Appendix 5

5.1 Cost Information for Non-EGU and area sources

Full details on controls can be found in Appendix 3

Low Emission Combustion (LEC)

The average cost effectiveness for large IC engines using LEC technology
was estimated to be $532/ton (ozone season).  The EC/R report on IC
engines (Ec/R, September 1, 2000) estimates the average cost
effectiveness for IC engines using LEC technology to range from
$420-840/ton (ozone season) for engines in the 2,000-8,000 bhp range. 
The key variables in determining average cost effectiveness for LEC
technology are the average uncontrolled emissions at the existing
source, the projected level of controlled emissions, annualized costs of
the controls, and number of hours of operation in the ozone season.  The
ACT document uses an average uncontrolled level of 16.8 g/bhp-hr, a
controlled level of 2.0 g/bhp-hr (87% decrease), and nearly continuous
operation in the ozone season.  The EPA believes the ACT document
provides a reasonable approach to calculating cost effectiveness for LEC
technology.

Leak Detection and Repair (LDAR) for Fugitive Leaks

The control efficiency is 80 percent reduction of VOC at an annualized
cost of $4,800 per ton.  We do not include the costs of this control
measure in our analyses in the Houston nonattainment area since these
controls are already included in the 8-hour Ozone SIP for this area. 

Enhanced LDAR for Fugitive Leaks

The control efficiency of this measure is estimated at 50 percent at a
cost of $3,050/ton of VOC reduced.  

Flare Gas Recovery

The control efficiency of this measure is 98 percent reduction of VOC
emissions at a cost of $2,700/ton. Costs may become negligible as the
size of the flare increases due to recovery credit.

Cooling Towers

There is not a general estimate of control efficiency for this measure;
one is to apply a continuous flow monitor until VOC emissions have
reached a level of 1.7 tons/year for a given cooling tower.  The
annualized cost for a continuous flow monitor is $63,000 – this is
constant over a variety of cooling tower sizes.  

Wastewater Drains and Separators

The control efficiency is 65 percent reduction of VOC emissions at a
cost of $3,050/ton.  This is based on actual sampling and cost data for
5 refineries in the Bay Area Air Quality Management District (BAAQMD).

5.2 Cost Information for EGU sources

Full details on controls can be found in Appendix 3

Cost of Controls as a Result of Lower Sub-regional Caps within the MWRPO
and OTC and other Local Controls outside of these Regions within CAIR 

As previously discussed, the power sector will achieve significant
emission reductions under the Clean Air Interstate Rule (CAIR) over the
next 10 to 15 years.  When fully implemented, CAIR (in conjunction with
NOx SIP Call) will reduce ozone season NOx emissions by over 60 percent
from 2003 levels within the CAIR states. These reductions will greatly
improve air quality and will lessen the challenges that some areas face
when solving nonattainment issues significantly.  

Power sector impacts analyzed in detail in the Final PM NAAQS RIA 15/35
(  HYPERLINK "http://www.epa.gov/ttn/ecas/ria.html" 
http://www.epa.gov/ttn/ecas/ria.html  ) provides the baseline for this
RIA. The analysis and projections in this section attempt to show the
potential impacts of the additional controls applied (see section 3.3.3
of this RIA) to facilitate attainment of the more stringent 8-hr ozone
standard of 0.070 ppm. Generally, the incremental impacts of these
controls on the power sector are marginal.

Projected Costs. EPA projects that the annual incremental cost of the
proposed new ozone standard approach is $0.2billion in 2020.  The
additional annual costs reflect additional retrofits (SCR and SNCR) and
generation shifts,. Annualized cost of CAIR is projected to be $6.17
billion in 2020. The proposed approach applied in this RIA would add
$0.2 billion incremental to this cost.

Projected Generation Mix. Coal-fired generation and natural
gas/oil-fired generation are projected to remain almost unchanged. 
Installation of approximately 3.7 GWs of SCR and 1.1 GWs of SNCR
incremental to the base case are projected as a result of the lower
sub-regional caps. There are very small changes in the generation mix.
Coal-fired generation increases about 12 GWh (an increase of
approximately 0.25% of the total generation) and gas-fired generation
decreases a similar amount.  Hydo, nuclear, other, and renewable based
generation projected to remain the same. Projected retirements of coal
units is marginal, accounting to about 0.4 GWs compared to the base case
approach.

Projected Nationwide Retail Electricity Prices. Retail electricity
prices are projected to change marginally, only about 1%. The extension
of the cap-and-trade approach in the form of lower sub-regional caps
allows industry to meet the requirements of CAIR in the most
cost-effective manner, thereby minimizing the costs passed on to
consumers. Retail electricity prices are projected to increase less than
1% within the MWRPO and OTC regions, and decrease about 1% in the rest
of the CAIR region. 

5.3 Cost information for Onroad and Nonroad Mobile Sources

Full details on controls can be found in Appendix 3

Diesel Retrofits and Vehicle Replacement 

To calculate costs for the use of selective catalytic reduction as a
retrofit technology, the assumption was made that all relevant vehicles
would be affected by the control. Therefore, all on-road heavy duty
diesel vehicles that received a retrofit were assumed to employ
selective catalytic reduction as a retrofit technology. The average cost
of a selective catalytic reduction system ranges from $10,000 to $20,000
per vehicle depending on the size of the engine, the sales volume, and
other factors (Pechan, 2003). For AirControlNET analysis, the average
estimated cost of this system is $15,000 per heavy duty diesel vehicle.
(Source: AirControlNET Documentation, III-160).  OTAQ conducted an
additional assessment of current SCR costs and calculated that for the
year 2020, the cost of SCRs will be approximately $13,000 per unit.  
This estimate reflects an economy of scale cost reduction of 33%, which
is consistent with trends in other mobile source control technologies
that enter large scale production.

The rebuild/upgrade kit is applied to nonroad equipment.  OTAQ estimates
the cost of this kit to be $2,000 to $4,000 per vehicle.  For this
analysis, the average estimated cost is $3,000 per vehicle. 

Table 5.3.1: Summary of Cost Effectiveness for Rebuild/Upgrade Kit for
Various Nonroad Vehicles

Nonroad Vehicle	Retrofit Technology	Range of $/ton NOx Emission Reduced
Range of $/ton HC Emission Reduced

Tractors/Loaders/Backhoes	Rebuild/  Upgrade kit	$1,300	$2,200	$9,600
$18,900

Excavators

$1,100	$4,200	$8,100	$43,400

Crawler Tractor/Dozers

$1,100	$4,200	$8,300	$43,500

Skid Steer Loaders

$1,000	$1,600	$7,400	$14,800

Agricultural Tractors

$1,200	$4,900	$9,300	$34,300



Table 5.3.2: Summary of Cost Effectiveness for SCR for Various Nonroad
Vehicles

Nonroad Vehicle	Retrofit Technology	Range of $/ton NOx Emission Reduced
Range of $/ton HC Emission Reduced

Tractors/Loaders/Backhoes	SCR	$2,900	$5,300	$32,200	$63,700

Excavators

$2,700	$10,400	$27,400	$146,200

Crawler Tractor/Dozers

$2,800	$10,400	$27,900	$146,700

Skid Steer Loaders

$2,600	$4,000	$24,900	$52,100

Agricultural Tractors

$3,000	$7,600	$31,200	$115,500



Table 5.3.3: Summary of Cost Effectiveness for SCR for Various Highway
Vehicles 

Highway Vehicle	Retrofit Technology	Range of $/ton NOx Emission Reduced
Range of $/ton HC Emission Reduced

Class 6&7 Truck	SCR	$5,600	$14,100	$46,900	$126,200

Class 8b Truck

$1,100	$2,500	$14,900	$44,600



Implement Continuous Inspection and Maintenance Using Remote Onboard
Diagnostics (OBD)

Continuous I/M can significantly lower test costs and “convenience”
costs of I/M programs.  Using radio frequency transmission, there is a
one-time cost for the Continuous I/M device and its installation.  In
the case of Oregon, this cost is $50.  The unit is then good for the
life of the vehicle.  Annual or biennial test fees are not required
beyond this initial fee to operate the system but there may be
additional operational costs to cover data processing, reporting, and
oversight.  

We can compare the costs of periodic testing to Continuous I/M.  The
cost of data processing, reporting and oversight is estimated to be $2
per vehicle per year in the typical I/M area.  If we assume an average
vehicle life span of 14 years, with the first test at 4 years of age,
vehicles will get 5 inspections in a biennial program and 10 in an
annual program (not including additional change of ownership
inspections, which are required in some areas).  Thus, in a Continuous
I/M program, an additional cost of $10-$20 will be incurred for each
vehicle over its life, assuming the same costs apply in a Continuous I/M
program as in a tailpipe test program.  

In addition to test costs, Continuous I/M avoids most of the convenience
costs associated with I/M – the time and fuel it takes to drive to the
station, get a test, and return home.  The one-time installation of the
transmitter requires a visit to the test station, but no further visits
are required after that.  So, if we assume, conservatively, that the
typical test cycle requires a total of two hours of time at $20 per hour
and a half-gallon of gas (10 miles round trip with an average fuel
economy of 20 mpg) at $3 per gallon gives us a cost of $41.50.  Over the
life of the vehicle that works out to $207.50 in a biennial program or
$415 in an annual program.  Compare this to the one time trip for
Continuous I/M OBD at a cost of $41.50 and substantial savings are
realized.

Putting it all together, the table below shows the lifetime inspection
and convenience costs of Continuous I/M versus periodic I/M  (assuming
the current mix of annual and biennial testing and current test costs). 
Periodic I/M testing costs about $20 billion over a 10 year lifecycle
with an additional $25 billion in convenience costs for a total of $45
billion.  By contrast, Continuous I/M has a test and installation cost
of $3 billion dollars over the same 10 year period, and a convenience
cost of $2.5 billion for a total of $5.5 billion.  Thus, nationwide
installation of Continuous I/M would save the nation’s motorists about
$38 billion in inspection and convenience costs over a 10 year period.

Lifetime Inspection and Convenience Costs of I/M

	Test/Install Cost	Convenience Cost	Total

Cost

Continuous I/M	$20 billion	$25 billion	$45 billion

Remote I/M 	$4.3 billion	$2.5 billion	$6.8 billion

Savings	$15.7 billion	$22.5 billion	$38.2 billion



Given that Continuous I/M will actually reduce the cost of I/M,
implementation of this measure is highly cost-effective.  If we add
together the tons of NOx and HC reduced and assign the entire cost
savings to these two pollutants, this measure saves $X per ton of
pollution reduced.

Eliminating Long Duration Truck Idling

For purposes of this RIA, we identified this measure as a no cost
strategy i.e. $0/ton NOx.  Both TSEs and MIRTs have upfront capital
costs, but these costs can be fully recovered by the fuel savings.  The
examples below illustrate the potential rate of return on investments in
idle reduction strategies.

TSE

The average price of TSE technology is $11,500 per parking space.  The
average service life of this technology is 15 years.  Truck engines at
idle consume approximately 1 gallon per hour of idle.  Current TSE
projects are operating in environments where trucks are idling, on
average, for 8 hours per day per space for 365 days per year (or about
2,920 hours per year).  Since TSE technology can completely eliminate
long duration idling at truck spaces (i.e. a 100% fuel savings), this
translates into 2,920 gallons of fuel saved per year per space. At
current diesel prices ($2.90/gallon), this fuel savings translates into
$8,468.  Therefore, an $11,500 capital investment should be recovered
within about 17 months.  In this scenario, TSE investments offer over a
70% annual rate of return over the life of the technology. 

While it is technically feasible to electrify all parking spaces that
support long duration idling trucks, we should note that TSE technology
is generally deployed at a minimum of 25-50 parking spaces per location
to maximize economies of scale.  The financial attractiveness of
installing TSE technology will depend on the demonstrated truck idling
behavior – the greater the rates of idling, the greater the potential
emissions reductions and associated fuel and cost savings.  

MIRTs

The price of MIRT technologies ranges from $1,000-$10,000.  The most
popular of these technologies is the auxiliary power unit (APU) because
it provides air conditioning, heat, and electrical power to operate
appliances.  The average price of an APU is $7,000.  The average service
life of an APU is 10 years.  An APU consumes two-tenths of a gallon per
hour, so the net fuel savings is 0.80 gallons per hour.  EPA estimates
that trucks idle for 7 hours per rest period, on average, and about 300
days per year (or 2,100 hours per year).  Since idling trucks consume 1
gallon of fuel per hour of idle, APUs can reduce fuel consumption for
truck drivers/owners by approximately 1,680 gallons per year.  At
current diesel prices ($2.90/gallon), truck drivers/owners would save
$4,872 on fuel if they used an APU.  Therefore, a $7,000 capital
investment should be recovered within about 18 months.  In this
scenario, APU investments offer almost a 70% annual rate of return over
the life of the technology.

Cost-Effectiveness of Measure: $0/ton NOx 

Commuter Programs

We used the Transportation Research Board’s (TRB) cost-effectiveness
analysis of Congestion Mitigation and Air Quality Improvement Program
(CMAQ) projects to estimate the cost-effectiveness of this measure.  TRB
conducted an extensive literature review and then synthesized the data
to develop comparable estimates of cost-effectiveness of a wide range of
CMAQ-funded measures.  We took the average of the median
cost-effectiveness of a sampling of CMAQ-funded measures and then
applied this number to the overarching commuter reduction measure.  The
CMAQ-funded measures we selected were:

regional rideshares 

vanpool programs

park-and-ride lots

regional transportation demand management

employer trip reduction programs  

We felt that these measures were a representative sampling of commuter
reduction incentive programs.  There is a great deal of variability,
however, in the type of programs and the level of incentives that
employers offer which can impact both the amount of emissions reductions
and the cost of commuter reduction incentive programs.

We chose to apply the resulting average cost-effectiveness estimate to
one pollutant – NOx – in order to be able to compare commuter
reduction programs to other NOx reduction strategies. TRB reported the
cost-effectiveness of each measure, however, as a $/ton reduction of
both VOC and NOx by applying the total cost of the program to a 1:4
weighted sum of VOC and NOx [[total emissions reduction = (VOC * 1) +
(NOx * 4)).  There was not enough information in the TRB study to
isolate the $/ton cost-effectiveness for just NOx reductions, so we used
the combined NOx and VOC estimate.  The results are presented in Table
5.3.4.

Table 5.3.4

Cost-Effectiveness of Best Workplaces for Commuters Type Measures from
the 2002 TRB Study, Table E-5

$/ton (2000$) 1:4 VOC:NOx (reported in the RIA as $/ton NOx)

	 	Low	High	Median

Regional Rideshare	$1,200 	$16,000 	$7,400 

Vanpool Programs	$5,200 	$89,000 	$10,500 

Park-and-ride lots	$8,600 	$70,700 	$43,000 

Regional TDM	$2,300 	$33,200 	$12,500 

Employer trip reduction programs	$5,800 	$175,500 	$22,700 

Average of All Measures	$4,620 	$76,900 	$19,200 



Cost-Effectiveness of Measure: $19,200/ton NOx

Reduce Gasoline RVP from 7.8 to 7.0 in Remaining Nonattainment Areas

Cost-Effectiveness of Measure: Cost per ton will be $5,700 to $36,000 /
ton VOC

EMPAX-CGE Model Description, General Model Structure

This section provides additional details on the EMPAX-CGE model
structure, data sources, and assumptions.  The version of EMPAX-CGE used
in this analysis is a dynamic, intertemporally optimizing model that
solves in 5 year intervals from 2005 to 2050.  It uses the classical
Arrow- Debreu general equilibrium framework wherein households maximize
utility subject to budget constraints, and firms maximize profits
subject to technology constraints.  The model structure, in which agents
are assumed to have perfect foresight and maximize utility across all
time periods, allows agents to modify behavior in anticipation of future
policy changes, unlike dynamic recursive models that assume agents do
not react until a policy has been implemented.  

Nested CES functions are used to portray substitution possibilities
available to producers and consumers.  Figure 5.4.1 illustrates this
general framework and gives a broad characterization of the model. 
Along with the underlying data, these nesting structures and associated
substitution elasticities determine the effects that will be estimated
for policies.  These nesting structures and elasticities used in
EMPAX-CGE are generally based on the Emissions Prediction and Policy
Analysis (EPPA) Model developed at the Massachusetts Institute of
Technology (Babiker et al., 2001).  Although the two models are quite
different (EPPA is a recursive dynamic, international model focused on
national level climate change policies), both are intended to simulate
how agents will respond to environmental policies.

 

Figure 5.4.1.  General Production and Consumption Nesting Structure in
EMPAX-CGE

Given this basic similarity, EMPAX-CGE has adopted a comparable
structure.  EMPAX-CGE is programmed in the GAMS language (Generalized
Algebraic Modeling System) and solved as a mixed complementarity problem
(MCP)  using MPSGE software (Mathematical Programming Subsystem for
General Equilibrium).  The PATH solver from GAMS is used to solve the
MCP equations generated by MPSGE.

5.4.1 Data Sources

The economic data come from state level information provided by the
Minnesota IMPLAN Group and energy data come from EIA.  Although IMPLAN
data contain information on the value of energy production and
consumption in dollars, these data are replaced with EIA data for
several reasons.  First, the policies being investigated typically focus
on energy markets, making it essential to include the best possible
characterization of these markets in the model.  Although the IMPLAN
data are developed from a variety of government data sources at the U.S.
Bureau of Economic Analysis and U.S. Bureau of Labor Statistics, these
data do not always agree with energy information collected by EIA
directly from manufacturers and electric utilities.  Second, it is
necessary to have physical quantities for energy consumption in the
model to portray effects of environmental policies.  EIA reports
physical quantities, while IMPLAN does not.  Finally, although the
IMPLAN data reflect the year 2000, the initial baseline year for the
model is 2005.  Thus, AEO energy production and consumption, output, and
economic growth forecasts for 2005 are used to adjust the year 2000
IMPLAN data. 

EMPAX-CGE combines these economic and energy data to create a balanced
social accounting matrix (SAM) that provides a baseline characterization
of the economy.  The SAM contains data on the value of output in each
sector, payments for factors of production and intermediate inputs by
each sector, household income and consumption, government purchases,
investment, and trade flows.  A balanced SAM for the year 2005
consistent with the desired sectoral and regional aggregation is
produced using procedures developed by Babiker and Rutherford (1997) and
described in Rutherford and Paltsev (2000).  The methodology relies on
standard optimization techniques to maintain the calculated energy
statistics while minimizing the changes needed in the economic data to
create a new balanced SAM that matches AEO forecasts for the baseline
model year of 2005.

These data are used to define 10 regions within the United States, each
containing 40 industries.  Regions have been selected to capture
important differences across the country in electricity generation
technologies, while industry aggregations are controlled by available
energy consumption data.  Prior to solving EMPAX-CGE, these regions and
industries are aggregated up to the categories to be included in the
analysis.

Table 5.4.1.1 presents the industry categories included in EMPAX-CGE for
policy analysis.  Their focus is on maintaining as much detail in the
energy intensive sectors as is allowed by available energy consumption
data and computational limits of dynamic CGE models.  In addition, the
electricity industry is separated into fossil fuel generation and
nonfossil generation, which is necessary because many electricity
policies affect only fossil fired electricity.

Table 5.4.1.1  EMPAX-CGE Industries

EMPAX Industry	

NAICS Classifications



Coal	

2121



Crude Oila	

211111



Electricity (fossil and nonfossil)	

2211



Natural Gas	

211112, 2212, 4862



Petroleum Refining	

324



Agriculture	

11



Energy-Intensive Sector:  Food	

311



Energy-Intensive Sector:  Paper and Allied	

322



Energy-Intensive Sector:  Chemicals	

325



Energy-Intensive Sector:  Glass	

3272



Energy-Intensive Sector:  Cement	

3273



Energy-Intensive Sector:  Iron and Steel	

3311



Energy-Intensive Sector:  Aluminum	

3313



Other Manufacturing	

312-316, 321, 323, 326-327, 331-339



Services	

All Others



Transportationb	

481-488

a	Although NAICS 211111 covers crude oil and gas extraction, the gas
component of this sector is moved to the natural gas industry.

b	Transportation does not include NAICS 4862 (natural gas distribution),
which is part of the natural gas industry.

Figure 5.4.1.1 shows the five regions run in EMPAX-CGE in this analysis,
which have been defined based on the expected regional distribution of
policy impacts, availability of economic and energy data, and
computational limits on model size.  These regions have been constructed
from the underlying 10 region database designed to follow, as closely as
possible, the electricity market regions defined by the North American
Electric Reliability Council (NERC).  Note that, for purposes of
presenting results, the four regions; Northeast, Southeast, Midwest and
Plains, have been aggregated into an “East” region to approximate
the region of interest in this analysis.

Figure 5.4.1.1.  Regions defined in EMPAX-CGE

Production Functions

All productive markets are assumed to be perfectly competitive and have
production technologies that exhibit constant returns to scale, except
for the agriculture and natural resource extracting sectors, which have
decreasing returns to scale because they use factors in fixed supply
(land and fossil fuels, respectively).  The electricity industry is
separated into two distinct sectors:  fossil fuel generation and
nonfossil generation.  This allows tracking of variables such as heat
rates for fossil fired utilities (Btus of energy input per kilowatt hour
of electricity output).

All markets must clear (i.e., supply must equal demand in every sector)
in every period, and the income of each agent in the model must equal
their factor endowments plus any net transfers.  Along with the
underlying data, the nesting structures shown in Figure 5.4.1 and
associated substitution elasticities define current production
technologies and possible alternatives.

Utility Functions

Each region in the dynamic version of EMPAX-CGE contains four
representative households, classified by income, that maximize
intertemporal utility over all time periods in the model subject to
budget constraints, where the income groups are: 

$0 to $14,999, 

$15,000 to $29,999, 

$30,000 to $49,999, and 

$50,000 and above.  

These representative households are endowed with factors of production
including labor, capital, natural resources, and land inputs to
agricultural production.  Factor prices are equal to the marginal
revenue received by firms from employing an additional unit of labor or
capital.  The value of factors owned by each representative household
depends on factor use implied by production within each region.  Income
from sales of these productive factors is allocated to purchases of
consumption goods to maximize welfare.

Within each time period, intratemporal utility received by a household
is formed from consumption of goods and leisure.  All consumption goods
are combined using a Cobb Douglas structure to form an aggregate
consumption good.  This composite good is then combined with leisure
time to produce household utility.  The elasticity of substitution
between consumption goods and leisure depends on empirical estimates of
labor supply elasticities and indicates how willing households are to
trade off leisure time for consumption.  Over time, households consider
the discounted present value of utility received from all periods’
consumption of goods and leisure.

Following standard conventions of CGE models, factors of production are
assumed to be intersectorally mobile within regions, but migration of
productive factors is not allowed across regions.  This assumption is
necessary to calculate welfare changes for the representative household
located in each region in EMPAX-CGE.  EMPAX-CGE also assumes that
ownership of natural resources and capital embodied in nonfossil
electricity generation is spread across the United States through
capital markets.

5.4.4	Trade

In EMPAX-CGE, all goods and services are assumed to be composite,
differentiated “Armington” goods made up of locally manufactured
commodities and imported goods.  Output of local industries is initially
separated into output destined for local consumption by producers or
households and output destined for export.  This local output is then
combined with goods from other regions in the United States using
Armington trade elasticities that indicate agents make relatively little
distinction between output from firms located within their region and
output from firms in other regions within the United States.  Finally,
the domestic composite goods are aggregated with imports from foreign
sources using lower trade elasticities to capture the fact that foreign
imports are more differentiated from domestic output than are imports
from other regional suppliers in the United States.  

5.4.5	Tax Rates and Distortions

Taxes and associated distortions in economic behavior have been included
in EMPAX-CGE because theoretical and empirical literature found that
taxes can substantially alter estimated policy costs.  The IMPLAN
economic database used by EMPAX-CGE includes information on taxes such
as indirect business taxes (all sales and excise taxes) and social
security taxes.  However, IMPLAN reports factor payments for labor and
capital at their gross of tax values, which necessitates use of
additional data sources to determine personal income and capital tax
rates.  Information from the TAXSIM model at the National Bureau of
Economic Research (Feenberg and Coutts, 1993), along with user cost of
capital calculations from Fullerton and Rogers (1993), are used to
establish tax rates.

Along with these rates, distortions associated with taxes are a function
of labor supply decisions of households.  As with other CGE models
focused on interactions between tax and environmental policies (e.g.,
Bovenberg and Goulder [1996]; Goulder and Williams [2003]), an important
feature of EMPAX-CGE is its inclusion of a labor leisure choice—how
people decide between working and leisure time.  Labor supply
elasticities related to this choice determine, to a large extent, how
distortionary taxes are in a CGE model.  Elasticities based on the
relevant literature have been included in EMPAX-CGE (i.e., 0.4 for the
compensated labor supply elasticity and 0.15 for the uncompensated labor
supply elasticity).  These elasticity values give an overall marginal
excess burden associated with the existing tax structure of
approximately 0.3.

5.4.6	Intertemporal Dynamics and Economic Growth

There are four sources of economic growth in EMPAX-CGE:  technological
change from improvements in energy efficiency, growth in the available
labor supply (from both population growth and changes in labor
productivity), increases in stocks of natural resources, and capital
accumulation.  Energy consumption per unit of output tends to decline
over time because of improvements in production technologies and energy
conservation.  These changes in energy use per unit of output are
modeled as AEEIs, which are used to replicate energy consumption
forecasts by industry and fuel from EIA.   The AEEI values provide the
means for matching expected trends in energy consumption that have been
taken from the AEO forecasts.  They alter the amount of energy needed to
produce a given quantity of output by incorporating improvements in
energy efficiency and conservation.  Labor force and regional economic
growth, electricity generation, changes in available natural resources,
and resource prices are also based on the AEO forecasts.

 Savings provide the basis for capital formation and are motivated
through people’s expectations about future needs for capital.  Savings
and investment decisions made by households determine aggregate capital
stocks in EMPAX-CGE.  The IMPLAN dataset provides details on the types
of goods and services used to produce the investment goods underlying
each region’s capital stocks.  Adjustment dynamics associated with
formation of capital are controlled by using quadratic adjustment costs
experienced when installing new capital, which imply that real costs are
experienced to build and install new capital equipment.

Prior to investigating policy scenarios, it is necessary to establish a
baseline path for the economy that incorporates economic growth and
technology changes that are expected to occur in the absence of the
policy actions.  Beginning from the initial balanced SAM dataset, the
model is calibrated to replicate forecasts from AEO.  Upon incorporating
these forecasts, EMPAX-CGE is solved to generate a baseline consistent
with them through 2025.  Once this baseline is established, it is
possible to run “counterfactual” policy experiments.

5.5   Additional Regional Detail for Economic Cost Estimates

This appendix presents figures with additional regional economic impacts
related to the illustrative control strategies developed for the Ozone
070 standard.  Rather than showing an East-West separation for the
country, findings are given for all five regions in the EMPAX-CGE model.
 Figures 5.5.1 and 5.5.2 have additional detail on changes in energy
markets, and Figures 5.5.3 and 5.5.4 give disaggregated regional results
for the energy-intensive industries shown the RIA.  

Under the alternate standard, effects on energy production are quite
limited.  On average across all energy types, output, as measured in
quantity (or unit) terms in Figure 5.5.1, changes by less that one-half
of one percent (<0.5%).  Impacts on electricity generation are around
one-tenth of one percent (~0.10%), driven largely by modestly higher
costs in the South.  Along with spillover effects on energy consumption
in energy-intensive industries, electric utilities tend to use slightly
more coal and less natural gas in 2020.  Figure 5.5.2, which shows the
changes in terms of industrial gross revenues (these combine any
declines in output with any changes in production costs or prices),
generally go the same direction as the quantity changes – with the
exception of electricity in regions that have higher output quantities,
but lower gross revenues because of changes in production costs and
hence prices.  

Note that estimated percentage changes in output quantities are greatly
affected by the production base in each region.  For example, while
natural gas output declines by a larger percentage in the Midwest than
other regions, total production is relatively low in this area, compared
with other parts of the U.S.   Thus, as illustrated in Figure 5.5.2,
changes in gross revenues across the country measured in dollar terms
are relatively similar.

Figures 5.5.3 and 5.5.4 give the regional detail behind figures in the
main text.  These show that, although the controls for the Ozone 0.0070
ppm standard may tend to redistribute production around the nation (and
across states within model regions), the average impact on
energy-intensive industries is around two tenths of one percent (~0.2%).
 The disaggregated regional results do indicate, however, that
industries such as cement and glass could experience relatively larger
effects than other industries, especially within specific regions.   

As with the main findings in the EIA, it is important to note when
examining such findings that these impacts and redistributions are
directly related to the specific control option assumed in the Ozone
0.070 ppm standard analysis, and that attainment could be met through
alternative approaches.  Thus, while EPA provides this analysis as
guidance for States, it is expected that States will evaluate the best
strategies for achieving compliance and may choose options that could
significantly alter these regional effects.  Therefore, SIPs will most
likely be different than the strategy developed in this RIA and could be
designed to alleviate any disproportionate impacts on sensitive
industries.  

Figure 5.5.1.  Ozone 070 Impacts on Regional Energy Output Quantities,
2020

Source:	EMPAX-CGE

Figure 5.5.2.  Ozone 070 Impacts on Regional Energy Gross Output
Revenues, 2020

Source:	EMPAX-CGE

Figure 5.5.3.  Ozone 070 Impacts on Regional Energy-Intensive Output
Quantities, 2020

Source:	EMPAX-CGE

Figure 5.5.4.  Ozone 070 Impacts on Regional Energy-Intensive Gross
Output Revenues, 2020

Source:	EMPAX-CGE

 “NOx Emissions Control Costs for Stationary Reciprocating Internal
Combustion Engines in the NOx SIP Call States,”  E.H. Pechan and
Associates, Inc., Springfield, VA, August 11, 2000.  Available on the
Internet at   HYPERLINK
"http://www.epa.gov/ttn/ecas/regdata/cost/pechan8-11.pdf" 
http://www.epa.gov/ttn/ecas/regdata/cost/pechan8-11.pdf 

 “Suggested Short List and Evaluation of Point and Area Source
Emission Control Measures for the Houston-Galveston-Brazoria 8-Hour
Ozone Nonattainment Area,” Texas Council on Environmental Quality,”
Prepared by ENVIRON International Corp. for Lamar Univ.   June 15, 2006.
 Available on the Internet at    HYPERLINK
"http://www.h-gac.com/NR/rdonlyres/e4cgpdlu4wd3tiguvvxkg5ziefqy36adm2o5c
z5jpm36c67ksxbtfurvvwvgdquy362skyhnsel5uh4rdkfz2rusphd/Final+Short+List+
%26+Evaluations.pdf" 
http://www.h-gac.com/NR/rdonlyres/e4cgpdlu4wd3tiguvvxkg5ziefqy36adm2o5cz
5jpm36c67ksxbtfurvvwvgdquy362skyhnsel5uh4rdkfz2rusphd/Final+Short+List+%
26+Evaluations.pdf . 

 MARAMA Multipollutant Rule Basis for Flares, part of “Assessment of
Control Technology Options for Petroleum Refineries in the mid-Atlantic
Region.”  February 19, 2007.  Found on the Internet at   HYPERLINK
"http://www.marama.org/reports/021907_Refinery_Control_Options_TSD_Final
.pdf" 
http://www.marama.org/reports/021907_Refinery_Control_Options_TSD_Final.
pdf .    

 Reference 5. 

 Bay Area Air Quality Management District (BAAQMD).  Proposed Revision
of Regulation 8, Rule 8: Wastewater Collection Systems.  Staff Report,
March 17, 2004.   

 Transportation Research Board, National Research Council, 2002. The
Congestion Mitigation and Air Quality Improvement Program: assessing 10
years of experience, Committee for the Evaluation of the Congestion
Mitigation and Air Quality Improvement Program.

 Although it is not illustrated in Figure 5.4.1, some differences across
industries exist in their handling of energy inputs.  In addition, the
agriculture and fossil-fuel sectors in EMPAX-CGE contain equations that
account for the presence of fixed inputs to production (land and
fossil-fuel resources, respectively).

 See Brooke, Kendrick, and Meeraus (1996) for a description of GAMS ( 
HYPERLINK "http://www.gams.com/"  http://www.gams.com/ ).

 Solving EMPAX-CGE as a MCP problem implies that complementary slackness
is a feature of the equilibrium solution.  In other words, any firm in
operation will earn zero economic profits and any unprofitable firms
will cease operations.  Similarly, for any commodity with a positive
price, supply will equal demand, or conversely any good in excess supply
will have a zero price.  

 See Rutherford (1999) for MPSGE documentation (  HYPERLINK
"http://debreu.colorado.edu"  http://debreu.colorado.edu ).

 See   HYPERLINK "http://www.implan.com/index.html" 
http://www.implan.com/index.html  for a description of the Minnesota
IMPLAN Group and its data.

 These EIA sources include AEO 2003, the Manufacturing Energy
Consumption Survey, State Energy Data Report, State Energy Price and
Expenditure Report, and various annual industry profiles. 

 EIS industry categories are based on EIA definitions of
energy-intensive manufacturers in the Assumptions for the Annual Energy
Outlook 2003.

 Economic data and information on nonelectricity energy markets are
generally available only at the state level, which necessitates an
approximation of the NERC regions that follows state boundaries.  For
the IAQR analysis, these approximations include Northeast = NPCC + MAAC,
Southeast = SERC + FERC, Midwest = ECAR + MAIN, Plains = MAPP + SPP +
ERCOT, and West = WSCC.  See   HYPERLINK "http://www.nerc.com/" 
http://www.nerc.com/  for further discussion of these regions.

See Babiker et al. (2001) for a discussion of how this methodology was
used in the EPPA model (EPPA assumes that AEEI parameters are the same
across all industries in a country, while AEEI values in EMPAX-CGE are
industry specific).

 PAGE   

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Utility

Leisure

Goods (16 types)

Consumption

Household utility is a CES function of consumption and leisure.

Consumption is a Cobb-Douglas composite of the 16 types of goods.

Each consumption good is a CES composite of foreign and domestically
produced goods.

Foreign

Domestic

Domestic goods are a CES composite of locally produced goods and goods
from other regions.

Local Output

Regional Output

Intermediates

KLE

Most producer goods use fixed pro-portions of intermediate inputs and a
capital-labor-energy (KLE) composite.

Intermediate materials inputs are the 11 types of non-energy goods, in
fixed proportion for each industry.

Energy

Value Added

Labor

Capital

Energy  (5 Types)

The KLE composite is a CES function of energy and value-added (KL).

Value added is a Cobb-Douglas composite of capital and labor.

Energy is a CES composite of 5 types of fuel.  The structure of this
function varies across industries. 

