## November 2010



# Regulatory Impact Analysis: Proposed National Emission Standards for Hazardous Air Pollutants (NESHAP) for Mercury Emissions from Mercury Cell Chlor Alkali Plants

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Benefit and Cost Group (MD

- C439

- 02) Research Triangle Park, NC 27711

### iii CONTENTS

Section Page

Section 1 Introduction and Executive Summary

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

- 1

1.1 Executive Summary

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

- 1

1.2 Organization of this Report

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

- 4

Section 2 Industry Profile

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

- 1

2.1 Supply Side

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

- 1
2.1.1 Chlorine Production Process

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

- 1
2.1.2 Product Types

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

- 3
2.1.3 Materials and Costs of Production

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

- 3
2.1.4 Chlor

- alkali Transportation

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

- 5

2.2 The Demand Side

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

- 6
2.2.1 Who Uses Chlorine, Caustic Soda, and Caustic Potash?

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

- 6
2.2.2 What Factors Influence Demand?

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

- 9

2.3 Market Characteristics

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

- 10
2.3.1 Geography

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

- 10
2.3.2 Product Differentiation

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

- 10
2.3.3 Entry

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

- 11
2.3.4 Concentration

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

- 11

2.4 Overvi ew of Businesses Affected by the Rule

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

- 11
2.4.1 Mercury Cell Plants

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

- 11
2.4.2 Ultimate Parent Companies

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

- 13

Section 3 Engineering Cost Analysis

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

- 1

3.1 Capital Costs

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

- 2

3.2 Annual Costs

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

- 6
3.2.1 Capital Recovery Cost

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

- 7
3.2. 2 Annual Costs Associated with Storage of Unused Mercury

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

- 9

### iv 3.2.3 Costs Savings Associated with the Elimination of Mercury

- Related Environmental Compliance Costs

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

- 11
3.2.4 Energy Savings and Costs

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

- 14
3.2.5 Overall Annual Costs

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

- 16

3.3 Uncertainty Analysis

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

- 17

3.4 Summary of Results

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

- 19

Section 4 Economic Impact Analysis (EIA)

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

- 1

4.1 Conversion Versus Plant Closure

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

- 1

4.2 Market Concentration and Pricing Behavior

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

- 3

4.3 Impact on Employment

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

- 4

4.4 Transportation Issues Associated with Plant Closure

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

- 6

4.5 Small Entity Impact Analysis

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

- 6
4.5.1 Identify Affected Small Entities

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

- 7
4.5.2 Sales Test Analysis

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

- 7
4.5.3 Profit Test Analysis

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

- 8

4.6 Conclusions

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

- 8

4.7 References

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

- 10

Section 5 Human Health Benefits of Emissions Reductions

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

- 1

5.1 Synopsis

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

- 1

5.2 Mercury Benefits

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

- 1

5.3 Energy Savings Co

- benefits

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

- 4
5.3.1 PM 2.5 

### Co

- benefits

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

- 4
5.3.2 Social Cost of Carbon and Greenhouse Gas Co

- benefits

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

- 13
5.3.3 Total Monetized Co

- benefits of Energy Savings

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

- 16

5.4 Characterization of Uncertainty in the Mon etized PM 2.5 

### Co

- Benefits

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

- 16

5.6 References

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

- 22

Section 6

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

- 1

### v Comparison of Co

- Benef its and Costs

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

- 1

Appendixes

A: Mercury Emissions Memo

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

- 1

B: Emissions Reductions Associated with Reduced Mercury Cell Chlor

- alkali Plant Electricity Consumption

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

- 1

### vi LIST OF FIGURES

Number Page

2

- 1. Industry

- wide Uses of Chlorine by Industry

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

- 7 2

- 2. Use of Caustic Soda by Industry

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

- 7 2

- 3. Use of Caustic Potash by Industry

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

- 8 2

- 4. Income (or Loss) as Net a Percentage of Sales, Basic Chemicals, Resins, and Synthetics (NAICS 3251 and 3252): 2000

– 2005

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

- 14 3

- 1. Capital Cost Factor ($/ton Cl2 produced) vs. Capacity

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

- 5 4

- 1. Price Trends: Alkalis and Chlorine Manufacturing: 2000 to 2009

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

- 2 4

- 2. Unemployment Rates by County: 2000

– 2008

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

- 5 4

- 3. Unemployment Rate Differences from National Average: May 2009 and Historical Average: 2000

– 2008

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

- 6 4

- 4. Histor ical ECU Cash Margins

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

- 9 4

- 5. Profit Tests: ECU Cash Margin Reductions Associated with Conversion Costs

....... 4

- 10 5

- 1. Breakdown of Monetized PM 2.5 

### Health Co

- Benefits using Mortality Function from Pope et al. (2002) a



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

- 10 5

- 2. Total Monetized PM 2.5 

### Co

- Benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (Option 1)

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

- 13 5

- 3. Breakdown of Monetized Co

- benefits by Pollutant at 3% discount rate (Option 1)

.. 5

- 17 5

- 4. Percentage of Adult Population by Annual Mean PM 2.5 

### Exposure (pre

- and post

policy policy)

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

- 20 5

- 5. Cumulative Distribution of Adult Population at Annual Mean PM 2.5 

### levels (pre

and post

- policy policy)

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

- 20 6

- 1. Net Benefits for the Proposed Mercury Chlor Alkali NESHAP at 3% Discount Rate (Option 1) a



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

- 3
6

- 2. Net Benefits for the Proposed Mercur y Chlor Alkali NESHAP at 7% Discount Rate (Option 1) a



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

- 4

### vii LIST OF TABLES

Number Page

1

- 1. Summ ary of the Monetized Co

- Benefits, Social Costs, and Net benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (millions of 2007$) a



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

- 4 2

- 1. Comparison of the Processes

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

- 2 2

- 2. Approximate Energy Requirements of Different Cell Types

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

- 4 2

- 3. Olin Corporation Products and Services and Major End Uses: 2008

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

- 9 2

- 4. 2002 Economic Census Sales and Employment by Establishment Size

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

- 12 2

- 5. Estimated Plant

- Level Sales and Employment: 2008

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

- 12 2

- 6. Ultimate Parent Company Sales and Employment: 2008

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

- 13 3

- 1. Cost Factors for Conversion of Mercury Cell Chlor

- Alkali Facilities to Membrane Cell Technology ($/ton Cl2 produced)

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

- 4 3

- 2. Estimated Capital Costs for Existing Mercury Cell Chlor

- Alkali Facilities

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

- 6 3

- 3. Estimated Capital Recovery Costs for Varying Finance Periods (Using 7 percent interest rate)

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

- 8 3

- 4. Estimate of Annual Mercury Storage Costs

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

- 9 3

- 5. Environmental Compliance Cost Savings Factors from Elimination of the Mercury Process

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

- 13 3

- 6. Annual Cost Savings from Elimination of the Merc ury Processes, by Facility

......... 3

- 14 3

- 7. Energy Savings Associated with Conversion from the Mercury Cell to the Membrane Cell Process

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

- 16 3

- 8. Ranges of Individual Annual Cost/Savings Elements for the Conversion of Mercury Cells to Membrane Cells

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

- 17 3

- 9. EPA Estimates of the Annual Costs of Conversion of Mercury Cells to Membrane Cells

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

- 18 3

- 10.Results of the Monte Carlo Simulation Compared to the EPA Estimates

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

- 19 4

- 1. May 2009 County Employment Data (Not Seasonally Adjusted)

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

- 5 5

- 1. Human Health and Welfare Eff ects of PM 2.5


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

- 5 5

- 2. Summary of Monetized PM 2.5 

### Energy Co

- benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (2007$) ( Option 1)

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

- 11 5

- 3. Summary of Reductions in Health Incidences from PM 2.5 

### Co

- Benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (Option 1) a



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

- 11 5

- 4. All Monetized PM 2.5 

### Co

- Benefits Estimates for the Proposed Mercury Chlor Alkali NESHAP at discount rates of 3% and 7% in 2013 (in millions of 2007$) (Option 1) a



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

- 12 5

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

### ) f or 2013 a



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

- 15 5

- 6. Monetized Co

- benefits of CO 2 

### Emission Decreases in 2013 (Option 1) a



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

- 16

### viii 6

- 1. Summary of the Monetized Co

- Benefits, Social Costs, and Net benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (thousands of 2007$) a



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

- 2
B

- 1. Estimated Changes in Electricity Generation

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

- 2 B

- 2. Share of Electricity Generation by Fuel 2009

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

- 3
B

- 3. Power Company Emission Factors: Fuel=Coal (Short tons per kWh)

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

- 4
B

- 4. Power Company Emission Factors: Fuel=Natural Gas (Short tons per kWh)

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

- 4 B

- 5. Emission Factor Calculations: 2010

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

- 5
B

- 6. Emission Reductions by Plant (22 percent reduction)

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

- 6 B

- 7. Emission Estimates by Plant (37 percent reduction)

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

- 6

### 1

- 1 SECTION 1
INTRODUCTION AND EXECUTIVE SUMMARY

The U.S. Environmental Protection Agency (EPA) is proposing amendments to the national emission standards for hazardous air pollutants (NESHAP) for mercury emissions from mercury cell chlor

- alkali plants. On June 11, 2008, EPA proposed N ESHAP amendments in response to a petition for reconsideration filed by the Natural Resources Defense Council (NRDC); this action supplements the June 11, 2008, proposal. The proposed rule would amend the NESHAP to prohibit emissions of mercury from the me rcury cell process within 2 years from promulgation of the final rule and cease operation of mercury recovery units 3 years from promulgation of the final rule.

In 2003, when the maximum achievable control technology (MACT) was promulgated,
there were 12 mercury cell plants. EPA believes that by 2010 there will only be four plants still using the mercury cell process. The four plants using the mercury cell process are currently owned by three ultimate parent companies:

 

### ASHTA Chemicals, Inc.: one plant (Ohi o)

 

### Olin Corporation: two plants (Georgia and Tennessee)

 

### PPG Industries, Inc.: one plant (West Virginia)

ASHTA Chemicals is a small company with approximately 100 employees. Olin Corporation operates 10 North American chlor

- alkali plants; its other line of business is Winchester Ammunition. Currently, Olin employs over 3,600 people within both business segments. PPG’s commodity chemicals segment operates two chlor

- alkali plants but operates over 100 coatings and glass manufacturing facilities and employees o ver 40,000 people

company

- wide.

As part of the regulatory process of preparing these amendments, EPA is required to develop a regulatory impact analysis (RIA). This report documents the RIA methods and results.

1.1 Executive Summary
The key results of the RIA are as follows:

Engineering Cost Analysis: For Option 1 (Non

- mercury Technology Option), the engineering cost analysis estimates the total capital costs of conversion to be just under $300 million, which results in annual costs of $28 million per year annualized over 20 years. Individual plant

- level annualized capital conversion cost estimates

### 1

- 2 range from $3

– 15 million per year. All estimates are in 2007$. The annual cost to store mercury is approximately $250,000 per year. The individual plant

- level annual energy savings due to conversion may range from $1 million to $14 million per year depending on the energy consumption reduct ion level assumed. The total annual cost of conversion for all four facilities, considering the associated savings, is around $13 million per year. For Option 2 (Enhanced Work Practice Standards Option) the capital cost is $120 thousand and t he annual cost s are $25 thousand . Because of the small magnitude of Option 2 compared to Option 1, the market analysis only addresses Option 1.

 

### Plant Closure : EPA does not have sufficient data to predict whether individual companies would choose to convert or close dir ectly affected plants. In some cases, if a plant were to close, the closure may affect other closely related manufacturing operations. For example, ASHTA uses potassium hydroxide made from the mercury cell process to make potassium carbonate. If the A SHTA plant were to choose not to convert and close the mercury cell process, it is unclear whether existing technology and economics would allow the company to consider an alternative source of potassium hydroxide to continue making potassium carbonate. The eco nomics of making potassium carbonate could influence ASHTA ’s decision on how to respond to this rulemaking.

 

### Local Job Effects : Because EPA is unable to estimate plant closures, job losses associated with any closures are not estimated nor are any changes i n employment associated with conversion.

 

### Market Analysis: Of the three major chlor

- alkali products, the market for potassium hydroxide is the most highly concentrated. As of 2005, there were only four producers of potassium hydroxide (KOH) in the United St ates and approximately 50% is produced using mercury cells. A s a result, closure of one or more mercury cell plants could raise concerns about competitiveness and subsequent pricing behavior in the potassium hydroxide market. In highly concentrated markets , non

- mercury cell producers could have more scope to influence market prices above competitive levels by altering production decisions. EPA’s review of publicly available sources also suggests that closures would have similar market concentration conseque nces for a downstream commodity: potassium carbonate. Competitive issues have also been raised in other regulatory contexts; the Federal Trade Commission set conditions on a proposed asset purchase that reduced the number of competitors in the potassium hy droxide and potassium carbonate markets.

 

### Small Business Analysis: EPA performed an impact analysis that compares annual conversion costs to sales. For Option 1 (Non

- mercury Technology Option), EPA estimates that the one small entity’s cost

- to

- sales ratio (CSR) would range from 1 to 2%, which suggests a significant economic impact. The other three plants are owned by large ultimate parent companies with significant
company

- wide sales. As a result, the CSRs for the large ultimate parent companies are below 1%. In contrast, when plant

- only sales are considered, the

### 1

- 3 CSRs for plants owned by large ultimate parent companies range from 4 to 10%. EPA also considered industry profitability effects by comparing annual conversion costs to reported industry margins fo r a representative electrochemical unit (ECU). The analysis confirms the results of the sales tests; plant conversion costs will likely have an economically significant effect. In a typical year, conversion costs could reduce the margins by 10 to 20%. Howe ver, as explained in section 3, we do not consider this to be a significant impact on a substantial number of small businesses. For Option 2 (Enhanced Work Practice Standards Option) CSR for the only plant estimated to incur costs is less than 0.1 % and is not a significant economic impact.

 

### Benefits Analysi s: In the year of full implementation (2013), EPA estimates the monetized energy c o

- benefits of Option 1 are $22 million to $43 million and $ 21 million to $ 40 million, at 3% and 7% discount rates, respectively. All estimates are in 2007$ for the year 2013. Using alternate relationships between PM 2. 5 

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

- based estimates fall between these estimates. There are no emission reductions for Option 2.

 

### Net Benefits: The net benefits for Option 1 are $ 9 million to $30 million and $ 8 million to $2 7 million, at 3% and 7% discount rates, respectively (Table 1

- 1). The net benefits for Option 2 are $

- 2 5 ,000

. All estimates are in 2007$ for the year 2013.

### 1

- 4 Table 1

- 1. Summary of the Monetized Co

- Benefits, Social Costs, and Net benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 ( thousands of 2007$) a

3% Discount Rate 7% Discount Rate
Option 1 : Non

- mercury Technology Option

Total Monetized Benefits b $22 ,000 to $43 ,000 $ 21,000 to $ 40,000

Total Social Costs c $13 ,000 $13 ,000

Net Benefits $ 9,000 to $30 ,000 $ 8,000 to $2 7,000

Non

- monetized Benefits 656 pounds of mercury (including energy co

- benefits)

Health effects from NO 2 and SO 2 exposure Ecosystem effects
Visibility impairment
Option 2: Enhanced Work Practice Standards

Total Monetized Benefits b $0 $0

Total Social Costs c $25 $25

Net Benefits $

- 25 $

- 25

a All estimates are for the implementation year (2013), and are rounded to two significant figures.
b The total monetized co

- benefits reflect the human health co

- benefits associated with reducing exposure to PM 2.5 . It

is important to note that the monetized co

- benefits include many but not all health effects associated with PM 2.5 exposure. It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because there is no clear scientific evidence that would suppo rt the development of differential effects estimates by particle type. CO 2 - related benefits were calculated using the social cost of carbon (SCC) , which is discussed in Section 5 . The net present value of reduced CO 2 emissions is calculated differently tha n other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. This table shows monetized CO 2
co

- benefits at discount rates of 3 and 7 percent that were calculated using the global average SCC estimate at a 3% discount rate because the interagency workgroup on this topic deemed this marginal value to be the central value. In Section 5 , we also provide the monetized CO 2 co

- benefits using discount rates of 5 percent (average), 2.5 percent (average), and 3 percent (95th percentile)

. c The annual compliances costs serve as a proxy for the annual social costs of this rule. The compliance cost of a

regulation is generally a sl ight overestimate of social cost for competitive markets. The size of the overestimate is roughly one half the price change times the quantity change. The markets for chlorine and sodium hydroxide are expected to be competitive with the market share provid ed by the affected facilities quite small. However, the market share for potassium hydroxide is large and the market may not be competitive. In this case, social cost might be larger than compliance cost. Because insufficient data was available to estimate price and quantity changes the compliance cost was used as a rough estimate of the social cost.



### 1.2 Organization of this Report
The remainder of this report supports and details the methodology and the results of the
RIA:

 

### Section 2 presents a profile of t he affected industry and focuses on plants affected by the proposed rule. Although extensive individual plant information would help

### 1

- 5 assess the economic impacts of the rule, in most cases, detailed information is not publicly available. As a result, we fre quently rely on parent company information provided in company annual reports (e.g., form 10

- K), local press and industry trade publications, and company Web sites.

 

### Section 3 describes the engineering cost analysis.

 

### Section 4 describes the economic impact analysis and small business impact analysis.

 

### Section 5 presents the benefits estimates.

### 2

- 1 SECTION 2
INDUSTRY PROFILE

There are four mercury cell chlor

- alkali plants currently operating in the United States in 2010 that have the combined capacities to produce approximately 500,000 tons of chlorine per year. These plants are Olin Corporation in Augusta, Georgia (Olin GA); Olin Corporation in Charleston, Tennessee (Olin TN); PPG Industries in New Martinsville, West Virginia (PPG); and ASHTA Chemicals in Ashtabula, Ohio (ASHTA).

EPA has developed an industry profile to provide the reader a general understanding of
the techni cal and economic aspects of the industry. We outline processes and costs of getting chlorine products to market; identify key uses, consumers, and market characteristics; and provide economic data for the plants and owning companies affected by the propose d rule. Although we prefer to have extensive individual plant information, in most cases this information is not publicly available. As a result, we frequently rely on parent company information provided in company annual reports (e.g., form 10

- K), local p ress and industry trade publications, and company websites.

2.1 Supply Side

2.1.1 Chlorine Production Process
Although chlorine can be generated as a by

- product in a variety of chemical reactions, brine electrolysis accounts for 95% of total world chlorine production (World Chlorine Council, 2002). Through a chemical reaction catalyzed by an electric current in a series of electrolytic cells, a salt

- based solution containing either sodium or potassium chloride yields elemental chlorine gas, a caustic soluti on, and hydrogen gas in fixed proportions (“Special Focus on Chlorine,” 2007). The electrolysis occurs when electric current flows between the negatively charged cathodes and the anodes, which are the positive electrodes from which the chlorine is
collecte d (World Chlorine Council, 2002).

Three types of electrolytic cells can host this reaction: the diaphragm cell, the membrane cell, or the mercury cell. Although the three cell types share the basic chemical production formula, they are differentiated by th e way in which the chlorine is kept separate from the co

products generated in the cathode in the chemical process (“Special Focus on Chlorine,” 2007). The predominant manufacturing process in North America involves diaphragm cell technology,
which is char acterized by the asbestos

- based diaphragm structure that keeps the cathodes separate from the anodes. The membrane cell is structurally similar, though it uses an ion

-

### 2

- 2 exchange membrane to separate the anodes and chlorine gas from the caustic product. Mercu ry cell technology uses two distinct cells and functions through a sodium

- mercury stream that forms an amalgam and serves as the cathode (“Special Focus on Chlorine,” 2007).

Of the three electrolytic processes, the diaphragm and membrane processes are the most similar (Table 2

- 1). Both share the advantage of lower electricity consumption, but the unique
ion

- exchange structure enables membrane cells to produce a purer caustic product than the
asbestos

- based diaphragm. Another disadvantage of the diaphragm ce ll is the caustic limitations; although mercury and membrane cells can use sodium or potassium chloride for brine solution, diaphragm cells require sodium chloride. Mercury cells produce a high

- quality caustic solution and chlorine gas, but emit harmful me rcury pollutants into the environment. New plant construction has favored membrane cell construction because of low capital investment and operating costs relative to diaphragm and mercury processes (“Special Focus on Chlorine,” 2007).

Table 2

- 1. Compariso n of the Processes

Cell Type
Diaphragm Mercury Membrane
Separation Technique Asbestos

- based diaphragm Sodium

- mercury amalgam Ion

- exchange membrane
Caustic Capabilities Sodium hydroxide Sodium hydroxide, potassium hydroxide Sodium hydroxide, potassium hydroxide
Advantages • Use of well brine (no solid salt needed)

• Low electricity

requirements

• Simplest brine system • High purity caustic, chlorine, hydrogen

• Simple purification of

brine • Low energy consumption

• Low capital investment

• Easily operable cells

• High purity caustic

• Immune from variations

in cell load and shutdown
Disadvantages • Asbestos use

• High steam requirements

for caustic concentration • Mercury emissions and related environmental and regulatory costs

• Solid salt requirement

• Expensive cell

operations

• Large physical floor

space requirement • Solid salt requirement

• High purity brine

requirements

• Expensive membrane

• High oxygen

contamination in chlorine product

Source: “Special Focus on Chlorine.” October 2007. Chemical Business . pp. 27

- 40. Accessed July 29, 2009, from

Business Source Corporate database.

### 2

- 3 2.1.2 Product Types

2.1.2.1 Chlorine
Chlorine takes form in a greenish

- yellow poisonous gas at STP. It has a pungent odor and is 2.48 times denser than air. When liquefied, it becomes a yellow mobile fluid with low electrical conductivity. As a solid, chlorine retains the yellow color in the form of rhombic crystals. Chlorine is soluble in cold water and in salt solutions, although the solubility decreases with salt strength and temperature. Chlorine is sto red and transported as a liquefied gas (“Special Focus on Chlorine,” 2007).

2.1.2.2 Caustic Products: Sodium Hydroxide and Potassium Hydroxide
Sodium hydroxide, commonly referred to as caustic soda, is produced as a co

- product with chlorine and usually sol d in the form of a 50% solution. All three cell types can use sodium chloride brine in the electrolysis process to derive sodium hydroxide, but the variations between the cell structures require different degrees of evaporation and purification to reach th e 50% concentration. Only mercury cells generate the 50% liquid caustic solution directly; diaphragm cells yield a solution of 12% that is processed through filters and evaporators to reach the acceptable grade, while membrane cells produce a 30% liquid th at is evaporated to the 50% level ( Occidental Chemical Corporation , 2008b).

While most chlor

- alkali production involves sodium chloride brine, potassium hydroxide or caustic potash is the caustic solution produced when potassium chloride is used as a feeds tock in the brine electrolysis process (Sim, 2005). Membrane and mercury cell electrolysis can produce a caustic potash solution that is evaporated to 45% or 50% concentration for sale. The solution can also be concentrated to dry form and sold as flakes o r crystals ( Occidental Chemical Corporation, 2008a).

2.1.3 Materials and Costs of Production
Energy, salt brine, and raw materials represent the majority of the production costs in the
chlor

- alkali industry. The recent U.S. Census Bureau’s 2007 Economic Ce nsus provides industry

- level expenditures on production inputs for North American Industry Classification System (NAICS) code 325181 (Alkalis and Chlorine Manufacturers). The cost of materials (including electricity and purchased fuels) was 62.7% of the to tal production costs and nearly half of the industry’s total value of shipments. Electricity and fuels made up over 31% of total production costs. Labor expenses account for around 12.5% of the total reported costs in the industry.

### 2

- 4 Although the census dat a provide insight into the chlor

- alkali industry cost structure on the aggregate level, there is variation between companies in the industry because of size, structure, and technology. Olin Corporation, (2008) operates a number of chlor

- alkali facilities

a cross the country and cites electricity and salt as the largest share of raw material expenditures, which represent 55% of total production costs for the company. Other principle materials include potassium chloride, sulfur dioxide, and hydrogen (Olin Corp oration, 2008).

Although energy costs are important for all producers, each cell type requires a different combination of energy inputs. For example, membrane cell technology uses 25% less electricity than mercury cells but requires steam to concentrate t he caustic product that mercury cells do not (“Special Focus on Chlorine,” 2007). Table 2

- 2 illustrates the approximate energy requirements of the different cell types as well as the average electricity prices for the states in which the relevant plants ar e located.

Table 2

- 2. Approximate Energy Requirements of Different Cell Types

Cell Type
Diaphragm Mercury Membrane Electricity (kWh) 2,800

– 3,000 3,200

– 3,600 2,600

– 2,800 Steam equivalent (kWh) 800

– 1,000 0 100

– 200

Total kWh 3,600

– 4,000 3,200

– 3,600 2,700

– 3,000 Relative energy 100 92 75
Average Electricity Price for Industrial Sector (2007 cents/kWh) Georgia (Olin GA) 5.53
Ohio (ASHTA) 5.76
Tennessee (Olin TN) 5.19 West Virginia (PPG) 3.95

Sources: “Special Focus on Chlorine.” October 2007. Chemical Business . pp. 27

- 40. Accessed July 29, 2009, from

Business Source Corporate database. Energy Information Administration. 2009. “Electric Power Annual 2007: State Data Tables.” http://www.eia.doe.gov/cneaf/electricity/epa/epa_sprdshts.html. Accesse d July 31, 2009.



### Individual facility capital costs are also contingent on the specific requirements of the electrolytic cells used in the production process. For example, the complex mercury cell has a higher current density than the diaphragm or membrane and demands a rel atively higher capital investment. The variations in purification requirements impose the highest brine costs for membrane cell plants, whereas the relatively simple brine system accounts for only 3 to 4% of a diaphragm cell facility’s capital costs (“Spec ial Focus on Chlorine,” 2007). A mid

- sized mercury

### 2

- 5 cell plant not only bears the additional costs of the mercury itself, but also spends 10 to 15% of the total capital investment on equipment to prevent the hazardous emissions and to remove the mercury fro m the finished products (“Special Focus on Chlorine,” 2007).

2.1.4 Chlor

- alkali Transportation
A critical element in any production industry is the successful transfer of final products from the facility to the client. However, the hazardous nature of chem ical products requires
chlor

- alkali companies to devote extra resources to developing a geographic network of facilities and shipping points that integrates both cost considerations and safety concerns. Industry experts are acutely aware of the risks assoc iated with chemical production and strive to maintain security of their products, the safety of their employees and consumers, and the well

- being of local
communities.

In addition to complying with carefully developed industry standards and stewardship

pr actices, chlor

- alkali facilities are also subject to federal regulations regarding the proper handling of dangerous chemicals. The U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA) regulates the transportati on of chlorine and its derivatives under the Hazardous Material Regulations (49 CFR 171

- 180) (U.S. Department of Transportation, 2009a and 2009b). The PHMSA and other federal regulatory agencies control the movement of hazardous materials by all applicable modes of transportation. Chlorine and its derivatives are shipped via rail, highway, water vessel, and pipeline. According to the American Chemistry Council, rail hosts the majority of bulk chlorine shipments in the United States (30,000 tank car/yr) and is the safest mode of chlorine transportation (American Chemistry Council, 2009). Although accidents are rare, a 2005 Norfolk Southern freight train collision in South Carolina that killed or injured over 200 people illustrates real risks that chlorine tra nsportation poses to civilians and communities (Environmental News Service, 2005). Even though industry experts work in conjunction with transportation authorities to ensure that procedures and policies are carefully executed, the risk of an accident can n ever be completely eliminated.

One way to mitigate the risk of an accident is to minimize the distance from the producer
to the end

- user, which effectively curtails the potential for unexpected events or hazardous leaks during transit. Cutting down on tra vel distance and time is not only cost

- effective for producers, but it also prevents unnecessary exposure of civilian communities to the dangers associated with a collision. Familiarity with travel routes and transportation contractors may also aid chlorin e producers in reducing the potential for unforeseen circumstances that could threaten the security of the cargo.

### 2

- 6 In light of these issues, companies in the chlor

- alkali industry have selected existing facility locations based on the financial, logistic, a nd safety concerns involved with shipping their products. Plants, warehouses, and distribution centers are deliberately constructed in specific locations to serve customers safely and efficiently. ASHTA’s production facility is located adjacent to the tita nium dioxide plant to which it sells 100% of its chlorine (U.S. House of Representatives Committee on Commerce and Energy, 2009). The company also has warehouses, liquid distribution points, and sales headquarters placed strategically across the
United Sta tes to cater to nationwide potassium hydroxide demand (ASHTA, 2009a). PPG pioneered 1,100

- ton capacity barges as a safe mode of chlorine transport from the Natrium plant, which is ideally situated on the Ohio River easily accessible by the CSX rail line (P PG, 2009a). Olin Corporation’s Charleston plant produces potassium hydroxide and uses rail, barge and trucks to ship the product to customers and the terminal facility located in nearby Chattanooga (Olin Corporation, 2009c).

2.2 The Demand Side
A variety of applications use chlor

- alkali products to make other goods. As a result, the
demand for chlor

- alkali products indirectly depends on conditions in downstream end

- use markets. The organic chemicals industry uses chlorine to make polyvinyl chloride (PVC), and the construction industry subsequently uses PVC materials. The pulp and paper industry uses caustic soda during manufacturing processes that create paper and other products. Electronic products (e.g., televisions and computer monitors) requiring glass cathode ray tubes use potassium carbonate made from caustic potash. As a result of these links, growth or downturns in
end

- use markets ultimately influence the demand for chlor

- alkali products.

2.2.1 Who Uses Chlorine, Caustic Soda, and Caustic Potash?

Pla nt

- specific information regarding end

- users and other within/between plant relationships is not generally available or is limited. According to company annual reports (Olin Corporation, 2009a), industry

- wide data show that the organic chemical industry use s over 60% of chlorine (Figure 2

- 1). Vinyl chemical producers are industrial consumers (37%), and other organic chemical producers use an additional 28%. For caustic soda (Figure 2

- 2), organic chemicals, pulp and paper, and inorganic chemicals are signific ant end users; each industry accounts for more than 20% of caustic soda demand. For caustic potash (Figure 2

- 3), potassium carbonate (28%), other potassium chemicals (15%), and potassium phosphates (10%) are the most significant end uses (The Innovation Gr oup [TIG], 2009). The remaining applications are
diverse and all account for less than 10% of the market.

### 2

- 7 Figure 2

- 1. Industry

- wide Uses of Chlorine by Industry
Source: Olin Corporation, 2009a.



### Figure 2

- 2. Use of Caustic Soda by Industry

Source: Olin Corporation, 2009a. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Vinyls Organics Chorinated intermediates Inorganics Water
treatment Pulp and
paper Distribution and otherShare

Uses

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

Pulp and
paper Alumina Organics Detergents and Textiles Inorganics Water Treatment OthersShare

Uses

### 2

- 8 Figure 2

- 3. Use of Caustic Potash by Industry
Source: The Innovation Group (TIG). 2009. “Caustic Potash Chemical Profile.” http://www.the

- innovation

group.com/welcome.htm. Accessed July 2009.



### Olin Corporation (2008) also reports detai ls regarding company

- specific end uses and
customers for their chlor

- alkali products in the company’s annual reports (Table 2

- 3). This information applies to all 10 North American chlor

- alkali plants and may not necessarily be representative of the end

- use rs associated with the mercury cell plant. However, it is the best publicly available information EPA was able to identify. Olin sells a majority of its chlor

- alkali products to third parties; in 2008, no single customer bought more than 8% of total sales (Olin Corporation, 2008). Chlorine is also sold to a relatively small number of industrial customers; In contrast, caustic soda is sold to a large number of users or distributors (Olin Corporation, 2008).

Trade publications report that ASHTA supplies chlo rine to a nearby titanium dioxide facility originally owned by Lyondell Chemicals (Sim, 1995); the plant is now owned by Cristal’s Millennium Inorganic Chemicals business and continues to produce titanium dioxide (Millennium Inorganic Chemicals, 2009). A S H TA reports that all of its chlorine is sold to “a company located adjacent to ASHTA’s facility” (U.S. House of Representatives Committee on Commerce and Energy, 2009). Some of ASHTA’s other alkali products are used within the 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Share

Uses

### 2

- 9 Table 2

- 3. Olin Corporation P roducts and Services and Major End Uses: 2008

Products and Services Major End Uses Location
Chlorine/sodium hydroxide (caustic soda) Pulp and paper processing, chemical manufacturing, water purification, manufacture of vinyl chloride, bleach, swimming pool chemicals, and urethane chemicals Augusta, GA a Becancour, Quebec Charleston, TN a Henderson, NV McIntosh, AL Niagara Falls, NY St. Gabriel, LA
Potassium hydroxide (caustic potash) Fertilizer manufacturing, soaps, detergents and cleaners, battery manufacturing, food processing chemicals, and deicers Charleston, TN a

a Denotes plant using mercury technology.



### company to produce other chemicals. For example, liquid potassium hydroxide is used to make potassium carbonate. ASHTA (2009a) also reports that it is a manufacturer and marketer in the following chemical markets:

 

### 45% and 50% liquid potassium hydroxide

 

### anhydrous potassium carbonate K 2 

### CO 3

 

### anhydrous potassium carbonate flake

 

### chlorine

 

### 47% liquid potassium carbonate

 

### anhydrous potassium hydroxide walnut (briquette)

 

### potassium hydroxide

 

### chloropicrin

2.2.2 What Factors Influence Demand?

The chlor

- alkali industry is consid ered a procyclical industry where demand for product depends on trends in overall economic activity. For example, Olin Corporation (2008) notes:

The business of most of our customers, particularly our vinyl, urethanes, and pulp and paper customers are, to varying degrees, cyclical and have historically experienced periodic downturns. These economic and industry downturns have been characterized by diminished product demand, excess manufacturing capacity and, in some cases, lower average selling prices. Ther efore, any significant downturn in our customers’ businesses or in global economic conditions could

### 2

- 10 result in a reduction in demand for our products and could adversely affect our results of operations or financial condition. (p. 8)

Recent research has als o suggested that environmental regulations affecting chlorine end

use markets have reduced the demand for chlorine and help explain the exit of selected chlorine plants (Snyder, Miller, and Stavins, 2003). Chlorine plants that were co

- located with pulp and paper mills and plants affected by the Montreal Protocol were more likely to shutdown (11 to 15% higher than the average chlorine plant) (Snyder, Miller, and Stavins, 2003).

In addition to overall demand conditions, selected chlor

- alkali product markets a re very sensitive to price changes of close substitute products. For example, when the price of caustic soda rises, soda ash can be substituted for caustic soda in certain uses (e.g., pulp and paper, water treatment, and certain chemical sectors) (U.S. Geo logical Survey [USGS], 2009).

2.3 Market Characteristics
A review and description of market characteristics (i.e., geography, product differentiation, entry barriers, and degree of concentration) can enhance our understanding of
how chlor

- alkali markets op erate. These characteristics provide indicators of a firm’s ability to influence market prices by varying the quantity of product it sells. For example, in markets with large numbers of sellers and identical products, firms are unlikely to be able to influ ence market prices via their production decisions (i.e., they are “price takers”). However, in markets with few firms, significant barriers to entry (e.g., licenses, legal restrictions, or high fixed costs), or products that are similar but can be differen tiated, a firm may have some degree of market power (i.e., set or significantly influence market prices).

2.3.1 Geography
Most chlorine is not sold on the market and is consumed internally by the manufacturer. However, marketed chlorine and potassium hydro xide compete in a North American market; imports are limited by transportation costs and customer requirements for service and product availability (Olin Corporation, 2008; U.S. Federal Trade Commission [FTC], 2005). In contrast, caustic soda producers sel l their product in a worldwide market (Olin Corporation, 2008).

2.3.2 Product Differentiation
Product differentiation can occur both from differences in product attributes and quality and from brand name recognition of products. For a given end use, indust rial consumers are likely to view chlor

- alkali products produced by different firms as very good substitutes. As a result, the products are likely to be sold primarily on a price basis (Olin Corporation, 2008). In

### 2

- 11 addition, a close substitute for caustic s oda exists (soda ash) and limits the ability of caustic soda producers to significantly influence caustic soda prices by varying the quantities produced (USGS, 2009).

2.3.3 Entry

Short

- term entry into the chlor

- alkali market is difficult because new plant s require significant capital expenditures and take time to build. For example, the FTC concluded that new entry in the potassium hydroxide market would not be “timely, likely, or sufficient” in response to significant price increases (FTC, 2005, p. 5). Hi storically, imports have only played a major competitive role in the caustic soda markets only.

2.3.4 Concentration

Of the three chlor

- alkali products, the market for potassium hydroxide is the most highly concentrated. As of 2005, there were only four pr oducers of potassium hydroxide in the United States (FTC, 2005b). Approximately 50% is produced using mercury cells; ASHTA Chemicals reports a market share of approximately 15% (U.S. House of Representatives Committee on Commerce and Energy, 2009). The FTC (2005a, b) has also assessed competitive effects of proposed company acquisitions in this sector in some detail. Given these characteristics and conclusions of the FTC study, significant changes in production levels at a single company using mercury cells are likely to influence market prices, particularly over short periods.

2.4 Overview of Businesses Affected by the Rule
The proposed rule affects four mercury cell plants operating in the United States. We provide basic economic statistics for each plant and the ultimate parent companies that own each plant. These data are subsequently used for the economic impact analysis described in Section 3.

2.4.1 Mercury Cell Plants
The four mercury cell plants are located in four different states: Georgia, Ohio,
Te nnessee, and West Virginia. Annual plant sales ranges were estimated using economic data from the 2002 Economic Census (Table 2

- 4) and employment data. Given the uncertainties with sales data, we report a low and high range estimate for each plant. Lower

- b ound sales were obtained by multiplying the average value of shipments per employee by the number of employees. The upper

- bound sales represent the average establishment value of shipments for the appropriate establishment size. In cases where census data were not disclosed, we used the industry average. As shown, annual plant sales estimates range from $30 to $130 million (Table 2

- 5).

### 2

- 12 Table 2

- 4. 2002 Economic Census Sales and Employment by Establishment Size

Plant Establishments Employees Value of Shipments ($Million) $ per Employee $ per Establishment

($ Million)

All establishments 40 6,022 $2,702 $448,679 $68
1 to 4 employees 7 19 $8 $411,842 $1
5 to 9 employees 2
10 to 19 employees 4 52 $19 $358,231 $5
20 to 49 employees 7 246 $105 $428,415 $15
50 to 99 employees 1
100 to 249 employees 12 1,901 $980 $515,679 $82
250 to 499 employees 5 1,571 $662 $421,701 $132

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31:

Manufacturing: Industry Series: Industry Statistics by Employment Size: 2002.” <http://factfinder.census.gov>; accessed July, 2009.



### Table 2

- 5. Estimated Plant

- Level Sales and Employment: 2008

Plant State Owned by Small Entity Sales Range ($ million) Employment Employment Source

ASHTA OH Yes $40

– $70 ~100 U.S. House of Representatives Committee on Commerce and Energy, 2009

Olin GA No $40

– $70 85 Pavey, 2008

Olin TN No $120

– $130 280 Cleveland/Bradley Chamber of Commerce, Economic Development Council, 2009

PPG WV No $30

– $70 540 a West Virginia Development Office, 2009

a Ward (2007) suggests that 60 employees are associated with mercury cell operations.

### 2

- 13 2.4.2 Ultimate Parent Companies
EPA has reviewed industry information and publicly available sales and employment databases to identify the chain of ownership by accounting for subsidiaries, divisions, and joint ventures to appropriately group companies by size. Table 2

- 6 provides sales and employment data for three ultimate parent companies operating mercury plants. EPA classified each parent company as a small or large entity using Small Business Administration (SBA) general size standard definitions for NAICS codes. These size standards are presented either by number of employees or by annual receipt levels, dependi ng on the NAICS code. The chlor

- alkali companies are covered by NAICS code 325181 and are categorized as small if the total number of employees at the ultimate parent company is fewer than 500; otherwise, the ultimate parent company is classified as large. As shown in Table 2

- 6 ASHTA Inc. is considered a small
business.

Table 2

- 6. Ultimate Parent Company Sales and Employment: 2008

Plant State Parent Company Owned by Small Entity Sales Employment

ASHTA OH ASHTA Chemicals Inc. Yes <$75 million <100

Olin GA, TN Olin Corporation No $1.8 billion 3,600

PPG WV PPG Industries Inc. No $15.8 billion 44,900

Sources: Olin Corporation. December 31, 2008. Form 10

- K. Filed February 25, 2009. http://b2i.api.edgar

online.com/EFX_dll/EdgarPro.dll?FetchFilingConvPDF1?SessionID=DOVFWfuqOuZBE

- 9&ID=6436012. Accessed July 2009. PPG Industries, Inc. December 31, 2008. For m 10

- K. Filed February 19, 2009. http://corporateportal.ppg.com/ NR/rdonlyres/EAC49DFC

- DB9C

- 4320

- B9A3

- 421E40162220/0/10KYREND2008.pdf. Accessed July 2009.



### 2.4.2.1 Company Profitability Measures
Company profitability trends significantly influence the abil ity and desire to take on large

- scale capital projects. Although data on profits for individual companies are limited, historical government statistics about industry groups and publicly traded companies provide accounting measures of profit in annual repo rts. In Figure 2

- 4, we present information from the U.S. Census Quarterly Manufacturing Reports about the Basic Chemicals, Resins, and Synthetics (NAICS 3251 and 3252) (U.S. Census Bureau, 2000

– 2006). This is the finest level of detail reported by the Cens us and includes other chemical industries in addition to the chlor

- alkali industry. As shown, there is substantial variation in the profitability rates for the industry; before

tax profits as a percentage of net sales range from 1 to 10% during this period , with an average of
5% and a standard deviation of 3%.

### 2

- 14 Figure 2

- 4. Income (or Loss) as Net a Percentage of Sales, Basic Chemicals, Resins, and Synthetics (NAICS 3251 and 3252): 2000

– 2005

Source: U.S. Census Bureau. 2001

– 2006. Quarterly Financial Report for Manufacturing, Mining, and Trade

Corporations, 1st Quarter reports. http://www.census.gov/prod/www/abs/qfr

- mm.html. Accessed July 2009.



### Census data also show that smaller companies (defined by Census as companies with less the $25 million in assets) ha ve profit rates that are smaller than industry averages. For example, in a representative year during this period (2000), before

- tax profits as a percentage of net sales for small companies were less than 1% compared to approximately 6% for the industry as a whole.

To assess how representative the census data were for individual companies, EPA examined annual reports for Olin Corporation and PPG Industries and found before

- tax profit rates were comparable with quarterly financial report (QFR) 2000 industry averages. Olin reported a rate of approximately 8% and PPG reported a rate of 11% (Olin Corporation, 2001; PPG, 2001). These values are based on income statements and include all operations in addition
to the chlor

- alkali segment. Olin reported similar pro fit rates for its chlor

- alkali segment (~7%), but PPG did not report separate profitability information for their chemical commodity segment. 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005Income (or loss) (percent of net sales)

Year

After Tax Before Tax

### 3

- 1 SECTION 3
ENGINEERING COST ANA LYSIS

Most of this section addresses the cost a nalysis for For Option 1 (Non

- mercury Technology Option) . The other option In order to estimate the capital and annual costs associated with a conversion from mercury cell chlor

- alkali technology to membrane cell technology, information was used from the following resources:

 

### EPA’s Mercury Report to Congress 1

 

### Recent news reports regarding plants converting or considering converting 2

, 3

, 4

, 5

, 6
, 7
, 8

 

### EPA waste minimization study 9

 

### Fact sheet from the conversion of a Canadian facility 10

 

### General chlor

- alkali reference 11

1 U.S. EPA. Mercury Study Report to Congress Volume VIII: An Evaluation of Mercury Control Technologies and Costs. U.S. Environmental Protection Agency, Office of Air Quality Planning and Stan dards, Research Triangle Park, NC. December 1997.

2 Bradbury, D. Portrait of a Polluter

. Portland Press Herald. 1997.

3 Markets

— News Release. Houston Chronicle. January 30, 2007.

4 McCoy, M. PPG to Convert Chlorine Cells

. Chemical and Engineering News. Au gust 8, 2005.

5 Wise, A. Port Edwards sells School Forest Land to ERCO Worldwide; Nekoosa Considers Similar Deal. Wisconsin Rapids Tribune. January 13, 2009. Available on the Internet at: www.wisconsinrapidstribune.com

. 6 Westlake Chemical Corporation (2003, December 31). Form 10

- K, United States Securities and Exchange Commission. Available on the Internet at http://www.sec.gov/Archives/edgar/data/1262823/000095012904001565/h13929e10vk.htm

7 Chlorine/Chlorate Newsletter. “Tessenderlo Upgrades Chlorine; Expands VCM, PVC.” March 2002. p. 10

- 11. Available on the Internet at: http://www.eltechsystems.com/newsletter/ELTECH%20Newsletter%20March%202002.pdf

. Accessed 3/2/2007.

8 Hydrocarbon Processing (2007, November 19). "Uhde Opens New Electrolysis Plant."

9 D rabkin, M. and Rissmann, E. Project Summary Waste Minimization Audit Report: Case Studies of Minimization of Mercury Bearing Wastes at a Mercury Cell Chlor

- alkali Plant

. EPA/600/S2

- 88/011. U.S. Environmental Protection Agency, Hazardous Waste Engineering R esearch Laboratory, Cincinnati, OH. March 1988.

10 Fact Sheet No. 12. PPG Canada

. St Lawrence Vision 2000, Beauhamois, Quebec, Canada. 1996.

11 Modern Chlor

- Alkali Technology, Volume 2. Jackson, C., Editor. Society of Chemical Industry, London, England. 1983.

### 3

- 2  

### European Best Available Techniques Document 12

 

### Oceana 13



### In addition to reviewing the Oceana correspondence, all directly relevant references cited by Oceana in their comments were obtained, reviewed, and considered. These references are compiled and available in the docket as one docket entry. and the Chlorine industry

14



### 3.1 Capital Costs
Initially, to determine capital costs of conversion to non

- mercury technology, EPA collected capital cost data for mercury cell conversion projects that occurred between 1992 and 2008 from the search of the resources listed above. In their September 15, 20 09 memorandum, Oceana pointed out that several of the capital cost data points were not appropriate because either the costs were from unsubstantiated estimates or they were developed from conversions that were done so long ago that they did not apply to m embrane cell technologies currently being
used.

Initially, Oceana identified seven capital cost data points for conversion to non

- mercury technology that were derived from estimates rather than actual conversions. These were:

 

### One 1997 estimate published b y a HoltraChem facility where the conversion did not in actuality occur;

 

### One 1988 estimate for a facility in the U.S. that could not be identified;

 

### One 1992 estimate for a “model plant” representing a mid

- range facility size, with no documentation included ; and

 

### Four European estimates for conversions to non

- mercury technology, which did not include information on plant production capacities.

In addition, Oceana stated that conversion project costs prior to 1995 are not applicable to

current

- day conversion costs. According to Oceana, newer membrane

- cell technology uses

12 Reference Document on Best Available Techniques in the Chlor

- Alkali Manufacturing Industry. European Integrated Pollution Prevention and Control (IPPC) Bureau, Seville, Spain. December 2001. Available on the Internet at http://eippcb.jrc.es/reference/cak.html

. 13 Oceana is a large international organization focused on ocean conservation ( www.oceana.org). They have provided comments on EPA’s analyses of the costs and other impacts of converting mercury cell chlor

- alkali plants to membrane cells on multiple occasions, along with providing substantial data and analyses to support their comments.
14 Memorandum from Brown, H.P., EC/R Incorporated, Chapel Hill, NC, to Docket EPA

- HQ

- OAR

- 2002

- 0017. Capit al and Annual Costs References Cited By Oceana in Their Comments on EPA’s Cost Analysis of the Conversion of Mercury Cells to Membrane Cells. (Docket No. EPA

- HQ

- OAR

- 2002

- 0017). March 15, 2010.

### 3

- 3 bipolar electrolyzers, which were not commercially available until around 1997. The conversions prior to 1995 would have used the older monopolar electrolyzers. Therefore, we agree with
Ocean a’s position that for this analysis it is not appropriate to include the data points derived from conversions that took place prior to 1995; thus these data points were not used in our current analysis.

We developed capital cost factors for conversion to m embrane cell technology in units of dollars per ton ($/ton) of chlorine (Cl 2 

### ) production using data from conversions anywhere in the world that took place after 1995, excluding the cost data described above. These costs and cost
factors are shown in Table 3

- 1. The cost factors ranged from $267 to $941/ton Cl 2 

### produced and averaged $535/ton Cl 2 

### . In addition, Table 3

- 1 shows the average of the cost factors from only the most recent US conversions (since 2000), which is $596/ton Cl 2


### .

Since membrane cells are s maller than mercury cells, in some conversions the companies have elected to use the extra available space to install additional membrane cells, thus increasing the chlorine and caustic production capacity of the facility. Therefore, for some conversion

pr ojects included in Table 3

- 1, an increase in production capacity was also included. Oceana indicated in their comments that a conversion cost analysis should either exclude the costs attributed to expanding capacity or include the cost saving benefits deri ved from expanding capacity. We do not believe that it is appropriate to make an assumption about savings because any benefits derived from additional sales due to increased capacity assumes that product demand would simultaneously increase. We do not disa gree that the additional incremental costs of the expanded capacity should be excluded. However, the available information does not provide a breakdown of the costs associated with conversion versus the costs associated with the increased capacity. Therefo re, the capital costs for some conversions may include expansion costs. This could result in the capital costs based on these data being biased high.

When providing comments on the June 2009 memorandum, ASHTA stated that differences in size, the new or exp anded infrastructure required, and the end products desired (e.g., vapor or liquid chlorine, dilute or merchant concentration of caustic) all impact the actual cost of conversion. ASHTA suggested that, minimally, conversion cost estimates should take
into account economies of scale. However, Oceana expressed concern with the use of an equation that relates increasing size to lower conversion costs to estimate the cost of conversion for the four remaining facilities, since extrapolation of the costs to very small or very large capacities produces illogical conclusions. But we do believe that there could be some economies of scale. An equation we developed using the limited data set of the recent U.S. conversions

### 3

- 4 Table 3

- 1. Cost Factors for Conversion of Merc ury Cell Chlor

- Alkali Facilities to Membrane Cell Technology ($/ton Cl2 produced)

Year of Estimate Location of Conversion Capital Cost (as provided) Capacity (ton Cl 2

/yr) Capital Cost Factor ($/ton Cl 2 produced/yr)

Reference Base Year 2007 a

1997 Europe $104,946,640 173,063 $606 $824 12

1997 Europe $32,856,014 110,231 $298 $405 12

1998 Europe $16,334,213 66,139 $247 $333 12

1998 Europe $13,094,369 66,139 $198 $267 12

1998 Europe $29,923,557 44,092 $679 $915 12

1998 US (OxyTech) a $57,327,236 135,033 $425 $573 12

1998 Asia $39,775,832 182,983 $217 $293 12

1999 Europe $131,159,254 330,693 $397 $533 12

1999 Europe $31,026,058 82,673 $375 $505 12

2002 US (Westlake, KY) $86,100,000 205,000 $420 $497 6

2004 Belgium $171,032,583 363,762 $470 $556 7

2005 c US (PPG, LA) $90,000,000 275,000 $327 $367 4

2007 c US (Pioneer, LA) $117,000,000 c 197,000 $594 $594 3

2007 c Germany $109,678,733 250,000 $439 $439 8

2008 c US (ERCO, WI) $107,112,676 b 118,000 $908 $908 5
Average $535
Average of Recent US Conversions b $596

N/A = Not Available

a The conversion to 2007 dollars was estimated using the Chemical Engineering Plant Cost Index Economic

Indicators. Base year costs were adjusted to 2007 dollars using the ratio of the 2007 economic indicator and the base year economic indicator. See Refere nce. 15

b Refers to conversions that occurred in the United States after 2000.

c Used in this analysis to develop an average factor for near future U.S. conversions.

15 Chemical Engineering Plant Cost Index

. Chemical Engineering. February 2009. Available on the Internet at www.che.com

.

### 3

- 5 showed a good correlation (correlation coefficient (R 2 

### ), 16 

### = 0.9981) of costs to facility size/capacity. 17



### Figure 3

- 1 presents these cost factors versus plant chlorine capacity for all the facilities.
As shown in Figure 3

- 1, there is little correlation between cost factor and plant chlorine capacity (R 2 

### = 0.0102) for all global conversions with documented data. Therefore, we agree with Oceana that it is most appropriate to use a single cost factor to estimate the capital costs of conversion. However, when the entire data set (in Table 3

- 1) was plotted, a similar
correlation was not observed.

Figure 3

- 1. Capital Cost Factor ($/ton Cl2 produced) vs. Capacity

16 T he correlation coefficient is a measure of how accurately a regression predicts a value. The regression is most accurate when R 2 is equal to one (1).

17 Memorandu m from Jones, D.L., PhD, EPA, Research Triangle Park, NC, Brown, H.P. and Norwood, P., EC/R

Incorporated; Chapel Hill, NC; to Project File

— Mercury Chlor

- Alkali NESHAP. Revised Conversion Costs and Baseline Emissions

— Conversion From Mercury Cell Chlor

- Alkali to Membrane Cell Technology

. (Docket Item No. EPA

- HQ

- OAR

- 2002

- 0017

- 0105). September 15, 2009. R2 = 0.0102

0 100 200 300 400 500 600 700 800 900 1,000

0 100,000 200,000 300,000 400,000Capital Cost Factor ($/ton Cl2 produced)

Capacity (ton Cl2/yr) Capital Cost Factor ($/ton Cl2 produced/yr) vs Capacity (ton Cl2 produced/yr)

### 3

- 6 In order to develop capital cost factors for the current four mercury cell facilities, the chlorine production capacities from the 1997 Directories of Chemical Producers 18



### Table 3

- 2. Estimated Capital Costs for Existing Mercury Cell Chlor

- Alkali Facilities were obtained
and are shown in Table 3

- 2. The capital costs of converting to membrane cell technology for the four facilities in current operation were then estimated using these capacities. As shown in
Table 3

- 2, capital costs were est imated using both the average capital cost factor for the entire data set ($535/ton Cl 2 

### ) in Table 3

- 1, and then also using the average of only the four most recent U.S. conversions ($596/ton Cl 2 

### ) since 2000.

Facility Cl 2 Production Capacity (ton/yr) a Capital Costs ($)

Based on Entire Data Set b Based on Recent U.S. Conversions c,d

ASHTA 47,421 25,375,938 28,246,460

Olin GA 119,031 63,695,899 70,901,169

Olin TN 262,451 140,442,954 156,329,838

PPG 72,848 38,982,173 43,391,830

Totals 268,496,964 298,869,297

a From Reference 18

. b Calculated using average capital cost factor of $535/ton Cl 2 for entire data set.

c Calculated using average capital cost factor of $596/ton Cl 2 for recent (i.e., after 2000) U.S. Conversi ons.
d Used in this analysis to develop costs for near future U.S. conversions.



### Because of several known factors, including environmental regulations and process safety management, we do not believe the broad range of data represented by all the facilities
shown in Table 3

- 1 to be as accurate as the recent conversions that have occurred since 2000 in the U.S. to use to estimate the potential capital costs of conversion for the four remaining U.S. facilities. We believe that the average cost factor from the four recent U.S. conversions results in the best estimate of the capital costs that would occur for all four mercury cell chlor

- alkali plants to convert to membrane cells. The total estimated capital cost of conversion for all four facilities, therefore, i s approximately $300 million, which is also shown in Table 3

- 2.

3.2 Annual Costs
The annual costs associated with the conversion of mercury cells to membrane cells include costs related to capital recovery, unused mercury storage costs, and energy costs du e to the need for increased steam required to further concentrate caustic. However, some of these

18 1997 Directory of Chemical Producers

— United States of America. Stanford Research Institute International. Menlo Park, CA. 1997.

### 3

- 7 additional annual costs will be offset by savings realized due to the elimination of mercury, e.g.,

mercury

- related environmental compliance costs and the red uced energy needs for the membrane cell process. Each of these costs and savings are described below.

3.2.1 Capital Recovery Cost
Annualization of the capital costs involves establishing an annual “payment” sufficient to finance the investment over the exp ected lifetime of the equipment or loan period. This payment is typically referred to as the “capital recovery cost.” The three key inputs into the capital recovery costs are the capital costs, the interest rate, and the finance period. The estimates for

c apital costs were discussed above; the remainder of this discussion is focused on the interest rate and the finance period.

As pointed out by Olin in their February 2, 2010, correspondence “access to capital and
available interest rates are a function of m any things, including the overall financial environment, the financial condition of the prospective borrower (including its debt load), and the amount and length of the loan.” In their June 25, 2009 correspondence, Olin also stated that
“a 7 percent intere st rate is simply not available to industry in this economy.” They suggested replacing the 7 percent rate with a 10 percent, or even a 14 percent rate. In their February 2010 correspondence, Olin provided an example to support the use of an interest rate h igher than
7 percent. Olin reported that they sold $150 million in Senior Notes, and that these notes had an interest rate of 8.875 percent and a maturity date of August 2019. According to Olin, the effective interest rate was over 9 percent considering th e costs and discount.

The interest rate can result in significant differences in total annualized costs. For example, for a finance period of 15 years, the difference in capital recovery costs is approximately 20 percent higher for a 10 percent interest ra te than for a 7 percent interest rate. This difference is even more profound for longer finance periods.

EPA’s Office of Air Quality Planning and Standards always provides an interest rate of
7 percent for cost analysis. While we do not disagree with Olin’s information, we feel that it is important to maintain uniformity to allow decision

- makers to consider c onsistent information across various regulatory decisions. Therefore, in this present analysis, an interest rate of 7 percent was used in the development of the capital recovery cost. It should be noted that for a particular firm at a particular time, this interest rate of 7% might not be available because of either the firms financial health or temporary economic condition. In previous versions of this analysis, a finance period of 15 years was used. Oceana commented that this underestimates the
useful lif e of the equipment and overestimates the annualized costs. Oceana suggested that a 30

-

### 3

- 8 year finance period was more appropriate, and cited a previous analysis for the mercury cell industry where EPA used a 30

- year period to estimate the capital recovery cos ts. 19



### To illustrate the importance of the choice of finance period, consider the fact that the capital recovery costs for a 30

- year finance period at 7

- percent interest rate would be approximately 30 percent lower than those for a 1 5 year finance period. Table 3

- 3 shows the annual capital recovery costs for both a 15

- year and 30

- year period, using the capital cost estimates based on the cost factor from the average of recent U.S. conversions shown in
Table 3

- 2 of the previous section . While we recognize that a 15

- year finance period may understate the lifetime of the membrane cell process equipment, we believe that the use of a 30

- year period will bias the annualized costs unreasonably low. Therefore, we chose to base our capital reco very cost estimates on a 20

- year finance period which is higher than our previous estimate of 15 percent. This results in total annualized costs for the four U.S. facilities of approximately $30 million/yr, which we believe represent the best estimate of t he capital recovery costs, as shown in Table 3

- 3. However, it is important to note that the use of a 30

- year finance period in the analysis cited by Oceana was for a stationary building and not an operating industrial process. Therefore, we do not agree that this example establishes a precedent that is pertinent to our analysis.

Table 3

- 3. Estimated Capital Recovery Costs for Varying Finance Periods (Using 7 percent interest rate)

Plant Best Estimate of Capital Cost ($) a Capital Recovery Costs ($/yr)
15 years (CRF b = 0.1098) 30 years (CRF b = 0.0806) 20 years c (CRF b = 0.0944)

ASHTA $28,246,460 $3,101,309 $2,276,281 $2,666,266

Olin GA $70,901,169 $7,784,567 $5,713,670 $6,692,569

Olin TN $156,329,838 $17,164,176 $12,598,059 $14,756,431

PPG $43,391,830 $4,764,190 $3,496,792 $4,095,882

Totals $32,814,242 $24,084,802 $28,211,147

a Using the average cost factor from recent U.S. Conversions (See Table 3

- 2).

b CRF = Capital Recovery Factor.

c Used in this analysis to develop costs for near future U.S. conversions.

19 Me morandum from Brown

, H.P., EC/R Incorporated, Chapel Hill, NC, to D.L. Jones, U.S. Environmental Protection Agency, Research Triangle Park, NC. Documentation of Building Replacement Cost Estimates. June 5. 2008.

### 3

- 9 3.2.2 Annual Costs Associated with Storage of Unused Mercury
According to the Chlorine Institute, there were 1,376 tons of mercury available at the five mercury cell plants operating in 2008. 20



### Table 3

- 4. Estimate of Annual Mercury Storage Costs These plants operated a total of 264 mercury cells, which equates to 5.2 to ns of mercury per cell (ton Hg/cell) on the average. To estimate the amount of mercury available at each of the four currently operating facilities, that would need to be stored if they converted to membrane cells, this average factor was multiplied by the number
of cells in each of the facilities. As shown in Table 3

- 4, this results in an estimated total of 1,272 tons of mercury to be stored, ranging from 125 tons at ASHTA to over 550 tons at Olin TN.

Fac ility Number Cells a Available Mercury

(ton) b Mercury Storage Costs ($/yr)
Using EPA

Factor ($0.134/yr/lb) Using DOD Factor ($0.0561/yr/lb) Midpoint of EPA and DOD Factors c

ASHTA 24 125 $33,399 $14,035 $23,717

Olin GA 60 313 $83,498 $35,088 $59,293

Olin TN 106 552 $147,513 $61,989 $104,751

PPG 54 281 $75,148 $31,579 $53,364

Totals 244 1,271 $ 339,558 $ 142,691 $ 241,125

a Reference 18

. b Total amount of available mercury (5.28 tons/cell) distributed by number of mercur y cells (see Reference 20 ).

c Used in this analysis to develop costs for near future U.S. conversions.



### There were two sources of information identi fied that provided estimates of mercury storage costs. EPA’s Office of Pollution Prevention and Toxics and the EPA Office of Solid Waste and Emergency Response 21 

### has estimated future mercury storage costs at a private facility would be between $0.081 and $0.186 per year per pound of mercury stored, with an average of $0.134 per year per pound. The Defense National Stockpile Center also has presented
information in dicating that the cost associated with the federal mercury storage is $0.0561/yr/lb Hg. 22

20 The Chlorine Institute, Inc. “Chlor

- Alkali Industry 2008. Mercury Use and Emissions in the United States (Twelfth Annual Report).” August 2009.

21 Mercury Storage Cost Estimates

— Final Report. U.S. Environmental Protection Agency. Office of Pollution Prevention and Toxics and Office of Solid Waste a nd Emergency Response, Washington, DC. November 6, 2007. Page 28, Exhibit 15. Available on the Internet: http://www.epa.gov/mercury/stocks/Storage_Cost_Draft_Updated_ 11

- 6

- final.pdf

22 Lynch, Dennis, Environmental Protection Specialist. Presentation made at the Asia Mercury Storage Project Inception Workshop on March 4, 2009 at the Defense National Stockpile Center, Fort Belvoir, VA.

### 3

- 10 Table 3

- 4 shows the estimated storage costs using both the average factor from EPA ($0.134/yr/lb) and the factor from the Department of Defense ($0.0561/yr/lb). While there is a considerable difference in these estimates, the sources of the information are reliable. Use of either factor could bias the annual cost estimates high or low. Therefore, we assumed that these two points represent the upper and lower bounds of t he storage costs, and that the midpoint of the range of storage costs represents the best estimate of the mercury storage costs that would be incurred. These midpoint costs, which are also shown in Table 3

- 4, total just over $240,000 per year for all four plants.

The Mercury Export Ban Act (MEBA), which became public law on October 14, 2008 (Public Law No. 110

- 414), is intended to reduce the availability of elemental mercury in domestic and international markets. MEBA prohibits the export of elemental mercury as of January 1, 2013, and requires the Department of Energy (DOE) to designate and operate a facility (or facilities) for the purpose of long

- term management and storage of elemental mercury generated in the United States. MEBA also prohibits the transfer of e lemental mercury held by federal agencies as of the date of enactment in order to further control the flow of elemental mercury in the domestic market. MEBA covers elemental mercury but not mercury compounds.

MEBA requires DOE to publish guidance establis hing procedures and standards for the receipt, management and long

- term storage of elemental mercury by October 1, 2009. DOE issued an Interim Guidance document to fulfill this requirement, which is available at http://mercurystorageeis.com/Elementalmercurystorage%20Interim%20Guidance%20(dated%20
2009

- 11

- 13).pdf (See also Notice of Availability, November 16, 2009, 74 FR 58952). DOE must designat e a facility or facilities for long

- term management and storage of elemental mercury by January 1, 2010 according to MEBA. DOE has issued a Draft Long

- Term Management and Storage of Elemental Mercury Environmental Impact Statement (EIS) which analyzes a nu mber of candidate facilities. The draft Mercury Storage EIS is available at http://www.mercurystorageeis.com/ (See also Notice of Availability, January 29, 2010, 75 FR 4801). DOE is due to release a draft env ironmental impact statement (dEIS) for evaluating potential storage sites by December 2010.

MEBA requires that DOE to assess and collect fees for providing management and storage of elemental mercury. The amount of the fees will be made publicly available by October 1, 2012. MEBA also requires a designated facility or facilities to be operational by January 1, 2013; the facility will be subject to RCRA Subtitle C requirements, except for the storage prohibition contained in § 3004(j) of the Solid Waste Dis posal Act. MEBA requires the storage facility to accept custody, for the purpose of management and storage, of elemental mercury

### 3

- 11 generated within the United States and delivered to the facility, and that persons who deliver elemental mercury to the storage facility are indemnified against any liability related to the release or threatened release of mercury after delivery to the facility. MEBA also requires EPA or an authorized state to issue a final decision on a RCRA permit application for a DOE facility by January 1, 2015.

In addition, MEBA authorizes DOE to establish terms and conditions for the long

- term management and storage of elemental mercury. DOE has indicated that elemental mercury to be stored at the DOE facility or facilities must be of a puri ty of 99.5 percent or greater by volume (see DOE Interim Guidance document and Draft Mercury Storage EIS). Therefore, elemental mercury of lower purity would need to be further refined before it could be stored at the DOE facility (or facilities), but expo rt or federal agency transfer of such mercury is still prohibited. MEBA does not affect RCRA or RCRA regulations except that elemental mercury stored at the DOE facility, or elemental mercury that is destined for the DOE facility and accumulated for 90 day s or less is not subject to the RCRA storage prohibition, 3004(j) of the Solid Waste Disposal Act (42 U.S.C. 6924(j)). (See MEBA Sec. 5(g)(2)).

The elemental mercury that chlor

- alkali facilities store on

- site remains a commercial chemical product and its s torage is not barred by federal environmental statutes as long as it is not discarded. This is the case whether the facility continues to operate a mercury cell process,

converts to a non

- mercury process or shuts down the plant. State or local restrictions may apply, however. After the establishment and operation of the DOE facility, mercury being stored at
chlor

- alkali plants can continue to be stored as a product. However, if storage continues over a long period of time, or if the supply of commodity grad e mercury exceeds domestic demand, EPA may assess on a case

- by

- case basis the legitimacy of a claim that elemental mercury storage does not constitute discard. For a full discussion, see http://www.epa.gov/boston/topics/cleanup/compendium/2002

- Mercury

- from

- the

- HoltraChem

Manufacturing

- Co.pdf The EPA encourages responsible stewardship of the elemental mercury designated to go to a DOE facil ity, which could involve arranging secure temporary storage,
such as a RCRA

- permitted facility with the capacity to store mercury on a short term basis, until the company can send it to DOE for long

- term storage.

3.2.3 Costs Savings Associated with the El imination of Mercury

- Related Environmental Compliance Costs
There are some expected costs savings related to the conversion from a mercury

containing process to the membrane cell process due to the elimination of compliance costs
associated with the use of mercury. These savings can be categorized into three environmental

### 3

- 12 elements: air, waste, and water. Air

- related environmental compliance cost savings are realized when the facility no longer has to comply with mercury

- related air regulations, as well as l abor due to the elimination of mercury

- related air monitoring, reporting, abatement measures, and occupational exposure monitoring. Waste

- related environmental compliance cost savings are realized when a facility no longer incurs costs due to recycling, re torting, transporting, storing and/or disposing of mercury wastes. Water

- related environmental compliance cost savings are realized when the facility no longer is required to operate mercury wastewater treatment

programs.

We used information from the lite rature 23 

### (with costs converted from Euros to U.S. dollars using the average 2004 exchange rate 24 

### of 1.2439) and cost information presented to the State of Ohio by ASHTA as a part of their consent order 25

18 

### to estimate the cost savings.
Information related to an nual compliance costs associated with reducing mercury air emissions from the hydrogen and end box vent streams, presented to the Ohio EPA by ASHTA under the consent order, indicated that ASHTA would spend approximately $228,000 annually on air compliance. This value was converted to a $/ton Cl 2 

### value based on ASHTA’s capacity of 47,421 ton Cl 2 

### /yr, resulting in a cost factor of $4.81/ton Cl 2 

### capac ity. Since these costs would no longer be incurred after conversion, this factor represents a savings. In contrast, European information indicates that a savings of approximately $2.20/ton Cl 2 

### in expenditures related to air compliance could be realized fro m the elimination of mercury. 23



### In addition to the air

- related compliance costs described above, ASHTA presented to the Ohio EPA that they would b e spending approximately $256,500 annually on fugitive mercury
emission

- related activities (including the implementation of leak detection and repair standards). Upon conversion to membrane cells, this would represent a savings. These costs and savings are not relevant for the other three facilities because ASHTA is the only facility that did not install a cell room instrumental monitoring program and, therefore, is required to perform a leak program.

ASHTA also presented information to the Ohio EPA related to costs for their water treatment system. According to ASHTA, they spend approximately $98,000 annually on their storm water recovery system. This converts to a cost/savings factor of $2.07/ton Cl 2 

### capacity for

23 Maxon, Peter, Director. Concorde East/West Sprl. Status Report: Mercury Cell Chlor

- Alkali Plants in Europe. Prepared for European Environmental Bureau, Brussels, Belgium. October 2006.

24 Historic Exchange Rates. Prepared by GOCurrency.com. Available on the Internet at www.gocurrency.com

. Accessed April 1, 2009 and April 30, 2009.

25 ASHTA Chemicals. Early MACT Compliance. Consent Agreement. Exhibits B and C. (Docket No. EPA

- HQ

OAR

- 2002

- 0017). August 2004.

### 3

- 13 elimination of water

- related compliance cos ts due to mercury use. The European information indicates that a cost savings of approximately $1.92/ton Cl 2 

### could be realized by the eliminations of a mercury wastewater treatment system. 23



### Waste

- related environmental compliance cost savings are realized by facilities no longer being required to recycle, retort, transport, store and/or dispose of mercury

- containing wastes. The only information available was the European data that indicated that facilities could expect a cost savings of $6,470/ton waste, at an average mercury waste production rate of 4.2x10
- 5 

### ton waste per ton Cl 2


### . 23 

### This results in a waste

- related cost savings of $0.27/ton Cl 2 

### capacity for
elimination of waste

- related compliance costs due to mercury use.

Table 3

- 5 presents the cost savings factors for each environmental c ompliance element discussed above from both the information from ASHTA as well as the European literature. A total environmental compliance cost savings factor was obtained from the sum of all the individual elements in each data set. Since ASHTA did not p rovide data related to annual cost of handling mercury

- related wastes, the value from the literature was used to obtain a total from all media. With this approach, a value of $7.15/ton Cl 2 

### shown in Table 3

- 5, was developed from data mostly from ASHTA, and a value of $4.63/ton Cl 2 

### was developed from the European
literature.

Table 3

- 5. Environmental Compliance Cost Savings Factors from Elimination of the Mercury Process

Environmental Element Cost Saving Factors from Elimination of Mercury ($/ton Cl 2 )

ASHTA Factor a Literature Factor b

Air 4.81 2.20

Water 2.07 2.16

Waste 0.27 c 0.27

Totals 7.15 c,d 4.63

a See Reference 25

. b See Reference 23

.

c Since ASHTA did not provide data related to annual cost of compliance for mercury

- related wastes, the value from

the literature was used for this element.

d Used in this analysis to develop costs for near future U.S. conversions.

### 3

- 14 Table 3

- 6 presents the estimated annual cost savings from the elimination of mercury

related compliance costs for each of the four U.S. facilities using both total environmental compliance saving cost factors from Table 3

- 5. Since the cost data found in the literature were based on facilities in Europe and not from actual U.S. facilities, we believe that the annual cost savings from the elimination of mercury that is based mostly on the ASHTA cost factors represent the best estimate of the cost savings for the remaining U.S. facilities.

Table 3

- 6. Annual Cost Savings from Elimination of t he Mercury Processes, by Facility

Facility Chlorine Capacity (tpy) Savings from Hg Elimination ($/yr)

ASHTA Factor a Literature Factor b

ASHTA c 47,421 595,386 595,386

Olin GA 119,031 850,634 550,600

Olin TN 262,451 1,875,561 1,214,017

PPG 72,848 520,592 336,970

Totals 3,842,173 d 2,696,973

a Using the ASHTA factor of 7.15 $/ton Cl2 presented in Table 3

- 5.

b Using the literature factor of 4.63 $/ton Cl2 presented in Table 3

- 5.

c As discussed above, the ASHTA compliance costs savings include $256,500/yr related to their fugitive emissions

compliance costs (that includes a leak detection and repair element, as well as the installation of a temporary canopy for the cell room); where as, the other facilities do not include these costs because ASHTA was the only facility that did not install an instrumental monitoring program to comply with the NESHAP.

d Used in this analysis to develop costs for near future U.S. conversions.



### 3.2.4 Ene rgy Savings and Costs
The membrane cell process requires less electricity than the mercury cell process. Therefore, there are reduced energy requirements with membrane cells as compared to mercury cells. Energy cost savings due to reduced electricity usage were estimated using information from literature and average State electricity prices. 12 

### Although there could be variations in local electricity c osts, according to the information in the literature, the electricity used by the membrane cell technology is approximately 2,500 kilowatt hours per ton chlorine produced (kWh/ton Cl 2 

### ), which is about 700 kWh/ton Cl 2 

### less than that used by the mercury cell process (3,230 kWh/ton Cl 2 

### ). 12 

### This equates to an approximately 22 percent reduction in electricity usage. The total chlorine production capacity of the four mercury cell plants is 500,000 tons per year. Using the 700 kWh/ ton decrease in electricity usage, the total estimated electricity reduction after conversion of the four plants would be around 350 million kWh/year, or 350,000 Megawatt hours/year.

### 3

- 15 Oceana provided information that indicated that a 37.5 percent reduction was Applying a 37.5 percent reduction to the mercury cell process electricity usage estimate of 3,230 kWhr/ton
Cl 2 

### would yield a reduction in energy usage of approximately 1,211 kWhr/ton Cl 2


### .
These energy cost savings from reduced electricity usage are sli ghtly offset by an increase in energy required to concentrate the caustic co

- product generated in the membrane cell process. Mercury cells produce caustic at the market concentration of 50 percent, and membrane cells produce caustic at a 33 percent concent ration. Therefore, there is an increase in energy use (in the form of steam) due to the need to further concentrate the caustic produced with the membrane cell technology.

The energy requirement (in the form of steam) to concentrate the caustic to 50 perce nt
was estimated to be 601 kWh/ton Cl 2 

### ($13.6/ton Cl 2 

### ), as obtained from the average of estimates provided for three U.S. facilities. This cost was deducted from the energy savings described above to develop an estimate of overall energy cost savings with membrane cell technology due to reduced energy needs. The energy cost savings are shown in Table 3

- 7.

Further evaluation of Oceana’s claim of a 37.5 percent energy cost reduction 26



### In their September 29, 2009, letter, Oceana stated that the steam cost was an overestimate of the required energy needs to concentrate the caustic. Oceana argued that even though additional energy is required to gener ate steam, the steam could be obtained without additional expense. According to Oceana, facilities could use byproduct hydrogen as fuel to generate the necessary steam. While it may be possible to assume that byproduct hydrogen could be used to generate th e steam required to concentrate caustic from a membrane cell, we agree with industry’s claim that increases in the demand for and the value of high purity chemical hydrogen make this use of byproduct hydrogen economically impractical. Therefore, we have no t made adjustments to the energy usage costs (in the form of steam) based on the use of byproduct hydrogen as fuel. shows that although the estimate is for an actual facility, it is not clear whether steam costs are included. In addition, Oceana also indicated in their September 16, 2009, letter that a 25 percent reduction was commonplace. Therefore, we believe that the total energy cost savings based on the
22 percent electricity usage reduct ion represents the best cost estimate.

26 Government of India Bure au of Energy Efficiency. 2006. “The Travancore

- Cochin Chemicals Limited.” December 14. http://www.bee

india.nic.in/sidelinks/EC%20Award/eca06/Award2006_CD/06ChlorAlkali/TheTravancore

CochinChemicalsLimited.pdf

.

### 3

- 16 Table 3

- 7. Energy Savings Associated with Conversion from the Mercury Cell to the Membrane Cell Process

Facility Chlorine Capacity

(tpy) Average Industrial

Retail Electricity Cost (cents/kWh) a Steam Cost ($/yr) b Annual Energy Savings ($/yr) Total Energy Cost Savings ($/yr) f

22% Energy Reduction c,d 37.5% Energy Reduction e 22% Energy Reduction c,d 37.5% Energy Reduction e

ASHTA 47,421 5.77 644,788 1,911,268 3,313,692 1,266,480 2,668,904

Olin GA 119,031 5.48 1,618,476 4,556,346 7,899,639 2,937,870 6,281,163

Olin TN 262,451 5.40 3,568,575 9,901,023 17,166,059 6,332,448 13,597,484

PPG 72,848 3.90 990,515 1,986,964 3,444,931 996,449 2,454,416

Totals 11,533,247 25,001,967

a Average retail price of electricity to ultimate customers by end

- use sector, by state (Reference 27

b Steam cost associated with concentrating caustic produced by membrane cells from 33 percent to 50 percent at an

average cost of $13.60/ton Cl 2 (Reference ).

12 ).

c Calculated using a 22 percent energy savings associated with the conversion, at 699 kWh/ton Cl 2 . (Reference 12 ).

d Used in this analysis to develop costs for near future U.S. conversions.

e Calculated using a 37.5 percent energy savings associated with the conversion, at 1,211 kWh/ton Cl 2 . f Energy Cost Savings = Energy Savings ($/yr)

– Steam Cost ($/yr).



### 3.2.5 Overall Annual Costs
For each of the costs and savings elements discussed in the previous sections, there is credible information that would result in different estimated levels of costs/savings. For each element, this resulted in a high

- and low

- end esti mate. These values are shown in Table 3

- 8.

For each element, we chose the method of estimation that we believe is the best estimate of the cost or savings, as follows:

 

### For capital recovery costs and mercury storage costs, we concluded that neither the high

- end nor the low

- end of the ranges represented the best estimate of the costs that would be incurred. Instead, we concluded that using a 20

- year finance period provided the best estimate. (See Table 3

- 3 above.)

 

### For the mercury storage costs, we determined that the midpoint of the range of data from two sources (list) was the best estimate. (See Table 3

- 4 above.)

27 U.S. Department of Energy. Average Retail Price of Electricity to Ultimate Customers by End

- Use Sector, by State for 2006

. Available on the Internet at http://www.eia.doe.gov/cneal/electricity/epm/table5_6_b.html

. Accessed February 24, 2009.

### 3

- 17 Table 3

- 8. Ranges of Individual Annual Cost/Savings Elements for the Conversion of Mercury Cells to Membrane Cells

Facility Capital Recovery Costs ($/yr) a Mercury Storage Costs ($/yr) b Environmental Compliance Savings ($/yr) Energy Savings ($/yr)

7% and 30
years 7% and 15
years EPA Factor DOD Factor ASHTA Factors c Literature Factors 22% Energy Reduction c 37.5% Energy Reduction

ASHTA $2,276,281 $3,101,309 $33,399 $14,035 ($595,386) ($595,386) ($1,266,480) ($2,668,904)

Olin GA $5,713,670 $7,784,567 $83,498 $35,088 ($850,634) ($550,600) ($2,937,870) ($6,281,163)

Olin TN $12,598,059 $17,164,176 $147,513 $61,989 ($1,875,561) ($1,214,017) ($6,332,448) ($13,597,484)

PPG $3,496,792 $4,764,190 $75,148 $31,579 ($520,592) ($336,970) ($996,449) ($2,454,416)

Totals $ 24,084,802 $ 32,814,242 $ 0 $ 339,558 ($ 3,842,173 ) ($ 2,696,973 ) ($ 11,533,247 ) ($ 25,001,967 )

a A recovery period of 20 years and 7% was use in the final analysis.

b The midpoint of the EPA and DOD factor range was used in the final analysis.

c These data were used in the final analysis.

 

### For the environmental compliance savings element, we chose the higher estimated savings, using the cost factors developed mostly from ASHTA data to provide the best estimate for these U.S. facilities. (See Table 3

- 6 above.)

 

### For the energy savings, we believe that the lower savings level based on a 22 percent electricity usage reduction provides the best estimate. (See Table 3

- 7 above.)

Table 3

- 9 shows the estimates for each facility for each individual cost element that incorporate the choices described above, and also shows the total annual costs calculated using these estimates for each facility for conversion to non

- mercury technology. The values in
Table 3

- 9 represent our best estimate of the annual costs that would be incurred due to the
conversion of the four currently operating mercury cell facilities to membrane cells. These annual costs range from less than $1 million per year for ASHTA to almost $7 million per year for Olin TN, and a total estimate of $13 million for all four facilities.

3.3 Uncertainty An alysis
While we selected the values that we believe provide the best estimates of the costs that would be incurred and savings that would be realized in the conversion from mercury cells to membrane cells at these four facilities, there is some uncertainty associated with the estimates. While most of the information obtained and provided by commenters appears to be credible and representative of actual conversions that have occurred, there is a high amount of variability. This is most likely due to the fact that the conditions associated with the potential conversion of

### 3

- 18 each mercury cell plant are different, resulting in potentially significant differences in the costs and savings.

Table 3

- 9. EPA Estimates of the Annual Costs of Conversion of Mercury Cells to Membrane Cells

Facility Capital Recovery Costs ($/yr) a Annual Costs

Total Annual Costs ($/yr) Mercury Storage Costs ($/yr) b Environmental Compliance Savings ($/yr) c Energy Savings ($/yr) d

ASHTA $2,666,266 $23,717 ($595,386) ($1,266,480) $ 828,117

Olin GA $6,692,569 $59,293 ($850,634) ($2,937,870) $ 2,963,358

Olin TN $14,756,431 $104,751 ($1,875,561) ($6,332,448) $ 6,653,173

PPG $4,095,882 $53,364 ($520,592) ($996,449) $ 2,632,205

Totals $28,211,147 $ 241,125 ($ 3,842,173 ) ($ 11,533,247 ) $ 13,076,853

a Using 20 year finance period with a resulting CRF of 0.0944

b Using midpoint of EPA and DOD factors.

c Using ASHTA factors for air and water compliance costs and a literature value for waste compliance costs not

provided in ASHTA data.

d Using an estimate of 22 percent energy reduction with membrane process.



### In order to consider the uncertainties, we condu cted a simple Monte Carlo simulation. 28

28 Monte Carlo simulation is a computerized mathematical technique that is used to account for uncertainty in quantitative analyses and is often used in decision making and risk analysis. The simulation technique builds models of possible results by substituting a range of values

– often a probability distribution

— for any factor that has inherent uncertainty, a nd then recalculates the results numerous times, each time using a different set of random values from the probability functions. The results of Monte Carlo analyses are distributions of possible outcome values. 

### For this simulation, we assumed a normal distribution for each variable. We set the peak of the distribution at our best estimate. For the elements where we believe one end of the range provides the best estimate (i.e ., the environmental compliance savings and the energy savings), we truncated the distribution so that the peak of the distribution curve was at the best estimate. We ran the simulation with 100,000 calculations to create distributions of the individual pl ant
annual costs over all annual costs. The results are shown in Table 3

- 10. As can be seen, the results of this analysis are very close to our best estimated costs. This shows that collectively, considering the ranges of each parameter/input to the annual costs, a mean value is obtained from the simulation that is only slightly lower (4 percent) than our estimate.

### 3

- 19 Table 3

- 10. Results of the Monte Carlo Simulation Compared to the EPA Estimates

Facility EPA Estimate Total Annual Costs ($/yr) Monte Carlo Sim ulation Mean Total Annual Costs ($/yr)

ASHTA $828,117 $736,743

Olin GA $2,963,358 $2,821,114

Olin TN $6,653,173 $6,349,942

PPG $2,632,205 $2,608,708

Totals $13,076,853 $12,516,508



### 3.4 Summary of Results
For Option 1 (Non

- mercury Technology Option)

, t his section provided estimated costs for the conversion of the four currently operating mercury cell chlor

- alkali facilities to membrane cells. The EPA estimate of the total capital cost for this conversion is slightly under $300 million, with the EPA es timate of total annual costs at approximately $13 million. Per facility estimated costs range from a low of $828,117 (ASHTA) to $6,653,173 (Olin TN).

For Option 2 (Enhanced Work Practice Standards Option) , only the ASHTA facility
incurs costs. These costs are estimated to be $120 thousand in capital costs and $25 thousand in
total annualized costs.

### 4

- 1 SECTION 4
ECONOMIC IMPACT ANAL YSIS (EIA)

EPA prepares an EIA to provide information about a new environmental policy’s

un favorable effects (i.e., social costs) and how these costs are distributed among stakeholders (EPA, 2000). Although several tools are available to estimate the economic costs of direct performance standards, the proposed regulation is unique in that it ban s emissions of mercury from the mercury cell process within 2 years from final promulgation and bans operation of mercury recovery units 3 years from final promulgation. EPA’s Guidelines note that this type of regulation can be more difficult to assess whe n compared with traditional direct performance standards because it is hard to predict how stakeholders will respond (EPA, 2000). Bans may lead to shutdowns that will involve adjustment costs for people working at the affected plants. Affected plants may a lso have strong links with other firms or downstream markets; as a result, secondary consequences of the regulation become more important to consider.

4.1 Conversion Versus Plant Closure
The facilities currently in operation that do not already have plans to convert or close
would have to convert to one of the non

- mercury technologies, such as membrane cells. As a result, the owners of a plant have to make an investment decision that compares the costs of
conversion with future benefits of the conversion. A lthough there is historical evidence that owners have converted from mercury technologies in Europe and United States, other mercury plant owners have, to date, concluded the investment decision was currently not in their company’s interest given their ass essment of future economic conditions (U.S. House of Representatives Committee on Commerce and Energy, 2009; PPG, 2009). The extremely concentrated markets (few suppliers) for some chlor

- alkali products may provide manufacturers with an ability to pass on cost increases. ASHTA is one of only three producers of liquid potcarb and domestic entry is very difficult.

Although EPA does not have quantitative data to evaluate investment decisions (e.g., using capital budgeting techniques), the economics literature has emphasized three characteristics that influence the owner’s best investment decision (Dixit and Pyndick, 2004):

 

### Uncertainty : the future rewards of an investment are unknown

 

### Irreversibility : the resources used to make the investment typically cannot be undone

### 4

- 2  

### Timing : owners who delay investment get more information that they value; as a result, there is an “op portunity cost” associated with making an investment decision now versus later

Supply and demand conditions in the chlor

- alkali product markets are highly variable;
chlor

- alkali product prices regularly spike and fall during short periods (see Figure 4

- 1). Olin Corporation reports that the average product price for chlorine

- caustic soda co product (electrochemical unit or ECU) dropped from approximately $1,300 per short ton to just under $600 per short ton in the first 6 months of 2009 (Olin Corporation, 20 09). Figure 4.1 (below) shows that the long term price trend for chlor alkali products shows that prices have more than doubled since 2004, even when considering the recent price decreases.

Figure 4

- 1. Price Trends: Alkalis and Chlorine Manufacturing: 2 000 to 2009
Source: U.S. Bureau of Labor Statistics. 2009a. Producer Price Index Industry Data, Series ID = PCU

PCU325181325181. http://data.bls.gov/PDQ/outside.jsp?survey=pc. Accessed August 18, 2009.



### The sharp price changes highlight the risks associated with chlor

- alkali capital investments. For example, OxyChem completed an announced $60 million investment at its
membrane chlor

- alkali plant in Taft, Louisiana, to produce KOH instead of caustic potash (Access Intelligence, LLC, 2008). Almost a year later , an announcement suggests the plant has idled because of unstable market conditions (Access Intelligence, LLC, 2009). 0 50 100 150 200 250 300 350 400 450

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009PPIAlkalies and chlorine manufacturing(1980_12= 100)

### 4

- 3 Given the current market conditions, the benefits of delaying investment and waiting for new information are likely large if plants can d elay conversion decisions. However, if plants delay, they risk other manufacturers entering the market before them and losing profits and market share. If for example, there are industrial consumers of chlorine nearby, it is unlikely that
chlorine manufact urers will not meet that demand. And it appears to be cost

- effective to produce chlor alkali products close to large industrial consumers. Firms would like to require rates of return that exceed the cost of capital to make new capital investments now. As r eported above, prices for large industrial consumers of these facilities’ products could be sufficient to justify the
investment in conversion

To our knowledge, the only existing economics literature that examines regulation

induced plant closures in the c hlor

- alkali industry is a study by Snyder and colleagues (2003). Environmental regulations affecting chlorine end

- use markets have reduced the demand for chlorine and help explain the exit of selected chlorine plants (Snyder, Miller, and Stavins, 2003).
Fo r example, chlorine plants that were co

- located with pulp and paper mills and plants affected by the Montreal Protocol were more likely to shutdown (11 to 15% higher than the average chlorine plant) (Snyder, Miller, and Stavins, 2003). In our case, the dem and for chlorine and other products is not changing with the regulation. In contrast, regulatory factors did not appear to influence the decision to adopt membrane technologies at existing plants. EPA does not have sufficient data to predict whether indivi dual companies would choose to convert or close, historical responses suggest that plant closure may be an option considered by some of these facilities. Of the last four plants to stop using this process, two converted and two closed.

4.2 Market Concentra tion and Pricing Behavior
Of the three major chlor

- alkali products discussed in Section 2, the market for potassium hydroxide is the most highly concentrated. As of 2005, there were only four producers of potassium hydroxide in the United States (FTC, 2005 b). Approximately 50% is produced using mercury cells; ASHTA Chemicals, Inc. reports a market share of approximately 15% (U.S. House of Representatives Committee on Commerce and Energy, 2009). Producers of potassium hydroxide may have more ability to pass on costs of conversion than producers of other chlor

alkali products. Any closure of one or more mercury cell plants could raise concerns about competitiveness and subsequent firm pricing behavior in the KOH market; the remaining companies could have more scope to influence market prices by altering production levels. Given the small number of remaining KOH producers, removing an extra ton of KOH from the market may raise the market price and increase the profits on all the other KOH sold. If the price
effe ct is large enough, the remaining companies may find it is more profitable to reduce

### 4

- 4 production levels below the levels that they would be choose in a market with a large number of competitors.

EPA’s review of publicly available sources also suggests that any closure would have similar market concentration consequences for a downstream commodity: potassium carbonate. ASHTA is one of only three producers of liquid potcarb and domestic entry is very difficult because it is very expensive; foreign entry (i.e. , imports) has historically been limited (FTC, 2009b). In the absence of other market condition changes if ASHTA’s were to close, it would likely reduce competition and lead to higher prices in the liquid potcarb market. Although EPA
does not have data to quantify the effect of higher potassium carbonate prices, we recognize that end users of potassium carbonate (e.g., specialty glasses including television tubes and the ceramics industry [Armond Products Company, 2009; ERCO Worldwide, 2009]) and the

consum ers of these goods could indirectly be affected by a price increase. 29



### 4.3 Impact on Employment Industry sources already suggest that high energy costs and reduced domestic demand in the video glass industry has contributed potassium carbonate industry decline although there is som e evidence that other “niche” end users are emerging (Hoffmann, 2004).

EPA does not have enough information to estimate whether individual plants will convert or close and therefore no estimate of changes in employment are provide d. In the following section we provide information on each plants’ job contributions relative to the size of local
labor markets and current and historical conditions. In Table 4

- 1, we use the latest monthly statistics about county labor forces and show th e relative contributions of each plant in the context of current labor market conditions. In Figure 4

- 2, we compare the historical unemployment rates in the United States with unemployment rates for the counties where mercury cells are located. As shown in Figure 4

- 3, annual unemployment rates for three counties (Ashtabula, Richmond, and Wetzel) diverge and become higher than the national average beginning in 2004. Recent data for the month of May 2009 also suggest the relative difference
between Ashtabula County and the United States is higher than historical differences.

29 Companies using potassium products may als o be concern ed about other events that tend to increase the cost of potassium products (e.g., recent increases in th e input costs of potash used to make potassium hydroxide)

. For example, Superior Plus (2008) notes that a current contract to purchase potas h on favorable terms expired in 2009

, and production costs are likely to rise once potash is purchased at current (higher) market prices. Superior plus owns ERCO Worldwide, one of the four companies that compete in the U.S. potassium hydroxide market.

### 4

- 5 Table 4

- 1. May 2009 County Employment Data (Not Seasonally Adjusted)

Plant Location Plant Employees County Persons Employed Plant Share of Persons Employed County Persons Unemployed

ASHTA Ashtabula County, OH 100 42,481 0.2% 6,526

Olin Richmond County, GA 85 82,416 0.1% 8,634

Olin Bradley County, TN 280 42,132 0.7% 4,397

PPG Wetzel County, WV 540 5,663 9.5% 731

Source: U.S. Bureau of Labor Statistics. 2009b. LAU Local Area Unemployment Statistics Series Id:

LAUPS39011004, LAUPS39011005, LAUPA47035004, LAUPA47035005, LAUCN13245004, LAUCN13245005, LAUCN54103004, LAUCN54103005. Accessed August 2009.



### Figure 4

- 2. Unemployment Rates by County: 2000

– 2008
Sources: U.S. Bureau of Labor Statistics. 2009c. LAU Local Area Unemployment Statistics Series Id:

LAUPS39011006, LAUPA47035006, LAUCN13245006, and LAUCN54103006. Accessed August 2009. U.S. Bureau of Labor Statistics . 2009d. Current Employment Statistics

— CES (National) Series Id: LNU04000000. Accessed August 2009.

### 4

- 6 Figure 4

- 3. Unemployment Rate Differences from National Average: May 2009 and Historical Average: 2000

– 2008

Source: U.S. Bureau of Labor Statistics. 2009c . LAU Local Area Unemployment Statistics Series Id:

LAUPS39011006, LAUPA47035006, LAUCN13245006, and LAUCN54103006. Accessed August 2009.



### 4.4 Transportation Issues Associated with Plant Closure
If one or more mercury cell plants were to close, customers wi th existing arrangements with the plants will have to identify another outside supply source to maintain chemical production. For example, ASHTA’s production facility sells 100% of its chlorine to an adjacent titanium dioxide plant (U.S. House of Represent atives Committee on Commerce and Energy, 2009). Although other chlorine supply sources may be available, transportation will likely be more expensive because of increased shipping distances between the supply source and its destination. Depending on the mo de of transportation selected, this may also indirectly increase the volume of existing shipments passing through certain urban areas.

4.5 Small Entity Impact Analysis
The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a regulator y flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other statute unless the agency - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Ashtabula Bradley Richmond WetzelUnemployment Rate Difference From National Average

County

May 2009 Historical Average (2000

– 2008)

### 4

- 7 certifies that the rule will not have a significant economic impact on a substantial numb er of small entities (SISNOSE). The first step in this assessment was to determine whether the rule will have SISNOSE. To make this determination, EPA used an impact analysis to indicate whether EPA can certify the rule as not having a SISNOSE. The element s of this analysis
included

 

### identifying affected small entities,

 

### selecting and describing the measures and economic impact thresholds used in the analysis, and

 

### completing the assessment and determining the SISNOSE certification category.

4.5.1 Identify Aff ected Small Entities
For the purposes of assessing the impacts of the proposed rule on small entities, a small entity is defined as (1) a small business as defined by the Small Business Administration’s regulations at 13 CFR 121.201; according to these siz e standards, ultimate parent companies owning mercury cell chlor

- alkali facilities are categorized as small if the total number of employees at the ultimate parent company is fewer than 500; otherwise, the ultimate parent company is classified as large. As discussed in the previous section, ASHTA, Inc. is the sole entity that meets these qualifications.

4.5.2 Sales Test Analysis
EPA assessed how the regulatory program may influence the profitability of ultimate parent companies by comparing annual conversi on costs to total sales (i.e., a “sales” test). To do this, we divided an ultimate parent company’s total annualized compliance costs by its reported

revenue:

j n

i 

### TR

TACC
CSR 

# ∑

**=** 

### (4.1)

where

CSR = cost

- to

- sales ratio

TACC = total annualized compliance costs

TR j 

### = total sales from all operations of ultimate parent company j or annual government revenue

### 4

- 8 This method assumes the affected entity cannot shift pollution control costs to consumers (in the form of higher market prices). Instead, the owning entity experiences a one

- for

- one reduction in profits. For Option 1, EPA estimates that ASHTA’s CSR would r ange from 1 to 2%, which suggests a significant economic impact. For Option 2, EPA estimates that ASHTA’s CSR
would be less than 0.1%. The other three plants are owned by large ultimate parent companies with significant company

- wide sales. As a result, the CSRs for the large companies are below 1%. However, when plant

- only sales are considered, the CSRs for plants owned by large ultimate parent companies range from 4 to 9%.

4.5.3 Profit Test Analysis

Economic models assume that a chlor

- alkali company acts i n the best interest of its shareholders and tries to make the company’s profit (total revenue minus total costs) as large as possible. To measure of chlor

- alkali profits, EPA used trade publication data for a representative ECU called the “cash margin” (CM AI, 2008). The measure approximates the average profit per
metric ton of chlor

- alkali product made. As shown in Figure 4

- 4, historical data suggest that cash margins can fluctuate over time. Assuming the market prices for chlor

- alkali products do not chang e as a result of the proposed rule, the annual conversion costs ($/metric ton) associated with the proposed rule will reduce the average profit for chlor

- alkali products. To assess the size of this effect, EPA compared the conversion costs with economic da ta for a typical year (using historical industry ECU margins and capacity utilization rates from 1991 to 2005). 30



### 4.6 Conclusions As shown, conversion costs could reduce the margins by 10 to 20% (Figure 4

- 5). EPA also considered data in weak and strong margin years; as shown, margin reductions range from 5 to 100% for these years. As a result, the analysis confirms the results assoc iated with the sales test; plant conversion costs will likely have an economically significant effect.

The EIA suggests Option 1 requirements are likely to have significant economic costs. Plants will either convert or close. Predicting whe ther, no plant, one plant, two plants, three plants, or all four plants will close or convert is not possible with the available data.

30 Fo r a typical year, we used the observed average industry ECU cash margin (2008 dollars) and average industry utilization rate between 1991 and 2005. For weak and strong years, we considered cash margins and industry utilization rates one standard deviation (lower and higher) from the average.

### 4

- 9 Figure 4

- 4. Historical ECU Cash Margins
Source: EPA estimates using data from CMAI (2008).



### Closure of one or more merc ury cell process plants also raises concerns about competitiveness and subsequent pricing behavior in the potassium hydroxide and potassium carbonate markets. In highly concentrated markets with few producers, nonmercury cell producers may be able to influ ence market prices above competitive levels by altering production decisions. Similar competitive issues have also been raised in other regulatory

contexts.

The small business impact analysis shows that one of the four plants is owned by a small
business. A sales test suggests this small company will be significantly affected (CSR would range from 1 to 2% for Option 1 ). EPA also considered profitability effects by comparing annual conversion costs to reported industry margins for a representative ECU. Even in optimistic scenarios where ECU margins are higher than historical averages, conversion costs could significantly reduce the margins. $232

$0 $366

$225

$0 $132 $173 $456

$0 $50 $100 $150 $200 $250 $300 $350 $400 $450 $500

1990 1992 1994 1996 1998 2000 2002 2004 2006ECU margin

Year $/metric ton, 2008$

$/metric ton

### 4

- 10 Figure 4

- 5. Profit Tests: ECU Cash Margin Reductions Associated with Conversion Costs

4.7 References Access Intelligen ce, LLC. 2008. “Oxychem Completes Taft, LA KOH Conversion.” ChemWeek’s Business Daily , April 29, 2008.

Access Intelligence, LLC. June 16, 2009. Chlor

- alkali Market Wire.

American Chemistry Council. 2000. “Chlorine: Ensuring Rail Transportation Safety.” http://www.americanchemistry.com/s_acc/sec_article_acc.asp?CID=1695&DID=10018

. Accessed August 19, 2009.

Armand Products Company. 2009. “Potassium Carbonate Handbook.” http://www.armandproducts.com/pdfs/k2so3P1_12.pdf . Accessed August 2009.

ASHTA Chemicals Inc. 2009a. About ASHTA. http://www.ashtachemicals.com/aboutfs.htm. Accessed August 2009.

ASHTA Chemic als, Inc. 2009b. Potassium Hydroxide, Liquid. http://www.ashtachemicals.com/products/lkohfs.htm . Accessed August 2009. - 10%

- 14%

- 14%

- 20%

- 53%

- 75%

- 77%

- 100% - 5%

- 7%

- 7%

- 10%

- 100% - 95% - 90% - 85% - 80% - 75% - 70% - 65% - 60% - 55% - 50% - 45% - 40% - 35% - 30% - 25% - 20% - 15% - 10% - 5% 0% Ashta Olin GA Olin TN PPG Small Business Large BusinessesAnnualized Conversion Costs as a Share of ECU Cash Margin

Typical Year Weak Year Strong Year

### 4

- 11 Cleveland/Bradley Chamber of Commerce, Economic Development Council. 2009 . 2009 Industrial Directory. http://clevelandchamber.com/images/stories/pdf/2009inddir.pdf. Accessed August 7, 2009.

Dixit, A.K., and R.S. Pindyck. 1994. Investment under Uncertainty . Princeton University Press: Princeton, NJ.

Energy Information Administra tion. 2009. “Electric Power Annual 2007: State Data Tables.” http://www.eia.doe.gov/cneaf/electricity/epa/epa_sprdshts.html. Accessed July 31, 2009.

Environmental News Service. January 10, 2005. “Chlorine Gas from South Carolina Train Crash Kills Nine.” http://www.ens

- newswire.com/ens/jan2005/2005

- 01

- 10

- 04.asp . Accessed 18, 2009.

ERCO Worldwide. 2009. “Potassium Hydroxide.” http://www.ercoworldwide.com/ products_potassiumhydroxide.asp. Accessed August 2009.

Fullerton, D. 2008. Distributional Effects of Environmental and Energy Policy: An Introduction

. Cambridge, MA: National Bureau of Economic Research.

Hoffmann, J. April 19, 2004. “Potassium Carbonate Faces Pressures from the Video Gla ss.” Chemical Market Reporte r, p. 15.

Millennium Inorganic Chemicals. 2009. “Site Fact Sheet Ashtabula Complex.” http://www.millenniumchem.com/NR/rdonlyres/6AE8BF59

- D7CD

- 4768

- 951F

37AE6F1C7321/0/SiteFactSheetAshtabula.pdf. Accessed August 2009.

Occidental Chemical Corporation. 2008a. “OxyChem Caustic Potash Handbook.” http://www.oxy.com/Our_Businesses/chemicals/Pages/chem_products_basic_alkali.aspx #alk_koh. Accessed July 28, 2009.

Occidental Chemical Corporation. 2008b. “OxyChem Caustic Soda Handbook.” http ://www.oxy.com/Our_Businesses/chemicals/Documents/caustic_soda/caustic.pdf. Accessed August 6, 2009.

Olin Corporation. December 31, 2001. Form 10

- K405. Filed March 4, 2002. http://b2i.api.edgar

online.com/EFX_dll/EdgarPro.dll?FetchFilingConvPDF1?SessionID= n9ZFWjsyBGA3E

- 9&ID=1782286. Accessed August 2009.

Olin Corporation. December 31, 2008. Form 10

- K. Filed February 25, 2009. http://b2i.api.edgar

online.com/EFX_dll/EdgarPro.dll?FetchFilingConvPDF1?SessionID=DOVFWfuqOuZB E

- 9&ID=6436012. Accessed July 2009.

Ol in Corporation

. April 1, 2009a. “ BB&T Capital Markets Presentation Manufacturing and Materials Conference .” http://www.olin.com/ . Accessed July 31, 2009.

Olin Corporation. 2009b. “Olin Transportation of KOH.” http://koh.olinchloralkali.com/Facilities/chas/ChasLogistics.aspx . Accessed August 2009.

### 4

- 12 Pavey, R. 2008. “Bill Would End Mercury Use at Chlorine Plants.” http://chronicle.augusta.com/stories/031308/met_190792.shtml. Accessed August 7, 2009.

PPG Industries, Inc. December 31, 2001. Form 10

- K. Filed February 22, 2002. http://www.secinfo.com/dV5Ff.32Wq.7.htm#25t hPage. Accessed August 2009.

PPG Industries, Inc. December 31, 2008. Form 10

- K. Filed February 19, 2009. http://corporateportal.ppg.com/NR/rdonlyres/EAC49DFC

- DB9C

- 4320

- B9A3

- 421E40162220/0/10KYREND2008.pdf. Accessed July 2009.

PPG Industries. 2009a. “Chlor ine Product Bulletin.” http://corporateportal.ppg.com/NA/Chemicals/CAD/ProductInformation/Bull /English/EN. Accessed August 19, 2009.

PPG Industries

. August 11, 2009b . “PPG Reaches Agreement with State of Maryland.” PPG News Release. http://corporateportal.ppg.com/PPG/Newsroom/News/20090811.htm

. Accessed August 2009.

Sim, P.W. January 19, 2005. “ASHTA to Build Chlorine

- KOH Plant in Arkansas.” Chemical Week p. 13.

Snyder, L.D., N.H. Miller, and R.N. Stavins. 2003. “The Effects of Environmental Regulation on Technology Diffusion: The Case of Chlorine Manufacturing.” American Economic Review 93(2):431

- 435.

“Special Focus on Chlorine.” October 2007. Chemical Business . pp. 27

- 40. Busines s Source Corporate database. Accessed July 29, 2009.

Superior Plus. 2009. Annual Report 2008. http://www.olin.com/ . Accessed August 2009.

The Innovation Group (TIG). 2009. “Caustic Potash Chemical Profile.” http://www.the

innovation

- group.com/welcome.htm. Accessed July 2009.

U.S. Bureau of Labor Statistics. 2009a. Current Employment Statistics

— CES (National) Series Id: LNU04000000. Accessed August 2009.

U.S. Bureau of Labor Statistics. 2009b. LAU Local Area Unemployment St atistics Series Id: LAUPS39011004, LAUPS39011005, LAUPA47035004, LAUPA47035005, LAUCN13245004, LAUCN13245005, LAUCN54103004, LAUCN54103005. Accessed August 2009.

U.S. Bureau of Labor Statistics. 2009c. LAU Local Area Unemployment Statistics Series Id: LAUP S39011006, LAUPA47035006, LAUCN13245006, and LAUCN54103006. Accessed August 2009.

### 4

- 13 U.S. Bureau of Labor Statistics. 2009d. Producer Price Index Industry Data, Series ID = PCU PCU325181325181. http://data.bls.gov/PDQ/outside.jsp?survey=pc. Accessed August 18 , 2009.

U.S. Bureau of Labor Statistics. 2009e. Quarterly Census of Employment and Wages. Accessed August 2009.

U.S. Census Bureau. 2001

– 2006. Quarterly Financial Report for Manufacturing, Mining, and Trade Corporations, 1st Quarter reports. http://www.cen sus.gov/prod/www/abs/qfr

mm.html. Accessed July 2009.

U.S. Census Bureau. Economic Census. 2007. Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007. NAICS code 325181. http://factfinder.census.g ov/servlet/DatasetMainPageServlet?_program=ECN&_tabId=E CN1&_submenuId=datasets_4&_lang=en&_ts=24636668839. Accessed July 23, 2009.

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 31: Manufacturing: Industry Series: In dustry Statistics by Employment Size: 2002.” http://factfinder.census.go v. Accessed July 2009.

U.S. Department of Transportation. 2009a. “ Part 172

— Hazardous Materials Table, Special Provisions, Hazardous Materials Communications, Emergency Response Information, Training Requirements, And Security Plans .” http://ecfr.gpoaccess.gov/cgi/t/text/text

idx?c=ecfr&sid=1dbcd9fcab84b206ffd37360976a4c4f&tpl=/ecfrbrowse/Title49/49cfr17 2_main_02.tpl . Accessed August 2009.

U.S. Department of Transportation. 2009b. Pipeline and Hazardous Materials Safety Administration. http://www.phmsa.dot.gov/regulations . Accessed August 2009.

U.S. Environmental Protection Agency (EPA). September 2000. Guide lines for Preparing Economic Analyses . EPA 240

- R

- 00

- 003. Washington, DC: EPA. http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html.

U.S. Federal Trade Commission (FTC). 2005. “With Conditions, FTC Clears Occidental Chemical’s Purchase of Vulcan’ s Chemical Assets.” http://www.ftc.gov/opa/2005/06/vulcan.shtm. Accessed August 7, 2009.

U.S. Geological Survey (USGS). 2009. Soda Ash. http://minerals.usgs.gov/minerals/pubs/ commodity/soda_ash/mcs

- 2009

- sodaa.pdf. Accessed August 4, 2009.

U.S. House of Re presentatives Committee on Commerce and Energy, Subcommittee on Commerce, Trade, and Consumer Protection; Pertaining to the Mercury Pollution Reduction Act of 2009 (H. R. 2190), Testimony of Richard Jackson, Executive Vice President of Operations, ASHTA Ch emicals Inc., May 12, 2009. http://energycommerce.house.gov/Press_111/20090512/testimony_jackson.pdf. Accessed August 2009.

### 4

- 14 Ward, K. Jr. 2007. “Report Cites PPG Savings on Mercury Process Could Boost Efficiency, Phase Out Toxic Discharge at Marshall Plant. ” http://www.wvrivers.org/news2007/CharlestonGazette

- Oceana.pdf . Accessed August, 2009.

West Virginia Development Office. 2009. PPG Industries Invests Nearly $6 million in Marshal l County Plant.” http://www.wvopenforbusiness.com/OFB_0609/index.aspx#PPG. Accessed August 7, 2009.

World Chlorine Council. April 2002. “Chlor

- Alkali Manufacturing Processes.” The World Chlorine Council. http://www.worldchlorine.com/publications/pdf/mfg_pr ocesses.pdf. Accessed July 27, 2009.

### 5

- 1 SECTION 5
HUMAN HEALTH BENEFIT S OF EMISSIONS REDUC TIONS

5.1 Synopsis
In this section, we provide analysis of the benefits associated with the proposed Mercury
Chlor Alkali NESHAP. As there are no emission reductions or energy impacts associated with Option 2 (enhanced work practice standards), the benefits in this chapter correspond to Option 1 (non

- mercury technology option). The primary benefit of this regulation is the reduction of mercury emissions from these sources. Due to data, time, and resource limitations, we were unable to model mercury dispersion, deposition, methylation, bioaccumulation in fish tissue, and human consumption of mercury

- contaminated fish that would be need ed in order to estimate the human health benefits from reducing mercury emissions. In addition, this regulation would reduce electricity demand at these sources. Using benefit

- per

- ton estimates, we estimate the
monetized human health co

- benefits of reducin g cases of morbidity and premature mortality among populations exposed to particulate matter (PM) associated with the decreased electricity usage. Using the social cost of carbon (SCC), we estimate the monetized benefits associated with
reduced CO 2 

### emissio ns. At a 3% discount rate, we estimate the total monetized co

- benefits of the proposed NESHAP to be $22 million to $43 million in the implementation year (2013). At a 7% discount rate, we estimate the total monetized co

- benefits of the proposed NESHAP to b e $14 million to $33 million in 2013. All estimates are in 2007$. These estimates reflect EPA’s most current interpretation of the scientific literature. Higher or lower estimates of PM 2.5 

### co

- benefits are possible using other assumptions; examples of this are provided in Figure 5

- 2 and Table 5

- 6. Data, resource, and methodological limitations prevented EPA from monetizing the co

- benefits from several important benefit categories, including benefits from reducing ecosystem effects and visibility impairment. It is important to emphasize that these monetized co

- benefits do not include the benefits associated with reducing mercury emissions.

5.2 Mercury Benefits
Mercury is a highly neurotoxic contaminant that enters the food web as a methylated compound, methylm ercury (U.S. EPA, 2008 c ) . The contaminant is concentrated in higher trophic levels, including fish eaten by humans. Mercury is emitted to the air from various man

made and natural sources. These emissions transport through the atmosphere and eventually dep osit to land or water bodies. This deposition can occur locally, regionally, or globally, depending on the form of mercury emitted and other factors such as the weather. The form of mercury emitted from these sources is estimated to be about 98 percent ele mental and two percent divalent mercury. Gaseous elemental mercury can be transported very long distances,

### 5

- 2 even globally, to regions far from the emissions source (becoming part of the global “pool”) before deposition occurs. Inorganic ionic (divalent) mer cury has a shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions source. Furthermore, elemental mercury in the atmosphere can undergo transformation into ionic mercury, providing a significant pathway for deposition o f emitted elemental mercury.

This source category emitted about 640 pounds of mercury in the air in 2008 in the U.S. Based on the EPA’s National Emission Inventory, about 103 tons of mercury were emitted from all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We believe that total mercury emissions in the U.S. and globally in 2008 were about the same magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 0.3% of the total anthropogenic mercury emissions in the U.S. and about 0.02% of the global emissions. Overall, this rule would directly reduce mercury emissions by about 640 pounds per year from

curren t levels as well as an estimated 16 pounds per year indirectly through reduced electricity generation, and therefore, contribute to reductions in mercury exposures and health effects. Due to data, time, and resource limitations, we were unable to model mer cury dispersion, deposition, methylation, bioaccumulation in fish tissue, and human consumption of mercury

- contaminated fish that would be needed in order to estimate the human health benefits from reducing mercury
emissions.

Potential exposure routes to mercury emissions include both direct inhalation and consumption of fish containing methylmercury. In the U.S., the primary route of human exposure to mercury emissions from industrial sources is generally indirectly through the consumption of fish contain ing methylmercury. As described above, mercury that has been emitted to the air eventually settles into water bodies or onto land where it can either move directly or be leached into waterbodies. Once deposited, certain microorganisms can change it
into me thylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fish. Consumption of fish and shellfish are the main sources of methylmercury exposure to humans. Methylmercury builds up more in some types of fish and shellfish than in others. The levels of methylmercury in fish and shellfish vary widely depending on what they eat, how long they live, and how high they are in the food chain. Most fish, including ocean species and local freshwater fish, contain some methylmercury. For example, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, every fish sample d from 291 streams across the
country contained some methylmercury (Scudder, 2009).

### 5

- 3 The majority of fish consumed in the U.S. are ocean species. The methylmercury concentrations in ocean fish species are primarily influenced by the global mercury pool. However, the methylmercury found in local fish can be due, at least partly, to mercury emissions from local sources. Research shows that most people’s f ish consumption does not cause a

mercury

- related health concern. However, certain people may be at higher risk because of their routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families who rely heavily on fish for a substantial part of their diet). It has been demonstrated that high levels of methylmercury in the bloodstream of unborn babies and young children may harm the developing nervous system, making the child less able to think and learn. Moreover, mercury

exp osure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people of all ages.

Several studies suggest that the methylmercury content of fish may reduce these cardio

protective effects of fish consumption. Some of these studies a lso suggest that methylmercury may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of the literature concerning methylmercury health effects took note of two epidemiological studies
that found an association between d ietary exposure to methylmercury and adverse cardiovascular
effects. 31 

### Moreover, in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al. (1995) observed a relationship between methylmercury exposure via fish consumption and acute myocardial infarction (AMI or heart attacks), coronary heart disease, cardiovascular disease, and
all

- cause mortality. 32 

### The NRC also noted a study of 917 seven year old children in the Faroe Islands, whose initial exposure to methylmercury was in utero although post natal exposures may have occurred as well. At seven years of age, these children exhibited an increase in blood pressure and a decrease in heart rate variability. 33 

### Based on these and other studies, NRC concluded in 2000 that, while “the data base is not a s extensive for cardiovascular effects as it is for other end points (i.e., neurologic effects) the cardiovascular system appears to be a target for methylmercury toxicity.” 34

31 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washingt on, DC. pp. 168

- 173.

32 Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk

of myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91 (3):645

- 655

. 33 Sorensen, N, K. Murata, E. Budtz

- Jorgensen, P. Weihe, and Grandjean, P., 1999. “Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370

- 375.

34 National Research Council (NRC). 2000. Toxicological Effe cts of Methylmercury. Committee on the

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. p. 229.

### 5

- 4 Since publication of the NRC report there have been some 30 published papers

pre senting the findings of studies that have examined the possible cardiovascular effects of methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic investigations. Over a dozen review papers have also been published. I f there is a causal relationship between methylmercury exposure and adverse cardiovascular effects, then reducing exposure to methylmercury would result in public health benefits from reduced cardiovascular
effects.

In early 2010, EPA sponsored a workshop in which a group of experts were asked to assess the plausibility of a causal relationship between methylmercury exposure and cardiovascular health effects and to advise EPA on methodologies for estimating population level cardiovascular health impacts of reduced methylmercury exposure. The report from that workshop is in preparation.

5.3 Energy Savings Co

- benefits

Because the membrane cell process requires less energy than the mercury cell process

, this rule is anticipated to reduce electricity generated f or these facilities by over 375 million kilowatt hours. Assuming a 22% reduction in electricity demand, the emission reductions associated with reduced electricity generation are estimated to be 68 tpy of PM 2.5 

### , 0.008 tpy (16 lbs) of mercury, and 287,000 t py of CO 2 

### . The methodology for calculating these emission reductions is provided in Appendix B of this RIA.

5.3.1 PM 2.5 

### Co

- benefits
The energy savings anticipated from this rule would decrease emissions of PM, thus reducing human exposure, and the incidence of PM 2.5 

### - related health effects. Due to time and resource limitations, it was not possible to provide a comprehensive estimate of the PM 2.5 

### - related

co

- benefits. Instead, we used the “benefit

- per

- ton” approach to estimate these co

- benefits based

o n the methodology described in Fann, Fulcher, and Hubbell (2009). These PM 2.5 

### benefit

- per

- ton estimates provide the total monetized human health co

- benefits (the sum of premature mortality and premature morbidity) of reducing one ton of PM 2.5 

### from a specif ied source. EPA has used the benefit per

- ton technique in several previous RIAs, including the recent proposed Cement NESHAP (U.S. EPA, 2009a). For this analysis, we use the national average benefit

- per

- ton estimates associated with the electrical generati ng unit (EGU) sector. Because the anticipated emission reductions are assumed to occur across multiple EGUs, the national average benefit

per

- ton estimates best represent the regional scope of the anticipated emission reductions.

### 5

- 5 Table 5

- 1 identifies the q uantified and unquantified co

- benefits captured in those benefit

- per

- ton
estimates.

Table 5

- 1. Human Health and Welfare Effects of PM 2.5

Pollutant/

Effect Quantified and Monetized in Primary Estimates Unquantified Effects Changes in:

PM 2.5 Adult premature mortality
Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness
Minor restricted

- activity days
Work lo ss days Asthma exacerbations (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis

Non

- asthma respiratory emergency room visits Visibility
Househol d soiling



### Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM 2.5 

### co

- benefits
estimates utilize the concentration

- response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expert elicitation study as a sensitivity analysis.

 

### One estimate is based on the concentration

- response (C

- R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002), a study that EPA has previously used to generate its primary benefits estimate. When calculating the 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 Ai r 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 

### NAAQ S, has been used as an alternative estimate in the PM 2.5 

### NAAQS RIA and PM 2.5 

### benefits estimates in RIAs completed since the PM 2.5 

### NAAQS. When calculating the estimate, EPA applied the effect coefficient as reported in the study without an adjustment for as sumed concentration threshold of 10 μg/m 3 

### as was done in recent (2006

– 2009) RIAs.

 

### Twelve estimates are based on the C

- R functions from EPA’s expert elicitation study (IEc, 2006; 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

### 5

- 6 that study, twelve experts (labeled A through L) provided independent estimates of the PM 2.5 

### - mortality concentration

- response function. EPA practice has been to develop independent estimates of PM 2.5 

### - mortality estimates corresponding to the concentration

- response function provided by each of the twelve experts, to better characterize the degree of variability in the expert responses.

The effect coefficients are drawn from epidemiology studies e xamining two large population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six Cities cohort (Laden et al., 2006). 35 

### These are logical choices for anchor points in our presentation because, while both studies are well desi gned and peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for using both studies to generate co

benefits estimates. Previously, EPA had calculated co

- benefits based on these two empirical studies, but derived the range of co

- benefits, including the minimum and maximum results, from an expert elicitation of the relationship between exposure to PM 2.5 

### and premature mortality (Roman et al., 2008). 36



### Readers interested in reviewing the methodology for creating the benefit

- per

- ton estimates used in this analysis should consult Fann, Fulcher, and Hubbell (2009). As described in
the documentation for the benefit per

- ton estimates cited above, national per

- ton estimates are developed for selected pollutant/source category combinations. The per

- ton values calculated therefore apply only to tons reduced from those specific pollutant/sou rce combinations (e.g.,
NOx emitted from electric generating units; NO 2 

### emitted from mobile sources). Our estimate of
PM 2.5 

### control co

- benefits is therefore based on the total PM 2.5 

### emissions controlled by sector and multiplied by this per

- ton value. Within this assessment, we include the co

- benefits estimates derived
fr om the concentration

- response function provided by each of the twelve experts to better characterize the uncertainty in the concentration

- response function for mortality and the degree of variability in the expert responses. Because the experts used these cohort studies to inform
their concentration

- response functions, co

- benefits estimates using these functions generally fall between results using these epidemiology studies (see Figure 6

- 2). In general, the expert elicitation results support the conclusion that the co

- benefits of PM 2.5 

### control are very likely to be
substantial.

The b enefit

- per

- ton coefficients in this analysis were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, this analysis uses the benefit

- per

- ton method first applied in the Portland Ceme nt NESHAP RIA

35 These two studies specify multi

- pollutant models that control for NOX, among o ther pollutants.

36 Please see the Section 5.2 of the Portland Cement proposal RIA in Appendix 5A for more information regarding the change in the presentation of co

- benefits estimates.

### 5

- 7 (U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded geographic scope of the benefit

- per

- ton calculation, and the functions directly from the epidemiology studies without an adjustment for an assumed thre shold. 37



### EPA strives to use the best available science to support our benefits analyses, and we recognize that interpretation of the science regarding air pollution and health is dynamic and evolving. Based on our review of the current body of sci entific literature, EPA now estimates
PM

- related mortality without applying an assumed concentration threshold. EPA’s Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by EPA’s Clean Air Scientific Advisory Committee (U.S. EPA

- SAB, 2009a; U.S. EPA

- SAB, 2009b), concluded that the scientific literature consistently finds that a no

- threshold log

- linear model most adequately portrays the PM

- mortality concentration

- response relationship while recognizing potentia l uncertainty about the exact shape of the concentration

- response function. Since then, the Health Effects Subcommittee (U.S. EPA

- SAB, 2010) of EPA’s Council concluded, “The HES fully supports EPA’s decision to use a no

- threshold model to estimate
mortalit y reductions. This decision is supported by the data, which are quite consistent in showing effects down to the lowest measured levels. Analyses of cohorts using data from more recent years, during which time PM concentrations have fallen, continue to repo rt strong associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” In conjunction with the underlying scientific literature, this document provided a basis for reconsidering the application of thresholds in PM 2.5 

### co ncentration

- response functions used in EPA’s RIAs. For a summary of these scientific review statements and the panel members commenting on thresholds since 2002, please consult the Technical Support Document (TSD) Summary of Expert Opinions on the Existenc e of a Threshold (U.S. EPA, 2010c). Removing the threshold assumption is a key difference between the method used in this analysis of PM co

- benefits and the methods used in RIAs prior to Portland Cement, and we now calculate incremental co

benefits down to the lowest modeled PM 2.5 

### ai r quality levels.

Consistent with this finding, we have conformed the previous threshold sensitivity analysis to the current state of the PM science by incorporating a new “Lowest Measured Level” (LML) assessment . This information allows readers to determine the portion of population exposed to annual mean PM 2.5 

### levels at or above the LML of each study; in general, our confidence in the estimated PM mortality decreases as we consider air quality levels further below the LML in major cohort studies that estimate PM

- related mortality. While an LML

37 The benefit

- per

- ton estimates have also been updated since the Cemen t RIA to incorporate a revised VSL, as discussed on the next page.

### 5

- 8 assessment provides some insight into the level of uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues to quantify PM

- related mortality impacts using a full range of modeled air quality concentrations. It is important to emphasize that we have high confidence in PM 2.5 

### - related effects down to the lowest LML of the major cohort studies, which is 5.8 μg/m 3 

### . 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.

For this analysis, policy

- specific air quality data is not available due to time or resource limitations. For these rules, we are unable to estima te the percentage of premature mortality associated with this specific rule’s emission reductions at each PM 2.5 

### level. However, we believe that it is still important to characterize the distribution of exposure to baseline air quality levels. As a surrogat e measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 

### level using the most recent modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e). It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population, and change in air quality. In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the po pulation experiencing health impacts as a result of a specific emission reduction policy. The most important aspect, which we are unable to quantify for rules without air quality modeling, is the shift in exposure associated with this specific rule. Theref ore, caution is warranted when interpreting the LML assessment. For more information on the data and conclusions in the LML assessment for rules without policy

specific air quality modeling, please consult the LML TSD (U.S. EPA, 2010d). The results of
this analysis are provided in Section 5.4.

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods used to estimate air quality co

- benefits evolve over time to reflect the Agency’s most current interpretation of the scientific and ec onomic 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 studies. 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. T he $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta

-

### 5

- 9 analysis of 43 studies. The mean estimate of $5.5 million (2000$) 38



### During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a report from meta

- analytic experts to evaluate methodological questions raised by EPA and the SAB on combining estimates from the various data sources. In addition, the Agency consulted several times with the Science Advisory Board Environmental Economics Advisory Committee (SAB

- EEAC) on the issue. With input from the

meta

- analytic experts, the SAB

- EEAC advised the Agency to update its guidance using specific, appropriate meta

- analytic techniques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e., wage

- risk and stated preference) (U.S. EPA

- SAB, 2007). was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta

- analysis. However, the Agency neither changed its official guidance on the use of VSL in rule

- makings nor subjected the interim estimate to a scientific peer

- review process through the Science Advisory Board (SAB) or other peer

- review gr oup.

Until updated guidance is available, the Agency determined that a single, peer

- reviewed estimate applied consistently best reflects the SAB

- EEAC advice it has received. Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB i n the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) 39 

### while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 40



### Figure 5

- 1 illustrates the relative breakdown of the monetized PM 2.5 

### health co

- benefits by health endpoint. The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progr ess in responding to the SAB

- EEAC’s specific recommendations.

38 After adjusting the VSL for a different currency year (200 7 $) and to account for income growth to 2015 to the $5.5 million value, the VSL is $ 8.0 million.

39 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008 b ), EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. Therefore, this repo rt does not represent final agency policy.

40 In this analysis, we adjust the VSL to account for a different currency year (200 7 $) and to account for income growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $9.1 million

.

### 5

- 10 Figure 5

- 1. Breakdown of Monetized PM 2.5 

### Health Co

- Benefits using Mortality Functio n from Pope et al. (2002) a

a This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the

Laden et al. (2006) function for premature mortality, the percentage of total monetized co

- benefits due to adult

m ortality 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 5

- 2 provides a general summary of the monetized co

- benefits results by pollutant, including the emission reductions and benefits

- per

- ton estimates at discount rates of 3% and
7%. 41

41 To comply with Circular A

- 4, EPA provides monetized co

- benefits using discount rates of 3% and 7% (OMB, 2003). These co

- benefits are estimated for a specific analysis year (i.e., 2013), and most of the PM co

- benefits occur within that year with two exc eptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow

- up medical costs and lost wages. For premature mortality, we assume that there is a “cessation” lag between PM exposures and the total realization o f changes in health effects. Although the structure of the lag is uncertain, EPA follows the advice of the SAB

- HES to assume a segmented lag structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the
y ears 6 to 20 after the reduction in PM 2.5 (U.S. EPA

- SAB, 2004). Changes in the lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis ye ar and the valuation of premature mortalities that occur after the analysis year. As such, the monetized co

- benefits using a 7% discount rate are only approximately 10% less than the monetized

co

- benefits using a 3% discount rate. 

### Table 5

- 3 provides a summary of the reductions in health incidences anticipated as a result of the pollution reductions. In Table 5

- 4, we provide the monetized co

- benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the expert elicitation on PM mortality. Figure 5

- 2 provides a visual representation of the range of monetized co

- benefits estimates and the pollutant breakdown of the monetized co

- b enefits of the proposed option. As 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%

### 5

- 11 there are no emission reductions or energy impacts associated with Option 2 (enhanced work practice standards), the benefits in this chapter correspond to Option 1 (non

- mercury technology option).

Table 5

- 2. Summary of Mo netized PM 2.5 

### Energy Co

- benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (2007$) (Option 1)

Pollutant Emissions Reductions (tons) Benefit per ton (Pope,

3%) Benefit
per ton (Laden,

3%) Benefit per ton (Pope,

7%) Benefit
per ton (Laden,

7%) Mon etized PM 2.5 Co

- benefits (millions, 3%) Monetized PM 2.5 Co

- benefits (millions, 7%)

Direct PM 2.5 68 $220,000 $540,000 $200,000 $490,000 $15 to $37 $14 to $33

a All estimates are for the implementation year (2013), and are rounded to two significant figures so numbers may

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each t on of precursor reduced has a different propensity to become PM 2.5 . The monetized

co

- benefits incorporate the conversion from precursor emissions to ambient fine particles. Confidence intervals are unavailable for this analysis because of the benefit

- per

- t on methodology.



### Table 5

- 3. Summary of Reductions in Health Incidences from PM 2.5 

### Co

- Benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (Option 1) a

Avoided Premature Mortality

Pope et al. 2

Laden et al. 5
Avoided Morbidity

Chronic Bronchitis 1
Acute Myocardial Infarction 3
Hospital Admissions, Respiratory 0
Hospital Admissions, Cardiovascular 1
Emergency Room Visits, Respiratory 2

Acute Bronchitis 3
Work Loss Days 230

Asthma Exacerbation 31
Minor Restricted Activity Days 1,400
Lower Respiratory Symptoms 33
Upper Respiratory Symptoms 25

a All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant figures. All

fine particles are assumed to have equivalent health effects, but each PM 2.5 precursor pollutant has a different propensity to form PM 2.5 . Co nfidence intervals are unavailable for this analysis because of the benefit

- per

- ton methodology.

### 5

- 12 Table 5

- 4. All Monetized PM 2.5 

### Co

- Benefits Estimates for the Proposed Mercury Chlor Alkali NESHAP at discount rates of 3% and 7% in 2013 (in millions of 2007$ ) (Option 1) a

3% 7%

Benefit

- per - ton Coefficients derived from Epidemiology Literature

Pope et al. $15 $14

Laden et al. $37 $33

Benefit

- per - ton Coefficients Derived from Expert Elicitation

Expert A $39 $35
Expert B $30 $27
Expert C $30 $27
Expert D $21 $19
Expert E $48 $43
Expert F $27 $24
Expert G $18 $16
Expert H $22 $20
Expert I $29 $27
Expert J $24 $22
Expert K $6 $5
Expert L $22 $20

a All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they

were derived through the benefit

- per

- ton technique described above. The co

- benefits estimates from the Expert Elicitation are provided as a rea sonable characterization of the uncertainty in the mortality estimates associated with the concentration

- response function. Confidence intervals are unavailable for this analysis because of the benefit

- per

- ton methodology.

### 5

- 13 Figure 5

- 2. Total Monetized PM 2.5 

### Co

- Benefits for the Proposed Mercury Chlor Alkali NESHAP in 2013 (Option 1)

a This graph shows the estimated co

- 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 th ose studies.



### 5.3.2 Social Cost of Carbon and Greenhouse Gas Co

- benefits
EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 

### ) emissions using recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized
dam ages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosyst em services due to climate change. The SCC estimates used in this analysis were developed through an interagency process that included EPA and other executive branch entities, and concluded in February 2010. EPA
first used these SCC estimates in the co

- ben efits analysis for the final joint EPA/DOT Rulemaking to establish Light

- Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; see the rule’s preamble for discussion about application of $0 $10 $20 $30 $40 $50 $60 $70

Pope et al. Laden et al.Millions (2007$)

Benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR
7% DR

### 5

- 14 SCC (75 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. 42



### 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 43



### The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic chang e. Note that the interagency group estimated the growth rate of the SCC directly using the three integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally consistent with other m odeling assumptions. The SCC estimates for the analysis years of 2013, in 2007 dollars are provided in
Table 5

- 5. in 2010, in 2007 dollars. The first three values are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at several
discount rates are included because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context. The fourth value is the 95th percentile of the SC C from all three models at a 3 percent discount rate. It is included to represent higher

- than

- expected impacts from temperature change further out in the tails of the SCC distribution. Low probability, high impact events are incorporated into all of the SC C values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high
temperature ou tcomes, which in turn lead to higher projections of damages.

42 Docket ID EPA

- HQ

- OAR

- 2 009

- 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 Advisers, 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 Scie nce and Technology Policy, and Department of Treasury (February 2010). Also available at http://www.epa.gov/otaq/climate/regulations.htm
43 The interagency group decided that these estimates appl y only to CO 2 emissions. Given that warming profiles and

impacts other than temperature change ( e.g., ocean acidification) vary across GHGs, the group concluded “transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accurate estimates of the social costs of non

- CO 2 gases” (SCC TSD, pg 13).

### 5

- 15 Table 5

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

### ) for 2013 a

Discount Rate and Statistic SCC estimate (2007$)
5% Average $5.3
3% Average $22.8
2.5% Average $37.0

3% 95%ile $69.6

a The SCC values are dollar

- year and emissions

- year specific. SCC values represent only a partial accounting of

climate impacts.



### When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges. A recent report from the National Academies of Science (NRC, 2008) points out that any assessment will suffer from unc ertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) 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 limitations to the SCC analysis, including the incomplete way in which the integrated assessment models capture catastrophic and non

catastrophic impacts, their incomplete treatment of adaptation and technological c hange, 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 in teragency group hopes that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling. Additional details on these limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to updating the current estimates as the science and economic understanding of climate change and its impacts on society improves over time. Specif ically, the interagency group has set a preliminary goal of revisiting the SCC values within two years or at such time as substantially updated models become available, and to continue to support research in this area.

### 5

- 16 Applying the global SCC estimates to the estimated decreases in CO 2 

### emissions for the range of policy scenarios, we estimate the dollar value of the climate

- related co

- benefits captured by the models for each analysis year. For internal consistency, the annual co

- benefits are
discounted back to NPV terms using the same discount rate as each SCC estimate (i.e., 5%, 3%, and 2.5%) rather than 3% and 7%. 44



### Table 5

- 6. Monetized Co

- benefits of CO 2 

### Emission Decreases in 2013 (Option 1) a 

### These estimates are provided in Table 5

- 6.

Discount Rate and Sta tistic SCC

- derived benefits (millions of 2007$)
5% Average $1.5
3% Average $6.5
2.5% Average $11

3% 95%ile $20

a The SCC values are dollar

- year and emissions

- year specific. SCC values represent only a partial accounting of

climate impacts.



### 5.3.3 Total Monetized Co

- benefits of Energy Savings
The energy savings would decrease emissions of several pollutants. In this analysis, we
were able to monetize the co

- benefits associated with the decreased emissions of PM and CO 2 

### , but we were unable to monetiz e the co

- benefits associated with the decreased emissions of mercury. We estimate that the total monetized co

- benefits are $22 to $43 million and $14 to $33 million, at discount rates of 3% and 7% respectively. Figure 5

- 3 shows the breakdown of the
monetiz ed co

- benefits by pollutant.

5.4 Characterization of Uncertainty in the Monetized PM 2.5 

### Co

- Benefits
In any complex analysis, there are likely to be many sources of uncertainty. Many inputs
are used to derive the estimate of monetized co

- benefits, includin g emission inventories, air quality models (with their associated parameters and inputs), epidemiological estimates of
concentration

- response (C

- R) functions, estimates of values, population estimates, income estimates, and estimates of the future state of the world (i.e., regulations, technology, and human behavior). For some parameters or inputs it may be possible to provide a statistical representation of the underlying uncertainty distribution. For other parameters or inputs, the necessary
information i s not available. Because we used the benefit

- per

- ton approach for this analysis,
confidence intervals are unavailable.

44 It is possible that other co

- benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from th ose used to develop the S CC estimates.

### 5

- 17 Figure 5

- 3. Breakdown of Monetized Co

- benefits by Pollutant at 3% discount rate (Option 1)

There are uncertainties associated with each analytical step to calculate the human health benefits associated with reducing electricity consumption. Important uncertainties associated with estimating the reduction in electricity consumption include variability across facilities and variability in local energy costs. 45 

### Important uncertainties associated with estimating the emission reductions associated with a specific electricity consumption reduction include representativeness of historical emissions factors across regions and over time, transm ission losses, electricity market response to changes in demand, and emissions trading effects. 46 

### Important uncertainties associated with estimating the human health benefits associated with those emission reductions include assumptions regarding the PM 2.5 

### - related mortality relationship,
the value

- of

- a

- statistical

- life, and average benefit

- per

- ton estimates. 47

45 These uncertainties are described in detail in Appendix B of this RIA. 

### It should be noted that the method used to the change in PM 2.5 

### precursor emissions for this rule is consistent with the
method used to calculated chan ges in CO 2 

### emissions. Given the strong desire to estimate the effects of regulations on greenhouse gas (GHG) emissions, we will continue to investigate and improve the methodology for estimating all benefits categories associated with reducing electricity consumption, to the extent feasible.

46 These uncertainties are described in detail in Appendix B of this RIA.
47 These uncertainties are described in detail below.

### 5

- 18 Although this is the first RIA for which U.S. EPA’s Office of Air and Radiation has calculated the monetized human health benefits specifically associated with a reduction in electricity consumption, there are several p revious regulations that included the health benefits associated with energy changes. For example, the recently finalized Portland Cement NESHAP included the human health and climate disbenefits associated with increased electricity consumption and fuel us age from control devices (U.S. EPA, 2010i). U.S. EPA has previously calculated substantial human health benefits associated with reducing emissions from electrical generating units, including the proposed Federal Transport Rule (2010d), the SO 2 

### NAAQS (2010 g), and the Clean Air Interstate Rule (2005). In addition, several recent regulations from U.S. EPA’s Office of Transportation and Air Quality assessed the air quality changes and associated human health impacts from changes in fuel consumption and/or fuel production and distribution, including the Light Duty Vehicle GHG rule (U.S. EPA, 2010f), 48 

### Renewable Fuel Standard II rule (U.S. EPA, 2010e), 49 

### and the Locomotive Marine rule (U.S. EPA, 2008a). 50



### In addition to RIAs, other recent attempts to calculate the human health benefits associated with electricity consumption are available in the literature. For example, Levy et al. (2009) estimated the median monetized health

- related damages associated with emissions of
PM 2.5 

### , SO 2 

### , and NOx from coal

- fired power plants. In additi on, the National Research Council (NRC) estimated the monetized health benefits associated emissions of PM, SO 2 

### , and NOx to be for coal and natural gas power plants. It is important to note that there are important methodological differences between these two analyses and EPA’s approach to estimating PM

Furthermore, the Iron and Steel Foundries NESHAP (U.S. EPA, 2 003) included an estimate of the emission reductions associated with decreased energy consumption for alternate control devices, but no benefits analysis was conducted for that rule.

48 In addition to downstream emission reductions, the Light Duty Vehicle GHG rule accounted for the emissions associated with the processes involved in getting petroleum to the pump, including the extraction and transportation of crude oil, and the productio n and distribution of finished gasoline. Changes were anticipated in upstream emissions due to the expected reduction in the volume of fuel consumed. Less gasoline consumed means less gasoline transported, less gasoline refined, and less crude oil extracte d and transported to refineries. Thus, the analysis accounted for reductions in the emissions associated with each of these steps in the gasoline production and distribution process.
49 In addition to the effects of increased renewable fuel use on emissions from the vehicles and equipment that use the fuels, the RFS2 analysis accounted for shifts in the fuel production and transport/distribution methods that can have substantial impacts on emissions. These "upstream" emissions are associated with all stages of biofuel production and distribution, including biomass production (agriculture, forestry), fertilizer and pesticide production and transport, biomass transport, biomass refining (corn or cellulosic ethanol production facilities), biofuel transport to bl ending/distribution terminals, and distribution of finished fuels to retail outlets.
50 The locomotive and marine engine rule accounted for idle reduction technologies that provide substantial emission reductions as well as cost savings by reducing fuel co nsumption. Reduced idling time results in reduced fuel consumption and reduced idle emissions. The analysis estimated annual fuel savings, the associated cost savings, and the emissions reductions that would result from the idle reduction requirements.

### 5

- 19 related monetized benefits, including characterization of secondary formation of PM 2.5 

### , population growth, income growth, the VSL, the cessation lag assumed, among others. However, these studies provide addi tional examples of conceptually similar attempts to monetize the human health benefits associated with electricity consumption.

The annual benefit estimates presented in this analysis are also inherently variable due to the processes that govern pollutant emissions and ambient air quality in a given year. Factors such as hours of equipment use and weather are constantly variable, regardless of our ability to measure them accurately. As discussed in the PM 2.5 

### NAAQS RIA (Table 5.5) (U.S. EPA, 2006a), there ar e a variety of uncertainties associated with these PM co

- benefits. Therefore, the
estimates of annual co

- benefits should be viewed as representative of the magnitude of co

benefits expected, rather than the actual co

- benefits that would occur every year.

I t is important to note that the monetized benefit

- per

- ton estimates used here reflect specific geographic patterns of emissions reductions and specific air quality and co

- benefits modeling assumptions. For example, these estimates do not reflect local vari ability in population density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of these $/ton values to estimate co

- benefits associated with different emission control programs (e.g., for reducing emissions from large st ationary sources like EGUs) may lead to higher or
lower benefit estimates than if co

- benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific loca tion, as these are all based on national or broad regional emission reduction programs and therefore represent average co

- benefits

- per

- ton over the entire United States. The

co

- benefits

- per

- ton for emission reductions in specific locations may be very dif ferent than the estimates presented here.

PM 2.5 

### mortality co

- benefits are the largest benefit category that we monetized in this analysis. To better characterize the uncertainty associated with mortality impacts that are
estimated to occur in areas with low baseline levels of PM 2.5 

### , we included the LML assessment. Without policy

- specific air quality modeling, we are unable to quantify the shift in exposure associated with this specific rule. For this rule, as a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 

### level using the most recent modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e). A very large proportion of the population is exposed at or above the lowest LML of the cohort studies (Figures 5

- 4 and 5

- 5), increasing our confidence in the PM mortality analysis. Figure 5

- 4 shows a bar chart of the p ercentage of the population exposed to various air quality levels in the pre

and post

- policy policy. Figure 5

- 5 shows a cumulative distribution function of the same data.

### 5

- 20 Figure 5

- 4. Percentage of Adult Population by Annual Mean PM 2.5 

### Exposure (pre

- and post

- policy policy)

Figure 5

- 5. Cumulative Distribution of Adult Population at Annual Mean PM 2.5 

### levels (pre

- and post

- policy policy)

### 5

- 21 Both figures identify the LML for each of the major cohort studies. As the policy shifts the distribution of air quality levels, fewer people are exposed to PM 2.5 

### levels at or above the LML. Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM 2.5 

### level s at or above the LML of 7.5 μg/m 3 

### . Using the Laden et al. (2006) study, 40% of the population is exposed above the LML of 10 μg/m 3



### . As we model mortality impacts among populations exposed to levels of PM 2.5 

### that are successively lower than the LML of the lowest cohort study, our confidence in the results diminishes. However, the analysis above confirms that the great majority of the impacts occur at or above the lowest cohort study’s LML. It is important to emphasize that we have high confidence in PM 2.5 

### - r elated effects down to the lowest LML of the major cohort studies. Just because we have greater confidence in the benefits above the LML,
this does not mean that we have no confidence that benefits occur below the LML.

Above we present the estimates of th e total monetized co

- benefits, based on our interpretation of the best available scientific literature and methods and supported by the SAB

HES and the NAS (NRC, 2002). The co

- 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 monetized co

- benefits, we were able to quantify include the following:

1. PM 2.5 

### co

- benefits were derived through benefit p er

- ton estimates, which do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over

- estimate or under

estimate of the actual co

- benefits of controlling di rectly emitted fine particulates.

2. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 

### produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 

### released from diesel engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type.

3. We assume that the health impact functi on for fine particles is linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health

co

- benefits from reducing fine particles in areas with varied concentrations of PM 2.5, 

### including both regions that are in att ainment with fine particle standard and those that do not meet the standard down to the 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 co

- 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

### 5

- 22 transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 

### estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis. For more information on the uncertainties associated with PM 2.5 

### co

- benefits, please c onsult the PM 2.5 

### NAAQS RIA (Table 5

- 5).

This RIA does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA because we lack the necessary air quality input and monitoring data to run the co

benefits model. Moreover, it was not p ossible to develop benefit

- per

- ton metrics and associated estimates of uncertainty using the co

- benefits estimates from the PM RIA because of the significant differences between the sources affected in that rule and those regulated here. However, the resul ts of the Monte Carlo analyses of the health and welfare co

- benefits presented in Chapter 5 of the PM NAAQS RIA can provide some evidence of the uncertainty surrounding
the co

- benefits results presented in this analysis.

5.6 References Fann, N., C.M. Fulch er, B.J. Hubbell. 2009. The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution. Air Qual Atmos Health 2:169

- 176.

Kochi, I., B. Hubbell, and R. Kramer. 2006. “An Empirical Bayes Appr oach to Combining Estimates of the Value of Statistical Life for Environmental Policy Analysis.” Environmental and Resource Economics 34:385

- 406.

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.

Levy, J.I., L.K. Baxter, and J. Schwartz. 2009. “Uncertainty and variability in health

- related damages from coal

- fired power plants in the United States.” Risk Anal . 29(7) :1000

- 1014.

Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta

- Analysis.” Journal of Policy Analysis and Management 21(2):253

- 270.

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pol lution Regulations . Washington, DC: The National Academies Press.

National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Co

- benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC.

National Research Council. 2010. Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use. National Academies Press: Washington, D.C.: 466 pp.

### 5

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

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

- 1141.

Roman, H.A., K.D. Walker, T.L. Walsh, L. Conner, H.M. Richmond, J. Hubbell, and P.L. Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S.” Environ. Sci. Technol . 42(7):2268

- 2274.

Scudde r, B.C., Chasar, L.C., Wentz, D.A., Bauch, N.J., Brigham, M.E., Moran, P.W., and Krabbenhoft, D.P. 2009. Mercury in fish, bed sediment, and water from streams across the United States, 1998

– 2005: U.S. Geological Survey Scientific Investigations Report 2009

– 5109, 74 p.

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 a t
< http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 2003. Economic Impact Ana lysis of Final Iron and Steel Foundries NESHAP. Office of Air Quality Planning and Standards, Research Triangle Park. EPA

- 452/R

- 03

- 012. August. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/EIAs/foundry_report.pdf>.

U.S. Environmental P rotection Agency (U.S. EPA). 2005 . Regulatory Impact Analysis for the Final Clean Air Interstate Rule . EPA

- 452/R

- 05

- 002. Office of Air and Radiation. March. Available on the Internet at <http://www.epa.gov/cair/pdfs/finaltech08.pdf>.

U.S. Environmental Pro tection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter , Chapter 5. Available at

< http://www.epa.gov/tt n/ecas/regdata/RIAs/Chapter%205

-- Benefits.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Pe r Cylinder. EPA420

- R

- 08

- 001a. Office of Transportation and Air Quality. May. Available on the Internet at <http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Guidelines for Preparing Economic Analy ses: External Review Draft. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. Available at <http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE

- 0516

- 01.pdf/$File/EE

- 0516

- 01.pdf>.

U.S. Environmental Prot ection Agency (U.S. EPA). 2008 c

. Integrated Science Assessment for Oxides of Nitrogen and Sulfur

–

Ecological Criteria National (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R

- 08/139. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.

### 5

- 24 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 . Off ice of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at

< http://www.epa.gov/ttn/ecas/regdata/RIAs/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 at <http://cfpub.epa.gov/ncea/cfm/re cordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Final Regulatory Impact Analysis (RIA) for the NO 2 

### National Ambient Air Quality Standards (NAAQS) . Office of Air Quality Planning and Standards, Research Triangle Park, NC . January. Available at

< http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf >.
U.S. Environmental Protection Agency (U.S. EPA). 2010c. Summary of Expert Opi nions on the Existence of a Threshold in the Concentration

- Response Function for PM 2.5 

### - related Mortality: Technical Support Document . Compiled by Office of Air Quality Planning and Standards, Research Triangle Park, NC. July. Available at <http://www.epa.g ov/ttn/ecas/benefits.html>.

U.S. Environmental Protection Agency (U.S. EPA). 2010d. Lowest Measured Level (LML) Assessment for Rules without Policy

- Specific Air Quality Data Available: Technical Support Document . Office of Air Quality Planning and Standard s, Research Triangle Park, NC. July. Available at <http://www.epa.gov/ttn/ecas/benefits.html>.

U.S. Environmental Protection Agency (U.S. EPA). 2010e. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis . EPA

- 420

- R

- 10

- 006. Office of Transporta tion and Air Quality. March. Available on the Internet at <http://www.epa.gov/oms/renewablefuels/420r10006.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010f. Final Rulemaking to Establish Light

Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards Regulatory Impact Analysis . EPA

- 420

- R

- 10

- 009. Office of Transportation and Air Quality. April. Available on the Internet at <http://www.epa.gov/oms/climate/regulations/420r10009.pdf>.

U.S. Environmental Protection A gency (U.S. EPA). 2010g . Final Regulatory Impact Analysis (RIA) for the SO 2 

### National Ambient Air Quality Standards (NAAQS ). Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available on the Internet at <http://www.epa.gov/ttn /ecas/regdata/RIAs/fso2ria100602full.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2010h. Regulatory Impact Analysis for the Proposed Federal Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. July. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/proposaltrria_final.pdf>.

### 5

- 25 U.S. Environmental Protection Agency (U.S. EPA). 2010i. Regulatory Impact Analysis: Amendments to the National Emission Standards for Hazardous Air Pollutants and New Source Performan ce Standards (NSPS) for the Portland Cement Manufacturing Industry

. Office of Air Quality Planning and Standards, Research Triangle Park, NC. August. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf>.

U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2004. Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analysis

— Benefits and Costs of the Clean Air Act, 1990

– 2020. Advisory by the Heal th Effects Subcommittee of the Advisory Council on Clean Air Compliance Analysis. EPA

- SAB

- COUNCIL

- ADV

- 04

- 002. March. Available at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E540 0433D5D/$File/council_adv_04002.pdf>.

U.S. Environmenta l Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2007. SAB Advisory on EPA’s Issues in Valuing Mortality Risk Reductio n. EPA

- SAB

- 08

- 001. October. Available at

< http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F ile/sab

- 08

- 001.pdf >.
U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2009a. Review of EPA’s Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA

- COUNCIL

- 09

- 008. May. Available at

< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86 e/73ACCA834AB44A10852575BD0064346B/$File/EPA

- CASAC

- 09

- 008

- unsigned.pdf >.
U.S. Environmental Protection Agency

— Science Advisory Board (U.S. EPA

- SAB). 2009b. Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment . EPA

- COUNCIL

- 09

- 009. May. Available at

< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86 e/723FE644C5D758DF852575BD00763A32/$File/EPA

- CASAC

- 09

- 009

- 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.

### 6

- 1 SECTION 6

COMPARISON OF CO

- BENEFITS AND COSTS

Using a 3% discount rate, we estimate the total monetized co

- benefits of Option 1 of the Mercury Chlor Alkali NESHAP to be $22 million to $43 million in the implementation year (2013). Using a 7% discount rate, we estimate the total monetized co

- benefits o f this option to be $ 21 million to $ 40 million. The annualized social costs of th is option HAP are $13 million at a
7% interest rate. 51



### Option 2 does not have emission reductions, thus no monetized benefits. The annualized
social costs of th is option HAP are $ 25,000 at a 7% interest rate. Thus, the net benefits of this option are $

- 25,000

. Thus, the net benefits of this option are $ 9 million to $30 million at a 3% discount rate and $ 8 million to $2 7 million a t a 7% discount rate. All estimates are in 2007$ for the year 2013.

Table 6

- 1 shows a summary of the monetized co

- benefits, social costs, and net benefits for the proposed Mercury Chlor Alkali NESHAP, respectively. Figures 6

- 1 and 6

- 2 show the full range of net benefits estimates (i.e., annual c o

- benefits minus annualized costs) utilizing the 14
different PM 2.5 

### mortality functions at discount rates of 3% and 7%. It is important to emphasize
that these monetized co

- benefits do not include the benefits associated with reducing mercury
emissions. EP A believes that the co

- benefits are likely to exceed the costs under this rulemaking even when taking into account uncertainties in the cost and benefit estimates.

51 F or more information on the annualized social costs, please refer to Section 3 of this RIA.

### 6

- 2 Table 6

- 1

. Summary of the Monetized Co

- Benefits, Social Costs, and Net benefits for the Pro posed Mercury Chlor Alkali NESHAP in 2013 ( thousands of 2007$) a

3% Discount Rate 7% Discount Rate
Option 1 : Non

- mercury Technology Option

Total Monetized Benefits b $22 ,000 to $43 ,000 $ 21,000 to $ 40,000

Total Social Costs c $13 ,000 $13 ,000

Net Benefits $ 9,000 to $30 ,000 8,000 to $ 27,000

Non

- monetized Benefits 656 pounds of mercury (including energy co

- benefits)

Health effects from NO 2 and SO 2 exposure Ecosystem effects
Visibility impairment
Option 2: Enhanced Work Practice Standards

Total Monetized Benefits b $0 $0

Total Social Costs c $25 $25

Net Benefits $

- 25 $

- 25

a All estimates are for the implementation year (2013), and are rounded to two significant figures.
b The total monetized co

- benefits reflect the human health co

- benefits associated with reducing exposure to PM 2.5 . It

is important to note that the monetized co

- benefits include many but not all health effects associated with PM 2.5 exposure. It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chem ical composition, are equally potent in causing premature mortality because there is no clear scientific evidence that would support the development of differential effects estimates by particle type. CO 2 - related benefits were calculated using the social c ost of carbon (SCC)

. The net present value of reduced CO 2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. This table shows monetized CO 2 co

- benefits at discount rates of 3 and 7 percent that were calculated using the global average SCC estimate at a 3% discount rate because the interagency workgroup on this topic deemed this marginal value to be the central value. In Section 5 , we also provide the monetized CO 2 co

- benefits using discount rates of 5 percent (average), 2.5 percent (average), and 3 percent (95th percentile)

.

c The annual compliances costs serve as a proxy for the annual social costs of this rule given the lack of difference

between the two.

### 6

- 3 Figure 6

- 1

. Net Benefits for the Proposed Mercury Chlor Alkali NESHAP at 3% Discount Rate (Option 1) a

a Net Benefits are quan tified in terms of PM 2.5 co

- benefits for implementation year (2013). 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 co

- benefits incorporate the conversion from precursor emissions to ambient fine particles. The net benef its at a 3% discount rate also include CO 2 - related benefits calculated using the social cost of carbon. - $10

$0 $10 $20 $30 $40 $50

Pope et al. Laden et al.Millions (2007$)

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

### 6

- 4 Figure 6

- 2

. Net Benefits for the Proposed Mercury Chlor Alkali NESHAP at 7% Discount Rate (Option 1) a

a Net benefits are quantified in terms of PM 2.5 co

- benefits for implementation year (2013). This graph shows 14 co

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, b ut the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The monetized co

- benefits incorporate the conversion from precursor emissions to ambient fine particles. This table shows monetized CO 2
co

- benefits at discount rates of 3 and 7 percent that were calculated using the global average SCC estimate at a 3% discount rate because the interagency workgroup on this topic deemed this marginal value to be the central value. - $10 $0 $10 $20 $30 $40 $50

Pope et al. Laden et al.Millions (2007$)

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

## Net Benefits for Mercury Chlor Alkali in 2013 at 7% discount rate*

### A

- 1 APPENDIX A:
MERCURY EMISSIONS MEMO

### A

- 2 MEMORANDUM

TO: Donna Lee Jones, PhD, USEPA/OAQPS/SPPD/MMG

FROM: Phil Norwood, EC/R Incorporated

DATE: March 15, 2010

SUBJECT: Mercury Emission Reductions and Other Mercury Waste Impacts

— Conversion from Mercury Cell Chlor

- Alkali to Membr ane Cell Chlor Alkali Technology

The purpose of this memorandum is present the mercury air emission and other mercury waste impacts that would result from the conversion of the four mercury cell chlor

- alkali plants currently operating in the U.S. to non

- mercury (membrane cell) technology. These plants are ASHTA Chemicals in Ashtabula, Ohio (ASHTA), Olin Corporation in Augusta, Georgia (OLIN

GA), Olin Corporation in Charleston, Tennessee (OLIN

- TN), and PPG in New Martinsville, West Virginia (PPG). The first two sections address the baseline mercury air emissions and mercury releases to other media. In the event of conversion, these routine releases would be eliminated. This is followed by a discussion of the mercury releases and mercury

- contaminated

was tes that would be generated during the cleanup and decommissioning of the mercury cell plants. The final section summarizes these air and other media impacts.

BASELINE MERCURY AIR EMISSIONS

Table 1 shows the mercury air releases reported in EPA’s Toxics Re lease Inventory (TRI) for 2007 and 2008 for each of the currently operating mercury cell chlor

- alkali plants.
In addition to the TRI information available online, the companies operating these four facilities provided emission estimates (which matched the totals in Table 1 reported to TRI) in correspondence in the summer of 2009. 52

, 53

, 54

52 Letter. Jackson, R.L., ASHTA Chemicals Inc., to Jones, D.L., U.S. Environmental Protection Agency. RE: Docket No. EPA

- HQ

- OAR

- 2002

- 0017

— National Emission Standards for Hazardous Air Pollutants: Mercury Emissions from Mercury Cell Chlor

- Alkali Plants

— ASHTA Chemicals Inc. Comments and Rebuttal on Recent EC/R Study . June 30, 2009. 

### Later in 2009, EPA requested documentation of these emission estimates using their authority under section 114 of the Clean Air Act. In

53 Letter. Hall, R.K., Olin Chlor Alkali Products, to Jones, D.L., U.S. Environmental Prote ction Agency. June 25, 2009.
54 Letter. Smith, T.P., PPG Industries, Inc., to Jones, D.L., U.S. Environmental Protection Agency. Re: Docket No. EPA

- HQ

- OAR

- 2002

- 0017

—National Emission Standards for Hazardous Air Pollutants: Mercury Emissions from Mercury Ce ll Chlor

- Alkali Plants

— Memo: “Updated Cost Impacts Associated with the Conversion from Chlorine Production Utilizing Mercury Cell Chlor

- Alkali Technology to Membrane Cell Technology,” from Heather P. Brown, P.E. (EC/R Inc.) to Donna Lee Jones, PhD (USEPA), dated June 5, 2009.” July 13, 2009.

### A

- 3 February of 2010, this documentation w as submitted to the EPA. 55

, 56

, 57



### Table 1. Mercury Air Releases Reported in TRI for 2007 and 2008 This information, which
is available in Docket EPA

- HQ

- OAR

- 2002

- 0017, was carefully reviewed. More discussion on the conclusions of this review is provided below.

Plant TRI Mercury Air Releases (pounds/yr)

2007 2008
Fugitive Stack Total Fugitive Stack Total

ASHTA 0.0 0.0 0.0 0.0 0.2 0.2

OLIN

- GA 119 5 124 77 9 86

OLIN

- TN 353 67 420 241 43 285

PPG 145 55 200 149 58 207



### Summary of Reported Facility Emissions and Supporting Information

ASHTA

As shown in Table 1, ASHTA reported no mercury releases to the air in 2007 and
0.2 pounds in 2008. In the June 30, 2009 correspondence, ASHTA submitted emission estimates
for 2007 and 2008. The estimates for 2008 were consistent with those in the 2008 TRI. For 2007, ASTA reported 0.12 pounds of point source emissions. Consistent with the TRI, they reported
“0 (Trace) lbs./yr” fugitive emissions for both 2007 and 2008.

The EPA section 114 requested documentation fo r the point source emissions reported in the June 30, 2009 correspondence. It also questioned the report that the fugitive emissions are zero and requested that ASHTA provide a “precise, substantiated, fugitive emissions estimate.”

In their February 15, 20 10 response ASHTA provided details of the calculation of the point source (i.e., stack) emissions. ASHTA conducted emissions tests on their hydrogen and cell end box air emission streams in March 2006, and they provided a copy of the report for this

55 Letter. Jackson, R.L., ASHTA Chemicals Inc., to Jones, D.L., U.S. Environmental Protection Agency. RE: Docket Response to USEPA Questions. February 15, 2010.
56 Letter. Hall, R.K., Olin Chlor Alkali Products, to Jones, D.L., U.S. Environmental Protection Agency. Re: Information requested pursuant to section 114 of the Clean Air Act. February 2, 2010.
57 Letter. Smith, T.P., PPG Industries, Inc., to Jones, D.L., U.S. Environmental Protection Agency. Re: Response to Reques t for Information under Section 114 of the Clean Air Act, 42 U.S.C. § 7414, dated December 4, 2009, with regards to the Mercury Cell Chlor

- Alkali MACT and background data to support the estimates provided to USEPA by PPG in a letter dated July 13, 2009.” F ebruary 18, 2010.

### A

- 4 testin g. ASHTA using the periodic testing alternative to demonstrate compliance with the emission limitation for by

- product hydrogen streams and end

- box ventilation systems, which means that they perform tests three times per week. They use alternative methods f or this testing which have been approved by EPA. Results for each of the tests conducted in 2007 and 2008 were provided, along with the 52

- week rolling average calculations.

The calculations show point source mercury emissions of 0.09 pounds in 2007 and
0. 11 pounds in 2008. These estimates are lower than previously reported because the earlier estimates included emissions from a noncontact cooling tower and not from the mercury cell

process.

ASHTA did not provide any fugitive emission estimates in response to EPA’s request. They indicated that, since EPA does not have an approved method for estimating fugitive mercury emissions from a mercury cell chlor

- alkali plant, they cannot provide emission estimates. ASHTA indicated that the zero emissions levels repor ted were determined from the annual mercury balance, the low floor level mercury levels, and the operation of mercury control systems. They said that they are “confident that the fugitive emissions collection system installed on the cell room removes large amounts of off

- process mercury emissions” but they are “unable to provide the level of conclusion with any direct measurements.” The EPA estimate of these
emissions is discussed at the end of this section.

OLIN

The mercury emission estimates provided in O lin’s June 25, 2009 correspondence for 2007 and 2008 were consistent with those reported in the TRI for both their Augusta, Georgia, and Charleson, Tennessee facilities. In their February 2, 2010 correspondence, they provided
documentation for these estima tes.

At both facilities, Olin utilizes continuous mercury emission monitoring systems. These units were produced by Mercury Instruments GmbH Analytical Technologies of Germany, and are equipped with factory supplied calibration gas generators. Zero calibra tions are performed every hour the units are in operation as well as span checks at least once per day. These units measure and record the mercury concentrations for both the point sources and throughout the cell rooms. Stack velocity/flow rates are monito red for the point sources, and cell room flow rates are estimates using engineering equations.

For the point sources, Olin provided daily emission results for each point source, along
with detailed ten

- minute readings for representative days to illustrate how the daily emissions

### A

- 5 were determined. For the fugitive emissions, Olin provided daily measured concentrations, estimated flow rates, and emissions. They also provided detailed calculation sheets illustrating
how the emissions were calculated.

PPG

In the ir July 13, 2009 correspondence, PPG indicated that the mercury stack air releases in the TRI include 50 pounds/yr from coal

- fired boilers at the site. Therefore, they indicated that the 2007 stack emissions from the mercury cell chlor

- alkali plant should be 5 pounds for 2007 and 8 pounds for 2008. The fugitive emission estimates reported in this correspondence were consistent with those reported to TRI for 2007 (145 pounds) and 2008 (149 pounds). Therefore, PPG’s mercury emissions from mercury cell operati ons total 150 pounds in 2007 and 157 pounds
in 2008.

For their three point sources (Hydrogen De

- gas inlet box ventilation system, Chlorine
De

- gas end box ventilation system, and Hydrogen Purification system), PPG operates continuous mercury emission monito rs to measure the mercury concentration and flow meters to monitor the flow rate. In their February 28, 2010 correspondence PPG provided daily average measured concentrations, flow rates, and emission rates for 2007 and 2008. They also provided equations
i llustrating the calculations performed.

PPG also utilizes a continuous mercury monitoring system to measure mercury concentration throughout the cell room, and estimates the flow rate from the building based on the number of roof exhaust fans operating. Th ey provided average cell room concentrations, flow rates, and emission rates for each day in 2007 and 2008. They also provided equations illustrating the calculations performed.

PPG has entered into an agreement with the Attorney General of Maryland regard ing their West Virginia facility. As part of this agreement, PPG will limit mercury emissions to no more than 150 pounds/yr in 2011 and 2012, and no more than 145 pounds/yr in 2013 and beyond. Therefore, in 2011 and 2012 there will be around a 2 percent de crease from the average 2007/2008 levels and around a 6 percent decrease in 2013 and beyond.

Estimating Fugitive Emissions for ASHTA

Because ASHTA did not provide data documenting and substantiating their claim of zero fugitive emission estimates, we estim ated their fugitive emissions since it is not reasonable to assume there are no emissions. The approach we used was to develop a fugitive emissions factor

### A

- 6 based on the amount of mercury contained in their cells. Since a previous analysis found little

corre lation between chlorine production and fugitive mercury emissions, 58



### We developed the emissions factor for fugitive mercury emissions based on the estimated amount of mercury in the cells for the other three fa cilities and used this factor to estimate ASHTA’s fugitive mercury emissions. These data are shown in Table 2. The data used to calculate the average factor, at approximately 0.5 pounds of fugitive mercury emissions per year per ton of mercury in the cells , shows reasonable agreement between the factors developed for the three other mercury cell facilities. this other approach
was not considered viable.

At ASHTA, there are 24 mercury cells. 59



### Table 2. Development of Fugitive Mercury Emissions Factor Based on Mercury in Cells Multiplying this number of cells by the average
factor shown in Table 2 results in an estimated fugitive mercury emission estimate of 62 pounds per year for ASHTA. For purposes of estimating the emissions for the whole industry, this value was used for both the 2007 and 2008 in this analysis.

Plant Number of Cells a Mercury in Cells b

(tons) Fugitive Mercury Emissions (lb/yr) Fugitive Emissions

- to

Cell Mercury Factor (lb/yr emissions per ton mercury in cells) 2007 2008 Avg

OLIN

— GA 60 298 124 86 105 0.33576

OLIN

— TN 106 527 420 284 352 0.63712

PPG

— WV 54 268 145 149 147 0.52229
Average 0.49839

a From 1997 Directory of Chemical Producers (Reference 60

b Using a factor of 5.2 tons of mercury per cell, which is the amount of mercury in the Chlorine Institute’s 2008

process inventory (1,376 tons) divided by the number of operating cells in 2008 (264) (Reference ).

61

58 U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC. “ Summary of 2005 Cell Room Mercury Emissions Data for Occidental Chemical Company Mercury Cell Chlor

- Alkali Plants: Delaware City, Delaware and Muscle Shoals, Alabama .” March 2008. ).

59 Reference 8.
60 1997 Directory of Chemical Producers

— United States of America. Stanford Research Institute International. Menlo Park, CA. 1997.
61 The Chlorine Institute, Inc. “Chlor

- Alkali Industry 2008. Mercury Use and Emissions in the United States (Twelfth Annual Report).” August 2009.

### A

- 7 Summary of Baseline Air Emissions

Table 3 s ummarizes the mercury emissions from the four currently operating mercury
cell chlor

- alkali plants for 2007 and 2008.

Table 3. Baseline Mercury Emission Levels

Plant Mercury Emissions (pounds per year)

2007 2008 Average 2007

– 2008

Point Fugitive Total Point Fugitive Total Point Fugitive Total

ASHTA 0.09 62 62 0.11 62 62 0.10 62 62

OLIN

— GA 5 124 129 9 86 95 7 105 112

OLIN

— TN 67 420 487 43 284 327 55 352 407

PPG 5 145 150* 8 149 157* 7 147 154

Total 77 751 828 60 581 641 69 666 735

Note : PPG’s emissions is limited by a court settlement to 150 pounds per year in 2001 and 2012, and 145 pounds per

year in 2013 and thereafter.



### BASELINE NONAIR MERC URY RELEASES

Table 4 shows the nonair mercury releases reported to the TRI for 2007 and 2008. Th ese releases include the total amount of mercury released to water or land on or at a facility’s site and the amount that is transferred from a facility site to another site and then released to the
environment via water or land. Attachment 1 includes a de tailed analysis of TRI mercury release
data for these four facilities.

Table 4. Total Nonair Mercury Releases Reported in the TRI

Plant Total Nonair Mercury Releases (pounds/year)

2007 2008 Average

ASHTA 202 179 190

OLIN

- GA 82 105 93

OLIN

- TN 414 197 306

PPG 1,408 751 1,080

Total 2,106 1,232 1,669

### A

- 8 WASTE GENERATION FRO M CLEANUP/DECOMMISSI ONING

In addition to the elimination of the routine mercury releases discussed above, there
would be one

- time environmental impacts associated with the clean

- up and decommissioning of the mercury cell chlor

- alkali plants. The discussion and quantification of these impacts are
discussed below.

When cell rooms are closed and decommissioned, there will be some loss of mercury to
the environment. The extent of the los s will be influenced by cell room design and geographic location. However, once the cells are shut down, the cell room temperature will decrease, and the evaporation rate of any exposed mercury is reduced. 62



### There is little quantitative information available to estimate the amount of mercury

containing waste that would need to be disposed of during c leanup/decommissioning. When the Borregaard mercury cell chlor

- alkali plant in Norway was decommissioned, a large number of samples were taken to determine the quantity of mercury

- contaminated wastes. The total quantity of contaminated waste material was e stimated at 1,750 cubic meters, of which around 55 percent was contaminated process equipment such as steel and rubber

- lined steel. Therefore, we estimated that there would not be measured mercury air emissions during cleanup/decommissioning.

63



### However, information available for the Borregaard plant provided no estimate of the mass amount of mercury in the wastes. In order to obtain an estimate of the potential mass, th e release information in the TRI was examined for closed or converted facilities. There are six former mercury chlor

- alkali plants in the TRI that have at least five years worth of release data to analyze. For each of these facilities, the average air rele ases during the years prior to the last year for which release data were reported in the TRI was compared to the air releases in the last year. If those air releases in the last year were less than 50 percent of the average air releases for the previous ye ars for which TRI releases were reported, it was assumed that the mercury cell plant had ceased operations during that year and was in the process of cleanup/decommissioning. For those situations, the ratio of the offsite transfers during that year was cal culated to represent an estimate of the amount that offsite transfers could increase during cleanup/decommissioning.
This information is summarized in Table 5. Assuming this level of waste is representative, this would mean approximately 7,000 cubic meters of waste could be generate d during the conversion/closure of the four currently operation U.S. plants.

62 European Commission. Integrated Pollution Prevention and Control (IPPC). Reference Document on Best Available Techniques in the Chlor

- Alkali Manufacturing Industry. December 2001. p. 96
63 Reference 11, pp. 93, 97.

### A

- 9 Table 5. Summary of TRI Information Used to Estimate Mercury Cleanup/Decommissioning Wastes

Pla nt Last Yr Air Mercury Releases (pounds/yr) Offsite Mercury Transfers (pounds/yr)

Avg a Last Yr Last Year ÷

Avg Avg a Last Yr Last Year ÷

Avg

Westlake Calvert City, KY 2002 983 6 1% 10,108 27,777 275%

Occidental Muscle Shoals, AL 2008 667 322 48% 4,405 12,806 291%

Occidental New Castle, DE 2008 649 0.4 0% 3,166 16,520 522%

Holtrachem Orrington, ME 1999 660 116 18% 2,103 1,612 77%

Holtrachem Reigelwood, NC 1999 1,176 328 28% 171 176 103%

PPG Westlake, LA 2005 997 1,211 121% b b b

Average 253%

a Avg = average of all years for which data were reported except for the last year.

b Since the air releases in the last reporting year were greater than 50 percent of the average for the previous years, it

was assumed that the plant was operating during this year and the offsite transfers did not represent cleanup/decommissioning activiti es.



### In order to estimate the mercury contaminated wastes that would be generated during cleanup/decommissioning and transferred offsite for the four currently operating mercury cell
chlor

- alkali plants, the average level of nonair releases for reported for all years of operation are multiplied by 253 percent. These estimates are shown in Table 6.

Therefore, it is estimated that the closure/conversion of the four currently operating mercury cell chlor

- alkali plants in the U.S. could result in the generation of around 7,000 cubic meters of hazardous wastes containing over 6 tons of mercury. Given the unique aspects of each mercury cell plants, the extreme paucity of specific data on the wastes generated during mercury cell closures/conversions, and the major a ssumptions in this analysis, there is significant uncertainty in these estimates.

### A

- 10 Table 6. Estimated Cleanup/Decommissioning Wastes for Mercury Cell Plants

Plant Average Reported Nonair Releases Over All Years (pounds/yr) Estimated Cleanup/ Decommissioning Wastes (pounds/yr)

ASHTA 620 1,571

OLIN

— GA 1,264 3,203

OLIN

— TN 1,364 3,457

PPG 1,651 4,185

TOTAL 12,416



### SUMMARY

If the four currently operating mercury cell chlor

- alkali plants converted to membrane processes, the result would be the elimination of mercury emissions, the elimination of mercury releases to the water and other media, and the termination of the creation of mercury

contaminated wastes from the routine operation of the mercury cells. As discussed above, the
estimated levels of these ro utine releases are:

 

### Mercury air emissions of between 600 and 800 pounds per year.

 

### Nonair mercury releases of between 1,200 and 2,000 pounds per year.

During the cleanup and decommissioning, there will be mercury

- contaminated wastes
that will be created and that will need to be treated. A rough estimate is approximately 7,000 cubic meters of waste created during this process that contains over six tons (12,000 lb) of

mercury.

### A

- 11 ATTACHMENT 1 Detailed Analysis of Toxic Release Inventory (TRI) Data

# E C 

# /

R Incorporated Providing Environmental Technical Support Since 1989

501 Eastowne Drive, Suite 25 0  Chapel Hill, North Carolina 2751 4 Telephone: (919) 484

- 0222  Fax: (919) 484

- 0122 

### MEMORANDUM

DATE: January 5, 2010

SUBJECT: Summary of Mercury Cell Chlor

- Alkali Plant Mercury Releases Reported in the Toxics Release Inventory (TRI)

FROM: Phil Norwood, EC/R

TO: Dr. Donna Lee Jones, EPA/OAQPS/SPPD/MMG

The purpose of this memo is to pre sent mercury release information from EPA’s Toxics Release Inventory (TRI) ( http://www.epa.gov/tri/ ) from the four currently operating mercury cell
chlor

- alkali plants in the U.S.. These plants are ASHTA Chemicals in A shtabula, Ohio (ASHTA), Olin Corporation in Augusta, Georgia (OLIN

- GA), Olin Corporation in Charleston, Tennessee (OLIN

- TN), and PPG in New Martinsville, West Virginia (PPG). In addition to presenting historical release information, the final section prese nts what are estimated to be “typical” annual levels of Nonair mercury releases that would be eliminated in the event that these facilities ceased to operate.

TRI MERCURY RELEASE INFORMATION

These four mercury cell chlor

- alkali plants have been operating since the 1970s. The TRI contains mercury release data beginning in 1988. Attachment 1 contains annual release information for each of the four plants for the 21 years from 1988 to 2008. Table 1 shows the sum of the average annual releases from these four plants for the entire 21

- year period, the most recent ten years (1999

– 2008), and the most recent two years (1997 and 1998), and Figure 1 shows the 21 year trends for total mercury releases for each of the four plants. Table 2 provides more detailed plant

- s pecific averages. As can be seen in Tables 1 and 2, Figure 1, and Attachment A, mercury releases from these four mercury cell chlor

- alkali plants have steadily decreased in the 21 year period from 1988 to 2008.

There appears to be an anomaly in the offsite releases for the PPG facility in 2001 (see Figure 1 and Attachment A). In 2001, PPG reported almost 17,000 pounds released offsite (While not shown in the table, the specific release reported was attributed to “Offsite Landfill/Surface Impoundment). This value is over 12 times higher than the next highest level of offsite mercury release for any year in the 10

- year period. We believe it is reasonable to assume

### A

- 13 the activities in 2001 that resulted in this large release represent a unique and nonroutine even t. Therefore, the PPG average release for the period 1999

– 2008 was calculated excluding the 2001 values, and a separate total of the plant

- specific averages calculated. This was shown in Table 2. Therefore, both the 10

- year total and the 2007

– 2008 total No nair releases were just under 2,000 pounds per year.

Table 1. Total Average Mercury Releases from Mercury Cell Chlor

- Alkali Plants

Period Sum of Average Mercury Releases (pounds/yr)

Total Releases Total Air Releases Total Nonair Releases

1988

– 2008 9,410 4,510 4,900

1999

– 2008 6,839 3,459 3,380

2007

– 2008 2,331 661 1,669

Note : These plants are ASHTA Chemicals in Ashtabula, Ohio (ASHTA), Olin Corporation in Augusta, Georgia (OLIN

- GA), Olin Corporation in Charleston, Tennessee (OLIN

- TN), and PPG in New Martinsville, West Virginia (PPG).A-14



### Table 2. Average Mercury Release ValuesA-15 Average Mercury Releases (pounds/yr)

Total (On & Off Site) Total Nonair (On & Off Site) Onsite Air Onsite Nonair Onsite Water Offsite
21

- Year Average (1988

– 2008)

ASHTA 1,875 620 1,255 102 2 518

OLIN

– GA 2,263 1,264 1,000 10 10 1,254
OLIN

– TN 2,583 1,364 1,218 1,333 21 31
PPG 2,689 1,651 1,037 60 59 1,591 Total of Averages 9,410 4,900 4,510 1,505 92 3,394 Total of Averages (excluding PPG 2001) 8,646 4,148 4,498 1,507 94 2,641
10

- Year Average (1999

– 2008)

ASHTA 1,096 212 885 0 0 212
OLIN

– GA 761 106 655 10 10 96
OLIN

– TN 1,716 648 1,068 584 11 64

PPG 3,265 2,414 852 27 25 2,387 Total of Averages 6,839 3,380 3,459 621 46 2,759 Total of Averages (excluding PPG 2001) 5,207 1,794 3,413 621 46 1,173
2007

– 2008 Average

ASHTA 191 190 0 0 0 190
OLIN

- GA 199 93 105 11 11 82
OLIN

- TN 658 306 352 305 7 1

PPG 1,283 1,080 204 23 23 1,057
Total 2,331 1,669 661 340 41 1,329A-16 

### ATTACHMENT A. Annual Mercury Release Data from TRI 1988

– 2008

Facility Year Mercury Releases Reported in TRI (pounds/yr)

Onsite Releases

Off Site Releases Total Releases (On

& Off Site) Total Nonair Releases (On & Off Site) Total Onsite Releases Fugitive Air Stack Air Total Air Onsite Nonair Water Land

ASHTA 1988 1,569 1,046 516 1,562 7 6 1 1,877 3,446 1,884

ASHTA 1989 1,303 1,046 250 1,296 7 6 1 2,215 3,518 2,222

ASHTA 1990 1,804 1,046 750 1,796 8 6 2 0 1,804 8

ASHTA 1991 1,306 1,046 250 1,296 10 5 5 978 2,284 988

ASHTA 1992 1,653 1,046 595 1,641 12 7 5 792 2,445 804

ASHTA 1993 1,652 1,046 596 1,642 10 5 5 551 2,203 561

ASHTA 1994 2,871 1,046 614 1,660 1,211 6 1,205 1,177 4,048 2,388

ASHTA 1995 2,530 1,046 608 1,654 876 5 871 0 2,530 876

ASHTA 1996 1,658 1,046 607 1,653 5 5 0 524 2,182 529

ASHTA 1997 1,654 1,046 607 1,653 1 1 0 347 2,001 348

ASHTA 1998 1,653 1,046 607 1,653 0 0 0 297 1,950 297

ASHTA 1999 1,653 1,046 607 1,653 0 0 0 242 1,895 242

ASHTA 2000 1,390 1,046 344 1,390 0 0 0 219 1,609 219

ASHTA 2001 1,396 1,046 350 1,396 0 0 0 100 1,496 100

ASHTA 2002 1,395 1,046 349 1,395 0 0 0 173 1,568 173

ASHTA 2003 1,383 1,046 337 1,383 0 0 0 128 1,511 128

ASHTA 2004 813 464 349 813 0 0 0 134 947 134

ASHTA 2005 813 464 349 813 0 0 0 434 1,247 434

ASHTA 2006 2 0 2 2 0 0 0 306 308 306

ASHTA 2007 0 0 0 0 0 0 0 202 202 202A-17 

### ATTACHMENT A. Annual Mercury Release Data from TRI 1988

– 2008

Facility Year Mercury Releases Reported in TRI (pounds/yr)

Onsite Releases

Off Site Releases Total Releases (On

& Off Site) Total Nonair Releases (On & Off Site) Total Onsite Releases Fugitive Air Stack Air Total Air Onsite Nonair Water Land

ASHTA 2008 0 0 0 0 0 0 0 179 179 179

OLIN

- GA 1988 1,329 1,048 271 1,319 10 10 0 6,014 7,343 6,024

OLIN

- GA 1989 1,327 1,046 271 1,317 10 10 0 2,129 3,456 2,139

OLIN

- GA 1990 1,331 1,046 271 1,317 14 14 0 4,730 6,061 4,744

OLIN

- GA 1991 1,285 1,000 270 1,270 15 15 0 5,740 7,025 5,755

OLIN

- GA 1992 1,329 1,046 271 1,317 12 12 0 5,933 7,262 5,945

OLIN

- GA 1993 1,324 1,046 271 1,317 7 7 0 712 2,036 719

OLIN

- GA 1994 1,328 1,046 271 1,317 11 10 1 23 1,351 34

OLIN

- GA 1995 1,325 1,046 271 1,317 8 8 0 23 1,348 31

OLIN

- GA 1996 1,324 1,046 271 1,317 7 7 0 8 1,332 15

OLIN

- GA 1997 1,323 1,046 271 1,317 6 6 0 5 1,328 11

OLIN

- GA 1998 1,326 1,046 271 1,317 9 9 0 50 1,376 59

OLIN

- GA 1999 1,245 1,046 189 1,235 10 10 0 23 1,268 33

OLIN

- GA 2000 730 562 157 719 11 11 0 21 751 32

OLIN

- GA 2001 773 608 158 766 7 7 0 39 812 46

OLIN

- GA 2002 746 585 154 739 7 7 0 282 1,028 289

OLIN

- GA 2003 742 563 169 732 10 10 0 114 856 124

OLIN

- GA 2004 753 563 182 745 8 8 0 36 789 44

OLIN

- GA 2005 835 563 261 824 11 11 0 136 971 147

OLIN

- GA 2006 591 493 86 578 13 13 0 149 740 162A-18 

### ATTACHMENT A. Annual Mercury Release Data from TRI 1988

– 2008

Facility Year Mercury Releases Reported in TRI (pounds/yr)

Onsite Releases

Off Site Releases Total Releases (On

& Off Site) Total Nonair Releases (On & Off Site) Total Onsite Releases Fugitive Air Stack Air Total Air Onsite Nonair Water Land

OLIN

- GA 2007 138 119 5 124 14 14 0 68 206 82

OLIN

- GA 2008 96 77 9 87 9 9 0 96 192 105

OLIN

- TN 1988 4,421 1,046 282 1,328 3,093 26 3,067 0 4,421 3,093

OLIN

- TN 1989 5,750 1,045 249 1,294 4,456 25 4,431 0 5,750 4,456

OLIN

- TN 1990 5,236 1,045 240 1,285 3,951 25 3,926 5 5,241 3,956

OLIN

- TN 1991 3,780 1,045 378 1,423 2,357 22 2,335 12 3,792 2,369

OLIN

- TN 1992 4,353 1,045 180 1,225 3,128 17 3,111 3 4,356 3,131

OLIN

- TN 1993 2,777 1,045 192 1,237 1,540 26 1,514 0 2,777 1,540

OLIN

- TN 1994 1,704 1,045 464 1,509 195 53 142 0 1,704 195

OLIN

- TN 1995 1,719 1,045 488 1,533 186 43 143 0 1,719 186

OLIN

- TN 1996 1,868 1,045 249 1,294 574 40 534 0 1,868 574

OLIN

- TN 1997 2,165 1,045 428 1,473 692 31 661 0 2,165 692

OLIN

- TN 1998 3,279 1,045 255 1,300 1,979 15 1,964 0 3,279 1,979

OLIN

- TN 1999 2,640 1,045 584 1,629 1,011 13 998 0 2,640 1,011

OLIN

- TN 2000 2,185 1,045 369 1,414 771 7 764 1 2,186 772

OLIN

- TN 2001 2,126 1,045 85 1,130 996 8 988 0 2,126 996

OLIN

- TN 2002 2,137 1,045 85 1,130 1,007 14 993 375 2,512 1,382

OLIN

- TN 2003 1,711 1,046 85 1,131 580 18 562 26 1,737 606

OLIN

- TN 2004 1,455 1,049 105 1,154 301 14 287 30 1,485 331

OLIN

- TN 2005 1,517 1,046 204 1,250 267 12 255 5 1,522 272A-19 

### ATTACHMENT A. Annual Mercury Release Data from TRI 1988

– 2008

Facility Year Mercury Releases Reported in TRI (pounds/yr)

Onsite Releases

Off Site Releases Total Releases (On

& Off Site) Total Nonair Releases (On & Off Site) Total Onsite Releases Fugitive Air Stack Air Total Air Onsite Nonair Water Land

OLIN

- TN 2006 1,440 1,032 109 1,141 299 13 286 198 1,638 497

OLIN

- TN 2007 834 353 67 420 414 8 406 0 834 414

OLIN

- TN 2008 481 241 43 285 196 6 191 1 482 197

PPG 1988 1,500 1,000 250 1,250 250 250 0 4,300 5,800 4,550

PPG 1989 1,500 1,000 250 1,250 250 250 0 750 2,250 1,000

PPG 1990 1,500 1,000 250 1,250 250 250 0 250 1,750 500

PPG 1991 1,145 1,000 85 1,085 60 60 0 1,800 2,945 1,860

PPG 1992 1,163 1,045 85 1,130 33 33 0 250 1,413 283

PPG 1993 1,152 1,045 85 1,130 22 22 0 1,340 2,492 1,362

PPG 1994 1,256 1,045 193 1,238 18 18 0 230 1,486 248

PPG 1995 1,266 1,045 192 1,237 29 29 0 250 1,516 279

PPG 1996 1,275 1,045 190 1,235 40 40 0 191 1,466 231

PPG 1997 1,254 1,045 188 1,233 21 21 0 102 1,356 123

PPG 1998 1,258 1,045 189 1,234 24 24 0 80 1,338 104

PPG 1999 1,254 1,045 187 1,232 22 22 0 62 1,316 84

PPG 2000 1,239 1,045 182 1,227 12 12 0 93 1,332 105

PPG 2001 1,301 1,045 227 1,272 29 29 0 16,658 17,959 16,687

PPG 2002 1,267 1,045 188 1,233 34 34 0 900 2,167 934

PPG 2003 1,238 1,045 177 1,222 16 16 0 1,130 2,368 1,146

PPG 2004 1,248 1,045 171 1,216 32 32 0 1,145 2,393 1,177A-20 

### ATTACHMENT A. Annual Mercury Release Data from TRI 1988

– 2008

Facility Year Mercury Releases Reported in TRI (pounds/yr)

Onsite Releases

Off Site Releases Total Releases (On

& Off Site) Total Nonair Releases (On & Off Site) Total Onsite Releases Fugitive Air Stack Air Total Air Onsite Nonair Water Land

PPG 2005 425 169 231 400 25 25 0 657 1,082 682

PPG 2006 356 172 134 306 50 34 0 1,115 1,471 1,165

PPG 2007 230 145 55 200 30 30 0 1,378 1,608 1,408

PPG 2008 223 149 58 207 16 16 0 735 958 751

### B

- 1 APPENDIX B:
EMISSIONS REDUCTIONS ASSOCIATED WITH REDU CED MERCURY CELL
CHLOR

- ALKALI PLANT ELECTRI CITY CONSUMPTION

As discussed in Section 3 of the RIA, EPA analysis estimates that electricity requirements may be 700 to 1,211 kWh per short ton of chorine lower after conversion, which corresponds to a 22% and 37.5% reduction in electricity consumption, respectively. 64



### Due to tran smission and distribution losses, electrical generating units (EGU) supplying the electricity grid need to generate more electricity (and emissions) to meet each plant’s electricity consumption. EPA has assumed approximately 10% of power is lost during

tra nsmission and distribution. This factor is commonly used to approximate losses and selected using professional judgment. To account for the possible losses and convert consumption changes to equivalent electricity generation changes, we divided the kWh con sumption reductions by 0.9 (electricity generated = electricity consumed / (1

- fraction transmission and distribution loss)) (Table B

- 1). Therefore, the total amount of reduced electricity generation associated with converting the four existing mercury chlo r

- alkali plants to membrane cells is estimated to be 375 million kilowatt hours per year. Depending on assumed capacity factors, this is approximately equivalent to the electricity produced annually by a 40 to 60 megawatt power plant. Using the range of estimates and plant chlorine capacities, EPA calculated changes in the annual electricity consumption for four chlorine plants: OLIN Corporation (facilities in Augusta, GA and Charleston, TN), PPG Industries (New Martinsville, WV), and ASHTA Chemicals (Ashtabula, OH). This corresponds to a reduction of approximately 350 million kilowatt hours per year of reduced electricity consumption associated with converting the four existing mercury chlor

- alkali plants to membrane cells.

B.1 Share of Electri city Generation by Fuel Type
EPA recognizes there are alternative methods for determining electricity generation by fuel type (i.e., the fuel mix that produced the electricity). As a result, we have documented the
results and sources of the information to allow comparisons with the other methods (e.g., use of NERC region electricity grid generation statistics by fuel type). EPA’s analysis reviewed the

64 As discussed in Section 3, EPA believe s that the 22 percent electricity reduction represents is the best estimate of reduced electricity demand, but we provide estimates based on the 37.5 percent electricity reduction to represent the data provided by Oceana.

### B

- 2 Table B

- 1. Estimated Changes in Electricity Generation

Change in Electricity Generation (kWh) for a

Chlorine Plant Assumed Electricity Provider Capacity 22 % Consumption

reduction a 37.5 % consumption

reduction b

OLIN Corp. (Augusta, GA) Southern Company 47,421 36,883,000 63,807,590
OLIN Corp. (Charleston, TN) TVA 119,031 92,579,667 160,162,823

PPG Industries (New Martinsville,

WV) American Electric Power 262,451 189,839,557 c 328,422,433 c

ASHTA Chemicals (Ashtabula, OH) First Energy 72,848 56,659,556 98,021,031

Total 375,961,779 650,413,877

a Electricity Consumption = Capacity (tpy) × 700 (kwh /ton) / (0.9).

b Electricity Consumption = Capacity (tpy) × 1,211 (kwh/ton) / (0.9).

c PPG produces 70 percent of its electricity requirements. The remaining 30 percent is purchased from Wheeling

Electric Co., a subsidiary of American Electric Power.



### locat ion for the four facilities and used the Homeland Security Information Program (Gold), a multiple layer geospatial database, to identify the two closest power plants and companies (DHS, 2009). 65



### B.2 Electricity Generation by Fuel Using physical location information and engineering assessment s EPA selected a power company that most likely provides the electricity to each plant (Table B

- 1, Column 2). In the case of PPG Industries New Martinsville, WV facility generates 70% of their electricity demand. Therefore, EPA assumes only 30% of the plan t’s electricity requirements are provided by another power company.

For each power company, the share of electricity generation provided by fuel type is reported in Table B

- 2. EPA collected the latest historical informat ion (2009) from electricity provider websites including annual reports 66

65 http://proceedings.esri.com/library/userconf/feduc08/papers/hifld_hsip_overview_esri_feduc_feb_2008_jms.pdf 

### and the engineering assessment considers it representative for the analysis year (2013) and the fuel mix of the electricity generated to meet the chlorine plants electricity requiremen ts.

66 American Electric Power.,(2009), First Energy (2009), Southern Company (2009). And the Tennessee Valley Authority (2009).

### B

- 3 Table B

- 2. Share of Electricity Generation by Fuel 2009

Utility Coal Gas Nuclear Hydro Purchased
Southern Company 57% 23% 16% 4% NA

TVA 46% 2% 23% 7% 13%

American Electric Power 88% 6% 5% 1% NA
First Energy 54% 12% 29% 3% NA



### B.3 Emission Factors
EPA’s eGRID2007 Version 1.1 provided pollutant emission factors (e.g., short tons per kWh) for several pollutants, including NO X 

### , SO 2 

### , CO 2 

### , and Hg, from EGUs in the U.S. (U.S. EPA, 2008). 67

67 The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive inventory of environmental attributes of electric power systems. The preeminent so urce of air emissions data for the electric power sector, eGRID is based on available plant

- specific data for all U.S. electricity generating plants that provide power to the electric grid and report data to the U.S. government. eGRID integrates many diffe rent federal data sources on power plants and power companies, from three different federal agencies: EPA, the Energy Information Administration (EIA), and the Federal Energy Regulatory Commission (FERC). Emissions data from EPA are carefully integrated wi th generation data from EIA to produce useful values like pounds per megawatt

- hour (lb/MWh) of emissions, which allows direct comparison of the environmental attributes of electricity generation. eGRID also provides aggregated data by state, U.S. total, co mpany, and by three different sets of electric grid boundaries. For more information on eGRID, please see http://www.epa.gov/cleanenergy/energy

- resources/egrid/index.html 

### The emissions data contained within eGRID2007 Version 1.1 represent s the year 2005 and also includes operator, parent company, owner, and electric grid configuration as of December 31, 2007. It is the latest version available. eGRID2010 covering year 2007 data is expected to be published at the end of 2010. We applied the appropriate conversion factors to eGRID2007’s reported power company emissions factors in common units (short tons) (Table B

- 3 and Table B

- 4). The emissions data contained within eGRID reflect EPA’s Emissions Tracking System/Continuous Emissions Monitorin g data. 

### The engineering assessment considers the emissions factors representative and reasonable for the analysis given the physical location of chlorine plants and the power companies and the power companies fuel mix used to meet it customer’s electricity requirements.

### B

- 4 Table B

- 3. Power Company Emission Factors: Fuel=Coal (Short tons per kWh)

Power Company NO X SO 2 CO 2 Hg

American Electric Power Co 1.72E

- 06 5.71E

- 06 9.71E

- 04 2.96E

- 11
First Energy Corp 1.64E

- 06 6.41E

- 06 1.03E

- 03 3.76E

- 11
Southern Company Services Inc 1.39E

- 06 6.41E

- 06 1.03E

- 03 2.60E

- 11
Tennessee Valley Authority 1.89E

- 06 4.50E

- 06 1.07E

- 03 2.15E

- 11



### Table B

- 4. Power Company Emission Factors: Fuel=Natural Gas (Short tons per kWh)

Power Company NO X SO 2 CO 2
American Electric Power Co 1.48E

- 06 9.15E

- 09 6.30E

- 04
First Energy Corp 1.73E

- 07 2.45E

- 08 5.70E

- 04
Southern Company Services Inc 1.23E

- 07 2.68E

- 08 4.66E

- 04
Tennessee Valley Authority 1.14E

- 07 2.80E

- 09 4.86E

- 04



### Emission factors could not be identified for PM 2.5, 

### NH 3 

### , and VOCs in eGRID. As a result, EPA calculated the emission factors using information from two different sources. The first source, Co

- Benefits Risk Assessment model (COBRA), provided total U.S. emissions estimates by fuel type and by pollutant for 2010 (U.S. EPA, 2009). 68



### The second source, the EIA’s Annual Energy Outlook 2009 (Table 8), reports electricity supply, disposition, prices, and emissions and provides 2010 electricity generation (billion kWh) by fuel. EPA calculated emission factors by fuel type by dividing COBRA emissions estimates by the Energy Information Administration’s (EIA) electricity generation estimates (Table B

- 5) (EIA, 2009). The model contains detailed emissions estimates projected for the years 2010 and 2015 that were developed by EPA for its 2005 regulatory analysis of the Clean Air Interstate Rule (CAIR) (U.S. EPA, 2005). Emissions data undergoes quality assurance at both the state and federal level.

68 The Co

– Benefits Risk Assessment (COBRA) screening model is a stand

– alone Windows ap plication that enables policy analysts to quickly obtain a first

– order approximation of the costs and benefits of different emission scenarios and to compare outcomes in terms of changes in ambient particulate matter (PM) concentrations, related health eff ects, and monetary impacts. For more information on COBRA, please see http://www.epa.gov/statelocalclimate/resources/cobra.html

### B

- 5 Table B

- 5. Emission Factor Calculations: 2010

Variable Coal Gas Petroleum
Generation (billion kWh) AEO 2009
Electricity 2,057 814 56
Emissions (Short tons) COBRA Model

PM 2.5 506,294 6,696 17

NH 3 390 1,762 0

VOC 36,135 5,360 0
Emission Factor (Short tons per kWh) EPA Calculations

PM 2.5 2.46E

- 07 8.22E

- 09 3.02E

- 10

NH 3 1.90E

- 10 2.16E

- 09 0

VOC 1.76E

- 08 6.58E

- 09 0



### B.4 Emissions Reductions
Emission reductions were calculated in three steps. First, we allocate each change in electricity generation (Table B

- 1) by fuel source using the share data (Table B

- 2). The engineering assessment concluded it was reasonable to assume electricity demand de clines proportionally for all fuel types. In reality, power companies would presumably reduce generation at the EGU with the highest average production costs. However, data was not available to refine the analysis. Next, we multiplied the generation reduct ion by fuel by the appropriate fuel emission factors (Tables B

- 3, B

- 4, B

- 5). Since the reductions in kWh requirements per ton of chlorine is uncertain, EPA considered two scenarios: a 22% reduction (Table B

- 6) and 37% reduction (Table B

- 7).

It is important to note that we assume that these emission reductions occur across the multiple EGUs associated with the presumed electricity provider. Although we identify the location of the mercury chlor alkali plant that would consume less electricity in the tables
b elow, those locations do not reflect the location of the emission reductions from the EGUs. Because the emission reductions occur across multiple EGUs over a large geographic area, these estimates are representative of regional emission reductions.

### B

- 6 Table B

- 6. Emission Reductions by Plant (22 percent reduction)

Emission Sources (Short Tons)

Chlorine Plant PM 2.5 NH 3 VOC NO X SO 2 CO 2 Hg
OLIN Corp. (Augusta, GA) 5.2 0.02 0.4 30.2 135.0 25,508 0.001
OLIN Corp. (Charleston, TN) 10.5 0.01 0.8 80.7 191.4 46,400 0.001
PPG Industries (New Martinsville, WV) 45.1 0.04 3.2 320.5 1,045.0 179,882 0.005

ASHTA Chemicals (Ashtabula, OH) 7.6 0.02 0.6 51.3 196.4 35,207 0.001

Total 68.4 0.10 5.0 482.6 1,567.8 286,997 0.008



### Table B

- 7. Emission Estimates by Plant (37 percent reduction)

Emission Sources (Short Tons)

Chlorine Plant PM 2.5 NH 3 VOC NO X SO 2 CO 2 Hg
OLIN Corp. (Augusta, GA) 9.1 0.04 0.7 52.2 233.5 44,129 0.001
OLIN Corp. (Charleston, TN) 18.2 0.02 1.3 139.6 331.2 80,272 0.002
PPG Industries (New Martinsville, WV) 78.0 0.07 5.6 554.4 1,807.8 311,195 0.009

ASHTA Chemicals (Ashtabula, OH) 13.1 0.03 1.0 88.8 339.7 60,907 0.002

Total 118.3 0.17 8.7 835.0 2,712.3 496,504 0.014



### B.5 Uncertainties
This appendix provides sufficient documentation to reproduce the calculations of emission reductions anticipated to occur as a result of this proposed regulation. In any complex analysis, there can be many sources of uncertainty that could affect the results, especially if the analysis involves predicting future behaviors. In addition, there are always analytical choices that could have incorporated alternate assumptions. Below, we include a qualitative discussion of some of the main sources of uncertainty, including the direction and general magnitude of t he potential bias to the extent feasible given data limitations.

### B

- 7  

### There are several uncertainties associated with the estimate of reduced electricity demand. As shown in this appendix, assuming a 37.5% reduction as suggested by Oceana would nearly double th e magnitude of the emission reductions.

 

### EPA has assumed a 10 percent transmission and distribution loss factor. In 2007, the national average transmission loss was 6.5%, but varied by state (EIA, 2008). Using the EIA reported value lowers emission reductio ns by approximately 2 percent.

 

### The underlying historical data from analyses/models represent different points of time (between 2005 and 2010). EPA has assumed they are reasonable representations of the analysis year (2013). To our knowledge, forecasts of portfolio mixes and emission factors for the analysis year are not readily available. Although it is possible that current year or projected data may more accurately reflect the regulatory scenario, this analysis is based on the best available data.

 

### There are a variety of sources of uncertainty implicit within the emissions factors, including changes over time. For example, new emissions reduction technologies may be introduced earlier than planned and reduce emission factors. Other regulations on EGUs may result in lower emissions factors in the future. In contrast, higher natural gas prices relative to fuels like coal may change power company generation portfolios with higher emission rates. To the extent the emissions factors are higher than those used, the current analysis understates emissions reductions. In contrast, if the emissions factors are lower, the current analysis overstates emissions reductions.

 

### Since NOx and SO2 are covered by capped emissions trading programs, we're only estimating Pm2.5 e mission reductions from reduced electricity demand.

 

### The analysis of energy savings assumes that the four facilities all convert rather than close. If one or more were to close, the reduction in electricity use would be greater at the facility (e.g., rather than a 22% reduction for the process there would be a 100% reduction). However, if the facility closing no longer provided chlorine and caustic, most of it would be produced elsewhere with an associated increase in electricity use in another place. Since this would have to be at a facility that uses a process similar to the one the closing facility would have converted to, the national electricity savings would be expected to be similar for either conversion or closure although the resulting pollution redu ctions would differ spatially. If closure of the process led to closing other processes being produced at the same facility, the same reasoning would apply. Therefore, it is likely that in the event of closure, the emissions reductions would be similar. Ho wever if prices increased as a result closure and overall quantities decreased then emission reductions would have been underestimated by the amount of emissions associated with the reduced production.

### B

- 8  

### There are other pre

- existing conditions in electricity markets such as taxes and imperfect competition and taxes. EPA has only accounted for the externality associated with selected pollutant emissions reductions. While these other factors may influence benefit

- cost analysis, we believe that they are likely t o be small compared to the health effects associated with the emissions from the pollutants considered in the analysis.

B.6 References
American Electric Power. 2009. Available on the internet at http://www.aep.com/investors/edgar/docs/AEP_10K_2009.pdf

First Energy. 2009. Available on the internet at http://investors.firstenergycorp.com/phoenix.zhtml?c=102230&p=irol

- sec10k

Southern Company. 2009. Available on the internet at http://www.southerncompany.com/sotogo/SOtoGo.pdf

Tennessee Valley Authority. 200 9. Available on the internet at http://www.tva.com/finance/reports/pdf/fy2009ar.pdf

U.S. Department of Homeland Security. Homeland Security Information Program Gold (HSIP Gold), Version 2 (April 2007)

U.S. Energy Information Administration. 2008. State Ele ctricity Profiles. Table 10. Available on the internet at http://tonto.eia.doe.gov/ftproot/electricity/stateprofiles/07st_profiles/062907.pdf

U.S. Energy Information Administration. 2009. Supplemental Tables to the Annual Energy Outlook 2010. Table 2. Avai lable on the internet at http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html

U.S. Environmental Protection Agency (U.S. EPA). 2005. Clean Air Interstate Rule Emissions Inventory Technical Support Document. Office of Air Quality Planning and Standards. March. A vailable on the internet at http://www.epa.gov/CAIR/pdfs/finaltech01.pdf

U.S. Environmental Protection Agency (U.S. EPA). 2008. The Emissions and Generation Resource Integrated Database for 2007 (eGRID2007) Technical Support Document. Office of Atmospheri c Programs Climate Protection Partnerships Division. November. Available on the internet at http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2007TechnicalSupportDoc ument.pdf

U.S. Environmental Protection Agency (U.S. EPA). 2009. User’s Manual for th e Co

- Benefits Risk Assessment (COBRA) Screening Model. Climate Protection Partnerships Division State and Local Climate and Energy Programs. November. Available on the internet at http://www.epa.gov/statelocalclimate/documents/pdf/COBRA_manual.pdf