Global Economic Impacts of
Biofuels
by Thomas Hertel
Distinguished Professor of Agricultural Economics and
Executive Director, Center for Global Trade Analysis
Purdue University
In collaboration with Jayson Beckman, Dileep Birur, Alla
Golub, Roman Keeney, Farzad Taheripour, Wally Tyner,
Nelson Villoria, Purdue University
and Andy Jones, Michael O’Hare, Rich Plevin and Dan
Kammen, Energy Resources Group, UC Berkeley
Fanning Lecture: University of Georgia, November 13, 2009
Outline of Talk
• Biofuels and the changing economic landscape
facing agriculture
• How green are biofuels?
– US EISA; California ARB regulations require
answering this question
– Debate over “Indirect” Land Use Change (iLUC)
– Key role for market-mediated effects
• Global poverty impacts:
– How biofuels affect the poor in developing countries
– Estimated impacts
Biofuels have altered the agricultural
economy’s landscape
• Crop prices have become more closely linked to
energy prices:
– Historically main link was through input costs: fuel
and fertilizer – significant, but not enough to
dominate price linkage: corn-crude monthly price
correlation: 2001-2007 = 0.32
– However, with 1/3 of the US corn crop going to
ethanol, this has changed: 2007 – 2008 = 0.92
But we are now “hitting the Blend Wall”
• US consumes about 140 billion gallons of
gasoline type fuel annually, so a 10% blend
limit would be a max of 14 billion gallons of
ethanol
• However, the effective blend limit is lower due
to infrastructure limitations:
– at the wall, there is more ethanol capacity than
market absorptive capacity
– ethanol price falls to breakeven with corn for
marginal producer; ‘warm shutdown’
– ethanol tied closely to corn, but blend wall prevents
further growth in capacity
See also “Big time issues facing the ethanol industry” by Wally Tyner, 2009
From Bob Wisner, Iowa State
Biofuels have altered the agricultural
economy’s landscape
• Crop prices have become more closely linked to
energy prices:
– Historically main link was through input costs: fuel
and fertilizer – significant, but not enough to
dominate price linkage: corn-crude monthly price
correlation: 2001-2007 = 0.32
– However, with 1/3 of the US corn crop going to
ethanol, this has changed: 2007 – 2008 = 0.92
– The combination of lower oil prices/binding RFS
and hitting the blend wall loosens the corn-crude
link; don’t necessarily respond to a rise in oil prices
by boosting production: corn-crude price
correlation: 2008/09 = 0.56
The correlation between corn and oil prices has
changed over time as a consequence of ethanol
160
7
140
6
Oil
5
Corn
100
4
80
3
60
2
40
20
January 01 - August 07
Correlation = .32
Sep 07 Oct 08
r = .92
Nov 08 May 09
r = .56
0
0
7
Source: Beckman, Hertel and Keeney
1
Central Illinois No. 2, Yellow ($/bushel)
Cushing, OK Spot Price FOB ($/barrel)
120
Biofuels have altered the agricultural
economy’s landscape
• Crop prices closely linked to energy prices
• Ethanol industry has reached satiation
• The livestock industry has been squeezed:
– Hard hit by high commodity prices
– In an attempt to reduce costs, livestock industry has
become a major user of biofuel by-products; has
altered the relative cost competitiveness of sectors
– Livestock sectors’ ability to absorb by-products also
key to success of biofuel sectors, as this is important
revenue source for them:
• 16% for corn ethanol production
• 23% for rapeseed biodiesel
• 53% soydiesel
Changes in Livestock Production Due to the US and EU 2015
Biofuel Mandates: 2006-2015
US
EU
400.0
200.0
Millions of 2006 dollars
0.0
Others
Other regions suffer
because of higher crop
prices/limited access to
biofuel byproducts
EU non-ruminants get
a boost from lower
priced oilseed meal
-200.0
-400.0
-600.0
-800.0
-1000.0
Non-ruminants in US
suffer due to low rate of
displacement of DDGS
for corn
-1200.0
-1400.0
Dairy farms
Meat ruminant
Source: Taheripour, Hertel and Tyner, 2009
Non-ruminant
Biofuels have altered the agricultural
economy’s landscape
•
•
•
•
Crop prices closely linked to energy prices
Ethanol industry has reached satiation
The livestock industry has been squeezed
Introduced new sources of uncertainty into
agricultural markets:
– Petroleum prices increasingly key, but highly uncertain
Biofuels have altered the agricultural
economy’s landscape
•
•
•
•
Crop prices closely linked to energy prices
Ethanol industry has reached satiation
The livestock industry has been squeezed
Introduced new sources of uncertainty into
agricultural markets:
– Petroleum prices increasingly key, but highly uncertain
– Will the blend wall be adjusted?
– Will corn ethanol be allowed in place of cellulosic in the RFS if
the latter is not available in sufficient volume?
– Will my biofuel be green?
Outline of Talk
• Biofuels and the changing economic landscape
facing agriculture
• How green are biofuels?
– US EISA; California ARB regulations require
answering this question
– Debate over “Indirect” Land Use Change (iLUC)
– Key role for market-mediated effects
• Potential poverty impacts through higher
world food prices
– How biofuels affect the poor in developing countries
– Estimated impacts
iLUC Background (1)
• Prior to 2007, the general consensus was that corn
ethanol reduced greenhouse gasses:
– It is a renewable fuel – growth of corn sequesters carbon, this
is released when the ethanol is burned, then start process again
– Of course, corn production, transport, processing also requires
energy, so net energy additions are less than 100%, not fully
GHG neutral, but still deemed beneficial
• Add to this other important co-benefits:
– Boost farm incomes
– Reduce dependence on imported oil
• Looked like a real winner: Led to Renewable Fuel
Standards as part of Energy Investment and Security
Act (EISA) of 2007
US Renewable Fuel Standard: ensures
15byg corn ethanol... Provided it offers a
20% GHG improvement
40.00
35.00
Billions of Gallons
30.00
25.00
20.00
15.00
10.00
5.00
0.00
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Biomass-based Diesel
0.50 0.65 0.80 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Non-celulosic Advanced
0.10 0.20 0.30 0.50 0.75 1.00 1.50 2.00 2.50 3.00 3.50 3.50 3.50 4.00
Celulosic Advanced
0.10 0.25 0.50 1.00 1.75 3.00 4.25 5.50 7.00 8.50 10.5013.5016.00
Conventional Biofuels
4.00 4.70 9.00 10.5012.0012.6013.2013.8014.4015.0015.0015.0015.0015.0015.0015.0015.00
15
Net GHG contributions of corn ethanol (vs. net energy
contribution) in absence of iLUC
Source: Energy Resources Group, U.C. Berkeley
U.S. corn farmer switches
from corn/soy to
corn/corn
Presence of iLUC may greatly
increase GHG emissions
U.S. soy
exports go
down and
world soy
prices rise
Soy farmers everywhere
use more inputs to
increase yields
Additional land in
Brazil (for
instance) is put
into soy production
Indirect LUC
emissions
Indirect process
emissions
Process
emissions
17
Source: Energy Resources Group, UC Berkeley
iLUC Background (2)
• By the second half of 2007, the importance of
indirect land use change induced emissions was
circulating among professionals in the area
• The EISA included a requirement that indirect
land use changes be considered in estimating
total GHG impacts for biofuels
• In February 2008, Science published a paper by
Searchinger, Heimlich (USDA), Fabiosa,
El’Obeid, Lu, Tokoz, Hayes and Du (Iowa State
University) estimating the size of these effects;
greatly altered the GHG landscape for biofuels
Net GHG contributions in presence of iLUC
(Searchinger et al. estimates)
?
Source: Energy Resources Group, U.C. Berkeley
iLUC Background (3)
• Publication of Searchinger et al. has precipitated a series
of studies aimed at sharpening estimates of iLUC
• California Air Resources Board and UC Berkeley
approached Purdue to undertake a joint study of iLUC
for use in CARB’s Low Carbon Fuel Standard; chose to
use GTAP model since publicly available; supported by
consortium of world’s leading global economic orgs
• In April 2009, CARB passed the LCFS, inclusive of iLUC
estimates; the latter are based on GTAP analysis,
undertaken at Purdue University
• Model is publicly available for replication-critiqueimprovement
Land Conversion
(Ha) and Emissions
(TgCO2) due to
increased US corn
ethanol production
 Estimate cropland
expansion into accessible
forest land and pastures
 Greatest portion of land
conversion occurs in US
 Land cover elasticities
wrt to land rents based
on historical estimates
over period:1985 – 1997
 Emissions factors
based on Woods Hole
estimates
Source: CARB analysis, as documented in Hertel, Golub, Jones, O’Hare, Plevin and Kammen, 2009
What is the bottom line?
• To play a meaningful role in reducing GHG
emissions, must be significantly below gasoline:
– Gasoline = 94-96 gCO2e/MJ
– Direct emissions of US corn ethanol = 60-65g/MJ, according to
CARB; lower values are possible with new technologies
(possibly 45g/MJ)
– Indirect emissions (with 30 year time horizon):
• Searchinger et al = 100g/MJ
• Purdue-Berkeley estimate for CARB = 27g/MJ with std
deviation of 12g/MJ (considerable uncertainty remains)
• What explains these differences? Many factors
– explore here the market-mediated responses
Outline of Talk
• How green are biofuels?
– Background to debate over “Indirect” Land Use
Change (iLUC)
– Key role for global economic responses -- marketmediated effects
– The bottom line
• Potential poverty impacts through higher
world food prices
– How biofuels affect the poor in developing countries
– Preliminary estimates of impacts
Understanding the GTAP estimates of iLUC:
market-mediated effects are key
Source: Hertel, Golub, Jones, O’Hare, Plevin and Kammen, 2009
Determining the impact on corn
production of 13.25bgy rise in ethanol
(2001-2015 cumulative impact)
• Direct requirements:
– Need to boost cgrns production by 42%
– 15 Mha more land, ceteris paribus (holding all else
constant)
• But market-mediated adjustments:
– Ethanol production does not exhaust feed value of
corn; DDGS by products can be fed to livestock;
also substitute other feeds for higher priced corn;
43% decline in sales of corn for livestock feeding;
– Exports also fall, ceteris paribus
– Cuts required rise in US production from 42% to
17%
Determining the impact on US corn
area of 13.25bgy rise in ethanol
(2001-2015 cumulative impact)
• Increase in crop area required depends on
what happens to yields:
– Corn yields rise over time as a result of continuing
improvements in varieties/technology; so land
requirements to meet ethanol will fall over time
– Corn yields are also responsive to price: higher
prices boost yields above trend (intensive margin)
– Corn yields tend to fall when expand area (extensive
margin)
• Further reduces area required in US
Determining the impact on RoW corn
area of 13.25bgy rise in ethanol
(2001-2015 cumulative impact)
• Higher US prices, reduced exports, tend to
boost production in Rest of the World (Row)
• Where will production rise? Depends on the
geography of international trade:
–
–
–
–
Total current area (sole basis for naïve prediction)
Intensity of US exports to that country
Intensity of their exports to US
Intensity of competition in third markets
• To understand future, need to study past:
statistical analysis of last 40 years’ global area
changes in response to US price
Confidence intervals (95%) for the differences between the mean harvested areas
predicted by the IWM and the Armington model, by country:
Based on 1993 US cgrns production shortfall of 32 percent
Armington
Integrated World Market
Source: Villoria, N. (2009) "Understanding the Global Land Use Impacts of
Biofuels: The Role of Product Differentiation". Dissertation Essay #1.
Determining the impact on RoW Land
Area
• How much new cropland is required to offset
reduced US exports depends on relative yields
– Yields vary by country, so if new production arises in
countries with high yields, less area is required
International corn yields vary by region
10.000
9.000
RoW yields avg about 1/3 of US
8.000
7.000
6.000
5.000
GTAP
4.000
FAPRI
3.000
2.000
1.000
0.000
Comparison of corn yields (metric ton/ha) GTAP/SAGE is 1997-2003
average/ FAPRI is from the 2001/02 marketing year
30
Determining the impact on RoW Land
Cover
• How much new cropland is required to offset
reduced US exports depends on relative yields
– Yields vary by country
– Yields vary by Agro-Ecological Zone (AEZ)
Global Distribution of AEZs
Source: Lee et al. 2009
Yields vary within countries: China’s corn
yields by AEZ
9
8
7
6
5
4
3
2
1
0
4 AEZ4
AEZ5
AEZ6
AEZ7
AEZ8
AEZ9
AEZ10
AEZ11
AEZ12
AEZ13
AEZ14
AEZ15
AEZ16
AEZ17
Corn yields (metric ton/ha) GTAP/SAGE across AEZs for China
33
(no corn is grown in AEZ18; no AEZs 1-3 in China)
Determining the impact on RoW Land
Cover
• How much new cropland is required to offset
reduced US exports depends on relative yields
– Yields vary by country
– Yields vary by Agro-Ecological Zone (AEZ)
• Which type of crop cover is converted depends on
competition within AEZs; if pasture is
prominent, more pastureland will be converted,
similarly with forest land
Land Conversion
(Ha) and Emissions
(TgCO2) due to
increased US corn
ethanol production
 Geography matters:
Greatest portion of land
conversion occurs in US
 Overall, pasture
conversion is most
prominent
 Prominence of pasture
(vs. forest) further
reduces GHG emissions
 Finally, some modest
afforestration in AEZs
without biofuel feedstocks
Source: CARB analysis, as documented in Hertel, Golub, Jones, O’Hare, Plevin and Kammen, 2009
What is the bottom line?
• To play a meaningful role in reducing GHG
emissions, must be significantly below gasoline:
– Gasoline = 94-96 gCO2e/MJ
– Direct emissions of US corn ethanol = 60-65g/MJ, according to
CARB; lower values are possible with new technologies
(possibly 45g/MJ)
– Indirect emissions (with 30 year time horizon):
• Searchinger et al = 100g/MJ
• Purdue-Berkeley estimate for CARB = 27g/MJ with std
deviation of 12g/MJ (considerable uncertainty remains)
• Corn ethanol looks unlikely to make it in CA:
– CA-LCFS: need to reduce total emissions to 46g/MJ if going to
achieve desired 10% reduction based on 20% blend
– US-EPA: need 77g/MJ (20% of gasoline); but mostly
grandfathered in already – tied to climate change legislation
A focus on the consumption response
•
Impact of reduced consumption due to higher prices
plays a significant role in reducing land requirements for
biofuels; but largely overlooked
•
What if prevented reduction in consumption via food
subsidies? In our work with UCB-ERG:
•
•
Estimate twice as much forest land conversion and
•
50% higher GHG emissions from LUC when food
consumption is fixed (do not adjust to higher food
prices in the wake of increased biofuels)
However, most price responsive demand is in low income
countries, where rates of poverty and malnutrition are
highest; what are likely impacts of price rise on poverty?
37
Outline of Talk
• Biofuels and the changing economic landscape
facing agriculture
• How green are biofuels?
• Potential poverty impacts through higher
world food prices
– How biofuels affect the poor in developing countries
– Estimated impacts
Focus Countries and Stratification
for Biofuels/poverty study
• Survey data availability determines country selection:
– Disaggregated in v.6.2 GTAP data base
– Household survey available to the World Bank
– Disaggregation of income sources in survey
• Leads to eighteen countries (currently targeting 24):
– Africa: Malawi, Mozambique, Tanzania, Uganda, Zambia
– Asia: Cambodia, Bangladesh, Indonesia, Pakistan, Philippines,
Thailand, Vietnam
– Latin America: Brazil, Colombia, Chile, Mexico, Peru, Venezuela
• Stratification of households into 7 groups by earnings
specialization (> 95% of income from one source):
– Agr self-employment, non-agr self-employment, wage labor (rural
and urban), transfer dependent, diversified (rural and urban)
The poor spend a disproportionate share of income on
food and earn most of income from unskilled labor
Rural and transfer dependent
hhlds are disproportionately poor
Medium run price impacts of EU-US biofuels growth: 20012015 using GTAP model (% change relative to baseline)
Source: Hertel and Taheripour, 2009
Poverty impacts on individual strata are
rather consistent
Percent change
(Sign Consistency = SC = Avg/AAV-- darker portion of avg is
contribution of US biofuels)
43
Source: Hertel and Taheripour, 2009
But national poverty impacts vary widely
across 16 focus countries (% change)
Poverty reductions in Asia
Poverty rises in much
of Latin America
African impacts
smaller
44
Source: Hertel and Taheripour, 2009
Conclusions
• Biofuels have fundamentally altered the economic landscape facing
global agriculture
• Closer link between energy and farm commodity prices
• Introduces massive new uncertainties for agricultural entrepreneurs:
– Energy markets
– Biofuel policies
– Climate policies
• Controversy over GHG impacts of biofuels will continue due to
uncertainty about underlying economic parameters; key role of
market-mediated effects
• However, we deal with such uncertainty all the time:
– Confidence intervals
– Bounding analysis
– Focus on thresholds, not numerical values
• Global poverty impacts are mixed: those whose income is tied to
45
agriculture likely to gain, low income urban households likely to lose