APPENDIX 10-B. NATIONAL NET PRESENT VALUE OF CUSTOMER 

BENEFITS USING ALTERNATIVE EQUIPMENT PRICE FORECASTS AND 

ECONOMIC GROWTH SCENARIOS

TABLE OF CONTENTS

10-B.1 INTRODUCTION ...............................................................................................10-B-1

10-B.2 DESCRIPTION OF ALTERNATIVE 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 SCENARIOS................. 10-B-5

10-B.4 NET PRESENT VALUE RESULTS 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 AirCooled CUAC and CUHP Using Reference Equipment Price Forecast 

and AEO Reference Case Scenario, 3% Discount Rate (billion 2012$) .10-B-7

Table 10-B.4.2 Cumulative NPV Results for Small, Large, and Very Large AirCooled 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 AirCooled 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 AirCooled 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 AirCooled 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 AirCooled 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 Equipment Classes for 

Alternative Equipment Price Forecasts and Economic Growth Cases.. 10-B-12

10-B-i
LIST OF FIGURES

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 

Economic Growth Scenarios....................................................................10-B-6

10-B-ii
APPENDIX 10-B. NATIONAL NET PRESENT VALUE OF CUSTOMER BENEFITS 

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 decreasing product price forecasts. 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 forecasts 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, while 

the high NPV scenario was 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 ALTERNATIVE PRODUCT PRICE SCENARIOS

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 residential products, DOE has used 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. 

As experience (production) accumulates, the cost of producing the next unit decreases. The 

percentage reduction in cost that occurs with each doubling of cumulative production is known 

as the 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 academic literature on learning 

curves, and describes the application of a component-based learning curve 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-1
price indexes that are relevant 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 exponential 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 2007 and 2013.

10-B.2.1 Decreasing Price Scenario – Exponential Fit Approach

For the decreasing price scenario, DOE used an inflation-adjusted “unitary airconditioners, 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 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 ∙ ebX 

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-2

 
Figure 10-B.2.1 Deflated Unitary Air-Conditioners PPI versus Year, with

Exponential Fit from 1978 to 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 × 1011 ∙ e(−0.0133)X 

DOE then derived a price factor index for this scenario, renormalized with 2013 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.2.2 Increasing Price Scenario – Exponential Fit Approach

A similar approach was applied for the increasing price scenario. DOE used an inflationadjusted “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 represents 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 airconditioners, 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 ∙ ebX 

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-3
To estimate these exponential parameters, a least-square fit was performed on the 

inflation-adjusted CUAC price index versus year from 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) ∙ e0.0049X 

DOE then derived a price factor index for this scenario, renormalized with 2013 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 10-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-4
Figure 10-B.2.3 CUAC Price Trend Indexes

10-B.3 DESCRIPTION OF HIGH AND LOW ECONOMIC SCENARIOS

To generate NIA results reported in chapter 10, DOE uses the Reference case energy 

price projections 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 possible impacts of alternative 

economic growth assumptions on energy markets.3

 

Figure 10-C.2.1 shows electricity price projections based on the different AEO 2013

scenarios.

10-B-5
Figure 10-B.3.1 Electricity Price Projections for Reference Case, High, and 

Low Economic Growth Scenarios

10-B.4 NET PRESENT VALUE RESULTS USING ALTERNATIVE 

EQUIPMENT PRICE TRENDS

This section presents the NPV results for low and high scenarios using the alternative 

equipment price forecasts 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 high 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-6
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 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 36.23

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 11.03 13.50

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

0.15 0.35 0.49 0.81

Very Large Commercial Packaged Heat 

Pumps – ≥240,000 Btu/h and <760,000 

Btu/h Cooling Capacity

0.19 0.34 0.59 0.72

Total – All Classes 14.38 36.85 50.84 71.02

10-B-7
Table 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

0.92 1.98 2.86 4.80

Very Large Commercial Packaged Air 

Conditioners – ≥240,000 Btu/h and 

<760,000 Btu/h Cooling Capacity

1.00 1.83 3.26 3.90

Small Commercial Packaged Heat 

Pumps – ≥65,000 Btu/h and <135,000 

Btu/h Cooling Capacity

0.28 0.80 1.10 1.48

Large Commercial Packaged Heat 

Pumps – ≥135,000 Btu/h and <240,000 

Btu/h Cooling Capacity

0.05 0.11 0.16 0.27

Very Large Commercial Packaged Heat 

Pumps – ≥240,000 Btu/h and <760,000 

Btu/h Cooling Capacity

0.06 0.11 0.19 0.22

Total – All Classes 4.84 11.95 16.54 21.51

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$)

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 28.56 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

0.18 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 0.39 0.65 0.80

Total – All Classes 15.64 40.75 56.26 80.02

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$)

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

1.13 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-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$)

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 and <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-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$)

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 Packaged 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 36.85 50.84 71.02

NPV 7% 4.84 11.95 16.54 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-B-12
REFERENCES

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-6195E

3 Energy Information Administration, Macroeconomic Activity Module for Annual Energy 

Outlook 2013, 2013. Washington, DC. 

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

10-B-13