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# Regulatory Impact Analysis

Petroleum Refineries New Source Performance Standards Ja

January 2012

U.S. Environmental Protection Agency Office of Air and Radiation Office of Air Quality Pl anning and Standards Research Triangle Park, NC 27711

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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. Questions related to this document should be addressed to Alexande r Macpherson, U.S. Environmental Protection Agency, Office of Air Quality Planning and Stan dards, 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 sign ificant data and analys is to this document under contract number EP-D-06-003.

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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 EASON FOR T ODAY ’ S A CTION : M ARKET F AILURE OR O THER S OCIAL P URPOSE ........................................ 1-2 1.3 R ESULTS

.............................................................................................................................. ........................ 1-2 1.4 R EFERENCES

.............................................................................................................................. .................. 1-3
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, I nputs, and Outputs ....................................................................................... .... 2-1 2.2.2 Emissions and Controls in Petroleum Refining ................................................................................. 2-9 2.2.3 Costs of Pr oductio n ........................................................................................................... ............... 2-10 2.3 T HE D EMAND S IDE

.............................................................................................................................. ...... 2-14 2.3.1 Product Charac teris tics ....................................................................................................... ............. 2-14 2.3.2 Uses and Consumers ............................................................................................................ ............ 2-14 2.3.3 Substitution Possibilities in Consumption ..................................................................................... .. 2-16 2.4 I NDUSTRY O RGANIZATION ......................................................................................................................... 2-16 2.4.1 Market Structure .............................................................................................................. ................ 2-16 2.4.2 Characteristics of U.S. Petroleum Refineries and Petroleum Refining Companies ......................... 2-20 2.5 M ARKETS

.............................................................................................................................. ..................... 2-27 2.5.1 U.S. Petroleum Consumption .................................................................................................... ...... 2-27 2.5.2 U.S. Petroleu m Produc tion

..................................................................................................... ......... 2-33 2.5.3 Internationa l Trade ........................................................................................................... ................ 2-34 2.5.4 Market Pr ices ................................................................................................................. .................. 2-37 2.5.5 Profitability of Petr oleum Refineries ......................................................................................... ...... 2-38 2.5.6 Industry Trends ............................................................................................................... ................. 2-40 2.6 R EFERENCES

.............................................................................................................................. ................ 2-44
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
5 S MALL B USINESS A NALYSIS AND E XECUTIVE O RDERS ..................................................... 5-1

5.1 R ESULTS OF S MALL B USINESS A NALYSIS .................................................................................................... 5-1 5.2 U NFUNDED M ANDATES

.............................................................................................................................. . 5-1 5.3 E NVIRONMENTAL J USTICE ........................................................................................................................... 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 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 .......................................................................................................................... 6-1 8

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iv 6.5.1 Additional SO 2 and NO 2 Benefits .................................................................................................... 6-19 6.5.2 Ozone Bene fits ................................................................................................................ ................. 6-21 6.5.3 Visibility Im pairment ......................................................................................................... .............. 6-21 6.6 C HARACTERIZATION OF U NCERTAINTY IN THE M ONETIZED B ENEFITS ...................................................... 6-22 6.7 R EFERENCES

.............................................................................................................................. ................ 6-27
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

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v LIST OF TABLES

Table 2-1. Types and Characteristics of Raw Material s used in Petroleu m Refineries ....................................... 2-8 Table 2-2. Major Refinery Produ ct Catego ries ............................................................................................. ....... 2-8 Table 2-3. Labor, Material, and Capital Expenditures fo r Petroleum Refineries (NAICS 32 4110) .................. 2-12 Table 2-4. Costs of Materials Used in Pe troleum Refining Industry ................................................................. 2-13 Table 2-5. Major Refinery Products ....................................................................................................... ........... 2-15 Table 2-6. Market Concentration Measures of the Petr oleum Refining Industry : 1985 to 2007 ....................... 2-19 Table 2-7. Number of Petroleum Re fineries, by State ...................................................................................... . 2-21 Table 2-8. Full Production Capacity Utilization Ra tes for Petroleum Refineries .............................................. 2-22 Table 2-9. Characteristics of Small Businesses in the Petroleum Refini ng Industry ......................................... 2-28 Table 2-10. Total Petroleum Products Supplied (m illions of barrel s per year) ................................................... 2-33 Table 2-11. U.S. Refinery and Blender Net Productio n (millions of barrels per year) ........................................ 2-34 Table 2-12. Value of Product Shipment s of the Petroleum Re fining Indust ry .................................................... 2-35 Table 2-13. Imports of Major Petroleum Products (m illions of barrels per year) ................................................ 2-36 Table 2-14. Exports of Major Petroleum Products (m illions of barrels per year) ................................................ 2-36 Table 2-15. Average Price of Major Petroleum Products So ld to End Users (cents per gallon) ......................... 2-37 Table 2-16. Producer Price Index Industry Data: 1995 to 2010 .......................................................................... 2-3 8 Table 2-17. Mean Ratios of Profit before Taxes as a Percentage of Net Sales for Petroleum Refiners, Sorted by Value of Assets ............................................................................................................... .................. 2-39 Table 2-18. Net Profit Margins for Publicly Owned, Small Petroleum Re finers: 20 10 ...................................... 2-40 Table 2-19. Forecasted Average Price of Major Petroleum Products Sold to End Users in 2009 Currency (cents per gallon) ................................................................................................................... ..................... 2-41 Table 2-20. Total Petroleum Products Supplied (m illions of barrel s per year) ................................................... 2-41 Table 2-21. Selected Refinery Construc tion Projects: 2008–2014 ...................................................................... 2-42 Table 2-22. Full Production Capacity Utilization Ra tes for Petroleum Refineries .............................................. 2-43 Table 3-1. National Incremental Emission Reductions for Petroleum Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Ap plicability Da te) .......................... 3-2 Table 3-2. National Incremental Cost Impacts and Cost-Effectiv eness for Petroleum Refine ry Flares Subject to Amended Standards Under 40 CFR pa rt 60, subpart Ja (Fifth Year After Applicability Date) ......... 3-2 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 Reconsider ation*

.............................................................................................................. ................ 6-12 Table 6-4: All PM 2.5 Benefits Estimates for the Petroleum Refineries NSPS Reconsideration at discount rates of 3% and 7% in 2017 (i n millions of 2006$)*..................................................................................... 6-13 Table 6-5. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2017 a ............................................. 6-16 Table 6-6. Monetized SCC-Derived Benefits of CO 2 Emission Reductions in 2017 (millions of 2006$) a,b ..... 6-17 Table 7-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the Final Petroleum Refineries NSPS in 2017 (millions of 2006$) 1 ................................................................................... 7-1 Table A-1. Parent Company Information fo r Petroleum Refi neries .................................................................... A-2

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vi LIST OF FIGURES

Figure 2-1. Outline of the Refi ning Process ............................................................................................... .......... 2-3 Figure 2-2. Desalting Pr ocess ............................................................................................................. .................. 2-3 Figure 2-3. Atmospheric Dis tillation Pr ocess .............................................................................................. ......... 2-4 Figure 2-4. Vacuum Distilla tion Process ................................................................................................... ........... 2-5 Figure 2-5. Petroleum Refinery Expenditures ............................................................................................... ..... 2-10 Figure 2-6. Employment Distribution of Companies Owni ng Petroleum Refineri es (N=61) ............................ 2-23 Figure 2-7. Average Revenue of Companies Owning Petrol eum Refineries by Employment (N=61) .............. 2-24 Figure 2-8. Revenue Distribution of Companies Owni ng Petroleum Refineri es (N=61) ................................... 2-25 Figure 2-9. Total Petroleum Products Supplied (m illions of barrel s per year) ................................................... 2-32 Figure 6-1: Breakdown of Monetized PM 2.5 Health Benefits using Mortality Function from Pope et al. (2002)6-10 Figure 6-2: Total Monetized PM 2.5 Benefits of Final Petroleum Refine ries Reconsidera tion in 20 17 ............... 6-14 Figure 6-3: Breakdown of Monetized Benefits for Final Petroleum Refineries NSPS Reconsideration by Pollutant 18 Figure 6-4. Percentage of Adult Population by Annual Mean PM 2.5 Exposure in the Baseline ......................... 6-24 Figure 6-5. Cumulative Distribution of Adult Population by Annual Mean PM 2.5 Exposure in the Baseline .... 6-25 Figure 7-1. Net Benefits for Final Petroleum Refineries NS PS Reconsideration at 3% discount rate* ............... 7-2 Figure 7-2. Net Benefits for Final Petroleum Refineries NS PS Reconsideration at 7% discount rate* ............... 7-3

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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 perf ormance for petroleum refinery process units constructed, reconstructed, or modified after May 14, 2007. EPA subsequently received three petitions for reconsideration of these final rules. On September 26, 2008, EPA granted reconsideration and issued a stay for the issues raised in the petitions regarding process heaters and flares. On December 22, 2008, EPA addr essed those specific issues by proposing amendments to certain provisions for process heat ers and flares. This final regulation includes
emissions limits for new and modified/reconstructe d sources, and these limits are set for sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), volatile organic compounds ( VOC), and other pollutants.

The petroleum refining industry comprises esta blishments primarily engaged in refining crude petroleum into refined petroleum. Exam ples of refined petroleum products include gasoline, kerosene, asphalt, lubricants, solvents , and a variety of othe r products. Petroleum refining falls under the North American Indus trial 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, benef its, 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 Refinery Industry,

 Section 3: NSPS Regulatory Alternatives, and Costs and Emission Reductions From Complying with the NSPS

 Section 4: Economic Impacts of the NSPS,

 Section 5: Potential Impacts on Small Businesses,

 Section 6: Benefits of the NSPS, and

 Section 7: Comparison of Benefits and Costs

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1-2 1.2 Reason for Today’s Action: Market Failure or Other Social Purpose The petroleum refinery NSPS is of sufficient im pact to be considered as falling under the requirements for Executive Order 12866 (OMB, 2003). This final regulation is being issued in response to a decision between U.S. EPA and va rious parties requiring re view of the existing NSPS. Among the reasons a regulation such as this one may also be issued is to address market failure. The major types of market failure include : externality, market pow er, and inadequate or asymmetric information. Correcting market failure s is a reason for regula tion, but it is not the only reason. Other possible justifications in clude improving the functioning of government, removing distributional unfairness, or promoting privacy and personal freedom.

1.3 Results
EPA has characterized the fac ilities and companies potentia lly affected by the NSPS by examining existing refineries and the companies th at own them. EPA project s 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 exis ting refineries. EPA has collected data on 148 existing refineries, owned by 64 companies. Of th e affected parent companies, thirty-six are
identified as small entities based on the Small Bu siness Administration size standard criteria for NAICS 324110, for they employ 1,500 or fewer employees.

EPA estimates that complying with the fina l NSPS will have an annualized cost savings of approximately $79 million per year (2006 dollars ) in 2017. Given that there are cost savings, EPA anticipates that the NSPS will have no ne gative impacts on the market for petroleum products. Based on sales data obtai ned for the affected small en tities, as well as expected annualized cost savings, EPA estimates that the NSPS will not result in a SISNOSE (significant economic impact on a substantia l number of small entities).

In the year of full implementation (2017), EPA estimates that the tota l monetized benefits
of the final NSPS are $270 million to $580 million and $240 million to $530 million, at 3% and 7% discount rates, respectively (Table 1-1). All estimates are in 2006 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

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1-3 these estimates. In addition, direct exposure to SO 2 and NOx benefits, ozone benefits, ecosystem benefits, and visibility benefits have no t been monetized in this analysis.
In the year of full implementation (2017), EP A estimates the net benefits of the final
NSPS are $340 million to $660 million and $320 million to $610 million, at 3% and 7% discount rates, respectively (Table 1-1). All estim ates are in 2006 dollars for the year 2017.
Table 1-1. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Final Petroleum Refineries NSPS in 2017 (millions of 2006$) 1

3% Discount Rate 7% Discount Rate Final Major Source NESHAP

Total Monetized Benefits 2 $270 to $580 $240 to $530 Total Compliance Costs 3 -$79 -$79
Net Benefits $340 to $660 $320 to $610

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 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 effects, or visibility impairment. Huma n health benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume th at all fine particles, regardless of their chemical composition, are equally potent in causing premature mo rtality because the scientif ic 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 car bon (SCC) estimated at a 3 percent discount rate because the interagency workgroup deemed the SCC estimate at a 3 per cent discount rate to be the central value. 3 The engineering compliance costs are annualized using a 7 percent discount rate.

1.4 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.

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1-4 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 Mortal ity, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141.

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2-1 2 INDUSTRY PROFILE

2.1 Introduction
This industry profile of the petroleum refi ning industry provides information that will support this and subsequent regu latory impact analyses (RIAs) and economic impact analyses (EIAs) that will assess the im pacts 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, kero sene, asphalt, lubricants, a nd solvents, among others.

Firms engaged in petroleum refining are ca tegorized under the Nort h American Industry Classification System (NAICS) code 324110. In 2010, 148 establishments owned by 64 parent companies were refining petroleum in the con tinental United States. In 2009, the petroleum refining industry shipped products valued at ove r $436 billion (U.S. Census Bureau, Sector 31: 2009 and 2008).

This industry profile report is organized as follows. Section 3.2 provides a detailed description of the inputs, outputs, and proce sses involved in petroleum refining. Section 3.3 describes the applications and users of finish ed petroleum products. Section 3.4 discusses the organization of the industry and provides faci lity- and company-level data. In addition, small businesses are reported separately for use in ev aluating the impact on small business to meet the requirements of the Small Business Regulat ory Enforcement and Fairness Act (SBREFA). Section 3.5 contains market-level data on pri ces and quantities and discusses trends and projections for the industry.
2.2 The Supply Side
Estimating the economic impacts of any re gulation on the petroleum refining industry requires a good understanding of how finished petroleum products are produced (the “supply side” of finished petroleum pr oduct markets). This section desc ribes the production process used to manufacture these products as well as the inputs, outputs, and by-products involved. The section concludes with a desc ription of costs involved w ith the production process.
2.2.1 Production Process, Inputs, and Outputs
Petroleum pumped directly out of the ground, kn own as crude oil, is a complex mixture of hydrocarbons (chemical compounds that consis t solely of hydrogen a nd carbon) and various impurities such as salt. To manufacture the variet y of petroleum products recognized in everyday

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2-2 life, this tar-like mixture must be refined a nd processed over several stages. This section describes the typical stages involved in this process as well as th e inputs and outputs.

2.2.1.1 The Production Process
The process of refining crude oi l into useful petroleum products can be separated into two phases and a number of supporting operations. These phases are descri bed 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 include gasoline, kerosene, naphtha, and other products (EPA, 1995).

In the second phase, the distilled fractions ar e converted into petroleum products (such as gasoline and kerosene) using three different types of downstream processes: combining, breaking, and reshaping (EPA, 1995). An outline of the refining process is presented in Figure 2-
1.

Desalting. Before separation into fractions, crud e oil is treated to remove salts, suspended solids, and other impurities that c ould clog or corrode the downstream equipment. This process, known as “desalting,” is typically d one by first heating the crude oil, mixing it with process water, and depositing it into a gravity se ttler tank. Gradually, the sa lts 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 chem icals (a process known as chemical separation) and/or by applying an electric fiel d to concentrate the suspended water globules at the bottom of the settler tank (a proces s known as electrostatic se paration). The effluent water is then removed from the tank and sent to the refinery wastewater treatment fa cilities (EPA, 1995). This process is illustrated in Figure 2-2.

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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.

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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 atmo spheric pressure. After entering the tower, the lighter fractions flash into vapor and travels up the tower. This leaves only the heaviest fractions (which have a much higher boiling point) at th e bottom of the tower. These fractions include heavy fuel oil and asphalt residue (EPA, 1995).

As the hot vapor rises, its temperature is gr adually reduced. Lighter fractions condense onto trays located at successively higher portions of the tower. For example, motor gasoline will condense at higher portion of the tower than kerose ne because it condenses at lower temperatures. This process is illustrated in Figure 2-3. As thes e fractions condense, they will be drawn off their respective trays and potentially se nt downstream for further pro cessing (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.html>. As obtained on October 23, 2006.

Vacuum Distillation. The atmospheric distillation to wer cannot distil the heaviest fractions (those at the bottom of the tower) w ithout cracking under requis ite 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 volatization 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.

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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 refine d after distillation or “downstream.” These downstream processes change the molecular stru cture 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 cr acking, coking, catalytic cracking, catalytic hydrocracking, hydrotreating, alkylation, isomeri zation, polymerization, catalytic reforming, solvent extraction, merox, dewaxing, propane de asphalting and other operations (EPA, 1995).
2.2.1.2 Supporting Operations
In addition to the processes described above, there are other refine ry operations that do not directly involve the production of hydrocarbon fu els, but serve in a su pporting role. Some of the major supporting operations ar e described in this section.

Wastewater Treatment. Petroleum refining operations pr oduce a variety of wastewaters including process water (w ater used in process operations li ke 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 surf ace or leaks in the equipment that have collected in drains).

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2-6 Wastewater typically contai ns a variety of contamin ants (such as hydrocarbons, suspended solids, phenols, ammonia, sulfides, an d other compounds) and must be treated before it is recycled back into refini ng operations or discharged. Petrol eum refineries typically utilize two stages of wastewater treatment. In primary wastewater treatments, oil and solids present in the wastewater are removed. After this is comple ted, wastewater can be di scharged to a publicly owned treatment facility or under go secondary treatment before being discharged directly to surface water. In secondary treatment, microorgani sms are used to dissolve oil and other organic pollutants that are pres ent in the wastewater (EPA, 1995; OSHA, 2003).

Gas Treatment and Sulfur Recovery. Petroleum refinery operati ons such as coking and catalytic cracking emit gases with a high concen tration 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 Clean Air Act’s SO x emission limits and to recover saleable elemental sulfur.

Sulfur is recovered by first separating the fu el 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 under controlled conditions producing sulfur dioxide. A bauxite catalyst is then us ed to react w ith the sulfur dioxide and the unburned hydrogen sulfide to pr oduce elemental sulfur. However, the Claus process only removed 90% of the hydrogen sulfide present in the gas stre am, so other processes must be used to recover the remaining sulfur (EPA, 1995).

Additive Production. A variety of chemicals are a dded to petroleum products to improve their quality or add speci al characteristics. For example, ethers have been added to gasoline to increase octane levels an d reduce CO emissions since the 1970s.

The most common ether additives being used today are methyl tertiary butyl ether (MTBE), and tertiary amyl methyl ether (TAME) . Larger refineries tend to manufacture these additives themselves by reacting isobutylene (a by -product of several refinery processes) with methanol (OSHA, 2003).

Heat Exchangers, Coolers, and Process Heaters. Petroleum refineri es require very high temperatures to perform ma ny of their refining processes. To achieve these temperatures, refineries use fired heaters fueled by refinery or natural gas, distil late, and residual oils. This heat is managed through heat exchanges, where are composed of bundles of pipes, tubes, plate coils,

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2-7 and other equipment that surround heating or c ooling water, steam, or oil. Heat exchanges facilitate the indirect transfer of heat as needed (OSHA, 2003).

Pressure Release and Flare Systems. As liquids and gases e xpand and contract through the refining process, pressure must be activel y managed to avoid accid ent. Pressure-relief systems enable the safe handling of liquids and gases that that are rel eased by pressure-relieving 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 ma terial, such as blow-downs from process unit startups, furnace blow-downs, shutdowns , and emergencies” (OSHA, 2003).

Blending. Blending is the final operation in petrol eum refining. It is the physical mixture of a number of different liquid hydrocarbons to produce final petroleu m products that have desired characteristics. For example, additives such as ethers can be blended with motor gasoline to boost performance and reduce emissions. Produc ts 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 pro cess of petroleum products incl ude general inputs such as labor, capital, and water. The i nputs specific to this industry ar e crude oil and the variety of chemicals used in producing petroleum products. Th ese two specific inputs are discussed below.

Crude Oil. Contrary to popular conception, crud e oils are complex, heterogeneous mixtures. Crude oils contain many different hydr ocarbon compounds that vary in appearance and composition from one oil field to another. An “average” crude oil contai ns about 84% carbon; 14% hydrogen; and less than 2% sulfur, nitr ogen, oxygen, metals, and salts (OSHA, 2003).

In 2004, the petroleum refining industry used 5.6 billion barrels of crude oil in the production of finished petr oleum products (EIA, 2005). 1

Common Refinery Chemicals. In addition to crude oil, a variety of chemicals are used in the production of petroleum products. The sp ecific chemicals used will depend on specific characteristics of the product in question. Tabl e 2-1 lists the most common chemicals used by petroleum refineries, their character istics, and their applications.

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

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2-8 Table 2-1. Types and Characteri stics of Raw Materials used in Petroleum Refineries

Type Description Crude Oil Heterogeneous mixture of different hydrocarbon compounds. Oxygenates Substances which, when added to ga soline, increase the amount of oxygen in that gasoline blend. Ethanol, methyl tertiary butyl ether (MTBE), 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 te tramethyl 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 us ed 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 2004, the petroleum refining industry used 581 million barrels of natural gas liquids and other liquids in the production of fi nished petroleum products (EIA, 2005).
2.2.1.4 Types of Product Outputs
The petroleum refining industry produces a num ber of products that tend to fall into one of three categories: fuels, finished nonfue l products, and feedstock for the petrochemical industry. Table 2-2 briefly describes these produ ct categories. A more detailed discussion of petroleum fuel products can be found in Section 3.3.
Table 2-2. Major Refinery Product Categories

Product Category Description Fuels Finished Petroleum produ cts that are capable of rel easing 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, lubric ants (such as motor oil and industrial greases), and solvents (such as benzene, toluene, and xylene). Feedstock Many products 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.
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.

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2-9 2.2.2 Emissions and Controls in Petroleum Refining
Petroleum refining leads to emissions of me tals; spent acids; numerous toxic organic compounds; and gaseous pollutants, including carbon monoxide (CO), sulfur oxides, (SO x ), nitrogen oxides (NO x ), particulates, ammonia (NH 3 ), hydrogen sulfide (H 2 S), and volatile organic compounds (VOCs).

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 emissi ons from refineries include fugitive emissions of the volatile constituents in crude oil and its fractions, emissions from the burning of fuels in process heaters, and emissions from the various refinery processes themselves.

Fugitive emissions occur as a result of leaks throughout the refinery. Although individual leaks may be small, the sum of all leaks can result in a lot of hazardous emissions. These emissions can be reduced by purchasing leak-r esistant equipment a nd maintaining an ongoing leak detection and repa ir program (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 process 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).

The majority of gas streams exiting each re finery 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 pr evious section. Here, refinery fu el 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).

Emissions can also be created by the periodic regeneration of catalysts that are used in downstream processes. These proce sses generate streams that may contain relatively high levels of CO, particulates, and VOCs. Ho wever, these emissions are treated before being discharged to the atmosphere. First, the emissions are pro cessed through a CO boiler to burn CO and any VOCs, and then through an electros tatic precipitator or cyclone sepa rator to remove particulates (EPA, 1995).

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2-10 2.2.2.2 Wastewater and Other Wastes
Petroleum refining operations produce a variet y 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 coll ected 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-si te facilities before 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 incl uding incineration, land filling, and neutralization, among othe r treatment methods (EPA, 1995).
2.2.3 Costs of Production
Between 1995 and 2009, expenditures on input mate rials 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 materi als, containers, scrap, and supp lies used in produc tion or repair during the year, as well as the cost of all electricity and fuel consumed.

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>.

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2-11 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/prod/2006pubs/ U.S. Department of Commerce, Bureau of the Census. 2003a. 2001 Annual Survey of Manufacture s. M01(AS)-1. Washington, 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. Department 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; “S ector 31: Annual Survey of Manufactures: General Statistics: Statistics for Industry Groups and Industries: 2009 and 2008 “ Release Date: 12/3/10; (Data accessed on 10/10/11). [S ource for 2008 and 2009 numbers] http://factfinder.census.gov/servlet/IBQTa ble?_bm=y&-NAICSAS M=324110&-ds_name= AM0931GS101&-ib_type=NAICSASM&-_industry=324110&-_lang=en U.S. Census Bureau, American FactFinder; “S ector 31: Manufacturing: Industry Series: Detailed Statistics by Industry for the United Stat es: 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

Labor and capital accounted for the remaini ng expenses faced by petroleum refiners. Capital expenditures include permanent additions a nd alterations to facilities and machinery and equipment used for expanding plant capacity or replacing existing machinery. A detailed breakdown of how much petroleum refiners sp ent on each of these factors 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 refi ning is provided in Table 2-4.

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2-12 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://fact finder.census.gov/home/saff/main.html?_lang=en>. 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/prod/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: 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 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/ 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 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/ 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 In dustries: 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 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>

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2-13 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 petroleu m, 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 r eceived 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 Commer ce, 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; “Sect or 31: Manufacturing: Industry Series: Materials

Consumed by Kind for the United States: 2007” Re lease 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>

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2-14 2.3 The Demand Side
Estimating the economic impact the regulat ion will have on the petroleum refining industry also requires characteri zing various aspects of the demand for finished petroleum products. This section describes th e characteristics of finished pe troleum products, their uses and consumers, and possible substitutes.

2.3.1 Product Characteristics
Petroleum refining firms produce a variety of di fferent 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 3.1.4, finish ed petroleum products can be categorized into three broad groups based on their inte nded uses (EIA, 1999a):

 fuels —petroleum products 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, synthe tic 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 char acteristics and primary uses.

2.3.2 Uses and Consumers
Finished petroleum products are rarely consum ed as final goods in themselves. 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 incl ude fertilizers, pestic ides, paints, thinners, cleaning fluids, refrigerants, and synthetic fi bers (EPA, 1995). Similarly, fuels made from petroleum are used to run vehicles and industria l machinery and generate heat and electrical power. As a result, the demand for many finish ed 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

 Industrial sector—manufactur ing, construction, mining, agri cultural, and forestry establishments

 Transportation sector—private and pub lic vehicles that move people and commodities such as automobiles, ships, and aircraft

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2-15  Commercial sector—nonmanufacturing or nontransportation business establishments such as hotels, restaurants, retail stores , religious and nonprofit organizations, as well federal, state, and local government institutions

 Electric utility sector—priva tely and publicly owned esta blishments that generate, transmit, distribute, or se ll 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 gasoline 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 wate r 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, and 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 Products 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 mate rials, must be inert to most chemicals and weather conditions. Lubricants Lubricants are the result of a special re fining 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 compoun d 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.

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2-16 U.S. Department of Energy, Energy Information Administration (EIA). 1999.

Of these end users, the transportation sector consumes the largest share of petroleum products, accounting for 67% of total consump tion 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 petrol eum used in the United States (EIA, 1999a). In 2005, motor gasoline alone accounted for 49% of de mand 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 transpor tation sector, it is currently difficult to switch quickly from one fuel to another without costly and irreversible equipm ent changes, but other sectors can switch relatively qui ckly 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) th an in others (EIA, 1999a).

However, over time, demand for petroleum products could become more elastic. For example, automobile users could purchase more fu el-efficient vehicles or relocate to areas that would allow them to make fewer trips. Technolog ical advances could al so create new products that compete with petroleum products that curren tly have no substitutes. An example of such a technological advance would be the invention of ethanol (an alcohol pr oduced from biomass), which can substitute for gasoline in spark-ignition motor vehicles (EIA, 1999a).
2.4 Industry Organization
This section examines the organization of th e U.S. petroleum refining industry, including market structure, firm characteristics, plant location, and capacity util ization. Understanding the industry’s organization helps determine how it wi ll be affected by new emissions standards.

2.4.1 Market Structure

Market structure characterizes the leve l and type of competition among petroleum refining companies and determines their power to influence market prices for their products. For

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2-17 example, if an industry is perfectly competitiv e, then individual producers cannot raise their prices above the marginal cost of production without lo sing market share to their competitors. Understanding pricing behavior in the petroleum refining indust ry is crucial for performing subsequent EIAs.

According to basic microeconomic theor y, perfectly competitive industries are characterized by unrestricted entry and exit of firm s, large numbers of firms, and undifferentiated (homogenous) products being sol d. Conversely, imperfectly comp etitive industries or markets are characterized by barriers to entry and exit, a smaller number of firms, and differentiated products (resulting from either differences in pr oduct attributes or brand name recognition of products). This section considers whether the pe troleum refining industry is competitive based on these three factors.
2.4.1.1 Barriers to Entry
Firms wanting to enter the petroleum refi ning industry may face at least two major barriers to entry. First, according to a 2004 Federal Trade Commission staff study, there are significant economies of scale in petroleum refine ry operations. This means that costs per unit fall as a refinery produces more finished petrol eum products. As a result, new firms that must produce at relatively low levels will face higher av erage costs than firms that are established 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 di fficult 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 gove rnment, that give exclusive m onopoly to an inventor over his invention for a limited time period. In the pe troleum refining industry, firms rely heavily on process patents to appropriate re turns from their innovations. As a result, firms seeking to enter the petroleum refining industry must develop pro cesses that respect the novelty requirements of these patents, which could poten tially make entry more difficult for new firms (Langinier, 2004). A second example of a legal barrier would be envi ronmental regulations that apply only to new entrants or new pollution sources. Such regulatio ns 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 barrie rs is impossible for new entr ants to overcome, they can make it more difficult for new firms to enter the market for manufactured petroleum products. As a result, existing petroleum refiners could potentia lly raise their prices above competitive levels

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2-18 with less worry about new firms entering the market to compete away their customers with lower prices. It was not possible during this analysis to quantify how si gnificant these barriers would be for new entrants or what effect they would have on market prices. However, existing firms would still face competition from each 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 industry. Common measures include four-fir m 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 industry. The HHIs are the sums of the squared market shares of firms in the industry. These measures of industry concentrated are reporte d for the petroleum refining i ndustry (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 o ccurred 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 these concentration ratios. Howe ver, 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). Accordin g 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 c onsidered moderately concentrated (i.e., moderately competitive); and indus tries with higher HHIs are cons idered heavily concentrated. Based on this criterion, the petrol eum refining industry continues to be unconcentrated even in recent years.

A more rigorous examination of market c oncentration was conducted in a 2004 Federal Trade Commission (FTC) staff study. This study exp licitly accounted for the fact that a refinery in one geographic region may not exert competitiv e 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 duri ng World War II to help manage

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2-19 the allocation of fuels during wart ime. However, they have remained in use as a convenient way of organizing petroleum ma rket 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/oilme rgersrpt.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 accesse d on 10/12/11) [Source for 2007 numbers]<http://factfinder.census.gov/servle t/IBQTable?_bm=y&-ds_name=EC0731SR12&- NAICS2007=324110&-ib_type=NAICS2007&-NAICS2007sector=*6&-industrySel=324110&-geo_id=&- _industry=324110&-_lang=en>

This study concluded that these geographi c 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 combined regions was 789 in 2003, indicating a low concentration level. Conc entration in PADD IV (Rocky Mountains) was also low in 2003, with an HHI of 944. PADD V gr adually 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 pr ices for their product is through product differentiation. By differentiating one’s product and using marketi ng to establish brand loyalty, manufacturers can raise their prices above margin al cost without losing market share to their competitors.

While we saw in Section 3.3 that there are a wide variety of petr oleum products with many different uses, individual petroleum pr oducts 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 ro le of product differentia tion is probably quite small for many finished petroleum products. Howeve r, there are examples of relatively small

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2-20 refining businesses producing spec ialty products for small niche markets. As a result, there may be some instances where product differentiat ion is important for price determination.
2.4.1.4 Competition among Firms in the Petroleum Refining Industry
Overall, the petroleum industr y is characterized as produci ng largely generic products for sale in relatively unconcentrated markets. A lthough it is not possible to quantify how much barriers to entry and other fact ors will affect competition among firms, it seems unlikely that individual petroleum refiners w ould be able to significantly infl uence market prices given the current structure of the market.
2.4.2 Characteristics of U.S. Petroleum Refi neries and Petroleum Refining Companies
A petroleum refinery is a facility where labo r and capital are used to convert material inputs (such as crude oil and other materials) in to finished petroleum products. Companies that own these facilities are legal bus iness entities that conduct transact ions and make decisions that affect the facility. The terms “facility,” “establ ishment,” and “refinery” are synonymous in this report and 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 in formation on refineries, such 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 refineri es operating in the United States, spread across 32 states. The number of petroleum refineries located in each of these states is listed in Table 2-7. This table illustrate s that a significant portion of pe troleum refineries are located along the Gulf of Mexico region. The leading petrol eum 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 cap acity utilization rates. A capacity utilization rate is the ratio of actual production volumes to full-capacity production vo lumes. 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 po ssible output, the capacity utilization rate would be 75 percen t. On an industry-basis, capacity utilization is highly variable from year to year depending on economic condi tions. It is also variable on a company-by-

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2-21 company basis depending not only on economic conditions, but also on a company’s strategic position in its particular industry. While some pl ants may have idle production lines or empty floor space, others need a dditional space or capacity.

Table 2-8 lists the capacity ut ilization rates for petroleum refineries from 2000 to 2010. It is interesting to note the signifi cant drop in capacity u tilization in 2005. This would seem counter intuitive since there does not a ppear to be evidence that dema nd for petroleum products is not dropping. To understand why this might be the case, one must first realize that the capacity

Table 2-7. Number of Petrol eum 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 PA D 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/>

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2-22 utilization ratio in petroleum i ndustry represents the utilization of the at mospheric crude oil distillation units.

This is calculated for the petroleum industr y by dividing the gross input to atmospheric crude oil distillation units (all i nputs involved in atmospheric crude oil distillation, such as crude oil) by the industry’s operational capacity.

Table 2-8. Full Production Capacity Util ization 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) 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 Admini stration (EIA), “Refinery Utilizati on and Capacity.” Available at http://www.eia.gov/dnav/pet/pet_p np_unc_dcu_nus_a.htm; Release da te: 7/28/11; (Data accessed on 10/11/11). [Source for 2007-2010 numbers]

Operational capacity increased from 16,974,000 barrels per calendar day in 2004 to 17,196,000 barrels per calendar day in 2005. However, gross inputs fell from 15,783,000 barrels per calendar day in 2004 to 15,578,000 in 2005. This indicates that capacity utilization sagged due to a drop in production inputs. In 2006, gross inputs grew 0.15% to 15,602,000 barrels per day. However, since operational capacity grew much faster (from 17,196,000 to 17,385,000 or 1.00%), capacity utilization rates fo r the industry continued to fall. Capacity utilization rates continued to fall through 2009. From 2009 to 2010 gross inputs increased from 14,659,000 barrels per day to 15,177,000, increasing capacity utilizati on rates in 2010.
2.4.2.3 Characteristics of Small Businesse s Owning U.S. Petroleum Refineries
According to the Small Business Administ ration (SBA), a small business in the petroleum refining industry is defined for govern ment procurement purposes as having 1,500 or

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2-23 fewer employees and an Operable Atmospheric Cr ude Oil Distillation capac ity of no more than 125,000 barrels per calendar day total (SBA, 2011). We applied this definition in defining a small business refiner in our proposal RIA. Howeve r, as part of a response to a comment made on the proposal, we define a small business only as having 1,500 or fewer employees.

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 identif ied 64 parent companies owning refineries in the United States and were able to collect empl oyment and sales data for 61 (95%) of them.

The distribution of employment across compan ies is illustrated in Figure 2-6. As this figure shows, 36 companies (59% of the 61 tota l) employee fewer than 1,500 workers and would be 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 (Figure 2-7). A distribution of the number of fi rms earning different levels of revenue is presente d in Figure 2-8.

Figure 2-6. Employment Distribution of Co mpanies 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, estimated data for 2011, or occasionally 2009 values. 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

<100 100

‐ 250 250

‐ 500 500

‐ 1000 1000

‐ 1500 >1500 Share of firms (%)

Number of Employees

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2-24 Million Dollar Database online, 2011, accessed through University of South Ca rolina’s Moore School of Business Library. Million Dollar Online Data reflects e ither actual data for 2011 reported by companies or estimated data for 2011. Ward’s Business Directory of Pu blic 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 webs ites, 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 Je fferson Madison Regi onal Library, Charlottesville, Virginia. Copyright 2011 Experian Inform ation 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 Le xisNexis at University of Virginia’s Alderman Library.

Figure 2-7. Average Revenue of Compan ies 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, 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 Ca rolina’s Moore School of Business Library. Million Dollar Online Data reflects e ither actual data for 2011 reported by companies or estimated data for 2011. 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

‐ 1000 1000

‐ 1500 >1500 Average revenue ($ millions)

Number of Employees

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2-25 Ward’s Business Directory of Pu blic 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 webs ites, 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 Je fferson Madison Regi onal Library, Charlottesville, Virginia. Copyright 2011 Experian Inform ation 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 Le xisNexis at University of Virginia’s Alderman Library.

Figure 2-8. Revenue Distribution of Compan ies 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, estimated data for 2011, or occasionally 2009 values. Million Dollar Database online, 2011, accessed through University of South Ca rolina’s Moore School of Business Library. Million Dollar Online Data reflects e ither actual data for 2011 reported by companies or estimated data for 2011. Ward’s Business Directory of Pu blic 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 webs ites, 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 Je fferson Madison Regi onal Library, Charlottesville, Virginia. 0% 10% 20% 30% 40% 50% 60%

<5 5

‐ 10 10

‐ 50 50

‐ 100 100

‐ 500 500

‐ 1000 >1000 Share of firms (%)

Revenue ($millions)

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2-26 Copyright 2011 Experian Inform ation 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 Le xisNexis at University of Virginia’s Alderman Library.

Employment, crude capacity, and location inform ation 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 pe troleum refineries in Appendix A.

In Section 3.4.2.1, we discussed how petroleu m 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 refi ners 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 compe titive disadvantage by locating close to productconsuming areas to lower transportation cost s and serving niche pr oduct markets (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 fuels, lu bricants, and other products. A good example of a firm producing niche products is Calumet Specia lty 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. Ho wever, the firm’s special ty products unit is its largest unit (Hoovers, 2011 online). Also Arab ian American Developm ent Company’s South Hampton Resources facility specializes in produc ing high purity solvents for the plastics and foam industries.

However, recent developments are making thes e factors less important for success in the industry. For example, the entry of new product pi pelines is eroding the locational advantage of smaller refineries (FTC, 2004). Th is trend can possibly be illust rated by the fact that most refineries owned by small businesses tend to be lo cated in relatively rural areas (see Table 2-9). The median population density of counties occupied by small refine ries 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 focusing on developing specialty products or serving local customers as major parts of their business plan. For exam ple, in 2006 29 small refineries had a collective

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2-27 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, l ubricants, and petroleum coke. The remaining 79% was used to produce ga soline, 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 fr om gasoline from another refinery ), and they are also normally transported by pipeline.

2.5 Markets
This section provides data on the volume of petroleum products produced and consumed in the United States, the quantit y of products imported and expor ted, 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 2-9 illustrates the amount of petr oleum products supplied between 2000 and 2010 (measured in millions of barrels of oil). These data represent the approximate consumption of petroleum products because it measures the di sappearance of these products from primary sources (i.e., refineries, natu ral gas processing plants, blendi ng plants, pipelines, and bulk terminals).*** Draft – Do Not Cite, Quote, or Release During Review ***

2-28Table 2-9. Characteristics of Small Businesses in the Petroleum Refining IndustryParent CompanyParentCompanyTypeCumulativeCrudeCapacity(bbl/cd)ParentCompanyEmployment(#)Facility NameFacilityCityFacility StateFacility CountyFacilityCountyPopulationDensity (2000)FacilityCountyPopulationDensity (2010)AGE Refining, Inc. Private 14,021 124 AGE RefiningInc San Antonio TX Bexar County 1,117 1,383American Refining Group,Inc.Private 10,000 323AmericanRefining GroupIncBradford PA McKean County 47 44Arabian AmericanDevelopment CoPublic 0 145South HamptonResources Inc.Silsbee TX Hardin County 54 61BTB Refining Private 0 37 Trigeant Ltd.CorpusChristiTX Nueces County 375 406Calcasieu Refining Company Private 78,000 92CalcasieuRefining Co.LakeCharlesLA Calcasieu Parish 171 181Calumet Specialty ProductsPartners, L.P.Public 57,000 654CalumetShreveport LLCShreveport LA Caddo Parish 286 290Calumet Specialty ProductsPartners, L.P.Public 13,020 654CalumetLubricants Co LPCottonValleyLA Caddo Parish 286 290Calumet Specialty ProductsPartners, L.P.Public 8,300 654CalumetLubricants Co LPPrinceton LA Caddo Parish 286 290CHS, Inc. Public 59,600 287Cenex HarvestStatesLaurel MTYellowstoneCounty49 56Connacher Oil and Gas,LimitedPublic 10,000 170Montana RefiningCo.Great Falls MT Cascade County 30 30CVR Energy, Inc. Public 115,700 371CoffeyvilleResourcesRfg&Mktg LLCCoffeyville KSMontgomeryCounty56 55Countrymark Cooperative Private 26,500 425CountrymarkCooperative, Inc.Mt. Vernon IN Posey County 66 63Cross Oil Refining &Marketing, Inc.Private 7,500 110Martin MidstreamPartners LPSmackover AR Union County 44 40Foreland Refining Corp. Private 2,000 27Foreland RefiningCo.Ely NVWhite PineCounty1 1Frontier Oil Corp. Private 47,000 723Frontier RefiningIncCheyenne WY Laramie County 30 34(continued)*** Draft – Do Not Cite, Quote, or Release During Review ***

2-29Table 2-9. Characteristics of Small Businesses inthe Petroleum Refining Industry (continued)Parent CompanyParentCompanyTypeCumulativeCrudeCapacity(bbl/cd)ParentCompanyEmployment(#)Facility NameFacilityCityFacility StateFacility CountyFacilityCountyPopulationDensity (2000)FacilityCountyPopulationDensity (2010)Frontier Oil Corp. Private 138,000 723Frontier ElDorado RefiningCoEl Dorado KS Butler County 42 46Garco Energy Private 3,600 16Garco EnergyLLCDouglas WYConverseCounty3 3Gary-Williams EnergyCorporationPrivate 70,000 260WynnewoodRefining Co.Wynnewood OK Garvin County 34 34Goodway Refining, LLC Private 4,100 17GoodwayRefining LLCAtmore ALEscambiaCounty41 41Gulf Atlantic OperationsLLCPrivate 32Gulf AtlanticOperations LLCMobile Bay AL Mobile County 324 336Holly Corporation Public 25,050 1,321Holly Refining &Marketing CoWoodsCrossUT Davis County 785 1,026Holly Corporation Public 105,000 1,321Navajo RefiningCo.Artesia NM Eddy County 12 13Holly Corporation Public 70,300 1,321Holly Refining &Marketing CoTulsa East OK Tulsa County 988 1,058Holly Corporation Public 85,000 1,321Holly Refining &Marketing CoTulsa West OK Tulsa County 988 1,058Hunt Refining Co. Private 36,000 1,100 Hunt Refining Co. Tuscaloosa ALTuscaloosaCounty125 147Hunt Refining Co. Private 11,000 1,100Hunt SouthlandRefining CoSandersville MS Lamar County 79 112Kern Oil & Refining Co. Private 26,000 346Kern Oil &Refining Co.Bakersfield CA Kern County 81 103Lion Oil Co. Private 75,000 350 Lion Oil Co. El Dorado AR Union County 44 40National CooperativeRefinery AssociationPublic 85,500 612NationalCooperativeRefineryAssociationMcPherson KSMcPhersonCounty33 33NuStar Energy LP Public 70,000 1,492Nustar AsphaltRefining LLCPaulsboro NJ Gloucester 784 895NuStar Energy LP Public 28,000 1,492Nustar AsphaltRefining LLCSavannah GA Chatham 530 622(continued)*** Draft – Do Not Cite, Quote, or Release During Review ***

2-30Table 2-9. Characteristics of Small Businesses inthe Petroleum Refining Industry (continued)Parent CompanyParentCompanyTypeCumulativeCrudeCapacity(bbl/cd)ParentCompanyEmployment(#)Facility NameFacilityCityFacility StateFacility CountyFacilityCountyPopulationDensity (2000)FacilityCountyPopulationDensity (2010)Pasadena Refining SystemsInc.Private120,000348Pasadena RefiningSystems Inc.PasadenaTX Harris County 1967 2,402Pelican Refining Co LLC Private033Pelican RefiningCo. LLCLakeCharlesLA Calcasieu Parish 171 181Petro Star Inc. Private 19,700 400Petro Star Inc. North Pole AKFairbanks NorthStar11 13Petro Star Inc. Private 55,000 400 Petro StarInc. Valdez AK Valdez Cordova 0.3 0Placid Refining Private 57,000 207Placid RefiningInc.Port Allen LAWest BatonRouge Parish113 124San Joaquin Refining Co. Private 15,000 108San JoaquinRefining Co., Inc.Bakersfield CA Kern County 81 103Santa Maria RefiningCompanyPrivate 9,500 47 Greka Energy Santa Maria CASanta BarbaraCounty146 155Silver Eagle Refining Inc. Private 3,000 60Silver EagleRefiningEvanston WY Uinta County 10 10Silver Eagle Refining Inc. Private 10,250 60Silver EagleRefiningWoodsCrossUT Davis County 785 1,026Somerset Oil Inc. Private 5,500 11Somerset EnergyRefinery LLCSomerset KY Pulaski County 85 96US Oil & Refining Co. Private 38,800 182US Oil & RefiningCo.Tacoma WA Pierce County 417 476Ventura Refining &Transmission, LLCPrivate 12,000 37Ventura Refining& TransmissionLLCThomas OK Custer County 27 28Western Refining, Inc. Public 122,000 636Western RefiningCompany LPEl Paso TX El Paso County 671 791Western Refining, Inc. Public 66,300 636Western RefiningYorktown IncYorktown VA York County 533 625Western Refining, Inc. Public 16,800 636Western RefiningSouthwest IncBloomfield NM San Juan County 21 24Western Refining, Inc. Public 21,100 636Western RefiningSouthwest IncGallup NMMcKinleyCounty14 13(continued)*** Draft – Do Not Cite, Quote, or Release During Review ***

2-31Table 2-9. Characteristics of Small Businesses inthe Petroleum Refining Industry (continued)Parent CompanyParentCompanyTypeCumulativeCrudeCapacity(bbl/cd)ParentCompanyEmployment(#)Facility NameFacilityCityFacilityStateFacility CountyFacilityCountyPopulationDensity (2000)FacilityCountyPopulationDensity (2010)World Oil Marketing Co. Private 8,500 65Lunday-ThagardCo.South Gate CALos AngelesCounty2,344 2,420Wyoming Refining Co. Private 14,000 96WyomingRefining CoNew Castle WY Weston County 3 3Total1,875,641 11,264103Sources: U.S. Census Bureau, American Fact Finder, “2010 Census Summary File 1 Population, Housing Units, Area & Density: 2010-County – Census Tract100% Data 2010 Census” (Data accessed on10/21/2011); http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?ftp=tableHoovers 2011 Online Data, accessed through University of South Carolina’s Moore School of Business Library. Hoovers 2011 Online Data reflectseither 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 ofBusiness Library. Million Dollar Online Datareflects either actual data for 2011 reportedby companies or estimated data for 2011.Ward’s Business Directory of Public andPrivate Companies, 2011, accessed at JamesBranch 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 financialdata 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 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 be2010 or 2011.Global Duns Market Identifiers, accessed through LexisNexis 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 andIndividual Refinery as of January 1, 2011 <http://www.eia.gov/petroleum/refinerycapacity/>

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2-32 Between 2000 and 2004, U.S. consumption of petroleum products increased by 5%. Consumption leveled off by 2007 and dropped by 9% between 2007 and 2009 (Figure 2-9). This reduced growth was primarily the result of less je t fuel, residual fuel, di stillate fuel, and other products being consumed in recent years. C onsumption 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 cu mulative decrease in consumption over the 11 year period is 3% (Table 2-10).

Figure 2-9. Total Petroleum Products Supplied (millions of barrels per year) 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 Information Administration (EIA). 1996–2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on Oc tober 7, 2011) [Source for 2007–2010 numbers.] <http://www.eia.gov/dnav/pet/pet_sum_snd_d_nus_mbbl_a_cur.htm>.

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2-33 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 Oc tober 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 petroleu m products produced in the United States between 2000 and 2010. U.S. producti on of petroleum products at refineries and blenders grew steadily, resulting in a 7% cumu lative increase for the period However, in 2005 and 2009 production declined by slightly.

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2-34 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. U.S. Department of Energy. 2011 Energy Information Administration (EIA). 1996–2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on Oc tober 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 pos sibly the result of damage inflicted by two hurricanes (Hurricane Katrina and Hurricane R ita) on the U.S. Gulf Coast—the location of many U.S. petroleum refineries (Section 3.4.2). Acco rding to the American Petroleum Institute, approximately 30% of the U.S. re fining industry was shut down as a result of the damage (API, 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 report s the value of shipments of products produced by the petr oleum refining industry between 1997 and 2009.

2.5.3 International Trade

International trade trends are shown in Tables 3-13 and 3-14. Between 1995 and 2006, imports and exports of petroleum products in creased 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 recentl y, between 2008 and 2010 exports of petroleum products such as motor gasoline, jet fuel, distillate fuel oil and liquefied petroleum ga ses 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. Exports grew at an average annual rate of 12%. Exports in 2010 were 2.4 tim es the level of exports in 2001. The United States, however, still remains a net importer of petroleum products. In 2010, U.S. net imports

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2-35 were 98 million barrels. These imports made up 10% of the country’s imports and accounted for around 1% of total petroleum products consumed in that year.

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 produc er price index industry data for Total Manufacturing Industries (Table 2-16). Sources: U.S. Department of Commerce, Bureau of th e 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. 2003b. 2001 Annual Survey of Manufactures. M01(AS)-2. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as-2.p df>. 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 numbe rs] <http://factfinder.census.gov/servlet/ DatasetMainPageServlet?_lang=en&_ts=336651423017&_ds_name=AM0931GS101&_program=EAS>.

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2-36 Table 2-13. Imports of Major Petroleum Pr oducts (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 Administrati on (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 Information Administration (EIA). 1996–2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on Oct ober 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 Pr oducts (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.

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2-37 U.S. Department of Energy. 2011 Energy Information Administration (EIA). 1996–2011. “Petroleum Supply Annuals, Volume 1.” (Data accessed on Oc tober 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 petroleu m products sold to e nd users are provided for selected years in Table 2-15. 2 As these data illustrate, nomin al 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 pr ices increased. During the 2008–2010 period, the most volatile price was jet fuel price: it dec lined by 44% in 2009 and increased by 29% in 2010.

Table 2-15. Average 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, 2011.) [Source for 2007–2010 numbers.]<http://www.eia.gov/dnav/pet/pet_pri_refoth_dcu_nus_a.htm>.

The nominal prices domestic petroleum refine rs receive for their products have been volatile, especially compared to prices receiv ed by other U.S. manufacturers. This trend is demonstrated in Table 2-16 by comparing the pr oducer price index (P PI) for the petroleum refining industry against the index for all ma nufacturing industries. Be tween 1995 and 2010, prices received by petroleum refineries for their products rose by 288% , while prices received by all manufacturing firms rose by 41%. In 2009, both pr ice indexes experienced a decline from 2008 levels, however the decrease was 36% for petroleum refineries and 5% for all manufacturing firms.

2 Sales to end users are those made directly to the consum er of the product. This includ es bulk consumers, such as agriculture, industry, and utilities, as well as residential and commercial consumers.

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2-38 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. “Pro ducer Price Index Industry Da ta: Customizable Industry Data Tables.” Available at <http://www.bls.g ov/ppi/>. As obtained on October 11, 2007. U.S. Bureau of Labor Statistics (BLS). 2011. “Producer Price Index Industry Data: Customizable Industry Data Tables.” (Data accessed on Oct ober 11, 2011.) [S ource 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 sales 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 di viding net income into revenues for 44 firms for the 2006-2007 fiscal year and 43 firms for th e 2009-2010 fiscal year. They are broken down based on the value of assets owned by the reporting firms.

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2-39 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 report ed a greater value of a ssets 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 re turn 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 asset 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 pr ivately owned. However, some of the small, domestic petroleum refining firms identified in Section 3.4.2.3 are publ icly owned companies— the Arabian American Development Co., CVR Energy Inc., Calumet Specialty Products Partners, L.P., Holly Corporation, and Western Refining, Inc. Profit ratio s were calculated for these companies using data obtained from thei r publicly available 2010 income statements. These ratios are presented in Table 2-18.

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2-40 Table 2-18. Net Profit Margins for Publicly Owned, 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/1 428853845x0xS950123-11-18524/48039/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.corporateir.net/phoenix.zhtml?c=194293&p=irol-sec> Arabian American Develop Co., South Hampton Reso urces, Inc. Arabian American Development Co. Q4 & FY2010 Financial Results. (Data accessed on 10/21/11) [Source for 2010 numbers.] http://www.arabianamericandev.com/images/uploa ds/investor/2010eoyearningspresentation.pdf> Calumet Specialty Products Partners, Morningstar fo r Calumet Specialty Products Partners 10K. February 22, 2011. 10K for year ended Decem ber 31, 2010. (Data accessed on 10/ 21/11) [Source fo r 2010 numbers.] http://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/137 6139/000095012311022741/y90110e10vk.htm>
2.5.6 Industry Trends
The Energy Information Administration’s (EIA’s) 2011 Annual Energy Outlook provides forecasts of average petroleum prices, petrol eum product consumption, and petroleum refining capacity utilization to th e year 2035. Trends in these variable s 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 po licy decisions. As a result, the EIA evaluated a wide variety of cases based on different assump tions 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 re main 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 cr ude oil) are expected to steadily increase over the next 10 years as the amount of oil demande d by non-OECD and OECD countries increases. Since crude oil is the primary i nput in petroleum refining, an incr ease in its price would likewise represent an increase in production costs of pe troleum refiners. As a result, the prices of

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2-41 petroleum products sold to end users are expected to rise ove r the same period (Table 2-19). Higher prices and tighter fuel efficiency sta ndards (enlarged production of non-oil fuels) will moderate the growth in petroleum products co nsumed 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 product, motor gasoline, is projected to rise by 38% and its consumption is projec ted 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 (millions 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>.

In 2008, operational capacity of U.S. petroleum refineries was expected to grow for the foreseeable future. The expansion of dozens of petroleum refineries had been announced

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2-42 (Reuters, 2007). The Oil & Gas Journal’s (O&J) 2008 Worldwide Cons truction Update survey alone catalogued over 40 refining construction pr ojects that had been announced in the United States. Table 2-21 lists selected refinery construction projects from the 2008 and 2011 O&J Worldwide Construction Update surveys. The pr ojects listed include tw o refinery expansion projects and six new processes (distillation units, delayed cokers , and mild hydrocrackers) from the 2008 survey and two refinery expansion projects from the 2011 survey.

Table 2-21. Selected Refinery Construction Projects: 2008–2014

Company and Location Project Projected Added Capacity (barrels per day) Expected Completion
Cenex Harvest States, Laurel, MT New delayed coker unit N/A 2008
Frontier Oil Corp, El Dorado, KS New crude distillation unit 15,000 2008
New vacuum distillation unit 15,000 2008
Holly Corp, Woods Cross, UT New Mild hydrocracker 15,000 2008 Artesia, NM New Mild hydrocracker 15,000 2008
Motiva Enterprises LLC, Port Arthur, TX Refinery expansion 325,000 2010

Placid Refining Inc. New crude distillation unit 25,000 2010
Sinclair Oil Corp, Tulsa, OK Refinery Expansion 45,000 2011
Valero Energy Corp., Sunray, TX Refinery Expansion 25,000 2014
Tesoro Corp., Mandan, ND Refinery Expansion 10,000 2012

Sources: Oil and Gas Journal . April 7, 2008.Worldwide Construction Update. Oil and Gas Journal . April 4, 2011. Worldwide Construction Update http://bismarcktribune.com/news/local/artic le_52ec198a-53d8-11e0-ad90-001cc4c03286.html

In particular, several U.S. refineries are plan ning projects to expand their ability to handle cheaper and lower-quality vari eties of crude oil (known as “heavy crudes”). For example, ConocoPhillips will be expanding its capacity to handle heavy crude oils at its refinery in Billings, Montana, to 46,000 barrels per day (Reuters, 2007).

In addition to these expansions, in 2008 two entirely new refineries were potentially going to be constructed within th e next 5 years. The first was th e Arizona Clean Fuels Refinery in Phoenix. This facility was pr ojected to cost $3 billion to co nstruct and would be capable of producing 6 million gallons of gasoline, diesel, a nd jet fuel per day (Arizona Clean Fuels, 2007). Second, a proposal to construct the MHA Nation Clean Fuels Refinery in North Dakota was

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2-43 being reviewed. If constructed, th is facility would be capable of producing 15,000 barrels of fuel per day (EPA, 2006). However, O&J’s Worldwid e Construction Update Surveys for 2009 and 2010 showed projects on hold and completion da te delays due to lower petroleum product demand in North America. The April 2009 survey found that Marathon Oil Corp. planned to delay the expansion of its 100,000 barrels per day refinery in southwest Detroit. The company intended to finish the project in the fourth quarter of 2010. However two factors—namely a slowdown in gasoline demand and a delay in the Company’s Cana dian crude production— changed the date of completion to the middle of 2012. Construction on this project began in June of 2008, but is continuing at a slower pace than originally planned. Similarly the April 2009 Survey found that Motiva Enterpri ses LLC, which is a joint venture between Shell Oil Company and Saudi Refining Inc., has dela yed the expansion of its 285,000 barre ls per day refinery at Port Arthur, Texas from late 2010 to the first qu arter of 2012. The November 2009 Survey showed that Irving Oil Corporation and BP PLC susp ended plans to build a 300,000 barrels per day refinery at St. John, NB. The companies conduc ted an 18 month feasibility study and found the project “was not viable at a time of global economic recession and dampening forecasts of petroleum product demand in America.”

Overall, the EIA forecasts that U.S. operati onal capacity will decrea se by a total of 5% between 2011 and 2019 (Table 2-22). The rate of capacity utilizati on is projected to average 86% during this period.

Table 2-22. Full Production Capacity Util ization 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) 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 October 11, 2011) [Source for 2016–2019 numbers.] <http://www.eia.gov/forecasts/ aeo/pdf/0383( 2011).pdf>.

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2-44 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. Comp any Home Page. Available at <http://www.arizonacleanfuels.com/>. As obtained on February 5, 2007.
Arabian American Develop Co., South Hamp ton Resources, Inc. Arabian American Development Co. Q4 & FY2010 Financial Re sults. (Data accessed on 10/21/11) [Source for 2010 numbers.] <http://www.arabianamericandev.com/images/uploads/investor/ 2010eoyearningspresentation.pdf>
Arabian American Development Co. April 6, 2007. 10K for year ended December 31, 2006. EDGAR Database. Available at <htt p://www.sec.gov/Archives/edgar/data/ 7039/000095013407007709/0000950134-07-007709-index.htm>.
Calumet Specialty Products Partners, Morningsta r 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/AnnualReport/2010/12/31/t.aspx?t=XNAS:CLMT&ft=10- K&d=c7dd2813722445ded56c7e3aefebf2ca>
Calumet Specialty Products Partners. Febr uary 23, 2007. 10K for year ended December 31, 2006. EDGAR Database. Available at <htt p://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 (Vir ginia Commonwealth University).
Copyright 2011 Harte-Hanks Market Intelligen ce, 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 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>

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

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

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2-45 Energy Information Administration (EIA), Fo rm 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 Oct ober 31, 2011.) [Source for 2009–2011 numbers.] <http://www.eia.gov/petroleum/refinerycapacity/>
Federal Trade Commission (FTC). 2004. “The Petrol eum Industry: Mergers, Structural Change, and Antitrust Enforcement.” Availabl e at <http://www.ft c.gov/opa/2004/08/ oilmergersrpt.shtm>. As obtained on February 6, 2007.

Gale Research Inc. 2007. Ward’s Business Direct ory of U.S. Private and Public Companies. Detroit: Gale Research.
General Accounting Office (FTC). 2007. “Energy Markets: Factors That Influence Gasoline Prices.” Available at <http://www.gao.gov/ new.items/d07902t.pdf>. As obtained on February 6, 2007.
Global Duns Market Identifiers, accessed through LexisNexis at University of Virginia’s Alderman Library.
Holly Corporation. March 1, 2007. 10K for year ended December 31, 2006. EDGAR Database. Available at <http://www.sec.gov/Archives/edgar/data/48039/000095013407004555/ d44106e10vk.htm>.
Holly Corporation, EDGAR database for Holly Corporation 10K. February 25, 2011. 10K for year ended December 31, 2010. (Data acce ssed on 10/23/11) [Source for 2010 numbers.] <http://files.shareholder.com/downloads/FTO/1428853845x0xS950123-11- 18524/48039/filing.pdf>
Hoovers 2011 Online Data, accessed through Univers ity 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.
Langinier, Corrine. 2004. “Are Patent s Strategic Barriers to Entry?” Journal of Economics and Business 56:349-361.
MathTech, Inc. 1997. “Industry Profile for the Pe troleum Refinery NESHAP.” Available at <http://www.epa.gov/ttnecas1/regdata/ IPs/Petroleum%20Refinery%20 (Sulfur%20Recovery%20Units,%20Catalytic% 20Crackin.pdf>. As obtained on October 31, 2006.
Million Dollar Database online, 2011, accessed through University of South Carolina’s Moore School of Business Library. Million Dollar Onli ne Data reflects eith er actual data for 2011 reported by companies or estimated data for 2011.

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2-46 Nicholson, Walter. 2005. Microeconomic Theory: Basic Principles and Extensions . Mason, OH: South-Western Publishing.
Oil and Gas Journal, December 6, 2010
Oil and Gas Journal. April 4, 2011. Worldwide Construction Update
Oil and Gas Journal. April 7, 2008.W orldwide Construction Update.
Petroleum Refinery Emissions Information Coll ection 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.W orldwide Construction Update..
Risk Management Association (RMA). 2008. Annual Statement Studies 2007-2008. Pennsylvania: RMA, Inc.
Risk Management Association (RMA). 2011. Annual Statement Studies 2010–2011. Pennsylvania: RMA, Inc.
Reuters. July 19, 2007. “U.S. Refinery Expansion Plans.” Available at <http://uk.reuters.com/ article/oilRpt/idUKN1997 46120070719>. As obtained on February 5, 2007.

Small Business Administration. 2011. Table of Sm all Business Size Standards. Available at < http://www.sba.gov/sites/default/files/Size_St andards_Table.pdf>. As obtained October 2011.
U.S. Bureau of Labor Statistics (BLS), 2011. “Pr oducer Price Index Industry Data: Customizable Industry Data Tables.” (Data access ed on October 11, 2011.) [Source for 2007–2010 numbers]<http://data.bl s.gov/cgi-bin/dsrv?pc>.
U.S. Bureau of Labor Statistics (BLS). 2007. “Pr oducer Price Index Industry Data: Customizable Industry Data Tables.” Available at <http ://www.bls.gov/ppi/>. As obtained on October 11, 2007.
U.S. Census Bureau, using American FactFinder; “Sector 31: Annual Survey of Manufacturers: General Statistics: Statistics for Industr y 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=NAICS ASM&-_industry= 324110&-_lang=en>

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2-47 U.S. Census Bureau, using American FactFinde r; “Sector 31: Manufacturing: Industry Series: Detailed Statistics by Industr y for the United States: 2007” Release Date 10/30/09; (Data accessed on 10/11/11). [Source for 2007 numbers] <http://factfinder.census.gov/servle t/IBQTable?_bm=y&-geo_id=01000US&- ds_name=EC0731I1&-NAI CS2007=324110&-_lang=en>
U.S. Census Bureau, using American FactFinde r; “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 number s] <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>
U.S. Census Bureau, using American FactFinde r; “Sector 31: Manufactu ring: 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 number s]<http://factfinder.census.gov/ servlet/IBQTable?_bm=y&-ds _name=EC0731SR12&-NAICS2007=324110&- ib_type=NAICS2007&-NAICS2007sector =*6&-industrySel=324110&-geo_id=&- _industry=324110&-_lang=en>
U.S. Department of Commerce, Bureau of th e Census, FactFinder. 2011. 2009 Annual Survey of Manufactures. 1996–2011. Obtained through Amer ican FactFinder Database. (Data accessed on October 14, 2011.) [Source for 2007–2009 numbers] <http://factfinder.census.gov/servlet/Datas etMainPageServlet?_lang=en&_ts=336651423 017&_ds_name=AM0931GS101&_program=EAS>.
U.S. Department of Commerce, Bureau 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 the 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 Census. 2000. “Population Density by County: Census 2000 Summary File 1 (SF 1) 100-Per cent Data”. Available through American FactFinder <http://factf inder.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.

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2-48 U.S. Department of Commerce, Bureau 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 the 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/ec0231i 324110.pdf>. As obtained on October 23, 2006.
U.S. Department of Commerce, Bureau of the Census. 2006. 2005 Annual Survey of Manufacturer s. M05(AS)-1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2005pubs/am043 1gs1.pdf>. As obtained on October 23, 2006.
U.S. Department of Commerce, Bureau of the Census. 2007. 2006 Annual Survey of Manufacturers. Obtained through Am erican Fact Finder Database <http://factfinder.census.gov/home/saff/main.html?_lang=en>.
U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996–2011. “Petroleum Supply Annuals, Volume 1.” (Dat a 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.” (Dat a 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. “Petroleum Supply Annuals, Volume 1.” (Dat a accessed on October 7, 2011.) [Source for2007–2010 numbers.] <http://www.eia.gov/dnav/pet/pet_move_imp_dc_NUSZ00_mbbl_a.htm>
U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996–2011. “Petroleum Supply Annuals, Volume 1.” (Dat a accessed on October 7, 2011.) [Source for2007–2010 numbers] <http://www.eia.gov/dnav/pet/pet_move_exp_dc_NUSZ00_mbbl_a.htm>.
U.S. Department of Energy, 2011 Energy Information Administration (EIA). 1996–2011. “Petroleum Supply Annuals, Volume 1.” (Dat a accessed on October 7, 2011.) [Source for 2007–2010 numbers.]<http://www.eia.gov/dnav/ pet/pet_pri_refoth_dcu_nus_a.htm>.

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2-49 U.S. Department of Energy, Energy Informa tion Administration (EIA ). 1996–2007. “Petroleum Supply Annuals, Volume 1.” Availabl e at <http://www.eia.doe.gov/oil_gas/ petroleum/data_publications/petro leum_supply_annual/psa_volume1/psa_ volume1.html>. As obtained on October 31, 2007.
U.S. Department of Energy, Energy Informati on 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). Avai lable at <http://tonto.eia.doe.gov/ FTPROOT/petroleum/054599.pdf>. As obtained on October 23, 2006.
U.S. Department of Energy, Energy Informa tion Administration (E IA). 2006b. “Refinery Capacity Report 2006.” Available at <h ttp://www.eia.doe.gov/oil_gas/petroleum/ data_publications/refinery_capacity_data/ref capacity.html/>. As obtained on October 23, 2006.
U.S. Department of Energy, Energy Informati on Administration (EIA). 2006c. “Petroleum Supply Annual 2005, Volume 1.” Available at <http://www.eia.doe.gov/pub/oil_gas/ petroleum/data_publications/petroleum_s upply_annual/psa_volume1 /historical/2005/psa _volume1_2005.html>. As obt ained on October 31, 2007.
U.S. Department of Energy, Energy Informa tion 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 7, 2008.
U.S. Department of Energy, Energy Informa tion Administration (E IA). 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, Energy Informati on Administration (EIA). 2007c. “Annual Energy Outlook.” Available at <http://www.eia.doe .gov/oiaf/archive/ae o07/pdf/0383(2007).pdf>. As obtained on January 21, 2007.
U.S. Department of Energy, Energy Informa tion Administration (EIA). 2011, “Annual Energy Outlook.” (Data accessed on October 11, 2011) [Source for 2011–2019 numbers.]<http://www.eia.gov/fo recasts/aeo/pdf/0383(2011).pdf>.
U.S. Department of Justice and the Fede ral Trade Commission. 1992. Horizontal Merger Guidelines. Available at <http://www.ftc .gov/bc/docs/horizmer.htm>. As obtained on October 28, 2006.
U.S. Department of Labor, Occupational Sa fety and Health Administration (OSHA). 2003. OSHA Technical Manual, Section IV: Chapte r 2, Petroleum Refining Processes. TED 01- 00-015. Washington, DC: U.S. DOL. Availa ble at <http://www.osha.gov/dts/osta/ otm/otm_iv/otm_iv_2.html>. As obtained on October 23, 2006.

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2-50 U.S. Energy Information Administration (EIA), 2011a. “Refinery Utilization and Capacity.” Available at http://www.eia.gov/dnav/p et/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 Op erable 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: Profile of the Petroleum Refi ning 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.
U.S. Environmental Protection Agency. 2006. “P roposed MHA Nation Clean Fuels Refinery.” Available at <http://www.epa.gov/Region8/co mpliance/nepa/refinerydeis.html>. As obtained on February 6, 2007.
Ward’s Business Directory of Public and Pr ivate 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>.
Western Refining, Thomson Reuter s 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/phoe nix.zhtml?c=194293&p=irol-sec>

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3- 1 3 NSPS REGULATORY OPTIONS, CO STS AND EMISSION REDUCTIONS FROM COMPLYING WITH THE NSPS

The emissions reduction and cost impacts presen ted in this section fo r flares are revised estimates for the impacts of the final requirement s of 40 CFR part 60, subpart Ja for flares, as amended by this action. The impacts are expres sed as incremental differences between the impacts of petroleum refinery process units comp lying with the final amendments to subpart Ja and the current NSPS requirements of 40 CFR part 60, subpart J (i.e., base line). The impacts are presented for petroleum refinery flares th at commence construction, reconstruction or modification over the next 5 years. We anticipate that there will be 400 a ffected flares over the next 5 years, or about three fl ares per refinery, and we estimate that 30 percent of the 400 affected flares, or 120 flares, e ither would meet the definition of “emergency flare” in 40 CFR part 60, subpart Ja or would be equipped with a fl are gas recovery system. Therefore, the values presented in this section include the costs and em issions 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 w ith the short-term H2S c oncentration of 162 ppmv in the fuel gas are included in the baseline rather than the incremental impacts because this limit is unchanged from the requirements in subpart J. For further detail on the methodology of these calculations, see Documentation of Impact Estimat es for Fuel Gas Combustion Device and Flare Regulatory Options for Amendments to the Petr oleum Refinery NSPS, in Docket ID No. EPAHQ-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 and VOC by 3,400 tons/yr from the baseline. The estimated annual cost, including annualized capital costs, is a cost savings of about $79 million (2006 dollars) due to the replacemen t of some natural gas purchases with recovered flare gas and the retention of intermediate and product st reams due to a reduction in the number of malfunctions associated with refinery process units and ancillary equi pment connected to the
flare. The overall cost effectiveness is a co st savings of about $10,000 per ton of combined pollutants removed. The estimated nationwide 5-y ear incremental emissions reductions and cost impacts for the final standards are summa rized in Tables 3-1 and 3-2 below.

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3- 2 Table 3-1. National Incremental Emission Re ductions for Petroleum Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Applicability Date)

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 Revised Estimates for Amendments 460,000 100,000 -180,000 -79,000

Table 3-2. National Incremental Cost Impa cts and Cost-Effectiveness for Petroleum Refinery Flares Subject to Amended Standards Under 40 CFR part 60, subpart Ja (Fifth Year After Applicability Date)

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) Estimates from June 2008 Final Rule 80 6 200 -23,000 Revised Estimates for Amendments 3,200 1,100 3,400 -10,000

The cost, environmental and economic impacts for the final amendments to 40 CFR part 60, subpart Ja for process heaters ar e not expected to be different than those reported for the final June 2008 standards. We expect owners and opera tors to install the same technology to meet
these final amendments that we anticipated they would install to meet the June 2008 final subpart Ja requirements (i.e., ultra-low NO x burners). We did re vise our emissions estimates based on the type of process heater, creating se parate impacts for forced draft process heaters and natural draft process heaters. Dividing proce ss heaters into separate subcate gories, based on the draft type, required us to develop new distributions of base line emissions for each type of process heater.
The baseline emissions estimates for natural draf t 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 pro cess heaters, the baseline (i.e., uncontrolled) emissions rate for forced draft pr ocess heaters was revised slightly upward, based
on the available emissions data. B ecause of these differences, the mi x of controls needed to meet

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3- 3 a 40 ppmv emissions limit was no longer cost effectiv e for forced draft process heaters, but the emission reductions associated with process heat ers complying with the 60 ppmv standard were higher than those previously estimated for gene ric process heaters. T hus, 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 m onitoring controls in conjunction with ultralow NO x burners) and did not change the estimated compliance costs. As we do not have adequate data regarding the prevalence of natu ral draft process heaters versus forced draft process heaters that will become subject to th e rule, we used the emission reductions estimated for the 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/y r in the fifth year after promulgation (see
Revised NO x Impact Estimates for Process Heater s, in Docket ID No. EPA-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 we are finalizing here. Given the uncertainty in the emissions estimates, as well as the uncertainty in the relative number of natural dr aft process heaters versus forced draft process heaters, we concluded that the impacts previously developed for subpart Ja accurately represent the impacts for process heaters 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 s ubsequently determined that those estimates did not fully acc ount for the number of affected fl ares (which, at the time, were considered a subset of fuel gas combustion device s). Therefore, in the preamble to the December 2008 proposed amendments, we presented revise d emission reduction and cost estimates for affected fuel gas combustion devices. Because thes e final amendments consider flares to be a separate affected source, the em ission reductions and costs for fuel gas combustion devices are not affected by these final amendments and are not included in this ac tion. Rather, the 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 detail s of the analysis an d the final impacts are presented in Documentation of Impact Estimat es for Fuel Gas Com bustion Device and Flare Regulatory Options for Amendments to the Petr oleum Refinery NSPS, in Docket ID No. EPAHQ-OAR-2007-0011.

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3- 4 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.

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4-1 4 ECONOMIC IMPACT ANALYSIS: METHODS AND RESULTS

The economic impact analysis (EIA) is designed to inform decision makers about the potential economic consequences of a regulatory action. The analys is consists of estimating the social costs of a regulatory program and the di stribution of these costs across stakeholders (consumers and producers). As defined in EPA’s (2000) Guidelines for Preparing Economic Analyses, 1 social costs are the value of the goods a nd services lost by society resulting from using resources to comply with and impleme nt a regulation and re ductions in output.

For this rule, annual cost savings of $79 m illion (2006 dollars) are e xpected in the fifth year. Based on the cost savings estimated fo r this rule, no negative economic impacts are expected. We therefore did not include a detailed economic impact analysis of this rule in the
RIA.

This rule is not a “significant energy action” as defined in Executive Order 13211, “Actions Concerning Regulations That Significantly Affect Ener gy Supply, Distribution, or Use” (66 FR 28355 [May 22, 2001]) because it is not likely to have a significant adverse effect on the supply, distribution, or use of ener gy. Since there is a cost savings associated with compliance with this rule, there will be no increase in dom estic prices, and no significant increase in our dependence on foreign energy supplies. Finally, today’s action will have no adverse effect on crude oil supply, coal production, electricity pr oduction, and energy distribution. Based on the findings from the analysis of impacts on energy mark ets, we conclude that today’s action is not a “significant energy action” as defined in Executive Order 13211.

1 These guidelines are currentl y under review by the Agency.

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5- 1 5 SMALL BUSINESS ANALYSI S AND EXECUTIVE ORDERS

The Regulatory Flexibility Act (RFA) gene rally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other statute unless the agency certifies that the rule will not have a signifi cant economic impact on a substantial number of small entities. 1

In indicated in Section 2, 36 small companie s owning petroleum refine ries are classified as small according to the Small Business Admi nistration. As previously discussed, small businesses in the petroleum re fining industry (NAICS code 324110) are defined for the purposes of this rule as having 1,500 or fewer employees. 2
5.1 Results of Small Business Analysis
After considering the economic im pact of today’s action on small entities, we certify that this action will not have a significant economic im pact on a substantial number of small entities. Of the affected entities, none are estimated to incur any annua lized compliance cost, but should expect cost savings as mentione d in Section 4 of this RIA.

5.2 Unfunded Mandates

This rule does not contain a Federal manda te that may result in expenditures of $100 million or more for State, local, and tribal government s, 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 rule 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 requi rements of section 203 of UMRA because it contains no regulatory require ments that might significantly or uniquely affect small governments. The proposed amendments c ontain no requirements that apply to such governments, and impose no obligations upon them.

5.3 Environmental Justice
Executive Order 12898 (59 FR 7629, February 16, 1994) establishes Federal executive policy on environmental justice. Its main provi sion directs Federal agen cies, to the greatest extent practicable and permitted by law, to make environmental justice part of their mission by

1 Small entities include small businesses, small organizations, and small governmental jurisdictions.
2 Refer to http://www.sba.gov/content/table- small-business-size-standards for more information on SBA small

business size standards.

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5- 2 identifying and addressing, as appropriate, disp roportionately high and adverse human health or environmental effects of their programs, policie s, and activities on minority populations and lowincome 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 mi nority or low-income populations because it increases the level of environm ental protection for all affected populations without having any disproportionately high and adve rse human health or environm ental effects on any population, including any minority or low-income population. This rule is a nationwide standard that will yield reductions in various criter ia pollutant emissions from ne w, modified, and reconstructed process heaters and flares at pe troleum refineries, thus decreasi ng the amount of such emissions to which all affected populations are exposed.

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6-1 6 HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS

Synopsis

In this section, we provide an estimate of the monetized benefits for the final Petroleum Refineries NSPS Reconsideration associated w ith 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. These estimates reflect the monetized human health benefits of reducing ca ses of morbidity and premature mortality among populations exposed to PM 2.5 reduced by this reconsideration. We estimate the total monetized benefits to be $270 million to $580 million at a 3% discount rate and $240 million to $530 million at a 7% discount rate in the fifth year (2017). All estimates are in 2006$.

These estimates reflect EPA’s most current in terpretation of the scientific literature. Higher or lower estimates of benefits are possibl e 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 bene fit 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 Reconsiderati on would reduce emissions of SO 2 , NO X , and VOCs. Because these emissions are precursors to PM 2.5, reducing these emissi ons 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 provide a comprehensive estimate of PM 2.5 -related benefits or provide estimates of the health be nefits associated with direct exposure to SO 2 and NOx or exposure to ozone. Instead, we used th e “benefit-per-ton” approach to estimate these benefits. The methodology employed in this analysis is similar to the work described in Fann, Fulcher, and Hubbell (2009), but represents an improvement that EPA believes would provide more reliable estimates of PM 2.5 -related health benefits for em issions reductions in specific sectors. The key assumptions are de scribed 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 reducing one ton of PM 2.5 from a specified source. EPA has used the

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6-2 benefit per-ton technique in several previous RIAs, including the recent SO 2 NAAQS RIA (U.S. EPA, 2010).

The Integrated Science Assessment (ISA) for Particulate Matter (U.S. EPA, 2009b) identified the human health effects associated with ambient PM 2.5, which include premature morality and a variety of morbidity effects associ ated with acute and chronic exposures. Table 6- 1 shows the quantified and unquantified benefits cap tured in those benefit-per-ton estimates, but this table does not include entr ies for the unquantified health eff ects associated with exposure to ozone, SO 2 , and NOx nor welfare effects such as eco system effects and visibility that are described in section 6.2. It is important to emphasize that th e list of unquantified benefit categories is not exhaustive, nor is qu antification of each effect complete.

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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., pulmonaryfunction, non-asthma ER visits, nonbronchitis 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 qualitatively due to tim e and resource limitations for this analysis.
2 We assess these benefits qualitatively because we do not have sufficient co nfidence in available data or methods.
3 We assess these benefits qualitatively because current evid ence is only suggestive of ca usality or there are other significant concerns over the strength of the association.

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the benefits estimates utilize the concentration -response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expert elic itation study as a sens itivity analysis.

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6-4  One estimate is based on the concentrati on-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 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) Office of Air and Radiation RIAs.

 One estimate is based on the C-R function developed from 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 coeffici ent 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 func tions from EPA’s e xpert 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 experts (labeled A through L) provided independent estimates of the PM 2.5 -mortality concentration-response function. EPA pract ice has been to develop independent estimates of PM 2.5 -mortality estimates corresponding to the concentration-response function provided by each of the twelve expe rts, 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 al., 2002) and the Harvard Six Cities cohort (Laden et al., 2006). 6 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 beli eve argues for using both studies to generate benefits estimates. Previously, EPA had cal culated 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 relati onship between exposure to PM 2.5 and premature mortality (Roman et al., 2008). 7 Within this assessment, we include the benefits estimates derived from the

6 These two studies specify multi-pollutant models that control for SO 2 , among other co-pollutants.
7 Please see the Section 5.2 of the Portland Cement RIA in Appendix 5A for more information regarding the change in the presentation of benefits estimates.

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6-5 concentration-response function provided by each of the twelve experts to better characterize the uncertainty in the concentration-response functio n for mortality and the degree of variability in the expert responses. Because the experts used these cohor t studies to inform their concentration-response functions, be nefits estimates using these f unctions generally fall between results using these epidemiology studies (see Figu re 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 genera l methodology for creating the benefit-per-ton estimates used in this analys is should consult the draft Tech nical Support Document (TSD) on estimating the benefits per ton of reducing PM 2.5 and its precursors from the petroleum refineries sector (U.S. EPA, 2012). The primary difference betw een 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, an d Hubbell (2009) reflects broad pollutant/source category combinations, the sour ce apportionment modeling data used in this analysis is sector-specific. As a result, the benefit-per-ton estimates presented herein better reflect the geographic areas and population likely to be affected by the proposed rule. In this analysis, we apply these national benefit-per-ton estimates calculated for this sector separately for SO 2 and NOx and multiply them by the corresponding emission reductions.

These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premat ure mortality because the scientific evidence is not yet sufficient to allow differentiation of eff ects estimates by particle type. SO 2 and NOx are the primary PM 2.5 precursors affected by this rule. Even though we a ssume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary between precursors depending on the location and magnitude of their impact on PM 2.5 levels, which drive populati on exposure. The sectorspecific modeling does not pr ovide estimates of the PM 2.5 -related benefits associated with reducing VOC emissions, but these unquantified bene fits are generally small compared to other
PM 2.5 precursors (U.S. EPA, 2012).

The benefit-per-ton coefficients in this analys is were derived using modified versions of the health impact functions used in the PM NAA QS Regulatory Impact Analysis. Specifically, this analysis uses the benefit-per-ton method first applied in the Portland Cement NESHAP RIA (U.S. EPA, 2009a), which incorporated three up dates: a new population dataset, an expanded geographic scope of the benef it-per-ton calculation, and the f unctions directly from the

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6-6 epidemiology studies without an adjustment for an assumed threshold. 8 Removing the threshold assumption is a key difference between the method us ed in this analysis of PM benefits and the methods used in RIAs prior to Portland Ceme nt proposal, and we now calculate incremental benefits down to the lowest modeled PM 2.5 air quality levels.

Based on our review of the current body of sc ientific literature, EPA now estimates PMrelated mortality without applying an a ssumed 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 consis tently finds that a no-th reshold log-linear model most adequately portrays the PM-mortality concentration-response relationship while recognizing potential uncertainty about the exact shape of the c oncentration-response function.
Consistent with this finding, we have c onformed the previous threshold sensitivity analysis to the current state of the PM scienc e by incorporating a “Lowest Measured Level” (LML) assessment, which is a method EPA has em ployed in several recent RIAs including the Cross-State Air Pollution Rule (U .S. EPA, 2011b). 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 co nfidence in the estimated PM mortal ity decreases as we consider air quality levels further below the LML in major cohort studies that estim ate PM-related mortality. While an LML assessment provides some insight into the level of uncertainty in the estimated PM mortality benefits, EPA does not view the LM L as a threshold and continues to quantify PMrelated mortality impacts using a full range of mode led air quality concentra tions. 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 . Just because we have greater confidence in the benefits above the LML, this does not mean th at we have no confidence that benefits occur below the LML. For a summary of the scientif ic review statements regarding the lack of a
threshold in the PM 2.5 -mortality relationship, see the T echnical Support Document (TSD)
entitled Summary of Expert Opinions on the Existe nce of a Threshold in the ConcentrationResponse Function for PM 2.5 -related Mortality (U.S. EPA, 2010b).

8 The benefit-per-ton estimates have also been updated since the Portland Cement RIA to incorporate a revised VSL, as discussed below.

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6-7 For this analysis, policy-specific air quality data is not availa ble due to time or resource limitations. For these rules, we are unable to estimate the percentage of premature mortality associated with this specific ru le’s emission reductions at each PM 2.5 level. However, we believe that it is still important to characterize the distri bution of exposure to base line 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 apportionmen t 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 inciden ce rates population, and change in air quality. In other words, the per centage of the population exposed to air pollution below the LML is not the same as the percenta ge of the population experiencing health impacts as a result of a specific emission reduction polic y. The most important aspect, which we are unable to quantify for rules without rule-specifi c air quality modeling, is the shift in exposure associated with this specific rule. Therefor e, caution is warranted when interpreting the LML assessment for this rule. The results of this analysis 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 time ( 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 studi es. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) 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. Th e $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta-ana lysis of 43 studies. The mean estimate of $5.5 million (2000$) 9 was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysi s. However, the Agency neither changed its official guidance on the use of VSL in rulema kings nor subjected the interim estimate to a scientific peer-review process through the Scie nce Advisory Board (SAB ) or other peer-review

group.

During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a repo rt 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 consulte d several times with the Science Advisory Board

9 After adjusting the VSL for a different currency year (2006$) and to account for income growth to 2 015 of the $5.5 million value, the VSL is $7.4 million.

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6-8 Environmental Economics Advisory Committee (SAB-EEAC) on the i ssue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic tech niques to combine estimates from unique data sources and different studies, including those using differe nt methodologies (i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007).

Until updated guidance is availabl e, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has re ceived. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 10 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 st udies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 11 The Agency is committed to using scientifically sound, appropriately reviewed evid ence in valuing mortality risk reductions and has made significant progress in responding to the SAB-EEAC’s specific recommendations.

In implementing these rules, emission cont rols may lead to reductions in ambient PM 2.5 below the National Ambient Air Quality Standard s (NAAQS) for PM in some areas and assist other areas with attaining th e PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, there are important differences worth no ting in the design and anal ytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air quality standard nationwide based on an array of emission control st rategies for different sources. In short, NAAQS RIAs hypothesize, but do not pred ict, the control strate gies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS doe s not directly result in costs or benefits, and as such, the NAAQS RI As are merely illustrative and are not intended to be added to the costs and benefits of other regula tions that result in speci fic costs of control and emission reductions. However, some costs and be nefits estimated in this RIA account for the same air quality improvements as estimated in the illustrative PM 2.5 NAAQS RIA.

10 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 va luation guidance would be forthcoming in the near future. Therefore, this report does not represent final agency policy.
11 In this analysis, we adjust the VSL to account for a differe nt currency year ($2006) and to account for income growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $8.5 million.

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6-9 By contrast, the emission reductions for this RTR rule are from a specific class of wellcharacterized sources (i.e., petrol eum refining facilities). In general, EPA is more confident in the magnitude and location of the emission reductions for these rules. It is important to note that emission reductions anticipated from these rules do not result in emission increases elsewhere.
Emission reductions achieved unde r these and other promulgate d rules will ultimately be reflected in the baseline of future NAAQS an alyses, which would reduce the incremental costs and benefits associated with attaining th e NAAQS. EPA remains forward looking towards the next iteration of the 5-year re view cycle for the NAAQS, and as a result does not issue updated 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, 2010a).

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6-10 Figure 6.1 illustrates the relativ e breakdown of the monetized PM 2.5 health benefits.

Figure 6-1: Breakdown of Monetized PM 2.5 Health Benefits using Mortality Function 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 premat ure mortality, the percentage of total monetized benefits due to adult mortality would be 97%. This chart 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 th e monetized PM-related health benefits by precursor, including the emission re ductions and benefit-per-ton es timates at discount rates of 3% and 7%. 12 Table 6-3 provides a summary of the reducti ons in health incidences as a result of the pollution 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 fr om the expert elicitation on PM mortality. Figure

12 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 speci fic analysis year (i.e., 2017), and mo st of the PM benefits occur within that year with two excep tions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow-up medical co sts and lost wages. For premature mort ality, we assume that there is a “cessation” lag between PM exposures and the total reali zation 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 y ear, 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). Changes in the la g 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 prematur e mortalities that occur after the 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. Adult Mortality ‐ Pope et al. 93% Chronic Bronchitis 4%

AMI 2% Acute Respiratory Symptoms 0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio 0.2% Hospital Admissions, Resp 0.04% Asthma Exacerbation 0.01% Acute Bronchitis 0.01% Upper Resp Symp 0.00% Lower Resp Symp 0.00% ER Visits, Resp 0.00% Other 1%

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6-11 6-2 provides a visual repres entation 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 R econsideration (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%)

PM 2.5 Precursors

SO 2 3,050 $65,000 $160,000 $58,000 $140,000 $210 to $510 $190 to $460

NO X 590 $6,400 $16,000 $5,700 $14,000 $7.1 to $18 $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 $49 million (3% discount rate). All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary beca use 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. Confidence intervals are un available for this analysis because of the benefit-per- ton methodology.

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6-12 Table 6-3: Summary of Reductions in Health Incidences from PM 2.5 Benefits for 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 effect s because the scientific eviden ce is not yet sufficient to allow differentiation of effects estimate s by particle type. Confidence interval s are unavailable for this analysis because of the benefit-per-ton methodology.

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6-13 Table 6-4: All PM 2.5 Benefits Estimates for the Petroleum Refineries NSPS Reconsideration at discount rates of 3% and 7% in 2017 (in millions of 2006$)*

3% 7% Benefit-per-ton Coefficients Deri ved from Epidemiology Literature Pope et al. $220 $190
Laden et al. $530 $480 Benefit-per-ton Coefficients D erived 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 significan t figures. These estimates do not include CO 2 benefits estimated at $49 million (3% discount rate). The benefits estimates fro m the Expert Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimate s associated with the concentration-response function. Confidence intervals are unavailabl e for this analysis because of the benefit-per-ton methodology

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6-14 Figure 6-2: Total 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 estimated at $49 million (3% discount rate).

6.2 Social Cost of Carbon and Greenhouse Gas Benefits
EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 ) emissions using recent estimates of the “social cost of carbon” (S CC). The SCC is an estimate of the monetized damages associated with an in cremental increase in carbon emi ssions in a given year. It is intended to include (but is not limited to) change s in net agricultural pr oductivity, human health, property damages from increased flood risk, and the valu e 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 en tities, 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 Vehi cle Greenhouse Gas Emission Sta ndards and Corporate Average Fuel Economy Standards; see the rule’s preamb le for discussion about application of SCC (75 $0 $100 $200 $300 $400 $500 $600 $700

Pope et al. Laden et al.Millions(2006$)

PM 2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR
7% DR

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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. 13

The interagency group selected four SCC values for use in regulatory analyses, which we have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO 2 emissions 14 in 2010, in 2007 dollars. The first three valu es are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, a nd 5 percent, respectivel y. SCCs at several discount rates are included because the literatu re 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 95th percen tile of the SCC from all three models at a 3 percent disc ount rate. It is included to repr esent higher-than-expected impacts from temperature change further out in the ta ils of the SCC distribution. Low probability, high impact events are incorporated in to 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 sens itivity 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 sy stems become more stressed in response to greater climatic change. Note th at the interagency group estimated the growth rate of the SCC directly using the three integr ated 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.

13 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advi sers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of 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
14 The interagency group decided that these estimates apply only to CO 2 emissions. Given that warming profiles and impacts other than temperature cha nge (e.g., ocean acidifica tion) 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 estimate s of the social costs of non-CO 2 gases” (SCC TSD, pg 13).

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6-16 Table 6-5. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2017 a

Discount Rate and Statistic SCC estimate (2006$)
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 specifi c. SCC values represent on ly a partial accounting of

climate impacts.

When attempting to assess the incremen tal 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 climat e system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.

The interagency group noted a number of limita tions to the SCC analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion and technological 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 th ese gaps and that the SCC estimates used for regulatory analysis by the Federal government w ill continue to evolve with improvements in modeling. Additional details on these limita tions are discussed in the SCC TSD.

In light of these limitations , the interagency group has co mmitted to updating the current estimates as the science and economic understa nding of climate change and its impacts on society improves over time. Specifically, the inte ragency 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.

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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 va lue of the climate-relate d benefits captured by the models for each analysis year. For internal consistency, the annual benefits are discounted back to 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%. 15 These estimates are provided in Table 6-6.

Table 6-6. Monetized SCC- Derived Benefits of CO 2 Emission Reductions in 2017 (millions of 2006$) a,b

Discount Rate and Statistic Benefits (millions of 2006$)

Tons of CO 2 2,021,067 5% (Average) $12 3% (Average) $49 2.5% (Average) $78 3% (95 th percentile) $149

a The SCC values are dollar-year and emissions-year specifi c. SCC values represent on ly a partial accounting of

climate impacts.

b The tonnes of CO 2 emission reductions in this table have been reduced from 2,168,419 tonnes to reflect the

anticipated emission increases a ssociated with the energy disbenefits described in section 6.3.
6.3 Energy Disbenefits
In this section, we provide an estimate of the energy disbenefits associated with the increased emissions from additional energy usage. 16 We estimate that electricity usage associated with operation of the control devices would increase by 158 GWh/yr nationwide. 17 This electricity usage is anticipated to increase emi ssions of pollutants from electric utility boilers (EGU boilers) that supply electricity to th e non-EGU boiler facilities by 147,352 tonnes CO 2 . In the calculation of CO 2 benefits, we have alr eady subtracted these CO 2 emission increases associated with increased electricity usage from the CO 2 emission reductions associated with the regulatory requirements in the be nefits shown in Table 6-6.

15 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.
16 As we use the term “energy disbenefits” in this analysis, we are not 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.
17 See Documentation of 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.

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6-18 6.4 Total Monetized Benefits

The total monetized benefits of this rule making include both the PM-related health benefits as well as the CO 2 -related climate benefits. We estimate the total monetized benefits to be $270 million to $580 million at a 3% discount rate and $240 million to $530 million at a 7%
discount rate in th e fifth year (2017). All estimates are in 2006$. Fi gure 6-3 provides a visual representation of the breakdow n of total monetized benefits by pollutant.

Figure 6-3: Breakdown of Monetized Benefi ts for Final Petroleum Refineries NSPS Reconsideration by Pollutant * This chart uses the results ba sed on Pope et al. (2002) and a 3% discount rate as an example.
6.5 Unquantified Benefits

The monetized benefits estimated in this RIA onl y reflect a subset of benefits attributable to the health effect reductions as sociated with ambient fine part icles. Data, time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the benefits from several 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 SO2 79% NOX 3% CO2 18%

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6-19 also contribute to adverse welfare effects, in cluding acidic deposition 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. Unfo rtunately, 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 availabl e. 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 betw een respiratory health effects and short-term exposure to SO 2 (U.S. EPA, 2008b). 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 bronchocon striction” (U.S. EPA, 2009b). Asthmatics are more sensitive to the effects of SO 2 likely resulting from preex isting inflammation associated with this disease. A clear concentration-re sponse relationship has b een demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and per centage of asthmatics adve rsely affected. Based on our review of this informati on, we identified four short-term morbidity endpoints that the SO 2 ISA identified as a “causal relationship”: asth ma exacerbation, respiratory-related emergency department visits, and respiratory-related hospita lizations. The differing evidence and associated strength of the evidence for these differen t effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the rela tionship 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 ad justment for pollutants.

Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment (ISA) for Nitrogen Dioxide conc luded that there is a likely causal relationship between respiratory he alth effects and short-term exposure to NO 2 (U.S. EPA, 2008c). Persons with pr eexisting 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

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6-20 respiratory-related hospitalizations. The differi ng 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 relations hip between short-term NO 2 exposure and premature mortality was “suggestive but not sufficient to infer a causal rela tionship” because it is difficult to attribute the mortality risk effects to NO 2 alone. Although the NO 2 ISA stated that studies consistently reported a relationship between NO 2 exposure and mortality, the e ffect was generally smaller than that for other pollutants such as PM.

SO 2 and NOx emissions also contribute to a vari ety of adverse welfare effects, including acidic deposition, visibility impair ment, 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 invertebrates in aquatic ecosystems, as 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 cu ltural services, incl uding aesthetic and educational services and recreat ional fishing. Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum to xicity, which can cause reduced root growth, which restricts the ability of the plant to take up water and nutrient s. These direct effects can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leadi ng to increased mortality of can opy trees. Terrestrial acidification affects several important ecological services, in cluding declines in hab itat for threatened and endangered species (cultural), d eclines 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 nutri ent enrichment can lead to eu trophication. Eutrophication of estuaries can disrupt an importa nt source of food production, part icularly fish and shellfish production, and a variety of cultural ecosystem se rvices, including water-based recreational and aesthetic services. Terre strial nutrient enrichment is associ ated with changes in the types and number of species and biodiversit y in terrestrial systems. Exce ssive nitrogen deposition upsets the balance between native and nonnative plants, changing the ability of an area to support biodiversity. When the composition of species ch anges, then fire frequency and intensity can also change, as nonnative grasses fuel more fre quent and more intense wildfires. (U.S. EPA, 2008d)

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6-21 Mercury is a highly neurotoxic contaminant th at 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 evid ence 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 wate rsheds where mercury is present, increased sulfate deposition very likely results in met hylmercury 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 de position and increased mercury methylation 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 part icles with significant lightextinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). These suspended particles a nd gases degrade visibi lity by scattering and absorbing light. Higher visibility impairment levels in the Ea st are due to generally higher concentrations of fine particles, particularly sulf ates, and higher average relative humidity levels. In fact, particulate sulfate is th e largest contributor to regional ha ze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). In th e western U.S., particul ate sulfate contributes to 20-50% of regional haze (U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of daily activities a nd their overall sense of wellbeing. Good visibility increases the quality of life where individuals liv e and work, and where they engage in recreational activities.
6.5.2 Ozone Benefits
In the presence of sunlight, NOx and VOC s can undergo a chemical reaction in the atmosphere to form ozone. Reducing ambien t ozone concentrations is associated with significant human health benefits, including mort ality and respiratory morbidity (U.S. EPA, 2008a). Epidemiological researchers have associat ed ozone exposure with adverse health effects in numerous toxicological, clinical and epidemio logical studies (U.S. EPA, 2006c). These health effects include respiratory morbidity such as fewe r 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 pa rticles and gases degrade visibility by scattering and absorb ing light. Higher visibility impa irment levels in the East are

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6-22 due to generally higher concentrations of fine pa rticles, particularly su lfates, 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 wh ere they engage in recreational activities. Previous analyses (U.S. EPA, 2006; U.S. EPA, 2011a; U.S. EPA, 2011b) show that visibility benefits are a significant welfare benefit cate gory. Without air quality modeling, we are unable to estimate visibility related benefits, nor are we able to determine whethe r VOC emission reductions would be likely to have a significant impact on vi sibility in urban ar eas or Class I areas.
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 econo mic benefits, including emission inventories, air quality models (with their associated paramete rs and inputs), epidemio logical estimates of concentration-response (C-R) f unctions, estimates of values , population estimates, income estimates, and estimates of the future state of the world (i.e., regula tions, technology, and human behavior). For some parameters or inputs it may be possible to provide a statistical representation of the underlying unc ertainty distribution. For othe r parameters or inputs, the necessary information is not avai lable. As discussed in the PM 2.5 NAAQS RIA (Table 5.5) (U.S. EPA, 2006), there are a variety of uncertainties associated with th ese PM benefits. Therefore, the estimates of annual benefits should be viewed as representative of the magnitude of benefits expected, rather than the actual bene fits that would o ccur every year.
It is important to note that the monetized benefit-per-ton estimates used here reflect specific geographic patterns of emissions redu ctions and specific air quality and benefits modeling assumptions. For example, these estimates do not reflect local va riability in population density, meteorology, exposure, baseline health inci dence rates, or other local factors. Use of these $/ton values to estimate benefits associat ed with different emissi on control programs (e.g., for reducing emissions from large stationary s ources like EGUs) may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurr ing in any specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average bene fits-per-ton over the entire Unite d States. The benefits-per-ton for emission reductions in specific locations may be very different than the estimates presented here.

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6-23 PM 2.5 mortality benefits are the largest benef it category that we monetized in this analysis. To better characterize the uncertainty associated with mort ality 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-specifi c air quality data is not avai lable due to time or resource limitations, thus we are unable to estimate the per centage 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 6-4 and 6-5), increasing our conf idence in the PM mortality analysis. Figure 6-4 shows a bar chart of the percentage of the population exposed to various air quality levels in the pre- and post-policy policy. Figure 6-5 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies. As the policy shifts the distribution of air quality leve ls, 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 em phasize that we have high confidence in PM 2.5 -related effects down to the lowest LML of the major c ohort 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 baselin e exposure occurs 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 consid eration 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 tota l benefits estimate.

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6-24 Figure 6-4. Percentage of Adult Population by Annual Mean PM 2.5 Exposure in the Baseline

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6-25 Figure 6-5. 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 l iterature and methods and suppor ted 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 populati on density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over-estimate or underestimate of the actual benefits of control ling directly emitted fine particulates.

2. We assume that all fine particles, rega rdless of their chemical composition, are equally potent in causing pr emature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ

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6-26 significantly from direct PM 2.5 released from diesel engines and other industrial sources, but no clear scien tific grounds exist for s upporting differential effects estimates by particle type.

3. We assume that the health impact functi on for fine particles is linear down to the lowest air quality levels modeled in this an alysis. 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 th e lowest modeled concentrations.

4. To characterize the uncertainty in the 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 di verse 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 with PM 2.5 benefits, please consult the PM 2.5 NAAQS RIA (Table 5.5).

This RIA does not include the type of detail ed uncertainty assessment found in the PM NAAQS RIA because we lack the necessary air quality input and monitoring data to run the benefits model. In addition, we have not c onducted 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 th e PM RIA because of the significant differences between the sources affected in that rule and th ose regulated here. Howe ver, 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 su rrounding the benefits results presented in this analysis.

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6-27 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 be nefits 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.

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and spatial analysis of the American Cancer Soc iety study linking particulate air pollution and mortality. HEI Research Report, 140, Hea lth 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 Dete rmines the Value of Life? A Meta-Analysis.” Journal of Policy Analysis and Management 21(2):253-270.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The Na tional 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. Washington, 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. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortal ity, 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 Ambien t Fine Particulate Matter in the U.S.” Environ. Sci. Technol ., 42(7):2268-2274.

Sisler, J.F. 1996. Spatial and seasonal patterns and long-term variability of the composition of the haze in the United States: an analy sis of data from the IMPROVE network . CIRA Report, ISSN 0737-5352-32, Colorado State University.

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6-28 U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC . September. Available on the Internet at < http://yosemite.epa.gov/ee/epa/eed.nsf/we bpages/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. 2008a. Regulatory Impact Analysis, 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 Assessment, Research Triangle Park, NC. Available at <http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=198843>.
U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Pa rk, NC. July. Available at at <http://cfpub.epa.gov/ncea/cfm/reco rdisplay.cfm?deid=194645>.
U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessment for Oxides of Nitrogen and Sulfur–Ecological Criteria National (Final Report). National Center for Environmental Assessment, Research Tr iangle Park, NC. EPA/600/R-08/139. December. Available at <http://cf pub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.
U.S. Environmental Protection Agency (U.S. EPA). 2009a . Regulatory Impact Analysis: National Emission 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/R IAs/portlandcementria_4-20-09.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for Particulate Matter (Final Report) . EPA-600-R-08-139F. National Center for Environmental Assessment – RTP Division. December. Available on the Internet at

< http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=216546 >.
U.S. Environmental Protection Agency (U.S. EPA). 2010a. Final Regulatory Impact Analysis (RIA) for the SO 2 National Ambient Air Q uality Standards (NAAQS). Office of Air Quality Planning and Standards, Research Tria ngle Park, NC. June. Available at <http://www.epa.gov/ttn/ecas/ria.html>.
U.S. Environmental Protection Agency (U.S. EPA). 2010b. Technical Support Document: Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response

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6-29 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). 2010c. Guidelines for Preparing Economic Analyses . EPA 240-R-10-001. 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.ns f/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 Radiat ion, Washington, DC. March. <http://www.epa.gov/air/sect812/feb11/fullreport.pdf>. Accessed March 30, 2011.

U.S. Environmental Protection Agency (U.S. EPA). 2011b. Regulatory Impact Analysis for the Final Transport Rule . Office of Air Quality Planning a nd Standards, Research Triangle Park, NC. June. Available at <http:// www.epa.gov/airtransport/pdfs/FinalRIA.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2012. 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 Standard s, Research Triangle Park, NC. January.
U.S. Environmental Protection Agency - Scie nce Advisory Board (U.S. EPA-SAB). 2007. SAB Advisory on EPA’s Issues in Valu ing Mortality Risk Reduction. EPA-SAB-08-001. October. Available on the Internet at

< http://yosemite.epa.gov/sab/sabpro duct.nsf/4128007E7876B8F0852573760058A978/$File/ sab-08-001.pdf >.
U.S. Environmental Protection Agency - Scienc e Advisory Board (U.S. EPA-SAB). 2009a. Review of EPA’s Integrated Science Assessm ent for Particulate Matter (First External Review Draft, December 2008). EPA-COUNCIL-09-008. May. Av ailable on the Internet at

< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73 ACCA834AB44A10852575BD0064346B/$File /EPA-CASAC-09-008-unsigned.pdf >.
U.S. Environmental Protection Agency - Scie nce Advisory Board (U.S. EPA-SAB). 2009b. Consultation on EPA’s Particulate Matter Na tional Ambient Air Q uality Standards: Scope and Methods Plan for Health Ri sk and Exposure Assessment. EPA-COUNCIL-09-009. May. Available on the Internet at

< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/72 3FE644C5D758DF852575BD00763A32/$F ile/EPA-CASAC-09-009-unsigned.pdf >.

U.S. Environmental Protection Agency - Sc ience 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-10-001 . June. Available on the Internet at <http://yosemite.epa.gov/sab/sabproduc t.nsf/0/72D4EFA39E48CDB28525774500738776/$F ile/EPA-COUNCIL-10-001-unsigned.pdf>.
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.

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6-1 7 COMPARISON OF BENEFITS AND COSTS

EPA estimates the range of benefits of th is reconsideration to be $270 million to $580 million at a 3% discount rate and $240 million to $530 million at a 7% discount rate in 2017. The annualized costs are a cost savings of $79 milli on at a 7% interest rate . Thus, net benefits are $340 million to $660 million at a 3% discount rate for the benefits and $320 million to $610 million at a 7% discount rate. All estimates are in 2006$.

Table 7-1 shows a summary of the monetized benefits, social cost s, and net benefits. Figure 7-1 shows the full range of net benefits estimates (i.e., be nefits minus annualized costs) utilizing the 14 different PM 2.5 mortality functions at the 3% di scount rate, and Figure 7-2 shows this information at a 7% discount ra te. EPA believes that the benef its of this rule are likely to exceed the costs by a substantial margin even when taking into account uncertainties in the cost and benefit estimates.
Table 7-1. Summary of the Monetized Benefits , Social Costs, and Net Benefits for the Final Petroleum Refineries NSPS in 2017 (millions of 2006$) 1

3% Discount Rate 7% Discount Rate

Total Monetized Benefits 2 $270 to $580 $240 to $530 Total Compliance Costs 3 -$79 -$79
Net Benefits $340 to $660 $320 to $610

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 effects, or visibility impairment. Huma n health benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume th at all fine particles, regardless of their chemical composition, are equally potent in causing premature mo rtality because the scientif ic 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 car bon (SCC) estimated at a 3 percent discount rate because the interagency workgroup deemed the SCC estimate at a 3 per cent discount rate to be the central value.
3 The engineering compliance costs are annua lized using a 7 percent discount rate.

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6-2 Figure 7-1. Net Benefits for Final Petroleu m Refineries NSPS R econsideration 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 combinations are treated as independent and equally probable. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced ha s a different propensity to become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. $0 $100 $200 $300 $400 $500 $600 $700 $800 $900

Pope et al. Laden et al.Millions(2006$)

Cost estimate combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions

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6-3 Figure 7-2. Net Benefits for Final Petroleu m Refineries NSPS R econsideration 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 combinations are treated as independent and equally probable. 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 become PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. $0 $100 $200 $300 $400 $500 $600 $700 $800

Pope et al. Laden et al.Millions(2006$)

Cost estimate combined with total monetized benefits estimates derived from 2 epidemiology functions and 12 expert functions

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A-1 APPENDIX A PARENT COMPANY INFORMATION FOR PETROLEUM REFINERIES

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A-2 Table A-1. Parent Company Informat ion 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 Ba y 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 Char les 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 139,0 00 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 185,0 00 ConocoPhillips 198,655.00 29,700 (continued)

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A-3 Table A-1. Parent Company Information fo r 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 Hold ings LLC Not Listed Not Listed

Delek Refining Ltd Tyler TX 60,000 Dele k 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 ExxonMob il 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 Industrie s 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 Housto n 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)

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A-4 Table A-1. Parent Company Information fo r Petroleum Refineries (continued)

Facility Name City State Capacity (bbl/cd) Parent Company Name Parent Company Sales Parent Company Employment Kern Oil & Refining Co. Bake rsfield CA 26,000 Kern Oi l & 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. 169.60 350 Little America Refining Co. Evansville WY 24,500 Sinc lair 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 Marat hon 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 105,0 00 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 Pasade na 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. 887.00 400 Petro Star Inc. Valdez AK 55,000 Petro Star Inc. 887.00 400 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)

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A-5 Table A-1. Parent Company Information fo r 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 Jo aquin 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)

### *** Draft – Do Not Cite, Quot e, or Release During Review ***

A-6 Table A-1. Parent Company Information fo r 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 146,0 00 ConocoPhillips 198,655.00 29,700 WRB Refining LLC Wood River IL 362,0 00 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 Ca rolina’s Moore School of Business Library. Million Dollar Online Data reflects e ither actual data for 2011 reported by companies or estimated data for 2011. Ward’s Business Directory of Pu blic 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 webs ites, 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 Je fferson Madison Regi onal Library, Charlottesville, Virginia. Copyright 2011 Experian Inform ation 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 Le xisNexis at University of Virginia’s Alderman Library. Oil & Gas Journal, December 6, 2010 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 number s.] <http://www.eia.gov/petro leum/refinerycapacity/United States Environmental Protection Agency Office of Air and Radiation Office of Air Quality Pl anning and Standards Research Triangle Park, NC December 2011