1

MEMORANDUM

Subject: Correlating E85 consumption volumes with E85 price

From: David Korotney , U.S. EPA, Office of Transportation and Air Quality

To: EPA Air Docket EPA

- HQ

- OAR

- 201 5

- 0111

In support of efforts to determine the total volumes of renewabl e fuel that might be

achievable in 2016, we analyzed data on historical sales of E85 and correlated those sales with

the percentage reduction in the E85 retail price relative to E10 price. We procure d E85 sales

volumes from six states: Minnesota, Iowa, No rth Dakota, Kansas, California, and New York.

We combined the data from all six states to create correlations that were intended to reflect the

nation as a whole. We were also provided data covering multiple states from the National

Association of Conven ience Stores . In addition, we were provided with some limited sets of

proprietary data from individual companies that while too limited in scope for making national

estimates , provided further support for the trends seen in the publicly available data

.

A. Use of Price Data from E85prices.com

Most of the E85 sales volume data from individual states did not include information on

E85 or E10 prices , and was in the form of monthly totals/averages

. As a result, we needed an

alternative data source for E8 5 prices. The most complete publicly available source of data on

retail prices of E85 and E10 is the website E85prices.com. This is the source that we used for

E85 and E10 prices for all the analyses discussed in th is memorandum. D ata provided by the

Mi nnesota Department of Commerce did include both E85 volumes and E85 prices for

individual retail stations , but we determined that it would not be appropriate to use it to correlate

E85 sales volumes with E85 price discounts for the following reasons:

• I t did not include E10 prices , so the price discount of E85 relative to E10 could not be

determined

• It was based on survey data subsequently scaled up to represent all stations in the

state

• It covered a shorter timeframe than E85 sales volume data provid ed by the Minnesota

Department of Revenue

For development of our primary correlations between E85 price and E85 sales volumes for

Minnesota , therefore, we used the dataset from the Minnesota Department of Revenue (coupled

with price data from E85p r ices.co m ) covering a longer timeframe.2

In order to verify that th e E85 pric e discount data from E85prices.com was reasonably

accurate and appropriate to pair with E85 sales volume data from the states , we compared it to

three other sources of data on retail f uel prices as shown in Figures 1 and 2

. 1

Figure 1

Price Discount of E85 Compared to E10

Comparison of E85prices.com to Retail Price Data from

EERE 's Clean Cities Alternative Fuel Price Report

Source: http://www.afdc.energy.gov/fuels/prices.html

1 The third comparison used a proprietary data set from an E85 distributor in California. It demonstrated a

correlation coefficient of 0.58 in comparison to data from E85prices.com, demonstrating a reasonably close

correlation between the two.3 Figure 2

Price Discount of E85 Compared to E10

Comparison of E85prices.com to Retail Price Data from

National Association of Convenience Stores

(represents 298 retail stations selling E85 over 19 states)

Although the EERE fuels price data demonstrated a st rong correlation with the price data

from E85prices.com, the fact that the EERE data was only provided quarterly made it less

attractive as a basis for the correlations between E85 sales volumes and E85 price discounts.

With regard to the larger disparit y between data sources shown in Figure 2 for 2013, a

representative from NACS suggested that this may be the effect of retailers at the particular

locations included in the NACS survey who are able to blend their own E85 and thus benefit

from RIN values , p assing less of the value of those RINs along to customers . The retail stations

survey ed by NACS are typically the more innovative and larger firms who have the ability to

capitalize on such market opportunities. As such, they may not be representative of all retail

stations that offer E85.

Based on these comparisons, we believe that price data from E85prices.com is reasonably

accurate, and can be paired with E85 sales volumes from the six states to permit us to correlate

E85 volume and price. Neverthel ess, we acknowledge that this approach may include some bias

in that the average E85 prices for individual states from E85prices.com have not been weighted

by the volume of E85 sold at the stations where the price data was collected. This could have the

effect of muting the apparent consumer responses to greater reductions in E85 price, since the

stations with greater E85 sales volumes would also likely be the stations where E85 prices are

lowest. Since we do not have sufficient infor m ation at this time to investigate this potential

source of bias, we have used the price data from E85prices.com without alteration.

We recognize that the data provided through E85prices.com is the result of voluntary

submissions by individuals, and that it is not supplied consistently over time, nor is there any4

attempt to e nsure that the data represents a minimum sampling size for the number of retail

stations in a given state . In order to address the additional uncertainty and potential bias that

could result from using very small data sets, we chose to exclude from our an alysis any cases in

which E85 prices were collected on less than 5 separate retail stations for a given a month and

state, or less than 10 total price reports for a given month and state. Table 1 shows the number of

months that were excluded from the anal ysis due to a shortage of E85 price data.

Table 1

Data Excluded from Analysis

Total number of months of

available data on both E85 sales

volume and E85 price discount Number of months excluded from

analysis due to a paucity of data on

E85 price discounts

Minnesota 72 0

Kansas a 36 35

California 60 6

Iowa 84 0

New York 83 48

North Dakota 33 17

a Dropped from the analysis

B. Overview of Approach

The data for each of the five 2 states represented total E85 sales in that state. The raw data

provide d to EPA by each state is provided in the appendix. The data provided by the Iowa

Department of Revenue was for quarters rather than months. For the purposes of our analysis,

we estimated the monthly volumes by dividing each quarter's volume by three. T his allowed us

to pair monthly volumes with monthly estimates of E85 price reductions from E85prices.com.

T o correlate E85 sales with E85 price reduction in comparison to E10 price , we used a

per station analysis in which the total volume of E85 sold in a given state in a given month was

divided by the total number of retail stations offering E85 in that same state and month. Data on

the number of retail stations offering E85 was provided by DOE's Alternative Fuel Data Center

(AFDC) and is provided in th e appendix. Further discussion of the number of retail stations

offering E85 can be found in a separate memorandum. 3

Theoretically, we would expect that the correlations between E85 sales and E85 price

discount would be non

- linear, with poor er consumer response to E85 price reductions when E85

prices remain below energy parity in comparison to E10, and more dramatic consumer response

to E85 price reductions when E85 prices are near or above energy parity. Some have also

suggested that consumer response to E85 price discounts would exhibit an S

- shape , with the

2 After dropping Kansas data from the analysis due to a shortage of data on E85 price discounts.

3 " Memo to docket on E85 station counts ," memorandum from David Korotney to EPA Air Docket EPA

- HQ

- OAR

2015

- 0111.5

greatest increase occurring at the point where E85 price discount exceed energy parity . These

two theoretical non

- linear relationships are shown in Figure 3.

Figure 3

Theoretical Responses of FFV Owners to E85 Price Discounts

However, the available data did not exhibit nonlinear effect s in any obvious way. As a result, we

used linear correlations for all datasets. Additional investigations into potential nonlinearity are

discussed in Section F below.

C. State

- Specific Correlations

All correlations were of the following form:

Gallons of E85 per mo nth per station offering E85 = a × ( E85 price discount ) + b

where the "E85 price discount" refers to the % reduction in E85 price compared to E 10 price.

The values of the constants a and b for each of the five states is shown in Table 2.

Table 2

Results of State

- Specific Least

- Squares Regressions

Constant a Constant b R squared

Minnesota 12768

(p < 0.0001) 1031

(p = 0.0205) 0.28

California 5 392

(p = 0.6359) 7315

(p = 0.0001) 0.004

Iowa 14164

(p < 0.0001) 2693

(p < 0.0001) 0.33

New York 27046

(p = 0.0047 2153

(p = 0. 2103) 0.22

North Dakota 6134

(p < 0.0001) - 10

(p = 0.9398) 0.796

D

. Combined Correlations to Represent Average Impacts for Entire U.S.

The state

- specific correlations presented above are suggestive of consumer behavior

consistent with expectations: as the price of E85 decreases relative to the price of E10, sales of

E85 increase. The magnitude of th is effect is different for each state, however.7 Figure 4

Curve Fits for all States

These differences may be the result of different marketing efforts (e.g. signage, consumer

education), state

- level requirements for FFVs or renewable fuel consumptio n, differing cultural

attitudes towards renewable fuels, or differences in the way in which the data was collected.

Given the lack of any information explaining the reasons for these differences between states in

terms of consumer response to E85 price re ductions, we believe that the results for all five states

should be combined to estimate the effect of changes in the E85 price discount relative to E10 on

E85 s ales for the nation as a whole.

W e weighted the equations representing each of the five state s together based on the

estimated number of retail stations in each state that offered E85 in 2014 to arrive at a single

correlation representing all five states. The number of retail stations offering E85 that was used

in this weight ing is shown in Table 3

.

Table 3

Estimated Number of Retail Stations Offering E85 in 2014

E85 Retail Stations

Minnesota 281

California 94

Iowa 189

New York 80

North Dakota 60

Total for 5 states 704

Total nationwide 2722

Source: DOE Alternative Fuel Data Center estima te for July 2014

The resulting equation representing all five states is sho w n below:8

Gallons of E85 per mo nth per station offering E85 = 12495 × ( E85 price discount ) + 2463

A more useful equation may be total volume of E85 sold in 2 014 ra ther than per month per

station . This equation was derived by multiplying the above equation by 2722 stations and

converting to million gallons per year.

Million gallons of E85 in 2014 = 408 × ( E85 price discount ) + 80

According to E85prices.com, the nationwide, a verage E85 price discount was 17.5 % in 2014

. Based on this price discount, the equation above produces an estimate of about 150 mill gal of

E85 in 2014.

The equation above can also be generalized for any number of E85 sta tions, providing a

means for estimating potential E85 sales as a function of both E85 price discount and the number

of E85 stations :

Million gallons of E85 = [ 408 × (E85 price discount) + 80 ] × (# E85 stations / 2722)

As of August 2015, the total n umber of retail stations offering E85 was 2,941 according to

AFDC. Figure 5 below shows the correlation between E85 price discount and E85 sales for

2014 when there were 2,722 E85 stations , 2015 when there are 2,941 E85 stations , and two

hypothetical futu re case s for 3

, 100 and 3,300 E85 stations.

Figure 5

Nationwide E85 Sales a s a Function of E85 Price Discount

We also received a proprietary data set from the Fuels Institute that allowed us to verify

that the nationwide average correlations shown in Figure 5 are reasonable. This data was

collected daily on about 200 retail stations that offered E85 between January of 2013 and July

2014. The monthly average E85 price discounts relative to E10 and the monthly E85 sales

volumes per station are shown be low.9 Figure 6

Monthly E85 Sales and E85 Price Reductions from Fuels Institute

The linear least

- squares regression for the Fuels Institute data is as follows:

Gallons of E85 per month per station offering E85 = 17847 × (E85 price discount) + 2328

Once again we converted this equation into one representing the total volume of E85 sold in

2014 for the nation as a whole by multiplying the above equation by 2722 stations and

converting to million gallons per year , and then generalizing for any nu mber of E85 stations

Million gallons of E85 = [ 583 × (E85 price discount) + 7 6 ] × (# E85 stations / 2722)

A comparison between this equation derived from Fuels Institute data and that derived from the

analysis of data from five individual states discusse d earlier is shown below.10 Figure 7

Nationwide E85 Sales as a Function of E85 Price Discount in 2014

As described earlier, according to E85prices.com, the nationwide average E85 price

discount was 17.5% in 2014. Based on this price discount, the Fuels Institute correlation would

estimate a total E85 sales volume of about 180 mill gal of E85 in 2014. Given other analyses

that we have conducted, we believe that this is likely higher than the actual volume of E85

consumed in 2 014, which we believe was cl oser to about 150 mill gal. Nevertheless, the Fuels

Institute data does not appear to deviate significantly from the correlation derived from five

individual states.

E. Potential for Stronger Customer Response to E85 Price Reductions

Because we do not have any information regarding the reasons that the five different

states analyzed above exhibit different consumer responses to E85 price reductions, we have no

reason to think that consumer responses in other states will differ from the weighted average of

the five states we analyzed. Nevertheless, consumer responses in other states could in fact be

stronger or weaker than those shown in Figure 5. Moreover, we acknowledge that there may

some forms of bias in the data that was available that could make the correlations shown in

Figure 5 stronger or weaker than the true causal relationship between E85 price reductions and

E85 sales volumes. In Section A above we cited one form of potential bias related to the price

data from E85prices.com that could have the effect of muting the apparent consumer responses

to greater reductions in E85 price. It is also possible that the correlations shown in Figure 5 may

be biased away from the true causal relationship due to the fact that many retail stations offering

E 85 are isolated from one another and thus do not need to compete for customers by adjusting

their prices. Also, in some states there are mandates for the use of E85 and/or FFVs that could

similarly bias the observed relatio ns hip between E85 price discount and sales volumes away

from the true causal relationship. Although we have no data or information that would allow us

to investigate these potential sources of bias directly, we acknowledge that they could affect any

estimate for the nation as a whole of how demand for E85 might change as a function of E85

prices discounts. As a sensitivity analysis, therefore, we examined what E85 consumption levels11

might be possible if the response of consumers to E85 price discounts for the nation as a whole

was close to the strongest responses observed.

Of the five states analyzed, New York exhibited the strongest consumer responses to E85

prices. Using the regression coefficients shown in Table 2, we converted the equation that was

specific to New York into an equ ation that represents the nation as a whole, assuming that the

total number of retail stations offering E85 in 2014 was 2,722. We then modified the resulting

equation as before to allow estimates of E85 for larger numbers of E85 stations.

Million gallons of E85 = [ 883 × (E85 price discount) + 70 ] × (# E85 stations / 2722)

Figure 8

E85 Sales as a Function of E85 Price Discount if the Nationwide Consumer

Response to E85 Prices is Based on That Exhibited in New York

We also conducted a similar analysi s using the data from the Fuels Institute. We

separated out the 25% of retail stations exhibiting the strongest consumer response to E85 price

discounts and conducted a linear least

- squares regression on this subset. The results are shown

below, along wi th the original regression on the full dataset from Figure 6.12 Figure 9

Monthly E85 Sales and E85 Price Reductions from 25% of Stations from

Fuels Institute Dataset Exhibiting the Strongest Consumer Response to E85 Price Reductions

The linear least

- squa res regression for the Fuels Institute data subset and it's conversion to an

annual, nationwide correlation are as follows:

Gallons of E85 per month per station offering E85 = 35237 × (E85 price discount) + 1047

Million gallons of E85 = [ 1151 × (E85 price discount) + 34 ] × (# E85 stations / 2722)

Figure 10

E85 Sales as a Function of E85 Price Discount if the Nationwide Consumer

Response to E85 Price Discount s is Based on the 25% of Stations in the Fuels Institute Database

Exhibiting the Stronge st Consumer Response13

The E85 sales volumes shown in Figures 8 and 10 represent what may be possible if

retail station owners who offer E85 can modify their sales strategies so that the response of

consumers to E85 price discounts matches the strongest responses observed in the available data.

F

. Investigation of Linearity Assumption

The data from the five states that we used to develop the correlations shown in Figure 5

w ere available only in monthly aggregate s, or in some cases quarterly, at a state level. Based on

this data we developed linear correlations between the average price discount of E85 relative to

E10 and the total sales volume of E85 as discussed in the preceding sections. While this linear

relationship is well supported by the state data available to EPA, it has been suggested that if the

price of E85 was to be significantly lower than the price of E10 on an energy equivalent basis

, sales volumes of E85 could increase dramatically. Because very few of the months for which

EPA h as data had an average E85 price discount greater than the "energy parity" level ( 21.3 %

when compari ng E85 with 74% ethanol to E10), the data available from the six states is

insufficient to determine whether or not E85 sales do in fact increase significan tly when the price

of E85 is less than E10 on an energy equivalent basis. If the true relationship between E85 sales

volumes and E85 price discounts is in fact nonlinear, then u sing a linear correlation to estimate

the volume of E85 sold based on the expe cted average price of E85 relative to E10 could

underestimate the actual volume sold and the potential for higher E85 sales volume.

In order to investigate this issue

, we examined the station

- specific data available in the

Fuels Institute dataset. The s tation

- specific data offered the greatest likelihood of identifying

nonlinear correlation s if t hey legitimately exist , since the particular circumstances at e ach

l ocation (including s ignage, consumer bias, etc) are likely t o be unique and any resulting

nonli n e arities in the relationship between E85 price reductions and sales volumes m ay be muted

when data from multiple locations is pooled together. We focused on those retail stations that,

over the period January 2013 through July 2014, had a combination of the following:

 Highest E8 5 price reductions compared to E10

 Widest range of E85 price reductions compared to E10

Focusing on the stations that met these two criteria permitted us to observe a non

- linear

relationship between E85 sales and the discount for E85 relative to E10 i f one exists. From the

set of about 200 stations, we identified six that we believed were ideal for this investigation.

Weekly data, along with both linear and quadratic least

- squares regressions , are shown in the

graphs below for all six stations.14

The least

- squares regressions took the following form:

Gallons of E85 per week = a × (E85 price reduction) 2 + b × (E85 price reduction) + c

except that for the linear regressions the coefficient 'a' was not relevant. The results of the

regression s are shown in Table 4 below.

Table 4

Results of Least

- Squares Regressions

Station

#20 Station

#40 Station

#42 Station

#57 Station

#99 Station

#144

Linear regression

- R 2 0.481 0 0.6826 0.3464 0.5117 0.5358 0.386315

Linear regression

Estimate of coef

ficient 'b'

P

- value of coefficient 'b' 12561

0.000 0 6868

0.0000 3677

0.0000 10477

0.0000 2897

0.0000 1131

0.0000

Linear regression

Estimate of coefficient 'c'

P

- value of coefficient 'c' 1055

0.0000 142

0.1155 762

0.0000 720

0.0009 277

0.0000 182

0.0000

Quadratic regression

- R 2 0.481 5 0.7140 0.3500 0.5118 0.5467 0.3975

Quadratic regression

Estimate of coefficient 'a'

P

- value of coefficient 'a' - 4637

0.7933 20238

0.0048 - 4455

0.5207 2232

0.8852 4678

0.1681 - 2161

0.2239

Quadratic regression

Estimate of coefficient 'b'

P

- value of coefficient 'b' 13976

0.0145 967

0.6454 5154

0.0321 9755

0.0603 1507

0.1520 1879

0.0038

Quadratic regression

Estimate of coefficient 'c'

P

- value of coefficient ' c' 972

0.0113 481

0.0013 660

0.0009 770

0.0607 355

0.0000 129

0.0178

As expected, the addition of a second

- order term always increases the fit of the regression

equation in comparison to a linear regression, as measured by the r 2 value. However, this does

not necessarily mean that the incremental increase in the fit is significant. Indeed there are

indications that the use of a second

- order term may not be appropriate. For instance, for all six

stations, the quadratic regression has at least one term ('a' or 'b') that is not statistically significant

at the p = 0.05 level. For all except station #40, the non

- significant term is the second

- order

term. 4 In comparison, the linear term 'b' is always statistically significant for the linear

regress ion equations. In general, then, it would appear that a linear regression may be more

appropriate than a quadratic regression.

For station #40, the fact that it is the second

- order term in the quadratic regression that is

significant, rather than the first

- order term, suggests that the quadratic regression may be more

appropriate in this case

. To investigate this possibility further, we examined the Pearson

correlation coefficients between the weekly E85 sales volumes and either the E85 price reductio n

or the square of the E85 price reduction.

Table 5

Pearson Correlation Coefficients for Weekly E85 Sales Volume

Station

#20 Station

#40 Station

#42 Station

#57 Station

#99 Station

#144

(E85 price reduction) 0.6935 0.8262 0.5886 0.7153 0.7320 0.6216

(E 85 price reduction) 2 0.6624 0.8445 0.5561 0.6991 0.7314 0.5757

With the exception of station #40, the correlation coefficients for the squared term are lower than

those for the linear term. This result suggests that there is a stronger correlation for a first

- order

relationship than for a second

- order relationship for five of the six stations.

4 In the case of station #99, both terms in the quadratic regression equation were not significant.16

Finally, we conducted an F test to determine if the addition of a squared term to the

regression equation provides a statistically significant increase in the o verall fit to the data. The

results are shown below.

Table 6

F Test To Determine if Increased Fit Produced by Squared Term Was Significant

Station

#20 Station

#40 Station

#42 Station

#57 Station

#99 Station

#144

F 0.0691 8.4483 0.4164 0.0210 1.9340 1.5 023

Significance of F 0.7933 0.0048 0.5207 0.8852 0.1681 0.2239

For any case in which the significance of the F value is greater than 0.05, the increase in fit (as

represented by the r 2 value in Table 4) produced by the addition of a squared term to the

regression equation is deemed to not be statistically significant. Based on these results, station

#40 is the only case in which it would be appropriate to use a quadratic regression equation.

Based on this investigation, we would provisionally conclu de that only a minority of the

available data demonstrat e a nonlinear relationship between E85 price reduction and the E85

sales volume, and that this effect is unlikely to be identifiable in the larger data set based on data

from five states that was used to generate the correlations shown in Figure 5

. This suggests that,

at least at the curren t time, significant E85 sales volume increases are unlikely to occur as the

discount for E85 relative to E10 approaches and exceeds energy parity. It also supports EPA’s

methodology of using a linear correlation between E85 sales volumes and the E85 price discount

relative to E10 to estimate sales volumes of E85.17



### Appendix

Table A

- 1

Raw Data Provided by Minnesota Department of Revenue

Month E85 Volume

January

- 09 1,010,413

February

- 09 783,794

March

- 09 744,426

April

- 09 683,253

May

- 09 880,903

June

- 09 1,079,702

July

- 09 1,172,135

August

- 09 1,055,823

September

- 09 1,052,030

October

- 09 984,693

November

- 09 989,779

December

- 09 902,729

January

- 10 926,254

February

- 10 730,306

March

- 10 762,705

April

- 10 1,078,416

May

- 10 1,095,215

June

- 10 1,256,264

July

- 10 1,197,900

August

- 10 1,317,050

September

- 10 1,289,232

October

- 10 1,233,748

November

- 10 1,183,427

December

- 10 1,073,737

January

- 11 1,074,136

February

- 11 997,158

March

- 11 1,114,905

April

- 11 1,538,268

May

- 11 1,630,187

June

- 11 1,788,828

July

- 11 1,584,718

August

- 11 1,449,305

September

- 11 1,403,809

October

- 11 1,277,400

November

- 11 1,160,671

December

- 11 1,152,62818 January

- 12 1,443,844

February

- 12 526,600

March

- 12 918,604

April

- 12 1,090,863

May

- 12 1,184,102

June

- 12 1,229,913

July

- 12 1, 160,019

August

- 12 1,169,016

September

- 12 1,124,890

October

- 12 1,026,417

November

- 12 1,019,293

December

- 12 731,370

January

- 13 561,119

February

- 13 558,286

March

- 13 736,629

April

- 13 707,448

May

- 13 823,068

June

- 13 1,516,751

July

- 13 1,332,764

Augus t

- 13 1,415,664

September

- 13 1,484,826

October

- 13 1,479,898

November

- 13 1,359,508

December

- 13 1,078,148

January

- 14 884,916

February

- 14 874,969

March

- 14 869,041

April

- 14 1,126,464

May

- 14 1,039,814

June

- 14 1,215,212

July

- 14 1,191,822

August

- 14 1,3 49,410

September

- 14 1,233,608

October

- 14 1,282,936

November

- 14 1,361,354

December

- 14 1,011,39919 Table A

- 2

Raw Data Provided by Iowa Department of Revenue

1st Qtr. 2nd Qtr. 3rd Qtr. 4th Qtr.

2014 2,707,231 2,983,076 3,341,822 2,930,635

2013 1,83 4,947 2,623,949 3,610,895 2,784,326

2012 2,319,835 2,309,836 2,782,928 1,659,832

2011 2,524,037 3,698,872 2,565,969 1,940,119

2010 1,612,198 2,769,667 2,853,619 2,076,424

2009 1,118,985 1,792,203 1,956,328 1,659,519

2008 1,302,480 2,217,095 2,208,837 1,939,511

Table A

- 3

Raw Data Provided by North Dakota 's Ethanol Council (through " ND Ethanol Today " )

January 2009 14,030

February 2009 13,038

March 2009 12,897

April 2009 17,621

May 2009 22,552

June 2009 29,582

July 2009 28,077

August 2009 29,22 8

September 2009 26,619

October 2009 29,004

November 2009 27,340

December 2009 25,269

January 2010 23,316

February 2010 23,869

March 2010 29,931

April 2010 46,616

May 2010 56,260

June 2010 52,917

July 2010 58,483

August 2010 66,217

September 2 010 83,360

October 2010 72,657

November 2010 68,909

December 2010 81,192

January 2011 79,729

February 2011 83,884

March 2011 97,903

April 2011 113,90320 May 2011 129,180

June 2011 106,590

July 2011 120,279

August 2011 160,162

September 2011 114,3 59

Table A

- 4

Raw Data Provided by Kansas Department of Revenue

January

- 12 75,029

February

- 12 110,953

March

- 12 137,787

April

- 12 124,320

May

- 12 127,228

June

- 12 105,891

July

- 12 95,175

August

- 12 99,285

September

- 12 96,081

October

- 12 102,254

Novem ber

- 12 63,940

December

- 12 50,589

January

- 13 43,011

February

- 13 56,969

March

- 13 69,454

April

- 13 59,047

May

- 13 73,596

June

- 13 69,177

July

- 13 61,153

August

- 13 65,557

September

- 13 51,049

October

- 13 49,668

November

- 13 50,720

December

- 13 56,512

Jan uary

- 14 51,588

February

- 14 51,588

March

- 14 71,875

April

- 14 61,019

May

- 14 72,410

June

- 14 60,173

July

- 14 71,660

August

- 14 57,10721 September

- 14 68,621

October

- 14 51,707

November

- 14 44,199

December

- 14 40,023

Table A

- 5

Raw Data Provided by New York Department of Revenue

January

- 08 29,948

February

- 08 51,329

March

- 08 68,204

April

- 08 80,615

May

- 08 113,700

June

- 08 175,540

July

- 08 155,701

August

- 08 149,632

September

- 08 157,076

October

- 08 148,837

November

- 08 94,206

December

- 08 61,822

January

- 0 9 54,118

February

- 09 60,700

March

- 09 71,897

April

- 09 72,621

May

- 09 128,995

June

- 09 170,049

July

- 09 182,542

August

- 09 184,361

September

- 09 198,035

October

- 09 189,553

November

- 09 181,921

December

- 09 164,714

January

- 10 175,255

February

- 10 203,124

March

- 10 213,667

April

- 10 282,198

May

- 10 356,071

June

- 10 387,101

July

- 10 398,817

August

- 10 434,135

September

- 10 349,987

October

- 10 373,15722 November

- 10 336,971

December

- 10 386,318

January

- 11 417,685

February

- 11 385,752

March

- 11 649,823

April

- 11 656,798

May

- 11 816,680

June

- 11 749,740

July

- 11 725,414

August

- 11 672,301

September

- 11 641,722

October

- 11 610,445

November

- 11 543,453

December

- 11 540,819

January

- 12 407,170

February

- 12 592,646

March

- 12 813,499

April

- 12 862,358

May

- 12 841,607

June

- 12 776,908

July

- 12 746,567

August

- 12 758,558

September

- 12 739,541

October

- 12 801,877

November

- 12 516,705

December

- 12 566,423

January

- 13 541,209

February

- 13 556,828

March

- 13 656,002

April

- 13 592,750

May

- 13 629,470

June

- 13 606,074

July

- 13 523,867

August

- 13 623,550

September

- 13 610,161

October

- 13 624,313

November

- 13 600,933

December

- 13 560,083

January

- 14 528,375

February

- 14 498,579

March

- 14 343,86323 April

- 14 385,751

May

- 14 568,295

June

- 14 595,565

July

- 14 717,074

August

- 14 685,29 6

September

- 14 614,248

October

- 14 679,726

November

- 14 542,61524 Table A

- 6

Raw Data Provided by California A ir Resources Board

Month E85 Volume

January

- 10 172,952

February

- 10 157,082

March

- 10 241,521

April

- 10 236,450

May

- 10 238,824

June

- 10 287,523

July

- 10 2 94,064

August

- 10 329,212

September

- 10 246,356

October

- 10 275,228

November

- 10 202,507

December

- 10 248,315

January

- 11 251,501

February

- 11 288,212

March

- 11 455,582

April

- 11 501,783

May

- 11 559,961

June

- 11 551,854

July

- 11 429,277

August

- 11 405,151

September

- 11 427,720

October

- 11 420,280

November

- 11 362,763

December

- 11 370,245

January

- 12 295,144

February

- 12 430,895

March

- 12 590,116

April

- 12 541,623

May

- 12 556,642

June

- 12 593,032

July

- 12 520,956

August

- 12 594,021

September

- 12 514,362

Oct ober

- 12 716,295

November

- 12 603,761

December

- 12 526,02125 January

- 13 556,862

February

- 13 587,394

March

- 13 734,435

April

- 13 651,514

May

- 13 747,752

June

- 13 742,130

July

- 13 731,997

August

- 13 799,516

September

- 13 775,286

October

- 13 839,385

November

- 13 781,249

December

- 13 852,461

January

- 14 775,878

February

- 14 803,552

March

- 14 923,398

April

- 14 853,616

May

- 14 948,118

June

- 14 998,537

July

- 14 942,677

August

- 14 954,577

September

- 14 963,987

October

- 14 1,030,528

November

- 14 950,502

December

- 14 930,088

Table A

- 7

Monthly Average E85 Price Reductions f rom E85prices.com for Minnesota

Month E85 price

discount Number of

samples Number of unique

stations

January

- 09 16.38% 111 23

February

- 09 14.91% 103 27

March

- 09 16.42% 91 29

April

- 09 14.96% 66 16

May

- 09 16.92% 112 37

June

- 09 17.50% 83 21

July

- 09 17.55% 68 20

August

- 09 18.63% 58 22

September

- 09 18.27% 54 22

October

- 09 19.17% 45 20

November

- 09 18.45% 46 16

December

- 09 16.38% 45 2126 January

- 10 18.50% 47 13

February

- 10 18.56% 43 13

March

- 1 0 17.70% 44 11

April

- 10 18.25% 39 14

May

- 10 19.26% 48 11

June

- 10 21.34% 43 13

July

- 10 21.07% 39 12

August

- 10 21.15% 35 9

September

- 10 21.52% 28 12

October

- 10 20.28% 42 13

November

- 10 18.43% 45 10

December

- 10 16.50% 46 11

January

- 11 15.49% 37 10

February

- 11 16.95% 28 10

March

- 11 16.86% 49 25

April

- 11 16.59% 83 32

May

- 11 16.12% 55 29

June

- 11 15.01% 59 20

July

- 11 14.85% 40 11

August

- 11 15.45% 44 13

September

- 11 15.42% 40 18

October

- 11 16.66% 45 17

November

- 11 15.32% 38 9

December

- 11 14.39% 32 12

January

- 12 13.34% 26 9

February

- 12 12.88% 27 11

March

- 12 13.77% 60 31

April

- 12 13.90% 53 17

May

- 12 14.45% 31 10

June

- 12 14.98% 24 10

July

- 12 13.17% 45 12

August

- 12 13.28% 32 12

September

- 12 15.42% 27 10

October

- 12 13.72% 42 9

November

- 12 11.31% 22 8

December

- 12 10.32% 56 20

January

- 13 8.76% 45 16

February

- 13 8.88% 17 7

March

- 13 12.41% 37 13

April

- 13 14.81% 51 17

May

- 13 20.66% 63 1727 June

- 13 21.14% 69 21

July

- 13 23.47% 58 16

August

- 13 20.90% 140 49

September

- 13 23.58% 38 13

Octobe r

- 13 19.67% 39 16

November

- 13 19.37% 52 16

December

- 13 16.64% 53 17

January

- 14 17.14% 39 10

February

- 14 18.70% 35 10

March

- 14 23.15% 41 21

April

- 14 19.53% 50 16

May

- 14 18.87% 59 22

June

- 14 18.92% 61 23

July

- 14 19.72% 68 18

August

- 14 20.65% 39 12

September

- 14 23.01% 37 13

October

- 14 25.47% 51 17

November

- 14 22.50% 54 19

December

- 14 23.83% 63 2328 Table A

- 8

Monthly Average E85 Price Reductions from E85prices.com for Iowa

Month E85 price

discount Number of

samples Number of unique

stations

Jan uary

- 09 18.95% 21 12

February

- 09 14.57% 16 9

March

- 09 16.95% 25 21

April

- 09 17.64% 31 18

May

- 09 22.16% 38 20

June

- 09 24.42% 38 23

July

- 09 23.17% 21 12

August

- 09 26.34% 41 21

September

- 09 24.04% 26 14

October

- 09 22.54% 39 20

November

- 09 17.69% 16 11

December

- 09 14.95% 24 15

January

- 10 17.71% 29 18

February

- 10 19.47% 25 12

March

- 10 24.88% 44 21

April

- 10 29.88% 115 35

May

- 10 30.43% 217 38

June

- 10 28.37% 142 27

July

- 10 30.02% 129 30

August

- 10 28.97% 86 26

September

- 10 23.92% 97 24

October

- 1 0 16.54% 49 23

November

- 10 13.82% 36 22

December

- 10 19.01% 26 18

January

- 11 21.41% 57 30

February

- 11 23.02% 63 24

March

- 11 19.98% 58 25

April

- 11 25.94% 108 29

May

- 11 23.34% 63 39

June

- 11 18.91% 51 26

July

- 11 17.47% 19 11

August

- 11 17.00% 29 14

S eptember

- 11 16.15% 22 11

October

- 11 15.11% 17 9

November

- 11 11.64% 18 9

December

- 11 7.59% 28 12

January

- 12 8.43% 37 18

February

- 12 16.39% 32 1729 March

- 12 19.23% 44 26

April

- 12 17.09% 30 21

May

- 12 14.14% 15 11

June

- 12 16.47% 34 18

July

- 12 13.49% 20 16

August

- 12 12.37% 12 10

September

- 12 16.28% 18 9

October

- 12 12.07% 23 15

November

- 12 8.07% 26 18

December

- 12 6.18% 28 22

January

- 13 7.46% 30 16

February

- 13 15.65% 26 13

March

- 13 15.10% 20 12

April

- 13 13.73% 34 16

May

- 13 19.49% 61 25

June

- 13 1 6.82% 31 19

July

- 13 22.24% 41 25

August

- 13 24.85% 76 30

September

- 13 23.48% 53 25

October

- 13 19.82% 29 17

November

- 13 17.62% 38 18

December

- 13 15.93% 39 20

January

- 14 18.77% 23 16

February

- 14 22.91% 28 18

March

- 14 24.69% 65 37

April

- 14 18.24% 50 32

May

- 14 15.92% 33 24

June

- 14 20.19% 32 20

July

- 14 20.06% 37 24

August

- 14 19.03% 39 28

September

- 14 23.58% 30 24

October

- 14 24.38% 41 24

November

- 14 20.00% 40 24

December

- 14 3.59% 36 22

Table A

- 9

Monthly Average E85 Price Reductions from E85pri ces.com for North Dakota

Month E85 price

discount Number of

samples Number of unique

stations

January 2009 3.48% 13 8

February 2009 7.09% 18 1030 March 2009 9.40% 18 9

April 2009 7.69% 12 8

May 2009 10.96% 14 9

June 2009 15.69% 20 11

July 2009 16.50% 25 11

August 2009 16.67% 13 9

September 2009 16.25% 12 8

October 2009 15.99% 11 5

November 2009 19.00% 12 9

December 2009 13.69% 5 5

January 2010 15.35% 12 7

February 2010 15.66% 11 7

March 2010 19.06% 11 6

April 2010 21.56% 12 8

May 2010 24.45% 10 7

June 2010 21.35% 5 3

July 2010 21.62% 4 3

August 2010 23.79% 5 4

September 2010 25.25% 5 3

October 2010 16.54% 4 3

November 2010 11.24% 6 5

December 2010 11.15% 6 6

January 2011 11.59% 5 4

February 2011 11.60% 5 4

March 2011 16.17% 6 5

Apri l 2011 23.37% 7 4

May 2011 18.82% 8 6

June 2011 15.97% 3 3

July 2011 14.28% 8 6

August 2011 13.50% 3 3

September 2011 17.42% 4 4

Table A

- 10

Monthly Average E85 Price Reductions from E85prices.com for Kansas

Month E85 price

discount Number of

sample s Number of unique

stations

January

- 12 5.33% 9 4

February

- 12 10.01% 5 3

March

- 12 12.27% 16 6

April

- 12 9.73% 9 3

May

- 12 7.88% 2 231 June

- 12 4.92% 2 2

July

- 12 n/a 0 0

August

- 12 8.13% 2 2

September

- 12 10.83% 2 2

October

- 12 8.38% 2 2

November

- 12 n/a 0 0

December

- 12 13.78% 1 1

January

- 13 1.03% 2 2

February

- 13 17.62% 3 1

March

- 13 18.53% 1 1

April

- 13 3.97% 3 2

May

- 13 8.00% 1 1

June

- 13 1.94% 2 2

July

- 13 11.38% 7 4

August

- 13 7.74% 1 1

September

- 13 n/a 0 0

October

- 13 2.69% 1 1

November

- 13 9.40% 1 1

December

- 13 4.93% 1 1

January

- 14 10.75% 1 1

February

- 14 n/a 0 0

March

- 14 13.47% 1 1

April

- 14 n/a 0 0

May

- 14 n/a 0 0

June

- 14 n/a 0 0

July

- 14 n/a 0 0

August

- 14 n/a 0 0

September

- 14

- 0.91% 1 1

October

- 14 n/a 0 0

November

- 14 4.66% 1 1

December

- 14

- 20.08% 1 1

Table A

- 11

Monthly Average E85 Price Reductions from E85prices.com for New York

Month E85 price

discount Number of

samples Number of unique

stations

January

- 08 20.43% 7 2

February

- 08 21.23% 3 3

March

- 08 19.83% 1 1

April

- 08 17.66% 1 1

May

- 08 24.39% 12 432 June

- 08 26.49% 14 3

July

- 08 22.81% 15 5

August

- 08 23.23% 12 5

September

- 08 20.35% 17 5

October

- 08 18.68% 24 9

November

- 08 12.59% 25 6

December

- 08 10.18% 20 6

January

- 09 6.08% 14 9

February

- 09 11.57% 11 7

March

- 09 12.35% 3 2

A pril

- 09 12.44% 2 2

May

- 09 21.69% 27 9

June

- 09 22.61% 24 9

July

- 09 23.13% 15 5

August

- 09 24.35% 27 9

September

- 09 22.62% 22 11

October

- 09 18.61% 25 12

November

- 09 13.96% 25 14

December

- 09 12.26% 17 8

January

- 10 18.49% 6 3

February

- 10 16.49% 9 5

M arch

- 10 20.88% 16 6

April

- 10 24.50% 15 5

May

- 10 26.31% 20 8

June

- 10 24.95% 15 8

July

- 10 23.42% 9 6

August

- 10 21.83% 25 14

September

- 10 16.14% 12 7

October

- 10 14.74% 24 9

November

- 10 13.59% 22 9

December

- 10 16.82% 21 10

January

- 11 15.17% 17 7

Feb ruary

- 11 14.88% 18 9

March

- 11 15.98% 27 12

April

- 11 15.56% 23 11

May

- 11 16.18% 22 12

June

- 11 14.75% 17 9

July

- 11 14.88% 11 9

August

- 11 14.93% 11 7

September

- 11 13.20% 8 7

October

- 11 7.90% 2 233 November

- 11 3.95% 1 1

December

- 11 7.23% 6 6

January

- 1 2 12.36% 1 1

February

- 12 11.62% 4 2

March

- 12 15.28% 6 1

April

- 12 16.90% 7 5

May

- 12 15.97% 5 5

June

- 12 16.57% 9 7

July

- 12 15.06% 2 1

August

- 12 16.89% 5 5

September

- 12 18.69% 4 3

October

- 12 12.15% 2 2

November

- 12 7.31% 7 5

December

- 12 10.66% 3 2

January

- 13 8.86% 5 3

February

- 13 12.26% 4 3

March

- 13 12.85% 5 2

April

- 13 2.87% 1 1

May

- 13 11.58% 4 4

June

- 13 11.92% 2 2

July

- 13 14.52% 5 4

August

- 13 25.02% 5 2

September

- 13 11.58% 2 2

October

- 13 11.15% 3 2

November

- 13 9.89% 4 3

December

- 13 n/a 0 0

January

- 14 4.38% 1 1

February

- 14 n/a 0 0

March

- 14 n/a 0 0

April

- 14 11.49% 1 1

May

- 14 n/a 0 0

June

- 14 21.80% 2 2

July

- 14 14.95% 3 3

August

- 14 n/a 0 0

September

- 14 n/a 0 0

October

- 14 11.75% 2 1

November

- 14 n/a 0 0

Table A

- 12

Monthly Average E85 Price Reductions from E85prices.com for California34 Month E85 price

discount Number of

samples Number of unique

stations

January

- 10 14.69% 31 20

February

- 10 16.32% 15 9

March

- 10 16.74% 28 17

April

- 10 17.15% 23 15

May

- 10 19.80% 27 14

June

- 10 19.29% 16 10

July

- 10 19.40% 37 23

August

- 10 20.48% 32 21

September

- 10 14.60% 19 17

October

- 10 15.74% 20 13

November

- 10 11.07% 10 8

December

- 10 12.35% 18 13

January

- 11 13.72% 19 11

February

- 11 13.50% 22 12

March

- 11 16.60% 40 20

April

- 11 15.66% 41 16

Ma y

- 11 13.41% 24 13

June

- 11 13.89% 19 14

July

- 11 12.86% 11 9

August

- 11 7.15% 13 10

September

- 11 11.04% 7 7

October

- 11 11.39% 7 4

November

- 11 5.96% 4 4

December

- 11 8.64% 8 7

January

- 12 9.45% 3 2

February

- 12 12.10% 18 11

March

- 12 14.00% 11 8

April

- 1 2 13.30% 9 5

May

- 12 13.13% 12 7

June

- 12 13.26% 15 11

July

- 12 16.06% 14 8

August

- 12 13.36% 19 9

September

- 12 10.24% 13 6

October

- 12 16.51% 24 17

November

- 12 12.47% 12 6

December

- 12 11.55% 21 8

January

- 13 15.16% 19 9

February

- 13 14.23% 36 17

March

- 13 15.41% 37 1335 April

- 13 16.20% 28 14

May

- 13 15.51% 44 15

June

- 13 16.25% 28 11

July

- 13 18.45% 26 11

August

- 13 16.40% 30 13

September

- 13 14.39% 25 9

October

- 13 17.16% 31 13

November

- 13 15.30% 22 11

December

- 13 13.77% 18 10

January

- 14 15.29% 27 11

February

- 14 17.06% 23 8

March

- 14 12.92% 32 14

April

- 14 10.81% 26 12

May

- 14 15.43% 23 9

June

- 14 15.63% 25 13

July

- 14 15.00% 28 13

August

- 14 19.25% 17 8

September

- 14 16.58% 32 12

October

- 14 18.47% 46 13

November

- 14 15.31% 31 12

December

- 14 14.95% 33 11

Table A

- 8

Number of retail stations offering E85 from AFDC

Minnesota Iowa North

Dakota Kansas New

York California

January

- 08 322 78 23 20 8 6

February

- 08 322 78 23 20 8 6

March

- 08 322 86 23 21 8 7

April

- 08 338 88 24 23 9 7

May

- 08 338 88 24 23 11 8

June

- 08 338 89 24 23 11 8

July

- 08 339 89 24 23 14 9

August

- 08 348 94 26 23 14 10

September

- 08 352 96 26 24 14 10

October

- 08 353 96 29 25 16 10

November

- 08 353 103 29 23 17 11

December

- 08 353 107 30 23 17 13

January

- 09 357 107 30 24 17 19

F ebruary

- 09 363 109 31 32 30 19

March

- 09 363 108 31 33 32 2036 April

- 09 363 107 31 33 33 20

May

- 09 363 111 31 33 34 30

June

- 09 363 117 31 33 35 32

July

- 09 363 123 31 33 35 33

August

- 09 350 123 31 33 35 38

September

- 09 351 123 31 33 35 38

October

- 09 35 1 124 28 33 36 42

November

- 09 351 124 29 32 37 44

December

- 09 351 124 30 31 38 45

January

- 10 351 124 30 31 38 45

February

- 10 352 126 30 31 39 45

March

- 10 353 128 30 31 40 45

April

- 10 354 129 30 30 41 44

May

- 10 354 138 30 30 43 44

June

- 10 354 138 30 30 43 44

July

- 10 353 140 39 34 53 47

August

- 10 352 141 48 37 63 50

September

- 10 354 141 48 37 65 52

October

- 10 355 141 48 37 66 53

November

- 10 355 141 50 37 67 53

December

- 10 360 144 55 39 75 54

January

- 11 362 147 57 39 73 56

February

- 11 364 149 5 8 38 71 57

March

- 11 364 151 58 38 71 57

April

- 11 365 154 59 38 75 59

May

- 11 365 154 59 38 75 59

June

- 11 351 155 59 37 75 61

July

- 11 356 157 62 39 77 62

August

- 11 358 157 62 39 79 62

September

- 11 360 158 62 39 81 62

October

- 11 362 161 68 40 81 57

N ovember

- 11 363 163 68 40 82 58

December

- 11 364 167 74 40 82 58

January

- 12 366 171 75 39 79 59

February

- 12 367 172 75 39 80 59

March

- 12 362 172 75 39 81 59

April

- 12 361 171 75 39 80 60

May

- 12 361 175 75 39 80 60

June

- 12 360 178 75 39 81 61

July

- 12 3 60 179 75 39 81 60

August

- 12 360 179 75 33 80 6337

September

- 12 361 181 77 34 81 63

October

- 12 361 183 77 34 82 65

November

- 12 349 185 77 34 83 67

December

- 12 349 185 80 34 83 66

January

- 13 361 188 79 32 85 74

February

- 13 361 188 79 32 85 74

March

- 13 360 189 79 33 86 74

April

- 13 366 183 79 33 87 75

May

- 13 365 183 79 33 80 79

June

- 13 363 183 79 32 80 80

July

- 13 352 183 79 32 80 80

August

- 13 353 183 79 34 81 84

September

- 13 336 184 79 34 81 83

October

- 13 336 186 79 29 81 83

November

- 13 327 18 6 7 6 28 1 8 5

December

- 13 318 18 5 72 2 8 80 86

January

- 14 309 185 6 9 27 80 8 8

February

- 14 300 18 5 65 26 81 89

March

- 14 291 18 4 6 2 2 6 79 9 1

April

- 14 282 184 58 25 79 92

May

- 14 282 186 59 25 79 93

June

- 14 281 187 60 25 80 93

July

- 14 281 189 60 26 80 94

A ugust

- 14 281 191 61 26 81 94

September

- 14 281 192 62 26 81 95

October

- 14 280 194 63 26 81 96

November

- 14 280 196 64 26 82 96

December

- 14 280 197 64 27 82 97