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‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ | |Fw: ISIS TSD and ISIS Peer

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Keith W. Barnett USEPA/OAQPS/SPPD/MMG Mail Code D243

‐ 02 Research Triangle Park, NC 27711 919

‐ 541

‐ 5605 barnett.keith@epa.gov

‐‐‐‐‐ Forwarded by Keith Barnett/RTP/USEPA/US on 08/05/2010 03:17 PM

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‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ | |Keith Barnett/RTP/USEPA/US@EPA |

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‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ | |ISIS TSD and ISIS Peer

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Mike,

here is the last two documents that i have that will need to go into the docket: ISIS TSD for the NESHAP and NSPS analysis and the ISIS peer
‐ review response to comments document This last document needs to be converted into pdf before is added to the docket.With this email I complete sending you all of the documents that I have that belong to the Portland Cement NESHAP docket.

Thanks,

(See attached file: Final TSD ISIS NESHAP and NSPS August_5_2010.pdf)

(See attached file: 20100805 ISIS Cement_Response to Comments_Summary.doc)

Elineth Torres Environmental Protection Specialist U.S. EPA OAQPS/SPPD/Program Design Group 109 T.W. Alexander Dr. Durham, NC 27711 Phone: 919

‐ 541

‐ 4347 Fax: 919

‐ 541

‐ 5600Version 1 i EPA External Review 

## Industrial Sectors Integrated Solutions (ISIS) Model

for the Portland Cement Manufacturing Industry

Summary of Comments Received on the ISIS-Cement Documentation Package

U.S. Environmental Protection Agency Research Triangle Park, NC 27711

July 30, 2010Version 1 ii EPA External Review CONTENTS

1. Introduction .................................................................................................................. ........1
2. Market Structure ..................................................................................................................3
3. Transportation ................................................................................................................ ......5
4. AVC ........................................................................................................................... ..........6

5. Demand projections ...........................................................................................................12
6. Supply ........................................................................................................................ ........15

7. Emissions and Cost of Installing Controls ......................................................................... 19
8. Imports ....................................................................................................................... ........29
9. Calibration.................................................................................................................... ......32
10. Miscellaneous ................................................................................................................. ...33
11. Data Sources ......................................................................................................................41
12. Editorial Comments ...........................................................................................................50

Appendices: Appendix A Invitation Letter to Reviewers Appendix B Review Comments from Dallas Burtraw, Resources for the Future Appendix C Review Comments from Kevin Cullig an, US Environmental Protection Agency Appendix D Review Comments fr om Fereidun Feizollahi, CARB Economic Studies Section Appendix E Review Comments from Sikande r Khan, US Department of Energy Appendix F Review Comments from Andy O'Hare , Portland Cement Association (September 4, 2009) Appendix G Review Comments fr om Tyrone Wilson, Portland Ce ment Association (March 29, 2010) Appendix H Review Comments from Hendr ik van Oss, US Geological Survey Appendix I Review Comments fr om Hector Ybañez, Holcim Appendix J Review Comments from Eric Smith, US Environmental Protection Agency and Ernst Worrell, Ernest Orlando Lawrence Berkeley National Laboratory

List of Tables

Table 1-1. List of Commenters on IS IS Peer Review Documentation ............................................2Version 1 iii EPA External Review Acronyms and Abbreviations

AEO Annual Energy Outlook APCA American Portland Cement Alliance ARB Air Resources Board (see CARB) ASR Alkali-Silica Reactivity AVC Average Variable Cost AZ Arizona BLS Bureau of Labor Statistics CA California CARB California Air Resources Board CIF Cost, Insurance & Freight CKD Cement Kiln Dust CO Colorado DE Delaware DLI Dry Lime Injection EGFW Exit Gas Flow Rate EIA Energy Information Administration EPA Environmental Protection Agency ESP Electrostatic Precipitator FL Florida GA Georgia GDP Gross Domestic Product GHG Greenhouse Gas H 2 O Water HAP Hazardous Air Pollutant ID Idaho IL Illinois IPCC Intergovernmental Panel on Climate Change IPM International Petroleum Monthly ISIS Industrial Sectors Integrated Solutions LNB Low NO X Burner LWS Limestone Wet Scrubber MI Michigan MMBtu Million British Thermal Units MO Missouri MSA Mineralogical Society of America MYB Minerals Yearbook (USGS) NAA Nonattainment Area NAS National Academy of Science NC North Carolina NEI National Emission Inventory NESCAUM Northeast States for C oordinated Air Use Management NESHAP National Emission Standard s for Hazardous Air Pollutants NM New Mexico NO X Oxides Of NitrogenVersion 1 iv EPA External Review NRC National Research Council NV Nevada O&M Operations and Maintenance OK Oklahoma OMB Office of Management & Budget OR Oregon PA Pennsylvania PC Pulverized Coal PCA Portland Cement Association PM Particulate Matter PRB Powder River Basin PSD Prevention of Significant Deterioration RMC Ready-Mix Concrete SC South Carolina SCR Selective Catalytic Reduction SFC Specific Fuel Consumption SG&A Sales, General and Administration SLC Salt Lake City SNCR Selective Non-Catalytic Reduction SOx Oxides of Sulfur TDF Tire Derived Fuel USGS United States Geological Survey UT Utah WA Washington WBCSD World Business Council for Sustainable DevelopmentVersion 1 1 EPA External Review 

### 1. Introduction

In the National Academy of Science’s 2004 report, “Air Quality Management in the United States,” the Nation al Research Council (NRC) recommended to the U.S. Environmental Protection Agency (EPA) that standard setting, planning, and control strategy development should be based on integrated assessments that consider multiple pollutants, and that these integrated assessm ents should be conducted in a comprehensive and coordinated manner (NAS, 2004). With th ese recommendations, EPA began to move towards establishing multi-pollutant and sector-based approaches to manage air quality and environmental protection. The benefits of multipollutant and sector-based analyses and approaches include the ability to identi fy optimum strategies (c onsidering feasibility, costs, and benefits across all pollutant types such as criter ia, toxics, and others), while streamlining administrative and compliance complexities and reducing conflicting and redundant requirements.

The development of multipollutant and sect or-based approaches in managing emissions and air quality requires sophist icated analyses of relevant technical and economic factors. Such analyses are not possible withou t a sophisticated modeling framework. Accordingly, the Industrial Sectors Integrat ed Solutions (ISIS) model for industrial sectors has been developed at EPA. Currently, the ISIS model is populated with US cement manufacturing data, and efforts are unde rway to build repres entations of the US pulp and paper sector, and the US iron and steel sector.

EPA requested comments on the ISIS mode l on December 23, 2008 (six reviewers) and January 13, 2009 (one additional reviewer). A copy of the invitation letter is shown in Appendix A. In addition to the invitation le tter, the ISIS review package included the following files:

• Review documentation file – “ISIS P eer Review Docume ntation 12-23-2008.pdf”

• GAMS and ISIS instructions – “ GAMS Installation and ISIS Running Instructions.pdf”

• GAMS license file to run the trial version – “GAMSLICE.TXT”

• GAMS model files and Input/Output files – “ISIS_PeerReview.zip”

A total of nine sets of comments were received by EPA regarding the ISIS model. Two sets of comments were received from EPA (Febraury 6, 2009 and May 13, 2009) and two sets were received from the Portland Ceme nt Association (PCA) (September 4, 2009 and March 29, 2010). Some of the comment sets were signed or submitted on behalf of multiple commenters, with the most extens ive comments being received from Mr. Andy O’Hare and from Mr. Tyrone Wilson, both for the PCA. The first se t of PCA comments (September 4, 2009) were received as part of the Portland Ceme nt NESHAP Comment Period; only Chapter 13 pertains to the peer-review of ISIS.Version 1 2 EPA External Review Table 1-1 lists the names of the commenters and the commenters’ affiliations. Complete, as-received, review comments are gi ven in Appendices B through J.

Table 1-1. List of Commenters on ISIS Peer Review Documentation

Date Received Appendix Commenter Name and Affiliation Code used for Comments February 02, 2009 B Dallas Burtraw, Resources for the Future DB

February 06, 2009 C Scott Benolkin, US Environmental Protection Agency SB

March 02, 2009 D Fereidun Feizollahi, California Air Resources Board (CARB) Economic Studies Section FF

February 02, 2009 E Sikander Khan, US Department of Energy SK

September 04, 2009 F Andy O'Hare, Portland Cement Association AO

March 29, 2010 G Tyrone Wilson, Portland Cement Association TW

January 30, 2009 H Hendrik van Oss, US Geological Survey HO

January 29, 2009 I Hector Ybañez, Holcim HY

May 13, 2009 J Eric Smith, US Environmental Protection Agency Ernst Worrell of Ernest Orlando Lawrence Berkeley National Laboratory (contractor to US EPA) ES

The remainder of this document consists of responses to each substantive comment received by EPA on the proposed ISIS mode l. The comment summaries are grouped by topic in chapters of this document and are referenced as sequentially numbered comments with reviewer’s initials and the number of the chapter from the original documentation (issued to reviewers on December 23, 2008 and on January 13, 2009) to which the comment pertains. For example, comment number DB1.2 denotes a second comment pertaining to Chapter 1 in the original “I SIS Peer Review Docu mentation” that was received from Dallas Burtraw. The only exception to this is for the second set of comments received from PCA. These comments were additional general comments based on PCA’s experience in running the model, and are denoted only with the reviewer’s initials (TW for Tyrone Wilson).Version 1 3 EPA External Review 

### 2. Market Structure

Comment: Several comments were received that questioned or pertained to the regional market structure

AO2.1: Get rid of the market structure or if mark et structure is going to be used define markets as having geographic boundaries that more closely match the manner in which the data have been generated to reduce data manipulation.

• Instead of using the 20 EPA cement markets use the PCA states or metropolitan statistical areas (PCA’s consumption data)

• Instead of using the 20 EPA cement markets for cement sales price- use USGS 26 districts

• Instead of using the 20 EPA cement markets for imports – use USGS 38 custom districts Instead of markets- use demand centers and simulate each plant’s ability to compete as a function of the actual plant lo cation and transportation options and cost and distance to each demand center. Do not assign plants as ac tually being “in” a particular market or demand center.

Response: Regional markets have been replaced with demand centers, corresponding to states. Imports are now represented as im ports in the USGS imports-districts.

SB2.2 : The rationale behind the choice of region definitions needs more documentation...assuming uniform region-wide parameters could systematically underestimate the potential competition and exchange between ne ighboring plants in different regions....it would be nice to know or estimate how much of each plant’s commerce is within its ISIS region versus outside of it.

Response: The model outputs include quantity of cement traded-in and traded-out by each of the regions. However, the regional cement-markets have been replaced by demand centers; production locations (kilns) a nd import-districts are free to transport their production (or imports) to any demand center.

SK2.1 : How are the regions modeled for cemen t going to map into other industries...? Integration with other industries is going to be really vital...especially...if costs are higher for one industry than anothe r to control cert ain pollutants.

Response: Regions for one-industry may be entirely different from regions in another industry. ISIS-cement has regions pertinent to cement industry only. Future development on the ISIS framework including other industrial sub-sectors may take this into account. The regions have now been replaced with states as demand centers.

SB2.6: Given transportation costs and the fact that 10 companies account for 80% of production overall, is market power an issue in any regions or subregions?Version 1 4 EPA External Review Response: Discussions with the industry indicated the industry believes it operates in a perfectly competitive market and, therefore, th ere are no issues related to market power. In addition, historical demand exceeds capacity.

Instead of treating markets as isolated regi ons, we created an extensive transportation matrix, allowing an inter-market trade possible (which is more realistic) thereby reducing the possibility of market power. Also, we took into account comments in “EPA’s Draft Economic Analysis of Air Pollution Regula tions for the Portland Cement Industry” (APCA, January 1997) regarding choice of appropriate market structure for cement manufacturing.

DB1.4 : ...the threat of unfair inte rnational competition under climate policy... Since this is an emerging issue it would be a great enhancement to have it considered and modeled carefully in ISIS.

Response: The import formulation has been revised to accommodate imports from various countries in import di stricts. Structural imports (f rom Canada and Mexico) have been treated differently compared to the imports from the remainder of the exporting countries.

SK2.3 : Interregional trade seems like it would be very sensitive to fuel prices – using diesel prices avg. 1994-2005 may be misleadingly low.

Response: Yes, inter-regional trade is very se nsitive to transpor tation costs, and transportation costs are adjusted for 2005 based on increase in the average price of diesel, which is the main fuel used for trucking cement.

TW : Changing the assignment of kilns to mark ets and the cement transportation options available to particular kilns.

Response: The model has been reformulated. Any kiln can supply to any demand center, subject to market constraints such as transportation and production costs.Version 1 5 EPA External Review 

### 3. Transportation

AO2.2: Expand transportation matrix to include:

• barge,

• rail,

• transportation cost by truck, and

• ocean (for imports) Change the assumption that transportation within a market has no cost and that outside of the market transportation is based on the dist ance of the market main city to the other market main city.

Response: Transportation matrix has been comp letely revised taking into account various possible modes from units to demand cen ters and from import districts to demand
centers.

HO2.7 : In Figure 2.1, there are some very st range groupings re. the cement plants. For example: Why on Earth is Ash Grove, L eamington, UT grouped into Phoenix, but Holcim Devil’s Slide is Salt Lake City? Le t me assure you that the Leamington plant feeds markets primarily in UT (incl. SLC) and NV (Las Vegas) (plus v. minor tonnage, only, into CO and NM; nothing into AZ). In Las Vegas it competes with the S-CA producers. Why is Ash Grove, Durkee, OR groupe d with Salt Lake City—its market is primarily OR, WA, ID....

For example is known that some plants stra ddle more than one market: Lafarge’s Sugar Creek, MO plant can indeed feed westward into the Kansas City area, but it also ships a lot of cement down the Missouri River (i.e., eastward). Buzzi’s Cape Girardeau plant probably has the widest geographi c dispersal of its cement than any other plant. Overall, there are all sorts of flow s up and down, in and around, the entire Mississippi-MissouriOhio River system.

Response: In the ISIS-cement model markets are not isolated regions, we have created an extensive transportation matrix, allowing for inter-market trades. The transportation matrix includes transportati on by truck, rail, and barge.Version 1 6 EPA External Review 

### 4. AVC

Comment: A number of comments were received wh ich related to the average variable cost function and related costs and escalation factors used in the model

AO2.3: Expand average variable cost (AVC) f unction to account for SG&A, cost of shipping within a region and to terminals, property taxes and insurance, benefits on wages and salaries paid, freight out to customers, freight out to terminals, other cost of goods sold.

Repair and maintenance costs- EPA might want to escalate costs from 93 to 2005 by using a price index more specific to re pair/maintenance that GDP deflator.

Labor costs- are substantially low compared to benchmarking analysis- draw earning data from BLS consistent with PCA sugg estions (Page 19 of PCA comments).

Fuel cost- do not assume that is reasonable for a plant to use 100% coke or residual oil; use Staudt’s approach to estimate fuel c onsumption rather that RTI’s approach.

Escalation factors- 2008 EIA’s escalation f actors should not be us ed to escalate 2005 actual prices, run projections off the most recen t year for which data on actual prices and projection escalation ra tes are available.

AVC alone is not appropriate for estimating market-clearing prices over the long-run. In the long-run all costs are vari able including fixed costs.

Response: The AVC cost components have been updated and revised. Escalation factors have also been updated.

HO5.8 : Section 5.1.4...the cost analysis seems to s how that, of the tota l increase in costs ($1.58 billion), all but $0.01 billion is due to control techno logy. What about increased fuel costs?

Response: The increase in the cost can be attributed to several factor s, including cost of controls, changes in the fuel-use pattern, distribution of suppl y between domestic production and imports, and changes in the transportation modes and routes.

HY5.1 : Page 5-6. Table 5-1 control cost #’s need to be updated to differentiate between new capacity in the business-as-usual vs. policy scenario.

Response: Addressed in the updated ISIS model/documentationVersion 1 7 EPA External Review SB3.1: Equation 3.2.1 appears to set annual fixed costs proportional to a unit’s production rather than its capacity . If I am interpreting this correctly, it is a serious conceptual mistake.

Response: Addressed by adopting standard new kiln capacity of 1.2 million tons

DB4.1 : It would be helpful in Chapter 4 if an explicit equation showed the variable cost calculation with the calibr ator constant included...

Response: The equations will be included in the revised calibration methodology

.

SB4.2: Variable cost seems like an important parameter to be imputing a constant adjustment from a regression over (it appears) two years of data. How sensitive are the results to the choice of this constant (or th e choice of using a constant)?...my own limited investigations...show mo stly level shifts.

Response: The calibration methodology and re solution have been revised.

SB4.3: Constant escalation factors for fuel pri ces seem too simplistic...Ideally, a supply curve informs (of) the cost of fuels. Absent that, ISIS should use actual annual or multiannual projections rather than an annualized 20+-year average.

Response: Escalation factors have been updated as a function of time.

DB1.8 : It is clear from the optimization probl em that emissions policy compliance costs (taxes, permits) are included in the variable cost calculation...It s hould be mentioned in Chapter 1.

Response: This will be included in the revised version.

SK5.3 : Page 5-5, Figure 5-5: Can there be a s cenario, where the price of cement is affected to the point that it starts to affect demand?

Response: Yes, and it has been taken into account by way of incorporating demand response in the formulation of the model.

DB2.10 : Page 2-10. “By-product be nefits” Also costs?

Response : Costs of solid waste disposal have been included in the model.

DB2.13 : Page 2-12, Table 2-10. It may be usef ul to add an escalation factor for construction materials.

Response: Escalation factor of 5.16% for construc tion materials was added in Table 2- 10.Version 1 8 EPA External Review SB2.5: ...model’s energy prices escalation f actors reconcile with those in AEO 2008 Table A1..., they disagree with those in Tabl e A-3 (by sector),...mor e recent projections by EIA and IPM have much higher future pr ice increases (2–3% annualized) than AEO 2008.

Response: This is a user-defined pa rameter and can be changed.

SK2.8 : Page 2-11, Section 2.2.3.2:...wha t is the basis for ( the overall economic analysis ) these calculations: current or constant dollars? How are the construction times for the controls taken into account? Do es the model estimate some of the construction costs (e.g., allowance for funds used for constructio n),...? In Table 2-10, what does the annual escalation rate of 3.09% for the variable O& M cover – all variable costs or some of them? If all, then why different factors fo r limestone and gypsum? Is there a separate factor for lime as well?

Response: All costs are represented in constant (2005) dollars. Different factors for limestone and gypsum were obtained from hi storical price data published by USGS.

SB2.4: OMB’s 7% discount rate makes sense fo r analyzing the costs and benefits of policy results from ISIS, but it is not a ba sis for reflecting the discount rate cement companies use...

Response: Discount rate is user-defined parameter and can be changed.

SB2.8: In the real world, do producers buy at a world market price or benefit from multinational ownership?

Response: Since transportation is a large cost-component, there is no uniform “world market” price. Prices are regional in nature. Producers can benefit from multinational ownership by participation in imports.

HO4.1 : Section 4.1.3.1: I am not sure th at the assumption that Price imports = producer price is at all valid.......What is the per centage criteria for this designation ( importdominated markets )?...

Response: We calibrated import prices for each de mand region to arrive at historical import levels supported by data.

SK5.4 : Page 5-6 Table 5-1: The increase in th e combined import and control costs ($2 billions) does not match the increase in the total cost ($1.58 billions) for the example

case.

Response: Change in the total cost reflects co ntrol cost and the cost of supply (production, imports and transportation etc.). Should there be a drop in demand due toVersion 1 9 EPA External Review demand response, the change in the cost of c ontrol and imports can be different from the change in the total industry cost. SK5.5 : General Comment: The costs associated with the base and policy cases should be reported in terms of factors (e.g., $/ton of cli nker or $/ton of air po llutant removed) that can easily be understood and compared to the effects of othe r similar policies.

Response: Addressed in the updated ISIS model/documentation.

HOB.4 : Table 7: the unit electricity prices may not be valid for large industrial customers (they may get a lower rate).

Response : We have updated the electricity pric es to reflect electricity prices for industrial customers by state. At this time, we do not have any additional information regarding electricity prices for individual kilns.

TW : Similarly, there was no cell in the input sh eets for interest ra te, that user could change. Also, structure of the imports formul ation is coded in the model and cannot be easily changed. Further, variable cost co mponents assumed by EPA are incomplete. Several components, such as transportation within a region, SG&A costs, etc., are not explicitly included.

Response : Interest rates are now moved to th e input spreadsheet and can easily be modified by the user. Structure of the imports has been reformulated based on input from stakeholders and industry participants. Tr ansportation matrix has been extended to include transportation from each kiln and import district to demand centers. Variable cost components have also been revised.

Comment: Several comments were received whic h related to the banking algorithm and investment decisions in the model

DB1.3 : Can the model accommodate a bank that does not expire before the end of the second period, or is a terminal year date necessary for the banking algorithm to converge? How would the terminal year be determined? Is there a backstop technology that determines long-run abatement cost?

Response: Current version of the model uses a banking algorithm based on standard bank-exhaust condition at the end of the po licy horizon. However, we will consider incorporation of appropriate b ackstop technology to establish long-run abatement cost or steady-state equilibrium conditi on for the bank in the long-run.

DB1.7 : Are investment decisions using more than an annualized capital cost to consider in-place controls?

Response: Investments decisions use annualized cap ital cost and fixed and variable O&M costs.Version 1 10 EPA External Review Comment : Several comments were received th at related to fuel types and fuel efficiency used in the model

HY1.1 : Page 1-5. Statement about PC fuel efficiency in the first paragraph - replacement of both wet and certain dry pr ocess kiln capacity with m odern kiln processes yield substantial reductions in fuel use. This is true in term of fuel effi ciency but not in the absolute amount of fuel use. Newer kilns tend to be bigger kilns and are going to use more fuel.

Response: Addressed in the updated ISIS model/documentation.

DB2.5 : Page 2-2. “The ISIS model determines the optimal fuel type...” Does the model account for long run contracts for fuel, or are all projected fuel c hoices assuming a spot market for fuel?

Response: The model takes into account regional prices of the fuel as provided by the Energy Information Administration, and their air pollution impacts, to decide the optimal fuel-type for a given kiln. The model does not a ccount for long run contracts for fuel. All projected fuel choices are made assuming spot market for fuel.

HO2.13 : Figure 2-2: awfully crude breakout of fu els....Also, do the percentage data refer to tonnages or to the heat c ontribution (Btu)? If I use actua l industry-reported heats, my own data for 2005 show: Natural gas (3.72% to tal Btu); fuel oil (0.76% [seems low]); coal (64.1%); petcoke (19.3%); me tallurgical coke (nil); tire s (3.2%), other solid wastes (0.5%); liquid wastes (8.4%). Your report c ites total heat (EIA) of 451.2 trillion Btu. My (USGS) data for 2005 has 366.7 trillion Btu excluding electricity ,...Using actual data, if I include the electricity, then we jump to 413.4 trillion Btu. Thus, I suspect that the EIA data are either too high or that they, at the ve ry least, include electr icity. Based on the rest of your report,...skip the electric ity (in your heat analysis).

Response: New pie chart inserted utilizing numbers given by the reviewer.

HY2.5 : Page 2-11. Table 2-9 Are these #s fuel intensit y or absolute? We might think that the #s are intensity related not absolute. Wh en going from a wet to a PC you have more capacity, you produce more and therefore you will have more emissions in the absolute no matter that the process is more efficient.

Response: These numbers are intensities, i.e., repr esent mmBtu of fuel used per ton of clinker production.

SK2.5 : Page 2-2, First Paragraph: Itis stated that the ISIS model determines the optimal fuel type. What is the basis of this selectio n? Is there any information available for the existing fuel type, e.g., for the base case? Can the fuel type be an input to the model?

Response: The basis of selection is relative fuel -prices in a given region and emission characteristics of various fuels. Yes, fuel -type can be an input to the model, butVersion 1 11 EPA External Review prescribing a fuel-type might restrict the mode l’s ability to switch fuels in response to a policy.

HOA.19 : Page 35: Again, if I am reading this co rrectly, it appears that a claim is made that CemStar increases the electricity consumption by the finish mill. Well, if you assume that you get 5% more clinker for a 5% CemSta r (slag) introduction, th en, yes, it will take 5% more electricity to grind that extra clinker. But so what? This is like saying that if you get a salary increase, you will pay more ta xes even though the tax rate doesn’t change. This is not interesting. What w ould be interesting is if the use of slag (CemStar) made a clinker (perhaps a harder clinker) that was costlier to grind (more electricity per ton). But I do not think that this is the case: the cost (e ither per ton of clinker or per ton of cement) to grind the clinker into cemen t does not change as a result of using CemStar. There may be some additional electricity in the raw mill end of the plant to handle the extra ingredient (slag) but this is likely to be more than offset by the reduced electricity costs of comminution of the feed: unlike the limestone a nd other feeds, the slag does NOT need to be ground, just coarsely crushed. [the CemSta r patent “discovery” was not the chemical contribution to the clinker-making process. It is the fact that you don’t need to grind it— only crush it to 1-2” diameter. The utility of slag had been known for many decades, but it had not been popular earlie r because it was assumed that you had to grind it like everything else, and slag is i ndeed hard (costly) to grind].

Response: Raw material substitution and fuel reduction has been appropriately accounted for when CEMstar is applied.Version 1 12 EPA External Review 

### 5. Demand projections

AO2.4 : Data used by EPA is PCA forecast cement consumption, equivalent to cement usage. Agency’s “demand” terminology is misleading. EPA should refer more accurately to cement consumption rather than cement demand.

Consumption data is now outdated. Update consumption projections to latest data with the summer 2009 Forecast and 2009 Long-term forecast which reflects the deeper and prolonged slump for cement consumption because of the recession.

If cement demand centers are used a combin ation of PCA’s project ion by State and by MSA might be appropriate to allocate cement consumption rather than allocating consumption to different markets in a way that introduces uncertainty and errors (by state’s surface area, population or some other variable).

Developing year-by-year projections by f itting a polynomial equation for each region smoothes out variations in consumption ove r time that PCA believe are important.

Response: We are now using the demand projections provided by the PCA, and wherever needed, a linear interpolation scheme is used to arrive at data for missing years within the reported forecast horizon.

HO1.10 : Another related issue is the cement stockpiles: the comparison is between successive yearend (12/31) stocks. But you coul d have a significant drawdown or buildup due entirely to the early arrival or late arrival of a few ships—this would cause a stockpile shift entirely UNRELATED to any economic forces. So I do not find apparent consumption to be a very useful statistic.

Response: The ISIS-cement model uses demand pr ojections from PCA as a metric for future consumption of cement. The data from PCA reflects 2005 historical level of consumption of cement as reported by USGS . The historical data of apparent consumption in 2005 from USGS accounts fo r cement stockpiles as reported by USGS.

DB2.4 : Page 2-23. The elasticity value cited is -0.88. Is this to be interpreted as a short run or long run elasticity ? Is there reason to th ink they differ, or not? Are there long run substitutes for cement?

Response: The value of -0.88 is a short-run pric e elasticity of demand estimated by RTI as part of econometric modeling done for th e Portland Cement NESHAP promulgated in 1998. Given that there are very few substitu tes for cement, there are not likely to be long-run substitutes for cement. Further, the user can choose to do a sensitivity analysis by varying the elasticity values, as elasticity is a user-input that can be sp ecified in the workbook.Version 1 13 EPA External Review SB2.1: The documentation (Section 2.2.3.2) menti ons a cement demand elasticity of - 0.88,...model results show no changes in demand in the policy case, despite significant allowance prices. Is this a disabled feature?

Response: Demand response in the model can be enabled by choosing value of “Demand RESP” to be 1 in the corresponding “Policy” Tab in the Excel spreadsheet.

FF2.1: Demand projections - In ARB’s draf t calculations for California’s cement industry, ARB staff has assumed a 2% annual gr owth rate in cement demand. Due to the economic downturn, staff will be reev aluating the annual growth rate.

Response: In the ISIS model demand projection can be adjusted or changed by the user. At this time current demand projections in the ISIS-cement model use the latest PCA projections at the time of co mpletion of this documentation.

HO2.8 : Section 2.2.1.3: The 2005 demand for po rtland in 2005 was 122.4 Mt (million metric tons), and for portland + masonry, it was 127.9 Mt. Thus I question the 127.6 portland number in your report....PCA demand forecasts...change...at least quarterly....it is likely that you have used re ports predating the recent collapse of the economy...The PCA has revised its forecasts downwards....we have had plant closures in 2008 (I think 3) and announcemen ts of about 7 more in 2009,...

Response: The reason for the “127.6 number” is that Hawaii and Alaska are not included in Table 2-2.

HO3.4 : Pages 3-2 through 3-3: Equation 3.1.3. for regional demand: I suggest using the word “flows” instead of “trade” when referr ing to inter-State or inter-market movements of domestic cement.

Response: Since the market-based structure of th e model is no longer being used, intermarket trades are not relevant anymore. Howe ver, the new structure enables user to track transported quantities from each kiln to de mand center, and each import district to demand center.

HO3.5 : Equation 3.1.4 only holds if the import: to tal demand ratio is accurate, and I’m not sure this is the case.... How are you dete rmining where cement imported at location X is being consumed?

Response: The imports have been reformulated a nd the transportation cost matrix has been revised to allow imports to be transported to various demand centers.

HO5.5 : Section 5.1.3: The total demand in the n ear term has now likely been revised downwards by the PCA.

Response: Latest demand projections have been incorporated.Version 1 14 EPA External Review TW : PCA focused on making changes to the model to reflect the following changes: PCA’s most recent demand forecast; adopted PCA’s most recent long-term cement demand forecast, reflecting the impact of the recession.

Response: The new demand forecast has been incorpor ated in the model. Further, users can change the demand by going to the Inputs workbook.

TW : The impact of recession on loss of dome stic production in the presence of NESHAP is an important and “sensible” resu lt. It shows a drop in domestic production and increase in imports.

Response: The model has been revised to incorporate new demand.Version 1 15 EPA External Review 

### 6. Supply

AO2.5 Update for the cancellation or deferral of most of the industry’s previously planned capacity expansion project ions over the next few years.

Response : The planned capacity has been updated with latest available data.

DB1.11 : Page 1-5, bottom. How does one interpre t “kiln capacity?” Is output measured per unit time? I learn in the memo from Andover Technology Partners (September 23, 2008) in a footnote on Page 1 that capacity is normally expressed “per hour.” This should be added throughout the ISIS documentation.

Response: Kiln capacity comes from the PCA; U.S. and Canadian Portland Cement Industry: Plant Information Summary. December 31, 2004

Clinker capacity definitions:

Annual capacity – is daily capacity multiplied by 365 less normal downtime days (given in metric tons of clinker)

Daily capacity - is the normal clinker output a kiln can produce per day given a realistic work pattern

Normal downtime days - is the number of days of downtime required for maintenance, repair, or clean up.

HO1.3: It may be useful to discuss what is meant by “capacity” anyway! Daily capacity means per 24 hrs (and implies 24 hrs of contin uous operations) and is more or less unambiguous...except that you don’t know wh ether the cited number is the manufacturer’s rating on the kiln (usually a conservative figure), or actual “full blast— but normal operating—conditions. Commonly, the ra ted capacity is lower than the actual operational capacity. Annual capacity is not well defined. The USGS calculates an “apparent annual capacity” as follows:

Apparent annual capacity = (Reported daily capa city) * (X-Y) where: capacity is in tons; X is 365 days (or 366 in leap years) and Y is the number of days of downtime for routine, scheduled, maintenance. Thus, (X-Y) is th e scheduled operational year. The PCA, I believe, uses the same formula, but (in my opinion) does not always check to distinguish routine maintenance downtime from total downtime. Clearly, there can be a large variation in apparent annual cap acity if there is any mischaracterization of the routine maintenance downtime or the daily capaci ty. The USGS calculates—for the plant overall—the capacity utilizati on wrt. the actual clinker out put: we query (question) any utilization percentages in excess of 100%.Version 1 16 EPA External Review Because there is commonly downtime in excess of routine maintenance (extended upgrades, shutdowns for unplanned repairs...), the USGS feels that a utilization rate of 85% or more represents full practicable capacity operations.

[The industry reports to the USGS, for each kiln, the daily capacity, the total downtime, the subset downtime for routine maintenance, and the remainder for “other downtime”. We also ask for the dimensions of the kiln, whether it is wet or dry, whether it has a preheater, whether it has a pr ecalciner, and whether dust cont rol is via a baghouse or an ESP.]

Response: Refer to DB1.11 above – annual capacity definition is from PCA- and was added.

DB2.2 : What assumption can be made or is ma de about the location of the new unit and the expanded unit in each region ? Are all input costs uniform for all plants within a region?...model user will have to have info rmation about which plant within a region expands, or where new plants are located. A reasonable assump tion may be to distribute new capacity proportionately at the location of existing facilities.

Response: As recommended by reviewer, the peer-r eviewed version of the ISIS-cement model distributes new capacity proportionate ly at the location of existing facilities.

HO2.11: The phrase “...ISIS endoge nous capacity growth can also lead to endogenous retirement of kilns.” conjures up all sorts of imagery unrelated to cement...Couldn’t you just say something like: “capacity expansi on projects can include the retirement of existing kilns...”?

Response: Language changed to describe that capaci ty growth may lead to retirement of kilns.

DB3.3 : Page 3-2....What is the relationshi p between the chosen values ( of the constraints limiting capacity changes ) and historical experience?

Response: Average values of changes in the capacity have been estimated and used in the model.

DB3.5 : Page 3-2, middle. I don’t see how the constraints relate.

Response: Please refer to revised ISIS documentation.

DB3.6 : Bottom. “Note that replacements are associated with individual units....expansion...changes are...in regional capacity.” Then why are they summed?

Response: These have been summed to ensure th at the total changes in replacement capacity confirm the historically observed aggregate values.Version 1 17 EPA External Review HO3.3 : Production and Capacity Changes: the lead-in sentence is a bit unclear: “Regional production can be from existing kiln s, kilns added at a plant (i.e., expansion kilns), newer kilns replacing kilns at a plant (i.e., replacement kilns), and new kilns.” I think you mean to say something like: “...Future production...existing kilns, upgrades of existing kilns (debottlenecking, major techno logical upgrades), new kilns at existing plants (perhaps replacing existing ki lns), and new plants altogether.” It is unclear if your discussion, and projected capacity changes, fully account for plant shutdowns.... Have capacities been properly/unif ormly defined for all new kilns (are they summed on the same basis)?

Response: Replacement capacity is related to existi ng kilns. New capacity is part of the capacity expansion options, and total capacity change in a given year is constrained, based on historical observed values.

TW : We thus suggest that ISIS treat as unavoidable all the subsequent recurring payments resulting from any capital investment -- whether for pollution abatement or for new capacity -- that is simulated as o ccurring during the modeling time horizon.

Response: The capital cost accounting in ISIS has been revised.

TW : ISIS does not now include costs rela ting to “sunk” (i.e., pre-2013) capital investments at kilns as among the costs that will need to be covered in determining regional cement prices.

Response: While the sunk costs are an important as pect of decision making, they are not a determinant of market prices in a competitive market framework.

TW : A user should not need to pay a substantia l cost in order to run the ISIS model.

Response: The solver and GAMS languages used to run the ISIS model are proprietary tools and are sold for a fix price from GAMS Corporation.

TW : The section showing “Capital Cost” (lines 14 - 69) apparently gives the one-time capital cost of the control measure, and not the annualized cost equi valent that would be charged each year. Where in ISIS_inputs can one find and adjust the discount rate and useful life assumptions for each sort of control measure that are then used to convert the one-time cost into the annualized cost figures that show up in ISIS_outputs? It would be helpful to show the discount rate and useful life assumptions on this input worksheet and be able to adjust them at this point.

Response: Discount rate is now a user-defined el ement and can be changed by the user. Control cost equations are clearly listed in the Inputs workbook and can be easily modified by the user. Useful life of each c ontrol can now be modified in the Inputs workbook by going to the wo rksheet “Controls”.Version 1 18 EPA External Review TW : New cement production capacity: Make severa l changes so the model reflects the true costs for new, expanded or replacement cap acity rather than unrealistically low costs as originally assumed.

Response: Capital costs of new kiln have been revised based on industry comments and the capital cost for various capacity addition opt ions is now a parameter that the user can change.Version 1 19 EPA External Review 

### 7. Emissions and Cost of Installing Controls

Comment: The following comments relate to the cost of installing controls

AO2.6: Cost of installing new equipment, substitution of new/expansion/upgrade production capacity for existing capacity, fu el switching and process changes in the model are less expensive that th ey actually are. Other pote ntial industry’s responses are simulated as less expensive than they actua lly are. In most cases conversions and replacements cost 4 to 8 times more than EPA’s estimated costs per ton.

Response: Based on the new data received thru the ISIS peer-review, the costs equations of installing new kilns and substitu tion of kilns have been revised.

DB3.2 : Accounting of capital cost is not tran sparent...It is not apparent whether the variable cost in year 10 includes capita l cost for the newly built capacity (long run marginal cost). Perhaps the more appropriate question is whether annual fixed costs is included in the calculation of market prices. Is annual fixed cost considered a variable cost when the model is solv ing in annual time steps?

Response: The annual cost associated with mee ting the demand of cement includes the annualized capital costs associated with capacity growth (i.e., replacement units, expansion units, and new capacity. More inform ation on cost formulations is available in Chapter 2 of the final ISIS

TW : The desired changes by PCA require changes in the structure of the model, which PCA is unable to do. Some changes – specifi cally control cost information – are not doable due to incompatible structure of the model with that desired by PCA. Therefore, PCA tested the “directional” response of the model to changes in th e cost-function inputs. PCA believes that EPA has mis-specified the f unctional form of the cost equation as well as the values of the c onstants in the equation.

Response: The control cost equations are formulat ed as a generic equation to encompass all different components of co st relevant for various cont rol technologies. By choosing different values for various coefficients and parameters, user can choose a different specification of the cost function. User can copy the control equation from the ISIS_Inputs.xls workbook, and plug in vari ous cost components relevant for the particular control type, to arrive at the control cost.

TW : PCA is unable to change some inputs eith er they are not in cluded in the input sheets and are part of the model code (w ritten in GAMS which requires specialized knowledge to make any changes). Some inputs, while present in th e worksheet, were not linked to the model. Therefore, making any ch anges in the worksheet did not impact the results. One example of such input is mon itoring cost, which appear s in the input sheet, but any changes to the values do not affect model run and results.Version 1 20 EPA External Review Response: We have reorganized the information and moved all possible user-inputs to the ISIS_Inputs.xls workbook, so user can ea sily change these parameters and can conduct sensitivity analyses by varying various parameters.

TW : Control technologies: Revise the estimated cost and effectiveness of air pollution control technologies to reflect PCA’s September comments.

Response: Control costs have been revised based on the data received in the peer-review process coupled with data gathered by EP A’s engineers thru the Portland Cement NESHAP rulemaking.

TW : PCA did new runs with increased cost of controls and reduced control efficiency. This caused an increase in the cement price mo re than that predicted by the EPA run, and resulted in a higher dr op in consumption.

Response: The behavior found in PCA’s runs is consis tent with the model. If the cost of controls is increased, and efficiency re duced, the impacts are a higher drop in consumption and an increase in the prices.

TW : The model results are very se nsitive to the cost of controls and it is important to reflect and characterize thes e cost elements accurately.

Response: Control costs have been revised based on the data received in the peer-review process coupled with data gathered by EP A’s engineers thru the Portland Cement NESHAP rulemaking.

TW : Change some of the ancillary elements of control technology costs, including the electricity requirements and gyps um credit for wet scrubbers, the fuel requirement for RTOs, etc.

Response: Control costs and electricity costs are us er inputs and can be modified by the user. Various cost and credits have now b een included in the model based on the data gathered by EPA’s engineers thru th e Portland Cement NESHAP rulemaking.

TW : Changing the assumptions regarding disco unt rate and useful life for control measures, and Change the cost-per-ton at which kiln replacement can occur.

Response: These assumptions and costs are user i nputs and can be modified by the user. In the ISIS runs, EPA is using the discount rate and useful life for control measures determined by our engineers based on data gathered for the Portland Cement NESHAP rulemaking.Version 1 21 EPA External Review TW : The correct way to assess the impact of po licy would be to look at changes in the social welfare (consumer and producer surplus) which should be reported.

Response: Welfare loss under the policy case is now calculated and reported in the model output.

Comment: The following comments relate to emissions

HO2.17 : Table 2-8: low-NOx burners are great, ..., but lots of plants report...lower clinker output when using these burners.

Response: Low NOx burners control fuel and air mixing and -as a result- reduce peak flame temperature and form less NOx. The longer, less-intense flames resulting from lower temperature reduce thermal NOx form ation by about 30%. Low NOx burners may increase the amount of un-burnt carbon. By reducing excess air from 10 to 5%, NOx emissions can be reduced by approximately 15%. Reducing excess air levels results in indirect reduction of NOx emissions pe r amount of clinker produced (ACT 2008).

SB2.3 : Uncontrolled NO X emissions intensities va ry...by a factor of...Have you examined the sensitivity of results to these parameters ?...how much investment in monitoring infrastructure would kilns currently need to comply with either cap-and-trade or rate-based policies?

Response: NO x emission intensities have been revise d to reflect emission intensities for different kiln types. Monitoring and control cost is available for inclusion as a parameter, depending upon the policy being analyzed.

SKA.4 : Page 7, Table 4: The range value (1.9-3.1) for the wet kiln NOx emissions does not cover the average value (6.2).

Response: NO x emission intensity estimates have been revised.

DB1.5 : ...NOx “trimming”...The background techni cal memos discuss it somewhat, but it does not appear in the model documentation.

Response: NOx “trimming” meaning that small ch anges in combustion temperature are used to reduce the formation of heat rela ted NOx. For example, by reducing excess air from 10 to 5% (oxygen exhaust levels from 2 to 1%), NOx emissions can be reduced by approximately 15% (1994 ACT). Reducing excess air levels results in indirect reduction of NOx emissions per amount of clinker produced (ACT 2008).

SKA.8 : Page 16, Gas Treatment: Injection of bi osolids, a potential NOx control option, has not been addressed.Version 1 22 EPA External Review Response: Key NO x control options available in the model are listed in the Inputs_Controls sheet, and can be modified by the user, if appropriate cost and efficiency information is available.

HO5.1 : Figure 5-1 and the discussion of it lead to the conclusion that the NOx cap may be too restrictive....the emissions from a gi ven plant typically swing rather widely on a minute-by-minute basis....it has always been an issue as to how one is to determine the actual emissions volumes over a specified pe riod, as it could affect whether a plant exceeds its NOx cap or not. I think this report needs to discuss this. In contrast, Figure 5- 2 would seem to illustrate the relative ease in controlling SOx.

Response: Emissions are estimated on an annual basis.

HO5.2 : Figure 5-3:...it is confusing why th e emissions caps and banking are not shown...I do not know if the projections in Fi gure 5-3 have made allowance for clinker imports and changes therein. Gi ven a likelier harshe r reality re. plant closures, these projections may need to be redone.

Response: The clinker imports and their impact are included.

HO1.13 : The text in this ar ea ought to mention CO 2 . In fact, the whole section is awfully brief—but perhaps you could refer the reader to the discussion in the Appendix.

Response: Discussion of CO 2 emissions from cement kilns has been added ending with the referral to Appendix A fo r a more detailed discussion.

SB1.1: Lacking control technologies for CO 2 , ISIS Cement is not yet ready to address climate policy except as it relates to elec tricity and import prices....the richness of import-price-related modeling will be critic ally important to assessing the cement industry’s response to, for example, a price on carbon emissions. Both CO2 mitigation technology and product substitution in demand response are sine qua non for modeling the cement industry’s response to climate legislation.

Response: CO 2 mitigation technologies as well as product substitution have been included in the peer-reviewed version of the ISIS-cement model. The ISIS-cement model is ready to address c limate related modeling.

ES2.5: The model seems originally to be aimed at dealing with criteria air pollutants, to which now greenhouse gas emission calcula tions have been added. ... While NOx emission controls seem to be modeled very detailed, the measures to control CO 2 emissions are more limited. The “big ticket” it ems (i.e. major kiln re builds) are included, but especially many of the smaller measur es are missing ..... raw ma terial grinding and high-efficiency classifiers may improve en ergy efficiency. Moreover, measures like changes in fuel mix, addition of limestone to cement, and blending may be preferred measures to react to a cap and trade syst em. The discussion of electricity related measures is very limited and focuses on onl y two items, while leaving out an importantVersion 1 23 EPA External Review one: choice of grinding mill type. ....ISIS mo del would not work well for modeling the impacts of a GHG cap and trade system, unl ess the likely response measures of the cement industry are included in the model. A change in fuel mix ... is especially important to model... accurately to use the ISIS model for GHG assessments. Note that some of the alternative fuels may have “negat ive” costs for the kiln operator. Also, nontraditional biomass may be used (e.g. slaughterhouse wastes).

Response: Same as SB 1.1.

SK1.2 : Are there any ways to add CO 2 capture options?...incorporation of domestic and international (CO2) offsets are going to be really important to include.

Response: The peer-reviewed version of th e ISIS-cement model includes CO 2 mitigation technology and product substitution. The peer-reviewed version of the ISIS model also includes treatment of allowances (offsets).

SK2.6 : Page 2-7, Section 2.2.1.8: In the first paragra ph, it is stated that ISIS model uses emission intensities to estimate NOx, SO 2 , and CO 2 emission projections. However, from the model input file...; it is not obvio us if this has been done with the CO 2 emissions. It appears that CO 2 emissions for the year 2005 are calculated by multiplying the clinker yearly production rate with the same factor (0.45 for fuel) for all kilns. Also, the 2005 baseline figures for NOx and SO 2 emissions are estimated based on a constant multiplier for all kilns. Notes 10, 11, 12, and 14 are not provided, which may have explained the

reasons.

Response: In the ISIS-cement model CO2 emissions from calcination of raw materials is calculated based on emission factors. CO2 em issions from fuel are based on the chosen fuel type and fuel intensity factor.

SK2.7 : Page 2-9, Table 2-7: This table lists different CO 2 emission rates for PRB and bituminous coals...However, in the model input file, only one type of coal...is used. It is assumed that the model has the capab ility to use different coal types...

Response: Use of different fuel types require deta iled cost and emission characteristic of the fuels as input. Given this information, th e model can use any fuel user wishes to include.

HY2.4 : Page 2-10. Table 2-8 Are CO 2 reductions total CO 2 (process and fuel)? If so, express it to be total CO2. Are numbers absolu te #s or intensities? Can sewage impacts be included? PCA has given EPA a workbook with the fuel and power use for the cement industry. This workbook can be used to compare #s. The workbook is part of the WBCSD initiative. Hector will find out who at EPA PCA sent the workbook data.

Response: Numerical values of CO 2 emission reductions (intensiti es) are for fuel use, as tires produce slightly less CO 2 than coal. Sewage imp acts are not included in CO 2 emission reduction.Version 1 24 EPA External Review DBS2.11 : Page 2-10, Table 2-8....Is there no (Low NOx burners) penalty in a kiln?...there is no CO 2 penalty identified with the use of a Wet Scrubber, but later on Page 3-5, a penalty associat ed with “generation of CO 2 in a wet SO 2 scrubber” is described.

Response: Table 2-8 modified to reflect slight CO 2 increases for Low NOx burner and wet scrubber.

DB2.12 : Table 2-8 also seems inconsistent with the memo from Andover Technology Partners with respect to tires and the CO 2 implications.

Response: The following footnote was added to Table 2-8: “Tires are made of biomass, so there is an incremental CO 2 emission benefit. Because of the high heating value of tires compared to commonly used fuels, CO 2 emissions are lower. Tires produce slightly less CO 2 than coal.”

HO2.15 : Page 2-8. Your calcination emissions factor of 0.53 t CO 2 per ton of clinker is slightly too high. Instead of adopting this Andover T echnology default...you should use the IPCC default (which is what the EP A usues for the USA GHG Inventory....So use 0.51 or 0.52 (the latter includes a 2% addition for “lost” [not returned to kiln] CKD).

Response: Emission factor of 0.52 is now being used.

SK3.12 : ...SO 2 and NO x have lots of data on their emissions factors, but CO 2 does not. The emissions factors for CO 2 (rows 36-38) seem lower than those in EIA’s Table 6-1...

Response: To be addressed in the upda ted ISIS model/documentation.

HO5.9 : Basically OK. Perhaps you need to again mention here ( Summary ) why CO 2 emissions could drop under SO 2 -reduction strategies...

Response: Fuel switching and collateral emission benefits and energy efficiency options can result in CO 2 emissions

.

HO6.1 : The Summary seems to lack discussion of CO 2 —you need to at least mention it (and then you can say that your cost m odeling was confined to NOx and SOx.

Response: CO 2 emissions are included.

HOA.10 : Regarding the use of tires,...I am not sure that I agree that tires yield slightly less CO 2 than coal: perhaps on a dry weight basi s they yield slightly more. Depends on how you relate the emissions: per wei ght of fuel, per ton of clinker...

Response: Emissions are estimated on basis of energy intensity of the fuel.Version 1 25 EPA External Review HOA.17 : CemStar: I would add a bullet for the CO 2 reduction! (...) What you need to do is to compare the cost of making clinker w ithout using CemStar vs the cost to make clinker using CemStar. (...) The reduction in CO 2 emissions will be, on a per-ton-ofclinker basis, (...)

Response: Addressed in the updated ISIS model/documentation

HOA.18 : What this all means is that the statement on Page 36 that the CO 2 reduction (overall, on a per ton clinker or cement basis) will be direc tly proportional to the percent slag substitution is not quite safe—it will hold for the calcination part of the emissions, but may be different for the fuel side of the emissions.

Response: Fuel reduction and raw material substitution as a result of CEMstar implementation have been accounted for separately.

SKA.3 : Page 6, Total CO 2 : What is the assumption used in the model with regards to the cement kiln dust, which is also a source of CO 2 emission?

Response: Currently the model includes two mode s of CO emissions; calcinations related emissions and fuel consumption related CO emissions.

SKA.12 : Page 34, Process Modifications: It appe ars that the performance improvement options that may help reduce CO 2 emissions have not been fully developed. Options that may improve performance of raw material preparation systems and clinker grinding systems have not been mentioned at all. While conversions from one type of kiln to another, more efficient type have been covere d, they have not been included in the input file. Such conversions may not be viable at all sites. However, such limitations are not addressed. Use of high efficiency motors is included in the input file. However, such motors may already exist in some (or parts of some) newer kilns. It appears that a study to identify a reasonable level of performance improvement that would apply to most kilns on an average basis would be highly beneficial.

Response: An extended list of energy efficiency options are now included in the model.

DB2.3 : On Page 2-7, bottom paragraph, I had a hard time understanding the algorithm described.

Response: Description of SO 2 emissions has been expanded in the updated ISIS’ documentation.

HO1.12 : If you are going to quote the total weig ht of PM10 emissions, put it in the context of something—such as the weight of the total raw materials and fuels.

Response: Quarrying operations (the crushing and grinding of raw materials and clinker), along with kiln line operations result in particul ate matter (PM) emissions. The NEI estimates show that, in 2002, th e sector released 36,000 tons of PM 10 emissions.Version 1 26 EPA External Review HY1.4 : Page 1-9. Are the PM emissions (36,000 tons) emissions from the kiln or for the whole plant (including grinding)

Response: Quarrying operations (the crushing and grinding of raw materials and clinker), along with kiln line operations result in particul ate matter (PM) emissions. The NEI estimates show that, in 2002, th e sector released 36,000 tons of PM 10 emissions. Same as HO1.12

SK1.1: A model of the domestic industrial sector that encompasses a comprehensive approach to emission of pollu tants for all three media (air , water, and solids) would provide significantly greater valu e than a more limited focus.

Response: The ISIS peer-reviewed model includes multimedia impacts.

SK2.4 : Page 2-7; Section 2.2.1.8 Emissions: This section only discusses air emissions. A discussion of all substances emitted to all media would provide greater value.

Response: The model now calculates emissions of solid waste and water consumption associated with production of cement and use of controls.

ES2.6: The current estimates are based on direct emissions from kilns only. Note that a few plants operate other equipment. At least a plant in California and Florida also operate power generating equipment.

Response: Onsite power generation is not included.

SK4.1 : Is there any plan for showing the indirect effect on air emissions of changes in the electricity consumption caused by modificati ons required by policy decisions at cement plants?

Response: Electricity consumption by the plants is now estimated. User can use appropriate emission factors to estimate related offsite CO 2 emissions

.

DB5.1 : What constraints affect the choice of technologies for pollution reduction? Can only one technology be chosen? Can it be changed? This appears to be happening in Figure 5-4 on Page 5-4...the rise and fall of LNB+CEMStar. When this can happen, how are capital costs considered? Are opportunity costs properl y accounting for only marginal going forward costs (avoidable costs)?

Response: Control technology application al gorithms have been revised.

SK5.2 : Page 5-1; Section 5.1.1: The emissions cap and trade example provided is overly simplistic....all banked allowances are consumed by the end of the second phase of the rule...banking of allowances would most lik ely continue beyond the end of the forecast horizon being modeled.Version 1 27 EPA External Review Response: At this time, long-term steady state banked allowance information is not available, and can be included when available

.

Comment: The following comments all relate to Figure 1-7 (Architecture of ISIS)

DB1.6 : Page 1-11, Figure 1-7....emissions inputs are describing baseline or uncontrolled emissions at baseline levels of production. This should be explaine d in the context of Figure 1-7.

Response: Same as HO1.13. Figure 1-7 has been modified to reflect the comment.

HO1.14 : Figure 1-7: should you add (flowsheet box for emissions) HAP and PM?

Response: Same as DB1.6-

HY1.5 : Page 1-11

. Figure 1-7 – Emissions as inpu ts to the model are baseline uncontrolled emissions at base line levels of production.

Response: Revised figure is now Figure 2-2.

DB1.12 : Also, the “Production Technologies...” node could have more information in the box. Is technology choice a de cision made in this node?

Response: Figure 1-7 has been modified. Produ ction, Technologies and Emission Abatement Options addresses applicability, co st algorithms, and performance. These data are inputs in ISIS; the model determines which option is needed to meet the emission reduction requirement.

Comment: The following comment relates to nonattainment and PSD areas

DB1.13 : Page 1-9, bottom. Local constraints su ch as nonattainment areas and PSD areas are described, but there is no indication whet her these are represented in the model or have been ignored. Response: These local constraints such as NAA ar eas, class I Areas are not represented in ISIS at this time. Future version of ISIS will incorporat e these air quality considerations or constraints.

Comment: The following comment relates to transport-related emissions and emissions trading programs

DB2.1 : Are transport-related emissions included in the emissions calculations?...transport of fuel and material to the plant, and transport of cement to market....it wasn’t obvious to me whether a nd how transportation costs are included in the model. Are the costs of all materials and fuels the delivered costs?...it is not described how are costs of transporting product between regions accounted for...Version 1 28 EPA External Review Response: Currently transport related emissions ar e not included in the model. Subject, to availability of data, we will consider inclus ion of such emissions in a future version of the model. In the ISIS model transportation co st matrix allows commodity to be shipped from various production centers and import dist ricts to demand centers, and the cost is accounted for.

DB1.1 : A potentially valuable improvement in th e model capability would be to allow for emissions trading programs (or regulatory sta ndards) that have a location and/or time dimension ...They ( episodic controls ) could be implemented within an incentive-based (trading or tax) approach to policy...Could the model be used to simulate this type of regulatory issue by incorporating informati on about the cost of adjusting production schedules or fuels? Also, could the tradi ng program represented in the model have a regional dimension...?...Can this ( episodic operation of the facility ) be addressed in the narrative now and in a future version of the model?

Response: Capability to take into account episodic trading, controls and operation will be added to a future version of the ISIS Model.Version 1 29 EPA External Review 

### 8. Imports

Comments were received requesting enha ncement of imports granularity by adding terminal capacity and cost characteristics. Al so requesting the development of elasticity values for imports.

AO2.7: Test for additional years (other than 2004). How does the model do in matching import data for 2005, for 2006, for 2007 and for 2008? Do better alignment of Custom Districts from USGS with Regional Cement Markets. Add import terminals and ocean shipping. The latter far more important.

Response: Addressed in Chapter 4 of the fina l ISIS documentation dated August 2010. At the time of calibration, only data for 2005, 2006 and 2007 was available. In the future versions, the calibration can be done to incl ude latest available data. Regional markets have been replaced with th e concept of demand centers.

HO1.8 : Page 1-7: your disc ussion of imports is misleadi ng: imports can be by domestic producers seeking to feed markets where dom estic (particularly their own domestic) production is inadequate (or where they wish to capture market share) AND by independent importers who compete against domestic production in some markets, and help feed markets not easily serviced by domestic production.

Response: Text was modified to address the issue of domestic producers wishing to capture market share.

HO1.11 : Oddly enough, there is a lot of flexibility in how the industry views the cost of imports vs. cost of local production. A compa ny might continue to import—even at a loss or minimal profit—for at least a while if this is the only way they can keep their customers happy (i.e., not goi ng to another supplier)... ...the data (in Table 1-3) are for imports of “hydraulic cement and clinker”—meaning, all types of hydraulic cement, and all types of hydraulic clinker--so they are not just Portland cement imports. This (cement and clinker) issu e applies, I think, to all import data that you use in this report and represents a fairly serious problem for your model.

Response: The peer-reviewed version of the ISIS model incorporates a revised methodology for modeling imports.

HY1.2 : Page 1-7. Figure 1-4 Add import’s cust om districts to the map

Response: Map has been updated to adequately dis cern localities with more than one plant in a cluster and import cust om districts have been added.

FF2.2: Treatment of imports - California cement facilities will be part of a cap and trade system to reduce greenhouse gas (GHG) emissi ons. Details of the cap and trade system are under regulatory development. In additi on, ARB staff is considering the development of a regulation to establish blending re quirements of supplementary cementitousVersion 1 30 EPA External Review materials at concrete batch plants. The treatment of imported cements will be considered as part of that effort.

Response: The ISIS model has the capability of analyzing cap-and-trade policies for CO2. The peer-reviewed version of the model incorporates CO 2 mitigation measures including blending cement.

SK2.2 : The model seems to rely heav ily on imports...to meet emissions requirements....is there a cost curve that increases as more imported cement is demanded?...( how were ) import levels in future years ( estimated )? Does ( model ) proportion future imports the same way they were divided in 2005?

Response: Model proportions future imports ba sed 2005 import levels. However, these proportions can be modified by the modeler based on additional information, if available.

DB3.4 : ...on Page 3-3...imports are constrained to historic experience. However, many are concerned that under strict domestic climate policy there will be infusions of imports from countries that have not take on climate -related obligations...At l east; this constraint should be tagged...

Response: The imports formulation in the ISIS model has been revised by adding import elasticity based on historical data.

HO3.2 : Again, the import data that you are us ing appear to be for both cement and clinker. The factor imports (t,r) is likely prone to large error because we simply do not know how much cement coming into a certain Customs District is consumed in that district.....

Response: Imports methodology has been revised. See the model documentation for additional information. Imports arrive at an impo rt district and no assumption is made regarding their consumption in that import district. A transportation matrix allows for the imported commodity to be transported to various demand centers.

HO3.6 : Page 3-3: the price data for imports (Equation 3.2.5.) are very weak. You only have import values reported on a CIF basi s (USGS monthly and annual reports) and Customs Value (USGS annual reports). The CIF value does not represen t the actual price because it has not yet been offloaded from the sh ip: that is, the true price is CIF + various port charges + markup....High import ratios exist for Florida, Los Angeles, San Francisco, San Antonio—yet the Table ( 4.1 ) designates (**) only Fl orida and Seattle as having high ratios....The PCA may have predicte d higher import ratios in the future, but they certainly have fallen in 2007-09 a nd likely will stay down in 2010 as well....

Response: Port handling costs have been added to the CIF values.

TW : Revise the approach for simulating imports in order to obtain results that better match actual experience.Version 1 31 EPA External Review Response: Imports methodology has been revised to account historical data by using the elasticity of imports.

TW : Drop in demand results in drop in impor ts and production. PCA believes that the drop in imports will be more pronounced th an reflected in the model results, since imports are a “swing” supply.

Response: The imports formulation and calibration of the model has been revised to account for historical behavior on imports.

TW : The structure of the imports is such that th e user cannot change it. It is modeled as residual supply – and a function of price-ra tio. We used this parameter to test the sensitivity of imports to change in the prices.

We conclude from these new runs that the NESHAP version of IS IS-Cement behaves in the right direction regarding imports. We believe, though, that ISIS needs a fundamentally different approach to simulati ng imports than what exists in the NESHAP version of the model.

Response: The structure of the imports modelin g in ISIS has been revised.

TW : Change the manner in which imports are simulated, including the prices at which they can enter various domestic markets, the markets which they can enter, the constraints on increasing import quan tities from year to year, etc.

Response: Import formulation has been revised based on historical data by USGS import district.Version 1 32 EPA External Review 

### 9. Calibration

AO2.8: Use data set that span for many years for calibration and va lidation (“entire business cycle”) instead of one year. Calibrate for three or four years beginning around 2000 and then validate across pe riod that includes good years, mediocre year, and bad
year.

“The calibration constant” will be eliminated if the AVC function is updated to include the missing elements (SG&A, cost of shipping within a region and to terminals, property taxes, insurance). Adopt a long-run perspectiv e rather that a short- run perspective when modeling cement prices over the long-run. Es timate all the compone nts of costs that producers will seek to cover in the long-run in stead of using the “calibration factor” of 1.9.

Implement process for judging the degree of uncertainty and explicitly portray the level of uncertainty.

Perform sensitivity analysis on key assumpti ons and design choices for the model to show their impact on model resu lts (use ranges in the outpu ts to reflect uncertainty).

Response: The model has been re-calibrated. Calibration methodology and findings can be found in the final documentation of ISIS da ted August 2010. ISIS can also be used in sensitivity analyses. The user has the abil ity to change various input parameters and explore implications of input pa rameters in the model results.

DB4.2 : Page 4-4, middle...Why is a single calibrati on constant used to adjust variable cost of production?

Response: At the time of the first version of th e model the data for all the components that are part of the cost of producing cemen t were not available. We have updated the cost of producing cement to account for the missing factors in the enhanced version of the model.

SB4.1: Estimating and predicting based on a range of years rather than just one for each would improve the calibration procedure. What makes ±10% a reasonable range for predicting values one year later...?

Response: The number of years for calibration ha s been increased based on available data. New calibration methodology and results can be found in the final documentation of ISIS dated August 2010.Version 1 33 EPA External Review 

### 10. Miscellaneous

DB1.2 : An important dimension of environmenta l impact of a facility is its stack characteristics. These are not mentioned anywhere. What is the stack height?

Response: Stack parameters are needed for disp ersion modeling. The ISIS model does not incorporate dispersion modeling. Stack parameters for cement facilities can be found in the National Emission Inventory (NEI).

DB1.9 : Some short ( CEMStar ) summary should be includ ed in the ISIS model documentation.

Response: Brief text on CEMStar and a reference were added.

HO5.3 : One important issue with CemStar is that steel slag may not be available locally.

Response: This issue has been addressed by making CemStar applicable for the kilns that have steel production facility within a reasonable distance.

HO5.4 : Figure 5-4...why there is such a huge drop in LNB + TDF in 2011 wrt. 2010. Are the subsequent (2011--) years in addition to the 2010 levels? (i.e., are they just shown as margins?). Was there a scale change post-2010?

Response: To be addressed in the upda ted ISIS model/documentation.

HO5.6 : Figure 5-5:...why the red dot has been displaced rightwards...and what happened to the red dot for 2025?...I do not s ee much difference between the base case and the policy levels.

Response: To be addressed in the upda ted ISIS model/documentation.

HO5.7 : Figure 5-6: Unless I am missing somethi ng, the blue dots are not needed at all— the sum is always the top of a stacked bar graph....I do not understand how you have determined the (foreign) imports into the various markets...So this figure needs some work....

Response: Addressed in the updated ISIS model/documentation. The import methodology has been revised. Imports are no longer a function of market, but of import district.

HOA.6 : Page 15: perhaps this is a semantic poi nt, but Dragon Cement’s switch to dry technology involved shortening the existing k iln tube, not by bri nging in a new tube.

Response: Addressed in the updated ISIS model/documentation.Version 1 34 EPA External Review HOA.7 : While it is true that there is regiona l variation in the sulfur content of “limestone” (and the sulfur in “limestone” is from 3 main sources: su lfide minerals such as pyrite; kerogens; and gypsum/anhydrite.), th ere is as much, or greater variation from the S-contribution from the coals: there is a major difference in the S-content of coals (S in coal is from kerogens and su lfides like pyrite and marcasite).

Response: Addressed in the updated ISIS model/documentation.

HOA.8 : The sulfur content of the “limestone ...would not normally be a criteria for selection of a new quarry.

Response: Addressed in the updated ISIS model/documentation.

HOA.28 : You offer the notion that the USGS has/will map out suitable limestone deposits. Unlikely. Cement companies have their own staff of geologists, or will hire a consulting geologist, for this purpose (they may use USGS an d/or State geological maps to broadly identify where the limestone may be found).

Response: Addressed in the updated ISIS model/documentation.

HOA.13 : Page 26: LWS scrubber: basically fine. Here and elsewhere, use calcite, or calcium carbonate, instead of limestone. Limestone is a rock, only very rarely is it pure calcium carbonate.

Response: Addressed in the updated ISIS model/documentation.

HOA.14 : Page E-31: minor issue: gypsum is used to make finished cement, not finished concrete. Another issue: CaSO 4 is the formula for the mineral anhydrite. Gypsum has the formula: CaSO 4 ·2H 2 O.

Response: Addressed in the updated ISIS model/documentation.

HOA.21 : Page 38: states: “Water consumption w ould also drop. Moisture added to the materials of a wet kiln amount to about 0.75 to ns per short ton of clinker.” This is too high (too much water). Wet and dry kilns both need about 1.6-1.7 tons of raw materials per ton of clinker. Typical water content of wet kiln slurries is 35-40% by weight. So a 35-40% water slurry containing 1.6-1.7 t of raw materials (destined to make 1 ton of clinker) would contain 0.56 – 0.68 t water.

But, of course, water is also used in cement plants as a motor coolant and in the finish mill (as a spray) to make sure the calcium sulfate in the cement remains (mostly) gypsum and does not dehydrate to pl aster or to anhydrite.

Response: Addressed in the updated ISIS model/documentation.Version 1 35 EPA External Review HOA.9 : Given the small number of kilns in most of the markets, I am not sure that one ought to read too much in the mean vs. me dian emissions data in Tables 8 and 9.

Response: Addressed in the updated ISIS model/documentation.

HOA.11 : I agree that tires are not fossil fuel, pe r se, but most involve a considerable input of the products of the petroche mical industry in their manufacture.

Response: Addressed in the updated ISIS model/documentation.

HOA.12 : Page 13: Preheaters and pr ecalciners are described as “post-kiln” combustion technologies....but most people would...call preheaters and precalciners: “pre-kiln” technologies (that’s why “pre-“ is in their name!). I think mo st calciners are designed to burn about 60% of the (total kiln line) fuel , not 50%; though perhap s the oft’ quoted 60% includes riser duct firing.

Response: Addressed in the updated ISIS model/documentation.

HOA.22 : Page 42: The question is asked why ha ven’t all the existing preheater kilns been converted to preheater-precalciner technol ogy? There are at leas t 3 answers to this: 1) if the preheater kiln is small (capacity), the conversion boost in capacity might be insufficient to justify the cost;

2) significant projects like this typically require a litmus test of the plant still having at least 50 years of lim estone reserves.
3) significant projects like this likely will put the plant into a stricter regulatory framework re. emissions (the existing plant may have grandfathered in).

Response: Addressed in the updated ISIS model/documentation.

HOA.15 : Page 30: CKD is described as being made up of oxides of Ca, K, and Mg. You’ve left out Si!

Response: Addressed in the updated ISIS model/documentation.

HOA.16 : Page 30 Paragraph 2 states that CKD disposal is not an issue with drytechnology kilns. This is not true. Any kiln technology can burn CKD and any can have a problem burning CKD (...) You can certainly burn CKD in a wet kiln: the slurry viscosity can be an issue, but you can also add a bit more water. CKD was burned at Dragon’s wet kiln, for example, but only in re latively modest amounts (they did not burn all of it).

Response: Addressed in the updated ISIS model/documentation.Version 1 36 EPA External Review HOA.23 : General comment: One significant omission from this study—unless I missed it somewhere (it is a long report!) are certain upgrades to capacity that simply involve debottlenecking. (...)

Response: Addressed in the updated ISIS model/documentation.

HOA.26 : Item #2 (reserves). 100 years for a greenfie lds project is about right. To this I would again note that major capex projects n eed c. 50 years of remaining reserves or more (some companies may be satisfied with 35 years).

Response: Addressed in the updated ISIS model/documentation.

HOA.27 : Item #3 labor: I do not thi nk labor is important...(...)

Response: Addressed in the updated ISIS model/documentation.

HOA.29 : Page 46: Decision tree: add: consider ad ding a terminal (ins tead of a plant); consider swap arrangements (to supply cem ent to customers outside your own plant’s “range”.)

Response: Addressed in the updated ISIS model/documentation.

Comment: The following comments pertain to defini tions or requests for clarification in the text

DB3.7 : Page 3-5. What is “priceration” in Equation 3.2.5?

Response: Addressed in the updated ISIS model/documentation.

DB3.8 : Page 3-5, middle. Energydisl...and polbases ubfuel...Are these the same thing? If not, what is the difference?

Response: To be addressed in the upda ted ISIS model/documentation.

DB3.9 : Equation 3.3.2. What is “cp”?

Response: To be addressed in the upda ted ISIS model/documentation.

DB3.10 : Page 3-7....Can some narrative be added? Why is the sum in Equation 3.3.9 divided by the number of pollutants? What are units throughout these equations?

Response: To be addressed in the upda ted ISIS model/documentation.

DB3.11 : Page 3-9....“catconsumptcost”...is not defined as a price times a quantity. However, see Equation 3.4.11a...cost is defined as a price times a quantity.Version 1 37 EPA External Review Response: To be addressed in the upda ted ISIS model/documentation.

Comment: The following comments pertain to questions encountered with model parameters or the spreadsheet during test runs.

SK3.1 : Chapter 3, Can the model accommodate different safety valves built into a policy framework. Can a case for cap-and-trade with a limit on allowance price (with other required caveats) be run?

Response: To be addressed in the upda ted ISIS model/documentation.

SK3.9 : In the CO 2 case I ran...ISIS expanded capacity...and built...new capacity and seemed to keep all existing capacity,...why w ould the model build new capacity and not use it if it is not retiri ng older dirtier capacity?

Response: To be addressed in the upda ted ISIS model/documentation.

SK3.11 : Would be helpful to be able to adjust diesel prices – using AEO2005 may not be the most reliable data.

Response: To be addressed in the upda ted ISIS model/documentation.

SK3.13 : For the % reduction policy options inputs (cells F5:H22), what if the % reduction compared to? Is it a year-by-year?...

Response: To be addressed in the upda ted ISIS model/documentation.

SK3.14 : Another useful input would be to be ab le to manually ente r an emissions cap...

Response: To be addressed in the upda ted ISIS model/documentation.

SK3.15 : K5:M5: don’t understand “m iner” in these cells.

Response: This parameter is added to be able to specify a minimum emission reduction requirement by each production unit.

DB4.3 : Page 4-4, top. “the marginal <?> of the regional...”

Response: To be addressed in the upda ted ISIS model/documentation.

SB4.4 : Short of process changes like CEMStar, are marginal changes in feedstocks available to kilns in the real world? How much difference does this make?Version 1 38 EPA External Review Response: To be addressed in the upda ted ISIS model/documentation.

SK5.1 : An ability to store multiple scenario cases would be good.

Response: To be addressed in the upda ted ISIS model/documentation. TW : On the output side, the model does not re port loss in consumer and producer surplus – which is a very important quant ity - resulting from implementing a policy. Also, reporting of control cost is missing.

Response: Model output is now reformulated to calculate and report consumer and producer surplus.

TW : We have had a series of difficulties in work ing with ISIS in an attempt to modify the model and generate results to better estimate what the economic impacts from the proposed NESHAP would be. These difficulties have involved the model’s structure, the set of inputs that the user can change, the se t of outputs that the us er receives, and the mechanics of actually changing and running the model. These difficulties are sufficient to convince us that the model at this point cannot be used to generate reliable estimates for the impacts of the proposed NESHAP. The m odel needs substantial improvements (many of which EPA has under way) before we can ha ve any confidence in the accuracy of the results it generates.

Response: Based on peer-review by industry and others, the model has been reconfigured to allow users to ch ange various inpu ts with ease.

TW : Tracking and understanding of impacts in IS IS with regard to new capacity would be greatly facilitated for the user if the IS IS outputs included a kiln-by-kiln display of when the model simulates the addition of new capacity and when it simulates the closure of existing capacity.

Response: Kiln-by-kiln output of various quantities of interest is now reported in the model output.

TW : The cells to change cost of new and repl acement kilns in the model are not working correctly.

Response: Addressed in the new version of the model.

TW : The change in total cost for obtaining the amount of cement consumed in the U.S. is inappropriate as the basic measure of the cost of the environmental requirement being considered. ISIS should generate a more meani ngful measure that refl ects both the cost of obtaining cement and the value of the cement that is obtained. EPA should reconfigure ISIS so that it generates an output measure th at directly parallels the total social cost measure that OAQPS has traditionally estimated in its RIAs for potential regulations. TheVersion 1 39 EPA External Review producer surplus calculated in the model should include only the surplus realized by domestic cement producers. Any increased surplus generated by foreign cement producers via an increase in U.S. cement imports should not count as a benefit to the U.S. The model is not set up to generate the output quantity the user would most want to see – the net change in domestic producer and c onsumer surplus resulting from the proposed environmental requirement. Instead, the model generates as a featured output measure the total cost of obtaining all the cement consumed in the U.S. This is not a meaningful quantity with which to judge the impact of the proposed NESHAP or any other potential environmental policy.

Response: Welfare loss under the policy case is now calculated and reported in the model output.

TW : The model needs extensive clean-up and de-bugging before it will run smoothly as intended. Better documentation is also needed.

Response: Model and documentation have been revised.

TW : Some cells in the input sheets are “dead” in the sense that they do not affect model output.

Response: This has been corrected.

TW : Input data should be easil y modifiable by the user.

Response: User can now change input data and assumptions.

TW : Model user should not need to search th rough numerous spreadsheets and perform further calculations in order to obtain the key ISIS outputs. The various tables and cells containing model outputs should be clearly labeled, as we descri be more fully later in this

memo.
Response: Key ISIS outputs are reported in “Aggregate Results” worksheet in ISIS_Outputs.xls workbook, and de tailed results are reported in relevant spreadsheets.

TW : Several aspects of the ISIS input and output spreadsheets should be clarified. It is not clear in many of the ISIS spreadsheets ex actly what quantities are being referred to. Labels for each table would be helpful, indi cating things such as: whether the quantity is in short tons or metric tons; clinker or cement; and before or after addition of gypsum, limestone or other materials; in nominal dollars or real dollars (if in real dollars, indicate the base year); an annual quant ity or a present discounted valu e of a future stream; a cost that actually occurs in the y ear in question or an annualized cost chargeable in that year; and in hundreds, thousands, millions, or other units.Version 1 40 EPA External Review Response: ISIS Inputs and Output sh eets have been revised.Version 1 41 EPA External Review 

### 11. Data Sources

Comment: The following comments pertain to questions about the specific data sources being used.

HO2.1 : In many places, you have used PCA data instead of the USGS data....Any Statelevel (and USA overall) consumption or pr oduction data published by the PCA are taken from USGS data and you should cite th e USGS for such data, not the PCA.

Response: Generally, in this document USGS sources are used for historical data and PCA sources are used for projections.

HO2.3 : The USGS, not the PCA, should be cited as the source of the value data and the consumption numbers. For 2005, Portland cement sales to final customers (these are the data that the PCA calls “consumption” were worth $11 billion; masonry cement sales $0.68 billion; total about $12 billion.

Response: This is comment to Chapter 1. Intr oduction. Numbers were changed to address $12 billion market.

HO2.4 : Cite the USGS also for the statement that the top 10 cement companies had 80% of the production; ditto for all production by State data.

Response: Moved to Editorial Comments (HOED2.1).

HO2.5 : References:...USGS Mineral Commodities Summaries cited—these are “quick & dirty” summaries that are meant to be just that. We prefer, where possible, that data be obtained from the Minerals Yearbook, as this is a MUCH more au thoritative publication.

Response: Minerals Yearbook data was used Re ference corrected to USGS, 2007a.

ES2.3: Also, note that there is a difference between USGS and PCA data which is difficult to explain (see the Energy Star Gu ide), which brings in another source of uncertainty (which is now hidden due to the use of PCA data/assumptions only).

Response: We recognize this uncertainty due to discrepancy in the data-sources. Users can conduct sensitivity analysis to test implications of various assumptions and differences in the data sources used.

ES2.4 : It is interesting to see that EIA data is used for the fuel mix (Fig. 2-2), while the USGS has a much detailed overview of fuel used on an annual basis. A calibration to USGS data would be essential .... would be a better basis for emission estimates.

Response: The ISIS-cement model uses USGS fuel mix information. The model documentation has been updated to refl ect USGS data for fuel usage.Version 1 42 EPA External Review SB2.7: Are PCA estimates...corroborated by independent sources?...is the PCA still projecting 27 new kilns through 2010?

Response : Addressed in the updated ISIS model/documentation.

HO1.5 : I would note that the USGS Minerals Yearbook chapters on cement (MYB) also allow the average capacity to be discer ned: it would calculate as 0.577 Mt/yr (million metric tons per year) in 2005 (MYB, Table 5).

Response: Reviewer is correct, average kiln capacity could be inferred from data presented in Table 5 of th e USGS Mineral Yearbook 2005.

HOB.1 : Page 3-4: I would note that the USGS data pertaining to prices for crushed stone ($/t) are for sales by crushed stone quarries. Thus, they would be broadly applicable to prices for some of the raw materials that are purchased by the cement company, but likely exceed what a cement company “pays” for its own limestone. I would think that the basic limestone cost (quarry, primary crushing, convey to raw mill) would be typically ≤ $5/st.

Response: Addressed in the updated ISIS model/documentation.

DB1.10 : What is the plan for updating the data inputs and technology characteristics?...For example, Page 1-7, top...

Response: Data inputs and technology characteristics will be updated as data becomes available per EPA’s sector strategy and co rrespondent regulatory actions for each industrial sector.

Comment : The following comments pertain to qu estions about the di fferent types of cement and kilns.

HO1.1 : Section 1.1. Introduce this with portland and blended cement (both are used in concrete), and then state that you will focus on portland. But be careful: the USGS monthly data (1/98 onwards) split out the two (Table 2a vs. 2b) and they should be added together for discussions of consumption. I do not know if the PCA, in using our data, has combined these in all cases. The USGS a nnual data includes ble nded cement within the portland umbrella. The annual report (Mineral Yearbook) shows the combined monthly sales (consumption) data in Table 9.

The model uses consumption data from PCA not from USGS. Ed Sullivan, chief economist of PCA told EPA (oral communication) that their consumption data is for Portland cement only, it doesn’t include blended cement.

Response: Text in Section 1.1.1 has been revise d. Mineral Yearbook data for cement production are for Portland cement (including bl ended cement) and masonry cement (for example 99,319,000 metric tons for 2005 shown in Table 1).Version 1 43 EPA External Review SB1.2: How substitutable are the different vari eties of Portland cement with each other and with masonry cement? How well do pri ces and demands for different varieties correlate? What about other substitutes for cement?

Response: Gray Portland cement is the most comm only supplied type of cement because of the wide variety of applications to whic h it can be put. Gray cement held a 77% share of US consumption in 2005 (from Regulator y Impact Analysis for proposed Portland cement NESHAP, 2009). Sulfate resisting or what is known as Title V cement is the second largest concrete type at 15% of consumption in 2005.

HO1.2 :... it is NOT acceptable to use “dry” to mean (only) a dry kiln lacking a preheater and/or preheater/precalciner. You should call this type of kiln a “long dry” kiln. The 3 general categories for dry kilns are: 1) long dry kiln; 2) preheater kiln; 3) preheater/precalciner kiln (which, after spelling out the first time, you may shorten to precalciner kiln). Para 1 below Figure 1- 3: Here’s where you are tripping over your misuse of the term “Dry” kiln. First, our Table 5 did show 16.3% from wet plants, but this is NOT quite the same thing as saying that wet kilns did 16.3% of the clinker. This is because there is a separate line for the rela tively small output from “combination” plants (those that operate both wet and dry kilns). Likewise, the 11.8 Mt of clinker form Dry plants understates the output of the dry kilns because a coupl e of these are in the line for combination plants. And you misstate the 11.8 Mt “Dry” as being from dry kilns lacking a preheater or precalciner.

Response: Information in the text has been revi sed to state that 2005 clinker production was as follows: 13.5% from wet kilns, 81% from dry kilns (3 types) and 5.5% from plant operating wet and dry kilns. Data base on USGS Mineral Yearbook 2005, Table 7.

HO2.6 : Section 2.2.1.1: The USGS has a kiln count of 183, not 181 (=PCA number?)— but the difference is likely kilns active during year (183) vs still ac tive at yearend (181).

Response: We use PCA kiln count. The difference (183 vs. 181) is due to the difference of “active” kiln definition.

Comment: The following comments pertain to questions about table and figures

.

HO1.4 : Table 1-1: The data are typical averages , but are not universal. The reader may think these are fixed data. The EPA source site d likely used either USGS or PCA data.

Response: Table 1-1 title changed to: Typical Aver age Heat Input by Cement Kiln Type Commenter is correct, data in Table 1-1 ar e typical averages. Data in Table 1-1 comes from Table 3-3 in the 2007 ACT. In tu rn, Table 3-3 in the ACT quotes EPA’s 2000 document below:

U.S. Environmental Protection Agency. NOx Control Technologies for the Cement Industry . Prepared by EC/R Incorporated. September 19, 2000. Available atVersion 1 44 EPA External Review www.epa.gov/ttn/naaqs/ozone/ozonetech/cement_nox_update_09152000.pdf.

http://epa.gov/ttn/catc/dir1/cement_updt_1107.pdf

http://www.epa.gov/ttn/naaqs/ozon e/ozonetech/cem ent_nox_update_09152000.pdf

SK1.3 : Page 1-5, Table 1-1: Use of two sets of numbers for kiln specific heat input (or energy intensity) in this ta ble and Table 2-4 is confus ing. An explanation of the difference would be helpful.

Response: Table 2-4 has been modified and is now consistent with Table 1-1.

HO2.9 : Table 2-3: put the data back into mt. Da ta include clinker. These are U.S. Census trade data (the USGS just compile s them into convenient tables).

Response: Data in Table 2-3 is now in metric tons.

HO2.14 : Table 2-4: shouldn’t these data match those of Table 1-1 (or vice versa)?...the PCA data on unit consumption of heat...gene rally are based on an “equivalent ton” which is a bit of an apples & oranges mix.... You can recalculate to a per-ton-of-clinker basis, but I do not know if you have done this in Table 2-4. Also, I don’t recall the PCA’s Labor & Energy surveys mentioning gas flows—so it is unclear if the NM 3 /kg data are from the PCA or some other source....you should cite the actual page(s) from which the data are taken.

Response: Table 2-4 has been revised to match data in Table 1-1.

HY2.2 : Page 2-7. Table 2-5 Update with the most recent ACT data (2008 ACT) and show data in lb/ton clinker

Response: Table 2-5 was updated.

DB2.7 : Page 2-7, Table 2-5. Expl ain sources in this table and why they diverge.

Response: Table 2-5 was modified and numbers ar e given in terms of lb NOx/ton of clinker and in terms of lb NOx/MMBtu. The source is 2007 ACT document.

HO4.2 : Table 4-1: why 2004 data and not 2005? Ag ain, switch to metric tons! What is the basis of the production data in Table 4-1?....

Response: A ddressed in the updated ISIS model/documentation.

HO4.3 : Table 4-2: where are you getting your price data; and why are you showing 2003-2004 and not 2005? Need to cite a data source.

Response: Addressed in the updated ISIS model/documentation.Version 1 45 EPA External Review HO4.4 : Table 4-3: The emissions shown are too precise.

Response: Addressed in the updated ISIS model/documentation.

HOA.1 : Table 1: the exit flow gas rates would s how a lot of variation; not sure how “good” an average these rates are. By the wa y, Bhatty et al. (PCA 2004) cited is a huge volume, so a page reference would have been helpful.

Response: Addressed in the updated ISIS model/documentation.

SKA.1 : Page 1, Table 1: It needs to be clarif ied if the specific fuel consumption data provided includes only kiln fuel or non-kiln fuel sources, such as raw material dryers, auxiliary boilers, etc.....

Response: Addressed in the updated ISIS model/documentation.

HOA.2 : Table 2: although the heat values shown ar e reasonable, it should be pointed out that, in reality, most fuel categories have a range of heat values,...I would also comment that the values in Table 2 ar e expressed with more precisi on than is warranted....Table 2 appears to be at the upper end of the range.

Response: Addressed in the updated ISIS model/documentation.

SKA.11 : Page 33, Table 9: Suggest that you reso lve the following concerns that have been taken from other analyses on kilns, especially the above BART-related document:

• SCR and SNCR may not work on all wet and long dry kilns.

• Dry lime injection may not work on all wet and long dry kilns.

• In many cases only a tail-end type SCR syst em, with requirement for reheating of flue gas, may be required. This may result not only in additional costs, but also additional air emissions, if fuel is used for the reheating.

• Spray dryer technology may also be used for kilns other than wet type kiln. Depending on site specific factors, th is may be a more viable option.

Response: ISIS_Inputs workbook now contains a matrix of incompatible controls that prevents installation of incompatible controls . Further, the controls sheet specifies which controls are applicable to what kiln type (long-dry, wet, preheater, or precalciner). Availability of any control in a given year is also factored in.

HO1.7 : Figure 1-4: this plant location map is in complete because it fails to adequately discern localities with more th an one plant in a cluster. For example: WA has 3 plants (2 integrated plants @ Seattle and a grinding pl ant at Bellingham); CA has 11 plants; FL has 6 plants, etc...

Response: Map shows just integrated facilities.Version 1 46 EPA External Review Map will be updated to adequately discern localities with more than one plant in a cluster HO1.9 : Figure 1-5 needs a citation for the data source (USGS I presume). The curve for import share is misleading and represents a problem with the USGS data: we compare the level of cement imports to that of total cons umption (apparent consumption calculated as cement production + cement imports – cement e xports – change in cement stocks). We exclude the imports of clinker because the pr oduction of cement includes cement made in the USA from imported clinker. But we do not include changes in c linker stockpiles (but should) mainly because we did not collect such data prior to a few years ago. Done “properly” the import reliance would be 1-2% higher than what we s how currently in the data series.

Response: Figure 1-5 has been modified in that the “Import Share” curve has been removed. Data points for 2004 and 2005 agree with the ones on USGS Mineral Yearbook but there is no older data in the Yearbook.

HOA.5 : Figures 5a-5c: Author is being “kind” in his charact erization of the data: the correlations would appear to be not statistically significant....

Response: Addressed in the updated ISIS model/documentation.

HOB.5 : Table 10: a few comments (reflecting newer info):

• The Dixon, IL and Charlevoix, MI plants are now owned by St. Marys Cement.

• Hanson plant, CA is now owned by Lehigh SW

• RMC plant in CA is now owned by CEMEX

• Lehigh Allentown, PA plant is ac tually their Evansville plant

• Holcim (US) now controls all St. Lawrence facilities in the USA.

• Essroc’s Nazareth III plant in PA shut its kilns in 2005 (only kilns are at Naz. I)

• Lafarge’s Atlanta, GA plant shut its kiln in 2004; now only grinds some clinker to make masonry cement, regrinds Type I into Type III Portland cement, and grinds granulated slag.

• Dragon Cement was a dry plant in 2005 (the wet to dry conversion was completed 4/04).

Response: Addressed in the updated ISIS mode l/documentation and ISIS_inputs.xls workbook.

Comment: Miscellaneous comments were received regarding data accuracy.

ES2.1 : The model models each individual cement plant, without plant specific data but on the basis of a number of assumptions. Th e key assumptions are: Energy intensity ofVersion 1 47 EPA External Review the kiln (now based on type of kiln), Fuel mix (industry average), Capacity utilization factor. How are these assumptions used to estimate plant-specific energy use and emissions? Especially energy intensity and fuel mix can still vary within a relatively large range. .... I also think that thes e uncertainties are larger than the impacts of some of the control technologies modeled. Given these key uncertainties, I think that a thorough uncertainty analysis is essential. Moreover, it is questionable if the results should be presented on the level of individual plants. The results may be less uncertain on a regional level

Response: In developing the ISIS-cement model we have used the best data publicly available to characterize the cement industry. We are aware that without plant specific data as the basis of the model the level of uncertainty increases. For this reason we calibrate the results of the model to known hi storical levels. At this time, PCA is reviewing the ISIS’ model, we expect to receive comments on these assumptions.

ES2.2: The average energy intensity values gi ven in Table 2-4 are for good operation practices and do not seem to re flect the industry practice. .... How representative are these figures and it would be good to validate the assumptions using statistical data.

Response: At this time not enough data is availa ble to validate these assumptions using statistical methods.

HO2.2 : Somewhere, you should discuss the issue of data accuracy. In many places, data ought to be rounded. Few data reported are good to better than +/- 1%.

Response: Data was taken from different sources. Some data were measured while some were calculated.

HO3.1 : Page 3-2: the factor: produ_r(t,r)/0.92 has a clinker ratio (clinker/portland) of 0.92. This is too low for a good average. Even including the blended cements and masonry cements, the USGS data show the ratio to be right on at 0.946-0.950.... Anyway, 0.92 implies 8% gypsum and/or other additions, and this is too high right now. We can approach a 92% clinker factor hypothe tically if we assumed that we had a 95% market share for true Portland cement @ 95% clinker + 5% gypsum, and 5% market share of masonry cement averaging 50% clinke r (this is probably a bit low): (0.95 x 0.95) + (0.05 x 0.5) = 0.9275.

Response: Addressed in the updated ISIS model/documentation.

HOA.3 :... I do not like the default...(0.53 t CO 2 / t clinker) because it is based on an average CaO content of clinker of 67.6%. This is too high—...EPA in the USA GHG emissions inventory uses the IPCC default (w / CKD) of 0.52 and I would suggest that the ISIS model do the same.

Response: Addressed in the updated ISIS model/documentation.Version 1 48 EPA External Review HOA.4 : Page 8:...I agree with Staudt that the NO x data show a lot of scatter. I would note that his observation that the larger kilns are more effi cient (lower NOx) is perhaps not supported by his graph—yes it shows this, but there are only 3 data points for the kilns > 1 Mt/yr capacity. Is this statistically valid?

Response: Addressed in the updated ISIS model/documentation.

SKA.2 : Page 4, Second Paragraph: The figures quoted in this paragraph are based on pure limestone. It’s not clear if all of the figures in this paragraph, e.g., for the steelmaking slag, are based on pure limestone.

Response: Addressed in the updated ISIS model/documentation.

SKA.9 : Page 26, Limestone Variable Costs: The cost of $15/ton is for limestone, which is not pure CaCO 3 . Since all other data quoted in this section is for pure CaCO 3 , the cost of limestone use would be higher than $25.5/ton of SO 2 removed, taking into consideration the impurities in limestone.

Response: Addressed in the updated ISIS model/documentation

SKA.5 : Page 27, Appendix 4: The heating value of natural gas, 26,037 Btu/lb, shown in the last column is too high. This value is generally between 21,000 and 22,000 Btu/lb....

Response: Addressed in the updated ISIS model/documentation

HOB.3 : Table 6: The electricity da ta seem high. First, wherea s the logic of the PCA’s “equivalent tons” may be sound for heat, it makes less sence as the denominator for cement, given that roughly 1/3 of the consump tion is in the finish mill. The USGS data relate the unit consumption to total cement (not just portland) production. We show for 2005: wet plants = 135.4 kWh/mt; dry plants (all types) = 139.0 kWh/mt; combination plants (using wet & dry kilns) = 153 kWh/m t; and all integrated plants combined = 138.8 kWh/mt. Grinding plants = 84 kWh/mt.

Response: Addressed in the updated ISIS model/documentation

SKA.6 : ...Many of the cost sources used in this document are from years prior to 2005 and they have been adjusted to 2005 dollars using the Chemical Pl ant Engineering Index. These costs are too old and they do not reflec t the large price increases in environmental controls that have occurred during the last fe w years. Since ISIS is going to be used for projecting policy impacts in the future years, the proper way would have been to estimate the costs based on current prices and then ad just them back to the year 2005 using the above index.

Response: Addressed in the updated ISIS model/documentation.Version 1 49 EPA External Review SKA.10 : Page 28, DLI: The reagent for this system would normally be hydrated lime (which is what is quoted in the example in the second-last paragraph). The cost of $75/ton appears to be for pebble lime, not hydrated lime.

Response: Addressed in the updated ISIS model/documentation

HOB.2 : The average hourly wage table look a bit low for a cement plant, but I am not sure. Need to get some actual cement plant data . My main issue is that the typical cement plant worker has 10+ years of experience, I would think, and so would likely draw a higher wage.

Response: Addressed in the updated ISIS model/documentation.

SKA.7 : ...This document has established averag e effectiveness levels for various environmental controls for application on cement kilns. Some of these levels are different (usually higher) than those presented in another EPA document: “A ssessment of Control Options for BART-Eligible Source by NESCAU M, March 2005.” Can th is be justified?

Response: Addressed in the updated ISIS model/documentationVersion 1 50 EPA External Review 

### 12. Editorial Comments

Comment: The following comments pertain to ed itorial questions about table and figures.

HO1.6 : Page 1-6: Figure 1-3: the color scheme would be improved by using blue for wet and red(dish) for dry. Cite the data source.

Response: Thank you. We will incorporate the suggested color change in the plot and add the data source.

HY2.1 : Page 2-6

. Table 2-4: Spell acronyms in the ta ble (SFC, EGFW) to be consistent with the table in the appendix. The EGFW in Nm 3 /Kg for dry seems low. Hector will share Holcim’s calculation method at 10% oxygen to recal culate the EGFW.

Response: Acronyms spelled out.

DB2.9 : Page 2-8, Table 2-6. The colors do not a ppear consistent. For example, why does Birmingham, with SO2 emissions of 0.09 for the PC technology, have a white color in the cell?

Response: Addressed in the updated ISIS model/documentation.

HO2.16 : Table 2-7: I’m not sure you’ve made clear why you are showing H 2 O production.

Response: H 2 O is product of combustion of fuels.

HY2.3 : Page 2-9. Table 2-7 Label symmetrically lbmolesH 2 O/MMBtu, H 2 O is missing in some places

Response: Done.

HY2.6 : Page 2-12. Table 2-10 Specify in the model that these parameters are to be chosen by the user.

Response: Comment added.

Comment: The following comments pert ain to the spreadsheet.

HY3.1 : Comments on Spreadsheet. Add plot on retirement, new capacity, etc

Response: To be addressed in the upda ted ISIS model/documentation.Version 1 51 EPA External Review SK3.10 : Cell C2 – do you mean Newark, DE?

Response: Addressed in the updated ISIS model/documentation.

SK3.2 : Cell F6 should have some sort of label telling what it is.

Response: Addressed in the updated ISIS model/documentation.

SK3.3 : Fuel savings (cell J4) was not able to generate any scenarios where that was populated - what is it?

Response: Addressed in the updated ISIS model/documentation.

SK3.4 : Rows 9-24 would be more helpful if the formatting had commas, truncated decimals to one or two places.

Response: Addressed in the updated ISIS model/documentation.

SK3.7 : Cells AL30:45 – these are not labeled, and it’s not clear what this is.

Response: Addressed in the updated ISIS model/documentation.

SK3.8 : Rows 53-68 and 72-87 what are the units ? Any significance for the red font?

Response: Addressed in the updated ISIS model/documentation.

Comment: Miscellaneous editorial comments were received.

DB2.6 : Page 2-5. “In ISIS, projected <delete: kiln> retirements of existing kilns...”

Response: Addressed in the updated ISIS model/documentation

DB2.8 : Page 2-7, bottom. “...location (i.e. feedstoc k) appears...” Does this mean to say: “location and the associated f eedstock (fuel type) appears...”?

Response: Feedstock refers primarily to the ra w material- limestone. Text modified.

HO2.10: Page 2-5: Cement plants can certainly be older than 50 y ears!.... Generally, you wouldn’t build a plant on a site having < 100 y ears of limestone reserves. So the age can be a “site age” or an “equipment age”.

Response: Language changed to reflect that pl ants may be 50 years old or more.

HO2.12 : Page 2-6: “...such as coke...” Be sure specify petroleum coke (petcoke). Our old tables showed for many year s just “coke”, then we attempted to differentiate petcoke from metallurgical coke (i.e., from coal) wi thout much success (not sure the respondentsVersion 1 52 EPA External Review understood the difference). Likely that all coke use (outside of major blast-furnace/steel complex areas) has been of petcoke, and I am not sure that metallur gical coke has ever been in common use even in the steel areas—it’s too expensive.

Response: Footnote added to explain that PCA does not specify if “coke” is metallurgical coke or petroleum coke . Authors believe it is the latter.

HOA.20 : Page 37: The wording (Conversion from wet to dry) is a bit imprecise. You can convert a plant from wet to dry by replacing wet kilns with dry k ilns and/or by converting wet kilns to dry technology. (...) Regarding the 300,000 ton example—I would only comment that one would never build a kiln of such small capacity today, nor would it be worthwhile anymore to convert a 300,000 t wet to a 300,000 dry kiln.

Response: Addressed in the updated ISIS model/documentation.

DB3.1 : One could move the Objective function in Section 3.6 up to the beginning of Chapter 3. I would create a table...Then, the specifics of the algorithm can be explained in the other parts of Chapter 3.

Response: Addressed in the updated ISIS model/documentation.

SK3.5 : What does “eps” mean?

Response: Addressed in the updated ISIS model/documentation.

SK3.6 : I ran a CO 2 reduction case but no allowance price was calculated for CO 2 in the Output spreadsheet – I think th is would be a useful output.

Response: Addressed in the updated ISIS model/documentation.

HOA.24 : Page 44: don’t need to capitalize greenfield(s) and brownfield(s).

Response: Addressed in the updated ISIS model/documentation.

HOA.25 : Page 45: Not sure that “attainment” st atus is clear to all readers—may be environmental jargon.

Response: Addressed in the updated ISIS model/documentation.

HOA.30 : Round the midpoint costs to 2 signifi cant-figures: e.g., $280 instead of $277.

Response: Addressed in the updated ISIS model/documentation.Version 1 A-1 EPA External Review 

## Appendix A

Invitation Letter to ReviewersVersion 1 A-2 EPA External ReviewVersion 1 A-3 EPA External ReviewVersion 1 B-1 EPA External Review 

## Appendix B

Review Comments from Dallas Burtraw, Resources for the Future

This review focuses on the operations of the ISIS model with a specific example applied to the cement industry. The model makes available to the regulatory process an integrated framework for policy evaluation of environmental perform ance. The accomplishments in this model are substantial. To be constructiv e and useful, however, I focus my comments on points where there appears to be room for improvement. My comment s focus on three areas. One is the exposition of the documentation narrative. This is important in order for the model to be widely understood and to realize its full potential impact. A second is the logic in the underlying algorithm and potential issues in the banking function of the emissions m odule and in the investment algorithm. Third, in some cases the algorithm seems to limit the appli cability of the model with respect to what may emerge as important policy questions in the next few years. Data constraints may not require these limits in the model. The two policy applications I mention specifically are episodic NOx controls and potential climate policies that could not be addressed without some modifications to the algorithm.

Before mentioning specifics, let me summarize fu rther by saying that I am thrilled that the modeling effort has been undertaken. Without knowing the resources that have been committed one cannot say the effort is cost effective; howev er, from my research perspective and judging by the product, the effort to develop a model that can be used for policy and regulatory analysis has been successful. Let me focus on what mi ght be opportunities for improvement.

The narrative in the documentation is inconsisten tly written. Chapter 1 is very accessible. Chapter 3 is more technical, but nonetheless has subs tantial opportunities for improvement. I give suggestions below for reorganization. Moreover, there appears to be somewhat loose or inconsistent translation of information in me mos from Andover Technology Partners into Chapter 2 and elsewhere. Some examples are mentioned below.

I am concerned that accessibility is harmed importantly by the inconsistent inclusion of units of measure in the narrative . Adding units or examples is th e easiest way to help the reader understand the model. In many cases I was unsure a bout units. Table 2-2, 2-4 (SPC), and other tables do not have units labeled clearly. However, note that the corresponding table in the documentation from Andover Technology Partners (Sept. 23 2008) has units labeled. These should be copied into the ISIS documentation. Table 2-7 has lb CO2/MMBTU for CO2 but lbs/MMBTU for H20. Why not label them symmetrically?

It also is difficult to track the units through equations in Chapter 3 and the introduction of new data elements and variables seems to have units ar e inconsistently described. It might be helpful if the labeling of variables consistently used a label such as ‘F’ for a factor when a variable is unit free (such as percentages).

The emissions policies that can be accommodated in the model represent important options facing policy makers. However, evidence from other research indicates that air pollution problems associated with conventional air pollutants have changing characteristics as a consequence of aVersion 1 B-2 EPA External Review changing climate. Ozone may become increasingl y an episodic problem with “peakier” peak ozone concentrations. Similarly, longer periods of stagnant air may have regional characteristics. A potentially valuable improvement in the model capability would be to allow for emissions trading programs (or regulatory standards) th at have a location and/or time dimension . For example episodic controls on NOx emissions may be an element of ozone policy in the future. Episodic controls could be in effect for a period of days, or they could apply during early morning hours of a given day. They could be implemente d within an incentive-based (trading or tax) approach to policy through the trigger of a differe nt permit or tax level for a period of time. Could the model be used to simulate this type of regulatory issue by incorporating information about the cost of adjusting production schedules or fuels? Also, could the trading program represented in the model have a regional dimension, such that di fferent emissions allowance prices are in effect in different regions and seasons of the year ? The memo from Andover Technology Partners (Sept. 23, 2008), page 5 bottom, indicates that calculations “apply for any period of time so long that you are consistent with the time...” One might infer that episodic operation of the facility would lead to potentially important differences in its operating characteristics. Can this be addressed in the narrative now and in a future vers ion of the model? Care could be taken now in the narrative so that the model is not used inappropriately or its results misunderstood.

An important dimension of environmental impact of a facility is its stack characteristics. These are not mentioned anywhere. This is important if the integrated production cost ISIS model is to be combined with a broader environmental integr ated assessment. What is the stack height? Do emissions of NOx from a kiln have similar para meters (velocity, elevation, temperature) as emissions from a coal fired power plant? Users of the model for integrated assessment will need to know this, and how it vari es across plants and regions.

In the policy experiment that is illustrated, th e emissions bank expires before the end of the second period as a consequence of the policy that is modeled. Can the model accommodate a bank that does not expire before the end of the second period, or is a terminal year date necessary for the banking algorithm to converge ? How would the terminal year be determined? Is there a backstop technology that determines long-run abatement cost?

An important additional dimensi on to emissions policy is the way that allowances are allocated. In a competitive market it should make no diffe rence to product price (absent capital/liquidity constraints) whether they are grandfathered or au ctioned, as long as there is no free allocation for new sources and, under grandfathering, there is no surrender of allocations for changes in utilization or retirement of existing facilities. Ho wever an important emerging policy issue is the threat of unfair international competition under climate policy, and a proposed remedy (including recent proposed legislation from Rep. Inslee) is output based, updating, free allocation of allowances to industries exposed to unfair competition . This type of allocation provides an incentive to keep production onshore. Since this is an emerging issue it would be a great enhancement to have it considered and modeled carefully in ISIS.

In the power sector, an important part of NOx emissions reductions has occurred through NOx “trimming” meaning that small changes in co mbustion temperature are used to reduce the formation of heat related NOx. This leads to s lightly less efficient combustion and higher CO2 per unit of electricity generated. I am confused as to whether it is a factor in cement. The background technical memos discuss it somewh at, but it does not appear in the model documentation.

P. 1-11, Figure 1-7. Here and in the narrative the reader might be initially surprised to see “Emissions” as an input to the ISIS engine. Emi ssions would seem to be an output of technologyVersion 1 B-3 EPA External Review and production choices, and indeed they appear in the “Model Outputs” node. I understand, and it is explained in a subsequent chapter, that the emissions inputs are describing baseline or uncontrolled emissions at baseline levels of produc tion. This should be explained in the context of Figure 1-7. For example, at some facilities there already are low-NOx burners, and at others there are not. Technologies introduce an emissions modification factor that leads to ultimate emission estimates.

What constraints affect the choice of technologies for pollution reduction? Can only one technology be chosen? Can it be changed? That is, can a technology chosen in year 5 and then removed and replaced in year 10 with another t echnology choice? This appears to be happening in Figure 5-4 on page 5-4. See, for example, the rise and fall of LNB+CEMStar. When this can happen, how are capital costs considered? Are opportunity costs properly accounting for only marginal going forward costs (avoidable costs)?

It is not clear to me how costs are considered in the model. Are investment decisions using more than an annualized capital cost to consider in-place controls? It must be true that the expected value of the stream of future revenues and costs justify an investment; not just the revenues and costs in one year, considered one-year-at-a-time. It should be that investments cannot be reversed, and the calculation of net benefits includes the present discounted value over all options.

One could move the Objective function in Section 3.6 up to the beginning of Chapter 3. I would create a table, so that on the right hand colu mn each term definition is provided as one works one’s way down the equation. That is, put de finitions following “where” in the right hand column, spaced appropriately. Then one can “read ” this objective function and see the ways that economic intuition is satisfied in the formulation of the objective function. Then, the specifics of the algorithm can be explained in the other parts of Chapter 3.

Are transport-related emissions included in the em issions calculations? This concerns transport of fuel and material to the plant, and transport of cement to market. Presumably not, but this could be indicated.

In the narrative it wasn’t obvious to me whether and how transportation costs are included in the model. Are the costs of all materials and fuels th e delivered costs? Secondly, it is not described how are costs of transporting product between regions accounted for in the narrative, although cost differences are described. From the Excel spreadsheet input data files it is clear there is a region-to-region transportation da taset. This should be explained in the model documentation. The optimization problem introduces the new term “transcostt.” Regarding this and other new terms, it would be helpful if these terms are not e xplicitly introduced earlier, at least to indicate earlier that the related considerations are included in the model explicitly.

Location in a region should matter. What assumption can be made or is made about the location of the new unit and the expanded unit in each regi on? Are all input costs uniform for all plants within a region?

Further, what can be said about the location of ne w capital investment is relevant to subsequent modeling of emissions impacts because latitude and longitude info rmation is required for the next step in an integrated environmental assessment. The model user will have to have information about which plant within a region expands, or where new plants are located. A reasonable assumption may be to distribute new capacity propor tionately at the location of existing facilities. Probabilistically this may represent the future. Wh atever may be reasonable, the model narrative could provide some guidance for the user about what assumption is recommended.Version 1 B-4 EPA External Review It is clear from the optimization problem that em issions policy compliance costs (taxes, permits) are included in the variable cost calculation. But I did not see this stated explicitly anywhere in the narrative. It should be mentioned in chapter 1.

CEMStar is referred to in passing in various places. Is footnote (a) in Table 2-8 the description? It is described in Andover Technology Partners (9 /2508) page 34. Some short summary should be included in the ISIS model documentation.

Accounting of capital cost is not transparent to me . I see the market-clearing price is determined by the variable cost of the marginal capacity on an annual basis. The marginal capacity in year 10 may be the newly added capacity (e.g. capacity built in year 5). It is not apparent whether the variable cost in year 10 includes capital cost fo r the newly built capacity (long run marginal cost). The proper choice depends on the measure of co mpetitiveness in the industry and the role of business cycles. In a downturn, say in year 10, the recent capital investment is sunk, and production should depend only on year 10 variable cost. However, in the absence of business cycles one might expect to see long run marginal cost as a proxy for the expected (or average) marginal cost in future years. See for example the discussion on page 3-2: “Note that capacity is added endogenously...” The determinat ion of variable cost and product price is critical to this endogenous calculation. See also page 3-4, the d escription of annual fixed cost. Perhaps the more appropriate question is whether annual fixed costs is included in the calculation of market prices. Is annual fixed cost considered a variable cost when the model is solving in annual time steps? The narrative here could be strengthened.

Calibration is a standard operation to account for aspects of market structure and performance that are unobservable in the input data. It would be helpful in Chapter 4 if an explicit equation showed the variable cost calculation with the calibrator constant include d so that one could see the level of detail in the calibration. Also, see Tabl e 4-1. The “Total” in the table is labeled as a % Change of 5.1 but the numbers indicate a change much less in the total.

P. 4-4, middle. “A single calibration constant...” Why is a single calibration constant used to adjust variable cost of production? The justif ication for calibration may be market power, which is likely to be regionally specific . The assumption of the model is competition. If there is not a perfectly competitive market, or if there are regi onal rents associated with transportation costs, presumably these would be captured by the calib ration process. It appears calibration does not occur regionally or seasonally, which may be an important limitation of the model.

What is the plan for updating the data inputs a nd technology characteristics? The vintage of these inputs should be indicated. For example, P. 1-7, top. The projections for capacity expansion are likely out of date now, due to financial market issues. If this projection cannot be updated, at least a note should be added to indicate that these proj ections predate economic events of late 2008.

On page 2-7, bottom paragraph, I had a ha rd time understanding the algorithm described. I interpreted this to say that pyroprocessing cap tures sulfates, but sulfates are usually not considered a gas, and the sentence describes g as passing through the calcinations zone. (In the memo from Andover Technology Partners (September 23, 2008) p. 14, it is stated “SO2 released from feed materials...” It does not mention sulfat es in this instance.) Further, preheater and precalciner kilns are more effective...“Accordingly,...” Does this mean that in these types of kilns or in the dry and wet kilns? Also, loca tion “appears to have a significant role in SO2 emissions...” Does the feedstock in reference not include fuel?Version 1 B-5 EPA External Review P.2-23. The elasticity value cited is -0.88. Is th is to be interpreted as a short run or long run elasticity? Is there reason to think they differ, or not? Are there long run substitutes for cement?

P. 3-2. It is crucial to indicate when the c onstraints limiting capacity changes actually bind when reporting model results. What is the relationshi p between the chosen values and historical experience? I am concerned about these constrai nts because if they bind under a strict climate policy then one might ask if the inclusion of th e constraints and the role of these constraints should be considered in sensitivity analysis. If the constraints are binding a nd this is not apparent to the model user and policy analyst it could lead to an inappropriate prediction of what might happen under strict climate policy.

Similarly, on P. 3-3 we learn that imports are c onstrained to historic experience. However, many are concerned that under strict domestic climate policy there will be infusions of imports from countries that have not take on climate-related obligations. ISIS could not be used to inform this question. At least, this constraint should be tagged and output should be clear when, if ever, it binds.

Minor comments:

P. ix, middle. “...requirement compared to...” Does this mean “in lieu of...”?

P. 1-5, top. “...raw material feed...” This might be easier to read if it said “feedstock.”

P. 1-5, bottom. How does one interpret “kiln capac ity?” Is output measured per unit time? I learn in the memo from Andover Technology Partners (S eptember 23, 2008) in a footnote on page 1 that capacity is normally expressed “per hour.” This should be added throughout the ISIS documentation.

Also, the “Production Technologies...” node coul d have more information in the box. Is technology choice a decision made in this node?

Page 1-9, bottom. Local constraints such as nona ttainment areas and PSD areas are described, but there is no indication whether these are represented in the model or have been ignored.

Page 2-2. “The ISIS model determines the optim al fuel type...” Does the model account for long run contracts for fuel, or are all projected fuel choices assuming a spot market for fuel?

P. 2-5. “In ISIS, projected <delete: kiln> retirements of existing kilns...”

P. 2-7, Table 2-5. Explain sources in this table and why they diverge.

P. 2-7, bottom. “...location (i.e. feedstock) app ears...” Does this mean to say: “location and the associated feedstock (fuel type) appears...”?

P. 2-8, Table 2-6. The colors do not appear c onsistent. For example, why does Birmingham, with SO2 emissions of 0.09 for the PC technology, have a white color in the cell?

P. 2-10. “By-product benefits” Also costs?

P. 2-10, Table 2-8. Low NOx burners are usually thought to reduce efficiency in coal-fired electricity generation. Is there no penalty in a kiln? I expect this would appear under CO2.Version 1 B-6 EPA External Review However, the memo from Andover Technology Partners (9/25/08) page 5 indicates that there is a penalty of 0.09 kW per short ton, which does not appear in Table 2-8. Also, there is no CO2 penalty identified with the use of a Wet Scrubber, but later on page 3-5, a penalty associated with “generation of CO2 in a wet SO2 scrubber” is described.

Table 2-8 also seems inconsistent with th e memo from Andover Technology Partners with respect to tires and the CO2 implications. The memo clarifies that the use of tires must be compared against the fossil fuel that is being replaced.

P. 2-12, Table 2-10. It may be u seful to add an escalation factor for construction materials. This is to be newly added to the AEO, I understand.

P. 3-2, middle. I don’t see how the constraints relate. It appears that 2% can be replaced in a given year and no more than 10% over a 3-year pe riod. But no more than 6% could be replaced in a 3-year period, based on the first constraint.

Bottom. “Note that replacements are associate d with individual units....expansion...changes are...in regional capacity.” Then why are they summed?

P. 3-5. What is “priceration” in equation 3.2.5?

P. 3-5, middle. Energydisl refers to secondary fuel and polbasesubfuel refers to substitute fuel. Are these the same thing? If not, what is the di fference? Elsewhere the term “fuel displacement” is used. These are awkward labels. It would be simpler to refer to fuel substitution.

Equation 3.3.2. What is “cp”?

P. 3-7. I finally lost the thread in equations 3.3.7 to 3.3.9. Can some narrative be added? Why is the sum in 3.3.9 divided by the number of pollutants? What are units throughout these equations?

P. 3-9. An example is “catconsumptcost,” wh ich is not defined as a price times a quantity. However, see 3.4.11a. There, cost is defined as a price times a quantity. This inconsistency makes it unclear when rates, percentages or actual units are relevant to various equations.

P. 4-4, top. “the marginal <?> of the regional...”Version 1 C-1 EPA External Review 

## Appendix C

Review Comments from Kevin Culligan,

US Environmental Protection Agency

To: Wojciech Jozewicz, ARCADIS From: Scott Benolkin, Clean Air Markets Division, EPA Date: 9 February 2009 Subject: ISIS Cement Comments

ISIS Cement is a thoughtful, well-executed effort to directly address sector compliance issues that other models assume away. It will fill a gap in EPA’s modeling and meet an increasingly important need for detailed analysis of the environmental and economic impacts of air policy. As the first in a planned family of models, ISIS Ce ment also serves as prototype for a more general concept, and therefore, its structure and implementation bear more significance than those of a one-off model. With this in mind, I offer the following comments and questions:

1. The documentation (2.2.3.2) mentions a cement demand elasticity of -0.88, yet the model results show no changes in demand in th e policy case, despite significant allowance prices. Is this a disabled feature? Certainly, demand response will be an important factor in air policy impacts, especially for greenhouse gases.

2. Lacking control technologies for CO2, ISIS Cement is not yet ready to address climate policy except as it relates to electricity and import prices. For the former, electricity use should be a more prominent output. For the latter, given the cement industry’s energy intensity and the role of imports in meet ing cement demand even without greenhouse gas policy, the richness of import-price-related modeling will be critically important to assessing the cement industry’s response to, for example, a price on carbon emissions. Both CO2 mitigation technology and produc t substitution in demand response are sine qua nons for modeling the cement industr y’s response to climate legislation.

3. The rationale behind the choice of region definitions needs more documentation, especially because the regions differ in so me cases from those in the EPA (1998) RIA cited (e.g., see Attachment 8 of that documen t). Given the location of many plants far from their regional market’s center (see Figure 2-1), assuming uniform regionwide parameters could systematically underestimate the potential competition and exchange between neighboring plants in different regi ons. As a check of the assumption, it would be nice to know or estimate how much of each plant’s commerce is within its ISIS region versus outside of it. This is likely to be a significant assumption for other ISIS industry models as well.

4. Uncontrolled NOX emissions intensities vary not only among but also between average estimates by a factor of 2, ranging from the EPA (2000) estimates shown in Table 2-5 to those Andover Technology Partners provides. Have you examined the sensitivity of results to these parameters? More practica lly, how much investment in monitoring infrastructure would kilns currently need to comply with either cap-and-trade or ratebased policies?Version 1 C-2 EPA External Review 5. OMB’s 7% discount rate makes sense for analyzing the costs and benefits of policy results from ISIS, but it is not a basis for reflecting the discount rate cement companies use to make decisions. IPM, for example, deri ves its capital charge rate from assessments of actual discount rates faced in financing projects.

6. Equation 3.2.1 appears to set annual fixed costs proportional to a unit’s production rather than its capacity. If I am interpreting this co rrectly, it is a serious conceptual mistake.

7. Estimating and predicting based on a range of years rather than just one for each would improve the calibration procedure. What ma kes ±10% a reasonable range for predicting values one year later—i.e., how does it compare to the typical annual variances of production, imports, emissions, and prices? It see ms like a much easier target for some numbers than for others.

8. Variable cost seems like an important parameter to be imputing a constant adjustment from a regression over (it appears) two years of data. How sensitive are the results to the choice of this constant (or the choice of using a constant)? [Note: my own limited investigations tweaking that parameter in I nputs_Imports.gms show mostly level shifts.]

9. Constant escalation factors for fuel prices seem too simplistic and, negative factors contradict recent projections (see comment 10). Ideally, a supply curve informs the cost of fuels. Absent that, ISIS should use actual annual or multi-annual projections rather than an annualized 20+-year average. IPM, for instance, can supply such projections by region and coal type under a given policy.

10. Also, while the model’s energy prices escalation factors reconcile with those in AEO 2008 Table A1 (with the exception of using mine mouth rather than delivered coal prices), they disagree with those in Table A3 (by sect or), which for instance has coal rising, not falling, in price. Notably, more recent pr ojections by EIA and IPM have much higher future price increases (2–3% annualized) than AEO 2008.

General questions:

1. How substitutable are the different varieties of Portland cement with each other and with masonry cement? How well do prices and demands for different varieties correlate? What about other substitutes for cement? At the very least, knowledge of these options can inform more realistic demand response (as in comment 2 above).

2. Given transportation costs and the fact that 10 companies account for 80% of production overall, is market power an issue in any regions or subregions?

3. Are PCA estimates (e.g., of demand and capacity expansion) corroborated by independent sources? More immediately, is the PCA still projecting 27 new kilns through 2010?

4. In the real world, do producers buy at a wo rld market price or benefit from multinational ownership?

5. Short of process changes like CEMStar, are ma rginal changes in feedstocks available to kilns in the real world? How much difference does this make?Version 1 D-1 EPA External Review 

## Appendix D

Review Comments from Fereidun Feizollahi, CARB Economic Studies Section

Overall, this tool could probably be put to some use, however, at the moment I can not tell if the model is functioning correctly or if there are problems with the output spreadsheet.

With cap and trade, the results appear to be as e xpected in terms of direction of change and the resulting price effects. However, some of th e results (e.g., policy production and policy imports) did not seem to be influenced by the required reduc tions, that is, I got the same results for a small CO2 cap as I did for a sizable NOx and SO2 cap and a small CO2 cap. Some variables such as the average policy price did change but not signi ficantly given the difference in the scenario.

Differences were seen when I allowed for banking. With banking, policy production and policy imports were affected, but only for years 2010- 2016. Years 2017-2025 were the same as the no banking results. The price difference between a banking scenario vs. a no banking scenario seemed a bit excessive: far greater than that seen with a significant change in the program scope (NOx, SO2 and CO2 vs. CO2 only). Also note than when banking is not allowed, the resulting allowance price is reported as essentially zero (eps).

When comparing different fees, again production a nd imports seemed to be the same regardless of the amount of the fee.

Comments on EPA’s ISIS Model

• Demand projections - In ARB’s draft calculations for California’s cement industry, ARB staff has assumed a 2% annual growth rate in ceme nt demand. Due to the economic downturn, staff will be reevaluating the annual growth rate.

• Production and control costs and associated escala tion rates - ARB has limited information in this area and can not provide comments.

• Treatment of imports - California cement facilities will be part of a cap and trade system to reduce greenhouse gas (GHG) emissions. Details of the cap and trade system are under regulatory development. In addition, ARB staff is considering the development of a regulation to establish blending requirements of supplementary cementitous materials at concrete batch plants. The treatment of importe d cements will be considered as part of that effort.Version 1 E-1 EPA External Review 

## Appendix E

Review Comments from Sikander Khan, US Department of Energy

Comments submitted by Sikander Khan included co mments by Chris Nichols and Charles J. Drummond

Comments by Chris Nichols and Charles J. Drummond U.S. Department of Energy, National Energy Technology Laboratory January 29, 2009

General Comments

A model of the domestic industrial sector that encompasses a comprehensive approach to emission of pollutants for all three media (air, water, and solids) would provide significantly greater value than a more limited focus. All three media need to be represented to truly understand pollution reduction approaches that mini mize environmental impact and avoid simply reducing air pollution by increasing water pollution. Current descriptions of the ISIS model reference all three media; however, this focus n eeds to be expanded. I recommend that, consistent with NAS 2004 recommendations, a long-term vision for the ISIS model be established with an overall objective to optimize air, water, and so lid releases of pollutants from industrial sectors. This is not a trivial problem. However, the signi ficant value of achieving this vision motivates the effort that would be required. It is critical to establish this high level vision now so that a model framework capable of accomplishing the vision can be established. Efforts to build the model can be planned on an incremental basis as long as th e framework is established at the beginning. It would be too difficult to adapt a model developed primarily to optimize reductions of air emissions to conduct optimization of all three me dia if the concept for this capability was not included in the initial design of the framework.

An ability to store multiple scenario cases woul d be good. For example, if you ran two policy cases scenarios back to back (e.g., a CO 2 cap & trade vs. tax), it would be useful to be able to compare the two easily.

How are the regions modeled for cement going to map into other industries (especially NERC regions for electricity generation)? Integration with other industries is going to be really vital for getting valid results in any cap & trade scenario s in this model. This would be especially important if costs are higher for one industry than another to control certain pollutants.

The model seems to rely heavily on imports in very restrictive scenarios in order to meet emissions requirements. I could not find more de tailed information on how imports are treated – is there a cost curve that increases as more imported cement is demanded? Also, the documentation states that “selected base year (2 005) regional import information was used to estimate import levels in future years.” How? Does it proportion future imports the same way they were divided in 2005?Version 1 E-2 EPA External Review Interregional trade seems like it would be very sens itive to fuel prices – using diesel prices avg. 1994-2005 may be misleadingly low.

Are there any ways to add CO 2 capture options? This is going to be really important for future integration, I think. Also, when modeling CO 2 scenarios, incorporation of domestic and international offsets are going to be really important to include.

Specific Comments - ISIS Overview Presentation (1/14/2009)

Slide 6 – I agree that the objective should be to minimize costs of both production and emission control subject to meeting both demand for the commodity and emissions requirements. All of these criteria are important for a realistic model.

Slide 7 – Inclusion of both emissions contro ls and modification of industrial production equipment as choices that the model can make to reduce emissions is necessary and valuable.

Slide 14 – Starting and ending conditions for banking of emissions credits are not realistic. The model needs to establish constraints on banking that make the emissions banking process sustainable beyond the forecast horizon. It is unr ealistic (and overly simplis tic to the point of being meaningless) that all banked credits are cons umed by the end of the forecast period. This would only occur in reality if the end of th e forecast period coincided with the end of the emissions reduction requirement.

Specific Comments - ISIS Model Documentation (12/23/2008)

Page 2-7; Section 2.2.1.8 Emissions: This section only discusses air emissions. A discussion of all substances emitted to all media would provide greater value.

Page 5-1; Section 5.1.1: The emissions cap a nd trade example provided is overly simplistic. It does not provide insight into the application of the ISIS model to actual policy analysis. In the example given, all banked allowances are consumed by the end of the second phase of the rule. In actual application of the model, banking of a llowances would most likely continue beyond the end of the forecast horizon being modeled. The cap abilities of ISIS to model a realistic scenario should be provided in an example. Constraint s on conditions at the end of the forecast period would be needed to maintain realistic quan tities of banked allowances that would sustain industry’s approach to meeting the rule beyond the end of the forecast horizon.

Specific Comments - ISIS Model Output Spreadsheet

AggregateResults tab:

• Cell F6 should have some sort of label telling what it is.

• Fuel savings (cell J4) was not able to generate any scenarios where that was populated - what is it?

• Rows 9-24 would be more helpful if the forma tting had commas, truncated decimals to one or two places.

• What does “eps” mean? That occurs frequen tly throughout the output spreadsheet.Version 1 E-3 EPA External Review • I ran a CO 2 reduction case (50% 2010-2014, 70% 2015- 2025) but no allowance price was calculated for CO 2 in the Output spreadsheet – I think this would be a useful output.

• Cells AL30:45 – these are not labeled, and it’s not clear what this is.

• Rows 53-68 and 72-87 what are the units? Any significance for the red font?

• In the CO 2 case I ran above, in 2025, IS IS expanded capacity by 23.5 and built 5.3 in new capacity and seemed to keep all existing capacity, compared to 22.4 and 3.8 respectively in the base case. This is counter-intuitive to me since overall domestic production is less in the policy case – why would the model build new capacity and not use it if it is not retiring older dirtier capacity?

Specific Comments - ISIS Model Input Spreadsheet

TransCosts tab:

• Cell C2 – do you mean Newark, DE?

• Would be helpful to be able to adjust diesel prices – using AEO2005 may not be the most reliable data.

Emissions tab:

• Looking at the documentation, SO 2 and NO x have lots of data on their emissions factors, but CO 2 does not. The emissions factors for CO 2 (rows 36-38) seem lower than those in EIA’s Table 6-1, Documentation for Emissions of Greenhouse Gases in the U.S. 2006.

Policy tab:

• For the % reduction policy options inputs (cells F5:H22), what if the % reduction compared to? Is it a year-by-year? So when 60% is ente red for 2015, is it 60% of the baseline’s 2015 emissions, or 60% of the starting year’s emissions?

• Another useful input would be to be able to manually enter an emissions cap, i.e., enter the maximum emissions level for each year.

• K5:M5: don’t understand “miner” in these cells.

Additional Comments by U. S. Department of Energy February 2, 2009

Comments on Industrial Sector Integrated Solutions Model, December 23, 2008:

1. Page 1-5, Table 1-1: Use of two sets of numbers for kiln specific heat input (or energy intensity) in this table and Table 2.4 is confus ing. An explanation of the difference would be helpful.Version 1 E-4 EPA External Review 2. Page 2-2, First Paragraph: It is stated that the ISIS model determines the optimal fuel type. What is the basis of this selection? Is ther e any information available for the existing fuel type, e.g., for the base case? Can the fuel type be an input to the model?

3. Page 2-7, Section 2.2.1.8: In the first paragra ph, it is stated that ISIS model uses emission intensities to estimate NOx, SO 2 , and CO 2 emission projections. However, from the model input file (Spreadsheets “Units” and “Emissions”); it is not obvious if this has been done with the CO 2 emissions. It appears that CO 2 emissions for the year 2005 are calculated by multiplying the clinker yearly production rate with the same factor (0.45 for fuel) for all kilns. Also, the 2005 baseline figures for NOx and SO 2 emissions are estimated based on a constant multiplier for all kilns. Notes 10, 11, 12, and 14 are not provided, which may have explained the reasons.

4. Page 2-9, Table 2-7: This table lists different CO 2 emission rates for PRB and bituminous coals, as estimated by J. Staudt of Andover T echnology Partners in Appendix A. However, in the model input file, only one type of coal (b ituminous) is used. It is assumed that the model has the capability to use different coal t ypes, if input data were so prepared.

5. Page 2-11, Section 2.2.3.2: The economic parameters listed do not fully report the calculations built into the model for the overall economic analysis. For example, what is the basis for these calculations: current or constant dollars? How are the construction times for the controls are taken into account? Does the model estimate some of the construction costs (e.g., allowance for funds used for construction) , since such costs may not be included in the data provided in the Appendices? In Table 2- 10, what does the annual escalation rate of 3.09% for the variable O&M cover – all variable costs or some of them? If all, then why different factors for limestone and gypsum? Is there a separate factor for lime as well?

6. Chapter 3, ISIS Mathematical Framework: Can the model accommodate different safety valves built into a policy framework. Can a cas e for cap-and-trade with a limit on allowance price (with other required caveats) be run?

7. Page 5-5, Figure 5-5: Can there be a scenario, where the price of cement is affected to the point that it starts to affect demand?

8. Page 5-6 Table 5-1: The increase in the comb ined import and control costs ($2 billions) does not match the increase in the total cost ($1.58 billions) for the example case.

9. General Comment: The costs associated with the base and policy cases should be reported in terms of factors (e.g., $/ton of clinker or $/t on of air pollutant removed) that can easily be understood and compared to the effects of other similar policies.

10. Appendix A, Andover Technology Memorandum, September 23, 2008:

• Page 1, Table 1: It needs to be clarified if the specific fuel consumption data provided includes only kiln fuel or non-kiln fuel s ources, such as raw material dryers, auxiliary boilers, etc. These non-kiln sources can add significant CO 2 emissions.

• Page 4, Second Paragraph: The figures quot ed in this paragraph are based on pure limestone. It’s not clear if all of the figures in this paragraph, e.g., for the steelmaking slag, are based on pure limestone.Version 1 E-5 EPA External Review • Page 6, Total CO 2 : What is the assumption used in the model with regards to the cement kiln dust, which is also a source of CO 2 emission?

• Page 7, Table 4: The range value (1.9-3.1) for the wet kiln NOx emissions does not cover the average value (6.2).

• Page 27, Appendix 4: The heating value of na tural gas, 26,037 Btu/lb, shown in the last column is too high. This value is generally between 21,000 and 22,000 Btu/lb. The reason for this may be the assumption that there is only carbon and hydrogen in the gas. This is not true, since there may be CO 2 , nitrogen, and other impurities present.

11. Appendix B, Andover Technology Memorandum, September 25, 2008:

• General Comment: Many of the cost sources used in this document are from years prior to 2005 and they have been adjusted to 2005 dollars using the Chemical Plant Engineering Index. These costs are too old and they do not reflect the large price increases in environmental controls that have occurred during the last few years. Since ISIS is going to be used for projecting polic y impacts in the future years, the proper way would have been to estimate the costs based on current prices and then adjust them back to the year 2005 using the above index.

• General Comment: This document has established average effectiveness levels for various environmental controls for application on cement kilns. Some of these levels are different (usually higher) than those presen ted in another EPA doc ument: “Assessment of Control Options for BART-Eligible Source by NESCAUM, March 2005.” Can this be justified?

• Page 16, Gas Treatment: Injection of bioso lids, a potential NOx control option, has not been addressed.

• Page 26, Limestone Variable Costs: The co st of $15/ton is for limestone, which is not pure CaCO 3 . Since all other data quoted in this section is for pure CaCO 3 , the cost of limestone use would be higher than $25.5/ton of SO 2 removed, taking into consideration the impurities in limestone.

• Page 28, DLI: The reagent for this system would normally be hydrated lime (which is what is quoted in the example in the second-l ast paragraph). The cost of $75/ton appears to be for pebble lime, not hydrated lime.

• Page 33, Table 9: Suggest that you resolve the following concerns that have been taken from other analyses on kilns, especially the above BART-related document:

o SCR and SNCR may not work on all wet and long dry kilns.

o Dry lime injection may not work on all wet and long dry kilns.

o In many cases only a tail-end type SCR syst em, with requirement for reheating of flue gas, may be required. This may resu lt not only in additional costs, but also additional air emissions, if fuel is used for the reheating.Version 1 E-6 EPA External Review o Spray dryer technology may also be used for kilns other than wet type kiln. Depending on site specific factors, th is may be a more viable option.

• Page 34, Process Modifications: It appears that the performance improvement options that may help reduce CO 2 emissions have not been full y developed. Options that may improve performance of raw material preparation systems and clinker grinding systems have not been mentioned at all. While conversi ons from one type of kiln to another, more efficient type have been covered, they have not been included in the input file. Such conversions may not be viable at all sites. However, such limitations are not addressed. Use of high efficiency motors is included in the input file. However, such motors may already exist in some (or parts of some) newer kilns. It appears that a study to identify a reasonable level of performance improvement that would apply to most kilns on an average basis would be highly beneficial.

12. Is there any plan for showing the indirect effect on air emissions of changes in the electricity consumption caused by modifications required by policy decisions at cement plants?Version 1 F-1 EPA External Review 

## Appendix F

Review Comments from Andy O'Hare, Portland Cement Associat ion (September 4, 2009)

The review and comments of the PCA (39 pages) are summarized below within the following eight categories: market structure, transportation, averag e variable cost, demand projection, supply, cost of installing controls/conversions, im ports, and calibration-and-validati on. Complete comments are given after the summary of the eight categories.

Market Structure

1. Get rid of the market structure or if market stru cture is going to be used define markets as having geographic boundaries that more closely match the manner in which the data have been generated to reduce data manipulation.

a. Instead of using the 20 EPA cement markets use the PCA states or metropolitan statistical areas (PCA’s consumption data)

b. Instead of using the 20 EPA cement mark ets for cement sales price- use USGS 26 districts

c. Instead of using the 20 EPA cement markets for imports – use USGS 38 custom districts

2. Instead of markets- use demand centers and simu late each plant’s ability to compete as a function of the actual plant location and transportation options and cost and distance to each demand center. Do not assign plants as actually being “in” a particular market or demand center.

Transportation

1. Expand transportation matrix to include:

a. Barge

b. Rail

c. Transportation cost by truck

d. Ocean (for imports)

2. Change the assumption that transportation within a market has no cost and that outside of the market transportation is based on the distance of th e market main city to the other market main city.Version 1 F-2 EPA External Review AVC

1. Expand average variable cost (AVC) function to account for SG&A, cost of shipping within a region and to terminals, property taxes and insura nce, benefits on wages and salaries paid, freight out to customers, freight out to terminals, other cost of goods sold.

2. Repair and maintenance costs- EPA might want to escalate costs from 93 to 2005 by using a price index more specific to repair/maintenance that GDP deflator

3. Labor costs- are substantially low compared to benchmarking analysis- draw earning data from BLS consistent with PCA suggestions (Page 19 of PCA comments)

4. Fuel costa. do not assume that is reasonable for a plant to use 100% coke or residual oil

b. Use Staudt’s approach to estimate fuel consumption rather that RTI’s approach

5. Escalation factors- 2008 EIA’s escalation factors should not be used to escalate 2005 actual prices, run projections off the most recent year for which data on actual prices and projection escalation rates are available.

6. AVC alone is not appropriate for estimating market-clearing prices over the long-run. In the longrun all costs are variable including fixed costs.

Demand projections

1. Data used by EPA is PCA forecast cement consump tion, equivalent to cement usage. Agency’s “demand” terminology is mislea ding. EPA should refer more accurately to cement consumption rather that cement demand.

2. Consumption data is now outdated. Update cons umption projections to latest data with the summer 2009 Forecast and 2009 Long-term forecast which reflects the deeper and prolonged slump for cement consumption because of the recession.

3. If cement demand centers are used a combination of PCA’s projection by State and by MSA might be appropriate to allocate cement consumption rather than allocating consumption to different markets in a way that introduces un certainty and errors (by state’s surface area, population or some other variable)

4. Developing year-by-year projections by fitting a polynomial equation for each region smoothes out variations in consumption over time that PCA believe are important.

Supply

1. Update for the cancellation or deferral of most of the industry’s previously planned capacity expansion projections over the next few yearsVersion 1 F-3 EPA External Review Cost of Installing Controls/or Conversion

1. Cost of installing new equipment, substitution of new/expansion/upgrade production capacity for existing capacity, fuel switching and process changes in the model are less expensive that they actually are.

2. Other potential industry’s responses are simulated as less expensive than they actually are.

3. In most cases conversions and replacements cost 4 to 8 times more than EPA’s estimated costs per ton.

Imports

1. Test for additional years (oth er than 2004). How does the model do in matching data for 2005, for 2006, for 2007 and for 2008?

2. Do better alignment of Custom Districts from USGS with Regional Cement Markets.

3. Add import terminals and ocean shipping. The latter far more important.

Calibration and Validation

1. Use data set that span for many years for calibration and validation (“entire business cycle”) instead of one year. Calibrate for three or four years beginning around 2000 and then validate across period that includes good years, mediocre year, and bad year.

2. “The calibration constant” (fudge factor) will be eliminated if the AVC function is updated to include the missing elements (SG&A, cost of shi pping within a region and to terminals, property taxes, insurance). Adopt a long-run perspective ra ther that a short-run perspective when modeling cement prices over the long-run. Estimate all the components of costs that producers will seek to cover in the long-run instead of using the “calibration factor” of 1.9.

3. Implement process for judging the degree of uncertainty and explicitly portray the level of uncertainty

4. Perform sensitivity analysis on key assumptions a nd design choices for the model to show their impact on model results (use ranges in the outputs to reflect uncertainty)Version 1 F-4 EPA External Review Review and Comments on EPA’s ISIS Model

These comments on EPA’s ISIS model are preliminary. EPA provided the NESHAP version of ISIS for public review and put it in the docket relatively late during the public comment period. There has not been sufficient time between when EPA made the NESHAP version of the model available and the close of the public comment period for us to: 1) Learn how to run it, including getting the advice needed from EPA on “work-arounds” that allow it to run in practice; 2) Run the model under various assumptions regarding input data and control scenarios; 3) Understand the results; and 4) Provide our conclusions in the form of written comments. ISIS-cement is a large and extremely complex model. Any complex model with numerous inter-relating elements or modules is imp ossible to understand thoroughly without actually being able to run it and see how its outputs respond to changes in inputs or assumptions. We have not yet had the time to do this. Our comments at this point on ISIS-cement and on the model outputs relating to the proposed NESHAP are thus not based on having worked with the model, but instead reflect our having only:

• Reviewed the documentation for the model (mod el documentation exists largely for to the “peer review” version of the model, which addre sses NOx and SO2, rather than for the NESHAP version which addresses the relevant HAPs);

• Reviewed the inputs to the model; and

• Evaluated the outputs from EPA’s NESHAP run of the model.

We intend to conduct a more complete review of the NESHAP version of ISIS-cement in September, during which we will test the model using what we think to be better input data and more appropriate assumptions. For example, we intend to run the model assuming more accurate projections for cement demand in future years that reflect the current recession, in co ntrast to the projections in the model currently that do
not.

We look forward to providing more complete comme nts on ISIS in September. In the meantime, the comments below provide our preliminary assessment of the model.

Summary

ISIS offers the potential eventually to provide a significant improvement over EPA’s previous economic modeling for the cement industry. At present, though, ISIS is subject to numerous shortcomings in data, assumptions, structure and validation. We do not believe that the current version of ISIS provides believable estimates of likely economic impacts from the proposed NESHAP or from other potential environmental requirements. ISIS is so complex an d its deficiencies so pe rvasive that, until it is substantially improved, other methods should be used to assess the likely impacts of the proposed NESHAP.

In this section of the PCA comments, we provide an initial technical review of the current version of ISIS that EPA has used to estimate the costs an d economic impacts of the proposed rule. We provide comments and criticisms regarding the model and suggestions for improving it.

In some instances we provide data with which EPA can improve the model; in some instances we suggest data that we and EPA might work together to develop in September, in other instances we point EPA to sources from which the Agency might obtain the recommended data.Version 1 F-5 EPA External Review In a planned addition section of the PCA comments that will be provided in September, we will suggest how the economic impacts from the proposed NESHAP as estimated using ISIS would likely change if the model were improved as we believe is necessary. Most notably, we believe that a corrected and property validated ISIS would show much higher costs to comply with the proposed NESHAP and greater levels of cement imports and domestic plant closures than are shown by the current version of ISIS.

We commend EPA for undertaking the development of a model like ISIS. If EPA can eventually develop an acceptable version of ISIS, it will be valuable to have the capability to analyze in a single modeling system the impact on an industry of multiple regula tions applying to multiple pollutants in a framework that simultaneously considers the full range of possible responses to regulation: install controls, adjust production processes, shift to new and cleaner production capacity, change fuels, import. We particularly appreciate some substantial improvements reflected in ISIS relative to EPA’s previous modeling approach for the cement industry. 1 In contrast to EPA’s previous approach, ISIS;

• No longer models the U.S. cement industry unrealistically as a collection of 20 completely independent regional cement markets, with each cement producer selling product in only their own home market;

• Models cement producer behavior as competitive rath er than oligopolistic, dropping the assumption that producers restrain output in order to raise prices;

• Allows a greater potential role for imports, consistent with historical experience;

• Simulates endogenously the full range of potential responses to regulation instead of a ssuming many of them as exogenous inputs to the model; and

• In many respects uses updated and better data.

However, notwithstanding its commendable modeling objectives and the improvements over previous modeling efforts, the current version of ISIS that is used for the NESHAP analysis needs substantial improvement before its outputs ca n be considered to realistically forecast the impact of EPA regulatory options. We suggest the following:

• Further improvements are needed in modeling regional cement markets. EPA’s choice of 20 regional markets is arbitrary. Important markets are omitted, ov erlapping markets are ignored, and many plants are wrongly assigned to particular markets. The chosen restrictive market structure misses many plants’ ability to compete across markets through bulk shipping to distant terminals. It appears that GAMS, the impressive linear program modeling system used in running ISIS, has the computing power to handle a more realistic approach. Instead of simulating each cement plant as selling preferentially into only one of 20 semi-independent cement ma rkets, ISIS could instead model each plant’s ability to ship to any of scor es of cement “demand centers”.

• EPA’s “calibration” procedure that is intended to compensate for potentially inaccurate or missing cost elements and incomplete model struct ure has an indeterminate impact and is wholly inadequate. Addressing this serious deficiency requires additions and improvements to the ISIS model, and cannot be accomplished by use of a simple “fudge factor”, as currently.

1 The partial equilibrium static model that was originally developed to support the cement kiln dust rulemaking and that was most recently used along with an engineering compliance cost analys is as an alternative to ISIS in estimating the economic impact s of the proposed NESHAP.Version 1 F-6 EPA External Review • ISIS must be adequately valida ted before its results can responsibly be used to inform EPA’s decision-making process. ISIS should be run for a period much longer than the year or two over which it has been back-tested to compare its outputs with actual observed data in order to reliably evaluate the accuracy of the model. This validation process should al so include an explicit evaluation of the capabilities of ISIS to provide realistic forecasts over the course of an entire business cycle, and in particular the ability of ISIS to reflect the potential differential impact of the imposition of EPA requirements at different poi nts in the business cycle. Further, EPA should implement a process for judging the degree of uncertainty in ISIS’ projections, and the Agency should explicitly portray the resulting level of uncertainty.

• Kiln operating cost functions are inaccurate. Ther e are substantial inaccuracies in the five specific cost components that ISIS simulates: raw materials, repair and maintenance, labor, fuel and electricity. Other cost components are completely omitted: SG&A, costs of shipping within a region and to terminals, property taxes, insurance.

• Cement demand and supply projections are outd ated. The baseline cement demand projections that drive the model do not reflect the substantial impact of the recession and do not reflect the latest forecasts from PCA. A curve-fitting approach used in the model has a further impact in hiding some of the sharp reduction in demand with the recession. On the supply side, ISIS should reflect the cancellation or deferral of most of the industry’s previously planned capacity expansion projects over the next few years.

• Compliance options other than installing control eq uipment are viewed too optimistically. The model simulates many of the potential industry responses to regulation other than installing air pollution control equipment – i.e., substitution of new/expansion/upgrade production capacity for existing capacity, fuel switching, process changes – as being less expensive than they actually are. Excessive optimism regarding these lower cost compliance options causes ISIS to underestimate the frequency and cost with which the cement industry will need to install emission control equipment as a result of the NESHAP or other regulations.

• Cement and clinker imports are simulated in an unrealistic manner. ISIS simulates both the price and quantity of imports using additional “calibration c onstants” that reflect conditions in 2003 and 2004. This procedure has unknown impact for future years. Absent the “calibration constants,” imports are simulated as being available in unlimited quantity in each regional cement market at a price equal to that of the highest cost local domestic producer, a highly questionable assumption. The availability and price of imports for the U.S. market in fact depend heavily on world supply/demand balances and bulk commodity ocean shipping rates, two factors that will be exceedingly difficult to predict or model.

• Simulation of cement transportation costs need s improvement. Costs are estimated for truck transport only. Rail and water shipping and terminals are not included in the model. Transportation of cement from plant to customers within the plant’s region are assumed to have no
cost.

• Assumptions regarding cost and performance of em ission controls need revision. In general, industry believes that control measures will be less effective and have higher unit costs than is assumed in EPA’s ISIS run evaluating the proposed NESHAP.

In the following pages we expand on these general comments regarding ISIS.Version 1 F-7 EPA External Review 20 Regional Cement Markets

In ISIS, each cement plant in the U.S. is assigned to one of 20 regional markets:

There is no particular rationale for EPA’s choice to divide the country into these 20 specific markets. The list of markets was established by EPA based roughly on two academic studies in 1988 and 1991 that assumed there were a limited number (20 in one case and 25 in the other) of metropolitan areas that constituted markets. Neither of the two studies nor EPA based its selection of market areas on any data on the amounts of cement produced, shipped or consumed in different geographic areas. Neither the academics nor EPA considered any alternative schemes for delineating markets other than those which were asserted. EPA provided no rationale for its selection of 20 markets that are somewhat different than the set in either of the two earlier studies.

Other organizations that study and provide data on cement markets divide the country in further differing ways: the PCA provides data for States and for SMSAs; the USGS provides data for 29 producing districts and 57 shipment destinations (mostly reflecting states or combinations of states) and 38 Customs Districts; while the Engineering News Record compiles cement price information for 20 cities that differ from EPA’s 20 (e.g., ENR’s 20 cities do not include EPA’s Florida, Phoenix, Salt Lake City and San Antonio markets). In short, EPA’s delineation of 20 markets is arbitrary; only one of many possible ways of defining the geographic locations in which cement is consumed or produced. Many cities larger than some of those chosen by EPA might perhaps be better choices as “markets” or might reasonably serve as additional markets: Houston, San Diego, Indianapolis, etc.

EPA assigns each cement plant to one of the Agency’s 20 markets in a manner that is inappropriate in many instances. Some plants are clearly mis-assi gned. For example, the Ash Grove Cement plant in Leamington, Utah actually serves the Salt Lake C ity market, not Phoenix as EPA assumes. The Buzzi plant in Greencastle, Indiana is assigned by EPA to the Chicago market (150 miles away) and the Lehigh Cement plant in Mitchell, Indiana is assigned by EPA to the Cincinnati market (100 miles away). In reality, though, both of these plants compete in a different market than EPA assi gns: both ship primarily to the Indianapolis market (only 50 and 85 miles away from the two plants, and closer than either of the markets to which they are assigned by EPA). Indeed, there are at least eight cement plants located within 200 miles (EPA’s assumed trucking di stance for effective comp etition) of Indianapolis that might reasonably compete with each other in that city. EPA nevertheless does not include an Indianapolis market in ISIS, and instead assigns these eight plants among three different markets, thus sharply understating the competiti on that in reality occurs among th ese plants. In fact, many cementVersion 1 F-8 EPA External Review plants compete routinely in multiple markets, particularly via terminals and/or water transport, and assignment of these plants to a single market is clearly wrong.

PCA has previously provided EPA with extensive comm ents and examples of this nature on the Agency’s cement industry economic modeling. 2 EPA has nevertheless retained for ISIS the basic approach that we criticized previously -- ISIS assumes the sa me 20 regional cement markets and assigns cement plants to markets in an identical manner as previously. We thus refer the Agency again to our previous comments. For the sake of brevity, we simply reiterat e here some of the section headings in our previous
comments:

• “EPA’s choice of 20 markets is arbitrary and assignment of plants exclusively to them is inaccurate ...

• The model omits im portant markets ...

• Many plants serve more than one of EPA’s markets ...

• Examples of plants with multiple markets – truck transport ...

• Examples of plants with multiple markets – water transport ...

• EPA’s approach sharply under- represents the level of comp etition in the industry ...

• Cement markets are interdependent ...

• Cement markets overlap and cement plants serve multiple markets ...

• Examples of real and potential ripple effects [across markets] ...

• Cement markets are not oligopolistic ...

• The magnitude of errors stemming from EPA’s in correct market assumptions is significant”

However, we want to note that in developing ISIS, EPA has made some significant changes that mitigate to some degree these continuing concerns of ours abou t the Agency’s economic modeling. ISIS retains the arbitrary model structure of 20 regional markets and numerous inappropriate assignments of individual plants, but the model is notable in now:

1. Simulating each cement plant as able to compete and sell product outside of its own regional market, at a price reflecting the cost of production plus shipping to the new market. Thus, markets are no longer independent, but they are instead linked by simulating the ability for plants to compete across market boundaries.

2. Simulating each market as competitive rather than oligopolistic, with competition from producers located inside the region, producers located outside the region (adding a transportation cost to get their product into the region), imports, new capacity, etc..

2 APCA (1997). Comments on EPA’s Draft Economic An alysis of Air Pollution Regulations for the Portland Cement Industry (May 1996), prepared for American Portland Cement Alliance by Environomics, January 29, 1997. EPA has included this document in the rulemaking docket for the proposed NESHAP.Version 1 F-9 EPA External Review These are important steps toward increasing th e accuracy with which EPA’s economic modeling simulates the dynamics of cement markets. EPA’ s assumed market structure still causes significant distortions in the modeling, however.

In ISIS, a plant is assumed able to co mpete in its home market area with other plants within that market area without paying any transportation costs. A plant is also assumed as able to compete in other markets by paying a transportation cost for trucking cement based on the distance from the plant’s home market city to the named city for the other market . This approach of assigning a plant to a specific market results in significant distortions, particularly for plants near the edge of their assigned markets. Consider, for example, the Buzzi plant in Greencastle, Indiana. ISIS assigns this plant to the Chicago market. This plant is assumed to incur no cost to ship its

product some 150 miles to Chicago but to incur a si gnificant cost to ship its cement over the border into the adjoining Cincinnati market. The transporta tion cost in ISIS for shipping anywhere into the Cincinnati market is estimated based on the 297 mile distance by road from Chicago to Cincinnati. In fact, as mentioned previously, this plant actually ships much of its cement to the Indianapolis area (in the Cincinnati market), a distance of roughly 50 miles. ISIS thus simulates this plant as competing in Chicago with no transportation cost and as able to compete in Cincinnati at significant transportation cost. In fact, the plant competes primarily in the Indianapolis area, at modest transportation cost.

Such distortions in simulating a cement plant’s competiti ve options exist in ISIS for the great majority of plants. These distortions result largely because of the decision to assign each plant to a particular market and then to judge the plant’s competitive options as somehow reflecting this assignment. Why not instead judge a plant’s competitive options based simply on its location? Instead of inaccurately locating the Buzzi plant as “in” the Chicago market or the Cincinnati market or even the Indianapolis market, why not simply assign the plant appropriate transportation costs that it would need to pay based on its actual location in order to compete in any of these three markets?

Additional distortions ensue because EPA has chosen only 20 specific geogra phic areas as markets. EPA’s 20 markets divide the country in a geographically different manner from the way that other organizations that provide key cement industry data divide the country. As a result, EPA must re-shuffle much of the publicly available cement industry data in order to match it to the particular 20-market geographic structure the Agency has adopted. This reshuffling results in unpredictable distortions in the data. For example, ISIS uses the following information:

• Projections developed by the PCA of future cement consumption for each of the 50 States. EPA must crosswalk from this state-by-state data to the 20 regions. Many states are split among more than one of EPA’s regional markets. For example, EPA defines most of the State of Florida as being in the “Florida market”, but some of the State of the Florida (the panhandle?) is defined as being a part of the “Birmingham market.” EPA must thus guess at what portion of Florida’s cement consumption as estimated by PCA should be assigned to the Birmingham market rather than the Florida market. EPA guesses 10%. This guess may or may not be accurate. If EPA were to define its markets as having geographic boundaries that more closely match the manner in which the data have been generated, the need for such guesses would be reduced.

• Data on average cement sales prices developed by the USGS, which is organized by 26 districts. Most of the USGS districts include states or groups of states; again EPA must guess at how to reshuffle the USGS data in order to match it to the 20 regions.Data on imported cement developed also by USGS is organized by 38 Customs Districts. Here the crosswalk that EPA must perform to transform the data to fit the 20 regions is particularly difficult and uncertain. EPA must estimateVersion 1 F-10 EPA External Review which region or regions the imported cement entering the country in a particular Customs District will be shipped to.

• EPA estimates, for example, that half of th e cement imported via the New Orleans Customs District goes to the Dallas region and half goes to the Birmingham region. This is incorrect, we believe; much of the cement imported through New Orleans goes up the Mississippi by barge to the Saint Louis region or farther.

We suggest that EPA could avoid most of th ese problems we have been discussing – misassignment of plants, inaccurate simulation of a plant’s competitive options, re-shuffling data – by eliminating from ISIS the c oncept of assigning plants to ma rkets. ISIS should instead be structured around the existence of numerous “cement demand centers”, and the model would simulate each cement plan t’s ability to compete in each of the de mand centers as a function of the plant’s actual location and transportation op tions/distance to each demand center. The set of “demand centers” should be stru ctured in a manner to reflect both the geographic areas where there are significant concentrations of cement c onsumption and the geograph ic distinctions that are employed by the organizations that generate the key cement data se ts needed for modeling – perhaps some combination of SMSAs and States.

EPA staff has mentioned to us this possibility of re-orienti ng ISIS around “cement demand centers” rather than assignment of plants to markets. We would st rongly support such a reorientation. There appears to be no reason mathematically why ISIS could not simulate many more markets or demand centers (perhaps some thing like the top 50 SMSAs plus remaining portions of States) and as sign an appropriate shippi ng cost for each plant to reach each potential market/demand center. Th e optimization solver th at ISIS uses could ha ndle more constraints (e.g., geographic locations within each of which cement demand must be satisfied) than exist in the model currently. We do not believe there is any need to assign each plant as actually being “in” a particular market or demand center. We believe the model could be made to work if each plant is assigned a set of tran sportation costs for reaching each of the plausible demand centers it could serve.

PCA would be pleased to participate with EP A in devising this “demand center” approach. Attachment A provides one sort of helpful da ta compilation that PCA could provide in implementing this approach – a summary of cemen t consumption by SMSA in addition to our breakout by State.

Such an improved modeling approach should also simulate the transportation options actually available to each plant in a more realistic manne r. For example, the revised model could include bulk shipping from plants to term inals and shipping options via water and rail in addition to truck. Later in this paper we will provide furt her suggestions for better simulating transportation in ISIS.Version 1 F-11 EPA External Review Failure to Recognize the Difference Between Short- and Long-Run Perspective When Estimating Price
ISIS uses the average variable cost (AVC) for the marginal source of cement to determine the market-clearing price in each regional market. 3 The theory behind this approach is that, in the short run, a producer sh ould be willing to produ ce any marginal unit of output for which the market price exceeds the variable cost of producing that unit. As discussed in the next section, ISIS fails to accurately a nd fully estimate the AVC. 4 However, even if AV C were accurately and fully estimated, AVC alone would not be appropriate for estimating market-clearing prices in a model that attempts to make projections over the long run – from 2013 to 2018 in the NESHAP run of ISIS and out to 2 030 in ISIS-cement more generally. In the long run, all costs are ariable, including fixed costs. No invest or would decide to replace equi pment, expand capacity, build a new plant, or invest in long-lived pollution cont rol measures without the expectation that prices would be sufficient and sustaina ble to cover not only variable costs, but also fixed costs 5 and to provide in addition an adequate return on capital. In fact, the IS IS documentation acknowledges that actual prices do indeed syst ematically exceed the AVC estimated in ISIS, calling the “discrepancy” between predicted and observed prices the “producer’s margin.” (page 4-4)

Instead of attempting to model accurately what EP A refers to as the producer’s margin (i.e., reflecting all appropriate costs and the impact on price of the bala nce between supply and demand), the Agency applies a “fudge factor” (a multiplier of 1.9) to two of the five components of AVC which it estimates. The fu dge factor multiplier raises the calculated AVC to a figure closer to observed actual price. The ISIS document ation refers to this app lication of what really is a fudge factor as a “calibration” of the model to address any issues rega rding the “accuracy of the model’s technical structure” and the “quality of the input data.”

There is no acceptable rational e for the manner in which EPA develops and applies a fudge factor to compensate for the discrepancy betwee n the model’s predicted prices and actual prices. The model’s “calibration constant” of 1.9 was estimated by determining the magnitude of the multiplier that would be needed to apply to raw materials costs and maintenance and repair costs in order to bring the model’s estimated prices in 2003 closer to the act ual observed values in 2003. EPA claims to validate the estimated fudg e factor by applying it to data for 2004 and determining for that year also that predicte d regional prices using the fudge factor seem reasonably close to actual regional prices.

We disagree that EPA’s predicted prices for ei ther 2003 or 2004 are acceptably close to actual prices. The average difference fo r both 2003 and 2204 between the predicted regional price of

3 In ISIS, the operating cost functions determine the market-clearing price in each regional market as follows: 1) the kilns serving the market are rank-ordered by their AVC to produce a ton of clinker (or cement = clinker/0.92); 2) other sources are also simulated as poten tially serving the market (e.g., import s, existing kilns in nearby markets with transport costs added to their AVC, new kilns, expa nsion kilns, replacement kilns) and they also are rankordered by their AVC; 3) the least expens ive sources are selected first to fulfill regional demand; and 4) the AVC for the marginal source – the source that s upplies the last ton of cement needed to meet regional demand – establishes the market price for cement in the region.
4 Examples of line items omitted from EPA’s variable cost estimates include benefits on wages and salaries paid, freight out to customers, freight out to terminals, and other cost of goods sold. 5 Examples include equipment costs, property taxes, in surance, and SG&A.Version 1 F-12 EPA External Review cement and the actual price of cement is 8.5% across EPA’s 20 regional markets. We view errors of this magnitude as large rather than small:

• Year-over-year price ch anges from 2003 to 20 04 average much less than 8.5% across the regions. In other words, EPA would do better in predicting 2004 re gional prices by simply assuming they would be identical to 2003 regional prices than the Agency does in simulating 2004 regional prices by applying the model including the fudge factor.

• The policy responses the Agency is ultimately interested in simulating – for example, the percentage increase in cement prices that might occur as a result of the NESHAP – are small relative to the average error in predic ting regional prices (i.e., typically much less than 8.5%). The noise inherent in inaccura tely estimating regional prices is likely to obscure the signal that EP A is trying to measure.
ISIS Exhibit 4-2 is reproduced below, showing the differences between actual regional prices and regional prices predicted by ISIS using the “calibration constant”.

Average divergence from actual price: 8.5% 8.5%Version 1 F-13 EPA External Review EPA then proceeds to apply the “calibration constant” of 1.9 (which was established using data for the year 2003 only) when estima ting cement price in each market area in each future year through 2030. There is no theory or rationale that would support the use of such a simplistic fudge factor to serve as an ac curate substitute for developing an adequate mode l structure and complete data, especially when projections are to be made over a long time horizon.
As appropriate to match the tim e period over which the model aims to simulate cement producers’ decisions, EPA should adopt a lon g-run perspective rather than a short-run perspective. In predicting prices over the long run, EPA should aim to estimate in ISIS all the components of cost that producers will seek to cover over the long run. A simplistic fudge factor that is estimated based on one year’s worth of data is inadequate in this regard, both in terms of theory and in terms of empirical accuracy of the predictions that are generated.Version 1 F-14 EPA External Review Failure to Validate the ISIS Model and Explicit ly Specify Uncertainty
A complex model such as ISIS that is intended to offer predictions ac ross many future years should ideally be calibrated using a data set that spans many years and then be validated or backtested across another data set that also spans many years. Both the calibration period and the validation period should be long in duration and should reflect the same sort of variation in conditions as might be expected to prevail duri ng the future period th at will be forecasted.

ISIS as it exists currently meets none of these goals. The mode l has been calibrated over only one year, 2003. It has been validated also only for one year, 2004. The calibration and validation periods consist of only very good years economically for the indus try; years that are among the best ever. The time period to which IS IS is intended to predict, however, includes some of what are likely to be among the wors t years in recent histor y for the industry. The current recession will likely cause annual U.S. cement consumption to decline some 40% from its peak in 2005 to a low point in 2009. Cement consumption is not likely to recover to 2005 levels until sometime be tween 2015 and 2020. 5 The period to which ISIS is being asked to predict could hardly be more different than th e period over which it has been developed and tested.

The ISIS model is intended to provide a slate of outputs over a 25 year period. ISIS must be adequately validated as to whether it provides reliable outputs over such a timeframe before its results can responsibly be used to inform EPA’s decisions regard ing regulations. Importantly, an adequate validation proce dure also has the potential for genera ting insights into how ISIS can be improved to provide more reliable outputs.

For all of the components and outputs of ISIS for wh ich it is feasible to do so, ISIS should be run for sufficiently long prior periods to compare its outputs with actual observed data in order to reliably evaluate the accuracy of the model. An ideal procedure might be to run the model for the past 25 years, through 2008. If the ISIS model can be cali brated to provid e outputs that adequately reflect actual observa tions for a past period that is comparable in length to the intended forecast horizon for the model, then this would provide a good degree of assurance for the accuracy of the model’s projections. Or, at a more practical scale, perhaps ISIS could be calibrated for three or four years beginning around the year 2000, and then validated across a period that includes th e good years of 2005 and 2006, the mediocre year of 2007 and the bad year of 2008.

Along these lines, in addition to evaluating the accuracy of th e ISIS outputs over the modeling horizon, the validation should include careful evaluation of the ability of the model to appropriately handle ec onomic cycles. In partic ular, the model should have the capability to allow EPA to evaluate th e impact of the timing of the rule’s implementation relative to the timing of economic cycles (and the model’s ability to adequately reflect these). The ability of the model to delineate such imp acts over the course of the modelin g horizon should be carefully evaluated.Finally, a comprehensive validation procedure would test the robustness of the model by performing sensitivity an alysis on key assumptions and design choices for the model to show

5 PCA. Long-Term Cement Consumption Outlook. Summer 2009

.Version 1 F-15 EPA External Review their impact on the model outputs. Further, along thes e lines, it is not realistic to display point estimates for the outputs of a co mplex model, especially a model with such a long forecasting time horizon. One thing about whic h we can be certain is that the outputs will have a degree of uncertainty associated with them. The extent of th is uncertainty should be explicitly re flected in the outputs, such as th rough the use of ranges. Such ranges ca n be established th rough the use of sensitivity analysis, scenarios, or more sophis ticated modeling technique s (such as Monte Carlo analysis).Version 1 F-16 EPA External Review Kiln Operating Cost Functions
EPA estimates the average variable cost (AVC) for each kiln to produce a ton of clinker, as follows:

Raw materials cost + Repair and maintenance cost + Labor cost + Electricity cost + Fuel cost = AVC

The first four of these cost comp onents are determined exogenously as a function of each kiln’s characteristics and prices for ke y inputs, which vary by region and escalate over time. The fifth of these components – a ki ln’s fuel cost – is de termined endogenously in the model, as the model simulates each kiln’s option to switch fuels in order to reduce production costs

As we have discussed, kilns’ av erage variable costs as defined in this mann er serve in ISIS to determine the market-clearing price in each regional market:

• The kilns serving the market are rank-orde red by their AVC to produce a ton of clinker (or cement; clinker/0.92 = cement)

• Other sources are also simulated as potentially serving that market (e.g., imports, existing kilns in nearby markets with transport cost added to their AVC, new kilns, expansion kilns, replacement kilns) . They also are rank-o rdered by their AVC

• The least expensive source s are selected first to fulfill regional demand

• The AVC for the marginal source – the source that supplies the last ton of cement needed to meet regional demand – establishes th e market price for cement in the region

What’s wrong with this approach? The major empirical problem is that market price in practice is much higher than operating costs (AVC) defined as th e sum of EPA’s five components. EPA’s estimated base year (2005) AVC ranges across kilns from roughly $35 - $65 per metric ton, whereas the actual price of cement in 2005 ranged from roughly $75 to $100 per metric ton in different markets.

What’s missing from EPA’s calculation? What gets re flected in market price in addition to the quantities that EPA includes in AVC? A report published annually by PCA – the Financial Benchmarking Report -- is helpful here. The Benchmarking report is compiled by a contractor, who surveys cement industry companies and faci lities to obtain a wide range of financial information, including costs, income statemen ts, balance sheets, fina ncial ratios, etc.. The information is collect ed on a confidenti al basis: data obtained for an individual facility or company cannot be released, and statistics are publ ished only on an aggreg ated basis. Statistics are published for entities at the median and the upper and lower quartiles and for the industry aggregate average. Statistics are published for companies, for corporate cement divisions (someVersion 1 F-17 EPA External Review companies conduct operations in addition to cement production), and for cement plants. The following table provides income statement data from the Benchmarking report for 2007 for all for all corporate cement divisions. Respondents to the Benchmarki ng survey were responsible for more than 95% of all the cement shipped in the U.S.
PCA Benchmarking Report (2007 Data – Corporate Cement Divi sion, Industry Aggregate Results)

This table gives an indication about the categor ies of cement production costs that are left out in EPA’s attempt to estimate AVC: freight out to customers, freight out to terminals, SG&A expenses, property taxes, perhaps payroll taxes and employee fringe benefits, and some portion of “purchased materials costs” and “o ther cost of goods so ld”, to the extent th at they exceed raw materials costs. Further categories contribute to the difference between production costs and sales price, including de preciation (a book-keeping charge ra ther than an ac tual cost) and operating profit. The cost categories clearly omitted by EPA (freight, property taxes, SG&A, total of $16.50 per short ton) plus depreciation ($6.70 per short ton) and operating profit ($25.40 per short ton) approximately match the difference between EPA’s estimated AVC and cement price ($40 or so per metric ton).

In simulating cement prices in ISIS, as we indi cated previously, EPA attempts to make up for errors and omissions in estimat ing costs by “calibra ting” estimated AVC so as to make the modeled regional cement price match the actual observed regional cement price. Based on 2003 data, EPA estimated a “calibration constant” of 1.9 which, when multiplied by two components of AVC (raw materials cost + ma intenance and repair cost) serv es to bring mo deled price as close as possible to actual price. This factor of 1.9 is then used in ISIS in estimating the price of cement in each market in each future year through 2030.Version 1 F-18 EPA External Review We have several objectio ns to EPA’s use of a “c alibration constant” (rea lly a “fudge factor” in colloquial terms) in this manner:Version 1 F-19 EPA External Review • There is no acceptable rationale for adjusti ng the sum of raw materials costs plus maintenance and repair costs to compensate for: a) Omitting other variable costs plus fixed costs; b) Errors in estimating those variable costs that ar e included in EPA’s formulation; c) Ignoring fixed costs that must be recovered in the long run.

• There is no reason to believe that this ad justment, even if correct nationally for 2005, would be correct for different regions in 2005 or for different years in the future

• EPA’s predicted prices, even after this “calibra tion” procedure, are not particularly close to actual prices, and typically miss by an amount that is significant relative to the price impacts that EPA projects as occ urring due to the proposed NESHAP.

In short, EPA’s “calibrat ion” procedure has litt le conceptual justification and does not yield accurate estimates for regional prices. The large magnitude of the calculated calibration adjustment (factor of 1.9) provides evidence of the inaccuracy of EPA’s simulation of kiln operating costs.

We can offer to EPA several possibilities for improv ing this important part of ISIS. The aim of each approach would be to develo p data with which EPA could more completely simulate all the costs that a cement producer needs to cover in th e long run in order to remain in business. We could:

• Work with the Benchmarking report contractor to develop data for EPA to represent the cost categories that make up the difference between AVC as EPA calculates it (without including the calibration factor) and price. Th is data would need to be aggregated in some manner that respects the confidentiality agreements under which it was obtained; it could not be provided for indi vidual plants or companies. Perhaps regional aggregations could be provide, perhaps aggregations by ty pe of kiln, or perhaps other approaches would be appropriate.

• Alternatively, we would work with the contractor to provide data with which to simulate total long-run production costs fo r various aggregations of kiln s. This data would replace rather than supplement EPA’s AVC estimates. Or, altern atively, we could develop data to replace some portions of EPA’s AVC estimat es. We could, for example, develop data to replace all cost components but kiln en ergy costs, which we assume EPA would continue to want to develop endogenously within ISIS.

• Or, we could have the Benchmarking contract or run a correlation analysis between the Benchmarking data on costs by plant agains t EPA’s simulated AVC for the same plants. We anticipate that this analys is would indicate th e sorts of plants or kilns for which EPA’s cost estimates were reasonably accurate and the sorts of plants or kilns for which EPA’s estimates were far off base. We co uld then perhaps provide suggestions for improving EPA’s simulation of costs for the particular sorts of plants or kilns for which the Agency’s estimates are poor.Version 1 F-20 EPA External Review In the following pages, we provide additional comments on the five specific elements that comprise AVC as EPA estimates it in ISIS.Version 1 F-21 EPA External Review Raw materials costs

We do not have any data that would help signi ficantly in improving the manner in which ISIS simulates raw materials costs. Raw materials costs do not correspond to any of the categories in the Benchmarking study income statements, so this po tential data source would be of no assistance. We have no significant objection to the steps by which kil n-by-kiln raw materials costs are estimated in ISIS, includin g the estimates to the effect that:

• 1.67 metric tons of raw materials are n eeded per metric ton of cement produced

• The cost of the needed 1.67 tons of raw materials is represented by the price per ton of crushed stone as reported by USGS ( 2005 Minerals Yearboo k, Crushed Stone ) in the State in which the kiln is located. The great majority of crushed stone consists of limestone, so this appears to be a reasonable representation for the price of most of the raw material input for cement plants.

EPA may wish to represent in ISIS the possibility of changing specifications that allow for additional quantities of materials to be adde d to clinker in produc ing portland cement.

Repair and maintenance costs

EPA’s approach for simulating repair and maintenance costs is reasonable. EPA generates estimates as follows:

These estimates are roughly simila r to the figure given in the Benchmarking study of $9.40 per short ton for “Plant Ma intenance” (ind ustry aggregate for 2007). This Benchmarking is probably somewhat low, as some maintenance materials ma y be included in another cost category, “Total Purchased Materials Costs”.

We question whether EPA might es calate maintenance costs from 1993 to 2005 by using a price index more specific to repair/maintenance than the GDP deflator.

Note that in more recent ISIS documentation, EPA refers to maint enance and repair costs instead as “operation and maintenance costs”. We assume this represents an er ror in terminology, and that this quantity is not really intended to be “operation and maintenance costs”. Note also that EPA is inaccurate in treating repair and mainte nance costs as one of the five components ofVersion 1 F-22 EPA External Review short-run variable costs. Maintenance and repair costs are not are truly variable in the short run with plant output. They represent, in effect, a fi xed annual cost that vari es with kiln size. The other four of EPA’s components of AVC (e.g., ra w materials costs) do vary with plant output.
Maintenance and repair costs likely represen t a fruitful area for investigation of the Benchmarking data, since the benchmarking study includes a cost category that matches fairly well against maintenance and repair costs as EPA defines them. We could ask the Benchmarking study contractor to look at the costs reported for maintenance as a function of: 1) kiln or plant size; 2) kiln or plant process; 3) correlation of benchmarking data by region against EPA’s assumed regional cost variations; 4) other possibilities.

Labor costs

The approach taken in ISIS for simulating a ki ln’s labor costs is reasonable in concept.

As a first step informat ion on the numb er of labor hours required pe r metric ton of equivalent capacity (85% clinker, 15% finish ed cement) is drawn from PCA’s Labor-Energy Survey

The number of labor hours required per ton of capacity is then multiplied by average hourly earnings by State, drawn from th e Bureau of Labor Statistics. EP A thus estimates labor cost per ton of capacity, which varies by state, type of kiln and kiln size. The cost range is $3.97 per ton of capacity (TX) to $12.17/ton (MI) with a median of $6.50/ton. The weig hted average, based on clinker production, is lower at $5.75/ton.
These figures are all substantially low relative to labor costs as reported in the Benchmarking study, which are shown below for 2007.Version 1 F-23 EPA External Review Line Item $ / Ton Salaries for Plant Employees (Exempt) $ 2.80 Wages for Plant Employees (Non-Exempt) 5.20 Payroll Taxes (FICA, unemp., workers’ comp) 0.90 Employee Fringe Benefits 1.50 Total Plant Labor $ 10.40

The figures above (which include plant level ad ministrative personnel) are substantially higher than the EPA’s $5.75 weighted av erage cost per ton. Non-plant labor costs, such as sales, general management and non-plant level administ ration are captured in separate line items:

Line Item $ / Ton G&A Salaries and Bonuses $ 1.70 Sales Staff Salaries and Commissions 0.70 Payroll Taxes, Group Ins., Fringe Benefits 0.80 Total SG&A Pa y roll Ex p ense $ 3.20

Together, Plant Labor and SG&A Payroll Ex pense total more than $13.00 per ton:

Line Item $ / Ton Total Plant Labo r $ 10.40 Total SG&A Payroll Expense $ 3.20 Total Labor Cost $ 13.60

Total labor cost at $13.60 per ton is 2.4 times higher than the EPA average of $5.75. We believe the difference may be due to several factors:

1. Hourly earnings figure that represents hourly but not salaried employees. The closest line item to EPA’s $5.75 cost is PCA’s $5.20 “Wages for Pl ant Employees.” As these numbers are similar, EPA may ha ve drawn from BLS a wage ra te that reflects earnings per hour by hourly-wage workers only. A higher hourly earnings rate would be appropriate to reflect the actual mix of hourly and salaried employees.

2. Omission of costs for taxes and benefits

. It appears that EPA drew figures from BLS that include direct earnings by employees but no t the value of benefits and employer-paid taxes. As per the Benchmarking report, pl ant payroll taxes and plant employee fringe benefits add $0.90 and $1.50 re spectively to the amount of workers’ earni ngs, while nonplant payroll Taxes + Fringe Be nefits add $0.80. In total, benefits per the PCA report add $3.20.

3. Earnings figures representing industries othe r than cement. EPA may have used average hourly earnings figures from BL S that represent employees in industries other than or in addition to cement.Version 1 F-24 EPA External Review We expect that EPA would arri ve at labor costs for kilns th at are much cl oser to the Benchmarking data if the Agency draws earn ings data from BLS consistent with these suggestions.
Electricity costs

A kiln’s electricity costs are simulated as follows:

• Average figures for electricity consumption pe r metric ton of equiva lent capacity (92% clinker, 8% finished cement) for kilns of va rious types/capacities is drawn from PCA’s Labor-Energy Survey.

• These electricity requirements are multiplied by the average electricity price per kWh for industrial users in 2005 by State, drawn from EIA.

This process results in estimated electricity costs ranging across kilns from about $6 to $12 per metric ton ($17+ in CA) of cement. These estima tes seem a little high compared with figures from PCA’s Benchmarking studies: $6.60 per ton on average fo r electricity in 2007 and $7.60 in 2008. We have not investigated why this variance might exist.

Fuel costs

It is not clear to us which of two approaches documented by EPA is actually used in ISIS to simulate fuel costs.
Under the first approach, described in the RT I memorandum providin g backup for the AVC calculations, a kiln’s fuel input rate is a ffected by the age of the kiln as follows: 7

where the constant has been estimated as 0.0087. The fuel input for a new kiln is:

7 RTI International, “Documentation for Portland De ment Kiln Cost Functions (2005)”. August 31, 2007.Version 1 F-25 EPA External Review This approach ultimately results in fuel costs ra nging from about $4 to ab out $13 per short ton of cement, and in a few cases as low as $3 or as high as $35 per ton.
Under a second and different approach describe d in Andover (2008), fuel consumption per short ton of clinker does not depend on the age of the kiln, and is as follows:

For comparison, figure s are shown below calculated from PCA’s 2008 Labor-Energy Survey : 5.9 5.0 4.0

3.6

This second set of estimates for fuel c onsumption appears reasonably close to the Labor-Energy Survey figures. The first set of es timates gives fuel consumption substantially lower than the Labor-Energy Survey figures for kilns of less than 20-30 ye ars in age. We are thus skeptical of the first approach.

EPA’s procedure for estimating fuel costs then includes:

• Determine from PCA’s Plant Information Summary the primary fuel that the kiln used in 2005

• Assume that the kiln used its primary fuel for 100% of its fuel needs

• Multiply the kiln’s estimated fuel consumption per ton of cement (from one of the two methods described above) by the regional price for the primary fuel, obtained from EIA for 2005

o Regional coal prices ranged from $1.21 to $3.71 per MMBtu

o Petroleum coke price estimated uniformly at $1.80 per MMBtu

o Regional residual oil price ranged from $7.1 8 to $8.33 per MMBtu

We have compared the results of EPA’s fuel co st simulation, as shown in ISIS_inputs “Units” worksheet against average fuel costs of $7. 50 per ton of cement as shown in PCA’s Benchmarking report for 2007. We believe this figure from Benchmarking to be reasonably
accurate.

• The average estimated fuel cost per ton of ceme nt appears to be in the same ballpark as the Benchmarking estimate via EPA’s first method; but appears generally too low via EPA’s second method

• It does not appear reasonable to assume that any plant co uld go to 100% petroleum coke or residual oil, t hough roughly 15 kilns are pr ojected as doing soVersion 1 F-26 EPA External Review • Some plants are simulated as having unrealisti cally high fuel costs – e.g., several kilns projected as using 100% residual oil at $7.65/ MMBtu are simulated as having fuel costs of over $30 per ton of cement

More work is needed in order for ISIS properly to simulate kiln-by-ki ln fuel costs. We are willing to work with EPA, perhaps providing information fr om the Labor-Energy Survey on actual kiln fuel usage (but aggregated in some manner to respect the confidentiality agreements under which this information was obtained), to imp rove the data and procedur es used by ISIS in estimating fuel usage and cost.

Escalation of input prices over time
Each of the five inputs to the kiln operating cost functions are assigned prices that change across the simulation years from 2005 through 2030:

We presume that “Variable O&M” as shown in th is table is actually intended to mean “repair and maintenance”. This is an instance, we gue ss, of EPA’s documentation having shifted over time in how it refers to this particular cost element. Assuming that “repair and maintenance” is what is really intended here, we question whether it is reasonable to estimate this as escalating over time at a rate of 3.1% per year in real terms.
These escalation rates look like they would make energy costs less important in the future than they are at present relative to raw material and labor costs. This will give more weight in the future to elements of operating costs that EPA k nows less about. It will also give more weight in the future to the two elements of operating co sts to which EPA applies the 1.9 “calibration factor.”

The projections for declining re al energy prices in the future are surprising. Applying these escalation rates drawn from 2008 references to actual input prices beginning in the base year of 2005 may be problematic. First, EIA’s predicted es calation factors for years subsequent to 2008Version 1 F-27 EPA External Review are intended to be applied to 2008 actual prices , not to 2005 actual prices. Second, the 2008 projected prices for fuels in ISIS might be quite different from the actual 2008 prices for these fuels. We suggest in projecting future year pri ces in ISIS that EPA may want to run the projections off of the most recent year for whic h data on actual prices and projected escalation factors are available. Note that EIA’s 2009 Annual Energy Outlook projects increasing future real energy prices, in contrast to the projections for decreasing future real energy prices that EPA drew from the 2008 Annual Energy Outlook

.Version 1 F-28 EPA External Review Cement Demand Projections

In the NESHAP version of ISIS-cement, EPA si mulates the response of cement markets to the proposed regulation for each year from 2005 (the base year for the model) thr ough 2018. In ISIS-cement more generally, EPA provides the opportunity to simulate the response to environmental requirements all the way through the year 2030. In generating such long-term projections, ISIS requires as a key input a projection of base case (i.e., absent the environmental policy being analyze) cement consumption through 2030. 6 EPA obtains these long-term projections of future cement c onsumption from PCA’s periodically issued cement market economic forecasts.

At present, both the NESHAP version of ISIS and the peer review version draw their projections from PCA’s Long-Term Cement Co nsumption Outlook dated 1/31/08. These projections developed more than 1 1/2 years ago are now outdated, and EPA should use as input to ISIS the most recent available projections. In particular, the January, 2008, projections now used by EPA were developed and issued before the depth of th e current economic rece ssion became apparent. Subsequently issued PCA projections, particularly the summer 2009 Forecast and 2009 LongTerm Forecast , are quite different in projecting a deeper and prolonged slump in cement consumption. It is critically important that the baseline future projections for cement consumption that EPA uses in ISIS re flect the large impact of the recession. The current projections used in ISIS do not do so. For exam ple, the following tabl e shows tota l national cement consumption as currently assumed in IS IS compared with PCA’s current projections. 7

Projected U.S. Cement Consumption (millions of metric tons) 2005 2008 2010 2013 ISIS (based on PCA 1/31/08) 128.3 127.7 130.6 135.2 PCA current (2009 Long-Term Forecast ) 127.8 96.4 83.4 113.3 Difference 0.4% -24.5% -36.1% -14.0%

The cement consumption figures assumed in the current version of ISIS show essentially no change in annual consumpti on from 2005 through 2009, and then modest annual growth through 2013. In contrast, actual cement consumption has declined by more than 40% from 2005 through the present, and PCA’s latest projections for future years indicate a slow recovery in consumption such that 2013 consumption will be still remain more than 10% below 2005 levels. PCA projects, in effect, that the effect of the recession will continue st rongly into and beyond 2013, EPA’s assumed first year for full compliance with the proposed NESHAP and the key year for which most of the Agency’s economic impact projections are developed.

6 PCA specifically forecasts domestic cement consumption , equivalent to cement usage. In the ISIS documentation, however, EPA refers to cement demand rather than cement consumption , and the Agency uses PCA’s consumption projections to represent what the Agency calls future cement demand . We believe the Agency’s terminology is misleading, and suggest that EPA change and refer more accurately to cement consumption ra ther than cement demand. Demand has a specific meaning to economists that is different from consumption, and we believe the concept that EPA deals with as an input to ISIS should more accurately be referr ed to as consumption.
7 Cement consumption projections in ISIS are in short tons and exclude Alaska and Hawaii, which are not modeled. The ISIS figures shown in the table have been adjusted to convert to metric tons and add AK and HI so as to provide an appropriate comparison against the current PCA projections, which are in metric tons and include these two States.Version 1 F-29 EPA External Review By relying on outdated cement consumption proj ections, the current version of ISIS fails to reflect the huge impact of the recession on cement markets. Not only does ISIS rely on 2005 -- the year with the highest-ever totals for U.S. domestic cement production and consumption -- as the base year for simulating cement industry prod uction economics, the model further misses the disastrous market conditions th at the industry will subsequen tly face all the way through the period of NESHAP implementation and complian ce. The estimates of regulatory impacts in future years that are developed in ISIS cannot appro ach being credible wi thout this problem being fixed.

We see several additional problems in the ma nner by which EPA translates PCA’s cement consumption projections (to the year 2030) into ba seline cement consumption figures as needed for ISIS.

First, PCA provides consumption figures by State, but ISIS, b ecause of the regional market structure that EPA has adopted, n eeds figures broken out by ISIS’ 20 markets. To the extent that EPA’s market boundaries do not follow State bo undaries, EPA must do some sort of adjustment in order to use PCA’s figures. EPA has chosen to crossw alk geographically from PCA State figures to EPA market figures based somehow on th e percentage of each State located within the geographic boundarie s of each EPA market. It’s not clear whether the percentage reflects the State’s surface area, population, or some other variable. 10 Thus, for example:

• EPA’s “demand” for the Atlanta market = PC A’s consumption for the following States: NC + GA + SC + 1/3 of TN + 30% of VA

• Florida = 90% of FL

• Birmingham = AL + MS + 10% of FL + 1/3 of TN

• Dallas = LA + 70% of AR + 50% of OK + 40% of TX

• San Antonio = 60% of TX + 10% of OK + 80% of NM

This allocation process introduces some unknowa ble amount of error. EPA could avoid this error if the Agency were to define markets in a manner that follows State boundaries and matches the manner in which PCA’s figures are generated.
As an alternative, PCA also generates ce ment consumption proj ections broken out by Metropolitan Statistical Ar eas (e.g., Oklahoma City , Albany-Schenectady-T roy, etc.) as well as broken out by State. If EPA were to move toward the “cement dema nd center” approach that we mentioned earlier, some combination of PCA’s projections by State and by MSA might be appropriate.

10 EPA provides no documentation that we can find indicating how this crosswalk is performed and which quantity -- population, surface area or something else -- it is based on. The mathematics of this crosswalk can be determined from EPA’s ISIS_inputs Excel workbook, but the workbook does not explain which quantity provides the basis for this step in the analysis.Version 1 F-30 EPA External Review Another problem in how ISIS cu rrently uses PCA’s cement consum ption projections involves a step in which EPA translates PCA figures given at 5-year intervals (e.g., for 2015, for 2020) into figures at annual intervals. EPA develops year-by-year projections by f itting a polynomial equation for each region to regional consumption projections for every fifth year, and then using the fitted equations to then project consumption for every individual year. This curve-fitting approach has the impact of smoothing out variati ons in consumption over time that PCA believes are important. EPA’s regional re gressions consistently overestimate consumption in 2010, not catching even the small projected slump due to th e recession that PCA projected in the 1/31/08 figures that EPA uses. EPA’s to tal national projected consumpti on in 2010 after applying the curve-fitting procedure is 141.84 million short tons vs. the PCA figure of 1 38.31 that EPA used as input, a 2.6% overestimate by EPA. Other year s are better fits – e.g., for 2015 EPA’s figure in ISIS after curve-fitting is 0.7% below the PCA estimate that EPA started w ith, in 2020 EPA is 0.3% above PCA’s estimate, in 2 025 EPA is 1.3% too high, etc.. In order to get year-by-year figures, why does EPA not simply interpolate within the figures provi ded by PCA at 5-year intervals? Alternatively, if EPA wishes, PCA can de velop and provide proj ections for future cement consumption that have annual time st eps rather than 5-year intervals.

To summarize our comments regarding the cement demand projections in ISIS:

• EPA should use PCA’s latest demand projections. They best reflect the impact of the recession. It is critically im portant in assessing the impact of the proposed NESHAP to model the cement industry and cement market s not as they might be if the economic “good times” of 2005 were to continue into the fut ure, but instead as they are really likely to be given the recession.

• PCA can provide year-by-year projections as well as projections by 5-year intervals. PCA can also provide projections by MSA as well as by State.

• EPA should not use the curve-fitting procedure.Version 1 F-31 EPA External Review Future Changes in Cement Production Capacity
One of EPA’s hopes in developing ISIS-cement and other industrial sector models is that they can accurately simulate the proc ess of capacity growth and renewal in an industry. Replacing older, inefficient and generally more highly pol luting facilities with new and more efficient production capacity is a key process by which any industry reduces its emissions over time. In typical regulatory impact analyses using the traditional engineering costing approach, EPA assumes a rate at which this capital turnover or capital ex pansion will occu r. Thus, in the engineering cost analysis for th e Portland Cement NESHAP, EPA assumes that in the baseline in 2013 (the first year of full compliance), all kiln s existing as of 2005 will continue to produce without any changes, and an additional 20 new kiln s will have come on li ne. ISIS, in contrast, determines endogenously as part of the modeling the extent to which existing kilns expand, change their production process or shut down, and the extent to which new kilns are built. Environmental requirements such as the proposed NESHAP can affect the rate of capital turnover in an industry. Th e ability of ISIS (at leas t in theory) to simulate this process rather than having to assume it is a major advantage fo r this sort of industrial sector modeling.

In general, we believe that the current version of ISIS-cement simulates installation of new or expanded cement production capacity as being less costly than it really is. The model overestimates the degree to which the industry will respond to the proposed NESHAP by bringing new, cleaner capacity on line and underes timates the degree to which the industry will need to spend money to bring existing plants into compliance.

ISIS simulation of capacity changes -- in general

ISIS simulates changes in clinker production capacity of three sorts:

• “replacement kilns”, meaning conversion of an existing kiln from one process to another (e.g., from wet to preheater/precalciner), usually accompanied by a capacity increase. Although many sorts of kiln conversions/repla cements are addressed in EPA’s background documentation for ISIS, onl y two seem to be simulated:

o Wet to precalciner with 20% increase in capacity; and

o Long dry to precalciner with 20% increase in capacity;

• “expansion kilns”, meaning replacement of an existing kiln with a new kiln at the same site; and

• “new kilns”, meaning new Greenfield kilns.
The ISIS documentation says that: “capacity is added endogenously in the regional markets, provided total annualized cost (i.e., annualized capital and fixed and variable O&M costs) of incremental capacity is less than the total annu al cost associated with the corresponding existing capacity.” ( Industrial Sector Integrated Solutions Model , December 23, 2008, page 3-2). We cannot find further docume ntation on exactly how this comparison is ma de or on what is included in “fixed and variable O&M costs.” Annualized capital costs for incremental capacityVersion 1 F-32 EPA External Review are figured as capital co sts multiplied by a ca pital recovery f actor based on 25 years of plant useful life and a 7% real discount rate. EPA staff have indicated that new capacity in a region isVersion 1 F-33 EPA External Review simulated as having variable costs equal to those of the lowest-AVC kiln in the region, including in the calculated AVC the “calibration factor” of 1.9.
In ISIS, capacity additions projected to occu r from 2005 through the year 2010 are taken from some unidentified PCA source (perhaps U.S. and Canadian Portland Cement Industry: Plant Information Summary , December 31, 2006?) and are hard-wir ed into the model. Capacity additions subsequent to the year 2010 are determined en dogenously within the model.

The following table shows the total national capacity changes projected for the years 2013 through 2018 in EPA’s NESHAP run of ISIS-cement.

Base Case

Year Existing Capacity Replacement Capacity Expansion Capacity New Capacity Total Capacity

2005 102.90

-- -- -- 102.90
2013 100.17 3.65 1.49 1.43 106.74

2014 98.35 5.55 2.24 1.77 107.91

2015 97.26 6.95 2.99 4.57 111.77

2016 96.87 8.64 3.73 1.55 110.80

2017 96.47 10.08 4.48 1.69 112.73

2018 95.29 12.74 5.23 2.75 116.02

With Pro p osed NESHAP

Year Existing Capacity Replacement Capacity Expansion Capacity New Capacity Total Capacity

2005 -- -- -- -- -- 2013 99.32 4.36 1.49 2.27 107.44
2014 94.68 5.84 2.24 3.43 106.18
2015 92.98 8.23 2.99 4.57 108.77
2016 90.20 9.20 3.73 5.52 108.65
2017 91.40 10.73 4.48 4.83 111.44

2018 91.17 12.47 5.23 4.91 113.78

It is apparent that capacity additions are rather important in the modeling, with the amount of total capacity represented by existing kilns going down over nearly the entire simulation period, but with total capac ity increasing. (Note that these ISIS runs are base d on projections for future cement consumption that do not reflect the impact of the recession, as discussed previously.) Replacement capacity seems particularly important, accounting for nearly 10% of total capacity in 2018 in the “policy case” (i.e., with the NESHAP).
We have several questions and observations about these simulation results:

• Why should any sort of added new capacity dec line from one year to the next (e.g., in the base case, new capacity is 4. 57 million tons in 2015, but then d eclines to 1.55 million tons in the next year)? We would have thought that any new capacity added in one year because it can produce less costly cement than existing capacity would continue to be less expensive in subsequent years and would thus continue to be used.Version 1 F-34 EPA External Review • The retirements of existing capaci ty that are simulated in the ISIS base case are not unreasonable. PCA itself projects that a substantial share of existing wet kiln capacity will be retired by the year 2020, at a rate of approximately 0.7 million tons per year.
We also offer several comments regarding EPA’s approach in simulating new capacity:

• The total annualized cost fo r any sort of new capacity (replacement, expansion or greenfield) will be simulated as unreasonably low, if only due to EP A’s assumptions in annualizing capital costs that the useful life is 25 years and the real discount rate is 7%. This combination gives a capital recovery factor of only 0.086. It would be more realistic to assume a real hurdle rate of return of some 15 – 20% per year, pa rticularly during the current tight credit conditions and depresse d outlook for the U.S. cement market. Such skepticism about capital investments in cement production capacity will likely continue for the next several years. This discount rate will yield a capital recovery factor ranging from 0.155 to 0.202, roughly double or more what EPA has assume d currently for its ISIS-cement runs. We are awar e that OMB recommends use of 3% and 7% per year as real discount rates, but these recommendati ons are for discounting of social costs and benefits. ISIS in this instance is not discoun ting social costs or benefits, but instead is attempting to simulate capital investment decisions to be made by cement producers. In order to simulate producers’ d ecisions accurately, ISIS must choose a discount rate that accurately reflects the rate th at producers will actu ally apply. Note th at we believe this high real discount rate appli cable to cement industry capita l investments should apply to capital investments in air pollution control equipment as well as to capital investments in new production capacity.

• It’s not clear what EPA means to include in “fixed and variable O&M costs” for any sort of new capacity. We believe th at a cement producer will invest in new capacity only if he will recover all the costs of operating that new capacity over the plan t’s projected life, assuming an appropriate disc ount rate. These costs should include all variable costs (EPA’s 5 categories of variable costs, plus shipping costs) plus all fixed costs (plant SG&A, property taxes, insurance) plus a contribution to corpor ate overhead (corporate SG&A, legal, environmental), plus recovery of the capital costs.

• The most recent PCA assessment of planne d industry capacity ch anges suggests that some of the previously pla nned expansions/replacements/ new capacity from now through 2010 that are included in ISIS have been ab andoned are now unlikel y to occur. PCA will provide EPA with our latest projections regarding capacity changes over the next few years, and the ISIS inputs should be change d accordingly. See the discussion elsewhere in these PCA comments on “Impact of NES HAP Emission Changes on Domestic Cement Capacity” for further informat ion on planned industry capacity changes over the next several years.

EPA’s capital cost functi ons for capacity changes
We are particularly concerned with what ISIS assumes regarding the capital cost of capacity changes. Andover (2008), pages 37 – 47, provides documentation and cost equations for the fourVersion 1 F-35 EPA External Review varieties of capacity changes that are simulated in ISIS. The cost equations are as follows. All figures are in year 2005 dollars.Version 1 F-36 EPA External Review • “Replacement”: Wet to PH/PC, including +20% capacity:

o Capital cost is $93 per ton per year of annual clinker capacity, where capacity is measured in short tons/yr for the final (expanded) kiln size

o This $93 per ton per year figure is appl icable for a 300,000 tpy expanded kiln. Costs for expanded ki lns of larger or smaller size are determined by applying an exponent of 0.6 to reflect scale economies, resulting in a cost per ton of capacity that is lower or higher than the $93 figure.

• “Replacement”: Long dry to PH/PC, including +20% capacity

o Capital cost is $61 per ton per year of annual clinker capacity , applicable for a 250,000 tpy expanded kiln, 0.6 exponent

• “Expansion”: Assumed only PH/PC o Capital cost is $130 per ton per year of annual clinker capacity

o No indication is provided indicating the kiln size for which the $130 per ton of capacity figure is intended, nor is there any mention of the intended exponent to reflect economies of scale. EP A staff have stated in conv ersation that kiln size is assumed at 1.2 million tpy and that the 0.6 exponent for scale economies applies for all capital cost functions.

• “New”, Greenfield: Assumed only PH/PC

o Capital cost is $277 pe r ton per year of annu al clinker capacity

o Again, there is no wr itten indication of kiln size or exponent for scale economies. Again, based on conversation with EPA staff, we assume a new kiln size of 1.2 million tpy and a 0.6 expone nt for scale economies.

We believe that three of these four capital cost functions project costs that are unrealistically low. We arrive at this conclusion by identifying numerous recent planned or completed projects involving changes in cement prod uction capacity, and then compar ing the capital cost estimated for each project by its sponsoring company against the capital cost that would be estimated for that project using EPA’s ISIS cost functions. This anal ysis is presented below.

PCA collects a variety of publicly available press releases, news clippings , wire service reports (INTERCEM, CEMNET, and Industrial Info.com ) and other materials on cement industry capacity expansion projects. Informa tion on the size and projected co st of such proj ects has been compiled in the table below. Please note that thes e estimates are subject to wide variation. The projected size and costs of projects change over time, and we are not certain in all cases that the estimates below are the most recent , nor that the cost figure show n for a project is intended to match the size that is shown for the project (e.g., it is possible th at the cost figu re shown for a project derives from one source wh ile the size figure derives from another source, perhaps with a change in project scope having occurred betwee n the times when the cost and size estimates were developed).

Shown below are the size and estimated cost fo r 23 cement production capacity projects. In the next-to-rightmost column, th e raw cost per ton of ca pacity for each project is scaled using the 0.6Version 1 F-37 EPA External Review exponent so as to estimate what the cost per ton as implied by the project data would be for a project of identical scale as EP A’s benchmark. Thus, fo r example, for the ve ry large 4.8 millionVersion 1 F-38 EPA External Review ton/year greenfield St. Genevieve plant, the raw cost per ton of capacity is $138 ($660 million cost divided by 4.8 million tons/year capacity). However, EPA’s $277 estimated cost per ton for new greenfield capacity is premi sed on a 1.2 million ton per ye ar plant. The St. Genevieve figure of $138 per ton of capacity applies for a 4.8 million ton pe r year plant; scaling this raw cost per ton to a 1.2 million ton per year plant w ould result in a much higher cost per ton – $239 per ton as shown in the next-to-rightmost column.

EXPANSION Scaled to 1.2 million TPY

GREEN FIELD NEW Scaled to 1.2 million TPY

CONVERSION: LONG DRY TO PREHEATER / PRECALCINER Scaled to 250,000 TPY

CONVERSION: WET TO PREHEATER / PRECALCINER Scaled to 300,000 TPY

Plant Company Year Online Cost ($MM) Size (MM tons) Raw Cost per Ton Foreman, T X Ash Grove 2009 $ 190 0.7 $ 271 Hannibal, MO Continental 2008 138 0.6 230 Fernley, NV Eagle 2008 178 0.5 395 Laramie, Wy Eagle 2008 142 0.4 395 Lasalle, IL Eagle 2008 65 0.4 151 Martinsburg, VA Essroc 2008 320 1.0 320 Bath, PA Giant 165 0.3 550

We conclude the following from this analysis:

• The three expansion projects all cost more than EPA’s estimate of $130 per ton for a 1.2 million ton expansion. The three projects sugge st an average cost for expansions at $180 per ton scaled to 1.2 million tons.

• The seven greenfield projects all cost less than EPA’s esti mate of $277 per ton for a 1.2 million ton expansion. The seven projects suggest an average cost for new greenfield projects at $208 per ton scaled to 1.2 million tons. Plant Company Year Online Cost ($MM) Size (MM tons) Raw Cost per Ton

Brooksville. FL New Braunfels, TX Newberr y , FL Cemex Cemex Florida Rock 2008 2009 2009 $ 150 220 165 1.0 1.0 0.7 $ 150 222 244 Cost per Ton Cost per Ton (Industry) (EPA 2005$)
$ 139 $ 130 206 130 194 130

Plant Company Year Online Cost ( $MM ) Size (MM tons ) R a w Cost p er Ton Sumterville

, FL American 2008 $ 200 1.2 $ 167 Seligman, AZ Cemex 2012 400 1.9 211 St. Genevieve, MO Holcim 2008 660 4.8 138 Perry, GA Houston Cem e 2010 200 0.9 222 Pueblo, Co GCC 2009 200 1.0 200 Center Hill, FL Sumter 2009 229 1.7 135 Castle Hayne, NC Titan 2012 469 2.4 195 Cost per Ton (Industry) Cost per Ton (EPA 2005$) $ 167 $ 277 253 277 239 277 198 277 186 277 155 277 258 277

Plant Company Year Online Cost ( $MM ) Size (MM tons ) Raw Cost p er Ton Selma

, MO Buzzi 2008 $ 262 1.0 $ 262 Grand Chain, IL Lafarge 2010 415 1.6 253 Harleyville, SC Lafarge 2009 291 1.5 194 Mitchell, IN Lehigh 2012 400 0.8 488 New Braunfels, TX TXI 2009 350 1.5 233 Oro Grande, CA TXI 2008 358 1.0 358 Cost per Ton ( Industr y) Cost per Ton ( EPA 2005$ ) $ 456 $ 61 536 61 397 61 785 61 478 61 623 61

Cost per Ton

(Industry) Cost per Ton (EPA 2005$) $ 381 $ 93 303 93 465 93 425 93 175 93 518 93 550 93Version 1 F-39 EPA External Review • The 13 conversion or replacement projects all cost far more than EPA’s estimated figures of $61 and $93 per ton. In most cases, these conversions or repl acements cost 4 to 8 times more than EPA’s estimated costs pe r ton. This is partly a function of these conversions or replacements (particularly for long dry kilns) typically being far larger than EPA’s assumed 250,000 or 300,000 tpy size. Another possibility is that these projects are generally not conve rsions, as EPA thinks of them, at all. In most of these cases the existing old kiln is not being conver ted to a preheater/prec alciner, but instead the old kiln is actually abandoned and a new kiln is built to replace it. In this sense, most of these projects represent what EPA thinks of as expansions rather than conversions. Perhaps EPA should abandon the “conversion” concept altogether, and instead assume only expansion/replacement or greenfield as possible capacity changes. Expansion/replacement for exis ting wet kilns might be size d at 0.5 million tpy, the median among the wet kiln conver sion/replacements shown above. Expansion/replacement for other sorts of kiln s might be sized at 1.0 million tpy, the median size across the three expansions and six long-dry replacements shown above.

We believe based on the data presented abov e that EPA should revise their assumed cost functions for capacity changes. Note that each of EPA’s cost functions is currently based on only a single plant or reference; our data abov e covers 23 projects, all for which we have information. We would be willing, if EPA is in terested in work ing with this in formation, to make further efforts to: 1) Obtain better data directly from the sponsoring companies on the size and cost of each project; and 2) Better understand the exact na ture of each of the conversion/replacement projects.

Constraints in ISIS on sel ection of capacity changes

EPA includes the following statement in the ISIS documentation:

“ISIS framework also assumes that: (1) at the star t of the policy, up to 2% of the existing capacity can be replaced in a year, or added to by new or expansion capacity, (2) up to 10% of the existing capacity in each 3-year block can be replaced or added to by expansion capacity, (3) up to 5% of the existing capacity in each 5-year block can be added to by new capacity, and (4) for each of replacement, expansion, or new capacity types, the capacity growth in the block of simulation years remaining after the last 3- or 5-year block, as appropriate, cannot be more that 5% of the existing capacity in the block of remaining years. Implicit in these assumptions are the considerations that replacement and expansion ca pacity changes likely get completed in 3 years, while 5 years are needed for new capacity with site development requirements (Staudt, 2008). Additionally, it is not unreasonable to expect that between the finalization of the policy start year and implementation, some preparation will be undertaken by the affected units, and this may result in modest capacity growth.”

These limits on the amount and rate of capacity chan ges that can occur in ISIS may be important. It is not clear to us exactly what the preceding statement means and what th ese limits are. It is also not clear which of these constraints, if any, end up as binding in the ISIS model runs.Version 1 F-40 EPA External Review Furthermore, discussion with EPA staff indicates that the Agency may have dropped some or all of these constraints in the NESHAP version of ISIS . If so, what other constraints, if any, have replaced these? EPA has provided no documentation on this issue.Version 1 F-41 EPA External Review This is but one example of a general comment: we hope that EPA will provide thorough and clear documentation for ISIS, wr itten primarily in understandable text rather th an programming notation and equations.Version 1 F-42 EPA External Review Imports
The supply function for importing cement and clinker into domestic cement markets would be exceptionally difficult to simulate accurately.

In our view, imports result from one of three scenarios:

• Most imports are arranged by domestic producers as a means of continuing to serve customers and maintaining market share when domes tic sources of supply are insufficient. This sort of imports had accoun ted for well above half of all imports in recent years through 2006, but has declin ed rapidly beginning in 2007 as U.S. construction activity fell and cement plant cap acity utilization began to drop. This class of imports might be viewed as a sort of “swi ng supply” – rising quickly to meet peaks in demand, and then declining sharply when demand falls.

• Some imports, a small minority nationally but a significant share in some areas, are arranged routinely by domestic producers or indepe ndent importers in order to make up for persistent shortfalls in domestic supply to some particular markets

• Additional imports are arranged opportunistica lly by independent importers as a function of U.S. market conditions, global cement supply/demand balances, and ocean shipping
rates.

All three sorts of imports depend to varying degr ees on instances when th ere are excesses of U.S. demand over domestic supply, on episodes of excess world production capacity, on ocean shipping rates, and occasionally on trade policies. These factors would be virtually impossible to model or to predict successfully.

EPA has avoided addressing these very difficult modeling i ssues in ISIS by tr eating the price of imports not as a variable that is simulated in the model, but instead as a va riable that are set in the model to take on a value that makes the re mainder of the model matc h observed data for the period over which the model is calibrated. Said perhaps more cl early, values are assigned for import price in each regional market that make th e rest of the model work in matching historical data. More specifically:

• It is assumed that imports can enter a U.S. regional market in unlimited quantity (up to a maximum amount that we will discuss shortly ) at the import price, which is to be determined;

• Initially, imports are assumed to be available at a price equa l to the AVC of the highest cost kiln that is locat ed in the particular regional marke t. The import price ratio (ratio of import price to production pric e for highest cost local ki ln) is initially set to 1;

• The import price ratio for each market is then adjusted upward or downward by a regional “calibration constant” so as to change the import price in a manner th at results in domestic production in the mark et and imports to the market matching actual data for the calibration year as closely as possible.Version 1 F-43 EPA External Review This calibration process is performed using data for the year 2004. An import price ratio is determined for each regional cement market for which there were imports in 2004. The regional import price ratios that are dete rmined in this manner are then used in running the model for future years.
In addition to establishing thes e import price ratios for each domestic cement market with imports, EPA also establishes another constant that limits the level of imports – the “imports ratio”. Historical data from 1999 – 2007 was ex amined and it was determined that the highest ratio of imports to total nati onal cement consumption was 0.27 . EPA decided that imports in future modeled years could account for no grea ter percentage of regional cement consumption than this historical maximum percentage figure (27%) plus an additional 20% more. In ISIS modeling, then, imports ar e constrained in each re gional cement market to provide no more than 32.4% of consumption in any year.

The supply function in ISIS for im ports in a regional market in any year is thus perfectly elastic (a horizontal line) up to a maximum that is assumed as equal to 32.4% of regional cement consumption for that year. The price at which impo rts are available in each region is given by the regional calibration constant multiplied by the AVC of the highest cost kiln serving that market.

It should be apparent that there is no s ound theoretical basis for presuming such a supply function for imports. This postulated supply function has nothin g at all to do with the factors that determine the actual supply function in reality: the local bala nce between supply and demand, world excesses of supply over demand, ocean shipping rates, etc.. The import supply function as specified in ISIS is simply a set of ma thematical relationships that yielded predicted values for domestic production, imports and price in each regional market in 2004 (the calibration year) that matc hed actual values for th ese quantities as closely as possible. Absent any theoretical justification for such an import s upply function, there is no reason to believe it will apply when ISIS simulates regional cement markets in future ye ars. The mathematical relationship that worked well in matching actual data for the calibration year of 2004 may or may not do equally well in future years; no one can know. The import supply function postulated in ISIS is simply a shot in the dark – it is aimed at where the target was in 2004, but whether it will come close to the target in future years is simply unknown.

Holding this view, we believe it is vitally important to assess how this postulated import supply function works in causing the model to match obse rved data for additional years. Presuming the import price ratios established in testing for 2004, how does the model do in matching data for 2005, for 2006, for 2007 and for 2008? We strongly belie ve that the model needs to be tested (validated) for these additional years. In fact, 2007 and 2008 provide a part icularly important test for the model, si nce they represent very different cement market conditions (a severe recession rather than good times) and very different conditions regarding imports in comparison with the calibration year of 2004. If the model using the import price ratios established for 2004 does a poor job in simulating 2007 a nd 2008, then we suggest that the model will either need to be recalibrated (i.e., calibrate ac ross multiple years fro m 2004 through 2008 rather than for 2004 alone) or the approach to simulating an impor t supply function will n eed to be changed.Version 1 F-44 EPA External Review Our bottom line in evaluating how IS IS simulates imports is thus a judgment that no evaluation can be provided until the model is tested for additi onal years. There is no ba sis in theory for theVersion 1 F-45 EPA External Review import supply functions postulated in ISIS. A set of supply functions has been devised that “worked” for 2004. Ther e is no reason a priori to believe that they will work or not work for additional years. They need to be tested for additional years, and particularly so for additional years that are different in character from 2004.

In the remainder of this sect ion, we offer severa l additional comments about simulation of imports in ISIS:

• EPA makes some errors in crosswalking the US GS data on imports by Customs District to the regional cement markets in which th e imports are eventually consumed. For example, a substantial share of imports to th e New Orleans District goes far up rivers to markets other than Birmingham or Dallas. We reco mmend that EPA work with USGS to revise these crosswalking assumptions.

• Imports have consistently arrived at several of the U.S. regional markets that EPA has classified as “no import” regions. It is not cl ear to us that any region should be specified as “no import”.

• EPA staff have verbally expr essed some interest in perha ps modeling th e impact of import terminals. This could pe rhaps be a part of the approach if EPA were to attempt to model import supply functions substantively, in contrast to the current approach where they are determined mathematically in the “calibration” procedure. PCA could, if desired, provide information to EPA regarding import terminals. We believe generally, though, that import terminals ar e not very expensive nor very time-consuming to arrange, and hence they can be established or de-commissioned without great difficulty. We thus do not foresee data on existing import terminal locations and capacities as being key in modeling the supply of impo rts. Modeling ocean shipping rates would be far more important (and difficult).Version 1 F-46 EPA External Review Cement Transportation Costs
ISIS models the possibili ty of shipping cement from kilns lo cated in another nearby market as one of several means of satisfying demand for cement within a market. ISIS assumes that a kiln can ship cement at no cost within its own market . Costs to ship to anot her market are assumed as follows:

Distance Cost ($/ton) Source
101-200 14.53 Ref

201-300 18.86 Ref
301-500 22.36 Avg of 18.86 and 25.85 500 or higher 25.85 Ref

The reference for these costs is rather obscure, a 1994 case study by American University on Cemex, apparently involving truck transportatio n only. Costs provided in this reference were escalated from 1994 to 2005 based on changes in th e price of diesel. To calculate freight costs from a kiln in one market to another market, ISIS applies these figures to the road distance from the named city in the kiln’s market (e.g., Phoenix) to the named city in the destination market (e.g., Los Angeles). Using this approach, a kiln’s simulated tran sportation cost is unaffected by where the kiln is located within the market.

This approach is not realistic in various resp ects. In simulating the transportation of cement, ISIS should reflect the impact of terminals an d the frequent use of modes other than truck:

• Nearly half of cement shipments reach customers via terminals rather than direct. Shipments to terminals are more than 80% by rail or water rather than by truck. The importance of terminals and bulk shipping modes is increasing over time.

Shipments of Portland Cement fr om Mills by Type of Carrier

(Percent Distribution)

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007**
Percent of Total Shipments

Terminal to consumer 38.3 37.6 39.8 41.1 43.2 43.6 43.9 44.6 46.3 44.9 45.2

Plant to consumer 61.7 62.5 60.3 59.0 56.8 56.4 56.1 55.4 53.7 55.1 54.8
Shipments from plant to terminal (Bulk only)

Rail 44.8 47.4 44.1 49.2 48.2 49.3 49.7 50.7 47.8 47.6 42.6
Truck 14.5 17.3 16.4 17.5 10.8 11.0 18.4 15.6 13.4 20.1 21.5 Barge & Boat 33.0 35.4 39.6 33.5 41.0 39.6 32.2 33.7 35.7 32.3 35.9 Unspecified 7.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0

**2007 based on preliminary data

Source: U.S. Geological Survey

• Nearly half of total cement freight cost is to terminals. PCA’s Benchmarking survey for 2007 found that freight cost to terminals amounted to 4.5% of total cement sales revenues, on average. Freight costs from plan t or terminal to customer amounted to 5.1% of total cement sales revenues. For 2008, re flecting higher energy prices, the comparable figures were 6.6% to termin als and 6.9% to customers.

• The following references provide information on cost per ton mile for bulk shipping via rail and barge. They also provide informa tion on costs for truck shipment, serving as possible supplemental references in addition to the source cited by EPA.Version 1 F-47 EPA External ReviewVersion 1 F-48 EPA External Review 1 (Bureau of Transportation Statistics) General Bulk Transportation "...can only be considered the upper boundary of transport costs for each mode assuming each industry is opera ting at-cost or at a profit"
2 (Washington State Department of Transportation) Cost study to examine options for the transport of wh eat (Jessup and Casavant , 1998) 3 (North American Cement Transportation) Report covers cement specific transportation costs, assumes short tons--converted to m etric

• We suggest that EPA may want to improve the simulation of truck transportation costs as a function of distance by developing a small model. Cost per ton-mile can be estimated as a function of: truck capacity, ro und trip miles from plant or terminal to customer, driver time and wages, fuel cost, and other costs (truck amortization, maintenance and insurance, etc.)

In general, we suggest that EPA should improve the manner in which ISIS simulates the options for shipping cement. At a minimu m, ISIS should include in the AVC for each kiln some cost for shipping within the kiln’s market area, in additi on to costs for shipping ou t of the kiln’s market area. Ideally ISIS would simulate each cement plant’s shipping options to all demand centers the plant might reasonably reac h. This should reflect the termin als to which a plant has access and the location of these terminals. There are on the order of 250 cement terminals in the U.S. that receive domestic shipments and/or imports and exports. Many of these terminals have regular networks consisting of specific plants from which shipments are received and specific demand centers to which shipments are se nt. PCA would be will ing to assist EPA in listing and modeling these terminals and networks. ISIS should also si mulate the bulk shipping options available to each plant (water and rail) in addition to devel oping improved cost estimates for truck shipping. Regarding bulk shipping, note that USGS collect s extensive information on shipping volume by mode for individual cement plants. It is possible that USGS would share this information with EPA, likely aggregated in various ways to prese rve confidentiality. Obtaining such information would assist substantially in improving the accuracy with which IS IS simulates cement transportation. We believe, for example, that at least a dozen cement plants located on the Great Lakes or major rivers in the Midwest have far more market reach than EPA simulates in ISIS, using inexpensive water transport to ship the majority of their product to terminals and/or
customers. Bulk Shipment Cost (Cents per Metric Ton per Mile)

Truck
Rail

Barge Journal of Transport Geography Report 2005 N&S American Cement 2004 ISIS/American University 1994
BTS

1 WDOT 2 NACT 3

33.06 7.64 11.02 7.18
2.78 3.32 5.51
0.89 1.05 2.20Version 1 F-49 EPA External Review Emissions and Cost and Effect iveness of Cont rol Measures

ISIS simulates in a single model deci sions involving both producing cement and controlling emissions. Most of our commenta ry on ISIS has focused on how the model simulates cement markets. Equally important to the predictions that ISIS generates regarding the impact of the proposed NESHAP, however, are the emissions control aspects of the model: the baseline emissions estimates for the NESHAP HAPs and other relevant pollutants, and the assumptions regarding effectiveness and cost of the various possible emissions control measures.

The NESHAP version of ISIS includes iden tical information on baseline emissions and on control cost and effectiveness as th at which EPA has used for the Agency’s traditional engineering cost analysis of the pr oposed rule. We provide extensive comments on all of this information in another section of this economics appendix to the PCA comments.

In September, we intend to re-run ISIS in an attempt to generate an industry perspective on the likely economic impacts from the proposed NESHAP, using as inputs to ISIS both what we believe is better economic data and what we believe is better information regarding emissions cont rol. We intend use the commentary on emissions and control technology cost and effectiveness that is included in the other section of this appendix for this re-run of ISIS.Version 1 G-1 EPA External Review 

## Appendix G

Review Comments from Tyrone Wilson, Portland Cement Associ ation (March 29, 2010)Version 1 G-2 EPA External Review Ms. Elineth Torres Environmental Protection Specialist U.S. Environmental Protection Agency OAQPS/SPPD/Program Design Group 109 T.W. Alexander Dr. Durham, NC 27711

RE: General Comments – Furt her Comments on the ISIS-C ement Model, Based on PCA Experience in Running the Model

Dear Ms. Torres:

This memo supplements the comments on the model that PCA filed in September, 2009, as a part of PCA’s comments on the proposed Portland Ce ment NESHAP regulation. Specifically, this general comment document further expands on issues PCA has identified regarding EPA’s NESHAP version of the ISIS-Cement model. Bo th the September 2009 comments on ISIS and these additional and more general comments furth er respond to EPA’s invitation to PCA to provide peer review of the ISIS-Cement model. Furthermore, as you know, PCA and EPA have been engaged in a cooperative process in which PCA is providing data and suggesting approaches that EPA can use in revising portions of the model in res ponse to our September
comments.

General Background

Portland cement is the powder which acts as the glue or bonding agent that, when mixed with water, sand, gravel and other ma terials, forms concrete. Ceme nt is produced from various naturally abundant raw ma terials, including limes tone, shale, clay, and silica sand. Portland cement is an essential construction material a nd a basic component of our nation’s infrastructure. It is utilized in numerous markets, including the constr uction of highways, streets, bridges, airports, mass transit systems, commercial and residential buildings, dams, and water resource systems and facilities. The low cost and universal availability of portland cement ensure that concrete remains one of the world’s most essential and widely used construction materials.

Further Review of ISIS Based on New Model Runs
PCA’s comments in September were based on evaluating the model’s structure, data and assumptions, but at that time we weren’t able yet to run the mode l and see how it responds to revised inputs. ISIS-Cement is a large and very complex model. Any such model with March 29 , 2010Version 1 G-3 EPA External Review numerous inter-relating elements and modules is impossible to unde rstand thoroughly without actually being able to run it and see how its outputs respond to ch anges in inputs and assumptions.

Since September we have succeeded in getting the model to run (with some difficulty, and with some help from EPA) and have attempted to make the following key changes to the model:

• Demand forecast: Adopt PCA’s most recent long-term cement demand forecast, reflecting the impact of the recession;

• Control technologies: Revise the estimated cost and effectiveness of air pollution control technologies to reflect PCA’s September comments;

• Imports: Revise the approach fo r simulating imports in order to obtain results that better match actual experience;

• New cement production capacity: Make several changes so the model reflects the true costs for new, expanded or re placement capacity rather than unrealistically low costs as originally assumed.

While we provided comments in September sugg esting many more change s to the model beyond these, we believe these four sorts of changes to be among the most important, and they comprise the set of changes that we have attempted to implement in ISIS. In making each of these changes, we attempted to adjust the ISIS-Cement input data and/or parameters to reflect the change, run the model under the new conditions, and then evaluate the resulting model output.

We had hoped that this effort to modify and re-run ISIS would generate a more accurate assessment than has been available previously regarding the economic impact of the proposed NESHAP. At the time of proposal, EPA estimated using a combination of ISIS and traditional engineering cost analysis-based regulatory impact analysis (RIA) techniqu es, that the proposed NESHAP would result in the year 2013 in annualized emission control expe nditures by domestic cement kilns of $222 million (ISIS) or $370 million (RIA), and that total social costs in that year would be $684 million (RIA ). We had hoped, in re -running ISIS using what we think to be more accurate data and assumptions, to generate new and better estimates for the compliance costs, social costs and other impacts of the proposed rule. The initial goal we set for ourselves in modifying and re-running ISIS wa s to generate our own estimates of the impacts of the proposed NESHAP and to compare them against the es timates that EPA had developed in 2009.

In fact, though, we have not be en able to make most of the changes to the input data, assumptions and structure that we think the model needs, and then re -run it under these revised conditions. While we have comp leted more than 30 new runs of the NESHAP version of ISISCement, we have not been able to get very close to running the model using a full set of input assumptions that PCA believes is generally appr opriate. Many of the changes that PCA would like to make to the model are simply not achie vable given the model’s structure and state of development as of mid-summer 2009, when it was given to us. Some of the problems we have had in revising and re-ru nning ISIS are structural in the sense that ISIS is not designed in a manner that will accommodate some of the changes we want to make, and some are operationalVersion 1 G-4 EPA External Review in the sense that ISIS has not been debugged suffic iently so that it will actually run after some of the desired changes have been input. In short, as in September of 2009, we now still have little confidence in the accuracy of the model’s conclusi ons, largely because we have been able to implement few of the changes to input data, assu mptions and structure th at we think the model needs.

As a result, this memorandum on our results in running ISIS-Cement is different in nature from what we originally envisioned, and perhaps different also from what you expected. We do not conclude in this memo anything f rom our new ISIS runs about what we believe the impact of the proposed NESHAP will likely be. We do report in this memo the results of some of the new ISIS runs that we have made, but we regard th ese results as in dicating only the directions in which ISIS will likely respond wh en it is improved, and not th e results that the model will predict when ISIS is up and running satisfactorily. With the limited changes that we have been able to implement in the model, ISIS is still very far from being able to generate reliable and accurate estimates regarding the economic impacts from the proposed NESHAP. EPA is reworking the model considerably, as we ha ve been discussing now for several months.

Given the difficulties we have had in making the changes that we would like to the model and getting it to run with these changes, we repor t extensively on the problems that we have encountered in working with ISIS. We emphasize in this memo not the s ubstantive results that we have obtained in our new r uns (e.g., the specific projections about economic impacts that the proposed NESHAP would cause), but instead the observations that we have developed while working with ISIS. We believe th at these observations will be useful to EPA as the Agency works to further improve the model.

This memorandum contains three sections. In the firs t section, we discuss why it is that we have been unable to make many of the changes to the model that we think it needs, run the model incorporating these changes, and then evaluate the results. In the second section, we summarize the new runs that we have been able to implem ent and discuss some implications of the results from these runs. In the third section, we provide observations and suggestions from a user’s perspective that we have gained from working with ISIS; we hope that EPA will revise the model consistent with these suggestions so that it will more smoothly meet the needs of future

users.

Section 1: Why we Cannot Chan ge ISIS as We Wish and Th en Re-run it to get More Accurate Results

We want to apologize at this point for the extend ed discussion that will fo llow in this section. The following may seem like a tedious list of complaints about how the NESHAP version of ISIS is structured and about what we can’t do with the model but wished we could. We believe, though, that these “use r complaints” about the model will have value to EPA as the Agency considers how to redesign the model. And, we want to be clear about why we have not been able to accomplish what we set out to do in revising and re-running the model so as to generate more accurate estimates of the economic impacts from the proposed NESHAP.Version 1 G-5 EPA External Review There are four major reasons why we have not been able to: 1) ru n the NESHAP version of ISIS Cement using PCA’s preferred data and assumptions ; 2) obtain results from these runs that are different from (and more accurate than, in our op inion) the results that EPA generated in 2009; and 3) compare our new results agai nst EPA’s previous ISIS results.

1. Some of our desired change s would require substantiall y re-programming the overall structure of the model, something we are not able to do

For example, simulating imports in a more realistic manner, or si mulating cement kilns as being able to serve multiple demand centers instead of being lo cated in discrete markets are two changes that we would like to make, but doing so would requi re rewriting large portions of the model code and then debugging and recalibrati ng the entire model. Ma king changes such as these that would involve a different approach to a large section of the model would take hundreds of hours of time and specialized GAMS programming expertise that we do not have. EPA is currently making many changes of this sort in extensively reworking the model.

2. We can’t make and test some other desired changes to the model’s inputs because these changes don’t fit the particular stru cture that ISIS demands for these inputs

Others of our desired changes are smaller and could seemingly be accommodated within the broad design of the NESHAP versio n of ISIS-Cement, but neverthe less have proven to require an excessive amount of work to pr ogram into the model. Most no tably in this vein, we would like to change the cost equations for air pollution c ontrol equipment in ISIS so as to reflect the control cost informatio n that PCA submitted in comments in September, 2009. (PCA provided information that suggests significantly higher ca pital and operating costs for control equipment than what EPA has estimated.) However, to change the control cost equations without reprogramming the model requires that PCA’s cost information be manipulated so as to fit the particular structure and fo rmat that EPA has specified for each cost equation in ISIS. In the time available for our work we have not been able to fit PCA’ s cost information into the structure that ISIS demands, and hence have not been able to re-run the model to reflec t control cost functions consistent with the information PCA provided in September. However, we are continuing our work on this issue, and plan soon to provide a supplement to this memorandum in which we critique EPA’s control cost functions in ISIS, indicate the problems that we have had in revising them to reflect the in formation PCA has submitte d, and summarize the ISIS runs that we have been able to conduct using im proved control cost functions.

The following is a detailed example – for one particular control cost function -- of why we have been unable thus far to revise EPA’s cost functio ns in ISIS to reflect the information from PCA and then to re-run the model.

ISIS requires the function estima ting the capital cost of a limestone wet scr ubber for a kiln to be written in the following form:

Capital cost of LWS = A x (the kiln’s DSCFM) BVersion 1 G-6 EPA External Review Where A and B are constants and the kiln’s dry ga s flow (DSCFM) is a particular function of the kiln’s type (e.g., wet, long dry) , clinker capacity in tons per ye ar, EGFW (wet or actual exhaust gas flow rate), and SFC (specifi c fuel consumption, in million Bt u per ton of clinker). In the NESHAP version of ISIS-Cement, the value of the c onstant A is specified as 8,650 and the value
of the constant exponent B is specified as 0.60. We, or any other ISIS user, could easily change A and B to any other values we thought might be more accurate, and then run the model using the cost function with these different cons tant values. However, in reviewing EPA’s documentation supporting the ISIS control co st functions, we found major errors and inconsistencies in how EPA estimates a kiln’s dry gas flow (DSCFM) and how dry gas flow is presumed to relate to the kil n’s type, clinker cap acity, fuel consumption and wet gas flow. 1 To correct the ISIS capital cost function for LWS consistent with the information available to PCA, it is not sufficient to change only the values of the constants A and/or B. More importantly, we need also to significantly change: i) the underlying relationships that are used to estimate gas fl ow; and ii) the form of the cost function, making capital cost a function of actual or wet gas flow rather than dry gas flow, si nce it is the actual gas flow that determines how add-on control equipment must be sized. However, changing the underlying gas flow relationships or changing the functional form of the equation cannot be accomplished in any sort of direct manner in the NESHAP version of ISIS Cement. Ther e is no location in the model’s input spreadsheets where these relationships appear and are accessible and changeable. Short of finding where these underlying relationships and the equation’s function form are sp ecified in the voluminous ISIS modeling code, we can’t change them and we can’t subs titute into the model a capital cost function for LWS to reflect what PCA believes woul d be correct. In effect, we can’t “fix” the cost function to our satisfac tion and then re-run the model to see what it predicts assuming PCA’s estimate regarding the capital cost of installing LWS as opposed to assuming EPA’s estimate.

This situation we have described regarding the capital cost function for LWS is the case also for all the other control cost functions in ISIS – fo r LWS, for ACI, for RTOs, for baghouses, and for the cost functions for capital co sts, for operating and maintenance costs, and for monitoring costs (CEMS). Absent digging into the model code and changing it, we can’t revise any of these cost functions in ISIS to reflect what we think is be tter information on the actu al costs of installing and operating this equipment. Unable to run ISIS so as to reflect PCA’s cost information, we have taken two other steps that represent, we think, th e best we can do in the time available, short of reprogramming the model:

First, we have tested how the model responds directionally to changes in the control cost functions. While we cannot change the control cost functions in ISIS exactly to reflect what PCA believes to be most accura te, we can test how ISIS respo nds and what economic impacts are predicted to result from the proposed NESHAP when we generally incr ease or decrease the control cost relationships. We can test, for exam ple, the impact of doubling what EPA estimates as the capital cost of an LWS for a kiln by doubling the value of the constant “A” in the cost function that we described above and re-running ISIS using this doubled value of “A”. Or, as

1 As one example of the problems we found in the information underlying EPA’s control cost functions, the amount of gas flow assume d per ton of clinker for most kilns is about twice as high in EPA’s emissions calculations for the NESHAP as in EPA’s control cost calculations in ISIS and the RIA. InVersion 1 G-7 EPA External Review effect, then, EPA estimates approximately twice as much gas flow and tonnage of emissions needing control (emissions are estimated as exit concentration of the pollutant multiplied by gas fl ow) as could be handled by control equipment sized at the gas flow for which EP A has estimated costs. Review ing EPA’s documentation for the control cost functions in ISIS a nd identifying any issues is very time-consuming, and determining how to address any issues using the cost information submitted by PCA takes even longer.Version 1 G-8 EPA External Review another example, we could test the impact of a 20% reduction in the estimated capital cost of LWS by reducing “A” by 20% from the value currently assigned in ISIS (8, 650) to a value that is 20% lower (6,920). We can investigate the gene ral issue of whether ISIS responds in a reasonable manner to control cost relationships that are either higher or lower than those assumed by EPA in the NESHAP version of ISIS, even thou gh we cannot test specifically how ISIS would respond to the particular cost functions that PCA be lieves to be most accurate.

To this end, in this memorandum , we will provide the re sults from a new ISIS run that we have conducted where we have dou bled all of the control cost relationships that are accessible in ISIS without changing the modeling code. Note that in performing this run PCA is not saying that we believe most of the control cost functions in ISIS in fact should be doubled; we are only testing what the model would predict if cost functions were increased in this manne r. To repeat: we can test how ISIS responds to a dir ectional change in th e cost functions, but we cannot (absent much more effort) input PCA’s preferred control cost relationships into ISIS and see how the model responds. We cannot express PCA’s cost inform ation in the particul ar format de manded for input into the current version of ISIS, nor can we reprogram the mode l code to change the cost function format that the model demands to match what we think it should be. 2

The second step that we are taking to address the difficulty of incorpor ating PCA’s control cost information into ISIS is to prepare an additional memorandum on this issue. Within a week or so we will provide EPA with a memorandum that expa nds on PCA’s September, 2009, comments regarding problems in EPA’s control cost functi ons in ISIS, and reports on the limited progress we have been able to make in revising thes e cost functions and running the NESHAP version of ISIS-Cement using the revised co st functions. We have decided to report on this work in a separate memorandum because this work has lagged behind our other efforts to revise and re-run ISIS. We do not want to delay providing to EP A the current general memorandum on our ISIS runs, as would be necessary if we were to incorporate into this overall memorandum our findings also specifically with regard to mo difying the control cost functions.

2 One might ask why PCA could not avoid the issue of changing the structure/functional form of the cost fu nctions by instead simply adjusting the constant term “A” in each of the cost functions so as to roughly approximate what PCA thinks might be more accurate cost estimates? For example, in PCA’s September, 2009 comments, we provided capital cost information based on actual vendor quotes for LWS for four cement kilns and concluded that PCA’s capital cost projections for these kilns are “1.8 to 3.8 times those calculated by EPA’s equations” (PCA comments, Appendix 12, page 8). Why can’t we simply increase the constant term “A” in the ISIS cost function for cap ital costs of LWS by a factor somewhere in this range of 1.8 to 3.8, re-run ISIS making only this simple change to the cost functi on that can easily be input into the model, and see what results? Wouldn’t this approach at least approximate an ISIS run that reflects PCA’s capital cost information for LWS? The answer is “not necessarily”. Whereas the partic ular set of 4 kilns that PCA cited in our cost comments would have capital costs for LWS that range from 1.8 to 3.8 times higher than what EPA would estimate, other kilns of different sizes and types that are addressed in ISIS would have capital costs for LWS that are likely far outside of this range relative to EPA’s estimates. The problem is that we believe EPA has mis-specified the functional form of the cost equation as well as the values of the constants in the equation. If we make a scalar adjustment only to the constant term in such circumst ances, it is likely that the resulting “approximately adjusted” equation would fit reasonably well for kilns within the ra nge represented by the four kilns for which we cited LWS capital cost estimates, but the “approximately adjusted” equation would likely fit poorly for kilns outside this range. Testing the fit of an “approximately adjusted” equati on across the full range of kilns to which the equation would be applied in ISIS woul d represent a significant task.Version 1 G-9 EPA External Review 3. Other elements of ISIS that we would like to change are not capable of being changed, either because of programming errors that have not been corrected, or because these elements are not included in the model’s input spreadsheets

ISIS-Cement has been designed such that the user may change input data and assumptions via a large set of input spreadsheets in Microsoft Excel. The spreadsheets are organized generally by topic area, for example, kiln operating costs, control equipment performance and costs, cement demand projections, etc.. Each input spreadsheet is intended to list all the input data and the key assumptions for each topi c area. A user can change the inpu t assumptions for an ISIS run by locating the appropriate input spreadsheet or spre adsheets and changing the relevant values or relationships in the appropriate ce lls in these sheets. After the i nput spreadsheets are changed, the model itself will then run pulling the requ isite data and relationships from the revised spreadsheets.

However, this process does not work for many of the changes that we would like to make. One common problem is that a data cell in an input sp readsheet in the NESHAP version of ISIS that we are working with is somehow “dead”. The user changes the value of an input cell, but the model does not then pick up this change and ru n with it, either perhaps because the model is altogether disconnected from this cell, or perhap s because the value that has been entered in the cell is somehow over-written by something in the c ode. Examples of input data elements that we would like to change but that are “dead” in this manner include: the costs of monitoring a kiln for the NESHAP pollutants, the cost per ton at which new greenfield cement capacity can be built, the cost per ton for kiln expans ions, and more. There is no way in the NESHAP version of ISIS that the user can change these and other input da ta elements short of lo cating the faults in the model code that render these cells dead and fixing them.

Another somewhat different reason why we cannot change certain model inputs as we would like occurs when there is simply no location in th e input spreadsheets where the input of interest occurs. One can’t change an i nput data element that does not appear anywhere in the input spreadsheets. Changes to the model that we would like to make but can’t because of the lack of any corresponding cell in the input spreadsheets include, for example:

• Changing the manner in which im ports are simulated, including the prices at which they can enter various domestic markets, the ma rkets which they can and cannot enter, any constraints on the quantity of impo rts able to enter a market, etc.

• Adding into the model the seve ral categories of kiln variable costs that appear to be omitted, including the costs of transportation within the kiln’s home region, cost of transportation to terminals, SG&A costs, etc. In fact, because of the lack of any locations among the input spreadsheets where data on omitted categories of operating costs could be entered, we cannot at all test the impact of assuming ki ln operating co sts consistent with PCA’s “Benchmarking” survey in contrast to the rather different operating costs that EPA has assumed.

4. The model is not set up to generate the output quantity the user would most want toVersion 1 G-10 EPA External Review see – the net change in domestic producer and consumer surplus resulting from the proposed environmental requir ement. Omission of this key quantity and other importantVersion 1 G-11 EPA External Review elements from what ISIS disp lays as outputs makes it extr emely difficult to understand and interpret any new runs of ISIS that are performed

. The final reason why PCA has been unable to imp rove the model and run it to generate credible economic impact estimates for the proposed NE SHAP involves difficulties with the model’s outputs rather than with the model’s inputs. The outputs th at ISIS is set up to display are not those that we or any other user would most like to see. The model does not display as outputs the measures that we believe are most important for judgi ng the impact of the proposed NESHAP or any other environmental requirement th at one might simulate using ISIS. We can’t use the model effectively to evaluate the imp act of the proposed NESHAP because ISIS doesn’t tell us in a language that we can understand what the impact of the NESHAP is predicted to be.

In the NESHAP version of ISIS -Cement that we have been wo rking with, the primary model output that is displayed is the total cost of obta ining all the cement consumed in the U.S. This is not the most meaningful quantity with which to judge the impact of th e proposed NESHAP or any other potential environmental policy and, as we will show later in this paper, it often portrays regulatory impacts as desi rable when in fact they are undesi rable. We believe that it is a misleading measure, and certainly not one that s hould be featured as the primary output of the model.

We believe instead that the most important quantity that the model should display as reflecting the impact of the environmental policy under consider ation is the change in U.S. social welfare -- the net change in domestic producer and consumer surplus -- that will result from the proposed environmental requirement. OAQP S also has long belie ved that the net cha nge in producer and consumer surplus (social cost ) is the appropriate fundamental measure of the impact of a regulation. However, the current version of IS IS does not anywhere display the impact on consumer and producer surplus as an output of the model, nor can this key quantity be calculated by the user from the other outputs that are available in ISIS. ISIS should be reconfi gured so as to estimate this quantity and display it as the key output from each model run.

The NESHAP version of ISIS al so does not clearly display as an output of each run another quantity that is critical to understanding the proj ected impact of the proposed NESHAP – the control costs that affected kilns are estimated to incur in order to comply with the regulation. Among the ISIS output spreadsheets there nowh ere appears a simple summary of what the projected control costs are estimated to be. We have calculate d using various intermediate ISIS outputs what we guess the projected control co sts might be for a given run, but there are various locations among the intermediate outputs where different and conflicting values for different portions of total control costs are provided. We ca n’t be sure either th at we are choosing the correct source for a particular portion of total co ntrol costs, nor that we are putting together values from all the correc t sources in order to reach the correct total. We’ll say more later in this memorandum about this problem also. At this point, we want to add only two more comments on this issue:

1. In the second part of this memorandum, we discuss some results we have obtained in new ISIS runs that we have perf ormed. For many of these runs , we show in the summary of the run results the estimated total national cont rol costs for kilns. These estimated controlVersion 1 G-12 EPA External Review costs that we show should be regarded as our guesses about what ISIS is predicting
control costs to be, based on one possible mode of choosing among and putting together the various intermediate and partial control cost estimat es that ISIS generates.

2. We expect that we are wrong in these guesses about how to assemble an estimate of total control costs from the various potential intermediate outputs that are available from ISIS. We spent substantial time working with th e particular run of ISIS-Cement that EPA provided to us and that the Agency represente d as the run that gene rated the control cost estimates referenced in the RIA and Preamble for the propos ed rule. Using the various intermediate outputs from this particular run, we could not match the total control cost figures that EPA said in the RIA and the Preamble that th e run had generated. We thus believe that we are probably assembling various ISIS outputs into an estimate of total control costs in some manner that is different from what EPA intends.

In sum, we have had a series of difficulties in working with IS IS in an attemp t to modify the model and generate results so as to better estimate what the economic im pacts from the proposed NESHAP are likely to be. These difficulties have invo lved the model’s structure, the set of inputs that the user can change, the set of outputs that the user receives, and the mechanics of actually changing and running the m odel. These difficulties are suffi cient to convince us that the model at this poi nt cannot be used to generate reliable estimates for the impacts of the proposed NESHAP. The model needs substantial improv ements (many of whic h EPA has under way) before we can have any confidence in the accuracy of the results it generates.

Section 2: Discussion of Re sults from New ISIS Runs

In this section of the memorandum we provide comments on several of the new ISIS runs that we have performed. We have se lected for discussion one or more new run for each of the four major sorts of changes that we have made to the model, including:

• Demand forecast: Adopt PCA’s most recent long-term cement demand forecast, reflecting the impact of the recession;

• Control technologies: Revise the estimated cost and effectiveness of air pollution control technologies to reflect PCA’s September comments;

• Imports: Revise the approach fo r simulating imports in order to obtain results that better match actual experience;

• New cement production capacity: Make several changes so the model reflects the true costs for new, expanded or re placement capacity rather than unrealistically low costs as originally assumed.

For each of these new runs that we discuss, we have included in Appendix B a page summarizing the results or output from the run. These pages show key model outputs for:

1) The original EPA base case (i.e., without the propos ed NESHAP) run of ISIS;Version 1 G-13 EPA External Review 2) The original EPA “policy” case (i .e., with the proposed NESHAP);Version 1 G-14 EPA External Review 3) The revised base case as represented in our new run. The outputs from the revised base case in our new run will differ from those for the original EPA base case if the new run we are performing involve s some change to EPA’ s original base case , for example , if our new run involves changing the baseline as sumptions regarding fu ture cement demand.

4) The revised policy case as represented in our new run.

The estimated impact of the propos ed NESHAP is given for EPA’s or iginal run by the change in outputs from (1) to (2). In our revised run, the imp act of the proposed NESHAP is given by the change in outputs from (3) to (4). Appendix A to this memo provides further background and explanation on the structure of ISIS-C ement and the policy vs. base case.

In this section, we summarize the results of several of our new runs and provide comments suggesting to EPA what we beli eve to be the implications of the run for furthe r EPA work to improve ISIS-Cement.

Results of new ISIS run: Changing the assumptions about cement demand forecast

EPA’s original run of the NESHAP version of IS IS-Cement, performed to support the proposed rule in April of 2009, assumed that future cem ent demand would decline modestly from 2005 through 2008, then grow fairly steadily from 2008 through 2013. This a ssumption was based on PCA’s cement demand projections as of January, 2008, reflecting PCA’s view as of that time that any recession would be mild and brief.

In our new ISIS run, we substituted PCA’s more recent projections regarding future cement demand, prepared in the summer of 2009, projecting instead a much deeper and prolonged slump in cement consumption as a result of the recession.

As shown on the first page in Appendix B, our new ISIS run using PCA’s more recent cement demand forecast shows total U.S. cement consumption as 124.3 million tons in 2013, in contrast to EPA’s original ISIS run which shows this fi gure as 147.5 million tons. Our new run shows that about half of this 23.2 million ton reduction in cement consumption in 2013 will occur in domestic production (11.7 million tons) and about half will occur via reduced imports (11.5 million tons). This resu lt does not seem appropriate to us. As has be en observed in 2008 and 2009 with the recession, the impact of a reduction in cement consumption below high levels such as prevailed in 2005 and 2006 will be incurred largely as a reduction in imports, and likely much less as a reduction in domestic production. A large share of cement imports to th e U.S. are arranged by the multinational comp anies that own most domestic U.S. cement plants; these companies generally prefer to serve the U.S. ma rket via their domestic plants. Cement imports serve largely as “swing supply”, increasing wh en cement demand incr eases at a time when domestic capacity utilizati on is high and it is di fficult to further increa se domestic production, but decreasing sharply when demand falls and it is easy to meet domestic demand with domestic production. In our view, the results of this new ISIS run suggest a need to revise the manner in which import supply is simula ted in ISIS. ISIS should be re vised such that imports function more as “swing supply”.Version 1 G-15 EPA External Review The new ISIS run simulating the impact of reduced future cement demand with the recession shows the following regarding the impact of the NESHAP. Assuming EPA’s original projections for growing future cement demand, the NESHAP w ould cause a reduction in 2013 cement consumption of 2.8 million tons, consisting of a decline in domestic production of 3.2 million tons and an incr ease in imports of 0.5 million tons. Assuming instead PC A’s more recent recession-affected declining projections for futu re cement demand, our ne w ISIS run shows the NESHAP as causing a reduction in 2013 cem ent consumption of only 1.7 million tons, consisting of a decline in domest ic production of 3.5 million tons and an increase in imports of 1.8 million tons. Comparing th ese two runs, the NESHAP appe ars to cause more economic damage for domestic cement produ cers with the recession than wi thout it. With the recession, the NESHAP appears to cause both a greater loss in domestic cement production (the 3.2 million tpy decline in domestic productio n grows to 3.5 million tpy) an d a greater increase in imports (the increase in imports is 0. 5 million tpy from th e NESHAP without th e recession, but 1.8 million tpy with the recession). This is an im portant result, and one that seems entirely sensible to us. If ISIS is changed from the NESHAP version that EPA ra n in 2009 to a version instead that accurately reflects the impact of the recession in reduc ing projected future cement demand, the model will likely show even more advers e impacts from the NESH AP on domestic cement producers. We expect that the lo ss in social welfare (summed ch anges in producer and consumer surplus) that ISIS projects to resu lt from the NESHAP will be substantially higher if the model accurately represents the impact of the recession in comparison to what the model projected in 2009 when EPA ran it without fully reflec ting the impact of the recession.

Results of new ISIS run: Chang ing the assumed cost and eff ectiveness of air pollution control measures

EPA’s original run of the NESH AP version of ISIS-Cement included a set of assumptions regarding the performance of each potential NE SHAP control measure (percentage control efficiency achieved by LWS, AC I, RTO and baghouses), and a set of cost functions (f or both capital and operating and maintenance costs) for these control measures and monitoring equipment for the NESHAP pollutants. PC A provided comments in September of 2009 suggesting that EPA adopt different assumptio ns regarding control performance, and also included information suggesting likely much higher costs of installing and operating these controls and monitors.

PCA has been very interested in running ISIS to see what the projected impacts of the NESHAP would be assuming PCA’s information regarding the cost and performance of control measures rather than EPA’s estimat es. As discussed previously, though, we have had substantial difficulty in revising the ISIS in puts and running the model to do th is. We will provide a supplementary memo to EPA within several days that reports fu rther on our efforts to revise the control cost functions and re-run ISIS. In th is memorandum, we re port briefly on a new ISIS run that we have done in which we:

• Revised all of the control equipment performance estimates consistent with PCA’s September, 2009 comments; and

• Doubled (relative to EPA’s cost functions) th e estimated costs for most aspects of mostVersion 1 G-16 EPA External Review control measures. Because of various programmi ng errors in ISIS and the impossibility of changing some elements of EPA’s cost functi ons via the ISIS input spreadsheets, we were unable to double all of EPA’s cost func tions. In this new run on which we are now reporting, we were able to double EPA’s estimated capital and operating costs for ACI and RTOs, and to double EPA’s estimated capital costs for LWS. We were able to double only some of EPA’s estimated operating costs for LWS (we were not able to access and change in the model some aspect s of LWS operating costs, including the power cost, the gypsum credit and water costs) . We were unable to change in any way the capital or operating costs for baghouses and for monitors.

In effect, then, this new run of ISIS that we performed re presents a simulation of the impact of: 1) PCA’s assumptions regarding control equipm ent performance; and 2) Doubled costs for probably most of the cont rol measures. We have discussed prev iously in this memo some of the reasons why we could not perform a run that gets closer than this to reflecting all of PCA’s September comments.

The results of this new run as suming increased cost and redu ced effectiveness of control measures are summarized on the second page of Appendix B. The results show:

• Assuming increased cost and reduced effectiveness of control measures, as expected, causes the projected economic impacts of th e NESHAP to intensify. Whereas under EPA’s assumptions the NESHAP causes cement price in 2 013 to increase by $1.19 per ton and cement consumption to decline by 2. 8 million tons, using assumptions more in line with PCA’s commen ts causes cement price to increa se more (the increase nearly doubles, to $2.17 per ton) and cement consumption to fall by much more (the drop in consumption increases from 2.8 million tpy to 41.2 million tpy). These ISIS results are in the expected direction, but we are very surprised at the model’s prediction that cement consumption will fall so much more (41.2 milli on tpy vs. 2.8 mi llion tpy) assuming lesser effectiveness/higher costs for controls than what is predicted assuming EPA’s effectiveness and cost estimates. This resu lt needs further investig ation. Why should the change in assumed control effectiveness/cost cause cement consumption to decline so drastically? Is it the change in assumed control effectiven ess that causes this, or the change in assumed control costs? We presume that the answer has to do with how ISIS simulates the response to the NESHAP of kilns whose baseline emissions of some HAPs are so high that there is no control availabl e that is sufficiently effective to reduce emissions of each HAP to the level required under the NESHAP. We suspect that ISIS simulates that such a kiln that can’t control down to the NESHAP level will close. This might explain the result we observe in our new run: perhaps with PCA’s assumed lower control effectiveness, many k ilns will be simulated as un able to meet the NESHAP requirements, these kilns will close, and (perhaps) cement consumption will fall sharply. On the other hand, we would expect imports to increase to replace cement supply from the closed domestic kilns, whic h does not seem to be occurring in this new run. Again, we are investigating this surpri sing result in the further runs we are doing and will report on in the forthcoming supplementary memo. In these runs, we are investigating, among other things, what ISIS proj ects when we reduce control e ffectiveness or increase control costs separately, rather than together.Version 1 G-17 EPA External Review • In any case, it is clear that revised assumptions regarding c ontrol cost and effectiveness can have a very significant impact on what ISIS estimates in terms of economic impacts of the NESHAP. This high sensitivity of ISIS results to assumptions about control cost and effectiveness emphasizes the need for EPA to run the model using accurate and realistic estimates regarding co ntrol cost and effectiveness.

• The results of this run also emphasize the im portance of a point we make later in this memo. We believe that the NESHAP ve rsion of ISIS-Cement focuses on an inappropriate quantity as the bottom-line for any model run. EPA’s NESHAP version of ISIS-Cement is set up so that the primary output of each run that is provided to the user is the total cost to the nation of obtaining the quantity of cement that is consumed in any given year. The total cost of obtaining cement is, largely, the sum of the cost for all the nation’s cement that is produced domestically plus the cost of all the nation’s cement that is imported. Looking at the rightmost columns describing this new run (on the second page of Appendix B), we can see that EPA finds us ing the Agency’s assumptions regarding cost and performance of controls that the NESHAP causes the cost of obtaining the nation’s cement to increas e by $52 million in 2013 – th e cost of domestically produced cement increases by $15 million (consist ing of control cost s of $218 million offset by a reduction in production costs of , presumably, $203 million, as less cement is produced domestically) while the cost of obtaining imported cement increases by $37 million. In contra st, our new run pr ojects that, using different assumptions about higher cost of controls and lower effectiveness, the co st of obtaining the nation’s cement in 2013 will decline by nearly $2 billion per year (cons isting of a reduction in the cost of producing domestic cement of mo re than $2 billion per year, pl us an increase in the cost of imported cement of $34 million per year ). Focusing on EPA’s bottom line in the NESHAP version of ISIS-Cement that is de fined as the total cost of obtaining the nation’s cement, it appears that that incr easing the assumed co st and reducing the estimated effectiveness of controls makes the NESHAP appear less onerous rather than more onerous. In our new run, the total cost of obtaining the na tion’s cement goes down sharply when we revise the assumptions rega rding control cost an d effectiveness. We view this as strong evidence to the effect that EPA has chosen the wrong bottom line. Yes, the cost of obtaining the nation’s cement goes down in this new run, but this is because the nation has obtained much less cement. EPA needs to focus on a bottom line in the outputs that the model provides that is mo re meaningful than the total cost of obtaining the nation’s cement supply. As we discussed previously, we believe that the correct bottom line for assess ing the impact of the NESHAP should be the change in social welfare (sum of producer and consumer surplus) resulting f rom the NESHAP, and certainly not the change in cost of obtaining the nation’s cement.

Results of new ISIS runs: Changing the manner in which imports are simulated
We commented previously that we have not be en able to revise the manner in which ISIS simulates cement imports and re-run the model acco rdingly, since the mode l is not set up to allow the user to change the fundamental manner in which imports are simulated. In the NESHAP version of ISIS-Ce ment, imports are not modeled as ha ving a supply fu nction for eachVersion 1 G-18 EPA External Review regional cement market, as is the approach for do mestic kilns. Instead, imports are modeled as supplying whatever is the residual between the quantity of cement consumed in a domesticVersion 1 G-19 EPA External Review market and the quantity of cement provided by domestic kilns serving that market. In effect, the price and quantity of imports supplied to any do mestic market are estimated in the model as whatever gives the best fit relative to actual hist orical market data on price and quantity after domestic kiln costs and quantities are accounted for. For the ISIS user to change this sort of residual approach to simulating imports, the user would need to rewrite large portions of the model code and then debug and recalibrate the en tire model. We have not attempted to do so.

Instead, we have performed 9 ne w runs of ISIS intended genera lly to test the manner in which the model responds to increased assumed prices for imported cement. ISIS models the price of imports in each domestic cement market assuming th at there is a ratio of the import price to the cost per ton for the highes t cost domestic producer in that region – the “import price ratio”. EPA estimated the import price rati o for each region by fitting to 2005 data on cement price, consumption and import volume; the “import price ra tio” in ea ch region was estimated as whatever value made the model predictions fit most closely to actua l data. We tested in our new runs what happens if we increase the estimated im port price ratios in each region by a specified percentage. We chose this approach for investigating how ISIS re sponds to chan ges in the procedure for simulating imports for two reasons: 1) The import price ratio for each region is one of the few elements of import si mulation in ISIS that is reasonably ac cessible and subject to being changed; and 2) Increasing the import price ratio is a straightforward way to test how the model might respond to higher prices for imports.

Pages 3 and 4 of Appendix B provide a summary of our results from 9 ISIS runs in which we increased the import price ratio in each of the 17 regional cement markets where ISIS presumes that imports play some role (ISIS [wrongly] presu mes that imports play no role in several other regional cement markets). In the 9 runs, as shown on page 4, we increased the regional import price ratios by 0 % (i.e., matching EPA’s ISIS base case run), by 1%, by 2%, by 3%, by 4%, by 5%, by 10%, by 15%, and by 20%. For each of these percentage increases in impo rt price ratio, we plot the resulting change in quantity of imports that the mode l estimates is supplied to the U.S. in total. The results of th ese model runs appear generally reasonable. As the price at which imports can be obtained in the U.S. increases, the estimated quantity of imports that is consumed declines. Judging from the two new runs at the extremes (0% and 20% increase in import price ratio), a 20% increase in impor t prices in all U.S. markets would cause approximately a 60% decrease in import consumption (from 205 million tons over 2013 to 2018 to 80 million tons over this period). This implies a price elasticity of substitution for imports of about 3, which is at least plausible (some references suggest higher figures).

We conclude from these new runs that the NESHAP version of ISIS-Cement at least behaves in the right direction regarding imports. We beli eve, though, that ISIS needs a fundamentally different approach to simulating imports than what exists in the NESHAP version of the model. As we have discussed in the PCA comments in September in 2009 and in several recent conversations with you, we think that imports should be modeled as being more of a “swing supply”, with the elasticity of s ubstitution being lower when domes tic kiln capacity utilization is high and higher when domestic kiln capacity utilization is low. A change in the price at which imports can be obtained will af fect the quantity consumed in the U.S. by much more when domestic kiln capacity ut ilization is low than it will when domestic kiln capacity utilization isVersion 1 G-20 EPA External Review high. We believe from our disc ussions with you that EPA is proceeding along these lines in revising ISIS.Version 1 G-21 EPA External Review Results of new ISIS runs: Making new, replacement and expansion kilns more expensive

We commented previously that the NESHAP vers ion of ISIS-Cement appears to make the costs of additional domestic cem ent production capacity unrealistical ly inexpensive. We anticipate that this may cause ISIS to overestimate the extent to which domestic cement producers will respond to the NESHAP by building new capacity (which is less expensive to control) rather than paying the higher costs of controlling ex isting capacity. We conducted new ISIS runs to investigate how the model responds to assuming higher and, we th ink, more realistic costs for new capacity rather than what was assumed by EPA in the NESHAP vers ion of the model. The results of several of these new runs are summarized on pages 5 – 7 of Appendix B.

In the new run summarized on page 5 of Appendix B, we simulate the impact of increasing the hurdle rate of return that domestic cement pr oducers need in order to make new capital investments from 7%/yr on a real basis to 14%/year on a real basis. 8 We believe the 14% figure is likely more realistic. This run shows an impact in the expected direct ion when one assumes a higher required rate of return for investing in ne w capacity. In the base case assuming a 7% rate of return is needed for investi ng in new capacity, domestic cement produc tion in 2013 is 112.8 million tons. Assuming instead that a higher 14% rate of return is required, the model simulates domestic production as on ly 110.2 million tons. The higher as sumed rate of return apparently shifts 2.6 million tpy of prod uction from domestic kilns (presu mably new domestic kilns that would have been built with a 7% required rate of return that would not be built with a 14% required rate of return) to imports. This resu lt is plausible and in the expected direction.

The impact of this change in required rate of re turn on the estimated impact of the NESHAP is interesting. In EPA’s base case, assuming a 7% re quired rate of return, the NESHAP results in a $15 million increase in the co st of producing domestic cement in 2013, c onsisting of $218 million in additional control cost and a nearly offsetting reduction of $203 million in cost of producing this cement (the cost of producing domestic cement decl ines, despite th e control costs prompted by the NESHAP, because the NESHAP causes 3.2 million tons less domestic cement to be produced). The results of our new run assuming a 14% required rate of return, in contrast, show the NESHAP as caus ing a lesser increase in c ontrol costs ($216 million vs. $218 million in 2013), but also a lesser reduction in production costs ($21 6 - $33 = $183 million reduction relative to $203 million reductio n in EPA’s base case). Eviden tly the simulated increase in

8 Note that we are uncertain about the categories of capital investment to which ISIS-Cement applies this hurdle rate of return. We are certain that it a pplies in simulating the decision to i nvest or not in new greenfield cement production capacity. It may or may not appl y in simulating the decisi on to invest or not in pollution control capital equipment – the discount rate that applies in annualizing control equipment capital costs may be set in ISIS as whatever value is input in this hurdle rate cell in the ISIS input spreadsheets, or the discount rate for annualization may be hard-wired elsewhere in the model as 7%/year real. The ISIS documentation is uncl ear on this subject. It is also not clear that the change that we are making in this run to the hurdle rate of return applies to kiln replacement and to kiln expansion as well as to new greenfield capacity. It has become apparent to us that some portions of the ISIS simulation of kiln replacement and kiln expansion are disconnected from the ISIS inpu t spreadsheets and instead are somehow over-written by the model code – in whic h case changing the hurdle rate of return as we do in this run will have no impact on the model’s predictions re garding kiln replacement and/or expansion. Thus, because of shortcomings in the model documentation and possible problems in the model code, we cannot be sure exactly how far the change we make in hurdle rate in this new run goes in affecting ISIS’ simulation of investments in controls and in kiln repl acement and/or expansion.Version 1 G-22 EPA External Review hurdle rate of return caus es some new, low cost ki lns that were estimated in EPA’s base case to come on line despite the NESH AP to no longe r be built, thus reducing control costs but increasing production costs. This is interest ing and, we guess, plau sible. Tracking and understanding these sorts of impacts in ISIS w ith regard to new capacity would be greatly facilitated for the user if the ISIS outp uts included an ea sy-to-follow, kiln-by-kiln display of when the model simulates the addition of new capacity and when it simulates the closure of existing capacity.

On pages 6 and 7 of Appendix B, we summarize th e results of another run where we change the costs at which ISIS assu mes another form of ne w capacity can be built. As we commented in 2009, in the NESHAP version of ISIS-Ceme nt, EPA had made kiln replacement very inexpensive in terms of the cost per ton of capacity to convert an old, wet or long dry kiln to a larger preheater/precalciner kiln. In EPA’s base case ISIS run analyzing the proposed NESHAP, 3.65 million tons/year of such “r eplacement” capacity were simu lated as installed by the year 2013. In our revised run of the model, though, we make th e cost of such kiln replacement extremely high, changing the cost per ton to convert a wet kiln from $93 to $1,000,000 and changing the cost per ton to convert a long dry k iln from $61 to $1,000,000 also. In the new run, we expected that because these replacements are being made so expensive, the model would not simulate any such kiln repla cements at all as occurring. We were wrong; th e model under the new run continues to simulate exactly the same 3.65 million tons/year of kiln replacement capacity as coming on line by 2013 as was simulate d in the original run. Page 7 shows that the model output in the new run is completely uncha nged relative to EPA’s base case NESHAP run. This is clear evidence th at something is wrong with the mode l. We believe, as we will discuss later, that the cell in the ISIS input spreadsh eet where the user can specify the cost per ton for replacement capacity is somehow “dead” – either the model code does not cause this input cell to be pulled from, or the model code somehow overwrites whatever value the user has entered into this input cell. Nothing ha ppened in our new run when we changed the cost per ton in this cell to $1,000,000. This is an example of one of the ma ny apparent modeling errors that EPA will need to fix.

Section 3: Comments on the Mo del from a User’s Perspective

In this concluding section of the memo, we consider our experience in attempting to change and run the NESHAP version of ISIS-Cement, a nd provide suggestions for improvements, from a user’s perspective. We address topics such as:

• Does the model run smoothly and produce reasonable results?

• Does the model generate the out puts the user would most like to see, in an easy-tounderstand format?

• Can the user easily change the data inputs and assumptions of greatest interest, and do the model’s outputs change in a re asonable and expected manner?
The remainder of this memo includes our comments on these topics.Version 1 G-23 EPA External Review 1. The model is not set up to generate the output quantity the user would most want to see – the net change in domestic pr oducer and consumer surplus resulting from the proposed environmental requirement. Instead, the model generates as a featured output measure the total cost of obtaining all the cement consumed in the U.S. This is not a meaningful quantity with which to judge the impact of the proposed NESHAP or any other potential environmental policy

ISIS simulates the impact of a wide variety of potential actio ns by U.S. cement producers and consumers – operate or close dom estic cement kilns; add new or replacement or expansion capacity; implement emission controls as necessa ry to meet environmental requirements; import cement; transport cement to meet regional de mands – on the price and quantity of cement consumed in the U.S. The central mathematical operation performed by ISIS is to select a combination of all these actions so as to minimize the total cost of obtaining th e quantities of cement projected to be consumed in the U.S. in each future year. The total cost that the model seeks to minimize is defined essentially as follows:

Total cost of obtaining cement for the U.S. = opera ting costs for each U.S. kiln that is producing + capital costs of all new capacity that operates + costs of environmental controls + costs of all imported cement + costs of any inter-regional cement transportation

When used to assess the impact of a propos ed environmental requirement (e.g., the proposed Portland Cement NESHAP), the model estimates th e least-cost means of obtaining all the cement needed by the U.S. in the “base case” (i.e., without the environmental requirement) and compares this against the least-cost means of obta ining all the cement needed by the U.S. in the “policy case” (i.e., with the environmental requirement). The total cost of the environmental requirement or policy is then estimated in the model as:

Estimated total cost of the environmental requirements/policy = (Total cost of obtaining the cement consumed in the policy case) – (Total cost of obtaining the cement consumed in the base case)

These quantities are the key model outputs from th e NESHAP version of ISIS-Cement, shown in the ISIS model output spreadsheets as “Policy Total Cost” and “Base Case Total Cost” on the “AggregateResults” worksheet.

At first glance this difference in the cost of obtai ning all the cement consum ed in the U.S. might appear reasonable as the basic measure for the net impact of the environmental requirement or policy. In view of the mu ltiple ways of obtaining cement that might be affe cted in response to an environmental requirement (i.e., install controls at existing kiln s; build new, cleaner production capacity; import cement rather than produce it domestically from controlled kilns; transport cement from one region where there may be a surplus of produ ction to another region where there may be a deficit), it seems reasonable to as sess the total cost of obtaining cement across all of these options and then to compare the total cost after the environmental requirement against the total cost before the environmental requirement.Version 1 G-24 EPA External Review But what if the quantity of cemen t that is consumed in the U. S. changes as a result of the environmental requirement, as will often occur? The compliance costs associated with the environmental policy being analyzed will cause the cost of produci ng a ton of cement to increase for many domestic kilns, which will result in so me post-policy equilibr ium combination of a higher market price for cement and less ceme nt being consumed. It is possible that the environmental policy will result in a sufficient reduction in domestic cement consumption so that the total cost of obtaining all th e cement consumed in the policy case is less than the total cost of obtaining all the cement consumed in the base cas e, despite the increase in the cost per ton of producing cement domestic ally. Would such a reduction in th e total cost of obtaining all the cement consumed in the U.S. represent an impr ovement in the economic well-being of the U.S. as a result of the envi ronmental requirement? Not necessarily. A reduction in the total cost of all the cement consumed in the U.S. would seem to be a posi tive result, but on the other hand, this lower cost would be paid for a lesser quantity of cement than woul d have been consumed without the environmental requir ement. Any judgment about ec onomic well-being as it is affected by the environmental requirement should reflect some consideration regarding both the cost of obtaining the cement that the U.S. cons umes, and the quantity of cement that is obtained and for which this cost is paid. The change in total cost for obtaining the amount of cement consumed in the U.S. is inappropriate as the basic measure of the cost of the environmental requirement being considered. ISIS should generate a more meaningful meas ure that reflects both the cost of obtaining cement and the value of the cement that is obtained.

OAQPS has long recognized that the social cost of ac tions taken by privat e sector entities to comply with an environmental regulation is gi ven by the net change in consumer and producer surplus in the markets affected by the regulation. 4 For many years, OAQP S’ Regulatory Impact Analyses (RIAs) have estimated the social co st of a regulation under consideration by estimating how the proposed regulation would affect th e equilibrium price and quantity for goods in affected markets, and th en by summing the resulti ng expected changes in consumer and producer surplus. For example, in the April, 2009, RIA for the proposed Portland Cement NESHAP, OAQPS estimated the total social cost of the rule in 2013 as $605 million, consisting of: 5

• -$402 million (loss) in U.S. consumer surplus;

• -$204 million (loss) in producer surplus among domestic cement producers. This consists of a loss of pr oducer revenues of $341 million, partly of fset by a reduction in producer costs of $137 million (consisting of a reduction of $372 million in cement production costs partly offset by an increase of $235 million in regu latory compliance costs);

• $89 million gain in produce r surplus among foreign ce ment producers. (Foreign producers gain from a regulation affecting U.S. producers, since the regulation would cause equilibrium cement prices in the U.S. to increase and forei gn producers could then export cement to the U.S. and sell it for a higher price than would have been the case without the regulation.) (We would argue that OAQPS should estimate specifically the

4 See, for example, the OAQPS Economic Analysis Resource Document , April 1999, pages 5-45 through 5-49.Version 1 G-25 EPA External Review 5 Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industr y. Final Report. April, 2009. Pages 3-10 and 3-15. + $89 - $88 - $402 - $204 = - $605, the total loss in consumer surplus th at OAQPS estimated for the proposed NESHAP. domestic social cost of a regulat ion, leaving out any impact on foreign consumer or producer surplus. If OAQPS had done so for th e Portland Cement NESHAP, this estimated $89 million gain in foreign producer surplus woul d have been omitted from the social cost calculation.)

• $88 million in direct compliance costs for wh ite cement kilns and new kilns, two cement industry segments that were not addressed in the cement market anal ysis that estimated changes in producer and consumer surplus.

EPA should re configure ISIS so th at it generates an output measure that directly parallels the total social cost measure that OAQPS has traditionally estimated in its RIAs for potential regulations.

We would be pleased to work with EPA staff in determining how ISIS might estimate the changes in domestic consumer and producer surplus for the environmental requirement being simulated. We believe generally that the model should estimate domestic consumer and producer surplus in the base case, then estimate them in the policy case, and then determine the net change between the two cases. The inform ation needed for estimating producer surplus seems easily available in the model as revenues for each producer (quant ity of cement sold in each region multiplied by its re gional price) less the pro ducer’s production (including environmental) and transportation costs. Note th at the producer surplus calculated in the model should include only the surplus realized by domestic cement producers. An y increased surplus generated by foreign cement producers via an increase in U.S. cement imports should not count as a benefit to the U.S.

The process for estimating domest ic consumer surplus would ap pear to be somewhat more difficult. Regional price mu ltiplied by regional quantity consumed will give consumers’ expenditures in each region, but it is not clear to us how then to determine the entire regional demand curve as would be necessary to estimat e consumer surplus. The estimate of demand elasticity given in the model now provides a start. We would be willing to assume as a first approximation that this demand elasticity is co nstant across all cement regions or demand centers. However, we are uncertain whether this el asticity estimate should be regarded as a point estimate, as an interval estimate, or whether the entire demand curve should be regarded as constant elasticity. We would pleased to disc uss with EPA how producer and consumer surplus might be estimated given the informati on already included in ISIS-Cement.

2. The model needs extensive clean-up and de-bugging before it will run smoothly as intended. Better documentation is also needed.

In the version of the model we were given, hidden er rors in the modeling code prevented ISIS from running at all until they we re identified and fixe d. An asterisk impr operly removed from one line of the code prevented the policy case (a key portio n of the model needed to estimate the impacts of any environmental requirement being investigated) from runn ing. The model often ran intolerably slowly, in some instances taking more than 24 hours to conclude a run whenVersion 1 G-26 EPA External Review using a PC with less than an exceptional amount of RAM. Apparently there are “switches” in the code that affect: 1) the number of iterations the GAMS solver performs in finding an optimalVersion 1 G-27 EPA External Review solution and 2) whether the model performs additi onal iterations toward finding an equilibrium price and quantity for cement after estimatin g the initial impact in cement markets.

In these and other instances, the model would not run smoothly until we or EPA found and fixed the problem or developed “work-ar ounds.” We expect that additiona l portions of the model that we have not yet tested are like ly to have similar issues. In ge neral, we would hope that ISIS would be debugged so that it can be run smoothly by a user who has no e xpertise in the GAMS programming language and without any need to alter the GAMS modeling code.

In the NESHAP version of the model provided by EPA, many elements in the Excel input spreadsheets used to specifying the input conditions for a model run are “dead”, in the sense that changing them yields no changes in the model outputs. For example, as we discussed previously, one Excel input spreadsheet lists the cost-per-ton at which new cement production capacity can be purchased. If the user changes this cost-per-ton i nput, it should presumably affect the frequency with which the model simulates clean new kilns being brought on line. An increase in the cost-per-ton should decrease the purchase of new capacity. This does not prove to be the case, however, when we actually run the NESHAP version of ISIS – the model outputs do not change when a user changes the assumed cost of new cement production capacity in the input spreadsheet. The model is similarly non-responsive when the user changes EPA’s assumptions regarding the cost of monitori ng for the NESH AP pollutants. The model includes an input spreadsheet show ing estimated capital and annual costs of the four monitors necessitated by the proposed NESHAP. 9 Changing any of these monitoring costs included on this input spreadsheet has no impact on the model’s results. It also appears that the model does not respond to changes in the input spreadsheets regarding the cost-per-ton for kiln expansions, 10 nor does it respond to ch anges to the “switch” de termining whet her or not the model simulates a further demand response to the first-round si mulated changes in cement price resulting from the environmental policy being investigated. 11 The only way to change any of these “dead” Excel inputs is to change the values directly within the GAMS programming code. These and likely other instances that we ha ve not identified wher e the input spreadsheets are over-written by the GAMS c ode should all be found and fixed.Many other input data items that the user might like to change cannot be modified via the input spreadsheets for other reasons. The issue here is not that a cell in the input spread sheet is somehow “dead” and disconnected from the model (as in the previous), but instead the problem is that there is no location in the input spreadsheets where the inpu t of interest appears and can be changed. One cannot easily add into the model the several categor ies of kiln variable costs that appear to be omitted (e.g., cost of transportation within the kiln’s home region, cost of transportation to terminals, SG&A costs), because assumptions rega rding these cost elements do not appear in and are not accessible via the input spreadsheets. User-defined changes of this sort might perhaps be accomplished by modifying th e GAMS model code, but the typical ISIS user will not have the ability to do th is, and the model should not be designed in a mann er such that this is necessary. Other input changes th at a user might likely want to make that cannot easily be accomplished via the input spreadsheets include:

9 Hg CEMS, THC CEMS, HCl CEMS, and bag leak detectors. See the newControls tab in the ISIS_inputs.xls workbook.
10 Worksheet: Econinputs, Cell: L2, Variable: Expcapcost
11 Worksheet: Policy, Cell: A30, Variable: DemandrespVersion 1 G-28 EPA External Review • Changing the manner in which imports are si mulated, including the prices at which they can enter various domestic markets, the markets which they can enter, the constraints on increasing import quantities from year to year, etc.

• Changing some of the ancillary elements of control technology costs, including the electricity requirement s and gypsum credit fo r wet scrubbers, the fuel requirement for RTOs, etc.

• Changing the assumptions regarding discount rate and useful life for control measures,

• Changing the cost-per-ton at which kiln replacement can occur, 12

• Changing the assignment of kilns to markets and the cement transportation options available to particular kilns.

All of these difficulties that the user has in working with th e current version of ISIS-Cement are heightened by the inadequate and incomplete nature of the written documentation for the model. Much of the documentation reli es on reproducing equa tions that exist in the GAMS code, using difficult mathematical names for variables that in clude multiple subscripts. Much more text to explain the content and intent of each equation and variable would be very helpful. Some sort of user guide on how to work with the model would be useful in addition to the documentation about what is in the model.

3. More work is needed to improve IS IS’ output capabilities and display.

While the Excel workbook presenting the results from a run of ISIS-Cement is extensive, including seventeen different worksheets, many re sults that the user wo uld like to see are not available in the workbook or ar e presented in a less-than-idea l manner. We suggest that EPA improve the model outputs in the following ways:

• It would be helpful for a user to be able to see the model outputs for the years 2005 through 2012, in addition to the results as currently show n for the years 2013 to 2018. Showing model results for the years 2005 through 2009 would allow the user to test the model’s estimated results against actual historical data for these years. This is important for model validation an d calibration. Showing the mode l results for 2010 through 2012 would enable the user to see the ramp-up of regulatory impacts over the years before full implementation of the NESHAP in 2013.

12 Note that the user’s inability to change kiln replacemen t costs occurs for a reason different from the previously mentioned inability to change the cost-per-ton for new ki lns and for expanding kilns. For new and expansion kiln costs there are cells provided in the i nput spreadsheets that are intended to be available for change, but these cells appear unintentionally to be “dead”. For kiln replacement cost s, though, no cell appears to be available in any input spreadsheet that would allow a user to change the value assumed by the model.Version 1 G-29 EPA External Review •

• Many key outputs are shown only as discounted sums across the six years from 2013 t hrough 2018. It would be useful instead to see the undiscounted values for each year, as well as the discount ed total across this period.

• It would be useful to be able to see the total nationwide co st of the compliance measures (emission controls plus monitoring) simulate d by the model in each future year. Also available should be the split of these cost s by capital vs. O&M. Although the total annualized cost of the compliance measures in each year is a key summary measure, it will also be useful to know the capital cost component sp ecifically. For example, we would like to know what the m odel projects as the total capita l investment that will be required of the U.S. cement industry to co mply with the propose d NESHAP over some period of future year s. Likewise, the costs of all new capacity (rep lacement, expansion, greenfield, etc.) projected as installed in futu re years should be sh own both in annualized form and in terms of capital and O&M. Ultimat ely, we are interested to see an estimate of the projected total capital requirement for the U.S. cement industry as a result of the NESHAP (capital investments directly for compliance plus additional capital for new capacity) and to compare this against historical rates of capital investment by the industry.

• The model should show explicitly , as outputs, each of the qu antities that EPA reported in April, 2009, as ISIS “results” in the docke t for the proposed rule. We want to trace how EPA got to the results the Agency reported and then investigate how the results would change if model inputs or assumptions were changed. We can’t do this, though, with the limited set of model outputs that are now shown in the model’s output spreadsheets. By ISIS “results” we mean each of the quanti ties reported in the pr eamble to the proposed NESHAP (e.g., pages 21165 – 21 167) and those reported in th e ISIS Technical Support Document ( Industrial Sector Integrated Solutions Model. Report. USEPA, OAQPS. April 2009). For example, we cannot find in the ISIS_outputs Excel workbook (which provides the outputs from ISIS-Cement) any estimate of total control costs due to the NESHAP to compare against the $222 million figure that the pream ble (pages 21166 and 21167) and the ISIS TSD (page 33) cite as IS IS’ estimate for total projected control costs in 2013. Where among the available output s from ISIS did EPA obtain this figure? 10

10 With some painstaking work with va rious of the ISIS_outputs spreadsheets and the GAMS GDX files (these provide intermediate ISIS m odel results that the model th en uses to generate the output spreadsheets) and some effort at learning and applying GAMS programming code, we attempted to put together numbers from the IS IS-Cement NESHAP run that EPA sent to us so as to match the $222 million figure that EPA cites in the docket as the ISIS estimate for total cement industry control costs in 2013. However, we could match only some of the components of this $222 million total. We could match the $63 million that the ISIS TSD cites as the total variable control cost. We could not match the $131 million cited in the docket as the annualized capital cost figure for 2013, obtaining ins tead a figure of $74 million as the capital cost figure that is developed as a component of “Policy Total Cost”, or a figure of $175 m illion as another estimate of cap ital costs in 2013 that can be derived from the GDX files. Likewise we obtained a different value for th e annualized cost of monitoring in contrast to the $27 million figure cited in the docket -- $80 million as the cost of mon itoring included in “Policy Total Cost” for 2013. The $27 million figure can be derived from the ISIS-NE SHAP GDX files, but does not appear in ISIS_outputs. See Appendix C for further information on the apparent differences between the ISIS control cost results cited in the docket and those we could derive by working with the model’s outputs.Version 1 G-30 EPA External Review Similarly, we could not find in the ISIS_ouptuts workbook any figure direct ly matching the docket statement to the effect that the proposed NESHAP will result in 139 U.S. k ilns projected to be active in the U.S. in 2013. Using theVersion 1 G-31 EPA External Review Our general point here is that the model user should not need to search t hrough numerous spreadsheets and perform furt her calculations in order to obtain the key ISIS outputs. Each of the important results generated by th e model should be displayed clearly in the output spreadsheets, along with sufficient detail about the elements or components that give rise to each result so th at the user can see how the resu lt is derived. In the case of “total control costs”, for exam ple, the ISIS output spreadsh eets should somewhere clearly display for each model year and each kiln the estimated total control costs, the values for each of the three components (variable costs, capital costs, monitoring costs), and how the figures for each kiln su m to the national total.

• It would be helpful if one of the output spreadsheets were organized so as to present the kiln-by-kiln results of the simulation. Extensive kiln-by- kiln “input” detail is already available on the Units worksheet of the ISIS_inputs Excel workbook -- it would be useful to have kiln-by-kiln “output” data in a simila r format. It would be helpful to be able to see across each of the simulation years whether each kiln operat es or not, what controls it installs, what fuel it uses, wh at production costs it incurs (va riable operating costs, other operating costs, capital costs, all on both an annualized and an actua l basis), what control costs it incurs (capital cost s, operating costs, monitori ng costs, by-product recovery credits, allowance costs, etc., again on both an annualized and an actual basis), what the kiln’s capacity is, and how much clinker or cement it produces. This spreadsheet would sum each of these variables across all kilns, thus showing how the national total for each variable is derived.

• The various tables and cells containing mode l outputs should be clearly labeled, as we describe more fully later in this memo.

4. We cannot explain several puzzling result s that we obtain in running the model.

These puzzling results ma y represent errors in th e model that should be fixed, or they may instead represent errors that we have made in running the model or in understanding the results. In any case, we would like to provide further details on these issues to EPA. EPA can then investigate these issues and ideally would then let us know whether th ere is some appropriate explanation for them. Th e surprising results we have obtained include:

• The estimated control costs for the year 2013 that we obtain when we run ISIS using EPA’s April, 2009, NESHAP data and assumptions are somewhat different from the results cited by EPA in the preamble and in the ISIS TSD. We have already mentioned this issue. When we run ISIS using the NES HAP data and assumptions as specified in the ISIS_inputs workbook that EPA provided us with as representing the Agency’s NESHAP analysis, we get the following (see Appendix C):

GDX files, we can find that 139 kilns are projected as active in 2013 and that incur both capital costs and variable costs for air pollution control measures. However, the number of kilns projected in this year as incurring monitoringVersion 1 G-32 EPA External Review costs for air pollution control equipment appears to be 155 ($27 million in monitoring costs) at one point in the GDX files, or 460 ($80 million in monitoring costs) as is used in “Policy Total Cost”.Version 1 G-33 EPA External Review o Annualized capital cost of controls: e ither $175 million or $74 million, drawing from two different places in the output s preadsheets. EPA’s figure cited in the docket is $131 million.

o Variable cost of controls: $63.2 million. This matches EPA’s figure in the docket.

o Annualized cost of monitoring: either $80 million or $27 million. EPA’s figure in the docket is $27 million.

• The model appears consisten tly to show simulated tota l U.S. cement production in a year as exceeding the summed simulated production capacity of U.S. kilns in that year. How can this be? For example, referring to the 2013 base case figures shown on the AggregateResults tab of ISIS_outputs for EPA’s NESHAP run, total cement production is 112.8 million tons, while further down the worksheet to tal capacity (sum of existing capacity, replacement capacity, expansion capac ity and new capacity) is equal to only 106.7 million tons. Similar result s are apparent in other model runs, and for other years and for the policy case also. How can kilns consis tently produce more cement than their capacity?

• When we look at the intermediate result s that the model generates before the ISIS_outputs.xls workbook is develo ped, it is apparent that many kilns are projected as incurring costs that are zero in some year s during the 2013-2018 modeling period and positive in other years. The model apparently a llows an existing kiln to operate and incur costs in some years, and not to operate and not to incur costs in other years. The same is true for the years after the mo del projects that a new, expa nsion or replac ement kiln will be constructed – in some years subsequent to construction the new capacity may operate and incur costs, but in other years the new capacity can apparently be idled and not incur any costs. If we are interpreting the intermed iate modeling results correctly in this regard, we question EPA’s ration ale for assuming that all of a kiln’s costs will switch on or off depending on whether the kiln is projected to be oper ating or not. In general we agree that a kiln may avoid incurring most of its operating costs (both its variable costs of producing cement and the co sts of operating its pollution control equipment) during a year in which the kiln elects to be idle, but we disagree that idling a kiln will eliminate the capital costs that the kiln would otherwise need to pay:

o Capital costs of poll ution control equipment and monito ring. If a kiln decides that it wants to operate at any time subsequent to the co mpliance deadline for the NESHAP (presumed to be 2013 at the latest), it must ma ke the capital investment in air pollution controls and monitori ng equipment needed for compliance. Once this investment is made, the kiln cannot avoid paying the ensuing capital costs in subsequent years, even if the kiln does not operate in some of these subsequent years. EPA assumes in ISIS in effect that all capital costs are financed over the useful life of the ca pital equipment, and the model converts a capital cost incurred in a specific year into an equivalent st ream of recurring annualized costs in subsequent years. The company owning th e kiln cannot walk away from this subsequent stream of capital co st obligations either for a si ngle year if the kiln is idled for that year or forever if the kiln is closed permanently. The capital costVersion 1 G-34 EPA External Review obligation has presumably be en incurred by the company rather than the kiln; if the company wants to stay in business it mu st continue to service all of its debtsVersion 1 G-35 EPA External Review whether the particular facil ity that prompted a particular debt remains active or not. 11

o Capital costs for new, repl acement or expansion cement production capacity. Just as ISIS simulates so me companies during the modeling time horizon as electing to make pollution control and monitoring capital investments, some companies may also elect to make new investments in additional or modernized cement production capacity. And (short of declaring bankruptcy), just as a company can’t walk away from a debt incurred for pollution control/monitoring capital equipment, a company likewise cannot walk away from a debt incurred for enhanced production capacity.

We thus suggest that ISIS tr eat as unavoidable all the su bsequent recurring payments resulting from any capital investment -- whether for pollution abatement or for new capacity -- that is simulated as occurr ing during the modeling time horizon.

We also suggest that EPA consider extending this general point further. ISIS appears to assume that all costs associated with a kiln will be avoided in any year in which the kiln is idled. We believe, at a minimum as we have discussed above, that re curring payment obligations relating to capital investments made during the modeling time horizon should not be regarded as avoidable. In our view, though, there are likely also other sorts of costs that are unavoidable during a year when a kiln is idled: property taxes and some insurance, costs for some staff and maintena nce activities needed to preserve the option of operating the kiln again in the future, and maybe more.

We further suggest that EPA consider whet her debt service payments during the ISIS modeling time horizon for kiln capital investments made before the modeling time horizon should also be counted as costs that are unavoidable and must be paid. ISIS does not now include costs relating to “sunk” (i.e ., pre-2013) capital investments at kilns as among the costs that will need to be covered in determinin g regional cement prices. This strikes us as perhaps an excessively “short run” point of view, probably inappropriate for a model that aims to simulate cement producer behavior all the way from 2005 through 2030. Over this “long run”, cement producers will stay in business only if prices are sufficient to cover all their costs, incl uding costs relating to sunk investments.

5. A user should not need to pay a substant ial cost in order to run the ISIS model.

To run ISIS-Cement, the user needs a license from the GAMS Development Corporation that includes the particular solver (CPLEX) used by the model. The Corporation will provide an “evaluation license” for 60 days for free wh ich will allow the user te mporarily to run ISIS. Beyond this, a license costs roughly $10,000, and more if the user wants a multi-user license allowing the software to be run on multiple computers. It seems inappropriate to us for EPA to build a major model that will cost a user $10,0 00 or more to be able to run. EPA should somehow enable free use of the model by the public.

11 Virtually all U.S. cement plants ar e owned by companies that have multipleVersion 1 G-36 EPA External Review cement plants and/or other lines of business. Only in case of bankruptcy for the entire company can the company avoid honoring a debt that it has in curred for a particular facility.Version 1 G-37 EPA External Review 6. Several aspects of the ISIS input and output spreadsheets should be clarified.

It is not clear in many of the ISIS spreadshee ts exactly what quantities are being referred to. Labels for each table would be helpful, in dicating for example whet her the quantity being referenced is:

• In short tons or metric tons;

• Clinker or cement; and before or after additi on of gypsum, limestone or other materials;

• In nominal dollars or real dollars (if in real dollars, indicate the base year);

• An annual quantity or a present discounted value of a fu ture stream; a cost that actually occurs in the year in ques tion or an annualized cost chargeable in that year;

• In hundreds, thousands, m illions, or other units.

All cells in ISIS_inputs.xls that are active for user input should be clearly identified, perhaps by color coding. All active output cells in ISIS_outputs should al so be clearly identified. Consistent, thorough labe ling throughout the prov ided Excel wo rkbooks would make the model more easily understandable and usable.

A second, more specific issue involves the format used in ISIS for specifying the cost equations for the various potential kiln emission contro l measures (see ISIS_inputs.xls, “newControls” tab). The format is very difficult to follow, in the sense that substantia l effort is required from the user in order to determine exactly what the result ing cost equations are for each of the potential NESHAP control measures. The format in one respect is flexible insofar as it can apparently be used to generate cost equations based on any of kiln capacity, gas flow, clinker production, fuel usage or other factors, but it appe ars that other portions of the model may “hard wire” a variety of questionable presumed relations hips among these factor s. In another respect the format is rigid in requiring separate equations for capital costs, fixed costs and variable costs.

We have spent considerable time in attempting to apply the format and deduce the resulting control cost equations, and then trace these equati ons back to their source materials (the Andover Technology memos and Summary of Environmenta l and Cost Impact of Proposed Revisions to Portland Cement NESHAP ). Doing so is very difficult. As we discussed previously, the unclear derivation of these formulas and the difficulty in working with their complex format has prevented us from doing some ne w runs in which we would test how ISIS responds to the information on control costs th at industry suggested in our September, 2009 comments. We intend to provide soon a supplemen tary memo on these issues.

We think it is important for the next versi on of ISIS-Cement to offer users the opportunity to substitute a user’s control cost equation for EPA’s. Reaching this goal will likely require some changes to the format for the control cost equa tions in the newControls tab of ISIS_inputs.xls.Version 1 G-38 EPA External Review At this point we have only a few suggestions; for the most part we would like to defer more specific identification of is sues and potential solutions to our forthcoming memo.Version 1 G-39 EPA External Review • The section showing “Capital Cost” (lines 14 - 69) apparently gives the one-time capital cost of the control measure, and not the annualized cost equiva lent that would be charged each year. Where in ISIS_inputs can one find and adjust the discount rate and useful life assumptions for each sort of control measure that are then used to convert the one-time cost into the annualized cost figures that show up in ISIS_outputs? It would be helpful to show the discount rate and useful life assumptions on this input worksheet and be able to adjust them at this point.

• We do not understand why EPA wants to have separate sectio ns and equations for “Fixed Costs” (lines 72 - 99) and “Variable Costs” (lines 103 - 175). The intended distinction between the two is not really clear (see Andover Technology Partners memo of September 25, 2008, page 2). For only one co ntrol technology does EPA maintain this distinction (e.g., for LWS the fixed O&M costs for taxes, insurance, labor and materials are separated from the variable costs involving limestone, power, water and gypsum); for the other NESHAP control technologies EPA si mply lumps all O&M costs together (e.g., the discussions on ACI, RTOs and baghouses on page 14 of the Summary of Environmental and Cost Impacts ). For LWS, EPA separate s out power costs as an element of variable costs, yet for RTOs bo th power and fuel cost s are lumped into a broader category of “annual costs”.

12 Or maybe “annualized cost” for a monitor doesn’t include an annual capital charge, despite what the “annualized cost” term suggests . Page 17 of the Summary of Environmental and Cost Impacts document indicates that the $34,748 figure for a PM bag leak detector (this figure is shown on the newContro ls tab of ISIS_inputs.xls as the • EPA’s approach to control cost functions appears to be inconsistent and is confusing regarding whether the annualized cost equivalents stemming from a one-time capital cost are intended to be included in annual costs. For the NESHAP control measures, as shown in the newControls tab of ISIS_inputs. xls, one-time capital costs, fixed O&M costs, and variable O&M costs represent three separate sets of cost functions, and annualized capital costs are not presented anywhere. However, on the same newControls worksheet, at the bottom of the sheet, cost s for monitoring are apparently treated differently -- these costs are shown as cons isting of capital costs and “annualized cost”, the latter apparently including an annualized capital charge as well as annual O&M costs. 12 EPA’s cost e q uation documentation is also inconsistent and confusin g on this issue. The Andover Technology memo addressi ng costs for LWS is clear in discussing one-time capital costs and annual O&M costs, with annualized costs and/or annual capital charges not being mentioned. The cost documentation for LWS in this memo can easily be matched against the ISIS cost equati ons for LWS because bot h the memo and the ISIS_inputs spreadsheet use the same categories of cost s. However, for ACI, RTOs and baghouses, the Summary of Environmental and Cost Impacts document adopts an approach that is inconsistent with the ISIS-inputs spreadsheet by addressing total capital investment and, variously, “total annualized costs”, “annual cost”, “TAC”, and “total annual cost”, each of which, we guess, includes a capital charge (see pages 14 - 16). Assuming that these various terms for annual cost s are intended to include a capital charge, we then have substantial difficulty in trying to match the cost equations for ACI,Version 1 G-40 EPA External Review annualized cost of this monitor) represents its “annual operation and main tenance costs”, which would seem not to include an annual capital charge.Version 1 G-41 EPA External Review RTO and baghouses shown in the Summary of Environmen tal and Cost Impacts (which include capital charges) against the cost equations for these technologies in ISIS_inputs that are not supposed to include capital ch arges. EPA could elim inate this confusion by consistently presenting annual O&M costs ex cluding any capital charges, and using the same approach in both ISIS a nd the various source documents.

A final point of confusion in ISIS involves th e treatment of “capacity”. When a table refers to the “capacity” of a kiln, presumably its annual production capacity of clinker or cement as the case may be, there should be a note either in the table or in the ISIS documentation indicating how this capacity has been estimated. In th e ISIS tables showing year-by-year new, replacement, expansion, and reti red capacity, it should be expl ained whether the figure shown represents the gross change in that quantity during the year, the net change since the previous year, or the cumulative change over the mode ling period up to the point in question.

* * * * * *

We appreciate the opportunity to provide these comments on the ISIS model, and we look forward to continuing to coopera te with EPA in improving the m odel. The remainder of this memo consists of three appendi ces as discussed earlier. We wi ll soon also p rovide to EPA a supplementary memo disc ussing our experience in attempting to run the mode l with revised estimates for the cost and e ffectiveness of air pollution control measures, and providing comments based on this experience about what we suggest that EPA should do in ISIS to improve the simulation of cost and effectiveness of controls.

Sincerely,

Tyrone Wilson, Ph.D. Director, Regulatory Affairs, Energy & Environment
cc: Andy O’Hare, PCAVersion 1 G-42 EPA External Review Appendix A
Background on the St ructure of ISIS-Cement and How th e User Interacts With the Model

The model is structured in three parts:

• A large set of 22 input spreadsheets in Excel. The user can change the input data for the model as desired by changing the figures in various locations in these spreadsheets

• A “black box” model, writ ten in GAMS programming lan guage, that us es a powerful linear programming solver to find the lowest cost way to meet future U.S. regional cement demand subject to a set of environmental regulatory constraints

• A large set of 17 output spread sheets in Excel. The model dumps the outputs into these spreadsheets. The user looks am ong the output spreadsheets to see various aspects of the model results.

In using ISIS to estimate the impact of a set of environmental requiremen ts, the user runs it twice:

• Run the “base case”. The model determines which kilns produce how much cement at what cost to meet the user’s specified re gional cement demands for future years. The model also determines the pric e for cement in each region al market, import quantities, installation of new cement production capacity, kiln closures, cross-regional shipments,
etc.

• Run the “policy case”. Specify the set of new environmental requirements (using one of the input spreadsheets) and es timate again which kilns will produce how much cement at what cost, prices, imports, etc..

The user will then want to compare the simulate d outcomes in the policy case against those in the base case. The model does this automa tically. The impact of the environmental requirements is given as:

Impact = (outcomes under the policy case) – (outcomes under the base case)

The output spreadsheets show this difference: (policy) – (base)

### Appendix BVersion 1G-44EPA External ReviewAppendix B: Results from Some New ISIS RunsRevised ISIS Run – Use PCA’s Latest Forecast forFuture Cement Demand,Reflecting the Impact of the Recession“ISIS_Outputs.xls” ResultsBase CasePolicy Case-Including Proposed NESHAPImpact of NESHAPOriginalOriginalOriginalRevisedΔRevisedΔRevisedEPAEPAEPACost of Domestic Production 2013 (millions)$ 6,943 $ 6,083 $ (860)$ 6,958 $ 6,073 $ (885)$ 15 $ (10)Cost of Controlsincluded above2013 (millions)$ - $ - $ -$ 218 $ 202 $ (16)$ 218 $ 202Import Costs 2013 (millions)$ 2,455 $ 1,587 $ (869)$ 2,492 $ 1,721 $ (771)$ 37 $ 134Value of Lost Cement 2013 (millions)$ - $ 1,710 $ 1,710$ - $ 1,634 $ 1,634$ - $ (76)Lost Producer / Consumer Surplus 2013 (millions) TBD TBD TBDTBD TBD TBDTBD TBDTotal Social Cost 2013 (millions)TBD TBD TBDTBD TBD TBDTBD TBDPrice 2013$ 75.37 $ 73.58 $ (1.79) $ 76.56 $ 73.86 $ (2.70) $ 1.19 $ 0.28Price 2018$ 77.91 $ 76.16 $ (1.76)$ 79.06 $ 76.40 $ (2.67)$ 1.15 $ 0.24Consumption 2013 (million tons)147.5 124.3 (23.2) 144.8 122.6 (22.2) (2.8) (1.7)Consumption 2018 (million tons)162.5 145.0 (17.5)160.3 143.5 (16.9)(2.2) (1.5)U.S. Production 2013 (million tons)112.8 101.1 (11.7)109.6 97.6 (12.0)(3.2) (3.5)U.S. Production 2018 (million tons)123.1 113.6 (9.4)120.5 110.9 (9.5)(2.6) (2.7)Version 1G-45EPA External ReviewImports 2013 (million tons)34.7 23.2 (11.5)35.2 25.0 (10.2)0.5 1.8Imports 2018 (million tons)39.4 31.4 (8.0)39.9 32.5 (7.3)0.4 1.1Comments:•Results appear sensible: with reduced future demand, consumption and price both decline•About half of the decline occurs in domestic production (11.7 million tons in 2013), about half occurs inimports (11.5 million tons in2013). In reality, ashas been observed in 2008 and 2009 with the recession,imports would likely bear a higher percentage share of the decline and domestic production a lower share. Wesuggest revising the manner in which imports are simulated to make them behave as “swing supply”.Version 1G-46EPA External ReviewRevised ISIS Run – Increase Assumed Cost and Reduce Assumed Effectivenessof Control Measures, Consistent with PCA’s 9/09 Comments“ISIS_Outputs.xls” ResultsBase CasePolicy Case-Including Proposed NESHAPImpact of NESHAPOriginalEPARevisedΔOriginalEPARevisedΔOriginalEPARevisedCost of Domestic Production 2013 (millions)$ 6,943 $ 6,943 $ -$ 6,958 $ 4,821 $ (2,137)$ 15 $ (2,122)Cost of Controlsincluded above2013 (millions)$ - $ - $ -$ 218 $ 443 $ 225$ 218 $ 443Import Costs 2013 (millions)$ 2,455 $ 2,455 $ - $ 2,492 $ 2,489 $ (2)$ 37 $ 34Value of Lost Cement 2013 (millions)$ - $ - $ -$ - $ 2,939 $ 2,939$ - $ 2,939Lost Producer / Consumer Surplus 2013 (millions) TBD TBD TBDTBD TBD TBDTBD TBDTotal Social Cost 2013 (millions)TBD TBD TBDTBD TBD TBDTBD TBDPrice 2013$ 75.37 $ 75.37 $ - $ 76.56 $ 77.54 $ 0.98 $ 1.19 $ 2.17Price 2018$ 77.91 $ 77.91 $ -$ 79.06 $ 78.17 $ (0.90)$ 1.15 $ 0.25Consumption 2013 (million tons)147.5 147.5 - 144.8 106.3 (38.4) (2.8) (41.2)Consumption 2018 (million tons)162.5 162.5 -160.3 106.1 (54.2)(2.2) (56.4)U.S. Production 2013 (million tons)112.8 112.8 -109.6 71.3 (38.3)(3.2) (41.5)U.S. Production 2018 (million tons)123.1 123.1 -120.5 68.3 (52.2)(2.6) (54.8)Version 1G-47EPA External ReviewImports 2013 (million tons)34.7 34.7 -35.2 35.1 (0.2)0.5 0.3Imports 2018 (million tons)39.4 39.4 -39.9 37.8 (2.0)0.4 (1.6)Comments:•Results appear generally sensible: higher costs for controls in Revised Policy Case cause price to increaseeven more and consumption to fall even more than in Original EPA Policy Case. All the impact falls ondomestic producers, not on imports•But would consumption fall by only 2.8 million tons (2013) underEPA’s control cost/effectivenessassumptions vs. 41.2 million tonsunder PCA’s control cost/effectiveness assumptions? Needs looking into•Note that EPA would count the total cost of NESHAP asonly $52 million in 2013 (increased cost of domesticproduction plus imports), missing the values lost inconsuming only 144.8 milliontons of cement in 2013 vs.147.5 million tonsVersion 1 G-48 EPA External Review 

### Revised ISIS Runs Regarding Imports

ISIS models imports using a ratio of import price to the cost per ton for the highest cost domestic producer in a region – “the import price ratio”. EPA estimated this ra tio for each region based on fitting to 2005 data. We think the model simulates import be havior somewhat inappropriately. For example, in the new run using the latest PCA demand forecasts, ISIS does not show imports falling off as sharply with reduced domestic demand as we think they will. We have been testing how the model responds to differing values of the “import price ratio”. We have tested whether increasing the price of imports in the model (simulated by increasing the “import price ratio” for each region) causes the simulated level of imports to decline. The model responds appropriately to this change.

Screenshot Update (increase ‘priceratio’ by +10%)

Parameter priceratio (r) *$ontext Priceratio( Atlanta )= 0.6478 ; Priceratio( Baltimore/Philadelphia )= 0.82 ; Priceratio( Birmingham ) = 0.615 ; Priceratio( Chicago )= 0.82 ; Priceratio( Dallas ) = 0.9143 ; Priceratio( Detroit )= 0.7708 ; Priceratio( Florida )= 1.148; Priceratio( Kansas City )= 0.82 ; Priceratio( Los Angeles )= 0.82 ; Priceratio( Minneapolis )= 0.82 ; Priceratio( New York/Boston )= 0.82 ; Priceratio( Pittsburgh )= 0.82 ; Priceratio( Salt Lake City ) = 0.82 ; Priceratio( San Antonio )= 0.82 ; Priceratio( San Francisco ) = 0.82 ; Priceratio( Seattle )= 0 .8856 ; Priceratio( St. Louis )= 0.82 ; *$offtext Parameter priceratio (r) *$ontext Priceratio( Atlanta )= 0.7126 ; Priceratio( Baltimore/Philadelphia )= 0.9020 ; Priceratio( Birmingham )= 0.6765 ; Priceratio( Chicago )= 0.9020 ; Priceratio( Dallas )= 1.0057 ; Priceratio( Detroit )= 0.8479 ; Priceratio( Florida )= 1.2628 ; Priceratio( Kansas City )= 0.9020 ; Priceratio( Los Angeles )= 0.9020 ; Priceratio( Minneapolis )= 0.9020 ; Priceratio( New York/Boston )= 0.9020 ; Priceratio( Pittsburgh )= 0.9020 ; Priceratio( Salt Lake City )= 0.9020 ; Priceratio( San Antonio )= 0.9020 ; Priceratio( San Francisco )= 0.9020 ; Priceratio( Seattle )= 0.9742 ; Priceratio( St. Louis )= 0.9020 ; *$offtextVersion 1 G-49 EPA External Review 

### Imports—Impact

Scatterplot Results

Changes in Import 'priceratio'

0% 5% 10% 15% 20% % Increase in 'priceratio' -Total Imports 2013 to 2018 (m) 250

200

150

100

50 y = 1262.5x 2 - 882.35x + 203.97 R

2 = 0.9447Version 1G-50EPA External ReviewRevised ISIS Run – Make New/Replacement/Expansion Capacity MoreExpensive by Increasing Rate of ReturnRequired for New Investment – From7% to 14%“ISIS_Outputs.xls” ResultsBase CasePolicy Case -Including Proposed NESHAPImpact of NESHAPOriginalEPARevisedΔOriginalEPARevisedΔOriginalEPARevisedCost of Domestic Production 2013 (millions)Cost of Controlsincluded above2013 (millions)Import Costs 2013 (millions)Value of Lost Cement 2013 (millions)Lost Producer / Consumer Surplus 2013 (millions)Total Social Cost 2013 (millions)$6,943$ -$2 455$6,911$ -$2 612$ (32)$ -$ 157$ -TBDTBD$ 6,958$ 218$ 2,492$ -TBDTBD$ 6,944$ 216$ 2,649$ -TBDTBD$ (14)$ (2)$ 157$ -TBDTBD$ 15$ 218$ 37$ -TBDTBD$ 33$ 216$ 37$ -TBDTBDPrice 2013$ 75.37 $ 82.99 $ 7.62 $ 76.56 $ 83.45 $ 6.89 $ 1.19 $ 0.46Price 2018$ 77.91 $ 85.32 $ 7.41$ 79.06 $ 86.42 $ 7.36$ 1.15 $ 1.09Consumption 2013 (million tons)147.5 147.5 - 144.8 144.9 0.2 (2.8) (2.6)Consumption 2018 (million tons)162.5 162.5 -160.3 160.6 0.3(2.2) (1.9)U.S. Production 2013 (million tons)112.8 110.2 (2.6)109.6 107.2 (2.4)(3.2) (3.1)U.S. Production 2018 (million tons)123.1 120.3 (2.7)120.5 118.0 (2.4)(2.6) (2.3)Version 1G-51EPA External ReviewImports 2013 (million tons)34.7 37.3 2.635.2 37.8 2.60.5 0.5Imports 2018 (million tons)39.4 42.1 2.739.9 42.6 2.70.4 0.4Comments:•Results appear sensible: higher threshold rate of return required for making new capacity investmentsresults in less domestic investment in new capacity and less ultimate U.S. production and more imports•Doesn’t seem to affect the estimated cost of the NESHAP as much as we might have guessed,thoughVersion 1 G-52 EPA External Review 

### Revised ISIS Run – Make Kiln Replacement More Costly

EPA had made kiln replacement very ine xpensive in terms of cost per ton of capacity to convert an old, wet or l ong dry kiln to a larger preheater precalciner. In this revised run of the model, we make the cost of such replacement very high. In the new run, the model presumably s houldn’t elect such replacements.

• 2005 Replacement Cost per ton for Wet Ki lns was changed from $93 to $1,000,000

• 2005 Replacement Cost per ton for Dry Kilns was changed from $61 to $1,000,000

Supporting Analysis
November 2009

Plant Company Year Online Cost ($m) New Tons (m) Raw Cost per Ton Selma

, MO Buzzi 2008 $ 262 1.0 $ 262 Grand Chain, IL Lafarge 2010 415 1.6 253 Harleyville, SC Lafarge 2009 291 1.5 194 Mitchell, IN Lehigh 2012 400 0.8 488 New Braunfels, TX TXI 2009 350 1.5 233 Ore Grande, CA TXI 2008 358 1.0 358

CONVERSION: WET TO PREHEATER / PRECALCINER Scaled to 300,000 TPY

Plant Company Yea r Online Cost ($m) New Tons (m) Raw Cost per Ton Foreman

, TX A sh Grove 2009 $ 190 0.7 $ 271 Hannibal, MO Continental 2008 138 0.6 230 Fernley, NV Eagle 2008 178 0.5 395 Laramie, Wy Eagle 2008 142 0.4 395 Lasalle, IL Eagle 2008 65 0.4 151 Martinsburg, VA Essroc 2008 320 1.0 320 Bath, PA Giant 165 0.3 550 Cost per Ton Cost per Ton (Industry) (EPA 20 05$) $ 381 $ 93 303 93 465 93 425 93 175 93 518 93 550 93 CONVERSION: LONG DRY TO PREHEATER / PRECALCINE R Scaled to 250,000 TP Y
Cost per Ton ( Industr y) $ 456 536 397 785 478 623 Costper Ton (EPA 2005$) $ 61 61 61 61 61 61Version 1 G-53 EPA External Review



### Increased Kiln Replacement Costs—Impact

“ISIS_Outputs.xls” Results

Base Case Policy Case

- Including Proposed NESHAP Impact of NESHAP
Original

EPA Revised Δ Original EPA Revised Δ Original EPA Revised
Cost of Domestic Production 2013 (millions)

Cost of Controls included above 2013 (millions) Import Costs 2013 (millions) Value of Lost Cement 2013 (millions) Lost Producer / Consumer Surplus 2013 (millions) Total Social Cost 2013 (millions) $6,943 $6,943 $ - $ - $ - $ - $2,455 $2,455 $ - $ - $ - $ - TBD TBD TBD

TBD TBD TBD $ 6,958 $ 6,958 $ - $ 218 $ 218 $ - $ 2,492 $ 2, 492 $ - $ - $ - $ - TBD TBD TBD

TBD TBD TBD $ 1 5 $ 15 $ 218 $ 218 $ 37 $ 37 $ - $ -
TBD TBD TBD TBD

Price 2013 $ 75.37 $ 75.37 $ - $ 76.56 $ 76.56 $ - $ 1.19 $ 1.19 Price 2018 $ 77.91 $ 77.91 $ - $ 79.06 $ 79.06 $ - $ 1.15 $ 1.15

Consumption 2013 (million tons) 147.5 147.5 - 144.8 144.8 - (2.8) (2.8) Consumption 2018 (million tons) 162.5 162.5 - 160.3 160.3 - (2.2) (2.2) U.S. Production 2013 (million tons) 112.8 112.8 - 109.6 109.6 - (3.2) (3.2) U.S. Production 2018 (million tons) 123.1 123.1 - 120.5 120.5 - (2.6) (2.6) Imports 2013 (million tons) 34.7 34.7 - 35.2 35.2 - 0.5 0.5 Imports 2018 (million tons) 39.4 39.4 - 39.9 39.9 - 0.4 0.4

Comments:

• Something is wrong. The model does not respond at all to the change in kiln replacement costs

• We tried it again with increasing kiln replacement costs to $1 trillion per ton. Again no responseVersion 1 G-54 EPA External Review 

### Appendix C—Differing Estimates for Control Costs in 2013, as Obtained from Va rious Sources in ISIS

Estimated Control Technology Cost Amounts in 2013 $ Millions

Control Cost Item ISIS per Docket 1 ISIS_Outputs.xls (provided by EPA) ISIS_Outputs.xls (from GDX files, using items included in PTC 2 ) ISIS_Outputs.xls (from GDX files, using other items not included in PTC 2 )

Variable 63 63 63 63
Capital 131 175 74 175
Monitoring 27 Not shown 80 27

Total 222 Not shown 218 266

Sources for Estimated Contro l Technology Costs in 2013 Excel Location or GMS Variable Name(s)

Control Cost Item ISIS per Docket 1 ISIS_Outputs.xls (provided by EPA) ISIS_Outputs.xls (from GDX files, using items included in PTC 2 ) ISIS_Outputs.xls (from GDX files, using other items not included in PTC 2 ) Variable n/a SUM(Matrix B2:EJ2) cntrlFOMcostEPr + cntrlVOMcost sumFinalCCVar

Capital n/a SUM(ControlMatrix AF4:AK142) cntrlcapcostEPr SumFinalCCCap

Monitoring n/a n/a totalmonitoringcost TotMonitoringCost

1 “Industrial Sector Integrated Solutions Model Report” US EPA OAQPS April 2009, page 33, and Preamble, pages 21166 and 21167.

2 PTC = Policy Total Cost (Excel Workbook: ISIS_Outputs.xls, Worksheet: AggregateResults, Cell: F5)Version 1 H-1 EPA External Review 

## Appendix H

Review Comments from Hendrik van Oss, US Geological Survey

Hendrik G. van Oss, Ceme nt Commodity Specialist U.S. Geological Survey, Reston, VA 20192 January 29, 2009

I have broadly reviewed the ISIS document package that you sent to me on a CD ROM (as well as the ancillary/repla cement Excel tables). As I not ed to you earlier, I did not have time to run the actual software. I am not trained in finance, so I have not looked at your costing algorithms in detail. Overall, the package is impressive—it represen ts a lot of work! I have made a number of general and specific comments (see below), but , overall, and assuming certain changes are made, I think that the project will prove to be very useful in evaluating options/estimating the costs of various (chiefly environmental-related) upgrades at plants. General Comments Editing The text needs some editing, with especial atte ntion to the use of commas (you need more commas) and the common omission of the indefinite article “the”. The word “data” is always plural: th us “data are/were/show....”, never “data is/was/shows...”. There is no need to capita lize portland cement (c.f., Po rtland cement), although it is certainly OK to do so. The word “since” has a time context and should be avoided where the meaning really is “because”. Global 1) It is the U.S. Geological Survey , not U.S. Geological Service.

2) Very important: the U.S. Govt. has a mandate to use metric units as much as possible. This report has taken data that are largel y published as metric tons (e.g., recent PCA data, USGS data, Census trade data) and c onverted them to short tons. Then, in the text, you have always provided a pa renthetical conversion: “...9,217,487 tons (8,361,966 metric tons)...” –this is not only visually distracting (to the flow of the text) but greatly adds to the length of the te xt. The use of short tons in the tables has prevented me from conveniently checking ma ny of your data against my own (USGS) publications. I think that th e whole report (maybe not the Anderson addendum report) needs to be recast in (only) metric units. On e exception can be the use of Btus instead of joules—as Btus are almost universally understood.

Use degrees Celsius (T°C), not °F.

All units, in all tables, must be de fined. “Ton” does not imply short ton.Version 1 H-2 EPA External Review All (certainly most) tables need to be checked to see that they include citations for the data source(s). 3) As much as possible, avoid the use of acronyms in the text.

4) As much as possible, recast the equations into standard mathematical notation rather than “computer printout” notation. For example: IPG * CAPACITY ^0.4 is not as clear as it should be. Probably CAPACITY was defined near the equation, as was IPG, and * is fairly universally unders tood. But ^0.4 is not: is it the CAPACITY 0.4 or is it CAPACITY x 10 0.4 ? At least define it somewhere.

5) Virtually everywhere, you use SO 2 , where SOx would probably be the better term. But you could state that, for comput ational purposes, you have used SO 2 because it is “rigid” stoichiometrically and, in any case, makes up the majority of the SOX. Ditto for CO 2 including CO.

6) In many places, you have used PCA data instead of the USGS data. In some cases, this is fine—the PCA does its own survey of energy use & plant capacity (and the USGS holds all plant-specific data proprieta ry). But you may wish to point out to the reader (potentially familiar with both data sets) that there are some important differences. On the kiln count, for example, the USGS counts all kilns active for at least 1-day during the year, whereas the PCA only counts the kilns th at were active at yearend (we count 183 kilns in 2005, the PCA just 181). Many of the PCA energy data exclude white (portland) cement, wher eas these are included in the USGS data. The PCA provides the cement companies with heat conversion fact ors for the various fuels, whereas the USGS asks the industry to directly supply the Btus realized (at whatever HHV (gross heat) conversion factor the plant normally uses). Any Statelevel (and USA overall) consumption or production data published by the PCA are taken from USGS data and you should cite the USGS for such da ta, not the PCA.

7) Avoid using MM for million, just use M. I th ink this is an issu e only with your Btu discussions. In my experience, M for thousa nd survives in the world only in old Latin texts and for Copyrights, and in billing for natural gas (MCF).

8) Somewhere, you should discuss the issue of data accuracy. In general, the cement industry calculates, but does not weigh, its clinker and cement production. They weigh their sales, however! In many pl aces, data ought to be rounded. Few data reported are good to better than +/- 1%.

Specific Comments Summary The summary is a little casually written. A lthough it is debatable as to what constitutes “pyroprocessing,” it is generally more appli cable to the combination of calcination and sintering (clinkering), and not so much to preheating and drying. You talk about “criteria polluta nts” and HAP almost as if they are interc hangeable. It reads as if NOx and SOx are HAP. The Summary seems to lack discussion of CO 2 —you need to at least mention it (and then you can say that your cost modeling was confined to NOx and SOx.Version 1 H-3 EPA External Review Sec. 1.1 Introduce this with portland a nd blended cement (both are used in concrete), and then state that you will focus on portland. But be careful: the USGS monthly data (1/98 onwards) split out the two (Table 2a vs. 2b) and they should be added together for discussions of consumption. I do not know if the PCA, in using our data, has combined these in all cases. The USGS annual data includes blended cement within the portland umbrella. The annual report (Mineral Yearbook) shows th e combined monthly sales (consumption) data in Table 9. Para 1: say calcium aluminate hydrate, not calcium aluminum hydrate. Sec. 1.1.1 “....rotary kiln...cylindrical furnace...”—I presume that the latter is an explanation of the former, so put it in parentheses. The materi al temperature is generally taken to 1450°C, not 1500°C (white cement kilns probab ly do bring the material to 1500°C). “...the heart of the process generally is the rotary kiln....” The low end of the length range would more typically be c. 150 ft for a precalciner kiln of c. 0.7 Mt/yr capacity. You state that the kiln is fired at the lower end, but you might wish to also mention midkiln firing. The term “clinkerization” is not much used; more commonly, you see “clinkering” or “sintering”. (Also “burning” but this is to be avoided as too ambiguous). Very important (Global ): it is certainly OK to distingui sh wet vs. dry kilns. But it is NOT acceptable to use “dry” to mean (only) a dry kiln lacking a preheater and/or preheater/precalciner. You should call this type of kiln a “long dry” kiln. The 3 general categories for dry kilns are: 1) long dry kiln; 2) preheater kiln; 3) preheater/precalciner kiln (which, after spelling out the first time, you may shorten to precalciner kiln). (Semiwet vs. semidry: frankly, these kilns ca n go either way. If the kiln has a preheater and/or precalciner, the USGS lumps them within the overall Dry kiln category. I am aware of only 1 plant of this type (Buzzi’s Greencastle, IN plant). Figure 1-2: in the caption, you might want to state that the schema tic for a precalciner kiln would be essentially identical. Table 1.1: The data are typical averages, but are not universal. Th e reader may think these are fixed data. The EPA source sited likely used either USGS or PCA data. Global : if at all possible, avoid using PC for “precalciner” as it is a common acronym for portland cement. p. 1-5: last para: make sure it is unders tood that “smaller” means lower capacity, not smaller dimensions on the kiln tube. I would note that the USGS Minerals Year book chapters on cement (MYB) also allow the average capacity to be discerned: it w ould calculate as 0.577 Mt/yr (million metric tons per year) in 2005 (MYB, table 5). It may be useful to discuss what is mean t by “capacity” anyway! Daily capacity means per 24 hrs (and implies 24 hrs of conti nuous operations) and is more or less unambiguous...except that you don’t know wh ether the cited number is the manufacturer’s rating on the kiln (usually a conservative figure), or actual “full blast— but normal operating—conditions. Commonly, the ra ted capacity is lower than the actual operational capacity. Annual capacity is not well defined. The USGS calculates an “apparent annual capacity” as follows:Version 1 H-4 EPA External Review Apparent annual capacity = (Reported daily capa city) * (X-Y) where: capacity is in tons; X is 365 days (or 366 in leap years) and Y is the number of days of downtime for routine, scheduled, maintenance. Thus, (X-Y) is th e scheduled operational year. The PCA, I believe, uses the same formula, but (in my opinion) does not always check to distinguish routine maintenance downtime from total downtime. Clearly, there can be a large variation in apparent annual cap acity if there is any mischaracterization of the routine maintenance downtime or the daily capaci ty. The USGS calculates—for the plant overall—the capacity utilizati on wrt. the actual clinker out put: we query (question) any utilization percentages in excess of 100%. Because there is commonly downtime in excess of routine maintenance (extended upgrades, shutdowns for unplanned repairs...), the USGS feels that a utilization rate of 85% or more represents full practicable capacity operations. [The industry reports to the USGS, for each kiln, the daily capacity, the total downtime, the subset downtime for routine maintenance, and the remainder for “other downtime”. We also ask for the dimensions of the kiln, whether it is wet or dry, whether it has a preheater, whether it has a pr ecalciner, and whether dust cont rol is via a baghouse or an ESP.] p. 1-6: Figure 1-3: the color scheme woul d be improved by using blue for wet and red(dish) for dry. Cite the data source. Para 1 below Figure 1-3: Here’s where you are tripping over your misuse of the term “Dry” kiln. First, our table 5 did show 16.3% from wet plants , but this is NOT quite the same thing as saying that wet kilns did 16.3% of the clinker. This is because there is a separate line for the relatively small output fr om “combination” plants (those that operate both wet and dry kilns). Likewise, the 11.8 Mt of clinker form Dry plants understates the output of the dry kilns because a couple of th ese are in the line for combination plants. And you misstate the 11.8 Mt “Dry” as bein g from dry kilns lacking a preheater or precalciner. Not so—the datum is for dry plants of all 3 types (long dry, preheater kilns, preheater/precalciner kilns). The USGS, not the PCA, should be cited as the source of the value data and the consumption numbers. For 2005, portland cement sales to final customers (these are the data that the PCA calls “consumption” were worth $11 billion; masonry cement sales $0.68 billion; total about $12 billion. Cite the USGS also for the statement that the top 10 cement companies had 80% of the production; ditto for all production by State data. Figure 1-4: this plant location map is incomp lete because it fails to adequately discern localities with more than one plant in a cl uster. For example: WA has 3 plants (2 integrated plants @ Seattle and a grinding pl ant at Bellingham); CA has 11 plants; FL has 6 plants, etc... p. 1-7: your discussion of im ports is misleading: imports can be by domestic producers seeking to feed markets where domestic (par ticularly their own domestic) production is inadequate (or where they wish to captur e market share) AND by independent importers who compete against domestic production in so me markets, and help feed markets not easily serviced by domestic production. Oddly enough, there is a lot of flexibility in how the industry views the cost of imports vs. cost of local production. A company might continue to import—even at a loss orVersion 1 H-5 EPA External Review minimal profit—for at least a while if this is the only way they can keep their customers happy (i.e., not going to another supplier). The existence of imports can affect where domestic production is ta rgeted for marketing. A good example is the Los Angeles basin: a big chunk of the demand there is met by imports, which then free up a lot of the actua l S-CA production to be sent into the Las Vegas market and, to a smaller extent, into Arizona. Table 1-3: The data in this table represent U. S. Census trade data (as supplied to and then compiled by the USGS). The original data were in metric tons. DO NOT CONVERT THESE TO SHORT TONS!!! Also, as best I can tell, the data are for imports of “hydraulic cement and clinker”—meaning, all ty pes of hydraulic cement, and all types of hydraulic clinker--so they are not just portland cement imports. This (cement and clinker) issue applies, I think, to all impo rt data that you use in this report and represents a fairly seriou s problem for your model

. The percentage column needs clarification: percentage of what (USA total imports). Figure 1-5 needs a citation for the data source (USGS I presume). The curve for import share is misleading and represents a problem with the USGS data: we compare the level of cement imports to that of total consumption (apparent consumption calculated as cement production + cement imports – cement e xports – change in cement stocks). We exclude the imports of clinker because the pr oduction of cement includes cement made in the USA from imported clinker. But we do not include changes in c linker stockpiles (but should) mainly because we did not collect such data prior to a few years ago. Done “properly” the import reliance would be 1-2% higher than what we s how currently in the data series. Another related issue is the cement stockpile s: the comparison is between successive yearend (12/31) stocks. But you could have a significant drawdown or buildup due entirely to the early arrival or late arriva l of a few ships—this would cause a stockpile shift entirely UNRELATED to any econom ic forces. So I do not find apparent consumption to be a very useful statistic. Sec. 1.2: Emissions Para 1: a bit clumsy explaining where S comes from—it is from sulfur-bearing compounds in both the fuels and the raw mate rials. Many organic compounds (in fuels) contain sulfur, as do kerogens in sedimentar y rocks like limestone. Both fuels (especially coal) and raw materials can c ontain sulfide minerals (chief ly pyrite or marcasite); and sedimentary rocks may cont ain sulfates like gypsum. If you are going to quote the total weight of PM10 emissions, put it in the context of something—such as the weight of th e total raw materials and fuels. Figure 1-6 is small to the point of uselessnes s. Why not make it into 3 Figures (1-6a, 1- 6b, 1-6c)? The text in this area ought to mention CO 2 . In fact, the whole s ection is awfully brief— but perhaps you could refer the reader to the discussion in the Appendix. Sec. 1.3: ISIS Figure 1-7: should you add (flowsheet box for emissions) HAP and PM? References: I am always a bit nervous when I see the USGS Mineral Commodities Summaries cited—these are “quick & dirty” summa ries that are meant to be just that. We prefer, where possible, that data be obtained from the Minerals Yearbook, as this is a MUCH more author itative publication.Version 1 H-6 EPA External Review Ch. 2: Data used in ISIS 2.2.1.1: The USGS has a kiln count of 183, not 181 (=PCA number?)—but the difference is likely kilns active during year ( 183) vs still active at yearend (181). Figure 2.1: I suppose that your ma rket grouping has its virtues, but there are some very strange groupings re. the cement plants. For example: Why on Earth is Ash Grove, Leamington, UT grouped into Phoenix, but Holcim Devil’s Slide is Salt Lake City? Let me assure you that the Leamington plant feeds markets primarily in UT (incl. SLC) and NV (Las Ve gas) (plus v. minor tonnage, only, into CO and NM; nothing into AZ). In Las Vegas it competes with the S-CA producers. Why is Ash Grove, Durkee, OR grouped with Salt Lake City—its market is primarily OR, WA, ID. Some plants straddle more than one markets: Lafarge’s Sugar Creek, MO plant can indeed feed westward into the Kansas City area, but it also ships a lot of cement down the Missouri River (i.e., eastward). Buzzi’s Cape Girardeau plant probably has the widest geographic dispersal of its ce ment than any other plant. Overall, there are all sorts of flows up and down, in a nd around, the entire MississippiMissouri-Ohio River system. Not sure so me of these market breakouts in the midcontinent make any sense. 2.2.1.3: The 2005 demand for portland in 2005 was 122.4 Mt (million metric tons), and for portland + masonry, it was 127.9 Mt. Thus I question the 127.6 portland number in your report. I would urge extreme caution in using the PCA demand forecasts. First, they change them at least quarterly. Second, it is likely that you have used reports predating the recent collapse of the economy. Cement sales fell nearly 10% in 2007, another 10-11% in 2008, and will likely drop around 15% in 2009. The PCA has revised its forecasts downwards. The fall in sales in 2007 was accommodated by a huge drop in imports and just a small drop in production. There was less flexibility in 2008—imports continued to plummet, but production has fallen substantially, as well. Both will likely fall further in 2009. Secondly (and related), we have had pl ant closures in 2008 (I think 3) and announcements of about 7 more in 2009, and all/most but the most advanced expansion/greenfields projects are now on hold or have been cancelled. American Cement’s (FL) brand new plant is just sitt ing there; the kiln as yet unfired. Sumter Cement (FL) is on indefinite hold. Votorantim near Macon, GA will likely never materialize. Titan in NC will likely be delayed. Cemex at Seligman, AZ was probably never more than a bluff, anyway. Ash Grove at Moapa (Las Vegas) was cancelled a year
ago. Of the closures, it is as yet unclear how many are permanent—but the closures include Buzzi’s Independence, KS plant and Fredoni a (I think), St Marys Dixon, IL, plant, Holcim’s Dundee, MI and Clarksville, MO plants (both are we t plants), Cemex @ Brooksville, FL; TXI at Crestmore, CA a nd, temporarily, at Or o Grande; Cemex at Davenport, CA (likely tempor ary); some of the kilns at TXI (or was it Ash Grove) @ Midlothian, TX; Essroc at Fred erick, MD (no surprise--is due to upgrade project at their nearby Martinsburg, WV plant)...etc...And lots of plants are taking extended maintenance shutdowns, which augurs for lower capacity utilization percentages in 2008 and 2009. So any forecasts of capacity growth would be of dubious merit, at the moment; you need to use caution in relying on the PCA forecasts.Version 1 H-7 EPA External Review Table 2-3: put the data back into mt. Data include clinker. These are U.S. Census trade data (the USGS just compiles them into convenient tables). p. 2-5: Cement plants can certainly be older than 50 years! There is a definitional issue. You could have had a cement plant on site for nearly a century, but it may have been upgraded along the way. Generally, you wouldn’ t build a plant on a site having < 100 years of limestone reserves. So the age can be a “site age” or an “equipment age”. Avoid like the plague the us e of the word “endogenous”. The phrase “...ISIS endogenous capacity growth can also lead to endogenous retirement of kiln s.” conjures up all sorts of imagery unrelated to cement...Couldn’t you just say something like: “capacity expansion projects can include the reti rement of existing kilns...”? p. 2-6: “...such as coke...” Be sure specify petroleum coke (petcoke). Our old tables showed for many years just “coke”, then we attempted to differentiate petcoke from metallurgical coke (i.e., from coal) wit hout much success (not sure the respondents understood the difference). Likely that all coke use (outside of major blast-furnace/steel complex areas) has been of petcoke, and I am not sure that metallur gical coke has ever been in common use even in the steel areas—it’s too expensive. Figure 2-2: awfully crude brea kout of fuels. Where’s the pe tcoke (in with the coal???). What is “Petroleum”---do you mean petroleum products? Crude petroleum? If the former, do you just mean distillate and/or residual fuel oil, or do you include all sorts of waste oils and other petrochemical waste fuels? Where are the alternative fuels? Also, do the percentage data refer to tonna ges or to the heat contribution (Btu)? If I use actual industry-reported heats, my own data for 2005 show: Natural gas (3.72% total Btu); fuel oil ( 0.76% [seems low]); coal (64.1%); petcoke (19.3%); metallurgical coke (nil ); tires (3.2%), other solid wastes (0.5%); liquid wastes (8.4%). Your report cites total heat (EIA) of 451.2 trillion Btu. My (USGS) data for 2005 has 366.7 trillion Btu excluding electricity , or 375.4 trillion Btu if I impose some “standard” heat conversions for the fuels. Using actual data , if I include the elec tricity, then we jump to 413.4 trillion Btu. Thus, I suspect that the EIA data are either too high or that they, at the very least, include electricity. Based on th e rest of your report, I do not think that you should be converting electricity (kWh) into Btus ; use the Btus just for the fuels burned at the cement plant. That is, skip the el ectricity (in your heat analysis). Table 2-4: shouldn’t these data match those of table 1-1 (or vice versa)? Important: as you may realize, the PCA data on unit consumption of heat (Btu/mt—and be sure you know which ton they are using!) generally are based on an “equivalent ton” which is a bit of an apples & oranges mix. At one point, years back, I figured out why they use this, but I have forgotten and they seem to have forgotten as well. You can recalculate to a per-ton-of-cli nker basis, but I do not know if you have done this in table 2-4. Also, I don’t recall the PCA’s Labor & Ener gy surveys mentioning gas flows—so it is unclear if the NM 3 /kg data are from the PCA or some other source. PCA 2004 (Bhatty et al.) is a huge tome (very good book!), so you should cite the actual page(s) from which the data are taken. Table 2-5: define “mg” (milligram or microgram?)Version 1 H-8 EPA External Review See my earlier comments on SOx. Regarding NOx, I thnk you should note that it is extremely difficult to get precise data on NOx outputs, given that output varies (wide swings) minute-by-minute. But your averag es for NOx and SOx are reasonable. While I agree that raw material s play a significant role (and hence there can be regional trends) in the SOx emissions, it really applies to the potential emissions (actual emissions also vary with scrubber technology, if any, kiln technology, and, of course, the fuels). Coal is hugely important—is the pl ant burning a low- or high-S coal? p. 2-8. Your calcination emi ssions factor of 0.53 t CO 2 per ton of clinke r is slightly too high. Instead of adopting th is Andover Technology default (see my comments on Appendix A) you should use the IPCC default (w hich is what the EPA usues for the USA GHG Inventory. The Andover Technology average CaO content for clinker is a bit too high. So use 0.51 or 0.52 (the latter in cludes a 2% addition for “los t” [not returned to kiln] CKD). The average CO 2 emissions per ton of fuel noted in this section should be rounded! Remember, fuel consumption data are not all that precise, and one is never told if the reported weight is on a dry basi s (as in lab-dried), standard dry basis (i.e., actual dry feed to kiln—typically has several % moisture), or is some wet slop at an initial handling point. Further, one can ask for—but the re porting may not always be—as gross or high heat. You may be getting net or low heat. These are among the (many) wo es of data collecting! Table 2-7: I’m not sure you’ve made clear why you are showing H 2 O production. Table 2-8: low-NOx burners are great, in theo ry, but lots of plants report problems in their actual use (have lower clinker output when using these burners). Ch. 3: ISIS Mathematical Framework p. 3.2: the factor: produ_r(t,r)/0.92 has a clinker ratio (clinke r/portland) of 0.92. This is too low for a good average. Even including the blended cements and masonry cements, the USGS data show the ratio to be right on at 0.946-0.950 ( you can’t ignore these cements because the USGS raw materials data is for portland & blended cement). Anyway, 0.92 implies 8% gypsum and/or other additions, and this is too high right now. If the up-to-5% limestone addition becomes widespread (current version of ASTM C150), then 8% may become reasonable. We can approach a 92% clinker factor hypothe tically if we assumed that we had a 95% market share for true portland cement @ 95% clinker + 5% gypsum, and 5% market share of masonry cement averaging 50% clinke r (this is probably a bit low): (0.95 x 0.95) + (0.05 x 0.5) = 0.9275. Again, the import data that you are using a ppear to be for both cement and clinker. The factor imports (t,r) is likely prone to la rge error because we simply do not know how much cement coming into a certain Customs Dist rict is consumed in that district. For example, there is considerable barging of cement along the midAtlantic and Gulf Coasts; ditto along the NW (Pacific) Coast. One of the few places where the assumption (of local consumption) is valid is Florida. The worst case is with New Orleans: imports into New Orleans can reach Chicago! And then we have the whole issue--sad ly, very common--of cement swaps among companies.Version 1 H-9 EPA External Review Production and Capacity Changes : the lead-in sentence is a bit unclear: “Regional production can be from existing kilns, kilns ad ded at a plant (i.e., expansion kilns), newer kilns replacing kilns at a plant (i.e ., replacement kilns), and new kilns.” I think you mean to say something like: “...Future production...existi ng kilns, upgrades of existing kilns (debottlen ecking, major technological upgra des), new kilns at existing plants (perhaps replacing existing ki lns), and new plants altogether.” Here’s how I look at things: Present kilns Production? Future production Idle kilns: No Maybe will be restarted Active yes yes (same capcity); and/or Upgrade kiln line; modify feeds (eg CemStar); and/or Debottlenecking; and/or New kiln at existing plant; Shut down old kiln; and/or New (greenfields) plant. It is unclear if your discussion, and projected capacity changes, fully account for plant shutdowns (now seen to be a much faster sc hedule than was anticipated even 1-2 years ago). Have capacities been properly/uniform ly defined for all new kilns (are they summed on the same basis)? p. 3.2-3.3: Equation 3.1.3. for regional demand: I suggest using the word “flows” instead of “trade” when referring to inter-State or inter-market movement s of domestic cement. Equation 3.1.4 only holds if the import: total demand ratio is accurate, and I’m not sure this is the case. Again, I would note the fact that imports into the New Orleans Customs District feed the entire Mi ssissippi-Missouri-Ohio river sy stem. How are you determining where cement imported at location X is being consumed? p. 3-3: the price data for imports (Equati on 3.2.5.) are very weak. You only have import values reported on a CIF basis (USGS mont hly and annual reports) and Customs Value (USGS annual reports). The CI F value does not represent th e actual price because it has not yet been offloaded from the ship: that is, the true price is CIF + various port charges + markup. And the CIF (and Customs) value data themselves are full of errors: sad but true. Not much that can be done about this. Much of Ch. 3 is not exactly reviewer-friendly (!) and I really was unable to do much with it absent detailed experien ce with running the actual model. Ch. 4: Calibration of Cement ISIS Model 4.1.3.1. I am not sure that the assumption that Price imports = producer price is at all valid. Dumping issues come to mind. I have found th at imports can be cheaper, the same, or higher than those from “local” producers. One issue is transportation costs. Another may be differences in the type of portland cem ent: local production might be Type I and the imports Type III. One thing I will say is that “cheap” imports do not necessarily equate to cheap sales prices—importers ar e very fond of large margins. You cite a VERY limited number of examples of import-dominated markets, and without really defining what this mean s. What is the percentage cr iteria for this designation? Seattle may not be an es pecially good example.Version 1 H-10 EPA External Review USGS data for 2005 (MYB Table 9) show consumption in WA as 2.348 Mt and OR as 1.237 Mt; against imports into Seattle Cust oms District (CD) of 1.489 Mt and 0.867 Mt into the Columbia-Snake CD. This looks lik e a high import ratio, bu t do you really know where these imports are being consumed? Again, things fall apart for New Orleans and points upstream. Table 4-1: why 2004 data and not 20 05? Again, switch to metric tons! What is the basis of the produc tion data in table 4-1? The US GS data do not allow for it, as our District groups do not match your market groupings. The PCA normally does not publish production data othe r than in broad groupings (kilns > 500,000 tpy, etc...). High import ratios exist for Florida, Los Angeles, San Francisco, San Antonio—yet the table designates (**) only Florida and Seattle as ha ving high ratios. 4.1.3.2. The PCA may have predicted higher import ratios in the future, but hey certainly have fallen in 2007-09 and likely will stay down in 2010 as well. Thereafter, I venture
not. Table 4-2: where are you getting your price data; and why are you showing 2003-2004 and not 2005? Need to cite a data source. Table 4-3: The emissions shown are too precise. Ch. 5: Illustrative Analysis Figure 5-1 and the discussion of it lead to the conclusion th at the NOx cap may be too restrictive. Whether this is valid or not, however, is hard to determine. Apart from the difficulty for a plant in precisely controlling NOx emissions, in typical operation, the emissions from a given plant typically swing rather widely on a minute-by-minute basis. Because of this, it has always been an issue as to how one is to determine the actual emissions volumes over a specified period, as it could affect whethe r a plant exceeds its NOx cap or not. I think this report needs to discuss this. In contrast, Figure 5-2 would seem to illust rate the relative ease in controlling SOx. Figure 5-3: Figures should sta nd alone—thus it is confusi ng why the emissions caps and banking are not shown (I know the answer—jus t, for now, leave these off the caption). I do not know if the projections in Figure 5- 3 have made allowance for clinker imports and changes therein. Given a likelier harsher reality re. plant cl osures, these projections may need to be redone. 5.1.2.: In the text, it is important to minimize the use of acronyms, es pecially for readers not conversant with these technologies. The savings of space are not that much! One important issue with CemStar is that steel slag may not be available locally. Figure 5-4 is impossible to read for those of us equipped with standard or substandard human eyesight. After staring at this long enough, I am left wondering as to why there is such a huge drop in LNB + TDF in 2011 wrt. 2010. Are the subsequent (2011--) years in addition to the 2010 levels? (i.e., are they ju st shown as margins?). Was their a scale change post-2010? 5.1.3: The total demand in the near term ha s now likely been revised downwards by the PCA. Figure 5-5: it is not immediat ely clear why the red dot has been displaced rightwards (I can guess, but it looks strange). And wh at happened to the red dot for 2025? Looking at this figure, I do not see much diffe rence between the base case and the policy levels.Version 1 H-11 EPA External Review Figure 5-6: Unless I am missing something, the blue dots are not needed at all—the sum is always the top of a stacked bar graph. The Legend is confusing, as I only see one market that has either a trad e-in or trade-out (and, again, I would urge calling this an inflow or outflow) and that is Birmingham. Su rely, there are more intermarket flows than that! And the data for imports are either essentially all missing or you are projecting zero imports. I do not understand how you have dete rmined the (foreign) imports into the various markets—such as Dallas. The trade data for that area are under the HoustonGalveston Customs District, which is in your San Antonio Market area (yes?). Why are no foreign imports shown for Birmingham? Florida???, Phoenix?? St. Louis?? So this figure needs some work.... 5.1.4. the cost analysis seems to show that, of the total increase in costs ($1.58 billion), all but $0.01 billion is due to control technology. What a bout increased fuel costs? Ch. 6: Summary Basically OK. Perhaps you need to again mention here here why CO 2 emissions could drop under SO 2 -reduction strategies (somewhere earlier it was stated that there could be decreased CO 2 using limestone wet scrubbers (LWS)— it is unclear why this would be so. I would think that reduced CO 2 would instead be—as stated elsewhere—from changes in the fuel types that might occu r in a SOx-reduction program. Appendix A: Staudt, Jim, Ando ver Technology Partners, “NO X , SO 2 , and CO 2 emissions from Cement Kilns”: This is a rather good report with a few places that bothered me. Table 1: the exit flow gas rate s would show a lot of variat ion; not sure how “good” an average these rates are. By the way, Bhatty et al. (PCA 2004) cited is a huge volume, so a page reference would have been helpful. Table 2: although the heat values shown are reas onable, it should be pointed out that, in reality, most fuel categories have a range of heat values, and that it is NOT guaranteed that the coal burned by plant X in year Y w ill be the same coal as burned in year Z. Further, the reporting of fuels by cement plants has problems, among which are: a) who is reporting: a lab tec hnician, a plant manager, a controller/accountant? Same person each year?

b) is the weight of (e.g., coal) th e actual weight going into the kiln? In it expressed as the actual mass or converted to a constant moisture (> 0%) content basis or to a zero moisture basis?

c) Is the heat value provided or not? If s o, is it a gross (= HHV) basis or net (LHB) basis?—the former is preferre d for emissions calculations.

d) Is the heat value provided as a Btu/st (or other unit) ratio (USGS then multiplies it out), or did the plant report th e full Btus? If the former (Btu/t ), was it “real” or simply calculated by the plant: tons x Btu/t = Btu – and if so, was the same Btu/t used for all the coal used during the year, or were period ic checks/adjustments made to the Btu/t?

e) for whole-tire fuel, the moisture content can vary dramatically.

f) Have any waste fuels been identified pr operly: if no heat was provided, how do you impose a heat estimate for “liquid waste ?” (undifferentiated). How about for anVersion 1 H-12 EPA External Review identification given simply as: “used solven ts?” Or “off-spec oil and lubes, inks, and solvents”. What about things like: “(so lid wastes: oily rags, street sweepings; contaminated soils)”. Or even “high-carbon fly ash”?

These are significant problems because the carbon factors for fuels are generally expressed as “ amount of carbon per X amount of heat” Anyway, I would also comment that the valu es in Table 2 are expressed with more precision than is warranted. From table 2: coal at 12,456 Btu /lb becomes 27,460,772 Btu (HHV)/mt and 26,340,824 Btu (LHV)/mt. USGS data (asked for as HHV) typically show a range of 22-28 MBtu/mt. So table 2 appears to be at the upper end of the range. CO 2 from calcination: Although Staudt’s calc ulation is arithmetic ally sound (within rounding parameters), I do not like th e default he derives (0.53 t CO 2 / t clinker) because it is based on an average CaO content of clinke r of 67.6%. This is too high—it is near the upper end of the CaO range in modern clinke rs. As was exhaustively dealt with by the IPCC, the average is a bit lower. No great prec ision is justified owing to variation in the chemistry of clinker at various plants and am ong plants, but 65% is a better, more useful average. This drops the default (using the sa me assumptions as to CaO source) to 0.51. If you incorporate the IPCC-suggested 2% additional CO 2 for CKD “lost” (not returned to the kiln) to the system, it becomes 0.52%. Ne ither protocol adjusts for MgO contributed from a carbonate source, but this is OK b ecause the assumption that the CaO is 100% from CaCO 3 already causes a compensating overestimate; MgO is always kept low anyway (0.5-2%; seldom higher), and MgO is commonly from silicate phases instead of/in addition to a carbonate phase. In any case, the EPA in the USA GHG emi ssions inventory uses the IPCC default (w/ CKD) of 0.52 and I would suggest that the ISIS m odel do the same. It should be noted that the CaO content of the actual “limestone” burned by the cement industry varies a lot, both across country and even within some individual quarries. Staudt has been incautious in equating “limestone” and calci um carbonate (calcite): the former is a rock mostly made up of calcite; the latter is a mineral of formula CaCO 3 . His stoichiometry applies to calcite or calcium carbonate, not to limestone. I found much of Staudt’s repor t very interesting and useful . Although he eventually gets to the important issue of the age of the kilns (as in, newer vs. older technology), there is a related factor that he appear s not to discuss—unless I missed it: the issue of plant-level reporting of fuel and electrical consump tion (etc...) for plants operating single vs. multiple kilns. USGS data are fairly close to the PCA data (but ours include white kilns): we generally see a ranking (1= least efficient → 4 most efficient) as follows: total heat (total fuel consumption): 1 wet → 2 long dry → 3 preheater → 4 preheater/precalciner (precalciner). Electricity: 1 precalciner → 2 preheater → 3 long dry → 4 wet. But we see other patterns: for two plan ts of 1 Mt/yr capacity wher e each has similar technology kilns, a plant having a single kiln will be more efficient than the plant with multiple kilns. This relates not only to economies of scale but, for electricity, on the fewer motors needed for the single kiln. p. 8: while it is true that, to some degree, flame temperatures in precalciners may be a bit lower than in the kiln tube itself, the di fferent proportion of NOx type (mostly thermal NOx in the kiln tube; mostly fuel and/or feed NOx in the calciner) also derives from the fact that: 1) the burner for the calciner generates a shorter flame, which may reduceVersion 1 H-13 EPA External Review thermal NOx, and the calciner is dealing with higher surface area particles, so does the calcining far more efficiently than the kiln tube could do. Thus there will be less NOx overall for the calciner on a per ton of clinke r basis. And, because of this efficiency, the kiln tube itself will be smaller on a precalciner kiln line, which makes for better efficiencies all around (for example, redu ced heat loss throug h the kiln shell). The higher NOx/MBtu for wet kilns mentioned by Staudt may also be related to the fact that wet kilns these days almost invariable burn a relatively high proportion of waste fuels. Also, there could be some correlati on with things like the use of primary vs secondary air. And, many of the wet plants operate multiple kilns. And, being older, and larger tubes, with more motors/tires (hence kinking), there may be more frequent stoppages, hence more use of warmup fuels (natural gas, for example, makes a lot of thermal NOx because of the exceptionally high flame temperature). Finally, the wet kiln plants may have older NOx monitors a nd maybe these are skewing data (???). I agree with Staudt that the NOx data show a lot of scatter. I would note that his observation that the larger kiln s are more efficient (lower NOx) is perhaps not supported by his graph—yes it shows this, but there are only 3 data points for the kilns > 1 Mt/yr capacity. Is this statistically valid? Figures 5a-5c: Author is being “kind” in his characteriza tion of the data: the correlations would appear to be not statistically significant. In his discussion of SOx capture, the author sa ys that synthetic gypsum is useful to the cement plant (saves them buying outside gypsum ). Yes and no. In realitity, some syngyp is dirty. A common complaint about its use in the finish mill is that it is sticky. So far, not that many plants are using it (but, in fairness , they are not required to split syngyp from natural gypsum in their raw materials reporting to the USGS, so we may not be seeing the full extent of syngyp use). p. 15: perhaps this is a semantic point, but Dragon Cement’s switch to dry technology involved shortening the existing kiln t ube, not by bringi ng in a new tube. While it is true that there is regional variati on in the sulfur conten t of “limestone” (and the sulfur in “limestone” is from 3 main sources: sulfide minerals such as pyrite; kerogens; and gypsum/anhydrite.), there is as much, or greater variation from the Scontribution from the coals: there is a major di fference in the S-content of coals (S in coal is from kerogens and sulfides like pyrite and marcasite). The sulfur content of the “limestone (BTW —Dragon actually burns marble; and much of the limestone burned in N-CA is also a marb le, or nearly so) would not normally be a criteria for selection of a new quarry. Given the small number of kilns in most of th e markets, I am not sure that one ought to read too much in the mean vs. median emissions data in tables 8 and 9. Appendix A continued: Re: Cost s and Performance of Controls This is also a good report. Regarding the use of tires, I would commen t that not all plants burn tires because: 1) there can be local competition for tires —you need an “abundant” supply, not just the occasional tire;

2) permitting issues

3) the requisite tire supply may not be available in all areas (not near enough to a large city);Version 1 H-14 EPA External Review 4) Capex for the tire-burning and feeding equipment.

I am not sure that I agree th at tires yield slightly less CO 2 than coal: perhaps on a dry weight basis they yield slightly more. Depends on how you relate the emissions: per weight of fuel, pe r ton of clinker... I agree that tires are not fossil fuel, per se, but most involve a considerable input of the products of the petrochemical industry in their manufacture. p. 13: Preheaters and precalcine rs are described as “post-k iln” combustion technologies. I guess that they would be if you are looking up- kiln (i.e., from the flame and gas flow directional perspective), but most people would follow the raw materials and clinker instead and would call preheate rs and precalciners: “pre-kiln ” technologies (that’s why “pre-“ is in their name!). I think most calciners are desi gned to burn about 60% of the (total kiln line) fuel, not 50%; though perhaps the oft’ quoted 60% includes riser duct firing. As stated in para 1 of p. 13, there is an implication that most thermal NOx is avoided in preheater and precalciner kilns—as stated, this is incorrect. First, there is a lot of thermal NOx generated in the kiln tube from th e main burner. Secondly, a preheater kiln commonly uses hot combustion air for the preh eating (which is full of thermal NOx but this may be destroyed if reducing conditions exis t in the preheater). What is true is that NOx generated in the calciner (and hence also carried into the pr eheater cyclones in a preheater-precalciner system) is dominated by fu el and/or feed NOx. So the issue is that the precalciner (location) doesn’t generate a lot of thermal NOx—the same cannot be said for the whole precalciner kiln line. p. 26: LWS scrubber: basically fine. Here and elsewhere, use calcite, or calcium carbonate, instead of limestone. Limestone is a rock, only very rare ly is it pure calcium carbonate. p. E.31: minor issue: gypsum is used to make finished cement, not finished concrete. Another issue: CaSO 4 is the formula for the mineral a nhydrite. Gypsum has the formula: CaSO 4 ·2H 2 O . p. 30: CKD is described as being made up of oxides of Ca, K, and Mg. You’ve left out Si! Para. 2 states that CKD disposal is not an issue with dry-technology kilns. This is not true. Any kiln technology can burn CKD a nd any can have a problem burning CKD—it depends primarily on things like the alkali c ontent of the CKD and the resulting clinker. If there is a regional ASR problem with the local aggregates (in concrete) then you need to keep the alkali content of the clinker dow n. This may be well-controlled if you have an alkali bypass system, but if you do not, then your ability to burn CKD may be severely limited. You can certainly burn CKD in a wet kiln: th e slurry viscosity can be an issue, but you can also add a bit more water. CKD was bur ned at Dragon’s wet kiln, for example, but only in relatively modest amounts (they did not burn all of it). CemStar: I would add a bullet for the CO 2 reduction! On the negative side, CemStar may add to a plants emissions of Cr +6 . I think this is why Cemex’s Davenport (N-CA) plant is presently idle. Good for you in getting the $16/ton royalty fi gure: I am in the business of collecting proprietary data and I never co uld get anyone to tell me this number (I heard grunts and mutterings about it, it was high; I heard $12 mentioned once but it was not definitive).Version 1 H-15 EPA External Review The high royalty definitely limited interest in the technology. But, there is an important difference between what TXI got/continues to get for the CemStar contracts that were signed with TXI, and what the slag pr ocessing company Levy (who bought the CemStar patent in 2004) gets for cont racts it develops. Levy is primar ily interested in promoting the slag use (and selling the sl ag to the cement plant) and charges (they tell me) a much lower royalty (but they don’t te ll me what it is). My point is that, in your cost analysis, it is likely that future (new) users of CemSta r will be paying <<$16/ton royalty (if any) for the process. At $16/st, CemStar becomes quite expensive relati ve to the cost of the steel furnace slag (a few dollars per ton only, + tr ansportation) and its handling. I do not understand this $72.59/st cost for clinker (seems excessively high), nor why it is even relevant. If I understand the report correc tly, it is being said that this clinker cost minus $10/ton for the slag, yields a savi ngs of $62.59/ton or $45- 50/ton including the CemStar royalty. I just don’t see how this relates to evaluating the use of CemStar. What you seem to be doing is pretending that you stick slag into the finish mill to make cement (that it competes with clinke r) when, in fact, it is “comp eting” with (some of the) limestone as a feed to the kiln

. What you need to do is to compare the cost of making clinker without using CemStar vs the cost to make clinker using CemStar. CemS tar always involves inco rporating steel slag into the raw mix as a partial (3-10 %) substitute for limestone (etc...). Without CemStar: cost = A (c ost to quarry, comminute, & blend the raw feed materials) plus P (the cost of pyroprocessi ng (fuel costs etc...) to make c linker. This is the cost to make the clinker, period. With CemStar: for every ton of clinker made, you are quarrying, comminuting, and blending less “normal” feed, so A is lower by an amount B. But, against this, is the cost (A’) of: buying the slag, the royalty for the pro cess, crushing the slag to 1-2” size, and blending. So the resu lting feed cost b ecomes (A-B + A’). The pyroprocessing cost is the co st of calcining the original feed mix (in the now smaller amount), plus the cost of melting/disassocia ting the slag, plus the cost of sintering. Because there is less carbonate to calcine (per ton of clinker), the fuel (heat) required for calcination will be less. (Calcination is where 60% or more of the heat is consumed). Also, because the slag melts/disassociates easily, you are getting so me of your silica, iron, and aluminum (oxide) for less heat (a nd maybe faster) than would be the case burning clays, shale, and quartz sand. So th e cost savings are related to the reduced heat/fuel charges of the pre-sint ering stages of pyroprocessing (P’). For the sintering part of the clinker-forming process, there pr obably will not be any savings. So the pyroprocessing cost becomes P-P’. So the overall comparison is A + P vs. (A-B + A’ + P – P’). And the overall savings will be nowhere near $45- 50/ton of clinker! The reduction in CO 2 emissions will be, on a per-ton-of-c linker basis, will be something like: for calcination emissions: (1-C ’)*C, where C is the calcination CO 2 for the carbonates burned in feed that does not incorpor ate CemStar, and C’ is the percentage of carbonate substitution by slag. That is, 5% (C’=0.05) substitution yields: (1-.05)*C = 0.95C. i.e., this reduction is stoichiometrically straightforward. The fuel emissions reduction is a bit less so. If F is the non- CemStar fuel emission for the pre-sintering stages, the new emission (w/ CemStar) becomes something like: ((1- C’)*F) + G + SVersion 1 H-16 EPA External Review where the first term relates to the reduced heat of calcination and low-temperature pyroprocessing of the normal feed mix; G is the low temperature fuel requirements to deal with the pre-sintering di sassociation of the slag; and S is the fuel requirement for sintering. I am assuming that S does not change (i.e., fuel emissions without CemStar = F + S). The CemStar savings on fuels stems from the fact that G << C’*F. What this all means is that the statement on p. 36 that the CO 2 reduction (overall, on a per ton clinker or cement basis) will be directly pr oportional to the percent slag substitution is not quite safe—it will hold for the calcination part of the emissions, but may be different for the fuel side of the emissions. p. 35: Again, if I am reading this correctly, it appears that a claim is made that CemStar increases the electricity consumption by the fi nish mill. Well, if you assume that you get 5% more clinker for a 5% CemStar (slag) in troduction, then, yes, it will take 5% more electricity to grind that extr a clinker. But so what? This is like saying that if you get a salary increase, you will pay more taxes even though the tax rate doesn’t change. This is not interesting. What would be interesting is if the use of slag (CemStar) made a clinker (perhaps a harder clinker) that was costlier to grind (more electr icity per ton). But I do not think that this is the case: the cost (eithe r per ton of clinker or per ton of cement) to grind the clinker into cement does not change as a result of using CemStar. There may be some additional electricity in the raw mill end of the plant to handle the extra ingredient (slag) but this is likely to be more than offset by the reduced electricity costs of comminution of the feed: unlike the limestone a nd other feeds, the slag does NOT need to be ground, just coarsely crushed. [the CemSta r patent “discovery” was not the chemical contribution to the clinker-making process. It is the fact that you don’t need to grind it— only crush it to 1-2” diameter. The utility of slag had been known for many decades, but it had not been popular earlie r because it was assumed that you had to grind it like everything else, and slag is i ndeed hard (costly) to grind]. p. 37 Conversion from wet to dry The wording is a bit imprecise. You can convert a plant from wet to dry by replacing wet kilns with dry kilns and/or by converting wet kilns to dry tech nology. Dragon Cement in Maine was a true kiln conversion: the old wet k iln tube was retained (but about 1/3 of its length was cut off), and a preheater/calciner tower was added. Holcim @ Holly Hill, SC was a replacement of the 2 we t kilns (which were shut dow n) with a completely new, unrelated, dry kiln (p recalciner kiln). Regarding the 300,000 ton example—I would only comment that one would never build a kiln of such small capacity today, nor w ould it be worthwhile anymore to convert a 300,000 t wet to a 300,000 dry kiln. p. 38: states: “Water consumption would also drop. Moisture added to the materials of a wet kiln amount to about 0.75 tons per short ton of c linker.” This is too high (too much water). Wet and dry kilns both need about 1.6-1.7 tons of raw materials per ton of clinker. Typical water content of wet kiln slurries is 35-40% by weight. So a 35-40% water slurry containing 1.6-1.7 t of raw mate rials (destined to make 1 t on of clinker) would contain 0.56 – 0.68 t water. But, of course, water is also used in cement plants as a motor coolant and in the finish mill (as a spray) to make sure the calcium sulfate in the cement remains (mostly) gypsum and does not dehydrate to pl aster or to anhydrite.Version 1 H-17 EPA External Review p. 42: The question is asked why haven’t all the existing preheater kilns been converted to preheater-precalciner technology? There are at l east 3 answers to this: 4) if the preheater kiln is small (capacity), the conversion boost in capacity might be insufficient to justify the cost;

5) significant projects like this typically require a litmus test of the plant still having at least 50 years of lim estone reserves.
6) significant projects like this likely will put the plant into a stricter regulatory framework re. emissions (the existing plant may have grandfathered in).

General comment : One significant omission from this study—unless I missed it somewhere (it is a long report!) are certain upgrades to capacity that simply involve debottlenecking. This could involve things like: 1) upgrade/replace clinker cooler

2) upgrade raw mill

3) upgrade finish mill (e.g., insta ll roller mill instead of ba ll mill—it will greatly reduce electricity consumption)

4) Upgrade storage facilities (raw feeds, clinker, cement) and overall materials handling.

5) Upgrade dust collectors

p. 44: don’t need to capitalize gr eenfield(s) and brownfield(s). p. 45: Not sure that “attainment” status is clear to all eaders—may be environmental jargon. Item #2 (reserves). 100 years for a greenfields project is about righ t. To this I would again note that major capex projects need c. 50 years of remaining reserves or more (some companies may be satisfied with 35 years). Item #3 labor: I do not think la bor is important...Look, they ha ve cement plants all over the world. Labor can always be found. The more skilled positions w ill draw engineers (et al.) from far away. When I ha ve visited cement plants, I am commonly struck by the fact that most of the workers have been ther e for ages—they have had or are planning on having their whole career there. They evidentl y like the work. The pay is adequate and steady; it’s relatively clean, a nd relatively safe. It’s a more recession-proof industry than most. So there’s not necessa rily a lot of overturn (oddl y enough, except for the plant managers). People learn the work; they get trained. Modern plants do not need a huge workfor ce anyway. And, normally, you would not cite a cement plant in the middle of the Arctic wilderness. Unlike certain metals mines (where you simply must put the mine where the depos it is located or else the mine will fail badly), you generally have a c hoice as to where you site the cement plant (limestone is abundant). Generally, there will be some sort of community nearby the proposed cement plant that can supply the “blue-collar” labor. You offer the notion that the USGS has/wi ll map out suitable limestone deposits. Unlikely. Cement companies have their own sta ff of geologists, or will hire a consultingVersion 1 H-18 EPA External Review geologist, for this purpose (they may use USGS and/or State geological maps to broadly identify where the limestone may be found). p. 46: Decision tree: add: consid er adding a terminal (instead of a plant); consider swap arrangements (to supply cement to cust omers outside your own plant’s “range”.) Round the midpoint costs to 2 signifi cant-figures: e.g., $280 instead of $277. Appendix B: (memo from RTI) p. 3-4: I would note that the USGS data pertaining to prices for crushed stone ($/t) are for sales by crushed stone quarries. Thus, they w ould be broadly appli cable to prices for some of the raw materials that are purchased by the cement company, but likely exceed what a cement company “pays” for its own limestone. I would think that the basic limestone cost (quarry, primary crushing, convey to raw mill) would be typically ≤ $5/st. The average hourly wage table look a bit low fo r a cement plant, but I am not sure. Need to get some actual cement plant data. My main issue is that the typical cement plant worker has 10+ years of experience, I woul d think, and so would likely draw a higher
wage. Table 6: The electricity data seem high. First, whereas the logic of the PCA’s “equivalent tons” may be sound for heat, it makes less sence as the denominator for cement, given that roughly 1/3 of the consumption is in the finish mill. The USGS data relate the unit consumption to total cement (not just portl and) production. We show for 2005: wet plants = 135.4 kWh/mt; dry plants (all types) = 139.0 kWh/mt; combination plants (using wet & dry kilns) = 153 kWh/mt; and all integrated plants combined = 138.8 kWh/mt. Grinding plants = 84 kWh/mt. Table 7: the unit electricity pr ices may not be valid for la rge industrial customers (they may get a lower rate). Table 10: a few comments (reflecting newer info):

• The Dixon, IL and Charlevoix, MI plants are now owned by St. Marys Cement.

• Hanson plant, CA is now owned by Lehigh SW

• RMC plant in CA is now owned by CEMEX

• Lehigh Allentown, PA plant is ac tually their Evansville plant

• Holcim (US) now controls all St. Lawrence facilities in the USA.

• Essroc’s Nazareth III plant in PA shut its kilns in 2005 (only kilns are at Naz. I)

• Lafarge’s Atlanta, GA plant shut its kiln in 2004; now only grinds some clinker to make masonry cement, regrinds Type I into Type III portland cement, and grinds granulated slag.

• Dragon Cement was a dry plant in 2005 (the wet to dry conversion was completed 4/04).

This completes my review comments.Version 1 I-1 EPA External Review 

## Appendix I

Review Comments from Hector Ybañez, Holcim

Transcription of comments submitted verbally to EPA during the meeting on January 29, 2009

Page 1-3 and 1-4: Figures 1-1 and 1-2 Update cement diagram – add fabric filters (for PM controls) instead of ESPs and simplif y the diagram with just the cement making
process
Page 1-5: Statement about PC fuel efficiency in the first paragraph... replacement of both wet and certain dry process kiln capacity with modern kiln processes yield substantial reductions in fuel use. This is true in term of fuel efficiency but not in the absolute amount of fuel use. Newer kilns tend to be bigger kilns and are going to use more fuel.

Page 1-7: Figure 1-4 Add import’ s custom districts to the map

Page 1-8: Do these imports in clude imports from Mexico (imports by rail and trucks)?

Page 1-9: Are the PM emissions (36,000 tons) emissions from the kiln or for the whole plant (including grinding)/

Page 1-11: Figure 1-7 – Emissions as input s to the model are baseline uncontrolled emissions at baseline levels of production.

Holcim will provide EPA with capital recovery cost for 15-20 years old kilns and some information on capital co st and interest rates

Page 2-3 and 2-4: Tables 2- 2 and 2-3 specify the units

Page 2-6: Table 2-4- Spell acronyms in the ta ble (SFC, EGFW) to be consistent with the table in the appendix. The EGFW in Nm 3 /Kg for dry seems low. Hector will share Holcim’s calculation method at 10 % oxygen to recalcu late the EGFW.

Page 2-7: Table 2-5 Update with the most recent ACT data (2008 ACT) and show data in lb/ton clinker

Page 2-8: Table 2-6 Change red color to be light green.Version 1 I-2 EPA External Review Page 2-9: Table 2-7 Label symmetrically, lbmolesH 2 O/MMBtu, H 2 O is missing in some places

Page 2-10: Table 2-8 Ar e CO2 reductions total CO 2 (process and fuel)? If so, express it to be total CO2. Are numbers absolute #s or intensities? Can sewage impacts be included? PCA has given EPA a workbook with the fuel and power use for the cement industry. This workbook can be used to compare #s. The workbook is part of the WBCSD initiative. Hector will find out w ho at EPA PCA sent the workbook data.

Page 2-11: Table 2-9 Are these #s fuel intensit y or absolute? We might think that the #s are intensity related not absolute. When going from a wet to a PC you have more capacity, you produce more and therefore you will have more emissions in the absolute no matter that the process is more efficient.

Page 2-12: Table 2-10 Specify in the model that these parameters are to be chosen by the

user.
Page 5-6: Table 5-1 control cost #’s need to be updated to differentiate between new capacity in the business-as-usual vs. policy scenario.

Comments on Spreadsheet Add plot on retirement, new capacity, etc

.Version 1 J-1 EPA External Review 

## Appendix J

Review Comments from Eric Smith, US Environmental Protection Agency and Ernst Worrell, Ernest Orlando Lawrence Be rkeley National Laboratory

1. The model models each individual cement plant, without plant specific data but on the basis of a number of assu mptions. The key assumptions are:

a. Energy intensity of the kiln (now based on type of kiln) b. Fuel mix (industry average)

c. Capacity utilization factor

How are these assumptions used to estimate plant-specific energy use and emissions?

Especially energy intensity and fuel mix can still vary within a relatively large range. The energy intensity assumptions given in Table 2-4 demonstrate this point for energy intensity. In the US there are a number of kilns that used more petcoke (i.e. plants operated by Cemex), while othe rs are larger users of alternative fuels (depending on the permit). In Europe, kiln s with up to 50% of biomass and 80% alternative fuels can be found. While, this va riation is smaller in the US, this is a factor likely to change in the future (if permitted). I also think that these uncertainties are larger than the impacts of some of the control technologies modeled.

Given these key uncertainties, I think th at a thorough uncertainty analysis is essential. Moreover, it is questionable if the results should be presented on the level of individual plants. The results ma y be less uncertain on a regional level

2. The average energy intensity values gi ven in Table 2-4 are for good operation practices and do not seem to reflect the industry practice. For example, the average fuel intensity for wet kilns given in Table 2-4 is lower than the average as can be calculated from USGS data (see al so the Energy Star Guide for the cement industry). How representative are these figures and it would be good to validate the assumptions usi ng statistical data.

3. Also, note that there is a difference between USGS and PCA data which is difficult to explain (see the Energy Star Gu ide), which brings in another source of uncertainty (which is now hidden due to the use of PCA data/assumptions only).Version 1 J-2 EPA External Review 4. It is interesting to see that EIA data is used for the fuel mix (Fig. 2-2), while the USGS has a much detailed overview of fuel used on an annual basis. A calibration to USGS data would be essentia l, but moreover it gi ves a good feel for the trends in the use of alternative fuels (broken down by wet and dry kilns, and in 3 fuel types: tires, solid and liquid wastes ), and hence would be a better basis for emission estimates.

5. The model seems originally to be aimed at dealing with criteria air pollutants, to which now greenhouse gas emission calculati ons have been added. This is OK. However, in the modeling of abatement/contr ol technologies the original “bias” of the model is clearly visible. While NOx emission controls seem to be modeled very detailed, the measures to control CO2 emissions are more limited. The “big ticket” items (i.e. major kiln rebuilds) are included, but especially many of the smaller measures are missing, e.g. combus tion/process controls, more efficient burners, kiln shell measures, seals, CKD-related measures, cooler optimization/replacement, and oxygen enrichment. Also, measures as improved raw material grinding and high-effici ency classifiers may improve energy efficiency. Moreover, measures like change s in fuel mix, additi on of limestone to cement, and blending may be preferred m easures to react to a cap and trade system. The discussion of electricity rela ted measures is very limited and focuses on only two items, while leaving out an important one: choice of grinding mill type.

It is unlikely that a cap and trade system will trigger the replacement of a kiln, as also noted in the report, but more by regional markets, production and maintenance costs (including producti on stops), and remaining limestone reserves. Hence, the ISIS model would not work well for modeling the impacts of a GHG cap and trade system, unless the like ly response measures of the cement industry are included in the model.

A change in fuel mix is the most likel y first response to a GHG cap and trade system. Hence, it is especially important to model this accurately to use the ISIS model for GHG assessments. Note that so me of the alternative fuels may have “negative” costs for the kiln operator. Also, non-traditional biomass may be used (e.g. slaughterhouse wastes).

6. The current estimates are based on direct emissions from kilns only. Note that a few plants operate other equipment. At leas t a plant in California and Florida also operate power generating equipment.

# DRAFT Technical Support Document The Industrial Sectors Integrated Solutions (ISIS) Model and the Analysis for the National Emission Standards for Hazardous Air Pollutants and New

Source Performance Standards for the Portland Cement Manufacturing Industry

U.S. Environmental Protection Agency

Sectors Policies and Program Division and

Air Pollution Prevention and Control Division

Research Triangle Park, NC 27711

August 2010ii 

## Table of Contents

List of Tables .................................................................................................................

.............................. iv List of Figures............................................................................................................................................... v Conversion Table – English Units to SI Units...................................................................................

.......... ix Chapter 1 Introduction ............................................................................................................................... 1-1

1.1 The US Cement Industry.........................................................................................................

... 1-1

1.1.1 Cement Types and Categories............................................................................................ 1-1

1.1.2 Overview of the Cement Manufacturing Process .............................................................. 1-2

1.1.3 Kiln Types and Their Use .................................................................................................. 1-5

1.1.4 Portland Cement Production in the U.S. ............................................................................ 1-7

1.1.5 Imports of Portland Cement in the U.S.............................................................................. 1-8

1.1.6 Cement Demand Centers ................................................................................................. 1-10

1.2 Emissions from the U.S. Cement Industry and Applicable Regulations ................................. 1-10

1.3 Overview of ISIS ...............................................................................................................

...... 1-12

References for Chapter 1 .......................................................................................................

.................. 1-14 Chapter 2 ISIS Mathematical Framework ................................................................................................. 2-1

2.1 Indexes, Sets, and Mappings....................................................................................................

.. 2-3

2.2 Objective Function.............................................................................................................

........ 2-4

2.3 Supply ........................................................................................................................................ 2-7

2.4 Production Capacity and Supply Costs.................................................................................... 2-10

2.5 Emissions ................................................................................................................................. 2-14

2.6 Controls and Costs ................................................................................................................... 2-16

2.7 Costs of Energy E fficiency Measures...................................................................................... 2-23

2.8 Policy Options.......................................................................................................................... 2-26

2.9 Optimization and Post-Processing ........................................................................................... 2-27

References for Chapter 2 .......................................................................................................

.................. 2-29 Chapter 3 Input Data for ISIS-Cement Model .....................................................................................

...... 3-1

3.1 Data Requirements..............................................................................................................

....... 3-1

3.2 Cement-Specific Data ...........................................................................................................

..... 3-1

3.2.1 Industry, Fuel, and Emissions............................................................................................ 3-1

3.2.2 Control Technologies and Emission Abatement Approaches.......................................... 3-13

3.2.3 Policy and Economic Parameters..................................................................................... 3-13

References for Chapter 3 .......................................................................................................

.................. 3-24 Chapter 4 Model Calibration ....................................................................................................

................. 4-1iii 4.1 Calibration Methodology ........................................................................................................

... 4-1

4.2 Data for Calibration ...........................................................................................................

........ 4-1

4.2.1 Cement Prices in USGS Districts....................................................................................... 4-2

4.2.2 Cement Production in USGS Districts............................................................................... 4-2

4.2.3 Cement Imports by Import Districts .................................................................................. 4-2

4.3 Results of Calibration.........................................................................................................

........ 4-2

4.4 Conclusions.............................................................................................................................. 4-10

References for Chapter 4 .......................................................................................................

.................. 4-11 Chapter 5 Analysis and Results of th e Portland Cement NESHAP and NSPS.......................................... 5-1

5.1 Affected Sources...............................................................................................................

......... 5-1

5.2 Emission Limits ................................................................................................................

......... 5-2

5.3 Control Technologies...........................................................................................................

...... 5-3

5.4 ISIS Runs and Results..........................................................................................................

...... 5-3

5.4.1 ISIS Results...................................................................................................................

..... 5-4

References for Chapter 5 .......................................................................................................

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

Disclaimer................................................................................................................................................ 5-11iv 

### List of Tables

Table 1-1. Typical Average Heat Input by Cement Kiln Type ........................................................... 1-5

Table 1-2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2009 ........................................... 1-6
Table 1-3. Largest Hydraulic Cement and Clinker Import Custom Districts in the U.S. in 2005....... 1-9
Table 3-1. Summary of Kilns Modeled in the ISIS Cement Industry Model...................................... 3-1
Table 3-2. Portland Cement Demand in Millions of Metric Tons....................................................... 3-4
Table 3-3. Bulk Shipment Costs (Cents per Metric Ton per Mile) ..................................................... 3-6
Table 3-4. Portland Cement and Clinker Imports in Milli on Metric Tons, by Customs District in 2005................................................................................................................................... 3-6
Table 3-5. Specific Fuel Consumption and Total Exhaus t Gas Flow Rate (wet) for Various Kiln Types ................................................................................................................................. 3-9
Table 3-6. Estimated Uncontrolled NO X Emission Intensities for Cement Kilns ............................. 3-10
Table 3-7. Average SO 2 Emissions for Each Kiln Type in Each State ............................................. 3-11
Table 3-8. Approximate CO 2 and H 2 O Produced from Combustion of Fuels................................... 3-12
Table 3-9. NO X Control Technologies for Cement Kilns.................................................................. 3-15

Table 3-10. SO 2 Control Technologies for Cement Kilns................................................................... 3-17
Table 3-11. CO 2 Control Technologies for Cement Kilns .................................................................. 3-19
Table 3-12. HCl, Hg, and THC Control Technologies for Cement Kilns........................................... 3-20
Table 3-13. Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation 1

...... 3-20
Table 3-14. Energy Efficiency Measures fo r Raw Materials Preparation........................................... 3-21
Table 3-15. Energy Efficiency C linker Making Measures.................................................................. 3-22
Table 3-16. Energy Efficiency Measur es for Finish Grinding ............................................................ 3-23
Table 3-17. Energy Efficiency Pl ant-wide Measures.......................................................................... 3-23

Table 4-1. Cement Prices ($/short ton) for USGS Districts ................................................................ 4-4

Table 4-2. Cement Production (short tons) for Various USGS Districts ............................................ 4-5
Table 4-3. Cement Imports (except from Canada and Mexico) for Import Districts (short tons)....... 4-6

Table 4-4. Reported and Calculated Cement Pr ices in USGS Districts (2005)................................... 4-7
Table 4-5. Reported and Calculated Cement Pr ices in USGS Districts (2006)................................... 4-8
Table 4-6. Reported and Calculated Cement Pr ices in USGS Districts (2007)................................... 4-9

Table 4-7. Aggregate production (reported a nd modeled) for 2005-2007 .......................................... 4-9
Table 5-1. Final NESHAP Emission Limits .......................................................................................

...... 5-2
Table 5-2. Final NSPS Emission Limits .........................................................................................

.......... 5-2 Table 5-3. Control Technology and Control Efficiency Matrix by Pollutant ........................................... 5-3 Table 5-4 Policy Impact on U.S. Cement Demand in 2013....................................................................... 5-5 Table 5-5 Policy Impact on U.S. Cement Production..............................................................................

.. 5-5 Table 5-6 Policy Impact on U.S. Cement Imports.................................................................................

.... 5-5 Table 5-7 Policy Impact on U.S. Cement Prices..................................................................................

...... 5-5v Table 5-8 Control Technology Installation......................................................................................

.......... 5-6
Table 5-9 Control Cost.........................................................................................................

...................... 5-6 Table 5-10 Cement Capacity ..................................................................................................................... 5-6 Table 5-11 Kiln Population.....................................................................................................

................... 5-7

Table 5-12 Number of Cement Facilities.........................................................................................

.......... 5-7 Table 5-13 Change in Employee Hours............................................................................................

......... 5-7
Table 5-14 PCA’s Labor Data: 2005 and 2008.....................................................................................

..... 5-8 Table 5-15 Employment Impacts............................................................................................................... 5-8
Table 5-16 Total Social Cost ...................................................................................................

.................. 5-8
Table 5-17 Emissions................................................................................................................................. 5-9



### List of Figures

Figure 1-1. Schematic of the Wet Cement Process............................................................................... 1-3 Figure 1-2. Schematic of the Dry Cement Process with Cycl one Preheater......................................... 1-4 Figure 1-3. Trends in Cement Kiln Type and Ca pacity in the U.S. (1995 to 2008) ............................. 1-6 Figure 1-4. Portland Cement Plant Locations....................................................................................... 1-8 Figure 1-5. Imports of Clinker and Cement from 1998 to 2008 (PCA, 2009a).................................. 1-10 Figure 1-6. Portland Cement Facilities and O 3 NAA, PM 2.5 NAA, and Class 1 Areas ...................... 1-12 Figure 1-7. An Integrated View of Pollution Generation Pathways, Emissions Abatement Approaches, and Multimedia Impacts for an Industrial Sector ....................................... 1-14 Figure 2-1. Consumer and Producer Surplus in a Market .................................................................... 2-2 Figure 2-2. Broad Modules and Associated Information Fl ows Utilized in the ISIS Framework........ 2-2 Figure 2-3. Stepwise Calculation of the Demand Curve ...................................................................... 2-5 Figure 3-1. Distribution of Cement Kilns in the United States as of 2009........................................... 3-3 Figure 3-2. Commercial Fuel Use Profile by U. S. Cement Industry in 2005....................................... 3-8 Figure 5-1 Portland Cement Facilities and K ilns Modeled in ISIS-Cement........................................ 5-1vi Acronyms and Abbreviations

ACI Activated Carbon Injection
AEO Annual Energy Outlook
APCA American Portland Cement Alliance
ARB Air Resources Board (see CARB)
ASD Adjustable Speed Drives
ASR Alkali-Silica Reactivity ASTM American Society for Testing and Materials
AVC Average Variable Cost
AZ Arizona BACT Best Available Control Technology
BAU Business As Usual

BLD Bag Leak Detector
BLS Bureau of Labor Statistics
BTS Bureau of Transportation Statistics
CA California
CARB California Air Resources Board
CC Carbon Capture CEMBUREAU European Cement A ssociation based in Brussels CEMS Continuous Emission Monitoring System
CGC Convert to Reciprocating Grate Cooler
CIF Cost, Insurance & Freight
CKD Cement Kiln Dust
CO Colorado
CO Carbon Monoxide
CO 2 Carbon Dioxide
CSI Combustion System Improvement
DE Delaware

DLI Dry Lime Injection EGFW Exhaust Gas Flow Rate (wet)
EIA Energy Information Administration EMCS Energy Management and Control System
EMD Efficient Mill Drives EMPC Energy Management and Process Control
EPA U.S. Environmental Protection Agency
ESP Electrostatic Precipitator
ETS Efficient Transport System
FL Floridavii FOM Fixed Operation and Maintenance GA Georgia GAMS General Algebraic Modeling System
GDP Gross Domestic Product
GHG Greenhouse Gas

H 2 O Water
HAP Hazardous Air Pollutant HCl Hydrochloric Acid HEC High-Energy Classifiers HEM High Efficiency Motors HERM High Efficiency Roller Mill Hg Mercury HPRP High-Pressure Roller Press HRPG Heat Recovery for Power Generation
ID Idaho
IF Indirect Firing IGMBM Improved Grinding Media (Ball Mills)
IL Illinois IPCC Intergovernmental Panel on Climate Change
IPM International Petroleum Monthly
ISIS Industrial Sectors Integrated Solutions KJ Kilojoules
LI Lime Injection
LNB Low NO X Burner
LWS Limestone Wet Scrubber MBR Membrane Bags Retrofit MI Michigan
MMBtu Million British Thermal Units
MO Missouri
MSA Mineralogical Society of America MYB Minerals Yearbook (USGS)
NAA Nonattainment Area NACT North American Cement Transportation
NAS National Academy of Science
NATA National Air Toxics Assessment
NC North Carolina
NEI National Emission Inventory NESCAUM Northeast States for Coordinated Air Use Managementviii NESHAP National Emission Standards for Hazardous Air Pollutants
NM New Mexico
NO X Oxides of Nitrogen
NSPS New Source Performance Standard
NRC National Research Council
NV Nevada O&M Operations and Maintenance OCAS Optimization of Compressed Air Systems
OGR Optimize Grate Cooler
OK Oklahoma OMB U.S. Office of Management and Budget OR Oregon PA Pennsylvania
PC Pulverized Coal
PCA Portland Cement Association
PCVM Process Control Vertical Mill
PM Particulate Matter
PM Preventive Maintenance

PRB Powder River Basin
PSD Prevention of Significant Deterioration RMB Raw Materials Blending RMC Ready-Mix Concrete RMTHEC Raw Materials Transport High Energy Classifiers RTO Regenerative Thermal Oxidizer
SBH Slurry Blending and Homogenization
SC South Carolina
SCR Selective Catalytic Reduction
SFC Specific Fuel Consumption SG&A Sales, General and Administration
SHLR Shell Heat Loss Reduction
SLC Salt Lake City SNCR Selective Non-Catalytic Reduction
SO X Oxides of Sulfur SPE Spatial Price Equilibrium
SR Seal Replacement

TDF Tire Derived Fuel
THC Total Hydrocarbons TRAGIS Transportation Routing Analysis Geographical Information Systemix USGS United States Geological Survey
UT Utah WA Washington WBCSD World Business Council for Sustainable Development WDOT Washington State Department of Transportation
WMCCC Wash Mills with Closed Circuit Classifier



### Conversion Table – English Units to SI Units

To Obtain From Multiply by
m ft 0.3048

m 2 ft 2 9.29 × 10 -2

m 3 ft 3 2.83 × 10 -2

°C °F 5/9 × (°F – 32) kg lb 0.454 J/kg Btu/lb 1.33 × 10 -4

m 3 /s cfm 4.72 × 10 -4

m 3 /s gpm 6.31 × 10 -5

J/kWh Btu/kWh 1055.056 mills $ 0.001 kg/m 2 in. Hg 345.31
metric ton short ton 0.9071-1 

## Chapter 1 Introduction

In the National Academy of Science’s 2004 report, “A ir Quality Management in the United States,” the National Research Council (NRC) recommended to th e U.S. Environmental Protection Agency (EPA) that standard setting, planning, and control stra tegy development should be based on integrated assessments that consider multiple pollutants, and that these integrated assessments should be conducted in a comprehensive and coordinated manner (NAS , 2004). With these recommendations, EPA began to move toward establishing multipollutant and sect or-based approaches to manage air quality and environmental protection. The benefits of multi pollutant and sector-based analyses and approaches include the ability to identify optimum strategies (cons idering feasibility, costs, and benefits across all pollutant types such as criteria, toxics, and others ), while streamlining administrative and compliance complexities and reducing conflic ting and redundant requirements.

The development of policy options for managing emi ssions and air quality can be made more effective and efficient through sophisticated analyses of releva nt technical and economic factors. Such analyses are greatly enhanced by the use of an appropriate modeling framework. Accordingly, the Industrial Sectors Integrated Solutions (ISIS) model has been developed at EPA. Currently, the ISIS model is populated with US cement manufacturing data, and efforts are underway to build representations of the US pulp and paper sector and the US iron and steel sector. This document describes the framework of EPA’s ISIS model and its application to the US cement manufact uring industry and the analysis performed for the Portland Cement National Emission Standards for Hazardous Air Pollutants (NESHAPs) and the New Source Performance Standards (NSPS) for the Portland cement manufacturing industry

.


### 1.1 The US Cement Industry

1.1. 1 Cement Types and Categories

Cement is a finely ground powder which, when mixed with water, forms a hardening paste of calcium silicate hydrates and calcium aluminate hydrates. Cement is used in mortar (to bind together bricks or stones) and concrete (bulk rock-like building materi al made from cement, aggregate, sand, and water). Concrete production uses the majority of cement produced.

Portland cement and blended cement are used in concrete production, but Portland cement is by far the most common type of cement used for concrete produ ction. By modifying the raw material mix and, to some degree, the temperatures utilized in manufacturi ng, slight compositional va riations can be achieved to produce Portland cements with slightly different properties. In the U.S., the different varieties of Portland cement are denoted per the American Soci ety for Testing and Materials (ASTM) Specification C-150. The ASTM standard C-150 recognizes eight types of Portland cement:

• Type I is for use in general construction (e.g., buildings, bridges, floors, etc.).

• Type IA is similar to Type I with th e addition of an air-entraining agent.

• Type II generates less heat at a slower rate and has a moderate sulfate-attack resistance.

• Type IIA is similar to Type II with th e addition of an air-entraining agent.

• Type III is used when concrete mu st set and gain strength rapidly.1-2 • Type IIIA is similar to Type III with th e addition of an air-entraining agent.

• Type IV has low heat of hydration and slow strength development.

• Type V is used when concrete must resist high sulfate concentration in soil and groundwater.

Portland cements are usually gray, but a more expensive white Portland cement (generally within the Type I or II designations) can be obtained by pro cessing only raw materials with very low iron and transition-elements content. In addition to the eight types of Portland cement listed above, small volumes of specialty cements are also manufactured. Other types of specialty cement include blended cement (Portland cement mixed together with blast furnace slag or other pozzolan), pozzolan-lime cement, masonry cement, and aluminous cemen t (PCA, 2008a). The common industry practice, and that of the US Geological Survey (USGS), includes, within the Port land cement designation, a number of other cements not within ASTM C-150, which are composed largel y of Portland cement and are used for similar applications (e.g., concrete) (U SGS, 2005). These cements include blended cement, block cement, expansive cement, oil well cement, regulated fast setting cement, and waterproof cement. Plastic cements and Portland-lime cements are being grouped within the masonry cement designation. Because Portland cement accounts for approximately 95% of the ceme nt industry’s total production (van Oss, 2008), and because the costs and trends of this industry sector can be adequately captured by describing the market processes associated with the production, distribution, and use of Portland cement, in this work the focus is on Portland cement. In 2006, Portland cement’s market share in the U.S. was 94%, while masonry cement’s market share comprised the remaining 6% (USGS, 2007a).



### 1.1.2 Overview of the Cement Manufacturing Process

Portland cement is produced from raw materials such as limestone, chalk, shale, clay, and sand. These raw materials are quarried, crushed, finely ground, and blended to the correct chemical composition. Small quantities of iron ore, alumina, and other mine rals may be added to adjust the raw material composition. The fine raw material is fed into a large ro tary kiln (cylindrical furn ace) where it is heated to extremely high temperatures [about 2640 °F (a bout 1450 °C)]. The high temperature causes the raw material to react and form a hard nodular material called “clinker”. Clinker is cooled and ground with approximately 5% gypsum and other minor additives to produce Portland cement. The main steps in the cement manufacturing process are illustrated in Figures 1-1 and 1-2 showing the wet process and the dry process with cyclone preheater. The schematic for a pr ecalciner kiln would be very similar to that shown in Figure 1-2, with the addition of a calciner vessel.

The heart of the clinker production process is the kiln , which can be rotary or vertical shaft designs. Rotary kilns are commonly used in the U.S. and elsew here. These kilns are 6-8 m in diameter and 60 m to well over 100 m long. The kilns are set at a slight incline and rotate at 1 to 3 revolutions per minute. The kiln is fired at the lower end and the feed materi als move toward the flame as the kiln rotates. The materials reach temperatures between 1400-1500 °C in the kiln. Three steps occur with the raw material mixture during pyroprocessing. First, all moisture is driven off from the materials. Then the calcium carbonate in limestone dissociates into CO 2 and calcium oxide (free lime) during calcination. Finally, the lime and other minerals in the raw materials react to form calcium silicates and calcium aluminates, the main components of clinker.1-3Figure 1-1. Schematic of the Wet Cement ProcessSource: CEMBUREAU, 19991-4Figure 1-2. Schematic of the Dry Cement Process with Cyclone PreheaterSource: CEMBUREAU, 19991-5 

### 1.1.3 Kiln Types and Their Use

Rotary kilns are broadly categorized as dry- and we t-process kilns, depending on how the raw materials are prepared. Wet-process kilns are fed raw material slurry with moisture content ranging between 30 and 40%. A wet-process kiln needs additional length to evaporate the water contained in the raw material feed. Nearly 33% additional kiln energy is consumed in evaporating the water in the slurry.

In dry-process kilns, raw material is fed as dry powder. There are three major variations of dry-process kilns in operation in the U.S.: long dry kilns, preheater kilns, and preheater/precalciner kilns. In preheater kilns and preheater/precalciner kilns, the early stag es of pyroprocessing occur before the materials enter the rotary kiln. Preheater and preheater/precalcine r kilns have higher production capacities and greater fuel efficiency compared to other types of cement k ilns. Table 1-1 shows heat input in terms of millions of British Thermal Units (MMBtu)/ton 1 of clinker for various types of kilns. As the data clearly demonstrate, preheater/precalciner kilns provide great er fuel efficiency. The replacement of wet and (certain) dry process kiln capacity with modern k iln processes can yield, theoretically, substantial reductions in fuel use due to fuel efficiency gains. As the industry moves toward more efficient processes, replacement of wet and long dry process capacity w ith more efficient kiln process technologies is expected.

Table 1-1. Typical Average Heat Input by Cement Kiln Type

Kiln Type Heat Input,
MMBtu/ton of clinker

Wet 6.0
Long Dry 4.5

Preheater 3.8

Preheater/Precalciner 3.3
Source: EPA, 2007b (Table 3-3)

As expected, a recent trend in the cement sector has shown the replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient kilns. This trend is expected to continue. In the U.S., the overall number of kilns decreased by 11 % from 1995 to 2004. During the same period, total clinker production capacity increased by 18.6%. The Portland Cement Association (PCA) data show that average kiln capacity also incr eased by 27%, from 405,000 to 556,000 t ons per year between 1995 and 2004. The number of kilns operating in the U.S. in 2005 compared with the number of kilns in operation in 2009 is shown in Table 1-2. The trend in kiln d esign and average kiln capacity is shown in Figure 1-3.

1 PLEASE NOTE: for the purposes of this document, short tons will be referred to simply as “tons” and represents 2000 lbs. A unit conversion table is prov ided in the front matter of this document.1-6 Table 1-2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2009
Kiln Type Number of Kilns (2005) Number of Kilns (2009)

Wet 50 41 Dry 48 32

Preheater 36 29

Precalciner 47 66

Source: PCA, 2006 and 2009a

0 100 200 300 400 500 600

0 25 50 75 100 125 150 175 200 225

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Average Capacity, thousands of metrictons/yearNumber of kilns

Dry Kilns Wet Kilns Average Kiln Capacity

Figure 1-3. Trends in Cement Kiln Type and Capacity in the U.S. (1995 to 2008) Note: The PCA Plant Information Summary became biennial in 2004. The 2005 data were obtained by personal communications between EPA and the PCA.

As reported by the USGS, in 2005, the U.S. Portla nd cement industry produced approximately 87 million metric tons of clinker, of which 13.5% was produced by plants with wet process kiln systems while 81% was produced by plants operating only dry kilns of all three types (long-dry, preheater and preheater/precalciner). The remaining 5.5% was produced by plants that operate both wet and dry kilns (USGS, 2007b: Table 7).1-7 

### 1.1.4 Portland Cement Production in the U.S.

The cement industry remains a vital industry in the U.S. and throughout the world. In the U.S., Portland cement is a $12 billion industry (van Oss, 2008). In 2005, the U.S. consumed a record 128 million metric tons of Portland cement (USGS, 2007b: Table 1).

In 2005, Portland cement was produced at 107 plants in the U.S., including 37 states and Puerto Rico. The locations of the clinker-producing Portland cement pl ant facilities in 2005 are shown in Figure 1-4. The cement manufacturing sector in the U.S. is concentr ated among a relatively small number of companies, many of which are owned by or are subsidiaries of foreign companies (USGS, 2007b). Together, ten companies accounted for about 80% of the total cement production in U.S. for 2005 (USGS, 2007b). California, Texas, Pennsylvania, Florida, and Alab ama are the five leading cement-producing states, which accounted for about 43% of the total production in 2005 (USGS, 2007b: Table 3).

In 2009, PCA projected a capacity expansion of 27 million tons between 2008 and 2013, an 18% increase in existing capacity from 2006. PCA’s projected capacity expansions are to come from 23 kilns; 5 came on-line in 2008 and 18 more are expected to come on-line between 2009 and 2013 (PCA, 2009b). The investment in these projected capacity expansions is pr ojected at $6.9 billion. Note that a typical project for a new facility (greenfield) from ground breaking to startup has a timeframe of about 2 to 3 years. Building a new kiln in an existing facility (brownfi eld) can take approximately 1 year (EPA, 2007b). If permitting for mining and (re)construction is accounted, a typical project can take as long as 4 to 6 years for a greenfield facility and up to 3 years for a brownfield facility (Staudt, 2009).1-8 Figure 1-4. Portland Cement Plant Locations



### 1.1.5 Imports of Portland Cement in the U.S.

Portland cement is not only produced and consumed domes tically, but it is also traded internationally. In 2005, the U.S. (not including Puerto Rico) produced 94 million metric tons of cement (USGS, 2007b: Table 3) and imported 34 million metr ic tons of hydraulic cement and clinker (USGS, 2007b: Tables 3 and 18). The level of imports to the U.S. is highly cyclical, with domestic producers importing primarily when domestic plants are at full capacity and cannot meet excess demand. Imports also occur when domestic producers seek to capture market share. Gene rally, imported cement and clinker make up 20 to 27% of domestic cement consumption. In 2005, total imports of cement and clinker (especially clinker) increased, owing to continued high demand; imported cement accounted for about 24% of the total cement sales in the U.S. (USGS, 2007b).

In 2005, the ten leading international cement and cli nker suppliers to the U.S. were, in descending order, Canada, China, Thailand, Greece, the Republic of Korea, Venezuela, Mexico, Colombia, Taiwan, and1-9 Sweden. The ten busiest ports of entry within cu stoms districts existing in 2005 were, in descending order, New Orleans, Tampa, Los Angeles, Houston-Galveston, San Francisco, Miami, Seattle, Detroit, New York, and Charleston (South Carolina) (USGS, 2007b). Table 1-3 shows the major customs districts for hydraulic cement and clinker imports in the U.S. Figure 1-5 shows the imports of clinker and cement from 1998 to 2008.

Table 1-3. Largest Hydraulic Cement and Clinker Import Custom Districts in the U.S. in 2005
Import of Hydraulic Cement and Clinker
Custom District
thousands of metric to ns thousands of tons Percentage of Total US

Imports

New Orleans, LA 4,095 4,514 12.31
Tampa, FL 3,478 3,834 10.46 Los Angeles, CA 3,053 3,365 9.18
Houston-Galveston, TX 2,619 2,887 7.87
San Francisco, CA 2,363 2,605 7.10
Miami, FL 2,265 2,497 6.81
Seattle, WA 1,489 1,641 4.48
Detroit, MI 1,317 1,452 3.96
New York, NY 1,264 1,394 3.80
Charleston, SC 1,102 1,215 3.31
Total 23,046 25,404* 69.29% * rounding, original data in metric tons Source: USGS, 2007b (Table 18)1-10 Figure 1-5. Imports of Clinker and Ce ment from 1998 to 2008 (PCA, 2009a)



### 1.1.6 Cement Demand Centers

Because of the relatively high transportation costs, th e U.S. cement industry is structured around statespecific cement demand centers. PCA reports that the v ast majority of cement produced in the U.S. is being transported less than 300 miles by truck due to cement's low value by weight and high cost of transport (PCA, 2005). However, cement may be tran sported over longer distances, especially when the less expensive rail and water transporta tion modes are available (APCA, 1997).



### 1.2 Emissions from the U.S. Cement In dustry and Applicable Regulations

Criteria pollutants, hazardous air pollutants, and carbon dioxide (CO 2 ) are released during cement manufacturing. Nitrogen oxide (NO x ) emissions from cement kilns result primarily from the combustion process: oxidation of fuel nitrogen (fuel NO x ) and the oxidation of nitrogen in the combustion air (thermal NO x ). EPA’s 2005 National Air Toxics Assessment (NATA) Inventory reports that cement kilns released 181,000 metric tons (200,000 short tons) of NO X emissions from the combustion of fuels (EPA, 2009).

Sulfur dioxide (SO 2 ) emissions 2 from cement kilns result from the combustion of sulfur-bearing compounds in coal, oil, and petroleum coke, as well as sulfur compounds in raw materials. Sulfur in the fuel will oxidize to SO 2 during pyroprocessing and a significant amount is likely to be captured in the form of sulfates as the flue gas p asses through the calcination zone. Compared to long dry and wet kilns, preheater and precalciner kilns tend to be more effective at capturing fuel-generated SO 2 . Accordingly,

2 Small amounts of SO 3 may be released in addition to bulk SO 2 but the SO 3 emissions are treated as SO 2 for computational purposes. Small amounts of CO may be released in addition to bulk CO 2 but CO emissions are treated as CO 2 for computational
purposes.1-11 oxidation of sulfur in the feed materials is likely to be the major component of total SO 2 emissions. The 2005 NATA Inventory reflects that cement kilns released 133,000 metric tons ( 147,000 short tons) of SO 2 emissions in 2005.

Particulate matter (PM) emissions result from quarr ying operations (the crus hing and grinding of raw materials and clinker) as well as kiln line operations . The 2005 NATA Inventory estimates show that, in 2005, cement kilns released 10,000 tons of PM 10 emissions. The cement industry is also a source of HAPs (e.g., hydrochloric acid vapor and chlorine), as well as metals including but not limited to mercury, antimony, cadmium, and lead (EPA, 2008).

The cement manufacturing process is also a source of carbon dioxide (CO 2 ) emissions. CO 2 emissions are a product of the combustion of fuel as well as the calcination of the limestone in the raw meal. The CO 2 from fuel combustion can be calculated from heat input and fuel characteristics combined with clinker production. The CO 2 from calcination can be calculated taking into account the amount of limestone used as a source of clinker calcium. Limestone currently is the predominant source of calcium for the clinker. Pure limestone would produce 0.44 tons of CO 2 for every ton of limestone completely calcined to calcium oxide. Substitute materials may be used in lieu of limestone with the effect of reducing the CO 2 emissions from clinker production. The CO 2 emissions reduction would be proportional to the amount of substitute materials added.

Multiple regulatory requirements to reduce criteria ai r pollutants and HAPs emissions currently apply to the cement industry sector. The New Source Performance Standards (NSPS) and the National Emissions Standards for Hazardous Air Pollutants (NESHAPs) are two of the federal requirements that apply to cement facilities. Additionally, state and local regulat ory requirements might apply to individual cement facilities depending on their locations. In 2008, 44 cement facilities were located within ozone (O 3 ) nonattainment areas (NAA), while 20 facilities were within PM 2.5 nonattainment areas. Seventeen facilities were found to be located in (or within 30 miles of) Class 1 areas, as shown in Figure 1-6. Class 1 areas are areas of special natural, scenic, recreational, or historic (national or regional) value for which the Prevention of Significant Deterioration (PSD) regulations provide special protection.1-12 Figure 1-6. Portland Cement Facilities and O 3 NAA, PM 2.5 NAA, and Class 1 Areas



### 1.3 Overview of ISIS

ISIS, a sector-based dynamic programming model, will facilitate the analyses of emission reduction strategies for multiple pollutants while taking into account plant-level economic and technical factors such as the type of emission units (for cement –kiln), associated capacity, location, cost of production, applicable controls, and their costs. For each of th e emission reduction strategi es under consideration, the model is able to provide information on the following:

• Optimal (least cost) industry operation.

• Cost-effective controls to meet the demand for cement.

• Emission reduction requirements over the time period of interest.1-13 ISIS design allows for incorporating multiple industries within a multi-market, multi-product, multipollutant, and multi-region emissions trading fra mework. The objective function in ISIS maximizes total surplus and uses an elastic formulation of the demand function to estimate area under the demand

curve.
The ISIS code is written in the General Algebr aic Modeling System (GAMS) language. Input data, organized in various spreadsheets of a Microsoft Excel workbook, are passed on to the GAMS files. These input data consist of an industry database, which provides unit-level production, capacity, production cost, and emissions information. The co ntrols database provides information regarding applicable air pollution control technologies and their cost and emission control characteristics. A policy module is used to specify various parameters of in terest to the policy analyst, such as emissions cap, emission reduction scenarios, and discount rate. The i nput data, control data, and policy parameters are then transmitted to the optimization part of the ISIS model, where they are used to solve the selected base and policy cases. After solving, the results are post- processed to calculate values of various outputs of interest. The output data are exported to Excel spreadsheets for further analyses and graphical representation of selected results.

Within an industrial sector, generally emissions ari se from four pathways: (1) on-site emissions due to combustion of fossil fuels for energy at plants; (2 ) on-site emissions due to processing of certain raw materials (e.g., limestone calcination in cement pl ants, non-energy uses of fossil fuels in chemical processing and metal smelting); (3) off-site emissions due to combustion of fossil fuels at power plants to generate the electricity needed by the industria l sector; and (4) overseas emissions associated with imports. These pathways are depicted in Figure 1-7.

Also shown in Figure 1-7 are the potential options for abating emissions from industrial sectors and multimedia impacts. The options shown in green ar e pollution prevention measures and the ones in red are mitigation measures. Clearly, the integrated pi cture presented in Figure 1-7 makes a compelling case for considering commodity production/supply activitie s along with emissions while developing holistic emission reduction strategies. While developing the ISIS framework, care has been taken to build the emission pathways and abatement options shown in Fi gure 1-7. Example emission reduction policies that can be evaluated using ISIS are:

• Criteria pollutants (NO X , SO 2 , PM, CO) –emission limits and/or cap-and-trade.

• Hazardous Air Pollutants (e.g. Hg, HCl) – emission limits.

• CO 2 – cap-and-trade and/or emission taxes.

• Long and short time horizons: CO 2 (decades), criteria pollutants (annual).

• Regional or national requirements.1-14 Pollution Controls (NOx, SO 2 ,PM, CO 2 , VOC, .. )



### Industrial Plants
Improvements 

### Fuel(s) Pollution Reduction Policy (Rate-based, cap-and-trade, emission taxes)



### Air emissions

Product(s)
Product Substitution Fuel Substitution/ Reduction (NOx, SO2, PM, CO2, ...) 

### Raw Materials

Raw Material Substitution



### Electricity Change in use (Power plant emissions) 

### Water

Waste

Market(s) Imports (Overseas emissions)

Figure 1-7. An Integrated Vi ew of Pollution Generation Pathways, Emissions Abatement Approaches, and Multimedia Impa cts for an Industrial Sector



### References for Chapter 1

APCA (1997). Comments on EPA’s Draft Economic An alysis of Air Pollution Regulations for the Portland Cement Industry (May 1996), prepared for American Portland Cement Alliance by Environomics, January 29, 1997.

CEMBUREAU (1999). Best Available Techniques for the Cement Industry, CEMBUREAU Report, The European Cement Association, December 1999. D/1999/5457/December, Brussels. http://www.cembureau.be

EPA (1998). June 1998. Regulatory Impact Analysis of Cement Kiln Dust Rulemaking . Washington, DC: U.S. Environmental Protection Agency. http://www.epa.gov/osw/nonhaz/industria l/special/ck d/ckd/ckdcostt.pdf , accessed October 21, 2008.

EPA (2007a). Personal communication from Alvaro Li nero, Florida Department of Environmental Protection to Keith Barnett, US EPA, on December 4, 2007.

EPA (2007b). Alternative Control Techniques Document Update – NO X Emissions from New Cement Kilns. EPA-453/R-07-006, November 2007. http://www.epa.gov/ttn/catc/dir1/cement_updt_1107.pdf

, accessed October 21, 2008.

EPA (2008). AP 42, Fifth Edition, Compilation of Air Pollutant Emission Factors, Volume I, Chapter 11: Mineral Products Industry. http://www.epa.gov/ttn/chief/ap42/ch11/final/c11s06.pdf , accessed October 21, 2008. Complete document: http://www.epa.gov/ttn/chief/ap42/ , accessed October 21, 2008.1-15 EPA (2009). EPA 2005 National Air Toxics Assessment Inventory. U.S. Environmental Protection Agency. October 2009.

NAS (2004). Air Quality Management in the United States. National Research Council (U.S.), Committee on Air Quality Management in the United States, National Academies Press, Washington, 2004. http://books.nap.edu/catal og.php?record_id=10728 , accessed October 21, 2008.

PCA (2005). Letter from David S. Hubbard, Director, Legislative Affairs, Portland Cement Association. RE: Hours of Service of Drivers; Proposed Ru le (Docket Number FMCSA-2004-19608), March 10, 2005. http://www.cement.org/exec/DHOS%20Comments%203.10.05.pdf , accessed October 21, 2008.

PCA (2006). U.S. and Canadian Portland Cement Industry: Plant Information Summary. Portland Cement Association, Skokie, IL, 2006.

PCA (2008a). History & Manufacture of Portland Cement. Portland Cement Association. http://www.cement.org/basics/concretebasics_history.asp , accessed October 21, 2008.

PCA (2008b). Practical Application of PCA Economic Forecast and Market Assessments. Portland Cement Association, Education & Training, August 12-13, 2008, Skokie, IL. (Ed Sullivan PowerPoint presentation).

PCA (2008c). Forecast Report: Long-Term Cement C onsumption Outlook. By Ed Sullivan January 31, 2008

. http://www.cement.org/econ/pdf/Long-TermFlashwinter2007nonmember.pdf , accessed October 21, 2008.

PCA (2009a). PCA Annual Yearbook 2009. North Am erican Cement Industry. Portland Cement Association.

PCA (2009b). PCA Capacity Report. Flash Report. Portland Cement Association’s Economic Research Department. Updated October 19, 2009.

Staudt, J. (2009). NO X , SO 2 and CO 2 Emissions from Cement Kilns. Andover Technology Partners, Memorandum to Ravi Srivastava, March 10, 2009.

USGS (2005). Background Facts and Issues Concerning Cement and Cement Data. U.S. Geological Survey, Open-File Report 2005-1152. http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf , accessed October 21, 2008.

USGS (2007a). Mineral Commodity Summaries. pp. 40-41, January 2007, http://minerals.usgs.gov/minerals/ pubs/commodity /cement/cemenmcs07.pdf , accessed September 24, 2009.

USGS (2007b). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, http://minerals.usgs.gov/minerals/ pubs/commodity /cement/cemenmyb05.pdf , accessed September 24, 2009.

van Oss, H.G. (2008). Personal communication from He ndrik G. van Oss, USGS, to Elineth Torres, US EPA, on July 7, 2008.1-16 This page intentionally left blank.2-1 

## Chapter 2 ISIS Mathematical Framework

ISIS is a sector-based dynamic linear programming model that can determine optimal sector operation for meeting demand and pollution reducti on requirements over specified time periods. The objective in an ISIS simulation is to maximize total surplus (see Figure 2-1) over a horizon of interest for an industry, which, in general, can be a multi-product one.

The general concept of spatial price equilibrium (SPE) in a network, where the mutual influences of production, transportation, and cons umption patterns are given full consideration, has been developed over the past 6 decades. In SPE network models, in terregional economies are simulated by finding the balance of demand, supply, and trade that will r esult in competitive market equilibrium among the regions. Enke (1951) first demonstrated how the co st of transportation might be included in an equilibrium analysis of spatially separated markets by means of analogy with resistance to the flow of current in an electric circuit. Shortly after Enke, Samuelson (1952) analyzed interregional flows of commodities and market equilibrium using a linear programming formulation. In this type of formulation, the equilibrium for each market of a sector is equivalent to the quantities and prices that result while maximizing the sum of consumer and producer surp luses for each market of the sector. This sum is referred to as the total surplus or net social payo ff of the sector; McCarl and Spreen (1980) provide interpretation and justification. The linear program ming formulation of the SPE problem was developed by Duloy and Norton (1975).

Using Figure 2-1, the definition of the suppliers’ surp lus corresponding to a quantity Q of a commodity is the difference between the total revenue and the to tal cost of supplying the commodity. This surplus (gross profit) is given by the area under the horizontal line P1-P minus the area under the inverse supply curve up to point P. Similarly, the consumers’ surp lus corresponding to a quantity Q is given by the area under the inverse demand curve up to point C minus th e area under the horizontal line C1-C. This area is the cumulative opportunity gain of all consumers w ho purchase the commodity at a price lower than the price they would have been willing to pay. It is evident from Figure 2-1 that the total surplus is maximized exactly when Q is equal to the equilibrium quantity QE. This is a very useful result, as it provides a method for computing the equilibrium.

The total surplus concept has long been a mainstay of social welf are economics because it takes in to account the interests of both consumers and of producers (Samuelson and Nordhaus, 1977).

The broad modules and associated information flows u tilized in the ISIS framework are shown in Figure 2-2. While ISIS structure permits accounting for multiple products, the descr iption below is provided relative to one product to bring out the important elements of the formulation and not burden the reader with many details. Also, to make the description mo re readable and understandable, the input data (i.e., those supplied by the user, or derived from user-supplied data) are shown in bold and the variables, whose values are determined in th e optimization process, are shown in italics . This scheme helps in organizing the numerous data elements and variables, and hopefully makes it easier for the reader to relate the data element to descriptions in the previ ous chapters. Finally, the names of variables and data elements have been chosen to be self-explanatory as far as possible.2-2 Price
Supply curve

Quantity Demand curve Equilibrium Consumer

surplus

Producer

surplus 

### C1

P1

Q Q E 

### P

C

Figure 2-1. Consumer and Producer Surplus in a Market

ISIS Engine Industry Information • Products • Production capacities and

costs • Demand • Imports parameters • Transportation costs • Noted retirements and growth • Energy Intensity Production Technologies and Emission Abatement Approaches

Availability, applicability,

cost, and performance Policy Parameters • Time horizon • Policy type

Model Outputs • Base case and controlled emissions • Production, Inter-market trades, imports • Commodity and allowance prices • Retirements and capacity growth • Controls installed • Costs • Other Economic Parameters • Discount rate • Escalation rates • Demand elasticity • Economic life

Fuels • Primary: coal, coke,

natural gas, coal+TDF • Substitute: tires, TDF Emissions • Sulfur dioxide • Nitrogen oxides • Carbon dioxide

Figure 2-2. Broad Modules and Associated Information Flows Utilized in the ISIS Framework2-3 

### 2.1 Indexes, Sets, and Mappings

Before we start the description of the mathematical st ructure of ISIS, it is helpful to define the sets of relevant entities and mappings describing th e relationships among various entities.

Indexes (one-dimensional sets)

• ISIS data structures (sets and parameters), vari ables, and equations use the following indexes:

• t is the set of years in the time horizon of interest;

• i is the set of all units, including existing, re placement, expansion, and new units. Subsets of i described below define more-specific populations of units;

o iE(i) is the set of existing units

o irepl(i) is the set of replacement units

o iExp(i) represents the set of expansion units

o iNew(i) represents the set of new units

• f is the set of fuels applicable to the sector;

• m is the set of raw materials specific to the sector;

• j is the set of sector-specific pollutants of interest;

• k is the set of sector-specific controls relevant to the emission reduction policy of interest;

• ee is the set of energy efficiency measures; and

• r is the set of geographic regions where units are located.

Mappings

The mappings used in ISIS are:

• ri(i,r) is the mapping relating a unit i to its geographic location r ;

• poltikjee(t,i,k,j,ee) is the mapping relating availability, applicability, and the ability to change emissions of controls k and energy efficiency measures ee to unit i, pollutant j , and year t ;

• poltik(t,i,k) is the mapping relating availability and applicability of controls k to unit i and year t ;

• poltiee(t,i,ee) is the mapping relating availability and applicability of energy efficiency measures ee to unit i and year t ;

• poltirk(t, ri(i,r),k) is the mapping relating availability and applicability of controls k to unit i located in geographic region r and year t ; and2-4 • poltiree(t, ri(i,r),ee) is the mapping relating availability and applicability of energy efficiency

measures ee to unit i located in geographic region r and year t

.


### 2.2 Objective Function

As mentioned above, the objective function solved in the ISIS model corresponds to maximizing the total surplus (or minimizing the negative of the total surplu s) for the sector of interest over the selected time horizon. This objective function is:



## ∑ ∫ ∑ ∑ ∑ ∑ ∑ ∑

⎥ ⎦ ⎤ ⎢ ⎣ ⎡

⋅ ⋅ − + ⋅ ⋅ + ⋅ + ⋅ + ⋅ + ⋅ + ⋅=

t dc t,j t,i,ee t,i,k t,id t,dc t,i

dc) ddemand(t, )price(t,dc ions(t,j) totalemiss ee)scost(t,i, teemeasure st(t,i,k) tcontrolco st(t,id) timportsco (t,dc) ttranscost )dncost(t,i tannualpro z

dis(t) j)alprice(t, dis(t) dis(t) dis(t) dis(t) dis(t) dis(t) Minimize

(2.2.1)

where the quantities appearing in the equation above are defined for year t as follows,

dis(t) is the discount factor,

tannualprodncost(t,i) is the annual production cost ($) for production unit i

, ttranscost(t,dc) is the cost ($) of transporting the product to demand center dc

, timportscost(t,id) is the cost ($) of importing foreign product in import district id

, tcontrolcost(t,i k) is each unit’s total control cost ($) using control technology k

, teemeasurescost(t, i, ee) is each unit’s total cost ($) usi ng the energy efficiency measures ee , alprice(t,j) is the allowance price input by the user,

totalemissions(t,j) is emission of pollutant j from the sector, 

## ∫

⋅

dc dctddemand

dctprice ),( ),( is the area under the demand-price curves for all markets dc

.2-5 Note that in the objective function the term with alprice(t,j) comes into effect only if the user provides values for alprice(t,j) . If these values are specified, the model runs as described under the “Allowance Price Inputs” in a later section.

A stepwise approximation of the demand curves (FPL-PELPS, 2003) is used in ISIS so that relevant area can be computed in a linear programming schem e. This approximation is explained below.

Stepwise Approximation of Demand Curves in ISIS

For clarity, the subscripts t and dc corresponding to time and demand center are dropped in the following explanation.

The relationship between demand and price in a market dc is expressed as

σ 1

D0 P0 ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= D DP

)( (2.2.2)

where

D is the demand for the commodity with corresponding price P ( demand ),

σ is the elasticity of demand relative to price, and

D0 and P0 are the initially-specified demand quantity and price, respectively.

Figure 2-3 shows a representation of the above equa tion and also reflects how a stepwise approximation of the price-demand curve can be created.

P ( d 1 )

P ( d 2 )

P ( d 3 ) P ( d 4 )

P 0

D min D 0 D max d 1 d 2 d 3 d 4 P ( D )

D wid

D wid Price

Quantity

Figure 2-3. Stepwise Calculation of the Demand Curve2-6 First the range of the demand-price curve is defined using

range)-(1D0D

min ⋅= (2.2.3)

range)(1D0D

max +⋅= (2.2.4)

where

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval D min - D max within which the new equilibrium demand quantity is expected to be found. Note range should be large enough to ensure that the solution does fall in the interval D min - D max. On the other hand a smaller value of range can increase precision of the stepwise approximation. In ISIS a value of 0.5 is used for range

. Next, a series of steps is defined within the interval D min - D max with the width of each step given by

stepsofNumber D-D dwidth min max = (2.2.5)

where

Number of steps is another user-supplied parameter. Incr easing the value of this parameter will increase precision of the stepwise approximation, but will increase the model size. In ISIS, a value of 100 is used for Number of steps

. Now the demand quantity at the center of the slice dstep inside the interval is

2 1)-dstep(2dwidth DD min

dstep ⋅⋅ += (2.2.6)

Using the above information, the price-demand curve is determined

σ 1 dstep dstep D0 D P0DP ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= )( (2.2.7)

Finally, the approximated area under the price-demand curve is



## )( dstep dstep 

## DD P stepsofNumber

1dsteps



# ∑

= 

## ⋅ (2.2.8)2-7 Substituting (2.2.8) in (2.2.1), the objective function becomes



## ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑

⎥ ⎦ ⎤ ⎢ ⎣ ⎡

⋅ ⋅ − + ⋅ ⋅ + ⋅ + ⋅ + ⋅ + ⋅ + ⋅=

t dstepdc t,j t,i,ee t,i,k t,id t,dc t,i

dstepdctdemand ions(t,j) totalemiss ee)scost(t,i, teemeasure st(t,i,k) tcontrolco st(t,id) timportsco (t,dc) ttranscost )dncost(t,i tannualpro z

dstep)dc,, P(demand(t dis(t) j)alprice(t, dis(t) dis(t) dis(t) dis(t) dis(t) dis(t)

, ),,( Minimize

(2.2.9)

The above objective function is minimized with the constraints and related equations described in the following sections to arrive at the relevant optimum solution.

Equation (2.2.7) is used to generate the price-de mand curves for all time periods and demand centers. However, this equation needs specifications of on e point on each demand function in each time period (i.e., [P0{t, dc}, D0{t, dc}]) . To determine such points, a single run of the inelastic version of the ISIS model (with exogenous D0[t, dc]) is made and then the resulting shadow prices P0(t, dc) are used in Equation (2.2.7) to generate price-demand curves for all time periods and demand centers. These curves are used in the last term of the objective function an d in the supply constraint, Equation (2.3.1), presented in the next section.



### 2.3 Supply

The demand for a commodity in a mark et can be satisfied by domestic production and foreign imports as follows:

.),,( ),,( ),,( 

## ∑ ∑ ∑ ≥ + dstep id i dstepdctdemand dcidtantity importedqu dcitity prodnquant (2.3.1)

where

prodnquantity(t,i,dc) is the quantity supplied from the domestic production unit i to demand center dc in year t
, importedquantity(t,oi,id,dc) is the quantity of commodity received from the origin (country) of imports oi at the domestic import district id and supplied from that district to demand center dc in
year t
, and

demand(t, dc,dstep) is the demand for the commodity at the dstep th level of the price-demand curve for the demand center dc in year t . The construction of the price-demand curves has been explained above.2-8 Note that transportation of quantity from production units and import districts to demand centers is implicit in the above equation.

The sum of all quantities in year t supplied from a kiln i to various demand centers dc must equal the production level of unit i in that year. Then,

. prodn(t,i) )ity(t,i,dc prodnquant

dc = 

## ∑ (2.3.2)

where

prodn(t, i) is the production level (tons/year) of manufacturing unit i in year t . Note that, in general, production can be from existing units, units added at a plant (i.e., expansion units), newer units replacing units at a plant (i.e., replacement units), an d new kilns. Treatment of these is explained in a subsequent section.

Production of a unit is constrained by its capacity. So,

i).,capacity(t ≤ ),( itprodn (2.3.3)

where

capacity(t, i) is the capacity (tons product/year) of manufacturing unit i in year t

. The sum of all imported quantities supplied from an import district id to various demand centers dc in
year t must equal the imports available at that import district in that year. Then, 

## ∑ ∑
= istepoi dc id,istep)oiImports(t, d,dc) antity(t,i importedqu

, , (2.3.4)

where

Importedquantity(t,id,dc) is the imported quantity supplied from import district id to demand center dc in year t, and

Imports(t,oi,id,istep) is the imported quantity available at the import district id in year t at the istep th level of the imports curve for country oi . Construction of the country (or region) and import district specific imports-cost curves is explained below.

Stepwise Approximation of Imports Curves in ISIS

The treatment of imports is similar to the treatment for elastic demand curves. Again, for clarity, the subscripts t, oi, and id corresponding to time, origin of imports, and import district are dropped in the following explanation.

The relationship between imports arriving from oi at id and their price is expressed as

α 1

Imports0 Iprice0 ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= Imports orts) Iprice(Imp (2.3.5)2-9 where

Imports is the imports of the commodity arriving from oi at id with corresponding imports price (value) Iprice
,



### α is the elasticity of imports relative to imports price, and

{Imports0, Iprice0} is a point on the applicable quantity-price curve.

First the range of the imports-price curve is defined using



### range)-(1 Imports0 I min 

### ⋅ = (2.3.6)

range)(1 Imports0 I
max +⋅ = (2.3.7)

where

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval I min - I max within which the new equilibrium imports quantity is expected to be found. Note range should be large enough to ensure that the solution does fall in the interval I min - I max. On the other hand a smaller value of range can increase precision of the stepwise approximation. In ISIS a value of 0.5 is used for range

. Next, a series of steps is defined within the interval I min - I max with the width of each step given by

steps ofNumber I-I Iwidth min max = (2.3.8)

where

Number of steps is another user-supplied pa rameter. Increasing the value of this parameter will increase precision of the stepwise approximation, but will increase the model size. In ISIS, a value of 100 is used for Number of steps

. Now the import quantity at the center of the slice istep inside the interval is

2 1)-istep(2Iwidth I Imports min istep ⋅⋅ += (2.3.9)

Using the above information, the imports-price curve is determined

α 1 istep istep Imports0 Imports Iprice0 Imports Iprice ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= ) ( (2.3.10)

Equation (2.3.10) is used to genera te the imports-cost curves for all time periods, origins of imports, and import districts. However, this equation needs specifi cations of one point on each import function in each time period (i.e., [Iprice0{t, oi, id}, Imports0{t, oi, id}]) . To determine such points, a single run of the inelastic version of the IS IS model (with exogenous Imports0[t, oi, id] corresponding to capacities of2-10 import districts) is made and then the resulting Iprice0(t, oi, id) are used in Equation (2.3.10) to generate the imports-cost curves for all time periods, origins of imports, and import districts.



### 2.4 Production Capacity and Supply Costs

The ISIS model includes constraints for ensuring that endogenous production capacity changes occur in a realistic way. This section describes how the capacity changes take place in the ISIS framework and the treatment of related costs. Note that various cost elem ents (e.g., capital cost of a unit [$/ton clinker] in the cement sector) are escalated appropriately to reflect values in years of interest.

Production Capacity Related Constraints

Added capacity in year t is given by

When t = 1,

(2.4.1a)

For t > 1,



## { } .iNew(i) iExp(i) irepl(i)i ants); techlifepl (vyear(v) tyear(t) vyear(v) ;i),capacity(t

∪∪ ∈ + <≤ ⋅ +

− = 

## ∑

v )tcap(t,i,v i)timeblock, addcap(t)addcap(t,i

(2.4.1b)

where

v is the set of vintages of unit i

, vyear(v) and tyear(t) are parameters with values corresponding to years in the selected time horizon,

tcap(t,i,v) is a binary variable that can bring v th vintage of unit i online in year t,

timeblock is the block of years used in simulation

, and

techlifeplants is the technical life of a unit.

Only one vintage of a unit is possible for the period starting when the vintage comes on line and ending with its technical life.2-11 (2.4.2)

The annual costs associated with meeting the demand for the commodity include: (1) annualized capital costs associated with capacity growth (i.e., repl acement units, expansion units, and new capacity) and projected units, (2) annual fixed operation and main tenance (FOM) costs, (3) annual variable costs associated with use of labor, raw material, fuel, electricity, and operation and maintenance, (4) annual transportation costs, and (5) annual cost of imports. These costs are described below.

Capital Recovery

If the existing units are paid for and do not have capital recovery costs, then 

## { } .iE(i)i ∈=

;0 st(t,i) plantcapco (2.4.3)

where

plantcapcost(t,i) is the annual capital cost of a unit.

For projected units, for which startup date is known, annual capital cost is given by



### { }

. ; iP(i)i nts); ecolifepla (tstart(i) tyear(t) tstart(i) i)t, pcapcostt( i),capacity(t CRFplant

∈ + ≤≤ ⋅ ⋅ = st(t,i) plantcapco (2.4.4)

where

pcapcostt(t,i) is the capital cost (e.g., $/ton of clinker for the cement sector) of i th unit in year t, and

CRFplant is the capital recovery factor calculated using an appropriate interest rate and time period, ecolifeplants , for capital recovery.

Annual capital costs for all populations except existing and projected is
CRFplant itAddcap st(t,i) plantcapco

⋅ ⋅ = i),capcostt(t),( (2.4.5)

where

capcostt(t,i) is the capital cost (e.g., $/ton of clinker for the cement sector) for the i th unit

.

Variable Costs

The annual variable cost at a unit is calculated using2-12 

## [ ]

. ),( ),( 

## ∑ + + + ⋅ + + + =

sw (t,i,SW) SWdispcost )tncost(t,i H2Oconsump itfc itprodn i)varcost(t, i)ELCt(t,i)LBRt(t,i)VOMt(t, i)RMTt(t, (2.4.6)

where

RMTt(t,i) is the raw material cost ($/ton product) at unit i in year t
, VOMt(t,i) is the cost of operation and maintenance ($/ton product) at unit i in year t
, LBRt(t,i) is the cost of labor ($/ton product) at unit i in year t
, ELCt(t,i) is the cost of electricity use ($/ton product) at unit i in year t
, fc(t, i) is the cost of fuel ($/ton product) at unit i in year t , and

varcost(t,i) is the annual variable cost ($/ton product) at unit i in year t.

The fuel cost for a unit is calculated as follows:

. 1 ]] pollutants ofNumber f)i,t,fuelcostt( y(i) [eintensit

ee)j,k,i,t,poltikjee( m, f

⋅ ⋅ ⋅ = 

# ∑

∑

,ee),i,f,m,j,k

varprodn(t fc(t,i)

(2.4.7)

where

eintensity(i) is the energy intensity (MMBtu/ton product) for unit i

, fuelcostt(t,i, f) is the cost of fuel f ($/MMBtu) at unit i in year t , and

varprodn(t,i,f,m,j,k,ee) is a production coefficient described in the next section.

Annual water consumption re lated cost is given by

1000/ 2 cos 2 ) H2Ocostt(t

⋅ = (t,i) Oconsumptn Ht(t,i) Oconsumptn H (2.4.8)

With annual water consumption given by

).,( 2 itprodn (t,i) Oconsumptn H

⋅ = ty(i)

H2Ointensi (2.4.9)

where

H2Ocostt(t) is the cost of water ($/1000 gallon) in year t , and

H2Ointensity(i) is the gallons of water needed to produce a ton of product at unit i

. Annual solids discharge related cost is given by2-13 sw)costt(t, SWdisposal

⋅ = sw) SWgen(t,i (t,i,sw) SWdispcost

, (2.4.10)

With annual solids discharges given by

).,( itprodn sw) SWgen(t,i,

⋅ = sw)on(i,

SWgenerati (2.4.11)

where

SWdisposalcostt(t,sw) is the cost of disposing of a solid discharge sw in year t , and

SWgeneration(i,sw) is the discharge of a solid sw (tons) in the process of producing a ton of product at unit i

.

Note that SWdisposalcostt(t,sw) value can be positive (disposal cost) or negative (sale price).

Total Annual Cost of Production

Using the above information, the total annual cost of production at unit i in year t is

. i)varcost(t, st(t,i) plantcapco )dncost(t,i tannualpro + = (2.4.12)

Annual Cost of Imported Commodity

The cost of imports of commodity at import district id in year t is calculated using the following equation



### ( ) )].,,,( [

, istepsidoitImports st(t,id) timportsco

istepsoi



## ∑

⋅ + + = d) handling(i (id) InsFreight isteps)id,oi,Iprice(t, (2.4.13)

where

Iprice(t,oi,id,isteps) is the price (value) of importing commodity from origin oi (foreign country) at the import district dc , at the istep th level of the relevant price-import curve.

InsFreight(id) is the insurance and freight at the import district.

handling(id) is the handling cost at the import district.

Total Annual Transportation Cost

The cost of transporting the commodity from unit i and import district id to demand center dc is calculated using

)].,,( )],,(

dcidtquantity Imported dcitquantity prodn (t,dc) ttranscost

id i 

# ∑ ∑

⋅ + ⋅ =
dc)id,(t,sportcostt [imprttran dc)i,(t,sportcostt [prodntran

(2.4.14)2-14 where

prodntransportcostt(t,i,dc) is the cost of transporting one ton of commodity ($/ton) from unit i to demand center dc , and

imprttransportcostt(t,id,dc) is the cost of transporting one ton of commodity ($/ton) from imprt district id to demand center dc.



### 2.5 Emissions

As discussed in the previous chapter, emissions can be generated from fuel firing and also from use of raw materials (e.g., CO 2 emissions from calcination of limestone in cement kiln). As such, both of these emission generation pathways are included in ISIS. Fu rther, the ISIS framework includes algorithms to account for tracking multiple pollutant streams asso ciated with uncontrolled emissions, controlled emissions, pollution prevention from process modifications and energy efficiency measures, and any controls-related effects (e.g., generation of CO 2 in a wet SO 2 scrubber). These algorithms are described below.



### k)/100].j,cp(i,[1ee)k,ity(i, modeintens j)f,yfuel(i, emintensit ee)k,j,f,i,polfuel(t,

−⋅ ⋅ =
(2.5.1)



### ee).,eHIdispl(i k)Ichange(i, secondaryH k)hange(i, primaryHIc (i)eintensity ee)k,ity(i, modeintens

+ + + =

(2.5.2)

where

polfuel(t,i,f,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from processing (firing) fuel f and application of any control k and/or energy efficiency measure ee at unit i in year t, taking into account whether a unit is available for operation in that year;

emintensityfuel(i,f,j) is the emission intensity (tons pollu tant per ton product) of pollutant j resulting from processing (firing) fuel f at unit i, taking in to account whether any controls (e.g., Best Available Control Technology [BACT]) are already installed on the unit;

modeintensity(i,k,ee) is the modified energy intensity (MMBtu per ton product) needed to produce 1 ton of product at unit i . This energy intensity takes into account any heat input changes accompanying a control k and/or an energy efficiency measure ee;
eintensity(i) is the energy intensity (MMBtu per ton product) needed to produce 1 ton of product at unit i;

primaryHIchange(i,k) is the change in primary heat input (MMBtu per ton product) due to application of control k at unit i;

secondaryHIchange(i,k) is the change in heat input (MMBtu per ton product) due to any secondary fuel addition resulting from application of control k at unit i;2-15 eHIdispl(i,ee) is the amount of heat input (MMBtu per ton product) displaced (reduced) due to application of energy efficiency measure ee at unit i; and

cp(i,j,k) is the reduction efficiency for pollutant j using control k at unit i

. Similarly,



### k)/100].j,cp(i,[1 k)/100 rcent(i, MTchangepe secondaryR k)/100 ent(i, changeperc primaryRMT 1 j )m,yrmt(i, emintensit ee)k, j ,m,i,polrmt(t,

− ⋅ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + + ⋅ =

(2.5.3)

where

polrmt(t,i,m,j,k,ee) is emission (tons pollutant pe r ton product) of pollutant j resulting from processing raw material m and application of any control k and/or energy efficiency measure ee at unit i in year t, taking into account whether a unit is available for operation in that year;

emintensityrmt(i,m,j) is the emission intensity (tons pollutant per ton product) of pollutant j resulting from processing raw material m at unit i, taking into account whether any controls (e.g., BACT) are already installed on the unit;

primaryRMTchangepercent(i,k) is the percent change in primary raw material input (tons raw material per ton product) due to application of control k at unit i; and

secondaryRMTchangepercent (i,k) is the percent change in raw material input (tons raw material per ton product) due to any secondary raw materi al addition resulting from application of control k at unit i

. Finally,



### k).j,m,i,polrmt(t, ee)k,j,f,i,polfuel(t, ee)k,j,m,f,i,pol(t, + = (2.5.4)

where

pol(t,i,f,m,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from processing fuel f and raw material m, and application of any control k and/or energy efficiency measure ee, at unit i in year t, taking into account whether a unit is available for operation in that year.

Emissions of pollutant j at unit i in year t are given by

.),,,,,,( ),,( 

# ∑

⋅ = ee)j,k,i,t,poltikjee( m,f, ee)k,j,m,f,i,pol(t, eekjmfitvarprodn jitemissions (2.5.5)

where2-16 varprodn(t,i,f,m,j,k,ee) is the production variable associated with use of k th control and/or ee th energy efficiency measure for j th pollutant at unit i using fuel f and raw material m in year t;

emissions(t,i,j) are the emissions (tons of pollutant per ton of clinker) of pollutant j at unit i ; and

poltikjee(t,i,k,j,ee) is the mapping described above.

Now total emissions from all units are

.),,( ),( 

## ∑
=

i jitemissions jtions totalemiss (2.5.6)

where

totalemissions(t,,j) are total emissions (tons of pollutant per ton of clinker) of pollutant j resulting from production activity in the entire sector.

Note that production associated with each pollutant is the same and therefore



### ;);,,,,( ),,,,( jjjjjmfitprodnpol jmfitprodnpol ≠ = (2.5.7)

with,

.),,,,,,( ),,,,(
),,,,( 

# ∑

= eejkitpoltikjee eekjmfitvarprodn jmfitprodnpol (2.5.8)

where

prodnpol(t,i,f,m,j,) production variable associated with pollutant j

.


### 2.6 Controls and Costs

The ISIS framework includes constraints to ensure th at endogenous applications of sector-based controls and energy efficiency options occur in a realistic ma nner. A description of these constraints and costs for controls is presented in this section. The treatment of energy efficiency measures is described in a subsequent section.

Controls Related Constraints

Only one vintage of a control on a unit is possible for the period starting when the vintage comes on line and ending with its technical life.

.ntrols(k)] techlifeco + [(vyear(v) tyear(t) vyear(v) ;1

<≤ ≤ 

## ∑

vt ,v)t,i,kc(poltikts

, )(_

(2.6.1)2-17 where

v is the set of vintages of a control for unit i

, vyear(v) and tyear(t) are parameters with values corresponding to years in the selected time horizon,

ts_c(t,i,k,v) is a binary variable that can bring v th vintage of control k for unit i online in year t, and

techlifecontrols(k) is the technical life of control k.

Control capacity is given by the following equations.

For t = 1,

.ntrols(k)) techlifeco (vyear(v) tyear(t) vyear(v) ;i),capacity(t

,_,

+ <≤ ⋅ = 

## ∑

v v)c(t,i,ktsk)(t,i ControlCap (2.6.2a)

For t > 1,

.ntrols(k)) techlifeco (vyear(v) tyear(t) vyear(v) ;i),capacity(t

,_, ,

+ <≤ ⋅ +

− = 

## ∑

v v)c(t,i,ktsk)itimeblock, (t ControlCap k)(t,i ControlCap (2.6.2b)

In any year, no two incompatible controls can coexist on a unit,

),( ),,( ),,( itcapacity kkit ControlCap kit ControlCap ≤ + (2.6.2c)

where

k and kk are incompatible controls.

Finally, the production coefficient asso ciated with a control is less than the capacity of the unit the control is installed on,

tyear(t). vyear(v) ;i),capacity(t

_ pollutants ofNumber ),,,,(

≤ ⋅ ≤ 

## ∑ ∑

v eejkitjeef,m,poltik c(t,i,k,v)ts ,ee),i,f,m,j,k varprodn(t

(2.6.3)

where

capacity(t,i) is the capacity of unit i in year t

. In general, the costs associated with controls comprise the following components: (1) capital and fixed operation and maintenance costs, (2) costs associated with any reagent and/or catalyst consumption, (3)2-18 costs associated with any reduction in fuel and/or raw material use, (4) cost associated with electricity consumption, (5) cost associated with byproduct(s) , and (6) costs associated with water use. The calculations of these costs are described below. Note that various cost elements (e.g., capital cost of a control [$/ton clinker] for the cement sector) are escala ted appropriately to use values in years of interest.

Capital Recovery and Fixed Cost

Annual recovery of capital cost of control k is given by

trols(k)]. ecolifecon [vyear(v) tyear(t) vyear(v) ;(k) CRFcontrol k)i,alcostt(t, cntrlcapit _

+ <≤ ⋅ ⋅ = (t,i,k) ControlCap , i,k)) c(poltik(t capcost

(2.6.4)

Similarly, annual FOM cost is given by

.ntrols(k)] techlifeco [vyear(v) tyear(t) vyear(v) ;k)i,costt(t, cntrlfixed ) (_

+ <≤ ⋅ = (t,i,k) ControlCap i,k)poltik(t, c FOMcost

(2.6.5)

where

cntrlcapitalcostt(t,i,k) is the capital cost ($/ton of clinker) of application of control k on i th unit

, cntrlfixedcostt(t,i,k) is the annual fixed operation and mainte nance (FOM) cost ($/ton of clinker) of application of control k on i th unit

, capcost_c(t,i,k) is the annualized capital cost ($) of k th control application on i th unit,

FOMcost_c(t,i,k) is the annual fixed operation and maintenance cost ($) of k th control application on i th unit, and

CRFcontrol(k) is the capital recovery factor calculated using an appropriate interest rate and time period, ecolifecontrols(k) , for capital recovery.2-19 Variable Costs

Change in fuel cost associated with application of controls is given by

. ] [

),,,( ),,,(,),(

pollutants ofNumber f)r,t,fuelcostt( k) Ichange(i, secondaryH fr,t,fuelcostt( k)hange(i, primaryHIc 

## ∑ ∑

⋅ ⋅ + ⋅ ⋅ =

j,eekitpoltikjee kf(k,f),m, j,eekitpoltikjee,mfifi ,ee),i,f,m,j,k varprodn(t ) ,ee),i,f,m,j,k varprodn(t i,r),k)) tirk(t,ri( ange_c(pol f

uelcostch

(2.6.6)

where

primaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of k th technology,

secondaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of k th technology,

fuelcostt(t,r,f) is the regional cost of fuel ($/MMBtu) in year t,

ri(i,r) is a set with elements corresponding to mappi ng between kiln and their geographic locations, and

kf(k,f) is a set with elements corresponding to mapping between fuels and technologies.

poltirk(t, ri(i,r),k) is a mapping described above.

Change in raw material cost associated w ith application of controls is given by

. ] 100/ 100/ [

),,,( ),( ),,,( ),(

pollutants ofNumber i)RMTt(t, rcent MTchangepe secondaryR i)RMTt(t, ent changeperc primaryRMT 

## ∑ ∑

⋅ ⋅ + ⋅ ⋅ =

j,eekitee,m,poltikjfifi

j,eekitee,m,poltikjfifi ,ee),i,f,m,j,k varprodn(t ,ee),i,f,m,j,k varprodn(t ,r),k)) irk(t,ri(i nge_c(polt R MTcostcha

(2.6.7)

where

primaryRMTchangepercent is the percent change in primary raw material with use of k th technology,

secondaryRMTchange(i,k) is the percent change raw material input corresponding to any secondary raw material addition with use of k th technology, and2-20 RMTt(t,i) is the unit-specific cost of raw material ($/ton clinker) in year t.

Annual reagent consumption costs are

rgnt).cet(t, reagentpri

⋅ = 

## ∑

rgnt rgntkitcpt rgntconsum kitpoltikcptcost rgntconsum ),,,(_ )),,((_

(2.6.8)

The annual consumption of a reagent given by

. ] [ ),,,(_

),,,,(),(

, ),,,,( ),(

pollutants ofNumber

rgnt)j,k,m,, sumptrmt(i reagentcon pollutants ofNumber rgnt)j,k,f,i, sumptfuel( reagentcon

],ee),i,f,m,j,k varprodn(t [ ,ee),i,f,m,j,k varprodn(t rgnt)kitc(poltikpt rgntconsum

eejkit,poltikjeefifi jm eejkitem,poltikje ,jfifi



## ∑ ∑ ∑

∑

⋅ + ⋅ =

(2.6.9)

where

reagentconsumptfuel(i,f,k,j,rgnt) is the reagent consumption due to control (tons reagent/tons clinker) associated with fuel -based emission intensity,

reagentconsumptrmt(i,m,k,j,rgnt) is the reagent consumption due to control (tons reagent/tons clinker) associated with raw material-based emission intensity,

reagentpricet(t,rgnt) is the price of reagent rgnt ($/ton of reagent) in year t , and

rgntconsumpt_c(t,i,k,rgnt) is the annual reagent consumption due to control (tons reagent/year).

Catalyst consumption cost is

;) ),,,(_ ),,((_ 

# ∑

⋅ =

cat catkitct catconsump )kitpoltikctcost catconsump catice(t, catalystpr (2.6.10)

with2-21 . ),,(_

),,,,( ),( pollutants ofNumber EGFW(i) cat)j,k, nsumpt(i, catalystco 

## ∑ ∑

⋅⋅ =

j eejkitee,m,poltikjfifi ,ee),i,f,m,j,k varprodn(t 10000 ,cat)kitc(poltikt catconsump

(2.6.11)

where

catalystconsumpt(i,k,j,cat) is the catalyst consumption rate (ft 3 catalyst/10000 ft 3 flue gas),

EGFW(i) is the exhaust gas flow rate (scf/ton clinker),

catalystpricet(t,cat) is the price of the catalyst ($/ft 3 ) in year t
, catconsumpt_c(t,i,k,cat) is the catalyst consumption rate (ft 3 /year), and

catconsumptcost_c(t,i,k) is the catalyst cost ($/year).

Annual cash flow associated with b yproduct generation disposal/sale is

byprod); ricet(t, byproductp

⋅ = 

## ∑

byprod prod) c(t,i,k,by en byproductg )kitc(poltik ost byproductc

_ ),,(_

(2.6.12)

with annual generation of a byproduct given by

. ),,(_

),,,,(),( ),,,,( ),(

pollutants ofNumber byprod)j,k,m, rodnrmt(i, byproductp pollutants ofNumber byprod)j,k,f,,rodnfuel(i byproductp

],ee),i,f,m,j,k varprodn(t [ ],ee),i,f,m,j,k varprodn(t [ ,byprod)kitc(poltiken byproductg

eejkit,poltikjeefifi m,j eejkitem,poltikje ,jfifi



## ∑ ∑ ∑

∑

⋅ + ⋅ =

(2.6.13)

where

byproductprodnfuel(i,f,k,j,byprod) is the byproduct generation due to control (tons reagent/tons clinker) associated with fuel -based emission intensity,

byproductprodnrmt(i,m,k,j,byprod) is the byproduct generation due to control (tons reagent/tons clinker) associated with raw material-based emission intensity,2-22 byproductpricet(t, byprod) is the price of disposing or selling the byproduct byprod ($/ton of reagent) in year t , and

byproductgen_c(t,i,k,byprod) is the annual byproduct generation due to control (tons reagent/year).

Note that byproductpricet value can be positive (disposal cost) or negative (sale price).

Annual electricity consumption (kWh/yr) due to control is

. ] [
),,( ), ( ) ,,,,(, pollutants ofNumber

k)f,i, kWhperton( 

## ∑ ∑

⋅

= fifi eejkitpoltikjeem ,ee),i,f,m,j,k varprodn(t
)kitik kwh_c(polt (2.6.14)

The cost of electricity consumption is

i).ycostt(t, electricit

⋅ = k)kwh_c(t,i, )kitpoltik kwhcost_c( ),,( (2.6.15)

where

KWhperton(i,f,k) is the electrical requirement (kWh per ton of clinker) for technology k , and

electricitycostt(t,r) is the electricity price ($/kWh) in year t at unit i.

Annual water consumption a ssociated with control k is

. _ 2

),,,,(),( ),,,,( ),(

pollutants ofNumber k)m,i, H2Ousermt( pollutants ofNumber k)f,(i, H2Ousefuel

],ee),i,f,m,j,k varprodn(t [ ],ee),i,f,m,j,k varprodn(t [ ,i,k)) c(poltik(t Oconsumpt H

eejkit,poltikjeefifi m,j eejkitem,poltikje ,jfifi



## ∑ ∑ ∑

∑

⋅ + ⋅ =

(2.6.16)

The cost of water consumption is

.1000/ ) H2Ocostt(t

⋅ = t_c(t,i,k)

H2Oconsump ,k))poltik(t,i H2Ocost_c( (2.6.17)

where

H2Ousefuel(i,f,k) is the water use due to control (tons r eagent/tons clinker) associated with fuelbased emission intensity,2-23 H2Ousermt(i,m,k) is the water use due to control (tons reagent/tons clinker) associated with raw material-based emission intensity,

H2Ocostt(t) is the price of water ($/1000 gallons) in year t , and

H2Oconsumpt_c(t,i,k) is the annual water use due to control (gallons/year).

Total Annual Cost

Using the above costs, the total annual cost of controls is

t,i,k) H2Ocost_c( t,i,k) kWhcost_c( k)ost_c(t,i, byproductc i,k)tcost_c(t, catconsump ,i,k) ptcost_c(t rgntconsum k)nge_c(t,i, RMTcostcha ,k) ange_c(t,i fuelcostch t,i,k) FOMcost_c( t,i,k) capcost_c( st(t,i,k) tcontrolco

+ + + + + + + + =

(2.6.18)



### 2.7 Costs of Energy Efficiency Measures

As described below, the treatment of energy efficiency measures in ISIS is similar to that for control.

Energy Efficiency Measures Related Constraints

As for controls, constraints are needed to ensure r ealistic applications of energy efficiency measures. These constraints are described below.

Only one vintage of an energy efficiency measure on a unit is possible for the period starting when the vintage comes on line and ending with its technical life.

(ee)]. eetechlife + [(vyear(v) tyear(t) vyear(v) ;1

<≤ ≤ 

## ∑

vt ,v)t,i,ee ee(poltiee ts

, )(

_

(2.7.1)

where

v is the set of vintages of measure ee , poltiee(tpolc(t) is the mapping described before,

vyear(v) and tyear(t) are parameters with values corresponding to years in the selected time horizon,

ts_ee(t,i,ee,v) is a binary variable that can bring v th vintage of measure ee online on unit i in year t, and

eetechlife (ee) is the technical life of measure ee.2-24 Energy efficiency measure capacity is given by the following equations.

For t = 1,

e)). techlife(e (vyear(v) tyear(t) vyear(v) ;i),capacity(t ),(

_ ,

ee v)t,i,ee ee(poltiee tsee) EECap(t,i

v + <≤ ⋅ = 

## ∑ (2.7.2a)

For t > 1,

e)). techlife(e (vyear(v) tyear(t) vyear(v) ;i),capacity(t ),(

_ , ,

ee v)t,i,ee ee(poltiee tsee)itimeblock, EECap(t ee) EECap(t,i

v + <≤ ⋅ +

− = 

## ∑ (2.7.2b)

In any year, no two incompatible measures can coexist on a unit, 

# [ ] 1 ;),,(_),,(_ 

# ∑ ≤ +

v ,v)eeeitee(poltiee ts,v)eeitee(poltiee ts (2.7.2c)

where

ee and eee are incompatible measures.

Finally, production coefficient associ ated with an energy efficiency me asure is less than the capacity of the unit the measure is installed on,

tyear(t) vyear(v) i);,capacity(t pollutants ofNumber

≤ ⋅ ≤ 

## ∑ ∑

v j,eekijee(t f,m,poltik v)ee(t,i,ee,ts ,ee),i,f,m,j,k varprodn(t

_ ),,,

(2.7.3)

where

capacity(t,i) is the capacity of unit i in year t.

Capital Recovery and Fixed Cost

For an energy efficiency measure ee , annual recovery of capital and annual FOM cost are given by Equations 2.7.4 and 2.7.5, respectively.

ee)]. ecolifeee( [vyear(v) tyear(t) vyear(v) ;CRFee(ee) ee)i,stt(t, ecapitalco _

+ <≤ ⋅ ⋅ = ee) EECap(t,i, (t, i,ee)) ee(poltiee capcost

(2.7.4)2-25 (ee)]. eetechlife [vyear(v) tyear(t) vyear(v) ;ee)i,t(t, efixedcost ) (_

+ <≤ ⋅ = ee) EECap(t,i, i,ee) poltiee(t,ee FOMcost

(2.7.5)

where

ecapitalcostt(t,i,ee) is the capital cost ($/ton of clinker) of ee th energy efficiency measure application

on i th unit

, efixedcostt(t,i,ee) is the annual fixed operation and mainte nance (FOM) cost ($/ton of clinker) of ee th energy efficiency measure application on i th unit

, ts_ee(t,i,ee,v) is a binary variable that can bring v th vintage of measure ee online on unit i in year t
, capcost_ee(t,i,ee) is the annualized capital cost ($) of ee application on i th unit, and

FOMcost_ee(t,i,ee) is the annual fixed operation and maintenance cost ($) of ee application on i th unit, and

CRFee(ee) is the capital recovery factor calculated using an appropriate interest rate and time period, ecolifeee(ee) , for capital recovery.

Variable Costs

Change in fuel cost associated with application of ee measures is given by

pollutants ofNumber fr,t,fuelcostt( ee),eHIdispi(i 

## ∑

⋅ ⋅ =

),,,,( _

eejkitjee f,m,poltik ) ,ee),i,f,m,j,k varprodn(t ),ee)) e(t,ri(i,r ee(poltire ange fuelcostch

(2.7.6)

where

eHIdispi(i,ee) is the displacement of primary heat input (MMBtu per ton clinker) with use of ee th energy efficiency measure,

fuelcostt(t,r,f) is the regional cost of fuel ($/MMBtu) in year t,

ri(i,r) is a set with elements corresponding to mappi ng between kiln and their geographic locations, and

poltiree(t, ri(i,r),ee) is a mapping described above.

Annual electricity consumption (kWh/yr) due to an energy efficiency measure is2-26 pollutants ofNumber ee)i, kWhperton( 

# ∑

⋅

= ),,,,(,, ),,(_ eejkitpoltikjeemf ,ee),i,f,m,j,k varprodn(t e )eeitee(poltiee kwh (2.7.7)

The cost of electricity consumption is

i)ycostt(t, electricit

⋅ = ,ee)

kwh_ee(t,i )eeit(poltiee kwhcost_ee ),,( (2.7.8)

where

eKWhperton(i,f,ee) is the electrical requirement (kWh per ton of clinker) for ee, and

electricitycostt(t,i) is the electricity price ($/kWh) in year t at unit i.

Total Annual Cost

Using the above costs, the total annual co st of energy efficiency measures is

ee(t,i,ee) kWhcost ee(t,i,ee) ange fuelcostch ee(t,i,ee) FOMcost ee(t,i,ee) capcost ee)scost(t,i, teemeasure

_ _ _ _ + + + =

(2.7.9)



### 2.8 Policy Options

ISIS can help design and evaluate a number of em issions reduction policy options including cap-andtrade, emissions taxes, and emissi ons limits. Additionally, appropriate combinations of these options can also be evaluated. The policy options included in ISIS are described below.

Cap-and-Trade

Under this option, an emissions cap is set on the amount of a pollutant that can be emitted. Sources, companies, or other groups are issued emission perm its (allowances) which represent the right to emit a specific amount of the pollutant. Allowances may be banked for use in future. The total amount of allowances available in the current period and those banked in previous periods cannot exceed the cap in the current period. Sources or companies that need to increase their emissions must buy allowances from those who pollute less. The transfer of allowances is referred to as a trade. In effect, the buyer is paying a charge for polluting, while the selle r is being rewarded for having redu ced emissions by more than was needed. Thus, in theory, those that can easily reduce emissions most cheaply will do so, achieving the pollution reduction at the lowest possible cost to society.

Generally, annual caps have been utilized in ongoing programs (e.g., Title IV SO 2 reduction program). However, ISIS does permit evaluation of potential pr ograms with caps over us er-selected time periods (e.g., 5-yearly caps). This evaluation is accomplished using,

),(),( ),( jperiodtbnkjtbnk jtions totalemiss +

− +≤ j)ec(t, (2.8.1)

where

ec(t,j) is the emission cap for pollutant j in year t , and2-27 bnk(t+period, j) are the allowances of the pollutant j banked in year t for the year t + period

. Cap-and-Trade with a Minimum Reduction Requirement

While designing a cap-and-trade program, there may be an interest in requiring a minimum level of emission reduction from each affected entity. Such a requirement may be able to help address any local emissions-related concerns. In ISIS, th is requirement can be imposed using

j)i,,ecpminer(t j)i,t, emissions( ≤ (2.8.2)

where

ecpminer(t,i,j) is the unit-specific minimum emission reduction requirement for pollutant j in year t

.

Emissions Limits

ISIS permits evaluation of the costs and emissions reductions associated with more traditional emission reduction programs utilizing unit-specific rate-based emission limits. Such requirements are imposed using

),( ),,( itprodn jitemissions ⋅≤ j)el(t, (2.8.3)

where

el(t,j) is the rate-based emission limit for pollutant j in year t and every affected unit complies with this limit.

Allowance Price Inputs

In some cases, there may be an interest in endoge nously determining the level of emission reduction corresponding to a certain allowance price. This info rmation may be useful, for example, in a situation where an allowance price is set for reducing emissions from many industrial sectors. In such a case the levels of emission reductions corres ponding to the same allowance price may be different for the sectors under consideration. The emissions response to a given a llowance price is driven by the following term in the objective function (see section 2.1),

),( )(

, jtions totalemiss tdis
jt 

## ∑

⋅ ⋅ j)alprice(t, (2.8.4)

where

alprice(t,j) is the exogenous allowance price of pollutant j in year t

. Note that the allowance price inputs scheme above is equivalent to emission-tax-based programs in which affected units or companies pay a tax for every unit of pollution they produce. Thus this scheme can also be used to evaluate such programs.



### 2.9 Optimization and Post-Processing

In ISIS, the input data are pre-processed to arrive at suitable input parameters for use in the model equations explained in this chapter. Once the data have been pre-processed, ISIS solves for the2-28 appropriate levels of production, im ports and controls required to meet the constraints associated with commodity demand and emissions, while maximizing total surplus. Once the surplus maximization problem has been solved, the results are post-processe d to obtain parameters and level values of the variables of interest. The key variables of interest are: production level of each production unit to meet regional demand, level of imports in each region, installation of various controls, emissions, and various costs. Output data are written in appropriate work sheets in an Excel workbook and further linked to various plots to enable visual presentation and analyses of the results.2-29 

### References for Chapter 2

Duloy, J. H.; Norton, R.D. (1975). Prices and incomes in linear programming models. American Journal of Agricultural Economics . 57(4): 591.600.

Enke, S. (1951). Equilibrium among spatially separated markets: solution by electric analogue. Econometrica . 19: 40.47.

FPL-PELPS, A price endogenous linear programming system for economic modeling, supplement to PELPS III, version 1.1, USDA Research paper FPL-RP-614, 2003.

McCarl, B.A.; Spreen, T.H. (1980). Price endogenous mathematical programming as a tool for sector analysis. American Journal of Agricultural Economics . 62: 87.102.

Samuelson, P.A. (1952). Spatial price equilibrium and linear programming. American Economic Review

. 42(3): 283.303.

Samuelson, P.A.; and W. Nordhaus. (1977). Economics (17th edition) , John Wiley.3-1 

## Chapter 3 Input Data for ISIS-Cement Model

3.1 Data Requirements

Data requirements for ISIS include sector-specific (in this case, cement-specific) data as well as policy and economic parameters. The cement-specifi c data requirements are discussed below.



### 3.2 Cement-Specific Data

The inputs to the ISIS model for the cement industry can be broadly categorized into three main components:

• Industry production, fuel, and emissions;

• Control technologies, and emission abatement approaches; and

• Policy and economic parameters.



### 3.2.1 Industry, Fuel, and Emissions

3.2.1.1 Existing, Planned/Committed, and Potential Units

The ISIS cement industry model contains information on 168 cement kilns that were in existence in 2009 and PCA’s projected capacity expansions from 2010 to 2012, as shown in Table 3-1 (PCA, 2009b). Additionally, the model also includes potential kiln representations that may come on line as a result of endogenous capacity addition [i.e., new production capac ity (by state) and replacements (by kilns)].

Table 3-1. Summary of Kilns Modeled in the ISIS Cement Industry Model
Kiln Population Number of Kilns Existing (2009) 168 3
PCA’s Projected New Kilns (2010 – 2012) 9

Each kiln modeled in ISIS is characterized by its locat ion, design (i.e., wet, dry, preheater, or precalciner), daily and annual clinker capacity 4 , vintage, and retirement information when available (PCA, 2006). In addition, each kiln is characterized by its average variable costs components (AVC).

3 Does not include 2 white cement kilns ne ither 2 cement kilns in Puerto Rico.
4 Annual clinker capacity as reported by PC A refers to the kiln daily capacity mul tiplied by 365 less normal downtime days, where daily capacity is the normal clinker output a kiln can pr oduce per day given a realistic work pattern and normal downtime days are number of days of downtime required for mainte nance, repair or cleanup.3-2 3.2.1.2 Average Variable Costs

In previous economic analyses, five input variab les in cement production have been identified to determine the kiln-level AVC functions: raw materials, repair and maintenance, labor, electricity, and fuel (Depro, 2007, Depro, 2010, Depro and Lentz, 2010). Raw materials serve as the kiln feed, and repair and maintenance are required for periodic upkeep of the kiln . Labor is used in the quarry, in the operation of the kiln, and for packing. Electricity is consumed ma inly by the auxiliary equipment and fuel is largely consumed in the kilns. The AVC for raw materials, labor, repair and maintenance, and electricity was determined following the methodology in the EPA regulatory impact analysis of the cement kiln dust rulemaking (EPA, 1998).

3.2.1.3 Cement Demand Centers

The U.S. cement markets are organized in st ate-specific demand centers. Figure 3-1 shows the distribution of Portland cement kilns operating in 2009. Each state containing at least one kiln is shaded. The ISIS-cement model simulates each cement plant’s ability to compete in each of the demand centers as a function of the plant’s production cost and transpor tation cost associated with supply to each demand
center.

3.2.1.4 Portland Cement Demand

One of the key data inputs for the ISIS-cement model is the demand projection for each demand center. In general, this demand is a function of gross domes tic product (GDP) growth, interest rates, special construction projects (e.g., highways), and public sector construction spending. Portland cement demand was 128 million metric tons in 2005. PCA expects cem ent demand will reach 192 million metric tons by 2035, which reflects an increase of nearly 64 million me tric tons with a compound annual growth rate of 1.4%. Cement demand through year 2035 is repor ted in the PCA Long-Term Cement Consumption Outlook (PCA, 2009c). PCA projections of cement de mand (in million metric tons) by state for 2005 through 2035, in 5-year increments, are shown in Table 3-2.3-3 Figure 3-1. Distribution of Cement Kiln s in the United States as of 20093-4 Table 3-2. Portland Cement Demand in Millions of Metric Tons

State Demand Center 2005 2010 2015 2020 2025 2030 2035

Alabama 1.92 1.45 2.02 2.16 2.32 2.49 2.67

Arizona 4.77 2.21 3.85 5.16 6.55 7.95 9.59

Arkansas 1.30 0.93 1.29 1.41 1.54 1.67 1.81

California 16.01 8.33 13.88 15.43 17.11 18.95 20.96

Colorado 2.55 1.74 2.76 3.08 3.47 3.90 4.33

Connecticut 0.82 0.54 0.77 0.83 0.88 0.94 0.98

Delaware 0.22 0.18 0.26 0.28 0.29 0.31 0.32

District of Columbia 0.21 0.15 0.18 0.18 0.17 0.18 0.18

Florida 12.28 4.77 8.99 11.05 13.47 16.33 19.67 Georgia 4.75 2.52 3.95 4.96 5.67 6.17 6.65

Hawaii 0.44 0.35 0.47 0.49 0.52 0.55 0.57

Idaho 0.70 0.44 0.70 0.82 0.92 1.03 1.15

Illinois 4.64 3.10 4.29 4.62 4.99 5.44 5.98

Indiana 2.27 1.69 2.30 2.49 2.73 2.98 3.25

Iowa 1.94 1.59 1.98 2.04 2.10 2.15 2.20

Kansas 1.55 1.36 1.65 1.71 1.78 1.85 1.91 Kentucky 1.60 1.06 1.48 1.59 1.71 1.84 1.97

Louisiana 2.23 2.44 2.91 3.15 3.29 3.42 3.52

Maine 0.35 0.20 0.29 0.31 0.32 0.34 0.35 Maryland 1.66 1.12 1.63 1.78 1.94 2.12 2.31

Massachusetts 1.26 0.81 1.11 1.21 1.31 1.43 1.56 Michigan 3.06 1.54 2.05 2.19 2.44 2.68 2.75

Minnesota 2.06 1.29 1.86 2.09 2.34 2.57 2.80 Mississippi 1.14 1.07 1.50 1.58 1.66 1.76 1.85

Missouri 2.87 2.03 2.71 3.18 3.58 3.90 4.29

Montana 0.38 0.30 0.42 0.45 0.48 0.51 0.53

Nebraska 1.37 1.14 1.46 1.55 1.66 1.76 1.87

Nevada 2.63 1.20 2.33 3.26 4.27 5.14 6.16 New Hampshire 0.37 0.23 0.33 0.36 0.39 0.42 0.45 New Jersey 2.06 1.45 1.89 1.99 2.09 2.18 2.28

New Mexico 0.91 0.65 0.94 1.00 1.07 1.12 1.153-5 State Demand Center 2005 2010 2015 2020 2025 2030 2035

New York 3.29 2.84 3.60 3.73 3.87 3.98 4.08

North Carolina 3.25 2.16 3.43 3.92 4.50 5.16 5.88

North Dakota 0.36 0.39 0.47 0.47 0.46 0.44 0.42

Ohio 4.06 2.59 3.61 3.75 3.90 4.02 4.12

Oklahoma 1.67 1.57 1.68 1.76 1.87 1.98 2.11

Oregon 1.24 0.83 1.22 1.40 1.61 1.86 2.15 Pennsylvania 3.44 2.61 3.49 3.64 3.78 3.89 3.97

Rhode Island 0.19 0.12 0.17 0.18 0.19 0.19 0.20

South Carolina 1.94 1.07 1.81 1.99 2.19 2.38 2.56

South Dakota 0.48 0.44 0.55 0.57 0.61 0.64 0.67

Tennessee 2.52 1.54 2.38 2.61 2.89 3.19 3.53

Texas 15.09 12.11 17.51 21.03 24.10 27.48 30.58

Utah 1.53 1.22 1.82 2.08 2.40 2.78 3.23

Vermont 0.16 0.11 0.16 0.17 0.18 0.20 0.21 Virginia 2.87 1.87 2.84 3.13 3.45 3.80 4.17 Washington 2.24 1.68 2.50 2.75 3.04 3.36 3.73 West Virginia 0.54 0.47 0.59 0.60 0.60 0.59 0.58

Wisconsin 2.37 1.59 2.18 2.32 2.45 2.59 2.74 Wyoming 0.47 0.43 0.55 0.55 0.55 0.55 0.54

Total 128.03 83.54 122.81 139.06 155.70 173.14 191.51

3.2.1.5 Transportation-Interregional Trade

In ISIS-cement, a transportation matrix is used to describe the costs for transporting cement from kiln and import district locations to demand centers. To deve lop these costs, information on distances between supply and demand points and costs of transportation mode s (truck, rail, or water transport) was obtained. In particular, the Transportation Routing Analysis GIS (TRAGIS) model (TRAGIS, 2003) was used to develop the origin-destination distances. Also, in the matrix, the applicable lowest cost transportation option is used to connect a supply point with a destination. While the cement demand centers are interlinked through a transportation matrix, the comp etition is generally mainta ined on a regional level because the cost of transporting cement is relatively high.

The cost of transportation for interregional trade takes into account the impact of terminals and the frequent use of modes other than truck. According to the USGS (2007), nearly half of cement shipments reach customers via terminals rather than direct. Shipments to terminals are more than 80% by rail or water, rather than by truck. Table 3-3 provides information on cost per ton mile for bulk shipping via truck, rail, and barge. In ISIS-cement, transportation costs for each mode of transportation (truck, rail, and barge) are calculated from each kiln and import di strict to each demand center. For a given origin-3-6 destination pair, the dominant mode (lowest cost of sh ipment) is used to determine the transportation cost for that route. The TRAGIS model was used to arrive at the origin-destination distances and feasible routes for rail and barge; Google Maps was used for truck transportation. The shipment costs shown in Table 3-3 were used to determine the tran sportation costs for the routes in ISIS.

Table 3-3. Bulk Shipment Costs (Cents per Metric Ton per Mile)

Mode Journal of Transport Geography Report, 2005 N&S American Cement, 2004 American University, 1994 Averages Used in Model

BTS 1 WDOT 2 NACT 3

Truck 33.06 7.64 11.02 7.18 14.73

Rail 2.78 3.32 5.51 N/A 3.87 Barge 0.89 1.05 2.20 N/A 1.38
1 (Bureau of Transportation Statistics) General Bulk Transportation "...can only be considered the upper boundary of transport costs for each mode assuming each i ndustry is operating at-cost or at a profit". 2 (Washington State Department of Transportation) Cost study to examine options for the transport of wheat (Jessup and Casavant, 1998). 3 (North American Cement Transportation) Report covers cem ent-specific transportation costs, assumes short tons-- converted to metric.

3.2.1.6 Imports

U.S. cement markets receive imported quantities of ce ment and clinker from a number of countries, and these imports arrive at more than 30 import district s (USGS, 2009). In ISIS-cement, international supplies from exporting countries to U.S. import districts are modeled using supply elasticity and then these imports are transported to the demand centers.

The five largest international suppliers of cement and clinker to the U.S. are China, Thailand, Venezuela, South Korea, and Greece. An econometric study was c onducted to provide an estimate of international supply elasticity for supplies from these countries and the rest of the world. The results of this study (Burtraw, 2010) reflected that the best estimate of th e international supply elastic ity of cement and clinker from China, Thailand, Venezuela, South Korea, Greece, and the rest of the world into the U.S. is 3.94. This value indicates that if the price of cement were to increase by 1% within any import district in the U.S., then, ceteris paribus , the quantity of cement imported from each of these five supply countries into that district would increase by 3.94%.

Table 3-4 shows the import levels by USGS Customs District, then state, for 2005, estimated by using USGS data (USGS, 2007).

Table 3-4. Portland Cement and Clinker Imports in Million Metric Tons, by Customs District in 2005

USGS Customs District Quantity, million metric tons
Alabama, Mobile 0.51 Alaska, Anchorage 0.14 Arizona, Nogales; Mexico 1.073-7 USGS Customs District Quantity, million metric tons California, Los Angeles 3.05

California, San Diego 0.72
California, San Francisco 2.36
Florida, Miami 2.27 Florida, Tampa 3.48 Georgia, Savannah 0.08
Hawaii, Honolulu 0.43 Illinois, Chicago 0.01
Louisiana, New Orleans 4.18
Maine, Portland 0.16 Maryland, Baltimore 0.13
Massachusetts, Boston 0.13 Michigan, Detroit 1.32 Minneapolis, MN, Canada 0.04
Minnesota, Duluth; Canada 0.16
Missouri, St. Louis 0.01
Montana, Great Falls 0.06
New York, Buffalo 0.82
New York, New York 1.26 New York, Ogdensburg 0.33 North Carolina, Wilmington 0.39
North Dakota, Pembina; Canada 0.18
Ohio, Cleveland 0.79 Oregon, Columbia-Snake 0.87 Pennsylvania, Philadelphia 0.49
Rhode Island, Providence 0.74
South Carolina, Charleston 1.1

Texas, El Paso; Mexico 0.72
Texas, Houston-Galveston 2.61

Texas, Laredo; Mexico 0.14

U.S. Virgin Islands 0.06
Vermont, St. Albans; Canada 0.133-8 USGS Customs District Quantity, million metric tons Virginia, Norfolk 0.7 Washington, Seattle 1.49
Wisconsin, Milwaukee; Canada 0.19

Total 33.33
Source: USGS, 2007. Data do not include Puerto Rico

3.2.1.7 Capacity Changes

Cement plants have a relatively long lifespan, ty pically 50 years or more (FLSmidth, 2007). Various factors, including (but not limited to) raw material availability in the quarry, technology changes, productivity, efficiency, longevity, reliability, maintenan ce, and long-term costs can affect the lifespan of a cement kiln/plant. In ISIS-cement, retirements from 2002 to 2009 and projected retirements after 2009 of existing kilns were based on information from PC A on capacity expansion estimates. These estimates were supplemented with information from individual cement companies on their plans for shut-downs, new construction, and kiln consolidation (PCA, 2004a ). Further, as mentioned earlier, ISIS-cement includes algorithms for endogenous capacity growth and retirement of kilns. To determine capital recovery factor for capital costs asso ciated with kiln capacity changes, an economic life of 25 years and an interest rate of 15% are used. Capital costs in 2005 $ per short ton of clinker for new, replacement of wet, and replacement of dry capacity are 208, 296, and 238, respectively (PCA, 2009a).

3.2.1.8 Fuel Intensity

The Annual Energy Outlook (2008) energy use profile for 2005 is shown in Figure 3-2. In 2005, the cement sector consumed 451.2 trillion Btu (476.0 trillion kJ) of energy (EIA, 2008c). As shown in Figure 3-2, the primary fuel being burned in kilns is coal. Co al is projected to remain the dominant fuel used by the U.S. cement industry. However, there has been an increasing trend towards using other fuels, particularly alternative fuels, such as coke, 5 waste tires, and other wastes, especially oily wastes.

3.72%

0.76%
64.11% 19.30%

3.20%
0.50%

8.40% Natural Gas
Fuel Oil

Coal

Petroleum Coke

Tires
Other Solid Wastes Liquid Wastes

Figure 3-2. Commercial Fuel Use Profile by U.S. Ce ment Industry in 2005.

5 PCA does not specify if “coke” is me tallurgical coke or petr oleum coke. Authors believe it is the latter.3-9 Source: EIA, 2008a

In the ISIS-cement model, to determine the fuel intens ity of each kiln, correlations of kiln type to heat input and/or gas flow were developed (see Appendix A). Once determined, the kiln’s specific fuel intensity is used to calculate fuel cost for each kiln . PCA’s data on heat input to various kilns by type were used to develop kilns’ fuel intensities. The data , expressed in heat input per unit of clinker (specific fuel consumption [SFC]) and exhaus t gas flow rate (wet) (EGFW) per unit of clinker, are summarized in Table 3-5.

Table 3-5. Specific Fuel Consumption and Total Exhaust Gas Flow Rate (wet) for Various Kiln Types

SFC EGFW Kiln Type MMBtu/ton a Nm 3 /kg SCF/ton a
Wet 6.0 3.4 108,990 Dry 4.5 1.8 57,701
Preheater 3.8 1.5 48,084
Preheater/Precalciner 3.3 1.4 44,878

a short t on Note: SFC=specific fuel consumption; Source: EPA, 2007a (Table 3-3) EGFW=exhaust gas flow rate (wet): Source: PCA, 2004 (original data in metric units)

For each individual kiln, the ISIS model determines the optimal fuel type(s) based on the regional cost of the chosen fuel and the kiln’s specific fuel intensity.

3.2.1.9 Emissions

The design of the ISIS Model can accommodate any num ber of pollutants of interest. In ISIS-cement, each kiln is characterized by its NO x , SO 2 , PM, hydrochloric acid (HCl), mercury (Hg), total hydrocarbon (THC), and CO 2 emission intensities. The characterization by kiln of the NOx, SO2, PM, HCl, mercury and THC emissions is presented in "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" (August 6, 2010) (EPA, 2010).

NO X emissions from cement kilns result primarily from the following combustion process: oxidation of fuel nitrogen (fuel NO X ) and the oxidation of nitrogen in the combustion air (thermal NO X ). Oxidation of nitrogen in the feed materials (feed NO X ) can also influence total NO X emissions. Table 3-6 shows NO X emission intensities for cement kilns in lb/ton of clinker and in lb/MMBtu (EPA, 2007a).3-10 Table 3-6. Estimated Uncontrolled NO X Emission Intensities for Cement Kilns

Uncontrolled NO x Emissions Kiln Type Heat Input,
MMBtu/ton of clinker lb/ton of clinker * lb/MMBtu

Wet 6.0 9.7 1.62
Long Dry 4.5 8.6 1.91

Preheater 3.8 5.9 1.55

Preheater/Precalciner 3.3 3.8 1.15

* Average Source: EPA 2007 (Table 3-3 and Table 6-1)

SO 2 emissions from cement kilns are the product of sulfur in the fuel as well as sulfur in the feed materials. Sulfur in the fuel will oxidize to SO 2 during pyroprocessing, and a significant amount is likely to be captured in the form of sulfates as the ga s passes through the calcination zone. Compared to long dry and wet kilns, preheater and preheater/precalciner kilns tend to be more effective at capturing fuelgenerated SO 2 . Accordingly, oxidation of sulfur in the feed materials is likely to be a major component of total SO 2 emissions. Table 3-7 shows average SO 2 emissions for each kiln type for each state (Andover Technology Partners, 2009a). State-sp ecific emission intensities of SO 2 were determined from emissions reported for the kilns in that state. For any state wh ere the emission intensity was not available for a kilntype, the national average emission intensity was assigned to kilns in that state.

CO 2 emissions from cement kilns result from limestone calcination and fuel combustion. Calcination releases 0.52 tons of CO 2 per ton of clinker produced, while fuel-based CO 2 emission factors range from 199.52 lb CO 2 /MMBtu for coal to 105.02 lb CO 2 /MMBtu for natural gas (Andover Technology Partners, 2009b; Andover Technology Partners, 2010). Table 3-8 shows approximate CO 2 and H 2 O produced from combustion for the most important fuels for cement kilns.3-11 Table 3-7. Average SO 2 Emissions for Each Kiln Type in Each State

SO 2 (lb/ton clinker) State Precalciner Preheater Dry Wet

AL 0.09 0.61 9.02 13.99

AZ 1.30 0.07 8.51 7.81

AR 2.94 2.32 3.45 7.59

CA 0.33 1.12 0.81 0.00

CO 0.32 0.10 1.07 4.24

CT 1.15 2.32 9.02 7.81

DE 1.15 2.32 9.02 7.81

FL 0.45 0.02 9.02 7.81

GA 0.95 2.73 11.42 12.25

ID 0.13 2.32 9.02 0.70

IL 5.58 5.77 5.88 9.50

IN 1.15 2.32 9.02 7.81

IA 1.15 2.32 9.02 7.81

KS 1.25 5.88 24.85 8.61

KY 0.19 5.04 12.53 7.81

LA 1.15 2.32 9.03 7.81

ME 0.30 0.30 ? 11.98

MD 3.14 3.95 7.31 7.49

MA 1.15 2.32 9.03 7.81

MI 5.23 2.32 5.31 18.00

MN 1.15 2.32 9.03 7.81

MS 0.09 0.61 9.02 13.99

MO 2.18 2.32 1.51 7.02

MT 0.13 2.32 9.02 0.70

NE 1.25 5.88 24.85 8.61

NV 0.49 0.05 1.51 7.81

NH 1.15 2.32 9.02 7.81

NJ 1.15 2.32 9.02 7.81

NM 1.30 0.07 8.51 7.81

NY 0.30 0.30 9.02 11.983-12 SO 2 (lb/ton clinker) State Precalciner Preheater Dry Wet

NC 1.15 2.32 9.02 7.81

ND 1.15 2.32 9.02 7.81

OH 0.19 5.04 12.53 7.81

OK 1.25 5.88 24.85 8.61

OR 0.13 2.32 9.02 0.70

PA 1.15 2.32 9.02 7.81

RI 1.15 2.32 9.02 7.81

SC 0.95 2.73 11.42 12.25

SD 0.32 0.10 1.07 4.24

TN 0.95 2.73 11.42 12.25

TX 1.15 2.32 9.02 7.81

UT 0.13 2.32 9.02 0.70

VT 1.15 2.32 9.02 7.81

VA 0.95 2.73 11.42 12.25

WA 0.53 2.32 9.02 3.80

WV 3.14 3.95 7.31 7.49

WI 1.15 2.32 9.02 7.81

WY 0.32 0.10 1.07 4.24

National Average 1.15 2.32 9.02 7.81

Table 3-8. Approximate CO 2 and H 2 O Produced from Combustion of Fuels

Variable Name Flue Gas Tires Petroleum Cokes Heavy Fuel Oil Rosemont PRB Logan, WV BIT Natural Gas

LBMCO2MMBTU lbmoles CO 2 /MMBtu 4.26 4.83 3.85 4.25 4.53 2.39

LBCO2MMBTU lb CO 2 /MMBtu 187.44 212.56 169.32 186.83 199.52 105.02

LBMH2OMMBTU lbmoles H 2 O/MMBtu 2.76 2.76 2.23 3.23 4.14 2.77

LBH2OMMBTU lbs H 2 0/MMBtu 49.71 49.73 40.26 58.21 74.64 49.92

PRB = Powder River Basin coal WV BI T = West Virginia bituminous coal3-13 

### 3.2.2 Control Technologies and Emi ssion Abatem ent Approaches

ISIS-cement contains information on abatement approaches for NO x , SO 2 , PM, HCl, Hg, THC, and CO 2 emissions described above. The three categories of abatement approaches included are: process modifications and upgrades, raw material and/or fu el substitution, and mitigation technologies. For each emission abatement approach, where possible, inform ation on the following parameters was developed (Andover Technology Partners, 2008) and included in the model: capital cost, fixed operating cost, variable operating cost, emission reduction performance fo r all of the pollutants, impacts on fuel and/or raw material use, impact on electricity consumption, byproduct generation and cost, and impact on water

use.
To estimate capital recovery factors for capital cost s associated with control technologies, economic life values of 15 and an interest rate of 7% are generally used, but different values can be selected by the user. Payback periods and technical life for the energy effi ciency measures shown in Tables 3-14 through 3-17 are given in Worrell and Galitsky (200 4). Economic life for each of these measures can be taken to be the average of the technical life and the payback period. Agai n, an interest rate of 7% can be used for capital recovery in the absence of more specific information.

Tables 3-9 through 3-12 show the NO X , SO 2, CO 2 , HCl, Hg, THC emissions control technologies being used in the ISIS-cement model. Tables also re flect the impacts of these technologies on pollution reduction, electricity use, and water use. Multimedia impacts of changes to the capacity of cement kilns are listed in Table 3-13. Tables 3-14 through 3-17 s how the electricity consumption and heat input changes accomplished as a result of implementation of energy efficiency measures for Raw Materials Preparation, Clinker Making, Finish Grinding, and Plant-Wide Measures, respectively.



### 3.2.3 Policy and Economic Parameters

3.2.3.1 Policy Parameters

The ISIS model framework allows the user to select a variety of potential policy options for evaluation. The user can select from cap-and-trade policy (with or without deminimis requirements), emissions charge policy, or rate-based policies. In a cap-and- trade policy scenario, separate caps on pollutants of interest can be specified. The user has the option to run a cap-and-trade policy scenario with or without banking of emissions. Further, a cap-and-trade policy scenario can include deminimis requirements, where the user defines a minimum level of emission reduction required for each emission unit. As mentioned before, it is also possible for the user to input an emission charge for pollutants of interest. Furthermore, traditional policy scenarios (rate-based policies) with unit specific emission reduction requirements specified by the user can be modeled in ISIS.

The user can specify the policy horizon (time period) to be used for the model runs. The user can also choose to run ISIS with blocks of years (e.g., 5-year blocks). The simulation horizon and blocks of years can be chosen by the user subject to availability of data.

3.2.3.2 Additional Economic Parameters

In the ISIS framework, the following additional econo mic parameters are used: discount rate, escalation rates, and demand elasticity. For the ISIS-cement model, the default discount rate has been chosen as 7%, as recommended by the U.S. Office of Manageme nt and Budget (OMB) for project evaluation (OMB, 1992). Escalation rates used can be found in the “ISI S_Inputs.xls” workbook. Escalation rates for labor are based on historical data from Bureau of Labor an d Statistics (BLS). Raw materials escalation factors are calculated from historical price data of crus hed sandstone and gypsum from USGS. The escalation3-14 factors for variable operating and maintenance costs are based on Chemical Engineering Index. Escalation factors for various fuels and electricity are esti mated based on data from the EIA’s Annual Energy Outlook 2008 (RTI, 2010). Demand for cement is relatively inelastic and an elasticity value of -0.88 is used in the model (EPA, 1998).3-15Table 3-9. NOXControl Technologies for Cement KilnsImpact on Emissions,±% changeElectricity Consumption,kWh/ton of cementControl TypeNOXSO2PM Hg Other Grinding Kiln OperationWaterConsumption,gal/ton ofcementLow NOXBurners –Indirect Firing-20% to -30%1No impact203-1.2403Mid KilnFiring-Tires-20% to -40%1May vary5030603Low NOXBurner + MidKiln Firing- Tires-20% to -40%1May vary503-1.24,603Low NOXBurners + TireDerived Fuel-20% to -40%7May vary503-1.24,603Low NOXBurner +Selective Non CatalyticReduction-50%2No data5No data8No data8No data80 -1.24,9+1.259Low NOXBurner +Selective CatalyticReduction-90%2Oxidation10No data8Oxidation10No data80 -1.24,9+1.259Low NOXBurners +CemStara-30%11-1.3 (wetprocess);-1.9 (dryprocess)12-1.24,12from LNBand-1.5 (wet process)or-2.2 (dry process)from CemStar130CemStar/Fly Ash Injection-20%14-1.3 (wetprocess);-1.9 (dryprocess)13-1.5 (wet process)or-2.2 (dry process)from CemStar130Notes to Table 3-91.See EPA (2004), Table 5-1.3-162.Andover Technology Partners 2009b.3.These technologies do not use and do not affect the consumption of water in raw mix preparation or significantly affect electric power consumption incement manufacturing processes.4.Conversion from direct firing systems typical of wet and dry process kilns and older preheater kilns to indirect firing systems, required to implementlow NOXburner (LNB) technology, could result in reductions in primary air fan and kiln-induced draft fan power requirements and concomitant slightincreases in coal conveying power requirements. A reduction in fan/blower power on the order of 100 hp might be anticipated fora moderately sized(300,000 – 500,000 clinker tons per year) kiln converted from direct firing to indirect firing. No power savings on adding an LNB would be anticipatedif the kiln system is already indirect-fired.5.SO2emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln operation. Sulfur content of TDF(typically, 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used in cement kilns,such as coal or coke.Therefore it is not practical to relate SO2emissions to the use of these NOXcontrol methods. With respect to use of an SO2wet scrubber using groundlimestone, it is assumed that, for wet process kiln systems, uncontrolled SO2emissions of 8.2 lb per ton of clinker (8.9 lb/ton of cement) (EPA Pub. AP42, Table 11.6-8, 1995) are treated by use of a stoichiometric amount (with respect to uncontrolled SO2) of limestone of 90% purity in a 15% limestoneslurry. Limestone and water consumption are 15.5 lb and 87.7 lb, respectively, per ton of cement produced. For a precalciner kiln, it is assumed thatuncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement)(EPA, 1995: Table Table 11.6-8)are treated similarly (stoichiometricamount limestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2.1 lb and 11.8 lb per ton of cement produced. For aprecalciner kiln, it is assumed that uncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995: Table 11.6-8) are treatedsimilarly (stoichiometric amount limestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2.1 lband 11.8 lb per ton ofcement produced.6.Kiln system and raw materials grinding electric power consumption would not be significantly affected by introduction of tiresor tire-derived fuel.7.Combined effect will vary. Low NOx20 to 40%, MKF 20 to 40%.8.While there may be theoretical or limited experimental bases toassume increases or decreases inemissions of various pollutants in connection withNOXemission controls, statistics on such effects are not available. See, generally, EPA (2007), Chapter 11.9.Assumes SNCR or SCR both stoichiometric additions of NH3in 20-percent solution. Typical precalciner NOXemissions of 4.2 lb/ton clinker beforetreatment (EPA 1995, Table 11.6-8),and 0.92 tons of clinker areused in each ton of cement. It should also be noted that SNCRis not applicable to wetprocess, long dry process, and many preheater kilns because the kiln gas exit temperatures are too low from those units for thenecessary reactions totake place. See EPA (2007a). SNCR is assumed to achieve 63% reduction in NOXemissions from applicable kiln systems. See EPA (2007a), Chapter 8.There may be an attendant small increase in kiln system electric power in connection with injection of water into the kiln system due to an increase ingas volume handled by the kiln’s fan system. The increase is not considered in the calculation.10.Typically, up to about 1% of SO2can be oxidized to SO3, Elemental form of Hg oxidized across SCR provided sufficient concentration of halogens influe gas.11.On a lb/ton of clinker basis.12.Use of theCemStarprocess may have multiple effects on electric power consumption, including reduced raw mix preparation costs in the event thatungroundCemStarmaterial is fed to the rotary kiln, and reduced fan power requirements resulting from reduced kiln gas volumes in connection withboth combustion gases and raw mix calcination. Assuming a 5% replacement of raw mix with carbonate-freeCemStarmaterial, a concomitant 5%reduction in raw mix preparation energy is also assumed. A very approximate resulting 5% reduction in kiln electrical energy consumption is assumedbased on a roughly 5% reduction in kiln exit gases from both calcination and combustion.13.CemStarand fly ash injection are expected to have similar effects onraw mix preparation and kiln process electrical energy requirements.3-1714.See NESCAUM (2000) for effect ofCemStaron NOXreductions from cement kilns. Investigation of the combined effects of multiple technologies onpollutant emissions was not carried out for this summary.General NotesA. Cement kiln processes do not use steam.B. Cement kiln dust (CKD) disposal rates as functions of NOXor SO2emissions control technology has not been reported. Typical CKD disposal ratesrange from 0.042 to 0.115 tons CKD/ton clinker (0.046 to 0.125 tons CKD/ton cement assuming 0.92 tons clinker per ton of cement).C. CKD disposal costs vary widely by region, CKD characteristics, CKD volumes, and location of disposal site (on-site or off-site). In addition, disposalcosts are expected to be of comparable cost to transportation costs in connection withoff-site disposal. For example, prices at three northern Californialandfills range from $26/short ton to $69/short ton of CKD plus transportation, whichmay range from approximately $500 to $1,200 per truck load. Pers.Comm., E. Leamer (ARCADIS) Feb. 26, 2009. CKD disposal prices would be expected to be generallyunaffected by the type of cement kiln pollutioncontrols employed.References for Table 3-9(references called out in the above notes can be found at the end of the chapter)Air Pollution Controls and Efficiency Improvement Measures for Cement Kilns, Prepared by ARCADIS Under Contract No. EP-C-04-023, March 31, 2008.Srivastava et al., ES&T, March 2006, pp.1385-1393.Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. 1992.Table 3-10. SO2Control Technologies for Cement KilnsImpact on Emissions,±% changeElectricity Consumption,kWh/ton of cementControl TypeNOXSO2PM Hg Other Grinding Kiln OperationWaterConsumption,gal/ton ofcementWet Scrubber-90% to -95%1-80% -99.9% for HCl+0.2(wet processkilns)2+52+12 (wetprocess); +1.5(dry process)3Dry Lime Injection-50%4-75% for HCl555Notes to Table 3-101.Andover Technology Partners 2009b.2.Electric power consumption in connection with wet scrubbing is assumed to be primarily a result of: 1) grinding of limestone tobelow 45 to 74micrometers in particle size and pumping limestone slurry; and 2)increased kiln exhaust gas fan power requirements. The latterenergy increase iscaused by increased pressure drop demand on kiln ID fans due to conveying kiln exit gases across the scrubber spray tower plusincreased gas volume3-18demand on kiln ID fans due to the addition of water in the limestone slurry. Slurry preparation and pumping is assumed to require 20 kWh per ton oflimestone. The spray tower pressure drop is assumed to add 500 hp to ID fan requirements on a 300,000 to 500,000 ton per year cement kiln process.3.SO2emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln operation. Sulfur content of TDF(typically 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used in cement kilns, such as coal or coke.Therefore it is not practical to relate SO2emissions to the use of these NOXcontrol methods. With respect to use of an SO2wet scrubber using groundlimestone, it is assumed that, for wet process kiln systems, uncontrolled SO2emissions of 8.2 lb per ton of clinker (8.9 lb/ton of cement) (EPA, 1995:Table 11.6-8) are treated by use of a stoichiometric amount (with respect to uncontrolled SO2) of limestone of 90% purity in a 15% limestone slurry.Limestone and water consumption are 15.5lb and 87.7 lb, respectively, per ton ofcement produced. For aprecalciner kiln, it is assumed thatuncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement)(EPA, 1995 Table 11.6-8) are treated similarly (stoichiometric amountlimestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2.1 lb and 11.8 lb per ton of cement produced.4.20 percent at Ca:S stoichiometry of 2:1 to 5:1. Dry Sorbent Injection report.(ARCADIS, 1992)5.Dry lime injection technology is not well developed for the cement industry and no statistics are available.General Notes: See Table 3-9References for Table 3-10(references called out in the above notes can be found at the end of the chapter)Andover Technology Partners, Cost and Performance of Controls, March 10, 2009.Srivastava et al., ES&T, March 2006, pp.1385-1393.Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. ARCADIS, 1992.EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplements A-F, AP-42. U.S.Environmental Protection Agency, Research Triangle Park, North Carolina.3-19Table 3-11. CO2Control Technologies for Cement KilnsImpact on Emissions,+/- % changeControl TypeNOXSO2PM Hg CO2OtherElectricityConsumption,MWh/ton ofclinkerProcess WaterConsumption,ton/ton ofclinkerCooling WaterConsumption,ton/ton of clinkerSolvent CC1New -99% -99% -85% -0.0220.4294.82Solvent CC1Retrofit -99% -99% -85% -0.1030.1604.82Oxy-combustion -99% -99% -85% 0.174 14.3Notes to Table 3-111.CC = carbon captureReferences for Table 3-11Andover Technology Partners, Cost and Performance of Controls, March 10, 2009.EPA, Report to Congress on Cement Kiln Dust, Chapter 3: CKD Generation and Characteristics; US Environmental Protection Agency:1993.3-20 Table 3-12. HCl, Hg, and THC Control Technologies for Cement Kilns
Impact on Emissions, ± % change Control Type 1

NO X SO 2 PM Hg Other

ACI 2 -99.9% -90% -50% for THC Membrane Bag -99.9%

RTO 3 -98% for THC Dry Lime Injection -50% -75% for HCl

Wet Scrubber -90% to -95% -80%

Notes to Table 3-12 1. Feasible combinations of the above control technologies may be utilized as necessary. 2. ACI = activated carbon injection. 3. RTO = regenerative thermal oxidizer

Table 3-13. Multimedia Impacts of Process Capa city Replacement on Cement Kiln Operation 1

Electricity Consumption,
kWh/ton of cement Waste

Kiln Type Water Consumption, gal/ton of cement 2 Grinding
3 Kiln Operation 4 Generatio n Rate, ton/ton of

cement 5 Disposal Cost, $/ton of cement

Wet to Precalciner -214 2,6 -12 8 -7 8 -0.072 see notes 9,10

Long Dry to Precalciner No change 2 Not available 7 Not available 7 -0.062 see notes 9,10

Preheater to Precalciner No change 2 Not available 7 Not available 7 0 11 see notes 9,10

Notes to Table 3-13. 1. Unless specifically noted otherwise, impacts are pres ented on a per-short-ton-of-cement basis and generallymake use of published data reported on a per-short-ton-of-clinker basis. These values are converted to a per-tonof-cement basis by assuming that cement consists of 92-per cent clinker. All weight units in this table are short
tons. 2. Water is not normally consumed in dry process cement kiln processes, except on an emergency basis to prevent damage to process equipment by hot gas or solid process streams. Water is used in non-contact cooling processes that are common in both wet process and dry process cement plants, some of which may or may not use closed circuit cooling systems w ith no evaporation losses. Water is consumed in direct contact cooling processes such as cement grinding in both wet process and dry process plants. However, the nature and amount of such water consumption is not intrinsically different between wet process and dry process plants. 3. Power data reported in Worrell and Galitsky (2004) are used for raw mix preparation and kiln process electrical energy consumption for wet and all dry processes.3-21 4. Air pollution control device contributions to electric power consumption data on cement kiln process systems are assumed to include only existing particulate matter control devices and not scrubbers for SO 2 or other criteria pollutants. 5. Solid waste from all process types is assumed to be cement kiln dust (CKD). Data used here are as reported in EPA (1993). 6. Cement is assumed to consist of 92-percent clinker. Clin ker is assumed to require 1.42 tons of raw kiln feed (dry) per ton of clinker. Kiln feed loss on ignition is assu med to be 35 percent. Kiln feed slurry is assumed to contain 36-percent water. 7. Industry-wide statistics on electrical energy use do not distinguish among the various cement processing stages between the various dry processes – all dry process plants are averaged together. Published data that distinguish between various process types combine all process phases without indicating energy use for individual process phases. Therefore, electrical energy use is reported here to be the same for all dry process plants. See Worrell and Galitsky (2004). 8. As stated in note 7, industry data on electric power use in raw materials preparation and kiln processing are not available for every dry process. Data are for all dry processes are combined. 9. Data reported in EPA (1993) on CKG generation by cement plants doe not distinguish between preheater and precalciner kilns with respect to net CKD generation rates. 10. CKD disposal prices vary widely by location. Transportation of CKD is a significant component of the cost of disposal if disposal is off-site. For example, an estimated off-site disposal cost ranging from $26 to $69 per ton of CKD for disposal, plus transportation costs ranging from $500 - $1,500 per truck load for one northern California location, depending on the landfill chosen. (ARCADIS, 2009) On-site disposal costs would include costs of transportation, dust control, and landfill operation. These costs have not been determined and would most certainly vary widely by location based on terrain, site geology, landfill operating requirements, and permit and future closure costs. 11. Modern preheater cement kilns and pr ecalciner cement kilns generate simila r volumes of CKD. Older preheater cement kilns are similar in CKD generation to long dry process cement kilns.

Table 3-14. Energy Efficiency Measures for Raw Materials Preparation
Electricity Consumption Change, kWh/ton clinker Energy Efficiency Improvement Method Dry Wet Preheater Precalciner

ETS ( Efficient Transport System) -3.20 -3.20 -3.20
RMB (Raw Materials Blendi ng) -2.70 -2.70 -2.70

PCVM ( Process Control Vertical Mill) -0.90 -0.90 -0.90 HERM (High Efficiency Roller Mill) -11.05 -11.05 -11.05 SBH (Slurry Blending and Ho mogenization) -0.35
WMCCC (Wash Mills with Closed Circuit Classifier) -12.00 RMTHEC (High-Efficiency Classi fiers) -5.05 -5.05 -5.05 -5.053-22 Table 3-15. Energy Efficiency Clinker Making Measures
Electricity Consumption Change, kWh/ton clinker Heat Input Change, MMBtu/ton of clinker Energy Efficiency

Improvement Method Dry Wet Preheater Precalciner Dry Wet Preheater Precalciner

EMCS ( Energy Management and Control System) -1.90 -1.50 -1.90 -1.90 -0.15 -0.21 -0.15 -0.15

SR (Seal Replacement) -0.02 -0.02 -0.02 -0.02

CSI ( Combustion System Improvement) -0.25 -0.35 -0.25 -0.25

IF ( Indirect Firing) -0.16 -0.16 -0.16 -0.16

SHLR ( Shell Heat Loss Reduction) -0.20 -0.20 -0.20 -0.20

OGR (Optimize Grate Cooler) 0.90 0.90 0.90 -0.09 -0.10 -0.09 -0.09

CGC (Convert to reciprocating grate cooler) 2.40 2.40 2.40 2.40 -0.23 -0.24 -0.23 -0.23

HRPG ( Heat Recovery for Power Generation) -18.0

EMD ( Efficient Mill Drives) -2.00 -1.70 -2.00 -2.003-23 Table 3-16. Energy Efficiency Measures for Finish Grinding
Electricity Consumption Change, kWh/ton clinker Energy Efficiency Improvement Method Dry Wet Preheater Precalciner EMPC (Energy Management and Process Control) -1.60 -1.60 -1.60 -1.60 IGMBM (Improved Grinding Media [Ba ll Mills]) -1.80 -1.80 -1.80 -1.80 HPRP (High-Pressure Roller Press)

- 16.00 -16.00 -16.00 -16.00

HEC (High-Efficiency Classifi ers) -3.85 -3.55 -3.85 -3.85

Table 3-17. Energy Efficiency Plant-wide Measures
Electricity Consumption Change, kWh/ton clinker Energy Efficiency Improvement Method Dry Wet Preheater Precalciner
PM *(Preventive Maintenance) -2.50 -2.50 -2.50 -2.50 HEM (High Efficiency Motors) -2.50 -2.50 -2.50 -2.50 ASD (Adjustable Speed Drives) -6.25 -6.00 -6.25 -6.25 OCAS (Optimization of Compressed Ai r Systems) -1.00 -2.50 -1.00 -1.00 *This is the only occurrence wherein “PM” does not stand for “particulate matter”3-24 

### References for Chapter 3

American University (2008). TED Case Studies: Cemex Case

. http://www.american.edu/TED/cemex.htm

, accessed October 21, 2008.

Andover Technology Partners (2008). Cost and Perfo rmance of Controls, memorandum to U.S. EPA, September 25, 2008.

Andover Technologies Partne rs (2009a). Memorandum: NO X , SO 2 and CO 2 Emissions from Cement Kilns (Emissions Memo) , from Jim Staudt to Ravi Srivastava, Samudr a Vijay, Elineth Torres. Dated March 10, 2009 (see Appendix A of this document).

Andover Technology Partners (2009b). Memorandum: Costs and Performance Controls , from Jim Staudt to Ravi Srivastava, Samudra Vijay, Elineth Torre s. Dated March 10, 2009 (see Appendix A of this document).

Andover Technologies Partners (2010). Memorandum: Wet Scrubber Cost Algorithms , from Jim Staudt to Ravi Srivastava, Elineth Torres, Keith Barnett. Dated February 26, 2010 (see Appendix A of this document).

ARCADIS (1992). Theoretical Approach for Enhan ced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. ARCADIS, 1992.

ARCADIS (2009). Personal Comm. with E. Leamer of ARCADIS, 26 February, 2009.

Burtraw, D. (2010). Supply Elasticity Estimation, me morandum to Ravi Srivastava, U.S. EPA, March 10, 2010.

Depro, B.M. (2007). RTI International. “ Documentation for Portland Cement Kiln Cost Functions (2005 )”, Memorandum to Keith Barnett, US EPA, August 31, 2007.

Depro, Brooks. (2010). RTI International. “ISIS Cement Production Costs”, Memorandum to Elineth Torres, US EPA, March 31, 2010.

Depro, Brooks and Lentz, Anthony. (2010). RTI International. “ISIS Cement Production Costs”, Excel Spreadsheet Attachment to Memo of Same Name from Brooks Depro (2010), February 16, 2010.

BLS (2008). Bureau of Labor Statistics, Databases, Tables & Calculators by Subject, Major Sector Productivity and Costs Index. Series ID : PRS30006112, Sector: Manufacturing. http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_num be rs&series_id=PRS30006112

, accessed November 24, 2008.

EIA (2008) Annual Energy Outlook 2008, DOE/EIA-0383(2008), June 2008. http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2008).pdf , accessed October 21, 2008.

EPA (1993). Report to Congress on Cement Kiln Dust , Chapter 3: CKD Generation and Characteristics; US Environmental Protection Agency: 1993.

EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplements A-F, AP-42. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.3-25 EPA (1998). June 1998. Regulatory Impact Analysis of Cement Kiln Dust Rulemaking . Washington, DC: U.S. Environmental Protection Agency. http://www.epa.gov/osw/nonhaz/industria l/special/ck d/ckd/ckdcostt.pdf , accessed October 21, 2008.

EPA (2004). Alternative Control Techniques Document - NO X Emissions from Cement Manufacturing, dated March 1994 (EPA-453/R-94-004)

EPA (2005). EPA 2002 National Emission Inventory v3. http://www.epa.gov/ttn/chief/net/2002inventory.htm l , accessed October 21, 2008

EPA (2006). EPA 2005 National Emissions Inventory Version 2. http://www.epa.gov/ttn/chief/net/2005inventory.html accessed November 17, 2008

EPA (2007). November 2007. Alternative Control Techniques Document Update - NO X Emissions from New Cement Kilns. EPA-453/R-07-006 . Research Triangle Park, NC. US Environmental Protection Agency.

EPA (2010). August 6, 2010. "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" Memorandum prepared for Keith Barnett by RTI. US Environmental Protection Agency.

FLSmidth & Co. A/S (2007). Q2 Report 2007. August 2007 http://hugin.info/2106/ R/1148414/219358.pdf , accessed October 21, 2008.

NESCAUM (2000). Status Report on NO X Controls for Gas Turbines, Cement Kilns, Industrial Boilers, Internal Combustion Engines: Technologies & Cost E ffectiveness. Northeast States for Coordinated Air Use Management, Decem ber 2000. Accessed at http://www.nescaum.org/documents/nox-2000.pdf/

OMB (1992). Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs. OMB Circular No.A-94 (Revised). U.S. Office of Management and Budget, October 29, 1992. http://www.whitehouse.gov/omb/circulars/a094/a094.html#8 , accessed October 21, 2008.

PCA (2004). Innovations in Portland Cement Manufacturi ng. Portland Cement Association. Edited by J. I. Bhatty, F. M. Miller, and S. H. Kosmatka. 2004.

PCA (2004a). U.S. and Canadian Portland Cement I ndustry: Plant Information Summary of December 31, 2004.

PCA (2006). U.S. and Canadian Portland Cement I ndustry: Plant Information Summary. Portland Cement Association, Skokie, IL, 2006.

PCA (2008). Forecast Report: Long-Term Cement Cons umption Outlook. By Ed Sullivan January 31, 2008

. http://www.cement.org/econ/pdf/Long- Term Flashwinter2007nonmember.pdf , accessed October 21, 2008.

PCA (2009a). PCA Capacity Report. Flash Report. Portland Cement Association’s Economic Research Department. Updated October 19, 2009.

PCA (2009b). Forecast Report: Long-Term Cement Cons umption Outlook. By Ed Sullivan. October 28, 2009. Portland Cement Association.3-26 Ryan, S. (2006). The Costs of Environmental Regula tion in a Concentrated Industry. Department of Economics, MIT, Cambridge, MA, October 5, 2006. http://econ-www.mit.edu/files/116 6 , accessed October 21, 2008.

RTI (2010). ISIS Input Price Escalation Factors, Excel Spreadsheet.

TRAGIS (2003). Transportation Routing Analysis Ge ographic Information System (TRAGIS) User’s Manual, Revision 0, Oak Ridge Na tional Laboratory, June 2003. https://tragis.ornl.gov/TRAGISmanual.pdf

. USGS (2005). Background Facts and Issues Concer ning Cement and Cement Data. U.S. Geological Survey, Open-File Report 2005-1152

. http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf , accessed October 21, 2008.

USGS (2007a). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, http://minerals.usgs.gov/minerals/ pubs/commodity /cement/cemenmyb05.pdf , accessed October 21, 2008

USGS (2007b). Mineral Commodity Summaries: Cement, U.S. Geological Survey, pp. 40-41, January 2007. http://minerals.er.usgs.gov/minerals /pubs/com modity/cement/cemenmcs07.pdf , accessed October 21, 2008.

USGS (2009). 2005 Minerals Yearbook: Cement. U.S. Geological Survey (Table 17), pp. 16.21. http://minerals.usgs.gov/minerals/pubs /commodity /cement/myb1-2007-cemen.pdf

. Worrell, E.; Galitsky, C. (2004). Energy Efficiency Improvement Opportunities for Cement Making; Lawrence Berkeley National Laboratory, LBNL-54036, Jan 2004. An ENERGY STAR ® Guide for Energy and Plant Managers, http://www.osti.gov/ene rgycitations/servlets/purl/821915-Re2kcK/native/.

Yates, J.R. et al. (2003). CemStar Process and Technology for Lowering Greenhouse Gases and Other Emissions while Increasing Cement Production. http://www.hatch.ca/Environment_Community/Susta inable_Development/Projec ts/Copy%20of%20Cem Star-Process-final4-30-03.pdf accessed February 27, 2009.4-1 

## Chapter 4 Model Calibration

Large techno-economic models of ISIS framework size require model calibration as they utilize extensive amount of data which comes from different sources. This chapter outlines calibration methodology that was used, discusses data used for calibration, presents calibration results, and gives further recommendations.



### 4.1 Calibration Methodology

Calibration methodology utilizes the concept of calibra tion constant. The calibration constant has been developed to account for possible errors in costs. The value of calibration constant, calconst(i) , is set by trial and error during calibration. The objective of the trial and error approach is to minimize the absolute difference in the reported and model-pr edicted prices (which are marginal values of the supply equation) for each USGS district.

In the first step of calibration, the model is set to run for 2005-2007 by making appropriate changes in the input “Policy” worksheet, and GAMS input files. The import quantities are then adjusted equal to reported import quantity for each of the import dist rict, except for those of Mexico and Canada.

In the next step, impact of changing the calibration constant is monitored. This impact of the calibration constant is assessed on estimated production quantities to keep the difference between reported and model predicted production values within reasonable limits 6 . The calibration constant modifies each kiln’s variable cost of production. “Calibration” worksheet within the Inputs workbook has values of calibration constant assigned for each USGS district. Finally, in the input GAMS file, the values are assigned for a given USGS district to each of the kilns located in that USGS district.

The model is first calibrated for year 2005, to obtain values of the calibration parameter calconst(i) for the year. Next, calconst(i) values for 2005 are used as starting point and obtain values for the same parameter for 2006. Similarly, the process is repeated to obtain the values for 2007. Then, an average of the calconst(i) values over the three years is taken and used fo r the future model runs. Current values of the parameter Calconst(i) being used in the model runs can be found in the worksheet “ Calibration ” of the “ISIS_Inputs.xls” workbook.

Calibration is a dynamic process, and it is recommend ed that model calibration be performed periodically. In this fashion, any available new production, imports, or price data could be utilized in t he model..



### 4.2 Data for Calibration

Production quantities in various USGS production distri cts, import levels in the import districts and reported cement prices for USGS districts are th e key quantities used for calibration of the model. Reported data for years 2005, 2006 and 2007 were us ed to calibrate the model and obtain values of appropriate calibration parameters.

6 There is no standard method to guide user in determining acceptable level of “error” in the reported and predicted values for the purpose of calibration. In this works, we have set acceptable level for the absolu te gap between the individual reported and predicted values to ± 15%, although effort ha s been made to keep this level below 10% for most of the quantities. However, due to discontinuities in the transportation matrix, error in the re ported data, or other unknowns, th e gap in the estimated and re ported values may be higher in certain markets.4-2 

### 4.2.1 Cement Prices in USGS Districts

Reported cement prices in various USGS district s are shown in Table 4-1 for years 2005 through 2007. For a given year, the reported cement price shows significant variation across USGS districts. For most districts the cement price has increased over the thr ee year period. USGS districts assignation is the location of the reporting production facilities.



### 4.2.2 Cement Production in USGS Districts

Reported cement production in various USGS distri cts is shown in Table 4-2 for years 2005 through 2007. For a given year, the reported cement production shows significant variation across USGS districts. Generally production dropped fro m 2005 to 2007, due to a decrease in demand resulting from the economic downturn.



### 4.2.3 Cement Imports by Import Districts

Reported cement import quantity in various import districts is shown in Table 4-3 for years 2005 through 2007. For a given year, the reported cement imports show a significant variation across import districts. Generally imports dropped from 2005 to 2007, due to a decrease in demand resulting from the economic downturn. New Orleans (LA), San Francisco (CA) an d Tampa (FL) suffered large drops in the quantities of cement imported in 2007 compared to that in 2005.



### 4.3 Results of Calibration

Reported and calculated values of cement prices by USGS district are shown for years 2005, 2006, and 2007 in Table 4-4, Table 4-5, and Table 4-6, respectivel y. As can be seen from these tables, in most of the districts reported and calculated cement prices are with in 10% range, whereas in a few they are higher. It should be noted that in general the price differentials are smaller in the year 2005 and have increased in 2007. In some USGS districts the reported and calcula ted prices are different than the model predicted prices due to several factors, including error in reported prices, discontinuities in the transportation matrix, and/or unique factors for some specific markets.

Specifically, the Arizona and New Mexico USGS di strict has a price differential due to capacity constraints in New Mexico. New Mexico has only two small, very old kilns with a capacity half than the demand in New Mexico. The balance demand is met by kilns in Texas, and imports, which incur a significant transportation cost, resulting in a high pric e for cement in this market. Similarly, Oregon and Washington USGS District is dependent on supplies from other states. While demand in Washington is met by domestic production, and imports from Canada , Oregon, relies on imports from Canada, and other nearby states, resulting in higher cost.

Generally, individual market prices are within the cr iteria specified in the QA document, the aberrations are explained by the demand-supply gap and transpor tation cost. Moreover, root-mean-square values of the price difference for 2005 and 2006 are about 12% and 10% respectively, and 18% for 2007.

Further while calibrating for price, aggregate production level is also tracked to make sure that it is within reasonable limits. The reported and predicted aggreg ate production levels for 2005-2007 are shown in Table 4-7.4-3 The current set of calibration constant values are averaged over the years of calibration, and are available in the “Calibration” worksheet in the “ISIS_Inputs.xls” workbook.4-4 Table 4-1. Cement Prices ($/short ton) for USGS Districts

USGS District 2005 2006 2007

Alabama 74.61 81.74 87.09

Illinois 80.29 89.81 91.17

Indiana 72.03 80.04 79.99

Kansas 76.58 86.50 92.87 Maryland 74.76 81.19 80.18

Missouri 78.92 86.61 89.36

Ohio 82.08 90.24 91.47

South Carolina 68.57 80.46 87.54

Maine and New York 80.74 92.53 96.62 Pennsylvania 79.73 89.99 90.85 Michigan and Wisconsin 79.83 84.82 90.12
Iowa, Nebraska and South Dakota 78.72 89.83 93.08

Florida 77.56 90.44 95.55 Georgia, Virginia and We st Virginia 84.49 95.93 93.60 Kentucky, Mississippi and Tennessee 84.37 89.81 88.90

Arkansas and Oklahoma 89.00 96.99 97.74

Texas 74.72 85.10 88.33

Arizona and New Mexico 75.76 84.16 86.64 Colorado and Wyoming 82.24 92.90 92.73
Idaho, Montana, Nevada and Utah 83.61 90.26 95.25

California 88.20 99.09 100.24 Oregon and Washington 79.38 90.72 90.724-5 Table 4-2. Cement Production (short tons) for Various USGS Districts

USGS District 2005 2006 2007 Alabama 5,647,141 5,733,121 5,578,650

Illinois 3,568,182 3,425,984 3,434,254 Indiana 3,370,868 3,334,492 3,285,999 Kansas 3,182,373 3,310,241 3,039,164 Maryland 2,813,098 2,922,227 3,305,160 Missouri 5,877,524 5,776,111 5,763,479

Ohio 1,086,879 1,064,833 1,010,077 South Carolina 3,601,251 3,654,162 4,057,414
Maine and New York 3,572,591 3,699,357 3,471,388 Pennsylvania 6,931,334 6,631,505 6,239,551 Michigan and Wisconsin 6,171,841 5,993,267 6,047,588 Iowa, Nebraska and South Dakota 4,962,606 5,024,335 4,889,607 Florida 6,311,835 6,477,181 6,076,137 Georgia, Virginia and West Vi rginia 2,612,478 2,696,253 2,528,648 Kentucky, Mississippi and Tennessee 3,649,753 3,849,271 3,770,250 Arkansas and Oklahoma 3,097,495 2,979,547 2,879,982
Texas 12,737,207 12,510,131 12,038,919
Arizona and New Mexico 3,073,244 2,809,792 2,902,131 Colorado and Wyoming 2,918,920 2,842,861 2,797,240 Idaho, Montana, Nevada and Utah 3,400,630 3,354,333 3,308,645 California 12,747,128 12,069,207 11,941,198 Oregon and Washington 2,175,963 2,101,005 2,103,4814-6 Table 4-3. Cement Imports (except from Canada and Mexico) for Import Districts (short tons)
Import District 2005 2006 2007 Baltimore, MD 146,300 203,500 18,700

Boston, MA 145,200 50,600 3,300 Buffalo, NY 6,600 4,400 0 Charleston, SC 1,212,200 1,100,000 363,000

Chicago, IL 1,100 1,100 0 Cleveland, OH 0 1,100 0 Columbia-Snake, ID-OR-WA 831,600 1,115,400 1,180,300 Detroit, MI 59,400 0 0 Duluth, MN 0 0 0 El Paso, TX 0 0 0 Great Falls, MT 0 0 0 Houston-Galveston, TX 2,880,900 3,705,900 3,641,000 Laredo, TX 0 0 0 Los Angeles, CA 3,357,200 3,759,800 2,029,500 Miami, FL 2,395,800 2,311,100 980,100 Minneapolis, MN 0 0 0 Mobile, AL 565,400 573,100 0 New Orleans, LA 4,503,400 5,090,800 1,171,500

New York, NY 1,390,400 1,327,700 810,700

Nogales, AZ 0 0 0 Norfolk, VA 767,800 793,100 447,700 Ogdensburg, NY 0 0 0

Pembina, ND 0 0 0 Philadelphia, PA 544,500 665,500 342,100

Portland, ME 0 0 0 Providence, RI 814,000 680,900 509,300 San Diego, CA 620,400 708,400 427,900 San Francisco, CA 2,599,300 3,081,100 1,524,600

Savannah, GA 88,000 204,600 376,200

Seattle, WA 368,500 733,700 644,600 St. Albans, VT 0 0 0 St. Louis, MO 0 0 3,300

Tampa, FL 3,825,800 3,791,700 1,523,500 Wilmington, NC 427,900 416,900 284,9004-7 Table 4-4. Reported and Calculated Ce ment Prices in USGS Districts (2005)

USGS District Reported Calculated % Δ

Alabama 74.61 78 5

Illinois 80.29 83 3

Indiana 72.03 84 16

Kansas 76.58 81 6 Maryland 74.76 74 -1

Missouri 78.92 82 4

Ohio 82.08 83 1

South Carolina 68.57 76 10

Maine and New York 80.74 73 -9 Pennsylvania 79.73 77 -3 Michigan and Wisconsin 79.83 81 2
Iowa, Nebraska and South Dakota 78.72 81 3

Florida 77.56 75 -3 Georgia, Virginia and West Virginia 84.49 80 -6 Kentucky, Mississippi and Tennessee 84.37 81 -4

Arkansas and Oklahoma 89.00 87 -2

Texas 74.72 78 4

Arizona and New Mexico 75.76 105 39 Colorado and Wyoming 82.24 98 20
Idaho, Montana, Nevada and Utah 83.61 70 -16

California 88.20 85 -4 Oregon and Washington 79.38 69 -134-8 Table 4-5. Reported and Calculated Ce ment Prices in USGS Districts (2006)

USGS District Reported Calculated % Δ

Alabama 81.74 85 4

Illinois 89.81 83 -8

Indiana 80.04 85 6

Kansas 86.50 83 -4 Maryland 81.19 80 -1

Missouri 86.61 81 -6

Ohio 90.24 83 -8

South Carolina 80.46 85 6

Maine and New York 92.53 78 -16 Pennsylvania 89.99 80 -11 Michigan and Wisconsin 84.82 80 -6
Iowa, Nebraska and South Dakota 89.83 81 -10

Florida 90.44 82 -10 Georgia, Virginia and We st Virginia 95.93 86 -10 Kentucky, Mississippi and Tennessee 89.81 84 -6

Arkansas and Oklahoma 96.99 91 -6

Texas 85.10 81 -4

Arizona and New Mexico 84.16 105 25 Colorado and Wyoming 92.90 100 8
Idaho, Montana, Nevada and Utah 90.26 83 -8

California 99.09 85 -14 Oregon and Washington 90.72 74 -184-9 Table 4-6. Reported and Calculated Ce ment Prices in USGS Districts (2007)

USGS District Reported Calculated % Δ

Alabama 87.09 73 -17

Illinois 91.17 72 -21

Indiana 79.99 75 -7

Kansas 92.87 71 -24 Maryland 80.18 74 -8

Missouri 89.36 71 -21

Ohio 91.47 76 -17

South Carolina 87.54 69 -21

Maine and New York 96.62 74 -24 Pennsylvania 90.85 75 -17 Michigan and Wisconsin 90.12 76 -15
Iowa, Nebraska and South Dakota 93.08 71 -23

Florida 95.55 66 -31 Georgia, Virginia and We st Virginia 93.60 74 -20 Kentucky, Mississippi and Tennessee 88.90 71 -20

Arkansas and Oklahoma 97.74 88 -10

Texas 88.33 80 -10

Arizona and New Mexico 86.64 101 17 Colorado and Wyoming 92.73 89 -4
Idaho, Montana, Nevada and Utah 95.25 83 -13

California 100.24 81 -20 Oregon and Washington 90.72 75 -18

Table 4-7. Aggregate production ( reported and modeled) for 2005-2007

Year Reported Production (million short tons) Calculated Production (million short tons) % Δ

2005 111.84 112.54 0.63

2006 110.30 111.23 0.84

2007 108.17 101.95 -5.754-10 

### 4.4 Conclusions

If any of the key input parameters, specifically those relating to production quantities and costs, are refined or otherwise modified or additional observed data becomes available, the calibration of the model should be repeated. Transportation matrix, modes, and cost of transportation also have significant impact on the behavior of production distribution across the districts and production prices. Therefore, if any further modifications or refinements are made to th e transportation matrix, the model needs to be recalibrated.

At the time of calibration of the current version of th e model, production, import and price values for only 2005 to 2007 were available. As discussed above, when the new values become available, the model needs to be calibrated again. Calibration of the mode l should be repeated as soon as new information or new observed data become available. Due to practical limitations it is recommended that the calibration of the model be repeated every two years. Further, since the calibration data is available only for three years, equal weight was given to the parameters obtained fo r each year. Once larger data-set is available, a modified weighing system can be adopted to give highest weight to the most-recent year data.4-11 

### References for Chapter 4

United States Geological Service. Mineral Yearbook 2008. Table 5. http://minerals.usgs.gov/mine rals/pubs/commodity /cement/

United States Geological Service. Mineral Yearbook 2008. Table 3. http://minerals.usgs.gov/mine rals/pubs/commodity /cement/

United States Geological Service. Mineral Yearbook 2008. Table 11. http://minerals.usgs.gov/mine rals/pubs/commodity /cement/5-1 

## Chapter 5 Analysis and Results of the Po rtland Cement NES HAP and NSPS

5.1 Affected Sources

EPA is finalizing amendments to the NESHAP for the Portland cement manufacturing industry and the NSPS for Portland cement plants. The final amendmen ts to the NESHAP add or revise, as applicable, emission limits for mercury (Hg), total hydrocarbo ns (THC), and particulate matter (PM) from nonhazardous waste (nhw) kilns located at major or area sources, and hydrochloric acid (HCl) from nhw kilns located at major sources. The amendments to the N SPS add or revise, as applicable, emission limits for PM, NOx and SO2 for facilities that commence construc tion, modification, or reconstruction after June 16, 2008.

ISIS-cement was used to conduct an integrated engineering and economic analysis for the final Portland Cement NESHAP and final NSPS. Figure 5-1 shows the cement facilities and the kilns modeled in ISIS for the final NESHAP and final NSPS analysis. Ev en though the Portland cement NESHAP affects only nhw kilns, in ISIS-cement all cement kilns projected to be in the U.S. in 2013 are modeled to meet the cement demand.

Figure 5-1 Portland Cement Faciliti es and Kilns Modeled in ISIS-Cement ISIS Inputs: 2013

Total Cement Facilities = 108

Total Cement Kilns = 177

Total Cement Capacity = 127.6 Million metric tons

Non-hazardous Waste Facilities = 97

Total Non-hazardous Waste Kilns = 157

Total Cement Capacity = 115.2 Million metric tons Hazardous Waste Facilities = 11

Total Kilns = 20

Total Cement Capacity = 12.4 Million metric tons

Existing Facilities in 2009 = 93

Existing Kilns = 148

Total Cement Capacity = 103.8 Million metric tons Projected New Facilities in 2010-2012 = 4

Projected New Kilns = 9

Total Cement Capacity = 11.4 Million metric tons5-2 From the 108 Portland cement facilities modeled in IS IS-cement for the final NESHAP and final NSPS analysis, 97 facilities or 157 nhw kilns are projected to be subject to the final NESHAP. From these 157 nhw kilns projected to be subject to the NESHAP, 16 k ilns are projected to be subject to the limits for new sources while 137 kilns are projected to be subj ect to the limits for existing sources. Seven facilities or 7 kilns are projected to be subject to the final NSPS. 7



### 5.2 Emission Limits

Table 5-1 shows the emission limits for the final NES HAP analyzed in ISIS, while Table 5-2 shows the emission limits for the final NSPS. Emission limits in tables 5-1 and 5-2 are average annual emissions. EPA’s analysis using ISIS-cement focused on the indivi dual rules and also on the combination of the final NESHAP and the final NSPS. The limits used in th e analysis were input as lbs/ton cement produced annually.

Table 5-1. Final NESHAP Emission Limits

NESHAP Limits for ISIS' Runs Existing New Pollutant Average Average Units

31.7 14 lb/MM ton clinker 0.0000317 0.000014 lb/ton clinker Hg 0.000029164 0.00001288 lb/ton cement 5 5 ppmv 0.050856164 0.050856164 lb THC/ton clinker THC 0.046787671 0.046787671 lb/ton cement 0.4 0.4 ppmv 0.003378699 0.003378699 lb HCl/ton clinker HCl 0.003108403 0.003108403 lb/ton cement 0.012 0.005 lb/ton clinker PM 0.01104 0.0046 lb/ton cement

Table 5-2. Final NSPS Emission Limits

NSPS Limits for ISIS' Runs New Pollutant Limit Average Units 1.5 1.5 lb/ton clinker NOx 1.38 1.38 lb/ton cement 0.4 0.4 lb/ton clinker SO2 0.368 0.368 lb/ton cement

7 Under the NESHAP any source that commenced constr uction after May 6, 2009 is a new source. Under the NSPS, any source constructed, reconstructed or modified after June 16, 2008 is a new source.5-3 

### 5.3 Control Technologies

Kilns subject to the final NESHAP and/or final NSPS ar e expected to add one or more control devices to comply with the final emission limits. Table 5-3 shows control technologies and control efficiencies used in the final NESHAP and final NSPS analysis. Th e cost of these control technologies can be found in the "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS". August 6, 2010. The final NESHAP and final NSPS ru les also include additional testing and monitoring requirements for affected sources. In the final NESH AP, in addition to meeting the emission limits, each affected kiln will be required to install Continuous Emission Monitoring Systems (CEMS) to monitor Hg, THC and HCl, while bag leak detector (BLD) will be required to monitor pe rformance of all baghouses (fabric filters). In the final NSPS, in addition to meeting the emission limits, each kiln affected will be required to install CEMS for NOx and SO2. The cost of these devices can also be found in "Summary of Environmental and Cost Impacts for Final Por tland Cement NESHAP and NSPS", August 6, 2010

. Table 5-3. Control Technology and Co ntrol Efficiency Matrix by Pollutant

Pollutants Control Technologies Hg THC HCl PM SO2 NOx Limestone Wet Scrubbers (LWS) 80% 99% 90% Activated Carbon Injection (ACI) with polishing baghouse 90% 80% 99.9% Regenerative Thermal Oxidizer (RTO) 98%
Dry Lime Injection (DLI) 75% 70%

Low NOx Burner (LNB) + Selective Non-Catalytic Reduction (SNCR) 65% Baghouse with Membrane Bags (BHMB) 99.9% Membrane Bags Retrofit (MBR) 99.9% ACI + LWS 90% 80% 99% 99.9% 90% LWS + RTO 80% 98% 99% 90%



### 5.4 ISIS Runs and Results

EPA’s analysis focuses on the results of the final NESHAP and final NSPS. We also present additional information on different combinations of the regulatory programs to help stakeholders better understand the size and scope of each. The three policies analyzed are:

o Final NSPS only,

o Final NESHAP only, and

o Final NSPS and NESHAP.

Because all existing sources under the NESHAP will be required to come into compliance in 2013, the analysis presented here reflects the impact of the final NESHAP and final NSPS in 2013.5-4 As explained in Chapter 3, one of the key data inputs for the ISIS-cement model is the demand forecast for each demand center. For cement, the demand is a function of gross domestic product (GDP) growth, interest rates, special construction projects (e.g., hi ghways), and public sector construction spending. In 2005, Portland cement demand reached a record high of 128 million metric tons. The economic distress in the US during 2008 and 2009 resulted in a decline in cement demand. The demand for cement compared with 2005 levels declined from nearly 89% in 2007 to less than 60% in 2009 (PCA, 2009). Per PCA, cement demand is not expected to reach past cycli cal peak levels (2005) until 2015 (PCA, 2010). Since multiple years were not modeled, the average de mand for the years 2013 th rough 2018 of PCA’s 2009 Long-Term Cement Consumption Outlook (PCA, 2009 ) was used as the exogenous demand in 2013. This average demand is expected to be more repres entative of what will be typical after implementation of the regulation.

On the domestic supply side, ISIS uses the industry data for all the kilns operating in 2009 (see Figure 5- 1) and optimizes the industry in 2013 taking into account retirements, replacements, and new kilns projected by PCA from 2010 to 2013. For the policy scenarios analyzed here, ISIS simultaneously estimated 1) optimal industry operation to meet dema nd and emission reduction requirements; 2) the suite of control technologies needed to meet the emission lim its; 3) the engineering cost of controls; and 4) the economic impacts of the policies.

In the analysis shown below, EPA assumed that all the projected new kilns shown in Figure 5-1 will be active in 2013. To accommodate for this assumption, in the NSPS policy run, ISIS required the 7 new kilns projected to be subject to the final NSPS to r un in both the reference and policy case. In the NSPS policy case these 7 kilns were required to meet the NSPS limits by installing controls. In the NESHAP and the combination of NSPS and NESHAP, ISIS required the 16 new kilns 8 projected to be subject to the NESHAP limits for new sources to run in both the reference and policy case. In the NESHAP policy case these 16 kilns were required to meet the NESHAP limits for new sources by installing controls. On the other hand, in the NSPS and NESHAP combinati on the 16 kilns projected to be subject to the NESHAP limits for new sources as well as the 7 kilns projected to be subject to the NSPS were required to meet both the NSPS and NESHAP limits for new sources by installing controls.



### 5.4.1 ISIS Results

5.4.1.1 Cement Demand

As mentioned in Chapter 3, in ISIS-cement the U.S. cement industry is organized in state-specific demand centers. The model simulates each cement kiln’s ab ility to compete in each of the demand centers as a function of the kiln’s production cost and transporta tion cost associated with supplying to each demand center. At the same time the cement demand at each demand center can be met by local production, foreign imports, or by inter-market tradi ng (i.e., shipping from other demand center).

The exogenous cement demand used in ISIS for th is analysis was the average demand for 2013 through 2018 or 123.6 million metric tons of cement. Table 5-4 shows the impact of the two policies analyzed on the cement demand in 2013. As shown in Table 5-4, in 2013 the US cement demand could drop 0.65% under the NSPS, 5.70% under the NESHAP, and 6.27 un der the combination of the NSPS and NESHAP.

8 Seven of the 16 new kilns projected to be subject to the NES HAP limits for new sources are also kilns projected to be subject to the NSPS.5-5 Table 5-4 Policy Impact on U.S. Cement Demand in 2013
Cement Demand (metric tons of cement) Policy Run Reference Case Policy Ca se Change in Demand % Change NSPS 123,653,270 122,844,362 -808,907 -0.65 NESHAP 123,653,270 116,608,341 -7,044,929 -5.70 NSPS and NESHAP 123,653,270 115,894,658 7,758,612 -6.27

5.4.1.2 Cement Production

Table 5-5 shows the impact of the policies analyzed on the US cement production in 2013. As shown in Table 5-5, U.S. cement production could drop 1.4% under the NSPS, 9.60% under the NESHAP, and 10.62% under the combination of NSPS and NESHAP.

Table 5-5 Policy Impact on U.S. Cement Production
Cement Production (metric tons of cement) Policy Run Reference Case Policy Ca se Change in Production % Change NSPS 94,036,939 92,680, 715 -1,356,224 -1.4 NESHAP 94,141,619 85,101,425 -9,044,929 -9.60 NSPS and NESHAP 94,141,619 84, 145,488 -9,996,131 -10.62

5.4.1.3 Cement Imports

Table 5-6 shows the impact of the policies analyzed on US cement imports in 2013. As shown in Table 5-6, U.S. cement imports could rise 1.84% under the NSPS, 6.76% under the NESHAP, and 7.58 under the combination of NSPS and NESHAP.

Table 5-6 Policy Impact on U.S. Cement Imports
Cement Imports (metric tons of cement) Policy Run Reference Case Policy Ca se Change in Imports % Change NSPS 29,616,331 30, 163,647 547,316 1.84 NESHAP 29,511,650 31,506,916 1,995,266 6.76 NSPS and NESHAP 29,511,650 31,749,170 2,237,520 7.58

5.4.1.4 Cement Prices

Table 5-7 shows the estimated average national pri ce in 2013 for Portland cement under the policies analyzed. As shown in Table 5-7, cement prices may increase 0.74% under the NSPS, 6.83% under the NESHAP, and 7.58% under the combination of NSPS and NESHAP.

Table 5-7 Policy Impact on U.S. Cement Prices
Cement Price (S) Policy Run Reference Case Policy Ca se Change in Price % Change NSPS 87.77 88.42 0.65 0.745-6 NESHAP 84.81 90.60 5.79 6.83 NSPS and NESHAP 84.81 91.24 6.42 7.58

5.4.1.5 Control Technology Installation

Table 5-8 shows the projected control technology installation in 2013 for the policies analyzed.

Table 5-8 Control Technology Installation

Controls Policy Run LNB + SNCR LWS ACI LWS + ACI MBR RTO LWS + RTO DLI BHMB

NSPS 7 7 - - - - - - - NESHAP 0 30 42 29 6 15 6 1 2 NSPS and NESHAP 7 27 41 22 8 11 12 1 1

Table 5-9 shows control cost associated with the cont rol installation shown in Table 5-8. Table 5-9 also shows the monitoring cost associated wi th the three policy scenarios analyzed.

Table 5-9 Control Cost
Control Cost ($) Policy Run Control Cost Monitoring Total Cost NSPS 32,135,140 1, 386,821 33,521,691 NESHAP 333,536,975 17,236,198 350,773,173 NSPS and NESHAP 325,654,798 16,839,964 342,494,762

5.4.1.6 Cement Capacity

One important feature of the ISIS-cement model is that the cement capacity is dynamically optimized by endogenously installing new capacity, replacing exis ting capacity and retiring non-competitive existing capacity. Table 5-10 shows the cement capac ity in 2013 for the policies analyzed.

Table 5-10 Cement Capacity Capacity (metric tons cement) Policy Run Reference Case Policy Ca se Capacity Change % Change NSPS 94,036,939 92,680, 715 -1,356,224 -1.4 NESHAP 94,141,619 85,101,425 -9,044,929 -9.60 NSPS and NESHAP 94,141,619 84, 145,488 -9,996,131 -10.62

With respect to the reference case in 2013 ISIS identif ied that 1.3 million metric tons of existing capacity may idle under the NSPS, 9.0 million metric tons of existing capacity may idle under NESHAP, while 9.9 million metric tons of existing capacity may idle under the combination of NSPS and NESHAP.5-7 Table 5-11 shows the number of kilns active in each policy analyzed in ISIS. A kiln that is active in ISIScement will run at 100% of its capacity after ta king into account normal downtime days. A kiln is considered to be idling if it was producing cement in the reference case but is not in the policy case. As shown in Table 5-11, 1 kiln may idle under the N SPS, 12 kilns may idle under the NESHAP, and 14 kilns may idle under the combination of NSPS and NESHAP. If kilns owners decide to run their kilns at a lower utilization rate a lower number of kilns will be expected to idle under each policy scenario.

Table 5-11 Kiln Population

Kilns Policy Run Reference Case Polic y Case Kilns Idling % Change NSPS 127 126 1 -0.78 NESHAP 113 101 12 -11.88 NSPS and NESHAP 113 99 14 -12.38

Table 5-12 shows the number of cement facilities that may idle as result of the policies analyzed in ISIS. The changes in cement capacity and kiln population may cause 4 facilities to idle under the NESHAP, and may cause 8 facilities to idle under the combination of NSPS and NESHAP. A facility is considered idling if one or more kilns at the facility were pr oducing cement in the reference case but they are not producing in the policy case.

Table 5-12 Number of Cement Facilities

Facilities Policy Run Reference Case Policy Case Facilities Idling % Change NSPS 86 86 0 0.00 NESHAP 81 77 4 -4.93 NSPS and NESHAP 81 73 8 -9.87

5.4.1.7 Employment Impacts

Table 5-13 shows the total number of employee hours for each policy analyzed. The number of employee hours for each policy is a function of cement producti on. In each policy analyzed, the number of employee hours is proportional to the cement production. The difference between the number of hours in the reference and policy case in each scenario gives an estimate of the change in the number of employee hours due to the policy analyzed. Using PCA’s labor us e data, we can calculate the number of jobs that could potentially be affected by each policy under an alysis. Table 5-14 shows PCA’s labor use data for 2005 and 2008 (PCA 2005, 2008). Table 5-15 shows the estimated impact on employment for each policy under analysis.

Table 5-13 Change in Employee Hours
Employee Hours Policy Run Reference Case Polic y Case Difference % Change NSPS 24,586,999 24,3 85,731 -201,269 -0.82 NESHAP 24,086,384 21,735,357 -2,351,111 -9.76 NSPS and NESHAP 24,086,384 21, 484,727 -2,601,741 -10.805-8 Table 5-14 PCA’s Labor Data: 2005 and 2008

Year Process Labor hours: Direct Number of Employees Number of Direct Hours per Employee Wet Process 3,970,188 1,962 2,024 Dry Process 35,448,534 16,562 2,140 Total 39,418,722 18,524 2,128 2005
Average 19,709,361 9,262 2,082 Wet Process 3,607,766 1,769 2,039 Dry Process 34,767,447 16,504 2,107 Total 38,375,213 18,273 2,100 2008
Average 19,187,607 9,137 2,073

Table 5-15 Employment Impacts
Employment Impacts Policy Run Change in Employee Hours Change in Employment NSPS -201,269 94-97

NESHAP -2,351,111 1,105-1,134

NSPS and NESHAP -2,601,741 1,223-1,255

5.4.1.8 Total Social Cost

In ISIS, total social cost is the cost to comply with the policy under analysis. This cost includes installation of controls and monitoring requirements as well as the cost to society due to the changes in supply, demand, employment and price effects. Table 5-16 shows the change in surplus for each policy analyzed in ISIS. As shown in Table 5-16 the su rplus for the cement industry may fall by $95 million under the NSPS, $520 million under the NESHAP, while it may fall $385 million under the combination of NSPS and NESHAP.

Table 5-16 Total Social Cost Surplus Loss (Million Dollars) Policy Run Reference Case Policy Ca se Change in Surplus % Change NSPS 10,050 9,955 -95 -0.95 NESHAP 9,229 8,808 -421 -4.56 NSPS and NESHAP 9,229 8,844 -385 -4.17

5.4.1.9 Emissions

Table 5-17 shows the estimated emission changes in 2013 for Hg, THC, PM, HCl, SO 2 and NO x for each policy analyzed.5-9 Table 5-17 Emissions

Emissions (tpy) Policy Run Pollutant Reference Case Policy Case Change in Emissions Percent Change PM 7,645 7,561 83 1.09 Hg 5.78 5.32 0.46 7.94 THC 12,207 12,185 22 0.18 HCl 4,611 3,925 686 14.88 NO x 126,751 116,043 10,708 8.45 NSPS

SO 2 122,825 114,481 8,344 6.79

PM 6,349 620 5,729 90.23 Hg 7.25 1.24 6.01 82.91 THC 11,915 1,106 10,809 90.72 HCl 4,434 128 4,307 97.12 NO x 123,372 109,214 14,159 11.48 NESHAP

SO 2 115,163 34,919 80,245 69.68

PM 6,349 622 5,727 90.20 Hg 7.25 1.25 6.00 82.76 THC 11,915 1,060 10,8545 91.10 HCl 4,434 137 4,297 96.91 NO x 123,372 96,154 27,218 22.06 NSPS and NESHAP

SO 2 115,164 35,775 79,389 68.945-10 

### References for Chapter 5

EPA (2010). August 6, 2010. "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" Memorandum prepared for Keith Barnett by RTI. US Environmental Protection Agency.

PCA. December 2005. U.S. and Canadian Labor -Energy Input Survey 2005 . Skokie, IL: PCA’s Economic Research Department. Portland Cement Association

PCA. December 2008. U.S. and Canadian Labor -Energy Input Survey 2008 . Skokie, IL: PCA’s Economic Research Department. Portland Cement Association

PCA. 2009. Forecast Report: Long-Term Cement Consumption Outlook . By Ed Sullivan. October 28, 2009. Portland Cement Association

PCA. 2010. Forecast Report: Capacity Update. By Ed Sullivan. April 28, 2010. Portland Cement Association5-11 

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