EPA Land Use Emissions for Corn 

Ethanol

1

AIR, Inc. and RFA

July 14, 2009
Overview

• The following slides are informational only 

and are intended to highlight the wide 

variability and uncertainty in LUC modeling 

results

• While AIR and RFA are attempting to conduct 

LUC modeling, we still have major concerns 

about the scientific basis, including the 

assumptions and methodology, for including 

international indirect land use changes in a 

lifecycle GHG analysis

2
Overview

• AIR’s GTAP results for CARB with modified elasticities

• EPA’s results

• AIR’s GTAP results using EPA ethanol shock

• Distillers grains assumptions

3

• Corn ethanol sensitivity modeling

• Avoided emissions from unconventional fuels
CARBÕs Table IV-10 from ISOR Š LUCs for Corn Etha nol

Scenario A B C D E F G Mean

Economic Inputs

ETOH pro d increase 13.25 13.25 13.25 13.25 13.25 13.25 13.25

Elasticity of crop yields 

wrt area expan sion

0.5 0.75 0.5 0.5 0.5 0.66 0.75 0.59

Crop y ield elasticity 0.4 0.4 0.2 0.4 0.4 0.25 0.2 0.32

Elasticity of land 

transform ation

0.2 0.2 0.2 0.3 0.1 0.2 0.2 0.2

Elasticity of harvested 

acreage response

0.5 0.5

Mode l Results

Total land conver ted 4.03 2.68 5.48 4.56 3.01 3.83 3.66 3.89

4

Total land conver ted 4.03 2.68 5.48 4.56 3.01 3.83 3.66 3.89

 Forest land 1.04 0.37 1.46 0.89 1.00 0.73 0.55 0.86

 Pasture land 3.00 2.32 4.02 3.65 2.01 3.10 3.10 3.03

US land conve rted 1.74 1.16 2.01 2.12 1.14 1.46 1.32 1.56

 Forest land 0.7 0.36 0.82 0.81 0.48 0.46 0.40 0.58

 Pasture land 1.04 0.79 1.19 1.31 0.66 1.00 0.92 0.99

LUC carbon intensity (g 

CO2 eq/MJ)

33.6 18.3 44.3 35.3 27.1 27.4 24.1 30

CARB did adjust these values for the 8.7% improvement in yields to 

2007, but did not adjust them for the storage derating factor of 10% 

(adjustment for this factor would yield average of 28 g/MJ)
RFA Mtg with CARB on April 16 before 

Board Hearing on 23rd

• RFA presented information from Informa showing elasticity with 

resepct to area transformation was ~1, not 0.5 to 0.7

• In this meeting, Purdue (Hertel) suggested that the elasticity 

with respect to area expansion should be set to 1.0 and the 

price-yield elasticity should be set to ~ 0

– Same assumption as FASOM/FAPRI

• AIR was able to run GTAP with ARB’s ethanol shock of 13.25 

5

bgy, with the area expansion elasticity at 0.95 and the priceyield at 0.1 

– Model would not reach a solution for us with 1.0/0.0

• We obtained an LUC for corn of 17.1 g/MJ (as opposed to 

CARB’s 30 g/MJ). 12.3% benefit vs gasoline (MW dry mill, nat 

gas, dried DGs). 

– This accounts for the 8.7% yield improvement and the 10% storage 

derating factor which CARB intended to include

– Does NOT account for updated DG land use credits, reduced rice 

methane, livestock methane
EPA’s Results Using FAPRI/FASOM

• Converted emissions to g/MJ for ease of comparison

• Annualized emissions

– For 30 year, divided by 30, and for 100 year, divided by 100 

(after discounting using 2%) 

6
0.10

0.15

0.20

0.25

0.30

0.35 2022 Cropland Change, FASOM/FAPRI, 30/0%

Total=1.93 million hectares

7

-0.05

0.00

0.05

0.10
6

8

10

12

14 2022 LUC Emissions, FASOM/FAPRI, 30/0%, Annualized

Total=63.29 gCO2e/Mj

8

-2

0

2

4
1.5

2.0

2.5

3.0

3.5

4.0 2022 LUC Emissions, FASOM/FAPRI, 100/2%, Annualized

Total=19.25 gCO2e/Mj

9

-0.5

0.0

0.5

1.0

1.5
AIR GTAP Run with EPA Ethanol 

“Shock”

• GTAP 2001 database

• EPA reference is 12.4 bgy, control is 15 bgy, for 

difference of 2.6 bgy

• GTAP is at 1.75 bgy in 2001

10

• We shocked model for increase from 1.75 to 4.34 

(increase of 2.6, same as EPA)

• Use price yield elasticity of 0.1 (close to 0), and area 

expansion elasticity of 0.95
0.05

0.10

0.15

0.20

0.25 Cropland Change, GTAP, 2.6 bgy Ethanol Shock, ETA=0.95, YDEL=0.1

Total=0.67 million hectares

11

FASOM/FAPRI indicate 1.93 mha converted, GTAP

indicates 0.67 mha

-0.05

0.00
0

2

4

6

8

10

12

Forestry

Livestock

LUC Emissions, GTAP, 2.6 bgy Ethanol Shock, ETA=0.95, YDEL=0.1

Total= 20.98 gCO2e/Mj

12

The above results not corrected for yield increase or storage 

derating factor. When these are included, total = 18 g/MJ

-4

-2
Observations on Differences Between GTAP 

and FASOM/FAPRI Results

• Big differences in amount of land converted

– FAPRI much higher than GTAP

• Big differences in where the land is converted

– FAPRI indicates its outside of North America, GTAP 

13

indicates most of land conversion within U.S.

• These differences can have a large impact on LUC 

for corn (and other feedstocks)
Distillers Grains in FASOM/FAPRI

• Two things affecting the land use credit: mass 

replacement ratio and % soy

• FASOM assumes 1 lb of DG replaces 1 lb of feed, 

with 8.5% soy, 91.5% corn mix

14

• FAPRI more disaggregated

– 61% of DG used for beef cattle, 0.97 lb replaced, 3% soy

– 21% for dairy, 0.97 lb replaced, 3% soy

– 9% for swine, 0.89 lb replaced, 11% soy

– 9% for poultry, 0.79 lb replaced, 21% soy

– These need to be weighted together to compare with other 

sources
1 acre 8400 lbs corn

2400 lbs 

DGs

2400 lbs corn

15

3216 lbs 

animal feed

772 lbs soy

0.28 acre

0.42 acre

0.7 acre
DG Estimates

Parameter FASOM FAPRI GTAP Argonne Shurson

Mass 

Replacement 

Ratio

1.0 0.87 1.0 1.28 1.22

16

Ratio

% Corn 91.5% 94.7% 100% 76% 75%

% Soy 8.5% 5.3% 0% 24% 25%

FASOM, FAPRI, and GTAP replacement ratios and % soy are too low. 

FAPRI estimates especially troubling, since FAPRI’s land use estimates 

drive the corn land use numbers. And, why would EPA perform modeling 

using different values for FAPRI and FASOM?
50

60

70

80

0.90

1.00

1.10

1.20

1.28

Land Use Credit versus Percent Soy in Base Feed and DG Ratio

DG Ratio

Argonne

Shurson

17

20

30

40

0 5 10 15 20 25 30

Percent Soy in Base Feed Air Improvement Resource, Inc.

GTAP

FASOM

FAPRI
DG Conclusions

• GTAP, FASOM, and FAPRI DG assumptions should 

be revised to the Argonne or Shurson estimates

• External correction of GTAP to Argonne DG 

estimates would reduce emissions from 18 g/MJ to 8 

18

g/MJ
EPA FASOM, FAPRI LUC Sensitivity Cases 

for Corn Ethanol

• There currently are none

– Economic sensitivity for higher yield and elimination of CRP 

floor

– AIR has requested complete data for these two cases so we 

19

can see impact on LUCs. Availability?

• RFA suggested sensitivity cases

– DG assumptions

– Projected corn yields

– Elimination of U.S. CRP floor
Other Issues with FAPRI

• The fact that the model runs cannot be 

replicated by anyone outside of the 

CARD system is a big problem

20
Other Issues RFA is Working On

• Comparing Winrock emissions to Woods Hole

• Having FASOM/FAPRI run by sponsors with 

improved DG assumptions and a variety of 

projected corn yields

21

• Dr. Shurson is reviewing FASOM/FAPRI DG 

assumptions
Questions

• What things is EPA trying to fix in GTAP?

• Will GTAP modeling be done for final rule?

• Is EPA doing a Winrock vs Woods Hole emissions 

comparison?

22

• Will EPA include the social cost of carbon concept in 

its time accounting?
What do biofuels displace?

• Biofuels displace and delay the need for highcarbon unconventional fuels

• Most traditional lifecycle GHG analyses compare 

biofuels to average (conventional) fuels

• Biofuels GHGs should be compared to the fuels 

they replace at the margin of the fuel market
Liquid Fuel Supply Growth, 2007-2030

4.4

12.4

80

90

100

110

mbpd

Liquid Fuels Supply Growth, 2007-2030

•Unconventional fuels nearly triple

•Conventional fuels grow 14%

81.8

93.0

50

60

70

2007200820092010

2011

2012201320142015201620172018201920202021202220232024202520262027202820292030

mbpd

Conventional Unconventional

•Conventional fuels grow 14%

•Unconventional = 42% of growth

•Conventional = 58% of growth

Source: EIA 2009 AEO
Unconventional growth sources

8

10

12

14

mbpd

Unconventional Fuel Growth, 2007-2030

0

2

4

6

2007 2030

mbpd

Biofuels Bitumen Extra-heavy oil CTL GTL Shale Other

What would 

be used if 

these 

biofuels 

didn’t exist?

Source: EIA 2009 AEO
Unconventional growth, adjusted to exclude biofuels

Unconventional Source Share of Unconventional Supply Growth

Bitumen (Tar Sands) 55.5%

Extra Heavy Crude 10.7%

Biofuels 0.0%

Coal-to-Liquids 24.2%

Gas-to-Liquids 5.6%

Oil Shale 3.3%

Other 0.7%
What will biofuels displace in the aggregate?

Source Worst Case Best Case

Conventional 58.0% 69.6%

Bitumen (Tar Sands) 23.3% 16.9%

Extra Heavy Crude 4.5% 3.3%

Coal-to-Liquids Liquids 10.2% 7.4%

Gas-to-Liquids 2.4% 1.7%

Oil Shale 1.4% 1.0%

Other Unconventional 0.3% 0.2%
Lifecycle GHG Emissions Compared

Gas-to-Liquids [a]

Bitumen (Tar Sands) [a]

Extra-Heavy [a]

Coal-to-Liquids [a]

Oil Shale [a]

Lifecycle GHG Emissions

0 50 100 150 200 250

Conventional Gasoline ...

Cellulosic Ethanol [c]

Corn Ethanol [b]

Gas-to-Liquids [a]

g CO2e/MJ

Notes:

•Farrell & Sperling. A Low Carbon Fuels Standard for California. Part I: Technical Analysis. Aug. 1, 2007.

•Low est. = Midwest nat. gas dry mill (Liska et al., 2009). High est. = Midwest wet mill (CARB, 2009).

•Low estimate = Huang, 2007. High estimate = Wang et al., 2007.

•EPA, 2009.

• DIRECT EMISSIONS ONLY
Results

Conventional Gasoline [a]

Best Case Displaced, Low GHG

Worst Case Displaced, Low GHG

Best Case Displaced, High GHG

Worst Case Displaced, High GHG

Avoided Emissions Above Conventional Baseline Gasoline

Avoided 

emissions above

conventional 

gasoline baseline 

range from 8 to 

22 g CO2e/MJ

depending on 

assumptions 

used.

+22

+8

0 20 40 60 80 100 120

Cellulosic Ethanol [c]

Corn Ethanol [b]

Conventional Gasoline [a]

g CO2e/MJ

Notes:

•Farrell & Sperling. A Low Carbon Fuels Standard for California. Part I: Technical Analysis. Aug. 1, 2007.

•Low est. = Midwest nat. gas dry mill (Liska et al., 2009). High est. = Midwest wet mill (CARB, 2009).

•Low estimate = Huang, 2007. High estimate = Wang et al., 2007.

•EPA, 2009..

• DIRECT EMISSIONS ONLY
Questions

• Is EPA considering a marginal fuel displacement 

approach?

• What methodology and assumptions are being used?

• Is this encompassed by the NEMS modeling being 

30

• Is this encompassed by the NEMS modeling being 

performed?

• Ideas/current thinking on how to reconcile inequity of 

2005 petroleum baseline?