§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-1

CHAPTER CONTENTS

B2E-1 Summary Insights from this Profile . . . . . . . . . . B2E-2

B2E-2 Domestic Production . . . . . . . . . . . . . . . . . . . . . B2E-3

B2E-2.1 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-4

B2E-2.2 Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-7

B2E-2.3 Number of Facilities and Firms . . . . . . . . . B2E-8

B2E-2.4 Employment and Productivity . . . . . . . . . B2E-12

B2E-2.5 Capital Expenditures . . . . . . . . . . . . . . . . B2E-14

B2E-2.6 Capacity Utilization . . . . . . . . . . . . . . . . B2E-16

B2E-3 Structure and Competitiveness . . . . . . . . . . . . . B2E-17

B2E-3.1 Geographic Distribution . . . . . . . . . . . . . B2E-17

B2E-3.2 Facility Size . . . . . . . . . . . . . . . . . . . . . . B2E-18

B2E-3.3 Firm Size . . . . . . . . . . . . . . . . . . . . . . . . B2E-20

B2E-3.4 Concentration Ratios . . . . . . . . . . . . . . . B2E-20

B2E-3.5 Foreign Trade . . . . . . . . . . . . . . . . . . . . . B2E-21

B2E-4 Financial Condition and Performance . . . . . . . B2E-24

B2E-5 Facilities Operating Cooling Water Intake

Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-26

B2E-5.1 Waterbody and Cooling System Type . . . B2E-27

B2E-5.2 Facility Size . . . . . . . . . . . . . . . . . . . . . . B2E-28

B2E-5.3 Firm Size . . . . . . . . . . . . . . . . . . . . . . . . . B2E-29

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-30

Chapter B2E: Aluminum (SIC 333/5)

EPA’s Detailed Industry Questionnaire,

hereafter referred to as the DQ, identified two 4-

digit SIC codes in the nonferrous metals

industries (SIC codes 333/335) with at least one

existing facility that operates a CWIS, holds a

NPDES permit, withdraws equal to or greater

than two million gallons per day (MGD) from a

water of the United States, and uses at least 25

percent of its intake flow for cooling purposes

(facilities with these characteristics are hereafter

referred to as facilities potentially subject to the

Phase III regulation or “potential Phase III

facilities”). 

For each of the two SIC codes, Table B2E-1

below provides a description of the industry

segment, a list of products manufactured, the

total number of detailed questionnaire

respondents (weighted to represent national

results), and the number and percent of potential

Phase III facilities within the estimated national

total of facilities in the respective industry SIC

code groups.

Table B2E-1: Potential Phase III facilities in the Aluminum Industries (SIC 333/335)

SIC SIC Description Important Products Manufactured

Number of Facilitiesa

Total Potential Phase

III facilities b %

3334 Primary Production

of Aluminum

Producing aluminum from alumina and in refining

aluminum by any process 31 11 35.5%

3353 Aluminum Sheet,

Plate, and Foil

Flat rolling aluminum and aluminum-base alloy basic

shapes, such as rod and bar, pipe and tube, and tube

blooms; producing tube by drawing

57 10 17.5%

Total 88 21 24.0%

a

 Number of weighted detailed questionnaire survey respondents. b

 Individual numbers may not add up due to independent rounding.

Source: U.S. EPA, 2000; Executive Office of the President, 1987.

As reported in the table, EPA estimates that 21 out of 88 facilities (or 24 percent) in the Aluminum Industries

(SIC 333/335) are potentially subject to this proposed regulation. EPA also estimated the percentage of total

production that occurs at facilities potentially subject to the proposed regulation. Total value of shipments for the

Aluminum Industries (SIC 333/335) from the 1998 Annual Survey of Manufacturers is $19.3 billion. Value of

shipments, a measure of the dollar value of production, was selected for the basis of this estimate. Because value

of shipments data were not collected using the DQ, these data were not available for the sample of Phase III

manufacturing facilities potentially subject to the proposed regulation. Total revenue, as reported on the DQ, was
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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used as a close approximation for value of shipments for these facilities. EPA estimated the total revenue of

facilities in the aluminum industry subject to the proposed regulation is $12.1 billion. Therefore, EPA estimates

that 63 percent of total domestic aluminum production occurs at facilities potentially subject to the proposed

regulation.

Table B2E-2 provides the cross-walk between SIC codes and the new NAICS codes for the profiled aluminum

SIC codes. The table shows that both of the profiled 4-digit SIC codes in the aluminum industry have a one-toone relationship to NAICS codes.

Table B2E-2: Relationships between SIC and NAICS Codes for the Aluminum Industries (1997)

SIC

Code SIC Description NAICS

Code NAICS Description Number of

Establishments

Value of

Shipments

($1000)

Employment

3334 Primary aluminum 331312 Primary aluminum

production 235 7,565,377 15,763

3353 Aluminum sheet,

plate, and foil 331315 Aluminum sheet, plate, and

foil manufacturing 70 13,755,566 25,111

Source: U.S. DOC, 1997.

B2E-1 SUMMARY INSIGHTS FROM THIS PROFILE

A key purpose of this profile is to provide insight into the ability of aluminum industry firms subject to the 316(b)

regulation to absorb compliance costs without material adverse economic/financial effects. Two important factors

in the industry’s ability to withstand compliance costs are: (1) the extent to which the industry can shift

compliance costs to its customers through price increases, and (2) the financial health of the industry and its

general business outlook. 

Likely Ability to Pass Compliance Costs Through to Customers

As reported in the following sections of this profile, the aluminum industry is moderately concentrated. This

potentially supports firms in this industry passing through to customers a significant portion of their compliancerelated costs. However, the domestic Primary Aluminum Production segment faces significant competition from

imports into the U.S. market. Facilities in the Aluminum Sheet, Plate, and Foil segment have a notable reliance

on foreign markets. The substantial competitive pressure from abroad weakens the potential of firms in this

industry to pass through to customers a significant portion of their compliance-related costs. As discussed above,

the proportion of total value of shipments in the industry potentially subject to the proposed regulation is 63

percent. The actual proportion of total value of shipments subject to regulation-induced compliance costs would

be smaller since not all of the facilities would be subject to the national categorical requirements of the proposed

regulation: that is, facilities below the proposed design intake flow (DIF) would be subject to permitting based on

best professional judgement (BPJ) rather than based on national standards, and several facilities currently employ

baseline technologies that meet the requirements of the proposed regulation. Given the likelihood that these

percentages represent upper bound estimates, EPA believes that the theoretical threshold for justifying the use of

industry-wide CPT rates in the impact analysis of existing Phase III aluminum facilities has not been met. For

these reasons, in its analysis of regulatory impacts for the aluminum industry, EPA assumes that complying firms

would be unable to pass compliance costs through to customers: i.e., complying facilities must absorb all

compliance costs within their financial condition at the time of compliance (see following sections and Appendix

3 to Chapter B3: Economic Impact Analysis for Manufacturers for further information).

Financial Health and General Business Outlook

Over the past decade, the aluminum industry, like other U.S. manufacturing industries, has experienced a range of

economic/financial conditions, including substantial challenges. In the early 1990s, the domestic aluminum
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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industry was adversely affected by reduced U.S. demand and the dissolution of the Soviet Union, resulting in

large increases in Russian aluminum exports. Although domestic market conditions improved by mid-decade,

weakness in Asian markets, along with growing Russian exports, dampened performance during the latter half of

the 1990s. Demand for aluminum industry products declined again in 2000 through 2002, reflecting weakness in

both the U.S. and world economies, and again resulted in oversupply and declining financial performance. More

recently, as the U.S. economy began recovering from economic weakness, the domestic aluminum industry is

showing signs of recovery with higher demand levels and improving financial performance over the course of

2003. Although the industry has weathered difficult periods over the past few years, the strengthening of the

industry’s financial condition and general business outlook suggest improved ability to withstand additional

regulatory compliance costs without imposing significant financial impacts.

B2E-2 DOMESTIC PRODUCTION

Commercial production of aluminum using the electrolytic reduction process, known as the Hall-Heroult process,

began in the late 1800s. The production of primary aluminum involves mining bauxite ore and refining it into

alumina, one of the feedstocks for aluminum metal. Direct electric current is used to split the alumina into molten

aluminum metal and carbon dioxide. The molten aluminum metal is then collected and cast into ingots. 

Technological improvements over the years have improved the efficiency of aluminum smelting, with a particular

emphasis on reducing energy requirements. Currently, no commercially viable alternative exists to the

electrometallurgical process (Aluminum Association, 2001).

In 2003, aluminum recovered from purchased scrap was about 2.8 million tons, of which about 60% came from

new (manufacturing) scrap and 40% from old scrap (discarded aluminum products). Aluminum recovered from

old scrap was equivalent to about 17% of apparent consumption (USGS, 2004a). Recycling consists of melting

used beverage cans and scrap generated from operations. Recycling saves approximately 95 percent of the energy

costs involved in primary smelting from bauxite (S&P, 2001). In contrast to the steel industry, aluminum

minimills have had limited impact on the profitability of traditional integrated aluminum producers. Aluminum

minimills are not able to produce can sheet of the same quality as that produced by integrated facilities. As a

result, they are able to compete only in production of commodity sheet products for the building and distributor

markets, which are considered mature markets. According to Standard & Poor’s (2001), construction of new

minimill capacity is unlikely given the potential that added capacity would drive down prices in the face of slow

growth in the markets for minimill products. No secondary smelters (included, along with secondary smelting of

other metals, in SIC code 3341) were reported in EPA’s Detailed Industry Questionnaire. These facilities are

therefore not addressed in this profile.

Facilities in SIC code 3353 produce semifabricated products from primary or secondary aluminum. Examples of

semifabricated aluminum products include (Aluminum Association, undated):

< sheet (cans, construction materials, and automotive parts);

< plate (aircraft and spacecraft fuel tanks);

< foil (household aluminum foil, building insulation, and automotive parts);

< rod, bar, and wire (electrical transmission lines); and 

< extrusions (storm windows, bridge structures, and automotive parts). 

U.S. aluminum companies are generally vertically integrated. The major aluminum companies own large bauxite

reserves, mine bauxite ore and refine it into alumina, produce aluminum ingot, and operate the rolling mills and

finishing plants used to produce semifabricated aluminum products (S&P, 2001).

As noted, the production of primary aluminum is an electrometallurgical process, which is extremely energy

intensive. Electricity accounts for approximately 30 percent of total production costs for primary aluminum

smelting. The aluminum industry is therefore a major industrial user of electricity, spending more than $2 billion

annually. The industry has pursued opportunities to reduce its use of electricity as a means of lowering costs. In
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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the last 50 years, the average amount of electricity needed to make a pound of aluminum has declined from 12

kilowatt hours to approximately 7 kilowatt hours. (Aluminum Association, undated).

B2E-2.1 Output

The largest single source of demand for aluminum is the transportation segment, primarily the manufacture of

motor vehicles. Demand for lighter, more fuel efficient vehicles has increased demand for aluminum in auto

manufacturing, at the expense of steel (S&P, 2001). Until 1996, containers were the largest U.S. market for

aluminum. Production of beverage cans is a major use of aluminum sheet, and aluminum has entirely replaced

steel in the beverage can market. Other major uses of aluminum include construction (including aluminum siding,

windows, and gutters) and consumer durables (USGS, 2001a).

Demand for aluminum reflects the overall state of the domestic and world economies, as well as long-term trends

in materials use in major end-use sectors. Because aluminum production involves large fixed investments and

capacity adapts slowly to fluctuations in demand, the industry has experienced alternating periods of excess

capacity and tight supplies. The early 1980s was a period of oversupply, high inventories, and excess capacity. 

By 1986, excess capacity was closed, inventories were low, and demand increased substantially. The early 1990s

were affected by reduced U.S. demand and the dissolution of the Soviet Union, resulting in large increases in

Russian exports of aluminum. By the mid-1990s, global production declined, demand rebounded, and aluminum

prices rose. Subsequent increased production reflected an overall increase in the demand for aluminum with

stronger domestic economic growth, driven by increased consumption by the transportation, container, and

construction segments. The economic crises in Asian markets in the later 1990s, along with growing Russian

exports, again resulted in a period of oversupply, although U.S. demand for aluminum remained strong. Demand

declined again in 2000 through 2002 due to slower growth in both the U.S. and the world economy, resulting in

oversupply. The surplus was mitigated somewhat as demand in the automotive and housing markets remained

relatively high through mid-2003. In addition, the California energy crisis in 2000 and 2001 reduced production

from primary smelters located in the Pacific Northwest (Aluminum Association, 1999; USGS, 1999c; USGS,

1998e; USGS, 1994c; Value Line, 2001). Production in China increased during this period, and although

increased Chinese consumption helped reduce the surplus slightly, the country switched from being a net importer

to a net exporter. Additionally, interest rates are likely to increase which may decrease U.S. demand for

aluminum from major industrial end markets (aerospace, automotive, home-construction, and commercialconstruction). However, with the economy showing signs of recovery the aluminum industry saw higher demand

levels in 2003. If the economy remains strong, demand is expected to continue at 2003 levels (Value Line, 2003;

S&P 2004).

Table B2E-3 shows trends in output of aluminum by Primary Aluminum producers and recovery of aluminum

from old and new scrap. Secondary production grew from 37 percent to over half of total domestic production

over the period from 1990 to 2003. Of total secondary production in 2003, 1,170 thousand metric tons (MT) or

40 percent, is from old scrap (discarded aluminum products), as opposed to new scrap (from manufacturing). 

Primary production of aluminum recorded a net decrease over the 13-year period, but declined sharply in 2001

compared to 2000. As noted above, this decrease reflects reduced domestic and world demand for aluminum, and

curtailed production at a number of Pacific Northwest mills caused by the California energy crisis (S&P 2001a;

USGS, 2001a). Production remained fairly constant for the final three years of the period.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

1

 Terms highlighted in bold and italic font are further explained in the glossary.

B2E-5

Table B2E-3: U.S. Quantities of Aluminum Produced

Year

Aluminum Ingot

Primary Production Secondary Production 

(from old & new scrap) Total Production

Thousand

MT % Change Thousand

MT % Change Thousand

MT % Change

1990 4,048 n/a 2,390 n/a 6,438 n/a

1991 4,121 1.8% 2,290 -4.2% 6,411 -0.4%

1992 4,042 -1.9% 2,760 20.5% 6,802 6.1%

1993 3,695 -8.6% 2,940 6.5% 6,635 -2.5%

1994 3,299 -10.7% 3,090 5.1% 6,389 -3.7%

1995 3,375 2.3% 3,190 3.2% 6,565 2.8%

1996 3,577 6.0% 3,310 3.8% 6,887 4.9%

1997 3,603 0.7% 3,550 7.3% 7,153 3.9%

1998 3,713 3.1% 3,440 -3.1% 7,153 0.0%

1999 3,779 1.8% 3,700 7.6% 7,479 4.6%

2000 3,688 -2.4% 3,450 -6.8% 7,138 -4.6%

2001 2,637 -28.5% 2,970 -13.9% 5,607 -21.4%

2002 2,707 2.7% 2,930 -1.3% 5,637 0.5%

2003 2,703 -0.1% 2,930 0.0% 5,633 -0.1%

Total percent change

1990-2003 -33.2% 22.6% -12.5%

Average annual

growth rate -3.1% 1.6% -1.0%

Source: USGS, 2001b-2003b; USGS 1996a-2004a; USGS 2002d

Value of shipments and value added are two common measures of manufacturing output1. They provide insight

into the overall economic health and outlook for an industry. Value of shipments is the sum of the receipts a

manufacturer earns from the sale of its outputs; it indicates the overall size of a market or the size of a firm in

relation to its market or competitors. Value added measures the value of production activity in a particular

industry. It is the difference between the value of shipments and the value of inputs used to make the products

sold.

Figure B2E-1 reports constant dollar value of shipments and value added for the Primary Aluminum, and

Aluminum Sheet, Plate, and Foil segments between 1987 and 2001.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

VOS-Primary Aluminum Production

(SIC 3334)

VOS-Primary Aluminum Production

(Estimated from NAICS)

VOS-Aluminum Sheet, Plate, and

Foil (SIC 3353)

VOS-Aluminum Sheet, Plate, and

Foil (Estimated from NAICS)

0

200

400

600

800

1,000

1,200

1,400

1,600

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

VA-Primary Aluminum

Production (SIC 3334)

VA-Primary Aluminum

Production (NAICS to SIC 3334)

VA-Aluminum Sheet, Plate, and

Foil (SIC 3353)

VA-Aluminum Sheet, Plate, and

Foil (NAICS to SIC 3353)

Figure B2E-1: Value of Shipments and Value Added for Profiled Aluminum Segments 

(millions,$2003)

Value of Shipments

Value Added

a Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the

North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the

SIC code classifications us`ing the 1997 Economic Census Bridge Between NAICS and SIC.

Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992 and 1997.

The value of Primary Aluminum shipments shows generally the same pattern as the quantity data shown in Table

B2E-3. Trends in production reflect trends in demand for aluminum, growth since 1990 in the percentage of

domestic demand provided by imports, and increasing secondary production of aluminum, which substitutes in

some but not all markets for primary production. Value added by aluminum production excludes the value of

purchased materials and services (including electricity), and shows less fluctuation since 1990 than value of

shipments. 

Demand for semifinished aluminum products reflects demand from the transportation, container, and building

industries. Real value of shipments of Aluminum Sheet, Plate, and Foil declined from the late 1980s through

1993, and then recovered by mid-decade, before turning down again in the late 1990s. Demand for semifinished
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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products has been affected by strong growth in both the container and packaging segment and the auto segment

(S&P, 2001). 

Both industry segments show lower values for the constant dollar value of shipments and value added at the end

of the 15-year analysis period than at the beginning of the period. These declining values reflect the overall

maturity of the aluminum production industry and the increasing role of foreign production in meeting total U.S.

demand.

B2E-2.2 Prices

The producer price index (PPI) measures price changes, by segment, from the perspective of the seller, and

indicates the overall trend of product pricing, and thus supply-demand conditions, within a segment.

The price trends shown for Primary Aluminum in Figure B2E-2 reflect the fluctuations in world supply and

demand discussed in the previous section. During the early 1980s, the aluminum industry experienced

oversupply, high inventories, excess capacity, and weak demand, resulting in falling prices for aluminum. By

1986, much of the excess capacity had been permanently closed, inventories had been worked down, and

worldwide demand for aluminum increased strongly. This resulted in price increases through 1988, as shown in

Figure B2E.2.

In the early 1990s, the dissolution of the Soviet Union had a major impact on aluminum markets. Large quantities

of Russian aluminum that formerly had been consumed internally, primarily in military applications, were sold in

world markets to generate hard currency. At the same time, world demand for aluminum was decreasing. The

result was increasing inventories and depressed aluminum prices.

The United States and five other primary aluminum producing nations signed an agreement in January 1994 to

curtail global output, in response to the sharp decline in aluminum prices. At the time of the agreement, there was

an estimated global overcapacity of 1.5 to 2.0 million metric tons per year (S&P, 2000).

By the mid-1990s, production cutbacks, increased demand, and declining inventories led to a sharp rebound of

prices. Prices declined again though during the late 1990s, when the economic crises in Asian markets reduced

the demand for aluminum (USGS, 2001b). During 2000, prices rebounded sharply despite the continuing trend of

high Russian production and exports. However, economic recession caused prices to fall again through 2002. 

Prices began to recover during 2003 and are expected to continue on an upward trend as the economy recovers. 

(S&P, 2001-2004).
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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80

90

100

110

120

130

140

150

160

170

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Primary Aluminum

Production (SIC 3334)

Aluminum Sheet, Plate, and

Foil (SIC 3353)

Figure B2E-2: Producer Price Indexes for Profiled Aluminum Segments

Source: BLS, 2002.

B2E-2.3 Number of Facilities and Firms

U.S. Geological Survey data indicate that the number of Primary Aluminum facilities and the number of firms

that own them remained fairly constant over the period 1995 through 2001, as shown in Table B2E-4. The

number of domestic companies and plants sharply declined in 2002 compared to 2001. In 2002, the 10 domestic

producers had a total of 7 smelters that were either temporarily or permanently idled. The bulk of the idled

capacity resulted from curtailed production at a number of Pacific Northwest mills caused by the California

energy crisis. Most of the smelters outside of this region continued to operate at or near their engineered

capacities (S&P 2001; USGS, 2001a; USGS, 2002c).
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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Table B2E-4: Primary Aluminum Production - Number of Companies and

Plants

Year Number of Companies Number of Plants

1995 13 22

1996 13 22

1997 13 22

1998 13 23

1999 12 23

2000 12 23

2001 12 23

2002 7 16

2003 7 15

Source: USGS, 1996a-2004a.

Statistics of U.S. Businesses covers a larger number of facilities classified under SIC 3334 than do the USGS

data, and also provide data on SIC 3353 (Aluminum Sheet, Plate, and Foil). These data, shown in Table B2E-5

and B2E-6, show more fluctuation in the number of establishments and the number of firms.

Table B2E-5 shows that the number of Primary Aluminum facilities decreased by 30 percent between 1991 and

1995, with the majority of this decrease, 27 percent, occurring between 1991 and 1993. The number of facilities

in the Aluminum Sheet, Plate, and Foil segment showed a more consistent trend, increasing each year except in

1993. In 1998, the number of facilities decreased in both segments, but have continued to grow since then.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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Table B2E-5: Number of Facilities for Profiled Aluminum Segments

Year

Primary Aluminum Production Aluminum Sheet, Plate, and Foil 

Number of

Establishments Percent Change Number of

Establishments Percent Change

1989 56 n/a 61 n/a

1990 54 -3.6% 64 4.9%

1991 57 5.6% 73 14.1%

1992 52 -8.8% 73 0.0%

1993 44 -15.4% 63 -13.7%

1994 41 -6.8% 69 9.5%

1995 40 -2.4% 76 10.1%

1996 51 27.5% 81 6.6%

1997 34 -33.3% 91 12.3%

1998 28 -17.6% 83 -8.8%

1998a 28 -17.6% 79 -13.2%

1999a 29 3.6% 93 17.7%

2000a 32 10.3% 103 10.8%

2001a 38 18.8% 111 7.8%

Total Percent Change

1989-2001 -32.1% 82.0%

Average Annual

Growth Rate -3.2% 5.1%

a

 Before 1998, these data were compiled in the SIC system; since 1998, these data have been compiled in the North

American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to

the SIC code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.

Source: U.S. SBA, 1989-2001.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-11

The trend in the number of firms over the period between 1989 and 2001 is similar to the trend in the number of

facilities in both industry segments. Table B2E-6 on the following page presents SUSB information on the

number of firms in each segment between 1989 and 2001.

Table B2E-6: Number of Firms for Profiled Aluminum Segments

Year

Primary Aluminum Production Aluminum Sheet, Plate, and Foil

Number of Firms Percent Change Number of Firms Percent Change

1990 38 n/a 43 n/a

1991 41 7.9% 53 23.3%

1992 36 -12.2% 53 0.0%

1993 33 -8.3% 45 -15.1%

1994 30 -9.1% 47 4.4%

1995 30 0.0% 51 8.5%

1996 40 33.3% 56 9.8%

1997 23 -42.5% 66 17.9%

1998 19 -17.4% 60 -9.1%

1998a 19 -17.4% 56 -15.2%

1999a 20 5.3% 66 17.9%

2000a 22 10.0% 73 10.6%

2001a 28 27.3% 82 12.3%

Total Percent Change 

1990-2001 -26.3% 90.7%

Average Annual

Growth Rate -2.7% 6.0%

a

 Before 1998, these data were compiled in the SIC system; since 1998, these data have been compiled in the North

American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the

SIC code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.

Source: U.S. SBA, 1989-2001.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

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10,000

12,000

14,000

16,000

18,000

20,000

22,000

24,000

26,000

28,000

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Primary Production of Aluminum

(SIC 3334)

Primary Aluminum Production

(NAICS to SIC 3334)

Aluminum Sheet, Plate, and Foil

(SIC 3353)

Aluminum Sheet, Plate, and Foil

(NAICS to SIC 3353)

B2E-2.4 Employment and Productivity

Figure B2E-3, below, provides information on employment from the Annual Survey of Manufactures for the

Primary Aluminum and Aluminum Sheet, Plate, and Foil segments. Trends in Primary Aluminum facility

employment reflect trends in both production and producers’ efforts to improve labor productivity to compete

with less labor-intensive minimills (McGraw-Hill, 2000). The figure shows that employment in the Primary

Aluminum segment has declined steadily since 1992, even in years of increased production.

Employment in the Aluminum Sheet, Plate, and Foil segment declined from 1987 through 1994, but rose between

1995 and 1997, before declining again during 1997 to 2001. Employment in the Primary Aluminum Production

segment increased during the 1987 to 1992 period, but fell persistently over the remainder of the 1990s decade

and through 2001. For both industry segments, the low employment level in 2001 resulted from the idled

capacity from curtailed production at a number of Pacific Northwest mills caused by the California energy crisis.

Figure B2E-3: Employment for Profiled Aluminum Segments

a Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the

North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the

SIC code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.

Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992, and 1997.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-13

Table B2E-7 presents the change in value added per labor hour, a measure of labor productivity, for the Primary

Aluminum Production and Aluminum Sheet, Plate, and Foil segments between 1987 and 2001. The trend in labor

productivity in both segments showed volatility over this period, reflecting variations in capacity utilization. 

Value added per hour in the Primary Aluminum segment showed a 3.3 percent net increase over the entire period

1987 and 2001. Value added per hour in the Aluminum Sheet, Plate, and Foil segment saw a three percent

decrease over the whole period between 1987 and 2001.

Table B2E-7: Productivity Trends for Profiled Aluminum Segments ($2003)

Year

Primary Production of Aluminum Aluminum Sheet, Plate, and Foil

Value

Added

(millions)

Production

Hours

(millions)

Value Added/Hour Value

Added 

(millions)

Production

Hours

(millions)

Value Added/Hour

($/hour) Percent

Change ($/hour) Percent

Change

1987 821 28 30 n/a 1,317 40 33 n/a

1988 933 32 29 -2% 1,345 41 33 0%

1989 964 30 32 9% 1,336 41 33 0%

1990 971 32 30 -5% 1,322 40 33 2%

1991 971 32 30 0% 1,251 39 32 -4%

1992 986 32 31 1% 1,241 40 31 -2%

1993 870 29 30 -2% 1,224 39 32 2%

1994 827 27 31 2% 1,148 37 31 -1%

1995 855 28 30 -2% 1,178 38 31 -2%

1996 844 29 30 -2% 1,215 39 31 1%

1997 783 26 30 1% 1,302 41 32 1%

1998a 800 27 30 0% 1,187 39 31 -3%

1999a 742 26 29 -5% 1,158 37 32 3%

2000a 700 24 29 0% 1,110 35 32 1%

2001a 607 19 31 9% 1,011 32 32 -2%

Total Percent Change

 1987-2001

-26.1% -32.1% 3.3% -23.2% -20.0% -3.0%

Average Annual

Growth Rate

-2.1% -2.7% 0.2% -1.9% -1.6% -0.2%

a

 Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the North

American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code

classifications using the 1997 Economic Census Bridge Between NAICS and SIC.

Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992, and 1997.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-14

B2E-2.5 Capital Expenditures

Aluminum production is a highly capital-intensive process. Capital expenditures are needed to modernize,

replace, and when market conditions warrant, expand capacity. Environmental requirements also require major

capital expenditures.

Capital expenditures in the Primary Aluminum Production and Aluminum Sheet, Plate, and Foil segments

between 1987 and 2001 are presented in Table B2E-8 below. The table shows that capital expenditures in the

Primary Aluminum segment increased throughout the early 1990s, reaching a high in 1992 and again in 1998. In

between these two periods of increased capital investment there was a significant decrease of 46 percent between

1992 and 1994. These decreases resulted from the production cutbacks and capacity reductions implemented in

response to oversupply conditions prevalent in the market for aluminum.

Capital expenditures in the Aluminum Sheet, Plate, and Foil segment also fluctuated considerably between 1987

and 2001, with the highest values occurring in 1990, two years earlier than in the Primary Aluminum segment. 

Although producers of Aluminum Sheet, Plate, and Foil reduced capital expenditures by approximately 50

percent between 1988 and 1997, outlays began to increase in 2001.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-15

Table B2E-8: Capital Expenditures for Profiled Aluminum Segments (millions, $2003)

Year

Primary Aluminum Production Aluminum Sheet, Plate, and Foil

Capital Expenditures Percent Change Capital Expenditures Percent Change

1987 250 n/a 634 n/a

1988 205 -18.2% 731 15.4%

1989 245 19.4% 742 1.5%

1990 243 -0.6% 882 18.9%

1991 260 6.8% 710 -19.6%

1992 263 1.2% 512 -27.9%

1993 200 -24.0% 289 -43.6%

1994 141 -29.2% 321 11.1%

1995 174 23.0% 435 35.5%

1996 233 34.0% 451 3.7%

1997 355 52.2% 367 -18.5%

1998a 432 21.8% 348 -5.3%

1999a 380 -12.2% 352 1.2%

2000a 371 -2.3% 367 4.1%

2001a 266 -28.4% 594 62.1%

Total Percent Change

 1987-2001 6.4% -6.3%

Average Annual

Growth Rate 0.4% -0.5%

a

 Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the

North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the

SIC code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.

Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992, and 1997.

B2E-2.6 Capacity Utilization

Capacity utilization measures actual output as a percentage of total potential output given the available capacity. 

Capacity utilization reflects excess or insufficient capacity in an industry and is an indication of whether new

investment is likely. Capacity utilization is also closely linked to financial performance for industries with

substantial fixed costs, such as the aluminum industry. Like integrated steel mills, the aluminum manufacturing

process requires a large capital base to transform raw material into finished product. Because of the resulting high

fixed costs of production, earnings can be very sensitive to production levels, with high output levels relative to

capacity needed for plants to remain profitable.

Figure B2E-4 shows capacity utilization from 1989 to 2002 for the Primary Aluminum Production and Aluminum

Sheet, Plate, and Foil segments. The figure shows that for most of the 1990s, the Primary Aluminum segment

was characterized by excess capacity. Although capacity utilization for this segment was in the high 90 percent

range between 1990 and 1992, domestic utilization fell sharply in 1993 as large amounts of Russian aluminum

entered the global market for the first time (McGraw-Hill, 1999). Capacity utilization remained at this lower level

through 1999. In 2000 and 2001, capacity utilization fell again reflecting the general weakening of product

demand during the Asian economic crisis and later, general economic weakness in the U.S. and world economies. 

Reflecting the economic recovery, product demand increased and capacity utilization rose during 2002.

A substantial amount of U.S. capacity remains idled, which could be brought on-line as demand improves. This

“overhang” idle capacity is likely to limit construction of new capacity and to limit price increases for aluminum
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-16

55

60

65

70

75

80

85

90

95

100

105

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Primary Production of Aluminum

(SIC 3334)

Primary Production of Aluminum

(NAICS to SIC 3334)

Aluminum Sheet, Plate, and Foil

(SIC 3353)

Aluminum Sheet, Plate, and Foil

(NAICS to SIC 3353)

(S&P, 2001). No new smelter capacity has been constructed in the United States since 1980 (McGraw-Hill,

1999). The ten domestic producers of primary aluminum had a total of 7 smelters that were either temporarily or

permanently idled in 2002. By the end of 2002, about 1.5 million metric tons per year of domestic primary

aluminum smelting capacity, equivalent to 35 percent of total capacity, was closed. Of this total, 270,000 metric

tons per year of capacity was permanently closed and the remainder was classified as temporarily idled. The bulk

of the idled capacity was due to curtailed production at a number of Pacific Northwest mills caused by the

California energy crisis (USGS, 2002c).

Although also experiencing year-to-year fluctuation, capacity utilization in the Aluminum Sheet, Plate, and Foil

segment grew overall between 1989 and 1998. This growth resulted largely from the continued strength of rolled

aluminum products, which account for more than 50 percent of all shipments from the aluminum industry. 

Increased consumption by the transportation segment, the largest end-use segment for aluminum sheet, plate, and

foil, is responsible for bringing idle capacity into production (McGraw-Hill 1999) However, falling demand in

these segments after 1998 and through 2001, led to a marked fall-off in capacity utilization. Again, reflecting the

economic recovery during 2002, capacity utilization rose substantially in this segment.

Figure B2E-4: Capacity Utilization Rates (Fourth Quarter) for Profiled Aluminum Segments

a

 Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the North

American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code

classifications using the 1997 Economic Census Bridge Between NAICS and SIC.

Source: U.S. DOC, 1989-2002.

B2E-3 STRUCTURE AND COMPETITIVENESS

Aluminum production is a highly-concentrated industry. A number of large mergers among aluminum producers

have increased the degree of concentration in the industry. For example, Alcoa (the largest aluminum producer)

acquired Alumax (the third largest producer) in 1998 and Reynolds (the second largest producer) in May 2000. 

Alcan acquired Algroup in 2000 and Pechiney in 2004. Three companies now account for just over 50 percent of

global aluminum output (S&P, 2004). Some sources speculate that, with increased consolidation resulting from

mergers, aluminum producers might refrain from returning idle capacity to production as demand for aluminum

grows, which could reduce the cyclical volatility in production and aluminum prices that has characterized the

industry in the past (S&P, 2000). 

B2E-3.1 Geographic Distribution
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

2

 The 1992 Census of Manufactures is the most recent data available by SIC code and State.

B2E-17

4

1

1

2

1

3

2

2

3

4

6

3

6

7

6 2

4

2

3

2

1

3

0

10

12

Number of Facilities

0

1 - 2

3 - 4

5 - 7

8 - 12

The cost and availability of electricity is a driving force behind decisions on the location of new or expanded

smelter capacity. The Primary Aluminum producers are generally located in the Pacific Northwest (OR, MT,

WA) and the Ohio River Valley (IL, IN, KY, MI, MO, OH, PA), where supplies of lower-priced hydroelectric

and coal-based electricity are abundant. In 2002, the 11 smelters east of the Mississippi River accounted for 75

percent of production; whereas the remaining 11 smelters, which included the 9 Pacific Northwest smelters,

accounted for only 25 percent (USGS, 2004a). Aluminum consumption was centered in the East Central United

States (USGS, 2004a). The Aluminum Sheet, Plate, and Foil segment is located principally in California and the

Appalachian Region (Alabama, Kentucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West

Virginia).

Figure B2E-5 shows the distribution of all facilities by State in both profiled aluminum segments (primary

smelters and Aluminum Sheet, Plate, and Foil producers), based on the 1992 Census of Manufactures.2

Figure B2E-5: Number of Facilities by State for Aluminum Segments (SIC 3334 and 3353)

Source: U.S. DOC, 1987, 1992, and 1997.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-18

B2E-3.2 Facility Size

Facility size can be expressed by the number of employees and/or by the total value of shipments, with the most

accurate depiction of size being a combination of both. 

Economic Census data include numerous small facilities (less than 10 employees) for the profiled aluminum

segments, as shown in Figure B2E-6. These facilities may not be production facilities. Value of shipments,

however, are dominated by large establishments (greater than 500 employees) for both the Primary Aluminum

and Aluminum Sheet, Plate, and Foil industry segments. Figure B2E-6 shows that 93 percent of the value of

shipments for the Primary Aluminum segment is produced by establishments with more than 250 employees. 

Approximately 88 percent of the value of shipments for the Aluminum Sheet, Plate, and Foil industry is produced

by establishments with more than 250 employees. Establishments in the Primary Aluminum Production and the

Aluminum Sheet, Plate, and Foil segments with more than 1,000 employees are responsible for approximately 37

and 53 percent of all industry shipments, respectively.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-19

0

2

4

6

8

10

12

14

16

1-9 10-19 20-49 50-99 100-249 250-499 500-999 1000-

2499

2500+

Primary Aluminum

Production (SIC 3334)

Aluminum Sheet, Plate,

and Foil (SIC 3353)

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

1-9 10-19 20-49 50-99 100-249 250-499 500-999 1,000-

2,499

2500+

Primary Aluminum Production

(SIC 3334)

Aluminum Sheet, Plate, and

Foil (SIC 3353)

Figure B2E-6: Number of Facilities and Value of Shipments in 1992a by Facility Employment Size

Category for Profiled Aluminum Segments

Number of Facilities

Value of Shipments (in millions)

a

 The 1992 Census of Manufactures is the most recent data available by SIC code and facility employment size.

Source: U.S. DOC, 1987, 1992, and 1997.

B2E-3.3 Firm Size

The Small Business Administration (SBA) defines a small firm for SIC codes 3334 and 3353 as a firm with 1,000

or fewer and 750 or fewer employees, respectively. The Statistics of U.S. Businesses (SUSB) provide

employment data for firms with 500 or fewer employees and do not specify data for companies with 500-750

employees for SIC 3353 and 500-1000 for SIC 3334. Therefore, based on 2001 data for firms with up to 500

employees,
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

3

 Note that the measured concentration ratio and the HHF are very sensitive to how the industry is defined. An industry with a high

concentration in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign producers

or if it competes with products produced by other industries (e.g., plastics vs. aluminum in beverage containers). Concentration ratios

based on share of domestic production are therefore only one indicator of the extent of competition in an industry.

B2E-20

< 18 of the 28 firms in the Primary Aluminum Production segment had less than 500 employees. 

Therefore, at least 64 percent of this segment’s firms are classified as small. These small firms owned 18

facilities, or 47 percent of all facilities in the segment.

< 67 of the 82 firms in the Aluminum Sheet, Plate and Foil segment had less than 500 employees. 

Therefore, at least 82 percent of this segment’s firms are classified as small. These small firms owned 69

facilities, or 62 percent of all facilities in the segment. 

Table B2E-9 below shows the distribution of firms and facilities in SIC 3334 and 3353 by the employment size of

the parent firm.

Table B2E-9: Number of Firms and Facilities by Employment Size Category

for the Profiled Aluminum Segments, 2001

Employment

Size Category

Primary Aluminum Production Aluminum Sheet, Plate, and Foil

Number of Firms Number of Facilities Number of Firms Number of Facilities

0-19 13 13 42 43

20-99 4 4 16 16

100-499 1 1 9 10

500+ 10 20 15 42

Total 28 38 82 111

Source: U.S. SBA, 2001.

B2E-3.4 Concentration Ratios

Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is closely

related to entry barriers with more concentrated industries generally having higher barriers.

The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) are common measures of

industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72

percent means that the four largest firms in the industry account for 72 percent of the industry’s total value of

shipments. The higher the concentration ratio, the less competition there is in the industry, other things being

equal3

. An industry with a CR4 of more than 50 percent is generally considered concentrated. The HHI indicates

concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market

shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market

shares of 60, 30, and 10 percent, respectively, the HHI of this industry would be equal to 4,600 (602

 + 302 + 102). 

The higher the index, the fewer the number of firms supplying the industry and the more concentrated the

industry. Based on the U.S. Department of Justice’s guidelines for evaluating mergers, markets in which the HHI

is under 1000 are considered unconcentrated, markets in which the HHI is between 1000 and 1800 are considered

to be moderately concentrated, and those in which the HHI is in excess of 1800 are considered to be concentrated. 

Table B2E-10 shows that Primary Aluminum has an HHI of 1231 and Aluminum Sheet, Plate and Foil has an

HHI of 1447. The Primary Aluminum and Aluminum Sheet, Plate, and Foil segments, with HHI values of 1231
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-21

and 1447, respectively, appear to be moderately concentrated. Thus, based on this factor, firms in the aluminum

industry may enjoy moderate amounts of market power, which may enable them to pass-through costs at a more

than negligible rate. However, an accurate assessment of the cost pass-through potential of firms in the

Aluminum industry must be considered in conjunction with other measures of market power.

The four largest firms in Primary Aluminum Production accounted for 59 percent of total U.S. primary capacity in

1992. Consolidation in the industry since the early 1990s has increased concentration. With the merger of Alcoa,

Inc. and Reynolds in May 2000, the single merged company accounted for 50 percent of domestic primary

aluminum capacity, and the four largest U.S. producers control 72 percent of the domestic capacity (Alcoa Inc. 

for 50 percent, Century Aluminum Co. for almost 10 percent, and Noranda Aluminum Inc. and Ormet Primary

Aluminum Corp. for 6 percent each) reported at the end of 2002 (USGS, 2002c).

Table B2E-10: Selected Ratios for the Profiled Aluminum Segments

SIC

Code Year

Total

Number

of Firms

Concentration Ratios

4 Firm

(CR4)

8 Firm

(CR8)

20 Firm

(CR20)

50 Firm

(CR50)

HerfindahlHirschman Index

3334

1987 34 74% 95% 99% 100% 1934

1992 30 59% 82% 99% 100% 1456

1997 13 59% 82% 100% 100% 1231

3353

1987 39 74% 91% 99% 100% 1719

1992 45 68% 86% 99% 100% 1633

1997 41 65% 85% 98% 100% 1447

Source: U.S. DOC, 1987, 1992, and 1997.

B2E-3.5 Foreign Trade

This profile uses two measures of foreign competition: export dependence and import penetration. 

Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic

markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that

industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests

that higher import penetration levels will reduce market power and pricing discretion because foreign competition

limits domestic firms’ ability to exercise such power. Firms belonging to segments in which imports account for

a relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to passthrough costs because foreign producers would not incur costs as a result of the Phase III regulation. The

estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for 2001 is 22 percent. 

For characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with

import ratios close to or above 22 percent would more likely face stiff competition from foreign firms and thus be

less likely to succeed in passing compliance costs through to customers. 

Export dependence, calculated as exports divided by value of shipments, measures the share of a segment’s sales

that is presumed subject to strong foreign competition in export markets. The Phase III regulation would not

increase the production costs of foreign producers with whom domestic firms must compete in export markets. 

As a result, firms in industries that rely to a greater extent on export sales would have less latitude in increasing

prices to recover cost increases resulting from regulation-induced increases in production costs. The estimated

export dependence ratio for the entire U.S. manufacturing sector for 2001 is 15 percent. For characterizing the

ability of industries to withstand compliance cost burdens, EPA judges that industries with export ratios close to
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-22

or above 15 percent are at a relatively greater disadvantage in potentially recovering compliance costs through

price increases since export sales are presumed subject to substantial competition from foreign producers.

Table B2E-11 reports export dependence and import penetration for both the Primary Aluminum Production and

the Aluminum Sheet, Plate, and Foil segments, since 1993. Imports of Primary Aluminum rose dramatically in

1994, primarily due to the large exports from Russian producers. Representatives of major aluminum producing

countries met in late 1993 and 1994 to address the excess global supply of primary aluminum. Those discussions

resulted in the Russian Federation’s agreement to reduce production by 500,000 MTs per year, and plans for other

producers to cut their production and to assist Russian producers to improve their environmental performance and

stimulate the development of internal demand for the Russian production (USGS, 1994c). Nonetheless, imports

have continued to represent a substantial and growing proportion of U.S. demand, reaching an estimated 44

percent in 2001 for Primary Aluminum Production. By 2002, Canada was the largest supplier of imports,

supplying more than one-half of total imports. Russia continued to be the second largest supplier of aluminum

materials to the U.S. (USGS, 2002c). The majority of U.S. exports (two-thirds) are shipped to Canada and

Mexico.

As discussed previously, the import penetration ratio for the Primary Aluminum Production segment in 2001 was

44 percent, which is twice the U.S. manufacturing segment average of 22 percent. The export ratio for Primary

Aluminum Production in 2001 was eight percent; therefore the segment will not likely be affected by competitive

pressures from abroad in export sales. On balance, the U.S. Primary Aluminum Production segment is subject to

significant international competitive pressure, largely manifesting though the penetration of foreign product into

domestic markets. This finding indicates a low likelihood that Primary Aluminum producers subject to the 316(b)

regulation would be able to pass a material share of compliance costs through to customers.

In 2001, the import penetration ratio for facilities in the Aluminum Sheet, Plate, and Foil segment was 11 percent,

which is one-half of the U.S. manufacturing segment average of 22 percent. In 2001, the export dependence ratio

for this segment was 15 percent, or approximately the average for U.S. manufacturers. This industry segment

appears to face lower competition from foreign producers in domestic markets than the Primary Aluminum

Production segment, but this segment competes more vigorously in foreign markets, where it is more exposed to

foreign competition than the Primary Aluminum Production segment. On balance, this industry segment is likely

to face moderate competitive pressure from foreign producers, whether in domestic or export markets, in

attempting to recover regulation-induced increases in production costs through price increase. 

Overall, the competitive pressure from foreign firms/markets may offset the finding, stated above, that the

aluminum industry would appear to possess market power from being a moderately concentrated industry. As a

result, from a total market perspective, the industry is not likely to possess any substantial market power

advantage in being able to pass compliance costs through to customers as price increases.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-23

Table B2E-11: Import Share and Export Dependence for the Profiled Aluminum Segments

Year

Value of Imports

(millions, $2003)

Value of Exports 

(millions, $2003)

Value of

Shipments 

(millions, $2003)

Implied

Domestic

Consumptiona

Import

Penetrationb

Export

Dependencec

Primary Aluminum Production

1993d 2,570 647 6,181 8,104 31.7% 10.5%

1994d 4,073 627 6,533 9,979 40.8% 9.6%

1995d 4,233 791 7,660 11,102 38.1% 10.3%

1996d 3,422 768 6,679 9,333 36.7% 11.5%

1997d 3,876 671 6,893 10,098 38.4% 9.7%

1997e 3,892 688 6,893 10,097 38.5% 10.0%

1998e 4,075 597 6,803 10,281 39.6% 8.8%

1999e 4,193 648 6,272 9,817 42.7% 10.3%

2000e 4,435 656 6,369 10,148 43.7% 10.3%

2001e 4,152 474 5,707 9,385 44.2% 8.3%

Total Percent Change

1993-2001

61.6% -26.7% 15.8% -20.7%

Average Annual

Percent Change 5.5% -3.4% 1.6% -2.5%

Aluminum Sheet, Plate, and Foil

1993d 1,001 1,770 14,401 13,632 7.3% 12.3%

1994d 1,267 2,162 13,479 12,584 10.1% 16.0%

1995d 1,885 3,004 13,024 11,905 15.8% 23.1%

1996d 1,444 2,681 11,563 10,326 14.0% 23.2%

1997d 1,682 3,041 12,381 11,022 15.3% 24.6%

1997e 1,402 2,670 15,938 14,670 9.6% 16.8%

1998e 1,502 2,507 14,231 13,226 11.4% 17.6%

1999e 1,521 2,354 15,179 14,346 10.6% 15.5%

2000e 1,672 2,372 14,297 13,597 12.3% 16.6%

2001e 1,466 2,071 13,833 13,228 11.1% 15.0%

Total Percent Change

1993-2001 46.5% 17.0% -3.0% 21.8%

Average Annual

Percent Change 4.3% 1.8% -0.3% 2.2%

a

 Calculated by EPA as shipments + imports - exports. b

 Calculated by EPA as imports divided by implied domestic consumption. c

 Calculated by EPA as exports divided by shipments.

d

 As no Census Trade data is available before 1997, Export and Import values are taken from USGS Mineral Yearbooks for years 1993-

1997. “Metals and Alloys, Crude” represent SIC 3334 and “Plate, Sheets, Bars, Strip, etc.” is equivalent to SIC 3353. e

 Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the North

American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code

classifications using the 1997 Economic Census Bridge Between NAICS and SIC.

Source: ASM 1997-2001 USGS 1993c-1997c.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-24

Table B2E-12 shows trends in exports and imports separately for aluminum metal and alloys and for semifinished

products separately. U.S. aluminum companies have a large overseas presence, which makes it difficult to

analyze import data. Reported import data may reflect shipments from an overseas facility owned by a U.S. firm. 

The import data therefore do not provide a completely accurate picture of the extent to which foreign companies

have penetrated the domestic market for aluminum. This table shows that imports have grown substantially in

both categories between 1993 and 2003. Exports of primary aluminum have generally declined, with some

fluctuation over the period. Exports to semifinished aluminum rose steadily until 1999, where they peaked for the

period, and have since declined.

Table B2E-12: Trade Statistics for Aluminum and Semifabricated Aluminum Products 

(Quantities in thousand metric tons; Values in $millions)

Year

Metals and Alloys, Crude Plate, Sheets, Bars, Strip, etc.

Importa Exportb Importa Exportb

Quantity Value Quantity Value Quantity Value Quantity Value

1993 1,840 2,150 400 541 400 837 594 1,481

1994 2,480 3,480 339 536 507 1,082 719 1,847

1995 1,930 3,690 369 690 622 1,643 812 2,619

1996 1,910 3,040 417 682 498 1,283 760 2,382

1997 2,060 3,500 352 606 562 1,519 882 2,746

1998 2,400 3,660 265 449 649 1,715 893 2,723

1999 2,650 3,760 318 515 735 1,777 907 2,564

2000 2,490 4,030 273 468 791 2,088 845 2,380

2001 2,560 3,930 192 320 683 1,762 751 2,120

2002 2,790 4,040 206 337 796 1,922 706 1,880

Total Percent Change 

1993-2002 51.6% 87.9% -48.5% -37.7% 99.0% 129.6% 18.9% 26.9%

Average Annual

Growth Rate 4.7% 7.3% -7.1% -5.1% 7.9% 9.7% 1.9% 2.7%

Source: USGS 1994c-2002c. a

Table 10: U.S. Imports for Consumption of Aluminum, by Class b

Table 9: U.S. Exports of Aluminum, by Class

B2E-4 FINANCIAL CONDITION AND PERFORMANCE

The financial performance and condition of the aluminum industry are important determinants of its ability to

withstand the costs of regulatory compliance without material adverse economic/financial impact. To provide

insight into the industry’s financial performance and condition, EPA reviewed two key measures of financial

performance over the 12-year period, 1992-2003: net profit margin and return on total capital. EPA calculated

these measures as a revenue-weighted index of measure values for public reporting firms in the respective

industries, using data from the Value Line Investment Survey. Financial performance in the most recent financial

reporting period (2003) is obviously not a perfect indicator of conditions at the time of regulatory compliance. 

However, examining the trend, and deviation from the trend, through the most recent reporting period gives

insight into where the industry may be, in terms of financial performance and condition, at the time of

compliance. In addition, the volatility of performance against the trend, in itself, provides a measure of the
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-25

potential risk faced by the industry in a future period in which compliance requirements are faced: all else equal,

the more volatile the historical performance, the more likely the industry may be in a period of relatively weak

financial conditions at the time of compliance.

Net profit margin is calculated as after-tax income before nonrecurring gains and losses as a percentage of sales or

revenues, and measures profitability, as reflected in the conventional accounting concept of net income. Over

time, the firms in an industry, and the industry collectively, must generate a sufficient positive profit margin if the

industry is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from

a several factors, including: variations in aggregate economic conditions (including international and U.S.

conditions), variations in industry-specific market conditions (e.g., short-term capacity expansion resulting in

overcapacity), or changes in the pricing and availability of inputs to the industry’s production processes (e.g., the

cost of energy to the aluminum production process). The extent to which these fluctuations affect an industry’s

profitability, in turn, depends heavily on the fixed vs. variable cost structure of the industry’s operations. In a

capital intensive industry such as the aluminum industry, the relatively high fixed capital costs as well as other

fixed overhead outlays, can cause even small fluctuations in output or prices to have a large positive or negative

affect on profit margin.

Return on total capital is calculated as annual net profit, plus one-half of annual long-term interest, divided by the

total of shareholders' equity and long-term debt (total capital). This concept measures the total productivity of the

capital deployed by a firm or industry, regardless of the financial source of the capital (i.e., equity, debt, or

liability element). As such, the return on total capital provides insight into the profitability of a business’ assets

independent of financial structure and is thus a “purer” indicator of asset profitability than return on equity. In the

same way as described for net profit margin, the firms in an industry, and the industry collectively, must generate

over time a sufficient return on capital if the industry is to remain economically viable and attract capital. The

factors causing short-term variation in net profit margin will also be the primary sources of short-term variation in

return on total capital.

Figure B2E-7 below shows net profit margin and return on total capital for the aluminum industry between 1992

and 2003. The graph shows considerable volatility. Performance was very low between 1988 and 1993,

reflecting general economic weaknesses and oversupply in the market (McGraw-Hill, 2000). By the mid-1990s,

performance improved as demand recovered and aluminum prices increased. Performance declined again though

in 2000 through 2002, reflecting economic downturn in both the U.S. and world economies. By 2003, financial

performance began to level off compared to the significant declines experienced in the three prior years. 

Improving financial performance on a quarter-to-quarter basis over the course of 2003 suggests that the

Aluminum industry is in position for improving financial performance in 2004 and beyond as U.S. economic

conditions continue to strengthen. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

4

 Data on cooling water use are from the 1982 Census of Manufactures. 1982 was the last year in which the Census of Manufactures

reported cooling water use.

5

 The proposed Phase III regulation also applies to existing electric generating facilities as well as certain facilities in the oil and gas

extraction industry and the seafood processing industry. See Chapters B4 and B5 and Part C of this document for more information on

these industries.

B2E-26

-2%

0%

2%

4%

6%

8%

10%

12%

14%

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Net Profit M argin -

A luminum Indus try

Return on Total Capital

- A luminum Indus try

Figure B2E-7: Net Profit Margin and Return on Total Capital for the Aluminum Industry

Source: Value Line, 1992-2003.

B2E-5 FACILITIES OPERATING COOLING WATER INTAKE STRUCTURES

Section 316(b) of the Clean Water Act applies to point source facilities that use or propose to use a cooling water

intake structure and that withdraws cooling water directly from a surface waterbody of the United States. In

1982, the Primary Metals industries as a whole (including Steel and Non-ferrous producers) withdrew 1,312

billion gallons of cooling water, accounting for approximately 1.7 percent of total industrial cooling water intake

in the United States4

. The industry ranked 3rd in industrial cooling water use, behind the electric power

generation industry, and the chemical industry (1982 Census of Manufactures).

This section provides information for facilities in the profiled aluminum segments potentially subject to the

proposed regulation. Existing facilities that meet all of the following conditions are potentially subject to the

proposed regulation:5

< Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or

arrangement with an independent supplier who has a cooling water intake structure; or their cooling water

intake structure(s) withdraw(s) cooling water from waters of the U.S., and at least twenty-five (25)

percent of the water withdrawn is used for contact or non-contact cooling purposes;

< Have an National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;

and

< Have a design intake flow of greater than 2 million gallons per day (MGD).

The proposed regulation also covers substantial additions or modifications to operations undertaken at such

facilities. While all facilities that meet these criteria are subject to the regulation, this section focuses on the 21
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

6

 EPA applied sample weights to the sampled facilities to account for non-sampled facilities and facilities that did not respond to the

survey. For more information on EPA’s 2000 Section 316(b) Industry Survey, please refer to the Information Collection Request (U.S.

EPA, 2000).

B2E-27

facilities nation-wide in the profiled aluminum segments identified in EPA’s 2000 Section 316(b) Industry Survey

as being potentially subject to this proposed regulation6

. Information collected in the Detailed Industry

Questionnaire found that an estimated 11 out of 31 Primary Aluminum producers (36 percent) and 10 out of 57

Aluminum Sheet, Plate, and Foil manufacturers (18 percent) meet the characteristics of a potential Phase III

facility.

B2E-5.1 Waterbody and Cooling System Type

Table B2E-13 shows the distribution of potential Phase III facilities in the profiled aluminum segment by type of

water body and cooling system. The table shows that three-quarters of the potential Phase III facilities use a

once-through cooling system (16, or 77%) and one-quarter use a recirculating system (5, or 23%). Ten of the 11

Section 316(b) Primary Aluminum producers obtain their cooling water from a freshwater stream or river. The

other Section 316(b) Primary Aluminum producer draws from a lake or reservoir. Seven of the Section 316(b)

Aluminum Sheet, Plate, and Foil manufacturers obtain their cooling water from either a freshwater stream or

river. The other three Section 316(b) Aluminum Sheet, Plate, and Foil manufacturers draw from one of the Great

Lakes. Seventy-six percent (16 facilities) of all Section 316(b) aluminum facilities withdraw their cooling water

from a freshwater stream or river. None of the facilities withdraw from an estuary, the most sensitive type of

waterbody.

Table B2E-13: Number of Potential Phase III facilities by Water Body Type and

Cooling System Type for the Profiled Aluminum Segments

Water Body Type

Cooling System

Recirculating Once-Through

Total

Number % of Total Number % of Total

Primary Production of Aluminum

Freshwater Stream or River 0 0% 10 100% 10

Lake or Reservoir 1 100% 0 0% 1

Total 1 9% 10 91% 11

Aluminum Sheet, Plate, and Foil

Freshwater Stream or River 3 43% 3 47% 7

Great Lake 0 0% 3 100% 3

Total 3 30% 7 70% 10

Total for Profiled Aluminum Facilities

Freshwater Stream or River 3 18% 13 82% 16

Lake or Reservoir 1 100% 0 0% 1

Great Lake 0 0% 3 100% 3

Total 5 24% 16 78% 21

Source: U.S. EPA, 2000.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-28

0 0

7

3

5

7

0

1

2

3

4

5

6

7

8

9

10

<500 500-999 >=1000

Primary Production of

Aluminum (SIC 3334)

Aluminum Sheet, Plate, and

Foil (SIC 3353)

B2E-5.2 Facility Size

Section 316(b) sample aluminum facilities are larger than facilities in their industries as a whole, as reported in

the Census and discussed previously:

< Sixty-five percent of all facilities in the aluminum segment had fewer than 500 employees in 1992; none

of the potential Phase III facilities in that segment fall into that employment category.

Figure B2E-8 shows the number of potential Phase III facilities by employment size category.

Figure B2E-8: Number of Potential Phase III facilities by Employment Size for the Profiled

Aluminum Segments

Source: U.S. EPA, 2000.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-29

B2E-5.3 Firm Size

EPA used the Small Business Administration (SBA) small entity size standards to determine the number of

existing Section 316(b) profiled aluminum industry facilities owned by small firms. Firms in the Primary

Production of Aluminum segment are defined as small if they have 1000 or fewer employees; firms in the

Aluminum Sheet, Plate, and Foil segment are defined as small if they have 750 or fewer employees. Table B2E14 shows that three (or 27 percent) of the Section 316(b) Primary Aluminum producers are owned by small firms. 

The remaining eight (or 73 percent) Primary Aluminum producers are owned by large firms. All of the Section

316(b) Aluminum Sheet, Plate, and Foil producers are owned by large firms.

Table B2E-14: Number of Potential Phase III facilities by Firm Size for the Profiled

Aluminum Segments

SIC Code

Large Small

Total

Number % of SIC Number % of SIC

3334 8 73% 3 27% 11

3353 10 100% 0 0% 10

Total 18 86% 3 14% 21

Source: U.S. EPA, 2000; U.S. SBA, 2000; D&B, 2001.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-30

REFERENCES

The Aluminum Association. Undated. Aluminum: An American Industry in Profile.

The Aluminum Association. 2001. The Aluminum Situation. September 2001.

The Aluminum Association. 1999. “Northwest Smelter Restarts Are Seen Unlikely”, Industry News. October

29, 1999.

Bureau of Labor Statistics (BLS). 2002. Producer Price Index. Series: PCU33__#-Primary Metal Industries.

Dun and Bradstreet (D&B). 2001. Data extracted from D&B Webspectrum August 2001.

Executive Office of the President. 1987. Office of Management and Budget. Standard Industrial Classification

Manual.

Federal Reserve Board. 2004. Industrial Production and Capacity Utilization. Data extracted May 11, 2004.

McGraw-Hill and U.S. Department of Commerce, International Trade Administration. 2000. 

U.S. Industry & Trade Outlook ‘00.

McGraw-Hill and U.S. Department of Commerce, International Trade Administration. 1999. 

U.S. Industry & Trade Outlook ‘99.

Standard & Poor’s (S&P). 2000. Industry Surveys - Metals: Industrial. January 20, 2000.

Standard & Poor’s (S&P). 2001a. Industry Surveys - Metals: Industrial. July 12, 2001.

Standard & Poor’s (S&P). 2004. Sub Industry Outlook- Alcoa Inc.. February 21, 2004.

U.S. Department of Commerce (U.S. DOC). 1989-2002. Bureau of the Census. Current Industrial Reports. 

Survey of Plant Capacity.

U.S. Department of Commerce (U.S. DOC). 1988-1991, 1993-1996, and 1998-2001. Bureau of the Census. 

Annual Survey of Manufactures.

U.S. Department of Commerce (U.S. DOC). 1987, 1992, and 1997. Bureau of the Census. Census of

Manufactures.

U.S. Department of Commerce (U.S. DOC). 1997. Bureau of the Census. 1997 Economic Census Bridge

Between NAICS and SIC.

U.S. Environmental Protection Agency (U.S. EPA). 2000. Detailed Industry Questionnaire: Phase II Cooling

Water Intake Structures.

U.S. Environmental Protection Agency (U.S. EPA). 1995. Office of Enforcement and Compliance Assurance. 

Profile of the Nonferrous Metals Industry, EPA Office of Compliance Segment Notebook Project. EPA 310-R95-010. September, 1995.

United States Geological Survey (USGS). 1996a-2004a. Mineral Commodity Summaries. Aluminum. Author:

Patricia Plunkert.

United States Geological Survey (USGS). 2001b-2003b. Mineral Industry Surveys. Aluminum. Author: Patricia

Plunkert.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-31

United States Geological Survey (USGS). 1993c-2002c. Minerals Yearbook. Aluminum. Author: Patricia

Plunkert.

United States Geological Survey (USGS). 2002d. Historical Statistics for Mineral and Material Commodities in

the United States. Aluminum. Authors:

United States Geological Survey (USGS). 1998e. Metal Prices in the United States through 1998.

U.S. Small Business Administration. 1989-2001. Statistics of U.S. Businesses.

U.S. Small Business Administration (U.S. SBA). 2000. Small Business Size Standards. 13 CFR section 121.201.

Value Line. 1992-2003. Value Line Investment Survey.

Value Line. 2001. Metals & Mining (Diversified) Industry. July 27, 2001.

Value Line. 2003a. Metal Fabricating Industry. March 28, 2003.

Value Line. 2003b. Metals & Mining (Diversified) Industry. April 25, 2003.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2E: Aluminum

B2E-32

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§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2F: Facilities in Other Industries

B2F-1

CHAPTER CONTENTS

B2F-1 Facilities Operating Cooling Water Intake

Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2F-2

B2F-1.1 Waterbody and Cooling System Types . . . . B2F-3

B2F-1.2 Facility Size . . . . . . . . . . . . . . . . . . . . . . . . B2F-3

B2F-1.3 Firm Size . . . . . . . . . . . . . . . . . . . . . . . . . . . B2F-4

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2F-5

Chapter B2F: Facilities in Other Industries

(Various SICs)

The preceding profile sections focus on the five

Primary Manufacturing Industries – Paper and

Allied Products, Chemicals and Allied Products,

Petroleum and Coal Products, Steel, and

Aluminum – identified, after electric power

generators, as using the largest amount of

cooling water in their operations and most likely,

after electric power generators, to be subject to

the proposed regulation. However, facilities in

other industries use cooling water and would therefore also be subject to the proposed regulation if they meet the

regulation’s specifications. This section of the profile provides information on a sample of facilities in these

Other Industries.

Although EPA targeted its Detailed Industry Questionnaire at the electric power industry and the five Primary

Manufacturing Industries, the Agency received 22 questionnaire responses from facilities with business

operations in industries other than these major cooling water-intensive industries. EPA originally believed these

facilities to be non-utility electric power generators; however, inspection of their responses indicated that the

facilities were better understood as cooling water-dependent facilities whose principal operations lie in businesses

other than the electric power industry or the Primary Manufacturing Industries. Unlike the sample facility

observations for the five Primary Manufacturing Industries, the sample of observations from Other Industries is

not based on a scientifically framed sample and the information from this sample of observations may not be

reliably extrapolated beyond these facilities. As a result, EPA’s profile of information for the Other Industries

facilities is restricted to these 22 sample facilities and is not presented as national estimates.

The 22 Other Industries facilities fall in a wide range of businesses, as defined by 2-digit SIC industry group. 

Table B2F-1, following page, presents the number of responses received from facilities in the Other Industries by

industry group.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2F: Facilities in Other Industries

1

 The proposed Phase III regulation also applies to existing electric generating facilities as well as certain

facilities in the oil and gas extraction industry and the seafood processing industry. See Chapters B4 and B5 and

Part C of this document for more information on these industries.

B2F-2

Table B2F-1: Facility Observations in Other Industries by 2-digit SIC code

No. of

Facilities

SIC

Code SIC Description Important Operations

1 01 Agriculture production -

crops

Crops, plants, vines, and trees (excluding forestry operations); sod farms, and

cranberry bogs; mushrooms, bulbs, flower seeds, and vegetable seeds; and

growing of hydroponic crops.

3 10 Metal mining

Mining, developing mines, or exploring for metallic minerals (ores); ore dressing

and beneficiating operations, whether performed at mills operated in conjunction

with the mines served or at mills, such as custom mills, operated separately.

1 14

Mining and quarrying of

nonmetallic minerals,

except fuels

Mining or quarrying, developing mines, or exploring for nonmetallic minerals,

except fuels; certain well and brine operations, and primary preparation plants,

such as those engaged in crushing, grinding, washing, or other concentration

12 20 Food and kindred

products

Manufacturing or processing foods and beverages for human consumption, and

certain related products, such as manufactured ice, chewing gum, vegetable and

animal fats and oils, and prepared feeds for animals and fowls.

1 22 Textile mill products

Preparation of fiber and subsequent manufacturing of yarn, thread, braids, twine,

and cordage; manufacturing broadwoven fabrics, narrow woven fabrics, knit

fabrics, and carpets and rugs from yarn; dyeing and finishing fiber, yarn, fabrics,

and knit apparel; coating, waterproofing, or otherwise treating fabrics; integrated

manufacture of knit apparel and other finished articles from yarn; manufacture of

felt goods, lace goods, non-woven fabrics, and miscellaneous textiles.

2 24 Lumber and wood

products, except furniture

Cutting timber and pulpwood; merchant sawmills, lath mills, shingle mills,

cooperage stock mills, planing mills, and plywood mills and veneer mills

engaged in producing lumber and wood basic materials; manufacturing finished

articles made entirely or mainly of wood or related materials.

1 34

Fabricated metal products,

except machinery and

transportation equipment

Ferrous and nonferrous metal products, such as metal cans, tinware, handtools,

cutlery, general hardware, nonelectric heating apparatus, fabricated structural

metal products, metal forgings, metal stampings, ordnance (except vehicles and

guided missiles), and a variety of metal and wire products, not elsewhere

classified.

1 37 Transportation equipment Equipment for transportation of passengers and cargo by land, air, and water.

Source: U.S. EPA, 2000; Executive Office of the President, 1987.

B2F-1 FACILITIES OPERATING COOLING WATER INTAKE STRUCTURES

Section 316(b) of the Clean Water Act applies to point source facilities that use or propose to use a cooling water

intake structure and that withdraws cooling water directly from a surface waterbody of the United States. This

section provides information for facilities in Other Industries potentially subject to the proposed regulation. The

proposed regulation applies to existing facilities that meet all of the following conditions:1

< Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or

arrangement with an independent supplier who has a cooling water intake structure; or their cooling water

intake structure(s) withdraw(s) cooling water from waters of the U.S., and at least twenty-five (25)

percent of the water withdrawn is used for contact or non-contact cooling purposes;
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2F: Facilities in Other Industries

B2F-3

< Have an National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;

and

< Have a design intake flow of greater than 2 million gallons per day (MGD).

The proposed regulation also covers substantial additions or modifications to operations undertaken at such

facilities. While all facilities that meet these criteria are subject to the regulation, this section focuses on the 22

facilities nation-wide in Other Industries identified in EPA’s 2000 Section 316(b) Industry Survey as being

potentially subject to this proposed regulation.

B2F-1.1 Waterbody and Cooling System Types

Table B2F-2 reports the distribution of the Other Industries facilities by type of water body and cooling system. 

The majority of these facilities have either a once-through system (10, or 46 percent) or recirculating system (7, or

33 percent). In addition, a majority of these facilities draw water from a freshwater stream or river (12, or 55

percent). Of the four facilities that withdraw from an estuary, the most sensitive type of waterbody, three use a

once-through cooling system. Plants with once-through cooling water systems withdraw between 70 and 98

percent more water than those with recirculating systems.

Table B2F-2: Number of Sampled Facilities by Water Body and Cooling System 

Type for Facilities in Other Industries

Water Body Type

Cooling System

Recirculating Combination Once-Through Other

Totala

Number % of

Total Number % of

Total Number % of

Total Number % of

Total

Other Industries

Freshwater Stream/ River 6 50% 1 8% 3 25% 2 17% 12

Estuary/ Tidal River 1 25% 0 0% 3 75% 0 0% 4

Lake / Reservoir 0 0% 0 0% 1 100% 0 0% 1

Great Lake 0 0% 2 50% 2 50% 0 0% 4

Ocean 0 0% 0 0% 1 100% 0 0% 1

Totala 7 32% 3 14% 10 45% 2 9% 22

a

 Individual numbers may not add up to total due to independent rounding.

Source: U.S. EPA, 2000.

B2F-1.2 Facility Size

Figure B2F-1 shows the employment size category for the 22 sampled facilities identified as having primary

operations outside of the power generation and Primary Manufacturing Industries already profiled. Half of the

sampled facilities have between 100 and 500 employees and five have over 1,000 employees.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2F: Facilities in Other Industries

B2F-4

2

5

8

3

4

0

1

2

3

4

5

6

7

8

<100 100-249 250-499 500-999 >=1000

Other Industries

Figure B2F-1: Number of Sampled Facilities in Other Industries by Employment Size

Source: U.S. EPA, 2000.

B2F-1.3 Firm Size

EPA used the Small Business Administration (SBA) small entity size standards to determine the number of

sampled facilities in Other Industries that are owned by small firms. Depending on their SIC code, firms are

defined as small based on either revenues or number of employees. Table B2F-3 shows that Section 316(b)

facilities in Other Industries are predominantly owned by large firms. Overall, 19 facilities (86 percent) are

owned by large firms, and 3 facilities (14 percent) are owned by small firms.

Table B2F-3: Number of Sampled Section 316(b) Facilities in Other Industries by Firm Size

Large Small Total

Other Industries 19 3 22

Source: U.S. EPA, 2000; U.S. SBA, 2000; D&B, 2001.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2F: Facilities in Other Industries

B2F-5

REFERENCES

Dun and Bradstreet (D&B). 2001. Data extracted from D&B Webspectrum August 2001.

Executive Office of the President. 1987. Office of Management and Budget. Standard Industrial Classification

Manual.

U.S. Environmental Protection Agency (U.S. EPA) 2000. Detailed Industry Questionnaire: Phase II Cooling

Water Intake Structures.

U.S. Small Business Administration (U.S. SBA). 2000. Small Business Size Standards. 13 CFR section 121.201.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2F: Facilities in Other Industries

B2F-6

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§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2: Glossary

B2Glos-1

GLOSSARY

Capital expenditures: As reported in the Economic Censuses, reflects permanent additions and major

alterations, as well as replacements and additions to capacity, for which depreciation, depletion, or Office of

Minerals Exploration accounts are ordinarily maintained. Reported capital expenditures include work done on

contract, as well as by the mine forces. Totals for expenditures include the costs of assets leased from other

concerns through capital leases. Excluded are expenditures for land and cost of maintenance and repairs charged

as current operating expenses. Also excluded are capital expenditures for mineral land and rights which are

shown as a separate item.

Capacity utilization: Indicates the extent to which plant capacity is being used and shows potential excess or

insufficient capacity. This profile reports capacity utilization as published by the U.S. Bureau of Census in the

Survey of Plant Capacity published in the Current Industrial Reports. The utilization rate is equal to an output

index divided by a capacity index. Output is measured by seasonally adjusted indexes of industrial production,

and is based on actual output in 1992. The capacity indexes attempt to capture the concept of sustainable

practical capacity, which is defined as the greatest level of output that a plant can maintain within the framework

of a realistic work schedule, taking account of normal downtime, and assuming sufficient availability of inputs to

operate the machinery and equipment in place.

Concentration ratio: The combined percentage of total industry output accounted for by the largest producers

in the industry. For example, the four-firm concentration ratio (CR4) refers to the market share of the four largest

firms. The higher the concentration ratio, the more concentrated the industry. A market is generally considered

highly concentrated if the CR4 is greater than 50 percent.

Coverage ratio: The ratio of primary products shipped by the establishments classified in the industry to the

total shipments of such products that are shipped by all manufacturing establishments, wherever classified. An

industry with a high coverage ratio accounts for most of the value of shipments of its primary products, whereas

an industry with a low coverage ratio produces a smaller portion of the total value of shipments of its primary

products produced by all sources.

Employment: Total number of full-time equivalent employees, including production workers and nonproduction workers.

Export dependence: The share of shipments by domestic producers that is exported; calculated by dividing the

value of exports by the value of domestic shipments. 

Herfindahl-Hirschman index (HHI): An alternative measure of concentration. Equal to the sum of the

squares of the market shares for the largest 50 firms in the industry. The higher the index, the more concentrated

the industry. The Department of Justice uses the HHI for antitrust enforcement purposes. The benchmark used

by DOJ is 1,000, where any industry with an HHI less than 1,000 is considered to be unconcentrated. The

advantage of the HHI over the concentration ratio is that the former gives information about the dispersion of

market share among all the firms in the industry, not just the largest firms (Arnold, 1989).

Import penetration: The share of all consumption in the U.S. that is provided by imports; calculated by

dividing imports by reported or apparent domestic consumption (the latter calculated as domestic value of

shipments minus exports plus imports). 

Labor productivity: Amount of output produced per unit of labor input on average. Calculated in this profile as

real value added divided by production hours. This measure indicates how an industry uses labor as an input in

the production process. Changes over time in labor productivity may reflect changes in the relative use of labor

versus other inputs to produce output, due to technological changes or cost-cutting efforts. Changing patterns of

labor utilization relative to output are particularly important in understanding how regulatory requirements may

translate into job losses, both in aggregate and at the community level.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B2: Glossary

B2Glos-2

Nominal values: Dollar values expressed in current dollars.

Operating margin: Measure of the relationship between input costs and the value of production, as an indicator

of financial performance and condition. Everything else being equal, industries and firms with lower operating

margins will generally have less flexibility to absorb the costs associated with a regulation than those with higher

operating margins. Operating margins were calculated in this profile by subtracting the cost of materials and total

payroll from the value of shipments. Operating margin is only an approximate measure of profitability, since it

does not consider capital costs and other costs. It is used to examine trends in revenues compared with production

costs within an industry; it should not be used for cross-industry comparisons of financial performance.

Primary product shipments: An establishment is classified in a particular industry (4-digit SIC codes) if its

shipments of the primary products of that industry exceed in value its shipments of the products of any other

single industry. An establishment’s primary product shipments are those products considered primary to its

industry. 

Producer production indexes (PPI): A family of indexes that measures the average change over time in

selling prices received by domestic producers of goods and services (Bureau of Labor Statistics, PPI Overview). 

Used in this profile to convert nominal values into real 1997 dollar values. 

Real values: Nominal values normalized using a price index to express values in a single year’s dollars. 

Removes the effects of price inflation when evaluating trends in dollar measures.

Secondary product shipments: An establishment’s products that are considered secondary to the industry in

which the establishment is classified and primary to other industries. For example, a petroleum refinery classified

in SIC code 2911 would produce petroleum products as primary products, but might produce organic chemicals as

secondary products. 

Value added: A measure of manufacturing activity, derived by subtracting the cost of purchased inputs

(materials, supplies, containers, fuel, purchased electricity, contract work, and contract labor) from the value of

shipments (products manufactured plus receipts for services rendered), and adjusted by the addition of value

added by merchandising operations (i.e., the difference between the sales value and the cost of merchandise sold

without further manufacture, processing, or assembly) plus the net change in finished goods and work-in-process

between the beginning-and end-of-year inventories. Value added avoids the duplication in value of shipments as

a measure of economic activity that results from the use of products of some establishments as materials by

others. Value added is considered to be the best value measure available for comparing the relative economic

importance of manufacturing among industries and geographic areas.

Value of shipments: Net selling values of all products shipped as well as miscellaneous receipts. Includes all

items made by or for an establishments from materials owned by it, whether sold, transferred to other plants of the

same company, or shipped on consignment. Value of shipments is a measure of the dollar value of production,

and is often used as a proxy for revenues. This profile uses value of shipments to indicate the size of a market and

how the size differs from year to year, and to calculate operating margins.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

1

 EPA applied sample weights to 199 sample facilities to account for non-sampled facilities and facilities that did not respond to the

survey. For more information on EPA’s 2000 Section 316(b) Industry Survey, please refer to the Information Collection Request (U.S.

EPA, 1999a). 

B3-1

Chapter B3: Economic Impact Analysis for

Manufacturers

INTRODUCTION

This chapter assesses the expected economic effect of

the proposed section 316(b) Regulation for Phase III

Facilities on the Manufacturers that would be subject to

national categorical requirements under the proposed

regulation. The analysis focuses on impacts in five key

manufacturing industries – Paper, Chemicals,

Petroleum, Aluminum, Steel (the “Primary

Manufacturing Industries”) – in which a substantial

number of facilities are expected to be subject to the

proposed regulation. EPA’s analysis of the regulation’s

expected impact in these industries is based on a

statistically valid sample survey of facilities in these

five industries. The sample survey indicates that the

regulation would potentially subject as many as 532

facilities in the Primary Manufacturing Industries1

 to

national requirements.

This chapter also considers the effect of the regulation on facilities in other industries (“Other Industries”) that are

expected to be within the scope of the regulation. The facility impact analysis for Other Industries is restricted to

a sample of 22 facilities for which EPA received surveys, but which are not part of the statistically valid sample. 

As a result, EPA’s analysis for the Other Industries group is limited to these known facilities. EPA has not

estimated the number of facilities in the Other Industries group that may be subject to the regulation because EPA

does not believe that this number can be reliably extrapolated from the sample of known facilities in this group. 

However, because the statistically valid survey group of six industries (i.e., for the five Primary Manufacturing

Industries and Electric Generators) reflects 99% of total cooling water withdrawals, EPA believes that few

additional facilities in the Other Industries group are potentially subject to the proposed regulation. 

Although EPA was able to undertake impact analysis for the Other Industries group using only the sample of

known facilities for this group, EPA believes that its analysis for the Other Industries group provides a sufficient

basis for regulation development. EPA’s review of the engineering characteristics of cooling water intake and use

in the Other Industries group indicates that cooling water intake and use in these industries do not differ

materially from cooling water intake and use in the electric power industry and the Primary Manufacturing

Industries. In addition, EPA specifically analyzed the economic impacts of the three proposed options on the 22

sample facilities in the Other Industries group and found no economic impact of the proposed options on these

facilities. For these reasons, EPA believes that its findings of no economic impact to the known facilities in the

Other Industries group, and thus the practicability of the three proposed options, are generally applicable to the

full breadth of industries, including the Other Industries group, within the regulation’s scope.

Based on the sum of the sample-weighted estimate of 532 facilities in the Primary Manufacturing Industries and

the 22 known facilities in the Other Industries group, EPA included a total of 554 potentially regulated facilities

CHAPTER CONTENTS

B3-1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . B3-3

B3-2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . B3-3

B3-2.1 Market-Level Impacts . . . . . . . . . . B3-5

B3-2.2 Impact Measures . . . . . . . . . . . . . . B3-5

B3-3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-15

B3-3.1 Baseline Closures . . . . . . . . . . . . B3-15

B3-3.2 Number of Facilities with Regulatory

Requirements . . . . . . . . . . . . . . . . B3-16

B3-3.3 Post-Compliance Impacts . . . . . . B3-17

B3-3.4 Compliance Costs . . . . . . . . . . . . B3-17

B3-3.5 Summary of Facility Impacts . . . B3-18

B3-3.6 Firm Impacts . . . . . . . . . . . . . . . . B3-19

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-21

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-22

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-23

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A-i
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-2

in the economic impact analysis for the Manufacturers segment. The total number of Manufacturers segment

facilities considered in the economic impact analysis (554) differs from the number of facilities potentially subject

to regulation (566), as reported in Chapter A1. EPA determined that the survey responses of 14 sample facilities

lacked certain financial data needed for the facility impact analysis while containing sufficient data to support

estimates of facility counts and compliance costs. EPA therefore retained these sample facilities (37 sample

weighted facilities) in the analyses to estimate the total number of Manufacturers facility potentially subject to

regulation but excluded them from the economic impact analysis. When these sample facilities were excluded

from the impact analysis, the sample weights for remaining facilities within the affected sample frames were

adjusted upwards to account for their removal. The difference in the reported facility totals in the impact and

social cost analyses reflects the removal of these 14 facilities and the use of adjusted sample weights. The

removal of specific sample facilities from the analysis universe and simultaneous adjustment of sample weights to

account for their removal yields the same estimate of the total combined population of Manufacturers and Electric

Generators for the analysis. However, as a result of the sample stratification methodology, the estimates of the

total facility populations for Manufacturers only differ slightly between the two sample facility cases. Both

values are valid statistical estimates of the same, but unknown, value of the Manufacturers facility population. 

EPA undertook the economic impact analysis to aid in assessing the economic achievability of alternative

regulatory options and, on the basis of that assessment, to aid in defining the proposed regulation. Measures of

economic impact include facility closures and associated losses in employment, financial stress short of closure

(“moderate impacts”), and firm-level impacts. Severe impacts are facility closures and the associated losses in

jobs at facilities that close due to the regulation. EPA also assessed moderate economic impacts to support its

evaluation of regulatory options and to understand better the regulation's economic impacts. Moderate impacts

are adverse changes in a facility's financial position that are not threatening to its short-term viability. The firm

impact analysis assesses whether firms that own multiple facilities are likely to incur more significant impacts

than indicated by the facility impact analysis. Impacts may be more significant at the firm level than at the

facility level if a firm owns a number of facilities that incur significant cost. In addition, a firm-level analysis is

needed to assess impacts on small businesses, as required by the Regulatory Flexibility Act and SBREFA. Other

chapters consider the impacts on small entities (Chapter D1: Regulatory Flexibility Analysis) and impacts on

governments (Chapter D2: UMRA Analysis).

This chapter presents the impact analysis results for the three proposed options: the “50 MGD for All

Waterbodies” option (“50 MGD All”), the “200 MGD for All Waterbodies” option (“200 MGD All”), and the

“100 MGD for Certain Waterbodies” Option (“100 MGD CWB”). These options differ with regard to (1) their

design intake flow (DIF) applicability thresholds (50, 100, and 200 MGD, respectively); and (2) the type of

waterbodies to which they would apply (the options with the 50 and 200 MGD applicability thresholds would

apply to all waterbody types while the 100 MGD applicability threshold option would apply only to certain

waterbody types – an ocean, estuary, tidal river/stream, or one of the Great Lakes). Facilities meeting these

applicability criteria would be required to meet similar requirements to those required in the final Phase II

regulation, including a 80-95% reduction in impingement mortality and a 60-90% reduction in entrainment. 

Facilities not meeting these criteria would continue to be subject to 316(b) requirements established by permit

writers based on their Best Professional Judgment (BPJ). As a result, the number of facilities required to meet the

national categorical requirements would vary under each of the three proposed options. Of the three options

presented here, the 100 MGD for Certain Waterbodies Option would subject the smallest number of facilities to

national categorical requirements, with the 200 MGD for All Waterbodies Option and 50 MGD for All

Waterbodies Option subjecting successively larger numbers of facilities to national requirements. 

As outlined in Chapter A1: Introduction, EPA considered several additional regulatory options based on varying

flow regimes and waterbody types, in arriving at the proposed options. Summary results for five additional

options can be found in Appendix 1 to this chapter.

This chapter describes the methodology used to assess economic impacts for the Manufacturers facilities, and

presents the results of the analyses.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

2

 See Appendix 2 to Chapter B3 for details of the downtime cost calculation.

3

 The annualization approach used for the facility impact analysis differs from that used to develop the social cost estimate presented

in Chapter B1: Summary of Cost Categories and Key Analysis Elements for Existing Facilities. For the analysis of the social cost of the

proposed regulation, the present value of total cost and the constant annual equivalent to that present value (annualized cost) were

calculated as of the expected effectiveness date of the final regulation for Phase III facilities, beginning of year 2007. In contrast, for the

impact analysis, the present value and annualized value of compliance cost were determined as of the first year of compliance of each

facility (for this analysis, assumed to be 2010 to 2014).

B3-3

B3-1 DATA SOURCES 

The economic impact analyses rely on data provided in the financial portion of the detailed questionnaires

distributed by EPA to facilities potentially subject to the Phase III regulation under the authority of Section 308 of

the Clean Water Act. The survey financial data included facility and parent firm income statements and balance

sheets for the three years 1996, 1997, and 1998. 

In addition to the survey data, a number of secondary sources were used to characterize economic and financial

conditions in the industries subject to the proposed Phase III regulation. Secondary sources used in the analyses

include:

< Department of Commerce economic census and survey data, including the Census of Manufactures,

Annual Surveys of Manufactures, and international trade data;

< U.S. Industry and Trade Outlook, published by McGraw-Hill and the U.S. Department of Commerce;

< Value Line Investment Survey;

< Annual Statement Studies, published by Risk Management Association (RMA); and

< Statistics of U.S. Businesses (SUSB).

B3-2 METHODOLOGY

The impact analysis starts with compliance cost estimates from the EPA engineering analysis and then calculates

how these compliance costs would affect the financial condition of Section 316(b) Manufacturers. EPA included

the following compliance cost categories in this analysis: capital cost, annual operating and maintenance

cost, administrative cost, and the loss of business income from potential shutdown of facilities during installation

of compliance equipment2

. Of these cost categories, only operating and maintenance and certain administrative

costs recur annually. The remaining costs occur only once at the beginning of compliance or on a multi-year

interval over the period of the compliance analysis. Some of the impact analyses require combining the annually

recurring and non-recurring costs into a single, annual equivalent value. For combining the annually recurring

and non-recurring costs in this analysis, EPA calculated the annual equivalent cost of the non-recurring cost

categories and added these annualized costs to the annually recurring operating and maintenance cost. 

To derive the constant annual value of the non-annual costs, EPA annualized each cost component over the

component’s estimated useful life, using a 7.0% discount rate. The cost of compliance equipment, which includes

fine-mesh traveling screens, with and without fish handling, and fish handling and return systems, was annualized

over 10 years; initial permitting cost and the income loss from installation shutdown were annualized over 30

years; and repermitting cost was annualized over 5 years3

. For more information on the compliance cost

components developed for this analysis, see Chapter B1: Summary of Cost Categories and Key Analysis Elements

for Existing Facilities and the § 316(b) Technical Development Document (U.S. EPA, 2004).
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

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As discussed in Chapter B1: Summary of Cost Categories and Key Analysis Elements for Existing Facilities, the

various economic information used in this analysis were initially provided in dollars of different years. For

example, facility financial data obtained in the 316(b) survey for Manufacturers are for the years 1996, 1997, and

1998, while the technology costs of regulatory compliance were estimated in dollars of the year 2002. To support

a consistent analysis using these data that were initially developed in dollars of different years, EPA needed to

bring the dollar values to a common analysis year. For this analysis, EPA adjusted all dollar values to constant

dollars of the year 2003 (average or mid-year, depending on data availability) using an appropriate inflation

adjustment index. For adjusting compliance costs, EPA used the Construction Cost Index (CCI) published

by the Engineering News-Record. For financial statement information, EPA used the Gross Domestic

Product Implicit Price Deflator (GDP Deflator) to bring dollar values to 2003. The values used to adjust

the dollar values to constant dollars can be found in Chapter B1.

For the impact analysis, EPA first eliminated from analysis those facilities showing materially inadequate

financial performance in the baseline, that is, in the absence of the regulation. EPA judged these facilities, which

are referred to as baseline closures, to be at substantial risk of financial failure regardless of any financial

impacts of the 316(b) regulation. Second, for the remaining facilities, EPA evaluated how compliance costs

would likely affect facility financial health. A facility is identified as a regulatory closure if it would have

operated under baseline conditions but would fall below an acceptable financial performance level when subject

to the new regulatory requirements.

EPA’s analysis also identified facilities that would likely incur moderate impacts from compliance with the

regulation. EPA anticipates that these facilities would experience moderate deterioration of financial performance

but not at a level sufficient to cause the facility to fail financially. The test of moderate impacts examined two

financial ratios – pre-tax return on assets and interest coverage ratio – calculated on a baseline and

post-compliance basis. Incremental moderate impacts are attributed to the regulation if both financial ratios

exceeded threshold values in the baseline (i.e., no moderate impacts in the baseline), but at least one financial

ratio fell below the threshold value in the post-compliance case.

For the assessment of firm-level effects, EPA compared annualized after-tax compliance cost to firm revenue and

reports the estimated number and percentage of firms incurring compliance cost in three cost-to-revenue ranges:

less than 1.0%; at least 1.0% but less than 3.0%; and 3.0% or greater. Although EPA’s sample-based data support

specific estimates of the number of facilities, these data do not support a specific estimate of the number of

entities that own these facilities. As a result, EPA estimated the number of entities owning facilities in the

manufacturing industries as a range, based on alternative assumptions about the potential ownership of regulated

facilities. In its comparison of compliance cost to firm revenue, EPA also used this same range concept, which

yields approximate upper and lower bound estimates of the value of compliance cost that might be incurred by an

entity, based on the number of regulated facilities that it owns.

Key steps in the facility- and firm-level impact methodologies are described in the following discussions. In

addition, seven appendixes to this chapter provide detail of specific aspects of the impact analysis methodologies.

B3-2.1 Market-Level Impacts

Increased cost from the regulation may affect industry-level prices and output. In some instances, facilities

incurring compliance costs may be able to pass part of these costs through to customers as price increases and

thus reduce the compliance cost burden borne directly by complying facilities. On the basis of analysis presented

in Appendix 3 to Chapter B3 and the findings from the industry profile analyses as discussed in the preceding

chapter, Chapter B2: Profile of Manufacturers, EPA determined that an assumption of zero cost pass-through

is appropriate for its analysis of the effect of the 316(b) regulation on manufacturing industries. The assumption

of zero cost pass-through is conservative in that the analysis assumes that facilities must bear all compliance costs

within baseline cash flow. Because facilities may be able to pass compliance costs through to consumers in some

markets, this assumption may overstate impacts to affected facilities and understate the ability of facilities to

withstand the cost of 316(b) regulatory compliance without material financial impact.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-5

B3-2.2 Impact Measures

a. Test of Severe Impacts

The analysis of severe impacts estimates the number of facilities that could potentially close due to the regulation. 

EPA predicted that a facility would close if compliance costs cause the facility's overall financial performance and

resulting implied financial value to fall below a specified threshold level.

The assessment of severe impacts for 316(b) manufacturing facilities is based on the change in the facility’s

estimated business value, as determined from a discounted present value analysis of baseline cash flow and the

change in cash flow resulting from regulatory compliance. If the estimated discounted cash flow value of the

facility is positive before considering the effects of regulatory compliance but becomes negative as a result of

compliance outlays, then the facility is considered a regulatory closure. In this impact test, the estimated ongoing

business value of the facility is compared with a threshold value of zero for the closure decision: as long as the

discounted cash flow value of the facility is greater than zero, the business is earning its cost of invested capital

and continuation of the business is warranted. If the discounted cash flow value of the facility is less than zero in

the baseline or becomes less than zero as a result of compliance outlays, then the business would not earn its cost

of invested capital and the business owners would be better off financially by terminating the business. As noted

in earlier discussion, facilities for which EPA estimated a negative baseline value were considered baseline

closures and were not tested for additional adverse impacts from regulatory compliance. 

In an alternative, theoretically more accurate, formulation of this concept, business owners would compare the

discounted cash flow value of the facility with the value that the facility’s assets would bring in liquidation. In

this case, the estimated ongoing business value would be compared with a value that may be different from zero: 

liquidation value could be positive or negative. When liquidation value is positive, business owners might

benefit financially by terminating a business and seeking its liquidation value even when the ongoing business

value is positive but less than the estimated liquidation value. With negative liquidation value – which generally

would result from business termination liabilities (e.g., site clean-up) – the opposite result could occur: business

owners may find it financially advantageous to remain in business even though the business earns less than its

cost of invested capital, if the liquidation value of the business is “more negative”, and thus less in value, than the

ongoing business based on the discounted cash flow analysis. EPA attempted to implement this alternative

impact test formulation. EPA judges that the liquidation value estimates are substantially speculative and subject

to considerable error. For these reasons, EPA decided against using liquidation value for comparison with

ongoing business value in the closure test.

The cash flow concept used in calculating ongoing business value for the closure analysis is free cash flow

available to all capital. Free cash flow is the cash available to the providers of capital – both equity owners and

creditors – on an after-tax basis from business operations, and takes into account the cash required for ongoing

replacement of the facility’s capital equipment. Free cash flow is discounted at an estimated after-tax total cost

of capital to yield the estimated business value of the facility. Details of the calculation of free cash flow and

the discounting of free cash flow to yield the facility’s estimated value are explained in the following sections.

 ̃ Calculation of Baseline Free Cash Flow and Performance of Baseline Closure Test

Calculation of baseline free cash flow and performance of the baseline closure test involved the following steps:

1. Average survey income statement data over response years and convert to mid-year 2003 dollars: EPA first

adjusted facility income statement data for 1996, 1997, and 1998 to the year 1998, using the GDP Deflator. 

These data were then averaged over the months and/or years for which survey respondents reported data to

develop an annual average income statement in 1998 constant dollars. For example, if a facility reported

income statement data for 1996, 1997, and 1998, then a simple average was calculated for the three reported

years. The annual average income statement in 1998 was then brought forward from 1998 to 2003, again

using the GDP Deflator.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-6

2. Calculate after-tax income excluding the effects of financial structure: The questionnaire responses include a

calculation of after-tax income in accord with conventional accounting principles. However, this calculation

reflects the financial structure of the business, which may include debt financing and thus interest charges

against income. Because the cash flow concept to be discounted in the business value analysis is cash flow

available to all capital, it is necessary to restate after-tax income to exclude the effects of debt financing, or on

a before-interest basis. This restatement involves: (1) increasing after-tax income by the amount of interest

charges and (2) increasing taxes (and thereby reducing after-tax income) by the amount of tax reduction

provided by interest deductibility. This adjustment amounts to adding tax-adjusted interest expense to aftertax income and yields an estimate of after-tax income independent of capital structure or financing effects. In

calculating the tax adjustment for interest, EPA used a combined federal/state corporate income tax rate. For

this calculation, EPA used a tax rate that integrates the federal corporate income tax rate (35%) and statespecific state corporate income tax rates, based on facility location.

The combined federal/state corporate income tax rate was calculated as follows:

J = JS + JF - (JS * JF) (B3-1)

where:

J = estimated combined federal-state tax rate;

JS = state tax rate; and

JF = federal tax rate (35%).

After-tax income, before interest, was calculated as follows:

ATI-BI = ATI + I - JI or

ATI-BI = ATI + (1 - J)I 

(B3-2)

where:

ATI-BI = after-tax income before interest;

ATI = after-tax income from baseline financial statement;

I = interest charge from baseline financial statement; and

J = estimated combined federal-state tax rate.

3. Calculate after-tax cash flow from operations, before interest, by adjusting income for non-cash charges: The

calculation of after-tax income may include a non-cash charge for depreciation (and potentially amortization). 

To convert income to after-tax cash flow (ATCF) from operations, it is therefore necessary to add back

any depreciation charge to the calculation of after-tax income, before interest. Cash flow, before interest, was

calculated as follows:

ATCF-BI = ATI-BI +D (B3-3a)

where:

ATCF-BI = after-tax cash flow before interest;

ATI-BI = after-tax income before interest; and

D = baseline depreciation.

As a final step in the calculation of after-tax cash flow before interest, EPA eliminated the implied cash flow

benefit of any negative taxes, as reported in the facility’s income statement and after adjustment for removal of

interest. That is, in these calculations, negative taxes increase after-tax income and cash flow, and thus appear to

improve the financial performance and value of the facility in terms of cash flow from operations. However,

whether and when the implied cash flow benefit of negative taxes can be realized depends on the overall
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-7

profitability and tax circumstances of the total enterprise, including any other facilities owned by the same firm,

and the extent of profitability in periods before or after the survey data periods. To be conservative in this

analysis, EPA therefore assumed that a facility would not receive the implied cash flow benefit from negative

taxes – negative taxes, after adjustment for interest, were set to zero in the baseline analysis. This assumption is

consistent with a later step in the post-compliance analysis in which EPA limited the cash flow benefit of tax

deductions on compliance outlays not to exceed the amount of taxes paid as reported in the baseline income

statement (and adjusted for interest). In theory, the application of this limit could cause some facilities that would

otherwise pass the baseline closure analysis, instead to fail the analysis if the reported amount of negative tax,

after adjustment for interest, would be sufficient to offset the negative cash flow from operations independent of

taxes. In practice, though this limitation did not affect the findings of the baseline closure analysis.

4. Adjust after-tax cash flow to reflect estimated real change in business performance from the time of survey

data collection to the present: EPA adjusted facility baseline cash flow to reflect the estimated real change

(i.e., independent of inflation) in business performance in the manufacturing industries from the time of the

facility survey, 1996-1998, to the present. This adjustment is intended to address two potential concerns that

could lead to biased findings from the regulatory impact assessment: 

C First, EPA was concerned that facility survey data might have been collected during a period that deviated

cyclically from the longer-term trend of business performance for the 316(b) manufacturing industries. 

Given the knowledge that U.S. business conditions during the latter half of the 1990s were cyclically

strong, EPA was particularly concerned that business conditions during the 316(b) survey period

(1996-1998) might be abnormally favorable for some of the five Primary Manufacturing Industries. In

this case, the business performance and valuation measures, based on survey data, used to assess the

burden of regulatory compliance costs might overstate industry's ability to bear these costs and therefore

understate the potential impact of the proposed regulation.

C Second, apart from the issue of short-term deviation from trend caused by a cyclically strong economy,

EPA was also aware from its profile analyses that some of the industries might be experiencing a

longer-term trend of deteriorating performance. Using sample facility data that don't reflect such possible

trends would again potentially overstate industry's ability to bear compliance costs and therefore

understate the potential impact of the proposed regulation.

To calculate the adjustment factor, EPA collected data on after-tax cash flow for public firms in the 316(b)

manufacturing industry sectors over a 12-year period and developed adjustment factors by industry and/or

key industry segment (details of this analysis are contained in Appendix 4 to Chapter B3). Adjusted after-tax

cash flow is calculated as follows:

ATCF-BIADJ = ATCF-BI * Adj (B3-3b)

where:

ATCF-BIADJ = after-tax cash flow before interest adjusted to reflect the real change in business

performance; a

ATCF-BI = after-tax cash flow before interest; and

Adj = adjustment factor to reflect the real change in business performance.

5. Calculate free cash flow by adjusting after-tax cash flow from operations for ongoing capital equipment

outlays: The measure of after-tax cash flow from the previous step, cash flow from operations, reflects the

cash receipts and outlays from ordinary business operations, but includes no allowance for replacement of the

facility’s existing capital equipment. To sustain ongoing operations, however, a business must expend cash

for capital replacement. Accordingly, to understand the true cash flow of a business and thus provide a

conceptually valid cash flow measure for business valuation, it is necessary to reduce cash flow from

operations by an allowance for capital replacement. For the calculation of free cash flow, EPA estimated

baseline capital outlays from a regression analysis of capital expenditures by public firms in the 316(b)
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-8

industry sectors over an 11-year period (details of this analysis and estimation framework are contained in

Appendix 5 to Chapter B3). Free cash flow is calculated as follows:

FCF = ATCF-BIADJ - CAPEX (B3-3b)

where:

FCF = free cash flow

ATCF-BIADJ = after-tax cash flow before interest adjusted to reflect the real change in business performance;

and

CAPEX = estimated baseline capital outlays.

Or on a more detailed accounting statement basis:

FCF = REV - TC - T - JI - CAPEX (B3-3c)

where:

FCF = free cash flow

REV = revenue

TC = total operating costs, excluding interest, depreciation, and taxes

T = baseline income tax

J = estimated combined federal-state tax rate;

I = interest charge from baseline financial statement; 

JI = the increase in tax liability resulting from calculating income on a pre-interest basis; and 

CAPEX = estimated annual baseline capital outlays.

This calculation of free cash flow is based on a static representation of a facility’s business. With the

exception of bringing estimated cash flow forward from the time of the survey, 1996-1998, to approximately

the present, 2003, the facility impact analysis assumes, in effect, that the facility’s business will continue in

the future – absent the effects of regulation – exactly as reflected in the baseline financial statements provided

in the survey questionnaire

6. Calculate baseline facility value as the present value of free cash flow over a 10-year analysis horizon: To

calculate baseline business value, EPA discounted free cash flow over a 10-year period at an estimated real

(i.e., excluding the effects of inflation), after-tax cost of capital of 7.0%. The use of 10 years as the

discounting horizon reflects the expected useful life of capital equipment to be installed for 316(b) regulation

compliance. Facility baseline business value is calculated as follows:

(B3-4)

where:

VALUE = estimated baseline business value of the facility

FCF = free cash flow

CoC = after-tax cost-of-capital (7.0%); and 

t = year index, t = 0-9 (10-year discounting horizon).

In the present value calculation, yearly cash flows accrue at the beginning of the year. As a result, the first

year of cash flows is not discounted – i.e., t = 0 for the first year of the analysis – and cash flows in the tenth

and final year of the analysis period are discounted over a 9-year period – i.e., t = 9 in the final year of the

analysis.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

4

 For the facility cash flow analysis, EPA treated the income loss from installation downtime on an annual equivalent basis even

though this financial event occurs only once, and at the beginning of the assumed analysis period. EPA treated the installation downtime

on an annualized basis for two reasons. First, the installation downtime is assumed to have a useful “financial life” of 30 years to reflect

the total potential business life of the facility (note that reinstallation of the basic capital equipment, which is assumed to recur on a 10-year

interval, does not require a new round of downtime). Since compliance capital equipment is assumed to have a 10-year useful life and the

discounted cash flow analysis is accordingly structured as a 10-year analysis, including the income loss from installation downtime (which

is assumed to have a 30-year useful life) as a one-time up-front cost would overstate its impact in the discounted cash flow calculation. 

Second, calculation of the downtime cost on an annual basis allows the tax effect from the one-time income loss to be summed with other

annual tax effects for applying the limit to tax offsets, as explained in the next step of the analysis.

B3-9

As explained above, EPA considered a facility to be a baseline closure if its estimated business value was

negative before incurring regulatory compliance costs. Baseline closures were neither tested for adverse impact in

the post-compliance impact analysis nor were their compliance costs included in the tally of total costs of 316(b)

regulatory compliance.

 ̃ Calculation of Post-Compliance Free Cash Flow and Performance of Post-Compliance Closure Test

For the post-compliance closure analysis, EPA recalculated annual free cash flow, accounting for changes in

annual expenses and taxes that are estimated to result from compliance-related outlays. EPA combined the postcompliance free cash flow value and the estimated compliance capital outlay in the present value framework to

calculate business value on a post-compliance basis.

Calculation of post-compliance free cash flow and performance of the post-compliance closure test involved the

following steps:

1. Adjust baseline annual free cash flow to reflect compliance expense effects: Compliance-related effects on

annual free cash flow include: annually recurring operating and maintenance costs; the annual equivalent of

permitting and repermitting costs, which recur on other than an annual basis over the life of the analysis; the

annual equivalent of the income loss from installation downtime (see Appendix 2); and related changes in

taxes4

. The change in taxes includes: (1) the tax effect of these annually recurring and annualized expenses

and (2) the tax effect from depreciation of initial compliance outlays. For calculating the tax effect of

depreciation, EPA assumed that compliance capital outlays would be depreciated for tax purposes on a 10-

year straight-line schedule. Post-compliance free cash flow was calculated as follows:

FCFPC = FCFBL - )TC - J(- )TC - )D) (B3-5)

where:

FCFPC = post-compliance free cash flow;

FCFBL = baseline free cash flow, as calculated above;

)TC = change in total facility annual costs (excluding interest, depreciation and taxes), calculated as the

cost of operating and maintaining compliance equivalent plus the annual equivalent of certain

non-annual costs, as described above;

J = marginal tax rate for calculating compliance-related tax effects (combined federal-state tax rate);

and

)D = change in depreciation expense, calculated as compliance capital outlay (CC) divided by 10.

2. Limit tax adjustment to not exceed taxes as reported in baseline financial statement: The tax effect of

compliance outlays is to reduce tax liability. As a result, in the free cash flow calculation, the tax adjustment

generally increases cash flow and business value and, all else equal, reduces the likelihood that a facility will

fail the post-compliance closure test. However, the extent to which a facility would realize this contribution

to cash flow depends on its tax circumstances. In particular, some businesses may not be paying sufficient

taxes in the baseline to take full benefit of the implied tax reduction at the facility level – unless the unused

tax loss can be transferred to other, profitable business units in the firm, these businesses would not be able to

use fully the implied tax reduction on a current basis. Also, the marginal tax rate for businesses with

relatively lower pre-tax income may be less than the combined Federal/State tax rate used in the analysis. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

5

 For the analysis of options presented in this chapter, none of these impact measures (e.g., employment loss, output loss) were in fact

relevant because none of the three primary presentation options resulted in regulatory closures.

B3-10

While businesses may be able to carry forward tax losses to reduce taxes in later years, EPA recognizes that

the implied cash flow benefit from tax reduction may not be fully realized, particularly in circumstances

involving single-facility firms. To be conservative in its analysis, EPA therefore limited the amount of tax

reduction from compliance outlays to be no greater than the amount of tax paid by facilities as reported in the

baseline financial statement. The analysis effectively assumes that facilities will not be able to offset an

implicit negative tax liability against positive tax liability elsewhere in the owning firm’s operations or to

carry forward (or back) the negative income and its implicit negative tax liability to other positive

income/positive tax liability operating periods. On average, this approach overstates impacts on facilities,

because some businesses may be able to benefit from tax reductions that exceed facility baseline taxes,

especially if the facility is owned by a multiple-site firm. Accordingly, EPA constrained the tax effect term in

the free cash flow calculation, [-J( - )TC - )D)] as specified above, to be no greater than baseline financial

statement tax liability, T.

3. Calculate post-compliance facility value, including post-compliance free cash flow and the compliance

capital outlay: As in the baseline analysis, EPA calculated post-compliance facility value as the present value

of free cash flow and accounting for the compliance capital outlay as an undiscounted cash outlay in the first

analysis period. Facility post-compliance business value was calculated as follows:

(B3-6)

where:

VALUEPC = estimated post-compliance business value of the facility

FCFPC = estimated post-compliance free cash flow

CoC = after-tax cost-of-capital (7.0%);

t = year index, t = 0-9 (10-year discounting horizon); and

CC = compliance capital outlay. 

EPA considered a facility to be a post-compliance closure if its estimated business value was positive in the

baseline but became negative after adjusting for compliance-related cost, revenue and tax effects. In addition to

tallying closure impacts in terms of the number of estimated facility closures, EPA also measured the significance

of closures in terms of losses in employment and output. Employment losses equal the number of employees

reported by closure facilities in survey responses; output losses equal total revenue reported for regulatory closure

facilities. EPA estimated national results by multiplying facility results by facility sample weights.5

b. Test of Moderate Impacts

EPA also conducted an analysis of financial stress short of closure to identify the regulation’s moderate impacts. 

Facilities incurring moderate impacts are not projected to close due to the proposed Section 316(b) regulation. 

The regulation, however, might reduce their financial performance to the point where they incur greater difficulty

and higher costs in obtaining financing for future investments.

The analysis of moderate impacts examined two financial measures:

Pre-Tax Return on Assets (PTRA): ratio of pre-tax operating income – earnings before interest and

taxes (EBIT) – to assets. This ratio measures the operating performance and profitability of a business’ assets

independent of financial structure and tax circumstances. PTRA is a comprehensive measure of a firm's

economic and financial performance. If a firm cannot sustain a competitive PTRA on a post-compliance

basis, it will likely face difficulty financing its investments, including the outlay for compliance equipment.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-11

Interest Coverage Ratio (ICR): ratio of pre-tax operating cash flow – earnings before interest, taxes, and

depreciation (EBITDA) – to interest expense. This ratio measures the facility's ability to service its debt on

the basis of current, ongoing financial performance and to borrow for capital investments. Investors and

creditors will be concerned about a firm whose operating cash flow does not comfortably exceed its

contractual obligations. The greater the ICR, the greater the firm's ability to meet interest payments, and,

generally speaking, the greater the firm's credit-carrying ability. ICR also provides a measure of the amount

of cash flow available for equity after interest payments.

Creditors and equity investors review the above two measures as criteria to determine whether and under what

terms they will finance a business. PTRA and ICR also provide insight into a firm's ability to generate funds for

compliance investments from internally-generated equity, i.e., from after-tax cash flow. The measures are defined

as follows:

Pre-Tax Return on Assets

(B3-7)

where:

PTRA = pre-tax return on assets,

EBIT = pre-tax operating income, or earnings before interest and taxes, and

TA = total assets.

Or, stated in terms of 316(b) income statement accounts,

(B3-8)

where:

PTRA = pre-tax return on assets;

REV = revenue;

TC = total operating costs (excluding interest, taxes, and depreciation/amortization);

D = depreciation; and 

TA = total assets.

Interest Coverage Ratio

(B3-9)

where:

ICR = interest coverage ratio;

EBITDA = pre-tax operating cash flow, or earnings before interest, taxes, and depreciation (and

amortization) and

I = interest expense.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-12

Or, stated in terms of 316(b) income statement accounts,

(B3-10)

where:

ICR = interest coverage ratio;

REV = revenue;

TC = total operating costs (excluding interest, taxes, and depreciation/amortization); and

I = interest expenses.

Including the effects of 316(b) compliance costs, post-compliance PTRA and ICR are: 

(B3-11)

(B3-12)

where:

PTRApc = pre-tax return on assets, post-compliance;

ICRpc = interest coverage ratio, post-compliance;

)TC = change in total facility operating costs (excluding interest, depreciation and taxes), calculated

as operating and maintenance costs of compliance;

)D = change in depreciation expense, calculated as compliance capital outlay (CC) divided by 10;

CC = compliance capital outlay (assuming all of the outlay would be capitalized and reported as an

addition to assets on the balance sheet); and

)I = incremental interest expense from financing of compliance capital outlay. As a simplifying,

conservative assumption, incremental interest expense is calculated assuming that the

compliance capital outlay is fully debt financed at the overall real cost-of-capital of 7.0%. 

The annual incremental interest value is calculated as the annualized value of interest

payments over 10 years, assuming a constant annual payment of principal and interest.

In calculating the baseline values of the PTRA and ICR measures, EPA applied the same cash flow adjustments as

described above for the facility closure analysis, to the numerators of the PTRA and ICR measures. In the same

way as described for the facility closure analysis, these adjustments are intended to capture the change in the

financial performance of firms in the Primary Manufacturing Industries between the time of the 316(b) Phase III

survey and 2003 (see Appendix 4 to Chapter B3).

For evaluating 316(b) manufacturing facilities according to the moderate impact measures, EPA compared

baseline and post-compliance PTRA and ICR to 316(b) industry-specific thresholds that were developed from

data compiled by Risk Management Association, Inc. (RMA). RMA compiles and reports financial statement

information by industry as provided by member commercial lending institutions. The threshold values represent

the 25th percentile values of PTRA and ICR for statements received by RMA for the eight years from 1994 to

2001 within relevant industries. EPA developed 316(b) industry-level values by weighting and summing the
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

6

 See Appendix 6 to Chapter B3 for details of moderate impact threshold development and sector-specific threshold values.

B3-13

RMA industry values according to the definition of 316(b) industries6. Thresholds by sector ranged from 1.8% to

2.9% for PTRA and from 2.0 to 2.4 for ICR. Because the financial statements received by RMA are for

businesses applying for credit from member institutions, the data don’t represent a random sample. In particular,

the RMA data likely exclude representation from the financially weakest businesses, which are unlikely to seek

financing from RMA member lending institutions. As a result, EPA views the threshold values as being relatively

conservative and likely to overstate the occurrence of moderate impacts.

Both measures are important to financial success and firms’ ability to attract capital. Facilities failing at least one

of the moderate impact measures in the baseline were deemed to be already experiencing moderate financial

weakness and were not tested for additional financial impact in the moderate impact analysis. Facilities that

passed both moderate impact tests in the baseline but failed one or both threshold comparisons, post-compliance,

were considered to incur moderate financial impacts, short of closure, as a result of the proposed Section 316(b)

regulation.

c. Firm Level Impacts

The analysis of impact on firms builds on the facility impact analysis to assess whether firms that own multiple

facilities are likely to incur more significant impacts than indicated by the facility impact analysis. For the

assessment of firm-level effects, EPA calculated annualized after-tax compliance costs as a percentage of firm

revenue and reports the estimated number and percentage of affected firms incurring compliance costs in 3 costto-revenue ranges: less than 1.0%; at least 1.0% but less than 3.0%; and 3.0% or greater.

EPA’s sample-based facility analysis supports specific estimates of (1) the number of facilities expected to be

subject to the regulation and (2) the total compliance costs expected to be incurred in these facilities. However,

the sample-based analysis does not support specific estimates of the number of firms that own manufacturing

facilities. In addition and as a corollary, the sample-based analysis does not support specific estimates of the

number of regulated facilities that may be owned by a single firm, or of the total of compliance costs across

regulated facilities that may be owned by a single firm. 

For the firm level analysis, EPA therefore considered two cases based on the sample weights developed from the

facility survey. These cases provide approximate upper and lower bound estimates on: (1) the number of firms

incurring compliance costs and (2) the costs incurred by any firm owning a regulated facility. The cases are as

follows:

Case 1: Upper bound estimate of number of firms owning facilities that face requirements under the

regulation; lower bound estimate of total compliance costs that a firm may incur. 

For this case, EPA assumed (1) that a firm owns only the regulated sample facility(ies) that it is known to own

from the sample analysis and (2) that this pattern of ownership, observed for sampled facilities and their owning

firms, extends over the facility population represented by the sample facilities. This case minimizes the

possibility of multi-facility ownership by a single firm and thus maximizes the count of affected firms, but also

minimizes the potential cost burden to any single firm.

For each firm that owns one sample facility, no firm is assumed to own more than one regulated facility, and the

analysis is straightforward: the firm owns one regulated facility and incurs compliance costs only for that facility. 

This configuration is assumed to exist as many as times as the facility's sample weight. However, EPA found that

28% of the firms identified as owning a sample facility, own more than one sample facility. Where the multiple

facilities owned by the same firm have the same sample weight, the analysis is also straightforward: the firm is

assumed to own and incur the compliance costs of the identified sample facilities, and the configuration is

assumed to exist as many times as many times as the uniform sample weight of the multiple facilities. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-14

CC CC firm i

i

= ∑

CC CC firm i

i

= × ∑ Wi

In some instances, however, the sample facilities that are owned by the same firm have different sample weights. 

In these cases, which required a more complex analysis, EPA accounted for the ownership of multiple sample

facilities by a single firm, but restricted the count of the multiple facilities and their configuration of ownership

for the firm-level cost analysis based on the sample weights of the individual sample facilities. Specifically, the

firm is assumed to exist on a sample-weighted basis as many times as the highest of the sample weights among the

sample facilities known to be owned by the firm. However, sample facilities with a smaller sample weight, and

their compliance costs, can be included in the total instances of ownership by the firm for only as many times as

their sample weights. Otherwise, the total facility count implied in the firm analysis would exceed the

sample-based estimated total of facilities; correspondingly, the total of compliance costs accounted for in the firm

level analysis would exceed the sample-based estimated total of facility compliance costs. For implementation,

this concept means that all of the sample facilities known to be owned by the same firm, and their compliance

costs, can be included in the ownership configuration for only as many sample weighted instances as the smallest

sample weight among the multiple facilities owned by the firm. Once the sample weight of the smallest sample

weight facility is "used up," a new multiple facility ownership is configured including only the costs for those

facilities with weights greater than the weight of the smallest sample weight facility. This configuration is

assumed to exist for as many sample weighted instances as the difference between the lowest sample weight and

the next higher sample weight among the facilities owned by the firm. This process is repeated – with successive

removal of the new lowest sample weight facility, and its compliance cost– as many times as necessary until only

the highest sample weight facility remains in the ownership configuration.

The survey asked respondents to provide firm-level revenue for the parent firm. For single-facility firms, firm

revenue and compliance costs are identical to those for the facility. For multi-facility firms, EPA grouped

together all facilities with a common parent firm from the surveys. For each firm in the analysis, firm-level

compliance cost is:

(B3-13)

where:

CCfirm = firm-level compliance cost

CCi = compliance cost for the surveyed facility i, known to be owned by the firm

Case 2: Lower bound estimate of number of firms owning facilities that face requirements under the

regulation; upper bound estimate of total compliance costs that a firm may incur.

For this case, EPA inverted the prior assumption and assumed that any firm owning a regulated sample

facility(ies), owns the known sample facility(ies) and all of the sample weight associated with the sample

facility(ies). This case minimizes the count of affected firms, while tending to maximize the potential cost burden

to any single firm.

For this case, EPA grouped together all facilities with a common parent firm from the surveys and sample

weighted the facility compliance costs. EPA calculated the firm-level compliance cost as:

(B3-13)

where:

CCfirm = firm-level compliance cost

CCi = compliance cost for surveyed facility i owned by the firm

Wi = sample weight for surveyed facility i owned by the firm
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-15

As stated above, for the analysis of firm-level impacts, EPA calculated annualized after-tax compliance costs as a

percentage of firm revenue. EPA judged that firms with annualized after-tax compliance cost of less than 1.0% of

revenue would not be materially affected by the regulation. EPA identified firms as subject to potentially more

serious impacts if annualized compliance cost exceeded 3.0% of revenue.

B3-3 RESULTS

This section presents the results of the facility impact analysis. The first section presents the results of the

baseline closure analysis. The subsequent sections report the impact analysis results for the three proposed

options. Section B3-3.2 presents the number of facilities with regulatory requirements under the different options. 

Section B3-3.3 discusses post-compliance closures. Section B3-3.4 presents the number of facilities with

moderate impacts by industry. Section B3-3.5 summarizes total annualized compliance costs on an after-tax basis

by option. Section B3-3.6 summarizes the estimated impacts by option, including facility impacts and total

annualized compliance costs on both a pre-tax and after-tax basis. Section B3-3.7 presents the results of the firmlevel analysis for the two analytic cases described above.

B3-3.1 Baseline Closures

Table B3-1 reports estimated baseline closures for facilities in the Primary Manufacturing Industries and the

additional known facilities in Other Industries. EPA determined that 76 facilities (or 14%) of the estimated 532

regulated facilities in the five Primary Manufacturing Industries have a negative business value before incurring

regulatory compliance costs. The highest percentages of baseline closures occur in the Steel industry sector

(43%) and Aluminum industry sector (33%). An additional four facilities (or 18%) of the 22 known facilities in

Other Industries are baseline closures. These facilities are projected to close in the baseline and are not

considered in the analysis of impacts attributable to the regulation.

Appendix 7 to Chapter B3 provides information on typical establishment closure rates in the 316(b) industries. 

EPA compared the percentage of facilities assessed as baseline closures to typical establishment closure rates in

the five Primary Manufacturing Industries, as reported in Statistics of U.S. Businesses (SUSB). SUSB data

indicate that between 1.4% and 12.5% of all facilities in these industries, close annually. The percentage of

facilities assessed as baseline closures in the Steel and Aluminum industries is higher than the typical closure rates

in the industries. However, EPA based its analysis on survey data provided by the facilities and believes these

data and analysis provide an accurate representation of the financial condition of the facilities that would subject

to the Phase III regulation.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-16

Table B3-1: Summary of Baseline Closures by Sector

Sector Total Number of

Facilities

Number of Baseline

Closures

Percentage Closing

in Baseline

Number Operating

in Baseline

Paper 230 32 13.9% 198

Chemicals 178 4 2.2% 173

Petroleum 36 5 13.9% 30

Steel 68 29 42.6% 40

Aluminum 21 7 33.3% 14

Total Facilities in Primary

Manufacturing Industries 532 76 14.3% 456

Additional known facilities

in Other Industries 22 4 18.2% 18

Source: U.S. EPA analysis, 2004.

B3-3.2 Number of Facilities with Regulatory Requirements

Of the three options presented here, the 50 MGD Option for All Waterbodies, would the largest number of

facilities, 133 facilities, or 127 facilities in the Primary Manufacturing Industries and 6 known facilities in Other

Industries to national categorical requirements (see Table B3-2). The 200 MGD for All Waterbodies, would

subject a smaller set of Manufacturers to national requirements: 24 facilities, or 22 facilities in the Primary

Manufacturing Industries and 2 of the known facilities in Other Industries. The 100 MGD for Certain

Waterbodies Option, would subject the smallest number of facilities to national requirements: 20 facilities, or 18

facilities in the Primary Manufacturing Industries and 2 known facilities in the Other Industries.

Table B3-2: Number of Facilities with Regulatory Requirements by Sector and Option

Sector

Total

Operating

in Baseline

Number of Facilities with Regulatory Requirements

50 MGD All 200 MGD All 100 MGD CWB

Number Percentage Number Percentage Number Percentage

Paper 198 37 18.7% 3 1.5% 0 0.0%

Chemicals 173 52 30.1% 5 2.9% 7 4.0%

Petroleum 30 13 43.3% 3 10.0% 5 16.7%

Steel 40 22 55.0% 9 22.5% 6 15.0%

Aluminum 14 5 35.7% 1 7.1% 0 0.0%

Total Facilities in Primary

Manufacturing Industries 456 127 27.9% 22 4.8% 18 3.9%

Additional known facilities

in Other Industries 18 6 33.3% 2 11.1% 2 100.0%

Source: U.S. EPA analysis, 2004.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-17

B3-3.3 Post-Compliance Impacts

Of the 474 facilities potentially subject to regulation after baseline closures, EPA estimated that no facilities

would close or incur employment losses as a result of any of the three proposed options.

EPA also found that none of the Manufacturers would incur a moderate impact ( i.e, financial stress short of

closure) under any of the three proposed options.

B3-3.4 Compliance Costs

Table B3-3 reports the estimated total after-tax compliance cost to facilities in the Primary Manufacturing

Industries and the known facilities in Other Industries by sector and regulatory option. The reported costs exclude

costs in baseline closures. The total annualized, after-tax compliance cost reported in Table B3-3 represents the

cost actually incurred by complying firms, assuming no recovery of costs from customers through increased

prices and taking into account the reductions in tax liability resulting from incurrence of compliance outlays. The

after-tax analysis uses a combined Federal/State tax rate, and accounts for facilities’ baseline tax circumstances. 

Specifically, tax offsets to compliance costs are limited to not exceed facility-level tax payments as reported in

facility questionnaire responses. The total annualized, after-tax compliance cost reported here is the sum of

annualized, after-tax costs by facility at the year of compliance. This cost calculation differs in concept from the

calculation of compliance costs as included in the calculation of the total social costs of the regulation. For the

social cost calculation, which is presented in Chapter E1: Summary of Social Costs, the year-by-year stream of

total pre-tax compliance costs for all facilities is discounted to the assumed year of promulgation of the 316(b)

final regulation for Phase III facilities – i.e., beginning of year 2007 – and then annualized. Two social discount

rate values, 3% and 7%, are used in the social cost analysis.

Of the three options described here, the 50 MGD Option for All Waterbodies, has the highest total after-tax

compliance cost, $38.0 million: $32.8 million for facilities in the Primary Manufacturing Industries, and $5.2

million for known facilities in Other Industries. The 100 MGD for Certain Waterbodies has the next higher total

after-tax compliance cost, $16.4 million: $15.8 million for facilities in the Primary Manufacturing Industries, and

$0.6 million for known facilities in Other Industries. The 200 MGD Option for All Waterbodies, would have the

lowest cost, $14.5 million: $13.7 million for facilities in the Primary Manufacturing Industries, and $0.7 million

for known facilities in Other Industries.

Table B3-3: Total Annualized Facility Compliance Costa

 by Sector and Regulatory Option 

(millions, 2003$)

Sector

After-Tax Costs

50 MGD All 200 MGD All 100 MGD CWB

Paper $5.0 $2.1 $0.0

Chemicals $15.9 $3.6 $9.0

Petroleum $4.7 $3.0 $3.2

Steel $6.3 $5.0 $3.6

Aluminum $0.8 $0.0 $0.0

Total Facilities in Primary

Manufacturing Industries $32.8 $13.7 $15.8

Additional known facilities in Other

Industries $5.2 $0.7 $0.6

a

 This table reflects the cost incurred by complying businesses and does not represent the cost to society from

regulatory compliance. Chapter E1: Summary of Social Costs discusses the social cost of the proposed regulation and

the other options. The values in this table exclude baseline closures.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-18

Source: U.S. EPA analysis, 2004.

B3-3.5 Summary of Facility Impacts

Table B3-4 summarizes the estimated impacts of the three proposed regulatory options for Manufacturers, as

reported in the preceding sections.

Table B3-4: Regulatory Impacts for All Facilities by Option, National Estimates

50 MGD All 200 MGD All 100 MGD CWB

Primary Manufacturing Industries

Number of Facilities Operating in Baseline 456 456 456

Number of Facilities with Regulatory Requirements 127 22 18

Percentage of Facilities with Regulatory Requirements 27.9% 4.8% 3.9%

Number of Closures (Severe Impacts) 0 0 0

Percentage of Facilities with Regulatory Requirements

Predicted to Close 0.0% 0.0% 0.0%

Number of Facilities with Moderate Impacts 0 0 0

Percentage of Facilities with Regulatory Requirements

with Moderate Impacts 0.0% 0.0% 0.0%

Annualized Compliance Costs (after tax, million $2003) $32.8 $13.7 $15.8

Additional Known Facilities in Other Industries

Number of Facilities Operating in Baseline 18 18 18

Number of Facilities with Regulatory Requirements 6 2 2

Percentage of Facilities with Regulatory Requirements 33.3% 11.1% 11.1%

Number of Closures (Severe Impacts) 0 0 0

Percentage of Facilities with Regulatory Requirements

Predicted to Close 0.0% 0.0% 0.0%

Number of Facilities with Moderate Impacts 0 0 0

Percentage with Moderate Impacts 0.0% 0.0% 0.0%

Annualized Compliance Costs (after tax, million $2003) $5.2 $0.7 $0.6

Source: U.S. EPA analysis, 2004.

B3-3.6 Firm Impacts

As previously discussed, EPA’s analysis of firm-level impacts considered two analytic cases: 

< Case 1: Approximate upper bound estimate of number of firms owning facilities that face

requirements under the regulation; approximate lower bound estimate of total compliance costs that a

firm may incur, and 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-19

< Case 2: Lower bound estimate of number of firms owning facilities that face requirements under the

regulation; approximate upper bound estimate of total compliance costs that a firm may incur.

Based on these two analytic cases, EPA estimated the number of firms owning regulated facilities in the Primary

Manufacturing Industries to range from 100 (Case 2 estimate) to 313 (Case 1 estimate), depending on the assumed

ownership cases outlined above. An additional 14 firms are known to own facilities in Other Industries. EPA

included the additional known facilities in Other Industries in the firm impact analyses but since these facilities

have no sample weight (i.e., they are not modeled to represent facilities other than themselves), the upper and

lower bound estimates were not applicable to them.

Under both Case 1 and Case 2, no firms are estimated to incur total compliance costs equal to or exceeding 1.0%

of annual revenue under any of the three proposed options (See Table B3-5, below).

Table B3-5: Firm-Level After-Tax Annual Compliance Costs as a Percentage of Annual Revenue

Number of

Firms in the

Analysis

Number and Percentage with After Tax Annual Compliance Costs/Annual Revenue Equal to:

Total

No Costs Less than 1% 1-3% At Least 3%

Number % Number % Number % Number %

Primary Manufacturing Industriesa

Case 1: Upper bound estimate of number of firms owning facilities that face requirements under the regulation; lower bound estimate of

total compliance costs that a firm may incur

50 MGD All 313 208 66% 105 34% 0 0% 0 0%

200 MGD All 313 292 93% 21 7% 0 0% 0 0%

100 MGD CWB 313 293 94% 21 7% 0 0% 0 0%

Case 2: Lower bound estimate of number of firms owning facilities that face requirements under the regulation; upper bound estimate of

total compliance costs that a firm may incur

50 MGD All 100 54 54% 46 46% 0 0% 0 0%

200 MGD All 100 86 86% 14 14% 0 0% 0 0%

100 MGD CWB 100 88 88% 12 12% 0 0% 0 0%

Other Industries

50 MGD All 14 10 71% 4 29% 0 0% 0 0%

200 MGD All 14 13 93% 1 7% 0 0% 0 0%

100 MGD CWB 14 13 93% 1 7% 0 0% 0 0%

a

 Two known facilities in Other Industries are owned by firms that own facilities in the Primary Manufacturing Industries and are

included in this category.

Source: U.S. EPA analysis, 2004.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-20

GLOSSARY

after-tax cash flow (ATCF): The cash generated from business operations, after-tax, that is available to

providers of capital – equity and debt – or for reinvestment in the business.

baseline closures: Facilities showing inadequate financial performance in the baseline, that is, in the absence

of the regulation. EPA’s analysis assumes these closures would have occurred with or without the regulation. 

Construction Cost Index (CCI): Measures the cost of a hypothetical package of general construction goods

and services compared a base year. The CCI can be used where labor costs are a high proportion of total costs. 

The CCI uses 200 hours of common labor, multiplied by the 20-city average rate for wages and fringe benefits. 

(http://www.enr.com/cost/costfaq.asp)

cost of capital: Costs incurred for a firm to obtain financing from all capital sources including, in particular,

equity and debt.

cost pass-through: Calculates the percentage of compliance costs that EPA expects firms subject to

regulation to recover from customers through increased revenue.

facility: A contiguous set of buildings or machinery on a piece of land under common ownership.

free cash flow: Cash flow generated by the company that is available to all providers of the company’s capital,

both creditors and shareholders.

Gross Domestic Product (GDP) Implicit Price Deflator: The GDP Deflator is a quarterly series that

measures the implicit change in prices, over time, of the bundle of goods and services comprising gross domestic

product.

interest coverage ratio (ICR): Ratio of cash operating income to interest expenses. This ratio measures the

facility's ability to service its debt and borrow for capital investments.

liquidation value: Net amount that could be realized by selling the assets of a firm after paying debt.

moderate impacts: Adverse changes in a facility's financial position that weaken financial performance and

may increase cost of financing but are not threatening to short-term viability.

operating and maintenance: Costs estimated to result from operating and maintaining pollution controls

adopted to comply with effluent guidelines. Operating costs include the costs of monitoring.

pre-tax return on assets (PTRA): Ratio of cash operating income to assets. This ratio measures facility

profitability.

regulatory closure: A facility that is predicted to close because it can not afford the costs of complying with

the regulation.

severe impacts: Facility closures and the associated losses in jobs, earnings, and output at facilities that close

due to the regulation.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-21

ABBREVIATIONS

ATCF: after-tax cash flow

CCI: construction cost index

ICR: interest coverage ratio

PTRA: pre-tax return on assets
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities B3: Impact Analysis for Manufacturers

B3-22

REFERENCES

Engineering News-Record (ENR). 2004. Construction Cost Index.

Available at: http://enr.construction.com/features/conEco/costIndexes/constIndexHist.asp

McGraw-Hill and U.S. Department of Commerce, International Trade Administration. U.S. Industry & Trade

Outlook.

McKinsey & Company, Inc. et al. 2000. Valuation: Measuring and Managing the Value of Companies, 3rd

edition. New York: John Wiley & Sons, Inc.

Risk Management Association (RMA). 1997-1998. Annual Statement Studies.

Risk Management Association (RMA). 2000-2001. Annual Statement Studies.

U.S. Bureau of Economic Analysis (U.S. BEA). 2004. Gross Domestic Product. Table 1.1.9: Implicit Price

Deflators for Gross Domestic Product (GDP). Last Revised on February 27, 2004.

Available at: http://www.bea.doc.gov/bea/dn/nipaweb/TableView.asp#Mid

U.S. Department of Commerce (U.S. DOC). 2001. Bureau of the Census. International Trade Administration.

U.S. Department of Commerce (U.S. DOC). 1989, 1992, and 1997. Bureau of the Census. Census of

Manufactures.

U.S. Department of Commerce (U.S. DOC). 1988-1991, 1993-1996, and 1998-2001. Bureau of the Census. 

Annual Survey of Manufactures.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Technical Development Document for the Proposed

Section 316(b) Rule for Phase III Facilities. EPA-821-R-04-015. November.

U.S. Small Business Administration (U.S. SBA). 1989-2001. Statistics of U.S. Businesses. 

Available at: http://www.sba.gov/advo/stats/int_data.html

Value Line. 1992-2003. Value Line Investment Survey.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendices to Chapter B3

B3A-i

Appendices to Chapter B3

Appendix 1: Summary of Results for Alternative Options

B3A1-1 Number of Facilities with Regulatory Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-1

B3A1-2 Post-Compliance Closures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-2

B3A1-3 Moderate Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-2

B3A1-4 After-Tax Compliance Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-3

B3A1-5 Overview of Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-4

B3A1-6 Firm Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-5

Appendix 2: Calculation of Installation Downtime Cost

B3A2-1 Estimated Shut-Down Period for Installing Compliance Equipment . . . . . . . . . . . . . . . . . B3A2-1

B3A2-2 Calculating the Impact of Installation Downtime on Complying Facilities . . . . . . . . . . . . B3A2-2

B3A2-3 Calculating the Cost to Society of Installation Downtime . . . . . . . . . . . . . . . . . . . . . . . . . B3A2-4

Appendix 3: Cost Pass-Through Analysis

B3A3-1 The Choice of Firm-Specific versus Sector-Specific CPT Coefficients . . . . . . . . . . . . . . . B3A3-1

B3A3-2 Market Structure Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-3

B3A3-2.1 Industry Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-4

B3A3-2.2 Import Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-6

B3A3-2.3 Export Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-7

B3A3-2.4 Long-Term Industry Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-8

B3A3-2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-9

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-11

Appendix 4: Adjusting Baseline Facility Cash Flow

B3A4-1 Background: Review of Overall Business Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-2

B3A4-2 Framing and Executing the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-4

B3A4-2.1 Identifying the Financial Data Concept to Be Analyzed . . . . . . . . . . . . . . . . . . . . . . . . B3A4-4

B3A4-2.2 Selecting Appropriate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-5

B3A4-2.3 Selecting Industry Groups and Firms for Use in the Analysis . . . . . . . . . . . . . . . . . . . . B3A4-7

B3A4-2.4 Structuring the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-9

B3A4-3 Summary of Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-10

B3A4-4 Developing an Adjustment Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-14

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-18

Appendix 5: Estimating Capital Outlays for Section 316(b) Phase III Manufacturing Sectors Discounted

Cash Flow Analyses

B3A5-1 Analytic Concepts Underlying Analysis of Capital Outlays . . . . . . . . . . . . . . . . . . . . . . . B3A5-2

B3A5-2 Specifying Variables for the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-4

B3A5-3 Selecting the Regression Analysis Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-7

B3A5-4 Specification of Models to be Tested . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-9

B3A5-5 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-12

Attachment B3A5.A: Bibliography of Literature Reviewed for this Analysis . . . . . . . . . . . . . . . . . . B3A5-17

Attachment B3A5.B: Historical Variables Contained in the Value Line Investment Survey Dataset B3A5-18

Appendix 6: Summary of Moderate Impact Threshold Values by Industry

B3A6-1 Developing Threshold Values for Pre-Tax Return on Assets . . . . . . . . . . . . . . . . . . . . . . . B3A6-2

B3A6-2 Developing Threshold Values for Interest Coverage Ratio . . . . . . . . . . . . . . . . . . . . . . . . B3A6-2

B3A6-3 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A6-4

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A6-5

Appendix 7: Analysis of Baseline Closure Rates
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendices to Chapter B3

B3A-ii

B3A7-1 Annual Establishment Closures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A7-1

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A7-2
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendices to Chapter B3

B3A-iii

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§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-1

Appendix 1 to Chapter B3:

Summary of Results for Alternative Options

INTRODUCTION

This appendix presents results for 5 additional

regulatory options considered by EPA. For these

options, facility counts and other results include only

those Phase III Manufacturers that are (1) nonbaseline closures and (2) subject to national

categorical requirements under the option. See the

main body of this chapter for a description of data

sources and methodologies used in these analyses. In

the following tables, the results for these additional options are presented in order of increasing estimated total

annualized cost of compliance.

B3A1-1 NUMBER OF FACILITIES WITH REGULATORY REQUIREMENTS

Table B3A1.1: Number of Facilities with Regulatory Requirements by Sector and Option

Sector Total Operating

in Baseline

Number of Facilities with Regulatory Requirements

Option 3 Option 4 Option 2 Option 1 Option 6

Paper 198 117 46 117 117 198

Chemicals 173 116 75 116 116 173

Petroleum 30 18 13 18 18 30

Steel 40 33 23 33 33 40

Aluminum 14 8 5 8 8 14

Total Facilities in Primary

Manufacturing Industries 456 292 162 292 292 456

Additional known facilities

in Other Industries 18 9 7 9 9 18

Source: U.S. EPA analysis, 2004.

APPENDIX CONTENTS

B3A1-1 Number of Facilities with Regulatory

Requirements . . . . . . . . . . . . . . . . . . . . . . B3A1-1

B3A1-2 Post-Compliance Closures . . . . . . . . . . . . B3A1-2

B3A1-3 Moderate Impacts . . . . . . . . . . . . . . . . . . . B3A1-2

B3A1-4 After-Tax Compliance Costs . . . . . . . . . . B3A1-3

B3A1-5 Overview of Impacts . . . . . . . . . . . . . . . . B3A1-4

B3A1-6 Firm Impacts . . . . . . . . . . . . . . . . . . . . . . B3A1-5
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-2

B3A1-2 POST-COMPLIANCE CLOSURES

For a description of this analysis, see section B3-2.3 above.

Table B3A1.2: Number of Facilities Estimated as Post-Compliance Closures 

by Sector and Option

Sector Total Operating

in Baseline

Number of Post-Compliance Closures

Option 3 Option 4 Option 2 Option 1 Option 6

Paper 198 0 0 0 0 0

Chemicals 173 0 0 0 0 0

Petroleum 30 0 0 0 0 0

Steel 40 0 0 0 0 0

Aluminum 14 0 0 0 0 0

Total Facilities in Primary

Manufacturing Industries 456 0 0 0 0 0

Additional known facilities

in Other Industries 18 0 0 0 0 0

Source: U.S. EPA analysis, 2004.

B3A1-3 MODERATE IMPACTS

For a description of this analysis, see section B3-2.3 above.

Table B3A1.3: Number of Facilities Estimated as Moderate Impacts by Sector and Option

Sector Total Operating

in Baseline

Number of Moderate Impacts

Option 3 Option 4 Option 2 Option 1 Option 6

Paper 198 0 0 0 0 0

Chemicals 173 0 0 0 0 0

Petroleum 30 0 0 0 0 0

Steel 40 0 0 0 0 0

Aluminum 14 0 0 0 0 0

Total Facilities in Primary

Manufacturing Industries 456 0 0 0 0 0

Additional known facilities

in Other Industries 18 0 0 0 0 0

Source: U.S. EPA analysis, 2004.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-3

B3A1-4 AFTER-TAX COMPLIANCE COSTS

For a description of this analysis, see section B3-2.3 above.

Table B3A1.4: Total Annualized Facilitya

 After-Tax Compliance Cost 

by Sector and Option (millions, 2003$)

Sector Option 3 Option 4 Option 2 Option 1 Option 6

Paper $8.5 $7.2 $9.6 $11.3 $22.9

Chemicals $28.3 $35.1 $35.9 $35.9 $42.0

Petroleum $4.7 $4.7 $4.7 $4.7 $4.8

Steel $7.2 $7.4 $8.1 $8.1 $8.1

Aluminum $0.8 $0.8 $0.8 $0.8 $0.9

Total Facilities in Primary

Manufacturing Industries $49.5 $55.3 $59.1 $60.8 $78.8

Additional known facilities

in Other Industries $5.6 $5.4 $5.6 $5.6 $5.8

a

 This table reflects the cost incurred by complying businesses and does not represent the cost to society from regulatory compliance. 

Chapter E1: Summary of Social Costs discusses the social cost of the proposed rule and the other options. The estimates in this table

exclude baseline and include regulatory closures, and are after-tax.

Source: U.S. EPA analysis, 2004.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-4

B3A1-5 OVERVIEW OF IMPACTS

For a description of this analysis, see section B3-2.3 above.

Table B3A1.5: Regulatory Impacts for All Facilities by Option, National Estimates

Option 3 Option 4 Option 2 Option 1 Option 6

Primary Manufacturing Industries

Number of Facilities Operating in Baseline 456 456 456 456 456

Number of Facilities with Regulatory

Requirements 292 162 292 292 456

Percentage of Facilities with Regulatory

Requirements 64.0% 35.5% 64.0% 64.0% 100.0%

Number of Closures (Severe Impacts) 0 0 0 0 0

Percentage of Facilities with Regulatory

Requirements Predicted to Close 0.0% 0.0% 0.0% 0.0% 0.0%

Number of Facilities with Moderate Impacts 00000

Percentage of Facilities with Regulatory

Requirements with Moderate Impacts 0.0% 0.0% 0.0% 0.0% 0.0%

Annualized Compliance Costs (after tax,

million $2003)

$49.5 $55.3 $59.1 $60.8 $78.8

Additional Known Facilities in Other Industries

Number of Facilities Operating in Baseline 18 18 18 18 18

Number of Facilities with Regulatory

Requirements 9 7 9 9 18

Percentage of Facilities with Regulatory

Requirements 50.0% 38.9% 50.0% 50.0% 100.0%

Number of Closures (Severe Impacts) 0 0 0 0 0

Percentage of Facilities with Regulatory

Requirements Predicted to Close 0.0% 0.0% 0.0% 0.0% 0.0%

Number of Facilities with Moderate Impacts 00000

Percentage of Facilities with Regulatory

Requirements with Moderate Impacts 0.0% 0.0% 0.0% 0.0% 0.0%

Annualized Compliance Costs (after tax,

million $2003) $5.6 $5.4 $5.6 $5.6 $5.8

Source: U.S. EPA analysis, 2004.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-5

B3A1-6 FIRM IMPACTS

For a description of this analysis, see section B3-2.3 above.

Table B3A1.6: Firm-level After-Tax Annual Compliance Costs as a Percentage of Annual Revenue

Number of

Firms in the

Analysis

Number and Percentage with After Tax Annual Compliance Costs/Annual Revenue Equal to:

Total

No Costs Less than 1% 1-3% At Least 3%

Number % Number % Number % Number %

Primary Manufacturing Industriesa

Case 1: Upper bound estimate of number of firms owning facilities that face requirements under the regulation; lower bound estimate of

total compliance costs that a firm may incur

Option 3 313 114 36% 199 64% 0 0% 0 0%

Option 4 313 180 58% 133 42% 0 0% 0 0%

Option 2 313 114 36% 199 64% 0 0% 0 0%

Option 1 313 114 36% 199 64% 0 0% 0 0%

Option 6 313 0 0% 313 100% 0 0% 0 0%

Case 2: Lower bound estimate of number of firms owning facilities that face requirements under the regulation

Option 3 100 31 31% 69 69% 0 0% 0 0%

Option 4 100 48 48% 52 52% 0 0% 0 0%

Option 2 100 31 31% 69 69% 0 0% 0 0%

Option 1 100 31 31% 69 69% 0 0% 0 0%

Option 6 100 0 0% 99 99% 1 1% 0 0%

Other Industries

Option 3 14 8 57% 6 43% 0 0% 0 0%

Option 4 14 9 64% 5 36% 0 0% 0 0%

Option 2 14 8 57% 6 43% 0 0% 0 0%

Option 1 14 8 57% 6 43% 0 0% 0 0%

Option 6 14 0 0% 14 100% 0 0% 0 0%

a

 Two known facilities in Other Industries are owned by firms that own facilities in the Primary Manufacturing Industries and are

included in this category.

Source: U.S. EPA analysis, 2004.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-6

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§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-1

Appendix 2 to Chapter B3: 

Calculation of Installation Downtime Cost

INTRODUCTION

Depending on the engineering design of a facility’s

cooling water intake system, installation of some of

the compliance technologies considered for the

proposed regulation could require a one-time,

temporary shut-down of the facility’s cooling water

system. During this period, the facility’s coolingwater dependent operations would most likely be

halted, with a potential loss of revenue and income

from those operations. Accordingly, a key element of the cost to facilities in complying with the 316(b) Phase III

regulation is the loss in income from installation downtime. Installation downtime may also present a cost to

society, depending upon assumptions about the cost structure of the production to replace the goods and services

not produced by complying facilities during the installation downtime.

Unlike the capital and operating cost elements of total compliance cost, this cost element is not estimated based

solely on engineering analysis of compliance technology specifications. Instead, the cost of installation downtime

depends on a number of factors additional to the engineering assessment of compliance requirements. 

Specifically, the cost of installation downtime depends on the estimated length of time that a facility’s cooling

water intake system would be removed from service, the extent to which the facility’s business operations depend

on cooling water, and the revenue and operating cost structure of those cooling water dependent operations. Of

these items, the length of time that the facility’s cooling water intake system would be out of service was

estimated as part of the engineering analysis of compliance requirements. The remaining items – the extent to

which the facility’s business operations depend on cooling water, and the revenue and operating cost structure of

the facility’s cooling water dependent operations – were obtained from the facility’s response to the economic/

financial section of the 316(b) Phase III questionnaire. EPA used this information to calculate the pre-tax income

loss from installation downtime. 

The following sections of this appendix presents the methodology used to estimate the income loss from

installation downtime. 

B3A2-1 ESTIMATED SHUT-DOWN PERIOD FOR INSTALLING COMPLIANCE EQUIPMENT

Installation of some of the compliance technologies considered for the proposed regulation would require a onetime, temporary shut-down of the facility’s cooling water intake system. Table B3A2-1, below, lists the estimated

durations of net system downtime, in weeks, for each of the compliance technology modules considered for

compliance with the 316(b) Phase III regulation. The net downtime duration accounts for any expected annual

period of cooling water system downtime for regular maintenance and repair – the net downtime is the number of

weeks the cooling water system would need to be out of service above and beyond any regular maintenance

downtime period. Most of the technology modules are expected to be able to be installed without any additional

net system downtime. However, several of the technology modules are expected to require a net downtime

ranging from a month or less to nearly three months.

APPENDIX CONTENTS

B3A2-1 Estimated Shut-Down Period for Installing

Compliance Equipment . . . . . . . . . . . . . B3A2-1

B3A2-2 Calculating the Impact of Installation Downtime

on Complying Facilities . . . . . . . . . . . . . B3A2-2

B3A2-3 Calculating the Cost to Society of Installation

Downtime . . . . . . . . . . . . . . . . . . . . . . . . B3A2-4
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-2

Table B3A2.1: Estimated Average Cooling Water System Downtime by Technology Module

Module # Description Net Downtime (Weeks)

1 Fish handling and return system 0

2 Fine mesh traveling screens with fish handling and return 0

3 New larger intake structure with fine mesh, handling and return 2 - 4

4 Passive fine mesh screens with 1.75 mm mesh size at shoreline 9 - 11

5 Fish barrier net 0

6 Gunderboom 0

7 Relocate intake to submerged offshore with passive fine mesh screen with 1.75 mm

mesh size 9 - 11

8 Velocity cap at inlet of offshore submerged 0

9 Passive fine mesh screen with 1.75 mm mesh size at inlet of offshore submerged 0

10 Shoreline tech for submerged offshore 0

11 Double-entry, single-exit with fine mesh and fish handling and return 0

12 Passive fine mesh screens with 0.75 mm mesh size at shoreline 9 - 11

13 Relocate intake to submerged offshore with passive fine mesh screen with 0.75 mm

mesh size 0

14 Passive fine mesh screen at inlet of offshore submerged with 0.75 mm mesh size 9 - 11

Source: U.S. EPA analysis, 2004.

B3A2-2 CALCULATING THE IMPACT OF INSTALLATION DOWNTIME ON COMPLYING

FACILITIES

Installation downtime may affect a facility’s business operations in several ways:

1. The facility will be unable to perform production or other business operations that depend on cooling

water.

2. The facility will lose revenue from the production and sale of the goods and services that otherwise would

have been produced by the affected production operations during the period of downtime.

3. The facility will shed the variable cost of producing the goods and services not able to be produced during

the period of installation downtime. However, the facility will continue to incur the fixed costs of

production associated with the affected operations.

4. If, as part of its cooling water dependent operations, the facility generates electricity for its own use, and

some part of this self-generated electricity continues to be needed during the period of installation

downtime, the facility may need to purchase replacement electricity.

Together, these effects lead to a loss in pre-tax income, which EPA calculated and used as the cost of installation

downtime in its analysis of facility impacts. EPA calculated the loss in pre-tax income by first calculating the

annual loss in revenue in cooling water dependent operations less the variable production costs associated with

those operations plus the cost of purchasing electricity to replace any own-generated electricity that is used by the

facility. Second, EPA adjusted this annual pre-tax loss value to reflect the length of net installation downtime as

estimated in the engineering analysis of compliance technology requirements. Specific elements of these

calculations are summarized below for: (1) business effects not associated with electric power generation and (2)

electric power generation-related effects.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-3

Business Effects Not Associated with Electric Power Generation

The 316(b) Phase III questionnaire included a series of questions aimed at understanding the potential financial

effect of temporary or permanent shutdown of a facility’s cooling water intake system. A key data item obtained

from the questionnaire response is the fraction of a facility’s non-electric revenue that depends on cooling water. 

This information coupled with facility income statement information obtained from the questionnaire response

provided the basis for calculating the income loss in non-electric power-related operations. Steps in the

calculation are as follows: 

1. Calculate the annual revenue loss from curtailment of cooling water-dependent operations by multiplying

the fraction of cooling water-dependent revenue times total reported non-electric revenue. 

2. Calculate the variable production cost offset to this revenue loss by multiplying materials expense, as

reported on the facility’s income statement provided in the questionnaire, times the fraction of cooling

water-dependent revenue, as described above. This approach assumes that the variable production cost

structure for cooling water-dependent operations is the same as that for non-cooling water-dependent

operations. The use of materials expense as the only component of facility operating costs that may be

shed during a period of installation downtime, is relatively conservative as other cost accounts might also

be able to be curtailed or the services provided by those accounts – e.g., labor – applied to some other

beneficial service within the enterprise. 

3. Calculate annual loss in pre-tax income from curtailment of the facilities cooling water intake system

from non-electric power-related operations as estimated revenue loss less estimated reduction in variable

production cost.

4. Calculate pre-tax income loss in non-electric power-related operations, from installation downtime, by

multiplying the annual pre-tax income loss by the fraction of the year indicated as the net downtime

required for installing compliance equipment.

Business Effects Associated with Electric Power Generation

The analysis of installation downtime costs for cooling water-dependent electric power generation activities is the

same in concept as that outlined for non-electric power-related operations, with the exception that facilities may

need to incur an additional cost for purchasing replacement electricity if some of the facility’s electric power

needs were met from its own generation. Key information obtained from facility questionnaires for calculating

the income loss in electric power-related operations includes: (1) annual electric revenue reported as cooling

water dependent, (2) the fuel cost of electric power generation, which is assumed to be shed during the period of

curtailed operations, (3) the quantity of electricity consumed by the facility, and (4) the quantity of electricity 

generated by the facility. The remaining key input required for this analysis is the unit price of replacement

electricity: for this item, EPA used the average electricity price for industrial customers by state, using data from

the Department of Energy, Energy Information Administration, for 2002/2003. EPA calculated the pre-tax

income loss effect for electric power generation activities as follows. 

1. Annual electric revenue from cooling water-dependent generation is obtained directly from the facility

questionnaire. This value is assumed to be the annual revenue loss in electric power generation, from

curtailment of cooling water-dependent operations. 

2. Annual fuel cost of electric power generation is obtained directly from the facility questionnaire. This

value is assumed to be shed during the period of curtailed operations. 

3. Calculate own-generated electricity that is consumed by the facility as the lesser of (a) the facility’s own

electricity generation or (b) the electricity used within the facility. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-4

4. Calculate the quantity of replacement electricity to be purchased by the facility, by multiplying the

quantity of own-generated electricity that is consumed by the facility times the fraction of non-electric

revenue that is cooling water dependent but subject to a maximum reduction in electricity need of 75

percent. That is, the facility is assumed to need replacement electricity in proportion to the fraction of

non-electric revenue that is not cooling water-dependent. As the fraction of revenue dependent on

cooling water, and thus affected by installation downtime, increases, the need for replacement electricity

decreases. However, even in the case where the fraction of revenue that is cooling water dependent is

very large (e.g., 100 percent), the analysis assumes that the facility will not shed all of its electricity need: 

the facility is assumed to always require 25 percent of its baseline electricity consumption from owngenerated electricity. The assignment of the 25 percent minimum electricity replacement need is

somewhat arbitrary but reflects the reality that less electricity is likely to be needed to serve a lower level

of operations during a cooling water system shutdown, while also acknowledging that all electricity need

cannot be shed, regardless of the reduction in non-electric generating activity. The numerical

consequence on imposing the 25 percent electricity requirement floor (as opposed to a floor of zero

percent) is very small. 

5. Calculate the cost of electricity purchased to replace own-generated electricity used by the facility by

multiplying the quantity of replacement electricity times the average electricity price, by state, for

industrial customers. 

6. Calculate annual loss in pre-tax income for electric power-related operations as estimated revenue loss

from cooling water-dependent generation less estimated annual fuel cost of electric power generation plus

cost of electricity purchased to replace own-generated electricity.

7. Calculate pre-tax income loss in electric power-related operations, from installation downtime, by

multiplying the annual pre-tax income loss by the fraction of the year indicated as the net downtime

required for installing compliance equipment.

These values are summed to yield the total pre-tax income loss to the facility from installation downtime.

Under the 50 MGD All Option, 19 manufacturing facilities have non-zero downtime. Of these, 18 have non-zero

cost of downtime. The facility with no downtime costs reported that none of its revenue was cooling water

dependent. Of the 18 facilities with non-zero downtime cost, downtime cost as a fraction of annual revenue

ranges from 0.3% to 14.6%. Under the 200 MGD All Option, 3 manufacturing facilities have non-zero

downtime. Of these, all 3 have non-zero cost of downtime. Of the 3 facilities with non-zero downtime cost,

downtime cost as a fraction of annual revenue ranges from 0.3% to 3.0%. Under the 100 MGD SWB Option, 9

manufacturing facilities have non-zero downtime. Of these, all 9 have non-zero cost of downtime. Of the 9

facilities with non-zero downtime cost, downtime cost as a fraction of annual revenue ranges from 0.3% to 3.0%.

B3A2-3 CALCULATING THE COST TO SOCIETY OF INSTALLATION DOWNTIME

The preceding discussion describes the calculation of the pre-tax income loss from installation downtime as used

in the facility impact analysis. For the analysis of cost to society, the concept of cost of installation downtime

differs from that for the private impact analysis. Specifically, under the assumption that the total quantity of

goods and services produced and sold by the affected industries would not change as a result of the regulation (see

Chapter E1 for further detail on the social cost analysis framework), the cost to society from installation downtime

is the increase in cost for producing the goods and services that would otherwise have been produced by the

affected facilities. That is, other producers are assumed to replace the production of goods and services lost due

to installation downtime, and the cost to society is the amount, if any, by which the cost of these goods and

services exceeds the cost at which the affected facilities would have produced these goods and services. 

In concept, the cost to society could vary over a broad range depending on the structure of, and character of

competition in, the production of goods and services in the individual markets affected by the 316(b) Phase III

regulation. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-5

< At the low end of this possible range, if the replacement goods and services can be provided by

other producers at the same variable production cost as otherwise would have been incurred by

the affected 316(b) Phase III facilities, then the cost to society of installation downtime would be

zero. Because the cost for alternative producers is the same as for the producers incurring

downtime, society incurs no incremental resource cost when other producers provide the

replacement goods and services. In this case, although the affected 316(b) Phase III facilities

incur a financial impact from installation downtime, this impact – the loss in pre-tax income

described in the preceding section – becomes a transfer from the producers incurring installation

downtime losses to the producers who make up the lost production.

< At the high end of this possible range, the cost to society would be approximately equal to the

pre-tax income loss incurred by facilities due to installation downtime. That is, the cost to society

would again be the lost revenue from installation downtime less the variable cost of producing the

goods and services not produced due to the installation downtime. In this case, the variable

production cost for other producers to replace the lost goods and services is assumed to be

essentially the same as the price received for the sale of the goods and services not produced by

the facilities incurring the installation downtime. This assumption is consistent with a

competitive market model of increasing marginal production cost, such that the variable

production cost of the marginal supplier of goods and services produced and sold in any period is

approximately equal to the price received for those goods and services in the market. 

The likely reality is that the cost to society from installation downtime lies somewhere between these cases. 

Lacking specific knowledge of the overall production cost structure of the affected industries and for the

numerous goods and services provided by the affected industries, to be conservative in its analysis, EPA adopted

the latter of the two analytic cases outlined above for its analysis. That is, EPA assumed that the cost to society

from installation downtime would be the same as that estimated as the pre-tax cost of installation downtime for

Manufacturers facilities. To the extent that the variable production cost for replacement goods and services is less

than the selling price of those goods and services, this assumption overstates the cost to society of installation.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

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§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-1

Appendix 3 to Chapter B3: 

Cost Pass-Through Analysis

INTRODUCTION

This appendix presents the assessment of cost passthrough (CPT) potential for five key manufacturing

industries in which a substantial number of facilities

are expected to be subject to the Section 316(b)

Phase III regulation. The five industry sectors

considered in this analysis are:

< SIC 26: Paper and allied products

< SIC 28: Chemicals and allied products

< SIC 29: Petroleum and coal products

< SIC 331: Steel

< SIC 333/5: Aluminum

The purpose of the CPT analysis is to estimate the extent to which cost increases incurred by facilities in

complying with the proposed Section 316(b) Phase III regulation can be reasonably expected to be passed on to

consumers in the form of higher prices.

This appendix begins with a review of approaches for assessing CPT potential associated with market-wide cost

increase scenarios. Next, a description of the methodology and specific metrics used to assess CPT potential are

discussed and the results for each sector provided. Finally, conclusions are presented.

From this analysis, EPA concluded that an assumption of zero cost pass-through is appropriate for analyzing the

impact of the Phase III regulation on facilities in the manufacturing industries. Performance of the financial

impact analysis under this assumption means that facilities must absorb all compliance-related costs and operating

effects (e.g., income loss from facility shutdown during equipment installation) within their baseline cash flow

and financial condition. To the extent that facilities would be able to pass on some of the compliance costs to

customers through price increases, the analysis overstates the potential impact on complying facilities. Thus, this

assumption is conservative in avoiding potential overstatement of the manufacturing industries’ ability to absorb

the costs of 316(b) regulation compliance without material adverse economic/financial impact.

B3A3-1 THE CHOICE OF FIRM-SPECIFIC VERSUS SECTOR-SPECIFIC CPT COEFFICIENTS

One method of examining the ability of a firm to pass-through compliance-related cost increases associated with

the Phase III regulation is to review the firm’s historical performance in passing on previous cost increases to

consumers. For example, Ashenfelter et al. (1998) estimate the cost pass-through rate facing an individual firm,

and distinguish that rate from the rate at which a firm passes through cost changes common to all firms in an

industry, by regressing the price a firm charges on both its costs and the costs of another firm in the industry. The

estimated firm-specific CPT rate relates a change in the prices charged by a specific firm to a change in its

production costs, assuming no changes in the production cost for rival producers of that product. However,

estimating firm specific CPT rates is extremely complex. For example, in order to estimate firm-specific CPT

rates for every Phase III manufacturing firm included in the sample of Detailed Industry Questionnaire (DQ)

respondents, EPA would require, for each firm, detailed information on the products sold, the markets in which

these products are sold, as well as information identifying major competitors in each market. The Detailed

Industry Questionnaire did not obtain this information from surveyed facilities. And even if such information

were available, the analysis would remain highly challenging and subject to significant analytic error. As such, it

APPENDIX CONTENTS

B3A3-1 The Choice of Firm-Specific versus Sector-Specific

CPT Coefficients . . . . . . . . . . . . . . . . . . . B3A3-1

B3A3-2 Market Structure Analysis . . . . . . . . . . . . B3A3-3

B3A3-2.1 Industry Concentration . . . . . . . . . B3A3-4

B3A3-2.2 Import Competition . . . . . . . . . . . . B3A3-6

B3A3-2.3 Export Competition . . . . . . . . . . . . B3A3-7

B3A3-2.4 Long-Term Industry Growth . . . . . B3A3-8

B3A3-2.5 Conclusions . . . . . . . . . . . . . . . . . . B3A3-9

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-11
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-2

is neither possible nor practical to develop firm-specific CPT coefficients for the sample of Phase III

manufacturers. 

Moreover, even if the Agency possessed the data necessary to estimate firm-specific CPT rates, it is questionable

whether these rates would be the appropriate measure of CPT potential for compliance-related cost increases

stemming from the Phase III regulation. The Phase III regulation would force multiple firms in each of the

industry sectors considered in this analysis to incur compliance-related cost increases, which implies that for most

firms the cost increases would not only apply to them, but also to several of their competitors. Not surprisingly,

previous studies have found that the CPT rate for changes to an individual firm’s cost differs from the rate at

which a firm would pass through cost changes that are common to all, or a substantial fraction of, firms in an

industry (Ashenfelter et al., 1998). It can be reasonably expected that the higher the share of firms incurring the

cost increase, or more appropriately the higher the share of total output produced by such firms, the greater the

ability of those firms to pass on a greater portion of those costs to the consumer.

In cases where an industry-wide cost shock occurs, an industry-wide CPT rate would be an appropriate and

practical way of assessing the potential of all firms in that industry to pass through that cost increase to consumers

(EPA, 2003). An industry-wide CPT rate provides an estimate of the change in each facility’s output prices as a

function of the increase in its production costs, assuming that the same cost increase is experienced by all firms in

the industry. Such an industry-wide rate is relatively easier to estimate than firm-specific cost pass-through rates

if one assumes that perfect competition exists in the industry. Among other things, perfect competition implies

the existence of product homogeneity within the industry, homogeneity of production technology among firms in

the industry, and homogeneity of production costs among firms (i.e., pricing is at marginal cost). Under these

conditions, the price response to a general industry-wide change in production costs is likely to be industry-wide

and similar across all firms. For example, in support of the recently promulgated Metal Products &Machinery

(MP&M) industry effluent guidelines, EPA’s Office of Water (OW) estimated industry-specific CPT rates since a

large fraction of establishments in these industries were expected to be subject to the regulation. EPA estimated

these CPT rates by regressing annual output price indices on annual input cost indices for the MP&M industry. 

The estimated CPT coefficients were validated by a market structure analysis which assessed, for each industry,

the potential market power enjoyed by firms in the industry and the consequent implications it had on their ability

to pass through compliance-related costs.

Industry-wide CPT rates can be estimated for the Phase III manufacturing sectors based on the methodology used

for deriving industry-wide CPT rates for industries covered by the MP&M regulation. However, because the

proposed regulation would only affect those facilities that operate a CWIS to withdraw cooling water from

surface waterbodies, only a subset of facilities in each industry sector would incur compliance-related cost

increases. As the cost increase associated with the proposed regulation is not industry-wide, it is questionable

whether industry-wide CPT rates are appropriate for estimating the price response of firms in the five industry

sectors considered in the analysis of Phase III impacts. If a substantial portion of production in each industry

occurs at facilities not subject to the proposed regulation, then the use of industry-wide CPT rates may grossly

overestimate the ability of firms in these industries to pass-through compliance-related costs to consumers. 

To assess the reasonableness of using industry-wide CPT rates in the analysis of impacts to Phase III

manufacturers, EPA estimated the percentage of total production in each of the five industry sectors considered in

this analysis that occurs at facilities potentially subject to compliance-related cost increases. Value of shipments,

a measure of the dollar value of production, was selected for the basis of this estimate. Because value of

shipments data were not collected using the DQ, these data were not available for the sample of Phase III

manufacturing facilities potentially subject to the proposed regulation. As such, total revenue, as reported on the

DQ, was used as a close approximation to value of shipments for these facilities. EPA estimated the total revenue

subject to the proposed regulation by multiplying the 1998 revenue of facilities in the sample of Phase III

manufacturers that were determined to be potentially subject to the proposed regulation by their facility sample

weights and summing across all facilities. Total value of shipments estimates for each industry were obtained

from the 1998 Annual Survey of Manufacturers. Table B3A3.1 summarizes the findings of this analysis.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-3

Table B3A3.1: Proportion of Value of Shipments Potentially Subject to Compliance-Related 

Costs Associated with the Phase III Regulation (1998)

SIC Industry Sector

Revenue for Facilities

Subject to Phase III

Regulation 

(Millions 1998$)

Total Value of

Shipments 

(Millions 1998$)

Proportion of Total

Value of Shipments

Potentially Subject to

Phase III Regulation

26 Paper and allied products $55,143 $84,911 65%

28 Chemicals and allied products $61,179 $268,000 23%

29 Petroleum and coal products $47,832 $118,156 40%

331 Steel $38,423 $76,182 50%

333/35 Aluminum $12,096 $19,266 63%

Notes: For the purpose of this analysis, facility revenue was used as an appropriate surrogate in the absence of value of shipments for

sample facilities.

Source: Section 316(b) Detailed Industry Questionnaire and 1998 Annual Survey of Manufacturers.

As shown in Table B3A3.1, the proportion of total value of shipments potentially subject to the Phase III

regulation ranges from 23 percent to 65 percent depending on the industry considered. The actual proportion of

total value of shipments subject to regulation-induced compliance costs would be smaller since not all of the

potentially regulated facilities would be subject to meet the national categorical requirements of the proposed

Phase III regulation: that is, facilities below the proposed design intake flow (DIF) would be subject to permitting

based on best professional judgement (BPJ) rather than based on national standards, and several facilities

currently employ baseline technologies that meet the requirements of the proposed regulation. Given that less

than 65 percent of the total value of shipments in each of the five industries considered in this analysis would be

subject to regulation induced compliance costs, and the likelihood that these percentages represent upper bound

estimates, EPA believes that the theoretical threshold for justifying the use of industry-wide CPT rates in the

Phase III impact analysis has not been met. The Agency believes that using industry-wide CPT rates in the

analysis of Phase III impacts would overestimate the cost pass-through ability of firms incurring regulationinduced compliance costs, and thus underestimate impacts. At the other end of the spectrum, however, an

assumption of zero CPT would provide a conservative estimate as it would assume that all facilities incur one

hundred percent of cost impacts.

Given the inability to estimate firm-specific CPT rates and the finding that the use of industry-wide CPT rates

would not be appropriate, EPA next conducted a market structure analysis to investigate the extent to which firms

in the five industry sectors enjoy sufficient market power to pass compliance-related costs on to consumers in the

form of higher prices.

B3A3-2 MARKET STRUCTURE ANALYSIS

Information on the competitive structure and market characteristics of an industry provide insight into the likely

ranges of supply and demand elasticities and the sensitivity of output prices to input costs. For example, when

input costs increase, the profit-maximizing firm attempts to maintain its profits by increasing output prices, to the

extent permitted by market power. The amount of the cost increase that the firm can pass on as higher prices

depends on the relative market power of the firm and its customers. The market structure analysis described in

this section attempts to measure the market power enjoyed by firms in each of the five industries. This analysis is

combined with information from industry review documents such as McGraw Hill’s U.S. Industry and Trade

Outlook to reach conclusions regarding the CPT ability of firms in each industry.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

1

 The Herfindahl-Hirschman Index was chosen because it provides a more complete picture of industry concentration compared to

other measures such as the four-firm and eight-firm concentration ratios. The HHI uses the market shares of all the firms in the industry,

and these market shares are squared in the calculation to place more weight on the larger firms. In contrast, the four- and eight-firm

concentration ratios do not use the market share of all firms in the industry, and nor do they provide information about the distribution of

firm size. For example, if there were a significant change in the market shares among the firms included in the ratio, the value of the

concentration ratio would not change.

B3A3-4

The market structure analysis consists of a review of economic data for the following four indicators of market

power: industry concentration; import competition; export competition; and long term growth. Each of these

indicators is discussed in detail below. EPA notes that the impact of each of these four indicators of market

power varies from industry to industry. Furthermore, the results presented for each indicator must be interpreted

with caution because even though for a particular industry an indicator may predict high cost pass-through

potential, the specific features of the industry may result in the indicator having diminished significance in

predicting market power.

B3A3-2.1 Industry Concentration

The extent of concentration among a group of market participants is an important determinant of that group's

market power. A group of many small firms typically has less market power than a group of a few large firms,

because the latter are in a more advantageous position to collude with each other. All else being equal, highlyconcentrated industries are therefore expected to pass-through a higher proportion of the compliance costs that

would result from the Phase III regulation.

This analysis uses the Herfindahl-Hirschman Index (HHI) as a measure of market concentration1

. The HHI is

calculated by squaring the market share of each firm competing in the market and then summing the resulting

numbers. For example, for a market consisting of four firms with shares of thirty, thirty, twenty and twenty

percent, the HHI is 2600 (302

 + 302 + 202 + 202 = 2600). The HHI takes into account the relative size and

distribution of the firms in a market and approaches zero when a market consists of a large number of firms of

relatively equal size. The HHI increases both as the number of firms in the market decreases and as the disparity

in size between those firms increases. Based on the U.S. Department of Justice’s guidelines for evaluating

mergers, markets in which the HHI is under 1000 are considered unconcentrated, markets in which the HHI is

between 1000 and 1800 are considered to be moderately concentrated, and those in which the HHI is in excess of

1800 are considered to be concentrated.

The accuracy of any analysis of market power originating from industry concentration depends to a large extent

on properly defining the relevant market. A well-defined market requires the inclusion of all competitors and the

exclusion of all non-competitors. Defining the relevant market too narrowly overstates market power, while

defining the market too broadly would underestimate it. The four-digit SIC category, while not a perfect

delineation, is most often used by industrial organization economists in their studies because, among publicly

available data sources, these industries appear to correspond most closely to economic markets (Waldman &

Jensen, 1997). Therefore, in Table B3A3.2 below, industry concentration data is presented for each of the fourdigit SIC codes that include at least one potentially regulated Phase III facility for which DQ data are available. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-5

Table B3A3.2: Herfindahl-Hirschman Index for Four-Digit SIC

SIC SIC Description Industry HHI

Unconcentrated Markets (HHI < 1,000)

2679 Converted Paper Products, N.E.C. Paper and Allied Products 143

2899 Chemical Preparations, N.E.C. Chemicals and Allied Products 190

3317 Steel Pipe and Tubes Steel 194

3315 Steel Wire and Related Products Steel 201

2821 Plastics Materials and Resins Chemicals and Allied Products 284

2869 Industrial Organic Chemicals, N.E.C. Chemicals and Allied Products 336

2834 Pharmaceutical Preparations Chemicals and Allied Products 341

2621 Paper Mills Paper and Allied Products 392

2911 Petroleum Refining Petroleum and Coal Products 414

2865 Cyclic Crudes and Intermediates Chemicals and Allied Products 428

2631 Paperboard Mills Paper and Allied Products 438

3312 Blast Furnaces and Steel Mills Steel 551

3316 Cold Finishing of Steel Shapes Steel 604

2819 Industrial Inorganic Chemicals, N.E.C. Chemicals and Allied Products 677

2873 Nitrogenous Fertilizers Chemicals and Allied Products 792

2611 Pulp Mills Paper and Allied Products 858

Moderately Concentrated Markets (1,000 < HHI < 1,800)

3313 Electrometallurgical Products Steel 1,103

2676 Sanitary Paper Products Paper and Allied Products 1,451

3334 Primary Aluminum Aluminum 1,456

2874 Phosphatic Fertilizers Chemicals and Allied Products 1,528

2841 Soap and Other Detergents Chemicals and Allied Products 1,584

2813 Industrial Gases Chemicals and Allied Products 1,629

3353 Aluminum Sheet, Plate, and Foil Aluminum 1,633

Concentrated Markets (1,800 < HHI)

2816 Inorganic Pigments Chemicals and Allied Products 1,910

2812 Alkalies and Chlorine Chemicals and Allied Products 1,994

2824 Organic Fibers, Noncellulosic Chemicals and Allied Products 2,158

2833 Medicinals and Botanicals Chemicals and Allied Products 2,999

Notes: HHI not available for SIC 2823.

Source: Economic Census 1992.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-6

Table B3A3.2 reveals that based on their HHI, 16 four-digit SIC markets can be classified as unconcentrated, 7

can be classified as moderately concentrated, and only 4 can be classified as concentrated. Notably, all 4 of the

four-digit SIC categories listed as being concentrated belong to the Chemicals and Allied Products industry. 

From a market power perspective, Table B3A3.2 seems to suggest that at the four-digit SIC level only four SIC

categories are sufficiently concentrated to argue that firms may possess sufficient market power to pass-through a

portion of their compliance-related costs assuming that competitor firms in the same industry do not incur similar

cost increases.

To further examine the level of concentration in each of the five industry sectors, EPA decided to analyze HHI at

the industry level as well. The Industry-level HHI for each sector was calculated as the average of each four-digit

SIC HHI belonging to that sector, weighted by the value of shipments for that SIC. EPA notes that aggregating

HHI for four-digit SIC categories into industry HHI are likely to yield estimates that in general understate market

power. Nonetheless, estimated industry HHI should still provide meaningful insight into market power of firms

in the industry because firms in each industry still produce similar or related products (for example, paper

products, chemicals, and etc.). Estimated Industry HHI are presented below in Table B3A3.3.

Table B3A3.3: Herfindahl-Hirschman Index by Industry

SIC Industry HHI

29 Petroleum and Coal Products 414

331 Steel 478

26 Paper and Allied Products 643

28 Chemicals and Allied Products 715

333/5 Aluminum 1,570

Source: U.S. EPA Analysis, 2004.

Table B3A3.3 reveals that, at the industry level, the estimated HHI for four of the five industries are quite small,

implying that they are unconcentrated markets and within these industries individual firms do not enjoy much

market power. Notably, the Chemicals and Allied Products industry has a low HHI, which suggests that the 4

four-digit SIC categories that were classified as having concentrated markets in reality make up a very small

segment of the Chemicals and Allied Products industry. Thus, from the perspective of the Phase III regulation,

the majority of firms in this industry have small market power. In addition, EPA notes that only 23 percent of

production in this is industry would potentially be subject to compliance-related cost increases, which suggests

that the cost pass-through potential of firms from this sector incurring such expenses would be severely curtailed.

An important finding in Table 3 is that the Aluminum industry, which is categorized at the three-digit SIC level,

appears to be moderately concentrated. Thus, based solely on an analysis of industry concentration, it would

appear that firms in the Aluminum industry may enjoy moderate amounts of market power, which may enable

them to pass through costs at a more than negligible rate. However, as cautioned at the beginning of the market

structure analysisok, an accurate judgement of the market power enjoyed by firms in an industry must be reserved

until all indicators have been analyzed.

B3A3-2.2 Import Competition

Theory suggests that imports as a percent of domestic sales are negatively associated with market power because

competition from foreign firms limits domestic firms’ ability to exercise such power. Firms belonging to sectors

in which imports make up a relatively large proportion of domestic sales would therefore be at a relative

disadvantage in their ability to pass-through costs compared to firms belonging to sectors with lower levels of

import penetration, the measure of import competition used in this analysis. Import penetration, the ratio of
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-7

imports in a sector to the total value of domestic consumption in that sector, is particularly relevant because

foreign producers would not incur costs as a result of the Phase III regulation. 

In this market structure analysis, EPA assumes that higher import penetration will generally imply that firms are

exposed to greater competition from foreign producers and would thus possess less market power to increase

prices in response to regulation-induced increases in production costs. EPA estimated import penetration ratios

for each industry as total imports in an industry divided by total value of domestic consumption in that industry;

where domestic consumption equals domestic production plus imports minus exports. Import penetration ratios

estimated using 1998 census data for the five industry sectors considered in this analysis are presented below in

Table B3A3.4.

Table B3A3.4: Import Penetration by Industry

SIC Industry Imports

(Millions of 1998$)

Implied Domestic

Consumption

(Millions of 1998$)

Import

Penetration

26 Paper and Allied Products $13,137 $85,865 15%

28 Chemicals and Allied Products $44,570 $263,404 17%

29 Petroleum and Coal Products $10,711 $120,112 9%

331 Steel $19,221 $88,645 22%

333/5 Aluminum $5,189 $20,063 26%

Notes: Implied Domestic Consumption = Value of Shipments + Imports - Exports.

Source: 1998 U.S. Bureau of Census data.

The estimated import penetration ratios for the five industries range from 9 percent to 26 percent for the year

1998. The estimated import penetration ratio for the entire U.S. manufacturing sector (SIC 20-39) for the same

year is 19 percent. Considering that the United States is an open economy, EPA believes it is reasonable to

assume that in industries with import penetration ratios close to or above 19 percent domestic firms most likely

face stiff competition from foreign firms. Such competition is likely to curtail the market power enjoyed by

domestic firms and given the scenario that regulation-induced cost increases are not incurred by foreign producers

would limit the ability of domestic firms to pass-through such costs. Thus, based on the import penetration ratios

presented in Table 4, only firms in the Petroleum and Coal Products Industry appear to be in a position to passthrough to consumers a significant portion of compliance-related costs associated with the Phase III regulation. 

However, given the low HHI for this industry EPA believes that existing market competition among domestic

firms most likely nullifies any favorable influence the lack of foreign competitors would have on increasing the

market power of firms in this industry. EPA also highlights the above average import penetration ratios for the

Steel and Aluminum industries which suggest low market power for firms in this industry. With respect to the

Aluminum industry, this fact may offset – from a market power perspective – the finding that the industry was

identified above as being moderately concentrated. Thus, even though there are relatively few domestic

producers in the U.S. Aluminum industry, the notable presence of foreign producers in U.S. markets is likely to

markedly reduce their the market power. 

B3A3-2.3 Export Competition

The Phase III regulation would not increase the production costs of foreign producers with whom domestic firms

must compete in export markets. As a result, firms in industries that rely to a greater extent on export sales would

have less latitude in increasing prices to recover cost increases resulting from regulation-induced increases in

production costs. They would therefore have a lower CPT potential, all else being equal.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

2

 The period from 1989 to 1998 represents the most recent ten year period that includes data consistent with the survey period for the

Detailed Industry Questionnaire (1996-1998).

B3A3-8

This analysis uses export dependence, defined as the percentage of shipments from an industry that is exported, to

measure the degree to which a sector is exposed to competitive pressures abroad in export sales. Firms in

industries with relatively high export dependence are expected to have lesser market power than those in

industries with relatively low export dependence due to their relatively larger reliance on sales in export markets. 

Estimated export dependence ratios estimated using 1998 census data for the five industry sectors considered in

this analysis are presented below in Table B3A3.5.

Table B3A3.5: Export Dependence by Industry

SIC Industry Exports

(Millions of 1998$)

Value of Shipments

(Millions of 1998$) Export Dependence

26 Paper and Allied Products $10,051 $82,778 12%

28 Chemicals and Allied Products $49,932 $268,765 19%

29 Petroleum and Coal Products $5,038 $114,439 4%

331 Steel $5,268 $74,692 7%

333/5 Aluminum $2,951 $17,825 17%

Source: 1998 U.S. Bureau of Census data.

The estimated export dependence ratios for the five industries range from 4 percent to 19 percent for the year

1998. The estimated export dependence ratio for the entire U.S. manufacturing sector for the same year is 23

percent. Thus, for all five industries, their export dependence ratio is below the average for the U.S.

manufacturing sector. This finding implies that none of the five industries are characterized by strong competitive

pressures from foreign firms/markets, and thus market power and CPT potential are not diminished by export

dependence. However, it is questionable whether this effect works as strongly in the opposite direction, i.e., firms

in an industry will have a comparatively high cost pass-through potential simply because firms in that industry are

not active in export markets. From the standpoint of firms gaining market power, EPA believes that the finding of

low export dependence diminishes the importance of export competition as an indicator of market power. Thus,

the other three indicators must be relied upon to gauge the amount of market power that firms in each industry are

expected to hold. For example, even though the Petroleum and Coal Products and Steel industries have extremely

low export dependence, the low market concentration in these industries leads EPA to believe that market power

held by individual firms is likely to be quite small. In addition, as discussed later in this memo, recent trends in

the Steel industry provide good reason to believe that firms in this industry are unlikely to be able to pass through

a notable portion of regulation-induced cost increases given the current business environment they face.

B3A3-2.4 Long-Term Industry Growth

An industry’s competitiveness and the ability of firms to engage in price competition are likely to differ between

declining and growing industries. Most studies have found that recent growth in revenue is positively related to

profitability (Waldman & Jensen, 1997), which suggests a greater ability to recover costs fully.

To examine trends in long-term growth for each of the five industry sectors considered in this analysis, EPA

estimated the average annual growth rate in the value of shipments between 1989 and 1998 for each industry

using data available from the U.S. Bureau of Census2

. EPA expects firms in sectors with higher growth rates to

be better positioned to pass through compliance costs rather than being forced to absorb such cost increases in

order to retain market share and revenue. The results of this analysis are presented in Table B3A3.6.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-9

Table B3A3.6: Average Annual Growth Rates by Industry

SIC Industry

Average Annual Growth

Rate in Value of

Shipments (1989 to 1998)

26 Paper and Allied Products -0.3%

28 Chemicals and Allied Products 1.5%

29 Petroleum and Coal Products 0.6%

331 Steel 0.4%

333/5 Aluminum -2.2%

Notes: Average Annual Growth Rate for the Petroleum and Coal Products industry

has been estimated for the years 1989 to 1997. The reported value of shipments for

this industry in 1998 is significantly lower than in earlier years and therefore was

excluded from this analysis as an outlier.

Source: U.S. Bureau of Census data.

Table B3A3.6 shows that of the five industries specifically considered for this analysis, two industries

experienced negative growth over the 1989 to 1998 time period and another two experienced only marginal

growth. Only the Chemicals and Allied Products industry experienced what may be qualified as moderate

growth, displaying an average annual growth rate of 1.5 percent. Based on the U.S. Industry and Trade Outlook

‘99, annual growth in manufactures over the period 1993 to 1998 was over 2 percent in all years, and over 4

percent in four of the six years in that period (the figures reported for 1997 and 1998 are estimates). An

examination of the growth rate for the Chemicals and Allied Products industry for the same six year period (1993-

98) revealed a growth rate of 2.4 percent. Comparing the average annual growth rate for the Chemicals and

Allied Products industry with that of the entire manufacturing sector, and comparing overall growth in this sector

with the growth experience by the manufacturing sector taken as a whole between 1993 and 1998 suggests that its

performance in general was below average. Thus, in the absence of strong growth performance during the 1990s

for all five industries, it is unlikely that firms in any of these industries acquired significant market power on

account of growing demand for their products. In effect, the long-term growth performance of all five industries

does not support a conclusion that firms in these industries are in a strong position to pass on a significant portion

of their compliance costs. 

B3A3-2.5 Conclusions

Given that less than 65 percent of the total value of shipments in each of the five industries considered in this

analysis would be subject to regulation-induced compliance costs, and the likelihood that these percentages

represent upper bound estimates, the likelihood that firms incurring such costs would be able to pass through to

consumers a material portion of 316(b) compliance costs is small. To validate this hypothesis, EPA undertook the

market structure analysis presented in the previous section. In general, the weight of evidence from the market

structure analysis suggests that firms in all five industries are unlikely to posses significant amounts of market

power, thereby lending support to EPA’s hypothesis that most firms would not be in a position to pass-through a

significant portion of compliance costs.

The analysis of individual indicators under the market structure analysis did reveal a few exceptions to the general

finding of low market power in all industries. However, considering the combined impact of all four indicators of

market power together with information on recent economic trends in these industries suggests that on the whole,

firms in each of the five industries hold relatively low market power and CPT potential. For example, the

estimated HHI for the Aluminum industry indicated that this sector is moderately concentrated, which would

potentially allow firms in this industry to pass through a significant portion of their compliance-related costs. In
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-10

contrast, however, the market structure analysis also found that the domestic Aluminum industry witnessed a

sustained decline in production during the 1990s and also faces stiff competition from foreign producers in its

U.S. markets. As discussed in the profile of this industry, in the early 1990s the domestic Aluminum industry was

affected by reduced U.S. demand and the dissolution of the Soviet Union, which resulted in dramatic increases in

Russian exports of aluminum. The recovery that followed was subsequently affected by the economic crises in

Asian markets in the second-half of the 1990s, which along with growing Russian exports, again resulted in a

period of oversupply. These trends, which are reflected in the negative average annual growth rate and high

import penetration for the domestic Aluminum industry, suggest that domestic firms in this industry hold

relatively low market power and are not in a position to pass through significant portions of their compliancerelated cost increases. Overall, the balance of the argument in favor of and against high cost pass-through in the

Aluminum industry rests with the indicators that argue against it; the lack of domestic competition in the industry

is more than offset by the existence of stiff competition from foreign producers and the general decline witnessed

by the domestic industry. Similarly, in the case of all other exceptions found in the market structure analysis, the

weight of evidence – when all four indicators of market power are considered together – rests with the indicators

that suggest low market power and CPT potential.

Based on the findings of the market structure analysis, EPA decided to assume a zero CPT rate for all five

industries in the analysis of Phase III impacts. EPA believes that this assumption is reasonable given the results

of the market structure analysis and is definitely superior to using industry-wide CPT rates. In addition, EPA

notes that by assuming a CPT rate of zero for all industries, the analysis of Phase III impacts takes a conservative

approach in that the analysis assumes that facilities would incur one hundred percent of compliance costs. Thus,

whereas an overstated CPT rate may erroneously underestimate impacts for facilities incurring compliance-related

cost increases, the use of a conservative CPT rate of zero errs on the side of caution, thus potentially overstating

impacts to affected facilities. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-11

REFERENCES

Ashenfelter, Orley; David Ashmore; Jonathan B. Baker; and Signe-Mary McKernan. (1998), "Identifying the

Firm-Specific Cost Pass-Through Rate," FTC Working Paper No. 217, January.

McGraw-Hill (1999), U.S. Industry and Trade Outlook ‘99. McGraw-Hill Companies.

Waldman, Don E. and Elizabeth J. Jensen (1997), Industrial Organization: Theory and Practice. 

Addison-Wesley.

U.S. Department of Commerce (U.S. DOC). 1989, 1992, and 1997. Bureau of the Census. Census of

Manufactures.

U.S. Department of Commerce (U.S. DOC). 1988-1991, 1993-1996, and 1998. Bureau of the Census. Annual

Survey of Manufactures.

U.S. EPA, Office of Water (2003), Effluent Limitations Guidelines and New Source Performance Standards for

the Metal Products and Machinery Point Source Category.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-12

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§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-1

CHAPTER CONTENTS

B3A4-1 Background: Review of Overall Business

Conditions . . . . . . . . . . . . . . . . . . . . . . B3A4-2

B3A4-2 Framing and Executing the Analysis . . B3A4-4

B3A4-2.1 Identifying the Financial Data Concept to

Be Analyzed . . . . . . . . . . . . . . . B3A4-4

B3A4-2.2 Selecting Appropriate Data . . . . B3A4-5

B3A4-2.3 Selecting Industry Groups and Firms for

Use in the Analysis . . . . . . . . . . B3A4-7

B3A4-2.4 Structuring the Analysis . . . . . . B3A4-9

B3A4-3 Summary of Findings . . . . . . . . . . . . . B3A4-10

B3A4-4 Developing an Adjustment Concept . B3A4-14

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-18

Appendix 4 to Chapter B3:

Adjusting Baseline Facility Cash Flow

INTRODUCTION

To support its analysis of the economic impact of the

316(b) Phase III regulation, EPA collected

economic/financial data for the three years 1996-1998

from a sample of facilities in the manufacturing

industries primarily expected to be subject to the Phase

III regulation. These facility economic/financial data

are used to gauge the potential economic/financial

impact of regulatory compliance: the facilities and their

financial data serve as models for testing the financial

effect of regulatory alternatives. For this analysis to

provide valid insight into the ability of the affected

industries to meet regulatory requirements without

material adverse impact, the sample facility data should reflect business conditions that might be reasonably

anticipated at the time of compliance. 

In performing its impact analyses using these data, EPA was concerned in two ways that the facility survey data

might yield erroneous conclusions. 

< First, knowing that U.S. business conditions during the latter half of the 1990s were cyclically strong,

EPA was concerned that business conditions during the 316(b) survey period (1996-1998) might be

abnormally favorable for some of the five manufacturing sectors covered in the Phase III analysis. In this

case, the business performance and valuation measures, which are based on survey data, used to assess the

burden of regulatory compliance costs might overstate industry's ability to bear these costs and therefore

understate the potential impact of the Phase III regulation.

< Second, apart from the issue of short-term deviation from trend caused by a cyclically strong economy,

EPA was also aware from its profile analyses that some of the industries might be experiencing a

longer-term trend of deteriorating performance. Using sample facility data that don't reflect such possible

trends would again potentially overstate industry's ability to bear compliance costs and therefore

understate the potential impact of the Phase III regulation.

Given these concerns, EPA analyzed for the manufacturing industries (1) whether business conditions were

“abnormally favorable” during the survey period and (2) whether business performance over a longer term might

be following a non-neutral – in particular, negative – trend. This analysis validated EPA’s concerns that use of

unadjusted survey data might yield erroneous conclusions from the facility impact analysis. From the findings of

this analysis, EPA developed a basis for adjusting survey financial data to account for these effects: short-term

deviation from trend and non-neutral trend. 

This appendix documents EPA’s analysis and development of adjustment factors for the 316(b) Phase III

manufacturing industries.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-2

B3A4-1 BACKGROUND: REVIEW OF OVERALL BUSINESS CONDITIONS

As background for its analysis, EPA reviewed general economic data over the past several years to assess whether

business conditions during the survey data collection period of 1996-1998 might be generally perceived as

abnormally favorable for the U.S. economy, as a whole. This review confirmed the concern that business

conditions in 1996-1998 were generally more favorable than the average of conditions over a longer time period. 

Figures B3A4.1-B3A4.3 present annual and average values for the period 1985-2003 for three measures of

general economic performance: 

Figure B3A4.1: Growth in Real Gross Domestic Product, 1985-2003. This exhibit, based on data published

by the Department of Commerce, Bureau of Economic Analysis, focuses on the growth trend of the broad

economy, including all sectors. Growth stronger than the average trend would indicate a strongly expanding

economy and would generally indicate strong business performance.

Figure B3A4.2: Capacity Utilization in Manufacturing Industries, 1985-2003. This exhibit, based on U.S.

Federal Reserve Bank data, reports the rate of capital utilization for all manufacturing sectors. All else equal,

when the rate of capital utilization is higher than the average trend, demand for manufacturing output is strong

and manufacturing business performance would be generally strong.

Figure B3A4.3: Growth in Industrial Production, 1985-2003. Like the preceding exhibit, this exhibit is based

on data published by the U.S. Federal Reserve Bank and reports the rate of growth in the Federal Reserve's

Industrial Production Index, which is a measure of the real output of the manufacturing industries. Growth

stronger than the average trend would indicate a strong expansion in the manufacturing industries and would

generally indicate strong manufacturing business performance.

In each case, the annual values in the period 1996-1998 are above the average trend line, indicating stronger

overall economic performance in the survey data collection period than for the longer period presented in the

charts. The data show a consistent year-by-year pattern over the 1996-1998 period:

< 1996: The values for 1996 are above the longer-term average trend but are the lowest of the values for the

three years. 

< 1997: The values for 1997 are the highest of the values for the three years.

< 1998: The values for 1998 fall between the 1996 and 1997 values. In the case of industrial production

and capacity utilization in manufacturing industries, 1997 is the peak performance year over the 1990s

decade and is followed by a decline in 1998 and subsequent years leading to the recession period in 2001. 

In the case of GDP growth, the fall-off in 1998 (from 1997) is followed by one more year of strong

growth in 1999. Afterwards, GDP growth turns sharply lower during 2000, the recession year of 2001,

and subsequent years. As is widely acknowledged in general business conditions analyses, economic

weakness during the 2000-2003 period began earlier in the manufacturing industries than in the general

economy.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-3

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Year to Year Growth in GDP, Percent

Grow th, Real GDP Average Grow th, GDP

70.00

72.00

74.00

76.00

78.00

80.00

82.00

84.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Capacity Utilization, Percent

Capacity Utilization Average Capacity Utilization

Figure B3A4.1: Growth in Real Domestic Product, 1985-2003

Figure B3A4.2: Capacity Utilization in Manufacturing Industries, 1985-2003
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-4

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Year to Year Growth in Industrial Production

Grow th, Industrial Production Average Grow th, Industrial Production

Figure B3A4.3: Growth in Industrial Production, 1985-2003

B3A4-2 FRAMING AND EXECUTING THE ANALYSIS

The objective of this analysis was to understand (1) the extent to which the business conditions and financial

performance of the Phase III manufacturing industries reflected cyclically favorable conditions during the 316(b)

survey period and (2) whether these industries show a non-neutral longer term trend in economic/financial

performance – e.g., deterioration in performance over time independent of cyclical variation. If either or both of

these conditions were found, then the data used to test for these conditions would be used to adjust relevant

survey data items to a level consistent with normal business conditions and/or the longer term of performance.

To meet these objectives, EPA set, as its overall approach, identification and analysis of a financial performance

data series for the 316(b) manufacturing industries. This data series would be used to test whether financial

performance at the time of the 316(b) survey differed from the longer term trend. At the outset, EPA recognized

that, in all likelihood, such a data series would not report financial performance at the level of the individual

facility – which is the unit of analysis for the 316(b) facility impact analysis – but would report performance for

individual firms or for some industry aggregate. As a result, EPA would need to infer the trend of performance in

facility financial performance from firm- or industry-level performance and, in turn, apply adjustments, if needed,

to facility financial data based on analysis of the firm- or industry-level performance. Although the use of firmor industry-level information for adjusting facility data necessarily represents a limitation in this analysis, EPA

judges that the effort is warranted given: (1) the potential for the facility impact analysis to yield erroneous

findings if it is based on data that reflect cyclically favorable conditions and (2) the absence of facility data to

support a more precise analysis.

Key steps in framing and executing the analysis are described below.

B3A4-2.1 Identifying the Financial Data Concept to Be Analyzed

EPA determined that the financial data concept to be analyzed should be equivalent, or close in concept, to the

business performance and valuation metrics used in the Phase III impact analysis. For the facility impact analysis,

the key financial metric is after-tax, pre-interest cash flow, calculated as income before interest, depreciation and

amortization, and adjusted to be on an after-tax basis. In the facility impact analysis, this metric is used to
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-5

calculate the business value of a sample facility, on both a baseline – i.e., before imposition of compliance costs –

and post-compliance basis. Using this, or a closely related, measure in the analysis of financial performance at

the time of the facility survey would therefore support a direct test of whether and how the survey financial data –

to be subsequently used in the facility impact analysis – might reflect cyclically favorable conditions or differ

from the longer term trend of financial performance in an analysis. If either or both of these conditions were

found, the data would also readily support development of a necessary adjustment to offset these potential biases

in the survey data.

EPA recognized that the after-tax, pre-interest cash flow measure used in the facility impact analysis would very

likely not be directly available from financial datasets that might be practically used in this analysis. However,

reasonable surrogates for this measure that would likely be available include: after-tax cash flow from operations

(net income plus depreciation and amortization); earnings before interest, taxes, depreciation and amortization

(EBITDA); net income; and earnings before interest and taxes (EBIT).

B3A4-2.2 Selecting Appropriate Data

Other key requirements of the data to be used in the analysis include:

< The financial data need to be a time series, preferably annual, over a sufficiently long period (and

including the survey period) to allow testing of (1) whether survey period business conditions were

cyclically favorable; and (2) whether financial performance in the industries exhibits a longer-term, nonneutral trend.

< The data need to be at a sufficient level of industry resolution to account for variations in business

conditions and performance not only across the five manufacturing sectors but also within certain sectors,

where there may be substantial variation in performance by important segments. Of particular importance

is the ability to segment the chemicals sector into its segments such as basic chemicals and

pharmaceuticals, and the primary metals sector into the ferrous and non-ferrous metals segments.

Based on these requirements, EPA selected the Value Line Investment Survey firm financial dataset as the data

source for this analysis. The Value Line dataset meets analysis requirements as follows:

< The general company dataset of the Value Line Investment Survey (VL) reports summary financial

information for nearly all publicly traded companies in the United States for a 12-year period, 1992-2003,

which includes the 1996-1998 Phase III survey period. The individual years in this 12-year period may

be categorized in three broad categories of economic performance: (1) six years of "normal" economic

performance - 1992-1996 and 2000; (2) three years of "subnormal" economic performance - 2001-2003;

and (3) three years of years of "supra-normal" economic performance - 1997-1999. The 12-year period

thus captures reasonable diversity of business conditions before, after, and during the survey period. By

including financial results for full-year 2003, the dataset also comes as close as possible to the present

(2004) and thus would provide a basis for adjusting facility baseline financial data to essentially current

conditions. 

< VL identifies and groups companies in a business content classification scheme that approximates 3-digit

SIC or 4-digit NAICS classifications. These business classifications support identification of firms within

the Phase III manufacturing industries at a level of sector detail sufficient for this analysis. Because (1)

the dataset is by company instead of by aggregate groups and (2) the business classifications are defined

by practical business content instead of in a rigid SIC or NAICS classification scheme, the VL dataset
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

1

 As described in the industry profiles, the change from SIC-based to NAICS-based reporting of economic data by federal

government and other data sources at around 1997/98 created difficulties in aligning and ensuring consistency of time series data that are

organized within these frameworks.

B3A4-6

avoids the challenge confronted elsewhere in the Phase III analysis of the change in economic

classification schemes and resulting inconsistency of aggregated data series over the year of the change1

. 

< The VL dataset reports key accounting items that will readily support calculation of a financial metric,

after-tax cash flow, that very nearly matches the principal financial metric (after-tax, pre-interest cash

flow) underlying the Phase III facility impact analysis. 

EPA recognizes that the VL dataset, by definition, excludes firms that are not publicly traded. The studied

industries include private, non-publicly traded firms, for which no comparable database of financial information is

available. As a result, use of the VL dataset in this analysis could yield findings that are not representative of the

overall industry, including the non-publicly traded firms, to the extent that non-public firms in the studied

industries faced materially different business conditions or achieved materially different business performance

than publicly traded firms in the same industries. Overall, EPA expects that the business conditions faced by, and

performance achieved by, non-public firms in the studied industries are not likely to have been materially

different from those of the public firms. As a result, EPA judges that use of the VL dataset for this analysis is

appropriate and likely to yield reasonably representative findings for to overall industries, including publicly

traded and non-traded firms.

In addition to the VL dataset, EPA considered a range of other data sources, including:

< Economic and business performance data published by the Federal Reserve, in particular the Federal

Reserve Economic Data (FRED II) data series compiled by the Federal Reserve Bank of St. Louis.

< The Quarterly Financial Report for Manufacturing, Mining, and Trade Corporations (QFR) published by

the U.S. Census Bureau.

< Data series from the Bureau of Economic Analysis and data specifically available in The Survey of

Current Business.

These data sources were each deficient for the analysis in some material way, including: 

< Data being too aggregate to provide the industry sector and sub-sector level of resolution needed to assess

business conditions and trends within the Phase III manufacturing sectors.

< Data items being descriptive of general economic/financial conditions in an industry but not being

sufficiently close to the financial performance concept needed for the analysis.

< Data being reported in inconsistent economic classification frameworks over the desired analysis period. 

In addition to the problem of the SIC/NAICS break itself, data are sometimes reported at different levels

of resolution before and after the SIC/NAICS break – e.g., at a 4-digit or finer level in the NAICS

framework but only 2-digit level of resolution in the SIC framework.

< Data not being readily available in an electronic format needed for efficient performance of the analysis.

B3A4-2.3 Selecting Industry Groups and Firms for Use in the Analysis

As discussed above, VL organizes firms by industry groups, which, in most instances, approximate 3-digit SIC or

4-digit NAICS classifications. From review of the VL industry groups and the 316(b) Phase III manufacturing

industries, EPA selected 10 VL industry groups and the firms within these industry groups as candidates for this
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

2

 EPA considered removing the non-recurring item from the income statement but, because of uncertainty about the correct tax

adjustment, rejected this approach.

B3A4-7

analysis. Following review of the firms within these industry groups, EPA retained 6 firms for use in this

analysis. Key considerations in selecting the firms are as follows:

< The selected VL industry groups are those that most closely correspond to the 316(b) Phase III

manufacturing industries.

< Within the industry groups, only those firms whose business operations reasonably match the profile of

business activities of the 316(b) Phase III manufacturing industries were considered candidates for the

analysis. In some industry groups, a substantial number of firms included in the VL industry groups

were excluded from the analysis:

< VL includes Aluminum industry firms in its Metals and Mining industry group. However, most firms

in this VL industry group are not involved in the Aluminum industry and thus were excluded from the

analysis dataset.

< EPA retained from the Paper and Forest Products group only those firms engaged in pulp mill, paper

mill and/or paper and paperboard manufacturing operations. Firms engaged only in timber and

lumber production were excluded from the analysis.

< EPA retained only those firms that are based in the United States or Canada, and for which financial

information is available in U.S. dollars. 

< After defining an initial set of firms according to these procedures and criteria, EPA retained only those

firms for which a full 12 years of data were available.

< Finally, EPA excluded firms that had undergone a significant restructuring – e.g., a merger or

acquisition – which materially disrupted the continuity of financial reporting. Since the analysis to be

performed would start from a time series of cash flow, measured in absolute dollars – as opposed, for

example, to a time series of profit percentage values – including data from firms whose continuity of

financial reporting had been affected by merger or acquisition activity would tend to bias the analysis. In

particular, firms engaging in mergers and acquisitions that were accounted for on a purchase-accounting

basis instead of a pooling-of-interests basis, would be likely to show sudden jumps in revenue, net

income, and cash flow. These sudden jumps would bias the analysis by suggesting greater business

growth than could be reasonably be achieved by the firm or facilities within the firm on a simple, organic

growth basis. Similarly, large contractions in business volume resulting from divestiture or termination of

a line of business would bias the analysis in the downward direction. To apply this restriction, EPA

examined the year-to-year revenue profiles for all firms over the 12-year analysis period. EPA researched

annual reports and other financial reporting for those firms showing large increases or decreases from

year to year and excluded those firms where a material business event was found that would otherwise

disrupt the continuity of financial reporting. EPA followed this rule with only two exceptions. First, EPA

kept firms in the analysis when the only business event/disruption of financial reporting occurred in the

last year of financial reporting – 2003. EPA kept these firms in the analysis but excluded the final year of

data from the analysis. Second, in its research on one firm in the paper industry, EPA found that the firm

had recorded an unusual, non-recurring stock gain transaction in 1995 that caused revenue and net

income to increase abnormally in that year. Although the VL net income item used in the analysis

generally excluded income from unusual, non-recurring events, the VL data series did not exclude income

from this transaction. Because EPA had already set aside a substantial number of firms from the paper

industry, EPA decided to keep this firm in the analysis but exclude the single year of unusual financial

performance from the analysis dataset2

. Applying this restriction substantially reduced the number of
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

3

 Because no Aluminum industry firms were able to be retained in the analysis, EPA was unable to develop an after-tax cash flow

adjustment factor for facilities in the Aluminum industry. EPA considered adjusting the pre-event financial statements for the Aluminum

industry firms – in effect, converting the purchase-accounting treatment of transactions to a pooling basis – but rejected this approach as

requiring too many judgments. However, EPA assessed the potential effect of applying a cash flow adjustment factor to facilities in this

industry by testing hypothetical factor values that substantially exceeded the adjustments – both for decrease and increase – estimated and

applied for the facilities in other industries. This analysis found that the facility impact analysis results for the Aluminum industry did not

change over this wide range of hypothesized cash flow adjustment factors.

B3A4-8

firms that were included in the analysis dataset. In particular, for the Aluminum industry, all of the firms

in the initial VL dataset were found to have some significant discontinuity of financial reporting.3

EPA organized these 60 firms into six 316(b) Industry Groups for the analysis. Table B3A4.1, below, lists the

VL industry groups, the 316(b) Phase III manufacturing industries and/or industry segments (as discussed in the

industry profiles) to which the VL industry groups approximately correspond, the 316(b) Industry Groups for this

analysis, and the number of VL firms used in the analysis for each industry group.

Table B3A4.1: Value Line Industry Groups Selected for Analysis

Value Line Industry

Group

316(b) Phase III 

Manufacturing

Industry

316(b) Industry Segment(s)

 (as relevant)

316(b) Phase III

Industry Group for

Analysis

Number of Firms

Used in Analysis

Metals and Mining Aluminum Aluminum none

Paper and Forest

Products Pulp and Paper Mills Pulp and Paper Mills 6

Chemical (basic) Chemicals Industry Organic Chemicals

Inorganic Chemicals

Industrial Chemicals 15

Chemical (diversified) Chemicals Industry Organic Chemicals

Inorganic Chemicals

Chemical (specialty) Chemicals Industry Plastics Material and Resins Plastics Material and

Resins 15

Biotechnology Chemicals Industry Pharmaceuticals

Pharmaceuticals 4

Drug Chemicals Industry Pharmaceuticals

Petroleum (integrated) Petroleum Refining Petroleum Refining 6

Steel (General) Steel

Steel 14

Steel (Integrated) Steel

Source: Value Line Investment Survey and U.S. EPA analysis, 2004.

B3A4-2.4 Structuring the Analysis

The general objectives of this analysis were to:

< Test, by 316(b) Industry Group, whether after-tax cash flow performance deviated, during the 316(b)

survey data collection years, from normal performance over the 12-year analysis period.

< Test, by 316(b) Industry Group, whether after-tax cash flow performance might be following a nonneutral trend over the 12-year analysis period.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-9

< Given a finding that either or both of these conditions are true, to develop an adjustment to baseline aftertax cash flow to account for these effects, and, to yield after-tax cash flow values for the facility impact

analysis that more closely reflect current financial performance in the 316(b) Phase III industries. 

The overall approach to the analysis was to analyze, for each industry group, the trend of financial performance

over the 12-year analysis period and to assess where the industry’s financial performance lay relative to that trend

during the 316(b) survey data collection years of 1996-1998. For each industry group. EPA used as analysis

observations, an index of constant dollar-adjusted after-tax cash flow for each of the firms in the industry group. 

To analyze the trend, EPA calculated a simple regression of the index values against time. The estimated

regression relationship provided a direct measure of the real (i.e., inflation-adjusted) trend of financial

performance for each industry group. The 1996-1998 average of index values for each industry group were then

compared with the trend values predicted from the estimated regression coefficients – both for the 1996-1998

years and for the end of the analysis period – to determine the extent to which 1996-1998 survey values should be

adjusted to reflect (1) the deviation from trend at 1996-1998 and (2) the trend from 1996-1998 to the end of the

analysis period.

Specific steps in this analysis were as follows:

< Calculate After-Tax Cash Flow (ATCF) as Net Profit plus Depreciation for each firm by year. As

discussed above, EPA sought to analyze ATCF as a close approximation of the key financial metric –

after-tax, pre-interest cash flow – used in the facility impact analysis. EPA calculated ATCF on a yearby-year basis for each firm in the analysis dataset as the sum of the VL data items: Net Profit and

Depreciation. In the VL data framework, Net Profit is defined as net income from continuing operations

and excluding non-recurring items. Depreciation includes both non-cash items, depreciation and

amortization. 

< Adjust ATCF to constant dollar values at mid-year 2003, using the GDP deflator. To eliminate the effects

of inflation in analyzing the trend of financial performance, EPA deflated the ATCF values for all firms to

mid-year 2003 using the GDP Deflator series published by the Department of Commerce, Bureau of

Economic Analysis. 

< Calculate an index of each company’s ATCF values by year using, as an index numerator, the average

ATCF value for the company over the 12-year period. As summarized above, the overall approach of the

analysis involved a regression analysis of the trend of ATCF values for the firms in an industry group

over the 12-year analysis period. To allow individual firms’ ATCF values to be combined in a single

regression requires eliminating the scale effect of the different sizes of firms. For this reason, the

inflation-adjusted ATCF values for each firm were normalized to an index series by dividing the yearly

values by the average of values for each firm over the 12-year period. EPA used the 12-year average of

values for this index calculation instead of the value for a single year to prevent anomalously large swings

in the index series when the ATCF value for the year selected as the base year for the index calculation

was very small relative to other values in the 12-year series. In addition, in calculating the index values

for the 12-year series, EPA first removed any negative values from the series for each firm by adding to

each value in the firm’s 12-year series, the absolute value of the most negative value for the firm plus the

absolute value of the smallest non-negative value in the series. This adjustment has the effect of

“vertically” shifting the ATCF values for a firm so that all values are positive while retaining the

mathematical “shape” of the series for the trend analysis. This adjustment was necessary to prevent the

undesirable inversion of the index trend that would occur if a negative index numerator is combined with

a positive series values in calculating the index series. 

< Regress ATCF index values against year by industry group to calculate the time trend of constant dollar

ATCF over the period 1992-2003. The preceding calculations yield a constant dollar series of ATCF

indexed to one and with an average value of one over the 12-year analysis period. To calculate the trend

indicated by these index ATCF values, EPA estimated a weighted linear regression of the index ATCF

values against year by industry group. These regressions were performed on a revenue-weighted basis –
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

4

 In addition to testing a simple linear model of index ATCF against time, EPA also used a logarithm-adjusted series of the ATCF

values to test for an exponential trend in ATCF. The log model provided no improvement in the estimated regression relationships. As a

result, EPA used the coefficients estimated from the linear model for its analysis of the ATCF trend.

B3A4-10

i.e., each ATCF value was weighted by the firm’s revenue value for the year – so that each firm’s

individual ATCF trend carries a weight in proportion to its revenue in estimating the trend relationship. 

As a result, the estimated trend relationship reflects a revenue-weighted average of the ATCF trends of

the individual firms, instead of an arithmetic average, which would overweight the presence of smaller

firms in estimating the trend relationship. The estimated ATCF index coefficient from the regression for

each industry indicates the trend in constant dollar ATCF over the analysis period: a negative coefficient

indicates declining constant dollar ATCF over the analysis period; a positive coefficient indicates

growing constant dollar ATCF over the analysis period.4

< Calculate the predicted trend of ATCF index values for each industry group. Used together, the estimated

ATCF index coefficient and regression intercept yield a predicted trend line of ATCF index values for the

12-year period , for each industry group analyzed. The actual ATCF index values for an industry group

can then be compared with predicted trend line to assess whether the ATCF values during the 1996-1998

survey data collection period deviate from the trend. The predicted trend line also indicates where the

ATCF index values would be at the end of the analysis period if the ATCF index values followed the

predicted trend. 

< Calculate the revenue-weighted average of actual ATCF index values by industry group for the 1996-

1998 period. The revenue-weighted average of actual ATCF values for the 1996-1998 period is

compared with the predicted trend values to assess the extent to which the actual ATCF index values

deviate from trend and to provide a basis for estimating the adjustment needed to bring the ATCF values

to the trend, or to the predicted trend value at the end of the analysis period. These values were calculated

by, first, averaging the 1996, 1997, and 1998 ATCF index values for each firm, and, second, averaging

these firm-average values over the firms in an industry group using, as weights, the 1996-1998 average

revenue of each firm in the industry group. 

B3A4-3 SUMMARY OF FINDINGS

Table B3A4.2, below, summarizes key results from the analyses outlined above. Items reported in the table are as

follows:

< Estimated Trend: the revenue-weighted average of annual change in ATCF Index values for firms in the

industry group over the analysis period, 1992-2003. This value is the estimated coefficient of ATCF

Index against time from the simple linear regression, as described above. Because the trend is estimated

from an index series with an average value of one over the analysis period, the estimated trend may also

be interpreted as equal approximately to the annual percent change in ATCF Index. A negative estimated

trend value indicates that ATCF Index values decline, on average, over the analysis period; a positive

estimated trend value indicates that ATCF Index values increase, on average, over the analysis period.

< Average of ATCF Index Values at 1996-1998: the revenue-weighted average of actual ATCF Index

values for firms in the industry analysis set over the 1996-1998 period. The values in this column are

compared with values in the next two columns, respectively, to assess (1) the extent to which financial

performance during the 1996-1998 survey data period deviated from the analysis period trend at 1996-

1998 and (2) the overall change in financial performance from the 1996-1998 survey data period to the

end of the analysis period resulting from the combination of deviation from analysis period trend and the

trend, itself (see following paragraphs for further discussion).

< Average of Predicted Trend Values at 1996-1998: the average of predicted ATCF Index values over the

1996-1998 survey data period as predicted from the estimated regression terms. If ATCF performance

for an individual firm or industry matched the industry trend over time, the actual ATCF Index values at
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-11

1996-1998 would equal this value. Material deviation of the actual ATCF Index values from this value

suggests, for an industry, that ATCF performance during the 1996-1998 survey data period was: (1)

abnormally favorable, if the actual ATCF Index values exceed the average of predicted values, or (2)

abnormally unfavorable, if the actual ATCF Index values are less than the average of predicted values.

< Predicted Trend Value at 2003: the ATCF Index value at the end of the analysis period as predicted from

the estimated regression terms. This value is the (statistically) expected value of ATCF Index at 2003 for

a firm or for the industry, based on the estimated trend relationship. Material deviation of this value from

the Average of Predicted Trend Values at 1996-1998 indicates a general trend going forward from the

1996-1998 survey data period, which, apart from cyclical deviation, which would further affect ATCF

performance for firms in the industry group. For an industry, if this value is less than the Average of

Predicted Trend Values at 1996-1998, then financial performance, as indicated by ATCF Index, generally

deteriorated from 1996-1998 forward to 2003, the end of the analysis period. In addition to the cyclical

deviation effect, this “trend” effect might also be taken into account in adjusting ATCF data calculated

from the 316(b) survey responses. In this case, comparison of the average of actual ATCF Index values

over the 1996-1998 survey data period with the Predicted Trend Value at 2003, would indicate the total

potential adjustment, accounting for both the cyclical deviation and trend effects.

Table B3A4.2: Key Results from Analysis of After-Tax Cash Flow Trends by 316(b) Industry for

1992-2003

316(b) Phase III Industry

Group

Estimated

Trend

Average of ATCF

Index Values at 1996-

1998

Average of Predicted

Trend Values at 1996-

1998

Predicted Trend

Value at 2003

Aluminum Analysis not undertaken for the Aluminum industry

Pulp and Paper Mills -0.00040 0.98953 1.03058 1.02880

Industrial Chemicals 0.00120 1.10059 1.00837 1.01567

Plastics Material and Resins 0.03180 1.03865 0.99332 1.18383

Pharmaceuticals 0.05070 0.98775 0.99703 1.30096

Petroleum Refining 0.02300 0.92346 1.01441 1.15248

Steel -0.00420 1.25879 1.03948 1.01414

Source: U.S. EPA analysis, 2004.

The following page presents charts, by industry group, of the yearly ATCF Index Values and the estimated

predicted trend values over the analysis period.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-12

Iron and Steel

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

ATCF Index

Time Trend

Index ATCF

Industrial Chemicals

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1992

1993

1994

19951996

1997

1998

1999

2000

2001

20022003

Year

ATCF Index

Time

Trend

Index

ATCF

Petroleum Refining

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

ATCF Index

Time

Trend

Index

ATCF

Pharmaceuticals

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

ATCF Index

Time

Trend

Index

ATCF

Plastic Material and Resins

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1992

1993

1994

1995

1996

19971998

1999

2000

2001

2002

2003

Year

ATCF Index

Time

Trend

Index

ATCF

Pulp & Paper Mills

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

ACTF Index

Time

Trend

Index

ATCF

Figure B3A4-4: ATCF Index vs Trend
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-13

By industry, these results indicate the following:

< Pulp and Paper Mills. The analysis indicates a very slight annual decline, approximately -0.04 percent, in

constant dollar ATCF over the analysis period, meaning financial performance declined very modestly

over this period. The analysis also indicates that the survey data collection years of 1996-1998 showed

weaker performance than the predicted trend performance in those years. Specifically, the average actual

ATCF index value at 1996-98 is 4.0 percent below the predicted ACTF trend value for those years. With

negative growth in ATCF over the analysis period, the predicted ATCF index value at the end of the

analysis period, 2003, declines slightly from the 1996-1998 period. As a result, the average actual ATCF

index value at 1996-98 is approximately 3.8 percent below the predicted ACTF trend value in 2003.

< Industrial Chemicals. The analysis for the Industrial Chemicals segment of the Chemicals industry

indicates a very slight annual increase, approximately 0.12 percent, in constant dollar ATCF over the

analysis period, meaning financial performance improved slightly over this period. In contrast to the

finding for the Paper and Allied Products industry, the analysis indicates that the Industry Chemicals

segment achieved higher financial performance during the survey data collection years of 1996-1998 than

the predicted trend performance in those years. Specifically, the average actual ATCF index value at

1996-98 is 9.1 percent above the predicted ACTF trend value for those years. With slight positive growth

in ATCF over the analysis period, the predicted ATCF index value at the end of the analysis period, 2003,

increase slightly from the 1996-1998 period. As a result, the average actual ATCF index value at 1996-

98 is approximately 8.4 percent above the predicted ACTF trend value in 2003.

< Plastics Material and Resins. This segment of the Chemicals industry shows a moderate increase, 3.2

percent annually, in constant dollar ATCF over the analysis period. From this analysis, the Plastic

Material and Resins segment, like the Industrial Chemicals segment, also achieved higher financial

performance during the survey data collection years of 1996-1998 than the predicted trend performance in

those years. Specifically, the average actual ATCF index value at 1996-98 is 4.6 percent above the

predicted ACTF trend value for those years. However, with relatively strong positive growth in ATCF,

the predicted ATCF index value at the end of the analysis period, 2003, increases sufficiently to reverse

this relationship. As a result, by 2003, the average actual ATCF index value at 1996-98 is 12.3 percent

below the predicted ACTF trend value in 2003.

< Pharmaceuticals. This third segment of the Chemicals industry also shows a strong increase, 5.1 percent

annually, in constant dollar ATCF over the analysis period. Financial performance during the survey data

collection years very nearly equaled the predicted ATCF index value: the average ATCF index value at

1996-98 is 0.9 percent below the predicted ACTF trend value for those years. With strong growth in

ATCF over the analysis period, by 2003, the predicted ATCF value is substantially higher than the

average ATCF index value at 1996-98: the average ATCF index value at 1996-1998 is 24.1 percent below

the predicted trend value at 2003.

< Petroleum Refining. The analysis indicates a moderate increase, 2.3 percent annually, in constant dollar

ATCF over the analysis period. The analysis also indicates that the Petroleum Refining industry

achieved, on average, weaker financial performance during the survey data collection years than the

predicted trend performance in those years: the average ATCF index value for 1996-1998 is 9.0 percent

below the predicted trend value during those years. However, the individual yearly values during 1996-

1998 show considerable volatility relative to the trend, suggesting weak confidence in this finding. Like

Pharmaceuticals, with relatively strong growth in ATCF over the analysis period, by 2003, the predicted

ATCF value is substantially higher than the average ATCF index value at 1996-98: the average ATCF

index value at 1996-1998 is 19.9 percent below the predicted trend value at 2003.

< Steel. The analysis indicates declining performance, approximately -0.4 percent annually, in constant

dollar ATCF over the analysis period. The analysis also indicates that financial performance during the

survey data collection years substantially exceeded, by 21.1 percent, trend-based predicted performance

during those years. With declining trend-based performance through the end of the analysis period, by
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-14

2003, the gap between the average ATCF value at 1996-1998 and the predicted trend value at 2003

widens to 24.1 percent.

Table B3A4.3, below, summarizes these findings. 

Table B3A4.3: Estimated Relationship Between Actual ATCF at Survey Period and Trend

Predicted Values at Survey Period and End of Analysis Period

316(b) Phase III

Industry Group

Percentage Difference in Actual and

Predicted ATCF Index Values at 1996-

1998 

Percentage Difference in Actual ATCF

Index Values at 1996-1998 and Trend

Predicted Value at 2003

Aluminum Analysis not undertaken for the Aluminum industry

Pulp and Paper Mills -4.0% -3.8%

Industrial Chemicals 9.1% 8.4%

Plastics Material and

Resins 4.6% -12.3%

Pharmaceuticals -0.9% -24.1%

Petroleum Refining -9.0% -19.9%

Steel 21.1% 24.1%

Source: U.S. EPA analysis, 2004.

From these results, the industries and/or segments where financial performance during the 1996-1998 survey data

collection period exceeds trend-predicted performance and thus for which survey data may overstate the

industry’s ability to withstand compliance burdens in comparison to the predicted trend values at 1996-1998 are:

< Pulp and Paper Mills industry,

< Industrial Chemicals segment of the Chemicals industry,

< Plastics Material and Resins segment of the Chemicals industry , and

< Steel industry.

Looking to the end of the analysis period, 2003, the industries and/or segments where financial performance

during the 1996-1998 survey data collection period exceeds trend-predicted performance at 2003, and thus for

which survey data may overstate the industry’s ability to withstand compliance burdens at a time closer to the

point of regulatory implementation are:

< Industrial Chemicals segment of the Chemicals industry, and

< Steel industry.

B3A4-4 DEVELOPING AN ADJUSTMENT CONCEPT

On the basis of these findings, EPA considered whether and how to adjust after-tax cash flow, as derived from the

facility survey responses for use in the facility impact analysis. Given that several of the industries, or segments

within industries, were found to have financial performance during the survey data collection period that

exceeded the predicted trend financial performance for that period or that exceeded the predicted trend financial

performance at the end of the analysis period, EPA concluded that development and application of an adjustment

to baseline after-tax cash flow was warranted. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-15

In deciding how to adjust cash flow, EPA considered three primary adjustment concepts:

1. Adjust baseline cash flow to account for deviation from predicted trend at the time of the survey data

collection. 

2. Adjust baseline cash flow to account for deviation from predicted trend at the end of the analysis period. 

3. Adjust baseline cash flow to a future estimated period of compliance based on the estimated trend of change

in constant dollar after-tax cash flow over time. 

EPA decided to apply the ATCF adjustment according to the second of these three adjustment concepts: Adjust

baseline cash flow to account for deviation from predicted trend at the end of the analysis period. This adjustment

concept addresses both concerns that (1) business performance during the survey data collection period diverged

from the predicted trend performance during the survey data collection period, and (2) business performance from

the time of survey data collection period followed a non-neutral trend to the present. EPA considered extending

the trend projection to the estimated time of compliance (concept 3) but rejected this approach since it was

deemed speculative in attempting to forecast business performance into the future. In addition, the greater the

number of years over which ATCF results are projected based on historical trend, the less likely that the predicted

changes in ATCF reflect the performance of a static set of facilities and instead reflect capital additions, new

facilities, facility closures, etc. For these reasons, EPA decided to restrict the adjustment to the end of the ATCF

analysis period.

EPA also considered whether to apply the indicated adjustment factors asymmetrically – i.e., only to reduce the

ATCF values as calculated from facility survey responses – or symmetrically – i.e., both to increase or to reduce

the ATCF values as calculated from facility survey responses. In the case of asymmetric adjustment, the

adjustment would correct for business performance during the survey data collection period that exceeded the

predicted trend value – whether for the survey period or some future period – and would thus attempt to avoid

overstating the a facility’s ability to bear the costs of regulation compliance without material financial impact. In

the case of a symmetric adjustment, the adjustment would additionally address the potential that business

performance during the survey data collection period fell short of the predicted trend value and would thus

attempt to avoid understating the ability of an industry or facility to bear the costs of regulatory compliance

without material financial impact.

On this question, EPA decided to apply the ATCF adjustment on a symmetric basis, potentially reducing or

increasing facility cash flow on the basis of the estimated adjustment factor. EPA based its decision on the

principle of avoiding both overstatement and understatement of the ability of facilities in an industry to bear the

costs of regulatory compliance without material financial impact.

Based on these decisions, EPA calculated the adjustment factors by dividing the Predicted Trend Value at 2003

by the Average of ATCF Index Values at 1996-1998, as reported in Table B3A4-2, above. In the facility closure

analysis, as described in Chapter B3: Economic Impact Analysis for Manufacturers, facility after-tax cash flow is

simply multiplied by the appropriate adjustment factor based on the industry or industry segment to which a

facility is assigned. The resulting adjusted ATCF is carried forward in the baseline and post-compliance closure

analyses. Where the Predicted Trend Value at 2003 is less than Average of ATCF Index Values at 1996-1998, the

resulting adjustment factor value is less than one and the effect of the ATCF adjustment is to reduce the calculated

value of cash flow used in the facility impact analysis. Where the Predicted Trend Value at 2003 is greater than

Average of ATCF Index Values at 1996-1998, the resulting adjustment factor value is greater than one and the

effect of the ATCF adjustment is to increase the calculated value of cash flow used in the facility impact analysis. 

EPA also used the adjustment factors to adjust the numerator values of the measures used in the facility moderate

impact analysis: pre-tax return on assets and interest coverage ratio. In both cases, the numerators of the measures

are closely related to cash flow, but are calculated on a pre-tax basis. As a result, in this case, the ATCF-based

adjustment factor does not match as closely in concept the financial measures to which it is applied as is the case

for the ATCF measure used in the facility closure analysis. Nevertheless, use of the adjustment for these
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-16

measures is appropriate because the pre-tax measures used in the facility moderate impact analysis will still move

closely with the after-tax cash flow measure on which the adjustment factor is based. In addition, it is important

that EPA recognize the potential effect of change in financial condition since the survey data collection period in

the facility moderate impact analysis as well as in the facility closure analysis.

Table B3A4.4, below, summarizes the adjustment factors implemented for each of the industries, and within the

chemical industry, for the industry segments. The table also reports the number of baseline and post-compliance

closures (under the proposed regulatory option) estimated with and without application of the ATCF adjustment

factor.

Table B3A4.4: Using After-Tax Cash Flow Adjustment Factors in the Facility Closure Analysis

Adjustment

Factor

Number of

Facilities

Analyzed

Summary Results from Closure Analysis

Using Adjustment Factors Not Using Factors

Baseline

Closures

Regulatory

Closures

Baseline

Closures

Regulatory

Closures

Aluminum 1.0000 21 7 0 7 0

Pulp and Paper Mills 1.0397 230 32 0 32 0

Chemicals Industry

Industrial Chemicals 0.9228 138 4040

Plastics Material and

Resins 1.1398 34 0000

Pharmaceuticals 1.3171 6 0000

Petroleum Refining 1.2480 36 5051

Steel 0.8056 68 29 0 25 0

Total 532 76 0 73 1

Adjustment factor not developed for Aluminum industry, so the results with use of adjustment factors are necessarily the same

as those without use of the factors.

All results are sample weighted. The reported totals may differ from the apparent sums of individual data items due to

rounding.

Source: U.S. EPA analysis, 2004.

As reported in Table B3A4.4, the adjustment factors for the Steel industry and Industrial Chemicals segment of

the Chemicals industry are less than one, at 0.8056 and 0.9228, respectively. For these industries, the effect of the

adjustment factor is to reduce the cash flow values calculated from facility survey responses. As described above,

EPA did not calculate an adjustment factor for the Aluminum industry due to data limitations; accordingly, this

industry’s “adjustment factor” is simply 1.0000. The adjustment factors for the remaining industries are greater

than one, ranging from a value of 1.0397 for the Pulp and Paper Mills industry to 1.3198 for the Pharmaceuticals

segment of the Chemicals industry. For these industries, the effect of the adjustment factor is to increase the cash

flow values calculated from facility survey responses. 

In terms of effect on analytic results, the use of the ATCF adjustment factors caused the number of baseline

closures to change in only one 316(b) industry group, the Steel industry. For this industry, the reduction in cash

flow resulting from the adjustment causes an additional 4 facilities, on a sample weighted basis, to fail the

baseline closure analysis. 
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-17

Under the 50 MGD All Option for existing manufacturing facilities, the use of the ATCF adjustment factors

eliminated one regulatory closure, in the Petroleum Refining industry. Because the calculated adjustment factor

for this industry is quite large, 1.248, EPA reviewed closely the effect of the adjustment factor on the facility

closure calculation. In particular, EPA was concerned that the regulatory closure was being eliminated by

application of a large ATCF adjustment. From this review, EPA determined that the single Petroleum Refining

industry closure, without the ATCF adjustment, is a very marginal closure. Specifically, an ATCF adjustment

factor of 1.0195 (compared to the calculated 1.248) provides a sufficient increase in baseline cash flow to

eliminate the closure under the compliance requirements of the 50 MGD All Option. Another way of

understanding the 1.0195 adjustment factor is to calculate the annual trend factor (year-to-year change in

predicted trend ATCF Index) that would yield the 1.0195 value. In this case, the year-to-year change required to

generate the 1.0195 value is 0.0038, or an annual change factor that is very close to zero (approximately 0.38

percent annual average change). Given these findings, EPA concludes that the extent of improvement in cash

flow needed to eliminate the regulatory closure in the Petroleum Refining industry is extremely small and is thus

quite plausible within the overall improving business performance trend exhibited by the Petroleum Refining

industry. Under the other two co-proposed options, the 200 MGD All Option and the 100 MGD SWB Option, the

use of the ATCF adjustment factors would have no closure effect.
§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-18

REFERENCES

Board of Governors of the Federal Reserve System. Release G.17, Industrial Production and Capacity

Utilization: Capacity Utilization: Manufacturing (NAICS) and Industrial Production Index quarterly data series. 

2004.

U.S. Department of Commerce. Bureau of Economic Analysis. Table 1.1.6. Real Gross Domestic Product,

Chained Dollars. 2004.

U.S. Environmental Protection Agency (U.S. EPA). 2004. Information Collection Request for Cooling Water

Intake Structures, Phase II Existing Facility Final Rule. ICR Number 2060.02. February 2004.

Value Line Investment Survey. Value Line Publishing, Inc. 220 East 42nd Street, New York, N.Y. 10017-589. 

2003 and 2004.