U.S. Department of Energy May 1999

INDIRECT EMPLOYMENT

Indirect employment impacts from ballast standards are defined as net jobs created or eliminated

in the general economy as a consequence of reduced spending by commercial and industrial

sector businesses on electricity, increased spending on the purchase price of ballasts and reduced

spending on new power plants by the utility industry (and the associated indirect effects of those

three factors). Figure 1 shows the estimated national indirect employment impact of four

different electronic ballast standards scenarios which are described in the NES Results section.

Figure 1

To develop these results we used an input/output model of the U.S. economy to estimate the

effects on different major sectors of the U.S. economy most relevant to buildings and their net

impact on jobs. The model was developed by the Office of Building Technologies and State

Programs. This software, ImBuild, is a PC-based economic analysis system that characterizes

the interconnections among 35 sectors as national input-output structural matrices. The model

can be applied to future time periods. The ImBuild output includes employment and wage

income. The impacts of new ballast standards are estimated in the NES spreadsheet as energy

savings (reduced electricity use), energy cost savings, and increased ballast purchase price.

These three (see Figures 2-4 below) impacts are output from NES and input to ImBuild. Direct

employment impacts, those that would occur at ballast manufacturing plants, are discussed in the

EE-RM-97-500 COMMENT 67M

u.s. Department of Energy May 1999 

INDIRECT EMPLOYMENT 

Indirect employment impacts from ballast standards are defmed as net jobs created or eliminated 

in the general economy as a consequence of reduced spending by commercial and industrial 

sector businesses on electricity, increased spending on the purchase price of ballasts and reduced 

spending on new power plants by the utility industry (and the associated indirect effects of those 

three factors). Figure 1 shows the estimated national indirect employment impact of four 

different electronic ballast standards scenarios which are described in the NES Results section. 

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To develop these results we used an input/output model of the U.S. economy to estimate the 

effects on different major sectors of the U.S. economy most relevant to buildings and their net 

impact on jobs. The model was developed by the Office of Building Technologies and State 

Programs. This software, ImBuild, is a PC-based economic analysis system that characterizes 

the interconnections among 35 sectors as national input-output structural matrices. The model 

can be applied to future time periods. The ImBuild output includes employment and wage 

income. The impacts of new ballast standards are estimated in the NES spreadsheet as energy 

savings (reduced electricity use), energy cost savings, and increased ballast purchase price. 

These three (see Figures 2-4 below) impacts are output from NES and input to ImBuild. Direct 

employment impacts, those that would occur at ballast manufacturing plants, are discussed in the 
Manufacturer Impact Analysis Results section.

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EE-RM-97-500 COMMENT 67M

Manufacturer Impact Analysis Results section. 

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EE-RM-97-500 COMMENT 67M

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&:S. Departmentof'.1 ner y April .1.9.99

LIFE CYCLE COST RESULTS

Summary of Results

The life cycle cost savings for product classes and design options analyzed are summarized in the

table below. The column titled Delta LCC gives the change in LCC when switching from the

baseline option of EEM ballast to the listed design option. The column titled % Winners

represents the probability of the design option resulting in reduced LCC.

We performed probability based LCC analysis with a computer program called Crystal Ball. For

each input to the LCC model, a probability based distribution of the input is defined to account

for the variability of the input. Instead of a single "average" value being used to represent the

input in its entirety, the whole distribution is used to calculate the LCC. The output of the LCC

model is a mean LCC savings for each product class as well as a probability distribution or

likelihood of LCC reduction or increase. The input and output distributions are shown in the

Input Distributions and Results section.

Inputs required for calculating LCC include end-user prices for ballasts (which are discussed in

the Price - Efficiency Results section), electricity rates ($/kWh), ballast lifetimes, annual lighting

operating hours, lumens and coefficient of utilization which are discussed below.

Table 1. Su [ULM ary of Delta LCC* Results

Product Class Design Option Sector Delta LCC

Mean (1997$) %Winners**

1F40

T12 CC Commercial -4 7

T12ERS Commercial 4 67

T8 ERS Commercial 16 98

2F40

T12 CC Commercial -2 30

T12 ERS Commercial 6 80

T8 ERS Commercial 25 99

3F40

Tandem-Wired T12 CC Commercial -1 40

T12 ERS Commercial 3 58

T8 ERS Commercial 40 99

Not Tandem-Wired T12 CC Commercial -3 26

EE-RM-97-500 COMMENT 67M

US'. Department of Energy April 1999 

LIFE CYCLE COST RESULTS 

Summary of Results 

The life cycle cost savings for product classes and design options analyzed are summarized in the 

table below. The column titled Delta LCC gives the change in LCC when switching from the 

baseline option of EEM ballast to the listed design option. The column titled % Winners 

represents the probability of the design option resulting in reduced LCC. 

We performed probability based LCC analysis with a computer program called Crystal Ball. For 

each input to the LCC model, a probability based distribution of the input is defined to account 

for the variability of the input. Instead of a single "average" value being used to represent the 

input in its entirety, the whole distribution is used to calculate the LCC. The output of the LCC 

model is a mean LCC savings for each product class as well as a probability distribution or 

likelihood ofLCC reduction or increase. The input and output distributions are shown in the 

Input Distributions and Results section. 

Inputs required for calculating LCC include end-user prices for ballasts (which are discussed in 

the Price - Efficiency Results section), electricity rates ($/kWh), ballast lifetimes, annual lighting 

operating hours, lumens and coefficient of utilization which are discussed below. 

Table 1. Summary of Delta LCC* Results 

Product Class Design Option Sector DeltaLCC 

Mean (1997$) %Winners** 

1F40 

T12CC Commercial -4 7 

T12ERS Commercial 4 67 

T8 ERS Commercial 16 98 

2F40 

T12CC Commercial -2 30 

T12ERS Commercial 6 80 

T8 ERS Commercial 25 99 

3F40 

Tandem-Wired T12CC Commercial -1 40 

T12ERS Commercial 3 58 

T8 ERS Commercial 40 99 

Not Tandem-Wired T12CC Commercial -3 26 
U.S. Department of Energy April .1.999

T12 ERS Commercial 19 98

T8 ERS Commercial 67 100

4F40

T12 CC Commercial 1 47

T12 ERS Commercial 15 90

T8 ERS Commercial 77 100

2F96

T12 EIS Commercial 7 75

T12 EIS Industrial 0 45

2F96HO

T12 CC Commercial 10 89

T12 ERS Commercial 27 98

T12 CC Industrial 1 51

T12 ERS Industrial 15 94

*A positive Delta LCC implies a LCC savings whereas a negative number implies an increase in

LCC

** % ballasts with reduced life cycle cost (winners), noted as "certainty level" by Crystal Ball.

Marginal Electricity Rates

We estimated consumer marginal electricity prices by applying a sample of utility commercial

tariffs to a sample of commercial building energy and demand usage levels. The sample of

electric utility commercial tariffs represented approximately 21 % of commercial customers in the

US. The information obtained for each utility tariff included fixed charges (such as customer

charges), block structure, rates per kilowatt (kWh) by block, and demand charges. We obtained

building energy and demand from a sample of 1323 buildings from a proprietary database

(DAISY) for commercial buildings. We chose the building sample to reflect the distribution of

buildings by type in CBECS and calculated electricity bills for each building with the tariffs that

applied to that building. Then we compared each building's bill with the bill recalculated to

reflect a reduction in energy and demand (such as would be associated with ballast efficiency

improvements). The change in the bill divided by the change in energy usage yields the marginal

electricity price. We derived a distribution of epsilons (the percentage difference between the

corresponding marginal electricity price and average electricity price) weighted by building

energy use and by the energy associated with each tariff.

We used those epsilons along with a distribution of average commercial sector electricity prices

EE-RM-97-500 COMMENT 67M

u.s. Department a/Energy April 1999 

TI2ERS Commercial 19 98 

T8 ERS Commercial 67 100 

4F40 

TI2CC Commercial 1 47 

TI2ERS Commercial 15 90 

T8 ERS Commercial 77 100 

2F96 

TI2 EIS Commercial 7 75 

TI2 EIS Industrial 0 45 

2F96HO 

TI2CC Commercial 10 89 

TI2 ERS Commercial 27 98 

TI2CC Industrial 1 51 

TI2ERS Industrial 15 94 

* A positive Delta LCC implies a LCC savings whereas a negative number implies an increase in 

LCC 

** % ballasts with reduced life cycle cost (winners), noted as "certainty level" by Crystal Ball. 

Marginal Electricity Rates 

We estimated consumer marginal electricity prices by applying a sample of utility commercial 

tariffs to a sample of commercial building energy and demand usage levels. The sample of 

electric utility commercial tariffs represented approximately 21 % of commercial customers in the 

US. The information obtained for each utility tariff included fixed charges (such as customer 

charges), block structure, rates per kilowatt (kWh) by block, and demand charges. We obtained 

building energy and demand from a sample of 1323 buildings from a proprietary database 

(MAISY) for commercial buildings. We chose the building sample to reflect the distribution of 

buildings by type in CBECS and calculated electricity bills for each building with the tariffs that 

. applied to that building. Then we compared each building's bill with the bill recalculated to 

reflect a reduction in energy and demand (such as would be associated with ballast efficiency 

improvements). The change in the bill divided by the change in energy usage yields the marginal 

electricity price. We derived a distribution of epsilons (the percentage difference between the 

corresponding marginal electricity price and average electricity price) weighted by building 

energy use and by the energy associated with each tariff. 

We used those epsilons along with a distribution of average commercial sector electricity prices 
L:S Department of'.Ener`y April 19.99

(which were obtained from EIA data on over 3000 utilities) to generate a distribution of initial

marginal electricity prices for the LCC analysis. The rate of decrease of this initial price is

obtained from the AEO 1999 Reference Case.

We also investigated whether there is any relationship between electricity price and the

percentage of new ballast sales that are magnetic or electronic since NEMA submitted data

which showed these percentages on a state by state basis. To determine if there is a statistically

significant correlation between a state's electricity rate and the percent of ballast or fixture sales

in the state that is electronic, we plotted the percent electronic for ballasts and fixtures versus

average electricity price for all fifty states and performed a linear regression analysis. We found

that the r2 values for the ballast and fixture regressions were 0.05 and 0.02, respectively, showing

that there is not a statistically significant correlation between electricity rate and percent of sales

that are electronic (or magnetic). We therefore believe it is appropriate to use the electricity

price distributions for all electricity customers to represent the distributions for ballast end users

who will be impacted by standards.

Ballast Lifetime

In the 1997 Report, we used an average ballast lifetime of 45,000 hours for both magnetic and

electronic ballasts. A triangular distribution of lifetimes was used in the probability based

sensitivity analysis. The high and low points of the triangle were 80,000 and 33,000 hours. The

topic of ballast lifetimes was discussed at the April 28, 1998 Ballast Workshop. A ballast lifetime

of 50,000 hours for both magnetic and electronic ballasts was agreed on by most stakeholders at

this workshop. We therefore used an average ballast lifetime of 50,000 hours for both magnetic

and electronic ballasts in the analysis. For the variability analysis, we used a Weibull

distribution based on suggestions by the ballast industry that the Weibull distribution most

closely represents the failure pattern for ballasts.

Annual Lighting Hours

The values we used for annual lighting hours in the 1997 Report were based on average values

from energy audits performed by Xenergy, Inc. on over 25,000 building between 1990 and 1994,

as described in Section A.4 of the 1997 Report.

Based on comments, we have performed additional analysis and found that for 4-foot

'commercial ballasts in the Xenergy data set, there is a significant difference in the lighting hours

for magnetic ballasts (annual average 3600 hours/year) and electronic ballasts (annual average

4100 hours/year). We found similar results for other ballast types where enough data were

available to make a comparison (4-foot industrial and 8-foot commercial), and for the Green

Lights data set. This is consistent with the proposal that magnetic ballast end users have lower

average operating hours because users with higher hours are more likely to switch to electronic

ballasts.

EE-RM-97-500 COMMENT 67M

u.s. Department afEnergy April 1.9.9.9 

(which were obtained from EIA data on over 3000 utilities) to generate a distribution of initial 

marginal electricity prices for the LCC analysis. The rate of decrease of this initial price is 

obtained from the ABO 1999 Reference Case. 

We also investigated whether there is any relationship between electricity price and the 

percentage of new ballast sales that are magnetic or electronic since NEMA submitted data 

which showed these percentages on a state by state basis. To determine if there is a statistically 

significant correlation between a state's electricity rate and the percent of ballast or fixture sales 

in the state that is electronic, we plotted the percent electronic for ballasts and fixtures versus 

average electricity price for all fifty states and performed a linear regression analysis. We found 

that the r values for the ballast and fixture regressions were 0.05 and 0.02, respectively, showing 

that there is not a statistically significant correlation between electricity rate and percent of sales 

that are electronic ( or magnetic). We therefore believe it is appropriate to use the electricity 

price distributions for all electricity customers to represent the distributions forballast end users 

who will be impacted by standards. 

Ballast Lifetime 

In the 1997 Report, we used an average ballast lifetime of 45,000 hours for both magnetic and 

electronic ballasts. A triangular distribution oflifetimes was used in the probability based 

sensitivity analysis. The high and low points of the triangle were 80,000 and 33,000 hours. The 

topic of ballast lifetimes was discussed at the Apri128, 1998 Ballast Workshop. A ballast lifetime 

of 50,000 hours for both magnetic and electronic ballasts was agreed on by most stakeholders at 

this workshop. We therefore used an average ballast lifetime of 50,000 hours for both magnetic 

and electronic ballasts in the analysis. For the variability analysis, we used a Weibull 

distribution based on suggestions by the ballast industry that the Weibull distribution most 

closely represents the failure pattern for ballasts. 

Annual Lighting Hours 

The values we used for annual lighting hours in the 1997 Report were based on average values 

from energy audits performed by Xenergy, Inc. on over 25,000 building between 1990 and 1994, 

as described in Section AA of the 1997 Report. 

Based on comments, we have performed additional analysis and found that for 4-foot 

'commercial ballasts in the Xenergy data set, there is a significant difference in the lighting hours 

for magnetic ballasts (annual average 3600 hours/year) and electronic ballasts (annual average 

4100 hours/year). We found similar results for other ballast types where enough data were 

available to make a comparison (4-foot industrial and 8-foot commercial), and for the Green 

Lights data set. This is consistent with the proposal that magnetic ballast end users have lower 

average operating hours because users with higher hours are more likely to switch to electronic 

ballasts. 
US. Department of'Fner }- April .1.9.99

Since standards would impact only those end users who have not already switched to electronic

ballasts, the distribution and point value that should be used for the analysis are those that would

be representative of magnetic ballast users in the year that a ballast efficiency standard would go

into effect. Simply using the distribution and average values for magnetic ballasts from the

Xenergy data set is not likely to be representative of the lighting hours for magnetic users in the

year standards would be implemented because the Xenergy data were taken at a point in time

when only 5% of those surveyed had switched to electronic. Over time , it is expected that the

lighting hours for magnetic users will shift downward, as users with higher lighting hours shift to

electronic ballasts. Lighting hours for electronic ballasts should also shift downward over time,

since it is expected that the earliest adopters will be at the highest hours, and later adopters will

have somewhat lower average hours.

We assumed that 50% of the ballasts in the building stock would be electronic at the time a

standard goes into effect leaving 50% as magnetic. The goal, then, was to estimate the

distribution of lighting hours for magnetic ballasts at a point when their percentage penetration in

buildings is 50%, as follows. It was assumed that the overall lighting hours distribution for all

ballasts would not change as the penetration shifts from magnetic to electronic ballasts.

Therefore, if electronic ballasts were to achieve 100% penetration, their lighting hours

distribution would be the same as the total distribution for electronics plus magnetics. Since the

electronic ballast distribution is expected to shift from the curve derived from the Xenergy data,

where their penetration is 5%, to the curve for all ballasts derived from the Xenergy data with

100% penetration of electronics, the assumption was made that at 50% penetration of electronics,

each point in the distribution would be halfway between the distribution for all ballasts and the

.distribution for electronics at 5% penetration. The magnetic lighting hours distribution was then

estimated for 50% penetration by subtracting the distribution estimated for 50% electronic

penetration from the distribution for all ballasts.

Using this procedure, the projected average lighting hours for electronic ballasts at 50%

penetration is shifted upward from the overall average by half the amount that the 5% average is

shifted upward. For example, the average lighting hours for 4ft commercial electronic ballasts at

5% penetration is 4100 hours per year, which is 14% higher than the average for all ballasts of

approximately 3600 hours per year. The projected lighting hours for 4ft commercial electronic

ballasts at 50% penetration is 7% higher than the total, or 3900 hours/yr (rounded to nearest 100).

At 50% penetration, the estimated magnetic ballast hours are shifted downward from the total by

the same amount that the electronic ballasts are shifted upward (in this case, 7%) so their

estimated operating hours are approximately 3350 and rounded up to 3400 hrs/yr in the table

below. This value was therefore used as the point estimate of lighting hours for 4 ft commercial

magnetic ballasts in the Life Cycle Cost analysis, and the distribution was used in the sensitivity

analysis.

The same procedure was followed to derive magnetic distributions for 8ft commercial and 4ft

industrial ballasts.

EE-RM-97-500 COMMENT 67M

u.s. Department afEnergy April 1999 

Since standards would impact only those end users who have not already switched to electronic 

ballasts, the distribution and point value that should be used for the analysis are those that would 

be representative of magnetic ballast users in the year that a ballast efficiency standard would go 

into effect. Simply using the distribution and average values for magnetic ballasts from the 

Xenergy data set is not likely to be representative of the lighting hours for magnetic users in the 

year standards would be implemented because the Xenergy data were taken at a point in time 

when only 5% of those surveyed had switched to electronic. Over time, it is expected that the 

lighting hours for magnetic users will shift downward, as users with higher lighting hours shift to 

electronic ballasts. Lighting hours for electronic ballasts should also shift downward over time, 

since it is expected that the earliest adopters will be at the highest hours, and later adopters will 

have somewhat lower average hours. 

We assumed that 50% of the ballasts in the building stock would be electronic at the time a 

standard goes into effect leaving 50% as magnetic. The goal, then, was to estimate the 

distribution oflighting hours for magnetic ballasts at a point when their percentage penetration in 

buildings is 50%, as follows. It was assumed that the overall lighting hours distribution for all 

ballasts would not change as the penetration shifts from magnetic to electronic ballasts. 

Therefore, if electronic ballasts were to achieve 100% penetration, their lighting hours 

distribution would be the same as the total distribution for electronics plus magnetics. Since the 

electronic ballast distribution is expected to shift from the curve derived from the Xenergy data, 

where their penetration is 5%, to the curve for all ballasts derived from the Xenergy data with 

100% penetration of electronics, the assumption was made that at 50% penetration of electronics, 

each point in the distribution would be halfway between the distribution for all ballasts and the 

distribution for electronics at 5% penetration. The magnetic lighting hours distribution was then 

estimated for 50% penetration by subtracting the distribution estimated for 50% electronic 

penetration from the distribution for all ballasts. 

Using this procedure, the projected average lighting hours for electronic ballasts at 50% 

penetration is shifted upward from the overall average by half the amount that the 5% average is 

shifted upward. For example, the average lighting hours for 4ft commercial electronic ballasts at 

5% penetration is 4100 hours per year, which is 14% higher than the average for all ballasts of 

approximately 3600 hours per year. The projected lighting hours for 4ft commercial electronic 

ballasts at 50% penetration is 7% higher than the total, or 3900 hours/yr (rounded to nearest 100). 

At 50% penetration, the estimated magnetic ballast hours are shifted downward from the total by 

the same amount that the electronic ballasts are shifted upward (in this case, 7%) so their 

estimated operating hours are approximately 3350 and rounded up to 3400 hrs/yr in the table 

below. This value was therefore used as the point estimate oflighting hours for 4 ft commercial 

magnetic ballasts in the Life Cycle Cost analysis, and the distribution was used in the sensitivity 

analysis. 

The same procedure was followed to derive magnetic distributions for 8ft commercial and 4ft 

industrial ballasts. 
US. Department of'.Ener gy April .19.99

For commercial 8ft HO and industrial 8ft and 8ftHO ballasts, there were not enough data on

electronic ballasts to use this approach. For these ballast types, we shifted all points in the total

distribution down by 7% to represent the magnetic distribution. We chose 7% because it is the

average shift we obtained from total to magnetic lighting hours for 4ft commercial, 4ft industrial,

and 8ft commercial. The table below shows the new and previous values for average annual

operating hours.

Table 2. Previous Values and New Values for Annual Lighting Hours for Ballast Analysis.

Ballast Type and Sector 1997 Report Value (hours/year) New Values (hours/year)

4ft, Commercial 3600 3400

8ft, Commercial 3600 3300

4ft, Industrial 4200 4100

8ft, Industrial 4000 3700

8ftHO, Commercial 4000 3700

8ftHO, Industrial 5000 4600

Lumens and Co-efficient of Utilization

In the previous version of the LCC and NES spreadsheets, we assumed that all fixtures would be

equipped with either T12 or T8 lamps only. The current version of these spreadsheets includes

an additional end-user option - a four-foot fixture equipped with T 12 or T8 lamp(s) and

compatible electronic ballast(s). A T-8 lamp 's diameter is one-half inch less than that of a T-12

lamp. As a result, the photometric data for a fixture equipped with T-8 lamps is different from

the photometric data for the same fixture equipped with T-12 lamps. Therefore, when T12

lamp/ballast systems are compared to T8 lamp/ballast systems, we need to adjust for equal light

output of the two systems by using ratios of mean lumens and coefficient of utilitzation (CU) as

shown in the table below.

Table 3. Mean Lumens and CUs for each Lamp/Ballast System

Level Description Lamp Type Mean Lumens CU

4ft 1 LAMP FIXTURE

0 EEM 1F40T12/ES 2290 0.28

1 cc 1F40T12/ES 2290 0.28

2 ERS T12 1F40T12/ES 2290 0.28

3 ERS T8 1F32T8/RE70 2560 0.29

4 ft 2 L1 FIXTURE

EE-RM-97-500 COMMENT 67M

us. Department of Energy April.l999 

For commercialSft HO and industrialSft and SftHO ballasts, there were not enough data on 

electronic ballasts to use this approach. For these ballast types, we shifted all points in the total 

distribution down by 7% to represent the magnetic distribution. We chose 7% because it is the 

average shift we obtained from total to magnetic lighting hours for 4ft commercial, 4ft industrial, 

and Sft commercial. The table below shows the new and previous values for average annual 

operating hours. 

Table 2. Previous Values and New Values for Annual Lighting Hours for Ballast Analysis. 

Ballast Type and Sector 1997 Report Value (hours/year) New Values (hours/year) 

4ft, Commercial 3600 3400 

Sft, Commercial 3600 3300 

4ft, Industrial 4200 4100 

Sft, Industrial 4000 3700 

SftHO, Commercial 4000 3700 

SftHO, Industrial 5000 4600 

Lumens and Co-efficient oj Utilization 

In the previous version of the LCC and NES spreadsheets, we assumed that all fixtures would be 

equipped with either T12 or TSlamps only. The current version of these spreadsheets includes 

an additional end-user option - a four-foot fixture equipped with Tl2 or T8Iamp(s) and 

compatible electronic ballast(s). A T-81amp's diameter is one-half inch less than that ofa T-12 

lamp. As a result, the photometric data for a fixture equipped with T -8 lamps is different from 

the photometric data for the same fixture equipped with T-121amps. Therefore, when T12 

lamp/ballast systems are compared to TS lamp/ballast systems, we need to adjust for equallight 

output of the two systems by using ratios of mean lumens and coefficient ofutilitzation (CU) as 

shown in the table below. 

Table 3. Mean Lumens and CUs for each Lamp/Ballast System 

Level Description Lamp Type Mean Lumens CU 

4ft I LAMP FIXTURE 

0 EEM 1 F40TI2IES 2290 0.28 

1 CC 1 F40TI2IES 2290 0.28 

2 ERS TI2 1 F40TI2IES 2290 0.28 

3 ERST8 IF32T81RE70 2560 0.29 

4 ft :2 LAMPI FIXTURE 
U.S. Department of'Ener`,, April 1999

0 EEM 2F40T12/ES 4580 0.38

1 CC 2F40T12/ES 4580 0.38

2 ERS T12 2F40T12/ES 4580 0.38

3 ERS T8 2F32T8/RE70 5120 0.41

4 ft 3 LAMP FIXTURE (tandem wired)

0 EEM 1.5X2F40T12/ES 6870 0.47

1 CC 1.5X2F40T12/ES 6870 0.47

2 ERS T12 1.5X2F40T12/ES 6870 0.47

3 ERS T8 1.5X2F40T12/ES 7680 0.51

4 ft 3 LAMP FIXTURE (not tandem wired)

0 EEM 1F40+2F40T12/ES 6870 0.47

1 CC 1F40+2F40T12/ES 6870 0.47

2 ERS T12 3F40T12/ES 6870 0.47

3 ERS T8 3F32T8/RE70 7680 0.51

4 ft 4 LAMP FIXTURE

0 EEM 2X2F40T12/ES 9160 0.69

1 CC 2X2F40T12/ES 9160 0.69

2 ERS 12 4F40T12/ES 9160 0.69

3 ERS T8 4F32T8/RE70 10240 0.79

EE-RM-97-500 COMMENT 67M

US. Department of Energy April 1999 

0 EEM 2F40TI2IES 4580 0.38 

1 CC 2F40TI2IES 4580 0.38 

2 ERS TI2 2F40TI2IES 4580 0.38 

3 ERST8 2F32T81RE70 5120 0.41 

4 ft 3 LAMP FIXTURE (tandem wired) 

0 EEM 1.5X2F40TI2IES 6870 0.47 

1 CC 1.5X2F40TI2IES 6870 0.47 

2 ERS TI2 1.5X2F40TI21ES 6870 0.47 

3 ERST8 1.5X2F40TI2IES 7680 0.51 

4 ft 3 LAMP FIXTURE (not tandem wired) 

0 EEM IF40+2F40TI21ES 6870 0.47 

1 CC 1 F40+2F40TI2IES 6870 0.47 

2 ERS TI2 3F40TI2IES 6870 0.47 

3 ERST8 3F32T81RE70 7680 0.51 

4 ft 4 LAMP FIXTURE 

0 EEM 2X2F40TI21ES 9160 0.69 

1 CC 2X2F40TI21ES 9160 0.69 

2 ERS12 4F40TI2IES 9160 0.69 

3 ERST8 4F32T81RE70 10240 0.79