8

- J

- i APPENDIX 8
- J. FORECAST OF EQUIPMEN T PRICE TRENDS FOR C ENTRAL AIR CONDITIONERS, HEAT P UMPS AND FURNACES TABLE OF CO NTENTS 8

- J.1 INTRODUCTION

................................ ................................ ................................ .. 8

- J

- 2 8

- J.2 PRICE, COST AND MARK ET STRUCTURE

................................ ..................... 8

- J

- 3 8

- J.3 DERIVATION OF LEARNI NG RATES

................................ ............................... 8

- J

- 4 8

- J.4 EQUIPMENT PRICE TREN DS FORECAST

................................ ....................... 8

- J

- 8 LIST OF TABLES Table 8

- J.3.1 Learning Parameter as a Function of Time Period

................................ ..... 8

- J

- 8 LIST OF FIGURES Figure 8

- J.3.1 Historical Normalized Prices of Warm Air Furnaces

................................ . 8

- J

- 4 Figure 8

- J.3.2 Historical Normalized Prices of Unitary Air Conditioners and Heat Pumps

................................ ................................ ................................ .......... 8

- J

- 5 Figure 8

- J.3.3 Historical and Projected Total Shipments of Warm Air Furnaces

............. 8

- J

- 6 Figure 8

- J.3.4 Historical and Projected Total Shipments of Unitary Air Conditioners

..... 8

- J

- 6 Figure 8

- J.3.5 Relative Price versus Cumulative Shipments of Warm Air Furnaces, with Power Law Fit

................................ ................................ ..................... 8

- J

- 6 Figure 8

- J.3.6 Relative Price versus Cumulative Shipments of Unitary Air Conditioners, with Power Law Fit

................................ .............................. 8

- J

- 78

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- 2 APPENDIX 8
- J. FORECAST OF EQUIPMEN T PRICE TRENDS FOR C ENTRAL AIR CONDITIONERS, HEAT P UMPS AND FURNACES

8
- J.1 INTRODUCTION In its Notice of Data Availability (NODA) published on February 22, 2011 ( 76 FR 9696) , DOE stated that it may consider improv ing regulatory analysis by addressing equipment price trends . Consistent with the NODA, DOE examined historical producer price indices (PPI) for air cond itioning equipment and furnaces. For this equipment, DOE found consistent negative real price trends. Therefore, DOE concluded that the real prices of air conditioning equipment and furnaces have a different long term trend than prices in the economy as a whole . In the analysis for the direct final rule, DOE concluded that the manufacturer selling prices of products meeting various efficiency levels may not remain fixed, in real terms, after 2010 (the year for which the engine ering analysis estimated costs ). DOE maintained the constant real price trend as a sensitivity analysis to evaluate how the impact of potential standards might change under this scenario. DOE stated in the NODA that examination of historical price data for certain appliances and equi pment that have been subject to energy conservation standards indicates that the assumption of constant real prices and costs may, in many cases, over

- estimate long

- term appliance and equipment price trends. E conomic literature and historical data suggest that the real costs of these products may in fact trend downward over time according to “learning” or “experience” curves , or alternatively that the price trends for certain sectors of the US economy may be different than the price trends for the economy a s a whole

. A draft paper , “Using the Experience Curve Approach for Appliance Price Forecasting , ” posted on the DOE web site at http://www1.eere.energy.gov/buildings/appliance_standards/supplemental_info_equipment_pric e_forecasting.html , provides a summary of the data and literature currently available to DOE that is relevant to price forecasts for selected appliances and equipment

. The extensive literature on the “learning” or “experience” curve phenomenon is typically based on observations in the manufacturing sector. a In the experience curve method, the real cost of production is related to the cumulative production or “experie nce” with a manufactured product. This experience is usually measured in terms of cumulative production. A common functional relationship used to model the evolution of production costs in this case is : Y = aX - b where a is an initial price (or cost), b is a positive constant known as the learning rate parameter, X is cumulative production , and Y is the price as a function of cumulative production. Thus, as experience (production) accumulates, the cost of prod ucing the next unit decreases. The

a In addition to the draft paper mentioned above, see Weiss, M., Junginger, H.M., Pat el, M.K., Blok, K., (2010a). A Review of Experience C urve Analyses for Energy Demand T echnologies. Technological Forecasting & Social Change . 77:411

- 428

.8

- J

- 3 percentage reduction in cost that occurs with each doubling of cumulative production is known as the learning rate (LR), given by: LR = 1

– 2 - b In typical learning curve formulations, the learning rate parameter is derived using two historical data series: cumulat ive production and price (or cost). 8
- J.2 PRICE, COST AND MARK ET STRUCTURE DOE uses a cost

- based analysis i n estimating equipment prices. To estimate equipment prices in both the standards and the baseline or no

- standards case, DOE develops engineering cost es timates that DOE then uses to estimate manufacturer selling price. The manufacturer selling price includes direct manufacturing production costs (labor, material, and overhead estimated in DOE’s manufacturer production cost s) and all non

- production costs (SG&A, R&D, and interest), along with profit . The process of the cost

- based method for developing the manufacturer selling prices is described in the engineering analysis described in Chapter 5 of this TSD. To convert the manufacturer selling price to an equipment price for the consumer, DOE performs an analysis of distribution chain markups and estimates markups on both the baseline and incremental manufacture selling prices to determine equipment prices after distribution to the consumer. In analyzing e xperience curves to estimate price trends, DOE uses producer price indices as a key data input and analyzes this data to estimate the experience curve exponent. This approach has only one model parameter to describe the price trend and assumes a simple re lationship between producer price and retail equipment price. Specifically, the approach assumes that producer prices, distribution chain markups and equipment prices all scale proportionally over time for the same product. DOE could have developed a mor e complex price trend forecasting model with more parameters that could explain different trends in different equipment price and cost components over time. But the relatively few available data points presents a risk that a fit with multiple parameters wo uld “overfit” the data. Overfitting occurs when there are too many degrees of freedom in a statistical model compared to the data and the fits are sensitive to random noise unrelated to long term trends. Due to the risk of overfitting the available data, DOE has decided to not develop a more complex multi

- parameter price trend estimation model at this time. Due to the simple nature of the price trend estimation model, there are several well known economic and market phenomenon that will not be captured in detail by the price trend forecast. Some effects might lead to an overestimate of the long term price trend and other effects may lead to an underestimate. For example, if there has been increasing market concentration historically on the part of manufacturers, this may have resulted in increasing manufacturer and wholesale markups over time. This would result in an observed historical producer price trend t hat did not decrease as fast as the underlying industrial learning rate. Depending on if market concentration accelerated8

- J

- 4 or decelerated into the future this could lead to an over

- or under

- estimation of future price trends. Similarly, if there are cost components that have relatively slow long term price trends that have an increasing impact on price over time, the decreasing share of costs that are declining rapidly can result in a change in the empirically estimated experience curve exponent over time

. 8
- J.3 D ERIVATION OF LEARNIN G RATES To derive learning rate parameter s for HVAC products, DOE obtained historical Producer Price Index (PPI) data for warm air furnaces and unitary air conditioners from the Bureau of Labor Statistics’ (BLS). Because PPI data specific to air

- source heat pumps were not available prior to 2008, DOE used PPI data for unitary air conditioners a s representative of both unitary air conditioner and heat pump prices. For warm air furnaces, DOE used PPI data spanning th e time period 19 9 0

- 2010, while fo r unitary air conditioners and heat pumps, DOE use data f rom 19 78

- 2010 . Inflation

- adjusted price indices for warm air furnaces and unitary air conditioners and heat pumps were calculated by dividing the PPI series by the Co nsumer Price Index ( CPI ) “all items” index for the same years . These inflation

- adjusted price indices (shown in Figures 8

- J.3. 1 and 8

- J.3. 2) were used in subsequent analysis steps.

Figure 8
- J.3.1 Historical Normalized Prices of Warm Air Furnaces8

- J

- 5 Figure 8
- J.3.2 Historical Normalized Prices of Unitary Air Conditioners and Heat Pumps DOE assembled a time

- series of annual shipments for 1953

- 2009 for unitary air conditioners and furnaces using data from AHRI and the Gas Appliance Manufacturers Association. (Chapter 9 in the fi nal rule TSD describes the data sources for 1972

- 2009. For shipments before 1972, DOE used earlier data from the same sources.) Heat pump shipments data were also available but were not utilized in this analysis due to the unavailability of PPI data. (Proj ected heat pump prices were based on the learning rate derived from the PPI and shipments data for unitary air conditioners.) For furnaces, shipments prior to 1953 were extrapolated backward based on a linear trend to the historical shipments. The annual s hipments data were used to estimate cumulative shipments (production). Projected shipments after 2009 were obtained from the base case projections made for the NIA (see chapter 9 of this TSD)

. Figures 8

- J.3. 3 and 8

- J.3. 4 show the shipments time series used in the analysis.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

1920 1940 1960 1980 2000 2020 2040 2060Annual Total Shipments (millions)

Year Historical Projected Extrapolated8

- J

- 6 Figure 8
- J.3.3 Historical and Projected Total Shipments of Warm Air Furnaces

Figure 8
- J.3.4 Historical and Projected Total Shipments of Unitary Air Conditioners

To estimate a learning rate parameter, a least

- squares power - law fit was performed on the unified price index versus cumulative shipments. See Figures 8

- J.3. 5 and 8

- J.3. 6.

Figure 8
- J.3.5 Relative Price versus Cumulative Shipments of Warm Air Furnaces , with Power Law Fit 0 1 2 3 4 5 6 7

1940 1960 1980 2000 2020 2040 2060Ammual Total Shipments (millions)

Year Historical Projected

y = 13.184x - 0.527 R2 = 0.954

0.8 0.9 1.0 1.1 1.2 1.3 1.4

60 80 100 120 140 160Relative Price (2010=1)

Cumulative Shipments (millions) Historical Learning Fit8

- J

- 7 Figure 8
- J.3.6 Relative Price versus Cumulative Shipments of Unitary Air Conditioners, with Power L aw Fit The form of the fitting equation is: P(X) = P o X

- b , where the two parameters, b (the learning rate parameter) and P o (the price or cost of the first unit of production), are obtained by fitting the model to the data. DOE notes that the cumulative shipments on the right hand side of the equation can have a dependence on price, so there is an issue with simultaneity where the independent variable is not truly independent. DOE’s use of a simple least squares fit is equivalent to an assumption of no significant first price elasticity effects in the cumulative shipments variable. The parameter values obtained are:
P o = 13

. 18

− 3

. 02 + 3

. 92 (95% confidence) for furnaces and 3

. 77

− 0

. 31 + 0

. 34 (95% confidence) for unitary air conditioners, and b = 0.527±0.056 (95% confidence) for furnaces and 0.288±0.021 (95% confidence) for unitary air conditioners. For warm air furnaces, the estimated learning rate (defined as the fractional reduction in price expected from each doubling of cumulative production) is 30

. 6

− 2

. 7 + 2

. 6 % (95% confidence)

. For unitary air conditioners, the estimated learning rate is 18

. 1

− 1

. 2 + 1

. 2 % (95% confidence)

. Projected heat pump prices were based on the learning rate derived for unitary air conditioners. y = 3.775x - 0.288 R2 = 0.960

0.6 0.8 1.0 1.2 1.4 1.6 1.8

0 20 40 60 80 100 120 140Relative Price (2010=1)

Cumulative Shipments (millions) Historical Learning Fit8

- J

- 8 DOE recognizes that there is uncertainty in its estimates of equipment price trends. Uncertainty arises from potentially systematic long term changes in the trend. To provide a potential indication of long term changes in the trend, DOE performed price trend fits to two component pe r iods in the historical data. DOE analyzed years in which the historical efficiency standards for the product changed that were approximately in the middle of the time series of the available data. For warm air furnaces, the selected year was 2000, and for central air conditioners, the selected year was 1992. Table 8
- J.3.1 Learning P arameter as a F unction of T ime P eriod Product Period Learning Parameter (%) Furnaces 1990

- 2010 30.6 (medium) Furnaces 1990

- 1999 33.3 (high) Furnaces 2000

- 2010 19.2 (low) Central Air Conditioners 1978

- 2010 18.1 (medium) Central Air Conditioners 1978

- 1991 20.5 (high) Central Air Conditioners 1992

- 2010 11.5 (low)

8
- J.4 EQUIPMENT PRICE TREN DS FORECAST DOE derived a price factor index, with 2010 equal to 1, to forecast prices in each future year in the analysis period. The index value in a given year is a function of the LR and the cumulative production forecast through that year. DOE applied the same value to forecast prices for each furnace and air conditioner product class at each consider ed efficiency level. For the LCC and PBP analysis, DOE used the medium learning rates to forecast product prices in 201 6 . These rates were also used as default values to forecast prices through 2045 for the national impact analysis (NIA). For the NIA , DOE also examined the impacts of potential standards using high and low values of the learning rate parameter. T he “high learning” rates are 20.5 percent for central air conditioners and heat pumps and 33.3 percent for furnaces. The “low learning” rates are 11.5 percent for central air conditioners and heat pumps and 19.2 percent for furnaces. In addition, DOE examined a constant real price scenario as a sensitivity calculation

.