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  |Keith Barnett/RTP/USEPA/US                                                                

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  |Cortney_Higgins@omb.eop.gov                                                               

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  |08/05/2010 03:17 PM                                                                       

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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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  |Elineth Torres/RTP/USEPA/US                                                               

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  |Michael Laney <mnl@rti.org>                                                               

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  |Keith Barnett/RTP/USEPA/US@EPA                                                            

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  |08/05/2010 03:09 PM                                                                       

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  |ISIS TSD and ISIS Peer‐review response to comments document for the docket                

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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: 919‐541‐5600
Version 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, 2010 
Version 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 Culligan, US Environmental Protection Agency 

Appendix D Review Comments from Fereidun Feizollahi, CARB Economic Studies Section 

Appendix E Review Comments from Sikander Khan, US Department of Energy 

Appendix F Review Comments from Andy O'Hare, Portland Cement Association (September 

4, 2009) 

Appendix G Review Comments from Tyrone Wilson, Portland Cement Association (March 

29, 2010) 

Appendix H Review Comments from Hendrik van Oss, US Geological Survey 

Appendix I Review Comments from 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 ISIS Peer Review Documentation ............................................2 
Version 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 

H2O 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 NOX 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 Coordinated Air Use Management 

NESHAP National Emission Standards for Hazardous Air Pollutants 

NM New Mexico 

NOX Oxides Of Nitrogen 
Version 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 Development 
Version 1 1 EPA External Review 

1. Introduction 

In the National Academy of Science’s 2004 report, “Air Quality Management in the 

United States,” the National 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 assessments should be conducted in a comprehensive 

and coordinated manner (NAS, 2004). With these 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 identify optimum strategies (considering feasibility, 

costs, and benefits across all pollutant types such as criteria, toxics, and others), while 

streamlining administrative and compliance complexities and reducing conflicting and 

redundant requirements. 

The development of multipollutant and sector-based approaches in managing emissions 

and air quality requires sophisticated analyses of relevant technical and economic factors. 

Such analyses are not possible without a sophisticated modeling framework. 

Accordingly, the Industrial Sectors Integrated 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 underway to build representations of the US 

pulp and paper sector, and the US iron and steel sector. 

EPA requested comments on the ISIS model 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 letter, the ISIS review package included the 

following files: 

• Review documentation file – “ISIS Peer Review Documentation 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 Cement 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 extensive comments being received from Mr. Andy 

O’Hare and from Mr. Tyrone Wilson, both for the PCA. The first set of PCA comments 

(September 4, 2009) were received as part of the Portland Cement 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 given 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 model. 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 “ISIS Peer Review Documentation” 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 market 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 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. 

Response: Regional markets have been replaced with demand centers, corresponding to 

states. Imports are now represented as imports 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 neighboring 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) and import-districts are free to transport 

their production (or imports) to any demand center. 

SK2.1: How are the regions modeled for cement 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 another to control certain 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, there are no issues related to market power. 

In addition, historical demand exceeds capacity.

Instead of treating markets as isolated regions, 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 Regulations for the Portland Cement Industry” 

(APCA, January 1997) regarding choice of appropriate market structure for cement 

manufacturing. 

DB1.4: ...the threat of unfair international 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 districts. Structural imports (from 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 sensitive to transportation 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 markets 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 distance of the market main city to the other 

market main city.

Response: Transportation matrix has been completely revised taking into account 

various possible modes from units to demand centers and from import districts to demand 

centers.

HO2.7: In Figure 2.1, 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 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 grouped with Salt Lake City—its market is 

primarily OR, WA, ID.... 

For example is known that some plants straddle 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 geographic dispersal of its cement than any other plant. Overall, 

there are all sorts of flows 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 transportation by truck, rail, and barge. 
Version 1 6 EPA External Review 

4. AVC 

Comment: A number of comments were received which related to the average variable 

cost function and related costs and escalation factors used in the model

AO2.3: Expand average variable cost (AVC) function 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 repair/maintenance that GDP deflator. 

Labor costs- are substantially low compared to benchmarking analysis- draw earning data 

from BLS consistent with PCA suggestions (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 consumption rather that RTI’s approach. 

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. 

AVC alone is not appropriate for estimating market-clearing prices over the long-run. In 

the long-run all costs are variable 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 show that, of the total increase in costs 

($1.58 billion), all but $0.01 billion is due to control technology. What about increased 

fuel costs? 

Response: The increase in the cost can be attributed to several factors, including cost of 

controls, changes in the fuel-use pattern, distribution of supply 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/documentation 
Version 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 calibrator 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 the choice of using a constant)?...my own limited 

investigations...show mostly level shifts. 

Response: The calibration methodology and resolution have been revised.

SB4.3: Constant escalation factors for fuel prices 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 problem that emissions policy compliance costs 

(taxes, permits) are included in the variable cost calculation...It should 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 scenario, 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 benefits” 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 useful to add an escalation factor for 

construction materials. 

Response: Escalation factor of 5.16% for construction materials was added in Table 2-

10. 
Version 1 8 EPA External Review 

SB2.5:...model’s energy prices escalation factors reconcile with those in AEO 2008 

Table A1..., they disagree with those in Table A-3 (by sector),...more recent projections 

by EIA and IPM have much higher future price increases (2–3% annualized) than AEO 

2008. 

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

SK2.8: Page 2-11, Section 2.2.3.2:...what 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? Does the model estimate some of the construction costs (e.g., 

allowance for funds used for construction),...? 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? 

Response: All costs are represented in constant (2005) dollars. Different factors for 

limestone and gypsum were obtained from historical price data published by USGS. 

SB2.4: 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... 

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 that the assumption that Priceimports = producer 

price is at all valid.......What is the percentage criteria for this designation (importdominated markets)?... 

Response: We calibrated import prices for each demand region to arrive at historical 

import levels supported by data. 

SK5.4: Page 5-6 Table 5-1: The increase in the 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 control cost and the cost of supply 

(production, imports and transportation etc.). Should there be a drop in demand due to 
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demand response, the change in the cost of control 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 clinker or $/ton of air pollutant removed) that 

can easily be understood and compared to the effects of other 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 prices 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 sheets for interest rate, that user could 

change. Also, structure of the imports formulation is coded in the model and cannot be 

easily changed. Further, variable cost components 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 the 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. Transportation 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 which 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 policy horizon. However, we will consider 

incorporation of appropriate backstop technology to establish long-run abatement cost or 

steady-state equilibrium condition 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 capital cost and fixed and variable 

O&M costs. 
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Comment: Several comments were received that 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 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. 

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 choices 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 account 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 fuels....Also, do the percentage data refer 

to tonnages 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,...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 very least, include electricity. Based on the rest 

of your report,...skip the electricity (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 intensity 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. 

Response: These numbers are intensities, i.e., represent mmBtu of fuel used per ton of 

clinker production. 

SK2.5: 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 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, but 
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prescribing a fuel-type might restrict the model’s ability to switch fuels in response to a 

policy. 

HOA.19: Page 35: Again, if I am reading this correctly, 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% CemStar (slag) introduction, then, 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 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 electricity per ton). But 

I do not think that this is the case: the cost (either 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 and other feeds, the slag does NOT need to 

be ground, just coarsely crushed. [the CemStar 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 earlier because it was assumed that you had to grind it like 

everything else, and slag is indeed hard (costly) to grind]. 

Response: Raw material substitution and fuel reduction has been appropriately 

accounted for when CEMstar is applied.
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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 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 uncertainty and errors (by 

state’s surface area, population or some other variable). 

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. 

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 could 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 projections 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 for 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 think they differ, or not? Are there long run 

substitutes for cement? 

Response: The value of -0.88 is a short-run price elasticity of demand estimated by RTI 

as part of econometric modeling done for the Portland Cement NESHAP promulgated in 

1998. Given that there are very few substitutes 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 specified in the 

workbook. 
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SB2.1: The documentation (Section 2.2.3.2) mentions 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 draft calculations for California’s cement 

industry, ARB staff has assumed a 2% annual growth rate in cement demand. Due to the 

economic downturn, staff will be reevaluating 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 completion of this documentation. 

HO2.8: Section 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....PCA demand forecasts...change...at least 

quarterly....it is likely that you have used reports 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 announcements 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 referring to inter-State or inter-market movements 

of domestic cement. 

Response: Since the market-based structure of the model is no longer being used, intermarket trades are not relevant anymore. However, the new structure enables user to track 

transported quantities from each kiln to demand center, and each import district to 

demand center. 

HO3.5: Equation 3.1.4 only holds if the import: total demand ratio is accurate, and I’m 

not sure this is the case.... How are you determining where cement imported at location 

X is being consumed? 

Response: The imports have been reformulated and 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 near term has now likely been revised 

downwards by the PCA. 

Response: Latest demand projections have been incorporated. 
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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 incorporated in the model. Further, users 

can change the demand by going to the Inputs workbook. 

TW: The impact of recession on loss of domestic production in the presence of 

NESHAP is an important and “sensible” result. It shows a drop in domestic production 

and increase in imports. 

Response: The model has been revised to incorporate new demand. 
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6. Supply 

AO2.5 Update for the cancellation or deferral of most of the industry’s previously 

planned capacity expansion projections 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 interpret “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 continuous operations) and is more or less 

unambiguous...except that you don’t know whether 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 rated 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 capacity) * (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 the 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 capacity if there is any mischaracterization of the routine 

maintenance downtime or the daily capacity. The USGS calculates—for the plant 

overall—the capacity utilization wrt. the actual clinker output: we query (question) any 

utilization percentages in excess of 100%. 
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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 precalciner, and whether dust control 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 made 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 information about which plant within a region 

expands, or where new plants are located. A reasonable assumption may be to distribute 

new capacity proportionately at the location of existing facilities. 

Response: As recommended by reviewer, the peer-reviewed version of the ISIS-cement 

model distributes new capacity proportionately at the location of existing facilities.

HO2.11: The phrase “...ISIS endogenous 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 expansion projects can include the retirement of 

existing kilns...”? 

Response: Language changed to describe that capacity growth may lead to retirement of 

kilns. 

DB3.3: Page 3-2....What is the relationship 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 that the total changes in replacement 

capacity confirm the historically observed aggregate values.
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HO3.3: Production and Capacity Changes: the lead-in sentence is a bit unclear: 

“Regional production can be from existing kilns, 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 technological upgrades), new kilns at existing 

plants (perhaps replacing existing kilns), and new plants altogether.” 

It is unclear if your discussion, and projected capacity changes, fully account for plant 

shutdowns.... Have capacities been properly/uniformly defined for all new kilns (are they 

summed on the same basis)? 

Response: Replacement capacity is related to existing 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 occurring during the modeling time horizon. 

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

TW: 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 determining 

regional cement prices. 

Response: While the sunk costs are an important aspect 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 substantial 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 equivalent 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 element 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 control can now be modified in the Inputs 

workbook by going to the worksheet “Controls”. 
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TW: New cement production capacity: Make several changes so the model reflects the 

true costs for new, expanded or replacement capacity 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 options is now a parameter that the user can 

change. 
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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, fuel switching and process changes in the 

model are less expensive that they actually are. Other potential industry’s responses are 

simulated as less expensive than they actually 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 substitution of kilns have been revised. 

DB3.2: Accounting of capital cost is not transparent...It is not apparent whether the 

variable cost in year 10 includes capital 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 solving in annual time steps? 

Response: The annual cost associated with meeting the demand of cement includes the 

annualized capital costs associated with capacity growth (i.e., replacement units, 

expansion units, and new capacity. More information 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 – specifically 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 the cost-function inputs. 

PCA believes that EPA has mis-specified the functional form of the cost equation as well 

as the values of the constants in the equation.

Response: The control cost equations are formulated as a generic equation to encompass 

all different components of cost relevant for various control 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 various cost components relevant for the 

particular control type, to arrive at the control cost. 

TW: PCA is unable to change some inputs either they are not included in the input 

sheets and are part of the model code (written in GAMS which requires specialized 

knowledge to make any changes). Some inputs, while present in the worksheet, were not 

linked to the model. Therefore, making any changes in the worksheet did not impact the 

results. One example of such input is monitoring cost, which appears in the input sheet, 

but any changes to the values do not affect model run and results.
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Response: We have reorganized the information and moved all possible user-inputs to 

the ISIS_Inputs.xls workbook, so user can easily 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 EPA’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 more than that predicted by the EPA run, and 

resulted in a higher drop in consumption. 

Response: The behavior found in PCA’s runs is consistent with the model. If the cost of 

controls is increased, and efficiency reduced, the impacts are a higher drop in 

consumption and an increase in the prices. 

TW: The model results are very sensitive to the cost of controls and it is important to 

reflect and characterize these cost elements accurately. 

Response: Control costs have been revised based on the data received in the peer-review 

process coupled with data gathered by EPA’s engineers thru the Portland Cement 

NESHAP rulemaking. 

TW: Change some of the ancillary elements of control technology costs, including the 

electricity requirements and gypsum credit for wet scrubbers, the fuel requirement for 

RTOs, etc. 

Response: Control costs and electricity costs are user inputs and can be modified by the 

user. Various cost and credits have now been included in the model based on the data 

gathered by EPA’s engineers thru the Portland Cement NESHAP rulemaking. 

TW: Changing the assumptions regarding discount 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 inputs 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. 
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TW: The correct way to assess the impact of policy 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 formation 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 per amount of clinker produced (ACT 2008). 

SB2.3: Uncontrolled NOX emissions intensities vary...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: NOx emission intensities have been revised 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: NOx emission intensity estimates have been revised. 

DB1.5:...NOx “trimming”...The background technical memos discuss it somewhat, but 

it does not appear in the model documentation. 

Response: NOx “trimming” meaning that small changes in combustion temperature are 

used to reduce the formation of heat related 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 biosolids, a potential NOx control option, 

has not been addressed. 
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Response: Key NOx 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 given 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 period, 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 the emissions caps and banking are not 

shown...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 closures, these 

projections may need to be redone. 

Response: The clinker imports and their impact are included.

HO1.13: The text in this area ought to mention CO2. In fact, the whole section is awfully 

brief—but perhaps you could refer the reader to the discussion in the Appendix. 

Response: Discussion of CO2 emissions from cement kilns has been added ending with 

the referral to Appendix A for a more detailed discussion. 

SB1.1: Lacking control technologies for CO2, ISIS Cement is not yet ready to address 

climate policy except as it relates to electricity and import prices....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 product substitution in demand response are sine qua non for modeling 

the cement industry’s response to climate legislation. 

Response: CO2 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 climate related modeling. 

ES2.5: The model seems originally to be aimed at dealing with criteria air pollutants, to 

which now greenhouse gas emission calculations have been added. ... 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 ..... raw material grinding and 

high-efficiency classifiers may improve energy 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 system. The discussion of electricity related 

measures is very limited and focuses on only two items, while leaving out an important 
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one: choice of grinding mill type. ....ISIS model would not work well for modeling the 

impacts of a GHG cap and trade system, unless 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 “negative” 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 CO2 capture options?...incorporation of domestic and 

international (CO2) offsets are going to be really important to include. 

Response: The peer-reviewed version of the ISIS-cement model includes CO2

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 paragraph, it is stated that ISIS model uses 

emission intensities to estimate NOx, SO2, and CO2 emission projections. However, from 

the model input file...; it is not obvious if this has been done with the CO2 emissions. It 

appears that CO2 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 SO2 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 emissions 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 CO2 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 capability to use different coal types... 

Response: Use of different fuel types require detailed cost and emission characteristic of 

the fuels as input. Given this information, the model can use any fuel user wishes to 

include. 

HY2.4: Page 2-10. Table 2-8 Are CO2 reductions total CO2 (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 who at EPA PCA sent the workbook data. 

Response: Numerical values of CO2 emission reductions (intensities) are for fuel use, as 

tires produce slightly less CO2 than coal. Sewage impacts are not included in CO2

emission reduction.
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DBS2.11: Page 2-10, Table 2-8....Is there no (Low NOx burners) penalty in a 

kiln?...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. 

Response: Table 2-8 modified to reflect slight CO2 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 CO2 implications. 

Response: The following footnote was added to Table 2-8: “Tires are made of biomass, 

so there is an incremental CO2 emission benefit. Because of the high heating value of 

tires compared to commonly used fuels, CO2 emissions are lower. Tires produce slightly 

less CO2 than coal.” 

HO2.15: Page 2-8. Your calcination emissions factor of 0.53 t CO2 per ton of clinker is 

slightly too high. Instead of adopting this Andover Technology default...you should use 

the IPCC default (which is what the EPA 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:...SO2 and NOx have lots of data on their emissions factors, but CO2 does not. 

The emissions factors for CO2 (rows 36-38) seem lower than those in EIA’s Table 6-1... 

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

HO5.9: Basically OK. Perhaps you need to again mention here (Summary) why CO2

emissions could drop under SO2-reduction strategies... 

Response: Fuel switching and collateral emission benefits and energy efficiency options 

can result in CO2 emissions.

HO6.1: The Summary seems to lack discussion of CO2—you need to at least mention it 

(and then you can say that your cost modeling was confined to NOx and SOx. 

Response: CO2 emissions are included.

HOA.10: Regarding the use of tires,...I am not sure that I agree that tires yield slightly 

less CO2 than coal: perhaps on a dry weight basis they yield slightly more. Depends on 

how you relate the emissions: per weight of fuel, per ton of clinker... 

Response: Emissions are estimated on basis of energy intensity of the fuel. 
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HOA.17: CemStar: I would add a bullet for the CO2 reduction! (...) What you need to do 

is to compare the cost of making clinker without using CemStar vs the cost to make 

clinker using CemStar. (...) The reduction in CO2 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 CO2 reduction 

(overall, on a per ton clinker or cement basis) will be directly 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 CO2: What is the assumption used in the model with regards to the 

cement kiln dust, which is also a source of CO2 emission? 

Response: Currently the model includes two modes of CO emissions; calcinations 

related emissions and fuel consumption related CO emissions. 

SKA.12: Page 34, Process Modifications: It appears that the performance improvement 

options that may help reduce CO2 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 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. 

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 SO2 emissions has been expanded in the updated ISIS’ 

documentation. 

HO1.12: If you are going to quote the total weight 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 particulate matter (PM) emissions. The 

NEI estimates show that, in 2002, the sector released 36,000 tons of PM10 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 particulate matter (PM) emissions. The 

NEI estimates show that, in 2002, the sector released 36,000 tons of PM10 emissions. 

Same as HO1.12 

SK1.1: 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. 

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 modifications 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 CO2 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 properly accounting for only marginal 

going forward costs (avoidable costs)? 

Response: Control technology application algorithms 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 likely continue beyond the end of the forecast 

horizon being modeled. 
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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 explained 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 inputs to the model are baseline 

uncontrolled emissions at baseline 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 decision made in this node? 

Response: Figure 1-7 has been modified. Production, Technologies and Emission 

Abatement Options addresses applicability, cost 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 such as nonattainment areas and PSD areas 

are described, but there is no indication whether these are represented in the model or 

have been ignored. 

Response: These local constraints such as NAA areas, class I Areas are not represented 

in ISIS at this time. Future version of ISIS will incorporate 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 and 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... 
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Response: Currently transport related emissions are not included in the model. Subject, 

to availability of data, we will consider inclusion of such emissions in a future version of 

the model. In the ISIS model transportation cost matrix allows commodity to be shipped 

from various production centers and import districts to demand centers, and the cost is 

accounted for. 

DB1.1: A potentially valuable improvement in the model capability would be to allow for 

emissions trading programs (or regulatory standards) 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 information about the cost of adjusting production 

schedules or fuels? Also, could the trading 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. 
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8. Imports 

Comments were received requesting enhancement of imports granularity by adding 

terminal capacity and cost characteristics. Also 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 final 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 include latest available data. Regional markets 

have been replaced with the concept of demand centers.

HO1.8: Page 1-7: your discussion of imports is misleading: imports can be by domestic 

producers seeking to feed markets where domestic (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 company 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 going 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) issue 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 custom districts to the map 

Response: Map has been updated to adequately discern localities with more than one 

plant in a cluster and import custom 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) 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 
Version 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 CO2 mitigation measures 

including blending cement. 

SK2.2: The model seems to rely heavily 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 based 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 least; 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 using 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 import 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 basis (USGS monthly and annual reports) and 

Customs Value (USGS annual reports). The CIF value does not represent the actual price 

because it has not yet been offloaded from the ship: 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 Florida and Seattle as 

having high ratios....The PCA may have predicted higher import ratios in the future, but 

they certainly have fallen in 2007-09 and 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. 
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Response: Imports methodology has been revised to account historical data by using the 

elasticity of imports. 

TW: Drop in demand results in drop in imports and production. PCA believes that the 

drop in imports will be more pronounced than 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 the user cannot change it. It is modeled as 

residual supply – and a function of price-ratio. 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 ISIS-Cement behaves in 

the right direction regarding imports. We believe, though, that ISIS needs a 

fundamentally different approach to simulating imports than what exists in the NESHAP 

version of the model. 

Response: The structure of the imports modeling 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 quantities from year to year, etc. 

Response: Import formulation has been revised based on historical data by USGS 

import district. 
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9. Calibration 

AO2.8: 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. 

“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 perspective rather 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. 

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

of uncertainty. 

Perform sensitivity analysis on key assumptions and design choices for the model to 

show their impact on model results (use ranges in the outputs to reflect uncertainty). 

Response: The model has been re-calibrated. Calibration methodology and findings can 

be found in the final documentation of ISIS dated August 2010. ISIS can also be used in 

sensitivity analyses. The user has the ability to change various input parameters and 

explore implications of input parameters in the model results.

DB4.2: Page 4-4, middle...Why is a single calibration constant used to adjust variable 

cost of production? 

Response: At the time of the first version of the model the data for all the components 

that are part of the cost of producing cement 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 has 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 environmental impact of a facility is its stack 

characteristics. These are not mentioned anywhere. What is the stack height? 

Response: Stack parameters are needed for dispersion 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 included 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 updated 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 see much difference between the base case 

and the policy levels. 

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

HO5.7: 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....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 point, but Dragon Cement’s switch to dry 

technology involved shortening the existing kiln tube, not by bringing in a new tube. 

Response: Addressed in the updated ISIS model/documentation.
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HOA.7: While it is true that there is regional variation in the sulfur content 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 S-contribution from the coals: there is a major difference in the S-content of coals (S 

in coal is from kerogens and sulfides 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 and/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: CaSO4 is the formula for the mineral anhydrite. Gypsum has the 

formula: CaSO4·2H2O. 

Response: Addressed in the updated ISIS model/documentation.

HOA.21: Page 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 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 plaster or to anhydrite. 

Response: Addressed in the updated ISIS model/documentation.
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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. median 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, per se, but most involve a considerable 

input of the products of the petrochemical industry in their manufacture. 

Response: Addressed in the updated ISIS model/documentation.

HOA.12: Page 13: Preheaters and precalciners 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 most calciners are designed to 

burn about 60% of the (total kiln line) fuel, not 50%; though perhaps 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 haven’t all the existing preheater kilns 

been converted to preheater-precalciner technology? There are at least 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 limestone 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 relatively modest amounts (they did not burn 

all of it). 

Response: Addressed in the updated ISIS model/documentation.
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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 greenfields project is about right. 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). 

Response: Addressed in the updated ISIS model/documentation.

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

Response: Addressed in the updated ISIS model/documentation.

HOA.29: Page 46: Decision tree: add: consider adding a terminal (instead of a plant); 

consider swap arrangements (to supply cement to customers outside your own plant’s 

“range”.) 

Response: Addressed in the updated ISIS model/documentation.

Comment: The following comments pertain to definitions 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 polbasesubfuel...Are these the same thing? If 

not, what is the difference? 

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

DB3.9: Equation 3.3.2. What is “cp”? 

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

SK3.9: In the CO2 case I ran...ISIS expanded capacity...and built...new capacity and 

seemed to keep all existing capacity,...why would the model build new capacity and not 

use it if it is not retiring older dirtier capacity? 

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

SK3.14: Another useful input would be to be able to manually enter an emissions cap... 

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

SK3.15: K5:M5: don’t understand “miner” 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 updated 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 updated ISIS model/documentation. 

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

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

TW: On the output side, the model does not report loss in consumer and producer 

surplus – which is a very important quantity - 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 working 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 set of outputs that the user 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 model needs substantial improvements (many 

of which EPA has under way) before we can have 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 change various inputs with ease. 

TW: Tracking and understanding of impacts in ISIS with regard to new capacity would 

be greatly facilitated for the user if the ISIS 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 replacement 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 meaningful measure that reflects 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 that directly parallels the total social cost 

measure that OAQPS has traditionally estimated in its RIAs for potential regulations. The 
Version 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 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. 

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 easily modifiable by the user. 

Response: User can now change input data and assumptions. 

TW: Model user should not need to search through 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 describe more fully later in this 

memo. 

Response: Key ISIS outputs are reported in “Aggregate Results” worksheet in 

ISIS_Outputs.xls workbook, and detailed 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 exactly what quantities are being referred to. 

Labels for each table would be helpful, indicating 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 quantity or a present discounted value of a future stream; a cost 

that actually occurs in the year 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 sheets 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 production data published by the PCA are taken 

from USGS data and you should cite the 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. Introduction. 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 authoritative publication. 

Response: Minerals Yearbook data was used Reference 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 Guide), 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 reflect 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 discerned: 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 the 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 correspondent regulatory actions for each 

industrial sector. 

Comment: The following comments pertain to questions about the different 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 annual data includes blended 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 revised. Mineral Yearbook data for cement 

production are for Portland cement (including blended 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 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? 

Response: Gray Portland cement is the most commonly supplied type of cement because 

of the wide variety of applications to which it can be put. Gray cement held a 77% share 

of US consumption in 2005 (from Regulatory 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 relatively 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 couple 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 revised 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 active 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 sited likely used either USGS or PCA data. 

Response: Table 1-1 title changed to: Typical Average Heat Input by Cement Kiln Type 

Commenter is correct, data in Table 1-1 are typical averages. Data in Table 1-1 comes 

from Table 3-3 in the 2007 ACT. In turn, 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 at 
Version 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/ozone/ozonetech/cement_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 table and Table 2-4 is confusing. 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. Data include clinker. These are U.S. Census 

trade data (the USGS just compiles 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...generally 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 NM3

/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. Explain sources in this table and why they diverge. 

Response: Table 2-5 was modified and numbers are 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? Again, switch to metric tons! What is 

the basis of the production data in Table 4-1?.... 

Response: Addressed 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 show a lot of variation; 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. 

Response: Addressed in the updated ISIS model/documentation.

SKA.1: Page 1, Table 1: It needs to be clarified 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 are 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 are expressed with more precision 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 resolve 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 system, 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, this 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 incomplete because it fails to adequately 

discern localities with more than one plant in a cluster. For example: WA has 3 plants (2 

integrated plants @ Seattle and a grinding plant 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 consumption (apparent consumption calculated as 

cement production + cement imports – cement exports – change in cement stocks). We 

exclude the imports of clinker because the production of cement includes cement made in 

the USA from imported clinker. But we do not include changes in clinker 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 show 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 characterization 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 actually 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 model/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. The key assumptions are: Energy intensity of 
Version 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 these 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 historical 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 given in Table 2-4 are for good operation 

practices and do not seem to reflect 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 available 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 hypothetically 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% clinker (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 CO2/ 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 NOx data show a lot of scatter. I would 

note that his observation that the larger kilns 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? 

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 CaCO3. Since all other data quoted in this section is for pure CaCO3, the cost 

of limestone use would be higher than $25.5/ton of SO2 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 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 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/mt; 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 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 policy 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. 

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 average 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: “Assessment of Control 

Options for BART-Eligible Source by NESCAUM, March 2005.” Can this be justified? 

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

12. Editorial Comments 

Comment: The following comments pertain to editorial 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 table (SFC, EGFW) to be consistent 

with the table in the appendix. The EGFW in Nm3

/Kg for dry seems low. Hector will 

share Holcim’s calculation method at 10% oxygen to recalculate the EGFW. 

Response: Acronyms spelled out. 

DB2.9: Page 2-8, Table 2-6. The colors do not appear 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 H2O 

production. 

Response: H2O is product of combustion of fuels. 

HY2.3: Page 2-9. Table 2-7 Label symmetrically lbmolesH2O/MMBtu, H2O 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 pertain to the spreadsheet.

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

Response: To be addressed in the updated 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. feedstock) appears...” Does this mean to say: 

“location and the associated feedstock (fuel type) appears...”? 

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

HO2.10: Page 2-5: Cement plants can certainly be older than 50 years!.... 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”. 

Response: Language changed to reflect that plants 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 years just “coke”, then we attempted to differentiate petcoke 

from metallurgical coke (i.e., from coal) without much success (not sure the respondents 
Version 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 metallurgical 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 kilns 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 CO2 reduction case but no allowance price was calculated for CO2 in the 

Output spreadsheet – I think this 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” status 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 significant-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 Reviewers 
Version 1 A-2 EPA External Review 
Version 1 A-3 EPA External Review 
Version 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 performance. The accomplishments in this model are 

substantial. To be constructive and useful, however, I focus my comments on points where there 

appears to be room for improvement. My comments 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 module and in the investment algorithm. Third, in 

some cases the algorithm seems to limit the applicability 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 further 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; however, 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 might be opportunities for improvement. 

The narrative in the documentation is inconsistently written. Chapter 1 is very accessible. Chapter 

3 is more technical, but nonetheless has substantial opportunities for improvement. I give 

suggestions below for reorganization. Moreover, there appears to be somewhat loose or 

inconsistent translation of information in memos 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 the easiest way to help the reader 

understand the model. In many cases I was unsure about 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 are 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 a 
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changing climate. Ozone may become increasingly 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) that 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 implemented within an incentive-based (trading or tax) 

approach to policy through the trigger of a different 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 different 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 version 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 integrated assessment. What is the stack height? Do 

emissions of NOx from a kiln have similar parameters (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 varies across plants and regions. 

In the policy experiment that is illustrated, the 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 dimension to emissions policy is the way that allowances are allocated. 

In a competitive market it should make no difference to product price (absent capital/liquidity 

constraints) whether they are grandfathered or auctioned, 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. However 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 combustion temperature are used to reduce the 

formation of heat related NOx. This leads to slightly 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 somewhat, 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. Emissions would seem to be an output of technology 
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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 production. 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 technology 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 column each term definition is provided as one works 

one’s way down the equation. That is, put definitions 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 emissions 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 the 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 dataset. 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 explicitly 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 region? Are all input costs uniform for all plants 

within a region? 

Further, what can be said about the location of new capital investment is relevant to subsequent 

modeling of emissions impacts because latitude and longitude information 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 proportionately at the location of existing facilities. 

Probabilistically this may represent the future. Whatever may be reasonable, the model narrative 

could provide some guidance for the user about what assumption is recommended. 
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It is clear from the optimization problem that emissions 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 for the newly built capacity (long run marginal cost). 

The proper choice depends on the measure of competitiveness 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 determination of variable cost and product price is critical to this 

endogenous calculation. See also page 3-4, the description 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 included so that one could see 

the level of detail in the calibration. Also, see Table 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 justification 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 regional rents associated with transportation costs, 

presumably these would be captured by the calibration 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 and 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 projections predate economic events of late 2008. 

On page 2-7, bottom paragraph, I had a hard time understanding the algorithm described. I 

interpreted this to say that pyroprocessing captures sulfates, but sulfates are usually not 

considered a gas, and the sentence describes gas 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 sulfates 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, location “appears to have a significant role in SO2 

emissions...” Does the feedstock in reference not include fuel? 
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P.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 think they differ, or not? Are there long run substitutes for cement? 

P. 3-2. It is crucial to indicate when the constraints limiting capacity changes actually bind when 

reporting model results. What is the relationship between the chosen values and historical 

experience? I am concerned about these constraints because if they bind under a strict climate 

policy then one might ask if the inclusion of the constraints and the role of these constraints 

should be considered in sensitivity analysis. If the constraints are binding and 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 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. 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 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. 

Also, the “Production Technologies...” node could have more information in the box. Is 

technology choice a decision made in this node? 

Page 1-9, bottom. Local constraints such as nonattainment 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 optimal 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) appears...” Does this mean to say: “location and the 

associated feedstock (fuel type) appears...”? 

P. 2-8, Table 2-6. The colors do not appear consistent. 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. 
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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 the 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 useful 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 period. But no more than 6% could be replaced in 

a 3-year period, based on the first constraint. 

Bottom. “Note that replacements are associated 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 difference? 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,” which 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...” 
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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 Cement 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 the 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 meeting 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 product substitution in demand response are sine 

qua nons for modeling the cement industry’s response to climate legislation. 

3. The rationale behind the choice of region definitions needs more documentation, 

especially because the regions differ in some cases from those in the EPA (1998) RIA 

cited (e.g., see Attachment 8 of that document). 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 regions. 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 practically, how much investment in monitoring 

infrastructure would kilns currently need to comply with either cap-and-trade or ratebased policies? 
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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, derives 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 correctly, 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 makes ±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 seems 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 Inputs_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 minemouth rather than delivered coal prices), 

they disagree with those in Table A3 (by sector), which for instance has coal rising, not 

falling, in price. Notably, more recent projections 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 world market price or benefit from multinational 

ownership? 

5. Short of process changes like CEMStar, are marginal changes in feedstocks available to 

kilns in the real world? How much difference does this make? 
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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 expected in terms of direction of change and the 

resulting price effects. However, some of the results (e.g., policy production and policy imports) 

did not seem to be influenced by the required reductions, 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 significantly 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 and 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 cement demand. Due to the economic downturn, 

staff will be reevaluating the annual growth rate. 

• Production and control costs and associated escalation 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 imported cements will be considered as part of that 

effort. 
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Appendix E 

Review Comments from Sikander Khan, 

US Department of Energy 

Comments submitted by Sikander Khan included comments 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 minimize 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 needs 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 solid releases of pollutants from industrial sectors. 

This is not a trivial problem. However, the significant 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 the 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 media if the concept for this capability was not 

included in the initial design of the framework. 

An ability to store multiple scenario cases would be good. For example, if you ran two policy 

cases scenarios back to back (e.g., a CO2 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 scenarios 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 detailed 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 (2005) 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? 
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Interregional trade seems like it would be very sensitive to fuel prices – using diesel prices avg. 

1994-2005 may be misleadingly low. 

Are there any ways to add CO2 capture options? This is going to be really important for future 

integration, I think. Also, when modeling CO2 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 controls 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 unrealistic (and overly simplistic to the point of 

being meaningless) that all banked credits are consumed by the end of the forecast period. This 

would only occur in reality if the end of the 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 and 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 allowances would most likely continue beyond the 

end of the forecast horizon being modeled. The capabilities of ISIS to model a realistic scenario 

should be provided in an example. Constraints on conditions at the end of the forecast period 

would be needed to maintain realistic quantities 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 formatting had commas, truncated decimals to one or 

two places. 

• What does “eps” mean? That occurs frequently throughout the output spreadsheet. 
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• I ran a CO2 reduction case (50% 2010-2014, 70% 2015-2025) but no allowance price was 

calculated for CO2 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 CO2 case I ran above, in 2025, ISIS 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, SO2 and NOx have lots of data on their emissions factors, but 

CO2 does not. The emissions factors for CO2 (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 entered 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 confusing. 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 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? 

3. Page 2-7, Section 2.2.1.8: In the first paragraph, it is stated that ISIS model uses emission 

intensities to estimate NOx, SO2, and CO2 emission projections. However, from the model 

input file (Spreadsheets “Units” and “Emissions”); it is not obvious if this has been done with 

the CO2 emissions. It appears that CO2 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 SO2 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 CO2 emission rates for PRB and bituminous 

coals, as estimated by J. Staudt of Andover Technology Partners in Appendix A. However, in 

the model input file, only one type of coal (bituminous) is used. It is assumed that the model 

has the capability to use different coal types, 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 case 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 combined 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 $/ton 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 sources, such as raw material dryers, auxiliary 

boilers, etc. These non-kiln sources can add significant CO2 emissions. 

• 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. 
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• Page 6, Total CO2: What is the assumption used in the model with regards to the cement 

kiln dust, which is also a source of CO2 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 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. 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 CO2, 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 policy 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 presented in another EPA document: “Assessment of 

Control Options for BART-Eligible Source by NESCAUM, March 2005.” Can this be 

justified? 

• Page 16, Gas Treatment: Injection of biosolids, a potential NOx control option, has not 

been addressed. 

• Page 26, Limestone Variable Costs: The cost of $15/ton is for limestone, which is not 

pure CaCO3. Since all other data quoted in this section is for pure CaCO3, the cost of 

limestone use would be higher than $25.5/ton of SO2 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-last 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 system, 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. 
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, this may be a more viable option. 

• Page 34, Process Modifications: It appears that the performance improvement options 

that may help reduce CO2 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 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? 
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Appendix F 

Review Comments from Andy O'Hare, 

Portland Cement Association (September 4, 2009) 

The review and comments of the PCA (39 pages) are summarized below within the following eight 

categories: market structure, transportation, average variable cost, demand projection, supply, cost of 

installing controls/conversions, imports, and calibration-and-validation. Complete comments are given 

after the summary of the eight categories. 

Market Structure 

1. Get rid of the market structure or if market 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. 

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 markets 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 simulate 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 the market main city to the other market main 

city. 
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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 insurance, 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 consumption, equivalent to cement usage. Agency’s 

“demand” terminology is misleading. EPA should refer more accurately to cement consumption 

rather that cement demand. 

2. 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. 

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 uncertainty 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 years 
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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 (other 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 shipping within a region and to terminals, property 

taxes, insurance). Adopt a long-run perspective rather 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 and design choices for the model to show their 

impact on model results (use ranges in the outputs to reflect uncertainty) 
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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 impossible 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 (model documentation exists largely for to the 

“peer review” version of the model, which addresses 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 contrast to the projections in the model currently that do 

not. 

We look forward to providing more complete comments 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 and its deficiencies so pervasive 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 and 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. 
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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 regulations 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 rather 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 assuming 

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 can 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, overlapping 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 markets, ISIS could instead model each plant’s 

ability to ship to any of scores of cement “demand centers”. 

• EPA’s “calibration” procedure that is intended to compensate for potentially inaccurate or 

missing cost elements and incomplete model structure 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 analysis as an alternative to ISIS in 

estimating the economic impacts of the proposed NESHAP.
Version 1 F-6 EPA External Review 

• ISIS must be adequately validated 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 also 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 points 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. There 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 outdated. 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 equipment 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 constants” 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 needs 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 emission 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. 
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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-assigned. For example, the Ash Grove Cement plant in 

Leamington, Utah actually serves the Salt Lake City 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 assigns: 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 distance for effective competition) 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 competition that in reality occurs among these plants. In fact, many cement 
Version 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 comments 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 same 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 reiterate 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 important 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 competition 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 incorrect 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 about 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 Analysis 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.
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These are important steps toward increasing the 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 compete 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 significant cost to ship its cement over the border 

into the adjoining Cincinnati market. The transportation 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 competitive 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 geographic 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 estimate 
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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 the 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 these problems we have been discussing – misassignment of plants, inaccurate simulation of a plant’s competitive options, re-shuffling data – 

by eliminating from ISIS the concept of assigning plants to markets. ISIS should instead be 

structured around the existence of numerous “cement demand centers”, and the model would 

simulate each cement plant’s ability to compete in each of the demand centers as a function of 

the plant’s actual location and transportation options/distance to each demand center. The set of 

“demand centers” should be structured in a manner to reflect both the geographic areas where 

there are significant concentrations of cement consumption and the geographic distinctions that 

are employed by the organizations that generate the key cement data sets needed for modeling – 

perhaps some combination of SMSAs and States. 

EPA staff has mentioned to us this possibility of re-orienting ISIS around “cement demand 

centers” rather than assignment of plants to markets. We would strongly support such a 

reorientation. There appears to be no reason mathematically why ISIS could not simulate many 

more markets or demand centers (perhaps something like the top 50 SMSAs plus remaining 

portions of States) and assign an appropriate shipping cost for each plant to reach each potential 

market/demand center. The optimization solver that ISIS uses could handle 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 transportation costs for reaching each of the plausible demand centers it 

could serve. 

PCA would be pleased to participate with EPA in devising this “demand center” approach. 

Attachment A provides one sort of helpful data compilation that PCA could provide in 

implementing this approach – a summary of cement 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 manner. For example, the revised model could include 

bulk shipping from plants to terminals and shipping options via water and rail in addition to 

truck. Later in this paper we will provide further 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 should be willing to produce 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 and fully estimate the AVC.4 However, even if AVC 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 2030 in ISIS-cement more generally. In the long run, all costs are ariable, 

including fixed costs. No investor would decide to replace equipment, expand capacity, build a 

new plant, or invest in long-lived pollution control measures without the expectation that prices 

would be sufficient and sustainable to cover not only variable costs, but also fixed costs5 and to 

provide in addition an adequate return on capital. In fact, the ISIS documentation 

acknowledges that actual prices do indeed systematically 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 EPA refers to as the producer’s margin (i.e., 

reflecting all appropriate costs and the impact on price of the balance 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 fudge factor multiplier raises the calculated AVC to a figure 

closer to observed actual price. The ISIS documentation refers to this application of what really 

is a fudge factor as a “calibration” of the model to address any issues regarding the “accuracy of 

the model’s technical structure” and the “quality of the input data.” 

There is no acceptable rationale for the manner in which EPA develops and applies a fudge 

factor to compensate for the discrepancy between 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 actual observed values in 

2003. EPA claims to validate the estimated fudge factor by applying it to data for 2004 and 

determining for that year also that predicted regional prices using the fudge factor seem 

reasonably close to actual regional prices. 

We disagree that EPA’s predicted prices for either 2003 or 2004 are acceptably close to actual 

prices. The average difference for 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 potentially serving the market (e.g., imports, existing kilns in nearby markets 

with transport costs added to their AVC, new kilns, expansion kilns, replacement kilns) and they also are rankordered by their AVC; 3) the least expensive sources are selected first to fulfill regional demand; and 4) the AVC for 

the marginal source – the source that supplies 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, insurance, 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 changes from 2003 to 2004 average much less than 8.5% across the 

regions. In other words, EPA would do better in predicting 2004 regional 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 predicting regional prices (i.e., typically much less 

than 8.5%). The noise inherent in inaccurately estimating regional prices is likely to 

obscure the signal that EPA 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 estimating 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 accurate substitute for developing an adequate model structure and 

complete data, especially when projections are to be made over a long time horizon. 

As appropriate to match the time period over which the model aims to simulate cement 

producers’ decisions, EPA should adopt a long-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 Explicitly Specify Uncertainty 

A complex model such as ISIS that is intended to offer predictions across 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 during the future period that will be forecasted. 

ISIS as it exists currently meets none of these goals. The model 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 industry; years that are 

among the best ever. The time period to which ISIS is intended to predict, however, includes 

some of what are likely to be among the worst years in recent history 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 between 2015 and 2020.5

 The period to which ISIS is being asked to 

predict could hardly be more different than the 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 regarding regulations. Importantly, an 

adequate validation procedure also has the potential for generating insights into how ISIS can be 

improved to provide more reliable outputs. 

For all of the components and outputs of ISIS for which 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 calibrated to provide outputs that 

adequately reflect actual observations 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 the 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 the ISIS outputs over the modeling 

horizon, the validation should include careful evaluation of the ability of the model to 

appropriately handle economic cycles. In particular, the model should have the capability to 

allow EPA to evaluate the 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 impacts over the course of the modeling horizon should be carefully 

evaluated.Finally, a comprehensive validation procedure would test the robustness of the 

model by performing sensitivity analysis 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 these lines, it is not realistic to display point 

estimates for the outputs of a complex model, especially a model with such a long forecasting 

time horizon. One thing about which we can be certain is that the outputs will have a degree of 

uncertainty associated with them. The extent of this uncertainty should be explicitly reflected in 

the outputs, such as through the use of ranges. Such ranges can be established through the use of 

sensitivity analysis, scenarios, or more sophisticated modeling techniques (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 components are determined exogenously as a function of each kiln’s 

characteristics and prices for key inputs, which vary by region and escalate over time. The fifth 

of these components – a kiln’s fuel cost – is determined 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’ average variable costs as defined in this manner serve in ISIS to 

determine the market-clearing price in each regional market: 

• The kilns serving the market are rank-ordered 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-ordered by their AVC 

• The least expensive sources 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 the 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 the 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 reflected 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 facilities to obtain a wide range of financial 

information, including costs, income statements, balance sheets, financial ratios, etc.. The 

information is collected on a confidential basis: data obtained for an individual facility or 

company cannot be released, and statistics are published only on an aggregated 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 (some 
Version 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 Benchmarking survey were responsible 

for more than 95% of all the cement shipped in the U.S. 

PCA Benchmarking Report 

(2007 Data – Corporate Cement Division, Industry Aggregate Results) 

This table gives an indication about the categories 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 “other cost of goods sold”, to the extent that they exceed raw 

materials costs. Further categories contribute to the difference between production costs and 

sales price, including depreciation (a book-keeping charge rather than an actual 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 indicated previously, EPA attempts to make up for 

errors and omissions in estimating costs by “calibrating” 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 + maintenance and repair cost) serves to bring modeled 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 objections to EPA’s use of a “calibration constant” (really a “fudge factor” in 

colloquial terms) in this manner: 
Version 1 F-19 EPA External Review 

• There is no acceptable rationale for adjusting 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 are 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 adjustment, 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 “calibration” 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 occurring due to the proposed NESHAP. 

In short, EPA’s “calibration” procedure has little 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 improving this important part of ISIS. The aim of 

each approach would be to develop data with which EPA could more completely simulate all the 

costs that a cement producer needs to cover in the 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. This 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 individual plants or companies. Perhaps regional aggregations 

could be provide, perhaps aggregations by type 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 for various aggregations of kilns. This data would replace 

rather than supplement EPA’s AVC estimates. Or, alternatively, we could develop data 

to replace some portions of EPA’s AVC estimates. We could, for example, develop data 

to replace all cost components but kiln energy costs, which we assume EPA would 

continue to want to develop endogenously within ISIS. 

• Or, we could have the Benchmarking contractor run a correlation analysis between the 

Benchmarking data on costs by plant against EPA’s simulated AVC for the same plants. 

We anticipate that this analysis would indicate the 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 could 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 significantly 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 potential data source would be of no 

assistance. We have no significant objection to the steps by which kiln-by-kiln raw materials 

costs are estimated in ISIS, including the estimates to the effect that: 

• 1.67 metric tons of raw materials are needed 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 Yearbook, 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 added to clinker in producing 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 similar to the figure given in the Benchmarking study of $9.40 per 

short ton for “Plant Maintenance” (industry aggregate for 2007). This Benchmarking is probably 

somewhat low, as some maintenance materials may be included in another cost category, “Total 

Purchased Materials Costs”. 

We question whether EPA might escalate 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 maintenance and repair costs instead 

as “operation and maintenance costs”. We assume this represents an error 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 maintenance costs as one of the five components of 
Version 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 fixed annual cost that varies with kiln size. The 

other four of EPA’s components of AVC (e.g., raw materials costs) do vary with plant output. 

Maintenance and repair costs likely represent 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 kiln’s labor costs is reasonable in concept. 

As a first step information on the number of labor hours required per metric ton of equivalent 

capacity (85% clinker, 15% finished 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 the Bureau of Labor Statistics. EPA 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 weighted 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 administrative personnel) are substantially higher 

than the EPA’s $5.75 weighted average cost per ton. Non-plant labor costs, such as sales, 

general management and non-plant level administration 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 Payroll Expense $ 3.20 

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

Line Item $ / Ton 

Total Plant Labor $ 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 Plant Employees.” As these 

numbers are similar, EPA may have drawn from BLS a wage rate 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 not the value of benefits and employer-paid 

taxes. As per the Benchmarking report, plant payroll taxes and plant employee fringe 

benefits add $0.90 and $1.50 respectively to the amount of workers’ earnings, while nonplant payroll Taxes + Fringe Benefits add $0.80. In total, benefits per the PCA report add 

$3.20. 

3. Earnings figures representing industries other than cement. EPA may have used average 

hourly earnings figures from BLS that represent employees in industries other than or in 

addition to cement. 
Version 1 F-24 EPA External Review 

We expect that EPA would arrive at labor costs for kilns that are much closer to the 

Benchmarking data if the Agency draws earnings data from BLS consistent with these 

suggestions. 

Electricity costs

A kiln’s electricity costs are simulated as follows: 

• Average figures for electricity consumption per metric ton of equivalent capacity (92% 

clinker, 8% finished cement) for kilns of various 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 estimates seem a little high compared with figures 

from PCA’s Benchmarking studies: $6.60 per ton on average for 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 RTI memorandum providing backup for the AVC 

calculations, a kiln’s fuel input rate is affected 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 Dement Kiln Cost Functions (2005)”. August 31, 2007. 
Version 1 F-25 EPA External Review 

This approach ultimately results in fuel costs ranging from about $4 to about $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 described 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, figures 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 consumption appears reasonably close to the Labor-Energy 

Survey figures. The first set of estimates gives fuel consumption substantially lower than the 

Labor-Energy Survey figures for kilns of less than 20-30 years 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 $7.18 to $8.33 per MMBtu 

We have compared the results of EPA’s fuel cost 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 cement 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 could go to 100% petroleum coke 

or residual oil, though roughly 15 kilns are projected as doing so 
Version 1 F-26 EPA External Review 

• Some plants are simulated as having unrealistically 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-kiln fuel costs. We are 

willing to work with EPA, perhaps providing information from 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 improve the data and procedures 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 this table is actually intended to mean “repair 

and maintenance”. This is an instance, we guess, 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 knows less about. It will also give more weight in 

the future to the two elements of operating costs to which EPA applies the 1.9 “calibration 

factor.” 

The projections for declining real 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 escalation factors for years subsequent to 2008 
Version 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 prices in ISIS that EPA may want to run the 

projections off of the most recent year for which 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 simulates the response of cement markets to the 

proposed regulation for each year from 2005 (the base year for the model) through 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 consumption 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 Consumption 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 the current economic recession 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 reflect the large impact of the recession. The current 

projections used in ISIS do not do so. For example, the following table shows total national 

cement consumption as currently assumed in ISIS 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 consumption 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 strongly 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 rather 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 referred 

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.
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By relying on outdated cement consumption projections, 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 production economics, the model further misses the 

disastrous market conditions that the industry will subsequently face all the way through the 

period of NESHAP implementation and compliance. The estimates of regulatory impacts in 

future years that are developed in ISIS cannot approach being credible without this problem 

being fixed. 

We see several additional problems in the manner by which EPA translates PCA’s cement 

consumption projections (to the year 2030) into baseline cement consumption figures as needed 

for ISIS. 

First, PCA provides consumption figures by State, but ISIS, because of the regional market 

structure that EPA has adopted, needs figures broken out by ISIS’ 20 markets. To the extent that 

EPA’s market boundaries do not follow State boundaries, EPA must do some sort of adjustment 

in order to use PCA’s figures. EPA has chosen to crosswalk geographically from PCA State 

figures to EPA market figures based somehow on the percentage of each State located within the 

geographic boundaries 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 = PCA’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 unknowable 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 cement consumption projections broken out by 

Metropolitan Statistical Areas (e.g., Oklahoma City, Albany-Schenectady-Troy, etc.) as well as 

broken out by State. If EPA were to move toward the “cement demand 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. 
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Another problem in how ISIS currently uses PCA’s cement consumption 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 fitting 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 variations in consumption over time that PCA believes 

are important. EPA’s regional regressions consistently overestimate consumption in 2010, not 

catching even the small projected slump due to the recession that PCA projected in the 1/31/08 

figures that EPA uses. EPA’s total national projected consumption in 2010 after applying the 

curve-fitting procedure is 141.84 million short tons vs. the PCA figure of 138.31 that EPA used 

as input, a 2.6% overestimate by EPA. Other years 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 with, in 2020 EPA is 

0.3% above PCA’s estimate, in 2025 EPA is 1.3% too high, etc.. In order to get year-by-year 

figures, why does EPA not simply interpolate within the figures provided by PCA at 5-year 

intervals? Alternatively, if EPA wishes, PCA can develop and provide projections for future 

cement consumption that have annual time steps 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 important in assessing the impact of the proposed NESHAP to 

model the cement industry and cement markets not as they might be if the economic 

“good times” of 2005 were to continue into the future, 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. 
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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 process of capacity growth and renewal in an industry. Replacing 

older, inefficient and generally more highly polluting 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 expansion will occur. Thus, in the 

engineering cost analysis for the Portland Cement NESHAP, EPA assumes that in the baseline in 

2013 (the first year of full compliance), all kilns existing as of 2005 will continue to produce 

without any changes, and an additional 20 new kilns will have come on line. 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. The ability of ISIS (at least in theory) to simulate this process rather 

than having to assume it is a major advantage for 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 underestimates 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/replacements are addressed in EPA’s 

background documentation for ISIS, only 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 annual cost associated with the corresponding existing 

capacity.” (Industrial Sector Integrated Solutions Model, December 23, 2008, page 3-2). We 

cannot find further documentation on exactly how this comparison is made or on what is 

included in “fixed and variable O&M costs.” Annualized capital costs for incremental capacity 
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are figured as capital costs multiplied by a capital recovery factor based on 25 years of plant 

useful life and a 7% real discount rate. EPA staff have indicated that new capacity in a region is 
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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 occur 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-wired into the model. Capacity 

additions subsequent to the year 2010 are determined endogenously 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 Proposed 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 capacity increasing. (Note that these ISIS runs are based 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 decline from one year to the next (e.g., in the 

base case, new capacity is 4.57 million tons in 2015, but then declines 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 capacity 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 for any sort of new capacity (replacement, expansion or 

greenfield) will be simulated as unreasonably low, if only due to EPA’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, particularly during the 

current tight credit conditions and depressed 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 assumed currently for its 

ISIS-cement runs. We are aware that OMB recommends use of 3% and 7% per year as 

real discount rates, but these recommendations are for discounting of social costs and 

benefits. ISIS in this instance is not discounting social costs or benefits, but instead is 

attempting to simulate capital investment decisions to be made by cement producers. In 

order to simulate producers’ decisions accurately, ISIS must choose a discount rate that 

accurately reflects the rate that producers will actually apply. Note that we believe this 

high real discount rate applicable to cement industry capital 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 that a cement producer will invest in new capacity only if 

he will recover all the costs of operating that new capacity over the plant’s projected life, 

assuming an appropriate discount 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 corporate overhead (corporate 

SG&A, legal, environmental), plus recovery of the capital costs. 

• The most recent PCA assessment of planned industry capacity changes suggests that 

some of the previously planned expansions/replacements/new capacity from now through 

2010 that are included in ISIS have been abandoned are now unlikely to occur. PCA will 

provide EPA with our latest projections regarding capacity changes over the next few 

years, and the ISIS inputs should be changed accordingly. See the discussion elsewhere 

in these PCA comments on “Impact of NESHAP Emission Changes on Domestic Cement 

Capacity” for further information on planned industry capacity changes over the next 

several years. 

EPA’s capital cost functions 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 four 
Version 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 applicable for a 300,000 tpy expanded kiln. 

Costs for expanded kilns 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. EPA staff have stated in conversation 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 per ton per year of annual clinker capacity 

o Again, there is no written 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 exponent 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 production capacity, and then comparing 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 analysis 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. Information on the size and projected cost of such projects has been 

compiled in the table below. Please note that these 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 shown for a project is intended to 

match the size that is shown for the project (e.g., it is possible that the cost figure shown for a 

project derives from one source while the size figure derives from another source, perhaps with a 

change in project scope having occurred between the times when the cost and size estimates 

were developed). 

Shown below are the size and estimated cost for 23 cement production capacity projects. In the 

next-to-rightmost column, the raw cost per ton of capacity for each project is scaled using the 0.6 
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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 EPA’s benchmark. Thus, for example, for the very large 4.8 million 
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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 premised on a 1.2 million ton per year plant. The St. Genevieve 

figure of $138 per ton of capacity applies for a 4.8 million ton per year plant; scaling this raw 

cost per ton to a 1.2 million ton per year plant would 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, TX 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 suggest 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 estimate 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 

Newberry, 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)

Raw Cost

per 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

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

Oro Grande, CA TXI 2008 358 1.0 358

Cost per Ton 

(Industry)

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 93 
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• 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 replacements cost 4 

to 8 times more than EPA’s estimated costs per 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 conversions, as EPA thinks of them, at all. In most of these 

cases the existing old kiln is not being converted to a preheater/precalciner, 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 existing wet kilns might be sized at 0.5 million tpy, the 

median among the wet kiln conversion/replacements shown above. 

Expansion/replacement for other sorts of kilns 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 above 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 above covers 23 projects, all for which we have 

information. We would be willing, if EPA is interested in working with this information, 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 nature of each of the 

conversion/replacement projects. 

Constraints in ISIS on selection of capacity changes

EPA includes the following statement in the ISIS documentation: 

“ISIS framework also assumes that: (1) at the start 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 capacity 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 changes that can occur in ISIS may be important. 

It is not clear to us exactly what the preceding statement means and what these limits are. It is 

also not clear which of these constraints, if any, end up as binding in the ISIS model runs. 
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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. 
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This is but one example of a general comment: we hope that EPA will provide thorough and 

clear documentation for ISIS, written primarily in understandable text rather than programming 

notation and equations. 
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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 domestic sources of supply are 

insufficient. This sort of imports had accounted for well above half of all imports in 

recent years through 2006, but has declined rapidly beginning in 2007 as U.S. 

construction activity fell and cement plant capacity utilization began to drop. This class 

of imports might be viewed as a sort of “swing 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 independent importers in order to make up 

for persistent shortfalls in domestic supply to some particular markets 

• Additional imports are arranged opportunistically 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 degrees on instances when there 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 issues in ISIS by treating the price of 

imports not as a variable that is simulated in the model, but instead as a variable that are set in 

the model to take on a value that makes the remainder of the model match observed data for the 

period over which the model is calibrated. Said perhaps more clearly, values are assigned for 

import price in each regional market that make the 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 equal to the AVC of the highest 

cost kiln that is located in the particular regional market. The import price ratio (ratio of 

import price to production price for highest cost local kiln) 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 that results in 

domestic production in the market and imports to the market matching actual data for the 

calibration year as closely as possible. 
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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 determined in this manner are then used in running the model for 

future years. 

In addition to establishing these 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 examined and it was determined that the highest 

ratio of imports to total national cement consumption was 0.27. EPA decided that imports in 

future modeled years could account for no greater percentage of regional cement consumption 

than this historical maximum percentage figure (27%) plus an additional 20% more. In ISIS 

modeling, then, imports are constrained in each regional cement market to provide no more than 

32.4% of consumption in any year. 

The supply function in ISIS for imports 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 imports 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 sound theoretical basis for presuming such a supply 

function for imports. This postulated supply function has nothing at all to do with the factors 

that determine the actual supply function in reality: the local balance 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 mathematical relationships that yielded predicted 

values for domestic production, imports and price in each regional market in 2004 (the 

calibration year) that matched actual values for these quantities as closely as possible. Absent 

any theoretical justification for such an import supply function, there is no reason to believe it 

will apply when ISIS simulates regional cement markets in future years. 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 observed 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 believe that the model needs to be tested 

(validated) for these additional years. In fact, 2007 and 2008 provide a particularly important 

test for the model, since 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 and 2008, then we suggest that the model will either need to 

be recalibrated (i.e., calibrate across multiple years from 2004 through 2008 rather than for 2004 

alone) or the approach to simulating an import supply function will need to be changed. 
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Our bottom line in evaluating how ISIS simulates imports is thus a judgment that no evaluation 

can be provided until the model is tested for additional years. There is no basis in theory for the 
Version 1 F-45 EPA External Review 

import supply functions postulated in ISIS. A set of supply functions has been devised that 

“worked” for 2004. There 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 section, we offer several additional comments about simulation of 

imports in ISIS: 

• EPA makes some errors in crosswalking the USGS data on imports by Customs District to 

the regional cement markets in which the imports are eventually consumed. For 

example, a substantial share of imports to the New Orleans District goes far up rivers to 

markets other than Birmingham or Dallas. We recommend 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 clear to us that any region should be specified 

as “no import”. 

• EPA staff have verbally expressed some interest in perhaps modeling the impact of 

import terminals. This could perhaps 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 are 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 imports. Modeling ocean shipping rates would be far more 

important (and difficult). 
Version 1 F-46 EPA External Review 

Cement Transportation Costs 

ISIS models the possibility of shipping cement from kilns located 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 another 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 transportation only. Costs provided in this reference were 

escalated from 1994 to 2005 based on changes in the 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 transportation cost is unaffected by 

where the kiln is located within the market. 

This approach is not realistic in various respects. In simulating the transportation of cement, 

ISIS should reflect the impact of terminals and 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 from 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 plant or terminal to customer amounted to 5.1% 

of total cement sales revenues. For 2008, reflecting higher energy prices, the comparable 

figures were 6.6% to terminals 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 information on costs for truck shipment, serving as 

possible supplemental references in addition to the source cited by EPA. 
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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 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 cement specific transportation costs, assumes short tons--converted to metric 

• 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, round 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 minimum, ISIS should include in the AVC for each kiln some cost for 

shipping within the kiln’s market area, in addition to costs for shipping out of the kiln’s market 

area. Ideally ISIS would simulate each cement plant’s shipping options to all demand centers the 

plant might reasonably reach. This should reflect the terminals 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 sent. PCA would be willing to assist EPA in listing and modeling 

these terminals and networks. ISIS should also simulate the bulk shipping options available to 

each plant (water and rail) in addition to developing improved cost estimates for truck shipping. 

Regarding bulk shipping, note that USGS collects 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 preserve confidentiality. Obtaining such information 

would assist substantially in improving the accuracy with which ISIS 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 

BTS1 WDOT2 NACT3

33.06 7.64 11.02 7.18 

2.78 3.32 5.51 

0.89 1.05 2.20 
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Emissions and Cost and Effectiveness of Control Measures 

ISIS simulates in a single model decisions involving both producing cement and 

controlling emissions. Most of our commentary 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 identical information on baseline emissions 

and on control cost and effectiveness as that which EPA has used for the Agency’s 

traditional engineering cost analysis of the proposed 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 control. 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. 
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Appendix G 

Review Comments from Tyrone Wilson, 

Portland Cement Association (March 29, 2010) 
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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 – Further Comments on the ISIS-Cement 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 Cement NESHAP regulation. Specifically, this 

general comment document further expands on issues PCA has identified regarding EPA’s 

NESHAP version of the ISIS-Cement model. Both the September 2009 comments on ISIS and 

these additional and more general comments further 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 response 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 materials, forms concrete. Cement is produced from various 

naturally abundant raw materials, including limestone, shale, clay, and silica sand. Portland 

cement is an essential construction material and a basic component of our nation’s infrastructure. 

It is utilized in numerous markets, including the construction 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 model and see how it responds to 

revised inputs. ISIS-Cement is a large and very complex model. Any such model with 

March 29, 2010 
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numerous inter-relating elements and modules is impossible to understand thoroughly without 

actually being able to run it and see how its outputs respond to changes 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 for 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 replacement capacity rather than unrealistically low costs as 

originally assumed. 

While we provided comments in September suggesting many more changes 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) techniques, that the proposed 

NESHAP would result in the year 2013 in annualized emission control expenditures 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 was to generate our own estimates of the impacts of the proposed 

NESHAP and to compare them against the estimates that EPA had developed in 2009. 

In fact, though, we have not been 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 completed 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 appropriate. Many of the changes that PCA would 

like to make to the model are simply not achievable 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-running 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 operational 
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in the sense that ISIS has not been debugged sufficiently 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 conclusions, largely because we have been able to 

implement few of the changes to input data, assumptions and structure that 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 from 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 these results as indicating only the directions in 

which ISIS will likely respond when it is improved, and not the 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 have 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 report extensively on the problems that we have 

encountered in working with ISIS. We emphasize in this memo not the substantive results that 

we have obtained in our new runs (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 that these observations will be useful to EPA as the Agency 

works to further improve the model. 

This memorandum contains three sections. In the first 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 implement 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 Change ISIS as We Wish and Then Re-run it to get More 

Accurate Results 

We want to apologize at this point for the extended discussion that will follow 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 “user 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. 
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There are four major reasons why we have not been able to: 1) run 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 opinion) the results that EPA generated in 2009; 

and 3) compare our new results against EPA’s previous ISIS results. 

1. Some of our desired changes would require substantially 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 simulating cement kilns as being 

able to serve multiple demand centers instead of being located in discrete markets are two 

changes that we would like to make, but doing so would require rewriting large portions of the 

model code and then debugging and recalibrating the entire model. Making 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 structure 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 version of ISIS-Cement, but nevertheless have proven to require 

an excessive amount of work to program into the model. Most notably in this vein, we would 

like to change the cost equations for air pollution control equipment in ISIS so as to reflect the 

control cost information that PCA submitted in comments in September, 2009. (PCA provided 

information that suggests significantly higher capital 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 format 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 reflect 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 information PCA has submitted, and summarize the ISIS runs that we have 

been able to conduct using improved 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 functions in ISIS to reflect the information from PCA 

and then to re-run the model. 

ISIS requires the function estimating the capital cost of a limestone wet scrubber for a kiln to be 

written in the following form: 

Capital cost of LWS = A x (the kiln’s DSCFM)B
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Where A and B are constants and the kiln’s dry gas flow (DSCFM) is a particular function of the 

kiln’s type (e.g., wet, long dry), clinker capacity in tons per year, EGFW (wet or actual exhaust 

gas flow rate), and SFC (specific fuel consumption, in million Btu per ton of clinker). In the 

NESHAP version of ISIS-Cement, the value of the constant 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 constant values. However, in reviewing EPA’s 

documentation supporting the ISIS control cost 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 kiln’s type, clinker capacity, 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 flow; and ii) the form of the cost function, making capital cost a function of 

actual or wet gas flow rather than dry gas flow, since 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. There 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 specified in the 

voluminous ISIS modeling code, we can’t change them and we can’t substitute into the model 

a capital cost function for LWS to reflect what PCA believes would be correct. In effect, we 

can’t “fix” the cost function to our satisfaction 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 – for LWS, for ACI, for RTOs, for baghouses, and for 

the cost functions for capital costs, 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 better information on the actual 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, the 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 accurate, we can test how ISIS responds and what economic impacts 

are predicted to result from the proposed NESHAP when we generally increase or decrease the 

control cost relationships. We can test, for example, 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 assumed 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. In 
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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 flow) as could be handled by 

control equipment sized at the gas flow for which EPA has estimated costs. Reviewing EPA’s documentation for 

the control cost functions in ISIS and identifying any issues is very time-consuming, and determining how to 

address any issues using the cost information submitted by PCA takes even longer. 
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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 general 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 though we cannot test specifically how 

ISIS would respond to the particular cost functions that PCA believes to be most accurate. 

To this end, in this memorandum, we will provide the results from a new ISIS run that we have 

conducted where we have doubled 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 manner. To repeat: we can 

test how ISIS responds to a directional change in the 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 information in the particular format demanded for 

input into the current version of ISIS, nor can we reprogram the model 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 incorporating 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 expands on PCA’s September, 2009, comments 

regarding problems in EPA’s control cost functions in ISIS, and reports on the limited progress 

we have been able to make in revising these cost functions and running the NESHAP version of 

ISIS-Cement using the revised cost 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 EPA 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 modifying the control cost functions. 

2

 One might ask why PCA could not avoid the issue of changing the 

structure/functional form of the cost functions 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 capital 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 function 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 particular 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 circumstances, it is likely that the resulting “approximately adjusted” 

equation would fit reasonably well for kilns within the range 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” equation across the full range of kilns to which the equation 

would be applied in ISIS would represent a significant task. 
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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 topic area. A user can change the input assumptions for an ISIS run by 

locating the appropriate input spreadsheet or spreadsheets and changing the relevant values or 

relationships in the appropriate cells in these sheets. After the input spreadsheets are changed, 

the model itself will then run pulling the requisite 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 spreadsheet 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 run with it, either perhaps because the model is 

altogether disconnected from this cell, or perhaps because the value that has been entered in the 

cell is somehow over-written by something in the code. 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 expansions, and more. There is no way in the NESHAP version of ISIS 

that the user can change these and other input data elements short of locating 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 the input spreadsheets where the input of interest 

occurs. One can’t change an input 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 imports are simulated, including the prices at which they 

can enter various domestic markets, the markets which they can and cannot enter, any 

constraints on the quantity of imports able to enter a market, etc. 

• Adding into the model the several 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 kiln operating costs 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 to 
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see – the net change in domestic producer and consumer surplus resulting from the 

proposed environmental requirement. Omission of this key quantity and other important
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elements from what ISIS displays as outputs makes it extremely difficult to understand 

and interpret any new runs of ISIS that are performed. 

The final reason why PCA has been unable to improve the model and run it to generate credible 

economic impact estimates for the proposed NESHAP involves difficulties with the model’s 

outputs rather than with the model’s inputs. The outputs that 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 judging the impact of the proposed 

NESHAP or any other environmental requirement that one might simulate using ISIS. We can’t 

use the model effectively to evaluate the impact 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 working with, the primary model 

output that is displayed is the total cost of obtaining all the cement consumed in the U.S. This is 

not the most meaningful quantity with which to judge the impact of the proposed NESHAP or 

any other potential environmental policy and, as we will show later in this paper, it often portrays 

regulatory impacts as desirable when in fact they are undesirable. We believe that it is a 

misleading measure, and certainly not one that should 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 consideration 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. OAQPS also has long believed that the net change in producer and 

consumer surplus (social cost) is the appropriate fundamental measure of the impact of a 

regulation. However, the current version of ISIS 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 reconfigured so as to 

estimate this quantity and display it as the key output from each model run. 

The NESHAP version of ISIS also does not clearly display as an output of each run another 

quantity that is critical to understanding the projected 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 nowhere appears a simple summary of what the 

projected control costs are estimated to be. We have calculated using various intermediate ISIS 

outputs what we guess the projected control costs 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 can’t be sure either that we are choosing the 

correct source for a particular portion of total control costs, nor that we are putting together 

values from all the correct 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 performed. For many of these runs, we show in the summary of 

the run results the estimated total national control costs for kilns. These estimated control 
Version 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 estimates 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 the particular run of ISIS-Cement that EPA 

provided to us and that the Agency represented as the run that generated the control cost 

estimates referenced in the RIA and Preamble for the proposed 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 the 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 ISIS in an attempt to modify the 

model and generate results so as to better estimate what the economic impacts from the proposed 

NESHAP are likely to be. These difficulties have involved 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 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 model needs substantial improvements (many of which EPA has under way) 

before we can have any confidence in the accuracy of the results it generates. 

Section 2: Discussion of Results 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 selected 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 for 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 replacement 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 proposed NESHAP) run of ISIS; 
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2) The original EPA “policy” case (i.e., with the proposed NESHAP); 
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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 involves some change to EPA’s original base case, for example, if our 

new run involves changing the baseline assumptions regarding future cement demand. 

4) The revised policy case as represented in our new run. 

The estimated impact of the proposed NESHAP is given for EPA’s original run by the change in 

outputs from (1) to (2). In our revised run, the impact 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-Cement 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 believe to be the implications of the run for further 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 ISIS-Cement, performed to support the proposed 

rule in April of 2009, assumed that future cement demand would decline modestly from 2005 

through 2008, then grow fairly steadily from 2008 through 2013. This assumption 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 figure 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 result does not seem appropriate to us. As has been 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 the U.S. are 

arranged by the multinational companies that own most domestic U.S. cement plants; these 

companies generally prefer to serve the U.S. market via their domestic plants. Cement imports 

serve largely as “swing supply”, increasing when cement demand increases at a time when 

domestic capacity utilization is high and it is difficult to further increase 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 simulated in ISIS. ISIS should be revised such that imports function 

more as “swing supply”. 
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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 would 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 increase in imports of 0.5 million tons. Assuming instead PCA’s more recent 

recession-affected declining projections for future cement demand, our new ISIS run shows the 

NESHAP as causing a reduction in 2013 cement consumption of only 1.7 million tons, 

consisting of a decline in domestic production of 3.5 million tons and an increase in imports of 

1.8 million tons. Comparing these two runs, the NESHAP appears to cause more economic 

damage for domestic cement producers with the recession than without 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 production grows to 3.5 million tpy) and a greater increase in imports 

(the increase in imports is 0.5 million tpy from the NESHAP without the recession, but 1.8 

million tpy with the recession). This is an important result, and one that seems entirely sensible 

to us. If ISIS is changed from the NESHAP version that EPA ran in 2009 to a version instead 

that accurately reflects the impact of the recession in reducing projected future cement demand, 

the model will likely show even more adverse impacts from the NESHAP on domestic cement 

producers. We expect that the loss in social welfare (summed changes in producer and consumer 

surplus) that ISIS projects to result 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 reflecting the impact of the recession. 

Results of new ISIS run: Changing the assumed cost and effectiveness of air pollution 

control measures 

EPA’s original run of the NESHAP version of ISIS-Cement included a set of assumptions 

regarding the performance of each potential NESHAP control measure (percentage control 

efficiency achieved by LWS, ACI, RTO and baghouses), and a set of cost functions (for both 

capital and operating and maintenance costs) for these control measures and monitoring 

equipment for the NESHAP pollutants. PCA provided comments in September of 2009 

suggesting that EPA adopt different assumptions 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 estimates. As discussed previously, though, we have had substantial difficulty 

in revising the ISIS inputs and running the model to do this. We will provide a supplementary 

memo to EPA within several days that reports further on our efforts to revise the control cost 

functions and re-run ISIS. In this memorandum, we report 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) the estimated costs for most aspects of most 
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control measures. Because of various programming errors in ISIS and the impossibility 

of changing some elements of EPA’s cost functions via the ISIS input spreadsheets, we 

were unable to double all of EPA’s cost functions. 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 aspects 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 represents a simulation of the impact of: 

1) PCA’s assumptions regarding control equipment performance; and 2) Doubled costs for 

probably most of the control measures. We have discussed previously 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 assuming increased cost and reduced 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 the NESHAP to intensify. Whereas under 

EPA’s assumptions the NESHAP causes cement price in 2013 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 comments causes cement price to increase 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 million tpy vs. 2.8 million tpy) assuming lesser 

effectiveness/higher costs for controls than what is predicted assuming EPA’s 

effectiveness and cost estimates. This result needs further investigation. Why should the 

change in assumed control effectiveness/cost cause cement consumption to decline so 

drastically? Is it the change in assumed control effectiveness 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 available 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 kilns will be simulated as unable 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, which does not seem to be occurring in this new run. Again, 

we are investigating this surprising 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 projects when we reduce control effectiveness or increase control 

costs separately, rather than together. 
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• In any case, it is clear that revised assumptions regarding control 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 control cost and effectiveness. 

• The results of this run also emphasize the importance of a point we make later in this 

memo. We believe that the NESHAP version 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 using the Agency’s assumptions 

regarding cost and performance of controls that the NESHAP causes the cost of obtaining 

the nation’s cement to increase by $52 million in 2013 – the cost of domestically 

produced cement increases by $15 million (consisting of control costs 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 contrast, our new run projects that, using different assumptions about higher 

cost of controls and lower effectiveness, the cost of obtaining the nation’s cement in 2013 

will decline by nearly $2 billion per year (consisting of a reduction in the cost of 

producing domestic cement of more than $2 billion per year, plus 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 defined as the total cost of obtaining the 

nation’s cement, it appears that that increasing the assumed cost 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 nation’s cement goes down 

sharply when we revise the assumptions regarding control cost and 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 more meaningful than the total cost of 

obtaining the nation’s cement supply. As we discussed previously, we believe that the 

correct bottom line for assessing the impact of the NESHAP should be the change in 

social welfare (sum of producer and consumer surplus) resulting from 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 been able to revise the manner in which ISIS 

simulates cement imports and re-run the model accordingly, since the model is not set up to 

allow the user to change the fundamental manner in which imports are simulated. In the 

NESHAP version of ISIS-Cement, imports are not modeled as having a supply function for each 
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regional cement market, as is the approach for domestic kilns. Instead, imports are modeled as 

supplying whatever is the residual between the quantity of cement consumed in a domestic 
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market and the quantity of cement provided by domestic kilns serving that market. In effect, the 

price and quantity of imports supplied to any domestic market are estimated in the model as 

whatever gives the best fit relative to actual historical 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 entire model. We have not attempted to do so. 

Instead, we have performed 9 new runs of ISIS intended generally 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 that there is a ratio of the import price to the 

cost per ton for the highest cost domestic producer in that region – the “import price ratio”. EPA 

estimated the import price ratio for each region by fitting to 2005 data on cement price, 

consumption and import volume; the “import price ratio” in each region was estimated as 

whatever value made the model predictions fit most closely to actual data. We tested in our new 

runs what happens if we increase the estimated import price ratios in each region by a specified 

percentage. We chose this approach for investigating how ISIS responds to changes 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 simulation in ISIS that is reasonably accessible 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] presumes 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 import price ratio, 

we plot the resulting change in quantity of imports that the model estimates is supplied to the 

U.S. in total. The results of these 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 import 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 believe, 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 substitution being lower when domestic 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 affect the quantity consumed in the U.S. by much more when 

domestic kiln capacity utilization is low than it will when domestic kiln capacity utilization is 
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high. We believe from our discussions with you that EPA is proceeding along these lines in 

revising ISIS. 
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Results of new ISIS runs: Making new, replacement and expansion kilns more expensive

We commented previously that the NESHAP version of ISIS-Cement appears to make the costs 

of additional domestic cement production capacity unrealistically 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 existing capacity. We conducted new ISIS runs to 

investigate how the model responds to assuming higher and, we think, more realistic costs for 

new capacity rather than what was assumed by EPA in the NESHAP version 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 producers 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 direction when one assumes a 

higher required rate of return for investing in new capacity. In the base case assuming a 7% rate 

of return is needed for investing in new capacity, domestic cement production in 2013 is 112.8 

million tons. Assuming instead that a higher 14% rate of return is required, the model simulates 

domestic production as only 110.2 million tons. The higher assumed rate of return apparently 

shifts 2.6 million tpy of production from domestic kilns (presumably 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 result is plausible and in the expected direction. 

The impact of this change in required rate of return on the estimated impact of the NESHAP is 

interesting. In EPA’s base case, assuming a 7% required rate of return, the NESHAP results in a 

$15 million increase in the cost of producing domestic cement in 2013, consisting 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 declines, despite the 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 causing a lesser increase in control costs ($216 million vs. $218 million in 

2013), but also a lesser reduction in production costs ($216 - $33 = $183 million reduction 

relative to $203 million reduction in EPA’s base case). Evidently 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 applies in simulating the decision to invest or not in new greenfield cement 

production capacity. It may or may not apply in simulating the decision 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 unclear 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 input spreadsheets and 

instead are somehow over-written by the model code – in which case changing the hurdle rate of return as we do in 

this run will have no impact on the model’s predictions regarding 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 replacement and/or expansion.
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hurdle rate of return causes some new, low cost kilns that were estimated in EPA’s base case to 

come on line despite the NESHAP to no longer be built, thus reducing control costs but 

increasing production costs. This is interesting and, we guess, plausible. Tracking and 

understanding these sorts of impacts in ISIS with regard to new capacity would be greatly 

facilitated for the user if the ISIS outputs included an easy-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 the results of another run where we change the 

costs at which ISIS assumes another form of new capacity can be built. As we commented in 

2009, in the NESHAP version of ISIS-Cement, 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 “replacement” capacity were simulated as installed by the year 

2013. In our revised run of the model, though, we make the 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 kiln 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 replacements at all as occurring. We were wrong; the 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 simulated in the original run. Page 7 shows that the 

model output in the new run is completely unchanged relative to EPA’s base case NESHAP run. 

This is clear evidence that something is wrong with the model. We believe, as we will discuss 

later, that the cell in the ISIS input spreadsheet 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 happened 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 many apparent modeling errors that EPA will need 

to fix. 

Section 3: Comments on the Model 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, and 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 outputs 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 reasonable and expected manner? 

The remainder of this memo includes our comments on these topics. 
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1. 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 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 actions by U.S. cement producers and 

consumers – operate or close domestic cement kilns; add new or replacement or expansion 

capacity; implement emission controls as necessary to meet environmental requirements; import 

cement; transport cement to meet regional demands – 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 the 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. = operating 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 proposed environmental requirement (e.g., the proposed 

Portland Cement NESHAP), the model estimates the 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 obtaining 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 the 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 obtaining all the cement consumed 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 multiple ways of obtaining cement that might be affected in response to an 

environmental requirement (i.e., install controls at existing kilns; 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 production to another region where 

there may be a deficit), it seems reasonable to assess 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. 
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But what if the quantity of cement 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 producing a ton of cement to increase 

for many domestic kilns, which will result in some post-policy equilibrium combination of a 

higher market price for cement and less cement 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 the cement consumed in the policy case is less than the total cost of 

obtaining all the cement consumed in the base case, despite the increase in the cost per ton of 

producing cement domestically. Would such a reduction in the total cost of obtaining all the 

cement consumed in the U.S. represent an improvement in the economic well-being of the U.S. 

as a result of the environmental requirement? Not necessarily. A reduction in the total cost of 

all the cement consumed in the U.S. would seem to be a positive result, but on the other hand, 

this lower cost would be paid for a lesser quantity of cement than would have been consumed 

without the environmental requirement. Any judgment about economic 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. consumes, 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 measure 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 actions taken by private sector entities to 

comply with an environmental regulation is given by the net change in consumer and producer 

surplus in the markets affected by the regulation.4

 For many years, OAQPS’ Regulatory Impact 

Analyses (RIAs) have estimated the social cost of a regulation under consideration by estimating 

how the proposed regulation would affect the equilibrium price and quantity for goods in 

affected markets, and then by summing the resulting 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 producer revenues of $341 million, partly offset 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 regulatory compliance 

costs); 

• $89 million gain in producer surplus among foreign cement 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 foreign 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. 
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5

 Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland 

Cement Manufacturing Industry. Final Report. April, 2009. Pages 3-10 and 3-15. + $89 - $88 - $402 - $204 = - 

$605, the total loss in consumer surplus that OAQPS estimated for the proposed NESHAP. 

domestic social cost of a regulation, leaving out any impact on foreign consumer or 

producer surplus. If OAQPS had done so for the Portland Cement NESHAP, this 

estimated $89 million gain in foreign producer surplus would have been omitted from the 

social cost calculation.) 

• $88 million in direct compliance costs for white cement kilns and new kilns, two cement 

industry segments that were not addressed in the cement market analysis that estimated 

changes in producer and consumer surplus. 

EPA should re configure ISIS so that 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 information needed for estimating producer surplus 

seems easily available in the model as revenues for each producer (quantity of cement sold in 

each region multiplied by its regional price) less the producer’s production (including 

environmental) and transportation costs. Note that the 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 process for estimating domestic consumer surplus would appear to be somewhat more 

difficult. Regional price multiplied 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 estimate 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 constant across all cement regions or demand 

centers. However, we are uncertain whether this elasticity 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 discuss with EPA how producer and consumer surplus 

might be estimated given the information 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 errors in the modeling code prevented ISIS 

from running at all until they were identified and fixed. An asterisk improperly removed from 

one line of the code prevented the policy case (a key portion of the model needed to estimate the 

impacts of any environmental requirement being investigated) from running. The model often 

ran intolerably slowly, in some instances taking more than 24 hours to conclude a run when 
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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 optimal 
Version 1 G-27 EPA External Review 

solution and 2) whether the model performs additional iterations toward finding an equilibrium 

price and quantity for cement after estimating 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-arounds.” We expect that additional portions of the model that 

we have not yet tested are likely to have similar issues. In general, we would hope that ISIS 

would be debugged so that it can be run smoothly by a user who has no expertise 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 input, 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 monitoring for the NESHAP pollutants. 

The model includes an input spreadsheet showing 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 changes to the “switch” determining whether or not 

the model simulates a further demand response to the first-round simulated 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 have not identified where the input spreadsheets 

are over-written by the GAMS code 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 spreadsheet 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 input of interest appears and can be changed. One 

cannot easily add into the model the several categories 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 regarding 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 the GAMS model code, but the typical ISIS user will not have 

the ability to do this, and the model should not be designed in a manner such that this is 

necessary. Other input changes that 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: Demandresp
Version 1 G-28 EPA External Review 

• Changing 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 quantities from year to year, etc. 

• Changing some of the ancillary elements of control technology costs, including the 

electricity requirements and gypsum credit for 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 the current version of ISIS-Cement are 

heightened by the inadequate and incomplete nature of the written documentation for the model. 

Much of the documentation relies on reproducing equations that exist in the GAMS code, using 

difficult mathematical names for variables that include 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 ISIS’ output capabilities and display. 

While the Excel workbook presenting the results from a run of ISIS-Cement is extensive, 

including seventeen different worksheets, many results that the user would like to see are not 

available in the workbook or are presented in a less-than-ideal 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 shown 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 and calibration. Showing the model 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 replacement costs occurs for a reason different from the previously 

mentioned inability to change the cost-per-ton for new kilns and for expanding kilns. For new and expansion kiln 

costs there are cells provided in the input spreadsheets that are intended to be available for change, but these cells 

appear unintentionally to be “dead”. For kiln replacement costs, though, no cell appears to be available in any input 

spreadsheet that would allow a user to change the value assumed by the model.
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• • Many key outputs are shown only as discounted sums across the six years from 

2013 through 2018. It would be useful instead to see the undiscounted values for each 

year, as well as the discounted total across this period. 

• It would be useful to be able to see the total nationwide cost of the compliance measures 

(emission controls plus monitoring) simulated by the model in each future year. Also 

available should be the split of these costs 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 specifically. For example, we 

would like to know what the model projects as the total capital investment that will be 

required of the U.S. cement industry to comply with the proposed NESHAP over some 

period of future years. Likewise, the costs of all new capacity (replacement, expansion, 

greenfield, etc.) projected as installed in future years should be shown both in annualized 

form and in terms of capital and O&M. Ultimately, 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 quantities that EPA reported in 

April, 2009, as ISIS “results” in the docket 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 quantities reported in the preamble to the proposed 

NESHAP (e.g., pages 21165 – 21167) and those reported in the 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 preamble (pages 21166 and 

21167) and the ISIS TSD (page 33) cite as ISIS’ estimate for total projected control costs 

in 2013. Where among the available outputs from ISIS did EPA obtain this figure?10

10 With some painstaking work with various of the ISIS_outputs spreadsheets 

and the GAMS GDX files (these provide intermediate ISIS model results that the model then uses to generate the 

output spreadsheets) and some effort at learning and applying GAMS programming code, we attempted to put 

together numbers from the ISIS-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 instead 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 million as another estimate of capital costs in 2013 that can 

be derived from the GDX files. Likewise we obtained a different value for the annualized cost of monitoring in 

contrast to the $27 million figure cited in the docket -- $80 million as the cost of monitoring included in “Policy Total 

Cost” for 2013. The $27 million figure can be derived from the ISIS-NESHAP 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 directly matching the docket statement to the 

effect that the proposed NESHAP will result in 139 U.S. kilns projected to be active in the U.S. in 2013. Using the 
Version 1 G-31 EPA External Review 

Our general point here is that the model user should not need to search through numerous 

spreadsheets and perform further calculations in order to obtain the key ISIS outputs. 

Each of the important results generated by the 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 that the user can see how the result is derived. In the case of 

“total control costs”, for example, the ISIS output spreadsheets 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 sum 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 similar format. It would be helpful to be able to 

see across each of the simulation years whether each kiln operates or not, what controls it 

installs, what fuel it uses, what production costs it incurs (variable operating costs, other 

operating costs, capital costs, all on both an annualized and an actual basis), what control 

costs it incurs (capital costs, operating costs, monitoring 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 model outputs should be clearly labeled, as we 

describe more fully later in this memo. 

4. We cannot explain several puzzling results that we obtain in running the model. 

These puzzling results may represent errors in the 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 there is some appropriate 

explanation for them. The 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 NESHAP 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 monitoring 
Version 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: either $175 million or $74 million, drawing 

from two different places in the output spreadsheets. 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 consistently to show simulated total 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 total capacity (sum of existing 

capacity, replacement capacity, expansion capacity and new capacity) is equal to only 

106.7 million tons. Similar results are apparent in other model runs, and for other years 

and for the policy case also. How can kilns consistently produce more cement than their 

capacity? 

• When we look at the intermediate results that the model generates before the 

ISIS_outputs.xls workbook is developed, it is apparent that many kilns are projected as 

incurring costs that are zero in some years during the 2013-2018 modeling period and 

positive in other years. The model apparently allows 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 model projects that a new, expansion or replacement 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 intermediate modeling results correctly in this 

regard, we question EPA’s rationale for assuming that all of a kiln’s costs will switch on 

or off depending on whether the kiln is projected to be operating 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 costs 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 pollution control equipment and monitoring. If a kiln decides that 

it wants to operate at any time subsequent to the compliance deadline for the 

NESHAP (presumed to be 2013 at the latest), it must make the capital investment 

in air pollution controls and monitoring 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 capital equipment, and the model converts a capital cost incurred 

in a specific year into an equivalent stream of recurring annualized costs in 

subsequent years. The company owning the kiln cannot walk away from this 

subsequent stream of capital cost obligations either for a single year if the kiln is 

idled for that year or forever if the kiln is closed permanently. The capital cost 
Version 1 G-34 EPA External Review 

obligation has presumably been incurred by the company rather than the kiln; if 

the company wants to stay in business it must continue to service all of its debts 
Version 1 G-35 EPA External Review 

whether the particular facility that prompted a particular debt remains active or 

not.11

o Capital costs for new, replacement or expansion cement production capacity. Just 

as ISIS simulates some 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 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 occurring 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 recurring 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 maintenance activities needed to preserve the option 

of operating the kiln again in the future, and maybe more. 

We further suggest that EPA consider whether 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 determining 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, including costs relating to sunk investments. 

5. A user should not need to pay a substantial 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 which will allow the user temporarily 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 $10,000 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 are owned by companies that have multiple 
Version 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 incurred for a particular facility. 
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6. Several aspects of the ISIS input and output spreadsheets should be clarified. 

It is not clear in many of the ISIS spreadsheets exactly what quantities are being referred to. 

Labels for each table would be helpful, indicating for example whether the quantity being 

referenced 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 quantity or a present discounted value of a future stream; a cost that actually 

occurs in the year in question or an annualized cost chargeable in that year; 

• In hundreds, thousands, millions, 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 also be clearly identified. 

Consistent, thorough labeling throughout the provided Excel workbooks 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 control measures (see ISIS_inputs.xls, “newControls” 

tab). The format is very difficult to follow, in the sense that substantial effort is required from 

the user in order to determine exactly what the resulting 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 appears that other portions of the model may “hard 

wire” a variety of questionable presumed relationships among these factors. 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 equations back to their source materials (the Andover 

Technology memos and Summary of Environmental 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 new runs in which we would test how ISIS responds to the 

information on control costs that industry suggested in our September, 2009 comments. We 

intend to provide soon a supplementary memo on these issues. 

We think it is important for the next version 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 equations 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 issues 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 equivalent 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 sections 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 control 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 simply 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 separates out power costs as an 

element of variable costs, yet for RTOs both power and fuel costs 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 newControls 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, costs for monitoring are apparently treated 

differently -- these costs are shown as consisting of capital costs and “annualized cost”, 

the latter apparently including an annualized capital charge as well as annual O&M

costs.12EPA’s cost equation documentation is also inconsistent and confusing on this 

issue. The Andover Technology memo addressing 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 equations for LWS because both the memo and the 

ISIS_inputs spreadsheet use the same categories of costs. 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 costs 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 maintenance 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 Environmental and Cost Impacts (which 

include capital charges) against the cost equations for these technologies in ISIS_inputs 

that are not supposed to include capital charges. EPA could eliminate this confusion by 

consistently presenting annual O&M costs excluding any capital charges, and using the 

same approach in both ISIS and the various source documents. 

A final point of confusion in ISIS involves the 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 the ISIS tables showing year-by-year new, 

replacement, expansion, and retired capacity, it should be explained 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 modeling 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 cooperate with EPA in improving the model. The remainder of this 

memo consists of three appendices as discussed earlier. We will soon also provide to EPA a 

supplementary memo discussing our experience in attempting to run the model with revised 

estimates for the cost and effectiveness 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, PCA 
Version 1 G-42 EPA External Review 

Appendix A 

Background on the Structure of ISIS-Cement and How the 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, written in GAMS programming language, that uses 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 spreadsheets in Excel. The model dumps the outputs into these 

spreadsheets. The user looks among the output spreadsheets to see various aspects of the 

model results. 

In using ISIS to estimate the impact of a set of environmental requirements, 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 regional cement demands for future years. The 

model also determines the price for cement in each regional 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 estimate again which kilns will produce how much cement at 

what cost, prices, imports, etc.. 

The user will then want to compare the simulated outcomes in the policy case against those in 

the base case. The model does this automatically. 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 B 
Version 1 G-44 EPA External Review 

Appendix B: Results from Some New ISIS Runs Revised ISIS Run – Use PCA’s Latest Forecast for Future Cement Demand, Reflecting the Impact of the Recession 

“ISIS_Outputs.xls” Results 

Base Case Policy Case - Including Proposed NESHAP Impact of NESHAP 

Original Original Original

Revised Δ Revised Δ Revised EPA EPA EPA

Cost of Domestic Production 2013 (millions) $ 6,943 $ 6,083 $ (860) $ 6,958 $ 6,073 $ (885) $ 15 $ (10)

Cost of Controls included above 2013 (millions) $ - $ - $ - $ 218 $ 202 $ (16) $ 218 $ 202 

Import Costs 2013 (millions) $ 2,455 $ 1,587 $ (869) $ 2,492 $ 1,721 $ (771) $ 37 $ 134 Value of Lost Cement 2013 (millions) $ - $ 1,710 $ 1,710 $ - $ 1,634 $ 1,634 $ - $ (76)Lost Producer / Consumer Surplus 2013 (millions) TBD TBD TBD TBD TBD TBD TBD TBD Total Social Cost 2013 (millions) TBD TBD TBD TBD TBD TBD TBD TBD Price 2013 $ 75.37 $ 73.58 $ (1.79) $ 76.56 $ 73.86 $ (2.70) $ 1.19 $ 0.28 Price 2018 $ 77.91 $ 76.16 $ (1.76) $ 79.06 $ 76.40 $ (2.67) $ 1.15 $ 0.24 Consumption 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 1 G-45 EPA External Review 

Imports 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 1.1

Comments: 

•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 in 

imports (11.5 million tons in 2013). In reality, as has 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. We suggest revising the manner in which imports are simulated to make them behave as “swing supply”. 
Version 1 G-46 EPA External Review 

Revised ISIS Run – Increase Assumed Cost and Reduce Assumed Effectiveness of Control Measures, Consistent with PCA’s 9/09 Comments “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) $ 6,943 $ 6,943 $ - $ 6,958 $ 4,821 $ (2,137) $ 15 $ (2,122)

Cost of Controls included above 2013 (millions) $ - $ - $ - $ 218 $ 443 $ 225 $ 218 $ 443 

Import Costs 2013 (millions) $ 2,455 $ 2,455 $ - $ 2,492 $ 2,489 $ (2) $ 37 $ 34 Value of Lost Cement 2013 (millions) $ - $ - $ - $ - $ 2,939 $ 2,939 $ - $ 2,939 Lost Producer / Consumer Surplus 2013 (millions) TBD TBD TBD TBD TBD TBD TBD TBD Total Social Cost 2013 (millions) TBD TBD TBD TBD TBD TBD TBD TBD Price 2013 $ 75.37 $ 75.37 $ - $ 76.56 $ 77.54 $ 0.98 $ 1.19 $ 2.17 Price 2018 $ 77.91 $ 77.91 $ - $ 79.06 $ 78.17 $ (0.90) $ 1.15 $ 0.25 Consumption 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 1 G-47 EPA External Review 

Imports 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 increase 

even more and consumption to fall even more than in Original EPA Policy Case. All the impact falls on domestic producers, not on imports 

•But would consumption fall by only 2.8 million tons (2013) under EPA’s control cost/effectiveness 

assumptions vs. 41.2 million tons under PCA’s control cost/effectiveness assumptions? Needs looking into 

•Note that EPA would count the total cost of NESHAP as only $52 million in 2013 (increased cost of domestic 

production plus imports), missing the values lost in consuming only 144.8 million tons of cement in 2013 vs147.5 million tons 
Version 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 ratio for each region based on fitting 

to 2005 data. We think the model simulates import behavior 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; 

*$offtext 
Version 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 = 1262.5x2

 - 882.35x + 203.97 

R2 = 0.9447 
Version 1 G-50 EPA External Review 

Revised ISIS Run – Make New/Replacement/Expansion Capacity More Expensive by Increasing Rate of Return Required for New Investment – From 7% to 14% “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 $ - 

$

2 455

$

6,911 $ - 

$

2 612

$ (32)$ - $ 157 $ - 

TBD TBD 

$ 6,958 $ 218 $ 2,492 $ - 

TBD TBD 

$ 6,944 $ 216 $ 2,649 $ - 

TBD TBD 

$ (14)$ (2)$ 157 $ - 

TBD TBD 

$ 15$ 218 $ 37 $ - 

TBD TBD

$ 33$ 216 $ 37 $ - 

TBD TBD

Price 2013 $ 75.37 $ 82.99 $ 7.62 $ 76.56 $ 83.45 $ 6.89 $ 1.19 $ 0.46 Price 2018 $ 77.91 $ 85.32 $ 7.41 $ 79.06 $ 86.42 $ 7.36 $ 1.15 $ 1.09 Consumption 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 1 G-51 EPA External Review 

Imports 2013 (million tons) 34.7 37.3 2.6 35.2 37.8 2.6 0.5 0.5

Imports 2018 (million tons) 39.4 42.1 2.7 39.9 42.6 2.7 0.4 0.4

Comments: 

• Results appear sensible: higher threshold rate of return required for making new capacity investments 

results 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, 

though 
Version 1 G-52 EPA External Review 

Revised ISIS Run – Make Kiln Replacement More Costly 

EPA had made kiln replacement very inexpensive in terms of cost per ton of 

capacity to convert an old, wet or long 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 shouldn’t elect such replacements. 

• 2005 Replacement Cost per ton for Wet Kilns 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 Year

Online 

Cost 

($m) 

New 

Tons (m) 

Raw Cost 

per Ton 

Foreman, TX 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 

Cost per Ton Cost per Ton 

(Industry) (EPA 2005$) 

$ 381 $ 93

303 93

465 93

425 93

175 93

518 93

550 93

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

Cost per Ton

(Industry)

$ 456

536 

397 

785 

478 

623 

Costper Ton

(EPA 2005$) 

$ 61

61 

61 

61 

61 

61 
Version 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 OriginalEPA 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

$ 15 $ 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 response 
Version 1 G-54 EPA External Review 

Appendix C—Differing Estimates for Control Costs in 

2013, as Obtained from Various Sources in ISIS 

Estimated Control Technology Cost Amounts in 2013 

$ Millions 

Control 

Cost Item 

ISIS per 

Docket1

ISIS_Outputs.xls 

(provided by EPA) 

ISIS_Outputs.xls 

(from GDX files, using items

included in PTC2) 

ISIS_Outputs.xls 

(from GDX files, using other

items not included in PTC2) 

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 Control Technology Costs in 2013 

Excel Location or GMS Variable Name(s) 

Control 

Cost Item 

ISIS per 

Docket1

ISIS_Outputs.xls 

(provided by EPA) 

ISIS_Outputs.xls 

(from GDX files, using items

included in PTC2) 

ISIS_Outputs.xls 

(from GDX files, using other

items not included in PTC2) 

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” USEPA 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, Cement 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/replacement Excel tables). As I noted 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 represents 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 attention to the use of commas (you need more 

commas) and the common omission of the indefinite article “the”. 

The word “data” is always plural: thus “data are/were/show....”, never “data 

is/was/shows...”. 

There is no need to capitalize portland cement (c.f., Portland 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 largely published as metric tons (e.g., recent PCA 

data, USGS data, Census trade data) and converted them to short tons. Then, in the 

text, you have always provided a parenthetical 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 text. The use of short tons in the tables has 

prevented me from conveniently checking many of your data against my own (USGS) 

publications. I think that the whole report (maybe not the Anderson addendum report) 

needs to be recast in (only) metric units. One 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 defined. “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 understood. But ^0.4 is not: is it the CAPACITY0.4 or 

is it CAPACITY x 100.4? At least define it somewhere. 

5) Virtually everywhere, you use SO2, where SOx would probably be the better term. 

But you could state that, for computational purposes, you have used SO2 because it is 

“rigid” stoichiometrically and, in any case, makes up the majority of the SOX. Ditto 

for CO2 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 proprietary). 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 that were active at 

yearend (we count 183 kilns in 2005, the PCA just 181). Many of the PCA energy 

data exclude white (portland) cement, whereas these are included in the USGS data. 

The PCA provides the cement companies with heat conversion factors 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 data, not the PCA. 

7) Avoid using MM for million, just use M. I think this is an issue only with your Btu 

discussions. In my experience, M for thousand 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 places, data ought to be rounded. Few data 

reported are good to better than +/- 1%. 

Specific Comments

Summary 

The summary is a little casually written. Although it is debatable as to what constitutes 

“pyroprocessing,” it is generally more applicable to the combination of calcination and 

sintering (clinkering), and not so much to preheating and drying. 

You talk about “criteria pollutants” and HAP almost as if they are interchangeable. It 

reads as if NOx and SOx are HAP. 

The Summary seems to lack discussion of CO2—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 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 annual data includes blended cement within the portland 

umbrella. The annual report (Mineral Yearbook) shows the 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 material temperature is generally taken to 1450°C, 

not 1500°C (white cement kilns probably 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 distinguish 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 can 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 schematic for a precalciner 

kiln would be essentially identical. 

Table 1.1: The data are typical averages, but are not universal. The 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 understood that “smaller” means lower capacity, not 

smaller dimensions on the kiln tube. 

I would note that the USGS Minerals Yearbook chapters on cement (MYB) also allow 

the average capacity to be discerned: it would calculate as 0.577 Mt/yr (million metric 

tons per year) in 2005 (MYB, table 5). 

It may be useful to discuss what is meant by “capacity” anyway! Daily capacity means 

per 24 hrs (and implies 24 hrs of continuous operations) and is more or less 

unambiguous...except that you don’t know whether 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 rated 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 capacity) * (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 the 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 capacity if there is any mischaracterization of the routine 

maintenance downtime or the daily capacity. The USGS calculates—for the plant 

overall—the capacity utilization wrt. the actual clinker output: 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 precalciner, and whether dust control is via a baghouse or an 

ESP.] 

p. 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. 

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 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 couple 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. 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 incomplete because it fails to adequately discern 

localities with more than one plant in a cluster. For example: WA has 3 plants (2 

integrated plants @ Seattle and a grinding plant at Bellingham); CA has 11 plants; FL has 

6 plants, etc... 

p. 1-7: your discussion of imports is misleading: imports can be by domestic producers 

seeking to feed markets where domestic (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. 

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 or 
Version 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 targeted 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 actual 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 types 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 import data that you use in this report and 

represents a fairly serious 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 exports – change in cement stocks). We 

exclude the imports of clinker because the production of cement includes cement made in 

the USA from imported clinker. But we do not include changes in clinker 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 show currently in the 

data series. 

Another related issue is the cement stockpiles: 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 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. 

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 materials. Many organic compounds (in fuels) 

contain sulfur, as do kerogens in sedimentary rocks like limestone. Both fuels (especially 

coal) and raw materials can contain sulfide minerals (chiefly pyrite or marcasite); and 

sedimentary rocks may contain 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 the total raw materials and fuels. 

Figure 1-6 is small to the point of uselessness. Why not make it into 3 Figures (1-6a, 1-

6b, 1-6c)? 

The text in this area ought to mention CO2. In fact, the whole section 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” 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 authoritative publication. 
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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 market 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 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 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 cement than any other plant. 

Overall, there are all sorts of flows up and down, in and around, the entire MississippiMissouri-Ohio River system. Not sure some 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 plant 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 sitting 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 Fredonia (I think), St Marys Dixon, IL, plant, 

Holcim’s Dundee, MI and Clarksville, MO plants (both are wet plants), Cemex @ 

Brooksville, FL; TXI at Crestmore, CA and, temporarily, at Oro Grande; Cemex at 

Davenport, CA (likely temporary); some of the kilns at TXI (or was it Ash Grove) @ 

Midlothian, TX; Essroc at Frederick, 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. 
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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 use of the word “endogenous”. The phrase “...ISIS endogenous 

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 expansion 

projects can include the retirement 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) without 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 metallurgical coke has ever 

been in common use even in the steel areas—it’s too expensive. 

Figure 2-2: awfully crude breakout of fuels. Where’s the petcoke (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 tonnages 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 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 very least, include electricity. Based on the 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 electricity (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-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 NM3

/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?) 
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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 averages for NOx and SOx are reasonable. 

While I agree that raw materials 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 plant burning a low- or high-S coal? 

p. 2-8. Your calcination emissions factor of 0.53 t CO2 per ton of clinker is slightly too 

high. Instead of adopting this Andover Technology default (see my comments on 

Appendix A) you should use the IPCC default (which 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 includes a 2% addition for “lost” [not returned to kiln] 

CKD). 

The average CO2 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 basis (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 reporting may not always be—as gross or high 

heat. You may be getting net or low heat. 

These are among the (many) woes of data collecting! 

Table 2-7: I’m not sure you’ve made clear why you are showing H2O production. 

Table 2-8: low-NOx burners are great, in theory, 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 (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 (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 hypothetically 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% clinker (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 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. 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--sadly, very common--of cement swaps among 

companies. 
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Production and Capacity Changes: the lead-in sentence is a bit unclear: “Regional 

production can be from existing kilns, 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 technological upgrades), new kilns at existing 

plants (perhaps replacing existing kilns), 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 schedule than was anticipated even 1-2 years 

ago). Have capacities been properly/uniformly 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 movements 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 Mississippi-Missouri-Ohio river system. How are you determining 

where cement imported at location X is being consumed? 

p. 3-3: the price data for imports (Equation 3.2.5.) are very weak. You only have import 

values reported on a CIF basis (USGS monthly and annual reports) and Customs Value 

(USGS annual reports). The CIF value does not represent the 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 experience with running the actual model. 

Ch. 4: Calibration of Cement ISIS Model

4.1.3.1. I am not sure that the assumption that Priceimports = producer price is at all valid. 

Dumping issues come to mind. I have found that 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 cement: 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 are very fond of large margins. 

You cite a VERY limited number of examples of import-dominated markets, and without 

really defining what this means. What is the percentage criteria for this designation? 

Seattle may not be an especially good example. 
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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 Customs District (CD) of 1.489 Mt and 0.867 Mt 

into the Columbia-Snake CD. This looks like a high import ratio, but 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 2005? Again, switch to metric tons! 

What is the basis of the production data in table 4-1? The USGS data do not allow for it, 

as our District groups do not match your market groupings. The PCA normally does not 

publish production data other 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 having 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 that 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 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. 

Figure 5-3: Figures should stand alone—thus it is confusing why the emissions caps and 

banking are not shown (I know the answer—just, 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 closures, these projections may need to be 

redone. 

5.1.2.: In the text, it is important to minimize the use of acronyms, especially 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 just shown as margins?). Was their a scale 

change post-2010? 

5.1.3: The total demand in the near term has now likely been revised downwards by the 

PCA. 

Figure 5-5: it is not immediately clear why the red dot has been displaced rightwards (I 

can guess, but it looks strange). And what happened to the red dot for 2025? 

Looking at this figure, I do not see much difference between the base case and the policy 

levels. 
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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 trade-in or trade-out (and, again, I would urge calling this an 

inflow or outflow) and that is Birmingham. Surely, 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 determined 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 about increased fuel costs? 

Ch. 6: Summary 

Basically OK. Perhaps you need to again mention here here why CO2 emissions could 

drop under SO2-reduction strategies (somewhere earlier it was stated that there could be 

decreased CO2 using limestone wet scrubbers (LWS)—it is unclear why this would be so. 

I would think that reduced CO2 would instead be—as stated elsewhere—from changes in 

the fuel types that might occur in a SOx-reduction program. 

Appendix A: Staudt, Jim, Andover Technology Partners, “NOX, SO2, and CO2 

emissions from Cement Kilns”:

This is a rather good report with a few places that bothered me. 

Table 1: the exit flow gas rates would show a lot of variation; 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 reasonable, 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 will 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 technician, a plant manager, a controller/accountant? Same 

person each year? 

b) is the weight of (e.g., coal) the 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 so, is it a gross (= HHV) basis or net (LHB) 

basis?—the former is preferred 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 the 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 periodic 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 properly: if no heat was provided, how do you 

impose a heat estimate for “liquid waste?” (undifferentiated). How about for an 
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identification given simply as: “used solvents?” Or “off-spec oil and lubes, inks, and 

solvents”. What about things like: “(solid 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 values 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. 

CO2 from calcination: Although Staudt’s calculation is arithmetically sound (within 

rounding parameters), I do not like the default he derives (0.53 t CO2/ t clinker) because it 

is based on an average CaO content of clinker of 67.6%. This is too high—it is near the 

upper end of the CaO range in modern clinkers. As was exhaustively dealt with by the 

IPCC, the average is a bit lower. No great precision is justified owing to variation in the 

chemistry of clinker at various plants and among plants, but 65% is a better, more useful 

average. This drops the default (using the same assumptions as to CaO source) to 0.51. If 

you incorporate the IPCC-suggested 2% additional CO2 for CKD “lost” (not returned to 

the kiln) to the system, it becomes 0.52%. Neither protocol adjusts for MgO contributed 

from a carbonate source, but this is OK because the assumption that the CaO is 100% 

from CaCO3 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 emissions inventory uses the IPCC default (w/ 

CKD) of 0.52 and I would suggest that the ISIS model 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 calcium carbonate (calcite): the 

former is a rock mostly made up of calcite; the latter is a mineral of formula CaCO3. His 

stoichiometry applies to calcite or calcium carbonate, not to limestone. 

I found much of Staudt’s report 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 appears not to discuss—unless I missed it: the issue of plant-level 

reporting of fuel and electrical consumption (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 plants of 1 Mt/yr capacity where 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 different 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 reduce 
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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 clinker 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, reduced heat loss through 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 correlation 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 and 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 kilns 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 characterization of the data: the correlations 

would appear to be not statistically significant. 

In his discussion of SOx capture, the author says 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 tube, not by bringing in a new tube. 

While it is true that there is regional variation in the sulfur content 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 difference 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 marble, or nearly so) would not normally be a 

criteria for selection of a new quarry. 

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. median emissions data in tables 8 and 9. 

Appendix A continued: Re: Costs and Performance of Controls 

This is also a good report. 

Regarding the use of tires, I would comment 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 that tires yield slightly less CO2 than coal: perhaps on a dry 

weight basis they yield slightly more. Depends on how you relate the emissions: per 

weight of fuel, per 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 precalciners are described as “post-kiln” 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 preheaters and precalciners: “pre-kiln” technologies (that’s why 

“pre-“ is in their name!). 

I think most calciners are designed 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 the main burner. Secondly, a preheater kiln 

commonly uses hot combustion air for the preheating (which is full of thermal NOx but 

this may be destroyed if reducing conditions exist in the preheater). What is true is that 

NOx generated in the calciner (and hence also carried into the preheater cyclones in a 

preheater-precalciner system) is dominated by fuel 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 rarely is it pure calcium 

carbonate. 

p. E.31: minor issue: gypsum is used to make finished cement, not finished concrete. 

Another issue: CaSO4 is the formula for the mineral anhydrite. Gypsum has the formula: 

CaSO4·2H2O . 

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 and any can have a problem burning CKD—it 

depends primarily on things like the alkali content 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 down. 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: 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 relatively modest amounts (they did not burn all of it). 

CemStar: I would add a bullet for the CO2 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 figure: I am in the business of collecting 

proprietary data and I never could 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 processing company Levy (who bought the CemStar 

patent in 2004) gets for contracts it develops. Levy is primarily interested in promoting 

the slag use (and selling the slag to the cement plant) and charges (they tell me) a much 

lower royalty (but they don’t tell me what it is). My point is that, in your cost analysis, it 

is likely that future (new) users of CemStar will be paying <<$16/ton royalty (if any) for 

the process. 

At $16/st, CemStar becomes quite expensive relative to the cost of the steel furnace slag 

(a few dollars per ton only, + transportation) 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 correctly, it is being said that this clinker cost 

minus $10/ton for the slag, yields a savings 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 clinker) when, in fact, it is “competing” 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. CemStar always involves incorporating steel slag 

into the raw mix as a partial (3-10%) substitute for limestone (etc...). 

Without CemStar: cost = A (cost to quarry, comminute, & blend the raw feed materials) 

plus P (the cost of pyroprocessing (fuel costs etc...) to make clinker. 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 process, crushing the slag to 1-2” size, and 

blending. So the resulting feed cost becomes (A-B + A’). 

The pyroprocessing cost is the cost of calcining the original feed mix (in the now smaller 

amount), plus the cost of melting/disassociating 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 some of your silica, 

iron, and aluminum (oxide) for less heat (and maybe faster) than would be the case 

burning clays, shale, and quartz sand. So the cost savings are related to the reduced 

heat/fuel charges of the pre-sintering stages of pyroprocessing (P’). For the sintering part 

of the clinker-forming process, there probably 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 CO2 emissions will be, on a per-ton-of-clinker basis, will be something 

like: for calcination emissions: (1-C’)*C, where C is the calcination CO2 for the 

carbonates burned in feed that does not incorporate 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 + S 
Version 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 disassociation 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 CO2 reduction (overall, on a per 

ton clinker or cement basis) will be directly 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. 

p. 35: Again, if I am reading this correctly, 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% CemStar (slag) introduction, then, 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 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 electricity per ton). But I do 

not think that this is the case: the cost (either 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 and other feeds, the slag does NOT need to 

be ground, just coarsely crushed. [the CemStar 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 earlier because it was assumed that you had to grind it like 

everything else, and slag is indeed 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 technology. Dragon Cement in 

Maine was a true kiln conversion: the old wet kiln 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 wet kilns (which were shut down) with a completely new, 

unrelated, dry kiln (precalciner kiln). 

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. 

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 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 plaster 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 least 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 limestone 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., install roller mill instead of ball 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 greenfield(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 right. 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 labor is important...Look, they have cement plants all over 

the world. Labor can always be found. The more skilled positions will draw engineers (et 

al.) from far away. When I have visited cement plants, I am commonly struck by the fact 

that most of the workers have been there for ages—they have had or are planning on 

having their whole career there. They evidently like the work. The pay is adequate and 

steady; it’s relatively clean, and relatively safe. It’s a more recession-proof industry than 

most. So there’s not necessarily a lot of overturn (oddly enough, except for the plant 

managers). People learn the work; they get trained. 

Modern plants do not need a huge workforce 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 deposit is located or else the mine will fail 

badly), you generally have a choice 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/will map out suitable limestone deposits. 

Unlikely. Cement companies have their own staff of geologists, or will hire a consulting 
Version 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: consider adding a terminal (instead of a plant); consider swap 

arrangements (to supply cement to customers outside your own plant’s “range”.) 

Round the midpoint costs to 2 significant-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 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. 

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. 

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 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/mt; and all integrated plants combined = 138.8 kWh/mt. Grinding 

plants = 84 kWh/mt. 

Table 7: the unit electricity prices may not be valid for large 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 actually 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 simplify 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 include 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 inputs 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 cost 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 table (SFC, EGFW) to be consistent with the 

table in the appendix. The EGFW in Nm3

/Kg for dry seems low. Hector will share 

Holcim’s calculation method at 10% oxygen to recalculate 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, lbmolesH2O/MMBtu, H2O is missing in some 

places 

Page 2-10: Table 2-8 Are CO2 reductions total CO2 (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 who at EPA PCA sent the workbook data. 

Page 2-11: Table 2-9 Are these #s fuel intensity 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 Berkeley National Laboratory 

1. The model models each individual cement plant, without plant specific data but 

on the basis of a number of assumptions. 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 others are larger users of alternative fuels 

(depending on the permit). In Europe, kilns with up to 50% of biomass and 80% 

alternative fuels can be found. While, this variation 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 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 

2. The average energy intensity values given 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 also the Energy Star Guide for the cement 

industry). How representative are these figures and it would be good to validate 

the assumptions using statistical data. 

3. Also, note that there is a difference between USGS and PCA data which is 

difficult to explain (see the Energy Star Guide), 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 essential, but moreover it gives 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 calculations have been added. This is OK. 

However, in the modeling of abatement/control 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. combustion/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-efficiency classifiers may improve energy 

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 

system. The discussion of electricity related 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 production 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 likely response measures of the cement 

industry are included in the model. 

A change in fuel mix is the most likely 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 some 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 least 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 2010 
ii

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 Efficiency 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 .....................................................................................................................4-1
iii

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 the 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................................................................................................................................................5-11
iv

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 Million Metric Tons, by Customs District in 

2005...................................................................................................................................3-6

Table 3-5. Specific Fuel Consumption and Total Exhaust Gas Flow Rate (wet) for Various Kiln 

Types .................................................................................................................................3-9

Table 3-6. Estimated Uncontrolled NOX Emission Intensities for Cement Kilns .............................3-10

Table 3-7. Average SO2 Emissions for Each Kiln Type in Each State .............................................3-11

Table 3-8. Approximate CO2 and H2O Produced from Combustion of Fuels...................................3-12

Table 3-9. NOX Control Technologies for Cement Kilns..................................................................3-15

Table 3-10. SO2 Control Technologies for Cement Kilns...................................................................3-17

Table 3-11. CO2 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 Operation1 ......3-20

Table 3-14. Energy Efficiency Measures for Raw Materials Preparation...........................................3-21

Table 3-15. Energy Efficiency Clinker Making Measures..................................................................3-22

Table 3-16. Energy Efficiency Measures for Finish Grinding ............................................................3-23

Table 3-17. Energy Efficiency Plant-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 Prices in USGS Districts (2005)...................................4-7

Table 4-5. Reported and Calculated Cement Prices in USGS Districts (2006)...................................4-8

Table 4-6. Reported and Calculated Cement Prices in USGS Districts (2007)...................................4-9

Table 4-7. Aggregate production (reported and 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........................................................................................5-5
v

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 Cyclone Preheater.........................................1-4

Figure 1-3. Trends in Cement Kiln Type and Capacity 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 O3 NAA, PM2.5 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 Flows 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 Kilns Modeled in ISIS-Cement........................................5-1
vi

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 Association 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 

CO2 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 Florida 
vii

FOM Fixed Operation and Maintenance 

GA Georgia 

GAMS General Algebraic Modeling System 

GDP Gross Domestic Product 

GHG Greenhouse Gas 

H2O 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 NOX 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 Management 
viii

NESHAP National Emission Standards for Hazardous Air Pollutants 

NM New Mexico 

NOX 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 

SOX Oxides of Sulfur 

SPE Spatial Price Equilibrium 

SR Seal Replacement 

TDF Tire Derived Fuel 

THC Total Hydrocarbons 

TRAGIS Transportation Routing Analysis Geographical Information 

System 
ix

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 

m2 ft2 9.29 × 10-2 

m3 ft3 2.83 × 10-2 

°C °F 5/9 × (°F – 32) 

kg lb 0.454 

J/kg Btu/lb 1.33 × 10-4 

m3/s cfm 4.72 × 10-4

m3

/s gpm 6.31 × 10-5

J/kWh Btu/kWh 1055.056 

mills $ 0.001 

kg/m2 in. Hg 345.31 

metric ton short ton 0.907 
1-1

Chapter 1 

Introduction 

In the National Academy of Science’s 2004 report, “Air Quality Management in the United States,” the 

National 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 assessments should be conducted 

in a comprehensive and coordinated manner (NAS, 2004). With these recommendations, EPA began to 

move toward establishing multipollutant 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 identify optimum strategies (considering feasibility, costs, and benefits across all 

pollutant types such as criteria, toxics, and others), while streamlining administrative and compliance 

complexities and reducing conflicting and redundant requirements. 

The development of policy options for managing emissions and air quality can be made more effective 

and efficient through sophisticated analyses of relevant 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 manufacturing 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 material 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 production. By modifying the raw material mix and, to 

some degree, the temperatures utilized in manufacturing, slight compositional variations 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 Society 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 the 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 the addition of an air-entraining agent. 

• Type III is used when concrete must set and gain strength rapidly. 
1-2

• Type IIIA is similar to Type III with the 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 processing 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 cement (PCA, 2008a). The common industry practice, and that of the US 

Geological Survey (USGS), includes, within the Portland cement designation, a number of other cements 

not within ASTM C-150, which are composed largely of Portland cement and are used for similar 

applications (e.g., concrete) (USGS, 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 cement 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 minerals may be added to adjust the raw material 

composition. The fine raw material is fed into a large rotary kiln (cylindrical furnace) where it is heated to 

extremely high temperatures [about 2640 °F (about 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 precalciner 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 elsewhere. 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 materials 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 CO2 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. 
1-3 

Figure 1-1. Schematic of the Wet Cement Process Source: CEMBUREAU, 1999 
1-4 

Figure 1-2. Schematic of the Dry Cement Process with Cyclone Preheater Source: CEMBUREAU, 1999 
1-5 

1.1.3 Kiln Types and Their Use 

Rotary kilns are broadly categorized as dry- and wet-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 stages of pyroprocessing occur before the materials enter 

the rotary kiln. Preheater and preheater/precalciner kilns have higher production capacities and greater 

fuel efficiency compared to other types of cement kilns. Table 1-1 shows heat input in terms of millions 

of British Thermal Units (MMBtu)/ton1

 of clinker for various types of kilns. As the data clearly 

demonstrate, preheater/precalciner kilns provide greater fuel efficiency. The replacement of wet and 

(certain) dry process kiln capacity with modern kiln 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 with 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 increased by 27%, from 405,000 to 556,000 tons 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 design 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 provided 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 2008

Average Capacity, thousands of metric 

tons/year

Number 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. Portland 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 plant facilities in 2005 are shown in Figure 1-4. The 

cement manufacturing sector in the U.S. is concentrated 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 Alabama 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 projected 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 (brownfield) 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 domestically, 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 metric 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. Generally, 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 clinker suppliers to the U.S. were, in descending order, 

Canada, China, Thailand, Greece, the Republic of Korea, Venezuela, Mexico, Colombia, Taiwan, and 
1-9 

Sweden. The ten busiest ports of entry within customs 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 tons 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 Cement from 1998 to 2008 (PCA, 2009a) 

1.1.6 Cement Demand Centers 

Because of the relatively high transportation costs, the U.S. cement industry is structured around statespecific cement demand centers. PCA reports that the vast 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 transported over longer distances, especially when the 

less expensive rail and water transportation modes are available (APCA, 1997). 

1.2 Emissions from the U.S. Cement Industry and Applicable Regulations 

Criteria pollutants, hazardous air pollutants, and carbon dioxide (CO2) are released during cement 

manufacturing. Nitrogen oxide (NOx) emissions from cement kilns result primarily from the combustion 

process: oxidation of fuel nitrogen (fuel NOx) and the oxidation of nitrogen in the combustion air (thermal 

NOx). EPA’s 2005 National Air Toxics Assessment (NATA) Inventory reports that cement kilns released 

181,000 metric tons (200,000 short tons) of NOX emissions from the combustion of fuels (EPA, 2009). 

Sulfur dioxide (SO2) emissions2

 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 SO2 during pyroprocessing and a significant amount is likely to be captured in the 

form of sulfates as the flue gas passes through the calcination zone. Compared to long dry and wet kilns, 

preheater and precalciner kilns tend to be more effective at capturing fuel-generated SO2. Accordingly, 

 

2

 Small amounts of SO3 may be released in addition to bulk SO2 but the SO3 emissions are treated as SO2 for computational 

purposes. Small amounts of CO may be released in addition to bulk CO2 but CO emissions are treated as CO2 for computational 

purposes. 
1-11 

oxidation of sulfur in the feed materials is likely to be the major component of total SO2 emissions. The 

2005 NATA Inventory reflects that cement kilns released 133,000 metric tons (147,000 short tons) of SO2

emissions in 2005. 

Particulate matter (PM) emissions result from quarrying operations (the crushing 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 PM10 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 (CO2) emissions. CO2 emissions are 

a product of the combustion of fuel as well as the calcination of the limestone in the raw meal. The CO2

from fuel combustion can be calculated from heat input and fuel characteristics combined with clinker 

production. The CO2 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 CO2 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 CO2 emissions 

from clinker production. The CO2 emissions reduction would be proportional to the amount of substitute 

materials added. 

Multiple regulatory requirements to reduce criteria air 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 regulatory requirements might apply to individual cement 

facilities depending on their locations. In 2008, 44 cement facilities were located within ozone (O3) 

nonattainment areas (NAA), while 20 facilities were within PM2.5 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 O3 NAA, PM2.5 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 the emission reduction strategies 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 framework. 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 Algebraic 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 controls 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 interest to the policy analyst, such as emissions cap, 

emission reduction scenarios, and discount rate. The input 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 arise 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 plants, 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 industrial 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 are pollution prevention measures and the ones in red 

are mitigation measures. Clearly, the integrated picture presented in Figure 1-7 makes a compelling case 

for considering commodity production/supply activities 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 Figure 1-7. Example emission reduction policies that 

can be evaluated using ISIS are: 

• Criteria pollutants (NOX, SO2, PM, CO) –emission limits and/or cap-and-trade. 

• Hazardous Air Pollutants (e.g. Hg, HCl) – emission limits. 

• CO2 – cap-and-trade and/or emission taxes. 

• Long and short time horizons: CO2 (decades), criteria pollutants (annual). 

• Regional or national requirements. 
1-14 

Pollution Controls (NOx, SO2,PM, 

CO2, 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 View of Pollution Generation Pathways, Emissions Abatement 

Approaches, and Multimedia Impacts for an Industrial Sector 

References for Chapter 1 

APCA (1997). Comments on EPA’s Draft Economic Analysis 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/industrial/special/ckd/ckd/ckdcostt.pdf, accessed October 21, 2008. 

EPA (2007a). Personal communication from Alvaro Linero, Florida Department of Environmental 

Protection to Keith Barnett, US EPA, on December 4, 2007. 

EPA (2007b). Alternative Control Techniques Document Update – NOX 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/catalog.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 Rule (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 Consumption 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 American 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). NOX, SO2 and CO2 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 Hendrik G. van Oss, USGS, to Elineth Torres, US 

EPA, on July 7, 2008. 
1-16 

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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 reduction 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 consumption patterns are given full consideration, has been developed 

over the past 6 decades. In SPE network models, interregional economies are simulated by finding the 

balance of demand, supply, and trade that will result in competitive market equilibrium among the 

regions. Enke (1951) first demonstrated how the cost 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 surpluses for each market of the sector. This sum is 

referred to as the total surplus or net social payoff of the sector; McCarl and Spreen (1980) provide 

interpretation and justification. The linear programming formulation of the SPE problem was developed 

by Duloy and Norton (1975). 

Using Figure 2-1, the definition of the suppliers’ surplus corresponding to a quantity Q of a commodity is 

the difference between the total revenue and the total 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’ surplus corresponding to a quantity Q is given by the area 

under the inverse demand curve up to point C minus the area under the horizontal line C1-C. This area is 

the cumulative opportunity gain of all consumers who 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 welfare 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 utilized in the ISIS framework are shown in Figure 

2-2. While ISIS structure permits accounting for multiple products, the description 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 more 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 the 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 previous 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 QE

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 Framework 
2-3 

2.1 Indexes, Sets, and Mappings 

Before we start the description of the mathematical structure of ISIS, it is helpful to define the sets of 

relevant entities and mappings describing the relationships among various entities. 

Indexes (one-dimensional sets)

• ISIS data structures (sets and parameters), variables, 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, replacement, 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; and 
2-4 

• 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 surplus) 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

)price(t,dc ddemand(t, dc)

totalemissions(t,j)

teemeasure ee)scost(t,i,

tcontrolcost(t,i,k)

timportsco st(t,id)

ttranscost(t,dc)

z tannualpro )dncost(t,i

dis(t)

dis(t) j)alprice(t,

dis(t)

dis(t)

dis(t)

dis(t)

Minimize dis(t)

(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 ($) using 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

dctprice ),( dctddemand ),( 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 scheme. 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 equation and also reflects how a stepwise approximation 

of the price-demand curve can be created. 

P(d1)

P(d2)

P(d3)

P(d4)

P0

Dmin D0 Dmax

d1 d2 d3 d4

P(D)

Dwid

Dwid

Price

Quantity

Figure 2-3. Stepwise Calculation of the Demand Curve 
2-6 

First the range of the demand-price curve is defined using 

min ⋅= range)-(1D0D (2.2.3) 

max +⋅= range)(1D0D (2.2.4) 

where 

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval 

Dmin - Dmax 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 Dmin - Dmax. 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 Dmin - Dmax with the width of each step given by 

stepsofNumber

D-D dwidth max min = (2.2.5) 

where 

Number of steps is another user-supplied parameter. 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 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 P DD

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

totalemissions(t,j)

teemeasure ee)scost(t,i,

tcontrolcost(t,i,k)

timportscost(t,id)

ttranscost(t,dc)

z tannualpro )dncost(t,i

dis(t) P(demand(t dstep)dc,,

dis(t) j)alprice(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-demand curves for all time periods and demand centers. 

However, this equation needs specifications of one 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 and in the supply constraint, Equation (2.3.1), presented 

in the next section. 

2.3 Supply 

The demand for a commodity in a market can be satisfied by domestic production and foreign imports as 

follows: 

∑ ),,( +∑ ),,( ≥ ∑ .),,(

i id dstep

prodnquant dcitity importedqu dcidtantity dstepdctdemand (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 dstepth 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, 

prodnquant )ity(t,i,dc prodn(t,i). 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), and new kilns. Treatment of these is explained in a 

subsequent section. 

Production of a unit is constrained by its capacity. So, 

itprodn ),( ≤ i).,capacity(t (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, 

∑ = ∑

dc istepoi

importedquantity(t,id,dc) id,istep)oiImports(t, ,, (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 istepth

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 Iprice(Imports) (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 

I Imports0 range)-(1 min = ⋅ (2.3.6) 

Imax = Imports0 +⋅ range)(1 (2.3.7) 

where 

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval 

Imin - Imax 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 Imin - Imax. 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 Imin - Imax with the width of each step given by 

steps ofNumber 

I-I Iwidth max min = (2.3.8) 

where 

Number of steps is another user-supplied parameter. 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 Imports Imin

istep ⋅⋅ += (2.3.9) 

Using the above information, the imports-price curve is determined 

α

1

istep

istep

Imports0

Imports Iprice Imports Iprice0 ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ( ) = (2.3.10) 

Equation (2.3.10) is used to generate the imports-cost curves for all time periods, origins of imports, and 

import districts. However, this equation needs specifications 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 ISIS model (with exogenous Imports0[t, oi, id] corresponding to capacities of 
2-10 

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 elements (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, 

{ } irepl(i)i iExp(i) .iNew(i)

vyear(v) tyear(t) (vyear(v) techlifepl ants);

;i),capacity(t

∈ ∪∪

<≤ +

= − + ∑ ⋅ v

addcap(t)addcap(t,i i)timeblock, )tcap(t,i,v

 (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 vth 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., replacement units, expansion units, and new capacity) and 

projected units, (2) annual fixed operation and maintenance (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 

plantcapcost(t,i) ;0 ∈= { }.iE(i)i (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 

{ }.

;

tstart(i) tyear(t) (tstart(i) ecolifepla iP(i)i nts);

CRFplant i),capacity(t pcapcostt( i)t,

≤≤ + ∈

plantcapco st(t,i) = ⋅ ⋅ (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 

plantcapco st(t,i) = itAddcap ⋅ i),capcostt(t),( ⋅ CRFplant (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 using 
2-12 

[ ]

),( .

),(

+ +∑

= + + + ⋅ +

sw

itfc H2Oconsump )tncost(t,i SWdispcost(t,i,SW)

i)varcost(t, i)RMTt(t, i)ELCt(t,i)LBRt(t,i)VOMt(t, itprodn

 (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 ]] ofNumber pollutants

[eintensity(i) f)i,t,fuelcostt(

m, ee)j,k,i,t,poltikjee(

f

⋅

= ⋅ ⋅

∑

∑

varprodn(t ,ee),i,f,m,j,k

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 related cost is given by 

H 2Oconsumptn cos = Ht(t,i) 2Oconsumptn(t,i)⋅H2Ocostt(t) 1000/ (2.4.8) 

With annual water consumption given by 

H 2Oconsumptn(t,i) = H2Ointensity(i) ⋅ itprodn ).,( (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 by 
2-13 

SWdispcost (t,i,sw) = SWgen(t,i ,sw)⋅SWdisposal sw)costt(t, (2.4.10) 

With annual solids discharges given by 

SWgen(t,i,sw) = SWgenerati sw)on(i, ⋅ itprodn ).,( (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 

tannualpro )dncost(t,i = plantcapcost(t,i) + i)varcost(t, . (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

timportscost(t,id)

istepsoi

∑ + + ⋅

=

isteps)id,oi,Iprice(t, InsFreight(id) handling(id) (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 istepth 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 

)].,,(

)],,(

Imported dcidtquantity

ttranscost(t,dc) prodn dcitquantity

id

i

∑

∑

⋅

= ⋅ +

[imprttran dc)id,(t,sportcostt

[prodntran dc)i,(t,sportcostt

(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., CO2 emissions from calcination of limestone in cement kiln). As such, both of these 

emission generation pathways are included in ISIS. Further, the ISIS framework includes algorithms to 

account for tracking multiple pollutant streams associated with uncontrolled emissions, controlled 

emissions, pollution prevention from process modifications and energy efficiency measures, and any 

controls-related effects (e.g., generation of CO2 in a wet SO2 scrubber). These algorithms are described 

below. 

emintensit j)f,yfuel(i, modeintens k)/100].j,cp(i,[1ee)k,ity(i,

ee)k,j,f,i,polfuel(t,

⋅ −⋅

=

(2.5.1)

ee).,eHIdispl(i

(i)eintensity primaryHIc k)hange(i, secondaryH k)Ichange(i,

modeintens ee)k,ity(i,

+

+ +

=

(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 pollutant 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

secondaryRMTchangepercent(i, k)/100

1 primaryRMTchangepercent(i, k)/100

j,m,i,polrmt(t, ee)k, emintensit j)m,yrmt(i,

−

⋅ ⎥

⎦

⎤ ⎢

⎣

⎡ + +

= ⋅

(2.5.3)

where 

polrmt(t,i,m,j,k,ee) is emission (tons pollutant per 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 material addition resulting from application of control k at 

unit i. 

Finally, 

ee)k,j,m,f,i,pol(t, = ee)k,j,f,i,polfuel(t, + k).j,m,i,polrmt(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 

),,( = ∑ ⋅ .),,,,,,(

m,f, ee)j,k,i,t,poltikjee(

jitemissions ee)k,j,m,f,i,pol(t, eekjmfitvarprodn (2.5.5) 

where 
2-16 

varprodn(t,i,f,m,j,k,ee) is the production variable associated with use of kth control and/or eeth 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

totalemiss jtions jitemissions (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 

jmfitprodnpol ),,,,( = ≠ jjjjjmfitprodnpol ;);,,,,( (2.5.7) 

with, 

),,,,( .),,,,,,(

),,,,(

= ∑

eejkitpoltikjee

jmfitprodnpol eekjmfitvarprodn (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 that endogenous applications of sector-based controls 

and energy efficiency options occur in a realistic manner. 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. 

vyear(v) tyear(t) techlifeco + [(vyear(v) .ntrols(k)]

;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 vth 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, 

vyear(v) tyear(t) (vyear(v) techlifeco .ntrols(k))

,_, ;i),capacity(t

<≤ +

= ∑ ⋅ v

ControlCap v)c(t,i,ktsk)(t,i (2.6.2a) 

For t > 1, 

vyear(v) tyear(t) (vyear(v) techlifeco .ntrols(k))

, ,_, ;i),capacity(t

<≤ +

= − +∑ ⋅ v

ControlCap k)(t,i ControlCap (t v)c(t,i,ktsk)itimeblock,

 (2.6.2b) 

In any year, no two incompatible controls can coexist on a unit, 

ControlCap kit ),,( + ControlCap kkit ),,( ≤ itcapacity ),( (2.6.2c) 

where 

k and kk are incompatible controls. 

Finally, the production coefficient associated with a control is less than the capacity of the unit the control 

is installed on, 

vyear(v) tyear(t).

_ ;i),capacity(t ofNumber pollutants

),,,,(

≤

≤ ∑ ⋅

∑

v

eejkitjeef,m,poltik c(t,i,k,v)ts

varprodn(t ,ee),i,f,m,j,k

(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 escalated appropriately to use values in years of interest. 

Capital Recovery and Fixed Cost

Annual recovery of capital cost of control k is given by 

tyear(t) vyear(v) [vyear(v) ecolifecontrols(k)].

cntrlcapit k)i,alcostt(t, CRFcontrol ;(k)

_

<≤ +

⋅ ⋅

=

ControlCap(t,i,k)

capcost c(poltik(t, i,k))

(2.6.4)

Similarly, annual FOM cost is given by 

tyear(t) vyear(v) [vyear(v) techlifeco .ntrols(k)]

cntrlfixed ;k)i,costt(t,

(_ )

<≤ +

⋅

=

ControlCap(t,i,k)

FOMcost i,k)poltik(t, c

(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 maintenance (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 kth control application on i

th unit, 

FOMcost_c(t,i,k) is the annual fixed operation and maintenance cost ($) of kth 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 

.

]

[

),,,(

),,,(,),(

ofNumber pollutants

secondaryH Ichange(i, k) f)r,t,fuelcostt(

primaryHIc k)hange(i, fr,t,fuelcostt(

∑

∑

+ ⋅ ⋅

⋅ ⋅

=

kf(k,f),m, j,eekitpoltikjee

j,eekitpoltikjee,mfifi

varprodn(t ,ee),i,f,m,j,k

varprodn(t ,ee),i,f,m,j,k )

fuelcostchange_c(poltirk(t,ri(i,r),k))

(2.6.6)

where 

primaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of kth

technology, 

secondaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of kth

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 mapping 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 with application of controls is given by 

.

100/ ]

[ 100/

),( ),,,(

),( ),,,(

ofNumber pollutants

secondaryRMTchangepercent i)RMTt(t,

primaryRMTchangepercent i)RMTt(t,

∑

∑

+ ⋅ ⋅

⋅ ⋅

=

j,eekitee,m,poltikjfifi

j,eekitee,m,poltikjfifi

varprodn(t ,ee),i,f,m,j,k

varprodn(t ,ee),i,f,m,j,k

RMTcostchange_c(poltirk(t,ri(i,r),k))

(2.6.7)

where 

primaryRMTchangepercent is the percent change in primary raw material with use of kth

technology, 

secondaryRMTchange(i,k) is the percent change raw material input corresponding to any secondary 

raw material addition with use of kth technology, and 
2-20 

RMTt(t,i) is the unit-specific cost of raw material ($/ton clinker) in year t. 

Annual reagent consumption costs are 

⋅reagentpri rgnt).cet(t,

=

∑

rgnt

rgntconsum rgntkitcpt

rgntconsum kitpoltikcptcost

),,,(_

)),,((_

(2.6.8)

The annual consumption of a reagent given by 

.

[ ]

),,,(_

, ),,,,(),(

),( ),,,,(

ofNumber pollutants

reagentcon sumptrmt(i rgnt)j,k,m,,

ofNumber pollutants

reagentcon sumptfuel( rgnt)j,k,f,i,

[ varprodn(t ],ee),i,f,m,j,k

varprodn(t ,ee),i,f,m,j,k

rgntconsum rgnt)kitc(poltikpt

jm eejkit,poltikjeefifi

,jfifi eejkitem,poltikje

∑ ∑

∑ ∑

⋅

+

⋅

=

(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

catconsump catkitct

catconsump )kitpoltikctcost

catalystpr catice(t,

(2.6.10)

with 
2-21 

.

),,(_

),( ),,,,(

ofNumber pollutants

EGFW(i) catalystconsumpt(i, cat)j,k, ∑ ∑ ⋅⋅

=

j eejkitee,m,poltikjfifi

varprodn(t ,ee),i,f,m,j,k 10000

catconsump ,cat)kitc(poltikt

(2.6.11)

where 

catalystconsumpt(i,k,j,cat) is the catalyst consumption rate (ft3

 catalyst/10000 ft3 flue gas), 

EGFW(i) is the exhaust gas flow rate (scf/ton clinker), 

catalystpricet(t,cat) is the price of the catalyst ($/ft3

) in year t, 

catconsumpt_c(t,i,k,cat) is the catalyst consumption rate (ft3

/year), and 

catconsumptcost_c(t,i,k) is the catalyst cost ($/year). 

Annual cash flow associated with byproduct generation disposal/sale is 

⋅byproductpricet(t, byprod);

=

∑

byprod

byproductgen c(t,i,k,byprod)

byproductcost )kitc(poltik

_

),,(_

(2.6.12)

with annual generation of a byproduct given by 

.

),,(_

),,,,(),(

),( ),,,,(

ofNumber pollutants

byproductp rodnrmt(i, byprod)j,k,m,

ofNumber pollutants

byproductp byprod)j,k,f,,rodnfuel(i

[ varprodn(t ],ee),i,f,m,j,k

[ varprodn(t ],ee),i,f,m,j,k

byproductg ,byprod)kitc(poltiken

m,j eejkit,poltikjeefifi

,jfifi eejkitem,poltikje

∑ ∑

∑ ∑

⋅

+

⋅

=

(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 

.

[ ]

),,( ( ) ), ,,,,(,

ofNumber pollutants

∑ kWhperton( k)f,i, ⋅ ∑ = fifi eejkitpoltikjeem

varprodn(t ,ee),i,f,m,j,k

kwh_c(polt )kitik

(2.6.14)

The cost of electricity consumption is 

kwhcost_c( ),,( )kitpoltik = k)kwh_c(t,i, ⋅ electricit i).ycostt(t, (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 associated with control k is 

.

2 _

),,,,(),(

),( ),,,,(

ofNumber pollutants

H2Ousermt( k)m,i,

ofNumber pollutants

H2Ousefuel k)f,(i,

[ varprodn(t ],ee),i,f,m,j,k

[ varprodn(t ],ee),i,f,m,j,k

H Oconsumpt c(poltik(t,i,k))

m,j eejkit,poltikjeefifi

,jfifi eejkitem,poltikje

∑ ∑

∑ ∑

⋅

+

⋅

=

(2.6.16)

The cost of water consumption is 

H2Ocost_c( ,k))poltik(t,i = H2Oconsump t_c(t,i,k) ⋅ H2Ocostt(t ) .1000/ (2.6.17) 

where 

H2Ousefuel(i,f,k) is the water use due to control (tons reagent/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 

kWhcost_c(t,i,k) H2Ocost_c(t,i,k)

rgntconsumptcost_c(t,i,k) catconsump i,k)tcost_c(t, byproductc k)ost_c(t,i,

fuelcostchange_c(t,i,k) RMTcostcha k)nge_c(t,i,

capcost_c(t,i,k) FOMcost_c(t,i,k)

tcontrolcost(t,i,k)

+ +

+ + +

+ +

+

=

(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 realistic 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. 

vyear(v) tyear(t) eetechlife + [(vyear(v) (ee)].

;1

<≤

∑ ≤ vt

ts ee(poltiee ,v)t,i,ee ,_ )(

(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 vth 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, 

vyear(v) tyear(t) (vyear(v) techlife(e e)).

, _ ),( ;i),capacity(t

ee

EECap(t,i tsee) ee(poltiee v)t,i,ee v

<≤ +

= ∑ ⋅ (2.7.2a) 

For t > 1, 

vyear(v) tyear(t) (vyear(v) techlife(e e)).

, , _ ),( ;i),capacity(t

ee

EECap(t,i ee) EECap(t tsee)itimeblock, ee(poltiee v)t,i,ee v

<≤ +

= − +∑ ⋅ (2.7.2b) 

In any year, no two incompatible measures can coexist on a unit, 

∑[ ] + ≤ ;),,(_),,(_ 1

v

ts ts,v)eeitee(poltiee ,v)eeeitee(poltiee (2.7.2c) 

where 

ee and eee are incompatible measures. 

Finally, production coefficient associated with an energy efficiency measure is less than the capacity of 

the unit the measure is installed on, 

vyear(v) tyear(t)

i);,capacity(t ofNumber pollutants

≤

≤ ∑ ⋅

∑

v

f,m,poltik j,eekijee(t v)ee(t,i,ee,ts

varprodn(t ,ee),i,f,m,j,k

_ ),,,

(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. 

tyear(t) vyear(v) [vyear(v) ecolifeee(ee)].

ecapitalco ee)i,stt(t, ;CRFee(ee)

_

<≤ +

⋅ ⋅

=

EECap(t,i,ee)

capcost ee(poltiee(t, i,ee))

(2.7.4)
2-25 

tyear(t) vyear(v) [vyear(v) eetechlife(ee)].

efixedcost ;ee)i,t(t,

(_ )

<≤ +

⋅

=

EECap(t,i,ee)

FOMcost poltiee(t,ee i,ee)

(2.7.5)

where 

ecapitalcostt(t,i,ee) is the capital cost ($/ton of clinker) of eeth energy efficiency measure application 

on i

th unit,

efixedcostt(t,i,ee) is the annual fixed operation and maintenance (FOM) cost ($/ton of clinker) of eeth

energy efficiency measure application on i

th unit,

ts_ee(t,i,ee,v) is a binary variable that can bring vth 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 

ofNumber pollutants

ee),eHIdispi(i ⋅ ∑ ⋅ fr,t,fuelcostt(

=

),,,,(

_

f,m,poltik eejkitjee

varprodn(t ,ee),i,f,m,j,k )

fuelcostch ange ee(poltire e(t,ri(i,r),ee))

(2.7.6)

where 

eHIdispi(i,ee) is the displacement of primary heat input (MMBtu per ton clinker) with use of eeth

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 mapping 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 is 
2-26 

ofNumber pollutants

kWhperton( ee)i, ⋅ ∑ = ),,,,(,, ),,(_ eejkitpoltikjeemf

e varprodn(t ,ee),i,f,m,j,k

kwh )eeitee(poltiee

(2.7.7)

The cost of electricity consumption is 

kwhcost_ee ),,( )eeit(poltiee = kwh_ee(t,i ,ee) ⋅ electricit i)ycostt(t, (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 cost of energy efficiency measures is 

fuelcostch ange ee(t,i,ee) kWhcost ee(t,i,ee)

capcost ee(t,i,ee) FOMcost ee(t,i,ee)

teemeasure ee)scost(t,i,

_ _

_ _

+

+ +

=

(2.7.9)

2.8 Policy Options 

ISIS can help design and evaluate a number of emissions reduction policy options including cap-andtrade, emissions taxes, and emissions 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 permits (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 seller is being rewarded for having reduced 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 SO2 reduction program). 

However, ISIS does permit evaluation of potential programs with caps over user-selected time periods 

(e.g., 5-yearly caps). This evaluation is accomplished using, 

totalemiss jtions ),( j)ec(t, +≤ − + jperiodtbnkjtbnk ),(),( (2.8.1) 

where 

ec(t,j) is the emission cap for pollutant j in year t, and 
2-27 

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, this requirement can be imposed using 

emissions( j)i,t, ≤ j)i,,ecpminer(t (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 

jitemissions ),,( j)el(t, ⋅≤ itprodn ),( (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 endogenously determining the level of emission reduction 

corresponding to a certain allowance price. This information 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 corresponding to the same allowance price may be different for the sectors 

under consideration. The emissions response to a given allowance price is driven by the following term in 

the objective function (see section 2.1), 

)( ),(

,

tdis totalemiss jtions

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 the 
2-28 

appropriate levels of production, imports 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-processed 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 worksheets 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-specific 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 capacity (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) 1683

PCA’s Projected New Kilns (2010 – 2012) 9 

Each kiln modeled in ISIS is characterized by its location, design (i.e., wet, dry, preheater, or precalciner), 

daily and annual clinker capacity4

, 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 neither 2 cement kilns in Puerto Rico. 4

 Annual clinker capacity as reported by PCA refers to the kiln daily capacity multiplied by 365 less normal downtime days, 

where daily capacity is the normal clinker output a kiln can produce per day given a realistic work pattern and normal downtime

days are number of days of downtime required for maintenance, repair or cleanup. 
3-2 

3.2.1.2 Average Variable Costs 

In previous economic analyses, five input variables 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 mainly 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 state-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 transportation 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 domestic 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 cement demand will reach 192 million metric tons by 

2035, which reflects an increase of nearly 64 million metric tons with a compound annual growth rate of 

1.4%. Cement demand through year 2035 is reported in the PCA Long-Term Cement Consumption 

Outlook (PCA, 2009c). PCA projections of cement demand (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 Kilns in the United States as of 2009 
3-4 

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 1.15 
3-5 

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 develop these costs, information on distances between 

supply and demand points and costs of transportation modes (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 competition is generally maintained 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 district to each demand center. For a given origin-
3-6 

destination pair, the dominant mode (lowest cost of shipment) 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 transportation 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 

 BTS1 WDOT2 NACT3 

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 industry 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 cement-specific transportation costs, assumes short tons--

converted to metric. 

3.2.1.6 Imports 

U.S. cement markets receive imported quantities of cement and clinker from a number of countries, and 

these imports arrive at more than 30 import districts (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 conducted 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 the international supply elasticity 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 1.07 
3-7 

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 0.13 
3-8 

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, typically 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, maintenance, 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 PCA 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 associated 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. Coal 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%

NaturalGas

FuelOil

Coal

PetroleumCoke

Tires

Other Solid Wastes

Liquid Wastes

Figure 3-2. Commercial Fuel Use Profile by U.S. Cement Industry in 2005. 

 

5

 PCA does not specify if “coke” is metallurgical coke or petroleum coke. Authors believe it is the latter. 
3-9 

Source: EIA, 2008a 

In the ISIS-cement model, to determine the fuel intensity 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 exhaust 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/tona

 Nm3/kg SCF/tona

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 ton 

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 number of pollutants of interest. In ISIS-cement, 

each kiln is characterized by its NOx, SO2, PM, hydrochloric acid (HCl), mercury (Hg), total hydrocarbon 

(THC), and CO2 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). 

NOX emissions from cement kilns result primarily from the following combustion process: oxidation of 

fuel nitrogen (fuel NOX) and the oxidation of nitrogen in the combustion air (thermal NOX). Oxidation of 

nitrogen in the feed materials (feed NOX) can also influence total NOX emissions. Table 3-6 shows NOX

emission intensities for cement kilns in lb/ton of clinker and in lb/MMBtu (EPA, 2007a). 
3-10 

Table 3-6. Estimated Uncontrolled NOX Emission Intensities for Cement Kilns 

Uncontrolled NOx 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) 

SO2 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 SO2 during pyroprocessing, and a significant amount is likely 

to be captured in the form of sulfates as the gas 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 SO2. Accordingly, oxidation of sulfur in the feed materials is likely to be a major component of 

total SO2 emissions. Table 3-7 shows average SO2 emissions for each kiln type for each state (Andover 

Technology Partners, 2009a). State-specific emission intensities of SO2 were determined from emissions 

reported for the kilns in that state. For any state where the emission intensity was not available for a kilntype, the national average emission intensity was assigned to kilns in that state. 

CO2 emissions from cement kilns result from limestone calcination and fuel combustion. Calcination 

releases 0.52 tons of CO2 per ton of clinker produced, while fuel-based CO2 emission factors range from 

199.52 lb CO2/MMBtu for coal to 105.02 lb CO2/MMBtu for natural gas (Andover Technology Partners, 

2009b; Andover Technology Partners, 2010). Table 3-8 shows approximate CO2 and H2O produced from 

combustion for the most important fuels for cement kilns. 
3-11 

Table 3-7. Average SO2 Emissions for Each Kiln Type in Each State 

SO2 (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 11.98 
3-12 

SO2 (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 CO2 and H2O Produced from Combustion of Fuels 

Variable Name Flue Gas Tires Petroleum 

Cokes 

Heavy 

Fuel 

Oil 

Rosemont 

PRB 

Logan, 

WV 

BIT 

Natural 

Gas 

LBMCO2MMBTU lbmoles 

CO2/MMBtu 4.26 4.83 3.85 4.25 4.53 2.39 

LBCO2MMBTU lb CO2/MMBtu 187.44 212.56 169.32 186.83 199.52 105.02 

LBMH2OMMBTU lbmoles 

H2O/MMBtu 2.76 2.76 2.23 3.23 4.14 2.77 

LBH2OMMBTU lbs H20/MMBtu 49.71 49.73 40.26 58.21 74.64 49.92 

PRB = Powder River Basin coal WV BIT = West Virginia bituminous coal 
3-13 

3.2.2 Control Technologies and Emission Abatement Approaches 

ISIS-cement contains information on abatement approaches for NOx, SO2, PM, HCl, Hg, THC, and CO2 

emissions described above. The three categories of abatement approaches included are: process 

modifications and upgrades, raw material and/or fuel substitution, and mitigation technologies. For each 

emission abatement approach, where possible, information 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 for 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 costs 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 efficiency measures shown in Tables 3-14 through 3-17 

are given in Worrell and Galitsky (2004). Economic life for each of these measures can be taken to be the 

average of the technical life and the payback period. Again, 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 NOX, SO2, CO2, HCl, Hg, THC emissions control technologies being 

used in the ISIS-cement model. Tables also reflect 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 show 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 economic 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 Management and Budget (OMB) for project evaluation (OMB, 

1992). Escalation rates used can be found in the “ISIS_Inputs.xls” workbook. Escalation rates for labor 

are based on historical data from Bureau of Labor and Statistics (BLS). Raw materials escalation factors 

are calculated from historical price data of crushed sandstone and gypsum from USGS. The escalation 
3-14 

factors for variable operating and maintenance costs are based on Chemical Engineering Index. Escalation 

factors for various fuels and electricity are estimated 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). 
3-15 

Table 3-9. NOX Control Technologies for Cement Kilns 

Impact on Emissions, 

±% change 

Electricity Consump

kWh/ton of ceme

Control Type 

NOX SO2 PM Hg Other Grinding Kiln Ope

Low NOX Burners – 

Indirect Firing -20% to -30%1 No impact2 03 -1.2

Mid Kiln 

Firing-Tires -20% to -40%1 May vary5 03 06 

Low NOX Burner + Mid 

Kiln Firing- Tires -20% to -40%1 May vary5 03 -1.2

Low NOX Burners + Tire 

Derived Fuel -20% to -40%7 May vary5 03 -1.2

Low NOX Burner + 

Selective Non Catalytic 

Reduction 

-50%2 No data5 No data8 No data8 No data8 0 -1.2

Low NOX Burner + 

Selective Catalytic 

Reduction 

-90%2 Oxidation10 No data8 Oxidation10 No data8 0 -1.2

Low NOX Burners + 

CemStara -30%11

-1.3 (wet process); 

-1.9 (dry 

process) 12

-1.24,12 fro

and 

-1.5 (wet p

or 

-2.2 (dry p

from Cem

CemStar/Fly Ash Injection -20%14

 -1.3 (wet 

process); 

-1.9 (dry 

process) 13

-1.5 (wet p

or 

-2.2 (dry p

from Cem

Notes to Table 3-9 

1. See EPA (2004), Table 5-1. 
3-16 

2. 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 electri

cement manufacturing processes. 

4. Conversion from direct firing systems typical of wet and dry process kilns and older preheater kilns to indirect firing systems, 

low NOX burner (LNB) technology, could result in reductions in primary air fan and kiln-induced draft fan power requirement

increases in coal conveying power requirements. A reduction in fan/blower power on the order of 100 hp might be anticipated 

(300,000 – 500,000 clinker tons per year) kiln converted from direct firing to indirect firing. No power savings on adding an Lif the kiln system is already indirect-fired. 

5. SO2 emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln operation. 

(typically, 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used in cement kilnsTherefore it is not practical to relate SO2 emissions to the use of these NOX control methods. With respect to use of an SO2 we

limestone, it is assumed that, for wet process kiln systems, uncontrolled SO2 emissions of 8.2 lb per ton of clinker (8.9 lb/ton o

42, Table 11.6-8, 1995) are treated by use of a stoichiometric amount (with respect to uncontrolled SO2) of limestone of 90% p

slurry. Limestone and water consumption are 15.5 lb and 87.7 lb, respectively, per ton of cement produced. For a precalciner kuncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995: Table Table 11.6-8) are treated sim

amount limestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2.1 lb and 11.8 lb per ton oprecalciner kiln, it is assumed that uncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995: T

similarly (stoichiometric amount limestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2cement produced. 

6. Kiln system and raw materials grinding electric power consumption would not be significantly affected by introduction of tire

7. Combined effect will vary. Low NOx 20 to 40%, MKF 20 to 40%. 

8. While there may be theoretical or limited experimental bases to assume increases or decreases in emissions of various pollutan

NOX emission controls, statistics on such effects are not available. See, generally, EPA (2007), Chapter 11. 

9. Assumes SNCR or SCR both stoichiometric additions of NH3 in 20-percent solution. Typical precalciner NOX emissions of 4.

treatment (EPA 1995, Table 11.6-8), and 0.92 tons of clinker are used in each ton of cement. It should also be noted that SNCR 

process, long dry process, and many preheater kilns because the kiln gas exit temperatures are too low from those units for the take place. See EPA (2007a). SNCR is assumed to achieve 63% reduction in NOX emissions from applicable kiln systems. See 

There may be an attendant small increase in kiln system electric power in connection with injection of water into the kiln systegas volume handled by the kiln’s fan system. The increase is not considered in the calculation. 

10. Typically, up to about 1% of SO2 can be oxidized to SO3, Elemental form of Hg oxidized across SCR provided sufficient conc

flue gas. 

11. On a lb/ton of clinker basis. 

12. Use of the CemStar process may have multiple effects on electric power consumption, including reduced raw mix preparation 

unground CemStar material is fed to the rotary kiln, and reduced fan power requirements resulting from reduced kiln gas volu

both combustion gases and raw mix calcination. Assuming a 5% replacement of raw mix with carbonate-free CemStar materi

reduction in raw mix preparation energy is also assumed. A very approximate resulting 5% reduction in kiln electrical energy based on a roughly 5% reduction in kiln exit gases from both calcination and combustion. 

13. CemStar and fly ash injection are expected to have similar effects on raw mix preparation and kiln process electrical energy r
3-17 

14. See NESCAUM (2000) for effect of CemStar on NOX reductions from cement kilns. Investigation of the combined effects of 

pollutant emissions was not carried out for this summary. 

General Notes 

A. Cement kiln processes do not use steam. B. Cement kiln dust (CKD) disposal rates as functions of NOX or SO2 emissions control technology has not been reported. Typic

range 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 cemenC. CKD disposal costs vary widely by region, CKD characteristics, CKD volumes, and location of disposal site (on-site or off-sitcosts are expected to be of comparable cost to transportation costs in connection with off-site disposal. For example, prices at three 

landfills range from $26/short ton to $69/short ton of CKD plus transportation, which may range from approximately $500 to $1,20

Comm., E. Leamer (ARCADIS) Feb. 26, 2009. CKD disposal prices would be expected to be generally unaffected by the type of ccontrols 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-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. SO2 Control Technologies for Cement Kilns 

Impact on Emissions, 

±% change 

Electricity Consumption, 

kWh/ton of cement Control Type 

NOX SO2 PM Hg Other Grinding Kiln Operati

Wet Scrubber -90% to -95%1 -80% -99.9% for HCl 

+0.2 

(wet process 

kilns) 2

+52

Dry Lime Injection -50%4 -75% for HCl 5 5 

Notes to Table 3-10 

1. 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 t

micrometers in particle size and pumping limestone slurry; and 2) increased kiln exhaust gas fan power requirements. The latt

caused by increased pressure drop demand on kiln ID fans due to conveying kiln exit gases across the scrubber spray tower plu
3-18 

demand on kiln ID fans due to the addition of water in the limestone slurry. Slurry preparation and pumping is assumed to reqlimestone. The spray tower pressure drop is assumed to add 500 hp to ID fan requirements on a 300,000 to 500,000 ton per ye

3. SO2 emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln operation. 

(typically 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used in cement kilnsTherefore it is not practical to relate SO2 emissions to the use of these NOX control methods. With respect to use of an SO2 we

limestone, it is assumed that, for wet process kiln systems, uncontrolled SO2 emissions of 8.2 lb per ton of clinker (8.9 lb/ton o

Table 11.6-8) are treated by use of a stoichiometric amount (with respect to uncontrolled SO2) of limestone of 90% purity in a 

Limestone and water consumption are 15.5 lb and 87.7 lb, respectively, per ton of cement produced. For a precalciner kiln, it i

uncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995 Table 11.6-8) are treated similarly (s

limestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2.1 lb and 11.8 lb per ton of cemen

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 3-9 References 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, 

EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplem

Environmental Protection Agency, Research Triangle Park, North Carolina. 
3-19 

Table 3-11. CO2 Control Technologies for Cement Kilns 

Impact on Emissions, 

+/- % change Control Type 

NOX SO2 PM Hg CO2 Other 

Electricity 

Consumption, 

MWh/ton of 

clinker 

Process Water 

Consumption, 

ton/ton of 

clinker 

Solvent CC1 New -99% -99% -85% -0.022 0.429 

Solvent CC1 Retrofit -99% -99% -85% -0.103 0.160 

Oxy-combustion -99% -99% -85% 0.174 

Notes to Table 3-11 

1. CC = carbon capture 

References for Table 3-11 

Andover 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 Ag
3-20 

Table 3-12. HCl, Hg, and THC Control Technologies for Cement Kilns 

Impact on Emissions, 

Control Type ± % change 1

NOX SO2 PM Hg Other 

ACI2 -99.9% -90% -50% for THC 

Membrane Bag -99.9% 

RTO3 -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 Capacity Replacement on Cement Kiln Operation1

Electricity Consumption, 

kWh/ton of cement 

Waste 

Kiln Type 

Water 

Consumption, 

gal/ton of 

cement2 Grinding

3

Kiln 

Operation4

Generatio

n Rate, 

ton/ton of 

cement5

Disposal Cost, 

$/ton of cement 

Wet to 

Precalciner -2142,6 -128

 -78 -0.072 see notes 9,10 

Long Dry to 

Precalciner No change2 Not 

available7 Not available7

 -0.062 see notes 9,10 

Preheater to 

Precalciner No change2 Not 

available7 Not available7

 011 see notes 9,10 

Notes to Table 3-13. 

1. Unless specifically noted otherwise, impacts are presented on a per-short-ton-of-cement basis and generally 

make 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-percent 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 with 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 SO2 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. Clinker is assumed to require 1.42 tons of raw kiln feed 

(dry) per ton of clinker. Kiln feed loss on ignition is assumed 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 precalciner cement kilns generate similar 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 Blending) -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 Homogenization) -0.35 

WMCCC (Wash Mills with Closed Circuit Classifier) -12.00 

RMTHEC (High-Efficiency Classifiers) -5.05 -5.05 -5.05 -5.05 
3-22 

Table 3-15. Energy Efficiency Clinker Making Measures 

Electricity Consumption Change, 

kWh/ton clinker 

Heat Input Change, MMBtu/ton of Energy Efficiency clinker 

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 -2.00 
3-23 

Table 3-16. Energy Efficiency Measures for Finish Grinding 

Electricity Consumption Change, kWh/ton 

Energy Efficiency Improvement Method clinker 

Dry Wet Preheater Precalciner 

EMPC (Energy Management and Process Control) -1.60 -1.60 -1.60 -1.60 

IGMBM (Improved Grinding Media [Ball 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 Classifiers) -3.85 -3.55 -3.85 -3.85 

Table 3-17. Energy Efficiency Plant-wide Measures 

Electricity Consumption Change, kWh/ton 

Energy Efficiency Improvement Method clinker 

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 Air 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 Performance of Controls, memorandum to U.S. EPA, 

September 25, 2008. 

Andover Technologies Partners (2009a). Memorandum: NOX, SO2 and CO2 Emissions from Cement Kilns 

(Emissions Memo), from Jim Staudt to Ravi Srivastava, Samudra 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 Torres. 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 Enhanced 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, memorandum 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_numbers&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/industrial/special/ckd/ckd/ckdcostt.pdf, accessed October 21, 2008. 

EPA (2004). Alternative Control Techniques Document - NOX 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.html, 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 - NOX 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 NOX Controls for Gas Turbines, Cement Kilns, Industrial Boilers, 

Internal Combustion Engines: Technologies & Cost Effectiveness. Northeast States for Coordinated Air 

Use Management, December 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 Manufacturing. Portland Cement Association. Edited by J. 

I. Bhatty, F. M. Miller, and S. H. Kosmatka. 2004. 

PCA (2004a). U.S. and Canadian Portland Cement Industry: Plant Information Summary of December 

31, 2004. 

PCA (2006). U.S. and Canadian Portland Cement Industry: Plant Information Summary. Portland Cement 

Association, Skokie, IL, 2006. 

PCA (2008). Forecast Report: Long-Term Cement Consumption Outlook. By Ed Sullivan January 31, 

2008. http://www.cement.org/econ/pdf/Long-TermFlashwinter2007nonmember.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 Consumption Outlook. By Ed Sullivan. October 28, 

2009. Portland Cement Association. 
3-26 

Ryan, S. (2006). The Costs of Environmental Regulation in a Concentrated Industry. Department of 

Economics, MIT, Cambridge, MA, October 5, 2006. http://econ-www.mit.edu/files/1166, accessed 

October 21, 2008. 

RTI (2010). ISIS Input Price Escalation Factors, Excel Spreadsheet. 

TRAGIS (2003). Transportation Routing Analysis Geographic Information System (TRAGIS) User’s 

Manual, Revision 0, Oak Ridge National Laboratory, June 2003. 

https://tragis.ornl.gov/TRAGISmanual.pdf. 

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). 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/commodity/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/energycitations/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/Sustainable_Development/Projects/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 calibration 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-predicted 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 district, 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 limits6

. 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 for 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 recommended 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 districts, import levels in the import districts and 

reported cement prices for USGS districts are the key quantities used for calibration of the model. 

Reported data for years 2005, 2006 and 2007 were used 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 absolute gap between the individual reported and 

predicted values to ± 15%, although effort has been made to keep this level below 10% for most of the quantities. However, due 

to discontinuities in the transportation matrix, error in the reported data, or other unknowns, the gap in the estimated and reported 

values may be higher in certain markets. 
4-2 

4.2.1 Cement Prices in USGS Districts 

Reported cement prices in various USGS districts 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 three 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 districts 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 from 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) and 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, respectively. As can be seen from these tables, in most of the 

districts reported and calculated cement prices are within 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 calculated 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 district 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 price 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 criteria specified in the QA document, the aberrations 

are explained by the demand-supply gap and transportation 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 aggregate 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 West 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 90.72 
4-5 

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 Virginia 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 2,103,481 
4-6 

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 427,900 416,900 284,900 
4-7 

Table 4-4. Reported and Calculated Cement 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 -13 
4-8 

Table 4-5. Reported and Calculated Cement 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 West 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 -18 
4-9 

Table 4-6. Reported and Calculated Cement 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 West 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 -5.75 

 
4-10 

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 the transportation matrix, the model needs to be recalibrated. 

At the time of calibration of the current version of the 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 model 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 for 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/minerals/pubs/commodity/cement/

United States Geological Service. Mineral Yearbook 2008. Table 3. 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/

United States Geological Service. Mineral Yearbook 2008. Table 11. 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/
5-1

Chapter 5 

Analysis and Results of the Portland Cement NESHAP 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 amendments to the NESHAP add or revise, as applicable, 

emission limits for mercury (Hg), total hydrocarbons (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 NSPS add or revise, as applicable, emission limits for 

PM, NOx and SO2 for facilities that commence construction, 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. Even 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 Facilities 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 tons 
5-2

From the 108 Portland cement facilities modeled in ISIS-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 kilns are projected to be subject to the limits for 

new sources while 137 kilns are projected to be subject 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 NESHAP 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 individual rules and also on the combination of the final 

NESHAP and the final NSPS. The limits used in the 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 

Hg 0.0000317 0.000014 lb/ton clinker 

0.000029164 0.00001288 lb/ton cement 

5 5 ppmv 

THC 0.050856164 0.050856164 lb THC/ton clinker 

0.046787671 0.046787671 lb/ton cement 

0.4 0.4 ppmv 

HCl 0.003378699 0.003378699 lb HCl/ton clinker 

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 construction 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 are 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. The 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 rules also include additional testing and monitoring 

requirements for affected sources. In the final NESHAP, 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 performance 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 Portland Cement NESHAP and NSPS", August 6, 2010. 

Table 5-3. Control Technology and Control 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., highways), 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 cyclical peak levels (2005) until 2015 (PCA, 2010). Since 

multiple years were not modeled, the average demand for the years 2013 through 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 representative 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 demand and emission reduction requirements; 2) the suite 

of control technologies needed to meet the emission limits; 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 run 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 kilns8

 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 combination 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 ability to compete in each of the demand centers as a 

function of the kiln’s production cost and transportation 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 trading (i.e., shipping from other demand center). 

The exogenous cement demand used in ISIS for this 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 under the combination of the NSPS and NESHAP. 

 

8

 Seven of the 16 new kilns projected to be subject to the NESHAP 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 Case 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 Case 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 Case 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 price 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 Case Change in Price % Change 

NSPS 87.77 88.42 0.65 0.74 
5-6

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 control installation shown in Table 5-8. Table 5-9 also 

shows the monitoring cost associated with 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 existing capacity and retiring non-competitive existing 

capacity. Table 5-10 shows the cement capacity in 2013 for the policies analyzed. 

Table 5-10 Cement Capacity 

Capacity (metric tons cement) 

Policy Run Reference Case Policy Case 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 identified 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 taking 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 NSPS, 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 Policy 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 producing 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 production. 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 use data, we can calculate the number of jobs that 

could potentially be affected by each policy under analysis. 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 Policy Case Difference % Change 

NSPS 24,586,999 24,385,731 -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 -10.80 
5-8

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 surplus 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 Case 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, SO2 and NOx for each 

policy analyzed. 
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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 

NOx 126,751 116,043 10,708 8.45 

NSPS 

SO2 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 

NOx 123,372 109,214 14,159 11.48 

NESHAP 

SO2 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 

NOx 123,372 96,154 27,218 22.06 

NSPS and 

NESHAP 

SO2 115,164 35,775 79,389 68.94 
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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 

Association 
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Disclaimer 

Reference herein to any specific commercial products, process, or service by trade name, trademark, 

manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or 

favoring by the United States Government. The views and opinions of authors expressed herein do not 

necessarily state or reflect those of the United States Government, and shall not be used for advertising or 

product endorsement purposes.