April 2009 

Regulatory Impact Analysis: 

National Emission Standards for 

Hazardous Air Pollutants from the 

Portland Cement Manufacturing 

Industry

Final Report

Prepared for 

Tom Walton 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709 

RTI Project Number 0209897.003.067 
_________________________________ 

RTI International is a trade name of Research Triangle Institute. 

 RTI Project Number 

 0209897.003.067 

Regulatory Impact Analysis: 

National Emission Standards for 

Hazardous Air Pollutants from the 

Portland Cement Manufacturing 

Industry

Final Report

April 2009

Prepared for 

Tom Walton 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709 
iii 

CONTENTS 

Section Page

1 Introduction ................................................................................................................... 1-1 

1.1 Executive Summary ............................................................................................. 1-1 

1.2 Organization of this Report .................................................................................. 1-3 

2 Industry Profile ............................................................................................................. 2-1 

2.1 The Supply Side ................................................................................................... 2-1 

2.1.1 Production Process ................................................................................... 2-1

2.1.2 Types of Portland Cement ....................................................................... 2-3

2.1.3 Production Costs ...................................................................................... 2-3

2.2 The Demand Side ................................................................................................. 2-8 

2.3 Industry Organization ........................................................................................ 2-10 

2.3.1 Market Structure .................................................................................... 2-10

2.3.2 Manufacturing Plants ............................................................................. 2-11

2.3.3 Firm Characteristics ............................................................................... 2-16

2.4 Markets .............................................................................................................. 2-20

2.4.1 Market Volumes..................................................................................... 2-21

2.4.2 Market Prices ......................................................................................... 2-22

2.4.3 Future Projections .................................................................................. 2-22

3 Economic Impact Analysis ........................................................................................... 3-1 

3.1 Regulatory Program Costs ................................................................................... 3-1 

3.2 Partial-Equilibrium Analysis for Costs Applying to Existing Kilns .................... 3-2 

3.2.1 Regional Structure and Baseline Data ..................................................... 3-3

3.2.2 Seller Pricing Behavior ............................................................................ 3-6

3.2.3 Economic Impact Analysis Results ......................................................... 3-7

3.3 Direct Compliance Cost Method for White Cement Kilns and Kilns 

Coming On Line in 2013 ................................................................................... 3-11 
iv 

3.4 Social Cost Estimates ......................................................................................... 3-13 

3.5 Energy Impacts .................................................................................................. 3-14 

4 Small Business Impact Analysis ................................................................................... 4-1 

4.1 Identify Affected Small Entities .......................................................................... 4-1 

4.2 Sales and Revenue Test Screening Analysis ....................................................... 4-1 

4.3 Additional Market Analysis ................................................................................. 4-3 

5 Human Health Benefits of Emissions Reductions ........................................................ 5-1 

5.1 Summary .............................................................................................................. 5-1 

5.2 Calculation of Human Health Benefits ................................................................ 5-2 

5.3 Assumptions regarding Thresholds in the Health Impact Function ..................... 5-7 

5.4 Updating the Benefits Data Underlying the Benefit-per-Ton Estimates ............. 5-9 

5.5 Results of Benefits Analysis ................................................................................ 5-9 

5.6 Characterization of Uncertainty in the Benefits Estimates ................................ 5-12 

5.7 Comparison of Benefits and Costs ..................................................................... 5-18 

6 References ..................................................................................................................... 6-1 

Appendixes 

A Regional Portland Cement Markets and Economic Model ................................. A-1 

B Model of the Cement Plant’s Production Decision ..............................................B-1 

C Social Methodology ............................................................................................C-1 
v 

LIST OF FIGURES 

Number Page

2-1. Simplified Flow Sheet of Clinker and Cement Manufacture........................................ 2-2

2-2. Labor Costs per Metric Ton of Cement ($2005) .......................................................... 2-7

2-3. Distribution of Energy Consumption ............................................................................ 2-8

2-4. End Uses of Cement: 1975 to 2003 .............................................................................. 2-9

2-5. Producer Price Indices for Competitive Building Materials: 2003 to 2008................ 2-10

2-6. Distribution of Cement Kilns in the United States ..................................................... 2-13

2-7. Historical U.S. Cement Price ...................................................................................... 2-23

2-8. Deviation from National Average Cement Price per Metric Ton by Region: 

2005............................................................................................................................. 2-24

3-1. Distribution of Annualized Direct Compliance Costs per Metric Ton of Clinker 

Capacity: Existing Grey and White Cement Kilns (2005$) .......................................... 3-3

3-2. Hypothetical Cost-to-Sales Ratios for a Representative Kiln Coming On Line in 

2013............................................................................................................................. 3-12

4-1. SBREFA Screening Analysis Results ........................................................................... 4-3

5-1. Monetized Human Health Co-Benefits of Proposed Portland Cement NESHAP 

in 2013a ......................................................................................................................... 5-2

5-3. Monetized Health Benefits of the Proposed Portland Cement NESHAP by PM2.5

Precursor in 2013 using the no-threshold model a ....................................................... 5-12

5-4. Monetized Human Health Benefits of Proposed Portland Cement NESHAP in 

2013 based on the no-threshold model (preferred approach) a ................................... 5-17

5-5. Monetized Health Benefits for Proposed Portland Cement NESHAP in 2013 

based on the threshold model (without technical updates) (sensitivity analysis)a

 ...... 5-18

5-6. Net Benefits for Proposed Portland Cement NESHAP in 2013 at 2 Discount 

Rates using the no-threshold modela ........................................................................... 5-19
vi 

LIST OF TABLES 

Number Page

2-1. Portland Cement Shipped from Plants in the United States to Domestic 

Customers, by Typea, b .................................................................................................. 2-4

2-2. Raw Material Input Ratios for the U.S. Cement Industry: 2000 to 2005 ..................... 2-5

2-3. Raw Material Costs by Market and State: 2005 ........................................................... 2-6

2-4. Labor Productivity Measures for the U.S. Cement Industry by Process Type: 

2000 to 2005 (employee hours per metric ton) ............................................................. 2-6

2-5. Energy Consumption by Type of U.S. Cement Plant (million BTU per metric 

ton) ................................................................................................................................ 2-8

2-6. Number of Kilns and Clinker Capacity by State: 2005 .............................................. 2-12

2-7. Number of Kilns and Clinker Capacity by Age and Process Type ............................. 2-14

2-8. Clinker Capacity, Production, and Capacity Utilization in the United States: 

2000 to 2005 ............................................................................................................... 2-15

2-9. Capacity Utilization Rates by State: 2005 .................................................................. 2-17

2-10.Cement Manufacturing Employment (NAICS 327310): 2000 to 2005 ...................... 2-18

2-11. Ultimate Parent Company Summary Data: 2005 ........................................................ 2-19

2-12. Historical U.S. Cement Statistics (106 metric tons) .................................................... 2-21

2-13. U.S. Cement Trade Data: 2000 to 2007 ...................................................................... 2-22

3-1. Summary of Direct Compliance Costs (106 2005$) ...................................................... 3-2

3-2. Portland Cement Prices by Market ($/metric tons): 2005 ............................................ 3-4

3-3. Portland Cement Markets (106 metric tons): 2005 ....................................................... 3-5

3-4. National-Level Market Impacts: 2005 .......................................................................... 3-7

3-5. Regional Compliance Costs and Market Price Changes ($/metric ton of 

cement): 2005................................................................................................................ 3-8

3-6. Summary of Regional Market Impacts: 2005 ............................................................... 3-9

3-7. National-Level Industry Impacts: 2005 ...................................................................... 3-10

3-8. Distributional of Industry Impacts: 2005 .................................................................... 3-10

3-9. Cement Plants with Significant Utilization Changes: 2005 ........................................ 3-11

3-10. Distribution of Social Costs ($106): 2005 ................................................................... 3-14

3-11. U.S. Cement Sector Energy Consumption (Trillion BTUs)a: 2013 ............................ 3-15

4-1. Small Entity Summary Data: 2005 ............................................................................... 4-2

4-2. Summary Statistics for Small Business Regulatory Enforcement Fairness Act 

(SBREFA) Screening Analysis ..................................................................................... 4-4

4-3. Market Analysis—Small Entity Impacts: 2005 ............................................................ 4-4
vii 

5-1. Summary of PM2.5 Health Co-Benefits of the Proposed Portland Cement 

NESHAP using the no-threshold model (preferred approach)a

 .................................. 5-10

5-2. Summary of Health Benefits of the Proposed Portland Cement NESHAP, using 

the threshold model (without technical updates) (sensitivity analysis)a ..................... 5-11

5-3. Summary of Reductions in Health Incidences of the Proposed Portland Cement 

NESHAPa

 .................................................................................................................... 5-11

5-4. All Benefits Estimates for Proposed Portland Cement NESHAP in 2013 (in 

millions of 2005$) for no-threshold model (preferred approach)a .............................. 5-15

5-5. All Benefits Estimates for proposed Portland Cement NESHAP in 2013 (in 

millions of 2005$) for the threshold model (without technical updates) 

(sensitivity analysis) a .................................................................................................. 5-16
1-1 

SECTION 1

INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) is proposing amendments to the 

current National Emission Standards for Hazardous Air Pollutants (NESHAP) for the Portland 

cement manufacturing industry. The proposed amendments add or revise, as applicable, emission 

limits for mercury, total hydrocarbons, hydrogen chloride, and particulate matter from kilns and 

in-line/raw mills located at a major or area sources. The proposed amendments also remove the 

operating limit for the average hourly recycle rate for cement kiln dust and remove the work 

practice requirement for using certain mercury-containing fly ash in cement kilns. EPA 

developed these proposed amendments in response to the notice of reconsideration published on 

December 20, 2006, and other requirements. As part of the regulatory process of preparing these 

amendments, EPA is required to develop a regulatory impact analysis (RIA). This RIA includes 

an economic impact analysis (EIA) and a small entity impacts analysis. This report documents 

the RIA methods and results. 

1.1 Executive Summary 

The key results of the RIA are as follows: 

 Engineering Cost Analysis: EPA estimates total annualized costs with the NESHAP 

will be $368 million (2005$). 

 Market Analysis: The partial-equilibrium economic model suggests the average 

national price for Portland cement could be 4% higher with the NESHAP, or $3.30 

per metric ton, while annual domestic production may fall by 8%, or 7 million tons 

per year. Because of higher domestic prices, imports rise by 2 million metric tons per 

year. 

 Industry Analysis: As domestic production falls, cement industry revenues are 

projected to decline by 4%, or $341 million. Overall, net production costs also fall by 

$137 million with compliance cost increases ($235 million) offset by cost reductions 

associated with lower cement production. Operating profits fall by $204 million, or 

16%. Other consequences include reduced demand for labor. Employment falls by 

approximately 8%, or 1,167 employees. EPA identified six domestic plants with 

negative operating profits and significant utilization changes that could temporarily 

idle until market demand conditions improve. The plants are small capacity plants 

with unit compliance costs close to $5 per ton and $50 million total change in 

operating profits. Since these plants account for approximately 2.5% of domestic 

capacity, a decision to permanently shut down these plants would reduce domestic 

supply and lead to additional projected market price increases. 

 Social Cost Analysis: The estimated social cost of the proposed amendments is $605 

million (2005$). This estimate includes: the results for existing kilns included in the 

partial-equilibrium analysis ($606 million surplus loss in domestic surplus and $89 
1-2 

million surplus gain for other countries producing cement); the direct compliance 

costs for white cement kilns ($2 million); and the direct compliance costs for 20 

additional kilns projected to come on line in 2013 ($86 million). The social cost 

estimates are significantly higher than the engineering analysis estimates, which 

estimated annualized costs of $368 million. This is a direct consequence of EPA’s 

assumptions about existing domestic plants’ pricing behavior discussed extensively in 

previous cement industry rulemakings, Section 2, Appendix A, and Appendix B of 

this RIA. Under baseline conditions without regulation, the existing domestic cement 

plants are assumed to choose a production level that is less than the level produced 

under perfect competition. As a result, a preexisting market distortion exists in the 

markets covered by the proposed rule (i.e., the observed baseline market price is 

higher than the [unobserved] market price that a model of perfect competition would 

predict). The imposition of additional regulatory costs tends to widen the gap between 

price and marginal cost in these markets and contributes to additional social costs. 

 Energy Impacts: EPA concludes that the rule when implemented will not have a 

significant adverse effect on the supply, distribution, or use of energy. The cement 

industry accounts for less than 0.3% of the U.S. total energy consumption. Although 

EPA estimates the additional add-on controls may increase electricity consumption by 

926 million kWh, this is less than 0.1% of the Annual Energy Outlook (AEO) 2009 

(Department of Energy [DOE], 2008) 2013 electricity forecasts of total electricity use 

(4,091 billion kWh). 

 Small Business Analysis: EPA performed a screening analysis for impacts on small 

entities by comparing compliance costs to average company revenues. EPA’s analysis 

found that the ratio of compliance cost to company revenue falls below 1% for one of 

the four small entities included in the screening analysis. Two small entities 

(including a tribal government) would have an annualized cost of between 1% and 

3% of sales. One small business would have an annualized cost greater than 3% of 

sales. In addition to the screening analysis, EPA also examined small entity effects 

after accounting for market adjustments. Under this assumption, the entities recover 

some of the regulatory program costs as the market price adjusts in response to higher 

cement production costs. Even after accounting for these adjustments, small entity 

operating profits fall by $4 million, or 9%. As cement production falls, employment 

may decline by up to 23 employees, a 5% reduction. 

 Benefits Analysis: In the year of full implementation (2013), EPA estimates the 

benefits of this proposal are $4.4 billion to $11 billion and $4.0 billion to $9.7 billion, 

at 3% and 7% discount rates respectively.1

 Annualized domestic social costs are $694 

million at a 7% discount rate as mentioned in Section 4 of this RIA.2

 Thus, the net 

benefits (i.e., benefits in 2013 minus annualized costs) are $3.7 billion to $11 billion 

and $3.3 billion to $9.0 billion, at 3% and 7% discount rates respectively. 

 

1

 The benefits are discounted to account for the cessation lag in PM2.5 benefits frompremature mortality and acute 

myocardial infarctions (AMIs), rather than a discounted stream of future benefits; whereas discounting the costs 

reflects the lifetime costs of the equipment. For this reason, it is appropriate in this context to use two different 

discount rates for the benefits and costs. 2

 The domestic cost does not include the estimated $89 million surplus gain for foreign producers. 
1-3 

1.2 Organization of this Report 

The remainder of this report supports and details the methodology and the results of the 

EIA: 

 Section 2 presents a profile of the affected industry. 

 Section 3 describes the economic impact analysis and energy impacts. 

 Section 4 describes the small business impact analysis. 

 Section 5 presents the benefits estimates. 

 Appendix A describes the regional Portland cement markets and the economic model. 

 Appendix B discusses the model of the cement plant’s production decision. 

 Appendix C presents the social cost methodology. 
2-1 

SECTION 2

INDUSTRY PROFILE 

Hydraulic cement (primarily Portland cement) is a key component of an important 

construction material: concrete. Concrete is used in a wide variety of applications (e.g., 

residential and commercial buildings, public works projects), and cement demand is influenced 

by national and regional trends in these sectors. Recent data for 2007 show that the U.S. cement 

industry produced over 90 million metric tons of Portland cement (Department of Interior [DOI], 

U.S. Geological Survey [USGS], 2008b). The value of total U.S. sales, including imported 

cement, was about $11.8 billion, with an average value of approximately $100 per metric ton. 

The vast majority of cement sales went to ready-mixed concrete producers and concrete product 

manufacturers (88%). Since 2003, the United States has relied on cement imports to meet 

approximately 20% to 23% of its consumption needs. However, this share dropped to 

approximately 17% in 2007 as overall construction demand for cement fell (DOI, USGS, 2008b). 

The remainder of this section provides an introduction to the Portland cement industry. 

The purpose is to give the reader a general understanding of the technical and economic aspects 

of the industry that must be addressed in the economic impact analysis. Section 2.1 provides an 

overview of the production processes and costs data. Section 2.2 discusses the uses, consumers, 

and substitutes for cement. Section 2.3 summarizes the organization of the Portland cement 

industry. The industry profile concludes with a discussion of historical market data and the 

current industry outlook. 

2.1 The Supply Side 

2.1.1 Production Process 

As shown in Figure 2-1, the manufacturing process of an integrated cement plant includes 

 quarrying and crushing the raw materials, 

 grinding the carefully proportioned materials to a high degree of fineness, 

 firing the raw materials mixture in a rotary kiln to produce clinker, and 

 grinding the resulting clinker to a fine powder and mixing with gypsum to produce 

cement. 
2-2 

Figure 2-1. Simplified Flow Sheet of Clinker and Cement Manufacture 

There are two processes for manufacturing cement: the wet process and the dry process. 

In the wet process, water is added to the raw materials during the blending process and before 

feeding the mixture into the rotary kiln. In contrast, the dry process feeds the blended materials 

directly into the rotary kiln in a dry state. Newer dry process plants also use preheater and 

precalciner technologies that partially heat and calcine the blended raw materials before they 

enter the rotary kiln. These technologies can increase the overall energy efficiency of the cement 

plant and reduce production costs. 

The fuel efficiency differences between the wet and dry processes have led to a 

substantial decline in clinker capacity provided by the wet process over the last 3 decades. 

Historical data show capacity shares falling from 52% in 1980 to approximately 22% in 2000 

(Van Oss and Padovani, 2002). Data also show that the number of wet process plants fell from 

32 in 2000 to 23 in 2005 (Van Oss and Padovani, 2002; DOI, USGS, 2007). 
2-3 

2.1.2 Types of Portland Cement 

Portland cement manufacturers produce a variety of types of cement in the United States 

designed to meet different requirements. The American Society for Testing Materials (ASTM) 

specification C-150 provides for eight types of Portland cement: five standard types (I, II, III, IV, 

V) and three additional types that include air-entraining properties (IA, IIA, IIIA) (PCA, 2008a). 

We describe these below. 

Types I and IA: These types are the usual product used in general concrete construction, 

most commonly known as gray cement because of its color. 

Types II and IIA: These types are intended for use when moderate heat of hydration is 

required or for general concrete construction exposed to moderate sulfate action. 

Type III and IIIA: These types are made from raw materials with a lime-to-silica ratio 

higher than that of Type I cement and are ground finer than Type I cements. They contain a 

higher proportion of tricalcium silicate than regular Portland cements. 

Type IV: This type contains a lower percentage of tricalcium silicate and tricalcium 

aluminate than Type I, thus lowering the heat evolution. Consequently, the percentage of 

tetracalcium aluminoferrite is increased. Type IV cements are produced to attain a low heat of 

hydration. 

Type V: This type resists sulfates better than the other four types. 

As shown in Table 2-1, the vast majority of Portland cement shipments1

 in 2005 were 

Types I and II grey cement. However, Type V (sulfate-resisting) is a growing market (DOI, 

USGS, 2007a); since 2000, Type V cement has increased its share of shipments from 4% to 

15%. Shipment shares for other types of cement remained constant during this period. 

2.1.3 Production Costs

Portland cement is produced using a combination of variable inputs such as raw 

materials, labor, electricity, and fuel. U.S. Census data for the cement industry (North American 

Industry Classification System [NAICS] 32731: cement manufacturing) provides an initial 

overview of aggregated industry expenditures on these inputs (Department of Commerce [DOC], 

Bureau of the Census, 2008). In 2006, the total value of shipments was $10.7 billion, and the 

industry spent approximately $2.1 billion on materials, parts, and packaging, or 20% of the value 

of shipments. Total compensation for all employees (includes payroll and fringe benefits) 

 

1

 USGS notes these shipment data include cement imports (primarily Types I, II, and V). 
2-4 

Table 2-1. Portland Cement Shipped from Plants in the United States to Domestic 

Customers, by Typea, b

Type 2000 Share 2005 Share 

General use and moderate heat (Types I and II) (gray)c 90,644 88% 93,900 77% 

High early strength (Type III) 3,815 4% 3,960 3% 

Sulfate resisting (Type V)c

 4,453 4% 18,100 15% 

Whited

 894 1% 1,190 1% 

Blended 1,296 1% 3,160 3% 

Expansive and regulated fast setting 60 0% 6 0% 

Othere

 1,786 2% 1,997 2% 

Totalf 102,947 100% 122,000 100% 

a Includes imported cement. 

b Data are rounded to no more than three significant digits; may not add to totals shown. 

c Cements classified as Type II/V hybrids are now commonly reported as Type V. 

d Mostly Types I and II but may include Types III through V and block varieties. 

e Includes block, oil well, low heat (Type IV), waterproof, and other Portland cements. 

f Data are based on an annual survey of plants and importers. 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Table 15. 

U.S. Department of the Interior, U.S. Geological Survey. 2002. 2001 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Table 15. 

Amounted to $1.3 billion (12%).1 Fuels and electricity expenditures were approximately $1.6 

billion (15%). 

2.1.3.1 Raw Material Costs 

According to the USGS, approximately 159.7 million tons of raw materials were required 

to produce approximately 95.5 million tons of cement in 2005 or 1.67 tons of raw materials per 

ton of cement. Table 2-2 summarizes the amount of raw material inputs used per ton of cement 

produced in the United States between 2000 and 2005. As the data show, the amount of raw 

materials required to produce one ton of cement has remained essentially constant during this 

6-year period. 

 

1

 Wages paid to production workers were $0.7 billion (6% of the value of shipments) at an average hourly rate of 

$25. 
2-5 

Table 2-2. Raw Material Input Ratios for the U.S. Cement Industry: 2000 to 2005 

 2000 2001 2002 2003 2004 2005 

Raw material input (103 metric tons) 144,949 147,300 153,100 150,500 158,200 159,700 

Cement production (103 metric tons) 85,178 86,000 86,817 89,592 94,014 95,488 

Metric tons of raw material input per 

ton of cement 

1.70 1.71 1.76 1.68 1.68 1.67 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2002–2007a. 2001–2005 Minerals Yearbook, 

Cement. Table 6. Washington, DC: U.S. Department of the Interior. 

U.S. Department of the Interior, U.S. Geological Survey. 2002–2007a. 2001–2005 Minerals Yearbook, 

Cement. Table 3. Washington, DC: U.S. Department of the Interior. 

The price of these raw materials varies across regions. Table 2-3 lists the average price of 

raw materials per metric ton by state. In 2005, the prices of raw materials were highest in Hawaii 

where they sold for an average of $13.34 per metric ton. The prices of raw materials were lowest 

in Michigan, where they sold for an average of $3.89 per metric ton. 

2.1.3.2 Labor Costs 

In 2005, the Portland Cement Association (PCA) reported labor productivity measures (in 

terms of metric tons of cement per employee hour)1 for 2000 to 2005 in its U.S. and Canadian 

Labor-Energy Input Survey. Using these data, we computed a measure of labor hour 

requirements to produce cement (see Table 2-4). As these data show, wet process plants are 

typically more labor intensive, requiring approximately 45% more labor hours to produce a 

metric ton of cement than dry process plants. 

In addition, labor productivity has been improving more quickly in dry process plants 

than in those using a wet manufacturing process. Between 2000 and 2005, labor requirements 

decreased by 15% in dry process plants, while in wet process plants labor requirements remained 

constant. As a result, the wet process labor costs relative to dry process plants labor costs have 

risen in recent years (Figure 2-2).2

 

1

 Throughout this report, we use PCA’s method to calculate labor and energy efficiency. This measure is a weighted 

sum of clinker and finished cement production. Weights for labor are 85% clinker and 15% finished cement 

production. Weights for energy are 92% clinker and 8% finished cement production (PCA, 2005). 2

 The labor costs reported in Figure 2-3 were calculated by first multiplying the number of employee hours per 

metric ton of cement reported in Table 2-4 by the average hourly earnings of production workers for each year 

(BLS, 2007). Next, these cost estimates were adjusted for inflation and expressed in 2005 dollars by using the 

consumer price index (CPI) (DOC, BLS, 2008). 
2-6 

Table 2-3. Raw Material Costs by Market and State: 2005 

State(s) 

Price of Raw Materials

($/metric ton)a State(s) 

Price of Raw Materials

($/metric ton)a

AK 6.60 MT $4.76 

AL 6.57 NC $8.59 

AR $6.29 ND $4.45 

AZ $5.75 NE $7.10 

CA $8.37 NH $8.02 

CO $6.85 NJ $7.04 

CT $9.19 NM $6.67 

DE $6.89 NV $7.17 

FL $8.67 NY $8.44 

GA $7.63 OH $5.82 

HI $13.34 OK $5.67 

IA $7.27 OR $6.01 

ID $5.37 PA $6.67 

IL $7.16 RI $7.74 

IN $5.40 SC $7.61 

KS $7.20 SD $4.60 

KY $7.24 TN $7.55 

LA $8.18 TX $6.15 

MA $9.19 UT $5.58 

MD $8.28 VA $9.03 

ME $6.85 VT $6.75 

MI $3.89 WA $6.92 

MN $8.30 WI $5.83 

MO $7.37 WV $6.86 

MS $11.90 WY $5.68 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2006b. 2005 Minerals Yearbook, Crushed Stone.

Table 4. Washington, DC: U.S. Department of the Interior. 

Table 2-4. Labor Productivity Measures for the U.S. Cement Industry by Process Type: 

2000 to 2005 (employee hours per metric ton) 

Year 2000 2001 2002 2003 2004 2005 

All plants 0.394 0.388 0.360 0.347 0.338 0.338 

Wet process 0.469 0.457 0.450 0.465 0.452 0.463 

Dry process 0.376 0.375 0.342 0.328 0.318 0.318 

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 
2-7 

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$10.00

2000 2001 2002 2003 2004 2005

All Plants Wet Process Dry Process

Figure 2-2. Labor Costs per Metric Ton of Cement ($2005) 

Sources: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 

U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2007a. “Current Employment Statistics 

(National): Customizable Data Tables” Available at <http://www.bls.gov/ces/>. As obtained on March 14, 

2008. 

U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2008. “Consumer Price Index All Items – 

U.S. City Average Data: Customizable Data Tables.” Available at <http://www.bls.gov/cpi/>. As obtained 

on March 14, 2008. 

2.1.3.3 Energy Costs 

Figure 2-3 provides a detailed breakdown of U.S. energy consumption in 2005. As this 

figure shows, the vast majority of energy in U.S. cement plants is derived from coal and coke 

(75%). The remaining 25% of energy consumption is derived from electricity, waste, natural gas, 

and petroleum products. 

PCA also reported energy consumption data by type of U.S. cement plant (in terms of 

millions of BTUs per metric ton of cement) (see Table 2-5). As these data show, wet process 

plants are typically more energy intensive, consuming approximately 44% more energy per ton 

of cement than dry process plants. In addition, the trends in energy consumption continue to 

show that dry plants have become more energy efficient than wet process plants. Between 2000 

and 2005, energy consumption per ton of cement in dry process plants decreased by 5%; in 

contrast, wet process plants’ energy consumption increased slightly during this period. 
2-8 

Coal & Coke

75.4%

Petroleum 

Products

0.8%

Electricity

11.0%

Natural Gas

3.6%

Waste

9.2%

Figure 2-3. Distribution of Energy Consumption 

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 

Table 2-5. Energy Consumption by Type of U.S. Cement Plant (million BTU per metric 

ton) 

Year 2000 2001 2002 2003 2004 2005 

All plants 4.982 4.93 4.858 4.762 4.755 4.699 

Wet process 6.25 6.442 6.676 6.647 6.807 6.387 

Dry process 4.673 4.655 4.498 4.433 4.407 4.433 

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 

2.2 The Demand Side 

The demand for Portland cement is considered a “derived” demand because it depends on 

the construction demands for its end product—concrete. A recent study by the U.S. International 

Trade Commission suggests that 0.192 metric tons of grey Portland cement were used per $1,000 

of construction in 1998 (USITC, 2006). Given cement prices at this time (approximately $75 per 

metric ton), Portland cement costs represented only a small share of the total value of 

construction expenditures (less than 2%). 

Concrete is used in a wide variety of construction applications, including residential and 

commercial buildings, and public works projects such as the national highway system. As shown 

in Figure 2-4, ready-mixed concrete producers have historically accounted for over half of the 

Portland cement consumption. Although government and miscellaneous expenditures saw 

substantial increases in the early 1990s, their consumption share returned to pre-1990s levels 

after 1996. The latest USGS use data show that ready-mixed concrete producers accounted for 
2-9 

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

1975 1980 1985 1990 1995 2000

Metric tons

Ready-mix concrete Concrete products Contractors

Building material dealers Oil well, mining, and waste stabilization Government and miscellaneous

Masonry cement

Figure 2-4. End Uses of Cement: 1975 to 2003 

Source: Kelly, T. and G. Matos. 2007a. “Historical Statistics for Mineral and Material Commodities in the United 

States: Cement End Use Statistics.” U.S. Geological Survey Data Series 140, Version 1.2. Available at 

http://minerals.usgs.gov/ds/2005/140/. 

74% of cement sales in 2005, followed by concrete product manufacturers (14%), contractors 

(6%), and other (6%) (Kelly and Matos, 2007a). 

Cement competes with other construction materials such as steel, asphalt, and lumber. 

Lumber is the primary substitute in the residential construction market, while steel is the primary 

substitute in commercial applications. Asphalt is a key substitute in transportation projects such 

as road and parking lot surfacing. However, concrete has advantages over these substitutes 

because it tends to be available locally and has lower long-term maintenance costs (Van Oss and 

Padovani, 2002). 

The PCA regularly reports price trends for these competing building materials (PCA, 

2008b). As shown in Figure 2-5, steel and asphalt have risen sharply relative to cement since 

2003 while lumber has declined. 
2-10 

Figure 2-5. Producer Price Indices for Competitive Building Materials: 2003 to 2008 

Source: Portland Cement Association. 2008b. “Market Research: Producer Price Indices—Competitive Building 

Materials.” Available at <http://www.cement.org/market/>. 

2.3 Industry Organization 

2.3.1 Market Structure 

A review and description of market characteristics (i.e., degree of concentration, entry 

barriers, and product differentiation) can enhance our understanding of how U.S. cement markets 

operate. These characteristics provide indicators of a firm’s ability to influence market prices by 

varying the quantity of cement it sells. For example, in markets with large numbers of sellers and 

identical products, firms are unlikely to be able to influence market prices via their production 

decisions (i.e., they are “price takers”). However, in markets with few firms, significant barriers 

to entry (e.g., licenses, legal restrictions, or high fixed costs), or products that are similar but can 

be differentiated, the firm may have some degree of market power (i.e., set or significantly 

influence market prices). 

Cement sales are often concentrated locally among a small number of firms for two 

reasons: high transportation costs and production economies of scale.1

 Transportation costs 

significantly influence where cement is ultimately sold; high transportation costs relative to unit 

value provide incentives to produce and sell cement locally in regional markets (USITC, 2006). 

 

1

 The 2002 Economic Census reports that the national Herfindahl-Hirschman Index (HHI) for cement (North 

American Industry Classification System [NAICS] 32731) is 568. However, this measure is likely not 

representative of actual concentration that exists in regional markets. 
2-11 

To support this claim, the empirical literature has typically pointed to Census of Transportation 

data showing over 80% of cement shipments were made within a 200-mile radius (Jans and 

Rosenbaum, 1997)1

 and reported evidence of high transportation costs per dollar of product value 

from case studies (Ryan, 2006). The cement industry is also very capital intensive and entry 

requires substantial investments. In additional, large plants are typically more economical 

because they can produce cement at lower unit costs; this reduces entry incentives for smallsized cement plants. Using recent data for planned capacity expansions between 2008 and 2012, 

the PCA reports these expansions will cost $5.9 billion and add 25 million metric tons (PCA, 

2007), or $240 per metric ton, of new capacity. 

For a given construction application, consumers are likely to view cement produced by 

different firms as very good substitutes. American Society for Testing and Materials (ASTM) 

specifications tend to ensure uniform quality, and recent industry reviews (USITC, 2006) suggest 

that there is little or no brand loyalty that allows firms to differentiate their products. 

2.3.2 Manufacturing Plants 

During 2005, 107 cement manufacturing plants with 186 cement kilns were operating in 

the United States. This section describes the location, age, production capacity, and employment 

of these manufacturing facilities. Section 2.3.2 concludes with a discussion of future trends. 

Section 2.3.3 provides a detailed discussion of the characteristics of the firms owning these 

facilities. 

2.3.2.1 Location 

Table 2-6 summarizes the geographic location of cement kilns in the United States and 

clinker capacity. The top five states in order of clinker capacity are California, Texas, 

Pennsylvania, Florida, and Alabama. Together these states account for 75 (40%) of the kilns in 

the United States and 41 million metric tons (44%) of clinker capacity. Figure 2-6 provides a 

graphical depiction of the number of kilns distributed by state. 

Fourteen states (Alaska, Hawaii, Connecticut, Louisiana, New Hampshire, North Dakota, 

Wisconsin, Delaware, Massachusetts, New Jersey, Rhode Island, Minnesota, North Carolina, and 

Vermont) and the District of Columbia had no clinker-producing facilities in 2005. 

 

1

 A recent USITC study of California cement markets found more than 75% of gray Portland cement shipments in 

the state were shipped to customers within 200 miles of the cement producer (USITC, 2006). 
2-12 

Table 2-6. Number of Kilns and Clinker Capacity by State: 2005 

 No. Kilns Clinker Capacity (103

 metric tons per year) 

AK 0 

AL 5 5,375 

AR 3 831 

AZ 8 2,809 

CA 20 12,392 

CO 2 2,117 

CT 0 

DE 0 

FL 7 5,489 

GA 2 1,020 

HI 0 

IA 4 2,672 

ID 2 260 

IL 8 2,770 

IN 8 3,191 

KS 9 2,835 

KY 1 1,365 

LA 0 

MA 0 

MD 4 2,538 

ME 1 392 

MI 8 4,243 

MN 0 

MO 6 5,169 

MS 1 419 

MT 2 573 

NC 0 

ND 

NE 2 845 

NH 0 

NJ 0 

NM 2 432 

NV 2 452 

NY 4 2,886 

OH 3 1,115 

OK 7 1,869 

OR 1 816 

(continued) 
2-13 

Table 2-6. Number of Kilns and Clinker Capacity by State: 2005 (continued) 

 No. Kilns Clinker Capacity (103

 metric tons per year) 

PA 21 6,414 

RI 0 

SC 6 3,480 

SD 3 851 

TN 2 1,438 

TX 22 11,688 

UT 2 1,514 

VA 1 1,120 

VT 0 

WA 2 1,100 

WI 0 

WV 3 708 

WY 2 597 

Total 186 93,785 

Source: Portland Cement Association (PCA). 2004. U.S. and Canadian Portland Cement Industry: Plant Information 

Summary. Skokie, IL: PCA’s Economic Research Department. 

Figure 2-6. Distribution of Cement Kilns in the United States 

Source: Portland Cement Association (PCA). December 2004. U.S. and Canadian Portland Cement Industry: Plant 

Information Summary. Skokie, IL: Portland Cement Association Economic Research Department. 

2.3.2.2 Age 

In 2005, 72% (134) of all kilns in the United States used the dry manufacturing process, 

and it accounted for 83% (78 million metric tons) of national clinker capacity. The growing 

prevalence of the dry process among cement manufacturers is part of a long-term trend. As the 

data in Table 2-7 indicate, no new wet clinker capacity has been added within the past 30 years. 
2-14 

Table 2-7. Number of Kilns and Clinker Capacity by Age and Process Type 

 No. Kilns 

Clinker Capacity (103

 metric 

tons per year) Average Annual Capacity per Kiln 

Total 

0–10 26 28,144 1,082.5 

11–15 3 2,176 725.3 

16–20 5 3,345 669.0 

21–25 16 14,982 936.4 

26–30 18 11,843 657.9 

31–35 16 5,786 361.6 

36–40 21 9,285 442.1 

41–45 29 8,971 309.3 

46–50 32 6,564 205.1 

51–55 6 991 165.2 

56–60 6 800 133.3 

60+ 8 898 112.3 

Total 186 93,785 504.2 

Dry Process 

0–10 26 28,144 1,082.5 

11–15 3 2,176 725.3 

16–20 5 3,345 669.0 

21–25 16 14,982 936.4 

26–30 18 11,843 657.9 

31–35 10 3,962 396.2 

36–40 12 5,498 458.2 

41–45 14 3,800 271.4 

46–50 16 2,651 165.7 

51–55 4 682 170.5 

56–60 6 800 133.3 

60+ 4 328 82.0 

Total 134 78,211 583.7 

Wet Process 

0–10 0 

11–15 0 

16–20 0 

21–25 0 

26–30 0 

31–35 6 1,824 304.0 

36–40 9 3,787 420.8 

41–45 15 5,171 344.7 

(continued) 
2-15 

Table 2-7. Number of Kilns and Clinker Capacity by Age and Process Type (continued) 

 No. Kilns 

Clinker Capacity (103

 metric 

tons per year) Average Annual Capacity per Kiln 

Wet Process (cont.) 

46–50 16 3,913 244.6 

51–55 2 309 154.5 

56–60 0 

60+ 4 570 142.5 

Total 52 15,574 299.5 

Source: Portland Cement Association (PCA). 2004. U.S. and Canadian Portland Cement Industry: Plant Information 

Summary. Skokie, IL: PCA’s Economic Research Department. 

All 68 kilns that have become operational within the past 30 years use the dry manufacturing 

process. These new kilns account for 64% (60 million metric tons) of national clinker capacity. 

2.3.2.3 Production Capacity and Utilization 

Between 2000 and 2005, apparent annual clinker capacity grew approximately 17%, 

while clinker production grew by approximately 14% (Table 2-8). Because capacity tends to 

grow more rapidly than production, total capacity utilization decreased slightly in this period 

from 87.5% in 2000 to 85.4% in 2005. 

Table 2-8. Clinker Capacity, Production, and Capacity Utilization in the United States: 

2000 to 2005 

2000 2001 2002 2003 2004 2005 

Apparent annual capacity (103 

metric tons) 

89,264 100,360 101,000 102,000 105,000 104,000 

Production (103 metric tons) 78,138 79,979 82,959 83,315 88,190 88,783 

Capacity utilization (%) 87.5% 79.7% 82.1% 81.7% 84.0% 85.4% 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2000–2005. Minerals Yearbook, Cement. Table 5. 

Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/pubs/ 

commodity/cement/>. As obtained on March 14, 2008. 

Much of the vast majority of the growth in clinker capacity came in 2001 when existing 

Portland cement plants completed major capacity upgrade projects, resulting in a 12% increase in 

clinker capacity over the previous year (USGS, 2002). As a result, capacity utilization fell to 

79.7% that year. After 2001, clinker capacity grew an average of 1% each year, while production 

grew an average of 2%. As a result, capacity utilization has risen slowly since 2001. However, 
2-16 

throughout these movements in clinker capacity and production, capacity utilization tended to 

remain between 80% and 85%. 

Capacity utilization often varies by geographic region as a result of fluctuations in 

regional construction activity. For example, 2005 data show that Idaho, Montana, and Nevada 

shared a capacity utilization rate of 95.5%—well above the national average. In contrast, South 

Carolina used only 64.5% of its clinker capacity. Table 2-9 provides a complete listing of 

capacity utilization rates by state in 2005. 

2.3.2.4 Employment 

Each year, the Annual Survey of Manufactures (ASM) collects employment, payroll, 

sales, and other data for all manufacturing establishments. Table 2-10 summarizes the 

employment data collected by the ASM for the cement manufacturing industry (NAICS 327310) 

from 2000 to 2005. As these data indicate, total employment fell approximately 2% over this 

6-year period, from approximately 17,000 employees in 2000 to 16,900 in 2005. 

2.3.2.5 Trends 

As previously discussed, clinker capacity has been increasing at a slower pace since 

2001. However, according to the PCA, the cement industry has announced that it will increase 

clinker capacity by nearly 25 million metric tons between 2007 and 2012. This represents a 27% 

increase over U.S. 2006 clinker capacity and amounts to a $5.9 billion investment (PCA, 2007). 

In addition to these expected capacity expansions, likely changes in U.S. specifications 

allowing the use of limestone in Portland cement could also increase production capacity. 

According to the PCA, domestic cement supply could increase by as much as 2 million 

additional tons by 2012. Increases in EPA production variances could also add another 1.1 

million metric tons of domestic supply (PCA, 2007). 

2.3.3 Firm Characteristics 

EPA has reviewed industry information and publicly available sales and employment 

databases to identify the chain of ownership by accounting for subsidiaries, divisions, and joint 

ventures to appropriately group companies by size. Table 2-11 provides sales and employment 

data for 27 ultimate parent companies operating Portland cement manufacturing plants in 2005. 

 
2-17 

Table 2-9. Capacity Utilization Rates by State: 2005 

State USGS Geographic Area 

Utilization Rate 

(percent) 

AL Alabama 86.7 

AR Arkansas and Oklahoma 90.9 

AZ Arizona and New Mexico 87 

CA California, northern and southern 88.8 

CO Colorado and Wyoming 79.5 

FL Florida 85.9 

GA Georgia, Virginia, West Virginia 78.4 

IA Iowa, Nebraska, South Dakota 85.5 

ID Idaho, Montana, Nevada, Utah 95.5 

IL Illinois 91.4 

IN Indiana 86.8 

KS Kansas 89.1 

KY Kentucky, Mississippi, Tennessee 87.4 

MD Maryland 89.1 

ME Maine and New York 83.6 

MI Michigan 85.5 

MO Missouri 90.3 

MS Kentucky, Mississippi, Tennessee 87.4 

MT Idaho, Montana, Nevada, Utah 95.5 

NE Iowa, Nebraska, South Dakota 85.5 

NM Arizona and New Mexico 87 

NV Idaho, Montana, Nevada, Utah 95.5 

NY Maine and New York 83.6 

OH Ohio 84.7 

OK Arkansas and Oklahoma 90.9 

OR Oregon and Washington 83.3 

PA Pennsylvania, eastern and western 83.7 

SC South Carolina 64.5 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2007b. 2005 Minerals Yearbook, Cement. 

Table 5. Washington, DC: U.S. Department of the Interior. 
2-18 

Table 2-10. Cement Manufacturing Employment (NAICS 327310): 2000 to 2005 

Year Number of Employees 

2000 17,175 

2001 17,220 

2002 17,660 

2003 17,352 

2004 16,883 

2005 16,877 

Sources: U.S. Department of Commerce, Bureau of the Census. 2006. 2005 Annual Survey of Manufactures. 

M05(AS)-1. Washington, DC: Government Printing Office. Available at 

<http://www.census.gov/prod/2003pubs/m01as-1.pdf>. As obtained on March 14, 2008. 

 U.S. Department of Commerce, Bureau of the Census. 2003. 2001 Annual Survey of Manufactures. 

M05(AS)-1. Washington, DC: Government Printing Office. Available at 

<http://www.census.gov/prod/2003pubs/m01as-1.pdf>. As obtained on March 14, 2008. 

2.3.3.1 Distribution of Small and Large Companies 

Firms are grouped into small and large categories using Small Business Administration 

(SBA) general size standard definitions for NAICS codes. These size standards are presented 

either by number of employees or by annual receipt levels, depending on the NAICS code. The 

manufacture of Portland cement is covered by NAICS code 327310 for cement manufacturing. 

Thus, according to SBA size standards, firms owning Portland cement manufacturing plants are 

categorized as small if the total number of employees at the firm is less than 750; otherwise, the 

firm is classified as large. As shown in Table 2-11, potentially affected firms range in size from 

160 to 71,000 employees. A total of 4 firms, or 15%, are categorized as small, while the 

remaining 23 firms, or 75%, are large.1

2.3.3.2 Capacity Share 

As shown in Table 2-11, the leading companies in terms of capacity at the end of 2005 

were Holcim (U.S.) Inc.; CEMEX, Inc.; Lafarge North America, Inc.; Buzzi Unicem USA, Inc.; 

HeidelbergCement AG (owner of Lehigh Cement Co.); Ash Grove Cement Co.; Texas 

Industries, Inc.; Italcementi S.p.A.; Taiheiyo Cement Corporation; Titan Cement; and VICAT. 

The top 5 had about 57% of total U.S. clinker capacity, and the top 10 accounted for 83% of total 

capacity. Small companies accounted for less than 5% of clinker capacity.

 

1

 In cases where no employment data were available, we used information from previous EPA analyses to determine 

firm size. 
2-19 

Table 2-11. Ultimate Parent Company Summary Data: 2005 

Ultimate Parent 

Name 

Annual 

Sales 

($106

) 

Employment Type Small 

Business Plants Kilns 

Clinker 

Capacity 

(103

 metric 

tons per 

year) 

Capacity 

Share 

Holcim, Inc $14,034 59,901 Public No 14 17 13,089 14.0% 

CEMEX, S.A. de 

C.V. 

$18,290 26,679 Public No 13 21 12,447 13.3% 

Lafarge S.A. $22,325 71,000 Public No 13 23 12,281 13.1% 

BUZZI UNICEM 

SpA 

$3,495 11,815 Private No 10 19 8,129 8.7% 

HeidelbergCement 

AG 

$12,182 45,958 Public No 10 13 7,786 8.3% 

Ash Grove Cement 

Company 

$1,190 2,600 Private No 9 15 6,687 7.1% 

Texas Industries, 

Inc. 

$944 2,680 Public No 4 15 5,075 5.4% 

Italcementi S.p.A. $5,921 20,313 Public No 6 16 4,442 4.7% 

Taiheiyo Cement 

Corporation 

$7,710 2,061 Private No 3 7 3,375 3.6% 

Titan Cement $1,589 1,834 Public No 2 2 2,612 2.8% 

VICAT $2,137 6,015 Public No 2 2 1,933 2.1% 

Eagle Materials $922 1,600 Public No 3 5 1,651 1.8% 

Mitsubishi Cement 

Corporation 

$1,134 NA Joint 

venture 

No 1 1 1,543 1.6% 

Rinker Materials $4,140 11,193 Private No 2 2 1,533 1.6% 

Hanson America 

Holdings 

$3,000 14,872 Private No 1 1 1,497 1.6% 

Salt River Materials 

Group a

$150b

 <750 Tribal 

Govern

ment 

Yes 1 4 1,477 1.6% 

Grupo Cementos de 

Chihuahua, S.A. de 

C.V. 

$663 2,591 Public No 2 5 1,283 1.4% 

Cementos Portland 

Valderrivas, S.A. 

$1,159 2,674 Public No 2 6 1,257 1.3% 

Zachary 

Construction 

$152 1,200 Private No 1 2 868 0.9% 

RMC Pacific 

Materials 

$160 800 Private No 1 1 812 0.9% 

(continued) 
2-20 

Table 2-11. Ultimate Parent Company Summary Data: 2005 (continued) 

Ultimate Parent 

Name 

Annual 

Sales 

($106

) 

Employment Type Small 

Business Plants Kilns 

Clinker 

Capacity 

(103

 metric 

tons per 

year) 

Capacity 

Share 

Monarch Cement 

Company 

$154 600 Public Yes 1 2 787 0.8% 

Florida Rock 

Industries 

$1,368 3,464 Public No 1 1 726 0.8% 

Votorantim Group 

and Anderson 

Columbia Company 

$9,518 30,572 Joint 

venture 

No 1 1 682 0.7% 

Dyckerhoff AG $1,876 6,958 Public No 1 1 586 0.6% 

Continental Cement 

Company, LLC 

$50b

 <750 Private Yes 1 1 549 0.6% 

Cementos Del 

Norte 

NA NA Private No 1 1 392 0.4% 

Snyder Associate 

Companies 

$29 350 Private Yes 1 2 286 0.3% 

a

 Enterprise is owned by Salt River Pima-Maricopa Indian Community. 

b

 EPA estimate. 

Sources: Dun & Bradstreet, Inc. 2007. D&B million dollar directory. Bethlehem, PA. 

LexisNexis. LexisNexis Academic [electronic resource]. Dayton, OH: LexisNexis. 

2.3.3.3 Company Revenue and Ownership Type 

Cement manufacturing is a capital-intensive industry. The vast majority of stakeholders 

are large global companies with sales exceeding $1 billion. In 2005, ultimate parent company 

sales ranged from $30 million to $22.3 billion (Table 2-11), with average (median) sales of 

$4,565 ($1,589) million. Small companies accounted for 0.3% share by sales. Ultimate parent 

companies were either privately or publicly owned or jointly operated by several companies. A 

majority of the companies (52%) were publicly owned. Private companies had a slightly smaller 

share (41%), and only two (or 7%) were joint ventures. 

2.4 Markets 

Portland cement is produced and consumed domestically as well as traded internationally. 

The United States meets a substantial fraction of its cement needs through imports; in contrast, it 

exports only a small fraction of domestically produced cement to other countries. We provide 

value, quantity, and price trends over the past decade for Portland cement when detailed statistics 
2-21 

are available. In the case of international trade, we can report data only for hydraulic cement, 

which includes Portland and masonry cement. 

2.4.1 Market Volumes 

2.4.1.1 Domestic Production 

In 2007, the domestic shipments of Portland cement were 90.6 million metric tons, 

reflecting an 8.5% increase from 2000 and, more recently, a 3% decrease from 2006 (see 

Table 2-12). Year-end stocks remained relatively level during this period at 7.4 million metric 

tons. Stocks fell slightly by 5% since 2006 and equaled 8.9 million tons in 2007. As Table 2-12 

shows, shipments to final customers increased steadily since 2000, reaching 128 million tons in 

2006. However, affected by declines in the housing market, the shipments fell by 9% in 2007. 

Table 2-12. Historical U.S. Cement Statistics (106 metric tons) 

 2000 2001 2002 2003 2004 2005 2006 2007 

Production 

Clinker 78.1 78.5 82.0 81.9 86.7 87.4 88.6 87.2 

Portland cement 83.5 84.5 85.3 88.1 92.4 93.9 93.2 90.6 

Masonry cement 4.3 4.5 4.4 4.7 5.0 5.4 5.0 4.9 

Total cement 87.8 88.9 89.7 92.8 97.4 99.3 98.2 95.5 

Shipments to final customers 110.0 113.1 110.0 112.9 120.7 127.4 127.9 116.0 

Stocks, cement, year end 7.6 6.6 7.6 6.6 6.7 7.4 9.4 8.9 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2008b. Minerals Commodity Summaries, Cement 

2008. Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/ 

pubs/commodity/cement/mcs-2008-cemen.pdf>. 

U.S. Department of the Interior, U.S. Geological Survey. 2003. 2002 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/ 

commodity/cement/>. 

2.4.1.2 International Trade 

Cement imports are a significant share of domestic consumption (approximately 20%); 

they also grew by 30% from 2000 to 2006 (see Table 2-13). Major importing countries in 2007 

included Canada (18% of total imports in 2006), China (16%), and Thailand (11%) (DOI, USGS, 

2008b). In 2007, the falling value of the dollar and construction activity declines in the housing 

market tempered the quantity of import demanded. As a result, the share of U.S. consumption 

met by imports fell to its lowest level in 10 years. 
2-22 

Table 2-13. U.S. Cement Trade Data: 2000 to 2007 

 2000 2001 2002 2003 2004 2005 2006 2007 

Exports (106 metric tons) 0.7 0.7 0.9 0.8 0.7 0.8 1.5 1.9 

Imports (106 metric tons) 24.6 23.6 22.5 21.0 25.4 30.4 32.1 21.3 

Net import share of apparent 

consumption (%) 

20.0 21.0 19.0 20.0 21.0 23.0 23.0 17.0 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2008b. Minerals Commodity Summaries, Cement 

2008. Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/ 

pubs/commodity/cement/mcs-2008-cemen.pdf>. 

U.S. Department of the Interior, U.S. Geological Survey. 2003. 2002 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Available at 

<http://minerals.er.usgs.gov/minerals/pubs/>. 

During the period from 2000 to 2005, U.S. exports remained relatively constant at about 

800,000 tons and typically did not exceed 1% of production. However, the level of U.S. exports 

has increased during the last 2 years. In 2007, U.S. exports totaled 1.9 million metric tons. The 

vast majority of U.S. exports of hydraulic cement are supplied to Canada: U.S. producers 

shipped a total of 650,000 tons to Canada in 2005, or 85% of total U.S. exports. The remaining 

fraction of U.S. exports in 2005 went to the Bahamas, Mexico, and 33 other countries around the 

world (DOI, USGS, 2008b). 

2.4.2 Market Prices 

Correcting for the effects of inflation, we find that the real price of cement per metric ton 

(2005 dollars) has typically ranged between $75 and $95 since 1990 (see Figure 2-7). However, 

data for the last 2 years suggest the average price of cement is at its highest level in over 2 

decades (approximately $100). Because of transportation constraints, there are regional 

differences in the price of cement across states. For example, remote locations such as Alaska 

and Hawaii had the highest deviation from the national average ($48 in 2005) (see Figure 2-8). 

In the contiguous states, prices in Arizona, New Mexico, and California were higher than the 

national averages, while prices in Texas, Indiana, and South Carolina were among the lowest. 

2.4.3 Future Projections 

Although estimates of future cement demand are not publicly available, the Energy 

Information Administration provides projections for the real value of shipments for the stone, 

clay, and glass industry in its AEO (DOE, 2007). The forecasted annual average growth rate for 

2005 to 2030 is approximately 1.7%. 
2-23 

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

$110

F.O.B. Cement Price ($/metric ton)

Year

Current U.S. Dollars

Constant 2005 U.S. Dollars

Figure 2-7. Historical U.S. Cement Price 

Sources: 1990–2003: Kelly, T. and G. Matos. 2007b. “Historical Statistics for Mineral and Material Commodities in 

the United States: Cement Supply and Demand Statistics.” U.S. Geological Survey Data Series 140, 

Version 1.2. Available at <http://minerals.usgs.gov/ds/2005/140/>. Last modified April 11, 2006. 

2004–2007: U.S. Department of the Interior, U.S. Geological Survey. 2008b. Minerals Commodity 

Summaries, Cement 2008. Washington, DC: U.S. Department of the Interior. Available at 

<http://minerals.usgs.gov/minerals/pubs/commodity/cement/mcs-2008-cemen.pdf>. 
2-24 

-$20 -$15 -$10 -$5 $0 $5 $10 $15 $20 $25 $30 $35 $40 $45 $50 $55

South Carolina

Indiana

Texas, southern

Alabama

Maryland

Arkansas and Oklahoma

Texas, northern

Kansas

Georgia, Virginia, West Virginia

Kentucky, Mississippi, Tennessee

Missouri

Pennsylvania, eastern

Colorado and Wyoming

Oregon and Washington

Illinois

Maine and New York

Pennsylvania, western

Ohio

Florida

Iowa, Nebraska, South Dakota

Michigan and Wisconsin

Idaho, Montana, Nevada, Utah

California, southern

California, northern

Arizona and New Mexico

Alaska and Hawaii

Dollar Difference From National Average 

F.O.B. Cement Price ($90 per metric ton): 2005

Figure 2-8. Deviation from National Average Cement Price per Metric Ton by Region: 

2005 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Table 11. Available at 

<http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. 
3-1 

SECTION 3

ECONOMIC IMPACT ANALYSIS 

EPA prepares an EIA to provide decision makers with a measure of the social costs of 

using resources to comply with a program (EPA, 2000). The social costs can then be compared 

with estimated social benefits (as presented in Section 5). As noted in EPA’s (2000) Guidelines 

for Preparing Economic Analyses, several tools are available to estimate social costs and range 

from simple direct compliance cost methods to the development of a more complex market 

analysis that estimates market changes (e.g., price and consumption) and economic welfare 

changes (e.g., changes in consumer and producer surplus). 

The Office of Air Quality Planning and Standards (OAQPS) has adopted the standard 

industry-level analysis described in the Office’s resource manual (EPA, 1999a). This approach is 

consistent with previous EPA analyses of the Portland cement industry (EPA, 1998; EPA, 

1999b) and uses a single-period static partial-equilibrium model to compare prepolicy cement 

market baselines with expected postpolicy outcomes in these markets. The benchmark time 

horizon for the analysis is the intermediate run where producers have some constraints on their 

flexibility to adjust factors of production. This time horizon allows us to capture important 

transitory impacts of the program on existing producers. Key measures in this analysis include 

 market-level effects (market prices, changes in domestic production and 

consumption, and international trade), 

 industry-level effects (changes in revenues, costs, profits, employment), 

 facility-level effects (plant utilization changes), and 

 social costs (changes in producer and consumer surplus). 

3.1 Regulatory Program Costs 

EPA is proposing the new emission limits for mercury, total hydrocarbons (THC), 

particulate matter, and hydrochloric acid. For the year 2013, EPA’s engineering cost analysis 

estimates the total annualized costs of the proposed rule are $368 million (in 2005 dollars) (see 

Table 3-1). These costs include a variety of pollution control expenditures: equipment 

installation, operating and maintenance, recordkeeping, and performance-testing activities. 

Figure 3-1 illustrates the distribution of annualized compliance costs per metric ton of capacity 

for existing grey and white cement kilns. For analytical convenience, the analysis assumes the 
3-2 

Table 3-1. Summary of Direct Compliance Costs (106 2005$) 

Type Value EIA Social Cost Method 

Existing Grey Cement Kilns 

Total: $280 

Partial-equilibrium model 

(baseline year 2005) 

Mercury $28 

THC $111 

PM $17 

HCl (maximum achievable control 

technology [MACT] option) 

$124 

Existing White Cement Kilns 

Total: $2 

Direct compliance cost method 

Mercury <$1 

THC <$1 

PM <$1 

HCl (MACT option) <$1 

20 Additional New Kilns in 2013 

Total: $86 

Direct compliance cost method 

Mercury $25 

THC $20 

PM $5 

HCl (MACT option) $36 

Total, All Kilns: $368 

capital costs will take place at the beginning of 2013. However, costs may actually begin being 

phased in a year or two earlier.1

3.2 Partial-Equilibrium Analysis for Costs Applying to Existing Kilns 

The partial-equilibrium analysis performed for this rule develops a cement market model 

that simulates how stakeholders (consumers and firms) might respond to the additional 

regulatory program costs. In this section, we provide an overview of the economic model. 

Appendix A provides additional details on the behavioral assumptions, data, parameters, and 

model equations. 

 

1

 These costs are actually draft compliance costs. The final compliance cost estimate for this rule are somewhat 

lower, at $317 million; this difference is not expected to have an impact on the results of the market analysis, 

emissions reductions, or on the expected distribution of social costs among stakeholders. 
3-3 

0

5

10

15

20

25

30

35

40

45

50

<$0.10 $0.10 to

<$0.50

$0.50 to

<$1.00

$1.00 to

<$1.50

$1.50 to

<$2.00

$2.00 to

<$2.50

$2.50 to

<$3.00

$3.00 to

<$3.50

$3.50 to

<$4.00

$4.00 to

<$4.50

$4.50 to

<$5.00

>$5.00

Range ($/metric ton of clinker capacity)

Number of Kilns (N=186)

Figure 3-1. Distribution of Annualized Direct Compliance Costs per Metric Ton of Clinker 

Capacity: Existing Grey and White Cement Kilns (2005$) 

3.2.1 Regional Structure and Baseline Data 

Cement sales are often concentrated locally among a small number of firms for two 

reasons: high transportation costs and production economies of scale.2 Transportation costs 

significantly influence where cement is ultimately sold; high transportation costs relative to unit 

value provide incentives to produce and sell cement locally in regional markets (USITC, 2006). 

To support this claim, the empirical literature has typically pointed to Census of Transportation 

data showing over 80% of cement shipments were made within a 200-mile radius (Jans and 

Rosenbaum, 1997)3 and reported evidence of high transportation costs per dollar of product value 

from case studies (Ryan, 2006). Based on this literature, the Agency assumes that the U.S. 

Portland cement industry is divided into a number of independent regional markets with each 

having a single market-clearing price. 

 

2

 The 2002 Economic Census reports that the national Herfindahl-Hirschman Index (HHI) for cement—North 

American Industry Classification System (NAICS) 32731—is 568. However, this measure is likely not 

representative of actual concentration that exists in regional markets. 3

 A recent USITC study of California cement markets found more than 75% of gray Portland cement shipments in 

the state were shipped to customers within 200 miles of the cement producer (USITC, 2006). 
3-4 

The need for a complete set of statistics makes the use of a 2005 baseline the best choice; 

it was the latest year for which the PCA had published their plant information summary and 

complete statistics for updating variable cost functions were available. However, EPA recognizes 

that the demand for cement is a derived demand because it is dependent on demand for sectors 

such as housing and construction. As a result, business cycles also influence the cement industry. 

If 2013 is more or less favorable for the cement industry than 2005, then impacts would be 

expected to change accordingly. 

The freight-on-board (f.o.b.) price of Portland cement for each regional market is derived 

as the production weighted average of the state level f.o.b. prices reported by the USGS for 

cement (see Table 3-2). The production of Portland cement within each market is the sum of 

estimated individual kiln production levels (see Appendix A for additional details) (see Table 3-

3). We obtained estimates of Portland cement imports from the USGS and mapped them to each 

market based on the port of entry. 

Table 3-2. Portland Cement Prices by Market ($/metric tons): 2005 

Market Price ($/metric ton) 

Atlanta $81 

Baltimore/Philadelphia $86 

Birmingham $83 

Chicago $86 

Cincinnati $84 

Dallas $83 

Denver $89 

Detroit $93 

Florida $91 

Kansas City $86 

Los Angeles $97 

Minneapolis $92 

New York/Boston $89 

Phoenix $99 

Pittsburgh $88 

St. Louis $87 

Salt Lake City $91 

San Antonio $82 

San Francisco $97 
3-5 

Seattle $88 

Table 3-3. Portland Cement Markets (106 metric tons): 2005 

Market U.S. Production 

Importsa

Canada Rest of World Total 

Atlanta 6.1 — 2.3 8.4 

Baltimore/Philadelphia 8.0 — 0.6 8.6 

Birmingham 5.9 — 2.2 8.1 

Chicago 4.3 0.2 <0.1 4.5 

Cincinnati 3.7 — — 3.7 

Dallas 8.2 — 2.4 10.6 

Denver 3.4 — — 3.4 

Detroit 4.8 1.3 0.1 6.1 

Florida 5.6 — 5.8 11.4 

Kansas City 5.3 — 0.0 5.3 

Los Angeles 9.6 — 3.8 13.4 

Minneapolis 1.7 0.4 — 2.1 

New York/Boston 3.2 0.6 2.1 6.0 

Phoenix 4.1 — — 4.1 

Pittsburgh 1.5 1.6 <0.1 3.1 

St. Louis 5.4 — — 5.4 

Salt Lake City 2.4 0.1 — 2.4 

San Antonio 5.7 — 4.6 10.3 

San Francisco 3.4 — 2.8 6.2 

Seattle 1.1 1.3 1.2 3.6 

 Total, Grey 93.6 5.4 27.9 126.8 

 Total, White 0.3 0.3 1.5 2.1 

Total 93.9 5.7 29.4 129.0 

a Hydraulic cement. The vast majority of these imports are Portland cement (approximately 29 million metric tons, 

or 86%). Excludes Puerto Rico. 
3-6 

3.2.2 Seller Pricing Behavior 

Once the markets were defined, we examined the evidence supporting the appropriate 

supplier pricing behavior assumptions in these markets. For example, the degree of 

concentration, entry barriers, and product differentiation can indicate a firm’s ability to influence 

market prices by varying the quantity of cement it sells. In markets with large numbers of sellers 

and identical products, firms are unlikely to be able to influence market prices via their 

production decisions (i.e., they are “price takers”). However, in markets with few firms, 

significant barriers to entry (e.g., licenses, legal restrictions, or high fixed costs) or with products 

that are similar but can be differentiated, the firm may have some degree of market power (i.e., 

set or significantly influence market prices). 

Although perfect competition on the supply side (and demand side) is widely accepted 

for modeling many industries (EPA, 2000), the cement industry has unique characteristics that 

lead us to use an alternative assumption about supplier pricing behavior. First, high 

transportation costs and other production economics limit the number of sellers, so each seller 

has a substantial market share. Potential entry is constrained by the high capital costs that involve 

purchases and construction of large rotary kilns that are not readily movable or transferable to 

other uses. In addition, large plants are typically more economical because they can produce 

cement at lower unit costs; this reduces entry incentives for small-sized cement plants. Second, 

cement producers offer very similar or identical products. American Society for Testing and 

Materials (ASTM) specifications tend to ensure uniform quality, and recent industry reviews 

(USITC, 2006) suggest that there is little or no brand loyalty that allows firms to differentiate 

their products. Given this evidence, EPA continued to use the oligopoly framework used in 

previous economic analyses (1998, 1999b). 

One consequence of this assumption is that the seller individually chooses an output level 

that is less than the level produced under perfect competition. As a result, the baseline market 

price will be higher than a model of perfect competition, and there is a preexisting market 

distortion in the industry being regulated.4

 The size of the existing distortion depends on the 

seller’s market share and how responsive cement consumers are to changes in the cement price. 

Economic theory suggests that in the model EPA selected for this analysis, the market distortion 

 

4

 This ultimately influences the partial-equilibrium model’s estimates of the social cost of the regulatory program 

since bigger existing market distortions tend to widen the gap between price and marginal cost in these markets 

and lead to higher deadweight loss estimates than under the case of perfectly competitive markets. The Office of 

Management and Budget (OMB) explicitly mentions the need to consider market power–related welfare costs in 

evaluating regulations under Executive Order 12866 (EPA, 1999a). 
3-7 

will typically be higher the smaller the number of sellers and in markets where the quantity 

demanded is less sensitive to price (i.e., the demand elasticity is inelastic) (see Appendix A). 

3.2.3 Economic Impact Analysis Results 

3.2.3.1 Market-Level Results 

Market-level impacts include the regional price and quantity adjustments for Portland 

cement, including the changes in imports for the appropriate regions. As shown in Table 3-4, the 

average national price for Portland cement increases by 4% higher, or $3.30 per metric ton, while 

domestic production falls by 8%, or 7 million tons per year. 

Table 3-4. National-Level Market Impacts: 2005 

 Changes from Baseline 

 Baseline Absolute Percent 

Market Price ($/metric ton) $88.35 $3.30 3.7% 

Market Output (106 metric tons) 127 −4 −3.3% 

Domestic production 94 −7 −7.8% 

Imports 33 2 7.1% 

As shown in Table 3-5, price increases are the highest in regions with high compliance 

costs per metric ton. For example, the Cincinnati market price increase ($8.00 per metric ton) 

also includes kilns with higher average compliance costs and a kiln with the highest per-unit 

compliance costs ($15.10 per metric ton). 

Imports of Portland cement increase in response to higher domestic cement prices. As 

shown in Table 3-4, imports increase by 7%, or 2 million metric tons. Imports also tend to limit 

price increases in certain regions. Cement plants in these regions have more difficulty passing on 

compliance costs in the form of higher prices when compared with similar plants operating in 

regions without import competition. As shown in Table 3-6, median price increases in regions 

with imports are approximately 17% lower than the median price increases in regions without 

import competition. 
3-8 

Table 3-5. Regional Compliance Costs and Market Price Changes ($/metric ton of 

cement): 2005 

Market 

Compliance Costs Market Price Change 

Mean Minimum Maximum Absolute Percent 

Atlanta $3.20 $0.00 $6.50 $2.10 2.6% 

Baltimore Philadelphia $4.70 $0.00 $8.10 $4.60 5.3% 

Birmingham $2.30 $0.00 $4.50 $2.50 3.1% 

Chicago $3.40 $0.00 $7.10 $3.50 4.1% 

Cincinnati $7.30 $2.40 $15.10 $8.00 9.5% 

Dallas $2.70 $0.00 $6.40 $4.30 5.2% 

Denver $2.90 $1.30 $4.90 $4.80 5.4% 

Detroit $3.50 $0.00 $6.20 $3.80 4.1% 

Florida $2.30 $1.10 $3.10 $1.90 2.1% 

Kansas City $4.90 $0.00 $9.30 $4.90 5.7% 

Los Angeles $4.00 $2.30 $6.20 $2.80 2.9% 

Minneapolis $3.60 $2.40 $5.60 $4.80 5.2% 

New York Boston $2.50 $1.30 $4.20 $1.60 1.8% 

Phoenix $2.80 $1.20 $5.60 $3.30 3.3% 

Pittsburgh $4.90 $4.50 $5.30 $3.40 3.9% 

St Louis $2.10 $0.00 $4.10 $2.40 2.7% 

Salt Lake City $4.60 $2.90 $6.60 $5.50 6.0% 

San Antonio $4.00 $1.50 $7.50 $3.10 3.8% 

San Francisco $2.40 $1.60 $3.20 $2.00 2.1% 

Seattle $1.60 $1.20 $2.00 $0.80 0.9% 

National $3.00 $0.00 $15.10 $3.30 3.7% 

3.2.3.2 Industry-Level Results 

As domestic production falls, cement industry revenues are projected to decline by 4%, 

or $341 million (see Table 3-7). Overall, net production costs also fall by $137 million with 

compliance cost increases ($235 million) offset by cost reductions associated with lower cement 

production. Operating profits fall by $204 million, or 16%. Other consequences include reduced 

demand for labor. Employment falls by approximately 8%, or 1,167 employees. 
3-9 

Table 3-6. Summary of Regional Market Impacts: 2005 

 Regional Markets 

 With Imports Without Imports All Markets 

Change in Market Price 

Absolute ($/metric ton) 

Mean $3.20 $4.60 $3.30 

Median $3.30 $4.00 $3.30 

Minimum $0.80 $2.40 $0.80 

Maximum $5.50 $8.00 $8.00 

Percentage of baseline price 

Mean 3.7% 5.2% 3.7% 

Median 3.8% 4.4% 3.8% 

Minimum 0.9% 2.7% 0.9% 

Maximum 6.0% 9.5% 9.5% 

Change in Domestic Production 

Absolute (103

 metric tons) 

Mean −408.6 −189.8 −207.5 

Median −333.1 −160.5 −179.4 

Minimum −79.9 −129.1 −27.2 

Maximum −931.5 −309.0 −486.7 

Percentage of baseline production 

Mean −8.7% −4.8% −8.0% 

Median −7.8% −4.3% −7.4% 

Minimum −3.8% −2.4% −2.4% 

Maximum −21.8% −8.4% −21.8% 

As shown in Table 3-8, compliance costs vary by cement plant, and this variation 

suggests some plants will be more adversely affected than others. To assess these differences, 

EPA collected industry operating profit data and identified plants with operating profit increases 

and losses. Absent plant-specific data, EPA assumed each plant’s baseline profits were consistent 

with the median operating profit margin reported by the PCA (2008c, Table 44). In 2005, this 

value was $18 per metric ton, or 15.7%. Using this assumption, total operating profits for 67 

plants (64%) decrease by $328 million with regulation. These plants tend to be larger major 

sources and have higher unit compliance costs. The remaining plants’ compliance burden is 
3-10 

Table 3-7. National-Level Industry Impacts: 2005 

 Changes from Baseline 

 Baseline Absolute Percent 

Revenues ($106) $8,261 −$341 −4.1% 

Costs ($106) $6,966 −$137 −2.0% 

Cement production $6,966 −$372 −5.3% 

Regulatory program $0 $235 NA 

Operating Profits ($106

) $1,294 −$204 −15.8% 

Employment 15,440 −1,167 −7.6% 

NA = Not available. 

Table 3-8. Distributional of Industry Impacts: 2005 

 Changes in Total Operating Profit: 

 Plants with Loss Plants with Gain All Plants 

Number 67 38 105 

Cement Capacity (106 metric tons) 

Total 64,757 35,668 100,424 

Average per plant 967 939 956 

Compliance Costs 

Total ($103

) $230,894 $46,829 $277,722 

Average ($/metric cement) $3.57 $1.31 $2.77 

Capacity Utilization (%) 

Baseline 93.4% 92.9% 93.2% 

With regulation 80.4% 95.9% 85.9% 

Change in total operating profits ($106

) −$329 $125 −$204 

Change in Employees −1,345 178 −1,167 

offset by higher cement prices, and total plant operating profits increase by $125 million. These 

plants are typically smaller area sources and have lower unit compliance costs compared with 

their competitors. 

Within the group of plants with operating losses, EPA identified six domestic plants with 

negative operating profits and significant utilization changes that could temporarily idle until 

market demand conditions improve (see Table 3-9). The plants are small capacity plants with 

unit compliance costs close to $5 per ton; they account for approximately 2.5% of domestic 
3-11 

Table 3-9. Cement Plants with Significant Utilization Changes: 2005 

 Total 

Number 6 

Cement Capacity (106 metric tons) 

Total 2,860 

Average per plant 477 

Compliance Costs 

Total ($103

) $14,185 

Average ($/metric cement) $4.96 

Capacity Utilization (%) 

Baseline 94.7% 

With regulation 39.3% 

Change in Operating Profit ($106

) −$50 

Change in Employees −208 

capacity. If the plant owners did decide to permanently shut down these plants, the reduction in 

domestic supply would lead to additional projected market price increases. Reducing national 

supply by an additional 2 million metric tons and holding other domestic and foreign supply 

fixed, EPA calculations suggest the national cement price could rise by 6.5% ($5.80 per metric 

ton). 

3.3 Direct Compliance Cost Method for White Cement Kilns and Kilns Coming On 

Line in 2013 

The partial-equilibrium analysis is an illustrative example of the economic impacts 

associated with the engineering cost analysis for existing kilns ($280 million in direct 

compliance costs). In addition, EPA developed a separate engineering cost analysis for four 

white cement kilns and an additional 20 kilns that are likely to come on line by 2013. These costs 

were not included in the EIA because of uncertainties associated with cement market conditions 

in 2013. 

The total annualized costs for white cement kilns are $1.5 million, or $4.90 per metric ton 

of clinker capacity. Using reported 2005 data from the USGS on the average mill net value of 

white cement ($176 per metric ton), this cost represents between 2.5 and 2.8% of the product 

value. 
3-12 

Using a model kiln with a clinker capacity of 1.2 million metric tons, EPA estimates the 

total annualized costs for 20 additional kilns coming on line in 2013 to be $86 million. The 

average cost per ton of clinker capacity is approximately $4.00.5

Using a range of historical national price data and unit cost data, EPA conducted sales 

tests for a representative kiln: 

Sales Test Ratio = Control Costs ($/ton)/F.O.B Cement Prices ($/ton). 

The USGS reports that the real price of cement per metric ton (2005 dollars) has typically 

ranged between $75 and $100 since 1990. A sales test using these data shows cost-to-sales ratios 

(CSRs) between 3 and 5% (see Figure 3-2). 

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

12%

13%

14%

15%

AL

AR

AZ

CO

FL

IL

MO

NVP

A

SC

TX

WVWY

U.S. High Price (90-07)

U.S. Low Price (90-07)

Cost-to-Sales Ratio (%)

Region

Figure 3-2. Hypothetical Cost-to-Sales Ratios for a Representative Kiln Coming On Line 

in 2013 

 

5

 Using a cement/clinker ratio of 1.14, this is approximately $3.50 per ton of cement. 
3-13 

From 2000 to 2006, the PCA reports that the average operating profit rates for the 

industry ranged from 17 to 21% (PCA, 2008c). If these profit data are representative of operating 

profit rates for new kilns, kilns could potentially significantly reduce their operating profit rates. 

As a result, companies may have the incentive to look for less expensive alternatives to meet the 

emission standards. If these alternatives are limited or not cost effective, the NESHAP may lead 

companies to consider delaying rates of construction of these new kilns until market conditions 

change (e.g., increases in demand that lead to rising cement prices) to cover these additional 

control costs. 

3.4 Social Cost Estimates 

The market adjustments in price and quantity were used to estimate the changes in 

aggregate economic welfare using applied welfare economics principles (see Appendix C). Table 

3-10 presents the estimates of the social costs and their distribution. Higher cement prices and 

reduced consumption lead to consumer welfare losses ($402 million). Domestic producers (in 

aggregate) experience a net loss of $204 million. As noted in the previous section, individual 

domestic producers will gain or lose depending on the change in costs versus the change in the 

regional market prices. The total domestic surplus loss (consumer and producers) totals $606 

million. Other countries selling cement to the United States will benefit from higher cement 

prices (a surplus gain of $89 million). The resulting net change in total surplus estimated by the 

partial-equilibrium analysis is $517 million. 

The estimated social cost of the proposed NESHAP is $606 million. This estimate 

includes the results for existing kilns included in the partial-equilibrium analysis ($517 million) 

and the direct compliance costs for white cement kilns ($2 million) and 20 additional kilns 

projected to come on line in 2013 ($86 million). The social estimates are significantly higher 

than the engineering analysis estimate of annualized costs totaling $368 million. This is a direct 

consequence of EPA’s assumptions about existing domestic plants’ pricing behavior discussed 

extensively in previous cement industry rulemakings and in Section 2 and Appendix B of this 

RIA. Under baseline conditions without regulation, the existing domestic cement plants are 

assumed to choose a production level that is less than the level produced under perfect 

competition. As a result, a preexisting market distortion exists in the markets covered by the 

proposed rule (i.e., the observed baseline market price is higher than the [unobserved] market 

price that a model of perfect competition would predict). The imposition of additional regulatory 

costs tends to widen the gap between price and marginal cost in these markets and contributes to 

additional social costs. The above social costs for 2013 include annualized capital costs over the 

expected lifetime of the equipment and an opportunity cost of capital (7%) discount rate. To 
3-14 

facilitate comparisons of benefits and costs when estimates vary of time across multiple years, 

EPA typically estimates a “consumption equivalent” present value measure of costs. This could 

be computed using a consumption rate of interest of 3% and 7%. However, this calculation was 

not necessary since the cost and benefit analyses only produce estimates for a single year 

(OAQPS, 1999a). 

Table 3-10. Distribution of Social Costs ($106

): 2005 

Partial-Equilibrium Model 

Change in consumer surplus −$402 

Change in domestic producer surplus −$204

Change in domestic surplus −$606 

Change in foreign producer surplus $89 

Net change in total surplus −$517 

Direct Compliance Costs Method 

White cement (not modeled) −$2 

New kilns (not modeled) −$86 

Change in total surplus −$605 

3.5 Energy Impacts 

Executive Order 13211 (66 FR 28355, May 22, 2001) provides that agencies will prepare 

and submit to the Administrator of the Office of Information and Regulatory Affairs, OMB, a 

Statement of Energy Effects for certain actions identified as “significant energy actions.” Section 

4(b) of Executive Order 13211 defines “significant energy actions” as any action by an agency 

(normally published in the Federal Register) that promulgates or is expected to lead to the 

promulgation of a final rule or regulation, including notices of inquiry, advance notices of 

proposed rulemaking, and notices of proposed rulemaking: (1) (i) that is a significant regulatory 

action under Executive Order 12866 or any successor order, and (ii) is likely to have a significant 

adverse effect on the supply, distribution, or use of energy; or (2) that is designated by the 

Administrator of the Office of Information and Regulatory Affairs as a significant energy action. 

This rule is not a significant energy action as designated by the Administrator of the 

Office of Information and Regulatory Affairs because it is not likely to have a significant adverse 

impact on the supply, distribution, or use of energy. EPA has prepared an analysis of energy 

impacts that explains this conclusion below. 
3-15 

To enhance understanding regarding the regulation’s influence on energy consumption, 

EPA examined publicly available data describing the cement sector’s energy consumption. The 

AEO 2009 (DOE, 2008) provides energy consumption data. As shown in Table 3-11, this 

industry accounts for less than 0.4% of the U.S. total energy consumption. As a result, any 

energy consumption changes attributable to the regulatory program should not significantly 

influence the supply, distribution, or use of energy. EPA has also estimated the 

Table 3-11. U.S. Cement Sector Energy Consumption (Trillion BTUs)a

: 2013 

Quantity Share of Total Energy Use 

Residual fuel oil 1.05 0.0% 

Distillate fuel oil 7.97 0.0% 

Petroleum coke 53.70 0.1% 

Other petroleumb

 34.35 0.0% 

Petroleum subtotal 97.07 0.1% 

Natural gas 22.55 0.0% 

Steam coal 227.33 0.2% 

Metallurgical coal 7.70 0.0% 

Coal subtotal 235.03 0.2% 

Purchased electricity 44.79 0.0% 

Total 399.44 0.4% 

Delivered Energy Use 74,045 72.2% 

Total Energy Use 102,581 100.0% 

a

 Fuel consumption includes consumption for combined heat and power. 

b

 Includes petroleum coke, lubricants, and miscellaneous petroleum products. 

Source: U.S. Department of Energy, Energy Information Administration. 2008. Supplemental Tables to the Annual 

Energy Outlook 2009. Table 10 and Table 39. Available at 

<http://www.eia.doe.gov/oiaf/aeo/supplement/supref.html>. 

amount of additional electricity consumption associated with add-on controls. The analysis 

shows electricity consumption may increase by 926 million kWh per year as a result of these 

controls. This is less than 0.1% of AEO 2013 electricity forecasts of total electricity use (4,091 

billion kWh). 
4-1 

SECTION 4

SMALL BUSINESS IMPACT ANALYSIS 

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule subject to notice and comment rulemaking 

requirements under the Administrative Procedure Act or any other statute unless the agency 

certifies that the rule will not have a significant economic impact on a substantial number of 

small entities (SISNOSE). The first step in this assessment was to determine whether the rule 

will have SISNOSE. To make this determination, EPA used a screening and market analysis to 

indicate whether EPA can certify the rule as not having a SISNOSE. The elements of this 

analysis included 

 identifying affected small entities, 

 selecting and describing the measures and economic impact thresholds used in the 

analysis, and 

 completing the assessment and determining the SISNOSE certification category. 

4.1 Identify Affected Small Entities 

For the purposes of assessing the impacts of the proposed rule on small entities, small 

entity is defined as (1) a small business as defined by the Small Business Administration’s 

regulations at 13 CFR 121.201; according to these size standards, ultimate parent companies 

owning Portland cement manufacturing plants are categorized as small if the total number of 

employees at the firm is fewer than 750 (see Table 4-1 for list); (2) a small governmental 

jurisdiction that is a government of a city, county, town, school district, or special district with a 

population of less than 50,000; and (3) a small organization that is any not-for-profit enterprise 

that is independently owned and operated and is not dominant in its field. As reported in Section 

2, EPA has identified four small entities (see Table 4-1). One of the four entities is owned by a 

small Tribal government (Salt River Pima-Maricopa Indian Community). The remaining three 

entities are small businesses. 

4.2 Sales and Revenue Test Screening Analysis 

In the next step of the analysis, EPA assessed how the regulatory program may influence 

the profitability of ultimate parent companies by comparing pollution control costs to total sales 

(i.e., a “sales” test). To do this, we divided an ultimate parent company’s total annualized 

compliance costs by its reported revenue: 
4-2 

Table 4-1. Small Entity Summary Data: 2005 

Owner 

Entity 

Type 

Annual 

Sales ($106) Employees Plants Kilns 

Clinker 

Capacity (103

metric tons 

per year) 

U.S. 

Capacity 

Share 

Salt River 

Materials Groupa

Tribal 

government 

$184b

 NA 1 4 1,477 1.6% 

Monarch Cement 

Company 

Business $154 600 1 2 787 0.8% 

Continental 

Cement 

Company, LLC 

Business $50c

 <750 1 1 549 0.6% 

Snyder Associate 

Companies 

Business $29 350 1 2 286 0.3% 

a

 Enterprise is owned by Salt River Pima-Maricopa Indian Community. 

b

 EPA estimate. Estimate uses revenue data for four of the six enterprises owned by Salt River Pima-Maricopa 

Indian Community. 

c

 EPA estimate. Estimate uses cement production levels and average market prices. 

j

n

i

TR

TACC

CSR

∑

= (4.1) 

where 

CSR = cost-to-sales ratio, 

TACC = total annualized compliance costs, 

i = index of the number of affected plants owned by company j, 

n = number of affected plants, and 

TRj = total sales from all operations of ultimate parent company j or annual 

government revenue. 

This method assumes the affected entity cannot shift pollution control costs to consumers 

(in the form of higher market prices). Instead, the owning entity experiences a one-for-one 

reduction in profits. For small entities, the total reduction in profits under this method is 

approximately $7, and the average loss is $0.7 million per kiln. 
4-3 

The results of the screening analysis, presented in Figure 4-1 and Table 4-2, show that 

one small business has a CSR greater than 3%. One small business and one small government 

have an estimated CSR between 1 and 3%. The average (median) CSR for small entities is 2.0% 

(1.5%), and the average and median CSR for all large entities is 0.5% (0.3%). 

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

<0.1% 0.1% to <

0.5%

0.5% to <

1.0%

1.0% to <

1.5%

1.5% to <

2.0%

2.0% to <

3.0%

3.0% to <

5.0%

5.0% to < 10% >10.0%

Cost-to-Sales Range

Number of Ultimate Parent Companies (N=26)

Large Small

Figure 4-1. SBREFA Screening Analysis Results 

4.3 Additional Market Analysis 

In additional to the screening analysis, EPA also examined small entity effects after 

accounting for market adjustments. Under this assumption, the entities recover some of the 

regulatory program costs as the market price adjusts in response to higher cement production 

costs. Even after accounting for these adjustments, small entity operating profits fall by $4 

million, or 9% (see Table 4-3). However, all nine cement kilns continue to operate under withregulation conditions. As cement production falls, employment may decline by up to 23 

employees, a 5% reduction. 
4-4 

Table 4-2. Summary Statistics for Small Business Regulatory Enforcement Fairness Act 

(SBREFA) Screening Analysis 

Small Large

Number Share (%) Number Share (%)

Companies 4 100% 22 100% 

Compliance costs are <1% of sales 1 25% 18 82% 

Compliance costs are ≥1% to 3% of sales 2 50% 4 18% 

Compliance costs are ≥3% of sales 1 25% 0 0% 

Annualized Compliance Cost Summary 

Total ($106) $7 $273 

Average ($106

 per kiln) $0.7 $1.5 

Average ($ per metric ton) $2.22 $3.23 

Cost-to-Sales Ratios (%) 

Average 2.0% 0.5% 

Median 1.5% 0.3% 

Maximum 4.8% 1.9% 

Minimum 0.0% 0.0% 

Table 4-3. Market Analysis—Small Entity Impacts: 2005 

 Changes from Baseline 

 Baseline Absolute Percent 

Revenues ($106) $293 −$10 −3.6% 

Costs ($106) $0 −$6 −2,602.4% 

Cement production $247 −$12 −4.8% 

Proposed NESHAP $0 $6 NA 

Operating profit ($106)

a $46 −$4 −8.7% 

Employment 490 −23 −4.6% 

a Estimates using median results of cement operations reported by PCA (2007, Table 44) ($18 per metric ton, or 

15.7%). 
5-1 

SECTION 5

HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS 

5.1 Summary 

EPA benefits estimates are the monetized human health co-benefits of reducing cases of 

morbidity and premature mortality among populations exposed to PM2.5 from installing controls 

to limit hazardous air pollutants (HAPs), such as mercury, hydrochloric acid, and hydrocarbons. 

For the proposed Portland Cement NESHAP, EPA estimates the PM2.5-related co-benefits of to 

be $4.4 billion to $11 billion (2005$) in the year of full implementation (2013).1 These are our 

preferred estimates, which reflect EPA’s most current interpretation of the scientific literature on 

PM2.5 and mortality. They reflect our updated benefits methodology (i.e., a no-threshold model 

that calculates incremental benefits down to the lowest modeled PM2.5 air quality levels and 

incorporates two technical updates) compared to the estimates in previous RIAs that did not 

include these changes. 2 The anchor points for these estimates are derived from two empirical 

(epidemiological) studies of the relationship between ambient PM2.5 and premature mortality (the 

extended analyses of the Harvard Six Cities study by Laden et al (2006) and the American 

Cancer Society cohort by Pope et al (2002)). Since 2006, EPA had calculated benefits based on 

these two empirical studies, but derived the range of benefits, including the minimum and 

maximum results, from an expert elicitation of the relationship between exposure to PM2.5 and 

premature mortality (Roman et al., 2008). Using alternate relationships between PM2.5 and 

premature mortality supplied by experts, higher and lower benefits estimates are plausible (see 

Figure 5-1 below), but most of the expert-based estimates fall between the two epidemiologybased estimates (Roman et al., 2008). Methodological limitations prevented EPA from 

quantifying the monetized benefits of emissions reductions from HAPs. 

 

1

 These benefits results use a 3% discount rate. Using a 7% discount rate, the benefits are about 9% to 10% less. 

2

 Using the previous methodology (i.e., a threshold model at 10 μg/m3 without two technical updates), EPA 

estimates the PM2.5-related co-benefits of the proposed Portland Cement NESHAP to be $ 3.1 billion to $ 6.5 

billion (2005$) in the year of full implementation (2013). 
5-2 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

Pope et al  

Laden et al

Millions (2005$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR

7% DR

Figure 5-1. Monetized Human Health Co-Benefits of Proposed Portland Cement NESHAP 

in 2013a

a

 This graph shows the estimated benefits using the no-threshold at discount rates of 3% and 7% using effect 

coefficients derived from the Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived 

from EPA’s expert elicitation on PM mortality. The results shown are not the direct results from the studies or 

expert elicitation; rather, the estimates are based in part on the concentration-response function provided in those 

studies. 

5.2 Calculation of Human Health Benefits 

To estimate the PM2.5-related human health benefits of reducing emissions from the 

proposed NESHAP for Portland Cement kilns, EPA used the benefits transfer approach it created 

for the regulatory impact analysis (RIA) accompanying the recent National Ambient Air Quality 

Standards (NAAQS) for Ozone.1,2 This methodology incorporates the best available science, 

which is described in detail below. In that RIA, EPA developed and applied PM2.5 benefit-perton coefficients to estimate the PM2.5 co-benefits resulting from reductions in emissions of NOX. 

 

1

 U.S. EPA, 2008c. Technical Support Document: Calculating Benefit Per-Ton estimates, Ozone NAAQS Docket 

#EPA-HQ-OAR-2007-0225-0284. Available on the Internet at http://www.regulations.gov. 2

 U.S. EPA, 2008b. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-level 

Ozone, Chapter 6. Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/6-

ozoneriachapter6.pdf. 
5-3 

EPA has followed that same approach to estimate the health benefits for the projected emission 

reductions of PM2.5 precursor pollutants associated with this proposal, but has made incremental 

updates to the benefit-per-ton estimates to reflect new science and data, as discussed below. 

EPA did not perform an air quality modeling assessment of the emission reductions 

resulting from installing controls on these kilns because of the time and resource constraints and 

the limited value of such an analysis for the purposes of developing the regulatory approach for 

this proposal. This lack of air quality modeling limited EPA’s ability to perform a 

comprehensive benefits analysis for this proposal because our benefits model BenMAP requires 

either air quality modeling or monitoring data. In the absence of formal air quality modeling, we 

applied PM2.5 benefit-per-ton coefficients to estimate benefits. In addition to the 2008 Ozone 

NAAQS RIA, this benefit-per-ton approach has been used in RIAs prepared for a number of 

previous EPA rulemakings (e.g., the 2002 large industrial spark ignition engine and recreational 

vehicles rule, the 2004 Industrial Boilers and Process Heaters MACT, and the 2008 Petroleum 

Refineries NSPS). 

The benefit per-ton estimates provide the total monetized human health benefits (the sum 

of premature mortality and morbidity related benefits) of reducing one ton of PM2.5 or PM2.5

precursor emissions from a specified source. We include direct PM2.5 and PM2.5 precursor 

emissions (SOX). These PM benefits are actually co-benefits, which result from the installing 

controls to limit hazardous air pollutants (HAPs). Methodological limitations prevented EPA 

from quantifying the monetized benefits of other emissions reductions from this proposed 

NESHAP, including 8 tons of mercury, 3,900 tons of hydrochloric acid, and 14,780 tons of total 

hydrocarbons annually. In addition, these monetized benefits do not incorporate additional 

emission reductions that would occur if cement facilities temporarily idle or reduce capacity 

utilization as a result of this regulation. Using the benefit-per-ton approach, we are unable to 

monetize the anticipated improvements in visibility due to reductions in PM2.5 and PM2.5 

precursors. 

The PM co-benefits estimates in this proposal analysis utilize the same concentrationresponse functions as described in the PM NAAQS RIA analysis.1

 Each data source is described 

below: 

 One estimate is based on the concentration-response (C-R) function developed from 

the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope 

 

1

 U.S. EPA, 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate 

Matter, Chapter 5. Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--

Benefits.pdf. 
5-4 

et al. (2002), a study that EPA has previously used to generate its primary benefits 

estimate. When calculating the preferred estimate, EPA applied the effect coefficient 

as reported in the study without an adjustment for assumed concentration threshold of 

10 μg/m3

 as was done in recent (post 2006) RIAs. 

 One estimate is based on the C-R function developed from the extended analysis of 

the Harvard Six Cities cohort, as reported by Laden et al (2006). This study, 

published after the completion of the Staff Paper for the 2006 PM NAAQS, has been 

used as an alternative estimate in the PM NAAQS RIA and PM co-benefits estimates 

in RIAs completed since the PM NAAQS. When calculating the preferred estimate, 

EPA applied the effect coefficient as reported in the study without an adjustment for 

assumed concentration threshold of 10 μg/m3

 as was done in recent (post 2006) RIAs. 

 Twelve estimates are based on the C-R functions from EPA’s expert elicitation 

study1,2

 on the PM-mortality relationship and interpreted for benefits analysis in 

EPA’s final RIA for the PM NAAQS. For that study, twelve experts (labeled A 

through L) provided independent estimates of the PM-mortality concentrationresponse function. EPA practice has been to develop independent estimates of PMmortality estimates corresponding to the concentration-response function provided by 

each of the twelve experts, to better characterize the degree of variability in the expert 

responses. 

The effect coefficients are drawn from epidemiology studies examining two large 

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six 

Cities cohort (Laden et al., 2006)3

. These are logical choices for anchor points in our 

presentation because, while both studies are well designed and peer reviewed, there are strengths 

and weaknesses inherent in each, which we believe argues for using both studies to generate 

benefits estimates. Using estimates from both cohorts substantially narrows the range of benefits 

estimates when compared to the range of estimates from the expert elicitation.. Because the 

experts used these studies based on these cohorts to inform their concentration-response 

functions, benefits estimates using these functions generally fall between results using these 

epidemiology studies. As Figure 5-1 illustrates, the Pope et al. (2002) and Laden et al. (2006) 

estimates, based on the ACS and Six Cities cohorts, respectively, capture the mass of expert 

 

1

 Industrial Economics, Inc., 2006. Expanded Expert Judgment Assessment of the Concentration-Response 

Relationship Between PM2.5 Exposure and Mortality. Prepared for the U.S. EPA, Office of Air Quality Planning 

and Standards, September. Available on the Internet at 

http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf. 2

 Roman et al., 2008. Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate 

Matter in the U.S. Environ. Sci. Technol., 42, 7, 2268–2274. 3

 The American Cancer Society (ACS) cohort analyzed by Pope et al. includes a larger number of cities, and a 

greater population size, than the Harvard Six Cities cohort. However, the ACS cohort is also more affluent and 

less diverse, than the average population. Alternately, the Six Cities cohort offers a superior estimate of PM 

exposure. 
5-5 

opinion, while preserving the empirical basis of our estimates. This presentation style is flexible 

enough to incorporate future epidemiology studies based on these cohorts. 

In recent RIAs, EPA presented benefits estimates using concentration response functions 

derived from the PM2.5 Expert Elicitation as a range from the lowest expert value (Expert K) to 

the highest expert value (Expert E) (Roman et al., 2008; IEc, 2006). Although this approach 

characterized the bounds of the expert elicitation, it generated a range of benefits estimates 

extending nearly an order of magnitude. In addition, this approach did not indicate the agency’s 

judgment on what the best estimate of PM benefits may be. According to EPA’s Science 

Advisory Board, this presentation was misleading because: 

1. “...[T]his is, in fact, a form of aggregation that assigns positive weight to the most 

extreme judgments and zero weight to all the others...,”1 and 

2. “...[T]he casual reader could easily infer substantial differences in scientific opinion 

when, in fact, there was a pronounced central cluster of views on PM2.5 mortality.”2

The SAB advice captures the tension between providing readers with a coherent estimate 

of total benefits while also respecting the underlying uncertainty in the epidemiology-derived 

estimates of PM mortality. For this reason, above we present the cohort-based benefits estimates 

as well as the results of the Expert Elicitation jointly (see Figure 5-1).3 

 The effect coefficients are drawn from epidemiology studies examining two large 

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six 

Cities cohort (Laden et al., 2006).4 These two studies are logical choices, given that EPA has 

previously applied effect coefficients from each analysis and these studies informed the 

judgment of the twelve experts as they developed their estimates.5

 While both studies are well 

designed and peer reviewed, there are strengths and weaknesses inherent in each, which we 

 

1

 U.S. Environmental Protection Agency Science Advisory Board, 2008. Characterizing Uncertainty in Particulate 

Matter Benefits Using Expert Elicitation. EPA-COUNCIL-08-002. Pp 6. 2

 U.S. Environmental Protection Agency Science Advisory Board, 2008. Characterizing Uncertainty in Particulate 

Matter Benefits Using Expert Elicitation. EPA-COUNCIL-08-002. Pp ii. 3

 In the near term, we are using two alternate PM mortality estimates as a surrogate for a single central estimate, 

which will likely be interpreted as a range. We are still trying to determine how to present two alternate benefits 

estimates without implying a range, and we will continue to develop methods to improve the clarity of the 

benefits presentation. 4

 The American Cancer Society (ACS) cohort analyzed by Pope et al. includes a larger number of cities, and a 

greater population size, than the Harvard Six Cities cohort. However, the ACS cohort is also more affluent and 

less diverse, than the average population. Alternately, the Six Cities cohort offers a superior estimate of PM 

exposure. 5

 According to the expert elicitation report, “All of the experts cited the long-term cohort-based studies as major 

evidence in support of a positive relationship between ambient annual average PM2.5 concentrations and 

mortality” (IEc, 2006, page 3-10). 
5-6 

believe argues for using both studies to generate the benefits estimates. Using both estimates 

from each cohort substantially narrows the range of benefits estimates. Because the experts used 

these studies based on these cohorts to inform their concentration-response functions, benefits 

estimates using these functions generally fall between results using these epidemiology studies. 

As Figure 5-1 illustrates, the Pope et al. (2002) and Laden et al. (2006) estimates, based on the 

ACS and Six Cities cohorts, respectively, capture the mass of expert opinion, while preserving 

the empirical basis of our estimates. This presentation style is flexible enough to incorporate 

future epidemiology studies based on these cohorts. Finally, this approach is generally 

consistent with advice from both the National Academy of Sciences (NRC, 2002) and the 

Science Advisory Board (U.S. EPA-SAB, 2004) directing EPA to rely upon prospective cohort 

studies as the basis for estimating PM mortality effects. 

Within this benefits chapter, EPA presents independent estimates of PM-mortality 

estimates corresponding to the concentration-response function provided by each of the twelve 

experts to better characterize the degree of variability in the expert responses. Because in this 

RIA we estimate benefits using benefit-per-ton estimates, technical limitations prevent us from 

providing the associated credible intervals with the expert functions. EPA believes that the 

estimates derived from the expert elicitation are indicative of the uncertainty associated with a 

major component of the health impact functions; whereas, the benefits represented by estimates 

derived from Pope et al. and Laden et al. represent the preferred estimates of PM co-benefits. In 

general, the expert elicitation results support the conclusion that the benefits of PM2.5 control are 

very likely to be substantial. 

To develop the estimate of the co-benefits of reducing emissions from this proposal, we 

calculated the monetized benefits-per-ton of emissions reduction estimates for direct PM2.5 and

each PM2.5 precursor pollutant. Readers interested in the complete methodology for creating the 

benefit-per-ton estimates used in this analysis may consult the Technical Support Document 

(TSD) accompanying the final Ozone NAAQS RIA (U.S. EPA, 2008c). In the TSD, we describe 

in detail how we generated the benefit-per-ton estimates. In summary, we used a model to 

convert emissions of direct PM2.5 and PM2.5 precursors (i.e., SO2, NOX, and VOCs) into changes 

in PM2.5 air quality. Next, we used the benefits model (BenMAP) to estimate the changes in 

human health based on the change in PM2.5 air quality. Finally, the monetized health benefits 

were divided by the emission reductions to create the benefit-per-ton estimates. Even though all 

fine particles are assumed to have equivalent health effects, the benefit-per-ton estimates vary 

between precursors because each ton of precursor reduced has a different propensity to form 

PM2.5. For example, SOX has a lower benefit-per-ton estimate than direct PM2.5 because it may 
5-7 

form PM2.5 further from population centers than directly emitted PM2.5 and thus and the 

monetized health benefits would be lower. After generating the benefit-per-ton estimate, we 

then multiply this estimate by the number of tons of each pollutant reduced to derive an overall 

monetary value of benefits. 

It is important to note that the monetized benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

modeling assumptions. Use of these $/ton values to estimate benefits associated with different 

emission control programs (e.g., for reducing emissions from large stationary sources like EGUs) 

may lead to higher or lower benefit estimates than if benefits were calculated based on direct air 

quality modeling. Great care should be taken in applying these estimates to emission reductions 

occurring in any specific location, as these are all based on national or broad regional emission 

reduction programs and therefore represent average benefits-per-ton over the entire United 

States. The benefits-per-ton for emission reductions in specific locations may be very different 

from the national average. 

5.3 Assumptions regarding Thresholds in the Health Impact Function 

The preferred benefit-per-ton coefficients in this analysis were derived using modified 

versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis. 

Specifically, EPA selected the same mortality and morbidity studies as were used in that RIA, 

but used models both with and without an assumed threshold for PM2.5 related threshold adverse 

effects to test the sensitivity of this assumption. The Agency’s peer review process conducted 

through the Science Advisory Board has provided advice regarding analytic treatment of 

thresholds for PM multiple times. 

 In 1999, the SAB Advisory Council on Clean Air Compliance Analysis (ACCACA) 

concluded that there was currently no scientific basis for selecting any specific 

threshold (EPA-SAB, 1999). 

 In 2004, the Health and Environmental Effect subcommittee of ACCACA concluded, 

“it is reasonable for EPA to assume a no threshold model down to, at least, the low 

end of the concentrations reported in the studies” (EPA-SAB, 2004). 

 In 2005, CASAC indicated that “[t]he available epidemiological database on daily 

mortality and morbidity does not establish either the presence or absence of threshold 

concentrations for adverse health effects” (EPA-SAB, 2005). 

In addition, in 2002, as a part of their review of EPA benefits methods, the National 

Research Council concluded that there is no evidence for any departure from linearity in the 

observed range of exposure to PM10 or PM2.5, nor any indication of a threshold (NRC, 2002). 
5-8 

They cite the weight of evidence available from both short- and long-term exposure models and 

the similar effects found in cities with low and high ambient concentrations of PM. 

These conclusions were based on a wide body of peer-reviewed literature on health 

effects of short and longer term PM exposures (Daniels et al., 2000; Pope, 2000; Pope et al., 

2002; Rossi et al., 1999; Schwartz and Zanobetti, 2000; Schwartz et al., 2002; Smith et al., 2000; 

Krewski et al., 2000). 

In the time since the CASAC advice was received, the EPA, with close OMB 

collaboration, conducted a PM2.5 Expert Elicitation in which experts were asked to describe the 

true relationship between PM2.5 exposure and premature mortality (Roman, 2008; I.Ec., 2006). 

Of the 12 experts included in the elicitation, only one expert (Expert K) elected to specify a 

threshold, as the rest cited a lack of empirical and/or theoretical basis for a population threshold. 

Expert K specified a 50% chance of no threshold, a 40% chance that there would be a threshold 

at a concentration of less than 5 μg/m3, and only a 10% chance that there would be a threshold 

between 5 and 10 μg/m3. No expert thought that there was any chance that there would be a 

threshold in excess of 10 μg/m3. In addition, a recent extended follow-up of the Harvard Six 

Cities cohort concluded that the concentration response function is linear with no threshold 

(Schwartz, 2008). 

In its December 2008 draft Integrated Science Assessment (ISA), EPA's Office of 

Research and Development concluded that the scientific literature consistently finds that a nothreshold log-linear model most adequately portrays the PM-mortality concentration-response 

relationship, while recognizing potential uncertainty about the exact shape of the concentrationresponse function. CASAC is currently considering EPA's assessment (in the PM2.5 ISA) of the 

body of evidence on the shape of the C-R function relating PM2.5 and mortality, including the 

evidence regarding the existence of a threshold. The CASAC is also currently considering EPA's 

draft scope and methods document for the PM risk and exposure assessment, which proposes to 

use the no-threshold model as the primary model for estimating mortality risk from PM2.5 

exposures. The ISA is being revised and will be reviewed again by the CASAC in October 

2009, concurrent with the review of the first draft risk assessment. Although this document does 

not represent final agency policy that has undergone the full agency scientific review process, it 

provides a basis for reconsidering the application of thresholds in PM2.5 concentration-response 

functions used in EPA’s RIAs. 
5-9 

5.4 Updating the Benefits Data Underlying the Benefit-per-Ton Estimates 

As described above, the estimates provided are derived through a benefits transfer 

technique that adapts monetized benefits from reductions in PM2.5 precursor pollutants that were 

estimated for the Ozone RIA utilizing nationally distributed emissions reductions. Our preferred 

benefit-per-ton estimates for this analysis have been updated since the Ozone RIA was 

completed, and they reflect EPA’s most current interpretation of the scientific literature on PM2.5 

and mortality. These estimates include a new population dataset, an expanded geographic scope 

of the benefit-per-ton calculation, and the functions directly from the epidemiology studies 

without a threshold adjustment. They reflect our updated benefits methodology (i.e., a nothreshold model that calculates incremental benefits down to the lowest modeled PM2.5 air 

quality levels and incorporates two technical updates) compared to the estimates in previous 

RIAs that did not include these changes. Because the benefits are sensitive to the assumption of a 

threshold, we also provide a sensitivity analysis using the previous methodology (i.e., a threshold 

model at 10 μg/m3 without the two technical updates) as a historical reference. Approximately 

75% of the difference between the previous methodology and the updated methodology for this 

rule is due to removing thresholds with 25% due to the two technical updates. This percentage 

breakdown would vary for other rules depending on the combination of emission reductions 

from different sources and PM2.5 precursor pollutants.

EPA is currently in the process of generating localized benefit-per-ton estimates to better 

account for the spatial heterogeneity of benefits for a small number of urban areas. EPA believes 

that these estimates may better represent the localized benefits of emission reductions at a 

specific location than benefits estimates that use national averages. However, because the kilns 

affected by this rule are widely distributed nationally, we believe that the national estimates are 

most appropriate for this analysis. 

5.5 Results of Benefits Analysis 

Using the preferred no-threshold model, in the year of full implementation (2013), EPA 

estimates the benefits of this proposal to be $4.4 billion to $11 billion and $4.0 billion to $9.7 

billion, at 3% and 7% discount rates respectively.1

 Using the threshold model without technical 

updates, EPA estimates the benefits of this proposal to be $3.1 billion to $6.5 billion and $2.8 

billion to $5.9 billion, at 3% and 7% discount rates respectively. Because the benefits are 

sensitive to the assumption of a threshold, we present the results using the threshold model below 

 

1

 The benefits are discounted to account for the cessation lag in PM2.5 benefits frompremature mortality and acute 

myocardial infarctions (AMIs), rather than a discounted stream of future benefits; whereas discounting the costs 

reflects the lifetime costs of the equipment. For this reason, it is appropriate in this context to use two different 

discount rates for the benefits and costs. 
5-10 

to show the sensitivity of this assumption. Tables 5-1 and 5-2 provide general summaries of the 

results by precursor pollutant, including the emissions reductions and monetized benefits-per-ton 

using the no-threshold model and the threshold model (without technical updates), respectively.1, 

2

 Table 5-3 provides a summary of the reductions in health incidences associated with the 

benefit per ton estimates. Incidence estimates using the threshold-based benefit-per-ton 

methodology have not been calculated. Figure 5-3 provides a visual representation of the range 

of benefits estimates by precursor pollutant at a 3% discount rate using the no-threshold model. 

More details on the regulatory scenario, emissions, and emission reductions can be found in 

Section 4 of this RIA. 

Table 5-1. Summary of PM2.5 Health Co-Benefits of the Proposed Portland Cement 

NESHAP using the no-threshold model (preferred approach)a

 3% Discount Rate 7% Discount Rate 

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope) 

Benefit 

per ton

(Laden) 

Total Monetized 

Benefits 

(millions 2005$) 

Benefit 

per ton

(Pope) 

Benefit 

per ton 

(Laden) 

Total Monetized 

Benefits 

(millions 2005$) 

Direct PM2.5 6,345 $180,000 $440,000 $1,200 to $2,800 $160,000 $400,000 $1,000 to $2,500

PM2.5 Precursors 

SO2 139,240 $23,000 $57,000 $3,300 to $8,000 $21,000 $52,000 $3,000 to $7,200

Grand Total $4,400 to $11,000 $4,000 to $9,700

a

 All estimates are for the analysis year (2013), and are rounded to two significant figures so numbers may not sum 

across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates 

vary between precursors because each ton of precursor reduced has a different propensity to form PM2.5. The 

monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. This analysis 

assumes the PM2.5 fraction is 45%. 

 

1

 The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in 

part on the concentration-response function provided in those studies. 2

 Because of the absence of data, we are unable to quantify the amount of reductions in condensable PM. PM 

emissions consist of both a filterable fraction and a condensable fraction, which exists as a gas in an exhaust 

stream and condenses to form particulate once the gas enters the ambient air. Most condensable PM is PM2.5. In 

this analysis, all emission reductions and the corresponding benefits estimates reflect only the filterable fraction. 
5-11 

Table 5-2. Summary of Health Benefits of the Proposed Portland Cement NESHAP, using 

the threshold model (without technical updates) (sensitivity analysis)a

 3% Discount Rate 7% Discount Rate 

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope) 

Benefit 

per ton 

(Laden) 

Total Monetized 

Benefits 

(millions 2005$) 

Benefit 

per ton 

(Pope) 

Benefit 

per ton 

(Laden) 

Total Monetized 

Benefits 

(millions 2005$) 

Direct PM2.5 6,345 $150,000 $320,000 $940 to $2,000 $140,000 $290,000 $860 to $1,800 

PM2.5 Precursors 

SO2 139,240 $15,000 $32,000 $2,100 to $4,500 $14,000 $29,000 $1,900 to $4,100 

Grand Total $3,100 to $6,500 $2,800 to $5,900 

a

 All estimates are for the analysis year (2013), and are rounded to two significant figures so numbers may not sum 

across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates 

vary between precursors because each ton of precursor reduced has a different propensity to form PM2.5. The 

monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. This analysis 

assumes the PM2.5 fraction is 45%. 

Table 5-3. Summary of Reductions in Health Incidences of the Proposed Portland 

Cement NESHAPa

Avoided Premature Mortality

Pope 620 

Laden 1,600 

Woodruff (Infant Mortality) 3 

Avoided Morbidity

Chronic Bronchitis 420 

Acute Myocardial Infarction 1,000 

Hospital Admissions, Respiratory 150 

Hospital Admissions, Cardiovascular 320 

Emergency Room Visits, Respiratory 590 

Acute Bronchitis 1,000 

Work Loss Days 82,000 

Asthma Exacerbation 11,000 

Acute Respiratory Symptoms 490,000 

Lower Respiratory Symptoms 12,000 

Upper Respiratory Symptoms 9,000 

a

 All estimates are for the analysis year (2013) and are rounded to two significant figures. All fine particles are 

assumed to have equivalent health effects, but each PM2.5 precursor pollutant has a different propensity to form 

PM2.5. 
5-12 

$‐

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

Pope et al Laden et al

Millions of 2005$

PM2.5 Mortality Function

SO2NonEGU Direct PM2.5 NonEGU

Figure 5-3. Monetized Health Benefits of the Proposed Portland Cement NESHAP by 

PM2.5 Precursor in 2013 using the no-threshold model a

a

 This graph shows the estimated benefits by precursor pollutant using effect coefficients derived from the Pope et 

al. study and the Laden et al, study at a 3% discount rate. The breakdown by precursor pollutant would be similar 

at a 7% discount rate and using the threshold model. All fine particles are assumed to have equivalent health 

effects, but the benefit-per-ton estimates vary because each ton of precursor reduced has a different propensity to 

become PM2.5

5.6 Characterization of Uncertainty in the Benefits Estimates 

In any complex analysis, there are likely to be many sources of uncertainty. Many inputs 

are used to derive the final estimate of economic benefits, including emission inventories, air 

quality models (with their associated parameters and inputs), epidemiological estimates of 

concentration-response (C-R) functions, estimates of values, population estimates, income 

estimates, and estimates of the future state of the world (i.e., regulations, technology, and human 

behavior). There is uncertainty at each stage of the analytic process to generate benefits 

estimates. For some parameters or inputs, it may be possible to provide a statistical 

representation of the underlying uncertainty distribution. For other parameters or inputs, the 
5-13 

necessary information is not available. Therefore, it is difficult to estimate the relative 

importance of each source of uncertainty, particularly when using benefit-per-ton estimates. 

The annual benefit estimates presented in this analysis are also inherently variable due to 

the processes that govern pollutant emissions and ambient air quality in a given year. Factors 

such as hours of equipment use and weather are constantly variable, regardless of our ability to 

measure them accurately. As discussed in the PM2.5 NAAQS RIA (Table 5-5), there is a variety 

of uncertainties associated with these PM benefits. Therefore, the estimates of annual benefits 

should be viewed as representative of the magnitude of benefits expected, rather than the actual 

benefits that would occur every year. 

The benefits estimates are subject to a number of assumptions and uncertainties. For 

example, for key assumptions underlying the estimates for premature mortality, which typically 

account for at least 90% of the total PM benefits, we were able to identify the following 

uncertainties: 

1. Inhalation of fine particles is causally associated with premature death at 

concentrations near those experienced by most Americans on a daily basis. Although 

biological mechanisms for this effect have not been established definitively yet, the 

weight of the available epidemiological evidence supports an assumption of causality. 

2. All fine particles, regardless of their chemical composition, are equally potent in 

causing premature mortality. This is an important assumption, because PM produced 

via transported precursors emitted from EGUs may differ significantly from direct 

PM released from diesel engines and other industrial sources, but no clear scientific 

grounds exist for supporting differential effects estimates by particle type. 

3. The impact function for fine particles is approximately linear within the range of 

ambient concentrations under consideration. Thus, the estimates include health 

benefits from reducing fine particles in areas with varied concentrations of PM, 

including both regions that are in attainment with fine particle standard and those that 

do not meet the standard. 

4. The forecasts for future emissions and associated air quality modeling are valid. 

Although recognizing the difficulties, assumptions, and inherent uncertainties in the 

overall enterprise, these analyses are based on peer-reviewed scientific literature and 

up-to-date assessment tools, and we believe the results are highly useful in assessing 

this proposal. 

5. Benefits estimated here reflect the application of a national dollar benefit-per-ton 

estimate of the benefits of reducing directly emitted fine particulates from point 

sources. Because they are based on national-level analysis, the benefit-per-ton 

estimates used here do not reflect local variability in population density, meteorology, 
5-14 

exposure, baseline health incidence rates, or other local factors that might lead to an 

over-estimate or under-estimate of the actual benefits of controlling directly emitted 

fine particulates. 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA because we lack the necessary air quality input and monitoring data to run the 

benefits model (BenMAP). Moreover, it was not possible to develop benefit-per-ton metrics and 

associated estimates of uncertainty using the benefits estimates from the PM RIA because of the 

significant differences between the sources affected in that rule and those regulated here. 

However, the results of the Monte Carlo analyses of the health and welfare benefits presented in 

Chapter 5 of the PM RIA can provide some evidence of the uncertainty surrounding the benefits 

results presented in this analysis. In this analysis, we provide additional benefits results that use 

the functions obtained in the expert elicitation as a reasonable characterization of the uncertainty 

in the relationship between PM2.5 and mortality. Because this analysis uses benefit-per-ton 

estimates, we are only able to present the mean benefits results using the expert functions 

without the associated credible intervals. We recognize that this captures only a fraction of the 

overall uncertainty. We also recognize that the magnitude of the mortality C-R function is a 

critical parameter in the analysis, and the uncertainty in that parameter is likely to contribute a 

large fraction of the overall uncertainty in the benefits estimates. Tables 5-4 and 5-5 show all 14 

benefits estimates for the no-threshold and threshold models, including those based on expert 

functions, at discount rates of 3% and 7%.1 Figures 5-4 and 5-5 show the data from Tables 5-3 

and 5-4 in a graphical form. 

 

1

 Circular A-4 requires regulatory analyses to assess benefits using discount rates of 3% and 7%. Office of 

Management and Budget (OMB), 2003. Circular A-4: Regulatory Analysis. Washington, DC. Available on the 

internet at http://www.whitehouse.gov/omb/circulars/a004/a-4.html. 
5-15 

Table 5-4. All Benefits Estimates for Proposed Portland Cement NESHAP in 2013 

(in millions of 2005$) for no-threshold model (preferred approach)a

 3% Discount Rate 7% Discount Rate 

Benefit-per-ton Coefficients Derived from Epidemiology Literature 

Pope et al. $4,400 $4,000 

Laden et al. $11,000 $9,700 

Benefit-per-ton Coefficients Derived from Expert Elicitation 

Expert A $11,000 $10,000 

Expert B $8,700 $7,900 

Expert C $8,700 $7,900 

Expert D $6,200 $5,600 

Expert E $14,000 $13,000 

Expert F $7,900 $7,200 

Expert G $5,200 $4,700 

Expert H $6,600 $5,900 

Expert I $8,600 $7,800 

Expert J $7,000 $6,400 

Expert K $1,800 $1,600 

Expert L $6,300 $5,700 

*All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they 

were derived through the benefit-per-ton technique described above. The benefits estimates from the Expert 

Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated 

with the concentration-response function. 
5-16 

Table 5-5. All Benefits Estimates for proposed Portland Cement NESHAP in 2013 

(in millions of 2005$) for the threshold model (without technical updates) 

(sensitivity analysis) a

 3% Discount Rate 7% Discount Rate 

Benefit-per-ton Coefficients Derived from Epidemiology Literature 

Pope et al. $3,500 $3,200 

Laden et al. $7,500 $6,800 

Benefit-per-ton Coefficients Derived from Expert Elicitation 

Expert A $12,000 $11,000 

Expert B $9,100 $8,200 

Expert C $9,000 $8,100 

Expert D $6,400 $5,800 

Expert E $15,000 $13,000 

Expert F $8,200 $7,400 

Expert G $5,400 $4,900 

Expert H $6,800 $6,100 

Expert I $8,900 $8,100 

Expert J $7,300 $6,600 

Expert K $1,700 $1,600 

Expert L $6,500 $5,900 

a

 All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they 

were derived through the benefit-per-ton technique described above. The benefits estimates from the Expert 

Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated 

with the concentration-response function. These monetized benefits do not incorporate additional emission 

reductions that would occur if cement facilities temporarily idle or reduce capacity utilization as a result of this 

regulation or the unquantifiable amount of reductions in condensable PM. 
5-17 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

Pope et al  

Laden et al

Millions (2005$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12 expert functions

3% DR

7% DR

Figure 5-4. Monetized Human Health Benefits of Proposed Portland Cement NESHAP in 

2013 based on the no-threshold model (preferred approach) a 

a

 This graph shows the estimated at two discount rates using effect coefficients derived from the Pope et al. study 

and the Laden et al study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality. 

All fine particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary because 

each ton of precursor reduced has a different propensity to become PM2.5. These monetized benefits do not 

incorporate additional emission reductions that would occur if cement facilities temporarily idle or reduce 

capacity utilization as a result of this regulation or the unquantifiable amount of reductions in condensable PM. 
5-18 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

Pope et al  

Laden et al

Millions(2005$)

PM2.5 mortality benefits estimates derived from 2 epidemiology functions and 12

expert functions

3% DR

7% DR

Figure 5-5. Monetized Health Benefits for Proposed Portland Cement NESHAP in 2013 

based on the threshold model (without technical updates) (sensitivity analysis)a

a

 This graph shows the estimated benefits using the old methodology at discount rates of 3% and 7% using effect 

coefficients derived from the Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived 

from EPA’s expert elicitation on PM mortality. The results shown are not the direct results from the studies or 

expert elicitation; rather, the estimates are based in part on the concentration-response function provided in those 

studies. 

5.7 Comparison of Benefits and Costs 

Using the no-threshold model, in the year of full implementation (2013), EPA estimates 

the benefits of this proposal to be $4.4 billion to $11 billion and $4.0 billion to $9.7 billion, at 

3% and 7% discount rates respectively.1

 Annualized domestic social costs are $694 million at a 

7% discount rate as mentioned in Section 4 of this RIA.2 Thus, the net benefits (i.e., benefits in 

2013 minus annualized costs) are $3.7 billion to $11 billion and $3.3 billion to $9.0 billion, at 

3% and 7% discount rates respectively. Using alternate relationships between PM2.5 and 

premature mortality supplied by experts, higher and lower benefits estimates are plausible (see 

Figure 5-6), but most of the expert-based estimates fall between the two epidemiology-based 

 

1

 The benefits are discounted to account for the cessation lag in PM2.5 benefits frompremature mortality and acute 

myocardial infarctions (AMIs), rather than a discounted stream of future benefits; whereas discounting the costs 

reflects the lifetime costs of the equipment. For this reason, it is appropriate in this context to use different 

discount rates for the benefits and costs. 2

 The domestic social cost does not include the estimated $89 million surplus gain for foreign producers. 
5-19 

estimates. EPA believes that the benefits are likely to exceed the costs by a substantial margin 

under this proposal even when taking into account uncertainties in the cost and benefit estimates. 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Pope et al.

Laden et al.

Millions(2005$)

Net benefits are derived from 14 benefits estimates (2 epidemiology functions and 12 expert functions) minus

the cost estimate

3% DR

7% DR

All benefits are

greater than costs

Figure 5-6. Net Benefits for Proposed Portland Cement NESHAP in 2013 at 2 Discount 

Rates using the no-threshold modela

a

 Net Benefits are quantified in terms of PM2.5 benefits at a 3% discount rate and a 7% discount rate. This graph 

shows all of the benefits estimates combined with the cost estimate, specifically identifying the estimates based on 

Pope et al and Laden et al with green bars and the expert elicitation with translucent bars. 
6-1 

SECTION 6

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APPENDIX A 

REGIONAL PORTLAND CEMENT MARKETS AND ECONOMIC MODEL 
A-1 

The Office of Air Quality Planning and Standards (OAQPS) has adopted the standardindustry level analysis described in the Office’s resource manual (EPA, 1999a). This approach is 

consistent with previous EPA analyses of the Portland cement industry (EPA, 1998; EPA, 

1999b) and uses a single-period static partial-equilibrium model to compare prepolicy cement 

market baselines with expected postpolicy outcomes in these markets. The benchmark time 

horizon for the analysis is the intermediate run where producers have some constraints on their 

flexibility to adjust factors of production. This time horizon allows us to capture important 

transitory impacts of the program on existing producers. Key measures in this analysis include 

 market-level effects (market prices, changes in domestic production and 

consumption, and international trade), 

 industry-level effects (changes in revenues, costs, profits, employment), 

 facility-level effects (plant utilization changes), and 

 social costs (changes in producer and consumer surplus). 

The partial equilibrium analysis performed for this rule develops a cement market model 

that simulates how stakeholders (consumers and firms) may respond to the additional regulatory 

program costs. In this appendix, we provide details on the baseline data, behavioral assumptions, 

parameters, and model equations. 

A.1 Baseline Market Data 

Cement sales are often concentrated locally among a small number of firms for two 

reasons: high transportation costs and production economies of scale.1

 Transportation costs 

significantly influence where cement is ultimately sold; high transportation costs relative to unit 

value provide incentives to produce and sell cement locally in regional markets (USITC, 2006). 

To support this claim, the empirical literature has typically pointed to Census of Transportation 

data showing over 80% of cement shipments were made within a 200-mile radius (Jans and 

Rosenbaum, 1997) and reported evidence of high transportation costs per dollar of product value 

from case studies (Ryan, 2006). Based on this literature, the Agency assumed that the U.S. 

Portland cement industry is divided into a number of independent regional markets with each 

having a single market-clearing price. 

To estimate cement demand for each of the 20 cement markets, RTI collected the 

Portland Cement Association’s (2004) reported annual kiln clinker capacity data and state-level 

 

1

 The 2002 Economic Census reports that the national Herfindahl-Hirschman Index (HHI) for cement NAICS 32731 

is 568. However, this measure likely not representative of actual concentration that exists in regional markets. 
A-2 

utilization rates reported by the U.S. Geological Survey (USGS). Utilization rates are reported in 

Table A-1. For each kiln, we calculated clinker production as follows: 

 Kiln Clinker Production = State-Level Utilization Rate x Annual Clinker Capacity. 

Next, we summed clinker production across kilns to obtain domestic clinker production 

in the United States. However, to match the 2005 national clinker production data (87,405 metric 

tons) from the USGS (U.S. Department of the Interior, 2006a, Table 1), we applied a scaling 

factor to these USGS utilization rates (value = 1.103). When making this calculation, we did 

constrain each kiln’s clinker production capacity utilization rate to be 95% or less. With this 

adjustment, kiln clinker production is computed as follows: 

Kiln Clinker Production = 

Minimum[(State-Level Utilization Rate x 1.103 x Annual Clinker Capacity), (0.95 x Annual 

Clinker Capacity)]. 

Next, we summed clinker production across kilns in each market to obtain domestic 

clinker production in each cement market (m): 

Domestic Clinker Production Kiln Clinker Production m ( ) im i = ∑ . 

We calculated Portland cement production by applying a ratio of Portland cement 

production to clinker production (value = 1.07) that leads us to match the reported 2005 national 

Portland cement production data (93,904 metric tons) from the USGS: 

Domestic Portland Cement Production Kiln Clinker Production Cement Factor m ( ) im i= × ∑ . 

A portion of cement market demand is also met by international imports. We collected 

hydraulic cement import data by customs district from the USGS (2006a) and assigned these 

imports to markets using a district-to-market mapping shown in Table A-2. The USGS reports 

hydraulic cement imports were approximately 33.3 million metric tons in 2005. 

Total grey Portland cement demand was 126.8 million metric tons in 2005 and ranged 

from 3.6 million metric tons in the Seattle market to 13.4 million metric tons in the Los Angeles 

market (see Table A-3). White cement demand was 2.1 million metric tons. 
A-3 

Table A-1. Capacity Utilization Rates by State: 2005 

State USGS Geographic Area Utilization Rate (percent) 

AL Alabama 86.7 

AR Arkansas and Oklahoma 90.9 

AZ Arizona and New Mexico 87.0 

CA California, northern and southern 88.8 

CO Colorado and Wyoming 79.5 

FL Florida 85.9 

GA Georgia, Virginia, West Virginia 78.4 

IA Iowa, Nebraska, South Dakota 85.5 

ID Idaho, Montana, Nevada, Utah 95.5 

IL Illinois 91.4 

IN Indiana 86.8 

KS Kansas 89.1 

KY Kentucky, Mississippi, Tennessee 87.4 

MD Maryland 89.1 

ME Maine and New York 83.6 

MI Michigan 85.5 

MO Missouri 90.3 

MS Kentucky, Mississippi, Tennessee 87.4 

MT Idaho, Montana, Nevada, Utah 95.5 

NE Iowa, Nebraska, South Dakota 85.5 

NM Arizona and New Mexico 87.0 

NV Idaho, Montana, Nevada, Utah 95.5 

NY Maine and New York 83.6 

OH Ohio 84.7 

OK Arkansas and Oklahoma 90.9 

OR Oregon and Washington 83.3 

PA Pennsylvania, eastern and western 83.7 

SC South Carolina 64.5 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. Table 

5. Washington, DC: U.S. Department of the Interior. 

For cement prices, we used the district-level average value per metric ton data reported 

by USGS (2006a, Table 11) and assigned a value to each state. Values represent mill net or explant (free on board plant) valuations of total sales to final customers, including sales from plant. 

To calculate a price for the 20 cement markets in the model (see Table A-4), we computed a 

weighted average price using production and state price information for each kiln. 

Kiln Cement Production

Market price USGS State Price i

j ij i Total Market Domestic Production j

⎛ ⎞

= × ∑⎜ ⎟

⎝ ⎠
A-4 

Table A-2. Hydraulic Cement Imports by Market and Customs District (million metric 

tons): 2005 

Market Customs District Quantity 

Atlanta Charleston, SC 1.1 

 Norfolk, VA 0.7 

 Savannah, GA 0.1 

 Wilmington, NC 0.4 

Baltimore/Philadelphia Baltimore, MD 0.1 

 Philadelphia, PA 0.5 

Birmingham Mobile, AL 0.5 

 New Orleans, LAa 1.7 

Chicago Chicago, IL 0.0 

 Milwaukee, WI, Canada 0.2 

Dallas New Orleans, LAa 2.4 

Detroit Detroit, MI 1.3 

Florida Miami, FL 2.3 

 Tampa, FL 3.5 

 U.S. Virgin Islands 0.1 

Kansas City St. Louis, MO 0.0 

Los Angeles Los Angeles, CA 3.1 

 San Diego, CA 0.7 

Minneapolis Duluth, MN, Canada 0.2 

 Minneapolis, MN, Canada 0.0 

 Pembina, ND, Canada 0.2 

New York/Boston Boston, MA 0.1 

 New York, NY 1.3 

 Ogdensburg, NY 0.3 

 Portland, ME 0.2 

 Providence, RI 0.7 

 St. Albans, VT, Canada 0.1 

Pittsburgh Buffalo, NY 0.8 

 Cleveland, OH 0.8 

Salt Lake City Great Falls, MT 0.1 

San Antonio El Paso, TX, Mexico 0.7 

 Houston-Galveston, TX 2.6 

 Laredo, TX, Mexico 0.1 

 Nogales, AZ, Mexico 1.1 

San Francisco Honolulu, HI 0.4 

 San Francisco, CA 2.4 

Seattle Anchorage, AK 0.1 

 Columbia-Snake, OR 0.9 

 Seattle, WA 1.5 

Total 33.3 

Note: Excludes Puerto Rico.

a Imports for New Orleans were distributed between the Birmingham and Dallas markets using baseline domestic 

production levels in each market. 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. Table 

18. Washington, DC: U.S. Department of the Interior. 
A-5 

Table A-3. Portland Cement Demand by Market (106 metric tons): 2005 

Market U.S. Production 

Imports a

Canada Rest of World Total 

Atlanta 6.1 — 2.3 8.4 

Baltimore/Philadelphia 8.0 — 0.6 8.6 

Birmingham 5.9 — 2.2 8.1 

Chicago 4.3 0.2 0.0 4.5 

Cincinnati 3.7 — — 3.7 

Dallas 8.2 — 2.4 10.6 

Denver 3.4 — — 3.4 

Detroit 4.8 1.3 0.1 6.1 

Florida 5.6 — 5.8 11.4 

Kansas City 5.3 — 0.0 5.3 

Los Angeles 9.6 — 3.8 13.4 

Minneapolis 1.7 0.4 — 2.1 

New York/Boston 3.2 0.6 2.1 6.0 

Phoenix 4.1 — — 4.1 

Pittsburgh 1.5 1.6 0.0 3.1 

St. Louis 5.4 — — 5.4 

Salt Lake City 2.4 0.1 — 2.4 

San Antonio 5.7 — 4.6 10.3 

San Francisco 3.4 — 2.8 6.2 

Seattle 1.1 1.3 1.2 3.6 

 Total, Grey 93.6 5.4 27.9 126.8 

 Total, White 0.3 0.3 1.5 2.1 

Total 93.9 5.7 29.4 129.0 

a Hydraulic cement. The vast majority of these imports are Portland cement (approximately 29 million metric tons, 

or 86%). Excludes Puerto Rico. 

To illustrate this calculation, we use the following simple example for the Seattle market. 

There are two domestic kilns in Washington; one kiln produces approximately 660,000 metric 

tons of cement, while the other produces approximately 420,000 metric tons of cement per year. 

The USGS reports the same average value per metric ton of cement in Oregon and Washington 

($88 dollars). To derive the market price for the Seattle market, we perform the following 

calculation: 

$88 × 660/(660 + 420) + $88 × 420/(660 + 420) = $88. 

A.2 Cost Function Updates for 2005 

Previous EIAs (EPA, 1998; EPA, 1999) used a kiln-level average variable cost (AVC) 

function developed in a microeconomic study of kiln use and retirement (Das, 1992). Das 
A-6 

Table A-4. Portland Cement Prices by Market ($/metric tons): 2005 

Market Price ($/metric ton) 

Atlanta $81 

Baltimore/Philadelphia $86 

Birmingham $83 

Chicago $86 

Cincinnati $84 

Dallas $83 

Denver $89 

Detroit $93 

Florida $91 

Kansas City $86 

Los Angeles $97 

Minneapolis $92 

New York/Boston $89 

Phoenix $99 

Pittsburgh $88 

St. Louis $87 

Salt Lake City $91 

San Antonio $82 

San Francisco $97 

Seattle $88 

describes five variable inputs in cement production: raw materials, repair and maintenance, 

labor, electricity, and fuel. Raw materials serve as the kiln feed, repair and maintenance are 

required for periodic upkeep of the kiln, labor is used in the quarry and for packing, electricity is 

consumed mainly by the auxiliary equipment, and fuel is largely consumed by the kilns. 

The AVC function (expressed in dollars per metric ton of cement) can be written as 

 AVC = AVMI *Pm + AVRI * Pr + AVLI * w + AVFI *Pf f + AVEI * Pe, 

where AVRI, AVLI, AVFI, AVEI, and AVMI are the average variable inputs of materials, repair 

and maintenance, labor, fuel, and electricity, and Pm, Pr, w, Pf, and Pe are the prices of each 

variable input. 

For the updated model, RTI collected data from the Portland Cement Association (PCA, 

2005) and government statistical publications and updated selected cost function parameters. 

These updates are presented and discussed in this section. 
A-7 

A.2.1 Raw Materials 

In Table A-5, we report the quantities of raw material inputs, cement production, and the 

raw material input ratio for the U.S. cement industry. In 2005, approximately 1.67 metric tons of 

raw materials were required to make a metric ton of cement. 

Table A-5. Raw Material Input Ratios for the U.S. Cement Industry: 2005 

 Metric Tons (103) 

Raw material inputa 159,700 

Cement productionb 95,488 

Ratio (AVMI) 1.67 

a U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. Table 6. 

Washington, DC: U.S. Department of the Interior. 

b U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. Table 3. 

Washington, DC: U.S. Department of the Interior. 

State raw material prices for crushed stone range between $4 and $13 per metric ton 

(Table A-6). To calculate raw material costs, we multiplied state raw material prices by the raw 

material input ratio (1.67). 

A.2.2 Repair and Maintenance 

Rock Products (1994) magazine reports the 1993 values of annual repair and maintenance 

costs by kiln capacity (Table A-7). Since more recent repair and maintenance costs are not 

available, we adjusted these costs to 2005 dollars using the gross domestic product (GDP) 

deflator (U.S. BEA, 2007). 

A.2.3 Labor 

The Portland Cement Association reports labor productivity measures in terms of metric 

tons of cement per employee hour. We used these data to calculate the average variable labor 

inputs (AVLI): a measure of employee hours needed per ton of cement. AVLI estimates are 

computed by taking the inverse of each labor productivity measure. Table A-8 shows labor 

productivity continues to vary by process type and kiln size. Productivity is higher for dry 

process kilns compared with wet kilns; it is also higher for large-capacity kilns. 

State average hourly earnings for the durable goods industry are presented in Table A-9. 

To calculate labor costs, we multiplied state average hourly earnings by labor hours per metric 

ton. 
A-8 

Table A-6. Raw Material Costs by Market and State: 2005 

State(s) 

Price of Raw Materials 

($/metric ton)a

Cost of Raw Materials 

($/metric ton of cement) 

AL $6.57 $10.99 

AK $6.60 $11.04 

AZ $5.75 $9.62 

AR $6.29 $10.52 

CA $8.37 $14.00 

CO $6.85 $11.46 

CT $9.19 $15.37 

DE $6.89 $11.52 

FL $8.67 $14.50 

GA $7.63 $12.76 

HI $13.34 $22.31 

ID $5.37 $8.98 

IL $7.16 $11.97 

IN $5.40 $9.03 

IA $7.27 $12.16 

KS $7.20 $12.04 

KY $7.24 $12.11 

LA $8.18 $13.68 

ME $6.85 $11.46 

MD $8.28 $13.85 

MA $9.19 $15.37 

MI $3.89 $6.51 

MN $8.30 $13.88 

MS $11.90 $19.90 

MO $7.37 $12.33 

MT $4.76 $7.96 

NE $7.10 $11.87 

NV $7.17 $11.99 

NH $8.02 $13.41 

NJ $7.04 $11.77 

NM $6.67 $11.16 

NY $8.44 $14.12 

NC $8.59 $14.37 

ND $4.45 $7.44 

OH $5.82 $9.73 

OK $5.67 $9.48 

OR $6.01 $10.05 

PA $6.67 $11.16 

RI $7.74 $12.94 

SC $7.61 $12.73 

SD $4.60 $7.69 

TN $7.55 $12.63 

TX $6.15 $10.29 

UT $5.58 $9.33 

VT $6.75 $11.29 

VA $9.03 $15.10 

WA $6.92 $11.57 

WV $6.86 $11.47 

WI $5.83 $9.75 

WY $5.68 $9.50 a U.S. Department of the Interior, U.S. Geological Survey. 2007b. 2005 Minerals Yearbook, Crushed Stone. Table 

4. Washington, DC: U.S. Department of the Interior. 
A-9 

Table A-7. Annual Repair and Maintenance Costs by Kiln Capacity: 2005 

Kiln Capacity (short tons) 1993 ($/metric ton) 2005 ($/metric ton)a

<500,000 tons $10.54 $15.46 

500,000 to 750,000 tons $8.30 $12.17 

>750,000 tons $5.85 $8.59 

a Adjusted by chained (real) GDP obtained from the U.S. Department of Commerce, Bureau of Economic Analysis. 

2007. Current-Dollar and “Real” Gross Domestic Product. Available at 

<http://www.bea.gov/national/xls/gdplev.xls>. 

Table A-8. Labor Productivity Measuresfor the U.S. Cement Industry by Process Type 

and Kiln Size: 2005 

Process Type Metric Tons per Employee Houra, b

AVLI 

(employee hours 

per metric ton)a

 

Wet Process 2.16 0.4630 

≤500,000-ton capacity 1.87 0.5348 

>500,000-ton capacity 2.31 0.4329 

Dry Process 3.14 0.3185 

≤500,000-ton capacity 1.93 0.5181 

>500,000-ton capacity 3.19 0.3135 

Precalciner 3.36 0.2976 

Preheater 3.2 0.3125 

All Plants 2.96 0.3378 

a Following the PCA, the metric tons used to measure labor efficiency are an equivalent ton measure, composed of 

85% clinker production and 15% finished cement production (PCA, 2005). 

b Portland Cement Association (PCA). December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 

A.2.4 Electricity 

To reflect the differences in electricity consumption process type and kiln size, we 

collected energy consumption (kWh) and production data (Table A-10). We computed electricity 

consumption rates (AVEI) as follows: 

 AVEI = kWh/(0.92*Clinker Production + 0.08*Finished Cement Production). 

This approach follows the Portland Cement Association’s energy-efficiency measures 

using “equivalent tons” (PCA, 2005). This measure is composed of 92% clinker production and 

8% finished cement production. 
A-10 

Table A-9. Average Hourly Earnings by Market and State: 2005 

State 

U.S. BLS 

Series Report ID Average Hourly Earnings ($) 

AL SMU0100000310000006 $15.33 

AK SMU0200000300000006 $14.22 

AZ SMU0400000310000006 $14.72 

AR SMU0500000310000006 $13.98 

CA SMU0600000310000006 $16.47 

CO SMU0800000310000006 $16.17 

CT SMU0900000310000006 $19.57 

DE SMU1000000310000006 $23.20 

FL SMU1200000310000006 $13.94 

GA SMU1300000310000006 $15.96 

HI SMU1500000300000006 $14.34 

ID SMU1600000300000006 $14.96 

IL SMU1700000310000006 $16.35 

IN SMU1800000310000006 $18.90 

IA SMU1900000310000006 $15.95 

KS SMU2000000310000006 $17.82 

KY SMU2100000310000006 $17.50 

LA SMU2200000310000006 $16.62 

ME SMU2300000310000006 $16.77 

MD SMU2400000310000006 $18.67 

MA SMU2500000310000006 $18.48 

MI SMU2600000310000006 $23.07 

MN SMU2700000310000006 $16.99 

MS SMU2800000310000006 $14.26 

MO SMU2900000310000006 $19.12 

MT SMU3000000310000006 $14.87 

NE SMU3100000310000006 $15.30 

NV SMU3200000310000006 $15.29 

NH SMU3300000310000006 $15.95 

NJ SMU3400000310000006 $17.42 

NM SMU3500000300000006 $13.66 

NY SMU3600000310000006 $19.02 

NC SMU3700000310000006 $15.04 

ND SMU3800000310000006 $15.07 

OH SMU3900000310000006 $20.62 

OK SMU4000000310000006 $15.93 

OR SMU4100000310000006 $15.76 

PA SMU4200000310000006 $15.59 

RI SMU4400000310000006 $13.08 

SC SMU4500000310000006 $16.00 

SD SMU4600000300000006 $13.47 

TN SMU4700000310000006 $14.01 

TX SMU4800000310000006 $13.53 

UT SMU4900000310000006 $14.71 

VT SMU5000000310000006 $15.45 

VA SMU5100000310000006 $17.45 

WA SMU5300000300000006 $18.83 

WV SMU5400000310000006 $16.71 

WI SMU5500000310000006 $17.04 

WY SMU5600000300000006 $17.08 

Source: U.S. Department of Labor, Bureau of Labor Statistics. 2007. “State and Area Employment, Hours and 

Earnings 2005.” Washington, DC: U.S. Department of Labor. 
A-11 

Table A-10. Electricity Consumption of U.S. Cement Producers by Process Type: 2005 

Process Type 

Clinker Production 

(metric tons) 

Finished Cement 

Production 

(metric tons) 

Electricity 

Consumption 

(1,000 kWh) 

AVEI 

(kWh per 

metric ton)a

Wet Process 11,054,608 12,150,780 1,660,208 149.0 

≤500,000-ton capacity 3,228,213 3,563,013 515,451 158.4 

>500,000-ton capacity 7,826,395 8,587,767 1,144,757 145.1 

Dry Process 70,112,322 77,036,853 10,778,235 152.5 

≤500,000-ton capacity 1,563,774 1,690,568 276,358 175.6 

>500,000-ton capacity 68,548,548 75,346,285 10,501,877 152.0 

Precalciner 44,295,828 48,559,311 6,725,423 150.7 

Preheater 60,357,246 66,278,745 9,224,958 151.6 

All Plants 81,166,930 89,187,633 12,438,443 152.0 

a Following the PCA, the metric tons used to measure energy efficiency are an equivalent ton measure composed of 

92% clinker production and 8% finished cement production (PCA, 2005). 

Source: Portland Cement Association (PCA). December 2005. U.S. and Canadian Labor-Energy Input Survey 

2005. Skokie, IL: PCA’s Economic Research Department. 

Table A-11 presents state electricity prices. To calculate electricity costs, we multiplied 

these prices by AVEI. 

A.2.5 Fuel 

Das (1992) represents average variable fuel input rates using the following expression: 

 AVFI = (1 + constant)Age in 2005 * average variable fuel input of a new kiln. 

We used the calibration procedures described from EPA’s 1998 cement study to compute 

a revised average variable primary fuel input of a new kiln. The constant used remains 0.0087, 

and Table A-12 reports the average variable fuel input of a new kiln by its process type and its 

capacity. 

The AEO 2007 reports other industrial coal, natural gas, and residual fuel prices by 

census region (Table A-13) (Energy Information Administration, 2007). For petroleum coke 

price, we used the procedure outlined in the Energy Information Administration’s State Energy 

Consumption, Price, and Expenditure Estimates (SEDS): Technical Notes Prices and 

Expenditures Section 4 (DOE, 2006b). In the process described there, the average price was 

calculated by dividing the sum of the value of calcined and uncalcined petroleum coke exports 

by the sum of export quantities. We collected these data from the U.S. International Trade 

Commissions Database (USITC, 2007). To convert units to million 
A-12 

Table A-11. Electricity Price by Market and State: 2005 

State Cents per kWh 

AL 4.52 

AK 9.29 

AZ 5.85 

AR 4.74 

CA 9.55 

CO 5.74 

CT 9.40 

DE 6.21 

FL 6.46 

GA 5.28 

HI 15.79 

ID 3.91 

IL 4.61 

IN 4.42 

IA 4.56 

KS 4.85 

KY 3.60 

LA 6.71 

ME 7.28 

MD 7.01 

MA 9.22 

MI 5.32 

MN 5.02 

MS 5.37 

MO 4.54 

MT 4.83 

NE 4.43 

NV 7.71 

NH 11.48 

NJ 9.76 

NM 5.61 

NY 8.23 

NC 5.04 

ND 4.32 

OH 5.10 

OK 5.11 

OR 4.83 

PA 6.29 

RI 10.01 

SC 4.55 

SD 4.95 

TN 4.73 

TX 7.14 

UT 4.24 

VT 7.77 

VA 4.46 

WA 4.27 

WV 3.85 

WI 5.39 

WY 3.99 

Source: U.S. U.S. Department of Energy, Energy Information Administration. 2006a. Electric Power Annual 2005. 

Figure 7-7. Washington, DC: U.S. Energy Information Administration. 
A-13 

Table A-12. Calibrated Average Variable Primary Fuel Input for New Kiln by Process 

Type: 2005 

Process Type 

Average Variable Fuel Input for a New Kiln 

(million BTU per metric ton)

Wet Process 

≤500,000-ton capacity 2.84 

>500,000-ton capacity 3.34 

Dry Process 

≤500,000-ton capacity 3.43 

>500,000-ton capacity 2.61 

Precalciner 2.97 

Preheater 3.01 

BTUs, we converted from metric tons to barrels by multiplying by 5.51, and we converted from 

barrels to million BTUs by multiplying by 6.024. 

To calculate fuel costs, we used the primary fuel used by the kiln reported by the Portland 

Cement Association (2004) and multiplied fuel prices by the fuel input equation described above. 

A.2.6 Results 

RTI used the methods described above to revise the kiln-level AVC functions and to 

reflect the cost differences across kiln technologies and kiln size. The new AVC estimates for 

each kiln are reported in Table A-14. We report them in dollars per metric ton and dollars per 

short ton. 

A.3 Partial Equilibrium Model 

Once the markets were defined, we examined the evidence supporting the appropriate 

supplier pricing behavior assumptions in these markets. For example, the degree of 

concentration, entry barriers, and product differentiation can indicate a firm’s ability to influence 

market prices by varying the quantity of cement it sells. In markets with large numbers of sellers 

and identical products, firms are unlikely to be able to influence market prices via their 

production decisions (i.e., they are “price takers”). However, in markets with few firms, 

significant barriers to entry (e.g., licenses, legal restrictions, or high fixed costs), or products that 

are similar but can be differentiated, the firm may have some degree of market power (i.e., set or 

significantly influence market prices). 
A-14 

Table A-13. Fuel Prices by Census Region, State, and Fuel Type: 2005 

 Fuel Price ($/million BTU) 

Census Region State 

Other Industrial 

Coal a

Petroleum 

Cokeb Natural Gasa

 Residual Oila

East South Central AL $2.47 $1.80 $8.51 $7.81 

Pacific AK $2.24 $1.80 $7.46 $7.98 

Mountain AZ $1.73 $1.80 $7.71 $7.59 

West South Central AR $1.79 $1.80 $7.80 $7.02 

Pacific CA $2.24 $1.80 $7.46 $7.98 

Mountain CO $1.73 $1.80 $7.71 $7.59 

New England CT $3.71 $1.80 $10.47 $7.79 

Middle Atlantic DE $2.13 $1.80 $9.65 $8.33 

South Atlantic FL $2.83 $1.80 $9.11 $7.93 

South Atlantic GA $2.83 $1.80 $9.11 $7.93 

Pacific HI $2.24 $1.80 $7.46 $7.98 

Mountain ID $1.73 $1.80 $7.71 $7.59 

East North Central IL $2.24 $1.80 $8.52 $7.65 

East North Central IN $2.24 $1.80 $8.52 $7.65 

West North Central IA $1.21 $1.80 $7.86 $7.18 

West North Central KS $1.21 $1.80 $7.86 $7.18 

East South Central KY $2.47 $1.80 $8.51 $7.81 

West South Central LA $1.79 $1.80 $7.80 $7.02 

New England ME $3.71 $1.80 $10.47 $7.79 

South Atlantic MD $2.83 $1.80 $9.11 $7.93 

New England MA $3.71 $1.80 $10.47 $7.79 

East North Central MI $2.24 $1.80 $8.52 $7.65 

West North Central MN $1.21 $1.80 $7.86 $7.18 

East South Central MS $2.47 $1.80 $8.51 $7.81 

West North Central MO $1.21 $1.80 $7.86 $7.18 

Mountain MT $1.73 $1.80 $7.71 $7.59 

West North Central NE $1.21 $1.80 $7.86 $7.18 

Mountain NV $1.73 $1.80 $7.71 $7.59 

New England NH $3.71 $1.80 $10.47 $7.79 

Middle Atlantic NJ $2.13 $1.80 $9.65 $8.33 

Mountain NM $1.73 $1.80 $7.71 $7.59 

East North Central NY $2.24 $1.80 $8.52 $7.65 

South Atlantic NC $2.83 $1.80 $9.11 $7.93 

West North Central ND $1.21 $1.80 $7.86 $7.18 

East North Central OH $2.24 $1.80 $8.52 $7.65 

West South Central OK $1.79 $1.80 $7.80 $7.02 

Pacific OR $2.24 $1.80 $7.46 $7.98 

Middle Atlantic PA $2.13 $1.80 $9.65 $8.33 

New England RI $3.71 $1.80 $10.47 $7.79 

South Atlantic SC $2.83 $1.80 $9.11 $7.93 

West North Central SD $1.21 $1.80 $7.86 $7.18 

East South Central TN $2.47 $1.80 $8.51 $7.81 

East South Central TX $2.47 $1.80 $8.51 $7.81 

Mountain UT $1.73 $1.80 $7.71 $7.59 

(continued) 
A-15 

Table A-13. Fuel Prices by Census Region, State, and Fuel Type: 2005 (continued) 

 Fuel Price ($/million BTU) 

Census Region State 

Other Industrial 

Coal a

Petroleum 

Coke b Natural Gas a

 Residual Oil a

New England VT $3.71 $1.80 $10.47 $7.79 

South Atlantic VA $2.83 $1.80 $9.11 $7.93 

Pacific WA $2.24 $1.80 $7.46 $7.98 

South Atlantic WV $2.83 $1.80 $9.11 $7.93 

East North Central WI $2.24 $1.80 $8.52 $7.65 

Mountain WY $1.73 $1.80 $7.71 $7.59 

a Other industrial coal, natural gas, and residual oil: U.S. Department of Energy, Energy Information 

Administration. 2007. Annual Energy Outlook 2007. Supplemental Tables 12, 15, and 16. Washington, DC: U.S. 

Energy Information Administration. For kilns using waste oil as a primary fuel, we used the residual fuel oil price 

as a proxy. 

b Petroleum coke: Export trade data come from USITC Interactive Tariff and Trade DataWeb. 2007 

<http://dataweb.usitc.gov/scripts/user_set.asp >. HTS codes: 271311 and 271312. The price calculation is 

described in State Energy Consumption, Price, and Expenditure Estimates (SEDS): Technical Notes Prices and 

Expenditures Section 4 (U.S. Department of Energy, 2006b). 

<http://www.eia.doe.gov/emeu/states/sep_prices/notes/pr_petrol.pdf.>. 

Although perfect competition on the supply side (and demand side) is widely accepted 

for modeling many industries (EPA, 2000), the cement industry has unique characteristics that 

lead us to use an alternative assumption about supplier pricing behavior. First, high 

transportation costs and other production economics limit the number of sellers, so each seller 

has a substantial market share. Potential entry is constrained by the high capital costs that involve 

purchases and construction of large rotary kilns that are not readily movable or transferable to 

other uses. In addition, large plants are typically more economical because they can produce 

cement at lower unit costs; this reduces entry incentives for small-sized cement plants. Second, 

cement producers offer very similar or identical products. American Society for Testing and 

Materials (ASTM) specifications tend to ensure uniform quality, and recent industry reviews 

(USITC, 2006) suggest that there is little or no brand loyalty that allows firms to differentiate 

their products. Given this evidence, EPA continued to use the oligopoly framework used in 

previous economic analyses (EPA, 1998, 1999b). 
A-16 

Table A-14. Average Variable Costs by Kiln: 2005 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

GA101 Atlanta Lafarge North 

America 

Atlanta Plant Atlanta GA 42 C Dry 315 

TN101 Atlanta Buzzi Unicem 

USA, Inc. 

Chattanooga Plant Chattanooga TN 5 K Dry-C 771 

GA201 Atlanta Cemex Clinchfield Plant Clinchfield GA 6 C Dry-X 705 VA101 Atlanta Titan America LLC Roanoke Plant Cloverdale VA 9 C Dry-C 1,120 SC101 Atlanta Giant Cement 

Holding, Inc. 

Harleyville Plant Harleyville SC 53 C Wet 164 

SC102 Atlanta Giant Cement 

Holding, Inc. 

Harleyville Plant Harleyville SC 49 C Wet 156 

SC103 Atlanta Giant Cement 

Holding, Inc. 

Harleyville Plant Harleyville SC 45 C Wet 164 

SC104 Atlanta Giant Cement 

Holding, Inc. 

Harleyville Plant Harleyville SC 33 C Wet 164 

SC201 Atlanta Lafarge North 

America 

Harleyville Plant Harleyville SC 17 C Dry-C 972 

SC401 Atlanta Holcim (U.S.) Inc. Holly Hill Plant Holly Hill SC 2 C Dry-C 1,860 

TN201 Atlanta Cemex Knoxville Plant Knoxville TN 27 C Dry-C 667 PA601 Baltimore/ 

Philadelphia 

Giant Cement Holding, Inc. 

Bath Plant Bath PA 49 C Wet 103 

PA602 Baltimore/ 

Philadelphia 

Giant Cement Holding, Inc. 

Bath Plant Bath PA 49 C Wet 506 

PA801 Baltimore/ 

Philadelphia 

Lehigh Cement Company 

Allentown Plant Blandon PA 40 C Dry 470 

PA802 Baltimore/ 

Philadelphia 

Lehigh Cement Company 

Allentown Plant Blandon PA 40 C Dry 470 

MD101 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Frederick Plant Buckeystown MD 47 C Wet 155 

MD102 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Frederick Plant Buckeystown MD 47 C Wet 155 

MD301 Baltimore/ 

Philadelphia 

St. Lawrence Cement Company 

Hagerstown Plant Hagerstown MD 34 C Dry 513 
A-17 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

WV101 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Martinsburg Plant Martinsburg WV 50 C Wet 177 

WV102 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Martinsburg Plant Martinsburg WV 45 C Wet 177 

WV103 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Martinsburg Plant Martinsburg WV 40 C Wet 354 

PA501 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Nazareth Plant I Nazareth PA 27 C Dry-X 1,116 

PA502 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Nazareth Plant III Nazareth PA 48 C Dry 150 

PA503 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Nazareth Plant III Nazareth PA 48 C Dry 155 

PA504 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Nazareth Plant III Nazareth PA 56 C Dry 107 

PA505 Baltimore/ 

Philadelphia 

Essroc Cement Corp. 

Nazareth Plant III Nazareth PA 56 C Dry 107 

PA201 Baltimore/ 

Philadelphia 

Buzzi Unicem USA, Inc. 

Stockertown Plant Stockertown PA 26 K Dry-X 365 

PA202 Baltimore/ 

Philadelphia 

Buzzi Unicem USA, Inc. 

Stockertown Plant Stockertown PA 12 K Dry-X 543 

MD201 Baltimore/ 

Philadelphia 

Lehigh Cement Company 

Union Bridge Union Bridge MD 4 C Dry-C 1,715 

PA701 Baltimore/ 

Philadelphia 

Lafarge North America 

Whitehall Plant Whitehall PA 40 C Dry-X 419 

PA702 Baltimore/ 

Philadelphia 

Lafarge North America 

Whitehall Plant Whitehall PA 30 C Dry-X 283 

MS101 Birmingham Holcim (U.S.) Inc. Artesia Plant Artesia MS 31 C Wet 419 

AL301 Birmingham Lafarge North 

America 

Roberta Plant Calera AL 3 C Dry-C 1,498 

AL101 Birmingham Cemex Demopolis Plant Demopolis AL 28 C Dry-X 821 AL401 Birmingham Lehigh Cement 

Company 

Leeds Plant Leeds AL 29 C Dry-X 716 
A-18 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

AL501 Birmingham National Cement 

Co. of Alabama 

Ragland Plant Ragland AL 30 C Dry-C 900 

AL201 Birmingham Holcim (U.S.) Inc. Theodore Plant Theodore AL 24 C Dry-C 1,440 IA201 Chicago Lafarge North 

America 

Buffalo Plant Buffalo IA 24 C Dry-C 946 

IL201 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 IL202 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 IL203 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 IL204 Chicago Cemex Dixon Plant Dixon IL 40 K Dry 184 IN101 Chicago Buzzi Unicem 

USA, Inc. 

Greencastle Plant Greencastle IN 5 C Dry-C 1,190 

IL301 Chicago Eagle Materials La Salle Plant La Salle IL 31 K Dry-X 602 IN201 Chicago Essroc Cement 

Corp. 

Logansport Plant Logansport IN 43 K Wet 202 

IN202 Chicago Essroc Cement 

Corp. 

Logansport Plant Logansport IN 43 K Wet 202 

IL101 Chicago Buzzi Unicem 

USA, Inc. 

Oglesby Plant Oglesby IL 33 C Dry 569 

KY101 Cincinnati Cemex Kosmosdale Plant Louisville KY 5 C Dry-C 1,365 

IN402 Cincinnati Lehigh Cement 

Company 

Mitchell Plant Mitchell IN 55 C Dry-X 251 

IN401 Cincinnati Lehigh Cement 

Company 

Mitchell Plant Mitchell IN 45 C Dry-X 251 

IN403 Cincinnati Lehigh Cement 

Company 

Mitchell Plant Mitchell IN 29 C Dry-X 274 

IN301 Cincinnati Essroc Cement 

Corp. 

Speed Plant Speed IN 33 C Dry 279 

IN302 Cincinnati Essroc Cement 

Corp. 

Speed Plant Speed IN 27 C Dry-X 542 

OH101 Cincinnati Cemex Fairborn Plant Xenia OH 31 K Dry-X 661 
A-19 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

OK201 Dallas Holcim (U.S.) Inc. Ada Plant Ada OK 47 C Wet 278 OK202 Dallas Holcim (U.S.) Inc. Ada Plant Ada OK 47 C Wet 278 AR101 Dallas Ash Grove Cement 

Company 

Foreman Plant Foreman AR 47 C Wet 246 

AR102 Dallas Ash Grove Cement 

Company 

Foreman Plant Foreman AR 43 C Wet 246 

AR103 Dallas Ash Grove Cement 

Company 

Foreman Plant Foreman AR 41 C Wet 339 

TX201 Dallas Ash Grove Texas, 

L.P. 

Midiothian Plant Midiothian TX 39 C Wet 283 

TX202 Dallas Ash Grove Texas, 

L.P. 

Midiothian Plant Midiothian TX 36 C Wet 283 

TX203 Dallas Ash Grove Texas, 

L.P. 

Midiothian Plant Midiothian TX 33 C Wet 283 

TX701 Dallas Holcim (U.S.) Inc. Holnam Texas L.P. Midiothian TX 18 C Dry-C 1,036 TX702 Dallas Holcim (U.S.) Inc. Holnam Texas L.P. Midiothian TX 5 C Dry-C 990 TX901 Dallas Texas Industries 

Inc. 

Midiothian Plant Midiothian TX 45 C Wet 286 

TX902 Dallas Texas Industries 

Inc. 

Midiothian Plant Midiothian TX 42 C Wet 286 

TX903 Dallas Texas Industries 

Inc. 

Midiothian Plant Midiothian TX 38 C Wet 286 

TX904 Dallas Texas Industries 

Inc. 

Midiothian Plant Midiothian TX 33 C Wet 286 

TX905 Dallas Texas Industries 

Inc. 

Midiothian Plant Midiothian TX 4 C Dry-C 1,964 

OK301 Dallas Lafarge North 

America 

Tulsa Plant Tulsa OK 44 C Dry 347 

OK302 Dallas Lafarge North 

America 

Tulsa Plant Tulsa OK 42 C Dry 341 
A-20 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

CO201 Denver Holcim (U.S.) Inc. Portland Plant Florence CO 4 C Dry-C 1,631 WY101 Denver Eagle Materials Laramie Plant Laramie WY 9 C Dry 181 WY102 Denver Eagle Materials Laramie Plant Laramie WY 6 C Dry-X 416 CO101 Denver Cemex Lyons Plant Lyons CO 25 C Dry-X 486 SD101 Denver GCC of America, 

Inc. 

Dacotah Cement Rapid City SD 50 C Wet 147 

SD102 Denver GCC of America, 

Inc. 

Dacotah Cement Rapid City SD 48 C Wet 147 

SD103 Denver GCC of America, 

Inc. 

Dacotah Cement Rapid City SD 10 C Dry-X 557 

MI301 Detroit Lafarge North 

America 

Alpena Plant Alpena MI 43 C Dry 364 

MI302 Detroit Lafarge North 

America 

Alpena Plant Alpena MI 40 C Dry 364 

MI303 Detroit Lafarge North 

America 

Alpena Plant Alpena MI 40 C Dry 364 

MI304 Detroit Lafarge North 

America 

Alpena Plant Alpena MI 40 C Dry 536 

MI305 Detroit Lafarge North 

America 

Alpena Plant Alpena MI 40 C Dry 536 

MI101 Detroit Cemex Charlevoix Plant Charlevoix MI 26 C Dry-C 1,211 MI201 Detroit Holcim (U.S.) Inc. Dundee Plant Dundee MI 46 C Wet 431 MI202 Detroit Holcim (U.S.) Inc. Dundee Plant Dundee MI 46 C Wet 437 OH201 Detroit Lafarge North 

America 

Paulding Plant Paulding OH 49 A Wet 226 

OH202 Detroit Lafarge North 

America 

Paulding Plant Paulding OH 49 A Wet 228 

FL501 Florida Suwannee 

American Cement 

 Branford FL 2 C Dry-C 682 

FL101 Florida Cemex Brooksville Plant Brooksville FL 29 C Dry-X 550 FL102 Florida Cemex Brooksville Plant Brooksville FL 20 C Dry-X 506 FL301 Florida Rinker Materials Brooksville Plant Brooksville FL 18 C Dry-X 605 
A-21 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

FL601 Florida Titan America LLC Pennsuco Plant Medley FL 1 C Dry-C 1,492 FL401 Florida Rinker Materials Miami Plant Miami FL 5 C Dry-C 928 FL201 Florida Florida Rock 

Industries 

Thompson S. Baker Plant 

Newberry FL 6 C Dry-C 726 

KS101 Kansas City Ash Grove Cement 

Company 

Chanute Plant Chanute KS 4 C Dry-C 1,308 

KS301 Kansas City Lafarge North 

America 

Fredonia Plant Fredonia KS 69 A Wet 169 

KS302 Kansas City Lafarge North 

America 

Fredonia Plant Fredonia KS 49 A Wet 243 

KS401 Kansas City Monarch Cement 

Company 

Humbolt Plant Humbolt KS 32 C Dry-X 284 

KS402 Kansas City Monarch Cement 

Company 

Humbolt Plant Humbolt KS 30 C Dry-C 503 

KS201 Kansas City Buzzi Unicem 

USA, Inc. 

Independence Plant Independence KS 88 K Dry 82 

KS202 Kansas City Buzzi Unicem 

USA, Inc. 

Independence Plant Independence KS 88 K Dry 82 

KS203 Kansas City Buzzi Unicem 

USA, Inc. 

Independence Plant Independence KS 88 K Dry 82 

KS204 Kansas City Buzzi Unicem 

USA, Inc. 

Independence Plant Independence KS 88 K Dry 82 

NE101 Kansas City Ash Grove Cement 

Company 

Louisville Plant Louisville NE 29 C Dry-X 338 

NE102 Kansas City Ash Grove Cement 

Company 

Louisville Plant Louisville NE 23 C Dry-C 507 

OK101 Kansas City Buzzi Unicem 

USA, Inc. 

Pryor Plant Pryor OK 45 C Dry 189 

OK102 Kansas City Buzzi Unicem 

USA, Inc. 

Pryor Plant Pryor OK 43 C Dry 186 

OK103 Kansas City Buzzi Unicem 

USA, Inc. 

Pryor Plant Pryor OK 25 C Dry 250 
A-22 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

MO501 Kansas City Lafarge North 

America 

Sugar Creek Plant Sugar Creek MO 4 C Dry-C 930 

CA101 Los Angeles California Portland 

Cement 

Colton Plant Colton CA 43 C Dry 340 

CA102 Los Angeles California Portland 

Cement 

Colton Plant Colton CA 43 C Dry 340 

CA801 Los Angeles National Cement 

Co. of California 

Lebec Plant Lebec CA 6 K Dry-C 1,033 

CA701 Los Angeles Mitsubishi Cement 

Corporation 

Cushenbury Plant Lucerne Valley CA 23 C Dry-C 1,543 

CA201 Los Angeles California Portland 

Cement 

Mojave Plant Mojave CA 24 C Dry-C 1,363 

CA1101 Los Angeles Texas Industries 

Inc. 

Oro Grande Plant Oro Grande CA 57 C Dry 155 

CA1102 Los Angeles Texas Industries 

Inc. 

Oro Grande Plant Oro Grande CA 57 C Dry 155 

CA1103 Los Angeles Texas Industries 

Inc. 

Oro Grande Plant Oro Grande CA 57 C Dry 155 

CA1104 Los Angeles Texas Industries 

Inc. 

Oro Grande Plant Oro Grande CA 53 C Dry 155 

CA1105 Los Angeles Texas Industries 

Inc. 

Oro Grande Plant Oro Grande CA 53 C Dry 155 

CA1106 Los Angeles Texas Industries 

Inc. 

Oro Grande Plant Oro Grande CA 46 C Dry 155 

CA1107 Los Angeles Texas Industries 

Inc. 

Oro Grande Plant Oro Grande CA 46 C Dry 155 

CA601 Los Angeles Lehigh Southwest 

Cement Company 

Tehachapi Plant Tehachapi CA 14 C Dry-C 958 

CA301 Los Angeles Cemex Victorville Plant Victorville CA 22 C Dry-X 1,046 CA302 Los Angeles Cemex Victorville Plant Victorville CA 4 C Dry-X 1,681 IA101 Minneapolis Holcim (U.S.) Inc. Mason City Plant Mason City IA 39 C Dry 600 

IA102 Minneapolis Holcim (U.S.) Inc. Mason City Plant Mason City IA 39 C Dry 371 
A-23 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

IA301 Minneapolis Lehigh Cement 

Company 

Lehigh-Mason City Mason City IA 27 C Dry-C 755 

NY301 New York/ 

Boston 

St. Lawrence Cement Company 

Catskill Plant Catskill NY 41 C Wet 580 

NY101 New York/ 

Boston 

Glens Fall Lehigh Cement Co. 

Glens Falls Plant Glens Falls NY 32 C Dry-X 586 

NY201 New York/ 

Boston 

Lafarge North America 

Ravena Plant Ravena NY 43 C Wet 852 

NY202 New York/ 

Boston 

Lafarge North America 

Ravena Plant Ravena NY 43 C Wet 868 

ME101 New York/ 

Boston 

Dragon Products Company 

Thomaston Plant Thomaston ME 34 C Wet 392 

AZ201 Phoenix Phoenix Cement 

Company 

Clarkdale Plant Clarkdale AZ 46 C Dry-C 187 

AZ202 Phoenix Phoenix Cement 

Company 

Clarkdale Plant Clarkdale AZ 46 C Dry-C 187 

AZ203 Phoenix Phoenix Cement 

Company 

Clarkdale Plant Clarkdale AZ 44 C Dry-C 191 

AZ204 Phoenix Phoenix Cement 

Company 

Clarkdale Plant Clarkdale AZ 3 C Dry-C 912 

UT101 Phoenix Ash Grove Cement 

Company 

Leamington Plant Nephi UT 24 C Dry-C 810 

AZ101 Phoenix California Portland 

Cement 

Rillito Plant Rillito AZ 56 C Dry 121 

AZ102 Phoenix California Portland 

Cement 

Rillito Plant Rillito AZ 54 C Dry 121 

AZ103 Phoenix California Portland 

Cement 

Rillito Plant Rillito AZ 50 C Dry 121 

AZ104 Phoenix California Portland 

Cement 

Rillito Plant Rillito AZ 3 C Dry-C 969 

NM101 Phoenix GCC of America, 

Inc. 

Tijeras Plant Tijeras NM 46 C Dry-X 216 
A-24 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

NM102 Phoenix GCC of America, 

Inc. 

Tijeras Plant Tijeras NM 45 C Dry-X 216 

PA401 Pittsburgh Essroc Cement 

Corp. 

Bessemer Plant Bessemer PA 44 C Wet 225 

PA402 Pittsburgh Essroc Cement 

Corp. 

Bessemer Plant Bessemer PA 41 C Wet 339 

PA101 Pittsburgh Armstrong Cement 

& Sup. Corp. 

Cabot Plant Cabot PA 79 C Wet 143 

PA102 Pittsburgh Armstrong Cement 

& Sup. Corp. 

Cabot Plant Cabot PA 79 C Wet 143 

PA301 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 215 PA302 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 205 PA303 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 238 OR101 Salt Lake 

City 

Ash Grove Cement Company 

Durkee Plant Durkee OR 7 C Dry-C 816 

ID101 Salt Lake 

City 

Ash Grove Cement Company 

Inkom Plant Inkom ID 77 C Wet 115 

ID102 Salt Lake 

City 

Ash Grove Cement Company 

Inkom Plant Inkom ID 54 C Wet 145 

MT101 Salt Lake 

City 

Ash Grove Cement Company 

Montana City Plant Montana City MT 42 C Wet 293 

UT201 Salt Lake 

City 

Holcim (U.S.) Inc. Devil’s Slide Plant Morgan UT 8 C Dry-C 704 

MT201 Salt Lake 

City 

Holcim (U.S.) Inc. Trident Plant Three Forks MT 32 C Wet 280 

TX1201 San Antonio Texas-Lehigh 

Cement Company 

Buda Plant Buda TX 27 C Dry-C 1,125 

TX501 San Antonio Cemex Balcones Plant New Braunfels TX 25 K Dry-X 1,005 TX1001 San Antonio Texas Industries 

Inc. 

Hunter Cement Plant New Braunfels TX 26 C Dry-C 780 

TX601 San Antonio Cemex Odessa Plant Odessa TX 47 C Dry 256 TX602 San Antonio Cemex Odessa Plant Odessa TX 26 C Dry-X 285 
A-25 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

TX101 San Antonio Alamo Cement 

Company 

1604 Plant San Antonio TX 24 C Dry-C 821 

TX401 San Antonio Capitol 

Aggregates, Ltd. 

Capitol Cement Division 

San Antonio TX 40 C Wet 269 

TX402 San Antonio Capitol 

Aggregates, Ltd. 

Capitol Cement Division 

San Antonio TX 22 C Dry-C 599 

TX301 San Antonio Buzzi Unicem 

USA, Inc. 

Maryneal Plant Sweetwater TX 34 C Dry-X 153 

TX302 San Antonio Buzzi Unicem 

USA, Inc. 

Maryneal Plant Sweetwater TX 34 C Dry-X 153 

TX303 San Antonio Buzzi Unicem 

USA, Inc. 

Maryneal Plant Sweetwater TX 34 C Dry-X 162 

CA401 San 

Francisco 

Hanson Permanente Cemente 

Permanente Plant Cupertino CA 24 C Dry-C 1,497 

CA901 San 

Francisco 

RMC Pacific Materials 

Santa Cruz Plant Davenport CA 24 C Dry-C 812 

NV101 San 

Francisco 

Eagle Materials Nevada Cement Femley NV 41 C Dry 226 

NV102 San 

Francisco 

Eagle Materials Nevada Cement Femley NV 19 C Dry-X 226 

CA501 San 

Francisco 

Lehigh Southwest Cement Company 

Redding Plant Redding CA 24 C Dry-C 592 

WA101 Seattle Ash Grove Cement 

Company 

Seattle Plant Seattle WA 13 C Dry-C 675 

WA201 Seattle Lafarge North 

America 

Seattle Plant Seattle WA 38 K Wet 425 

MO101 St. Louis Buzzi Unicem 

USA, Inc. 

Cape Girardeau Plant Cape Girardeau MO 24 C Dry-C 1,265 

MO401 St. Louis Holcim (U.S.) Inc. Clarksville Plant Clarksville MO 38 C Wet 1,241 MO201 St. Louis Buzzi Unicem 

USA, Inc. 

Festus Plant Festus MO 40 K Dry 592 
A-26 

Table A-14. Average Variable Costs by Kiln: 2005 (continued) 

Kiln ID 

Market 

Name Company Name Plant Name City State 

Age in 

2005 

Primary 

Fuel IDa Processb

Clinker 

Capacity (103

metric 

tons/year) 

V

MO202 St. Louis Buzzi Unicem 

USA, Inc. 

Festus Plant Festus MO 36 K Dry 592 

IL401 St. Louis Lafarge North 

America 

Joppa Plant Grand Chain IL 42 K Dry 443 

IL402 St. Louis Lafarge North 

America 

Joppa Plant Grand Chain IL 30 K Dry 612 

MO301 St. Louis Continental 

Cement Co., Inc. 

Hannibal Plant Hannibal MO 39 C Wet 549 

CA1001 White 

Cement 

Texas Industries Inc. 

Crestmore Riverside CA 47 O Dry 51 

CA1002 White 

Cement 

Texas Industries Inc. 

Crestmore Riverside CA 45 O Dry 51 

TX801 White 

Cement 

Lehigh Cement Company 

Waco Plant Waco TX 37 G Wet 97 

PA901 White 

Cement 

Lehigh Cement Company 

York Plant York PA 43 O Wet 112 

a Primary fuel ID codes are coal (C), coke (K), oil (O), and waste (A). b Process codes are preheater (X) and precalciner (C). 
A-27 

One consequence of this assumption is that the seller individually chooses an output level 

that is less than the level produced under perfect competition. As a result, the baseline market 

price will be higher than a model of perfect competition, and there is a preexisting market 

distortion in the industry being regulated.1

 To provide some intuition about factors that influence 

the size of the existing distortion, we express a seller’s “best” supply decision as a function of the 

market price, the seller’s market share, the market demand elasticity, and the seller’s marginal 

costs (see Varian [1992], pp. 289–290): 

Price × (1 + Market Sharei/Demand Elasticity) = Marginal Costi

This equation shows the relationship between the oligopoly model and perfect competition. The 

market distortion will typically be higher the smaller the number of sellers and in markets where 

the quantity demanded is less sensitive to price (i.e., the demand elasticity is inelastic). 

A.4 Equations 

To estimate the economic impacts of the regulation, EPA used four linear equations to 

calculate the following unknown variables: 

 change in domestic plant production (dqi), 

 change in imports (dqimports), 

 change in cement market quantity (dQ), and 

 change in cement price (dP). 

Equation 1: Domestic Supply. For each plant, we describe its response to the regulatory 

program as follows. The total compliance cost per ton (ci) is applied to each kiln, and the 

difference in the highest cost kiln with-regulation and the highest cost kiln in the baseline 

approximates the plant’s change in the marginal cost of production (dMCi). In with-regulation 

equilibrium, the change in marginal revenue (dMRi) must equal the change in the marginal cost 

(dMCi) for each plant.2

dmarginal Revenuei = dmarginal Costi

or 

 

1

 This ultimately influence partial equilibrium model’s estimates of the social cost of the regulatory program since 

bigger existing market distortions tend tends to widen the gap between price and marginal cost in these markets 

and leads to the higher deadweight loss estimates than under the case of perfectly competitive markets. The 

Office of Management and Budget (OMB) explicitly mentions the need to consider market power–related 

welfare costs in evaluating regulations under Executive Order 12866. 2

 To highlight and make transparent the assumptions regarding seller behavior, this equation is formally derived in 

Appendix B. 
A-28 

( ) dmarginal cost

dem elasticity

price plant q market Q

dmarket Q

dem elasticity

price

market Q

dplant q

dem elasticity

mkt share dprice 1 2

i + × − × × = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

× +

Equation 2: Supply of Imports. If applicable to the market, an equation describing the supply 

of cement from other countries was included: 

dimports = import supply elasticity × (dprice/baseline price) × baseline imports. 

For import supply, EPA used the latest empirical work on how other countries who export (i.e., 

supply imports) to the United States respond to price changes. Broda et al. (2008) report that the 

export supply elasticity for commodities imported by the United States was approximately two. 

This implies that a 1% increase in prices results in a 2% increase in the volume of exports for a 

typical good. 

Equation 3: Market Supply. Market supply of Portland cement equals the change in domestic 

production and imports: 

dmarket Q = dtotal domestic production + dimports. 

This condition ensures that the market quantity is consistent with the individual supply decisions 

of domestic plants and imports in the new with-regulation equilibrium for each regional market. 

Equation 4: Market Demand. The demand for Portland cement is derived from the demand for 

concrete products, which, in turn, is derived largely from the demand for construction. Based on 

a linear demand equation, the market demand condition for Portland cement must hold based on 

the projected change in market price, that is, 

dMarketQ = demand elasticity (dprice/baseline price) × baseline consumption. 

The use of published estimates from previous rulemakings is appropriate in cases when the cost 

of preparing original estimates is high (EPA, 2000). In previous analyses, EPA econometrically 

estimated the demand elasticity for cement and reported a point estimate of −0.88 (EPA, 1998). 

This value suggests that a 1% increase in the cement price would lead to a 0.88% reduction in 

cement consumption. 
APPENDIX B 

MODEL OF THE CEMENT PLANT’S PRODUCTION DECISION 
B-1 

This appendix provides additional detail about the cement plant production decision used 

in the economic model (see Equation 1 in Section 3 of the RIA). Table B-1 identifies and 

describes the key variables of the cement plant’s profit function. 

Table B-1. Variable Descriptions 

P Market price 

Q=∑ i q Market output 

qi Domestic plant i’s output 

FCi Plant fixed costs 

VCi Plant variable costs 

Step 1: First, we assume the plant’s goal is to maximize profits: 

( ) ( ) i Q = P Q qi −VCi qi − FCi max ( )

qi

π . 

Step 2: The first-order conditions for a profit maximum are: 

( )

q 0

q

VC q

q

q

P(Q) P

q

π

i

i

i i

i

i i

i = ∂ − ∂

∂ = +

∂ . 

Step 3: Apply two key assumptions in the Cournot price model: 

 Plant’s (i) recognizes its own production decisions influence the market price: 

0

q

P

i

≠

∂

∂

 Plant (i) output decisions do not affect those of any other plant (j) (e.g., there is no 

strategic action among cement plants): 

0

q

q

i

j = ∂

∂

Step 4: Next, multiply second term by 

Q

P

P

Q 1 =
B-2 

( )

q 0

q

VC q

Q

P

P

Q q

q

P(Q) P i

i

i i

i

i

= ∂ −⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂

+ . 

Step 5: Rearranging terms: 

( )

q 0

q

VC q P

Q

q

q

Q

P

P(Q) P i

i

i i i

i

= ∂ − ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂

+ . 

Step 6: Use and apply the following definitions: 

inverse demand elasticity 1

q

Q

P

P(Q)

i

= = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂

η

q Q plant's market share

Q

q 1

i

i =⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − . 

We derive the following expression: 

( )

i

i i

i

q

Q VC q

q

1 ∂ =

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

+

η

P . 

Step 7: The total differential of this equation is determined and gives us the optimal 

decision rule for the plant: 

dMC Q 1

q

1 2

i

=⎥

⎦

⎤ ⎢

⎣

⎡ −⎥

⎦

⎤ ⎢

⎣

⎡

+

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

+

Q

P q dQ QP dP dq i

i

η η η . 
APPENDIX C 

SOCIAL COST METHODOLOGY 
C-1 

The Office of Air Quality Planning and Standards (OAQPS) has adopted the standard 

industry-level analysis described in the Office’s resource manual (EPA, 1999a). This approach is 

consistent with previous EPA analyses of the Portland cement industry (EPA, 1998; EPA, 

1999b) and uses a single-period static partial-equilibrium model to compare prepolicy cement 

market baselines with expected postpolicy outcomes in these markets. The benchmark time 

horizon for the analysis is the intermediate run where producers have some constraints on their 

flexibility to adjust factors of production. This time horizon allows us to capture important 

transitory impacts of the program on existing producers. The model provides an estimate of the 

social costs (changes in producer and consumer surplus) associated with controls applying to 

existing kilns (see Section 4). Since the social cost methodology is identical to the approach used 

in previous cement analysis (EPA, 1998, Appendix C), we have included elements of the 

previous report’s Appendix C in this RIA. 

Figure C-1 illustrates the conceptual framework for evaluating the social cost and 

distributive impacts under the imperfectly competitive structure of U.S. cement markets. The 

baseline equilibrium is given by the price, P0, and the quantity, Q0. Without the regulation, the 

total benefits of consuming cement are given by the area under the demand curve up to the 

market output, Q0. This equals the area filled by the letters ABCDEFGHIJ. The total variable 

cost to society of producing Q0 equals the area under the MC function, given by the area IJ. 

Thus, the total surplus value to society from the production and consumption of output level Q0

equals the total benefits minus the total costs, or the area filled by the letters ABCDEFGH. 

This total surplus value to society can be further divided into producer surplus and 

consumer surplus. Producer surplus accrues to the suppliers of cement and reflects the value they 

receive in the market for producing Q0 units of cement less their costs of production, i.e., their 

profits. As shown in Figure C-1, producer surplus is given by the area DEFGH, which is the 

difference between cement revenues (i.e., area DEFGHIJ) and production costs (area IJ). 

Consumer surplus accrues to the consumers of cement and reflects the value they place on 

consumption (total benefits of consumption) less what they must pay on the market, i.e., P0. 

Consumer surplus is thereby given by the area ABC. 
C-2 

Q1

P1

D

$

Output

P0

P1

c

P0

c

Q0

C B

A

D E

F

I J

H

G

MC + Control Costs

MR

MC

Figure C-1. Social Cost of Regulation Under Imperfect Competition 

The proposed rule will increase the marginal cost of producing cement and thereby shift 

this curve upward by the amount of the incremental compliance costs. As shown in Figure C-1, 

this results in a new market equilibrium that occurs at a higher market price for cement, P1, and a 

lower level of output, Q1. In this scenario, the total benefits of consumption are equal to area 

ABDFI and the total variable costs of production are equal to area FI. This yields a withregulation social surplus equal to area ABD with area BD representing the new producer surplus 

and area A being the new consumer surplus. The social cost of the regulation equals the total 

change in social surplus caused by the regulation. Therefore, the social cost of the regulation is 

represented by the area FGHEC in Figure C-1. 

The distributive effects are estimated by separating the social cost into producer surplus 

and consumer surplus losses. First, the change in producer surplus is given by 

ΔPS = B – F – (G+H+E) (C.1) 

Producers gain B from the increase in price (a transfer from consumers to producers), but lose F 

from the increase in production costs due to the incremental compliance costs. Furthermore, the 

reduction of cement production leads to foregone baseline profits of G+H+E. 
C-3 

The change in consumer surplus is given by 

ΔCS = – (B + C) (C.2) 

This change results from the reduction in consumer surplus from the baseline value of ABC to 

the with-regulation value of A. In this case, consumers lose area B as a transfer to producers 

through the increase in the price they pay for the with-regulation level of cement consumption, 

while the reduction in cement consumption due to regulation leads to foregone baseline value of 

consumption equal to area C. 

The social cost or total change in social surplus can then be derived simply by adding the 

changes in producer and consumer surplus, i.e., 

 Social Cost = ΔPS + ΔCS = – (F + G + H + E + C) (C.3) 

This estimate can be compared to the engineering estimate of incremental compliance cost to 

demonstrate the difference between these two estimates of social cost. The incremental 

compliance cost estimate is given by the area FGH, which is simply the constant cost per unit 

times the baseline output level of cement. The social cost estimate from Equation (C.3) above, 

however, exceeds the engineering estimate by the area EC. In other words, the incremental 

compliance cost estimate understates the social costs of the regulation. The reason for this 

follows directly from the imperfectly competitive structure of the markets for cement. A 

comparison with the outcome under perfect competition will assist in illustrating this point. 

Suppose that the MR curve in Figure C-1 was the demand function for a competitive 

market, rather than the marginal revenue function for an imperfectly competitive producer. 

Similarly, let the MC function be the aggregate supply function for all producers in the market. 

The market equilibrium is still determined at the intersection of MC and MR, but given the 

revised interpretation of MR as the competitive demand function, the baseline (competitive) 

market price, P0

C

, is now equal to MC and Q0 is now interpreted as the competitive level of 

cement demand. In this case, all social surplus goes to the consumer. This is because producers 

receive a price that just covers their costs of production. 

In the with-regulation perfectly competitive equilibrium, the market price would rise by 

the per unit control cost amount to P1

c

. The social cost of the regulation is given entirely by the 

loss in consumer surplus as given by area FG. As shown in Figure C-1, this estimate of social 

cost is less than the incremental compliance cost estimate (i.e., area FGH) so that the engineering 
C-4 

estimate overstates the social cost of the regulation under perfect competition. The overstatement 

results from the fact that the incremental compliance costs are estimated based on the baseline 

market level of cement output. With regulation, output is projected to decline to Q1, so that the 

actual incremental compliance costs incurred by the industry are given by area F. Area G 

represents the foregone value of cement consumption to consumers, also referred to as the 

deadweight loss (analogous to area C under the imperfect competition scenario). 

In addition, the estimate of social cost under perfect competition is less than the estimate 

under imperfect competition by the area HEC, i.e., 

 SCimp – SCperf = −[(F+G+H+E+C) – (F+G)] = −(H + E + C) (C.4) 

The difference between these two measures results from the fact that the price paid by consumers 

(i.e., marginal value to society for cement) exceeds the cost of producing cement (i.e., the 

marginal cost to society of producing cement). As shown in Figure C-1, this difference in social 

cost is equal to the area between the demand curve (D) and the marginal revenue curve (MR) 

that exist under imperfectly competitive market structure. This area does not exist under perfect 

competition because the MR curve is interpreted as the demand curve so that the price paid by 

consumers equals the marginal cost of producing cement. The pre-existing social inefficiency of 

imperfect competition is exacerbated as the regulation moves society further away from the 

socially optimal level of cement production, which results in social costs greater than the 

incremental compliance cost imposed on the cement industry.