United States Office of Water EPA

- 821

- R

- 15

- 005

Environmental Protection Washington, DC 20460 September 2015 Agency



# Benefit and Cost Analysis
for the Effluent Limitations
Guidelines and Standards
for the Steam Electric Power Generating Point Source Category

# Benefit and Cost Analysis for the Effluent
Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category

EPA

- 821

- R

- 15

- 005

September 2015

U.S. Environmental Protection Agency Office of Water (4303T) Engineering and Analysis Division 1200 Pennsylvania Avenue, NW Washington, DC 20460Benefit and Cost Analysis for Steam Electric Power Generating ELGs Acknowledgments and Disclaimer



### This report was prepared by the U.S. Environmental Protection Agency. Neither the United States Government nor any of its employees, contractors, subcontractors, or their employees make any warrant, expressed or implied, or assume any legal liability or responsibility for any third party’s us e of or the results of such use of any information, apparatus, product, or process discussed in this report, or represents that its use by such party would not infringe on privately owned rights.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 i 

## Table of Contents

1 I NTRODUCTION



### ................................ ................................ ................................ ................................ ....... 1

- 1

S TEAM 

### E LECTRIC 

### P OWER 

### P LANTS



### ................................ ................................ ................................ ... 1

- 2 1.1

R EGULATORY 

### O PTIONS 

### A NALYZED FOR THE 

### F INAL 

### ELG S



### ................................ ............................. 1

- 2 1.2

A NALYSIS 

### S CENARIOS



### ................................ ................................ ................................ ..................... 1

- 3 1.3

L OADING AND 

### W ITHDRAWAL 

### R EDUCTIONS



### ................................ ................................ .................... 1

- 4 1.4

1.4.1 Loading Reductions

................................ ................................ ................................ ............ 1

- 4

1.4.2 Water Withdrawal Reductions

................................ ................................ ............................ 1

- 4

1.4.3 Loading Reductions Used in Estimating Benefits

................................ .............................. 1

- 4

A NALYTIC 

### F RAMEWORK



### ................................ ................................ ................................ .................. 1

- 6 1.5

1.5.1 Constant Prices

................................ ................................ ................................ ................... 1

- 6
1.5.2 Discount Rate and Year

................................ ................................ ................................ ...... 1

- 7
1.5.3 Period of Analysis

................................ ................................ ................................ .............. 1

- 7
1.5.4 Population and Income Growth

................................ ................................ .......................... 1

- 7

O RGANIZATION OF THE 

### B ENEFIT AND 

### C OST 

### A NALYSIS 

### R EPORT



### ................................ ..................... 1

- 8 1.6

2 B ENEFITS 

### O VERVIEW



### ................................ ................................ ................................ ............................. 2

- 1

H UMAN 

### H EALTH 

### B ENEFITS 

### A SSOCIATED WITH 

### I MPROVEMENTS IN 

### S URFACE 

### W ATER 

### 2.1 Q UALITY



### ................................ ................................ ................................ ................................ .......... 2

- 3

2.1.1 Fish Consumption

................................ ................................ ................................ ............... 2

- 3

2.1.2 Dr inking Water Consumption

................................ ................................ ............................ 2

- 4

2.1.3 Complementary Measure of Human Health Benefits

................................ ......................... 2

- 5

E COLOGICAL 

### B ENEFITS 

### A SSOCIATED WITH 

### I MPROVEMENTS IN 

### S URFACE 

### W ATER 

### Q UALITY



### ......... 2

- 5 2.2

2.2.1 Improved Surface Water Quality

................................ ................................ ........................ 2

- 5

2.2.2 Benefits to Threatened and Endan gered Species

................................ ................................ 2

- 7

2.2.3 Reduced Sediment Contamination

................................ ................................ ..................... 2

- 8

B ENEFITS 

### A SSOCIATED WITH 

### I MPROVEMENTS IN 

### G ROUNDWATER 

### Q UALITY



### ................................ . 2

- 8 2.3

E CONOMIC 

### P RODUCTIVITY 

### B ENEFITS



### ................................ ................................ .............................. 2

- 9 2.4

2.4.1 Reduced Impoundment Releases

................................ ................................ ........................ 2

- 9

2.4.2 Enhanced Marketability of Coal Ash for Beneficial Use

................................ ................... 2

- 9
2.4.3 Water Supply and Use

................................ ................................ ................................ ...... 2

- 10

2.4.4 Reduced Sedimentation in Navigational Waterways

................................ ........................ 2

- 11

2.4.5 Commercial Fisheries

................................ ................................ ................................ ....... 2

- 11

2.4.6 Tourism

................................ ................................ ................................ ............................. 2

- 11

2.4.7 Property Values

................................ ................................ ................................ ................ 2

- 12

R EDUCED 

### A IR 

### P OLLUTION



### ................................ ................................ ................................ ............. 2

- 12 2.5

R EDUCED 

### W ATER 

### W ITHDRAWALS



### ................................ ................................ ................................ 2

- 13 2.6

S UMMARY OF 

### B ENEFITS 

### C ATEGORIES



### ................................ ................................ ........................... 2

- 13 2.7

3 H UMAN 

### H EALTH 

### B ENEFITS



### ................................ ................................ ................................ ................... 3

- 1

A FFECTED 

### P OPULATION



### ................................ ................................ ................................ ................... 3

- 2 3.1

P OLLUTANT 

### E XPOSURE FROM 

### F ISH 

### C ONSUMPTION



### ................................ ................................ ......... 3

- 4 3.2Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 ii



### 3.2.1 Fish Tissue Pollutant Concentrations

................................ ................................ ................. 3

- 4

3.2.2 Average Daily Dose

................................ ................................ ................................ ............ 3

- 4

B ENEFITS TO 

### C HILDREN FROM 

### R EDUCED 

### L EAD 

### E XPOSURE



### ................................ ............................ 3

- 6 3.3

3.3.1 Methods

................................ ................................ ................................ .............................. 3

- 6
3.3.2 Results

................................ ................................ ................................ ................................ 3

- 9

B ENEFITS TO 

### A DULTS FROM 

### R EDUCED 

### L EAD 

### E XPOSURE



### ................................ .............................. 3

- 10 3.4

3.4.1 Methods

................................ ................................ ................................ ............................ 3

- 10

3.4.2 Result s

................................ ................................ ................................ .............................. 3

- 14

B ENEFITS TO 

### C HILDREN FROM 

### R EDUCED 

### M ERCURY 

### E XPOSURE



### ................................ .................. 3

- 14 3.5

3.5.1 Methods

................................ ................................ ................................ ............................ 3

- 15

3.5.2 Results

................................ ................................ ................................ .............................. 3

- 16

R EDUCED 

### C ANCER 

### C ASES FROM 

### A RSENIC 

### E XPOSURE



### ................................ ................................ .. 3

- 16 

### 3.6

3.6.1 Methods

................................ ................................ ................................ ............................ 3

- 16

3.6.2 Results

................................ ................................ ................................ .............................. 3

- 19

T OTAL 

### M ONETIZED 

### H UMAN 

### H EALTH 

### B ENEFITS



### ................................ ................................ ........... 3

- 19 3.7

A DDITIONAL 

### M EASURES OF 

### H UMAN 

### H EALTH 

### B ENEFITS



### ................................ .............................. 3

- 20 3.8

I MPLICATIONS OF 

### R EVISED 

### S TEAM 

### E LECTRIC 

### P LANT 

### L OADING 

### E STIMATES



### ................................ 3

- 20 3.9

L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ............................... 3

- 21 3. 10

4 N ONMARKET 

### B ENEFITS FROM 

### W ATER 

### Q UALITY 

### I MPROVEMENTS



### ................................ .................... 4

- 1

W ATER 

### Q UALITY



### ................................ ................................ ................................ ............................. 4

- 1 4.1

4.1.1 WQI Calculation

................................ ................................ ................................ ................. 4

- 2

4.1.2 Sources of Data on Ambient Water Quality

................................ ................................ ....... 4

- 5

4.1.3 Baseline WQI

................................ ................................ ................................ ..................... 4

- 8

4.1.4 Es timated Changes in Water Quality (∆WQI) from the ELG Rule

................................ .... 4

- 9

M ETHODOLOGY FOR 

### E STIMATING 

### WTP FOR 

### W ATER 

### Q UALITY 

### I MPROVEMENTS



### ......................... 4

- 11 4.2

T OTAL 

### WTP FOR 

### W ATER 

### Q UALITY 

### I MPROVEMENTS



### ................................ ................................ .... 4

- 17 4.3

I MPLICATIONS OF 

### R EVISED 

### S TEAM 

### E LECTRIC 

### P LANT 

### L OADING 

### E STIMATES



### ................................ 4

- 20 4.4

L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ............................... 4

- 22 4.5

5 I MPACTS AND 

### B ENEFITS TO 

### T HREATENED AND 

### E NDANGERED 

### S PECIES



### ................................ ............ 5

- 1

I NTRODUCTION



### ................................ ................................ ................................ ................................ . 5

- 1 5.1

B ASELINE 

### S TATUS OF 

### F RESHWATER 

### F ISH 

### S PECIES



### ................................ ................................ .......... 5

- 1 5.2

T&E S PECIES 

### A FFECTED BY THE 

### F INAL 

### ELG S



### ................................ ................................ ................ 5

- 2 5.3

5.3.1 Identifying T&E Species Potentially Affected by the Fina l ELGs

................................ ..... 5

- 2

5.3.2 Estimating Benefits of T&E Species Improvements from the Final ELGs

........................ 5

- 4

E STIMATING 

### WTP FOR 

### T&E S PECIES 

### P OPULATION 

### I NCREASES



### ................................ ...................... 5

- 5 5.4

5.4.1 Economic Valuation Methods

................................ ................................ ............................ 5

- 5

5.4.2 Estimating WTP for Improved Protection of T&E Species

................................ ............... 5

- 5

R ESULTS



### ................................ ................................ ................................ ................................ ........... 5

- 7 5.5

I MPLICATIONS OF 

### R EVISED 

### S TEAM 

### E LECTRIC 

### P LANT 

### L OADING 

### E STIMATES



### ................................ .. 5

- 9 5.6

L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ................................ . 5

- 9 5.7

6 B ENEFITS FROM 

### A VOIDED 

### I MPOUNDMENT 

### F AILURES



### ................................ ................................ ......... 6

- 1

M ETHODS AND 

### D ATA



### ................................ ................................ ................................ ....................... 6

- 1 6.1Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 iii



### 6.1.1 Baseline Failure Probability and Release Quantity

................................ ............................ 6

- 2

6.1.2 Effects of the ELGs

................................ ................................ ................................ ............ 6

- 4

6.1.3 Costs of a Release

................................ ................................ ................................ ............... 6

- 6

R ESULTS



### ................................ ................................ ................................ ................................ ......... 6

- 10 6.2

I MPLICATIONS OF 

### R EVISED 

### S TEAM 

### E LECTRIC 

### P LANT 

### L OADING 

### E STIMATES



### ................................ 6

- 13 6.3

L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ............................... 6

- 13 6.4

7 A IR



### - R ELATED 

### B ENEFITS



### ................................ ................................ ................................ ........................ 7

- 1

D ATA AND 

### M ETHODOLOGY



### ................................ ................................ ................................ ............. 7

- 2 7.1

7.1.1 Changes in Air Emissions

................................ ................................ ................................ ... 7

- 2

7.1.2 NO

x 

### and SO 2


### ................................ ................................ ................................ ....................... 7

- 4

7.1.3 CO 2


### ................................ ................................ ................................ ................................ ..... 7

- 7

7.1.4 Estimating Total Air

- Related Benefits

................................ ................................ ............... 7

- 9

R ESULTS



### ................................ ................................ ................................ ................................ ......... 7

- 10 7.2

I MPLICATIONS OF 

### R EVISED 

### S TEAM 

### E LECTRIC 

### P LANT 

### L OADING 

### E STIMATES



### ................................ 7

- 11 7.3

L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ............................... 7

- 11 7.4

8 B ENEFITS FROM 

### R EDUCED 

### W ATER 

### W ITHDRAWALS



### ................................ ................................ ........... 8

- 1

G ROUNDWATER 

### W ITHDRAWALS



### ................................ ................................ ................................ ..... 8

- 1 8.1

8.1.1 Methods

................................ ................................ ................................ .............................. 8

- 1

8.1.2 Results

................................ ................................ ................................ ................................ 8

- 2

8.1.3 Implicat ions of Revised Steam Electric Plant Loading Estimates

................................ ...... 8

- 2

8.1.4 Limitations and Uncertainties

................................ ................................ ............................. 8

- 2

9 B ENEFITS FROM 

### A VOIDED 

### D REDGING 

### C OSTS



### ................................ ................................ ...................... 9

- 1

10 B ENEFITS FROM 

### E NHANCED 

### M ARKETABILITY OF 

### C OAL 

### C OMBUSTION 

### R ESIDUALS



### ...................... 10

- 1

M ETHODS



### ................................ ................................ ................................ ................................ ....... 10

- 1 10.1

10.1.1 Beneficial Use Applications

................................ ................................ ............................. 10

- 1

10.1.2 Marketable CCR by State

................................ ................................ ................................ . 10

- 2

10.1.3 Value to Society from Re

- Using CCR

................................ ................................ .............. 10

- 5

R ESULTS



### ................................ ................................ ................................ ................................ ......... 10

- 8 10.2

I MPLICATIONS OF 

### R EVISED 

### S TEAM 

### E LECTRIC 

### P LANT 

### L OADING 

### E STIMATES



### .............................. 10

- 10 10.3

L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ............................. 10

- 10 10.4

11 S UMMARY OF 

### T OTAL 

### M ONETIZED 

### B ENEFITS



### ................................ ................................ .................... 11

- 1

T OTAL 

### A NNUALIZED 

### B ENEFITS



### ................................ ................................ ................................ ..... 11

- 1 11.1

T IME 

### P ROFILE OF 

### B ENEFITS



### ................................ ................................ ................................ ........... 11

- 6 

### 11.2

12 S UMMARY OF 

### T OTAL 

### C OSTS



### ................................ ................................ ................................ ............... 12

- 1

O VERVIEW OF 

### C OSTS 

### A NALYSIS 

### F RAMEWORK



### ................................ ................................ ............. 12

- 1 12.1

K EY 

### F INDINGS FOR 

### R EGULATORY 

### O PTIONS



### ................................ ................................ .................. 12

- 2 12.2

13 B ENEFITS AND 

### C OSTS



### ................................ ................................ ................................ ........................... 13

- 1

C OMPARISON OF 

### B ENEFITS AND 

### C OSTS BY 

### O PTION



### ................................ ................................ ...... 13

- 1 13.1

A NALYSIS OF 

### I NCREMENTAL 

### B ENEFITS AND 

### C OSTS



### ................................ ................................ ...... 13

- 2 13.2

14 E NVIRONMENTAL 

### J USTICE



### ................................ ................................ ................................ .................. 14

- 1Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 iv



### S OCIO



### - ECONOMIC 

### C HARACTERISTICS OF 

### P OPULATIONS 

### R ESIDING IN 

### P ROXIMITY TO 

### 14.1

S TEAM 

### E LECTRIC 

### P OWER 

### P LANTS



### ................................ ................................ ................................ . 14

- 1

D ISTRIBUTION OF 

### H UMAN 

### H EALTH 

### I MPACTS AND 

### B ENEFITS



### ................................ ....................... 14

- 3 14.2

D ISTRIBUTION OF 

### B ENEFITS ACROSS 

### B ENEFITTING 

### P OPULATIONS



### ................................ ............... 14

- 4 14.3

S UBSISTENCE 

### F ISHERS



### ................................ ................................ ................................ ................... 14

- 6 14.4

EJ A NALYSIS 

### F INDINGS



### ................................ ................................ ................................ .................. 14

- 7 14.5

L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ............................... 14

- 7 14.6

15 R EFERENCES



### ................................ ................................ ................................ ................................ ......... 15

- 1

A PPENDIX 

### A. C HANGES TO 

### B ENEFITS 

### A NALYSIS SINCE 

### P ROPOSAL



### ................................ ..................... A

- 1

A PPENDIX 

### B. A NALYSIS FOR 

### S CENARIO WITHOUT 

### CPP R ULE



### ................................ .............................. B

- 1

B.1 H UMAN 

### H EALTH 

### B ENEFITS



### ................................ ................................ ................................ .............. B

- 1

B.1.1 Benefits to Children from Reduced Lead Exposure

................................ ........................... B

- 1

B.1.2 Benefits to Adults from Reduced Lead Exposure

................................ .............................. B

- 2

B.1.3 Benefits to Children from Reduced Mercury Exposure

................................ ..................... B

- 3

B.1.4 Reduced Cancer Cases from Arsenic Exposure

................................ ................................ . B

- 3

B.1.5 Total Monetized Human Health Benefits

................................ ................................ ........... B

- 4

B.1.6 Additional Measures of Human Health Benefits

................................ ................................ B

- 4

B.2 N ON



### - M ARKET 

### B ENEFITS FOR 

### W ATER 

### Q UALITY 

### I MPROVEMENTS



### ................................ ................... B

- 5

B.3 I MPACTS AND 

### B ENEFITS TO 

### T HREATENED AND 

### E NDANGERED 

### S PECIES



### ................................ ......... B

- 5

B.4 B ENEFITS FROM 

### A VOIDED 

### I MPOUNDMENT 

### F AILURES



### ................................ ................................ ..... B

- 7

B.5 A IR



### - R ELATED 

### B ENEFITS



### ................................ ................................ ................................ .................. B

- 7

B.6 B ENEFITS FROM 

### R EDUCED 

### W ATER 

### W ITHDRAWALS



### ................................ ................................ ...... B

- 11

B.7 B ENEFITS FROM 

### E NHANCED 

### M ARKETABILITY OF 

### C OAL 

### C OMBUSTION 

### R ESIDUALS



### ..................... B

- 11

B.8 T OTAL 

### M ONETIZED 

### B ENEFITS



### ................................ ................................ ................................ ....... B

- 14

B.9 T OTAL 

### C OSTS



### ................................ ................................ ................................ ................................ . B

- 19

B.10 M ONETIZED 

### B ENEFITS AND 

### C OSTS 

### C OMPARISON



### ................................ ................................ ......... B

- 21

B.10.1 Comparison of Total Monetized Benefits and Costs

................................ ........................ B

- 21

B.10.2 Analysis of Incremental Monetized Benefits and Costs

................................ ................... B

- 21

A PPENDIX 

### C. E STIMATION OF 

### E XPOSED 

### P OPULATION



### ................................ ................................ .......... C

- 1

A PPENDIX 

### D. D ERIVATION OF 

### A MBIENT 

### W ATER AND 

### F ISH 

### T ISSUE 

### C ONCENTRATIONS IN


### R ECEIVING AND 

### D OWNSTREAM 

### R EACHES



### ................................ ................................ ................................ .. D

- 1

D.1 M ETALS



### ................................ ................................ ................................ ................................ ........... D

- 1

D.1.1 Estimating Water Concentrations in each Reach

................................ ............................... D

- 1

D.1.2 Estimating Fish Tissue Concentrations in each Reach

................................ ...................... D

- 2

D.2 N UTRIENTS AND 

### S USPENDED 

### S EDIMENT



### ................................ ................................ ........................ D

- 3

A PPENDIX 

### E. D ETAILS ON 

### M ODELING OF 

### C ARDIOVASCULAR 

### D ISEASE 

### I NCIDENCE AND


### M ORTALITY 

### E

- 1

E.1 B ENEFITS TO 

### A DULTS FROM 

### R EDUCED 

### L EAD 

### E XPOSURE



### ................................ ................................ E

- 1

E.1.1 Hazard Reduction under Final ELGs

................................ ................................ .................. E

- 1

E.1.2 Estimating Premature Deaths Avoided Over Multiple Years

................................ ............. E

- 2

A PPENDIX 

### F. H UMAN 

### H EALTH 

### B ENEFITS 

### S ENSITIVITY 

### A NALYSIS



### ................................ ....................... F

- 1Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 v 

### F.1 A LTERNATIVE 

### F ISHING 

### D ISTANCE 

### A SSUMPTION



### ................................ ................................ ............. F

- 1

F.2 L OG



### - L INEAR 

### C ONCENTRATION 

### R ESPONSE 

### F UNCTION FOR 

### IQ I MPACTS TO 

### C HILDREN

FROM 

### L EAD 

### E XPOSURE



### ................................ ................................ ................................ .................... F

- 2

F.3 A LTERNATIVE 

### C ANCER 

### S LOPE 

### F ACTOR AND 

### C ASE 

### V ALUATION FOR 

### A RSENIC 

### A NALYSIS



### ............. F

- 3

A PPENDIX 

### G. WQI R EGIONAL 

### S UBINDICES



### ................................ ................................ ................................ . 1

A PPENDIX 

### H. D EVELOPMENT OF 

### M ETA



### - R EGRESSION 

### M ODELS OF 

### W ILLINGNESS TO 

### P AY FOR


### W ATER 

### Q UALITY 

### I MPROVEMENTS



### ................................ ................................ ................................ .............. H

- 1

H.1 L ITERATURE REVIEW TO IDENTIFY ADDITIONAL STUDIES



### ................................ .............................. H

- 1

H.2 V ARIABLE 

### D EVELOPMENT AND 

### C ODING



### ................................ ................................ ........................ H

- 5

H.2.1 Study Methodology and Year

................................ ................................ ............................ H

- 9

H.2.2 Region and Surveyed Populations

................................ ................................ ..................... H

- 9

H.2.3 Reconciliation of Water Quality Baseline and Change

................................ ................... H

- 10

H.2.4 Delineation of the Affected Resource and Substitut es

................................ .................... H

- 12

H.3 M ODEL 

### S PECIFICATION



### ................................ ................................ ................................ .................. H

- 15

H.3.1 Marginal Willingness to Pay (Model 1)

................................ ................................ .......... H

- 15

H.3.2 Marginal Willingness to Pay (Model 2)

................................ ................................ .......... H

- 16

H.4 R EGRESSION 

### R ESULTS



### ................................ ................................ ................................ ................... H

- 17

H.5 L IMITATIONS AND 

### U NCERTAINTY



### ................................ ................................ ................................ .. H

- 20

A PPENDIX 

### I. I MPACTS OF 

### S TEAM 

### E LECTRIC 

### P OLLUTANTS ON 

### A QUATIC 

### S PECIES



### .............................. I

- 1

A PPENDIX 

### J. S UPPORTING 

### D ATA FOR 

### I MPOUNDMENT 

### R ELEASE 

### A NALYSIS



### ................................ ........ J

- 1

A PPENDIX 

### K. M ETHODOLOGY FOR 

### B ENEFITS FROM 

### A VOIDED 

### D REDGING 

### C OSTS



### ............................. K

- 1

K.1 R EVIEW AND 

### A NALYSIS OF 

### H ISTORICAL 

### D REDGING 

### D ATA



### ................................ ............................ K

- 1

K.1.1 Dredging Location, Recurrence Interval, and Dredging Volumes

................................ .... K

- 2

K.1.2 Determining Affected Navigational Dredging Jobs and Unit Costs

................................ .. K

- 3

K.1.3 Determining Reservoir Dredging Locations

................................ ................................ ...... K

- 4

K.2 S EDIMENT 

### D EPOSITION IN 

### N AVIGABLE 

### W ATERWAYS AND 

### R ESERVOIRS



### ................................ ....... K

- 4

K.3 E STIMATING 

### D REDGING 

### C OSTS UNDER 

### B ASELINE AND 

### R EGULATORY 

### O PTIONS



### ........................... K

- 4

K.3.1 Estimating Annualized Dredging Costs for Navigational Waterbodies

............................ K

- 4

K.3.2 Estimating Annualized Dredging Costs for Reservoirs

................................ ..................... K

- 6

K.4 R ESULTS



### ................................ ................................ ................................ ................................ .......... K

- 7

K.4.1 Navigational Dredging Benefits

................................ ................................ ........................ K

- 7

K.4.2 Reservoir Dredging Benefits

................................ ................................ ............................. K

- 7
K.5 L IMITATIONS AND 

### U NCERTAINTIES



### ................................ ................................ ................................ K

- 8Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 vi 

## List of Figures

Figure 2

- 1: Summary of Benefits Resulting from the ELGs.

................................ ................................ .......... 2

- 2 Figure 3

- 1: Example changes in PbB through time under baseline, option B, and option D for cohorts in

the 20s and 60s (age as of 2014). Differences between cohorts are driven by differences in body mass. Example data represent modeled PbBs of male subsistence fishers residing in CBG 560419753004. The majority of cohorts nationwide experience a smaller reduction in

PbB due to the rule.

................................ ................................ ................................ .................... 3

- 12 Figu re 4

- 1: Comparison between the NHD and E2RF1 Network in a Single Watershed.

.............................. 4

- 7 Figure 6

- 1: Baseline Release Probability and Capac ity Factor Assumptions

................................ .................. 6

- 6 Figure 10

- 1: Cost Accounting Framework for the Beneficial Reuse Analysis.

................................ ............. 10

- 5 Figure E

- 1: Illustration of a Hypothetical Policy Effect on a Survival Curve.

................................ ................ E

- 3 Figure E

- 2: A Recursive Illustration of a Policy Effect on Survival Rates.

................................ ..................... E

- 4



## List of Tables

Table 1

- 1: Steam Electric ELG Regulatory Options

................................ ................................ ........................ 1

- 3

Table 1

- 2: Pollutant R emoval for Final ELGs Regulatory Options

................................ ................................ . 1

- 4

Table 1

- 3: Pollutant Removal for Final ELGs Regulatory Options Used in Estimating Be nefits

................... 1

- 5

Table 1

- 4: Impacts of Loading Reduction Revisions on Benefit Estimates

................................ .................... 1

- 5

Table 2

- 1: Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

........... 2

- 13
Table 3

- 1: Summary of Potentially Affected Population Living within 50 Miles of Affected Reaches

(baseline, as of 2010)

................................ ................................ ................................ .................... 3

- 4

Table 3

- 2: Summary of group

- specific assumptions for human health benefit analysis a



### ................................ 3

- 5

Table 3

- 3: Value of an IQ Point a 

### (2013$)

................................ ................................ ................................ ........ 3

- 8
Table 3

- 4: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead

................................ . 3

- 9
Table 3

- 5: Estimated Avoided C ost of Compensatory Education for Children with Blood Lead

Concentrations above 20  g/dL and IQ Less than 70

................................ ................................ 3

- 10
Table 3

- 6: Parameters Used to Apply the Leggett+ Model

................................ ................................ ........... 3

- 11
Table 3

- 7: Sample of Inputs for Age

- and Sex

- Specific Hazard Functions

................................ ................... 3

- 13
Table 3

- 8: Summary of Estimated Health Benefits due to Decreased Risk of CVD Mortality during

2019

- 2042 based on the Economic Value of Avoided Premature Mortality (VSL)

.................. 3

- 14
Table 3

- 9: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure

...................... 3

- 16
Table 3

- 10: Total Costs of Illness for Skin Cancer a



### ................................ ................................ ....................... 3

- 18
Table 3

- 11: Weighted Average Skin Cancer Cost of Illness a



### ................................ ................................ ........ 3

- 18
Table 3

- 12: Annual Benefits from Reduced Cancer Cases due to Arsenic Exposure

................................ ... 3

- 19

Table 3

- 13: Total Monetized Human Health Benefits for ELG Options (millions of 2013$)

....................... 3

- 19
Table 3

- 14: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric

P ollutants

................................ ................................ ................................ ................................ .... 3

- 20
Table 3

- 15: Estimated Aggregate Changes in Pollutant Loadings for Lead, Mercury and Arsenic

(Pounds per Year).

................................ ................................ ................................ ...................... 3

- 21
Table 3

- 16: Limitations and Uncertainties in the Analysis of Human Health Benefits

................................ . 3

- 22
Table 4

- 1: Freshwater Water Quality Subindices

................................ ................................ ............................ 4

- 3

Table 4

- 2: Freshwater Water Quality Subindex for Heavy Metals

................................ ................................ .. 4

- 4

Table 4

- 3: Water Quality Classif ications

................................ ................................ ................................ ......... 4

- 5Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 vii 

### Table 4

- 4: Water Quality Modeling Data used in Calculating the Baseline and Policy Metal, Nutrient

and Sediment Conc entrations

................................ ................................ ................................ ....... 4

- 6
Table 4

- 5: Water Quality Data used in Calculating the Baseline and Policy WQI

................................ .......... 4

- 8

Table 4

- 6: Percentage of Potentially Affected Inland Reach Miles by WQI Classification: Baseline

Scenario

................................ ................................ ................................ ................................ ........ 4

- 9
Table 4

- 7: Water Quality Improvements from Final ELGs in All Benefiting Reaches

................................ . 4

- 10

Table 4

- 8: Independent Variable Assignments for Surface Water Quality Meta

- Analysis

........................... 4

- 15

Table 4

- 9: Household Willingness

- to

- Pay for Water Quality Improvements

................................ ................ 4

- 18

Table 4

- 10: Total Willingness

- to

- Pay for Water Quality Improveme nts

................................ ...................... 4

- 19

Table 4

- 11: Estimated Aggregate Changes in Pollutant Loadings for Metals, Nutrients, and Suspended

Solids.

................................ ................................ ................................ ................................ ......... 4

- 21

Table 4

- 12: Number of Reaches with Baseline AWQC Exceedances Based on Initial Loadings.

................ 4

- 21

Table 4

- 13: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits

.............. 4

- 22

Table 5

- 1: T&E Species with Habitat Occurring within Waterbodies Affected by Steam Electric Power

Plants

................................ ................................ ................................ ................................ ............ 5

- 3
Table 5

- 2: T&E Species Whose Recovery May Benefit from the Final ELGs

................................ ............... 5

- 4
Table 5

- 3: Independent Variable Assignments for the T&E Meta

- Regression

................................ ............... 5

- 6
Table 5

- 4: Estimated Annualized Benefits to T&E Species from WQ Improvements (Millions 2013$) a,b



### ..... 5

- 8
Table 5

- 5: Limitations and Uncertainties in the Analysis of T&E Species Benefits

................................ ....... 5

- 9
Table 6

- 1: Probabilities of CCR impoundment releases, based on analysis of 49 historical release events

1995

– 2008

................................ ................................ ................................ ................................ .... 6

- 3
Table 6

- 2: Release Probability and Capacity Factor Assumptions for the ELGs

................................ ............ 6

- 4
Table 6

- 3: Steam Electric Impoundments by Size in ELG Baseline

................................ ............................... 6

- 5

Table 6

- 4: Documented Cleanup Costs from Impoundment Releases

................................ ............................ 6

- 7
Table 6

- 5: NRD Settlements Summary Statistics

................................ ................................ ............................ 6

- 9
Table 6

- 6: Studies Summarizing Tra nsaction Costs as a Share of Superfund Spending (for potentially

responsible parties)

................................ ................................ ................................ ....................... 6

- 9
Table 6

- 7: Unit Costs for Impoundment Re leases (2013$)

................................ ................................ ........... 6

- 10
Table 6

- 8: Total Expected Number of Releases in 2019 through 2042, by Failure Type

.............................. 6

- 11
Table 6

- 9: Expected Total Coal Combustion Residuals Volume Released Annually, by Failure Type

(Million Gallons)

................................ ................................ ................................ ........................ 6

- 12
Table 6

- 10: Estimated Annualized Benefits of Avoided Impoundment Failures by Release Type

(Millions; 2013$) a



### ................................ ................................ ................................ ....................... 6

- 13
Table 6

- 11: Limitations and Uncertainties in Analysis of Avoided Risk of Impoundment Failure

Benefits

................................ ................................ ................................ ................................ ....... 6

- 14

Table 7

- 1: Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to Increase

in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline

................................ . 7

- 2

Table 7

- 2: Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at Steam

Electric Power Plants, Relative to Baseline

................................ ................................ ................. 7

- 3

Table 7

- 3: Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity

Generation Profile, Relative to Baseline

................................ ................................ ...................... 7

- 3

Table 7

- 4: Estimated Net Changes in Air Pollutant Emissions due to Increase in Auxiliary Service at

Steam Electric Power Plants, Increased Trucking at Steam Electric Power Plants, and Changes in Electricity Generation Profile, Relative to Baseline

................................ .................. 7

- 4

Table 7

- 5: National Benefits per Ton Estimates for NOx and SO 2 

### Emissions (2013$/ton) from the

Benefits per Ton Analysis Reported by Fann et al. (2012) a, b, c



### ................................ .................... 7

- 7
Table 7

- 6: Social Cost of Carbon Values (2013$/metric ton CO 2 

### )

................................ ................................ .. 7

- 9Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 viii 

### Table 7

- 7: Estimated Benefits from Reduced Air Emissions for Selected Years (millions; 2013$) a



### ............ 7

- 10

Table 7

- 8: Estimated Annualized Benefits from Reduced Air Emissions (Millions; 2013$)

........................ 7

- 11

Table 7

- 9: Limitations and Uncertainties in Analysis of Air

- related Benefits

................................ .............. 7

- 12
Table 8

- 1: Estimated Annualized Benefits from Reduced Groundwater Withdrawals (Millions; 2013$)

...... 8

- 2
Table 8

- 2: Limitations and Uncertainties in Analysis of Reduced Groundwater Withdrawals

....................... 8

- 2
Table 10

- 1: Applicable Beneficial Use Applications of CCRs, by CCR Category

................................ ....... 10

- 1
Table 10

- 2: State

- level Market A pproximation (Short Tons).

................................ ................................ ....... 10

- 4
Table 10

- 3: Economic Value of CCR Handling Costs per Unit (2013$).

................................ ...................... 10

- 7
Table 10

- 4: Avoided Resource and Environmental Impacts per Ton of Virgin Material Produced.

............. 10

- 7
Table 10

- 5: Economic Value of Avoided Resource and Environmental Impacts per Unit of Impact

(2013$).

................................ ................................ ................................ ................................ ...... 10

- 8
Table 10

- 6: Estimated Beneficial Use Applications of CCRs, by CCR Category

................................ ......... 10

- 8
Table 10

- 7: Annual Avoided Resource and Environmental Impacts Given CCR Reuse in Concrete and

Fill Applications.

................................ ................................ ................................ ........................ 10

- 9
Table 10

- 8: Annualized Economic Value of Estimated Changes in Beneficial Use (Million 2013$) a,b



### . .... 10

- 10

Table 11

- 1: Summary of Total Annualized Benefits at 3 Percent (Millions; 2013$)

................................ .... 11

- 2

Table 11

- 2: Summary of Total Annualized Benefits at 7 Pe rcent (Millions; 2013$)

................................ .... 11

- 4

Table 11

- 3: Time Profile of Benefits at 3 Percent (Millions; 2013$) (Including Air

- Related Benefits for

Options B and D)

................................ ................................ ................................ ........................ 11

- 7
Table 11

- 4: Time Profile of Benefits at 7 Percent (Millions; 2013$) (Including Air

- Related Benefits for

Options B and D)

................................ ................................ ................................ ........................ 11

- 8

Table 12

- 1: Summary of Annualized Costs (Millions; $2013)

................................ ................................ ...... 12

- 2

Table 12

- 2: Time Profile of Costs to Society (Millions; $2013)

................................ ................................ ... 12

- 3
Table 13

- 1: Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions;

2013$)

................................ ................................ ................................ ................................ ......... 13

- 1
Table 13

- 2: Incremental Net Benefit Analysis (Millions; 2013$)

................................ ................................ . 13

- 3

Table 14

- 1: Socio

- economic Characteristics of Comm unities Living in Proximity to Receiving Reaches

.. 14

- 2

Table 14

- 2: Socio

- economic Characteristics of Affected Communities, Compared to State Average

.......... 14

- 3

Table 14

- 3: Characteristics of Population Potentially Exposed to Steam Electric Pollutants via

Consumption of Self

- caught Fish

................................ ................................ ............................... 14

- 4

Table 14

- 4: Distribution of Baseline IQ Point Decrements by Pollutant (2021 to 2042)

.............................. 14

- 5

Table 14

- 5: Distribution of Avoided IQ Point Decrements Relative to the Baseline, by Pollutant (2021

to 2042)

................................ ................................ ................................ ................................ ...... 14

- 5

Table 14

- 6: Distribution of Baseline IQ Point Decrements by Pollutant and Fishing Mode (2021 to

2042)

................................ ................................ ................................ ................................ ........... 14

- 6
Table 14

- 7: Distribution of Avoided IQ Point Decrements Relative to the Baseline by Fishing Mode,

and Pollutant (2021 to 2042)

................................ ................................ ................................ ...... 14

- 6Benefit and Cost Analysis for Steam Electric Power Generating ELGs List of Abbreviations

September 29, 2015 ix 

## List of Abbreviations

ADD Average daily dose

A SMFC Atlantic States Marine Fisheries Commission

As Arsenic

AF S C Alaskan Fisheries Science Center AWQC Ambient water quality criteria

BAT Best available technology economically achievable

BCA Benefit cost analysis

BEA Bureau of Economic Analysis

BenMAP Environmental Benefits Mapping and Analysis Program

BLS Bureau of Labor Services

BOD B iochemical oxygen demand

BPJ Best professional judgment

BPT B est practicable control technology currently available

C&D Construction and development

Cal OEHHA California Office of Environmental Health Hazard Assessment

CB G Census Block Group

CBI Confidential Business Information

CCI Construction Cost Index

CCME Canadian Council of Ministers of the Environment

CCR Coal combustion residuals CMAQ Co mmunity Multiscale Air Quality

CO 2 

### Carbon dioxide

COD Chemical oxygen demand

COI Cost of illness

COPD C hronic obstructive pulmonary disease

CPI Consumer Price Index

CSF Cancer slope factor

CVD Cardiovascular disease

DCN Document Control Number

DHHS Department of Health and Human Services

DO Dissolved oxygen

DOJ Department of Justice

EA Environmental Assessment

ECI Employment Cost Index

EGU Electric Generating Unit

EJ Environmental justice

ELGs E ffluent limitations guidelines and standards

EP A United States Environmental Protection Agency

ESA Endangered Species Act

FC Fecal coliform

FCA Fish consumption advisories

FGD F lue gas desulfurization

FGMC F lue gas mercury control

FR Federal Register

GDP Gross domestic product

GIS Geographic Information SystemBenefit and Cost Analysis for Steam Electric Power Generating ELGs List of Abbreviations

September 29, 2015 x 

### Hg Mercury

HR Hazard ratio

HUC Hydrologic unit code

IEUBK Integrated Exposure, Uptake, and Biokinetics

IPM Integrated Planning Model

IRIS Integrated Risk Information System IQ Intelligence quotient

LADD L ifetime average daily dose

MCL Maximum contaminant level

MCLG Maximum contaminant level goal

MDNR Minnesota Department of Natural Resources

MEPS Medical Expenditure Panel Survey NAAQS National Ambient Air Quality Standards

NEFSC Northeast Fisheries Science Center

NERC No rth American Electric Reliability Corporation

NESHAP National Emissions Standards for Hazardous Air Pollutants

NHD National hydrography dataset

NMFS National Marine Fisheries Service

NOAA National Oceanic and Atmospheric Administration

NO X 

### Nitrogen oxi des

NPDES National Pollutant Discharge Elimination System

NRC National Research Council

NRD Natural resource damages

NSPS New Source Performance Standards

NWIS National Water Information System OAQPS Office of Air Quality Planning and Standards

O&M Operation and maintenance

OMB Office of Management and Budget

OPPT Office of Pollution Prevention and Toxics

ORCR Office of Resource Conservation and Recovery

OSC On

- scene coordinator

PbB Blood lead concentration

PIFSC Pacific Islands Fisheries Science Center

ppm parts per million

PSES Pretreatment Standards for Existing Sources

PSNS Pretreatment Standards for New Sources QA Quality assurance QC Quality control

RIA Regulatory Impact Analysis

RSEI Risk

- Screening Environmental Indicators

SCC Social cost of carbon

Se Selenium

SEFSC Southeast Fisheries Science Center

SO 2 

### Sulfur dioxide

SPARROW SPAtially Referenced Regressions On Watershed attributes

STORET STOrage and RETrieval Data Warehouse

SWFSC Southwest Fisheries Science Center

T&E Th reatened and endangeredBenefit and Cost Analysis for Steam Electric Power Generating ELGs List of Abbreviations

September 29, 2015 xi 

### TDD Technical Development Document

TDS Total dissolved solids

THMs Trihalomethanes

TN Total nitrogen

TP Total phosphorus

TRI Toxic Release Inventory

TSD Technical Support Document

TSS Total suspended solids

TVA Tennessee Va lley Authority

U.S. FWS United States Fish and Wildlife Service

USGS United States Geological S urvey VSL Value of a statistical life WQI Water quality index WQL Water quality ladder

WTP Willingness to payBenefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 1 

## 1 Introduction

The U.S. Environmental Protection Agency ( EPA ) is promulgating a regulation that would strengthen the existing controls on discharges from steam electric power plants by revising technology

- based effluent limitations guidelines and standards ( ELGs ) for the steam electric power generating point source category , 40

CFR part 423

.
The analyses supporting the final ELGs for the Steam Electric Power Generating Point Source Category are based on data generated or obtained in accordance with EPA’s Quality Policy and Information Quality Guidelines. EPA’s quality assurance (QA) and quality control (QC) activities for this rulemaking include the development, approval and implementation of Quality Assurance Project Plans for the use of environmental data generated or collected from all sampling and analyses, existing databases and literature searches, and for the development of any models which used environmental data. Unless otherwise stated within this document, the data used and associated data analyses were evaluated as described in these quality assurance documents

to ensure t hey are of known and documented quality, meet EPA's requirements for objectivity, integrity and utility, and are appropriate for the intended use.

This document presents an analysis of the social benefits and social costs of the final rule and complements other analyses EPA conducted in support of the ELGs , described in separate documents:

 Environmental Assessment for the Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category (EA) ( U.S. EPA, 2015a ; DCN SE04527 )

. The EA summarizes the environmental and human health improvements that are expected to result from implementation of the ELGs

.  Technical Development Document for the Effluent Limitations Guidelines and Standards for the
Steam Electric Power Gener ating Point Source Category (TDD) ( U.S. EPA, 2015b ; DCN SE05904 )

. The TDD provides background on the final ELGs ; applicability and summary of the ELGs; industry description; wastewater characterization and identification of pollutants of concern; and treat ment technologies and pollution prevention techniques . It also documents EPA’s engineering analyses to support the final rule including facility specific compliance cost estimates, pollutant loadings, and

non

- water quality impact assessment.

 Regulatory Imp act Analysis for the Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category (RIA ) ( U.S. EPA, 2015c ; DCN SE05976 ) . The

RIA describes EPA’s analysis of the costs and economic impacts of the final rule. This analysis provides the basis for social cost estimates presented in this document. The RIA also provides information pertinent to meeting several legislative and administrative requirements , including the Regulatory Flexibility Act of 1980 ( as amended by the Small Business Regulatory Enforcement Fairness Act (SBREFA) of 1996 ), the Unfunded Mandates Reform Act of 1995

, Executive Order

13211 on Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or

Use , and others

.
The r est of this chapter discusses aspects of the final ELGs that are salient to EPA’s analysis of the social

benefits and social costs of the rule and summarizes key analytic assumptions used throughout this document.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 2 

### Steam Electric Power Plants 1.1

The final rul e establishes new limitations and standards for plants subject to the previously established ELGs for the Steam Electric Power Generating Point Source Category The ELGs apply to a subset of the electric power industry, namely those plants with discharges r esulting from the operation of a generating unit

“ primarily engaged in the generation of electricity for distribution and/or sale, which results primarily from a process utilizing fossil

- type fuels (coal, petroleum coke, oil, gas) or nuclear fuel in conjun ction with a thermal cycle employing the steam water system as the thermodynamic medium. ” 1



### Based on data EPA obtained from the 2010 Questionnaire for the Steam Electric Power Generating Effluent Guidelines ( industry survey; U.S. EPA, 2010c ) and other sources (see TDD ) , EPA estimates that there were 1,080 steam electric power plants in 2009

. 2 

### EPA projects that a subset of these plants will implement changes

to meet the final limitations (refer to the TDD and RIA for details).



### Regulatory Options Analyzed for the Final ELGs 1.2

EPA presents six regulatory options for the final rule (see Table 1

- 1 )

. These options differ in the wastestreams controlled by the regulation, the size of the units controlled, and the stringency of controls (see TDD for a detailed discussion of the options and the associated treatment technology bases ). Thus, EPA evaluated revising or establishing Best Available Tec hnology Economically Achievable (BAT) , New Source Performance Standards (NSPS), Pretreatment Standards for Existing Sources (PSES), and Pretreatment Standards for New Sources (PSNS) that apply to discharges of up to seven wastestreams: FGD wastewater, fly ash transport water, bottom ash transport water, combustion residual leachate from landfills and surface impoundments, wastewater from FGMC systems, wastewater from gasification systems, and nonchemical metal cleaning wastes

.
EPA is establishing limitation s and standards for existing sources (BAT/PSES) based on the technologies in Option D . For new sources, EPA selected the technologies in Option F as the basis for the NSPS and PSNS. The preamble that accompanies the final rule explains the rationale for EP A’s decision

.

1 The final rule contains three minor modifications to the wording of the previously established applicability provision in the steam electric power generating ELGs to reflect EPA’s longstanding interpretation and implementation of the rule. These revisions do not alter the universe of generating units regulated by the ELGs, nor do they impose compliance costs on the industry. Instead, they remove potential ambiguity in the regulations by revising the text to more clearly reflect EPA’s longstanding interpretation . See Section VIII of the preamble for more details.

2 The industry survey EPA conducted in 2010 requested data for several years of operation, up to the most recent complete calendar year at the time the survey was conducted: 2009.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 3 Table 1

- 1 : Steam Electric ELG Regulatory Options

W a s t e s t re a m s T ec hn ology B asis f or B A T /N S P S / P SES / P S NS
R e g u la t o r y O p t io n s

A B C D E F

F GD

W a s t e w at er Chemical

Precipitation Chemical

Precipitation + Biological

Treatment Chemical

Precipitation + Biological

Treatment Chemical

Precipitation + Biological

Treatment Chemical

Precipitation + Biological

Treatment Evaporation

F ly Ash Tr a nsp o rt W at er Dry Handling Dry Handling Dry Handling Dry Handling Dry Handling Dry handling

B ot t o m A s h
Tr a nsp o rt W at er Impoundment

(Eq u al to B P T ) Impoundment

(Eq u al to B P T ) Dry handling /

Closed loop (for

units >400 MW); Impoundment (Equal to BPT)(for units ≤400 MW) Dry Handling /

Closed loop Dry Handling /

Closed loop Dry handling /

Closed loop

F G M C

W a s t e w at er Dry Handling Dry Handling Dry Handling Dry Handling Dry Handling Dry handling

G a si f ic at i o n W a s t e w at er Evaporation Evaporation Evaporation Evaporation Evaporation Evaporation

Combustion
Residual Le a ch at e Impoundment

(Eq u al to B P T ) Impoundment

(Eq u al to B P T ) Impoundment

(Eq u al to B P T ) Impoundment

(Eq u al to B P T ) Chemical

Precipitation Chemical

Precipitation

Nonchemical Metal Cleaning
Wastes [Reserved] [Reserved] [Reserved] [Reserved] [Reserved] [Reserved]

Source: U.S. EPA, 2015



### In the remainder of this document, EPA presents the analytical results only for Options A through E for existing sources. During development of the final rule, EPA decided not to base the final rule on Option F for existing sources due primarily to the hig h cost of that option , particularly in light of the costs associated with other rulemakings expected to impact the steam electric industry (see Section VIII.C.1 of the preamble). As a result, EPA chose not to conduct particular analyses for Option F to the same extent that it did for some of the other options considered.

While EPA calculated the cost impacts of New Sources Performance Standards (NSPS) and Pretreatment Standards for New Sources (PSNS) for the final ELGs , no new coal steam plants have been a nnounced nor are projected (see RIA Section 3.2; U.S. EPA 2015c )

. The lack of any new coal steam plant in the near future

and the site

- specific nature of environmental effects and benefits make the assessment of load reductions and

benefits associated with new source requirements hypothetical and speculative. Accordingly, EPA focused the analysis of the benefits of the final ELGs on the BAT/PSES requirements for existing sources.



### Analysis Scenarios 1.3

EPA made every effort to appropriately account for other ru les in its many analyses for this rule. Since proposal, EPA has promulgated several other rules affecting the steam electric industry: the Cooling Water Intake Structures (CWIS) rule for existing facilities (79 FR 48300), the CCR rule (80 FR 21302), the CP P rule ( FR publication forthcoming ). At the time it conducted these analyses, the CPP rule had not yet beenBenefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 4 

### finalized, and thus EPA used the proposed CPP rule for its analyses as a proxy for the final CPP rule requirements

. In some cases, EPA performed two sets of parallel analyses to demonstrate how the other rules

affected the final ELGs

. For example, EPA conducted an assessment of the final ELGs both with and without accounting for the CPP rule. EPA approached analyses associated with each rulemaking car efully. EPA also recognizes that the steam electric industry complying with three regulations cumulatively in a very short period time may choose different compliance path than assumed in the analyses. The cumulative effect introduces uncertainty on the c ompliance path, and thereby on the benefits and costs associated with these

rules.

The results presented in the main body of this document are based on this scenario with the CPP rule

. The results of EPA’s analyses without accounting for the CPP rule are p resented in Appendix B

.



### Loading and Withdrawal Reductions 

### 1.4

1.4.1 Loading Reductions

EPA expects that final rule will reduce discharge load s of various categories of pollutants including conventional (such as total suspended solids (TSS), biochemical oxygen demand (BOD), and oil and grease), priority (such as mercury (Hg), arsenic (As), and selenium (Se)), and non

- conventional pollutants (such as phosphorus (TP), chemical oxygen demand (COD) and total dissolved solids (TDS)). Table 1

- 2 summarizes

the estimated pollutant reductions under each of the five regulatory options for existing sources

.

Table 1

- 2 : Pollutant R emoval for Final ELGs Regulatory Options

Regulatory Option Pollutant Load Reduction
(pounds per year)
Option A 123,814,202
Option B 132,342,281
Option C 306,198,515
Option D 371,152,958
Option E 382,032,630

Source: U.S. EPA Analysis, 2015



### 1.4.2 Water Withdrawal Reductions

T he regulatory options are also expected to eliminate or reduce water withdrawals associated with wet ash transport and wet FGD scrubbers. EPA estimates that the final BAT/PSES option (Option D) will reduce

surface water withdrawals a t steam electric power plant s by 57 billion gallons per year ( 155 million gallons per day) , and reduce withdrawals of 8 million gallons of groundwater per year (21,971 gallons per day).

1.4.3 Loading Reduction s Used in Estimating Benefits

EPA revised the estima ted steam electric power generating plant discharge loads to incorporate data submitted via public comments and following additional review conducted after completing the benefit analyses described in this report

. 3 

### The revisions affect baseline loadings as well as loading reductions

3 EPA reevaluated the bottom ash dataset for the final rule, including the addition of new data submitted via public comments. EPA subjected all data to its revised data editing criteria and as such, removed or replaced
some of the data included in the 1982 TDD . See TDD for details (U.S. EPA, 2015b)

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 5 

### estimated for each of the regulatory options . The load reductions presented in Table 1

- 2 above reflect these

revisions

.
Table 1

- 3 shows loading reductions calculated from the original loading data used in estimating the benefits of the regulatory opti ons ; these are 0.01 percent to 0.17 percent greater than the reductions shown Table 1

- 2

. The changes affect most pollutants of concern, including arsenic, cadmium, chromium, lead, mercury, nickel, total nitrogen, total phosphorus, selenium, total suspended solids, and zinc. See TDD for details ( U.S. EPA,
2015b )

.

Table 1

- 3 : Pollutant Removal for Final ELGs Regulatory Options Used

in Estimating Benefits

Regulatory Option Pollutant Load Reduction
(pounds per year)
Option A 124,030,659
Option B 132,558,737
Option C 306,236,745
Option D 371,220,336
Option E 382,110,008

Source: U.S. EPA Analysis, 2015



### R evisions to the plant loadings may affect estimated benefits in several categories . Implication of the changes

are summarized in Table 1

- 4 , and discussed at greater length in each of the relevant chapters.

Table 1

- 4 : Impacts of Loading Reduction Revisions on Benefit Estimates

Benefit Category Impact of Revised Loading Reduction on Benefit
Estimate Report Chapter

Human health benefits (from fish consumption) Revisions may r educe benefits, relative to estimates presented in Chapter 3

. 3

Nonmarket benefits from water quality improvements Revisions may reduce benefits, relative to estimates presented in Chapter 4

. 4

Benefits to threatened and endangered species Revisions may reduce benefits, relative to estimates presented in Chapter 5

. 5

Benefits from avoided impoundment
failures No impact . BCA estimates are unchanged. 6

Air

- related benefits No impact . BCA esti mates are unchanged. 7

Benefits from reduced water withdrawals No impact . BCA estimates are unchanged. 8
Benefits from avoided dredging costs Revisions may reduce benefits, relative to estimates presented in Chapter 9

. 9

Benefits from enhanced marketability of
coal combustion residuals No impact

. BCA estimates are unchanged. 10

Source: U.S. EPA Analysis, 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 6 

### Analytic Framework 1.5

The analytic framework of this benefit

- cost analysis ( BCA ) includes four basic components used consistently throughout the analysis of social benefits and social costs 4 

### of the final ELGs :

1. All values are presented in 2013 dollars;

2. Future benefits and costs are discounted using rates of 3 percent and 7 percent back to 2015 ;

3. Benefits and costs are analyzed over a 24

- year period ( 20 19 to 20 42); and

4. Future values account for annual U.S. population and income growth.

These components are discussed in the sections below.

EPA’s analysis of the final ELGs generally follows the methodology the Agency used previously to analyze the proposed ELGs (see Benefit and Cost Analysis for the Proposed Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category (U.S. EPA, 2013 a ; DCN

SE 03172 ). In analyzing the final ELGs, however, EPA made several important changes relative to the analysis of the proposed rule:

 EPA used revised inputs that reflect the costs and loads estimated for the final regulatory options (see

TDD and RIA for details )

.  EPA updated the universe and characteristics of steam electric power plants to reflect generating units

that have been announced to retire or convert (e.g., to natural gas) during the period of analysis as well as the anticipated effects of other regulations affecting the power sector and which may change operations and wastestreams of steam electric power plants

.  EPA updated the baseline scenario to reflect the projected effects of the CCR rule, including regarding the residual environmental risks from CCR impoundments.

 EPA revised assumptions to use more recent data ( e.g. , analysis year, compliance period, dollar year adjustments).

 Finally, EPA made certain changes to the methodologies to address comments EPA received on the proposed rule ( e.g. , Environmental Justice analysis)

, to be consistent with approaches used by the Agency for other rules , and to incorporate recent advances in the health risk and resource valuation

research

. These changes are descri bed in the relevant sections of this document, and summarized in Appendix A

.

T he benefits and social cost analyses presented in this document are generally based on loading reductions and other inputs generated for individual steam electric power plants (f or all plants that were surveyed)

. These inputs are used to determine surface waters and other resources affected by steam electric power plant discharges, estimate changes in pollutant levels, identify populations exposed to steam electric pollutants, etc

.

1.5.1 Constant Prices

This BCA applies a year 2013 constant price level to all future annual monetary values of costs and benefits. Some monetary values of benefits and costs are based on actual past market price data ( i.e. , prior to 2013 ),

and in those inst ances, EPA updated the prices to 2013 by multiplying them by appropriate indexes, or specific sub

- components of these general indexes (index

- updated prices). However, not all dollar

- monetized

4 Unless otherwise noted, costs represented in this document are social costs.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 7 

### benefits and costs in this BCA are based on actual market prices of goods or services. Several categories of benefits presented in this report are estimated based on household willingness

- to

- pay (WTP) surveys, such as WTP for surface water quality improvements for monetizing ecological benefits of the final ELGs . This B C A updates these non

- market prices as needed using appropriate indexes ( e.g. , Consumer Price Index (CPI)).

1.5.2 Discount Rate and Year

This B C A estimates t he annualized value of future benefit s using two discount rates: 3 percent and 7 percent. The 3 percent discount rate reflects society’s valuation of differences in the timing of consumption; the

7 percent discount rate reflects the opportunity cost of capital to society. In Circular A

- 4, the Office of Management and Budget (OMB) recommends that 3 percent be used when a regulation affects private consumption, and 7 percent in evaluating a regulation that will mainly displace or alter the use of capital in the private sector ( U.S. OMB, 2003; updated 2009). T he same discount rate s are used for both benefits and

costs

.
All future cost and benefit values are discounted back to 2015

.

1.5.3 Period of Analysis

Benefits are expected to begin accruing when each plant implements the control technologies needed to comply with any applicable new effluent limits or standards. As discussed in the RIA (in Chapter 3: Compliance Costs ) , for the purpose of the economic impact and benefit analysis, EPA assumes that plants will implement control technologies to meet the final rule limitations and standards as their permits are

renewed over the period of 2019 through 2023

. This schedule recognizes that control technology implementation is likely to be staggered over time across the universe of steam electric power plant s.

As discussed in the relevant sections of this document, for sever al benefit categories where environmental changes are not provided on a plant

- specific basis ( e.g. , reduced air emissions or changes in surface water quality attributed to multiple plants) , EPA was not able to use plant

- specific assumed compliance years in the benefits analysis but instead used the mid

- point of the compliance period (2021) as the assumed starting year when benefits begin accruing. As presented in the RIA (Table 3

- 1; U.S. EP A 2015c), over half of the plants potentially incurring costs for the final ELGs (under Option E) have their technology implementation year in

2021 or earlier.

The period of analysis extends to 2042 to capture the life of the longest

- lived compliance tech nology at any steam electric power plant (20 years), and the last year of technology implementation ( 202 3 ).

1.5.4 Population and Income Growth

To account for future population growth or decline, EPA used the population forecasts i n Woods & Poole (2012), which d eveloped county

- level forecasts for each year from 2000 through 2040, by age and gender for

non

- Hispanic White, African

- American, Asian

- American, and Native

- American and for all Hispanics. 5 

### EPA aggregated the population forecasts across all ages, genders, races, and ethnicities for the entire U.S. and used the aggregated growth projections to adjust affected population estimates for future years ( i.e. , from 2019 to

204 0 )

. EPA used a linear extrapolation approach to forecast population values between 20 4 0 an d 204 2

. Also, since WTP is expected to increase as income increases, EPA took into account income growth for estimating the value of avoided premature mortality based on the value of a statistical life (VSL) and WTP for

5 Woods and Poole (2012); t he detailed documentation can be found at http ://www.woodsandpoole.com/pdfs/CED12.pdf.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 8 

### water quality (WQ) improvements

. To develop adjustment factors for VSL, EPA first used income growth

factors in the Environmental Benefits Mapping and Analysis Program ( BenMAP ) database between 1990 and 2024 to estimate a linear regression model. Using coefficient estimates from the linear regression, EPA extrapolated the income gro wth factors for years 2025

- 2042. EPA applied the projected income data along with the income elasticity for the respective models (VSL and meta

- regression) to adjust the VSL and WQ

meta

- analysis estimates of WTP i n future years

. 6



### Organization of the Benefit and Cost Analysis Report 1.6

This B C A report presents EPA’s analysis of the benefits of the ELGs , assessment of the total costs , and comparison of the costs and monetized benefits.

The remainder of this report is organized as follows:

 Chapter 2 : Benefits Overview provides an overview of the main benefits expected to result from the implementation of the ELGs

.  Chapter 3 : Human Health Benefits details the methods and results of EPA’s analysis of the human

health benefits.

 Chapter 4 : Non market Benefits from Water Quality Improvements discusses EPA’s analysis of the

surface water quality improvements resulting from the ELGs

.  Chapter 5 : Impacts and Benefits to Threatened and Endangered Species discusses expected benefits to threatened and endangered (T&E) species.

 Chapter 6 : Benefits from Avoided Impoundment Failures assesses the benefits of reducing the impacts of any future CCR releases from impoundments used by some steam electric power plant s to manage their CCR waste.

 Chapter 7 : Air

- Related Benefits describes EPA’s analysis of benefits associated with changes in emissions of air pollutants due to increased electricity consumption, transportation, and changes in the profile of electricity generation.

 Chapter 8 : Benefits from Reduced Water Withdrawals discusses benefits arising from reduced surface

water intake and groundwater use

.  Chapter 9 : Benefits from Avoided Dredging Costs describes benefits from reduced maintenance dredging of navigational channels and reservoirs.

 Chapter 10 : Benefits from Enhanced Marketability of Coal Combustion Residuals discusses benefits arising from the enhanced ability by plants to market dry coal combustion ashes

.  Chapter 11 : Summary of Total Monetized Benefits summarizes results across benefit categories

.  Cha pter 12 : Summary of Total Costs summarizes costs of the E LGs

.  Chapter 13 : Benefits and addresses the requirements of Executive Orders that EPA is required to

s atisfy for this proposal, notably Executive Order 12866, which requires EPA to compare the benefits

and costs of its actions.

6 These extrapolated income growth factors were originally developed for EPA’s COBRA tool (http://epa.gov/statelocalclimate/resources/cobra.html). The latest public version is 2.613 released in September
2014.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1

- 9 

###  Chapter 14 : Environmental Justice details EPA’s analysis of the distribution of benefits across socioeconomic groups to fulfill requirements under Executive Order (E.O.) 12898.

 Chapter 15 provides references cited in the text.

Several appendices provide additional details on selected aspects of analys e s described in the main text of the
report. In particular, Appendix B presents the results of EPA’s analysis of the benefits for an alternate scenario using a baseline that excludes the incremental conversions, retirements, and other changes projected to occur in respons e to the CPP rule.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 1 

## 2 Benefits Overview

This chapter provides an overview of the potential benefits to society resulting from implementatio n of the

ELGs . EPA expects that benefits will accrue to society in several broad categories, including enhanced

surface water quality, reduced health risks, and increased productivity in economic activities that are adversely affected by steam electric dis charges. These effects follow directly from changes in effluent limits

and standards , which will reduce pollutant loadings to receiving waters . Benefits of the ELGs also include

other effects of the implementation of control technologies or other changes in plant operations, such as reduction in emissions of air pollutants ( e.g. , carbon dioxide ( CO 2 

### )

, nitrogen oxides ( NO X 

### ), and sulfur dioxide ( SO 2 

### ) ) which provide benefits in the form of reduced mortality and CO 2 

### impacts on environmental quality and economic activities; reduction in water use, which provide benefits in the form of increased availability of

surface water and groundwater; and reduction in the use of surface impoundment to manage CC R wastes, with benefits in the form of avoided cleanup and other costs associated with impoundment releases

. This chapter also provides a brief discussion of the steam electric pollutants, their human health and ecological effects, and a framework for unde rstanding the benefits expected to be achieved by the steam

electric ELGs

. For a more detailed description of steam electric pollutants, their fate

, transport

, and impacts on human health and environment, see the Environmental Assessment document ( U.S. EPA

, 2015a ).

Figure 2

- 1 summarizes the potential effects of the ELGs , the expected environmental changes, and categories of benefits, as well as EPA’s approach to analyzing those benefits. EPA was not able to bring the same depth of analysis to all categories of benefits because of imperfect understanding of the link between discharge

reductions or other environmental effects of the ELGs and benefit categories, and how so ciety values some of

the benefits . EPA was abl e to quantify and monetize some benefits, quantify but not monetize other benefits, and assess still other benefits only qualitatively. The remainder of this chapter provides a qualitative discussion of the benefit categories applicable to th is rule, inclu ding human health benefits , ecological

benefits

, improved groundwater quality, economic productivity

, and reduced air pollution and water

withdrawal s

. Some benefits estimates presented in this document rely on complex models that embed a variety of assumpt ions, limitations and uncertainties discussed in more details in chapters 3 through 10 for the relevant benefit categories.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2-2 Figure 2-1: Summary of Benefits Resulting from the ELGs. 

### --------------------------------------------------------------------------------------------------, 

# ! 1

.. ·1.

"' ::J Reduced toxic,

b i oaccumulat i ve,

a n d other ha r mfu l

waters



## a

... Qj



## .... 

## "' 

# s

Reduced re l iance on impoundments to

ma n ageCCR

Cha n ge in:

Aux i liary power

use Transportat i on Electr i city
generat i on

Conversion to dry
systems

Reduced water use Reduced fish tissue

co nt amination

I mproved surface

water quality

Reduced CCR waste managed in impoun d ments

Reduced air

emiss i ons of C02,

NOx:

, and SOx

Enhanced ability to

market as h for

be n eficia l use

Reduced groundwate r

w i thdrawal s

Reduced surface

water withdrawals Improved Human Health COl

Avoided cardiovascu l ar d i sease from l ead and a r senic exposure VSL

Avoided cance r cases from arse n ic expos u re Va l ue of an IQ point

Avoided I Q l osses i n childre n from mercury and l ead exposure Count of human health cr i teria

Reduced cases of other cancer and n on

- cancer h ealth effects exceedances (non-monet i zed)

Improved Ecological Conditions

I mproved recreationa l and non

- use va l ues Threatened and enda n gered (T&E) species protect i on

Reduced sediment deposition i n chan n e l s a n d reservoirs

Improved Economic Productivity

I mproved touri s m
I ncreased commercial f i shery y i elds

• Reduced need fo r water treatment

• Enha n ced property values

Fewer and less consequent i al impo u ndme n t fai l ures

Reduced premature morta li ty, non

- fatal heart attacks, hospita l

admissions

, emergency department visits, upper and lowe r respiratory symptoms, acute bro n chit i s, aggravated asthma, lost

work days a n d acute resp i ra t o r y symptoms

Reduced C02 impacts

Avoided disposa l costs

Avoided life

- cycle impacts and costs of v i rgin raw materials

I ncreased grou n dwater availability

Reduced vulnerab i lity to drought

Reduced imp i ngement and e n trai n ment morta lit y WTP for use and n on-use values of

su r face water quality improvements

WTP for T&E pop ul atio n inc r eases

Avoided maintenance d r edging costs

Avoided cleanup costs, natural resource damage, and transaction costs

Benefits-per

- ton
Soc i a l cost of ca r bon

Avoided disposal costs
Avoided raw material costs and life

cycle production impacts (var i ous)

Avoided cost of water purchase

Qualita t ive discussion

C O l = C o st of illn ess; VS L = V a l ue of Sta t i s t i ca l lif e; WTP =W ill i ng n ess t o Pay; CCR = Co al C om bu st i o n R es i dua l sBenefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 3 

### Human Health Benefits Associated with Improvements in Surface Water Quality 2.1

P ollutants present in steam electric plant discharges can cause a wide variety of adverse human health effects arising , for example, from consuming contaminated fish tissue. Toxic bioaccumulative pollutants are of particular concern because they do not volatilize, do not biodegrade, can be toxic to plants, invertebrates and fish, ad sorb to sediments, and bio

- concentrate in fish tissues ( U.S. EPA, 200 3 ). More details on the fate, transport, and exposure risks of steam electric pollutants are provided in the EA ( U.S. EP A, 201 5 a ).

Reducing pollutant discharges to the nation’s waterways provides human health benefits by several mechanisms. The most important and readily analyzed benefits stem from reduced risk of illness associated with the consumption of water, fish, she llfish, and other aquatic organisms that are taken from waterways affected by steam electric discharges. Human health benefits are typically analyzed by estimating the change in the expected number of adverse human health events in the exposed population r esulting from a reduction in effluent discharges. While some health effects ( e.g. , cancer or mortality from cardiovascular disease (CVD) ) are relatively well understood and can be quantified in a benefits analysis, others are less well

characterized and cannot be assessed with the same rigor, or at all.

The ELG s provide human health benefits by reducing exposure to pollutants in water via two principal exposure pathways discussed below : (1) consumption of fish and shellfish taken from waterways affected b y steam electric discharges, and (2) consumption of water from surface waters affected by steam electric power plant discharges. T he ELGs also provide human health benefits by reducing air emissions of pollutants via changes in the profile of electricity g eneration; these benefits are discussed separately in Section 2.5

.

2.1.1 Fish Consumption

Recreational anglers and subsistence fishers (and their househo ld members) who consume fish caught in the reaches receiving steam electric power plant discharges are expected to benefit from reduced pollutant concentrations in fish tissue. EPA analyzed the following five direct measures of change in risk to human

heal th from exposure to contaminated fish tissue:

1. Neurological effects to children ages 0 to 7 and incidence of cardiovascular disease in adults from exposure to lead;

2. Neurological effects to infants from in

- utero exposure to mercury ;

3. Incidence of skin cancer and cardiovascular disease from exposure to arsenic ; and

4. Reduced risk of other cancer and non

- cancer toxic effects

. EPA was able to monetize only the first three of these four measures. The Agency evaluated l ead and mercury impacts to children in t erms of potential intellectual impairment as measured by estimated changes in intelligence quotient (IQ). Incidence of cardiovascular diseases was translated into an expected level of avoided early mortality and, on that basis, monetized. Incidence of canc er was translated into an expected

number of avoided cases and monetized based on avoided costs. Chapter 3 of this report provides d etails on these analyses

.
The fift h effect (reduced risk of other cancer and non

- cancer toxic effects from fish consumption) is addressed indirectly in EPA’s assessm ent of changes in exceedances of ambient water quality criteria (see Section 3.8 ).

The value of health benefits is the monetary value that society is willing to pa y to avoid the adverse health

effects. WTP to avoid morbidity or mortality is generally considered to be a comprehensive measure of the costs of health care, losses in income, and pain and suffering of affected individuals and their caregivers. ForBenefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 4 

### example , the value of a statistical life (VSL) (see Section 3.4 ) is based on estimates of society’s WTP to avoid the risk of premature mortality. Alternatively, the cost

- o f

- illness (COI) approach, which is used to estimate the value of avoided skin cancer cases (see Section 3.6 ) , is a less comprehensive measure of cost: it allows valuation of a particular type of non

- fatal illness by placing monetary values on metrics, such as lost productivity and the cost of healt h care and medications that can be monetized.

Some health benefits of reduced exposure to steam electric pollutants, such as neurological effects to children and infants exposed to lead and mercury, are measured based on avoided IQ losses . Changes in IQ c annot be valued based on WTP approaches since a vailable economic research provides little empirical data on society’s WTP to avoid IQ losses

. Instead , EPA calculated monetary values for avoided neurological and cognitive damages based on the impact of an a dditional IQ point on an individual’s future earnings and the cost of compensatory education for children with learning disabilities

. These estimates represent only one component of society's WTP to avoid adverse neurological effects and therefore produce a partial measure of

benefits from reduced exposure to lead and mercury . Employed alone, these monetized benefits will underestimate society's WTP , and perhaps significantly so . See Sections 3 .3 and 3.5 for applications of this method to valuing benefits to children and infants from reduced exposure to lead and mercury.

EPA expects that there c ould also be material health benefits via the fish consumption pathway arising from reduced discharges of other steam electric pollutants, such as cadmium, selenium, and zinc. Analyses of these

health benefits are not possible due to lack of data on a quantitative relationship between ingestion rate and potential adverse health effects.

Despite numerous studies conducted by EPA and other researchers , dose

- response functions are available only for a handful of heal th endpoints associated with steam electric pollutants. In addition, t he available research does not always allow complete economic evaluation, even for quantifiable health effects. For example, EPA’s analysis of health benefits omits the following health effects: low birth weight and neonatal mortality from in

- utero exposure to lead (U.S. EPA, 2009 d ); additional effects to adults from exposure to lead ( e.g. , nervous system disorders

, anemia and blood disorders ) ( U.S. EPA, 2009 d ; 2013a ); effects to adults f rom exposure to mercury

, including vision defects

, hand

- eye coordination, hearing loss, tremors, cerebellar changes, and others ( Mergler, et al., 2007; CDC, 2009 ) ; and other cancer and non

- cancer effects from exposure to other steam electric pollutants. Th erefore, the total monetized human health benefits included in this analysis represent only a subset of the potential health benefits that are expected to result from the ELGs

.

2.1.2 Drinking Water Consumption

Steam electric pollutants discharged to surface wat ers may affect the quality of water used for public drinking supplies. However, public drinking water supplies are subject to legally enforceable maximum contaminant levels (MCLs) established by EPA (U.S. EPA, 2012a ). As the term implies, an MCL for drinki ng water specifies the highest level of a contaminant that is allowed in drinking water. The MCL is based on the MCL Goal (MCLG), which is the level of a contaminant in drinking water below which there is no known or expected risk to human health. EPA sets the MCL as close to the MCLG as possible, with consideration for the best available treatment technologies and costs.

Pursuant to MCLs, public drinking water supplies are already treated for pollutants that pose human health

risks. Detection of the pollu tants is subject to imperfect monitoring and treatment may not remove all contaminants from the drinking water supplies , as evidenced by reported MCL violations for inorganic and

other contaminants at community water systems (U.S. EPA, 2013d) . T here may th erefore be some

incremental health

- related benefits associated with reduced concentrations arising from the final ELGs

. However, EPA ’s screening level analysis suggests that these benefits would not be substantial. As a first step
in assessing the potential benefits , EPA determined that 27 directly receiving reaches have metalBenefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 5 

### concentrations above the MCL in the baseline. N one of those reaches, however, has an associated drinking water intake. Based on the analysis of the larger set of reaches (incl uding downstream reaches) there are 817 reaches with MCL exceedances in the baseline; 250 of these reaches improve under the final ELG s . However, again, none of these improving reaches has an associated drinking water intake. Accordingly, EPA restricted

th e analysis of monetized health benefits from improved surface water quality to benefits arising from the consumption of contaminated fish tissue.

2.1.3 Complementary Measure of Human Health Benefits

EPA quantified but did not monetize the expected reduction of p ollutant concentrations in excess of human

health

- based ambient water quality criteria ( AWQC ) limits. This analysis provides a measure of the change in

cancer and non

- cancer health risk by comparing the number of receiving reaches exceeding health

- based

AW QC for steam electric pollutants in the baseline to the number exceed ing AWQC under the regulatory options ( Section 3.8 ).

Because AWQC are set at levels to protect human health through ingestion of water and aquatic organisms, reducing the frequency at which human health

- based AWQC are exceeded should transla te into reduced risk

to human health. This measure should be viewed as an indirect indicator of reduced risk to human health because it does not reflect the size of the exposed population and does not quantify changes in human health risk per se.



### Ecologic al Benefits Associated with Improvements in Surface Water Quality 2.2

The composition of steam electric power plant wastewater depend s on a variety of factors

, such as fuel composition, air pollution control technologies used, and waste management techniques u sed ; wastewater
often contains metals such as aluminum, arsenic, boron, cadmium, chromium, copper, iron, lead, manganese, mercury, nickel, selenium, thallium, vanad ium, and zinc (U.S. EPA, 201 5 a ) . Discharges of these pollutants to

surface water has a wide variety of environmental effects, including fish kills, reduction in the survival and growth of aquatic organisms, behavioral and physiological effects in wildlife, and degradation of aquatic habitat in the vicinity of steam electric power plant discharges (U.S. EPA, 201 5 a ). The adverse effects associated with releases of steam electric pollutants depend on many factors such as the chemical

- specific properties of the effluent, the mechanism, medium, and timing of releases, and site

- specific environmental

co nditions.

EPA expects the ecological benefits from the ELGs to include enhanced habitat for fresh

- and saltwater plants, invertebrates, fish, and amphibians, as well as terrestrial wildlife and birds that prey on aquatic organisms exposed to steam electri c pollutants. The reduction in pollutant loading s is expected to reestablish productive ecosystems in damaged waterways and to protect resident species, including threatened and endangered species. EPA expects the regulation to enhance the general health of fish and invertebrate populations, increase their propagation to waters currently impaired, and expand fisheries for both commercial and recreational purposes. Improvements in water quality will also favor recreational activit ies such as swimming, boating, fishing, and water skiing. Finally, the Agency expects the regulation to augment nonuse values ( e.g. , option, existence, and bequest values) of the affected water resources.

2.2.1 Improved Surface Water Quality

The steam electric ELG s are expected to provide ecological benefits through improvements in the habitats or ecosystems (aquatic and terrestrial) that are affected by steam electric power plant discharges. Society value s such ecological improvements by a number of mechanisms, including increased frequency and value of use ofBenefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 6 

### the improved habitat for recreational activities. In addition, individuals also value the protection of habitats and species that are adversely affected by effluent discharges

, even when those individuals do not use or anticipate future use of the affected waterways for recreational or other purposes , resulting in nonuse values

. Recreational activities that may be enhanced by reducing steam electric discharges to surface waters include:

 Recreational Fishing

. Degraded water can reduce fish populations by inhibiting reproduction , growth, and survival of an aquatic species (Friedman et al. 1996; Niimi and Kissoon 1994; U.S. EPA, 2009 d ; U.S. EPA, 2011 a ) resulting in fewer and smaller fish and thereby reducing th e value of a fishing trip. Reducing pollutant loads in steam electric power plant discharges is expected to improve aquatic

habitat and thus increase the number, size, diversity, and heal th of recreational fish species and, as a result, the value of recrea tional fishing. S tudies have shown that the value of water resources for recreational fishing increases with declining level of toxic contamination in fish tissue (Phaneuf et al., 1998; and Jakus et al., 1997). In addition, improved aesthetic qualities of the water body ( e.g. , from reduced nutrient loadings) and k nowledge that the water is cleaner and does not contain any or contains fewer pollutants that harm humans and aquatic life, increases individuals’ enjoyment of their recreational experience.

 Outings. Participants in other recreational activities such as hiking, jogging, picnicking, and wildlife viewing also benefit from improved abundance and diversity of aquatic and terrestrial species. For example, wildlife viewers benefit from improved abun dance of piscivorous birds ( e.g. , osprey , eagle )
and waterfowl whose population s are likely to increase due to a reduction of mercury and other heavy

metals in the food web and an increase in the forage fish populations (Schoch et al., 2011; U.S. EPA,

2011 a ). In addition, improved aesthetic quality of surface waters ( e.g. , clarity and odors) enhance s the recreational experience of wildlife viewers and other recreational users. (Schoch et al., 2011; U.S. EPA, 2011 a ).

 Boating. Boaters benefit from enhanced o pportunities for companion activities, such as fishing and wildlife viewing ( e.g. , piscivorous birds)

, and from improved aesthetic quality

.  Swimming

. Swimmers benefit from improved aesthetic quality of surface waters including water clarity and odor thereb y enhancing swimmer’s aesthetic enjoyment of a water body.

 Hunting. Waterfowl hunters benefit from improved aesthetic enjoyment of a water resource

, an increase in the number and quality of game available , and the removal of waterfowl consumption advisories. Reducing nutrient loadings from steam electric power plant s is likely to benefit diving ducks populations by reducing eutrophication and turbidity in the affected waters and improving their food sources. Diving ducks rely upon undisturbed and abundant plant and invertebrate sources to prepare for migration. Exces sive nutrient loadings can lead to eutrophic and turbid waters, with few plants and invertebrates food sources ( Minnesota Department of Natural Resources ( MDNR ) , 2010). Waterfowl populations are adversely affected by consuming contaminated fish or inverteb rates; zebra mussels are an attractive food source for ducks and have been found to have high concentrations of methyl mercury (MDNR, 2010). High mercury levels have led to duck consumption advisories (Utah Department of Natural Resources , 2005). Reduction in metal loading to surface waters and of their presence in the food web may benefit waterfowl reproduction and lead to removal of duck consumption advisories.

EPA quantified potential ecological impacts from the final ELG option s by estimating in

- waterwa y concentrations of nutrients and other harmful pollutants discharged by steam electric power plant s and

transla ting water quality measurements into a single numerical indicator (water quality index (WQI)). EPA

used the estimated change in WQI as a quantit ative measure of ecological benefit for this regulatory analysis

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 7 

### Section 4.1 of this report provides detail on the parameters used in formulating the WQI and the WQI methodology and calculations.

A variety of primary methods exist for estimating recreational use values, including both revealed and stated preference methods (Freeman, 2003). Where appropriate data are available or can be collected, revealed preference methods can represent a preferred set of methods for estimating use values. These methods use observed behavior to infer users’ values for environmental goods and services. Examples of revealed preference methods include t ravel cost, hedonic pricing, and random utility (or site choice) models.

In contrast to direct use values, nonuse values are considered more difficult to estimate. Stated preference methods, or benefit transfer based on stated preference studies, are the generally accepted techniques for estimating these values (U

. S

. EPA

, 201 0 a ; U.S. OMB

, 2003). Stated preference methods rely on carefully designed surveys, which either (1) ask people about their WTP for particular ecological improvements, such

as increased protection of aquatic species or habitats with particular attributes, or (2) ask people to choose between competing hypothetical “packages” of ecological improvements and household cost (Bateman et al.

, 200 6 ). In either case, values are estimated by stati stical analysis of survey responses.

Although the use of primary research to estimate values is generally preferred because it affords the opportunity for the valuation questions to closely match the policy scenario , the realities of the regulatory

proces s often dictate that benefit transfer is the only option for assessing certain types of non

- market values (Rosenberger and Johnston

, 2007). Thus, EPA developed a benefit transfer approach based on a meta

- analysis

of surface water valuation studies to evalu ate the use and non

- use benefits of improved surface water quality resulting from the final rule . This analysis is presented in Chapter 4

. Benefit transfer is described as the “practice of taking and adapting value estimates from past research ... and using them ... to assess the value of a similar, but separate, change in a different resource” (Smith et al. 2002, p. 134). It involves adapting

research conducted fo r another purpose to estimate values within a particular policy context (Bergstrom and De Civita, 1999). In the benefit transfer used for analyzing non

- market benefits associated with water quality improvements, EPA used a regression

- based meta

- analysis of 1 40 estimates of total WTP (including both use and nonuse values) for water quality improvements, provided by 51 original studies conducted between 1981

and 2011

. 7 

### The estimated econometric model allows calculation of total WTP for improvements in a varie ty of environmental services affected by water quality and valued by humans, including enhanced recreational fishing , other water

- based recreation, and existence services such as aquatic life, wildlife, and habitat designated uses. The model also allows EP A to adjust WTP values based on the core geospatial factors predicted by theory to influence WTP, including: scale (the size of affected resources or areas), market extent (the size of the market area over which WTP is estimated) and the availability of su bstitutes

.

2.2.2 Benefits to Threatened and Endangered Species

For threatened and endangered (T&E) species vulnerable to future extinction, even minor changes to reproductive rates and small levels of mortality may represent a substantial portion of annual popu lation growth. Consequently, steam electric power plant discharges may either lengthen recovery time, or hasten the demise of these species. By reducing the discharge of steam electric pollutants to aquatic habitats, the ELGs are expected to enhance the su rvivability of some T&E species living in these habitats. These T&E species may have both use and nonuse values. However, given the protected nature of T&E species and the fact that

7 A lthough the potential limitations and challenges of benefit transfer are well established (Desvousges et al.

, 1998), benefit transfers are a nearly universal component of benefit cost analyses conducted by and for government agencies. As noted by Smith et al. (2002 ; p. 134), “nearly all benefit cost analyses rely on benefit transfers, whether they acknowledge it or not.”Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

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### use activities general constitute take, which is illegal unless permitted, the majority of the economic value for T&E species comes from nonuse values.

Species

- specific estimates of nonuse values held for the protection of T&E species can be derived only by primary research using stated preference techniques. As a second

- best al ternative, 8 

### EPA used a benefit transfer approach that relies on information from existing studies (U.S. EPA, 2010a ). This benefit transfer approach is based on a meta

- analysis of 31 stated preference studies valuing threatened, rare, or endangered fish, bi rd or mammal species (Richardson and Loomis, 2009). EPA used t he estimated WTP equation provided in this meta

- analysis to estimate the monetary value of the potential increases in T&E populations resulting from the ELGs

. This analysis and results are prese nted in Chapter 5

. WTP values for improvements in water quality discussed in the preceding section may inherently include benefits to T&E species

. Although

there may be some overlap between WTP estimates for T&E species and the WTP estimates for improvements in water quality

, this overlap is likely to be minimal , h owever, since none of the studies in
EPA’s meta

- analysis of WTP for water quality impro vements specifically mentioned or otherwise prompted respondents to include benefits to T&E species populations (see Chapter 4 )

.

2.2.3 Reduced Sediment Contamination

Effl uent discharges from steam electric power plant s can also contaminate waterbody sediments. For example, adsorption of arsenic, selenium, and other pollutants found in steam electric power plant discharges

can result in accumulation of contaminated sediment on stream and lake beds (Ruhl, et al., 2012), posing a particular threat to benthic ( i.e. , bottom

- dwelling) organisms. These pollutants can later be re

- released into the water column and enter organisms at different trophic levels; concentrations of selen ium and other steam electric pollutants in fish tissue of organisms of lower trophic levels can bio

- magnify through higher trophic levels, posing a threat to the food chain at large (Ruhl, et al., 2012).

By reducing discharges of pollutants to receiving r eaches, the ELGs are expected to reduce the future contamination of waterbody sediments, thereby mitigating impacts to benthic organisms and reducing the probability that the pollutants would later be released into the water column and affect surface water quality and the waterbody food chain. Due to data limitations, EPA did not quantify or monetize this benefit

.



### Benefits Associated with Improvements in Groundwater Quality 2.3

Impoundments used by steam electric power plant s to manage their wastewater can lea ch pollutants into groundwater aquifers, degrading water quality and potentially creating health hazards to households drawing drinking water from affected aquifers. The operational changes prompted by the ELGs are expected to result in plants ceasing or s ignificantly reducing their use of impoundments to manage coal combustion residuals (CCR). EPA estimated benefits from reducing the risk of groundwater contamination as part of its analysis of the proposed ELG options (U.S. EPA, 2013 a )

. In December 2014, E PA promulgated the CCR rule which specifically addresses risks to groundwater quality from leaking impoundments. The CCR rule establishes technical requirements for CCR surface impoundments , including composite liners, groundwater monitoring,

c orrective ac tion, and closure/post closure care , among others. For example, the rule requires any existing unlined CCR surface impoundment that is contaminating groundwater above a regulated constituent’s groundwater protection standard to stop receiving CCR and eithe r retrofit or close , and that corrective action be taken to address contamination from leaking clay

- or composite

- lined impoundments. The final ELGs may still provide groundwater protection benefits, however, by reducing the potential impacts of future

8 T he cost, administrative burden, and time required to develop primary research estimates to value effects of the regulation on T&E spec ies were beyond the schedule and resources available for this rulemaking.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

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### imp oundment leaks, thereby avoid ing future corrective action costs. EPA does not have sufficient data to

model the avoidance of future leaks of clay

- or composite

- lined im poundments that may no longer receive CCR as a result of the final ELGs. EPA’s analysis of the CCR rule showed significantly lower lifetime risk of groundwater contamination from lined impoundments than from unlined impoundments (4 and 300 times smaller risk for clay

- and composite

- lined impoundments respectively) , which suggests that correct ive actions associated with lined impoundments should be infrequent

. Accordingly, EPA estimates that the residual risk to aquifers after implementation of the CCR rule is small and did not estimate incremental benefits for the

ELGs.



### Economic Productivity B enefits 2.4

The economic productivity benefits expected to result from the ELGs include reduced impacts of impoundment releases of CCR and the reduction in the costs associated with the resulting cleanup, environmental damages, and transaction costs

. Conversion to dry handling systems to comply with the ELG are expected to provide economic benefits by enhancing the ability of steam electric power plant s to market the ash for beneficial use ( e.g. , in concrete or fill), thereby reducing the disposal cost s otherwise incurred by steam electric power plant s and displacing resource intensive virgin materials. Other economic productivity benefits may stem from reduced contamination of public drinking water supplies and irrigation water; increased tourism; incr eased commercial fish harvests; and increased property values

.

2.4.1 Reduced Impoundment Releases

Steam electric power plant s manage CCR such as fly ash and bottom ash through either wet or dry handling. For plants that use wet handling, the waste is typically s luiced to one or more surface impoundments ( e.g. , settling ponds), where the solids settle out of the water. Many plants also use surface impoundments to manage their flue gas desulfurization (FGD) wastewater. In addition to solids associated with the ash and

FGD wastes , these impoundments typically contain water with high concentrations of steam electric pollutants, including dissolved metals.

The operational changes prompted by the ELGs , such as conversion to dry handling, are expected to cause some plan t owners to reduce their reliance on impoundments to handle CCR. These changes could affect the

volume of CCR released in the event of a failure and/or the future probability of impoundment releases

. Benefits arising from the reduced risk of impoundment re leases include avoided cleanup costs, environmental damage, and transaction costs.

EPA quantified and monetized these benefits based on expected future impoundment release rates , the

volumes of CCR that would be released in an incident

, and the costs of c leanup, natural resource damages,

and transaction costs

. Chapter 6 describes this analysis

.

2.4.2 Enhanced Marketability of Coal Ash for Beneficial Use

EPA anticipates that the final ELGs will prompt certain plants to convert from wet handling of fly ash, bottom ash, and/or FGD waste to dry handling. This change would in turn allow plants to more readily market the

CCR to beneficial uses. EPA quantified a nd monetized changes in the marketability of two CCR wastestreams, and two end uses: (1) fly ash as a substitute for Portland cement in concrete production and (2) fly

- and bottom ashes as substitutes for sand and gravel in fill applications. The changes a re based on the tonnage of fly and bottom ash handled dry instead of wet, with benefits derived from plants avoiding certain costs associated with disposing of the ashes as waste and society or users of the ash avoiding the cost and life

cycle effects asso ciated with the displaced virgin material. Chapter 10 describes this analysis.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

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### 2.4.3 Water Supply and Use

The ELG s are expected to reduce loading of steam electric pollu tants to surface waters and thus enhance uses of these waters for drinking water supply and agriculture:

 D rinking water treatment costs

. The ELG s have the potential to reduce costs of drinking water treatment ( e.g. , filtration and chemical treatment ) by reducing metal concentrations and

e utrophication in source waters. Eutrophication is one of the main causes of taste and odor impairment in drinking water , which has a major negative impact on public perception s of drinking water safety

. Additional treatme nt to address foul tastes and odors can significantly increase the cost of public water supply

. The Agency conducted screening

- level assessment to evaluate the potential for cost saving to public drinking water systems and concluded that such savings, whil e they exist, may not be significant

. The assessment involved identifying the pollutants for which treatment costs may vary depending on source water quality, and using data from EPA’s ELG analysis and the location of drinking water intakes to determine wh ether modeled water quality improvements have the potential to reduce drinking water treatment costs. During the first step in the assessment, EPA determined that water utilities may see reduced costs for the removal of arsenic, cadmium, copper, lead, merc ury, selenium, thallium, nitrogen, and total suspended solids. During the second step in the assessment, EPA determined that few drinking water systems are currently drawing water at levels that exceed one or more MCLs and would improve under the policy op tions. And similarly, few reaches with elevated total nitrogen (TN) levels will see those levels decline significantly under the regulatory options to result in substantive cost savings. Accordingly, EPA did not conduct detailed analysis of cost savings to publicly operated treatment systems.

 Reduction in bromide concentrations . P ublic drinking water sources do not always effectively remove bromides (a steam electric pollutant) from raw surface waters. While bromide itself is not thought to be toxic at levels present in the environment, lab studies and case reports show that bromide found in source water can react during routine drinking water treatment to generate harmful disinfection byproducts (DBPs) (Richardson, et al. , 2007; U.S. EPA, 201 2a ). For example, McTigue et al. (2014) estimate that 96 drinking water treatment plants using surface water are downstream of 57 coal

- fired power plants using wet scrubbers. If existing water treatment is not suffic ient, an alternate water

source needs to be substituted or developed, or alternate disinfection technologies need to be adopted (Watson, et al. , 2012 ) . Long

- term solutions might require the development of new raw water supplies,

which would involve costs for the acquisition of land (if available), regulatory review and permitting, development of infrastructure (dams, pumps, pipes), and watershed protection. Thus , increased bromide levels in raw source water could translate into additional drinking water tr eatment costs at some plants, and potentially pose human health risk. In this Final Rule, EPA is not proposing technology

- based effluent limits for bromide for steam electric industry. However, NPDES permit developers could specify effluent limits for brom ide on a plant

- by

- plant basis based on site

- specific considerations that include the potential to affect public drinking water sources. Benefits of any plant

specific limits in terms of avoided treatment costs or human health risk would need to be determin ed based on more detailed information about the receiving waters and drinking water system.

 Irrigation and other agricultural uses : Reducing steam electric pollutants discharges can improve agricultural productivity by improving water quality used for irri gation and livestock watering (Clark

et al.

, 1985). Although elevated nutrient concentrations in irrigation water would not adversely affect its usefulness for plants, concerns exist for potential residual effects due to steam electric pollutants entering the food chain. Further

, eutrophication promotes cyanobacteria blooms that can kill livestockBenefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

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### and wildlife that drink the contaminated surface water. EPA did not quantify or monetize benefits from enhanced quality agricultural water sources arising from th e ELGs due to data limitations.

 Reservoir Capacity

. Reservoirs serve many functions, including storage of drinking and irrigation

water supplies , flood control, hydropower supply, and recreation. Streams can carry sediment into reservoirs, where it can set tle and cause b uildup of silt layers over time, at a recorded average rate of 1.2 billion kilograms per reservoir every year (USGS, 2007 b ). Sedimentation reduces reservoir capacity (Graf et al., 2010) and the useful life of reservoirs unless measures such as dredging are taken to reclaim capacity (Clark et al., 1985). EPA expects that by reducing total suspended solids ( TSS ) concentrations, the ELGs will provide modest cost savings by reducing dredging activity to reclaim capacity at existing reservoirs.

2.4.4 Reduced Sedimentation in Navigational Waterways

Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of the United States’ transportation network. Navigable channels are prone to reduced functionality due to sediment

build

- up, which can reduce the navigable depth and width of the waterway ( Clark et al., 1985 ). For many navigable waters, periodic dredging is necessary to remove sediment and keep them passable. Dredging of navigable waterways can be costly.

EPA expects that the ELGs will reduce sediment loadings to surface waters and reduce dredging of navigational waterways and reservoirs

. EPA quantified and monetized these benefits based on the avoided cost for expected future dredging volumes. Chapter 9 describes this analysis

.

2.4.5 Commercial Fisheries

P ollutants in steam electric power plant discharges can reduce fish populations by inhibiting reproduction and survival of aquatic species. These changes may negatively affect commercial fishing industries as well as

consumers of fish , shellfish, and fish and seafood products. Estuaries are particularly important breeding and nursery areas for commercial fish and shellfish species. In some cases, excessive pollutant loadings can lead to the closures of shellfish beds, thereby reducing shellfish harvests. Improve d water quality due to reduced discharges of steam electric pollutants would enhance aquatic life habitat and , as a result, contribute to reproduction and survival of commercially harvested species and larger fish and shellfish harvest, which in

turn lead to an increase i n producer and consumer surplus.

EPA did not quantify or monetize benefits to commercial fisheries from the ELGs . EPA’s EA (see U.S. EPA,

201 5 a ) shows that a small number of steam electric power plant s discharge to estuaries or marine waters

. Although ben efits to local fish populations and commercial harvest may be positive

, the overall benefits to commercial fisheries arising from the ELGs are likely to be negligible

. Most species of fish have numerous close substitutes. The literature suggests that when there are plentiful substitute fish products, numerous fishers, and a strong ex

- vessel market, individual fishers are generally price takers. Therefore, the measure of consumers welfare (consumer surplus) is unlikely to change as a result of small changes in fish landings, such

as those EP A expects under the final rule.

2.4.6 Tourism

The ELGs may also benefit local economies by contributing to the tourism industries ( e.g. , sales of fishing equipment) in the areas surrounding affected waters due to improved recreational opportunities. The effects of water quality on tourism are likely to be highly localized . Moreover, s ince s ubstitute tourism locations may be available, inc reased tourism in the vicinity of steam electric power plant s may lead to a reduction in tourismBenefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 12 

### in other locations. Due to these factors EPA believes that benefit from an increase in tourism would be limited to communities in the vicinity of steam electri c power plant s; although tourism revenue is potentially important to these communities, the overall societal benefits are likely to be small. Therefore, EPA did not quantify or monetize this benefit category.

2.4.7 Property Values

The ELG s are expected to impro ve the a esthetic quality o f land and water resources by reducing pollutant discharges and thus enhancing water clarity, odor, and color in the receiving and downstream reaches. S everal

s tudies (Boyle et al., 1 999 ; Poor et al., 2001; Leggett and Bockstael

, 2000 ; Bin and Czakowski, 2013; Walsh et al., 2011; Tuttle and Heintzelman, 2014 ) suggest that waterfront property is more desirable when located near unpolluted water. T herefore , the value of properties located in proximity to waters contaminated with

stea m electric pollutants may increase due to reduced steam electric discharges. Although this benefit would accrue to the current property owners only, it represents an overall increase in societal wealth.

Due to data limitations, EPA was not able to quantify or monetize the potential increase in property values

associated with the ELGs . The magnitude of the potential increase depends on many factors, including the number of housing units located in the vici nity of the affected waterbodies, community ( e.g. , residential density) and housing stock ( e.g. , single family or multiple family) and the effects of steam electric pollutants on aesthetic quality of surface water

. Given that the main benefit of the final ELG is reduction in metal concentrations that do not affect aesthetic quality of surface water, changes in property values are expected to be small. In addition, there may be overlap between changes in property values and the estimated total WTP

for surfac e water quality improvements summarized in Section 2.2.1

.



### Reduced Air Pollution 2.5

The ELGs are expected to affect air pollution through three main mec hanisms: 1) additional auxiliary electricity use by steam electric power plant s to operate wastewater treatment, ash handling, and other systems needed to comply with the new effluent limits and standards; 2) additional transportation

- related

emissions due to the increased trucking of CCR waste to on

- si t e or off

- site landfills; and 3) the change in the profile of electricity generation due to relatively higher cost to generate electricity at plants incurring compliance costs for the ELGs . The different prof ile of generation can result in lower or higher air pollutant emissions due to differences in emission factors for coal or natural gas combustion, or nuclear or hydroelectric power generation.

Of the three mechanisms above, the change in the emissions prof ile of electricity generation at the market level is the most significant. S mall reductions in coal

- based electricity generation as a result of the final ELGs are compensated by increases in generation using other fuels or energy sources

– biomass, landfil l gas, natural gas, nuclear power, oil, and wind power. The changes in air emissions reflect the differences in emissions factors for these other fuels, as compared to coal

- fueled generation . Overall for the three mechanisms (auxiliary services, transporta tion, and market

- level generation), EPA estimates a net reduction in CO 2 

### and

SO 2 

### , and a slight increase in NO X 

### emissions.

NO X 

### , and SO 2 

### are known precursors to PM2.5, a criteria air pollutant that has been associated with a variety

of adverse health effects , including premature mortality and hospitalization for cardiovascular and respiratory

diseases ( e.g. , asthma, chronic obstructive pulmonary disease (COPD), and shortness of breath) . To estimate benefits of reducing NO X 

### , and SO 2 

### emissions, EPA used estimates of national monetized benefits per ton of

emissions avoided. CO 2 

### is an important greenhouse gas that is linked to clima te change effects including global warming, sea level rise, increased frequency of extreme weather events, ocean acidification, etc. EPABenefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 13 

### used estimates of the social cost of carbon (SCC) obtained from the Interagency Working Group on Social Cost of Carbon (see Interagency Working Group on Social Cost of Carbon ( IWGSCC ), 201 3 b ) to derive benefits per ton for CO 2

. 

### The SCC reflects a broad range of climate change impacts, including changes in agricultural productivity, human health risks, property damage from increased flood frequencies, the loss of ecosystem services, and others. Chapter 7 details this analysis.



### Reduced Water Withdrawals 2.6

Steam electric power plant s use water for wet ash transport and for operating wet FGD scrubbers. By eliminating or reducing water used in sluicing operations or prompting the recycling of water in FGD wastewater treatment systems, the ELG s are expected to reduce demand on aquifers by plants that rely on groundwater sources.
Additionally, r educed surface water intake would reduce impingement and entrainment mortality. Due to data limitations, EPA did not quantify and monetize these benefits as part of this analysis. For more details on the impacts of surface water w ithdrawals, see paper titled “Water Withdrawals in Water Stressed Areas: Impacts of the Steam Electric Effluent Limitations Guidelines” (DCN SE05943) in the record for the final steam

electric ELG rule.

Reduced water use from groundwater sources by steam e lectric power plant s would result in greater availability of groundwater supplies for alternative uses. EPA used the state specific prices for bulk drinking water supplies to value the increased quanti t y of groundwater. This analysis is presented in Chapte r 8

.



### Summary of Benefits Categories 2.7

Table 2

- 1 summarizes the benefits of the ELGs and the level of analysis applied to each category. As indicated in the table, only a subset of anticipated benefits can be quantified and monetized (in wh ich case the table identifies the section of the report that discusses the analysis) . The monetized benefits include reductions

in some human health risks, use and non

- use values from improved surface water quality, benefits to T&E species, reduced impacts from impoundment releases

, increase in the amount of ash marketed for beneficial

uses, reduced costs for dredging navigational waterways, reduced air pollution, and reduced water withdrawals. Other benefit categories, including expected reduction of pollu tant concentrations in excess of

human health

- based AWQC limits , can be quantified but not monetized . Finally, EPA was not able to quantify or monetize other benefits , including drinking water treatment costs and benefits to commercial

fisheries ; EPA evalu ated these benefits qualitatively as discussed above in Sections 2.1 through 2.6

.

Table 2

- 1 : Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

Category Effect of ELGs Benefits Analysis

Quantified Monetized Methods (Report Chapter or Section where Analysis is
Detailed )

Human Health Benefits from Surface Water Quality Improvements

Reduced IQ losses to
children ages 0 to 7 Reduced childhood exposure to lead from fish consumption 

###   IQ point valuation ( Section 3.3 )

Reduced need for specialized education Reduced childhood exposure to lead from fish consumption 

###   Avoided cost ( Section 3.3 )

Reduced incidence of
cardiovascular disease Reduced exposure to lead from fish consumption 

###   VSL ( Section 3.4 )Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 14 Table 2

- 1 : Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

Category Effect of ELGs Benefits Analysis

Quantified Monetized Methods (Report Chapter or Section where Analysis is
Detailed )

Reduced IQ losses to
infants Reduced in

- utero mercury exposure from maternal fish consumption 

###   IQ point valuation ( Section 3.5 )

Reduced incidence of

cancer Reduced exposure to arsenic from fish consumption 

###   COI ( Section 3.6 )

Reduced other adverse health effects (cancer
and non

- cancer) Reduced exposure to other pollutants (arsenic, lead, etc.) via fish consumption 

###  Human health
criteria exceedances ( Section 3.8 )

Reduced adverse health
effects Reduced exposure to pollutants from
recreational water uses Qualitative
discussion
Ecological Conditions and Recreational use Benefits from Surface Water Quality Improvements
Improved aquatic and
wildlife habitat a Improved ambient water quality in receiving reaches



###   Benefit transfer ( Chapter 4 ) Water

- based recreation a Enhanced swimming, fishing, boating, and near

- water activities from improved water quality

Aesthetics a Increased aesthetics from improved water clarity, color, odor, including nearby site amenities (residing, working, traveling)

Non

- use values a Enhanced existence, option, and bequest values from improved ecosystem health Aquatic and wildlife Reduced risks to aquatic life from exposure to steam electric pollutants Improved protection of T&E species Improved T&E habitat and thus potential increase in T&E population 

###   Benefit transfer ( Chapter 5 )

Reduced sediment
contamination Reduced deposition of toxic pollutants to sediment Qualitative
discussion
Groundwater Quality Benefits
Groundwater quality Reduced groundwater contamination Qualitative
discussion

Market and Productivity Benefits
Impoundment releases Reduced risk of impoundment
releases due to changes in the use of impoundment 

###   Avoided cost of cleanup, natural resource damages,
and transaction

costs ( Chapter 6 ) Reduced dredging costs Reduced costs for maintaining navigational waterways and reservoir capacity 

###   Avoided dredging costs ( Chapter 9 )Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2

- 15 Table 2

- 1 : Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

Category Effect of ELGs Benefits Analysis

Quantified Monetized Methods (Report Chapter or Section where Analysis is
Detailed )

Beneficial use of ash Reduced disposal costs and avoided
life

- cycle impacts from displaced virgin material 

###   Avoided disposal
cost and avoided
resource use and
environmental impacts ( Chapter
10 )

Reduced water treatment costs for drinking water and irrigation water Improved quality of source water used for drinking and irrigation Qualitative
discussion

Commercial fisheries Improved fisheries yield and harvest quality due to aquatic habitat improvement Qualitative
discussion

Benefits to tourism
industries Increased participation in water

based recreation Qualitative
discussion Property values Increased property values from water quality improvements Qualitative
discussion

Air

- Related Benefits

Reduced air emissions of
NO

x , SO 2 Reduced mortality and morbidity from exposure to NO

x , SO 2 and particulate matter (PM 2.5 ) 

###   Benefit per ton of air pollutant
removed ( Chapter
7 )

Reduced air emissions of
CO 2 Avoided climate change /global warming impacts 

###   Social cost of carbon (SCC) ( Chapter 7

)

Reduced Water Withdrawal Benefits
Reduced groundwater
withdrawals Increased availability of groundwater resources 

###   Avoided c ost ( Chapter 8 )

Reduced surface water
withdrawals Reduced vulnerability to drought
and r educed impingement and
entrainment mortality Qualitative
discussion

a. These values are implicit in the total WTP for water quality improvements.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 1 

## 3 Human Health Benefits

EPA expects that the ELGs will yield a range of human health benefits by reducing effluent discharges to surface waters and, as a result, ambient pollutant concentrations in the receiving reaches. EPA’s EA (U.S

. EPA, 2015a) provides details on the health effects of steam electric pollutants. Recreational anglers and subsistence fishers (and their household members) who consume fish caught in the reaches receiving steam electric discharges are expected to benefit from reduced pollutant concentrations in fish tissue. This chapter presents EPA’s analysis of human health benefits from reduced exposure to steam electric pollutants via the fish consumption pathway. 9 

### The analyzed health benefits include:

 Reduced exposu re to lead:

‒ A voided neurological and cognitive damages in children (ages 0

- 7) based on the impact of an additional IQ point on an individual’s future earnings and the cost of compensatory education for children with learning delays

‒ Reduced incidence of CVD in adults

 Reduced exposure to mercury:

‒ Reduced neurological and cognitive damages in infants from exposure to mercury in

- utero

 Reduced exposure to arsenic:

‒ Reduced incidence of cancer cases.

The total quantified human health benefits included in this a nalysis represent only a subset of the potential health benefits expected to result from the final ELGs . While additional adverse health effects are also associated with steam electric pollutants (such as kidney damage from cadmium or selenium exposure,

ga strointestinal problems from zinc, thallium, or boron exposure, and others), the lack of data on dose

response relationships 10 

### between ingestion rates and these effects precluded EPA from quantifying the

associated benefits

. EPA’s analysis of the monetary v alue of human health benefits is based on data and methodologies described

in the EA (U.S. EPA, 2015a). The relevant data include COMIDs 11 

### for receiving waters, baseline and post

compliance annual plant

- level loadings of each discharged pollutant, ambient pollutant concentrations in receiving reaches and downstream reaches, pollutant concentrations in fish tissue, fish consumption rates among different racial and ethnic cohorts for affected recreational anglers and subsistence fishers, and the average daily dose (ADD) or lifetime average daily dose (LADD) of pollutants for each age cohort for recreational anglers and subsistence fishers.

Section 3.1 d escribes how EPA identified the population potentially exposed to pollutants from steam electric discharges via fish consumption. Section 3.2 descr ibes the methods for determining fish tissue pollutant concentrations and exposure via fish consumption in the affected population. Sections 3.3 to 3.7 describe

9 The analysis of human health benefits focuses on the fish consumption pathway only, since EPA assumed that drinking water is treated to reduce pollutant concentrations below MCLs. See Chapter 2 for further discussion.

10 A dose response relationship is an increase in incidences of an adverse health outcome per unit increase in exposure to a toxin.

11 A COMID is a unique numeric identifier for a given waterbody, assigned by a joint effort of the United States Geological Survey, EPA, and Horizon Systems, Inc.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 2 

### EPA’s analysis of benefits of various human health endpoints affected by the final ELGs. The human health benefits estimates rely on models of concentration

- response and exposure pathways that embed assumptions

and involve limitations and uncertainties. Section 3.9 describes the limitations and uncertainties in the health

benefit analyses.



### Affected Population 3.1

The affected population ( i.e. , individuals potentially exposed to steam electric pollutants via consumption of

contaminated fish tissue) includes recreational anglers and subsistence fishers who fish reaches affected by steam electric discharges (including receiving and downstream reaches) , as well as their household members. EPA estimated the number of people who are likely to fish affected reaches based on typical travel distances to a fishing site, presence of substitute fishing locations, data on the locations and status of fish consumption advisories (FCAs) for affected reaches , and information on anglers’ awareness and adherenc e to FCAs. Since fish consumption rates vary across different racial and ethnic groups and across fishing mode (recreational versus subsistence fishing), EPA estimated benefits separately for a number of age

- , ethnicity

- , and mode

specific cohorts.

First, for each Census Block Group (CB G ) within 50 miles of an affected reach, EPA pulled 2010 Census data on the number of people in 7 age categories (0 to 1, 2, 3 to 5, 6 to 10, 11 to 15, 16 to 21, and 21 or higher), and then subdivided each group accord ing to 7 racial/ethnic categories: 12 

### 1) White non

- Hispanic; 2)

African

- American non

- Hispanic; 3) Tribal/Native Alaskan non

- Hispanic; 4) Asian/Pacific Islander non

Hispanic; 5) Other non

- Hispanic (including multiple races), 6) Mexican Hispanic, and 7) Other Hispanic 13



### . Within each racial/ethnic group, EPA further subdivided the population according to recreational and subsistence groups, assuming that 5 percent of the population practices subsistence fishing. 14 

### Finally, EPA also subdivided the affected populati on by income into poverty and non

- poverty groups, based on the share of people below the federal poverty line. 15 

### After subdividing population groups by age, race, fishing mode, and the poverty indicator, each CB G has 196 unique population cohorts (7 age groups × 7 ethnic/racial groups × 2 exposure cohorts [recreational vs. subsistence fishing] × 2 poverty status designations).

EPA distinguished the exposed population by racial/ethnic group and poverty status to s upport analysis of potential Environmental Justice (EJ) considerations in baseline exposure to pollutants in steam electric power plant discharges, and to allow evaluation of the effects of the regulatory options on mitigating any EJ

concerns. See Chapter 14 for details of the EJ analysis. As noted below, distinguishing the exposed population in this manner also allows the Agency to account for differ ences in exposure among demographic groups, where supported by data.

Equation 3

- 1 shows how EPA estimated the affected population, ExPop(i)(s) (c), for CB G i in state s for

cohort c

.

12 The racial/ethnic categories are based on available fish consumption data as well as the breakout of ethnic/racial populations in Census data, which distinguishes racial group s within Hispanic and non

- Hispanic categories

. 13 The Mexican Hispanic and Hispanic block group populations were calculated by applying the Census tract percent Mexican Hispanic and Hispanic to the underlying block

- group populations, since these data were not available at the block

- group level.

14 Data is not available on the share of the fishing population that practices subsistence fishing. EPA assumed that 5% of people who fish practice subsistence fishing, based on the assumed 95 th percentile fish consu mption rate for this population (see U.S. EPA, 2011b).

15 Poverty status is based on data from the Census Bureau’s American Community Survey which determines poverty status by comparing annual income to a set of dollar values called poverty thresholds that vary by family size, num ber of children, and the age of the householder.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 3 

### Equation 3

- 1

. ExPop ( i ) ( s ) ( c ) = Pop ( i ) ( c ) × % Fish ( s ) × A ( i ) × CaR ( c )

Where:

Pop(i) (c) = Total CB G population in cohort c. Age and racial/ethnicity

- specific populations in each

CB G are based on data from the U .S. Census Bureau’s 2010 Census

. The Census data provides population numbers for each CB G broken out by age and racial/ethnic group separately. To estimate the population in each age

- and ethnicity/race

- s pecific group, EPA calculated the share of the population in each racial/ethnic group and applied the percentages to the population in each age group.

%Fish(s) = Fraction of people who live in households with anglers. To determine what percentage of the

to tal population participates in fishing, EPA used region

- specific U.S. Fish and Wildlife Service (U.S. FWS, 2011) estimates of the population 16 and older who fish. 16 

### EPA assumed that the share of households that includes anglers is equal to the fraction of people over 16 who are anglers.

A(i) = Adjustment for f

ish consumption advisories

. EPA further adjusted the affected angler population to reflect the presence of FCAs , where applicable for a given reach . Based on EPA’s review of studies documenting angler s ’ awareness of FCA and their behavioral responses to FCAs, 57.0 percent to
61.2 percent of anglers are aware of FCAs

, and 7 1.6 percent to 76.1 percent of those who are aware ignore FCAs

. Conservatively assuming that 61.2 percent of anglers are aware of app licable FCAs and that 71.6 percent of aware anglers ignore them, the number of anglers exposed to steam electric pollutants would be 17.4 percent lower for reaches with FCAs. 17 

### Therefore, for receiving reaches with FCAs, EPA reduced the affected populations by 17.4 percent.

CaR(c) = Adjustment for catch

- and

- release practices

. According to U.S. FWS (2006) data, approximately 23.3 percent of anglers release all the fish they catch (“catch

- and

- release” anglers). Anglers practicing “catch

- and

- release” would not be exposed to steam electric pollutants via consumption of contaminated fish. For all recreational anglers, EPA reduced the affected population by

23.3 percent . EPA assumed that subsistence fishers do not practice “catch

- and

- release” fishing.

Table 3

- 1 summarizes the population living within 50 miles of reaches affected by steam electric power plant discharges (see Section 3.2.1 for a discussion of this distance buffer) and EPA’s estimate of the population potentially exposed to the pollutants via consumption of recreationally

- caught fish (based on 2010 population data and not adjuste d for population growth during the analysis period)

. Of the total population, 14 percent live within 50 miles of an affected reach and participate in recreational and/or subsistence fishing, and

10 percent are potentially exposed to fish contaminated by st eam electric pollutants.

16 The share of the population who fishes ranges from 9 percent in the Pacific region to 23 percent in the West North Central region. Other regions include the Middle Atlantic (11 percent), New England (12 percent), South Atlantic (13 percent), Mountain (15 percent), West South Central (16 percent), East North Central (16 percent), and East South Central (17 percent).

17 This is calculated as 6 1

. 2 percent aware of advisories times 28.4 percent (100%

- 71.6%) who choose not to fish or otherwise don’t eat fish caught in waterbodies affected by advisories.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 4 Table 3

- 1 : Summary of P otentially A ffected P opulation Living within 50 Miles of Affected
R eaches (baseline , as of 2010 )

Total population 306,707,864

Total a ngler population a 42,710,492

P opulation potentially exposed to contaminated fish b , c 29,655,404

Source: U.S. EPA Analysis, 2015
a. Total population living within 50 miles of an affected reach times the state

- specific share of the population who
fishes based on U.S. FWS (2011; between 9% and 23%).
b. Total angler population adjusted to reflect lower fishing/consumption rates for reaches with fish consumption
advisories and catch

- and

- release practices.
c. Analysis accounts for projected population growth so that the avera ge affected population over the period of 2019 through 2042 is 21.3 percent higher than population i n 2010 presented in the table , or 36 million people.



### Pollutant Exposure from Fish Consumption 3.2

EPA calculated an average fish tissue concentration for each pollutant for each CB G based on a length

weighted average concentration for all reaches within 50 miles. For each combination of pollutant, cohort and

CB G , EPA calculated ADD and LADD consumed via the fish consumption pathway.

3.2.1 Fish Tissue Pollutant Conce ntrations

The set of reaches that may represent a source of contaminated fish for recreational anglers and subsistence

fishers in each CB G depends on the typical travel distance anglers travel to fish. EPA assumed that anglers typically travel up to 50 mil es to fish, using this distance to estimate the relevant fishing sites for the population of anglers in each CB G . See Appendix F for a sensitivity analysis using a travel distance of

100 mile s . Based on data from the National Survey on Recreation and the Environment, about 80 percent of

all water

- based recreation occurs with 100 miles of the users’ homes (Viscusi et al., 2008).

Anglers may have several fishable sites to choose from within 50 miles of travel

. To account for the effect of substitute sites, E PA assumed that anglers are uniformly distributed among all the available fishing sites
within 50 miles from the CB G (travel zone) and alternate their travels across all the sites. For each CB G , EPA
identified all fishable COMIDs within 50 miles (where dis tance was determined based on the Pythagorean

distance between the centroid of the CB G and the midpoint of the COMID) and the COMID length in miles

EPA then calculated

, for each CB G , the reach

- length weighted average fish tissue concentration of arsenic ( As), mercury (Hg), and lead (Pb) based on all fishable sites within the 50 mile buffer. Appendix D describes the approach used to derive ambient water and fish tissue concentrations of steam electric pollutants in the baseline and under each of the regulat ory options.

For each CB G , EPA then calculating the reach length ( Lengt h i 

### ) weighted fish filet concentration ( C Fish_Filet 

### (CB G ) ) based on all fishable COMIDS within the 50 mile radius according to Equation 3

- 2 :

Equation 3

- 2

. C Fis h

_ Filet e 

### ( CBG ) = ∑ C Fis h

_ Filet ( i ) ∗ Lengt h i n i = 1 ∑ Lengt h i n i = 1



### 3.2.2 Average Daily Dose

Exposure to steam electric pollutants via fish consumption depends on the cohort

- specific fish consumption

rates. Table 3

- 2 summarizes the fish consumption rates , expressed in daily grams per kilogram of bodyBenefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 5 

### weight, according to the race/ethnicity and fishing mode. For more details on these fish consumption rates

, see U.S. EPA (2015a).

Table 3

- 2 : Summary of group

- specific assumptions for human health benefit analysis a

Race/ Ethnicity EA Cohort b Consumption Rate (g/ kg BW/day)

Recreational Subsistence

White (non

- Hispanic) Non

- Hispanic White 0.67 1.9
African American (non

- Hispanic) Non

- Hispanic Black 0.77 2.1
Asian/Pacific Islander (non

- Hispanic) Other, including Multiple Races 0.96 3.6
Tribal/Native Alaskan (non

- Hispanic) Other, including Multiple Races 0.96 3.6

Other non

- Hispanic Other, including Multiple Races 0.96 3.6
Mexican Hispanic Mexican Hispanic 0.93 2.8
Other Hispanic Other Hispanic 0.82 2.7

a. Each group is also subdivided into seven age groups (0

- 1, 2, 3

- 5, 6

- 10, 11

- 15, 16

- 20, Adult (21 or higher) and two income groups (above and below the poverty threshold).

b. U.S. EPA (2015a).



### Equation 3

- 3 and Equation 3

- 4 show the cohort

- and CB G

- specific calculation of the ADD and LADD based

on fi sh tissue concentrations, consumption rates, and other assumptions about exposure duration and averaging periods, as shown below.

Equation 3

- 3

. ADD ( c ) ( i ) = C Fis h

_ Filet ( i ) × CR Fish ( c ) × F Fish

1000



### Where:

ADD ( c )( i ) = Average daily dose of pollutant from fish consumption for cohort c in CB G i (milligrams[mg] per kilogram [kg] body weight [BW] per day)

C fish_filet 

### ( i ) = average fish filet pollutant concentration consumed by humans for CB G i (mg per kg)

CR fis h 

### ( c ) = Consumption rate of fish for cohort c (grams per kg BW per day); see Table 3

- 2



### .

F fish 

### = fraction of fish from contaminat ed source (percent; assumed value of 100%)

Equation 3

- 4

. LADD = ADD × ED × EF

AT × 365



### Where:

LADD ( i )( c ) = Lifetime average daily dose (mg per kg BW per day) for cohort c in CB G i

ADD ( i )( c ) = Average daily dose (mg per kg BW per day) for cohort c in CB G i

ED ( c ) = exposure duration (years) for cohort c

EF = exposure frequency (days; assumed value of 350)

AT = averaging time (years; assumed value of 70)

EPA used the doses of steam electric po llutants from recreational caught fish thus obtained in its analysis of

benefits associated with the various human health endpoints described below.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 6 

### Benefits to Children from Reduced Lead Exposure 3.3

Lead is a highly toxic pollutant that can damage neurologic al, cardiovascular, and other major organ systems

. In particular, e levated lead exposure may induce a number of adverse neurological effects in children, including hyperactivity, behavioral and attentional difficulties, delayed mental development, and moto r and perceptual skill deficits (see U.S. EPA, 2015a ). Elevated blood lead (PbB) concentrations in children may

also result in metabolic effects such as impaired heme sy nthesis, anemia, and slowed growth (U.S. EPA,

1990 ; National Academy of Sciences, 1993; Kim et al. 1995 ). Severe lead poisoning may result in renal effects, seizures, impaired coordination, recurrent vomiting, coma, and acute lead encephalopathy, a potentially fatal condition (Piomelli et al., 1984 ; National Academy of Sciences, 1993; CDC , 2 005 ). Studies have also found a relationship between lead exposure in expectant mothers and lower birth weight in newborns (Zhu et al., 2010; Bornschein et al., 1989; and Dietrich et al. 1987). Because of data limitations, EPA estimated only the benefits f rom reducing neurological and cognitive damages to pre

- school (ages 0 to 7) children using the dose

- response relationship for IQ decrements (S c hwartz 1994 )

. EPA estimated benefits from reduced exposure to lead to preschool children using PbB as a biomarke r of lead exposure. EPA first modeled PbB under the baseline and post

- compliance scenarios, and then used a

concentration

- response relationship between PbB and IQ loss to estimate avoided IQ losses in the affected population of children and reduced inciden ces of extremely low IQ scores (less than 70, or two standard deviations below the mean). EPA calculated the monetary value of benefits to children based on the impact of an additional IQ point on an individual’s future earnings and the cost of compensator y education for children with learning disabilities (including children with IQ less than 70 and PbB levels above 20  g/dL).

EPA used the methodology described in Section 3.1 to estimate the population of pre

- school children who live in recreational angler and subsistence fisher households and are potentially exposed to lead via consumption of contaminated fish tissue. Since this benefit category ap plies to children up to the seventh birthday only, EPA restricted the analysis to the relevant age cohorts of angler household members.

3.3.1 Methods

This analysis considers children who are born after implementation of the ELGs and live in recreational angler and subsistence fisher households. It relies on EPA’s Integrated Exposure, Uptake, and Biokinetics (IEUBK) Model for Lead in Children (U.S. EPA, 2009 c ), which uses lead concentrations in a variety of

media

– including soil, dust, air, water, and diet

– to estimate total exposure to lead for children in seven one

year age cohorts from birth through the seventh birthday. Based on this total exposure, the model generates a predicted geometric mean PbB for a population of children exposed to similar lead levels

. The BCA report for the proposed ELG provides a more detailed description of the IEUBK model and describes EPA’s application of the model to estimating benefits to pre

- school children from reduced exposure to lead contaminated fish (U.S. EPA, 2013a )

.
For each CB G , EPA used the cohort

- specific ADD based on Equation 3

- 3 . Lead bioavailability and uptake after consumption varies for different chemical forms. Many factors complicate the estimation of bioavailability, including nutritional status and timing of meals relative to lead intake. For this analysis, EPA

used the default media

- specific bioavailability factor provided in the IEUBK model, which is 50 percent for oral ingestion. EPA used the IEUBK model to generate the geometric mean PbB for each cohort in each CB G under the baseline and post

- compliance scenarios. Note t he IEUBK model processes daily intake to two decimal places (ug/day). For this an alysis, this means that some of the change between the baseline and regulatory options is missed by using the model ( i.e. , it does not capture very small changes) , since theBenefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 7 

### benefits are driven by very small changes across large populations

. 18 

### As such, the benefits shown in this section are likely to underestimate the actual lead

- related benefits to children arising from the ELG.

3.3.1.1 Estimating of Avoided IQ Point Losses



### In a pooled data analysis, Lanphear et al. (2005; as cited in Agency for Toxic Substances a nd Disease Registry ( ATSDR ) , 2007) found that the greatest IQ losses per 1  g/dL occur at the lowest ranges of PbB. When the authors grouped IQ losses data for children with PbB below and above 7.5  g/dL, they found that the IQ losses were 2.94 points per  g/dL for children with PbB concentrations below 7.5  g/dL and

0.16 points per  g/d for children with PbB concentrations above 7.5  g/dL.

Given the baseline PbB levels estimated using the IEUBK model ( mean PbB of approximately 2.7 μg/dL ),

EPA used the dose

- response factor of 2.94 points per  g/dL from Lanphear et al. to estimate changes in IQ

losses between the baseline and post

- compliance scenarios

. 19 

### Comparing the baseline and post

- compliance

results provides the avoided IQ loss per child. Multiplying the result by the number of affected pre

- school children yields the total increase in the number of IQ points for the affected population of children for the baseline and each regulatory option.

The IEUBK model estimates the mean of the PbB d istribution in children, assuming a continuous exposure pattern for children from birth through the seventh birthday. The U.S. Census Bureau ( 2010 ) indicates that children ages 0 to 7 are approximately evenly distributed by age. To get an annual estimate o f the number of children that would benefit from implementation of the final rule , EPA divided the estimated number of affected pre

- school children by 7. This division adjusts the equation to apply only to children age 0 to 1. The estimated avoided IQ loss is thus an annual value ( i.e. , it would apply to the cohort of children born each year after implementation). 20 

### Equation 3

- 5 shows this calculation for the annual increase in total IQ points.

Equation 3

- 5

. ∆ IQ ( i ) ( c ) = ( ∆ GM ( i ) ( c ) ∗ CRF ∗ ( ExCh ( i )

7 

### ) )

Where:

∆IQ(i)(c) = the difference in total IQ points between the baseline and regulatory option scenarios for

cohort c in CB G i

∆GM(i)(c) = the change in the average PbB in affected population of children (μg/dL) in cohort c in

CB G i

CRF = concentration response function (2.94 IQ points lost per ug/dL increase in PbB)

ExCh(i) = the number of affected children aged 0 to 7 for CB G i

The a vailable economic literature provides little empirical data on society’s overall WTP to avoid a decrease

in children ’s IQ. To determine the value of avoided IQ losses, EPA used estimates of the chang es in a child ’s future expected lifetime earnings per one IQ point reduction and the cost of compensatory education f or children with learning disabilities

.

18 For example, the average intake across all affected children is reduced from 0.333 ug/kg BW/day under t he baseline to 0.332 under Option D.

19 See Appendix F for a sensitivity analysis using a log

- linear concentration

- response function.

20 Dividing by seven undercounts overall benefits. Children from ages 1 to 7 are not accounted for in the base year
of th e analysis, although they are presumably affected by lead exposure.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 8 

### Salkever (1995) and Schwartz (1994) estimate that a one point IQ reduction reduces expected lifeti me earnings by 2.38 percent and 1.76 percent , respectively . D ata from the U.S. Census Bureau for 2009 indicate that lifetime earnings are approximately $147,462 when discounting future earnings at 7 percent, and $ 656,737 when discounting future earnings at 3 percent. 21 

### The resulting estimated value s of an IQ point are summarized in Table 3

- 3

.

Table 3

- 3 : Value of an IQ Point a ( 2013$)

Discount Rate Assumed Reduction in Expected Lifetime Earnings (percent per IQ point)

1.76 percent/IQ point ( Schwartz, 1994 ) 2.38 percent/IQ point ( Salkever, 1995 )

3 percent $ 11,559 $ 15,630
7 percent $2,559 $3,510

a. Values are not adjusted for the cost of education.



### Decreased IQ also results in less education and, therefore, reduced education costs. Data from the National Center for Education Statistics (2014) indicate that the average expenditure per student in 2010/2011 was $12,600 (in 2013$). Schwartz (1994) and Sa lkever (1995) estimate that a one IQ point reduction results in 0.131 and 0.101 fewer education years, respectively; this represents lifetime cost savings between $377 and $970, discounting the avoided costs over 18 years. EPA subtracted these education co sts from the value of lifetime earnings per IQ point in Table 3

- 3

. Subtracting education costs is done for accounting purposes only and does not suggest that this is a desirable , positive outcome.

The value of an IQ point reduction adjusted for the avoided cost of education ranges between $2,107 (following Schwarz (1994) and discounting at 7 percent) to $14,883 (following Salkever (1995) and discounting at 3 percent). This effect represents only one component of society’s WTP to avoid IQ decreases,

and thus underestimates the total value of benefits to children from reduced exposure to lead.

3.3.1.2 Reduced Expenditures on Compensatory Education



### Children whose PbB exceeds 20  g/dL are more likely to have IQs less than 70, which means that they would require compensat ory education tailored to their specific needs. EPA’s IEUBK model can generate probabilities that a child would have a PbB in excess of a specific threshold.

EPA estimated the number of children that would have PbB above 20  g/dL for each CB G under the ba seline and each analyzed regulatory option. EPA assumed that 20 percent of children with PbB above 20 ug/dL would have IQs less than 70 and require compensatory education. 22 

### Equation 3

- 6 shows the calculation of the number of children requiring compensatory education for each cohort and CB G . Summing across all cohorts

and CB G s provides the total number of children who would require special education

.

21 EPA updated lifetime earnings to 2013 dollars using the Consumer Price Index (2013=236.384; 2009 = 214.537).

22 This assumption follows the methodology used by EPA in Economic and Envir onmental Benefits Analysis Document for the Final Effluent Limitations Guidelines and Standards for the Metal Products and Machinery Point Source Category (U.S. EPA, 2003).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 9 

### Equation 3

- 6

. CompEd ( i ) ( c ) = ExCh ( i ) ∗ Pr 20 ( i ) ( c ) ∗ S

Where:

CompEd(i) = the number of children with PbB over 20  g/dL and IQ less than 70 (who would need compensatory education) for cohort c in CB G i

ExCh(i) = the number of affected children for cohort c in CB G i

Pr20(i) = the probability that a child’s PbB is above 20  g/dL for cohort c in CB G i

S = S hare of children with PbB over 20  g/dL that would have IQ scores less than 70 (20%)

The U.S. Department of Education (Chambers, et al., 2003) estimated that average annual expenditures for a student with mental retardation are approximately $8,484 higher than for an average student. Updating to

2013 dollars 23 

### yields annual costs of $16,075 per chil d. EPA assumed that children with IQ less than 70 would incur these additional costs each year for 12 years. Discounting future costs using a 3 percent discount rate yields a total compensatory education cost of approximately $164,806 per child with an IQ score less than 70 ($136,613 per child if using a 7 percent discount rate).

3.3.2 Results

Table 3

- 4 shows the benefits associated with avoided IQ losses from lead exposure via fish consumption

, and

Table 3

- 5 shows the benefits associat ed with a reduced need for specialized education. T he final BAT/PSES
opt ion will generate annualized benefits of $0. 8 million to $ 1.1 million using a 3 percent discount rate , and $0.1 million to $0.2 million using a 7 percent discount rate

.

Table 3

- 4 : Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead

Regulatory Option Average

Annual
Number of
Affected
Children 0 to 7 Total

Avoided IQ Losses
, 2021 to
2042 Annual ized Value of Avoided IQ Point Losses a (Millions 2013$ )

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Option A 3,326,127 853 $0.34 $0.48 $0.05 $0.08 Option B 3,326,127 853 $0.34 $0.48 $0.05 $0.08 Option C 3,326,127 1,285 $0.51 $0.72 $0.08 $0.12 Option D 3,326,127 1,985 $0.79 $1.11 $0.13 $0.19 Option E 3,326,127 1,985 $0.79 $1.11 $0.13 $0.19

Source: U.S. EPA Analysis, 2015

a . Low bound assumes that the loss of one IQ point results in the loss of 1.76% of lifetime earnings (following Schwartz, 1994 ); high bound assumes that the loss of one IQ point results in the loss of 2.38% of lifetime earnings (following Salkever, 1995).

23 Updated to 2013 dollars using the Consumer Price Index for Education (2013 = 224 .521; 1999 = 107.0).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 10 Table 3

- 5 : Estimated Avoided Cost of Compensatory Education for Children with Blood Lead
Concentrations above 20  g/dL and IQ Less than 70

Regulatory Option Number of

Affected
Children 0 to 7 Decrease in Number

of Cases of IQ < 70,
in 2021 to 2042 Avoided Annual Co st (Millions; 201 3 $) a

3% Discount Rate 7% Discount Rate

Option A 3,326,127 1 $0.00 $0.00
Option B 3,326,127 1 $0.00 $0.00
Option C 3,326,127 1 $0.01 $0. 0 0
Option D 3,326,127 2 $0.01 $0.01
Option E 3,326,127 2 $0.01 $0.01

Source: U.S. EPA Analysis, 2015

a. “

- “ indicates that a value was not estimated and “$0.00” indicates that avoided annual cost is less than $0.01 million

.



### Benefits to Adults from Reduced Lead Exposure 3.4

The public health literature suggests that a wide spectrum of adverse health outcomes can occur from lead exposure in people of all ages. For example, recent evidence has suggested that exposure to lead in adults can

result in CVD impacts; specifically, increases in hypertension, coronary heart dis ease, CVD, and CVD

- related mortality (National Toxicology Program, 2012; U.S. EPA, 2013 b ). EPA has developed and externally peer

reviewed concentration response functions for CVD

- related mortality resulting from adult exposure to lead (Abt Associates, 2015 and U.S. EPA, 2015 f ). EPA is using these concentration response functions to estimate

human health benefits from reduc ed adult exposure to lead following the implementation of the final ELGs.

3.4.1 Methods

This section summarizes EPA’s approach for estimating the benefits to human health due to reductions in adult exposure to lead following implementation of the final ELGs. EPA relied on two models to estimate baseline and post

- compliance exposure for each population cohort:

 The first model estimates PbB in adu lts as a function of air

- based background lead exposure and consumption of contaminated fish tissues.

 The second model is a population life table model that estimates the gains in life years due to

decreased risk of CVD mortal ity from the PbB reductions.

EPA then aggregated the resulting cohort

- specific gains in life expectancy to represent the total magnitude of the expected human health benefits for each ELG option. EPA estimated monetized benefits by applying a constant value per statistical life (VSL) to the estimated number of premature deaths avoided in each analysis

year.

3.4.1.1 Modeling Blood Lead Concentrations in Adults



### The Leggett model (Leggett, 1993), a physiologically

- based pharmacokinetic model, has been used to

estimate bone and blood lead levels in adults by U.S. EPA and others ( California Office of Environmental Health Hazard Assessment ( Cal OEHHA ) , 2013a, 2013b)

. 24 

### The model predicts PbB by explicitly modeling lead absorption, distribution, metabolism, and excretion for 21 body tissue compartment s on a daily time step. Therefore, the model is able to address time

- dependent conditions ( e.g. , changing exposures through time).

24 EPA Office of Pollution Prevention and Toxics (OPPT) is using the Legget t+ model for the upcoming renovation and repair rule.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 11 

### Since its original publication, the model has been updated and improved to better fit observed data. Most recently, the Cal O EHHA has published an updated version of the Leggett model, Leggett+, which they have used for occupational health standards in California. EPA implemented the updated model in the R programming language using publicly available documentation and MATLAB co de (Cal OEHHA, 2013a, 2013b). The Agency modified the model inputs to more easily alter lead exposure due to contaminated fish consumption, and to reduce the complexity (and therefore computational time) of background exposure from

air. 25 

### Internal model par ameters with respect to the transit of lead through the 21 body tissues are unchanged.

Table 3

- 6 presents and describes parameters used to apply the L eggett + model.

Table 3

- 6 : Parameters Used to Apply the Leggett+ Model

Parameter Units Value Notes

Age at Start of Exposure Years 18 The Leggett+ model is parameterized to model
PbB only for adults

Age in 2014 Years 25

– 65 in 10

- year increments Data used for all individuals within decadal
cohort

Duration of Exposure to
Contaminated
Fish Years Within age cohort, number of years between 18 and year of ELG implementation. Pre

- 2014 exposures limited to 27 years (based upon examination of model results). Pre

- 2014 exposure calculated as: Duration = Age in 2014

– 18.

Initial Blood
Lead
Concentration μg/dL 1.5 CDC 2009; Schober 2006

Background Lead
Intake μg/day 1.8 Calculated to maintain PbB at 1.5 μg/dL with no fish exposure for a body mass of 74 kg (Cal OEHHA 2013a)

Body Mass kg Age Men Women Values from Table 8

- 24 of EPA (2011 b)

. Values are held c onstant for all age s within
each decade. Affects quantity of contaminated
fish tissue consume d (see below). 20s 82.5 71.4

30s 85.7 76.3

40s 88.0 76.3

50s 88.5 77.8

60s 88.2 75.6

Lead Intake from
Contaminated
Fish Tissue mg/kg body

mass / day Average daily doses c alculated

as described in Section 3.2.2 Baseline ( i.e

. , no ELG) values used until 2021 ; thereafter, option

- specific values.

Initial Tissue Compartment
Parameters Various Various Values used were obtained from CAL OEHHA (2013a).



### EPA ran the models for several population cohorts defined by sex, age, exposure (recreational and subsistence fishers), and geography (215,460 CB G s) to characterize variability in the relevant population characteristics.

The model was run on a daily time

- step for each of the combination s of input parameters (6 scenarios [baseline + 5 ELG options)] x 2 sexes [male/female] x 215,460 CB G s x 2 exposure cohorts [recreational vs. subsistence fishing] x 5 age cohorts [20s

– 60s, by decade]). Simulation duration included two separate

25 The Leggett+ model was designed to model airborne workplace exposure to air; in adapting the model to this analysis, EPA excised all parameters directly related to workplace exposure (including workplace airborne lead concentrations, breathing rate, transfer fraction of inhaled lead to blood, etc.) to improve model runtime. Background airborne ingestion re mained consistent with the baseline Leggett+ model inputs.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 12 

### components: exposures pre

- and post

- 20 15 . Exposure occurring before 20 15 (beginning at the age of 18 for each decadal age group) was run once for each combination of input parameters. These parameter values were then used as initial parameters for all model runs (bas eline and the five ELG options) simulating exposure

after 201 5 . Exposure prior to 201 5 was limited to 27 years in duration, because inspection of model results suggested that additional lead accumulation in body tissues was minimal beyond 25 years. Consequ ently,

simulations under no

- ELG conditions ranged in duration from 34 years (for the 20s age cohort: 7 years of pre

201 5 exposure plus 201 5

– 204 2 , inclusive) to 54 years (for 50s and 60s age cohorts, 27 years of pre

- 201 5 exposure plus 201 5

- 204 2 , inclusiv e), while the duration for all ELG

- option simulations was 27 years (201 5

- 204 2 , inclusive).

For each model run, EPA averaged forecasted PbB outputs within forecast year. Figure 3

- 1 illustrate s changes

in PbB over time for two selected male subsistence fishing cohorts within a single CB G under the baseline and two of the regulatory options

.

Figure 3

- 1 : Example changes in PbB through time under baseline, option B , and option D for cohorts in the 20s and 60s (age as of 2014). Differences between cohorts are driven by differences in body mass. Example d ata represent modeled PbB s of male subsistence fishers residing in CB G 560419753004

. The majority of cohorts nationwide experience a smaller reduction in PbB due to the rule.



### Relative to baseline, PbB was reduced for approximately 1. 1 percent of the simulated population under options A and B, 2 .0 percent of simulations under option C, and 2

. 3 percent of simulated individuals under options D and E . The extent of the reductions followed the spatial distribution and magnitude of loading reductions across the options. When reductio ns did occur

, they were typically small ( e.g. , < 0.0 3 μg/dL in

204 2 for options A

– C, and < 0. 08 μg/dL in 2042 for options D

- E ).

Relative to baseline, PbB was reduced by more than 1 percent of baseline

– a decrease EPA considers to be a meaningful reduc tion level based upon CVD mortality rat es

– for between 0.0 3 percent to 1 .1 8 per cent of the simulated population under options A to E , respectively

, with the final BAT/PSES (Option D) showing 1 .18
percent of simulated individuals with a reduction of greater than 1 percent of baseline PbB

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 13 3.4.1.2 Estimating Hazard Reduction under the Final ELGs



### For each sex, single

- year age, and exposure cohort (hereafter cohort), the analysis characterizes two basic survival analys e s under the baseline and option scenarios: a death hazard function and a survival function (details of the analysis are provided in Appendix E )

. 26 

### To estimate changes in mortality following ELG implementation

, EPA characterized :

1. the baseline CVD death hazard function;

2. the baseline non

- CVD death ha zard function; and

3. an option

- specific CVD death hazard function.

The main source of data for hazard estimation in key simulation elements (1) and (2) above is a life table, a collection of statistics that shows age

- specific probabilities of survival and fecundity. 27 

### EPA obtained life table data from two sources: the CDC’s National Center for Health Statistics (Arias, 2014) and the CDC’s Underlying Cause of Death Database (CDC 2012). 28 

### After obtaining all necessary data, EPA calculated age

and sex

- specific baseline hazards for CVD mortality ; a sample of life table data used in calculations is presented in Table 3

- 7

.

Table 3

- 7 : Sample of I nputs for A ge - an d Sex

- Specific Hazard Functions

Age Sex Proportion of Population Mean Body Mass (kg) CVD Rate

(per 1,000)

20s M 0.0875 82.5 0.072149

30s M 0.0906 85.7 0.242882

40s M 0.0977 88.0 0.833447

50s M 0.0925 88.5 2.209863

60s M 0.0630 88.2 4.633724

20s F 0.0852 71.4 0.037076

30s F 0.0909 76.3 0.123911

40s F 0.0995 76.3 0.392972

50s F 0.0973 77.8 0.940202

60s F 0.0693 75.6 2.303575



### Using CVD rates collected, EPA then calculated option

- specific CVD death hazard functions based on the relationship between PbB and the CVD hazard ratio. To do this, EPA used a concentration

- response function from a peer reviewed study which found a n adjus ted relative hazards of CVD mortality of 1.53 (1.21

- 1.94) per 3.4

- fold increase in PbB (Menke et al. 2006).

3.4.1.3 Estimating Premature Deaths Avoided Over Multiple Years



### The VSL is the marginal rate of substitution between wealth and mortality risk in a defined time period, usually taken to be one year. Therefore, the product of VSL and the estimated aggregate reduction in risk of

26 Collett (2003), pp. 10

- 12

. 27 An extensive discussion of life tables can be found in Shryock et al. (1980) Chapter 15.

28 Database query parameters included: Dataset: Underlying Cause of Death, 1999

- 2010; Autopsy: All; Gender: Female, Male; ICD

- 10 Codes: I00

- I99 (Diseases of the Circulatory System); Place of Death: All; Race: All; Single

- year ages 20

– 100+, inclusive; Years: 2006

- 2010; States: All; Urbanization: All; Calculate Rates Per: 100,00 0; Rate Options: Default intercensal populations for years 2001

- 2009

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 14 

### premature death represents the affected population’s aggregate WTP to reduce its probability of death in one year. EPA estimated the b enefits of multi

- year mortality risk as the product of:

1. The reduction in initial age

- specific mortality rate ( i.e. , the proportion of people alive at exact age x , who will die before attaining exact age x + 1 in year t ) ; and

2. The number of individuals surv iving to the beginning of year t . This value is calculated as the initial cohort population size in 201 5 multiplied by the probability that these individuals survive to age x , and are alive at the beginning of year t to enjoy the benefits of the year’s mortality risk reduction.

It is important to note that WTP for a specific reduction in risk may depend on (i) the time of payment , (ii) the conditions under which the individual can save and borrow against future income

, and (iii) whether the individual knows about the change in survival probability ahead of time (Hammitt 2007). T he earlier an individual learns of the altered probability of survival , the earlier she “can adjust by reallocating her planned future consumptio n and risk

- reducing expenditures.” (Hammitt 2007).

3.4.2 Results

Table 3

- 8 summarizes the magnitude and economic value of human health benefits from reduced incidence of

cardiovascular disease connected to adult lead exposure due to ELG implementation during the period 201 9

- 204 2 . At a 3 percent discount rate, the final BAT/PSES (O ption D ) has annualized benefits of $ 1 2.8 million ; the annualized benefits are $ 10. 7 million at a 7 percent discount rate.

Table 3

- 8 : Summary of Estimated Health Benefits due to Decreased Risk of CVD Mortality during 201 9

- 204 2 based on the Economic V alue of A voided P remature M ortality (V SL)

Regulatory Option Aggregate Reduction
in Risk of Premature
Death Annualized Benefits (millions 2013$)

3 Percent 7 Percent

Option A 10. 7 $3. 77 $ 3.14 Option B 10. 7 $3. 77 $ 3.14 Option C 2 3.6 $ 8.35 $6. 97 Option D 3 6.2 $1 2.80 $ 10. 68 Option E 3 6.2 $1 2.80 $ 10. 68

Source: EPA Analysis, 2015



### Benefits to Children from Reduced Mercury Exposure 3.5

Mercury can have a variety of adverse health effects on adults and children (see U.S. EPA, 2015a). The final ELGs are expected to reduce the discharge of mercury to surface waters by steam electric power plant s and therefore provide a range of human health benefits. Due to data limitations, however, EPA estimated only the

benefits from redu ced IQ losses among children exposed to mercury in

- utero as a result of maternal consumption of contaminated fish.

EPA identified the population of children exposed in

- utero starting from the CB G

- specific affected population described in Section 3.1 . Because this analysis focuses only on infants born after implementation of the ELGs, EPA further limited the affected population by estimating the number of women between the ages of 15 and 42 potentially exposed to contaminated fish caught in the affected waterbodies, andBenefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 15 

### multiplying the result by ethnicity

- specific average fertility rates. 29 

### This yields the cohort

- specific annual

number of births fo r each CB G

. The U.S. Department of Health and Human Services provides fertility rates by race for 2012 in the N ational Vital Statistics Report (Martin, et al., 2013) . The fertility rate measures the number of births occurring per 1,000 women between the a ges of 15

- 44 in a particular year. F ertility rates were highest for Hispanic women at 74.4, followed by African Americans at 65.0, Asian or Pacific Islanders at 62.2, Caucasians at 58. 6 , and

Tribal/Other at 47.0.

3.5.1 Methods

EPA used the same ethnicity

- and m ode

- specific consumption rates shown in Table 3

- 2 and calculated the

CB G

- and cohort

- specific mercury ADD based on Equation 3

- 3 . In this analysis, EPA used a linear dose

response relationship between maternal mercury hair content and subsequent childhood IQ loss from Axelrad et al. (2007). Axelrad et al. (2007) developed a dose

- response function based on data from three epidemiological studies in the Faroe Islands, New Zealand, and Seychelle Islands. According to their results, there is a 0.18 point IQ loss for each 1 part

- per

- million (ppm) increase i n maternal hair mercury.

To estimate maternal hair mercury concentrations based on the daily intake (see Section 3.2.2 ), EPA used the

median convers ion factor derived by Swartout and Rice (2000), who estimated that a 0.08 μg/kg body weight increase in daily mercury dose is associated with a 1 ppm increase in hair concentration. Equation 3

- 7 shows
EPA’s calculation of the total annual IQ decrement for a given receiving reach.

Equation 3

- 7

. IQL ( i ) ( c ) = InExPop ( i ) ∗ MADD ( i ) ( c ) ∗ ( 1

Conv 

### ) ∗ DRF

Where:

IQL(i) = IQ losses associated with in

- utero exposure to mercury from maternal consumption of fish

contaminated with mercury for cohort c in CB G i

InExPop(i) = affected population of infants in CB G i (the number of births)

MADD(i)(c) = maternal ADD for cohort c in CB G i (μg/kg BW/day)

Conv = conversion factor for hair mercury concentration based on maternal mercury exposure (0.08 ug/kg BW/day per 1 ppm increase in hair mercury)

DRF = dose response fun ction for IQ decrement based on marginal increase in maternal hair mercury (0.18 point IQ decrement per 1 ppm increase in hair mercury)

Summing estimated IQ losses across all analyzed CB G s yields the total number of IQ points lost due to in

utero mercury e xposure from maternal fish consumption under each analyzed regulatory option. The benefits of the ELGs are calculated as the reduction in IQ points lost between the baseline and modeled post

compliance conditions.

The a vailable economic literature provides little empirical data on society’s overall WTP to avoid a decrease

in children ’s IQ. To determine the value of avoided IQ losses, EPA used estimates of the changes in a child ’s future expected lifetime earnings per one IQ point reduction and the c ost of additional education. EPA used the same values of an IQ point presented in Section 3.3.1 , which range from $2,107 to $14,883.

29 EPA acknowledges that fertility rates vary by age. However, the use of a single average fertility rate for all ages is not expected to bias results because the average fertility rate reflects the underlying distribution of fertility rates by age.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 16 

### 3.5.2 Results

Table 3

- 9 show s the estimated benefits of avoided IQ point losses for infants exposed to mercury in

- utero

. T he final BAT/PSES option will generate annualized benefits of $ 2.9 million to $ 4. 0 million at a 3 percent discount rate, and $0.5 million to $0.7 million at a 7 percent discount rate

.

Table 3

- 9 : Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure

Regulatory Option Number of

A ffected Infants per
Year Total Avoided

IQ Losses
, 2021 to 2042 Annual ized Value of Avoided IQ Point Losses a (Millions 2013$ )

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound
Option A 418,953 3,239 $1.29 $1.81 $0.21 $0.31
Option B 418,953 3,311 $1.32 $1.85 $0.21 $0.32
Option C 418,953 6,001 $2.38 $3.35 $0.39 $0.58
Option D 418,953 7,219 $2.87 $4.03 $0.47 $0.69
Option E 418,953 7,898 $3.14 $4.41 $0.51 $0.76

Source: U.S. EPA Analysis, 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 1995).



### Reduced Cancer Cases from Arsenic Exposure 3.6

Among steam electric pollutants analyzed in the EA, arsenic is the only confirmed carcinogen with a published dose response function (see U.S. EPA, 2010 b ). 30 

### EPA estimated the number of annual cancer cases associated with consumption of fish contaminated with arsenic from steam electric discharges under the baseline and each regulatory option. The reduction in the number of cancer cases from the baseline to po st

compliance represents human health benefits attributable to the final ELGs

.

3.6.1 Methods

EPA used the cohort

- specific arsenic LADD (see Section 3.2.2 ) to calculate the total number of cancer cases

for each cohort for each CB G under the baseline and each of the regulatory options, based on Equation 3

- 8

. Equation 3

- 8

. CC ( i ) ( c ) = ExPop ( i ) ( c ) ∗ CSF ∗ LADD ( i ) ( c )

Where:

CC(i)(c) = the number of cancer cases for cohort c in CB G i

ExPop(i)(c) = the number of people affected for cohort c in CB G i

CSF = Cancer Slope Factor f or skin cancer from arsenic [1.5 (mg/kg BW/day)
- 1 

### ].

LADD(i)(c) = Lifetime Average Daily Dose of arsenic for cohort c in CB G i (mg/kg BW/day).

For this analysis, EPA used the current Integrated Risk Information System (IRIS) CSF, which is based on

incidences of skin cancer. EPA is currently revising its cancer assessment of arsenic to reflect new data on

30 Although other pollutants, such as cadmium, are also likely to be carcinogenic (see U.S. Department of Health and Human Services (U.S. DHHS), 2008), EPA did not identify dose

- response funct ions to quantify the effects of changes in these other pollutants.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 17 

### internal cancers. It is possible that the revised combined (lung and bladder cancer) CSF would be higher ( e.g. , the draft value is 25.7 per mg/kg BW/day), suggesting that the use of the current IRIS value may bias benefits

downward.

Summing the number of cancer cases across all cohorts and all CB G s yields the total number of annual cancer cases under the baseline and each of the regulatory options. To estimate the number of avoided cancer cases, EPA subtracted the estimated number of cancer cases for each analyzed regulatory option from the estimat ed

number of cancer cases under the baseline.

In the analysis of the proposed Steam Electric ELGs, EPA had used VSL to place a monetary value on avoiding a cancer case. This monetization approach inherently assumes that all cases would be fatal. G iven

that the majority of skin cancer cases (which are the basis for the health benefits arising from reduced arsenic exposure) are not fatal, this approach is likely to bias benefit estimates upward. For the analysis of the final rule, therefore, EPA revised the m onetization approach to value skin cancer cases based on a COI approach.

Based on a literature review, EPA developed COI estimates for the skin cancer health endpoint associated with oral arsenic exposure (Abt Associates, 2014). The Agency found the follow ing distribution of non

melanoma skin cancers associated with arsenic:

 Basal cell carcinoma: 15%;

 Invasive squamous cell carcinoma: 19%;

 Non

- invasive squamous cell carcinoma : 58%; and

 Combined: 8%.

These types of skin cancers have very low fatality rates. Diagnosis involves medical histories, physical exams, and skin biopsies, while treatments consist of minor surgeries and periodic follow

- up visits.

The COI estimates for skin cancers associated wit h arsenic include both direct medical expenditures and indirect opportunity costs. The direct medical costs were based on Medical Expenditure Panel Survey (MEPS) data on office

- based provider visits and outpatient visits between 1996 and 2010, and represen t the mean expenditures per patient. 31 

### Diagnosis and surgery are one

- time costs, while follow

- up visits are periodic for all years after the surgery, with frequency depending on the type of cancer. The other component of the cost of an illness is the opport unity cost

– i.e. , the value of time lost during the illness. For non

- melanoma skin cancer, EPA assumes that all patients in this analysis incur opportunity costs for diagnosis, surgery, and

follow

- up doctor visits. Table 3

- 10 shows the total costs for different types of skin cancer.

31 Expenditures are for entire procedures; for example, expenditures for a surgery include the surgery itself as well as the associated hospital stay.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 18 Table 3

- 10 : Total Costs of Illness for Skin Cancer a

Type of Cost Cost in First Year Cost in Subsequent Years

Basal Cell Skin Cancer

Medical care $1,112 $307

Opportunity cost $282 $39

Total cost for BCC $1,394 $347

Non

- invasive Squamous Cell Skin Cancer

Medical care $1,419 $266

Opportunity cost $322 $34

Total cost for non

- invasive SCC $1,741 $300

Invasive Squamous Cell Skin Cancer

Medical care $3,020 $388

Opportunity cost $460 $50

Total cost for invasive SCC $3,480 $439

a. Updated to 2013$ using the consumer price index for medical care



### Table 3

- 11 shows the weighted average skin cancer cost of illness estimate, based on the proportion of cases for each type of skin cancer. Overall, a skin cancer case from arsenic exposure results in costs of approximately $ 2,056 in the first year and $ 338 in the subsequent 14 years. Using a 3 percent discount rate, this equates to a $ 5,877 value of a skin cancer case, and using a 7 percent discount rat e, the value is $ 5,015

.

Table 3

- 11 : Weighted Average Skin Cancer Cost of Illness a

Type Proportion of Cases COI (first year) COI (subsequent years) b

Non

- invasive squamous cell 58% $1,741 $300

Invasive squamous cell 19% $3,480 $439

Basal cell 15% $1,394 $347

Combination c 8% $2,205 $362
Total/Weighted Average 100% $2,056 $338

a. updated to 2013$ using the consumer price index for medical care.

b. Assumes 14 subsequent years.

c . For “combination,” EPA calculated COI based on the average of basal cell, invasive squamous cell, and non

- invasive squamous
cell cases.



### A reduction in pollutant loadings does not immediately result in cessation of adverse health effects. There is a lag between the time when exposures are reduced and the time when a reduction in risk occurs. Additionally, there may be a latency period between the initial exposure and the onset of the illness. T he latency period

between low

- dose arsenic exposure and skin cancer is unknown (Kara gas et al., 20 01; Shannon and Strayer,

19 8 9), though some researchers postulate it could range from several years to decades (Karagas et al

. , 1998). U.S. EPA (2010b) notes that the cessation lag for skin cancer from arsenic is unknown, but that the cessati on lag for internal cancers from arsenic may be longer than for skin cancer, ranging from 15 to 50 years. For this analysis, EPA assumed that cancer cases resulting from arsenic would not occur for ten years after exposure

and discounted the value of avoid ed cancer cases by an additional ten years.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 19 

### 3.6.2 Results

Table 3

- 12 shows the estimated changes in incidence of cancer cases from exposure to arsenic in fish tissue under the ELGs and the annualized benefits calculated using 3 percent and 7 percent discount r ates. Under both discount rates, annualized benefits are under $0.01 million.

Table 3

- 12 : Annual Benefits from Reduced Cancer Cases due to Arsenic Exposure

Regulatory Option Annual

Affected Population Reduced Cancer Cases
,

2019 to 2042 Benefits (Millions 2013$) a

3% Discount 7% Discount

Option A 35,972,005 0.03 $0.00 $0.00
Option B 35,972,005 0.03 $0.00 $0.00
Option C 35,972,005 0.06 $0.00 $0.00
Option D 35,972,005 0.09 $0.00 $0.00
Option E 35,972,005 0.1 $0.00 $0.00

Source: U.S. EPA Analysis, 2015

a . “

- “ indicates that a value was not estimated and “$0.00” indicates that annual benefi ts are less than $0.01 million.



### Total Monetized Human Health Benefits 3.7

Table 3

- 13 presents total monetized human health benefits for the final BAT/PSES (Option D) and alternate regulatory options. Using a 3 percent discou nt rate, benefits under Option D range from $ 16.5 million to $ 18.0 million ($ 11.3 million to $ 11.6 million using a 7 percent discount rate) . Reduced lead exposure for adults and reduced mercury exposure for children represent the majority of total monetized human health

benefits.

Table 3

- 13 : Total Monetized Human Health Benefits for ELG Options (millions of 2013$)

Discount
Rate Option Reduced L ead

E xposure for
C hildren a Reduced

L ead
E xposure for A dult s Reduced M ercury

E xposure for
C hildren a Reduced

C ancer C ases from
A rsenic b Total a

L ow High Low H igh L ow H igh

3% A $0.34 $0.48 $3.77 $1.29 $1.81 $0.00 $5.40 $6.06

B $0.34 $0.48 $3.77 $1.32 $1.85 $0.00 $5.43 $6.10

C $0.52 $0.73 $8.35 $2.38 $3.35 $0.00 $11.25 $12.43

D $0.80 $1.12 $12.80 $2.87 $4.03 $0.00 $16.47 $17.95

E $0.80 $1.12 $12.80 $3.14 $4.41 $0.00 $16.74 $18.33

7% A $0.05 $0.08 $3.14 $0.21 $0.31 $0.00 $3.40 $3.53

B $0.05 $0.08 $3.14 $0.21 $0.32 $0.00 $3.40 $3.54

C $0.08 $0.12 $6.97 $0.39 $0.58 $0.00 $7.44 $7.67

D $0.14 $0.20 $10.68 $0.47 $0.69 $0.00 $11.29 $11.57

E $0.14 $0.20 $10.68 $0.51 $0.76 $0.00 $11.33 $11.64

Source: U.S. EPA Analysis, 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 1995).

b. “$0.00” indicates that annual benefits are less than $0.01 million.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 20 

### Additional Measures of Human Health Benefits 3.8

The benefits described above are only some of the human health effects expected to improve as a result of the final ELGs for which EPA was able to identify dose

- response relationships . As noted in the introduc tion to

this Chapter, pollutants in steam electric power plant discharges have been linked to additional adverse human

health effects. To provide an additional measure of the potential health benefits of the final ELGs, EPA also estimated the expected redu ction in the number of receiving reaches with pollutant concentrations in excess of

human health

- based AWQC. This analysis and its findings are not additive to the preceding analyses in this chapter, but represent another way of characterizing potential he alth benefits resulting from reduced exposure to steam electric pollutants. This analysis compares in

- stream pollutant concentrations estimated for the baseline and each analyzed ELG option in receiving reaches and downstream reaches (see the EA; U.S. EPA, 2015a) to criteria established by EPA for protection of human health. EPA compared in

- water concentrations

of arsenic , copper, nickel, selenium, thallium, and zinc to EPA’s national recommended water quality criteria protective of human health used by sta tes and tribes (U.S. EPA, 2012b). Pollutant concentrations in excess of these values indicate potential risks to human health. For another four steam electric pollutants (cadmium, chromium, lead, and mercury) for which there are no recommended criteria, EP A instead compared concentrations to MCLs (U.S. EPA, 2012a).

Table 3

- 14 shows the results of this analysis. EPA estimates that in

- stream concentratio ns of steam electric pollutants exceed human health criteria or MCLs for at least one pollutant in 3,673 reaches nationwide as a result of baseline steam electric pollutant di scharges. EPA expects that the final BAT/PSES ( Option D ) will

reduce the occurrence of concentrations in excess of human health

- based criteria for 1,973 of the reaches

, and

eliminate all e xceedances for 1, 870 of those reaches

. While Option D reduces concentrations in the remaining

103 reaches relative to the baseline level s , and thereby improves water quality in these reaches , the reductions are not sufficient to bring concentrations below the human health criteria o r MCLs.

Table 3

- 14 : Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric
Pollutants

Regulatory Option Number of Reaches with

Steam Electric Pollutant a Concentrations Exceeding
Human Health Criteria for at
Least One Pollutant Number of Reaches with Improved Water Quality,
Relative to Baseline

Number of Reaches with
Reduced Number of
Exceedances b Number of Reaches with
All Exceedances
Eliminated

Baseline 3,673

-- -- Option A 3,128 631 545
Option B 3,128 633 545
Option C 2,270 1, 540 1, 403
Option D 1,803 1,973 1, 870
Option E 1,625 2, 088 2, 048

Source: U.S. EPA Analysis, 2015

a. Pollutants includ e arsenic, copper, nickel, selenium, thallium, zinc, cadmium, chromium, lead, and mercury.

b. T he number of reaches with exceedances reduced includes those reaches where all exceedances are eliminated.



### Implications of Revised Steam Electric Plant Loading Estimates 3.9

As described in Section 1.4.3 , EPA revised its estimates of pollutant loadings in steam electric power plant discharges after completing the benefit analyses. The revisions affect baseline discharges of the threeBenefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 21 

### pollutan ts explicitly modeled to quantify and monetize human health benefit s in this Chapter : lead, mercury,

and arsenic. Table 3

- 15 summarizes changes in loading estimates between the original loads used in estimating benefits in this Chapter and the revised loads. The table shows total industry loads of lead, mercury

and arsenic in the baseline and under the final ELGs ( Option D ) , as well as pollutant removals achieved by

the final ELGs

.

Table 3

- 15 : Estimated Aggregate Changes in Pollutant Loadings for Lead, Mercury and Arsenic (Pounds per Year)

.

Pollutant Baseline Option D Removal under Option D

Original
Values Revised

Values %

Change Original

Values Revised

Values %

Change Original

Values Revised

Values %

Change

Lead 14,588 7,674

- 47% 350 331

- 5% 14,238 7,343

- 48%

Mercury 1,176 992

- 16% 31 31

- 1% 1,145 961

- 16%

Arsenic 22,219 20,138

- 9% 1,483 1,480 0% 20,737 18,658

- 10%

Source: U.S. EPA Analysis, 2015



### As shown in Table 3

- 15 , the revisions mainly reduced the baseline loadings of lead, mercury and arsenic

, and

had a smaller relative impact on loadings post

- compliance

. Because baseline pollutant loads are lower tha n previously estimated

, EPA expects the adverse health effects estimated in the baseline to also be lower than previously calculated, which correspondingly reduces the incidence of adverse health effects avoided by implementing the final ELGs and therefore expected improvements from the final ELG s

.
For several reasons

— notably the fact that revisions do not affect all plants equally, exposure also depends on other point sources, and many model functions are not linear

— it would be inappropriate to simply s ca le

the monetized benefits based on the aggregate changes in loadings. While EPA cannot readily recalculate the benefits, t he direction and magnitude of the change in pollutant removals in Table 3

- 15 indicate that it is likely that revisions to the loadings affect benefit estimates and that new benefit estimates would be lower than presented in this Chapter. The magnitude of this reduction is, however, uncertain.



### Limitations and Uncertainties 3.10

The analysis presented in this chapter does not include all possible human health benefits associated with
post

- compliance reductions in pollutant discharges due to lac k of data on a dose

- response relationship between ingestion rates and potential adverse health effects. Therefore, the total quantified human health benefits included in this analysis represent only a subset of the potential health benefits expected to res ult

from the final rule

. Additionally, the methodologies and data used in the analysis of health benefits associated with reduced incidences of adverse health outcomes due to consumption of fish contaminated with steam electric pollutants

involve limitati ons and uncertainties , that add to the limitations and uncertainties inherited from the EA analysis and data (see U.S. EPA, 2015a)

. Table 3

- 16 summari zes the limitations and uncertainties and indicates the direction of the potential bias.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 22 Table 3

- 16 : Limitations and Uncertainties in the Analysis of Human Health Benefits

Uncertainty/Assumption Effect on Benefits
Estimate Notes

The analysis is based loadings that were subsequently revised by EPA Overestimate Revised loadings of lead, mercury, and arsenic are lower than the loadings used to estimate benefits. The changes indicate that the incidence of adverse health effects are likely lower in the baseline, and therefore that improvements due to the ELGs may be correspondingly lower. The analysis does not consider the suitability of alternate fishing
sites. Uncertain Estimating the number of anglers fi shing on receiving
and downstream reaches based on the ratio of reach length to the total number of reach miles within the
same 50

- mile buffer area recognizes the effects of the quantity of competing fishing opportunities on the likelihood of fishing a giv en reach, but does not account for the differential quality of fishing sites. If the quality of substitute sites is distinctly worse or better ( e.g. , some sites have better access or designated fishing areas), the estimated benefits may be overstated

or un derstated. Anglers are assumed to be distributed evenly (over the reach miles) over all available fishing
sites within the 50

- mile travel
distance. Uncertain EPA assumed that all anglers travel up to 50 miles and
distribute their visits over all fishable sites within the area. In fact, recreational anglers may have preferred sites ( e.g. , a site located closer to their home) that they visit more frequently. The characteristics of these sites, notably ambient water concentrations and fishing advisories, aff

ects exposure to pollutants, but EPA does not have data to support a more detailed analysis of fishing visits. The impact of the assumption on
benefit estimates is uncertain since fewer/more anglers may be exposed to higher/lower fish tissue
concentrations than assumed by EPA in the analysis.

The number of subsistence fishers was assumed to equal 5 percent of the total number of anglers fishing
the affected reaches. Uncertain The magnitude of subsistence fishing in the United
States or individual states is not known. Assuming
5 percent may understate or overstate the number of potentially affected subsistence fishers (and their households) overall, and ignores potential variability in subsistence rates across racial/ethnic groups.

EPA used a CSF for arseni c of 1.5 cases per mg/kg BW/day based on skin cancer only

. Uncertain This is the current IRIS value and was based on incidences of skin cancer. EPA is currently revising its
cancer assessment of arsenic to reflect new data on
internal cancers.

There is a linear 0.18 point IQ loss for each 1 ppm increase in maternal hair mercury. Uncertain This dose

- response function may over

- or underestimate IQ impacts arising from mercury exposure if a linear function is not the best representation of the relationship between maternal body burden and IQ losses.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3

- 23 Table 3

- 16 : Limitations and Uncertainties in the Analysis of Human Health Benefits

Uncertainty/Assumption Effect on Benefits
Estimate Notes

For the mercury

- and lead

- related benefits analyses, EPA assumed that IQ losses are an appropriate endpoint for quantifying adverse cognitive and neurological effects resulting from childhood or in

utero exposur es to lead and mercury (respectively). Underestimate IQ may not be the most sensitive endpoint. Additionally, there are deficits in cognitive abilities that are not reflected in IQ scores, including acquisition and retention of information presented verbal ly and many motor skills (U.S. EPA, 2005). To the extent that these impacts create disadvantages for children exposed to mercury at current exposure levels or result in the absence of (or independent from) measurable IQ losses, this analysis may underestim ate the benefits of the ELGs of reduced lead and mercury exposure. The IEUBK model processes daily intake from “alternative sources” to 2 decimal places (ug/day). Underestimate Since the intakes are very small, some variation is missed by using the model ( i.e. , it does not capture very small changes). EPA did not quantify the benefits
associated with reduced adult exposure to mercury. Underestimate The scientific literature suggests that exposure to mercury may have significant adverse health effects for adults; if measurable effects a re occurring at current exposure levels, excluding the benefits of reduced adult exposure results in an underestimate of benefits.

EPA assumed c onstant body mass
for all males and females in the

a dult Pb and As analyses Uncertain Male and female body mass estimates used in analyses
are national estimates obtained from the CDC. The
extent to which CB G

- specific body masses varies will
have an unknown effect on benefits Uniform application of data from
national life tables in a dult Pb and As analyses Uncertain By applying national averages, EPA assumes that mortality rates of all modeled cohorts (specific to location, fishing cohort, etc.) do not differ from the national mortality experience. EPA also assumes that

age

- specif

ic mortality rates are constant through time.

CVD

- related benefits of reduced Pb exposure are not tracked for individuals younger than age 20 in
2014 Underestimate Benefits accruing to younger individuals are not tracked because the Leggett + model is unable to model PbBs in youth and adolescents.

EPA assumed n o cessation lag for
PbB effects Uncertain EPA assumed no time lag between changes in PbB and risk reductions. The accuracy of this assumption is
unknown.

Concentration

- response functions used in adult Pb and As analyses Uncertain The overall applicability of the concentration

- response functions are subject to uncertainty. Generalized
concentration

- response functions are used to model

age

- specific hazard ratios.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 1 

## 4 Non market Benefits from Water Quality Improvements

As discussed in the EA document (U.S. EPA, 2015 a ) , heavy metals, nutrients , and other pollutants discharged by steam electric power plant s have a wide range of effects on water resources located in the vicinity and

downstream from the plants. These environmental changes affect environmen tal goods and services valued by humans, including recreation; commercial fishing; public and private property ownership; navigation; water supply and use; and existence services such as aquatic life, wildlife, and habitat designated uses. Some
environment al goods and services ( e.g. , commercially caught fish ) are traded in markets, and thus their value can be directly observed. Other environmental goods and services ( e.g. , recreation and support of aquatic life)

cannot be bought or sold directly and thus do not have observable market values. These second types of

e nvironmental goods and services are classified as “nonmarket”

. The expected changes in the nonmarket values of the water resources affected by the final ELG s (hereafter nonmarket benefits) are addi tive to the market benefits ( e.g. , avoided costs of producing various market goods and services) and benefits from improved groundwater quality estimated in other chapters (Freeman

, 2003 )

. EPA’s approach to estimating the nonmarket benefits from water quality improvements resulting from the

final ELG s involves 1) characterizing baseline and post

- compliance water quality using a water quality index

a nd 2) monetizing changes in the nonmarket v alue of affected water resources attributable to the final ELGs using a meta

- analysis of surface water valuation studies that provide data on the public’s WTP for water quality improvements. The analysis accounts for improvements in water quality resulting from changes in

nutrient , sediment

, and metals concentrations in reaches affected by discharges from steam electric power plant s.



### Water Quality 4.1

To link water quality changes from reduced metal

, nutrient and sediment discharges to effects on human uses

an d support for aquatic and terrestrial species habitat, EPA used a water quality index (WQI) which translates water quality measurements, gathered for multiple parameters that are indicative of various aspects of water quality, into a single numerical indic ator.

The WQI provides the link between specific pollutant levels, as reflected in individual index parameters ( e.g. , dissolved oxygen (DO) concentrations), and the presence of aquatic species and suitability for particular uses

. The WQI value, which is measured on a scale from 0 to 100, reflects varying water quality, with 0 for poor quality and 100 for excellent.

The WQI used in this analysis is the same as was used for the proposed rule analysis. 32 

### The WQI includes the six para meters of the WQI previously used for the Final Construction and Development Rule

– DO, biochemical oxygen demand (BOD), fecal coliform (FC), total nitrogen (TN), total phosphorus (TP) , and total suspended solids (TSS)

– and one additional subindex for met als, for a total of seven parameters

. 33

, 34 

### As

32 The WQI modifies the WQI used by EP A in the Environmental Impact and Benefits Assessment for Final Effluent Guidelines and Standards for the Construction and Development Category (also referred to as the C&D rule; U.S. EPA, 2009 a ), which builds on McClelland (1974) and on the methodology developed by Dunnette (1979) and subsequently updated by Cude (2001) to better account for spatial and morphologic variability in the
natural characteristics of streams. A more detailed discussion of the history of the WQI framework is found in Chapter 10 of the C&D report (U.S. EPA, 2009b).

33 EPA modified the WQI for freshwater waterbodies from the C&D analysis to include metals. This was done by incorporating elements of the WQI developed by th e Canadian Council of Ministers of the EnvironmentBenefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 2 

### discussed in Chapter 4 of the EA document (U.S. EPA, 2015 a ), lotic systems such as rivers and streams, account for the vast majority ( 82 percent) of water bodies receiving direct discharges from steam electric power plants , with 183 of the 222 immediately receiving waters . Lentic freshwater systems such as lakes (with the exception of the Great Lakes) , ponds, and reservoirs, represent 12 p ercent, the Great Lakes another

5 percent, and estuaries the remaining 1 percent. EPA focused the national level model on rivers/streams and lakes/ponds/reservoirs as the most common affected waterbodies. Because of the specific hydrodynamics and

scale of the analysis required to appropriately model and quantify receiving water concentrations in the Great Lakes and estuary systems , EPA looked at the changes in pollutant loadings and impacts to these systems in

selected case studies (see EA document for deta ils; U.S. EPA, 2015a) . EPA did not quantify the benefits to these systems, leading to an underestimate of the benefits discussed in this chapter.

4.1.1 WQI Calculation

Implementing the WQI methodology involves three key steps: 1) obtaining water quality levels for each of

seven parameter s included in the WQI; 2) transforming parameter levels to subindex values expressed on a common scale; and 3) aggregating the individual parameter subindices to obtain an overall WQI value that reflects waterbody conditions acr oss the seven parameters. Th ese steps are repeated to calculate the WQI

value for the baseline ( i.e. , in the absence of the final ELGs ), and for each analyzed regulatory option.

The first step in the implementation of the WQI involves obtaining water quali ty levels for each parameter

, and for each waterbody , under both baseline conditions and post

- compliance conditions ( see Section 0 ). Some
parameter levels are field measurements while others are modeled values.

The second step involves transforming the parameter measurements into subindex values that express water quality conditions on a common scale of 0 to 100. EPA used the subindex transformation c urves developed by Dunnette (1979) and Cude (2001) for the Oregon WQI for BOD, DO, and FC. For TSS, TN, and TP concentrations, EPA adapted the approach developed by Cude (2001) to account for the wide range of natural or background nutrient and sediment co ncentrations that result from the variability in geologic and other region

- specific conditions, and to reflect the national context of the analysis. TSS, TN, and TP subindex curves were developed for each Level III ecoregion (U

. S

. EPA

, 2009 a ) using baselin e TSS, TN, and TP

concentrations calculated in SPARROW at the E2RF1 reach level. 35

, 36

, 37 

### For each of the 85 Level III ecoregions intersected by the E2RF1 reach network, EPA derived the transformation curves by assigning a

(CCME) wherein the index values are calculated based on the scope, frequency, and amplitude of exceedances of specified numeric thresholds (CCME, 2001).

34 EPA analyzed changes to water quality resulting f rom the implementation of the final ELG s on receiving freshwater reaches. While steam electric plants also discharge to estuarine and coastal reaches, EPA did not estimate benefits from reducing pollutant loadings to these waterbodies due to the relatively small changes in concentrations expected.

35 The SPARROW (SPAtially Referenced Regressions On Watershed attributes) model was developed by the United States Geological Survey (USGS) for the regional interpretation of water

- quality monitoring data. The
mod el relates in

- stream water

- quality measurements to spatially referenced characteristics of watersheds, including contaminant sources and factors influencing terrestrial and aquatic transport. SPARROW empirically estimates the origin and fate of contaminant s in river networks and quantifies uncertainties in model predictions. More information on SPARROW can be found at http://water.usgs.gov/nawqa/sparrow/FAQs/faq.html#1

36 EPA’s E2RF1 (Enhanced River File Version 2.0) is a digital stream networks used in SPA RROW models. This dataset extends over the continental United States and includes approximately 62,000 stream reaches.

37 Following the approach EPA used for the C&D analysis, t he selected data exclude outlier TSS concentration s , defined as values that exc eed the 95 th percentile based on the universe of all E2RF1 reaches modeled in SPARROW (U.S. EPA, 2009a)

. In the C& D analysis, the USGS and EPA had determined that these outlier values corresponded to headwater reaches and were an artifact of the model rather than expected concentrations.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

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### score of 100 to the 25th percentile of the reach

- level TSS concentrations in the ecoregion ( i.e. , using the 25th percentile as a proxy for “reference” concentrations), and a score of 70 to the median concentration. An exponential equation was then fitted to the two concentration points follo wing the approach used in Cude (2001)

. For this analysis, EPA also used a metals

- specific subindex curve based on the number of Ambient Water Quality Criteria (AWQC) exceedances for metals in each waterbody. National f reshwater chronic AWQC

values are ava ilable for arsenic, cadmium, chromium, lead, mercury, nickel, selenium, and zinc. T o develop this subindex curve, EPA used an approach developed by the Canadian Council of Ministers of the

Environment (CCME 2001). The CCME water quality index is based on t hree attributes of water quality that relate to water quality objectives: scope (number of monitored parameters that exceed water quality standard or toxicological benchmark); frequency (number of individual measurements that do not meet objectives,

relati ve to the total number of measurements for the time period of interest) and amplitude ( i.e. , amount by

which measured values exceed the standards or benchmarks). Following the CCME approach , EPA ’s metal

subindex considers the number of parameters with exceed a nces of the relevant water quality criterion

. With regards to frequency, EPA modeled long

- term annual average concentrations in ambient water (see Section 0

and Appendix D for details), and therefore any exceedance of an AWQC may indicate that ambient concentrations exceed AWQCs most of the time (assumed to be 100 percent of the time). EPA did not consider amplitude, because if the annual aver age concentration exceeds the chronic AWQC then the water is impaired for that constituent and the level of exceed a nce is of secondary concern

. Using this approach, t he

subindex curve for metals assigns the lowest subindex score of 0 to waters where exceedances are observed for all eight metals analyzed , and a maximum score of 100 to waters where there are no exceedances

. Intermediate values are distributed evenly between 0 and 100.

Table 4

- 1 presents parameter

- specific functions used for transforming water quality data into water quality subindices for freshwater waterbodies for the six traditional pollutants. Table 4

- 2 presents the subindex values for metals. The equation parameters for each of the 85 ecoregion

- specific TSS, TN, and TP subindex curves are provided in Appendix G

.

Table 4

- 1 : Freshwater Water Quality Subindices

Parameter Concentrations Concentration
Unit Subindex

Dissolved Oxygen (DO)

DO saturation ≤100%

DO DO ≤ 3.3 m g/L 10

DO 3.3 < DO < 10.5 m g/L

- 80.29+31.88 × DO

- 1.401 × DO 2

DO DO ≥ 10.5 m g/L 100
100% < DO saturation ≤ 275%

DO N/A m g/L 100 × exp((DOsat

- 100) ×

- 1.197 ×10 - 2 )

275% < DO saturation

DO N/A m g/L 10
Fecal Coliform (FC)

FC FC > 1,600 Lbs/100 mL 10

FC 50 < FC ≤ 1,600 Lbs/100 mL 98 × exp ( (FC

- 50) ×

- 9.9178 ×10 - 4 )

FC FC ≤ 50 Lbs/100 mL 98Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 4 Table 4

- 1 : Freshwater Water Quality Subindices

Parameter Concentrations Concentration
Unit Subindex

Total Nitrogen (TN) a

TN TN > TN 10 m g/L 10

TN TN 100 < TN ≤ TN 10 m g/L a × exp(TN × b); where a and b are ecoregion

specific values in Appendix G

TN TN ≤ TN 100 m g/L 100
Total Phosphorus (TP) b

TP TP > TP 10 m g/L 10

TP TP 100 < TP ≤ TP 10 m g/L a × exp(TP × b); where a and b are ecoregion

specific values in Appendix G

TP TP ≤ TP 100 m g/L 100
Total Suspended Solids (TSS) c

TSS TSS > TSS 10 m g/L 10

TSS TSS 100 < TSS ≤ TSS 10 m g/L a × exp(TSS × b); where a and b are ecoregion

specific values in Appendix G

TSS ≤ TSS 100 m g/L 100
Biochemical Oxygen Demand, 5

- day (BOD)

BOD BOD > 8 m g/L 10

BOD BOD ≤ 8 m g/L 100 × exp(BOD ×

- 0.1993)

a. TN10 and TN100 are ecoregion

- specific TN concentration values that correspond to subindex scores of 10 and 100, respectively.
Use of 10 and 100 for the lower and upper bound s of the WQI subindex score follow the approach in Cude (2001)

b. TP10 and TP100 are ecoregion

- specific TP concentration values that correspond to subindex scores of 10 and 100, respectively. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)

c. TSS10 and TSS100 are ecoregion

- s pecific TSS concentration values that correspond to subindex scores of 10 and 100, respectively

. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)

Source: EPA analysis using methodology in Cude (2 001)

.

Table 4

- 2 : Freshwater Water Quality Subindex for Heavy Metals

Number of Metals with AWQC

Exceedances Subindex

0 100.0

1 87.5

2 75.0

3 62.5

4 50.0

5 37.5

6 25.0

7 12.5

8 0.0



### The final step in implementing the WQI involves combining the individual parameter subindices into a single WQI value that reflects the overall water quality across the parameters. EPA calculated the overall WQI for a given reach using a geometric mean function and assig ned all WQ parameters an equal weight of 0.143 (1/7 thBenefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 5 

### of the overall score)

. Unweighted scores for individual metrics of a WQI ha ve previously been used in Cude ( 2001 ) , CCME ( 2001 ), and Carruthers and Wazniak (2003).

Equation 4

- 1 presents EPA’s calculation of the overall WQI score.

Equation 4

- 1

. WQI

r 

### = the multiplicative water quality index (f rom 0 to 100) for reach r

Q i 

### = the water quality subindex measure for parameter i

W i 

### = the weight of the i

- th parameter (0.143)

n = the number of parameters ( i.e. , seven)

Once an overall WQI value is calculated, it can be related to suitability for potential uses. Vaughan (1986) developed a water quality ladder (WQL) that can be used to indicate whether water quality is suitable for various human uses ( i.e. , boating, rough fishing, game fishing, swimming, and drinking without treatment). Vaughan iden tified “minimally acceptable parameter concentration levels” for each of the five potential uses. Vaughan used a scale of zero to 10 instead of the WQI scale of zero to 100 to classify water quality based on its suitability for potential uses. Therefore, t he WQI value corresponding to a given water quality use classification equals the WQL value multiplied by 10. Table 4

- 3 presents water use classificati ons and the corresponding WQL and WQI values.

Table 4

- 3 : Water Quality Classifications

Water Quality Classification WQL Value WQI Value ... drinking without treatment 9.5 95 ... swimming 7.0 70 ... game fishing 5.0 50 ... rough fishing 4.5 45 ... boating 2.5 25

Source: Vaughan (198 6)



### 4.1.2 Sources of Data on Ambient Water Quality

EPA used the following data sources to obtain ambient concentrations for the seven parameters included in the WQI:

 Outputs from USGS’s SPARROW models provided baseline and post

- compliance concentrations of total nitrogen

, total phosphorus, and total suspended solids. These calibrated national models are the same models previously used by EPA in the C&D rule analysis (U.S. EPA, 2009c). See Appendix D

for further details

.  EPA estimated baseline and post

- compliance metal concentrations using the water quality model component of EPA’s Risk

- Screening Environmental Indicators (RSEI) model (U.S. EPA, 2012c).

EPA used estimates of metal loadings discharged from steam electric plants to directly receiving

reaches under the baseline and the five analyzed options (see EA for discussion of directly receiving reaches; U.S. EPA, 2015a ) . EPA input the loadings from steam electric plant s in the R SEI model to estimate the long

- term average concentrations in directly receiving and downstream reaches. TheseBenefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

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### loadings are used to complement discharges already included in RSEI for other facilities that report to

TRI. See Appendix D for details. The numb er of exceedances per waterbody (each reach) was calculated by comparing baseline and post

- compliance concentrations with EPA’s freshwater chronic aquatic life criteria values for each metal

. 38 

### If the concentration was greater than the aquatic life

criteria value for a given metal

, EPA categorized the waterbody as having an AWQC exceedance for

that metal. EPA then summed t he total number of metals with AWQC exceedances (up to eight) for each waterbody under the baseline and under each analyzed regulatory opt ion

.  The USGS National Water Information System (NWIS) provided concentration data for three parameters: 1) fecal coliform, 2) dissolved oxygen, and 3) biochemical oxygen demand. 39 

### EPA’s Storage and Retrieval (STORET) data warehouse provided additional da ta on fecal coliform counts and biochemical oxygen demand whe re NWIS data was unavailable (U

. S

. EPA

, 2008 a ). 40



### Note that the concentration data input into the WQI typically represent long

- term average concentrations.

Table 4

- 4 summarizes the water quality modeling data used for estimating baseline and post

- compliance

metal , nutrient and sediment concentrations for reaches directly receiving steam electr ic plant discharge and

for downstream reaches.

Table 4

- 4 : Water Quality Modeling Data used in Calculating the Baseline and Policy Metal

, Nutrient and Sediment Concentrations

Reach Input Data Water Quality Model Model Output

Reaches directly receiving steam electric plant discharge and
downstream reaches (18,622
NHD reaches total) Baseline and p olicy metal l oading s

to inland r eaches d irectly r eceiving

s team e lectric plant d ischarges (U.S. EPA, 2015a). Metal loadings from other TRI dischargers in 201 2

. Concentrations m odel ed in
RSEI In

- steam metal
concentrations at the NHD
level

Baseline and p olicy nutrient and
sediment l oading s to inland

r eaches d irectly r eceiving s team electric plant d ischarges (U.S. EPA, 2015a)
Baseline values for other nutrient
and sediment sources in inland
reaches ( e.g. , urban, agricultural and forested land, animal agriculture (nutrients), streambed (sediment), atmospheric deposition (nitrogen)). Concentrations m odel ed in
SPARROW In

- stream nutrient and
sediment concentrations at
the E2RF1 level

38 RSEI utilizes the USGS’s National Hydrology Dataset (NHD) which defines a rea ch as a continuous piece of surface water with similar hydrologic characteristics. In the NHD each reach is assigned a reach code; a reach may be composed of a single feature, like a lake or isolated stream, but reaches may also be composed of a
number of contiguous features. Each reach code occurs only once throughout the nation and once assigned, a reach code is permanently associated with its reach. If the reach is deleted, its reach code is retired. The NHD reaches in this analysis range from 0.003 mile s to 9.11 miles in length.

39 USGS’s NWIS dataset provides information on the occurrence, quantity, quality, distribution, and movement of surface and underground waters based on data collected at approximately 1.5 million sites in all 50 States, the
Distr ict of Columbia, and U.S. territories. More information on NWIS can be found at http://waterdata.usgs.gov/nwis/

40 EPA’s STORET (STOrage and RETrieval) Data Warehouse is a repository for water quality, biolog ical, and physical water monitoring data. More information can be found at http://www.epa.gov/storet/Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

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- 7 

### EPA used two different reach classification frameworks to assess in

- stream water quality under the baseline

and each of the regulatory options : the National Hydrography Dataset (NHD) network and the USGS’s

E nhanced River File 1 (E2RF1). M etal concentrations were estimated for reaches indexed to the NHD

network. In contrast , the SPARROW, NWIS, and STORET data are available for reaches indexed to the E2RF1 network and to USGS’s Hydrological Unit Code (HUC) watersheds. The WQI and benefits are ultimately calculated at the resolution of NHD reaches, but with adjustments made to data available only at the E2RF1 level to reflect differences in spatial scale. Thus, t o reconcile the two levels of resolution, EPA mapped all modeled reaches from the E2RF1 to the NHD network using GIS and assigned the closest E2RF1

ID to each NHD reach. Figure 4

- 1 illustrates the differences in scale between the E2RF1 network and the

NHD network.

Figure 4

- 1 : Comparison between the NHD and E2RF1 Network in a Single Watershed

.



### The water quality analysis included a tota l of 18,622 NHD reaches, totaling 27,421 miles, that are potentially affected by steam electric plants under baseline conditions. Baseline concentrations for all WQI parameters

were available for over 9 5 percent of the potentially affected NHD reaches . EPA used a successive average approach to address the data gaps in WQI parameters not described above ( i.e. , DO, BOD, fecal coliform) in the remaining inland reaches. The approach involves assigning the av erage of ambient concentrations for a WQI parameter within a hydrologic unit to reaches within the same hydrologic unit with missing data

, and progressively expanding the geographical scope of the hydrologic unit (HUC8, HUC6, HUC4, and HUC2) toBenefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

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- 8 

### fill in all missing data. 41 

### This approach assumes that reaches located in the same watershed generally share similar characteristics. Using this estimation approach, EPA compile d baseline water quality data for all analyzed NHD reaches. Table 4

- 5 summarizes the data sources used to estimate baseline and post

compliance values by water quality parameters.

Table 4

- 5 : Water Q uality D ata used in C alculating the B aseline and Policy WQI

Parameter Baseline value Post

- compliance value

TN From SPARROW output (baseline run)

matched to NHD level From SPARROW output ( regulatory option run)
matched to NHD level

TP From SPARROW output (baseline run)

matched to NHD level From SPARROW output ( regulatory option run)
matched to NHD level

TSS From SPARROW output (baseline run)

matched to NHD level From SPARROW output ( regulatory option run)
matched to NHD level

DO Baseline value at the E2RF1 level matched to

NHD level a No change. Regulatory option value equal
baseline value

BOD Baseline value at the E2RF1 level matched to

NHD level a No change. Regulatory option value equal
baseline value

Fecal Coliform Baseline value at the E2RF1 level matched to

NHD level a No change. Regulatory option value equal
baseline value

Metals Baseline exceedances at NHD level based on

RSEI model outputs Regulatory option exceedances at NHD level based on RSEI model outputs

.

a. Values based on STORET and NWIS data, complemented with data available for progressively larger geographical units (HUC8, HUC6, HUC4, and HUC2), as needed to fill in all missing data.



### 4.1.3 Baseline WQI

The water quality analysis included a total of 18,622 NHD reaches that are potentially affected by s team

e lectric power generating plants under baseline conditions. Based on the estimated WQI value under the

baseline scenario (WQI

- BL) , EPA categorized each of these 18,622 NHD reach es using five WQI ranges (WQI < 25, 25≤WQI< 45

, 4 5≤WQI< 50 , 50≤WQI<70, and 7 0≤WQI). WQI values of less than 25 indicate that water is not suitable for boating (the recreational use with the lowest required WQI), whereas WQI values

g reater than 7 0 indicate that waters are swimmable ( the recreational use with the highest required WQI ). 42

41 Hydrologic Unit Codes (HUCs) are cataloguing numbers that uniquely identify hydrologic features such as
surf ace drainage basins. The HUCs consist of 8 to 14 digits, with each set of 2 digits giving more specific information about the hydrologic feature. The first pair of values designate the region (of which there are 21), the next pair the subregion (total of 2 22), the third pair the basin or cataloguing unit (total of 352), and the fourth pair the subbasin, or accounting unit (total of 2,262) (USGS, 2007 a ). Digits after the first eight offer more detailed information, but are not always available for all waters . In this discussion, a HUC level refers to a set of waters that have that number of HUC digits in common. For example, the HUC6 level includes all reaches for which the first six digits of their HUC are the same.

42 EPA did not separately categorize water s where the WQI was greater than or equal to 90 (drinkable water), because the analysis did not examine other pollutants that may impact the drinkability of a water.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

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- 9 Table 4

- 6 : Percentage of Potentially Affected Inland Reach Miles by WQI Classification: Baseline Scenari o

Water Quality
Classification Baseline WQ Number of
Reaches Percent of

Potentially
Affected
Reaches Number of

Reach Miles Percent of

Potentially
Affected Reach
Miles

Unusable WQI<25 0 0.0% 0 0.0%

Suitable for Boating 25≤WQI<45 677 3.6% 781 2.8%

Suitable for Rough Fishing 45≤WQI<50 649 3.5% 722 2.6%

Suitable for Game Fishing 50≤WQI<70 6,771 36.4% 10,187 37.2%

Suitable for Swimming 70≤WQI 10,525 56.5% 15,731 57.4%

Total 18,622 100.0% 27,421 100.0%

Source: U.S. EPA Analysis, 2015



### 4.1.4 Estimated Changes in Water Quality ( ∆ WQI) from the ELG Rule

To estimate benefits of water quality improvements expected to result from the final ELGs , EPA calculated the WQI for each analyzed regulatory option. As discussed in Section 0

, EPA estimated changes in ambient

concentrations of TN

, TP and TSS using the USGS ’s SPARROW model and metal concentrations using the water quality component of the RSE I model. In calculating the post

- compliance WQI (WQI

- PC) , the Agency used option

- specific metal, TN, TP , and TSS concentrations. The estimated post

- compliance metal

, nutrient

, and sediment concentration s reflect the expected reduction in pollutant discharg es under each analyzed regulatory option. Although the final ELGs would also improve levels of other WQI parameters , such as BOD

and DO , the se other parameters were held constant in this analysis for all regulatory options , due to data

limitations.

The difference in the WQI between baseline conditions and a given regulatory option (hereafter denoted as ∆WQI) is a measure of the change in water quality attributable to the final ELGs

. Table 4

- 7 presents water quality changes for all reaches. The water quality analysis included a total of 18,622 NHD reaches that are potentially affected by s team e lectric power generating plants under baseline conditio ns. Of these potentially

affected reaches

, 104 have fewer AWQC exceedances for metals under O ption D. The combination of these

reaches and reaches with reductions in nutrient or sediment concentrations modeled in SPARROW amount to

a total of 13,229 unique NHD reaches , or 71 percent of reaches with ∆WQI > 0 under Option D, comprising a

total of 19,573 reach miles , or 71 percent of all reach miles . Among other options analyzed , the largest

number of reaches affected by the ELGs occurs under Option E. Under Option E, there are 13,537 inland reaches with ∆WQI > 0, totaling 20,300 miles . Note that the changes are based on annual average concentrations and represent changes expected after compliance with the final ELGs. As discussed in Section

1.5.3 , the changes are assumed to start in 2021 and remain constant thereafter over the period of analysis through 2042.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

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- 10 Table 4

- 7 : Water Quality Improvements from Final ELGs in All Benefiting Reaches

Change i n WQI Number of Inland
Reaches Percentage of

Potentially
Affected Inland
Reaches
(18,622 Reaches) Reach Miles Percentage of Potentially Affected
Inland Reach Miles
(27,421 Miles)

Option A
ΔWQI = 0 13,843 74.34% 19,244 70.18% 0 < ΔWQI < 0.1 4,679 25.13% 7,998 29.17% 0.1 ≤ ΔWQI < 1 48 0.26% 99 0.36% 1 ≤ ΔWQI < 5 43 0.23% 72 0.26% 5 ≤ ΔWQI < 10 5 0.03% 3 0.01% 10 ≤ ΔWQI 4 0.02% 5 0.02%

Total 18,622 100.00% 27,421 100.00% Option B
ΔWQI = 0 9,928 53.31% 13,991 51.02% 0 < ΔWQI < 0.1 7,148 38.38% 11,115 40.53% 0.1 ≤ ΔWQI < 1 1,428 7.67% 2,165 7.89% 1 ≤ ΔWQI < 5 100 0.54% 121 0.44% 5 ≤ ΔWQI < 10 11 0.06% 10 0.04% 10 ≤ ΔWQI 7 0.04% 20 0.07%

Total 18,622 100.00% 27,421 100.00% Option C
ΔWQI = 0 7,922 42.54% 11,005 40.13%

0 < ΔWQI < 0.1 8,519 45.75% 13,122 47.85%

0.1 < ΔWQI < 1 2,011 10.80% 3,053 11.13%

1 ≤ ΔWQI < 5 147 0.79% 202 0.74%

5 ≤ ΔWQI < 10 14 0.08% 19 0.07%

10 ≤ ΔWQI 9 0.05% 21 0.08%

Total 18,622 100.00% 27,421 100.00% Option D
ΔWQI = 0 5,393 28.96% 7,848 28.62%

0 < ΔWQI < 0.1 10,915 58.61% 16,066 58.59%

0.1 < ΔWQI < 1 2,111 11.34% 3,223 11.76%

1 ≤ ΔWQI < 5 178 0.96% 242 0.88%

5 ≤ ΔWQI < 10 13 0.07% 18 0.07%

10 ≤ ΔWQI 12 0.06% 23 0.09%

Total 18,622 100.00% 27,421 100.00%Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 11 Table 4

- 7 : Water Quality Improvements from Final ELGs in All Benefiting Reaches

Change i n WQI Number of Inland
Reaches Percentage of

Potentially
Affected Inland
Reaches
(18,622 Reaches) Reach Miles Percentage of Potentially Affected
Inland Reach Miles
(27,421 Miles)

Option E
ΔWQI = 0 5,085 27.31% 7,121 25.97%

0 < ΔWQI < 0.1 11,218 60.24% 16,777 61.18%

0.1 < ΔWQI < 1 2,106 11.31% 3,215 11.73%

1 ≤ ΔWQI < 5 187 1.00% 262 0.95%

5 ≤ ΔWQI < 10 13 0.07% 18 0.07%

10 ≤ ΔWQI 13 0.07% 28 0.10%

Total 18,622 100.00% 27,421 100.00%

Source: U.S. EPA Analysis, 2015



### Methodology for Estimating WTP for Water Quality Improvements 4.2

To estimate the nonmarket benefits of the water quality improvements resulting from the final ELGs , EPA

used results from a meta

- analysis of stated preference studies summarized below and described in greater

detail in Appendix H

. This meta

- analysis is a revised version of the 2009 meta

- analysis used in the benefit

cost analysis of the proposed ELGs (U.S. EPA, 2013a)

. As described in the EPA report (U.S. EPA 2015e),

EPA made a number of improvements to the meta

- regression model. In particular, the revised meta

- model

satisfies the adding

- up condition, a theore tically desirable property. 43 

### This condition ensures t hat if the model were used to estimate WTP for the cumulative water quality change resulting from a number of CWA regulations, the benefits estimates would be equal to the sum of benefits from using the model to estimate WTP for water quality changes separ ately for each rule. EPA used the revised meta

- analysis to estimate the

sum of use and non

- use values for water quality improvements resulting from the final ELGs

. In addition,

EPA views the meta

- analysis here as a work in progress and the current m odel is not the final product. Nonetheless, EPA views the results reported here as a clear improvement over the analysis used at proposal.

The meta

- analysis is based on a meta

- dataset of 51 stated preference studies , published between 1985 and

2011

. Each of these studies used a stated preference approach to elicit survey respondents’ willingness to pay for water quality improvements

. EPA entered values for all existing variables in the meta

- data for each study

included in the meta

- data set

. In addition to the study variables included in the 2009 meta

- data, EPA developed and coded new variables (for all studies) that address gaps in the 2009 meta

- data. While the 2009

meta

- data largely relied on binary variables to distinguish broad categories of affected resources ( e.g. , single

vs

. multiple riv ers, regional freshwater) , EPA developed new variables to captur e geospatial factors including extent of sampled market, surveyed populations and affected resources

. EPA report “ Meta

- analysis of the
Willi ngness

- to

- Pay for Water Quality Improvements” provides detail on variable development and coding (U.S. EPA 2015e). The variables in the revised MRM fall into four general categories :

43 For a WTP function WTP (WQI 0 , WQI 2 , Y 0 ) to satisfy the adding

- up property, it must meet the simple condition that WTP(WQI 0 , WQI 1 , Y 0 ) + WTP( WQI 1 , WQI 2 , Y 0 - WTP(WQI 0 , WQI 1 , Y 0 ) ) = WTP( WQI 0 , WQI 2 , Y 0 ) for all possible values of baseline water quality (WQI 0 ), potential future water quality levels (WQI 1 and WQI 2 ), and baseline income (Y 0 ).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 12 

### 1. Study methodology and year variables characterize such features as the ye ar in which a study was conducted, payment vehicle and elicitation formats, WTP estimation method, and publication type. These variables are included to explain differences in WTP across studies but are not expected to vary

across benefit transfer for diff erent policy applications.

2. Region and surveyed populations variables characterize such features as the geographical region

within the United States in which the study was conducted, the average income of respondent households and the representation of use rs and nonusers within the survey sample.

3. Sampled market and affected resource variables characterize features such as the geospatial scale (or size) of affected waterbodies , the size of the market area over which populations were sampled, as

well as land cover and the quantity of substitute waterbodies.

4. Water quality (baseline and change) variables characterize baseline conditions and the extent of the water quality change. To standardize the results across these studies, EPA expressed water quality (base line and change) in each study using the 100

- point WQI , if they did not already employ the WQI or WQL.

Using this meta

- dataset, EPA then developed a meta

- regression model that predicts how marginal WTP for water quality improvements depends on a variety o f methodological, population, resource, and water quality change characteristics

. In other words, the meta

- regression model predicts the marginal WTP values that would be generated by a stated preference survey with a particular set of characteristics chos en to represent the water quality improvements and other specifics of the ELGs where possible, and best practices where not possible

. EPA developed two versions of the meta

- regression model

. Model 1 is used to provide EPA ’ s central estimate of non

- market benefits a nd Model 2 is used to develop a range of estimates to account for uncertainty in the resulting WTP values. The two models differ only in how they account for the magnitude of the water quality improvements presented to respondents in the original stated preference studies:

 Model 1 assumes that individuals’ marginal WTP depends on the level of water quality, but not on the magnitude of the water quality change specified in the survey. This restriction means that, the

meta

- model satisfies the adding

- up condition, a theoretically desirable property.

 Model 2 allows marginal WTP to depend not only on the level of water quality but also on the magnitude of the water quality change specified in the survey. T he model allows for the possibility that margina l WTP for improving from 49 to 50 on the water quality index depends on whether respondents were asked to value a total water quality change of 10, 20, or 50 points on a WQI scale

. This model provides a better statistical fit to the meta

- data, but it satis fies the adding

- up conditions only if the same magnitude of the water quality change is considered ( e.g. , 10 points)

. To uniquely define the demand curve and satisfy the adding

- up condition using this model, EPA treats the water quality change variable as a methodological variable and therefore must make an assumption about the size of the water quality change that would be appropriate to use in a stated preference survey designed to value water quality changes resulting from the final ELG s . When the water quality change is fixed at the mean of the meta

- data, the predicted WTP is very close to the central estimate

from Model 1.

EPA used the two meta

- regression model s in a benefit transfer approach that follows standard methods

described by Johnston et al. (2 005)

, Shrestha et al. (2007), a nd Rosenberger and Phipps ( 2007)

. The benefitBenefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 13 

### transfer approach uses census block groups ( CBGs ) as the geographic unit of analysis. 44 

### The transfer approach involved projecting benefits in each CBG and year, based on the following general benefit function:

Equation 4

- 2

. ln ( MWTP Y

, B )
= Intercept + ∑ ( coefficien t i ) × ( independent variable valu e i )



### Where

ln(MWTP Y,B 

### ) = T he predicted natural log of marginal household WTP for a given year ( Y ) and CBG ( B ).

coefficient = A vector of variable coefficients from the meta

- regression

. independent
variable values = A vector of independent variable values. Variables include baseline water

quality level ( WQI

- BL Y,B 

### ) and expected water quality under the regulatory option ( WQI

- PC Y,B 

### ) for a given year and CBG

.

Here, ln( M WTP Y

,B 

### ) is the dependent variable in the meta

- analysis

— the log of approximated marginal WTP per household

, in a given CBG B for water quality in a given year Y

. 45 

### The b aseline water quality level ( WQI

BL Y,B 

### ) and expected water quality under the regulatory option ( WQI

- PC Y,B 

### ) were based on water quality at

waterbodies within a 100

- mile buffer of the centroid of each CBG . A buffer of 100 miles is consistent with Viscusi et al. (2008) and with the assumption that the majority of recreational trips will occur within a 2

- hour

drive from home. Because marginal WTP is assumed to depend, according to Equation 4

- 2 , on both ba seline water quality level ( WQI

- BL Y,B 

### ) and expected water quality under the regulatory option ( WQI

- PC Y,B 

### )

, EPA

estimated t he marginal WTP for water quality improvements resulting from the final ELG s at the mid

- point of the range over which water quality was changed, WQI Y,B 

### = (1/2)( WQI

- BL Y,B 

### + WQI

- PC Y,B 

### ) )

. In this analysis, EPA estimated WTP for the households in each CBG for waters within a 100

- mile radius of

that CBG ’s centroid

. EPA chose the 100 m ile

- radius because households are likely to be most familiar with waterbodies and their qualities within the 100 mile distance . However, this assumption may be an underestimate of the distance beyond which households have familiarity with and WTP for water bodies affected by steam electric discharges and their quality. By focusing on a buffer around the CBG as a unit of analysis, rather than buffers around affected waterbodies, each household is included in the assessment exactly once, eliminating the potent ial for double

- counting of households. 46 

### Total national WTP is calculated

as the sum of estimated CBG

- level WTP across all block groups that have at least one affected waterbody
within 100 miles. Using this approach

, EPA is unable to analyze the WTP for CBG s with no affected waters

within 100 miles. Appendix C describes the methodology used to identify the relevant populations

. In each CBG and year, predicted WTP per household is tailored by choosing appropriate input values for the

meta

- analysis parameters describing the resource(s) value d, the extent of resource improvements ( i.e.

, WQI

PC Y,B 

### ), the scale of resource improvements relative to the size of the buffer and relative to available substitutes, the characteristics of surveyed populations ( e.g. , users, nonusers)

, and other methodological

44 A Census Block group is a group of Census Blocks (the smallest geographic unit for the Census) in a contiguous area that never crosses a State or county boundary. A block group typically contains a population between 600 and 3,000 individuals. There are 217,740 block groups in the 2010 Census. See http://www.census.gov/geo/maps

- data/data/tallies/tractblock.html

. 45 To satisfy the adding

- up condition , as noted above, EPA normalized WTP values reported in the studies
included in the meta

- data so that the dependent variable is MWTP per WQI point

. This ‘average’ marginal WTP value is an approximation of the MWTP value elicited in each survey scenario

. 46 Population double

- counting issues can arise when using “distance to waterbody” to assess simultaneous improvements to many waterbodies.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 14 

### variables

. For example, EPA assumed that household income (an independent variable) changes over time, resulting in household WTP values that vary by year.

Table 4

- 8 provides details on how EPA used the meta

- analysis to predict household WTP for each CBG and

year

. The table presents t he estimated regression equation intercept, variable coefficients ( coefficient i 

### ) for the

two models , and the corresponding independent variable names and assigned values

. The meta

- regression allows the Agency to forecast WTP based on assigned values for model variables that are chosen to represent a resource change in the final ELG s ’ policy context. EPA assigned a value to each model variable corresponding with theory, characteristics of the w ater resources, and sites affected by the final ELGs and the policy context. This follows general guidance from Bergstrom and Taylor (2006) that meta

- analysis benefit transfer should incorporate theoretical expectations and structures, at least in a weak f orm.

In this instance, EPA assigned six study and methodology variables, ( thesis, volunt, nonparam, non_reviewed, lump_sum, and WTP_median ) a value of zero . One methodological variable, outliers_trim

, was included with an assigned value of 1

. Because the interpretation of the study year variable ( Lnyear ) is uncertain

, EPA gave
the variable a value of 3.0796 , which is the 75 th 

### percentile of the year values in the meta

- data

. This value assignment reflects an equal probability that the variable represents a real time trend ( in which case its value should be set to the most recent year of the analysis ) and spurious effects (in which case its values should be

set to the mean value from the meta

- data)

. The choice experiment variable (c e ) was set to 1 to reflect recent trends in the use of choice experiments within the environmental valuation literature. Model 2 includes an additional variable, water quality change ( ln_quality _ch ) , which as discussed above allows the function to reflect differences in marginal WTP based on difference s in the magnitude of changes presented to survey respondents when eliciting values

. To ensure that the benefit transfer function satisfies the adding

- up condition, this variable was treated as a demand curve shifter, similar to the met hodological control variables,

and held fixed for the benefit calculations

. To estimate low and high values of WTP for water quality improvements resulting from the final ELG s , EPA estimated marginal WTP using two alternative settings of

the ln_quality var iable :  WQI = 5 units and  WQI = 50 units , which represent the low and high end of the range of values observed in the meta

- data

. All but one of the region and surveyed population variables vary based on the characteristics of each CBG

. For median household income, EPA used CBG

- level median household income data from the U.S. Census

2010 (American Community Survey 5

- year data) and used a stepwise autoregressive forecasting method to

estimate future annual state level median household in come. EPA set the variable nonusers _only to zero because water quality improvements are expected to enhance both use and non

- use values of the affected

resources and thus benefit both users and nonusers (a nonuser value of 1 implies WTP values that are

rep resentative of nonusers only, whereas the default value of 0 indicates that both users and nonusers are included in the surveyed population)

. EPA set the variable r iver to 1 and mul t_ type to 0 because the analysis

focuses only on rivers and streams. Other waterbody types (e.g., lakes and estuaries) are excluded from the analysis

.
The geospatial variables corresponding to the sampled market and scale of the affected resource s ( ln_ar_agr

, ln_ar_ratio

, sub_proportion ) vary based on attributes of the CBG and attributes of the nearby affected

resources

. For all options, the affected resource is based on the 18,622 NHD reaches potentially affected by steam electric power generating plant discharge s under baseline conditions. The affected resource for each

CBG is the portion of the 18,622 reaches that fall with in the 100

- mile buffer of the CBG . Spatial scale is held fixed across regulatory options. The variable corresponding to the sampled market ( ln_ar_ratio ) is set to the

mean value across all CBGs included in t he analysis of benefits from water quality improvements resulting

from the final ELG s , and thus does not vary across affected CBGs

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 15 

### Because data on specific recreational uses of the water resources affected by the final ELG s are not available,

the recreational use variables ( swim_use, gamefish, boat_use ) are set to zero, which corresponds to “unspecified” or “all” recreational uses in the meta

- data. 47 

### Water quality variables ( Q and lnq uality_ch ) vary across CBGs and regulatory options based on the ma gnitude of the reach

- length weighted average water quality improvement at affected resources within the 100

- mile buffer of each CBG

.

Table 4

- 8 : Independent Variable Assignments for Surface Water Quality Meta

- Analysis

Variable Type Variable Coefficient Assigned
Value Explanation Model
1 Model

2
Study Methodology
and Year intercept

- 1.040

- 6.14

C e 0.377 0.423 1 Binary variable indicating that the study is a choice experiment. Set to one to reflect that choice experiments represent current state

- of

- art methods in stated preference literature

.

t hesis 0.866 0.774 0 Binary variable indicating that the study is a thesis
or dissertation. Set to zero because studies published in peer

- reviewed journals are preferred

.

l nyear

- 0.412

- 0.5 3.0796 Natural log of the year in which the study was conducted ( i.e. , data w ere collected), converted to an index by subtracting 1980. Set to the natural log
of the 75 th percentile of the year index value for studies in the metadata ( 21.7 ) to reflect uncertainty in the variable interpretation. If the variable represents a real time t rend, the appropriate value should reflect the most recent year of the analysis. If it represents spurious effects, the values should
reflect the mid

- point from meta

- data

. Both interpretations are equally probable.

v olunt

- 1.390

- 1.184 0 Binary variable indicating that WTP was estimated using a payment vehicle described as voluntary as opposed to, for example, property taxes. Set to zero because hypothetical voluntary payment mechanisms are not incentive compatible (Mitchell and Carson 1989).

outliers_ trim

- 0.367

- 0.291 1 Binary variable indicating that outlier bids were excluded when estimating WTP

. Set to one
because WTP estimates that exclude outlier bids are preferable.

n onparam

- 0.408

- 0.39 0 Binary variable indicating that regression analysis
was not used to model WTP. Set to zero because
use of the regression analysis to estimate WTP
values is preferred

.

non_reviewed

- 0.709

- 0.871 0 Binary variable indicating that the study was not published in a peer

- reviewed journal. Set to zero because studies published in peer

- reviewed journals are preferred.

47 If a particular recreational use was not specified in the survey instrument, EPA assumed that survey respondents were thinking of all relevant uses.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 16 Table 4

- 8 : Independent Variable Assignments for Surface Water Quality Meta

- Analysis

Variable Type Variable Coefficient Assigned
Value Explanation Model
1 Model

2

lump_sum 0.843 0.773 0 Binary variable indicating that the study provided
WTP as a one

- time, lump sum or provided annual WTP values for a payment period of five years or
less. Set to zero to reflect that the majority of
studies from the meta

- data estimated an annual
WTP , and to produce an annual WTP prediction

.

wtp _median

- 0.161

- 0.151 0 Binary variable indicating that the WTP measure from the study is the median. Set to zero because only average or mean WTP values in combination
with the number of affected households will mathematically yield total benefits if the distribution of WTP is not perfectly symmetrical. Region and Surveyed Population n ortheast 1.180 0.593 Varies Binary variable indicating that the affected population is located in a Northeast U.S. state, defined as ME, NH, VT, MA, RI, CT, and NY. Set
based on the state in which the CBG is located.

central 0.561 0.726 Varies Binary variable indicating that the affected population is located in a Central U.S. state, defined as OH, MI, IN, IL, WI, MN, IA, MO, ND, SD, NE, KS, MT, WY, UT, and CO. Set based on
the state in which the CBG is located.

south 1.400 1.563 Varies Binary variable indicating that the affected population is located in a Southern U.S. state, defined as NC, SC, GA, FL, KY, TN, MS, AL, AR, LA, OK, TX, and NM. Set based on the state
in which the CBG is located.

nonusers_only

- 0.586

- 0.54 0 D ummy variable indicat ing that the sampled population included nonusers only; the alternative
case includes all households

. Set to zero to estimate the total value for aquatic habitat improvements for all households

, including users
and nonusers

.

lnincome 0.333 0.96 Varies Natural log of m edian household income value s assigned separately for each CBG

. V ar ies by year based on the estimated income growth in future

years. Sampled
Market and
Affected
Resource mult_ type a

- 0.827

- 0.63 0 Binary variable indicating that multiple waterbody types are affected ( e.g. , river and lakes ). Set to zero because calculations are based exclusively on
rivers.

R iver

- 0.079

- 0.174 1 Binary variable indicating that rivers are affected.
Set to one because calculations are based exclusively on stream miles. EPA did not estimate water quality changes for other waterbody types ( e.g. , lakes and estuaries).

swim_use

- 0.234

- 0.27 0 Binary variables that identify studies in which swimming, gamefish, and boating uses are specifically identified. Since data on specific
recreational uses of the reaches affected by steam G amefish 0.233

- 0.01 0Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 17 Table 4

- 8 : Independent Variable Assignments for Surface Water Quality Meta

- Analysis

Variable Type Variable Coefficient Assigned
Value Explanation Model
1 Model

2

boat_use

- 0.725

- 0.32 0 electric plant discharges are not available, set to

zero , which corresponds to all recreational use s

.

ln_ar_agr

- 0.271

- 0.413 Varies Natural log of the proportion of the affected resource area which is agricultural based on National Land Cover Database, reflecting the nature of development in the area surrounding the

resource. Use d Census county boundary layers to identify counties that intersect affected resources
within the 100

- mile buffer of each CBG . For intersecting counties, calculate d the fraction of total land area that is agricultural using the National Land Cover Dataset (NLCD). The ln_ar_agr variable was coded in the metadata to reflect the area surrounding the affected resources.

ln_ar_ratio

- 0.034

- 0.057 1.238 The natural log of the ratio of the sampled area ( sa_area ) relative to the affected resource area (defined as the total area of counties that intersect the affected resource(s)) ( ar_total_area ). Set to the
mean value from the CBG’s with 100

- mile buffers containing waters affected by the final ELGs

.

sub_proportion 1.100 0.607 Varies The size of the affected resources relative to
available substitutes. Calculate d as the ratio of
affected reaches miles to the total number of reach miles within the buffer that are the same order(s)
as the affected reaches within the buffer. Its va lue can range from 0 to 1.

Water Quality

Q

- 0.015

- 0.004 Varies Because marginal WTP is assumed to depend on both baseline water quality and expected water quality under the regulatory option

, this variable is

set to t he mid

- point of the range of water quality change s due to the final ELG s , WQI Y,B = (1/2)(WQI

- BL Y,B + WQI

- PC Y,B )

. Calculate d as the length

- weighted average WQI score for all potentially affected COMIDs within the 100

- mi le
buffer of each C BG.

lnquality _ch NA

- 0.746 ln(5) or ln(50) Ln

_ quality _ch was set to the natural log of

 WQI =5 or  WQI =50 for high and low estimates of the marginal WTP, respectively

.

a. The meta

- data includes six waterbod y categories (1) river and stream, (2) lake, (3) all freshwater, (4) estuary, (5) river and lake, (6 ) salt pond/marshes, Variable multi

- type t akes on a value of 1 if the study focused on waterbody categories (3) and (6). EPA notes that the overall effect of this variable should be considered in conjunction with the regional dummies ( e.g. , a study of the Lake
Okeechobee basin in Florida) and that only eight percent of all observations in the meta

- data fall in the multiple waterbody categor ies

.



### Total WTP for Water Quality Improvements 4.3

EPA estimated e conomic values of water quality improvements at the CBG level. For each block group

, EPA multiplied the coefficient estimates for each variable

, taken from meta

- analysis results ( Table 4

- 8

, Coefficients: Model 1 and Model 2 )

, by the variable levels calculated for each CBG or fixed at the levelsBenefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 18 

### indicated above ( Table 4

- 8

, column 4). The sum of these products represents the predicted natural log of marginal household WTP ( ln_ M WTP ) for a representative household in each CBG , as indicated by Table 4

- 8

. Equation 4

- 3 provides the discount formula used to calculate household benefits for each CBG

. Equation 4

- 3

. 

### HWTP Y

, B 

### = MWTP

Y

, B 

### × ∆ WQI

B



### where:

HWTP Y



### , B 

### = Annual household WTP in 201 3$ in year Y for households located in

the CBG ( B ),

M WTP Y

,B 

### = Marginal WTP for water quality for a given year ( Y ) and the CBG ( B ) estimated by the meta

- analysis function and evaluated at the midpoint of the range over which water quality is changed

, ∆WQI B 

### = Estimated annual average water quality change for the CBG ( B ).

As summarized in Table 4

- 9

, average annual household WTP estimates for the final ELGs (Option D) range from $0. 32 on the low end (Model 2) to $ 1. 77 on the high end (Model 2), with a central estimate of $0.3 5 (Model 1)

. The average is calculated based on the 84.5 million households affected by Option D

. We note that

the central estimate does not fall at the midpoint of the range, but instead represents the value from Model 1

which falls between the low and high bound estimates provided by Model 2.

Table 4

- 9 : Household Willingness

- to

- Pay for Water Quality Improvements

Regulatory Option Improving Reach

Miles Number of Affected

Households (Millions) Average Annual WTP Per Household (2013$)

Low Central High
Option A 8,177 30.3 $0.17 $0.20 $0.92
Option B 13,431 60.3 $0.28 $0.37 $1.58
Option C 16,417 67.9 $0.33 $0.46 $1.85
Option D 19,573 84.5 $0.32 $0.45 $1.77
Option E 20,300 94.3 $0.31 $0.45 $1.75

Source: U.S. EPA Analysis, 2015



### To estimate total WTP (TWTP) for water quality improvements for each CBG , EPA multiplied the per

household WTP values for the estimated water quality improvement by the number of households within each block group in a given year. EPA then calculated annualized total WTP values for each CBG with both a

3 percent and 7 percent discount rate as shown below in Equation 4

- 4 . As discussed in Chapter 1 , benefits from water quality changes are estimated for all years betwee n 2021 and 2042. F or reaches directly receiving steam electric plant discharges, benefits are expected to begin accruing according to the technology implementation period of 2019 through 2023 described in Chapter 3 of the RIA (U.S. EPA, 2015b)

. D ownstream reaches, however, can be affected not only by discharges from a given plant discharging directly

to the reach , but also by any change in discharge s from plants located upstream , which may have different compliance years

. Therefore, f or this analysis

, EPA u sed a simplified assumption that all benefits begin accruing in 20 21 , which is the midpoint of the compliance period

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 19 Equation 4

- 4

.



### TWTP B 

### = ( ∑ HWTP Y

, B 

### × HH Y

, B 

### ( 1 + i ) Y

− 2015 2042

T = 2021 

### )

× ( i × ( 1 + i ) n 

### ( 1 + i ) n + 1



### − 1 )

where:

T WTP B 

### = Total household WTP in 201 3$ for households located in the CBG ( B ),

H WTP Y, B 

### = Annual household WTP in 201 3$ for households located in the CBG

( B ) in year ( Y

)

,

HH Y, B 

### = the number of households residing in the CBG ( B ) in year ( Y )

,

T = Y ear when benefits are realized

i = Discount r ate (3 or 7 percent)

n = Duration of the analysis (22 years) 48



### EPA generated annual household counts for each CBG through the period of analysis based on projected population growth following the method described in Section 1.5.4

. Table 4

- 10 present s the results for the

3 percent and 7 percent discount rates

.

Table 4

- 10 : Total Willingness

- to

- Pay for Water Quality Improvements

Regulatory Option Number of Affected

Households (Millions) 3% Discount Rate 7% Discount Rate

Low Central High Low Central High

Option A 30.3 $4.2 $4.9 $23.4 $3.3 $3.9 $18.6
Option B 60.3 $15.0 $18.9 $83.7 $12.0 $15.2 $66.7
Option C 67.9 $19.6 $26.0 $109.4 $15.7 $20.9 $87.3
Option D 84.5 $23.2 $31.3 $129.5 $18.5 $25.1 $103.4
Option E 94.3 $25.1 $34.0 $140.0 $20.0 $27.3 $111.7

Source: U.S. EPA Analysis, 2015



### EPA estimated that 19,573 reach miles would improve under the final ELGs. The total annualized benefits of water quality improvements resulting from reduced metal , nutrient and sediment discharges in these reaches range from $ 23.2 million to $ 129.5 million with a central estimate of $ 31.3 million using a 3 percent discount

rate and $ 18.5 million to $ 103.4 million with a central estimate of $ 25.1 million using a 7 percent discount

rate.

Readers may wonder why the central estimate is closer to the low end of the range than the high end. EPA

tested several different functional forms for Model 2 , and found that the model has the highest explanatory power (R

- squared) when water quality change is included in logged form. This implies that water quality change has a nonlinear effect on MWTP. In particular, small initial increases in the scale of the water quality change scenario have a larger effect on MWTP than subsequent increases. This is the reason why the central estimate of MWTP (based on a water quality change scenario of approximately +20 units) is closer to the low MWTP estimate (based on a water quality change scenario of +50) than to the high MWTP estimate (based on a water quality cha nge scenario of +5). In addition, when Model 2 is used in a benefits transfer application with a water quality change of +20, the mean of the meta

- data, the results are very close to the results of

48 See Section 1.5. 3 for detail on the period of analysis.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 20 

### Model 1. This sensitivity analysis is included because a w ater quality change of +5 is closer to the size of water quality changes projected to result from the ELGs than the +20 analog to the central estimate, while the +50 represents the upper end of water quality changes in existing surveys (and the lower end o f the sensitivity benefits range), for completeness.

In addition, EPA views its revisions to the meta

- analysis employed here as a work in progress. As part of the revisions, EPA has identified some issues that require further analysis, and intends to conti nue this work after the Steam Electric ELGs are promulgated. EPA also intends to have the SAB review the results of that progress when it has reached a point of completion where SAB review is appropriate. In particular, EPA has identified the following iss ues to address: conducting additional robustness tests and cross validation, investigating model over

- fitting, investigating whether a distance decay effect can be gleaned from the meta

data (to substitute for the 100

- mile radius assumption used in the ben efits transfer application here), and to consider employing Bayesian estimation techniques. However, EPA views the results presented here as a sufficient improvement over the results presented at proposal to warrant their inclusion in this report.



### Implicat ions of Revised Steam Electric Plant Loading Estimates 4.4

As described in Section 1.4.3 , EPA revised its estimates of pollutant loadings in steam elect ric power plant discharges after completing the benefit analyses. The revisions affect baseline discharges of the several pollutants explicitly modeled to quantify and monetize the water quality improvement benefits in this Chapter: the eight metals, TN, T P and TSS. Changes in baseline metal loadings range from a 47 percent

reduction ( for lead ) to a 1 percen t increase (for selenium) while changes in TN, TP, and TSS loadings are all less than 1 percent. The overall effect of changes in TN, TP, and TSS loadin gs is likely to be trivial given the magnitude of loading reductions (i.e., less than 1 percent). Impacts of the revised metal loadings on the WQI score is difficult to assess since the score is not based on a continuous scale, but depends instead on whet her

the modeled instream concentration s metals exceed the relevant AWQC s

. T o the extent that baseline concentrations calculated based on the original loads exceed the relevant AWQC s only slightly , small

reductions in these concentrations due to the revised loads could increase the baseline WQI score, all else being equal , and therefore reduce the magnitude of improvements attributable to the ELGs

.
For several reasons

— notably the fact that revisi ons do not affect all plants equally, the metals subindex score is based on counts of exceedances, and the model functions are not linear

— it would be inappropriate to simply scale the monetized benefits based on the aggregate changes in loadings. However , comparison of the initial and revised baseline loads, on the one hand, and the ratios of modeled concentrations based on the initial loads to AWQCs, on the other hand, provides insight on the potential significance of the revisions on

benefit estimates.

Table 4

- 11 summarizes changes in loading estimates between the original loads used in estimating benefits for Option D in this Chapter and the revised loads. The table shows total industry loads of the eight metals included in the WQI subindex, for the baseline and under the final ELGs (Option D )

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 21 Table 4

- 11 : Estimated Aggregate Changes in Pollutant Loadin gs for Metals, Nutrients, and Suspended Solids

.

Pollutant Baseline Loadings Option D

Initial Revised % Change Initial Revised % Change

Arsenic 22,219 20,138

- 9% 1,483 1,480 0%

Cadmium 10,925 8,289

- 24% 636 630

- 1%

Chromium (VI) 119 119 0% 0 0 0%

Lead 14,588 7,674

- 47% 350 331

- 5%

Mercury 1,176 992

- 16% 31 31

- 1%

Nickel 94,201 61,933

- 34% 1,781 1,697

- 5%

Selenium 112,999 114,533 1% 3,122 3,129 0%

Zinc 145,045 124,333

- 14% 6,951 6,893

- 1%
Total nitrogen 13,134,797 13,134,797 0% 69,969 70,285 0%
Total phosphorus 153,972 154,519 0 % 31,630 31,667 0 %
Total suspended
solids 14,182,480 14,286,431 1 % 1,661,665 1,665,345 0 %

Source: U.S. EPA Analysis, 2015



### While EPA cannot readily recalculate the benefits, the direction and relative magnitude of the change in pollutant removals in Table 4

- 11 suggest there could be reaches where modeled concentrations would no longer exceed the applicable AWQS after revising the loadings. The changes are most significant for the baseline and are most likely to affect benefit estimates in instances where the con centrations were only slightly above the AWQC. However, r eview of baseline exceedances shows that, of the 1,023 exceedances present in the baseline ( in 567 reaches), 774 exceedances result from instream concentrations being more than

1.5 times the relevant AWQC and 690 exceedances result from instream concentrations that are more than

twice the AWQC. Instream concentrations would need to decline by at least 33 percent and 50 percent, respectively, to eliminate such baseline exceedances. Based on this data r eview, EPA concludes that the values presented in this Chapter based on the initial loadings provide reasonable insight into the water quality improvement benefits expected from the final ELGs.

Table 4

- 12 : Num ber of Reaches with Baseline AWQC Exceedances Based on Initial Loadings

.

Pollutant Ratio of [baseline concentration]/[AWQC]

>1.0 >1.5 >2.0

Arsenic 167 128 124

Cadmium 151 100 75

Chromium (VI) 10 7 5

Lead 124 97 62

Mercury 17 11 7

Nickel 216 147 138

Selenium 91 68 47

Zinc 247 216 167

Source: U.S. EPA Analysis, 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 22 

### Limitations and Uncertainties 4.5

Table 4

- 13 summarizes the limitations and uncertainties in the analysis of benefits associated with improved surface water quality and indicates the direction of any potential bias.

Table 4

- 13 : Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits

Issue Effect on Benefits

Estimate Notes

Revisions to p ollutant l oads used to e stimate w ater q uality i mprovements The analysis is based loadings that were subsequently revised by EPA Overestimate Revised loadings of parameters included in the WQI (notably the eight metals), are lower than the loadings used to estimate benefits. The changes indicate that number of exc eedances of AWQCs may be lower in the baseline, and therefore the resulting improvements due to the ELGs may be lower. As discussed in

Section 4.4 , however, the mag nitude of the overstatement may be

small.

Limitations inherent to the meta

- analysis model and benefit transfer

Use of 100

- mile buffer for calculating water quality benefits for

each CBG Underestimate T he distance between the surveyed households and the affected waterbodies is not well measured by any of the explanatory

variables in the meta

- regression model. EPA would expect values for water quality improvements to diminish with distance (all else equal ) betw een the home and affected

water body

. The choice of 100 miles is based on typical driving distance to recreational sites ( i.e. , 2 hours or 100 miles). Therefore, EPA used 100 miles to approximate the distance decay effect.

Selection of the WQI
parameter value for estimating low and high WTP values Uncertain EPA set  WQI to 5 and 50 units to estimate high and low benefit

values based on Model 2. These values were based on the lowest and highest water quality changes included in the meta

- data

. To

the extent that  WQI = 50 is significantly larger than the water quality expected from the final ELG s it is likely to significantly

understate the estimated WTP value.  WQI = 5 is more consistent with the magnitude of water quality changes resulting

f rom the final ELGs

. Whether p otential hypothetical bias is present in underlying stated preference

results Uncertain Following standard benefit transfer approaches, this analysis proceeds under the assumption that each source study provides a valid, unbiased estimate of the welfare measure under consideration (cf. Moeltner et al. 2007; Rosenberger and Phipps 2007). To minimize potential hypothetical bias underlying stated preference studies included in meta

- data , EPA set independent

variable values to reflect best benefit transfer practices.

Use of different water quality measures in

the underlying meta

data Uncertain The estimation of WTP may be sensitive to differences in the environmental water quality measures across studies in the meta

data. Stu dies that did not use the WQI were mapped to the WQI so a comparison could be made across studies. In preliminary model runs, EPA tested a dummy variable (WQI) that captures the effect of a study using (WQI=1) or not using (WQI=0) the WQI. The variable coe fficient was not statistically different from

zero , indicating no systematic bias in the mapping of studies that did not use the WQI.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non

- Market Benefits

September 29, 2015 4

- 23 Table 4

- 13 : Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits

Issue Effect on Benefits

Estimate Notes

Transfer error Uncertain Transfer error may occur when benefit estimates from a study site are adopted to forecast the b enefits of a policy site. Rosenberger and Stanley (2006) define transfer error as the difference between the transferred and actual, generally unknown, value. While

meta

- analysis is fairly accurate when estimating benefit function, transfer error may be a problem in cases where the sample size is

small. M eta

- analyses have been shown to outperform other

function

- based transfer methods in many cases, but this result is not universal (Shrestha et al. 2007). This notwithstanding, results reviewed by Rosenberger and Phipps (2007) are “very promising” for the performance of meta

- analytic benefit transfers

relative to alternative transfer methods.
Use of the WQI to link water quality changes to effects on human uses and support for aquatic and terrestrial species

Omission of lakes and

estuaries from analysis

of benefits from water quality improvements Underestimate 12 percent of s team e lectric power generating plants discharge to

the Great Lakes or estuaries

. Due to limitations of the water quality models used in the analysis of the final ELG s , these waterbodies were excluded from the analysis

. This omission is likely to underestimate benefits of water quality improvements

from the final ELG s

. Changes in WQI reflect only reductions

in metal

, nutrient , and

total suspended

sediment

concentrations Uncertain The estimated changes in WQI reflect only water quality improvements resulting directly from reductions in metal

, nutrient and sediment concentrations . They do not include improvements in other water quality para meters ( e.g. , BOD, dissolved oxygen) that are part of the WQI

. If the o mitt ed water quality parameters also improve, then the analysis underestimat es the expected water quality changes.

In

- stream metal

concentrations are based only on loadings

from steam electric power generating plants and other TRI dischargers Uncertain In

- stream concentrations for heavy metals were estimated based

on loadings from steam electric plant and other TRI dischargers only and, as a result, do not account for background

concen trations of these pollutants from other sources, such as contaminated sediments, non

- point sources, point sources that are not required to report to TRI, air deposition, etc . Not including other contributors to background metal concentrations in the analysis is likely to result in understatement of baseline concentrations of these pollutants and therefore of AWQC

exceedances . The overall impact of this limitation on the estimated WTP for water quality improvement is uncertain but is expected to be sma ll since the WTP function used in this analysis

is most sensitive to the change in water quality.

Use o f nonlinear

subindex curves Underestimate The methodology used to translate in

- stream sediment and nutrient concentrations into subindex scores employs nonlinear transformation curves. Water quality changes that fall outside of the sensitive part of the transformation curve ( i.e. , above/below the upper/lower bounds , respectively ) yield no benefit in the analysis.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5

- 1 

## 5 Impacts and Benefits to Threatened and Endangered Species

Introduction 5.1

Threatened and endangered ( T&E ) species are species vulnerable to future extinction or at risk of extinction in the near future, respectively. These designations reflect low or rapidly declining population levels, loss of essential habitat, or life history stages that are particularly v ulnerable to environmental alteration. In many cases, T&E species are given special protection due to inherent vulnerabilities to habitat modification, disturbance, or other human impacts. This chapter examines the environmental impacts of steam electric

p ower plant discharges on T&E species and the benefits associated with improvements resulting from the

final ELGs and other regulatory options

. As described in the EA (U.S. EPA, 2015a ), the chemical constituents of steam electric waste streams can pose

ser ious threats to ecological health due to the bioaccumulative nature of many pollutants , high concentrations, and high loadings. Pollutants such as selenium, arsenic and mercury have been associated with fish kills, disruption of growth and reproductive cyc les and behavioral and psychological alterations in aquatic organisms (U.S. EPA, 2009 d ; Appendix H ). Additionally, high nutrient loads can lead to the eutrophication of

waterbodies. Eutrophication can lead to increases in the occurrence and intensity of water column phytoplankton, including harmful algal blooms ( e.g. , nuisance and/or toxic species), which have been found

to cause fatal poisoning in other animals, fish, and birds (Will iams et al., 2001). Eutrophication may also result in the loss of critical submerged rooted aquatic plants (or macrophytes), and reduced DO, levels, leading to anoxic or hypoxic water s

. For species vulnerable to future extinction, even minor changes to gro wth and reproductive rates and small levels of mortality may represent a substantial portion of annual population growth. Consequently, steam electric power plant discharges may either lengthen recovery time, or hasten the demise of these species. For

this reason, the final ELGs may have a significant impact on T&E species populations.

From an economic perspective, T&E species affected by steam electric power plant discharges may have both use and nonuse values. However, given the protected nature of T&E sp ecies and the fact that use activities generally constitute take, which is illegal unless permitted, the majority of T&E species do not have direct uses, the majority of the economic value for T&E species comes from nonuse values. Species

- specific

estimate s of nonuse values held for the protection of T&E species can be most accurately derived by primary research using stated preference techniques. However, the cost, administrative burden, and time required to develop primary research estimates to value effe cts of the final rule on T&E species are beyond the schedule and resources available to EPA for this rulemaking. As an alternative, EPA used a benefit transfer approach that relies on information from existing studies (U.S. EPA, 2010 c ).

In this chapter, E PA explores the current status of major freshwater taxa, identifies the extent to which the final ELGs can be expected to benefit species protected by the Endangered Species Act (ESA) , and applies economic valuation studies to these T&E species to estimate WTP for these benefits.



### Baseline Status of Freshwater Fish Species 5.2

Reviews of aquatic species’ conservation status over the past three decades have documented the effect of cumulative stressors on freshwater aquatic ecosystems, resulting in a significant decline in the biodiversity and condition of indigenous communities (Deacon et al., 1979; Williams et al., 1989; Williams et al., 1993; Taylor et al., 1996; Taylor et al., 2007; Jelks et al., 2008). Overall, aquatic species are disproportionatelyBenefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5

- 2 

### imperiled relative to terrestrial species. For example, while 39 percent of freshwater and diadromous fish species (Jelks et al., 2008) are classified as T&E, a similar status review found that only 7 percent of North American bird and mammal species are currently imperiled (Wilcove and Master, 2005).

Approximately 39 percent of described fish species in North America are imperiled, with 700 fish taxa classified as vulnerable (230), threatened (190), or endangered (280) in addition to 61 taxa presumed extinct

or fun ctionally extirpated from nature (Jelks et al., 2008). These data show that the number of T&E species have increased by 98 percent and 179 percent when compared to similar reviews conducted by the American Fisheries Society in 1989 (Williams, Johnson et al . 1989) and 1979 (Deacon et al., 1979), respectively. Despite recent conservation efforts, including the listing of several species under the ESA, only 6 percent of the fish taxa assessed in 2008 had improved in status since the 1989 inventory (Jelks et al ., 2008).

Several families of fish have strikingly high proportions of T&E species. Approximately 46 percent and

44 percent of species within families Cyprinidae (carps and true minnows) and Percidae (darters and perches) are imperiled, respectively. Some families with few, wide

- ranging species have even higher rates of imperilment, including the Acipenseridae (sturgeons; 88 percent) and Polyodontidae (paddlefish;

100 percent). Families with species important to sport and commercial fisheries ranged from a low of

22 percent for Centrarchidae (sunfishes) to a high of 61 percent for Salmonidae (salmon) (Jelks et al., 2008)

.



### T&E Species Affected by the Final ELGs 5.3

To assess the potential effects of the final ELGs on T&E species, EPA constructed databases to dete rmine which species are found in waters expected to improve due to a reduction in pollutant discharge from steam electric power plant s . Notably, these databases exclude all species considered threatened or endangered by scientific organizations [ e.g. , the American Fisheries Society (Williams et al.

, 1993; Taylor et al.

, 2007; Jelks

et al.

, 2008)] but not protected by the ESA. The se databases allowed EPA to estimate the potential for adverse impacts of steam electric power plant discharges on T& E species, as well as benefits associated with the final

ELGs

.

5.3.1 Identifying T&E Species Potentially Affected by the Final ELGs

To estimate the effects of the final ELGs on T&E species, all affected species must first be identified. EPA identified all speci es currently listed or in consideration for listing under the ESA using the U.S. FWS Environmental Conservation Online System (U .S. FWS, 201 4 a ). Whenever possible, EPA obtained the geographical distribution of T&E species in geographic information system ( GIS) format as polygon (shape) files, line files (for inhabitants of small creeks and rivers) and as a subset of geodatabase files. Data sources

include U.S. FWS (2014b), the National Oceanic and Atmospheric Administration’s ( NOAA’s ) Office of Response and Restoration (NOAA, 2010 ), NatureServe (NatureServe, 2014), and NOAA National Marine Fisheries Service (NMFS, 201 4 a; NMFS, 201 4 b; NMFS, 201 4 c). For several freshwater species, geographic ranges were available only as 6

- digit HUC s (NatureServe, 2014; U.S. F WS, 2014b). For these species, EPA created GIS data layers using a GIS HUC database obtained from the USGS (Steeves and Nebert, 1994).

To determine the probability that individual T&E species could benefit from the final ELGs, EPA compiled

data on locatio ns of steam electric power plant s and receiving waterbodies. The Agency used plant and outfall coordinates it had obtained through its 2010 Questionnaire for the Steam Electric Power Generating Effluent

Guidelines (the industry survey) and georeferenced th ese coordinates to waterbodies ( see EA for details; U.S. EPA, 2015a ). The result of this analysis consists of the NHD Plus (COMIDs) identifiers of waterbodies that receive discharges from steam electric power plant s and indicators of water quality under the baseline and each analyzed regulatory option. EPA queried these data to identify “affected areas” as those habitats where

1 ) receiving waters do not meet water quality metrics recognized to cause harm in organism s under baselineBenefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5

- 3 

### conditions; and 2 ) receiving waters exceed water quality metrics under the most stringent regulatory option EPA analyzed (Option E )

. EPA used these data in ArcGIS to determine the T&E species with habitat extents overlapping the affected a reas.

EPA constructed a screening database using the spatial data . This database included all T&E species whose habitat overlaps those waterbodies receiving effluent discharges from steam electric power plant s. A buffer of

500 m was chosen when constructin g this database to account for any minor errors in outfall location and habitat maps.

After identifying T&E species potentially affected by the final ELGs, EPA classified the species on the basis of their vulnerability to changes in water quality. Species were classified as follows:

 High vulnerability

– species living in aquatic habitats for several life history stages and/or species that

obtain a majority of their food from aquatic sources

.  Moderate vulnerability

– species living in aquatic habitats for one life history stage and/or species

that obtain some of their food from aquatic sources

.  Low vulnerability

– species whose habitats overlap bodies of water, but whose life history traits and

food sources are terrestrial

. Life history data used to classif y species were obtained from a wide variety of sources (Froese and Pauly

, 2009; NatureServe

, 20 14 ; Alaska Fisheries Science Center ( AFSC )

, 2010; Atlantic States Marine Fisheries Commission ( ASMFC )

, 2010; Northeast Fisheries Science Center ( NEFSC )

, 2010; Pa cific Islands Fisheries Science Center ( PIFSC )

, 2010 a ; PIFSC

, 2010 b ; Southeast Fisheries Science Center ( SEFSC )

, 2010; Southwest Fisheries Science Center ( SWFSC )

, 2010; U

. S

. FWS

, 2010).

The results of the spatial analysis and vulnerability classification p rocess (as described above) are presented in

Table 5

- 1

. Appendix I list s all T&E species potentially affected by the final ELGs.

Table 5

- 1 : T&E Species with Habitat Occurring within Waterbodies Affected by Steam Electric Power
Plants

Species Group Species Vulnerability Species Count Low Moderate High
Amphibians 0 2 2 4

Arachnids 4 0 0 4

Birds 9 2 1 1 2

Clams 0 0 33 33

Crustaceans 0 1 1 2

Fishes 0 0 17 17

Insects 10 2 1 1 3

Mammals 1 7 4 1 22 Reptiles 3 1 5 9

Snails 8 0 1 4 22

Total 51 1 2 75 1 38

Source: U.S. EPA Analysis, 2015



### For the purposes of estimating benefits, EPA excluded all species with low and moderate vulnerability potentials based upon life history traits. For all species with high potential vulnerability, EPA conducted further analyses to identify those species likely to be affected by the final ELGs, rather than all species whoseBenefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5

- 4 

### life histories make them vulnerable. High vulnerability species meeting the following criteri a were removed

from further consideration:

 Sp ecies presumed to be extinct, including those not collected for a minimum of 30 years ( e.g. , Noturus trautmani ).

 E ndemic species living in waterbodies ( e.g. , isolated headwaters, natural springs) unlikely to be affected by steam electric power plant discharges ( e.g. , Gambusia georgei ).

 S pecies protected by the ESA whose recovery plans i) do not include pollution or water quality issues as factors preventing recovery, and ii) identify habitat destruction (due to d amming, stream channelization, water impoundments, wetland drainage, etc.) as a primary factor preventing recovery ( e.g. , Erimystax cahni ).

 Listings due to non

- native species introductions and/or hybridization with native or non

- native congeners ( e.g. , Onc orhynchus clarki somias )

 Listings where water quality issues are identified as the primary issue preventing recovery, but where a specific industry or entity not within the scope of the final ELGs is identified as the culprit. ( e.g. , Erimystax cahni due to siltation from coal mining activity).

 Species about which very little is known, including geographic distribution.

After eliminating the T&E species meeting these criteria, EPA identified a total of 15 species whose recovery may be enhanced by the final ELGs

.

5.3.2 Estimating Benefits of T&E Species Improvements from the Final ELGs

The final ELGs have the potential to positively affect the recovery trajectory for 15 T&E species. For each of these species, EPA estimated the magnitude of potential benefits by identifying inhabited waterbodies likely to meet AWQC for aquatic life as a consequence of the final ELGs and comparing these areas to the overall area of habitat occupied by T&E species.

First, for each T&E species affected by steam electric power plant d ischarges, EPA examined water quality in each of the waterbodies inhabited by each T&E species under baseline conditions, and under conditions projected to exist following implementation of the final ELGs. For each analyzed regulatory option, EPA

identifie d waterbodies that 1) do not meet AWQC for wildlife under baseline conditions, but 2) have no wildlife AWQC exceedances following implementation of the final ELGs. For 1 1 species, there were no waterbodies that met these conditions, leaving three T& E fish species and one salamander species in nine

states that may experience increases in population growth rates as a result of the final ELGs ( Table 5

- 2 )

.

Table 5

- 2 : T&E Species Whose Recovery May Benefit from the Final ELGs

Species Common Name State ( s )

Acipenser brevirostrum Shortnose Sturgeon MD, SC Cryptobranchus alleganiensis Hellbender salamander IL, KY, MO, OH, PA

Etheostoma chermocki Vermilion darter AL

Etheostoma etowahae Etowah darter GA

Source: U.S. EPA Analysis, 2015


### EPA did not identify d ata sufficient to explicitly model population growth rates as a function of water quality

for any of these species. Therefore, to estimate proportionate population increases as a result of the final

ELGs

, EPA identified the fraction of inhabited waterbodies that meet wildlife AWQC as a consequence of theBenefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5

- 5 

### final ELGs. This fraction was used to estimate relative population changes in estimating the WTP for T&E species recovery

.



### Estimating WTP for T&E Species Population Increases 5.4

5.4.1 Economic Valuation Methods

For several reasons, it is difficult to estimate the benefits of improving T&E species habitats resulting from the final ELGs. First, data required to estimate the response of T&E populations to improved habitats are rarely available. Second, the contribution of T&E species to ecosystem stability, ecosystem function, and life history remains relatively unknown. Third, much of the wildlife economic literature focuses on commercial and recreational benefits that are not relevant for many protected species ( i.e. , use values).There is a paucity

of economic data focused on the benefits of preserving habitat for T&E species because nonuse values comprise the principal source of benefit estimates for most T&E species.

Analysis of nonuse benefits for T&E species affected by changes in pollutant discharges from steam electric power plant s stemming from the final ELGs involves the following two steps: 1) quantifying the impacts of pollutant discharges from steam electric power plant s on T&E species and estimating the change in these impacts as a consequence of reducing steam electric dischar ges; and 2) estimating an economic value of improving T&E habitats and populations as a consequence of the final ELGs.

Benefit transfer involves extrapolating existing estimates of nonmarket values to the policy sites that potentially differ from the orig inal analytical situation in terms of geographic locations or affected species. Ideally, the resource in question ( i.e. , T&E species ), policy variable s (change in species status, recovery interval, population size , etc.), and the geographic location and be nefitting population ( i.e. , defined human population) are identical. Such a match rarely occurs. Despite differences in these variables, however, a benefit transfer approach can provide useful insights into the social benefits gained by reducing impacts o n T&E species

.

5.4.2 Estimating WTP for Improved Protection of T&E Species

To estimate the potential economic values of increased T&E species populations affected by the final ELGs

, EPA used a benefit transfer approach based on a meta

- analysis of 31 stated prefe rence studies eliciting WTP for changes in T&E populations (Richardson and Loomis

, 2009). This meta

- analysis is based on studies conducted in the United States that valued threatened, rare, or endangered fish, bird, reptile, or mammal species. Because the underlying meta

- data does not contain amphibian valuation studies, EPA was unable to monetize any benefits for potential population increases of Hellbender salamander as a result of the final

ELGs. Equation 5

- 1 contains the estimated WTP equation from the Richardson and Loomis ( 2009 ) paper that

EPA used to monetize potential population increases resulting from the final ELGs.

Equation 5

- 1

. ln WTP ( 2006$ ) = − 153

. 231 + 0

. 870 ln CHANGESIZE + 1

. 256 VISITOR + 1

. 020 FISH +

0

. 772 MARINE + 0

. 826 BIRD

– 0

. 603 ln RESPONSERATE + 2

. 767 CONJOINT +

1

. 024 CHA RISMATIC

– 0

. 903 MAIL + 0

. 078 STUDYYEAR

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5

- 6 

### Table 5

- 3 lists the assigned variable values and definitions used in estimating per household WTP for improved protection of T&E species resulting from the final ELGs.

Table 5

- 3 : Independent Variable Assignments for th e T&E Meta

- Regression

Variable Description Value Explanation

Intercept Intercept

- 153.231

-
ln ChangeSize Natural log of percentage change in the

populatio n of the species of interest Varies Log of percentage change in fish population

Visitor Dummy variable indicating if survey

respondents are visitors rather than full

time residents 0 Primary beneficiaries are expected to be
full

- time state residents

Fish Dummy variable indicating population

increases for fish species 1

Only freshwater T&E fi sh species are expected to be affected Marine Dummy variable indicating population

increases for marine mammals 0

Bird Dummy variable indicating population

increases for bird 0

Charismatic Dummy variable indicating a

charismatic species Varies Sturgeon species are considered charismatic; minnow species are not

Conjoint Dummy variable indicating conjoint

method surveys 0 Default value from Richardson and Loomis (2009) as only one underlying

meta

- study used conjoint analysis ; t he rest
were CV studies ln ResponseRate Natural log of the survey response rate 3.912 Mean value from Richardson and Loomis (2009) follow ing the Johnston et al. (2006) approach where values for methodological
attributes are set at mean values from the
metadata Mail Indicates mail surveys 0.851

StudyYear Year of study 1992

Source: U.S. EPA Analysis, 2015



### EPA does not currently have either species

- specific estimates of the population effects of the final ELGs or population models to estimate future population changes for the affected T&E species due to improved aquatic habitat conditions. In the absence of such estimates, EPA used best professional judgment to assign a range of potential improvements in the T&E populations based on the expected reductions in AWQC exceedances under the post

- compliance scenario. T o estimate total population increases as a result of each analyzed regulatory option , EPA assumed minimal increase s in population size of 0.5, 1, or 1.5 percent. EPA then weighted these population growth estimates within states by the proportion of reaches used by T&E species expected to meet wildlife

- based AWQC under each option . The natural log of these weighted population growth estimates under each scenario was used to assign a value to the ChangeSize parameter
estimate. EPA used the approach described in Johnston et al. (2006) and assigned mean study values from Richardson and Loomis (2009) for the methodological variables ( ln ResponseRate , Mail, and StudyYear)

. Although it could not find published literature to support dose

- response relationships for a ny of the species assessed or any other numerical estimates of benefit that might occur to T&E species because of the rule, EPA believes that its low (0.5 percent), medium (1.0 percent) and high (1.5 percent) estimates of population growth for T&E species ( occurring within affected reaches exceeding AWQC at baseline) because of the final

ELG are reasonable. This is because of the high number of species filtered from further assessment (as

described in Section s 5.3.1 and 5.3.2, only four species met all crit eria for inclusion ), because population
increases were estimated to occur only in reaches meeting AWQC because of the rule (the majority of habitatBenefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5

- 7 

### used by T&E species assessed meets AWQC at baseline) , and because few individuals must be saved to attain

th ese growth estimates. For example, consider a T&E species with a state

- level population of 10,000

individuals (reasonable for threatened species, likely an over

- estimate for endangered species and endemic species) , residing in 10 reaches

- only one of which does not meet AWQC criteria at baseline (but does under rule options), and a population growth rate of 0 in the baseline . This species would achieve EPA’s low, medium and high population increases if the ELG results in one fe wer premature mortality every 5 years (low growth) and approximately one fewer premature mortality every 1.5 year s (high growth) between 2019 and

2042. This low level of effect needed to meet growth assumptions, when combined with known effects of

wildlife living in areas with poor water quality, make this level of population increase reasonable.

Because population growth was assessed at the state level, EPA was unable to attribute benefits to a specific steam electric power plant and therefore to a ccount f or the timing of benefits based on the assumed control technology implementation year

. EPA assumed that benefits begin accruing in 20 21 for all states . This year is the midpoint of the period of 2019 through 2023 when plants are assumed to implement contro l technologies to comply with the revised effluent limits and standards

. For each state, EPA estimated household WTP for improved protection of T&E species resulting from the final ELGs using Equation 5

- 1 and the independent variable assignments presented in Table 5

- 3

. EPA estimated total annual benefits for the years between 2021 and 2042 by multiplying household WTP by the number of households in each state for a given year. EPA then calculated the value of benefits for each year and the annualized total WTP values for each state using 3 percent and 7 percent discount rates

.



### Results 5.5

Table 5

- 4 present s the annualized total benefits calculated using discount rates of 3 percent and 7 percent. The

monetized b enefits to T&E species of Option D are concentrated in two states : Alabama (AL) and Georgia (GA) . The annualized benefits of Option D are $0.0 2 million for the medium population increase using a 3 percent discount rate

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: Threatened and Endangered Species Benefits

September 29, 2015 5

- 8 Table 5

- 4 : Estimated Annualized Benefits to T&E Species from WQ Improvements (Millions 201 3 $) a ,b

Discount
Rate State Option A Option B Option C Option D Option E

Low Medium High Low Medium High Low Medium High Low Medium High Low Medium High

3% AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01

MD $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01

SC $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 < $ 0.01 $ 0.01 <$0.01 < $ 0.01 $ 0.01 <$0.01 < $ 0.01 $ 0.01

Total $0.01 $0.01 $0.02 $0.01 $0.01 $0.02 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03 $0.01 $0. 02 $0.03

7% AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01

MD $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01

SC $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

Total $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.0 2 $0.0 3 $0.01 $0.0 2 $0.0 3 $0.01 $0.0 2 $0.0 3

Source: U.S. EPA Analysis, 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: Threatened and Endangered Species Benefits

September 29, 2015 5

- 9 

### Implications of Revised Steam Electric Plant Loading Estimates 5.6

As described in Section 1.4.3 , EPA revised its estimates of pollutant loadings in steam electric power plant discharges after completing the benefit analyses. The revisions affect baseline discharges of several pollutants with wildlife AWQC exceedances. For several reasons

— notably the fact that revisions do not affect all plants eq ually, and that species vulnerability to steam electric pollutants is determined based on threshold

effects

— it would be inappropriate to simply scale the monetized benefits based on the aggregate changes in loadings. The impacts of the loading revision s on estimated benefits to E&T specifies may be similar to that

discussed in Section 4.4 for water quality improvement benefits.



### Limitations and Uncertainties 5.7

Table 5

- 5 summarizes the caveats, omissions, biases

, and uncertainties known to affect EPA’s estimates of the benefits to T&E species and indicates the direction of the potential bias

.

Table 5

- 5 : Limitations and Uncertainties in the Analysis of T&E Species Benefits

Issue Effect on Benefits
Estimate Notes

The analysis is based loadings that were subsequently revised by EPA Overestimate Revised loadings are lower than the loadings used to estimate benefits. The changes indicate that number of exceedances of AWQCs may be lower in the baseline, and therefore th e improvements resulting from the ELGs may be lower. As

discussed in Section 4.4 , however, the magnitude of the overstatement may be small. Change in T& E populations

due to the effect of s team

e lectric ELGs is uncertain Uncertain Data necessary to quantitatively estimate population changes are unavailable. Therefore, EPA used best professional judgment to

assess reasonable changes in T&E populations. Actu al effects of

the final rule may be larger or smaller than p rojected changes in the population of T&E species assumed in this analysis

. Only those T&E species

listed as threatened or endangered on the Endangered Species Act are included in the analysis Underestimate The databases used to estimate benefits to T&E species exclude all species considered threatened or endangered by scientific organizations but not protected by the ESA . The magnitude of the underestimate is likely to be significant, since the proportion of imperiled fish and mussel species is high ( e.g. , Jelks et al 2008, Taylor et al 2007).

Benefit estimates do not

include monetized values for potential population increases

in Hellbender salamander ( Cryptobranchus alleganiensis a ) Underestimate It is likely that population increases in Cryptobranchus alleganiensis have value to the public. In addition to bequest, altruistic, and existence values, salamanders may have aesthetic

or cultural values. Salamanders also provide beneficial ecological services through indirect biotic control of species diversity and ecosystem processes, connection of energy and matter between aquatic and terrestrial landscapes, contributing to soil dynamics, and providing available st ores of energy and nutrients for tertiary consumers (David and Welsh 2004).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: Threatened and Endangered Species Benefits

September 29, 2015 5

- 10 Table 5

- 5 : Limitations and Uncertainties in the Analysis of T&E Species Benefits

Issue Effect on Benefits
Estimate Notes

Benefit estimates are likely to include only a subset of species that may be affected Underestimate EPA did not consider species for which water quality was not listed as an impor tant factor to species recover y . Because w ater quality issues may be important to species recovery even if not listed explicitly in species recovery plans this analysis may omit species that are likely to benefit from the final ELG

. Benefit transfer introduces

uncertainties Uncertain Value may over

- or understate true WTP values (See Section 4 for more details). Ecological roles filled by

T&E species Underestimate WTP values are unlikely to include changes to food

- webs and ecosystem stability as a consequence of the restoration (or loss) of T&E species. Overlap between WTP estimates for T&E species

and the WTP estimates for improvements in wa ter quality Overestimate There may be some overlap between WTP estimates for T&E species and the WTP estimates for improvements in water quality because WTP values for improvements in water quality

may inherently include benefits to T&E species. However, n one of the studies in EPA’s meta

- analysis of WTP for water quality improvements specifically mentioned or otherwise prompted respondents to include benefits to T&E species populations (see Chapter 4 ); therefore, any overlap is likely to be minimal

. WTP estimates do not take into account possible

substitution for effects for similar species. Overestimate WTP estimates may be affected by the availability of related species (Hoehn and Loomis, 1993), however Kahneman and Knetsch (1992) argue that substitution effects may not apply to the values associated with endangered species, because their uniqueness is the essence of their existence va lue.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

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- 1 

## 6 Benefits from Avoided Impoundment Failures

EP A has promulgated several rules affecting the steam electric industry recently : the Cooling Water Intake Structures (CWIS) rule for existing facilities (79 FR 48300), the CCR rule (80 FR 21302), and the CPP rule ( FR publication forthcoming). EPA approached analyses associated with each rulemaking carefully. EPA also recognizes that the steam electric industry complying with three regulations cumulatively in a very short period of time may choose a different compliance path than assumed in the analyses. The cumulative effect introduces uncertainty on the compliance path, and thereby on the benefits and costs associated with these

rules.

EPA expects that the operational changes prompted by the final ELGs will cause some plant owners to reduce their reliance on impoundments to manage coal combustion residuals. The CPP rule is likely to have similar effect on plant owners’ reliance on impoun dments. These changes will affect the future probability and/or magnitude of impoundment failures and the resulting accidental, and sometimes catastrophic, releases of coal

combustion residuals

. This rule takes the CPP rule into account in the baseline whe reas the CCR rule did not

, though it included the proposed CPP rule as a sensitivity analysis. Because the timing was such that the CCR rule did not include compliance with CPP in its main analysis, the benefits and costs for the ELGs of complying with the full set of rules may be higher than reported here, although EPA has done its best to incorporate the effect of all three rules in this analysis

. Benefits from the reduced risk of impoundment failures include avoided cleanup costs, environmental damage, a nd transaction costs. EPA’s analysis of the monetary value of avoided impoundment failures is

based on the identification of impoundments that would be affected by each of the regulatory options. EPA estimated benefits from avoided impoundment failures bas ed on the probability of a release for each impoundment in a given year, the capacity of the impoundment, and the cost (including cleanup costs, natural resource damages (NRD) and transaction costs) per gallon of coal combustion residuals slurry spilled.

B enefits are calculated as the difference between expected failure costs for a regulatory option and expected

failure costs under baseline conditions, over the period of 201 9 through 204 2

.



### Methods and Data 6.1

This section describes the methodology and data use d to determine the baseline and post

- compliance probability of impoundment failures, assign costs to the releases , and estimate the total present and annualized values of benefits resulting from the final ELGs

. As described below, the ELG analysis follows an approach similar to that used in the final CCR rule analysis (U.S. EPA, 2014), but uses ELG

- specific data and assumptions that reflect differences in the impoundment universe and failure probability. For example, the final ELG analysis considers a unive rse of 1,070 impoundments at 1,080 steam electric plants to which the ELGs apply (see Section 6.1.2 ) , which is larger than the 735 impoundments analyzed for the CC R rule. Fifty

- three of the impoundments in the Steam Electric universe are projected to close as a result of the CCR rule, leaving 1,017 impoundments in the ELG analysis

baseline. The ELG analysis also uses assumptions that reflect implementation of the CC R rule in the ELG baseline. Thus, the risk of an impoundment failure applied to big impoundments subject to wall breaches (0.044 percent ; see Section 6.1.1 ) reflects the residual risk remaining after implementation of the CCR rule, as compared to the higher h istorical rate of 0.09 percent used as baseline in the CCR rule analysis

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

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- 2 

### 6.1.1 Baseline Failure Probability and Release Quantity

EPA estimated the future pr obability of coal combustion residuals releases from impoundments based on historical trends, accounting for the primary types of releases applicable to different types of impoundments. The approach builds on the methodology used by EPA Office of Resource Conservation and Recovery (ORCR) for the analysis of final regulations governing the disposal of CCR in impoundments ( i.e. , “CCR rule”; U.S. EPA, 2014).

To determine the frequency of releases, EPA used data from a survey of impoundments conducted in suppo rt of the CCR rule (U.S. EPA, 2012d ). The surveyed plants had a total of 656 CCR impoundments, and the survey obtained information from owners and operators about impoundment releases between 1999 and 2008. Two of the survey respondents also provided data for two additional releases that occurred in 1995 and 1998. In total, the survey provides data for 49 relevant historical releases 49 

### over a total of 6,565 impoundment

- year observations. 50



### In response to the benefit analysis for the proposed ELGs, EPA receiv ed several comments noting that seepage events, which account for 13 of the 49 release events, are not likely to result in significant cleanup or other types of costs. EPA excluded these 13 seepage events from the historical release data used to determine the frequency of relevant releases. The remaining 36 releases are summarized in Appendix J

. EPA sorted the 36 releases into two categories based on the circumstances and cause of the release: wall

breaches (4 releases), which are structural failures of ext ernal perimeter embankments, and other releases (32 releases). These other releases include overtopping (8 failures); miscellaneous causes (13 failures) such as sink hole, stack failure, pump failure, hydraulic dredging pipe failure, liner perforation, int ernal dike breach (not perimeter), seal failure, discharge structure disturbed during maintenance, and embankment slough; or unknown causes (11 releases).

EPA assumes, and the historical release data shows, that the potential for wall breaches exists only for large impoundments that meet certain structural integrity “factor of safety” design criteria specified in the CCR rule. These impoundments (labeled big for the purpose of the analysis) are those with:

 Height (impounding elevation) of five feet or mor e above the upstream toe, and s torage volume of 20

acre

- feet or more, or

 Height (impounding elevation) of 20 feet or more above the upstream toe.

Impoundments that do not meet either criterion (labeled small ) are assumed not susceptible to failure by wall breach, but susceptible only to other types of releases. Based on these criteria, the ORCR survey (U.S. EPA,

2012d ) includes 444 big impoundments and 212 small impoundments. EPA calculated the frequency of

releases based on the 36 historic releases for eac h type of failure (wall breach vs. other) and type of impoundment (big vs. small).

49 This differs from the risk of failure estimated for the proposed ELG. For the proposed rule analysis, EPA had counted 42 CCR pond damage cases rather than the current count of 49 cases. EPA corrected the survey database for nine cases that had been entered as single incidents but were in fact separate incidents, leading to
an interim total of 52 cases. Two of these 52 cases did not involve coal combustion residuals ponds (they
inv olved a metal cleaning waste basin and a coal pile sump) and one case did not involve the release of impounded coal combustion residuals material (it involved an oil and grease exceedance due to servicing of a pump located near a coal combustion residuals pond).

50 The survey responses provided 10 years of data for 656 impoundments, an additional 4 years of data for one impoundment, and an additional year of data for another impoundment, for a total of 6,565 observations (656 × 10 + 1×4 + 1×1).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 3 

### The probability of a release is assumed to be uniform over time and across all impoundments within a category (big or small), irrespective of other impoundment characterist ics such as age, amount of coal

combustion residuals managed, etc. In practice, the probability of a release may depend on impoundment characteristics and could therefore change as a result of the final rule . However, EPA did not have sufficient

data to mo del the probability as a function of impoundment characteristics.

The impoundment survey conducted by ORCR (see U.S. EPA, 2012d ) provides data on the release volume and impoundment capacity for 17 of the 36 documented releases. For each type of release and impoundment category, EPA calculated a “capacity factor” that represents the ratio of gallons of coal combustion residuals

released compared to the design capacity of the impoundment involved in the release

.
Table 6

- 1 summarizes the historical probability of impoundment release and the capacity factor by release and impoundment type.

Table 6

- 1 : Probabilities of CCR impoundment releases, based on analysis of 49 historical release events 1995

– 2008

Impoundment Type

– Big

( at least 5 feet high AND at least 20 acre

- feet OR at least 20 feet high independent of volume)
Impoundments with observations for 10 years 444
Additional observations (impoundment

- years) 4

Total number of observations 4,444
Release Type Wall Breach Other

Number of releases 4 26
Probability of a release

– number of releases per impoundment per year 0.09% 0.59%
Capacity factor

– volume released as percent of capacity 27.42% 2.91%

Impoundment Type

– Small ( Does not fit "big" criteria)
Impoundments with observations for 10 years 212
Additional observations (impoundment

- years) 1

Total number of observations 2,121
Release Type Wall Breaches Other

Number of releases Not applicable a 6
Probability of a release

– number of releases per impoundment per year Not applicable a 0.28%
Capacity factor

– volume released as percent of capacity Not applicable a 0.41%

a. Small impoundments only incurred "other" releases (no wall breaches).
Source: U.S. EPA Analysis, 2015



### EPA adjusted the release probabilities shown in Table 6

- 1 to account for implementation of the final CCR

rule. 51 

### The rule establishes minimum national criteria for the storage of CCR in surface impoundments at

coal

- fired electric utility plants, regu lating CCR as a non

- hazardous waste. It requires standardized pollution control measures as well as monitoring and corrective actions in the event of a leak. Additionally, it subjects all impoundments to location restrictions, design and operating conditio ns, groundwater monitoring, closure requirements, and post

- closure care. For more details on the CCR rule, see U.S. EPA (2014).

As detailed in U.S. EPA ( 2014 ), some plant owners may close their existing impoundments in response to the CCR rule, effectivel y eliminating the probability of a future release from these impoundments. For

51 The CCR rule is not expected to affect the capacity factors in the event of a release.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 4 

### impoundments that remain operational, the CCR rule is expected to reduce the release rate through the implementation of structural integrity programs, including regular inspecti ons and other safeguards. For these impoundments, EPA estimated that the CCR rule would reduce the probability of releases from big impoundments to 0.044 percent annually for wall breaches and to 0.16 percent for other releases (compared

with 0 .09 percent and 0.59 percent, respectively, in Table 6

- 1 ). For small impoundments, EPA expects that the CCR rule will reduce the probability of a release from 0.2 8 percent ( Table 6

- 1 ) to 0.07 percent. See U.S. EPA (2014) for more detail on these release probability assumptions.

Table 6

- 2 summarizes the release probability and capacity factor assumptions used for big and small impoundments in this analysis , taking the effects of the CCR rule into account

.

Table 6

- 2 : Release Probability and Capacity Factor Assumptions for the ELGs

Type Annual Probability of Release Capacity Factor

Wall Breach Other Release Wall Breach Other Release
Big 0.044% 0.16% 27.42% 2.91%

Small NA 0.07% NA 0.41%

Source: U.S. EPA Analysis, 2015



### 6.1.2 Effects of the ELG s

The 1,080 steam electric power plant s subject to the Steam Electric ELG s have a total of 1,070 impoundments. Following implementation of the CCR rule, EPA expects 1,017 of these impoundments to continue operation as of 2023

. As discussed in Section 1.3 , EPA included the effects of the CPP rule in the baseline for the ELG analysis. EPA projects that the CPP rule will result in some generating unit ceasing operation (retiring) or converting, which will also affect impoundments that receive wastestream from the units. Due to the uncertainty in plant owners’ decision to continue to operate impoundments at plants where some but not all generating units are converted or retire, EPA estimated the i mpacts of the CPP in two ways:

 Low bound estimate: EPA a djusted impoundment capacity only for those plants where all units that send wastewater streams to an impoundment retire as a result of the CPP rule

.  High bound estimate: EPA adjusted impoundment capa city considering the share of the generating capacity of units that retire as a result of the CPP rule

.
This CPP

- adjusted universe of impoundments (883 to 925 impoundments) represents the baseline for the ELG analysis , i.e., EPA evaluates the incremental impacts of the ELG on those impoundments EPA determined

would handle coal combustion residuals wastestreams by the time the plant would comply with the ELG s after accounting for both the CCR and CPP rule s

. EPA categorized these impoundments as big or smal l following the criteria outlined in Section 6.1.1 . EPA used the Steam Electric Industry survey (U.S. EPA, 2010c ) as the primary source of impoundment size data, supplemented by information collected in the CCR survey (see U.S. EPA 2012d ) for impoundments that are

common to both data sets. 52 

### Table 6

- 3 summarizes the results

.

52 To categorize each of the impoundments as big or small, EPA imputed impoundment height based on the volume, difference in elevation, and the reported maximum berm height from U.S. EPA (2010b). Together with this height data, EPA used the reported capacity to categorize impoundments that are clearly either big or small. EPA used ORCR impoundment data (from U.S. EPA 2010c) to categorize an additional 69 impoundments
where there was clear overlap between the two impoundment data sets. EPA categorized an additional 187 impoundments as big based on at least one height indicator being over 20 feet or at least one height indicatorBenefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 5 Table 6

- 3 : Steam Ele ctric Impoundments by Size in ELG Baseline

Type Number of Impoundments Impoundment Capacity

Count Percent of
Total Total (million gallons) Percent of
Total Low Bound High Bound Low Bound High Bound
Big 632 668 72% 687,758 713,317 95%

Small 251 257 28% 34,336 34,348 5%

Total 883 925 100% 722,094 747,665 100%

Source: U.S. EPA Analysis, 2015 

### For each of these impoundments, EPA estimated the expected number of magnitude of releases for each year between 2019 and 2042, and estimated the expected costs associated with the releases. EPA did this

calculation for the baseline and under each of the f ive regulatory options.

Specifically, EPA used the probability of release established based on the historic data and revised to account for implementation of the CCR rule ( Table 6

- 2 ) to estimate the expected number of releases from each of the steam electric power plant impoundments in the baseline. EPA used the applicable capacity factor to estimate the volume involved in a release from each impoundme nt. For example, EPA assumes that an “other” release from a big impoundment involves a volume of coal combustion residuals equal to 2.91 percent of the impoundment’s capacity. Figure 6

- 1 illustrates assumptions used for the baseline scenario.

The Steam Electric ELG is anticipated to change how plant owners or operator handle their coal combustion

residuals , potentially reducing the quantity of FGD soli ds or ash managed using impoundments. This lower

coal combustion residuals volume is in turn expected to reduce the amount of coal combustion residuals accumulating in the impoundments in any given year. For this analysis, EPA assume d that the amount of co al

combustion residuals released in the event of a wall breach or other release is reduced in proportion to the

reduction in the amount of coal combustion residuals handled by the impoundment. Thus, for each scenario, EPA assumed that an impoundment has the same expected number of releases post

- compliance, but calculated the volume released by multiplying the capacity factor by an adjusted volume that reflects the

reduction in the amount of coal combustion residuals handled wet (see U.S. EPA, 2015g; DCN S E05831 (CBI
version / DCN SE05832 (non

- CBI version ) for the calculation s)

. EPA assumed that the reduction in volume occurs in the same year as the technology implementation year assumed in other parts of the analysis (see

Section 1.5.3 ).

being over 5 feet together with capacity over 20 acre

- feet. For impoundments for which no height data was provided, EPA categorized impoundments based on capacity only.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 6 Figure 6

- 1 : Baseline Release Probability and C apacity Factor Assumptions

1 Changes in the risk of impoundment failure in impoundments affected by the CPP rule are not attributable to the ELGs and do n ot generate benefits attributable to the ELGs in this analysis.



### 6.1.3 Costs of a Release

The following sections discuss three categori es of costs associated with impoundment releases: cleanup, NRD, and transaction costs. All dollar values are presented in year 2013 dollars. 53

6.1.3.1 Cleanup Costs



### EPA estimated per

- gallon cleanup costs based on five historical impoundment failures. The average unit cost

associated with these historical incidents is $ 1.35 per gallon released ( see Table 6

- 4 ). Details on the five incidents are provi ded below and reflect additional research EPA conducted since the proposed ELG analysis.

53 As needed, costs were updated to 2013 dollars using the Construction Cost Index from the Engineering News Report (unless otherwise indicat ed).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

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- 7

Table 6

- 4 : Documented Cleanup Costs from Impoundment Releases

Incident Volume Spilled

(million gallons) Cleanup Cost

( millions; 201 3 $) Unit Cleanup

Cost (201 3 $)

Oak Creek 5 $13 $2.83

TVA Widows Creek 6 $10 $1.64

Martins Creek 100 $62 $0.62
Massey 230 $89 $0.39
TVA Kingston 1,100 $1,379 $1.25
Average 288 $311 $1.35



### The Oak Creek release occurred in October 2011 when a bluff collapsed near an ongoing construction project at the Wisconsin Energy Oak Creek power plant, releasing 22,720 cubic yards (or 4.6 million gallons) of coal ash into Lake Michigan. The collapse als o carried “ debris from the construction worksite, including vehicles, heavy machinery, a filter press, a frac tank and four miscellaneous conex boxes filled with unknown amounts of miscellaneous equipment

, down the bluff and into Lake Michigan .” (Wisconsin Department of Justice (DOJ), 2013). Wisconsin Energy’s immediate response to the spill included the placement of 1,500 feet of containment berms and booms and a geotechnical analysis of the stability of the bluff (Wisconsin DOJ, 2013; Jones and Behm, 2011 ). Additional response actions included excavation and removal of the material that had been spilled, with cleanup being complete by the end of November 2011 (Wisconsin DOJ, 2013). According

to the U.S. EPA On

- Scene Coordinator (U.S. EPA OSC, 2012), Wiscon sin Energy reported spending $12.1 million on cleanup and restoration. After updating to 2013$ (for a total of $13 million), unit costs are $2.83 per gallon spilled.

The Tennessee Valley Authority ( TVA ) Widows Creek spill involved the release of 6.1 millio n gallons of gypsum, water, and fly ash. Updated to 2013$, cleanup costs for dredging of the creek and other activities were approximately $10 million (TVA, 2009), or $1.64 per gallon spilled.

The Martins Creek release involved the discharge of 100 million gallons of slurry over the course of 3 days, resulting from the failure of a wooden stop log. In its 2006 annual report (PPL Corporation, 2006) , PPL Corporation estimated that the costs of the remediation effort were $48 million. After updating the revise d costs to 2013$ (for a total cost of $62 million), the unit cost of this spill is $0.62 per gallon spilled

. The Massey Coal slurry spill involved the collapse of a 2,000 acre

- foot (651.7 million gallon) surface impoundment on top of an idled underground mine. The Massey Energy 2002 annual financial report (10

- K; Massey Energy, 2002) states that the release invol ved 230 million gallons and that Massey incurred a total of $58.3 million of cleanup costs in connection with the spill. Updated to 2013$ (for a total cost of $89 million), the unit cost for this spill is $0.39 per gallon spilled.

The TVA Kingston release involved approximately 1.1 bill ion gallons of slurry being released from an ash pond onto 300 acres, primarily the Watts Bar Reservoir and shoreline property. The spill also damaged three homes, interrupted utility services, and blocked a local road (TVA, 2009). Cleanup activities included ash dredging and processing, ash disposition, infrastructure repair, dredge cell repair, dike reinforcement, construction of temporary ash storage basins, and others. Updated to 2013$, t he cleanup costs were approximatel y $1.38 billion (TVA, 2010; 2011), or $1.25 per gallon spilled.

In early February 2014, a coal ash release occurred at the Dan River power plant operated by Duke Energy in North Carolina. The release of 24 to 27 million gallons of ash and ash pond water wa s caused by a break in a

48

- inch stormwater pipe beneath the ash basin (Duke Energy, 2014). Cleanup involved the removal of 2,500Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 8 

### tons of ash and contaminated sediment that settled against a dam, and an additional 500 tons that settled in other parts of th e river and municipal water treatment settling tanks (Associated Press, 2014).

Duke Energy has stated that company investors and insurers will pay for the cleanup rather than ratepayers (Duke Energy, 2014). Duke Energy’s second quarterly financial report in 2014 reports that the company spent approximately $20 million in repairs and remediation related to the spill through June 2014, and completed cleanup in July. Additionally, according to a plea agreement signed by Duke Energy subsidiaries on May 14,

201 5, Duke Energy agreed to spend $34 million for environmental damages, including a $24 million payment to the National Fish and Wildlife Foundation to benefit riparian areas of North Carolina and Virginia, plus $10 million in wetland mitigation bank credits

. T otal cleanup costs , transaction costs, and NRD were not available at the time that EPA conducted this analysis

, however, and this incident was therefore not included in the cleanup costs shown in Table 6

- 4

, nor used in this analysis.

6.1.3.2 Natural Resource Damages



### Israel (2006) provides a detailed state

- by

- state summary of NRD programs, including some prominent cases (arising from oil spills, chemical spil ls, and other incidents) in each state . Israel (2013) provides an updated version of the accounting with additional cases identified since the 2006 study. Appendix J lists the 137 NRD cases from Israel (2006; 2013) that provide quantitative estimates of NR D restoration and compensation

costs. 54



### Releases resulting from impoundment failures may affect resources similar to those that were affected by some of the NRD cases identified in Israel (2006; 2013) and therefore would be expected to have similar

NRD cos ts. Of the 137 NRD settlements identified in Israel (2006; 2013), EPA identified 65 cases that are relevant to this analysis based on the resources affected and the general circumstances of the releases. In particular, EPA excluded as potentially less rele vant settlements for NRD, which, based on the description provided in Israel, involved damage to groundwater only or to ocean/coastal resources, or resulted from legacy pollution associated with Superfund sites.

Table 6

- 5 shows summary statistics for the 65 NRD settlements EPA retained as relevant to this analysis, compared to the summary statistics for the cleanup costs in Table 6

- 4 . As shown in the table, the mean NRD settlement is approximately $13.7 million, or 4 percent of the mean total cleanup cost.

54 NRD does not include cleanup costs (or legal and transaction costs, if reported) but includes only the resource restoration and compensation values. For example, in one case, Israel (2006) reported that "In total, the State's claim was $764 million , $342 million of which was restoration cost damages, $410 million of which was compensable value damages, and $12 million of which was assessment and legal costs." For this case, EPA used the sum of $342 million and $410 million as NRD ( i.e. , excluded ass essment and legal costs). EPA used values for individual cases discussed in the study, focusing exclusively on NRD and excluding or subtracting assessment costs when those costs were reported separately. EPA also excluded NRD values that represent the

aggr egate of several cases when it was not possible to discern NRD for individual cases.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 9 Table 6

- 5 : NRD Settlements Summary Statistics

Natural Resource Damages

(2013$) a Cleanup Costs (2013$) b NRD as a Percent of Cleanup Costs

Minimum $44,000 $10,000,000 0%

Median $1,965,000 $62,000,000 3%

Mean $13,723,000 $311,000,000 4%

Maximum $439,545,000 $1,379,000,000 32%

a. Based on Israel (2006; 2013); updated using changes in the GDP. If a year is not provided for a case, EPA updated the value
based on the year of the study that identified the case ( i.e.

, 2006 or 2013).
b. Based on cleanup costs in Table 6

- 4

.



### To estimate expected NRD costs for future impoundment releases, EPA assumed that the NRD varies depending on the magnitude of the release in proportion to cleanup costs. To derive a per

- gallon estimate of the expected NRD value for future releases, EPA divided the mean NRD settlement of $13.7 million by the mean cleanup cost ($311 million). T his calculation yields an NRD estimate of 4 percent of cleanup costs, or $0.05 per gallon spilled (4 percent of $1.35 /gallon)

. Note that this estimate provides an approximate value for NRD resulting from impoundment failures as a

function of the volume o f coal combustion residuals released, and is appropriate for analyses that look at NRD over a range of locations and circumstances. EPA expects that actual damages from any given release would be highly location

- and release

- specific.

6.1.3.3 Transaction Costs



### For this analysis, transaction costs include the costs associated with negotiating NRD, determining responsibility among potentially responsible parties, and litigating details regarding settlements and

remediation. 55 

### EPA estimated transaction costs based on d ata showing transaction costs as a share of total cleanup costs at Superfund sites and the share of spending that represents total transaction costs. Table 6

- 6

shows the data sources.

Table 6

- 6 : Studies Summarizing Transaction Costs as a Share of Superfund Spending (for potentially responsible parties)

Acton (1995) 27%
Acton and Dixon (1992) 17%
Dixon, et al. (1993) 32%
Steinhardt et al. (1994) 33%
Average 27%
Multiplier (transaction costs as a share of cleanup cost) a 37%

a. Multiplier is calculated as Average/(1

- Average)



### These data indicate that, on average, transaction costs represent 27 percent of total costs . As the purpose is to estimate unit costs per gallon spilled, transactions costs represent 37 percent of the subset of costs that are

55 These activities involve services, whether performed by the complying entity or other parties, that EPA expects would be required in the absence of this final rule in t he event of an impoundment failure . Accordingly, it is appropriate to account for the avoided resource cost of these services as social benefits in the benefit

- cost analysis. Note that the transaction costs do not include fines, cleanup costs, damages, or other costs that
constitute transfers or are already accounted for in the other categories analyzed separately.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 10 

### cleanup costs, calculated as 0.27/(1

- 0.27)

. There fore, the estimated transaction costs per gallon of coal

combustion residuals slurry spilled are $0.50 (37 percent of $1.35/gallon).

6.1.3.4 Total Release Costs



### Table 6

- 7 summarizes unit costs for cleanup costs, NRD, and transaction costs. Total impoundment release costs are $1.90 per gallon spilled. 56

Table 6

- 7 : Unit Costs for Impoundment Releases (2013$)

Cost Component Unit Cost ($/gallon spilled)

Cleanup costs $1.35

NRD $0.05

Transaction costs $0.50

Total costs $1.90



### Results 6.2

The final ELGs will provide benefits by reducing the impact of releases from impoundments that are expected

to see reduced utilization as a result of the final rule , but would continue to operate in the absence of the final

rule . For each of the impoundmen ts included in the analysis, EPA calculated the difference between the annualized costs from future expected failures under the baseline and each analyzed regulatory option. The calculation involves the following steps:

 Multiplying the release rate for a g iven i mpoundment and year by the capacity factor, impoundment volume (adjusted if needed to reflect the effects of the ELG), and total unit costs for the release (including cleanup, NRD, and transaction costs);

 For each regulatory scenario, subtracting th e costs of expected releases from the cost of expected releases under the baseline; and

 D iscounting for future years, aggregating across the analysis time horizon (201 9 to 204 2 ), and annualizing over a 24

- year period using rates of 3 percent and 7 percent

.
Table 6

- 8 shows the total number of impoundment failures estimated over the period of 2019

- 2042 in the baseline and under each of the five regulatory options

. E xpected failures are reported as a range to reflect the range of the anticipated effects of the CPP rule discussed in Section 6.1.2

. These values reflect the number and types of impoundments (big and small) and the associated failure probability for each type of failure (wall breach and other). Table 6

- 9 shows the estimated volume of coal combustion residuals released annually in these expected failures , after full compliance by all steam electric power generating plan ts. Estimates of the

coal combustion residuals volume released reflect the size of the impoundments involved in the expected failures and the capacity factor for each type of failure.

The number of failures estimated in the ELG baseline (approximately 8 wa ll breaches and 33 other failures over 24 years) is consistent with the post

- compliance failures estimated in the final CCR rule analysis (15 wall breaches and 91 other releases over 100 years; see U.S. EPA 2014), given differences in the impoundment unive rse noted in the introduction to this Chapter. Similarly, the number of avoided failures estimated to result from implementation of the final ELGs (Option D), which is approximately 2 wall

56 In the economic analysis of the proposed rule, EPA capped release costs for any single incident at $1.3 billion based on the total estimated cleanup costs of the TVA Kingston spill. However, EPA removed this cap for the final rule analysis, since future incidents could feasibly exceed t he damages associated with Kingston.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 11 

### breaches and 7 other releases over 24 years, seems consistent with the number of avoided failures estimated for the final CCR rule (32 wall breaches and 429 other releases over 100 years; see U.S. EPA 2014), given the differences in the universe of impoundments and in assumptions regarding the failure rate for the ELG

ana lysis.

Table 6

- 8 : Total Expected Number of Releases in 2019 through 2042

, by Failure Type

Regulatory Option Expected Number of Failures Reduction in Expected Number of Failures

Wall Breaches Other Releases Wall Breaches Other Releases

Low Bound Estimate of CPP Effects

Baseline 7.7 32.6 0.0 0.0

Option A 7.3 30.8 0.4 1.7

Option B 7.3 30.8 0.4 1.7

Option C 6.7 28.1 1.0 4.5

Option D 6.1 25.7 1.6 6.9

Option E 6.1 25.7 1.6 6.9
High Bound Estimate of CPP Effects

Baseline 7.7 32.6 0.0 0.0

Option A 7.2 30.4 0.5 2.1

Option B 7.2 30.4 0.5 2.1

Option C 6.5 27.4 1.2 5.2

Option D 5.8 24.4 1.9 8.1

Option E 5.8 24.4 1.9 8.1

Source: U.S. EPA Analysis, 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 12 Table 6

- 9 : Expected Total C oal C ombustion R esiduals Volume Release d Annually

, by Failure Type (Million Gallons)

Regulatory Option Estimated CCR Volume Released Estimated Reduction in CCR Volume
Released a

Wall
Breaches Other

Releases All Releases Wall
Breaches Other

Releases All Releases

Low Bound Estimate of CPP Effects

Baseline 125 48 173 0 0 0

Option A 115 45 160 9 4 13

Option B 115 45 160 9 4 13

Option C 88 34 122 37 14 51

Option D 82 32 114 42 16 59

Option E 82 32 114 42 16 59
High Bound Estimate of CPP Effects

Baseline 125 48 173 0 0 0

Option A 114 44 158 10 4

- 4 14

Option B 114 44 158 10 4

- 4 14

Option C 86 33 119 39 14

- 15 54

Option D 79 31 110 45 16

- 18 63

Option E 79 31 1 10 45 16

- 18 63

Source: U.S. EPA Analysis, 2015

a. The values reflect reductions in the volume of coal combustion residuals released in expected failures for years 2023

- 2042

, after compliance by all steam electric power generating plants. Reductions in years 2019

- 2022 are less than re ported in this table due to
the assumed distribution of permit renewals and control technologies implementation over the period of 2019 through 2023

.



### Table 6

- 10 shows the total benefits of avoided impoundment failures, calculated as the sum of the avoided release costs across all impoundments expected to be affected by the final rule under each analy zed regulatory option. The range of benefits reflects the range of the anticipated effects of the CPP rule discussed in Section

6.1.2 . For each option, avoided wal l breaches account for 72 percent of benefits, while other releases account for the remaining 28 percent.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 13 Table 6

- 10 : Estimated Annualized Benefits of Avoided Impoundment Failures by Release Type (Millions; 2013$) a

Discount
Rate Regulatory

Option Wall Breaches Other Releases All Releases

Low Bound High Bound Low Bound High Bound Low Bound High Bound

3% Option A $14.5 $16.5 $5.6 $6.4 $20.1 $22.9
Option B $14.5 $16.5 $5.6 $6.4 $20.1 $22.9
Option C $59.3 $62.7 $22.9 $24.2 $82.2 $86.9
Option D $68.8 $74.1 $26.7 $28.8 $95.6 $102.9
Option E $68.8 $74.1 $26.7 $28.8 $95.6 $102.9

7% Option A $11.6 $13.3 $4.5 $5.1 $16.1 $18.4
Option B $11.6 $13.3 $4.5 $5.1 $16.1 $18.4
Option C $47.9 $50.7 $18.5 $19.6 $66.4 $70.3
Option D $55.9 $60.3 $21.7 $23.4 $77.7 $83.7
Option E $55.9 $60.3 $21.7 $23.4 $77.7 $83.7

Source: U.S. EPA Analysis, 2015
a. Baseline value of total failure costs minus option value of total failure costs.



### Implications of Revised Steam Electric Plant Loading Estimates 6.3

As described in Section 1.4.3 , EPA revised its estimates of pollutant loadings in ste am electric power plant discharges after completing the benefit analyses. Estimated benefits from avoided impoundment failures do not depend on pollutant loadings and therefore the monetized benefit estimates provided in this Chapter are unaffected by revi sions to steam electric plant loading estimates.



### Limitations and Uncertainties 6.4

T able 6

- 11 summarizes the limitations and uncertainties in the analysi s of benefits associated with reduced impoundment failures arising from the final rule . The methodologies used in this analysis involve several simplifications and sources of limitations and uncertainties, as described below. These uncertainties add to the limitations and uncertainties inherited from the EA analysis and data (see U.S. EPA, 2015a)

. Whether these

limitations and uncertainties, taken together, are likely to result in an understatement or overstatement of the

estimated benefits is not known

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6

- 14 T able 6

- 11 : Limitations and Uncertainties in Analysis of Avoided Risk of Impoundment Failure Benefits

Uncertainty/Assumption Effect on Benefits
Estimate Notes

The analysis assumes that, in the
absence of the final rule , all impoundments would continue to operate in the baseline during the entire period of analysis. Overestimate Plant owners may close existing impoundments or make other changes to their operations that would reduce the baseline probability of f ailure. Not accounting for these baseline conditions may
overstate the benefits of the final rule

. The analysis accounts for projected closures of steam electric plant impoundments due to the CCR rule as of
2023 (53 impoundments). The analysis
does not account for additional projected
closures in 2024

- 2042 due to the CCR
rule

. Overestimate To the extent that additional impoundments are projected to close due to the CCR rule after implementation of the ELGs and EP A estimated benefits for these closures as part of the CCR rule analysis, EPA may be overstating the incremental
benefits of the ELGs. The magnitude of
overstatement is unknown but is expected to be small given the relatively few closures projected overall in
the CCR rule analysis (98 impoundments close by 2114 out of the total of 735 impoundments analyzed ; see U.S. EPA 2014 ), the timing of these projected
future closures

, and the effects of discounting on annualized benefit estimates . Further more , EPA’s
ad justment to the ELG benefit analysis to reflect impoundment closures due to the CCR rule is consistent with the adjustment to ELG compliance

costs. EPA estimated expected future impoundment releases from big and small impoundments based on uniform
release rates for wall breaches and other
releases . In practice, the probability of failure may depend on impoundment
characteristics and management practices

. Uncertain U sing a uniform failure rate may understate benefits
of the final rule

. Conversely, the historical failure rate may overstate projected failures under baseline conditions by not reflecting the effects of any recent changes in impoundment management practices ( e.g. , revised inspection and monitoring programs).

The analysis u ses a uniform cost of $ 1.90 per gallon spilled , including cleanup costs, natural resource damages,
and transaction costs. Uncertain There is significant uncertainty involved in estimating the costs of unknown future release incidents, and these estimates are based on a small and highly variable sample of historic releases. The costs of future releases may be substantially higher or lower than the cost estimates applied in this analysis, depending on site

- specific factors, including the ecosystems, infrastr ucture, and other resources damaged by the release.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 1 

## 7 Air

- Related Benefits

The final rule is expected to affect air pollution through three main mechanisms: 1) additional auxiliary electricity use by steam electric power plant s to operate wastewater treatment, ash handling, and other systems needed to comply with the new effluent limits and standards; 2) additional transportation

- related emissions due to the increased trucking of CCR waste to on

- si t e or off

- site landfills; and 3) the change in the profile of electricity generation due to relatively higher cost to generate electricity at plants incurring compliance costs for the ELGs . The different profile of generation can result in lower or higher air pollutant

emissions due to differences in emission factors. Thus, small reductions in coal

- based electricity generation as a result of the ELGs are compensated by increases in generation using other fuels or energy sources

– biomass, landfill gas, natural gas, nuclear power, oil, a nd wind power. For example, as detailed in Section 10.6 of the RIA (U.S. EPA, 2015c), IPM projects a 0.3 percent decline in electricity generation from coal ( 3,276 GWh) , as a result of the final ELGs (Option D); this decline is offset by a 0.1 percent incr ease in natural gas generation (1,964 GWh) and additional increases in electricity generation from waste coal, wind,

and biomass

. The changes in air emissions reflect the differences in emissions factors for these other fuels or sources of energy , as compa red to coal.

In this analysis, EPA estimated the human health and other benefits resulting from net changes in emissions of three pollutants: NO X 

### , SO 2 

### , and CO 2


### .
NO

x 

### and SO

x 

### ( which includ e SO 2 

### emissions quantified in this analysis) are known precursors to fine particles (PM 2.5 

### ) air pollution, a criteria air pollutant that has been associated with a variety of adverse health effects

– most notably, premature mortality. 57 

### In addition, in the presence of sunlight, NO X 

### and VOCs can undergo a

chemical reaction i n the atmosphere to form ozone. Depending on localized concentrations of volatile organic compounds (VOCs), r educing NO X 

### emissions would also reduce human exposure to ozone and the incidence

of ozone

- related health effects. Reducing emissions of SO 2 

### and NOx would also reduce ambient exposure to

SO 2 

### and NO 2 

### , respectively. For the purpose of this analysis, EPA quantified only those benefits from

associated reductions PM 2.5


### . 58



### CO 2 

### is an important greenhouse gas that is linked to climate change effects, i ncluding: an increase in temperature; sea level rise; changes in weather patterns toward an intensified water cycle with stronger floods and droughts ; and stress on ecosystems, especially in the Arctic, mountain and tropical areas, resulting in the

shift o f species habitat range. The expected economic losses from climate change include reduced agricultural yields , human health risks, property damages from increased flood frequencies, the loss of ecosystem services, etc. Increased CO 2 

### levels also affect biol ogical systems independent of climate change. For example, oceans become markedly more acidic, endangering coral reefs and potentially harming fisheries and other

marine life ( Intergovernmental Panel on Climate Change (I PCC ), 2014)

.

57 Sulfur oxides (SOx) include sulfur monoxide (SO), sulfur dioxide (SO 2 ), sulfur trioxide (SO 3 ) and other sulfur oxides. In this analysis, EPA analyzed changes in emissions of SO 2 only.

58 The Integrated Science Asses sment for Particulate Matter (PM ISA) ( U.S. EPA, 2009b ) identified the human health effects associated with ambient PM2.5 exposure, which include premature morality and a variety of morbidity effects associated with acute and chronic exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical Oxidants (Ozone ISA) ( U.S. EPA, 2013 c ) identified the human health effects associated with ambient ozone exposure, which include premature morality and a variety of morbidity effects

ass ociated with acute and chronic exposures.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 2 

### Data and Methodology 7.1

7.1.1 C hanges in Air Emissions

As discussed in the RIA (Chapter 5: Electricity Market Analyses) , EPA used the Integrated Planning Model (IPM) to estimate the electricity market

- level effects of two of the five regulatory options (Options B and D ;

see Chapter 5 in RI

A (U.S. EPA, 2015c )). IPM outputs include NO

x 

### , SO 2 

### , and CO 2 

### emissions to air from electricity generating units (EGU). Comparing these emissions to those projected for the base case provides an assessment of the changes in air emissions resulting from changes in the profile of electricity generation

under the final rule . EPA used four run years, 2020 , 2025, 2030, and 20 4 0, to represent the periods of 201 9

- 202 2

, 2023

- 2027, 2028

- 2033, and 20 34

- 20 42 , respectively (for a more detailed discussion of the IPM analysis years, refer to Chapter 5 in RIA )

. EPA developed separate estimates of air emissions associated with increases in electricity generation to power wastewater treatment systems by multiplying plant

- specific additional electricity consumption estimated as part of the engineering analysis by plant

- or North American Electric Reliability Corporation ( NERC )

- specific

emission factors obtained from IPM for each analysis year. EPA estimated air emissions associated with increased trucking by multiply ing the number of miles by average emission factors. Details of these two analyses are provided in the TDD

.
Table 7

- 1 through Table 7

- 3 summarize the estimated changes in emissions for the three mechanisms, the three pollutants, and the two regulatory options covered in this particular analysis. As shown in the table s , EPA estimates that changes in auxiliary service ( Table 7

- 1 ) and transportation ( Table 7

- 2 ) would result in an increase in emissions (positive val ues), while changes in the profile of electricity generation ( Table 7

- 3 )
would reduce CO 2 

### , SO 2 

### and NOx emissions (negative values). Table 7

- 4 presents the net emissions changes

across the three mechanisms.

The largest effect on projected air emissions is due to the change in the emissions profile of electricity generation at the market level. As presented in the RIA ( Section 10.6 : Executive Order 13211: Actions Concerning Regulations That Significantly Affect Energy Su pply, Distribution, or Use ), IPM projects small

reductions in electricity generation coming from coal as a result of the ELGs ( less than 0.1 percent for Option B ; 0.3 percent for Option D ), which is compensated with increases in generation using other fuel s or

energy sources

– biomass, landfill gas, natural gas, nuclear power, and wind power. The changes in air emissions reflect the differences in emissions factors for these other fuels, as compared to coal.

Table 7

- 1 : Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to Increase in Auxiliary Service at Steam Electric Power Plant s , Relative to Baseline

Regulatory Option Year Electricity Consumption (MWh) CO 2 (Metric

Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option B 2015

- 2018 0.0 0.0 0.0 0.0

2019 30,859.8 27,409.7 13.9 35.9

2020 41,529.5 36,972.7 20.5 45.4

2021 72,651.5 63,257.8 35.4 69.4

2022 86,488.4 75,759.4 45.2 75.6

2023

- 2042 102,168.7 89,632.5 59.6 86.7Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 3 Table 7

- 1 : Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to Increase in Auxiliary Service at Steam Electric Power Plant s , Relative to Baseline

Regulatory Option Year Electricity Consumption (MWh) CO 2 (Metric

Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option D 2015

- 2018 0.0 0.0 0.0 0.0

2019 60,680.1 55,222.0 35.3 59.2

2020 94,172.8 83,855.3 64.7 84.4

2021 152,706.0 134,385.6 95.7 132.0

2022 192,404.2 169,583.1 128.3 162.3

2023

- 2042 237,367.0 208,881.9 164.9 195.8

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table 7

- 2 : Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at Steam Electric Power Plant s , Relative to Baseline

Option Year CO 2 (Metric Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option B 2015

- 2018 0.0 0.0 0.0

2019 550.9 0.2 0.0

2020 691.5 0.3 0.0

2021 865.3 0.4 0.0

2022 925.2 0.4 0.0

2023

- 2042 982.5 0.4 0.0

Option D 2015

- 2018 0.0 0.0 0.0

2019 2,111.5 0.9 0.0

2020 2,480.3 1.1 0.0

2021 3,244.6 1.4 0.0

2022 3,641.2 1.6 0.0

2023

- 2042 3,767.8 1.6 0.0

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table 7

- 3 : Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity Generation Profile, Relative to Baseline

Regulatory Option Year CO 2 (Metric Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option B 2015

- 2018 0.0 0.0 0.0

2019

- 2022

- 2,057,293.5

- 3,534.5

- 4,620.6

2023

- 2027

- 1,437,164.1

- 3,323.9

- 527.5

2028

- 2033

- 246,295.3

- 1,361.2 1,315.7

2034

- 2042

- 1,186,462.9

- 1,500.2

- 1,608.4

Option D 2015

- 2018 0.0 0.0 0.0

2019

- 2022

- 4,869,524.3

- 14,614.0

- 5,662.8

2023

- 2027

- 2,555,361.0

- 11,615.0 2,238.4

2028

- 2033

- 2,089,591.4

- 8,826.0

- 984.2

2034

- 2042

- 3,193,009.0

- 10,638.8

- 4,243.9

Source: U.S. EPA Analysis, 2015; see TDD for details.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 4 Table 7

- 4 : Estimated Net Changes in Air Pollutant Emissions due to Increase in Auxiliary Service at Steam Electric Power Plant s, Increased Trucking at Steam Electric Power Plant s, and Changes in Electricity Generation Profile, Relative to Baseline

Regulatory Option Year CO 2 (Metric Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option B 2015

- 2018 0.0 0.0 0.0

2019

- 2,029,332.9

- 3,520.3

- 4,584.6

2020

- 2,019,629.3

- 3,513.7

- 4,575.1

2021

- 1,993,170.4

- 3,498.7

- 4,551.2

2022

- 1,980,609.0

- 3,488.9

- 4,545.0

2023

- 2027

- 1,346,549.1

- 3,263.9

- 440.8

2028

- 2033

- 155,680. 3

- 1,301. 2 1,402. 5

2034

- 2042

- 1,095,847. 9

- 1,440.2

- 1,521.7

Option D 2015

- 2018 0.0 0.0 0.0

2019

- 4,812,190.8

- 14,577.7

- 5,603.6

2020

- 4,783,188.7

- 14,548.2

- 5,578.4

2021

- 4,731,894.1

- 14,516.9

- 5,530.8

2022

- 4,696,300.0

- 14,484.1

- 5,500.5

2023

- 2027

- 2,342,711.3

- 11,448.4 2,434.3

2028

- 2033

- 1,876,941.7

- 8,659.4

- 788.3

2034

- 2042

- 2,980,359.3

- 10,472.3

- 4,048.0

Source: U.S. EPA Analysis, 2015



### 7.1.2 NO

x 

### and SO

2


### Detailed human health benefits analyses for air regulations typically involve the use of a sophisticated air quality model, such as the Community Multiscale Air Quality (CMAQ) Model, and BenMAP, EPA’s principal air pollution benefits analysis modeling tool . The air quality model estimates the changes in concentrations of criteria air pollutants in each cell of a grid resulting from changes in emissions to air ( e.g. , of NO

x 

### and SO 2 

### ) under various policy scenarios. These criteria air pollutant changes are the n input to BenMAP, which estimates the resulting changes in incidence in the exposed population of the adverse health effects associated with the pollutants and the corresonding monetized benefits (see ( Abt Associates

, 2012 ) for additional description of B enMAP). This detailed approach for human health benefits analysis of air regulations tends to be time

- and resource

- intensive.

Recognizing that a less resource

- and time

- intensive approach is sometimes desirable, EPA relied on the best available methods of benefits transfer, which is the science and art of adapting primary research from similar contexts to estimate benefits for the environmental quality change under analysis. EPA ’s Air Office developed estimates of national monetize d benefits per ton of emissions avoided for use in estimating benefits without the need to conduct detailed air quality and human health benefits modeling. The benefit per ton values repsent the total monetized human health co

- benefits, premature mortailit y and premature morbidity,

from the reduction in one ton of PM 2.5 

### (or PM 2.5 

### precursor such as NO X 

### or SO 2 

### )

. Because the benefits per ton of emissions depend on both the type of emissions ( e.g. , NO

x 

### vs. SO 2 

### ) and the geographic distribution (relative to population centers) of the emitting sources, EPA developed benefits per ton estimates for specific combinations of emission source categories and PM 2.5 

### precursor s . EPA used this approach, for example, in itsBenefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 5 

### a ssessment of the benefits of PM and SO 2 

### reductions for the Industrial Boiler and Process Heaters National Emissions Standards for Hazardous Air Pollutants ( NESHAP ) rule (U.S. EPA 2004a) and its analysis of the Mobile Source Area Toxics Rule (U.S. EPA 2004b ) (See also Fann et al.

, 2012 ; Fann et al.

, 2009 ; Levy et al.

, 2009 ; and Muller and Mendelsohn

, 2009).

EPA’s calculation of the benefits per ton values involved three principal steps, as described by Fann et al. (2009) and the Technical Support Document f or the calculation of benefit per

- ton estimates (U.S. EPA 2008b):

1. Using a n air quality model to estimate the changes in ambient PM 2.5 

### concentrations resulting from specified precursor emissions reductions under various scenarios and then calculating the to tal tons (of the precursor emissions) reduced under each scenario;

2. Using BenMAP to estimate the changes in incidence of the associated health effects and the monetized benefits of those incidence reductions under each scenario; and

3. Estimating national bene fits per ton by dividing the national monetized benefits by the total tons of

emissions reduced under each scenario.

In the current analysis, benefits per ton estimates are needed for four combinations of emission type and source category involving NO

x 

### or SO

x 

### :

 NO

x 

### from EGUs (to be applied to changes in market

- level NO

x 

### emissions projected by IPM, and changes in emissions from auxiliary service);
 SO

x 

### from EGUs (to be applied to changes in market

- level SO

x 

### emissions projected by IPM, and changes in emiss ions from auxiliary service);
 NO

x 

### from mobile sources (to be applied to changes in NO

x 

### emissions associated with transporting

CCR waste to landfills); and
 SO

x 

### from mobile sources (to be applied to changes in SO

x 

### emissions associated with transporting

CCR w aste to landfills).

As described by Fann et al. (2009), “ambient PM 2.5 

### is a complex mixture of primary and secondarily formed particles, resulting from interactions in the atmosphere and physical transport of emissions of particulate matter precursors, in cluding available SO 2 

### , NO

x 

### , and NH 3 

### , mete o rology (particularly temperature), and baseline levels and composition of PM 2.5 

### ” (Fann et al. 2009, p.170). NO

x 

### and SO

x 

### differ in their propensity for becoming PM 2.5


### . The benefits per ton estimates are based on the assum ption that all fine particulates have the same potency for causing premature mortality (U.S. EPA 2011 a ). 59



### Fann et al. (2012) reported benefits per ton estimates for a variety of emission type/source category combinations, including a ll of those listed above, that are relevant to the current analysis, for the years 2005 and 2016. Although they are not reported in Fann et al. (2012), EPA also obtained benefits per ton estimates for each of these categories for the years 2020, 2025, and 2030 directly from one of the study co

- authors. 60



### F or these additional years, the benefits per ton were calculated assuming the same change in ambient air quality as the author’s forecast for 2016, but accounting for the 2020, 2025 a n d 2030 projected baseli ne mortality and population to estimate the change in mortality risk

59 B enefits per ton estimates are available for other pollutants, such as direct PM 2.5 emissions, but they were not
included in this analysis because emissions factors were not available. The chemistry of PM format ion is complex and nonlinear.

60 Provided in personal communication with Charles Fulcher, EPA Office of Air Quality Planning and Standards (OAQPS), on October 19, 2012.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 6 

### To be consistent with the rest of the analysis of the costs and benefits of the final ELGs

, benefits per ton

estimates are needed for each year from 201 9 through 204 2 . Because the benefit s per ton 

### estimates for the years 2016, 2020, 2025, and 2030 are almost linear as a function of year

, EPA interpolated benefits per ton values for the intermediate years ( e.g. , between 2020 and 2025) and projected values for the years from 2031 through 204 2 by linear regression, using (year, benefits per ton ) data points for the years 2016, 2020, 2025, and 2030. Note, however, that the approximate linearity of the (year, benefits per ton ) data points may be an artifact of the inability to project meteorolog ical changes and thus changes in air quality for all years after 2016, noted above. Thus, additional uncertainty was generated by using benefits per ton estimates for the future years that did not account for meteorological and air quality changes.

Assumi ng that the geographic distribution of controlled emitting sources in a source category ( e.g. , EGUs)

and of emissions reductions in the current analysis are similar to the geographic distribution of emitting

sources and emissions reduction in the analysis in Fann et al. (2012), EPA can derive a rough estimate of benefits from changes in air emissions by applying these benefits per ton estimates to the changes (in tons) of emissions resulting from compliance with the final rule . For example, the benefits fro m reduced emissions of

NO

x 

### from EGUs under Option D can be estimated by multiplying emissions avoided under the regulatory option by the appropriate benefits per ton value.

As noted above, NO

x 

### and SO

x 

### are known precursors to PM 2.5

. 

### Several adverse health effects have been

associated with PM 2.5

, 

### including premature mortality, non

- fatal heart attacks, hospital admissions, emergency department visits, upper and lower respiratory symptoms, acute bronchitis, aggravated asthma, lost work days and acute respirato ry symptoms. All of these health effects were included in the estimation of benefits that

went into the calculation of benefits per ton in Fann et al. (2012).

A very large percentage , 98 percent, of the total monetized benefits of reducing PM 2.5 

### concentrat ions are attributable to avoided premature mortality. The appropriate method for valuing the reductions in premature mortality and development of an accepted value for projected reduction in the risk of premature mortality is

still a discussion in the econ omics and public policy communities. Fann et al. (2012) used data from Krewski et al. (2009), a study of mortality and long

- term exposure to PM 2.5 

### , to estimate the change in incidence of premature mortality associated with a given change in PM 2.5 

### concentra tions. This study is one of several credible peer

- reviewed long

- term exposure studies that EPA has used in benefits analyses of PM 2.5


### . When using long

- term exposure studies, EPA has traditionally assumed that premature mortality avoided as a

result of a re duction in PM 2.5 

### concentrations in a given year do not all occur in that year. Instead, EPA
assumes that the avoided PM 2.5 

### - related premature mortalities are distributed over a 20

- year period, with most occurring in the earlier years. EPA values avoided pre mature mortality using VSL and then discounts that value back to the year of the analysis. Thus the numerator (the benefits) of the benefits per ton estimate for a given year is the value of morbidity avoided in that year plus the present discounted value of the stream of

avoided premature mortalities over a twenty year period, discounted back to that year. For example, a benefits
per ton estimate from Fann et al. (2012) for a ton removed in 2016 is the value of avoided morbidity in 2016 plus the present discounted value of the stream of avoided premature mortalities associated with that ton from 2016 to 2036, discounted back to 2016.

EPA obtained two sets of benefits per ton estimates for th is analysis for the years 2005, 2016, 2020, 2025, and 2030: one set using a 3 percent discount rate and the other using a 7 percent discount rate. 61 

### All benefits per

ton estimates for years 201 9 through 204 2 were further discounted back to the year 201 5 (us ing a 3 percent or 7 percent discount rate, as appropriate). Because avoided premature mortalities are assumed to occur over a
twenty

- year period, and real income is likely to increase over time, these benefits per ton estimates reflect

61 Provided in personal communication with Charles Fulcher, EPA/OAQPS, on October 19, 2 012Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 7 

### EPA’s estimated inc reases in the value for mortal risk reductions with respect to increases in real income. The income growth adjustment factors used are those in BenMAP (Abt Associates Inc., 2012 ). Table 7

- 5
summarizes the benefits per ton estimates EPA used for the different emission type and source category combinations involving NO

x 

### and SO 2 

### in the analysis of the final rule

.

Table 7

- 5 : National Benefits per Ton Estimates for NOx and SO 2 Emissions (201 3 $ /ton ) from the Benefits per Ton Analysis Reported by Fann et al. (2012) a , b, c

Discount
Rate Year EGU Mobile Source (Onroad)

N O x SO 2 N O x SO 2

3% 2005 $3,791 $27,377 $4,633 $21,059

2016 $5,475 $36,853 $7,687 $20,006

2020 $5,686 $38,959 $8,108 $22,112

2025 $6,107 $42,118 $8,845 $24,218

2030 $6,528 $45,277 $9,582 $27,377

7% 2005 $3,475 $25,271 $4,107 $18,953

2016 $4,844 $32,641 $6,949 $17,900

2020 $5,159 $34,747 $7,476 $20,006

2025 $5,475 $37,906 $8,002 $22,112

2030 $5,897 $41,065 $8,634 $24,218

Source: U.S. EPA Analysis, 2015 based on Fann et al. (2012)

a

. Provided for this analysis by Charles Fulcher, EPA/OAQPS on October 19, 2012.

b

. Mortality benefits based on Krewski et al. (2009).

c

. Estimation of benefits per ton for 2016, 2020, 2025, and 2030 were based on year 2016 emissions modeling.



### 7.1.3 CO

2



### EPA estimate d the global social benefits of CO 2 

### emission reductions using the social cost of carbon (S CC )
estimates developed by the Interagency Working Group on the Social Cost of Carbon ( IWGSCC

, 2010,

2013 a , 2013b , 2015a ). This document refer s to these estim ates, which were developed by the U.S.

gove rnment, as “SC C estimates.” The SCC is a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 

### emissions in a given year. It includes a wide range of anticipated climate impac ts, such as net changes in agricultural productivity and human health, property damage from increased flood risk, and changes in energy system costs, such as reduced costs for heating and increased costs for air conditioning. It is used to quantify the ben efits of reducing CO 2 

### emissions, or the disbenefit from increasing emissions, in regulatory impact analyses.

The SCC estimates were developed over many years, using the best science available, and with input from the public. Specifically, an interagency w orking group (IWG) that included EPA and other executive branch agencies and offices used three integrated assessment models (IAMs) to develop the SCC estimates and recommended four global values for use in regulatory analyses. The SCC estimates were first released in February 2010 and updated in 2013 using new versions of each IAM. The 2013 update did not revisit the 2010 modeling decisions with regards to the discount rate, reference case socioeconomic and emission scenarios, and equilibrium climate sensi tivity distribution. Rather, improvements in the way damages are modeled are confined to those that have been incorporated into the latest versions of the models by the developers themselves and published in the peer

- r eviewed literature. The 2010 SC C Techn ical Support Document (TSD) (IWGSCC

, 2010) provides a complete discussion of the methods used to develop the se estimates and the current SC C TSD presents and discusses the 2013 update (including recent minor technicalBenefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 8 

### corrections to the estimates) (IWGSCC

, 2013 b ). In July 2015, IWGSCC published technical correction to the estimates (IWGSCC, 2015a); EPA uses these most current values for the benefit estimates presented in this chapter.

The 2010 SCC TSD noted a number of limitations to the SCC analysis, inclu ding the incomplete way in which the IAMs capture catastrophic and non

- catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Currently IAMs do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature due to a lack of precise information on the nature of damages and because the sci ence incorporated into these models understandably lags behind the most recent research. Nonetheless, these estimates and the discussion of their limitations represent the best available information about the social benefits of CO 2 

### reductions to inform ben efit

- cost analysis. The new versions of the models offer some improvements in these areas, although further work is

warranted.

Accordingly, EPA and other agencies continue to engage in research on modeling and valuation of climate impacts with the goal to improve these estimates. The EPA and other agencies also continue to consider

feedback on the SC C estimates from stakeholders through a range of channels, including public comments on Age ncy rulemakings that use the SC C in supporting analyses and through r egular interactions with

stakeholders and resear ch analysts implementing the SC C methodology used by the IWG. In addition, OMB’s Office of Information and Regulatory Affairs sought public comment on the approach used to develop the SCC estimates through a separate comment period that ended on February 26, 2014. See response to comment document (IWGCC, 2015b).

After careful evaluation of the full range of comments, the IWG continues to recommend the use of the SC C estimates in regulatory impact analysis. Wit h the release of the response to comments, the IWG announced plans to obtain expert independent advice from the National Academy of S ciences to ensure that the SCC

estimates continue to reflect the best available scientific and economic information on clim ate change. The NRC review will be informed by the public comments received and focus on the technical merits and challenges of potential approaches to improving the SC C estimates in future updates.

Concurrent with OMB’s publication of the response to com ments on SCC and announcement of the NRC process, OMB posted a revised TSD that includes two minor technical corrections to the current estimates. One technical correction addressed an inadvertent omission of climate change damages in the last year of

anal ysis (2300) in one model and the second addressed a minor indexing error in another model. On average the revised SCC estimates are one dollar less than the mean SCC estimates reported in the November 2013 TSD. The change in the estimates associated with t he 95th percentile estimates when using a 3 percent discount rate is slightly larger, as those estimates are heavily influenced by the results from the model that was affected by the indexing error.

The four SCC estimates are: $13, $46, $68, and $1 3 0 per m etric ton of CO2 emissions in the year 2020 (2013 dollars). 62 

### The first three values are based on the average SCC from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. Estimates of the SCC for several discount rates are included because t he

literature shows that the SC C is sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context (where costs and benefits are incurred by different generations). The fourth value i s the 95th percentile of the SC C across all three models at a 3 percent

62 The SCC TSDs provide SC C in 2007 dollars, which are adjusted to 2013 dollars using the GDP Implicit Price
Deflator. While t he SCC values reported in Table 7

- 6 have been ro unded to two significant digits, u nrounded
numbers were used to calculate the CO 2 benefits.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 9 

### discount rate. It is included to represent higher

- than

- expected impacts from temperature change further out in

the tails of the SCC distribution. The SCC increases ov er time because future emissions are expected to produce larger incremental damages as economies grow and physical and economic systems become more stressed in response to greater climate change.

These estimates are then discounted back to the year 2015 us ing the same discount rate used to estimate the

SCC. For internal consistency, the annual benefits are discounted back to net present value terms using the

same discount rate as each SC C estimate ( i.e. 5 percent, 3 percent, and 2.5 percent) rather than the discount rates of 3 percent and 7 percent used to derive the net present value of other streams of costs and benefits of

the final rule. 63



### EPA estimates the dollar value of the CO 2 

### - related benefits for each analysis year between 2019 and 2042 by applying t he global SCC estimates, shown in Table 7

- 6 , to the estimated reductions in CO 2 

### emissions under

the final rule.

Table 7

- 6 : S ocial C ost of C arbon Values (201 3 $ / metric ton CO 2 )

Year 5% Discount Rate,

Average 3% Discount Rate,

Average 2.5 % Discount

Rate, Average 3% Discount Rate,

95 th Percentile

2019 $13 $45 $67 $130

20 20 $13 $46 $68 $130

2021 $13 $46 $69 $140

202 2 $14 $47 $70 $140

2025 $15 $50 $7 4 $150

2030 $1 8 $55 $80 $170

20 35 $20 $60 $8 5 $180

20 40 $2 3 $66 $92 $200

Source: IWGSCC, 201 3 b (values updated to 201 3 dollars using GDP deflator (1.095) )

.



### 7.1.4 Estimating Total Air

- Related Benefits

EPA calculated the monetized air

- related benefits of the final rule , under options B and D

, in any given year (discounted back to the year 201 5 ) by (1) multiplying the tons of emissions avoided for a given emissions type/source category combination in that year by the benefits per ton for that emissions type/source category combination for that year, and then (2) summing the benefits across all e missions type/source category

combinations. The total benefit for year y , then, is calculated using Equation 7

- 1

.

Equation 7

- 1

.



### Where:

j = 1, 2, and 3 denote NO

x 

### , SO 2 

### , and CO 2 

### , respectively, from market

- level EGUs; j = 4, 5, and 6 denote NO

x 

### , SO 2 

### , and CO 2 

### , respectively, associated with auxiliary service; j = 7, 8, and 9 denote NO

x 

### , SO 2 

### , and CO 2 

### , respectively, associated with transportation; and

BPT y , j 2015 

### is the present discounted value, discounted to the year 201 5 , of the benefits per ton for the j th emissions type/source category combination.

63 See more discussion on the appropriate discounti ng of climate benefits using SC C in t he 2010 SCC TSD (IWGSCC, 2010).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 10 

### The t otal present discounted value of benefits, discounted to the year 201 5

, PDV 201 5 

### , is calculated using Equation 7

- 2

.

Equation 7

- 2

. .



### Results 7.2

Table 7

- 7 shows the estimated benefits from reductions in emissions of NO

x 

### , SO 2 

### , and CO 2 

### in each of several selected years for the two regulatory options EPA analyzed

.

Table 7

- 7 : Estimated B enefits from R educed Air Emissions for Selected Years (millions; 201 3 $) a

Option Year 3% Discount Rate 7% Discount Rate

Option B 2019 b $287.7 $266.3

2020 $291.1 $270.0

2025 $106.3 $102.4

2030

- $46.5

- $41.4

Option D 2019 b $514.5 $482.2

2020 $520.0 $488.8

2025 $85.4 $88.4

2030 $195.0 $186.2

Source: U.S. EPA Analysis, 2015

a. EPA used SCC values based on a 3 percent (average) discount rate to calculate total benefit values presented for both the 3 percent and 7 percent discount rate.

b

. The benefits per ton values used for year 201 9 benefit calculation is assumed to be the sam e as the 20 20 benefits per ton values.



### Table 7

- 8 shows the annual ized benefits from reductions in emissions of NO

x 

### , SO 2 

### , and CO 2 

### for the two regulatory options EPA analyzed . EPA annualized benefit estimates to enable consistent reporting ac ross benefit categories ( e.g. , benefits from improvement in water quality)

. The total air

- related benefits include

benefits from CO 2 

### emissions reductions calculated using average SCC values (average at 2.5 percent, average at 5 percent, and the 95 th 

### percen tile at 3 percent)

. The annualized benefits of Options B and D are $11 0.2 million and $2 8 4.5 million, respectively, using a discount rate of 3 percent ($ 103.6 million and $ 248.6 million, respectively, using a discount rate of 7 percent). 64

64 The 3 percent average SCC estimate was used to value reductions in CO 2 everywhere total benefits of the final rule are reported.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 11 Table 7

- 8 : Estimated Annualized B enefits from R educed Air Emissions ( M illions; 201 3 $)

ELG Option Pollutant 3% Discount Rate 7% Discount Rate

Option B NO

x $12.7 $10.6

SO 2 $45.0 $40.5

CO 2 3% Avg $52.5 $52.5

5% Avg $15.3 $15.3
2.5% Avg $77.4 $77.4

3% 95 th Percentile $157.7 $157.7

TOTAL 3% Avg $110.2 $103.6

5% Avg $73.1 $66.5
2.5% Avg $135.2 $128.6

3% 95 th Percentile $215.5 $208.9

Option D NO

x $63.1 $49.4

SO 2 $81.6 $59.4

CO 2 3% Avg $139.8 $139.8

5% Avg $40.4 $40.4
2.5% Avg $206.8 $206.8

3% 95 th Percentile $421.1 $421.1

TOTAL 3% Avg $284.5 $248.6

5% Avg $185.1 $149.2
2.5% Avg $351.4 $315.6

3% 95 th Percentile $565.8 $529.9

Source: U.S. EPA Analysis, 2015



### Implications of Revised Steam Electric Plant Loading Estimates 7.3

As described in Section 1.4.3 , EPA revised its estimates of pollutant loadings in steam electric power plant discharges after completing the benefit analyses. Estimated air

- related benefits do not depend on pollutant loadings to receiving waters and therefore the monetized benefit est imates provided in this Chapter are unaffected by revisions to steam electric plant loading estimates.



### Limitations and Uncertainties 7.4

T h is analysis is subject to the standard sources of uncertainty found in any air pollution benefits analysis

– uncertainti es surrounding the estimated emissions change s , the estimated changes in air pollutant concentrations resulting from changes in emissions, the estimated concentration

- response relationships between the air pollutant and various health effects in the expose d population , and the estimated value of each

health effect avoided

. There is additional uncertainty in the SCC estimates, which reflect the projection of future harm from climate change, and the benefits per ton estimates

. More details about the limitatio ns and

uncertainties associated with the air

- benefit analysis are discussed in Table 7

- 9

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 12 Table 7

- 9 : Limitations and Uncertainties in Analysis of Air

- related Benefits

Issue Effect on Benefits
Estimate Notes

U ncertainty in projecting the
future harm from climate change

. Uncertain When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A report from the National Research
Council (NRC

, 2009) points out that any assessment will suffer from uncertainty, s peculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.

The Intera gency Working Group on Socia l Cost of Carbon (IWGSCC, 2010, 201 3 a, 2013b ) noted a number of limitations to the SCC analysis, including the incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, the modeli ng of
inter

- regional and inter

- sectoral linkages, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of
research l inking climate impacts to economic damages makes the interagency modeling exercise even more difficult. This said, t he SCC estimates were developed using a defensible set of input assumptions that are grounded in the existing
literature. There is uncertainty
associated with the effects of compliance costs on the forecast change in emissions from the electricity sector. Uncertain Compliance costs (capital, fixed or variable) will influence marginal generation decisions of plants affected b y the final
rule . In order to model the electricity market effects of the
final rule

, EPA made certain modeling assumptions that may influence the pattern of generation across the electricity sector, and therefore emissions. For example, EPA converted engineering capital costs to annual fixed operation and maintenance (O&M) costs in order to model the cost of complying with the final rule in IPM.

See RIA Chapter 5, and Section 10.6 : Executive Order 13211: Actions Concerning Regulations That Significan tly Affect Energy Supply, Distribution, or Use for additional discussion of how modeling assumptions may influence the forecast air pollution changes from the IPM modeling.

Differences between modeled and actual quantities of electricity generated and em ission factors of dispatched generating units would affect the changes in air pollutants emissions and therefore the benefits resulting from these changes. EPA does not have information to quantify the magnitude of this uncertainty.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air

- Related Benefits

September 29, 2015 7

- 13 Table 7

- 9 : Limitations and Uncertainties in Analysis of Air

- related Benefits

Issue Effect on Benefits
Estimate Notes

There is uncertainty

a ssociated with the effects of
the Clean Power Plan rule Uncertain The final Clean Power Plan provides states considerable flexibility in developing state implementation plans to meet the rate or mass targets. This flexibility provides states great leeway to meet key priorities. However, it induces a considerable degree of uncertainty in what the future electric power market will look like and the overall economic impacts of the final ELGs. For example, states may choose to comply
with the Clean Power Plan in ways that will lead to fewer or
more coal

- fired steam ELG plant retirements than the IPM runs would indicate. Such differences may have an important impact on dispatch profiles and emission changes.

EPA used a reduced form approach ( benefits per ton ) t o value air

- related benefits of emissions changes

. Uncertain As Fann et al. (2012) note, “... implicit in the benefit per ton assessment is that the key attributes of the modeling

— e.g. population distribution, source parameters, etc.

— are not so differen t from the policy scenario as to affect the estimated benefits appreciably. Reduced form approaches assume a linear relationship between changes in emissions and benefits, an assumption that may not be valid for large changes in emissions” (Fann et al., 20 12, p. 142).

EPA used year

- specific benefits per ton estimates to derive values for each year within the analysis period

. Uncertain Use of year

- specific benefits per ton estimates from which to generate annual estimates introduces another layer of uncertainty into the analysis. In particular, because actual air quality modeling was carried out only for 2005 and 2016, the approximate linearity seen in the benefits per ton estimates for 2016, 2020, 2025, and 2030 may be an artifact of assuming
that ai r quality remains constant at 2016 levels. The benefits per ton estimates for intermediate years also do not take into account the likely non

- linearity involved. If each year

- specific benefits per ton is uncertain, then a n annual estimate incorporating ben efits per ton

- based estimates may be more uncertain. As a result, the annual estimates can be considered only rough estimates.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 8: Benefits from Reduced Water Withdrawals

September 29, 2015 8

- 1 

## 8 Benefits from Reduced Water Withdrawals

Steam electric power plant s use vast quantities of water for ash transport and for operating wet FGD

scrubbers.

By eliminating or reducing water used in sluicing operations or prompting the recycling of water in FGD wastewater treatment systems, the ELG s are expected to reduce wat er withdrawal from both surface waterbodies and aquifers. The reduction in water use depends on the regulatory option. 65 

### EPA estimates that power plants would reduce water withdrawals from between 45 billion gallons per year ( 0.12 million gallons per day) u nder Option and 209 to 222 billion gallons per year ( 0.57 to 0.61 million gallons per day) under Option E (see Chapter 11 of TDD for details)

. The final BAT/PSES option (Option D) will reduce water

withdrawals at steam electric power plant s by 143 to 155 billion gallons per year ( 0.39 to 0.42 million gallons per day)

.
The section below discuss es the benefits resulting specifically from reductions in groundwater withdrawals ( Section 8.1 ) . Benefits associated with surface water withdrawals are discussed qualitatively in Chapter 2 and in a separate report provid ed in the final rule record ( see DCN SE05943 ).



### Groundwater Withdrawals 8.1

Reduced water intake from groundwater sources by steam electric power plant s are expected to result in increased availability of groundwater for local municipalities that rely on groundwater aquifers for drinking water supplies. These municipalities are expected to avoid the cost of supplementing drinking water supplies through alternative means, such as bulk drinking water purchases. The following sections describe EPA’s

estimate of reduced groundwater withdrawal benefits.

EPA estimated the benefits of reduced groundwater withdrawals based on avoi ded costs of purchasing drinking water during perio ds of shortages in groundwater supply.

8.1.1 Methods

EPA’s analysis of the final ELG options (U.S. EPA, 2015 b ) indicate that one plant located in Nebraska will reduce the volume of groundwater withdrawn as a result of the ELGs

. EPA estimated that the plant will avoid withdrawing a total of 21,971 gallons per day (8 million gallons per year) by converting to dry handling for its bottom ash under Options D and E. Because the state is potentially or currently water

- stressed (Tetra Tech, 2011), the ELG s are likely t o generate benefits from improved groundwater recharge. To estimate the value of improved groundwater supply, EPA relied on state

- specific prices of bulk drinking water supplies

, since municipalities may need to purchase supplementary supplies in response to groundwater shortages arising

from excessive withdrawals. EPA recognizes that the assumption that a reduction in groundwater withdrawals

in the water

- stressed states may result in reduced groundwater shortages is somewhat speculative, but used

this assu mption to provide screening

- level estimates of the potential benefits.

To estimate the monetary value of reduced groundwater withdrawal, EPA relied on current state

- specific water prices ($ 1

, 192.06 per acre/foot for Nebraska). For each affected plant and r egulatory option, EPA

65 Depending on the BAT/PSES technology basis for fly and bottom ash wastewater, the regula tory option may eliminate or reduce water use associated with current wet sluicing operating systems at steam electric power plants. Specifically, reductions in intake flow are expected to occur at plants which convert to dry handling or recycle FGD wastew ater .Benefit and Cost Analysis for Steam Electric Power Generating ELGs 8: Benefits from Reduced Water Withdrawals

September 29, 2015 8

- 2 

### multiplied the reduction in groundwater withdrawal (in gallons per year) by the estimated price of drinking water per gallon. EPA used a conversion factor of 325,851 to convert acre foot to gallons.

8.1.2 Results

Table 8

- 1 shows estimated annual benefits from reduced groundwater withdrawals. The annual benefits from

the BAT/PSES option (Option D) for existing sources are $0. 0 2 million using a 3 percent discount ra te ($0.0 2 million using a 7 percent discount rate )

.

Table 8

- 1 : Estimated Annualized Benefits from Reduced Groundwater Withdrawals (Millions; 201 3 $)

Regulatory Option Reduction in
Groundwater Intakes (million gallons per year; full implementation) 3% Discount Rate 7% Discount Rate

Option A 0 .0 $0.00 $0.00
Option B 0.0 $0.00 $0.00
Option C 0 .0 $0.00 $0.00
Option D 8.0 $0.0 2 $0.0 2 Option E 8.0 $0. 02 $0.0 2

Source: U.S. EPA Analysis, 201 5



### 8.1.3 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3 , EPA revised its estimates of pollutant loadings in steam electric power plant discharges after completing the benefit analyses. Estimated water withdrawal benefits do not depend on pollutant loadings and therefore the monetized benefit estimates provided in this Chapter are unaffected by revisions to steam electric plant loading estimates.

8.1.4 Limitations and Uncertainties

Table 8

- 2 summarizes the limitations and uncertainties in the analysis of benefits associated with reduced groundwater withdrawals.

Table 8

- 2 : Limitations and Uncertainties in Analysis of Reduced Groundwater Withdrawals

Uncertainty/Assumption Effect on Benefits
Estimate Notes

EPA assumed that municipalities would need to replace lost groundwater supplies with bulk drinking water purchases. Uncertain

See below. Municipalities may not need to replace groundwater

withdrawn by steam electric power plant s (in which case the benefits of the ELG may be overstated) , or they may choose to replace the groundwater through other means, such as desalinization (in the case of Florida , in which case the benefits of the ELG may be understated ). EPA assumed a direct relationship between groundwater withdrawals
in water

- stressed states and groundwater shortages, i.e. , that reducing demand for limited groundwater supplies would result in avoided costs for purchased

water. Overestimate EPA assumed that demand for additional water supply exists in the affected areas (Florida and Nebraska) due to potential draughts. However, the extent of this demand is
uncertain.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 8: Benefits from Reduced Water Withdrawals

September 29, 2015 8

- 3 Table 8

- 2 : Limitations and Uncertainties in Analysis of Reduced Groundwater Withdrawals

Uncertainty/Assumption Effect on Benefits
Estimate Notes

Affected aquifer characteristics Uncertain If the affected aquifers are used for private wells only, the estimated benefits of improved groundwater recharge
could be under

- or overstated, depending on households WTP for protecting groundwater quantity.Ben efit and Cost Analysis for Steam Electric Power Generating ELGs 9: Avoided Dredging Costs

September 29, 2015 9

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## 9 Benefits from Avoided Dredging Costs

EPA expects that the final rule will reduce discharge loads of various categories of pollutants including total suspended solids (TSS), thereby reducing the rate of sediment deposition to affected waterbodies, including navigable waterways and reservoirs that require dredging for maintenance.

Navigable waterways, including rivers, lakes , bays, shipping channels and harbors, are an integral part of the United States’ transportation network. They are prone to reduced functionality due to sediment build

- up, which can reduce the navigable depth and width of the waterway (Clark, Haverkamp, an d Chapman, 1985). In many cases, costly periodic dredging is necessary to keep them passable. The final steam electric ELG could provide cost savings to government and private entities responsible for maintenance of navigable waterways by reducing the dred ging volume

. Reservoirs serve many functions, including storage of drinking and irrigation water supplies, flood control, hydropower supply, and recreation. Streams can carry sediment into reservoirs, where it can settle and cause buildup of silt layers o ver time, at a recorded average rate of 1.2 billion kilograms per reservoir every year (USGS, 2007 b ). Sedimentation reduces reservoir capacity (Graf, Wohl, Sinha, and Sabo, 2010) and the useful life of reservoirs unless measures such as dredging are taken to reclaim capacity (Clark, et al., 1985)

. EPA expects that the final ELG will provide cost savings by reducing dredging activity to reclaim capacity at existing reservoirs.

EPA estimates that the final ELG would result in modest cost savings from reducing the amount of sediment dredged from navigational waterways and reservoirs affected by pollutant discharges from steam electric power plants

. Under Option D, benefits from reduced navigational waterway dredg ing are less than one

thousand dollars , with both 3 and 7 percent discount rates; benefits from reduced reservoir dredgi ng are less

than two thousand dollars , with both 3 and 7 percent discount rates. EPA has revised sediment loadings following the comple tion of the benefit analysis However, because sediment loadings changed by approximately 1 percent the overall effect on the estimated benefits from reduced dredging of navigational waterways and reservoirs is likely to be trivial.

Appendix K provides a more detailed description of the methodology and results of this analysis. This appendix presents EPA’s analysis of the avoided dredging costs for navigable waterways and reservoirs under the five regulatory options. First, it describes EPA’s analysis of historic dredging locations and frequency. Next, it presents EPA’s approach for estimating sediment deposition and removal in dredged waterways and reservoirs under the ELG regulatory options and associated costs. It then presents estimated benefits.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

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## 10 Benefits from Enhanced Marketability of Coal Combustion Residuals

EPA expects that installation of waste stream treatment technologies to com ply with final ELGs will affect

the type of coal combustion residue (CCR) by prompting plants to convert from wet handling of fly ash, bottom ash, and/or flue gas desulfurization (FGD) waste to dry handling. Relative to wet ash, dry ash’s chemical and phys ical properties make it more suitable for re

- use in a variety of applications like structural fill, concrete, and wallboard ( U.S. EPA, 2015 d ; American Coal Ash Association, 2012 ). This change would in turn allow plants to more readily market CCR to benefic ial uses.

There are two main economic benefits to society of re

- using CCR. First, plants that are able to beneficially re

use CCRs are able to offset the CCR disposal costs ( e.g. , trucking and landfills) that EPA counted in its cost and economic impact an alyses of this final rule. These avoided costs are net of added costs that some plants may incur in preparing dry CCRs for reuse. Second, by replacing raw or virgin inputs with re

- used CCR materials during the production of structural fill and concrete, so ciety avoids the need for and cost of extracting and preparing the raw or virgin inputs. These benefits include both direct costs ( e.g. , operating machinery, costs of transport), and indirect costs ( e.g. , downstream environmental benefits).

This Chapter de scribes the methodology EPA used to estimate the avoided cost benefits resulting from the enhanced marketability of CCR. The approach builds on the methodology EPA used in analyzing changes in
beneficial use of CCRs under the final RCRA Final Rule for Disposal of Coal Combustion Residuals Generated by the Electric Utility Industry (U.S. EPA, 2014).



### Methods 10.1

10.1.1 Beneficial Use Applications

The methodology focuses on two CCR wastestreams, and two end uses that may be affected by the final

ELGs: 66 

### (1) fly ash a s a substitute for Portland cement in concrete production and (2) fly

- and bottom ashes as substitutes for sand and gravel in fill applications ( Table 10

- 1 ). 67



### In this analysis, EPA assumes that the ELGs do not materially affect the total quantity of CCR generated by steam electric power plants but instead change only the relative shares of the CCR handled dry instead of wet. Following the central assump tions of EPA’s cost and economic impact analyses for this rule ( U.S. EPA,

2015c ), EPA assumes that plants generate a constant amount of CCR each year in the analytic time period.

Table 10

- 1 : Applicable Benefi cial Use Applications of CCRs, by CCR Category

CCR Category Beneficial Use Application

Concrete Production Fill
Fly ash  

Bottom ash 

66 EPA does not expect the final ELGs to affect the quantity or handling of FGD gypsum, and this CCR use is therefore excluded from the benefit analysis

. 67 Note that this assumed pattern simplifies actual uses. The American Coal Ash Association (AC AA) reports other important uses ( i.e. , each more than 1 million short tons in 2012) for fly ash, bottom ash, and FGD gypsum, including waste stabilization (fly ash), blended cement/feed for clinker (fly ash, bottom ash, and FGD gypsum), and mining applica tions (fly ash, FGD gypsum) ( http://www.acaa

usa.org/Portals/9/Files/PDFs/revisedFINAL2012CCPSurveyReport.pdf ).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 2 

### 10.1.2 Marketable CCR by State

The demand for concrete and fill constrain s t he extent to which s team e lectric power plants will be able to shift CCRs from disposal to beneficial use. T ransportation costs are another main constraint and make marketing of CCR across long distances less likely. USGS Minerals Commodity Summaries ( e.g. , Bolen, 2014; van Oss, 2013 ) show that both sand and cement production vary across regions of the United States; thus, Steam Electric facilities in different parts of the country likely face different markets for CCR.

CCR end use sites include a mix of industrial facilities ( e.g. , to produce c ement) and dispersed sites ( e.g. , as structural fill). Without precise information about the location of these beneficial use sites within regions, EPA used sand and cement production within the state surrounding a plant to approximate the regional market demand each plant may face in marketing dry CCR. This approach assumes that plants only market their CCR to sites within the plant’s state , and that state

- level demand constrains the total amount of CCR that s team

e lectric power plants in that state, as a group, will be able to market to various end uses. To determine the maximum quantity of CCRs that s team e lectric power plants located in a given state will be able to market (“marketable CCRs”), EPA compared state

- level estimates of supply and demand for C CR. 68 

### Procedures to estimate demand and supply are described below. Due to heterogeneity in state sizes, this assumption implies the geographic extent of, and volume of, CCR markets vary across states.

10.1.2.1 Demand for CCR



### EPA assume d that the annual quantities of end products ( e.g. , cement, and construction sand and gravel ) produced and used in the U nited S tates represent the maximum annual demand for CCR in those applications, accounting for the share of the end products that may be repla ced with CCR.

 Total End Product Production . EPA estimated concrete and fill production based on U.S. Geological Survey “Minerals Commodity Summaries,” and developed 3

- year average production statistics for

cement and construction sand and gravel , respecti vely (Bolen, 2011, 2012, 2013, 2014; van Oss,

2009 , 201 0 , 2013). These are the same source s as used for the final CCR rule analysis

, but with more

current data. In estimating production, EPA used state

- level sand and gravel production statistics directly ( e.g. , Bolen, 2010). A nnual cement production statistics are presented by multi

- state region s , and separately report production for Portland and masonry cements ( e.g. , Van Oss, 2010). Because

Portland cement constitutes the majority of total cement production by weight (97% in 2013), EPA

use d total cement production as a proxy for Portland cement production. EPA downscale d regional cement production statistics to individual state(s) by state shares of regional population (US Census, 2012).

 End Produ ct Input Replaceable with CCR. For each end product, EPA then estimate d the portion of total production that could be replaced with CCR

. For cement, EPA assumed that :

‒ Fly ash could replace 25% of cement used in concrete

. This percentage represents the low
end of several replacement rate s reported in industry literature while remaining consistent
with t he final CCR rule and other federal government documents. For example, ERMCO (2013) report s that fly ash additives constitute 21.7% of cement in the United St ates, and We Energies (2013, p. 65) concluded that fly ash could be substituted for up to 40% of Portland concrete by weight in making structural grade concrete . Government replacement rate

68 The CCR Final Rule approach (Appendix S) used linear programming to estimate incremental changes in beneficial reuse due to the rule, accoun ting for county

- level capacity constraints (maximum demand).Although

our state

- level market approximations more coarsely delineate markets, they also serve to constrain maximum reuse to estimated market capacity.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 3



### estimates tend to be somewhat lower than industry estimates; for e xample, the CCR rule

assume d that “that fly ash makes up no more than 30% of

... cementitious material” (Appendix S in US EPA, 2014 ), based on resources from US EPA (2008) 69 

### , and the Federal Highway Administration Materials Group recommends a substitution rate of no more than 15 to 25 percent in pavement applications (FHWA, 2015)

.

‒ 73 percent of cement is used in ready

- mix concrete (ERMCO, 2013). Of the cement that fly ash can replace, only a portion is used in concrete; mortar and grout are other uses for

cemen t.

For f ill

, EPA assumed that :

‒ Fill is 36% of total sand and gravel production by state

. In 2012, 36% of national construction sand and gravel production was used in fill and fill

- like applications ( Bolen,
2013 ), which include concrete aggregates (includi ng concrete sand) ; asphaltic concrete aggregates and other bituminous mixtures ; road base and coverings ; fill ; and snow and ice
control.

‒ Beneficial reuse of CCRs in fill carries no stigma, and could potentially replace 100 percent of sand/gravel use as fi ll. Consistent with the analysis of the CCR final rule, EPA assume d no stigma around the reuse of CCRs in fill applications . EPA notes, however, that there may remain some potential for the public to negatively perceive CCR re

- used in fill or concrete
beca use they are unfamiliar with CCR and/or with re

- use practices, but consume media
information that incorrectly characterizes these products and practices as “toxic” (U.S. EPA, 2010d)

. Because EPA did not identify information estimating the national extent or impact of stigma around CCR reuse, EPA assumed no stigma in residential applications

. This approach
is consistent with the non

- hazardous determination applied in the Final CCR Rule , which is designed to avoid stigma around uses of “hazardous ” products. To the extent that stigma or perceptional effects does exist and leads to reduced marketability of CCRs, the analysis may

over

- state the re

- use of CCRs in some applications ( e.g. , residential contexts).

10.1.2.2 CCR Supply



###  Baseline Production . EPA estimated state

- level baseline CCR supply by ash type, including (1) the sum of plant

- level dry fly and bottom ash production marketed, as reported by Steam Electric plants located within the state in response to the 2010 Questionnaire for the Steam Electric Power

G enerating Effluent Guidelines (U.S. EPA, 2010a) ; and (2) The state

- level sum of additional CCRs available for beneficial reuse due to the final CCR rule. The CCR final rule analysis projected that in 2025, the 3

- y ear r olling a verage of the a nnual i ncrease in national CCR b eneficial u se s will total 0.26 million tons of fly ash in concrete, and 5.65 million tons of CCRs in structural fill

. 70 

### EPA allocate d

thes e national total supply of CCR to states with baseline cement and sand production. Using population a s a proxy for fill and concrete use (and thereby, a proxy for production), EPA allocate d the additional CCR to states based on each state’s population

.  Change in Dry CCR Supply due to Final Steam Electric ELGs . EPA used data on annual plant

- level changes i n the quantity of CCRs handled dry, by category

– fly ash and bottom ash

– and which could

69 U.S. EPA (2008). Study on Increasing the Usage of Recovered Mineral Components in Federally Funded Projects Involving Procurement of Cement or Concrete to Address the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users, Report to Congress, June 3, 2008, EPA530

- R

- 08

- 007.

70 CCR RIA, Exhibit 5

- L.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 4 

### be more readily marketed for beneficial use after making changes to meet the final ELGs under each of the regulatory options (see TDD for details). 71



### C onsistent with the screening

- level cost and economic impact analyses for the final rule which assume that

s team e lectric power plants generate a constant amount of electricity throughout the analysis period (see RIA;

U.S. EPA 2015 c ) , EPA assumed that steam electric power plants will produce the same quantity of dry CCRs every year in the analysis period, beginning in the technology implementation year

. EPA determine d the amount of plant

- level changes in fly

- and bottom ash CCRs marketable to the two benefic ial use categories assuming that fly ash is first marketed to concrete, as it is the use with higher market value, and then the remaining fly and bottom ash CCRs marketed to fill, as possible given remaining demand.

10.1.2.3 Marketable CCR



### Thirty six states produ ced cement in 2010, 2011, or 2012 (van Oss, 2009; 2010; 2013). Given baseline CCR supply, EPA estimate d that additional CCR production due to conversion to dry ash handling systems to meet

the final ELGs would be marketable in 14 (39 percent) of these stat es. Note that none of the steam electric

power plants expected to increase dry CCR production due to the ELGs are located in states without cement production in the baseline.

All 50 states produce sand and gravel, and EPA estimate d that the CCR supply (fro m existing marketing of CCR by s team e lectric power plants and projected CCR reuse) exceeds demand for fill in only one state (West Virginia). In all other states, demand data suggest that s team e lectric power plants can market all the

additional CCR handl ed dry

.
Table 10

- 2 reports “marketability” estimates for all states combined. Marginal changes in dry CCR represent approximately 5 percent of unmet demand for CCR as a substitute for Portland cement in concrete and

2 percent of unmet demand for CCR as a substitute for sand and gravel in fill. EPA expect s that some plants will market CCR to both cement and fill, and that a lack of demand for fly ash in cement will induce many to market fly ash to fill. Nearly all of the expected conversions to dry

- handled fly and bottom ash could potentially be marketed for beneficial reuse within the plant’s state.

Table 10

- 2 : State

- level M arket A pproximation ( S hort T ons ).

Application Baseline Production Marketable Changes due to ELGs

Production Unmet Demand (%

of production) Fly Ash
(% Total ΔCCR) Bottom Ash (% Total ΔCCR)

Concrete 13,400,062 3,277,184 (24%) 162

, 491 (12.2%) NA

Fill 320,701,683 312,705,505 (98%) 1

, 166

, 260 (87.8%) 4

, 314

, 946 (91.7%)

Source: U.S. EPA Analysis 2015



### EPA exclude d from subsequent valuation of tallied “incremental” marketable CCR any changes that occur at steam electric power plants which already market the wet ash in the baseline, and only calculate d benefits associated with plants that do not currently market their CCR wet. While these plants may convert to dry handling and may therefore find it easier to market their CCR in the future, EPA assume d for simplicity that the amount of ash they could market for beneficial use will not change as a result of this conversion. As a

71 Fly ash tonnages are a dry

- basis (not moisture conditioned), and were only calculated for steam electric units flagged for transport and disposal costs, excluding those units flagged for costs associated with additional conveyance capacity. Bottom ash tonn ages were estimated for all bottom ash handling conversions excluding units marked for bottom ash management costs. Tonnage estimates include the CCR population; conversions at plants flagged as a fly ash or bottom ash dry conversion under the CCR rule wer e zeroed.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 5 

### result of this assumption, the analysis values 100 percent of fly ash conversions m arketed to concrete, and

93 percent of fly ash and bottom ash conversions marketed to fill.

10.1.3 Value to Society from Re

- Using CCR

Replacing virgin materials like fill and cement with CCR wastes avoids some costs while introducing other

costs. EPA use d the soc ial cost accounting framework summarized in Figure 10

- 1 to track benefits and costs

of CCR re

- use , notably changes in production and disposal pre

- and post

- ELGs. The remainder of this section provides more detail on cost and benefit categories.

Figure 10

- 1 : Cost Accounting Framework for the Beneficial Reuse Analysis.

Without Reuse With Reuse Net Change Val uation Framework Raw Materials (sand, cement)

Extraction / mining No e xtraction / Mining  Raw material
extraction

Life Cycle Analysis a Processing No p rocessing  Raw materials processing

Transport to market No transport to market  Raw materials transportation
CCR from Steam Electric Power Generation Prepare CCR for disposal Beneficiat e CCR for re

- use  Beneficiation Estimate beneficiation

c osts Transport SE CCR to disposal site Transport SE CCR to
reuse site No net change in SE CCR transport costs b Qualitative uncertainty analysis

Disposal No Disposal  D isposal costs to meet SE ELG Estimate avoided disposal costs

a : As was done in the analysis of the final CCR rule, EPA selected the LCA approach to estimate costs that society avoids by replacing virgin materials with CCR , including environmental impacts. The market value of avoided raw materials is an alternative cost proxy which captures some, but not all of these production costs. LCA captures some, but not all costs from a market price framework, and thereby is also an approximate avoided social cost.

b : On net, EPA assume d no change in transportation cost between the base case and policy scenario

. In the base case, s ome plants dispose of CCR on

- site, while others di spose of it off

- site. Re

- use applications are assumed to occur off

- site; however, EPA also

assume d that

, on net to society, total costs of transportation to disposal and re

- use sites are approximately equal given our in

- state market framework

.



### Avoided Costs

. R e

- us ing CCRs avoids several costs , including the environmental costs of extracting virgin

materials and costs to s team e lectric power plants of disposing of their CCR.

 Compliance cost offset

. By redirecting the CCR to beneficial re

- use rather than disposing of it, steam
electric power plants can avoid disposal O&M costs attributed to meeting the ELG s in the economic impact analysis (see RIA; U.S. EPA 2015 c )

. EPA’s analysis of the Final CCR Rule assumed plants marketing CCR could avoid ce rtain per

- ton transportation and disposal costs based on the volume of CCR marketed, but that plants could not offset certain other costs of the rule ( e.g. , general liability insurance required under the CCR rule, but not under the Steam Electric ELGs). Fo r this Final Rule, EPA also assumed plants could not offset capital costs associated with disposal systems ( e.g. , landfills), but could offset O&M costs include tipping fees at off

- site landfills. In this analysis, EPA

assumes Steam Electric power plants m arket 100% of dry CCR produced following the rule. Since EPA assumes that plants that market dry CCR are able to re

- use 100% of ash by volume, EPA assumed plants could offset 100% of these O&M costs. To the extent that plants may not market
100% of ash in all years, this assumption produces an upper bound estimate of potential cost offsets

due to beneficial reuse.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 6 

###  Production cost offset

. Th is offset is the avoided cost of virgin materials costs 72 

### resulting from using CCR in place of the virgin materials ( e.g

. , Portland cement, sand and gravel). Depending on who uses the CCR for beneficial purposes, these cost offsets (transfers) may accrue to the steam electric power plant, the secondary CCR user who either paid for or received the CCR for free , a third

- party reseller,

etc.

 Environmental benefits . By avoiding the production of virgin materials needed for Portland cement and fill, society also gains a reduction in total environmental damages associated with extracting, transporting, and processi ng these raw materials ( e.g. , air emissions and resource consumption)

. Life Cycle Analysis (LCA) is a framework to assess the total environmental impact of a product based on an inventory of energy and material inputs and outputs associated with each step in producing the product. Typically, LCA addresses raw material acquisition, materials manufacture, production, use/reuse/maintenance, and waste management (SAIC, 2006), and estimates environmental impacts per unit of product produced ( e.g. , changes in ene rgy consumption, water consumption, and air pollutant emissions). To estimate the total environmental benefits of avoided Portland cement and fill production in this analysis, EPA applied the existing LCA impacts for cement and sand and gravel
used in EPA’ s CCR Final Rule.

Table 10

- 4 lists environmental impacts per ton of avoided virgin material, and Table 10

- 5 lists the

estimated unit value s of the avoided economic impact s of these environmental impacts. EPA applied

benefit transfer of unit economic values based on the Final CCR Rule’s LCA approach , the Social

Cost of Carbon (IWGSCC, 2013 b ) , and human health benefits from reducing emissions of PM 2.5


### precursors (U.S. EPA based on Fann et al., 2012)

.
As shown in Figure 10

- 1

, p roduction cost offse t s overlap with benefits from avoided life

- cycle impacts. Specifically, the life

- cycle impact framework captures many of the energy, water and other production costs that fill and concrete producers can av oid by re

- using CCR products and avoiding the production of virgin

materials. Since the life

- cycle framework also captures avoided environmental impacts of production, the remainder of this analysis reports only the monetized benefits from the life

- cycle f ramework to avoid potentially double

- counting benefits

.
Additional C osts

. Steam e lectric power plants that market CCRs for beneficial re

- use do so when the re

- use is economically preferable to disposal. Some plants may find it necessary to undertake additional preparation of dry CCRs before beneficial use ( e.g. , to bring CCR to ASTM standards), or may need to consider the cost of transporting CCR to a beneficial use destination . There is uncertainty in the deg ree to which s team e lectric

power plants incur one or more of these additional costs . To provide a conservative estimate of avoided costs (and benefits)

, EPA included additional costs for beneficiation.

 Beneficiation of fly ash prior to use in concrete : EP A assume d that 13 percent of fly ash requires beneficiation prior to use in concrete

. 73 

### Electric Power Research Institute ( 2005 ) reports a range of beneficiation costs per ton of fly ash; the CCR Final Rule analysis estimate d that a reasonable central

tende ncy value for the cost of beneficiation is approximately $10 per ton (in year 2004$)

. EPA update d thi s cost to 2013 dollars and applied this cost to 13 percent of marketable fly ash CCRs at

each s team e lectric power plant.

72 For reference, the 3

- year average (2008 to 2012) market price of sand and gravel used as fill is $4.36/ton (range: $2.38

-

$9.30 by state) (van Oss/USGS , 2010, 2011, 2013 ). The 3

- year average (2008 to 2012) market price of Portland cement

is $44.50/ton ($76.24

- $148.02 by state) ( Bolen

, 2011, 2012, 2013, 2014 ).

73 In the Final CCR rule analysis document, ORCR cites personal communication with David Goss , ACCA (June 25, 2008) as the source of this assumption.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 7 

### EPA calculate d monetary values of estimated changes in beneficial use of marketable CCRs based on the

avoided costs by s team e lectric power plant s, as well as the avoided resource impacts from displacing virgin

materials

. Table 10

- 3 lists rates and ratios for avoided costs and additionally

- incurred costs associated with re

using CCR for beneficial uses

. Table 10

- 4 and Table 10

- 5 list the quantity and value of avoided resource and environmental impacts.

Table 10

- 3 : Economic Value of CCR Handling Costs per Unit (2013$).

Cost Type Beneficial Use Cost/Benefit

Avoided Costs of Disposal Sand and Gravel Used as Fill;
Concrete Var ies by Steam Electric plant a

Additionally Incurred Costs of Beneficiation Concrete $11.95 per ton b

a. EPA estimated the annual cost of CCR disposal by Steam Electric plant and year (U.S. EPA, 2015)

. b. Electric Power Research Institute ( 200 5 ).

Table 10

- 4 : Avoided Resource and Environmental Impacts per Ton of Virgin Material Produced.

Impact Category Portland Cement a,c Fill b,c

Energy (MMBtu) 3.52

- 3.62* 0.04 Water (gal) 201

– 246,947* 1,208

Air Emissions Greenhouse gases (tons) 0.90

– 0.93 * 0.0032

NOx (tons) 0.0019 2.68 x 10 - 5

SOx (tons) 2.43 x 10 - 4 8.50 x 10 - 6

a. Assumptions are based on Final CCR rule analysis. For energy use, water use, greenhouse gases, carbon monoxide, and PM10 associated with portland cement, the range of values presented in this Table reflect data from Ecoinvent v2.2 as included in SimaPro 7.2 and the National Renewable Energy Laboratory’s U.S. Life Cycle Inventory Database, the latter of which is available at http://www.nrel.gov/lci/. The Portland cement values for NOx, PM2.5, SOx, and mercury were all derived from emissions data presented in U.S. EPA, Regulatory Impact Analysis: Amendments to the National Emission Standards for Hazardous Air Pollutants and New Source Performance Standards (NSPS) for the Portland Cement Manufacturing Industry Final Report, August 2010. The
value for lead wa s derived from Ecoinvent v2.2 as included in SimaPro 7.2.
b. Assumptions are based on Final CCR rule analysis. Values for fill reflect the average of values for sand and clay from Eco invent
v2.2 as included in SimaPro 7.2.

* Denotes cases in which EPA used the lower limit of the range for this scoping analysis.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 8 Table 10

- 5 : Economic Value of Avoided Resource and Environmental Impacts per Unit of Impact (2013$).

Impact Category Portland Cement Fill

Energy ($/MMBtu) a $4.94 $19.15 Water ($/gal) b $0.00003 Greenhouse gases ($/ton) c $ 36 to $62 NOx ($/ton) d $5,694 to $8,836 $5,694 to $8,836 SOx ($/ton) d $43,856 to $68,109 $43,856 to $68,109

a. Assumptions are based on Final CCR rule analysis. Derived from U.S. Department of Energy, Energy Consumption by Manufacturers, May 2013. Table 7.2. This is the same data source as that used for the final CCR rule, updated to 2013$ using GDP
deflator.
b. Assumptions are based on Final CCR rule analysis, updated to 201 3$ using GDP deflator. Value provided by H. Scott Matthews, Professor of Civil and Environmental Engineering and Engineering and Public Policy at Carnegie Mellon University and Research Director of Carnegie Mellon University’s Green Design Institute, Octob er 18, 2011.

c. Values shown are the range of yearly Social Cost of Carbon values from 2015 to 204 2 , using the Average Value at 3% discount rate (adjusted to 2013$ and converted from tonnes to tons ). Values are d erived from Interagency Working Group on Soc ial Cost of Carbon, United States Government, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, 2013 (revised June 2015)

. EPA also calculated benefits using the 5% average ( $10 to $22) , 2.5% average ($ 50 to $85 ) , and 3% 95 th percentile ( $85 to $188 ) SCC estimates

. d. Values shown are the range of yearly benefit per ton estimates (2015 to 2043) linearly interpolated and extrapolated from BenMap benefit per ton estimates for 2016, 2020, 2025, and 2030. Benefit per ton unit impacts were p rovided in personal communication with Charles Fulcher, EPA/OAQPS, on October 19, 2012 and updated to 2013$ using GDP deflator. EPA applied values for the “Cement Kilns” sector to Portland cement, and values for the “area sources” aggregate sector to fill. Values shown are the central tendency by sector, at 3% discount rate

.



### Results 10.2

Table 10

- 6 summarizes the estimated change in dry and marketable CCRs beneficially used in concrete and fill applications due to final ELGs and other regulatory options, for steam electric power plants not marketing

wet ash in the baseline. The analysis suggests that most marketable CCRs would be beneficially used in fill applications.

Table 10

- 6 : Estimated Beneficial Use Applications of CCRs, by CCR Category

ELG Option and CCR Category Beneficial Use Application (1,000 short tons)

Concrete Structural Fill

Option A or Option B
Fly ash 162 1,166

Bottom ash NA 0

Total 162 1,166
Option C
Fly ash 162 1,166

Bottom ash NA 2,813

Total 162 3,979
Option D or Option E
Fly ash 162 1,166

Bottom ash NA 3,763

Total 162 4,929Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 9 

### Table 10

- 7 r eport s the estimated life cycle benefits of avoiding virgin materials in concrete and fill production. Table 10

- 8 reports the estimated a nnualized economic value of increased marketable CCRs by value category. Relative changes in total CCR beneficial use and total life cycle impacts under the final ELGs

are consistent with the projected ef fects of the CCR rule on beneficial reuse (shown in last column of the table; U.S. EPA, 2014)

.

Table 10

- 7 : Annual Avoided Resource and Environmental Impacts Given CCR Reuse in Concrete and Fill Applications.

Impact Category Fly Ash Bottom Ash Option Total CCR RIA, 2030 3

- yr rolling avg

Option A or Option B

Energy (MMBtu) 618,618 0 618,618 910,000
Water (million gal) 1,442 0 1,442 21,000
Greenhouse gases (tons) 149,974 0 149,974 75,000

NOx (tons) 340 0 340 310

SOx (tons) 49 0 49 160

Option C

Energy (MMBtu) 618,618 112,536 731,154 910,000
Water (million gal) 1,442 3,399 4,840 21,000
Greenhouse gases (tons) 149,974 9,003 158,977 75,000

NOx (tons) 340 75 415 310

SOx (tons) 49 24 73 160

Option D or Option E

Energy (MMBtu) 618,618 150,515 769,133 910,000
Water (million gal) 1,442 4,546 5,987 21,000
Greenhouse gases (tons) 149,974 12,041 162,015 75,000

NOx (tons) 340 101 441 310

SOx (tons) 49 32 81 160

Note: Values in this table represent annual changes in a full

- compliance year ( e.g. , starting in 2023).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 10 Table 10

- 8 : Annualized E conomic Value of Estimated Changes in Beneficial Use ( Million 2013$) a ,b

. Regulatory Option Impact Category 3% 7%

Option A or
Option B Avoided Disposal Costs to Steam Electric Plants $0.80 $0.68

Beneficiation Costs

- $0.21

- $0.16
Avoided Life Cycle Costs of Virgin Materials $10.74 $13.79

Net Social Value $11.33 $14.31

Option C Avoided Disposal Costs to Steam Electric Plants $13.03 $10.87

Beneficiation Costs

- $0.21

- $0.16
Avoided Life Cycle Costs of Virgin Materials $12.18 $15.43

Net Social Value $25.00 $26.14

Option D or
Option E Avoided Disposal Costs to Steam Electric Plants $18.49 $15.46

Beneficiation Costs

- $0.21

- $0.16
Avoided Life Cycle Costs of Virgin Materials c $12.52 $15.81

Net Social Value $30.80 $31.11

Notes: a. Annualized over 24 years (2015

- 204 2 ). Values escalated using CCI and GDP through 2022; thereafter, assume no real change in prices above inflation. Avoided disposal costs to s team e lectric power plants include are annual O&M costs. B.

b

. EPA used SCC values based on a 3 percent (average) discount rate to calculate total benefit values presented for both the 3 percent and 7 percent discount rate.

c. EPA also estimated l ife cycle benefits for Options D or E, at other SCC estimates ( IWGSCC, 201 3 (revised June 2015)). Results of LCA benefits at 5% average, 2.5% average, and 3% 9 th percentile SCC estimat es range from $ 5.12 million (5% average) to $19.90 million (3% 95 th percentile) when discounted at 3%, and from $3.81 million (5% average) to $14.87 million (3% 95 th percentile).



### As shown in Table 10

- 8

, for Options requiring both fly and bottom ash technologies, much of the annualized economic value from changes in beneficial use comes from avoided disposal costs incurred by steam electric plants, ev en after accounting for any increased beneficiation costs.

The estimates above translate into net annualized benefits of approximately $ 30 .8 million (3 percent discount rate) for Options D or E. Estimated benefits for Option C are lower due to the reducti on in the amount of marketable bottom ash relative to Options D and E ( i.e. , BAT/PSES standards for bottom ash apply only to units with greater than 400 MW capacity), and benefits for Options A and B are lower yet, as they derive from the incremental marketability of fly ash only

.



### Implications of Revised Steam Electric Plant Loading Estimates 10.3

As described in Section 1.4.3 , EPA revised its estimates of pollutant loadings in steam electric power plant discharges after com

pleting the benefit analyses. Estimated benefits from the enhanced marketability of CCR do not depend on pollutant loadings to receiving waters and therefore the monetized benefit estimates provided in this Chapter are unaffected by revisions to steam elec tric plant loading estimates.



### Limitations and Uncertainties 10.4

Key uncertainties and limitations include:

 Benefits from marketed CCR are sensitive to assumptions about CCR generation. The analysis assumes that the amount of CCR generated by s team electric po wer plants is constant throughout the analysis, i.e. , coal

- fired plants generate a constant quantity of electricity. This is consistent with the screening

- level cost and economic impact analysis framework (see RIA report) . However, theBenefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10

- 11 

### Department of Energy and EPA both project that generation from steam electric plants will decline over time, due to a variety of market factors, including environmental regulations. This decline would reduce the quantity of CCR generated, available to be marketed for benefici al use, and the associated benefits estimated in this memo. The Agency accounted for disposal costs in its economic impact analysis assuming that a constant quantity of CCR would need to be disposed of. Therefore, the assumption of constant CCR generation does not result in an overstatement of avoided disposal benefits since they offset costs already calculated elsewhere. In contrast, non

- disposal related benefits could be lower when accounting for the reduction in CCR generation.

 As discussed above, EPA a s sum ed that conversions to dry handling at plants already marketing wet

ash in the baseline are not incremental economic benefits because there is no change in the total

amount of CCR that is beneficially used . Relaxing this assumption c ould increase estima ted cost

offsets to the extent that these plants avoid certain compliance or operational costs that have been counted against the ELGs

.  Steam electric plants may not market all of their CCR within a given year; for example, they may

instead temporarily sto re it . This affects both the time profile and value of beneficial reuse benefits. To the extent that steam electric plants may or may not be able to completely avoid the annual O&M costs of disposal, EPA’s analysis is an upper bound.

 Certain other dimensions of this analysis may lead to under

- estimated benefits of CCR re

- use. For example, EPA assumed that facilities only offset O&M costs of disposal . To the extent that some plants are actually able to avoid or offset capital costs of c ompliance or other longer

- term costs given reduced need for landfilling ( e.g. , costs of securing and developing additional disposal sites), EPA’s analysis is a lower

- bound on the benefits of dry handling CCR.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Be nefits

September 29, 2015 11

- 1 

## 11 Summary of Total Monetized Benefits

Total Annualized Benefits 11.1

Table 11

- 1 and Table 11

- 2 , on the next two pages, summarize the total annual monetized benefits using 3 percent and 7 percent discount rates , respectively

. Table 11

- 3 and Table 11

- 4 compile, for each of the five analyzed regulatory options, the time profiles of total (non

- discounted) monetized benefits . The table s also
report the calculated present and annualized values of benefits at 3 percent and 7 percent discount rates, respectively.

T he estimated total mone tized benefits of the five regulatory options EPA analyzed rang e from $ 41 .1 million to $ 565.6 million per year using a 3 percent discount rate, depending on the option ($ 37.2 million to $ 478.4 million per year using a 7 percent discount rate ) , and whether the analysis includes air

- related benefits

. Option D has total benefits (including air

- related benefits) of approximately $ 450.6 million to $ 565.6 million using a 3 percent discount rate and $ 387.3 million to $ 478.4 million using a 7 percent discount rate.

The monetized benefits of the final rule do not account for all benefits because they omit various sources of benefits to society from reduced steam electric pollutant discharges , such as reduction in certain non

- cancer health risk ( e.g. , effects of cadm ium on kidney functions and bone density ) and reduced cost of drinking water

treatment . See Chapter 2 for a discussion of categories o f benefits EPA did not moneti ze

. Finally, EPA was able to estimate air

- related benefits for Options B and D only (see Chapter 7 )

. Benefits for options A

, C and E are therefore understated to a greater degree than the other options ; in particular, EPA expects that the benefits for Option E would be higher than those fo r Option D if air

- related benefits were

included. See Section 13 .1 for an extrapolation of potential air

- related benefits for these options.

As desc ribed in Section 1.4.3 , EPA revised its estimates of pollutant loadings in steam electric power plant discharges after completing the benefit analyses. These revisions a re likely to reduce benefit estimates in the following four categories: human health, nonmarket water quality benefits, benefits to threatened and endangered species and avoided dredging costs. For several reasons

— notably the f act that revisions do not affect all plants equally and the model functions are not linear

— it would be inappropriate to simply scale the monetized benefits based on the aggregate changes in loadings. Therefore, EPA qualitatively assessed effects of pollu tant loading reductions on the relevant benefit categories (see Chapters 3

, 4

, 5 , and 9 for detail). The Agency concluded that although the r evised loadings are likely to reduce the estimated benefit in four benefit categories

, the magnitude of these reductions is likely to be small compared to the total benefits of the rule

. Chapters 3 through 10 provide more detail on limitation and uncertainty inherent in the analysis of each benefit category.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11

- 2 Table 11

- 1 : Summary of Total Annual ized Benefits at 3 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High

Human Health Benefits g $5.4 $5.7 $6.1 $5.4 $5.8 $6.1 $11.3 $11.8 $12.4 $16.5 $17.2 $17.9 $16.7 $17.5 $18.3
Reduced IQ losses in children from exposure to lead a ,g $0 .3 $0.4 $0.5 $0.3 $0.4 $0.5 $0.5 $0.6 $0.7 $0.8 $1.0 $1.1 $0.8 $1.0 $1.1

Reduced CVD in adults from exposure to lead g $3.8 $3.8 $8.4 $12.8 $12.8

Reduced IQ losses in children
from exposure to mercury a ,g $1.3 $1.6 $1.8 $1.3 $1.6 $1.9 $2.4 $2.9 $3.4 $2.9 $3.5 $4.0 $3.1 $3.8 $4.4

Avoided cancer cases from exposure to arsenic b ,g <$0. 1 <$0.1 <$0.1 <$0.1 <$0.1

Improved Ecological
Conditions and Recreational
Uses g $4.2 $4.9 $23.4 $15.0 $18.9 $83.7 $19.6 $26.0 $109.4 $23.3 $31.3 $129.5 $25.1 $34.0 $140.0

Use and nonuse values for water quality improvements g $4.2 $4.9 $23.4 $15.0 $18.9 $83.7 $19.6 $26.0 $109.4 $23.2 $31.3 $129.5 $25.1 $34.0 $140.0

Nonuse values of T&E species b ,g <$0.1 <$0.1 <$0.1 <$0. 1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1

Market and Productivity
Benefits $31.5 $32.8 $34.2 $31 .5 $32.8 $34.2 $107.2 $109.5 $111.9 $126.4 $130.0 $133.7 $126.4 $130.0 $133.7

Avoided impoundment failures $20.1 $21.5 $22.9 $20.1 $21.5 $22.9 $82.2 $84.5 $86.9 $95.6 $99.2 $102.9 $95.6 $99.2 $102.9
Reduced dredging costs b <$0.1 <$0.1 <$0.1 <$0.1 < $0.1
Ash marketing benefits $11.3 $11.3 $25.0 $30.8 $30.8

Air

- related benefits NE $110.2 NE $284.5 NE

Reduced human health effects NE $57.8 NE $144.7 NE

Reduced CO 2 emissions c NE $52.5 NE $139.8 NE

Reduced water withdrawals b <$0.1 <$0.1 <$0.1 <$0.1 <$0.1Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11

- 3 Table 11

- 1 : Summary of Total Annual ized Benefits at 3 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High
Total (excluding air

- related Benefits) d $41.1 $43.4 $63.7 $51.9 $57.5 $124.0 $138.1 $147.4 $233.7 $166.1 $178.5 $281.2 $168.3 $181.6 $292.0

Total (including air

- related Benefits) d, e NE NE NE $16 2.2 $167.8 $234.2 NE NE NE $450.6 $463.0 $565.6 NE NE NE

Source: U.S. EPA Analysis, 2015

“NE” indicates that EP A did not estimate the benefits. Air

- related benefits of Option A are expected to be less than those for Option B; air

- related benefits for Option C are expected to be between those of Options B and D; and air

- related benefits of Option E are expected to b e greater than those for Option D.

a. Value includes reduced IQ losses and avoided cost of compensatory education in children from exposure to lead. For details see Chapter 3

. b. “< $0.1” indicates that the monetized annual benefits are positive but less than $0.1 million.

c . For the valuation of benefits from reductions in CO 2 emissions EPA relied on the 3 percent average social cost of carbon estima te.

d . Values for individual benefit categories may not sum to the total due to independent rounding.

e . The total monetized benefits for options A, C, and E do not include air

- related benefits. This category of benefits was analyzed for Options B and D on ly (see Chapter 7 ).
f. EPA estimated use and nonuse values for water quality improvements using two different meta

- regression models of willingness

- to

- pay. One model provides the low and high bounds while a different model provides a central estimate (included in this table in the mid

- range column) . For this reason, the mid

- range estimate differs from the midpoint of the range for this benefit catego ry. For details, see Chapter 4

.
g. Estimates for this benefit category do not reflect revised pollutant loadings , which could result in lower monetized benefits . See Section 1.4.3 for details

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11

- 4 Table 11

- 2 : Summary of Total Annual ized Benefits at 7 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High

Human Health Benefits g $3.4 $3.5 $6.7 $3.4 $3.5 $14.2 $7.4 $7.6 $7.7 $11.3 $11.4 $11.6 $11.3 $11.5 $11.6
Reduced IQ losses in children from exposure to lea d a ,g $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.2 $0.2 $0.1 $0.2 $0.2

Reduced CVD in adults from exposure to l ead g $3.1 $3.1 $7.0 $10.7 $10.7

Reduced IQ losses in children
from exposure to mercury a ,g $0.2 $0.3 $3.5 $0.2 $0.3 $11.0 $0.4 $0.5 $0.6 $0.5 $0.6 $0.7 $0.5 $0.6 $0.8

Avoided cancer cases from exposure to arsenic b

, g <$0.1 <$0.1 <$0.1 < $0.1 <$0.1

Improved Ecological
Conditions and Recreational
Uses g $3.3 $3.9 $18.6 $12.0 $15.2 $66.7 $15.7 $20.9 $87.4 $18.6 $25.1 $103.4 $20.1 $27.3 $111.7

Use and nonuse values for water quality improvemen ts g $3.3 $3.9 $18.6 $12.0 $15.2 $66.7 $15.7 $20.9 $87.3 $18.5 $25.1 $103.4 $20.0 $27.3 $111.7

Nonuse values of T&E species b ,g <$0.1 <$ 0.1 <$0.1 <$0.1 < $0.1 <$0.1 <$0.1 < $0.1 <$0.1 <$0.1 < $0.1 <$0.1 <$0.1 < $0.1 <$0.1

Market and Productivity
Benefits $30.4 $31.6 $3 2.7 $30.4 $31.6 $32.7 $92.5 $94.4 $96.4 $108.8 $111.8 $114.8 $108.8 $111.8 $83.7

Avoided impoundment failures $16.1 $17.3 $18.4 $16.1 $17.3 $18.4 $66.4 $68.3 $70.3 $77.7 $80.7 $83.7 $77.7 $80.7 $83.7
Reduced dredging costs b < $0.1 < $0.1 < $0.1 < $0.1 < $0.1
Ash marketing benefits $14.3 $14.3 $26.1 $31.1 $31.1

Air

- R elated B enefits NE $103.6 NE $248.6 NE

Reduced human health effects NE $51.1 NE $108.8 NE

Reduced CO 2 emissions c NE $52.5 NE $139.8 NE

Reduced water withdrawals b <$0.1 <$0.1 <$0.1 <$0.1 <$0.1Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11

- 5 Table 11

- 2 : Summary of Total Annual ized Benefits at 7 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High
Total (excluding air

- related Benefits) d $37.2 $38.9 $58.0 $45.8 $50.2 $113.7 $115.6 $122.9 $191.4 $138.7 $148.4 $229.8 $140.2 $150.6 $207.1

Total (including air

- related Benefits) d, e NE NE NE $149.4 $153.8 $217.3 NE NE NE $387.3 $397.0 $478.4 NE NE NE

Source: U.S. EPA Analysis , 2015

“NE” indicates that EPA did not estimate the benefits. Air

- related benefits of Option A are expected to be less than those for Option B; air

- related benefits for Option C are expected to be between those of Options B and D; and air

- related benefits of Opti on E are expected to be greater than those for Option D.

a. Value includes r educed IQ losses and a voided cost of compensatory education in children from exposure to lead . For details see Chapter 3

. b

. “ < $0. 1 ” indicates that the monetized annual benefits are positive but less than $0.1 million.

c

. For the valuation of benefits from reductions in CO 2 emissions EPA relied on the 3 percent average social cost of carbon estimate.

d . Values for individual be ne fit categories may not sum to the total due to independent rounding.

e . The total mo ne tized be ne fits for options A

, C , and E do not include air

- related benefits . This category of benefits was analyzed for Options B and D only (see Chapter 7 )

. f

. EPA estimated use and nonuse values for water quality improvements using two different meta

- regression models of willingness

- to

- pay. One model provides the low and high bounds while a different model provides a central estimate (included in this table in the mid

- range column) . For this reason, the mid

- range estimate differs from the midpoint of the range f or this benefit category. For details, see Chapter 4

.
g. Estimates for this benefit category do not reflect revised pollutant loadings, which could result in lower monetized benefits. See Section 1.4.3 for details

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11

- 6 

### Time Profile of Benefits 11.2

Table 11

- 3 and Table 11

- 4 compile the time profiles of total (non

- discounted) monetized benefits including

air

- related benefits for Options B and D, and the calculated annualized values of benefits at 3 percent and 7 percent discount rates, respectively. The time profile is based on mid

- range (or central) estimates for benefits for which benefits are presented as a range in Table 11

- 1 and Table 11

- 2

. As shown in the tables, benefits under Option D increase from 2019 until 2022, then decline in 2023 before gradually increasing

. This 2023 decline is due to an IPM

- projected increase in SO 2 

### relative to ba seline (negative benefits). For Option B, there is a similar decline in 2023 as well as a larger decline in benefits in 2028. As with Option D, t hese declines are attributable to IPM

- projected increase s in SO 2 

### (negative benefits)

, but also occur due to smaller estimated CO 2 

### and NO

x 

### reductions when compared to earlier years

. For both Options B and D (excluding the increases in SO 2 

### in the given periods)

, IPM indicates a decline in net air emissions reductions between 2019 and 2033 followed by an increase i n net reductions in 2034. For more details on the IPM projections, see Chapter 7

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11

- 7 Table 11

- 3 : Time Profile of Benefits at 3 Percent (Millions; 201 3 $) (Including Air

- Related Benefits for Options B and D)

Year Option A a Option B Option C a Option D Option E a

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $3.8 $291.4 $16.4 $544.8 $30.3

2020 $5.5 $296.6 $39.9 $578.7 $58.7

2021 $18.7 $327.6 $83.3 $633.5 $115.1

2022 $31.0 $344.4 $155.8 $718.1 $193.2

2023 $57.2 $176.1 $189.9 $310.5 $232.6

2024 $58.0 $179.1 $191.6 $314.9 $235.1

2025 $58.4 $181.5 $192.3 $317.6 $235.9

2026 $58.6 $184.0 $192.6 $319.9 $236.2

2027 $58.7 $186.3 $192.7 $321.8 $236.2

2028 $58.7 $31.1 $192.7 $422.6 $236.0

2029 $58.6 $30.4 $192.5 $423.2 $235.5

2030 $58.5 $29.9 $192.3 $426.2 $235.1

2031 $58.6 $29.6 $192.3 $428.8 $234.8

2032 $58.6 $29.2 $192.2 $431.6 $234.4

2033 $58.6 $28.8 $192.1 $434.4 $234.1

2034 $58.6 $224.3 $192.1 $670.1 $233.8

2035 $58.7 $226.7 $192.1 $676.4 $233.6

2036 $58.7 $229.2 $192.1 $682.7 $233.5

2037 $58.8 $231.7 $192.2 $689.1 $233.4

2038 $58.9 $234.2 $192.4 $695.6 $233.4

2039 $59.0 $236.8 $192.5 $702.1 $233.4

2040 $59.2 $239.3 $192.7 $708.6 $233.5

2041 $59.3 $241.9 $192.9 $715.3 $233.7

204 2 $59.3 $243.2 $193.1 $718.6 $233.8
Annualized Benefits,
3% b $43.4 $167.8 $147.4 $463.0 $181.6

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A

, C and E do not include air

- related benefits. This category of benefits was only estimated for Options B and
D (see Chapter 7

)

. b. Total annualized year

- specific benefits may not sum to the total annualized benefits due to rounding.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11

- 8 Table 11

- 4 : Time Profile of Benefits at 7 Percent (Millions; 201 3 $) (Including Air

- Related Benefits for Options B and D)

Year Option A a Option B Option C a Option D Option E a

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $3.8 $270.0 $16.4 $512.5 $30.3

2020 $5.5 $275.5 $39.9 $547.5 $58.7

2021 $16.9 $304.5 $80.1 $597.4 $110.8

2022 $29.2 $321.3 $152.5 $682.1 $188.8

2023 $55.4 $170.3 $186.6 $309.7 $228.2

2024 $56.2 $173.3 $188.3 $314.0 $230.6

2025 $56.5 $175.7 $188.9 $316.4 $231.4

2026 $56.7 $178.2 $189.2 $318.8 $231.6

2027 $56.8 $180.4 $189.3 $320.7 $231.6

2028 $56.8 $34.2 $189.3 $409.6 $231.3

2029 $56.6 $33.5 $189.1 $410.2 $230.8

2030 $56.5 $33.0 $188.9 $413.0 $230.4

2031 $56.6 $32.7 $188.8 $415.6 $230.0

2032 $56.6 $32.3 $188.6 $418.3 $229.6

2033 $56.6 $31.9 $188.5 $421.1 $229.2

2034 $56.6 $214.9 $188.5 $641.9 $228.9

2035 $56.6 $217.3 $188.5 $648.1 $228.7

2036 $56.7 $219.8 $188.5 $654.4 $228.5

2037 $56.8 $222.3 $188.5 $660.7 $228.4

2038 $56.8 $224.8 $188.6 $667.1 $228.4

2039 $56.9 $227.3 $188.8 $673.5 $228.3

2040 $57.0 $229.8 $188.9 $680.0 $228.4

2041 $57.2 $232.4 $189.1 $686.6 $228.5

204 2 $57.2 $233.7 $189.2 $689.8 $228.5
Annualized Benefits,
7% b $38.9 $153.8 $122.9 $397.0 $150.6

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A

, C and E do not include air

- related benefits. This category of benefits was only estimated for Options B and
D (see Chapter 7

)

. b. Total annualized year

- specific benefits may not sum to the total annualized benefits due to rounding. Additionally, note that SCC values are annualized at 3 percent, whereas other benefits are annualized at 7 percent.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 12: Total Social Costs

September 29, 2015 12

- 1 

## 12 Summary of Total Costs

This chapter develops EPA’s estimates of the costs to society resulting from the final ELGs . As analyzed in this chapter, the costs of regulatory actions are the opportunity costs to society of employing resources to prevent the environmental damage otherw ise occurring from discharge s of wastewater containing metals, nutrients, and other pollutants

.



### Overview of Costs Analysis Framework 12.1

RIA Chapter 3: Compliance Costs presents EPA’s development of costs to the 1, 0 80 steam electric power plant s subject to the final ELGs (U.S. EPA, 2015c ). These costs are used as the basis of the social cost analysis. However, the compliance costs used to estimate total costs differ in their consideration of taxes from those reported in RIA Chapter 3 , which were calculated for the purpose of estimating the private costs and the

economic impacts of the ELGs . In the analysis of costs to society, compliance costs are considered without accounting for any tax effects. The costs to society are the full value of the re sources used, whether they are paid for by the regulated plants

, by taxpayers in the form of lost tax revenues , or by some combination

. 74



### As described in Chapter 1

, EPA assumed that steam electric power plant s, in the aggregate , would implement control technologies during a 5

- year period from 201 9 to 20 23

. For this analysis, EPA developed a year

explicit schedule of compliance outlays over the period of 2019 through 2042

. 75 

### After creating a cost

incurrence schedule for each cost component, EPA summed the costs expected to be incurred in each year for each plant, then aggregated these costs to estimate the total cost s for each year in the analysis period.

After complia nce costs were assigned to the year of occurrence, the Agency adjusted these costs for real change between their stated year and the year(s) of their incurrence as follows:

 A ll technology costs, except planning, were adjusted to their incurrence year(s) u sing the Construction Cost Index (CCI) from McGraw Hill Construction and the Gross Domestic Product (GDP) deflator index published by the U.S. Bureau of Economic Analysis (BEA) ;

 P lanning costs were adjusted to their incurrence year(s) using the Employment Cost Index (ECI) Bureau of Labor Statistics (BLS) and GDP deflator.

Note that the CCI and ECI adjustment factors were developed only through the year 2022 ; after these years,

EPA assumed that the real change in prices is zero

– that is, costs are expecte d to change in line with general inflation. EPA judges this to be a reasonable assumption, given the uncertainty of long

- term future price projections.

After developing the year

- explicit schedule of total costs and adjusting them for predicted real change to the year of their incurrence, EPA calculated the present value of these cost outlays as of the promulgation year by discounting the cost in each year back to 2015 , using both 3 percent and 7 percent discount rates. These discount rate values reflect gu idance from the OMB regulatory analysis guidance document, Circular A

- 4

74 For the impact analyses, compli ance costs are measured as they affect the financial performance of the regulated plants and firms. The economic impact analyses therefore consider the tax deductibility of compliance expenditures

, as appropriate depending on the tax status of the complyin g entity.

75 The end of the analysis period, 2042, was determined based on the life of the longest

- lived compliance technology implemented at any steam electric power plant (20 years), and the last year of technology implementation (2023).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 12: Total Social Costs

September 29, 2015 12

- 2 

### ( U.S. OMB, 2003). EPA calculated the constant annual equivalent value (annualized value), again using the two values of the discount rate, 3 percent and 7 percent, over a 24

- year socia l cost analysis period. EPA

assumed no re

- installation of compliance technology during the period covered by the social cost analysis.

To assess the economic costs of the ELGs to society, EPA relied first on the estimated costs to steam electric power pla nt s for the labor, equipment, material, and other economic resources needed to comply with the

ELGs . In this analysis, EPA assumed that the market prices for labor, equipment, material, and other compliance resources represent the opportunity costs to soci ety for use of those resources in regulatory compliance. Finally, EPA assumed in its social cost analysis that the final rule does not affect the aggregate quantity of electricity that would be sold to consumers and, thus, that the rule’s social cost would include no

loss in consumer and producer surplus from lost electricity sales by the electricity industry in aggregate. Given the small impact of the final rule on electricity production cost for the total industry, EPA believes that this assumption is rea sonable for the social cost analysis (for more details on the impacts of the final rule on electricity production cost, see RIA Chapter 5: Electricity Market Analys e s ). The social cost analysis considers

costs on an as

- incurred, year

- by

- year basis

– that is , this analysis associates each cost component to the

year ( s ) in which they are assumed to occur relative to the assumed promulgation and technology implementation years. 76



### Finally, a s discussed in RIA Chapter 10 (Section 10.7: Paperwork Reduction Act of 1 995 ) , the final ELG s are

not expected to result in additional administrative costs for plants to implement

, and s tate and federal NPDES permitting authorities to administer, the final ELGs

. As a result, the social cost analysis focuses on the resource cost of compliance as the only direct cost incurred by society as a result of the ELGs

.



### Key Findings for Regulatory Options 12.2

Table 12

- 1 presents annualized costs for each of the five regulatory options. At a 3 percent discount rate,

estimated annualized costs range between $ 120.5 million under Option A and $ 536.0 million under Option E

. The final BAT/PSES option s for existing sources (Option D) have annualized costs of $ 479.5 million at a
3 percent discount rate , and $ 471.2 million at a 7 percent discount rate

. 77

Table 12

- 1 : Summary of Annualized Costs (Millions; $ 201 3 )

Regulatory Option 3% Discount Rate 7% Discount Rate

Option A $120.5 $116.9
Option B $198.7 $194.7
Option C $383.5 $379.9
Option D $479.5 $471.2
Option E $536.0 $525.8

Source: U

.S. EPA Analysis, 201 5

.



### Table 12

- 2 provide s additional detail on the social cost calculations. The table compile s , for each of the five regulatory options EPA analyzed for the final ELG , the time profiles of compliance costs incurred. The table

76 The specific assumptions of when each cost component is incurred can be found in Chapter 3: Compliance
Costs of the RIA

. 77 Similarities in the values obtained when discounting costs using 3 percent and 7 percent are due to the time profile of the costs, specifically the timing and relative magnitude of upfront capital costs versus ongoing O&M expenditures.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 12: Total Social Costs

September 29, 2015 12

- 3 

### also report s the calculated annualized values of costs at 3 percent and 7 percent discount rates. 78 

### The maximum compliance outlays are incurred over the years 201 9 th rough 202 3

, i.e. , during the estimated

window when steam electric power plant s are expected to implement compliance technologies.

Table 12

- 2 : Time Profile of Costs to Society (Millions; $201 3)

Year Option A Option B Option C Option D Option E

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $388.8 $641.8 $1,143.4 $1,336.6 $1,427.1

2020 $132.3 $276.7 $631.1 $856.7 $950.3

2021 $390.2 $620.2 $1,243.4 $1,364.9 $1,498.0

2022 $201.7 $374.2 $882.5 $1,123.1 $1,331.8

2023 $286.6 $512.9 $1,058.8 $1,332.7 $1,497.4

2024 $75.7 $120.2 $215.3 $282.3 $317.9

2025 $83.0 $127.5 $222.9 $291.2 $327.5

2026 $78.3 $122.7 $219.0 $289.5 $326.0

2027 $80.9 $125.4 $219.9 $289.1 $325.9

2028 $79.0 $123.4 $218.6 $286.6 $324.1

2029 $84.9 $129.3 $230.9 $304.2 $341.2

2030 $83.2 $127.7 $227.1 $298.6 $334.2

2031 $85.4 $129.8 $232.1 $303.7 $340.0

2032 $84.6 $129.0 $229.5 $303.4 $340.0

2033 $85.1 $129.5 $230.7 $302.9 $339.7

2034 $77.8 $122.2 $218.0 $286.8 $324.2

2035 $83.1 $127.5 $223.8 $293.4 $330.5

2036 $79.0 $123.5 $221.5 $290.7 $326.3

2037 $82.6 $127.1 $224.0 $295.7 $332.0

2038 $81.4 $125.8 $224.0 $295.2 $331.5

2039 $84.4 $128.8 $229.4 $300.3 $336.7

2040 $83.9 $128.3 $226.5 $298.0 $334.5

2041 $83.8 $128.2 $226.9 $297.2 $333.2

2042 $83.3 $127.7 $224.9 $293.9 $329.5

Annualized Costs, 3% $120.5 $198.7 $383.5 $479.5 $536.0

Annualized Costs, 7% $116.9 $194.7 $379.9 $471.2 $525.8

Source: U.S. EPA Analysis, 2015

.

78 Whereas EPA calculated the time profile of benefits using discount rate

- specific social cost of carbon estimates and therefore obtained time profiles that are sp ecific to the discount rates (see Table 11

- 3 and Table 11

- 4 ), the time profile of costs does not depend on the discount rate.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 13: Benefits and Social Costs

September 29, 2015 13

- 1 

## 13 Benefits and Costs

This chapter compares total monetized benefits and costs for the five regulatory options analyzed for the final

ELGs

. Benefits and costs are compared on two bases: (1) for each of the options analyzed and (2) incrementally across options. The comparison of benefits and costs also satisfies the requirements of Executive Order 12866: Regulatory Planning and Review and Executive Order 13563: Improving Regulation and Regulatory Review (see Chapter 9: Other Administrative Requirements of the RIA ; U.S. EPA, 2015c ).



### Comparison of Benefits and Costs by Option 13.1

Chapter 11 and Chapter 12 present estimates of the benefits and costs , respectively, for the regulatory options evaluated in developing the final ELGs

.
Table 13

- 1 presents EPA’s estimates of benefits and costs of the regulatory options for existing steam electric power plant s, at 3 percent and 7 percent discount rates, and annualized over 24 years. These values are all in

20 1 3 dollars and are based on the discounting of costs and benefits to 201 5

, the rule promulg ation year.

As discussed in Chapter 11 , EPA did not analyze air

- related benefits for Options A, C, and E. T he total

monetized benefits for those options are therefore understated. To compare the costs and benefits of these options , EPA calculated the average ratio of total benefits with air

- related benefits to total benefits without air

related benefits for Options B and D, 79 

### then applied the average ratio to Options A, C, and E to extrapolate total monetized benefits including air

- related benefits for these options

. These extrapolated , approximate

benefits are shown in Table 13

- 1

.

Table 13

- 1 : Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions; 2013$)

Regulatory Option Total Monetized Ben efits Total Monetized Benefits, Including Extrapolated Values a Total
Costs Low Mid b High Low Mid b High

3% Discount Rate
Option A $41.1 $43.4 $63.7 $122.1 $122.2 $125.7 $120.5
Option B $162.2 $167.8 $234.2 $162.2 $167.8 $234.2 $198.7
Option C $138.1 $147.4 $233.7 $410.4 $414.5 $461.3 $383.5
Option D $450.6 $463.0 $565.6 $450.6 $463.0 $565.6 $479.5
Option E $168.3 $181.6 $292.0 $500.2 $510.8 $576.3 $536.0

79 This ratio averag ed 2.97 for low benefits, 2.81 for mid

- range, and 1.97 for high

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 13: Benefits and Social Costs

September 29, 2015 13

- 2 Table 13

- 1 : Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions; 2013$)

Regulatory Option Total Monetized Ben efits Total Monetized Benefits, Including Extrapolated Values a Total
Costs Low Mid b High Low Mid b High

7% Discount Rate

Option A $37.2 $38.9 $58.0 $110.5 $109.5 $114.4 $116.9
Option B $149.4 $153.8 $217.3 $149.4 $153.8 $217.3 $194.7
Option C $115.6 $122.9 $191.4 $343.7 $345.6 $377.8 $379.9
Option D $387.3 $397.0 $478.4 $387.3 $397.0 $478.4 $471.2
Option E $140.2 $150.6 $207.1 $416.8 $423.7 $408.7 $525.8

Source: U.S. EPA Analysis, 2015

.

a . EPA did not analyze air

- related benefits for Options A, C, and E. This category of benefits was only estimated for Options B and D (see Chapter 7

). EPA adjusted the total benefits estimated for Options A, C and E by multiplying the totals without air

- related benefits by the average ratio of [total with air

- related benefits]/[total with out air

- related benefits] for Options B and D.

b. EPA estimated use and nonuse values for water quality improvements using two different meta

- regression models of WTP . One model provides the low and high bounds while a different model provides a central es timate (included in this table under the mid

range column) . For this reason, the mid benefit estimate differs from the midpoint of the benefits range. For details, see Chapter 4

.



### Analysis of Incremental Benefits and Costs 13.2

In addition to comparing benefits and costs for each regulatory option, as presented in the preceding section, EPA also analyzed the benefits and costs of the options on an incremental basis. The comparison in the preceding section addresses the simple quantitative relationship between estimated benefits and costs for each option by itself: for a given option, which is greater

– costs or benefits

– and by how much? In contrast, incremental analysi s looks at the differential relationship of benefits and costs across options and poses a different question: as increasingly more costly options are considered, by what amount do benefits, costs, and

net benefits ( i.e. , benefits minus costs) change from o ption to option? Incremental net benefit analysis provides insight into the net gain to society from imposing increasingly more costly requirements.

EPA conducted the incremental net benefit analysis by calculating, for the five regulatory option s , the change in net benefits, from option to option, in moving from the least stringent option to successively more stringent options. As described in Chapter 1 , th e regulatory options differ in the technology basis used to determine

effluent limits and standards for different wastestream s . Thus, the difference in benefits and costs across the options derives from the characteristics of the wastestream s controlled by an option, the relative effectiveness

of the control technology in reducing pollutant loads, and the distribution and characteristics of steam electric power plant s that would implement the technolog ies and of the receiving waterbodies.

A s noted previous ly, however, the total monetized benefits for Options A

, C , and E do not include air

- related benefits; this benefit category is included in results for Options B and D only

. Therefore, to allow for

consistent calculation of incremental benefits as one move s from one option to the next, EPA used the extrapolated total benefits estimated for Options A

, C , and D to include the air

- related benefits for these options (see Table 13

- 1 )

. As reported in Table 13

- 2

, EPA estimates that the annual monetized costs exceed the mid

- range annual

monetized benefits for the final ELG by $ 16.5 million using a 3 percent discount rate and $ 74.2 million using a 7 percent discount rate.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 13: Benefits and Social Costs

September 29, 2015 13

- 3 

### Using a 3 percent discount rate, the incremental net annual monetized benefits moving from Option A to Option B is

- $ 33 million (the negative value indicates that the increase in costs is larger than the increase in benefits). Moving from Option B to Option C, the change is $ 62 million, with the positive value indicating

that the increase in benefits is larger than the increase in costs. Moving from Option C to Option D, and from Option D to Option E, the change is negative, at

- $ 48 million and

- $ 9 million, respectively.

Table 13

- 2 : Incremental Net Benefit A nalysis (Millions; 2013$)

Regulatory Option a Total Annual Monetized

Benefits, Including Adjusted or Inferred
Values Total

Social
Costs Net Annual Monetized

Benefits a Incremental Net Annual
Monetized Benefits b

L ow M id d H igh L ow M id d H igh L ow M id d H igh

3% Discount Rate
Option A $122.1 $122.2 $125.7 $120.5 $1.6 $1.6 $5.2

- - - Option B $162.2 $167.8 $234.2 $198.7

- $36.5

- $30.9 $35.6

- $38.1

- $32.6 $30.4
Option C $410.4 $414.5 $461.3 $383.5 $26.9 $31.0 $77.8 $63.4 $61.9 $42.2
Option D $450.6 $463.0 $565.6 $479.5

- $28.9

- $16.5 $86.1

- $55.8

- $47.5 $8.3
Option E $500.2 $510.8 $576.3 $536.0

- $35.8

- $25.2 $40.3

- $6.8

- $8.7

- $45.8

7% Discount Rate
Option A $110.5 $109.5 $114.4 $116.9

- $6.4

- $7.4

- $2.5

- - - Option B $149.4 $153.8 $217.3 $194.7

- $45.3

- $40.9 $22.5

- $38.9

- $33.5 $25.0
Option C $343.7 $345.6 $377.8 $379.9

- $36.2

- $34.3

- $2.1 $9.1 $6.6

- $24.6
Option D $387.3 $397.0 $478.4 $471.2

- $83.9

- $74.2 $7.2

- $47.7

- $39.9 $9.2
Option E $416.8 $423.7 $408.7 $525.8

- $109.0

- $102.1

- $117.1

- $25.1

- $27.9

- $124.3

Source: U.S. EPA Analysis, 2015

.

a

. EPA did not analyze air

- related benefits for Options A

, C , and E . This category of benefits was only estimated for Options B and D (see Chapter 7

). EPA adjusted the total benefits estimated for Options A, C, and E by multiplying the totals without air

- related benefits by the average ratio of [total with air

- related benefits]/[total wit hout air

- related benefits] for Options B and D.

b. Net benefits are calculated by subtracting total annualized costs from total annual monetized benefits.

c . Incremental net benefits are equal to the difference between net benefits of an option and net be nefits of the previous, less stringent option.

d. EPA estimated use and nonuse values for water quality improvements using two different meta

- regression models of WTP . One model provides the low and high bounds while a different model provides a central estimate (included in this table under the mid

range column) . For this reason, the mid benefit estimate differs from the midpoint of the benefits range. For details, see Chapter 4

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 1 

## 14 Environmental Justice

Executive Order (E.O.) 12898 (59 FR 7629, February 11, 1994) requires that, to the greatest extent practicable and permitted by law, each Federal agency must make the achievement of environmental justice (EJ) part of its mission. E.O. 12898 provides that each Federal agency must conduct its programs, policies, and activities that substantially affect human health or the environment in a manner that ensures such programs, policies, and activities do not have the effect of (1) excluding persons (including populations) from particip ation in, or (2) denying persons (including populations) the benefits of, or (3) subjecting persons (including populations) to discrimination under such programs, policies, and activities because of their race, color, or national origin.

To meet the object ives of E.O. 12898, EPA examined whether the benefits from the final ELGs may be differentially distributed among population subgroups in the affected areas. EPA considered the following factors in this analysis: population characteristics, proximity to af fected waters, exposure pathways, cumulative risk exposure, and susceptibility to environmental risk. For example, subsistence fishers rely on

self

- caught fish for a larger share of their food intake than do recreational fishermen, and as such may incur a larger share of benefits arising from the final ELGs.

As described in the following sections, EPA conducted two types of analyses to evaluate the EJ implications of the final ELGs: (1) summarizing the demographic characteristics of the households living in proximity to
reaches that receive steam electric power plant discharges ; and ( 2) analyzing the human health impacts from consuming self

- caught fish on minority and/or low

- income populations located within 5 0 miles of reaches

affected by steam electric pow er plant discharges. 80 

### This second analysis seeks to provide more specific insight on the distribution of adverse health effects and benefits and to assess whether minority and / or low

income populations incur disproportionally high environmental impacts and /or are disproportionally excluded from realizing the benefits of this final rule

. The following two sections describe ( 1) a comparison of the socio

- economic characteristics of the populations that live in proximity to steam electric power plant s to state and national averages, and ( 2) the evaluation of human health effects and benefits that accrue to populations in different socio

- economic cohorts.



### Socio

- economic Characteristics of Populations Residing in Proximity to Steam 14.1
Electric Power Plant s

For the first an al ysis, EPA assessed the demographic characteristics of the populations within specified

distances of reaches that receive steam electric power plant discharges. The receiving reaches are those to which plants discharge directly in the bas eline; for this first analysis, EPA did not include additional reaches located downstream from the receiving reaches (see BCA for a discussion of receiving and downstream reaches, U.S. EPA 2015a). The analysis is similar to the profile EPA had developed to support the proposed ELG, but looks at populations living within different, closer distances of steam electric power plant s. The change was made in part to addresses comments EPA received on the need to look at communities in closer proximity to the plant s, instead of the 100

- mile buffer the Agency had used at proposal (see comment response document; U.S. EPA 2015d). 81

80 As detailed in the Chapter 3 , EPA used a distance of 50 miles to determine the affected populati on

. 81 Commenters recommended that EPA consider the characteristics of populations within 4 miles, 5 miles,
15 miles and 30 miles of the plants (see U.S. EPA, 2015d).Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 2 

### EPA collected population

- specific Census data on:

 the percent of the population below the poverty threshold, 82 

### and

 the population categori zed in various racial/ethnic groups, from which EPA calculated the percent of the population that belongs to a minority racial/ethnic group. 83



### EPA compiled these data for CB G s located within specified distances ( e.g. , 1 mile, 3 miles, 15 miles, 30 miles, and 50 miles) of the reaches receiving steam electric power plant discharges. EPA compared demographic metrics to state and national averages to identify communities where EJ concerns may exist. EJ concerns may exist in areas where the percent of the population below the poverty threshold is higher than the state or national average or the percent of the population that is minority is above the state or national

average.
This first analysis considers the spatial distr ibution of low

- income and minority groups to determine whether these groups are more or less represented in the populations in proximity to reaches receiving steam electric power plant discharges. The specified distance buffers from the reaches are denoted below as the “benefit region

. ” Populations within the benefit regions may be affected by steam electric power plant discharges and other environmental impacts in the baseline, and would be expected to benefit from environmental improvements resulting from the final ELGs. If the population within a benefit region has a larger proportion of minority or low

- income families than the state average, it may indicate that the final ELGs may benefit communities that have been historically exposed to a disproportion ate share of environmental impacts and

thus contribute to redressing existing EJ concerns

. EPA used the U.S. Census Bureau’s American Community Survey (ACS) data for 2006 to 2010 to identify poverty status (Table C17002) at the state and CBG levels. EPA a lso used 2010 U.S. Census data (Summary File 1; Table 8

– P3) to identify the percent of the population that is minority at the CB G and state levels. EPA overlaid the data with GIS data of buffer zones of specified distances from receiving reaches to

chara cterize the affected communities living in proximity to the reaches. Table 14

- 1 summarizes th e socio

economic characteristics of benefit regions define d using radial distances of 1, 3, 10, 15, 30 and 50 miles from the receiving reaches.

Table 14

- 1 : Socio

- economic Characteristics of Communities Living in Proximity to Receiving
Reaches

Distance from receiving
reach Total population (millions) Percent minority Percent below poverty
level

1 mile 0. 2 20.7% 16.4%

3 miles 1.1 23.4% 15.3%

15 miles 14.0 29. 8 % 13. 6 %

30 miles 37.4 31.5% 13.1%

50 miles 57.3 29.9% 13.1%

United States 306.3 36.0% 13.9%

Source: U.S. EPA analysis, 2015

82 Poverty status is based on data from the Census Bureau’s American Community Survey whi ch determines poverty status by comparing annual income to a set of dollar values called poverty thresholds that vary by family size, num ber of children, and the age of the householder.

83 The racial/ethnic categories are based on available fish consumpti on data as well as the breakout of ethnic/racial populations in Census data, which distinguishes racial groups within Hispanic and non

- Hispanic categories

. Minority groups include: African American (non

- Hispanic); Asian/Pacific Islander (non

- Hispanic);
Tri bal/Native Alaskan (non

- Hispanic); Other non

- Hispanic; Mexican Hispanic, and Other Hispanic

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 3 

### As shown in Table 14

- 1

, approximately 200,000 people live within 1 mile of steam electric power plant s currently discharging to surface waters, over 1.1 million live within 3 miles, and nearly 37.4 million people live within 30 miles. The statistics also show that a greater fraction of the communities living in close proximity to steam electric power plant s is poor, when compared t o the national average. A pproximately

16.4 percent of households in communities within 1 mile of steam electric power plant s have income below the poverty level as compared to a national average of 13.9 percent. A smaller fraction of the population

within 1 mile of the plants belong s to minority racial or ethnic groups (20.7 percent), than the national average (36.0 percent). As one moves further away from the steam electric power plant s, the fraction of the community that is below the poverty threshold goe s down while the percent minority increases, so that the overall composition of the communities approaches that of the U.S. population overall. Thus, looking at
communities within 30 miles of steam electric power plant s, 13.1 percent of the population is b elow the poverty level (vs. 13.9 percent nationally) while 31.5 percent belong to a minority group (vs. 36.0 percent nationally).

The simple comparison to the national average masks important differences, however, between states, particularly given the no n

- uniform geographical distribution of plants across the country. EPA therefore also compared the demographic profile of affected communities to that of the state where they are located. Table

14

- 2 summarizes the results of this comparison. Of the 37 states with communities within 1 mile of steam electric power plant s, 11 states have communities with a higher percentage of households below the poverty

th reshold than the overall state, 17 have a higher percent of the population that is minority, and 10 have a higher proportion of poor and minority households. These results show the potential for localized differences indicative of potential EJ concern, but the overall comparison reveals no systematic difference in demographic characteristics of the populations living in proximity to the steam electric power plant s that would indicate that any communities with EJ concern would be precluded from the benefits of the final ELGs.

Table 14

- 2 : Socio

- economic Characteristics of Affected Communities, Compared to State Average

Distance from receiving
reach Number of States

with Affected
Communities a Number of States where Affected Communities...

are Poorer have a Higher Proportion of Minority Population are Poorer and have a Higher Proportion of Minority Population

... than the State Average

1 mile 37 11 17 10

3 miles 37 10 17 7

15 miles 38 21 16 19

30 miles 42 22 1 6 21

50 miles 45 19 9 12

Source: U.S. EPA analysis, 2015

a. “Affected communities” are Census Block Group s within the specified distance of one or more receiving reaches.



### Distribution of Human Health Impacts and Benefits 14.2

The second type of analysis looks at the distribution of environmental effects and benefits to further inform understanding of the potential EJ concerns and the extent to which the final rule may mitigate them.

A significant share of the benefits of the fi nal ELGs comes from reducing discharges of harmful pollutants to surface waters and associated reductions in fish tissue contamination. EPA quantified the human healthBenefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 4 

### benefits resulting from reducing exposure to pollutants in fish tissue in individuals wh o consume fish caught in reaches immediately receiving or downstream from steam electric plant discharges . The analysis relied on

CBG

- level data to estimate the number and characteristics of individuals exposed to steam electric pollutants through the con sumption of self

- caught fish, and race and ethnicity

- specific assumptions to estimate exposure. This analysis allows the agency to report the distribution of benefits across population subgroups, including subgroups who may have been historically exposed t o a disproportionate share of environmental impacts.

This section presents r esults for the two types of anglers analyzed: recreational anglers and subsistence

fishers. Chapter 3 provides more details on the approach used to identify the affected population, derive exposure, quantify health effects and monetize benefits.

EPA limited its analysis of the distribution of health effects and benefits to t wo pollutants (lead and mercury) due to the small benefits resulting from reducing arsenic discharges. Further, for recreational anglers, EPA focused on benefits accruing to infants and children due to the complexity of carrying the detailed socio

economic data through the models used to quantify the changes in premature mortality from cardiovascular disease (CVD) in adults. The outputs from the CVD benefit analyses do not provide sufficient information to assess changes across socio

- economic subgroups. How ever, EPA did account for changes in the incidence of CVD attributable to the final ELGs when comparing recreational and subsistence fishers (see Section 14.4 ).



### Distribution of Benefits across Benefitting Populations 14.3

Table 14

- 3 summarizes the estimated number of individuals exposed to steam electric pollutants through consumption of self

- caught fish in the general population and in population subgroups that may be i ndicative

of EJ concerns. As shown in the table, of the approximately 36.0 million people exposed to steam electric pollutants, 13.9 percent are poor, 34.4 percent are minority, and 6.5 percent are both poor and minority. Overall, 41.9 percent of potential ly exposed individuals are categorized in at least one or more EJ subgroup based on their poverty level or race/ethnicity , while 58.1 percent are neither minority nor poor

.

Table 14

- 3 : Characteristics of Popul ation Potentially Exposed to Steam Electric Pollutants via Consumption of Self

- caught Fish

S ubgroup Minority Non

- Minority Total

Poor 2,345,972 6.5% 2,668,083 7.4% 5,014,055 13.9%

Non

- Poor 10,042,390 27.9% 20,915,560 58.1% 30,957,950 86.1%

Total 12,388,362 34.4% 23,583,643 65.6% 35,972,005 100%

Source: U.S. EPA Analysis, 2015



### The distribution of adverse health effects is a function of the characteristics of the affected population ( Table

14

- 3 ), including age and sex, 84 

### ethnicity

- specific exposure factors, 85 

### and reach water quality. Table 14

- 4

84 Some adverse health effects are analyzed only for individuals in certain age groups. For example, IQ point decrements from exposure to lead are calculated for c hildren 0 to 7 years old and the baseline exposure therefore depends on the number of children within this age group in the affected population in each socio

economic subgroup. IQ point decrements from exposure to mercury are calculated for infants born wi thin the analysis period and baseline exposure depends on the number of women of childbearing age (and fertility rates) in the affected population.

85 Ethnicity

- specific factors that determine exposure to pollutants in fish tissue include the assumed fish consumption rates and average fertility rate. For example, as described in Chapter 2 of the BCA, Asian/PacificBenefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 5 

### shows the distribution of selected adverse health effects in the baseline. Table 14

- 5 shows the distribution of adverse health effects avoided under each of the five options EPA analyzed in developing the final ELGs.

Note that benefits follow the same distribution as avoided adverse health effects since each case is valued equally, irrespe ctive of the socio

- economic subgroup.

The two tables show results for three selected subgroups:

 Poor and minority (6.5 percent of the exposed population),

 Poor or minority ( i.e. , but not both; 35.3 percent of the exposed population), and

 All others ( i. e. , non

- poor white; 58.1 percent of the exposed population).

The first two subgroups are the primary interest of this analysis as potentially indicative of EJ concerns.

Table 14

- 4 : Distribution of Baseline I Q Point Decrements by Pollutant (2021 to 2042)

Pollutant Poor & Minority

(6.5% of Population) Poor or Minority

(35.3% of Population) All Others
(58.1% of Population) Total

Lead 6,266,344 8.0% 29,663,242 38.0% 42,214,217 54.0% 78,143,802 100%

Mercury 91,838 9.3% 439,403 44.6% 454,081 46.1% 985,322 100%

Source: U.S. EPA Analysis, 2015

Table 14

- 5 : Distribution of Avoided IQ Point Decrements Relative to the Baseline, by Pollutant (2021 to 2042)

Pollutant
and Exposed Population Regulatory

Option Poor & Minority

(6.5% of Population) Poor or Minority

(35.3% of Population) All Others

(58.1% of Population) Total

Children Exposed to
Lead Option A 78 9.2% 313 36.6% 462 54.2% 853 100%

Option B 78 9.2% 313 36.6% 462 54.2% 853 100%
Option C 128 9.9% 518 40.3% 640 49.8% 1,285 100%
Option D 155 7.8% 693 34.9% 1,137 57.3% 1,985 100%
Option E 155 7.8% 693 34.9% 1,137 57.3% 1,985 100%

Infants Exposed to Mercury Option A 287 8.9% 1,300 40.1% 1,652 51.0% 3,239 100%

Option B 293 8.9% 1,331 40.2% 1,687 50.9% 3,311 100%
Option C 471 7.9% 2,216 36.9% 3,314 55.2% 6,001 100%
Option D 540 7.5% 2,603 36.1% 4,076 56.5% 7,219 100%
Option E 591 7.5% 2,839 35.9% 4,469 56.6% 7,898 100%

Source: U.S. EPA Analysis, 2015



### The distribution of baseline heal t h effects and health improvements resulting from the ELGs can be compared to the relative share of the population exposed to steam electric pollutants (from Table 14

- 3 ) to asse ss the

Islander anglers have daily consumption rates that are 1.4 times and 1.9 times those of While (non

- Hispanic) anglers for recreational and subsis tence fishing modes, respectively.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 6 

### degree to which the regulatory options contribute to mitigating any EJ concerns that may be present in the

baseline.

The poor and minority subgroup represents 6.5 percent of the potentially affected population, but accounts for 8.0 percent and 9. 3 percent of the estimated IQ point decrements in the exposed population in the baseline. Under Option D, the poor and minority subgroup also sees a disproportional share of the imp rovements, with

7.8 percent and 7.5 percent of the avoided IQ point decrements from exposure to lead and mercury, respectively. Findings are similar for the poor or minority subgroup, i.e. , this subgroup also incurs a disproportionate share of baseline adv erse health effects, and of the improvements arising from the final

ELGs.



### Subsistence Fishers 14.4

In the analysis of health benefits (see Chapter 3 ), E PA assumed that 5 percent of the exposed population are subsistence fishers, and that the remaining 95 percent are recreational anglers. This is based on the assumed

95 th 

### percentile fish consumption rate for subsistence fishers. Subsistence fishers consume more self

- caught fish than recreational anglers and can therefore be expected to experience higher health risks associated with steam electric pollutants in fish tissue.

The results of the human health analysis suggest that subsistence fishers are dispro portionally exposed to pollutants in steam electric power plant discharges via fish consumption and disproportionally incur adverse health effects from this exposure. As shows in Table 14

- 6 and Table 14

- 7 , subsistence fishers incur 7 percent to 17 percent of the baseline IQ d ecrements

, even though they represent only 5 percent of the overall population, and account for 17 percent to 30 percent of the avoided health effects and benefits of the final

rule.

Table 14

- 6 : Distribution o f Baseline IQ Point Decrements by Pollutant and Fishing Mode (2021 to 2042)

Pollutant and Exposed Population Subsistence Fishers

(5 percent of population) Recreational Fishers
(95 percent of population) Total

Children Exposed to Lead 5,302,873 6.8% 72,840,929 93.2% 78,143,802 100%
Infants Exposed to Mercury 166,415 16.9% 818,907 83.1% 985,322 100%

Source: U.S. EPA Analysis, 2015

Table 14

- 7 : Distribution of Avoided IQ Point Decrements Relative to the Baseline by Fishing Mode, and Pollutant (2021 to 2042)

Pollutant
and Exposed Population Regulatory

Option Subsistence Fishers

(5 percent of population) Recreational Fishers
(95 percent of population) Total

Children Exposed to
Lead Option A 186 21.8% 667 78.2% 853 100%

Option B 186 21.8% 667 78.2% 853 100%
Option C 390 30.4% 895 69.6% 1,285 100%
Option D 605 30.5% 1,381 69.5% 1,985 100%
Option E 605 30.5% 1,381 69.5% 1,985 100%Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 7 Table 14

- 7 : Distribution of Avoided IQ Point Decrements Relative to the Baseline by Fishing Mode, and Pollutant (2021 to 2042)

Pollutant
and Exposed Population Regulatory

Option Subsistence Fishers

(5 percent of population) Recreational Fishers
(95 percent of population) Total

Infants Exposed to Mercury Option A 536 16.5% 2,703 83.5% 3,239 100%

Option B 548 16.5% 2,763 83.5% 3,311 100%
Option C 992 16.5% 5,009 83.5% 6,001 100%
Option D 1,194 16.5% 6,025 83.5% 7,219 100%
Option E 1,306 16.5% 6,592 83.5% 7,898 100%

Source: U.S. EPA Analysis, 2015



### EJ Analysis Findings 14.5

Based on the EJ analyses discussed above, EPA determined that the final ELGs will not deny communities

from the benefits of environmental improvements expected to result from compliance with the more stringent effluent limits. In fact, the distribution of avoided adverse health outcomes and benefits suggests that poor and minority communities may receive a greater share of the benefits from the final ELGs than their representation in the affected populations. The final ELGs may thus help redress environmental inequities that may be present in the baseline

. By reducing exposures to pollutants discharged by steam electric power p lants, a ll of the evaluated options
would benefit communities with potential EJ concerns. Of the five options, Options C, D, and E are expected to provide significantly greater human health benefits than Options A and B

, as indicated by higher reductions

i n adverse health effects from lead and mercury exposure. Improvements under Option C accrue to poor and minority population to a larger relative extent than do improvements under Options D and E, but Option D (on which the final limitations and standards a re based) provides greater benefits overall than Option C, including to poor and minority populations exposed to steam electric pollutants through consumption of self

caught fish. Thus, the final rule will further environmental justice objectives.



### Limitati ons and Uncertainties 14.6

This EJ analysis inherits the limitations and uncertainties of the human health benefit analysis (see Chapter 3 ) regarding po llutant exposure, health effects, and valuation. In addition, however, the analysis also embeds uncertainty derived from the application of uniform assumptions across the population exposed to pollutant discharges when factors may instead vary across socio economic characteristics, including:

 EPA assumed that all fishers travel up to 50 miles; in fact, some anglers stay closer to home and certain EJ or sensitive subpopulations may tend to stay closer to home ( e.g. , poor people and subsistence fishers). These people may be exposed to relatively higher concentrations of pollutants.

 EPA assumed that subsistence fishers are 5 percent of all anglers, with this assumption applied uniformly across all socioeconomic groups. In fact, a relatively higher share of EJ gr oups may be subsistence fishers. This would tend to increase the inequities already in the baseline and further increase the mitigating effect of the ELGs in addressing these inequities.Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14

- 8 

###  EPA applied uniform f ishing participation rates, FCAs, and catch and release practices across the entire population. However , diffe rences in behavior across socio economic groups may result in different distribution of baseline impacts and benefits.

In summary, use of average values across the entire US p opulation (or within a state) instead of assumptions that reflect specific socioeconomic conditions may understate inequities present in the baseline and benefits to poor or minority populations , to the extent that different socioeconomic groups may be more likely to be

expos ed to pollutants from steam electric power plant discharges

.Benefit and Cost Analysis for Steam Electric Power Generat ing ELGs 15: References

September 29, 2015 15

- 1 

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### *** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix A: Changes to Benefits Analysis

September 29, 2015 A

- 1 

## Appendix A. Changes to Benefits Analysis since Proposal

The table below summarizes the principal changes EPA made to its benefit s analysis for the final rule, as compared to the analysis of the proposed rule (in addition to changes to inputs such as costs and pollutant

loads )

. EPA made changes to address comments it received on the proposed rule analysis and to incorporate updated, more recent data.

Table A

- 1: Changes to Benefits Analysis Since Proposal

Report Section or Benefit Category Analysis Component
[Proposed rule analysis value ] Changes to Analysis for Final Rule
[Final rule analysis value ]
General assumptions Dollar year [all costs and benefits expressed in 2010 dollars] Updated dollar year [2013]

Promulgation year [all costs and revenue streams discounted back to 2014] Updated p romulgation year [2015]

Period of social

- costs and benefits analyses [2017

- 2040] Updated p eriod of social

- costs and benefits analyses [2019

- 2042] Technology implementation years [2017

- 2021] Updated t echnology implementation years [2019

- 2023]

General pollutant loadings and
concentration assumptions EA modeling of metal concentrations in immediately receiving reaches (national model) Updated immediately recei ving reaches (see EA for details)

RSEI modeling of metal concentrations in
immediate and downstream reaches model
across nation Adjusted loadings to reflect partitioning of pollutants in immediate reach, based on the
EA national model. Used updated b aseline that reflects TRI
releases for 2012

. SPARROW modeling of nutrient concentrations in receiving and downstream reaches [estimate changes in ambient pollutant (nutrient) concentrations in receiving and downstream reaches based on regression model] No changes for nutrients. Estimated changes in suspended sediments and channel deposition using SPARROW

Human health benefits associated with reductions in fish tissue contamination Assignment of populations to affected
waterbodies to determine potential exposure Potentially exposed population [people residing within 100 miles of an affected waterbody] Revised the analysis to focus on Census
Block Groups . Subdivided Census Block Group population according to
socioeconomic indicators. Used travel
distance of 50 miles Fishing practices [addressed by consumption estimate] For recreational anglers, adjust ed exposed population to account for catch and release practices Scope [immediately receiving reaches only] Included downstream reaches Pollutant exposure via consumption of self

caught fish Consumption rates [uniform consumption
rates for recreational/subsistence fishers ] Consumption rates [ethnicity and fishing
mode

- speci fic consumption rates]

Avoided IQ losses in
children from lead

exposure Exposure estimates and monetization Update d/verified the dose

- response relationship between PbB and IQ based on most recent data for low level exposure (to address comment)

### *** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix A: Changes to Benefits Analysis

September 29, 2015 A

- 2 Table A

- 1: Changes to Benefits Analysis Since Proposal

Report Section or Benefit Category Analysis Component
[Proposed rule analysis value ] Changes to Analysis for Final Rule
[Final rule analysis value ]

Monetization Use d updated estimates of the cost of
education
Avoided IQ losses in infants from mercury

exposure Exposure estimates [exposed population is infants born to women (15

- 50 years of age) in households that fish recreationally] Modified definition of cohort of women of
child

- bearing age (15

- 42 years old)

Monetization Use d updated estimates of the cost of
education

Avoided cancer cases from arsenic exposure Monetization [valuation based on value of statistical life (VSL)] Change d valuation to use cost of illness (COI) as basis for monetizing avoided cancer cases since skin cancer is generally not fatal

Avoided cardiovascular
disease in adults from lead exposure N/A Estimated changes in number of CVD (Leggett model) based on ingestion of recreationally

- caught fish. Monetization
based on VSL Avoided exposure in drinking water N/A Expanded discussion of the potential benefits of reducing pollutant concentrations below
MCLs

Other benefits

Non

- market benefits from surface water quality improvements Data sources Added changes in suspended solid
concentration ( from SPARROW)
New data for monetization to reflect parameters in new meta

- analysis function Water quality index N o change Willingness

- to

- pay function Revised meta

- analysis to include spatial
characteristics of the affected water resources: size of the market, waterbody characteristics (length and flow), availability of substitute sites, land use type in the abutting counties Benefitting population Test alternative definition of the unit of the analysis as the Census Block Group ; all households in a given Census Block Group value all water quality changes in a 100 mile
radius

Benefits from groundwater quali ty improvements Willingness

- to

- pay for water quality Did not estimate benefits given promulgation
of the final CCR rule Avoided human health hazards

Benefits to threatened and endangered species Categorical analysis based on habitat overlap/proximity Monetization [willingness to pay] No change

Benefits from avoided impoundment failures Projected impoundment use at s team e lectric

power plants Updated to account for projected effects of
the CCR rule Average failure rate and expected impacts of
failures Expressed risk of failure and impacts for two impoundment categories (small, big) depending on size and failure type

Residual failure rate Revised to reflect effects of the CCR rule Cleanup costs Updated to include more recent d ata and/or

more detailed review

### *** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix A: Changes to Benefits Analysis

September 29, 2015 A

- 3 Table A

- 1: Changes to Benefits Analysis Since Proposal

Report Section or Benefit Category Analysis Component
[Proposed rule analysis value ] Changes to Analysis for Final Rule
[Final rule analysis value ]

Transaction costs Updated to include more recent data and/or
more detailed review Natural resources damages Updated to include more recent data and/or
more detailed review

Reduced emissions of
NO X and SO 2 Changes in air pollutant levels from IPM and engineering analysis
Benefit

- per

- ton estimates for avoided human health impacts No change

Reduced emissions of
CO 2 Changes in air pollutant levels from IPM and engineering analysis No change

Monetization [based on social cost of carbon (SCC) (Interagency Working Group on Social Cost of Carbon, 2010)] Updated SCC to reflect most recent value in Interagency Working Group on Social Cost of Carbon (2013)

Benefits from reduced
water withdrawals Surface water withdrawals Expanded qualitative discussion

Groundwater withdrawals No change

Enhanced CCR marketability for
beneficial uses N/A Estimated changes in beneficial uses of CCR due to conversions from wet to dry handling. Estimated benefits based on approach used
for the final CCR rule analysis.

Avoided maintenance dredging costs N/A Estimated reductions in volume of sediment dredged from waterways and reservoirs.
Estimated benefits based on the avoided costs for maintenance dredging.

Reduced water treatment

costs Qualitative discussion Expanded qualitative discussion to provide more information on potential impacts of bromide discharges on disinfection byproducts (and benefits from reducing discharges)

Environmental Justice Environmental justice Profile of affected populations [compare demographic characteristics of population affected by steam electric plant discharges to the state population , using 100

- mile buffer
distance ] Use Census Block Group as the unit of analysis; use various distance buffers that

r eflect distance travelled by different socioeconomic groups (1, 3, 15, 30, and 50 miles), based on comments

N/A Evaluate d EJ considerations across the regulatory option s by explicitly analyzing distribution of human health benefits by
socioeconomic sub groupBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 1 

## Appendix B. Analysis for Scenario without CPP Rule

EPA included the anticipated effects of the Clean Power Plan (CPP) rule in its baseline for the final ELG rule analysis, as described in Chapter 1 . This baseline was developed based on information about conversions, retirements, and other changes EPA projected in response to the CPP rule, as proposed by EPA in June 2 014. In particular, EPA updated its steam electric power plants profile to account for additional changes due to CPP implementation that affect plant costs for meeting the ELGs and pollutant loads (see TDD for details). The results presented in the main bo dy of this document are based on this scenario with CPP.

This appendix presents the results of an alternative benefits analysis using a baseline that does not include the incremental conversions, retirements, and other changes projected to occur in respons e to the CPP rule. All other assumptions match those described in Chapters 3 through 10

.

Table B

- 1

. Pollutant Removal for Final ELGs Regulatory Options for Scenario without CPP

Regulatory Option Pollutant Load Reduction (pounds per year)

Conventional
Pollutants a Priority Pollutants Nonconventional
Pollutants b Toxic

- Weight ed Pound Equivalent
Option A 5,810,000 285,000 151,000,000 1,110,000
Option B 5,810,000 386,000 161,000,000 1,250,000
Option C 13,600,000 528,000 377,000,000 1,630,000
Option D 18,500,000 618,000 514,000,000 1,870,000
Option E 19,100,000 665,000 526,000,000 1,910,000

a. The loadings reduction for conventional pollutants includes BOD and TSS.

b. The loadings reduction for nonconventional pollutants excludes TDS and COD to avoid double

- counting removals for certain pollutants that would also be measured by these bulk parameters ( e.g. , sodium, magnesium).



### Additionally, u nder the scenario without CPP, EPA estimates that the final BAT/PSES option (Option D) will

reduce surface wate r withdrawals at steam electric power plant s by 209 to 222 billion gallons per year ( 0.57 to

0.61 million gallons per day) and will avoid withdrawals of 8 million gallons of groundwater per year (21,971 gallons per day )

.



### B.1 Human Health Benefits

B.1.1 Benefits to Children from Reduced Lead Exposure

Table B

- 2 . Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead for Scenario without CPP

Regulatory Option Average

Annual
Number of
Affected
Children 0 to 7 Total

Avoided IQ
Losses , 202 1
to 2042 Annual ized Value of Avoided IQ Point Losses a (Millions 2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound
Option A 3,326,127 893 $0.35 $0.50 $0.06 $0.09
Option B 3,326,127 893 $0.35 $0.50 $0.06 $0.09
Option C 3,326,127 3,940 $1.57 $2.20 $0.25 $0.38Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 2 Table B

- 2 . Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead for Scenario without CPP

Regulatory Option Average

Annual
Number of
Affected
Children 0 to 7 Total

Avoided IQ
Losses , 202 1
to 2042 Annual ized Value of Avoided IQ Point Losses a (Millions 2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound
Option D 3,326,127 4,693 $1.86 $2.62 $0.30 $0.45
Option E 3,326,127 4,693 $1.86 $2.62 $0.30 $0.45

Source: U.S. EPA Analysis, June 2015

a . Low bound assumes that the loss of one IQ point results in the loss of 1.76% of lifetime earnings (following Schwartz, 1994); high bound assumes that the loss of one IQ point results in the loss of 2.38% of lifetime earnings (following Salkever, 1995)

.

Table B

- 3 . Estimated Avoided Cost of Compensatory Education for Children with Blood Lead
Concentrations above 20  g/dL and IQ Less than 70 a for Scenario without CPP

Regulatory Option Number of

Affected
Children 0 to 7 Decrease in Number

of Cases of IQ < 70,
in 202 1 to 2042 Avoided Annual Cost (Millions; 201 3 $)

3% Discount Rate 7% Discount Rate

Option A 3,326,127 1 $0.00 $0.00
Option B 3,326,127 1 $0.00 $0.00
Option C 3,326,127 2 $0. 0 1 $0.01
Option D 3,326,127 3 $0.02 $0.01
Option E 3,326,127 3 $0.02 $0.01

Source: U.S. EPA Analysis, June 2015

a. “

- “ indicates that a value was not estimated and “$0.00” indicates that avoided annual cost is less than $0.01.



### B.1.2 Benefits to Adults from Reduced Lead Exposure

Table B

- 4

. Summary of Health Benefits due to Decreased Risk of CVD Mortality during 201 9

- 204 2 based on the Economic V alue of A voided P remature M ortality (VSL) for Scenario without CPP

Regulatory Option Avoided premature
deaths Annualized Benefits (millions 2013$)

3 Percent 7 Percent
Option A 11.4 $ 4.03 $ 3.36 Option B 11.4 $ 4.03 $3. 36 Option C 48.0 $1 6.98 $1 4.17 Option D 62.1 $ 21.93 $1 8.30 Option E 62.1 $ 21.93 $1 8.30

Source: EPA Analysis, June 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 3 

### B.1.3 Benefits to Children from Reduced Mercury Exposure

Table B

- 5 . Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure for Scenario without CPP

Regulatory Option Number of

Affected Infants per
Year Total Avoided

IQ Losses
, 2021 to 2042 Annual ized Value of Avoided IQ Point Losses a (Millions 2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound
Option A 418,953 4,519 $1.80 $2.52 $0.29 $0.43
Option B 418,953 4,610 $1.83 $2.57 $0.30 $0.44
Option C 418,953 11,851 $4.71 $6.62 $0.76 $1.14
Option D 418,953 13,351 $5.31 $7.46 $0.86 $1.28
Option E 418,953 14,189 $5.64 $7.92 $0.92 $1.36

Source: U.S. EPA Analysis, 2015
a. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 1995).



### B.1.4 Reduced Cancer Cases from Arsenic Exposure

Table B

- 6

. Annual Benefits from Reduced Cancer Cases due to Arsenic Exposure a for Scenario without CPP

Regulatory Option Annual

Affected Population Reduced Cancer Cases
,

2019 to 2042 Benefits (Millions 2013$)

3% Discount 7% Discount

Option A 35,972,005 0.04 $0.00 $0.00
Option B 35,972,005 0.04 $0.00 $0.00
Option C 35,972,005 0.13 $0.00 $0.00
Option D 35,972,005 0.16 $0.00 $0.00
Option E 35,972,005 0.17 $0.00 $0.00

Source: U.S. EPA Analysis, 2015

a . “

- “ indicates that a value was not estimated and “$0.00” indicates that annual benefits are less than $0.01 million.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 4 

### B.1.5 Total Monetized Human Health Benefits

Table B

- 7

. Total Monetized Human Health Benefits for ELG Options (millions of 2013$) a,b for Scenario without CPP

Discount
Rate Option Reduced L ead

E xposure for
C hildren Reduced

L ead
E xposure for A dults Reduced M ercury

E xposure for
C hildren Reduced

C ancer C ases from
A rsenic Total

L ow High Low H igh L ow H igh

3% A $0.35 $0.50 $4.03 $1.80 $2.52 $0.00 $6.18 $7.05

B $0.35 $0.50 $4.03 $1.83 $2.57 $0.00 $6.21 $7.10

C $1.58 $2.21 $16.98 $4.71 $6.62 $0.00 $23.27 $25.81

D $1.88 $2.64 $21.93 $5.31 $7.46 $0.00 $29.12 $32.03

E $1.88 $2.64 $21.93 $5.64 $7.92 $0.00 $29.45 $32.49

7% A $0.06 $0.09 $3.36 $0.29 $0.43 $0.00 $3.71 $3.88

B $0.06 $0.09 $3.36 $0.30 $0.44 $0.00 $3.72 $3.89

C $0.26 $0.39 $14.17 $0.76 $1.14 $0.00 $15.19 $15.70

D $0.31 $0.46 $18.30 $0.86 $1.28 $0.00 $19.47 $20.04

E $0.31 $0.46 $18.30 $0.92 $1.36 $0.00 $19.53 $20.12

Source: U.S. EPA Analysis, June 2015

a. “$0.00” indicates that annual benefits are less than $0.01 million.
b. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following S alkever, 1995).



### B.1.6 Additional Measures of Human Health Benefits

Table B

- 8 . Reaches Exceeding Human Health Criteria for Steam Electric Pollutants for Scenario without CPP

Regulatory Option Number of Reaches with

Steam Electric Pollutant a Concentrations Exceeding
Human Health Criteria for at
Least One Pollutant Number of Reaches with Improved Water Quality,
Relative to Baseline

Number of Reaches with
Fewer Exceedances b Number of Reaches with All
Exceedances Eliminated

Baseline 3,959

-- -- Option A ,3,334 712 625
Option B 3,334 714 625
Option C 2,400 1, 716 1, 559
Option D 1,904 2, 215 2, 055
Option E 1,753 2, 303 2, 206

Source: U.S. EPA Analysis, 2015

a. Pollutants include arsenic, copper, nickel, selenium, thallium, zinc, cadmium, chromium, lead, and mercury.

b. T he number of reaches with exceedances reduced includes those reaches where all exceedances are eliminated.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 5 

### B.2 Non

- Market Benefits for Water Quality Improvements

Table B

- 9 : Total Willingness

- to

- Pay for Water Quality Improvements

Regulatory Option Number of Affected

Households (Millions) 3% Discount Rate 7% Discount Rate

Low Mean High Low Mean High

Option A 39.9 $5.1 $6.0 $28.4 $4.0 $4.8 $22.6
Option B 70.4 $15.1 $19.2 $84.4 $12.1 $15.4 $67.3
Option C 76.3 $20.7 $27.8 $115.5 $16.6 $22.3 $92.2
Option D 94.8 $25.8 $35.3 $143.6 $20.6 $28.3 $114.6
Option E 101.4 $27.6 $38.0 $154.0 $22.1 $30.5 $122.9

Source: U.S. EPA Analysis, 2015



### B.3 Impacts and Benefits to Threatened and Endangered Species

Table B

- 10

. T&E Species with Habitat Occurring within Waterbodies Affected by Steam Electric Power
Plants for Scenario without CPP

Species Group Species Vulnerability Species Count Low Moderate High
Amphibians 0 4 2 6

Arachnids 4 0 0 4

Birds 10 2 1 13

Clams 0 0 37 37

Crustaceans 0 1 2 3

Fishes 0 0 21 21

Insects 10 2 1 1 3

Mammals 1 7 6 1 24 Reptiles 3 1 5 9

Snails 8 0 1 4 22

Total 52 16 84 152

Source: U.S. EPA Analysis, 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 6 Table B

- 11

. Estimated Annualized Benefits to T&E Species from WQ Improvements (Millions 201 3 $) a ,b for Scenario without CPP

Discount
Rate State Option A Option B Option C Option D Option E

Low Medium High Low Medium High Low Medium High Low Medium High Low Medium High

3% AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 < $0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01

MD $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01

SC $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 < $ 0.01 $ 0.01 <$0.01 < $ 0.01 $ 0.01 <$0.01 < $ 0.01 $ 0.01

Total $0.01 $0.01 $0.02 $0.01 $0.01 $0.02 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03 $0.01 $0. 02 $0.03

7% AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 < $0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01 <$0.01 $ 0.01 $ 0.01

MD $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01 < $0.01 $0.01 $0.01

SC $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 $0.0 0 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

Total $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.0 2 $0.0 3 $0.01 $0.0 2 $0.0 3 $0.01 $0.0 2 $0.0 3Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 7 

### B.4 Benefits from Avoided Impoundment Failure s

Table B

- 12 : Steam Electric Impoundments by Size in ELG Baseline for Scenario without CPP

Type Number of Impoundments Impoundment Capacity

Count Percent of Total Total (million gallons) Percent of Total
Big 732 7 2 % 1,031,833 96%

Small 285 2 8 % 44,202 4%

Total 1,017 100% 1,076,036 100%

Table B

- 13

. Estimated Annualized Benefits of Avoided Impoundment Failures by Release Type for Scenario without CPP (Millions; 2013$) a

Discount
Rate Regulatory Option Wall Breaches Other Releases All Releases

3% Option A $25.3 $9.8 $35.2

Option B $25.3 $9.8 $35.2 Option C $101.7 $39.4 $141.1 Option D $136.3 $52.9 $189.1 Option E $136.3 $52.9 $189.1

7% Option A $20.3 $7.9 $28.1

Option B $20.3 $7.9 $28.1 Option C $82.6 $31.9 $114.5 Option D $110.6 $42.9 $153.5 Option E $110.6 $42.9 $153.5

Source: U.S. EPA Analysis, 2015
a. Baseline value of total failure costs minus option value of total failure costs.



### B.5 Air

- Related Benefits

Table B

- 14 . Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to Increase in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline for Scenario without CPP

Regulatory Option Year Electricity Consumption (MWh) CO 2 (Metric

Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option B 2015

- 2018 0.0 0.0 0.0 0.0

2019 51,163.2 35,363.6 20.4 37.1

2020 69,022.4 47,657.8 28.2 51.5

2021 105,250.1 73,942.8 43.1 75.4

2022 119,561.0 86,906.2 54.3 81.7

2023

- 2042 139,648.1 102,655.1 70.9 93.0Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 8 Table B

- 14 . Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to Increase in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline for Scenario without CPP

Regulatory Option Year Electricity Consumption (MWh) CO 2 (Metric

Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option D 2015

- 2018 0.0 0.0 0.0 0.0

2019 95,434.4 68,584.9 48.3 66.1

2020 146,933.1 101,292.8 80.3 93.3

2021 223,365.9 153,405.3 112.5 144.4

2022 270,636.7 191,064.6 146.4 176.0

2023

- 2042 339,202.2 238,871.5 193.5 212.4

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table B

- 15 . Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at Steam Electric Power Plants, Relative to Baseline for Scenario without CPP

Regulatory Option Year CO 2 (Metric Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option B 2015

- 2018 0.0 0.0 0.0

2019 775.3 0.3 0.0

2020 918.4 0.4 0.0

2021 1,092.2 0.5 0.0

2022 1,143.4 0.5 0.0

2023

- 2042 1,218.8 0.5 0.0

Option D 2015

- 2018 0.0 0.0 0.0

2019 2,401.2 1.0 0.0

2020 2,772.4 1.2 0.0

2021 3,551.3 1.5 0.0

2022 3,972.7 1.7 0.0

2023

- 2042 4,277.3 1.9 0.0

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table B

- 16 . Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity Generation Profile, Relative to Baseline for Scenario without CPP

Regulatory Option Year CO 2 (Metric Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

With CPP (Also Applied to Without CPP Scenario)

Option B 2015

- 2018 0.0 0.0 0.0

2019

- 2022

- 2,057,293.5

- 3,534.5

- 4,620.6

2023

- 2027

- 1,437,164.1

- 3,323.9

- 527.5

2028

- 2033

- 246,295.3

- 1,361.2 1,315.7

2034

- 2042

- 1,186,462.9

- 1,500.2

- 1,608.4Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 9 Table B

- 16 . Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity Generation Profile, Relative to Baseline for Scenario without CPP

Regulatory Option Year CO 2 (Metric Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option D 2015

- 2018 0.0 0.0 0.0

2019

- 2022

- 4,869,524.3

- 14,614.0

- 5,662.8

2023

- 2027

- 2,555,361.0

- 11,615.0 2,238.4

2028

- 2033

- 2,089,591.4

- 8,826.0

- 984.2

2034

- 2042

- 3,193,009.0

- 10,638.8

- 4,243.9

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table B

- 17 . Estimated Net Changes in Air Pollutant Emissions due to Increase in Auxiliary Service at Steam Electric Power Plants, Increased Trucking at Steam Electric Power Plants, and Changes in Electricity Generation Profile, Relative to Baseline for Scenario without CPP

Regulatory Option Year CO 2 (Metric Tonnes/Year) NOx (Tons/Year) SO 2 (Tons/Year)

Option B 2015

- 2018 0.0 0.0 0.0

2019

- 2,021,154.6

- 3,513.7

- 4,583.5

2020

- 2,008,717.3

- 3,505.9

- 4,569.1

2021

- 1,982,258.5

- 3,490.9

- 4,545.2

2022

- 1,969,243.9

- 3,479.6

- 4,538.8

2023

- 2027

- 1,333,290.2

- 3,252.5

- 434.5

2028

- 2033

- 142,421.36

- 1,289.73 1,408.69

2034

- 2042

- 1,082,588.96

- 1,428.78

- 1,515.47

Option D 2015

- 2018 0.0 0.0 0.0

2019

- 4,798,538.2

- 14,564.6

- 5,596.7

2020

- 4,765,459.0

- 14,532.5

- 5,569.4

2021

- 4,712,567.7

- 14,499.9

- 5,518.4

2022

- 4,674,486.9

- 14,465.9

- 5,486.8

2023

- 2027

- 2,312,212.2

- 11,419.6 2,450.9

2028

- 2033

- 1,846,442.6

- 8,630.6

- 771.7

2034

- 2042

- 2,949,860.2

- 10,443.5

- 4,031.4

Source: U.S. EPA Analysis, 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 10 Table B

- 18

. Estimated B enefits from R educed Air Emissions for Selected Years for Scenario without CPP (millions; 2013$) a,b

Option Year 3% Discount Rate 7% Discount Rate

Option B 2019 c $287.2 $265.8

2020 $290.3 $269.2

2025 $105.3 $101.4

2030

- $47.5

- $42.4

Option D 2019 c $513.5 $481.3

2020 $518.7 $487.6

2025 $83.0 $86.1

2030 $192.4 $183.7

Source: U.S. EPA Analysis, 2015

a. EPA used SCC values based on a 3 percent (average) discount rate to calculate total benefit values presented for both the 3 percent and 7 percent discount rate.

b. EPA used the changes in a nnual a ir p ollutant e missions due to c hanges in e lectricity g eneration p rofile for scenario with CPP (from IPM analysis) when calculating benefits for the scenario without CPP

c. The benefits per ton values used for year 2019 benefit calculation is assumed to be the same as the 2020 benefits per ton values.

Table B

- 19 . Estimated Annualized B enefits from R educed Air Emissions for Scenario without CPP (Millions; 2013$) a

ELG Option Pollutant 3% Discount Rate 7% Discount Rate

Option B NO

x $12.7 $10.6

SO 2 $44.8 $40.4

CO 2 3% Avg $51.9 $51.9

5% Avg $15.1 $15.1
2.5% Avg $76.5 $76.5

3% 95 th Percentile $155.9 $155.9

TOTAL 3% Avg $109.3 $102.8

5% Avg $72.6 $66.1
2.5% Avg $134.0 $127.4

3% 95 th Percentile $213.4 $206.8

Option D NO

x $62.9 $49.3

SO 2 $81.0 $58.9

CO 2 3% Avg $138.4 $138.4

5% Avg $40.0 $40.0
2.5% Avg $204.7 $204.7

3% 95 th Percentile $416.9 $416.9

TOTAL 3% Avg $282.4 $246.7

5% Avg $183.9 $148.3
2.5% Avg $348.6 $313.0

3% 95 th Percentile $560.8 $525.2

Source: U.S. EPA Analysis, 2015

a

. EPA used the changes in a nnual a ir p ollutant e missions due to c hanges in e lectricity g eneration p rofile for scenario with CPP (from IPM analysis) when calculating benefits for the scenario without CPPBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 11 

### B.6 Benefits from Reduced Water Withdrawals

Table B

- 20 . Estimated Annualized Benefits from Reduced Groundwater Withdrawals (Millions; 2013$)

Regulatory Option Reduction in
Groundwater Intakes (million gallons per year; full implementation) 3% Discount Rate 7% Discount Rate

Option A 0 .0 $0.00 $0.00
Option B 0.0 $0.00 $0.00
Option C 0 .0 $0.00 $0.00
Option D 8.0 $0.0 2 $0.0 2 Option E 8.0 $0. 02 $0.0 2

Source: U.S. EPA Analysis, 2015



### B.7 Benefits from Enhanced Marketability of Coal Combustion Residuals

Table B

- 21 2

. State

- level M arket A pproximation ( S hort T ons).

Application Baseline Production Changes due to ELGs

Production Unmet Demand (%

of production) Fly Ash
(% ΔCCR) Bottom Ash
(% ΔCCR)

Concrete 13,400,062 3,277,184 (24%) 166,535 (9.6%) -

Fill 320,701,683 312

, 705

, 505 (98%) 1,575,450 (90.4%) 5,480,176 (92.7%)

Source: U.S. EPA Analysis 2015

Table B

- 23

. Estimated Beneficial Use Applications of CCRs, by CCR Category

ELG Option and CCR Category Beneficial Use Application (1,000 short tons)

Concrete Structural Fill

Option A or Option B
Fly ash 167 1,575

Bottom ash NA 0

Total 167 1,575
Option C
Fly ash 167 1,575

Bottom ash NA 3,431

Total 167 5,006
Option D or Option E
Fly ash 167 1,575

Bottom ash NA 4,821

Total 167 6,397Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 12 Table B

- 22 4

. Annual Avoided Resource and Environmental Impacts Given CCR Reuse in Concrete and Fill Applications for Scenario Without CPP

Impact Category Fly Ash Bottom Ash Option Total CCR RIA, 2030
3

- yr rolling avg Option A or Option B

Concrete (mill. tons) 0.167 NA 0.167 0.21
Structural fill (mill. tons) 1.58 0 1.58 6.93
Energy (MMBtu) 657,548 0 657,548 910,000
Water (million gal) 1,937 0 1,937 21,000
Greenhouse gases (tons) 1,442,070 0 1,442,070 75,000
NOx (tons) 359 0 359 310

SOx (tons) 54 0 54 59
Option C

Concrete (mill. tons) 0.167 NA 0.167 0.21
Structural fill (mill. tons) 1.58 3.43 5.06 6.93
Energy (MMBtu) 657,548 137,232 794,780 910,000
Water (million gal) 1,937 4,144 6,081 21,000

Greenhouse gases (tons) 1,442,070 10,979 1,453,048 75,000

NOx (tons) 359 92 451 310

SOx (tons) 54 29 83 160
Option D or Option E

Concrete (mill. tons) 0.167 NA 0.167 0.21
Structural fill (mill. tons) 1.58 4.82 6.39 6.93
Energy (MMBtu) 657,548 192,848 850,396 910,000
Water (million gal) 1,937 5,824 7,761 21,000
Greenhouse gases (tons) 1,442,070 15,428 1,457,498 75,000

NOx (tons) 359 129 488 310

SOx (tons) 54 41 95 160

Note: Values in this table represent annual changes in a full

- compliance year ( e.g. , starting in 2023).Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 13 Table B

- 25

. Annualized E conomic Value of Estimated Changes in Beneficial Use ( Million 2013$).

Regulatory Option Impact Category 3% 7%

Option A or
Option B Avoided Disposal Costs to Steam Electric Plants $6.44 $5.46

Beneficiation Costs

- $0.21

- $0.16
Avoided Life Cycle Costs of Virgin Materials $11.34 $14.52

Net Social Value $17.57 $19.82

Option C Avoided Disposal Costs to Steam Electric Plants $21.97 $18.39

Beneficiation Costs

- $0.21

- $0.16

Avoided Life Cycle Costs of Virgin Materials $13.25 $16.69

Net Social Value $35.01 $34.92

Option D or
Option E Avoided Disposal Costs to Steam Electric Plants $34.43 $28.59

Beneficiation Costs

- $0.21

- $0.16
Avoided Life Cycle Costs of Virgin Materials $13.94 $17.48

Net Social Value $48.16 $45.90

Notes: Annualized over 24 years (2015

- 204 2 ). Values escalated using CCI and GDP through 2022; thereafter, assume no real change in prices above inflation. Avoided disposal costs to s team e lectric power plants include annual O&M costs.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 14 

### B.8 Total Monetized Benefits

Table B

- 26 . Summary of Total Annual ized Benefits at 3 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High

Human Health Benefits $6.2 $6.6 $7.1 $6.2 $6.7 $7.1 $23.3 $24.5 $25.8 $29.1 $30.6 $32.0 $29.4 $31.0 $32.5
Reduced IQ losses in children from exposure to lead a $0.4 $0.4 $0.5 $0.4 $0.4 $0.5 $1.6 $1.9 $2.2 $1.9 $2.3 $2.6 $1.9 $2.3 $2.6

Reduced CVD in adults from exposure to lead $4.0 $4.0 $17.0 $21.9 $21.9

Reduced IQ losses in children
from exposure to mercury a $1.8 $2.2 $2.5 $1.8 $2.2 $2.6 $4.7 $5.7 $6.6 $5.3 $6.4 $7.5 $5.6 $6.8 $7.9

Avoided cancer cases from exposure to arsenic b $0.0 $0.0 $0.0 $0.0 $0.0

Improved Ecological
Conditions and Recreational
Uses $5.1 $6.1 $28.4 $15.2 $19.2 $84.4 $20.7 $27.8 $115.5 $25.8 $35.3 $143.7 $27.6 $38.0 $154.0

Use and nonuse values for water quality improvements $5. 1 $6.0 $28.4 $15.1 $19.2 $84.4 $20.7 $27.8 $115.5 $25.8 $35.3 $143.6 $27.6 $38.0 $154.0

Nonuse values of T&E species b $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

Mar ket and Productivity
Benefits $52.7 $52.7 $176.1 $237.3 $237.3

Avoided impoundment failures $35.2 $35.2 $141.1 $189.1 $189.1
Reduced dredging costs b $0.0 $0.0 $0.0 $0.0 $0.0
Ash marketing benefits $17.6 $17.6 $35.0 $48.2 $48.2

Air

- related benefits NE $109.4 NE $282.4 NE

Reduced human health effects NE $57.5 NE $143.9 NE

Reduced CO 2 emissions c NE $51.9 NE $138.4 NE

Reduced water withdrawals b $0.0 $0.0 $0.0 $0.0 $0.0
Total (excluding air

- related Benefits) d $64.0 $65.4 $88.2 $74.1 $78.6 $144.3 $220.1 $228.4 $317.4 $292.2 $303.2 $413.0 $294.4 $306.3 $423.8

Total (including air

- related Benefits) d, e NE NE NE $18 3.5 $188.0 $253.6 NE NE NE $574.6 $585.5 $695.4 NE NE NE

Source: U.S. EPA Analysis , 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 15 Table B

- 26 . Summary of Total Annual ized Benefits at 3 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High

“NE” indicates that EPA did not estimate the benefits. Air

- related benefits of Option A are expected to be less than those for Option B; air

- related benefits for Option C are expected to be between those of Options B and D; and air

- related benefits of Option E are expected to be greater than those for Option D.

a. Value includes r educed IQ losses and a voided cost of compensatory education in children from exposure to lead . For details see Chapter 3

. b

. “ < $0. 1 ” indicates that the monetized annual benefits are positive but less than $0.1 million.

c

. For the valuation of benefits from reductions in CO 2 emissions EPA re lied on the 3 percent average social cost of carbon estimate.

d . Values for individual be ne fit categories may not sum to the total due to independent rounding.

e . The total mo ne tized be ne fits for options A

, C , and E do not include air

- related benefits . Thi s category of benefits was analyzed for Options B and D only (see Chapter 7 )

. f. EPA estimated use and nonuse values for water quality improvements using two different meta

- regression models of willingness

- to

- pay. One model provides the low and high bounds
while a di fferent model provides a central estimate (included in this table in the mid

- range column) . For this reason, the mid

- range estimate differs from the midpoint of the range for this benefit category. For details, see Chapter 4

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 16 Table B

- 27 . Summary of Total Annual ized Benefits at 7 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High

Human Health Benefits $3.7 $3.8 $7.2 $3.7 $3.8 $22.2 $15.2 $15.4 $15.7 $19.5 $19.8 $20.0 $19.5 $19.8 $20.1
Reduced IQ losses in children from exposure to lead a $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.3 $0.3 $0.4 $0.3 $0.4 $0.5 $0.3 $0.4 $0.5

Reduced CVD in adults from exposure to lead $3.4 $3.4 $14.2 $18.3 $18.3

Reduced IQ losses in children
from exposure to mercury a $0.3 $0.4 $3.8 $0.3 $0.4 $18.7 $0.8 $1.0 $1.1 $0.9 $1.1 $1.3 $0.9 $1.1 $1.4

Avoided cancer cases from exposure to arsenic b $0.0 $0.0 $0.0 $0.0 $0.0

Improved Ecological
Conditions and Recreational
Uses $4.1 $4.8 $22.6 $12.1 $15.4 $67.3 $16.6 $22.3 $92.2 $20.6 $28.3 $114.6 $22.1 $30.5 $122.9

Use and nonuse values for water quality imp rovements $4.0 $4.8 $22.6 $12.1 $15.4 $67.3 $16.6 $22.3 $92.2 $20.6 $28.3 $114.6 $22.1 $30.5 $122.9

Nonuse values of T&E species b $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0
Market and Productivity
Benefits $47.9 $47.9 $149.4 $199.4 $199.4

Avoided impoundment failures $28.1 $28.1 $114.5 $153.5 $153.5
Reduced dredging costs b $0.0 $0.0 $0.0 $0.0 $0.0
Ash marketing benefits $19.8 $19.8 $34.9 $45.9 $45.9

Air

- R elated B enefits NE $102.8 NE $246.7 NE

Reduced human health effects NE $50.92 NE $108.28 NE

Reduced CO 2 emissions c NE $51.85 NE $138.42 NE

Reduced water withdrawals b $0.0 $0.0 $0.0 $0.0 $0.0
Total (excluding air

- related Benefits) d $55.7 $56.6 $77.7 $63.7 $67.1 $137.4 $181.2 $187.2 $257.4 $239.5 $247.5 $334.1 $241.0 $249.8 $296.5

Total (including air

- related Benefits) d, e NE NE NE $166.5 $169.9 $240.2 NE NE NE $486.2 $494.2 $580.8 NE NE NE

Source: U.S. EPA Analysis , 2015

“NE” indicates that EPA did not estimate th e benefits. Air

- related benefits of Option A are expected to be less than those for Option B; air

- related benefits for Option C are expected to be between those of Options B and D; and air

- related benefits of Option E are expected to be greater than those for Option D.

a. Value includes r educed IQ losses and a voided cost of compensatory education in children from exposure to lead . For details see Chapter 3

. b

. “ < $0. 1 ” indicates that the monetized annual benefits are positive but less than $0.1 million.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 17 Table B

- 27 . Summary of Total Annual ized Benefits at 7 Percent (Millions; 201 3 $)

Be ne fit Category Option A Option B Option C Option D Option E

Low Mid f High Low Mid f High Low Mid f High Low Mid f High Low Mid f High

c

. For the valuation of benefits from reductions in CO 2 emissions EPA relied on the 3 percen t average social cost of carbon estimate.

d . Values for individual be ne fit categories may not sum to the total due to independent rounding.

e . The total mo ne tized be ne fits for options A

, C , and E do not include air

- related benefits . This category of benefi ts was analyzed for Options B and D only (see Chapter 7 )

. f

. EPA estimated use and nonuse values for water quality improvements using two different meta

- regression models of willingness

- to

- pay. One model provides the low and high bounds
while a di fferent model provides a central estimate (included in this table in the mid

- range column) . For this reason, the mid

- range estimate differs from the midpoint of the range for this benefit category. For details, see Chapter 4

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 18 Table B

- 28 : Time Profile of Benefits at 3 Percent (Millions; 201 3 $) (Including Air

- Related Benefits for Options B and D) for Scenario Without CPP

Year Option A a Option B Option C a Option D Option E a



### 2015 $0.0 $0.0 $0.0 $0.0 $0.0


### 2016 $0.0 $0.0 $0.0 $0.0 $0.0


### 2017 $0.0 $0.0 $0.0 $0.0 $0.0


### 2018 $0.0 $0.0 $0.0 $0.0 $0.0


### 2019 $10.6 $297.9 $27.7 $555.8 $42.3



### 2020 $13.2 $303.5 $83.6 $630.0 $111.3



### 2021 $30.6 $337.8 $138.0 $714.3 $197.5



### 2022 $48.9 $360.5 $244.3 $833.9 $310.8


### 2023 $85.6 $202.5 $295.2 $471.8 $396.2



### 2024 $86.5 $205.5 $298.2 $477.4 $400.0


### 2025 $86.9 $208.0 $299.0 $480.2 $401.0


### 2026 $87.1 $210.4 $299.1 $482.3 $401.1


### 2027 $87.2 $212.7 $298.8 $483.7 $400.7


### 2028 $87.3 $57.5 $298.3 $583.9 $399.9


### 2029 $87.2 $56.9 $297.4 $583.9 $398.8



### 2030 $87.1 $56.3 $296.6 $586.2 $397.7



### 2031 $87.2 $56.0 $295.9 $588.0 $396.7


### 2032 $87.2 $55.6 $295.1 $590.1 $395.7


### 2033 $87.2 $55.2 $294.4 $592.3 $394.8


### 2034 $87.3 $250.6 $293.8 $827.4 $393.9


### 2035 $87.3 $253.1 $293.3 $833.1 $393.2



### 2036 $87.4 $255.6 $292.9 $838.9 $392.6



### 2037 $87.5 $258.1 $292.5 $844.8 $392.1



### 2038 $87.7 $260.6 $292.2 $850.8 $391.6


### 2039 $87.8 $263.1 $291.9 $856.8 $391.3



### 2040 $87.9 $265.7 $291.8 $863.0 $391.0


### 2041 $88.1 $268.3 $291.7 $869.2 $390.8


### 204 2 $88.2 $269.6 $291.5 $872.2 $390.6 

### Annualized Benefits,
3% $65.4 $188.0 $228.4 $585.5 $306.3

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A

, C and E do not include air

- related benefits. This category of benefits was only estimated for Options B and
D (see Chapter 7

)

.

Table B

- 29 : Time Profile of Benefits at 7 Percent (Millions; 2010$) (Including Air

- Related Benefits for Options B and D) for Scenario Without CPP

Year Option A a Option B Option C a Option D Option E a



### 2015 $0.0 $0.0 $0.0 $0.0 $0.0



### 2016 $0.0 $0.0 $0.0 $0.0 $0.0



### 2017 $0.0 $0.0 $0.0 $0.0 $0.0


### 2018 $0.0 $0.0 $0.0 $0.0 $0.0


### 2019 $10.6 $276.5 $27.7 $523.6 $42.3Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 19 Table B

- 29 : Time Profile of Benefits at 7 Percent (Millions; 2010$) (Including Air

- Related Benefits for Options B and D) for Scenario Without CPP

Year Option A a Option B Option C a Option D Option E a



### 2020 $13.2 $282.4 $83.6 $598.9 $111.3


### 2021 $28.2 $314.1 $131.1 $674.4 $189.2


### 2022 $46.5 $336.8 $237.2 $794.0 $302.4


### 2023 $83.2 $196.2 $288.1 $467.1 $387.7


### 2024 $84.0 $199.2 $291.0 $472.6 $391.4



### 2025 $84.4 $201.6 $291.8 $475.1 $392.3



### 2026 $84.6 $204.1 $291.8 $477.2 $392.4



### 2027 $84.7 $206.3 $291.5 $478.6 $391.9


### 2028 $84.7 $60.1 $290.8 $566.8 $391.0


### 2029 $84.6 $59.4 $289.9 $566.7 $389.9


### 2030 $84.5 $58.8 $289.0 $568.8 $388.7


### 2031 $84.6 $58.5 $288.2 $570.6 $387.6


### 2032 $84.6 $58.1 $287.4 $572.6 $386.5


### 2033 $84.6 $57.7 $286.7 $574.7 $385.5



### 2034 $84.6 $240.6 $286.0 $794.8 $384.6



### 2035 $84.6 $243.1 $285.4 $800.4 $383.8



### 2036 $84.7 $245.5 $284.9 $806.1 $383.1


### 2037 $84.8 $248.0 $284.5 $811.9 $382.5


### 2038 $84.9 $250.5 $284.1 $817.8 $382.0


### 2039 $85.0 $253.0 $283.8 $823.8 $381.5


### 2040 $85.1 $255.5 $283.6 $829.8 $381.2



### 2041 $85.3 $258.1 $283.4 $836.0 $380.9



### 204 2 $85.3 $259.4 $283.2 $838.8 $380.6 

### Annualized Benefits,

7% $56.6 $169.9 $187.2 $494.2 $249.8

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A

, C and E do not include air

- related benefits. This category of benefits was only estimated for Options B and
D (see Chapter 7

)

.



### B.9 Total Costs

Table B

- 30 : Su mmary of Annualized Costs for Scenario without CPP (Millions; $201 3 )

Regulatory Option 3% Discount Rate 7% Discount Rate

Option A $ 141.1 $ 137.1 Option B $ 238.9 $ 234.9 Option C $ 465.3 $ 461.4 Option D $ 640.5 $ 626.1 Option E $ 711.8 $ 695.8

Source: U.S. EPA Analysis, 2015

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 20 Table B

- 3 1 : Time Profile of Costs to Society (Millions; $201 3 ) , Scenario Without CPP

Year Option A Option B Option C Option D Option E

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $474.6 $820.7 $1,420.3 $1,758.7 $1,900.2

2020 $169.2 $377.9 $855.0 $1,218.1 $1,388.1

2021 $431.3 $696.7 $1,347.4 $1,657.5 $1,809.6

2022 $223.1 $415.8 $1,043.8 $1,367.5 $1,597.5

2023 $355.8 $618.1 $1,367.7 $1,843.9 $2,035.9

2024 $85.8 $141.8 $258.5 $391.0 $434.6

2025 $95.9 $151.9 $268.5 $401.8 $446.9

2026 $91.3 $147.3 $265.2 $401.5 $446.8

2027 $93.8 $149.8 $265.6 $400.0 $445.1

2028 $90.2 $146.2 $263.8 $398.0 $443.8

2029 $98.4 $154.4 $279.0 $419.2 $464.6

2030 $96.3 $152.3 $275.0 $414.1 $457.7

2031 $99.3 $155.2 $279.7 $419.0 $464.1

2032 $97.9 $153.9 $277.7 $416.8 $462.1

2033 $98.7 $154.7 $279.8 $420.4 $465.5

2034 $88.3 $144.2 $261.7 $395.6 $441.3

2035 $95.7 $151.7 $269.6 $404.1 $449.5

2036 $91.8 $147.8 $267.7 $404.2 $447.8

2037 $96.2 $152.2 $271.2 $407.8 $452.9

2038 $93.8 $149.8 $271.1 $408.9 $453.9

2039 $97.8 $153.8 $277.2 $415.4 $460.0

2040 $97.0 $153.0 $273.9 $411.3 $456.0

2041 $97.0 $153.0 $273.7 $409.1 $453.4

2042 $96.3 $152.3 $271.7 $405.2 $448.8

Annualized Costs, 3% $141. 1 $238.9 $465.3 $640.5 $711.8

Annualized Costs, 7% $137.1 $234.9 $461.4 $626.1 $695.8

Source: U.S. EPA Analysis, 2015

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 21 

### B.10 Monetized Benefits and Costs Comparison

B.10.1 Comparison of Total Monetized Benefits and Costs

Table B

- 32. Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions; 2013$)

Regulatory Option Total Monetized Benefits Total Monetized Benefits, Including Extrapolated Values a Total Costs

Low Mid High Low Mid High

3% Discount Rate
Option A $64.0 $65.4 $88.2 $145.4 $144.7 $152.8 $141.1
Option B $183.5 $188.0 $253.6 $183.5 $188.0 $253.6 $238.9
Option C $220.1 $228.4 $317.4 $499.9 $505.3 $549.8 $465.3
Option D $574.6 $585.5 $695.4 $574.6 $585.5 $695.4 $640.5
Option E $294.4 $306.3 $423.8 $668.6 $677.6 $734.0 $711.9

7% Discount Rate
Option A $55.7 $56.6 $77.7 $126.5 $125.1 $134.6 $137.1
Option B $166.5 $169.9 $240.2 $166.5 $169.9 $240.2 $234.9
Option C $181.2 $187.2 $257.4 $411.5 $414.1 $445.8 $461.4
Option D $486.2 $494.2 $580.8 $486.2 $494.2 $580.8 $626.1
Option E $241.0 $249.8 $296.5 $547.4 $552.6 $513.6 $695.8

Source: U.S. EPA Analysis, 2015

.

a . EPA did not analyze air

- related benefits for Options A, C, and E. This category of benefits was only estimated for Options B and D (see Chapter 7

). EPA adjusted the total benefits estimated for Options A, C and E by multiplying the totals without air

- related benefits by the average ratio of [total with air

- related benefits]/[total without air

- related benefits] for Options B and D.



### B.10.2 Analysis of Inc remental Monetized Benefits and Costs

Table B

- 33

. Incremental Net Benefit Analysis for Scenario without CPP (Millions; 2013$)

Regulatory Option a Total Annual Monetized

Benefits, Including Adjusted or Inferred
Values Total

Social
Costs Net Annual Monetized

Benefits a Incremental Net Annual
Monetized Benefits b

L ow M id d H igh L ow M id d H igh L ow M id d H igh

3% Discount Rate
Option A $145.4 $144.7 $152.8 $141.1 $4.3 $3.6 $11.7

- - - Option B $183.5 $188.0 $253.6 $238.9

- $55.4

- $50.9 $14.7

- $59.7

- $54.5 $3.0
Option C $499.9 $505.3 $549.8 $465.3 $34.6 $40.0 $84.5 $90.0 $90.9 $69.8
Option D $574.6 $585.5 $695.4 $640.5

- $65.9

- $55.0 $54.9

- $100.5

- $95.0

- $29.7
Option E $668.6 $677.6 $734.0 $711.9

- $43.3

- $34.3 $22.1 $22.7 $20.7

- $32.7

7% Discount Rate
Option A $126.5 $125.1 $134.6 $137.1

- $10.6

- $12.0

- $2.5

- - - Option B $166.5 $169.9 $240.2 $234.9

- $68.4

- $65.0 $5.3

- $57.8

- $53.0 $7.7
Option C $411.5 $414.1 $445.8 $461.4

- $49.9

- $47.3

- $15.6 $18.5 $17.6

- $20.9Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B

- 22 Table B

- 33

. Incremental Net Benefit Analysis for Scenario without CPP (Millions; 2013$)

Regulatory Option a Total Annual Monetized

Benefits, Including Adjusted or Inferred
Values Total

Social
Costs Net Annual Monetized

Benefits a Incremental Net Annual
Monetized Benefits b

L ow M id d H igh L ow M id d H igh L ow M id d H igh
Option D $486.2 $494.2 $580.8 $626.1

- $139.9

- $131.9

- $45.3

- $90.0

- $84.6

- $29.7
Option E $547.4 $552.6 $513.6 $695.8

- $148.4

- $143.2

- $182.2

- $8.5

- $11.3

- $136.9

Source: U.S. EPA Analysis, 2015

.

a

. EPA did not analyze air

- related benefits for Options A

, C , and E . This category of benefits was only estimated for Options B and D (see Chapter 7

). EPA adjusted the total benefits estimated for Options A, C, and E by multiplying the totals without air

- related benefits by the average ratio of [total with air

- related benefits]/[total without air

- related benefits] for Options B and D.

b. Net benefits ar e calculated by subtracting total annualized costs from total annual monetized benefits.

c . Incremental net benefits are equal to the difference between net benefits of an option and net benefits of the previous, le ss stringent option.

d. EPA estimated us e and nonuse values for water quality improvements using two different meta

- regression models of WTP . One model provides the low and high bounds while a different model provides the mid estimate. For this reason, the mid benefit estimate
differs from the m idpoint of the benefits range. For details, see Chapter 4.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix C: Estimation of Exposed Population

September 29, 2015 C

- 1 

## Appendix C. Estimation of Exposed Population

The assessment uses the Census Block Group as the geographic unit of analysis, assigning a radial distance ( e.g. , 50 miles or 100 miles ) from the Census Block Group centroid. EPA assumes that all modeled reaches within this range are viable fishing sites, with all unaffected reaches viable substitutes for affected reaches

within the area around the Census Block Group

.

B y focusing on distance from the Census Block Group , rather than distances from affected reaches , each household is only included in the assessment once, eliminating the potential for double

- counting of households that are near multiple affected waterbodies.

Figure C

- 1 presents a hypothetical example focu sing on two Census Block Group s (squa re at the center of each circular area), each near five waterbodies with water quality changes under the ELGs (thick red lines).

The same approach is used to identify populations for the analysis of non

- market benefits in Chapter 4

. In that case, the circles represent the outer edge of the 100

- mile buffer around each block group. Highlighted in red are the affected NHD reaches under regulatory options for which baseline WQI and ∆WQI would be

estimated

Figure C

- 1

. Illustration of Intersection of Census Block Group s and COMIDs

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue

September 29, 2015 D

- 1 

## Appendix D. Derivation of Ambient Water and Fish Tissue

Concentrations in Receiving and Downstream Reaches

This appendix describes the methodology EPA used to estimate in

- stream and fish tiss ue concentrations under the baseline and each of the five ELG regulatory options. The concentrations are used as inputs to estimate the water quality improvements and human health benefits of the final rule . Specifically, EPA in

- stream metal

concentrations to analyze non

- use benefits of water quality improvements (see Chapter 4 ), and to derive fish tissue concentrations used to analyze human health effects from consuming self

- caught fish (see Chapter 3 ). Nutrient and susp ended sediment concentrations are used to support analysis of non

- use benefits from water quality improvements (see Chapter 4 ).

The overall modelin g methodology is similar to that used at proposal ( see Chapter 4 in U.S. EPA (2013a) ) and builds on data and methods described in the Technical Development Document and Environmental Assessment documents for the final ELGs (U.S. EPA, 2015a; 2015b). The fol lowing sections discuss

calculations of the metal concentrations in streams and fish tissue and nutrient and sediment concentrations in

streams.



### D.1 Metals

D.1.1 Estimating Water Concentrations in each Reach

EPA first estimated the baseline and post

- compliance metal concentrations in reaches receiving steam electric power plant discharges and downstream reaches.

The water quality model component of EPA’s Risk

- Screening Environmental Indicators (RSEI) model (U.S. EPA, 2012c) was used to estimate water concentrations. The model tracks the fate and transport of discharged pollutants through a reach network defined based on the medium resolution National Hydrographic Dataset (NHD). 86 

### The hydrography network represented in RSEI consists of approximately 2.5 million reaches (unique COMIDs). 87

86 RSEI utilizes the USGS’s National Hydrology Dataset (NHD) which defines a reach as a continuous piece of surface water with similar hydrologic characteristics. In the NHD each reach is assigned a reach code; a reach may be composed of a single feature, lik e a lake or isolated stream, but reaches may also be composed of a number of contiguous features. Each reach code occurs only once throughout the nation and once assigned, a reach code is permanently associated with its reach. If the reach is deleted, its reach code is retired. The NHD reaches in this analysis range from 0.003 miles to 9.11 miles in length.

87 Reaches represented in RSEI are those determined to be potentially fishable based on type and physical characteristics. As documented in U.S. EPA (20 12c) : “Certain criteria were applied to the NHDPlus dataset to select the reaches to be used in the model. Specifically, because RSEI calculates the movement of a chemical release downstream using flow and velocity data, qualifying reaches must have at lea st one downstream or upstream connecting reach and have a non

- negative flow and velocity. RSEI will not calculate concentrations for certain types of reaches, such as coastlines, treatment reservoirs, and bays; the downstream path of any
chemical is assume d to stop if one of these types of reach is encountered. Additionally, some types of reaches are excluded from the set of fishable reaches, such as pipelines, aqueducts, and certain types of reservoirs. NHDPlus does not separate canals (presumably fishable ) and ditches (presumably not fishable), so RSEI excludes reaches in the canal/ditch category if the annual mean flow is less than 5 ft 3 /s. This is an arbitrary minimum, and is intended primarily to exclude ditches at the point of the facility discharge. F or reaches designated as not fishable in NHDPlus, the chemical is still assumed to travel downstream to the next reach, which may or may not be fishable.”Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue

September 29, 2015 D

- 2 

### The analysis involved the following key steps for the baseline and each of the five regulatory options:

 Summing plant

- level loadings to the COMID . EPA summed the plant

- level annual average loads (see TDD ) for each unique COMID receiving plant discharges from steam electric power plant s in the

baseline

. Chapter 4 in the EA report describes the approach EPA used to identify the receiving

waterbodies (U.S. EPA 2015a)

.  Specifying loads in the water quality model . RSEI includes data on annual average pollutant loadings to surface waters from facilities that reported to the Toxic Release Inventory (TRI) in 2012

. EPA replaced the loadings provided for Steam Electric plant dischargers in the TRI data set with those obtained in Step 1. Loadi ngs for other TRI reporters were left unchanged.

 Performing dilution and transport calculations

. RSEI’s water quality model uses a simple dilution

and first

- order decay equation ( where metals are treated as conservative substances) to estimate average annual water concentrations for each individual reach . In the model, a plant is assumed to

release its annual load at a constant rate throughout the year. Each source

- pollutant release is tracked downstream throughout the NHD reach net work until one of three conditions occurs: 1) the release

has traveled 300 km (186 miles) downstream ; 2) the release has traveled a distance equivalent to one week of travel time; or 3) the concentration reaches 1 x 10
- 9 

### mg/L. The model calculates the

conc entration of the pollutant in a given reach based on the total mass transported to the reach from upstream sources and the reach mean flow (provided in NHDPlus as an attribute of each COMID).

EPA used the approach above to estimate annual average concentra tions of ten metals: arsenic, cadmium, chromium VI, copper, lead, mercury, nickel, selenium, thallium, and zinc. The results show that, of the

2.5 million reaches represented in RSEI:

 77,414 reaches (117,518 km) have non

- zero concentrations, and

 18,773 o f these reaches (27,778 km) are affected by steam electric power plant discharges in the

baseline.

D.1.2 Estimating Fish Tissue Concentrations in each Reach

To support analysis of the human health benefits associated with water quality improvements (see Chapter 3 ), EPA estimated concentrations of arsenic, lead, and mercury in fish tissue based on the RSEI model outputs

discussed above.

The methodology follows the same general approach described in the EA document for estimating fish tissue concentrations for receiving reaches (U.S. EPA, 2015a), but applies the calculations to the larger set of reaches modeled using RSEI, which include not only the recei ving reaches analyzed in the EA, but also downstream reaches. Further, the calculations use RSEI

- estimated concentrations as inputs, which account not only for the steam electric discharges, but also other major dischargers that report to TRI.

The analysis involved the following key steps for the baseline and each of the five regulatory options:

 Obtaining the relationship between water concentrations and fish tissue concentrations . EPA used the results of the EA national model to parameter ize the linear rel ationship between water concentrations in receiving reaches and composite fish tissue concentrations (representative of trophic levels 3 and 4 fish consumed) in these same reaches for each of the three metals.

 Calculating fish tissue data for affected rea ches . For reaches for which RSEI provides non

- zero water concentrations ( i.e. , reaches affected by steam electric power plant s or other TRI dischargers),Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue

September 29, 2015 D

- 3 

### EPA used the relationship obtained in Step 1 to calculate a preliminary fish tissue concentration for

each metal.

 Imputing the fish tissue concentrations for all other modeled reaches

. For reaches for which RSEI does not calculate water concentrations, EPA assigned background fish tissue concentrations

based on the 10 th 

### percentile of the distribution of r eported concentrations in fish tissue samples in the National Listing Fish Advisory (NLFA) data 88 

### (see Table C

- 1 ). EPA found that the distribution of these samples was consistent with values reported in Wathen et al ( 2014 ) and used the 10 th 

### percentile

as re presentative of background, “clean” reaches not affected by point source discharges.

 Validating and a djusting the fish tissue concentrations based on empirical data , if needed . EPA then applied the same method used to validate and adjust estimated fish ti ssue data in the EA national model to ensure that the fish tissue concentrations calculated based on the RSEI outputs are reasonable when compared to measured data . The approach involves applying order

- of

- magnitude adjustments in cases where the preliminar y concentrations are greater than empirical measurements for a given reach or geographic area by an order of magnitude or more. Section 5.1.2 of the EA

describes the methodology in greater detail.

The analysis provides background metal

- specific composite fish fillet concentrations for each COMID

modeled in RSEI.

Table C

- 1: Assumed Background Fish Tissue Concentrations,

based on 10 th percentile

Parameter Pollutant Concentration (mg/kg)

As 0.039
Hg 0.058

Pb 0.039



### D.2 Nutrients and Suspended Sediment

EPA used the USGS’s SPARROW model to estimate nutrient and sediment concentrations in receiving and downstream reaches. The calibrated, national models used for this analysis are the same as those used to

estimate in

- stream concentrations of TN, TP and TSS in the Construction and Development Industry Category ELGs (s ee U.S. EPA, 2009 c )

. The approach involved the following steps:

 Referencing the receiving reaches to E2RF1 reaches . EPA overlaid the medium resolution NHD and E2RF1 features in GIS to develop th e crosswalk between the two hydrologic networks.

 Summing the loads for each E2RF1 . EPA summed the plant

- level loadings over each E2RF1 in the baseline and under each of the five regulatory options.

 Calculating the change in loading for each E2RF1 . EPA ca lculated the difference between the baseline and post

- compliance loadings under each of the five regulatory options.

 Specifying the change in loading in SPARROW . The national SPARROW models for nutrients do not have an explicit explanatory variable for poi nt source loadings in mass units. In the TN and TP SPARROW models, point sources ( e.g. , wastewater treatment plants) are represented by a population variable. The national calibrated models show contributions of 2.2514 kg TN/capita and

88 See http://map1.epa.gov/

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue

September 29, 2015 D

- 4 

### 0.2319 kg TP/capita for point sources. EPA used these calibrated loading factors to express the load reductions obtained under each of the regulatory options into population

- equivalent in SPARROW. This population

- equivalent loading was subtracted from the baseline population value for each reach when running the SPARROW model. For the suspended sediment model, EPA used the same approach as used for the C&D ELG analysis , which involved adjusting the mass flux attributed to the

urban land explanatory variable in the model to sub tract the change in loading achieved under each option, under the assumption that steam electric power plant loadings are implicitly accounted for in the urban land component of the model (see U.S. EPA, 2009 c ).

The model provides annual average post

- compli ance concentrations in each E2RF1, which EPA compared with baseline conditions obtained directly from the national, calibrated model.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

September 29, 2015 Internal Draft

– Deliberative, Predecisional

– Do not Quote, Cite, or Distribute E

- 1 

## Appendix E. Details on Modeling of Cardiovascular Disease Incidence

and Mortality

E.1 Benefits to Adults from Reduced Lead Exposure

E.1.1 Hazard Reduction under Final ELGs

For each sex, single

- year age, and exposure cohort (hereafter cohort), the analysis characterizes two basic survival analys e s under the baseline and option scenarios: a death hazard function and a survival function

. 89



### A death hazard function (HF)

, h ( x ) , for an individual surviving to age x years, is a function such that h ( x ) dx gives the probability of death between ages x and x + dx (where dx → 0 ). Specifically, h ( x ) dx = P ( x < X d 

### ≤ x + dx | X d 

### > x ) , where X d 

### is person’s exact age at death. The death hazard function is also commonly known as the hazard rate, and less commonly as the force of mortality . At any point in time, an individual is at risk of death from competing causes, of which cardiovascular disease ( CVD ) is only one. As discussed in Beyersmann et al. (2009), competing risks ca n be modeled using cause

- specific death hazard functions, which are additive (by the law of total probability):

Equation E

- 1

. h ( x ) = h CVD 

### ( x ) + h OTH 

### ( x )

A survival function, S ( x )

, is the probability that a person dies at some point after age x , specifically: S ( x ) = P ( X d 

### > x ) . The survival function and the hazard function are intrinsically linked, with the survival function determined completely by the hazard function:

S ( x ) = exp {

− ∫ h ( v ) dv

x

0 

### }

, where v represents a time period typically different than x. Thus, to estimate changes in mortality such as those resulting from implementation of the final ELGs , it is necessary to characterize:

(1) baseline CVD death hazard function;

(2) basel ine other cause ( i.e. , non

- CVD) death hazard function; and

(3) option

- specific CVD death hazard function.

The main source of data for hazard estimation in key simulation elements (1) and (2) above is a life table,
which is a collection of statistics that shows age

- specific probabilities of survival and fecundity. 90 

### Employed heavily in actuarial science, demography, population biology, ecology and epidemiology, data from life tables can be used to calculate probabilities of survival to a given age, age

- spec ific life expectancy, population growth rates, and many other demographic characteristics.

The statistics reported in a life table can be used to approximate the (baseline) hazard function. Of a particular interest are the estimates of the initial age

- spec ific mortality rate, q x 

### : the proportion of people alive at exact

age x , who will die before attaining exact age x + 1 . The initial mortality rate is closely related to the hazard

function:

Equation E

- 2

. q x 

### = 1

− S ( x + 1 ) S ( x ) ⁄ = 1

− exp {

− ∫ h ( v ) dv x + 1

x 

### }

.

89 Collett (2003), pp. 10

- 12

. 90 An extensive discussion of life tables ca n be found in Shryock et al. (1980) Chapter 15.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

September 29, 2015 Internal Draft

– Deliberative, Predecisional

– Do not Quote, Cite, or Distribute E

- 2 

### Assuming a constant hazard function within each integer age, ∫ h ( v ) dv x + 1

x 

### = ∫ h x 

### dv

x + 1

x 

### = h x 

### , estimates of
the initial age

- specific mortality rates q x 

### reported in the life table can be used to obtain an estimate of the

baseline ha zard function:

Equation E

- 3

. h x b


### = ln { 1

− q x



### ̂ }

At each integer age x , this overall hazard function can be further decomposed into CVD death hazard function

and other

- cause death hazard function. To this end, annual age

- speci fic CVD death rates (which are reported
by the Center for Disease Control and Prevention ( CDC) can be used to approximate baseline q x CVD

, b 

### and, consequently, obtain an estimate of the baseline CVD hazard function:

Equation E

- 4

. h x CVD

, b



### = ln { 1

− q x

CVD

, b 

### }

which is key simulation element (1) noted above.

Additionally, the estimate of the baseline other hazards function can be derived as:

Equation E

- 5

. h x OTH

, b 

### = h x

b



### − h x CVD

, b



### providing key simulation element (2) from above.

EPA used a concentration

- response function from a peer reviewed study, Menke et al. (2006). The study finds

a multivariate adjusted relative hazards of CVD mortality of 1.53 (1.21

- 1.94) per 3.4

- fol d increase in PbB

, based on a Cox proportional hazards model using the log of the blood lead level. The corresponding beta estimate ( β BLL 

### ) for this Hazard Ratio (HR) is 0.35. Cox proportional hazards models, such as the one estimated by Menke et al (20 06) are designed to estimate a multiplicative relationship between an outcome ( i.e. , the CVD death hazard function) and a set of predictors. Menke et al. (2006) assessed the HR in various subgroups, and noted that subgroup interaction terms were not statis tically significant, supporting the use of the coefficient for the overall population in benefits estimation. A key assumption of this model, the proportional hazards assumption [met in Menke et al (2006)], is that the HR is constant through time. Based on this information, EPA derive d an estimate for β BLL 

### and use d it to compute HRs for changes of different magnitudes

. EPA calculated HR associated with a known PbB change (due to an ELG regulatory option) as:

Equation E

- 6

. HR o 

### = exp { β BLL 

### ∙ ln [ BLL o

BLL b 

### ] }

H azard functions account ing for reduced CVD mortality following ELG implementation were then calculated

as:

Equation E

- 7

. h x CVD

, o 

### = h x

CVD

, b



### ∙ HR o 

### (eq. 7)

This equation provides key simulation element (3), above.

E.1.2 Estimating Premature Deaths Avoided Over Multiple Years

The value of a statistical life ( VSL ) is the marginal rate of substitution between wealth and mortality risk in a defined time period, usually take n to be one year. Therefore, the product of VSL and the estimated reduction

in risk of premature death represents the affected population’s aggregate willingness to pay (WTP) to reduce its probability of death in one year. EPA estimated the benefits of mul ti

- year mortality risk as the product of:

(1) The reduction in initial age

- specific mortality rate ( i.e. , the proportion of people alive at exact age x , who will die before attaining exact age x + 1 ; commonly represented as q x 

### ) in year t ; andBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

September 29, 2015 Internal Draft

– Deliberative, Predecisional

– Do not Quote, Cite, or Distribute E

- 3 

### (2) The number of individuals surviving to the beginning of year t . This value is calculated as the initial cohort population size in 2014 multiplied by the probability that these individuals survive to age x [commonly represented as S ( x ) ], and are alive at the beginning of year t to enjoy the benefits of the year’s mortality risk reduction. EPA used the survival probability under each ELG option ( i.e. , the survival probability reflecting cumulative reductions in mortality risk prior to year t ) in each

recur sive step of this calculation.

Each pattern of annual mortality rates corresponds to a unique survival curve. Thus, each intervention

- related change in mortality rates (over multiple years) will generate a unique change in the cohort’s survival curve.
Figure E

- 1 

### illustrates such a change for an individual whose baseline initial mortality rate of 0.3 ( i.e. , a 30 percent chance of death in one year) was reduced to 0.2 ( i.e. , a 20 percent chance of death in one year) by an intervention which begins in year 0 and continues indefinitely. The baseline survival curve is shown as a solid line, while the dashed line shows the effects of intervention.

Figure E

- 1 : Illustration of a Hypothetical Policy Effect on a Survival Curve.



### Each intervention

- related change in survival curve can be summarized by either the gain in life expectancy ( i.e. , the area between the survival curves) or b y the number of avoided premature deaths at each point in time (Hammitt 2007). The recursive calculation of the number of avoided premature deaths (described above) can be seen as a series of shifts of the survival curve. Each shift represents the incremen tal effect of a mortality risk reduction in year t (while keeping mortality rates in subsequent years at their baseline levels) on the survival curve. Therefore, these shifts are consistent with the cumulative impact of mortality risk reductions in

years 0 to t

− 1

. Figure E

- 2 illustrates two such shifts: (1) the dashed survival curve reflects the impact of reduced mortality rate in year 0 on the baselin e survival curve ( i.e. , the incremental impact of mortality risk reductions in year 0); (2) the dotted curve reflects the impact of reducing mortality rate in year 1 on the

dashed survival curve ( i.e. , the incremental impact of mortality risk reductions in year 1).

As noted earlier, the area between the survival curves represents the gain in life expectancy. Thus, each shift of the survival curve reflects an incremental gain in life expectancy ( e.g. , in Figure E

- 2 , the area between

solid and dashed lines represents the incremental gain in life expectancy because of reduced mortality in year 0; the a rea between dashed and dotted lines represent incremental gain in life expectancy because of reduced mortality in year 1). By design, the sum of these incremental gains is equal to the original gain in life expectancy from a multi

- year reduction. This conf irms that EPA’s procedure generates an estimated reductionBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

September 29, 2015 Internal Draft

– Deliberative, Predecisional

– Do not Quote, Cite, or Distribute E

- 4 

### in the risk of premature death that is consistent with the estimated aggregate gain in life expectancy from the

final ELGs.

Figure E

- 2 : A Recursive Illustration of a Policy Effect on Survival Rates.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix F: Human Health Sensitivity Analyses

September 29, 2015 F

- 1 

## Appendix F. Human Health Benefits Sensitivity Analysis

EPA conducted a series of sensitivity analyses to evaluate the impacts of varying assumptions related to a) the distance travelled by recreational and subsistence anglers, b) the form of the concentration

- response function for lead, and c) the cancer slope function and cancer case valuation approach for the arsenic analysis. These analyses are summarized bel ow.



### F.1 Alternative Fishing Distance Assumption

The set of reaches that may represent a source of contaminated fish for recreational anglers and subsistence

fishers in each CB G depends on the distance the typical angler travels to fish. In the human health ben efits analysis (Chapter 3 ), EPA assumed that anglers typically travel up to 50 miles to fish, using this distance to estimate the relevant fishing sites for the population of anglers in each CB G

. Viscusi et al. (2008) found that 78 percent of anglers live within 100 miles of their fishing destinations. EPA conducted a sensitivity analysis to assess the impact of the travel distance assumptions to th e health analysis results. Using a different travel distance assumption tends to increase the number of alternate fishing sites visited by anglers within each CB G , but also increases the availability of substitute sites.

Table F

- 1 shows the affected population using this alternative fishing travel distance, and Table F

- 2 shows the results for the health benefit categories for which EPA conducted this sensitivity analysis.

Table F

- 1

. Summary of Potentially Affected Population Living within 100 Miles of Affected Reaches (baseline)

Total population 306,740,261 Total angler population a 42,714,703 Angler population potentially exposed to
contaminated fish b 29,592,014

a. Total population living within 100 miles of an affected reach times the state

- specific share of the population who fishes based on U.S. FWS (2011; between 9% and 23%).
b. Total angler population adjusted to reflect lower fishing/consumption rates for reaches with fish consumption advisories and catch

- and

- release practices.

Table F

- 2

. Total Monetized Human Health Benefits for ELG Options Using a 100

- mile Buffer Zone (millions of 2013$) a,b

Regulatory Option Reduced lead

exposure for
children Reduced

lead exposure for
adults Reduced

mercury exposure for
children Reduced

cancer cases from

arsen ic Total

L ow High Low H igh Low H igh

3% Discount Rate

Option A $0.28 $0.40 $3.55 $1.37 $1.92 $0.00 $5.20 $5.87
Option B $0.28 $0.40 $3.55 $1.39 $1.95 $0.00 $5.22 $5.90
Option C $0.43 $0.60 $6.66 $2.36 $3.32 $0.00 $9.45 $10.58
Option D $0.57 $0.80 $8.98 $2.93 $4.11 $0.00 $12.49 $13.90
Option E $0.57 $0.80 $8.98 $3.30 $4.64 $0.00 $12.86 $14.42Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix F: Human Health Sensitivity Analyses

September 29, 2015 F

- 2 Table F

- 2

. Total Monetized Human Health Benefits for ELG Options Using a 100

- mile Buffer Zone (millions of 2013$) a,b

Regulatory Option Reduced lead

exposure for
children Reduced

lead exposure for
adults Reduced

mercury exposure for
children Reduced

cancer cases from

arsen ic Total

L ow High Low H igh Low H igh

7% Discount Rate

Option A $0.05 $0.07 $2.96 $0.22 $0.33 $0.00 $3.23 $3.36
Option B $0.05 $0.07 $2.96 $0.23 $0.33 $0.00 $3.24 $3.37
Option C $0.07 $0.11 $5.56 $0.38 $0.57 $0.00 $6.01 $6.24
Option D $0.10 $0.14 $7.50 $0.48 $0.71 $0.00 $8.07 $8.35
Option E $0.10 $0.14 $7.50 $0.54 $0.80 $0.00 $8.13 $8.44

Source: U.S. EPA Analysis, 2015

a. “

- “ indicates that a value was not estimated and “$0.00” indicates that annual benefits are less than $0.01 million. b. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 1995).



### F.2 Log

- Linear Concentration Response Function for IQ Impacts to Children from Lead Exposure

In the analysis of benefits to children from reduced le ad intake via fish consumption, EPA used a linear

concentration

- response function to quantify the relationship between blood lead concentrations (PbB) and intelligence quotient (IQ). This linear function is based on a concentration

- response function based on
children with PbB below 7.5  g/dL since the average PbB among affected children is approximately 2.7

 g/dL (see Section 3.3 ). EPA received severa l comments stating that this approach was inappropriate and

resulted in an overestimate of the benefits.

Given the uncertainty surrounding the lead concentration response relationship in children, EPA conducted a sensitivity analysis applying a log

- linear function used by EPA (2008b ) in the RIA for the Lead National Ambient Air Quality Standards ( NAAQS ) . This function uses a log

- linear function for PbB above 1.47 μg/dL and a linear slope for PbB levels below that cut

- point, as shown in Equation F

- 8 and Equation F

- 9

, respectively.

Equation F

- 8

. IQ Loss = β 1 

### × ln ( PbB
cutpoint 

### ) + β 2 

### × cutpoint

Equation F

- 9

. IQ Loss = β 2 

### × cutpoint

Where:

cutpoint = 1.47  g/dL

β 1 

### =

- 3.04 (log

- linear regression coefficient)

β 1 

### =

- 2.1 (linear regression coefficient)

Table F

- 3 shows the results of the analysis of avoided IQ point losses among children from exposure to lead using this alternative concentration

- response function (corresponding to Table 3

- 4 in Section 3.3 ).Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix F: Human Health Sensitivity Analyses

September 29, 2015 F

- 3 Table F

- 3 . Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead using Log

Linear Concentration Response Function (2013$)

Regulatory Option Average Annual

Number of
Affected
Children 0 to 7 Total Avoided

IQ Losses
, 2021 to 2042 Annual ized Value of Avoided IQ Point Losses a (Millions )

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound
Option A 3,326,127 324 $0.13 $0.18 $0.02 $0.03
Option B 3,326,127 324 $0.13 $0.18 $0.02 $0.03
Option C 3,326,127 486 $0.19 $0.27 $0.03 $0.05
Option D 3,326,127 816 $0.32 $0.46 $0.05 $0.08
Option E 3,326,127 816 $0.32 $0.46 $0.05 $0.08

Source: U.S. EPA Analysis, 2015

a . Low bound assumes that the loss of one IQ point results in the loss of 1.76% of lifetime earnings (following Schwartz, 1994 ); high bound assumes that the loss of one IQ point results in the loss of 2.38% of lifetime earnings (following S alkever, 1995).



### F.3 Alternative Cancer Slope Factor and Case Valuation for Arsenic Analysis

The Integrated Risk Information System (IRIS) reports a cancer slope factor (CSF) of 1.5 cases per mg/kg BW/day, which is based on incidences of skin cancer. EPA applied the 1.5 cases per mg/kg BW/day CSF to

estimate the benefits shown in Section 3.6

. EPA is currently revising its cancer assessment of arsenic to reflect new data on internal cancers including bladder and lung cancers associated with arsenic exposure via oral ingestion. The draft CSF is substantially higher

– at 25.7 per mg/kg BW/day for women and 16.9 per mg/kg BW/day for men (U.S. EPA, 2010b ).

EPA conducted a sensitivity analysis using the more sensitive CSF, together with monetizing the avoided cases using the value of a statistical life (VSL; $8.548 million), reflective of the higher mortality rates

associated with internal cancers. Table F

- 4 shows the results of this sensitivity analysis (corresponding to

Table 3

- 12 in Section 3.6 ).

Table F

- 4 . Annual Benefits from Reduced Cancer Cases due to Arsenic Exposure , using Alternative CSF and VSL

Regulatory Option Annual Affected

Population Reduced Cancer Cases
,

2019 to 2042 Benefits (Millions; 2013$)

3% Discount 7% Discount
Option A 35,972,005 0.55 $0.13 $0.07
Option B 35,972,005 0.55 $0.13 $0.07
Option C 35,972,005 1.05 $0.25 $0.14
Option D 35,972,005 1.59 $0.38 $0.21
Option E 35,972,005 1.72 $0.41 $0.23

Source: U.S. EPA Analysis, 2015Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G

- 1 

## Appendix G. WQI Regional Subindices

This appendix provides the ecoregion

- specific parameters used in estimating the TSS, TN, or TP water quality subindex, as follows:

 If [WQ Parameter] ≤ WQ Parameter 100 

### Subindex = 100

 If WQ Parameter 100 

### < [WQ Parameter] ≤ WQ Parameter 10 

### Subindex = a exp(b [WQ Parameter])

 If [WQ Parameter] > WQ Parameter 10 

### Subindex = 10

 Where [WQ Parameter] is the measured concentration of either TSS, TN, or TP and WQ Parameter 10 

### , WQ Parameter 100 

### , a, and b are specified in Table G

- 1 for TSS, Table G

- 2 for TN, and Table G

- 3 for TP.

Table G

- 1 : TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS 100 TSS 10 10.1.2 Columbia Plateau 126.56

- 0.0038 63 668

10.1.3 Northern Basin and Range 112.42

- 0.0007 160 3,457

10.1.4 Wyoming Basin 123.36

- 0.001 220 2,513

10.1.5 Central Basin and Range 121.22

- 0.0018 109 1,386

10.1.6 Colorado Plateaus 144.44

- 0.001 363 2,670

10.1.7 Arizona/New Mexico Plateau 126.76

- 0.0004 668 6,349

10.1.8 Snake River Plain 146.39

- 0.0027 142 994

10.2.1 Mojave Basin and Range 119.34

- 0.0015 121 1,653

10.2.2 Sonoran Desert 112.39

- 0.0002 567 12,097

10.2.4 Chihuahuan Desert 214.39

- 0.0005 1,419 6,130

11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 127.97

- 0.0012 205 2,124

11.1.2 Central California Valley 171.86

- 0.0044 122 646

11.1.3 Southern and Baja California Pine

- Oak Mountains 115.12

- 0.0007 197 3,491

12.1.1 Madrean Archipelago 261.35

- 0.0005 2,053 6,527

13.1.1 Arizona/New Mexico Mountains 120.98

- 0.0004 477 6,233

15.4.1 Southern Florida Coastal Plain 116.95

- 0.0405 4 61

5.2.1 Northern Lakes and Forests 157.76

- 0.0233 20 118

5.2.2 Northern Minnesota Wetlands 154.99

- 0.0186 24 147

5.3.1 Northern Appalachian and Atlantic Maritime Highlands 174.99

- 0.0261 21 110

5.3.3 North Central Appalachians 245.15

- 0.0176 51 182

6.2.10 Middle Rockies 144.64

- 0.0038 98 703

6.2.11 Klamath Mountains 238.9

- 0.0068 129 467

6.2.12 Sierra Nevada 185.36

- 0.0116 53 252

6.2.13 Wasatch and Uinta Mountains 124.28

- 0.0014 160 1,800

6.2.14 Southern Rockies 153.42

- 0.0031 140 881

6.2.15 Idaho Batholith 184.23

- 0.0142 43 205

6.2.3 Columbia Mountains/Northern Rockies 180.7

- 0.0168 35 172

6.2.4 Canadian Rockies 396.62

- 0.0308 45 119

6.2.5 North Cascades 240.95

- 0.0193 46 165

6.2.7 Cascades 192.94

- 0.0181 36 164

6.2.8 Eastern Cascades Slopes and Foothills 178.82

- 0.0145 40 199

6.2.9 Blue Mountains 148.35

- 0.0037 107 729

7.1.7 Strait of Georgia/Puget Lowland 181.06

- 0.0224 27 129

7.1.8 Coast Range 174.78

- 0.0114 49 251

7.1.9 Willamette Valley 210.3

- 0.0114 65 267Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G

- 2 Table G

- 1 : TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS 100 TSS 10 8.1.1 Eastern Great Lakes and Hudson Lowlands 144.62

- 0.0104 36 257

8.1.2 Lake Erie Lowland 112.79

- 0.0049 25 494

8.1.3 Northern Appalachian Plateau and Uplands 322.68

- 0.0113 103 307

8.1.4 North Central Hardwood Forests 148.68

- 0.0108 37 250

8.1.5 Driftless Area 117.97

- 0.0012 141 2,057

8.1.6 S. Michigan/N. Indiana Drift Plains 191.44

- 0.0143 46 206

8.1.7 Northeastern Coastal Zone 158.48

- 0.0164 28 168

8.1.8 Maine/New Brunswick Plains and Hills 156.02

- 0.025 18 110

8.1.10 Erie Drift Plain 133.08

- 0.0037 78 700

8.2.1 Southeastern Wisconsin Till Plains 121.34

- 0.0042 46 594

8.2.2 Huron/Erie Lake Plains 145.17

- 0.0058 65 461

8.2.3 Central Corn Belt Plains 187.95

- 0.0033 191 889

8.2.4 Eastern Corn Belt Plains 235.18

- 0.003 282 1,053

8.3.1 Northern Piedmont 175.82

- 0.0042 135 683

8.3.2 Interior River Valleys and Hills 149.68

- 0.0013 303 2,081

8.3.3 Interior Plateau 220.47

- 0.0037 217 836

8.3.4 Piedmont 224.11

- 0.0048 169 648

8.3.5 Southeastern Plains 205.3

- 0.0085 85 356

8.3.6 Mississippi Valley Loess Plains 492.49

- 0.0048 333 812

8.3.7 South Central Plains 184.36

- 0.0045 136 648

8.3.8 East Central Texas Plains 162.32

- 0.0013 362 2,144

8.4.1 Ridge and Valley 186.83

- 0.0063 99 465

8.4.2 Central Appalachians 166.76

- 0.0062 82 454

8.4.3 Western Allegheny Plateau 183.67

- 0.0032 190 910

8.4.4 Blue Ridge 216.16

- 0.0087 89 353

8.4.5 Ozark Highlands 175.16

- 0.0018 317 1,591

8.4.6 Boston Mountains 329.77

- 0.0062 193 564

8.4.7 Arkansas Valley 283.25

- 0.004 261 836

8.4.8 Ouachita Mountains 212.77

- 0.0048 157 637

8.4.9 Southwestern Appalachians 207.09

- 0.0071 103 427

8.5.1 Middle Atlantic Coastal Plain 182.17

- 0.0178 34 163

8.5.2 Mississippi Alluvial Plain 131.35

- 0.0029 93 888

8.5.3 Southern Coastal Plain 138.62

- 0.0144 23 183

8.5.4 Atlantic Coastal Pine Barrens 283.76

- 0.0463 23 72

9.2.1 Aspen Parkland/Northern Glaciated Plains 136.43

- 0.0005 640 5,226

9.2.2 Lake Manitoba and Lake Agassiz Plain 174.13

- 0.0042 131 680

9.2.3 Western Corn Belt Plains 135.01

- 0.0009 347 2,892

9.2.4 Central Irregular Plains 201.19

- 0.001 673 3,002

9.3.1 Northwestern Glaciated Plains 133.98

- 0.0006 483 4,325

9.3.3 Northwestern Great Plains 130.6

- 0.0004 636 6,424

9.3.4 Nebraska Sand Hills 289.85

- 0.0066 162 510

9.4.1 High Plains 125.61

- 0.0005 507 5,061

9.4.2 Central Great Plains 156.84

- 0.0005 925 5,505

9.4.3 Southwestern Tablelands 137.77

- 0.0003 1,280 8,743

9.4.4 Flint Hills 270.93

- 0.0009 1,084 3,666

9.4.5 Cross Timbers 134.97

- 0.0006 523 4,337

9.4.6 Edwards Plateau 173.77

- 0.001 544 2,855

9.4.7 Texas Blackland Prairies 134.23

- 0.0005 624 5,194Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G

- 3 Table G

- 1 : TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS 100 TSS 10 9.5.1 Western Gulf Coastal Plain 124.47

- 0.0025 88 1,009

9.6.1 Southern Texas Plains/Interior Plains and Hills with Xerophytic Shrub and Oak Forest 166.67

- 0.0003 1,602 9,378

Table G

- 2 : TN Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TN 100 TN 10 10.1.2 Columbia Plateau 116.58

- 0.663 0.23 3.70
10.1.3 Northern Basin and Range 126.97

- 0.626 0.38 4.06
10.1.4 Wyoming Basin 124.89

- 0.445 0.50 5.67
10.1.5 Central Basin and Range 116.66

- 0.335 0.46 7.33
10.1.6 Colorado Plateaus 146.41

- 0.588 0.65 4.56
10.1.7 Arizona/New Mexico Plateau 116.33

- 0.286 0.53 8.58
10.1.8 Snake River Plain 129.93

- 0.594 0.44 4.32
10.2.1 Mojave Basin and Range 136.69

- 0.593 0.53 4.41
10.2.2 Sonoran Desert 117.99

- 0.495 0.33 4.99
10.2.4 Chihuahuan Desert 104.2

- 0.45 0.09 5.21
11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 123.22

- 0.889 0.23 2.82
11.1.2 Central California Valley 126.07

- 0.548 0.42 4.62
11.1.3 Southern and Baja California Pine

- Oak Mountains 122.76

- 0.564 0.36 4.45
12.1.1 Madrean Archipelago 130.61

- 0.325 0.82 7.91
13.1.1 Arizona/New Mexico Mountains 141.64

- 0.541 0.64 4.90
15.4.1 Southern Florida Coastal Plain 1000000

- 29.36 0.33 0.39
5.2.1 Northern Lakes and Forests 141.98

- 0.985 0.36 2.69
5.2.2 Northern Minnesota Wetlands 142.55

- 0.781 0.45 3.40
5.3.1 Northern Appalachian and Atlantic Maritime Highlands 142.6

- 0.854 0.42 3.11
5.3.3 North Central Appalachians 180.92

- 0.897 0.66 3.23
6.2.10 Middle Rockies 136.51

- 0.991 0.31 2.64
6.2.11 Klamath Mountains 140.34

- 1.805 0.19 1.46
6.2.12 Sierra Nevada 143.02

- 1.424 0.25 1.87
6.2.13 Wasatch and Uinta Mountains 129.75

- 0.452 0.58 5.67
6.2.14 Southern Rockies 131.07

- 0.66 0.41 3.90
6.2.15 Idaho Batholith 149.42

- 1.775 0.23 1.52
6.2.3 Columbia Mountains/Northern Rockies 136.14

- 1.599 0.19 1.63
6.2.4 Canadian Rockies 151.95

- 2.098 0.20 1.30
6.2.5 North Cascades 155.86

- 1.231 0.36 2.23
6.2.7 Cascades 143.07

- 1.473 0.24 1.81
6.2.8 Eastern Cascades Slopes and Foothills 123.99

- 1.07 0.20 2.35
6.2.9 Blue Mountains 125.19

- 0.786 0.29 3.22
7.1.7 Strait of Georgia/Puget Lowland 121.09

- 0.723 0.26 3.45
7.1.8 Coast Range 136.15

- 1.021 0.30 2.56
7.1.9 Willamette Valley 135.01

- 0.809 0.37 3.22
8.1.1 Eastern Great Lakes and Hudson Lowlands 158.18

- 0.563 0.81 4.90
8.1.2 Lake Erie Lowland 156.27

- 0.38 1.18 7.23
8.1.3 Northern Appalachian Plateau and Uplands 431.78

- 0.435 3.36 8.66
8.1.4 North Central Hardwood Forests 163.4

- 0.599 0.82 4.66
8.1.5 Driftless Area 126.18

- 0.272 0.85 9.32
8.1.6 S. Michigan/N. Indiana Drift Plains 130.25

- 0.149 1.78 17.23Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G

- 4 Table G

- 2 : TN Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TN 100 TN 10 8.1.7 Northeastern Coastal Zone 125.75

- 0.159 1.44 15.92
8.1.8 Maine/New Brunswick Plains and Hills 139.55

- 0.553 0.60 4.77
8.1.10 Erie Drift Plain 148.99

- 1.256 0.32 2.15
8.2.1 Southeastern Wisconsin Till Plains 134.85

- 0.16 1.87 16.26
8.2.2 Huron/Erie Lake Plains 119.06

- 0.091 1.91 27.22
8.2.3 Central Corn Belt Plains 135.57

- 0.087 3.50 29.96
8.2.4 Eastern Corn Belt Plains 149.12

- 0.122 3.28 22.15
8.3.1 Northern Piedmont 146.34

- 0.314 1.21 8.55
8.3.2 Interior River Valleys and Hills 120.48

- 0.131 1.43 19.00
8.3.3 Interior Plateau 146.39

- 0.446 0.85 6.02
8.3.4 Piedmont 148.67

- 0.637 0.62 4.24
8.3.5 Southeastern Plains 138.73

- 0.727 0.45 3.62
8.3.6 Mississippi Valley Loess Plains 123.15

- 0.379 0.55 6.62
8.3.7 South Central Plains 149.84

- 0.706 0.57 3.83
8.3.8 East Central Texas Plains 136

- 0.344 0.89 7.59
8.4.1 Ridge and Valley 158.11

- 0.659 0.70 4.19
8.4.2 Central Appalachians 161.22

- 0.907 0.53 3.07
8.4.3 Western Allegheny Plateau 125.25

- 0.44 0.51 5.74
8.4.4 Blue Ridge 158.16

- 0.777 0.59 3.55
8.4.5 Ozark Highlands 145.69

- 0.513 0.73 5.22
8.4.6 Boston Mountains 168.59

- 1.108 0.47 2.55
8.4.7 Arkansas Valley 135.4

- 0.47 0.64 5.54
8.4.8 Ouachita Mountains 162.34

- 0.942 0.51 2.96
8.4.9 Southwestern Appalachians 143.42

- 0.645 0.56 4.13
8.5.1 Middle Atlantic Coastal Plain 123.43

- 0.444 0.47 5.66
8.5.2 Mississippi Alluvial Plain 119.57

- 0.31 0.58 8.00
8.5.3 Southern Coastal Plain 118.73

- 0.701 0.24 3.53
8.5.4 Atlantic Coastal Pine Barrens 110.04

- 0.482 0.20 4.98
9.2.1 Aspen Parkland/Northern Glaciated Plains 141.62

- 0.086 4.06 30.82
9.2.2 Lake Manitoba and Lake Agassiz Plain 119.49

- 0.082 2.18 30.25
9.2.3 Western Corn Belt Plains 129.28

- 0.074 3.48 34.59
9.2.4 Central Irregular Plains 142.81

- 0.184 1.93 14.45
9.3.1 Northwestern Glaciated Plains 120.91

- 0.386 0.49 6.46
9.3.3 Northwestern Great Plains 125.65

- 0.404 0.56 6.26
9.3.4 Nebraska Sand Hills 113.81

- 0.324 0.40 7.51
9.4.1 High Plains 121.41

- 0.161 1.21 15.51
9.4.2 Central Great Plains 129.36

- 0.178 1.44 14.38
9.4.3 Southwestern Tablelands 136.03

- 0.413 0.74 6.32
9.4.4 Flint Hills 142.74

- 0.343 1.04 7.75
9.4.5 Cross Timbers 130.87

- 0.278 0.97 9.25
9.4.6 Edwards Plateau 141.98

- 0.588 0.60 4.51
9.4.7 Texas Blackland Prairies 133.84

- 0.243 1.20 10.68
9.5.1 Western Gulf Coastal Plain 106.22

- 0.301 0.20 7.85
9.6.1 Southern Texas Plains/Interior Plains and Hills with Xerophytic Shrub and Oak Forest 102.35

- 0.374 0.06 6.22Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G

- 5 Table G

- 3 : TP Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TP 100 TP 10 10.1.2 Columbia Plateau 147.39

- 2.211 0.18 1.22

10.1.3 Northern Basin and Range 165.9

- 2.78 0.18 1.01

10.1.4 Wyoming Basin 143.83

- 1.57 0.23 1.70

10.1.5 Central Basin and Range 167.24

- 2.541 0.20 1.11

10.1.6 Colorado Plateaus 123.74

- 0.784 0.27 3.21

10.1.7 Arizona/New Mexico Plateau 168.68

- 3.39 0.15 0.83

10.1.8 Snake River Plain 140.75

- 1.106 0.31 2.39

10.2.1 Mojave Basin and Range 139.89

- 0.978 0.34 2.70

10.2.2 Sonoran Desert 122.92

- 1.578 0.13 1.59

10.2.4 Chihuahuan Desert 132.89

- 3.737 0.08 0.69

11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 125.05

- 1.918 0.12 1.32

11.1.2 Central California Valley 126.32

- 2.138 0.11 1.19

11.1.3 Southern and Baja California Pine

- Oak Mountains 212.01

- 0.941 0.80 3.25

12.1.1 Madrean Archipelago 140.62

- 1.331 0.26 1.99

13.1.1 Arizona/New Mexico Mountains 555.88

- 306 0.01 0.01

15.4.1 Southern Florida Coastal Plain 157.9

- 26.64 0.02 0.10

5.2.1 Northern Lakes and Forests 152.78

- 16.37 0.03 0.17

5.2.2 Northern Minnesota Wetlands 171.4

- 21.87 0.02 0.13

5.3.1 Northern Appalachian and Atlantic Maritime Highlands 260.92

- 21.53 0.04 0.15

5.3.3 North Central Appalachians 157.84

- 6.439 0.07 0.43

6.2.10 Middle Rockies 188.95

- 15.04 0.04 0.20

6.2.11 Klamath Mountains 205.2

- 19.13 0.04 0.16

6.2.12 Sierra Nevada 142.56

- 2.752 0.13 0.97

6.2.13 Wasatch and Uinta Mountains 141.72

- 5.463 0.06 0.49

6.2.14 Southern Rockies 185.94

- 21.89 0.03 0.13

6.2.15 Idaho Batholith 168.85

- 17.88 0.03 0.16

6.2.3 Columbia Mountains/Northern Rockies 197.1

- 27.87 0.02 0.11

6.2.4 Canadian Rockies 289.57

- 47.06 0.02 0.07

6.2.5 North Cascades 227.85

- 26.77 0.03 0.12

6.2.7 Cascades 154.67

- 10.55 0.04 0.26

6.2.8 Eastern Cascades Slopes and Foothills 141.59

- 3.31 0.11 0.80

6.2.9 Blue Mountains 165.33

- 13.83 0.04 0.20

7.1.7 Strait of Georgia/Puget Lowland 185.34

- 14.77 0.04 0.20

7.1.8 Coast Range 159.54

- 9.053 0.05 0.31

7.1.9 Willamette Valley 148.02

- 7.95 0.05 0.34

8.1.1 Eastern Great Lakes and Hudson Lowlands 230.09

- 9.614 0.09 0.33

8.1.2 Lake Erie Lowland 3440.2

- 8.887 0.40 0.66

8.1.3 Northern Appalachian Plateau and Uplands 317.21

- 13.87 0.08 0.25

8.1.4 North Central Hardwood Forests 132.65

- 4.905 0.06 0.53

8.1.5 Driftless Area 141.49

- 2.261 0.15 1.17

8.1.6 S. Michigan/N. Indiana Drift Plains 184.34

- 5.59 0.11 0.52

8.1.7 Northeastern Coastal Zone 174

- 9.944 0.06 0.29

8.1.8 Maine/New Brunswick Plains and Hills 174.73

- 28.94 0.02 0.10

8.1.10 Erie Drift Plain 151.79

- 3.59 0.12 0.76

8.2.1 Southeastern Wisconsin Till Plains 141.21

- 1.577 0.22 1.68

8.2.2 Huron/Erie Lake Plains 247.17

- 2.666 0.34 1.20

8.2.3 Central Corn Belt Plains 223.41

- 3.555 0.23 0.87

8.2.4 Eastern Corn Belt Plains 196

- 3.734 0.18 0.80Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G

- 6 Table G

- 3 : TP Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TP 100 TP 10 8.3.1 Northern Piedmont 160.97

- 2.567 0.19 1.08

8.3.2 Interior River Valleys and Hills 156.71

- 3.616 0.12 0.76

8.3.3 Interior Plateau 197.72

- 5.623 0.12 0.53

8.3.4 Piedmont 223.39

- 9.266 0.09 0.34

8.3.5 Southeastern Plains 177.2

- 5.69 0.10 0.51

8.3.6 Mississippi Valley Loess Plains 168

- 4.659 0.11 0.61

8.3.7 South Central Plains 166.39

- 1.677 0.30 1.68

8.3.8 East Central Texas Plains 178.13

- 6.407 0.09 0.45

8.4.1 Ridge and Valley 225.67

- 16.59 0.05 0.19

8.4.2 Central Appalachians 187.73

- 8.367 0.08 0.35

8.4.3 Western Allegheny Plateau 174.12

- 10.5 0.05 0.27

8.4.4 Blue Ridge 152.7

- 2.889 0.15 0.94

8.4.5 Ozark Highlands 204.88

- 7.364 0.10 0.41

8.4.6 Boston Mountains 287.21

- 5.786 0.18 0.58

8.4.7 Arkansas Valley 158.46

- 6.821 0.07 0.41

8.4.8 Ouachita Mountains 169.72

- 7.296 0.07 0.39

8.4.9 Southwestern Appalachians 153.95

- 6.816 0.06 0.40

8.5.1 Middle Atlantic Coastal Plain 141.25

- 3.807 0.09 0.70

8.5.2 Mississippi Alluvial Plain 144.74

- 7.676 0.05 0.35

8.5.3 Southern Coastal Plain 126.84

- 8.388 0.03 0.30

8.5.4 Atlantic Coastal Pine Barrens 156.13

- 0.69 0.65 3.98
9.2.1 Aspen Parkland/Northern Glaciated Plains 132.19

- 1.087 0.26 2.38

9.2.2 Lake Manitoba and Lake Agassiz Plain 197.19

- 1.683 0.40 1.77

9.2.3 Western Corn Belt Plains 200.96

- 1.994 0.35 1.50

9.2.4 Central Irregular Plains 134.12

- 1.646 0.18 1.58

9.3.1 Northwestern Glaciated Plains 143.32

- 1.267 0.28 2.10
9.3.3 Northwestern Great Plains 185

- 3.788 0.16 0.77
9.3.4 Nebraska Sand Hills 153.07

- 0.946 0.45 2.88

9.4.1 High Plains 188.57

- 1.178 0.54 2.49

9.4.2 Central Great Plains 139.57

- 0.972 0.34 2.71

9.4.3 Southwestern Tablelands 218.92

- 2.351 0.33 1.31
9.4.4 Flint Hills 131.7

- 0.78 0.35 3.31
9.4.5 Cross Timbers 159.98

- 1.384 0.34 2.00
9.4.6 Edwards Plateau 149.63

- 1.064 0.38 2.54

9.4.7 Texas Blackland Prairies 127.17

- 1.863 0.13 1.36

9.5.1 Western Gulf Coastal Plain 104.21

- 0.513 0.08 4.57

9.6.1 Southern Texas Plains/Interior Plains and Hills with Xerophytic Shrub and Oak Forest 147.39

- 2.211 0.18 1.22Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 1 

## Appendix H. Development of Meta

- Regression Models of Willingness to

Pay for Water Quality Improvements

This meta

- regression is a revised version of the 2009 meta

- regression used in the benefit analysis of the proposed ELGs to estimate the water quality improvement benefits of the pr oposed rule (U.S. EPA, 2013a). EPA made a number of improvements to the meta

- regression model, including updating the set of studies and introducing GIS explanatory variables into the analysis. In particular, the revised meta

- model satisfies the adding

- up condition, a theoretically desirable property. 91 

### This condition ensures that if the model were used to estimate willingness to pay (WTP) for the cumulative water quality change resulting from a number of CWA regulations, the benefits estimates would be equa l to the sum of benefits from using the model to estimate WTP for water quality changes separately for each rule. EPA used the revised meta

- analysis to estimate the

sum of use and non

- use values for water quality improvements resulting from the final ELGs.

The following sections describe EPA’s literature review to identify additional studies, meta

- data development and coding, model specification, regression results, and limitations and uncertainties associated with the

model.



### H.1 Literature review to identify additional studies

EPA used the 2009 meta

- data (U.S. EPA, 2013a) as the starting point for the current revisions and conducted a literature review for additional studies to include in the metadata. The Agency followed Stanley et al. (2013)’s guidelines fo r meta

- regression analyses in economics in documenting the literature search including (a) the exact databases and other sources searched, (b) the precise combination of keywords, and (c) date completed. To identify new studies, EPA relied on the following :

 Searches of general literature databases and search engines (EBSCO, Google Scholar, Google). Search keywords were selected to be sufficiently broad so not to miss relevant studies:

‒ First terms: (1) water quality, (2) water clarity, (3) nutrient removal/i mprovement, (4) water quality index/ladder, (5) clean water, (6) water pollution reduction, (7) water habitat improvement, and (8) stream flow.

‒ Second terms: (1) willingness to pay, (2) stated preference, (3) contingent valuation, (4) choice experiments, ( 5) contingent activity, and (6) conjoint analysis

 Searches of online reference and abstract databases (Environmental Valuation Resource Inventory (EVRI), Benefits Use Valuation Database (BUVD), AgEcon Search, RePEc/IDEAs, and the Oregon State University Co llege of Forestry Recreation Use Values Database);

 Visits to webpages of authors and university programs known to publish stated preference studies and/or water quality valuation research; 92

91 If WQI0 < WQI1 < WQI2, then for a WTP function WTP (WQI0, WQI2, Y0) to satisfy the adding

- up property, it must meet the condition t hat WTP(WQI0, WQI1 , Y0) + WTP( WQI1, W QI2 , Y0

- WTP(WQI0, WQI1, Y0) )
= WT P( WQI0, WQI2 , Y0) for all possible values of baseline water quality (WQI0), potential future water quality levels (WQI1 and WQI2), and baseline income (Y0).

92 This included G. Poe (Cornell), J. Bergstrom (University of Georgia), T. Haab (Ohio State), K. Viscusi (Vanderbilt), R. Carson (U.C. San Diego), W. H. Desvousges, J. Whitehead (Appalachian State), K. Boyle (Virginia Tech), R. Rosenberger (Oregon State), J. Loomis (Colorado State), J. Corrigan (Kenyon College), and R.H. von Haefen (North Carolina St ate).Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 2 

###  Searches of web sites for Resources for the Future and EPA’s Natio nal Center for Environmental Economics, both known to conduct environmental and resource economics valuation research;

 Searches of key resource economics journals for 2005 to 2013 (Land Economics, Environmental and Resource Economics, Marine Resource Econo mics, Journal of Environmental Economics and Management, Water Resources Research, and Ecological Economics). EPA focused these journal

specific searches on the recent period of 2005 to 2013 given the likelihood that older studies were captured under the s teps above or captured during literature reviews conducted for prior versions of the MRM; and

 Review of bibliographies of other valuation meta

- analyses (Van Houtven et al. 2007; Ge et al. 2013; Brander and Brouwer 2011).

Studies identified during the searc h were screened according to the following criteria prior to inclusion in the metadata to ensure validity, consistency, and applicability. The identification and review of studies was completed and verified by multiple individuals following recommendations of Stanley et al. (2013):

 Commodity consistency

– Study must value water quality changes affecting ecosystem services provided by waterbodies, including recreational activities (such as fishing, boating, and swimming), aquatic life support and other nonus e values. 93 

### EPA did not include studies that estimate WTP for improvements in surface waters used primarily for drinking water.

 Welfare consistency

– The study must use general accepted stated preference approaches and report theoretically comparable Hicksi an welfare measures (Boyle et al. 2013).

 Amenity detail

– As described by Johnston et al. (2005), “the study must provide sufficient information regarding resource, context, and study attributes to warrant inclusion in the meta

- data” (p.223).

 Study locat ion

– The study must be conducted in the U.S.

 Research methods

– The study must apply research methods that are supported by the literature and that provide WTP estimates consistent with neoclassical welfare theory.

 Duplicative studies

– Some studies may b e released in multiple forms, such as working papers, conference papers, journal articles, and book chapters. We included only the latest version of the study (e.g., journal article) when there are multiple versions. Studies are screened to remove duplicat ive analysis and analysis that were subsequently revised and published. Multiple values can be included for a study if they address a different component of the study data or estimate values for alternative resource changes.

Table H

- 1 summarizes studies in the revised metadata after identifying and screening additional studies. In total, the revised metadata includes 51 stated preference studies that estimated total WTP (use plus nonuse) per household for water quality changes in U.S. w aterbodies. The studies address various waterbody types including, rivers, lakes, salt ponds/marshes, and estuaries. The fifteen studies added to the metadata during the

current revisions are shaded in Table H

- 1 . Two of the new papers (Corrigan et al. 2009 ; Whitehead 2006) replaced unpublished versions of the same studies (Azevedo et al. 2001; Whitehead et al. 2002). The revised metadata excludes two studies by Viscusi et al. (2008) and Carson and Mitchell (1993). Carson and Mitchell (1993) estimate WTP for water quality improvements nation

- wide. It is excluded because its national scale

93 For example, a study that estimates WTP for recreational fishing improvements due to fish stocking would not
be included in the meta

- data. However, a study that estimates WTP for recreational fishing improvements due
to nutrient reductions would be included in the meta

- data, presuming it satisfies all other criteria.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 3 

### means that the size of the affected resource area is substantially different from other studies in the meta

- data, which at most, assess improvements within a region. On the other hand, Viscusi et al. (2008) asked respondents in a national survey to value water quality improvements within 100 miles of their home. To be included in the metadata, a WTP observation must correspond to a specific affected resource or set of

resourc es that can be delineated in GIS. Viscusi et al. (2008) does not fit into EPA’s data coding framework because the affected resource varies across the sample, with each respondent asked to think about their own area. The WTP estimates reported by Viscusi et al. (2008) reflect an average across many affected areas, rather than WTP for a specific affected resource. Many studies contribute multiple observations due to in

study variations in factors such as number or type affected waterbodies, magnitude of water quality improvement, sampled market area, and elicitation methods. The inclusion of multiple observations per study is standard in resource valuation metadata (Nelson and Kennedy 2009). In total, the 51 studies provide 140
WTP observations.

Table H

- 1: Primary Studies in the Metadata A,B,C,D

Author(s) and
Publication Year Obs. in

Metadata State(s) Water Body Type(s) Willingness to Pay per Household (2007$)

Mean Min. Max.
Aiken (1985) 1 CO river and lake 193.18 193.18 193.18

Anderson and

Edwards (1986) 1 RI salt pond/marshes 180.71 180.71 180.71

Banzhaf et al. (2006) 2 NY Lake 57.47 54.09 60.85 Banzhaf et al. (2011) 1 VA, WV, TN, NC, GA river/stream 31.30 31.30 31.30

Bockstael et al (1988) 1 DC, MD, VA Estuary 149.03 149.03 149.03

Bockstael et al. (1989) 2 MD Estuary 158.30 75.67 240.93 Borisova et al. (2008) 3 WV, VA river/stream 44.94 18.05 65.82 Cameron and Huppert (1989) 1 CA Estuary 49.53 49.53 49.53

Carson et al. (1994) 2 CA estuary 59.40 41.21 77.59

Clonts and Malone (1990) 3 AL river/stream 103.20 78.31 127.48

Collins and Rosenberger (2007) 1 WV river/stream 18.19 18.19 18.19

Collins et al. (2009) 7 WV river/stream 120.52 2.84 217.57 Corrigan et al. (2009) 1 IA Lake 123.30 123.30 123.30 Croke et al. (1987) 9 IL river/stream 77.47 61.31 93.68 De Zoysa (1995) 1 OH river/stream 70.18 70.18 70.18 Desvousges et al. (1987) 12 PA river/stream 59.19 19.84 137.26

Downstream Strategies (2008) 2 PA river/stream 12.74 10.70 14.77

Farber and Griner (2000) 6 PA river/stream 76.16 16.58 148.59

Hayes et al. (1992) 2 RI estuary 397.44 390.68 404.19 Herriges and Shogren (1996) 2 IA Lake 134.55 61.71 207.40

Hite (2002) 2 MS river/stream 60.08 58.24 61.93 Huang et al. (1997) 2 NC estuary 258.65 255.01 262.29Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 4 Table H

- 1: Primary Studies in the Metadata A,B,C,D

Author(s) and
Publication Year Obs. in

Metadata State(s) Water Body Type(s) Willingness to Pay per Household (2007$)

Mean Min. Max.
Irvin et al. (2007) 4 OH all freshwater 21.67 19.65 23.23 Johnston et al. (1999) 1 RI river/stream 180.95 180.95 180.95 Kaoru (1993) 1 MA salt pond/marshes 218.61 218.61 218.61

Lant and Roberts (1990) 3 IA, IL river/stream 143.93 124.04 154.31

Lant and Tobin (1989) 9 IA, IL river/stream 55.63 40.58 67.64 Lichtkoppler and Blaine (1999) 1 OH river and lake 41.93 41.93 41.93

Lindsey (1994) 8 MD estuary 66.80 33.40 102.20 Lipton (2004) 1 MD estuary 63.98 63.98 63.98

Londoño Cadavid and Ando (2013) 2 IL river/stream 38.68 35.93 41.44

Loomis (1996) 1 WA river/stream 93.07 93.07 93.07 Lyke (1993) 2 WI river and lake 78.75 59.75 97.74 Matthews et al. (1999) 2 MN river/stream 21.73 18.14 25.32 Opaluch et al. (1998) 1 NY estuary 138.47 138.47 138.47

Roberts and Leitch (1997) 1 MN, SD Lake 8.35 8.35 8.35

Rowe et al. (1985) 1 CO river/stream 134.59 134.59 134.59 Sanders et al. (1990) 4 CO river/stream 160.69 81.01 210.04 Schulze et al. (1995) 2 MT river/stream 20.84 17.34 24.33

Shrestha and Alavalapati (2004) 2 FL river and lake 156.46 137.97 174.95

Stumborg et al. (2001) 2 WI Lake 84.29 66.73 101.86

Sutherland and Walsh (1985) 1 MT river and lake 146.03 146.03 146.03

Takatsuka (2004) 4 TN river/stream 286.88 181.90 391.85 Wattage (1993) 3 IA river/stream 53.89 40.24 74.59 Welle (1986) 6 MN Lake 167.28 109.60 238.42 Welle and Hodgson (2011) 3 MN Lake 145.10 10.59 285.06

Wey (1990) 2 RI salt pond/marshes 147.26 63.95 230.58

Whitehead and Groothuis (1992) 3 NC river/stream 41.01 31.90 53.16

Whitehead (2006) 3 NC river/stream 187.18 27.52 365.54Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 5 Table H

- 1: Primary Studies in the Metadata A,B,C,D

Author(s) and
Publication Year Obs. in

Metadata State(s) Water Body Type(s) Willingness to Pay per Household (2007$)

Mean Min. Max.

Whitehead et al. (1995) 2 NC estuary 95.44 78.29 112.59

Whittington et al. (1994) 1 TX estuary 194.72 194.72 194.72

Notes:
(A): Shading indicates studies added since the 2009 meta

- analysis.
(B): Journal publications by Corrigan et al. (2009) and Whitehead (2006) replaced staff papers authored by Azevedo et al. (2001) and Whitehead (2002), respectively.
(C): A study by Olsen et al. (1991) from the 2009 meta

- analysis was excluded from the current metadata following robu stness testing and additional review of study details.
(D): The revised metadata excludes studies by Viscusi et al. (2008) and Carson and Mitchell (1993) that surveyed households
across the U.S.



### H.2 Variable Development and Coding

EPA entered values for all existing variables in the metadata for each new study identified during the literature review. Next, EPA developed and coded new variables (for all studies) that address gaps in the 2009 metadata. The variables in the revised MRM fall into four general ca tegories:

Study methodology and year variables characterize such features as the year in which a study was conducted, payment vehicle and elicitation formats, WTP estimation method, and publication type. These variables are included to explain differences in WTP across studies but are not expected to vary across benefit transfer for different policy applications.

Region and surveyed populations variables characterize such features as the US region in which the study was conducted, the average income of res pondent households and the representation of users and nonusers within the survey sample.

Sampled market and affected resource variables characterize features such as the geospatial scale (or size) of affected water bodies, the size of the market area over which populations were sampled, as well as land cover and the quantity of substitute water bodies.

Water quality (baseline and change) variables characterize baseline conditions and the extent of the water quality change.

Table H

- 2 presents and defines k ey variables from the metadata by category. Shading indicates that the variable was developed during these revisions. The development of new variables focused primarily on developing variables that capture geospatial factors including extent of sampled mar ket, surveyed populations

and affected resources. 94 

### The 2009 MRM largely relied on binary variables to distinguish broad categories of affected resources ( e.g. , single vs multiple rivers, regional freshwater, etc.). Extensive GIS mapping is required in orde r to quantitatively incorporate new spatial factors using continuous variables because many primary studies give broad outlines but omit the detailed geospatial information regarding affected resources and sampled populations. This type of supplementation has been used by others to include various types of

94 EPA also coded one new methodological variable, ce , to identify studies that used a choice experiment approach.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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- 6 

### information into valuation MRMs other than that necessary for the current metadata (e.g., Ghermandi et al. 2010; Ghermandi and Nunes 2013). By linking to publicly available GIS data layers, EPA provides a consistent basis for the construction and coding of the new, spatial variables. The following subsections describe both development and coding of water quality and spatial variables for surveyed populations, extent of sampled market, study site, and affec ted resources. In accordance with Stanley et al. (2013) guidelines, all variable coding was reviewed by multiple individuals and documented clearly in the metadata and data dictionary.

Annual WTP values are typically desired for policy analysis to support a comparison of annualized costs and benefits. The metadata includes total WTP as reported by the original study, adjusted to 2007$. 95 

### For over 80 percent of observations (114 studies), total WTP reflects annual payments in perpetuity (113 studies) and one

observation reflects annual WTP for a 10

- year payment period. For the remaining 26 observations, WTP

reflects a short

- term payment period (11 observations use a one

- time lump sum payment, 1 observation uses a

3

- year payment period, and 14 observations use a 5

- year payment period). EPA used the lump_sum binary

variable to account for short

- term payment periods ( lump_sum =1). EPA also tested models with WTP values

for short

- term payment periods converted to perpetual streams but found that results were sensiti ve to discount rate assumptions and that these models did not provide any practical advantages for benefit transfer over models using the lump_sum variable.

Table H

- 2 : Definition and Summary Statistics for Model Variables A

Variable Definition Units Mean St. Dev.
Study Methodology and Year

Ce Binary variable indicating that the study is a choice

experiment Binary (Range: 0 or 1) 0.1071 0.3104

Thesis Binary variable indicating that the study is a thesis. Binary (Range: 0 or 1) 0.1143 0.3193

Lnyear Natural log of the year in which the study was

conducted (i.e., data was collected), converted to an index by subtracting 1980. Natural log of years (year ranges from 1981 to 2011). 2.2127 0.9282

Volunt Binary variable indicating that WTP was estimated

using a payment vehicle described as voluntary as opposed to, for example, property taxes. Binary (Range: 0 or 1) 0.0857 0.2809

outliers_trim Binary variable indicating that outlier bids were

excluded when estimating WTP. Binary (Range: 0 or 1) 0.1929 0.3960

nonparam Binary variable indicating that WTP was estimated

using non

- parametric methods. Binary (Range: 0 or 1) 0.4286 0.4966

non_reviewed Binary variable indicating that the study was not

published in a peer

- reviewed journal. Binary (Range: 0 or 1) 0.2357 0.4260

lump_sum Binary variable indicating that the study provided

WTP as a one

- time, lump sum or provided annual

WTP valu es for a payment period of five years or

less.. This variable enables the benefit transfer analyst to estimate annual WTP values by setting lump_sum =0. Binary (Range: 0 or 1) 0.1857 0.3903

95 EPA used $2007 because the a majority of observations were already converted to $2007 as part of the 2009

meta

- analysis used ELGs (U.S. EPA, 2013a).Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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September 29, 2015 H

- 7 Table H

- 2 : Definition and Summary Statistics for Model Variables A

Variable Definition Units Mean St. Dev.
wtp_median Binary variable indicating that the WTP measure

from the study is the median. Binary (Range: 0 or 1) 0.0714 0.2585

Region and Surveyed Populations

northeast Binary variable indicating that the survey included

respondents from states within the Northeast U.S., defined as ME, NH, VT, MA, RI, CT, and NY. This is equivalent to EPA Region 1 plus NY or the Northeast USDA region. Binary (Range: 0 or 1) 0.0714 0.2585

Central Binary variable indicating that the survey included

respondents from states within the Central U.S., defined as OH, MI, IN, IL, WI, MN, IA, MO, ND, SD, NE, KS, MT, WY, UT, and CO. This is equivalent to EPA regions 5, 7, and 8 or the

Midwest and Mountain Plains USDA regions. Binary (Range: 0 or 1) 0.3643 0.4830

southB,C Binary variable indicating that the survey included

respondents from states within the Southern U.S, defined as NC, SC, GA, FL, KY, TN, MS, AL, AR, LA, OK, TX, and NM. This is equivalent EPA regions 4 and 6, or the Southea st and Southwest USDA regions. Binary (Range: 0 or 1) 0.1571 0.3652

nonusers Binary variable indicating that the survey was

implemented over a population of recreational nonusers (default category for th is variable is a survey of any population that includes both users and nonusers). Binary (Range: 0 or 1) 0.0857 0.2809

lnincome Natural log of the median income (in 2007$) for the

sample area of each study based on historical U.S. Census data. It was designed to provide a consistent income variable given differences in reporting of respondent income across studies in the metadata ( i.e., mean vs. median). Also, some studies do not report respondent income. This variable was estimated for all studies in the metadata regardless of whether the study reported summary statistics for respondent income. Natural log of

income (2007$) (sampl e area income ranges from $34,332 to $78,444) 10.7453 0.1731

Sampled Market and Affected Resource
mult_bod Binary variable indicating that the survey addressed

multiple waterbody types. The eight waterbody type categories are (1) river/stream, (2) lake, (3) all freshwater, (4) estuary, (5) wetlands, (6) river and lake, (7) salt pond/marshes, and (8) multiple (e stuary and fresh water). Takes on a value of 1 if the study is coded as category (3), (6), or (8). Binary (Range: 0 or 1) 0.0786 0.2700

River Binary variable that takes on a value of 1 if the

study affects a river, such that river length>0, and

zero oth erwise. Binary (Range: 0 or 1) 0.6857 0.4659

swim_use Binary variable indicating that the affected use(s)

stated in the study include swimming. Binary (Range: 0 or 1) 0.2643 0.4425Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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September 29, 2015 H

- 8 Table H

- 2 : Definition and Summary Statistics for Model Variables A

Variable Definition Units Mean St. Dev.
gamefish Binary variable indicating that the affected use

stated in the study is gamefishing. Binary (Range: 0 or 1) 0.0571 0.2329

boat_use Binary variable indicating that the affected use(s)

stated in the study includes boating. Binary (Range: 0 or 1) 0.1143 0.3193

ln_ar_agr Natural log of the proportion of the affected

resource area which is agricultural based on NLCD, reflecting the nature of development in the area surrounding the resource. The affected resource

area is defined as all counties that interse ct the affected resource(s). EPA also tested a variable for the fraction of lands that is developed land classes, but it did not improve model fit. Natural log of proportion (Range: 0 to 1) - 1.4329 0.9031

ln_ar_ratio A ratio of the sampled area, in km 2

, relative to the

affected resource area. When not explicitly reported in the study, the affected resource area is measured

as the total area of counties that intersect the affected resource(s), to create the variable ar_total_area . From here, ln_ar_ratio = log( sa_area / ar_total_area ), where sa_area is the size of the sampled area in km 2

. Natural log of

ratio (km2/km2) - 1.1278 2.6067

sub_proportion The proportion of water bodies of the same

hydrological type affected by the water quality change, within affe cted state(s). For rivers, this is measured as the length of the affected river reaches as a proportion of all reaches of the same order. For lakes and ponds, this is defined as the area of the affected water body as a proportion of all water

bodies of the same National Hydrography Dataset classification. For bays and estuaries, this is defined as the shoreline length of the water body as a proportion of all analogous (e.g., coastal) shoreline lengths. To account for observations where multiple waterbody ty pes are affected, the variable sub_proportion is defined as maximum of separate substitute proportions for rivers, lakes, and estuaries/bays. The affected resource appears in

both the numerator and denominator when calculating sub_proportion

. Proportion (Range: 0 to 1) (km/km) 0.1880 0.2911

Water Quality Baseline and Change lnquality_ch Natural log of the change in mean water quality

( quality_ch ), specified on the WQI (McClelland 1974; Mitchell and Carson 1989). WQI units 2.9070 0.6039

Lnbase Natural log of baseline water quality, specified on

the WQI (McClelland 1974; Mitchell and Carson 1989). WQI units 3.5889 0.6697Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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September 29, 2015 H

- 9 Table H

- 2 : Definition and Summary Statistics for Model Variables A

Variable Definition Units Mean St. Dev.

Notes:
(A): Shading indicates that the variable was developed and coded during the current set of revisions.
(B): EPA merged studies from the Southeast and Southwest region into a single group because the metadata includes only one observation from the Southwest region, Whittington et al. (1994) which studied estuaries in Texas
(C): The USDA regions omitted from the regional binary var iables are the Mid

- Atlantic region (NJ, DE, MD, DC, PA, WV, and VA) and the Western region (WA, OR, ID, CA, NV, AZ, and AK).



### H.2.1 Study Methodology and Year

As often found in meta

- analyses within the valuation and benefit transfer literature (Navrud and Ready 2007), a variety of study and methodology effects can be shown to influence WTP for water quality improvements. The 2009 metadata included numerous variables characterizing factors including, but not limited to publication type, elicitation method, respon se rate, treatment of outliers, payment period, and study year. EPA coded values for these variables for the new primary studies following the variable definitions from the 2009 MRM. The majority of the methodological variables in the metadata are binary, with 1 indicating that a given methodology was used in the primary study. Some others, such as study year, are continuous. Most of the methodological variables available in the metadata did not improve model fit and were not included in the

revised MRM. Gi ven that choice experiments are being used with increased frequency in the recent literature, EPA coded a new binary variable, ce , to flag observations that are based on a choice experiment framework.

H.2.2 Region and Surveyed Populations

The 2009 metadata incl uded variables describing the location of the study by state and region of the country although these were used in a fairly simplistic way in the meta

- analysis. EPA expanded the metadata by delineating sampled area for each of the 140 WTP observations as G IS polygons. The sampled area is defined as the geographic area over which the primary survey was fielded ( i.e. , the population it i s meant to be representative of). The Agency based its GIS delineation on the description of the study area from the study

d ocumentation and matched to polygon boundaries from publicly available GIS datasets. The sampled area can vary across observations from the same primary study if the WTP values are based on separate survey samples (this is true for 5 of 51).

The sampled ar eas are typically defined by either jurisdictional boundaries or watershed boundaries:

For sampled areas defined by jurisdictional boundaries ( e.g. , states, counties, or cities), EPA defined boundaries using the Census Topologically Integrated Geographic E ncoding and Referencing (TIGER) state and county shapefiles for 1990, 2000, and 2010. By using the shapefile vintage that most closely corresponds to the year in which the study was fielded, EPA is able to accurately identify boundaries at the time of the study.

For sampled areas defined by watersheds, EPA approximated sampled area boundaries by matching to the U.S. Geologic Survey Hydrologic Unit Code (HUC) Watershed Boundary Dataset (WBD) to approximate the boundaries of the study area. 96 

### The Agency first matched the watershed polygon in the HUC dataset based on

96 The HUC WBD is a package of watershed shapefiles for the contiguous U.S. subdivided to seven levels of resolution. Each resolution level is assigned a specific number of HUC digits, with the number of digits increasing with higher resolutions. The lowest resolution is the HUC 2

- digit, or HUC

- 2, watershed which divide the country into 21 distinct drainage re gions. The highest classification is the HUC12 dataset, consisting ofBenefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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### the watershed name presented in the study. If the name could not be matched, EPA consulted the survey materials or maps included in the study to either locate the corresponding HUC watershed or use the materials to trace a watershed polygon manually. If maps are not provided, then EPA used any additional locational information presented in the study to identify a HUC watershed to serve as a proxy for the study watershed. This approach generally invol ved more interpretation than if the study defined the sampled area based on jurisdictional boundaries (as in previous bullet). 97



### Income is inconsistently reported across primary studies, with some reporting median income, others reporting mean income, and o thers not reporting any income statistics for the sample. After delineating the sample area for all studies, EPA calculated income statistics for the population within the sampled area using georeferenced historic U.S. Census income data to overcome report ing deficiencies and improve consistency across observations. Historical median income data is available for 1980, 1990, 2000, and 2010. Each sampled area was matched to the data vintage that is nearest to the year in which the study was fielded. EPA

deriv ed sampled area income as a population

- weighted average of median income across counties that intersect the sampled are polygons. The population weights were adjusted based on the fraction of county area that is within the sample area. Resulting income est imates were adjusted to 2007$ using the Consumer Price Index (CPI).

The 2009 metadata included regional dummies for the study location based on the boundaries of U.S. Department of Agriculture (USDA) regions. 98 

### The USDA regions were used as the starting poi nt for regional assignments. The boundaries of the seven USDA regions are closely aligned with the ten EPA regions, EPA generated two new binary variables that combine some regions for which effects were found to be similar.

Northeast identifies studies th at sampled respondents in the Northeast U.S. (as far south as New York, central identifies studies that sampled in the Midwest or Mountain Plains regions of the U.S, and south identifies those that sampled in the Southeast and Southwest U.S. (as far west a s New Mexico). 99 

### The state boundaries

for each variable are listed in Table H

- 2

.
EPA also calculated the continuous variables sa_area , defined as the size of the area sample in square

kilometers. Sa_area feeds into the index variable, ln_ar_ratio , described in detail in the following subsection.

H.2.3 Reconciliation of Water Quality Baseline and Change

An important component metadata development is the reconciliation of variables across observations (Johnston et al. 2005; Smith and Pattanayak 2002; Smi th et al. 2002; Van Houtven et al. 2007). Although the calculation and reconciliation of most independent variables requires little explanation, there are some variables for which additional detail is warranted. These include variables characterizing surfa ce water quality and its measurement. To reconcile measures of water quality across studies, EPA adapted the prior approach

approximately 98,000 watersheds. Additional information about the HUC watershed and delineation process
can be found at http://water.us gs.gov/GIS/huc.html

. 97 EPA encourages authors of future studies to describe the sampled area clearly, to avoid the need for judgment or interpretation when including studies in future meta

- analyses.

98 Using ecoregion boundaries to define regional dummies is not feasible because it would result in a very small number of observations per region. Each USDA region includes several ecoregions and thus is better suited for grouping studies.

99 The default region correspond s to the Mid

- Atlantic states (DE, MD, NJ, PA, VA, WV). It also includes the state of California because only two studies (3 observations) correspond to California and because similarly to the
Mid

- Atlantic states it is a coastal state. No meta

- data observati ons correspond to other states in the Western U.S. (i..e, OR, WA, NV, ID, HI, AZ, AK).Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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### of Johnston et al. (2005) and van Houtven et al. (2007), mapping water quality changes to the 100

- point Water Quality Index (WQI).

A large number of the original studies in the metadata (10 studies and 31 percent of observations) include the

10

- point water quality ladder (WQL) measures as a native component of the original primary studies either using its numerical values ( e.g. , 2.5, 5 .0, etc.) or descriptive levels (e.g., boatable, gamefishing). 100 

### In other cases, EPA used descriptive information from studies to map baseline and post

- improvement to points on the WQL. To calculate 100

- point WQI values EPA multiplied the reported WQL value s by ten. In most cases, descriptions correspond closely to levels on WQL, rendering mapping straightforward. EPA used the following guidelines for mapping:

WQL assignments are made based on consideration of information provided in the primary study

docume ntation and, if available, the survey materials. The assignments are not supplemented by external water quality databases or reports. They should reflect the descriptions and metrics presented to respondents

within the valuation scenarios.

Identify recreat ional uses ( e.g. , boating, fishing, and swimming) provided in the description of baseline and improved (or declined) conditions. These uses may be stated directly or embedded in the definition of water quality metrics ( e.g. , low, moderate, high) and can in clude additional descriptive information such as effects on sensitive aquatic species or indication of presence of or amount of specific pollutants.

Only consider those metrics that can be reasonably mapped to the WQL. Ancillary improvements from the impro vement plans, such as shoreline trash pickup or terrestrial bird species, are not considered.

Exclude observations that describe an improvement in recreational use that is not tied to a water quality improvement ( e.g. , an increase in fish abundance not bas ed on water quality improvement).

Start by assigning values corresponding to use thresholds defined on the WQL ( e.g. , 2.5, 4.5). Assign intermediate values when the changes occur within a specific use category or changes extend beyond the minimum for provi sion of use. EPA notes that the majority of assignments directly match values corresponding to WQL use thresholds.

To the extent possible, assigned WQL values should reflect the “affected” portion of resource(s) described in the survey, that is, the porti on of the resource that is subject to water quality changes under the valuation

scenario.

In some cases, uses may be supported intermittently due to algae blooms, for example. If provided, use information regarding the frequency of service provision to cal culate intermediate WQL values by weighting
use threshold values.

For some types of environmental contamination, such as high acidity, the survey may state that the affected resource supports swimming but has degraded fisheries. It supports a higher use on the WQL (swimming), but not a lower use (gamefishing). . In these cases, we specify baseline WQL and WQL changes based on fishing conditions, the use that is actually improved under the valuation scenario ( e.g. , a baseline of 4.5 for rough fishing and a p ost

- improvement value of 5.0 for game fishing).

For more detail on water quality description from the original studies and the assigned baseline and improved water quality conditions for each study see Appendix B in Peer Review Package for Meta

- analysis of the Willingness

- to

- Pay for Water Quality Improvements (U.S. EPA, 2015e).

100 The WQL (Vaughan 1986) is expressed on a scale of 0 to 10 and can be mapped to the WQI by multiplying by 10. Refer to Chapter 10 and Appendix G of the benefits analy sis for the C&D Effluent Guidelines (U.S. EPA 2009) for additional detail on the WQI and WQL.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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### EPA did not identify any systematic variation in results associated with studies for which the WQI was a native component, versus those for which quality changes were mapped to the WQI. A binary variable indicating that the observation used the WQL ( CHNG_WQL ) was not individually significant and did not improve model fit.

H.2.4 Delineation of the Affected Resource and Substitutes

WTP for WQI changes is likely better explained in concert wi th geospatial factors, given the geographic heterogeneity in the metadata. In the context of water quality valuation, the geospatial scale of a water quality change reflects the area over which the environmental change occurs. EPA expects, all else being e qual, that WTP for water quality improvements would be directly related to the size and number of affected waterbodies. EPA defines an affected resource as a waterbody that experience a water quality change under the valuation scenarios presented in the pr imary study. The number of affected waterbodies assessed in the primary studies varies widely across the metadata from single waterbodies to all waterbodies throughout a state or small region. The existing metadata identifies the geographic scale of the af fected resources using four categories: (1) single waterbody, (2) multiple waterbodies, (3) a small region, or (4) a large region. Table

H

- 3 summarizes the number and percent of observations in each of the four geographic scale categories. As part of the r evisions, EPA delineated the affected waterbodies by matching waterbody descriptions and maps from the primary studies to publicly available georeferenced datasets. By delineating resources using GIS, EPA is able to incorporate detailed spatial and hydrolo gical features of the waterbodies. As with sampled area, some primary studies provide separate WTP values for multiple affected resources or sets of affected resources. These are always treated as separate observations within the metadata.

Table H

- 3 : Summ ary of Scale of Resource Changes in the Meta

- data.

Category Variable Description Observation
Count % of

Observations
Single Waterbody Single Single, discrete waterbody such as a river

or lake. 49 35.0%

Multiple

Waterbodies Multi Multiple discrete waterbodies ( e.g. , 2 rivers) 13 9.3%

Small Region Sm_reg Small region, includes watersheds and

state

- wide analyses 77 55.0%

Large Region Lg_reg Large region, includes one estuary analysis that includes multiple states. 1 0.7%

Total

- - 140 100.0%



### The datasets used to map affected resources vary by affected waterbody type. For rivers and lakes, the Agency opted to match to the NHDPlus dataset over other publicly available georeferenced hydrologic datasets ( e.g. , E2RF1) because it is a comprehensive, nat ional dataset of hydrologic features such as rivers, lakes, ponds and catchments, and contains tabular data that can be linked to these hydrologic features. For estuaries and coastlines, EPA used the National Oceanic and Atmospheric Administration (NOAA) G lobal

Self

- Consistent Hierarchical, High

- Resolution Geography Database (GSHHD). EPA attempted to match the name of the affected resource to the datasets, and if a match could not be found, used narrative descriptions and maps from the primary study to manu ally select the affected waterbodies in ArcGIS. Once delineated, EPA calculated affected shoreline length for all waterbody types as proxy for the human/water interface, although some uses actually occur in open water. For rivers, shoreline length is doubl e the reach length to

reflect both shorelines. EPA also calculated the area of affected lakes.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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### Observed mean WTP in a primary study is also related to the extent of market analyzed (Loomis 2000; Loomis and Rosenberger 2006). It is established in economic l iterature that mean WTP often declines as distance increases to an affected resource (i.e., distance decay) (Bateman et al. 2006; Jørgensen et al. 2013; Schaafsma et al. 2012). Because the scale of the sampled market area varies greatly across primary stud ies and past work has shown that such differences can have important implications for welfare estimates, the accuracy of benefit transfers depends on the ability to adjust for the distance between affected populations and the resource (Bateman et al. 2006; Johnston and Duke 2009; Johnston and Ramachandran 2013; Loomis and Rosenberger 2006). The size of the sampled area ( sa_area ) in the metadata varies greatly across the metadata mean of 49,065 mi 2 

### with 5 th 

### and 95 th 

### percentiles of 13.3 and 104,094 mi2, respe ctively. Sa_area is not correlated with the size of the affected resource because various factors affect researcher’s choice of survey region including budget and resource constraints and study objectives. As a result, the metadata does not provide clear i nformation regarding the extent of the market for the affected resources or distance effects.

Modeling results indicated that model performance was enhanced, in terms of model fit, variable significance, and consistency with theoretical expectations, when resource size was specified as a function of sampled market area ( sa_area ). The ln_ar_ratio variable reflects the size of the sampled market relative to the size of the affected resource, defined as the (natural log of the) size of the sampled market area ( sa_area ) divided by the geographic area encompassing the affected waters in square kilometers ( ar_total_area ). Ar_total_area is calculated based on the area of counties that insect the affected resource. For example, if a study valued all

freshwater in I owa, then ar_total_area would equal the area of Iowa. On other hand, ar_total_area would equal the area of a single Iowa county if the affected resource falls entirely within the county boundary. The area ratio, ln_ar_ratio , is expected to have negative si gn reflecting two effects. First, holding all else constant, stated preference surveys over larger market areas imply greater distances between individual households and affected waterbodies, and thus lower WTP per household. Secondly, improvements to more waters within the sampled area should be associated with greater WTP, ceteris paribus.

About 55 percent of observations in the metadata estimated WTP for water quality changes in a single watershed, group of watersheds, or a state. The other 45 percent f ocused on a single waterbody or small set of waterbodies ( e.g. , one lake or 3 lakes). Specifying the affected resource area requires some assumptions if the affected resource is defined as a river or lake ( e.g. , EPA assumed that counties intersecting the a ffected water bodies represent the affected resource area). As noted above, EPA used intersecting counties to define all affected resources, including discrete rivers, as geographic areas while avoiding the arbitrary selection of a boundary distance.

Relative to the alternatives, ln_ar_ratio provides a more intuitive means to capture the scale of the affected resource occurring throughout an area, such as watershed or region which is likely to be the case in the context of national rulemakings. Model f it decreases when the affected area ratio is included in non

- logged form and the variable ( ar_ratio ) is no longer individually significant. Logging the ratio is also intuitive because WTP is zero when the resource size is zero. EPA considered, but did not select, a variety of alternative specifications of the geographic scale and extent of market variables ( e.g. , shoreline length).

The availability and quality of substitutes in the surrounding geographic area are also expected to influence

welfare estimates (Loomis and Rosenberger 2006; Schaafsma et al. 2012). All else equal, WTP should be negatively related to substitute availability. EPA developed a continuous variable, sub_proportion , defined as

the ratio of affected waterbodies to available use and nonus e substitutes within the state. EPA expects that the relationship between distance and substitutes will differ for resource users and nonusers. For users, there is “a positive cost of access which depends on distance” (Hanley et al. 2003, p.300), therefore , sites that are farther away will generally be less attractive substitutes all other things being equal. On the other hand, “there seems no theoretical reason to expect such a [distance decay] trend to be seen within the responses of those who areBenefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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### present non

- users” (Bateman et al. 2006, p. 453). There is some evidence that a sense of spatial and cultural identity or “ownership” may be important for nonuse values for some environmental resources (Bateman et al. 2006; Hanley et al. 2003). Hanley et al. (200 3) provides the example that “I may have stronger non

- use values for Scottish wildlife sites if I am Scottish than for English wildlife sites” (p.300). In the U.S., this may be manifested as affiliation with one’s state of residence. Given these factors, E PA considers the state to be a reasonable basis for the calculating the substitute proportion.

The substitute proportion variable ( sub_proportion ) represents a potential advance over less sophisticated binary variables traditionally used to represent the size or scope of affected resources. For studies restricted to rivers, the calculation of substitutes is restricted to rivers of the same stream order(s) as the affected resource to ensure comparability. For non

- river inland waterbodies ( e.g. , lakes and po nds), substitute proportion is calculated based on area relative to area of lakes within the state(s). 101 For estuaries and coasts, EPA calculated the denominator as all coastline miles in the state(s). EPA also tested models with separate substitute variables for each waterbody type, but model performance was not improved.

By delineating an affected area, i n addition to the waterbodies themselves, EPA is also able to analyze the development characteristics of areas bordering the waterbodies. In particular, EPA estimated the fraction of the land in the study area used for agriculture ( ln_ar_agr ). EPA used the National Land Cover Dataset (NLCD) to develop a land cover profile of the affected resource area. The NLCD is a high

- resolution (30 meter) spatial dataset of land cover across the contiguous U.S. and available for the years 1992, 2001 and

2006. The NLCD h as 16 or 21 categories of land cover depending on the year. For the purpose of consistency and simplicity, EPA defined the agricultural land as the sum of “cultivated cropland” and “pasture/hay”. The Agency used the change in agricultural land between NLCD vintages to interpolate values for the actual year of the study. The agricultural fraction is calculated as the area

- weighted average of counties within the affected resource area. Areas dominated by agricultural land uses have particular characteristics which suggest that WTP for water quality improvements could be lower than other types of areas, ceteris paribus:

 First, unlike non

- agricultural rural areas, heavily agricultural areas have generally been altered from their natural ecosystem conditions, and do not tend to be highly prized for water

- based recreation (or characterized as pristine natural areas)

– this would be expected to decrease WTP for improvements in agricultural areas compared to many other rural areas.

 Second, unlike more heavily popula ted suburban or urban areas, agricultural lands do not have the population base to support well

- developed and used recreational areas. Improvements to water bodies in suburban areas, for example, are often highly valued because these areas support extensiv e recreational and other uses.

 Third, a greater proportion of the population in agricultural areas has employment linked to farm activities that may be associated directly or indirectly with water pollution. Those whose employment depends on farming activ ities may be hesitant to support programs to improve water quality (in stated preference surveys or otherwise), for fear that these policies may lead to greater restrictions on farms and farming activities.

For all of these reasons, one would expect areas dominated by agriculture to have lower WTP to improve water quality, again holding all else constant. This intuition is strongly supported by model results, as

discussed below. EPA also developed and tested an analogous variable based on the fraction of l and that is in developed land use categories but it not improve model fit.

101 

### NHDPlus does not provide widths of river and streams.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

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### H.3 Model Specification

EPA tested a number of meta

- regression models based on WTP estimates for improvements in water resources, derived from 51 original studies. However, only the m arginal WTP model (“Model 1”) and its variant (“Model 2”) are used in the analysis of benefits of the final ELG because these models satisfy adding

up conditions (Diamond 1996). Model specification for the marginal WTP model, results, and interpretation

of the results are described in the following sections. EPA’s Peer Review Package for Meta

- analysis of the Willingness

- to

- Pay for Water Quality Improvements (U.S. EPA, 2015e) and memorandum entitled Accounting for Scope in the MRM2 Meta

- Regression Model (Abt Associates 2015) provide additional detail on model selection, testing and alternative specifications. 102



### H.3.1 Marginal Willingness to Pay (Model 1)

In the meta

- regression model, marginal willingness

- to

- pay from observation i

, MWT P i 

### ≡ ∂WT P i ∂ Q 

### , is model ed

as follows:

ln ( MWT P

i 

### ) = α 0 

### + α 1 

### X i 

### + f ( Q i 

### ; β ) + ε i 

### (1)

In this equation, α 0 

### is a constant, X i 

### is a vector of study and resource characteristics that act as demand shifters, Q i 

### is the absolute water quality index level at which marginal wi llingness

- to

- pay is being evaluated,

and ε i 

### is a zero

- mean normally

- distributed error term. The function f (

⋅ ) expresses the core relationship between absolute water quality Q i 

### and the log of marginal WTP, and β is a vector of parameters that define

t he functional form of f (

⋅ ) . If f (

⋅ ) is assumed to be linear in Q i 

### :

f ( Q i 

### ; β ) ≡ β

⋅ Q i 

### (2)

then marginal WTP can be expressed as follows:

ln ( MWT P

i 

### ) = α 0 

### + α 1 

### X i 

### + β

⋅ Q i 

### + ε i 

### (3)

However, MWT P

i 

### is not observed directly in the meta

- data, and instead must be calculated from total willingness

- to

- pay as part of the estimation routine. This would require a nonlinear least squares or maximum likelihood estimation approach. As a simpler alternative, EP A assumed that marginal WTP can be approximated by average WTP per unit of water quality:

MWT P

i 

### ≈ WT P i
Δ Q i 

### (4)

with this approximation assumed to be valid at some point between Q i0 

### and Q i1 

### , here approximated by the midpoint of the water quality change valued for that meta

- data observation:

102 Note that Model 1 here is the model referred to as MRM2 in the Peer Review Package, while Model 2 her e is
the model referred to as MRM2

- S in the Accounting for Scope memorandum.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 16 

### Q i 

### = Q i 0 + Q i 1

2 

### (5)

Substituting Equations (4) and (5) into Equation (3) provides the version of the marginal WTP model that is ultimately used as a regression model (Model 1):

ln ( WT P i
Δ Q i 

### )

= α 0 

### + α 1 

### X i 

### + β

⋅ ( Q i 0 + Q i 1
2 

### ) + ε i 

### (6)

The simplest version of this regression uses an unweighted OLS approach to generate a vector of estimated parameter values ( α

̂ 0 

### , α ̂ 1 

### , β ̂

, σ ̂ 2 

### ) , where σ

̂ 2 

### is the sum of squared residuals divided by N

- K, where N is the number of observations and K is the number of parameters. Model 1 then substitutes these estimated values into Equation (3) above, to get an expression that can be used to predict the log of marginal WTP:

ln ( MWT P

i 

### ) = α ̂ 0 

### + α ̂ 1 

### X i 

### + β ̂

⋅ Q i 

### + ε i 

### (7)

Taking the exponent of both sides produces the following expression for marginal WTP evaluated for a particular set of study and resource characteristics X and a particular water quality index level Q :

MWTP = exp ( α

̂ 0 

### + α ̂ 1 

### X + σ ̂ 2 

### / 2 )

⋅ exp ( β

̂

⋅ Q ) ( 8)

Small changes in water quality (e.g., less than one unit) could be valued using an approximation that involves multiplying predicted MWTP from equation (8) by the amount of the water quality change. Larger water quality changes must account for the cur vature of the marginal WTP function, and so must be calculated using the integral:

WTP = ∫ exp ( α

̂ 0 

### + α ̂ 1 

### X + σ ̂ 2 

### / 2 )

⋅ exp ( β

̂

⋅ Q ) Q 1 Q = Q 0 

### dQ (9)

which is equal to:

WTP = exp ( α

̂ 0 

### + α ̂ 1 

### X + σ ̂ 2 

### / 2 )

⋅ exp ( β

̂

⋅ Q 1 )

− exp ( β

̂

⋅ Q 0 )

β ̂ 

### (10)

Because 98 percent of reach miles affected by the final ELG would experience WQI changes that range between 0 and 1 EPA estimated WTP for water quality improvements by multiplying MWTP by the amount of water quality changes expected from the final ELG, as described in Chapter 4

.

H.3.2 Marginal Willingness to Pay (Model 2)

A key feature of Model 1, as described in Equation (1) above, is that marginal WTP does not depend on the magnitude of the water quality change being valued. In other words, the model assumes that marginal WTP

for the one

- unit change from 49 to 50 on the water quality index is the same, regardless of whether survey respondents live in an area where baseline water quality is 35 or 49, and whether the expected total water quality change is 30 points or 2 points, respectively.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 1 7 

### The meta

- data, however, do show a relationship between marginal WTP and the water quality change. This relationship could occur for any number of reasons, including the transformations associated with studies that did not use the WQI or WQL directly, or an omitted variable that is correlated with the water quality improvement. More specifically, the meta

- data show that survey respondents express high marginal WTP for modest improvements in water quality, but then express much lower marginal willingness to pay for additional improvements. For more detail see a memorandum entitled Accounting for Scope in the MRM2

Meta

- Regr ession Model (Abt Associates 2015).

To address this relationship in the meta

- data while satisfying the adding up condition, EPA developed a variant of the marginal WTP model that includes water quality change as a study methodological

characteristic. Simil ar to other methodological parameters (e.g., the use of a choice experiment format, or a lump sum payment), the water quality change parameter could be set to any appropriate value when the meta

model is used for benefit transfer. Importantly, because wate r quality change would be treated as a methodological characteristic instead of as a scenario characteristic, the value assigned to the water quality change parameter need not correspond to the specific water quality change being valued in the benefit tran sfer application. For the same reason, the resulting model will still satisfy the adding

- up property.

The following equation presents an expanded version of marginal WTP that allows the water quality change

Δ Q i 

### to enter as a methodological characteristi c (hereafter Model 2):

ln ( MWT P

i 

### ) = α 0 

### + α 1 

### X i 

### + g ( Δ Q i 

### ; φ ) + f ( Q i 

### ; β ) + ε i 

### (11)

In this equation, Δ Q i 

### is the magnitude of the water quality change for meta

- data observation i , and φ is a vector of parameters that specify the functional form for the function g (

⋅ ) . For simplicity, EPA modeled the

function g (

⋅ ) as a linear function of the water quality change:

g ( Δ Q i 

### ; φ ) ≡ φ

⋅ Δ Q i 

### (12)

Substituting Equations (4), (5), and (12) into Equation (11) gives the following regression model:

ln ( WT P i
Δ Q i 

### )

= α 0 

### + α 1 

### X i 

### + φ

⋅ Δ Q i 

### + β

⋅ ( Q i 0 + Q i 1
2 

### ) + ε i 

### (13)

The estimated parameters from this equation can be used to generate an expression for both marginal WTP

and total WTP:

MWTP = e xp ( α

̂ 0 

### + α ̂ 1 

### X + φ

⋅ Δ Q + σ

̂ 2 

### / 2 )

⋅ exp ( β

̂

⋅ Q ) (14)

WTP = exp ( α

̂ 0 

### + α ̂ 1 

### X + φ

⋅ Δ Q + σ

̂ 2 

### / 2 )

⋅ exp ( β

̂

⋅ Q 1 )

− exp ( β

̂

⋅ Q 0 )

β ̂ 

### (15)

Because Δ Q is treated as a study methodological characteristic, not as a characteristic of the benefit transfer water quality improvement scenario, the parameter Δ Q need not be set equal to Q 1 

### − Q 0 

### in equations (14) and (15).



### H.4 Regression Results

Table H

- 4 presents regression results for Model 1 and Model 2. The models presented in Table H

- 4 were selected after the estimation of numerous preliminary mo dels with different specifications and groups ofBenefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 18 

### independent variables. The selection was based on both statistical fit and correspondence with theoretical expectations. EPA estimated the models using robust standard errors to account for study

- level depen dencies. Measures of fit for the illustrated models are good (R 2 

### = 0.641 and 0.773). These compare favorably to prior

meta

- analyses in the published literature.

The model performs well, with intuitive results for virtually all statistically significant variables. The model identifies numerous statistically significant coefficients for variables characterizing (1) study methodology, (2) region and surveyed populati ons, (3) extent of the market, study site, and affected resources, and 4) water quality. In total in Model 1, 15 out of 23 non

- intercept coefficient estimates are statistically significant at p<0.05, and 12 of 23 are significant at p<0.01. The sub_proporti on geospatial variable is significant at p<0.01 with signs on coefficients that are consistent with expectations.

Given the trans

- log functional form, these results for ln_ar_ratio imply diminishing marginal returns to scale ( e.g. , WTP per unit of water quality improvement declines as the scope of water quality change increases). Marginal WTP is lower among studies surveying only nonusers (nonusers), another expected result, because users may hold both use and nonuse values, while nonusers hold only nonus e values, by definition. Methodological variables also show expected patterns. For example, households are willing to pay a greater

nominal amount in a one

- shot payment ( lump_sum ) than they would be willing to pay in repeated annual
payments. 103 

### Marginal WTP also varies according to the type of uses potentially affected by the proposed changes (particularly boat_use ). In some cases, these variables may be correlated with water quality condition. However, counterexamples exist: for example, waters may be swimm able but the study focuses on improvements in game fishing (e.g., Banzhaf et al., 2006). EPA found that model fit was better with
swim_use

, boat_use , and gamefish included. 104 

### Thus, dropping these variables could result in omitted variable

bias.

As expected

, sub_proportion has a positive coefficient, indicating that WTP increases as the affected resource constitutes a larger fraction of available substitutes. The coefficient for ln_ar_ratio is negative because the sampled area ( sa_area ) is in the numerator i ndicating that marginal WTP decreases as the sampled market area increases relative to the affected resource. This is consistent with the expectation that a larger sample area will include a greater proportion of respondents who are resource nonusers, who are less familiar with the resource, and/or who live at a greater distance from the affected waters.

A negative coefficient on the agricultural area variable ( ln_ar_agr ) suggests that areas dominated by agriculture have lower WTP to improve water quality, holding all else constant. Because areas dominated by agriculture may be significantly different in terms of both resource and population characteristics, as discussed in the preceding section, this result is not surprising. EPA also notes that removing t his variable causes substantial changes elsewhere in the model, a sign that removing this variable could cause non

- trivial

omitted variables bias

. 105



### The absolute water quality variable ( Qavg2 ) shows that water quality has a larger effect on marginal WTP

und er Model 1, compared to Model 2. The coefficient is

- 0.017 under Model 1 and

- 0.004 under Model 2.

103 As discussed in Section 3, EPA also tested models with WTP values for short

- term payment periods converted to perpetual streams but found that results were sensitive to discoun t rate assumptions and that these models did not provide any practical advantages for benefit transfer over models including the lump_sum variable.

104 Peer Review Package for Meta

- analysis of the Willingness

- to

- Pay for Water Quality Improvements (U.S. EPA,
2015 e )

105 EPA notes that some peer reviewers questioned inclusion of this variable in response to the peer review (U.S. EPA, 2015e)

. In this analysis, the Agency followed the econometric guidance suggesting that if the inclusion of
a variable within a mo del is questionable, the default solution should be to include the variable (including an irrelevant variable only reduces efficiency; omitting a relevant variable creates bias )

.Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 19 

### This pattern suggests that after accounting for the change in water quality, absolute water quality may have less of an effect on marginal WTP. However, EPA notes that a t

- test would fail to reject the hypothesis that these coefficients are equal.

Model 2 also includes a water quality change variable: lnquality_ch . This variable is negative and highly significant. This pattern indicates that marginal WTP is de creasing in the size of the water quality change being valued. This is consistent with the patterns from the raw meta

- data, as discussed in detail in a memorandum entitled Accounting for Scope in the MRM2 Meta

- Regression Model (Abt Associates 2015).

Overal l, a comparison of the R 2 

### values from the two models indicates Model 2 has greater explanatory power

than Model 1. Model 1has a R 2 

### value of 0.641. In contrast, the R 2 

### value for Model 2 is 0.773. Given that Model 1 already has 23 variables and a constant, t his substantial increase in R 2 

### from one additional variable suggests that log water quality change is an important va riable to include in the model.

Table H

- 4 : Regression Results

Parameter Model 1 Model 2

Parameter Estimate Robust Standard
Error Parameter Estimate Robust Standard
Error

ce 0.3772 0.3241 0.4233* 0.1991

thesis 0.8664** 0.2721 0.7735** 0.1828
lnyear

- 0.4115** 0.1308

- 0.5000** 0.0911

volunt

- 1.3915** 0.2405

- 1.1837** 0.2040

OUTLIER_BIDS

- 0.3673 0.2025

- 0.2912* 0.1238

nonparam

- 0.4076** 0.1215

- 0.3902** 0.1322
non_reviewed

- 0.7094** 0.2008

- 0.8708** 0.1536
lump_sum 0.8427** 0.1895 0.7732** 0.1338
WTP_median

- 0.1612 0.3333

- 0.1507 0.1800

northeast 1.1785** 0.3389 0.5932* 0.2366

central 0.5607* 0.2154 0.7262** 0.1616

south 1.4028** 0.2361 1.5625** 0.1698

nonusers

- 0.5858** 0.1466

- 0.5403** 0.1133

lnincome1 0.3327 0.4756 0.9595** 0.3570
mult_bod

- 0.8273** 0.2124

- 0.6300** 0.1797

river1

- 0.0789 0.1748

- 0.1738 0.1194

swim_use

- 0.2342 0.1781

- 0.2697* 0.1223
gamefish 0.2331 0.3894

- 0.0103 0.2454
boat_use

- 0.7251** 0.2389

- 0.3204 0.1715
ln_ar_agr

- 0.2713* 0.1106

- 0.4134** 0.0875
ln_ar_ratio1

- 0.0340 0.0350

- 0.0573 0.0292
sub_proportion 1.0983** 0.2738 0.6066** 0.2003
Qavg2

- 0.0147* 0.0063

- 0.0041 0.0050
lnquality_ch

- 0.7456** 0.0947

_cons

- 1.0388 5.3335

- 6.1401 3.9505

Observations: 140 140

R 2 0.641 0.773Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 20 Table H

- 4 : Regression Results

Parameter Model 1 Model 2

Parameter Estimate Robust Standard
Error Parameter Estimate Robust Standard
Error
Weights Yes Yes

Random Effects: No No

Notes: * denotes p<.05; ** denotes p<.01. All standard errors are clustered by study.



### Application of Model 2 to estimating benefits of improved water quality resulting from the final ELG requires selection of an appropriate value for the water quality change parameter. There are several potential hypotheses that could explain why survey respondents’ implied marginal WTP appears to depend on the overall magnitude of the change in water quality conditions. 106 

### Overall, the meta

- data do not provide any simple way of testing or ruling out these different possibilities. This makes choosing a single value for the water qualit y change parameter in Model 2 difficult. Therefore, EPA conducted a sensitivity analysis, using the range of water quality change scenarios found in the meta

- dataset ( e.g. , water quality change equal to +5 units and +50 units). This analysis characterizes the range of values that would be generated if EPA were to

conduct a well

- designed stated preference survey to elicit marginal WTP for the types of water quality improvements expected under the final ELG and future CWA regulations. Note that a water qualit y improvement of +5 is closer to the levels of water quality improvement in the benefits transfer application

here.



### H.5 Limitations and Uncertainty

The validity and reliability of benefit transfer

— including that based on meta

- analysis

— depends on a variety

of factors. While benefit transfer can provide valid measures of use and nonuse benefits, tests of its performance have provided mixed results ( e.g. , Desvousges et al. 1998; Vandenberg et al. 2001; Smith et al. 2002; Shrestha et al. 2007). Nonetheless, benefi t transfers are increasingly applied as a core component of benefit cost analyses conducted by EPA and other government agencies (Bergstrom and De Civita 1999; Rosenberger and Phipps 2007). Moreover, Smith et al. (2002, p. 134) argue that “nearly all benef it cost analyses rely on benefit transfers, whether they acknowledge it or not.” Given the increasing [or as Smith et al. (2002) might argue, universal] use of benefit transfers, an increasing focus is on the empirical properties of applied transfer method s and models.

Although the statistical performance of the models is good, EPA notes several limitations applicable to both Model 1 and Model 2. These limitations stem largely from information available from the original studies, as well as degrees of freed om and statistical significance. An important factor in any benefit transfer is the ability of the study site or estimated valuation equation to approximate the resource and context under which benefit estimates are desired. As is common, the meta

- analysis model presented here provides a close but not perfect match to the context in which values are desired. Although all of the studies used in the meta

- analysis valued changes in water quality improvements, many studies did not specify the cause of water qua lity impairment in the baseline or focused on causes that are different from the pollutant of concern in the regulation ( i.e. , heavy metals and nutrients). Preliminary models, however, suggest no systematic patterns in WTP associated with such factors, at least in the present meta

- data.

106 Accounting for Scope in the MRM2 Meta

- Regression Model (Abt Associates 201 5).Benefit and Cost Analysis for Steam Electric Power Gen erating ELGs Appendix H: Meta

- Regression Models

September 29, 2015 H

- 21 

### Additional limitations relate to the paucity of demographic variables available for inclusion in the model. The only demographic variable incorporated in the analysis ( lnincome1 ) was not statistically significant. Other demographic variables are unavailable.

The estimated model produces statistically significant coefficients and allows estimation of WTP based on study and site characteristics. However, strictly speaking, model f indings are relative to the specific case studies considered, and must be viewed within the context of the 140

- observation data set, with all the appropriate caveats. Although this represents a fairly standard

- to

- large sample size for a meta

- analysis in th is context (the 51 studies in the analysis gather data from tens of thousands of respondents), it is relatively small relative to other statistical applications in resource and environmental economics. Model results are also subject to choices regarding fu nctional form and statistical approach, although many of the primary model effects are robust to reasonable changes in functional form and/or statistical methods. The rationale for the specific functional form chosen here is detailed above.

As in all cases , results of the meta

- analysis are dependent on the sample of studies available for the given resource change (Navrud and Ready 2007), and may be subject to various selection biases if the available literature does not provide a representative, unbiased pe rspective on welfare estimates associated with resource changes (Rosenberger and Johnston 2007). In this case, however, the Agency took various steps to ameliorate such potential biases, including the incorporation of both peer

- reviewed and gray literature to avoid possible publication biases (Rosenberger and Johnston 2007), and the use of a comprehensive literature review in the attempt to avoid

— as much as possible

— other types of selection biases

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I

- 1 

## Appendix I. Impacts of Steam Electric Pollutants on Aquatic Species

Table I

- 1 : Common C oal

- combustion W astewater P ollutants (adapted from EPA, 2009 d )

Compound Potential Environmental Concern

Arsenic Frequently observed in high concentrations in coal combustion wastewater; causes poisoning of the liver in fish and developmental abnormalities; is associated with an increased risk of
cancer in humans in the liver and bladder.

BOD Can cause fish kills because of a lack of available oxygen; increases the toxicity of other pollutants, such as mercury. Has been associated with FGD wastewaters that use organic
acids for enhanced SO2 removal in the scrubber.

Boron Frequently observed in high concentrations in coal combustion wastewater; leachate into groundwater has exceeded state drinking water standards; human exposure to high concentrations can cause nausea, vomiting, and diarrhea. Can be toxic to vegetation.

Cadmium Elevated levels are characteristic of coal combustion wastewater

- impacted systems; organisms with elevated levels have exhibited tissue damage and organ abnormalities.

Chlorides Sometimes observed at high concentrations in coal combustion wastewater (dependent on FGD system practices); elevated levels observed in fish with liver and blood abnormalities.

Chromium Elevated levels have been observed in groundwater receiving coal combustion wastewater leachate; invertebrates with elevated levels require more energy to support their metabolism and therefore exhibit diminished growth. Copper Coal combustion wastewater can contain high levels; invertebrates with elevated levels require more ene rgy to support their metabolism and therefore exhibit diminished growth.

Iron Leachate from impoundments has caused elevated concentrations in nearby surface water; biota with elevated levels have exhibited sublethal effects including metabolic changes and abnormalities of the liver and kidneys.

Lead Concentrations in coal combustion wastewater are elevated initially, but lead settles out quickly; leachate has caused groundwater to exceed state drinking water standards. Human exposure to high concentrat ions of lead in drinking water can cause serious damage to the brain, kidneys, nervous system, and red blood cells.
Manganese Coal combustion wastewater leachate has caused elevated concentrations in nearby groundwater and surface water; biota with elevat ed levels have exhibited sublethal effects including metabolic changes and abnormalities of the liver and kidneys.
Mercury Biota with elevated levels have exhibited sublethal effects including metabolic changes and abnormalities of the liver and kidneys; can convert into methylmercury, increasing the potential for bioaccumulation; human exposure at levels above the MCL for relatively short periods of time can result in kidney damage. Nitrogen Frequently observed at elevated levels in coal combustion wast ewater; may cause eutrophication of aquatic environments.
pH Acidic conditions are often observed in coal combustion wastewater; acidic conditions may cause other coal combustion wastewater constituents to dissolve, increasing the fate and transport potential of pollutants and increasing the potential for bioaccumulation in aquatic organisms. Phosphorus Frequently observed at elevated levels in coal combustion wastewater; may cause eutrophication of aquatic environments.

Selenium Frequently observed at high concentrations in coal combustion wastewater; readily bioaccumulates; elevated concentrations have caused fish kills and numerous sublethal effects ( e.g. , increased metabolic rates, decreased growth rates, reproductive failure) to aquatic and terr estrial organisms. Short term exposure at levels above the MCL can cause hair and fingernail changes; damage to the peripheral nervous system; fatigue and irritability in humans. Long term exposure can result in damage to the kidney, liver, and nervous and circulatory systems.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I

- 2 Table I

- 1 : Common C oal

- combustion W astewater P ollutants (adapted from EPA, 2009 d )

Compound Potential Environmental Concern

Total Dissolved Solids High levels are frequently observed in coal combustion wastewater; elevated levels can be a stress on aquatic organisms with potential toxic effects; elevated levels can have impacts on agriculture & wetlands.

Zinc Frequently observed at elevated concentrations in coal combustion wastewater; biota with elevated levels have exhibited sublethal effects such as requiring more energy to support their metabolism and therefore exhibiting diminished growth, and abnorm alities of the liver and kidneys.

Table I

- 2 : T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plant s

Species Species Group Vulnerability
Acipenser brevirostrum Fishes High Acipenser medirostris Fishes High Acipenser oxyrinchus desotoi Fishes High Acipenser oxyrinchus oxyrinchus Fishes High

Alasmidonta heterodon Clams High

Alasmidonta raveneliana Clams High

Amblema neislerii Clams High Amblyopsis rosae Fishes High Ambystoma bishopi Amphibians Moderate Ambystoma cingulatum Amphibians Moderate Ambystoma macrodactylum Amphibians Moderate Ambystoma tigrinum Amphibians Moderate

Ammodramus savannarum floridanus Birds Low Anguispira picta Snails Low

Antrobia culveri Snails High

Antrolana lira Crustaceans High Aphelocoma coerulescens Birds Low Athearnia anthonyi Snails High

Batrisodes texanus Insects Low Batrisodes venyivi Insects Low

Boloria acrocnema Insects Low Brachylagus idahoensis Mammals Low Brachyramphus marmoratus Birds Moderate Brychius hungerfordi Insects High

Bufo houstonensis Amphibians Moderate

Cambarus aculabrum Crustaceans High Campeloma decampi Snails High Campephilus principalis Birds Low Canis lupus Mammals Low

Canis rufus Mammals Moderate

Charadrius melodus Birds Moderate

Cicindela dorsalis dorsalis Insects Moderate

Cicindela nevadica lincolniana Insects Moderate Cicindela puritana Insects ModerateBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I

- 3 Table I

- 2 : T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plant s

Species Species Group Vulnerability

Cicurina baronia Arachnids Low

Cicurina madla Arachnids Low

Cicurina venii Arachnids Low Cicurina vespera Arachnids Low Corynorhinus (=Plecotus) townsendii ingens Mammals Low Corynorhinus (=Plecotus) townsendii virginianus Mammals Low Cryptobranchus alleganiensis Amphibians High Cyprinella caerulea Fishes High Cyprogenia stegaria Clams High Dendroica chrysoparia Birds Low

Dendroica kirtlandii Birds Low

Discus macclintocki Snails Low

Dromus dromas Clams High Drymarchon corais couperi Reptiles Low

Elimia crenatella Snails High Elliptio chipolaensis Clams High Elliptio spinosa Clams High Elliptio steinstansana Clams High Elliptoideus sloatianus Clams High Enhydra lutris nereis Mammals Moderate Epioblasma brevidens Clams High Epioblasma capsaeformis Clams High Epioblasma florentina curtisii Clams High Epioblasma florentina florentina Clams High Epioblasma florentina walkeri Clams High Epioblasma florentina walkeri (=E. walkeri) Clams High Epioblasma metastriata Clams High Epioblasma obliquata obliquata Clams High Epioblasma obliquata perobliqua Clams High Epioblasma othcaloogensis Clams High Epioblasma torulosa rangiana Clams High Epioblasma torulosa torulosa Clams High Epioblasma turgidula Clams High

Etheostoma chermocki Fishes High

Etheostoma chienense Fishes High

Etheostoma etowahae Fishes High

Etheostoma fonticola Fishes High Etheostoma nianguae Fishes High

Etheostoma nuchale Fishes High Etheostoma phytophilum Fishes High

Etheostoma scotti Fishes High

Etheostoma sellare Fishes High Eubalaena glacialis Mammals Low Eurycea nana Amphibians HighBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I

- 4 Table I

- 2 : T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plant s

Species Species Group Vulnerability

Fusconaia cor Clams High

Fusconaia cuneolus Clams High Gambusia georgei Fishes High Gammarus acherondytes Crustaceans Moderate Glaucomys sabrinus coloratus Mammals Low Glaucomys sabrinus fuscus Mammals Low Gopherus polyphemus Reptiles Low Graptemys flavimaculata Reptiles High Graptemys oculifera Reptiles High

Grus americana Birds Moderate Grus canadensis pulla Birds Moderate

Hemistena lata Clams High Heraclides aristodemus ponceanus Insects Low Herpailurus (=Felis) yagouaroundi cacomitli Mammals Low Hesperia leonardus montana Insects Low

Heterelmis comalensis Insects High

Juturnia kosteri Snails Low Lampsilis abrupta Clams High Lampsilis altilis Clams High Lampsilis higginsii Clams High Lampsilis perovalis Clams High Lampsilis subangulata Clams High Lampsilis virescens Clams High Lasmigona decorata Clams High Leopardus (=Felis) pardalis Mammals Low Leopardus (=Felis) wiedii Mammals Low Leptodea leptodon Clams High Leptonycteris nivalis Mammals Low Leptoxis ampla Snails High Leptoxis foremani Snails High Leptoxis plicata Snails High Leptoxis taeniata Snails High Lepyrium showalteri Snails High Lioplax cyclostomaformis Snails High Lycaeides melissa samuelis Insects Low Lynx canadensis Mammals Low Margaritifera hembeli Clams High

Medionidus acutissimus Clams High Medionidus parvulus Clams High Medionidus penicillatus Clams High Medionidus simpsonianus Clams High

Mesodon clarki nantahala Snails Low Mesodon magazinensis Snails Low Microhexura montivaga Arachnids LowBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I

- 5 Table I

- 2 : T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plant s

Species Species Group Vulnerability
Microtus pennsylvanicus dukecampbelli Mammals Moderate Mustela nigripes Mammals Low Mycteria americana Birds Moderate Myotis grisescens Mammals Moderate Neoleptoneta microps Arachnids Low Neonympha mitchellii francisci Insects Low Neonympha mitchellii mitchellii Insects Low

Neotoma floridana smalli Mammals Low Nicrophorus americanus Insects Low Notropis cahabae Fishes High Noturus crypticus Fishes High Noturus placidus Fishes High

Obovaria retusa Clams High Odocoileus virginianus clavium Mammals Moderate Odocoileus virginianus leucurus Mammals Moderate Oncorhynchus clarkii stomias Clams High Oncorhynchus clarkii stomias Fishes High Oncorhynchus kisutch Fishes High

Orcinus orca Mammals Low Orthalicus reses (not incl. nesodryas) Snails Low

Palaemonias alabamae Crustaceans Moderate Palaemonias ganteri Crustaceans Moderate

Panthera onca Mammals Low Pegias fabula Clams High

Percina antesella Fishes High

Percina aurolineata Fishes High

Percina rex Fishes High

Percina tanasi Fishes High Peromyscus gossypinus allapaticola Mammals Low Peromyscus polionotus ammobates Mammals Low Peromyscus polionotus niveiventris Mammals Low Peromyscus polionotus phasma Mammals Low

Phoebastria (=Diomedea) albatrus Birds Low

Phoxinus cumberlandensis Fishes High

Picoides borealis Birds Low

Plethobasus cicatricosus Clams High Plethobasus cooperianus Clams High Plethodon nettingi Amphibians Low

Pleurobema clava Clams High

Pleurobema collina Clams High

Pleurobema curtum Clams High

Pleurobema decisum Clams High

Pleurobema furvum Clams High Pleurobema georgianum Clams HighBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I

- 6 Table I

- 2 : T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plant s

Species Species Group Vulnerability
Pleurobema hanleyianum Clams High

Pleurobema marshalli Clams High Pleurobema perovatum Clams High Pleurobema plenum Clams High Pleurobema pyriforme Clams High

Pleurobema taitianum Clams High

Pleurocera foremani Snails High Polyborus plancus audubonii Birds Low Polygyriscus virginianus Snails Low Potamilus capax Clams High

Potamilus inflatus Clams High Pseudemys alabamensis Reptiles High Ptychobranchus greenii Clams High Ptychocheilus lucius Fishes High Puma (=Felis) concolor coryi Mammals Low Pyrgulopsis (=Marstonia) pachyta Snails High Pyrgulopsis neomexicana Snails High Pyrgulopsis ogmorhaphe Snails High Pyrgulopsis roswellensis Snails Low Quadrula fragosa Clams High
Quadrula intermedia Clams High Quadrula sparsa Clams High Quadrula stapes Clams High Rangifer tarandus caribou Mammals Low

Rhadine exilis Insects Low

Rhadine infernalis Insects Low Rhadine persephone Insects Low Rhinichthys osculus thermalis Clams High

Salvelinus confluentus Fishes High Scaphirhynchus albus Fishes High Scaphirhynchus suttkusi Fishes High Sciurus niger cinereus Mammals Low

Somatochlora hineana Insects High

Sterna antillarum Birds High Sternotherus depressus Reptiles High

Strix occidentalis caurina Birds Low

Strix occidentalis lucida Birds Low Stygobromus (=Stygonectes) pecki Crustaceans High Stygoparnus comalensis Insects Low Succinea chittenangoensis Snails Low Sylvilagus palustris hefneri Mammals High Tartarocreagris texana Arachnids Low Texamaurops reddelli Insects Low Texella cokendolpheri Arachnids LowBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I

- 7 Table I

- 2 : T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plant s

Species Species Group Vulnerability

Texella reddelli Arachnids Low Texella reyesi Arachnids Low Toxolasma cylindrellus Clams High

Trichechus manatus Mammals Low Triodopsis platysayoides Snails Low Tryonia alamosae Snails High Tulotoma magnifica Snails High Tympanuchus cupido attwateri Birds Low Typhlomolge rathbuni Amphibians High

Ursus americanus luteolus Mammals Low

Vermivora bachmanii Birds Moderate

Villosa trabalis Clams High Xyrauchen texanus Fishes High Zapus hudsonius preblei Mammals LowBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 1 

## Appendix J. Supporting Data for Impoundment Release Analysis

Table J

- 1: Historical Releases from EPA Survey (U.S. EPA, 2 012d )

Facility State Release
Year Unit

Height (feet) Storage

Capacity (ac

- ft) Category Gallons
Released Capacity

Factor Release Description

Meredosia Power
Station IL 2006 24 700 Big 500 0% other 2006

- less than 500 gallons spilled from the Fly Ash Pond

Chesterfield Power
Station VA 2005 19 740 Big U other 2005 unusual discharge

Cliffside Power
Station NC 2005 38 Big U other 10/7/2005 the Cliffside Steam Station experienced a significant localized flood
event. The floodwaters from the Suck
Creek entered into the retired Units 1

- 4 ash basin, topped the top of the dam and
washed away part of the basin's dike.
Notifications were

Bowen Power Station GA 2002 45 3676 Big U other 7/28/02 sink hole; 11 cubic yards of ash
sediment reached creek.

Bowen Power Station GA 2008 45 3676 Big U other 9/9/08 Stack Failure. Approximately 40 tons of ash left plant property and flowed to near by residential properties and approximately 2 tons of ash reached
Euharlee Creek

Harllee Branch
Power Station GA 2000 83 1050 Big 35000 0% other discharge of slurry 12/1/00

R. M. Schahfer
Power Station IN 1998 4 48 Small U other no details March 1998

Sherburne County
Power Station MN 2007 57 6198 Big 600 0% other 600 Gal spill May 2007

Sherburne County
Power Station MN 2008 41 620 Big 8000 0% other Spring 2008, the piping used to transmit
the fine fraction of the bottom ash from hydraulic dredging of the bottom ash pond broke and approximately 8000 gallons of water and as was discharged over the
Bottom Ash Pond embankment to the groBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 2 Table J

- 1: Historical Releases from EPA Survey (U.S. EPA, 2 012d )

Facility State Release
Year Unit

Height (feet) Storage

Capacity (ac

- ft) Category Gallons
Released Capacity

Factor Release Description

Dave Johnston P ower Station WY 2009 0 3 Small 14400 1% other Jan 2009

- 14,400 gallons of process water
overflowed the canal Naughton Power
Station WY 56 3370 Big 9249802 1% other 55,000 cu. Yds of fly ash was spilled outside the pond boundries

PPL Martins Creek
Power Station PA 2005 43 1085 Big 1E+08 28% other 2005

- Fly Ash spill (100 Million Gal)
estimated 10 acres Colstrip Steam
Electric Station MT 2003 25 245 Big 2700 0% other 3/18/03 spill +/

- 2700

Colstrip Steam
Electric Station MT 1995 88 4370 Big 100 0% other 10/11/95 small spill +/

- 100 gallons C
Cell Colstrip Steam
Electric Station MT 2000 88 4370 Big 50 0% other 8/29/00 small spill +/

- 50 gallons C Cell

Colstrip Steam
Electric Station MT 2006 88 4370 Big 2000 0% other 2/1/06 hole in HDPE liner C Cell +/

- 2000 gallons

Roxboro Power
Station NC 2008 37.5 53 Big U wall breach 2008 Pond Breach

W. H. Weatherspoon
Power Station NC 2001 28 1375 Big U other Breach of an internal dike 2001

Winyah Power
Station SC 2008 30 1700 Big U other 2/14/08 Release of wastewater

- seal
failure

Johnsonville Power
Station TN 2004 30 2702 Big U other Reported release of small quantity of ceneospheres on 03/27/2004 when discharge structure was disturbed during
maintenance.

Kingston Power
Station TN 2003 50 8907 Big U other 11/07/2003 an ash release occurred to land from slough in the Dredge Cell
embankment.

Kingston Power
Station TN 2006 50 8907 Big U other 11/01/2006 an ash release occurred to land from slough in the Dredge Cell
embankment.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 3 Table J

- 1: Historical Releases from EPA Survey (U.S. EPA, 2 012d )

Facility State Release
Year Unit

Height (feet) Storage

Capacity (ac

- ft) Category Gallons
Released Capacity

Factor Release Description

Kingston Power
Station TN 2008 50 8907 Big 1.1E+09 38% wall breach A release into the Emory River occurred on 12/22/2008 from the Dredge Cell
embankment failure. No reports found of
releases from the Main Ash Pond or S

Widows Creek Power
Station AL 2008 115 11709 Big U other Reported release of small quantity of ceneospheres 01/30/2008.

Widows Creek Power
Station AL 2004 115 11709 Big U other Reported release of small quantity of ceneosperes 12/10/2004 due to intense precipitation.

Widows Creek Power
Station AL 2009 150 10961 Big U other An abandoned decant weir in Pond 2B
failed on 01/09/2009 Eagle Valley Generating Station IN 2008 38 415 Big 30000000 22% wall breach 1 levee breache (1/30/2008)

- State
notified. 30 million gallons spill. Eagle Valley Generating Station IN 2007 28 415 Big 30000000 22% wall breach 1 levee breache (2/14/2007)

- State
notified. 30 million gallons spill.

Limestone Electric Generating Station TX 2000 0 50 Small 500 0% other May 19, 2000: Approximately 500 gallons
of water was discharged resulting from a
severe rainfall event. The pH of the discharge was 8.5 su, TSS was 88 mg/1 and Selenium was <0.010 mg/1. The discharge ultimately made its way to Lynn Creek. Event was repor LaCygne Generating
Station KS 2007 45 9298 Big U other July 2007 Due to unusual rainfall events

LaCygne Generating
Station KS 2007 45 9298 Big U other Sept 2007 Due to unusual rainfall events

LaCygne Generating
Station KS 2009 45 9298 Big U other May 2009

- Due to unusual rainfall events

Riverside Generating
Station IA 2002 15 140 Big U other April 14, 2002

- Caused by Mississippi River flooding, fixed using drilled groutBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 4 Table J

- 1: Historical Releases from EPA Survey (U.S. EPA, 2 012d )

Facility State Release
Year Unit

Height (feet) Storage

Capacity (ac

- ft) Category Gallons
Released Capacity

Factor Release Description

Interstate Power & Light Co

- M.L. Kapp Generating
Station IA 2009 10 2 Small U other 3/13/2009, IPL reported to the Iowa DNR an unpermitted release of water from this pond at a location which sealed the pond from a previous discharge channel. This water leakage was immediately repaired
on 3/13/2009.

PSCo Comanche
Station CO 2007 0 12 Small 2500 0% other April 9, 2007

- between 2,000

- 3,000 gallons spilled from a broken pipe

PSCo Valmont
Station CO 2008 0 16 Small 4204.455 0% other Feb 14, 2008

- 25 cu yd spill of bottom ash slurry

Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

1 AL $491,976 2006 $596,000 Shelby County Train
Derailment soybean spill leading to damage of aquatic life (fish, mussels, and snails) Included

2 AL $1,000,000,000 2011 $1,081,352,000 Deepwater Horizon oil spill in Gulf of Mexico Excluded Ocean

$600,000,000 2013 $600,000,000 Excluded Ocean

3 AK $644,017 1997 $1,257,000 M/V Kuroshima oil spill along coastline Excluded Ocean

4 AK $1,000,000,000 1989 $2,968,963,000 Exxon Valdez Exxon Valdez Excluded Ocean

5 AZ $7,000,000 2013* $7,000,000 ASARCO LLC three historic mining sites Excluded CERCLA

6 AZ $6,800,000 2012 $7,031,000 Freeport

- McMoRan Corp
Morenci Mine hazardous substance release Included

7 AR $2,000,000 1987 $6,898,000 Vertac Chemical Corporation Superfund
Site dioxin release from herbicide/pesticide plant Excluded CERCLA

8 CA $16,300,000 1990 $45,789,000 American Trader Oil Spill biological injury (effects on fish and birds) from a 416,598 gallon crude oil spill Excluded OceanBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 5 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

9 CA $14,000,000 2001 $22,133,000 Cantara Loop/ Dunsmuir Chemical Spill spill of 19,000 gallons of an herbicide which damaged habitat
and fish Included

10 CA $30,000,000 2005 $38,481,000 Montrose Chemical long

- time accidental and purposeful discharges of DDT Excluded CERCLA

11 CA $32,300,000 2007 $37,469,000 Cosco Busan Oil Spill spill of 53,000 gallons of bunker
fuel Excluded Ocean

12 CA $5,400,000 1986 $19,761,000 Apex Houston Oil Spill spill of 25,800 gallons of crude oil, killing 10,577 birds Excluded Ocean

13 CA $10,800,000 1988 $34,538,000 Shell/Martinez Oil Spill release of 400,000 gallons of crude
oil into creek Included

14 CA $2,700,000 1991 $7,346,000 Exxon Mobil/Santa Clara River Oil Spill spill of 74,000 gallons of crude oil Included

15 CA $1,400,000 1992 $3,596,000 Avila I Oil Spill spill of 24,200 gallons of crude oil
into Pacific Ocean Excluded Ocean

16 CA $1,400,000 1993 $3,419,000 McGrath Oil Spill release of 87,150 gallons of crude
oil into McGrath Lake and the
Pacific Ocean Excluded Ocean

17 CA $7,100,000 1994 $16,318,000 ARCO/Santa Clara River Oil Spill release of 190,000 gallons of crude
oil into 16 miles of river Included

18 CA $3,625,000 1996 $7,517,000 SS Cape Mohican Oil Spill 96,000 gallons of intermediate fuel oil released, with 40,000 gallons spilling into San Francisco Bay Excluded Ocean bay

19 CA $4,820,000 1997 $9,405,000 M/V Kure/Humboldt Bay Oil Spill bunker fuel oil spill killing birds and affecting saltmarsh, mudflats, kayaking, camping, surfing Excluded Ocean bay

20 CA $1,900,000 1997 $3,707,000 Torch/Platform Irene Oil Spill oil release into ocean, killing 700 birds, affecting sandy and rocky
shoreline habitat Excluded Ocean

21 CA $3,900,000 1998 $7,208,000 T/V Command Oil Spill 3,000 gallons of intermediate bunker oil discharged, damaging birds, shoreline habitat,
recreational beach use Excluded OceanBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 6 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

22 CA $6,710,000 1999 $11,661,000 M/V Stuvyesant/Humboldt Oil Spill 2,000 gallons of intermediate fuel oil spilled into Pacific Ocean, killing 2,405 birds, damaging coastal beaches, shrimp, fish, and
beach use Excluded Ocean

23 CA $358,000 2000 $584,000 East Walker River Oil Spill 3,600 gallons of fuel oil spilled, impacting 15 miles of river Included

24 CA $6,000,000 2000 $9,795,000 Avila II Oil
Contamination numerous pipeline leaks caused underground plume of oil products Excluded Groundwater only

25 CA $1,150,000 2004 $1,573,000 Kinder

- Morgan/Suisun Marsh Oil Spill spill of 70,000 gallons of diesel
into marsh Excluded Ocean

26 CA $950,000 2013* $950,000 Searles Valley
Minerals/Searles Lake (Trona) Hypersaline industrial wastewater discharged into large ponds Included

27 CA $9,000,000 2013* $9,000,000 Guadalupe Oil Field
Contamination pipeline leaks causing 80 plumes
of diluent Excluded Ocean

28 CA $9,000,000 2013* $9,000,000 Iron Mountain Mine
CERCLA Site acid mine drainage over many
decades Excluded CERCLA

29 CA $6,750,000 2013* $6,750,000 New Almaden Mine
CERCLA Site release of mercury in Guadalupe
River watershed and south San Francisco Bay Excluded CERCLA

30 CA $2,850,000 2013* $2,850,000 Chevron/Castro oil and mercury dischargers contaminating intertidal and sub

tidal mudflats Included

31 CA $850,000 2009 $990,000 Dubai Star/San Francisco Bay Spill 400 gallons of intermediate fuel oil spilled, affecting shoreline, birds,
human uses Excluded Ocean

32 CA $22,700,000 2007 $26,333,000 S.S. Jacob Luckenbach Oil Spill 457,000 gallons of fuel released
from a sunken ship, killing 51,569
birds between 1990 to 2003 and

sea otters Excluded Ocean

33 CO $194,000,000 2009 $226,019,000 California Gulch Site superfund site with surface water
and habitat remediation needs Excluded CERCLABenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 7 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

34 CO $27,500,000 1992 $70,639,000 Rocky Mountain Arsenal former weapons and chemicals manufacturing site Excluded CERCLA

35 CO $10,000,000 1994 $22,983,000 Rocky Flats former nuclear weapons manufacturer with plutonium, uranium, volatile organic compounds, metals, radionuclide materials, nitrates, and asbestos
contamination Excluded CERCLA

36 CT $15,000,000 1977 $120,787,000 Housatonic and
Connecticut Rivers PCB contamination from longtime discharges from a power plant Excluded

37 CT $2,700,000 1967 $52,632,000 Quinnipiac River Basin settlement funds to replace lost drinking water source from a
landfill Excluded CERCLA

38 DE $27,500,000 2010 $30,881,000 Athos I discharge of 265,000 gallons into
Delaware River and tributaries Included

39 FL $3,100,000 1999 $5,387,000 Tampa Bay Oil Spill 362,000 gallons of fuel oil and other petroleum products, damaging beaches of shellfish beds Excluded Ocean

40 FL $1,200,000 2006* $1,455,000 Sapp Battery metal contaminations in soil, surface water, groundwater, and
wetlands from battery salvage Excluded

41 FL $77,000 2013* $77,000 Whitehouse Oil Pits Superfund Site assessment costs and restoration of
a wetland Excluded CERCLA

42 FL $3,600,000 1997 $7,025,000 Alafia River discharge of 50 million gallons of process water from a phosphogypsum stack breach Included

43 GA $11,800,000 2006 $14,303,000 R.J. Schlumberger PCB contamination Excluded CERCLA

44 HI $5,800,000 2013 $5,800,000 M/V Cape Flattery oil spill damaging 20 acres of coral reef, fish, algae, sea urchins, and
other reef animals Excluded Ocean

45 ID $498,500,000 2009 $580,775,000 Bunker Hill Mining Superfund Site 100 million tons of mining wastes in a river system; wildlife habitat damage, poisoning of birds and
other wildlife Excluded CERCLABenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 8 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

46 ID $60,000,000 1995 $131,504,000 Blackbird Mine Superfund Site mining damage to surface water and wildlife (Chinook salmon) Excluded CERCLA

47 IL $1,843,000 2011 $1,993,000 Former Indian Refinery damages from various contaminants to groundwater, surface water, soils, and adjacent properties Included

48 IL $450,000 2007 $522,000 Saline Branch and Salt
Fork River 2 fish kills from sudden ammonia
releases Included

49 IL $263,000 2001 $416,000 Marathon Oil Company numerous spills of crude oil and refined petroleum products impacting 29 counties Included

50 IL $154,648 2001 $244,000 Vesuvius USA Corporation spill of an industrial chemical into
3 tributaries of the Embarras River Included

51 IL $105,000 2013* $105,000 Williams Pipeline Company pipeline leak of 10,000 gallons of gasoline and diesel oil into tributary of Salt Creek Included

52 IN $86,000,000 1998 $158,936,000 Grand Calumet dredging after PCB, oil, benzene, cyanide, and heavy metal
contamination from Stell manufacturer, plus restoration of
habitat Excluded CERCLA

53 IN $6,250,000 2000 $10,203,000 White River excessive chemical discharges killing 4.6 million fish; note that the settlement was for $14 million, $6.25 million of which was for

NRD Included

54 IN $600,000 2000 $979,000 American Chemical
Services discharge of chemicals from storage drums Included

55 IN $31,309 1985 $121,000 I. Jones Recycling, Inc. hazardous waste release Included

56 IN $200,000 2009 $233,000 Lakeland Disposal
Landfill waste leakage into groundwater, surface water, and sediments Included

57 IN $597,000 1999 $1,037,000 Waste Inc. Landfill liquid waste drainage into aquifer, creek, and surrounding wetlands IncludedBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 9 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

58 KS $1,200,000 2008 $1,369,000 Cherokee County Superfund Site runoff from zinc and lead mine tailings that entered local streams and contaminated groundwater Excluded CERCLA

59 KY $2,500,000 2007 $2,900,000 Russellville Plant PCB pollution in groundwater, streams, and rivers Included

60 LA $750,000 2011 $811,000 Calcasieu Estuary and Bayou Verdine release of hazardous substances to soil and water, impacting assorted
benthos and other marine resources Excluded CERCLA

61 ME $1,000,000 1996 $2,074,000 Julie N Oil Spill Oil spill; 130 acres of habitat enhancement/acquisition Included

62 ME $125,000 1994 $287,000 F. O'Connor Superfund
Site groundwater damage from PCBs
and solvents Excluded CERCLA

63 ME $930,000 1997 $1,815,000 Maine Yankee petroleum, solvents, and radiological materials from nuclear power plant Included

64 ME $160,440 2013* $160,000 S.D. Warren Facility waste from manufacture of coated paper which contaminated groundwater Excluded Groundwater only

65 MD $2,700,000 2000 $4,408,000 PEPCO Spill oil spill; restoration of wetlands, oyster beds, waterfowl nesting areas, and terrapin habitat Included

66 MD $507,300 2007 $588,000 Spectron, Inc. Superfund
Site contamination and oil from site which migrated to Little Elk Creek Excluded CERCLA

67 MA $20,200,000 1992 $51,888,000 AVX/New Bedford
Harbor water column, sediments, shellfish, birds, anadromous fish, recreational fishing, beach usage Excluded CERCLA

68 MA $1,353,000 1993 $3,304,000 Charles George Landfill landfill pollution, gases, leachate, contamination, migratory birds,
fish Included

69 MA $157,000 1995 $344,000 PSC Resources groundwater and wetlands Included

70 MA $3,100,000 1998 $5,729,000 Nyanza/ Sudbury River surface water (riverine habitat), wetlands, fisheries, other wildlife,
recreational use IncludedBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 10 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

71 MA $30,000 1999 $52,000 Hallmark/ Mystic River surface water (riverine habitat),
recreational use Included

72 MA $19,700,000 2000 $32,159,000 General Electric/
Housatonic River ground and surface water, nesting habitats, recreational fishing and boating, various aquatic organisms
and birds Included

73 MA $30,000 2003 $44,000 Sulfuric Acid Spill/
North River Various aquatic resources, aquatic fish, amphibians, invertebrates, and plant species Included

74 MA $500,000 2004 $684,000 Coal Tar Deposits/ CT
River various aquatic resources, endangered species Included

75 MA $142,000 2004 $194,000 Posavina Oil Spill/
Chelsea Creek coastal land and habitat, salt water vegetation, migratory birds, fish Excluded Ocean

76 MA $1,300,000 2007 $1,508,000 Textron Systems Corporation/Mass Military Reservation Superfund Site groundwater Excluded CERCLA

77 MA $312,500 2008 $357,000 Global/Irving Chelsea Creek Oil Spill surface water, shoreline, wetlands,
salt marsh Included

78 MA $747,000 2010 $839,000 Rubchinuk Landfill Site community use Included

79 MA $1,650,000 2010 $1,853,000 Sutton Brook Disposal Area Superfund Site groundwater, biological resources
and their habitats Excluded CERCLA

80 MA $1,094,000 2011 $1,183,000 Blackburn and Union Privileges Superfund Site groundwater, biological resources
and their habitats Excluded CERCLA

81 MA $6,076,000 2011 $6,570,000 Bouchard B

- 120 Buzzards Bay Oil Spill aquatic and shoreline, ram island shoreline, recreation and shellfish, and piping plovers Excluded Ocean bay

82 MA $875,000 2012 $905,000 GM Assembly Plant in Framingham streambed, banks, and surrounding wetlands; birds, wildlife, and
benthic macroinvertebrates Included

83 MA $4,250,000 2012 $4,395,000 Pharmacia Corp./Bayer CropScience Superfund
Site wetland, river, and lake habitat; fish, turtles, amphibians, and migratory birds Excluded CERCLABenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 11 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

84 MI $807,490 1989 $2,397,000 Verona Well Field groundwater contamination from leaking solvents Excluded Groundwater only

85 MI $26,200,000 1998 $48,420,000 Saginaw River and Bay PCB release into Saginaw River Included

86 MS $3,000,000 1999 $5,214,000 Genesis Pipeline Spill crude oil spill; surface water, sediments, shoreline habitat,
wildlife Included

87 MO $49,000 2013* $49,000 Cominco/Halliburton seven lead and copper metal concentrate spill sites Included

88 MO $20,100,000 2009 $23,417,000 Newton County Mine Tailings Superfund Site releases of cadmium, lead, and zinc to groundwater, surface water, sediments, aquatic and terrestrial plants and organisms, aquatic mammals, fish, aquatic and terrestrial invertebrates, and migratory birds Excluded CERCLA

89 MO $41,200,000 2009 $48,000,000 Southeast Missouri Lead Mining District four mine sites impacting surface
water, geological resources, groundwater, and aquatic and
terrestrial biota Excluded CERCLA

90 MT $138,000,000 1999 $239,823,000 Atlantic Richfield Company decades of mining and mineral processing releasing hazardous
substances Included $169,000,000 2008 $192,848,000 Included $5,900,000 2009 $6,874,000 Included

91 MT $37,000,000 2008 $42,221,000 Mike Horse Dam Dam failure in 1975 due to heavy rains. Contaminated tailings were washed into the Beartrap Creek and the Upper Blackfoot River Included

92 MT $1,600,000 2012 $1,654,000 Silvertip Pipeline Oil Spill 63,000 gallons of oil spilled into
Yellowstone River Included

93 NV $859,528 2013 $860,000 Rio Tinto Mine abandoned copper mine waste disposal in Mill Creek Included

94 NH $1,500,000 2005 $1,924,000 Coakley Landfille Superfund Site contamination of wetlands with various pollutants; restoration of 338 acres of degraded saltmarsh Excluded CERCLABenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 12 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

95 NJ $20,000 2013* $20,000 In re Former Owens

Illinois Closure Site contamination of groundwater Excluded Groundwater only

96 NJ $40,462,000 2013* $40,462,000 In re Phelps Dodge Site hazardous waste discharge into groundwater Excluded Groundwater only

97 NJ $150,000 2009 $175,000 In re Jimmie's Raceway
Service Station contamination of soil and groundwater from gas station Excluded Groundwater only

98 NJ $3,218,700 2009 $3,750,000 Combe Fill South Landfill Superfund Site contamination of groundwater and nearby brook Excluded CERCLA

99 NM $1,000,000 2000 $1,632,000 Sparton Technology Site damage to groundwater from discarded solvents and plating

wastes Excluded Groundwater only

100 NM $1,100,000 2006* $1,333,000 Albuquerque ATSF Site railroad tie treating plant; damages to groundwater and wildlife habitat Included

101 NM $13,000,000 2010 $14,598,000 Freeport McMoRan damages to groundwater, wildlife,
and wildlife habitat Excluded Groundwater only $5,500,000 2012 $5,687,000 Included

102 NM $30,000 2004 $41,000 SOHIO L

- Bar Facility uranium tailings which
contaminated groundwater Excluded Groundwater only

103 NM $7,500,000 2006 $9,091,000 State of New Mexico v
General Electric Company et al. contaminated groundwater from a Superfund site Excluded CERCLA

104 NY $20,300,000 2013 $20,300,000 St Lawrence River in
Massena PCB contamination in a river Excluded

105 NY $12,000,000 2006 $14,545,000 Lake Ontario System release of dangerous chemicals into Lake Ontario System Included

106 OH $13,750,000 2008 $15,690,000 Fernald Uranium
Products uranium products (over 1,000 acres) damaging groundwater Excluded CERCLA

107 OH $5,500,000 2012 $5,687,000 Ashtabula River remediation of contaminated
sediment Excluded CERCLA

108 OH $2,040,000 2006 $2,473,000 Ohio River restoration of damage to mussels, fish, and snails from contamination from a metals company IncludedBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 13 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

109 OK $300,000 2007 $348,000 Double Eagle Superfund
Site contamination of groundwater by
hazardous waste Excluded CERCLA

110 OR $50,000 2013* $50,000 Whitaker Slough Cleanup contamination of Whitaker Slough by electroplating wastewater Included

111 OR $100,000 2013* $100,000 Johnson Lake overflow from settling ponds and stormwater discharges Included

112 OR $300,000 2009 $350,000 Union Carbide Site Claims related to waste from
carbide and ferroallow processing contaminating the Columbia Slough. Included

113 PA $21,000,000 2009 $24,466,000 Palmerton Zinc Superfund Site injuries to aquatic and terrestrial
resources from zinc and other
metals Excluded CERCLA

114 PA $7,350,000 2006 $8,909,000 Sinnemahoning Creek
Watershed spill of sodium hydroxide from train derailment, damaging several
waterbodies Included

115 RI $8,000,000 2013* $8,000,000 North Cape Oil Spill 828,000 gallons of home heating oil spilled, killing at least 9 million lobsters, thousands of birds, and millions of clams, crabs, and fish Excluded Ocean

116 RI $1,415,000 2005 $1,815,000 Calf Pasture Point and
Allen Harbor Landfill discharge of chemical wastes (chlorinated hydrocarbons and VOCs) into groundwater Excluded Groundwater only

117 RI $6,000,000 2011 $6,488,000 Buzzard's Bay oil spill injuring shoreline and aquatic resources, piping plovers,
and recreational uses Excluded Ocean bay

118 RI $750,000 2013 $750,000 Davis Liquid Waste Superfund Site contamination of groundwater Excluded CERCLA

119 SC $121,000 2012 $125,000 Cooper River/Charleston
Harbo release of 12,500 gallons of fuel oil affecting shoreline habitats, sediments, migratory birds, shellfish beds, and recreational shrimp baiting IncludedBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 14 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

120 SD $4,000,000 2006* $4,848,000 South Dakota v Homestake Mining Company damage to surface and groundwater from metals (from mining) Included

121 TN $543,203 2002 $831,000 Obed Wild and Scenic
River Site oil spill and fire in tributaries of
the Obed Wild and Scenic River Included

122 TN $50,000 2010 $56,000 U.S. Department of Energy's Oak Ridge
Reservation damages to fishing from release of
hazardous substances and radioactive compounds Included

123 TX $3,120,000 2012 $3,226,000 Malone Service Co. Disposal Facility hazardous waste material that contaminated groundwater and migrated to Galveston Bay Excluded CERC LA

124 UT $37,000,000 1995 $81,094,000 Southwest Jordan Valley $28 million in restoration of groundwater, damaged by historic mining, $9 in compensation Included

125 UT $2,580,000 2007 $2,993,000 Ensign

- Bickford Trojan Facility discharges from explosives manufacturing facility into groundwater (3 mile plume) Excluded Groundwater only

126 UT $3,500,000 2011 $3,785,000 Red Butte Creek Oil Spill pipeline rupture releasing oil into
creek and other waters Included

127 VA $3,700,000 2003 $5,399,000 Tazewell County Spill 1,300 gallons of a rubber accelerant, which damaged endangered species of freshwater
mussels Included

128 VA $2,500,000 2001 $3,952,000 Powell River six million gallons of coal slurry release to river watershed (20 miles downstream), impacting fish, endangered mussels, aquatic habitat, bats, and migratory birds Included

129 WA $5,000,000 2006* $6,061,000 Elliot Bay/ Duwamish
River habitat development and restoration after damage from sewer discharges Included

130 WA $5,500,000 2005 $7,055,000 Skykomish Facility discharge of diesel fueil which
leaked to water table IncludedBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data

September 29, 2015 J

- 15 Table J

- 2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD

Amount Year Updated NRD
Amount Case Notes Included

or Excluded Reason for

Exclusion

131 WA $9,000,000 1994 $20,685,000 Tenyo Maru release of 354,800 gallones of fuel oil and 97,800 gallons of diesel fuel, affecting coastal waters and shorelines and killing 4,300
seabirds Excluded Ocean

132 WA $512,857 2008 $585,000 Crystal Mountan Emergency Generation Facility release of 18,200 gallons of diesel
fuel into creek Included

133 WV $2,040,000 2006 $2,473,000 Ohio River restoration of native freshwater mussels, snails, and fish in Ohio
River Included

134 WV $500,000 2009 $583,000 Consol Energy/Dunkard
Creek discharge of mining wastewater containing chloride, resulting in an algae bloom that killed thousands of fish, mussels, and amphibians Included

135 WI $35,000,000 2013* $35,000,000 Fox River/Green Bay contamination from PCB and other discharges from paper mills Excluded CERCLA

136 WI $1,900,000 2012 $1,965,000 Ashland Lakefront contaminated sediments Included

137 WY $50,000 2013* $50,000 Gasoline Spill restocking a surface water after a fish kills from a gasoline spill IncludedBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 1 

## Appendix K. Methodology for Benefits from Avoided Dredging Costs

The following subsections describe EPA’s methodology for estimating sediment dredging benefits by applying the unit dredging costs to changes in the volume of sediment depo sited to navigable waterways and reservoirs under the five regulatory options. The section is organized as follows:

 Review and analysis of historical dredging data including dredging locations, intervals, and costs;

 Estimation of sediment deposition to dre dged waterways and reservoirs using SPARROW; and

 Estimation of dredging costs savings under regulatory options based on changes in sediment deposition and unit cost of dredging.

EPA notes that the methodology presented here focuses solely on avoided dredgi ng costs based on the volume of sediment. Due to data limitations, the analysis does not quantify environmental benefits of reducing the frequency of maintenance dredging, nor does it account for potentially lower costs associated with disposing of dredged material spoils due to reduced discharge of toxic metals.



### K.1 Review and Analysis of Historical Dredging Data

EPA used data from the Unite d States Army Corps of Engineers (USACE) Dredging Information System (USACE, 2013) to analyze baseline navigational dredg ing activity and to estimate the cost per cubic yard of sediment dredged. The system catalogs all USACE dredging contracts and USACE

- conducted dredging jobs from 1998 to 2012, including the location of dredging activities, start and stop dates, the volume of sediment dredged, and the cost of the dredging job. The system does not report separate cost components, but costs typically include ( Sohngen & Rausch, 1998 ) :

 Cost of dredging sediment from the waterway’s channel bed and loading onto a boat,

 Cost of transporting dredged material to a disposal facility, and

 Cost of confining or disposing of the dredged material.

EPA reviewed available information about sediment removal methods at existing U.S. reservoirs. The revie w indicated that dredging is a practical and common approach to sediment removal in existing reservoirs. With the exception of draining a reservoir and excavating settled sediment (which is typically more expensive and less common than dredging), dredging is the only feasible option for sediment removal in many reservoirs (Morris and Fan, 1997) . Other methods to counteract sedimentation o r reclaim capacity at existing reservoirs

include:

 Sediment routing

– a group of techniques that allow sediment

- laden water to pass around or through a reservoir without allowing the sediment to settle, optimized to address sediment

- laden flows from

events such as storms and floods (Morris and Fan, 1997) . EPA was not able to find any information on the prevalence or effectiveness of these techniques. 107



###  Flushing

– the scouring of accumulated sediment from a reservoir by partially or fully draining it and allowing the erosive force of the draining water to carry sediment through and downstream of the reservoir. The practice does not appear to be very common in the United States as it creates

107 Including sediment pools during initial construction and maintaining these pools can also reduce sediment deposition (Crowder 1987).Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 2 

### extraordinarily high sediment concentrations downstream of the reservoir

, 108 

### and the reservoir may still require dredging (Morris and Fan, 1997)

. Theoretically, reductions in sediment loadings could delay the construction of new reservoir capacity and associated construction costs, including land for the reservoir itself, embankments, mit igation lands, appurtenances to the dam (if applicable), such as pump stations, and pipelines to connect the reservoir to the treatment plant or raw water users. In practice, it may not be feasible to construct new capacity in many cases due to space const raints or adverse ecological effects associated with reservoir construction. Furthermore, the ELG regulatory options result in only marginal changes in sediment deposition to existing reservoirs that are unlikely to be of sufficient magnitude to influence decision

- making regarding capacity expansion.

Based on this review, EPA focused its benefits analysis for reservoirs on dredging over other capacity reclamation approaches due to the frequent use and broad feasibility of dredging

. Given likely similarities in cost components for navigational dredging and reservoir dredging, EPA used regional estimates of cost per cubic yard of sediment dredged based on the USACE Dredging Information System as inputs for its analysis of avoided reservoir dredging costs. The Agency recognizes that dredging costs are highly variable and driven my factors including costs for dewatering sites, weather, topography, and characteristics of bottom sediments, and that at some sites dredging costs could exceed the costs of other altern atives, potentially including the unit cost of constructing new capacity. 109 

### EPA notes that the volume of sediment that can be dredged from reservoirs may also affected by the availability of funds or nearby disposal sites ( Morris & Fan, 1997 )

.

K.1.1 Dredging Location

, Recurrence Interval , and Dredging Volumes

Each observation in the Dredging Info rmation System corresponds to a given date range and location at which dredging occurred, referred to here as “dredging occurrence.” Many locations have multiple dredging occurrences because recurrent dredging is may be necessary to maintain navigability. EPA uses the term “dredging job” to refer to multiple dredging occurrences at the same location. For each dredging job, EPA

identified:

 The number of occurrences from 1998 to 2012 . EPA merged dredging occurrences at the same

location that were less than 30 days apart because these may be continuations of the same dredging occurrence, rather than a new dredging occurrence. If determined to be a continuation of prior occurrence, EPA summed the volume of sediment dredged and costs of the records to generate a single observation in the database.

 Average dredging volume from 1998 to 2012

. EPA divided the total quantity of sediment dredged for each job (single location) over the past 15 years by the number of occurrences of that job to calculate an average quanti ty of sediment dredged for an occurrence of that job. EPA assumed that this quantity of sediment would be dredged each time the job occurs in the future under the baseline scenario, and

that it would be reduced due to the final ELGs

108 Concentration can sometimes exceed 1,000,000 mg/L and thus may require special permissions ( Morris and Fan, 1997 )

109 Alan Plummer Associates (2005) compared the cost of dredging to the costs of constructing additional reservoir capacity for reservoirs in Texas and found that dredging unit costs are at least twice that of securing storage in new reservoirs. New reservoir costs are approximately $1 for each cubic yard of water stored in the conservation pool (2011$, updated based on the construction cost index ), whereas dredging costs for large

- sized dredging projects are approximately $2 per cubic yard (2011$, updated based on the construction cost inde x ) and, depending on variability, could cost more than two times that amount.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 3 

###  The time elapsed between dredging occurrences . EPA calculated an average frequency of recurrence for each dredging job in years by dividing 15 (the number of data years, 1998

- 2012) by the number of occurrences of that job.

The number of dredging jobs in the system varies b y region, and is likely to be influenced by the size of the region, the number of navigable waterways, and their economic importance.

Where the USACE Dredging Information System did not contain cost or quantity of sediment dredged for a listed dredging oc currence, EPA estimated the missing information from other jobs. The Agency
calculated 10 th 

### , 50 th 

### , and 90 th 

### percentile costs and quantity boundaries and used these to fill in all incomplete records in each EPA region. EPA used the 10 th 

### and 90th percentile cost and amount dredged estimates in the low and high total dredging cost estimates, respectively. The cost data were adjusted to 2013$ using the construction cost index.

K.1.2 Determining Affected Navigational Dredging Jobs and Unit Costs

For this analysis, EP A mapped each navigational dredging job with latitude

- longitude coordinates to the

nearest reach modeled in SPARROW (E2RF1 reaches), within a one

- mile radius

. For jobs for which latitude

longitude coordinates were not provided, EPA used alternate job locat ion information such as the name of the job (usually the waterway dredged) and the USACE district that performed the job. 110 

### EPA excluded dredging jobs that are greater than 1 mile from a SPARROW reach from the analysis. 111



### This approach matched 29 unique dre dging jobs and 98 dredging occurrences to analyzed E2RF1 reaches, equivalent to 0.8 percent of the dredging occurrences with coordinates reported in the Dredging Information System. The remaining 99 percent of occurrences were eit her greater than 1 mile fr om a modeled reach

. Table

K

- 1 summarizes dredging jobs and recurrence intervals in the affected reaches. The recurrence interval ranges

from 1 to 15 years across all affected reaches, with an average of 9.6 years

.
Table K

- 2 summarizes the average cost of d redging. Costs vary considerably across affected reaches, from approximately $$1.59 per cubic yard at Establishment Bar in North Carolina to $28.08 per cubic yard at Bonum Creek in Virginia. The average unit cost of dredging for the entire coterminous Unit ed States is $$5.56 per cubic yard.

110 EPA reviewed to the data to identify cases where latitude

- longitude coordinates appear to have been misreported based on district and reach information. Where possible, EPA po pulated missing coordinates by interpolating from other occurrences of the same dredging job

. 111 To identify the nearest reach segment, EPA used an unprojected version of the ERF 1.2 (Enhanced Reach File) from USGS. For this analysis, EPA researched latitu de

- longitude coordinates for jobs where they were not provided and linked them to Reach File Version 1.0 (RF1) reaches. Each latitude/longitude of interest was matched to the nearest point in the ERF 1.2 universe of points using a spherical model of the ea rth and a standard haversine distance formula. No reach types were excluded from consideration in the nearest reach
calculationBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 4 Table K

- 1 : Navigational Dredging Jobs and Recurrence Intervals from 1998 to 2012 within Affected Reaches

Number of Affected Dredging Jobs Number of Recurring
Jobs Number of Single

Occurrence Jobs Average Interval for

Single Occurrence and Recurring Jobs (years) Average Interval for Recurring Jobs (years)



### 29 15 15 9.6 3.7

Note: Includes dredging jobs with latitude

- longitude coordinates that could be mapped to affected E2RF1 reaches

. Source: Dredging Information System ( USACE, 2013 )

.

Table K

- 2 : Historic Dredging Costs from 19 9 8 to 2012 within Affected Reaches

Number of
Affected Reaches Total Sediment

Removed (millions of cubic yards) Total Cost (millions of
201 3 $) Average Cost per cubic yard (201 3 $)



### 29 72.0 $754.0 $10.5

Note: Only includes jobs EPA was able to map to affected reaches

. Source: Dredging Information System ( USACE, 2013 )

.



### K.1.3 Determining Reservoir Dredging Locations

EPA relied upon the “ r eservoir” flags within the E2RF1 dataset to identify reservoir locations. SPARROW models reservoirs as impoundments located on the main reach network. For the purposes of this analysis, EPA assumed, consistent with Crowder ( 1987 ) , that all sediment entering reservoirs must be removed in order to maintain current water storage capacity.



### K.2 Sediment Deposition in Navigable Waterways and Reservoirs

EPA estimated annual sediment deposition to waterways using sediment ation outputs from SPARROW. EPA assumed that all sediment deposited within historically dredged waterways will be dredged at some future point. Likewise, EPA assumes that all sediment deposited in the affected reaches with reservoirs will also be dredged.

As described in Chapter 4 , the SPARROW outputs reflect changes in annual sediment deposition based on the changes in sediment loadings under the fi nal ELGs. EPA assumed that reduced deposition starts in 2021, which is the midpoint of the period during which steam electric plants are expected to implement changes to

meet the final rule limitations and standards.



### K.3 Estimating Dredging Costs under Baseli ne and Regulatory O ptions

Benefits under the final ELGs are calculated for each year from 2021 to 204 2 as the difference between baseline dredging costs and post

- compliance dredging costs

. Each waterway or reservoir will have unique

benefits based on sedim ent deposition, dredging frequency, and the unit cost of dredging. Dredging costs only occur in years when the waterbody or reservoir is projected to be dredged.

K.3.1 Estimating Annualized Dredging Costs for Navigational Waterbodies

For each dredging job locati on, EPA assumes that future dredging occurrences will happen at the same frequency as in the past. The volume of sediment removed in each dredging occurrence is equal to the amountBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 5 

### of sediment deposited in that reach since the prior occurrence at that job location. For example, if dredging occurs every two years at given job location, then the first analyzed occurrence project for the second year of the analysis period (202 2 ). The volume of sediment dredged in that occurrence is the sum of sediment deposite d in the current year and prior year (202 1 and 202 2 ). The dredging cost incurred in 202 2 is the product of the cubic yards dredged and unit cost per cubic yard. By the end of the 204 2 , the reach will have been dredged ten times.

Equation J

- 1 presents the calculation of annual dredging costs for navigational waterways for the baseline and regulatory options. EPA discounted the costs following the analytic framework described in Chapter 1

.

Equation J

- 1 AV = ∑ ( Q b

, t × ( 1

− R ) × C ( 1 + d ) t

− 2015 

### ) I 

### × ( d × ( 1 + d ) n
( 1 + d ) n + 1

− 1 

### )

Where:

AV = Annualized value

I = The index for the dredging occurrence. The number of occurrences is

based on the total number of periods (22) and the recurrence interval for the dredging job. The recurrence interval is calculated by dividing 15

years by the number of occurrences of the job in the USACE dredging

data. Q b 

### = C ubic yards of dredged materials under the baseline scenario in a given

year t

T = Year of dredging occurrence

R = P ercentage of cubic yards of dredge material remaining under the

regulatory scenario relative to baseline

C = C ost per cubic yard of sediment dredged for the dredging job location

(2013$)

D = A nnual discount rate. EPA used both 3 percent and 7 percent, in

accordance with OMB guidance ( Office of Management and Budget,

2003 )

N = Number of periods for annualization (24 years for this benefits analysis)

EPA conducted a sensitivity analysis for navigational dredging by varying assumptions for projected future dredging occurrence, generating low, medium, and high estimates for navigational dredging:

 For medium and high estimates, EPA assumed that single occurre nce dredging jobs occur once every 15 years ( i.e. , the number of data years). Single occurrence jobs are dropped from future projections

for the low estimate.

 For low and medium estimates, dredging is interpreted as occurring at the end of each interval ( i .e. , 202 2 in the example described above ). For the high estimate, EPA assumes that dredging occurs at the beginning of the interval, rather than the end ( e.g. , 202 1 ). This second approach tends to generate greater discounted benefits because jobs occur soo ner and are discounted less.

 EPA varied costs for jobs lacking actual cost values and dredging volumes in the Dredging Information System. EPA assigned 10th, 50th, and 90th percentile costs for low, mean, and high estimates, respectively.

 For the mean esti mate, EPA assumed a minimum recurrence interval of 90 days. Occurrences that began less than 90 days from the end of the prior occurrence at the same job location were consideredBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 6 

### to be continuation, and combined in the data. EPA used intervals of 30 and 18 0 days for the low and high estimates, respectively.

These assumptions are summarized in Table K

- 3

.

Table K

- 3 : Parameters Used in Sensitivity Analysis of Avoided Dredging Costs

Parameter
Value Treatment of Single

Occurrences Treatment of Interval Percentile of Cost
Estimates Used for Jobs Lacking Cost
Data Minimum Recurrence

Interval

Low Excluded Start 10th Percentile 30 days

Mean Included End 50th Percentile 90 days High Included End 90th Percentile 180 days



### K.3.2 Estimating Annualized Dredging Costs for Reservoirs

The frequency of reservoir dredging is highly site

- specific, depending on many factors including the average sediment concentration of the influent river or stream, the flow regime, the size of the reservoir and excess
storag e capacity, and any sediment routing practices. For this analysis, EPA chose a general frequency of reservoir dredging based on information presented by the USACE in a Final Dredged Material Management Plan and Environmental Impact Statement for reservoirs in Washington ( USACE, 2002 ) . The report states that “dredging cycles may vary from 2 to 10 years” ( USACE, 2002, p. 66 ) . EPA used these frequencies as high and low estimates and 6 years as a mean estimate. This approach provides a range of benefits estimates to account for uncertainty in t he frequency of reservoir dredging.

EPA was unable to identify a comprehensive source of cost data for reservoir dredging. 112 

### The Agency used the average unit cost of dredging from the analysis of USACE Dredging Information System Data grouped by EPA region

. Because this cost is given per cubic yard, the sediment attenuation in reservoirs given by SPARROW will be converted from kilograms to cubic yards using a sediment density of 1.5 g/cm 3 

### (Hargrove 2007). This translates to a conversion of 1,147 kilograms p er cubic yard. Equation K

- 2 summarizes the

calculation of annualized avoided costs for an affected reservoir. The total annualized avoided cost is the sum of annualized cost savings at all affected reservoirs.

Equation K

- 2 : AC r 

### = ∑ [ ( Q r
, b

, t − Q r
, s
, t )

1 147 × C
( 1 + d ) t

− 2014 

### ]

I 

### × [ d × ( 1 + d ) n
( 1 + d ) d + 1

− 1 

### ]

Where:

AC = Annualized avoided cost of dredging all sediment settling in

reservoir r

r = Reservoir reach ID number

I = The assumed interval in years of reservoir dredging; varied

between 2, 6, and 10 Qb = Quantity of sediment present at baseline (kg) Qs = Quantity of sediment present under the regulatory option (kg) 1,147 = Number of kilograms per cubic yard of sediment

112 Some limited information on the costs of reservoir dredging is available in the literature. For example, Crowder (1987) provi des a unit cost but provides not empirical basis for the estimate.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 7 

### C = Regional average of historic dredging job cost per cubic yard,

1998

- 2012.

r = ELG regulatory option (Option A

– Option E)

t = Year of the dredging occurrence

d = Annual discount rate. EPA used both 3 percent and 7 percent.

n = Number of periods for annualization (24 years for the benefits

analysis)



### K.4 Results

Benefits under each re gulatory option are equal to avoided costs, calculated as the difference in total annualized dredging costs at baseline and under regulatory options. The subsections below summarize navigational dredging and reservoir dredging benefits.

K.4.1 Navigational Dredging Benefits

Table K

- 4 presents estimates of baseline sediment dredging from 202 1 to 204 2 and low, mean, and high cost estimates. Total baseline navigational dredging costs range from $ 36 thousands to $ 48 thousands per year, using a 3 percent discoun t rate, and between $ 29 to $ 42 thousands using a 7 percent discount rate. Table K

- 5 presents estimated benefits for navigational dredging for the five regulatory options. Annualized benefits for Option D are less than one thousand, with both 3 and 7 percent discount rate s

.

Table K

- 4 : Annualized Dredging Costs at Affected Reaches under the Baseline (Thousands of 201 3 $)

Total Sediment Dredged
(millions cubic yards) Costs at 3% discount rate
(thousands of 201 3 $ per year) Costs at 7% discount rate
(thousands of 2013 $ per year)

Low Mean High Low Mean High Low Mean High

96.4 97.3 125.5 36. 2 36.5 47. 9 29. 2 29.4 42. 1

Source: EPA analysis, 2015.

Table K

- 5 : Annualized Benef its from Reduced Dredging Costs (Thousands of 201 3$)

Regulatory Option Total Reduction in Sediment

Dredged (Baseline

– Option ; cubic yards) 3% discount rate
(thousands of 201 3 $ per year) 7% discount rate
(thousands of 201 3 $ per year)

Low Mean High Low Mean High Low Mean High 

### Option A 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

### Option B 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

### Option C 763. 8 819. 6 2,606. 4 0.4 0.4 0.9 0.3 0.3 0. 9 

### Option D 1,496.7 1,735

. 0 6,648.3 0.8 0.8 2.2 0.6 0.6 2.0 

### Option E 3,122.7 3,361.5 8,530. 1 1.5 1.5 3.0 1.1 1. 2 2.7

Source: EPA analysis, 2015.



### K.4.2 Reservoir Dredging Benefits

Table K

- 6 presents the total amount of sediment that is estimated to be dredged in 20 21 from reservoirs , and the estimated annualized cost of dredging under the baseline scenario, including low, mean, and high estimates. Estimated dredging costs for the reservoirs range betw een $ 477.6 million and $ 577.8 million with aBenefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 8 

### three percent discount rate and $ 344.4 million and $ 480.0 million with a seven percent discount rate under the

baseline scenario.

Table K

- 6 : Annualized Reservoir Dredging Costs under the Baseline (Millions 201 3$)

Total Sediment Dredged
( millions cubic yards) (202 1

-

204 2 ) 3% Discount Rate ($/year) 7% Discount Rate ($/year)

Low Mean High Low Mean High Low Mean High

2,934.6 2,641. 2 3,228.1 477. 6 469.5 577.8 344. 4 378.9 480.0

Source: EPA analysis, 2015.



### The difference between the anticipated dredging costs under the baseline and a particular regulatory option represents the avoided costs of that regulatory option

. Table K

- 7 present s reductions in sedimentation and subsequent avoided costs from reduced reservoir dredging for each regulatory option , including low, mean, and high estimates under these regulatory options. Because the range of estimates is relati vely small between the low and high estimates, the values presented below in the discussion below are mean estimates unless

otherwise stated.

Avoided costs from a reduction in reservoir sedimentation vary depending on the regulatory option , the

assumed fr equency of reservoir dredging, and the discount rate. Annualized benefits for Option D are less than two thousand dollars with both 3 and 7 percent discount rates

.

Table K

- 7 : Total Annualized Benefits of Reduced Reservoir Dredging (2013 $)

Regulatory Option Total Reduction in Sediment

Dredged (cubic yards) (202 1

-

2040) a 3% Discount Rate (thousands
of 201 3 $ per year) 7% Discount Rate thousands of
201 3 $ per year)

Low Mean High Low Mean High Low Mean High



### Option A 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

### Option B 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

### Option C 8,106.0 7,295.4 8,916.6 1.6 1.6 2.0 1.2 1.3 1.6 

### Option D 9,624.1 8,661.7 10,586.5 1.9 1.9 2.3 1.4 1.5 1.9 

### Option E 11,114.0 10,002.6 12,225.4 2.2 2.2 2.7 1.6 1.8 2.2

Source: EPA analysis, 2015.



### K.5 Limitations and Uncertainties

Table K

- 8 summarizes key uncertainties and limitations for the analysis of sediment dredging benefits. Note
that th e SPARROW model used to estimate sediment deposition also ha s a number of limitations , descri bed

in Environmental Impact and Benefits Assessment for Final Effluent Guidelines and Standards for the Construction and Development Category (U.S. EPA 2009c)

.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 9 Table K

- 8

. Limitations and Uncertainties in the Analysis of Avoided Costs of Navigational Waterway Dredging

Issue Effect on Benefits
Estimate Notes

Lack of standardized job
names in USACE Database Underestimate The USACE dredging database identifies dredging jobs by

name, usually the name of the dredged waterbody, but lacks standardized naming conventions. It is possible that the same waterbody is dredged under different job names. For the low cost estimates, this may result in the exclusion of dredging job names that only appear once in the database, but in fact were carried out in the same waterbodies as a differently named job. This effect would tend to underestimate benefits in EPA’s low
estimates. Analysis restricted to jobs reported in USACE
Database for 199 8 to 201 2 Underestimate The USACE database is limited to USACE dredging contracts from 1998 to 2012. It does not capture dredging jobs contracted by other organizations or jobs occurring before 1998 or after
2012.

Lack of latitude and longitude data in USACE
Database Underestimate Many dredging occurrences lack or have incomplete latitude coordinates. As a result, EPA was only able to map about 71 percent of all dredging occurrences with records in the data

. EPA di d not attempt to use other methods, such as Google Earth, to map dredging locations to the E2RF1 reaches due to
resource constraints. The result is a downward bias in benefits estimates because the analysis excludes some dredging jobs that may benefit unde r the final rule

. Do not estimate benefits to waterways that are not dredged Underestimate EPA’s dredging analysis is limited to navigable waterways that have been dredged in the past and reservoirs flagged within the
E2RF1 dataset. Other waterbodies not identified by these data could require dredging in the future and benefit from sediment reductions under the proposed rule. Thus, EPA’s estimates may
be underestimated.

Omission of reservoirs
located off E2RF1 channel s Un derestimate The benefits analysis for modeled watersheds explicitly omits any reservoirs that are not located on the E2RF1 network. The omission of other reservoirs is likely to bias estimated reservoir dredging benefits downward. Lack of site specific d ata on sediment density Uncertain EPA used a single sediment density estimate to convert between sediment mass and volume. This may reduce the accuracy of resulting benefits estimates because sediment density is related soil type in the area and is not un iform. The direction of this potential bias is uncertain.

Lack of data on the frequency of reservoir dredging and the amount of sediment dredged Uncertain There is significant uncertainty as to the types of reservoirs that are dredged and the unit cost of dredging. The appropriateness of benefits estimates for reservoirs is conditional on assumption that water storage volume will be maintained. Actual benefi ts may diverge from estimates presented here if reservoirs take response actions other than dredging to address sediment loads. Assumption that excess
sediment is removed from reservoirs by dredging rather than building new reservoir capacity Uncertain E PA’s analysis of reservoir benefits assumes that all excess sediment is removed by dredging. Reservoir capacity could be replaced with new reservoirs in some cases. The unit cost of constructing new reservoirs may be higher or lower than dredging depending on site

- specific characteristics.Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K

- 10 Table K

- 8

. Limitations and Uncertainties in the Analysis of Avoided Costs of Navigational Waterway Dredging

Issue Effect on Benefits
Estimate Notes

Omission of natural water storage facilities from the analysis Underestimate The SPARROW model does not take into account sediment
build

- up in natural water storage facilities such as glacial lakes and ponds. Any activi ty to mitigate sedimentation in these waterbodies is not included in this benefits analysis. This may
bias benefits estimates downward.