# 1 Technical Support Document (TSD)

for the final Transport Rule

Docket ID No. EPA

- HQ

- OAR

- 2009

- 0491



# Power Sector Variability

Final Rule TSD

U.S. Environmental Protection Agency

Office of Air and Radiation

July 201 1

# 2 Power Sector Variability

This Technical Support Document (TSD) provides information in support of

section VI.E, “Approach to Power Sector Emissions Variability,” of the preamble to the

final Transport Rule. This TSD is organized as follows:

1. Introduction

2. Estimating v ariability in power system operations and emissions

3. Numerical simulation of 1 and 3

- year variability

4. Results of an analysis using the air quality assessment tool

5. Variation in electric generation from fossil units relative to total generation.

1. Introductio n.

Section VI of the preamble to the final Transport Rule discusses EPA’s approach to

define “significant contribution” and “interference with maintenance” with respect to the

1997 8

- hour ozone and annual fine particle (PM 2.5 

# ) National Ambient Air Quality

Standards (NAAQS) and the 2006 24

- hour PM 2.5 

# NAAQS

. 

# As discussed in preamble

section VI, EPA has identified the emissions that must be reduced by each state to address

the state’s significant contribution and interference with maintenance. To facilitate

implementation of the requirement that these emissions be eliminated , EPA also developed

SO 2 

# , annual NO X 

# , and ozone

- season NO X 

# state budgets based on its projections of state

by

- state power sector emissions in an average year reflecting these emission redu ctions. 1



# However, because of inherent variability in year

- to

- year baseline emissions

–

resulting from the inherent variability in power system operations

– state

- level emissions

may vary somewhat from year to year even after all the significant contributi on and

interference with maintenance that EPA has identified in the Transport Rule has been

eliminated. Therefore, and for the reasons discussed in preamble sections VI.E and VI.F,

EPA has determined that it is appropriate to develop variability limits fo r each state

1 

## EPA developed annual SO 2 

## and NO X 

## budgets for each state covered for the annual and/or 24

- hour PM 2.5 

## NAAQS and o zone

- season NO X 

## budgets for each state covered for the ozone NAAQS , as discussed in

section VI.D of the final Transport Rule’s preamble

. Table III

- 1 in preamble section III l ists the 27 states

that are c overed by the Transport Rule. As discussed in pream ble section III, EPA will issue a supplemental

proposal to require ozone

- season NO X 

## reductions in 6 additional states.

# 3

budget. These limits are used to identify the range of emissions that EPA believes may

occur in each state following the elimination of all emissions identified by EPA as

significantly contributing to nonattainment or interfering with mainten ance in one or more

other states.

Preamble sections VI.A through VI.D discuss EPA’s approach to quantify for each

upwind state the emissions that significantly contribute to nonattainment or interfere with

maintenance downwind for the existing ozone and PM 2.5 

# NAAQS and to determine state

emissions budgets for SO 2 

# , annual NO X 

# , and ozone season NO X 

# . Preamble sections VI.E

and VI.F discuss the inherent variability in electric power system operations and EPA’s

approach to determine appropriate variability li mits on emissions for each state covered by

the final Transport Rule to account for this variability. As explained in preamble section

VI.E, EPA calculate d variability limits for each state emissions budget. The final

Transport Rule applies those variabi lity limits in addition to the state budgets (which are

based on expected average conditions) to determine the “assurance level” for each state in

each compliance period. The Agency believes that because baseline power system

operations (and therefore emi ssions) are variable at the state level, emissions after the

elimination of all significant contribution and interference with maintenance are also

variable and thus it is appropriate to take this variability into account while assuring that

each state mak es necessary reductions.

In the final Transport Rule, EPA derived “1

- year” variability limits from an

assessment of historical year

- to

- year variability in heat input, which was originally

described in the “Power Sector Variability” TSD from the proposed Transport Rule and is

re

- analyzed in this TSD for all states included in the final Transport Rule, as well as for the

additional states EPA is proposing, through a supplemental proposal, to include in the

Transport Rule ozone program. For the reasons disc ussed in section VI.E of the preamble,

although EPA proposed to implement both 1

- year variability limits and 3

- year average

variability limits, the final Transport Rule decided to finalize only the 1

- year limits

.

Section 3 of this TSD presents an analysis of 1 and 3

- year variability that supports EPA’s

decision to finalize only the 1

- year variability limits in the final rule.

# 4 Section 4 of this TSD describe s an analysis using the air quality assessment tool

(AQAT) to estimate th e resulting air quality effec ts in a given year assuming several

potential degrees of variability in year

- to

- year annual emissions from the power sector

.

Section 5 of this TSD describes an assessment of electric generation from the fossil

sector (i.e., coal, petroleum, and gas) compar ed with the total electric generation of all

sectors (e.g., hydroelectric, nuclear, coal). The results of this analysis are used to put the

variability in generation from the units in the Transport Rule in the broader context of total

electric generation . This reinforces the conclusion that all states have the potential to

experience the same degree of variability observed in the states with the highest historic

variability

.

2. Estimating variability in power system operations and emissions.

This section describes the method that the Agency used to estimate the year

- to

- year

(“1

- year”) variability in power system operations and thereby annual SO 2 

# , annual NO X 

# and

ozone season NO X 

# emissions. As discussed above, the goal of this assessment is to

deter mine the amount of variability in state

- level emissions that is due to inherent

variability in power system operations. To quantify this expected variability, EPA used

variation in historic heat input. EPA considered the use of variation in historic emis sions

but determined this to be inappropriate because it reflected factors such as the installation

of new emission controls and changes in the operation of existing controls. These factors

are not directly related to variability in power system operation s. In fact, these factors are

activities that EPA is attempting to regulate with the implementation of the state budgets

themselves. EPA believes that all controls (existing or newly installed) should be operated

with consistency under the rule’s program s. As such, EPA is assuming a constant

relationship between heat input variability and emission variability for the purposes of this

historic variability analysis (i.e., variation in heat input is a proxy for variation in emissions

holding the effectivene ss of emission control in that state constant). This section in the

TSD provides information on:

The historical data set EPA established on a state

- by

- state basis of yearly heat input

values applicable to each of the pollutants regulated in the final Tran sport Rule

(SO 2 

# , annual NO

x 

# , and ozone season NO

x 

# ). EPA used these historical heat input

# 5 values to estimate the inherent variability in emissions due to power system

operation.

The method EPA utilized to estimate the year

- to

- year variability in the heat i nput

values. The year

- to

- year variability in heat input was estimated on a state

- by

- state

basis.

The approach EPA used to link inherent heat input variability with projected

pollutant emission levels to estimate the resulting variation expected in future

pollutant emissions on a year

- to

- year basis.

(a) Establishing a historical data set for use in estimating inherent variability in

emissions.

The objective of this section is to describe the inherent year

- to

- year (1

- year)

variability in emissions by char acterizing the year

- to

- year variance in total annual heat

input for each state in the Transport Rule. EPA is concerned with variation in total

emissions from year

- to

- year (or the variation in total emissions from one ozone season to

the next), as complian ce with the Transport Rule programs must be demonstrated for total

tonnage emitted during a single year or ozone season. For this assessment, EPA used total

yearly heat input values equaling the sum of heat input from all units operating in each

state dur ing a particular year.

E PA estimated the expected variation in power sector emissions for a yearly time

period based on the “standard deviation” of yearly power sector heat input (HI) assessed

over a n 11

- year time frame (200 0 through 20 10 ). EPA believes s uch data would capture

inherent variation in year

- to

- year emissions due to factors such as variation in power

demand, timing of maintenance activities, and unexpected shutdown of units. These

factors are strongly correlated with heat input and variation i n electric generation.

EPA chose the time period 2000 through 2010 for the analysis of variation in heat

input because it features nearly comprehensive reporting of heat input across many states

and EGU s for the longest available time period

. This is res ponsive to commenters who

said that the variability analysis should begin in 2000 (i.e., utilizing all available data since

EPA began collecting such data from a substantial majority of the fleet starting in that

# 6

year), and that any uneven increases or dec reases in heat input due to changes in fleet

composition and utilization are representative of long

- run year

- to

- year variability in power

system operation. F itting a regression line to this historic data accounts for a systematic

trend in heat input over time, thereby holding that longer

- run trend constant while allowing

for an assessment of year

- to

- year variability around that trend. Similarly, the use of all

available data since 2000, including annual heat input data now available from 2009 and

2010, is responsive to comments suggesting that the analysis include all available data (i.e.,

including heat input from 2009 and 2010) to improve the long

- run approximation of year

to

- year variability impacting the power sector at the state level.

For each year o f the 11

- year time period, EPA estimated total power sector heat

input on a state

- by

- state basis using the sum of reported heat input 2 

# for all units expected to

be covered by the Transport Rule

. Total a nnual heat input values ( in million MMBtu ) for

these units for each state can be found in Table 1. Ozone

- season heat input values for all

units can be found in Table 2. EPA assessed the inherent year

- to

- year variability in annual

annual NO

x 

# , annual SO 2 

# , and ozone season NO

x 

# emissions using the total yearly and ozone

season heat input s summed across all EGU s (Tables 1 and 2 , respectively)

.

In the proposed Transport Rule, the Agency assessed year

- to

- year variability in

annual SO 2 

# emissions using as a proxy the yearly heat input s from coal

- fired EGU s.

Howev er, for the final rule EPA has determined that annual historic heat inputs across all

covered units offer a straightforward and sufficient basis for anticipating potential year

- to

year variability in SO 2 

# emissions at the state level, considering that all s uch units will be

regulated for SO 2 

# control under the Transport Rule. Consequently, EPA analyzed annual

variation in historic heat input for all units to inform the determination of variability limits

for both annual NO

x 

# and SO 2 

# in the final Transport Rul e.

The development of the final Transport Rule included identification of the units

expected to be subject to the Transport Rule programs. Consequently, EPA updated its

variability analysis to include historic heat input at these relevant units. For the final

variability assessment, EPA also removed the limitation which excluded units that had not

operated continuously throughout the time

- period to give a more complete representation

of the year

- to

- year variation in total heat input in each state. EPA n otes that the regression

2 

# As reported to the US EPA Clean Air Markets Division.

# 7

line accounts for longer

- run trends in heat input resulting from the addition of new

generation capacity or increased utilization of existing units.

# 8

Table 1 : Total Annual Heat Input* (million MMBtu ) from All Units Expected to b e in the

Final Transport Rule

.

State 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Average

HI

AL 883.7 845.9 901.2 916.4 904.9 937.0 964.2 1,000.2 939.2 814.6 945.9 913.9

GA 843.1 777.8 842.4 823.9 854.1 949.4 968.9 1,033.6 958.7 860.4 928.2 894.6

IA 392.4 388.8 393.2 390.4 391.2 401.3 396.3 438.1 447.1 398.9 440.0 407.1

IL 887.6 892.3 965.5 974.3 1,040.5 1,051.2 1,036.6 1,083.4 1,063.7 989.5 1,039.8 1,002.2

IN 1,313.3 1,239.7 1,217.3 1,219.1 1,243.8 1,277.2 1,267.6 1,268.2 1,249.0 1,110.7 1,185.3 1,235.6

KS 416.2 398.0 449.7 448.7 429.4 427.0 397.0 429.3 398.6 384.2 391.2 415.4

KY 1,006.9 1,000.1 961.2 934.3 943.4 987.5 1,002.0 1,000.2 990.8 904.2 994.8 975.0

MD 339.5 326.4 338.6 343.6 331.8 346.0 308.3 318.8 288.9 254.8 283.7 316.4

MI 758.4 746.5 751.5 776.7 781.2 811.3 779.5 825.5 813.2 779.2 806.7 784.5

MN 333.6 325.3 355.3 379.5 354.0 360.1 366.2 374.0 372.4 354.0 339.7 355.8

MO 713.2 739.0 752.1 788.7 792.2 813.7 812.5 800.6 770.9 762.2 797.8 776.6

NC 727.0 695.8 723.1 726.2 745.4 773.6 752.8 795.2 791.2 688.2 785.0 745.8

NE 212.0 235.7 230.3 241.6 234.0 245.8 241.2 233.3 240.6 263.2 253.3 239.2

NJ 242.1 228.3 260.7 2 46.4 256.1 265.3 251.0 272.1 273.8 227.2 274.1 254.3

NY 697.6 698.0 667.8 650.5 652.6 689.1 627.4 658.8 600.9 532.0 590.4 642.3

OH 1,311.4 1,236.0 1,304.7 1,327.4 1,261.7 1,351.0 1,327.9 1,367.1 1,324.9 1,169.8 1,228.0 1,291.8

PA 1,111.1 1,082.9 1,181.5 1,232.3 1,305.1 1,329.4 1,302.4 1,364.4 1,340.3 1,316.6 1,406.3 1,270.2

SC 396.2 377.3 418.6 412.0 451.3 464.7 471.3 483.1 472.9 435.1 480.7 442.1

TN 633.3 610.5 635.7 581.4 592.0 593.0 613.7 624.5 582.6 411.8 452.8 575.6

TX 2,581.8 2,550.6 2,778.0 2,866.6 2,897.2 3,034.3 3,050.9 3,100.2 3,103.7 2,978.1 3,074.1 2,910.5

VA 395.5 411.7 401.2 447.2 452.2 4 63.4 404.4 460.0 429.4 383.5 437.3 426.0

WI 490.8 479.4 467.5 487.4 491.8 528.8 494.9 499.7 487.4 451.2 491.1 488.2

WV 893.9 788.9 900.5 908.9 859.0 870.2 871.5 902.8 869.4 689.6 769.3 847.6



# * Source: EPA, June 201 1 . All relevant units in the Transport Rule region. These data are available at

http://www.epa.gov/airmarkets/ th rough Data and Maps.

# 9

Table 2 : Total H eat I nput* (million MMBtu ) from All Units Expected to be in the Final

Transport Rule f

or Ozone Season.

State 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Average

HI

AL 398.0 391.2 427.5 429.0 423.8 427.5 457.0 470.1 432.8 374.2 450.3 425.6

AR 162.5 146.3 136.9 157.6 154.1 146.6 175.9 173.2 153.9 170.9 189.4 160.7

FL 622.2 623.3 701.6 731.3 750.0 788.9 801.5 80 7.8 797.6 785.0 858.1 751.6

GA 410.7 372.1 404.1 383.6 407.4 462.5 469.7 490.1 450.1 414.9 457.3 429.3

IA 164.5 169.5 170.8 161.8 161.8 183.5 168.9 189.9 182.4 165.5 191.4 173.6

IL 404.8 410.2 449.9 427.1 449.7 479.1 458.9 473.4 453.7 420.4 449.9 443.4

IN 543.7 547.1 539.0 530.2 524.5 563.1 551.8 545.3 536.9 461.4 528.4 533.8

KS 188.4 183.2 196.0 192.5 186.1 193.1 184 .1 190.9 172.8 167.7 180.8 185.1

KY 438.1 442.1 432.1 400.1 401.1 434.4 436.5 437.3 419.1 390.2 431.6 423.9

LA 308.4 281.6 296.5 273.9 303.4 305.9 296.2 304.4 300.2 287.0 330.1 298.9

MD 148.8 153.6 163.2 140.1 148.2 164.1 140.3 143.8 130.2 107.2 141.1 143.7

MI 344.1 336.2 346.5 351.0 355.5 387.0 378.2 379.7 354.3 335.8 374.4 358.4

MO 312.7 323.9 337.7 347.6 348.0 354. 3 362.8 357.7 342.0 333.2 358.5 343.5

MS 122.5 145.6 164.2 138.3 155.2 165.2 173.9 190.4 1 71.9 168.7 205.3 163.7

NC 312.3 319.9 334.8 324.6 332.0 359.0 359.6 371.4 363.4 304.8 367.0 340.8

NJ 110.8 112.5 131.1 112.8 128.8 135.1 128.6 136.6 135.8 104.4 139.6 125.1

NY 314.7 330.9 319.0 290.4 288.7 336.2 307.6 302.7 282.7 232.1 304.6 300.9

OH 560.5 531.7 580.2 560.4 542.8 580.0 567.8 602.5 559.1 479.1 559.2 556.7

OK 258.7 254.7 267.0 277.9 262.8 307.9 305.5 29 7.7 303.3 302.6 306.3 285.9

PA 470.8 463.6 530.5 529.1 558.2 606.2 591.9 610.1 576.8 565.5 631.4 557.6

SC 176.4 176.1 203.4 187.6 213.8 223.8 225.2 231.7 230.3 200.8 237.6 209.7

TN 277.8 270.8 291.2 245.2 256.2 270.6 273.8 279.7 261.3 176.2 210.0 255.7

TX 1,240.2 1,234.2 1,323.6 1,361.9 1,361.8 1,465.3 1,493.8 1,445.4 1,486.2 1,464.3 1,515.5 1,399.3

VA 171.8 178.4 183.1 209.9 223.9 227.6 206.9 231.5 200.2 167.0 217.3 201.6

WI 213.1 211.7 214.7 207.2 202.6 240.0 224.6 221.0 205.4 181.1 222.9 213.1

WV 372.8 352.3 379.5 390.9 368.0 382.3 379.5 396.6 373.4 275.8 333.4 364.1



# * Source: EPA, Jun e 2011; All relevant units in the Transport Rule region. These data are available at

http://www.epa.gov/airmarkets/ through Data and Maps.

# 10 (b) Estimating the state

- by

- state variability in heat input using historical heat

input data sets.

This subsecti on describes the method EPA used to estimate the variability in heat

input for each state covered by the final Transport Rule. It repeats some of the relevant

data and calculations presented in the proposed Transport Rule TSD “Power Sector

Variability” wi th updates reflecting the states covered by the final Transport Rule and

updates to the heat input estimates.

In the final Transport Rule, EPA used the method presented in the proposed

Transport Rule and assessed the year

- to

- year variability over the 11

- year time period of the

yearly total heat input values or ozone season heat input values by deriving a standard

deviation for each state while “screening out” overall growth or decline in heat input over

that time period. This method is described in detai l in this and the following subsections

and was selected for two reasons: First, it held constant any longer trend in growth or

decline in a state’s heat input over the baseline period analyzed ; second , a statistical

approach (i.e., using the standard devi ation) is less sensitive to data anomalies present in

finite data sets

- in other words, it effectively screens out extraneous information to

characterize long

- run variability.

For each state, it was important to first adjust the data set to screen out trends

(growth or decline) in heat input over the 11

- year time period, so that the data would then

reflect year

- to

- year variability independent of trends in electricity demand. After

controlling for growth in heat input over time, the year

- to

- year variati on in heat input was

assessed, as the differences between the actual yearly heat input values and the “yearly”

average heat input values estimated according to the trend. T o account for trends , a simple

least

- squares linear regression equation was fit to the heat input data as a function of time.

In other words, this step of the analysis attempts to define the amount of variability in the

data set for each state that can be attributed to a trend over time, which EPA is attempting

to hold constant in this analysis to determine year

- to

- year variability independent of

longer

- run trends in heat input (e.g., rising electricity demand).

This process fits a straight line to the data points using an equation of the form (y =

mx+b). In this equation, “y” is the es timated heat input (million MMBtu) for a particular

# 11

year “x”, m is the slope of the line (with units of million MMBtu/year), and b is the “y

intercept” (the heat input value when the line is extrapolated to x = 0). The value of r 2 

# , the

square of the Pears on product moment correlation coefficient, describes the proportion of

variance in “y” that is accounted for by knowing “x”. It indicates how significant a trend

(growth or decline) is present in the data. Large r 2 

# values (r 2 

# values are maximized at 1)

i ndicate that the least squares fit picks up a strong trend (upward or downward) in the data

which should be removed by the procedure described below. This is important for states

where there was substantial growth or decline in heat input over the time pe riod (i.e., for

states that have slopes for the regression line that are substantially different than zero).

Small r 2 

# values, on the other hand, indicate that the least squares fit did not detect a strong

trend (upward or downward) in the heat input data. Consequently, the procedure described

below has less of a corrective role to play, since there is no strong trend that needs to be

controlled for (held constant) when characterizing the data set’s year

- to

- year variability.

For each state, t he slopes of the regression equations , y

- intercepts, and r 2 

# values can

be found in Tables 3 and 4. Using the slope and y

- intercept from the regression equation

for each state, yearly heat input values were estimated for each state for each year from

200 0 through 20 10 (Tables 3 and 4)

. Year

- to

- year v ariation was assessed for each year or

for each ozone season as the difference between the actual heat input (from Tables 1 and 2)

and the heat input estimated using the regression equation (Tables 3 and 4)

.

# 12

Table 3 : Tot al Annual H eat I nput (million MMBtu ) from 

# All Units Expected to be in the Final Transport Rule f or Each Year Estimated Using

t he Regression Equation.

State Slope of

Linear

Regression

(million

MMBtu/year) Intercept r 2

Value 2000 2001 2002 2003 2004 2005 20 06 2007 2008 2009 2010

AL 4.79

- 8,689 0.09 890.0 894.8 899.6 904.4 909.1 913.9 918.7 923.5 928.3 933.1 937.9

GA 14.90

- 28,986 0.41 820.1 835.0 849.9 864.8 879. 7 894.6 909.5 924.4 939.3 954.2 969.1

IA 4.91

- 9,445 0.52 382.5 387.4 392.3 397.2 402.1 407.1 412.0 416.9 421.8 426.7 431.6

IL 15.08

- 29,242 0.56 926.8 941.9 957.0 972.1 987.1 1,002.2 1,017.3 1,032.4 1,047.5 1,062.6 1,077.6

IN

- 8.53 18,339 0.28 1,278.2 1,269.7 1,261.2 1,252.6 1,244.1 1,235.6 1,227.0 1,218.5 1,210.0 1,201.4 1 ,192.9

KS

- 3.67 7,781 0.28 433.8 430.1 426.4 422.7 419.1 415.4 411.7 408.0 404.4 400.7 397.0

KY

- 1.50 3,986 0.02 982.5 981.0 979.5 978.0 976.5 975.0 973.5 972.0 970.5 969.0 967.5

MD

- 7.15 14,659 0.65 352.2 345.0 337.9 330.7 323.5 316.4 309.2 302.1 294.9 287.8 280.6

MI 5.94

- 11,123 0.55 754.8 760.8 766.7 772.6 778.6 784.5 790.5 796.4 802.3 808.3 814.2

MN 1.79

- 3,241 0.12 346.9 348.7 350.5 352.3 354.0 355.8 357.6 359.4 361.2 363.0 364.8

MO 5.60

- 10,461 0.33 748.6 754.2 759.8 765.4 771.0 776.6 782.2 787.8 793.4 799.0 804.6

NC 5.54

- 10,368 0.24 718.1 723.6 729.2 734.7 740.2 745.8 751.3 756.9 762.4 768.0 773.5

NE 3.07

- 5,923 0.60 223.8 226.9 230.0 233.0 236.1 239.2 242.3 245.3 248.4 251.5 254.5

NJ 2.19

- 4,139 0.18 243.3 245.5 247.7 249.9 252.1 254.3 256.5 258.7 260.9 263.1 265.2

NY

- 12.81 26,330 0.69 706.3 693.5 680.7 667.9 655.1 642.3 629.5 616.7 603.8 591.0 578.2

OH

- 4.33 9,976 0.06 1,313.5 1,309.1 1,304.8 1,300.5 1,296.1 1,291.8 1,287.5 1,283.1 1,278.8 1,274.5 1,270.2

PA 28.62

- 56,114 0.82 1,127.1 1,155.7 1,184.3 1,213.0 1,241.6 1,270.2 1,298.8 1,32 7.4 1,356.1 1,384.7 1,413.3

SC 8.90

- 17,393 0.65 397.6 406.5 415.4 424.3 433.2 442.1 451.0 459.9 468.8 477.7 486.6

TN

- 15.90 32,450 0.51 655.1 639.2 623.3 607.4 591.5 575.6 559.7 543.8 527.9 512.0 496.1

TX 52.45

- 102,244 0.76 2,648.3 2,700.7 2,753.2 2,805.6 2,858.1 2,910.5 2,962.9 3,015.4 3,067.8 3,120.3 3,172.7

VA 1.44

- 2,456 0.03 418.8 420.2 421.7 423.1 424.6 426.0 427.4 428.9 430.3 431.7 433.2

WI

- 0.22 926 0.00 489.3 489.0 488.8 488.6 488.4 488.2 488.0 487.7 487 .5 487.3 487.1

WV

- 10.12 21,141 0.23 898.2 888.1 878.0 867.9 857.8 847.6 837.5 827.4 817.3 807.1 797.0

# 13

Table 4 : Heat I nput (million MMBtu ) from All Units Expected to be in t he Final Transport Rule 

# f or Ozone Season Estimated Using

t he Regression Equation.

State Slope of

Linear

Regression

(million

MMBtu/year) Intercept r 2 Value 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

AL 2.95

- 5,493 0.12 410.8 413.8 416.7 419.7 422.6 425.6 428.5 431.5 434.4 437.4 440.3

AR 3.06

- 5,978 0.43 145.4 148.4 151.5 154.5 157.6 160.7 163.7 166.8 169.9 172.9 17 6.0

FL 21.08

- 41,516 0.85 646.2 667.2 688.3 709.4 730.5 751.6 772.6 793.7 814.8 835.9 857.0

GA 7.44

- 14,481 0.41 392.1 399.6 407.0 414.4 421.9 429.3 436.8 444.2 451.6 459.1 466.5

IA 1.97

- 3,768 0.35 163.8 165.8 167.7 169.7 171.7 173.6 175.6 177.6 179.5 181.5 183.5

IL 3.45

- 6,479 0.22 426.1 429.6 433.0 436.5 439.9 443.4 446.8 450.3 453.7 457.2 460.6

IN

- 3.34 7,235 0.18 550.5 547.1 543.8 540.5 537.1 533.8 530.4 527.1 523.8 520.4 51 7.1

KS

- 1.59 3,372 0.37 193.0 191.4 189.8 188.2 186.6 185.1 183.5 181.9 180.3 178.7 177.1

KY

- 1.54 3,507 0.08 431.6 430.0 428.5 426.9 425.4 423.9 422.3 420.8 419.3 417.7 416.2

LA 1.77

- 3,260 0.15 290.0 291.8 293.6 295.4 297.1 298.9 300.7 302.5 304.2 306.0 307.8

MD

- 2.94 6,037 0.38 158.4 155.4 152.5 149.6 146.6 143.7 140.7 137.8 134.9 131.9 129.0

MI 2.30

- 4,256 0.17 346.9 349.2 351.5 353.8 356.1 358.4 360.7 363.0 365.3 367.6 369.9

MO 2.86

- 5,386 0.37 329.2 332.0 334.9 337.8 340.6 343.5 346.3 349.2 352.1 354.9 357.8

MS 5.93

- 11,731 0.72 134.1 140.0 145.9 151.9 157.8 163.7 16 9.7 175.6 181.5 187.5 193.4

NC 3.82

- 7,324 0.28 321.7 325.5 329.3 333.2 337.0 340.8 344.6 348.5 352.3 356.1 359.9

NJ 1.57

- 3,025 0.17 117.2 118.8 120.4 121.9 123.5 125.1 126.7 128.2 129.8 131.4 132.9

NY

- 4.65 9,621 0.30 324.1 319.5 314.8 310.2 305.5 300.9 296.2 291.6 286.9 282.3 277.6

OH

- 1.55 3,672 0.03 564.4 562.9 561.3 559.8 558.2 556.7 555.1 553.6 552.0 550.4 548.9

OK 5.65

- 11,035 0.75 257.6 263.3 268.9 274.6 280.2 285.9 291. 5 297.1 302.8 308.4 314.1

PA 14.05

- 27,608 0.72 487.4 501.5 515.5 529.5 543.6 557.6 571.7 585.7 599.8 613.8 627.9

SC 5.32

- 10,456 0.62 183.1 188.4 193.7 199.1 204.4 209.7 215.0 220.3 225.7 231.0 236.3

TN

- 6.55 13,385 0.41 288.5 281.9 275.4 268.8 262.3 255.7 249.2 242.6 236.1 229.5 223.0

TX 28.04

- 54,811 0.85 1,259.1 1,287.1 1,315.2 1,343.2 1,371.2 1,399.3 1,427.3 1,455.3 1,483.4 1,511.4 1,539.5

VA 2.37

- 4,541 0.11 189.8 192.1 194.5 196.9 199.2 201.6 204.0 206.3 208.7 211.1 213.4

WI

- 0.47 1,164 0.01 215.5 215.0 214.5 214.1 213.6 213.1 212.6 212.2 211.7 211.2 210.7

WV

- 4.53 9,443 0.19 386.7 382.2 377.6 373.1 368.6 364.1 359.5 355.0 350.5 345.9 341.4



## .

# 14 On a state

- by

- state basis, EPA calculated the difference between the actual heat

input and the estimated heat input using the regression equation for each y ear (2000

through 2010) . The differences for annual and ozone season heat input can be found in

Tables 5 and 6 , respectively. Some of these differences are positive, while others are

negative ; the size of the variation on either side of the regression li ne is meaningful for this

analysis

. EPA a ssess ed the differences between actual and estimated heat input across all

years for each state by calculating the standard deviation.

The standard deviation is defined as the square root of the variance (the sum of the

square of the differences 3 

# divided by the number of samples minus one). The state

- and

pollutant

- specific representative differences (defined using the standard deviation) were

used for the remaining steps in the variability analyses. In using th e standard deviation as

a representative difference, we assume that: (1) differences between the actual and

modeled heat inputs are “normally” distributed; and (2) yearly mean values are

independent.

For each state, t he standard deviation of the differen ces is a conventional statistical

measure of the year

- to

- year (1

- year) variation in heat input

. The statistical definition of a

standard deviation in this context conveys that, on average, 68 percent of all the year

specific heat input values over the lon g run w ould be no farther from the average annual

heat input (either higher or lower) than by the amount measured as the standard deviation

.

The standard deviation can also be used to estimate, on average, the probability of

larger variations in heat in put (for example a difference that we would expect less than 1

percent of the time). Using the standard deviation, “confidence levels ” representing the

variability difference in heat input that could be expected at different probabilities were

found for e ach state for each pollutant. Following the analysis from the proposed

Transport Rule and after reviewing all relevant public comment, t he two

- tailed 9 5 th 

# percent

confidence level was selected as the appropriate level of variation. The two

- tailed 95 th



# pe rcent confidence level indicates that we could expect that , on average, the total heat

3 

## On a s tate

- by

- state basis, the standard deviation was calculated from the set of yearly difference values. As

described, the yearly values were the difference between the actual heat input and the estimated heat input

( using the regression equation ) for each year.

# 15

input for a particular year for a particular state will be within 1.960 standard deviations of

the variation from its mean value 9 5 percent of the time. This statistica l confidence

interval is very commonly used to make future projections from data sets similar to EPA’s

purpose in this analysis. As described in the proposal, EPA believes that using the 95 th



# percent confidence level provides a high degree of confidence t hat sources subject to the

rule will be able to operate within the constraints of the variability limits while still

meeting their obligations to provide a reliable electricity supply across the states affected.

The 95 th 

# percent upper confidence level he at inputs , fractions (the 95 th 

# percent heat

input difference divided by the average heat input over the 200 0

- 20 10 time period) , and

percentages can be found in Tables 5 and 6 for annual and ozone season heat input,

respectively

.

The standard deviation in the heat input in million MMBtu can be found in Tables

5 and 6

. The standard deviation of the heat input was divided by the average heat input

value , resulting in a fraction (when the fraction is multiplied by one hundred, the result is

the standard devi ation percent variation).

# 16

Table 5 : Difference b etween T otal Annual H eat I nput (million MMBtu ) M easured and E stimated from All Units Expected to be in the

Final Transport Rule f

or Each Year Using t he Regression Equation.

State 2000 2001 2002 2003 2004 200 5 2006 2007 2008 2009 2010 Standard

Deviation

of Heat

Input

(million

MMBtu) Average

Heat Input

(2000

- 2010,

from

Table 1) Standard

Deviation

as a

Fraction

of

Average

HI 95%

Confidence

Level

Variability

in HI

(million

MMBtu) 95%

Confidence

Level

Variability

in HI (as a

Fraction of

Avg. HI) 95%

Confidence

Level

Variability

in HI (as a

Percentage

of Avg.

HI)

AL

- 6.3

- 48.8 1.7 12.0

- 4.2 23.0 45.5 76.7 10.9

- 118.5 8.0 50.3 913.9 0.055 98.6 0.108 11%

GA 23.0

- 57.2

- 7.4

- 40.9

- 25.6 54.9 59.4 109.2 19.4

- 93. 8

- 40.9 59.6 894.6 0.067 116.8 0.131 13%

IA 9.9 1.4 0.9

- 6.9

- 11.0

- 5.7

- 15.7 21.2 25.3

- 27.8 8.4 15.8 407.1 0.039 30.9 0.076 8%

IL

- 39.2

- 49.6 8.6 2.2 53.3 49.0 19.3 51.0 16.2

- 73.0

- 37.8 44.0 1,002.2 0.044 86.2 0.086 9%

IN 35.1

- 30.0

- 43.9

- 33.5

- 0.3 41.6 40.6 49.7 39.0

- 90.7

- 7.6 45.6 1,235.6 0.037 89.5 0.072 7%

KS

- 17.6

- 32.1 23.2 25.9 10.3 11.6

- 14.7 21.3

- 5.8

- 16.5

- 5.8 19.5 415.4 0.047 38.2 0.092 9%

KY 24.4 19.1

- 18.3

- 43.8

- 33.1 12.5 28.4 28.2 20.3

- 64.9 27.3 33.8 975.0 0.035 66.3 0.068 7%

MD

- 12.7

- 18.6 0.7 12.9 8.3 29.6

- 1.0 16.7

- 6.0

- 32.9 3.1 17.4 316.4 0.055 34.1 0.108 11%

MI 3.6

- 14.3

- 15.2 4.1 2.6 26.8

- 11.0 29.1 10.9

- 29.1

- 7.6 17.8 784.5 0.023 34.9 0.045 4%

MN

- 13.2

- 23.4 4.9 27.3 0.0 4 .2 8.6 14.6 11.2

- 9.0

- 25.2 16.2 355.8 0.046 31.8 0.089 9%

MO

- 35.4

- 15.2

- 7.7 23.2 21.2 37.1 30.2 12.8

- 22.6

- 36.8

- 6.8 26.3 776.6 0.034 51.5 0.066 7%

NC 8.9

- 27.8

- 6.0

- 8.5 5.2 27.9 1.5 38.3 28.8

- 79.7 11.6 32.5 745.8 0.044 63.7 0.085 9%

NE

- 11.8 8.8 0.3 8.6

- 2.1 6.6

- 1.1

- 12.0

- 7.8 11.7

- 1.3 8.3 239.2 0.035 16.2 0.068 7%

NJ

- 1.2

- 17.2 13.0

- 3.5 4.0 11.1

- 5.5 13.4 12.9

- 35.8 8.8 15.3 254.3 0.060 30.1 0.118 12%

NY

- 8.7 4.5

- 12.9

- 17.5

- 2.5 46.8

- 2.1 42.2

- 2.9

- 59.1 12.2 2 8.6 642.3 0.045 56.1 0.087 9%

OH

- 2.1

- 73.1

- 0.1 27.0

- 34.5 59.2 40.4 83.9 46.1

- 104.7

- 42.2 58.6 1,291.8 0.045 114.8 0.089 9%

PA

- 16.0

- 72.8

- 2.8 19.3 63.5 59.2 3.5 37.0

- 15.8

- 68.1

- 7.0 44.5 1,270.2 0.035 87.2 0.069 7%

SC

- 1.4

- 29.2 3.2

- 12.3 18.1 22.6 20.3 23.2 4.1

- 42.6

- 5.9 21.6 442.1 0.049 42.4 0.096 10%

TN

- 21.7

- 28.7 12.4

- 26.0 0.6 17.4 54.0 80.7 54.7

- 100.2

- 43.3 51.7 575.6 0.090 101.4 0.176 18%

TX

- 66.4

- 150.2 24.9 61.0 39.2 123.8 87.9 84.8 35.9

- 142.2

- 98.6 97.1 2,910.5 0.033 190.4 0.065 7%

VA

- 23.3

- 8.5

- 20.4 24.1 27.7 37.4

- 23.0 31.2

- 0.9

- 48.2 4.1 27.6 426.0 0.065 54.1 0.127 13%

WI 1.6

- 9.7

- 21.4

- 1.3 3.4 40.7 7.0 11.9

- 0.1

- 36.1 4.0 19.3 488.2 0.040 37.8 0.078 8%

WV

- 4.4

- 99.2 22.5 41.0 1 .3 22.6 34.0 75.4 52.1

- 117.6

- 27.7 60.6 847.6 0.071 118.8 0.140 14%

# 17

Table 6 : Difference b etween Total Heat Input Measured a nd Estimated ( m illion MMBtu ) f rom All Units Expected to be in the Final

Transport Rule f

or Ozone Season Using t he Regressio n Equation.

State 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Standard

Deviation

of Heat

Input

(million

MMBtu) Average

Heat

Input

(200 0

-

20 10

, from

Table 2 ) Standard

Deviation

as a

Fraction of

Average

HI 95%

Confidence

Level

Variability in

HI (mi llion

MMBtu) 95%

Confidence

Level

Variability in

HI (as a

Fraction of

Avg. HI) 95%

Confidence

Level

Variability in

HI (as a

Percentage of

Avg. HI)

AL

- 12.8

- 22.6 10.8 9.4 1.1 1.9 28.5 38.6

- 1.7

- 63.2 10.0 27.0 425.6 0.063 52.9 0.124 12%

AR 17.2

- 2.1

- 14.6 3.1

- 3.5

- 14.1 12.2 6.4

- 16.0

- 2.0 13.4 11.7 160.7 0.073 22.9 0.142 14%

FL

- 24.0

- 44.0 13.3 21.9 19.5 37.3 28.8 14.1

- 17.2

- 50.9 1.2 29.8 751.6 0.040 58.3 0.078 8%

GA 18.5

- 27.5

- 2.9

- 30.8

- 14.5 33.2 33.0 46.0

- 1.6

- 44.2

- 9.2 29.3 429.3 0.068 57.5 0.134 13%

IA 0.7 3.7 3.1

- 7.9

- 9.9 9.9

- 6.7 12.3 2.9

- 16.0 7.9 9.0 173.6 0.052 17.6 0.101 10%

IL

- 21.3

- 19.3 16.9

- 9.4 9.8 35.7 12.0 23.1 0.0

- 36.8

- 10.7 21.7 443.4 0.049 42.6 0.096 10%

IN

- 6.8

- 0.1

- 4.8

- 10.2

- 12.6 29.3 21.4 18.2 13.2

- 59.0 11.4 24.0 533.8 0.045 47.1 0.088 9%

KS

- 4.6

- 8.2 6.2 4.3

- 0. 5 8.0 0.6 9.1

- 7.5

- 11.0 3.7 6.9 185.1 0.038 13.6 0.074 7%

KY 6.5 12.1 3.6

- 26.9

- 24.3 10.5 14.2 16.5

- 0.1

- 27.5 15.4 17.6 423.9 0.041 34.5 0.081 8%

LA 18.4

- 10.2 3.0

- 21.4 6.3 7.0

- 4.5 2.0

- 4.0

- 19.0 22.4 13.8 298.9 0.046 27.0 0.090 9%

MD

- 9.6

- 1.8 10.7

- 9.5 1.5 20.5

- 0.5 6.0

- 4.6

- 24.7 12.1 12.4 143.7 0.086 24.3 0.169 17%

MI

- 2.8

- 13.0

- 5.0

- 2.8

- 0.6 28.5 17.5 16.7

- 11.0

- 31.9 4.5 16 .6 358.4 0.046 32.6 0.091 9%

MO

- 16.5

- 8.2 2.8 9.8 7.3 10.8 16.5 8.5

- 10.1

- 21.7 0.8 12.4 343.5 0.036 24.2 0.071 7%

MS

- 11.6 5.5 18.3

- 13.6

- 2.6 1.5 4.2 14.8

- 9.7

- 18.8 11.9 12.3 163.7 0.075 24.2 0.148 15%

NC

- 9.4

- 5.6 5.5

- 8.5

- 5.0 18.2 15.0 23.0 11.1

- 51.3 7.1 20.3 340.8 0.060 39.8 0.117 12%

NJ

- 6.4

- 6.3 10.7

- 9.2 5.3 10.0 1.9 8.3 6.0

- 27.0 6.7 11.4 125.1 0.091 22 .3 0.178 18%

NY

- 9.4 11.4 4.1

- 19.7

- 16.8 35.3 11.4 11.1

- 4.2

- 50.2 26.9 23.8 300.9 0.079 46.7 0.155 16%

OH

- 3.9

- 31.2 18.9 0.6

- 15.4 23.4 12.7 48.9 7.1

- 71.4 10.3 31.5 556.7 0.057 61.8 0.111 11%

OK 1.0

- 8.6

- 1.9 3.3

- 17.5 22.1 14.0 0.5 0.5

- 5.8

- 7.7 10.8 285.9 0.038 21.2 0.074 7%

PA

- 16.6

- 37.9 15.0

- 0.4 14.6 48.6 20.2 24.3

- 23.0

- 48.3 3.5 29.0 557.6 0.052 56.8 0.102 10%

SC

- 6.7

- 12.4 9.7

- 11.5 9.4 14.1 10.2 11.3 4.7

- 30.2 1.3 13.7 209.7 0.065 26.9 0.128 13%

TN

- 10.6

- 11.1 15.8

- 23.6

- 6.1 14.9 24.6 37.1 25.3

- 53.3

- 12.9 26.3 255.7 0.103 51.6 0.202 20%

TX

- 18.9

- 52 .9 8.4 18.7

- 9.5 66.0 66.4

- 10.0 2.8

- 47.2

- 23.9 39.2 1,399.3 0.028 76.8 0.055 5%

VA

- 18.0

- 13.7

- 11.4 13.0 24.6 26.0 3.0 25.1

- 8.4

- 44.0 3.9 21.8 201.6 0.108 42.8 0.212 21%

WI

- 2.4

- 3.3 0.2

- 6.9

- 11.0 26.9 12.0 8.8

- 6.3

- 30.1 12.1 14.9 213.1 0.070 29.2 0.137 14%

WV

- 13.9

- 29.9 1.9 17.8

- 0.6 18.3 20.0 41.6 22.9

- 70.2

- 8.0 30.6 364.1 0.084 60.1 0.165 16%

# 18

(c) Estimation of variability i n emissions using the year

- to

- year variability in

historical heat input.

The analysis described in the sections above results in estimated 95 th 

# percent confidence

level historic variabilities in heat input for each state and pollutant (Tables 5 and 6). A s

described in the preamble in section VI.E.2, from these variability values, for both annual and

ozone season, EPA identified a specific variability percentage level for each state defined as the

maximum value assessed across all states. This was 18 perc ent , for annual NO

x 

# and SO 2 

# and 21

percent for ozone season NO

x 

# . The 95 th 

# percent confidence level percentage variability in each

state as well as the final variability percentage can be found in Table 7.

EPA believes that it is reasonable to treat the m aximum state variability percentage

figures for the Transport Rule states for 2000

- 2010 as representative of the level of variability

that may occur in any of these states in the future. Although Tennessee and Virginia have the

highest variability percent ages for 2000 through 2010 among the Transport Rule states, a

multiplicity of factors (such as, for example, level of economic activity, weather, percentage of

generation comprising fossil

- fuel fired generation, and length and number of unplanned outages)

affects a state's total heat input for a given year and thus the variability in a state's heat input over

a period of years. Neither Tennessee nor Virginia seems to be unusual

--- with to regard to these

types of factors

--- among the Transport Rule state s. For example, based on total heat input, total

EGU emissions of SO 2 

# or NO

x 

# , geographic area, or population, neither of these states could be

described as comparable in size to the states EPA classified as “small” in the proposed Transport

Rule (e.g., Co nnecticut and Delaware). By further example, with the central location of these

states within the Transport Rule domain, and moderate latitude and longitudes, EPA expects

other more

- northerly, more

- southerly, or more

- westerly states could experience large r

climatological extremes. Consequently, EPA maintains that the percentage variability

experienced in Tennessee and Virginia can reasonably be regarded as a level that is

representative of what percentage level may occur in any other Transport Rule state in the future.

Moreover, because the percentage variability level is then applied to the individual state

- specific

budgets, the resulting variability limit , expressed in tons for each state, is a state

- specific value.

For these reasons, and for the rea sons described in preamble section VI.E, EPA is taking the

# 19

straight forward approach of applying to all Transport Rule states the highest historical

percentage variability for the Transport Rule states for 2000 through 2010.

As described in section VI.E. 2 of the preamble, the final step in estimating state

- by

- state

year

- to

- year (1

- year) variability in emissions is to convert the variability percentage level in each

state into variability in pollutant emissions (i.e., a “variability limit”). EPA did this by

multiplying the variability percentage level for each pollutant by the state emissions budgets for

each pollutant (section VI.D of the preamble discusses EPA’s approach to determine the final

Transport Rule budgets, and lists the budgets in Tables VI.D

- 3 and VI.D

- 4). For each state,

Tables VI.F

- 1, VI.F

- 2, and VI.F

- 3 list the resulting variability limits for each state for SO 2 

# ,

annual NO X 

# and ozone

- season NO X 

# , respectively.

# 20

Table 7 . Relationship between 95% Confidence Level Historic Variability in Hea t Input

Percentage and Variability Limit Percentage Used in the Transport Rule *

.

SO 2 

## and A nnual NO X 

## Ozone

- Season NO X



## State Historic Variability Transport Rule

Variability Limit Historic Variability Transport Rule

Variability Limit

AL 11% 18% 12% 21%

A R 14% 21%

FL 8% 21%

GA 13% 18% 13% 21%

IL 9% 18% 10% 21%

IN 7% 18% 9% 21%

IA* 8% 18% 10% 21%

KS* 9% 18% 7% 21%

KY 7% 18% 8% 21%

LA 9% 21%

MD 11% 18% 17% 21%

MI* 4% 18% 9% 21%

MN 9% 18%

MS 15% 21%

MO* 7% 18% 7% 21%

NE 7% 18 %

NJ 12% 18% 18% 21%

NY 9% 18% 16% 21%

NC 9% 18% 12% 21%

OH 9% 18% 11% 21%

OK* 7% 21%

PA 7% 18% 10% 21%

SC 10% 18% 13% 21%

TN 18% 18% 20% 21%

TX 7% 18% 5% 21%

VA 13% 18% 21% 21%

WV 14% 18% 16% 21%

WI* 8% 18% 14% 21%

*Indicates a state (IA, KS, MI, OK, WI, and MO) that is included in the supplemental notice of proposed

rulemaking for ozone

- season NO

x 

## emission reductions. See the preamble for details.

# 21

3. Numerical simulation of 1

- and 3

- year variability

For the reasons discussed in s ection VI.E of the preamble, although EPA proposed to

implement both 1

- year variability limits and 3

- year average variability limits, the final Transport

Rule includes only the 1

- year limits

. In the proposal, EPA first established 1

- year variability

limit s (following the procedure outlined above) and then established corresponding 3

- year

variability limits to be statistically indistinguishable from the 1

- year variability limits. In other

words, the proposal’s 3

- year variability limits were calculated on a statistical basis to offer states

the same degree of future variability (based on historic variability) as the 1

- year variability

limits. For the final rule, EPA analyzed the relationship between the 1

- year and 3

- year

variability limits, performing a num erical verification of equal likelihood of potential violations

of a state’s assurance level when evaluating random variation (10 percent at the 95 th 

# percent two

tailed confidence level) around a constant budget.

As discussed in section VI.E of the final Transport Rule preamble, the state

- by

- state

emission s budgets are based on the availability of emission reductions at an equal cost threshold

(within each of the programs). As such, EPA exp ects the sources in each covered state to make

these cost

- effectiv e reductions and to meet the emission budgets each year

. O n average EPA

does not expect any systematic bias in emissions (against the established state budgets) to occur

.

Thus, a non

- biased assessment of variability is appropriate. EPA simulated 1

- and 3

- year

variation around a constant budget for a hypothetical state, and found, as described below,

comparable results affirming that on a statistical basis, the 3

- year variability limits are no more

likely to be exceeded than the 1

- year variability limits.

Using a random

- number generator within Microsoft Excel, EPA numerically simulated

random variation using the 95 th 

# percent two

- tailed confidence level 1

- year variability of 10

percent . The 1

- year variability standard deviation is 10 percent divided by 1.9 60, or 5.1 percent

.

Using the formula, = NORMINV(RAND(),100, (10/1.960) ) , EPA simulated yearly emissions for

a “state” with a budget of 100, with random variation of 5.1 percent . EPA simulated five

consecutive “years” of emissions, then calculated three 3

- year consecutive averages (“years” 1,

2, and 3; 2, 3, and 4; and 3, 4, and 5) . EPA, then, assessed the average number of

“exceedances” of the 1

- year and 3

- year “budgets plus variability limits”. The 1

- year budget plus

variability limit is 110, while th e 3

- year budget plus variability limit (as defined in the proposal)

# 22

is 105.77. EPA simulated 10,000 5

- year time

- periods, and averaged the number of exceedances

of the 1

- year (50,000 data points) and 3

- year limits (30,000 data points). For both the 1

- year and

3

- year cases, the average number of exceedances was 2.5 percent , conforming to the expected

95 th 

# percent two

- tailed confidence exceedance value of 2.5 percent

.

This finding confirms that the proposed 3

- year variability limits are statistically

indisti nguishable from the 1

- year variability limits for each state; therefore, EPA has determined

that the 3

- year variability limits are redundant with the 1

- year variability limits and are thus

unnecessary.

4

. Results of an analysis using the air quality asses sment tool

The objective of this section is to estimate the possible effects of the variability limits on

air quality in 2014. This analysis used the air quality assessment tool, or AQAT, and state

- by

state emissions to estimate downwind state

- by

- state air quality contributions at receptors with

nonattainment and maintenance problems in the CAMx air quality modeling of the 2012 base

case (section VI.C of the final Transport Rule preamble discusses AQAT; section V of the final

Transport Rule preamble discuss es air quality modeling). This analysis is similar to the analyses

performed for the proposed Transport Rule (and reported in the proposed Transport Rule “Power

Sector Variability” TSD). It concludes that, as estimated using AQAT, while variability in

po wer system operation (and thereby SO 2 

# emissions from EGUs under the Transport Rule) does

have perceptible downwind air quality effects on PM 2.5 

# concentrations, these effects are minimal.

EPA believes that variation in other factors such as SO 2 

# and NO

x 

# emi ssion patterns from non

EGU sources, contributions from other pollutants (i.e., ammonia, elemental and organic carbon

for PM 2.5 

# and volatile organic carbon for ozone), and meteorological factors play important roles

in downwind air quality.

For this analys is, EPA varied the EGU SO 2 

# emissions of upwind states included in the

final Transport Rule that are “linked” to downwind receptors (as described in section V of the

preamble). The variation in emissions was designed to reflect the allowance of inherent ye ar

- to

year (1

- year) variability in power system operations above average state

- level emissions equal to

the $2,300/ton and $500/ton SO 2 

# cost thresholds for group 1 and group 2 states respectively

(“$2,300/ton” for the purposes of this discussion). Details on this scenario

( TR_SO2_2300_Final ) can be found in appendix A of the Significant Contribution and State

# 23

Emissions Budgets Final Rule TSD . EPA estimated the air quality impacts using the change in

the maximum daily PM 2.5 

# design value. This analysis fo cused on variability limits related to

annual SO 2 

# emissions and the 24

- hour PM 2.5 

# concentrations for the same reasons listed in the

proposed Transport Rule “Power Sector Variability” TSD, with the addition that the improved

“calibrated” AQAT used in the fi nal Transport Rule only allows SO 2 

# emissions to be varied for

estimating downwind ammonium sulfate concentrations (which is discussed further in section

VI.C in the final rule’s preamble).

In contrast to the analyses in the proposed Transport Rule, this an alysis focuses on the

“worst

- case” emissions variability in 2014. This is intended to estimate upper bounds for the

effects of year

- to

- year variability in power system operations on air quality. For this analysis,

the “worst case” situation is defined a s the hypothetical scenario in which all upwind states that

are linked to a particular receptor increase their SO 2 

# emissions to a “variability maximum” equal

to the 2014 emissions equal to the final Transport Rule cost thresholds plus a fixed percentage

(v ariability level) of the state budget. States that were not linked to the receptor were held at

base case SO 2 

# emission levels. This analysis focused exclusively on the downwind air quality

effects of variation in emissions from “linked” upwind states, si nce it is variation in these

emissions that are relevant to Clean Air Act (CAA) section 110(a)(2)(D)(i)(I) . Thus, the

emissions from the state containing the receptor (i.e., the “home” state) were maintained at a

constant level, equal to its final Transpo rt Rule cost threshold. In this assessment, four

variability levels w ere examined (5, 10, 15, and 20 percent ). The 20 percent level analysis

increases emissions from linked upwind states further than the allowed variability in the final

Transport Rule pr ograms, which is limited to 18 percent . Therefore, estimated air quality

impacts from the 20 percent level analysis would exceed the expected air quality impacts from a

scenario in which all of the upwind linked states emitted at their variability limits in a given year

This variability analysis does not balance emission increases in some states with emission

decreases in other states (as was done in the analysis for the proposed Transport Rule).

Specifically, this analysis does not account for correspond ing decreases from other states

included in the final Transport Rule that are not contributing above the 1 percent threshold to a

particular receptor.

EPA specifically notes that this analysis is intended to demonstrate the potential air

quality impacts from year

- to

- year variability under “extreme” worst

- case scenarios, where states

# 24

linked to a downwind receptor simultaneously need to utilize the maximum power sector

variability; it is distinct from the “typical” operating conditions represented in an “av erage” year

under which significant contribution and interference with maintenance are examined to inform

EPA’s determination of emission reductions available at various cost thresholds. In other words,

the Transport Rule state budgets are established on t he basis of conditions in an average year,

while the variability provisions address the inherent year

- to

- year fluctuations of power sector

operations around those average conditions. This analysis examines the fluctuations in

downwind air quality that m ay be attributable to various potential degrees of year

- to

- year power

sector emission variability around upwind state budgets. Such an analysis is fundamentally

different from the Transport Rule’s analysis to determine significant contribution and

interfe rence with maintenance, which includes an examination of downwind air quality impacts

not on a fluctuating year

- to

- year basis, but on the basis of the degree of stringency of upwind

emissions control under expected (i.e., average) operating conditions as r epresented by EPA’s

power sector modeling projections. Therefore, the results of this TSD’s analysis are not

pertinent to the Agency’s consideration of downwind air quality impacts as one factor in its

multifactor analysis of significant contribution and interference with maintenance.

The results of the analysis can be found in Table 8 and in Figure 1, below. The estimated

increases in maximum design value PM 2.5 

# concentrations across the range of variability levels

examined are relatively small, with an a verage increase of 0.19 g/m 3 

# and a maximum increase

of 0.41 g/m 3 

# for receptor number 420031008 in Allegheny

, Pennsylvania at 20 percent

variability. In particular, the differences in a ir quality increases between 15 percent a nd 20

percent variability levels are small, with no projected overall changes in receptor nonattainment

status. Based on these results, EPA can determine that downwind air quality impacts of linked

upwind states all using their full variability limits (of 18 percent or 21 percent ) in a given year

would ha ve limited impacts on downwind air quality that would not undermine the upwind

state’s elimination of significant contribution to nonattainment or interference with maintenance

at the downwind receptors . These estimates were confirmed in two ways. First, using the same

methodology used for 5, 10, 15, and 20 percent variability (Table 8), AQAT was used to estimate

the effects on air quality for the 18 and 21 percent variability level s . The results can be seen in

Table 9, again showing relatively small air quality impacts for both cases . Second, EPA used

IPM to estimate emissions when an assurance level at 18 percent is implemented ( i.e., a remedy

# 25

sensitivity run ). The resulting air quality estimates can be found in Table 1 of Appendix E of the

“Significa nt Contribution and State Emissions Budget Final Rule” TSD . The air quality

differences between this run and the final Transport Rule remedy (as modeled in AQAT) are

small

.

For most locations, the largest contributor to increased PM 2.5 

# concentration from a single

upwind state will be from sources within the state containing the receptor itself (this conclusion

is drawn from comparing the Worst Case (All States) and Worst Case (Upwind States) columns

in Table 22 from the “Power Sector Variability” TSD from the proposed Transport Rule.

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22).

**The eight sites include the six sites listed abov e as well as Cuyahoga, OH (38) and Wayne, MI (33).



# Figure 1. Increase in maximum PM 2.5 

# design value ( g/m 3 

# ) in 2014 at the $2300/ton cost

threshold level as the variability level is increased from 0 to 20 percent, individually for the eight

sites (left panel) as well as for various combinations of sites (right panel). The combinations of

sites can be fou nd in Table 8. The state containing the receptor is held constant with no

variability at the 2014 $2300/ton cost emission level.

# 26

Table 8. Maximum PM 2.5 

# DVs ( g/m 3 

# ) in 2014 with Linked States at the Final Transport Rule

Cost Threshold Level with Variou s Additional Amounts of Emissions Due to Variability with

the Home State Held Constant with No Variability at the 2014 Final Transport Rule Cost

Threshold.

Site ID State County Variability (%) Difference in A ir Q uality B etween

the 0% V ariability L evel and

A nother L evel.

0 5 10 15 20 0 5 10 15 20

avg. of 6 sites* 38.39 38.42 38.46 38.49 38.52 0.00 0.03 0.07 0.10 0.13

avg. of 8 sites** 37.62 37.66 37.69 37.73 37.76 0.00 0.03 0.07 0.10 0.14

max of all 2012 base case 48.63 48.69 48.78 48.87 48.95 0.00 0.10 0.21 0.31 0.41

420030064 Pennsylvania Allegheny 48.63 48.69 48.78 48.87 48.95 0.00 0.06 0.14 0.23 0.32

420030093 Pennsylvania Allegheny 34.80 34.88 34.95 35.03 35.10 0.00 0.08 0.15 0.23 0.30

390350038 Ohio Cuyahoga 35.41 35.42 35.44 35.46 35.47 0.0 0 0.02 0.03 0.05 0.06

261630016 Michigan Wayne 35.65 35.67 35.68 35.69 35.70 0.00 0.01 0.02 0.04 0.05

390350060 Ohio Cuyahoga 33.04 33.07 33.11 33.13 33.14 0.00 0.04 0.07 0.09 0.10

170311016 Illinois Cook 36.54 36.60 36.67 36.73 36.79 0.00 0.06 0.13 0.1 9 0.25

261630033 Michigan Wayne 35.23 35.29 35.35 35.41 35.47 0.00 0.06 0.12 0.18 0.24

180890022 Indiana Lake 36.51 36.55 36.58 36.60 36.62 0.00 0.05 0.07 0.09 0.11

540090011 West

Virginia Brooke 30.02 30.12 30.21 30.31 30.41 0.00 0.10 0.19 0.29 0.39

4 20710007 Pennsylvania Lancaster 37.18 37.18 37.18 37.19 37.19 0.00 0.00 0.01 0.01 0.02

390350045 Ohio Cuyahoga 27.60 27.64 27.69 27.74 27.79 0.00 0.05 0.09 0.14 0.19

390811001 Ohio Jefferson 28.03 28.11 28.19 28.27 28.35 0.00 0.08 0.16 0.25 0.33

2616300 19 Michigan Wayne 35.83 35.84 35.86 35.87 35.88 0.00 0.01 0.03 0.04 0.06

390350065 Ohio Cuyahoga 27.00 27.06 27.13 27.19 27.26 0.00 0.06 0.13 0.19 0.26

170313301 Illinois Cook 32.84 32.88 32.93 32.97 33.01 0.00 0.04 0.08 0.13 0.17

420070014 Pennsylvania Beaver 28.70 28.75 28.82 28.90 28.99 0.00 0.05 0.12 0.20 0.29

420033007 Pennsylvania Allegheny 28.81 28.87 28.93 29.00 29.06 0.00 0.06 0.13 0.19 0.26

010730023 Alabama Jefferson 32.12 32.15 32.19 32.22 32.26 0.00 0.03 0.07 0.10 0.14

550790026 Wisconsin Milwaukee 33.21 33.25 33.30 33.34 33.38 0.00 0.04 0.09 0.13 0.18

180970043 Indiana Marion 27.82 27.87 27.93 27.98 28.03 0.00 0.05 0.11 0.16 0.21

261470005 Michigan St Clair 33.38 33.40 33.42 33.44 33.45 0.00 0.02 0.04 0.05 0.07

550790043 Wisconsin Milw aukee 33.92 33.95 33.98 34.01 34.04 0.00 0.03 0.06 0.09 0.12

180890026 Indiana Lake 33.37 33.39 33.42 33.44 33.46 0.00 0.02 0.05 0.07 0.10

180970081 Indiana Marion 27.59 27.67 27.74 27.80 27.85 0.00 0.08 0.15 0.21 0.27

180970066 Indiana Marion 29.13 29. 22 29.30 29.37 29.43 0.00 0.09 0.17 0.24 0.30

171191007 Illinois Madison 30.66 30.75 30.80 30.85 30.90 0.00 0.09 0.14 0.19 0.24

550790010 Wisconsin Milwaukee 33.13 33.17 33.22 33.26 33.31 0.00 0.04 0.09 0.13 0.18

390170003 Ohio Butler 27.33 27.39 27.48 27.56 27.65 0.00 0.06 0.15 0.24 0.32

170316005 Illinois Cook 34.82 34.85 34.88 34.91 34.93 0.00 0.03 0.06 0.09 0.12

420031008 Pennsylvania Allegheny 25.62 25.72 25.83 25.93 26.03 0.00 0.10 0.21 0.31 0.41

261610008 Michigan Washtenaw 29.33 29.35 29.38 29 .40 29.42 0.00 0.02 0.05 0.07 0.09

170312001 Illinois Cook 32.33 32.38 32.42 32.46 32.50 0.00 0.04 0.08 0.12 0.16

170310052 Illinois Cook 30.31 30.34 30.37 30.40 30.43 0.00 0.03 0.06 0.09 0.12

421330008 Pennsylvania York 33.91 33.93 33.94 33.95 33.97 0. 00 0.01 0.03 0.04 0.05

261630015 Michigan Wayne 31.99 32.01 32.02 32.04 32.06 0.00 0.02 0.03 0.05 0.06

010732003 Alabama Jefferson 31.91 31.94 31.97 32.00 32.03 0.00 0.03 0.06 0.09 0.12

390618001 Ohio Hamilton 26.73 26.79 26.85 26.91 26.97 0.00 0.06 0.1 2 0.18 0.24

171190023 Illinois Madison 29.50 29.56 29.62 29.68 29.73 0.00 0.06 0.12 0.17 0.23

420031301 Pennsylvania Allegheny 26.15 26.21 26.27 26.32 26.38 0.00 0.06 0.11 0.17 0.23

391130032 Ohio Montgomery 24.62 24.69 24.75 24.82 24.89 0.00 0.07 0.13 0.20 0.27

420030116 Pennsylvania Allegheny 26.34 26.40 26.46 26.53 26.59 0.00 0.06 0.12 0.19 0.25

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22).

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).

# 27

In conclusion, EPA found that year

- to

- year variability reflecting inherent fluctuations in

reliable operation of the power sector yield small impacts to downwind air quality , even with

varia bility of up to 2 1 percent . This analysis confirms that even under a program allowing up to

21% of year

- to

- year variability around a budget in any given state’s power sector emissions, the

downwind air quality goals of the program are very unlikely to be compromised.

The 18 percent variability limits allowed under the Transport Rule annual

programs to account for year

- to

- year variability baseline power sector operation

,

and the corresponding 21 percent variability limits accounting for ozone

- season

operati ons, are bounded by the variability levels examined in this analysis. At all

levels examined, the resulting variation in air quality is small.

Higher levels of emission variability (beyond what EPA has calculated here as

year

- to

- year variability due to ba seline operation of the power sector), such as

could theoretically occur under an unlimited interstate cap

- and

- trade emission

program, would not undermine the program’s achievement of downwind air

quality improvements. This is especially true if coupled w ith emission reductions

in the state containing the receptor.

The heterogeneity of where, when, and how emission reductions occur (processes

not explicitly considered under the Transport Rule, such as intra

- state shifting of

emissions) will possibly have l arger downwind air quality impacts at a given

receptor than the minor variations in air quality attributable to the small shifts in

state

- level emissions due to inherent year

- to

- year variability.

As was described in the proposed Transport Rule “Power Sect or Variability” TSD, the

variations in downwind air quality found in this analysis of potential year

- to

- year variability in

upwind EGU emissions are much smaller than documented year

- to

- year variability in air quality

(as measured at the receptors and expr essed as the differences between the average and

maximum design values), indicating that other factors such as meteorology play a much larger

role in the variability of downwind air quality outcomes (once emissions are broadly controlled

at a given level o f stringency). Consequently, EPA finds that allowing variation in emissions

under the final variability limits in the Transport Rule allows the power sector to address

inherent fluctuations in electric generation without negatively affecting downwind air quality.

# 28

Table 9 . Maximum PM 2.5 

# DVs ( g/m 3 

# ) in 2014 with Linked States at the Final Transport Rule

Cost Threshold Level with 18 percent and 21 percent Additional Amount s of Emissions Due to

Variability with the Home State Held Constant with No Variability at the 2014 Final Transport

Rule Cost Threshold.

Site ID State County Variability (%) Difference in

A ir Q uality

B etween the

0%

Variability

Level and the

18% L evel. Variability (%) Difference in A ir

Q uality B etween

the 0%

Variability Level

and the 21%

L ev el.

0 18 0 21

420030064 Pennsylvania Allegheny 48.63 48.92 0.29 48.63 48.97 0.34

420030093 Pennsylvania Allegheny 34.80 35.07 0.27 34.80 35.12 0.32

390350038 Ohio Cuyahoga 35.41 35.46 0.06 35.41 35.47 0.07

261630016 Michigan Wayne 35.65 35.70 0.0 4 35.65 35.71 0.05

390350060 Ohio Cuyahoga 33.04 33.13 0.10 33.04 33.14 0.10

170311016 Illinois Cook 36.54 36.77 0.23 36.54 36.80 0.26

261630033 Michigan Wayne 35.23 35.45 0.22 35.23 35.48 0.25

180890022 Indiana Lake 36.51 36.61 0.11 36.51 36.62 0.12

540090011 West Virginia Brooke 30.02 30.37 0.35 30.02 30.43 0.41

420710007 Pennsylvania Lancaster 37.18 37.19 0.01 37.18 37.19 0.02

390350045 Ohio Cuyahoga 27.60 27.77 0.17 27.60 27.80 0.20

390811001 Ohio Jefferson 28.03 28.32 0.29 28.03 28.37 0.34

261 630019 Michigan Wayne 35.83 35.88 0.05 35.83 35.89 0.06

390350065 Ohio Cuyahoga 27.00 27.23 0.23 27.00 27.27 0.27

170313301 Illinois Cook 32.84 32.99 0.15 32.84 33.02 0.18

420070014 Pennsylvania Beaver 28.70 28.95 0.25 28.70 29.00 0.30

420033007 Pennsy lvania Allegheny 28.81 29.04 0.23 28.81 29.08 0.27

010730023 Alabama Jefferson 32.12 32.24 0.13 32.12 32.26 0.15

550790026 Wisconsin Milwaukee 33.21 33.37 0.16 33.21 33.39 0.19

180970043 Indiana Marion 27.82 28.01 0.19 27.82 28.04 0.22

261470005 Michig an St Clair 33.38 33.45 0.07 33.38 33.46 0.08

550790043 Wisconsin Milwaukee 33.92 34.03 0.11 33.92 34.04 0.12

180890026 Indiana Lake 33.37 33.46 0.09 33.37 33.47 0.10

180970081 Indiana Marion 27.59 27.83 0.24 27.59 27.87 0.28

180970066 Indiana Marion 2 9.13 29.40 0.27 29.13 29.44 0.31

171191007 Illinois Madison 30.66 30.88 0.22 30.66 30.91 0.25

550790010 Wisconsin Milwaukee 33.13 33.29 0.16 33.13 33.31 0.19

390170003 Ohio Butler 27.33 27.62 0.29 27.33 27.67 0.34

170316005 Illinois Cook 34.82 34.92 0. 11 34.82 34.94 0.12

420031008 Pennsylvania Allegheny 25.62 25.99 0.37 25.62 26.05 0.43

261610008 Michigan Washtenaw 29.33 29.42 0.08 29.33 29.43 0.10

170312001 Illinois Cook 32.33 32.48 0.15 32.33 32.51 0.17

170310052 Illinois Cook 30.31 30.42 0.11 30. 31 30.44 0.12

421330008 Pennsylvania York 33.91 33.96 0.05 33.91 33.97 0.06

261630015 Michigan Wayne 31.99 32.05 0.06 31.99 32.06 0.07

010732003 Alabama Jefferson 31.91 32.02 0.10 31.91 32.03 0.12

390618001 Ohio Hamilton 26.73 26.94 0.22 26.73 26.98 0. 25

171190023 Illinois Madison 29.50 29.71 0.21 29.50 29.75 0.24

420031301 Pennsylvania Allegheny 26.15 26.36 0.21 26.15 26.39 0.24

391130032 Ohio Montgomery 24.62 24.86 0.24 24.62 24.90 0.28

420030116 Pennsylvania Allegheny 26.34 26.56 0.22 26.34 26.60 0.26

# 29

5

. Variation in electric generation from fossil units relative to total generation.

Many individual units across many sectors (e.g., hydroelectric, fossil, nuclear, wind)

provide generation to meet electric generation demands in the U.S. Howeve r, only a subset of

these units is expected to be included in the final Transport Rule. As described in section VI.E

of the preamble and in this TSD, there are many reasons why emissions within a state may vary

from year to year (or, from ozone season to ozone season). One reason is the relative proportion

of electric generation from fossil units relative to the total generation. For example, in some

states, there are large capacity units that have only marginal, or even zero, SO 2 

# and NO X



# emission rates

. These could be, for example, nuclear units. As electric demand varies from one

year to the next, it is possible that the proportion of th at demand that is met by fossil units could

vary. For example, for a state with substantial nuclear generation ( ty pically operating as

relatively constant baseload units), it is possible that fossil generation could experience larger

year

- to

- year variation in proportion to its baseline generation share as compared to year

- to

- year

variation in total electricity demand in that state

.

Using electric generation d ata from the En ergy Information Administration 4 

# , EPA

assessed the electric generation for the fossil generation sector and for the total generation from

all sectors for each of the states expected to be in the Tra nsport Rule program for the time period

2000 through 2009 (Tables 1 0 and 11 ). Data for the year 2010 was not included because it was

not available at the time of this final rulemaking

. As shown in Table 12

, for each year, EPA

calculated the percentage of total generation provided by fossil fuel

- fired generators in each

state

. The results for Tables 10, 11, and 12 are summarized in Table 13.

The results show that there is considerable variation in fossil generation from year to

year, variation in total generation from year to year, and that the proportion of total g eneration

from fossil units also varies from year to year. Some states have high percentages of their

generation from fossil (e.g., Indiana) while others (e.g., Illinois) have lower percentag es (Table

12). For all states, the proportion of fossil generation varies from one year to the next (i.e., the

standard deviation from Table 12), with little or no obvious trend across states with regard to

total generation, total fossil generation, or ge neral (in any given year) proportion of fossil

4 

## Source: State Electricity Profiles, 2009 Edition; EIA; April 2011

(http://www.eia.gov/cneaf/electricity/st_profiles/e_profiles_sum.html)

# 30

generation to total generation (Table 13)

. For example, a large proportion of Iowa’s total

generation is from fossil. However, it has a relatively high year

- to

- year variability in the fossil to

total generat ion of 3.6% (Table 13). Kansas has a very similar proportion of fossil generation, a

similar total generation, and yet has less than half (i.e., 1.7%) of the year

- to

- year variation

experienced by Iowa (Table 13). However, as would be expected, states with almost complete

fossil generation to total generation (i.e., 98%) appeared to have slightly less variable fossil

generation to total generation percentages.

Since the proportion of fossil generation to total electric generation is one of the factors

that could potentially affect the state

- by

- state assessment of heat input variability (see section 2

of this TSD for details of the heat input assessment), it is important to examine how Tennessee

and Virginia compare with all of the other states expected to be in the Transport Rule. These

states demonstrated the highest 95 th 

# percent confidence level variabilities in heat input over the

time period from 2000 through 2010.

In examining electric generation of fossil relative to other generation, Tennessee and

V irginia are representative of most other states expected to be in the Transport Rule (being

within one standard deviation of average percent fossil generation relative to total generation for

the time period 2000

- 2009 assessed across all states) and show s imilar patterns of year

- to

- year

variability in the percent fossil relative to total compared with other states ( i.e., the standard

deviation for each of these states being within two standard deviations for percent of fossil

variation compared with all oth er states). During the 2000

- 20 0 9 timeframe, t hese states

happened to establish the highest value observed for year

- to

- year variability in fossil generation.

This is comparable to what EPA determined for the year

- to

- year variation in heat input over the

2 000

- 2010 timeframe (as described above in section 2 ). In this generation share assessment, the

year

- to

- year variability values in fossil generation share for both Tennessee and Virginia fall

within the 95 th 

# percent confidence level of the distribution of all states’ year

- to

- year variability

levels calculated in this analysis

. That is to say, neither Tennessee nor Virginia present an

“outlier” finding for year

- to

- year variability among the Transport Rule states analyzed. Thus,

EPA finds that their proport ion of generation, and variation in fossil generation relative to total

generation, are representative of all Transport Rule states

. EPA interprets this finding as

statistical evidence that the year

- to

- year variations exhibited by Tennessee and Virginia c ould be

exhibited in the future by other states covered in the Transport Rule programs

.

# 31

In conclusion, EPA finds that both Virginia and Tennessee have representative fossil to

other generation mixes and demonstrate year

- to

- year fluctuations in that generat ion ratio that are

representative of year

- to

- year variability in other states. Based in part on this case st udy of year

to

- year variability in the fossil fuel

- fired share of a state’s generation (a relevant factor informing

year

- to

- year variability in sta te EGU emissions ), EPA believes it is reasonable to accommodate

the statistical potential in all states to experience the maximum observed historic year

- to

- year

variability among those states in the analysis presented above in section 2.

# 32

Table 10

. Fossil Electric Industry Generation Sector by Year (Mega watthours) (Coal, Petroleum, and Gas Generation )

State * 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Average

(2000

- 2009)



## South Carolina 40,587,323 38,183,421 41,746,692 39,551,749 43,591,379 45,730,31 8 45,777,840 47,764,530 47,449,150 44,781,189 43,516,359



## New Jersey 28,269,374 27,772,751 29,495,528 26,362,066 27,748,311 27,996,979 26,910,083 29,576,322 30,264,384 26,172,779 28,056,858



## Illinois 88,005,813 86,018,711 96,219,615 93,208,355 98,803,288 9 9,905,430 97,212,331 103,072,379 101,100,510 94,662,485 95,820,892



## New York 79,769,670 78,755,767 73,224,414 71,029,014 71,351,947 76,483,124 69,879,757 75,234,282 66,755,913 57,186,776 71,967,066



## Virginia 47,353,187 47,427,595 46,261,913 47,495,691 47,2 47,427 47,984,304 42,342,803 48,421,535 42,241,799 38,888,332 45,566,459



## Pennsylvania 123,257,783 119,112,469 123,923,647 126,571,924 131,715,015 137,220,631 138,172,520 143,908,754 137,861,607 136,047,080 131,779,143



## Arkansas 28,251,936 28,350,764 27,86 1,034 31,093,932 30,961,268 29,250,905 33,626,179 34,202,584 34,639,352 36,385,133 31,462,309



## Tennessee 63,444,284 60,604,095 60,456,740 55,956,927 58,556,257 59,827,766 61,335,979 61,204,754 57,753,073 42,241,501 58,138,138



## North Carolina 77,901,547 7 5,148,857 79,341,500 77,140,309 78,715,678 82,113,946 79,133,706 84,935,370 80,312,350 70,231,526 78,497,479



## Maryland 34,767,108 33,601,840 33,713,429 35,032,267 34,087,166 35,337,813 32,091,398 33,302,504 29,810,049 26,529,272 32,827,285



## Alabama 83,142, 223 82,434,492 88,370,318 89,447,001 91,285,610 92,437,882 97,827,165 101,560,826 97,375,571 87,579,809 91,146,090



## Nebraska 18,963,946 20,615,626 20,377,695 21,384,309 20,776,485 21,652,165 21,460,666 20,775,698 22,272,915 23,684,230 21,196,374



## Minnesota 35,487,886 33,643,796 36,164,954 38,373,030 36,241,242 36,433,421 36,125,436 36,463,293 34,879,236 32,263,080 35,607,537



## Georgia 85,972,180 79,597,593 86,921,839 84,109,539 87,019,143 97,986,385 100,298,809 107,164,700 99,661,230 90,633,619 91,936,504



## M ichigan 82,063,526 82,192,176 83,666,519 80,218,113 84,648,878 85,336,546 80,004,350 84,932,522 80,179,418 75,868,993 81,911,104



## Mississippi 25,239,704 42,090,307 31,892,758 28,223,492 31,913,860 33,460,964 34,253,614 39,184,193 37,407,555 36,266,297 33,9 93,274



## Wisconsin 44,987,855 43,935,204 42,306,475 44,668,394 45,349,157 48,862,617 46,352,158 47,530,414 47,880,609 43,476,586 45,534,947



## Louisiana 73,293,099 66,231,383 73,313,913 73,948,305 76,263,521 72,693,078 69,795,311 71,027,828 72,850,498 70,155, 103 71,957,204



## Kansas 35,739,739 34,336,478 37,667,319 37,299,518 36,278,743 36,604,591 35,171,928 38,590,021 36,363,175 35,032,695 36,308,421



## Florida 151,711,197 152,378,116 162,784,858 173,183,074 178,708,928 183,324,770 184,530,490 188,433,327 180,167 ,254 181,553,197 173,677,521



## Iowa 35,602,734 35,360,575 35,990,642 36,234,176 36,205,522 36,882,772 37,013,529 41,388,360 42,734,425 38,620,904 37,603,364



## Texas 336,018,948 329,588,630 342,954,605 340,415,604 343,571,908 350,877,744 349,849,160 351,720,2 85 344,812,945 333,226,991 342,303,682



## Missouri 66,111,416 70,259,930 71,508,299 76,994,700 78,086,967 81,474,061 81,245,146 80,127,334 78,787,805 75,121,522 75,971,718



## Ohio 131,048,463 125,856,299 135,315,403 137,185,397 131,238,121 141,209,958 137,493, 780 138,542,595 134,877,563 119,711,673 133,247,925



## Oklahoma 53,274,124 52,802,543 57,134,398 58,705,640 57,155,277 64,987,956 68,092,606 67,764,810 70,121,839 68,699,869 61,873,906



## Kentucky 90,669,222 91,552,565 87,716,454 87,449,856 90,323,151 94,422,8 41 95,719,860 95,075,440 95,477,555 86,936,622 91,534,357



## West Virginia 91,699,645 80,858,445 93,664,643 93,164,499 88,255,268 92,012,899 92,062,841 92,510,654 89,481,340 68,394,521 88,210,476



## Indiana 127,101,358 121,868,463 125,065,281 123,853,265 126,6 70,732 129,431,154 129,345,084 129,576,104 128,206,437 114,118,178 125,523,606



## *This table is sorted in order of increasing average fraction of fossil relative to the total electric industry for the state (seen in Table s 12 and 13)

.

# 33

Table 11

. Total Ele ctric Industry Generation for All Sectors by Year (Mega watthours).

State * 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Average

(2000

- 2009)

South Carolina 93,346,240 89,158,987 96,563,498 93,772,678 97,939,929 102,514,665 99,267,606 103,402,142 100,97 8,005 100,125,486 97,706,924

New Jersey 58,085,215 59,421,260 61,569,386 57,399,351 55,882,342 60,549,583 60,700,139 62,671,245 63,674,789 61,811,239 60,176,455

Illinois 178,496,081 179,249,285 188,054,449 189,055,260 191,957,778 194,120,146 192,426,958 200,260,681 199,475,178 193,864,357 190,696,017

New York 138,079,075 143,914,559 139,591,689 137,643,316 137,964,794 146,887,419 142,265,432 145,878,687 140,322,100 133,150,550 140,569,762

Virginia 77,189,370 74,104,750 75,005,652 75,309,420 78,900,040 7 8,943,045 73,069,537 78,360,507 72,678,531 70,082,066 75,364,292

Pennsylvania 201,687,980 196,576,591 204,322,878 206,349,514 214,658,501 218,091,125 218,811,595 226,088,340 222,350,925 219,496,144 212,843,359

Arkansas 43,875,766 47,192,035 47,611,645 50 ,401,102 51,927,632 47,794,509 52,168,703 54,596,236 55,050,528 57,457,739 50,807,590

Tennessee 95,838,584 96,221,976 96,114,262 92,221,790 97,594,542 97,117,165 93,911,102 95,113,409 90,663,312 79,716,889 93,451,303

North Carolina 122,274,356 117,495,85 0 124,468,029 127,582,320 126,329,957 129,748,578 125,214,784 130,115,301 125,239,063 118,407,403 124,687,564

Maryland 51,145,380 49,062,340 48,279,088 52,244,237 52,052,770 52,661,600 48,956,880 50,197,924 47,360,953 43,774,832 49,573,600

Alabama 124,40 5,340 125,345,113 132,920,670 137,487,222 137,354,771 137,948,581 140,895,441 143,826,271 145,869,895 143,255,556 136,930,886

Nebraska 29,109,863 30,485,212 31,618,494 30,455,984 32,008,709 31,464,734 31,669,969 32,442,699 32,373,522 34,001,892 31,563,108

Minnesota 51,423,339 48,523,226 52,777,967 55,050,996 52,364,127 53,018,995 53,237,789 54,477,646 54,763,360 52,491,849 52,812,929

Georgia 123,877,413 118,316,789 126,512,215 124,076,834 126,812,715 136,667,892 138,010,208 145,155,158 136,173,395 128,69 8,376 130,430,100

Michigan 104,209,594 111,845,610 117,889,087 111,347,060 118,487,269 121,619,771 112,556,739 119,309,936 114,989,806 101,202,605 113,345,748

Mississippi 37,614,563 53,446,452 42,888,812 40,148,278 43,662,613 45,067,453 46,228,847 50,043 ,686 48,205,711 48,701,484 45,600,790

Wisconsin 59,644,417 58,763,431 58,431,438 60,122,424 60,444,933 61,824,664 61,639,843 63,390,630 63,479,555 59,959,060 60,770,040

Louisiana 92,865,635 87,894,377 94,970,963 94,885,040 98,172,309 92,616,878 90,921,82 9 92,578,329 92,453,141 90,993,676 92,835,218

Kansas 44,815,905 44,748,523 47,188,446 46,567,561 46,782,659 45,862,696 45,523,736 50,122,196 46,630,321 46,677,308 46,491,935

Florida 191,815,840 190,945,344 203,352,774 212,610,012 218,117,928 220,256,412 223,751,621 225,416,060 219,636,818 217,952,308 212,385,512

Iowa 41,542,010 40,658,512 42,528,385 42,116,192 43,248,189 44,156,160 45,483,462 49,789,217 53,086,786 51,860,063 45,446,898

Texas 377,742,365 372,580,002 385,628,541 379,199,685 390,299,132 39 6,668,722 400,582,878 405,492,296 404,787,781 397,167,910 391,014,931

Missouri 76,593,939 79,544,873 81,162,197 87,225,087 87,632,910 90,828,230 91,686,343 91,153,081 91,028,795 88,354,272 86,520,973

Ohio 149,060,280 142,261,807 147,068,849 146,638,128 1 48,345,905 156,976,323 155,434,075 155,155,545 153,412,251 136,090,225 149,044,339

Oklahoma 55,571,957 55,249,450 59,183,419 60,626,856 60,729,560 68,607,827 70,614,880 72,819,095 76,328,908 75,066,809 65,479,876

Kentucky 93,006,083 95,417,626 92,106,668 91,718,820 94,529,947 97,822,419 98,792,014 97,225,319 97,863,340 90,630,427 94,911,266

West Virginia 92,865,176 81,836,725 94,761,753 94,711,554 89,749,562 93,626,285 93,815,804 93,933,109 91,123,097 70,782,514 89,720,558

Indiana 127,819,516 122,569,67 3 125,608,139 124,888,218 127,770,396 130,371,573 130,489,788 130,637,999 129,510,294 116,670,280 126,633,588



## *This table is sorted in order of increasing average fraction of fossil relative to the total electric industry for the state (seen in Table s 12 and 13)

.

# 34

Table 12 . Percent of Fossil Electric Industry Generation Relative to Total Electric Industry

Generation by Year

State 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Average

(2000

- 2009) Standard

Deviation

(2000

- 2009)

South Carolina 43% 43% 43% 42% 45% 45% 46% 46% 47% 45% 44% 1.6%

New Jersey 49% 47% 48% 46% 50% 46% 44% 47% 48% 42% 47% 2.1%

Illinois 49% 48% 51% 49% 51% 51% 51% 51% 51% 49% 50% 1.3%

New York 58% 55% 52% 52% 52% 52% 49% 52% 48% 43% 51% 4.0%

Virginia 61% 64% 62% 63% 60% 61% 58 % 62% 58% 55% 60% 2.6%

Pennsylvania 61% 61% 61% 61% 61% 63% 63% 64% 62% 62% 62% 1.1%

Arkansas 64% 60% 59% 62% 60% 61% 64% 63% 63% 63% 62% 2.0%

Tennessee 66% 63% 63% 61% 60% 62% 65% 64% 64% 53% 62% 3.7%

North Carolina 64% 64% 64% 60% 62% 63% 63% 65% 64% 59% 63% 1.8%

Maryland 68% 68% 70% 67% 65% 67% 66% 66% 63% 61% 66% 2.7%

Alabama 67% 66% 66% 65% 66% 67% 69% 71% 67% 61% 67% 2.5%

Nebraska 65% 68% 64% 70% 65% 69% 68% 64% 69% 70% 67% 2.3%

Minnesota 69% 69% 69% 70% 69% 69% 68% 67% 64% 61% 67% 2.7%

Georg ia 69% 67% 69% 68% 69% 72% 73% 74% 73% 70% 70% 2.4%

Michigan 79% 73% 71% 72% 71% 70% 71% 71% 70% 75% 72% 2.7%

Mississippi 67% 79% 74% 70% 73% 74% 74% 78% 78% 74% 74% 3.6%

Wisconsin 75% 75% 72% 74% 75% 79% 75% 75% 75% 73% 75% 1.8%

Louisiana 79% 75% 77% 78% 78% 78% 77% 77% 79% 77% 77% 1.1%

Kansas 80% 77% 80% 80% 78% 80% 77% 77% 78% 75% 78% 1.7%

Florida 79% 80% 80% 81% 82% 83% 82% 84% 82% 83% 82% 1.6%

Iowa 86% 87% 85% 86% 84% 84% 81% 83% 80% 74% 83% 3.6%

Texas 89% 88% 89% 90% 88% 88% 87% 87% 85% 84% 88 % 1.8%

Missouri 86% 88% 88% 88% 89% 90% 89% 88% 87% 85% 88% 1.4%

Ohio 88% 88% 92% 94% 88% 90% 88% 89% 88% 88% 89% 1.9%

Oklahoma 96% 96% 97% 97% 94% 95% 96% 93% 92% 92% 95% 1.9%

Kentucky 97% 96% 95% 95% 96% 97% 97% 98% 98% 96% 96% 1.0%

West Virginia 99 % 99% 99% 98% 98% 98% 98% 98% 98% 97% 98% 0.6%

Indiana 99% 99% 100% 99% 99% 99% 99% 99% 99% 98% 99% 0.5%

# 35

Table 13. Average Fossil Generation, Average Total Generation, Ratio of Average Fossil

Generation to Average Total Generation, and Standard Deviati on of Year

- to

- Year Ratio of

Fossil Generation to Total Generation for each State in the Transport Rule.

State Average Fossil

Generation

( 2000

- 2009 )

(MWH)

(See Table 10) Average Total

Generation

( 2000

- 2009 )

(MWH)

(See Table 11) Av erage

Fossil to T otal

( See Table 12) St andar d

Deviation

( F ossil to

T otal)

(See Table 12)

South Carolina 43,516,359 97,706,924 44% 1.6%

New Jersey 28,056,858 60,176,455 47% 2.1%

Illinois 95,820,892 190,696,017 50% 1.3%

New York 71,967,066 140,569,762 51% 4.0%

Virginia 45,566 ,459 75,364,292 60% 2.6%

Pennsylvania 131,779,143 212,843,359 62% 1.1%

Arkansas 31,462,309 50,807,590 62% 2.0%

Tennessee 58,138,138 93,451,303 62% 3.7%

North Carolina 78,497,479 124,687,564 63% 1.8%

Maryland 32,827,285 49,573,600 66% 2.7%

Alabama 91, 146,090 136,930,886 67% 2.5%

Nebraska 21,196,374 31,563,108 67% 2.3%

Minnesota 35,607,537 52,812,929 67% 2.7%

Georgia 91,936,504 130,430,100 70% 2.4%

Michigan 81,911,104 113,345,748 72% 2.7%

Mississippi 33,993,274 45,600,790 74% 3.6%

Wisconsin 45,534 ,947 60,770,040 75% 1.8%

Louisiana 71,957,204 92,835,218 77% 1.1%

Kansas 36,308,421 46,491,935 78% 1.7%

Florida 173,677,521 212,385,512 82% 1.6%

Iowa 37,603,364 45,446,898 83% 3.6%

Texas 342,303,682 391,014,931 88% 1.8%

Missouri 75,971,718 86,520,973 88% 1.4%

Ohio 133,247,925 149,044,339 89% 1.9%

Oklahoma 61,873,906 65,479,876 95% 1.9%

Kentucky 91,534,357 94,911,266 96% 1.0%

West Virginia 88,210,476 89,720,558 98% 0.6%

Indiana 125,523,606 126,633,588 99% 0.5%

Median 71% 1.9%

Avera ge 73% 2.1%

Standard Deviation 16% 0.9%

One Standard

Deviation . B elow

Average 57%

Upper 95%

Confidence

Variability Level 3.9%