National Efficiency Benchmarking Study for … Report National Efficiency Benchmarking Study for...

Final Report

National Efficiency

Benchmarking Study for

Residential Air Conditioning

Prepared for: National Grid

June 13, 2008

National Efficiency Benchmarking Study for Residential Air Conditioning

Prepared by: Charles Bicknell

Scott Dimetrosky Dorianne Reinhardt Paul

Matei Perussi James Stewart James Thayer

C:\Documents and Settings\stevep\My Documents\nat grid national efficiency benchmarking sp rev.doc


Prepared for National Grid by The Cadmus Group, Inc. 3

CONFIDENTIALITY

© 2008 The Cadmus Group, Inc. All rights reserved. This report and all its contents are protected by copyright and owned by The Cadmus Group, Inc. Redistribution or publication of any part of this report for any purpose other than internal use shall be considered a copyright violation. Permission to use any part of this report must be obtained, in writing, from The Cadmus Group, Inc.



TABLE OF CONTENTS

ABSTRACT............................................................................................................................5

EXECUTIVE SUMMARY..........................................................................................................6

INTRODUCTION .....................................................................................................................8

SALES/SHIPMENTS ................................................................................................................8

PROGRAM EFFECTIVENESS ...............................................................................................14

AWARENESS OF INCENTIVE PROGRAMS ..............................................................................21

ATTITUDES ABOUT INCENTIVE PROGRAMS .........................................................................23

NATE CERTIFICATION........................................................................................................25

USING SIZING TOOLS.........................................................................................................26

SUMMARY...........................................................................................................................26

EVALUATION METHODOLOGY ............................................................................................27

Bibliography ......................................................................................................................32

APPENDIX 1 — COMPARISON AREAS ...............................................................................34

APPENDIX 2 — HVAC DEALER QUESTIONNAIRE ............................................................35



ABSTRACT The National Efficiency Benchmarking Study is an independent, nationwide assessment of

energy-efficiency programs and their impacts on the sale of residential central air conditioning

equipment. The study investigates trends in sales and market share of residential central air

conditioning units, seasonal energy efficiency ratio (SEER) level, for utility service territories

across the United States. The study examines sales in locales where there has been intense utility

or agency-sponsored program activity, and compares these sales to areas of less aggressive or

no program activity.

The National Efficiency Benchmarking Study incorporates data from over 1,100 equipment

dealers and distributors in 30 regions nationwide, and matches these to utility program

information to provide utilities with the relative impact of their energy efficiency programs. The

study looks across regions to understand the dynamics of residential air conditioning markets.



EXECUTIVE SUMMARY In 2007, approximately 4.5 million central air conditioning units were shipped in the United States.1 Within the last 18 months, the minimum federal efficiency level was raised to 13 SEER; units 13 SEER or higher are now considered high-efficiency. Nationally, air conditioning dealers report that an average of 32 percent of the air conditioning units installed in 2007 were high-efficiency units.

For areas with incentive programs, dealers report that an average of 39 percent of air conditioning units installed were high-efficiency units; in areas running “upstream” programs, such as incentives to the dealer (rather than the customer) and/or dealer training and education,

dealers reported a high-efficiency installation rate of 32 percent. In areas not providing

incentives for the installation of high-efficiency equipment, dealers reported that 26 percent of units installed were high-efficiency units.

In National Grid’s service territory, dealers reported that 30 percent of the units installed were high-efficiency units. While this is lower than the national average reported installation rate for other areas running rebate programs, a comparison showing the installation rates by US Census Region shows that this national average is skewed by higher than average installation rates in the Midwest, and that National Grid’s installation rates are actually quite similar to the installation rates in program areas in the South and West.

Additionally, simple comparisons of installation rates from one program area to another are instructive, but can be misleading. Necessary for interpreting such comparisons are considerations of the different variables both internal and external to the programs that will affect the installation rates, such as the length and severity of the cooling season, housing and income characteristics, as well as program characteristics. Because program managers are most interested in how program design affects the installation rates, this study adopts a regression modeling approach to identify the effects of incentive program design on high-efficiency percentage sales while controlling for other external factors.

Output from the study’s regression model demonstrates that, although percentage sales of high-efficiency air conditioning units in the National Grid service area are slightly below the national average, the

effects of National Grid’s incentive program on percentage sales are large. Increasing the current

1 Statistical Release, Air Conditioning, Heating, and Refrigeration Institute. January 2007 - December 2007, http://www.ari.org/Content/MonthlyShipmentReports_375.aspx

National Grid’s high- and premium-

efficiency installation rates are comparable

to other regional installation rates.

The marginal effects of rebates on percentage

sales in the National Grid service territory are

among the highest in the country.



rebate of $300 to $400 would raise percentage sales by 7.1 percentage points; the establishment of a typical upstream rebate program would increase percentage sales by 17.1 percentage points. Viewed relative to percentage sales, the marginal effects of rebates on percentage sales in the National Grid service territory are among the highest in the country.



INTRODUCTION Despite the presence of numerous energy-efficiency programs across the United States, quantitative evaluations of these programs have been an expensive prospect, focused exclusively on individual regions. Many studies are conducted to satisfy the requirements of state regulatory bodies and focus primarily on cost recovery instead of best practices in program design. With increasing fuel prices and the advent of legislatively mandated integrated resource planning targets in some states, it is important that utilities review the most efficient practices in program design so that they can maximize the savings achievable from their programs.

The National Benchmarking Study focuses on residential central air conditioning equipment. Central air conditioning is a major contributor to the summer system coincident peak load in many areas of the United States, and reducing demand on the power system during this season is a critical priority for many utilities. Therefore, many utilities provide incentives to repair or replace aging, less-efficient equipment, or provide incentives to residential customers who not only replace their aging, less-efficient, or non-functional equipment, but also provide additional savings by installing new, high- or premium-efficiency units that exceed the federally-mandated minimum efficiency requirements.

The goal of the National Benchmarking Study is to provide an independent, nationwide assessment of energy-efficiency programs and their impacts on the sale of residential central air conditioning equipment. The study investigates market share of residential central air conditioning units by SEER efficiency level for utility service territories across the United States. Further, the study examines market share in locales where there has been intense utility or agency-sponsored program activity, and compares these shares to areas of less aggressive or no program activity to allow utilities to assess the effectiveness of their programs.

SALES/SHIPMENTS In 2007, approximately 4.5 million central air conditioning units were shipped in the United States.2 In 2007, approximately 49,000 residential air conditioning units were shipped to Massachusetts, with approximately 30,000 shipped to dealers in National Grid’s Massachusetts service territory and approximately 6,000 to National Grid’s Rhode Island service territory. Table 1 presents a breakdown of unit sales by efficiency level. Nationally, air conditioning dealers report an average of 32 percent of the air conditioning units installed in 2007 were high-efficiency units. For areas with incentive programs, dealers report an average of 39 percent of air conditioning units installed were high-efficiency units; areas running “upstream” programs, such

2 Statistical Release, Air Conditioning, Heating, and Refrigeration Institute. January 2007 - December 2007, http://www.ari.org/Content/MonthlyShipmentReports_375.aspx



as incentives to the dealer (rather than the customer) and/or dealer training and education, had a high-efficiency installation rate of 32 percent. In areas not providing incentives for the installation of high-efficiency equipment, only 26 percent of units installed were high-efficiency.

Table 1: Sales of Units by Efficiency Level

Benchmark Comparables

Benchmark Comparables

Baseline Comparables

State (RI)

Territory

(Program Areas)

(Upstream Program

Areas)

(Non-Program Areas)

State (MA)

Region Nation Efficiency Level

Shipments Percent Percent Percent Percent Percent Percent Percent Percent

13 SEER 25,200 70 63 67 74 71 73 62 68

14+ SEER 10,800 30 39 33 26 29 27 38 32

14 SEER 4,700 13 20 19 11 10 13 24 16

15 SEER 2,500 7 9 5 4 7 10 6 6

16 SEER 1,800 5 5 5 7 7 1 5 6

17 + SEER 1,800 5 4 5 5 5 3 4 4

Total 36,000 100 100 100 100 100 100 100 100

National Grid’s installation rate of high-efficiency units for 2007 was compared to a benchmark group of utilities (those who also ran incentive programs for purchasing high-efficiency air conditioning units) as well as a baseline group (those not providing incentives to purchase high-efficiency equipment). Additional comparisons are presented at state, regional, and national levels. Of the units sold in National Grid’s Territory, 30 percent were high-efficiency and premium-efficiency units. While National Grid’s installation rate is lower than the national average for other areas running rebate programs, Figure 1 shows that this national average is skewed by higher than average installation rates in the Midwest, and that National Grid’s installation rates are actually similar to the installation rates in program areas in the South and West.



Figure 1: Equipment Sales in Program Areas by US Census Region

30%30%

38%

51%

32%

28%

39%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

National Grid CoolSmart Northeast Midwest South West National

These data raise the question of why some program areas have higher installation rates than others. Certainly, program design elements will contribute to the difference in installation rates;

however, there are other influential externalities, such as economic, demographic, and environmental factors.

One of the most significant external factors is the implementation of new minimum efficiency standards. On January 23, 2006, the US Department of Energy raised the minimum efficiency level for all air conditioning units manufactured after that date for the first time in approximately 15 years. Any unit manufactured after January 23, 2006 is required to have a minimum efficiency level of 13 SEER; consequently, any unit providing a higher efficiency than 13 SEER is considered a high-efficiency unit. The previous minimum efficiency level, in effect since 1992, was 10 SEER. Across the United States, installation rates of what would be considered premium-efficiency units — that is, those units with an efficiency rating of 16 SEER or greater — are consistent at around 10 percent. All comparison areas show that the majority of high-efficiency units purchased have been 14 SEER units. At first glance, it would appear that rebate programs are having little effect; however, given that the federal standards were raised only 18 months ago, it is clear that the market has not had time to adjust to the raised efficiency requirement. One dealer in the national survey remarked that his customers are still asking for the 10 and 12 SEER units and would buy the old 6 SEER units if they were available.

National Grid’s high- and premium-

efficiency installation rates are comparable

to other regional installation rates.



Figures 2 through 4 provide a graphic depiction of sales distribution by SEER level between National Grid and benchmark and baseline comparison groups. The most noticeable difference between National Grid’s sales distribution and the benchmark comparison distribution is the percent of 13 SEER and 14 SEER units installed. As a result of the recent increase to the federal minimum efficiency standard for central air conditioners to 13 SEER, sales of 13 SEER equipment clearly dominate the residential air conditioning market.

Figure 2: Sales of Units in National Grid Territory

13 SEER

70%

14 SEER

13%

15 SEER

7%

16 SEER

5%

17 SEER

5%

Figure 3: Comparison of National Grid SEER Sales Distribution by State

Massachusetts

13 SEER

69%

14 SEER

13%

15 SEER

5%

16 SEER

7%

17 SEER

6%

Rhode Island

13 SEER

73%

14 SEER

13%

15 SEER

10%

16 SEER

1%

17 SEER

3%

Figure 3 provides a comparison of the installation rates in National Grid’s Massachusetts and Rhode Island service territories.



Figure 4: Sales of Units in Comparable Areas

70% 70%

63%

74%

13% 12%

20%

11%

7% 8% 9% 4%

5% 5% 5%7%

5% 5% 4% 5%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

National Grid CoolSmart National (Benchmark) National (Baseline)

17 SEER

16 SEER

15 SEER

14 SEER

13 SEER

Why do 13 SEER air conditioning units dominate the market? Another external factor influencing installation rates is price. Customers may opt for 13 SEER units not because they are the least efficient, but because they are the least costly. Because of the recent shift of 13 SEER equipment from high-efficiency to baseline equipment, the least efficient, least costly option has become more expensive for the consumer. Comments from the dealer survey mentioned the needs of customers to “put in a more reasonable air conditioner.” Another dealer found: “About 25 percent of my customers want the high SEER systems and the rebates. The others are just breaking down and they want the cheapest they can get — they just don't have that kind of money.” Still another dealer responded: “People want price, they want cheap as they can get and still get a rebate, everything’s price.”

Table 2. Incremental Cost of High-Efficiency Units over the Baseline Price

13 SEER 14 SEER 15 SEER 16 SEER 17 SEER

CoolSmart $2,300 $500 $800 $1,200 $2,200

Northeast Region $2,300 $500 $1,000 $1,400 $2,200

Midwest Region $2,200 $400 $800 $1,100 $2,000

South Region $3,100 $600 $1,100 $1,900 $2,900

West Region $2,300 $400 $900 $1,300 $2,100USA $2,600 $500 $1,000 $1,500 $2,400



Table 2 shows the average base price for a typical 3-ton, 13 SEER air conditioner in the CoolSmart program area, along with the incremental prices to move up to each of the higher-efficiency units. For comparison, prices are provided by U.S. Census regions around the country, along with a national summary. The average base price for a typical 3-ton, 13 SEER unit in the CoolSmart program area is about $2,300. The average incremental cost to move up each incremental efficiency level is approximately $400-$500 (with the exception of 17 SEER units, which cost roughly $1000 more than 16 SEER units).

The $300 CoolSmart incentive amount covers more than half of the full incremental difference in base price of moving up from a 13 SEER to a 14 SEER unit; however, it covers less than half of the difference of moving up to a 15 SEER unit and approximately one-quarter of the difference of moving up to a 16 SEER unit. After implementation of the new federal minimum efficiency standards, customers already face a higher price for the least costly unit. They may simply choose the 13 SEER model because it is all they can afford.

Related to unit price is simple payback. One utility program manager remarked that, given that the average stay in one home is seven years, the savings must be high enough so that the simple payback occurs in seven years or less. Buyers who are not certain they will remain in a home for that long will be unwilling to make the investment in a higher-efficiency unit when they will not be present to reap the savings.

Another external factor influencing installation rates is the local economy. The recent economic downturn and home mortgage crisis had an impact on sales of high-efficiency equipment. In its December 2007 monthly statistical release,3 the Air Conditioning and Refrigeration Institute (ARI) reported shipments of air conditioning systems were down 9 percent over the previous year. J. P. Morgan Securities Inc. October 2007 semi-annual HVAC distributor survey4 reported distributors saw weaker demand for equipment, largely attributable to weakness in the U.S. housing market. A majority of the respondents to this survey also report seeing a negative impact in sales from the economy, regardless of the downturn in the housing market.

Conversations with utility program managers in different parts of the country revealed concerns about the lack of participation in their programs. One program manager from a summer-peaking utility, concerned about the effects the mortgage crisis was having on program participation, remarked that it stood to reason that if your house payment had increased from $1,200 per month to $3,500 per month, you would be unlikely — or even able — to spend extra money for a high-efficiency air conditioner; in fact, you might even forgo air conditioning altogether and just buy a fan. Another program manager in an area hit hard by the mortgage crisis reported customers in his service territory were just not replacing or even maintaining their air conditioning units because of the high foreclosure rate. Respondents to the J.P. Morgan Securities HVAC Distributors Survey noted more customers appear to be repairing systems and deferring the installation of new units.

3 Statistical Release, Air Conditioning, Heating, and Refrigeration Institute. December 2007, http://www.ari.org/Content/MonthlyShipmentReports_375.aspx

4 Tusa, C. Stephen Jr., CFA, Curt Woodworth, CFA. 2007. HVAC Industry. North America Equity Research for J.P. Morgan Securities, Inc. October 16.



Yet another external factor influencing the installation rate of high-efficiency equipment is the dealer’s percent of installations in residential new construction versus existing construction. Dealers were asked to estimate the percent of their installations in residential new construction versus existing construction. Nationally, the average percent of installations in new construction is 27 percent. Dealers in National Grid’s service territory reported an average of 38 percent of their installations in new construction. Because builders will not reap the savings from a higher-efficiency unit and are typically concerned with keeping the price of the homes they build as low as possible, baseline efficiency air conditioning units are usually installed. The higher-than-average percent of new construction installations could explain why a greater percentage of 13 SEER air conditioning units were installed in National Grid’s service territory in 2007.

PROGRAM EFFECTIVENESS While Figure 1 shows percentage sales of high-efficiency units in the CoolSmart program area fall slightly below the national average, this does not mean that the CoolSmart program is less effective than other incentive programs. Percentage sales of high-efficiency units are affected by many factors, so simple comparisons of percentage sales between program areas can be misleading. This is known as omitted variable bias. A program area’s economic, demographic, and environmental characteristics also affect percentage sales. In this section, we adopt a regression modeling approach to identify the effect of incentive programs on high-efficiency percentage sales while controlling for these other factors. The regression modeling approach for this study builds upon the previous work of Rosenberg (2003) and Wilson-Wright et al. (2005) in evaluating ENERGY STAR® promotions.

THE LOGIT MODEL

Cadmus estimated program incentives’ effects on sales of 14+ SEER air conditioning units using a logit probability model. In this model, the conditional probability of an event follows an extreme value distribution (similar in shape to the normal distribution), and predicted values are constrained to lie between zero and one.

Let s be the share of high-efficiency air conditioner sales, X be a vector of demographic, economic and environmental characteristics affecting high-efficiency sales, and Z be a vector of incentives affecting sales. The high-efficiency share of air conditioning sales in program area i is:

)(1

)X ,F(Z iii Β+Θ+

Β+Θ+

+==

ii

ii

XZ

XZ

e

eS

α

α

where α, θ, and β are coefficients to be estimated and indicate how incentives and demographic, environmental, and economic characteristics affect high-efficiency air conditioner sales.

The vector Zi includes the following end-user and dealer incentive variables:



• Tier1_3Ton: This rebate in dollars is offered to buyers of 3- ton, high-efficiency air conditioning units with 14 SEER rating. For program areas without rebates, this variable equals zero. Tier1_3Ton should have a positive effect on the percentage sales.

• Upstream_program Mean: This is a dummy variable (1=YES, 0=NO) indicating whether incentives were offered to dealers or installers. An upstream program should have a positive effect on percentage sales.

• Loan_Program: This is a dummy variable (1=YES, 0=NO) indicating whether financing was offered to purchasers of high-efficiency units. It should have a positive effect on percentage sales.

Xi includes the following environmental, demographic, and economic variables:

• Cdd65: These are cooling degree days (65 degrees as a base). Higher cooling degree days should increase percentage sales.

• Pct_college_edu: This variable indicates the percentage of adults who are college educated in the program area. College educated persons should have higher incomes and greater awareness of energy; thus, they are more likely to purchase high-efficiency air conditioning units.

• Pct urban: This variable measures the percentage of population living in urban areas within the program service area. It should have a negative sign as urban populations occupy smaller housing units than suburban and rural populations and therefore have less demand for high-efficiency air conditioning units. Urban areas also have larger impoverished populations who cannot afford expensive high-efficiency units.

• Unemp06: This is the program areas’ average unemployment rate in 2006. In program areas with high unemployment, incomes should have been lower and economic uncertainty higher; thus purchases of high-end durable goods such as high-efficiency air conditioning units should have been lower.

• Number_of_installers: This variable is the average number of installers working for dealers in the program area. It captures the size of the dealership. Larger dealers are better able to offer a wide range of air conditioning equipment, including high-efficiency units. This variable should have a positive sign.

• Years_ac_business_mean: This variable is the weighted average of years in business of AC dealers in the program area. This variable captures dealer knowledge about air conditioning units and should have a positive effect on percentage sales of high-efficiency units.

If k denotes the kth variable in X (or Z), then the marginal effect of independent variable Xk is:

δs/δXk = (exp α +Zθ +Xβ / (1 + exp α +Zθ +Xβ)2)βk = s(1-s) βk



Note in the logit model, the marginal effects of the independent variables are not equal to the estimated coefficients. The marginal effects depend on the values of independent variables. This means the marginal effects of the independent variables will vary between program areas.

MODEL RESULTS

The model was estimated with 26 program area observations of high-efficiency air conditioning sales.5 The regression results are displayed in Table 3, and estimates of the marginal effects are displayed in Table 4 through 6.

Table 3: Model Parameters Estimates

Parameter estimate Standard error T stat P value

Intercept -3.492 2.210 -1.580 0.134

Rebates 0.003 0.001 2.349 0.032

Upstream Program 0.742 0.341 2.177 0.045

Loan Program 0.204 0.322 0.633 0.536

cdd65 0.00013 0.00016 0.821 0.424

Percent College Educated 0.0776 0.0293 2.641 0.018

Percent Urban -0.0426 0.0149 -2.860 0.011

Unemployment -0.060 0.179 -0.333 0.744

Number of Installers 0.119 0.052 2.262 0.038

Years in AC Business 0.060 0.035 1.696 0.109

R2 0.559

N 26

The results show that the model’s explanatory variables explain much of the variation in high-efficiency air conditioning sales between program areas. The R2 is fairly high (0.56) and not an artifact of the logit transformation of the dependent variable. When the model is estimated as a linear probability model, the R2 is also high. The model predicts the share of high-efficiency air conditioning sales for a large number of observations. Almost 50 percent of predicted values lie within 5 percentage points of the true values. The model predicts very well (<2.5 percentage points) high-efficiency sales in the National Grid program areas.

All estimated coefficients have the predicted signs, and most coefficients are statistically significant at the 10 percent level or better. Rebates, upstream programs, and loan programs increase the share of high-efficiency air conditioning sales, although the effect of loan programs is not statistically significant. Program areas with more college graduates, high summer

5 The logit model was estimated by least squares. First the model was transformed, so

log(si /(1- si)) (the log-odds ratio of si) was a linear function of the observable characteristics:

log(si /(1- si)) = α + Ziθ +Xiβ + εi (1)

where the log indicates the natural logarithm function. The right hand side of (1) now includes εi, the error term representing random, unobservable factors affecting percentage sales. Because si is an average of high-efficiency sales of dealers in program area i, the error term is heteroskedastic (i.e., the error term has non-constant variance). The model is thus estimated by weighted least squares, where the weight on observation i is the square root of the number of dealers in program area i.



temperatures, low unemployment, and rural populations have a greater percentage of sales of high-efficiency air conditioning units. CDDs and the 2006 unemployment rate, however, do not have statistically significant effects. The size and experience of dealers in the program area have positive and statistically significant impacts on high-efficiency sales. Dealers with more employees and experience in the marketplace increase the share of high-efficiency units sold.

THE MARGINAL EFFECTS

Table 4: Marginal Effects

Marginal effects: (δF/δX)

Rebates Upstream Program

Loan Program

CDD Percent College

Educated

Percent Urban

Unemployment No. of Installers

Years in Business

National Grid 0.07% 16.13% 4.43% 0.003% 1.69% -0.93% -1.30% 2.58% 1.30%

Average 0.07% 16.55% 4.54% 0.003% 1.73% -0.95% -1.33% 2.64% 1.33%

Table 4 shows the marginal effects of the independent variables on the share of high-efficiency units sold in the National Grid program area. The marginal effect of the independent variable Xk

for program area i is the derivative of the probability function s with respect to Xk (δs/δXk) evaluated using the program area’s independent variables. For instance, the model predicts in the National Grid territory a $1.00 increase in the rebate increases the share of high-efficiency units sold by 0.07 percentage points. A cooling degree day increases the share of high-efficiency sales by 0.003 percentage points.

Table 4 also shows the average of the marginal effects in the sample of program areas. For instance, the average marginal effect of a loan program is to increase the share of high-efficiency sales by 4.5 percentage points. The marginal effects in the National Grid and the average of the effects in the other program areas are similar.

Marginal Effects: Discrete Changes in the Independent Variables

The marginal effects in Tables 4 reflect instantaneous (very small) changes in the independent variables. Panel A of Table 5 shows the effects on high-efficiency percentage sales of discrete changes in the rebate, the offering of a loan program, the offering of an upstream program, and the unemployment rate for different initial rebate levels. For instance, starting with a rebate of $300 (the amount currently offered by National Grid) or another amount, we asked what effect an increase in the rebate to $400 or $500 or a decrease to $0, $100, or $200 would have on percentage sales.



Table 5: Marginal Effects — Discrete Changes in Rebates, Upstream Programs, Loan

Programs, and the Unemployment Rate

Panel A: NGRID Marginal effects: F(Z1, X) - F(Z0,X)

ORIGINAL

REBATE

AMOUNT

NEW

REBATE

$0

NEW

REBATE

$100

NEW

REBATE

$200

NEW

REBATE

$300

NEW

REBATE

$400

NEW

REBATE

$500

UPSTREAM

PROGRAM

LOAN

PROGRAM

UNEMPL.

RATE TO

3.0%

UNEMPL.

RATE TO

6.0%

-$ 0.0% 4.5% 10.0% 16.4% 23.5% 31.2% 12.3% 2.9% 1.2% -1.2%

100$ -4.5% 0.0% 5.5% 11.8% 19.0% 26.6% 14.5% 3.5% 1.5% -1.4%

200$ -10.0% -5.5% 0.0% 6.4% 13.5% 21.1% 16.3% 4.1% 1.8% -1.7%

300$ -16.4% -11.8% -6.4% 0.0% 7.1% 14.8% 17.7% 4.6% 2.0% -1.9%

400$ -23.5% -19.0% -13.5% -7.1% 0.0% 7.6% 18.3% 4.9% 2.2% -2.1%

500$ -31.2% -26.6% -21.1% -14.8% -7.6% 0.0% 18.1% 5.1% 2.2% -2.2%

PANEL B: Program areas average marginal effects

ORIGINAL

REBATE

AMOUNT

NEW

REBATE

$0

NEW

REBATE

$100

NEW

REBATE

$200

NEW

REBATE

$300

NEW

REBATE

$400

NEW

REBATE

$500

UPSTREAM

PROGRAM

LOAN

PROGRAM

UNEMPL.

RATE TO

3.0%

UNEMPL.

RATE TO

6.0%

-$ 0.0% 6.9% 14.1% 21.5% 28.7% 35.4% 16.3% 4.4% 1.9% -1.9%

100$ -6.9% 0.0% 7.3% 14.6% 21.8% 28.6% 17.3% 4.7% 2.1% -2.0%

200$ -14.1% -7.3% 0.0% 7.4% 14.6% 21.3% 17.6% 4.8% 2.1% -2.1%

300$ -21.5% -14.6% -7.4% 0.0% 7.2% 13.9% 17.1% 4.8% 2.1% -2.1%

400$ -28.7% -21.8% -14.6% -7.2% 0.0% 6.7% 16.0% 4.5% 2.0% -2.0%

500$ -35.4% -28.6% -21.3% -13.9% -6.7% 0.0% 14.4% 4.1% 1.8% -1.9%

In the National Grid service territory, significant changes in the rebate have large effects on percentage sales of high-efficiency units. For instance, the model predicts that increasing the rebate from $300 to $400 increases percentage sales by 7.1 percentage points. If the rebate were to increase by $200 instead, high-efficiency sales would increase by 14.8 percentage points. Decreasing the rebate has the opposite effect. For instance, reducing the rebate to $200 would reduce percentage sales 6.4 percentage points, all else equal. Changes in the rebate from different starting rebate levels produce effects on percentage sales of similar magnitude. For instance, a change in the rebate from $0 to $200 would increase percentage sales by 10.0 percentage points.

Cadmus also estimated the effect of establishing an upstream program for different rebate amounts. Panel A of Table 5 shows that when the rebate is $300, the effect of establishing an incentives program targeting dealers is to increase sales by 17.1 percentage points. The effect of establishing a loan program is much smaller, however. It would increase sales by only 4.6 points. If the rebate were raised to $400, the effect of establishing an upstream program would be slightly greater (18.3 percentage points). Panel A suggests a positive interaction between the rebate level and an upstream program over a range of rebates. The returns to upstream programs increase with the rebate level for rebates below $500.

Cadmus also asked what effect changes in the unemployment rate from full employment (4.5 percent unemployment rate) to above full employment (3.0 percent) or below full employment (6.0 percent) have on sales of high-efficiency units. In the National Grid service area, a 1.5 percentage point decrease in the unemployment rate results in a 2.0 percentage point increase in percentage sales. An increase in the unemployment rate of 1.5 percentage points has exactly the opposite effect. These effects are small because purchasers of high-efficiency air conditioning units tend to be high income earners and highly educated and thus least affected by economic downturns.

In the National Grid service territory,

discrete changes in the rebate have

large effects on percentage sales of

high-efficiency units.



Panel B of Table 5 compares the marginal effects in the National Grid service territory with the marginal effects in other program areas in the United States. The marginal effects of incentive programs in the National Grid service territory are approximately equal to the average of the marginal effects in other program areas. For instance, the average effect of increasing a rebate from $300 to $400 is to increase sales by 7.2 percentage points. The average effect of increasing the rebate to $500 is to increase high-efficiency sales by 13.9 percentage points, slightly smaller than the effect in the National Grid service territory. The average effect of establishing an upstream program (17.1 percentage points) is also slightly smaller than the effect in the National Grid area (17.7 percentage points).

Cadmus also expressed the marginal effects of the independent variables relative to the sales predicted by the model. This shows the effect of a change in the rebate on percentage sales (henceforth, relative marginal effect). For example, the relative marginal effect of increasing the rebate from $300 to $400 is the marginal effect of increasing the rebate in Panel A divided by the model’s predicted percentage sales when the rebate is $300. Table 6 shows these calculations.

Table 6: Relative Marginal Effects — Discrete Changes in Rebates, Upstream Programs,

Loan Programs, and the Unemployment Rate Expressed Relative to Model Predicted

Relative Sales

Panel A: NGRID Relative Marginal Effects: (F(Z1, X) - F(Z0,X))/F(Z0,X)

ORIGINAL

REBATE

AMOUNT

NEW

REBATE $0

NEW

REBATE

$100

NEW

REBATE

$200

NEW

REBATE

$300

NEW

REBATE

$400

NEW

REBATE

$500

UPSTREAM

PROGRAM

LOAN

PROGRAM

UNEMPL.

RATE TO

3.0%

UNEMPL.

RATE TO

6.0%

-$ 0% 29% 65% 106% 151% 201% 79% 18% 8% -7%

100.00$ -23% 0% 27% 59% 94% 132% 72% 17% 7% -7%

200.00$ -39% -21% 0% 25% 53% 83% 64% 16% 7% -6%

300.00$ -51% -37% -20% 0% 22% 46% 55% 14% 6% -6%

400.00$ -60% -49% -35% -18% 0% 20% 47% 13% 5% -5%

500.00$ -67% -57% -45% -32% -16% 0% 39% 11% 5% -5%

Panel B: Program Area Average Relative Marginal Effects: (F(Z1, X) - F(Z0,X))/F(Z0,X)

ORIGINAL

REBATE

AMOUNT

NEW

REBATE $0

NEW

REBATE

$100

NEW

REBATE

$200

NEW

REBATE

$300

NEW

REBATE

$400

NEW

REBATE

$500

UPSTREAM

PROGRAM

LOAN

PROGRAM

UNEMPL.

RATE TO

3.0%

UNEMPL.

RATE TO

6.0%

-$ 0.0% 22.1% 46.0% 70.9% 95.8% 119.6% 60.7% 14.4% 6.1% -5.9%

100.00$ -18.0% 0.0% 19.5% 39.6% 59.8% 79.0% 52.5% 12.8% 5.5% -5.3%

200.00$ -31.2% -16.2% 0.0% 16.8% 33.4% 49.3% 44.4% 11.2% 4.8% -4.7%

300.00$ -40.9% -28.0% -14.2% 0.0% 14.1% 27.6% 36.8% 9.5% 4.1% -4.1%

400.00$ -47.9% -36.7% -24.7% -12.3% 0.0% 11.6% 29.8% 7.9% 3.4% -3.5%

500.00$ -53.1% -43.0% -32.3% -21.2% -10.3% 0.0% 23.7% 6.5% 2.8% -2.9%

The effect of incentive programs on high-efficiency sales in the National Grid service territory appears very large when the marginal effects are expressed in relative terms. The model predicts the effect of increasing the current rebate by $100 is to increase sales of high-efficiency units by 22 percent. Increasing the rebate by $200 would lift high-efficiency sales by 46 percent. The establishment of an upstream program would increase percentage sales by 55 percent. In relative terms, the marginal impacts of incentive programs in National Grid’s service territory are among the largest in our sample. For example, the relative effect of an increase in the rebate from $300 to $400 in the National

The marginal effects of rebates on percentage sales

in the National Grid service territory are among

the highest for the utilities running rebate

programs.



Grid service territory is in the 90th percentile of relative effects in the sample. The same is true for the effect of establishing a rebate program.

Summary

Although percentage sales of high-efficiency air conditioning units in the National Grid service area are slightly below the national average, the effect of incentive programs on percentage sales is large. The model predicts increasing the CoolSmart rebate of $300 to $400 would raise percentage sales by 7.1 percentage points or 22 percent relative to percentage sales with a $300 rebate. The establishment of a typical upstream rebate program would increase percentage sales by 17.1 percentage points or 55 percent. Viewed relative to percentage sales, the marginal effects of rebates in the National Grid area are among the highest in the county.

The Impact of Tiered Rebates

As requested, we also performed a separate analysis in which we estimated the effect of tiered rebates on percentage sales of premium-efficiency units (SEER 15+). For this analysis, if a program area had a flat rebate structure, then the Tier 2 rebate equals the Tier 1 (flat) rebate. Tiered rebates are intended to increase the sales of premium-efficiency units. Accordingly, we first checked whether rebates had an effect on sales of premium-efficiency units by separately regressing the log odds ratio of percentage sales of SEER 15+, SEER 15, SEER 16, and SEER 17 units on the Tier 2 rebate and the control variables used in the preceding analysis. The results (not included in this report) show that rebates have an effect that is small (and less than the effect for high-efficiency (SEER 14) units) and not statistically significant. The most important explanatory variables were the percentage college educated and the dealer number of installers. Next, we estimated the same models but included a dummy variable for whether the Tier 2 rebate was part of a tiered rebate structure. If tiered rebates increase percentage sales, the coefficient on the dummy variable should have a positive sign. None of the tiered rebate variables had an effect, however. To check the robustness of this result, we estimated the same models, except we replaced the dummy variable with the difference between the Tier 1 rebate and the Tier 2 rebate. Again, the difference between rebate levels had no effect. We also checked whether tiered rebate structures increase the percentage of premium-efficiency units (SEER 15+) sold relative to high-efficiency units (SEER 14) sold while leaving the total number of efficiency units sold unchanged. We regressed the logit transformed ratio of percentage sales of SEER 15+ units to the percentage sales of SEER 14+ units on the Tier 2 rebate, a dummy variable indicating the rebate is part of a tiered rebate structure, and the set of control variables. The results (not included in this report) indicate a tiered rebate structure has a statistically insignificant effect on the sales of premium-efficiency units relative to high-efficiency units. We performed the same analysis separately for percentage sales of SEER 15,

Tiered rebate structures do not increase the

percentage sales of premium-efficiency units.



SEER 16, and SEER 17 units relative to percentage sales of SEER 14+ units and obtained the same result.

In summary, Cadmus finds that tiered rebate structures do not increase the percentage sales of premium-efficiency units. This is consistent with the small effect of rebates on premium-efficiency percentage sales. From the standpoint of designing utility programs, the results suggest a high flat

rebate is superior to a tiered rebate structure with a low Tier 1 rebate because rebates have their greatest impact on SEER 14 (not premium-efficiency) units. Yet many rebate programs do the opposite. They have tiered rebate structures and low Tier 1 rebates. In our sample, the average Tier 1 rebate in tiered rebate programs is $205, while the average rebate in flat rebate programs is $240.

Some Caveats about the Model

Here are some limitations of this study’s econometric approach. First, Cadmus did not attempt to model the decision to purchase high-efficiency air conditioning units formally, so our approach is reduced-form. Thus, the results are best interpreted as correlations rather than in causal terms. Second, the size of the sample is small. This limited the number of regressors Cadmus could include in the models and thus the ability to test alternative specifications. Third, the offer of a rebate or other incentive in a program area may have been related to expectations about the incentive’s success in increasing sales. This has the potential to bias the model’s estimates. For instance, if rebates were offered where it was believed they would be successful in increasing sales, the weighted least squares estimate of the rebate’s effect would be biased upward.

Despite these limitations, Cadmus has confidence in the results. The model explains a large share of the variation in percentage sales between program areas. The estimated marginal effects of the independent variables have plausible magnitudes and signs. Also, the results appear robust to changes in model specification (e.g., the inclusion or exclusion of certain independent variables).

AWARENESS OF INCENTIVE PROGRAMS The Nationwide Dealer Survey checked for dealer awareness of incentive programs available in their operating areas. Although some customers do their homework and already know about available incentives at the time they receive their bid, many do not. In these instances, it becomes the dealers’ responsibility to make customers aware of what is available to them. Nationwide, 83 percent of dealers working in areas where incentive programs exist for purchasing high-efficiency air conditioning equipment report they are aware of the incentive programs available to them. In National Grid’s service territory, 85 percent of dealers surveyed reported awareness of the incentive programs available. Of those dealers reporting awareness, 71 percent (or 60 percent of all dealers) reported they use the incentive programs in conjunction with sales of their residential air conditioning units. In National Grid’s

In National Grid’s service territory,

over half of dealers surveyed

reported awareness of the incentive programs available.

A high flat rebate is superior

to a tiered rebate structure.



Massachusetts territory alone, 95 percent of dealers reported awareness of the incentive programs; 83 percent of those dealers (73 percent of all Massachusetts dealers) reported they use the incentive programs in conjunction with their sales of high-efficiency equipment. In Rhode Island, 70 percent of dealers surveyed reported awareness of the programs; of those, 56 percent (or approximately 40 percent of all Rhode Island dealers) reported they use the programs in conjunction with their sales.

Figure 5: Incentive Awareness and Usage Comparison

60%

74%

52%

25%

9%

10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

National Grid Rebate Upstream

Use Incentives Don't Use Incentives



Figure 6: Incentive Awareness and Usage Comparison – Massachusetts and Rhode Island

73%

39%

21%

31%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Massachusetts Rhode Island

Use Incentives Don't Use Incentives

ATTITUDES ABOUT INCENTIVE PROGRAMS Dealers surveyed were asked their opinion on the extent that incentive programs successfully convinced buyers to buy more efficient units. Figure 7 compares the attitudes of dealers in

National Grid’s service territory to those in the benchmark and baseline comparison groups. Overall, 67 percent of dealers surveyed in National Grid’s service territory feel incentive programs help to convince buyers to purchase more efficient air conditioning units. These dealers felt the incentives

helped them “upsell.” One dealer commented “people wouldn't buy without the incentives.” Another dealer remarked that incentives have “a huge impact, because more often than not, people are driven towards higher SEER units based on the incentives available to them.” One dealer commented that the incentives would have “more impact in future, since [the price of] energy is not going down.”

“They [incentives] have a

significant impact, they allow us

to upsell.”



Figure 7: Incentive Awareness and Usage Comparison

33%36%

56%

47%

28%

10%

20%

36% 34%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

National Grid Rebate Program Upstream Program

Large to Significant

Some Extent

Less Significant to No Extent

For those dealers less enthusiastic about the effect of incentives on their ability to sell high-efficiency equipment, comments included complaints about the amount of paperwork involved with the rebates. Others commented that rebates helped in high-end installations, but the rebates were only for the high-efficiency models, and that middle-class homeowners could not afford these units, even with the rebates. Another dealer remarked: “…it all boils down to how long does it take to pay for itself.”

In nationwide comments, some dealers expressed a lack of enthusiasm for high-efficiency models and the federal increase in the minimum efficiency standard. The feeling was the high-efficiency models were unnecessary and did not pay for themselves in savings. Given rising fuel prices, however, these attitudes may change.

In addition, one program manager interviewed commented that even a well-designed program will fail if it does not have the support of local dealers. The utility company may provide wonderful incentives, but, to move the market to higher-efficiency units, dealers in the field need to work in concert with the utility program to increase the number of high-efficiency units sold. The importance and impact of dealer cooperation can be seen in the high-efficiency sales percentage for utilities running upstream programs. Upstream programs educating dealers and their installers, motivating and, in some cases, providing incentives for them to recommend high-

“People wouldn’t buy without the incentives.”



efficiency equipment, resulted in 33 percent reported installation rates for high-efficiency units. Even without a rebate to the consumer, educated and motivated dealers have been able to increase the installation of high-efficiency equipment over the 26 percent baseline installation rate.

NATE CERTIFICATION

Figure 8: Percent of AC Installers who are NATE-certified

23%

25%

36%

29%

32%

0%

5%

10%

15%

20%

25%

30%

35%

40%

National Grid CoolSmart Midwest Region South Region West Region

With the recognition that savings come not only from the manufacturer’s efficiency rating, but from proper installation and sizing practices, the competence and practices of installers has come to the attention of program managers. The North American Technician Excellence (NATE) Inc. provides an independent, third-party certification via a series of rigorous tests to certify the competence of HVAC installers and maintain system savings through the proper installation techniques. In National Grid’s service territory, an average of 23 percent of the air conditioning installers are NATE-certified. At the national level, an average of 32 percent of installers are NATE-certified. The NATE Web site provides consumers with a searchable online database of HVAC contractors who fall into one of two categories: 75-100 percent of the company’s installers are NATE-certified, or 25-75 percent of the company’s installers are NATE-certified. In the CoolSmart program area, five respondents to the National Benchmarking Survey are listed in this database; four are in the group with 75-100 percent of installers NATE-certified, and three of these dealers are in National Grid’s service territory. When examining NATE certification



rates by size of contractor, a clear pattern emerges across the country in that larger dealers (over $500,000 in annual sales) have a higher percentage of NATE-certified installers.

USING SIZING TOOLS Also vital in achieving the desired energy savings from a new air conditioning unit is correct sizing of the unit. In National Grid’s service territory, 88 percent of the dealers report they size air conditioning units they install using the Manual J sizing procedure. Nationally, the average is 85 percent. Larger dealers are more likely to use Manual J. Although proper sizing is recognized to be important, there is some resistance to methods such as Manual J. Resistance to using any computerized sizing tool comes from many fronts. One dealer commented using Manual J is a waste of time and takes a lot of time. Another dealer remarked he has been in the business for a number of years and did not need a computer to size equipment. Other dealers mentioned resistance from their customers; the customer wants a certain size unit, and that is what they want to buy. In the end, the dealer sells the customer what they want. One dealer remarked that the calculation “always comes out to 500 sq. ft. per ton” and so just began to use that value as a rule of thumb when sizing equipment.

In conversations with utility program managers, they revealed feedback they received from air conditioning contractors was the time spent training installers to use sizing tools takes away from valuable time that could be spent conducting installations. Sizing tools are also resisted because they require an up-front investment in special equipment. Some contractors do not see how they, personally, will benefit from the investment, which can run into the thousands of dollars, because they get paid for the installation whether they use the new sizing tool or some other method.

SUMMARY • National Grid’s CoolSmart incentives have a strong, positive effect. The model predicts

that for every $100 change in the rebate amount offered, a 7 percent change in sales occurs.

• National Grid’s installation rate of high- and premium-efficiency equipment is comparable to those in other U.S. regions.

• Incentives are effective in moving customers up-market, but they need to be high enough for customers to reap the savings from a high-efficiency unit before they move on to their next home.

• Study results suggest a high flat rebate is superior to a tiered rebate structure.

National Grid’s dealers match the national

average for installations sized using

Manual J.



• Over 2/3 of the dealers in National Grid’s service territory feel rebates provide an incentive for customers to choose high-efficiency equipment rather than settle for units meeting the lowest efficiency allowed.

• The majority of dealers in National Grid’s service territory are aware of the rebates and use them when selling air conditioning equipment. National Grid should continue its

efforts to keep dealers aware and educated about rebates available for customers’

purchase of high-efficiency equipment.

• The 2007 economy affected sales of all efficiencies of air conditioning equipment, but especially high-efficiency equipment. Customers will only install what they can afford, regardless of how “green” they may be.

• Sales/installations of high-efficiency equipment lagged as the market adjusted to the recent increase in the federal minimum efficiency standard to 13 SEER.

• Dealer support is critical to the success of any incentive program to move sales of air conditioning equipment up-market from baseline efficiency to high-efficiency

EVALUATION METHODOLOGY To address researchable issues, Cadmus collected information through primary and secondary data sources. Primary and secondary data collection activities include telephone surveys of over 1,100 equipment dealers nationwide, and program database analysis.

DEALER TELEPHONE SURVEYS

Cadmus conducted surveys of over 1,100 air conditioning equipment dealers nationwide. The survey asked dealers questions about the sales of equipment by SEER in their sales territory as well as questions about sizing tools, certification of installers, and brands of equipment sold.

The sampling frame for the National Benchmarking Study focused on 30 geographic regions of the country to represent differing climates and air conditioning needs. These 30 regions were defined by first selecting states with higher annual cooling degree days to focus data collection efforts on regions where high-efficiency equipment would be most cost-effective. The U.S. Department of Energy’s EIA database was then used to select the service territories of electric utilities with the highest residential sales in each of the initial target states. Data mapping of electric utility company service territories to ZIP codes were purchased from Platts; so service territory targets could be geographically defined. The goal was to obtain 40 completed surveys in each of the 30 regions for a total of 1,200 completed surveys.

A sample of air conditioning equipment dealers was purchased from Dun & Bradstreet for each of the 30 regions. Cadmus purchased sample lists of firms in the following SIC categories:

171117 Air conditioning contractors and systems



572212 Household appliance stores – residential AC units

In addition, National Grid provided a list of all dealers participating in the CoolSmart program, which were matched or added to the purchased sample.

Collecting this data required more effort and resources than was initially envisioned. Over 43,000 calls were necessary to obtain the approximately 1,100 completed surveys, and it took approximately 38 calls and 12 records to obtain one completed survey. In 18 of the 30 regions, all available samples were purchased. Surveyors found many dealers, particularly on the East Coast, unwilling to provide any information, and their refusal to participate in the survey were often vehement.

Program Database Analysis

Cadmus assembled publicly available data from the DSIRE database at North Carolina State University, plus researched individual utility and public service commission websites about current energy-efficiency programs nationwide, then analyzed the data to create comparison groups of programs with similar characteristics. Utilities were characterized as having no rebate program, having a rebate program, or having an upstream program. In the event a utility ran both a rebate program and an upstream program, they were characterized as a rebate program. In addition, a database of utility level weather 1970-2000 normals data was assembled from the National Oceanic and Atmospheric Administration’s (NOAA’s) National Climatic Data Center; and income, housing, and employment statistics were gathered from the U.S. Bureau of Labor Statistics, the U.S. Census Bureau, and the U.S. Bureau of Economic Analysis Web sites. Interviews with program managers of selected utilities were conducted to further understand the impacts of upstream and rebate programs.

Survey Data Cleaning

In the 30 survey regions, 1,138 surveys were completed. Of these, 112 (10 percent) were dropped because dealers reported they served only utility territories outside of the analysis utilities (e.g., cooperatives, public utility districts, etc.). Another 35 dealers were remapped from the original ZIP code-mapped utility because dealers self-reported they worked primarily in the service territory of another nearby utility in the sample. If there was any conflict between the original ZIP code mapping and what a dealer reported, preference was given to the information reported by the dealer. Some adjacent utilities have overlapping service territories within a ZIP code; it was assumed the dealers know where they are doing most of their installations.

After data cleaning, the final analysis data set consisted of 1,026 records. For National Grid’s service territory, the analysis data set contains 22 completed surveys.

Dealer Survey Weighting Methodology

Final Weight

The final case weight for dealer i in utility strata j with sales size k:

FINAL_DEALER_WEIGHT ijk=DEALER_WEIGHTijk * UTILITY_WEIGHTij



where

DEALER_WEIGHTijk = Dealer_Population_Adjustment_Case Weightjk * Dealer Sales Weightik

UTILITY_WEIGHT ij = Utility_Total_Salesi * Dealer_Self_Reported_Utility_Percentij * (1 /

nstrataj)

The final weight is the product of the Dealer and Utility Weights. This assures dealers are properly weighted in the final analysis.

Development of the dealer and utility weights are described in more detail below:

Dealer Weight Component

Because the dealer respondent distribution can be different than the population of sites, weights were determined first at the dealer level. From the sample data of air conditioning equipment dealers purchased from Dun& Bradstreet, Cadmus received total sales information that provided information about the size of the dealer. There were 11 categories ranging from 1: <$500,000 to 11: >$1,000,000,000. The number of employees for each dealer were also available, but too much of this information was missing; so sales data were used as a measure of the size of the business. The sales categories were combined into the following three categories because some of the higher level categories had little representation in the sample:

• <$500,000

• $500,000-$1,000,000

• $1,000,000 +

If the sample distribution of the dealers matched the population distribution of dealer sizes, then the sample case weight adjustments were set to one. However, if there was less sample representation in a stratum building size category, the resultant weights were set to greater than one. If there was more sample representation, the dealer weights were set to less than one.

Additionally, because a small dealer is different from a large one, another sales based weight was created based on midpoints of the sales values:

• $250,000

• $750,000

• $5,000,000

The upper end of the distribution was obtained by calculating the average of midpoints based on the population and excluding outliers. For about 10 percent of the records, Dun & Bradstreet sales categories were missing; these records were assigned a dealer weight of 1 and a dealer sales average of approximately $2,000,000. They were then assigned to the largest stratum: stratum 3.

Utility Level Weight Component

There were three components to the utility weight: the dealer utility, the self-reported weight, and the percent from the survey.



The first component was the self-reported sales percentage attributed to the utility strata. Thus, a dealer serving only 30 percent of a utility service territory has less weight than a dealer whose business operates wholly in one utility service territory.

The next weight was the GWH sales associated with each of the 30 utilities, available from the Energy Information Administration (EIA). Thus, a utility with 5 million MWH does not have as much weight as a utility of 50 million MWH.

Since Cadmus was unable to fill the quota of completed surveys for some of the 30 strata targeted in the surveys, the final sales weight was divided by the sample n to allocate sales evenly across dealer responses. Otherwise, a stratum with only 20 dealers responding would have less weight than one where the survey quota was met with 40 respondents. With this normalization, each dealer entry represents the appropriate fraction of utility sales.

Comments from Dealers in National Grid’s Service Territory

These are the verbatim comments in response to the question: “To what extent do the incentive programs successfully convince buyers to buy more efficient air conditioning units?”

The first group of comments are from dealers who felt incentive programs have made an impact on their sales of high-efficiency equipment:

• A great extent.

• I mean it does help to sell them because the customers do want to hear that they can get a rebate. But it all boils down to how long does it take to pay for itself.

• I think they do have a significant influence on sales of energy efficient units. We use the rebate program on gas furnaces extensively, and if Western Massachusetts Electric had a high-efficiency rebate program, I'm sure we'd be using that. Federal program that ended in 07 didn't have.

• I wish Western Massachusetts Energy Co did it.

• I would say 50 percent.

• It’s nice for the customers to receive money back.

• On a high-efficiency, they have quite a bit of an impact.

• People wouldn’t buy without the incentives.

• A good deal of impact; well they’re attractive to encourage people to buy better equipment; instead of a generic brand you might have an insulated as opposed to an uninsulated...better quality machine.

• A great extent.



• I would say 100 percent. We’re trying to sell higher SEER equipment, and we mention the rebate almost all the time.

• If it’s great enough, it would be interesting to many customers.

• It has a huge impact, because more often than not, people are driven towards higher SEER units based on the incentives available to them.

• It has a moderate impact.

The next group of comments are from dealers who felt incentives either did not affect their sales at all or affected them only a minimal extent:

• Some. Because it has an effect on some people, others it doesn’t. Matter of income.

• [Incentive programs effect sales] 20 percent.

• I think they do have some extent. I mean not a major extent.

• It’s really not that much.

• Not much at all.

• Not much.

• Not much. Because customers don’t give the papers to me every time, and we don’t speak about them every time. [Q - Do you offer them to customers?] No. [Q - Why not?] Because I have a terrible sales team.

• People only want to put in the minimum amount of money. They don’t want paperwork. Bryant has nice system though.

• Probably 5 percent. A small amount.

• They are all right, but a lot of paperwork and don’t remember when the customers receive them.

• Very little; they offer incentives, typically the incentives are not that great for the customer...and so...there are contractor incentives that are really good for us [e.g.] right-sizing, Manual J, quality installation and verification of equipment.

• We haven’t used them.

• Well, it depends on the high end jobs...300-500k house...they want the highest air equipment anyway; but if you’re talking working middle class, maybe like smaller homes...usually, unless the rebate is substantial, they’re not going to push for a 13 or 16...ins.



• It might have some impact with some people, maybe with someone who’s pushing them a customer might be interested, not that it matters since it just bumps up the price anyway.

• None.

• Not much.

• Not really, they would be helping the builders.

• They take too much time and paper work to apply for the manufacturer’s rebates. We advise, but would have to hire another person to do all the paperwork.

• Very little, I don’t push it.

The last group of comments is from dealers who were ambivalent about the impact of incentive programs on their sales of high-efficiency equipment:

• I do a lot of bid work, pretty much most of it is determined by the engineer or architect and in value engineering the first thing to go is the efficiency. I believe they’re all through the utility companies...National Grid.

• I don’t know. Depends on the person whom you are selling it to.

• I really don’t know how to answer that. It doesn’t have an impact on my sales really because I am not in any incentive program.

• Not applicable.

• Not really in effect in a majority of the state.

• Not aware of programs.

BIBLIOGRAPHY Rosenberg, M. 2003. “The impact of regional incentive and promotion programs on the market penetration of ENERGY STAR appliances.” International Energy Program Evaluation

Conference, Madison, WI.

Statistical Release, Air Conditioning, Heating, and Refrigeration Institute. January 2007–December 2007, http://www.ari.org/Content/MonthlyShipmentReports_375.aspx

Tusa, C. Stephen Jr., CFA, Curt Woodworth, CFA. 2007. HVAC Industry. North America Equity Research for J.P. Morgan Securities, Inc. October 16.



Wilson-Wright, L., S. Feldman, L. Hoefgen, and A. Li 2005. “Front-loading Marketing: Assessing Cumulative Effects of ENERGY STAR Appliance Promotions on State-by-State Market Penetration Levels.” Energy Program Evaluation Conference, New York.

Wilson-Wright, L., S. Feldman, and L. Hoefgen 2004. “Can We Distinguish the Effects of Specific Program Support Activities on Market Penetration.” In the Proceeding of the Annual

Meeting of Association of Energy Service Professionals, Chapter 3, Appliances and Energy

Efficiency. Pensacola, FL: Association of Energy Service Professionals.



APPENDIX 1 — COMPARISON AREAS Rebate Program National Grid Rebate Program National Grid Rebate Program Arizona Public Service (APS) Rebate Program Florida Power and Light Rebate Program MidAmerican Rebate Program Alliant (IPL) Rebate Program Xcel Energy Rebate Program New Jersey Clean Energy Program Rebate Program Nevada Power Company Rebate Program Rocky Mountain Power Rebate Program Wisconsin Focus on Energy (WEPCO) Upstream Program PG&E Upstream Program SCE Upstream Program SDGE Upstream Program NYSERDA Upstream Program CenterPoint Energy (CNP) Upstream Program Oncor Upstream Program Entergy Texas Without Rebate Program Southern Company Alabama Without Rebate Program Entergy Arkansas Without Rebate Program Southern Company Georgia Without Rebate Program ComEd Without Rebate Program Ameren Without Rebate Program Entergy Louisiana Without Rebate Program Entergy Mississippi Without Rebate Program American Electric Power Without Rebate Program PPL Electric Utilities Corp Without Rebate Program Dominion (Virginia Electric & Power Company)



APPENDIX 2 — HVAC DEALER QUESTIONNAIRE CMDI: <<state>>

UTILITYLIST 1 Alabama Power Co. 2 Arizona Public Service Co. 3 Atlantic City Electric Co. 4 Boston Edison Co. 5 CenterPoint Energy Houston Electric, LLC 6 Central Hudson Gas & Electric Corp. 7 Columbus Southern Power Co. 8 Commonwealth Edison Co. 9 Commonwealth Electric Co. 10 Consolidated Edison Co. Of New York Inc. 11 Entergy Arkansas, Inc. 12 Entergy Gulf States, Inc. 13 Entergy Louisiana, Inc. 14 Entergy Mississippi, Inc. 15 Florida Power & Light Co. 16 Georgia Power Co. 17 Illinois Power Co. 18 Interstate Power & Light Co. 19 Jersey Central Power & Light Co. 20 Massachusetts Electric Co. 21 MidAmerican Energy Co. 22 Nantucket Electric Co. 23 Narragansett Electric Co. 24 Nevada Power Co. 25 New York State Electric & Gas Corp. 26 Niagara Mohawk, a National Grid Co. 27 Northern States Power Co. 28 Ohio Power Co. 29 Orange & Rockland Utilities, Inc. 30 Pacific Gas and Electric Co. 31 PacifiCorp. 32 PPL Electric Utilities Corp. 33 Public Service Electric and Gas Co. 34 Rochester Gas & Electric Corp. 35 San Diego Gas & Electric Co. 36 Southern California Edison Co. 37 TXU Electric Delivery Co. 38 TXU SESCO Co. 39 Virginia Electric & Power Co. 40 Western Massachusetts Electric Co.



41 Wisconsin Electric Power Co.

[Interviewer confirm HVAC dealership]

Intro1.

Hi, my name is _______ and I am calling from Cadmus, an independent research firm in Oregon. We are conducting a very brief study with HVAC dealerships on the types and numbers of residential AC units sold in <<state>>. This will take less than five minutes of your time and all your answers will be confidential.

Intro2.

May I speak to a person in your company who is knowledgeable about the types and number of AC units sold in your firm?

1. Respondent on the phone or coming to phone 2. Schedule callback – SET CALLBACK

9. Refusal

Intro3.

May I ask you these few questions now?

Q1.

Thanks, please remember all your answers will be kept confidential and there are no right or wrong answers.

How many AC Installers does your company employ?

___________ (Record number)

888. Don’t know

999. Refused

Q2.

What percentage of your AC installers are NATE certified?

[Interviewer: NATE is the National North American Technician Excellence, Inc, a certification program for technicians in the HVAC industry].

_________ percent (Record percentage)

888. Don’t know



999. Refused

Q3.

What brands of AC units does your company install?

[Do not read list; Check all mentioned]

1. Armstrong 2. Bard Manufacturing Co. 3. Carrier Corp. 4. International Comfort Products 5. Lennox Industries, Inc. 6. Nordyne 7. Rheem Manufacturing Co. 8. Trane UPG – Light Commercial 9. Trane UPG – Residential 10. York International 11. Other ____________________________________

Q4.

Which AC brands are most commonly sold in the state of <<insert state>>?

[Do not read list; Check brand mentioned]

1. Armstrong 2. Bard Manufacturing Co. 3. Carrier Corp. 4. International Comfort Products 5. Lennox Industries, Inc. 6. Nordyne 7. Rheem Manufacturing Co. 8. Trane UPG — Light Commercial 9. Trane UPG — Residential 10. York International 11. Other __________________________________

Q5.

To be best of your knowledge and thinking of your total company sales as 100 percent, what percent of your sales of AC units comes from residential new construction, what percent comes from residential existing homes and what percent from sales to non-residential entities?

[Interviewer: clarify that we are looking for percent of units sold, not dollar sales]

1. ____ percent from residential new construction

2. ____ percent from residential existing homes



3. ____ percent from non-residential

[Total has to add up to 100 percent]

If Answer to Q5 = 3 and is (larger) > 75 skip to Term1

Q6_1.

I would like to ask you about your total residential AC sales by SEER category. In 2007, what PERCENT of your total residential AC sales were ...?

[Interviewer: If respondent does not know percent, ask:

“Would you be able to estimate your residential AC sales in number of units sold?”]

888. Respondent does not know percentage, but can give number of units sold

If Answer = 888 skip to Q6_2

a) What PERCENT of your total residential AC sales were SEER 13: ________(record percent)

888. Respondent does not know percentage, but can give number of units sold

999. Refused

If Answer = 888 skip to Q6_2

b) What PERCENT of your total residential AC sales were SEER 14: ________(record percent)

888. Don’t know

999. Refused

c) What PERCENT of your total residential AC sales were SEER 15: ________(record percent)

888. Don’t know

999. Refused

d) What PERCENT of your total residential AC sales were SEER 16: ________(record percent)

888. Don’t know

999. Refused

d) What PERCENT of your total residential AC sales were SEER 17 or higher: ________(record percent)

888. Don’t know



999. Refused

All skip to Q7a

Q6_2.

In 2007, how many units of your total residential AC sold were …?

a) SEER 13: ________(record number)

888. Don’t know

999. Refused

b) How many units of your total residential AC sold were SEER 14? ________(record number)

888. Don’t know

999. Refused

c) How many units of your total residential AC sold were SEER 15? ________(record number)

888. Don’t know

999. Refused

d) How many units of your total residential AC sold were SEER 16? ________(record number)

888. Don’t know

999. Refused

d) How many units of your total residential AC sold were SEER 17 or higher? ________(record number)

888. Don’t know

999. Refused

Q7a.

In which electric utility’s service territory do you install MOST of your residential AC units?

Show utility list (41 list items) / single choice

Q7b.

What percentage of your residential AC installations would you say is installed in <<insert Q7a response>>?

record answer ______________ percent



888. DK

If Answer > 75 skip to Q10 (but less than 888)

If Answer = 888 skip to Q9

Q8a.

In what other utility service territories do you sell residential AC equipment?

(Interviewer: Record names of utilities)

Show utility list (41 list items) / multiple choice

Q8b.

What percentage of your residential AC installations are in those service territories?

<<insert Q8a response_1>> ______________ percent

<<insert Q8a response_2>> ______________ percent

888. DK

If Q4b = 888 skip to Q9, else skip to Q10

Q9.

Could you provide a breakdown of the percent of your residential AC sales by county?

a) County 1 _______________(record name of county), ___________record percent

888. Don’t know

999. Refused

b) County 2 _______________(record name of county) ___________record percent

888. Don’t know

999. Refused

c) County 3 _______________(record name of county) ___________record percent

888. Don’t know

999. Refused

Allow for up to three counties to be entered — for each we need to ask the percent of sales



Q10.

For your residential units, excluding installation and taxes …?

[Interviewer: if unsure: “Your best estimate is fine?”]

a) What is the base price of a typical 3-ton, 13 SEER residential air conditioning unit? $____

888. Don’t know

999. Refused

b) What is the base price of a typical 3-ton, 14 SEER AC unit? $_____

888. Don’t know

999. Refused

c) What is the base price of a typical 3-ton, 15 SEER AC unit? $_____

888. Don’t know

999. Refused

d) What is the base price of a typical 3-ton, 16 SEER AC unit? $_____

888. Don’t know

999. Refused

e) And finally what is the base price of a typical 3-ton, 17+ SEER AC unit? $_____

888. Don’t know

999. Refused

Q11.

Can you estimate the total number of residential AC units you sold in the last 12 months?

__________ (Record number)

888. Don’t know

999. Refused



Q12.

How long has your company been selling residential AC units?

_______(number of years)

888. Don’t know

999. Refused

Q13.

Are you aware of any incentive programs in your sales area that apply to the sales of residential AC units?

1. Yes

2. No

8. Don’t know

9. Refused

If Q13 > 1 skip to Q15

Q14.

Do you use these programs in conjunction with the sales of your residential AC units?

1. Yes

2. No

8. Don’t know

9. Refused

If Q14 > 1 skip to Q16

Q15.

To what extend do the incentive programs successfully convince buyers to buy more efficient AC units? Would you say …?

1. To a large extent?

2. To a significant extent?

3. To some extent?

4. Less significant extent?



5. To no extent?

8. Don’t know

9. Refused

[Interviewer: read the following if respondent cannot answer using above scale:

Would you say your sales are affected by incentive programs to …?

1. More than 70 percent?

2. 39 percent to 69 percent?




Q16.

What percentage of your residential AC sales would you estimate are sized within one-half ton (6,000 btu/hr) of the calculated cooling load as determined using the Air Conditioning Contractors of America (ACCA) Manual J sizing procedure?

________ percent (enter percentage)

888. Don’t know

999. Refused

If Q16 = 100 percent skip to Q18

Q17.

What are some of the reasons that you did not size all your units according to Manual J specifications?

Q18.

Which of the following is the most common method you use for sizing residential AC units you install?

1. Square foot calculation 2. Eyeball/ roughly estimate 3. Rule of thumb 4. Other (specify)

8. DK/REF

THANK YOU

And those are all the questions I have for you, thank you very much for taking the time to help us with our study. Goodbye



Int

Interviewer ID

Date post:	02-May-2018
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