# Chapter 5: Engineering Cost Estimates

## Synopsis

This chapter summarizes the data sources and methodology used to
estimate the engineering costs of attaining the alternative more
stringent levels for the ozone primary standard analyzed in this RIA.
This chapter estimates the engineering costs of 0.065 ppm, 0.070 ppm,
0.075 ppm, and 0.079 ppm. The chapter presents engineering cost
estimates for the illustrative modeled control strategy outlined in
Chapter 3 (which uses currently available known controls). The modeled
control strategy discussion is followed by a presentation of estimates
for the engineering costs of the additional tons of emissions that are
needed to move to full attainment of the alternate standards analyzed,
referred to as Extrapolated Costs (methodology and numbers discussed in
Chapter 4).

As noted in Chapter 3, EPA first modeled an illustrative control
strategy aimed at attaining a tighter standard of 0.070 ppm in 2020. EPA
modeled the lower end of the proposed range to capture a larger number
of geographic areas that may be affected by a new ozone standard. These
known controls were insufficient to bring all areas into attainment with
0.070 ppm, and EPA then developed methodology to estimate additional
tons of emissions needed to attain 0.079 ppm, 0.075 ppm, 0.070 ppm, and
0.065 ppm. This chapter presents the engineering costs associated with
each portion of the control analysis, clearly identifying the relative
engineering costs of modeled versus extrapolated emissions reductions as
well as providing an estimate of the total engineering cost of
attainment nationwide in 2020. Nationwide attainment refers to all areas
of the nation that are required to attain the current ozone standard by
the year 2020. It does not reflect full attainment for the two areas of
California, which have attainment dates for the current standard post
2020. For a complete discussion attainment for these two areas of
California see the Appendix 9b. Section 5.1 summarizes the methodology
and the engineering costs associated with applying known and
supplemental controls to partially attain a 0.070 ppm alternative
standard, incremental to reaching the current baseline (effectively
0.084 ppm) in 2020.

Section 5.2 describes the methodology used to estimate the engineering
costs of extrapolated tons needed to reach attainment of the final 0.075
ppm standard as well as the three alternatives and provides estimates of
how much additional engineering costs will be associated with moving
from the modeled partial attainment scenario (i.e. modeled control
strategy) to the nationwide attainment scenario (see Chapter 4 for
discussion of extrapolated tons needed to attain 0.079, 0.075, 0.070,
and 0.065 ppm).

The engineering costs described in this chapter generally include the
costs of purchasing, installing, and operating the referenced
technologies. For a variety of reasons, actual control costs may vary
from the estimates EPA presents here. As discussed throughout this
report, the technologies and control strategies selected for analysis
are illustrative of one way in which nonattainment areas could meet a
revised standard. There are numerous ways to construct and evaluate
potential control programs that would bring areas into attainment with
alternative standards, and EPA anticipates that state and local
governments will consider programs that are best suited for local
conditions. Furthermore, based on past experience, EPA believes that it
is reasonable to anticipate that the marginal cost of control will
decline over time due to technological improvements and more widespread
adoption of previously niche control technologies. Also, EPA recognizes
the extrapolated portion of the engineering cost estimates reflects
substantial uncertainty about which sectors, and which technologies,
might become available for cost-effective application in the future.
This is explained in further detail in Section 5.3. The appendix to
Chapter 5 includes detailed cost and control efficiency information on
different control measures applied as part of our modeled control
strategy, and also includes summary results from applications of
specific control measures.

It is also important to recognize that the engineering cost estimates
are limited in their scope. Because we is not certain of the specific
actions that states will take to design State Implementation Plans to
meet the revised standards, we do not present estimated costs that
government agencies may incur for managing the requirement and
implementation of these control strategies or for offering incentives
that may be necessary to encourage or motivate the implementation of the
technologies, especially for technologies that are not necessarily
market driven. This analysis does not assume specific control measures
that would be required in order to implement these technologies on a
regional or local level.

We use EMPAX-CGE to estimate the economic impacts and the social costs
associated with the modeled control strategy. EMPAX uses as input the
engineering costs estimated for the modeled control strategy to
calculate its economic impacts and social costs. Economic impacts are
estimates of changes in price and output for those industries and
consumers of their output affected by the modeled control strategy.
Social costs are costs from changes in household welfare due to impacts
from the costs of the controls in the modeled control strategy. For more
details on the economic impacts and social costs, please refer to
Appendix 5B.

5.1 Modeled Controls

### 5.1.1 Sector Methodology

#### 5.1.1.1 NonEGU Point and Area Sources: AirControlNET

After designing a national hypothetical control strategy using the
methodology discussed in Chapter 3 (see sub-section 3.2.1), EPA used
AirControlNET to estimate engineering control costs. AirControlNET
calculates engineering costs using three different methods: (1) by
multiplying an average annualized cost per ton estimate against the
total tons of a pollutant reduced to derive a total cost estimate; (2)
by calculating cost using an equation that incorporates information
regarding key plant information; or (3) by using both cost per ton and
cost equations. Most control cost information within AirControlNET has
been developed based on the cost per ton approach. This is because
estimating engineering costs using an equation requires more data, and
parameters used in other non-cost per ton methods may not be readily
available or broadly representative across sources within the emissions
inventory. The costing equations used in AirControlNET require either
plant capacity or stack flow to determine annual, capital and/or
operating and maintenance (O&M) costs. Capital costs are converted to
annual costs, in dollars per ton, using the capital recovery factor.[^1]
Where possible cost calculations are used to calculate total annual
control cost (TACC) which is a function of the capital (CC) and O&M
costs. Capital costs are converted to annual costs, in dollars per ton,
using the capital recovery factor (CRF). The capital recovery factor
incorporates the interest rate and equipment life (in years) of the
control equipment. Operating costs are calculated as a function of
annual O&M and other variable costs. The resulting TACC equation is TACC
= (CRF \* CC) + O&M.

Engineering costs will differ based upon quantity of emissions reduced,
plant capacity, or stack flow which can vary by emissions inventory
year. Engineering costs will also differ by the year the costs are
calculated for (i.e., 1999\$ versus 2006\$). For capital investment, we
do not assume early capital investment in order to attain standards by
2020. For 2020, our estimate of annualized costs represents a "snapshot"
of the annualized costs, which include annualized capital and O&M costs,
for those controls included in our modeled control strategy. Our
engineering cost analysis uses the equivalent uniform annual costs
(EUAC) method, in which annualized costs are calculated based on the
equipment life for the control measure along with the interest rate by
use of the CRF as mentioned previously in this chapter. Annualized costs
are estimated as equal for each year the control is expected to operate.
Hence, our annualized costs for nonEGU point and area sources estimated
for 2020 are the same whether the control measure is installed in 2019
or in 2010. We make no presumption of additional capital investment in
years beyond 2020. The EUAC method is discussed in detail in the EPA Air
Pollution Control Cost Manual (found at
<http://epa.gov/ttn/catc/products.html#cccinfo>). Applied controls and
their respective engineering costs are provided in the Ozone NAAQS RIA
docket.

The modeled control strategy for nonEGU Point and Area sources
incorporated annualized engineering cost per ton caps. These caps were
defined as the upper cost per ton for controls of nonEGU point and area
sources. The caps were calculated by examining the marginal cost curves
for each pollutant for the geographic areas (approximately 1,300
counties for NOx controls, see Figure 3.5 and approximately 120 counties
for VOC controls, see Figure 3.6) being analyzed for this analysis. For
reductions of NOx emissions the cap (see Figure 5.1) was set at
\$23,000/ton (2006\$). At this cap, ninety-eight percent of the possible
reductions from known measures are achieved at eighty-two percent of the
total annualized engineering cost. There were only two controls whose
cost per ton were greater than this cap, and subsequently not included
in this analysis, due to the large capital component of installing these
controls. A similar process was followed for reductions from VOCs. The
relative air quality effectiveness of reductions in VOC was considered,
and the marginal cost curve (Figure 5.2) was analyzed. Subsequently, the
cap was set at approximately \$5,000/ton (2006\$). At this cap,
forty-six percent of the possible reductions are achieved at fifteen
percent of the total engineering cost. It is important to note that as
part of the extrapolated cost analysis the VOC cap was raised to
\$15,000/ton (for geographic areas where the supplemental air quality
modeling showed VOC control to be beneficial). At this cap (2006\$)
ninety-eight percent of the possible reductions could be achieved.

Figure 5.1: Marginal Cost Curve for Modeled Control Strategy Geographic
Areas\
(NOX nonEGU Point and Area Source Controls Prior to Cut Points)

![](media/image1.wmf){width="6.497916666666667in"
height="2.167361111111111in"}

Figure 5.2: Marginal Cost Curve for Modeled Control Strategy Geographic
Areas\
(VOC nonEGU Point and Area Source Controls Prior to Cut Points)

![](media/image2.wmf){width="6.502777777777778in"
height="2.2354166666666666in"}

#### 5.1.1.2 EGU Sources: the Integrated Planning Model

Engineering costs for the electric power sector are estimated using the
Integrated Planning Model (IPM). The model determines the least-cost
means of meeting energy and peak demand requirements over a specified
period, while complying with specified constraints, including air
pollution regulations, transmission bottlenecks, fuel market
restrictions, and plant-specific operational constraints. IPM is unique
in its ability to provide an assessment that integrates power,
environmental, and fuel markets. The model accounts for key operating or
regulatory constraints (e.g., emission limits, transmission
capabilities, renewable generation requirements, fuel market
constraints) that are placed on the power, emissions, and fuel markets.
IPM is particularly well-suited to consider complex treatment of
emission regulations involving trading and banking of emission
allowances, as well as traditional command-and-control emission
policies.[^2] Applied controls and their respective engineering costs
are provided in the docket. IPM is described in further detail in
Appendix 3.

#### 5.1.1.3 Onroad and Nonroad Mobile Sources: National Mobile Inventory Model (NMIM) and Various Studies

Engineering cost information for mobile source controls was taken from
studies conducted by EPA for previous rulemakings and studies conducted
for development of voluntary and local measures that could be used by
state or local programs to assist in improving air quality. Applied
controls and their respective engineering costs are provided in the
docket.[^3]

Engineering costs, in terms of dollars per ton emissions reduced, were
applied to emission reductions calculated for the onroad and nonroad
mobile sectors that were generated using the NMIM. NMIM is an EPA model
for estimating pollution from highway vehicles and nonroad mobile
equipment. NMIM uses current versions of EPA's model for onroad mobile
sources, [MOBILE6](http://www.epa.gov/otaq/m6.htm), and nonroad mobile
sources, [NONROAD](http://www.epa.gov/otaq/nonrdmdl.htm), to calculate
emission inventories [^4].

### 5.1.2 Modeled Controls---Engineering Cost by Sector

In this section, we provide engineering cost estimates of the control
strategies identified in Chapter 3 that include control technologies on
nonEGU stationary sources, area sources, EGUs, and onroad and nonroad
mobile sources. Engineering costs generally refer to the capital
equipment expense, the site preparation costs for the application, and
annual operating and maintenance costs.

The total annualized cost of control in each sector in the control
scenario is provided in Table 5.1. These numbers reflect the engineering
costs across sectors annualized at a discount rate of 7% and 3%,
consistent with the guidance provided in the Office of Management and
Budget's (OMB) (2003) Circular A-4. However, it is important to note
that it is not possible to estimate both 7% and 3% discount rates for
each source (see section 5.1.3). In Table 5.1, an annualized control
cost is provided to allow for comparison across sectors, and between
costs and benefits. A 7% discount rate was used for control measures
applied to nonEGU point, area, and mobile sources. Engineering costs
from EGU sources, which are calculated using the IPM model and variable
interest rates, are captured in this table at an annualized 7% discount
rate.[^5]

Table 5.1: Annual Control Costs by Sector and Region, for the\
Modeled Control Strategy (2006\$) ^a,\ b,\ e^

![](media/image3.wmf){width="6.414583333333334in"
height="5.033333333333333in"}

^a^ All estimates rounded to two significant figures. As such, totals
will not sum down columns. The modeled control strategy is that strategy
applied to reach attainment of the 0.070 alternate primary standard, and
is described in detail in Chapter 3.

^b^ All estimates provided reflect the engineering cost of the modeled
control strategy, incremental to a 2020 baseline of compliance with the
current standard of 0.084 ppm.

^c^ The total cost is negative in the west for the modeled control
strategy due to an electricity generation shift. The west generates less
electricity and exports from the east.

^d^ Total annualized costs were calculated using a 3% discount rate for
controls which had a capital component and where equipment life values
were available. For this modeled control strategy, data for calculating
annualized costs at a 3% discount was only available for NonEGU point
sources. Therefore, the total annualized cost value presented in this
referenced cell is an aggregation of engineering costs at 3% and 7%
discount rate.

^e^ Sacramento Metro, CA geographic area also contains the South Coast
and San Joaquin Valley Areas. These two areas will still be reducing
emissions to meet the 0.08 ozone standard, and therefore the costs of
these emission reductions are not incurred as part of meeting a new
ozone standard. The difference between the emission reductions needed in
Table 4.7a and this table are accounted for by the tons that South Coast
and San Joaquin need to reduce to reach the current standard, and to
help Sacramento attain a new ozone standard.

^f^ This average cost/ton estimate is based on ozone season NOx
reductions from EGUs from controls that operate year-round as explained
in Chapter 3. By counting NOx reductions in the ozone season while
operation of NOx controls is modeled as year-round, our cost/ton
estimate may spread out reductions and thus affect the average cost/ton
estimate. It should be noted that the resulting cost/ton of the controls
applied within EGU control strategy is practically the same as that in
2020 for the final CAIR rule (\$1,900 in 2006 dollars).

Total annualized costs were calculated using a 3% discount rate for
controls which had a capital component and where equipment life values
were available. In this RIA, the nonEGU point source sector was the only
sector with available data to perform a sensitivity analysis of our
annualized control costs to the choice of interest rate. Sufficient
information on annualized capital calculations was not available for
area source and mobile controls to provide a reliable 3 percent discount
rate estimate. As such, the 3% value in Table 5.1 is representative of
the sum of the nonEGU Point Source sector at a 3% discount rate, and the
EGU, mobile, and Area Source sector at a 7% discount rate. It is
expected that the 3% discount rate value is overestimated due to the
addition of cost sectors at a higher discount rate. With the exception
of the 3 % Total Annualized Cost estimate on Table 5.1, engineering cost
estimates presented throughout this and subsequent chapters are based on
a 7% discount rate.

The total annualized engineering costs associated with the application
of known and supplemental controls, incremental to the baseline, are
approximately \$2.8 billion using a 7% discount rate.

### 5.1.3 Limitations and Uncertainties Associated with Engineering Cost Estimates

EPA bases its estimates of emissions control costs on the best available
information from engineering studies of air pollution controls and has
developed a reliable modeling framework for analyzing the cost,
emissions changes, and other impacts of regulatory controls. The
annualized cost estimates of the private compliance costs are meant to
show the increase in production (engineering) costs to the various
affected sectors in our control strategy analyses. To estimate these
annualized costs, EPA uses conventional and widely-accepted approaches
that are commonplace for estimating engineering costs in annual terms.
However, our engineering cost analysis is subject to uncertainties and
limitations.

One of these limitations is that we do not have sufficient information
for all of our known control measures to calculate cost estimates that
vary with an interest rate. We are able to calculate annualized costs at
an interest rate other than 7% (e.g., 3% interest rate) where there is
sufficient information---available capital cost data, and equipment
life---to annualize the costs for individual control measures. For the
vast majority of nonEGU point source control measures, we do have
sufficient capital cost and equipment life data for individual control
measures to prepare annualized capital costs using the standard capital
recovery factor. Hence, we are able to provide annualized cost estimates
at different interest rates for these point source control measures as
we have done for the proposed ozone RIA and the PM2.5 RIA last year.

For area source control measures, the engineering cost information is
available only in annualized cost/ton terms. We have extremely limited
capital cost and equipment life data for area source control measures.
We know that these annualized cost/ton estimates reflect an interest
rate of 7% because these estimates are typically products of technical
memos and reports prepared as part of rules issued by our office (OAQPS)
over the last 10 years or so, and the costs estimated in these reports
have followed the policy provided in OMB Circular A-4 that recommends
the use of 7% as the interest rate for annualizing regulatory costs.
Capital cost

Figure 5.3: Total Annualized Costs by Emissions Sector and Region for
Modeled Control Strategy in 2020^a,\ b,\ c,\ d^

![](media/image4.wmf){width="6.497916666666667in"
height="4.4527777777777775in"}\
^a^ Total costs presented above are for a seven percent discount rate.

> ^b^ All estimates provided reflect the engineering cost of the modeled
> control strategy, incremental to a 2020 baseline of compliance with
> the current standard of 0.084 ppm.
>
> ^c^ The total cost is negative in the west for the modeled control
> strategy due to an electricity generation shift. The west generates
> less electricity and exports from the east.
>
> ^d^ Sacramento Metro, CA geographic area also contains the South Coast
> and San Joaquin Valley Areas. These two areas will still be reducing
> emissions to meet the 0.08 ozone standard, and therefore the costs of
> these emission reductions are not incurred as part of meeting a new
> ozone standard. The difference between the emission reductions needed
> in Table 4.7a and this table are accounted for by the tons that South
> Coast and San Joaquin need to reduce to reach the current standard,
> and to help Sacramento attain a new ozone standard.

information for these area source controls, however, is often limited
since these measures are often not the traditional add-on controls where
the capital cost is well known and convenient to estimate. Such area
source controls can include reformulation of coatings to reduce VOC, as
one example. The limited availability of useful capital cost data for
such control measures has led to our use of annualized cost/ton
estimates to represent the engineering costs of these controls in our
cost tools and hence in the PM2.5 and ozone RIAs.

For mobile source measures, the situation is very much like that for our
area source measures. We do not have sufficient capital cost information
from what our mobile source office (OTAQ) has sent us to compute
annualized costs for different interest rates other than 7%. Finally, It
should be noted that the annualized capital costs for EGUs are prepared
at an interest rate other than 7%. Information on the annualization of
EGU control costs is presented later in this chapter.

There are some unquantified costs that are not adequately captured in
this illustrative analysis. These costs include the costs of federal and
State administration of control programs, which we believe are less than
the alternative of States developing approvable SIPs, securing EPA
approval of those SIPs, and Federal/State enforcement. Additionally,
control measure costs referred to as "no cost" may require limited
government agency resources for administration and oversight of the
program not included in this analysis; those costs are generally
outweighed by the saving to the industrial, commercial, or private
sector. The Agency also did not consider transactional costs and/or
effects on labor supply in the illustrative analysis.

The economic impacts of the cost of these modeled control strategy is
included in Appendix 5b of this analysis. The illustrative analysis does
quantify the potential for advancements in the capabilities of pollution
control technologies as well as reductions in their engineering costs
over time. This is discussed in Section 5.4.

For purposes of this analysis, we assume attainment by 2020 for all
areas except San Joaquin Valley and South Coast air basins in
California. The state has submitted plans to EPA for implementing the
current ozone standard which propose that these two areas of California
meet that standard by 2024. We have assumed for analytical purposes that
the San Joaquin Valley and South Coast air basin would attain a new
standard in 2030. There are many uncertainties associated with the year
2030 analysis. Between 2020 and 2030 several federal air quality rules
are likely to further reduce emissions of NOx and VOC, such as, but not
limited to National rules for Diesel Locomotives, Diesel Marine Vessels,
and Small Nonroad Gasoline Engines. These emission reductions should
lower ambient levels of ozone in California between 2020 and 2030.
Complete emissions inventories as well as air quality modeling were not
available for this year 2030 analysis. Due to these limitations, it is
not possible to adequately model 2030 air quality changes that are
required to develop robust controls strategies with associated costs and
benefits. In order to provide a rough approximation of the costs and
benefits of attaining 0.075 ppm and the alternate standards in San
Joaquin and South Coast air basins, we've relied on the available data.
Available data includes emission inventories, which do not include any
changes in stationary source emissions beyond 2020, and 2020
supplemental air quality modeling. This data was used to develop
extrapolated costs and benefits of 2030 attainment. These results
indicate that it would cost between \$680 million and \$1 billion
dollars to meet a new ozone standard of 0.075 ppm in 2030. To view the
complete analysis for the San Joaquin Valley and South Coast air basins
see Appendix 9b.3

2.  Extrapolated Engineering Costs

### 5.2.1 Methodology

This section presents the methodology and results of the extrapolated
engineering cost calculations of attainment of a new ozone standard of
0.075 ppm and analyses of three alternative standards, a less stringent
0.079 ppm and two more stringent options (.065 and 0.070 ppm).

As discussed in Chapter 3, the application of the modeled control
strategy was not successful in reaching nationwide attainment of the
alternate ozone standards. Many areas remained in nonattainment for all
four alternate standard scenarios; therefore, the engineering costs
detailed in Section 5.1 represent only the costs of partial attainment.

The estimation of engineering costs for unspecified emission reductions
needed to reach attainment many years in the future is inherently a
difficult issue. As described later in this chapter, our experience with
Clean Air Act implementation shows that technological advances and
development of innovative strategies can make possible emissions
reductions that are unforeseen today, and to reduce costs of emerging
technologies over time. But we cannot quantitatively predict the amount
of technology advance in the future. For areas needing significant
additional emission reductions, much of the control must be for sources
that historically haven't been controlled. The relationship of the cost
of such control to the cost of control options available today is not at
all clear. Available, current known control measures increase in cost
beyond the range of what has ever been implemented and would still not
provide the needed additional control for full attainment in the
analysis year 2020. In the absence of technological change, the needed
control for full attainment in 2020 would not be available.

The degree to which unknown controls are needed to achieve attainment
depends significantly upon variables in the analysis, such as attainment
date assumptions. We will better understand the true scope of the issue
in the future as states conduct detailed area-by-area analyses to
determine available controls and attainment dates that are appropriate
under the Clean Air Act. We do not attempt to determine specific
attainment dates in this analysis. The Clean Air Act provides
flexibility for a nonattainment area to receive an attainment date up to
20 years after designation if earlier attainment is not practical based
on controls that are reasonably available considering cost. Although we
assume attainment in 2020 (except for two California areas), areas that
face difficulty attaining could qualify under the Clean Air Act for an
attainment date as late as 2030 (assuming designations in 2010). This
would give such areas additional time to take advantage for national
standards to reduce emissions from onroad and nonroad mobile sources
through fleet turnover, and to take advantage of technological
innovation in cleaner technologies after 2020.

*Prior to presenting the methodology for estimating costs for
unspecified emission reductions, it is important to provide information
from EPA's Science Advisory Board Council Advisory,*[^6] *dated June 8,
2007, on the issue of estimating costs of unidentified control
measures.*

> *[812 Council Advisory, Direct Cost Report, Unidentified Measures
> (charge question 2.a)]{.underline}*
>
> *"The Project Team has been unable to identify measures that yield
> sufficient emission reductions to comply with the National Ambient Air
> Quality Standards (NAAQS) and relies on unidentified pollution control
> measures to make up the difference. Emission reductions attributed to
> unidentified measures appear to account for a large share of emission
> reductions required for a few large metropolitan areas but a
> relatively small share of emission reductions in other locations and
> nationwide.*
>
> *"The Council agrees with the Project Team that there is little
> credibility and hence limited value to assigning costs to these
> unidentified measures. It suggests taking great care in reporting cost
> estimates in cases where unidentified measures account for a
> significant share of emission reductions. At a minimum, the components
> of the total cost associated with identified and unidentified measures
> should be clearly distinguished. In some cases, it may be preferable
> to not quantify the costs of unidentified measures and to simply
> report the quantity and share of emissions reductions attributed to
> these measures.*
>
> *"When assigning costs to unidentified measures, the Council suggests
> that a simple, transparent method that is sensitive to the degree of
> uncertainty about these costs is best. Of the three approaches
> outlined, assuming a fixed cost/ton appears to be the simplest and
> most straightforward. Uncertainty might be represented using
> alternative fixed costs per ton of emissions avoided."*

EPA has considered this advice and the requirements of E.O. 12866 and
OMB circular A-4, which provides guidance on the estimation of benefits
and costs of regulations.

To generate estimates of the costs and benefits of meeting alternative
standards, EPA has assumed the application of unspecified future
controls that make possible the emissions reductions needed for
attainment in 2020 (excluding two California areas). By definition,
there is no cost data in existence for unidentified future technologies
or innovative strategies.

EPA used two methodologies for estimating the costs of unspecified
future controls: a new hybrid methodology and a fixed-cost methodology.
Both approaches assume that innovative strategies and new control
options make possible the emissions reductions needed for attainment by
2020. The fixed cost methodology was preferred by EPA's Science Advisory
Board over two other options, including a marginal-cost-based approach.
The hybrid approach has not yet been reviewed by the SAB.

The hybrid approach creates a marginal cost curve and an average cost
curve representing the cost of unknown future controls needed for 2020
attainment. This approach explicitly estimates the average per-ton cost
of unspecified emissions reductions assumed for each area, with a higher
average cost-per-ton in areas needing a higher proportion of unknown
controls relative to known modeled controls. This requires assumptions
about the average cost of the least expensive unspecified future
controls, and the rate at which the average cost of these controls rises
as more extrapolated tons are needed for attainment (relative to the
amount of reductions from known, modeled controls). These factors in
turn depend on implicit assumptions about future technological progress
and innovation in emission reduction strategies.

The fixed cost methodology utilizes a national average cost per ton of
future unspecified controls needed for attainment, as well as two
sensitivity values (presented in Appendix 5a.4.3). The range of
estimates reflects different assumptions about the cost of additional
emissions reductions beyond those in the modeled control strategy. The
alternative estimates implicitly reflect different assumptions about the
amount of technological progress and innovation in emission reduction
strategies.

The hybrid methodology has the advantage of using the information about
how significant the needed reductions from unspecified control
technology are relative to the known control measures and matching that
with expected increasing per unit cost for going beyond the modeled
technology. Under this approach, the relative costs of unspecified
controls in different geographic areas reflect the expectation that
average per-ton control costs are likely to be higher in areas needing a
higher ratio of emission reductions from unspecified and known controls.

The fixed cost methodology reflects a view that because no cost data
exists for unspecified future strategies, it is unclear whether
approaches using hypothetical cost curves will be more accurate or less
accurate in forecasting total national costs of unspecified controls
than a fixed-cost approach that uses a range of national cost per ton
values.

Technological change will provide new control possibilities that can be
employed to provide the additional unspecified control needed to reach
attainment. These new technologies will make control possible where
control has not been available for estimating our known control. An
example might be the development of a new control technology for a type
of emissions that have never been controlled. Technological change is
also expected to reduce the cost of known controls that currently have
prohibitive costs. For example, suppose a source that was not chosen for
control because the estimated cost was \$60,000 per ton but
technological change reduces the cost to \$16,000 per ton. Finally,
control technologies may change so that higher control efficiencies may
be obtained without a significant increase in per unit costs of control.

Both approaches (the hybrid and the fixed) estimate costs using national
level parameters and local area information about needed emission
reductions. Because cost changes due to technological change will be
available on a national level, it makes sense to use national level
estimates of these parameters. Local areas have different levels of
needed emission reductions and different inventories of uncontrolled
emissions and estimates of needed emission reductions are used in both
models. The hybrid model also uses information about the amount of
modeled control estimated for the local area.

The hybrid approach has yet to be peer reviewed and reflects a range of
views about the likely cost of future techniques and strategies that
reduce air pollutant emissions. Section 5.4 discusses historical
experience which has shown numerous technological advances in emission
reduction technologies, and provides a few examples of today's emerging
technologies.

#### 5.2.1.1 Initial Steps 

The first step involved identifying supplemental known controls not
included in the modeled control strategy. These controls include the
controls discussed in Appendix 3a.1.6, as well as additional controls
applied to select EGU sources, and VOC controls up to \$15,000/ton for
select geographic areas. For the more stringent alternative of 0.065 ppm
additional geographic areas were included, and therefore additional
known measures were available to be applied as well.

For the other three alternatives, there were geographic areas that were
"over controlled" and controls were removed from the analysis. For a
complete discussion of the supplemental and "over control" emission
reductions and costs see Appendix 5a.4.1 and 5a4.2 respectively. After
the supplemental controls are applied, any remaining emission reductions
needed are classified as additional tons from unknown control measures.

Supplemental controls were applied in addition to the known controls in
this illustrative analysis in order to achieve the highest possible
known emission reduction from NonEGU point and Area sources.
Supplemental control measures are those controls that are 1) applied in
these analyses but are not found in AirControlNET, and 2) are in
AirControlNET but whose data have been modified to better approximate
their applicability to source categories in 2020. The controls and
associated data such as control cost estimates not found in
AirControlNET are taken from technical reports prepared to support
preliminary 8-hour ozone State Implementation Plans (SIPs) prepared by
States and from various reports prepared by the staffs of various local
air quality regulatory agencies (e.g., Bay Area Air Quality Management
District). The reports that are the sources of additional controls data
are included within footnotes in the Chapter 3 Appendix. Modification of
control data, including percent reduction levels and control cost data,
in AirControlNET occurred as a result of a review of the nonEGU point
and area NOx control measures by technical staff. The changes EPA
supplied are provided later in the Chapter 3 Appendix.

Next. we classified the areas needing additional controls by attainment
date. Because two areas in California require no incremental additional
progress towards attainment by 2020 for a more stringent standard (their
requirements to reach attainment of the current standard by 2024 will be
the requirement that is binding) we separated the requirements to attain
more stringent standards for those two areas from the analysis for the
rest of the nation. A highly uncertain estimate of the extrapolated
engineering cost in 2030 is provided in Appendix 5a.5.

#### 5.2.1.2 Theoretical Model for Hybrid Approach

A simple model of how marginal costs increase with increasing control
requirements was developed. The model relies on emission estimates of
unspecified emissions (E~1~) needed to reach attainment and the modeled
control emission estimates. These unspecified emissions vary both with
the area and standard being analyzed. The modeled emissions vary by
area. The ratio (R) of unspecified emissions (E~1~) to controlled
emissions estimates (E~0~) is thus unique to each area and standard
being analyzed. The model of cost also includes two parameters developed
for use that don't vary across analyses of areas and standards. One is a
national projected dollar per ton cost for the last ton controlled for
the controlled emissions (N or jumping off price). The other is a
constant multiplier (M) to determine an average cost per ton that
increases as size of the needed unknown controls (E~1~) increase
relative to the modeled controls (R). The following equations show how
Average cost (AC), Total Cost (TC), and marginal (MC) are modeled in the
hybrid approach. See the appendix for a more detailed explanation.

> AC = N(1+RM)
>
> TC = AC(E~1~)
>
> MC = N(1+ 2RM)

For the controlled emissions estimated in the modeled control, costs
increase at an increasing rate as more control is applied. The shape of
the control cost curve for 2020 after technological change is unknown
but would also be expected to increase at an increasing rate. With all
of the uncertainty and as part of the trade-off between
simplicity/transparency and model richness we chose a proportional per
unit cost increase. This model assumes per unit costs increase at a
constant rate proportional to R.

#### 5.2.1.3 Parameter Estimation for Hybrid Approach

The jumping off price (N) used is \$15,000/ton (2006\$). To determine
this number we calculated the marginal costs for the last control
applied in all geographic areas for nonEGU and Area known controls[^7]
and averaged them for both the modeled control strategy and an alternate
primary standard of 0.065 ppm, this allowed for consistency with the
modeled control strategy marginal costs. These calculations showed a
range of \$14,500 to \$16,000 per ton (2006\$), with \$15,00 falling in
the middle. The February 2007 report, "Direct Cost Estimates for the
Clean Air Act Second Section 812," uses \$10,000 (1999\$) per ton. For
simplicity and comparability we used the \$15,000/ton. In addition the
marginal cost curve for the modeled control strategy NOx nonEGU and
Area, 90% of the controls applied are below \$15,000/ton. The jumping
off price (N) should be interpreted as the cost of the very first ton
needed from the unknown control[^8]. We chose the value \$15,000/ton and
not the \$23,000/ton applied for NOx nonEGU point and Area source
controls because the \$23,000/ton was calculated as an extreme upper
limit for NOx nonEGU controls and is not representative of the upper
limit of controls applied across all emissions sectors. It is important
to note that the cost/ton numbers calculated above are specific to the
geographic areas, source types, and emission projections of this
analysis. In an ideal world, we would have more complete information
about the available control options in each area and we would be able to
estimate what the next control to be employed (the "jumping off"
control) would be for each area needing control beyond the modeled known
control.

We have to estimate R and E information for each area and each standard.
Figure 5.4 shows how for phase 1 supplemental air quality modeling areas
how R varies based upon the level of the standard and the local
geographic area emissions.

We have no way to econometrically estimate M. The constant multiplier
(M) incorporates many different influences on the unit costs of control
such as technological change in control technology, change in energy
technology, learning by doing, relative price changes, and distribution
of sources with uncontrolled emissions. Using a high value for marginal
cost we can solve for M based on this value and our parameter estimate
of \$15,000 for N, and our highest value of R[^9] (2.19)for areas
meeting the current standard in 2020. For the modeled control we used a
maximum marginal cost of control of \$23,000 dollars/ton. At this cost
98% of the possible reductions NOx from nonEGU point and area were
applied. To arrive at a high value we doubled the maximum marginal cost
value (\$46,000). A number this high is rarely seen in either
implemented controls or other RIAs (e.g. the 1997 Ozone RIA highest cost
per ton was \$10,000 (1990\$) which is \$14,000 (2006\$)). This leads to
our estimate of M of 0.47. We calculated an M of 0.24, using a maximum
marginal cost value of \$31,00/ton, for the middle estimate and a value
of 0.12 for the lower estimate. The middle estimate of 0.24 is the one
expected to lead to the most dependable estimate, because it falls
between the higher estimate that involves control costs so high that
they are rarely seen in implemented controls and the lower estimate that
does not have marginal costs that increase much beyond the jumping off
price. The lower value of M is consistent with an optimistic scenario of
technical change and the higher value of M with a pessimistic scenario
of technical change. The middle value of M aims at being consistent with
our expectations of technical change. Estimates using the high and low
value of M are reported in the appendix.

Figure 5.4: Ratio of Unspecified Emission Reductions to Known Emission
Reductions Across Various Standards for Phase 1 Areas^a,\ b,\ c^

![](media/image5.wmf){width="6.4944444444444445in"
height="4.450694444444444in"}

> ^a^ Phase 1 Areas are defined in Chapter 4 Section 4.1.1
>
> ^b^ There are values of R for both NOx and VOC for the Eastern Lake
> Michigan, IL-IN-WI. This is the only geographic area where unknown
> control costs were calculated for VOC.
>
> ^c^ Houston did not meet the current standard after the modeled
> control strategy.

The cost of the last ton needed for the unknown control is N(1+2RM).
Thus, the per unit control cost for the unspecified tons in an area
starts with N and linearly increases with R. The ratio of needed unknown
control to modeled control (R) can be interpreted as a measure of "the
degree of difficulty" (see Figure 5.4). For example, the per unit
control costs would be expected to be higher if the unknown control
needed is twice the modeled control than if it is half the modeled
control. Table 5.2 shows how the cost of the last ton controlled for the
highest R value would vary with different values of M. Figure 5.5 also
depicts how the average cost per ton would vary.

^Table\ 5.2:\ Marginal\ Cost\ and\ Average\ Cost\ Values\ Used\ in\ Calculating\ Ma^

  ---------------------------- -------------- -------------- --------------
                               **Highest                     
                               Annual                        
                               Cost/Ton                      
                               Values                        
                               (2006\$),                     
                               Given R =                     
                               2.19**                        

                               **M = 0.12**   **M = 0.24**   **M = 0.47**

  Marginal Cost (MC)           \$23,000       \$31,000       \$46,000

  Average Cost (AC)            \$19,000       \$23,000       \$30,000
  ---------------------------- -------------- -------------- --------------

^a^ Marginal and average costs could be higher than the values presented
above for tighter ozone standards.

Figure 5.5 shows the range of average cost/ton values across geographic
areas and standards. This helps graphically illustrate the interplay of
all the variables to create a geographically specific average cost/ton
that is then multiplied by the amount of unspecified emissions
reductions needed to attain. These average cost per ton values

Figure 5.5: Ranges of Hybrid (Mid) Average Cost/Ton Values across
Geographic Areas and Standards

![](media/image6.wmf){width="6.503472222222222in"
height="4.197222222222222in"}

#### 5.2.1.4 Fixed Cost Approach

As discussed above the Science Advisory Board advice favored a fixed
cost per ton approach as the simplest and most straightforward. The
extrapolated cost equation involves only unspecified emissions (E~1~)
and Fixed Cost per ton (F). Thus the total cost (TC) equation is:

> TC= E~1~F

The primary estimate of F is \$15,000. The \$15,000 per ton amount is
commensurate with that used in the 1997 RIA in using current dollars. It
is also consistent with what an advisory committee to the Section 812
second prospective analysis on the Clean Air Act Amendments suggested.

Values of \$10,000/ton and \$20,000/ton are used for the sensitivity
analyses found in Appendix 5a.4.3.

### 5.2.2 Results

#### 5.2.2.1 Emission Reductions Needed to Attain Various Standards

Application of supplemental control measures (for a complete discussion
see Chapter 5 Appendix section 5a.4) mentioned above resulted in some
geographic areas no longer needing extrapolated tons to attain various
alternate primary standards. Table 5.3 shows the emission reductions
needed by geographic area, pollutant, and standard. Eastern Lake
Michigan is the only area with both NOx and VOC emission reductions
estimates. For the other areas additional control of NOx only is
expected to be a less expensive approach than controlling both NOx and
VOC. As expected, more areas need extrapolated emission reductions when
the alternative standards are more stringent.

Table 5.3: Extrapolated Emission Reductions Needed (Post Application of
Supplemental Controls) to Meet Various Alternate Standards in 2020

  -----------------------------------------------------------------------------------------------------------------
  **2020 Extrapolated Cost     **Additional                                                               
  Area**                       Emission                                                                   
                               Reductions                                                                 
                               Needed (annual                                                             
                               tons/year)**                                                               
  ---------------------------- -------------- --------- --------- --------- --------- --------- --------- ---------
                               **0.065 ppm**            **0.070             **0.075             **0.079   
                                                        ppm**               ppm**               ppm**     

                               **NOX**        **VOC**   **NOX**   **VOC**   **NOX**   **VOC**   **NOX**   **VOC**

  Ada Co., ID                  2,800                                                                      

  Atlanta, GA                  5,500                                                                      

  Baton Rouge, LA              160,000                  49,000                                            

  Boston-Lawrence-Worcester,   8,500                                                                      
  MA                                                                                                      

  Buffalo-Niagara Falls, NY    18,000                   3,700                                             

  Campbell Co., WY             50                                                                         

  Charlotte-Gastonia-Rock      47,000                                                                     
  Hill, NC-SC                                                                                             

  Cincinnati-Hamilton,         (40)^a^                                                                    
  OH-KY-IN                                                                                                

  Cleveland-Akron-Lorain, OH   78,000                   11,000                                            

  Dallas-Fort Worth, TX        48,000                   \(30\)                                            
                                                        ^a^                                               

  Denver-Boulder-Greeley-Ft    1,600                                                                      
  Collins-Love, CO                                                                                        

  Detroit-Ann Arbor, MI        100,000                  8,700                                             

  Dona Ana CO., NM             410                                                                        

  Eastern Lake Michigan,       320,000        320,000   250,000   250,000   74,000    49,000    \(60\)    \(50\)
  IL-IN-WI                                                                                      ^a^       ^a^

  El Paso Co., TX              \- ^a^                                                                     

  Houston, TX                  180,000                  160,000             110,000             81,000    

  Huntington-Ashland, WV-KY    800                                                                        

  Jackson Co., MS              \(200\) ^a^                                                                

  Jefferson Co, NY             6,200                                                                      

  Las Vegas, NV                3,900                                                                      

  Memphis, TN-AR               1,100                                                                      

  Norfolk-Virginia             21,000                                                                     
  Beach-Newport News                                                                                      

  Northeast Corridor,          340,000                  220,000             65,000                        
  CT-DE-MD-NJ-NY-PA                                                                                       

  Phoenix-Mesa, AZ             \(60\) ^a^                                                                 

  Pittsburgh-Beaver Valley, PA 13,000                                                                     

  Richmond-Petersburg, VA      \(600\) ^a^                                                                

  Sacramento Metro, CA ^b^     130,000                  89,000              44,000              1,800     

  Salt Lake City, UT           430                                                                        

  San Juan Co., NM             1,300                                                                      

  St Louis, MO-IL              17,000                                                                     

  Toledo, OH                   \(90\) ^a^                                                                 
  -----------------------------------------------------------------------------------------------------------------

^a^ negative or zero values indicate the supplemental measures applied
yielded equal or greater emission reductions than were needed for the
geographic area to attain the standard being analyzed.

^b^ Sacramento Metro, CA geographic area also contains the South Coast
and San Joaquin Valley Areas. These two areas will still be reducing
emissions to meet the 0.08 ozone standard, and therefore the costs of
these emission reductions are not incurred as part of meeting a new
ozone standard. The difference between the emission reductions needed in
Table 4.7a and this table are accounted for by the tons that South Coast
and San Joaquin need to reduce to reach the current standard, and to
help Sacramento attain a new ozone standard.

#### 5.2.2.2 Fixed Cost Approach Extrapolated Costs

Figure 5.6 and Table 5.4 presents the extrapolated cost estimates
regionally for the various alternative standards for a fixed cost
approach of \$15,000/ton. These costs are the values from Table 5.3
multiplied by \$15,000. See the Appendix 5a.4.3 for sensitivity analyses
of varying the fixed dollar per ton to values other than \$15,000. When
we evaluate the portion of costs for the extrapolated costs fixed
approach by supplemental air quality modeling phase (as described in
Chapter 4), 100% of the costs are allocated to phase 1 geographic areas
for the 0.075 ppm and 0.079 ppm standard. For the 0.065 ppm and 0.070
ppm standards 73% to 94% are allocated to phase 1 areas, 22% to 6% in
phase 2 areas, and only 5% to 0% for phase 3 areas. The sensitivity
analysis for the fixed cost approach at \$10,000/ton and \$20,000/ton
resulted in extrapolated costs of \$3.4 to \$6.8 billion dollars for the
0.075 ppm standard.

#### 5.2.2.3 Hybrid Approach Extrapolated Cost Results

Table 5.5 presents the extrapolated cost estimates regionally for the
various alternative standards for the hybrid approach (mid). See the
Appendix 5a.4.4 for sensitivity analyses of values of M of 0.47 and
0.12. A value of 0.24 is used for M because R goes up with the
stringency of the standard, the differences in costs between cost areas
increase with the stringency of the

Figure 5.6: Extrapolated Cost by Region to Meet Various Alternate
Standards\
Using Fixed Cost Approach (\$15,000/ton)

![](media/image7.wmf){width="6.503472222222222in"
height="4.450694444444444in"}

Table 5.4: Extrapolated Cost by Region to Meet Various Alternate
Standards\
Using Fixed Cost Approach (\$15,000/ton) ^a,\ b^

  ---------------------------------------------------------------------------------
  **2020 Extrapolated     **Fixed Cost                                
  Cost by Region**        Approach                                    
                          Extrapolated                                
                          Cost (M                                     
                          2006\$)**                                   
  ----------------------- -------------- -------------- ------------- -------------
                          **0.065 ppm**  **0.070 ppm**  **0.075 ppm** **0.079 ppm**

  East                    \$25,000       \$14,000       \$4,500       \$1,200

  West                    \$160          \-             \-            \-

  California              \$2,000        \$1,300        \$660         \$28

  **Total Extrapolated    **\$27,000**   **\$16,000**   **\$5,100**   **\$1,200**
  Cost**                                                              
  ---------------------------------------------------------------------------------

^a^ All estimates rounded to two significant figures. As such, totals
will not sum down columns.

^b^ These estimates do not reflect benefits or costs for the San Joaquin
Valley or South Coast Air Basins. Please see Appendix 9b for analysis of
these areas.

alternative being considered. When we evaluate the portion of costs for
the extrapolated costs fixed approach by supplemental air quality
modeling phase (as described in Chapter 4), 100% of the costs are
allocated to phase 1 geographic areas for the 0.075 ppm and 0.079 ppm
standard. For the 0.065 ppm and 0.070 ppm standards 74% to 95% are
allocated to phase 1 areas, 21% to 5% in phase 2 areas, and only 5% to
0% for phase 3 areas.

Figure 5.7: Extrapolated Cost by Region to Meet Various Alternate
Standards Using Hybrid Approach (Mid)

![](media/image8.wmf){width="5.988888888888889in"
height="4.104166666666667in"}

Table 5.5: Extrapolated Cost by Region to Meet Various Alternate
Standards Using Hybrid Approach (Mid) ^a,\ b^

  ----------------------- -------------- ----------- ----------- -----------
  **2020 Extrapolated     **Hybrid                               
  Cost by Region**        Approach                               
                          Extrapolated                           
                          Cost (M                                
                          2006\$)**                              

                          **0.065 ppm**  **0.070     **0.075     **0.079
                                         ppm**       ppm**       ppm**

  East                    \$36,000       \$20,000    \$5,500     \$1,700

  West                    \$170                                  

  California              \$2,800        \$1,700     \$770       \$28

  **Total Extrapolated    \$39,000       \$22,000    \$6,300     \$1,800
  Cost**                                                         
  ----------------------- -------------- ----------- ----------- -----------

^a^ All estimates rounded to two significant figures. As such, totals
will not sum down columns.

^b^ These estimates do not reflect benefits or costs for the San Joaquin
Valley or South Coast Air Basins. Please see Appendix 9b for analysis of
these areas.

## 5.3 Summary of Costs

Table 5.6 presents a summary of the total national cost of attaining
0.079, 0.075, 0.070, and 0.065 ppm standards in 2020. This summary
includes the engineering costs presented above from the modeled controls
and the extrapolated costs. The range presented in the extrapolated
costs and the total costs represent the upper and lower bound cost
estimates. Consistent with OMB Circular A-4, costs are presented at a 7%
discount rate. It is more consistent to present the extrapolated costs
at the same discount rate as the modeled control costs, for which a 7%
rate was determined to be more representative of actual costs (see
section 5.1.3). Although the amount of reduction assumed to occur using
unknown controls increases, the uncertainty of the associated costs and
benefits calculations increases.

^Table\ 5.6:\ Total\ Costs\ of\ Attainment\ in\ 2020\ for\ Alternate\ Levels\ of\ the\
Ozone\ Standard\ a,\ b,\ c^

  -------------- ------------ -------------- -------------- -------------- -------------- ------------- ------------- ------------- -------------
                 **Region**   **Annual                                                                                              
                              Engineering                                                                                           
                              Costs (M                                                                                              
                              2006\$)**                                                                                             

                              **0.065 ppm**                 **0.070 ppm**                 **0.075 ppm                 **0.079 ppm   
                                                                                          ^d^**                       ^d^**         

  Known Control  East         \$4,100                       \$3,100                       \$2,400                     \$960         
  Costs (\$B)                                                                                                                       

                 West         \$230                         \$14                          -\$4                        -\$5          

                 California   \$160                         \$160                         \$160                       \$160         

  Known Control               \$4,500                       \$3,300                       \$2,500                     \$1,100       
  Costs^e^                                                                                                                          

  Extrapolated                Fixed          Hybrid         Fixed          Hybrid         Fixed         Hybrid        Fixed         Hybrid
  Costs (\$B)                                                                                                                       

                 East         \$25,000       \$36,000       \$14,000       \$20,000       \$4,500       \$5,500       \$1,200       \$1,700

                 West         \$160          \$170          \$0            \$0            \$0           \$0           \$0           \$0

                 California   \$2,000        \$2,800        \$1,300        \$1,700        \$660         \$770         \$28          \$28

  Extrapolated                \$27,000       \$39,000       \$16,000       \$22,000       \$5,100       \$6,300       \$1,200       \$1,800
  Costs                                                                                                                             

  **Total Cost                **\$32,000**   **\$44,000**   **\$19,000**   **\$25,000**   **\$7,600**   **\$8,800**   **\$2,400**   **\$2,900**
  Range**                                                                                                                           
  -------------- ------------ -------------- -------------- -------------- -------------- ------------- ------------- ------------- -------------

^a^ All estimates rounded to two significant figures. As such, totals
will not sum down columns.

^b^ These estimates do not reflect benefits or costs for the San Joaquin
Valley or South Coast Air Basins. Please see Appendix 9b for the
analysis of these areas.

^c^ These estimates assume a particular trajectory of aggressive
technological change. An alternative storyline might hypothesize a much
less optimistic technological trajectory, with increased costs, or with
decreased benefits in 2020 due to a later attainment date.

^d^ Known control costs for 0.079 ppm and 0.075 ppm include the modeled
EGU cap and trade strategy, and therefore contain greater emission
reductions than are needed to attain for some geographic areas.
Therefore these results represent an overestimate of the costs of
attainment.

^e^ Known control costs consist of modeled control strategy costs
presented in Table 5.1, as well as supplemental costs and "giveback"
costs presented in Appendix 5a.4.1 and 5a.4.2.

Our estimates of costs of attainment in 2020 assume a particular
trajectory of aggressive technological change. This trajectory leads to
a particular level of emissions reductions and costs which we have
estimated based on two different approaches, the fixed cost and hybrid
approaches. An alternative storyline might hypothesize a much less
optimistic technological change path, such that emissions reductions
technologies for industrial sources would be more expensive or would be
unavailable, so that emissions reductions from many smaller sources
might be required for 2020 attainment, at a potentially greater cost per
ton. Under this alternative storyline, two outcomes are hypothetically
possible: Under one scenario, total costs associated with full
attainment might be substantially higher. Under the second scenario,
states may choose to take advantage of flexibility in the Clean Air Act
to adopt plan with later attainment dates to allow for additional
technologies to be developed and for existing programs like EPA's Onroad
Diesel, CAIR, Nonroad Diesel, and Locomotive and Marine rules to be
fully implemented. If states were to submit plans with attainment dates
beyond our 2020 analysis year, benefits would clearly be lower than we
have estimated under our analytical storyline. However, in this case,
state decision makers, seeking to maximize economic efficiency, would
not impose costs, including potential opportunity costs of not meeting
their attainment date, when they exceed the expected health benefits
that states would realize from meeting their modeled 2020 attainment
date. In this case, upper bound costs are difficult to estimate because
we do not have an estimate of the point where marginal costs are equal
to marginal benefits plus the costs of nonattainment.

Figure 5.8 shows the total costs for both the fixed and hybrid
approaches broken out by region.

Figure 5.8: Annual Total Costs by Region^a^

![](media/image9.wmf){width="5.988888888888889in"
height="4.104166666666667in"}

^a^ These estimates assume a particular trajectory of aggressive
technological change. An alternative storyline might hypothesize a much
less optimistic technological trajectory, with increased costs, or with
decreased benefits in 2020 due to a later attainment date.

Figure 5.9 separates the total cost under both the fixed and
extrapolated cost approaches into the known control costs and the
extrapolated costs. This shows graphically the increasing portion of
costs that comes from unknown controls as the standard tightens.
Depending upon the standard and extrapolated cost methodology (fixed or
hybrid) the costs from unknown control technologies ranges from 50% to
89% of the total costs.

Figure 5.9: National Known Control Costs and Extrapolated Costs for\
Various Standards^a,\ b^

![](media/image10.wmf){width="6.503472222222222in"
height="4.456944444444445in"}

^a^ Known control costs consist of modeled control strategy costs
presented in Table 5.1, as well as supplemental costs and "giveback"
costs presented in Appendix 5a.4.1 and 5a.4.2.

^c^ These estimates assume a particular trajectory of aggressive
technological change. An alternative storyline might hypothesize a much
less optimistic technological trajectory, with increased costs, or with
decreased benefits in 2020 due to a later attainment date.

Lastly, Figure 5.10 shows the total cost range by standard. For the
final standard of 0.075 ppm the total cost ranges from \$7.6 to \$8.8
billion.

Figure 5.10: Total Cost Ranges for Various Standards^a^

## ![](media/image11.wmf){width="6.4944444444444445in" height="4.450694444444444in"}

^a^ These estimates assume a particular trajectory of aggressive
technological change. An alternative storyline might hypothesize a much
less optimistic technological trajectory, with increased costs, or with
decreased benefits in 2020 due to a later attainment date.

## 5.4 Technology Innovation and Regulatory Cost Estimates

There are many examples in which technological innovation and "learning
by doing" have made it possible to achieve greater emissions reductions
than had been feasible earlier, or have reduced the costs of emission
control in relation to original estimates. Studies[^10] have suggested
that costs of some EPA programs have been less than originally estimated
due in part to inadequate inability to predict and account for future
technological innovation in regulatory impact analyses.

Technological change will affect baseline conditions for our analysis.
This change may lead to potential improvements in the efficiency with
which firms produce goods and services, for example, firms may use less
energy to produce the same quantities of output. In addition,
technological change may result in improvements in the quality of health
care, which can have impacts on the baseline health of the population,
potentially reducing the susceptibility of the population to the effects
of air pollution. While our baseline mortality incidence rates account
for increasing life expectancy, and thus reflect projected improvements
in health care, our baseline incidence rates for other health endpoints
such as hospital admissions do not reflect any future

advances in health care, and thus, our estimates of avoided health
impacts for these endpoints will potentially be overstated. For other
endpoints, such as asthma, there has been an observed upward trend in
prevalence, which we have not captured in our incidence rates. For these
endpoints, our estimates will potentially be understated. In general,
for non-mortality endpoints, there is increased uncertainty in our
estimates due to our use of current baseline incidence and prevalence
rates.

Constantly increasing marginal costs are likely to induce the type of
innovation that would result in lower costs than estimated early in this
chapter. Breakthrough technologies in control equipment could by 2020
result in a rightward shift in the marginal cost curve for such
equipment (Figure 5.11)[^11] as well as perhaps a decrease in its slope,
reducing marginal costs per unit of abatement, and thus deviate from the
assumption of one constantly increasing marginal cost curve. In
addition, elevated abatement costs may result in significant increases
in the cost of production and would likely induce production
efficiencies, in particular those related to energy inputs, which would
lower emissions from the production side.

Figure 5.11: Technological Innovation Reflected by Marginal Cost Shift

![](media/image12.wmf){width="3.751388888888889in"
height="2.6458333333333335in"}

### 5.4.1 Examples of Technological Advances in Pollution Control

There are numerous examples of low-emission technologies developed
and/or commercialized over the past 15 or 20 years, such as:

-   Selective catalytic reduction (SCR) and ultra-low NOx burners for
    NOx emissions

-   Scrubbers which achieve 95% and even greater SO2 control on boilers

-   Sophisticated new valve seals and leak detection equipment for
    refineries and chemical plans

-   Low or zero VOC paints, consumer products and cleaning processes

-   Chlorofluorocarbon (*CFC)* free air conditioners, refrigerators, and
    solvents

-   Water and powder-based coatings to replace petroleum-based
    formulations

-   Vehicles far cleaner than believed possible in the late 1980s due to
    improvements in evaporative controls, catalyst design and fuel
    control systems for light-duty vehicles; and treatment devices and
    retrofit technologies for heavy-duty engines

-   Idle-reduction technologies for engines, including truck stop
    electrification efforts

-   Market penetration of gas-electric hybrid vehicles, and clean fuels

These technologies were not commercially available two decades ago, and
some were not even in existence. Yet today, all of these technologies
are on the market, and many are widely employed. Several are key
components of major pollution control programs.

What is known as "learning by doing" or "learning curve impacts" have
also made it possible to achieve greater emissions reductions than had
been feasible earlier, or have reduced the costs of emission control in
relation to original estimates. Learning curve impacts can be defined
generally as the extent to which variable costs (of production and/or
pollution control) decline as firms gain experience with a specific
technology. Such impacts have been identified to occur in a number of
studies conducted for various production processes. Impacts such as
these would manifest themselves as a lowering of expected costs for
operation of technologies in the future below what they may have been.

The magnitude of learning curve impacts on pollution control costs has
been estimated for a variety of sectors as part of the cost analyses
done for the Draft Direct Cost Report for the second EPA Section 812
Prospective Analysis of the Clean Air Act Amendments of 1990.[^12] In
that report, learning curve adjustments were included for those sectors
and technologies for which learning curve data was available. A typical
learning curve adjustment example is to reduce either capital or O&M
costs by a certain percentage given a doubling of output from that
sector or for that technology. In other words, capital or O&M costs will
be reduced by some percentage for every doubling of output for the given
sector or technology.

T.P. Wright, in 1936, was the first to characterize the relationship
between increased productivity and cumulative production. He analyzed
man-hours required to assemble successive airplane bodies. He suggested
the relationship is a log linear function, since he observed a constant
linear reduction in man-hours every time the total number of airplanes
assembled was doubled. The relationship he devised between number
assembled and assembly time is called Wright's Equation (Gumerman and
Marnay, 2004).[^13] This equation, shown below, has been shown to be
widely applicable in manufacturing:

Wright's Equation: C~N~ = C~o~ \* N^b^,

where

N = cumulative production

C~N~ = cost to produce N^th^ unit of capacity

C~o~ = cost to produce the first unit

B = learning parameter = ln (1-LR)/ln(2), where

LR = learning by doing rate, or cost reduction per doubling of capacity
or output.

The percentage adjustments can range from 5 to 20 percent, depending on
the sector and technology. Learning curve adjustments were prepared in a
memo by IEc (2007) supplied to US EPA and applied for the mobile source
sector (both onroad and nonroad) and for application of various EGU
control technologies within the Draft Direct Cost Report.[^14] Advice
received from the SAB Advisory Council on Clean Air Compliance Analysis
in June 2007 indicated an interest in expanding the treatment of
learning curves to those portions of the cost analysis for which no
learning curve impact data are currently available. Examples of these
sectors are non-EGU point sources and area sources. The memo by IEc
outlined various approaches by which learning curve impacts can be
addressed for those sectors. The recommended learning curve impact
adjustment for virtually every sector considered in the Draft Direct
Cost Report is a 10% reduction in O&M costs for two doubling of
cumulative output, with proxies such as cumulative fuel sales or
cumulative emission reductions being used when output data was
unavailable.

For this RIA, we do not have the necessary data for cumulative output,
fuel sales, or emission reductions for sectors included in our analysis
in order to properly generate control costs that reflect learning curve
impacts. Clearly, the effect of including these impacts would be to
lower our estimates of costs for our control strategies in 2020, but we
are not able to include such an analysis in this RIA.

### 5.4.2 Influence on Regulatory Cost Estimates

Studies indicate that it is not uncommon for pre-regulatory cost
estimates to be higher than later estimates, in part because of
inability to predict technological advances. Over longer time horizons,
such as the time allowed for areas with high levels of ozone pollution
to meet the ozone NAAQS, the opportunity for technical advances is
greater.

-   *Multi-rule study:* Harrington et al. of Resources for the
    Future (2000) conducted an analysis of the predicted and actual
    costs of 28 federal and state rules, including 21 issued by EPA and
    the Occupational Safety and Health Administration (OSHA), and found
    a tendency for predicted costs to overstate actual implementation
    costs. Costs were considered accurate if they fell within the
    analysis error bounds or if they fall within 25 percent (greater or
    less than) the predicted amount. They found that predicted total
    costs were overestimated for 14 of the 28 rules, while total costs
    were underestimated for only three rules. Differences can result
    because of quantity differences (e.g., overestimate of pollution
    reductions) or differences in per-unit costs (e.g., cost per unit of
    pollution reduction). Per-unit costs of regulations were
    overestimated in 14 cases, while they were underestimated in six
    cases. In the case of EPA rules, the agency overestimated per-unit
    costs for five regulations, underestimated them for four regulations
    (three of these were relatively small pesticide rules), and
    accurately estimated them for four. Based on examination of eight
    economic incentive rules, "for those rules that employed economic
    incentive mechanisms, overestimation of per-unit costs seems to be
    the norm," the study said.

Based on the case study results and existing literature, the authors
identified technological innovation as one of five explanations of why
predicted and actual regulatory cost estimates differ: "Most regulatory
cost estimates ignore the possibility of technological innovation ...
Technical change is, after all, notoriously difficult to forecast ... In
numerous case studies actual compliance costs are lower than predicted
because of unanticipated use of new technology."[^15]

It should be noted that many (though not all) of the EPA rules examined
by Harrington had compliance dates of several years, which allowed a
limited period for technical innovation. Much longer time periods
(ranging up to 20 years) are allowed by the statute for meeting the
ozone NAAQS in areas with high ozone levels, where a substantial
fraction of the estimated cost in this analysis is incurred.

-   *Acid Rain SO2 Trading Program*: Recent cost estimates of the Acid
    Rain SO2 trading program by Resources for the Future (RFF) and MIT
    have been as much as 83 percent lower than originally projected by
    EPA.[^16] Note that the original EPA cost analysis also relied on an
    optimization model like IPM to approximate the results of emissions
    trading. As noted in the RIA for the Clean Air Interstate Rule, the
    ex ante numbers in 1989 were an overestimate in part because of the
    limitation of economic modeling to predict technological improvement
    of pollution controls and other compliance options such as fuel
    switching. The fuel switching from high-sulfur to low-sulfur coal
    was spurred by a reduction in rail transportation costs due to
    deregulation of rail rates during the 1990'sHarrington et al. report
    that scrubbing turned out to be more efficient (95% removal vs.
    80-85% removal) and more reliable (95% vs. 85% reliability) than
    expected, and that unanticipated opportunities arose to blend low
    and high sulfur coal in older boilers up to a 40/60 mixture,
    compared with the 5/95 mixture originally estimated.

  ---------------------------------------- ------------------------------
  **Phase 2 Cost Estimates**               

  Ex ante estimates                        \$2.7 to \$6.2 billion^a^

  Ex post estimates                        \$1.0 to \$1.4 billion

  ^a^ 2010 Phase II cost estimate in       
  \$1995.                                  
  ---------------------------------------- ------------------------------

-   *EPA Fuel Control Rules:* A 2002 study by two economists with EPA's
    Office of Transportation and Air Quality[^17] examined EPA vehicle
    and fuels rules and found a general pattern that "all ex ante
    estimates tended to exceed actual price impacts, with the EPA
    estimates exceeding actual prices by the smallest amount." The paper
    notes that cost is not the same as price, but suggests that a
    comparison nonetheless can be instructive.[^18] An example focusing
    on fuel rules is provided:

Table 5.7: Comparison of Inflation-Adjusted Estimated Costs and Actual
Price Changes for EPA Fuel Control Rules ^A^

+----------------------+-------+-------+-------+---------+-----------+
|                      | **Inf |       |       |         | **Actual  |
|                      | latio |       |       |         | Price     |
|                      | n-adj |       |       |         | Changes   |
|                      | usted |       |       |         | (c/gal)** |
|                      | Cost  |       |       |         |           |
|                      | Esti  |       |       |         |           |
|                      | mates |       |       |         |           |
|                      | (c/g  |       |       |         |           |
|                      | al)** |       |       |         |           |
+----------------------+-------+-------+-------+---------+-----------+
|                      | **    | **    | **    | **      |           |
|                      | EPA** | DOE** | API** | Other** |           |
+----------------------+-------+-------+-------+---------+-----------+
| **Gasoline**         |       |       |       |         |           |
+----------------------+-------+-------+-------+---------+-----------+
| > Phase 2 RVP        | 1.1   | 1.8   |       | 0.5     |           |
| > Control (7.8       |       |       |       |         |           |
| > RVP---Summer)      |       |       |       |         |           |
| > (1995\$)           |       |       |       |         |           |
+----------------------+-------+-------+-------+---------+-----------+
| > Reformulated       | 3.    | 3.    | 8.2   | 7.4     | 2.2       |
| > Gasoline Phase 1   | 1-5.1 | 4-4.1 | -14.0 | (CRA)   |           |
| > (1997\$)           |       |       |       |         |           |
+----------------------+-------+-------+-------+---------+-----------+
| > Reformulated       | 4.    | 7.6   | 10.8  | 12      | 7.2 (5.1, |
| > Gasoline Phase 2   | 6-6.8 | -10.2 | -19.4 |         | when      |
| > (Summer) (2000\$)  |       |       |       |         | corrected |
|                      |       |       |       |         | to 5yr    |
|                      |       |       |       |         | MTBE      |
|                      |       |       |       |         | price)    |
+----------------------+-------+-------+-------+---------+-----------+
| > 30 ppm sulfur      | 1.    | 2.    | 2.6   | 5.7     | N/A       |
| > gasoline (Tier 2)  | 7-1.9 | 9-3.4 |       | (NPRA), |           |
|                      |       |       |       | 3.1     |           |
|                      |       |       |       | (AIAM)  |           |
+----------------------+-------+-------+-------+---------+-----------+
| **Diesel**           |       |       |       |         |           |
+----------------------+-------+-------+-------+---------+-----------+
| > 500 ppm sulfur     | 1.    |       | 3.3   | 2.2     |           |
| > highway diesel     | 9-2.4 |       | (     |         |           |
| > fuel (1997\$)      |       |       | NPRA) |         |           |
+----------------------+-------+-------+-------+---------+-----------+
| > 15 ppm sulfur      | 4.5   | 4.    | 6.2   | 4.2-6.1 | N/A       |
| > highway diesel     |       | 2-6.0 |       | (NPRA)  |           |
| > fuel               |       |       |       |         |           |
+----------------------+-------+-------+-------+---------+-----------+

^a^ Anderson et al., 2002.

-   Chlorofluorocarbon (*CFC) Phase-Out:* Number range CHAPTEREPA used a
    combination of regulatory, market based (i.e., a cap-and-trade
    system among manufacturers), and voluntary approaches to phase out
    the most harmful ozone depleting substances. This was done more
    efficiently than either EPA or industry originally anticipated. The
    phaseout for Class I substances was implemented 4-6 years faster,
    included 13 more chemicals, and cost 30 percent less than was
    predicted at the time the 1990 Clean Air Act Amendments were
    enacted.[^19]

The Harrington study states, "When the original cost analysis was
performed for the CFC phase-out it was not anticipated that the
hydrofluorocarbon HFC-134a could be substituted for CFC-12 in
refrigeration. However, as Hammit (1997) notes, 'since 1991 most new
U.S. automobile air conditioners have contained HFC-134a (a compound for
which no commercial production technology was available in 1986) instead
of CFC-12" (p.13). He cites a similar story for HCFRC-141b and 142b,
which are currently substituting for CFC-11 in important foam-blowing
applications."

-   Additional examples of decreasing costs of emissions controls
    include: SCR catalyst costs decreasing from \$11k-\$14k in 1998 to
    \$3.5k-\$5k in 2004, and improved low NOx burners reduced emissions
    by 50% from 1993-2003 while the associated capital cost dropped from
    \$25-\$38/kw to \$15/kw (ICF, 2005).

We can not estimate the interplay between EPA regulation and technology
improvement, but it is clear that a *priori* cost estimation often
results in overestimation of costs because changes in technology
(whatever the cause) make less costly control possible.

## 5.5 References 

Anderson, J.F., and Sherwood, T., 2002. "Comparison of EPA and Other
Estimates of Mobile Source Rule Costs to Actual Price Changes," Office
of Transportation and Air Quality, U.S. Environmental Protection Agency.
Technical Paper published by the Society of Automotive Engineers. SAE
2002-01-1980.

Babiker, M.H., and T.F. Rutherford. 1997. "Input Output and General
Equilibrium Estimates of Embodied CO2: A Data Set and Static Framework
for Assessment." University of Colorado at Boulder, Working Paper 97 2.
Available at http://debreu.colorado.edu/papers/gtaptext.html.

Babiker, M.H., J.M. Reilly, M. Mayer, R.S. Eckaus, I.S. Wing, and R.C.
Hyman. 2001. "The MIT Emissions Prediction and CO2 Policy Analysis
(EPPA) Model: Revisions, Sensitivities, and Comparisons of Results." MIT
Joint Program on the Science and Policy of Global Change, Report No. 71.
Available at http://web.mit.edu/globalchange/www/eppa.html.

Bovenberg, L.A., and L.H. Goulder. 1996. "Optimal Environmental Taxation
in the Presence of Other Taxes: General Equilibrium Analysis." American
Economic Review 86(4):985-1000. Available at
\<http://www.aeaweb.org/aer/\>.

Brooke, A., D. Kendrick, A. Meeraus, and R. Raman. 1998. GAMS: A User's
Guide. GAMS Development Corporation. Available at http://www.gams.com.

Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L.
Palmer. 2000. "Sulfur Dioxide Control by Electric Utilities: What Are
the Gains from Trade?" *Journal of Political Economy* 108(#6):1292-1326.

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits:
The U.S. SO2 Cap-and-Trade Program. Massachusetts Institute of
Technology Center for Energy and Environmental Policy Research.

Feenberg, D., and E. Coutts. 1993. "An Introduction to the TAXSIM
Model." Journal of Policy Analysis and Management 12(1):189 194.
Available at http://www.nber.org/\~taxsim/.

Fullerton, D., and D. Rogers. 1993. "Who Bears the Lifetime Tax Burden?"
Washington, DC: The Brookings Institute. Available at
http://bookstore.brookings.edu/ book_details.asp?product%5Fid=10403.

Goulder, L.H., and R.C. Williams. 2003. "The Substantial Bias from
Ignoring General Equilibrium Effects in Estimating Excess Burden, and a
Practical Solution." Journal of Political Economy 111:898 927. Available
at <http://www.journals.uchicago.edu/JPE/home.html>

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. "On the Accuracy
of Regulatory Cost Estimates." *Journal of Policy Analysis and
Management* 19(2):297-322.

Hammit, J.K. (1997). "Are the costs of proposed environmental
regulations overestimated? Evidence from the CFC phaseout." Unpublished
paper, Center for Risk Analysis, Harvard School of Public Health,
Cambridge, MA.

Holmstead, Jeffrey, 2002. "Testimony of Jeffrey Holmstead, Assistant
Administrator, Office of Air and Radiation, U.S. Environmental
Protection Agency, Before the Subcommittee on Energy and air Quality of
the committee on Energy and Commerce, U.S. House of Representatives, May
1, 2002, p. 10.

ICF Consulting. October 2005. The Clean Air Act Amendment: Spurring
Innovation and Growth While Cleaning the Air. Washington, DC. Available
at http://www.icfi.com/Markets/Environment/doc_files/caaa-success.pdf.

Minnesota IMPLAN Group. 2003. State Level Data for 2000. Available from
<http://www.implan.com/index.html>.

Nestor, D.V., and C.A. Pasurka. 1995. The U.S. Environmental Protection
Industry: A Proposed Framework for Assessment. U.S. Environmental
Protection Agency, Office of Policy, Planning, and Evaluation. EPA
230-R-95-001. Available at
http://yosemite.epa.gov/ee/epa/eermfile.nsf/11f680ff78df42f585256b45007e6235/41b8b642ab9371df852564500004b543/\$FILE/EE
0217A 1.pdf.

U.S. Department of Energy, Energy Information Administration. Undated
(b). State Energy Price and Expenditure Report. Washington DC. Available
at http://www.eia.doe.gov/emeu/states/price_multistate.html.

U.S. Department of Energy, Energy Information Administration. 2001.
Manufacturing Energy Consumption Survey 1998. Washington DC. Available
at http://www.eia.doe.gov/emeu/mecs/.

U.S. Department of Energy, Energy Information Administration. January
2003. Annual Energy Outlook 2003. DOE/EIA 0383(2003). Washington DC.
Available at
http://www.eia.doe.gov/oiaf/archive/aeo03/pdf/0383(2003).pdf.

U.S. Department of Energy, Energy Information Administration. January
2004. Annual Energy Outlook 2004. DOE/EIA-0383(2003). Washington, DC.
Available at http://www.eia.doe.gov/oiaf/aeo/pdf/0383 (2001).

U.S. Environmental Protection Agency. June 2007. Advisory Council on
Clean Air Compliance Analysis (COUNCIL), Council Advisory on OAR's
Direct Cost Report and Uncertainty Analysis Plan. Washington, DC.
http://www.epa.gov/sab/pdf/council-07-002.pdf.

[^1]: For more information on this cost methodology and the role of
    AirControlNET, see Section 6 of the 2006 PM RIA, AirControlNET 4.1
    Control Measures Documentation (Pechan, 2006b), or the EPA Air
    Pollution Control Cost Manual, Section 1, Chapter 2, found at
    <http://www.epa.gov/ttn/catc/products.html#cccinfo>.

[^2]: The application of the 0.070 EGU control strategy results in
    annual NOx allowance price decreasing from \$1618/ton in the
    baseline to \$641/ton. See Technical Support Document on EGU Control
    Strategies for more details. Further detailed information on IPM is
    available in Section 6 of the 2006 PM RIA or at
    <http://www.epa.gov/airmarkets/epa-ipm>

[^3]: The expected emissions reductions from SCR retrofits are based on
    data derived from EPA regulations (Control of Emissions of Air
    Pollution from 2004 and Later Model Year Heavy-duty Highway Engines
    and Vehicles published October 2000), interviews with component
    manufacturers, and EPA's Summary of Potential Retrofit Technologies
    available at
    [www.epa.gov/otaq/retrofit/retropotentialtech.htm]{.underline}.

    For more information on mobile idle reduction technologies (MIRTs)
    see EPA's Idle Reduction Technology page at
    [http://www.epa.gov/otaq/smartway/idlingtechnologies.htm]{.underline}.

[^4]: More information regarding the National Mobile Inventory Model
    (NMIM) can be found at http://www.epa.gov/otaq/nmim.htm

[^5]: A different plant-specific interest rate is applied in estimating
    control costs within IPM. See PM RIA for details.

[^6]: U.S. Environmental Protection Agency. June 2007. Advisory Council
    on Clean Air Compliance Analysis (COUNCIL), Council Advisory on
    OAR's Direct Cost Report and Uncertainty Analysis Plan. Washington,
    DC.

[^7]: NOx NonEGU point and Area controls were used for this calculation
    due to availability of detailed data across all emission sectors.

[^8]: Although \$15,000/ton (2006\$) represents the cost of the very
    first ton of unknown control needed, marginal costs for the last ton
    of unknown control could be as high as \$46,000/ton (2006\$)

[^9]: The R for Eastern Lake Michigan was 2.19 for the 0.065 ppm
    alternative standard. The R for Houston was higher, yet this value
    was not used when calculating the highest value of M because Houston
    is the only area in our analysis for 2020 that did not meet the
    current standard, and therefore not representative of the majority
    of areas needing to reach a new ozone standard.

[^10]: Harrington et al. (2000) and previous studies cited by
    Harrington.

[^11]: Figure 5.2 shows a linear marginal abatement cost curve. It is
    possible that the shape of the marginal abatement cost curve is
    non-linear.

[^12]: E.H. Pechan and Associates and Industrial Economics, Direct Cost
    Estimates for the Clean Air Act Second Section 812 Prospective
    Analysis: Draft Report, prepared for U.S. EPA, Office of Air and
    Radiation, February 2007. Available at
    <http://www.epa.gov/oar/sect812/mar07/direct_cost_draft.pdf>.

[^13]: Gumerman, Etan and Marnay, Chris. Learning and Cost Reductions
    for Generating Technologies in the National Energy Modeling System
    (NEMS), Ernest Orlando Lawrence Berkeley National Laboratory,
    University of California at Berkeley, Berkeley, CA. January 2004,
    LBNL-52559.

[^14]: Industrial Economics, Inc. Proposed Approach for Expanding the
    Treatment of Learning Curve Impacts for the Second Section 812
    Prospective Analysis: Memorandum, prepared for U.S. EPA, Office of
    Air and Radiation, August 13, 2007.

[^15]: Harrington et al., 2000.

[^16]: Carlson et al., 2000; Ellerman, 2003.

[^17]: Anderson et al, 2002.

[^18]: The paper notes: "Cost is not the same as price. This simple
    statement reflects the fact that a lot happens between a producer's
    determination of manufacturing cost and its decisions about what the
    market will bear in terms of price change."

[^19]: Holmstead, 2002.