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Energy efficiency improvements and investment behavior in small commercial buildings
0360-5442/89 $3.00 + 0.00 CopyrightCJ 1989 Pergamon Press plc
Energy Vol. 14, No. 11, pp. 697-707, 1989 Printedin Great Britain. All rights reserved
Energy Efficiency Improvements and Investment Behavior in Small Commercial Buildings MARVIN J. HOROWITZ ERC International, Portland, OR 97205, USA. (Received
7 October
1988;
received
for
publication
10 August
1989)
Abstract -- Commercial-sector energy-efficiency programs offer many benefits to utilities, including better understanding of the client base and the determinants of program participation and investment, more precise knowledge of the supply of conservation acquired or available at given cost levels, and feedback on program design. A study of the Northeast Utilities EnergyCHECK audit program provides insiits on a number of these issues. Analysis of program data reveals that over 40% of recommended measures were adopted within two years of building audits; that owner occupancy of commercial buildings is an important determinant in consexvation decisions; and that the rate of increase in electrical energy use was slowed for participating buildings.
1. INTRODUCTION
Utility-sponsored energy conservation programs take many forms, from public information campaigns to outright financial subsidies. The wide array of program and financial incentive designs reflects the difficulties utilities are experiencing in developing successful long-term commercial sector conservation strategies. In the commercial and industrial sectors, the great breadth of building types and end uses makes assessments of technical and economical conservation potential highly uncertain; a multiplicity of different financial management and tenure arrangements makes program penetration rates unpredictable.’ As a result, commitments to major conservation acquisition efforts are often discouraged in favor of less ambitious and ad hoc programs. The issues that must be addressed before a major conservation program can be put in place are by now well-known: which end uses and building types have the largest potential savings, which receive the largest number of energy efficiency improvement recommendations, and which recommendations are most cost-effective and have the highest rates of implementation.2~3*4 In the study described here, we explore these issues by analyzing a commercial sector program sponsored by Northeast Utilities. The purposes of the study were to clarify the nature and magnitude of the conservation potential; to identify factors affecting program participation and investment in energy efficiency investments; and to provide qualitative estimates of program-related energy savings.
2. STUDY SAMPLES AND DATABASES
The EnergyCHECK Audit program of Northeast Utilities, first developed in 1981, delivers lowcost energy audits to qualified smaIl commercial customers. The program, which is voluntary and does not provide financial incentives to building owners for making the recommended improvements, employs XENCAP audit software developed by Northeast Utilities and Xenergy, Inc. For research purposes, Northeast Utilities selected four groups of buildings for comparison and analysis (Table l), two of which are samples for which EnergyCHECK audits were received: (a) PAV-I is a sample of 139 building owners who requested and received audits in the first year of the program (1982) and received post-audit visits between one and two years later (post-audit interviews were used primarily to determine which measures had been implemented and at what cost); 697
698
MARVIN J. HOROWITZ
(b) PAV-II is a sample of 108 building owners who received audits in the second year of the program (1983) and received a post-audit visit about one year later; (c) CON-I, the first control group, is a set of 144 building owners who did not request an audit; (d) CON-II is a set of 117 building owners who did not request an audit, but instead were given a free audit in 1985 as part of the evaluation data collection process. The database for this study includes: (1) 54 months of electric utility bills for all four groups; (2) XENCAP audits for PAV-I, PAV-II and CON-II; (3) post-audit visit interviews conducted with building owners/managers in PAV-I (in 1983 and 1985), PAV-II (1985) and CON-II (1985). For many of the analyses, the data collected from the PAV-I and PAV-II groups was combined into a single dataset, since few substantive differences were found in a comparison of sample characteristics. Table 1. Data analysis sample characteristics.
3. IMPLEMJZ~ATION ANALYSIS
Before the XENCAP audit data could be analyzed, a number of decisions were required concerning building and end use groupings. Practically speaking, groups should be broad enough to allow for large samples (so that uncertainty levels in each group are minimized), yet sufficiently narrow that they preserve distinctive group identities. Based on the subsample sizes for the audit program, the original 20 XEZNCAPfacility types were collapsed into six categories: (1) offices, (2) retail stores, (3) food stores, (4) restaurants, (5) schools, and (6) miscellaneous buildings. A second important typology, that of energy conservation measures, was retained in its original XENCAP form. This consisted of eight end use categories, the five major ones being lighting, hot water, air conditioning, heating, and envelope; and the three minor ones being refrigeration, cooking, and miscellaneous. For the first part of this analysis, PAV-I and PAV-II buildings were combined into one experimental group and summary statistics were generated for each end use. Table 2 summarizes the recommendations for all facilities combined. Median values are reported for five descriptive variables related to the overall sample of buildings. Data on the audits and follow-up interviews provides a profile of the number and types of recommendations made and their actual average costs. The analysis reveals that an average of nine recommendations were made per building, and 33% of all recommendations were implemented between the time of the audit and the first year follow-up interviews. Further, an average of $3400 was invested in those buildings in which non-zero cost measures were implemented. More than half of the implemented measures were reported as having zero cost. Many of these recommendations were for operations and maintenance measures. In other cases, the opportunity cost for providing the materials and/or labor may have been minimal, perhaps because these resources might have gone unused otherwise. Of the five major end uses, hot-water measures were the least expensive, averaging $250 per measure, whereas envelope measures were the most expensive, averaging $2,566 per measure.
Energy
efficiency improvements and investment behavior
699
Table 2. Measures recommended and implemented in tbe first year (PAV-I and PAV-II)? Total Racanndcd
Enduses
w
(A) 200
Totals
210
99
136 171 182 31 19 2
~
lnplanmlted
wigs.
Lightina Rot Uatar Air Conditioning Heating Envelope Rafri@aration Cooking RiscelLaneous
1lp1amntad Ranauras
TOW Raawraa
IIeaUWC1)
BldgS.
WI
(Cl
II (D)
f:,
43a 149
103 59 63 116 76 11 5
1
17D 69 04 174 106 11 7 2
39% 46% 35% 43% 19% 26% 21% 33%
176
623
33%
z 564 :
6
, I
Ian
RonZero (F)
212
Average
,Aver8ae
Zero -
cost WI
287
Invastnn?nt Witding
Co8tpar
neargic,
per
S
Bldg. cost, s (II)
(1)
46
1175
:z 37 54 5 na 1
250 70D 2826 3516 310
2% 2566 310
1:
1:
106
S3393
51728
(J)
z
alha PAV-I (PostI) and PAV-II (PostZ) experimentalsapI_ uara colkinad;eadian EUI * 75; median sq ft= 6950; median kyh 0 29500; median full-timean@oyacs = 10; I&ion Dart-time bal+Vaas=3. For non-zero cost mnsuras.
A second analysis involved a comparison of implementation rates one and two years after the XENCAP audit. For this analysis, only data from PAV-I buildings was available. Table 3 presents the PAV-I findings derived from the second post-audit visit using the same forms and surveys as in the first visit. Column B indicates the number of additional recommendations implemented since the first post-audit visit. Table 3. Measures recommended and implemented after the first year (PAV-I).B Avarapa coat pq Mus~ra S End Urea
(J)
Lighting Hot Uatar Air Conditioning Heating Envelopa Refrigeration Cooking RiscclIanaous Totals
I
112 5a
226 811
28 6
106 19 9 2
ii
246 327 20 16 6
:: 25 2
120
1081
144
I
15 1
10 4
15 1
312 15
13% -
1: 25 2 0 _
1: 0 1 1 _
I! 6 2 _ _
1756 1521 1949 1250 _ _
11%
I 64
32
34 7 17 27 34 3
15% It% 12% 11% 10% 11%
ot
0 2
58
124
1
41
SW66
312 15 1756 1404 1z
1
SIIW
'PAWI (Post-21 cxperimantalea#a, eedian full tioe arployecs= ID, median EUI = 60, median part time an@oyns = 3, wdian sq ft = 7300. b For non-zero cost uaasuras.
This comparison shows that: (1) overall implementation rates increased by an average of 11%; (2) the percentage of zero cost measures dropped substantially; and (3) the average costs of implemented measures and the average investment per building are somewhat similar after accounting for a few unusually large investments. The data supports the expectation that zero cost measures are the first to be implemented. Only 32 measures implemented in the second year were zero cost measures (one-third of all implemented measures), as opposed to 188 (58% of those implemented) in the first year. 4.
DECISION ANALYSIS
The decision analysis is designed to isolate the driving forces behind the two key decisions: (1) the decision of a buildii owner/manager to request an audit; and (2) the decision to act on the audit recommendations. The findings from this analysis can be used to develop preliminary market penetration estimates, providing an essential element for long-term resource planning
MARVINJ. Ho~owrrz
700
Model of program participation The model of program participation is formalized by the equation Yi = B’Xi + Ui,
(I)
where Yj is the unobserved response of the owner/manager of building i to choosing to request a audit for a given building; B’ is a vector of unknown parameters; Xi is a vector of explanatory variables related to the decision to request an audit; and ui is an error term.’ Though Yi is unobserved, we can distinguish buildings in the audit program from those which are not; hence Yi = 1 if a building is in the PAV-I or PAV-II group, and 5 = 0 for buildings in the CON-II group. Based on this formulation, the probability of requesting an audit can be viewed as Prob(Y = 1) = Prob(uJ > -B’Xi = l- F(-B’Xi),
(2)
where F is the cumulative distribution function for u. If we assume that the response function has certain realistic properties, such as that it is bounded by 0 and 1 and is flatter at the extremes than it is in the middle, a logistic cumulative distribution for ui can be specified, giving rise to a “logit” model. This model is formalized by F(-B’Xi) = 1 / [l + exp(B’Xi)J or l_ F(-B’X,) = exp(B’Xi) / 1 + exp(B’XJ.
(3)
Many characteristics of buildings and owners/managers might be expected to influence the probability of requesting an audit. For this model, the independent variables selected were the total number of audit recommendations made for each building (SUMREC), the total square footage of the building as reported in the XENCAP audit (TOTFQ, and two categorical variables representing whether or not the occupant owned the building (OWNER) and whether or not the occupant planned to relocate within the next five years (STAYER). Table 4 displays the outcome of the logistic regression model. A positive and statistically significant relationship was found between ownership and the probability of requesting an audit, indicating that this variable is a critical determinant for program participation. An expected positive relationship was also found between the variables representing square footage and tenure and the probability of requesting an audit. However, these variables did not enter significantly into the equation. Table 4. Program participation model: PAV-I and PAV-II (combined) vs. CON-II? Intercept
(Std. Error)
0.583
0.436
SWREC -0.065b (Std. Error) 0.032 PAV = 0 PAY = 1 Total N
74 187 261
STAYER (Std. Error)
1.191 1.065
CUNER (Std. Error)
0.78ab 0.407
Model Chi-Sqare d-f. Probability
TOTFTZ (Std. Error)
0.000 0.000
14.15 4 p = 0.0068
%pendent variable = probabilityof requestingan audit. bbistributeda8 Chi-Square,significantfor p < .lO.
The model indicates that the more measures were recommended, the less likely it was for a building to be in the audit-request group. This finding is an artifact of changes in the XENCAP audit form; an additional 21 conservation measures were suggested to the CON-II group. When these new measures are eliminated from the data set (58 out of 849, no difference is found between groups based on the number of recommendations made per building.
Energy efficiency improvements
701
and investment behavior
Modelsof investmentdecisions Several approaches can be used to analyze energy conservation investment decisions. Qualitatively, one approach is to interview decision-makers to identity the criteria they use to make investrnents’j3’ Quantitatively, analysis can focus on the total dollars invested in conservation or the level of investment, or to view the investment decision as a discrete choice. The first approach is problematic unless a model can be specified in sufficient detail that it can control for the large number of important differences between building types, construction materials, usage patterns and end uses in the commercial sector. For this reason, the latter approach is more practical. Rather than attempting to explain variations in the amount of dollars invested, the analysis focuses on the probability of an investment viewed as a function of its costs and benefits. To initiate this analysis, average simple payback, average audit-estimated savings and average audit-estimated costs per measure were computed for the eight end uses for all buildings combined. These values, as derived from the first year follow-up interviews for PAV-I and PAV-II buildings, are presented for both measures taken and those not taken. Simple payback is defined as total cost of the investment divided by the estimated annual benefit. This assumes that the lifespan of the measure is infinite and that the measure is maintenance-free; that the dollars saved on energy use are constant from year to year; and that the total cost of the measure was incurred in its first year of use. As indicated in Table 5, average payback per measure is lower for the measures adopted than for the measures not taken in each end use. Adopted measures averaged slightly under 18 months payback, whereas avoided measures averaged 28 months. Clearly, the most cost-effective measures in each end use category were, on average, the ones selected for adoption. However, the range of paybacks is relatively broad between end uses, indicating that no consistent payback criterion or hurdle rate exists. Table 5. Payback analysis: PAV-I (Post-l) Total
Total Measures Taken
RecW Weasures B&S.
and PAV-II (Post-2) experimental
atdgs.
Y
AVeregea Payhckb (Years)
Average Estimateg Savings
Average Estima&,ed cost
Yes (G)
Yes (1)
NO (H)
N
Yes
No
(0)
(E)
(F)
1
2
1.54 0.33 0.91 0.79 3.59 _ -
1.70 0.53 1.23 1.54 4.07 1.03 _ 0.18
5571 S 446 224 257
63 :
127 58 55 133 84 J
188
469
1.43
2.34
End Uses
(A)
(6)
Hot Uater Air Conditioning Heating Envelopa Refrigeration Cookirq Iriscellanecus
Lighting
154 88 118 148 159 24 16 2
327 130 171 326 450 :;
::
6
Total
188
1464
CC)
z
samples combined.
z 433
1:; 400
z 313
3: 7462
5560
S574
$820
118
180 1300 2475 _
$1238
No (J) $529 272 273 1335 1943 75 _ 50 1133
'The average payback is the individualcost divided by the individualyearly savings smmd over the unbar of total measures and dividad by the ran&r of maasures. % es = maasures installad,No = measures not installed.
Within each end use, mean audit-estimated paybacks were calculated for buildings in which all, some and none of the recommended measures were implemented. The statistics in Table 6 show several patterns. For all five end uses, the “some” and “all” groups have lower paybacks, on average, than the “none” group. In three of the five end uses the mean payback for the “some” group is lower than for the “all” group. These statistics suggest the possibility of systematic differences in investment behaviors based on the number of measures implemented.
MARVIN J. HOROWITZ
702
Table 6. Paybacks by end use: audit-estimated cost/savings for recommended measures. Payback (Years)
End Use
3.03 2.28 2.91
Lighting
Hot uater
ALL Scl?BZ NOM
0.51 0.11 0.65
Air Conditioning
ALL SOW NOM
1.02 1.23 1.81
Heating
All some N0ll.Z
1.15 1.17 3.79
EllVdopc
ALL S0b.W NaW
5.35 4.69 0.24
As a result of this analysis, three categories of investors were defined for the investment choice modeling effort. These categories segregated investors implementing all recommendations made in an end use (ALL), those implementing at least one recommendation but not all (SOME), and those not implementing any recommendations (NONE). The investment decision models were grouped so that each set of decisions within an end use was considered to be independent of the choices made within the other four end uses. Thus, a building owner’s decision to implement all the lighting recommendations is assumed to be unaffected by the decisions made for the other end use recommendations. This assumption is not unreasonable given that no consistent payback or hurdle rate was found between end uses. The models are also grouped by investor categories. Based on the ordering of paybacks within end uses, those building owners who discriminated among recommendations (i.e., those who declined at least one recommended measure) appear to share more in common with each other than they do with owners who implemented all the recommendations for the end use. Using this general framework and the different pairings of the three groups of investors, several sets of discrete choice models were estimated. Formalization of these models follows the binomial logit program participation model described above, after replacing the dependent variable “program participation” with the probability of being one or the other type of investor. For this analysis, the combined set of PAV-I and PAV-II groups was used. Findings
Findings for the first set of five separate models are presented in Table 7. The independent variables for these models are the total number of specific end use recommendations for each building and the average payback of these recommendations for each building. In general, all five end use models present similar findings. As the number of recommendations per end use increases, the probability of an investment in at least one of them also increases. Further, as the average rate of return increases, the probability of an investment occurring in that end use increases. In the next set of models, we combine the two groups of NONE and SOME of the previous model into one group, and pairs it against the group of buildings for which all the recommended measures in a given end use were implemented. In addition to the two previous independent variables, these models included the number of operating hours for each building and the building age. As shown in Table 8, the models again are generally consistent. As in the previous models, the variable representing the number of end use recommendations for a building is statistically significant.
Energy efficiency improvements
Table 7. Implementation
and investment behavior
703
models by end use.8
Parameter
Lighting
Intercept (Std. Error)
-4.256b 0.911
Recomnendations (Std. Error)
1.894b 0.365
I
Aversge Payback (Std. Error)
Heating
Envelope
Hot water
Air Cond.
-1.35sb 0.536
-l.913b 0.501
-&946b 3.205
-5.6D8b 1.203
0.7lOb 0.217
I
-0.690b 0.307
b I
I
-0.44P 0.189
-0.16p 0.092
5.122b 1.703
2.62Bb 0.625
I
-7.221b 2.472
-0.113 0.191
Dependent
Variable
= 0
74
53
93
34
69
DepemJent Total I
Variable
= 1
113 39
102 49
1:;
::
:
60.17 2
17.76 2
29.36 2
Model Chi-Square (d.f.) Probability 'Dependent
p = 0.0 variable
4. lstrlbuted .
= probability
as Chi-Square,
p = 0.0000
p = 0.0001 of taking
significant
30.26 2
31.76 2
p = 0.0
sane vs. no recmnded
p = 0.0000
measures.
for p < .lO.
However, the findings now indicate that as the number of recommendations increases, the probability of implementing all recommended measures in an end use declines. Most importantly, the coefficient of the variable representing financial criteria is not statistically significant in any of the five end use models. Table 8. Implementation
models by end use.a
Paremeter
Lighting
Heating
Intercept (Std. Error)
-2.228b 0.841
l.16P 0.791
I
Recamwsndations
Envelope
Hot water
1.085 1.847
Air Cond.
5.405b 1.385
,
1.492 1.062
,
-0.35P
-l.OPPb
-1.729b
(Std. Error)
0.204
0.264
0.626
0.650
-0.466 0.442
Average Payback (Std. Error)
0.095 0.148
-0.098 0.121
0.057 0.134
0.320 0.277
-0.236 0.187
OPRCUR (Std. Error)
O.Ollb 0.006
o.Do5 0.006
-0.021 0.019
-O.OICb 0.007
-0.018 0.011
AGEX (Std. Error)
0.330b 0.167
-0.274 0.167
0.133 0.287
0.709b 0.273
-0.341 0.212
Model Chi-Square (d.f.1
10.56 4
I
20.45 4
I
Probability *Dependent
p = 0.0319 variable
bD.lstributed
= probsbility
es Chi-Square,
]
p = 0.0000
of taking
significant
-2.3Wb
16.53 4
29.99 4
I
9.35 4
,
,
I
1 p = 0.0024
f p = 0.0000
1 p = 0.0530
all recomsanded
measures
in en end use.
for p ( .lO.
The latter finding strongly suggests that decision-makers who implement all recommendations for a given end use base their decisions on something beyond simple rate of return. For example, these investors may find it less difficult to contract for a complete lighting renovation rather than to contract separately for individual end use recommendations. Alternatively, the lighting overhaul may have been on their agenda previous to the audit, perhaps for cosmetic rather than energy efficiency reasons. Hence, the first set of models deals with investors who are rational in the narrow sense of the word, i.e., their investment choices can be directly related to simple rates of return. The second set of models reveals another group of investors who ignore narrow rate of return criteria, perhaps for
704
MARVINJ. HOROWITZ
broader goals. Of course, the same decision-makers who are in the NONE or SOME category for one end use may well be found in the ALL category for another. This is a reminder that simplistic notions of what constitutes rationality may not have much explanatory power in the face of complex, multi-level choices. Finally, the variables representing building age (AGEX, an age index from 1 to 9) and the number of building operating hours (OPHOUR) exhibit inconsistency across end uses. This finding is a further reminder of the complexity of analyzing investment choice. It indicates that building characteristics that increase the probability of investment in one end use can mitigate against investment in another.
5. ENERGY-SAVINGS
ANALYSIS
The final area of analysis, estimation of program-related electricity savings, is the one least amenable to study. Commercial sector programs target multiple end uses and building types. Hence, inspection of energy consumption histories provides little information about changes in energy use for specific building types and end uses. Disaggregation can only be accomplished by metering or by studying a fuel which is designated for one end use exclusively. Savings by building type can only be studied if a large sample of a particular building type is available.8t9 The energy savings analysis is based on monthly billing data representing electricity consumption for the period from May 1981 to December 1985. Four groups of buildings are used in this analysis. Three of the groups, PAV-I, PAV-II and CON-II, have been studied in the previous analyses which relied on XENCAP audit and/or follow-up interview data. A fourth group of buildings, CON-I, is now introduced. For this group, only utility billing records were available. To use the most recently collected consumption data, four periods were designated. Due to middle-of-the-month and irregular meter reading cycles, these periods begin no earlier than December 1 of each year and end no later than January 1 of the next year. By spanning 13 months each period is thus assured of at least one full year of data. For comparisons to be made between the study groups for pre- and post-program electricity consumption, the periods are divided in a way which roughly conforms to the timing of audits and interviews. In addition, to insure valid inter-year comparisons, it was necessary that each year consist of identical months. The initial period or “PERIODI” dates from December 1981 to January 1983 (in other words, PERIOD1 is slightly more than the full year of 1982). The second, third and fourth periods, referred to as PERIOD2 (1983) PERIOD3 (1984) and PERIOD4 (1985) follow the same annual cycle. This scheme resulted in approximately the same number of days in each annual period for each building. The analysis of annual changes in consumption was performed using the data in its raw form. Weather-adjustment or normalization of the data in any way was not called for, since many of the targeted end uses are not weather-related. Moreover, few buildings in the sample were heated electrically. A preliminary attempt was made to compute average electricity consumption by facility type for each study period. However, the large variation in electricity consumption within each category, coupled with the small sample sizes, rendered the results of this analysis unreliable. As an altemative, in each of the four study groups buildings were differentiated by low, medium and high levels of electricity use. This breakdown allows for roughly equal numbers of buildings in each group, and avoids the problem of analyzing buildings with highly disparate energy consumption profiles. Only buildings for which billing histories were present for more than 270 days per study period are included in the energy savings analysis. Buildings in Level 1 are those in which base year usage fell between 1,000 and 25,ooO kWh. Level 2 buildings are those with base usage between 25,000 and 125,000 kWh/year, and Level 3 buildings consumed more than 125,000 kWh in the base year.
Energy
efficiency
improvements
and investment behavior
705
To study the effects of the EnergyCHECK program on electricity use, annual percentage changes were computed for each building. The following are the relevant comparisons within each consumption level: (a) Level 1: for the change from PERIOD1 to PERIOD5 PAV-I must be compared to PAV-II, CON-I and CON-II; (b) Level 2: for the change from PERIOD2 to PERIOD3, PAV-I must be compared to CON-I and CON-II; now that PAV-II buildings have received audits, the program effects for PAV-II are viewed by comparing its changes to those of CON-I and CON-II; (c) Level 3: for the change from PERIOD3 to PERIOD4, PAV-I and PAV-II must be compared to CON-I only; now CON-II is also a treated group, so it too can be compared to CON-I. The small sample for CON-I in the Level 3 category renders these sets of comparisons somewhat unreliable. The average percentage changes for each group in each period are reported in Table 9 for Levels 1,2 and 3, respectively. As revealed in the tables, in only a few cases did electricity consumption actually decline. Thus, rather than viewing energy savings in terms of net reductions in consumption, “savings’,must be defined in terms of relative rates of increase in consumption. As a general explanation of this phenomenon, it should be pointed out that an upward trend in non-weather related energy use in the commercial sector has been observed by many utilities. This upward trend is attributed to the growing proliferation of office computers, copy machines and other electrical appliances and devices. Table 9. Energy-savings analysis. Mean Usage Net (k\h) Sample Level
N
Period
1
Change in Usage By Period
1-2
2-3
: 23%
ax
3% 0% 6%
3-4
I
PAV-II PAV-I cw-I cow-11
ax
z
13,800 15,300 9,900 10,700
59 38
54,200 66,600
1:3t
-2% 4::
10% 5%
Km-1 COW-11
:t
61,700 52,200
::
4%
E
Level III P&V-I PAW11
519,cOO c53.600
5%
::
-3% -4%
-1;
4 24
241,300 517,500
:z 10%
Level 11 PAV-I PAV-II
CON-II
CON-I
25 20
-:Xx
E -E
-ax -5%
The results of the energy savings analysis are strongest for the change from PERIOD1 to PERIOD2. The evidence indicates that the program did have the effect of slowing the increase in energy use. With one exception (in Level II, where the rate of change for PAV-I was equal to that of CON-I), PAV-I experienced less of a percentage increase in electricity use across all three levels than any of the other three study groups. Looking at consumption changes from PERIOD2 to PERIOD3, PAV-I consumption now appears to be roughly equal to the control groups. Hence, after the initial slowing in electricity consumption, PAV-I buildings appear to have kept pace with the other groups. This indicates that the effect of the program was to produce an initial slowing of the rate of consumption in the year following the audit. Electricity “savings” can thus be said to have remained stable in the second year of the program. The findings are less clear for the PAV-II group. For this group, the increase in electricity use either remains equal to or is midway between those of the remaining study groups. In other words, there does not appear to be any slowing of the increase or “savings” in consumption for the PAV-II group between PERIOD2 and PERIOD3. However, some slowing in the rate of increase in electric
706
MARVINJ. HOROWITZ
consumption can be seen for the PAV-II group in the last period, perhaps indicating that this group experienced a longer lag between the audits and implementation of the recommended measures. Finally, comparison of CON-II with the control group reveals distinct program effects. In Level 1 electric use actually declines, while in Level 2 the rate of increase is one-third of that for the control group. The findings of the model of program participation in the previous section may offer a possible explanation for this result. AS the analysis revealed, the CON-II group received additional recommendations due to an expanded XENCAP audit. This means that, on average, the CON-II buildings were provided with more opportunities for energy efficiency improvements than were the PAVI and PAV-II buildings. Thus, the findings may reflect the fact that more “energy savings” were available to the CON-II buildings. To summarize, the energy savings analysis indicates that, with few exceptions, electricity use increased in all groups at all levels in all years for which data was available. This may be attributable to a general growth trend in non-weather related electricity use in the commercial sector. However, the electricity increase in the group that had just received the energy audit was typically lower than the increase for the control groups.
6. CONCLUSIONS
Our study offers a number of insights into the nature of energy savings in small commercial buildings and the determinants of program participation and investment behavior. These issues are central to planning and implementing long-term commercial sector conservation programs. Our findings indicate that approximately one-third of the recommendations were adopted in the first year after the audit, and another 10% were implemented in the second year. Analysis of the decision to participate in the EnergyCheck program (i.e., to request a low-cost energy audit) revealed that owner occupancy was a relatively important factor in program penetration. Analysis of investment decisions indicated that payback criteria are relevant for implementation decisions within end use categories, but not between categories. Further investigation found that those who adopted all the recommendations within an end use were less sensitive to payback criteria than those who selectively implemented recommendations within an end use. The energy-savings analysis suggested that electrical energy use has been trending upward over time in most commercial buildings. Hence, the impact of the investments in energy efficiency was to slow the rate of change in energy consumption, rather than to cause an absolute decline in consumption levels. Taken together, the findings provide planners and program managers with a good indication of what might be expected from conservation in the small commercial building sector. In addition, this study demonstrates how various analytical tools and databases can be brought together and used to study potential and actual energy conservation.
Energy efficiency improvements
and investment behavior
707
REFERENCES
1. P. G. Sassone and M. V. Martucci, Energy 9,427 (1984). 2.
K. E. Train, P. Ignelzi and M. Kumm, Energy lo,1079 (1985).
3.
K. E. Train and P. C. Ignelzi, Energy 12,543 (1987).
4.
Portland Energy Conservation Inc., “Results of the Evaluation Project of the Commercial Building Energy Audit Loan Program,” Portland, OR (1984).
5.
G. C. Chow, Econometrics, McGraw-Hill, N.Y. (1983).
6.
Applied Management Sciences, Criteria for Building Owners to Invest in Energy Conservation Measures for Their Buildings,” Report C-91, Silver Spring, MD 20910 (1980).
7.
L. A Nieves and J. M. Fang, “Literature Review on Conservation Investment in Non-Residential Buildings,” draft prepared for the Bonneville Power Administration, Battelle Pacific Northwest Laboratory, Richland, WA 99352 (1984).
8.
David M. Dornbusch & Co., “Impact Evaluation: Bonneville Power Administration’s Institutional Buildings Program,” San Francisco, CA (1987).
9.
Washington State Energy Office, ‘The Institutional Buildings Program in Washington State: An Analysis of Program Impacts,” Report WAOENG-87-34 (1987).
Energy Vol. 14, No. 11, pp. 697-707, 1989 Printedin Great Britain. All rights reserved
Energy Efficiency Improvements and Investment Behavior in Small Commercial Buildings MARVIN J. HOROWITZ ERC International, Portland, OR 97205, USA. (Received
7 October
1988;
received
for
publication
10 August
1989)
Abstract -- Commercial-sector energy-efficiency programs offer many benefits to utilities, including better understanding of the client base and the determinants of program participation and investment, more precise knowledge of the supply of conservation acquired or available at given cost levels, and feedback on program design. A study of the Northeast Utilities EnergyCHECK audit program provides insiits on a number of these issues. Analysis of program data reveals that over 40% of recommended measures were adopted within two years of building audits; that owner occupancy of commercial buildings is an important determinant in consexvation decisions; and that the rate of increase in electrical energy use was slowed for participating buildings.
1. INTRODUCTION
Utility-sponsored energy conservation programs take many forms, from public information campaigns to outright financial subsidies. The wide array of program and financial incentive designs reflects the difficulties utilities are experiencing in developing successful long-term commercial sector conservation strategies. In the commercial and industrial sectors, the great breadth of building types and end uses makes assessments of technical and economical conservation potential highly uncertain; a multiplicity of different financial management and tenure arrangements makes program penetration rates unpredictable.’ As a result, commitments to major conservation acquisition efforts are often discouraged in favor of less ambitious and ad hoc programs. The issues that must be addressed before a major conservation program can be put in place are by now well-known: which end uses and building types have the largest potential savings, which receive the largest number of energy efficiency improvement recommendations, and which recommendations are most cost-effective and have the highest rates of implementation.2~3*4 In the study described here, we explore these issues by analyzing a commercial sector program sponsored by Northeast Utilities. The purposes of the study were to clarify the nature and magnitude of the conservation potential; to identify factors affecting program participation and investment in energy efficiency investments; and to provide qualitative estimates of program-related energy savings.
2. STUDY SAMPLES AND DATABASES
The EnergyCHECK Audit program of Northeast Utilities, first developed in 1981, delivers lowcost energy audits to qualified smaIl commercial customers. The program, which is voluntary and does not provide financial incentives to building owners for making the recommended improvements, employs XENCAP audit software developed by Northeast Utilities and Xenergy, Inc. For research purposes, Northeast Utilities selected four groups of buildings for comparison and analysis (Table l), two of which are samples for which EnergyCHECK audits were received: (a) PAV-I is a sample of 139 building owners who requested and received audits in the first year of the program (1982) and received post-audit visits between one and two years later (post-audit interviews were used primarily to determine which measures had been implemented and at what cost); 697
698
MARVIN J. HOROWITZ
(b) PAV-II is a sample of 108 building owners who received audits in the second year of the program (1983) and received a post-audit visit about one year later; (c) CON-I, the first control group, is a set of 144 building owners who did not request an audit; (d) CON-II is a set of 117 building owners who did not request an audit, but instead were given a free audit in 1985 as part of the evaluation data collection process. The database for this study includes: (1) 54 months of electric utility bills for all four groups; (2) XENCAP audits for PAV-I, PAV-II and CON-II; (3) post-audit visit interviews conducted with building owners/managers in PAV-I (in 1983 and 1985), PAV-II (1985) and CON-II (1985). For many of the analyses, the data collected from the PAV-I and PAV-II groups was combined into a single dataset, since few substantive differences were found in a comparison of sample characteristics. Table 1. Data analysis sample characteristics.
3. IMPLEMJZ~ATION ANALYSIS
Before the XENCAP audit data could be analyzed, a number of decisions were required concerning building and end use groupings. Practically speaking, groups should be broad enough to allow for large samples (so that uncertainty levels in each group are minimized), yet sufficiently narrow that they preserve distinctive group identities. Based on the subsample sizes for the audit program, the original 20 XEZNCAPfacility types were collapsed into six categories: (1) offices, (2) retail stores, (3) food stores, (4) restaurants, (5) schools, and (6) miscellaneous buildings. A second important typology, that of energy conservation measures, was retained in its original XENCAP form. This consisted of eight end use categories, the five major ones being lighting, hot water, air conditioning, heating, and envelope; and the three minor ones being refrigeration, cooking, and miscellaneous. For the first part of this analysis, PAV-I and PAV-II buildings were combined into one experimental group and summary statistics were generated for each end use. Table 2 summarizes the recommendations for all facilities combined. Median values are reported for five descriptive variables related to the overall sample of buildings. Data on the audits and follow-up interviews provides a profile of the number and types of recommendations made and their actual average costs. The analysis reveals that an average of nine recommendations were made per building, and 33% of all recommendations were implemented between the time of the audit and the first year follow-up interviews. Further, an average of $3400 was invested in those buildings in which non-zero cost measures were implemented. More than half of the implemented measures were reported as having zero cost. Many of these recommendations were for operations and maintenance measures. In other cases, the opportunity cost for providing the materials and/or labor may have been minimal, perhaps because these resources might have gone unused otherwise. Of the five major end uses, hot-water measures were the least expensive, averaging $250 per measure, whereas envelope measures were the most expensive, averaging $2,566 per measure.
Energy
efficiency improvements and investment behavior
699
Table 2. Measures recommended and implemented in tbe first year (PAV-I and PAV-II)? Total Racanndcd
Enduses
w
(A) 200
Totals
210
99
136 171 182 31 19 2
~
lnplanmlted
wigs.
Lightina Rot Uatar Air Conditioning Heating Envelope Rafri@aration Cooking RiscelLaneous
1lp1amntad Ranauras
TOW Raawraa
IIeaUWC1)
BldgS.
WI
(Cl
II (D)
f:,
43a 149
103 59 63 116 76 11 5
1
17D 69 04 174 106 11 7 2
39% 46% 35% 43% 19% 26% 21% 33%
176
623
33%
z 564 :
6
, I
Ian
RonZero (F)
212
Average
,Aver8ae
Zero -
cost WI
287
Invastnn?nt Witding
Co8tpar
neargic,
per
S
Bldg. cost, s (II)
(1)
46
1175
:z 37 54 5 na 1
250 70D 2826 3516 310
2% 2566 310
1:
1:
106
S3393
51728
(J)
z
alha PAV-I (PostI) and PAV-II (PostZ) experimentalsapI_ uara colkinad;eadian EUI * 75; median sq ft= 6950; median kyh 0 29500; median full-timean@oyacs = 10; I&ion Dart-time bal+Vaas=3. For non-zero cost mnsuras.
A second analysis involved a comparison of implementation rates one and two years after the XENCAP audit. For this analysis, only data from PAV-I buildings was available. Table 3 presents the PAV-I findings derived from the second post-audit visit using the same forms and surveys as in the first visit. Column B indicates the number of additional recommendations implemented since the first post-audit visit. Table 3. Measures recommended and implemented after the first year (PAV-I).B Avarapa coat pq Mus~ra S End Urea
(J)
Lighting Hot Uatar Air Conditioning Heating Envelopa Refrigeration Cooking RiscclIanaous Totals
I
112 5a
226 811
28 6
106 19 9 2
ii
246 327 20 16 6
:: 25 2
120
1081
144
I
15 1
10 4
15 1
312 15
13% -
1: 25 2 0 _
1: 0 1 1 _
I! 6 2 _ _
1756 1521 1949 1250 _ _
11%
I 64
32
34 7 17 27 34 3
15% It% 12% 11% 10% 11%
ot
0 2
58
124
1
41
SW66
312 15 1756 1404 1z
1
SIIW
'PAWI (Post-21 cxperimantalea#a, eedian full tioe arployecs= ID, median EUI = 60, median part time an@oyns = 3, wdian sq ft = 7300. b For non-zero cost uaasuras.
This comparison shows that: (1) overall implementation rates increased by an average of 11%; (2) the percentage of zero cost measures dropped substantially; and (3) the average costs of implemented measures and the average investment per building are somewhat similar after accounting for a few unusually large investments. The data supports the expectation that zero cost measures are the first to be implemented. Only 32 measures implemented in the second year were zero cost measures (one-third of all implemented measures), as opposed to 188 (58% of those implemented) in the first year. 4.
DECISION ANALYSIS
The decision analysis is designed to isolate the driving forces behind the two key decisions: (1) the decision of a buildii owner/manager to request an audit; and (2) the decision to act on the audit recommendations. The findings from this analysis can be used to develop preliminary market penetration estimates, providing an essential element for long-term resource planning
MARVINJ. Ho~owrrz
700
Model of program participation The model of program participation is formalized by the equation Yi = B’Xi + Ui,
(I)
where Yj is the unobserved response of the owner/manager of building i to choosing to request a audit for a given building; B’ is a vector of unknown parameters; Xi is a vector of explanatory variables related to the decision to request an audit; and ui is an error term.’ Though Yi is unobserved, we can distinguish buildings in the audit program from those which are not; hence Yi = 1 if a building is in the PAV-I or PAV-II group, and 5 = 0 for buildings in the CON-II group. Based on this formulation, the probability of requesting an audit can be viewed as Prob(Y = 1) = Prob(uJ > -B’Xi = l- F(-B’Xi),
(2)
where F is the cumulative distribution function for u. If we assume that the response function has certain realistic properties, such as that it is bounded by 0 and 1 and is flatter at the extremes than it is in the middle, a logistic cumulative distribution for ui can be specified, giving rise to a “logit” model. This model is formalized by F(-B’Xi) = 1 / [l + exp(B’Xi)J or l_ F(-B’X,) = exp(B’Xi) / 1 + exp(B’XJ.
(3)
Many characteristics of buildings and owners/managers might be expected to influence the probability of requesting an audit. For this model, the independent variables selected were the total number of audit recommendations made for each building (SUMREC), the total square footage of the building as reported in the XENCAP audit (TOTFQ, and two categorical variables representing whether or not the occupant owned the building (OWNER) and whether or not the occupant planned to relocate within the next five years (STAYER). Table 4 displays the outcome of the logistic regression model. A positive and statistically significant relationship was found between ownership and the probability of requesting an audit, indicating that this variable is a critical determinant for program participation. An expected positive relationship was also found between the variables representing square footage and tenure and the probability of requesting an audit. However, these variables did not enter significantly into the equation. Table 4. Program participation model: PAV-I and PAV-II (combined) vs. CON-II? Intercept
(Std. Error)
0.583
0.436
SWREC -0.065b (Std. Error) 0.032 PAV = 0 PAY = 1 Total N
74 187 261
STAYER (Std. Error)
1.191 1.065
CUNER (Std. Error)
0.78ab 0.407
Model Chi-Sqare d-f. Probability
TOTFTZ (Std. Error)
0.000 0.000
14.15 4 p = 0.0068
%pendent variable = probabilityof requestingan audit. bbistributeda8 Chi-Square,significantfor p < .lO.
The model indicates that the more measures were recommended, the less likely it was for a building to be in the audit-request group. This finding is an artifact of changes in the XENCAP audit form; an additional 21 conservation measures were suggested to the CON-II group. When these new measures are eliminated from the data set (58 out of 849, no difference is found between groups based on the number of recommendations made per building.
Energy efficiency improvements
701
and investment behavior
Modelsof investmentdecisions Several approaches can be used to analyze energy conservation investment decisions. Qualitatively, one approach is to interview decision-makers to identity the criteria they use to make investrnents’j3’ Quantitatively, analysis can focus on the total dollars invested in conservation or the level of investment, or to view the investment decision as a discrete choice. The first approach is problematic unless a model can be specified in sufficient detail that it can control for the large number of important differences between building types, construction materials, usage patterns and end uses in the commercial sector. For this reason, the latter approach is more practical. Rather than attempting to explain variations in the amount of dollars invested, the analysis focuses on the probability of an investment viewed as a function of its costs and benefits. To initiate this analysis, average simple payback, average audit-estimated savings and average audit-estimated costs per measure were computed for the eight end uses for all buildings combined. These values, as derived from the first year follow-up interviews for PAV-I and PAV-II buildings, are presented for both measures taken and those not taken. Simple payback is defined as total cost of the investment divided by the estimated annual benefit. This assumes that the lifespan of the measure is infinite and that the measure is maintenance-free; that the dollars saved on energy use are constant from year to year; and that the total cost of the measure was incurred in its first year of use. As indicated in Table 5, average payback per measure is lower for the measures adopted than for the measures not taken in each end use. Adopted measures averaged slightly under 18 months payback, whereas avoided measures averaged 28 months. Clearly, the most cost-effective measures in each end use category were, on average, the ones selected for adoption. However, the range of paybacks is relatively broad between end uses, indicating that no consistent payback criterion or hurdle rate exists. Table 5. Payback analysis: PAV-I (Post-l) Total
Total Measures Taken
RecW Weasures B&S.
and PAV-II (Post-2) experimental
atdgs.
Y
AVeregea Payhckb (Years)
Average Estimateg Savings
Average Estima&,ed cost
Yes (G)
Yes (1)
NO (H)
N
Yes
No
(0)
(E)
(F)
1
2
1.54 0.33 0.91 0.79 3.59 _ -
1.70 0.53 1.23 1.54 4.07 1.03 _ 0.18
5571 S 446 224 257
63 :
127 58 55 133 84 J
188
469
1.43
2.34
End Uses
(A)
(6)
Hot Uater Air Conditioning Heating Envelopa Refrigeration Cookirq Iriscellanecus
Lighting
154 88 118 148 159 24 16 2
327 130 171 326 450 :;
::
6
Total
188
1464
CC)
z
samples combined.
z 433
1:; 400
z 313
3: 7462
5560
S574
$820
118
180 1300 2475 _
$1238
No (J) $529 272 273 1335 1943 75 _ 50 1133
'The average payback is the individualcost divided by the individualyearly savings smmd over the unbar of total measures and dividad by the ran&r of maasures. % es = maasures installad,No = measures not installed.
Within each end use, mean audit-estimated paybacks were calculated for buildings in which all, some and none of the recommended measures were implemented. The statistics in Table 6 show several patterns. For all five end uses, the “some” and “all” groups have lower paybacks, on average, than the “none” group. In three of the five end uses the mean payback for the “some” group is lower than for the “all” group. These statistics suggest the possibility of systematic differences in investment behaviors based on the number of measures implemented.
MARVIN J. HOROWITZ
702
Table 6. Paybacks by end use: audit-estimated cost/savings for recommended measures. Payback (Years)
End Use
3.03 2.28 2.91
Lighting
Hot uater
ALL Scl?BZ NOM
0.51 0.11 0.65
Air Conditioning
ALL SOW NOM
1.02 1.23 1.81
Heating
All some N0ll.Z
1.15 1.17 3.79
EllVdopc
ALL S0b.W NaW
5.35 4.69 0.24
As a result of this analysis, three categories of investors were defined for the investment choice modeling effort. These categories segregated investors implementing all recommendations made in an end use (ALL), those implementing at least one recommendation but not all (SOME), and those not implementing any recommendations (NONE). The investment decision models were grouped so that each set of decisions within an end use was considered to be independent of the choices made within the other four end uses. Thus, a building owner’s decision to implement all the lighting recommendations is assumed to be unaffected by the decisions made for the other end use recommendations. This assumption is not unreasonable given that no consistent payback or hurdle rate was found between end uses. The models are also grouped by investor categories. Based on the ordering of paybacks within end uses, those building owners who discriminated among recommendations (i.e., those who declined at least one recommended measure) appear to share more in common with each other than they do with owners who implemented all the recommendations for the end use. Using this general framework and the different pairings of the three groups of investors, several sets of discrete choice models were estimated. Formalization of these models follows the binomial logit program participation model described above, after replacing the dependent variable “program participation” with the probability of being one or the other type of investor. For this analysis, the combined set of PAV-I and PAV-II groups was used. Findings
Findings for the first set of five separate models are presented in Table 7. The independent variables for these models are the total number of specific end use recommendations for each building and the average payback of these recommendations for each building. In general, all five end use models present similar findings. As the number of recommendations per end use increases, the probability of an investment in at least one of them also increases. Further, as the average rate of return increases, the probability of an investment occurring in that end use increases. In the next set of models, we combine the two groups of NONE and SOME of the previous model into one group, and pairs it against the group of buildings for which all the recommended measures in a given end use were implemented. In addition to the two previous independent variables, these models included the number of operating hours for each building and the building age. As shown in Table 8, the models again are generally consistent. As in the previous models, the variable representing the number of end use recommendations for a building is statistically significant.
Energy efficiency improvements
Table 7. Implementation
and investment behavior
703
models by end use.8
Parameter
Lighting
Intercept (Std. Error)
-4.256b 0.911
Recomnendations (Std. Error)
1.894b 0.365
I
Aversge Payback (Std. Error)
Heating
Envelope
Hot water
Air Cond.
-1.35sb 0.536
-l.913b 0.501
-&946b 3.205
-5.6D8b 1.203
0.7lOb 0.217
I
-0.690b 0.307
b I
I
-0.44P 0.189
-0.16p 0.092
5.122b 1.703
2.62Bb 0.625
I
-7.221b 2.472
-0.113 0.191
Dependent
Variable
= 0
74
53
93
34
69
DepemJent Total I
Variable
= 1
113 39
102 49
1:;
::
:
60.17 2
17.76 2
29.36 2
Model Chi-Square (d.f.) Probability 'Dependent
p = 0.0 variable
4. lstrlbuted .
= probability
as Chi-Square,
p = 0.0000
p = 0.0001 of taking
significant
30.26 2
31.76 2
p = 0.0
sane vs. no recmnded
p = 0.0000
measures.
for p < .lO.
However, the findings now indicate that as the number of recommendations increases, the probability of implementing all recommended measures in an end use declines. Most importantly, the coefficient of the variable representing financial criteria is not statistically significant in any of the five end use models. Table 8. Implementation
models by end use.a
Paremeter
Lighting
Heating
Intercept (Std. Error)
-2.228b 0.841
l.16P 0.791
I
Recamwsndations
Envelope
Hot water
1.085 1.847
Air Cond.
5.405b 1.385
,
1.492 1.062
,
-0.35P
-l.OPPb
-1.729b
(Std. Error)
0.204
0.264
0.626
0.650
-0.466 0.442
Average Payback (Std. Error)
0.095 0.148
-0.098 0.121
0.057 0.134
0.320 0.277
-0.236 0.187
OPRCUR (Std. Error)
O.Ollb 0.006
o.Do5 0.006
-0.021 0.019
-O.OICb 0.007
-0.018 0.011
AGEX (Std. Error)
0.330b 0.167
-0.274 0.167
0.133 0.287
0.709b 0.273
-0.341 0.212
Model Chi-Square (d.f.1
10.56 4
I
20.45 4
I
Probability *Dependent
p = 0.0319 variable
bD.lstributed
= probsbility
es Chi-Square,
]
p = 0.0000
of taking
significant
-2.3Wb
16.53 4
29.99 4
I
9.35 4
,
,
I
1 p = 0.0024
f p = 0.0000
1 p = 0.0530
all recomsanded
measures
in en end use.
for p ( .lO.
The latter finding strongly suggests that decision-makers who implement all recommendations for a given end use base their decisions on something beyond simple rate of return. For example, these investors may find it less difficult to contract for a complete lighting renovation rather than to contract separately for individual end use recommendations. Alternatively, the lighting overhaul may have been on their agenda previous to the audit, perhaps for cosmetic rather than energy efficiency reasons. Hence, the first set of models deals with investors who are rational in the narrow sense of the word, i.e., their investment choices can be directly related to simple rates of return. The second set of models reveals another group of investors who ignore narrow rate of return criteria, perhaps for
704
MARVINJ. HOROWITZ
broader goals. Of course, the same decision-makers who are in the NONE or SOME category for one end use may well be found in the ALL category for another. This is a reminder that simplistic notions of what constitutes rationality may not have much explanatory power in the face of complex, multi-level choices. Finally, the variables representing building age (AGEX, an age index from 1 to 9) and the number of building operating hours (OPHOUR) exhibit inconsistency across end uses. This finding is a further reminder of the complexity of analyzing investment choice. It indicates that building characteristics that increase the probability of investment in one end use can mitigate against investment in another.
5. ENERGY-SAVINGS
ANALYSIS
The final area of analysis, estimation of program-related electricity savings, is the one least amenable to study. Commercial sector programs target multiple end uses and building types. Hence, inspection of energy consumption histories provides little information about changes in energy use for specific building types and end uses. Disaggregation can only be accomplished by metering or by studying a fuel which is designated for one end use exclusively. Savings by building type can only be studied if a large sample of a particular building type is available.8t9 The energy savings analysis is based on monthly billing data representing electricity consumption for the period from May 1981 to December 1985. Four groups of buildings are used in this analysis. Three of the groups, PAV-I, PAV-II and CON-II, have been studied in the previous analyses which relied on XENCAP audit and/or follow-up interview data. A fourth group of buildings, CON-I, is now introduced. For this group, only utility billing records were available. To use the most recently collected consumption data, four periods were designated. Due to middle-of-the-month and irregular meter reading cycles, these periods begin no earlier than December 1 of each year and end no later than January 1 of the next year. By spanning 13 months each period is thus assured of at least one full year of data. For comparisons to be made between the study groups for pre- and post-program electricity consumption, the periods are divided in a way which roughly conforms to the timing of audits and interviews. In addition, to insure valid inter-year comparisons, it was necessary that each year consist of identical months. The initial period or “PERIODI” dates from December 1981 to January 1983 (in other words, PERIOD1 is slightly more than the full year of 1982). The second, third and fourth periods, referred to as PERIOD2 (1983) PERIOD3 (1984) and PERIOD4 (1985) follow the same annual cycle. This scheme resulted in approximately the same number of days in each annual period for each building. The analysis of annual changes in consumption was performed using the data in its raw form. Weather-adjustment or normalization of the data in any way was not called for, since many of the targeted end uses are not weather-related. Moreover, few buildings in the sample were heated electrically. A preliminary attempt was made to compute average electricity consumption by facility type for each study period. However, the large variation in electricity consumption within each category, coupled with the small sample sizes, rendered the results of this analysis unreliable. As an altemative, in each of the four study groups buildings were differentiated by low, medium and high levels of electricity use. This breakdown allows for roughly equal numbers of buildings in each group, and avoids the problem of analyzing buildings with highly disparate energy consumption profiles. Only buildings for which billing histories were present for more than 270 days per study period are included in the energy savings analysis. Buildings in Level 1 are those in which base year usage fell between 1,000 and 25,ooO kWh. Level 2 buildings are those with base usage between 25,000 and 125,000 kWh/year, and Level 3 buildings consumed more than 125,000 kWh in the base year.
Energy
efficiency
improvements
and investment behavior
705
To study the effects of the EnergyCHECK program on electricity use, annual percentage changes were computed for each building. The following are the relevant comparisons within each consumption level: (a) Level 1: for the change from PERIOD1 to PERIOD5 PAV-I must be compared to PAV-II, CON-I and CON-II; (b) Level 2: for the change from PERIOD2 to PERIOD3, PAV-I must be compared to CON-I and CON-II; now that PAV-II buildings have received audits, the program effects for PAV-II are viewed by comparing its changes to those of CON-I and CON-II; (c) Level 3: for the change from PERIOD3 to PERIOD4, PAV-I and PAV-II must be compared to CON-I only; now CON-II is also a treated group, so it too can be compared to CON-I. The small sample for CON-I in the Level 3 category renders these sets of comparisons somewhat unreliable. The average percentage changes for each group in each period are reported in Table 9 for Levels 1,2 and 3, respectively. As revealed in the tables, in only a few cases did electricity consumption actually decline. Thus, rather than viewing energy savings in terms of net reductions in consumption, “savings’,must be defined in terms of relative rates of increase in consumption. As a general explanation of this phenomenon, it should be pointed out that an upward trend in non-weather related energy use in the commercial sector has been observed by many utilities. This upward trend is attributed to the growing proliferation of office computers, copy machines and other electrical appliances and devices. Table 9. Energy-savings analysis. Mean Usage Net (k\h) Sample Level
N
Period
1
Change in Usage By Period
1-2
2-3
: 23%
ax
3% 0% 6%
3-4
I
PAV-II PAV-I cw-I cow-11
ax
z
13,800 15,300 9,900 10,700
59 38
54,200 66,600
1:3t
-2% 4::
10% 5%
Km-1 COW-11
:t
61,700 52,200
::
4%
E
Level III P&V-I PAW11
519,cOO c53.600
5%
::
-3% -4%
-1;
4 24
241,300 517,500
:z 10%
Level 11 PAV-I PAV-II
CON-II
CON-I
25 20
-:Xx
E -E
-ax -5%
The results of the energy savings analysis are strongest for the change from PERIOD1 to PERIOD2. The evidence indicates that the program did have the effect of slowing the increase in energy use. With one exception (in Level II, where the rate of change for PAV-I was equal to that of CON-I), PAV-I experienced less of a percentage increase in electricity use across all three levels than any of the other three study groups. Looking at consumption changes from PERIOD2 to PERIOD3, PAV-I consumption now appears to be roughly equal to the control groups. Hence, after the initial slowing in electricity consumption, PAV-I buildings appear to have kept pace with the other groups. This indicates that the effect of the program was to produce an initial slowing of the rate of consumption in the year following the audit. Electricity “savings” can thus be said to have remained stable in the second year of the program. The findings are less clear for the PAV-II group. For this group, the increase in electricity use either remains equal to or is midway between those of the remaining study groups. In other words, there does not appear to be any slowing of the increase or “savings” in consumption for the PAV-II group between PERIOD2 and PERIOD3. However, some slowing in the rate of increase in electric
706
MARVINJ. HOROWITZ
consumption can be seen for the PAV-II group in the last period, perhaps indicating that this group experienced a longer lag between the audits and implementation of the recommended measures. Finally, comparison of CON-II with the control group reveals distinct program effects. In Level 1 electric use actually declines, while in Level 2 the rate of increase is one-third of that for the control group. The findings of the model of program participation in the previous section may offer a possible explanation for this result. AS the analysis revealed, the CON-II group received additional recommendations due to an expanded XENCAP audit. This means that, on average, the CON-II buildings were provided with more opportunities for energy efficiency improvements than were the PAVI and PAV-II buildings. Thus, the findings may reflect the fact that more “energy savings” were available to the CON-II buildings. To summarize, the energy savings analysis indicates that, with few exceptions, electricity use increased in all groups at all levels in all years for which data was available. This may be attributable to a general growth trend in non-weather related electricity use in the commercial sector. However, the electricity increase in the group that had just received the energy audit was typically lower than the increase for the control groups.
6. CONCLUSIONS
Our study offers a number of insights into the nature of energy savings in small commercial buildings and the determinants of program participation and investment behavior. These issues are central to planning and implementing long-term commercial sector conservation programs. Our findings indicate that approximately one-third of the recommendations were adopted in the first year after the audit, and another 10% were implemented in the second year. Analysis of the decision to participate in the EnergyCheck program (i.e., to request a low-cost energy audit) revealed that owner occupancy was a relatively important factor in program penetration. Analysis of investment decisions indicated that payback criteria are relevant for implementation decisions within end use categories, but not between categories. Further investigation found that those who adopted all the recommendations within an end use were less sensitive to payback criteria than those who selectively implemented recommendations within an end use. The energy-savings analysis suggested that electrical energy use has been trending upward over time in most commercial buildings. Hence, the impact of the investments in energy efficiency was to slow the rate of change in energy consumption, rather than to cause an absolute decline in consumption levels. Taken together, the findings provide planners and program managers with a good indication of what might be expected from conservation in the small commercial building sector. In addition, this study demonstrates how various analytical tools and databases can be brought together and used to study potential and actual energy conservation.
Energy efficiency improvements
and investment behavior
707
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