3.6 shows the forecasted shipments of standard-size freezer in the base case (without new energy conservation st andards) and the historical sh ipments DOE used to calibrate the forecast. The figure also presents forecasted shipments disaggregated into its modeled market segments. Figure 9.3.7 presents shipment forecasts of standard-size freez ers in the base case, disaggregated into pr oduct class groups. a

a DOE grouped product classes 10 and 10A into one group.

9-191990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

1 99

0 1 99

5 2 00

0 2 00

5 2 01

0 2 01

5 2 02

0 2 02

5 2 03

0 2 03

5 2 04

0Millions 4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0 EHA Shipments NC Shipments
Retirements Historical Shipments Modeled Shipments

Figure 9.3.6 Standard-size Freezers: Historical and Base Case Shipments Forecast by Market Segment

0. 0 0. 5 1. 0 1. 5 2. 0 2. 5 3. 0 3. 5 4. 0 4. 5Millions PC: 10, 10A
PC9
PC8 Historical Shipments Modeled Shipments

Figure 9.3.7 Standard-size Freezers: Base Case Shipments Forecast by Product Class

9-209.3.3 Compact Refrigerators and Refrigerator-Freezers
DOE analyzed five product classes of comp act refrigerators for this rulemaking. DOE’s shipments model for these products utilizes aggregate shipments for all five product classes as the basis for its forecasts. In other words, DOE did not develop a separate shipments model for each product class. Instead, DOE used various data and assumptions to disaggregate total shipments into product classes.

9.3.3.1 Historical Shipments
DOE used data on historical shipments (domes tic shipments and imports) to calibrate its shipments model. It develope d historical shipments data based on data submitted by AHAM 7 and various issues of Appliance magazine. 2,9 DOE built up a total stock of compact refrigerators by integrating historical shipments. Over time, some units are retired and removed from the stock, triggering the shipment of new units to repl ace them. Because of the relationship between retirements and total stock, there is a strong corre lation between past and fu ture shipments. Table 9.3.9 summarizes the historical shipme nts data for compact refrigerators.

Table 9.3.9 Compact Refrigerators: Historical Shipments
Year Shipments (millions) Year Shipments (millions)
1983 0.568 1996 1.070
1984 0.602 1997 1.110
1985 0.783 1998 1.186
1986 0.810 1999 1.498
1987 0.830 2000 1.530
1988 1.000 2001 1.355
1989 0.925 2002 2.038
1990 0.933 2003 2.844
1991 0.925 2004 2.567
1992 0.950 2005 2.792
1993 1.030 2006 1.641
1994 0.950 2007 2.194
1995 1.032
Sources: 2005–2007: estimates based on AHAM Data Submittal, 2009;and 1983–2004: Appliance Magazine, “Statistical Review,” various issues.

9.3.3.2 Markets and Model Calibration
The market for compact refrigerators is primar ily comprised of units that replace products that have been retired from service, and units installed in new housi ng, new lodging in the commercial sector (such as hotel rooms and dormitories), and in other new construction in the

9-21commercial sector. Total compact refrigerator shipments are represented by the following equation:

Ship ( j ) = Rpl ( j ) + NH ( j ) + NLodg ( j ) + NOthComm ( j ) CRRF CRRF CRRF CRRF CRRF

Where:

Ship CRRF (j) = total shipments of compact refrigerators in year j
, Rpl CRRF (j) = replacement shipments in year j
, NH CRRF (j) = shipments to new households in year j
, NLodg CRRF (j) = shipments to new lodging units in year j , and NOthComm CRRF (j) = shipments to other new comm ercial establishments in year j

. The following sections discuss these markets.

New Housing. To estimate shipments to new housing in each year, DOE multiplied forecasted housing starts by the estimated satu ration of compact refrigerators in new housing units. DOE forecasted market saturation for this segment using the saturation of compact refrigerators in newly built homes in RECS 2001 and 2005 (2.7 percent and 3.2 percent, respectively). For years beyond 2005, DOE mainta ined the growth in new housing saturation measured between 2001 and 2005.

New Lodging and Other Commercial New Construction . To estimate shipments to new commercial establishments, DOE used forecasts of new c onstruction in lodging and other commercial establishments coupled with market saturation data (in units per building). DOE obtained the saturation data from th e American Lodging Association (ALA) 11 and the EIA’s Commercial Building Energy C onsumption Survey (CBECS). 12 For lodging, DOE used saturations from ALA for the ye ars 1998, 2003, and 2008. For future years, DOE maintained the growth in saturation rates seen between 2003 and 2008. For other commercial applications, DOE used saturations from CBECS for the years 1999 and 2003. DOE maintained the growth in saturation rates seen between 1999 and 2003 for subsequent years.

Figure 9.3.8 presents the forecast for satura tion of compact refrigerators for the three market segments for new construction.

9-22120%

100%

80%

60%

40%

20%

0% Lodging Other Commercial Residential

2003 - 72%

2003 - 48%

2005 - 3.2%199019931996199920022005200820112014201720202023202620292032203520382041

Figure 9.3.8 Forecast of Saturation of Compact Refrigerators in New Construction Market Segments

Replacements. DOE determined shipments to the replacement market using an accounting method that tracks the to tal stock of units by vintage. DOE integrated historical shipments to estimate each year’s stock of compact refrigerators by vintage. Over time, some of the units are retired and removed from the stock, triggering the shipment of a new unit. Because of the relationship between retire ments and total stock, there is a strong correlation between past and future shipments, independe nt of conservation standards.

A certain percentage (depending on the age) of units will fail each year and need to be replaced. To determine when a compact refrigerat or fails, DOE used a product survival function based on a lifetime distribution with an average value of 5.6 years. Chapter 8 presents a more thorough discussion of product lifet imes for compact refrigerator s. Figure 9.3.9 shows the survival and retirement functions that DOE used to estimate replacement shipments.

9-23100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% % Surviving % Retiring

0 5 10 15 20 25 30 Age Figure 9.3.9 Compact Refrigerators: Survival and Retirement Functions

Model Calibration. To calibrate estimated shipments with the historical data, DOE utilized compact refrigerator stock data for 2003. DOE obtained commercial stock data from CBECS 2003 and estimated the 2003 residential stock from various years of RECS. Based on these sources, DOE estimated that in 2003 the stock was split 30 percent, 18 percent, and 52 percent between residential, lodging, a nd other commercial s ectors, respectively

.

9.3.3.3 Disaggregation into Separate Compact Refrigerator Product Classes
DOE based its product class market shares for compact refrigerators on data submitted by AHAM 7 and CEC data 13 on available compact refrigerator models. Table 9.3.10 presents the market share forecast used for disaggregating total modeled shipments. DOE used these estimated market shares th roughout the forecast period.

Table 9.3.10 Product Class Market Shares of Compact Refrigerators
Year PC11 PC12 PC13 PC14 PC15
2008 84.40% 5.92% 9.03% 0.32% 0.32%

9.3.3.4 Base Case Shipments
Figures 9.3.10 shows the forecasted shipments of compact refrigerator s in the base case (without new energy conservation st andards) along with the histor ical shipments DOE used to

9-2419 9 0 1993 1 996 19 9 9 2 002 2 005 20 0 8 2 011 20 1 4 20 1 7 2 020 20 2 3 2026 2 029 20 3 2 2 035 2 038 20 4 1

1 9 90 1 9 93 1 9 96 1 9 99 2 0 02 2 0 05 2 0 08 2 0 11 2 0 14 2 0 1 7 2 0 2 0 2 0 2 3 2 0 2 6 2 0 2 9 2 0 3 2 2 0 3 5 20 3 8 20 4 1 calibrate the forecast. The figure also presents forecasted shipments disaggregated into modeled market segments. Figure 9.3.11 presents th e base case shipments forecast for compact refrigerators disaggregated by pr oduct class. (Product classes 14 and 15 are not visible in the figure because they account for a very small share of shipments.)

7.00
New other commercial

0.00 1.00 2.00 3.00 4.00 5.00 6.00Millions New Lodging New housing Replacements Historical Shipments Modeled Shipments

Figure 9.3.10 Compact Refrigerators: Base Case Shipments Forecast by Market Segment

7.0
PC15

0.0 1.0 2.0 3.0 4.0 5.0 6.0Millions PC14 PC: 13, 13A PC12 PC: 11, 11A Historical Shipments Total Shipments

Figure 9.3.11 Compact Refrigerators: Base Case Shipments Forecast by Product Class

9-259.3.4 Compact Freezers
DOE considered three product classes of co mpact freezers for this analysis. DOE based its forecast shipments compact freezers on aggregat e shipments, that is, shipments for all three product classes. In other words, DOE did not develop a separate shipments model for each product class. Instead, DOE used various data an d assumptions to disaggregate total estimated shipments into product classes.

9.3.4.1 Historical Shipments
DOE used data on historical shipments ( domestic shipments and imports) to populate and calibrate its shipments model for compact freezers. It obtained historical shipments data from data submitted by AHAM 7 and various issues of Appliance magazine 2,9 ( Table 9.3.11). DOE built up a total stock of compact freezers by integr ating historical shipments. Over time, some units are retired and removed from the stock, tr iggering the shipment of new units to replace them. Because of the relationshi p between retirements and tota l stock, there is a strong correlation between past and future shipments.

Table 9.3.11 Compact Freezers: Historical Shipments
Year Shipments (millions) Year Shipments (millions)
1983 0.237 1996 0.335
1984 0.242 1997 0.378
1985 0.234 1998 0.393
1986 0.247 1999 0.434
1987 0.255 2000 0.474
1988 0.287 2001 0.490
1989 0.325 2002 0.520
1990 0.337 2003 0.512
1991 0.333 2004 0.696
1992 0.319 2005 0.591
1993 0.326 2006 0.524
1994 0.304 2007 0.500
1995 0.324
Sources: 2005–2007: estimates based on AHAM Data Submittal, 2009; and 1983–2004: Appliance Magazine, “Statistical Review,” various issues.

9.3.4.2 Markets and Model Calibration
The market for compact freezers is primarily comprised of replacement units for products that have been retired from service and units pur chased by new owners (not new construction) in both residential and commercial sectors. Total compact freezer shipments are represented by the following equation:

9-26Ship ( j ) = Rpl ( j ) + NR ( j ) + NC ( j ) CF CF CF CF

Where:

Ship CF (j) = total shipments of compact freezers in year j
, Rpl CF (j) = replacement shipments in year j
, NR CF (j) = shipments to new residential owners in year j , and NC CF (j) = shipments to new commercial owners in year j

. The following sections discuss th ese markets in further detail.
Replacements . DOE determined shipments to the replacement market using an accounting method that tracks the to tal stock of units by vintage. DOE integrated historical shipments to estimate each year’s stock of comp act freezers by vintage. Over time, some units are retired and removed from the stock, triggering the shipment of a replacement unit. A certain percentage (depending on the age) of units will fail each year and n eed to be replaced. To determine when a compact freezer fails, DOE used a product survival function based on a lifetime distribution with an average value of 7.46 years. Chapter 8 provides a more thorough discussion of product lifetimes for compact free zers. Figure 9.3.12 shows the survival and retirement functions that DOE used to estimate shipments of replacement units.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % Surviving
% Retiring

0 5 10 15 20 25 30 Age Figure 9.3.12 Compact Freezers: Survival and Retirement Functions

New Owner. In the absence of data on saturati on of compact freezers in homes or commercial applications, DOE estimated historical shipments to new owners based on the

9-271990 1993 1996 1999 2 0 02 2005 2008 2011 2014 2017 2020 2023 20 2 6 2029 2032 2035 2038 2 0 4 1 difference between total shipments of compact freezers and estimated replacement shipments. DOE forecast new owner shipments using a 3-ye ar moving average method. DOE assumed an even split between residential and commercial new owners for this segment.

9.3.4.3 Disaggregation into Separate Compact Freezer Product Classes
DOE used California Energy Commission (CEC ) data on available freezer models to estimate market shares of product classes 16, 17, and 18. 14 Table 9.3.11 presents the market share forecast used in this analysis for disagg regating total modeled shipments. DOE used the 2008 market share estimates thr oughout the forecast period.

Table 9.3.12 Product Class Market Shares of Compact Freezers
Year PC16 PC17 PC18
2008 50.0% 30.0% 20.0%

9.3.4.4 Base Case Shipments
Figure 9.3.13 shows the forecasted compact freezer shipments in the base case (without new energy efficiency standards), along with the historical shipme nts DOE used to calibrate the forecast. The figure presents compact freezer ship ments disaggregated into the modeled market segments. Figure 9.3.14 presents forecasted base case compact freezer shipments disaggregated into the product classes.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Millions New Owner Replacements Historical Shipments Modeled Shipments

Figure 9.3.13 Compact Freezers: Historical and Base Case Shipments Forecast by Market Segment

9-2819 90 1993 1996 1999 2002 20 05 2008 2011 2014 2017 20 20 2023 2026 2029 2032 20 35 2038 2041 1.2 PC18
PC17
1.0 PC16

Figure 9.3.14 Compact Freezers: Base Case Shipments Forecast Disaggregated by Product Class

9.4 PURCHASE PRICE, OPERATING COST, AND HOUSEHOLD INCOME IMPACTS

DOE conducted a literature revi ew and an analysis of applia nce price and efficiency data to estimate the combined effects on product ship ments from increases in product purchase price, decreases in product operating co sts, and changes to household income. Appendix 9-A provides a detailed explanation of the methodology DOE used to quantify the impacts from these variables.

In the literature, DOE found onl y a few studies of appliance markets that are relevant to this rulemaking analysis. DOE identified no studies that use time-series da ta of product price and shipments data after 1980. The information that can be summarized from the literature suggests that the demand for appliances is price-inelastic. Other information in the literature suggests that appliances are a normal good, such that risi ng incomes increase the demand for appliances. Finally, the literature sugges ts that consumers use relativ ely high implicit discount rates b when comparing appliance prices and appliance operating costs.

b A high implicit discount rate with regard to operating costs means that consumers do not put much economic value on the operating cost savings realized from more-efficient appliances. In ot her words, consumers are much more concerned with higher purchase prices. 0.0 0.2 0.4 0.6 0.8Millions Historical Shipments Total Shipments

9-29DOE found insufficient data on product purchase price and op erating cost to perform a thorough analysis of dynamic cha nges in the appliance market. Ra ther, it used purchase price and efficiency data specific to residential refriger ators, clothes washers, and dishwashers over the period 1980–2002 to evaluate broad market trends and conduct simple regression analyses. These data indicate that there ha s been a rise in appliance ship ments and a decline in appliance purchase price and operating costs over the time pe riod. Household income has also risen during this time. To simplify the analysis, DOE combin ed the available economic information into one variable, termed the relative price , and used this variable in an an alysis of market trends, as well as to conduct a regression analysis. The relative price is defined with the following expression:

TP PP + PVOC RP = = Income Income

Where:

RP = relative price, TP = total price, Income = household income, PP = appliance purchase price, and PVOC = present value of operating cost.

In this equation, DOE used an implicit di scount rate of 37 percent to determine the present value of operating costs.

DOE’s analysis of market trends suggests that the relative price elasticity of demand for the three appliances is relatively inelastic (i.e., under 1.0) . DOE’s regression analysis suggests that the relative price elasticity of demand, averaged over the three appliances, is -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, a nd household income), the impact from any single effect is mitigated by changes from the other two effects. The relative price elasticity of -0.34 is consistent with estimates in the literature. Neve rtheless, 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, operati ng costs, and household income, explain most of the trend in applian ces per household in the United States since 1980. Changes in appliance quality a nd 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 reason able assessment of the impact that purchase price, ope rating cost, and household inco me have on product shipments.

Because DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time period, it need ed to consider how the relative price elasticity is affected once a new standard takes effect. DOE considered the relative price elasticity provided by the preceeding

9-30analysis to be a short-run value. It was unable to identify sources specif ic to household durable goods, such as appliances, to indicate how shor t-run and long-run price elasticities differ. Therefore, to estimate how the relative price elasticity changes over time, DOE relied on a study pertaining to automobiles. 15 This study shows that the autom obile price elasticity of demand changes in the years following a purchase price ch ange. 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 9.4.1 shows the relative change in the price elasticity of demand fo r 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 Table 9.4.1, DOE performed a linear interpolation to obtain the relative price elasticity.

Table 9.4.1 Change in Relative Price Elasticity following a Purchase Price Change Years Following Price Change 1 2 3 5 10 20 Relative Change in Elasticity to 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

Based on the following equation, DOE estimated standards case shipments by incorporating the impact of the relative price into the base case shipments forecast. Note that in this equation, the relative price and the relative price elasticity are functions of the year because both change with time.

_ Ship ( j ) = 

# ( Rpl ( j ) + NI ( j ) + M ( j ) 

# ) × 

# ( 1 e ( j ) × Δ RP( j ) 

# ) STD _ p BASE _ p BASE _ p BASE _ p RP

Where:

Ship STD_p (j) = total shipments under the standards case of product p in year j
, Rpl BASE_p (j) = units of product p under the base case retire d and replaced in year j
, NI BASE_p (j) = number of new construction instal lations under the base case of product p in year j
, M BASE_p (j) = units installed in market M under the base case of product p in year j ( M represents purchases for existing homes fo r standard-size freezers, and purchase of an additional refrigerator for standard-size refrigerators),

e RP (j)= relative price elasticity in year j (equals -0.34 for year 1), and Δ RP(j)= change in relative price due to a standard level in year j

.

9.5 AFFECTED STOCK
In addition to the forecast of product sh ipments under both the base case and the standards case, the affected stoc k is a key output of DOE’s shipme nts models. The affected stock

9-31(stock that is affected by a standa rds level) consists of those in-s ervice units that are purchased in or after the year the standard has taken eff ect, as described by the following equation:

_ j Std _ yr Aff Stock ( j ) = Ship ( j ) + 

# ∑ Stock ( age ) p p p age =1 Where:

Aff Stock p (j) = affected stock of units of product p of all vintages that are in service in year j
, Ship p (j) = shipments of product p in year j
, Stock p (j) = stock of units of product p of all vintages that are in service in year j
, age = age of the units (years), and Std_yr = effective date of the standard.

For the NES and NPV results presented in Chapter 10, DOE assumed that new energy efficiency standards will become effective in the year 2014. Thus, all appliances purchased starting on the first day of the year 2014 are affected by the standard level.

9.6 RESULTS
This section presents the shipments forecasts for the various standard levels that DOE considered for each of the four refrigeration product types.

Figure 9.6.1 shows the standard-size refriger ator-freezer shipment forecasts for the base case and for two standard levels for which an im pact is evident. The differences between the base case and standard level shipments forecas ts represent the annual shipments reductions attributable to the standard levels.

9-3225.0

5.0 10.0 15.0 20.0Millions

Base

CSL6

CSL8

0.0

1990 2000 2010 2020 2030 2040

Figure 9.6.1 Standard-Size Refrigerator-Freezers: Base Case and Standards Case Shipments Forecasts

Figure 9.6.2 shows the standard-size freezer sh ipment forecasts for the base case and for several standard levels for which an impact is evident. The differences between the base case and standard level shipments forecasts represent the annual shipments reductions attributable to the standard levels.

9-331990 1995 20 00 2005 2010 20 15 2020 2025 20 30 2035 2040 4.5

4.0

3.5

3.0

2.5ons

2.0Milli

1.5 CSL5

1.0 CSL7
CSL8
0.5 Base

0.0

Figure 9.6.2 Standard-size Freezers: Base Case and Standards Case Shipments Forecasts

Figure 9.6.3 shows the compact refrigerator shipment forecasts for the base case and for those standard levels for which an impact is evident. The differences between the base case and standard level shipments forecasts represent the annual shipments reductions attributable to the standard levels.

9-341 990 19 9 3 1 996 19 9 9 2 002 20 0 5 2 008 20 1 1 2 014 20 1 7 2 020 20 2 3 2 026 20 2 9 2 032 20 3 5 2 038 20 4

1 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0Miliions CSL6 CSL7 CSL8 Base

Figure 9.6.3 Compact Refrigerators: Base Case and Standards Case Shipments Forecasts

Figure 9.6.4 shows the compact freezer ship ment forecasts for the base case and for selected standard levels for which an impact is evident. The differences between the base case and standard level shipments forecasts represent the annual shipme nts reductions attributable to the standard levels.

9-35199 0 1993 1996 199 9 2002 2005 200 8 2011 2014 201 7 2020 2023 202 6 2029 2032 203 5 2038 2 041 0.

0.

0.

0.

0.

0.

0.

0.

0.

0. 90

80

70

60

50

40

30

20

10

00 Base CS L3 CS L5 CS L8Millions

Figure 9.6.4 Compact Freezers: Base Case and Standards Case Shipments Forecasts

9-36REFERENCES

1 U.S. Department of Energy- Energy Information Administration. An Updated Annual Energy Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and Reinvestment Act and Recent Changes in the Economic Outlook , April, 2009. Washington, DC. SR/OIAF/2009-03.

< http://www.eia.doe.gov/oiaf/servicerpt/stimulus/pdf/sroiaf(2009)03.pdf >

2 Statistical Review. Appliance Magazine , April, 1993.

3 Statistical Review. Appliance Magazine , April, 1990.

4 Statistical Review. Appliance Magazine , April, 1982.

5 Association of Home Ap pliance Manufacturers. AHAM 2005 Fact Book , 2005. Washington, DC.

6 Association of Home Ap pliance Manufacturers. AHAM 2003 Fact Book , 2003. Washington, DC.

7 Association of Home Appliance Manufacturers. Aggregated Refrigerator

‐ Freezer Shipments and Efficiency Data for DOE Refrigerator

‐ Freezer Energy Efficiency Standards Rulemaking , January 16, 2009

. Memo to Detlef Westphalen, Navigant Consulting, Inc.

8 U.S. Department of Energy–Ene rgy Information Administration. Annual Energy Outlook 2009 with Projections to 2030 . March 2009. U.S. DOE–EIA: Washington, D.C. DOE/EIA0383(2009). < http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2009).pdf >

9 U.S. Department of Energy–Ene rgy Information Administration. Residential Energy Consumption Survey. Washington, D.C. Surveys for 1990, 1993, 1997, 2001, and 2005.

< www.eia.doe.gov/emeu/recs/contents.html >

10 Statistical Review. Appliance Magazine , April, 1997.

11 American Hotel & Lodging Association. 2008 Lodging Survey: Lodging Services, Facilities, and Trends , 2008. Washington, DC. p. 52, Percenta ge of Hotels with Refrigerators in Room.

12 U.S. Department of Energy–Ene rgy Information Administration. Commercial Building Energy Consumption Survey. Washington, D.C. Surveys for 1999 and 2003. <http://www.eia.doe.gov/emeu/cbecs/>

13 California Energy Commission. Ap pliances Database—Refrigeration.

< www.energy.ca.gov/appliances/database/excel_based_files/Refrigeration/ > (Last accessed April 25, 2009.)

9-3714 California Energy Commission. Ap pliances Database—Refrigeration.

< www.energy.ca.gov/appliances/database/excel_based_files/Refrigeration/ > (Last accessed April 25, 2009.)

15 S. Hymans. Consumer Durable Sp ending: Explanation and Prediction, Brookings Papers on Economic Activity , 1971. Vol. 1971, No. 1, pp. 234-239.

9-38APPENDIX 9-A. RELATIVE PRIC E ELASTICITY OF DEMAND FOR APPLIANCES

TABLE OF CONTENTS

9-A.1 INTRODUCTION .................................................................................................9-A-1 9-A.2 LITERATURE REVIEW ......................................................................................9-A-1 9-A.2.1 Price .................................................................................................................. .....9-A-1 9-A.2.2 Income................................................................................................................. ...9-A-1 9-A.2.3 Appliance Efficiency and Discount Rates .............................................................9-A-2 9-A.3 VARIABLES DESCRIBING THE MARKET FOR REFRIGERATORS, CLOTHES WASHERS, AND DISHWASHERS .................................................9-A-2 9-A.3.4 Physical Household/Ap pliance Variables..............................................................9-A-3 9-A.3.5 Economic variables................................................................................................9-A-3 9-A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE SHIPMENTS .........................................................................................................9-A-4 9-A.4.1 Broad Trends..........................................................................................................9 -A-5 9-A.4.2 Model Specification...............................................................................................9-A-7 9-A.4.3 Model Discussion...................................................................................................9-A- 8 9-A.4.4 Analysis Results.....................................................................................................9- A-8 9A.4.4.1 Individual Appliance Model ................................................................9-A-8 9A.4.4.2 Combined Appliance Model................................................................9-A-9 9-A.4.5 Additional Regression Speci fications and Results ..............................................9-A-10 9A.4.5.1 Lower Consumer Discount Rate........................................................9-A-10 9A.4.5.2 Disaggregated Variables ....................................................................9-A-10 9-A.5 LONG RUN IMPACTS.......................................................................................9-A-11 9-A.6 SUMMARY.........................................................................................................9-A-12 9-A.7 DATA USED IN THE ANALYSIS ....................................................................9-A-13

LIST OF TABLES

Table 9-A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile and Appliance Sales...............................................................9-A-2 Table 9-A.3.1 Physical Househol d/Appliance Variables..................................................9-A-3 Table 9-A.3.2 Economic Variables...................................................................................9-A-4 Table 9-A.4.1 Simple Estimate of Tota l Price Elasticity of Demand ...............................9-A-6 Table 9-A.4.2 Tabular Estimate of Relative Price Elasticity of Appliance Demand........9-A-7 Table 9-A.4.3 Individual App liance Model Results .........................................................9-A-9 Table 9-A.4.4 Combined App liance Model Result...........................................................9-A-9 Table 9-A.4.5 Combined and Individual Results, 20 percent discount rate....................9-A-10 Table 9-A.4.6 Disaggregated Regression Results, 37 percent discount rate...................9-A-11 Table 9-A.5.1 Change in Price Elasticity of Demand for Automobiles following a Purchase Price Change.............................................................................9-A-12

9A-iTable 9-A.5.2 Change in Relative Price Elasticity for Home Appliances following a Purchase Price Change.............................................................................9-A-12

LIST OF FIGURE

Figure 9-A.3.1 Trends in Appliance Shipment , Housing Starts and Replacements...........9-A-4 Figure 9-A.4.1 Residual Shipments and Appliance Price ..................................................9-A-6

9A-iiAPPENDIX 9-A. RELATIVE PRICE ELASTICITY OF DEMAND FOR APPLIANCES

9-A.1 INTRODUCTION
This appendix summarizes DOE’s study of the price elasticity of demand for home appliances, including refrigerators, clothes washers and dishwashers. DOE chose this particular set of appliances because of the availability of da ta to determine a price elasticity. This appendix begins with a review of th e existing economics literature de scribing the impact of economic variables on the sale of durable goods in section 9A.2. In sec tion 9A.3, the market for home appliances and changes in it over the past 20 years is described. In section 9A.4, DOE summarizes the results of its regression analysis a nd presents estimates of the price elasticity of demand for the three appliances. In section 9A.5, DOE presents development of an ‘effective’ purchase price elasticity. DOE’s interpretation of its results is presented in section 9A.6. Finally, section 9A.7 describes the data used in DOE’s analysis.

9-A.2 LITERATURE REVIEW
There are relatively few studies measuring the impact of price, income and efficiency on the sale of household appliances. In this section DOE provides a short review of this literature which suggests the likely impor tance of these variables.

9-A.2.1 Price
The goal of many of the studies covered in this review is to measure the impact of price on sales in a dynamic market. One study of the au tomobile market prior to 1970 finds the price elasticity of demand to decline ove r time. The author explains this as the result of buyers delaying purchases after a pric e increase but eventually making the purchase (Table 9A.2.1). 1 A contrasting study of household white goods also prior to 1970, finds the elasticity of demand to increase over time as more price- conscious buyers enter the market. 2 A recent analysis of refrigerator market survey data finds that co nsumer purchase probability decreases with survey asking price. 3 Estimates of the price elasticity of demand for different brands of the same product tend to vary. A review of 41 studies of the impact of price on market share found the average price elasticity to be -1.75. 4 The average estimate of price elasticity of demand reported in these studies is -0.33 in th e appliance market and -0.47 in the combined automobile and appliance markets.

9-A.2.2 Income
Higher income households are more likely to own household appliances. 5 The impact of income on appliance shipments is explored in two econometric studies of the automobile and appliance markets. 1,2 The average income elasticity of demand is 0.50 in the appliance study cited in the literature revi ew, much larger in the au tomobile study (Table 9.A.2.1).

9-A-19-A.2.3 Appliance Efficiency and Discount Rates
Many studies estimate the impact of appliance efficiency on consumer appliance choice. Typically, this impact is summari zed by the implicit discount rate, i. e., the rate consumers use to compare future appliance operating cost savings against an appliance pur chase price premium. One early and much cited study c oncludes that consumers use a 20 percent implicit discount rate when purchasing room air conditioners (Table 9A.2.1). 6 A survey of several studies of different appliances suggests that the c onsumer implicit discount rate has a broad range and averages about 37 percent. 7

Table 9-A.2.1 Estimates of the Impact of Price, Income and Efficiency on Automobile and Appliance Sales

Durable Good Price Elasticity Income Elasticity Brand Price Elasticity Implicit Discount

Rate Model Data Years Time Period

Automobiles 1 -1.07 3.08

- - Linear Regression, stock adjustment - Short run

Automobiles 1 -0.36 1.02

- - Linear Regression, stock adjustment - Long run
Clothes Dryers 2 -0.14 0.26

- - Cobb-Douglas, diffusion 1947-1961 Mixed Room Air Conditioners 2 -0.37 8 0.45

- - Cobb-Douglas, diffusion 1946-1962 Mixed

Dishwashers 2 -0.42 0.79

- - Cobb-Douglas, diffusion 1947-1968 Mixed
Refrigerators 3 -0.37

- - 39% Logit probability, survey data 1997 Short run

Various 4

- - -1.76 9

- Multiplicative regression

- Mixed Room Air Conditioners 5 - - -1.72

- Non-linear diffusion 1949-1961 Short run
Clothes Dryers 5

- - -1.32

- Non-linear diffusion 1963-1970 Short run Room Air Conditioners 6 - - - 20% Qualitative choice, survey data - -
Household Appliances 7 - - - 37% 10 Assorted

- -
Sources: 1 S. Hymens. 1971; 2 P. Golder and G. Tellis, 1998; 3 D. Revelt and K. Train, 1997;
4 G. Tellis, 1988; 5 D. Jain and R. Rao; 6 J. Hausman; 7 K. Train, 1985. Notes: 8 Logit probability results are not directly comparab le to other elasticity estimates in this table.
9 Average brand price elasticity across 41 studies.
10 Averaged across several household appliance studies referenced in this work.

9-A.3 VARIABLES DESCRIBING THE M ARKET FOR REFRIGERATORS, CLOTHES WASHERS, AND DISHWASHERS

In this section DOE evaluates variables that appear to account for refrigerator, clothes washer and dishwasher shipments, including physical household/app liance variables, and economic variables.

9-A-29-A.3.4 Physical Household/Appliance Variables

Several variables influence the sale of refr igerators, clothes washers and dishwashers. The most important for explaining appliance sales trends are the annual number of new households formed (housing starts) and the numbe r of appliances reaching the end of their operating life (replacements). Housing starts influence sales because new homes are often provided with, or soon receive, new appliances , including dishwashers and refrigerators. Replacements are correlated with sales because ne w appliances are typically purchased when old ones wear out. In principle, if households ma intain a fixed number of appliances, shipments should equal housing starts plus appliance replacements.

9-A.3.5 Economic variables
Appliance price, appliance operating cost and household income are important economic variables affecting shipments. Low prices and costs encourag e household appliance purchases and a rise in income increases householder ability to purchase appliances. In principle, changes in economic variables should explain changes in the number of appliances per household.

During the 1980–2002 study period, annual ship ments grew 69 percent for clothes washers, 81 percent for refriger ators and 105 percent for dishwash ers (Table 9A.3.1). This rising shipments trend is explained in part by hous ing starts, which increased 6 percent and by appliance replacements, which rose between 49 percent and 90 percent, depending on the appliance, over the period (Table 9A.3.1). a For mature markets such as these, replacements exceed appliance sales associated with new housing construction.

Table 9-A.3.1 Physical Household/Appliance Variables Shipments 1 (millions) Housing Starts 2 (millions) Replacements 3 (millions) Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change Refrigerators 5.124 9.264 81% 1.723 1.822 6% 3.93 5.84 49% Clothes Washers 4.426 7.492 69% 1.723 1.822 6% 3.66 5.50 50% Dishwashers 2.738 5.605 105% 1.723 1.822 6% 1.99 3.79 90%

1 Shipments: Number of units sold. Sources: AHAM Fact Book and Appliance Magazine.

2 Housing Starts: Annual number of new homes constructed. Source: U.S. Census.

3 Replacements: Average of annual lagged shipments, with lag equal to expected appliance operating life, ± 5 years.
Nevertheless, it is apparent that appliance shipments increased somewhat more rapidly than housing starts and replacements. This is shown by comparing the beginning and end points of lines representing “starts plus replacements” (uppermost solid line in Figure 9A.3.1) and “shipments” (diamond linked line in Figure 9A.3.1). In 1980 the “shipment” line begins below the “starts plus replacements” line. In 2002, the “shipments” lin e ends above the “starts plus replacements” line. This more rapid increase in shipments, compared to housing starts plus replacements, suggests that the appliance per household ratio increased over the study period.

a Appliance replacements are determined from the expected operating life of refrigerators (19 years), clothes washers (14 years), and dishwashers (12 years) and from past shipments. Replacements are further discussed in section 9A.3. 9-A-3Refrigerator Shipments

0 1 2 3 4 5 6 7 8 9 10

1980 1984 1988 1992 1996 2000Millions New Housing

Retirements Shipments Clothes Washer Shipments

0 1 2 3 4 5 6 7 8

1980 1984 1988 1992 1996 2000Millions Dishwasher Shipments

0 1 2 3 4 5 6

1980 1984 1988 1992 1996 2000Millions

Figure 9-A.3.1 Trends in Appliance Shipment, Housing Starts and Replacements

Economic variables, including price, cost and income, may explain this increase in appliances per household. Over the period, applian ce prices decreased 40 percent to 50 percent, operating costs fell between 33 percent and 72 pe rcent, and median household income rose 16 percent (Table 9A.3.2).

Table 9-A.3.2 Economic Variables Price 1 (1999$) Operating Cost 2 (1999$) Household Income 3 (1999$) Appliance 1980 2002 Change 1980 2002 Change 1980 2002 Change Refrigerators 1208 726 -40% 333 94 -72% 37,447 43,381 16% Clothes Washers 779 392 -50% 262 175 -33% 37,447 43,381 16% Dishwashers 713 369 -48% 183 95 -48% 37,447 43,381 16%

1 Price: Shipment weighted retail sales price. Sources: AHAM Fact Book and Appliance Magazine.

2 Operating Cost: Annual electricity pri ce times electricity consumption. Source: AHAM Fact Book.

3 Income: Mean Household income. Source: U.S. Census.

9-A.4 REGRESSION ANALYSIS OF VARIABLES AFFECTING APPLIANCE SHIPMENTS
Little data is available for estimating the impact of economic variables on the demand for appliances. Industry operating co st data is incomplete—applian ce energy use data is available for only 12 years of the 1980-2002 study period. Industry price data is also incomplete— available for only 8 years of the study period for each of the appliances.

The lack of data suggests that regression anal ysis can at best evalua te broad data trends, utilizing relatively few explanator y variables. This section be gins by describing broad trends apparent in the economic and physical household data sets and then specifies a simple regression model to measure these trends, making assumpti ons to minimize the number of explanatory

9-A-4variables. Finally, results are presented of the regression analys is and the estimate of the price elasticity of demand for applian ces. In this section (specifi cally section 9A.4.5), DOE also presents the results of regression analysis performed with more co mplex models, and used to test assumptions made to specify the simple mode l. These results support the simple model specification, and estimates of the price elasti city of appliance demand measured with that model.

9-A.4.1 Broad Trends

In this section DOE reviews trends in the physical househol d and economic data sets and posit a simple approach for estimating the price el asticity of appliance demand. As noted above, the physical household variables (starts and ap pliance replacements), explain most of the variability in appliance shipments over the period. b DOE assumes the rest of the variability in shipments (referred to as “resid ual shipments”) is explained by economic variables, and present a tabular method for measuring price elasticities described below.

To illustrate this tabular approach, DOE de fines two new variables—residual shipments and total price. Residual shipments are defi ned as the difference between shipments and physical household demand (starts plus replacemen ts). Total price, repr esented by the following equation, is defined as applianc e price plus the present value of lifetime appliance operating
cost: c

TP = PP + PVOC

where:

TP = Total price, PP = Appliance purchase price, and PVOC = Present value of operating cost.

Over the study period, residual sh ipments increase 30 percent for refrigerators, 19 percent for clothes washers, and 23 percent fo r dishwashers in proportion to to tal shipments. At the same time, total prices decline 47 percent, 45 percent a nd 48 percent for refrigerators, clothes washers, and dishwashers, respectively. Assuming that to tal price explains the entire change in per household appliance usage, a rough estimate is calc ulated of the total pr ice elasticity of demand equal to -0.48 for refrigerators, -0.32 for cl othes washers and -0.37 for dishwashers (Table 9A.4.1).

b A log regression of the form: Shipments = a + b • Ho using Starts + c • Retirements, indicates that these two variables explain 89 percent of the variation in refrigerator shipments, 97 percent of the variation in clothes washer shipments, and 97 percent of the variation in dishwasher shipments.

c Present value operating cost is calculated assuming a 19 y ear operating life for refrigerat ors, 14 year operating life for clothes washers, and a 12 year oper ating life for dishwashers. A 37 percen t discount rate is used to sum annual operating costs into a present value operating cost. 9-A-5Table 9-A.4.1 Simple Estimate of Total Price Elasticity of Demand Residual Shipments (millions) Total Price (199$) Elasticity Appliance 1980 2002 Difference Change 1980 2002 Change Refrigerators -0.5 1.6 2.1 30% 1541 820 -61% -0.48 Clothes Washers -1.0 0.2 1.1 19% 1042 567 -59% -0.32 Dishwashers -1.0 -0.01 1.0 23% 896 464 -64% -0.37

The negative correlation betw een total price and residual shipments suggested by these negative price elasticities is illustrated in a gr aph of residual shipments on the y-axis and total price on the x-axis (Figure 9A.4.1).

2000 Refrigerators

1200 Clothes Washers

1000 Dishwashers

1600ents 1000ments 800ments

1200Shipm

800Shipual
600Shipual

800Residual

600Resid

400Resid

400 400 200

-2.0 -1.0 0.0 1.0 2.0 -1.5 -1.0 -0.5 0.0 0.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5
Total Price Total Price Total Price
Yellow points are observed price data; re d points are interpolated price data. Figure 9-A.4.1 Residual Shipments and Appliance Price

Household income rose during the study period, making it easier for households to purchase appliances. Assuming that a rise in income has a similar impact on shipments as a decline in price, the impact of income is in corporated by defining a third variable, termed relative price, calculated as total price divided by household income and represented by the following equation: d

RP = Income TP

where:

RP = Relative price, TP = Total price, and Income = Household income.

d Recall that the income elasticity of demand cited in the literature review is 0.50 and the price elasticity of demand cited in the review averages -0.35. This suggests that combining the effects of income and price will yield an elasticity less negative than price elasticity alone. 9-A-6The percent decline in relative price for the three appliances divided by the per cent decline in residual shipments sugge sts a rough estimate of relative price elasticity equal to -0.40 for refrigerators, -0.26 for clothes washers a nd -0.30 for dishwashers (Table 9A.4.2).

Table 9-A.4.2 Tabular Estimate of Relative Pric e Elasticity of Appliance Demand Residual Shipments (millions) Relative Price (1999$) Elasticity Appliance 1980 2002 Change 1980 2002 Change Refrigerators -0.532 1.597 30% 0.041 0.019 -74% -0.40 Clothes Washers -0.953 0.174 19% 0.028 0.013 -72% -0.26 Dishwashers -0.974 -0.005 23% 0.024 0.011 -76% -0.30

9-A.4.2 Model Specification
The limited price data suggests using a simple regression model to estimate the impact of economic variables on shipments, using few expl anatory variables. The following equation chosen for this analysis includes one physical ho usehold variable (starts plus replacements) and

one relative price variable (the sum of purchase pri ce plus operating cost, divided by income).

Ship = a + b × RP + c × 

## [ Starts + Rplc 

## ] Eq. 9A.1

where:

Ship = Quantity of appliance sold, RP = Relative price, Starts = Number of new homes, and Rplc = Number of appliances at the end of their operating life.

The natural logs are taken of all variables so that the estimated coefficients for each variable in the model may be interpreted as the percent change in shipments associated with the percent change in the variable. Thus, the coe fficient b in this model is interpreted as the relative price elasticity of demand fo r the three appliances.

The following combined regression equation is used to estimate an average price elasticity of demand across the th ree appliances, using pooled da ta in a single regression. A combined regression specification is justified, gi ven limited data availability and similarity in price and shipment behavior ac ross appliances (see Figure 9A.4.1). Thus, the model represented by the combined regression equation is consider ed the basic model in DOE’s analysis of appliance shipments.

Ship = a + b × RP + c × 

## [ Starts + Rplc 

## ] + d × CW + e × DW Eq. 9A.2

where:

CW = Quantity of clothes washers sold, and DW = Quantify of dishwashers sold.

9-A-79-A.4.3 Model Discussion
The most important assumption used to spec ify this model is that changes in economic variables over the study period—income, price, and operating co st—are responsible for all observed growth in residual appliance shipments. In other words, DOE assumes other possible explanations, such as changing consumer pr eferences and increases in the quality of appliances—had no impact. This assumption seems unlikely but without additi onal data, the impact of this assumption on th e price elasticity of demand cannot be measured. DOE effectively assumes that changes in consumer preferences and appliance characteristics, while affecting which specific models are purchased, have rela tively little impact on the total number of appliances purchased in a year.

Three additional assumptions used to sp ecify this model deserve comment. The relative price variable is specified in the model, assumi ng that (1) the correct implicit discount rate is used to combine appliance price and operating cost and that (2) rising income has the same impact on shipments as falling total price. The “starts + replacements” variable is specified, assuming (3) that starts and replacements have similar impacts on shipments.

To investigate the first assumption about di scount rates, DOE calculated “present value operating cost” using a 20 per cent implicit discount rate and performed a second regression analysis based on the models described in equations. 9A.1 and 9A.2. The results of this analysis, presented in section 9A.4.5, indica te that the elasticity of relative price is relatively insensitive to changes in the discount rate.

To investigate the second and third a ssumptions, DOE specified a regression model separating income from total price and replacem ents from starts, thus adding two additional explanatory variables to the basic mode l as shown in the following equation:

Ship = a + b × TP + c × Incone + d × Start + e × Rplc + f × CW + g × DW Eq. 9A.3

The results of the regression analysis of this model are also presented in section 9A.4.5. These results suggest that the elas ticity of total price (coefficient b) is relatively insensitive to changes in the treatment of income and “starts + replacements” in the model.

9-A.4.4 Analysis Results

9A.4.4.1 Individual Appliance Model
The individual appliance regression equations are specified as followed (as shown earlier as Eq. 9A.1):

Ship = a + b × RP + c × 

## [ Starts + Rplc 

## ]

In regression analysis of this model, the elasticity of relative price (b) is estimated to be

9-A-8-0.40 for refrigerators, -0.31 for clothes wash ers and -0.32 for dishwashers (Table 9A.4.3), averaging -0.35. These elasticities are similar to those reported in th e literature survey for appliances (Table 9A.2.1). They are remarkably similar to the price elasticity calculated using a tabular approach presented above (Table 9A.4.2).

The estimated coefficient associated with the “starts + replacements” variable is close to one. A coefficient equal to one for this variable would imply that shipme nts increase in direct proportion to an increase in “starts + replacemen ts”, holding economic variables constant. The high R squared values (above 95) and t statistics (above 5) in the results provide a measure of confidence in this analysis, despite the very small data set.

Table 9-A.4.3 Individual Appliance Model Results Refrigerator Clothes Washer Dishwasher Variable Coefficient t-stat Coe fficient t-stat Coefficient t-stat Intercept -1.51 -7.26 -1.47 -8.23 -2.08 -16.78 Relative Price -0.40 -6.60 -0.31 -5.69 -0.32 -7.03 Starts + Replacements 1.05 5.90 1.08 6.41 1.35 11.46 R 2 0.954 0.954 0.975 Observations 23 23 23

9A.4.4.2 Combined Appliance Model
The combined appliance regression equation is specified as follows (as shown earlier as Eq. 9A.2):

Ship = a + b × RP + c × 

## [ Starts + Rplc 

## ] + d × CW + e × DW

This regression analysis indicates that the model fits the existing shipments data well (high R squared) and that the va riables included in the model are statistically significant (Table 9A.4.4). The elasticity of relative price estimated with this model is -0.34, close to the average value estimated in the individual appliance models (-0.35). It is also similar to elasticity estimates reported in the literature survey and calculated using the ta bular approach above.

Table 9-A.4.4 Combined Appliance Model Result Variable Coefficient t-stat Intercept -1.60 -15.54 Relative Price -0.34 -10.74 Starts + Replacements 1.21 13.95 CW -0.20 -9.04 DW -0.32 -6.58 R 2 0.983 Observations 69

9-A-99-A.4.5 Additional Regression Sp ecifications and Results
As described above in section 9A.4.3, DOE used three assumptions to specify its appliance models. The first is that the implicit price variable in the ba sic regression model is specified using a 37 percent imp licit discount rate, to aggregat e appliance price and operating cost. The second states that the implicit price va riable is defined assuming that rising income has the same impact on shipments as falling total price. The third states that the “starts + replacements” variable is defined assuming th at housing starts have a similar impact on shipments as appliance replacements.

9A.4.5.1 Lower Consumer Discount Rate
To investigate the first assumption about di scount rates, DOE calculated “present value operating cost” using a 20 per cent implicit disc ount rate and performed a second regression analysis based on the models described in equati ons 9A.1 and 9A.2. The estimated coefficient associated with the relative price variable in these regressi ons is almost identical to the coefficients estimated for same variable reported above using a 37 percent implicit discount rate. The elasticity of relative price calculated using a 20 percen t discount rate is -0.33 in the combined regression and averages -0.35 for the th ree appliances (Table 9A.4.5). The elasticity of price calculated using a 37 percent discount rate is -0.34 in the combined regression and averages -0.35 for the three appliances. DOE concludes from this analysis that the elasticity of relative price is relatively insensitive to changes in the discount rate.

Table 9-A.4.5 Combined and Individual Results, 20 percent discount rate Three Appliances V ariable Intercept Total Price / Income Starts + Retirements CW DW Coefficient -1.53 -0.33 1.20 -0.18 -0.32 t-Stat -14.61 -10.69 13.65 -8.69 -6.57

R 2 Observations 0.982 69

V ariable Intercept Total Price / Income Starts + Retirements Refrigerator Coefficient -1.36 -0.38 1.04 t-Stat -6.26 -6.50 5.73 Clothes Washers Coefficient t-Stat -1.41 -7.49 -0.32 -5.29 1.06 5.83 Dishwasher Coefficient -2.04 -0.33 1.34 t-Stat -17.23 -7.30 11.64

R 2 Observations 0.953 23 0.950 23 0.977 23

9A.4.5.2 Disaggregated Variables
To investigate the second and third assu mptions, DOE constructed a regression model separating income from total price and replacem ents from starts, thus adding two additional explanatory variables to the basic mo del (as shown earlier as Eq. 9A.3).

9-A-10Ship = a + b × TP + c × Incone + d × Start + e × Rplc + f × CW + g × DW

The estimated coefficient associated with the total price variable in these regressions is almost identical to the coefficients estimated for the relative price variable reported above. The elasticity of total price in the above equation is -0.36 in the combined appliance regression and averages -0.35 for the three appliances (Table 9A.4.6). The elasticity of relative price based on the model described in equation 9A.2 is -0.34 in the combined regression (Table 9A.4.4) and averages -0.35 across the individual appliances (Table 9A.4.3). DOE concludes that the price elasticity calculated in this an alysis is relatively insensitive to the specification of household income and “starts + replacements” variables in the model.

Table 9-A.4.6 Disaggregated Regression Resu lts, 37 percent discount rate Three Appliances V ariable Coefficient t-Stat Intercept -2.92 -1.26 Income 0.58 2.92 Total Price -0.36 -7.06 Housing Starts 0.44 10.02 Retirements 0.62 8.12 CW -0.24 -9.25 DW -0.46 -7.68

R 2 0.985 Observations 69

Refrigerato r Clothes Washers Dishwasher V ariable Coefficient t-Stat Coefficient t-Stat Coefficient t-Stat Intercept -6.19 -2.24 -6.64 -1.63 1.00 0.23 Income 0.89 3.80 0.87 2.31 0.20 0.52 Total Price -0.35 -5.48 -0.27 -2.51 -0.43 -5.18 Housing Starts 0.41 7.38 0.25 3.29 0.62 8.24 Retirements 0.56 6.06 0.56 2.09 0.65 5.86

R 2 0.984 0.958 0.979 Observations 23 23 23

9-A.5 LONG RUN IMPACTS

As noted above in Table 9A.2.1 in section 9A.2, the literature re view provides price elasticities over short and long time periods, also referred to as short run and long run price elasticities. As noted in the firs t two rows of Table 9A.2.1, one source (i.e., Hymans) shows that the price elasticity of demand is significantly different over the shor t run and long run for automobiles. 1 Because DOE’s forecasts of shipments and national impacts due to standards is over a 30-year time period, considerat ion must be given as to how the relative price elasticity is affected once a new standard takes effect.

DOE considers the relative price elasticities determined abov e in section 9A.4 to be short run elastcities. DOE was unable to identify sources specific to household durable goods, such as appliances, to indicate how short run and long run pr ice elasticities differ. Therefore, to estimate how the relative price elasticity changes over time, DOE relied on the Hymans study pertaining to automobiles. Based on the Hymans study, Table 9A.5.1 shows how the automobile price

9-A-11elasticity of demand changes in the years follo wing a purchase price change. With increasing years after the price change, the price elasticity becomes more inelastic until it reaches a terminal value around the tenth year after the price change.

Table 9-A.5.1 Change in Price Elasticity of Dema nd for Automobiles following a Purchase Price Change Years Following Price Change 1 2 3 5 10 20 Price Elasticity of Demand -1.20 -0.93 -0.75 -0.55 -0.42 -0.40 Relative Change in Elasticity to 1 st year 1.00 0.78 0.63 0.46 0.35 0.33

Source: Hymans, 1971.

Based on the relative change in the automobile price elasticity of de mand shown in Table 9A.5.1, DOE developed a time series of relative price elasticities for home appliances. Table 9A.5.2 presents the time series.

Table 9-A.5.2 Change in Relative Price Elasticity for Home Appliances following a Purchase Price Change Years Following Price Change 1 2 3 5 10 20 Relative Change in Elasticity to 1 st year 1.00 0.78 0.63 0.46 0.35 0.33

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

9-A.6 SUMMARY
This appendix describes the results of a lite rature search, tabular analysis and regression analysis of the impact of price and other variab les on appliance shipments. In the literature, DOE finds only a few studies of appliance market s that are relevant to this analysis, and no studies using time series price and shipments data after 1980. The information that can be summarized from the literature, suggests that the demand for appliances is price inelastic. Other information in the literature suggests that applia nces are a normal good, such that rising incomes increase the demand for appliances. Finally, the l iterature suggests that consumers use relatively high implicit discount rates, when comparing ap pliance prices and appl iance operating costs.

There is not enough price and operating cost da ta available to perform complex analysis of dynamic changes in the appliance market. In this analysis, DOE us es data available for refrigerators, clothes washers and dishwashers to evaluate broad market trends and to perform simple regression analysis.

These data indicate that ther e has been a rise in applianc e shipments and a decline in appliance price and operating cost over the period. Household inco me has also risen during this time. To simplify the analysis, DOE combined the available economic information into one

9-A-12variable, termed relative price, and used this variable in a ta bular analysis of ma rket trends, and a regression analysis.

DOE’s tabular analysis of trends in the num ber of appliances per household suggests that the price elasticity of demand for the three applia nces is inelastic. Our regression analysis of these same variables suggests that the relative price elasticity of demand is -0.34. The price elasticity is consistent with estimates in the lit erature. Nevertheless, DOE stresses that the measure is based on a small data set, using very simple statistical analysis. More important, the measure is based on an assumption that econom ic variables, including price, income and operating costs, explain most of the trend in appliances per hous ehold in the United States since 1980. Changes in appliance quality and consumer preferences may have occurred during this period, but they are not accounted for in this analysis.

9-A.7 DATA USED IN THE ANALYSIS

• Appliance Shipments: Shipments are defined as the annu al number of units shipped in millions. These data were collected from th e Association of Home Appliance Manufacturers (AHAM) 8 and Appliance Magazine 9 as annual values for each year, 1980–2002. AHAM was used for the period 1989–2002 while Applia nce Magazine was used for the period 1980– 1988.

• Appliance Price: Price is defined as the shipments weighted retail sales price of the unit in 1999 dollars. Price values for 1980, 1985, 1986, 1991, 1993, 1994, 1998, and 2002 were collected from AHAM Fact Books. 10 Price values for other ye ars were interpolated from these eight years of data.

• Housing Starts: Housing starts data were collected from U.S. Census construction statistics (C25 reports) as annual values for each year, 1980–2002. 11

• Replacements: Retirement-driven replacements are estimated with the assumption that some fraction of sales arise from consumers replacing e quipment at the end of its useful life. Since each appliance has a different expected lifespan (19 years for refrigerators 12 , 14 years for clothes washers 13 , 12 years for dishwashers 14 ), replacements are calculated differently for each appliance type. Replacements are estimated as the average of shipments 14–24 years previous for refrigerators, 9–19 years previous for clothes wash ers, and 7–17 years previous for dishwashers. Historical shipments data were collected from AHAM and Appliance Magazine.

• Annual Electricity Consumption: Electricity Use (UEC) is defined as the energy consumption of the unit in kilowatt-hours. Elec tricity consumption is dependent on appliance capacity and efficiency. These data were provided by AHAM for 1980, 1990–1997 and 1999–2002. 15 Data were interpolated in the years for which data were not available.

9-A-13• Operating Cost: Operating Cost is the present value of the electricity consumption of an appliance over its expected lifespan. The lif espans of refrigerators, clothes washers and dishwashers are assumed to be 19, 14, and 12 years respectively. Discount rates of 20 percent 6 and 37 percent 16 were used, producing similar estim ates of price el asticity. A study by Hausman recommended a discount rate of “about 20 percent” in its introduction, and presented results ranging from 24.1 percent to 29 percent based on his calculations for room air conditioners. A study by Train suggests a range of implicit discount rates averaging 35 percent for appliances.

• Income: Median annual household income in 2003 dolla rs. This data was collected for each year, 1980–2002, from Table H-6 of the U.S. Census. 17

9-A-14REFERENCES

1 S. Hymans. Consumer Durable Sp ending: Explanation and Prediction, Brookings Papers on Economic Activity , 1971. Vol. 1971, No. 1, pp. 234-239.

2 P. Golder and G. Tellis. Beyond Diffusion: An Affordability Model of the Growth of New Consumer Durables, Journal of Forecasting , 1998.

3 D. Revelt and K. Train. Mixed Logit w ith Repeated Choices: Household's Choice of Appliance Efficiency Level, Review of Economics and Statistics , 1997.

4 G. Tellis. The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric Models of Sales, Journal of Marketing Research , 1988.

5 U.S. Department of Energy-Energy Information Agency. The Effect of Income on Appliances in U.S. Households, 2002. Washington, DC.

< http://www.eia.doe.gov/emeu/recs/appliances/appliances.html >

6 J. Hausman. Individual discount rates and the purchase and utilization of energy using durables, The Bell Journal of Economics , 1979.

7 K. Train. Discount rates in consumer' energy related decisions: a revi ew of the literature, Energy , 1985.

8 Association of Home Ap pliance Manufacturers. AHAM Fact Books , 2000 and 2003. Washington, DC.

9 Statistical Review. Appliance Magazine , April, 1990.

10 Association of Home Ap pliance Manufacturers. AHAM Fact Books , 1987, 1988, 1993, 1995, 2000, and 2003. Washington, DC.

11 U.S. Census Bureau. Current Construction Reports, C25 – New One-Family Houses Sold. Washington, DC. < http://www.census.gov/prod/www/abs/c25.html >

12 Duemling, Reubin. Product Life of Refrigerators , 1999. University of California, Berkeley, Energy and Resources Group masters thesis.

13 U.S. Department of Energy-Office of Building Research and Standards. Final Rule Technical Support Document (TSD): Energy Efficiency Standards for Consumer Products: Clothes Washers, December, 2000. Washington, DC. Chapter 7.

14 U.S. Department of Energy- Office of Codes and Standards. Technical Support Document: Energy Conservation Standards for Consumer Products: Dishwashers, Clothes Washers, and Clothes Dryers, December, 1990. Washington, DC. DOE/CE-02999P. Chapter 4.

9-A-1515 Association of Home Ap pliance Manufacturers. AHAM 2003 Fact Book , 2003. Washington, DC.

16 K.E. Train, and Atherton, T. Rebates, loans, and customers’ choice of appliance efficiency level: combining stated- a nd revealed-preference data, The Energy Journal , 1995.

17 U.S. Census Bureau. Income, Historical Income Tables – Households, Table H-6. Washington, DC. < http://www.census.gov/hhes/www/in come/histinc/inchhtoc.html >

9-A-16