# Regulatory Impact Analysis

Petroleum Refineries New Source Performance Standards Ja

June 2012

U

. S

. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Planning and Standards Research Triangle Park, NC 27711

### ii CONTACT INFORMATION

This document has been prepared by staff from the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency and RTI International . Que stions related to this document should be addressed to Alexander Macpherson, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, C445

- B, Research Triangle Park, North Carolina 27711 (email: macpherson.alex@epa.gov ).

ACKNOWLEDGEMENTS

In addition to EPA staff from the Office of Air Quality Planning and Standards, personnel from RTI International contributed significant data and analysis to this document
under contract number EP

- D

- 06

- 003

.

### iii TABLE OF CONTENTS

T ABLE OF 

### C ONTENTS



### ................................ ................................ ................................ .................... III


### L IST OF 

### T ABLES



### ................................ ................................ ................................ .............................. V


### L IST OF 

### F IGURES



### ................................ ................................ ................................ ........................... VI


### 1. E XECUTIVE 

### S UMMARY



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

- 1

1.1 I NTRODUCTION

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

- 1 1. 2 R ESULTS

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

- 2 1. 3 R EFERENCES

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

- 6


### 2 I NDUSTRY 

### P ROFILE



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

- 1

2.1 I NTRODUCTION

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

- 1 2.2 T HE S UPPLY S IDE

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

- 1 2.2.1 Production Process, Inputs, and Outputs

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

- 1 2.2.2 Emissions and Controls in Petroleum Refining

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

- 9 2.2.3 Costs of Production

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

- 10 2.3 T HE D EMAND S IDE

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

- 15 2.3.1 Product Characteristics

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

- 15 2.3.2 Uses and Consumers

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

- 15 2.3.3 Substitution Possibilities in Consumption

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

- 17 2.4 I NDUSTRY O RGANIZATION

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

- 17 2.4. 1 Market Structure

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

- 17 2.4.2 Characteristics of U.S. Petroleum Refineries and Petroleum Refining Companies

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

- 21 2.5 M ARKETS

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

- 30 2.5.1 U.S. Petroleum Consumption

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

- 30 2.5.2 U.S. Petroleum Production

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

- 36 2.5.3 International Trade

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

- 37 2.5.4 Market Prices

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

- 39 2.5.5 Profitability of Petroleum Refineries

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

- 41 2.5.6 Industry Trends

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

- 43 2.6 R EFERENCES

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

- 45


### 3 NSPS R EGULATORY 

### O PTIONS 

### , C OSTS AND 

### E MISSION 

### R EDUCTIONS FROM 

### C OMPLYING
WITH THE 

### NSPS

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

- 1

4 E CONOMIC 

### I MPACT 

### A NALYSIS 

### : M ETHODS AND 

### R ESULTS



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

- 1

4 .1 INTRODUCTION .......................................................................................................4

- 1 4.2 C OMPLIANCE COST ESTIMATES

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

- 1 4.3 SMALL BUSINESS IMPACT ANALYSIS.........................................................................4

- 4 4.3.1 Small Entity Economic Impact Measures...................................................

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

- 4 4.3.2 Small Entity Economic Impact Analysis......................................................

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

- 5 4.4 REFERENCES

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

- 8


### 5 E XECUTIVE 

### O RDERS



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

- 1

5.1 UN FUNDED MANDATES

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

- 1 5.2 ENVIRONMENTAL JUSTICE

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

- 1 5.3 SIGNIFICANT ENERGY ACTIONS

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

- 1


### 6 H UMAN 

### H EALTH 

### B ENEFITS OF 

### E MISSIONS 

### R EDUCTIONS



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

- 1

6.1 C ALCULATION OF PM 2.5 - R ELATED H UMAN H EALTH B ENEFITS

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

- 1

### iv

6.2 S OCIAL C OST OF C ARBON AND G REENHOUSE G AS B ENEFITS

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

- 14 6.3 E NERGY D ISBENEFITS

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

- 17 6.4 T OTAL M ONETIZED B ENEFITS

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

- 18 6.5 U NQUANTIFIED B ENEFITS

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

- 19 6.5.1 Additional SO 2 and NO 2 Benefits

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

- 19 6.5.2 Ozone Benefits

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

- 22 6.5.3 Visibility Impairment

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

- 22 6.6 C HARACTERIZATION OF U NCERTAINTY IN THE M ONETIZED B ENEFITS

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

- 23 6.7 R EFERENCES

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

- 28


### 7 C OMPARISON OF 

### B ENEFITS AND 

### C OSTS



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

- 1

A PPENDIX 

### A P ARENT 

### C OMPANY 

### I NFORMATION FOR 

### P ETROLEUM 

### R EFINERIES



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

- 1

### v LIST OF TABLES

Table 1

- 1 National Incremental Cost Impacts , Emission Reductions, and Cost Effectiveness for Petroleum

Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Effective Date of Final Rule Amendments )

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

- 3 Table 1

- 2 Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Final Petroleum

Refineries NSPS in 2017.........................................................

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

- 5 Table 2

- 1. Types and Characteristics of Raw Materials used in Petroleum Refineries

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

- 7 Table 2

- 2. Major Refinery Product Categories

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

- 9 Table 2

- 3. Labor, Material, and Capital Expenditu res for Petroleum Refineries (NAICS 324110)

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

- 12 Table 2

- 4. Costs of Materials Used in Petroleum Refining Industry

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

- 14 Table 2

- 5. Major Refinery Products

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

- 16 Table 2

- 6. Market Concentration Measures of the Petroleum Refining Industry: 1985 to 2007

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

- 20 Tab le 2

- 7. Number of Petroleum Refineries, by State

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

- 23 Table 2

- 8. Full Production Capacity Utilization Rates for Petroleum Refineries

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

- 24 Table 2

- 9. Characteristics of Small Businesses in the Petroleum Refining Industry

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

- 31 Table 2

- 10. Total Petroleum Products Supplied (millions of barrels per year)

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

- 36 Table 2

- 11. U.S. Refinery and Blender Net Production (millions of barrels per year)

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

- 36 Table 2

- 1 2. Value of Product Shipments of the Petroleum Refining Industry

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

- 38 Table 2

- 13. Imports of Major Petroleum Products (millions of barrels per year)

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

- 38 Table 2

- 14. Exports of Major Petroleum Products (millions of barrels per year)

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

- 39 Table 2

- 15. Average Price of Major Petroleum Products Sold to End Users (cents per gallon)

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

- 40 Table 2

- 16. Producer Price Index Industry Data: 1995 to 2010

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

- 41 Table 2

- 17. Mean Ratios of Prof it before Taxes as a Percentage of Net Sales for Petroleum Refiners, Sorted by Value of Assets

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

- 42 Table 2

- 18. Net Profit Margins for Publicly Owned, Small Petrol eum Refiners: 2010

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

- 43 Table 2

- 19. Forecasted Average Price of Major Petroleum Products Sold to End Users in 2009 Currency (cents

per gallon)

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

- 44 Table 2

- 20. Total Petroleum Products Supplied (millions of barrels per year)

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

- 44 Table 2

- 21. Full Production Capacity Utilization Rates for Petroleum Refineries

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

- 45 Table 3

- 1. National Incremental Cost Impacts for Petroleum Re finery Flares Subject to Amended Standards

Under 40 CFR part 60, subpart Ja (Fifth Year After Effective Date of Final Rule Amendments )

..... 3

- 2 Table 3

- 2. National Incremental Emissions Reductions and Cost

- Effectiveness for Petroleum Refinery Flares

Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Effective Date of Final Rule Amendments )

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

- 3 Table 4

- 1

. Rates of Return on Inves t ment for Refineries.........................................................

. ...... . 4

- 2 Table 4

- 2

. Impact Levels of Proposed NSPS Amendments on Small Firms...............

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

- 6 Table 4

- 3

. Summary of Sales Ratios for Firms Affected by Proposed NSPS Amendments........................4

- 7 Table 6

- 1

. Human Health Effects of PM 2.5 ................................ ................................ ................................ .......... 6

- 3 Table 6

- 2

. General Summary of Monetized PM

- Related Health Benefits Estimates for Petroleum Refineries

NSPS Reconsideration (millions of 2006$) *

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

- 11 Table 6

- 3

. Summary of Reductions in Health Incidences from PM 2.5 Benefits for Petroleum Refineries NSPS

Reconsideration*

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

- 12 Table 6

- 4

. All PM 2.5 Benefits Estimates for the Final Petroleum Refineries NSPS Reconsideration at discount

rates of 3% and 7% in 2017 (in millions of 2006$)*

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

- 13 Table 6

- 5. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2017 a

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

- 16 Table 6

- 6. Monetized SCC

- Derived Benefits of CO 2 Emission Reductions in 2017 a,b

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

- 17 Table 7

- 1. National Incremental Cost Impacts, Emission Reductions, and Cost Effectiveness for Petroleum

Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Effective Date of Final Rule Amendments)

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

- 2 Table 7

- 2

. Summa ry of the Monetized Benefits, Social Costs, and Net Benefits for the Final Petroleum

Refineries NSPS in 2017

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

- 3 Table A

- 1. Parent Company Information for Petroleum Refineries

................................ ................................ .... A

- 2

### vi LIST OF FIGURES

Figure 2

- 1. Outline of the Refining Process

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

- 3 Figure 2

- 2. Desalting Process

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

- 3 Figure 2

- 3. Atmospheric Distillation Process

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

- 4 Figure 2

- 4. Vacuum Distillation Process

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

- 5 Figure 2

- 5. Petroleum Refinery Expendit ures

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

- 11 Figure 2

- 6. Employment Distribution of Companies Owning Petroleum Refineries (N=61)

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

- 25 Figure 2

- 7. Average Revenue of Companies Owning Petroleum Refineries by Employment (N=61)

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

- 26 Figure 2

- 8. Revenue Distribution of Large Companies Owning Petroleum Refineries (N= 25 )

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

- 27 Figur e 2

- 9. Revenue Distribution of Small Companies Owning Petroleum Refineries (N=36).........

. .........2

- 28 Figure 2

- 10

. Total Petroleum Products Supplied (millions of barrels per year)

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

- 35 Figure 6

- 1: Breakdown of Monetized PM 2.5 Health Benefits using Mortality F unction from Pope et al. (2002) 6

- 10 Figure 6

- 2: Monetized PM 2.5 Benefits of Final Petroleum Refineries Reconsideration in 2017

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

- 14 Figure 6

- 3: Breakdown of Monetized Benefits for Final Petroleum Refineries NSPS Reconsideration by Pollutant

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

- 18 Figure 6

- 4. Breakdown of Monetized Benefits for Final P etroleum Refineries NSPS Reconsideration by

Requirement and Flare Size ...................................................................................6

- 19 Figure 6

- 5

. Percentage of Adult Population by Annual Mean PM 2.5 Exposure in the Baseline

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

- 25 Figure 6

- 6

. Cumulative Distribution of Adult Population by Annual Mean PM 2.5 Exposure in the Baseline

.... 6

- 26 Figure 7

- 1. Net Benefits for Final Petroleum Refineries NSPS Reconsideration at 3% discount rate*

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

- 4 Figure 7

- 2. Net Benefits for Final Petroleum Refineries NSPS Reconsideration at 7% discount rate*

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

- 5

### 1

- 1 1. EXECUTIVE SUMMARY

1.1 Introduction

On June 24, 2008, EPA promulgated amendments to the Standards of Performance for

Petroleum Refineries and new standards of performance for petroleum refinery process units constructed, reconstructed, or modified after May 14, 2007. EPA subsequently received three petitions for reconsideration of the se final rule s . On September 26, 2008, EPA granted reconsideration and issued a stay for the issues raised in th e petitions regarding process heaters

and flares. On December 22, 2008, EPA addressed those specific issues by proposing

amendments to certain provisions for process heaters and flares . This final regulation include s
emissions limits for new and modified /reconstructed sources, and these limits are set for sulfur
dioxide (SO 2 

### ), nitrogen oxides (NO x 

### ), volatile organic compoun ds (VOC), and other pollutants.

The petroleum refining industry comprises establishments primarily engaged in refining crude petroleum into refined petroleum. Examples of refined petroleum products include gasoline, kerosene, asphalt, lubricants, solvents, and a variety of other products. Petroleum refining falls u nder the North American Industrial Classification System ( NAICS) 324110.

This regulatory impact analysis (RIA) was prepared in response to requirements under Executive Order 12866. The RIA presents the results of analyses undertaken in support of this

final rule including compliance costs, benefits, economic impacts, and impacts to small

businesses. This RIA is organized as follows :

 

### Section 1: Executive Summary,

 

### Section 2 and an Appendix A: Profile of the Petroleum Refin ing Industry,

 

### Section 3 : NSPS Regulatory Al ternatives, and Costs and Emission Reductions From Complying with the NSPS

,

 

### Section 4 : Economic Impact Analysis: Methods and Results

,

 

### Section 5: Executive Orders,

 

### Section 6 : Benefits of the NSPS , and

 

### Section 7 : Comparison of Benefits and Costs

.

### 1

- 2

1.2 Result s
EPA has characterized the facilities and companies potentially affected by the NSPS by examining existing refineries and the companies that own them. EPA projects that new refineries and processes will be similar to existing ones, and that the companies owning new sources will also be similar to the companies owning existing refineries. EPA has collected data on 1 48 existing refineries, owned by 64 companies. Of the affected parent companies, thirty

- six are
identified as small entities based on the Small Business Administration size standard criteria for NAICS 324110, for they employ 1,500 or fewer employees.

The total annualized engineering compliance costs of the NSPS are estimated at $96 million. The total annual savings from offset natural gas purcha ses and product recovery credits that arise as a result of complying with the rule are estimated at $180 million. EPA estimates

that complyi ng with the final NSPS will yield an annualized cost savings of approximately $ 79 million per year (2006 dollars) in 2017

. The estimated nationwide 5

- year incremental emissions reductions and cost impacts for the final standards are summarized in Table 1

- 1 below. Given that there are cost savings, EPA anticipates that the NSPS will hav e no negative impacts on the

market for petroleum products . Based on sales data obtained for the affected small entities , as well as expected annualized cost savings , EPA estimates that the NSPS will not result in a SISNOSE (significant economic impact on a substantial number of small entities).

### 1

- 3 Table 1

- 1. National Incremental Cost Impacts , Emission Reductions, and Cost Effectiveness for Petroleum Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Effective Date of Final Rule Amendments ) 1

Subpart Ja Requirements Total

capital cost (millions) Total

annual cost without credit (millions/yr) Natural

gas offset/ product recovery credit (millions) Total

annual cost (millions/yr) Annual

emission reductions (tons SO 2 /yr) Annual

emission reductions (tons NO X /yr) Annual

emission reductions (tons VOC/yr) Cost

effective

ness ($/ton emissions reduced) Annual

emission reductions (metric tons CO 2 /yr) 2 Monetized

Benefits (millions , 3% discount rate for health and climate benefits ) Monetized Benefits (millions , 3% discount rate for health and 7% for climate benefits ) Small Flares Flare Monitoring $72 $12 $0 $12 0 0 0 0 $0 to $0 $0 to $0
Flare gas
recovery $0 $0 $0 $0 0 0 0 0 $0 to $0 $0 to $0

Flare Management $0 $0.79 $0 $0.79 0 0 270 $2,900 0 $0 to $0 $0 to $0

SO2 RCA/CA $0 $1.9 $0 $1.9 2,600 0 0 $760 0 $170 to $410 $150 to $370
Flowrate RCA/CA $0 $0.90

- $6.7

- $5.8 3.4 50 390

- $13,000 98,000 $2.9 to $3.7 $2.9 to $3.6

Total $72 $16

- $6.7 $9.0 2,600 50 660 $2,700 98,000 $170 to $410 $150 to $370 Large Flares Flare Monitoring $12 $2.0 $0 $2.0 0 0 0 0 $0 to $0 $0 to $0
Flare gas
recovery $380 $78

- $170

- $90 380 1,100 2,700

- $22,000 1,800,000 $74 to $120 $71 to $110

Flare Management $0 $0.0 88 $0 $0.088 0 0 30 $2,900 0 $0 to $0 $0 to $0

SO2 RCA/CA $0 $0.22 $0 $0.22 290 0 0 $760 0 $18 to $45 $17 to $41
Flowrate RCA/CA $0 $0.10

- $0.74

- $0.64 0 6 43

- $13,000 11,000 $0 to $0 $0 to $0

Total $390 $81

- $170

- $88 660 1,100 2,800

- $ 20 ,000 1,800,000 $93 to $160 $88 to $150 All Flares Flare Monitoring $84 $14 $0 $14 0 0 0 0 $0 to $0 $0 to $0
Flare gas
recovery $380 $78

- $170

- $90 380 1,100 2,700

- $22,000 1,800,000 $74 to $120 $71 to $110

Flare Management $0 $0.88 $0 $0.88 0 0 300 $ 2,900 0 $0 to $0 $0 to $0

SO2 RCA/CA $0 $2.2 $0 $2.2 2,900 0 0 $760 0 $180 to $450 $170 to $410
Flowrate RCA/CA $0 $1.00

- $7.4

- $6.4 3.4 56 430

- $13 ,000 110,000 $3.2 to $4.0 $3.1 to $3.9

Total $460 $96

- $180

- $79 3,200 1,100 3,400

- $ 10 ,000 1,900,000 $260 to $580 $240 to $520

1 All estimates are for the implementation year ( 2017 ), and are rounded to two significant figures.
2 The emission reductions of CO 2 reflect the anticipated emission increases associated with the energy disbenefits from additional electricity consumption.

### 1

- 4

EPA estimates that the total monetized benefits of the final NSPS are $ 260 million to
$ 580 m illion and $ 240 million to $ 520 m illion, at 3% and 7% discount rates, respectively (Table

1

- 2 ). All estimates are in 200 6 dollars for the year 2017 . Using alternate relationships between

PM 2.5 

### and premature mortality supplied by experts, higher and lower benefits estimates are plausible, but most of the expert

- based estimates fall between these estimates. In addition, direct exposure to SO 2 

### and NOx benefits, ozone benefits, ecosystem benefits

, and visibility benefits have not been monetized in this analysis.

EP A estimates the net benefits of the final NSPS are $ 340 m illion to $ 660 m illion and
$ 320 m illion to $ 600 m illion, at 3% and 7% discount rates, respectively (Table 1

- 2 )

. All

estimates are in 200 6 dollars for the year 2017

.

### 1

- 5

Table 1

- 2

. Summary of the Monetized B enefits, Social Costs, and Net B enefits for the Final Petroleum Refineries NSPS in 2017 (millions of 2006$) 1

3% Discount Rate 7% Discount Rate
Final Major Source N SPS

Total Monetized Benefits 2 $ 260 to $580 $240 to $ 520

Total Compliance Costs 3

- $79

- $79

Net Benefits $340 to $660 $320 to $ 600

Non

- monetized Benefits Health effects from SO 2

, NO 2 , and ozone exposure Health effects from PM exposure from VOCs Ecosystem effects Visibility impairment

1 All estimates are for the implementation year ( 2017 ), and are rounded to two significant figures.
2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of PM 2.5 precursors such as NOx and SO 2 as well as C O 2 benefits . It is important to note that the monetized benefits do not include the reduced health effects from direct exposure to SO 2 and NOx, ozone exposure, ecosystem effe cts, or visibility impairment. Human health b enefits are shown as a range from Pop e et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effects estimates by particle type. The net present value of reduced CO 2 emissions is calculated differently than other benefits. This table includes monetized climate benefits using the global average social cost of carbon ( SCC ) estimate d at a 3 percent discount rate because the interagency workgroup deemed th e SCC estimate at a 3 percent discount rate to be the central value. 3 The engineering compliance costs are annualized using a 7 percent discount rate.



### Alternatively, if no refin er ies install flare gas recovery systems, EPA estimates the costs would be $10.7 million with monetized benefits of $190 to $460 million and $170 to $410 million at a discount rates of 3% and 7% respectively. Thus, net benefits without flare gas

recovery s ystems would be $ 180 m illion to $ 450 m illion and $ 160 m illion to $ 400 m illion, at 3% and 7% discount rates, respectively. All estimates are in 200 6 dollars for the year 2017

.

For small flares, we estimate the monetized benefits are $170 million to $410 million (3

- percent discount rate) and $150 million to $370 million (7% discount rate for health benefits and

3% discount rate for climate benefits). For large flares, we estimate the monetized benefits are $93 million to $160 million (3% discount rate) an d $88 million to $150 million (7% discount rate

for health benefits and 3

- percent discount rate for climate benefits). All estimates are in 200 6 dollars for the year 2017

.

### 1

- 6

1

. 3 References

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine. 173: 667

- 672.

Office of Management and Budget. Circular A

- 4, September 17, 2003. Available on the
Internet at http://www.whitehouse.gov/omb/circulars_a004_a

- 4

. Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long

- term Exposure to Fine Particulate Air Pollution.” Journ al of the American Medical Association 287:1132

- 1141.

### 2

- 1 2. PROFILE OF THE PETRO LEUM REFININGINDUSTR Y

2.1 Introducti on This industry profile of the petroleum refining industry provides information that will support this and subsequent regulatory im pact analyses (RIAs) and economic impact analyses (EIAs) that will assess the impacts of these standards.

At its core, the petroleum refining industry comprises establishments primarily engaged in refining crude petroleum into finished petroleum products. Examples of these petroleum products include gasoline, kerosene, asphalt, lubricants, and solvents, among others.

Firms engaged in petroleum refining are categorized under the North American Industry Classification System (NAICS) code 324110. In 2010, 14 8 establishments owned by 64 parent companies were refining petroleum in the continental United States. In 2009, the petroleum refining industry shipped products valued at over $436 billion (U.S. Census Bureau, Sector 31:
2009 and 2008 ).

This industry profile report is organized as follows. Section 2.2 provides a detailed description of the inputs, outputs, and processes involved in petroleum refining. Section 2.3 describes the applications a nd users of finished petroleum products. Section 2.4 discusses the organization of the industry and provides facility

- and company

- level data. In addition, small businesses are reported separately for use in evaluating the impact on small business to meet the requirements of the Small Business Regulatory Enforcement and Fairness Act (SBREFA).
Section 2.5 contains market

- level data on prices and quantities and discusses trends and projections for the industry.

2.2 The Supply Side Estimating the economic impact s of any regulation on the petroleum refining industry requires a good understanding of how finished petroleum products are produced (the “supply side” of finished petroleum product markets). This section describes the production process used
to manufactur e these products as well as the inputs, outputs, and by

- products involved. The section concludes with a description of costs involved with the production process.

2.2.1 Production Process, Inputs, and Outputs

Petroleum pumped directly out of the ground, known as crude oil, is a complex mixture of hydrocarbons (chemical compounds that consist solely of hydrogen and carbon) and various impurities such as salt. To manufacture the variety of petroleum products recognized in everyday

### 2

- 2

life, this mixture must be refined and processed over several stages. This section describes the typical stages involved in this process as well as the inputs and outputs.

2.2.1.1 The Production Process
The process of refining crude oil into useful petroleum products can be separated into two phases and a number of supporting operations. These phases are described in detail in the following section. In the first phase, crude oil is desalted and then separated into its various hydrocarbon components (known as “fractions”). These fractions in clude gasoline, kerosene, naphtha, and other products (EPA, 1995 ).

In the second phase, the distilled fractions are converted into petroleum products (such as gasoline and kerosene) using three different types of downstream processe s: combining, breaking, and reshaping (EPA, 1995 ). An outline of the refining process is presented in Figure 2

- 1.

Desalting. Before separation into fractions, crude oil is treated to remove salts, suspended solids, and other impurities that could clog or corrode the downstream equipment. This process, known as “desalting,” is typically done by first heating the crude oil, mixin g it with process water, and depositing it into a gravity settler tank. Gradually, the salts present in the oil will be dissolved into the process water (EPA, 1995 ). After this takes place, the process water is separated from the oil by adding demulsifier chemicals (a process known as chemical separation) and/or by applying an electric field to concentrate the suspended water globules at the bottom of the settler tank (a process known as electrostatic separation). The effluent water i s then removed from the tank and sent to the refinery wastewater treatment facilities (EPA, 1995 ). This process is illustrated in Figure 2

- 2.

### 2

- 3

Figure 2

- 1 Outline of the Refining Process Source: U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003 . OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Available at <http://www.osha.gov/dts/osta/otm/ otm_iv/otm_iv_2.html>. As obtained on October 23, 2006.

Figure 2

- 2 Desalting Process Source: U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003 . OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Available at <http://www.osha.gov/dts/osta/otm/otm_iv/otm_iv_2.html>. As obtained on October 23, 2006.

### 2

- 4

Atmospheric Distillation. The desalted crude oil is then heated in a furnace to 750°F and fed into a vertical distillation column at atmospheric pressure. After entering the tower, the lighter fractions flash into vapor and travel up the tower. This leaves only the heaviest fractions (which have a much higher boiling poi nt) at the bottom of the tower. These fractions include heavy fuel oil and asphalt residue (EPA, 1995 ).

As the hot vapor rises, its temperature is gradually reduced. Lighter fractions condense onto trays located at successively highe r portions of the tower. For example, motor gasoline will condense at higher portion of the tower than kerosene because it condenses at lower temperatures. This process is illustrated in Figure 2

- 3. As these fractions condense, they will be drawn off their respective trays and potentially sent downstream for further processing (OSHA, 2003 ; EPA, 1995 ).

Figure 2

- 3 Atmospheric Distillation Process Source: U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003 . OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Available at <http://www.osha.gov/dts/osta/otm/otm_iv/otm_iv_2.htm l>. As obtained on October 23, 2006.



### Vacuum Distillation. The atmospheric distillation tower cannot distill the heaviest fractions (those at the bottom of the tower) without cracking under requisite heat and pressure. So these fractions are separated using a process called vacuum distillation. This process takes place in one or more vacuum distillation towers and is similar to the atmospheric distillation process, except very low pressures are used to increase volatilization and separation. A typical
first

- phase vacuum tower may produce gas oils or lubricating

- oil base stocks (EPA, 1995 ). This process is illustrated in Figure 2

- 4.

### 2

- 5

Figure 2

- 4 Vacuum Distillation Process Source: U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003 . OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Available at <http://www.osha.gov/dts/osta/otm/otm_iv/otm_iv_2.html >. As obtained on October 23, 2006.



### Downstream Processing. To produce the petroleum products desired by the market place, most fractions must be further refined after distillation or “downstream” processes. These downstream processes change the molecular structure of the hydrocarbon molecules by breaking them into smaller molecules, joining them to form larger molecules, or shaping them into higher quality molecules (EPA, 1995 ).

Downstream processes include thermal cracking, coking, catalytic cracking, catalytic hydrocracking, hydrotreating, alkylation, isomerization, polymerization, catalytic reforming, solvent extraction, merox, dewaxing, propane deasphalting and other operations (EPA, 1995 ).

2.2.1.2 Supportin g Operations
In addition to the processes described above, there are other refinery operations that do not directly involve the production of hydrocarbon fuels, but serve in a supporting role. Some of the major supporting operations are described in this section.

Wastewater Treatment. Petroleum refining operations produce a variety of wastewaters including process water (water used in process operations like desalting), cooling water (water used for cooling that does not come into direct contact with the o il), and surface water runoff (resulting from spills to the surface or leaks in the equipment that have collected in drains).

### 2

- 6

Wastewater typically contains a variety of contaminants (such as hydrocarbons, suspended solids, phenols, ammonia, sulfides, and other compounds) and must be treated before it is recycled back into refining operations or discharged. Petroleum refineries typically utilize two stages of wastewater treatment. In primary wastewater treatments, oil and solids present in
the wastewater ar e removed. After this is completed, wastewater can be discharged to a publicly owned treatment facility or undergo secondary treatment before being discharged directly to surface water. In secondary treatment, microorganisms are used to dissolve oil and ot her organic pollutants that are present in the wastewater (EPA, 1995 ; OSHA, 2003 ).

Gas Treatment and Sulfur Recovery. Petroleum refinery operations such as coking and catalytic cracking emit gases with a high concentration of hydrogen sulfide mixed with light refinery fuel gases (such as methane and ethane). Sulfur must be removed from these gases in order to comply with the Clean Air Act’s SO x 

### emission limits and to recover saleable elemental
sulfur.

Sulfur is recovered by first separating the fuel gases from the hydrogen sulfide gas. Once this is done, elemental sulfur is removed from the hydrogen sulfide gas using a recovery system known as the Claus Process. In this process, hydrogen sulfide is burned unde r controlled conditions producing sulfur dioxide. A bauxite catalyst is then used to react with the sulfur dioxide and the unburned hydrogen sulfide to produce elemental sulfur. However, the Claus process only removes 90% of the hydrogen sulfide present in the gas stream, so other processes must be used to recover the remaining sulfur (EPA, 1995 ).

Additive Production. A variety of chemicals are added to petroleum products to improve their quality or add special characteristics. For ex ample, ethers have been added to gasoline to increase octane levels and reduce CO emissions since the 1970s.

Heat Exchangers, Coolers, and Process Heaters. Petroleum refineries require very high temperatures to perform many of their refining processes. To achieve these temperatures, refineries use fired heaters fueled by refinery or natural gas, distillate, and residual oils. This heat is managed through heat exchangers, which are composed of bundles of pipes, tubes, plate coils,
and other equipment that surround heating or cooling water, steam, or oil. Heat exchangers facilitate the indirect transfer of heat as needed (OSHA, 2003 ).

Pressure Release and Flare Systems. As liquids and gases expand and contract th rough the refining process, pressure must be actively managed to avoid accident. Pressure

- relief systems enable the safe handling of liquids and gases that are released by pressure

- relieving

### 2

- 7

devices and blow

- downs. According to the OSHA Technical Manual, “ pressure relief is an automatic, planned release when operating pressure reaches a predetermined level. A blow

- down normally refers to the intentional release of material, such as blow

- downs from process unit startups, furnace blow

- downs, shutdowns, and em ergencies” (OSHA, 2003 ).

Blending. Blending is the final operation in petroleum refining. It is the physical mixture of a number of different liquid hydrocarbons to produce final petroleum products that have
desired characteristics. For example, additives such as ethers can be blended with motor gasoline to boost performance and reduce emissions. Products can be blended in

- line through a manifold system, or batch blended in tanks and vessels (OSHA, 2003 ).

2.2. 1.3 Inputs
The inputs in the production process of petroleum products include general inputs such as labor, capital, and water. 1 

### The inputs specific to this industry are crude oil and the variety of chemicals used in producing petroleum products. These two specific inputs are discussed below.

Crude Oil. Crude oils are complex, heterogeneous mixtures and contain many different hydrocarbon compounds that vary in appearance and composition from one oil field to another. An “average” crude oil contains about 8 4% carbon; 14% hydrogen; and less than 2% sulfur, nitrogen, oxygen, metals, and salts (OSHA, 2003 ). The proportions of crude oil elements vary over a narrow limit: the proportion of carbon ranges from 83 to 87 percent; hydrogen ra nges from 10 to 14 percent; nitrogen ranges from 0.1 to 2 percent; oxygen ranges from 0.5 to 1.5 percent; and sulfur ranges from 0.5 to 6 percent (Speight 2006).

In 2010, the petroleum refining industry used 5.4 billion barrels of crude oil in the producti on of finished petroleum prod ucts ( EIA 2010 ). 2



### Common Refinery Chemicals. In addition to crude oil, a variety of chemicals are used in the production of petroleum products. The specific chemicals used will depend on specific
characte ristics of the product in question. Table 2

- 1 lists the most common chemicals used by petroleum refineries, their characteristics, and their applications.

Table 2

- 1 Types and Characteristics of Raw Materials used in Petroleum Refineries

1 Crude oil processing requires large volumes of water, a large portion of which is continually recycled. The amount of water used by a refinery can vary significantly, depending on process configuration, refinery complexity, capability for recycle, degree of sewer segregation, and local rainfall. In 1992, the average amount of water use d in refineries was estimated between 65 and 90 gallons per barrel of crude oil processed ( OGJ 1992a )

. 2 A barrel is a unit of volume that is equal to 42 U.S. gallons

.

### 2

- 8 Type Description Crude Oil Heterogeneous mixture of different hydrocarbon compounds. Oxygenates Substances which, when added to gasoline, increase the amount of oxygen in that gasoline blend. Ethanol, ethyl tertiary butyl ether (ETBE), and methanol are common oxygenates. Caustics Caustics are added to desalting water to neutralize acids and reduce corrosion. They are also added to desalted crude in order to reduce the amount of corrosive chlorides in the tower overheads. They are used in some refinery treating processes to remove contaminants from hydrocarbon streams. Leaded Gasoline Additives Tetraethyl lead (TEL) and tetramethyl lead (TML) are additives formerly used to improve gasoline octane ratings but are no longer in common use except in aviation gasoline. Sulfuric Acid and Hydrofluoric Acid Sulfuric acid and hydrofluoric acid are used primarily as catalysts in alkylation processes. Sulfuric acid is also used in some treatment processes.

Source: U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003 . OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Available at <http://www.osha.gov/dts/osta/otm /otm_iv/otm_iv_2.html>. As obtained on October 23, 2006.



### In 2010, the petroleum refining industry used 971 million barrels of natural gas liquids and other liquids in the production of finished petroleum products (EIA 2010 ).

2.2.1.4 Types of Product Outputs
The petroleum refining industry produces a number of products that fall into one of three categories: fuels, finished nonfuel products, and feedstock for the petrochemical industry. Table
2

- 2 briefly describes these product catego ries. A more detailed discussion of petroleum fuel products can be found in Section 2.3.

### 2

- 9

Table 2

- 2 Refinery Product Categories Product Category Description Fuels Finished Petroleum products that are capabl e of releasing energy. These products power equipment such as automobiles, jets, and ships. Typical petroleum fuel products include gasoline, jet fuel, and residual fuel oil. Finished nonfuel products Petroleum products that are not used for powering machines or equipment. These products typically include asphalt, lubricants (such as motor oil and industrial greases), and solvents (such as benzene, toluene, and xylene). Feedstock

Sulfur Many produc ts derived from crude oil refining, such as ethylene, propylene, butylene, and isobutylene, are primarily intended for use as petrochemical feedstock in the production of plastics, synthetic fibers, synthetic rubbers, and other products. Commercial uses ar e primarily in fertilizers , because of the relatively high requirement of plants for it, and in the manufacture of sulfuric acid , a primary industrial chemical.

Source: U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003 . OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Available at <http://www.osha.gov/dts/osta/otm/otm_iv/otm_iv_2.html>. As obtained on October 23, 2006.



### 2.2.2 Emissions and Controls in P etroleum Refining
Petroleum refining results in emissions of hazardous air pollutants (HAPs), criteria air pollutants (CAPs), and other pollutants. The HAPs include metals and toxic organic compounds; the CAPs include carbon monoxide (CO), sulfur oxides ( SO x 

### ), nitrogen oxides (NO x 

### ), particulates, and volatile organic compounds (VOCs); and the other pollutants include sp ent acids, gaseous pollutants, ammonia (NH 3 

### ), and hydrogen sulfide (H 2 

### S).

2.2.2.1 Gaseous and VOC Emissions
As previously mentioned, CO, SO x 

### , NO x 

### , NH 3 

### , and H 2 

### S emissions are produced along with petroleum products. Sources of these emissions from refineries include fugitive emissions of the volatile constituents in crude oil and its fractions, emissions from the burning of fuels in process hea ters, and emissions from the various refinery processes themselves. Fugitive emissions occur as a result of leaks throughout the refinery and can be reduced by purchasing
leak

- resistant equipment and maintaining an ongoing leak detection and repair progra m (EPA,
1995 ).

The numerous process heaters used in refineries to heat process streams or to generate steam (boilers) for heating or other uses can be potential sources of SO x 

### , NO x 

### , CO, and hydrocarbons emissions. Emissions are low when p rocess heaters are operating properly and using clean fuels such as refinery fuel gas, fuel oil, or natural gas. However, if combustion is not complete, or the heaters are fueled using fuel pitch or residuals, emissions can be significant (EPA, 1995 ).

### 2

- 10

The majority of gas streams exiting each refinery process contain varying amounts of refinery fuel gas, H 2 

### S, and NH 3 

### . These streams are directed to the gas treatment and sulfur recovery units described in the previous section. Here, ref inery fuel gas and sulfur are recovered using a variety of processes. These processes create emissions of their own, which normally
contain H 2 

### S, SO x 

### , and NO x 

### gases (EPA, 1995 ). For additional details on refinery fuel, or waste,

gas composition, see Table 12 of the January 25, 2012 Impact Estimates for Fuel Gas Combustion Device and Flare Regulatory Options for Amendments to the Petroleum Refinery
NSPS available in the docket.

Emissions can also be created by the periodic regeneration of catalysts that are used in downstream processes. These processes generate streams that may contain relatively high levels of CO, particulate, and VOC emissions. However, these emissions are treated before being discharged to the atmosphere. First, the emissions are processed through a CO boiler to burn CO and any VOC, and then through an electrostatic precipitator or cyclone separator to remove particulates (EPA, 1995 ).

2.2.2.2 Wastewater and Other Wastes
Petroleum refining operat ions produce a variety of wastewaters including process water (water used in process operations like desalting), cooling water (water used for cooling that does not come into direct contact with the oil), and surface water runoff (resulting from spills to the surface or leaks in the equipment that have collected in drains). This wastewater typically contains a variety of contaminants (such as hydrocarbons, suspended solids, phenols, NH 3 

### , sulfides, and other compounds) and is treated in on

- site facilities be fore being recycled back into the production process or discharged.

Other wastes include forms of sludges, spent process catalysts, filter clay, and incinerator ash. These wastes are controlled through a variety of methods including incineration, land fill ing, and neutralization, among other treatment methods (EPA, 1995 ).

2.2.3 Costs of Production
Between 1995 and 2009, expenditures on input materials accounted for the largest cost to petroleum refineries

— amounting to 95% of total expenses (Figure 2

- 5). These material costs included the cost of all raw materials, containers, scrap, and supplies used in production or repair during the year, as well as the cost of all electricity and fuel consumed.

### 2

- 11 Average Percentage (1995

– 2009)

Figure 2

- 5 Petroleum Refinery Expenditures Sources: U.S. Department of Commerce, Bureau of the Census. 2007. 2006 Annual Survey of Manufactures. Obtained through American Fact Finder Database <http://factfinder.census.gov/home/saff/main.html?_lang=en>. U.S. Department of Commerce, Bureau of the Census. 200 6. 2005 Annual Survey of Manufacture s. M05(AS)

- 1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2006pubs/ U.S. Department of Commerce, Bureau of the Census. 2003a. 2001 Annual Survey of Manufacture s. M01(AS)

- 1. Washi ngton, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/ U.S. Department of Commerce, Bureau of the Census. 2001. 1999 Annual Survey of Manufacture s. M99(AS)

- 1 (RV). Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2001pubs/ U.S. Department of Commerce, Bureau of the Census. 1998. 1996 Annual Survey of Manufacture s. M96(AS)

- 1 (RV). Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/3/98pubs/ U.S. Depart ment of Commerce, Bureau of the Census. 1997. 1995 Annual Survey of Manufacture s. M95(AS)

- 1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2/manmin/ U.S. Census Bureau, American FactFinder; “Sector 31: Annual Survey o f Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2009 and 2008 “ Release Date: 12/3/10; (Data accessed on 10/10/11). [Source for 2008 and 2009 numbers] http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- NAICSASM=324110&

- ds_name= AM0931GS101&

- ib_type=NAICSASM&

- _industry=324110&

- _lang=en U.S. Census Bureau, American FactFind er; “Sector 31: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007” Release Date 10/30/09; (Data accessed on 10/11/11). [Source for 2007 numbers] http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- geo_id=01000US&

- ds_name=EC0731I1&

- NAICS2007=324110&

- _lang=en

### 2

- 12

Labor and capital accounted for the remaining expenses faced by petroleum refine rs. Capital expenditures include permanent additions and alterations to facilities and machinery and equipment used for expanding plant capacity or replacing existing machinery. A detailed breakdown of how much petroleum refiners spent on each of these fac tors of production over
this 15

- year period is provided in Table 2

- 3. A more exhaustive assessment of the costs of materials used in petroleum refining is provided in Table 2

- 4.

Table 2

- 3 Labor, Material, and Capital Expenditures for Petroleum Refineries (NAICS 324110)

Payroll ($millions) Materials ($millions) Total Capital ($millions)
Year Reported 2005 Reported 2005 Reported 2005 1995 3,791 4,603 112,532 136,633 5,937 7,209 1996 3,738 4,435 132,880 157,658 5,265 6,247 1997 3,885 4,595 127,555 150,865 4,244 5,020 1998 3,695 4,415 92,212 110,187 4,169 4,982 1999 3,983 4,682 114,131 134,146 3,943 4,635 2000 3,992 4,509 180,568 203,967 4,685 5,292 2001 4,233 4,743 158,733 177,838 6,817 7,638 2002 4,386 4,947 166,368 187,646 5,152 5,811 2003 4,752 5,227 185,369 203,893 6,828 7,510 2004 5,340 5,635 251,467 265,369 6,601 6,966 2005 5,796 5,796 345,207 345,207 10,525 10,525 2006 5,984 5,751 396,980 381,546 11,175 10,741 2007 6,357 5,885 470,946 435,965 17,105 15,834 2008 6,313 5,415 649,784 557,380 17,660 15,148 2009 6,400 5,776 398,679 359,790 16,824 15,183
Note: Adjusted for inflation using the producer price index industry for total manufacturing industries (Table 5

- 6).

Sources: U.S. Department of Commerce, Bureau of the Census. 2007 . 2006 Annual Survey of Manufactures. Obtained through American Fact Finder Database <http://factfinder.census.gov/home/saff/main.html?_lang=e n>. U.S. Department of Commerce, Bureau of the Census. 2006

. 2005 Annual Survey of Manufacture s. M05(AS)

- 1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/pro d/2006pubs/ am0531gs1.pdf>. As obtained on October 23, 2007. U.S. Department of Commerce, Bureau of the Census. 2003a

. 2001 Annual Survey of Manufacture s. M01(AS)

- 1. Washington, DC: Governme nt Printing Office. Available at <http://www.census.gov/prod/2003pubs/ m01as

- 1.pdf>. As obtained on October 23, 2006. U.S. Department of Commerce, Bureau of the Census. 2001

. 1999 Annual Surv ey of Manufacture s. M99(AS)

- 1 (RV). Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2001pubs/ m99

- as1.pdf>. As obtained on October 23, 2006. U.S. Department of Commerce, Bureau of the Census. 1998

. 1996 Annual Survey of Manufacture s. M96(AS)

- 1 (RV). Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/3/98pubs/ m96

- as1.pdf>. As obtained on October 23, 2006. U.S. Department o f Commerce, Bureau of the Census. 1997

. 1995 Annual Survey of Manufacture s. M95(AS)

- 1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2/manmin/ asm/m95as1 .pdf>. As obtained on October 23, 2006. U.S. Census Bureau, American FactFinder; “Sector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2009 and 2008 “ Release Date: 12/3/10; (Data accessed on 10/10/1 1). [Source for 2008 and 2009 numbers] http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- NAICSASM=32411 0&

- ds_name=AM0931GS101&

- ib_type=NAICSASM&

- _industry=324110&

- _lang=en

### 2

- 13

U.S. Census Bureau, American FactFinder; “Sector 31: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007” Release Date 10/30/09; (Data accessed on 10/11/11). [Source for 2007 numbers] http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- geo_id=01000US&

- ds_name=EC0731I1&

- NAICS2007=324110&

- _lang=en

### 2

- 14

Table 2

- 4 Costs of Materials Used in Petroleum Refining Industry 2007 2002

Material Delivered Cost ($10 3 ) Percentage

of Material Costs Delivered

Cost ($10 3 ) Percentage

of Material Costs Petroleum Refineries NAICS 324110 Total materials 440,165,193 100.00% 157,415,200 100.00% Domestic crude petroleum, including lease condensate 133,567,383 30.3% 63,157,497 40.1%

Foreign crude petroleum, including lease condensate 219,780,279 49.9% 69,102,574 43.9%

Foreign unfinished oils (received from foreign countries for further processing) D 2,297,967 1.5%

Ethane (C2) (80% purity or more)

— D Propane (C3) (80% purity or more)

— 118,257 0.1% Butane (C4) (80% purity or more) 7,253,910 1.7% 1,925,738 1.2% Gas mixtures (C2, C3, C4)

— 1,843,708 1.2% Isopentane and natural gasoline 5,117,182 1.2% 810,530 0.5% Other natural gas liquids, including plant condensate 3,356,718 0.8% 455,442 0.3%

Toluene and xylene (100% basis) 1,801,972 0.4% 159,563 0.1% Additives (including antioxidants, antiknock compounds, and inhibitors) D 40,842 0.0%

Other additives (including soaps and detergents) — 709 0.0%

Animal and vegetable oils

— D Chemical catalytic preparations D D Fats and oils, all types, purchased 87,038 0.0%

— — Sodium hydroxide (caustic soda) (100% NaOH) 209,918 0.1% 129,324 0.1%

Sulfuric acid, excluding spent (100% H 2 SO 4 ) 67,458 0.0% 189,912 0.1%

Metal containers D 9,450 0.0% Plastics containers D D Paper and paperboard containers 1,819 0.0% D Cost of materials received from petroleum refineries and lube manufacturers 20,951,741 4.8% 8,980,758 5.7%

All other materials and components, parts, containers, and supplies 24,839,320 5.6% 5,722,580 3.6%

Materials, ingredients, containers, and supplies 4,745,614 1.1% 576,175 0.4%

Sources: U.S. Department of Commerce, Bureau of the Census. 2004. 2002 Economic Census, Industry Series

— Shipbuilding and Repair . Washington, DC: Government Printing Office. Available at <http://www.census.gov/ prod/ec02/ec0231i324110.pdf>. As obtained on October 23, 2006. U.S. Census Bureau, American FactFinder; “Sector 31: Manufacturing: Industry Series: Materials Consumed by Kind for the United States: 2007” Release Date 10/30/09; (Data accessed on 10/11/11 ). [ Source for 2007 numbers] <http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- ds_name=EC0731I3&

- NAICS2007=324110&

- ib_type=NAICS2007&

- geo_id=&

- _industry=324110&

- _lang=en&

- fds_name=EC0700A1>

### 2

- 15

2.3 The Demand Side
Estimating the economic impact the regu lation will have on the petroleum refining industry also requires characterizing various aspects of the demand for finished petroleum products. This section describes the characteristics of finished petroleum products, their uses and consumers, and possibl e substitutes.

2.3.1 Product Characteristics
Petroleum refining firms produce a variety of different products. The characteristics these products possess largely depend on their intended use. For example, the gasoline fueling our
automobiles has different characteristics than the oil lubricating the car’s engine. However, as discussed in Section 2.1.4, finished petroleum products can be categorized into three broad groups based on their intended uses (EIA, 1999a ):

 

### fuels

—

petroleum pro ducts that are capable of releasing energy such as motor gasoline

 

### nonfuel products

—

petroleum products that are not used for powering machines or equipment such as solvents and lubricating oils

 

### petrochemical feedstocks

— petroleum products that are used as a raw material in the production of plastics, synthetic rubber, and other goods
A list of selected products from each of these groups is presented in Table 2

- 5 along with a description of each product’s characteristics and primary uses.

2.3.2 Uses and Consu mers Finished petroleum products are rarely consumed as final goods. Instead, they are used as primary inputs in the creation of a vast number of other goods and services. For example, goods created from petroleum products include fertilizers, pesticides, paints, thinners, cleaning fluids, refrigerants, and synthetic fibers (EPA, 1995 ). Similarly, fuels made from petroleum are used to run vehicles and industrial machinery and generate heat and electrical power. As a result, the
demand for many finished petroleum products is derived from the demand for the goods and services they are used to create.

The principal end users of petroleum products can be separated into five sectors:

 

### Residential sector

— private homes and residences

 

### Industri al sector

— manufacturing, construction, mining, agricultural, and forestry establishments

 

### Transportation sector

— private and public vehicles that move people and commodities such as automobiles, ships, and aircraft

### 2

- 16  

### Commercial sector

— nonmanufacturing or nontr ansportation business establishments such as hotels, restaurants, retail stores, religious and nonprofit organizations, as well federal, state, and local government institutions

 

### Electric utility sector

— privately and publicly owned establishments that gener ate, transmit, distribute, or sell electricity (primarily) to the public; nonutility power producers are not included in this sector

Table 2

- 5 Major Refinery Products
Product Description Fuels Gasoline A blend of refined hydrocarbons, motor gasoline ranks first in usage among petroleum products. It is primarily used to fuel automobiles and lightweight trucks as well as boats, recreational vehicles, lawn mowers, and other equipment. Other forms of gasolin e include Aviation gasoline, which is used to power small planes. Kerosene Kerosene is a refined middle

- distillate petroleum product that finds considerable use as a jet fuel. Kerosene is also used in water heaters, as a cooking fuel, and in lamps. Liquefied petroleum gas (LPG) LPG consists principally of propane (C 3 H 8 ) and butane (C 4 H 10 ). It is primarily used as a fuel in domestic heating, cooking, and farming operations. Distillate fuel oil Distillate fuel oil includes diesel oil, heating oils, an d industrial oils. It is used to power diesel engines in buses, trucks, trains, automobiles, as well as other machinery. Residual fuels Residual fuels are the fuels distilled from the heavier oils that remain after atmospheric distillation; they find their primary use generating electricity in electric utilities. However, residual fuels can also be used as fuel for ships, industrial boiler fuel, and commercial heating fuel. Petroleum coke Coke is a high carbon residue that is the final product of thermal decomposition in the condensation process in cracking. Coke can be used as a low

- ash solid fuel for power plants. Finished Nonfuel Prod ucts Coke In addition to use as a fuel, petroleum coke can be used a raw material for many carbon and graphite products such as furnace electrodes and liners. Asphalt Asphalt, used for roads and roofing materials, must be inert to most chemicals and weather conditions. Lubricants Lubricants are the result of a special refining process that produce lubricating oil base stocks, which are mixed with various additives. Petroleum lubricating products include spindle oil, cylinder oil, motor oil, and industrial greases. Solvents A solvent is a fluid that dissolves a solid, liquid, or gas into a solution. Petroleum based solvents, such as Benzyme, are used to manufacture detergent and synthetic fibers. Other solvents include toluene and xylene.

Feedstock Ethylene Ethylene is the simplest alkene and has the chemical formula C 2 H 4 . It is the most produced organic compound in the world and it is used in the production of many products. For example, one of ethylene’s derivatives is ethylene oxide, which is a primary raw material in the production of detergents. Propylene Propylene is an organic compound with the chemical formula C 3 H 6 . It is primarily used the production of polypropylene, which is used in the production of food packaging, ropes, and textiles.

Sources: U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003 . OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Available at <http://www.osha.gov/dts /osta/otm/otm_iv/otm_iv_2.html>. As obtained on October 23, 2006. U.S. Department of Energy, Energy Information Administration (EIA). 1999

.

### 2

- 17

Of these end users, the transportation sector consumes the largest share of petroleum produ cts, accounting for 67% of total consumption in 2005 (EIA, 2006a ). In fact, petroleum products like motor gasoline, distillate fuel, and jet fuel provide virtually all of the energy consumed in the transportation sector (EIA, 1999a ).

Of the three petroleum product categories, end

- users primarily consume fuel. Fuel products account for 9 out of 10 barrels of petroleum used in the United States (EIA, 1999a ). In 2005, motor gasoline alone accounted for 49% of demand for finished petroleum products (EIA,
2006a ).

2.3.3 Substitution Possibilities in Consumption
A major influence on the demand for finished petroleum products is the availability of
substitutes. In some sectors, like the transportation sector, it is currently difficult to switch quickly from one fuel to another without costly and irreversible equipment changes, but other sectors can switch relatively quickly and easily (EIA, 1999a )

.
For example, equipment at large manufacturing plants often can use either residual fuel oil or natural gas. Often coal and natural gas can be easily substituted for residual fuel oil at electricity utilities. As a result, we would expect demand in these industries to be more sensitive to price (in the short run) than in others (EIA, 1999a ).

However, over time, demand for petroleum products could become more elastic. For example, automobile users could purchase more fuel

- efficient vehicles or relocate to areas that would allow them to make fewer trips. Technological advances could also create new products that compete with petroleum products that currently have no substitutes. An example of such a technological advance would be the invention of ethanol (an alcohol produced from biomass), which can substitute for gasoline in spark

- ignition motor vehicles (EIA, 1999a ).

2.4 Industry Organization
This section examines the organization of the U.S. petroleum refini ng industry, including market structure, firm characteristics, plant location, and capacity utilization. Understanding the industry’s organization helps determine how it will be affected by new emissions standards.

2.4.1 Market Structure
Market structure characterizes the level and type of competition among petroleum refining companies and determines their power to influence market prices for their products. For example, if an industry is perfectly competitive, then individual producers ca nnot raise their

### 2

- 18

prices above the marginal cost of production without losing market share to their competitors. Understanding pricing behavior in the petroleum refining industry is crucial for performing subsequent EIAs.

According to basic microeconomic th eory, perfectly competitive industries are characterized by unrestricted entry and exit of firms, large numbers of firms, and undifferentiated (homogenous) products being sold. Conversely, imperfectly competitive industries or markets are characterized by barriers to entry and exit, a smaller number of firms, and differentiated products (resulting from either differences in product attributes or brand name recognition of products). This section considers whether the petroleum refining industry is competitiv e , based
on these three factors.

2.4.1.1 Barriers to Entry
Firms wanting to enter the petroleum refining industry may face at least two major barriers to entry. First, according to a 2004 Federal Trade Commission staff study, there are significant economie s of scale in petroleum refinery operations. This means that costs per unit fall as a refinery produces more finished petroleum products. As a result, new firms that must produce at relatively low levels will face higher average costs than firms that are e stablished and produce at higher levels, which will make it more difficult for these new firms to compete (Nicholson, 2005 ). This is known as a technical barrier to entry.

Second, legal barriers could also make it difficult for new firms to enter the petroleum refining industry. The most common example of a legal barrier to entry is patents

— intellectual property rights, granted by the government, that give exclusive monopoly to an inventor over his invention for a limited time perio d. In the petroleum refining industry, firms rely heavily on process patents to appropriate returns from their innovations. As a result, firms seeking to enter the petroleum refining industry must develop processes that respect the novelty requirements of these patents, which could potentially make entry more difficult for new firms (Langinier, 2004 ). A second example of a legal barrier would be environmental regulations that apply only to new entrants or new pollution sources. Such regulations would raise the operating costs of new firms without affecting the operating costs of existing ones. As a result, new firms may be less competitive.

Although neither of these barriers is impossible for new entrants to overcome, they can
make i t more difficult for new firms to enter the market for manufactured petroleum products. As a result, existing petroleum refiners could potentially raise their prices above competitive levels with less worry about new firms entering the market to compete aw ay their customers with lower

### 2

- 19

prices. It was not possible during this analysis to quantify how significant these barriers would be for new entrants or what effect they would have on market prices. However, existing firms would still face competition from e ach other. In an unconcentrated industry, competition among existing firms would work to keep prices at competitive levels.

2.4.1.2 Measures of Industry Concentration
Economists often use a variety of measures to assess the concentration of a given
indust ry. Common measures include four

- firm concentration ratios (CR4), eight

- firm concentration ratios (CR8), and Herfindahl

- Hirschmann indexes (HHI). The CR4s and CR8s measure the percentage of sales accounted for by the top four and eight firms in the industr y. The HHIs are the sums of the squared market shares of firms in the industry. These measures of industry concentrated are reported for the petroleum refining industry (NAICS 324110) in Table
2

- 6 for selected years between 1985 and 2007.

Between 1990 and 2000, the HHI rose from 437 to 611, which indicates an increase in market concentration over time. This increase is partially due to merger activity during this time period. Between 1990 and 2000, over 2,600 mergers occurred across the petroleum industry; 13% of these mergers occurred in the industry’s refining and marketing segments (GAO, 2007 ). From 2000 to 2007 the HHI rose again.

Unfortunately, there is no objective criterion for determining market structure based on
the values of the se concentration ratios. However, accepted criteria have been established for determining market structure based on the HHIs for use in horizontal merger analyses (U.S. Department of Justice and the Federal Trade Commission, 1992 ). According to these criteria, industries with HHIs below 1,000 are considered unconcentrated (i.e., more competitive); industries with HHIs between 1,000 and 1,800 are considered moderately concentrated (i.e

., moderately competitive); and industries with higher HHIs are considered heavily concentrated. Based on this criterion, the petroleum refining industry continues to be unconcentrated even in
recent years.

A more rigorous examination of market concentrati on was conducted in a 2004 Federal Trade Commission (FTC) staff study. This study explicitly accounted for the fact that a refinery in one geographic region may not exert competitive pressure on a refinery in another region if transportation costs are high . This was done by comparing HHIs across Petroleum Administration for Defense Districts (PADDs). PADDs separate the United States into five geographic regions or districts. They were initially created during World War II to help manage

### 2

- 20

the allocation of fu els during wartime. However, they have remained in use as a convenient way of organizing petroleum market information (FTC, 2004 ).

Table 2

- 6 Market Concentration Measures of the Petroleum Refining Industry: 1985 to 2007

Measure 1985 1990 1996 2000 2001 2002 2003 2007

Herfindahl

- Hirschmann Index (HHI) 493 437 412 611 686 743 728 807

Four

- firm concentration ratio (CR4) 34.4 31.4 27.3 40.2 42.5 45.4 44.4 47.5 Eight

- firm concentration ratio (CR8) 54.6 52.2 48.4 61.6 67.2 70.0 69.4 73.1

Sources: Federal Trade Commission (FTC). 2004 . “The Petroleum Industry: Mergers, Structural Change, and Antitrust Enforcement.” Available at <http://www.ftc.gov/opa/2004/08/oilmergersrpt.shtm >. As obtained on February 6, 2007. U.S. Census Bureau, American FactFinder; “Sector 31: Manufacturing: Subject Series: Concentration Ratios: Share of Value of Shipments Accounted for by the 4, 8, 20, and 50 Largest Companies for Industries: 2007 “ Release Date 1/7/2011; (Data accessed on 10/12/11) [Source for 2007 numbers]<http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- ds_name=EC0731SR12&

- NAICS2007=324110&

- ib_type=NAICS2007&

- NAICS2007sector=*6&

- industrySel=324110&

- geo_id=&

- _industry=324110&

- _l ang=en>



### This study concluded that these geographic markets were not highly concentrated. PADDs I, II, and III (East Coast, Midwest, and Gulf Coast) were sufficiently connected that they exerted a competitive influence on each other. The HHI for these combi ned regions was 789 in 2003, indicating a low concentration level. Concentration in PADD IV (Rocky Mountains) was also low in 2003, with an HHI of 944. PADD V gradually grew more concentrated in the 1990s after a series of significant refinery mergers. By 2003, the region’s HHI was 1,246, indicating a growth to a moderate level of concentration (FTC, 2004 ).

2.4.1.3 Product Differentiation
Another way firms can influence market prices for their product is through product
differentiation. B y differentiating one’s product and using marketing to establish brand loyalty, manufacturers can raise their prices above marginal cost without losing market share to their competitors.

While we saw in Section 3.3 that there are a wide variety of petrole um products with many different uses, individual petroleum products are by nature quite homogenous. For example, there is little difference between premium motor gasoline produced at different refineries (Mathtech, 1997 ). As a result , the role of product differentiation is probably quite small for many finished petroleum products. However, there are examples of relatively small

### 2

- 21

refining businesses producing specialty products for small niche markets. As a result, there may
be some ins tances where product differentiation is important for price determination.

2.4.1.4 Competition among Firms in the Petroleum Refining Industry
Overall, the petroleum industry is characterized as producing largely generic products for sale in relatively unc oncentrated markets. Although it is not possible to quantify how much barriers to entry and other factors will affect competition among firms, it seems unlikely that individual petroleum refiners would be able to significantly influence market prices given the
current structure of the market.

2.4.2 Characteristics of U.S. Petroleum Refineries and Petroleum Refining Companies
A petroleum refinery is a facility where labor and capital are used to convert material inputs (such as crude oil and other materials ) into finished petroleum products. Companies that own these facilities are legal business entities that conduct transactions and make decisions that affect the facility. The terms “facility,” “establishment,” and “refinery” are synonymous in this
report a nd refer to the physical location where products are manufactured. Likewise, the terms “company” and “firm” are used interchangeably to refer to the legal business entity that owns one or more facilities. This section presents information on refineries, su ch as their location and capacity utilization, as well as financial data for the companies that own these refineries.

2.4.2.1 Geographic Distribution of U.S. Petroleum Refineries
There are approximately 148 petroleum refineries operating in the United Sta tes, spread across 32 states. The number of petroleum refineries located in each of these states is listed in
Table 2

- 7. This table illustrates that a significant portion of petroleum refineries are located along the Gulf of Mexico region. The leading petr oleum refining states are Texas, Louisiana, and
California.

2.4.2.2 Capacity Utilization
Capacity utilization indicates how well current refineries meet demand. One measure of capacity utilization is capacity utilization rates. A capacity utilization rate is the ratio of actual production volumes to full

- capacity production volumes. For example, if an industry is producing as much output as possible without adding new floor space for equipment, the capacity utilization rate would be 100 percent. On the othe r hand, if under the same constraints the industry were only producing 75 percent of its maximum possible output, the capacity utilization rate would be 75 percent. On an industry

- basis, capacity utilization is highly variable from year to year depending o n economic conditions. It is also variable on a company

- by

-

### 2

- 22

company basis depending not only on economic conditions, but also on a company’s strategic position in its particular industry. While some plants may have idle production lines or empty floor space , others need additional space or capacity.

Table 2

- 8 lists the capacity utilization rates for petroleum refineries from 2000 to 2010. It is interesting to note the declines in capacity utilization from 2007 to 2008 and again from 2008
to 2009. These declines seem counter intuitive because there does not appear to be evidence that demand for petroleum products is dropping. To understand this better, it is important to realize that the capacity utilization ratio in the petroleum industry represents the utilization of the atmospheric crude oil distillation units. This ratio is calculated for the petroleum industry by dividing the gross input to atmospheric crude oil distillation units (all inputs involved in atmospheric crude oil distillation, such as cr ude oil) by the industry’s operational capacity.

### 2

- 23

Table 2

- 7 Number of Petroleum Refineries, by State

State Number of Petroleum Refineries Alabama 3 Alaska 6 Arkansas 2 California 20 Colorado 2 Delaware 1 Georgia 1 Hawaii 2 Illinois 4 Indiana 2 Kansas 3 Kentucky 2 Louisiana 19 Michigan 1 Minnesota 2 Mississippi 3 Montana 4 Nevada 1 New Jersey 5 New Mexico 3 North Dakota 1 Ohio 4 Oklahoma 6 Pennsylvania 5 Tennessee 1 Texas 26 Utah 5 Virginia 1 Washington 5 West Virginia 1 Wisconsin 1 Wyoming 6 Total 148

Source: U.S. Energy Information Administration (EIA), Form EIA

- 820, “Annual Refinery Report. Table 1. Number and Capacity of Operable Petroleum Refineries by PAD District and State as of January 1, 2011” Release Date: June 24, 2011; (Data accessed on 10/12/11). [Source for 2011 numbers.] http:/ /www.eia.gov/petroleum/refinerycapacity/

### 2

- 24

Table 2

- 8 Full Production Capacity Utilization Rates for Petroleum Refineries

Year Petroleum Refineries

Capacity Utilization Rates (NAICS 324110) Gross Input to Atmo spheric Crude Oil Distillation Units (1,000s of barrels per day) Operational Capacity (1,000s of barrels per day) 2000 92.6 15,299 16,525 2001 92.6 15,352 16,582 2002 90.7 15,180 16,744 2003 92.6 15,508 16,748 2004 93.0 15,783 16,974 2005 90.6 15,578 17,196 2006 89.7 15,602 17,385 2007 88.5 15,450 17,450 2008 85.3 15,027 17,607 2009 82.9 14,659 17,678 2010 86.4 15,177 17,575
Sources: U.S. Department of Energy, Energy Information Administration (EIA). 2007a

. “Refinery Utilization and Capacity.” Available at <http://tonto.eia.doe.gov/dnav/pet/ pet_pnp_unc_dcu_nus_m.htm>. As obtained on January, 2007. U.S. Energy Information Administration (EIA), “Refinery Utilization and Capacity.” Available at http://www.ei a.gov/dnav/pet/pet_pnp_unc_dcu_nus_a.htm; Release date: 7/28/11; (Data accessed on 10/11/11). [Source for 2007

- 2010 numbers]



### From 2007 to 2008 operational capacity increased from 17,450,000 barrels per calendar day to 17,607,000 barrels per calendar day at the same time gross inputs fell from 15,450,000 barrels per calendar day to 15,027,000 barrels per calendar day resulting in a 3.6 percent
decrease in utilization. Similarly, from 2008 to 2009 operational capacity increased from 17,607,000 barrels per ca lendar day to 17,678,000 barrels per calendar day at the same time gross inputs fell from 15,027,000 barrels per calendar day to 14,659,000 barrels per calendar day resulting in a 2.8 percent decrease in utilization.

2.4.2.3 Characteristics of Small Busine sses Owning U.S. Petroleum Refineries
Under Small Business Administration (SBA) regulations, a small refiner is defined as a refinery with no more than 1,500 employees. 3 

### For this analysis we applied the small refiner definition of a refinery with no more than 1,500 employees. For additional information on the Agency’s application of the definition for small refiner, see the June 24, 2008 Federal Register Notice for 40 CFR Part 60, Standards of Performance for Petroleum Refineries (Volume 73, Number 122, pa ge 35858).

As of January 2011, there were 148 petroleum refineries operating in the continental United States and US territories with a cumulative capacity of processing over 17 million barrels of crude per calendar day (EIA, 2011a ). We identified 64 parent companies owning refineries in

3 See Table in 13 CFR 121.201, NAICS code 324110.

### 2

- 25

the United States and were able to collect employment and sales data for 61 (95%) of them. We were not able to collect employment and sales data for Ten By Inc., PBF Holdings LLC, and
Northern Tier Ener gy LLC, representing 2.36% of refining capacity.

The distribution of employment across companies is illustrated in Figure 2

- 6. As this figure shows, 36 companies (59% of the 61 total) employee fewer than 1,500 workers and would
be considered small business es. These firms earned an average of $1.36 billion of revenue per year, while firms employing more than 1,500 employees earned an average of $82.5 billion of revenue per year (Figure 2

- 7). Distributions of the number of large and small firms earning
diffe rent levels of revenue are presented in Figures 2

- 8 and 2

- 9.

Figure 2

- 6 Employment Distribution of Companies Owning Petroleum Refineries (N=61)

Sources: Employment Data from Petroleum Refinery Emissions Information Collection, where available, Component 1, OMB Control No. 2060

- 0657. Hoovers 2011 Online Data, accessed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Online Data reflects either actual data for 2010/2011 reported by companies, estima ted data for 2011, or occasionally 2009 values. Million Dollar Database online, 2011, accessed through University of South Carolina’s Moore School of Business Library. Million Dollar Online Data reflects either actual data for 2011 reported by companies o r estimated data for 2011. Ward’s Business Directory of Public and Private Companies, 2011, accessed at James Branch Cabell Library (Virginia Commonwealth University). Ward’s Business Directory compiles financial data from several sources such as annual r eports, company websites, and phone interviews. If financial data from private companies is unavailable, Ward’s staff estimates the information. Copyright 2011 Harte

- Hanks Market Intelligence, Inc., All Rights Reserved. CI Technology Database, September 2 011. Reference USA, accessed thru Jefferson Madison Regional Library, Charlottesville, Virginia.

14 firms

6 firms 9 firms

4 firms 3 firms 25 firms

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

<100 100

- 250 250

- 500 500

- 1,000 1,000

- 1,500 >1,500 Share of firms (%)

Number of Employees

### 2

- 26 Copyright 2011 Experian Information Solutions, Inc., Experian Business Reports accessed through LexisNexis at James Branch Cabell Library (Virginia Commonwea lth University). Experian Business Reports reflects the most recent data reported by a company, which may be 2010 or 2011. Global Duns Market Identifiers, accessed through LexisNexis at University of Virginia’s Alderman Library.

Figure 2

- 7 Average Revenue of Companies Owning Petroleum Refineries by Employment (N=61)

Sources: Employment Data from Petroleum Refinery Emissions Information Collection, where available, Component 1, OMB Control No. 2060

- 0657.
Hoovers 2011 Online Data, acces sed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Online Data reflects either actual data for 2010/2011 reported by companies, estimated data for 2011, or occasionally 2009 values.
Million Dollar Database online, 20 11, accessed through University of South Carolina’s Moore School of Business Library. Million Dollar Online Data reflects either actual data for 2011 reported by companies or estimated data for 2011. Ward’s Business Directory of Public and Private Compani es, 2011, accessed at James Branch Cabell Library (Virginia Commonwealth University). Ward’s Business Directory compiles financial data from several sources such as annual reports, company websites, and phone interviews. If financial data from private comp anies is unavailable, Ward’s staff estimates the information. Copyright 2011 Harte

- Hanks Market Intelligence, Inc., All Rights Reserved. CI Technology Database, September 2011. Reference USA, accessed thru Jefferson Madison Regional Library, Charlottesvi lle, Virginia. Copyright 2011 Experian Information Solutions, Inc., Experian Business Reports accessed through LexisNexis at James Branch Cabell Library (Virginia Commonwealth University). Experian Business Reports reflects the most recent data reported b y a company, which may be 2010 or 2011. Global Duns Market Identifiers, accessed through LexisNexis at University of Virginia’s Alderman Library. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000

<100 100

- 250 250

- 500 500

- 1,000 1,000

- 1,500 >1,500

Average revenue ($ millions)

Number of Employees

### 2

- 27

Figure 2

- 8 Revenue Distribution of Large Companies Owning Petroleum Refineries (N=25)

Sources: Employment Data from Petroleum Refinery Emissions Information Collection, where available, Component 1, OMB Control No. 2060

- 0657. Hoovers 2011 Online Data, accessed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Online Data reflects either actual data for 2010/2011 reported by companies, estimated data for 2011, or occasionally 2009 values. Million Dollar Database online, 2011, accessed through University of South Carolina’s Moore School of Business Library . Million Dollar Online Data reflects either actual data for 2011 reported by companies or estimated data for 2011. Ward’s Business Directory of Public and Private Companies, 2011, accessed at James Branch Cabell Library (Virginia Commonwealth University) . Ward’s Business Directory compiles financial data from several sources such as annual reports, company websites, and phone interviews. If financial data from private companies is unavailable, Ward’s staff estimates the information. Copyright 2011 Harte

- Hanks Market Intelligence, Inc., All Rights Reserved. CI Technology Database, September 2011. Reference USA, accessed thru Jefferson Madison Regional Library, Charlottesville, Virginia. Copyright 2011 Experian Information Solutions, Inc., Experian Busine ss Reports accessed through LexisNexis at James Branch Cabell Library (Virginia Commonwealth University). Experian Business Reports reflects the most recent data reported by a company, which may be 2010 or 2011.
Global Duns Market Identifiers, accessed th rough LexisNexis at University of Virginia’s Alderman Library. 1 firm 1 firm 23 firms

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

<5 5

- 10 10

- 50 50

- 100 100

- 500 500

- 1,000 >1,000
Revenue ($milllions)

### 2

- 28

Figure 2

- 9 Revenue Distribution of Small Companies Owning Petroleum Refineries (N=36)

Sources: Employment Data from Petroleum Refinery Emissions Information Collection, where available, Component 1, OMB Control No. 2060

- 0657. Hoovers 2011 Online Data, accessed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Online Data reflects either actual data for 2010/2011 reported by companies, estimated data for 2011, or occasionally 2009 values. Million Dollar Database online, 2011, accessed through University of South Carolina’s Moore School of Business Library. Million Dollar Online Data reflects either actual data for 2011 reported by compa nies or estimated data for 2011. Ward’s Business Directory of Public and Private Companies, 2011, accessed at James Branch Cabell Library (Virginia Commonwealth University). Ward’s Business Directory compiles financial data from several sources such as an nual reports, company websites, and phone interviews. If financial data from private companies is unavailable, Ward’s staff estimates the information. Copyright 2011 Harte

- Hanks Market Intelligence, Inc., All Rights Reserved. CI Technology Database, Septe mber 2011. Reference USA, accessed thru Jefferson Madison Regional Library, Charlottesville, Virginia. Copyright 2011 Experian Information Solutions, Inc., Experian Business Reports accessed through LexisNexis at James Branch Cabell Library (Virginia Com monwealth University). Experian Business Reports reflects the most recent data reported by a company, which may be 2010 or 2011. Global Duns Market Identifiers, accessed through LexisNexis at University of Virginia’s Alderman Library.



### Employment, crude c apacity, and location information are provided in Table 2

- 9 for each refinery owned by a parent company employing 1,500 employees or less. Similar information can be found for all 64 companies owning petroleum refineries in Appendix A.

5 firms

1 firm 8 firms

2 firms 7 firms

4 firms 9 firms

0% 5% 10% 15% 20% 25% 30%

<5 5

- 10 10

- 50 50

- 100 100

- 500 500

- 1,000 >1,000
Revenue ($millions)

### 2

- 29

In Section 3.4.2.1, we discussed how petroleum refining operations are characterized by
economies of scale

— that the cost per unit falls as a refinery produces more finished petroleum products. This means that smaller petroleum refiners face higher per unit costs than larger refining operations because they produce fewer petroleum products. As a result, some smaller firms have sought to overcome their competitive disadvantage by locating close to product

consuming areas to lower transportation costs and serving niche product m arkets (FTC, 2004 ).

A good example of a firm locating close to prospective customers is Countrymark Cooperative, Inc., which was started in the 1930s for the express purpose of providing farmers in Indiana with a consistent supply of fuel s, lubricants, and other products. A good example of a firm producing niche products is Calumet Specialty Product Partners. The firm produces both basic fuels like gasoline, diesel fuel and jet fuel and specialty products like lubricating oils, solvents, waxes, and other petroleum products. However, the firm’s specialty products unit is its largest unit (Hoovers, 2011 online). Also Arabian American Development Company’s South Hampton Resources facility specializes in producing high purity solvents for th e plastics and
foam industries.

However, recent developments are making these factors less important for success in the industry. For example, the entry of new product pipelines is eroding the locational advantage of smaller refineries (FTC, 2004 ). This trend can possibly be illustrated by the fact that most refineries owned by small businesses tend to be located in relatively rural areas (see Table 2

- 9). The median population density of counties occupied by small refineries is 103 people per square mile. This could suggest that refineries do not rely on the population surrounding them to support their refining operations.

Capacity information for the refineries owned by small businesses also suggests that
fewer small businesses are focu sing on developing specialty products or serving local customers as major parts of their business plan. For example, in 2006 29 small refineries had a collective crude refining capacity of 778,920 barrels per calendar day or 857,155 barrels per stream day (EIA, 2006c ). Approximately 21% of this total capacity was devoted to producing specialty products or more locally focused products such as aromatics, asphalt, lubricants, and petroleum coke. The remaining 79% was used to produce gasolin e, kerosene, diesel fuel, and liquefied petroleum gases. Similarly, in 2011, approximately 20% of small businesses’ total capacity was dedicated to producing specialty products and 80% was dedicated to producing fuel products.
As discussed in Section 3.4. 1.3, fuel products tend to be quite homogenous (gasoline from one refinery is not very different from gasoline from another refinery), and they are also normally transported by pipeline.

### 2

- 30

2.5 Markets
This section provides data on the volume of petroleum products produced and consumed in the United States, the quantity of products imported and exported, and the average prices of major petroleum products. The section concludes with a discussion of future trends for the petroleum refining industry.

2.5.1 U.S. Petroleum Consumption
Figure 2

- 10 illustrates the amount of petroleum products supplied between 2000 and 2010 (measured in millions of barrels of oil). These data represent the approximate consumptio n of petroleum products because it measures the disappearance of these products from primary sources (i.e., refineries, natural gas processing plants, blending plants, pipelines, and bulk terminals).2
- 31 Table 2

- 9 Characteristics of Small Businesses in the Petroleum Refining Industry

Parent Company Parent Company Type Cumulative

Crude Capacity (bbl/cd) Parent

Company Employment (#) Facility N ame Facility City Facilit y State Facility County Facility County Population Density (2000) Facility

County Population Density (2010) AGE Refining, Inc. Private 14,021 124 AGE Refining Inc San Antonio TX Bexar County 1,117 1,383 American Refining Group, Inc. Private 10,000 323 American Refining Group Inc Bradford PA McKean County 47 44

Arabian American Development Co Public 0 145 South Hampton Resources Inc. Silsbee TX Hardin County 54 61

BTB Refining Private 0 37 Trigeant Ltd. Corpus Christi TX Nueces County 375 406

Calcasieu Refining Company Private 78,000 92 Calcasieu Refining Co. Lake

Charles LA Calcasieu Parish 171 181

Calumet Specialty Products Partners, L.P. Public 57,000 654 Calumet Shreveport LLC Shreveport LA Caddo Parish 286 290

Calumet Specialty Products Partners, L.P. Public 13,020 654 Calumet Lubricants Co LP Cotton

Valley LA Caddo Parish 286 290

Calumet Specialty Products Partners, L.P. Public 8,300 654 Calumet Lubricants Co LP Princeton LA Caddo Parish 286 290

CHS, Inc. Public 59,600 287 Cenex Harvest States Laurel MT Yellowstone County 49 56

Connacher Oil and Gas, Limited Public 10,000 170 Montana Refining Co. Great Falls MT Cascade County 30 30

CVR Energy, Inc. Public 115,700 371 Coffeyville Resources Rfg&Mktg LLC Coffeyville KS Montgomery County 56 55

Countrymark Cooperative Private 26,500 425 Countrymark Cooperative, Inc. Mt. Vernon IN Posey County 66 63

Cross Oil Refining & Marketing, Inc. Private 7,500 110 Martin Midstream Partners LP Smackover AR Union County 44 40

Foreland Refining Corp. Private 2,000 27 Foreland Refining Co. Ely NV White Pine County 1 1

Frontier Oil Corp. Private 47,000 723 Frontier Refining Inc Cheyenne WY Laramie County 30 34



### (continued)2
- 32 

### Table 2

- 9. Characteristics of Small Businesses in the Petroleum Refining Industry (continued)

Parent Company Parent Company Type Cumulative

Crude Capacity (bbl/cd) Parent

Company Employment (#) Facility Name Facility City Facilit y State Facility County Facility County Population Density (2000) Facility

County Population Density (2010) Frontier Oil Corp. Private 138,000 723 Frontier El Dorado Refining Co El Dorado KS Butler County 42 46

Garco Energy Private 3,600 16 Garco Energy LLC Douglas WY Converse County 3 3

Gary

- Williams Energy Corporation Private 70,000 260 Wynnewood Refining Co. Wynnewood OK Garvin County 34 34

Goodway Refining, LLC Private 4,100 17 Goodway Refining LLC Atmore AL Escambia County 41 41

Gulf Atlantic Operations LLC Private 32 Gulf Atlantic Operations LLC Mobile Bay AL Mobile County 324 336

Holly Corporation Public 25,050 1,321 Holly Refining & Marketing Co Woods

Cross UT Davis County 785 1,026

Holly Corporation Public 105,000 1,321 Navajo Refining Co. Artesia NM Eddy County 12 13

Holly Corporation Public 70,300 1,321 Holly Refining & Marketing Co Tulsa East OK Tulsa County 988 1,058

Holly Corporation Public 85,000 1,321 Holly Refining & Marketing Co Tulsa West OK Tulsa County 988 1,058

Hunt Refining Co. Private 36,000 1,100 Hunt Refining Co. Tuscaloosa AL Tuscaloosa County 125 147

Hunt Refining Co. Private 11,000 1,100 Hunt Southland Refining Co Sandersville MS Lamar County 79 112

Kern Oil & Refining Co. Private 26,000 346 Kern Oil & Refining Co. Bakersfield CA Kern County 81 103

Lion Oil Co. Private 75,000 350 Lion Oil Co. El Dorado AR Union County 44 40 National Cooperative Refinery Association Public 85,500 612 National Cooperative Refinery Association McPherson KS McPherson County 33 33

NuStar Energy LP Public 70,000 1,492 Nustar Asphalt Refining LLC Paulsboro NJ Gloucester 784 895

NuStar Energy LP Public 28,000 1,492 Nustar Asphalt Refining LLC Savannah GA Chatham 530 622



### (continued)2
- 33 

### Table 2

- 9. Characteristics of Small Businesses in the Petroleum Refining Industry (continued)

Parent Company Parent Company Type Cumulative

Crude Capacity (bbl/cd) Parent

Company Employment (#) Facility Name Facility City Facilit y State Facility County Facility County Population Density (2000) Facility

County Population Density (2010) Pasadena Refining Systems Inc. Private 120,000 348 Pasadena Refining Systems Inc. Pasadena TX Harris County 1967 2,402

Pelican Refining Co LLC Private 0 33 Pelican Refining Co. LLC Lake

Charles LA Calcasieu Parish 171 181

Petro Star Inc. Private 19,700 400 Petro Star Inc. North Pole AK Fairbanks North Star 11 13

Petro Star Inc. Private 55,000 400 Petro Star Inc. Valdez AK Valdez Cordova 0.3 0 Placid Refining Private 57,000 207 Placid Refining Inc. Port Allen LA West Baton Rouge Parish 113 124

San Joaquin Refining Co. Private 15,000 108 San Joaquin Refining Co., Inc. Bakersfield CA Kern County 81 103

Santa Maria Refining Company Private 9,500 47 Greka Energy Santa Maria CA Santa Barbara County 146 155

Silver Eagle Refining Inc. Private 3,000 60 Silver Eagle Refining Evanston WY Uinta County 10 10

Silver Eagle Refining Inc. Private 10,250 60 Silver Eagle Refining Woods

Cross UT Davis County 785 1,026

Somerset Oil Inc. Private 5,500 11 Somerset Energy Refinery LLC Somerset KY Pulaski County 85 96

US Oil & Refining Co. Private 38,800 182 US Oil & Refining Co. Tacoma WA Pierce County 417 476

Ventura Refining & Transmission, LLC Private 12,000 37 Ventura Refining & Transmission LLC Thomas OK Custer County 27 28

Western Refining, Inc. Public 122,000 636 Western Refining Company LP El Paso TX El Paso County 671 791

Western Refining, Inc. Public 66,300 636 Western Refining Yorktown Inc Yorktown VA York County 533 625

Western Refining, Inc. Public 16,800 636 Western Refining Southwest Inc Bloomfield NM San Juan County 21 24

Western Refining, Inc. Public 21,100 636 Western Refining Southwest Inc Gallup NM McKinley County 14 13



### (continued)2
- 34 

### Table 2

- 9. Characteristics of Small Businesses in the Petroleum Refining Industry (continued)

Parent Company Parent Company Type Cumulative

Crude Capacity (bbl/cd) Parent

Company Employment (#) Facility Name Facility City Facility

State Facility County Facility County Population Density (2000) Facility

County Population Density (2010) World Oil Marketing Co. Private 8,500 65 Lunday

- Thagard Co. South Gate CA Los Angeles County 2,344 2,420

Wyoming Refining Co. Private 14,000 96 Wyoming Refining Co New Castle WY Weston County 3 3

Total 1,875,641 11,264 103

Sources: U.S. Census Bureau, American Fact Finder, “2010 Census Summary File 1 Population, Housing Units, Area & Density: 2010

- County

– Census Tract 100% Data 2010 Census” (Data accessed on 10/21/2011); http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?ftp=table Hoovers 2011 Online Data, accessed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Onli n e Data reflects either actual data for 2010/2011 reported by companies, estimated data for 2011, or occasionally 2009 values. Million Dollar Database online, 2011, accessed through University of South Carolina’s Moore School of Business Library. Milli on D ollar Online Data reflects either actual data for 2011 reported by companies or estimated data for 2011. Ward’s Business Directory of Public and Private Companies, 2011, accessed at James Branch Cabell Library (Virginia Commonweal th University). Ward’s Bu siness Directory compiles financial data from several sources such as annual reports, company websites, and phone interviews. If financial data from private companies is unavailable, Ward’s staff estimates the information. Copyright 2011 Harte

- Hanks Marke t Intelligence, Inc., All Rights Reserved. CI Technology Database, September 2011. Reference USA, accessed thru Jefferson Madison Regional Library, Charlottesville, Virginia. Copyright 2011 Experian Information Solutions, Inc., Experian Business Reports accessed through LexisNexis at James Branch Cabell Library (Virginia Commonwealth University). Experian Business Reports reflects the most recent data reported by a company, which may be 2010 or 2011. Global Duns Market Identifiers, accessed through Lexis Nexis at University of Virginia’s Alderman Library. Employment Data from Petroleum Refinery Emissions Information Collection, where available, Component 1, OMB Control No. 2060

- 0657. Energy Information Administration (EIA), Form EIA 820, “Annual Refinery Report,” Table 3. Capacity of Operable Petroleum Refineries by State and Individual Refinery as of January 1, 2011 <http://www.eia.gov/petroleum/refinerycapacity/>

### 2

- 35 Between 2000 and 2004, U.S. consumption o f petroleum products increased by 5%. Consumption leveled off by 2007 and dropped by 9% between 2007 and 2009 (Figure 2

- 10). This reduced growth was primarily the result of less jet fuel, residual fuel, distillate fuel, and other products being consumed in recent years. Consumption of all petroleum products, except for motor gasoline, increased between 2009 and 2010, but the total consumption of petroleum products did not reach 2000

- 2004 levels. The cumulative decrease in consumption over the 11 year peri od is 3% (Table 2

- 10).

Figure 2

- 1 0 Total Petroleum Products Supplied (millions of barrels per year)

Sources: U.S. Department of Energy. 2011 Energy Information Administration (EIA). 1996

– 2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on February 23, 2012) [Source for 2000

– 2010 numbers.] < http://www.eia.gov/dnav/pet/pet_cons_psup_dc_nus_mbbl_a.htm >. 6,400 6,600 6,800 7,000 7,200 7,400 7,600 7,800

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Millions of barrels
per year

Year

### 2

- 36 Table 2

- 10 Total Petroleum Products Supplied (millions of barrels per year)

Year Motor

Gasoline Jet Fuel Distillate Fuel Oil Residual

Fuel Oil Liquefied

Petroleum Gases Other

Products Total
2000 3,101 631 1,362 333 816 967 7,211 2001 3,143 604 1,404 296 746 978 7,172 2002 3,229 591 1,378 255 789 969 7,213 2003 3,261 576 1,433 282 757 1,003 7,312 2004 3,333 597 1,485 316 780 1,076 7,588 2005 3,343 613 1,503 336 741 1,057 7,593 2006 3,377 596 1,522 251 749 1,055 7,551 2007 3,389 592 1,532 264 761 1,011 7,548 2008 3,290 563 1,444 228 715 896 7,136 2009 3,284 509 1,325 187 749 799 6,852 2010 3,282 523 1,387 195 793 820 7,001

Sources: Annuals, Volume 1.” Available at <http://www.eia.doe.gov/oil_gas/petroleum/data_publications/ petroleum_supply_annual/psa_volume1/psa_volume1.html>. As obtained on October 31, 2007. U.S. Department of Energy. 2011 Energy Information Administration (EIA). 1996

– 2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on October 11, 2011) [Source for 2007

– 2010 numbers.]

< http://www.eia.gov/dnav/pet/pet_sum_snd_d_nus_mbbl_a_cur.htm>.



### 2.5.2 U.S. Petroleum Production

Table 2

- 11 reports the number of barrels of major petroleum products produced in the United States between 2000 and 2010. U.S. production of petroleum products at refineries and blenders grew steadily, resulting in a 7% cumulative increase for the period. However, in 2005 and 2009 production declined by slightly.

Table 2

- 11 U.S. Refinery and Blender Net Production ( millions of barrels per year)

Year Motor

Gasoline Jet Fuel Distillate Fuel Oil Residual

Fuel Oil Liquefied

Petroleum Gases Other

Products Total 2000 2,910 588 1,310 255 258 990 6,311 2001 2,928 558 1,349 263 243 968 6,309 2002 2,987 553 1,311 219 245 990 6,305 2003 2,991 543 1,353 241 240 1,014 6,383 2004 3,025 566 1,396 240 236 1,057 6,520 2005 3,036 564 1,443 229 209 1,015 6,497 2006 3,053 541 1,475 232 229 1,032 6,561 2007 3,051 528 1,509 246 239 464 6,568 2008 3,129 546 1,572 227 230 950 6,641 2009 3,207 510 1,478 218 227 1,418 6,527 2010 3

, 306 517 1,542 213 240 1,747 6,735

Sources: U.S. Department of Energy, Energy Information Administration (EIA). 1996

– 2007. “Petroleum Supply Annuals, Volume 1.” Available at < http://www.eia.doe.gov/oil_gas/petroleum/data_publications/ petroleum_supply_annual/psa_volume1/psa_volume1.html>. As obtained on October 31, 2007.

### 2

- 37 U.S. Department of Energy. 2011 Energy Information Administration (EIA). 1996

– 2011. “Petroleum Supply Annua ls, Volume 1.” (Data accessed on October 7, 2011) [Source for 2007

– 2010 numbers.] <http://www.eia.gov/dnav/pet/pet_pnp_refp_dc_nus_mbbl_a.htm>.



### The 2005 decline in production (0.35%) was possibly the result of damage inflicted by two hurricanes (Hurrica ne Katrina and Hurricane Rita) on the U.S. Gulf Coast

— the location of many U.S. petroleum refineries (Section 3.4.2). According to the American Petroleum Institute, approximately 30% of the U.S. refining industry was shut down as a result of the damage (AP I,
2006 ). The 2009 decline in production (1.72%) was probably the result of the global economic crisis. Additional production data are presented in Table 2

- 12, which reports the value of shipments of products produced by the petroleum r efining industry between 1997 and 2009.

2.5.3 International Trade

International tr ade trends are shown in Tables 2

- 13 and 2

- 14. Between 1995 and 2006, imports and exports of petroleum products increased by 123% and 51% respectively. Between 1995 and 2006, while imports of most major petroleum products grew at approximately the same rate, the growth of petroleum product exports was driven largely by residual fuel oil and other petroleum products. More recently, between 2008 and 2010 exports of petroleum prod ucts such as motor gasoline, jet fuel, distillate fuel oil and liquefied petroleum gases have also increased.

Since 2006, industry import and export trends have diverged significantly. Between 2006 and 2010 imports declined by 28%, returning close to 2001 levels

. In 2010, U.S. net imports
were 98 million barrels , accounting for 10% of the country’s imports and around 1% of total petroleum products consumed in that year. Exports grew at an average annual rate of 12% and in
2010 were 2.4 times the level of exports in 2001.

In 2011, U.S. net imports of crude oil, based on a four

- week average, ranged from 8,138 to 9,474 thousand barrels per day. And while 2011 started out with the U.S. as a net importer of total petroleum products, from July 2011 through December 2011 the U.S. became a net exporter of total petroleum products. From July to December 2011, based on a four

- week average, the U.S. exported an average of 405,000 barrels per day with a maximum of 809,000 barrels per day of total petroleum produ cts (EIA 2012). 4

4 Data for 2011 located on the Energy Information Administration’s website at http://www.eia.gov/dnav/pet/pet_move_wkly_dc_NUS

- Z00_mbblpd_4.htm

.

### 2

- 38 Table 2

- 12 Value of Product Shipments of the Petroleum Refining Industry
Year Millions of $Reported Millions of $2005
1997 152,756 180,671 1998 114,439 136,746 1999 140,084 164,651 2000 210,187 237,425 2001 195,898 219,476 2002 186,761 210,647 2003 216,764 238,425 2004 290,280 306,328 2005 419,063 419,063 2006 489,051 470,037 2007 551,997 510,996 2008 682,756 585,664 2009 436,974 394,348

Note: Numbers were adjusted for inflation using producer price index industry data for Total Manufacturing Industries (Table 2

- 16).
Sources: U.S. Department of Commerce, Bureau of the Census. 2007. 2006 Annual Survey of Manufactures. Obtained through American Fact Finder Database <http://factfind er.census.gov/home/saff/main.html?_lang=en>. U.S. Department of Commerce, Bureau of the Census. 2003b. 2001 Annual Survey of Manufactures. M01(AS)

- 2. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as

- 2.pdf >. As obtained on March 4, 2008. U.S. Department of Commerce, Bureau of the Census, FactFinder. 2011. 2009 Annual Survey of Manufactures. 1996

– 2011. Obtained through American Fact Finder Database. (Data accessed on October 14, 2011.) [Source for 2007

– 2009 numbers] <http://factfinder.census.gov/servlet/ DatasetMainPageServlet?_lang=en&_ts=336651423017&_ds_name=AM0931GS101&_program=EAS>.

Table 2

- 13 Imports of Major Petroleum Products (millions of barrels per year)

Year Motor

Gasoline Jet Fuel Distillate Fuel Oil Residual

Fuel Oil Liquefied

Petroleum Gases Other

Products Total 1995 97 35 71 68 53 262 586 1996 123 40 84 91 61 322 721 1997 113 33 83 71 62 345 707 1998 114 45 77 101 71 324 731 1999 139 47 91 86 66 344 774 2000 156 59 108 129 79 343 874 2001 166 54 126 108 75 400 928 2002 182 39 98 91 67 396 872 2003 189 40 122 119 82 397 949 2004 182 47 119 156 96 520 1,119 2005 220 69 120 193 120 587 1,310 2006 173 68 133 128 121 687 1,310 2007 151 79 111 136 90 688 1,255 2008 110 38 78 128 93 700 1,146 2009 82 29 82 121 66 597 977 2010 49 36 83 134 56 584 942

Sources: U.S. Department of Energy, Energy Information Administration (EIA).1996

– 2007. “Petroleum Supply Annuals, Volume 1.” Available at <http://www.eia.doe.gov/oil_gas/petroleum/data_publications/ petroleum_supply_annual/psa_volume1/psa_volume1.html>. As obtained on October 31, 2007.

### 2

- 39 U.S. Department of Energy. 2011 Energy Information Administration (EIA). 1996

– 2011. “Petroleum Supply Ann uals, Volume 1.” (Data accessed on October 7, 2011.) [Source for 2007

– 2010 numbers.] http://www.eia.gov/dnav/pet/pet_move_imp_dc_NUS

- Z00_mbbl_a.htm

Table 2

- 14 Exports of Major Petroleum Products (millions of barrels per year)

Year Motor

Gasoline Jet Fuel Distillate Fuel Oil Residual

Fuel Oil Liquefied

Petroleum Gases Other

Products Total 1995 38 8 67 49 21 128 312 1996 38 17 70 37 19 138 319 1997 50 13 56 44 18 147 327 1998 46 9 45 50 15 139 305 1999 40 11 59 47 18 124 300 2000 53 12 63 51 27 157 362 2001 48 10 44 70 16 159 347 2002 45 3 41 65 24 177 356 2003 46 7 39 72 20 186 370 2004 45 15 40 75 16 183 374 2005 49 19 51 92 19 183 414 2006 52 15 79 103 21 203 472 2007 46 15 98 120 21 213 513 2008 63 22 193 130 25 216 649 2009 71 25 214 152 36 224 723 2010 108 31 239 148 48 270 843

Sources: U.S. Department of Energy, Energy Information Administration (EIA). 1996

– 2007. “Petroleum Supply Annuals, Volume 1.” Available at <http://www.eia.doe.gov/oil_gas/petroleum/data_publications/ petroleum_supply_annual/psa_volume1/psa_volume1.html>. As obtained on October 31, 2007. U.S. Department of Energy. 2011 Energy Infor mation Administration (EIA). 1996

– 2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on October 7, 2011.) [Source for 2007

– 2010 numbers] <http://www.eia.gov/dnav/pet/pet_move_exp_dc_NUS

- Z00_mbbl_a.htm>.



### 2.5.4 Market Prices
The average nominal prices of major petroleum products sold to end users are provided for selected years in Table 2

- 15. 5 

### As these data illustrate, nominal prices rose substantially between 2005 and 2008. In 2009 there was a drop in prices, resulting in a return to 2005 price levels for most products. In 2010 nominal prices increased. During the 2008

– 2010 period, the most volatile price was jet fuel price: it declined by 44% in 2009 and increased by 29% in 2010.

5 Sales to end users are those made directly to the consumer of the product. This includes bulk consumers, such as agriculture, industry, and utilities, as well as residential and commercial consumers.

### 2

- 40 Table 2

- 15 Averag e Price of Major Petroleum Products Sold to End Users (cents per gallon)
Product 1995 2000 2005 2008 2009 2010 Motor gasoline 76.5 110.6 183 278 189 230 No. 1 distillate fuel 62.0 98.8 183 298 214 271 No. 2 distillate fuel 56.0 93.4 178 314 184 232 Jet fuel 54.0 89.9 174 305 170 220 Residual fuel oil 39.2 60.2 105 196 134 171

Note: Prices do not include taxes.

Sources: U.S. Department of Energy, Energy Information Administration (EIA). 2007b. “Refiner Petroleum Product Prices by Sales Type.” Available at <http://tonto.eia.doe.gov/dnav/pet/pet_pri_refoth_dcu_nus_m.htm>. As obtained on January 11, 2008. U.S. Department of Energy. 2011 Energy Information Administration (EIA). 1996

– 2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on October 7, 2 011.) [Source for 2007

– 2010 numbers.]<http://www.eia.gov/dnav/pet/pet_pri_refoth_dcu_nus_a.htm>.



### The nominal prices domestic petroleum refiners receive for their products have been volatile, especially compared to prices received by other U.S. manufacturer s. This trend is
demonstrated in Table 2

- 16 by comparing the producer price index (PPI) for the petroleum refining industry against the index for all manufacturing industries. Between 1995 and 2010, prices received by petroleum refineries for their product s rose by 288%, while prices received by all manufacturing firms rose by 41%. In 2009, both price indexes experienced a decline from 2008 levels, however the decrease was 36% for petroleum refineries and 5% for all manufacturing firms.

### 2

- 41 Table 2

- 16 Producer Price Index Industry Data: 1995 to 2010 Petroleum Refining (NAICS 32411) Total Manufacturing Industries

Year PPI Annual Percentage Change in PPI PPI Annual Percentage Change in PPI
1995 74.5 3% 124.2 3%
1996 85.3 14% 127.1 2%
1997 83.1 −3% 127.5 0%
1998 62.3 −25% 126.2 −1%
1999 73.6 18% 128.3 2%
2000 111.6 52% 133.5 4%
2001 103.1 −8% 134.6 1%
2002 96.3 −7% 133.7 −1%
2003 121.2 26% 137.1 3%
2004 151.5 25% 142.9 4%
2005 205.3 36% 150.8 6%
2006 241.0 17% 156.9 4%
2007 266.9 11% 162.9 4%
2008 338.3 27% 175.8 8%
2009 217.0 −36% 167.1 −5%
2010 289.4 33% 175.4 5%

Sources: U.S. Bureau of Labor Statistics (BLS). 2007. “Producer Price Index Industry Data: Customizable Industry Data Tables.” Available at <http://www.bls.gov/ppi/>. As obtained on October 11, 2007. U.S. Bureau of Labor Statistics (BLS). 2011. “Producer Price Ind ex Industry Data: Customizable Industry Data Tables.” (Data accessed on October 11, 2011.) [Source for 2007

– 2010 numbers]<http://data.bls.gov/cgi

- bin/dsrv?pc>.



### 2.5.5 Profitability of Petroleum Refineries
Estimates of the mean profit (before taxes) to net s ales ratios for petroleum refiners are reported in Table 2

- 17 for the 2006

– 2007 and 2009

- 2010 fiscal years. These ratios were calculated by Risk Management Associates by dividing net income into revenues for 44 firms for
the 2006

- 2007 fiscal year and 43 fi rms for the 2009

- 2010 fiscal year. They are broken down based on the value of assets owned by the reporting firms.

### 2

- 42 Table 2

- 17 Mean Ratios of Profit before Taxes as a Percentage of Net Sales for Petroleum Refiners, Sorted by Value of Assets

Fiscal Year Total

Number of Statements 0 to

500,000 500,000 to

2 Million 2 Million

to 10 Million 10 Million

to 50 Million 50 Million

to 100 Million 100 Million

to 250 Million All

Firms
4/1/2006

– 3/31/2007 44

— — 4.6 6.5

— — 6.7

4/1/2009

– 3/31/2010 43

— — 5.5

— — — 4.1

Sources: Old Source: Risk Management Association (RMA). 2008. Annual Statement Studies 2007

– 2008

. Pennsylvania: RMA, Inc. New Source: Risk Management Association (RMA). 2011. Annual Statement Studies 2010

– 2011

. Pennsylvania: RMA, Inc.



### As these ratios demonstrate, firms that reported a greater value of assets also received a greater return on sales. For example, for the 2006

– 2007 fiscal year, firms with assets valued between $10 and $50 million received a 6.5% average return on net sales, while firms with assets valued between $2 and $10 million only received a 4.6% average return. Firms with assets valued between $2 and 10 million received 5.5% average return between 2009 and 2010. The
data for other as set size categories is not shown for the fiscal year 2009

– 2010 because RMA received fewer than 10 financial statements in those categories and RMA does not consider those samples to be representative. The average return on sales for the entire industry was 6.7% during
the 2006

– 2007 fiscal year and declined to 4.1% during the 2009

– 2010 fiscal year.

Obtaining profitability information specifically for small petroleum refining companies can be difficult as most of these firms are privately owned. However, some of the small, domestic petroleum refining firms identified in Section 3.4.2.3 are publicly owned companies

— the Arabian American Development Co., CVR Energy Inc., Calumet Specialty Products Partners, L.P., Holly Corporation, and Western Refining, Inc. Prof it ratios were calculated for these companies using data obtained from their publicly available 2010 income statements. These ratios are presented in Table 2

- 18.

### 2

- 43 Table 2

- 18 Net Profit Margins for Publicly Ow ned, Small Petroleum Refiners: 2010

Company Net Income ($millions) Total Revenue

($millions) Net Profit Margin (%) Arabian American Development Co. 2.69 139.11 1.93% Calumet Specialty Products Partners 16.70 2,190.80 0.76% CVR Energy Inc. 14.30 4,079.80 0.35% Holly Corporation 133.10 8,323.00 1.60% Western Refining, Inc. −17.05 7,965.10 −0.21%

Sources: Holly Corporation, EDGAR database Holly Corporation 10K. February 25, 2011. 10K for year ended December 31, 2010. (Data accessed on 10/23/11) [Source for 2010 numbers.] http://files.shareholder.com/downloads/FTO/1428853845x0xS950123

- 11

- 18524/48039/filing.pdf> Western Refining, Thomson Reuters Western Refining, Inc. 10K. March 8, 2011. 10K for year ended December 31, 2010. (Data accessed on 10/23/11) [Source for 2010 numbers.] http://phx.corporate

ir.net/ph oenix.zhtml?c=194293&p=irol

- sec> Arabian American Develop Co., South Hampton Resources, Inc. Arabian American Development Co. Q4 & FY2010 Financial Results. (Data accessed on 10/21/11) [Source for 2010 numbers.] http://www.arabianamericandev.com/images/uploads/investor/2010eoyearningspresentation.pdf> Calumet Specialty Products Partners, Morningstar for Calumet Specialty Products Partners 10K. F ebruary 22, 2011. 10K for year ended December 31, 2010. (Data accessed on 10/21/11) [Source for 2010 numbers.] ht tp://quote.morningstar.com/stock

- filing/Annual

- Report/2010/12/31/t.aspx?t=XNAS:CLMT&ft=10

- K&d=c7dd2813722445ded56c7e3aefebf2ca > CVR Energy Inc., EDGAR database for CVR Energy Inc. 10K. March 7, 2011. 10K for year ended December 31, 2010. (Data accessed on 10/23/11) [Source for 2010 numbers.] <http://www.sec.gov/Archives/edgar/data/1376139/000095012311022741/y90110e10vk.htm>



### 2.5.6 I ndustry Trends
The Energy Information Administration’s (EIA’s) 2011 Annual Energy Outlook provides forecasts of average petroleum prices, petroleum product consumption, and petroleum refining capacity utilization to the year 2035. Trends in these variables are affected by many factors that are difficult to predict, such as energy prices, U.S. economic growth, advances in technologies, changes in weather patterns, and future public policy decisions. As a result, the EIA evaluated a wide variety of cases base d on different assumptions of how these factors will behave in the future. This section focuses on the EIA’s “reference case” forecasts, which assume that current policies affecting the energy sector will remain unchanged throughout the projection period ( EIA, Form EIA

- 820 ).

According to the 2011 Annual Energy Outlook’s reference forecast, world oil prices (defined as the average price of low

- sulfur, light crude oil) are expected to steadily increase over the next 10 years as the amount of oil demanded by non

- OECD and OECD countries increases. Since crude oil is the primary input in petroleum refining, an increase in its price would likewise represent an increase in production costs of petroleum refiners. As a result, the prices of
petr oleum products sold to end users are expected to rise over the same period (Table 2

- 19).

### 2

- 44 Higher prices and tighter fuel efficiency standards (enlarged production of non

- oil fuels) will moderate the growth in petroleum products consumed by the recovering US economy (Table 2

- 20). Between 2011 and 2019, the prices of major petroleum products are expected to rise approximately from 32% to 49%, while consumption of all of those products is expected to rise by 7%. In particular the price of the most supplied pro duct, motor gasoline, is projected to rise by 38% and its consumption is projected to slightly increase by 2%.

Table 2

- 19 Forecasted Average Price of Major Petroleum Products Sold to End Users in 2009 Currency (cents per gallon)
Product 2011 2012 2013 2014 2015 2016 2017 2018 2019 Motor gasoline 286.1 291.3 312.3 326.9 342.1 353.6 369.5 382.9 395.5 Jet fuel 233.0 252.2 261.8 270.4 280.3 297.9 314.5 330.8 346.1 Distillate fuel 302.6 289.8 301.9 313.9 326.9 345.8 364.1 382.3 400.4 Residual fuel oil 186.2 183.1 191.3 202.9 213.6 225.9 236.8 249.1 259.9 LPGs 178.9 180.5 186.7 193.6 200.4 208 217 226.2 235.4

Sources: U.S. Department of Energy, Energy Information Administration (EIA). 2007c. “Annual Energy Outlook.” Available at <http://www.eia.doe.gov/oiaf/archive/aeo07/pdf/0383(2007).pdf>. As obtained on January 21, 2007. U.S. Department of Energy, Energy Information Administration (EIA). 2011. “Annual Energy Outlook.”; (Data accessed on October 11, 2011) [Source for 2011

– 2019 numbers.] <http://www.eia.gov/forecasts/aeo/pdf/0383(2011).pdf>.

Table 2

- 20 Total Petroleum Products Supplied (million s of barrels per year)

Year Motor

Gasoline Jet Fuel Distillate Fuel Oil Residual

Fuel Oil Liquefied

Petroleum Gases Other

Products Total 2011 3,317 515 1,367 199 768 806 6,972 2012 3,407 554 1,451 219 823 838 7,291 2013 3,424 555 1,501 218 835 866 7,400 2014 3,429 560 1,501 216 843 876 7,425 2015 3,432 564 1,509 217 848 888 7,459 2016 3,438 569 1,524 217 850 891 7,490 2017 3,416 575 1,538 218 850 891 7,487 2018 3,392 580 1,550 218 850 881 7,472 2019 3,371 585 1,563 218 850 876 7,463

Sources: U.S. Department of Energy, Energy Information Administration (EIA). 2007c. “Annual Energy Outlook.” Available at <http://www.eia.doe.gov/oiaf/archive/aeo07/pdf/0383(2007).pdf>. As obtained on January 21, 2007. U.S. Department of Energy, Energy Information Administration (EIA). 2011. “Annual Energy Outlook.”; (Data accessed on October 11, 2011) [Source for 2016

– 2019 numbers.] <http://www.eia.gov/forecasts/aeo/pdf/0383(2011).pdf>.



### Overall, the EIA forecasts that U.S. operational capacity will decrease by a total of 5% between 2011 and 2019 (Table 2

- 21 ). The rate of capacity utilization is projected to average 86% during this period.

### 2

- 45 Table 2

- 21 Full Production Capacity Utilization Rates for Petroleum Refineries

Year Petroleum Refineries

Capacity Utilization Rates (NAICS 324110) Gross Input to Atmospheric Crude Oil Distillation Units (1,000s of barrels per day) 6 Operational Capacity (1,000s of barrels per day) 2011 85.0% 14,946 17,583 2012 83.2% 14,672 17,635 2013 84.2% 14,836 17,626 2014 84.7% 14,851 17,524 2015 84.9% 14,847 17,497 2016 85.6% 14,853 17,342 2017 86.5% 14,827 17,142 2018 87.5% 14,778 16,887 2019 88.2% 14,743 16,706

Sources: U.S. Department of Energy, Energy Information Administration (EIA). 2007c. “Annual Energy Outlook.” Available at <http://www.eia.doe.gov/oiaf/archive/aeo07/pdf/0383(2007).pdf>. As obtained on January 21, 2007. U.S. Department of Energy, Energy Information Administration (EIA). 2011. ”Annual Energy Outlook.”; (Data accessed on February 23, 2012) [Source for 2011

– 2019 numbers.] < Capacity utilization

-- http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2011&subject=0

- AEO2011&table=11

- AEO2011®ion=0

- 0&cases=ref2011

- d020911a and Domestic Refinery Distillation Capacity

-- http://www.eia.gov/forecasts/aeo/excel/aeotab_11.xls >.



### 2.6 References American Petroleum Institute (API). 2006. “FYI on Refineries: Operating at Record Levels.” Available at < http://new.api.org/aboutoilgas/sectors/refining/upload/ FYI_Refineries.pdf>. As obtained on October 31, 2006.

Arizona Clean Fuels. 2007. Company Home Page. Available at <http://www.arizonacleanfuels.com/>. As obtained on February 5, 2007.

Arabian American Develop Co., South Hampton Resources, Inc. Arabian American Development Co. Q4 & FY2010 Financial Results. (Data accessed on 10/21/11) [Source for 2010 numbers.] <http://www.arabianamericandev.com/images/uploads/investor/ 2010eoyearningspr esentation.pdf>

Arabian American Development Co. April 6, 2007. 10K for year ended December 31, 2006. EDGAR Database. Available at <http://www.sec.gov/Archives/edgar/data/ 7039/000095013407007709/0000950134

- 07

- 007709

- index.htm>.

Calumet Specialty Products Partners, Morningstar for Calumet Specialty Products Partners 10K. February 22, 2011. 10K for year ended December 31, 2010. (Data accessed on 10/21/11) [Source for 2010 numbers.] <http://quote.morningstar.com/stock

- filing/Annual

Report/2010/12/31/t.aspx?t= XNAS:CLMT&ft=10

- K&d=c7dd2813722445ded56c7e3aefebf2ca>

6 For each year, the gross input to atmospheric crude oil distillation units is calculated by multiplying the capacity utilization rate by the operational capacity.

### 2

- 46 Calumet Specialty Products Partners. February 23, 2007. 10K for year ended December 31, 2006. EDGAR Database. Available at <http://www.sec.gov/Archives/edgar/data/ 1340122/000095013407003992/h43776e10vk .htm>.

Copyright 2011 Experian Information Solutions, Inc., Experian Business Reports accessed through LexisNexis at James Branch Cabell Library (Virginia Commonwealth University).

Copyright 2011 Harte

- Hanks Market Intelligence, Inc., All Rights Reserved. CI Technology Database, September 2011.

CVR Energy Inc. 2006. Google Finance. Available at <http://finance.google.com/finance?q=NYSE:CVI>. As obtained on February 28, 2008.

CVR Energy Inc., EDGAR database for CVR Energy Inc. 10K. March 7, 2011. 10K for yea r ended December 31, 2010. (Data accessed on 10/23/11) [Source for 2010 numbers.] <http://www.sec.gov/Archives/edgar/data/1376139/000095012311022741/y90110e10vk. htm>

Dun & Bradstreet Small Business Solutions. Small Business Database. Available at <http://s mallbusiness.dnb.com/default.asp?bhcd2=1107465546>.

Dun & Bradstreet. 2007. 2007 D&B Million Dollar Directory . Pennsylvania: Dun & Bradstreet Inc.

Energy Information Administration (EIA), Form EIA

- 820, “Annual Refinery Report.” Table 3. Capacity of Operab le Petroleum Refineries by State and Individual Refinery as of January 1, 2011. (Data accessed on October 31, 2011.) [Source for 2009

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### 2

- 47 Holly Corporat ion, EDGAR database for Holly Corporation 10K. February 25, 2011. 10K for year ended December 31, 2010. (Data accessed on 10/23/11) [Source for 2010 numbers.] <http://files.shareholder.com/downloads/FTO/1428853845x0xS950123

- 11

- 18524/48039/filing.pdf>

Hoove rs 2011 Online Data, accessed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Online Data reflects either actual data for 2010/2011 reported by companies or estimated data for 2011.

Hoovers. 2007. Free Content, Company Information. Available at <http://www.hoovers.com/free/>. As obtained on April 11, 2007.
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Million Dollar Database online, 2011, accessed through Universit y of South Carolina’s Moore School of Business Library. Million Dollar Online Data reflects either actual data for 2011 reported by companies or estimated data for 2011.

Nicholson, Walter. 2005. Microeconomic Theory: Basic Principles and Extensions . Mason, OH: South

- Western Publishing.

Oil & Gas Journal , September 21, 1992. K.S. Eble and J. Feathers, “Process Water Reuse, Part I.” OGJ 1992a

Oil and Gas Journal, December 6, 2010

Petroleum Refinery Emissions Information Collection Request, Component 1, OMB Control No. 2060

- 0657

Reference USA, accessed thru Jefferson Madison Regional Library, Charlottesville, Virginia.

Oil and Gas Journal. April 7, 2008.Worldwide Construction Update.

Risk Management Association (RMA). 2008. Annual Statement Studies 2007

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Risk Management Association (RMA). 2011. Annual Statement Studies 2010

– 2011. Pennsylvania: RMA, Inc.

### 2

- 48 Reuters. July 19, 2007. “U.S. Refinery Expansion Plans .” Available at <http://uk.reuters.com/article/oilRpt/idUKN199746120070719>. As obtained on February 5, 2007.

Small Business Administration. 2011. Table of Small Business Size Standards. Available at < http://www.sba.gov/sites/default/files/Size_Standards _Table.pdf>. As obtained October 2011.
Speight, James G. 2006. “The Chemistry and Technology of Petroleum.” 4 th 

### Edition, page 178.
U.S. Bureau of Labor Statistics (BLS), 2011. “Producer Price Index Industry Data: Customizable Industry Data Tables.” (D ata accessed on October 11, 2011.) [Source for 2007

– 2010 numbers]<http://data.bls.gov/cgi

- bin/dsrv?pc>.
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U.S. Census Bureau, using American FactFinder; “Sector 31: Annual Survey of Manufacturers: General Statistics: Statistics for Industry Groups and Industries: 2009 and 2008 “ Release Date: 12/3/10 ; (Data accessed on 10/10/11). [Source for 2008 and 2009 numbers] <http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- NAICSASM=324110&

- ds_name=AM0931GS101&

- ib_type=NAICSASM&

- _industry=324110&

- _lang=en>
U.S. Census Bureau, using American FactFinder; “Sector 31: Manufacturing : Industry Series: Detailed Statistics by Industry for the United States: 2007” Release Date 10/30/09; (Data accessed on 10/11/11). [Source for 2007 numbers] <http://factfinder.census.gov/servlet/IBQTable?_bm=y&

- geo_id=01000US&

- ds_name=EC0731I1&

- NAICS2007= 324110&

- _lang=en>
U.S. Census Bureau, using American FactFinder; “Sector 31: Manufacturing: Industry Series: Materials Consumed by Kind for the United States: 2007” Release Date 10/30/09; (Data accessed on 10/11/11). [Source for 2007 numbers] <http://factf inder.census.gov/ servlet/IBQTable?_bm=y&

- ds_name=EC0731I3&

- NAICS2007=324110&

- ib_type=NAICS2007&

- geo_id=&

- _industry=324110&

- _lang=en&

- fds_name=EC0700A1>
U.S. Census Bureau, using American FactFinder; “Sector 31: Manufacturing: Subject Series: Concentration Ratios: Share of Value of Shipments Accounted for by the 4, 8, 20, and 50 Largest Companies for Industries: 2007.” Release Date 1/7/2011; (Data accessed on 10/12/11) [Source for 2007 numbers]<http://factfinder.census.gov/ servlet/IBQTable?_bm=y&

- ds_name=E C0731SR12&

- NAICS2007=324110&

- ib_type=NAICS2007&

- NAICS2007sector=*6&

- industrySel=324110&

- geo_id=&

- _industry=324110&

- _lang=en>

### 2

- 49 U.S. Department of Commerce, Bureau of the Census, FactFinder. 2011. 2009 Annual Survey of Manufactures. 1996

– 2011. Obtained throug h American FactFinder Database. (Data accessed on October 14, 2011.) [Source for 2007

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U.S. Department of Commerce, Bure au of the Census. 1997. 1995 Annual Survey of Manufacturer s. M95(AS)

- 1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2/manmin/asm/m95as1.pdf>. As obtained on October 23, 2006.

U.S. Department of Commerce, Bureau of t he Census. 1998. 1996 Annual Survey of Manufacturer s. M96(AS)

- 1 (RV). Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/3/98pubs/m96

- as1.pdf>. As obtained on October 23, 2006.

U.S. Department of Commerce, Bureau of the Ce nsus . 2000. “Population Density by County: Census 2000 Summary File 1 (SF 1) 100

- Percent Data”. Available through American FactFinder <http://factfinder.census.gov/home/saff/main.html?_lang=en>. As obtained on February 21, 2008.

U.S. Department of Commerce , Bureau of the Census. 2001. 1999 Annual Survey of Manufacturer s. M99(AS)

- 1 (RV). Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2001pubs/m99

- as1.pdf>. As obtained on October 23, 2006.

U.S. Department of Commerce, Bur eau of the Census. 2003a. 2001 Annual Survey of Manufacturer s. M01(AS)

- 1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as

- 1.pdf>. As obtained on October 23, 2006.

U.S. Department of Commerce, Bureau of th e Census. 2003b. 2001 Annual Survey of Manufacturers . M01(AS)

- 2. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as

- 2.pdf. As obtained on March 4, 2008.

U.S. Department of Commerce, Bureau of the Census. 2004. 2002 Economic Census, Industry Series

— Petroleum Refineries . Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/ec02/ec0231i324110.pdf>. As obtained on October 23, 2006.

U.S. Department of Commerce, Bureau of the Ce nsus. 2006. 2005 Annual Survey of Manufacturer s. M05(AS)

- 1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2005pubs/am0431gs1.pdf>. As obtained on October 23, 2006.

U.S. Department of Commerce, Bureau of the Census. 20 07. 2006 Annual Survey of Manufacturers. Obtained through American Fact Finder Database <http://factfinder.census.gov/home/saff/main.html?_lang=en>.

### 2

- 50 U.S. Department of Energy, Energy Information Administration (EIA). 2012. “Petroleum & Other Liquids, Wee kly Imports & Exports.”

< http://www.eia.gov/dnav/pet/pet_move_wkly_dc_NUS

- Z00_mbblpd_4.htm >.
U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996

– 2011

. “Petroleum Supply Annuals, Volume 1.” (Data accessed on October 7, 2011) [Source for 2007

– 2010 numbers.] <http://www.eia.gov/dnav/pet/pet_sum_snd_d_nus_ mbbl_a_cur.htm>.

U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996

– 2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on October 7, 2011) [Source for 2007

– 2010 numbers.] <http://www.eia.gov/dnav/pet/pet_pnp_refp_dc_nus_ mbbl_a.htm>.

U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996

– 2011. “Pet roleum Supply Annuals, Volume 1.” (Data accessed on October 7, 2011.) [Source for 2007

– 2010 numbers.] <http://www.eia.gov/dnav/pet/pet_move_imp_dc_NUS

Z00_mbbl_a.htm>

U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996

– 2011. “Petr oleum Supply Annuals, Volume 1.” (Data accessed on October 7, 2011.) [Source for 2007

– 2010 numbers] <http://www.eia.gov/dnav/pet/pet_move_exp_dc_NUS

Z00_mbbl_a.htm>.

U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996

– 2011. “Petro leum Supply Annuals, Volume 1.” (Data accessed on October 7, 2011.) [Source for 2007

– 2010 numbers.]<http://www.eia.gov/dnav/pet/pet_pri_refoth_dcu_nus_a.htm>.

U.S. Department of Energy, Energy Information Administration (EIA). 1996

– 2007. “Petroleum Supply Annuals, Volume 1.” Available at <http://www.eia.doe.gov/oil_gas/ petroleum/data_publications/petroleum_supply_annual/psa_volume1/psa_ volume1.html>. As obtained on October 31, 2007.

U.S. Department of Energy, Energy Information Administration (EIA). 2006a. “A Primer on Gasoline Prices.” Available at <http://www.eia.doe.gov/bookshelf/brochures/ gasolinepricesprimer/printerversion.pdf>. As obtained on October 31, 2007.
U.S. Department of Energy, Energy Information Administration (EIA). 1999a. “Petroleum : An Energy Profile 1999.” DOE/EIA

- 0545(99). Available at <http://tonto.eia.doe.gov/ FTPROOT/petroleum/054599.pdf>. As obtained on October 23, 2006.
U.S. Department of Energy, Energy Information Administration (EIA). 2006b. “Refinery Capacity Report 2006. ” Available at <http://www.eia.doe.gov/oil_gas/petroleum/ data_publications/refinery_capacity_data/refcapacity.html/>. As obtained on October 23, 2006.

### 2

- 51 U.S. Department of Energy, Energy Information Administration (EIA). 2006c. “Petroleum Supply Annual 200 5, Volume 1.” Available at <http://www.eia.doe.gov/pub/oil_gas/ petroleum/data_publications/petroleum_supply_annual/psa_volume1/historical/2005/psa _volume1_2005.html>. As obtained on October 31, 2007.

U.S. Department of Energy, Energy Information Administr ation (EIA). 2007a. “Refinery Utilization and Capacity.” Available at <http://tonto.eia.doe.gov/dnav/pet/ pet_pnp_unc_dcu_nus_m.htm>. As obtained on January 7, 2008.
U.S. Department of Energy, Energy Information Administration (EIA). 2007b. “Refiner Petrol eum Product Prices by Sales Type.” Available at <http://tonto.eia.doe.gov/ dnav/pet/pet_pri_refoth_dcu_nus_m.htm>. As obtained on January 11, 2008.
U.S. Department of Energy, Energy Information Administration (EIA). 2007c. “Annual Energy Outlook.” Availabl e at <http://www.eia.doe.gov/oiaf/archive/aeo07/pdf/0383(2007).pdf>. As obtained on January 21, 2007.
U.S. Department of Energy, Energy Information Administration (EIA). 2010. “Petroleum Supply Annual 2010, Volume 1.” Available at http://www.eia.gov/petroleum/supply/annual/volume1/archive/2010/pdf/table15.pdf

. U.S. Department of Energy, Energy Information Administration (EIA). 2011, “Annual Energy Outlook.” (Data a ccessed on October 11, 2011) [Source for 2011

– 2019 numbers.]<http://www.eia.gov/forecasts/aeo/pdf/0383(2011).pdf>.

U.S. Department of Justice and the Federal Trade Commission. 1992. Horizontal Merger Guidelines. Available at <http://www.ftc.gov/bc/docs/hor izmer.htm>. As obtained on October 28, 2006.

U.S. Department of Labor, Occupational Safety and Health Administration (OSHA). 2003. OSHA Technical Manual, Section IV: Chapter 2, Petroleum Refining Processes. TED 01

- 00

- 015. Washington, DC: U.S. DOL. Availabl e at <http://www.osha.gov/dts/osta/ otm/otm_iv/otm_iv_2.html>. As obtained on October 23, 2006.
U.S. Energy Information Administration (EIA), 2011a. “Refinery Utilization and Capacity.” Available at http://www.eia.gov/dnav/pet/pet_pnp_unc_dcu_nus_a.htm; Release date: 7/28/11; (Data accessed on 10/11/11). [Source for 2007

– 2010 numbers]

U.S. Energy Information Administration (EIA), Form EIA

- 820, “Annual Refinery Report. Table 1. Number and Capacity of Operable Petroleum Refineries by PAD District and State as of January 1, 2011” Release Date: June 24, 2011; (Data accessed on 10/12/11). [Source for 2011 numbers.] <http://www.eia.gov/petroleum/refinerycapacity/>
U.S. Environmental Protection Agency. 1995. “EPA Office of Compliance Sector Notebook Project: Prof

ile of the Petroleum Refining Industry.” EPA/310

- R

- 95

- 013. Washington, DC: U.S. EPA. Available at <http://www.epa.gov/compliance/resources/ publications/assistance/sectors/notebooks/petrefsn.pdf>. As obtained on October 23, 2006.

### 2

- 52 U.S. Environmental Protec tion Agency. 2006. “Proposed MHA Nation Clean Fuels Refinery.” Available at <http://www.epa.gov/Region8/compliance/nepa/refinerydeis.html>. As obtained on February 6, 2007.
Ward’s Business Directory of Public and Private Companies, 2011, accessed at James Branch Cabell Library (Virginia Commonwealth University).

Western Refining, Inc. March 8, 2007. 10K for year ended December 31, 2006. EDGAR Database. Available at <http://www.sec.gov/Archives/edgar/data/1339048/000095 013407005096/h44360e10vk.htm>.

Wester n Refining, Thomson Reuters Western Refining, Inc. 10K. March 8, 2011. 10K for year ended December 31, 2010. (Data accessed on 10/23/11) [Source for 2010 numbers.] <http://phx.corporate

- ir.net/phoenix.zhtml?c=194293&p=irol

- sec>

### 3

- 1

3 NSPS REGULATORY OPTI ONS, COSTS AND EMISS ION REDUCTIONS

FROM COMPLYING WITH THE NSPS

The emission s reduction and cost impacts presented in this section for flares are revised estimates for the impacts of the final requirements of 40 CFR part 60, subpart Ja for flares, as amended by this action. The impacts are expressed as incremental differences between the impacts of petroleum refinery process units complying with the se final amendments to subpart Ja

and the final June 2008 NSPS requiremen ts of 40 CFR part 60, subpart J a (i.e., baseline). The impacts are presented for petroleum refinery flares that commence construction, reconstruction or modification over the next 5 years. We anticipate that there will be 400 affected flares over the

next 5 years and most of the flares would become affected due to the modification provisions for flares in subpart Ja. For this analysis we assumed 90 percent of the flares would be modified or reconstructed and 10 percent of the flares would be newly constr ucted

.

Further, we estimate that 30 percent of the 400 affected flares, or 120 flares, either would meet the definition of “emergency flare” in 40 CFR part 60, subpart Ja or would be equipped with a flare gas recovery system such that robust sulfur and fl ow monitoring would not be required

. Therefore, the values presented in this section include the costs and emissions reductions for 400 flares to comply with the flare management plan and root cause and corrective action analyses requirements and for 280 flares to comply with the sulfur and flow monitoring requirements. The cost and emissions reductions for the affected flares to comply with the short

term H2S concentration of 162 ppmv in the fuel gas are included in the baseline rather than the

incrementa l impacts because this limit is unchanged from the requirements in subpart J. For further detail on the methodology of these calculations, see the January 25, 2012 memorandum

entitled “ Impact Estimates for Fuel Gas Combustion Device and Flare Regulatory Op tions for Amendments to the Petroleum Refinery NSPS ” , in Docket ID No. EPA

- HQ

- OAR

- 2007

- 0011.

We estimate that the final requirements for flares will reduce emissions of SO 2 

### by 3, 200 tons per year (tons/yr), NO x 

### by 1,100 tons/yr

, VOC by 3,400 tons/yr , and C O 2 

### by 1,900,000

metric tons/yr from the baseline. The estimated annual cost, including annualized capital costs for flare gas recovery systems , is $96 million (2006 dollars). The total annual savings from offset natural gas purchases and product recovery credits that arise as a result of complying with

### 3

- 2

the rule are estimated at $180 million. The estimated net annual cost is a cost savings of $79
million (2006 dollars)

. Note that not all refiners will realize a cost savings as only flare systems with high waste gas flows (about 10% of all flares) are expected to install vapor recovery
systems. The overall cost effectiveness is a cost savings of about $ 10 ,000 per ton of combined pollutants (excluding CO 2 

### ) r emoved. The estimated nationwide 5

- year incremental emissions reductions and cost impacts for the final standards are summarized in Table s 3

- 1 and 3

- 2 below

.

Table 3

- 1. National Cost Impacts for Petroleum Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year A fter the Effective Date of the Final Rule Amendments )

Subpart Ja Requirements Total capital cost ($1,000) Total annual

cost without credit ($1,000/yr) Natural gas

offset/ product recovery credit ($1,000) Total Annual

Cost ($1,000/ yr) Estimates from June 2008 Final Rule 40,000 ( 7,000 )

Total for Revised Estimates for Amendments 46 0,000 96
, 0 00 ( 180

, 0 00 ) ( 79
, 0 00 )

Flare Monitoring 84,000 14,000 0 14,000

Flare Gas Recovery 7 3 8 0 ,000 78,000 (1 70 ,000) (90,000)

Flare Management Plans

-- 880

-- 880 SO2 Root Cause Analysis/Corrective Action -- 2,200

-- 2,200 Flow Rate Root Cause Analysis/

Corrective Action -- 1,000 (7,400) (6,400)

Note: Costs presented in this table are estimated relative to the baseline used for the 2008 NSPS impacts analysis. Additionally, t otal s may not sum due to independent rounding.

7 Forty flare systems are estimated to have sufficient waste gas to install recovery systems.

### 3

- 3

Table 3

- 2

. National Emission Reductions and Cost

- Effectiveness for Petroleum Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After the Effective Date of the Final Rule Amendments )

Subpart Ja Requirements Annual emission reductions (tons SO 2 /yr) Annual

emission reductions (tons NO x

/yr) Annual emission reductions (tons VOC/yr) Cost

effectiveness ($/ton emissions reduced) Annual

emission reductions (metric tons CO 2 /yr) Estimates from June 2008 Final Rule 80 6 200 ( 23,000 ) --

Total for Revised Estimates for Amendments 3, 200 1,100 3,400 (10 ,000 ) 1,900,000

Flare Monitoring 0 0 0

-- -- Flare Gas Recovery 380 1,100 2,700 (22,000) 1,800,000

Flare Management Plans 0 0 300 2,900

-- SO2 Root Cause Analysis/Corrective Action 2,900 0 0 760

-- Flow Rate Root Cause

Analysis/ Corrective Action 3.4 56 430 (13,000) 98,000



### Alternatively, if no refineries install flare gas recovery systems

, the final requirements for

flares would reduce emissions of SO 2 

### by 2,82 0 tons/yr, NO x 

### by 56 tons/yr, VOC by 730 tons/yr,

and CO 2 

### by 98, 00 0 metric tons/yr from the baseline. The estimated annual cost would be about
$ 10.7 million (2006 dollars)

.

The provisions for flares and other fuel gas combustion devices (i.e., process heaters and boilers) from the final June 2008 standards were stayed. The analysis for this final rule includes the same unit costs for the flare provisions as the final June 2008 r ule but reflects recalculated total costs using data collected in the March 2011 ICR to update the number of flares. 8 

### Table 3

- 1 includes the recalculated cost estimates for flares, broken out by specific flare requirements, based on the updated number of flares. For the other fuel gas combustion devices, the total

annua l ized costs for those provisions were estimated at $24 million (2006 dollars) in the June
2008 rule and remain the same. As discussed below, because there are no additional incremental

cos ts associated with the other fuel gas combustion device provisions, we consider those annual costs accounted for in the final June 2008 standards. We are presenting them here again, even

8 For the June 2008 standards we estimated 40 flares. As indicated, for this analysis we anticipate that there will be 400 affected flares that will be subject to this final rule.

### 3

- 4

though we estimate no changes to these costs, since these provisions will become effective upon this final action to lift the stay on certain provisions in the June 2008 rule.

The cost, environmental and economic impacts for the final amendments to 40 CFR part 60, subpart Ja for process heaters are not expected to be diffe rent than those reported for the final June 2008 standards. We expect owners and operators to install the same technology to meet these final amendments that we anticipated they would install to meet the June 2008 final subpart

Ja r equirements (i.e., ultra

- low NO x 

### burners). We did revise our emission s estimates based on the type of process heater, creating separate impacts for forced draft process heaters and natural draft process heaters. Dividing process heaters into separate subcategories, based on the d raft type, required us to develop new distributions of baseline emissions for each type of process heater.

The baseline emission s estimates for natural draft process heaters are slightly lower than those developed for the existing subpart Ja requirements ( per affected process heater), but the average emission reduction achieved by ultra

- low NO x 

### burners was adjusted to 80 percent (rather than 75 percent used for generic process heaters). For forced draft process heaters, the baseline (i.e.,
uncontrolled) em issions rate for forced draft process heaters was revised slightly upward, based

on the available emissions data. Because of these differences, the mix of controls needed to meet a 40 ppmv emissions limit was no longer cost effective for forced draft proc ess heaters, but the emission reductions associated with process heaters complying with the 60 ppmv standard were higher than those previously estimated for generic process heaters. Thus, the creation of new subcategories of process heaters with different emissions limits for each subcategory did not impact the control or compliance methods used by the facilities (i.e., BSER in all cases was based on the performance of advanced combustion monitoring controls in conjunction with ultra

low NO x 

### burners) and di d not change the estimated compliance costs. As we do not have adequate data regarding the prevalence of natural draft process heaters versus forced draft process heaters that will become subject to the rule, we used the emission reductions estimated

for t he two different types of process heaters as a means to bound the range of anticipated NO x 

### emission reductions to be from 7,100 to 8,600 tons/yr in the fifth year after promulgation (see

Revised NO x 

### Impact Estimates for Process Heaters, in Docket ID No. EP A

- HQ

- OAR

- 2007

- 0011). We estimated the emission reductions to be 7,500 tons/yr for the June 2008 final standards, which falls well within the anticipated range of emissions reductions for the standards

### 3

- 5

we are finalizing here. Given the uncertainty in the e missions estimates, as well as the uncertainty in the relative number of natural draft process heaters versus forced draft process heaters, we concluded that the impacts previously developed for subpart Ja accurately represent the impacts for process heate rs in these final amendments.

We note that, in the preamble to the June 2008 final standards, we estimated costs and emissions reductions for 30 fuel gas combustion devices, but we subsequently determined that those estimates did not fully account for the number of affected flares (which, at the time, were considered a subset of fuel gas combustion devices). Therefore, in the preamble to the December 2008 proposed amendments, we presented revised emission reduction and cost estimates for affected fuel gas combustion devices. Because these final amendments consider flares to be a separate affected source, the emission reductions and costs for fuel gas combustion devices are not affected by these final amendments and are not included in this action . Rather, t he final emission reduction and cost estimates for fuel gas combustion devices are very close to the impacts presented in the June 2008 final rule; the details of the analysis and the final impacts are presented in the January 25, 2012 memorandum entitled “ Impact Estimates for Fuel Gas Combustion Device and Flare Regulatory Options for Amendments to the Petroleum Refinery

NSPS ” , in Docket ID No. EPA

- HQ

- OAR

- 2007

- 0011.

The final amendments to 40 CFR part 60, subpart J are technical corrections or clarifications to the existing rule and should have no negative emissions impacts.

### 4

- 1

4 ECONOMIC IMPACT ANAL YSIS: METHODS AND RE SULTS

4.1 Intr o duction

The economic impact analysis ( EIA ) is designed to inform decision makers about the potential economic consequences of a regulatory action. A n economic welfare analysis

estimates the social costs and consumer and producer surplus changes associated with a regulatory program. The welfare a nalysis identifies how the regulatory costs are distributed

across the two broad classes of stakeholders

-- consumers and producers

. As defined in EPA’s
(20 10 ) Guidelines for Preparing Economic Analyses

, 9 

### social costs are the value of the goods and services lost by society resulting from using resources to comply with and implement a regulation. In addition, national engineering compliance cost estimates can be used to approximate the welfare impacts of a regulatory program

.

Because th e proposed amendments apply to new or modifying sources, we are not able to predict which specific sources will trigger applicability. Also, assuming no refineries install fuel
gas recovery systems , the estimated, incremental annual cost s would be about $10.7 million (2006 dollars), which represents substantially less than 0.0 0 1 % of total refinery industry

revenues in 20 10

. If we assume a full cost pass through, we do not anticipate any resulting price

increases. As such, w e d id not empl oy a detailed economic model to estimate the social costs of the proposed amendments, nor to estimate the social cost distribution across stakeholders. For
this rule, based on our analysis and an estimated annual cost savings of $ 79 million (20 06 dollars) expected in the fifth year, no national

- level negative economic impacts are expected.

The remainder of this section includes a discussion of why firms may consider installing flare
gas recovery systems , as well as the small business impact analysis

.

4.2 Compliance Costs Estimates

As indicated in Section 3, we estimate there are approximately 500 flares industry

- wide

, and about 80 percent, or 400 flares, would be subject to the proposed amendments over the next five years. We further estimate that it would be cost

- effective to install compressors and

9 These guidelines are currently under review by the Agency.

### 4

- 2

recovery systems at 10 percent of the 400 flares, or at approximately 40 flares

. We assume that a flare gas recovery system would be considered for flares with average releases greater than 500,000 scf per 24

- hour period. And wh ile we do not have current data on where all of the potential 40 flares are located, data from the 2002 N ational Emissions Invento r y indicat e the

flares are likely located at large refineries

. 10



### T he total annualized engineering compliance costs are estimated at $ 96 million, and the estimated annual savings from offset natural gas purchases and product recove ry credits are estimated at $180 million, giving rise to the net annual savings of $79 million discussed in

Section 3. These costs can be broken down into costs required by the rule and costs that are not required by the rule. The annualized compliance c osts for actions required by the rule are estimated at $18 million, and the estimated savings from product recovery credits is $7.4 million. Using these figures, we estimate the implied rate of return for actions required by the rule to be

about

- 60 percent. Meanwhile, t he annualized compliance costs for actions not required by the rule are estimated at $78 million, and the estimated savings from offset natural gas purchases is
$ 168 million. Using these figures, we estimate the implied rate of retur n for actions not required by the rule to be about 120 percent and the overall rate of return to be about 9 0 percent

. T he

actual industry

- level rate of return varies greatly over time because of industry and economic

factors . See Table 4

- 1 for refinery r ates of return on investment from 2000 through 2009. It is readily observed that the rate of return related to the pollution control equipment and activities by this rule exceeds that of the rate of return associated with investments in the refining secto r.

Table 4

- 1 Rate s of Return on Investment for Refineries

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Rate of Return on Investment 9.6 14.5 (1.7) 9.3 18.4 23.2 25.3 21.2 2.4 (6.6)

(EIA 2009)

10 For details on where flares of this size were located as of the 2002 National Emissions Inventory, see Appendix A of the January 25, 2012 memorandum entitled “Impact Estimate s for Fuel Gas Combustion Device and Flare Regulatory Options for Amendments to the Petroleum Refinery NSPS.”

### 4

- 3

Assuming financially rational producers, standard economic theory suggests that refineries would incorporate all cost

- effective improvements, which they are aware of, without government intervention. The cost analysis of this RIA nevertheless is based on the observation

that process actions (i.e., installation of a flare gas recovery system) that appear to be profitable in our analysis have not been widely adopted. One explanation for why there appear s to be negative cost control technologies that are not generally adopted is imperfect information. If process improvements in the refining sect or are not well understood, firms may underestimate the potential financial returns to those improvements

. Another explanation is the cost associated

with the irrever sibility of implementing these process improvements is not reflected in the engineering cost estimates. Because of the high volatility of natural gas prices, it is important to recognize the value of flexibility taken away from firms when requiring them t o install and use a particular process technology.

If a firm has not adopted the technology on its own, then a regulation encouraging its use means the firm may require reprioritizing other non

- environmental investments

. Although the rule does not specifically require installation of flare gas recovery systems, we anticipate that owners and operators of flares receiving high waste gas flows will conclude, upon installation of monitors, implementation of their flare managem ent plans, and implementation of root causes analyses, that installing flare gas recovery would result in fuel savings by using the recovered flare gas where purchased natural gas is now being used to fire equipment such as boilers and process heaters. Th e flare management plan requires refiners to conduct a thorough review of the flare system so that flare gas recovery systems are installed and used where these systems are

warranted. As part of the development of the flare management plan, refinery owner s and operators must provide rationale and supporting evidence regarding the flare waste gas reduction options considered

, the quantity of flare gas that would be recovered or prevented by the option, the BTU content of the flare gas and the ability or ina bility of the reduction option to offset natural gas purchases

. In a ddition, regulatory requirements imply firms are unable to suspend use of the technology if it becomes unprofitable in the future. Therefore, the true cost of the regulation should inclu de the lost option value , as well as the engineering costs . In the absence of quantitative estimates of this option value

, the costs presented in this RIA may underestimate

the f ull costs faced by the affected firms.

### 4

- 4

4.3 Small Business Impact Analysis

The Regulatory Flexibility Act (RFA) as amended by the Small Business Regulatory Enforcement Fairness Act (SBREFA) generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements u nder the Administrative Procedure Act or any other statute, unless the agency certifies that the rule will not have a significant economic impact on a substantial number of small entities. Small entities include small businesses, small governmental jurisd ictions, and small not

- for

- profit enterprises. The petroleum refining industry (NAICS code 324110) does not include small governmental jurisdictions or small not

- for

- profit enterprises. For this analysis we applied the Small Business

Administration ’s sma ll refiner definition of a refinery with no more than 1,500 employees (SBA,

2011 ). For additional discussion of the Agency’s application of the definition for small refiner, see the June 24, 2008 Federal Register Notice for 40 CFR Part 60, Standards of Performance for Petroleum Refineries (Volume 73, Number 122, page 35858). 11



### 4. 3 .1 Small Entity Economic Impact Measures

The analysis provides EPA with an estimate of the magnitude of impacts that the provisions of the proposed standards m ay have on the ultimate domestic parent companies that own the small refineries. This section references the data sources used in the screening analysis and presents the methodology we applied to develop estimates of impacts, the results of the analysis, and conclusions drawn from the results.

The small business impacts analysis for Subpart Ja New Source Perform ance Standards amendments rely upon data collected through the March 2011 Information Collection Request
(ICR

-- OMB Control No. 2060

- 0657). Inf ormation collected through component 1 of the ICR includes facility location, products produced, capacity, throughput, process and emissions, and employment and sales receipt data. EPA performed a screening analysis for impacts on all

affected small refin eries by comparing compliance costs to revenues at the parent company level.

This is known as the cost

- to

- revenue or cost

- to

- sales ratio, or the “sales test.” The “sales test” is

11 Refer to http://www.sba.gov/sites/default/files/Size_Standards_Table.pdf for more information on SBA small business size standards.

### 4

- 5

the impact methodology EPA employs in analyzing small entity impacts as opp osed to a “profits test,” in which annualized compliance costs are calculated as a share of profits. The sales test is frequently used because revenues or sales data are commonly available for entities impacted by EPA regulations, and profits data normall y made available are often not the true profit earned by firms because of accounting and tax considerations. The use of a “sales test” for estimating small business impacts for a rulemaking is consistent with guidance offered by EPA on compliance with the RFA 12 

### and is consistent with guidance published by the U.S. SBA’s Office of Advocacy that suggests that cost as a percentage of total revenues is a metric for evaluating cost increases on small entities in relation to increases on large entities (U.S. SBA, 2010). 13



### 4. 3 .2 Small Entity Economic Impact Analysis

As discussed in Section 2 of this R IA, as of January 2011 there were 148 petroleum refineries operating in the continental United States and US territories with a cumulative capacity of processing over 1 7 million barrels of crude per calendar day (EIA, 2011b ). Sixty

- four (64) parent companies own these refineries, and EPA has employment and sales data for 61 (95%) of

the parent companies . Thirty

- six (36) companies (59% of the 61 total ) employ fewer than 1,500 workers and are considered small businesses. These firms earned an average of $1.36 billion of revenue per year, while firms employing more than 1,500 employees earned an average of $82.5 billion of revenue per year. 14

12 The RFA c ompliance guidance to EPA rule writers regarding the types of small business analysis that should be considered can be found at < http://www.epa.gov/sbrefa/documents/r faguidance11

- 00

- 06.pdf>
13 U.S. SBA, Office of Advocacy. A Guide for Government Agencies, Ho w to Comply with the Regulatory Flexibility Act, Implementing the President’s Small Business Agenda and Executive Order 13272, June 2010

. 14 The U.S. Census Bureau’s Statistics of U.S. Businesses include the following relevant definitions: (i) establishmen t

– a single physical location where business is conducted or where services or indus trial operations are performed; (ii) firm

– a firm is a business organization consisting of one or more domestic establishments in the same state and industry that were spe cified under common ownership or control. The firm and the establishment are the same for single

- establishment firms. For each multi

- establishment firm, establishments in the same industry within a state will be counted as one firm ; and (iii) enterprise

-- a n enterprise is a business organization consisting of one or more domestic establishments that were specified under common ownership or control. The enterprise and the establishment are the same for single

- establishment firms. Each multi

- establishme nt co mpany forms one enterprise

.

### 4

- 6

Based on d ata collected through the March 2011 ICR, EPA performed the sales test analysis for impacts on affected small refineries. F our of the 36 small refiners were removed from the analysis because we determined they were not major sources and would not be subje ct to the rules, and one of the 36 small refiners ( Gulf Atlantic Operations) w as not analyzed because we had no ICR and/or other publically available employment and sales data.

While we estimate d the natural gas recovery offsets or credit at a national level and

believe that larger firms are more likely to offset natural gas purchases , the revenues from natural

gas recovery offsets m ight mask disprop ortionate impacts on small refiners. At present to better identify disproportionate impacts

, we examined the potential impacts on refiners based on a scenario where no firms adopt flare gas recovery systems and comply with the NSPS through flare monitoring and flare management and root cause analysis actions. Refer to Tables 3

- 1 and

3

- 2 in the prior Section for details on the costs and emissions reductions associated with each of the potential compliance actions. Table 4

- 2 presents the distr ibution of estimated cost

- to

- sales ratios for the small firms in our analysis. The incremental compliance costs imposed on small refineries are not estimated to create significant impacts on a cost

- to

- sales ratio basis at the firm

level.

Table 4

- 2 Impact Levels of Proposed N SPS Amendments on Small Firms

Impact Level Number of Small Firms in Sample Estimated to be Affected % of Small Firms in

Sample Estimated to be Affected

Cost

- to

- Sales Ratio less than 1% 31 100%
Cost

- to

- Sales Ratio 1

- 3% 0

-- Cost

- to

- Sales Ratio greater than 3% 0

--



### For comparison, we calculated the cost

- to

- sales ratios for all of the affected refineries to determine whether potential costs would have a more significant impact on small refineries. As presented in Table 4

- 3 , for all large firms, the average cost

- to

- sales ratio is less than 0.01 percent; the median cost

- to

- sales ratio is less than 0.01 percent; and the maximum cost

- to

- sales

ratio is 0.0 2 percent. For small firms, the average cost

- to

- sales ratio is about 0.0 6 percent, the

median cost

- to

- sales ratio is 0.0 2 percent, and the maximum cost

- to

- sales ratio is 0. 63 percent.
While the potential costs show a somewhat larger impact on small refiners, the impacts on small

refiners are not significant . B ecause no small firms are expected to have cost

- to

- sales ratios

### 4

- 7

greater than one percent, we determined that the cost impacts for Subpart Ja New Source

Perform ance Standards amendments will not have a significant economic impact on a substantial
number of small entities (SISNOSE).

Table 4

- 3 Summary of Sales Test Ratios for Firms Affected by Proposed N SPS Amendments

Firm Size No. of Known

Affected Firms % of Tot al Known

Affected Firms Mean Cost

- to

- Sales Ratio Median Cost

- to

Sales Ratio Min.

Cost

- to

Sales Ratio Max. Cost

to

- Sales Ratio
Small 31 5 5 % 0.0 6 % 0.0 2 % <0.01% 0. 63 % Large 25 45 % <0.01% <0.01% <0.01% 0.0 2 %

All 5 6 100% 0.0 2 % <0.01% <0.01% 0. 63 %

### 4

- 8

4.4 References

U.S. Department of Energy, 20 09 Energy Information Administration (EIA). “ Performance Profiles of Major Energy Producers 2009 .” (Data accessed on March 5, 2012 .)

< http://www.eia.gov/cfapps/frs/frstables.cfm?tableNumber=9 >
U.S. Environmental Protection Agency, 2010. Guidelines for Preparing Economic Analyses, EPA 240

- R

- 10

- 001 . < http://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidelines.html > Accessed on: 11/14/11.

### 5

- 1 5 EXECUTIVE ORDERS

5

. 1 Unfunded Mandates
This rule does not contain a Federal mandate that may result in expenditures of $100 million or more for State, local, and tribal governments, in the aggregate, or the private sector in any one year. The costs of the final amendments would not increase costs associated with the final rule. Thus, this ru le is not subject to the requirements of sections 202 or 205 of the Unfunded Mandates Reform Act of 1995 (UMRA).

This rule is also not subject to the requirements of section 203 of UMRA because it contains no regulatory requirements that might significantl y or uniquely affect small governments. The proposed amendments contain no requirements that apply to such governments, and impose no obligations upon them.

5.2 Environmental Justice
Executive Order 12898 (59 FR 7629, February 16, 1994) establishes Feder al executive policy on environmental justice. Its main provision directs Federal agencies, to the greatest extent practicable and permitted by law, to make environmental justice part of their mission by identifying and addressing, as appropriate, dispropo rtionately high and adverse human health or environmental effects of their programs, policies, and activities on minority populations and low

income populations in the United States.

EPA has determined that this final rule will not have disproportionately high and adverse human health or environmental effects on minority or low

- income populations because it increases the level of environmental protection for all affected populations without having any disproportionately high and adverse human health or envi ronmental effects on any population, including any minority or low

- income population. This rule is a nationwide s tandard that will yield reductions in various criteria pollutant emissions from new, modified, and reconstructed process heaters and flares at petroleum refineries , thus decreasing the amount of such emissions to which all affected populations are exposed.

5.3 Significant Energy Action s

T h ese proposed amendments do not represent a “ significant energy action ” as defined in Executive Order 13211, “ Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use ” (66 FR 28355 [ May 22, 2001 ] ) because they are not likely to have

### 5

- 2

a significant adverse effect on the supply, distribution, or use of energy. Assuming no refineries install a flare gas recovery system, the estimated annualized compliance costs for the proposed requirements are substantially less than 0.001% of total refinery industry revenues in 2010

. A ssum ing a full cost pass through, we do not anticipate any resulting price increases , and we estimate there will be no significant increase in our dependence on foreign e nergy supplies

. Finally, today ’ s action will have no adverse effect on crude oil supply, coal production, electricity production, and energy distribution. Based on the findings from the analysis of impacts on energy markets, we conclude that today ’ s action is not a “ significant energy action ”
as defined in Executive Order 13211.

### 6

- 1 6 HUMAN HEA LTH BENEFITS OF EMIS SIONS REDUC T IONS

Synopsis

In this section, we provide an estimate of the monetized benefits for the final Petroleum
Refineries NSPS Reconsideration associated with reducing exposure to ambient particulate matter (PM) by reducing emissions of PM 2.5 

### precursors including SO 2 

### , NOx, and VOCs

, as well as climate benefits associated with reducing emissions of CO 2 

### . SO 2 

### , NOx, and VOCs are precursors to PM 2.5 

### , and NOx and VOCs are precursors to ozone

. T he se estimates reflect the monetized human health benefits of reducing cases of morbidity and premature mortality among populations exposed to PM 2.5 

### reduced by this reconsideration . We estimate the total monetiz ed
benefits to be $ 260 million to $ 5 8 0 million at a 3% discount rate and $ 240 million to $ 520
million at a 7% discount rate. All estimates are in 2006$ in the year 2017

.

These estimates reflect EPA’s most current interpretation of the scientific literatu re. Higher or lower estimates of benefits are possible using other assumptions; examples of this are provided in Figure 6

- 2 on page 6

- 14)

. Data, resource, and methodological limitations prevented EPA from monetizing the benefits from several important ben efit categories, including benefits from reducing direct exposure to NO 2 

### and SO 2 

### , ozone exposure, ecosystem effects, and visibility impairment.

6. 1 Calculation of PM 2.5 

### - Related Human Health Benefits

This final NSPS Reconsideration would reduce emissions of SO 2 

### , NO X 

### , and VOCs. Because these emissions are precursor s to PM 2.5, 

### reducing these emissions would also reduce
PM 2.5 

### formation, human exposure and the incidence of PM 2.5 

### - related health effects. Due to analytical limitations, it was not possible to pr ovide a comprehensive estimate of PM 2.5 

### - related
benefits or provide estimates of the health benefits associated with direct exposure to SO 2 

### and NOx or exposure to ozone . Instead, we used the “benefit

- per

- ton” approach to estimate these
benefits . The method ology employed in this analysis is similar to the work described in Fann, Fulcher, and Hubbell (2009) , but represents an improvement that EPA believes will provide
more reliable estimates of PM 2.5 

### - related health benefits for emissions reductions in specifi c

sectors. The key assumptions are described in detail below. These PM 2.5 

### benefit

- per

- ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity) of redu cing one ton of PM 2.5 

### f rom a specified source. EPA has used the

### 6

- 2

benefit per

- ton technique in several previous RIAs, including the recent S O 2 

### NAAQS RIA (U.S. EPA, 2010 ).

The Integrated Science Assessment (ISA) for Particulate Matter (U.S. EPA, 2009 b )
identified the human health effects associated with ambient PM 2.5

, 

### which include premature morality and a variety of morbidity effects associated with acute and chronic exposures. Table 6

- 1 shows the quantified and unquantified ben efits captured in those benefit

- per

- ton estimates , but this table does not include entries for the unquantified health effects associated with exposure to ozone, SO 2 

### , and NOx nor welfare effects such as ecosystem effects and visibility that are
described i n section 6.2

. It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete.

### 6

- 3

Table 6

- 1: Human Health Effects of PM 2.5

Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information (refers to CSAPR RIA) Improved Human Health

Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30) 

 

 Section 5.4

Infant mortality (age <1) 

 

 Section 5.4

Reduced incidence of morbidity from exposure to PM 2.5 Non

- fatal heart attacks (age > 18) 

 

 Section 5.4 Hospital admissions

—

respiratory (all ages) 

 

 Section 5.4 Hospital admissions

— cardiovascular (age >20) 

 

 Section 5.4 Emergency room visits for asthma (all ages) 

 

 Section 5.4
Acute bronchitis (age 8

- 12) 

 

 Section 5.4 Lower respiratory symptoms (age 7

- 14) 

 

 Section 5.4 Upper respiratory symptoms (asthmatics age 9

- 11) 

 

 Section 5.4 Asthma exacerbation (asthmatics age 6

- 18) 

 

 Section 5.4
Lost work days (age 18

- 65) 

 

 Section 5.4 Minor restricted

- activity days (age 18

- 65) 

 

 Section 5.4 Chronic Bronchitis (age >26)   Section 5.4 Emergency room visits for cardiovascular effects (all ages) -- -- Section 5.4 Strokes and cerebrovascular disease (age 50

- 79) -- -- Section 5.4 Other cardiovascular effects (e.g., other ages) -- -- PM ISA 2
Other respiratory effects (e.g., pulmonary function, non

- asthma ER visits, non

bronchitis chronic diseases, other ages and populations) -- -- PM ISA 2

Reproductive and developmental effects (e.g., low birth weight, pre

- term births, etc) -- -- PM ISA 2,3

Cancer, mutagenicity, and genotoxicity effects -- -- PM ISA 2,3

1 We assess these benefits qualitative ly due to time and resource limitations for this analysis.

2 We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
3 We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.



### Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the benefits esti mates
utilize the concentration

- response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expert elicitation study as a sensitivity analysis.

### 6

- 4  

### One estimate is based on the concentration

- response (C

- R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002) , a study that EPA has previously used to generate its primary benefits
estimate. When calculating the estimat e, EPA applied the effect coefficient as reported in the study without an adjustment for assumed concentration threshold of 10 μg/m 3 

### as was done in recent ( 2006

- 2009 ) Office of Air and Radiation RIAs.

 

### One estimate is based on the C

- R function developed fro m the extended analysis of the Harvard Six Cities cohort, as reported by Laden et al (2006). This study, published after the completion of the Staff Paper for the 2006 PM 2.5 

### NAAQS, has
been used as an alternative estimate in the PM 2.5 

### NAAQS RIA and PM 2.5 

### benefits estimates in RIAs completed since the PM 2.5 

### NAAQS. When calculating the estimate, EPA applied the effect coefficient as reported in the study without an adjustment for assumed concentration threshold of 10 μg/m 3 

### as was done in recent ( 2006

- 2009 ) RIAs.

 

### Twelve estimates are based on the C

- R functions from EPA’s expert elicitation study (Roman et al., 2008) on the PM 2.5 

### - mortality relationship and interpreted for benefits analysis in EPA’s final RIA for the PM 2.5 

### NAAQS. For that study, twelve exper ts (labeled A through L) provided independent estimates of the PM 2.5 

### - mortality
concentration

- response function. EPA practice has been to develop independent
estimates of PM 2.5 

### - mortality estimates corresponding to the concentration

- response function prov ided by each of the twelve experts, to better characterize the degree of variability in the expert responses.

The effect coefficients are drawn from epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Pope et a l., 2002 ) and the Harvard Six Cities cohort (Laden et al., 2006 ). 15 

### These are logical choices for anchor points in our presentation because, while both studies are well designed and peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for using both studies to generate
benefits est imates. Previously, EPA had calculated benefits based on these two empirical studies, but derived the range of benefits, including the minimum and maximum results, from an expert elicitation of the relationship between exposure to PM 2.5 

### and premature mort ality (Roman

15 These two studies specify multi

- pollutant models that control for SO 2 , among other co

- pollutants.

### 6

- 5

et al., 2008 ). 16 

### Within this assessment, we include the benefits estimates derived from the
concentration

- response function provided by each of the twelve experts to better characterize the uncertainty in the concentra tion

- response function for mortality and the degree of variability in the expert responses. Because the experts used these cohort studies to inform their
concentration

- response functions, benefits estimates using these functions generally fall between

res ults using these epidemiology studies (see Figure 6

- 2 ). In general, the expert elicitation results support the conclusion that the benefits of PM 2.5 

### control are very likely to be substantial.

Readers interested in reviewing the general methodology for cr eating the benefit

- per

- ton estimates used in this analysis should consult the draft Technical Support Document (TSD) on estimating the benefits per ton of reducing PM 2.5 

### and its precursors from t he petroleum refineries

s ector specifically (U.S. EPA, 201 2 )

. The primary difference between the estimates used in this analysis and the estimates reported in Fann, Fulcher, and Hubbell (2009) is the air quality modeling data utilized. While the air quality data used in Fann, Fulcher, and Hubbell (2009)
reflects bro ad pollutant/source category combinations, such as all non

- EGU stationary point sources, the air quality modeling data used in this analysis is sector

- specific. In addition, the updated air quality modeling data reflects more recent emissions data (2005 ra ther than 2001) and has a higher spatial resolution (12km rather than 36 km grid cells). As a result, the benefit

per

- ton estimates presented herein better reflect the geographic areas and populations likely to be affected by this sector. The benefits meth odology, such as health endpoints assessed, risk estimates applied, and valuation techniques

, applied did not change. As noted below in the characterization of uncertainty, these updated estimates still have similar limitations as all
national

- average bene fit

- per

- ton estimates in that they reflect the geographic distribution of the
modeled emissions , which may not exactly match the emission reductions in this rulemaking, and they may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location. In this analysis, we apply
the se national benefit

- per

- ton estimate s calculated for this sector separately for SO 2 

### and NOx and multiply them by the corresponding e mission reductions

.

These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient

to allow differentiation of effects estimate s by particle type. SO 2 

### and NOx are the primary PM 2.5 

### precursors affected by this rule. Even though we assume that all fine particles have equivalent

16 Please see the Section 5.2 of the Portland Cement RIA in Appendix 5A for more information regarding the change in the present ation of benefits estimates.

### 6

- 6

health effects, the benefit

- per

- ton estimates vary between precursors depending on the location and magnit ude of their impact on PM 2.5 

### levels, which drive population exposure

. The sector

specific modeling does not provide estimates of the PM 2.5 

### - related benefits associated with reducing VOC emissions, but these unquantified benefits are generally small compared to other
PM 2.5 

### precursors (U.S. EPA, 201 2 )

.

The benefit

- per

- ton coefficients in this analysis were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis (U.S. EPA, 2006) . Specifically, this analysi s uses the benefit

- per

- ton method first applied in the Portland Cement NESHAP RIA (U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded geographic scope of the benefit

- per

- ton calculation, and the functions directly fro m the epidemiology studies without an adjustment for an assumed threshold. 17 

### Removing the threshold assumption is a key difference between the method used in this analysis of PM benefits and the methods used in RIAs prior to Portland Cement proposal, and w e now calculate incremental benefits down to the lowest modeled PM 2.5 

### air quality levels.

Based on our review of the current body of scientific literature, EPA now estimates PM

related mortality without applying an assumed concentration threshold. EPA’ s Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S. EPA

- SAB, 2009a; U.S. EPA

- SAB, 2009b), concluded that the scientific literature consistently finds that a no

- threshold log

- linear model most adequately portrays the PM

- mortality concentration

- response relationship while recognizing potential uncertainty about the exact shape of the concentration

- response function.

Consistent with this finding, we have conformed the previous threshold sensitivity analysis to the current state of the PM science by incorporating a “Lowest Measured Level”
(LML) assessment

, which is a method EPA has employed in several recent RIAs including the

Cross

- State Air Pollution Rule (U.S. EPA, 2011 b ). This information allows readers to determine the portion of population exposed to annual mean PM 2.5 

### levels at or above the LML of each study; in general, our confidence in the estimated PM mortality decreases as we con sider air quality levels further below the LML in major cohort studies that estimate PM

- related mortality. While an LML assessment provides some insight into the level of uncertainty in the estimated

17 The benefit

- per

- ton estimates have also been updated since the Portland Cement RIA to incorporate a revised VSL, as discussed below

.

### 6

- 7

PM mortality benefits, EPA does not view the LML as a th reshold and continues to quantify PM

related mortality impacts using a full range of modeled air quality concentrations. It is important to emphasize that we have high confidence in PM 2.5 

### - related effects down to the lowest LML of the major cohort studies, which is 5.8 μg/m 3 

### . J ust because we have greater confidence in the
benefits above the LML, this does not mean that we have no confidence that benefits occur

below the LML. F or a summary of the scientific review statements regarding the lack of a

thres hold in the PM 2.5 

### - mortality relationship, see the Technical Support Document (TSD)

entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration

Response Function for PM 2.5 

### - related Mortality (U.S. EPA, 2010 b )

.

For this analysis, policy

- specific air quality data is not available due to time or resource limitations. For these rules, we are unable to estimate the percentage of premature mortality associated with this specific rule’s emission reductions at each PM 2.5 

### level. However, we believe that it is still important to characterize the distribution of exposure to baseline air quality levels. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 

### level using the source appor tionment modeling used to calculate the
benefit

- per

- ton estimates for this sector . It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population, and change in air qualit y. In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy. The most important aspect, whi ch we are unable to quantify for rules without rule

- specific air quality modeling, is the shift in exposure associated with this specific rule. Therefore, caution is warranted when interpreting the LML

assessment for this rule

. The results of this analys is are provided in Section 6. 3

.
As is the nature of RIAs, the assumptions and methods used to estimate air quality benefits evolve over time to reflect the Agency’s most current interpretation of the scientific and economic literature. For a period of tim e (2004

- 2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 million to $1 0 million (2000$) consistent with two meta

- analyses of the wage

- risk literature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta

- analysis of 33 studies. The $10 million value represented th e upper end of

### 6

- 8

the interquartile range from the Viscusi and Aldy (2003) meta

- analysis of 43 studies. The mean estimate of $5.5 million (2000$) 18 

### was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta

- analysis. Howe ver, the Agency neither changed its official guidance on the use of VSL in rulemakings nor subjected the interim estimate to a scientific peer

- review process through the Science Advisory Board (SAB) or other peer

- review

group.

During this time, the Agen cy continued work to update its guidance on valuing mortality risk reductions, including commissioning a report from meta

- analytic experts to evaluate methodological questions raised by EPA and the SAB on combining estimates from the various
data sources. In addition, the Agency consulted several times with the Science Advisory Board Environmental Economics Advisory Committee (SAB

- EEAC) on the issue. With input from the

meta

- analytic experts, the SAB

- EEAC advised the Agency to update its guidance using sp ecific, appropriate meta

- analytic techniques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e., wage

- risk and stated preference) (U.S. EPA

- SAB, 2007).

Until updated guidance is ava ilable, the Agency determined that a single, peer

- reviewed estimate applied consistently best reflects the SAB

- EEAC advice it has received. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines
for Pr eparing Economic Analyses (U.S. EPA, 2000) 19 

### while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published bet ween 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 20 

### The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB

- EEAC’s specific
recommendations.

In implementing these rules, emission controls may lead to reductions in ambient PM 2.5 

### below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist

18 After adjusting the VSL for a different currency year (200 6 $) and to account for incom e growth to 2015 of the $5.5 million value, the VSL is $ 7.4 million.
19 In revised Economic Guidelines (U.S. EPA, 2010c ), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. Therefore, this report does not represent final agency policy.
20 In this analysis, we adjust the VSL to account for a different currency year ($2006) and to account for income growth to 20 15 . After applying these adjustments to the $6.3 million value, the VSL is $8. 5 m illion

.

### 6

- 9

other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM
benefits, t here are important differences worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air quality standar d nationwide based on an array of emission control strategies for different sources. In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS does not dir ectly result in costs or benefits, and as such, the NAAQS RIAs are merely illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and

emission reductions. However, some costs and benefits estimated in this RIA account for the same air quality improvements as estimated in the illustrative PM 2.5 

### NAAQS RIA.

By contrast, the emission reductions for implementation rules are from a specific class of

well

- characterized sources. In gene ral, EPA is more confident in the magnitude and location of

the emission reductions for implementation rules rather than illustrative NAAQS analyses

. Emission reductions achieved under these and other promulgated rules will ultimately be

reflected in the b aseline of future NAAQS analyses, which would reduce the incremental costs and benefits associated with attaining the NAAQS. EPA remains forward looking towards the

next iteration of the 5

- year review cycle for the NAAQS, and as a result does not issue upd ated RIAs for existing NAAQS that retroactively update the baseline for NAAQS implementation. For more information on the relationship between the NAAQS and rules such as analyzed here, please see Section 1.2.4 of the SO 2 

### NAAQS RIA (U.S. EPA, 2010 a ).

### 6

- 10

Figure 6. 1 illustrates the relative breakdown of the monetized PM 2.5 

### he alth benefits.

Figure 6

- 1: Breakdown of Monetized PM 2.5 

### Health Benefits using Mortality F unction from Pope et al. (2002) * *This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult mortality would be 97%. This c hart shows the breakdown using a 3% discount rate, and the results would be similar if a 7% discount rate was used.



### Table 6

- 2 provides a general summary of the monetized PM

- related health benefits by

precursor , including the emission reductions and ben efit

- per

- ton estimates at discount rates of
3% and 7%. 21 

### Table 6

- 3 provides a summary of the reductions in health incidences as a result of the polluti on reductions. In Table 6

- 4, we provide the benefits using our anchor points of Pope
et al. and Laden et al. as well as the results from the expert elicitation on P M mortality. Figure

21 To comply with Circular A

- 4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). These benefits are estimated for a specific analysis year (i.e., 2017 ), and most of the PM benefits occur within that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortal ity. For AMIs, we assume 5 years of follow

- up medical costs and lost wages. For premature mortality, we assume that there is a

“ cessation ” lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, EPA follows the advice of the SAB

- HES to assume a segmented lag structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA

- SAB, 2004). C hanges in the lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis year and the valuation of premature mortalities that occur after th e analysis year. As such, the monetized benefits using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate.

### 6

- 11

6

- 2 provide s a visual representation of the range of PM 2.5 

### - related benefits estimates using
concentration

- response functions supplied by experts

.

Table 6

- 2: General Summary of Monetized PM

- Related Health Benefits Estimates for

Petroleum Refineries NSPS Reconsideration (millions of 2006$) *

Pollutant Emissions

Reductions (tons) Benefit

per ton (Pope, 3%) Benefit per ton (Laden, 3%) Benefit per ton (Pope, 7%) Benefit per ton (Laden, 7%) Total Monetized Benefits (millions of 2006$ at 3%) Total Monetized

Benefits (millions of 2006$ at 7%)

Small Flares PM 2.5 Precursors

SO 2 2,600 $65,000 $160,000 $58,000 $140,000 $170 to $410 $150 to $370

NO X 50 $6,400 $16,000 $5,700 $14,000 $.32 to $.79 $.29 to $.71

Total $170 to $410 $150 to $370

Large Flares PM 2.5 Precursors

SO 2 660 $65,000 $160,000 $58,000 $140,000 $43 to $100 $38 to $94

NO X 1,100 $6,400 $16,000 $5,700 $14,000 $6.80 to $17.00 $6.10 to $15.00

Total $49 to $120 $44 to $110

All Flares PM 2.5 Precursors

SO 2 3,200 $65,000 $160,000 $58,000 $140,000 $210 to $510 $190 to $460

NO X 1,100 $6,400 $16,000 $5,700 $14,000 $7.1 to $18.0 $6.4 to $16

Total $220 to $530 $190 to $480

* All estimates are for the analysis year (2017), and are rounded to two significant figures so numbers may not sum across columns. It is important to note that the monetized benefits do not include reduced health effects from direct exposure to SO 2 and NOx, ozone exposure, ecosystem effects, or visibility impairment. These estimates do not include CO 2 benefits estimated at $ 4 6 million (3% discount rate). All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to form PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. C onfidence intervals are unavailable for this analysis because of the benefit

- per

- ton methodology.

### 6

- 12

Table 6

- 3 : Summary of Reductions in Health Incidences from PM 2.5 

### B enefits for the Final

Petroleum Refineries NSPS Reconsideration*

Avoided Premature Mortality
Pope et al. 26

Laden et al. 66 Avoided Morbidity

Chronic Bronchitis 19 Emergency Department Visits, Respiratory 19 Hospital Admissions, Respiratory 5 Hospital Admissions, Cardiovascular 10

Acute Bronchitis 43 Lower Respiratory 550 Upper Respiratory 420 Minor Restricted Activity Days 23,000 Work Loss Days 3,800

Asthma Exacerbation 920 Acute Myocardial Infarction 27

* All estimates are for the analysis year (2017) and are rounded to whole numbers with two significant figures. All fine particles are assumed to have equivalent health effects because the scientific evidence is not yet sufficient to allow differentiation of effects estimates by particle type. Confidence intervals are unavailable for this analysis because of the benefit

- per

- ton methodology.

### 6

- 13

Table 6

- 4 : All PM 2.5 

### Benefits Estimates for the Final Petroleum Refineries NSPS

Reconsideration at discount rates of 3% and 7% in 2017 (in millions of 2006$)*

3% 7%

Benefit

- per - ton Coefficients Derived from Epidemiology Literature Pope et al. $220 $190

Laden et al. $530 $480

Benefit

- per - ton Coefficients Derived from Expert Elicitation
Expert A $560 $510
Expert B $430 $380
Expert C $430 $380
Expert D $300 $270
Expert E $700 $630
Expert F $390 $350
Expert G $260 $230
Expert H $320 $290
Expert I $420 $380
Expert J $350 $310
Expert K $83 $74
Expert L $290 $260

* All estimates are rounded to two significant figures. These estimates do not include CO 2 benefits estimated at $ 49 million (3% discount rate). The benefits estimates from the Expert Elicitation are provided as a reasonable characterization of the uncertain ty in the mortality estimates associated with the concentration

- response function. Confidence intervals are unavailable for this analysis because of the benefit

- per

- ton methodology

### 6

- 14

Figure 6

- 2: Monetized PM 2.5 

### Benefits of Final Petroleum Refineries Reconsideration in 2017

* This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration

- response function provided in those studies. These estimates do not include CO 2 benefits es timated at $ 46 million (3% discount rate).



### 6.2 Social Cost of Carbon and Greenhouse Gas B enefits
EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 

### ) emissions using recent estimates of the “social cost of carbon” (SCC). The SCC is an est imate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk , and the value of ecosystem services due to climate change. The SCC estimates used in this analysis were developed through an interagency process that included EPA and other executive branch entities, and concluded in February, 2010. EPA
first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking to establish Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; see the rule’s preamble for discussion about application of SCC (7 5

### 6

- 15

FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete discussion of the methods used to develop these SCC estimates. 22



### The interagency group selected four SCC values for use in regulatory analyses, which we have applied in t his analysis: $5, $21, $35, and $65 per metric ton of CO 2 

### emissions 23 

### in 2010, in 2007 dollars. The first three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at se veral discount rates are included because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context. The fourth value is the 9 5th percentile of the SCC from all three models at a 3 percent discount rate. It is included to represent higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution. Low probability, high impact events are incorpo rated into all of the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high temperature outcomes, which in turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response t o greater climatic change. Note that the interagency group estimated the growth rate of the SCC directly using the three integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. The SCC estimates for the analysis years of 2014, in 2005 dollars are provided in
Table 6

- 5.

22 Docket ID EPA

- HQ

- OAR

- 2009

- 0472

- 114577, Technical Support Document: Social Cost of Carbon for R egulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department o f Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010)

. Also available at http://www.epa.gov/otaq/climate/regulations.htm
23 The interagency group decided that these estimates apply only to CO 2 emissions. Given that warming profiles and impacts other than temperature change ( e.g., ocean acidification) vary across GHGs, the group concluded

“ transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accurate estimates of the social costs of non

- CO 2 gases ” (SCC TSD, pg 13).

### 6

- 16

Table 6

- 5. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 

### ) for 201 7 a

Discount Rate and Statistic SCC estimate (200 6 $)
5% (Average) $5.9
3% (Average) $24.1
2.5% (Average) $38.6

3% (95 th percentile) $73.9

a The SCC values are dollar

- year and emissions

- year specific. SCC values represent only a partial accounting of climate impacts.



### When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC, 2009 ) points out that any assessment will suffer from uncertainty, speculation, 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) t he 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 provisiona l.

The interagency group noted a number of limitations to the SCC analysis, including the incomplete way in which the integrated assessment models capture catastrophic and non

catastrophic impacts, their incomplete treatment of adaptation and technologica l change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impacts to economic damages makes the interagency modeling exercise even more difficult. The interagency group hopes that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling. Additional details on the se limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to updating the current estimates as the science and economic understanding of climate change and its impacts on society improves over time. Spe cifically, the interagency group has set a preliminary goal of revisiting the SCC values within two years or at such time as substantially updated models become available, and to continue to support research in this area.

### 6

- 17

Applying the global SCC estimates to the estimated reductions in CO 2 

### emissions for the range of policy scenarios, we estimate the dollar value of the climate

- related benefits captured by the models for each analysis year. For internal consistency, the annual benefits are discounted
back t o net present value ( NPV ) terms using the same discount rate as each SCC estimate (i.e., 5%, 3%, and 2.5%) rather than 3% and 7%. 24 

### These estimates are provided in Table 6

- 6.

Table 6

- 6. Monetized SCC

- Derived Benefits of CO 2 

### Emission Reductions in 2017 a,b

Discount Rate and Statistic Monetized Climate Benefits with flare gas recovery (millions of 2006$) Monetized Climate Benefits without flare gas recovery (millions of 2006$)

Ton ne s of CO 2 1,900,000 110,000
5% (Average) $11 $0.6
3% (Average) $46 $2.6
2.5% (Average) $73 $4.2
3% (95 th percentile) $140 $8.1

a All estimates have been rounded to two significant digits. The SCC values are dollar

- year and emissions

- year specific. SCC values represent only a partial accounting of climate impacts.

b The tonnes of CO 2 emission reductions in this table reflect the anticipated emission increases associated with the energy disbenefits described in section 6.3.



### 6.3 Energy Disbenefits
In this section, we provide an estimate of the energy disbenef its associated with the increased emissions from additional energy usage. 25 

### We estimate that electricity usage associated with operation of the control devices would increase by 1 23 GWh/ yr nationwide. 26 

### This

e lectricity usage is anticipated to increase emis sions of pollutants from electric utility boilers (EGU boilers) that supply electricity to the non

- EGU boiler facilities by 104,381 tonnes CO 2


### . In
the calculation of CO 2 

### benefits, we have already subtracted these CO 2 

### emission increases associated with increased electricity usage from the CO 2 

### emission reductions associated with the regulatory requirements in the benefits shown in Table 6

- 6

.

24 It is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from those used to develop the SCC estimates.
25 As we use the term “energy disbenefits” in this analysis, we are n ot referring to the cost of purchasing additional electricity or fuel, which are captured within the cost estimates. We use the term to refer to the increase in emissions associated with additional energy use.
26 See Impact Estimates for Fuel Gas Combusti on Device and Flare Regulatory Options for Amendments to the Petroleum Refinery NSPS , in Docket ID No. EPA

- HQ

- OAR

- 2007

- 0011.

### 6

- 18

6.4 Total Monetized Benefits
The total monetized benefits of this rulemaking i nclude both the PM

- related health
benefits as well as the CO 2 

### - related climate benefits. We estimate the total monetized benefits to be $ 260 million to $ 58 0 million at a 3% discount rate and $ 24 0 million to $ 520 million at a 7%
discount rate. All estimate s are in 2006$ for the year 2017

. Figure s 6

- 3 and 6

- 4 provide a visual representation of the breakdown of total monetized benefits by pollutant and requirement, respectively

. Alternatively, if no refineries install flare gas recovery systems, EPA estimates the
monetized benefits of $190 to $460 million and $170 to $410 million at discount rates of 3% and 7% respectively. For small flares only , we estimate the monetized benefits are $170 million to $410 million (3 % discount rate) and $150 millio n to $370 million (7% discount rate for health

benefits and 3% discount rate for climate benefits). For large flares, we estimate the monetized

benefits are $93 million to $160 million (3% discount rate) an d $88 million to $150 million (7%

discount rate fo r health benefits and 3

- percent discount rate for climate benefits). All estimates

are in 200 6 dollars for the year 2017

.

Figure 6

- 3: Breakdown of Monetized Benefits for Final Petroleum Refineries NSPS Reconsideration by Pollutant * This chart uses the results based on Pope et al. (2002) and a 3% discount rate as an example. 

### SO2 80% NOX 3% CO2

17%

### 6

- 19

Figure 6

- 4: Breakdown of Monetized Benefits for Final Petroleum Refineries NSPS

Reconsideration by Requirement and Flare Size

6. 5 Unquantified Benefits

The monetized benefits est imated in this RIA only reflect a subset of benefits attributable to the health effect reductions associated with ambient fine particles. Data, time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the benefits from

s everal important benefit categories, including benefits from reducing direct exposure to SO 2 

### and NOx, ozone exposure, ecosystem effects, and visibility impairment

. NO x 

### and SO 2 

### emissions also contribute to adverse welfare effects, including acidic depositi on in aquatic and terrestrial ecosystems and visibility impairment.

6. 5 .1 Additional SO 2 

### and NO 2 

### Benefits
In addition to being a precursor to PM 2.5 

### , SO 2 

### and NO x 

### emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health
benefits associated with reduced SO 2 

### and NO 2 

### exposure in this analysis because we do not have air quality modeling data available. Therefore, this analysis only quantifies and monetizes the
PM 2.5 

### benefits associated w ith the reductions in SO 2 

### and NO 2 

### emissions.

Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the U.S. EPA has concluded that there is a causal relationship between respiratory health 

### Flare Gas Recovery 

### 28% SO 2 

### RCA/CA 71% Flow Rate RCA/CA 1.2%

Flare Monitoring 0% Flare Management Plans 0% 

### Small flares 65% Large flares 35%

### 6

- 20

effects and short

- term exposure to SO 2 

### (U.S. EPA, 2008 b ). According to summary of the ISA in
EPA ’ s risk and exposure assessment (REA) for the SO 2 

### NAAQS, “the immediate effect of SO 2 

### on the respiratory system in humans is bronchoconstriction” (U.S. EPA, 2009 b ). Asthmatics are

m ore sensitive to the effects of SO 2 

### likely resulting from preexisting inflammation associated
with this disease. A clear concentration

- response relationship has been demonstrated in laboratory studies following exposures to SO 2 

### at concentrations between 2 0 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified four short

- term morbidity endpoints that the SO 2 

### ISA identified as a “causal relationship” : asthma exacerbation, respiratory

- related emergency department visits, and respiratory

- related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 

### ISA. The SO 2 

### IS A also concluded that the relationship between short

- term SO 2 

### exposure and premature mortality was “suggestive of a causal relationship” because it is difficult to attribute the mortality
risk effects to SO 2 

### alone. Although the SO 2 

### ISA stated that studies are generally consistent in reporting a relationship between SO 2 

### exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants.

Following an extensive evaluation of health evidence from epidemiologic an d laboratory studies, the Integrated Science Assessment (ISA) for Nitrogen Dioxide concluded that there is a likely causal relationship between respiratory health effects and short

- term exposure to NO 2 

### (U.S. EPA, 2008c ). Persons with preexisting respiratory disease, children, and older adults may be more susceptible to the effects of NO 2 

### exposure. Based on our review of this information, we
identified four short

- term morbidity endpoints that the NO 2 

### ISA identified as a “likely causal relationship” : asthma exacerbation, respiratory

- related emergency department visits, and respiratory

- related hospitalizations. The differing evidence and associated strength of the
evidence for these different effects is described in detail in the NO 2 

### ISA. The NO 2 

### ISA also concluded that the relationship between short

- term NO 2 

### exposure and premature mortality was “suggestive but not sufficient to infer a causal relationship” because it is difficult to attribute the mortality risk effects to NO 2 

### alone. Although the NO 2 

### I SA stated that studies consistently reported a relationship between NO 2 

### exposure and mortality, the effect was generally smaller than that for other pollutants such as PM.

SO 2 

### and NOx emissions also contribute to a variety of adverse welfare effects , incl uding acidic deposition, visibility impairment, nutrient enrichment (NOx only) and enhanced mercury methylation (SO 2 

### only)

. Deposition of sulfur and nitrogen causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro in vertebrates in aquatic ecosystems, as

### 6

- 21

well as a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple ( Acer saccharum ) in terrestrial ecosystems

. In the northeastern United States, the surface waters affected by acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesthetic and educational services and recreational fishing. B iological effects of acidification in terrestrial

e cosystems are generally linked to aluminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrients. These direct effects can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects several important ecological services, including declines in habitat for threatened and endangered species (cultura l), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating). (U.S. EPA, 2008d)

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of estuaries can disrupt an important source of food production, particularly fish and shellfish production, and a v ariety of cultural ecosystem services, including water

- based recreational and aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and number of species and biodiversity in terrestrial systems. Excessive nitrogen dep osition upsets the balance between native and nonnative plants, changing the ability of an area to support biodiversity. When the composition of species changes, then fire frequency and intensity can also change, as nonnative grasses fuel more frequent and more intense wildfires. (U.S. EPA, 2008d)

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008d) . The contaminant is concentrated in higher trophic levels, including fish eaten by humans. Experimental evidence has established that only inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA, 2008d) . Current evidence indicates that in watersheds where mercury is present, increased
sulfate deposi tion very likely results in methylmercury accumulation in fish (Drevnick et al., 2007; Munthe et al, 2007). The NOx/ SO x 

### Ecological ISA concluded that evidence is sufficient to infer a casual relationship between sulfur deposition and increased mercury met hylation in wetlands and aquatic environments (U.S. EPA, 2008d).

Reducing SO 2 

### and NO x 

### emissions and the secondary formation of PM 2.5 

### would improve the level of visibility throughout the United States. Fine particles with significant light

-

### 6

- 22

extinction effi ciencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). These suspended particles and gases degrade visibility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher concentrations of fine particles, particularly sulfates, and higher average relative humidity levels. In fact, particulate sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate contributes
to 20

- 50% of regional haze (U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality o f life where individuals live and work, and where they engage in recreational activities.

6. 5 .2 Ozone Benefits
In the presence of sunlight, NOx and VOCs can undergo a chemical reaction in the atmosphere to form ozone. Reducing ambient o zone concentratio ns is associated with significant human health benefits, including mortality and respiratory morbidity (U.S. EPA,
2008 a ). Epidemiological researchers have associated ozone exposure with adverse health effects in numerous toxicological, clinical and epidem io logical studies (U.S. EPA, 2006c ). These health effects include respiratory morbidity such as fewer asthma attacks, hospital and ER visits, school loss days, as well as premature mortality.

6. 5 .3 Visibility Impairment

Reducing secondary formation of PM 2.5 

### would improve visibility throughout the U.S. Fine particles with significant light

- extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Suspended particles and gases degrade visibility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher concentrations of fine particles, particularly sulfates, and higher average relative humidity levels. Visibility has direct significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreational activities. Previous analyses (U.S. EPA, 2006; U.S. EPA, 201 1a ; U.S. E PA, 2011b ) show that visibility benefits are a significant welfare benefit category. Without air quality modeling, we are unable to estimate visibility

- related benefits, nor are we able to determine whether VOC emission reductions would be likely to have a significant impact on visibility in urban areas or Class I areas.

### 6

- 23

6. 6 Characterization of Uncertainty in the Monetized Benefits
In any complex analysis, there are likely to be many sources of uncertainty. Many inputs
are used to derive the final estimate of economic benefits, including emission inventories, air quality models (with their associated parameters and inputs), epidemiological estimates of
concentration

- response (C

- R) functions, estimates of values, population estimates, income estimates, and e stimates of the future state of the world (i.e., regulations, technology, and human behavior). For some parameters or inputs it may be possible to provide a statistical representation of the underlying uncertainty distribution. For other parameters or in puts, the necessary information is not available. As discussed in the PM 2.5 

### NAAQS RIA (Table 5.5) (U.S. EPA, 2006) , there are a variety of uncertainties associated with these PM benefits. Therefore, the estimates of annual benefits should be viewed as rep resentative of the magnitude of benefits expected, rather than the actual benefits that would occur every year.

It is important to note that the monetized benefit

- per

- ton estimates used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these estimates do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of these $/ton values to estim ate benefits associated with different emission control programs (e.g., for reducing emissions from large stationary sources like EGUs) may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Gre at care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average benefits

- per

- ton over the entire United States. The benefits

- per

- ton for emission reductions in specific locations may be very different than the estimates presented
here.

PM 2.5 

### mortality benefits are the largest benefit category that we monetized in this analysis. To better characteri ze the uncertainty associated with mortality impacts that are
estimated to occur in areas with low baseline levels of PM 2.5 

### , we included the LML assessment. For this analysis, policy

- specific air quality data is not available due to time or resource
limit ations , thus we are unable to estimate the percentage of premature mortality associated with this specific rule’s emission reductions at each PM 2.5 

### level. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 

### level using the source apportionment modeling used to calculate the benefit

- per

- ton estimates for this sector

. A very large proportion of the population is exposed at or above the lowest LML of the cohort studies (Figures 6

- 5 and 6

- 6 ), increas ing our confidence in the PM mortality analysis. Figure 6

- 5

### 6

- 24

shows a bar chart of the p ercentage of the population exposed to various air quality levels in the

pre

- and post

- policy policy. Figure 6

- 6 shows a cumulative distribution function of the same
dat a. Both figures identify the LML for each of the major cohort studies. As the policy shifts the distribution of air quality levels, fewer people are exposed to PM 2.5 

### levels at or above the
LML. Using the Pope et al. (2002) study, the 77 % of the population is exposed to annual mean
PM 2.5 

### levels at or above the LML of 7.5 μg/m 3 

### . Using the Laden et al. (2006) study, 25 % of the population is exposed above the LML of 10 μg/m 3



### . As we model avoided premature deaths among populations exposed to levels of PM 2.5 

### , we have lower confidence in levels below the LML for each study. It is important to emphasize that we have high confidence in PM 2.5 

### - related effects down to the lowest LML of the major cohort studies. Just because we have greater
confidence in the benefits above the LML, this does not mean that we have no confidence that
benefits occur below the LML.

A large fraction of the baseline exposure occur s below the level of the National Ambient Air Quality Standard (NAAQS) for annual PM 2.5 

### at 15 μg /m 3 

### , which was set in 2006. It is important to emphasize that NAAQS are not set at a level of zero risk. Instead, the NAAQS reflect the level determined by the Administrator to be protective of public health within an adequate margin of safety, taking into consideration effects on susceptible populations. While benefits occurring below the standard may be less certain than those occurring above the standard, EPA considers them to be legitimate components of the total benefits estimate.

### 6

- 25

Figure 6

- 5

. Percentage of Adult Population by Annual Mean PM 2.5 

### Exposure in the Baseline

### 6

- 26

Figure 6

- 6

. Cumulative Distribution of Adult Population by Annual Mean PM 2.5 

### Exposure in the Baseline

Above we present the estimates of the total benefits, based on our interpretation of the best available scientific literature and methods and supported by the SAB

- HES and the NAS (NRC, 2002 ). The benefits estimates are subject to a number of assumptions and uncertainties. For example, for key assumptions underlying the estimates for premature mortality, which typically account for at least 90% of the total benefits, we were able to quantify include the following:

1. PM 2.5 

### benefits were derived through benefit per

- ton estimates, which do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over

- estimate or under

estimate of the actual benefits of controlling directly emitted fine particulates. We do not have data on the specific location of the air quality changes associated with this rulemaking; as such, it is not feasible to estimate the proportion of benefits occurring in different locations, such as designated nonattainment areas. In addition, the benefi t

- per

- ton estimates are based on emissions from existing sources. To the extent

### 6

- 27 that the geographic distribution of the emissions reductions for this rule are different than the modeled emissions, the benefits may be underestimated or overestimated. In
ge neral, there is inherently more uncertainty for new sources, which may not be included in the emissions inventory, than existing sources.

2. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

### produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 

### released from diesel engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type.

3. We assume that the health impact function for fine particles is linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5, 

### including both regions that are in attainment with fine particle standard and those that do not meet the standard down to the lowest modeled concentrations.

4. To characterize the uncertainty in th e relationship between PM 2.5 

### and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates. Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 

### estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis. For more information on the uncertainties associated wi th PM 2.5 

### benefits, please consult the
PM 2.5 

### NAAQS RIA (Table 5.5).

This RIA does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA because we lack the necessary air quality input and monitoring data to run the
benefits mode l. In addition, we have not conducted any air quality modeling for this rule. Moreover, it was not possible to develop benefit

- per

- ton metrics and associated estimates of uncertainty using the benefits estimates from the PM RIA because of the significant differences between the sources affected in that rule and those regulated here. However, the results of the Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty surroundi ng the benefits results presented in this analysis.

### 6

- 28

6. 7 References

Fann, N., C.M. Fulcher, B.J. Hubbell. 2009. “ The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution. ” Air Qual Atmos Health 2:169

– 176.

Kochi, I., B. Hubbell, and R. Kramer. 2006. “ An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Environmental Policy Analysis

. ” Environmental and Resource Economics. 34: 385

- 406.

Krews ki D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow

- up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine. 173: 667

- 672.

Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta

- Analysis.” Journal of Policy Analysis and Management 21(2):253

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National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press.

National Research Council (NRC). 2009. Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use. National Academies Press. Washington, DC.

Office of Management and Budget (OMB). 2003. Circular A

- 4: Regulatory Analysis. W ashington, DC. Available on the Internet at http://www.whitehouse.gov/omb/circulars/a004/a

- 4.html

. Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurst on. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long

- term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132

- 1141.

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. 2008. “ Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S. ” Environ. Sci. Technol ., 42(7):2268

- 2274.

Sisler, J.F. 1996. Spatial and seasonal patterns and l ong

- term variability of the composition of the haze in the United States: an analysis of data from the IMPROVE network . CIRA Report, ISSN 0737

- 5352

- 32, Colorado State University.

### 6

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U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 240

- R

- 00

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at < http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf >.

U.S. Environmental Protection Agency. 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter , Chapter 5. Available at

< http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205

-- Benefits.pdf >.

U.S. Environmental Protection Agency. 2008 a

. Regulatory Impact Analysi s, 2008 National Ambient Air Quality Standards for Ground

- level Ozone , Chapter 6. Available at

< http://www.epa.gov/ttn/ecas/regdata/RIAs/6

- ozoneriachapter6.pdf >.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for Sulfur Oxides

—

Health Criteria (Final Report). National Center for Environmental Assessm ent, Research Triangle Park, NC

. Available at <http://cfpub.epa.gov/ncea/cfm/recordi splay.cfm?deid=198843>.

U.S. Environmental Protection Agency (U.S. EPA). 2008 c

. Integrated Science Assessment for Oxides of Nitrogen

- Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. July. Available at at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>.

U.S. Environmental Protection Agency (U.S. EPA). 2008 d

. Integrated Science Assessment for Ox ides of Nitrogen and Sulfur

– Ecological Criteria National (Final Report). National Center for E nvironmental Assessment, Research Triangle Park, NC. EPA/600/R

- 08/139. December. Available a t <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.

U.S. Environmental Protection Agency (U.S. EPA). 2009a . Regulatory Impact Analysis: National Emissi on Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry . Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available on the Internet at

< http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4

- 20

- 09.pdf >.

U.S. Environmental Protection Agency (U.S. EPA). 2009 b

. Integrated Science Assessment for Particulate Matter (Final Report)

. EPA

- 600

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- 08

- 139F. National Center for Environmental Assessment

– RTP Division. December. Available on the Internet at

< http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 >.

U.S. Environmental Protection Agency (U.S. EPA). 2010 a

. Final Regulator y Impact Analysis (RIA) for the SO 2 

### National Ambient Air Quality Standards (NAAQS)

. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at <http://www.epa.gov/ttn/ecas/ria.html>.

U.S. Environmental Protection Agenc y (U.S. EPA). 2010b. Technical Support Document: Summary of Expert Opinions on the Existence of a Threshold in the Concentration

- Response

### 6

- 30

Function for PM 2.5 

### - related Mortality . Research Triangle Park, NC. June. Available on the Internet at < www.epa.gov/ttn/ ecas/regdata/Benefits/ threshold s tsd.pdf >.

U.S. Environmental Protection Agency (U.S. EPA). 2010 c

. Guidelines for Preparing Economic Analyses

. EPA 240

- R

- 1 0

- 001

. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. December

. Available on the Internet at

< http://yosemite.epa.gov/ee/epa/eerm.nsf/vwAN/EE

- 0568

- 50.pdf/$file/EE

- 0568

- 50.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 2011a. The Benefits and Costs of the Clean Air Act from 1990 to 2020 . Office of Air and Radiation, Washington, DC. March. <http://www.epa.gov/air/sect812/feb11/fullreport.pdf>. Accessed March 30, 2011.

U.S. Environmental Protection Agency (U.S. EPA). 201 1 b

. Regulatory Impact Analysis for the Final Transport Rule . Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at < http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 201 2

. Technical Support Document: Estimating the Benefit per ton of Reducing PM 2.5 

### Precursors from the Petroleum Refineries Sector . Office of Air Quality Planning and Standards, Research Triangle Park, NC. January

. U.S. Environmental Protection Agency

- Science Advisory Board (U.S. EPA

- SAB). 2007. SAB Advisory on EPA’s Issues in Valuing Mortality Risk Reduction. EPA

- SAB

- 08

- 001 . October. Available on the Internet at

< http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$File/ sab

- 08

- 001.pdf >.

U.S. Environmental Protection Agency

- Science Advisory Board (U.S. EPA

- SAB). 2009a. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA

- COUNCIL

- 09

- 008. May. Available on the Intern et at
< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73 ACCA834AB44A 10852575BD0064346B/$File/EPA

- CASAC

- 09

- 008

- unsigned.pdf >.

U.S. Environmental Protection Agency

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- SAB). 2009b. Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for He alth Risk and Exposure Assessment. EPA

- COUNCIL

- 09

- 009. May. Available on the Internet at

< http ://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/72 3FE644C5D758DF852575BD00763A32/$File/EPA

- CASAC

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- 009

- unsigned.pdf >.

U.S. Environmental Protection Agency

- Science Advisory Board (U.S. EPA

- SAB). 2010. Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. EPA

- COUNCIL

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- 001 . June. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500738776/$F ile/EPA

- COUNCIL

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Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5

- 76.

### 7

- 1 7 COMPARISON OF BENEFI TS AND COSTS

EPA estimates the range of benefits of this reconsideration to be $ 260 million to $ 580 million at a 3% discount rate and $ 240 million to $ 520 million at a 7% discount rate in 2017

. The annualized costs are a cost savings of $ 79 million at a 7% interest ra te. Thus, net benefits are $ 340 million to $ 660 million at a 3% discount rate for the benefits and $ 320 million to $ 600
million at a 7% discount rate. All estimates are in 2006$.

Table 7

- 1 provides details, by flare size, of the monetized benefits and Ta ble 7

- 2 shows a summary of the monetized benefits, social costs, and net benefits. Figure 7

- 1 shows the full range of net benefits estimates (i.e., benefits minus annualized costs) utilizing the 14 different
PM 2.5 

### mortality functions at the 3% discount ra te , and Figure 7

- 2 shows this information at a 7%
discount rate

. EPA believes that the benefits of this rule are likely t o exceed the costs by a substantial margin even when taking into account uncertainties in the cost and benefit estimates.

Alternatively, if no refineries install flare gas recovery systems, EPA estimates the costs would be $10.7 million with monetized benefits of $190 to $460 million and $170 to $410 million at a discount rates of 3% and 7% respectively. Thus, net benefits wi thout flare gas recovery systems would be $ 180 m illion to $ 450 m illion and $ 160 m illion to $ 400 m illion, at 3%

and 7% discount rates, respectively. All estimates are in 200 6 dollars for the year 2017

. For small flares, we estimate the monetized benefits are $170 million to $410 million (3

- percent discount rate) and $150 million to $370 million (7% discount rate for health benefits and

3% discount rate for climate benefits). For large flares, we estimate the monetized benefits are
$93 million to $160 milli on (3% discount rate) an d $88 million to $150 million (7% discount rate

for health benefits and 3

- percent discount rate for climate benefits). All estimates are in 200 6 dollars for the year 2017

.

### 7

- 2 Table 7

- 1. National Incremental Cost Impacts , Emission Reductions, and Cost Effectiveness for Petroleum Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Effective Date of Final Rule Amendments ) 1

Subpart Ja Requirements Total

cap ital cost (millions) Total

annual cost without credit (millions/yr) Natural

gas offset/ product recovery credit (millions) Total

annual cost (millions/yr) Annual

emission reductions (tons SO 2 /yr) Annual

emission reductions (tons NO X /yr) Annual

emission reductions (tons VOC/yr) Cost

effective

ness ($/ton emissions reduced) Annual

emission reductions (metric tons CO 2 /yr) 2 Monetized

Benefits (millions , 3% discount rate for health and climate benefits ) Monetized Benefits (millions , 3% discount rate for health and 7% for climate benefits ) Small Flares Flare Monitoring $72 $12 $0 $12 0 0 0 0 $0 to $0 $0 to $0
Flare gas
recovery $0 $0 $0 $0 0 0 0 0 $0 to $0 $0 to $0

Flare Management $0 $0.79 $0 $0.79 0 0 270 $2,900 0 $0 to $0 $0 to $0

SO2 RCA/CA $0 $1.9 $0 $1.9 2,600 0 0 $760 0 $170 to $410 $150 to $370
Flowrate RCA/CA $0 $0.90

- $6.7

- $5.8 3.4 50 390

- $13,000 98,000 $2.9 to $3.7 $2.9 to $3.6

Total $72 $16

- $6.7 $9.0 2,600 50 660 $2,700 98,000 $170 to $410 $150 to $370 Large Flares Flare Monitoring $12 $2.0 $0 $2.0 0 0 0 0 $0 to $0 $0 to $0
Flare gas
recovery $380 $78

- $170

- $90 380 1,100 2,700

- $22,000 1,800,000 $74 to $120 $71 to $110

Flare Management $0 $0.0 88 $0 $0.088 0 0 30 $2,900 0 $0 to $0 $0 to $0

SO2 RCA/CA $0 $0.22 $0 $0.22 290 0 0 $760 0 $18 to $45 $17 to $41
Flowrate RCA/CA $0 $0.10

- $0.74

- $0.64 0 6 43

- $13,000 11,000 $0 to $0 $0 to $0

Total $390 $81

- $170

- $88 660 1,100 2,800

- $ 20 ,000 1,800,000 $93 to $160 $88 to $150 All Flares Flare Monitoring $84 $14 $0 $14 0 0 0 0 $0 to $0 $0 to $0
Flare gas
recovery $380 $78

- $170

- $90 380 1,100 2,700

- $22,000 1,800,000 $74 to $120 $71 to $110

Flare Management $0 $0.88 $0 $0.88 0 0 300 $ 2,900 0 $0 to $0 $0 to $0

SO2 RCA/CA $0 $2.2 $0 $2.2 2,900 0 0 $760 0 $180 to $450 $170 to $410
Flowrate RCA/CA $0 $1.00

- $7.4

- $6.4 3.4 56 430

- $13 ,000 110,000 $3.2 to $4.0 $3.1 to $3.9

Total $460 $96

- $180

- $79 3,200 1,100 3,400

- $ 10 ,000 1,900,000 $260 to $580 $240 to $520

1 All estimates are for the implementation year ( 2017 ), and are rounded to two significant figures.
2 The emission reductions of CO 2 reflect the anticipated emission increases associated with the energy disbenefits from additional electricity consumption.

### 7

- 3

Table 7

- 2

. Summary of the Monetized B enefits, Social Costs, and Net B enefits for the

Final Petroleum Refineri es NSPS in 2017 (millions of 2006$) 1

3% Discount Rate 7% Discount Rate

Total Monetized Benefits 2 $ 260 to $580 $240 to $ 520 Total Compliance Costs 3

- $ 79

- $ 79

Net Benefits $340 to $660 $320 to $ 600

Non

- monetized Benefits Health effects from ozone, SO 2 , NO 2 exposure
Health effects from PM 2.5 exposure from VOCs Ecosystem effects Visibility impairment

1 All estimates are for the implementation year ( 2017 ), and are rounded to two significant figures.
2 The total monetized benefits reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of PM 2.5 precursors such as NOx and SO 2 as well as CO 2 benefits . It is important to note that the monetized benefits do not include the reduced health effects from direct exposure to SO 2 and NOx, ozone exposure, ecosystem effe cts, or visibility impairment. Human health b enefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical
compo sition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effects estimates by particle type. The net present value of reduced CO 2 emissions is calculated differently than o ther benefits. This table includes monetized climate benefits using the global average social cost of carbon ( SCC ) estimate d at a 3 percent discount rate because the interagency workgroup deemed th e SCC estimate at a 3 percent discount rate to be the centr al value.
3 T he engineering compliance costs are annualized using a 7 percent discount ra t e.

### 7

- 4

Figure 7

- 1

. Net Benefits for Final Petroleum Refineries NSPS Reconsideration at 3% discount rate * *Net Benefits are quantified in terms of PM 2.5 and CO 2 benefits at a 3% discount rate for 2017 . This graph shows 14 benefits estimates combined with the cost estimate. All fine particles are assumed to have equivalent health effects. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

### 7

- 5

Figure 7

- 2

. Net Benefits for Final Petroleum Refineries NSPS Reconsideration at 7% discount rate * *Net Benefits are quantified in terms of PM 2.5 benefits at a 7 % discount rate and CO 2 benefits at a 3% discount rate for 2017 . This graph shows 14 benefits estimates combined with the cost estimate. All fine particles are assumed to have equivalent health effects. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

### A

- 1 APPENDIX A
PARENT COMPANY INFOR MATION FOR PETROLEUM REFINERIES

### A

- 2

Table A

- 1. Parent Company Information for Petroleum Refineries

Facility Name City State Capacity (bbl/cd) Parent Company Name Parent Company Sales Parent Company

Employment
AGE Refining Inc San Antonio TX 14,021 AGE Refining, Inc. 65.00 124
Alon Bakersfield Operating Inc Bakersfield CA Alon Israel Oil Company LTD 7,283.06 12,000

Alon Refining Krotz Springs Inc Krotz Springs LA 80,000 Alon Israel Oil Company LTD 7,283.06 12,000

Alon USA Energy Inc. Big Spring TX 67,000 Alon Israel Oil Company LTD 7,283.06 12,000

American Refining Group Inc Bradford PA 10,000 American Refining Group, Inc. 737.19 323

Big West Oil Co. North Salt Lake UT 29,400 Flying J Oil & Gas Inc. 1,719.00 15,000 BP Exploration Alaska Inc Prudhoe Bay AK 12,780 BP PLC 297,107.00 12,633
BP Products North America Inc Whiting IN 405,000 BP PLC 297,107.00 12,633

BP Products North America Inc Texas City TX 406,570 BP PLC 297,107.00 12,633

BP West Coast Products LLC Los Angeles CA 253,000 BP PLC 297,107.00 12,633

BP West Coast Products LLC Ferndale WA 225,000 BP PLC 297,107.00 12,633

BP

- Husky Refining LLC Toledo OH 131,400 BP PLC 297,107.00 12,633 Calcasieu Refining Co. Lake Charles LA 78,000 Calcasieu Refining Company 291.74 92

Calumet Lubricants Co LP Cotton Valley LA 13,020 Calumet Specialty Products Partners, L.P. 2,190.75 654

Calumet Lubricants Co LP Princeton LA 8,300 Calumet Specialty Products Partners, L.P. 2,190.75 654

Calumet Shreveport LLC Shreveport LA 57,000 Calumet Specialty Products Partners, L.P. 2,190.75 654

Cenex Harvest States Laurel MT 59,600 CHS, Inc. 23.30 287 Chalmette Refining LLC Chalmette LA 192,500 ExxonMobil Corp. 383,221.00 83,600
Chevron USA Inc. Perth Amboy NJ 80,000 Chevron Corporation 204,928.00 58,267
Chevron USA Inc. Salt Lake City UT 45,000 Chevron Corporation 204,928.00 58,267
Chevron USA Inc. Pascagoula MS 330,000 Chevron Corporation 204,928.00 58,267
Chevron USA Inc. El Segundo CA 273,000 Chevron Corporation 204,928.00 58,267
Chevron USA Inc. Richmond CA 245,271 Chevron Corporation 204,928.00 58,267
Chevron USA Inc. Honolulu HI 54,000 Chevron Corporation 204,928.00 58,267 Citgo Corpus Christi TX 163,000 CITGO Petroleum Corporation 475.80 3,506

Citgo Petroleum Corp. Lake Charles LA 427,800 CITGO Petroleum Corporation 475.80 3,506

Coffeyville Resources Rfg & Mktg LLC Coffeyville KS 115,700 CVR Energy, Inc. 4,079.77 371

ConocoPhillips Sweeny TX 247,000 ConocoPhillips 198,655.00 29,700 ConocoPhillips Linden NJ 238,000 ConocoPhillips 198,655.00 29,700 ConocoPhillips Wilmington CA 139,000 ConocoPhillips 198,655.00 29,700 ConocoPhillips SF

— Rodeo CA 120,200 ConocoPhillips 198,655.00 29,700 ConocoPhillips Belle Chasse LA 247,000 ConocoPhillips 198,655.00 29,700 ConocoPhillips Alaska Inc Prudhoe Bay AK 15,000 ConocoPhillips 198,655.00 29,700 ConocoPhillips Co Trainer PA 185,000 ConocoPhillips 198,655.00 29,700 (continued)

### A

- 3

Table A

- 1. Parent Company Information for Petroleum Refineries (continued)

Facility Name City State Capacity (bbl/cd) Parent Company Name Parent Company Sales Parent Company

Employment ConocoPhillips Company Westlake LA 239,400 ConocoPhillips 198,655.00 29,700 ConocoPhillips Company Ponca City OK 198,400 ConocoPhillips 198,655.00 29,700 ConocoPhillips Company Billings MT 58,000 ConocoPhillips 198,655.00 29,700 ConocoPhillips Company Ferndale WA 100,000 ConocoPhillips 198,655.00 29,700 Countrymark Cooperative, Inc. Mt. Vernon IN 26,500 Countrymark Cooperative 862.97 425

Deer Park Refining LTD Partnership Deer Park TX 327,000 Royal Dutch Shell, PLC 378,152.00 93,000

Delaware City Refining Co LLC Delaware City DE 182,200 PBF Holdings LLC Not Listed Not Listed

Delek Refining Ltd Tyler TX 60,000 Delek US Holdings, Inc. 3,755.60 3,296 Equistar Chemicals LP Channelview TX Lyondell Basell Industries N.V. 27,684.00 14,000

Ergon Refining Inc. Vicksburg MS 23,000 Ergon, Inc. 3,830.00 1,625 Ergon West Virginia Inc. Newell WV 20,000 Ergon, Inc. 3,830.00 1,625
Excel Paralubes Westlake LA Koch Industries Inc 9,182.50 62,544 ExxonMobil Corp. Beaumont TX 344,500 ExxonMobil Corp. 383,221.00 83,600 ExxonMobil Corp. Torrance CA 149,500 ExxonMobil Corp. 383,221.00 83,600 ExxonMobil Oil Corp Baytown TX 560,640 ExxonMobil Corp. 383,221.00 83,600 ExxonMobil Refining & Supply Co Baton Rouge LA 502,000 ExxonMobil Corp. 383,221.00 83,600

ExxonMobil Refining & Supply Co Billings MT 60,000 ExxonMobil Corp. 383,221.00 83,600

ExxonMobil Refining & Supply Co Joliet IL 238,600 ExxonMobil Corp. 383,221.00 83,600

Flint Hills Resources Corpus Christi TX 290,078 Koch Industries Inc 9,182.50 62,544
Flint Hills Resources North Pole AK 219,500 Koch Industries Inc 9,182.50 62,544
Flint Hills Resources Saint Paul MN 262,000 Koch Industries Inc 9,182.50 62,544 Foreland Refining Co. Ely NV 2,000 Foreland Refining Corp. 12.55 27
Frontier El Dorado Refining Co El Dorado KS 138,000 Frontier Oil Corp. 5,884.91 723

Frontier Refining Inc Cheyenne WY 47,000 Frontier Oil Corp. 5,884.91 723 Garco Energy LLC Douglas WY 3,600 Garco Energy 11.44 16 Goodway Refining LLC Atmore AL 4,100 Goodway Refining, LLC 2.00 17 Greka Energy Santa Maria CA 9,500 Santa Maria Refining Company 2.80 47

Gulf Atlantic Operations Mobile Bay AL 20,000 Gulf Atlantic Operations LLC 76.99 32

Hess Corporation Port Reading NJ Hess Corporation 34,613.00 13,800 Holly Refining & Marketing Co Woods Cross UT 25,050 Holly Corporation 8,323.00 1,321

Holly Refining & Marketing Co Tulsa East OK 70,300 Holly Corporation 8,323.00 1,321

Holly Refining & Marketing Co Tulsa West OK 85,000 Holly Corporation 8,323.00 1,321

Houston Refining LP Houston TX 280,390 Lyondell Basell Industries N.V. 27,684.00 14,000

Hunt Refining Co. Tuscaloosa AL 36,000 Hunt Refining Co. 4,871.00 346 Hunt Southland Refining Co Sandersville MS 11,000 Hunt Refining Co. 4,871.00 1,100

(continued)

### A

- 4

Table A

- 1. Parent Company Information for Petroleum Refineries (continued)

Facility Name City State Capacity (bbl/cd) Parent Company Name Parent Company Sales Parent Company

Employment Kern Oil & Refining Co. Bakersfield CA 26,000 Kern Oil & Refining Co. 282.00 105 Lima Refining Company Lima OH 155,000 Husky Energy, Inc. 18,174.36 4,491
Lion Oil Co. El Dorado AR 75,000 Lion Oil Co. 169.60 35 0 Little America Refining Co. Evansville WY 24,500 Sinclair Co.’s 7,753.46 7,000

Lunday

- Thagard Co. South Gate CA 8,500 World Oil Marketing Co. 35.40 65
Marathon Petroleum Co. LLC Robinson IL 206,000 Marathon Oil Corp 73,621.00 29,677

Marathon Petroleum Co. LLC Catlettsburg KY 212,000 Marathon Oil Corp 73,621.00 29,677

Marathon Petroleum Co. LLC Detroit MI 106,000 Marathon Oil Corp 73,621.00 29,677

Marathon Petroleum Co. LLC Canton OH 78,000 Marathon Oil Corp 73,621.00 29,677

Marathon Petroleum Co. LLC Texas City TX 76,000 Marathon Oil Corp 73,621.00 29,677

Marathon Petroleum Co. LLC Garyville LA 464,000 Marathon Oil Corp 73,621.00 29,677

Martin Midstream Partners LP Smackover AR 7,500 Cross Oil Refining & Marketing, Inc. 265.00 110

Montana Refining Co. Great Falls MT 10,000 Connacher Oil and Gas, Limited 574.19 265

Motiva Enterprises Port Arthur TX 285,000 Motiva Enterprises LLC 26,400.00 2,900 Motiva Enterprises LLC Norco LA 233,500 Motiva Enterprises LLC 26,400.00 2,900 Motiva Enterprises LLC Convent LA 235,000 Motiva Enterprises LLC 26,400.00 2,900 Murphy Oil USA Inc. Superior WI 34,300 Murphy Oil Corp 23,345.07 8,994 Murphy Oil USA Inc. Meraux LA 125,000 Murphy Oil Corp 23,345.07 8,994 National Cooperative Refinery Association McPherson KS 85,500 National Cooperative Refinery Association 2,810.30 612

Navajo Refining Co. Artesia NM 105,000 Holly Corporation 8,323.00 1,321 Nustar Asphalt Refining LLC Paulsboro NJ 70,000 NuStar Energy LP 4,403.06 1,492

Nustar Asphalt Refining LLC Savannah GA 28,000 NuStar Energy LP 4,403.06 1,492

Paramount Petroleum Co Long Beach CA 31,500 Alon Israel Oil Company LTD 7,283.06 12,000

Paramount Petroleum Corp. Paramount CA 53,000 Alon Israel Oil Company LTD 7,283.06 12,000

Pasadena Refining Systems Inc. Pasadena TX 100,000 Pasadena Refining 31.00 348

Paulsboro Refining Co LLC Paulsboro NJ 160,000 PBF Holding Company LLC Not Listed Not Listed

PDV Midwest Refining Lemont IL 167,000 Petróleos de Venezuela S.A. (PDVSA) 75.00 102,750

Pelican Refining Co. LLC Lake Charles LA Pelican Refining Co LLC 4.00 33
Petro Star Inc. North Pole AK 19,700 Petro Star Inc. 887.00 4 00
Petro Star Inc. Valdez AK 55,000 Petro Star Inc. 887.00 4 00
Placid Refining Inc. Port Allen LA 57,000 Placid Refining 3,351.84 207 Premcor Refining Group Inc Memphis TN 180,000 Valero Energy 82,233.00 20,313

(continued)

### A

- 5

Table A

- 1. Parent Company Information for Petroleum Refineries (continued)

Facility Name City State Capacity (bbl/cd) Parent Company Name Parent Company Sales Parent Company

Employment Premcor Refining Group Inc Port Arthur TX 292,000 Valero Energy 82,233.00 20,313

San Joaquin Refining Co., Inc. Bakersfield CA 15,000 San Joaquin Refining Co. 3.50 108

Shell Chemical LP St. Rose LA 55,000 Royal Dutch Shell, PLC 378,152.00 93,000
Shell Chemical LP Saraland AL 80,000 Royal Dutch Shell, PLC 378,152.00 93,000
Shell Oil Products US Anacortes WA 145,000 Royal Dutch Shell, PLC 378,152.00 93,000
Shell Oil Products US Martinez CA 156,400 Royal Dutch Shell, PLC 378,152.00 93,000 Silver Eagle Refining Evanston WY 3,000 Silver Eagle Refining Inc. 225.87 60 Silver Eagle Refining Woods Cross UT 10,250 Silver Eagle Refining Inc. 225.87 60 Sinclair Wyoming Refining Co Sinclair WY 74,000 Sinclair Co.’s 7,753.46 7,000

Somerset Energy Refinery LLC Somerset KY 5,500 Somerset Oil Inc. 16.90 11

South Hampton Resources Inc. Silsbee TX Arabian American Development Co 139.11 145

St Paul Park Refining Co. LLC Saint Paul MN 74,000 Northern Tier Energy LLC Not listed Not Listed

Suncor Energy Inc. Commerce City West CO 67,000 Suncor Energy Inc. 34,398.09 12,978

Suncor Energy Inc. Commerce City East CO 36,000 Suncor Energy Inc. 34,398.09 12,978

Sunoco, Inc. Marcus Hook PA 178,000 Sunoco, Inc. 37,489.00 4,544 Sunoco, Inc. Toledo OH 160,000 Sunoco, Inc. 37,489.00 4,544 Sunoco, Inc. (R&M) Philadelphia PA 335,000 Sunoco, Inc. 37,489.00 4,544 Tenby Inc. Oxnard CA 2,800 Ten By Inc. NotListed NotListed
Tesoro Alaska Petroleum Co Kenai AK 68,000 Tesoro Corp 20,583.00 5,261

Tesoro Hawaii Corp Ewa Beach HI 93,500 Tesoro Corp 20,583.00 5,261 Tesoro Refining & Marketing Corporation Martinez CA 166,000 Tesoro Corp 20,583.00 5,261

Tesoro Refining & Marketing Corporation Wilmington CA 94,300 Tesoro Corp 20,583.00 5,261

Tesoro West Coast Mandan ND 58,000 Tesoro Corp 20,583.00 5,261
Tesoro West Coast Salt Lake City UT 57,500 Tesoro Corp 20,583.00 5,261
Tesoro West Coast Anacortes WA 120,000 Tesoro Corp 20,583.00 5,261
Total Petrochemicals Inc Port Arthur TX 232,000 Total SA 186,158.80 92,855 Trigeant Ltd. Corpus Christi TX BTB Refining 5.00 37 United Refining Co. Warren PA 65,000 United Refining Company 2,654.40 4,434

US Oil & Refining Co. Tacoma WA 38,800 US Oil & Refining Co. 26.90 182 Valero Energy Corporation Three Rivers TX 93,000 Valero Energy 82,233.00 20,313 Valero Energy Corporation Sunray TX 156,000 Valero Energy 82,233.00 20,313 Valero Refining Co California Benicia CA 132,000 Valero Energy 82,233.00 20,313

Valero Refining Co California Wilmington Asphalt Plant CA 6,300 Valero Energy 82,233.00 20,313

Valero Refining Co California Wilmington Refinery CA 78,000 Valero Energy 82,233.00 20,313

Valero Refining Co Oklahoma Ardmore OK 85,000 Valero Energy 82,233.00 20,313

(continued)

### A

- 6

Table A

- 1. Parent Company Information for Petroleum Refineries (continued)

Facility Name City State Capacity (bbl/cd) Parent Company Name Parent Company Sales Parent Company

Employment Valero Refining Co Texas LP Corpus Christi TX 142,000 Valero Energy 82,233.00 20,313

Valero Refining Co Texas LP Houston TX 88,000 Valero Energy 82,233.00 20,313

Valero Refining Co Texas LP Texas City TX 225,000 Valero Energy 82,233.00 20,313

Valero Refining New Orleans LLC Norco LA 205,000 Valero Energy 82,233.00 20,313

Ventura Refining & Transmission LLC Thomas OK 12,000 Ventura Refining & Transmission, LLC 3.10 37

Western Refining Company LP El Paso TX 122,000 Western Refining, Inc. 7,965.05 636

Western Refining Southwest Inc Bloomfield NM 16,800 Western Refining, Inc. 7,965.05 636

Western Refining Southwest Inc Gallup NM 21,100 Western Refining, Inc. 7,965.05 636

Western Refining Yorktown Inc Yorktown VA 66,300 Western Refining, Inc. 7,965.05 636

WRB Refining LLC Borger TX 146,000 ConocoPhillips 198,655.00 29,700 WRB Refining LLC Wood River IL 362,000 ConocoPhillips 198,655.00 29,700 Wynnewood Refining Co. Wynnewood OK 70,000 Gary

- Williams Energy Corporation 49.90 260

Wyoming Refining Co New Castle WY 14,000 Wyoming Refining Co. 125.00 96

Sources: Hoovers 2011 Online Data, accessed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Online Data reflects either actual data for 2010/2011 reported by companies, estimated data for 2011, or occasionally 2009 values. Million Dollar Database online, 2011, accessed through University of South Carolina’s Moore School of Business Library. Million Dollar Online Data reflects either actual data for 2011 reported by companies or estimat ed data for 2011. Ward’s Business Directory of Public and Private Companies, 2011, accessed at James Branch Cabell Library (Virginia Commonwealth University). Ward’s Business Directory compiles financial data from several sources such as annual reports, c ompany websites, and phone interviews. If financial data from private companies is unavailable, Ward’s staff estimates the information. Copyright 2011 Harte

- Hanks Market Intelligence, Inc., All Rights Reserved. CI Technology Database, September 2011.
Ref erence USA, accessed thru Jefferson Madison Regional Library, Charlottesville, Virginia. Copyright 2011 Experian Information Solutions, Inc., Experian Business Reports accessed through LexisNexis at James Branch Cabell Library (Virginia Commonwealth Unive rsity). Experian Business Reports reflects the most recent data reported by a company, which may be 2010 or 2011. Global Duns Market Identifiers, accessed through LexisNexis at University of Virginia’s Alderman Library. Oil & Gas Journal, December 6, 201 0 Energy Information Administration (EIA), Form EIA

- 820, “Annual Refinery Report.” Table 3. Capacity of Operable Petroleum Refineries by State and Individual Refinery as of January 1, 2011. (Data accessed on October 31, 2011.) [Source for 2009

– 2011 numbers.] < http://www.eia.gov/petroleum/refinerycapacity/

### United States Environmental Protection Agency Office of Air and Radiation Off ice of Air Quality Planning and Standards Research Triangle Park, NC June 2012