United States
Environmental Protection
Agency
Air and Energy Engineering
Research Laboratory
Research Triangle Park, NC 27711
Research and Development
EPA/600/SR-94/003 March 1994
v/EPA Project Summary
Evaluation and Reporting of
County Gasoline Use
Methodologies
Sharon L. Kersteter
The Emissions and Modeling Branch
(EMB) of EPA'a Air and Energy Engi-
neering Research Laboratory (AEERL)
has been investigating improvements
in allocating state-level gasoline sales
to counties in order to improve annual
county-level emissions estimates from
this source category. This report re-
views two EMB studies on improving
estimates of county gasoline sales. The
approaches given in these studies are
compared with the current approach
prescribed by EPA.
The studies reviewed in this report
attempted to develop improved proce-
dures for estimating county-level gaso-
line sales using data for several states
and counties. The first study developed
regression equations using county-level
data to estimate county gasoline sales,
while the second study analyzed pro-
portional allocation methods using state
and county-level data to estimate
county gasoline sales. Equations were
developed using various demographic
and vehicle-characteristic variables, and
were based on 1986 data.
Allocating state-level gasoline sales
to the county level using the regres-
sion equations was generally closer to
actual sales than the values estimated
using the existing EPA approach. How-
ever, since some coefficients used in
the regression equations were not sta-
tistically significant and since only 1
year of data were analyzed, these equa-
tions may not apply to years other than
1986. Using the proportional allocation
approach, several variables were found
to perform as well as the current EPA
methodology. When comparing the re-
sults using the EPA methodology to
actual gasoline sales, the EPA method-
ology consistently underestimated ac-
tual gasoline sales.
This Project Summary was developed
by EPA's Air and Energy Engineering
Research Laboratory, Research Tri-
angle Park, NC, to announce key find-
ings of the research project that is fully
documented in a separate report of the
same title (see Project Report ordering
information at back).
Introduction
Over the past 2 years, EMB has been
investigating improvements in allocating
state-level gasoline sales to counties in
order to improve annual county-level emis-
sions estimates from this source category.
This project reviewed results of two EMB
studies on improving estimates of county
gasoline sales. In addition, the approaches
given in these studies were compared to
the current approach prescribed by EPA.
Existing EPA Methodology
Current EPA guidance for estimating
emissions from gasoline distribution activ-
ities is based on county-level fuel con-
sumption estimates. The suggested
method for estimating fuel consumption at
the county level is to collect county-level
gasoline tax revenues or supplier data.
For example, since tax is collected on
each gallon of gasoline sold, actual total
gasoline sales within a county can be
back-calculated with tax formulas. In gen-
eral, it is assumed that county-level gaso-
line sales equal county-level gasoline con-
sumption. If these data are unavailable,
data from various national publications can
Printed on Recycled Paper
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be used to estimate state gasoline con-
sumption. Countywide estimates can be
determined by apportioning these state-
wide totals by the percent of state service
station sales occurring within each county.
Countywide service station gasoline
sales data are available from the Bureau
of the Census which reports sales data
by Standard Industrial Classification Code
(SIC) for counties containing more than
300 establishments in the SIC. Other ap-
portioning variables, such as registered
vehicles or vehicle miles traveled (VMT),
can be used if the inventorying agency
feels that their use results in more accu-
rate distributions of state totals to the
county level.
The use of fuel tax or supplier data
depends on both the availability of the
data at the county-level and the manner
in which the data are compiled. For ex-
ample, reported county fuel tax revenues
may not represent actual fuel sales, but
rather the portion of total state sales rev-
enues assigned or apportioned to that
county. In addition, fuel sales taxes may
vary from county to county within a state,
resulting in biased estimates of fuel sales
and consumption.
If sales data are unavailable, the inven-
torying agency may consider surveying
county suppliers; however, this process is
time-consuming and costly. Many suppli-
ers may not respond to a survey, causing
the agency to develop procedures to
"scale-up" the survey results to account
for the nonrespondents.
The alternative approach, using state-
level data to estimate county fuel sales,
also has advantages and disadvantages.
These state-level data are easily obtained
from national publications and are updated
regularly. However, this type of apportion-
ment assumes that the variables affecting
fuel sales in each county are the same
from county to county and have the same
effect in all counties. The two studies re-
viewed in this project reflect EPA's re-
search into improving the estimating meth-
odologies and assumptions.
General Description of the
Studies
In the studies, arbitrarily identified Stud-
ies 1 and 2, regression analyses and allo-
cation methodologies are used to identify
the demographic and geographic variables
(singly and in combination) which most
closely estimate actual county-level gaso-
line sales for 1986 for several states. The
equations are developed at the state level
and fit is evaluated by the resulting R2
values.
The data used in both studies were
initially collected for the Study 1 analyses
and were provided for the Study 2 analy-
ses. These data included demographic
variables (e.g., population, number of li-
censed drivers) and geographic variables
(e.g., land area, miles of highways). All 50
states were contacted in Study 1 to iden-
tify states collecting county-level highway
vehicle gasoline sales data. Some data
were available for only ten states; how-
ever, only 6 states had sufficiently com-
plete data for 1986. Results of the regres-
sions were compared to these county-
level data.
County-Level Motor Vehicle Fuel
Consumption (Study 1)
The purpose of this study was (1) to
develop an equation to estimate county-
level fuel consumption using demographic
and geographic variables as correlates,
and (2) to compare the results of this
equation with the current EPA methodol-
ogy.
For this study, fuel sales were consid-
ered an appropriate surrogate for fuel con-
sumption. The base year for the study
was 1986. Only six states were included
in the study, due primarily to the availabil-
ity of state-collected county gasoline sales
data: Arizona, Florida, Hawaii, Nevada,
New York, and Washington. Candidate
county-level variables for estimating
county-level gasoline consumption in-
cluded: taxable gasoline gallonage or
gasoline sales in gallons (GASOLINE); to-
tal population (POP); total population, aged
18 to 64, inclusive (AGE); number of per-
sons per square mile of land (DENSITY);
number of persons aged 18 to 64 (inclu-
sive) per square mile of land (RATIO);
total number of licensed drivers (DRIVER);
land area in square miles (AREA); total
number of miles of paved roads (MILE-
AGE); miles of roads classified as inter-
state highways (INTERSTATE); miles of
roads classified as principal arterials
(PRINART); miles of roads classified as
minor arterials (MINART); miles of roads
classified as collectors (COLLECTOR); to-
tal number of registered vehicles
(REGIST); eight weight classes of regis-
tered gasoline vehicles (RGVW1 through
RGVW8); and average engine size in li-
ters for gasoline vehicles for each of the
eight weight classes (SGVW1 through
SGVW8). The statistics on numbers of
registered vehicles and average engine
size in various weight classes were ob-
tained from R.L. Polk and Co.
Linear regression analyses were per-
formed to examine various combinations
of the variables and their ability to predict
county-level gasoline sales, with the goal
of developing a single equation for a state
which could be applied to all the counties
in the state. Equations were evaluated by
counting the number of counties for which
predicted gasoline sales deviated from
actual gasoline sales by more than 20%.
Because this is an atypical approach to
developing regression equations, the re-
sulting equations were not tested for
multicollinearity, heteroscedasticity, and
autocorrelation.
Study 1 Results
The analyses were performed for all
counties in Arizona, Hawaii, Nevada, and
Washington, 50 of the 67 counties in
Florida, and 53 of the 67 counties in New
York. In addition, a regression analysis
was performed on the combined state data
in an effort to identify national trends. Ap-
proximately one third of the 182 counties
included in this analysis exceeded the 20%
deviation between actual and predicted
fuel sales.
The variable SGVW2 (representing av-
erage engine size in liters for gasoline
vehicles weighing between 6,001 and
10,000 Ib) appears in the equations of
most of the analyses, followed by POP.
Population factors were present in all equa-
tions, represented either by POP or AGE,
but no equations used both POP and AGE.
(Since POP (total population) and AGF:
(population between ages 18 and 64) are
collinear, it is not advisable to develop an
equation that includes both variables.) The
R.L. Polk data were included in all equa-
tions, either as total gasoline-powered ve-
hicle registrations by a vehicle weight class
or as average engine size by a vehicle
weight class. Highway mileage categories
were not strongly represented in the equa-
tions.
A case study using the Florida data
included sales data (represented as the
variable SALES) as an independent vari-
able. The resulting equation included the
following variables: POP, PRINART, COL-
LECTOR, MILEAGE, SGVW2, SGVW8,
and SALES. No county had a variance in
excess of 20 %. This equation is judged
to be superior to the earlier equation which
did not use sales data for Florida, which
had errors as large as 31%.
Finally, the EPA methodology using SIC
554 data and the best fit regressions were
compared to actual consumption for three
states: Florida, New York, and Washing-
ton. In Florida, the regressions compare
favorably with the EPA methodology, with
the regressions yielding a significant re-
duction in outliers (i.e., counties with de-
viations greater than 20% from actual con-
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sumption). In New York, 19 counties had
estimates which deviated by more than
20% using the EPA method; the regres-
sion equation had only five such outliers.
Neither the EPA nor the regression model
predicted Washington county-level fuel
sales well. However, for Washington, as
for Florida and New York, the regression
analysis was more accurate at predicting
county-level fuel consumption.
Study 1 Conclusions
Overall, the state-level gasoline sales
regressions analyses demonstrated that,
for a given state, equations may be devel-
oped that predict gasoline sales better
than the current EPA allocation methodol-
ogy, with correlates varying by state. How-
ever, this statement can be made with
confidence only for the year 1986. A
comparison of the state studies and the
combined national study show that the
factors in the national equation included
correlates that were seldom used in the
state-level studies. Using this comparison,
Study 1 suggests that the correlates in
this study are insufficient to develop a
single national equation for estimating fuel
sales. An additional analysis of the com-
bined data showed the marginal effect of
adding variables to the equation. In this
analysis, a regression equation was de-
veloped using only two variables (SGVW1
and AGE), with an R2 of 0.940. Adding
four additional variables (SGVW6,
RGVW1, DENSITY, and SGVW2) in-
creased the R2to 0.964. Study 1 suggests
that this slight increase in the fit of the
equation (R2) resulting from the addition
of the four variables emphasizes the domi-
nance of the first two variables in the
equation.
Predicting County-Level Gasoline
Sales (Study 2)
The objective of this study was to iden-
tify a generally applicable allocation equa-
tion or set of equations that could be reli-
ably applied to estimate county-level gaso-
line sales, given state gasoline sales and
relevant county-specific information such
as population, number of registered driv-
ers, total highway mileage, and SIC 554
sales data. As in the Study 1, gasoline
sales are considered to be a surrogate for
gasoline consumption. The equation de-
veloped should be applicable across
states; i.e., the equation should not be
state-specific. A limiting factor in this study
was the availability of data for identifying
and validating prediction methods. This
study focused on relatively simple alloca-
tion methods, since such simple methods
are more likely to satisfy the criterion of
general applicability.
Study Design and Data
Twelve potential variables were identi-
fied from Study 1 for use in allocating
state gasoline sales to counties: SIC 554
revenue data (dollars) (SIC554 Sales);
county population estimates for 1986
(Population); county land area in square
miles (Area); miles of roads classified as
principal arteries (Artery); miles of roads
classified as collectors (Collector); miles
of roads classified as collectors, principal
arteries, or minor arteries (Mileage); num-
ber of licensed drivers (Drivers); total num-
ber of gasoline vehicles in all size classes
(Gas Fleet); combined engine size (liters)
of all registered gasoline vehicles (Total
Engine Size); combined total engine size
of all registered gasoline vehicles divided
by total number of gasoline vehicles in all
size classes (Average Engine Size); num-
ber of vehicles registered as passenger
cars, trucks, or buses (Total Registrations);
and number of registered passenger ve-
hicles (Total Passenger Registrations).
Four states were included in this study:
Florida, Hawaii, Nevada, and Washing-
ton. These states were chosen based on
the availability and completeness of the
variables identified above. While data on
all 12 variables were available for Florida,
only five variables (SIC554 Sales, Popu-
lation, Area, Mileage, and Total Popula-
tion) were available for the remaining
states (Hawaii, Nevada, and Washington).
The simple allocation methods evalu-
ated in Study 2 are proportional allocation
methods similar to the current EPA meth-
odology, which is a proportional allocation
method based on SIC 554 Sales. The
proportional allocation method takes the
form:
y
Y _ ^county Y
count/ ~" Y state
Astate
where Yc is the predicted gasoline for
the county, X, is the value of the vari-
able X for the°c"ounty, Xstat9 is the value of
variable X for the state, and Ystate is the
state gasoline total.
Study 2 evaluated the potential alloca-
tion methods in terms of their relative er-
rors of prediction (REs), defined as:
( predicted gasoline-actual gasoline
RE = 100 -
^ actual gasoline
For a given allocation method, the dis-
tribution of REs across all counties indi-
cates the method's performance. To com-
pare allocation methods, Study 2 used
differences between the absolute values
of the relative errors. A statistical test of
the average of the differences, TJ, was
obtained by calculating ZD as:
If ZD was near zero, it was concluded that
there was no significant difference between
the two prediction methods. Specific criti-
cal values were obtained from tables of
the standard normal distribution.
The study noted that the data are in-
complete with respect to variables (i.e,
not all variables are available for all states)
and observations (information may be
available for most, but not all, counties).
Study 2 states that, while missing data for
select counties probably have a slight ef-
fect on the identification of feasible state-
level allocation rules, these counties are
commonly smaller, less populated, and
have low gasoline sales. Low gasoline
sales are inherently more difficult to esti-
mate when the RE is the criterion used to
judge performance. Missing data for these
counties may have a more significant ef-
fect on the estimated performance of the
allocation equations.
Simple Allocation Method Results
Simple allocation methods were investi-
gated for Florida alone and for the com-
bined data for Hawaii, Nevada, and Wash-
ington. The analysis of the Florida data
included 48 of the 67 counties, since com-
plete county sales data were available for
only 48 counties. The 12 potential vari-
ables were plotted against actual county
gasoline sales, represented as the vari-
able GASOLINE, from the state files and
displayed as scatterplots. Seven poten-
tially useful variables were identified from
visual inspection of the scatterplots
SIC554 Sales, Population, Drivers, Gas
Fleet, Total Engine Size, Total Registra-
tions, and Total Passenger Registrations
The method derived from the SIC554
Sales is the basis of the current EPA
methodology and was employed as the
benchmark in the analysis; i.e., the re
maining six allocation methods were com
pared to the SIC554 Sales method by
comparing their relative errors of predic
tion. REs were calculated using predicted
sales based on the variable and the ac
tual sales (GASOLINE). In general, Study
2 concludes that methods based on Popu
lation, Drivers, or Total Registrations are
the most reasonable alternatives to the
SIC554 Sales method.
Hawaii, Nevada, and Washington had
relatively complete information for five pre
dictors: SIC554 Sales, Population, Area,
Mileage, and Total Registrations. The five
potential variables were plotted against
county gasoline sales from state files and
displayed as scatterplots. Only three po
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tentially useful variables were identified
from visual inspection of the scatterplots:
SIC554 Sales, Population, and Total Reg-
istrations. All counties in Hawaii, 14 of 17
counties in Nevada, and 34 of 39 counties
in Washington had complete data and
were included in the analyses. According
to Study 2, Population or Total Registra-
tions methods are potential alternatives to
the EPA (SIC554 Sales) method.
Study 2 concludes, from analyses of
simple allocation methods, that several
predictors in addition to SIC554 Sales can
be used. Statistical analyses suggest that
Population, Drivers, Gas Fleet, Total En-
gine Size, and Total Registrations are not
much different than SIC554 Sales for allo-
cating state-level gasoline sales to coun-
ties. Predictors such as Population are
readily available and can be used in place
of SIC554 Sales (i.e., the EPA methodol-
ogy) with little or no loss of accuracy. All
of the predictors analyzed, however, fail
to yield allocation equations with uniformly
small relative errors. Larger magnitude er-
rors are always associated with small
counties.
The Study 2 analysis of the Florida
data shows that REs from the SIC554
Sales and Population allocation methods
were generally less than 50%. However,
since small counties were excluded from
the Florida data, these results may be
misleading. For the combined data includ-
ing Hawaii, Nevada, and Washington,
small counties were better represented
and REs as large as 100% were not un-
common. Study 2 indicated that this result
is probably more representative of the per-
formance of the simple allocation methods
in general.
Other Prediction Methods
Study 2 also investigated whether there
are prediction equations depending on two
or more predictors that significantly out-
perform the best simple allocation rules
and are generally applicable. Three forms
of two-variable allocation equations were
investigated: (1) weighted averages of
two simple allocation equations; (2) gen-
eral linear combinations of two simple al-
location equations; and (3) linear combi-
nations of two simple allocation equations
including an intercept. The allocation equa-
tions investigated have parameters that
must be estimated from the data. For each
model, parameter estimates were obtained
by minimizing the sum of the squared
relative errors of prediction. This method
of estimation ensures that no other pa-
rameter values can result in better over-
all performance in terms of relative pre-
diction errors. The equations were limited
to two-variable equations since the more
parameters that are estimated from a given
state's data, the greater the likelihood that
the resulting equation that works well for
that state will not work well for other states.
The Florida data were analyzed first.
The intent of this analysis was to identify
useful variables and equations for the
Florida data and to establish a 'best' equa-
tion (or set of equations) as a benchmark
to compare with simpler allocation equa-
tions with the combined data. The analy-
ses show that the equations depending
on SIC554 Sales and Population have
estimated parameters that are very simi-
lar. This suggests an equation that is ob-
tained by averaging the simple allocation
equations based on SIC554 Sales and
Population. Since Population is highly cor-
related with Drivers and Total Registra-
tions, either predictor could be substituted
for Population in the equation with similar
results. Statistical analyses of the relative
errors of the equations indicated that the
three-parameter equation yielded slightly
smaller absolute relative errors than the
two-parameter equation, and that the two-
parameter equation slightly outperformed
the one-parameter equation.
The best allocation equations identified
using the Florida data were then applied
to the combined data for Hawaii, Nevada,
and Washington. However, since fewer
variables were available for the combined
data, only certain Florida equations were
used. The results of the comparisons indi-
cate that only the simple allocation equa-
tions based on SIC554 Sales, Population,
and Total Registrations, and two aver-
aged allocation equations (SIC554 Sales
and Population, and SIC554 Sales and
Total Registration) perform well for both
sets of data. The more complicated allo-
cation equations determined by fitting
equations to the Florida data result in bet-
ter estimates for the Florida data, but re-
sult in worse estimates for the combined
data. Comparisons of the results of the
one-, two-, and three-parameter equations
show that, when applied to the combined
data, the three-parameter equation results
in the least accurate estimates.
Per Capita Modeling of All Data
An additional analysis of the data was
performed in which variables were nor-
malized for state-to-state comparison by
creating per capita versions of the vari-
ables and Gasoline (i.e., all variables were
divided by Population). The Florida data
set and combined data set were merged,
and the five variables common to these
data sets were investigated. In this analy-
sis, the equation that best predicts per
capita gasoline for all the data was sought.
County Gasoline was then obtained by
multiplying the per capita prediction by
county population. The purpose of this
exercise was not to derive an allocation
equation, but to confirm that the equa-
tions identified by this analysis were simi
larto those obtained in the previous analy
ses.
Study 2 applied standard methods of
linear-model estimation and variable re
duction to the per capita data. Relative
prediction errors were used to judge the
equations' applicability. The results of this
exercise indicate that equations that fit
data from all states well do not need to
include more than two variables (SIC 554.
Sales and Population or Total Registra
tions), and the results are in general agree
ment with the conclusions of the other
equation analyses.
Study 2 Conclusions
Study 2 states that the analyses de
scribed in the previous sections suggest
two major conclusions. First, if SIC554
data are not available for a particular
county, any one of the simple allocation
equations based on Population, Total Reg
istrations, or Drivers can be used. The
resulting estimates are comparable to the
SIC554 Sales allocation method. In addi
tion, there is no evidence in the data ana
lyzed that the allocations can be improved
significantly by using more complex esti
mation schemes. This contrasts with Study
1 which adopted very complex, input vari
able-intensive equations.
Second, if SIC554 Sales data are avail
able, one of the averaged allocation equa
tions (SIC554 Sales and Population or
SIC554 Sales and Total Registrationsi
should be used. There is evidence thai
these equations yield better estimates
across states than any simple allocation
equation. There is no evidence that any
other allocation equation will work as well
for all states.
Conclusions
The two studies reviewed for and in-
cluded in this report attempted to develop
improved procedures for allocating state-
level gasoline sales to the county leve!
using data for several states. Study 1 de-
veloped regression equations using
county-level data to estimate county gaso
line sales, while Study 2 analyzed propor-
tional allocation methods using state and
county-level data to estimate gasoline
sales. Equations were developed using
various demographic and vehicle-charac-
teristic variables. These equations were
based on the 1986 data.
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Data and Study Design
The variables used in these studies were
initially identified and collected during
Study 1 and provided for Study 2. No
additional data were collected during Study
2. Although Study 1 did not explain how
the variables were chosen, subsequent
contact with the Study 1 researchers indi-
cated that the variables were chosen
based on the data Study 1 identified as
being inexpensive, easily obtained, and
regularly updated, and only includes de-
mographic (e.g., population) and vehicle-
characteristic (e.g., number of registered
gasoline-powered vehicles by weight class)
data.
Both studies were based on available
county-level gasoline sales data for sev-
eral states. Equations were developed us-
ing various demographic and vehicle-char-
acteristic variables that most closely pre-
dicted the available county gasoline sales
data. Study 1 does not discuss the reli-
ability of these state-supplied county-level
data. The manner in which these data are
collected and reported may differ between
states; in fact, some states do not report
actual county gasoline sales, but rather
the tax revenues received or assigned to
each county. Gasoline taxes that differ
between and within counties may not be
accurately accounted for. In addition,
county tax revenues may not reflect ac-
tual gasoline sales in that county, but rather
the amount of revenues received from the
state by that county based on highway
mileage or some other characteristic. Rev-
enue data thus recorded and used may
bias the equations and not reflect actual
conditions and activity.
Finally, the biases that may be intro-
duced into the equations by excluding from
the analyses those counties with missing
data have not been adequately addressed
in either study. It is likely that these coun-
ties are small, rural counties and would
generally not be of concern in State Imple-
mentation Plan (SIP) inventories. How-
ever, some of these counties may be part
of a nonattainment area, and neither study
provides guidance or suggestions for han-
dling this situation.
Overall Conclusions
Table 1 presents comparisons of the
Study 1 regression to the EPA method
and the Study 2 allocations to the EPA
method. Due to the nature of Studies 1
and 2, results can be reasonably com-
pared for only one state—Florida. Since
the Study 1 term "deviation" and the Study
2 term "RE" are equal to
(100)x
predicted-actuah
percent
actual )
the results can be compared directly. Study
2's use of the EPA methodology for the
Florida data results in 25% of the counties
deviating from actual gasoline sales by
more than 20%. Study 1 shows that 19%
of the counties deviated from the actual
value by more than 20%. This difference
may be due to the number of Florida coun-
ties included in the EPA methodology
(Study 1-53 and Study 2-48).
The results given in Table 1 appear to
suggest that the Study 1 regression equa-
tion provides the best estimates of actual
gasoline sales. However, this conclusion
is misleading since, in developing the re-
gression equations, Study 1 kept some
statistically non-significant coefficients. It
is not known exactly what effect this has
on the results. In addition, since only one
year of data was used in the analyses,
these resulting equations may not work
well for years other than 1986.
Table 2 presents more detailed infor-
mation on the deviations from actual seen
in Study 1. This analysis suggests that
the EPA method consistently underesti-
mates actual county-level gasoline sales.
Sixty percent of the counties analyzed by
Study 1 for the EPA methodology resulted
in underestimates of actual sales. This
may be an artifact of the retail outlet sales
data which may not be complete or may
include sales of items other than gasoline.
Based on the problems outlined above,
it is difficult to draw conclusions on rea
sonable alternate methods for estimating
county-level gasoline sales. For lack of a
proven alternative, the simple approach of
the existing EPA allocation methodology
may be best, although it may underesti
mate gasoline sales. However, if the in
ventorying agency plans to use the esti
mates for modeling, more detailed data
will be needed; i.e., the existing EPA
methodology may not be acceptable and
will not provide the necessary level of
detail.
Table 1. Comparisons of Studies' Results to the EPA Methodology
State
Study 1
Percent of counties analyzed
with deviations from actual > 20%
EPA Methodology
Study 1 regression
Study 2
Percent of counties analyzed
with RE > 20%
Study 2 allocation
EPA Methodology (average of proxies)
Florida
New York
Washington
Combined
Hawaii,
Nevada, and
Washington
19 (w/o sales)
19 (w/sales)
45
63
na
4 (w/o sales)
0 (w/sales)
9
18
na
25 29
na na
na na
50 58
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Table 2. Comparisons of Deviations in Study 1
EPA Methodology
Percent of counties analyzed
with deviations
Study 1 Regressions
Percent of counties analyzed
with deviations
State
Florida
New York
Washington
Overall
average
No. of
counties
analyzed
53
42
16
111
Above actual
43
33
44
40
No. of
counties
Below actual analyzed
57
67
56
60
(w/o sales)
50
(w/sales)
48
53
39
190
Above actual
56
46
51
44
49
Below actual
44
54
49
56
51
•&U.S. GOVERNMENT PRINTING OFFICE: 1994 - 550-067/80228
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S. Kersteteris with Southern Research Institute, P. O. Box 13825, Research Triangle
Park, NC 27709-3825.
Charles C. Masser is the EPA Project Officer (see below).
The complete report, entitled "Evaluation and Reporting of County Gasoline Use
Methodologies," (Order No. PB94-145455/AS; Cost: $27.00, subject to change)
will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Air and Energy Engineering Research Laboratory
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
United States
Environmental Protection
Agency
Center for Environmental Researchlnformation
Cincinnati, OH 45268
Official Business
Penalty for Private Use $300
BULK RATE
POSTAGE & FEES PAID
EPA
PERMIT NO. G-35
EPA/600/SR-94/003
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