### 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 27709iii 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 Co sts 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 ................................................................................... 3-11iv 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 Underlyi ng 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-1v LIST OF FIGURES

Number Page

2-1. Simplified Flow Sheet of Clinker and Cement Manufacture........................................ 2-2 2-2. Labor Costs per Metric T on of Cement ($2005) .......................................................... 2-7 2-3. Distribution of Ener gy 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 Complia nce Costs per Metric Ton of Clinker Capacity: Existing Grey and Wh ite Cement Kilns (2005$) .......................................... 3-3 3-2. Hypothetical Cost-to-Sales Ratios for a Re presentative 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 2013 a ......................................................................................................................... 5-2 5-3. Monetized Health Benefits of the Pr oposed Portland Cement NESHAP by PM 2.5 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 tech nical updates) (sensitivity analysis) a ...... 5-18 5-6. Net Benefits for Proposed Portland Ceme nt NESHAP in 2013 at 2 Discount Rates using the no-threshold model a ........................................................................... 5-19vi LIST OF TABLES

Number Page

2-1. Portland Cement Shipped from Plants in the United States to Domestic Customers, by Type a, 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 Mark et 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 Capaci ty by Age and Process Type ............................. 2-14 2-8. Clinker Capacity, Production, and Capaci ty 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 (10 6 metric tons) .................................................... 2-21 2-13. U.S. Cement Trade Data: 2000 to 2007 ...................................................................... 2-22

3-1. Summary of Direct Compliance Costs (10 6 2005$) ...................................................... 3-2 3-2. Portland Cement Prices by Market ($/metric tons): 2005 ............................................ 3-4 3-3. Portland Cement Markets (10 6 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 Mark et 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 ($10 6 ): 2005 ................................................................... 3-14 3-11. U.S. Cement Sector Energy Consumption (Trillion BTUs) a : 2013 ............................ 3-15

4-1. Small Entity Summar y 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 ............................................................ 4-4vii 5-1. Summary of PM 2.5 Health Co-Benefits of the Proposed Portland Cement NESHAP using the no-threshol d model (preferred approach) a .................................. 5-10 5-2. Summary of Health Benefits of the Proposed Portland Cement NESHAP, using the threshold model (without technica l updates) (sensitivity analysis) a ..................... 5-11 5-3. Summary of Reductions in Health Incidences of th e Proposed Portland Cement NESHAP a .................................................................................................................... 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 .................................................................................................. 5-161-1 SECTION 1 INTRODUCTION
The U.S. Environmental Protection Agency (EPA) is proposing amendments to the current National Emission Standards for Hazard ous Air Pollutants (NESHAP) for the Portland cement manufacturing industry. The proposed amendmen ts add or revise, as applicable, emission limits for mercury, total hydrocar bons, hydrogen chloride, and part iculate matter from kilns and in-line/raw mills located at a major or area s ources. 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 mercur y-containing fly ash in cement kilns. EPA developed these proposed amendments in response to the notice of rec onsideration published on December 20, 2006, and other requirements. As part of the regulatory process of preparing these amendments, EPA is required to develop a regulat ory 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 economi c 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. Over all, 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 change s 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 acc ount for approximately 2.5% of domestic capacity, a decision to permanently shut down these plants would reduce domestic supply and lead to additional proj ected 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 $891-2 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 lin e in 2013 ($86 million). The social cost estimates are significantly higher than th e engineering analysis estimates, which estimated annualized costs of $368 million. This is a dire ct consequence of EPA’s assumptions about existing domestic plants’ pricing behavior disc ussed extensively in previous cement industry rulemakings, S ection 2, Appendix A, and Appendix B of this RIA. Under baseline conditions wit hout regulation, the existing domestic cement plants are assumed to choose a production le vel that is less than the level produced under perfect competition. As a result, a pree xisting 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 perf ect competition would predict). The imposition of additional regulatory costs tends to widen the gap between price and marginal cost in these markets a nd contributes to additional social costs.

 Energy Impacts: EPA concludes that the rule when implemented will not have a significant adverse effect on the supply, di stribution, or use of energy. The cement industry accounts for less than 0.3% of th e 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 electr icity 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 analys is, EPA also examined small entity effects after accounting for market adjustments. U nder this assumption, the entities recover some of the regulatory program costs as the ma rket price adjusts in response to higher cement production costs. Even after accounting for these ad justments, 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 implemen tation (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 me ntioned in Section 4 of this RIA. 2 Thus, the net benefits (i.e., benefits in 2013 minus annua lized costs) are $3.7 billion to $11 billion and $3.3 billion to $9.0 billion, at 3% a nd 7% discount rates respectively.

1 The benefits are discounted to account for the cessation lag in PM 2.5 benefits from premature mortality and acute myocardial infarctions (AMIs), rather than a discounted stream of future benefits; whereas discounting the costs reflects the lifetime costs of the equipmen t. 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 a nd the results of the EIA:

 Section 2 presents a profile of the affected industry.

 Section 3 describes the economic imp act analysis and energy impacts.

 Section 4 describes the small business impact analysis.

 Section 5 presents the benefits estimates.

 Appendix A describes the regional Portla nd cement markets and the economic model.

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

 Appendix C presents the social cost methodology.2-1 SECTION 2 INDUSTRY PROFILE
Hydraulic cement (primarily Portland cemen t) 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 work s 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 va lue of total U.S. sales, including imported cement, was about $11.8 billion, with an averag e value of approximately $100 per metric ton. The vast majority of cement sales went to re ady-mixed concrete producers and concrete product manufacturers (88%). Since 2003, the United Stat es has relied on cement imports to meet approximately 20% to 23% of its consumpti on needs. However, this share dropped to approximately 17% in 2007 as overall constr uction demand for cement fell (DOI, USGS, 2008b).
The remainder of this section provides an introduction to the Po rtland cement industry. The purpose is to give the reader a general und erstanding of the techni cal 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 da ta. Section 2.2 discusses the uses, consumers, and substitutes for cement. Section 2.3 summari zes the organization of the Portland cement industry. The industry profile concludes with a di scussion 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 proc ess of an integrated cement plant includes

 quarrying and crushing the raw materials,

 grinding the carefully proportioned materi als 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 pr ocess and the dry process. In the wet process, water is added to the raw materials during the ble nding process and before feeding the mixture into the rota ry kiln. In contrast, the dry pr ocess feeds the blended materials directly into the rotary kiln in a dry state. Newer dry process plants also use preheater and precalciner technologies that part ially heat and calcine the blended raw materials before they enter the rotary kiln. These tec hnologies 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 falli ng 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 variet y of types of cement in the United States designed to meet different requirements. The Am erican Society for Testing Materials (ASTM) specification C-150 provides for eight types of Portla nd cement: five standard types (I, II, III, IV, V) and three additional types th at include air-entraining properties (IA, IIA, IIIA) (PCA, 2008a). We describe these below.
Types I and IA: These types are the usual pr oduct used in general concrete construction, most commonly known as gray cement because of its color.
Types II and IIA: These types ar e intended for use when mode rate heat of hydration is required or for general concrete construc tion 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 sili cate than regular Portland cements.
Type IV: This type contains a lower percen tage of tricalcium silicate and tricalcium aluminate than Type I, thus lowering the heat evolution. Consequentl y, 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 ma jority of Portland cement shipments 1 in 2005 were Types I and II grey cement. However, Type V (s ulfate-resisting) is a growing market (DOI, USGS, 2007a); since 2000, Type V cement has incr eased its share of sh ipments from 4% to 15%. Shipment shares for other types of ceme nt remained constant during this period.

2.1.3 Production Costs Portland cement is produced using a combin ation 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 employ ees (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 Plan ts in the United States to Domestic Customers, by Type a, 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%
White d 894 1% 1,190 1% Blended 1,296 1% 3,160 3% Expansive and regulated fast setting 60 0% 6 0%
Other e 1,786 2% 1,997 2%

Total f 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 Interi or, 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 milli on tons of raw materials were required to produce approximately 95.5 million tons of cem ent in 2005 or 1.67 tons of raw materials per ton of cement. Table 2-2 summarizes the amount of raw material inputs us ed 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 e ssentially 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 fo r the U.S. Cement Industry: 2000 to 2005

2000 2001 2002 2003 2004 2005 Raw material input (10 3 metric tons) 144,949 147,300 153,100 150,500 158,200 159,700 Cement production (10 3 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% mo re 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 ma nufacturing 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 pl ants labor costs have risen in recent y ears (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 aver age hourly earnings of produc tion workers for each year (BLS, 2007). Next, these cost estimates were adjusted for inflation and expr essed 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 Associatio n. December 2005. U.S. and Canadian Labor-Energy Inpu t 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 As sociation. 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 electricit y, 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) (s ee Table 2-5). As these data show, wet process plants are typically more energy intensive, c onsuming 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 ener gy 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 consumpti on 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 As sociation. 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 As sociation. 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 consider ed a “derived” demand because it depends on the construction demands for its end product—conc rete. A recent study by the U.S. International Trade Commission suggests that 0. 192 metric tons of grey Portla nd 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 represente d only a small share of the total value of construction expenditure s (less than 2%).
Concrete is used in a wide variety of cons truction applications, in cluding residential and commercial buildings, and public works projects su ch as the national highway system. As shown in Figure 2-4, ready-mixed concre te producers have historically accounted for over half of the Portland cement consumpti on. Although government and mis cellaneous expenditures saw substantial increases in the ear ly 1990s, their consumption shar e returned to pre-1990s levels after 1996. The latest USGS use data show that ready-mixed concrete producers accounted for2-9 0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000

1975 1980 1985 1990 1995 2000Metric 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 Sta tistics for Mineral and Material Commodities in the United States: Cement End Use Statistics.” U.S. Geological Su rvey Data Series 140, Ve rsion 1.2. Available at http://minerals.usgs.gov/ds/2005/140/

. 74% of cement sales in 2005, followed by concre te product manufacturers (14%), contractors (6%), and other (6%) (Kelly and Matos, 2007a).
Cement competes with other construction mate rials such as steel, asphalt, and lumber. Lumber is the primary substitute in the residentia l 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, conc rete 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 pr ice 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 Co mpetitive Building Materials: 2003 to 2008
Source: Portland Cement Association. 2008b. “Marke t 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 descriptio n of market characteristics (i.e ., degree of con centration, entry barriers, and product differentiat ion) 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 se lls. For example, in markets w ith large numbers of sellers and identical products, firms are unlikely to be able to influence market pric es via their production decisions (i.e., they are “price ta kers”). However, in markets with few firms, significant barriers to entry (e.g., licenses, legal rest rictions, or high fixed costs), or products that are similar but can be differentiated, the firm may have some degr ee of market power (i.e., set or significantly influence market prices).

Cement sales are often concen trated locally among a sma ll 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 empiri cal literature has typi cally pointed to Cens us of Transportation data showing over 80% of cement shipments we re made within a 200-mile radius (Jans and Rosenbaum, 1997) 1 and reported evidence of high transpor tation costs per dolla r of product value from case studies (Ryan, 2006). The cement industr y 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 co sts; this reduces entry incentives for smallsized cement plants. Using recent data for pl anned 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, consum ers are likely to view cement produced by different firms as very good substitutes. Ameri can Society for Testing and Materials (ASTM) specifications tend to ensure uniform quality, a nd recent industry reviews (USITC, 2006) suggest that there is little or no bra nd loyalty that allows firms to differentiate their products.
2.3.2 Manufacturing Plants During 2005, 107 cement manufacturing plants w ith 186 cement kilns were operating in the United States. This section describes the lo cation, age, production ca pacity, and employment of these manufacturing facilities. Section 2.3.2 co ncludes 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 ar e California, Texas, Pennsylvania, Florida, and Alabam a. 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 ha d 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 (10 3 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 (10 3 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 Asso ciation (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 St ates used the dry ma nufacturing process, and it accounted for 83% (78 million metric tons) of national clinker capacity. The growing prevalence of the dry process am ong cement manufacturers is part of a long-term trend. As the data in Table 2-7 indicate, no new wet clinker cap acity 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 (10 3 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 Capaci ty by Age and Process Type (continued)

No. Kilns Clinker Capacity (10 3 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 y ears use the dry manufacturing process. These new kilns account for 64% (60 milli on metric tons) of national clinker capacity.
2.3.2.3 Production Capacity and Utilization Between 2000 and 2005, apparent annual cli nker 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 utiliz ation decreased sligh tly 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 (10 3 metric tons) 89,264 100,360 101,000 102,000 105,000 104,000

Production (10 3 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% eac h year, while production grew an average of 2%. As a result, capacity utilization has risen sl owly since 2001. However,2-16 throughout these movements in clinker capacity a nd production, capacity u tilization tended to remain between 80% and 85%.
Capacity utilization often varies by geogra phic region as a result of fluctuations in regional construction activity. Fo r example, 2005 data show that Idaho, Montana, and Nevada shared a capacity utiliz ation rate of 95.5%—well above the na tional average. In contrast, South Carolina used only 64.5% of its clinker capacity . Table 2-9 provides a complete listing of capacity utilization ra tes by state in 2005.
2.3.2.4 Employment Each year, the Annual Survey of Manufactur es (ASM) collects employment, payroll, sales, and other data for all manufacturi ng establishments. Table 2-10 summarizes the employment data collected by the ASM for th e cement manufacturi ng 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 ha s 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 amount s to a $5.9 billion investment (PCA, 2007).

In addition to these expected capacity expans ions, likely changes in U.S. specifications allowing the use of limestone in Portland cem ent could also increas e production capacity. According to the PCA, domestic cement supply could increase by as much as 2 million additional tons by 2012. Increase s 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 sa les and employment databases to identify the chain of ownership by accounting for subsidiari es, divisions, and joint ventures to appropriately gr oup 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 Commer ce, Bureau of the Census. 2006. 2005 Annual Survey of Manufacture s. M05(AS)-1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as-1.p df>. As obtained on March 14, 2008. U.S. Department of Commerce, Bureau of the Census. 2003. 2001 Annual Survey of Manufacture s. M05(AS)-1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as-1.p df>. 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 ow ning 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 companie s 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 Ce ment Co.); Ash Grove Cement Co.; Texas Industries, Inc.; Italcementi S .p.A.; Taiheiyo Cement Corporatio n; Titan Cement; and VICAT. The top 5 had about 57% of total U.S. clinker capacity, and the top 10 acc ounted 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 us ed 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 ($10 6 ) Employment Type Small Business Plants Kilns Clinker Capacity (10 3 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 $150 b <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 Compan y Summary Data: 2005 (continued)

Ultimate Parent Name Annual Sales ($10 6 ) Employment Type Small Business Plants Kilns Clinker Capacity (10 3 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 $50 b <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, In c. 2007. D&B million dollar directory. Bethlehem, PA.

LexisNexis. LexisNexis Academic [elect ronic resource]. Dayton, OH: LexisNexis. 2.3.3.3 Company Revenue and Ownership Type Cement manufacturing is a capit al-intensive industry. The vast majority of stakeholders are large global companies with sales exceedi ng $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 owne d 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 (o r 7%) were joint ventures.

2.4 Markets Portland cement is produced and consumed dome stically 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 pr oduced cement to other countries. We provide value, quantity, and price trends over the past de cade for Portland cement when detailed statistics2-21 are available. In the case of international trade, we can repo rt 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 Portla nd 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 a nd equaled 8.9 million tons in 2007. As Table 2-12 shows, shipments to final customers increase d steadily since 2000, reaching 128 million tons in 2006. However, affected by declines in the hous ing market, the shipments fell by 9% in 2007.
Table 2-12. Historical U.S. Cement Statistics ( 10 6 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://m inerals.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 Ta ble 2-13). Major impor ting 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 an d construction activity d eclines 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 (10 6 metric tons) 0.7 0.7 0.9 0.8 0.7 0.8 1.5 1.9 Imports (10 6 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 pr oduction. However, the level of U.S. exports has increased during the last 2 years. In 2007, U.S. exports tota led 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 Ba hamas, 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 pric e of cement is at its highest level in over 2 decades (approximately $100). Because of tran sportation 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 Ca lifornia were higher than the national averages, while prices in Texas, Indi ana, and South Carolina were among the lowest.
2.4.3 Future Projections Although estimates of future cement dema nd are not publicly available, the Energy Information Administration provide s projections for the real valu e 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 $110F.O.B. Cement Price ($/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. “Histo rical 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 Interi or, 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 make rs 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 presente d 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 cons umer and producer surplus).
The Office of Air Quality Planning and Sta ndards (OAQPS) has adopted the standard industry-level analysis describe d 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 st atic partial-equilibrium model to compare prepolicy cement market baselines with expected postpolicy outcomes in thes e 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 produ cers. 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 reve nues, costs, profits, employment),

 facility-level effects (plant utilization changes), and

 social costs (changes in produ cer 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 th e year 2013, EPA’s engin eering cost analysis estimates the total annualized costs of the pr oposed rule are $368 million (in 2005 dollars) (see Table 3-1). These costs include a variety of pollution cont rol expenditures: equipment installation, operating and mainte nance, recordkeeping, and pe rformance-testing activities. Figure 3-1 illustrates the distribut ion of annualized compliance cost s per metric ton of capacity for existing grey and white cement kilns. For an alytical convenience, the analysis assumes the3-2 Table 3-1. Summary of Direct Compliance Costs (10 6 2005$)

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 fo r 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 complian ce costs. The final compliance cost es timate 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)NumberofKilns(N=186)

Figure 3-1. Distribution of A nnualized 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 concen trated locally among a sma ll 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 empiri cal literature has typi cally pointed to Cens us of Transportation data showing over 80% of cement shipments we re made within a 200-mile radius (Jans and Rosenbaum, 1997) 3 and reported evidence of high transpor tation costs per dolla r of product value from case studies (Ryan, 2006). Based on this li terature, the Agency assumes that the U.S. Portland cement industry is divided into a numbe r of independent region al 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 ma kes 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 f unctions 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 cem ent industry than 2005, then impacts would be expected to change accordingly.
The freight-on-board (f.o.b.) price of Portland cement for each regiona l market is derived as the production weighted average of the stat e level f.o.b. prices repo rted 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 A ppendix A for additional de tails) (see Table 3- 3). We obtained estimates of Portland cement im ports 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 $973-5 Seattle $88

Table 3-3. Portland Cement Markets (10 6 metric tons): 2005

Market U.S. Production Imports a

Total Canada Rest of World
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 ar e 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 examin ed the evidence supporting the appropriate supplier pricing behavior assumptions in th ese 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 take rs”). However, in markets with few firms, significant barriers to entry (e.g., li censes, legal restrictions, or hi gh fixed costs) or with products that are similar but can be differentiated, the fi rm may have some degree of market power (i.e., set or significantly infl uence 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 pr oduction economics limit the number of sellers, so each seller has a substantial market share. Po tential entry is constrained by th e high capital costs that involve purchases and construction of larg e rotary kilns that are not read ily movable or transferable to other uses. In addition, large plants are typi cally 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 ensu re 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 conti nued to use the oligopoly framework used in previous economic analyses (1998, 1999b).
One consequence of this assumption is that th e seller individually c hooses an output level that is less than the level produced under perfec t competition. As a result, the baseline market price will be higher than a mode l of perfect competition, and th ere is a preexisting market distortion in the indus try being regulated. 4 The size of the existing distortion depends on the seller’s market share and how responsive cement c onsumers are to changes in the cement price. Economic theory suggests that in the model EPA se lected 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 th e 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 de mand 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 increas es 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 (10 6 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 th e 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 complian ce costs and a kiln with the highest per-unit compliance costs ($15.10 per metric ton).

Imports of Portland cement increase in respons e 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 wh en 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 a pproximately 8%, or 1,167 employees.3-9 Table 3-6. Summary of Region al 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 (10 3 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 va ry by cement plant, and this variation suggests some plants will be more adversely aff ected than others. To assess these differences, EPA collected industry operating profit data and iden tified plants with operating profit increases and losses. Absent plant-specific data, EPA assume d each plant’s baseline profits were consistent with the median operating profit margin repo rted 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 milli on with regulation. These plants tend to be larger major sources and have higher unit compliance costs. The remaining plants’ compliance burden is3-10 Table 3-7. National-Level Industry Impacts: 2005

Changes from Baseline
Baseline Absolute Percent Revenues ($10 6 ) $8,261

− $341

− 4.1% Costs ($10 6 ) $6,966

− $137

− 2.0%

Cement production $6,966

− $372

− 5.3% Regulatory program $0 $235 NA Operating Profits ($10 6 ) $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 (10 6 metric tons) Total 64,757 35,668 100,424 Average per plant 967 939 956 Compliance Costs Total ($10 3 ) $230,894 $46,829 $277,722 Average ($/metric cemen t) $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 ($10 6 )

− $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 ha ve lower unit compliance costs compared with their competitors.
Within the group of plants with operating losse s, EPA identified six domestic plants with negative operating profits and signi ficant utilization changes that could temporar ily 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 approxi mately 2.5% of domestic3-11 Table 3-9. Cement Plants with Sign ificant Utilization Changes: 2005

Total
Number 6 Cement Capacity (10 6 metric tons)
Total 2,860 Average per plant 477 Compliance Costs Total ($10 3 ) $14,185 Average ($/metric cement) $4.96 Capacity Utilization (%)
Baseline 94.7% With regulation 39.3% Change in Operating Profit ($10 6 )

− $50 Change in Employees

− 208

capacity. If the plant owners did decide to perm anently shut down these plants, the reduction in domestic supply would lead to additional projected market pric e increases. Reducing national supply by an additional 2 million metric tons and holding other domestic and foreign supply fixed, EPA calculations suggest th e 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 analys is for existing kilns ($280 million in direct compliance costs). In addition, EPA developed a se parate 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 uncerta inties 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 fr om 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 kiln s coming on line in 2013 to be $86 million. The average cost per ton of clinke r 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%ALARAZCOFLILMONVPASCTXWVWYU.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 th e average operating profit rates for the industry ranged from 17 to 21% (PCA, 2008c). If th ese profit data are representative of operating profit rates for new kilns, kilns could potentially significantly reduce thei r operating profit rates. As a result, companies may have the incentive to look for less expensive alte rnatives to meet the emission standards. If these alternatives are limite d or not cost effective, the NESHAP may lead companies to consider delaying rates of constr uction 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 quantit y were used to estimate the changes in aggregate economic welfare usi ng applied welfare economics prin ciples (see Appendix C). Table 3-10 presents the estimates of the social cost s and their distribution. Higher cement prices and reduced consumption lead to consumer welfar e 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 lo se depending on the change in co sts versus the change in the regional market prices. The to tal domestic surplus loss (consu mer and producers) totals $606 million. Other countries selling cement to the Un ited States will benefit from higher cement prices (a surplus gain of $89 million). The result ing 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 cemen t kilns ($2 million) and 20 additional kilns projected to come on line in 2013 ($86 million). Th e social estimates are significantly higher than the engineering analysis estimate of annua lized 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 rulema kings and in Section 2 and Appendix B of this RIA. Under baseline conditions without regulati on, 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 marg inal cost in these markets and contributes to additional social costs. The a bove social costs for 2013 include annualized capital costs over the expected lifetime of the equipment and an opport unity cost of capital (7%) discount rate. To3-14 facilitate comparisons of benef its 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 anal yses only produce estimates for a single year (OAQPS, 1999a).

Table 3-10. Distribution of Social Costs ($10 6 ): 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 id entified as “significant energy actions.” Section 4(b) of Executive Order 13211 defines “significa nt energy actions” as an y 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, in cluding notices of inquiry, advance notices of proposed rulemaking, and notices of proposed rulema king: (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, di stribution, or use of energy; or (2) that is designated by the Administrator of the Office of Information and Re gulatory Affairs as a si gnificant energy action.
This rule is not a signifi cant 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 ener gy. EPA has prepared an analysis of energy impacts that explains this conclusion below.3-15 To enhance understanding regarding the regu lation’s influence on energy consumption, EPA examined publicly available data describi ng 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 attr ibutable to the regulatory pr ogram 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 petroleum b 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 consum ption 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 as sociated 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) gene rally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other statute unless the agency certifies that the rule will not have a 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 sm all 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 (s ee Table 4-1 for list); (2) a small governmental jurisdiction that is a government of a city, county, town, school distri ct, or special district with a population of less than 50,000; and (3) a small orga nization 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 Tabl e 4-1). One of the four entities is owned by a small Tribal government (Salt River Pima-Maric opa 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 asse ssed 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 th is, we divided an ultimate pa rent 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 ($10 6 ) Employees Plants Kilns Clinker Capacity (10 3

metric tons

per year) U.S. Capacity

Share
Salt River Materials Group a Tribal government $184 b NA 1 4 1,477 1.6%

Monarch Cement Company Business $154 600 1 2 787 0.8%

Continental Cement Company, LLC Business $50 c <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 pro duction 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 aff ected plants owned by company j,
n = number of affected plants, and

TR j = total sales from all operations of ultimate parent company j or annual government revenue.
This method assumes the affected entity canno t shift pollution contro l costs to consumers (in the form of higher market prices). Instea d, the owning entity expe riences a one-for-one reduction in profits. For small en tities, 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, presen ted 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 av erage (median) CSR for small entities is 2.0% (1.5%), and the average and median CSR for all large entitie s 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 RangeNumber 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 ad justs in response to higher cement production costs. Even after accounting for these adjustme nts, small entity operating profits fall by $4million, or 9% (see Table 4-3). However, all nine cement kilns continue to operate under withregulation conditions. As cement production fa lls, 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 ($10 6 ) $7 $273 Average ($10 6 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 ($10 6 ) $293

− $10

− 3.6% Costs ($10 6 ) $0

− $6

− 2,602.4% Cement production $247

− $12

− 4.8% Proposed NESHAP $0 $6 NA Operating profit ($10 6 ) 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 PM 2.5 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 PM 2.5 -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 curre nt interpretation of the scientific literature on PM 2.5 and mortality. They reflect our updated be nefits methodology (i.e., a no-threshold model that calculates incremental benefits down to the lowest modeled PM 2.5 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 estima tes are derived from two empirical (epidemiological) studies of th e relationship between ambient PM 2.5 and premature mortality (the extended analyses of the Harvard Six Cities st udy 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 PM 2.5 and premature mortality (Roman et al., 2008). Using alternate relationships between PM 2.5 and premature mortality supplied by experts, higher and lower benefits estimates are plausible (see Figure 5-1 below), but most of the expert-bas ed estimates fall between the two epidemiologybased estimates (Roman et al., 2008). Me thodological 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/m 3 without two technical updates), EPA estimates the PM 2.5 -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 alMillions(2005$)

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

Figure 5-1. Monetized Human He alth Co-Benefits of Proposed Portland Cement NESHAP in 2013 a

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 PM 2.5 -related human health benefits of reducing emissions from the proposed NESHAP for Portland Cement kilns, EPA us ed the benefits transf er approach it created for the regulatory impact analysis (RIA) acco mpanying 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 PM 2.5 benefit-perton coefficients to estimate the PM 2.5 co-benefits resulting from reductions in emissions of NO X .

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 Qua lity 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 PM 2.5 precursor pollutants associated with this proposal, but has made incremental updates to the benefit-per-ton estimates to refl ect new science and data , as discussed below.
EPA did not perform an air quality mode ling 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 propos al because our benefits model BenMAP requires either air quality modeling or monitoring data. In the absence of formal air quality modeling, we applied PM 2.5 benefit-per-ton coefficients to estimate benefits. In addition to the 2008 Ozone NAAQS RIA, this benefit-per-ton approach has b een used in RIAs prepared for a number of previous EPA rulemakings (e.g., the 2002 large indus trial spark ignition e ngine 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 PM 2.5 or PM 2.5 precursor emissions from a specified source. We include direct PM 2.5 and PM 2.5 precursor emissions (SO X ). These PM benefits are actually co-benefits, whic h result from the installing controls to limit hazardous air pollutants ( HAPs). Methodological lim itations prevented EPA from quantifying the monetized benefits of ot her 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 moneti zed benefits do not in corporate additional emission reductions that would occur if cement fa cilities temporarily id le or reduce capacity utilization as a result of this regulation. Usi ng the benefit-per-ton appr oach, we are unable to monetize the anticipated improvements in visibility due to reductions in PM 2.5 and PM 2.5 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 concentrati on-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 es timate, EPA applied the effect coefficient as reported in the study without an adjustme nt for assumed concentration threshold of 10 μg/m 3 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 Sta ff 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. Wh en calculating the preferred estimate, EPA applied the effect coefficient as repor ted in the study without an adjustment for assumed concentration threshold of 10 μg/m 3 as was done in recent (post 2006) RIAs.

 Twelve estimates are based on the C-R f unctions from EPA’s expert elicitation study 1 , 2 on the PM-mortality relationship and in terpreted for benefits analysis in EPA’s final RIA for the PM NAAQS. For that study, twelve experts (labeled A through L) provided independent estimate s of the PM-mortality concentrationresponse function. EPA practice has been to develop independent estimates of PMmortality estimates corresponding to the c oncentration-response function provided by each of the twelve experts, to better characte rize the degree of vari ability 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 beli eve argues for using both studies to generate benefits estimates. Using estimates from both c ohorts substantially narrows the range of benefits estimates when compared to the range of estim ates from the expert elicitation.. Because the experts used these studies based on these cohor ts to inform their co ncentration-response functions, benefits estimates us ing 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 PM 2.5 Exposure and Mortality . Prepared for the U.S. EPA, Office of Air Quality Planning and Standards, September. Av ailable 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 epidemio logy studies based on these cohorts.
In recent RIAs, EPA presented benefits estim ates using concentra tion response functions derived from the PM 2.5 Expert Elicitation as a range from th e 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 e xpert elicitation, it generated a range of benefits estimates extending nearly an order of magnitude. In addi tion, this approach did no t indicate the agency’s judgment on what the best estimate of PM be nefits may be. According to EPA’s Science Advisory Board, this presenta tion was misleading because:
1. “...[T]his is, in fact, a form of aggregati on 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 s ubstantial differences in scientific opinion when, in fact, there was a pronounced centr al cluster of views on PM2.5 mortality.” 2

The SAB advice captures the tension between pr oviding 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, abov e 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 fromepidemiology 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 morta lity 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 pr esent two alternate benefits estimates without implying a range, an d we will continue to develop meth ods 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 PM 2.5 concentrations and mortality” (IEc, 2006, page 3-10).5-6 believe argues for using both studi es to generate the benefits estimates. Using both estimates from each cohort substantially narro ws the range of benefits estimat es. Because the experts used these studies based on these cohorts to inform their concentration-response functions, benefits estimates using these functions generally fall be tween 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 pres entation style is flexible enough to incorporate future epidemiology studies based on these coho rts. Finally, this approach is generally consistent with advice from both the Nationa l Academy of Sciences (NRC, 2002) and the Science Advisory Board (U.S. EPA-SAB, 2004) di recting 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-re sponse function provided by each of the twelve experts to better character ize the degree of variability in the expert responses. Because in this RIA we estimate benefits using benefit-per-ton estimates, technical limita tions prevent us from providing the associated credible intervals with the expert f unctions. EPA believes that the estimates derived from the expert elicitation are indicative of the uncertainty associated with a major component of the health im pact functions; whereas, the bene fits represented by estimates derived from Pope et al. and Lade n et al. represent the preferred es timates of PM co-benefits. In general, the expert elicitation results suppor t the conclusion that the benefits of PM 2.5 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 benef its-per-ton of emissions reduc tion estimates for direct PM 2.5 and each PM 2.5 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 PM 2.5 and PM 2.5 precursors (i.e., SO 2 , NO X , and VOCs) into changes in PM 2.5 air quality. Next, we used the benefits model (BenMAP) to estimate the changes in human health based on the change in PM 2.5 air quality. Finally, the monetized health benefits were divided by the emission reductions to crea te the benefit-per-ton estimates. Even though all fine particles are assumed to have equivalent he alth effects, the benefit-per-ton estimates vary between precursors because each ton of precursor reduced has a different propensity to form PM 2.5 . For example, SO X has a lower benefit-per-ton estimate than direct PM 2.5 because it may5-7 form PM 2.5 further from population centers than directly emitted PM 2.5 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 redu ctions and specific air quality and benefits modeling assumptions. Use of thes e $/ton values to estimate bene fits associated with different emission control programs (e.g., for reducing emissi ons from large stationary sources like EGUs) may lead to higher or lower benef it 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-p er-ton over the entire United States. The benefits-per-ton for emission reductions in specific lo cations may be very different from the national average.

5.3 Assumptions regarding Thresholds in the Health Impact Function The preferred benefit-pe r-ton coefficients in this analys is 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 PM 2.5 related threshold adverse effects to test the sensitivity of this assumption. The Agency’s peer review process conducted through the Science Advisory Board has provide d advice regarding anal ytic treatment of thresholds for PM multiple times.

 In 1999, the SAB Advisory Council on Cl ean Air Compliance Analysis (ACCACA) concluded that there was currently no sc ientific basis for selecting any specific threshold (EPA-SAB, 1999).

 In 2004, the Health and Environmental Eff ect 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 reporte d in the studies” (EPA-SAB, 2004).

 In 2005, CASAC indicated that “[t]he avai lable epidemiological database on daily mortality and morbidity does not establish either the presen ce or absence of threshold concentrations for adverse h ealth effects” (EPA-SAB, 2005).
In addition, in 2002, as a part of their revi ew of EPA benefits methods, the National Research Council concluded that there is no ev idence for any departure from linearity in the observed range of exposure to PM 10 or PM 2.5 , nor any indication of a threshold (NRC, 2002).5-8 They cite the weight of eviden ce available from both short- a nd 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 te rm 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 PM 2.5 Expert Elicitation in which expe rts were asked to describe the true relationship between PM 2.5 exposure and premature mortality (Roman, 2008; I.Ec., 2006). Of the 12 experts included in th e elicitation, only one expert (E xpert 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/m 3 , and only a 10% chance that there would be a threshold between 5 and 10 μg/m 3 . No expert thought that there wa s any chance that there would be a threshold in excess of 10 μg/m 3 . In addition, a recent extende d follow-up of the Harvard Six Cities cohort concluded that th e concentration response functi on is linear with no threshold (Schwartz, 2008).
In its December 2008 draft Integrated Science Assessment (ISA), EPA's Office of Research and Development conclude d that the scientific literature consistently finds that a nothreshold log-linear model most adequately portrays the PM-mortality concentration-response relationship, while recognizing po tential uncertainty a bout the exact shape ofthe concentrationresponse function. CASAC is currently considering EPA's assessment (in the PM 2.5 ISA) of the body of evidence on the shape of the C-R function relating PM 2.5 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 ri sk and exposure assessment, which proposes to use the no-threshold model as the primary m odel for estimating mortality risk from PM 2.5 exposures. The ISA is being revised and w ill be reviewed again by the CASAC in October 2009, concurrent with the review of the first dr aft 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 th e application of thresholds in PM 2.5 concentration-response functions used in EPA’s RIAs.5-9 5.4 Updating the Benefits Data Underl ying 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 PM 2.5 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 in terpretation of the scientific literature on PM 2.5 and mortality. These estimates include a new population dataset, an expanded geographic scope of the benefit-per-ton calcula tion, and the functions directly from the epidemiology studies without a threshold adjustment. They re flect our updated benefits methodology (i.e., a nothreshold model that calculates incrementa l benefits down to the lowest modeled PM 2.5 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 met hodology (i.e., a threshold model at 10 μg/m 3 without the two technical upda tes) 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 dependi ng on the combination of emission reductions from different sources and PM 2.5 precursor pollutants. EPA is currently in the process of generating localized benefit-per-ton estimates to better account for the spatial heterogeneity of benefits fo r a small number of urban areas. EPA believes that these estimates may better represent the lo calized benefits of emission reductions at a specific location than benefits estimates that us e national averages. However, because the kilns affected by this rule are widely distributed nationally, we believ e 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 bill ion and $4.0 billion to $9.7 billion, at 3% and 7% di scount rates respectively. 1 Using the threshold model without technical updates, EPA estimates the benefits of this pr oposal to be $3.1 billi on 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 pr esent the results using the threshold model below

1 The benefits are discounted to account for the cessation lag in PM 2.5 benefits from premature mortality and acute myocardial infarctions (AMIs), rather than a discounted stream of future benefits; whereas discounting the costs reflects the lifetime costs of the equipmen t. 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. Tabl es 5-1 and 5-2 provide general summaries of the results by precursor pollutant, in cluding 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 in cidences 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, emi ssions, and emission reductions can be found in Section 4 of this RIA.

Table 5-1. Summary of PM 2.5 Health Co-Benefits of the Proposed Portland Cement NESHAP using the no-thresho ld 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 PM 2.5 6,345 $180,000 $440,000 $1,200 to $2,800 $160,000 $400,000 $1,000 to $2,500

PM 2.5 Precursors

SO 2 139,240 $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 y ear (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 PM 2.5 . The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. This analysis assumes the PM 2.5 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 reductio ns in condensable PM. PM emissions consist of both a filterable fraction and a conde nsable 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 PM 2.5 . In this analysis, all emission reductions and the correspondin g 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 techni cal 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 PM 2.5 6,345 $150,000 $320,000 $940 to $2,000 $140,000 $290,000 $860 to $1,800
PM 2.5 Precursors
SO 2 139,240 $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 y ear (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 PM 2.5 fraction is 45%.

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

Avoided Premature Mortalit y Pope 620 Laden 1,600 Woodruff (Infant Mortality) 3 Avoided Morbidit y Chronic Bronchitis 420 Acute Myocardial Infarction 1,000 Hospital Admissions, Respirator y 150 Hospital Admissions, Cardiovascula r 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 di fferent propensity to form PM2.5.5-12 $

‐ $2,000 $4,000 $6,000 $8,000 $10,000 $12,000

Pope et al Laden et alMillionsof2005$

PM2.5 Mortality Function

SO2 NonEGU Direct PM2.5 NonEGU Figure 5-3. Monetized Health Benefits of the Proposed Portland Cement NESHAP by PM 2.5 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 estimat es vary because each ton of precursor reduced has a different propensity to become PM 2.5

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 econo mic benefits, including emission inventories, air quality models (with their associated paramete rs and inputs), epidemio logical estimates of concentration-response (C-R) f unctions, estimates of values , population estimates, income estimates, and estimates of the future state of the world (i.e., regula tions, technology, and human behavior). There is uncertaint y 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 unc ertainty distribution. For othe r parameters or inputs, the5-13 necessary information is not available. Theref ore, it is difficult to estimate the relative importance of each source of uncer tainty, particularly when usi ng benefit-per-ton estimates.
The annual benefit estimates presen ted 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 a nd weather are constantly variable , regardless of our ability to measure them accurately. As discussed in the PM 2.5 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 num ber of assumptions a nd uncertainties. For example, for key assumptions underlying the esti mates for premature mortality, which typically account for at least 90% of the total PM benefits, we were ab le 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 ev idence supports an assumption of causality.
2. All fine particles, regard less of their chemical compos ition, are equally potent in causing premature mortality. This is an important assumption, because PM produced via transported precursors emitted from EG Us may differ significantly from direct PM released from diesel engines and othe r industrial sources, but no clear scientific grounds exist for supporting differential e ffects estimates by particle type.
3. The impact function for fine particles is approximately linear within the range of ambient concentrations under considerati on. 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 a ssociated air quality modeling are valid. Although recognizing the difficulti es, assumptions, and inhere nt uncertainties in the overall enterprise, these analyses are based on peer-reviewed scien tific 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 app lication 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 nati onal-level analysis, the benefit-per-ton estimates used here do not reflect local variability in population density, meteorology,5-14 exposure, baseline health incide nce rates, or other local fact ors 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 detail ed uncertainty assessment found in the PM NAAQS RIA because we lack the necessary air quality input and monitoring data to run the benefits model (BenMAP). Moreover, it was not possible to develop be nefit-per-ton metrics and associated estimates of uncertainty using the bene fits estimates from the PM RIA because of the significant differences between the sources affect ed 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 evid ence of the uncertainty surrounding the benefits results presented in this analysis. In this analys is, we provide additional benefits results that use the functions obtained in the expert elicitation as a reasonable characteriza tion of the uncertainty in the relationship between PM 2.5 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 in tervals. We recognize that this captures only a fraction of the overall uncertainty. We also recognize that th e magnitude of the mortality C-R function is a critical parameter in the analysis, and the uncertain ty in that parameter is likely to contribute a large fraction of the overall uncertainty in the bene fits estimates. Tables 5-4 and 5-5 show all 14 benefits estimates for the no-threshold and thre shold 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-thres hold model (preferred approach) a

3% Discount Rate 7% Discount Rate Benefit-per-ton Coefficients Deri ved from Epidemiology Literature Pope et al. $4,400 $4,000 Laden et al. $11,000 $9,700 Benefit-per-ton Coefficients D erived 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. Es timates 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 mort ality 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 Deri ved from Epidemiology Literature Pope et al. $3,500 $3,200 Laden et al. $7,500 $6,800 Benefit-per-ton Coefficients D erived 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. Es timates 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 mort ality estimates associated with the concentration-response function. These moneti zed 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 alMillions(2005$)

PM 2.5 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 usin g 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 PM 2.5 . 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 alMillions(2005$)

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

Figure 5-5. Monetized Health Benefits fo r Proposed Portland Cement NESHAP in 2013 based on the threshold model (without t echnical 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 bill ion 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 mentione d 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 bil lion to $9.0 billion, at 3% and 7% discount rates re spectively. Using alternat e relationships between PM 2.5 and premature mortality supplied by experts, higher and lower benefits estimates are plausible (see Figure 5-6), but most of the expert-based es timates fall between the two epidemiology-based

1 The benefits are discounted to account for the cessation lag in PM 2.5 benefits from premature mortality and acute myocardial infarctions (AMIs), rather than a discounted stream of future benefits; whereas discounting the costs reflects the lifetime costs of the equipm ent. 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 th e costs by a substantial margin under this proposal even when taking into account uncertain ties 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 Port land Cement NESHAP in 2013 at 2 Discount Rates using the no-threshold model a

a Net Benefits are quantified in terms of PM 2.5 benefits at a 3% discount rate and a 7% discount rate. This graph

shows all of the benefits estimates co mbined 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 REFERENCES
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Varian, H. 1992. Microeconomic Analysis. 3 rd Ed. New York: W.W. Norton & Company.APPENDIX A REGIONAL PORTLAND CEMENT MARKETS AND ECONOMIC MODELA-1 The Office of Air Quality Planning and Sta ndards (OAQPS) has adopted the standardindustry level analysis describe d 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 st atic partial-equilibrium model to compare prepolicy cement market baselines with expected postpolicy outcomes in thes e 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 produ cers. 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 reve nues, costs, profits, employment),

 facility-level effects (plant utilization changes), and

 social costs (changes in produ cer and consumer surplus). The partial equilibrium analysis performed for this rule develops a cement market model that simulates how stakeholders (consumers a nd firms) may respond to the additional regulatory program costs. In this appendix, we provide deta ils on the baseline data, behavioral assumptions, parameters, and model equations.

A.1 Baseline Market Data

Cement sales are often concen trated locally among a sma ll 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 empiri cal literature has typi cally pointed to Cens us of Transportation data showing over 80% of cement shipments we re made within a 200-mile radius (Jans and Rosenbaum, 1997) and reported evidence of high tr ansportation costs per dollar of product value from case studies (Ryan, 2006). Based on this li terature, the Agency assumed that the U.S. Portland cement industry is divided into a numbe r of independent region al 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 annua l 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 calcula ted clinker production as follows:

Kiln Clinker Production = St ate-Level Utilization Rate x Annual Clinker Capacity.
Next, we summed clinker production across k ilns to obtain domes tic clinker production in the United States. However, to match the 200 5 national clinker produc tion 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 utilizati on rates (value = 1.103). When making this calculation, we did constrain each kiln’s clinker pr oduction capacity utiliza tion rate to be 95% or less. With this adjustment, kiln clinker production is computed as follows:

Kiln Clinker Production = Minimum[(State-Level Utilizat ion Rate x 1.103 x Annual Cli nker Capacity), (0.95 x Annual Clinker Capacity)].
Next, we summed clinker production across k ilns in each market to obtain domestic clinker production in eac h 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 me t by international imports. We collected hydraulic cement import data by customs distri ct 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 approxim ately 33.3 million metric tons in 2005.
Total grey Portland cement demand was 126.8 m illion 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 Interi or, 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) valuat ions of total sales to final cust omers, 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 a nd state price information for each kiln.

Kiln Cement Production i Market price USGS State Price j ij Total Market Domestic Production i j

⎛ ⎞ ⎜ ⎟ = ×

∑ ⎝ ⎠A-4 Table A-2. Hydraulic Cement Imports by Mark et 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, LA a 1.7 Chicago Chicago, IL 0.0 Milwaukee, WI, Canada 0.2 Dallas New Orleans, LA a 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 Interi or, 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 (10 6 metric tons): 2005

Market U.S. Production Imports a
Total Canada Rest of World 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 ar e Portland cement (approximately 29 million metric tons,

or 86%). Excludes Puerto Rico.
To illustrate this calculation, we use the follo wing simple example for the Seattle market. There are two domestic kilns in Washington; one kiln produces appr oximately 660,000 metric tons of cement, while the other produces approx imately 420,000 metric tons of cement per year. The USGS reports the same average value per me tric ton of cement in Oregon and Washington ($88 dollars). To derive the market price fo r the Seattle market, we perform the following calculation:

$88 × 660/(660 + 420) + $88 × 420/(660 + 420) = $88.
A.2 Cost Function Updates for 2005 Previous EIAs (EPA, 1998; EPA, 1999) used a kiln-level average variable cost (AVC) function developed in a microeconomic study of kiln use and retirement (Das, 1992). DasA-6 Table A-4. Portland Cement Prices by Market ($/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 materi als, repair and maintenance, labor, electricity, and fuel. Raw materials serve as the kiln f eed, 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, a nd fuel is largely consumed by the kilns.
The AVC function (expressed in dollars per metric ton of cement) can be written as

AVC = AVMI *P m + AVRI * P r + AVLI * w + AVFI *P f f + AVEI * P e, where AVRI, AVLI, AVFI, AVEI, and AVMI are the av erage variable inputs of materials, repair and maintenance, labor, fu el, and electricity, and P m, P r , w, P f , and P e are the prices of each variable input.
For the updated model, RTI collected data fr om 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 qua ntities of raw material i nputs, cement production, and the raw material input ratio for the U.S. cement i ndustry. 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 (10 3 ) Raw material input a 159,700 Cement production b 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 pr ices 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 need ed 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 ($/metric ton) 2 005 ($/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 obtain ed from the U.S. Department of Comme rce, 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 Measures for the U.S. Cement Industry by Process Type and Kiln Size: 2005

Process Type Metric Tons per Employee Hour a, 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 c onsumption 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 measur e 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 Consum ption 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 ener gy efficiency are an equivalent ton measure composed of

92% clinker production and 8% finished cement production (PCA, 2005).
Source: Portland Cement Asso ciation (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 descri bed from EPA’s 1998 cement study to compute a revised average variable prim ary 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 Informa tion Administration, 2007). For petroleum coke price, we used the procedure outlined in the Energy Information Administration’s State Energy Consumption, Price, and Expenditure Estimat es (SEDS): Technica l 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 th ese data from the U.S. International Trade Commissions Database (U SITC, 2007). To convert units to millionA-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 Prim ary 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 ba rrels 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 primar y fuel used by the kiln reported by the Portland Cement Association (2004) and multiplied fuel pr ices by the fuel input equation described above.

A.2.6 Results

RTI used the methods described above to re vise the kiln-level AVC functions and to reflect the cost differences acr oss kiln technologies and kiln size. The new AVC estimates for each kiln are reported in Table A-14. We report th em in dollars per metric ton and dollars per short ton.
A.3 Partial Equilibrium Model Once the markets were defined, we examin ed the evidence supporting the appropriate supplier pricing behavior assumptions in th ese 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 take rs”). However, in markets with few firms, significant barriers to entry (e.g., li censes, legal restrictions, or hi gh 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 Coke b Natural Gas a Residual Oil a 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 o il: 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 USIT C Interactive Tariff and Trade DataWeb. 2007

<http://dataweb.usitc.gov/scripts/use r_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/s ep_prices/notes/pr_petrol.pdf.>.
Although perfect competition on the supply si de (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 pr oduction economics limit the number of sellers, so each seller has a substantial market share. Po tential entry is constrained by th e high capital costs that involve purchases and construction of larg e rotary kilns that are not read ily movable or transferable to other uses. In addition, large plants are typi cally 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 ensu re 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 conti nued to use the oligopoly framework used in previous economic analyses (EPA, 1998, 1999b).A-16Table A-14. Average Variable Costs by Kiln: 2005Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)GA101 AtlantaLafarge NorthAmericaAtlanta Plant Atlanta GA 42 C Dry 315 $60 $54TN101 AtlantaBuzzi UnicemUSA, Inc.Chattanooga Plant Chattanooga TN 5 K Dry-C 771 $38 $35GA201 Atlanta Cemex Clinchfield Plant Clinchfield GA 6 C Dry-X 705 $48 $43VA101 Atlanta Titan America LLC RoanokePlant Cloverdale VA 9 C Dry-C 1,120 $45 $41SC101 AtlantaGiant CementHolding, Inc.Harleyville Plant Harleyville SC 53 C Wet 164 $57 $51SC102 AtlantaGiant CementHolding, Inc.Harleyville Plant Harleyville SC 49 C Wet 156 $56 $51SC103 AtlantaGiant CementHolding, Inc.Harleyville Plant Harleyville SC 45 C Wet 164 $56 $51SC104 AtlantaGiant CementHolding, Inc.Harleyville Plant Harleyville SC 33 C Wet 164 $55 $50SC201 AtlantaLafarge NorthAmericaHarleyville Plant Harleyville SC 17 C Dry-C 972 $43 $39SC401 Atlanta Holcim (U.S.) Inc. Holly Hill Plant Holly Hill SC 2 C Dry-C 1,860 $41 $38TN201 Atlanta Cemex Knoxville Plant Knoxville TN 27 C Dry-C 667 $45 $41PA601Baltimore/PhiladelphiaGiant CementHolding, Inc.Bath Plant Bath PA 49 C Wet 103 $54 $49PA602Baltimore/PhiladelphiaGiant CementHolding, Inc.Bath Plant Bath PA 49 C Wet 506 $50 $45PA801Baltimore/PhiladelphiaLehigh CementCompanyAllentown Plant Blandon PA 40 C Dry 470 $56 $51PA802Baltimore/PhiladelphiaLehigh CementCompanyAllentown Plant Blandon PA 40 C Dry 470 $56 $51MD101Baltimore/PhiladelphiaEssroc CementCorp.Frederick Plant Buckeystown MD 47 C Wet 155 $62 $57MD102Baltimore/PhiladelphiaEssroc CementCorp.Frederick Plant Buckeystown MD 47 C Wet 155 $62 $57MD301Baltimore/PhiladelphiaSt. LawrenceCement CompanyHagerstown Plant Hagerstown MD 34 C Dry 513 $52 $48(continued)A-17Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)WV101Baltimore/PhiladelphiaEssroc CementCorp.Martinsburg Plant Martinsburg WV 50 C Wet 177 $54 $49WV102Baltimore/PhiladelphiaEssroc CementCorp.Martinsburg Plant Martinsburg WV 45 C Wet 177 $54 $49WV103Baltimore/PhiladelphiaEssroc CementCorp.Martinsburg Plant Martinsburg WV 40 C Wet 354 $53 $48PA501Baltimore/PhiladelphiaEssroc CementCorp.Nazareth Plant I Nazareth PA 27 C Dry-X 1,116 $43 $39PA502Baltimore/PhiladelphiaEssroc CementCorp.Nazareth Plant III Nazareth PA 48 C Dry 150 $57 $52PA503Baltimore/PhiladelphiaEssroc CementCorp.Nazareth Plant III Nazareth PA 48 C Dry 155 $57 $52PA504Baltimore/PhiladelphiaEssroc CementCorp.Nazareth Plant III Nazareth PA 56 C Dry 107 $58 $52PA505Baltimore/PhiladelphiaEssroc CementCorp.Nazareth Plant III Nazareth PA 56 C Dry 107 $58 $52PA201Baltimore/PhiladelphiaBuzzi UnicemUSA, Inc.Stockertown Plant Stockertown PA 26 K Dry-X 365 $49 $44PA202Baltimore/PhiladelphiaBuzzi UnicemUSA, Inc.Stockertown Plant Stockertown PA 12 K Dry-X 543 $45 $40MD201Baltimore/PhiladelphiaLehigh CementCompanyUnion Bridge Union Bridge MD 4 C Dry-C 1,715 $47 $43PA701Baltimore/PhiladelphiaLafarge NorthAmericaWhitehall Plant Whitehall PA 40 C Dry-X 419 $51 $46PA702Baltimore/PhiladelphiaLafarge NorthAmericaWhitehall Plant Whitehall PA 30 C Dry-X 283 $50 $46MS101 Birmingham Holcim (U.S.) Inc. Artesia Plant Artesia MS 31 C Wet 419 $61 $55AL301 BirminghamLafarge NorthAmericaRoberta Plant Calera AL 3 C Dry-C 1,498 $38 $35AL101 Birmingham Cemex Demopolis Plant Demopolis AL 28 C Dry-X 821 $41 $38AL401 BirminghamLehigh CementCompanyLeeds Plant Leeds AL 29 C Dry-X 716 $45 $41(continued)A-18Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)AL501 BirminghamNational CementCo. of AlabamaRagland Plant Ragland AL 30 C Dry-C 900 $40 $37AL201 Birmingham Holcim (U.S.) Inc. Theodore Plant Theodore AL 24 C Dry-C 1,440 $40 $36IA201 ChicagoLafarge NorthAmericaBuffalo Plant Buffalo IA 24 C Dry-C 946 $37 $33IL201 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 $49 $44IL202 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 $49 $44IL203 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 $49 $44IL204 Chicago Cemex Dixon Plant Dixon IL 40 K Dry 184 $53 $48IN101 ChicagoBuzzi UnicemUSA, Inc.Greencastle Plant Greencastle IN 5 C Dry-C 1,190 $37 $33IL301 Chicago Eagle Materials La Salle Plant La Salle IL 31 K Dry-X 602 $44 $40IN201 ChicagoEssroc CementCorp.Logansport Plant Logansport IN 43 K Wet 202 $49 $44IN202 ChicagoEssroc CementCorp.Logansport Plant Logansport IN 43 K Wet 202 $49 $44IL101 ChicagoBuzzi UnicemUSA, Inc.Oglesby Plant Oglesby IL 33 C Dry 569 $44 $40KY101 Cincinnati Cemex Kosmosdale Plant Louisville KY 5 C Dry-C 1,365 $39 $35IN402 CincinnatiLehigh CementCompanyMitchell Plant Mitchell IN 55 C Dry-X 251 $49 $44IN401 CincinnatiLehigh CementCompanyMitchell Plant Mitchell IN 45 C Dry-X 251 $48 $44IN403 CincinnatiLehigh CementCompanyMitchell Plant Mitchell IN 29 C Dry-X 274 $47 $42IN301 CincinnatiEssroc CementCorp.Speed Plant Speed IN 33 C Dry 279 $52 $47IN302 CincinnatiEssroc CementCorp.Speed Plant Speed IN 27 C Dry-X 542 $43 $39OH101 Cincinnati Cemex FairbornPlant Xenia OH 31 K Dry-X 661 $44 $40(continued)A-19Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketName Company Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)OK201 Dallas Holcim (U.S.) Inc. Ada Plant Ada OK 47 C Wet 278 $49 $45OK202 Dallas Holcim (U.S.) Inc. Ada Plant Ada OK 47 C Wet 278 $49 $45AR101 DallasAsh Grove CementCompanyForeman Plant Foreman AR 47 C Wet 246 $49 $44AR102 DallasAsh Grove CementCompanyForeman Plant Foreman AR 43 C Wet 246 $48 $44AR103 DallasAsh Grove CementCompanyForeman Plant Foreman AR 41 C Wet 339 $48 $44TX201 DallasAsh Grove Texas,L.P.Midiothian Plant Midiothian TX 39 C Wet 283 $54 $49TX202 DallasAsh Grove Texas,L.P.Midiothian Plant Midiothian TX 36 C Wet 283 $54 $49TX203 DallasAsh Grove Texas,L.P.Midiothian Plant Midiothian TX 33 C Wet 283 $54 $49TX701 Dallas Holcim (U.S.) Inc.Holnam Texas L.P. Midiothian TX 18 C Dry-C 1,036 $42 $38TX702 Dallas Holcim (U.S.) Inc. Holnam Texas L.P. Midiothian TX 5 C Dry-C 990 $41 $37TX901 DallasTexas IndustriesInc.Midiothian Plant Midiothian TX 45 C Wet 286 $55 $50TX902 DallasTexas IndustriesInc.Midiothian Plant Midiothian TX 42 C Wet 286 $54 $49TX903 DallasTexas IndustriesInc.Midiothian Plant Midiothian TX 38 C Wet 286 $54 $49TX904 DallasTexas IndustriesInc.Midiothian Plant Midiothian TX 33 C Wet 286 $54 $49TX905 DallasTexas IndustriesInc.Midiothian Plant Midiothian TX 4 C Dry-C 1,964 $41 $37OK301 DallasLafarge NorthAmericaTulsa Plant Tulsa OK 44 C Dry 347 $51 $46OK302 DallasLafarge NorthAmericaTulsa Plant Tulsa OK 42 C Dry 341 $51 $46(continued)A-20Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)CO201 Denver Holcim (U.S.) Inc. PortlandPlant Florence CO 4 C Dry-C 1,631 $39 $35WY101 Denver Eagle Materials Laramie Plant Laramie WY 9 C Dry 181 $47 $43WY102 Denver Eagle Materials Laramie Plant Laramie WY 6 C Dry-X 416 $43 $39CO101 Denver Cemex Lyons Plant Lyons CO 25 C Dry-X 486 $48 $44SD101 DenverGCC of America,Inc.Dacotah Cement Rapid City SD 50 C Wet 147 $44 $39SD102 DenverGCC of America,Inc.Dacotah Cement Rapid City SD 48 C Wet 147 $43 $39SD103 DenverGCC of America,Inc.Dacotah Cement Rapid City SD 10 C Dry-X 557 $36 $33MI301 DetroitLafarge NorthAmericaAlpena Plant Alpena MI 43 C Dry 364 $54 $49MI302 DetroitLafarge NorthAmericaAlpena Plant Alpena MI 40 C Dry 364 $54 $49MI303 DetroitLafarge NorthAmericaAlpena Plant Alpena MI 40 C Dry 364 $54 $49MI304 DetroitLafarge NorthAmericaAlpena Plant Alpena MI 40 C Dry 536 $42 $38MI305 DetroitLafarge NorthAmericaAlpena Plant Alpena MI 40 C Dry 536 $42 $38MI101 Detroit Cemex Charlevoix PlantCharlevoix MI 26 C Dry-C 1,211 $38 $35MI201 Detroit Holcim (U.S.) Inc. Dundee Plant Dundee MI 46 C Wet 431 $52 $47MI202 Detroit Holcim (U.S.) Inc. Dundee Plant Dundee MI 46 C Wet 437 $52 $47OH201 DetroitLafarge NorthAmericaPaulding Plant PauldingOH 49 A Wet 226 $78 $70OH202 DetroitLafarge NorthAmericaPaulding Plant PauldingOH 49 A Wet 228 $78 $70FL501 FloridaSuwanneeAmerican CementBranford FL 2 C Dry-C 682 $49 $45FL101 Florida Cemex Brooksville Plant Brooksville FL 29 C Dry-X 550 $53 $48FL102 Florida Cemex Brooksville Plant Brooksville FL 20 C Dry-X 506 $52 $47FL301 Florida Rinker Materials Brooksville Plant Brooksville FL 18 C Dry-X 605 $52 $47(continued)A-21Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)FL601 Florida Titan America LLC PennsucoPlant Medley FL 1 C Dry-C 1,492 $45 $41FL401 Florida Rinker Materials MiamiPlant Miami FL 5 C Dry-C 928 $46 $41FL201 FloridaFlorida RockIndustriesThompson S. BakerPlantNewberry FL 6 C Dry-C 726 $49 $45KS101 Kansas CityAsh Grove CementCompanyChanute Plant Chanute KS4 C Dry-C 1,308 $37 $34KS301 Kansas CityLafarge NorthAmericaFredonia Plant Fredonia KS 69 A Wet 169 $82 $74KS302 Kansas CityLafarge NorthAmericaFredonia Plant Fredonia KS 49 A Wet 243 $76 $69KS401 Kansas CityMonarch CementCompanyHumbolt Plant Humbolt KS 32 C Dry-X 284 $46 $42KS402 Kansas CityMonarch CementCompanyHumbolt Plant Humbolt KS 30 C Dry-C 503 $42 $38KS201 Kansas CityBuzzi UnicemUSA, Inc.Independence Plant Independence KS 88 K Dry 82 $58 $53KS202 Kansas CityBuzzi UnicemUSA, Inc.Independence Plant Independence KS 88 K Dry 82 $58 $53KS203 Kansas CityBuzzi UnicemUSA, Inc.Independence Plant Independence KS 88 K Dry 82 $58 $53KS204 Kansas CityBuzzi UnicemUSA, Inc.Independence Plant Independence KS 88 K Dry 82 $58 $53NE101 Kansas CityAsh Grove CementCompanyLouisville Plant Louisville NE 29 C Dry-X 338 $44 $40NE102 Kansas CityAsh Grove CementCompanyLouisville Plant Louisville NE 23 C Dry-C 507 $40 $36OK101 Kansas CityBuzzi UnicemUSA, Inc.Pryor Plant Pryor OK 45 C Dry 189 $51 $46OK102 Kansas CityBuzzi UnicemUSA, Inc.Pryor Plant Pryor OK 43 C Dry 186 $51 $46OK103 Kansas CityBuzzi UnicemUSA, Inc.Pryor Plant Pryor OK 25 C Dry 250 $50 $45(continued)A-22Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)MO501 Kansas CityLafarge NorthAmericaSugar Creek Plant Sugar Creek MO 4 C Dry-C 930 $37 $34CA101 Los AngelesCalifornia PortlandCementColton Plant Colton CA 43 C Dry 340 $66 $60CA102 Los AngelesCalifornia PortlandCementColton Plant Colton CA 43 C Dry 340 $66 $60CA801 Los AngelesNational CementCo. of CaliforniaLebec Plant Lebec CA 6 K Dry-C 1,033 $48 $43CA701 Los AngelesMitsubishi CementCorporationCushenbury Plant Lucerne Valley CA 23 C Dry-C 1,543 $50 $45CA201 Los AngelesCalifornia PortlandCementMojave Plant Mojave CA 24 C Dry-C 1,363 $50 $45CA1101 Los AngelesTexas IndustriesInc.Oro Grande Plant Oro Grande CA 57 C Dry 155 $67 $61CA1102 Los AngelesTexas IndustriesInc.Oro Grande Plant Oro Grande CA 57 C Dry 155 $67 $61CA1103 Los AngelesTexas IndustriesInc.Oro Grande Plant Oro Grande CA 57 C Dry 155 $67 $61CA1104 Los AngelesTexas IndustriesInc.Oro Grande Plant Oro Grande CA 53 C Dry 155 $67 $61CA1105 Los AngelesTexas IndustriesInc.Oro Grande Plant Oro Grande CA 53 C Dry 155 $67 $61CA1106 Los AngelesTexas IndustriesInc.Oro Grande Plant Oro Grande CA 46 C Dry 155 $66 $60CA1107 Los AngelesTexas IndustriesInc.Oro Grande Plant Oro Grande CA 46 C Dry 155 $66 $60CA601 Los AngelesLehigh SouthwestCement CompanyTehachapi Plant Tehachapi CA 14 C Dry-C 958 $49 $45CA301 Los Angeles Cemex Victorville Plant Victorville CA 22 C Dry-X 1,046 $51 $47CA302 Los Angeles Cemex Victorville Plant Victorville CA 4 C Dry-X 1,681 $50 $46IA101 Minneapolis Holcim (U.S.)Inc. Mason City Plant Mason City IA 39 C Dry 600 $41 $37IA102 Minneapolis Holcim (U.S.)Inc. Mason City Plant Mason City IA 39 C Dry 371 $50 $45(continued)A-23Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)IA301 MinneapolisLehigh CementCompanyLehigh-Mason City Mason City IA 27 C Dry-C 755 $37 $33NY301New York/BostonSt. LawrenceCement CompanyCatskill Plant Catskill NY 41 C Wet 580 $57 $52NY101New York/BostonGlens Fall LehighCement Co.Glens Falls Plant Glens Falls NY 32 C Dry-X 586 $55 $50NY201New York/BostonLafarge NorthAmericaRavena Plant Ravena NY 43 C Wet 852 $54 $49NY202New York/BostonLafarge NorthAmericaRavena Plant Ravena NY 43 C Wet 868 $54 $49ME101New York/BostonDragon ProductsCompanyThomaston Plant Thomaston ME 34 C Wet 392 $62 $56AZ201 PhoenixPhoenix CementCompanyClarkdale Plant Clarkdale AZ 46 C Dry-C 187 $46 $42AZ202 PhoenixPhoenix CementCompanyClarkdale Plant Clarkdale AZ 46 C Dry-C 187 $46 $42AZ203 PhoenixPhoenix CementCompanyClarkdale Plant Clarkdale AZ 44 C Dry-C 191 $46 $42AZ204 PhoenixPhoenix CementCompanyClarkdale Plant Clarkdale AZ 3 C Dry-C 912 $37 $33UT101 PhoenixAsh Grove CementCompanyLeamington Plant Nephi UT 24 C Dry-C 810 $35 $32AZ101 PhoenixCalifornia PortlandCementRillito Plant Rillito AZ 56 C Dry 121 $53 $48AZ102 PhoenixCalifornia PortlandCementRillito Plant Rillito AZ 54 C Dry 121 $52 $48AZ103 PhoenixCalifornia PortlandCementRillito Plant Rillito AZ 50 C Dry 121 $52 $47AZ104 PhoenixCalifornia PortlandCementRillito Plant Rillito AZ 3 C Dry-C 969 $37 $33NM101 PhoenixGCC of America,Inc.Tijeras Plant Tijeras NM 46 C Dry-X 216 $48 $43(continued)A-24Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)AverageVariableCosts($/short ton)NM102 PhoenixGCC of America,Inc.Tijeras Plant Tijeras NM 45 C Dry-X 216 $48 $43PA401 PittsburghEssroc CementCorp.Bessemer PlantBessemer PA 44 C Wet 225 $54 $49PA402 PittsburghEssroc CementCorp.Bessemer PlantBessemer PA 41 C Wet 339 $54 $49PA101 PittsburghArmstrong Cement& Sup. Corp.Cabot Plant Cabot PA 79 C Wet 143 $57 $52PA102 PittsburghArmstrong Cement& Sup. Corp.Cabot Plant Cabot PA 79 C Wet 143 $57 $52PA301 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 215 $57 $51PA302 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 205 $57 $51PA303 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 238 $57 $51OR101Salt LakeCityAsh Grove CementCompanyDurkee Plant Durkee OR 7 C Dry-C 816 $38 $34ID101Salt LakeCityAsh Grove CementCompanyInkom Plant Inkom ID 77 C Wet 115 $48 $44ID102Salt LakeCityAsh Grove CementCompanyInkom Plant Inkom ID 54 C Wet 145 $46 $42MT101Salt LakeCityAsh Grove CementCompanyMontana City Plant Montana City MT 42 C Wet 293 $46 $42UT201Salt LakeCityHolcim (U.S.) Inc. Devil’s Slide Plant Morgan UT 8 C Dry-C 704 $38 $34MT201Salt LakeCityHolcim (U.S.) Inc. Trident Plant Three Forks MT 32 C Wet 280 $45 $41TX1201 San AntonioTexas-LehighCement CompanyBuda Plant Buda TX 27 C Dry-C 1,125 $43 $39TX501 San Antonio Cemex Balcones PlantNew Braunfels TX 25 K Dry-X 1,005 $41 $38TX1001 San AntonioTexas IndustriesInc.Hunter Cement Plant New Braunfels TX 26 C Dry-C 780 $43 $39TX601 San Antonio Cemex Odessa Plant Odessa TX 47 C Dry 256 $58 $53TX602 San Antonio Cemex Odessa Plant Odessa TX 26 C Dry-X 285 $51 $46(continued)A-25Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)VariableCosts($/short ton)TX101 San AntonioAlamo CementCompany1604 Plant San Antonio TX 24 C Dry-C 821 $43 $39TX401 San AntonioCapitolAggregates, Ltd.Capitol CementDivisionSan Antonio TX 40 C Wet 269 $54 $49TX402 San AntonioCapitolAggregates, Ltd.Capitol CementDivisionSan Antonio TX 22 C Dry-C 599 $46 $42TX301 San AntonioBuzzi UnicemUSA, Inc.Maryneal Plant Sweetwater TX 34 C Dry-X 153 $52 $47TX302 San AntonioBuzzi UnicemUSA, Inc.Maryneal Plant Sweetwater TX 34 C Dry-X 153 $52 $47TX303 San AntonioBuzzi UnicemUSA, Inc.Maryneal Plant Sweetwater TX 34 C Dry-X 162 $52 $47CA401SanFranciscoHansonPermanenteCementePermanente Plant Cupertino CA 24 C Dry-C 1,497 $50 $45CA901SanFranciscoRMC PacificMaterialsSanta Cruz Plant Davenport CA 24 C Dry-C 812 $50 $45NV101SanFranciscoEagle Materials Nevada CementFemley NV 41 C Dry 226 $57 $52NV102SanFranciscoEagle Materials Nevada CementFemley NV 19 C Dry-X 226 $51 $46CA501SanFranciscoLehigh SouthwestCement CompanyRedding Plant Redding CA 24 C Dry-C 592 $54 $49WA101 SeattleAsh Grove CementCompanySeattle Plant Seattle WA 13 C Dry-C 675 $43 $39WA201 SeattleLafarge NorthAmericaSeattle Plant Seattle WA 38 K Wet 425 $51 $46MO101 St. LouisBuzzi UnicemUSA, Inc.Cape Girardeau Plant Cape Girardeau MO 24 C Dry-C 1,265 $38 $34MO401 St. Louis Holcim (U.S.) Inc. Clarksville Plant Clarksville MO 38 C Wet 1,241 $41 $38MO201 St. LouisBuzzi UnicemUSA, Inc.Festus Plant Festus MO 40 K Dry 592 $44 $40(continued)A-26Table A-14. Average Variable Costs by Kiln: 2005 (continued)Kiln IDMarketNameCompany Name Plant Name City StateAge in2005PrimaryFuel IDaProcessbClinkerCapacity (103metrictons/year)AverageVariable Costs($/metric ton)VariableCosts($/short ton)MO202 St. LouisBuzzi UnicemUSA, Inc.Festus Plant Festus MO 36 K Dry 592 $44 $40IL401 St. LouisLafarge NorthAmericaJoppa Plant Grand Chain IL 42 K Dry 443 $53 $48IL402 St. LouisLafarge NorthAmericaJoppa Plant Grand Chain IL 30 K Dry 612 $42 $38MO301 St. LouisContinentalCement Co., Inc.Hannibal Plant Hannibal MO 39 C Wet 549 $45 $41CA1001WhiteCementTexas IndustriesInc.Crestmore Riverside CA 47 O Dry 51 $96 $87CA1002WhiteCementTexas IndustriesInc.Crestmore Riverside CA 45 O Dry 51 $95 $86TX801WhiteCementLehigh CementCompanyWaco Plant Waco TX 37 G Wet 97 $78 $70PA901WhiteCementLehigh CementCompanyYork Plant York PA 43 O Wet 112 $79 $72aPrimary fuel ID codes are coal (C),coke (K), oil (O), and waste (A).bProcess codes are preheater(X) and precalciner (C).A-27 One consequence of this assumption is that th e seller individually c hooses an output level that is less than the level produced under perfec t competition. As a result, the baseline market price will be higher than a mode l of perfect competition, and th ere is a preexisting market distortion in the indus try being regulated. 1 To provide some intuition a bout 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 Share i /Demand Elasticity) = Marginal Cost i
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 pri ce (i.e., the demand elasticity is inelastic).
A.4 Equations To estimate the economic impacts of the regu lation, EPA used four linear equations to calculate the following unknown variables:

 change in domestic plant production (dq i ),

 change in imports (dq imports ),

 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 (c i ) 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 (dMC i ). In with-regulation equilibrium, the change in margin al revenue (dMRi) must equal th e change in the marginal cost (dMCi) for each plant. 2

dmarginal Revenue i = dmarginal Cost i

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 ment ions the need to consider market power–related welfare costs in evaluating regulations under Executive Order 12866. 2 To highlight and make transparent the assumptions regard ing seller behavior, this equation is formally derived in Appendix B.A-28 

### ( ) cost dmarginal elasticity dem price q plant Q market Q dmarket elasticity dem price Q market q dplant elasticity dem share

mkt 1 dprice 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 × (dpr ice/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 pri ce 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 indi vidual 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/ba seline price) × baseline consumption.
The use of published estimates from previous rule makings 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 ce ment 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 DECISIONB-1 This appendix provides additi onal detail about the cement plant production decision used in the economic model (see Equation 1 in Sect ion 3 of the RIA). Table B-1 identifies and describes the key variables of th e cement plant’s profit function.
Table B-1. Variable Descriptions

P Market price Q= 

# ∑ i q Market output

q i Domestic plant i’s output
FC i Plant fixed costs
VC i Plant variable costs

Step 1: First, we assume the pl ant’s goal is to maximize profits: 

### ( ) ( ) i i i i i FC q VC q Q P Q

− − = ) ( max

i q π
.

Step 2: The first-order conditi ons for a profit maximum are: 

### ( ) 0 q q q VC 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 d ecisions influence the market price:

0 q P

i ≠ ∂ ∂

 Plant (i) output decisions do not affect t hose 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 

### ( ) 0 q q q VC Q P P Q q q P(Q) P i i i i i i = ∂

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛
∂ ∂ +

.

Step 5: Rearranging terms: 

### ( ) 0 q q q VC P Q q q Q P P(Q) P i i i i i

i = ∂

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛
∂

∂ +

.

Step 6: Use and apply the following definitions:

elasticity demand inverse 1
q Q P P(Q)

i = = ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛
∂

∂
η

share market s plant' Q q 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 equa tion is determined and gives us the optimal decision rule for the plant:

dMC 1 Q q
1 2 i

= ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜

⎝ ⎛

+ Q q P dQ Q P dq dP i i η

η η .APPENDIX C

SOCIAL COST METHODOLOGYC-1 The Office of Air Quality Planning and Sta ndards (OAQPS) has adopted the standard industry-level analysis describe d 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 st atic partial-equilibrium model to compare prepolicy cement market baselines with expected postpolicy outcomes in thes e 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 pr oducers. The model provides an estimate of the social costs (changes in producer and consumer su rplus) associated with controls applying to existing kilns (see Section 4). Sin ce the social cost methodology is id entical to the approach used in previous cement analysis (EPA, 1998, Appendi x C), we have included elements of the previous report’s Appendix C in this RIA.
Figure C-1 illustrates the conceptual fram ework for evaluating the social cost and distributive impacts under the imperfectly compe titive structure of U.S. cement markets. The baseline equilibrium is given by the price, P 0 , and the quantity, Q 0 . Without the regulation, the total benefits of consuming cement are give n by the area under the demand curve up to the market output, Q 0 . This equals the area filled by the le tters ABCDEFGHIJ. The total variable cost to society of producing Q 0 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 Q 0 equals the total benefits minus the total cost s, 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 surplu s accrues to the suppliers of cem ent and reflects the value they receive in the market for producing Q 0 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) le ss what they must pay on the market, i.e., P 0 . Consumer surplus is thereby given by the area ABC.C-2 Q 1 P 1

D

$

Output P 0

P 1 c

P 0 c

Q 0 C B A

D E

F

J I H G MC + Control Costs

MR MC

Figure C-1. Social Cost of Regula tion 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 incremen tal compliance costs. As shown in Figure C-1, this results in a new market equilibrium that occurs at a higher market price for cement, P 1 , and a lower level of output, Q 1 . In this scenario, the total benefits of consumption are equal to area ABDFI and the total vari able 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 so cial cost of the regul ation equals the total change in social surplus caused by the regulation. Therefore, the so cial cost of the regulation is represented by the area FGHEC in Figure C-1.
The distributive effects are estimated by separa ting the social cost into producer surplus and consumer surplus losses. First, the change in producer surplus is given by

Δ PS = B – F – (G+H+E) (C.1)
Producers gain B from the increase in price (a tr ansfer from consumers to producers), but lose F from the increase in production co sts due to the incremental compliance costs. Furthermore, the reduction of cement production leads to fo regone baseline profits of G+H+E.C-3 The change in consumer surplus is given by

Δ CS = – (B + C) (C.2)
This change results from the reduction in cons umer surplus from the baseline value of ABC to the with-regulation value of A. In this case, c onsumers lose area B as a transfer to producers through the increase in the price they pay for the with-regulati on 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 su rplus can then be derived simply by adding the changes in producer and consumer surplus, i.e.,

Social Cost = Δ PS + Δ CS = – (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 competi tion will assist in i llustrating this point.
Suppose that the MR curve in Figure C-1 was the demand function for a competitive market, rather than the marginal revenue func tion for an imperfectly competitive producer. Similarly, let the MC function be the aggregate supply function fo r 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 compe titive demand function, the baseline (competitive) market price, P 0 C , is now equal to MC and Q 0 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 cove rs their costs of production.
In the with-regulation perfec tly competitive equilibrium, the market price would rise by the per unit control cost amount to P 1 c . The social cost of the regul ation 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 engineeringC-4 estimate overstates the social cost of the regu lation under perfect competition. The overstatement results from the fact that the incremental comp liance costs are estimated based on the baseline market level of cement output. With regula tion, output is projected to decline to Q 1 , so that the actual incremental compliance costs incurred by the industry are given by area F. Area G represents the foregone value of cement consump tion to consumers, also referred to as the deadweight loss (analogous to area C unde r 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.,

SC imp – SC perf = − [(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 cu rve (D) and the marginal revenue curve (MR) that exist under imperfectly competitive market structure. This area doe s 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 cem ent. The pre-existing social inefficiency of imperfect competition is exacerbated as the regul ation moves society further away from the socially optimal level of cement production, whic h results in social costs greater than the incremental compliance cost imposed on the cement industry.