APPENDIX 10

- B. NATIONAL NET PRESENT VALUE OF CUSTOMER BENEFITS USING ALTER NATIVE EQUIPMENT PRI CE FORECASTS AND ECONOMIC GROWTH SCEN ARIOS

TABLE OF CO NTENTS

10

- B.1 INTRODUCTION

................................ ................................ ............................... 10

- B

- 1 10

- B.2 DESCRIPTION OF ALTER NATIVE PRODUCT PRICE SCENARIOS

......... 10

- B

- 1 10

- B.2.1 Decreasing Price Scenario

– Exponential Fit Approach

................................ ...... 10

- B

- 2 10

- B.2.2 Increasing Price Scenario

– Exponential Fit Approach

................................ ....... 10

- B

- 3 10

- B.2.3 Summary

................................ ................................ ................................ .............. 10

- B

- 4 10

- B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

................. 10

- B

- 5 10

- B.4 NET PRESENT VALUE RESULT S USING ALTERNATIVE EQUIPMENT PRICE TRENDS

................................ ................................ ................................ .. 10

- B

- 6

LIST OF TABLES

Table 10

- B.2.1 Price Trend Sensitivities

................................ ................................ .......... 10

- B

- 4 Table 10

- B.4.1 Cumulative NPV Results for Small, Large, and Very Large Air

Cooled CUAC and CUHP Using Reference Equipment Price Forecast and AEO Reference Cas e Scenario, 3% Discount Rate (billion 2012$)

. 10

- B

- 7 Table 10

- B.4.2 Cumulative NPV Results for Small, Large, and Very Large Air

Coo led CUAC and CUHP Using Reference Equipment Price Forecast and AEO Reference Case Scenario, 7% Discount Rate (billion 2012$)

. 10

- B

- 8 Table 10

- B.4.3 Cumulative NPV Results for Small, Large, and Very Large Air

Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario, 3% Discount Rate (billion 2012$)

................................ ........................... 10

- B

- 9 Table 10

- B.4.4 Cumulative NPV Results for Small, Large, and Very Large Air

Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario, 7% Discount Rate (billion 2012$)

................................ ......................... 10

- B

- 10 Table 10

- B.4.5 Cumulative NPV Results for Small, Large, and Very Large Air

Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario, 3% Discount Rate (billion 2012$)

................................ ......................... 10

- B

- 11 Table 10

- B.4.6 Cumulative NPV Results for Small, Large, and Very Large Air

Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario, 7% Discount Rate (billion 2012$)

................................ ......................... 10

- B

- 12 Table 10

- B.4.7 Comparison of Total NPV across All Equipmen t Classes for Alternative Equipment Price Forecasts and Economic Growth Cases

.. 10

- B

- 12

10

- B

- iLIST OF FIGURE S

Figure 10

- B.2.1 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential Fit from 1978 to 2006

................................ ................................ .............. 10

- B

- 3 Figure 10

- B.2.2 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential Fit from 2007 to 2013

................................ ................................ .............. 10

- B

- 4 Figure 10

- B.2.3 CUAC Price Trend Indexes

................................ ................................ ..... 10

- B

- 5 Figure 10

- B.3.1 Electricity Price Projections for Reference Case, High, and Low Ec onomic Growth Scenarios

................................ ................................ .... 10

- B

- 6

10

- B

- iiAPPENDIX 10

- B. NATIONAL NET PRESENT VALUE OF CUSTOMER BE NEFITS
USING ALTERNATIVE EQUIPMENT PRICE FORECASTS AND ECONOMIC
GROWTH SCENARIOS

10

- B.1 INTRODUCTION

This appendix presents National Impact Analysis (NIA) results using alternative product price forecasts and economic growth scenarios. As will be discussed below, DOE developed increasing and de creasing product price forecast s . In addition, alternative economic growth scenarios were utilized based on the High Economic Growth case and the Low Economic Growth case from Energy Information Administration’s (EIA’s) Annual Energy Outlook 2013 ( AEO2013 ). 1 DOE developed low and high NPV scenarios based on the combination of product price forecast s and economic growth scenarios. DOE based the low NPV scenario on the combination of an increasing product price forecast and the Low Economic Growth case, wh ile the high NPV scenario w as based on the combination of a decreasing product price forecast and the High Economic Growth case. Results are presented for the above two combinations at the end of this appendix.

10

- B.2 DESCRIPTION OF ALTER NATIVE PRODUCT PRICE SCE NARIOS

DOE used a constant price assumption for the default forecast in the NIA described in Chapter 10. In order to investigate the impact of different equipment price forecasts on the consumer net present value (NPV) for the considered TSLs for CUAC and CUHPs, DOE also considered two alternative price trends for a sensitivity analysis. This appendix describes the alternative price trends and compares NPV results for these scenarios with the default forecast.

In recent rulemakings for several residenti al products, DOE has us ed the experience curve method to derive learning rates to forecast future prices . In the experience curve method, the real cost of production is related to the cumulative production, or experience, with a manufactured product. That experience usually is measured in terms of cumulative production. A s experience (production) accumulates, the cost of prod ucing the next unit decreases. The percentage reduction in cost that occurs with each doubling of cumulative production is known as th e learning rate . A recent report from Lawrence Berkeley National Laboratory by Taylor and Fujita provides an overview of some of the major findings of the academi c literature on learning curves, and describes the application of a component

- based learning c urve approach (by the Environmental Protection Agency and the National Highway Transportation Safety Administration ) and a price

- based learning curve approach (by DOE) in regulatory impact
assessment. 2

For some commercial and industrial equipment, there are insufficient data to apply a price

- based learning curve approach , particularly with respect to cumulative production . In such cases, DOE used a constant price assumption for the default forecast in the NIA , but made use of

10

- B

- 1price indexes that are relev ant for the equipment in question to derive alternative price trends for sensitivity analysis. a DOE is using this approach for CUACs.

DOE considered two alternative price trends for a sensitivity analysis. One that assumes decreasing prices used an exp onential fit on the deflated price index for “unitary air

- conditioners, except heat pumps” during the period of 1978 to 2006 . The other assumes rising prices and is based on the same deflated price index between 200 7 and 201 3

.

10

- B.2.1 Decreasing Price Scenario

– Exponential Fit Approach
For the decreasing price scenario, DOE used an inflation

- adjusted “unitary air

conditioners, except heat pumps” Producer Price Index (PPI) spanning the time period of 1978

- 2006 from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the explanatory variable. b The PPI during this period of time showed a continuing downward trend, so the exponential fit based on this part of the historical PPI represents the decreasing price scenario of future pr ice projection.

The PPI data reflect nominal prices, adjusted for equipment quality changes. An inflation

- adjusted (deflated) price index for “unitary air

- conditioners, except heat pumps” was calculated by dividing the PPI series by the Gross Domestic P roduct Chained Price Index

. In this case, the exponential function takes the form of:

Y = a ∙ e bX

where Y is the CUAC price index, X is the time variable, a is the constant and b is the slope parameter of the time variable.

To estimate these exponential parameters, a least

- square fit was performed on the inflation

- adjusted CUAC price index versus year from 1978 to 2006. See Figure 10

- B .2.1.

a See appendix 10C of the final rule TSD for distribution transformers. http://www.regulations.gov/#!documentDetail;D=EERE

- 2010

- BT

- STD

- 0048

- 0760
b Series ID PCU333415333415E ; http://www.bls.gov/ppi/

10

- B

- 2Figure 10

- B.2.1 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential Fit from 1978 t o 2006

The regression performed as an exponential trend line fit results in an R

- square of 0.95, which indicates a good fit to the data. The final estimated exponential function is:

Y = 3

. 28 × 10 11

∙ e (

− 0

. 0133 ) X

DOE then derived a price factor index for this scenario, renormalized with 201 3 equal to 1, to project prices in each future year in the analysis period considered in the NIA. The index value in a given year is a function of the exponential parameter and y ear

.

10

- B.2.2 Increasing Price Scenario

– Exponential Fit Approach
A similar approach was applied for the increasing price scenario. DOE used an inflation

adjusted “unitary air

- conditioners, except heat pumps” Producer Price Index (PPI) spanning the time period of 2007

- 2013 from the Bureau of Labor Statistics’ (BLS) to fit an exponential model with year as the explanatory variable. The PPI during this period of time shows a continuing upward trend, so the exponential fit based on this part of the historical PPI rep resents the increasing price scenario of future price projection. The PPI data reflect nominal prices, adjusted for equipment quality changes. An inflation

- adjusted (deflated) price index for “unitary air

conditioners, except heat pumps” was calculated by dividing the PPI series by the Gross Domestic Product Chained Price Index

. In this case, the exponential function takes the form of:

Y = a ∙ e bX

where Y is the CUAC price index, X is the time variable, a is the constant and b is the slope parameter of the time variable.

10

- B

- 3To estimate these exponential parameters, a least

- square fit was performed on the inflation

- adjusted CUAC price index versus year fr om 2007 to 2013. See Figure 10

- B .2.2.

Figure 10

- B.2.2 Deflated Unitary Air

- Conditioners PPI versus Year, with Exponential Fit from 2007 to 2013

The regression performed as an exponential trend line fit results in an R

- square of 0.39. The final estimated exponential function is:

Y = 5

. 24 × 10 (

− 5 )

∙ e 0

. 0049 X

DOE then derived a price factor index for this scenario, renormalized with 201 3 equal to 1, to project prices in each future year in the analysis period considered in the NIA. The index value in a given year is a function of the exponential parameter and year

.

10

- B.2.3 Summary

Table 1 0

- B.2.1 shows the summary of the average annual rates of change for the equipment price index in each scenario. Figure 10

- B.2.3 shows the resulting price trends.

Table 10

- B.2.1 Price Trend Sensitivities

Sensitivity Price Trend Average Annual Rate of Change % Medium (Default) Constant Price Projection 0.0 Decreasing Price Scenario Exponential Fit using data from 1978 to 2006

- 1.32
Increasing Price Scenario Exponential Fit using data from 2007 to 2013 0.49

10

- B

- 4Figure 10

- B.2.3 CUAC Price Trend Indexes

10

- B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCE NARIOS

To generate NIA results reported in chapter 10, DOE uses t he Reference case energy price projection s from AEO2013

. The reference case is a business

- as

- usual estimate, given known market, demographic, and technological trends. For AEO2013 , EIA explored the impacts of alternative assumptions in other scenarios with different macroeconomic growth rates, world oil prices, rates of technology progress, and policy changes.

To reflect uncertainty in the projection of U.S. economic growth, EIA’s AEO2013 uses High and Low Economic Growth scenarios to project the po ssible impacts of alternative economic growth assumptions on energy markets. 3

Figure 10

- C.2.1 shows electricity price projections based on the different AEO 20 13 scenarios.

10

- B

- 5Figure 10

- B.3.1 Electricity Price Projections for Reference Case, High, and Low Economic Growth Scenarios

10

- B.4 NET PRESENT VALUE RE SULTS USING ALTERNAT IVE EQUIPMENT PRICE TREN DS

This section presents the NPV results for low and high scena rios using the alternative equipment price forecast s in combination with the alternative economic growth scenarios. DOE based the low NPV scenario on the combination of an increasing product price forecast and the AEO Low Economic Growth case, while the h igh NPV scenario was based on the combination of a decreasing product price forecast and the AEO High Economic Growth case. Results are presented below for each equipment class. 0.90 0.95 1.00 1.05 1.10 1.15 1.20

2012 2017 2022 2027 2032 2037 Reference Case
High Economic Growth

Low Economic Growth

10

- B

- 6Table 10

- B.4.1 Cumulative NPV Results for Small, Large, and Very Large Air

- Cooled CUAC and CUHP Using Reference Equipment Price Forecast and AEO Reference Case Scenario , 3% Discount Rate (billion 2012 $)

Product Class Trial Standard Level 1 2 3 4 Small Commercial Packaged Air Conditioners

– ≥65,000 Btu/h and

< 135,000 Btu/h Cooling Capacity 6. 86 20.65 26.03 3 6.23

Large Commercial Packaged Air Conditioners

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 3. 04 6.85 9. 66 15. 57

Very Large Commercial Packaged Air Conditioners

– ≥240,000 Btu/h and

< 760,000 Btu/h Cooling Capacity 3. 39 6. 37 11.0 3 13.5 0

Small Commercial Packaged Heat Pumps

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 0. 75 2. 29 3.05 4.20

Large Commercial Packaged Heat Pumps

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 0 .1 5 0.3 5 0. 49 0.8 1

Very Large Commercial Packaged Heat Pumps

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 0.19 0.3 4 0.59 0.72

Total

– All Classes 14. 38 3 6.85 50. 84 71.02

10

- B

- 7Table 10

- B.4.2 Cumulative NPV Results for Small, Large, and Very Large Air

- Cooled CUAC and CUHP Using Reference Equipment Price Forecast and AEO Reference Case Scenario , 7% Discount Rate (billion 2012 $)

Product Class Trial Standard Level 1 2 3 4 Small Commercial Packaged Air Conditioners

– ≥65,000 Btu/h and

< 135,000 Btu/h Cooling Capacity 2. 53 7.11 8. 97 11. 84

Large Commercial Packaged Air Conditioners

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 0.9 2 1.98 2. 86 4.8 0

Very Large Commercial Packaged Air Conditioners

– ≥240,000 Btu/h and

< 760,000 Btu/h Cooling Capacity 1.0 0 1. 83 3. 26 3. 90

Small Commercial Packaged Heat Pumps

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 0.2 8 0.80 1. 10 1. 48

Large Commercial Packaged Heat Pumps

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 0.0 5 0.1 1 0.1 6 0.2 7

Very Large Commercial Packaged Heat Pumps

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 0.06 0.11 0.1 9 0.22

Total

– All Classes 4.84 11.9 5 1 6.54 21. 51

10

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- 8Table 10

- B.4.3 Cumulative NPV Results for Small, Large, and Very Large Air

- Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario , 3% Discount Rate (billion 2012 $)

Product Class Trial Standard Level 1 2 3 4 Small Commercial Packaged Air Conditioners

– ≥65,000 Btu/h and

< 135,000 Btu/h Cooling Capacity 7.18 22.65 2 8 .56 40.94

Large Commercial Packaged Air Conditioners

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 3. 46 7.70 11.04 17.70

Very Large Commercial Packaged Air Conditioners

– ≥240,000 Btu/h and

< 760,000 Btu/h Cooling Capacity 3. 80 7.22 12.33 15.26

Small Commercial Packaged Heat Pumps

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 0. 81 2. 41 3.11 4.39

Large Commercial Packaged Heat Pumps

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 0.1 8 0. 40 0.57 0.93

Very Large Commercial Packaged Heat Pumps

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 0. 21 0.3 9 0.6 5 0.80

Total

– All Classes 15.64 40.75 56.26 80.02

10

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- 9Table 10

- B.4.4 Cumulative NPV Results for Small, Large, and Very Large Air

- Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Decreasing Trend) and AEO High Economic Growth Case Scenario , 7% Discount Rate (billion 2012 $)

Product Class Trial Standard Level 1 2 3 4 Small Commercial Packaged Air Conditioners

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 2. 61 7.59 9 .68 13.29

Large Commercial Packaged Air Conditioners

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 1.07 2.24 3.32 5.50

Very Large Commercial Packaged Air Conditioners

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 1. 1 3 2.11 3.68 4.47

Small Commercial Packaged Heat Pumps

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 0. 31 0.86 1.12 1.52

Large Commercial Packaged Heat Pumps

– ≥135,000 Btu/h and < 240,000 Btu/h Cooling Capacity 0.06 0.13 0.19 0.31

Very Large Commercial Packaged Heat Pumps

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 0.07 0.12 0. 21 0.25

Total

– All Classes 5.25 13.04 18.19 25.35

10

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- 10Table 10

- B.4.5 Cumulative NPV Results for Small, Large, and Very Large Air

- Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario , 3% Discount Rate (billion 2012 $)

Product Class Trial Standard Level 1 2 3 4 Small Commercial Packaged Air Conditioners

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 6.61 19.20 24.33 33.21

Large Commercial Packaged Air Conditioners

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 2.77 6.32 8.81 14.24

Very Large Commercial Packaged Air Conditioners

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 3.07 5.73 10.01 12.16

Small Commercial Packaged Heat Pumps

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 0.74 2.14 2.78 3.74

Large Commercial Packaged Heat Pumps

– ≥135,000 Btu/h an d <240,000 Btu/h Cooling Capacity 0.14 0.33 0.46 0.77

Very Large Commercial Packaged Heat Pumps

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 0.17 0.31 0.54 0.65

Total

– All Classes 13.52 34.03 46.92 64.76

10

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- 11Table 10

- B.4.6 Cumulative NPV Results for Small, Large, and Very Large Air

- Cooled CUAC and CUHP Using Alternative Equipment Price Forecast (Increasing Trend) and AEO Low Economic Growth Case Scenario , 7% Discount Rate (billion 2012 $)

Product Class Trial Standard Level 1 2 3 4 Small Commercial Packaged Air Conditioners

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 2.47 6.77 8.54 10.99

Large Commercial Packaged Air Conditioners

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 0.84 1.84 2.61 4.42

Very Large Commercial Packaged Air Conditioners

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 0.90 1.64 2.96 3.49

Small Commercial Packaged Heat Pumps

– ≥65,000 Btu/h and <135,000 Btu/h Cooling Capacity 0.29 0.81 1.04 1.33

Large Commercial Package d Heat Pumps

– ≥135,000 Btu/h and <240,000 Btu/h Cooling Capacity 0.05 0.11 0.15 0.26

Very Large Commercial Packaged Heat Pumps

– ≥240,000 Btu/h and <760,000 Btu/h Cooling Capacity 0.05 0.10 0.17 0.20

Total

– All Classes 4.60 11.27 15.47 20.69

Table 10

- B.4.7 Comparison of Total NPV across All Equipment Classes for Alternative Equipment Price Forecasts and Economic Growth Cases Trial Standard Level
1 2 3 4 billion 2012$

Reference Case NPV 3% 14. 38 3 6.85 50. 84 71.02
NPV 7% 4.84 11.9 5 1 6.54 21. 51 Decreasing Price Trend + AEO High Economic Growth Case NPV 3% 15.64 40.75 56.26 80.02

NPV 7% 5.25 13.04 18.19 25.35 Increasing Price Trend + AEO Low Economic Growth Case NPV 3% 13.52 34.03 46.92 64.76

NPV 7% 4.60 11.27 15.47 20.69

10

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- 12REFERENCES

1 Energy Information Administration, Annual Energy Outlook 2013 with Projections to 2040

, 2013. Washington, DC. http://www.eia.gov/forecasts/aeo/pdf/0383(2013).pdf

2 Taylor, Margaret, and Sydny K. Fujita. Accounting for Technological Change in Regulatory Impact Analyses: The Learning Curve Technique . Berkeley: Lawrence Berkeley National Laboratory, 2013. LBNL

- 619 5E

3 Energy Information Administration, Macroeconomic Activity Module for Annual Energy Outlook 2013 , 2013. Washington, DC.

< http://www.eia.gov/forecasts/aeo/assumptions/p df/macroeconomic.pdf >

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- 13