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 

### u.s. Department of Energy May 1999 

### INDIRECT EMPLOYMENT Indirect employment impacts from ballast standards are defmed as netjobs created or eliminated in the general economy as a consequence ofreduced spending by commercial and industrial sectorbusinesses on electricity, increased spending onthe purchase price ofballasts and reduced spending onnewpowerplants by the utility industry (and the associated indirect effects ofthose three factors). Figure 1 shows the estimated national indirect employment impact offour different electronic ballast standards scenarios which are described inthe NES Results section.

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To develop these results we used an input/output model ofthe U.S. economy to estimate the effects on different major sectors ofthe U.S. economy mostrelevant to buildings and their net impact onjobs. The model was developed by the Office ofBuilding 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 canbe applied to future time periods. The ImBuild output includes employment and wage income. The impacts ofnewballast standards are estimated in the NES spreadsheet as energy savings (reduced electricity use), energy cost savings, and increased ballastpurchase 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 intheManufacturer Impact Analysis Results section.

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### &:S. Department of'.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 ofDelta 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 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 inthe tablebelow. The columntitled Delta LCC gives the change in LCC when switching from the baseline option ofEEMballast to the listed design option. The column titled 

### % Winners represents the probability ofthe design option resulting inreduced LCC. Weperformedprobabilitybased LCC analysis with a computerprogram called Crystal Ball. For each input to the LCC model, a probabilitybased distribution ofthe input is defined to account for the variability ofthe input. Instead ofa single "average" value beingused to represent the input in its entirety, the whole distribution is used to calculate the LCC. The output ofthe LCC model is a mean LCC savings for eachproduct class as well as a probability distribution or likelihood ofLCC reduction orincrease. The input and output distributions are shown inthe Input Distributions and Results section. Inputs required for calculating LCC include end-userprices for ballasts (which are discussed in the Price

- Efficiency Results section), electricityrates ($/kWh), ballast lifetimes, annual lighting operating hours, lumens and coefficient ofutilization which are discussed below.



### Table 1. SummaryofDeltaLCC* 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 26U.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



# 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 DeltaLCC implies a LCC savings whereas a negative number implies an increase in LCC 

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

MarginalElectricityRates We estimated consumer marginal electricity prices by applying a sample ofutility commercial tariffs to a sample ofcommercial building energy and demand usage levels. The sample of electric utility commercial tariffs represented approximately 21% ofcommercial customers inthe US. The information obtained for each utility tariffincluded fixed charges (such as customer charges), block structure, rates perkilowatt (kWh) byblock, 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 bytype in CBECS and calculated electricity bills for each building with the tariffs that

.applied to thatbuilding. Then we compared eachbuilding'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 inthebill divided bythe change in energy usage yields the marginal electricity price. We derived a distribution ofepsilons (the percentage difference between the corresponding marginal electricityprice and average electricity price) weighted bybuilding energy use and bythe energy associated with each tariff. We used those epsilons along with a distribution ofaverage commercial sector electricity pricesL: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.



# u.s. Department afEnergy April 1.9.9.9 (which were obtained from EIA data on over 3000 utilities) to generate a distribution ofinitial marginal electricityprices forthe LCC analysis. The rate ofdecrease ofthis initial price is obtained from the ABO 1999 Reference Case. We also investigated whether there is anyrelationship between electricityprice and the percentage ofnewballast sales that are magnetic or electronic since NEMA submitted data which showed these percentages on a stateby state basis. To determine ifthere is a statistically significant correlationbetween a state's electricity rate and the percent ofballast or fixture sales in the state that is electronic, we plotted thepercent electronic for ballasts and fixtures versus average electricityprice for all fifty states and performed a linearregression 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 ofsales that are electronic ( ormagnetic). We therefore believe itis appropriate to use the electricity price distributions for all electricity customers to represent the distributions forballast end users who will be impactedby standards. 

### BallastLifetime Inthe 1997 Report, we used an average ballast lifetime of45,000 hours for both magnetic and electronic ballasts. A triangular distribution oflifetimes was used intheprobability based sensitivity analysis. Thehigh and low points ofthe triangle were 80,000 and 33,000 hours. The topic ofballast lifetimes was discussed at the Apri128, 1998 Ballast Workshop. A ballast lifetime of50,000 hours for both magnetic and electronic ballasts was agreed onbymost stakeholders at this workshop. Wetherefore used an average ballast lifetime of50,000 hours for both magnetic and electronicballasts inthe analysis. Forthe variability analysis, we used a Weibull distributionbased on suggestions bythe ballast industry that the Weibull distribution most closelyrepresents the failure pattern for ballasts. 

### AnnualLightingHours The values weused for annual lighting hours in the 1997 Report were based on average values from energy audits performedbyXenergy, Inc. on over 25,000 buildingbetween 1990 and 1994, as described in Section

### AAofthe 1997 Report. Based on comments, we have performed additional analysis and found that for 4-foot 'commercial ballasts intheXenergy data set, there 

### is a significant difference inthe lightinghours for magneticballasts (annual average 3600 hours/year) and electronic ballasts (annual average 4100 hours/year). We found similar results for otherballast 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 operatinghours becauseusers with higherhours are more likelyto 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. 

## 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 beused for the analysis are those that would berepresentative ofmagnetic 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 ofthe lighting hours for magnetic users inthe 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. Overtime, it is expected that the lighting hours for magnetic users will shift downward, as users with higher lighting hours shift to electronicballasts. 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% ofthe ballasts inthe 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 oflightinghours for magnetic ballasts at a point when theirpercentagepenetration in buildings is 50%, as follows. Itwas assumed that the overall lighting hours distribution for all ballasts would not change as the penetration shifts from magnetic to electronic ballasts. Therefore, ifelectronic ballasts were to achieve 100% penetration, their lighting hours distribution would bethe 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 theirpenetrationis 5%, to the curve for all ballasts derived from the Xenergy data with 100% penetration ofelectronics, the assumption was made that at 50% penetration ofelectronics, each point inthe distribution would behalfway between the distribution for all ballasts and the distribution for electronics at 5% penetration. The magnetic lighting hours distribution was then estimated for 50% penetrationby subtracting the distribution estimated for 50% electronic penetration from the distribution for all ballasts. Using this procedure, theprojected average lighting hours for electronic ballasts at 50% penetration is shifted upward from the overall average byhalfthe 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 peryear, which is 14% higher than the average for all ballasts of approximately 3600 hours peryear. Theprojected 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 inthe table below. This value was therefore used as the point estimate oflightinghours for 4 ft commercial magneticballasts inthe Life Cycle Cost analysis, and the distribution was used in the sensitivity analysis. The sameprocedure 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 ofthe 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 

## 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. Forthese ballast types, we shifted all points inthe total distribution down by 7% to represent themagnetic distribution. We chose 7% because it is the average shift we obtained from total to magnetic lightinghours for 4ft commercial, 4ftindustrial, and Sft commercial. Thetable below shows the new and previous values for average annual operating hours. 

### Table 2. Previous Values andNew Values for AnnualLighting Hours for BallastAnalysis.

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 ojUtilization In the previous version ofthe LCC and NES spreadsheets, we assumed that all fixtures wouldbe equipped with either T12 orTSlamps only. The current version ofthese spreadsheets includes an additional end-user option - a four-foot fixture equipped with Tl2 orT8Iamp(s) and compatible electronic ballast(s). A T-81amp's diameter is one-halfinch less than that ofaT-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, weneed to adjust for equallight output ofthe two systems byusing ratios ofmean lumens and coefficient ofutilitzation (CU) as shown inthe 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 FIXTUREU.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 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 LAMPFIXTURE (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