APPENDIX 10 B

. NIA SENSITIVITY ANALYSIS FOR ALTERNATIVE

PRODUCT PRICE TREND SCENARIOS

TABLE OF CO NTENTS

10B.1 INTRODUCTION

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

10B.2 ALTERNATIVE SPVU PRI CE TREND SCENARIOS

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

10B.2.1 Exponential Fit Approach (High Price Scenario)

........................................... 10B -2

10B.2.2 Annual Energy Outlook 2013 Price Forecast (Low Price Scenario)

............. 10B -3

10B.2.3 Summary

................................ ................................ ........................................... 10B -3

10B.3 NPV RESULTS BY ALTERNATI VE PRICE TREND SCENA RIO

......................... 10B -4

LIST OF TABLES

Table 10B.2.1 Price Trend Sensitivities

................................ ...................................................... 10B -3

Table 10B.3.1. Summary of Cumulative Net Present Value for SPVUs: Low Price

Scenario (Seve n Percent Discount Rate)

..................................................... 10B -4

Table 10B.3.2. Summary of Cumulative Net Present Value for SPVUs: Low Price

Scenario (Three Percent Discount Rate)

...................................................... 10B -4

Table 10B.3.3. Summary of Cumulative Net Present Value for SPVUs: High Price

Scenario (Seven Percent Discount Rate)

..................................................... 10B -5

Table 10B.3.4. Summary of Cumulative Net Present Value for SPVUs: High Price

Scenario (Three Percent Discoun t Rate)

...................................................... 10B -5

LIST OF FIGURES

Figure 10B.2.1 Relative Price of SPVUs versus Year, with Exponential Fit

...................... 10B -2

Figure 10B.2.2 SPVU Price Forecast Indexes

................................ ....................................... 10B -4

10B-iAPPENDIX 10B. NIA SENSITIVITY ANAL YSIS F OR

ALTERNATIVE PRODUCT PRICE TREND SCENARIOS

10B.1 IN TR ODUCTION

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 product price forecasts on the

consumer net present value (NPV) for the considered T SLs for SPVUs , DOE also considered

two alternative price trends for a sensitivity analysis. Thi s 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 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 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 academi c 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) i n regulatory impact

assessment. 1

For some commercial and industrial products, 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 price

indexes that are relevant for the product in question to derive alternative price trends for

sensitivit y analysis. a This approach was used for SPVU.

10B.2 ALTERNATIVE SPVU PRICE TREND SCENARIO S

For SPVU, DOE considered tw o alternative price trends for sensitivity analysis. One of

these used an exponential fit on the deflated Producer P rice I ndex (PPI) for all other

miscellaneous refrigeration and air

- conditioning equipments

, and the other is based on the

“ deflator

— other d urables excluding medical ” that was forecasted for EIA’s Annual Energy

Outlook 201 3 ( AEO201 3)

.

a See appendix 10C of the final rule TSD for distribution transformers.

http://www.regulations.gov/#!documentDetail;D=EERE -2010- BT

- STD -0048-0760

10B-110B.2.1 Exponential Fit Approach ( High Price Scenari o)

For this scenario , DOE used an inflation

- adjusted all other miscellaneous refrigeration

and air

- conditioning equipment PPI from 2001

- 201 2 to fit an exponential model with year as the

explanatory variable. DOE obtained historical Producer Price Index (PPI) data all other

miscellaneous refrigeration and air

- conditioning equipment spanning the time period 2001

- 201 2

from the Bureau of Labor Statistics’ (BLS). b The PPI data reflect nominal prices, adjusted for

p roduct quality changes. An inflation

- adjusted (deflated) price index for all other miscellaneous

refrigeration and air

- conditioning equipment was calculated by dividing the PPI series by the

Gross Domestic Product Chained Price Index

. The deflated price in dex is now presented in 201 2

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

YY = aa ∙ ee bbbb

where Y is the SPVU price index, X is the time variable, a is the constant and b is the slope

parameter of the time variable. c

To estimate these exponential parameters, a least

- square fit was performed on the

inflation

- adjusted SPVU price index versus year from 2001 to 2012 . See Figure 10B.2 .1

.

Figure 10B.2 .1 Relative Price of SPVUs versus Year, with Exponential Fit

b Series ID PCU3334153334159 ; www.bls.gov/ppi/

c During analysis for the Fina l Rule, DOE will consider alternate fits to these data. y = 4E

- 14e 0.0153x

R2 = 0.7479

0.820 0.840 0.860 0.880 0.900 0.920 0.940 0.960 0.980 1.000 1.020

2000 2005 2010 2015Deflated Price Index (2012=1)

Year Exponential Fit on

Deflated PPI from

2001 to 2012

Expon. (Exponential

Fit on Deflated PPI

from 2001 to 2012)

10B-2The regression performed as an exponential trend line fit results in an R

- square of 0. 75

, which indicates a mode r ate fit to the data. The final estimated exponential func t ion is:

YY = 4. 0 54 × 10 (

− 14 ) ∙ ee 0

. 0153

D OE then derived a price factor index for this scenari o , 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

.

10B.2.2 Annual Energy Outlook 201 3 Price Forecast ( Low Price Scenario )

DOE also examined a forecast based on the “chained price index

— industrial equipment”

that was forecaste d for AEO201 3 out to 2040. This index is the most disaggregated category that

includes SPVUs. To develop an inflation

- adjusted index, DOE normalized the above index with

the “chained price index

— gross domestic product” forecasted for AEO201 3 . To extend the price

index beyond 2040, DOE used the average annual price growth rate in 2031 to 2040.

10B.2.3 Summary

Table 10B .2.1 shows the summary of the average annual rates of changes for the product

price index in each scenario

. Figure 10B .2.2 shows the resulting pric e trends.

Table 10B.2 .1 Price Trend Sensitivities

Sensitivity Price Trend Average Annual rate of

change %

Medium (Default) Constant Price Projection 0.0 0

Low Price Scenario AEO201 3

-- “ chained price index

— industrial equipment ” - 0. 26

High Price Scenario Exponential Fit using data from 2001 to

2012 1. 5 4

10B-3Figure 10B.2 .2 SPVU Price Forecast Index es

10B.3 N PV RESULTS BY ALTERNATIVE PRICE TREND SCENARIO

Table 10B.3 .1 . Summary of Cumulative Net Present Value for SPVUs : Low Price Scenario

(Seven Percent Discount Rate )

Equipment Class Net Present Value (Billion 201 3 $)

TSL 1 TSL 2 TSL 3 TSL 4

SPVAC <65,000 Btu/h 1 Phase 0.03 0.02 (0.24) (0.43)

SPVAC <65,000 Btu/h 3 Phase 0.01 0.01 (0.11) (0.19)

SPVHP <65,000 Btu/h 1 Phase 0.03 0.07 0.02 (0.07)

SPVHP <65,000 Btu/h 3 Phase 0.01 0.03 0.01 (0.03)

All Classes 0.09 0.13 (0.32) (0.73)

* Numbers in parentheses indicate negative NPV.

Table 10B.3 .2 . Summary of Cumulative Net Present Value for SPVUs : Low Price Scenario

(Three Percent Discount Rate )

Equipment Class Net Present Value (Billion 201 3 $)

TSL 1 TSL 2 TSL 3 TSL 4

SPVAC <65,000 Btu/h 1 Phase 0.10 0.11 (0.39) (0.66)

SPVAC <65,000 Btu/h 3 Phase 0.04 0.05 (0.17) (0.30)

SPVHP <65,000 Btu/h 1 Phase 0.09 0.22 0.12 (0.01)

SPVHP <65,000 Btu/h 3 Phase 0.04 0.10 0.06 (0.01)

All Classes 0.28 0.48 (0.39) (0.98)

* Numbers in parentheses indicate negative NPV.

0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400 1.600 1.800201320162019202220252028203120342037204020432046 Constant Price

Index_Default

Exponential Fit using

PPI deflated with GDP

(2001-2012)_ High

Price Case

AEO 2013_Low Price

Case

10B-4Table 10B.3 .3 . Summary of Cumulative Net Present Value for SPVUs : High Price Scenario

(Seven Percent Discount Rate )

Equipment Class Net Present Value (Billion 201 3 $)

TSL 1 TSL 2 TSL 3 TSL 4

SPVAC <65,000 Btu/h 1 Phase 0.01 (0.06) (0.46) (0.70)

SPVAC <65,000 Btu/h 3 Phase 0.00 (0.03) (0.21) (0.31)

SPVHP <65,000 Btu/h 1 Phase 0.02 0.04 (0.08) (0.19)

SPVHP <65,000 Btu/h 3 Phase 0.01 0.02 (0.04) (0.09)

All Classes 0.04 (0.03) (0.78) (1.28)

* Numbers in parentheses indicate negative NPV.

Table 10B.3 .4 . Summary of Cumulative Net Present Value for SPVUs : High Price Scenario

(Three Percent Discount Rate )

Equipment Class Net Present Value (Billion 201 3 $)

TSL 1 TSL 2 TSL 3 TSL 4

SPVAC <65,000 Btu/h 1 Phase 0.04 (0.08) (0.92) (1.27)

SPVAC <65,000 Btu/h 3 Phase 0.02 (0.03) (0.41) (0.57)

SPVHP <65,000 Btu/h 1 Phase 0.07 0.15 (0.10) (0.27)

SPVHP <65,000 Btu/h 3 Phase 0.03 0.07 (0.05) (0.12)

All Classes 0.15 0.11 (1.48) (2.23)

* Numbers in parentheses indicate negative NPV.

10B-5REFERENCES

1. Margaret Taylor and K. Sydny Fujita. Accounting for Technological Change in Regulatory

Impact Analyses: The Learning Curve Technique. Lawrence Berkeley National Laboratory,

Berkeley, CA. April 2013.

10B-6