Impact of Biofuel Production on Food Prices Miroslava

Impact of Biofuel Production on Food Prices Miroslava Rajcaniova Faculty of Economics and Management, Slovak University of Agriculture, Nitra, Slovak Republic

Definition of Biofuels are fuels derived from biomass that are provided by agriculture, forestry, or fishery as well as from wastes of agroindustry or food industry (FAO, 2008).

Background Production of biofuels tripled from 2000 to 2007 (OECD, 2008). Around 85 percent of the global production of liquid biofuels is in the form of ethanol. The production of ethanol tripled between 2000 and 2007 to reach over 60 billion liters, with Brazil and the United States accounting for most of this growth. Biodiesel output, mostly by the European Union, witnessed an even more pronounced expansion over the same period, having grown from less than one billion liters to almost 11 billion liters (FAO, 2009).

Development of Biofuel Production Source: Energy Information Administration (EIA)

Development of Bioethanol Production Source: Earth Policy Institute, F. O. Licht

Development of Biodiesel Production Source: Earth Policy Institute, F. O. Licht

World Ethanol Production by Country (Millions of U. S. liquid gallons per year) Source: RFA Industry Statistics

World Biodiesel Production by Country (in Thousand tonnes) Source: data from OECD. stat

Background Ethanol is an alcohol derived from sugar or starch crops (e. g. sugar beet, sugar cane or corn) by fermentation. Cellulosic materials (e. g. wood, grasses and some waste crop residues) can also be converted into bioethanol. Sugarcane is favorite raw material for ethanol production in Brazil, cereals and sugar beet in the USA, EU and other developed countries with temperate climate.

Feedstocks for Bioethanol in Europe Source: EU FAS posts

Background Biodiesel is derived from vegetable oils (e. g. rapeseed oil, soy or palm oil). Waste residues (e. g. waste cooking fat) can also be converted into biodiesel. Biodiesel can either be burnt directly in diesel engines or blended with diesel derived from fossil fuels.

Feedstocks for Biodiesel in Europe Source: EU FAS posts

Background Blends of biofuels and gasoline or diesel are applied into cars. Low ethanol blends from 5 to 22% applied without modifications of engines and with the existing infrastructure. E 10 used in USA, Brazil, E 5 popular in Europe. High ethanol blends of 85 % require special engine modifications and used in flexible fuel vehicles (FFV). Biodiesel application ranges from pure biodiesel known as B 100 to low biodiesel blends B 20.

Reasons to Produce Biofuels The development of biofuel production is partly influenced by the government support programs and partly by the development of oil prices.

Government Support Programs - - Consumer excise-tax exemptions at the gasoline pump Mandatory blending or biofuel consumption requirements (from domestic and import supplies) Import tariffs on biofuels Production subsidies for biofuel feedstocks (e. g. , maize) Production subsidies for biofuels (grants, loan guarantees, tax incentives, etc. ) Subsidies for R&D of new technologies Grants, loans. . .

Blending Mandates Brazil 1975 - ethanol blends 20– 25 percent, all diesel must contain 2% biodiesel and this share will increase to 5 percent by 2013.

Blending Mandates European Union The 2003 biofuel directive (The Directive 2003/30/EC) sets that by 2010 EU should reach 5. 75 percent share of biofuels in total transport fuel use (by year 2020 - 10 percent). At least 20% of the target in 2015 and 40% of the 2020 goal must be met from “nonfood and feed-competing” second-generation biofuels or from cars running on green electricity and hydrogen.

Blending Mandates United States The US Renewable Fuel Standard (RFS) mandates the minimum use of 36 billion gallons of ethanol by 2022. About 11 billion gallons are used at present.

Blending Mandates Source: US EPA (2010)

Tax Exemption / Tax Credit Tax credit in the US amounts to 52 cents per gallon. The blender receives a subsidy per gallon of biofuel blended with a fossil fuel. In the European Union and Brazil - tax reductions or exemptions for renewable fuels. Tax exemption on biodiesel in Germany was reduced from 0. 47 Euro per liter to 0. 29 Euro per liter between 2005 and 2009. Tax on biofuels for transport may be not less than 50 percent of the normal excise tax.

Reasons to Support Biofuels - - biofuels reduce the dependency of many countries on imported oil countries are worried about the stability of oil prices increased production of biofuels is expected to improve the environment and to contribute to the reduction of global climate change biofuel support might reduce the cost of agricultural support programs

Greenhouse Gas Emissions In theory, the production of biofuels is carbon neutral. Most of the carbon emitted to atmosphere is CO 2 which is greenhouse gas (GHG). Fossil fuels on the other hand release carbon that was stored for millions of years under the surface of the earth. At the same time production of food from maize or other feedstock used to produce biofuels is also carbon neutral (FAO, 2008).

Greenhouse Gas Emissions - To assess the net effect of a biofuel on greenhouse gas emissions life cycle analysis is used. - Life cycle analysis measures product’s environmental flows and potential impacts throughout the whole life time of the product. - Greenhouse gas emissions of biofuels are strongly dependent on raw material and technology of production and consumption (Lee, Clark, Devereaux, 2008).

Estimated GHG savings of First Generation Biofuels Biofuel % GHG saving Ethanol (corn) -5 – 35% Ethanol (wheat) 18 – 90% Ethanol (sugar cane) 70 – 100% Ethanol (sugar beet) 35 - 65% Biodiesel (rapeseed) 20 – 85% Biodiesel (palm oil) 8 – 84% Biodiesel (palm oil) -868% w/rainforest conversion Biodiesel (palm oil) -2070% w/peat forest conversion Biodiesel (soybean) -17 – 110% Biodiesel (sunflower) 35 – 110% Source: Scope, 2009

Estimated GHG Savings of Second Generation Biofuels Biofuel % GHG saving Cellulosic ethanol (switchgrass) 88 – 98% Cellulosic ethanol (poplar, switchgrass, forest residue) 10 – 102% Cellulosic ethanol (wheat straw) 84 – 98% Cellulosic ethanol (poplar) 70% Cellulosic ethanol (grass and wood) 65% Ethanol (various lignocellulose) 15 – 115% FT Diesel (various lignocellulose) 28 – 200% FT Diesel (residual wood) 80 – 96% Source: Scope, 2009

Energy Balance in Production of Biofuels The fossil energy balance expresses the ratio of energy contained in the biofuel relative to the fossil energy used in its production. A fossil energy balance of 1. 0 means that it requires as much energy to produce a litre of biofuel as it contains; in other words, the biofuel provides no net energy gain or loss. Fossil fuel energy balance of 2. 0 means that a litre of biofuel contains twice the amount of energy as that required in its production.

Energy Balance in Production of Biofuels Fuel Fossil energy balance Ethanol (corn) 1. 3 – 1. 8 Ethanol (wheat) 1. 2 – 4. 3 Ethanol (sugar beet) 2. 0 – 8. 3 Biodiesel (rapeseed) 1. 2 – 3. 7 Biodiesel (palm oil) 8. 7 – 9. 7 Biodiesel (soybean) 1. 4 – 3. 4 Biodiesel (waste vegetable oil) 4. 9 – 5. 9 Gasoline (crude oil) 0. 8 Diesel (crude oil) 0. 8 – 0. 9 Source: Worldwatch Institute 2006

Biofuels and Biodiversity Biofuel production can affect wild and agricultural biodiversity in some positive ways, such as through the restoration of degraded lands, but many of its impacts will be negative, for example when natural landscapes are converted into energy crop plantations or peat lands are drained, habitat loss following land conversions, agrochemical pollution and the dispersion of invasive species. . . (CBD, 2008, FAO, 2008).

Biofuels and Land Use -supply of food is constrained by fixed land - to increase the production area for energy crops, land conversions of different native ecosystems are needed, these can have substantial impact on the GHG balances of biofuels

Biofuels and Land Use Conversion of grassland to cultivated land can release 300 tons of carbon per hectare. When forestland is converted, 600 – 1000 tons of carbon per ha are emitted (OFID/IIASA 2009). Conversion of native ecosystems, such as grassland, forests and peatland, to energy crop lands, or through returning abandoned croplands to production is called direct land use change (LUC) while indirect LUC occurs when existing food/feed cropland is diverted to energy crops.

Economics of Biofuels are almost perfect substitutes to fossil fuels. The market price of biofuels should therefore be strongly dependent on the market price for gasoline. Perfect substitutes have the same prices, which means that price of gasoline (PG) = price of ethanol (PE), in energy terms PE = k. PG where in reality k is approximately 0. 7 when adjusted to an E-100 basis. (De. Gorter and Just, 2008)

Price relationships Excise tax imposed: PE + t= k(PG + t), t is an excise tax. Tax exemption of biofuels: PE + t - te = k (PG + t), te is tax exemption. PE = k. PG – t(1 -k) + te. To increase the price of ethanol and to stimulate its production the government can lower the excise tax on fuels, to increase the tax exemption. PE increases when PG goes up.

Ethanol is mainly used as an additive to gasoline and that the complementarity relationship is considered to be more dominant than the substitution relationship between ethanol and gasoline in the U. S. (Tokgoz and Elobeid, 2007). Coltrain (2001) found that ethanol price is typically 50 cents above the price of gasoline. Gallagher et al. (2003) support this finding, attributing the difference in ethanol and gasoline price to the U. S. federal excise tax.

Gasoline Ethanol Differential Source: Bloomberg - ethanol prices, EIA – gasoline prices, (Gasoline - ethanol differential without the taxes and tax credit)

The differentials below zero represent time periods when ethanol is more expensive than gasoline. Around September 2005, ethanol price dropped below the gasoline price. The same situation was observed in the US ethanol market as well. Hart (2005) attributes this fall in price to the expansion of ethanol production and to the expansion of ethanol products that directly compete with gasoline, such as E 85. There was an explosion in production in 2005 and 2006 with doubledigit growth rates.

Different studies have shown similar results, showing that ethanol is not competitive with gasoline without government policies (Kruse 2007, de Gorter, Just 2008, Hermanson 2008). The costs of biofuel production are declining however. The second generation of biofuels produced from cellulosic material is expected to be more efficient than the first generation of biofuels produced from agricultural feedstock like sugarcane, maize, wheat, plant oils etc. (OECD/IEA 2008).

Hypotheses From literature review the following hypotheses follow: Food prices are positively related to fuel prices. Biofuel prices are positively related to fuel prices. Food prices are positively related to biofuel prices. The main goal is to check whether the relationships among fossil fuel, biofuel and food prices are statistically significant as suggested in the literature.

Data - weekly data (April, 2005 to August, 2010) for oil, ethanol, corn, wheat and sugar prices - prices are expressed in USD per gallon of fuel and USD per ton of agricultural commodity. Two periods:

Development of Fuel and Food Prices Source: Bloomberg - ethanol prices, EIA – gasoline prices, oil prices, Deutsche Börse – corn, wheat and sugar prices

Methods The study evaluates the relationship among the following variables: fuel prices (oil, ethanol and gasoline) and selected food prices (corn, wheat and sugar). We conduct a series of statistical tests, starting with • tests for unit roots and stationarity, • estimation of cointegrating relationships between price pairs, • evaluating the inter-relationship among the variables in a Vector Autoregression (VAR) and • Impulse Response Function (IRF). The direction of causation in the variables runs from oil to gasoline to ethanol investigated by means of • Granger causality tests.

Correlation Matrix (2005 – 2010) Variabl e Etha Gaso Oil nol line Ethanol 1. 00 00 - - Corn Whe Suga at r - - - Gasolin 0. 60 e 95 1. 00 00 - Oil 0. 61 92 0. 95 44 1. 00 00 - - - Corn 0. 62 09 0. 70 54 0. 79 64 1. 00 00 - - Wheat 0. 69 90 0. 69 26 0. 75 03 0. 71 89 1. 00 00 - 0. 15 02 0. 06 13 0. 08 40 0. 24 69 0. 30 19 1. 00 00 Source: Own calculation Sugar

Correlation Results Abbot et al. , (2009), found the crude/corn price correlation to be high and positive at 0. 80 for the period 2006 -08 Campiche et al. (2007), examined the correlation coefficients computed for corn price series and crude oil prices for the 20032007 time period. Corn prices have a positive, but low correlation with crude oil prices in 2003 -2005. However, in 2007, the correlation between corn prices and crude oil prices was negative which causes the cointegrating relationship to be questionable. Sugar prices have an extremely positive and significant correlation with crude oil prices in 2003 -2006 and a high negative correlation in 2007.

Unit Root Tests Level Time series First Differences None Constant & Trend None Constant and Trend ADF - Ethanol 1. 159 -2. 018 -1. 074 -10. 097*** -10. 423*** -10. 453*** ADF - Gasoline -0. 317 -2. 493 -2. 501 -14. 268*** -14. 243*** -14. 208*** ADF - Oil -0. 771 -1. 927 -1. 652 -7. 121*** -7. 107*** -7. 113*** ADF - Corn -0. 335 -1. 723 -1. 645 -8. 115*** -8. 096*** -8. 102*** ADF - Wheat -0. 323 -1. 215 -0. 789 -9. 463*** -9. 442*** -9. 510*** ADF - Sugar -1. 174 -0. 525 -0. 671 -15. 536*** -15. 583*** -15. 622*** PP - Ethanol 1. 198 -1. 990 -1. 053 -12. 871*** -13. 127*** -13. 186*** PP - Gasoline -0. 193 -2. 165 -2. 151 -14. 268*** -14. 243*** -14. 208*** PP - Oil -0. 642 -1. 710 -1. 461 -14. 283*** -14. 257*** -14. 254*** PP - Corn -0. 318 -1. 690 -1. 577 -15. 530*** -15. 496*** -15. 491*** PP - Wheat -0. 319 -1. 256 -0. 843 -15. 383*** -15. 350*** 15. 407*** PP - Sugar -1. 165 -0. 600 -0. 754 -15. 536*** -15. 583*** -15. 622*** Source: own calculation, Notes: * significance at the 10% level, ** *** significance at the 1% level significance at the 5% level,

Unit Root Tests We use two tests to check for stationarity of time series: augmented Dickey Fuller (ADF) test and Phillips Perron (PP) test. The lags of the dependent variable were determined by Akaike Information Criterion (AIC). Both tests show that all the time series (oil, ethanol, corn, wheat and sugar prices) are integrated of order 1, i. e. non-stationary. To make them stationary we therefore take the first differences.

Johansen cointegration test results 2005 – July 2008 L-max Test August 2008 - 2010 Trace Test L-max Test Trace Test H 0: r=0 H 0: r=1 Ethanol - Oil 4. 07*** 1. 35 5. 42*** 1. 35 16. 66 0. 89** 17. 56 0. 89** Ethanol - Corn 5. 07*** 1. 51 6. 59*** 1. 51 12. 22 4. 44* 16. 66 4. 44** Ethanol - Wheat 5. 93*** 1. 14 4. 79*** 1. 14 9. 65*** 1. 45 11. 10*** 1. 45 Ethanol - Sugar 5. 61*** 1. 96 7. 57*** 1. 96 6. 80*** 1. 92 8. 71*** Oil - Corn 7. 93*** 1. 06 9. 02*** 1. 06 16. 23 1. 54** 17. 76 1. 54** Oil - Wheat 7. 48*** 1. 41 8. 89*** 1. 41 24. 73 2. 00** 26. 74 2. 00** Oil - Sugar 3. 75*** 1. 13 4. 88*** 1. 13 12. 95 2. 36* Source: own calculation, Notes: * significance at the 10% level, ** *** significance at the 1% level H 0: r=0 15. 31 significance at the 5% level, H 0: r=1 1. 92

Cointegration • Absence of relationship between oil and commodity prices – strange if we believe that oil prices affect commodity prices through inputs • Prices are related when biofuel markets get matured, price transmission from oil to biofuels to commodities works slowly

Cointegration Ciaian and Kancs (2009) tested the relationship between crude oil and nine major traded agricultural commodity prices including corn, wheat, rice, sugar, soybeans, cotton, banana, sorghum and tea. There were no cointegration relationships in the period 1994 -1998, the prices of crude oil and corn and crude oil and soybean were cointegrated in the period 1999 -2003 and all nine agricultural commodity prices and crude oil prices contained a cointegrating vector in the period 2004 -2008.

Cointegration Higgins et al. (2006) found a cointegrating relationship interconnecting ethanol price and corn price during the period of June of 1989 and August of 2005. The results indicate a nearly one toone relationship between corn and ethanol prices. Campiche et al. (2007) tested the cointegration of corn, soybean oil, palm oil, sugar and crude oil in two different time periods. No cointegrating relationship was observed in the time period 20032005. Corn and soybeans, but not soybean oil were found to be cointegrated with crude oil from 2006 through the first half of 2007.

Cointegration Results Cointegration tests in Serra (2008) support the existence of a (single) long-run relationship between US ethanol, US corn and US oil prices. Results from Zhang (2009) yield cointegration relationship between ethanol and corn prices for the 1989 -1999 period. In contrast, results indicate no long-run relation between ethanol and corn prices in the 2000 -2007 period. In contrast to popular belief, between 2000 and 2007 ethanol and corn do not appear to have any long-run price relationships.

Vector Error Correction Model Equation ∆ bioethanol ∆ gasoline ∆ oil ∆ maize ∆ wheat ∆ sugar ∆ bioethanolt-1 -0. 001 0. 192 -0. 089 -0. 035 0. 245 -0. 352* ∆ gasoline t-1 ∆ oil t-1 0. 038* -0. 083 0. 074 0. 091* -0. 017 0. 064 0. 004 0. 360*** 0. 021 -0. 156** -0. 052 -0. 078 ∆ maize t-1 ∆ wheat t-1 0. 051* -0. 039 0. 096 0. 063 -0. 029 0. 122 -0. 042 0. 091 0. 006 -0. 084 0. 018 0. 011 ∆ sugar t-1 0. 018 0. 006 -0. 037 -0. 065 -0. 034 -0. 030 ECT t-1 0. 003 0. 012* 0. 000 -0. 006 -0. 020*** -0. 027*** Source: Own calculation Significance: 1% (***), 5% (**), and 10% (*).

Vector Autoregression Ethanol Gasoline Oil Corn Wheat Sugar Ethanol t-1 0. 1582 (0. 033) 0. 0339 (0. 874) 0. 1581 (0. 500) 0. 0581 (0. 783) 0. 3376 (0. 109) -0. 3653 (0. 107) Gasoline t-1 0. 0180 (0. 438) -0. 1168 (0. 081) 0. 2248 (0. 002) 0. 0947 (0. 152) -0. 0159 (0. 810) 0. 0615 (0. 387) Oil t-1 -0. 0343 (0. 179) 0. 3923 (0. 000) -0. 0661 (0. 413) -0. 1461 (0. 044) -0. 0576 (0. 428) -0. 0593 (0. 447) Corn t-1 0. 0523 (0. 106) 0. 0720 (0. 440) 0. 1160 (0. 257) 0. 1387 (0. 132) -0. 0130 (0. 888) 0. 1577 (0. 111) Wheat t-1 -0. 0564 (0. 076) 0. 1020 (0. 265) -0. 0281 (0. 780) -0. 1613 (0. 074) -0. 0203 (0. 822) 0. 0099 (0. 918) Sugar t-1 -0. 0013 (0. 955) -0. 0366 (0. 591) -0. 0907 (0. 225) -0. 1273 (0. 058) -0. 0930 (0. 167) -0. 0563 (0. 436) Source: own calculation Note: p values in parentheses below the estimate

Cereal end-use in the EU Note: 1/3 from the bioethanol use goes into the feed chain indirectly as high protein cattle feed (DDGS) and replaces soy meal, about 1% of all cereals are used to produce fuel ethanol Source: European Bioethanol Fuel Association (Harvest 2008/2009 estimate)

Corn end-use in the EU Source: Kristensen, 2010

Granger Causality Results 2005 - 2008 - 2010 Oil → Ethanol*** Oil → Corn** Oil → Wheat* Oil → Sugar Ethanol → Oil*** Ethanol → Corn* Ethanol → Wheat* Ethanol → Sugar Source: own calculation, Notes: * significance at the 10% level, ** *** significance at the 1% level significance at the 5% level,

Causality • The Granger causality tests suggest unidirectional causality from fuel prices to commodity prices. • No Granger causality in opposite direction. • This result is also in line with Arshad and Hameed (2009), Ciaian and Kancs (2009)

Impulse Response Function

Impulse Response Function Sudden increase in ethanol price, would result in a slight decrease of gasoline and oil prices. It seems that the response disappears after about five weeks. This is also true for the response of oil price to the shock in gasoline prices. A sudden change in oil prices results in a small change in ethanol prices but the same shock in oil prices will lead to a strong response of the gasoline prices. After a sudden increase, gasoline prices will then start decreasing after 7 -10 days following the shock. It will eventually approaches zero within a ten-week period, which proves to be a temporary response.

Impulse Response Function Similar results were observed by Serra (2008) Ethanol responses usually reach a peak after about 10 days of the initial shock and fade away after around 30 -35 days (Serra, 2008).

Discussion • Busse (2009) tested the relationship between rapeseed oil and biodiesel prices in Germany. • strong error-correction until 2005 but becomes increasingly instable in 2006 and 2007. Strong overcapacity and norms requiring rapeseed oil use in biodiesel production are the main reasons for weak adjustment in rapeseed oil prices in 2006. In 2007, these effects are outweighed by overall price increases and substitution of rapeseed oil by soya oil in biodiesel production.

Discussion • Gardner (2007) models the interrelationship between biofuel markets and agricultural markets. He shows in a partial equilibrium model that increased demand for biofuels caused by biofuel subsidy leads to higher producer prices of ethanol while the buyers’ prices of ethanol fall. • Agricultural producers of commodities used for biofuel production (wheat, …) also gain because increased use of ethanol increases derived demand for wheat.

Discussion • Msangi et al. (2006) use the IMPACT model. The results show that when the demand for biofuels is growing very rapidly, holding crop productivity unchanged, world prices for crops increase substantially.

Discussion The long run behaviour of sugar prices was found to be determined by oil prices and, rather surprisingly, not ethanol prices (Rapsomanikis, Hallam, 2006).

Discussion • Price spikes are common in agricultural markets due to a combination of relatively inelastic demand volatile supply. • World wheat prices were 15% higher in 1995 and 1996 than the 2007 price spike. • Cereal consumption for ethanol in the EU in 2007/08 only accounted for 0. 09% of the global cereal production with over 40% of it being grown on set-aside land where food production was forbidden. • EU ethanol has had no discernible impact on the commodity price spike (ebio, 2008).

Discussion • Informa Economics demonstrates that packaging, processing, labor and other activities have the most impact on consumer food prices. • 4% of the change in the food CPI (Consumer Price Index) is explained by fluctuations in corn futures prices. • The so-called “marketing bill”—the portion of final food costs that excludes grains or other raw materials – has a higher correlation with the CPI for food than does corn (Ethanol Industry Outlook, 2008).

Discussion • An analysis made by the Energy Information Administration suggested that up to 16 billion gallons of corn-ethanol could still be produced in 2015 without affecting the corn price (EIA, 2007).

Conclusions • Several factors are believed contributed to the price rise. Food demand increases in growing Asian economies, supply suffered from the adverse weather, but increasing biofuel demand also contributed to rising food prices.

Conclusions • Energy prices affect prices for commodities. • Interdependencies between the energy and food markets are increasing over time. • The causality tests suggest that there is a Granger causality from oil to agricultural commodity prices, but not vice versa.

Conclusions • Agricultural commodity prices are affected by energy prices, including those that are not directly used for bioenergy production. • The impact of a positive oil price shock on agricultural commodities is considerably larger than vice versa.

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