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 2011
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 variability 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. Introduction.

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 (PM2.5) National Ambient Air Quality 

Standards (NAAQS) and the 2006 24-hour PM2.5 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 

SO2, annual NOX, and ozone-season NOX state budgets based on its projections of stateby-state power sector emissions in an average year reflecting these emission reductions.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 contribution 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 for each state 

 

1

 EPA developed annual SO2 and NOX budgets for each state covered for the annual and/or 24-hour PM2.5

NAAQS and ozone-season NOX 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 lists the 27 states 

that are covered by the Transport Rule. As discussed in preamble section III, EPA will issue a supplemental 

proposal to require ozone-season NOX 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 maintenance 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 PM2.5 NAAQS and to determine state 

emissions budgets for SO2, annual NOX, and ozone season NOX. Preamble sections VI.E 

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

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

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

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

Transport Rule applies those variability 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 emissions) 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 makes 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 discussed 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 describes an analysis using the air quality assessment tool 

(AQAT) to estimate the resulting air quality effects 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) compared 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 SO2, annual NOX and 

ozone season NOX emissions. As discussed above, the goal of this assessment is to 

determine 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 emissions 

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 operations. 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 programs. 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 effectiveness 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 Transport Rule 

(SO2, annual NOx, and ozone season NOx). 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 input 

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 characterizing 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 compliance 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 during a particular year.

EPA 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 an 11-year time frame (2000 through 2010). EPA believes such 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 in 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 EGUs for the longest available time period. This is responsive 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 decreases in heat input due to changes in fleet 

composition and utilization are representative of long-run year-to-year variability in power 

system operation. Fitting 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 yearto-year variability impacting the power sector at the state level.

For each year of the 11-year time period, EPA estimated total power sector heat 

input on a state-by-state basis using the sum of reported heat input2for all units expected to 

be covered by the Transport Rule. Total annual 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 NOx, annual SO2, and ozone season NOx emissions using the total yearly and ozone 

season heat inputs summed across all EGUs (Tables 1 and 2, respectively). 

In the proposed Transport Rule, the Agency assessed year-to-year variability in 

annual SO2 emissions using as a proxy the yearly heat inputs from coal-fired EGUs. 

However, 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-toyear variability in SO2 emissions at the state level, considering that all such units will be 

regulated for SO2 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 NOx and SO2 in the final Transport Rule.

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 notes 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 be 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 

 

246.4 

 

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 

 

463.4 

 

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 2011. All relevant units in the Transport Rule region. These data are available at 

http://www.epa.gov/airmarkets/ through Data and Maps.
9

Table 2: Total Heat Input* (million MMBtu) from All Units Expected to be in the Final 

Transport Rule for 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 

 

807.8 

 

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 

 

171.9 

 

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 

 

297.7 

 

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, June 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 subsection 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” with 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 detail 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 deviation) 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 variation 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. To 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 estimated 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 “yintercept” (the heat input value when the line is extrapolated to x = 0). The value of r2

, the 

square of the Pearson 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 (r2values are maximized at 1) 

indicate 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 period (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, the slopes of the regression equations, y-intercepts, and r2values 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

2000 through 2010 (Tables 3 and 4). Year-to-year variation 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: Total Annual Heat Input (million MMBtu) from All Units Expected to be in the Final Transport Rule for Each Year Estimated Using 

the 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 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 1,327.4 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 Input (million MMBtu) from All Units Expected to be in the Final Transport Rule for Ozone Season Estimated Using 

the 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 0.43 145.4 148.4 151.5 154.5 157.6 160.7 163.7 166.8 169.9 172.9 176.0 

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 0.18 550.5 547.1 543.8 540.5 537.1 533.8 530.4 527.1 523.8 520.4 517.1 

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 0.72 134.1 140.0 145.9 151.9 157.8 163.7 169.7 175.6 181.5 187.5 193.4 

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 year (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 line is meaningful for this 

analysis. EPA assessed 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 differences3divided 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 the 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, the standard deviation of the differences 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 yearspecific heat input values over the long run would 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 input (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 each state for each pollutant. Following the analysis from the proposed 

Transport Rule and after reviewing all relevant public comment, the two-tailed 95

th percent 

confidence level was selected as the appropriate level of variation. The two-tailed 95th

percent confidence level indicates that we could expect that, on average, the total heat 

 

3 On a state-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 95 percent of the time. This statistical 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 95th

percent confidence level provides a high degree of confidence that 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 95th percent upper confidence level heat inputs, fractions (the 95th percent heat 

input difference divided by the average heat input over the 2000-2010 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 deviation percent variation). 
16

Table 5: Difference between Total Annual Heat Input (million MMBtu) Measured and Estimated from All Units Expected to be in the 

Final Transport Rule for Each Year Using the Regression Equation.

State 2000 2001 2002 2003 2004 2005 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 28.6 642.3 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 between Total Heat Input Measured and Estimated (million MMBtu) from All Units Expected to be in the Final 

Transport Rule for Ozone Season Using the Regression Equation.

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

Standard 

Deviation 

of Heat 

Input 

(million 

MMBtu)

Average 

Heat 

Input 

(2000-

2010, 

from 

Table 2)

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 -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 in emissions using the year-to-year variability in 

historical heat input. 

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

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

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 percent, for annual NOx and SO2 and 21 

percent for ozone season NOx. The 95th 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 maximum 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 percentages 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 states. For example, based on total heat input, total 

EGU emissions of SO2 or NOx, 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., Connecticut 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 larger 

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 reasons 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 SO2, 

annual NOX and ozone-season NOX, respectively.
20

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

Percentage and Variability Limit Percentage Used in the Transport Rule*.

SO2 and Annual NOX Ozone-Season NOX

State Historic Variability Transport Rule 

Variability Limit Historic Variability Transport Rule Variability Limit

AL 11% 18% 12% 21%

AR 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 NOx emission reductions. See the preamble for details. 
21

3. Numerical simulation of 1- and 3-year variability

For the reasons discussed 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 includes only the 1-year limits. In the proposal, EPA first established 1-year variability 

limits (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 numerical verification of equal likelihood of potential violations 

of a state’s assurance level when evaluating random variation (10 percent at the 95th percent twotailed confidence level) around a constant budget. 

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

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

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

these cost-effective reductions and to meet the emission budgets each year. On 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 95th percent two-tailed confidence level 1-year variability of 10

percent. The 1-year variability standard deviation is 10 percent divided by 1.960, 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 the 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 

95th percent two-tailed confidence exceedance value of 2.5 percent.

This finding confirms that the proposed 3-year variability limits are statistically 

indistinguishable 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 assessment 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-bystate 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 discusses 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 

power system operation (and thereby SO2 emissions from EGUs under the Transport Rule) does 

have perceptible downwind air quality effects on PM2.5 concentrations, these effects are minimal. 

EPA believes that variation in other factors such as SO2 and NOx emission patterns from nonEGU sources, contributions from other pollutants (i.e., ammonia, elemental and organic carbon 

for PM2.5 and volatile organic carbon for ozone), and meteorological factors play important roles 

in downwind air quality.

For this analysis, EPA varied the EGU SO2 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 year-toyear (1-year) variability in power system operations above average state-level emissions equal to 

the $2,300/ton and $500/ton SO2 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 PM2.5 design value. This analysis focused on variability limits related to 

annual SO2 emissions and the 24-hour PM2.5 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 final Transport Rule only allows SO2 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 analysis 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 as the hypothetical scenario in which all upwind states that 

are linked to a particular receptor increase their SO2 emissions to a “variability maximum” equal 

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

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

base case SO2 emission levels. This analysis focused exclusively on the downwind air quality 

effects of variation in emissions from “linked” upwind states, since 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 Transport Rule cost threshold. In this assessment, four 

variability levels were 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 programs, 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 corresponding 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 “average” 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 the 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 may 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 

interference 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 represented 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 PM2.5 concentrations across the range of variability levels 

examined are relatively small, with an average increase of 0.19 g/m3and a maximum increase 

of 0.41 g/m3for receptor number 420031008 in Allegheny, Pennsylvania at 20 percent

variability. In particular, the differences in air quality increases between 15 percent and 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 have 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 levels. 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 

“Significant 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 PM2.5 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 above as well as Cuyahoga, OH (38) and Wayne, MI (33).

Figure 1. Increase in maximum PM2.5 design value ( g/m3) 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 found 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 PM2.5 DVs ( g/m3) in 2014 with Linked States at the Final Transport Rule 

Cost Threshold Level with Various 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 Air Quality Between 

the 0% Variability Level and

Another Level. 

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 0.00 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 0.19 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

420710007 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

261630019 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 Milwaukee 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 0.12 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 

variability of up to 21 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 

operations, 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 baseline 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 with 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 larger 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 Sector 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 expressed 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 of 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 PM2.5 DVs ( g/m3) in 2014 with Linked States at the Final Transport Rule 

Cost Threshold Level with 18 percent and 21 percent 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 

Air Quality 

Between the 

0% 

Variability 

Level and the 

18% Level.

Variability (%)

Difference in Air 

Quality Between 

the 0%

Variability Level 

and the 21% 

Level.

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 0.04 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

261630019 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 Pennsylvania 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 Michigan 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 29.13 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. However, 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, SO2 and NOX

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 that demand that is met by fossil units could 

vary. For example, for a state with substantial nuclear generation (typically 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 data from the Energy Information Administration4, 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 Transport Rule program for the time period 

2000 through 2009 (Tables 10 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 generation 

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 percentages (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 general (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 generation 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 95th 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 

Virginia 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 similar 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 other states). During the 2000-2009 timeframe, these 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 

2000-2010 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 95th 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 proportion 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 could 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 generation ratio that are 

representative of year-to-year variability in other states. Based in part on this case study of yearto-year variability in the fossil fuel-fired share of a state’s generation (a relevant factor informing

year-to-year variability in state 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 (Megawatthours) (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 45,730,318 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 99,905,430 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 47,247,427 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 27,861,034 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 75,148,857 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

Michigan 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 33,993,274

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 336,018,948 329,588,630 342,954,605 340,415,604 343,571,908 350,877,744 349,849,160 351,720,285 344,812,945 333,226,991 342,303,682

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 94,422,841 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 127,101,358 121,868,463 125,065,281 123,853,265 126,670,732 129,431,154 129,345,084 129,576,104 128,206,437 114,118,178 125,523,606

*This table is sorted in order of increasing average fraction of fossil relative to the total electric industry for the state (seen in Tables 12 and 13).
33

Table 11. Total Electric Industry Generation for All Sectors by Year (Megawatthours).

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 99,267,606 103,402,142 100,978,005 100,125,486 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 78,943,045 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 122,274,356 117,495,850 124,468,029 127,582,320 126,329,957 129,748,578 125,214,784 130,115,301 125,239,063 118,407,403 124,687,564

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 124,405,340 125,345,113 132,920,670 137,487,222 137,354,771 137,948,581 140,895,441 143,826,271 145,869,895 143,255,556 136,930,886

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 123,877,413 118,316,789 126,512,215 124,076,834 126,812,715 136,667,892 138,010,208 145,155,158 136,173,395 128,698,376 130,430,100

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 90,921,829 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 377,742,365 372,580,002 385,628,541 379,199,685 390,299,132 396,668,722 400,582,878 405,492,296 404,787,781 397,167,910 391,014,931

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 149,060,280 142,261,807 147,068,849 146,638,128 148,345,905 156,976,323 155,434,075 155,155,545 153,412,251 136,090,225 149,044,339

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 127,819,516 122,569,673 125,608,139 124,888,218 127,770,396 130,371,573 130,489,788 130,637,999 129,510,294 116,670,280 126,633,588

*This table is sorted in order of increasing average fraction of fossil relative to the total electric industry for the state (seen in Tables 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%

Georgia 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 Deviation 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)

Average

Fossil to Total

(See Table 12)

Standard

Deviation

(Fossil to 

Total)

(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%

Average 73% 2.1%

Standard Deviation 16% 0.9%

One Standard

Deviation. Below 

Average 57%

Upper 95% 

Confidence 

Variability Level 3.9%