state of wisconsin department of administration … of wisconsin department of administration ......

State of Wisconsin Department of Administration Division of Energy Focus on Energy Statewide Evaluation Business Programs: End-use Specific Attribution Factors Final: October 28, 2005

Evaluation Contractor: PA Government Services Inc.

Prepared by: Valy T. Goepfrich, KEMA Inc.

State of Wisconsin Department of AdministrationDivision of Energy Focus on Energy Statewide Evaluation Business Programs: End-use Specific Attribution Factors Final: October 28, 2005 © PA Knowledge Limited 2005

Liaison Contact: Dr. David Sumi PA Government Services Inc. 6410 Enterprise Lane, Suite 300 Tel: +1 608 443 2700 Fax: +1 608 661 5181 E-mail: [email protected]

Prepared by: Valy T. Goepfrich, KEMA Inc.

Acknowledgment: Ralph Prahl, Prahl & Associates, contributed critical review and analysis.

This report is the property of the State of Wisconsin, Wisconsin Department of Administration, Division of Energy, and was funded through the Wisconsin Focus on Energy Program.

TABLE OF CONTENTS

1. Executive Summary 1-1

2. Introduction 2-1

3. Study Background and Methods 3-1 3.1 Background 3-1 3.2 Methodology 3-1

4. Summary of the Findings 4-1 4.1 End-use Specific Attribution Factors 4-1 4.2 Other Attribution Factors 4-2

5. Detailed Findings 5-1 5.1 End Use 5-1 5.2 Participant Size 5-3 5.3 Number of Locations 5-8 5.4 Headquarters’ Location 5-10 5.5 Own/Lease 5-11

1-1

End-use Specific Attribution Factors 1/24/06

1. EXECUTIVE SUMMARY

The primary purpose of this report is to provide end-use specific attribution factor results based on Focus on Energy Business Programs’ impact evaluation data. Two sets of results are provided. One set of results is based on FY03 and FY04 impact evaluation data and the other set of results is based on FY05 impact evaluation data.

This study suggests the type of measure installed has consequences for the proportion of savings attributable to Focus Business Programs. It also suggests participant size, number of locations, headquarters’ location, and own/lease are secondary factors affecting attribution, if they are factors at all. The FY03/FY04 results and the FY05 results are summarized in Table 1–1. Based on both sets of results, attribution appears to vary by end use. In addition, when end-use mix is controlled for to the extent possible in this analysis, attribution does not appear to vary very much by participant size, number of locations, headquarters’ location, or own/lease.

Table 1–1 Summary of FY05 and FY03/FY04 Results

Characteristic FY03, FY04 FY05

Participant size Limited LimitedNumber of locations Limited--therms only NoneHeadquarters' location None Insufficient dataOwn/lease Limited--therms only None

Substantial, but different ranking:(Building shell, insufficient data)Lighting (excl CFLs), 65% or 70%HVAC, 36% (therms inconclusive)Mnfctrng prcss, 32% or 40% (therms insufficient data)CFLs

End use

Substantial, ranking smallest to largest:Building shellLighting (excl CFLs), 41% or 44%HVAC, 57% to 64%Mnfctrng prcss, 66% to 83%CFLs

Evidence Characteristic Affects Attribution

Both the FY03/FY04 and FY05 results suggest Business Programs should continue to rebate CFLs. In both analyses, compact fluorescent lamps (CFLs) have relatively high attribution factors, between 61 and 72 percent. Otherwise, the FY03/FY04 and FY05 results by end use differ. Based on the FY03/FY04 results, lighting measures have low attribution, but based on the FY05 results, lighting measures have relatively high attribution. Also, heating, ventilation, and air conditioning (HVAC) and manufacturing process measures have smaller attribution factors for electricity in FY05 than in FY03/FY04. Furthermore, it is not clear to what extent differences in the technology mix between the two time periods is the cause of these differences.

It is all but impossible to make additional recommendations based on the results by end use given the differences between the FY03/FY04 and FY05 results. In order to develop these recommendations, the evaluation team is currently pursing two tasks. We are comparing and contrasting the measures supported by Focus Business Programs with the measures supported by other similar programs. This task includes a review of incentive levels and the structure of incentives.

The evaluation team will also conduct a basic review of what Business Programs delivers and how they deliver it, with an eye toward the consequences for attribution. In particular, it appears the attribution factor for a specific technology is not necessarily constant. It may change as a result of changes in how it is delivered, changes in to whom it is delivered, and changes in exactly what technology is delivered.

1-1


1. Executive Summary

Although participant size does not appear to be a primary determinant of attribution, Business Programs staff should still proceed with caution when a project has large gross savings. The attribution associated with a large project will have a large effect on the relevant sector-level (i.e., Agriculture, Commercial, Industrial, or Institutional) attribution factor.

1-2


2. INTRODUCTION

The primary purpose of this report is to provide end-use specific attribution factor results based on Focus on Energy Business Programs’ impact evaluation data.1 Two sets of results are provided. One set of results is based on FY03 and FY04 impact evaluation data and the other set of results is based on FY05 impact evaluation data.

This report starts by providing the background for the analysis and a brief description of the methodology. The results are then summarized and some preliminary implications are provided. The report concludes with the detailed results.

1 The draft of this report consists of two September 2005 memos that provided end-use specific attribution factor results based on Business Programs’ impact evaluation data for two different time periods. The results based on FY03 and FY04 impact evaluation data were provided in a September 16, 2005, memo. The results based on FY05 impact evaluation data were provided in a September 30, 2005, memo.

2-1


3. STUDY BACKGROUND AND METHODS

In this section of the report we provide the background for the analysis. We also briefly describe the study methods.

3.1 BACKGROUND

The energy savings in the program-tracking database are gross savings. That is, they are an estimate of the annual energy savings of installing energy efficiency measures. The first step in the impact evaluation is to verify these gross savings. The next step in the impact evaluation is to estimate the proportion of verified gross savings that are in fact attributable to Focus Business Programs. We refer to this proportion as the attribution factor and the resulting savings as net savings. One hundred minus the attribution factor is the estimated free ridership rate.

If the attribution factor is 100 percent (or free ridership is 0 percent), none of the energy savings associated with the energy efficiency improvements would have been realized without the program. Without the program, customers either would have done nothing or what they would have done would not have resulted in any energy savings. On the other hand, if the attribution factor is 0 percent (or free ridership is 100 percent), all of the energy savings associated with the energy efficiency improvements would have been realized without the program. Without the program, customers would have done the same thing as they did with the program.

Clearly, the objective is to design a program that leads participants to make energy efficiency improvements that they would not have made without the program. At the same time, while measures furthest from standard practice are likely to have the highest attribution, they are also likely to be the most expensive to make happen. The goal then is to design a program that maximizes net savings given the program budget. As a first step in examining Business Programs attribution factors, at the request of Business Programs, this report discusses end-use specific results. We also discuss the results for a variety of other attribution factors as well.

The results discussed in this report only begin to tell us where we might look to improve attribution. Additional evaluation activities during FY06 will further explore Business Programs attribution and how it may be improved. The revised Business Programs Detailed Evaluation Plan will describe these additional evaluation activities.

3.2 METHODOLOGY

The attribution factor has already been described as an estimate the proportion of verified gross savings attributable to Focus Business Programs. It is also the ratio of net savings to verified gross savings. Net savings used to estimate the attribution factor are calculated on a measure-by-measure or an end use-by-end use basis using participant self-reported information about their plans and intentions. The calculation includes adjustments for the efficiency, quantity, and timing of measures that the participant may have installed without the program. (More details on the calculation are presented in Volume III, Impact Evaluation of the Business Programs Comprehensive Report, December 23, 2002.)

3-1


3. Study Background and Methods

This report presents two sets of attribution factor results based on Focus Business Programs’ impact evaluation data. One set of results is based on FY03 and FY04 impact evaluation data and the other set of results is based on FY05 impact evaluation data. Only attribution factors based on 10 or more observations are presented.

3.2.1 End Use and Other Attribution Factors

The primary purpose of this report is to discuss the end-use specific attribution factor results, which is of particular interest to Business Programs. However, we discuss the results for a variety of other attribution factors as well: number of employees, square footage, number of locations, headquarters’ location, and own or lease. For each characteristic, Table 3–1 gives the sample sizes (count of the number of participants) available for the analysis.

Table 3–1. Sample Sizes

kWh kW Therms kWh kW ThermsBuilding shell 3 1 10 2 0 7CFLs 85 85 NA 89 89 NALighting (excluding CFLs) 150 138 NA 27 22 NAHVAC 51 29 118 22 16 21Manufacturing process 79 66 26 18 14 7Miscellaneous 32 19 11 5 1 10Very small (<5) 92 85 30 40 36 3Small (5-49) 90 78 46 24 20 13Medium (50-249) 68 56 36 19 13 10Large (250-499) 23 21 11 1 1 4Very large (>499) 27 24 14 2 2 1Unknown 62 53 20 62 62Small (<5,000) 59 54 16 39 36 2Medium (5,001-50,000) 63 59 32 25 24 8Large (50,001-200,000) 57 47 34 20 14 11Very large (>200,000) 55 47 29 6 5 4Unknown 128 110 46 58 55 13One 167 148 65 98 95 11Multiple 162 142 81 39 30 18Franchise 8 8 1 4 2 4Unknown 25 19 10 7 7In Wisconsin 142 127 65 39 29 20Outside Wisconsin 27 22 17 8 7 3Unknown 25 19 10 3 3Own 293 260 127 119 106 30Lease 25 21 13 13 13 2Unknown 25 19 8 8 8 5

FY03 and FY04Sample Size

Number of locations

Own/lease

CharacteristicFY05

End use

Participant size# employees

Participant sizesquare footage

Headquarters' location

7

5

4

NA: Not applicable.

Larger participants would seem to have more resources and perhaps more incentive (larger energy bill and energy may even be a larger proportion of costs) to pursue energy efficiency improvements on their own compared with smaller participants. Hence, larger participants may have lower attribution than smaller participants.

Participants operating at multiple locations may have lower attribution than participants operating at a single location. Regardless of size, it seems operating at multiple locations

3-2


3. Study Background and Methods

requires more skilled management and may lead to more outsourcing. Either of which may result in more attention paid to energy costs. Also, participants operating at multiple locations have more opportunities to make the same energy efficiency improvement. The program may have led the participant to make the first energy efficiency improvement, but the program may be less influential in subsequent improvements of the same type.

There are energy efficiency programs in other states and these programs may have already influenced Business Programs participants with headquarters located outside of Wisconsin. Consequently, participants with headquarters located outside of Wisconsin may have lower attribution than participants with headquarters located in Wisconsin.

It seems participants that own the space they occupy would have more incentive to make most energy efficiency improvements than participants that lease. Therefore, participants that own the space they occupy may have lower attribution than participants that lease.

3.2.2 Approach to the Results

Separately for the FY03/FY04 attribution factors and the FY05 attribution factors, we tested for differences between the end-use specific attribution factors as well as for differences among the other sets of attribution factors (i.e., number of employees, square footage, number of locations, and own or lease). We conducted these tests at the 10 percent level of significance (p-value≤0.10). In addition, for a difference we identify among the other sets of attribution factors, we compared the end-use mix to see if that explains the difference.

We also tested for differences between the FY03/FY04 attribution factors and the FY05 attribution factors. These tests were also conducted at the 10 percent level of significance.

3-3


4. SUMMARY OF THE FINDINGS

This section of the report summarizes the study results. It also provides some preliminary implications.

This study suggests the type of measure installed has consequences for the proportion of savings attributable to Focus Business Programs. It also suggests participant size, number of locations, headquarters’ location, and own/lease are secondary factors affecting attribution, if they are factors at all. These results are not definitive primarily because this analysis has a limited ability to control for multiple factors that may affect attribution. However, the results remain suggestive.

The FY03/FY04 results and the FY05 results are summarized in Table 4–1. Based on both sets of results, attribution appears to vary by end use. In addition, when end-use mix is controlled for to the extent possible in this analysis, attribution does not appear to vary very much by participant size, number of locations, headquarters’ location, or own/lease.

Table 4–1. Summary of FY05 and FY03/FY04 Results

Characteristic FY03, FY04 FY05

Participant size Limited LimitedNumber of locations Limited--therms only NoneHeadquarters' location None Insufficient dataOwn/lease Limited--therms only None

Substantial, but different ranking:(Building shell, insufficient data)Lighting (excl CFLs), 65% or 70%HVAC, 36% (therms inconclusive)Mnfctrng prcss, 32% or 40% (therms insufficient data)CFLs

End use

Substantial, ranking smallest to largest:Building shellLighting (excl CFLs), 41% or 44%HVAC, 57% to 64%Mnfctrng prcss, 66% to 83%CFLs

Evidence Characteristic Affects Attribution

4.1 END-USE SPECIFIC ATTRIBUTION FACTORS

Both the FY03/FY04 and FY05 results suggest Business Programs should continue to rebate CFLs. In both analyses, compact fluorescent lamps (CFLs) have relatively high attribution factors, between 61 and 72 percent. Otherwise, the FY03/FY04 and FY05 results by end use differ. Based on the FY03/FY04 results, lighting measures have low attribution, but based on the FY05 results, lighting measures have relatively high attribution. Also, heating, ventilation, and air conditioning (HVAC) and manufacturing process measures have smaller attribution factors for electricity in FY05 than in FY03/FY04. Furthermore, it is not clear to what extent differences in the technology mix between the two time periods is the cause of these differences.

It is all but impossible to make additional recommendations based on the results by end use given the differences between the FY03/FY04 and FY05 results. In order to develop these recommendations, the evaluation team is currently pursing two tasks. We are comparing and contrasting the measures supported by Focus Business Programs with the measures supported by other similar programs. This task includes a review of incentive levels and the structure of incentives.

The evaluation team will also conduct a basic review of what Business Programs delivers and how they deliver it, with an eye toward the consequences for attribution. In particular, it appears the attribution factor for a specific technology is not necessarily constant. It may

4-1


4. Summary of the Findings

change as a result of changes in how it is delivered, changes in to whom it is delivered, and changes in exactly what technology is delivered.

4.2 OTHER ATTRIBUTION FACTORS

When end-use mix is controlled for to the extent possible in this analysis, attribution does not appear to vary very much by participant size, number of locations, headquarters’ location, or own/lease. The limited evidence attribution varies by participant size is more often in the unexpected direction. Based on FY03/FY04 impact evaluation data:

• Very large participants have a larger attribution factor for therms than small and medium participants.

• Medium participants have a larger attribution factor for therms than small participants.

• Very large participants have smaller attribution factors for electricity than large participants.

Based on the FY05 impact evaluation data, it appears medium participants have a larger attribution factor for kW than very small and small participants.

Although participant size does not appear to be a primary determinant of attribution, Business Programs staff should still proceed with caution when a project has large gross savings. The attribution associated with a large project will have a large effect on the relevant sector-level (i.e., Agriculture, Commercial, Industrial, or Institutional) attribution factor.

The FY03/FY04 analysis found limited evidence that attribution varies by number of locations and own/lease, whereas the FY05 analysis found no evidence. Based on the FY03/FY04 impact evaluation data:

• Participants operating at multiple locations have a smaller (expected direction) attribution factor for therms than participants operating at a single location.

• Participants that own the space they occupy have a larger (unexpected direction) attribution factor for therms than participants that lease.

The FY03/FY04 analysis found differences in the attribution factors for therms where it did not find differences for electricity. The FY05 results, however, do not suggest there are more differences in the attribution factors for therms than for electricity.

4-2


5. DETAILED FINDINGS

This section of the report provides the detailed findings.

5.1 END USE

Both the FY03/FY04 and FY05 results suggest attribution varies by end use (see Figure 5–1 and Figure 5–2, respectively). However, at the end use level, the FY03/FY04 and FY05 results are similar only for CFLs, with attribution factors in the neighborhood of 61 to 72 percent.

Figure 5–1. FY03/FY04 Attribution Factors by End Use

71%67%

57% 57%

41%

2%

72%

83%

66%

39%

54%

65% 64%

44%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

kWh kW therms

Att

ribut

ion

Fact

or CFL

Prcss

MIsc

HVAC

Ltg

Shll

CFL

Prcss

MIsc

HVAC

Ltg

Prcss

Misc

HVAC

Figure 5–2. FY05 Attribution Factors by End Use

61%

40% 36%

70%

62%

32%

53%

36%

65% 67%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

kWh kW therms

Att

ribut

ion

Fact

or

Ltg

CFL

Prcss

HVAC

MIsc

Ltg

CFL

Prcs

HVAC

HVAC

5-1


5. Detailed Findings

Consider the ranking of end uses from smallest to largest attribution factor based on the FY03/FY04 impact evaluation data:

1. Building shell measures have a very low attribution factor for therms at 2 percent.

2. Lighting measures (excluding CFLs) have low attribution factors at 41 (kWh) or 44 (kW) percent.

3. HVAC measures have moderate attribution factors ranging between 57 and 64 percent across kWh, kW, and therms.

4. Manufacturing process measures have moderate to high attribution factors ranging between 66 and 83 percent across kWh, kW, and therms.

5. CFLs have high attribution factors at 71 (kW) or 72 (kWh) percent.

6. Miscellaneous measures have low to moderate attribution factors ranging between 39 and 65 percent across kWh, kW, and therms. However, the sampling error associated with the electric estimates is sufficiently large that the true attribution factor could be high.

This ordering is supported by tests of the difference between two proportions at the 10 percent level of significance (p-value≤0.10).

In contrast, based on the FY05 impact evaluation data:

• Lighting measures have relatively high attribution factors at 65 (kW) or 70 (kWh) percent.

• HVAC measures have a low attribution factor for electricity at 36 percent.

• Manufacturing process measures have low attribution factors for electricity at 32 (kW) or 40 (kWh) percent.

CFLs also have relatively high attribution factors at 61 or 62 percent. The difference between any of the FY05 low proportions and any of the FY05 higher proportions is statistically significant at better than the 5 percent level (p-value<0.05).

For FY05 HVAC measures, with 90 percent confidence, the true attribution factor for therms is between 34 and 72 percent. For FY05 miscellaneous measures, with 90 percent confidence, the true attribution factor for therms is between 45 and 88 percent. Therefore, for these two end uses, the therms results are inconclusive.

We reviewed the technology detail behind the lighting, HVAC, and manufacturing process measures for possible explanations of why the attribution factors for these end uses changed between FY03/FY04 and FY05. It is not clear to what extent differences in the technology mix between the two time periods is the cause. Although the technology mix may have changed between FY03/FY04 and FY05, the attribution factors for specific technologies also appears to have changed. The attribution factor for a specific technology may have changed because the program changed how it delivers the technology or to whom it is delivering it. It is also possible that the specific technologies we defined (e.g., T8s, T8/T5s, compressed air, energy management systems) are not specific enough (e.g., T8 versus super T8) and the change in the attribution factor is reflecting changes in the underlying mix of still more specific technologies.

5-2



5.2 PARTICIPANT SIZE

Both the FY03/FY04 and FY05 results suggest attribution does not appear to vary very much by participant size, whether size is measured in terms of number of employees or square footage. A priori, we thought larger participants might have lower attribution than smaller participants.

The analysis used the number of employees or the total enclosed square footage at the location(s) the energy efficiency improvements were made. Results were produced for the employment and square footage categories listed in Table 5–1. These categories are based on the distribution of observations available in the FY03/FY04 data, the range end points coincide with the Commercial Buildings Energy Consumption Survey and the Manufacturing Energy Consumption Survey, and they seem reasonable.

Table 5–1. Employment and Square Footage Categories

Size # Employees Square FootageVery Small (VS) Less than 5Small (S) 5 to 49 Less than 5,000Medium (M) 50 to 249 5,001 to 50,000Large (L) 250 to 499 50,001 to 200,000Very Large (VL) More than 499 More than 200,000

5.2.1 FY03/FY04

The FY03/FY04 attribution factors by participant number of employees and by participant square footage are shown in Figure 5–3 and Figure 5–5, respectively. Although attribution does not vary very much by participant size, there appears to be some variation. The differences in attribution factors by participant size that do not seem to be explained by the difference in end-use mix are as follows (see Figure 5–4 and Figure 5–6):

1. Very large participants (measured in terms of number of employees or square footage) have a smaller attribution factor for kW than large participants (p-value<0.084). Very large participants, measured in terms of square footage, also have a smaller attribution factor for kWh than large participants (p-value=0.04).

2. Very large participants, measured in terms of number of employees, have a larger attribution factor for therms than small participants (p-value=0.0042). Very large participants, measured in terms of square footage, have a larger attribution factor for therms than medium participants (p-value=0.09).

3. Medium participants, measured in terms of number of employees, have a larger attribution factor for therms than small participants, 52 percent compared with 29 percent (p-value=0.02).

Other differences in attribution factors (p-value≤0.10) appear to be explained by the difference in end-use mix:

1. Very large participants, measured in terms of square feet, may have smaller attribution factors for electricity than medium participants because they have a smaller proportion of CFLs, which has high attribution, than medium participants.

2. Small participants, measured in terms of number of employees, may have a smaller attribution factor for therms than very small participants and large participants

5-3



because they have a smaller proportion of manufacturing process measures, which has moderate to high attribution, than very small and large participants.

Figure 5–3. FY03/FY04 Attribution Factors by Participant Number of Employees

56%

69%

48%

58%

29%

60%59%

77%

50% 48%

55%54% 52%

66%72%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

kWh kW therms

Att

ribut

ion

Fact

or

LVS

MVL

S

L

VS

M

VL

S

LVS

M

VL

S

Figure 5–4. FY03/FY04 End-use Mix by Participant Number of Employees

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

CFL Prcss HVAC Ltg Shll Misc

% P

artic

ipan

t-En

d U

se C

ombi

natio

ns

VSSMLVL

5-4



Figure 5–5. FY03/FY04 Attribution Factors by Participant Square Footage

58% 62%59%65% 61%

46% 47%52% 53%

66%72%

0%

20%

40%

60%

80%

100%

kWh kW therms

Att

ribut

ion

Fact

or

ML

SVL

M LS

VLM

L

VL

Note: The estimate of the small participant attribution factor for therms is not shown because the error associated with the estimate is so large that the estimate is essentially meaningless. Although the estimate is based on more than 10 observations (16), the negative therms savings as a result of fuel switching made it impossible to estimate the small participant attribution factor for therms with reasonable accuracy.

Figure 5–6. FY03/FY04 End-use Mix by Participant Square Footage

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%


% P

artic

ipan

t-En

d U

se C

ombi

natio

ns

SMLVL

5-5



5.2.2 FY05

The FY05 attribution factors by participant square footage and by participant number of employees are shown in Figure 5–7 and Figure 5–8, respectively. Medium participants measured in terms of number of employees have a larger attribution factor for kW than very small participants (p-value=0.03) as well as small participants (p-value=0.0061). This is the only evidence attribution varies by participant size, for all other comparisons p-value>0.10.

Furthermore, the difference in the kW attribution factors for medium participants (measured in terms of number of employees) and very small participants or small participants, does not appear to be explained by the difference in end-use mix (see Figure 5–9). In fact, both very small participants and small participants have a larger proportion of measures that have relatively a high attribution factor for kW (lighting and CFLs) and a smaller proportion of measures that have a low attribution factor for kW (manufacturing process and HVAC) than medium participants. In spite of this, however, medium participants have a larger attribution factor for kW than very small participants and small participants.

Figure 5–7. FY05 Attribution Factors by Participant Square Footage

70%

58% 55%61%64%

74%

0%

10%20%

30%40%

50%

60%70%

80%90%

100%

kWh kW

Attr

ibut

ion

Fact

or

SM

L SM

L

5-6



Figure 5–8. FY05 Attribution Factors by Participant Number of Employees

61%

50%

38%

77%

86%

55%64% 62%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

kWh kW therms

Att

ribut

ion

Fact

or

VS S

M

VSS

M

S

M

Figure 5–9. FY05 End-use Mix by Participant Number of Employees

70%

28%

2%

22%

31%

52%

17%

24%

54%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Ltg/CFLs Prcss/HVAC Misc/Shll

% P

artic

ipan

t-En

d U

se C

ombi

natio

ns

VS

S

M VSS

M

VS SM

5-7



5.3 NUMBER OF LOCATIONS

The FY05 analysis found no evidence and the FY03/FY04 analysis found limited evidence attribution varies depending on whether participants operate at a single location or multiple locations (see Figure 5–10 and Figure 5–11, respectively).2 For kWh, kW, and therms, the FY05 attribution factors for participants operating at a single location and participants operating at multiple locations are very similar.

Based on the FY03/FY04 impact evaluation data, it appears participants operating at a single location have a larger attribution factor for therms than participants operating at multiple locations, 81 versus 58 percent (p-value=0.05). A priori, we thought single-location participants might have higher attribution than multiple-location participants.

It appears the differences in the FY03/FY04 attribution factors for electricity are driven by the difference in end-use mix observed for the two types of participants. Single-location participants have a larger proportion of CFLs, which has high attribution, whereas multiple-location participants have a larger proportion of lighting measures (excluding CFLs), which has low attribution.

Figure 5–10. FY05 Attribution Results by Number of Locations

48% 48%51%

26% 25%

53%

0%

10%20%

30%40%

50%

60%70%

80%90%

100%

kWh kW therms

Attr

ibut

ion

Fact

or

OneMulti

One Multi

One Multi

2 Participants were placed into one of three categories: operate at a single location, operate at multiple locations, or a franchise organization. There were less than 10 franchise organizations available for the analysis so these results are not presented.

5-8



Figure 5–11. FY03/FY04 Attribution Factors by Number of Locations

64%

47% 49%58%

81%

61%

0%

10%20%

30%40%

50%

60%70%

80%90%

100%

kWh kW therms

Attr

ibut

ion

Fact

or

One

Multi

One

Multi

One

Multi

Figure 5–12. FY03/FY04 End-use Mix by Number of Locations

30%

5%

9%

34%

10%

2%

23%23%

17%

3%

28%

18%

0%

5%

10%

15%

20%

25%

30%

35%

40%


% P

artic

ipan

t-En

d U

se C

ombi

natio

ns

One

Multi

One Multi

One

Multi

One

Multi

One MultiOne

Multi

5-9



5.4 HEADQUARTERS’ LOCATION3

The FY05 impact evaluation data has fewer than 10 observations on participants’ headquarters located outside Wisconsin. Therefore, only the results based on the FY03/FY04 impact evaluation data are reported here.

Based on the FY03/FY04 impact evaluation data, attribution does not appear to vary very much depending on whether participants’ headquarters are located in Wisconsin or outside Wisconsin (see Figure 5–13). The two types of participants have similar attribution factors for electricity (p-value>0.10) and the difference between their attribution factors for therms may be driven by the difference in end-use mix. Participants headquartered in Wisconsin have a larger proportion of manufacturing process measures, which has moderate to high attribution.

Figure 5–13. FY03/FY04 Attribution Factors by Headquarters’ Location

62%

38%

55% 53%

44%

54%

0%

10%

20%

30%

40%

50%

60%

70%

kWh kW therms

Attr

ibut

ion

Fact

or WI

OutsdWI

WI OutsdWI

WI

OutsdWI

3 These data were only collected for firms that operate at multiple locations, including franchise organizations.

5-10



Figure 5–14. FY03/FY04 End-use Mix by Headquarters’ Location

9% 10%

20%

34%

0%

6%3%

33%

27%

18%

28%

12%

0%

5%

10%

15%

20%

25%

30%

35%

40%


% P

artic

ipan

t-En

d U

se C

ombi

natio

ns

WI

OutsdWI

WI

Out-sdWI

WI

OutsdWI

WI

OutsdWI

WI

Out-sdWI WI

Out-sdWI

5.5 OWN/LEASE

The FY05 analysis found no evidence and the FY03/FY04 analysis found limited evidence attribution varies depending on whether participants own or lease the space they occupy at the location(s) the energy efficiency improvements were made. The FY05 attribution factors for participant-owners and participants that lease are very similar (see Figure 5–15).

For electricity, the FY03/FY04 attribution factors for participant-owners and participants that lease are also very similar. On the other hand, participant-owners have an attribution factor for therms that is 33 percentage points higher than do participants that lease. Furthermore, this result does not appear to be explained by the end-use mix observed for the two types of participants (see Figure 5–17). Participant-owners and participants that lease appear to have a very similar mix of end-uses.

5-11



Figure 5–15. FY05 Attribution Factors by Own/Lease

49% 48%47% 45%

0%

10%20%

30%40%

50%

60%70%

80%90%

100%

kWh kW

Attr

ibut

ion

Fact

or

Ow nLease Ow n

Lease

Figure 5–16. FY03/FY04 Attribution Factors by Own/Lease/Both

69%

52% 50%53%55% 54%

36%

56%

0%

10%

20%

30%

40%

50%

60%

70%

80%

kWh kW therms

Attr

ibut

ion

Fact

or

Ow n Lease BothOw n Lease

Ow n

Lease

Both

5-12



Figure 5–17. FY03/FY04 End-use Mix by Own/Lease

21%

16%

25%

6%

30% 30%

0%

10%

3%

28%

13%

18%

0%

5%

10%

15%

20%

25%

30%

35%


% P

artic

ipan

t-En

d U

se C

ombi

natio

ns

Ow n

Lease

Ow n

Lse

Ow n

Lease

Ow nLease

Ow n LseOw n

Lse

5-13


state of wisconsin department of administration … of wisconsin department of administration ......

Documents